Vector_benchmarking/DGGS/dggs_join_classify_benchmark_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56525c39-20ce-44a7-a639-535f48332b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "from math import sqrt\n",
    "from pathlib import Path\n",
    "import geopandas as gpd\n",
    "import h3pandas\n",
    "from itertools import product\n",
    "import time\n",
    "import timeit\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e44cba71-8263-485c-863f-e2ec5f7615b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dir = Path('vector_1000')\n",
    "output_dir = Path.cwd()\n",
    "\n",
    "# Get a list of all GPKG files in the folder\n",
    "v_files = list(sorted(input_dir.glob('*.gpkg')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d70aba6d-d56d-4c4f-a0d3-840de1a372d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the number of files to open\n",
    "num_files_to_open = 2  #Layers to join\n",
    "h3_res=14   #Setting h3 resolution\n",
    "bench_runs=30  #Benchmark runs, should probably do 30 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "84362c3a-7b10-4053-bc3a-0ed5e5b7c03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through each GPKG file, index and join\n",
    "for i, file in enumerate(v_files[:num_files_to_open]):\n",
    "        col_name = file.stem.split('_')[1]  # Extracting the column name from the file name\n",
    "        df = gpd.read_file(file).to_crs(4326)\n",
    "        df = df.h3.polyfill(h3_res, explode=True).drop(columns=['geometry'])\n",
    "        df.columns = [col_name, *df.columns[1:]]  # Setting the column to the file\n",
    "        df = df.set_index('h3_polyfill')\n",
    "        if i == 0:\n",
    "            combined_df = df.copy()\n",
    "        else:\n",
    "            combined_df = combined_df.join(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d87714c-6e42-4fbd-a1ec-83a375f3ffc5",
   "metadata": {},
   "source": [
    "Benchmarking for joining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62ceddda-b76a-4419-8013-541e1cbdf6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "indexing_time= []\n",
    "joining_time = []\n",
    "\n",
    "def run_loop(num_files_to_open, v_files, h3_res):\n",
    "    index_time= []\n",
    "    join_time= []\n",
    "\n",
    "    start_time = time.time()\n",
    "    for i, file in enumerate(v_files[:num_files_to_open]):\n",
    "        \n",
    "        col_name = file.stem.split('_')[1]  # Extracting the column name from the file name\n",
    "        df = gpd.read_file(file).to_crs(4326)\n",
    "\n",
    "        #Indexing benchmark\n",
    "        index_Stime=time.time()\n",
    "        df = df.h3.polyfill(h3_res, explode=True).drop(columns=['geometry'])\n",
    "        index_Etime=time.time()\n",
    "        index_Ftime = index_Etime - index_Stime\n",
    "        index_time.append(index_Ftime)\n",
    "\n",
    "        #Joining benchmark\n",
    "        join_Stime=time.time()\n",
    "        df.columns = [col_name, *df.columns[1:]]  # Setting the column to the file\n",
    "        df = df.set_index('h3_polyfill')\n",
    "        if i == 0:\n",
    "            combined_df = df.copy()\n",
    "        else:\n",
    "            combined_df = combined_df.join(df)\n",
    "        join_Etime=time.time()\n",
    "        join_Ftime = join_Etime - join_Stime\n",
    "        join_time.append(join_Ftime)\n",
    "\n",
    "    # Calculate and print the time taken for each run\n",
    "    end_time = time.time()\n",
    "    time_taken = end_time - start_time\n",
    "    \n",
    "    indexing_time.append(sum(index_time))\n",
    "    joining_time.append(sum(join_time))\n",
    "    print(f'Run: index time - {sum(index_time)} , joining time - {sum(join_time)} seconds, Total time- {time_taken} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "986d5b86-1e5d-4ae4-b0fa-fafd47a80ee6",
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
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      "Run: index time - 0.5679895877838135 , joining time - 0.06353235244750977 seconds, Total time- 0.6600902080535889 seconds\n",
      "Run: index time - 0.5539288520812988 , joining time - 0.06429767608642578 seconds, Total time- 0.6407461166381836 seconds\n",
      "Run: index time - 0.5847799777984619 , joining time - 0.07134771347045898 seconds, Total time- 0.6810917854309082 seconds\n",
      "Run: index time - 0.6673462390899658 , joining time - 0.12244343757629395 seconds, Total time- 0.8124852180480957 seconds\n",
      "Run: index time - 0.7339503765106201 , joining time - 0.12044835090637207 seconds, Total time- 0.8848011493682861 seconds\n",
      "Run: index time - 0.7390246391296387 , joining time - 0.11064267158508301 seconds, Total time- 0.8817458152770996 seconds\n",
      "Run: index time - 0.7908780574798584 , joining time - 0.12148118019104004 seconds, Total time- 0.9424867630004883 seconds\n",
      "Run: index time - 0.763322114944458 , joining time - 0.10170602798461914 seconds, Total time- 0.8950331211090088 seconds\n",
      "Run: index time - 0.7934541702270508 , joining time - 0.11272072792053223 seconds, Total time- 0.9324796199798584 seconds\n",
      "Run: index time - 0.780778169631958 , joining time - 0.15546464920043945 seconds, Total time- 0.9674630165100098 seconds\n",
      "Run: index time - 0.8190402984619141 , joining time - 0.1023712158203125 seconds, Total time- 0.9526584148406982 seconds\n",
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      "Run: index time - 0.7768802642822266 , joining time - 0.11108851432800293 seconds, Total time- 0.9125797748565674 seconds\n",
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      "Run: index time - 0.7657606601715088 , joining time - 0.08481454849243164 seconds, Total time- 0.8913533687591553 seconds\n",
      "Run: index time - 0.7754497528076172 , joining time - 0.0822916030883789 seconds, Total time- 0.8934381008148193 seconds\n",
      "Run: index time - 0.839313268661499 , joining time - 0.11822676658630371 seconds, Total time- 0.9863295555114746 seconds\n",
      "Run: index time - 0.654815673828125 , joining time - 0.08566641807556152 seconds, Total time- 0.7694745063781738 seconds\n",
      "Run: index time - 0.6628084182739258 , joining time - 0.11226701736450195 seconds, Total time- 0.8063421249389648 seconds\n",
      "Run: index time - 0.8239254951477051 , joining time - 0.1097412109375 seconds, Total time- 0.9620888233184814 seconds\n",
      "Run: index time - 0.8133533000946045 , joining time - 0.10335302352905273 seconds, Total time- 0.9471502304077148 seconds\n"
     ]
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    {
     "data": {
      "text/plain": [
       "27.28289739997126"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit(lambda: run_loop(num_files_to_open, v_files, h3_res), number=bench_runs) #Benchmark run for joining"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc4011c-b87f-4731-890f-aaab9943bb35",
   "metadata": {},
   "source": [
    "Defining classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d54cd7f1-cd26-4c62-a018-fb3180466155",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_prime(n):\n",
    "    count = 0\n",
    "    if n > 1:\n",
    "        for i in range(1, n + 1):\n",
    "            if n % i == 0:\n",
    "                count += 1\n",
    "    return count == 2\n",
    "\n",
    "def is_polygonal(s, x):\n",
    "    assert s > 2 and s % 1 == 0 and x % 1 == 0\n",
    "    n = (sqrt(8 * (s - 2) * x + (s - 4) ** 2) + (s - 4)) / (2 * (s - 2))\n",
    "    return n % 1 == 0\n",
    "\n",
    "def is_fibonacci(n):\n",
    "    a, b = 0,1\n",
    "    while a < n:\n",
    "        a, b = b, a + b\n",
    "    return a == n\n",
    "\n",
    "def is_perfect(n):\n",
    "    sum = 1\n",
    "    i = 2\n",
    "    while i * i <= n:\n",
    "        if n % i == 0:\n",
    "            sum = sum + i + n/i\n",
    "        i += 1\n",
    "    return sum == n and n != 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69895183-7ad3-4153-8577-dcfe87930f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df['sum'] = combined_df.select_dtypes(include='number').sum(axis=1)\n",
    "classes = [\n",
    "    ('is_prime', is_prime),\n",
    "    ('is_triangular', partial(is_polygonal, 3)),\n",
    "    ('is_rectangular', partial(is_polygonal, 4)),\n",
    "    ('is_pentagonal', partial(is_polygonal, 5)),\n",
    "    ('is_hexagonal', partial(is_polygonal, 6)),\n",
    "    ('is_fibonacci', is_fibonacci),\n",
    "    ('is_perfect', is_perfect),\n",
    "]\n",
    "for i, func in enumerate(classes):\n",
    "    combined_df[func[0]] = combined_df['sum'].apply(func[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8680f9e-2947-4943-9800-83096693168a",
   "metadata": {},
   "outputs": [],
   "source": [
    "numerics = list(combined_df.select_dtypes(include='number'))\n",
    "combined_df = combined_df.drop(columns=numerics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19d694e4-ca2c-4228-b5a6-2abec704c85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "combinations = list(product([True, False], repeat=7))\n",
    "class_mapping = {tuple(row): f'Class_{i+1:03}' for i, row in enumerate(combinations)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b75a20-742e-4f5d-b686-435209920150",
   "metadata": {},
   "source": [
    "Benchmarking Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d955488e-b03e-4c6f-bc32-9ac5a1de3645",
   "metadata": {},
   "outputs": [],
   "source": [
    "classify_time=[]\n",
    "def classify_loop():\n",
    "    \n",
    "    start_time = time.time()\n",
    "\n",
    "    bench_class = combined_df.apply(lambda row: class_mapping[tuple(row.astype(int))], axis=1) #This should remain the same, as cell count remains static   \n",
    "\n",
    "    end_time = time.time()\n",
    "    \n",
    "    # Calculate and print the time taken for each run\n",
    "    time_taken = end_time - start_time\n",
    "    classify_time.append(time_taken)\n",
    "    print(f'Run: Time taken - {time_taken} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2ed3d33-ccfc-4cb7-bf29-b2fe0995105d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: Time taken - 7.766390323638916 seconds\n",
      "Run: Time taken - 8.018817663192749 seconds\n",
      "Run: Time taken - 8.353869199752808 seconds\n",
      "Run: Time taken - 8.14209771156311 seconds\n",
      "Run: Time taken - 7.976290702819824 seconds\n",
      "Run: Time taken - 7.862644195556641 seconds\n",
      "Run: Time taken - 7.609578371047974 seconds\n",
      "Run: Time taken - 7.1752989292144775 seconds\n",
      "Run: Time taken - 7.015469551086426 seconds\n",
      "Run: Time taken - 7.207256317138672 seconds\n",
      "Run: Time taken - 7.361089468002319 seconds\n",
      "Run: Time taken - 6.5390918254852295 seconds\n",
      "Run: Time taken - 6.434775352478027 seconds\n",
      "Run: Time taken - 6.001894235610962 seconds\n",
      "Run: Time taken - 5.7795090675354 seconds\n",
      "Run: Time taken - 5.759830474853516 seconds\n",
      "Run: Time taken - 5.897729396820068 seconds\n",
      "Run: Time taken - 5.671546697616577 seconds\n",
