demo_reader.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Install IPD package \n",
    "Note: Also installs notebook packages for running demos. Omit the optional `notebooks` group if you don't want this.\n",
    "### Option 1 (from source):\n",
    "1. Clone this github repo\n",
    "2. Run `pip install -e .[notebooks]` \n",
    "    1. Make sure you are using a compatible python version (tested with 3.9.2 and 3.10.14)\n",
    "    2. Recommend using a venv\n",
    "\n",
    "### Option 2 (not available yet):\n",
    "1. `pip install intrinsic-ipd[notebooks]`\n",
    "\n",
    "### Option 3:\n",
    "1. `pip install git+[github/http/url]@main[notebooks]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from intrinsic_ipd import IPDReader, IPDCamera, IPDImage, IPDLightCondition\n",
    "import intrinsic_ipd.constants # import CameraFramework, DATASET_IDS\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pprint import pprint\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(\n",
    "    level=logging.INFO,  # Set the logging level (INFO, DEBUG, WARNING, etc.)\n",
    "    format=\"%(asctime)s - %(name)s - %(levelname)s - %(filename)s - %(funcName)s - %(message)s\",\n",
    "    datefmt=\"%Y-%m-%d %H:%M:%S\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Download (if not exists) and load dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Select a dataset among list of dataset ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset_basket_0',\n",
       " 'dataset_basket_1',\n",
       " 'dataset_basket_2',\n",
       " 'dataset_basket_3',\n",
       " 'dataset_basket_4',\n",
       " 'dataset_basket_5',\n",
       " 'dataset_basket_6',\n",
       " 'dataset_basket_7',\n",
       " 'dataset_basket_8',\n",
       " 'dataset_basket_9',\n",
       " 'dataset_darkbg_0',\n",
       " 'dataset_darkbg_1',\n",
       " 'dataset_darkbg_2',\n",
       " 'dataset_darkbg_3',\n",
       " 'dataset_darkbg_4',\n",
       " 'dataset_darkbg_5',\n",
       " 'dataset_darkbg_6',\n",
       " 'dataset_darkbg_7',\n",
       " 'dataset_darkbg_8',\n",
       " 'dataset_texturedbg_0',\n",
       " 'dataset_texturedbg_1',\n",
       " 'dataset_texturedbg_2',\n",
       " 'dataset_texturedbg_3']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_id = intrinsic_ipd.constants.DATASET_IDS[20]\n",
    "intrinsic_ipd.constants.DATASET_IDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Select camera and lighting condition\n",
    "See `intrinsic_ipd.constants` for available options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dataset_texturedbg_1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camera = intrinsic_ipd.constants.IPDCamera.PHOTONEO\n",
    "lighting = intrinsic_ipd.constants.IPDLightCondition.ROOM \n",
    "dataset_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Read dataset (optionally, download)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `download` option allows you to download the dataset if it does not exist, otherwise this class will read in the dataset from the root path specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "corner_bracket2_symm.json: 0.00B [00:00, ?B/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "corner_bracket2_symm.json: 0.00B [00:00, ?B/s]\n",
      "2024-09-21 00:09:19 - root - ERROR - utils.py - download - url: https://storage.googleapis.com/akasha-public/industrial_plenoptic_dataset/cad_models/symmetries/corner_bracket2_symm.json failed to download with error: HTTP Error 404: Not Found\n",
      "corner_bracket5.stl: 0.00B [00:00, ?B/s]\n",
      "2024-09-21 00:09:19 - root - ERROR - utils.py - download - url: https://storage.googleapis.com/akasha-public/industrial_plenoptic_dataset/cad_models/corner_bracket5.stl failed to download with error: HTTP Error 404: Not Found\n",
      "corner_bracket5_symm.json: 0.00B [00:00, ?B/s]\n",
      "2024-09-21 00:09:19 - root - ERROR - utils.py - download - url: https://storage.googleapis.com/akasha-public/industrial_plenoptic_dataset/cad_models/symmetries/corner_bracket5_symm.json failed to download with error: HTTP Error 404: Not Found\n",
      "handrail_bracket_symm.json: 0.00B [00:00, ?B/s]\n",
      "2024-09-21 00:09:20 - root - ERROR - utils.py - download - url: https://storage.googleapis.com/akasha-public/industrial_plenoptic_dataset/cad_models/symmetries/handrail_bracket_symm.json failed to download with error: HTTP Error 404: Not Found\n",
      "hex_manifold_symm.json: 0.00B [00:00, ?B/s]\n",
      "2024-09-21 00:09:20 - root - ERROR - utils.py - download - url: https://storage.googleapis.com/akasha-public/industrial_plenoptic_dataset/cad_models/symmetries/hex_manifold_symm.json failed to download with error: HTTP Error 404: Not Found\n",
      "pegboard_basket_symm.json: 0.00B [00:00, ?B/s]\n",
      "2024-09-21 00:09:20 - root - ERROR - utils.py - download - url: https://storage.googleapis.com/akasha-public/industrial_plenoptic_dataset/cad_models/symmetries/pegboard_basket_symm.json failed to download with error: HTTP Error 404: Not Found\n",
      "t_bracket_symm.json: 0.00B [00:00, ?B/s]\n",
      "2024-09-21 00:09:20 - root - ERROR - utils.py - download - url: https://storage.googleapis.com/akasha-public/industrial_plenoptic_dataset/cad_models/symmetries/t_bracket_symm.json failed to download with error: HTTP Error 404: Not Found\n",
      "2024-09-21 00:09:20 - root - INFO - reader.py - __init__ - \n",
      "\tDataset:\tdataset_texturedbg_1\n",
      "\tDataset Path:\t./datasets/dataset_texturedbg_1/test\n",
      "\tCamera Type:\tPhotoneo (ID: 000)\n",
      "\tLighting:\tROOM\n",
      "\tNum Scenes:\t30\n"
     ]
    }
   ],
   "source": [
    "ipd_reader = IPDReader(\"./datasets\", dataset_id=dataset_id, camera=camera, lighting=lighting, download=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Examples of reading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Getting a list of available parts and object instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'corner_bracket0',\n",
       " 'corner_bracket6',\n",
       " 'gear1',\n",
       " 'handrail_bracket',\n",
       " 'wraparound_bracket'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd_reader.parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('corner_bracket0', 0),\n",
       " ('corner_bracket0', 1),\n",
       " ('corner_bracket0', 2),\n",
       " ('corner_bracket0', 3),\n",
       " ('corner_bracket0', 4),\n",
       " ('corner_bracket6', 0),\n",
       " ('corner_bracket6', 1),\n",
       " ('corner_bracket6', 2),\n",
       " ('corner_bracket6', 3),\n",
       " ('corner_bracket6', 4),\n",
       " ('gear1', 0),\n",
       " ('gear1', 1),\n",
       " ('gear1', 2),\n",
       " ('gear1', 3),\n",
       " ('handrail_bracket', 0),\n",
       " ('handrail_bracket', 1),\n",
       " ('handrail_bracket', 2),\n",
       " ('handrail_bracket', 3),\n",
       " ('handrail_bracket', 4),\n",
       " ('wraparound_bracket', 0),\n",
       " ('wraparound_bracket', 1),\n",
       " ('wraparound_bracket', 2),\n",
       " ('wraparound_bracket', 3)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Objects are identified by part and instance.\n",
    "ipd_reader.objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Getting ground truth pose annotations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Poses are stored as a Xarray DataArray with 4 dimensions, with the following coordinates: \n",
    "- `scene`: scene id integers in `reader.scenes.keys()`\n",
    "- `object` (pandas multi-index) \n",
    "   - `part`: part names in `reader.parts`\n",
    "   - `instance`: indicates which instance when there are multiple parts\n",
    "- `transform_major`: [0, 1, 2, 3]\n",
    "- `transform_minor`: [0, 1, 2, 3] \n",
    "\n",
    "Poses can be accessed by specifying the dimension and coordinate value.\n",
    "\n",
    "See [Xarray Tutorials](https://tutorial.xarray.dev/fundamentals/01_datastructures.html) for more details. This is a brief overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('scene', 'object', 'transform_major', 'transform_minor')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd_reader.o2c.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * scene            (scene) int64 30 31 32 33 34 35 36 ... 53 54 55 56 57 58 59\n",
       "  * object           (object) MultiIndex\n",
       "  - part             (object) object 'corner_bracket0' ... 'wraparound_bracket'\n",
       "  - instance         (object) int64 0 1 2 3 4 0 1 2 3 4 ... 3 0 1 2 3 4 0 1 2 3\n",
       "  * transform_major  (transform_major) int64 0 1 2 3\n",
       "  * transform_minor  (transform_minor) int64 0 1 2 3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd_reader.o2c.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting by scene:\n",
    "Will give you all the poses of all the objects (part & instance) in the scene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd_reader.o2c.sel(scene=30) #returns a DataArray\n",
    "ipd_reader.o2c.sel(scene=30).data #returns a numpy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting by part:\n",
    "Will give you all the poses for all the instances across all scenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (scene: 30, instance: 5, transform_major: 4,\n",
       "                   transform_minor: 4)&gt;\n",
       "array([[[[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "         [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "         [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3249,   -0.6633,   -0.6741, -166.4593],\n",
       "         [  -0.1151,    0.6797,   -0.7244,  220.2536],\n",
       "         [   0.9387,    0.3129,    0.1445, 1750.2346],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.5102,    0.7794,   -0.3637, -372.5465],\n",
       "         [   0.7988,    0.5861,    0.1355,  162.0151],\n",
       "         [   0.3188,   -0.2214,   -0.9216, 1967.5482],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3353,   -0.5541,   -0.7619, -201.6179],\n",
       "         [  -0.1137,    0.779 ,   -0.6166,  346.7942],\n",
       "         [   0.9352,    0.2934,    0.1982, 1776.7752],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "...\n",
       "\n",
       "        [[   0.0597,   -0.8167,   -0.574 ,   89.5117],\n",
       "         [  -0.0324,    0.5732,   -0.8188,  264.2713],\n",
       "         [   0.9977,    0.0674,    0.0078, 1883.1399],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.6905,    0.7158,   -0.1043, -158.7451],\n",
       "         [   0.7155,    0.697 ,    0.0468,  180.2697],\n",
       "         [   0.1061,   -0.0423,   -0.9934, 2039.3486],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.0704,   -0.7221,   -0.6882,   30.1857],\n",
       "         [  -0.0294,    0.6881,   -0.725 ,  384.2422],\n",
       "         [   0.9971,    0.0713,    0.0272, 1889.4999],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.9626,    0.2695,   -0.0292,  -88.3007],\n",
       "         [   0.2695,   -0.963 ,   -0.0033, -195.1056],\n",
       "         [  -0.029 ,   -0.0046,   -0.9996, 2032.7744],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]]]])\n",
       "Coordinates:\n",
       "  * scene            (scene) int64 30 31 32 33 34 35 36 ... 53 54 55 56 57 58 59\n",
       "  * instance         (instance) int64 0 1 2 3 4\n",
       "  * transform_major  (transform_major) int64 0 1 2 3\n",
       "  * transform_minor  (transform_minor) int64 0 1 2 3</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>scene</span>: 30</li><li><span class='xr-has-index'>instance</span>: 5</li><li><span class='xr-has-index'>transform_major</span>: 4</li><li><span class='xr-has-index'>transform_minor</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7986b25b-54b4-4f39-b692-0f417fc5e43f' class='xr-array-in' type='checkbox' checked><label for='section-7986b25b-54b4-4f39-b692-0f417fc5e43f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.1998 0.7808 0.5919 -209.3 -0.5991 ... 2.033e+03 0.0 0.0 0.0 1.0</span></div><div class='xr-array-data'><pre>array([[[[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "         [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "         [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3249,   -0.6633,   -0.6741, -166.4593],\n",
       "         [  -0.1151,    0.6797,   -0.7244,  220.2536],\n",
       "         [   0.9387,    0.3129,    0.1445, 1750.2346],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.5102,    0.7794,   -0.3637, -372.5465],\n",
       "         [   0.7988,    0.5861,    0.1355,  162.0151],\n",
       "         [   0.3188,   -0.2214,   -0.9216, 1967.5482],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3353,   -0.5541,   -0.7619, -201.6179],\n",
       "         [  -0.1137,    0.779 ,   -0.6166,  346.7942],\n",
       "         [   0.9352,    0.2934,    0.1982, 1776.7752],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "...\n",
       "\n",
       "        [[   0.0597,   -0.8167,   -0.574 ,   89.5117],\n",
       "         [  -0.0324,    0.5732,   -0.8188,  264.2713],\n",
       "         [   0.9977,    0.0674,    0.0078, 1883.1399],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.6905,    0.7158,   -0.1043, -158.7451],\n",
       "         [   0.7155,    0.697 ,    0.0468,  180.2697],\n",
       "         [   0.1061,   -0.0423,   -0.9934, 2039.3486],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.0704,   -0.7221,   -0.6882,   30.1857],\n",
       "         [  -0.0294,    0.6881,   -0.725 ,  384.2422],\n",
       "         [   0.9971,    0.0713,    0.0272, 1889.4999],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.9626,    0.2695,   -0.0292,  -88.3007],\n",
       "         [   0.2695,   -0.963 ,   -0.0033, -195.1056],\n",
       "         [  -0.029 ,   -0.0046,   -0.9996, 2032.7744],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-9023091a-b1b2-469a-882b-0c365d4e5932' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9023091a-b1b2-469a-882b-0c365d4e5932' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scene</span></div><div class='xr-var-dims'>(scene)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>30 31 32 33 34 ... 55 56 57 58 59</div><input id='attrs-f112cfde-12fa-46fa-96cc-2d8005762216' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f112cfde-12fa-46fa-96cc-2d8005762216' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b2c762-1496-4b57-a33d-bcacfc5b833b' class='xr-var-data-in' type='checkbox'><label for='data-e6b2c762-1496-4b57-a33d-bcacfc5b833b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,\n",
       "       48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>instance</span></div><div class='xr-var-dims'>(instance)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-6e79a48b-68f6-4a60-adae-9163a3f4b7f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e79a48b-68f6-4a60-adae-9163a3f4b7f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fd112a4-b671-47db-a55d-e41b5aa3bb5f' class='xr-var-data-in' type='checkbox'><label for='data-0fd112a4-b671-47db-a55d-e41b5aa3bb5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>transform_major</span></div><div class='xr-var-dims'>(transform_major)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-5c3c81db-977d-40ca-ba40-612165cddd36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c3c81db-977d-40ca-ba40-612165cddd36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b96344f-8a94-4327-b969-954c101e1f93' class='xr-var-data-in' type='checkbox'><label for='data-0b96344f-8a94-4327-b969-954c101e1f93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>transform_minor</span></div><div class='xr-var-dims'>(transform_minor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-553a6977-9481-42bd-bb01-548caa488683' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-553a6977-9481-42bd-bb01-548caa488683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dcaf91b-4196-4110-a604-785336c003ca' class='xr-var-data-in' type='checkbox'><label for='data-3dcaf91b-4196-4110-a604-785336c003ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd6eb0f1-7741-426c-9a47-1fc4c36ca2c2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd6eb0f1-7741-426c-9a47-1fc4c36ca2c2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (scene: 30, instance: 5, transform_major: 4,\n",
       "                   transform_minor: 4)>\n",