      "Run: Time taken - 5.760833263397217 seconds\n",
      "Run: Time taken - 5.684429168701172 seconds\n",
      "Run: Time taken - 5.826375722885132 seconds\n",
      "Run: Time taken - 5.5595293045043945 seconds\n",
      "Run: Time taken - 5.745591640472412 seconds\n",
      "Run: Time taken - 6.352838039398193 seconds\n",
      "Run: Time taken - 5.942759037017822 seconds\n",
      "Run: Time taken - 5.717104434967041 seconds\n",
      "Run: Time taken - 5.842856407165527 seconds\n",
      "Run: Time taken - 5.870246887207031 seconds\n",
      "Run: Time taken - 5.966681003570557 seconds\n",
      "Run: Time taken - 5.919342279434204 seconds\n"
     ]
    },
    {
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       "196.76690119993873"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeit.timeit(lambda: classify_loop(), number=bench_runs) #Benchmark run for classifying"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d99599d-b346-4b63-81b3-06586484416e",
   "metadata": {},
   "source": [
    "Writing benchmark results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "481ccbcc-be4b-4455-b7bf-af8f8b504dea",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'DGGS_benchmark_{num_files_to_open}_layers.csv', 'w', newline='') as csvfile: #Writing benchmark results\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerow(['Run', 'Indexing(s)', 'Joining(s)','Classify(s)'])\n",
    "    for i, (index_taken, join_taken, classify_taken) in enumerate(zip(indexing_time,joining_time, classify_time)):\n",
    "        writer.writerow([i+1, index_taken, join_taken, classify_taken])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf07ebd8-82a7-4525-9a74-08809708486c",
   "metadata": {},
   "source": [
    "Applying classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d9b39dd-2eff-46c4-bab5-79657963aeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df['class'] = combined_df.apply(lambda row: class_mapping[tuple(row.astype(int))], axis=1) #This should remain the same, as cell count remains static"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2713834-c66d-4111-a1e7-39c8d99f8cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "h3_df=combined_df[['class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "294e4946-915f-4761-afc7-45e1e4eda63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "h3_df=h3_df.h3.h3_to_geo_boundary() #NOT going to benchmark these yet. But this should remain the same, as cell count remains static this whole cell should take roughly 10s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00eca3ce-9f68-402d-90b4-67e28b898146",
   "metadata": {},
   "outputs": [],
   "source": [
    "h3_df=h3_df.dissolve(by='class').reset_index().to_crs(2193)  #Technically this is traditional method, do we even include this? I think not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ccebb4c-ed31-4c92-ab81-4a1c7e6e93f5",
   "metadata": {},
   "source": [
    "Writing Geopackage output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb1a53e5-464a-4a10-bfa4-982615935ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir.mkdir(parents=True, exist_ok=True)\n",
    "output = (output_dir / f'DGGS_{num_files_to_open}_layers.gpkg')\n",
    "# output.touch(exist_ok=True)\n",
    "h3_df.to_file(output, layer='benchmarking', driver='GPKG', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd97b38e-6a23-47d2-82c9-d003d3ccd698",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<Axes: >"
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
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e8/eGSq2iT0Y8dRUW7I0OLlQzxjAD5khTq/7i0mJJHZTMK6t+RSD4+xxXlN8FSKX8DlVWcNNHH7Z4uAI8MdLBw9nvBSpmyPm46HYW7wqn1nm+XlYL5RdiRHTMW3Uv+fWKVV+hfaaK0ZQ+sTLUYiicJe3BCZiuy+b311yHWtX9e1cJcg8hLq8Xz0UzT4Df7dVT6JwRAomkQUSFi3R+tqWl4gN4c38SIsYQSdaSEtcERfEpdIhKELD9fW+oxVA4h0qgbGw0e8rLcF60aJBtyKCMcpmx7HAevjYX1ALXfjEQj5DUxrmeT5FzGkM+ugaR1hWSN5WoWFFyQwikak1b8ikoXMhoXTT1B0pDLYYCYEyLIu3RyRyx1nHLkKGYtNqgjKsoPxmY1z+73XM2j8DCfdewpfbG4AkkAQ3iMP6U17/DNj/bEk6tb3SQJGofgdarbgWFC9GvOhNqERTOEvujceRmNq32dpUWB21cRfnJwMV7fRfz/hEt93ydwGHr1UGSKDC8QgLTP53EsuMdOwc7fSrMqqogSdU+yfp9hGuVrWyFtskyR1L6WV6oxbjsEYH0n8/iaNz5yWpGVHTQxleUnwzo1Wo/Wglc/0U6TjJllycQ6n0j+W/hHBrc/pkS/3xsFiL+vH/5sIspqBTLp0I71HqchGXGdt5QQVYS52azLt5Omf28h/f6M6fa2TKSHkX5ycCwxD7EGk2dthMReG7nbMQeHG750t4xPLcl3O/2b+41cNoRWqceo1BMsjmkIij0YGI1BhpPVnfeUEE20p+eTu0tWa2O3z18JCohODNXRfnJwPaiQqrtNr/arjilZlvtdTJL1HW8QiL/KriDT050/SeiEkK85ya6GRYXHI8xhd5Hyu66kLpEmTNjWsVjioDarCN8UEKr9qIA+jgzEUMSW50TtGp0sZ1PtHsUKoEDWWqOWGtbnTpeUxM0MXrukqMXU9LY0HmjC7j76wR23zKcCGG/TBJ1jQrPRN49NpglB3Xduv7tvCxeGr0FAVfnjWVAwMEvRm3mo/zeG1aiIA/xBhNFS9aFVAbD0xOJqXThM6hxhKtp0IrU+lzUu5uCyyeUD8UTrcduErCofVR5HNQ5HaSawulXLuKO1GI1CNTgpsJpo78hgrSjVnzxRhzhauq1PhJrRIp/uiqk77MtEuZmU3pLBlVWS5vnU4NYRFxRfjKQHOa/mbAJgcc3T+BvU08h0DXFKQd/ODiEfx3rvrvxsuMars+cx+ToT6UTqovUuuJpmk8rm38K5xlSpaLIE1pnKNcbOzn9k2FU2BvBBRfPEXNjPYCtVcKlQlsDheE0JWi6wLCUZ62FVJo6OttfUnnPe7SLAvhu6M9pa/uruy/yj/Ho+ElBkUcxe8rA0IRE9Oqu/fg2lKj43sbbWFH6XZmk6hyvEMejO+7n38cCv3Ee3xCPjUESSNU94nRFqAXF41PhPEaNhrr39oVaDBqOVTL420ZZx3D1Dd4Kyh8EjYD1z7PZ7u7YrBmh13fqLS8VivKTgV2lxa1yFfrD5lIVT26Moto7TgapOkZExc7aKaw8rZYkSNzqBoNQIoFk3UMrWMiOUlZ9CudxeDxETc8ItRgAFC3ZwRTkc+v3/KfnhHIYkiJIfXwa5XZrp22truBtlSjKTwb2l5cFdP2D60ciCl01nQbGz/d8nx+sab3Z3l2cPhX/PjNfsv66ihcDJ+uVlZ/CebRqNRWf9hylUPfyZtJN0t/nw0wxVG3pOUH8pl9NJTfVP6V229Dh6PwKFQscRfnJwOD4wJTI3ioVfz9+I2IQvh4RE1Xe8XyUr8Hpk3a8X20Lo8o7XtI+/UUrFvPnWYo7u8J5xnkjcFX754UdDBylFtK+lN46Er2xvMfsdEcMTmSPu87v9p8fOyKfMBehKD8ZyI4JPID2pV0mzgQhCfam6quZuHyUbP1rBHfnjWQiMzz02WYUegZalQrPsuA9WP2l7NM8ZlrCGGGKkaS/dFM4Rf/cJUlfgZK0YDj6h8e0meS/PRLNYTJK1BJF+clAoaVekn5+nNsfj9BHkr4uRkRFlXc8r+yRN9PFu8dGhCyIP15XCK0iqhQuRwYboqjadCrUYrRJ4a/W4P7ZevqZIwPuKyvf2SNWfSJQdlUS2zxdi9srslgIVpU9RfnJwMTUdKINhoD7ya9X8crBa7CKgyVVIF4hjmO22UxcPopDNfL+BP60X89R62xZx2gPi0e6PUyF3k3U5vJQi9AhziorCR8cRxtAHbsYvZGK9/dKJ1Q3iRyWRMIf53Hc2vVFwD0jRiEoGV56L58fO0Ktw9F5Qz/4W56OER9P55htliT9AWyrnsa1n/eTrL/O+NH6vrhID9p454jXbOeJkc7OGypc0iQZzZT+O/QhDp1RmXuCKZXdnzR7RB+mjOAlhm6P8DuGdXnFd47VJ/IllqZ9FOUnA3Em6dMN3b8uUxIFsqt+Pq/tiZNAIv/RqkS0hGbmPTC6c/dqhUubUruVhKsHhloMvyh5bSNDzN1XYA15oV3hRr89j92R3Z/4942Kkk6YTlCUnwxIteq7kFKrwM92XRlQtfRGMYfvrkpif3Vwv/ZTFhVrK0MT9jAkKrCwE4XeTz9zJGW9pISR1+ZG/7vdGDVd3+YY2WDAbZH+2eMPIpB4/RC+ddbQEECs3oGK4ClvRfnJwFVZ/QOy3bfHpyc0bK+b261rD1mv5lffTiJU6b6e3BhDgzg06OOW2kJvBlIILelH7aEWoUtYDpUz4UjXspwY1Bos7+6RSaLOSf3tXPbODdx5rthiwenpeoKQ7qAoPxlYdigPdxfce7vCo+sTsYqDu3SNiJonNqaz4lTo6uzZPAJ6Ifh5S0dHfsWsVKWy++VKrMFIxT9DpxS6S8EftzLK6H/4w3B1BA1HKmSUqG1EIDw7nr0RTmyewMOa+kZGKUHuvRk5vZVqnQIPbZrqdwaYRjGHO9bfxwlL6B2g/3NmmiSp07qCgJvMiODMJBV6HkMa9TjLQ58svqsIgOuFTUTo9H6113wemjCO5JuHsfehLCwuaRzLrsjsp3h79mbkjlPZXKri89J5ncuBhv+cHM3O8p7xNb+43Uype0rQx52RXBf0MRVCjwBY/vxtqMXoNo5SCyM3Wzot7pptjqL8q+AH8CdcNRDr3DRJ+9xSWCBpfx3RM56KlxhXZQ2QfYyfbY7k0R33U+sb3eZ5ETNXf3UfL+3qvoOMHDyQOxifIE02C385Whe8rBEKPYf+5ijspW3XjestFH+whwmqjvetzzgaiGijCK6c6BPDOXpjMnvs0hafrXUEb39WUX4ycKK2BrXMS3e7V2DlaTUPbhiDT2i50dwgDmPQx3ecNXWG3tx5IfFGEMS6oI55T9YaBkQpmV4uN0pcVszpvd/hqfZX60g2tT+B62+IwHIouCEOCXePosYpvaKKkiA5iL8oyk8GBsTEou+Gq3J32F2h4qOCq5tfu0jn/eOj8Io986vdVKJiX8M1QR1TLdZhVCvK73JjKOFYDvfszC7+4K61k/FZMfp2HEHitgU3h23ar+dwKlseJXXT4BxZ+m2LnvmE7OUsO3wQmzt4CZ2f32ai2DUNETM3rLqK1/b0LFPnxTySm4KDrCCO6GBuXyXTS09Bq1IRa2j7N2rUaEgymhlqjmbYRcmew7U6ssyRjDbEMNUXxcwqI33NEWhUKhKNJoaYoxmvi2G6M5JZJXp8Sw8G4+0EhfIvDjNxv7tVDcBkUxglHwTPmzV6dArboh2cscljTl594rgs/bZFz6t1fwkwJa0vv9++NWjjeUUVd68ZzIsT4smv7/nzmTK7wOL90/nV8BNBGU9AZGeFLihjKbTErNUyptGEqtKOp6Ae+8lq7AW1uGps5Cyai8rugXIb7sJ6bMersRXV4m5wYQfMmTHMmJWF83QtjUcrsZXUgwj1NP0BRGbGoK9oxGN14QQu5SlO0ZIdCBqBfm/P5eTZvJnZpSKFjuB4M6c9NoXCYWE4upGz01+CuUmjKD8ZOFkr7SawPxQ0Cnx/bXzQx+0uQ2ODG3j83f71rC8KrqPN5c4AcyQpxx0UvryqzfPFz7R9/NwD0HqqBuupmlbHL+Tc+Z61sy0fokck4V8nKL2pD6IIVe8Ex5tVBAqGhTUrXbnwBqmiAyhmT1mYkJImu8NLb+eZzRFYfMODNl64NnR1BS9XUvZaKHx5fajFuOSo/OY44+sMjBbDsRfLq4wAIob2oe7tmbIrPoD1Z07h8nYtu013UZSfDKgEIaheS70Tgfs3TEAkOObIidErmZMWnJtKARKMJsp6YWaV3kLhL9dQ9dxa2cfRx5kx3zeSGmdwcoZeO2CgkuGlN+MRfVicl/LugzQMjvYi0P0kuF1BFEyy1y5UOM/gSlXIkixfDggieK3y3juCRsD60hQ2qepkHedCYo3SV8RpD+VpIANfHDsqW27PS4kPjmo57ZCuTmFHqMRa5qQpps9gMHlVHSULc0MthkIAmDNjSH1sCmeswU0SUGW3BW0sRfnJwJVZ/UMtQq/hxxsG4BGSgjJWH5Ni9pSbHHM05SuP4AuSB6KCPEQ8NoHc9OB/h9uKlPRmvRqnx4NerTjS+kN+ncDSE3OCMtb9A1YQrlWC3eUkdktlqEVQCACVQUPaU9PJM4SmDFT/6Fh8QfL4VJSfDGhUKryiYvb0l4FRjUEZx+5LJoie1JcV4Vodk1XRlP5rb6hFUQiAPjcOJTfVRW2QHFwuZndZCY0BFMPtCoryk4GPDx3Eo+z5+c0j6+Oxky37OCahgH6RivaTg2HeMMofW6mYO3s5jbOSQzr+TYOGEKH3r4xToCjKTwauyx4YahF6FQ1ugWd2TA/CSD4yI5VJiRx4PzocahEUAkCfGE74X+Zx0FkXUjkOVQbPbK5sTMlAnV1x8e4qI2PbDg0R0eMVorH5EqhxxlJqj+RMo5lrUvKIUOXjog8WTxxVzmhKbGGcthjJr9fxmzGfoRZbJvwVsPHrMVv57OS0YLyly4YcczSVm7ddNllWLkUSvjeaXInLE3WH9KiooI2lKD8ZiDb27MTSPZGXdpkYmzAPi8tAQaOR/Ho9h2vUnKwXqHXCxQmsXtOPp845vt3K8JH6+dyaVUCWoWUgcJUrgaZkTcqjWgpmNIZT96dvERRrcq8l+u157PQ1QA+IBFpz8jgvzLgCjUp+o6Si/GSgf0wsCWYzFVZrqEXpVdy80v+q0LXOjpXX3/J0/C2vP/fnpHNH/1Ok63MBSNKfRKvKxq1YPwNGq1JRvnijEszeSxG0amImpbPDGfoV3znuHDYiKIoPFOUnC4cqKxTF10P4a56Ofx/N5sv5HuJ1p1DjJCMC8utCLVnvJlpvYKjVQImi+HoFKXePQZUZiTtGj9UoUCt4qHLbOR7E0mv+UNrQELSxFOUnA8eqg1tcUqFjGj0C0z/JYUH/gejVIgUWxewZKEM31lPyYW6oxVBoC5WAPs6EMT0aY79YNOmR5PXXUOmwATYITQifX8SbzUEbS1F+MtAvWimd0xNZdrzp525QaxhmiuCAreeYe3oTaaZwij9UHFx6AppwHUkPT8IbZ8QepqJe7aPG66TB7bqgOoILHMGJnQuU/x07yhMTpwRlLEX5yUBmdDQmrTao1dwV/GeoJgLnwi3MmDuAQ2PDz86IFfwl67SbwlALoUDMhHSMNw8mV1cPOOESsEAnms24vN6gVHZQlJ8M5FdXK4qvB1Or8SIW1GH7604G1g0n5cpUDjhqg1pIU26Gx/r48bBaUsyNxOrreWn3ED4/HdjtrhYEplUaqHpvt0RSKgSC9o6hbBAvLetFlc2GGKT7UFF+MrA1iMlZFbpOhE/NuVz1pcv2w7L9THtoErahMexw9c6HSZhGJCdWZGich/6RTsbFlZJhWNd8fvH4Ur4tn0uZvfvGyqHGaAoXfimFuAoBIGjVJLw2lwOaRoJUESxofDdnGHpNcNRSl3xKX3jhBQRBaPHXp0+fdtvn5ua2ai8IAkeOHGlus2TJEqZNm0Z0dDTR0dHMmTOHHTt2tOqruLiYu+66i9jYWEwmEyNHjmTXrl0AuN1ufvrTnzJs2DDMZjPJycncc889lJSUdOXtScbopNCmCFLomD32GpJvG9niWOFbW6l9cjU55miSjMHbdA+Ez649w64F2zjy3RXsuenvfDBjCc/lvMstqR+2UHwAOk7zlysCm5RFbakI6HoFaUi+fSRbfLU0BCkHZjBZefxY0MbqsorNyclhzZo1za/Vfthmjx49SkRERPPr+Pj45v/n5uZy++23M3nyZAwGA4sXL2bu3Lnk5eWRkpICQG1tLVOmTGHWrFmsXLmShIQETpw4QdTZbAA2m43du3fzy1/+khEjRlBbW8vjjz/O9ddfz7ffftvVtxgwyeERnTdSCCnqIXGtjvkcHhwPrKTfjTkMyklgXXyo3eJETBpICYPB0T6yo130i7CTYm4gQV9LnGYXAp6mmH0/GGJey31D7uLvh3TdkqZoSiwpwlgqlh9UYvtCRMriqzga6QH7pRlKlRoRGbSxuqz8NBpNh6u9tkhISGhWVBfzwQcftHi9ZMkSli1bxtq1a7nnnnsAePnll0lLS+Pdd99tbpeRkdH8/8jISL7++usW/fzhD39g/PjxFBQUkJ6e3iV5A6VSifHr8dhj21cAZZ/mIX6ax9TXr6YiHI5Z64In2FkERBZNbmBB6v8AK4K/Gq7DPt38dNjnrC36DmcsXTd/nrDWc2KEhtETriCiwEbhotyAZVLwH8OSa9jusOC0X7p1KY/XVOMTRVSC/L7EXQ6lz8/PJzk5mczMTG677TZOnjzZ6TWjRo0iKSmJ2bNns27dug7b2mw23G43MTHnwwVWrFjB2LFjueWWW0hISGDUqFEsWbKkw37q6+sRBKFdpQvgdDqxWCwt/qRgUmoasUaTJH0pyIOxsu1coucQgNInv0L3wlZmlRnoI5MpdHKSjweHOnltaj3Lriliw3fy2HfLRo5891MWpP4XgUZJFN851GIl711xpPOGHbDbUcOGRCep3xtLzKS+Ekmm0C4qgZTvjyXPWovTe+kqPoC7h48MiuIDEMQuuNasXLkSm81GdnY25eXlLFy4kCNHjpCXl0dsbGyr9kePHmXDhg2MGTMGp9PJP//5T/785z+Tm5vL9OltZ/F/+OGHWbVqFQcPHsRgMAA0//vkk09yyy23sGPHDh5//HH+8pe/NK8OL8ThcDB16lQGDRrE+++/3+77eeGFF3jxxRdbHa+vr29hpu0qnxw+xE++Xtnt6xXkJd5gIva5rbhr/TdrJt06gtIr+1Bgb8AdYLkqrUokIwJGxXv5zaj3mkyXQURExZITd7J4V+ATtCxzJPFONTU/XYPX7gbfpeMx21NIfHMeO6kL+HfXG7gqawBvX3t9QH1YLBYiIyM7fY53SfldjNVqJSsri2eeeYYnn3zSr2vmz5+PIAisWLGi1bnFixfz29/+ltzcXIYPH958XKfTMXbsWLZs2dJ87LHHHmPnzp1s3bq1RR9ut5tbbrmFgoICcnNzO3zzTqcTp/P8CsBisZCWlhaw8vvqeD4Pfdn6/SmEnhGmGBp++GW3A7TTfjoDW1+z316hN/f3cH1GDX2MFqJ19YSpq9BSgUBoYwtFjNyw5i4O1UiTR9Gk0TKu3kDRz7/uvLGCf6gEYiaks+f2lMumPuiDY8bx0ymBlTfzV/kF5FNqNpsZNmwY+fn5fl8zceLENldjr776KosWLWLNmjUtFB9AUlISQ4YMaXFs8ODBLF++vMUxt9vNrbfeyqlTp/jmm286VWB6vR69DIUTHZ7LqaBn70kVNsAcRcS6UgKpG1/48nq00UayFk/H4nW3CJAXEEkLg0HRPgbGeMiMsDMj8QiRqn2BCy8xAnaWXvEtY5eNQ4rvz+ZxsyNSZNSb8/B+dJiabwuVwrYBYEyLIu6WoeSme+AyUXwA35YUB22sgJSf0+nk8OHDTJvmf320PXv2kJSU1OLYK6+8wsKFC1m1ahVjx45tdc2UKVM4evRoi2PHjh2jb9/z+w3nFF9+fj7r1q1r0wwbLK7IzESv1uD0hubmFxBJCYORcV6GxrroG27nkxNRrC5UY9KIDI4RGRrrJSvSSXqYjSSjhdtWZ2HUwMBokSExbjIjHKSZrSQa63H5tDy6sR8Do0Syo130j7SRarYQr68lXF3Jt7UjWVsUw6BoB7VOtSTmNCnRqFTkGKLwvrCZkuL6gPtz19oRH1hF9k1DGTMunl/csYNIbRV6oRxBbECgdzysolR7WDxlIM9slsbDzu7xsIUaIu5Ip8+9A9H+JPeS8ApV6TXoE8KwF9a1OC7ntC/sp5PJ9fTOmNNAqHM4sLvdGLVa2cfqkvJ76qmnmD9/Punp6VRUVLBw4UIsFgv33nsvAM899xzFxcUsXboUgDfeeIOMjAxycnJwuVy8//77LF++vMWKbfHixfzyl7/kww8/JCMjg7KyMgDCwsIICwsD4IknnmDy5MksWrSIW2+9lR07dvDOO+/wzjvvAODxeFiwYAG7d+/m888/x+v1NvcTExODTtc91+7ukrtpP6OECHar63F5vWhUKsI0WqK1eiJ9GvbKUDTyrZk1jIopIExTg14oRyW2dN6ZkZCOarIHtViJ0Ebhru037kIQG9vdf/r8ynXtPtQnR59mcnTT/31CBKvOfJd9VcEpS9IZCUYTA+x6yn4oTXC2KED6I5MR+5hxRmop1Puod5uJ1RWgEgNXrMHmO6lf8ILmdmwe6R7jFpcTi8vJhJeuwFhgpei1DYie3rkXGPnneeR7G/GKIhMKVHhjDTjC1TRqRax4Sf74NLq+UQh9wvBE67CbVPgE4A+7MfWPRZsWiZhgAo2K4l9+jT4+HFN6FIasWFSReore3gY0KVhjUjhhgxLQZUazUwhedYOeRLTRGLQg9y6NUlRUxO23305VVRXx8fFMnDiRbdu2Na/ASktLKSg4H0jrcrl46qmnKC4uxmg0kpOTwxdffMG8efOa27z11lu4XC4WLFjQYqznn3+eF154AYBx48bxySef8Nxzz/HrX/+azMxM3njjDe68885muc7tIY4cObJFP+vWrWPmzJldeZsB49xbTuXTXzL+1hG4iuuxnarBUWlFdHtpAGb+3xTqhkRKpgS/vj6fvsbdqMS6pgNtPGd0FHQYD9Z8bTv4u5rxYeZEXc8wgw4yR9PncAOFr38jWZ8JVwwgN8sHNIAH8MC1n2fSx5jJ76eXMjxqD1qxSLLx5MYh9iFMCzYZjBTb3TVoU1SY3pzOwH8XUL3hlPSDyIRKryF2aibbHOfv0XXJoBbsxKoFMsNExsV4yf5/WvqGV5BkzKfIGs29axKaGt+fcfYqH9CIAET9YQaNblez44pBrSH7nXnU4qbO7aTR7Tp/i16m6RGvGzCwZ3p7Xur4u1HaGf97exVvPvzXDtsYkiIo/tVo6pzdMwslm0XemXmGOEMtcerWGXFCyW7LfG79KrRZbjQqFWM+KqVqk7QP3IghiZx4ZBAWV9uhEvfnuHh66P9QiXVB9+LsLt/Wz+e2VfJ+X/3NkSRZBEqeXoXoE3v8LnHa87PJjW4Zr6tX+Xh3Tjnjor5CaCOv2H8K7+DnW8ODJeIlyfT0DP5x480B9REUhxeFthk/b3SnbRylFoZva2DDqK7Ztscl+nh6VBnJxmr6aLd0fkEIiNdbgNApv1RTOP0LvRRKrPgALIfKGb0vgw1DVPjamDf+NU/H3/Ju4i+zqhkcVUaSdrPkMkjNmMiV3DPobpYekW974Li1nuNqSHz7CgZVqXqsV6gIZMzrx5VXVnN3ZAMppgu8dIVqBNGKQNuxdjOTTgHD2zyn4B9OrwdRFBGCsPpTlJ8MnDlUhEqtwuft2FRY9N4uptmG45yV6pfr/P+NdHBLZn6PVXrnSDIcw6geiN0b/Pn9GEMM5q0VFL4rX1q7wr9sZ/wf57GtHYcEEYEfrosj2RzLmmsL0Qj1PXo/UMDDcyNWs7FkHqcs8u7VltutVIermPKbK3GsOUnluhMhXwWKgC7CQPjgBIZNMDDjhx6mxLzXdsMOUPUSR6eejFYlfymjcyjKTwayRmZgDDNgre84lksASj7ej/brfPq8PJmydvL1PTPGxohYCyOjDqCn84w6oUYjlvLldUeZ9dlAghkGMd0Zif1fByneLn9VDZ3dC50s2kusAqOWXcV3sz0syCpliPkr2eXqLlqxmLdnnOLq/2XJPpbH52O9uYHE2zKIuSOb6E9PU73hBJ6G4CRqTrlrNIyIx2FWY9GKVHkd1Dgd+ESR31x/nH6Gtd3qt+cbc3s+tw0dHpRVHyjKTxbWvr+hU8V3Ie46OwPWVFI2pWWIgFrw8ccZtVyRuAG12Lsy6vcxnDy7+gvOeKmmcMp/uS5oD1Dv1mKE6WGdJh5z+lQsPaJj6ZG+rJw/h/7G7Qj0TE++LOM3PDM6mcW7jUEZr9xupRwrXBnJzAnTKfzVms4v6gIioDHrMKdHN3tekmDidIqa09b6pnJAF/xcBkeLZBp3+J0o/GI+OD5ICrEva77IP8q8AdlBGUtRfjIwZPLALl9T/PF+xp1JQ3/7UI6ZanhqtIWM8EZGR3ze7ZsxlKiwkxUFB6uDM17/Yh+FQVJ8AGX/Pcj0MVey3uy/Irv+i36Eafvx8VX5pBiPoxV7Vj10AR/3D/gfqwoWBD1UZXuMg4R35pL6ZTGOM3XU7el+sLO5Xyy+ZydQLbqodTlwNMfbioAV2jCwPDbSwb399yGI3Z+Y3JR5mj/t7/q9r3AeUxDi+86hKD8ZKM4v7fI1AlCzo5DEuDAe/ZWKm1L+Lb1gQUQtVvHGlHzmrJB/FqdTqyl5daPs41xM+csbGfDaDPKt/u3nuX0CtU6YsyKb+4Zk8NzQDxHoOMF2sFGJtbw9/SCT/xtcxw27x8MZj4UzM8PJNqeh/r8KvLb23f0FjYAhIRxTZkxTzFxqOJ44AzazijqVl5PWroUR3dt/H5GqvQG9B5vHEND1CtAYxBqFivKTgdFzhqPRqvG4/bf5JVw5AMPwPtRmh7OvqoLb0jW9xlW+PYpsUUEZZ7wrnFJr8At7ui0ObL7ufUd/P6Rjxal7eX1qBdkRJT0qXCVBs5XFU/pKlvmlqxyz1jHg91eQVurFtuo41ZtPN58TAbVBg/n3cznorLsg56Wj6a+LJRjVgo9rM8SAFR9AmCbU9R97PzuLi3B43Bg0PSzDi4J/fPHO111SfKnfG0vZlDiOWuvAXsPe4xq2ld3HbyeXMzHqM/kElZkpMau4JuMOVp6W14NL0xC6gOBBx92UpnTv2iq7wD1fJ5IelsBX88rQUtpm9p1Q8J2UT1iRfA+bSkKTqSffWk9+BOhuS2Hi/Gyc4RoadSK1opsapwOHRAki/npFNVNjP5ekr2TDIdRCNl6xZ2Q36o3MyMgMiuKDbtTzU+ic9MGpnTdSCSTfMpy0p6azY6yhSfFdQFGjwMO5idjovXsIAjaKG+T33PKEB2+f4GIKXtnASGNM5w076qNRYM4XV/PCvrspdU9B7AFzUgEnf5wS+pAal9fLBoOF7e4a8qy1lNgaL9jDC4xwrcjkuI1tBqx3h1O2EYriC5C+kVFBG0v5pmSg/HRlh+eNaVGkPTaFjVNM5Ka6sLdTBaLeJfDk1mn4hEhEgpufVCp+M7EQuT12bP8IbdUEnQQerSVWgQ+Oapn2yVCO2a5AJHjxTu0RJuTx99kd/5Z7M0+NtqIWpXl/Ngby+v4MSfq6nCm0BC8eVlF+MjDj1kltHg8bEE/ybSOJemISuRn+zV7XFKrJ+fi7rK0MrMBjqMgO24NJxoXMaEMMdQe67mAkJcY9VZL2d9uqDGZ9+QOqveNwC35YEWRkeux/uc7P32rvQuTG9N2S9OQRkhj+0QxWnwn9hKW3s7csePeyovxkoL6qAZ2hpSkufmYW+ifGsXGigU2q2i715/YJPLUxhgZxmJRiBgURFSlh8vVvXHkm5KHFRUt2MEWIlqy/BrdAUaPAhOWj+c2+uZL1212GxwXfmUhuFvT3YhKOSNJXbsUULk7mcHu2mzBNL4xRCjHjU4I32VOUnwyYIoycS/sYOTKZ5FuGc+b2TL+rf7dFo0fgB+sn4iQTkd6TPFcjlvLH6Sdk6dus1eI41TNqntUv3kKaSfrvpY8p9KuuCYmXnulzU7EanxAXcD+iEM5ru1tPfL7Tr5L/XXuUlyZb2rhKoT3GJAUvJ7Ci/GRg1d+/wePzkXDVQA7+IJONU0yU2gOpH97E7goVOR/N5X+l8zpv3IM43SDdquhCrG43+tQoWfruKp4GJ3Ue6WP2/p6nxyd0v8KIFOSYv+Inoy4tN/5+USIqMbB7st43gpcPLiC/vvVj9JH1ScTri1iQuozpyUrOT3/ZX14WtLEU5ScD0350BWlPTGXrNdG4vNLn9/r5lkhqvGMk71curohfJcsDINscRcVqaUxXgeK2OBhdLb1T0tNjGlsVJg4FD2SvZkjMpfMQ31KqIrcqsEnkhydG8Ne8tr9zH6A/G7ryhymbeG9OBUJvTNUUZE7VdW1LKBAU5ScDRThYlyxf5g67V+D2r8dQ6xuNTwjMzT4YCDTwu8nSB3EnH2joURXCi55fS7Y5StI+Pb5Q72g2oRHLeGv60VCLISkWV/dDZNxCKm/s1bd7vsouUO4eCYBZOMyUmE/486yaS2oCIQfJ4cGzcijKTwaSw8Nlr0Z8wiIwbtk4/nPmalnHkYoaV6yk/cUajBT+MfRxaC3wiWgldr9R9QzdB0CqbgOLp/Tc0kxd5cNj3d+jXV06pdOYPrXQ0uozO34Zn815l6v7Binbey9k9Yl8nO2EfkmNovxkICUigkSzOShjvbDdTIl7alDGCoRMwzfcOkC6H3VfITiVB7pKwrG2y1J1l+tS90raX6DcmPoVU5IujdXL5CQ3YhcfgSIa6nyjeGln56nffpA7vJVzmoCHVyfkolf5FDNoG9w2dDh6TXCSPCjKTwbO1NdT2hi4g4s/eEUV3109hFOOK/AKiUEZs7uMipfOaaJccKE2hD4TysUUvLKBsXrpTNGbK3Mk60sKVGIdv5/yLUlmkSlJPvSq3qsIvzqjA7o2Sa3yjGHssvGU2TtfkveL8NFWCQkDxzlwyzI+urqkS2NfDtQ5HEEbS1F+MrCnNLg/6lKrwJUrBvC346GPCeuIYdHS1TcaUEqHWf9DiffNXcQZAl+ZmjQiMxLkq0jfXaJUe9hw7bu8N20J/7m6iJXzT/DMGP/rV/YUPD4RjxDVpWv+cWyw321zi9S4SW/znEqsZWTEVyy/pogbs0IfztJTCNe3v48qNYryk4FhiaFZgS3eZeSorWcqwL0N1/L0lr6S9KVXq6n6S8+pgnAxoteHxR14YPj3h7jQc0oCiaTnXALuoWErGWBcwwNZyxgQ1bvMeKcsKt45eoXf7Z30Y+lh/z16DVrwdZCmTsDDiPAv+M2Yb5SA+LN8cSx4TlWK8pOBeJOZMG0ocnEK/GBtBjvqrg95WqwLEVHz3JZkDtdK470xRozAXhJ69//2aDhWyXh3973WnhjpYPk1hTw4aJuEUsmLgJ1/zdmJVtW7HuKDov3boz3hmM3Pd83C7vX/N1xlF3h+98xO2+k5xSfzjrNwUnC2Snoy6ZGRisNLb6assZFGCWb+3RrbLnDH6iReO3glYsgTfzVxxjG9zUDg7qLdL20uTTmo/s16MsxdV4Bvz6zmgex1jAj/EhM9I4bRX6JUe3h5Ss+dlLTF2iL/ahb+ans/Pj3R9T3mEXH+7WFlGr7hu2kfc9cgNwnG3jWBkJJiiwWvGJz3ryg/GVh7Sp50Xl3hr3k6dtdfF2oxqPeN5I95/STt0ztE2rAJOXBV20hZUeR3+74RIp/MK2BOwgp0FMgombycqO9d1cxLrCpE2t+fdZLJzd88wPby7j0qPzhqQsS/fSwBF88P/4D1139JetjlqQBvyRmKSavU8+u1TEmTZm8rUB7ekMRnJbfhJDMk43uFRKZ9Or5bM+b2UAsCjn/lSdafnGj7dZ7WbWIfH+9fWcbSKw4xLGylZLXlQsWPB22kTy9auWwqUbGlpu1Y2Tzr1cz76kr2VXX/MRmhA1HwP7O7gBOtWMTbM08hdymwnsiak8FbOCjKTwYig+ix1BFVdoGfbIrk57uuCEk9wG3Vk7F5pDW9DjNEU7uzUNI+5aLw95sZYm5fAf7fSAevTDrExOjPSNFtCqJk8mHgBH+Y0btc+FPNrVNqiUI4L+5M44wlsN9vo0tAELvuCZttWseR737Ow8PlyxTVE8mIikZUzJ69F4urZ/1gPz2hYWN1cE2gqytuYdGuwLPmX0z4ut71YDW9cxBzG2act2bW8MPsDSRpN4dAKnkZGbGKewb1nhXswdrz3tkiWqziYAb853Z2VwT+eDxcCycdk7t8nYAHjVjCo4NX8d6cisvGDHqgokzZ8+vNjE9OJdEsYxG7bvDExjjePHYvdrJlH6tRzOHR9VEcrZX25xVnMFK5qnfll9SYddgv8F5LDRP57zWFXJmwoseGMQSKgJufjfiKzIjeEQB/uKZpz88nRLHHcjUjPp7GxfX5uo/Av4+3HevnDxqxlCkxn7Bk1gkuBzPoXcNGolEFRy0pyk8Gvjl9knJrz3JbrncJvLnXwD/yJ8o+1senR3Wa97A7VDnsxEwLzf5ld6nIPcFYbTTjEpv29j6Yc5jh4V8i0LOsA1KjEUt5Z2boHb/8ISPSiVdI5KUD3+HWr5KRTvE10eBSBex5nWVcyx9n1PaqFXV32FYcvC0NRfnJQLBs1t0hwSRfVhQRAbeQxqKdJln672+OpGzFIVn6lgsBMB1v4JlRpWf39jaGWqSgkWFY3yvqAC7aGcdVn9/Nu4fk2RcP10mTx/PqxI/5+YgvyYrouc+XQLk2M3g5exXlJwMqoed+rMVW+RxfTtqvYMh/rpEtvjD1kLWHRC76T9ovZnF6sJGdFT2/9JTUCHj53oCeHaifaY5gdIWWJId87vXvH9HSIA6TpC+1WM57sw9yQz8PaqF3mJX9wagWuXWAh2sS/hq0MXvuU7oXMzktLWixKl1l5Sldh3FN3UVEw2v7M2VTfDF6I2V/63l5LttDn2Am+dWr2d7HzWmrhQFR0lZ76C04feYe+5AeYYoh5atSCl9Yi75SvoTKbp/A9qqBkvXXR7uF18b+jZ+P6335VNvjiVF2Fo36GypsiL7gvC9F+cnA1ydPYHP3zKTLI+J9gPSy1fuGsfpM+3kMA2Ww04DbEryM790lYmgfUn9zJWEvzGCjpq7Z2eWR3BicSBvsfyEiPXOyFaXaw+IpDaEWowU6tZoJ2hg8z2+idPkBADzR8oYn1Tqlt7jclfklB29d02scizrilsy9Z/+nAyE4YVmK8pOBKlvPneV/U6jCK8RL2meBcyZvHRopaZ8Xc8zgROzhNs+UO0ehfWg0680N7HDVtDjn9Kl4Zof/SZS7ymM77qXINR0H/WUbo7tcn7yMWak94wGdagpnUqOZqoe/xFF6PhVb/e+2Em+QZ68aIFIn/YRTJdZi4AT/uXJ3r8upeiHXZ3oJEw4CIBhvRBCUen69ljhTcArZdodHRtjRiKWS9ScK4Ty4Ppu/y+QscI6cChVCD76/0342i6KZCa2U3oWkh8uTsLfON4qVp9XM/HQwT26diUjPqnMo4OH3kzcSrQ/tFzjCFEPW7gaKnlvd6py1oI4ItXyr5+e2ROMVpI97BYhR7+KbG/J4fkLPnXR3xCPDTjU7BImOr4I2rqL8ZGBiSlqoRWiXjSXS7vedtI0nv07eJZlZq6X2n/tkHaNbqATSnp3RtLeX7Oaktb7D5m8f0FHrHSO5GA7v+e90daGaeavvY3XFLdTIMFZ3MXGE30+vCNn4WpUK7ZIDFP25bQccAYh3yme2r3cJlDjkK0ycpN3MXX3/zQ9yXIxJ6Bmr7M4RyYoQyTRc4AEtaJUML72Z4zXVqISeaaMbFiddnNDy4tt5ZIN8+1jnGCaG05hfKfs4XSF8UAKpC+eQ28fZYm+vYwQe3Twan9B5zs+uYFC3DCfIrxN4KDeGK/83Bqvof/FVuZkUvYI7BwZ/LzzDHMGU40KnafEsMq9M69zymVWhaYX9XM67/GV673AM+9lYO19d/WHLfLaim2AF8yvKTwaGJSb2mPyeF/PRUb0kD1+3kMovtoZLWqqoPXwfHZZ9jK6QfNtIdI+NZb2p644c28pUfF4ibcHhD0+07UZf7xK4bc00Ht1xP/W+kYjI+/DtDAEfPx+xNqipuiZoY0j9qpTCNztPI5dwSl6HqnBtcBy2olR7+MOMWiYn9dwVoIDIdzO/RaBlMhDBdAdCkELFFOUnA58fO0qto2d6Jj41xoJKbJ3It6t8XToZt0/+1W1fUwT1e4plH8df0n42k9LZiR3u7XVGv3Dpat55hTiWHGy/jNDhWoGVp9WMWTaBv5+4mTxr2xUMgoVGCF69v5k1Jlxv72726OwUjby/51/v7I9IcNIeXpP4Ee9N+6DHOBpdzG3ZHsxC60mt6Pg8aDIoyk8GBsdL600pJactga1IRTRYxOEs2hkljUCdUOJoJHJYUlDGag8RSH92JsmvXs3OFC/HO9nb64xHNqThFlIkkU0tVnHLAP9MiS/tMrFgZTrV3nG4yJBk/K4iiPXcli3vxDBSp2fGQS/FizdgOVjm93Ulr25kcAdVOAJlexk4Sey8oUQI2NCqe6aX2I+GHGv7hCp4ySAU5ScDBfWBPRzlJCUssD2Xau9oRn88iTJ7cPY0hxiiqN5yJihjtUXYgHjSfnMl6/o42KipwypB/GZRo8DredKYPp1k8vdD/nspun0CE5aP5sntc/is5DZJZOgKAj7uH/Apw2PlW5EMdZko+utOfI6uedf6XB5K3fIFWHtEsHnlU64XY/EN5+szPe8RPy7RR7JuV9snvcFziup5n8wlwIy+mRg0Pcvd/Bx/2BcWUG2/d48OkVCazonc4P/MXWqSbh2B4fFxrDdLH6S95KCWYtfUgPtRCw7Su2FJ++qMmp9simRd1c3YkC77iD+oRAvvzJTHe3fmUah9qXu1EUWPyCCffGFKXlGFSR34loO/vH9ipGwZlwLhiRHlCLST89WTh+gLTsiGovxk4KO8Azj88v4LPkPjfNBN5eekH/88EryiuH1NEZR+vD9o450j5e4xpP16DuVXJrHd3f29vY4RiNYF7sHqEc2UBrBYeeCbOCYsn8HaygVBVYJx6h28PEW6/T+jRsOY9wop+PM2nOU9K6PMhbx9ZFpQ4jBFTPzvVPt7wXKjV6uJ0rXeYtn4nYOMit6NnQFUeia02AMVMSBqxyKoghMn3TOXJ72c5PDwUIvQLqfqBERB3eWA8VOOK/jLoUzJK7N3REY1FDmDN4lo2tubwbF+WkpsjSDzBPSnO6bx5vgzCHR/D+zTwgkBOx7ZvQIProtlduo0/jDZjpbK9mfmEnJTysd8lvR9tpR2PgfXqFR4fG2bSkeYYvC+sIm6EksPXOe0ZE2hlscHG0GUV0EL2Jid6iK/Tj6v8xi9gTS1CbMDtBYXQoUdd0Ed1iOVNJ6oJuHKbFRzMtDUOhGLG1AlmLl1dQ4VtiHNJc8eGzmIBpeK/VUaTtXD90dP5eFxsoncAkX5yUBlD05v9sSoOlRi1/ckf7kji21lwTUUFMUF71Fm7hdL9A+aUpOJtuDU2lt5Ws327KuYGPVZt/tYVyhd0oK1RWqGfHQNv5nUwMjYSgaavpas77YQ8PD21M2M/Hhqh+Y5o0bD+FoDglfEHq/HUO9GKLdhGxiJudhO2Z9zcVVLs1enWnkK7VXRuNtRtIHS4BJwiXHokVf5OcnkvcPyWmmGFwsULvyS9iqXln95GL5s8ugUgag/z6PU6uDCeolv7m25Oj1SVSWPsG2gKD8ZmJ89iJc2bQi1GG3y1oFo5s7u2jWVnglBV3wAaYetBKO0ZdKC4bhnpbJeNhNn+6SaAtsDmpriYG2RtGain28NJ1wbxoYbh2FUVUmaDu9izMIhfj99KI9taOkIMsYQg2HlGWwnqrEX1FHY4GhlrVAZNNR4vIge6TwaK1Yfo++NcwL26G0Plw90lMvS94V8Uz4Ou1e+yWOUTk/N+3v9bz8sid2Ozu+vo1VViKKIEIQkIcqenwwUN1jQq3vmvGJiH3eX9hy8QhxvHwqukwtAvMFExft7gjJW7VUpMu7tdcz/bRrY7ZyPHqEPf9ovT+B6g1tg1MeTeGzbtWyruyEgJ6nOyKtpeg/DTDHMrDAwfZ8H3tpD2Wd5WA6W4ba0VnwAPodHUsUHIKgENDI+FqvsAqvKr5WtfwARM7/aFiXrGE6fF/MA/3+35u+N8KvdkISEoCg+UFZ+shBnNKPqoZsPO8s1kKMDOt9L8wkRjFx2k6wzyPYYbNFSXCG/+ThuVha77KELTSlsALq5U7W7djxVsoacCKw+o2b1mT7MSr2XewZWMy32v5L17hXiaPCmMSq+kaliH7wf51P4db5k/XcHn9ODD3kDwzeURHC1jOF++xpmUuuU/56t3VnkVztDUgR5Jif4sZswPT0jMKG6gLLyk4Flhw/6mesx+Dw1qhIB//ZH9tbPCIniA7D9+2BQxlHdNLBdRwq5+W62m6/n70Qtds/rc2TUXmkF6oB1RSq+vzaO/Q3zsDGo2/2IaPEJMdgZwLKCuYxdNp4frYsFoCLEiu8caQcbZXWcOV6vki3Ti4ieRbuSZen7QsY6w3DXde4UlXTzMLQvTqXG6Z8D1Y5i/xSqFCjKTwbm9c8OtQjt8snJWL/a5dvn8NqePjJL0z5hN8qfkNmYHME2b2jMnQA/HnKcSNXebl+/vmK8dML4hcBNK9N4YP00nt93X5dNoSJGvir/DoM/uplhH13Bz7ee94reJNSS+oMgufl1QtV/8whvw01fKpJMYqucllJR6h7H7gp5H+s6tRrb+/6ljLNdmcZem