       "array([[[[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "         [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "         [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3249,   -0.6633,   -0.6741, -166.4593],\n",
       "         [  -0.1151,    0.6797,   -0.7244,  220.2536],\n",
       "         [   0.9387,    0.3129,    0.1445, 1750.2346],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.5102,    0.7794,   -0.3637, -372.5465],\n",
       "         [   0.7988,    0.5861,    0.1355,  162.0151],\n",
       "         [   0.3188,   -0.2214,   -0.9216, 1967.5482],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3353,   -0.5541,   -0.7619, -201.6179],\n",
       "         [  -0.1137,    0.779 ,   -0.6166,  346.7942],\n",
       "         [   0.9352,    0.2934,    0.1982, 1776.7752],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "...\n",
       "\n",
       "        [[   0.0597,   -0.8167,   -0.574 ,   89.5117],\n",
       "         [  -0.0324,    0.5732,   -0.8188,  264.2713],\n",
       "         [   0.9977,    0.0674,    0.0078, 1883.1399],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.6905,    0.7158,   -0.1043, -158.7451],\n",
       "         [   0.7155,    0.697 ,    0.0468,  180.2697],\n",
       "         [   0.1061,   -0.0423,   -0.9934, 2039.3486],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.0704,   -0.7221,   -0.6882,   30.1857],\n",
       "         [  -0.0294,    0.6881,   -0.725 ,  384.2422],\n",
       "         [   0.9971,    0.0713,    0.0272, 1889.4999],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.9626,    0.2695,   -0.0292,  -88.3007],\n",
       "         [   0.2695,   -0.963 ,   -0.0033, -195.1056],\n",
       "         [  -0.029 ,   -0.0046,   -0.9996, 2032.7744],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]]]])\n",
       "Coordinates:\n",
       "  * scene            (scene) int64 30 31 32 33 34 35 36 ... 53 54 55 56 57 58 59\n",
       "  * instance         (instance) int64 0 1 2 3 4\n",
       "  * transform_major  (transform_major) int64 0 1 2 3\n",
       "  * transform_minor  (transform_minor) int64 0 1 2 3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part = 'corner_bracket0'\n",
    "\n",
    "## Using where\n",
    "# ipd_reader.o2c.where(ipd_reader.o2c.part == part, drop=True) \n",
    "\n",
    "## Using sel by \n",
    "# ipd_reader.o2c.sel(object=[o.item() for o in ipd_reader.o2c.object if o.part.item() == part])\n",
    "\n",
    "## Using sel\n",
    "ipd_reader.o2c.sel(part=part)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting a single pose:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "       [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "       [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "       [   0.    ,    0.    ,    0.    ,    1.    ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part = 'corner_bracket0'\n",
    "ipd_reader.o2c.sel(scene=30, part=part, instance=0).data #returns a numpy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Getting camera info and images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-21 00:09:22 - root - INFO - reader.py - _get_img_file - No image type specified, using IPDImage.PHOTONEO_HDR for camera PHOTONEO\n",
      "2024-09-21 00:09:22 - root - INFO - reader.py - get_img - Opening image from ./datasets/dataset_texturedbg_1/test/000030/000/rgb/000000.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2935547850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4ecvyyTz1lmQJGjSPsu7kM61fgH1dzVlogm4+l76CdN6WjvW6r5E3hvPIk2uvT1vtYJXRqssYWf0i1p+0btVxPEBp9QsAASAS8MmZNmmPtO0CMLEjNy/d8iAmNkLgf2s5Sc7C6O1saTo5G8KtddzxoGdPODqwzi/QZI0eOcKQ6VEp61OKpaGS2+64DZdllWWj4ErgozuURMvyGfPTMzhUGgyrnSfztJsHNFlW6zn5YtuUSiVUKhV0Op0ABi3jPS3lBQ/ynVUeD9Rlgak8vFlGMqb4ZRwNNtiGUjY00LdIKv0YaLdArNf2lkK2SCtaygDLx91mDMu0qWQLhcKEs3Veg6L1jebXKpc2Jh7AsMqm/UxkOAkwZdmYJ42MzoN6p9frBQNOEFooFNDv91Gr1TA3NxfOQ6Ge0jN7um5IelnXqiuZjjcIkHUlB3lSb8nZYpZFDjzlAFMbXQnGtQ6TPOl3WfrOkgkLPHpAIg9JYJJXb+Qhrw2kTFrHhFjfVt+fhm55EOM5qcrOzXcW2tezOGmahpHbYDAI513QWVT6qgCTW0y5vVPOsMi8S6VSWILic+kTww4ld3boKV2Wj8SOR2XF7YEEOVTWssNYQEFvF5QdX+bJON5Uqz58TCpp2UaarOf0WdDv5GhLGk4qZSoxHWeavPO8i5EGFPK5fK/jSF7zgjENXHS+WpnqNtPpyOc6Hc/QWDzpOFY7SpLyKcG4VQ4968lzdfi/XC6j2WweqEstl9yJxFGl9KfRPHsAVfcZ9mkpo1Y/4jKx3I6u86MeIU/0A6GOGQ6HQdZZxl6vF/z8CNT0iJh8sc68D+umWq2i2+1GjZsFYGTf1wBajtw98K3lUPtiaMAtD5FjfVrL7lIGrEGdJ7OSJ8mX/K2fWd86HatOsyjGo+bPK4fXfrpsMk6sTzNM7P+0dMuDGJJG+dpIxJS1RPIMx45PQ0og0+v1wvkJ2ogCmPCRsU6xpOKwnktAFRN22Rn1NG+pVArT5jKcBhieAZHfcrbFAoAAJqad0zSdOARK1pE32tGdJw/QkCNnPXVJABfzA7HqNc9v679FeQ28p0B0G1hKU4aX9S9lwlJKMv/DALs8IEzGl0Ddeq/je7Kg5Y8yzmd0ngf25GNubi4AYA24pfxJMMORZbfbPTA44W43Ovt7gJFgilvBmZ8crPCbzqRyGUTySb2UJEk4WTmm35JkbzAzOzt7wGndkgX6Bllnv+g4siysA/pEefH1Epbu9xrUWMDZ48kzutIxl0v8BIs8T0gubWkQw3Rkfppfto3Fs9bfGjxrfq36iVFWHUheZH17lBdgWH09iw+t5w5LtzyIyYMygf1RDX+TvLVeKoQ03dupxBNbB4MBut0uqtXqhEOvVFJydCPz4pZpz1FWdizy5nWGNE1D/lxGYhpyiYkCTeCkFZ80kjJfGUbzKhWxLq+sV8uAyLqVoyMZVuanDa+sA2nECDbr9fqBUyR1OTxAK59Jg6EVsEUxgBR7H1NErMcsiikTDbJ13pYhBHAAbFL+ObtHA7G1tXUAFIxGo4llJi3vWvlb9aV51QqfIJ3bcGU+9Xod1Wo11J8eHMQ+SZKgVquFuBIYSj8zrcTJp96dpXmX5Wo2mxPnR0n+5O9CYe/gR56Bo+tbpi1nYK0ZUMkzy2TNRjCMrJdyuRz44cwVz8sib14batDnHRbKMjAfq89J+SXpGfnRaBQ2ZvD4DOkfI2VS6lwNvvSSpbYjFsDRban7vrZLWbKv42WBg5jus9LzKKbD8ujEN4q+I0AMcHD7nqXYpVICJoVQz1oA+yfEDgYDDAYDlMvliRkZeU+N5Eeexms1sOWjY3Uiyb9WmFyW4jq2NCCSD3ZQ+opoBWB1Uj26tBQN+eS0taWoJJCRSlUrDU6n62lfTdYII0mSCafomOKLjRbkfwm8PAUg5cSSHYtnmZ+uL6v+LR4t+ZXv+M16kKN5bcC0AZdLHzI8wbDehceTVaUsczZDHk0QU76x+pW/tcGmzxpH2fSVmJmZwezs7IH7ZfJ+dF+S5faWMmSbcAZXv7P6lfYr023p1ZMnM1Y9WnpQppllqGQ4eSQE65rPOQNM3aj1MdOhHtLt6Rlegiwts9oNQPYnKSNaD8kTzqU+jAELySsBnNVGup61rrLqVOdhAR+pW7IAjPfOkgtPx1rPNL+St2l5mZZueRDDQ+eAfeUtZyWASWSvAYJcLuF/xmF8bnMeDAbhGPzBYBA6CdeepaBzhsIScm90LQWdvyUgkmVMkiRM5dKfRR/nLcusDyDzhJLx8oAIWb+6jMyL8byD+CRwke0mjxoH9petPOPg1aUFICSflhL3fFG0gpIKVitdwL+PRsqEdDaU7UyFL8s5Ho8nTnqWz/X/crk84eQpy2QZKSkjenmK7Sfr36rzQqEQllE0gJJ1oIGw5slSmjI+l2zG43GYBWT5Go3GgTur8nzYXrJt5D1ZTFPOcMo6tNKTPOv/GlzJNKy41nMPCHiASOeTBXzy8ECicaf8yB2esn5leNn+8r2WF62jYwZU6ifZTroeLDsgZ6W8sksQMxgMoEn2D6/eLTnRZfbqOQ/perHiZ+lM678Oaz230nu9dMuDGE4ZSqOp0a9UpHpqWwudBTBmZmZQqVTQ6/XCdupKpRKcaAlkZCcbj8cTvjEkzZt+rmeRdJoaKHHaVO+wkFPaXGqRaWrgwXfym/VjCbdlZPTIQZKewpXKhM/1TIz0S5CzLPI0XWmcCebyADJJevbHmlrXS3yyDuRIj7xw1CfPsOBIVvs2EJzIW9BnZmaC06nkn74UDKMBh0zTWyLQ7RVTmvzNZQOv7S3lLHfJaMCZR2FaRorbO9M0Dd+UAbmcaPHDNvLa1JJP2db0hQEwYSCtmVUNLuS3F9b77YEg/d8CL9ZvWcYYH54R8gwxMDl7zcEI9RSXADWIIlkypctlyZOnS9m+Wqdm1ZkebOkBgAdivPrKAjRZcfPk4eU5Dck4VlxPl+p4MZB5GLrlQUyapmGKkLMS2lFMKtM8nVz7lwAIZ0sQMHBNOE3TIMz6UDwaX21MiPitGQzrv3wuZzc4WyEPsZPvmRcBnp6JsvKUz3XH1/Wq42tn2hhZHYKKp9vtBhBgtRvDMh8J1trt9oSCIuCRozmro8r6kyBK7jCTIEoeo62NnUxb1re1xEjjaM1+6HJyFkb7eWiS/ka6nJJYt7KMuk9YMijTlN8EFXT8ZHtmyVjMmGljzRlR/pbv6/X6gS3fVtvIsvFbG1fLyJRKpeDvQ9mQ/miaLFCg05T9yTNqsXLo8lj5ejxpvqz31jMJEizepb6R5/HwWH9gcknZKoMuS5aR9/SJpz/ke5ZFAwymLz8cJOmtxVq2YvWnedfhZH46XJZe1elYv63wXj+PhdX2wYofa5+8dMuDGG1EWFmc9qOCksYOONgA0jB4gIeKWZ7kWygUwvQ2/UPkyI7KTo6y5dKBVKRaIWhFxjLJcrJjAfsOvnIGg7zILdRylG4pIi38zFuDFEsBSsNpfdge1gyCjMdyWp1LgjE5SkrTNNSBlaYEIlbdSkOm5USGtU6wtRSftXypDZiUhyzyFGWWopfkKXtPEUsDH0uT/UJecCnbTs5IabnP4hdAWKJot9tIkiRsOWZ8XhSoFaoFGjy5tJ7L8rH/yAGKHEToZd8s4ynTtuqTH/ZpS948WbUMsvVt9ZOsNtFLgJoIuPlb+qBw9ybrSx9+ptOWcmK1lS6PNZPN3xZZ6WgZknpWzhrruorlEQM5OkxMPmU+ltzE8vVI17GVprSJWfl6lDecplsexFSr1bBVURrlNN0fMeutxfq3NHqWEpSGj7MtBCV09uXFYQAmDr1jfP62BEsLjyUs2hHWGkHQaGqHRABh67H2P9H5Sb4t8Md3ecKRb5m2BAsk6atjGRTZNtLwyxkzrVQZT29b9QyLDOsR25IyI5/rfCXP2iHUIglkrPT42zNIWWXTvFoKWysyT2FZ8su+kKZpWEajPOjTqDXfOi1LBgaDAdrtdkhPG6x6vT7hKOuVP2YoLPAgeZV+Z8D+wEfPykxjTDwQop95AMV6pwGO9Ts2YLL6npazWDmAyaVOEttHOgJTT1uG3tLFFsDw6s4iGV73F6mrJc8aiOlyWwDDqherjuXzWN89TFmnCWvVhbSVOlyWPnsj6ZYHMVzWIaqXZ7Mkyf7x6AQzGqnr9U9pJBlOgxwqazoVcquhPGiOJPPVjpFWp7GUCp9LHmdmZgKA46mbJAtUcOpdpiMNsu4gkk8Zz3NWtYRdk6XUWBYJDDTAkSBAv/MUgyRrlG2F0XlnpRXLNzZasoyafO6N7mKjvizwdZh3VlgLwPCbfmHAvrzJU67pJ2bxzmfsx3IWtd/vo9PpTMwg6r5KEMP/Vj3LdpKDAC3fWqb5WxsyCcrZbnS6toBYnpGsB6hibWWlbcl8VlpZdab5ndaI6UGI1N2sS49Hy8BaPHj9i/l7fcxaEpThCVblLkqG0eG9urVItp23RBwDSTqdPHl6YWQaHoCcRhZl/NdDtzyIYePLUy85IpIKik5lUglZgiGBBYXbQueePwxnZLiLielwtCY7sXcZowYsnpLjlfFyFkHyLI/olnF1mXWe1nP5XwObmGGWecn/BDBa2KUxkaOgGPCIkafQ9DuPVx12GsWQp9PznaWwrbrx+MtbRg+0enG8smmSMsglVnllBtOTW/2lwmQdbG5uYjQaoV6vo1wuo9frhaWjND3ojzQej8OOJEvZy3azyqRnSeUsi/xmWPYrDU64rZxAzTqXSbdFrK9pg2gZG/1ep50FXmJAxuJLlndaACNJtrv0YZQzzNp/7LD5aSDKtCQPnk8TieH1YNQy7LrNvcGKlYc3Y6uBlwUqYmX3ymSF98quf2elkVf35aFbHsTojlsoFA5c+CY7Mqe2pfOZ9vWQ//U7fuS1API6AoILbseWTryWP4Ze72b61oyHLrNcQpM+CJawWeBDp+uRlbfXGWMkBVx+ZB56JGDxKuNqBeKl46UVK2uedxZgySJPWXh5yvB6LV4rs9hoUIf35MGrMwtIWWAqTdPgBE9nXxp39hstCwT14/EYJ06cQLvdxs7OTrjKg325Vquh0+lMDAbq9frEf6suLOWqyxQrjwTe2g+PxkfuMtPbe2NktYXmReZn7bbRYfJSzABb4TwdkzcfmRewPyBkvRKgSud0frTfmMUT/1s+d3r2xNJrum9oP0mZjqbXA+50Xcr8NOUFIBZPlr7Kw3dMr7xekJJFtzyIIWllw85O5aiRvb5hVa/XWztxZIdnPnIGhsqb//v9PiqVCoDJE1BlGtZvXSZJGrFLB12pGKRS9Mpg1aHOS/OXlyxAwd9ePhJAekpPl8vjTRt3/X/aslj/D5uWp3yttC0FrZ/FQIim2LssI6bBQEzRAgi7qbrdbtilIm9gl8Be7kbjoCBN07DVfDweo1arYTweh6WqJNm/uNArgzQ8WmlbvPOd5cdG+bSWOHl+jAeWNFjQH/lOhrd+W/rCkncvDZ1WTL4t3eHpLa8cnn5jWnLGWg/G0jSd2JptOcB7fMV0nMWHBST0+yTZ35lkDZSy8sqqI0surf/TAhDmo8s0TVz6ntIvLHYyuuUfeVi65UGMNvoaJcutfmwEvTOHKF8aUaanlbVUcCROI8ubr+noR4VNXvT0tOXUpkdlWrilL0uW4osp7Dz1GjOYkizAZ3VyT+ilQZCjH69MmifLeHggSqadVTdeebXc5UnLKrdXrzEwY+VjGZwsI5b1PAa2vLiybungm6b726GlX4lMl31QzlqOx2O0220UCvuH6PGmasavVCqZoM2SJd2/vLDSv0zyLwcREiix33tgwgIFmh+vjmMyofPKA2ay8rPy1HLxenQLv/UuPqlfZVip7+RsVJ568/j0dIT+zbZnWrEde1p/8xl5sGZypqnHPDpRpxkDK1ab677Z7XbR7XYPnAjPjS1emeX3YemWBzFyxCQBBytf+opIgEFAw7gEMRROTnHKXTPA5HIPG4dhOPsiL4mT//VpnyRLKXidQH40uLIUjs6Dvy2flCxUTj7ktxZ4D8jIutO8W2kznDV1Pk2HzFMuD3Dxv96ybcX38pVt5RnTGHlGb5pwMSVlhbHCS5nyjIGuBx7EyN0owP75IFKhS6DA2ZterxduaOZRBqPRCP1+f+LsHx5pIAcgug957WPVmTVgAPb7uD5vScokD+LjzIF+b/W3WF/NqnOZhpYvC8xn5e0ZvTwgTPM0zXOvHeR7CSSBSaAjB6EW71oudN6yvrSez+JR2wZL5zGOJYdeu+fpczp9612sv+bRabwQtdfrTQwygMlDQuUZaXzH/vJ66ZYHMSTLOOpTbDkTwtkRPTMDTI4KrTsypGOXFAL64hDIcJo7Tfedf+XBT1TgVM76tGGS1ak1iPF2DHnGi2XUMx4xYZflj51poju1fJ5n5OaNSHXcmBGWcTySdZiVN8N79aO/Y0Atpphi5dPx8/DnyY4HOnX6ljK2wvMj+4U0CvQRI5CX/ZL9gTMY7XY7yCbDAwjLTVxaGo/HEz5nVjvmNbh6MGAZPfpo6Hu+ZNl5d5BnqCyZ02H0Tsgs/q1wVtg8BtHKJ5ZvnriekfX49EiCGP3bmxXRjrJWv7T6reRP2wVdFu0kr9tYg0zyYX1b9cT3MT2VVzY0ab4kjcd7V3p0Op2Jm8r1DCr7rTwXyMrj9dAtD2KsUbIlOHzOb7mjSR9UJRtKbp/kf52eTJONyaUkbr3mtlF5CiwwuWavt27qssi8ZOfVowirvJpfnb9VVossMHLY9U+v81kdyyqDB5hixjnW4WUdyjrN6oSW4Y+NkKx4Md7zKCKL8iqwPAYuT71xBoVHDmhlz0MhKUPMSw4kKPvynBkNhhhXHywpy+2BfquONHiM6RPqDYIYzqzK37IuZXrasGXVp653y0DKdzKMZ+RkeT1jK+NYBjYGfD2+dRli4bwyS73I//oqDxpWGd/aUBGrV4s0aJX1mVU/fEed7fGQRXl0Sl7KApej0QjdbnfiagUOOqTNotwT4DOcBegOS7c8iCFJIYk1kKYkScKIj51BOi1J/xMAE1NpwOQBXuxUhUIBvV4vbLUul8vhuPRKpRJmMyQI0DuMmJ6clo4pLRnGGmlLReCRN5plHhZYiXUsD+Vb+ennOq43WsljdGNyoMFuFvDzDJAFCPKAAysdL39L8cbKZ8W1Rn95KQYOx+MxOp1O2Dk0NzdnHh7oDTrIkx4ly9lKzsJQmdIXjXWvp7B1/XtGxpI3XVe6L0plnqb7To9ZfdaSY82fDu+1u05HU56wsTBZQItl1HGm6XtZ/GjQo0EM20L7LElAqfWa5sOSRw045A4payZekpQXS9Y8/aG/Y2AxT5oxkvxr/ZemewMSzirGdEcePfd6gcwtD2KkAtKG2lJKVIp6HR3YP5jOGj3pjhATYCpY7syQN1/LHVMkdjo5GyPTY1myBEELYszoyHByWUmS3OWlSSoTzesbZSBl22qlYsWzOvK0IMpSorFOy3Qsg2PFy6O0Y21+WHBmlTOr/FbdWn1KfqSjupQH7SOSRVp+0zQNdyal6f7J2dIhn995lhG8QYHFnwZVctSfpmnwsdNtKQcmMQCjn+UlS6a8dC3AZIF3Hd76r9ORpNOMyZiVrlcOKQ+6j8m20/4afK4HGRZfepZE15OcsfeAr+TZK3feMB7lrWNdTl0mKy1PniRA1P3J6quvB7RouuVBDEl7uOtnrFQ2gjwldlqBlA2rt1rKvDkD0+/3J/xw5G24WrlKkmEkmIgpEL1uqzuuFrgs4BETcm8EI/OwRhZZHdlL0+MvT5qx97FOZwErK51plFkeiimpPPzmUYZWfjHyZI79qdlsBof4arU6YeytvqjTjdUtDQj/y3vAZN9mWJI1cveADNO23suBB8shR+fypN4sQGD91jx48S0+s+Lr315/9sCKl7duM/2J9dtYHtMYQcsfSu6AAyYvdfUGQlKnWXyz7T1/QA8oy/LE2lKnZf22/ltp6TJofe0BG74j+K5WqxgOhxOO6lm8e/y8HnrjNmt/mxIFQx78xorVU80SCGjjailRiex1Q8u8qVR1msViMTgf8qRSOkxpIeM7LSxWp8ja3qf5lXVlKXNZZv1bdygrXhYAs9Kywur8s4CH/M7qKJZi8YBenvTylCvvewto5uUjD02jaLQyt9pay7mU90ajEQ6fkx95kJlVdp2PdprVDvgSxMh+SLCjwXxM5rUxl2QBHxL5kne36frOAhoWkNBhPMoDOmLGU5YvlqfHm5W3l7/3PhbX4iEPXyTtCKw/ni6UaUnAKuXKytf6r9POKw+xMufVPXl0iAc0OTApl8vBBcKqpyzd79XtNHTLgxjAV1Ryx48EM7Jj87deU5UkDYs+jE2Gt86/mJmZCUJA8CIVnszDKw9JjiZiAhJTShZSzyIvD6+DeoJuAQWPjzwIXnb219NZLH6nUbaWw18svH5vASsvrPcsxq+noD2jo9PVQMMDkLIvWLKxsLCAc+fOTTjkMgzBh2UoCE4kH3KXIMkbBMT+y/JJ0KSfsTza4ZiDEl3mPH3LMnq6bj3j6JUhb95WPEm6j8pnWWXxeLVkKMaTVxdenVhyqR2+LTBjOYVrHUoZzBpAenUVK69OQ+ZpvcuT9jQykBVP8zStbL9e+o4AMRROKbQ09tIhVzvjxYwp4/O3BDwaTDAfKeAyPS4tSQDjTa1bHdEyHt6MjWXMLeSeB8FPQ9pgyY+ldLwyenxn5asNalZn85Spx2tefrx8DmMAZN3lUTZ5ecxSelbdWAbdK5MGNZVKBcvLy7j//vsxOzsbnH61jwEwueNBAheZr8WzNi5aBj0DY4EYz4Akyf6t9QRR3FYty+zVW1YezCdmqK2yW+SBgRjQiYGjLNDhpemB4qz0vTS9OsgaxFjt7+lM673mPVZG/s6jRz2ZjpXHAzeadyu8pXt12h4PeeyKxV9MPvLQLe8ToxvDOzNChk3TyW132hlQCqV0ALaAB+OT9DkPMo9KpYJer3cA7DCcHl3GDAt3Uel8vNGyVW8EYlac2LbpLAMmDbBuA+t/LB/Jr1cO+VvXgxfG49tKm996xCZ/6/y0c2ke/nXdWYbbyj9WDg/kxcgK69WzxwPDlUolnDp1CqdOncL8/DzG4zEuX74cQD3X3K17cdjf5JUFepaEYaXB0UsFetefRdoJVMsvv6vVatguTgAj+5DnO5CHtAHJA1Ss+FkAxZJJS3dYaWSFke8svaR5teJp/ZEHeMX6tVWX1uCKsqLzl+G98mbxFqs3C1jEdLDmJUtOZN6eTsojNzp/nZZuZ12OPLrHou+YmRjArnCpVORoie8sxz99SqckfWqk3s0kvbd12FKpFJaWZDiZv+TVUsIMr8GODK/vIonVWcxAxlB4njRlmbzRho6fB9y8UWGs8F59aP71J5aOF04DlphRspSuF16+ywIfsWdWOlnGx6qDra2tsGVzfn4ey8vLKJfLYc29Wq2iUqkEp2DNN/1OOAsiT/9lupzq17JntadXJjmjqs8FkeWmky9PJLaMtlWXHg95DJsXLouyZMZ7lje9acNNU47YIEADDC99Hc4C9VoHazug301DXp+w+PbiZFEWXxZY0+WxZFfy6JU/ZiM0qDks3fIzMdrAWw1ESpJJ73Rv67Dehp0kycSdESQqOolM5W+mI/9z+zW3ZFqGnwAoNqqj0pXl12WXI4vYWq4ul5WXV1/TkmwrD8DF0L+XptVRvfh5wZLmJ6tD5lXusbJaSsEa3cTy9epPp6Xf6XJ66cbS0lQoFNBqtXD16lWMRiO8+uqruHbtWgAxTIMHQUpAwW+ZP31SCCDknUw8ZK9araJarU5cDOnJmAcsyTuACVDDszM4g0SHfV0/eRR4XhmUukU/j4FW8mL9l8DYi2/Jal6jFOursfcyj1h/luGz6jH2PisPrU+zdJB+Fnsv5S3WL7Oe6bRicWL1zrDa7kjZyLITnoy+HjBzy4MY3RmBgx1VAg05A6KFiEpTKyFruliGlUrdWp7RIIeHdEne9ShPvvM6kLU93DMwXv1YfGqKPbf+e6g+K77XCWLvdZhY+l54Xdd8FgMHXlgpD3lAY1YdSL7y1KlMJ8sw5U0vVl9yNjNN07DMqY8DWFtbAwCsr6+H85LktR6yvFqmZT/ls93d3XCaKN9zq3Ov10OpVEK1WkWtVkO1Wo0CL6kLrPrnIIZ3IzEvufVUAi4tT7qeY4Yky+BO22YWKMjTl/PISVaZLODoGXKvfNZ7zVeWgdR92wOZ07SLjmv5KHqALAZm8vAj89d6P0Z52suzp7r+rLiWPXg94IX0HQFi+G0tHVkNZ83GWCMUyyjrpShrWUiH1UAnSZJwGaRHepZFp03AJNf/LdCk60OWUT636k5SHvAgy2yR5lEbKi8vD6DFwMs0ht/qnFoevDi6XDFFGPtvhWfeFojUfFnKx0vPK5dl+CyyjHyapuGY8pmZGVSr1bA8xCMGbty4gdnZWZTLZSwuLmIwGGBnZ8eUV0tGNf9cNtW+agQZnU4HvV4P5XI5gBn64mjgwo8EIpRn9jU5EyNvq85rUPR7Szd5ba3DWzKnZcADHl6/sNpA5me9s+LEAE9eucojg3kAjJV+Vl+V6fO3ZeAtHWvJlqQ8ANLiVYJ7izzA5OXr9SmPYvXMjSs84FWX/fUCmVsexMhdSVpRe2BExyXxnVyCkYIpDbQHfmIGWDasdFLU4awO46Fj6XhsCaZc1/f4i3UODZy8DueBJq/TWp3KOjhQ5mnF8epX8pQnfoysssm8Ymnk6ciyrS2lmEVZ4CWmeLN4zFJssm6HwyFarRbSNMXx48fDTAvBPkH76dOnMTs7i/X1dbRarYm85PIr4+rZzUKhgEajgVKpFMJzaUcu0/Kzu7uLVquFbreLSqWCWq124HJX2Z4ELFzekrOj/KZTr0xDO3PnbR8ZxmsbWd+W3GXJsRc2y8jq+tEyKdvFA1kxPrwwciZPp5uH71ide3WoQbN8r+tAvrfsg46bt8/JOHkc0vPqsKz4ecCW5FfzTzujL0d9I+iWBzFA3Ihaxh84qNzlb718pBG2nH2xRoG6oa3dT3QK3t3dNY2MXm7SecQ6j8WLJKmMdL5WRz5sG0wTV+ep2y9Ph7CUi/wvv/nbUzBePVhASaergZ8H5Cw+NUmQmlcpWIYvZiQtHj2QFgPB0umWIEHG47bqa9euYTAYBIfeTqcTwlj9R6dD/xmZLmd+KpUKAKDdbqPVak1cB0Dg0el0wlKTNpb6rBk+p3KWMzHyqhBPlqx6m6YdZD1Y+mda8vK2+oulJyQ/+jf1ln6eFc8La4F4r19bukNSVrtYaXrk8ebxGEv3MDrT0juWXYvx56Wn9YUOZ/0ejUZot9vo9/sTt9RbcnoYmb3lQYzeYURF5KFikqUo2Qm5ru8JthYQaWBkOH2Qkrz0ke/5XPOmBTFrBJJXqWUhbK+MnmBnUR7lkCddC8x4owLr3bR55YlDg+IZBv3fAjaazyzgaVEepZtlHGPAgRQzDkmSBGdaKjBJnM0goL9+/Xo4zdpLzzOi5JP9KUmSACjSNEWz2cS5c+dQLBZx8+ZNXL9+Hd1ud2I2pdVqodPphGWmWH1Rp3CJiruq9NEKMfKAgle3edrUy8PTEd6zLIM1Td+MybYMG+tTlpGO8SDLHavTWHmsPuC1SRYgzcqf72I2JQ9Z9TitHOm8DzODIvs7d+sVi0UUi8UwW1osFkPfm5Zu+S3WBB4aPEhEyQ9Hb9KvRSNquW1aH6AnR0HWVJ+FYq2RnZx606cK63JZ5dWCGzsxlnnKb6Zj/Y4902lOAwZiHTZrVJOXrxjizwtqphlBWd9eOnn4jtVDjBfGtXixfsfKk8WnbHfLaPAWdyst+bxYLKLb7YZZGBk+C4xaMyX8vbu7i3a7jY2NDSRJgnvuuQff//3fj+/+7u9Gs9mc6MvD4RDb29tYX18P5zfJviRPEOZz+vnIs23y9IUYsMgT30rPAy2xfOR3DLh6/Mb62LTvSXkMbyyMp7/yDFgsuZf9UH57YG9a4K/5zwO89Pus8ll9yctjWvBkpcedgZyN5VlKHDwABwf1eemWn4mRjSVvtNWKVo+YLUMqp4aByQPlGEaiX70zQ6crG4986PV9CiPX4PlcrsPr5SiSN/LRdWP9zzPyyBp551W8eYCItVySNZLJCieVkSyjp4is+s07QpoWXMWU22He6TRjo0kNsrP4t+rF4s3iQ/6X/SJN9w7Cs67gyDLw8mgDpqvLPhgMsLa2hu3tbdx+++04efIkqtUqrly5gitXrqDVaoV0er0eer3exE4mDRCYX71eD+BHXgipwZQ3wPHKOQ1Na0h1WKbhyXoWQJdpWXlP028Y3ksnFt7jMaYTpyVPL3i6VJI1QMp7GKIFVmL9e1pZ0jZoGiCtZV3yyRmXYrE44T82Go0mjjyYhm55EGOdkGv5k0hlrQGO1XGthrWUG7/l+TIS4BDkSBAlBUhv+WY+unwxoZXgKGakrfw9hcv3lvK1wsl8Xq/ikGl5viAxQKrDZBngGA9W+nmAhgfIYsAwBqAs0M3nXh15cq3TiRkPryx5AaJOl4MM+pRw2pkn4Oo6kv1N9i0efqd50f+HwyFeeeWVkFe9Xsdb3/pW3LhxA6+88gp6vR6APWC1s7ODXq+Her0+4fgr+w/Ppul2u+GdnCKX7ZSnrx0WyFhpWEZO8xWb3c0L1i3gkKccsXgxABjr35Iv+Z0Vl+/kJ0uf6LxkGnwuf0vbJONqHRHrkx4YswCSrpM8JPk5rN6O6Qd+CGi0r1weesOXkz7+8Y/jkUcewezsLJaXl/EDP/ADeOGFFybC9Ho9fPCDH8SxY8fQbDbxvve9D6urqxNhLl68iPe+972o1+tYXl7G3/pbfyu65ThGsiHkzImn+PkOOHjTacz4EJjI23hlmt4ylicoTIvLV7pzc3eEzkeWWYMqSxhjKNsyAjHyjOW0ytkzVDFlpuN6dZMnjDYumhePV0kx4ODJXSwdrx08ECfzy+LPA7ixOLpNYs+lPEryQCT7Ck/cLZfLaDabwU+GQEUu50hFSJ8UfvisVCqFD/PgduhWq4XV1VVcvXo1XIfQaDQmZGEwGGBraytMgzNfyYM8H8aahfGMu66jLDmzwsbaOk/eXjyL91g+byRllc2TrVhauh95+s0C49PybYFW+T/W/636z6s78wCPvLo9pku9/POEo12VOxWnpTd8Jub//J//gw9+8IN45JFHsLu7i5/+6Z/G933f9+H5559Ho9EAAPzkT/4kfvu3fxv/8T/+R8zPz+NDH/oQfvAHfxB/8Ad/AGBv1PPe974XJ0+exOc+9zlcvXoVP/qjP4pSqYRf/MVfnJonqaA1mNHggSM66RQoSS8fEU3r8yO0IOl1ej1Co2KWszX85pkxFnIHJp2XvRFWbATjCbs2nrpsMg3vv5emxSvz0unIb/KgTyKW5cxjtPPyGaubvMokBjKmUbz6mTc689KIARUNeiyw6MlQTMasOtDLslkAmks6AMKt71zWkf4o/M9+RUdiDga4M6LRaODYsWMYDAZotVrhfiOm0+/3w/bopaUllEolbG5uot/vI033ZnparVbYPcVdVSwHdzzpsmldozcYZIHbw4IDSyd4/7221HLg5ePpIS+vWNqW4dSyaOkkq1/ougf2ncl1XhZQl5Rn0OLpmhgg9PSh1nN5eInx56VzWF1kyYknu57elfZuWnrDQcynPvWpif//5t/8GywvL+Opp57Cn/pTfwpbW1v41//6X+M3fuM38D3f8z0AgE9+8pO455578PnPfx6PPvoofud3fgfPP/88fvd3fxcrKyt44IEH8Au/8Av4qZ/6Kfzcz/0cyuXyVDzpEYd+xt/WVkhrizT/yzSss2MYX2/T5Du5xMT33mwPt3BaxkgqcGuELpW9J1ye8cn7TAMJyZeXhtVx8hhiLz2dfx7+8+arwWseihn3rLx1ObyObyl1S8YPo+w88totxiNln/G8tX/2IdlX2E/SdP+8lyxe6/U6Go0GKpVKuCCyUqmgUqkgSRIcO3YMp0+fxsbGBr70pS+F+5WYp3TaLZfLWFpawvb2NtrtduCx2+1id3d3YtcVy6bLa9VvVp/Lq9A9MBjrB1kgVT7L05+sNDx+vDrJAldaf1p1rMuQB0zpMnjxYmDeChuruzxxrDaI5ZsX8Gg9GdMbmjcNUPLYDfncq0Papm/L3UlbW1sA9kY0APDUU09hOBzi3e9+dwhz991344477sDjjz8OAHj88cdx//33Y2VlJYR5z3veg+3tbTz33HNmPv1+H9vb2xMfYF8pkmKdU46mpK+KBA26k8hGTdN0wvlWC5VcFqJh5AiRylrmKdOw9tQzTSDfibrW0dckWTb5W5fRI62gdL3IPGKjkRhZbWflp8uW538WeeA1xqf+9tKUYbIUf1ZbaJnz8rTawDIaWXx7z620dnd30e12w9H8Oq7ko9vtTsyIyNOvGYY7HDqdDgaDQZDvmZkZ1Gq10Lfo67K8vIxjx45hbm4O/X4f1WoVd955J2677TY0m82wcypN93ceyQPyms0mZmdnJ/qczJ9h9a6kGJDx6l9SzLjkId0Xs8CC90yT14/zgJ7YM8tge/3b6g+xOpc8690wefqrBgCkLIfcLL3hAUL+lzbICmPxym9P93n6MkuXWmFiZNkQiydtq/PStxTEjMdj/MRP/ATe9a534b777gOAcLnbwsLCRNiVlRVcu3YthJEAhu/5zqKPf/zjmJ+fD58zZ86Ed3rKFsAEaNBAQTrZakHSPjJ8F1NUUgAt4x7bey8Bj3Z60kpET+Vagm6h87xKZxoDailDT9nERhm6E3oK11Ok04xmYvXklSNWH95ISD+bRjl56Xl1FOMrLxjT4N1LT/625KDT6aDVaoWDr3RezIf3G/EjZ0M1mCGI2N7eDoAHALrdLnZ2dgKgKJfLqFQqSNO9XU+DwQDPP/88nn32WTSbTRw/fhz1ej04A3NZSvu7VCoVNBqNicspR6NRmJWRS1PWiduxtrDkZRr5ioWLGYgs/eWdjmuFzdOH8shqDFhpMJInjax6jKWj28HTSXn6lebJG5jF+pvm7fW8z4qbp97yUB5dM016kr6lIOaDH/wgnn32Wfzmb/7mtzIbAMBHP/pRbG1thc+lS5cm3nsN4jWSFi4qJHmuiwQhEtTIeHxvARXtkCjjaj4462I5Pmn/Ag22LJ6s/x7pDib50r91mrHO7+Uh/0vwJ0FgXiDB91kGWKfrdao89eeBFw9E5QUVOk8PuFnp5jWm3jNt6A5jWGT/0QbeAqQ8aVe3iwQznMWUfUMu7W5ubqLb7SJJkgCckiQJB9jt7Oxga2sL8/PzmJ2dnQCV5EmCmdFohJmZGdTr9YlBTK/XQ7vdDjuYCGbytJdXV1afy2OYvfqPGU0dP096fGeN4mOyFwMzsf6VZ0Yga6Ak82C7erzo+NPMPMg4eepdpq/r0KqLPDzE3ln1FNPj06QzDTG8vB5kWvqWbbH+0Ic+hN/6rd/CZz/7Wdx+++3h+cmTJzEYDLC5uTkxG7O6uoqTJ0+GME888cREety9xDCauN5tkfY7sRpLChwVIMmakbB+W/HSdN/PRa7t8z0/0udFEuPJNPWJwRw5yrx0GtZv/d8zVNZMgeSfYbOAolePGqRYYS3KGilI8gyBdXJzzFjousjTib3ZFQugTasEdHqeX5T+beVlKWurbbz4sXIQOFBGS6WSGSZJ9m5xbzabIawGljpNCWSKxSKq1SrG4zGazSbG43Hwg+n1emg2myG9QqGAarUalqKpQKVTvz7gTvqllcvlcFM2fWRkn7baV7eDLr/1W7dfzDB7bSPTtPqylVfe/urlJfORAz9Pn8R4smRVg82scmXpQctwa0Cm9Wse/i2dZtWBlbf8/Xr0g6fbLR5jgC1L38bqIyZTAMI1O9PSGz4Tk6YpPvShD+E//+f/jN/7vd/DuXPnJt4/9NBDKJVK+MxnPhOevfDCC7h48SIee+wxAMBjjz2GZ555BtevXw9hPv3pT2Nubg733nvv1DxJY6MVDHmWz6Qwe0tOOh0pkHrKW59VY53qq0enlkOw3L5tCXPM50UqZct5SpdB8upR1iglS1lrBWQ9l2XLYwQ8UDGNEogBPS9snnRjCiDLoHg8xYDcNKM3nY5lEHTfsPL2/idJgkqlgtnZ2QPLMbptCoVCGJTIcBYREHHnUbPZRK1Ww+zsbPCDWV5eRprun6YL7C/hyn7GmRqGk31Rb6XWxyloOZADmjzt4MntGymvsq/mMUZ5BjiHoWnSkXo2K9zr5SPLITsPv5onmZ61nBfr23nIA255dJYVR9dJDNBIytM+WQDPcpnIQ2/4TMwHP/hB/MZv/Ab+63/9r5idnQ0+LPPz86jVapifn8eP//iP4yMf+QiWlpYwNzeHD3/4w3jsscfw6KOPAgC+7/u+D/feey9+5Ed+BL/0S7+Ea9eu4Wd+5mfwwQ9+0J1t8UgDGN0xrUPgYuFlGK0UPCVvdQw9OpGNJ5WnJHnuhRY2b1Siw0jnR6tMuuzTApSsDijT06DEimt1LD0KlAAtlqc3ysoCF3nAR56OrssZAw0WX7G2sIyMBdhjvGn51GSBf0/OdDw9KNBxdJ4a1Fr9U8enEy9/Mz/e0VIul5GmadiJ1O/3Qz8YDAYoFAoTjrzAwSVfaYw06NY8y2+rz8r6yVPuN4ryAKQ0nZx1yGvMGNYyhpZ8aj4s3mLyNi3ptKSes3ZwWrpV82/lIfOxNmW8EWWRuslKy7NR8p0V3ssrRjHdlMWb1AnfFufE/Oqv/ioA4M/8mT8z8fyTn/wk/spf+SsAgH/yT/4JCoUC3ve+96Hf7+M973kP/sW/+Bch7MzMDH7rt34LH/jAB/DYY4+h0Wjg/e9/Pz72sY9NzY+lXLzGkoaegqdnAPjOWvrRCt7Kx+oInoe8NtBscCpbC7ho5ZilKDwjbJXNe54l+B5w8UbtFn+WYrCMm8d3FmUp9BiQ897H6sxTjFl851GyltLyeLaMhiXHVrt5CspT/Dr9WD/02lH70sirO7hENRwOUa/XgwNvq9VCqVTCcDhEqVRCu90O/ajf74ddRuSJu5IkkNF+a3w2GAwOtIfWOV6dWKAwqx++XvLq2wsrec2bnkd6oBHTjXnJM9IyTyuO/J01mLL0i9WWVr4xsBID+1YYKVuadys9zVtMT8b40HnGKKbXY3HyhPPoDQcxeQSxWq3iE5/4BD7xiU+4Yc6ePYv/8T/+xxvCD8GHVITWjiV5Tgyfa18a6zRea72X4ESO2iwwxHCy8a04Mk+uHVqAxeqUOr6sF68jWgDOUhSWQtCGNjbS1PWmSSop3Ql13VizajpPiyePtLG1yqSNvObdU64yDcm3rvPYoXB5lZhXBxZw8cCHpRBjijpN97ZJN5tNLC8vYzQaYWtrC71eD0my5/dCHxme4zIe790PtrOzE7Ziz8/PI03T4HR77dq14Fw7Go3Q6/UwGo0wPz8ffF9KpVKYsf3GN76BmZkZLC8v4/jx42GGZjgcYjAYoFgsYnt7O8zEzMzMBMCjb5VP0/TANmpd73kAga47q10k6PcOucxD0xhK3Y4WwNIyJZfbdRm8fHUeMX49naDl3gMLWSA5Vq8xW+YBAittPWjJaksZVusfS8/LvGN6NKu8ugxWWFkm/dzTg1aakp/Dyjbplr87iRWkdy7wnQQcWmiydgIxDT3CIGkn3yRJJhQj07HuVtFASj6TS0pWZ9aCZilPb33WU65WPvK3p+R0WMmPLF8W+IopWI+8DmeVz3vmpZGVt8V7liKXz2i4POXjtZVu/yzlFVN8Ol6sfXQYynqr1cKZM2cwMzODjY0NtNttDIdDzM/Ph9ts5flJg8EA/X4f/X4fnU4nyDpnQjqdDnZ3d8OVG6PRCMViEXNzc8E3htcLAHt+Lrfddhu2t7exubmJN73pTWE2h8CJMyqFQiHMyBSLRQyHw1DW3d3dcL6NZ4QtA5Ml+7qdpm0fHdYDl55hihlVDzxPGyYLZFg8e/qG/7PKYNVznt+xfhDTcRZJ2+Ido6HBqkdeP4zJYp7yxMopy6HD5tEX+rmnh18v3fIghspK/tdIF5icppYVLmdM5JRylnGW8WR+etZG/pY7lCwULvOzZmPkMdoatXtpeobOEvKYMsrbFnlRd16eDpOvlY5WYh6vsdFPHmWbh1crrkceMJo2Xw1Gs8LqdKUsydnLXq+H559/HkmShAPpCoVCODNGpsc+Jm9nT9M0nC0DIMyQyMsgZ2dnUa1Ww+4kgpler4fd3d1wtsva2loATLw2gGlJB2IuMRHUEMDoW+utetZ9Kk+9x9Lib8+XKAugTyNzkicPcGjjlgVQst7LzQ2xfDy+rX6cBcrygEadrwdcLOMsbce0gy2rT8k8pQ2JAQYLEOXRKVlgJabfPKCi48bq7jB0y4MYbaTzKHzd8WIdVCt//peOs5IXOYJkeD7TBhHAgVkKKjP6xEj+rM4l+fLKKesmpvw85K95t+Ln6cx5wIbu1DEepwU8us50noA9u5aHYqBI1+k0fOdR2tZ/nU8WALLkw4pv8UKwLcFJ7IBHpkfwYOVPKpVK4RRdAvtCoYBGo4ErV66gUqng8uXLGAwGOHXqVNhhRB52dnbCBZOceSFwoTEg+JoWgE9DWq7zGPZYf/MM9hs1ALFAu5WH1WaWLHl62gMUVv1o3T0N4NJ5aD68+oiVP0kO7u7M21c1TTuYs8pu8WfVlRcvVgamw7TZz9jfvbhZ//PQt/zagT9q8gRant3CUZj2UZHvNJCIGTCvo0gDqI9Q151UAxLGledUeEhYH7LF51mgxTPcWkAtxTkteMjTCWMGOs9ITYeLhY+F03WZNx0L8OnnMaXq5eHxm5V3TJHF0vV4ygu4pKymaRoOgrPS1bKqL+iTVCgU0Gw2US6XJ/rpeDxGu93G1tYWkiTB4uJi2HadJHvnu9BPp9/vhxnMXq+HTqeD4XAYABRv0c5TJ7qedR+12kHG9/qPdZaRTjPGlwyb1WZ5+lvWewtgeP91uT3wklfWLAPthcvT97LCxNqau+Wsk9QPCxosGYtRzK5Yde3JSEz/sc/JazrkTe7a1smy6M9h6JYHMcCkQYw1kmwUjaIZT/rOaAdcpmsd0y07RJqmE9PXUqFLJ09dBqYjZ3G0AFjCInn06idvp/DCxJRYVhirg/C/1U7TdmSZxzSkQeth4uZ5p8GsVU8xQ2A9i8m6FcfLxwqflW6W0tf16fVFqzySV54nw+P+C4UC5ubmsLKygu3t7fBubW0tnDnT6/XCmTIcpBSLRfT7/bBExb49Ho9dwGWVK0/d5a0jWQfe9QVZBtbKPzYw0OHy8GjJjfc7lq+nA/S7vKA5Vk7vnaUz8xJ9tzgjSPmSR1pQ7rx8ZfmmyTtL9+r3efWBp6f0e86w6vOUWM+sCy/+66VbfjmJU8eyU+qZFD27QSCiFYecBtfoWv72BMFC7XoHk7VrSsbXYEpvtZbvZNqaP10neTtCHsVmlZdxpYLJq5C8/1kK2VO0MRCRpZRlmbIUdKyMMrwGrhpMS/LAixXOyn8a4Ofl4QEfq22lEY6N/KQMW3lb7VGpVCauGEjTNFwn0Ol0kKYpyuUyyuUyjh8/jsFgEIzI5uYmRqMRqtVq2CZNx1158CRP5NV5x8iS0Zgc878lhzpvD9hnURbotZ5NC8xkPMlvLC9dN1kykMeHRsfJ8y6vXrN0CJ3TvRkzrYdiuiKvzsgLXqfRS9PoBNafvFWefQfY12nc7SfzkAOZNwLIfEfMxEglygrkBzgIHuR7DRpkmroRrA6mR1UMp9Px0tSCK/OwTjPVxkMaCWksNI+yM2sQlmeEYHVQr+NrMBdTMjotzXOsE+j8PZ6tfLx0vTTzKpUYH/q/Ve8x+bDiZtWvZTQ84JOXZ8DvC1b6mh8Z3jNwvKka2BsJ8tLI4XCIK1euYDweo9/vh91KtVoNxWIxXNa4vb2NTqczMQNTq9VCWgAm7j+yyumBk1h4rw6z5Mdq92kMQExPWM9jADjGqwdCYjx5g6m86WcB/awBiZW3tBd5+oOWX6sep3Fi9uojBjwtHmQ58rSfRR4Q5E5CDxBxplQe5CoBjHwWK18W3fIzMVqZywaWzyxBJ2jQ08l8Lpd1gMkZHQBhNwOfEVRYs0N8rmdOrN1MFv9MQ/LPEYJVF9Z/+UznI9O16tird+u9Vl4eWLIEfRoFqds7lm6sHPq3Zai8kZZ8l6WgDkuWXHuKR+ZvxY/lESNLPrOMncWLDuMZkmq1ilKpdOAKj62tLVy/fh1JkoRbq2u1GtI0RbPZxNzcHFZXV7G5uRnSlT4v8pBLbre26kLLk65/HT5WB/K/ZbQsIGvJn6yvWD5WGppXrz2tvst41vlQeQFGlnxlgbaYvHvpeWAzxq+Xt6cnrd8xvab5oK3Rz2OgOFaGPDLHNGKAdnd3N/QPqx55eWu1Wp2YDIgtp1m856HvCBDDDgZMXitvffO3tRvBUyISaWsjopWwNjAScMTQshRkGdZaMuJzjciZv7z7xVMqMp4GarKsMs3YwWy6Dr3/Vv1avMUoC5xZ7eXFj3VymZYFMLP4ssqZtxN7PGue8igtqywWMPYMiTSi0qjrdD0ja/Egn+vwhUIhLBsxTKFQQLfbxZUrV5Cme8tICwsL6Pf7qNVqmJmZQblcDs6W5XIZ29vbIT86EHNZgDuSsniz6tQCOVZ95THKWXIVAyNee+YxGpZMWADFAqtZfdrTETHgy3feFnet4728vLqyBqpa7+XpJzoNrxxeWrE6iLW1B349PZUFHmP5eDItz2KrVqvBD02fc/ZG03fEchIwabw1sJBLRzI842hFIuNJ8kZBsnHlN8NrL269jKU7pUxHn4Gj+ddGU5LnsOopLq9TytmiwwiqzCeWnwXyPIXsKVKrE1oGyAurQV5W/cj09Xtrm7E3stGkw1lt7/3X4WMK3GvTmPHSMpelKL2yePUmnXS5hATsne/C5SDeTN1sNjE/P48TJ06gXq9jc3MzbKnmTdntdjvspuBpvPSF8Xiw6jGP7OftI5bRz8rf0lOWMbN40Ol44Nz6rfOfVqbz1ocMm6fes+rGCxcD2VZ9WLIubU2s7Fn9NFam2LM877zwHsCR9qhcLoerPaSTPC955bvY9uo3ir4jZmJ4DgsVlWXYvdGRhf41QCAQ0UpZghRtgOVBSBowybgaJMhvCX70EekyHW+Ln4fULWUUU3r8P62Q6jaQs0qe8Ot8vHexjhhTwFYcL76VVpbh8ZSYpei8axQ8JZvHsMTk3DNYup28eraMpcXjNEZUE0d5abp3qm6a7h2Gl