//3WLk1eM6CitlTBs704AwvI+I6d6u3M4B5/+sXsgDZMK2OuvflL2EU++hERIKXa/JChsaKpOi6F4wNTSbp1/cGLxXUhWwvV7G9XMWo+JsYGVtOhn5du20LnTP48+EsDlerOdMgUO+C9sy8h8eHk7wxnoZjoa3godKoMKu1WPyx03WDbwpVOMdnoueU5H3/+ZD8E2+jWgPqjp8NogAxo1LY7eyaQ1esUdqSax2hKD8ZyIqO6TAuKZR8edrEHX01CB3s+emFKobHiYiiyOBYLwMiXfznmJH8ehUCIvFGGBAlcrBaoN51/iYwqkXsXujuHtY5hophVMr8ANTFmjgS4yEI4WytGBjtY+GEIoQA9pYE0Ut2lI/8Ovlq0HXGU5siidRFMCe9H8+P3oyJI8BZ7+CqDE5ZDHxTqOdgtX+/hwq7jb5PjIMffymn2M2ItP1L9djcNHikK/11MZE6Avru28PGQD7Ol/+R3uB2dZo6Lv2nM9iW4sXn7dr2z9cnT+DwuDFo5CssfA5F+cnAvw7u65GKD8DuAZ8QhVpsP55GJday7IqlCBfMfL/TdwQawYVBVYlKrEfAzQn7bNyimnh9LWHqKrRCDWvK51Lv0tI3rJFEo4UP8jP58pSW4Qk+hsa4yIqw89npSFafUaNX+RgQLTA8zsOeCg2Ha5seRcKK46ASwCdfUt7EO0Zy2C5fHsf20Kt8LL/yGwycCLAnD7vKQ79rUe8SWH5cQ41jGs+OSqHMHsEHR2NZXdg9pbzTWcOsZ2fi3FdG+cojzccFjYA5I5awwQloM6Ig3oTtq3yqtpxBF20kPDseQ78Y1CkR+OIMOMM1OLXgW7QVU1Yc+r5RCH3C8MTqsZlV1Km9xLnVaFeeRpseidjHjCtKR4NBpNrnptEuj1kSoMwu8Jt9s3lxxN8l7feLojFBqbQySh9N7cGt7U5xw7PjOdFPh8PW9VjGBUNygqL4QFF+sjC9byb/2BccN/2uYtKCSqzrtJ1wkcknUnXWDHmBPsoyrm15kQhzEz5ucei5odv52dCWy6tZiekIE0U0YkXzOLXeMdi8JurdZr6nH8ig6/qj3V+JkBKOO1qP1SQQdcpG/f+OYMqOQ9s3Cl+iCQ5VU/TPXRhizYQNSUDwQdWm8+YkQatGF2nAWdVyL6FkeARYO/8cpOauwR4JFB+Uu8dQHoJVa3usK1Kxrqi/NH31cRDTN4mB8/ph1UMtbqpc9gucyHxAIynfG4Djnn7UOR14m6t/u5r+3IAbhMcHXvCTtTf9nbX8nwSYHUHTj7oRvMie1eccg2OkNan6hGh+tS045ZLCdlVR18ZxtUlL3JwBbLkiArqh+ADcXm9AsnUFRfnJwKm64CWv7Sr3D6nu0OQpNW2lydJR0KpYc7R6F9FqSNapGRqbxoYSBwxVA7amPxuQCNyfcfYKD2AhcmIYwqSZ1DsdiEBfcwQptw6m0QA1uKl02sgyRJB+zIo3zogjXE291sfRECg+gB8MlKYwb4LuMInGoZT1IAUoJTVOO1ux05FjcrGt89VZd2wHYwwx5PusWFzyZfr5W56J76bpW00yu0uNZ4Ak/fiDY2Tr+L6wAfFE3zWC3MjAVswaVfCsGaG3m1yCTEhJDbUI7fJwbixO+oVajHap9Y5kQ4n/P8t6l5O6s4oP4IzVwhZfDfttNRTZGnB6vRyy1rIuxcUGfT07XDUhUXypYSJfXHeSeI00FoGDluCVlbqcmOaJgrf2MLpI3kdjshlEQboEBVGaIkxBWsqoPjve4nXMhHR4elzAig/gq+PBC3fp0jf8wgsvIAhCi78+fdp3h8/NzW3VXhAEjhw5b8tfsmQJ06ZNIzo6mujoaObMmcOOHTta9VVcXMxdd91FbGwsJpOJkSNHsmvX+ZpQoijywgsvkJycjNFoZObMmeTl5XXl7UmGUaslTBe86gddwelT8dSOK/jdke/xxpF7cZFOrW90UDLN+8P7J4aGWgRZeGyEhYGmryWZ6YuoeHm3NMVwFc7T3xxJyTOrqNtfSsGidYw2yOdNu7lUxRel0tR0BNhWPaGF85lcDDJHUb76fCFaQ1IER+7JYF8Xwhk64uYhOZL04w9dfuLl5OSwZs2a5tdqdecb20ePHiUiIqL5dfwFlc5zc3O5/fbbmTx5MgaDgcWLFzN37lzy8vJISWm6wWtra5kyZQqzZs1i5cqVJCQkcOLECaKiopr7Wbx4Ma+//jr/+Mc/yM7OZuHChVx55ZUcPXqU8CBXWWhwOrFJUPRULlaeVgNN39sf918NCLw4YTBlNi3fzTpJH/1xGr2JqAUP4YJ/8TxS4KIvfz8oX3xV6BC5NqX1hK67CPi4qX8j28sjOm+s4BeTVdHwyWnKzzpZCYD4+13E/18OlQ55HKMywqQJsxHR8OKO7qXI6yrFLhuZmTFYT9UQf0V/tPP6k+euk6x/OeMrL6bLyk+j0XS42muLhISEForqQj744IMWr5csWcKyZctYu3Yt99xzDwAvv/wyaWlpvPvuu83tMjIymv8viiJvvPEGP//5z7npppsAeO+990hMTOTDDz/kwQcf7JK8gfJl/lF8onyeitLSNFt8fnvTZvmfDwxCZBAgMDLOx+y0URQ2anlx9FfYvbGEq08hyFSORUsp8/t5+Nex4Hh7BYtrM3zokK4avYiG9w4Hx7nhcsH6h29pzG8ZXmM5XE7OgX7kyrSd9vGJJIb5l/KyQ046ZnDKEpwdrHSdGeupGlLvG0fNxHgOSLTiO0eDjPusF9PlTyw/P5/k5GQyMzO57bbbOHnyZKfXjBo1iqSkJGbPns26de0HxALYbDbcbjcxMedNDitWrGDs2LHccsstJCQkMGrUKJYsWdJ8/tSpU5SVlTF37nkzgl6vZ8aMGWzZsqXdsZxOJxaLpcWfFDwyKpq3ZtYwIbEp3CFMIxKt7x3KsCmwvUkh7q1S8doeIx/laxj8n2sZvWwSS/K/w5+P38O39fPxCgmSFkAVcPGrUatZNKmRZHPv+Lz84YvTanKrbqTUPUWS/oqcUzhUo2zXS0GWOZKxHxS3UnznEJPkm2RoBDHgRBIiahbvzpRIos6J21FN+rMz2T/OLLniA9hYIN0ksTO6tPKbMGECS5cuJTs7m/LychYuXMjkyZPJy8sjNrZ12qykpCTeeecdxowZg9Pp5J///CezZ88mNzeX6dOntznGs88+S0pKCnPmzGk+dvLkSd5++22efPJJfvazn7Fjxw4ee+wx9Ho999xzD2VlZQAkJrbMFpuYmMiZM+1/mC+99BIvvvhiVz4CvzBrPcxNWM6MhAxUghONWEmFZwzbK/tiVHv4cW4MJg2khYscrT2vbHo2TTIu3n0uA4MBtXADYVqBxVOGcabByN1Z69CKgeXm04pF3Jr2AVenDOeIJYM7VicFKHfP4IFv4kgNi+XrawvQUBfQ6tmr+KlJwjRPFJaXv6X2ePsxr6WLckl7YyaF3XTd74ilR3R8t/+cgOom1npHsbYoeIkOxAHRrNPV+5WkujtkRkUHraSRIIrdt89ZrVaysrJ45plnePLJJ/26Zv78+QiCwIoVK1qdW7x4Mb/97W/Jzc1l+PDhzcd1Oh1jx45tsYp77LHH2LlzJ1u3bmXLli1MmTKFkpISkpLOPywfeOABCgsL+eqrr9qUxel04nSe/xYtFgtpaWnU19e32KPsKqJrJ2LNnW2fQ42bFLTUADa+rriZBKONfx7tw+enVPSLAK2aXjmzX9Dfw0uj35fMfRtgefHt7Cg3sfx4z3DICRSjWuTmAR6eH/5PBLq3L1zkms7MT+XPfXopY9RoyH7jCI0dKL5zJM4bzJa5kbLI8e2CHUSpuu8B/MaRe/njfkPnDSXgjzNq+N2eaE5Y5FNMqRERrLn7PnR++JK0h8ViITIystPneEBPFLPZzLBhw8jP9989deLEibz//vutjr/66qssWrSINWvWtFB80LSCHDKkZU25wYMHs3z5coDmPciysrIWyq+ioqLVavBC9Ho9er0MG6zaEaBKAl/rIqAC3qY4t7OcCwofNi6OV8faEbDSIA4lrz6LPkYLTq+Gaz/vuaEJF7K2UI1nTBxasViyPm9O+RfXp6bx9Igkpn0yMigZLOTE7hV4/4iWNPNtzEwuoZ9xG0IXI6vfPBC8mK5LkQHmSMQfr6bRz1qA5V8eZvJV89giSm/m21bZn6sTu6f8XGTwj0PBcBAR+Wr+SfoYSnnGJo3pvj3uGDoiIMXXFQJaXjidTg4fPtxC4XTGnj17WrV/5ZVX+H//7//x1VdfMXbs2FbXTJkyhaNHj7Y4duzYMfr27QtAZmYmffr04euvz5sPXC4X69evZ/LkyV15S9Lg2tKm4usItVjV/BAMFw4yMeozMvTrqHcFL9FroDw41C6p4juHViwkTr2Dz687wZOjLo2o7pd2mbj+i36M/+xO3EKa39e5SOerM6HL59nbSTaFkbLH0uUiuBaZbkOXr/uP4NyKcTR65J0MTk32sek7B+lvXMPGihxsMo+3r7xM1v4vpEuf/FNPPcX69es5deoU27dvZ8GCBVgsFu69914AnnvuuWYPTYA33niDTz/9lPz8fPLy8njuuedYvnw5jzzySHObxYsX84tf/IK///3vZGRkUFZWRllZGY2N5wMmn3jiCbZt28aiRYs4fvw4H374Ie+88w4PP/wwAIIg8Pjjj7No0SI++eQTDh48yPe+9z1MJhN33HFHQB9Qt/BJlxdwXNQqZqcGL+VPIKwuNCAi34M5y7CWHw/4kI3fOcjt2e5e7xTj9KmodQr8ctdcVpTe5tc1WkqYntIz88b2dIwaDQmv76fwra1dv/bdPMJliN39+ZZI6n0ju3ydiJGfb41udVwt+OheXpvW/HpiI3+csoU+2qbtpi9Oyx9a07edqAA56JLZs6ioiNtvv52qqiri4+OZOHEi27Zta16BlZaWUlBw3qTncrl46qmnKC4uxmg0kpOTwxdffMG8efOa27z11lu4XC4WLFjQYqznn3+eF154AYBx48bxySef8Nxzz/HrX/+azMxM3njjDe688/y+2jPPPIPdbuehhx6itraWCRMmsHr16qDH+AGgkm5MASeJZh/IqFSkYn6mHQF5FbWAhyTtZn49cifukX2Y++XVFDX2blPosuMalh2PJPmq+QyKOEmY0H5yhnrfUGXl1w3STOH0qxYoOlLRretrtp5hzKxMchOklcvhhTB1kd/6yi2kcf+GqzlcQ3O19qGxIn+adpgobRUGVSU7aqZyoCacrAg7qeZ61pf24a19RrJjREbEebC4VBfsobdd2+Kza88wMGwvGvH8SmxOWiNfnZFn7/Mca0+e4NkpbTtDSk1ADi+XGv5ulHaG6KtHrJwFojQrwE013+F7ayS+62Tgi+tOMdC0OqhjVnomcNSSzJMb46hx9j4noYvpYxRZO38t+naSX7uFVCZ+Mi8o2TwuFYabYojeUUnRX3cG1I+gEXC+NUdyz8811x8jw9BxCBg0FZh+clO/5uon5wjTiEzo4+OVSTuIEPa3uk7ECPiaHdHcQirVrnQitNUAvLxvCv2jXPQNs5FsshCrryFKdaCFQ5ZXiGP2FzfJPtEcHBfPx7fcjknb/VjfoDi8KLSDa5tkig/gSG3vCGjeXxPHQOnSFfpFvGY7cTEC22/Qs/DAHbx3uHcHyJfZBX6w4QoGx0znZ0Pfb+UR2uhNOFsQVsEfjBoNmj/soehA1/bg2+wrOZIGUXrLhtPX8W92W90NLDkUz+FqFRVt5HNt9AisLVLz6KYJvDu9oFXVlouTy2vFIvpoz4ckvTiipT9FW+RZxgfFwuIVxaAlt+79U+WeiEfaCs1XpJRI2p9cXJf6bUjGFRARcPDTYWv4+vp8jOrebczYVqbi3UM6VpbdRIFzZotzfzsynN4RFxp6+psjmXAS6iVQfAC2gjoGbpMmEcaF/GBtBl6htVf66opb+OXe+3h0fSLri9RtKr4L2Vyq4vOSqySXD2BIxG5Z+r2YBYNzeoe3p0I7aPpK2p1R7ZC0Pzm4Y6AbA8HLyN4WOgrINHzDn2Z2HrvVG3hsQxS3rs7GzgB8QhQiOg5U9+6VbTBJPdhI4eubJO2z6B/fMlklbcLrlHARlXheqYoYee3w93hmUzT/OqZt3tvzh6c3RVDumSSpfAA7ayZK3mdbfJ7f+SpUKhTlJweaoUg5O3d4e36y50+Oa/AKXcv5KgciOv50oHW2od5KlV1g/PJZPLVjAaBhcEzwajH2VjQqFTP2eih7d1fnjbuIIELVc2tIM0nn1La/SsBBGm4hlb+duJuf7LyTtw/ouxXG4BVV3LduaJsrye4iYuK1PfGdN5SASL0+aHmRlT0/OfAWIJW7MUCGcSdGdX/s3p5r7rovx4VGlMa8FAhl7nHsqri05nR2r8CKU2rGRtzGqYOxIEhvertUGGCOIv6TMxR9KU3R4LbwWl2kfVpEoYRZX74sHsP+KiMfHA18ZX+0VsVv9s1jXKKVaxI/Cri/Iuc49lYF556qdzoRRRGCkN7s0npK9BS0w0El3UzJLqZg1PTsfSyjpmfEnv3lsHSJtnsa33wdzumffM0YQwwZZqWcUVukHGygXEbFd47yLw8z3dHWdyASphEZGefj3sFuFvTvfKXu9gn8dHOEJIrvHEuP6Hh0fTSnHFcE3Fei/hRSTuY74q5hI1AHyeFFWfnJgWMV+NrOEt8dDEIF4XqBmuBV++gyja7Qz6PsZPPxsUs3Bq4s0wg+kboffUnijH6ULEjC5e0dCRCCwZj3iyjcLX2GobbQhOsQ91agmmTkqVE2rkk7Q6S2FpO6CrVY3RxWsKL0uyw7HhUUmdrih7lZfHnNsYASzhc7+hMsJ6sv8o+yYEhwClqH/ol1KSKxw4sgWpmQ2LMfcvdmyz/b7owvisfgDCBdVE+nz/765v9XrT/JsCUnmVkb5NiSCxAAcwDxWFJh1GiYqImh7tsi8Mm/QokcmUzSU9PYONGATxTZW60jTb+eCNV+NGJJi8Tuw2O6F1QvFacsKn6fN6fzhu0goua1fcHLLZwUFrykJJfukyKUeKXe+/KyrbRnr2hWFoU22bJPiOb5rb0jHrI7JBpNlL3fMgFy3f5SCl78hunOSAaao4ImyzBTDJNW1jL8TydIeWY7092RTHNHMbNUz/RdboaYo0kyhpEqoVNIe0Tp9EzI81DxyJeyj3UO9YMjyY08n4x89Rk1X5XfQp1vVKu2RdaooMnVHn8+qGe35bpuXSsKZmodga36tF0wY56uqwtorK6gmD3lQD8TBDOIXcvW3x4CHj64Mo9pnwTHHNBVBERu7Ns6s0Qw2Vo985Je9dm9XtIHxFGzo7DFcQEo/ukqosamEvODAdi9buwe+TxCZ9jCafjHXir2no89LX56VUuZ/iVg8ojETs+k/8xMNsba8crgwadXq0l/5QCFp2qCYpTTx5lJeHgC272tHY4eWR9DmGYcm77japGeblT0ESD02ZkeXJfMK1NuYlLcHvT4H4esEi2Eaf377jQqFaO0URgbvKgq7HiLLDhOVmM9U0fkL6YhiKCrc1GapOGotY4onR6tSk2lw9bcx8m6GixOJxFyVNu5WF7ZR7gcsX8imeI7R6S2ivby8IUaEfCJqpCJJmLmlSC5YocKvUpN3f72LQp13xYRtauI4c/NJDdBHuVnUGsoX7wJd13HlTXOVUyo3nCK6g2nmPbibE4mqSiQMC1YkjGM/hYNJaekLzPUHgl3jiQ32gbtfLyNHoEHN0xm6cwqVGIDAjY+LRgdNPk6otYpcP838SzofwUvjX4Pob03cRF1vpGsLfJvUjnQGEnNA22vwCsePX/c3C+WIY0OHBVW+lw9kCEz+iJU2sEn4p0eFxTFB4rZUx5U0nviGYRyBkRJ3m2HpJj8NSMKqAiht6egItHYNL5WpZIl+36oyfIY8Do6fmAJIhS/vonpe9wMNrfO+N9dBJryY2b8aleniq8tCp9fi+bBryU1zQ7c20jJ020XqZaD1IVXcmqoudN228tVDPzPDXxZNh+ArEjp0hxKwbLjGjZWX+93+0/P+B+zHLej2q921pPVOCusCED5V0cpem41ha9vpPCNTVx1LHjrMUX5yYGEJY3O4REjKA7SfRSp0zNeF8OAY/4lkZyV6iNcdUBmqTpAtFNs1RJrMDLRFcFwV+cPqd7GGa0Llabzh5DP4aH4vV0Yfr+H0cYYInSBzaI1KhUzqow4nliDu7b7tRQFEcLfPsDULfYu7QFdSD9zJKONMUz1RlH2r33dlqWrRAxJZEeUnTNWf+MrBZ7dHEmdbxTH6nrePvQTG+OwioP9antNWtsJ1i8m2RRG2b/3BiBVE4c3HQm4D39RlJ8cGK6RvEstJcxKlX911dcUwagSFdUPfYno5yx/U4mAg9A5vBQXjEd4ci+RP15H3ZvbyNPZOr+olzGwROxSAVbLwTLqH/yS0UUqVAEEDI/+dzGFi3LxWgPPpl27u5jSj/Yx9YyacXr/UoTp1GqSjGEMNcWgfmQt9Q9+SelPvurWCrQ7pP14IjVPjOjyPqrdK3D1F+N4Ly+JKL1BJum6R71L4KFNUxHp3ELytyOD/Oozu1jE0xD4b6TsdAU+X3CsSMqenxx4TwN6QMrAPEHWQPc+RjPRKh1RH5+gcPUxAAr/tpOJf5jHNk/H+yq3DPBg5JhssrXHjrrrWfRtH2L+VoKruhABiL1rFAdcl0a193OYtVpq3uteYuGixbnkJEei+sVk9tn83x+L0OkZhJnqLdu6NW5HFLy2EbVJy5Dfz+G4w9JmrKIAjPu4jNpdRfgcHkLxjYoCFI+O5Iy1vvPGbVBlF8jZX4cQoSc3Q1rZAmVzqYr/Ft/MzSn/areNkyz+eaTzUBaDWkPZ77dIIldyVh+EIGR3AUX5yYM6BQQtiNIpPwE3li4kuO0KWeZIwl7eRePxKi6MShJEqPnpGlJ+N51iW/s2V4tTQETj9yZ6oKwqv4X/nojk2woVfTWxFK/f3nwuP0MNl9jCz+p2E3NVNta/bO+88UWIHhFbQR2Rb+1l5o2D+TbeTaO74xl6simMgcfdFCyWL3zAa3OjeW4Dw9OjOHZ/f4b4zBhL7XhLGnDmV2M5XE51dei+yLAB8Zx8YjAN3VR85xAi9BT9cTPjxqXjvTeH3Y7gOeh0xs+2hOGeeCfXpOQRqdrb6vyq0rG4fZ0/c8a6wymVaCU+9TsTFOXXq7F/Kmk9v3PcM7iS1YXSJY8O1+kYKIRR+8CXtCet1+qi7+clFF/RvhNPpF4EmSu4Aywvvp3dlUb+d1KD7WzS36ht5Zzzq025dwwbOlDSvRWzVkvZvwIrxFq/t4T6vSWMfWo6J7LD2y3I2t8cSfRfD1OwvSCg8fzBWWXFWWVl1O5kqlfto7KwTvYx/cGYFkXED8fQ4KoLuC+x0YnoEanZeoawSisJTw+nwt4zZmdeUcUvtobxQfR4Ppt7skVlCYApCSeBjr1VtSoVzv8ckkymnav2cOU9MyTrryOUPT850AeeT68tTlmkc+RIN4UzpkxD7Y87n92XrzjENE9Uu+fvGXgGQabcfyIGVpTexquHvsfPtoTxn2PaZsWXZgqnbNn+s+2gemro46nkYLTDjNsiTVmrwlc3oP3h14w0tt5zSzWFIz6wipogKL4LKfrrTuw9RPEJGgH7ryawUVsnSX+2MedDcBqPVzFktzVoxVr95XCtwPunWnuAflWU3eF1aaZwplQZqNl6RjJZbJbgGbh71rdwqeCVtpjtOYbF1AbcRx+jmSHmaFI/KaTw19/4fV3xU1+RZW47i/2K02kBy3UxXiGBbypvZsnxW3lyYyR/PqjHK7b8uWYVefHamiqdx8/KIs8a+OfTExFV0pqBBIDffcuUdQ3EG0yEaXUMNcXQ/4C0sam9DXNmDKmPTeVEgKbOCzF80zKnZsFftjPo5TwmaqStCRgov9lpbFEH0CvE8cqujifbWac9XXqG+IMxLHjOQYrZUw40QwAdELj304WkGCuA9G5fn2WOJPy1PTQcqaC8i9cKQKxbTVuOz/cMyGvjaNfxCdEUO4ZT4Yhgb1UUL+1qP2+lQa2h7M2t5+W7eSB04pjTa5EhO0rDkQoajlQQviKPIT+aiH1fHoVbpJvB90aiHhpHrlY6xQfg87b0XBQAe3E9qle3kvyzMZT0EDO9V1Rx3RfD2H7jMVRiNSI6kswi+fVtT7zCtToqZaiXuOPLPdgb7RjDjJL3fTGK8pMB0f4RUis+gGj1Lp4ZncPi3V37YYRrdWSrw6h74EsCybFhKnVAG3Via1yRxAbwW/UKCdR5+vJtVQYPr/dvRjzWFda8yR6eHU+e0DMeInJgXbKn80bdRBCh6G3pPTp7E4JGIPWZmRwMd4E01uUL+m47J6/1dC1pS08wYHA8G4eqg1bAtSNqnQKv5F3PnJQqRkZtp7qDz2KE00xpqfR1JWfdNiUoig8Us6csCLrxsvWdV9O17CVppnDGVGqp+1HgnnsF/+8bRrWxVxSj695s2SvEYRWH8J8zVzJh+Wi/FR+ApvC8sou6cwQNnXgw9layzVE0nvIvc4ZC90i8bgi5CXaqHDLsN01KbvdU1aZTFC3ZwWRvlPTjdpMlB3XctiqJH2++iRpn2+pBLQjY3t0ry/ganZLhpXfjOS1b1+MT/btB+xjNDDZHk/Z5MYUvrJVkbAHgD7uJM5yfmQ2M9hGjPtilfkTCcZHObw/MZ8TH0/jVtq5nwfCmNF0jCnCgh5d7CoQip5Ww/nGhFuOSJe25mVRdK/2eNTTdL741pzttV/vKZmYcacpX2hMQEbAvqWk3veEoXTT1B6SuXNOE2+mWpd+2UMyecqCfSkD2xQ5ocnrp+CbpZ44k8vf7sBws6/LeXmdYDpYxNK8fuVlNr4/WCjSK/QkXOk9vJqLBJ0Rx7/rvsK1MoLuZsFWCQON7ewFIf2QyuXLM2HsIQ1XhVB8sC7UYlySacB3706HGWidL/waNBndV505EjlILRX/eRub1Q6i+MrpHFCjWT0ih72fFFF/Z2slNv0q+veHtX+5GFMWgxPopyk8WfCCEyRLrd7qx/RppYWf39uof+BLprfHnKfjDFib+sSnzyzUZvmbFJ6LBK8Rh8yZQ44qlwhFOodXM8XoDR2s0HKoVqLJDoOUfRuqjqTu4FZVGoHh4BMj08OoJlGncaOiJtTx6N31uyKH0ulRqJPTsvBi7x4M2xv9iw+UrDjFxylVs0Msnkz9E6PTUvL8P68lqps28ukXYR5Y5krIVXU+24C8zb52sBLn3akQfiBLvnJ9leuIxoPWeYpopnKwykcJfyV/UUwAaX9rMjIfGE729it+HPcDOKsirFmhwy//DNXx5GgFImDeYLZew4gNIcmupCrUQlxiiAI5r+nLSKr93sG5iCqz0P1lzxW83kLV4uqThFl1lqMdM+cmmfebip79iwp/mUanzctJaT/oRGwU++Zxz4lLb8KiTCWXPTwZEx2e0W/QrQHwXxbqdi9vru7KUwl+tkWXMtrAX11P086859W0jfzioYluZKiiKb6A5ivIVTRkl3NdkyD5eKFEJAiw7GmoxLikErZr6t2exK0hpxhzrTnepvbvWTuy7RzBpOs+pKRe+/57/zQlA1cNfEv9+PtF6AyXvyLfqA6gqDl64kqL8ZEAwXC1b3yrBC2ezqfQzR5L89mGcD6yk9JOuOZ1IhW5mRlDHS9rbNCOOGNqHg21U1L6UCNPq8IQwv6VUqPQajGlRiCG23RqSIkh9ejrVQdojjjMYsZ3peuKF6i1nGF8eOuWnu7Z/q2NV60+S8v92S1K5oSMObDwsa/8Xopg95UB0AGrkyHcZqTrK8NjJqG2RWB74ktDuDoB7ezF0kPdTShKNZso+bMoeH37XcOyeS1v5WVxONBE9qxxOV1GbdcS8PIdvfXVMsZhArcIZrkGzoZjqtflEDE5E9PmoljnAXgR0L0wl1x68lUW1w05WSiTWblSbFzopXCwXOrUa2wdt5+q0l8h/v6UNTFYcXno3OpAp12WVZziRZeGUPC3/3p4/uK/JAGdwHiiDatQU1dpRGTTsDndA8LyiQ0KOOZrKzdt6rbOLoFVT9/pUjp1VOOvDzjqAecA4PRzH5OGIQIY5guTv5mA9G0Gj++sBDP1iKfv0ID6HB5HuOfyIAuhjTEQMTsSQHcf6ICo+aHoCGIf3gU1dT3dY9Z/9mJ4Zhs0T3B95ji6S2p2FQR3zQvauO4jL4UJvDKwIsz8oyk8GRPt/AGkLMnqEJOy+ON49MhTbf0Jj4mwLc5kTouUfRyUI1Ly9A4CUx6eQ75ayVmLPJH5PHYWhT/zRLcz9Yom5dSi57VQwuLA47GmrhdNwvhTVHSkAjJ8+F4cG6nDT74gDT5EFTXoknoJ6Sj/ahyiALsqIxqQjdt5AhD5heKJ12E0qXDo3sWF20iNsbC3Tkl/XpD7Vgo9Ek8CAaB97K1TUu+SdWjTuLOq8URvYCuoYt8fB+mFtZ4iRixqVB41KABmdWjpi3v1zgqL4QFF+siAYrkW0L5OsPwf9+dHGWWwqUaFRqZiUFUvdt927qaTGkqQHh/wJkcdpoqkqqEOl13A6Sw/WS1/55Y80M+ChSVQsP4izXKbAUZkwPDWBXFdgK61d7ioyDALjErwMHOKib7idZFMFv9mZTcS8OcSY7QyP9JBtqcY8uJJE40lidTWEqctRi1UIZ1MMVueMw+XTEamtwiBUIIgNCHjYVnsDd30tXYmwi+lrjqAxf1+3ry/6207GvzWPHQF+jl0h1quhzieGzNpwOi94FUUU5ScHPml+rCImarw5TFh+vqaWx+fjxOx4jP+RZIiA0ITrKJQ6GWI7GI/UETUqBfN3c9hgvbT3+s5RbGukOBumPT2Fkqe+CrU4fqGNMtLn3tFs93X/O1p3wxES9IXoKEeg9cTqvVnfohLrEdqze1+0aIlV72zagr/o3ITo1dyYdQefnpDnMVjmsDJsQFxAZsTGX60nfuEEKh3BcXwyrjwTUj+CPhnBK0umeHvKgToxoMtFwrGTzfbaK1sovnMkiDoETeh3gjwNLlJUwXHIcA2IxHTXMDYYLg/FdyGbtfUk3zEq1GJ0SuzkviQ8O43cTG8Ls2ZXWNDfQ6p+A3pOtqn4gLOrusD3wgTsLBrzNX2M8pj4fKKIIbXtMmD+4qyyMvDrSrRBqAEYrtVhyw9tVOmGZdsQg5TkW1F+cqDpD0L3fvQeIYn/lc5j2Eez2jXJlOJEE8S6V+0RM6kveY66oIwlfH2GA6bgrDJ7Gj5R5NiMGNKen40xJbCHqZwId+YEPDn5v2FHZCuM3BY6Cvjr7NOy9O32+bAeqQy4n9LlB5jglf97b3C7MPYNwgZ+B9zw8NVBy/CiKD85cOeB2HXjgYMsfrz5Op7cGNVhO7vXQ9iA0Cc71t8yGLdPWseethhqjqb080PkuIJT6qQnUm63khttJeKnU0ItSiu0EQaSX7uagwEmtFULPuJ0wa8pOND0DQ8MlT5+bYg5mob8ioD7SXt+NoVm+fN9ZpujKF/VflIFQSNgSJI3rKmmrE7W/i9E2fOTA3dXqxwYqfUOYfzysX61VwENR6ROWd11xHUFMFv+GL/YzZXYRah8bg3pb8ykwNa7nD+kZIuvhpnPzsB9uIqyz6QpIhwI8bMH4Lt5ABtdNQGHnnhFFTXuNBI1wXXmEvDwdM4KVhfczBmLdKuOU84GBg1IwHK4+/dq9Nvz2CU20mCT36ks+VAjhT6R8EEJRM0dgJhgxB2hw2qAOjxUuuxEavUMzHdR+cFeHDLU84uMaz93sdQoKz850AzqtImICp8QjZ0B7Ky70m/FB1DncpJ447BAJJQE71UZso+Ragqn8B/fNo1nc5P63+B5g/VUcvs4OT4vEXNGaE1UAHxngGTeiJOTfCRo5Cvc2xEqsZr3Z0s7mdCq1Giiurc9ETE4EWHJVXzrrKHBJW9WFQGR9DCRpJkw+Ps5GB8eS24/L+vDGtniq2GfrYYzNgs2j5tSeyO5qS6in5HHArHuP5tl6bctlJWfHGgyafpoW276r6lcwLE6EwerdOyvUlFub6qd1R28PcDV36NGrhSmzfQ/4eJCXzlNYs+oeRZqyu020p6dhPH9w1RtOR38uCyVgOnP17DHKZ1v4P5KgQYxmwhhv2R9doUk7WaeG9OPl3b5X4mhI2weN86yrlsp0h6exNHhZkplTG5t0oisnn+QSG0VeqH8bPiHj6M5V3Lt552vvjYLtYyd1JeardKaqWOTY/B6vajV8sc3KspPDrxluIU+2Lyx1LhiKbFFcrLBzMIdRryiNIttdXhwAkE7Qiuz4ovWG6hcurvFsaL3dpEz9RryrF3PmXip8a2jBhYkMnNkHwr/uDVo44pA2oMTWG+TNv6s0SPwwPoJ/GtWISoxNN/vfVkf8dmpezhUE/h9OkIXRV0XU5sl/GEee9VW6uzSlUMbGedjSKyXgVFO+kVYSTLWE6OrJkJ1dpJxwbwp27SO/bcW8mbeJP6ap2u3z0idHk+d9A5o1cU1iD7xfGiKjCjKTwbsqjFc+cV3KW2Uvp7fObyjEuA92brvFGNKJHsFecMOcuwGSi/aVxAA05KDRHyvHxZX6Fe/PYF9OXoyBidiL7PgrpU3abMoND2gd9IAMqTeOlwj4BHD0REa5Sfg5J9X7GT88nEBT1SPeBvISIrwa29MFMDzlyvZbq/F5w18FX8uk83QOB+/n7gSrVjs13UCHkwc4amhdQyOnsmb+yPa3AetczkRHh8DD0qbZvG6B69Eow2OWlKUnwysOXlcVsUHoKsMrdu/vbiewdoIdnvkyz5h/0fb2TFqdhQyem5/ckPv8NojqHU6sD7Un8H6SDwPr0J0y+QZqBKwvz2b7fYa2YIRnF6RGncqfbSh29uNVO3llakDO/W67owIjQ61sePqDCLQ55pBHJqfSF2Ajly/Gt/INanHCdPUoBeqEMR6BHzdSjOsEcu4IfnfDIq6kms/79dmG50MaUc3fbKd+T++SvqO20BxeJGBBLP8+1K7U3xEj0uTfZz2CBsQz16HfLPzEaYYaveXtnveHRf6OMeehMvrZZ+thtT/k8cRQZ8YTtrjUym1N8oahecVVTyQO1TGEfxjftIy5qQFNolo9LgRPe33IQqQtuhK9l4bT12AVozl1xRye2Yu8ZrtGMlHJdY2Kb4AGWj6mpcmN3Bz/9Z7HDu8tcRMSA94jAtJ6idfurmLUZSfDNTY5a8X1uB24bXK6wXWEboYI2atfDXHDjvrCevXflVny+tb6WM0yzZ+b2VHhkjcjH6EDYiXtN+w56eRmx6cCgPjEt2IhK6eHYCAl9cnbSRa331VP8wbhq2gru3+tWp071zN5jBrwJUbJvbxMTx8jd+mza5yS+qH/GbMlwyIavlZ+ESRsFltrwq7y4l9pyXtryMU5ScDM/pmEK6T1yHFJ4pEXdd5SIVceG1uPDIGuI9UR2I9Wd3ueWthHVpB+flejM3jZvt3EnA/MxaVPvBdDW2EgZR7x7DHUxe4cH6y9IiOQ9bZQRvvHF4hDqs4mFL3FPY1XMua0pF4AviJH9RYEbQtPTdEAcIHJVD0+iSOWOskuYeeHV3UnMRbLjRiKX+deZjRCT7UwnmZvdHSPueqS2poqJV3y+gcyp6fDKw4doSGIDhjuE+GzuMx/Lah2D3yBZtrvmi/BlrMpL54v5fDbom9DS8lDllrmfn0NMre3Irb0r394aixqYRfP4jciEYIQiafC/nemnS23ZiIWpQ/mYOICTdxPL19Ll+cls7NMFFrRG3W4a5rsgRpwnUkfX8cG7J8+LzSuErHGUVywnIl6aszUnQb+WjmRpaevpNf72ja2tlibGD05L6SFSNOSI8jLCo4Fh1F+cmAJghJaAHUIxJAuspJXaLxk8NwV6osfWeboyhfub3Nc2mPT6UkJ4xjVkXxdcbGeAfa34xl2IeF3YrHUt83POCyRN2lf5SISpRnBVDlHc+npwdyrE7H4Ro1ZxrA5oHulcxtnxqvkz5GLe46O/o4M2G/ntH0eUq4afr/JtQgIP82y4XcmbmK/564idP1oFHrsBdJF4847upRSm7P3owvSLNkV1T7cThyY7xTvgwzyXmN7QZtnxhi5Ji1TraxLyW8oojD66HuvkFdMoFqo42k/r85HPSFLo3cznIVq8rnBdyPiAmLbzgFzpl8Wz+fT0tu4xc7hvPbXSb+e0LD4VoBm0dAasUHMKRRj73UQtiAeMoXjpe8Ll+YRmRGwg5J+/QHtVjFJ7P/xdob9mB32zA+O1myvvflBi9ln7Lyk4E5/frz83Vr8MlcmkPXKHOUeQeoXD6QYVszRm+k6E8bWh0XtGqSbxvJhss4r2d3ybfW0/cPV5BRDUXPre6wbfwV/VHdkM16sUb27D2d8czmaCbfOPx8MLafiKgQhXDcYhy7a4dx99eBlRjrLofDnAy+fSQbJxjAIf3q7L6hTnSEJiREoIFY9U7+36Qslu2ULubIUt2A1+NFrVEyvPRKTtTWoFGpcHnlzcR+IlaUQ//4hU8rj9FgeKWawjbi1BJfmcsWVX3Q954uFc7YLJwxwqSrBlLRQeZ+8eZstrh7hknZ7oEwdaHfZkK3kMpT26/mQJWKUhu4faGteTk1pYEvJumRIOKgTe7O6loCfTn4TsoXXJccw9N/S+HIrsCTb0fGhaNSB8cgqZg9ZSAzKhqTjGEA54gQNKgMwZ+/CFo1+w3SV5Y2a7XUvLu71XF9Yjjfqi2yepdeLhTfnE7kX+aBqqViUJu0xLw9j/1izykWLCLw52Pz2zmn5oxzJtvqbuDfhXfy6/3f56511/DFaTUFjULIFR/A9Rm1hMn0GJib5iVK3fpeCTYCDeiEM9zxxz6SPIuuuGNa0Pb8lJWfDKw4doQ6h/wZWKyiF70q+De56PYyxGdmG9J6tA73hVPRRi7EhB+N55BX/pIulwMFtgYKgCmvX03Z4yubjyffO4ZcZ89Y8V3I0VodIipABCEMp5hIvSeeElsst3yVEmrxOuS+tdLGWl7I06NOBrXob2e4wiDs93OJzG+k8NXW2xb+snXFTq763iwJJWsfRfnJwLT0vkEZp9xpY9iwJGq2B9fuH9Y/jgOC9Htv4vLW5rj0Z2ZwLEUA6RealzWbqWXKTUMp++9BEn8/j706GzhDmzKvLfZVqXhsx30cqlFRYOl+FZRgo1f5WHPDIaZ9In22mgGRPjIMGyXvNxCmxq7k/dgY1jk0TL13DLZDFdTuLOz8wovwBbE6iaL8ZOB4TXBm0EaNBuuJ9gPB5cLn8hCl1WN1S5fxY4g5mor121o82mKnZ7I+xYXPpiSwloPjVyVgumYu2221eJ09ZxVxIUWNAkWNQUjx7yfjdTGYKp2IZVYEIG+EiRi1juO2erwXOLi9MNGKzSNPCr5fjKtCCLRysMSIgp4D1Sp8osiGUVoGTc2Bbii/YJk8QVF+sjCqTxJGjQa7R153uVqng0HPz6DiUWkzq/tDjUvaVUK+vZ6hgxJbVL3W3DwYn9jzTHG9BQEI1+mJ0eqJEjWYHCK6OjeUNuI6VUvjkaPEzBvEqeyeqfh6GlE6PfbfbqH6gpRl4QJ4RJj20xk0ZpjZ7aolWqfl+tTtqAQHAlmSrlYjdSITYrdJGisoBSqxjn9eeYKrVvQH4Ii1jpm/noPtq/wuBcDvWXsAa70Vc6T8ge6K8pMBk+dTBse42FMhyG6mcYcgBWLsrcM54JE2nVKOPqqF4jMkRbBHbQm5u31PRqNSEabREq3VE+XTYLKLaGtdiKWNOI9X03i0EkdFI6LbSwPQlqFas+UM2kEZuBVnog4ZaoohZlM5xRfl6hTOKqHCl9ejizUxSKMmMdpD6ewMciuTJL//HxpmRyO2n/A9lERoWiYlyI1oRHd7KqP0GqrWnfCrj8k3jguK4oMuenu+8MILCILQ4q9Pn/azcOfm5rZqLwgCR44caW6zZMkSpk2bRnR0NNHR0cyZM4cdO1oGbvozbmNjI4888gipqakYjUYGDx7M22+/3ZW3JxkRpr78a9YKtt28h/tzXC1y4UnNCdGG2hRcDVj3+RHUEpsnwte3vKHjfzxe9pVzb2acPoZBvz1I3GMbUD2wGsuDX1L2+EoKX1xL0TvbqfzmOPbi+k7LG3nqHcTolQoZnRGzuZzipbs6bOOqtuEsb+DMUTs/WD+ahTulf4h/t1/oPTzbI1abT6Su5ZLU5fVSeUd/tBGGVnlO2yIipvMq8lLR5ZVfTk4Oa9asaX7tT7n5o0ePEhER0fw6Pv68F1Rubi633347kydPxmAwsHjxYubOnUteXh4pKee9uTob94knnmDdunW8//77ZGRksHr1ah566CGSk5O54YYbuvo2A8NzGrVYTqy6nGdzdnJb/1msKUrlT/uNNLilVRqJGiPeIG4SA0R8byReCV3i+5oiKFv+TYtjdYk6gpy1qUdh1GiI0uqJUekIdwro69wI5VbchfXYjlfTULSvOWdkIAgaNTU90NGlp2DSaBmxopySb/L9vib5lhFsskofMnLrAA9hQvAyoHQVmy8JVxtzrZPWegy/GctYTziaCjveOAM2s4rwYxZqPj1M2KAEdJlReKtsWKqDl8Siy8pPo9F0uNpri4SEBKKioto898EHH7R4vWTJEpYtW8batWu55557/B5369at3HvvvcycOROAH/7wh/zlL3/h22+/Db7yM8yGhoXNLzP06/hBloH7+hv5+/Hr+Py0kYPV0ijBIpeV/unRNByrlKQ/f1CV2SBDuv4yT7spdLZc5UVWu8Ek3Rg9DaNGQ4zOQCw6whygq3NB2XnlZiuqxd3gwgHIqZoa8isZZBjHASVJeCuGmWLQvrWXir0lXbrOOjsF7NJ/no8OPdJ5oxASJuTxytQhPLI+ptU5h9fDJqEWEgEc4ABVXwHfYwPOthCJHxaLxRiG1+dDHYT8yF1Wfvn5+SQnJ6PX65kwYQKLFi2iX7+OazqNGjUKh8PBkCFD+MUvfsGsWe3HcdhsNtxuNzExLT/AzsadOnUqK1as4L777iM5OZnc3FyOHTvG73//+3bHcjqdOJ3nPQktFolmaz4bCGFwQWJeAQeC6OD+rH/y3X7DKLYl8/DGvgG7b4uI6FMigqr8fMlmQJpktmatlvK/tE5iLRQ3woA2LuiljDTFELmnBvfpOqzHq7GX1OOxurAC7UUwBsXvTQCHjGb53opGpSIqt5SSLio+U3oUR9zSr/pGJ/joo2072XtPIkrnvy/AxekfKx02ZmdlBUXxQReV34QJE1i6dCnZ2dmUl5ezcOFCJk+eTF5eHrGxrQuPJiUl8c477zBmzBicTif//Oc/mT17Nrm5uUyfPr3NMZ599llSUlKYM2dOl8Z98803eeCBB0hNTUWj0aBSqfjrX//K1KlT230/L730Ei+++GJXPgL/ELQgtv8jCBcOMMh8gDVXa9hYfT3byyP4y8HuJSpzeDxYjwc33OF0lE+yuLvRdjMl1a07E1PDgZ6TbcRf1IJAjN5InFpPuFvA0OBFVePAtvUwRRtalmnqCRFrgghGUUn0dCHhOh0pP99Jca29y9+RtaCOfvpw8jzSlht7dnQZQi/w/hoRfQi9KgGnr3u/qf4x7RewlhpBFLuffdlqtZKVlcUzzzzDk08+6dc18+fPRxAEVqxY0erc4sWL+e1vf0tubi7Dhw/v0rivvvoqS5Ys4dVXX6Vv375s2LCB5557jk8++aSFIr2QtlZ+aWlp1NfXt9ij7Cq+ht+B1X9nG68Qj8MXx/fXT6XMKlBi7dotN6tET8Hi9V0Vs9sk/e6aJhNGgOjUaoa+c5L6A62918QlV3HSGvjqUqdWE6nVE63WEeFRofbB9rO5K40aDdE6A2ZBQ/4FlSIEwKzV0ehuPYHRqlTE6Y3EqHSEuQT0DR5UVQ68JQ3Y86toOFKOu8HVIxSbv6S9MJvcKCWDDjSt0BsfWono6d5jMXZ6JjtvSpQ0EiFaL7L9xuWoxODH9HaHN4/dy5t7u+dEdcewESyc1fbz2l8sFguRkZGdPscDCnUwm80MGzaM/Hz/N4MnTpzI+++/3+r4q6++yqJFi1izZk2Hiq+tce12Oz/72c/45JNPuPbaawEYPnw4e/fu5dVXX21X+en1evR66VNDC4ZrEbug/NRiJWahko9mHqbUPYWDtUn8ODcGf9cGvorgPbiMaVHkGexIkdlsuCaS2jYUH0BKvcDJAANxjBoNYzc3UvKfLXh9IrU0VSafPDUT2/EqbMVNpkdDehQz7xzZFCJwupaGo5Xo48zoHhyFvsGDUGnHW1iP9UglDfmVeB2eDvfiepPiA3DG6kFJH0eaKZywNcU0dFPxAWgWDEb0Sbvf98yYxl6j+ACs7u5bEg5WyF+8+BwBPV6cTieHDx9m2rRpfl+zZ88ekpKSWhx75ZVXWLhwIatWrWLs2LFdHtftduN2u1FdZCtWq9VBq63XAl8NoAa6XtUhSbuZPglqDt2awj9PzGBPlZ6vznTsUSskh3VPzm7gqrGRqDFSK4GHoH5V+8GvapsHur/4xqjRkP7cDkoaWq7e3BYH5V8ebn4tALaCOmwv5bZo5yi1wCNtK+beptw6Q3+4FrJDLUXoUAsCA0xRCM+up7QNE7y/GJIi2KtqkLSKg4DI9albpeswCDw9dCXL8q+n3tX1O6VvZBSiKAYl00uXlN9TTz3F/PnzSU9Pp6KigoULF2KxWLj33nsBeO655yguLmbp0qUAvPHGG2RkZJCTk4PL5eL9999n+fLlLF++vLnPxYsX88tf/pIPP/yQjIwMysrKAAgLCyMsLMyvcSMiIpgxYwZPP/00RqORvn37sn79epYuXcrrr78e+KfUVVSRBPKIFPCio4AfZP0Td/9UHhk2hPvWZlDtEPG2sT9jTQueW6RKreKkQ5q9OO2g9uuA+YzdT2mVZAwju1qgqEHaQPxLFW+NHbh8Y/2GG6KxPBB4liRHqYXBmnB2eaRb+Y1OENEJVT0uo0tHOHyxWLp5620uOIPN7cask79Qd5fWp0VFRdx+++0MHDiQm266CZ1Ox7Zt2+jbtymRc2lpKQUF55Msu1wunnrqKYYPH860adPYtGkTX3zxBTfddFNzm7feeguXy8WCBQtISkpq/nv11Vf9Hhfg3//+N+PGjePOO+9kyJAh/Pa3v+U3v/kNP/rRj7r94XQX0f4fpEpNohWLGGRazabrl7Ny/kkeGd56xWWqC95GeMINOZKVFnIdqGj/3Er/MkJczABzFFlrKyn62dfdFeuyQxV1+Sq+0cYYzF8XSdKXuV8s+1x1kvR1jl0VKv5XEnhF+2ASJuTx2yndi9dbMCQnKIoPAnR4udTwd6O0M0TXTsSaOyWU7IK+0VDknMKSI1msLtBQZRfQq9WM+FdRl3LodZew/nGceGJIm84gXWGQOQr3A1+1ez71vnGsH+7/6k+rUjHEEIX7lxtxlivV3ruCNtpI4cKxkiYq7w1E6w2kLNyDvViasJ3Ul+ay3ii9h7JRLZJ74x5i1Tsl71su9jZcy4KVqV2+bl7/bP44r+0ajv7i73Nc8XGWA2+xbF0LeEjTr+fFER+yYf5XLJzUSIzBgyYuOPnwou8eGbDiA0jcVdfheceJar9TqCUazUzwRtL4wy8VxdcNIgYnoApiNv2ewFBzNCMOOCVTfNooI4dj5LHA2L0Cd3w9Cp8QvDCAQOkXVkh3bLUJYcHzX1CUnxyoM2UfQsCBjgJuS/uAz6/ZwaPfryI8W77imQCiAHmJ0pg8hXMGh3aK8apvHtiiRExHDD7tpfSJlZ03VGiT6i1nGFN6+eS4nypGE77sJIVvSedI4rY4SJVx3/SEReCvx3uP+TNC2M9To7vuFPfFsaMEyxh5+fzig4k6GYRoEKUNdG2PSNVe9sfdzb6HTMyoHoZzexEVq49JPo4gQrZDT6UEEe77RxqJWnIVNp+HAWsqUadGULXsIPbCOkRAs70MxnUchqJVqZjkDKfq/Z6f+aKn4wsLQXmQEBCh01O3cDP2EonNkz6RSkFes3HPqWroH6ctXf9N3TV8RNBq+ikrPznwngma4jvHrJQSRCA31kbBgnQMS64hZlJftBGBz0ZFAfRxZuKmZnLEIE2myRqng5PWesrsVjZOMZHb14P3V5MQl1xF8u+uwT2lD0+MbH+sNFM4Uyr0FD+9CnthnSQyXc44vpB+stTTGG6KIeeTEukV31n6Nsirnv6434hb6Po+Wqj49eg1rao8dEYw952VlZ8cuHZ03kZijOrziqLY1kgxwHeTmKjJwXimkTOvbOhW8IXKoMH65kxO2RqbLPgO+UotHLY2TRhOCrAg3cND2f/h5szRRGir+fXuqejVIqctapy1cZg2l1P4XsclZhT8x5AWhUoQWuVbvFSY0RhOw9/2UNFOUgUpUHlEWZdnDW6BX++Zy69Hvo9Azw/jcYumLoc8GDXBU0nKyk8OtCODPqTD27aJcJunhi3pXhr+fAXJd4wiZnyaX/0ZkyOw/WUOpxePp+Sc4gsihQ0qQE0f7RZMHOW3o//GiyP+zphEF/VqL44JiYT1j0PQXF6OGnLhKKy7ZJ1ejBoN5Ys3tplGT0oqfrOeAeZIWcf41zEt+xuulHUMqTALh3lubNcmy1/mB88Coaz85ECdAGiB4CzhRVSkGQ9jVPfH7m39AHN6vVR6bVSO1zNg1hAS7xpK5dNf43V5mitRnyN2eiZVd2dT6HZQb29s1Vew6BfpRbio3oGLvvz9oJ5GT5OHnvBIf6a5xjT9/3gdRX/dgT4+TPH47AaNhyqI12dRGsLvXA5GGmNwPLsOt0X+moVui4PUHbXk58i7prhrdQrbbx6EiZ5d4ghgcmIFXal/lhYR2XNLGin4gbecYCg+EQ33b/ke+ytV1DrBn6wy+dZ68oHw301mVIMRDlRS/P5uUu4aTdWMRAp8LsokSCgdKAuyWub4O+W4gt/vz6TRc/49isAGXZOs5hE61H+cjkZQMTzfgxhvpPoP23BUNICg6rSi+eWOLs5MrevSKmobrTcQ9nURDbXBq4qsitAj971v9wpsrhjKlQk9X/nFGWrpivI7XV+LR1F+vRfRsUq2vjdU38QJi4nDNXoOVKnIr+/ej6TB5WKD3sXLj7iZ+VMNv80P43h1DWX2nmH6WlmQwIicpv9/VnIbf82L4HBt+7JduFGemw7gJu2FCdR5nAz1hqE/VgdaFYWvb2oKr/Bdmntb3aXhWCV9DaM5ekF1i96KAKSawjE+nkupLbiB+4VvbmHEn65in8zFge3e3uH7GafewTOjB7F4t9Gv9rflDEcfpH0/RfnJgKCfjmj/d8D9eIUEHL5Y6txxlNmjON0YxrObwwMqfnsx1/fNRSsWsTh2H43iYKZ/OokGd+gVYK1ThUgYv9h7K58eV+P0dV2mQluT+XMrNZAFevX/b++846sq78f/Pnfvm71DwgwQ9gaZgiiouLBKFUfV2lq1X/vVfvXXWm217lXbWiu1WutWEAeIiBL23oQVdiYhe9ybO5/fH4FAyLpJ7go579frvgjnPOd5Pue5557PMz5DInXB5ZS5HWQsyqf4e9+zkVzsCCFQXyR5/Ybro6j8+dIOhJXvPMLlQfnXHXBXWtuFO8Hv1lu55NrRXSLqy85S38OVZZ04xt0j2k5u4A9k5RcIpM4FmhYYcBLPTSsuY29pYBXRvqqhDDXnIeHGoChGr4LqMIhyFaH1ctA2gU8O+c//zOHxcPjMkq52UhrIyq8BSYDrIsjonmmMxPBdHqFcuDcOTkApST4HaegIZ6O+LJ11DKUoCVg7/uDlsasZnz+10ZZFS6RHRAZBonpk5RcIRPv3TjaUX8vCo9HsL1NwtFLC1YGZTkeI19W7F5x2j+X9wwMoDpNlz3f3qfj0UHrA6nebu4dTd3tIPOnkYNeJoNWEKK0O8fu1FIbY4Kngwx2MGT+bDZ7ALn0eqZJ4+/Asft77vwFtp7MIJBw+jqu2FxYELaXRxbHOEW5ox4MU0eJpBz054ZjKloo5LMq/mRf33cFDa+NYfETFwXJF0BQfwP1rMvFIMby+N5O/7/Z/Yt+O4hGKgC6/KmuDlwmjq+COCp/vvyNk2nRhY+lb+dQqorS+7XN1hpe36yhyTQh4O51BzyF+NtA3h7/bhgwLWoQXeeYXCOqWg6ho+K9AhZDMOEUUNZ5o3j04kDf3hseLxuGRqPGk8vGh7vUoHI+X5If/AtQFtZAcaik6Tu27u0ItQgPG9Chs7kDvHwi2zt2ASToY4HY6h4Tghl65/HNvnzbLbi3I5+ZBQ4Iglaz8AoNwNPx5wjGVO3/oR6GNoM7ofCWuwMIfvpyGuMCn7mLHKlTYVBLCLVt9nsWx5xQkB2/PxZ+M0EdRsWejH03BOkfZ5lzGXZVBliUwfpMRWsHNfZ2Ypb0Bqd/f6JSOtgsBPSPlPb8uzbd5mWw49jP2lys5XglljnD5STZGo1RS9o8tFNc66fHsJZy0hceSUTColNxoVEqEW17+PIumdxRdKmX4eei/PRFSI5fmKHppLRkvTw2I+8i/Ls1nmHmJ3+v1Ny4pma/zJnGwwrcl4Kzjx/jV6HEBlqoeWfkFgDEpPXlouRaXnzKeB4J4vZF+NWoKTlYA+Jw772KhxGlnwMB4yrcHLvdi0FFIaKMM1JXUdmgGVLJ4H/rHBmPvYgOCvsYIihZvDLUYTfA43Jxy+dfBXq0Q3NrfxRDL+rAdp3ikOHJqh1FQa2ZHiZl/7PF9i8fp8eDyeFArA+/HKCu/APD90cNhrfgABmyvIfef51IB9SiBY4Hfnw8bYjV6yrbnh80ymS9IKglNtAlDjwj0vaNRpVrwxOqxm5VUqryUeRxUuZxMOCbVO/O3E220Ab1S1eWUX/LuKnJDLUQzeB1uRmQ7yerrvzpv7e/id4PeCTvFJzBRI9KpdEWxoTiZx9a3nEG9NTRKZdBizMrKLwAUVAcmZYq/SNQbKfxwTaNjJa9vRP+7IV3uxddREhwqTodaiHaQ+sfp7I7zUu1ynDewctZ/HNR/zrC7r470YUlU7ixoVxsVuwsZergPK32LfR4WxOsNnPow/GZ9Z8n9+wZG/HM22+2dd3v47Qg7c3tm+0Eq/yBQgGTEJaJZe3okP18Z0+k6bxw4KCihzUBWfgEh0WQOtQgtMlwfhWVrCXnVjU2PdckWtF1w1N9RTms9CHyJhhoenEhSUlbrm1FSmaOOlPuGYX0DqvcX43X4/p164g0QdrtnLdO/TEVeaeeTKwcS15/Xo354YKdWg5KMgrv6Lg0Lh3YHvXhsy6VklyooqKXZYPodZWnOIX6SOdhv9bWGrPwCwMik8LUX136aQ94PTSObVGzNY9j03mQlhkCoEFDlcRGfGtFlEuGmlkkca4d3zG5bGdzRg6nF/ch9Jsvn69xZJ2FmYNPy+JOaj/ag0Knw1oXvoM08LKlT18/r5+K+zIMhUXwCNXmOCRTarZyoMZFToWVPqYotpwIzO9Opg6eSZOUXAI6WBzayQ0cZYIzk9MpNLZ73xOpptH52EWNQqLDnd40ZjjbOyH6rCzqQdGFbipf+Y1Ip2+zbrpiklLpUUts99/RiJMPRHiwn928bQi1Os5z6ah9jp81irVTeoevvyzxIonqdn6XyjTLPMKZ92T9o7dmDmMldjvASAEYnpWDS+B7MNRhMsVvQPL+l1WwGpS+sIUFvDKJUoSPPXk3s1N6hFsMn3LUuYpQdC4pQ7XRSeU8mCddmojK3/UwKr0B0EcUH9daBGzxlrM9QoF8wi4SrB2LuHxdqsZqgcLV/ydOqEWybu4kE9eYASOQb/80ZGNT2dhQVUlEXnBRUsvILAF8cyKbG6Vs4n2CgV6k4/fI6ag63vmziKK6lb1ZpkKQKLRmGCE7/eDjUYviE1+HipLPjztKHaitYN9VM4sOT2yx7+occ4rvgAMjp8bC3tpx10y1U/u8wtHHhcw8KrYodpva+0AWPja7GqtiJFKSk2BfiJJ1/7wtuJKoJqT2I0AXH7FxWfgGgT1T4RAfONEaS/NAGHCW+GUsoI3QBlig8iNsankvTzZE4d6hfDJE2RdcRM6312a6pZzR1nvDdP/OFY7VVKJ+eQsqfLwu1KACk/PoSqts5GF593T5uSF4cGIF8RMNxZqQG12UrxRy8/WZZ+QWAvKrwcXWIXlvcrizmeQs20cvYdQweOkKSwUTRR+ETB7Itytcdx6Lu/DK63e2m6Ke9Sb1vPOrI5kfX1TnFRKjDI+5sZ9hjK2OVsZrUX19C3Mx+IZNDoVVxtI/v/alVeJmZ5iFJvRaJ4GWgbw47ffnqWHCT5uYH0U1MVn4B4IrefYPmqNkSJrWGUdoo8t7b1q7rhFuQXNVVHAA6Rr8icFV1wHokVHgFZpV/9pBP1FaR1U8Q93+Tmm/KI4j2XDx2cFk9PeTNTUNt0YEi+M914o1DOGHz/YX+3MQq3hj7rwBK1DL/PjofIZkR1Kf7+iZvdNBl2H2qEJcnOGmIZeUXAErtNgzq0OaLG+I2Uf7LpUgdsF1QVobPfqW/USsUFL0SGsu5juKpc1Hu8q8V7jp9NQnXD2pyXBJQ++hKorQXz/J3rq2a4ufHE//qrKC3XTk5wceSgp9muJiZEJrM7GWekTyzVc+gz27mx9PX4JFieX2XKehyjE5OCUpoM5CVX0Awa7QhNxV3f7qvw9fmPbuKIYYoP0oTHsTo9IxzWHBVhHY5qb3E3zKcGpd/ByQeITh+RQKpT0xvYhziqrCTmVUe8tULf1LmsLNelJH8wuUkXtdU6QeC6Alp7Kur8Kns2HjBn4b+Gy1HAytUC7x9cDAg4fAqeHhtFFXuHoyJD84M7HyGxQfP0VhWfgFg0YFsbEH0V2kOw9iOx6gSLg+Rm7tS8C/fGLi2koJHloVajHZT9tU+dEr/L0Xm22rIiqwl6tGmVqAFn+xinNQ10xu1xmpNJTlXxBExPDngFqGaG32L6nLvIAfPjw9dTj6Bnq3F51aqql0St68cw6q84O/9Zp8uDlpbsvILAFf1C55TaEts76NAoe3ECzMEo36DSqBXBmbGnGwwkffB9oDUHWiifjYyoBaYaxXlJN85qsnx2iCvfMboBXf6mPG7M5yy29h2eyrWx6cErA11hJ5NyraDKCglLw8OXEWKZnXAZGkLCTtDYho/X7EnDQwLgT4+XBY8VytZ+QWA/KoqVEEKztoScWodSkPH9x1z/7WZUdrgLX0mGgVvTivmyyuP8LvR/k+s2/ekp0P7n2HBqcAnGj40zkriK7PQJZ6Lxq//8CAGVXD2rmemenhjSiGPDf6KtdftYUCkIEobWDP7LaIC3YJZJN08zO91Jzx0SZuzvolJXr67+jBajvm9/fZQRx8+Pnjuezaq1VT8ZxcnX1/H1ENSULdAUiwdywbREWTlFwASTaaQ75eUe5wodR1/cUkCylSBX/PXKwV9rV6+nb2OCZGL6aX7gTt6fsLtA/y3bGzWaCh5p2vO+gCID7zDdpG9lrWKcky/n4g48+hW7S3CGCTl9/9GHmKE5WsUopQE9Xq+uuxdll61i1cmVfLWtBICkcPH7fWSXVvOgalRpD54CYYeEX6pV23RcciHravfDM0nXbfSL212hmUFoxoFp1ZJCjSROiQBuW9sgGc2EBEkA6jVJ05Q5QhOiEVZ+QWAT7L34AySuW5LVDkdRE5I61QdiV/log2g5VWU1suOud/w7eULMEnnUrVIuPh/Q5aQaPTPC2+I09hl4ng2R25i8HytNrnK6HH/BABcNQ7iOhhWzVdi9ILFs0+SrNna6LiEkxjlZuYkfsy02MXsv+lb/jCmllnp/v9dna6zkdXLg/nhCSiNnXcpib95KAW21iPyrL9+F4PNqzrdVmfxShYe39B4tmX3uKkrqm74f+3xcobuCI6R2HX9B2DRBmevUVZ+AWBmbz9mr+wgLq+X0itSOlWH/XhgLP4MKsH1vd1suuZtVKL5nHNKcYpPZ2bz1ykdCwZ8Fgmwv9t1HNqbIznIwWj2ZGqJfHM22lgTRZ7A+kP+aoiNQaZvkWg5LZGEB7XI5bb093l17BKWXHWMRKNArfDvbHC9t4yCF8aRcnfn/NtOj25tmVBwd6aTONVGJAK/nN0Wm8unNUlJNBIrtpMVjY7lLtjEaG0UJj8EW2iNMnvwLLEvHm/WMOJEZUWoRQDA4JHojPlA7fFSemiHctBd4Rd5lJKXB4c6ua/fpz5Fr0hUryMhXsUDQ2/lr7s6NhocoYuiYmf4JjttC21MxzM6dJTSOjul2Bn2+wlkOATrAxRpZNGsXAZadrZrRVMlCskwFLLqqihy64bw5Yk0Xt/pvyW5GpeTA6PNJKyKpSan/RbPsdP7ssVW0eL5RbPywmLGd5YiW9NIP5odTe9bElDx0HLSowycfmIUp+yByaFo0QXPykqe+QWAgbHhEVW+Vunt1E6Ju9qJ+R97MPshQ4VeKfjmqmPc1+/DdoVtknDzQP/FDIjs2J3ovzvZoevCBbfNGfClx5bYaS9jvbdzM+/m0Cq8bJ27mcHmFS3O/NtCIcpI02bxQL//knXtfm4f4CJO75+Z4Cm7jYrfDif6jdntjwpzXV88zfr4CoZEexls/jEsZnxnSdA3/S26hzSfkd1b58ZeUEXGmoqAyfNtzkE8nUj62x5k5RcAIrQ6ovWGUItBvsuGdZCvESaapzL7FFHqzozGBOuu383uGz6kr35FhyLUK0QFb0/b3e7rehmtFCze2+7rwgmvy8sxR8czOnSW/152il03rmFItL9eSIJHR9uJUOygc+sS9Uh4SdGs5veDP2DVnCX8eXw1SX7YKz5RW8VmZxkpT88gef5In67Rp0aw0d38GvX9Qxwsmr4AKZhT+DYQqPjjlvhGxxSShPOT1gNk5L2/nQmKwPiA3jxoCMogWcrLyi8AlNltlAVoWaA9VDudGO8a3qk6vC4PUd6OrY5Hab0cvGkR8aoNSHTuBR6n2sjen/zAn8b5Xk+Pg/au695whoTrBuEIYZaFtYURGKV9vHvpJkyqznfmj9cc4ta0hX6QrDESDtQin5tSP+SbWRv5+PKCMwq7czKvMlSzd4IZc79YNNGtD2ijfjWm2ePX93Zze789nZIjEJx0TCSnovHMdpA+krINJ1q9TgKKf7OMVIPZ7zIFMzCWrPwCwMrjxwJgmN0xlHWdtI7zCrwvti+Z5sQkL/tvWsqma95HKVrPIdgedBxmXo+vGBvf9iwkQqOl6F+hiZPoTyo2nMAcYCOD1siMqh/EWaTdvDixjI4qE6XkZWKSlx7arIBnK7AodjPK+jULp/+Lf04rY26fzg0eyhx1ZN/fF+0zU1tcBtXGGDkQ2bSdNIvg+VGfEqkIL1cbgcRLu/o0OR650bd9TuEWpCw8ydQ8/z6bIohvTln5BYAJqT1CLUIDbkPnzeTNw5JQ+7gU8cqkCt6e9AVqkRuQl5xENX+btA21QpBkFExO8jZr9TekStvlYng2h3B7sapCl2Lou5PnzOBnxC3h4E1fMi2l/UugT4+v5d2JC/wpWptICKbHfs7TI79lx43rmJ7iIaaD+4Iur5ed9jKi/j6LlF+Ma3I+7pZhFF+w2jO3j5u/Tz6OJKqblA81ld5hfHu86bvBNjrW5zqKvztI7kurmWL3n2P690eDl2BatvYMAAa1Go1SGXJfP4CjOhcmnQpvXcdHv4Wf72bMxFmso2Xjh/dnFjEiYgdqSpBEYJd8IxXbyb7xBJKoQsLFrurZmFQOojQV/PvQYNYUaBDfnPKpLgGozRqER+CxhTYea3N4nW5OO0OnxEvsEgIzEtVI2FEKO3+ZsIYpX06m3OGbMchP+rq5KmVr2wUDhEoUYJYK+OeEvRx3TCO7PI7/WW1F0H43ni2OMrSZSkbfMJjafcVU7a9/zvIHmaG2olHZ3w7dRZQydPfdGl7R/KBYu7z9BmL5f/iBPm/M4HBtvS+tVuElwSRxogOp0XpFROHxeoOy7ycrvwBQ43QGLSdVWxTaa5h63zhyX1nbqXq8SglauKUtN2wlQrkvqMk3FeJcDMCh5qUNf/9mwHZu7DWF2+2jSBs3C5tBwiWB+o2dGHpHo0qxIGL1OCI0VGsFpV4nZc46RkhWTt+/tLmmQkr8vKHsd4dOKVt1Ai7YrzVwgL9M7s9t38c3f9F5DI3x8ufh7yK19PAEmXTtStISNFxxUwR/OzCbvaVqVua170Xr8HhYO8nIsJkjEfcuJfGaTNadp/h6Wrz8Y8oxIpXh61+qlNzUD/3OKaheRisFX2xs95BAuDxEv3MA9c3JeITgyXE1XJ6czcQvxmFzt6+2g6UlOD0e9LLy65p8eXB/2Oz5AQhb5w0mxMKD6G5IbAiwHKMXfDt7GyZVMWqR2+n6/YWE4G97e5NrqyZXggZ9fMfZpWgPUANu6j9n2EgZk28ZTv4HO4Iqb1uULTmI7jcDAxrYujVW5ipwjO2NjsbLUeMjv+LXw+aTXapmRW7zs4ifDXTy077Hw0bxnUXCiVIU8+uMd7HTl4K6Hly7pDd1Hto1G9xpLyPprRloImsh/9wv/qFhlfTRrwiE6H7jgyND4IJ7TTtgo6OOQaXrT9D3UAlxvxxLouEUFmk3L03M4L6s9sUF/UnmIPRByoUq7/kFgJm9mm4khxKFsfMP0+msI4wrqd/cvn2Ai5VXZxGp3BZWig+gVgzkiyMd2+fcPdGKNibwcTTbQ9SdI0Km+AAitaBoxiVBwssD/f7DX8b/2GwA6mcnVPHQoLWka0Mfu7I19OTQW/cDu+d+xGez8nlwWPtcERJNVbx1yQe8O6OYsfFevptzmMsTfwyQtP7BJaXy6o6m7ku1vTtnvekoqSXvuVXYy+qtYots7d+rXp8bvPeJPPPrBpRlmOut1LyN56OSWokuzoQhPRJdn2gUyRbcMVpsRgWW/VWULzmIqX8s6vQIRLyRG4YX8OLQA5iVhahEYYjupnUWnxzeob0cgApHHT3/PAXHL79t0lchI78aLKELkl5sl/DQ8ktMy1H+dWl/rv/2XP7I0fFe5qYsDuoyeGeRRDXDzEsYajZyc6/BPL55EAcrFOTVtN73j4/KQ6KaiVFfMGZKD9TkIYngOGl3lG8LJjT7GzHsK6ezCYWEy8O3RTHM6KPi5vQ1LMi+nMJa35/fnpHByyEpK79AEGYJsHfZypj87EwUtS5cUVpq9RKVCjflLgc1bhfuhogK9vqPHRQ9Jby/6n3muBeoprjExBWKbCQRnKjr7UEg8T+b72LFyc51/g57GVMfmMDJv6wLi69RpJq5cM8t2Byp6cMgU06L54eYl/LSxJvZUmwgzezixvTg7v/6E4la4lQbeXPCVmyiH1mnBvPwWisub9OnoYdJMNC8qcH7Q9PhRcPgUmxr6p4QrdOT+0ZWp+uOm9mPrwrqGHniJm5J+4B/Tj3JnCW+B9hfn3sSIQRSELLiyMovAIxMTCZar6c0iEFa22K1tpL6AbwNX95L3ma8TfeXSxyyTSHDsNzv8nWGWjGA+T9OZHepf1bxt/VTov/npSS+uJuaw/7zU+wIBfFKQh0NK1Hfdh9cm/QxVyanIISiyyiB1pBwY5T2cWXCPibfMJjVpwby0SELG4vOPWP/M7wKhQhy1HE/EGdoakBlVqrRxhhwFHfuYStZfZQec9N4YhNYtDdxuKJp7NDWuG3IsKAoPpD3/ALCmpPHw0rx+ROtMjzcAWz05y8H7+AXG+5mzneT/Kb4AKpdTortNjy/HYOkCu38LzEM3q1bS9J9KqcWeReF4rsQs7SHKxM+4b+T6/f2JibVR475V7b/I5wEGq9k5YWtTf3yHF4PXmfnl2sT5w7hpK3er3Fo5Cl+1X9lu8LNZZ8u7rQMviLP/AJAMNNyBJt7V/Zl6ax9KIVvfnT+psQzhuX5fdlcpOOb44F9fPfaypj6v5PJfT40UfhDkdGhOcqdoYswE05I2JgY9QVjJ6VS5U4guyK17YvCjN1VEymyNx3Q9TstkeeHoBCVkxOQbKVcluolVbsKCcEvB9fw+EbfBgo9rNZOy+ArsvILAFH69k31uxJWnWjkYxcsClwT2VOWyJfHrSw/EbzkruuSXQyfkEbp+tbjHQYCV42DeIWO063kugsGVk14zPbDBbXIJVqZy+TorhU+T6Dmyc3JTY6rFQpOv7bBL21El7n4x6wypscuQTqzGapV+j6jXH7kMA+MGe8XWdpCXvYMACMSktAEMAN6KNlerGBT+ZVBay/fOYlNFdfw0LoB/GpVVFAVH9SHtSq/cwDqiOAPaDx1boq8oV9FeH1n8EbjMoEj3zmevaVNZ3299Rbcdn8McAQvXfcd02MbW/pel7zEp3i8QFAjY8nKLwCszzsZFqHNAsWDq+OwkRHQNk65x7O3Zhbzvh/ALcsT2FYcukc1p7aC+EcmBr1dCcioC15yz5YYneBBtOLuINO1KXLZ0cV2zr9VZdYw465kLOY9SDT2S5Wo5onRx32qx+X1opANXrouB0tCayEYaMocElrJ/0ufAhUV3uEcts/gkkWDuXZpDwra4SMUSNaYaki4NjOobepTI8jWhn7m9+FBNburp4daDJlOIrUQdyrTZaD2eOeSFic+Mhnt/JaTeNe4fRvE3Zw5GJWcz6/r0ieqfSF9uh4Si/Mu81ttXimKWjGAA7UzGPX5GK74ujfh5izpFYKTs5PQJwdvCdDr9BChCg9jE4s6fJKwynSM1/f2a/a488PsDtcpJEieP5JDCXCiSoVoQaUMt3zPT/q2Hanom0MHOyxLe5GVXwDIjGs74G9XZ9FhE6KT9lICI6fc4+n3yVyGfjaZq9vhDBsKcm3VWB+ZELT2PDYHJc7wUDqfHg3v70amdarFIBYebrpfPsAYSemmjrunxE7uxeqRagpsNdw/6BgSze/tSdTxu2Hr26wvQhe8Zf52Kb8nn3wSSZIafRISElosn5WV1aS8JEkcOHCgocyCBQuYNGkSkZGRREZGMmPGDDZvbpw81dd29+/fz5w5c7BarZjNZsaNG8fJk8H3OzpR0bklhHBmVrqHN6aW8dKEfU3W9n1FoOKg7TKGLrqFSxYNJtxmea2xjnJS7h4dlLbc1U4yMQWlrbZIMzeN7ynTdfjgyAia+50dqasiYlhSh+pMuWcMzp/2B6C3RZCuW91q+QpXDG0lQy612xBBSufe7qF7ZmYmK1aci1iu9MGq8eDBg1gs5xwrY2PPJUzMyspi3rx5TJgwAZ1OxwsvvMDMmTPJzs4mOfmcWW5b7R45coSJEydy11138cc//hGr1cr+/fvRBXEkcZaxyalE6HRU1IXHqL3jCKK0gt4RMCjaTW+rk5lJBzuVo2xV6fU8sSmGUzaaDRnVFdg3ykzy2jiqDwTeIbdODYSBp8Ghcg10Pbc2GcAtJfLXnc0bLA1WW6nYWdDuOpV6NYdGWSi010dhuG1AbZuD4WTNGh4a1otXd7b8Tp4fxAgv7VZ+KpWq1dlec8TFxREREdHsuQ8++KDR/xcsWMDnn3/ODz/8wG233eZzu7/73e+YPXs2L7zwQsOxXr16tUtOf/Hlwf1dXvHplYJHRtqYn/4Jkh/evgvz5/HhQROHyiXsnq6p9M5yus5G2q9HwS8Dn/9P+dcdmH/Zl2pnaGdeGwpViKHmsMxKLtM2PcyQU9n0uP77XCraWVfMtN64bs7gkP1c+KEvjxn4aZq6zXfFLzO+5t/75lLpbP4dsDTnEFf1699OiTpGu/f8cnJySEpKomfPntx8880cPXq0zWuGDx9OYmIi06dPZ+XK1lOc2Gw2XC4XURcYjbTWrtfrZcmSJfTr14/LL7+cuLg4xo4dy+LFi1tty+FwUFVV1ejjD5ItTcMHhTsZkV6+ueoYm27YTvZPlrNr7ifMT/+404rv3WO3ct+Gu/n9BjO7ShRdXvGdZaujjNSHJwe8nco9hYwsDH0sij6RAkToLU9l2o9A0WJmBdWQli00myP14cmU/7Qv2+2N4+49OqLIp3eFzZuIsxUvsJQgvjvbpfzGjh3Le++9x3fffceCBQsoKipiwoQJlJY2b/aemJjIW2+9xcKFC1m0aBEZGRlMnz6d1atbXht+9NFHSU5OZsaMGT63W1xcTE1NDc899xxXXHEFy5cv57rrruP6669n1aqWQ1M9++yzWK3Whk9qqn/WdU7XhjgSsQ/olYK7M528NLGST68o4N/T9tLfsJxo5Ra0HEMhKjuxp6fnvydu5f/tuIs/bzGwPFfZZZc4W2NrT4gcHfi1QFdM6H3s1uQpcNGxvSGZ0LLy1ARqWsio7tzs+5KnAPb3VXPovKz1AJFawTBr28YsACYpm5cntWwTkVMWvGC2kujE7mJtbS29e/fmt7/9Lb/5zW98uubqq69GkiS++uqrJudeeOEFnnvuObKyshgyZIjP7RYUFJCcnMy8efP48MMPG8rNmTMHo9HIRx991Gw9DocDh+Ncep6qqipSU1OprKxstEfZXsrtdsa9/SYub3jl9VJKXnpZYEisl6nJ1cyK/9Sv9bulRJYWTGF/uZ4Fe8PDRD/QZBojqbvn24C2oU+NoOLx0eTZQrfkGKkVrL4mCz2HQiaDTPsRGBj75S2UOZrOczKMEbjvWeZTPTFTepF7Sy8KbM2l1xJkXXuAFE3rBi9n2Vh+Dbd+3/wWVorFwve33olW1fHVjqqqKqxWa5vv8U6tpxiNRgYPHkxOTsu5vi5k3LhxvP/++02Ov/TSSzzzzDOsWLGiVcXXXLsxMTGoVCoGDhzYqNyAAQNYu3Zti/VotVq0Wv+Pqj/J3hNWik+vFGy9YQUaipACGCdSKapYetzEityLM7Rbc2TXljP1TzPI/cOKtgt3EFeFjRp3aPf8yh0SaqmmLWM9mTAju2ZKs4oPIHFHJb7kTVdoVUg39qfA1vysLEYPyZptPss0NHIfemV8s1sg/aNjO6X42kOn/PwcDgf79+8nMTHR52t27NjRpPyLL77IU089xbJlyxg1alS729VoNIwePZqDBxs7SB46dIi0tOD7J+nVod2jeWpcNR9fXkDWtfvZdeMads5dhJajAVV8AEgKthdffMubbbHGWkvMtN5tF+wg7monQytCP5POrhoeahFk2olK0coGm67t95Skkqh+fQob3S0vRz45phypHUkn9eTw8RXNu6Bd0qOHz/V0lna9pR9++GGuvvpqevToQXFxMU8//TRVVVXcfvvtADz22GPk5+fz3nvvAfDaa6+Rnp5OZmYmTqeT999/n4ULF7Jw4cKGOl944QUef/xxPvzwQ9LT0ykqKgLAZDJhMpl8ahfgkUce4aabbmLy5MlMmzaNZcuW8fXXX5OVldWpDuoIde6O7ZV1BKMKUsxeBkR5GRjppJellikx3zbOpB2k0bokqnn/suPM/iY0VrahwiMEp37aG0XWEaQA9XXu776n71uXk1PbjMleENAqlRgdLvZKs+hhyMei2I1AicTFG8P2YsArWpnfOFp/TxnTI4maO5gse+uKzeFp/0pPsv4U0HRisrUgn9uHjmh3fR2hXcovLy+PefPmUVJSQmxsLOPGjWPjxo0Ns6vCwsJGTuVOp5OHH36Y/Px89Ho9mZmZLFmyhNmzZzeUeeONN3A6ncydO7dRW0888QRPPvmkT+0CXHfddbz55ps8++yzPPjgg2RkZLBw4UImTgx+QOLZfTJ4ft2aALciiNTCj3M2YJKyW4ysEGzidCVA91J+AMdrq5j48hUU/GZZQFz2JQF6Ebrl5CEqK89+PYRVxmqGRKdg0oxFrRQ8P3Y3pY4I+hnWIGFHoEFCdogPF57c0rxBVpzeQN5breep1D8ynixX2wYoakX73z0mZSFRWm+TJdnC6uDta3fK4OViw9eN0rZYc+I4d339BW4/7/v9aVwN0xKPY1GVo1OcRiHKwm7k7ZFiGb/42hb3GS52Jq61Ufj57oDU3ePRqaxMCI3/6ABjJGXeOk61kKh5Xj8Xx6qUHK2UePGS08TpqzGp7CSq1yFQhM3grDtR5JrAxC8GN3tuarmR3D/+0Ow5tUVHwvzhbBqgxN7mKpZg/01LUIv8dsuX75zIpV8OwHPe7HRmrz68edU17a7rfIJi8CLTPGkREagVik4rv+kpHiYm15FmspGgr6aXcS8qUVh/MkyHLHXeGKpd3W/f7yxHZ8ZhXWnAWer//dWTz2Ux8s3ZbKsLnjn4WfbXlvOPqaV4hcSvVjUN3P7RIXXD37eviAPiSDEJ+kcO4ESVxIcztlPmiuCU3cy4qFVIogqQOuxOI9M6Aol/7GvZWVycaH75PGpsDwxzMsgyVoMP2zdzenpRiY5FOkrQHiBWN5Ci88ZTa04ep6LOToQu8PkzZeUXAD7fl+3DiKkpWoWXeINE3wgv/aPc3NF3D5HK86yowlThnY9R2s9LE4fw69URoRal00RqBQOjBAOj3fS21pFitBGvq8akquWnK4bQP0rQP8LFgr3aM35UggJbDal/mkrJA9+C1/9fmKnUBZ1LvdZh3twbyeeXfsD0lJ/yQ17bS7B5NRJ5NfXlRi88Z8g2IXEuRyskHhhWw/i4AlxCSW/dD/IM0Y/U0ZcPDqpbPC+lNj8jUs4fxKpWjFsuZE2BArcU16GZn4SHNIugyH5usHx1v/5BUXwgK7+AMCU9nb9t2diOKwRPjavhprRvkUR1lx8NJxtrgIhQi9EhvptzmDhtMQbFqTPLys1/F8uv2NLwor6h5wQMyhoMyhKWF04kp9JBzD8HsqIqFbYUUfj5Lgypkagj9FTsaP9LohHHKmBQaPb+UkwCELwyfj1DP5tIRwOSry+sX+b63QYzkIFZLZic3JMDZQrem7EHu0dHsd3MsIjdaDiOoO2wWTKNUVJHjF5QYm/6HZnUGkre3d7omNqiI+73U9guNefH1zK/GGTvkOIDUIhy/j5pK+MXjW4IglHnCd67T1Z+AeB4RUWr54dEe/m/kcUk6CuJUJdjUJagEmVIF0n4qN6mE0BKqMVoFyPjvLwxaSdRyq31ST/bmLSdP0NJUJ+JbiFgdsKnkACe/jF88v115E224p44EZvbRZrRQlrZIBwmJS4luJ/fiLF3NJq0CKR4I7nPZCEk0Fj1OCvsTVRL6i/HcXCYEdqwvvMVrcJLD3N9aLuMSBc9LXaSDLXolC5u+b4naWbBgCgPfSOc9DTbSDZWAl6M0j4+vrw3N3/nn4gv1S6JJcfrFfoli875+I6Oj6fEDtNS3NyRkUO500SmcRkCCTC0y7y+u+HGQHmdoLkBymCvieLcikbHEuYNJUtT2e4g6jf22tVxIQGrYhdX9hzB4iP1qkitCN7ATlZ+AWB4QiIKScJ7xpYoySgYHuslM8pJusXOwIhTjaMhdIHlzPZQ6oil/qa6xt7f70bXclPPLRjwXyJNpSjhnWk7mfzFMBze+pnOidoqTmipf8G4QPp1v/O++jr6L5hFuddBocvJ2FP1fn3uKC02g0SVwsMOVx2VPis+gUEFqWbBgCgv/SKcpJvrSDFWE6stx6I6jYZioLbZDN+br9E3dpe5gJHWb7i+989YdCRwr5Atp+r77dg+Df/el4mE4Po+d7GvVEleDfz3slzcQkFPYx5WxS55hngenx8f2ciQ5HyylbXE61R46+pnWcnPX87hSC90IILQ1pK+XBrbGQOvxspuxdEjuDwe1D5kC+ossvILAFqVikidDrfHxuVpHp4Z/naoRQoqqfps9Mq+2MPLELVF/K34zhKt3MLLk9K5vxkDEWg65jlQey7mYVb02eUfG83rIIFZfU65ZUQ4STfbSDZWEastx6QsRk0pYGtWubVFa4qv/rzg6ZHf8/WxK4IWt1UgsfDwuVfWtUvrHaIHRiWhUYzBooFnx+2lxGFloDELiToEeqCuQ33QVfFIMfxpc8sbw0Nr9RScUXzG9Ei2GGux29q/3KiUvIyL7XgWeAAJF38e+SOLj8wE4LoBA4Oi+EBWfgEhSX+a9XM+RiFKu+VI1IuaJCMc8U+SjIAxKFqwcMZilB20VvOFmQnfMSXlJ6xqwUBErRD0MEH/KC8ZkU7e3ac94yZS78fZwyzoH+Whr9VJb4uNJGMl0ZoyjMrT9UvlgY7a0woaTvLVlUeZ9XUvQjnL31d2boZzdtn0hj63cKRCwYlqiZcnlhCrs2FR15CsWYtA1eX31S9EoMMjRWD3xrI7JwONUoXT40EhSRhVaiLUWiJQY64D2yf1M7XUe8eyZ7gBe13Htltu6ufBwIG2C7ZBpTuesytFBlXLRjr+RlZ+gaBuOUpRFGopQoZa5PPXycfCPtLLE6MLAqr4ABSiitfHr+MP2yaQEeEi3WIn2VhNXMPS42mgpmFmMrfnOHRKGwbFaZSiouUZWJhMZPrqV/CrIXfw992hzzxxPufPEH/2Q33y7ESjoJdlAAW18NFluyh1WOlj3IlSnEaceRV2RaWY75zEnKUDqHSCWa2l12v7GTfGhLOoBtvRUuyFVXjq3NigYaikMmsoHBVJ6QUZGtrDLwb6J8h5nGojDw3L4NWdOmyu4AVIkJVfINBOgZpXQy1FSDleExFqEVpEKXm5Ml0w1Lo2KErEKO3n5VH7fSobpzpjJRwmys0XHhzwLV8cuYaCFnLGhQuFtVJDXrtxC+vjlF7TqweldQr2lcJDw2sZG3cKt1dBP8P3XWKG6JHi+Muefg3JYR1eD6bMOPI/2NGo3NlvRkhgzUxgz8974e6E4gPIr40iKaJTVTRwb8Z3fHV0DlknjvMH/1TZJrLyCxhawNFmqYuVUdFHgeb3ukLNHQPdPJb5TpdSMOGMUpzi05l7WXCgP+/tV52xxuwafHn03Cvw8Y1mwIxJJRiXeDcHyhV8dFk2No+OU3YzoyJ3nnG90CHRdqSdfbVXMNDoW8qgjuCVIun/ybWN+tsrBKWrWk4w3uNX48nqI8AP0adSjSWdruMsAhV5NYJJaVF4vF6UisBHiJKVX0DwQpiPGAONRlFHOFp8/nKwgzv6dW6TXqYpCer1PD54PYmG+Ty3zRBqcTpFjVtqSMs16YtBDceHxVxGsV3i8jQXt/U9SrnTyBDz0kbO+QIlObZLyau18H/ro5mUdDd3D8yjtzEHLUf8KueG0qlNBhpjvVaKyptfKo+d3peCoVbo5IwP6l2DGlx8/IBa5PHixEqe2XYKu9uNSRP4LCay8gsAwr4QwizmZrBxifDaA4J6J+3/GfgNSuG/EatMY37W52sWHr6RnMqLL7brzpL6e3pnn4Z39vVHQnBF+t0cLpd4bdJxDEon+baIRolavzqm5KtjaSQZe7DiSjcaTnRaDoHEIdsMXtkZ0+i4WqHA+dG+Zq8x9Y1l2zWxuPyg+AAmJzkRSH61or0icRm6Sc8ERfGBrPwCgqSbjbB9EGoxQkqEYgc/zRjGh62EWAomN/Vzcc+AY7LiCzAKUcGHl21j8hejmk1WejEhkPj2jHP+lW0YdxXUSvzPhssYFOPknn4/ohJFHd5PtIn+zbaXqYugbEPT2VjCNZmI6Wm4vP6LCfvqTh1Runnk1WjYX6bm6bH70SvslDgjSdQVYpLqV1cEBp8tkiVRy9rcEmb085uYrSIrv0AgQmd+Hi5ICK7teZoPD/onCkhneSDzgF+XaWRaJlKxnWcu6ctDF0F8V3+yPFfJ8lw9r+yYzd+nlJMZeUGwCx9ZdHJks8ctqwppzk399FWpHKr1fzD0+j3SeiZ/kdnw99j4nkTqxrOzWMGYBC8PDjlCsd3MiRojkgTXp3yLS0RR44kmWrml4ToJD48M72T4v3YgK79AIIUo8nCYYdWEfhAQpxcsnrWbWNX2tgvL+I2rEpbweeI81hVefMufnac+M0aCPpJkcwZvT1mPXlGCUpxq9SqBmpf33cIHB1tYFhwSB+ctOFkGxqO/dyQbAqD4WmPTqXPfef2yb+Op3BOKGxuiHj01ri/HqrTsK1Px8oS9GMlGCA+SFHhHd/nJDASq/qCIC7UUIafGpQu1CPxmRDVxqo1ygtUgI1HDPyatIUorZ2loiSK7xLZiBcM+m8gLe2e3WT7PMYE392pbTBnm3VLY6P+WW4awXpSFnVHzWcUH9bPHf+/TsLFIwSWLhvBB3i+DovhAVn6BwbUTvIF1nu4KDDH/yIDI0P30Vl5zgBtSvglZ+90dAwd4fbL8O/CFt7M13L/pbg7YZlJHnybnBXpe2tW3xet7G62NkijH/H02uyK73oDvaHl524X8hKz8AoHnZKglCAsk7FzbK/iZKpSSl8lJXlK1q5BE+4P1yviPsZHfcUOf7u324yvLTii56puezF56KT+cnotbSkQg4ZHiGPj5/IbMF82Ruq8+4LmpbyzqBbPY7Cqjwtn1/IyTzS1nXvc3svILBMqulc4nkKSY2nYG9jcvTqzi7Yn/Cnq7Mk2RqOPpkcvP5AKU8YWTNRL3roxm7oqree3A7Ww8OqXFDA0AsToDxR/sQAARdwzjQG152C11+sqSHP8HmG8JWfkFAlU/kIKTjTjcOWUPjr+fWiFIMgpmpnm4PHFzt4riH+6oRS4fX7aXf116OtSidCkOlCvZfDCRl+eXMkYZ2WK5GrcTc2YCugWzWG/u2jkOI3Q6HO7grBTI1p6BwLUXLpLEtJ1lVkoOf9o8LKBtrLjmED10O5FEFVI3Dy4QriSo1xMfo+TeQfP5597wC4AQjmTorZTdsxQA959WMTzahP03wynzOim2N7ak1iZZ2FQbvP2yQFHtlANbd2mEa0fbhboJVW6T3+t8dkIVo2OLiFSXY1QWoxSFSEKe6YU7Eh4eylzOj3lXkVNxcTvA+4OYTSXknfnbUVyLo7gW7llGxo1DGJgZy7ooOy6vF7vbzerRF8eA4ubMwWhVwVFLsvILAJJ6kLzodoZeuix+MSiVNzsx2p+W4mV0nIN0i50kQw0DzNvOpSKSO7pLoRIF/GNyDjO+ClIYjy6ISa1hVLaLgo92NXu+4LPdiM8gI8mC+bGJbPAE148vkCzJOcgdw0YEpS1Z+QUCZXqoJQgbJDwcrGhfiDMJQawe0iyCgVEeHhq0qSFcEiArvC5Oum4lfxiT3Gq28e7MYK+J3DeWtlpGAuwFVUgvrif596PIt9UER7gAk2Dy/0pRS8jKLxB4um8i2+a4e2AxK/MS2i5IveHK4tnH6GdY41PamI7i76C8Mu3j1p7fsOT4jWwrlm3uzkcCPB83H5y6OWwnK+i1soT8saEPKOEPTlZWIoRAkgK/LC4rv0CgGQOKaPCWhlqSsECvdLFoVi5xugq+OtmbhTl6hsV5GBDp4GiVhgGRdcxMzsGkLEUtlZ4xXAmcYjpin87/ru/Nc+NOEKGpprgughTDKaKU2xCo8EixCFSoRW7AZOjuKEQFb0zaydiFwVni6gok6o1YH11PaQspiVqi4KOdTB5+Oas1lQGSLHjcNnR4UBQfgCSEbClwlqqqKqxWK5WVlVgsHXe2FLaFiKrH/CjZxcOF2bFdUjISXlSisJWr/MOWijm8sz+O3SUKiuyNf2B9IwRRWkF2qUSNW2Jmqocb+lRwolqP3a3gFxnLsHtjqHRFkqRZ35C/TaZzfHvqJzywqmUz/u7E1GI9uc+s7NC16gg9zhcnc7S2ayvAmb378OaV13SqDl/f4/LMLxAorKGWIGy5MI2LWgQnivtfDt7BR4c0lNibH1XWWx+eO1cfgT+64f+v7rym4fxDw3pztErF/ww+TKp2VSDFvui5In4x01Nu44e84MRzDEdUCgUDdBGcfmdth+twVdiJ+/gox66O7tKL+enWiKC1JSu/QCCH1AoLBHr+eugn7CnRsDKvsXJrP+eufXVn/f7KpsIMVs45HDQFfjEi4eTVCWsY8ukU2v/9CMxq6G0VDI2t9+/8z371mXoF8XoYEudlUJSTvWUalp8ITwWbqYug5uetG7j4wukfcpgyIZWs6NBnU+kou0+1ntnCn8jKLxBopwNaoOvF1rtYqBaDeXrHGBYeDtwjXmSXeHnvTB7NfCdgbXQHDBzkw5kZfHokloGRTnpba0k2VLH+VAJ/2Wmgf6RgSKybjAg7PUy1xGqriNCUnUkDVIFE/R6ZR4rlnv59sahL0UnFKMQ5p+/nsu8EwlP5WVcX4S9bzdw/r2TAP69gfxd1eC+sqcbucqFXBz4Jtqz8AoF9MbLiCx3bq67mtV0JrA9CLrl/Zau5Km0Wg0zfBryti5kxEV8x5oIcrb3TNcxPF0i4mr/ogvU9pThNgvp0s+emJZfxr2zfLI6DRYRGy7BciYJF2/xXqVegf30nqnt64vZ2vX3pZIsFnezk3pXpeg/dxcDKkhtYU2Bh2Qk1xS3s7fkfiRu+TWHpVdNJ0R9Fy7EgtXvx488cjEtPRLddKMhkOg3kPuv/QVPlnkImHu5FVi+/Vx1wLuvVO2jWnrKTTUCQuzWYCLS8fuh2frMmmvcOaIKo+OrxCAWXf92HP+64NKjtyviGV7KwqyT8xvl17+8JWN25r69jhD4qYPUHirUnTwStLfktHQh0M0ItQbfALSXy3vFbeWz7rby+U9dihutg8WmOijWl14dUBpmmKEQVGZHhE/BcrVAwXB9Fxda8tgt3Avfzmxj75WmMQdg/8xcVdcFLgSYrv0DgPky4bq5fLBS5JvD8niv402YjnwfQqKW9/HxlDLViQKjFkLmA0fHhYwF5Sb6a6l8Ffo+49mgpJSuPMGpb8HNqdhSrVkewXM9l5RcI5Hx+AWV16fX8dmMm7+zThFqUJri8EvetnYhAj1tKQCA/B+HAhkJDqEUAwKLRUvjGBoQ7eN54ef/ewhhN11gCvWFgZtD2/MJnyHwxYV8M4uIINBtOrDg9l/3lRt7dp6XSGb4pcdYVKhi1eD6VTnj+kmoiNC5SjFX0M/yIkCwoxMUThb8r4JaS2FAY+pWYXkYrPfLc5FYHL2fdWWr+sIrYp8dyui58ZsDN8d2RHGb1CU7GD1n5BQLN2FBL0IBLSqXclUysag8SXTPLs0eK54Njl/HsVgMub/gqvfM5q5z/b119eCW9Mgqr5mfUuuHjy49TZDcxIWYzKlEAaAIaxLu7oxTFjIjzsizETu5JywrIXRg4I5fWcJTU0m95MRVTLbjC2AVC0alAFO1DVn6BwBNac3eBAY9kxe6N4c19I/nnXi0PDstgcmIJQy2rcYlINJwMqYy+YKcvywtGsrdMH5ZLnO3B7pGwn4lXfOU3PQEYGjOL3GqYl+HiuvQTFNqtSAjGRi5BSOZGTtoyHafKOyjkiq+X0UrRl5tCKkPRor2MnTSLtVL4PlduETzFLAe2Pg+/BbZ25yFKLoeWnHMDhEBBVsl1PLw2mkonXBguSkKgU4JSgn9eeophEXvRcgSBxq8+Vf7geN00Pjic3uWVXnuREKSa4JRN8MkVeVS5tAyJyMEkZSPQN0QzkfEdjxTDpK+uD7oLzFnGqaJQrjhB0eLstgsHGLVFR93LkzleWxVqUZrFqtWx8a57O5XNXQ5sHUKE/ROCpfiKXBP49FhfDpSrOViuoKCGFpcGBRL2MxbftyxPYGRcHBV10xmX6ObegYdIVG9sEng6FPxwei5v7IlkV0n3s8cSSJysAZC4dmkPAAZExmHzXEKG1ctjI3I47bAw3FIfC1IifEz4wxWHiKY8hAGXHG9up2pveOT4dFXVEf/hEYpvSMLmDu7g3Bdm9enbKcXXHmTlFwAk1YCARlav8A4nuyKdkzV6lp0wsq6DYbzOJhI9UqXmg4OZPDSsNyeqVfwi8zjJuuBGKxHoWFc2i6NVBp7fasDh7X6KryX2l9cPZk5UKVme2x+AK9Lu5EiFxJNji0kxlHGqLoJEfRmJ6nUIdPIe4nnoOc6Y+MmsKwz+zG+ELoqKvRuDuJPVNiUrjzD60p6sMoef8os1GoPWlqz8AoHHv/tpXimCOhFPlSuaEoeVdw4k8eVR/391Z7MVLDrSh56WXjwxpoSTNQZuTvsWSdQEbGm0jt58kz+GR9d1fKm5u3F2D+uW5QlAfczKJGMiMbqB2N3w9qX7OV1nYbBlAwpR3q0z15e4h3V4gNgZpuZpKHp7ZVgpvrMU/DmLgX+9jH1hFgD7dG3wjPLkPb/z8N+e37Eze34dRyABBtxSBJ+dmMofNpo6VV9niNEL7hhQx+zUk9S6dQwwLvNLvVXeIewq78PaIitvZ3d+b8+k1jCqWI0rUsNmRSVmtYYYlQ6rW4FLATvt9S4GWqUSi1ob9mbf/uCKNA8nqyVuG1DDlPij1Hl03S4HoYOeDPr0sjO/qeBg1miIu38NeMP39WodlsTRe/tR4QifVYL0iEh+vO1nnapD3vMLJaIG0EEHl54O2Gby2/Vp5FZLVLugc3noOk+JXeKl7Xpe2p6BVuFlWurdPD4yG72yFpXkwMDBdtd5pG46b+9P59Mc/z2CDo8b9+4yipceoK9Vi6PEhscrKAOM6ZFcMiQRW04pNUdLsWTE0v/2oSjLHXDaRtaAcByfd56zM8T6WfUwzGrBdb17kV2m4h+TtuMSGkocVvoaD6DhOALFRZelvsKdENQ5b6rBTO9CL7lhrPgAKncWMOxIH7JSQi3JOaampQetLXnmdx5+m/m5DiFKr8NXo5cqMYSVhQM5WqVjX5ma7FIpZJZp7eWSRC9Tkuu4s9fneCULClHVqkWiQMO6siv5/cZY8mr8f49D/nKI2mPtdyLv8ehUViaEzwg42ExL8WJ3w6BoNz/vv4dyl5Xeuh9CLZZf+Dj3Fn6/ITgrJxIw4Ycqir7eF5T2OoukkrD8bRbb68Ij8MLzMy7nxoGDOlWHr+9xWfmdh7+Un7fqWbC1nuDUQW9O2HpRZDez7GSkX2dAoSBSKyh3wJvTypgR+3mT814pkl2Vl3C02tzg+B0IRuqiKP/lUqQOPNX6BbPYG2Z7IKFDcHemi/1lKv40Jgezqhq1wolZCo2TdmfIdUxh2pf9g9JWX6MV7z3fBaUtf6FPjaD49yMotod+G+Cu4SP53aSpnapDXvYMIZJuNuIC5SdQ45UiqfNGU+mO5qPDffjHHm2IJPQ/5Y76Wdwja6NYfe3gRi/JajGY5fmDAqr0zrKtroypvxhH7j82tvta9avb0f6yDw5PaN0HFJLEyA/zqbk7E7NLIkeyUR70fRmJf53Zh53+ZQYAI+K8TEoaQX6NiqdGLqfWE0O5M5J03Yaw9j/Uq4Ij21h1FJoV+RQEpTX/Yc+tYMCmaoqHhD4E3P6S00FrS1Z+gcB7bglBoACMrCm9jLt+iAnqpnsoqHZJ3LlyHJ9deoxKb19O1Cay8GgCHx4MXloVb03HTLj1faJxhljxAYxRRnJ60wbYdJJSYOD1g1APjmd3gpdIlYYjtZUhkWt7sYLtxfUWwZ8fnsXZveifDUzjZLWSuwYUkxlxCAMHQiJfS9R5dAFvQ61Q4PzbNkr2nwp4W4Eg950tjH99Nhs8oV3+7GGxBq0tWfkFAmUMha6JvLQrg31lSgproMYNoTZcCRY7SxS8e+xa1hbqWJUX/NFkyeQ4lAvVeGztU4KnvtrHuMtC+wJQShJ8kdPoWNGivYhFe4lSSXjdgql/nEFRkpqDteUY1GpqXaHw1zr3LP/7TBSeFbmJPDgskgf7NVZ+LimForreJOn2oxTFQZUS4P2c3gFvY6g6gvIuqvgAJAGlj64g5dUp5NmqQybHimNH+KPHg1oZ+PeGrPwCgTKNP20fzfcnQr+GHir+vCV4zqoXsq+2nKmPTiH3DyvafW3tyxtIfGwkhfbQZOVQSBKSqqlPmgQId/26Qe4TKzD2jGJArQNT/zgUPxnALqqwu0MfnWdfqbqJxeinx6fwxCYT8/r1xuGRGB1v58qUbdS6I4hVBT7e5fU98/hXdq+A1T+1UEvJh+sDVn+w8NQ66fFlHnmXBW/2dSHzBg0JiuIDWfkFhO2nqru14gsHvNqO/YBqj5fTb1s1hQNDM0t3eb0IR9tK7KxFq6P4GKw+xqgbh+AsrEZ7VV8KoxXk1FYEWNLmWZGrZHmvG7g8/jMEerZXzuDvu+sHQh8dql/6XnTEzGNMRavw8vDIvuRUaHl06BZUCidKHH6NLCRQ8vqeNL/VdyFRWh15L68Kan6+QHJqyX4mT7mc1ZrQLK2fqgneoFNWfgEguzj4SzsyjTkW6UVvaP/SJ4CUYgZCM/PLNEZSvKr94bAKPttd/8faY6gj9Uy9fzyiqIbDoy1UupzUuIIXuPxvuyO5ZEYmPxYN4jdrIlos5/AqGlYIPs0ZB0jMSvcwNbmW65M/Q0hmJFHdyXizXnpb3YD/ZxOxOgOZpyRyLxLFd5a8335H3wWXkxOCveUYQ/BWjGRXh/Pwl6vD5vw8bl74iR8lk+kIExRRnHpwabuvUxo1iNemheTHP3m7i/z3tvmtPqVBTewVGYipPVDZ3KzWBT6a/4g4L6PjXPxzb8etmVNMglM2+MvkcixqJ0mGyjORaQztzkv5wOa7+fa4f5WfQpIYu6SU4uWH/FpvuBAzsSc7bkoKugFYqsXKqjvu7lQdsqtDCEk2WzCpNUEdbcs0pULfsXGdp9ZJ9DsHkH6SGNTIICkGM/nvfe/XOj02F0WL9sKivQgJRo5MQXvbEGp04JEE2X72a1QrFJjXwEGjGVI6/vyfDYBwX1YUAFHaOKJ1falxS3ww4xDFdWaGR2xCKYoRqFqdHf6sfxHfHk/usCzNkWGIoHj5Br/WGU6UrD3GhEt7khUV3O2bRJMZh9sdlMwOsvILAMcry2XFFwYUe+qIT7RQV9j+2U7p+hOMm5cZVMvPPsdc5AawfklAxdY82JoHgLlfLJPH92DvODMGpZoqt5MqZ+dy/wxTR5D3j/rZ9vi/+s9ytsyhoOyMaJee8TucnHQNuTXw0ww7s1OPEK9qqowEWv6yO9EvMpxP3PZy8vxea3iR+6cfGfrWbHbZgvcbOG2rRQRpyNmuUOdPPvkkkiQ1+iQkJLRYPisrq0l5SZI4cOCcKfSCBQuYNGkSkZGRREZGMmPGDDZv3typdu+9914kSeK1115rz+35jTUnT4SkXZnGlNTZ0Tw5EUnd/iUvfbKVPYrg7ftFaHWUfrAzaO0BVB86Tf5/thH9P2vR/vx7RuyqY5I7osP1TS3SUvfsOatH3cnA9t/qAgXHqur3DScuGsy/j87n0e13UeYZhY3+1NEHUBCh8W+s0kS9kVMf7fJrneGK8o2dRGiCF4zjpszB6FTB8Qlu98wvMzOTFSvOmZArfTBLPXjwYKO119jY2Ia/s7KymDdvHhMmTECn0/HCCy8wc+ZMsrOzSU4+t1Tha7uLFy9m06ZNJCUlteu+/MmYpBTe2rYlZO3LnOOE10ZijLHds7+Yn41kryt4Sz6ZDj1FJyuC1t75eM9Yl+b+cxOSWsnwjFhUvxpBmcrjs0N9rM5Qb/XoOrdH5MwphWRzQGS+EIHEM1sNAHx+eAQgMS3Fy/RUGyer/ZvOqF+xRF5F+Ea08SeVOwsYdrAnWT2D0963hw/x85Gjg9JWu5WfSqVqddbVHHFxcURERDR77oMPPmj0/wULFvD555/zww8/cNttt7Wr3fz8fO6//36+++47rrzyynbJ6E/igpiQUaZ13EJg6BHRPuWnkMhJVUAQtzucH2UHr7FWEC5PfdbxXy4lYlQKlp/1bnUpVKNUMlxhpfSB7xopPoCixdlMHjczKEY2janfL1yZp2Blnn8DWqsVCopf6/o+fe0h9y/rGPv32WxyBX75M9VqRQiBJAXe1ajdQ6KcnBySkpLo2bMnN998M0ePHm3zmuHDh5OYmMj06dNZuXJlq2VtNhsul4uoqKh2tev1epk/fz6PPPIImZmZPt2Lw+Ggqqqq0ccflNm7x6iwK1DldFB9dyYKre/jvORbRpBvC66rg+Ha4ARebg8VW/MYcVQQpze0WGbCAS8lD3zbRPGdpe7ri8sacozXirO0+/nw1r26mQR94Af1h0pL8QbJAaFdym/s2LG89957fPfddyxYsICioiImTJhAaWlps+UTExN56623WLhwIYsWLSIjI4Pp06ezevXqFtt49NFHSU5OZsaMGe1q9/nnn0elUvHggw/6fD/PPvssVqu14ZOamurzta0xNjlFnv2FEQdry4md0dfn8mWT4gMoTVN0ShXVn+wNapu+kvvSaky/+JER+saDUbVCQX9jJCff2NB6wlYhMARpDycYqGpDH0UnFNQcLqHfmvKAB2icP2QYSoV/l6lbol2tzJo1ixtuuIHBgwczY8YMlixZAsB//vOfZstnZGRwzz33MGLECMaPH88bb7zBlVdeyUsvvdRs+RdeeIGPPvqIRYsWodOdC0bbVrvbtm3jL3/5C++++267psuPPfYYlZWVDZ/cXP/Y2i0/epji2vb5IskEDoUkISl8ey5ipvVmbxCt2wBUCgVqU/hm+JAEiNe2EanVoTrzYhqpiMB1z7dtpo4qXX+CMQUXj1G523jx3Et7yX9/OxM7YRDlC6tP+C+6T1t0SsUajUYGDx5MTk5O24XPMG7cuGbLv/TSSzzzzDMsX76cIUOGtKvdNWvWUFxcTI8ePVCpVKhUKk6cOMH//u//kp6e3mI9Wq0Wi8XS6OMP1IrQpwaROYdHCI5eleSb1efcjMALdAE1LieO4tBElPGVqv2nSH56B5fs9TBKF4X01WGfry362wZidPoAShc8nB+G5ww9WJS+uI50Y+BSk/WMiAxY3RfSKeXncDjYv38/iYm++9Hs2LGjSfkXX3yRp556imXLljFq1Kh2tzt//nx2797Nzp07Gz5JSUk88sgjfPdd8BNLerz+Na2W6TyF9hpSfzu51TLmfrHsEaGJaB9xg2/71KHEnl9J3lubKP/F0nZFNtHGm+uzVXRxhhmiKN0cSE/M8MdxqpqkhScC9n1uzs8PSL3N0a45/MMPP8zVV19Njx49KC4u5umnn6aqqorbb78dqF9GzM/P57333gPgtddeIz09nczMTJxOJ++//z4LFy5k4cKFDXW+8MILPP7443z44Yekp6dTVFQEgMlkwmQy+dRudHQ00dHRjWRVq9UkJCSQkRH8kfyktHT0KlVYRNmXOYcoa90QyTp/KDZ3aGZg3txgW0QGj5qc0/TfVsupEAUL9xfm7/MIXbKf8OHUsoNMGjedrAj/b+1UOx3YXC4M6sDvE7dr5peXl8e8efPIyMjg+uuvR6PRsHHjRtLS6qOmFxYWcvLkyYbyTqeThx9+mCFDhjBp0iTWrl3LkiVLuP766xvKvPHGGzidTubOnUtiYmLD5/x9wbbaDTe+zTkoK74wZO9QPaa+sc2fVEhkx4Yuka2iX1TbhbowuW9tYoLUde+xp9FyLni4DAUvrKa/McLv9Vq1WrRBSmkkB7Y+D38Ftv7X9q08s3aVHyWT8RfjVFGcvr9psOvU+8aT1S80PwWDSk2fV/ZRe7R5q+mLhaR5w1gzNvBZ1QPB1DwNuS+1bKXeHYkYnszuO9Nw+XGb5w+Tp3HHsBGdqsPX93hwbEq7GWZt+FrudXd0ZU1jrgoJ8oeHLoGnze0ickafkLUfLNxtLDuHK9E6PaeDHHquK1CxI59LjvlXhfxw7Ihf62sNWfkFgEk9wnM5Vga2RDuInnwuVpMu0ULqL8aFLPnrWer2Xfw5IIu/O8gUe+AsBQPFwBpth4KjdwdOvrqWoQb/LWe7PF6CtRgpK78AcLis7KKwbrsYqXW5qJp/zggq+n/Gsyr4NlFN0E/uHgOm0jc2B21Px19Uv+W//IoXGxIgntmA0U8GKipl8FSSrPwCwMDYOHnpM4yJcCiQ1EpSH7yE/db2Z3oPBF6LJtQiBAVdghm9sus4io/RRFF96HSoxQhrao+XM3Kr3S/RX27OHByUuJ4gK7+A8NXB/VTU1YVaDJkW2OguI/mJaWT18nC6LjziNCqqukf+x/ItuQzrIhm/FJKE6uu2YxfLQP47Wxmj7vzy5zc5B/0gjW/Iyi8ADEvwf/JMGf+yyhQ+EVWUkkTp+ztDLUbQOPn8KkZpw9/toa/ByqlvD7RdUAaA6j+tIb6VIOi+YFIHbwWk66w/dCGOV5QzKDaOFIuVHlYrJTYbiw7sQ6tUEWs0kGqxkmKxkmqxsj73JBvzczFrtKRYLKRarCRZLCSZzPx3906KaqqJNRpJMltIMptJMtX/+/TqLDQqJbEGI8lnzsUYjLyxdRMahRKzVkv8metSrVZcHg/v79mFVqkiSq9vaCvVamX/6dMsP3oYo1pNstlSL5vVSpLZzJcH9nOgtIRYg5EUi4VEs5lks4VEk5nXNq3H7fGekc9M4hn5/rZlI0pJwqzREmcykXzmGq1KxYLtW8/IoCPBZCbVWt8PRTU1LNyfjUGtJsFkauijFIuFrOPH2Jyfd0bu+vJJZjNJZgsLtm+l1G4j3mhq6KNks4W3d2ylzu3GotUSd965KL2ev27eiFapIkKnI8FkIvlMH9ldLt7bvRODWt1wv2f7aGdRIT8cO4pFqyXlgj76JHsvR8pKSTCZG76LZIuFj/fu4bStFqtWR7zJ1CBzosnMyxvWolEqz8hgZsLTfbHuLEepUvDJC1+iM2qJiLMSnxZLQnocCT3jKDhaxA//XY3erK8/3jOOhPQ44tNjWfvFJvatP0RUQgQJPeuPxaXGsOqzDeTnFGKw6IlNiSauRwxxqTHE9ojh4+e+wO10Y44yEZMSTXxaLLEp0ZijTLz35KfojFqsMRZiU6Mb2qopr+WrN5ahN+uJSY5qOJ7QM47dq/exeekOrDHm+uM944hPi2Xr8l3kbDuKzqAlNjWa+PRYkk/pmHzFJfxn1w4q6uxE6PQkms59TwkmM8+tW41epSJSryfRVN93qRYrAsE/t23BqNYQazSQYraSYrWSarGQU1rKV4cOYNVqSbbUH0uxWNl9qogNeblolEoSTWZSzhxPMptZtH8fJyoriNTpzv3OzBYyMPK+/keEEFhjLcSmxjT0n8Gi570nPkFn0hEZH0Fcj5iGfigvquDrN5djtBrqj5/po4IjRaz/cgtqjYqoxMhG39/6L7dweMcxjFYD8WmxxKfXfxfxabF8+uKX2KrsZ2Q49z1FJUby1iP/RW/SERFvJS41mrgzz4vL4eLTF7/CaDUQnXSuraqyan54fw1qjQprrKXhe0pIr//+dq/ah86obZArrkcscT1iWPLW9xSfLDnzTMbU98WZ/ljwf++jUiuJMBoYU2Skamw6yRYLWqWK1zatx6TRnPkNmkm1WPEIwYd7dqFRKInQ6xp+f6kWK2nWiKC9p2U/v/Pwl5/fhdQ4nVTW1RGl16NTqRqtaZ+21aKSFFh1OhQXrHUX1VQTodOhVaqarIOffamqFYqGc0IIjpSXEWMwYNJoG4IQAzjcbopra4nS69Gr1Y3aqqyrw+nxEKXXN4moXlxbg0mjRa9qWQbNBQYMOaWlxBqbyuD2eimoriJKb8BwgQy1Tic1TieRen2T+kptNjRKJSaNxmcZDpeVNtsPXiE4WVlBtN6AUaNpJIPD7aa8zt5sn1c56vAKgVWrayJDic2GWaNBq2o8ljxSVkqkXo/lvIDQZzlWUU5MMzJ43B5KC8uxRJvR6hvfr722DofNgTnShFLV+H6rSqvRGjRo9Y33mvNyCjFaDViiml6Tf7iQiDgrepMOxfl95PVSkleKOdqMzqBtJIPT4aK20oYpwoBK3biPaitrkRQK9KbGfVR0vBiVRkVknLWJDCcqKojU6zFd0A8AeVWVROkNTZ49t9dLud2ORatFo1Q27iOXC5fXg0mjbVTfqZoa3N76gdqFz8rJygoidLom15yV3RpradIPXq+X8lOVmCONqLXqRudcThf2mjqMFkOj+60sqcJWZScqMaLJ91R88jRagxZzlKnRdwFw6sRpLDFNvwshRP2zEmVqIoPH46G2wobBokelPvdc1lbZqCiuJCohAp2x8fdUWliOJIE11tIkWXjxydOYo0xNrgEoyS/FEm1uIoNXCMrtdsxnvqez1LldFNbUNPv8+wNf3+Oy8juPQCk/GRkZGZngIDu5y8jIyMjItICs/GRkZGRkuh2y8pORkZGR6XbIyk9GRkZGptshKz8ZGRkZmW6HrPxkZGRkZLodsvKTkZGRkel2yMpPRkZGRqbbISs/GRkZGZluh6z8ZGRkZGS6HbLyk5GRkZHpdsjKT0ZGRkam2yErPxkZGRmZboes/GRkZGRkuh1yMtvzOJvdqaqqKsSSyMjIyMh0hLPv77ay9cnK7zyqq6sBSE1NDbEkMjIyMjKdobq6GqvV2uJ5OZnteXi9XgoKCjCbzU2yFYcrVVVVpKamkpub2+0T8Mp9cQ65L84h98U5ukNfCCGorq4mKSkJhaLlnT155nceCoWClJSUUIvRISwWy0X7MLcXuS/OIffFOeS+OMfF3hetzfjOIhu8yMjIyMh0O2TlJyMjIyPT7ZCVXxdHq9XyxBNPoNVqQy1KyJH74hxyX5xD7otzyH1xDtngRUZGRkam2yHP/GRkZGRkuh2y8pORkZGR6XbIyk9GRkZGptshKz8ZGRkZmW6HrPyCxOrVq7n66qtJSkpCkiQWL17c5jUffPABQ4cOxWAwkJiYyJ133klpaWnD+QULFjBp0iQiIyOJjIxkxowZbN68uUPt7t+/nzlz5mC1WjGbzYwbN46TJ0925pZbJJz7oqamhvvvv5+UlBT0ej0DBgzgH//4R2dvuUVC1RfPPvsso0ePxmw2ExcXx7XXXsvBgwcblRFC8OSTT5KUlIRer2fq1KlkZ2f75b6bI1z7wuVy8X//938MHjwYo9FIUlISt912GwUFBX679wsJ1764kHvvvRdJknjttdc6eqshQ1Z+QaK2tpahQ4fyt7/9zafya9eu5bbbbuOuu+4iOzubzz77jC1btnD33Xc3lMnKymLevHmsXLmSDRs20KNHD2bOnEl+fn672j1y5AgTJ06kf//+ZGVlsWvXLh5//HF0Ol3Hb7gVwrkvHnroIZYtW8b777/P/v37eeihh3jggQf48ssvO37DrRCqvli1ahW/+tWv2LhxI99//z1ut5uZM2dSW1vbUOaFF17glVde4W9/+xtbtmwhISGByy67rCEGrr8J176w2Wxs376dxx9/nO3bt7No0SIOHTrEnDlz/NsB5xGufXE+ixcvZtOmTSQlJXX+hkOBkAk6gPjiiy9aLfPiiy+KXr16NTr2+uuvi5SUlBavcbvdwmw2i//85z/tavemm24St956a5tyB4Jw64vMzEzxpz/9qdGxESNGiN///vetyugPQtUXQghRXFwsALFq1SohhBBer1ckJCSI5557rqFMXV2dsFqt4s033/ThbjpHOPVFc2zevFkA4sSJE63K6A/CsS/y8vJEcnKy2Lt3r0hLSxOvvvpqm/cRbsgzvzBlwoQJ5OXlsXTpUoQQnDp1is8//5wrr7yyxWtsNhsul4uoqCif2/F6vSxZsoR+/fpx+eWXExcXx9ixY31aZgkWweoLgIkTJ/LVV1+Rn5+PEIKVK1dy6NAhLr/88s7ehl8IVF9UVlYCNJQ5duwYRUVFzJw5s6GMVqtlypQprF+/3k930zmC1RctlZEkiYiIiA7L70+C2Rder5f58+fzyCOPkJmZ6b+bCDah1LzdFXwYyQkhxGeffSZMJpNQqVQCEHPmzBFOp7PF8vfdd5/o3bu3sNvtPrdbWFgoAGEwGMQrr7widuzYIZ599lkhSZLIyspqz211iHDqCyGEcDgc4rbbbhOAUKlUQqPRiPfee8/X2+kUoeoLr9crrr76ajFx4sSGY+vWrROAyM/Pb1T2nnvuETNnzvTthjpBOPXFhdjtdjFy5Ehxyy23tCmfPwi3vnjmmWfEZZddJrxerxBCdNmZn6z8QoAvD3N2drZITEwUL7zwgti1a5dYtmyZGDx4sPjZz37WbPnnn39eREZGil27drWr3fz8fAGIefPmNTp+9dVXi5tvvtmn++kM4dQXQtQvH/Xr10989dVXYteuXeKvf/2rMJlM4vvvv2/PbXWIUPXFfffdJ9LS0kRubm7DsbPKr6CgoFHZu+++W1x++eW+31QHCae+OB+n0ymuueYaMXz4cFFZWenz/XSGcOqLrVu3ivj4+EaDIln5yfiMLw/zrbfeKubOndvo2Jo1a5p9Ib344ovCarWKLVu2tLtdh8MhVCqVeOqppxod/+1vfysmTJjQ+o34gXDqC5vNJtRqtfjmm28aHb/rrrvC5oXv7764//77RUpKijh69Gij40eOHBGA2L59e6Pjc+bMEbfddpsPd9M5wqkvzuJ0OsW1114rhgwZIkpKSny7ET8QTn3x6quvCkmShFKpbPgAQqFQiLS0tHbdV6iR9/zCFJvN1iQRo1KpBOpN0M/y4osv8tRTT7Fs2TJGjRrV7nY0Gg2jR49uYs586NAh0tLSOiC5/wlWX7hcLlwuV7Nteb3eDkjuf/zVF0II7r//fhYtWsSPP/5Iz549G53v2bMnCQkJfP/99w3HnE4nq1atYsKECf68pQ4TrL6A+mfjJz/5CTk5OaxYsYLo6Gg/303nCFZfzJ8/n927d7Nz586GT1JSEo888gjfffddAO4sgIRO73YvqqurxY4dO8SOHTsE0LC/dtZa7NFHHxXz589vKP/OO+8IlUol3njjDXHkyBGxdu1aMWrUKDFmzJiGMs8//7zQaDTi888/F4WFhQ2f6upqn9sVQohFixYJtVot3nrrLZGTkyP++te/CqVSKdasWdPt+mLKlCkiMzNTrFy5Uhw9elS88847QqfTiTfeeOOi6otf/vKXwmq1iqysrEZlbDZbQ5nnnntOWK1WsWjRIrFnzx4xb948kZiYKKqqqrpVX7hcLjFnzhyRkpIidu7c2aiMw+HoVn3RHPKyp0yrrFy5UgBNPrfffrsQQojbb79dTJkypdE1r7/+uhg4cKDQ6/UiMTFR3HLLLSIvL6/hfFpaWrN1PvHEEz63e5a3335b9OnTR+h0OjF06FCxePHiAPVEePdFYWGhuOOOO0RSUpLQ6XQiIyNDvPzyyw2b+xdLXzR3HhDvvPNOQxmv1yueeOIJkZCQILRarZg8ebLYs2dPQPohnPvi2LFjLZZZuXJlt+qL5uiqyk9OaSQjIyMj0+2Q9/xkZGRkZLodsvKTkZGRkel2yMpPRkZGRqbbISs/GRkZGZluh6z8ZGRkZGS6HbLyk5GRkZHpdsjKT0ZGRkam2yErPxkZGRmZboes/GRkZGRkgsbq1au5+uqrSUpKQpKkDuUOFULw0ksv0a9fP7RaLampqTzzzDPtqkPV7lZlZGRkZGQ6SG1tLUOHDuXOO+/khhtu6FAdv/71r1m+fDkvvfQSgwcPprKykpKSknbVIYc3k5GRkZEJCZIk8cUXX3Dttdc2HHM6nfz+97/ngw8+oKKigkGDBvH8888zdepUAPbv38+QIUPYu3cvGRkZHW5bXvaUkZGRkQkb7rzzTtatW8fHH3/M7t27ufHGG7niiivIyckB4Ouvv6ZXr15888039OzZk/T0dO6++27Kysra1Y6s/GRkZGRkwoIjR47w0Ucf8dlnnzFp0iR69+7Nww8/zMSJE3nnnXcAOHr0KCdOnOCzzz7jvffe491332Xbtm3MnTu3XW3Je34yMjIyMmHB9u3bEULQr1+/RscdDkdDAmGv14vD4eC9995rKPf2228zcuRIDh486PNSqKz8ZGRkZGTCAq/Xi1KpZNu2bQ2Z6M9iMpkASExMRKVSNVKQAwYMAODkyZOy8pORkZGR6VoMHz4cj8dDcXExkyZNarbMJZdcgtvt5siRI/Tu3RuAQ4cOAZCWluZzW7K1p4yMjIxM0KipqeHw4cNAvbJ75ZVXmDZtGlFRUfTo0YNbb72VdevW8fLLLzN8+HBKSkr48ccfGTx4MLNnz8br9TJ69GhMJhOvvfYaXq+XX/3qV1gsFpYvX+6zHLLyk5GRkZEJGllZWUybNq3J8dtvv513330Xl8vF008/zXvvvUd+fj7R0dGMHz+eP/7xjwwePBiAgoICHnjgAZYvX47RaGTWrFm8/PLLREVF+SyHrPxkZGRkZLodsquDjIyMjEy3Q1Z+MjIyMjLdDln5ycjIyMh0O2TlJyMjIyPT7ZCVn4yMjIxMt0NWfjIyMjIy3Q5Z+cnIyMjIdDtk5ScjIyMj0+2QlZ+MjIyMTLdDVn4yMjIyMt0OWfnJyMjIyHQ7/j957BFtPwaT8wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h3_df.plot('class', cmap='viridis')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}