6Yp+v0+qtVqkKH5+XkcP3483Jy9traG9fX1sIMCQDjwjkCIO530eUCxOvEGCdY7XT8xsOfJV1b/1DonVp8xWdaUx8DGZNMbAHh14PXhPLzkiZcXdMb+W++os+XSpMw/C4jptpC2wHpvyZYV7vWQ1o+86b1cLrs7kbIGADr9w/J6y4MYYL9z8ETc3d1dlEqlAyDBAhWAfZeRfC8r31J+MryHtuVR6FLIpSLiM6kAiIyt2ZBCYf9qc2sGSuch60C+t37LclkGUCtPbQALhcKEYybrRp6K7MX1FJjOzzLUMRCkn1mKNotkfWiw6dWNN7rKUgJ5AVqeeHp5MFZP0ihZbZy3rqbhkc94Jszu7i7K5TKAvfpZX18PB90tLS2hXq+jUqmg2Wyi0WhgMBhgY2MD3W43ABaWmRdF8kwYed0A07co1o+ssB6A5fsYkNRpeQDRkr8YaVm3wIZlyGM7cfRzT7fk4S1WB9PImSyHBNJW+WU5tJ735FL3aX3mmAbw+pmlD1jPuv4sAKP5s3Sn5jnWFlY5tX6QqwR50oq12esBW7c8iJEVzAqkwy1Hcpax8oRACqFW/JYgyudSoCWo4bfcLSUb1ZuVYFwqdR2G6/8sr+cXZKXtAQjrnSwD07fqVZa9Xq/jHe94B5rNJvr9flga6Ha76HQ66Ha7GAwGGAwG4Sh6AhwN8mS7WMZWP/M6vlcXXnqx+ovFk+QBGP3OqnsPVMaAqEdZRsgDNVkATPJjGfksQ6Tz5dkvPAU4TdOwU6nX66Hf74dbqhcWFjA7O4tSqYTr16+j1WqFawskcObyU5qmYTnJqt8Y71oX6LLnKZsXRpOVpgc2vDhZ/638PBmRz/L2iSyKlWWadD3ZzdsmWYDQakMad3kFDMN5A+a8ZYvJVF79IsN74EbyFkvDih/Tg98KuuVBjAYAxWJxQlFJ8KBJC74UTDll5hlKKbB6FsYCNzK+tfWaZI0aCFJ40JIsF59ROHmIV4yyhFfz4QEgT1HXajUcO3YMtVrtwAGC8tAkesH3+310u93wzd8cQUvD5s06sd1iZY6VUZ/hk9VZPcU5jQKW+Vm86bQ1z/J9Fs8WaIqBlVhZLDCvgb2XnqdcKddsZ57v0u12JwYErVYrnPmytLSEUqmEjY2N4KwrZYuABsDEjqS8hjomM7q/W+WU7/LKhdfuefqsTiOPcdayk1V+Kw2djtSRGiTJslg61vudVd4sWZbliQ3aYkQ5paxSXvMcmCh59fjK4v/1DFh0XjGdI8NmDdC+VeCFdMuDGGBSSaTp/g21u7u74RTFWGeyGpXGkoYtthVaO3h5xtBS/BrIaIMkz7SRz+nwa40OZV14daTLK/nV/y3l6Y0ymAeXBSRAY3gqAS4XWDywXgleePMxZ3P46fV64S4cjsIZP6ac5XMNRry60GXUaWfthpJ1EBt56jadZqTp8S7b3APm04KgLEPpyaJ+x9+838hantPgFQBu3ryJL37xi1hcXAwDELY/Z2KkD4we3Hh1JfnzyhUDMJryGmKdh5RhyafX/h4oOEy+Mk1+Z8mh5lP3sTwya/UdayepztMrg6e35Dutc3VaeoOEdOrVeeTVGdqeaD2bF6zGgGceAKtlJQtw6jz+X9B3BIixtt5xCUaeNmgtg+j/ngLwFJYMK/1eOHOijZxMj++s2YNisXgA3WvFpkGPDDczMxNmY7KMuH7n5WmNnmQYSdVq1TT0HujR9cPnlUrlQDgJGtnGPAxNghz+JwCSh5vJ77x8Sh5iIFE/89KzymzVZ5ZB1WlKXqxtxFpedDqeos/K23rmGT0JBNhn2J4yLHdFSJAi+xdnaTgyJjCWd7wQ2MjZScvQejzL3xoAWfEsnZIVXpMFeq2+HkvPKlsWyLZ0hAUErL4fA7q6XFk0rTGP5eEBKF2fVl3xuQYxlg60+lse/mR4S2by6OqsMmblZYXR5Xu9FJPdGH1HgBiSrHCCABoq7a/idT4NiPiOIzhvloVpesf9xxCvxZOFfCUvehu3rgO9d9/r2HkMlgfyPCOWJEkAMZovDYJiQE6nS5K+RaVSyawD6bTNy/+GwyEGg0EAOZ1O5wDIkZebWVsodR17il6XL1a/mrRcWsbJSkcrZQtwMpynFDVgjfFlvdN8xUCvDMc6130MQOh71WoV/X7fjM+ZlvF4jHK5HGSAcsDlJQ+ETktWGeXMrX7v1de0PFjykJdfSx4t0CLjZPHi5RMbCOg89TsL+HryldWOHjjIk4bFP8Nxhp8DI4JsDwwAkxtIPOAqZdPq3178w4DFmO6ydE+ePpP1/jBg6JYHMRI48D8ristKFA595ooGIPzN91ZYjgTlrAuFWAIHqaSsA/i8jsy85LR3bHs338stf7JuPKGyFGseBSTrSP7XcSuVSgAFjUYjOFlbde1RXoVtgT0pAwDCTimZHuuVI3w6gGoHZLlsxd0tBEe6Paw61WWx6k0/t9rBU25eflY9WvxkKUcLRGnFLPnV23+zSMaTS5BJkoSdhiR9ppLMfzgcIkn2BzAShHIWRtdrnrrUSl0+jy0zx2TXA5F56kmmocFQHiPjpaHrwksrS6/EfmuZiBlqHc8z/DHy4ug2tOoglraUJ6vdY23u6QgLsPG5/OTpo15aOowus17+98og0/H0SyzuNHTLgxiv0xDczMzMTOxU0B1fL/tIUGApCWurqvyvl5DkMwl4vAPImA7T1rMButMBOABimBbz0+nLOspSEll17wl6pVIJgI8zR1bY2MgjK4/Yf/3MCk+eeCaC10EpP5zJ4TJVp9MJHzmjo5cxrK36Vpl1G8QMjBfHexcz3FrxWvXm8aXfWQBegzwNQkql0gGZ5oBD7rzjAXVa9judTmg/psU2Yxg698oySRCSpWDJvx6oZBksXS8sy7R9LQ9lAVwZzvo9bfqeTomla4HrLFAUk1dPxvOWQfPtPfdAD8l6lkffMozlT2eBFZluVtsdBihntYeX/huRt0e3PIiRU7h6F5A2/KPRCMVi0fS9kHHkGQDMg+nrJQSCBYmU9ZKT5oXfOl/dGZkX86di59S5BAbkQeZDfnUZZZ3oZ9pYaYoBDplvuVzG7u7uxAVhabp3eupoNAqOv/KSS81rXqVsGd08owjZBjo8616emiyPwmca/JZOpHKHVbfbRbvdnpjR4WyOvNJep6uBa6w+Yu9l+TzQpMPItGIASafjkR4RarDCYwK4KylJkjCDl6Zp2B4t+4EE+EyzXC6HaX35zrojyQMweRT4YYC/biOr/+nwWnfo/L3wOmxssBKLx/dZg4w8lCcfjyzdaemumL7KGiR59eMBN+o5PUAmb1q+dH+OpSt512WR9ZH1XKbtlccKl1e+YgD+jQTptzyIobGhMdRbnaVToFRsFtDQKFSmxeUQeemj7lx69kZu2WY+1qjBWyvVnVXHkfnIMstOa8366LSs/7petIDHOkOhUAgOuRJojUYjvPzyy/jmN7+JUqmESqUS7uDgb/4vlUooFovhW9azzF+DkJiikKSVRR6wpPPRs0t6t5UGOVyykk7I3mwOfXTkjEIMDOgy5S2zZ0RiylRTDOjEjBT7lTzanI69w+EQm5ubB/qmBLxy9jFJkglfGMaTW6wtspZ6ZX6S1yyZ8uoiZjz5O6v/eelpmcjK+zAGygNRGvxa5ZAgwcpjGpmNgS9dn146WTIZy1/mw8GNPL8rT1t4NkDybdVXVtp5aRo9adWTJRc6jTcSwADfASBGN7hcupFh5O4HeWquJdyyQ0qwI6enGUd3JumcqEGHjKd3KlmKQi9DyDRnZmbCSaQ82I93WdAXhfzHOkCWwOVB1XoGjLyRL6bBqf92u31AgTE+R+YENWfOnMHZs2fdGSUd36rHvCBFd2brfB/rt/dM/me55GyOVmgEydxSzpkb+ufwwzN02M7a/0ODX0l6eSdWRlkfUkFljQjlb6ngdHhr9nA8HgdZljsLZfgk2T/5mQOFer0+MVhh+eWOJKt9ssoQk/084C0vefLkybV8puswy0gfhr8YeSBH8+DJUNaRFHne6TB5wESeMsUGDbIf0aZ4uvywde4BidgsniarzqYdtOWJ44Gh1wtubnkQI31M5Hq1npFhBXNUZi0ryUqWa9dMU18zLn09tPKw/lvrnpayp4OjDCPLQoM4OzuL9fX1YPy0QWTnkidLWpRXoK2RFnDwgkGCGI6uZTgeYKZHoeRdzlbI8nBniXSg5jtJo9EIW1tbSJI9/wjO5EheYqMOvj/syMfryDJdYHLpTIbndRnNZvNAupRruWzV7/cnAA5BDmd1eCKy9PPSfcMbSeUx8lbZ9HsP3HGGrdvthr5JOdYDD7k0ylktvmc6spyyvmSesbJ5bSXbwHufx8BOYwSmkT3P0MUMvdRrHsi3BhkWr1ae1gDPSjdGXnyvnuSgTacjfRV1mbRes0Chzof6SIbx5F3mYYW3eGI4L8y0cpIHtOn69uorJvvW+9dLtzyIASaFUU4P6y2P8p4hSTKOJbA0njoPCS74kUtKukNnjUosQZDPmLa8nEvmRSXOqU5L2HT6MbIUv35n/SeAkf4u/JbLcTKuXA6Ujsq1Wg2j0Qjb29vY3NxEq9VCqVQKS0/VahXlcjl8xuMxvvrVr2JzczP45nBWp16vTyxZVSoVlMvlAHZ0PVnKzOrYVlvmqdO89Sn/Zx0SSANv+edIoNNutydORLYOCcxbnjwKTD9nOQhEJGiVZ8XIj3SU1n2M8WU7eb4wHk8xo2zVtfzmb5m/7je6PvMYlhgf3jOrz+sBVMxAWeXRpOsx5tuV1T+sNsgCmPooDCuup0/z8CTliwMgXbd6mTcGXmXeMZmLlT1LdmJ1l1eGYjJv8a/5iqXjgbIsuuVBjD7N1kPQrEAqTXm9gIXS9SwMScblf5mX3umk+dLoX/JGgGR1QpL08bGEgsZfGyNN1vO8hkuWw1J6NE6aR84i6Hyq1SpOnjyJarWKbreL9fV1AHu3FFcqFaRpim63i5s3b2J9fR29Xg8AAlgrFosol8s4f/48Tp8+HWYi0jRFp9OZ4JOAtFAoBPCyvLyM+++/P4BcXUaSp0Q8xW8ZMv3bIiu9rHCybKwPi1/WPUGO3m3VbreDf450RJbn52iQY/EhP5Yxk0C3UqkgSZIwuiVg0VutZRkl4AUQdo+x3JQ1XVcxUBoz8jqMBRS8/7r+vXeaJ8toWDzGDJR1/pKW3Zi+scJ5zz3D5dWrJT/6mbdsYulW650H1rQ8WmGYVtbSjaXTZXzJk+wTXrlieegyTkteG3n5yOexAYAHJK10pqVbHsToSpEIXb6X4eiQlaZpmNUA9hUkjS+fyYaRPi+yo8kdTQRL2qfF+tbLUVTM0m9H8gYgOBlLQCDDSH6tThujLOWdJz6Nkq47fbGfDD8ajXD9+nUMBgOUSiUsLi5idnYWtVoNSbK3NHThwgX0ej08/fTT6Pf7KBQKOHHiBIbDITY2NiaMM9OXoI58sH15rsj8/DwAoNfrBWAk69EyeP1+H1tbW2GmRy5ZZdWTp1xlXUkloeUgjwLT4Zi2dIS3TkNmncm6pCOyXrbqdDphNodXP0gHeq8epDMvgDCzlqYpdnZ2Qh+QTs1SnmQZGK/X6wXZ4vUFstxWOTVZy715KdaeWc/0O91vrfjWcwtgxfKJ8Z9HhmPPLOBC/WZtRNB5W/3Bo7ztFWt7q+5i/GWl54WzgE4e/vJSFj9ZAEaXOSZzh+HrMH3rlgcxmrSylqfb8j0VIKenpf+J55jLuHQ81CMGPTLQTsCyEZmOJUAS2GihkctjwJ4vgaU4LADG91kOxTot/SymUJif3pnEPDiil+kBmDhNl0s8NFCs60ajEXaf8DCzer2Ot771reh0OnjqqadQLpeDMZX1naVweXPyl7/8ZQwGA5TL5QPLTrVaLczclMtlbG5u4oknnghGk3EYlh/utJL+ORYosv577ZLVTiy3/NbvtJGhbMj+UCqVUKvVTOVFkCEPCeTSld5xJR2R5Q3zEpQ3m82w1Zrgku8JlAjsuYuJO9xYlm63G5bSsrYvW/XB39MARc/o5U1D9ynPkFjGj8/0gE3HlbzGBim6DDE+LYr1Na2XdFl1Gh7Ys/jNY1w1D5KPaciqn6w61WGyQGQW2Hg9YCcrricveds2BtAOS7c8iNFnunCpx1IkegQA7Ctjy1lUKmzG16N6AhKZnhXGGinxv75xWx/OJeOl6Z7/QKFQwPb2NoCDwM0qg6UsrGU0+d6KZ4XRVK1WJwAMw/X7/QNbxtN0bxRNH4mFhQU0m03Mzs6GHSe9Xg/tdhvXrl3D9evXUSqVcO7cOVSr1eAnA+xtcWYeloOf5Js8zczMBL+bra0ttFqtiTZlWC5bNZtNPPzww+EE3/F4HM4x0YpaLneVSiWcOHEC9913X3DeZbistjqMwvIMgWckYv+tupQ+T1pZy/LIm8p55QNPReZnNBqFGTeCoEKhgHq9jqWlJXQ6HVy5cgW9Xm8CcFUqlTBzyv90HLfKpEn2qSwlbNWLByhjaWUBAYaJ1WkeQ5wlS1oXWeBNy+i0hlTGywLaXrksfuRzz0ldhs8DErMor5G26lTGk/89vqyySrIArUxj2neHpSwAmUfW89AtD2IkydEkdy9IRaUrnMaKt13HHHy9xpAzLRJMSZBgAQa5bKWXIGTHl7MvcnaHW1D1DiB5OZ52tssSbkvI8wiiLpccHTONNE0DwLDykD5KxWIxGL7xeIyLFy/i0qVL4QA9ADhx4gRKpRKeeOIJdLvdMNOxvb2NNN2/DsJyAJTtnCR7d/JwJkj6XjSbTZw5cwbr6+thqYsyRZ8bb4QI7O+WYVlmZ2cD6CEglm3v+S/s7OwgTff9SLh0FWuHLKPhKbSYstfvrWfyOctXLpdRr9cPlItGSM7q3HXXXWGZKk3TcF8SAGxuboZZO4JeOevGvHi/Uqwc1v+8pEerkqx6twCJbisLROYZMVPnxcJ5ICiWR57BS9YAyOIj9i7WPjF51YBgGsorA54elWAqL/+ezFh5ZAFBHd6rS0vHe3LhgSQrzzyA/PWCp1sexEhwoIVGAhnr9EQCCO2HQqVAw6fBjXa+1YpE//ZGO9JJV5ZHxrMETa4rS+JunkKhgE6nc6DTWCOtPApgGgEsFAqoVqvY3d09sItGzljI/FluLkuQJy4LbG9vo1ar4aGHHkKS7I3Wr169Gk7DHY1GYZcSgY+sS1kGAibZZhzBSz+KYrGIhYUF3H333Xj11Vdx8+ZNjEajsCRE52IJlPV5FzMzM1haWkKz2US320Wj0cB4PMbzzz+PTqcTdk3VarXwTR8bghUAePbZZ7G6uhqWsrjTih99SCDlKss/Z5p3lhK2RtLWQEHLn0xT11mz2TygREejEc6cORNmc7i8xO3lOzs7YWt5p9NBqVSKnoos+2Ge+rCMdl4AY42iPRBpGS4vv7yGIQZYvEGN984KY83SxYxgVroxkHxYOuzgLAs8HFYGdPt69RijGFDJC0xj8pQFhvLw5f2elm55EKMdMAlKgIOH0snfnAWRR/l7Z8sQOFBA5ChS72hhPOYlQYIWEss/hXlLXmQ8fQCYJrmtWaatebPIEmL931LoshMmyf7N0trXiLMXsrw876ZarWJubg7Hjh3DYDDAzZs3MRgMsLy8HEbi/X4/GOv19XWsr6+HdiS4kCAoNnqSfJdKpeBPAQCNRgNve9vbUK/XwxLSqVOncP369eC03O/3A2ggoGHa9CU5ceIETp8+DQBYW1sL7b2xsTGxbCVlhEtP1WoVd911F06fPo3t7W202+0D7UV5l0tW5XIZtVoNy8vLuOeee8wZG6t9s8gCu9Z/2baAL8cynJZ9bXg588QZPosvua2cy1Rra2t48sknQ/vEDKun4KUceUZWvtP1ZPUbTzbztkcs76wRt24vD5B66TJMFq9Wm1v1nAWkJQDNK7N5DWZWGKut9W9Lpr2Z9cPwO02Zs+JP0+c9Pe+BG0tmYnxNQ7c8iNEdTR/oxYOySNKZl/H43lqe0cpMAhgAB/LjbxoPC83rODI9zyjIGRs+t85+oPOrxT/DeIp4GuHzDBgNsHXUO8EIw5bLZSwuLuLEiRNoNpvY2trCK6+8EkbVd911V3D03NnZwec//3mUSiUsLS3h3nvvxeXLl/GNb3wjpFUoFEIemqyRLICwHZmHBibJ3vLSiRMnAmA5efIkZmdn8fjjj4f7fABgbm7uQJm4q4qzUN/85jdRLpexvb2NlZWVYGhPnTqFO++8E/1+H+12Gy+++GI4uwXYA6Nnz54Np/fGDNdoNJo4RJBACrCBvCy/JoIBeSmmVmiWTMt0Y0rTU6p5wJHFN//zgMVarYbZ2VkACKdGM5xnKDxAbuWn4+ny6EGVTlc/l2Fjeek60PzF6kgDLIv/PEZf5uWBWitPqz5kGK+epiEPcFgya4E7L00PwMj/+rmeKc+TZ5ZO9gYBVpxYmBhY0nl65PGflcdhAc0tD2IAhNG4NRriejuVslx+kgfDMR0JRqQQ6tuYPQUuG1AvZ7BzaodI68hq2eEskCR9N1he+vXQL4TgR/OmO/RhhMsyJkmSTJxRIpXReLx/C7HkIU1TbG1tBcdduYOlUqmEZSbO5PR6PdTr9bBbiXxwR5QHYrwRAg+7k/F2dnbw1FNPoV6vh6Ue8s9bkjudDjY3NwNITtO95a8bN27g5s2bYUaQsyPD4TAsiYzHY8zPz2NlZSUAr0uXLoUlQPJIXx25ZVyWx2oHlpPOyl/72tcwHA7Dbim5bEUjzw8ArK6u4plnnpm4y4r1wJ1j+r6jmHG2eLTKIcNZijcrDZ1fmqZotVoHHP21IdeG1DI8sbx0Gp4R5X+9bODlEQMXWUbCMt5ZRioLRFnlyWPsdPll2WI6NA/POi9rl5aM76WVB8zESOtWy5jH8sjTB/hMA0mdzmH1uUcxMO/JWUxu84BlTbc8iOGsh65kKotisRi29lJpp2k6AUhkB+CBW3oaXp/+KxtMz4hIoGOdLkrSHcs7hZL8yXS8A/poaOnfYXWCvIJkAZWsUYQ8/8PqdHTaTdMUg8EAN27cCMsF8rAz+tZo/xVgbznmi1/8IrrdbsiH54Xo80Gsjsb3codLv99HqVRCs9lEr9fDtWvXJmSAjsL1eh2j0Qj9fj8ASelAPBqNwrIH64PgkqCkWCyiVquFtAuFAm6//XYUi0Vcv34dabp/Bgrz0QpCKzmt/Gq1GnZ3d3Hx4kVsbW2FMsilJ86a1Wo1LC4u4vz582i1WtjY2JiQLwIVxpV3WxHkLC4u4uTJk6bMZMkPSR8hYKURI51+u92OHpbmkWWs9bfsz/o9f3uKX/Pq5afjxMgC6BbPVjwvvVjeeYylrKc8RnBaHmS4wxhuL08LhFjvrTr19LxXB3KwZ9kJqy3zAFtpe2LkgXIvfU/OZJjDAJUY3fIgBpjsqPKcF/1b3rfCj7xXiOBFLzmR9EF6fCcP9/IceRlO7jSywArLoBEtDQlH5dby03g8RrvdnvALkqRHQDHULN/nIYblso5c3iB4uf/++8OJsDxDRG5VBvavJaAPRL1eD/HkttxutxsAAYCJ2RJJlgGSdVCtVpEke7uNFhYW8Pa3vx2XLl3C17/+ddx9992oVCrodrvY2NjA5uYmqtXqxFH9LDMBAg16mu7NzNTr9bC8NDs7i3a7jePHj6Ner0/Uzb333otarYYbN26EZzyPRjulW3UvqVgsol6vTzhKJ0mCpaUl1Gq1cGZLr9fDzs4ORqMR2u02zp07F84eksaHII2zYgDQarUmZP6OO+7AyspKpuHRcq7LpB31px0hy7itVst07o4Z7phS1yBYl9MDlxbIOeyoVPOat4/G8tNtYAE0HfYwoEGmodOW/dKT8RhYscCEjJcnbBYo0SAjL7iM/ffiZKUda0u+99oub1oyPYabNr7m5TBy8x0BYoCD03j8LcGCvrSLZO0sIpCQMyA6nlZY2lFXO20yTAxpW7uTyI8sm9VJ0zSdOBTMQucexYSf72VZrNGjBDFy1ovgkDt1dD3Lc0R4YBqN7+7ublh2YX76bqB+v4+FhYWJE4E135JH2WacNeFt4EyvUChgeXkZ8/PzSNMUL730EjY2NsJOppmZGczNzWFubi6c2FutVtFsNicO+wMQZi+4pKnvP2q329jY2MCNGzdCO/MAPS4x6ZFVTHFwlxovfyTQuHDhAu68886JOl9fX8fTTz8dwBxnL7x2JmmgU6vVMB6PcfXqVSRJEpaeuPykwSPjjUYjvPTSS2GHGR23GS92cacFyknj8TicHyRlIUaeks7qN7G2sAYpOq42FJ7xyTK2sf9ZJOsyZngtneL1N4bXs2tWmfltlVHrQIt3TTK+x7P+Jq95626aAWCW7Mm6l5f26rrMytsD1/qZxVcWQPGAc1YfeD0ABvgOADHWNlJWqgQwXBqQTosMS2LjyCUn6aBreZ1z1kNu5ZaNanVgGUYac8m7FGrtnKkFKybQWpitkaQWfG/0EgNwfFatVgFM+vmQhsMhnn32WfT7/bCkIrcY1+t1zM/Po1QqYTQa4amnnsL29nYwhrVaLWwpZpzZ2VksLS1hZmYG6+vroc2lwrPamHXPGZzhcIh2u40/+IM/CL5Rzz77bAAlN2/eRJIk4VTZM2fOBL6YFp1LZbvKiyVJNNLkY21tDU8//fTEacbcZi39ZDzjIr/JY6VSCYDkzjvvxMLCAo4fPx5mjOgEy0sSCULkKdBSqUvlbi35cJntueeeQ6vVQqGwf4eTXnpi+83Pz2M8HuOVV17Bzs5O6F+SRwJDgkR56ScBjyX/nF2ylLmWXasvxIygDutRXqVtgWwvPQ208uShwZ6X/7QGOQZodD1qGdV8efKtgYsHNLyBVx7QHyuzl25Wnem6iAEIq9wEMnKTSJZsSN5j4aapi8PUXyzOtO0A/D8AMf/wH/5DfPSjH8Xf+Bt/A7/8y78MYO8Y+b/5N/8mfvM3fxP9fh/vec978C/+xb/AyspKiHfx4kV84AMfwO///u+j2Wzi/e9/Pz7+8Y+bW5ZjFNvOpn1YkiQJSxcSnOi4cuaGO5Z0w2gfGf2OgihnUCze9TsaYDn61kqOPEq+NemOZilqybuO41FslAYggBgrn9FohM3NzXAiK4Ei24PHyJ87dw4nT54M537I7cWy/IxDgHPixAm87W1vC/F4gaG8ikAv/fE6gTvvvBM7OzthFojbvFdXV1EoFLCwsICVlZVwFg3PM9GKjrN9ciZKnm5LkCHBTbfbnbhckWAwSZIJvx/dnlb+zLtcLuPGjRtIkgS333477rjjDhN88MRcghC5XVznJY2HlkkCIt5hJOPzLB8pv+VyGY8++igqlUpYopK79obDIbrdLra3t7G2tnag7EmS4I477ghnB2nZ5XKZltMsoyPLq+Pq8Fnp6frTbabj5gVGHgiT5ZDPPf0YK4fMx0rHMpoaYFlh9XNvkBWbgbDSPoxx9ICQlUdMN+aZhZCz0hY40s943Qo3pei8pgXWFgDWaXrtYfX5WNnzguG89C0FMU8++ST+1b/6V3jb29428fwnf/In8du//dv4j//xP2J+fh4f+tCH8IM/+IP4gz/4AwB7o6T3vve9OHnyJD73uc/h6tWr+NEf/VGUSiX84i/+4lQ86J1JwP5sh1a6XOag0pRARjvyciRJ4yeddaVQy1E3MCm0NEpySl+TFCwNYHQ4ma8eoVidwlJm3qjHSstTWl6+SZKE5RldnwSFw+EQFy5cwKlTp9Dv97G6uopXXnkFx44dw3g8xubmZnDE5iyB7ljkXxqqmZkZLC8v49y5cxN1wy3a+rbmXq8XZoTSNMWFCxcm6lYek89rHrgkJMurFY/c7SN3vlEWb9y4gY2NDZw5cybIA4EKwyVJEk645emz2gjK/DUfPL+GszhXr14NR/tz9oJLNQSI9Xo9tI9nGDwDz1kd6excKBRw7tw5nD9/Hl/72tdw6dIlnD17FsViEZcvX0aapuF0XQlwNDiyAD0Bnx7AyPhs35gCluWyyhkDM97MgeTD6j8eILIMm0V5DelhjEWWfvCes21iYa3ySd0ZCyvlTuqDLHAZG7xpPqxv75mVZ+x9FsiTfMtveeq4d96TlUZe/nQ9HkaGvLR1Xz4MyCR9y0BMq9XCD//wD+PXf/3X8ff//t8Pz7e2tvCv//W/xm/8xm/ge77newAAn/zkJ3HPPffg85//PB599FH8zu/8Dp5//nn87u/+LlZWVvDAAw/gF37hF/BTP/VT+Lmf+7kDJ71mkeUg6AEGKRhSCVoVTiMkBUl2Oj26JEllYPFBgyZnczSYsTq9XCqRMzx5AIkso/6tn+lRb15Kkv1dQlY8+mhwaaDRaKDX66FSqeDuu+8GsAeMeeKv9G/xRgWSKpUKWq0WvvrVrwbDKrcHLy4uBuNNEPSVr3wFX//618PFjY1GY8LYM45sD7leTf8W7tyxZIptPRwOcfnyZWxtbeHzn/98WBpZW1ubaKtCYe/eIIIpmbfnF8D4bINCYe/U5vF4jFdffRWvvfbagdmrSqUSZsW4E4oA3yPLgNAJmzeJsw7m5+cxOzsblrfOnz+P2dlZrK+vh4s2t7a2DsiypeylAWO4RqPh8tnpdNyyeCBFl8uL54WLvfd0gY6Xh3Q/0EArr8GIhfUMkwXsNFjgcytNS89mAUvLwOvlYssQa7BvlSerrmLAQA86LVAgZc0qo9QXOgyX5PVsjMV7XjsQa+8sQOIBEyueZWsOC2a+ZSDmgx/8IN773vfi3e9+9wSIeeqppzAcDvHud787PLv77rtxxx134PHHH8ejjz6Kxx9/HPfff//E8tJ73vMefOADH8Bzzz2HBx988EB+dLgkycsPJUqlYEiFpw2LBAK8jI+AhGmSisViWIKSSkjvVOIzPYL0wIJWarojalAj05ZxJBUKhYlD37zO7nU65u2NxrI6Ig26nIGQxBmWF154ARcvXkStVsPKygre8pa3oNVqhaUNeX2AVtixjkajfOXKlYlyS+NN4HD77bfj5MmT2NnZCTuCWD9y+Wdubg6Li4sTt1mnaRp8YVheuSzGepqZmcFgMMDVq1dx4sQJLC4uolarYTgcotPpYHt7O+yak21LECOXMz2FbyltzuJ0u11Uq1W85S1vwWg0QqfTwfr6Ora2tibi1ev1cCCf5VCoZUP/5sxPt9sNci+3kZ87dw4nTpzA7OxsmBnjbBCBlvb7ihHfx0BMu90+cLq1bOM8CjWPws1Kx+tbXpisdKRh9MCLZ9BihiYvvZ4RtQU2YjMTGrTq9pM8ZRnUPEaavzVfuj6zePfKrP9b9kb/li4Jul48fvKChbxtofM5LBg5LH1LQMxv/uZv4umnn8aTTz554N21a9fCbcSSVlZWcO3atRBGAhi+5zuLPv7xj+Pnf/7nDzy3EKHeH29tUZU+GTTC2s+FYelkRaVoGWjZUXR+lqEhn3oXErA/0ueSBA0d/3tKuFgsYnFxMZz86o1oLd69epV5ZSnHQmHv3BXrtN4kScIOIBqzVquFO++8E0tLS/j85z+Pra2t4AzK7bGx0ZpMmwBqe3sbhUIB9913X9geffXqVayurgYfi0KhgPn5+XClgR7VpmkatlBzWavRaGBmZgazs7O4cOFCOPSO8bgtn3zyLJkbN27g+vXrWFlZQbVaxUMPPRRO2CUw55Zz+vAMh0M0Go2J3UUaWHojqyRJwixOv99Hs9nEm9/85sDvCy+8gC996UsTaXFmRl4LYbWvpWzTNJ1wQgb2TjImiEmSBIuLi1hcXESSJNjc3MRgMMDc3BySJDHPcvGMhqwDAj1ZH5J3AkTrnVUmDwB4o2QrbN73eUCLlZbXDroOLKAp31v/8xhvzY8Ob+WhyTPgFni1+LXS8wZcXlzLZljpT5O2VR9ZgNxrR0nSTlkyoNtb85Alo4ehLCCYN05eesNBzKVLl/A3/sbfwKc//engxPn/gj760Y/iIx/5SPi/vb2NM2fOHFiSoTDI2QttzCVo0f4qUkjY+JzKk2vxJKYtl54soyP5AzAxutZn1zB/WQam7S01MX3pS2KNWPTylWUsZFhrlOMZVC7V0Ajrzs2lowcffHDiksgkSXDu3DlcunQJ7XY7nKA7jeDTENPP5eTJk5ibmwtgkYfI8SNPw9Xb2mUd9Pt9zMzMhG3XbI8bN26g1+uFG5r1luBCoYBGo4G5uTmcOnUKFy9exOrqKhqNxsSljbOzsyEOeU3TvUPyVldXD8wopWkarmsoFovBZ4eH7AFAs9kMs17cwtxoNFCpVCZmMFnWrJ1QlCNPqUsn5FKphIcffhjNZjMsUX3jG98IoEY6EgN7visxBSgHJVLe6AQuw/E7Tfdu/tZkGbLY6DILgFjGTaaVx0jlMfreoEWT7rN5Dajmz9IdOn2djgaBHoCw9IaXv3yu+6X1W/OaRZ7Rt3RxVjoeeMkjU7H3lHUPhMfSiQHavOnkAXFZ6VgyMQ294SDmqaeewvXr1/Hd3/3d4dloNMJnP/tZ/PN//s/xv/7X/8JgMMDm5ubEbMzq6ipOnjwJADh58iSeeOKJiXRXV1fDO4s4na9JdnLrjBWvQxAkEOXKhrJ2/ujwNDwauMj8ZXrWbiYpYOSJ4fQWZW/pSRONpzyq3gNTloLwBDZLmOk0zS3IVhkHg0Hwj2B90o/k7Nmz6HQ6IYw+tC6mOAEEv5Rerxd2/7B8PI2WSzi9Xs880l/OgJE/btXlTMb8/DySJAmzO+PxOIAYnvorfXF4L9Tq6iq2trYm0ubsEbccy5upa7Uaut3uxHkvabrng9NsNsMzzuoQ/PCkX5ar1+vhK1/5yoQcauXMm8/lbp6YcddyxPukut1uKB/btdPp4Bvf+EZwGKaMc0s3QQzTk3LGfmVtqa1Wq+b29STZuy9Nb6/Oolgf8EjqHFkf0xouzYfuq7JesgCP/h0bpEzDl07L4tkCBdOmGzO0Vrp55NPLy0pD2gcNdGP5ewBf5unF57cHfqmHrPLJcLE28OpJg5w8sjttO+gyTktvOIj53u/9XjzzzDMTz37sx34Md999N37qp34KZ86cQalUwmc+8xm8733vA4DgA/HYY48BAB577DH8g3/wD3D9+nUsLy8DAD796U9jbm4O995771T8yJkFKmn+lyQbSQqs3I/PCpbnwwCTW6Y5uqXBYP7SOGiBk4pIO/VKPjUI0wJJBy+O9FkGKlOmrx0IrRGirgsZzgI/WcogTff9Q/RsFN/3er2w9fib3/wmrly5gocffnhiJw6vLbB2lsi8NElfIIIp8n/69GmcOnUK4/EYL7/8crgbiEBAl9Uyptw6zdnHnZ0dFItFnDhxAuPx3l1KNJ7y6oDv+q7vwsmTJ8OMD9MlyOh0OhOAkkqrXC7jvvvuw5/9s392YlcVQRV9iOSSI9Msl8toNBp49NFHJ7ap87c8cZggLEn2ZlIsYKtlSLeBvIqh1+vhc5/7HM6cOYMHHnggbLmWMkGgzcstLbljXrpPU9ar1ap7HAN9jg5D2qDJZxYgyBoETEN5gIWuK29wMq2hsfLgb6vNs4yWZRQtGfKcnWN85TG0ugxap1n6S1NeMGalr3Wr5MmqX9k3svLR+snKaxrKivt60/aAdF56w0HM7Ows7rvvvolnjUYDx44dC89//Md/HB/5yEewtLSEubk5fPjDH8Zjjz2GRx99FADwfd/3fbj33nvxIz/yI/ilX/olXLt2DT/zMz+DD37wg+ZsS4zkFmsCDz06YsVJoMLKpdDo82AIMqxpfglkmIbVqaXSleE0DyTZ+bWfgMyPgEpTmqZhGYH/ZT7TjFjIT+y9Tk9u3dXEnTb9fh+XL1/G9evXg+8OAQcBSJIkEzMxlqLWo0ruaOMST5IkYSfQm970pjD7Q4BaLpexs7MzAT6tkQ2NKC965BZyXlPw1re+dSLMxsYGnnzyyTA7QrCkty7HFA/br16vh0sw6/U6lpeXJ5YeuSWa28e73S663S5arRaSJEGz2QwHAbLO5JZz+uPMzc0FHxrd/lo+NSXJ/hkxXMbkzqDxeBxmRKQMydkieaaM1b7aKPB3vV43+x1lR593YwER67mVn1V2D2zIeDFAkgUUJUixgIk1ILOMvKVjrO/XY6gsfaXTtOo/NmjySAIfq9/GwIinoyUv05JOr1gsTtgZ3XYekLF40P+lAzzz9kCrlunDlEXzEbMjWXE1T9PQH8mJvf/kn/wTFAoFvO9975s47I40MzOD3/qt38IHPvABPPbYY2g0Gnj/+9+Pj33sY4fOM2tkAOzP2ujdIHIrG3edsLNI5c84HAFqhWXNgGhhlr+1UZOzK9YUugZrWih4mJy3bq3zz1I8mu8YJUkSLmykz4M2CDRwX/nKV0LeTzzxRNjOvLGxgZWVFaRpai4nyQ4r27tQKIRdQ4PBAPPz8wCA9fV1rK6u4q677gppdDqdsFNJOrJ6dS2BDPPh+TStVguXL1/GyZMnA+CQdw8R9PT7fRNYx5Qrty3fvHkTTz/9NI4fP47bb78ds7OzE7JGnmZnZ8PZMJ/97GcDIOSOKt78zdusa7UaFhYWcOzYMRSLxQAorbbXSkzyOzMzc+CMmCRJcP36dXzhC1+YcNxlfC4pcmZJ5qfzl35tkg9595SmbrcbAJWscy1LFsUUtG4vL40s8O/l473LMkJ58pPpWIOlwxjzPIZY5695mAY8aX0pn1vpWmBSyq8HWK105X9P7mK60kvbA7KSGI7nSXG2OgYIDwtOY3Y0ll6e/A4LFP+fgJj//b//98T/arWKT3ziE/jEJz7hxjl79iz+x//4H687b61oSXKZBziITOXoFNjfgSQbQS4r0SeC22i5tCRvXmba0ifHMgTyZmYpdFTYHPVLIZeAimlZTsacUWKaeeovj5LMUjhJkoRZA5k/4/ACSO7E4W4c7shptVrY3d0NaXgXOep0WbcEUMPhELVaDTMzMzh//jxuu+22sNtpe3sbW1tbYblJHrGv60qCWGB/mzZBDA/me+aZZ7CwsBCMqgQsBCK8DoHpat6tESodlbe3t5Ek+8ueXFKkU/pgMAjLRbfddltY1uFnZ2dnAqwTAEl/nGq1ioWFBbz5zW8OF3TSYZjLTrpvME3pUL2yshJmYXZ3d3Ht2rUDOwXTNA1xbt686YJIXVeyPyfJ/hkx1ihRgyNLfqy8ZJtonqyy6/S9dK0+lqffWWGnNaJev9FlkmG1gX+j+Nb8WOnpvi3jevl6AES+t8ptlUXbiViZvP8WaLLSlu9iAIjpy1lj/U6X2StnVpt66ecFIJ58ZLVhjG75u5OAfbDhoWySFgKN2AlM6OvinV/B8HL7M2dn9BZtvQwkl6dIGujEULol0FoBeyMWTbHRU540dAekn4L2Vdja2kKr1UKlUsHc3ByOHTs2MSXMHVX9fj/sWpK3i2eNVjSIGY1G6Ha74TqCJElw6dIlPPPMM9jd3cWpU6eQJIm7pVjWoQSkvB2afhx33nkn5ubmJrb6DofDADhKpVKY8dFlkfJpARnG3d3dxenTp3Hs2LFQFgIszvQQhNRqNbTb7bALSBNljvXELefAHsh8xzveEXiT28u5VKVvIB8MBgEwLi0t4bHHHgvx9PZxAiNuryaIZL1IQB8bIbPs3P1myYS+vVqT7I9ZZ3Do9GNK2pqJtYxAXiAQM85SZqYFHzGgYBlhaxk+Rl65s+LI9D0evTrxQIDHt7YDVh+0nuUh3S6SR/3b+g/sH+Ph7Yb1+I7JXIw8cBbrE5KnLJoG/Gr6jgAxgN/YniBKIELSylQ3JICJaXwJKGi0POWrBVWP8pmO7JAEOxq8yFkjSzgkOJP5Wx051rE1UaFpg8My0M9BK9YrV67ghRdeCDuYaHjlCblc8qCvCoCJnV8xw0JjTkfdl156CZcvX564dJCnxCZJEpaevO29lhICEHgnf3fccQdWVlZCuMFggIWFBTz22GP4wz/8w+AfpC9VjCli/uZuq3K5HPLlNDJwEMRydoWzILpcWrFpOaVz7osvvog0TcMuqWq1ivn5+QngCewDoY2NDXz5y1+e2F3Fem82mzhx4sSEfMsdYNzSbbUrn8ujCNgfOIOkieWhr5PXtjKslael0C3+JKCMjT51XPmdZ5Ch0/R0mcV7LH/pNG0ZVuu5zC9mLPMY0Bjo0BQz1LItrXqdFjDGwKNl2LPATkx/aV05Hu8dwLq8vIxer4eNjQ3zoNOY7o/9zyq7fJYXdMRkblowa9EtD2Kks6ysKA0I5IhCAwVgUqC4a0MCDWBfyOS0vAQbesSiZ2X4XBLDWzuaNMhiuXiirQyXJXBWvjHl7KXhKXgCCfqbSOIFh2m657zbarXCO7kll8b61KlTePDBB8OOHH54MaN0rE7TNCzbVCoVXLhwIezEkctU0qeJ23ulD4hl5HW9EZQQ/PCSSN7/c/36dZTL5XBMALcuMx/LKU/XI4mn3fKAwLm5OQD7Mz1cUpLOvbVaDevr6xPgQIMjywDJbfkvvfRSOBCQwEieVkzfmtnZWZw8eRJJkuDixYsThzGSPwlW5dZxgt3BYBCWaWUfkobV2p1EfixZHI1G2NnZMY2MVweWbFtpa8Wu+7TVpnnBjcenFc8bkHhpenRYI+UBGJlmbBCnAYEFfLL49UCG1OuxutTxGN4aoOn8PcBolVnaJ720KsMlSRKWb0ulEur1OkqlEm7cuAEAYRbWK38M5Okwng2K1U0MHHr14T2blm55EKMbz9umrKeN+UwrSaYn07EUkwzLGQOecaKXtpiXdMjVI0IZXguVFH6WS/vixARc1lNe0OORN0rhtmD6pMjw+vRgmT/BBeuv2+3i9OnTWFlZCb5BfC9PupXghg6l1WoVb33rW0OdyWUq+o10Oh0sLS0F46/bUssTy1AoFDAcDvGlL30pHM//5S9/OcSV58M888wz2NrawokTJwDgAFiKGU8+42wRt6STCGK5G2swGOC1117D7u4ujh8/PrGUomXKM3oEdgQWi4uLOHHiBFZXV7GxsXHgJu0k2Tt753u/93uD/4nsQ2wnuWVbykmxWESz2cR3fdd34S1veUtYrpJtypOL2e5yhx/PiLGMDLe5T0uW4dNtZvXTrH4UM/JW/vp3nrRihtdKIwauNBjwdEmsPN5AxzPgMeNugTWZvgc0sp7F+JRxtCH3gKxMQwMj773mUctZvV7HmTNnwp1m+nb4LJ1vlTdPGA/keDIbk728chmj7wgQQ/K2y0pip7E6gxTcJEnC6DILuRK0UOFS2cr8JE/W+r8EKTIfaezpqyPv6cmqmxjKtgy3ztfrdDofXhegHabpaGopAU9pVKtVtNttfOUrXwm7b+RIvl6vY35+PjhY93o9PP3002E2Qh8a12g0sLi4OAEuNzc3J5SCNrT0uSAASZK9mZdvfvObIQxnSXjdw/z8fDjunmBOOynLOrBGOuSPF2myDi9dugQAmJ+fD/JDWUiSJGwrp08XZdwbRcm652m6nFE6deoUHnjgAXz5y1/G5uamqWC5tMW7jzy50PKVpmkAJ3NzcygUCuHUYoZl/Q2HQ/T7/QmA0263Q3hNSZIEX58shauf6zrRYWTbeOlOYyQs0OL1NQk8rDJo8sroGSNdThlG143UU9bOsRgYy9KjuvwevzHAxPdWmDxtnfXO48OLp0G895G2gcTre1qtVphRttKP9fO85cgCtzIvK//Ye53OtHTLgxjdqTTo0DMyMjyBCo0SG1MvA+mdThIIcc2e0+jcyaHPMtDLXlJwJKjSCoskl8L0PRrMJ4bw9TutpHRaMeVjtQF3/OizcGiQZDq6s+lOXqlU0Ov1cPPmzYnysx7pJ1Kr1XDhwgXMzs5ia2vrwFHzbBPph8OlkEqlgtOnT6PdbqPX64UtwuPxGEtLSzh79izW19extrY2cZs2jW2xWMSpU6fC0tjy8jIqlQpWV1fx3d/93fjd3/1d1Gq1MOOj69JT8JQ/zozwhutGo4FCoYDNzc0JWdjd3UWn00Gj0Qh1LS+N0/JmKRnt69NsNlEoFMIJvJxhk4fW0ZlZLl1ZitiSHYK0mZmZ4L/E5SqCUG67bzabAbBmAYY0TYPDsaeQtdK1AFheIyZJztjG8vbS99rJMvSAX68WCNA8efxl8WgNdrIAcl5gF3sm8/DStmRC16kui5efp6f4TA/wLBBlgRSLBw+47u7uYnNzM+zwk8vuVhpWmSyKyaUun/XeI0v2vYHatPQdAWIk6NDC6I1cJGDRIEdOb0qwwv+eQiCg4VIGBU7zxDSt0xn53hImec8PnYiz6sbrJDKMrCsZT4bxBJvfXPbQowXPiHtE59/BYICZmRm87W1vQ71eR7fbxcWLF7G2toZ6vY5er4fr16/jzJkzE8fsa+DHnTJyqzG3ez/66KPB8HPEz3NNTpw4gWvXrqFYLGI4HIYZFwliarUaHnzwQdy4cQOtVguj0Qi333472u12uFOJPjxe2S2lzCWTwWCA1dVV3HHHHTh79mwo00svvRTyG4/3ju5fWloKl2oyPz2r5+VLUEhAwgsku90u6vU6/uSf/JNot9v43Oc+F+SPpyzLk3G18orJHR1zNzc3sb6+PhGHvjVyKzivcDh//rx7ThL5obxphWoNFPIY9DwUM7BeGIbTh6PpOPJbhonNgFh569+WrvT41XnpMlijeWuQouPE9IGVls4nxpMGqFZdynfadcAqQx6goOvB63sxwMkl8F6vNzGDbIX3nlnyHZP3w5IH0vLwmIdueRDDZRW5lERFaO2rJxEs6LhyFgVAmLKXMysWyGEHoAHWN15L0GLNekgnYeabpmmYBZAovFAohCPnuYxgjSQsio1wLIG30rM6YaVSmTA+JLldOo+i58wJnUsXFhbCibJbW1tot9t46KGHsL6+ji9/+csHrg/QfOp2k/wOBgNcuXIlGMlarYZSqYTLly/jtddeQ7FYRJIkwWGZM008H2gwGOC+++7Dpz71KbRaLSwsLKBaraLX6+Gxxx7DsWPHcOPGjQNnrHjll2C4Wq2i1WoFMAcg+IEcP348+Nu0Wi1sbW2h2WwGmeP9VVKWLJ8w/ubWeM680B+n2WyiXC5jfn7+QNtxNxOXbqSsSP8Vb4TLpTY5k8N48pwdhrl58yYKhQLOnz8/wbvmiyAuNluT1Vc8MO/lKQ1yFph4I8gCJFZ+MQBkvc9jhLL4yjKYXn1qygJK1o4dGc8zqlb7Z4EM673MO6Yn84IPWS6eQcXLZ6UN0WnE2sh7lyX/MYAfq988PB2GbnkQI8GKXBICDk4nalCTJMnElk95uJ0GE0zDOylR5sXRu972zbD6aGppaC2h10JB4dbAJyZU3kjGC6+XuGSdWOF5XYRWFAR0On+PuEuJZSyVStjY2MDFixcxOzuLO+64A+12G+12G0mShIPs9KiLPjqzs7PBx0aChM3NTfR6Pbz66qtI0zT483B2Qy5B8ewbnpkyMzODs2fP4pFHHsHLL7+MNE1x++23Y21tDZcuXcL58+fD8g6dW/WSpKw7+ZszPqVSKZw/9NJLL4ULVBcWFsLMSZLsHfq2srKCWq2G0WiE+++/P+zK6nQ62NnZCX4rnkLizodOpxN2FZVKJTzyyCPh6IC5uTk89thj+MY3voGrV6+Gu4+kv48E41oOdL+s1+vY3d1Fr9c7wBvramVlBffeey8uXbqEr33taxPn8TBNSWmaotVqmc91Xcs6yDK0+p0sU8wIHIa8mQaLPwu06CMidByG03xqPWbllWfWRIezDL2Vl+bFA2rW/yyyePdmjrJ0ncUH5UAOOqXPli679eEuwDTd3wjBmUidh5Y/r+50eF0u67d+JgG6TF/naz2z0o6d3+TRLQ9igIMd0OrkUrCkv4BuIEt5cPRNI0Pw43U2joT1Vm0rrB4lkz/mRXBFPvmb1yPocmryOqNWwN4oQr6P5UNHVA3y9CyJp4BJ5XIZxWIxONR2u13cvHkTly9fxmOPPYYkSfD5z38+3JEkrw+QaVarVczOzuK2225Dmu5dR3D27Nkwsvn617+OmZkZLC4uolqthrrmCbjz8/OYm5vDcDjE+vp68Jm57bbb8NBDD+HChQtYXV3FzZs3cfLkSTz77LN47bXXkCQJ1tfXAwg6f/483vnOd6LVaoWLG+VhcVR6VFqsg1KphKWlJXz3d3/3xF1Ievdbr9fD9vY27rjjjjBjODs7i/n5ebTbbbz66qvY2tpy65yAgoArTVNcvXoVu7u7WFxcBLA3m1YqlXDHHXfg2rVrYbaGW96lfMiyeAqPedJ5l/Glvw2dt+UdSXS29gx8mqZhe7Uuo9UP8hpDmY9e+tFGWaarebR+x8JMQ1a8acpnGa8svaLLGzNgmk9tAD2+rHR0feXRg7G0vXS9eBY4IEm7oOtROkMzLfmhTiew1xtA0jQNukOX3aIsYJHVBodtY/lc2tpY2Bh9R4AYYLJyKEjyXZLYa6QarPC5HNXwd2zHhzUbRLAjBUb74Ojf0lFYghZu397d3Q1nbXDrq6Y8Ixlr15VVp1kdhfXG6wJ4Rgzjye3VltFhWNmRkyQJ58k8/vjjSNO9Ec43vvGNsCNmNBqh0WigWCyGTi35LJVKWFlZwbVr1wLg2d3dDX4qvDOISoHhCRrp67G0tBQuUTx16hTOnz+PNE1x7do1bG9vY2dnB1/+8pcDUAAwsdzUaDRw8uTJA9vFuZ1ZnoTL8224VMMzWVg2vauO8sIlp7W1NTz33HOhPYC95ZXYMiZBg7yu4Itf/CLuuusuvP3tb8fFixfx4osvhkMDV1dXUSqVUK1WsbW15cqGBOKevHDrKLDvMJ2mKc6dO4cLFy7g5Zdfxuc+97kwiyZPLNbEviLPILLCUN74HQPnWjfkHUXGQJZlxHU8nX8sji6bxb/mw8rHAwle2bRMeelpYCA/UvdZgNCrH62fZR1YQCQGyGR60wBBmSa/Zf+0yuvJjwxD4oCES6r07wMQNlHEeLPqVbeRJRfSrknbFqsPT6/ruF78LLrlQYw1q+J1Sq8BtJBpoZJpy63NUlHrsw4ogBzB07hbIEYLPX1f6KzKrbzcHcL0pD8C05MjYF0OC01n1Y389t4DCIeP6elPghhSlrKU559I59JyuRxmCKRzM/2DdB47Ozt45ZVXQn0PBgO8+uqrgceHHnoIFy9exJUrV3Du3LnQ/idOnECpVMJXv/rV4BTMJZtGo4GLFy8GP41nn30Wr7766oQzs9xFRd+WGzdu4JlnnkGpVEKj0UCz2USj0UC9Xsfs7CyWlpbCTBAB0uc+9znU63WcOHEiLKNwtw59cwCEk3FZn/Pz8+GgP9ahJ2sSxHBWhDI+HA6xvb2NtbW1sEOCtLCwEByBrTM+pEzq90myfy8UlwHPnDmD5eXlAOS41NTpdLCxsRFuOpfnD1k0HA7N3VIxYOAZLz0I8pSxZ7CtdCzjbZHWZfKZZaS9+JLXGG8eKMzKzwNqFvjQ+Vj1p9OJlSkPsMublkwvizxdlgdAWb8tWyNn+6nvKIO0NXqzhPx45ZZ86h2E0l6QCJ7ygCVZZmtwGpO1LLrlQQxwcMTEytWXIUqwYikFxvc6Pe9WYnp6pkbyQqNUKpWCcQAmO7nuiExPnivDXU48gyZN04mzMLJGD1pAvTgaXFl1q59L4EQQo5W/nPq0vjVVq9VwtgoB3QMPPID5+Xn0ej288soreO211wDsAycNlIC92ZCtra0DsjEej3H8+PFguCuVCu69915sbm7iySefRL1ex6lTp3Dy5MkwS3Pp0iW0220sLCygUqngm9/8Jr7+9a+j0+mYsxwk+uWsra3h+vXrE6cG0/elUqmEY/QXFxdx9913YzAY4MaNGygWi1hbW8PCwkLw4yGwWVpaCme10IdmeXkZi4uLaLVaePnll3H9+vWJ9rKUjtwJJQHHa6+9FkCjNmjcHs0ZEksB6nyk0y5HkvRrWllZwblz5wDsjUC/+MUv4qWXXsJ9992H06dP44tf/GJob4+SJAk+SzGyBjkaLOhnXn76vwY0Xt/MazAtgMDnsq9aMxIeyfxjYEobIKuds8CRZ+TykM7DkyeLZytOVh5WGroeSN6Az6oPHTemT2VY+lTqwSj1P/nwdL3Fqy5XXnCSRbrs+jiR10vfMSDGamzdWSVIkCBEpmGBDWDynBaexcHOlKbphLOuzEf6tUiULQWO/jbMh/kDCNOGRMUAwnJHkiQT/hGSspSHpcj53FIaXscF9kGMXA/mtzytVvJpdfokSUI6NJ6sy0Jh7+h9eQgcl7BkHrI8VpsC+0aYy0x6xqxQKITZEW5hbjQauH79Ol5++eVwnopOV+dPcCEPX6vX67hw4UK4UuHmzZthKardbuNNb3oThsNhcCTu9/tot9sBjLdaLdy4cSM4dnPGp9Fo4OGHH8b8/Hw4Y+XMmTMTlzYyPfpqjcdjFIvFcALu29/+9uC3I69uADAxIuSZNXI5UwNzXT+yjlj/nDW5cuUKRqMRzp49O1EmuSuKzsYyDy2/cnu1R1kg2gJkOrzVH3T5Y4MBzzB7aecxBLEBimVI8xh6r5xZdWi907rYMuyWfoilz49estTGehq+PX5j+TOc9U7aA/IpjbwMz/eyr1llkrs9ddms//J5rExeeMu2WPnpj5f3tHTLgxhZWXIrNA2+BB+sbBonvRQlSf6XwIOzMVxWsnadSABDoMH85A4oCQJk59ZCQidhWWbmrzuRnBHKKzAa8GkBzeosnHGw6pAOmnRi1evGsh6SZP82agDhQsOnn346zFbII/AJYvS5ILpOdF4EQrw1m4CGW9ZbrRaeeeYZDIdD1Ot1nD59GhsbG3j22Wcntu9mKUNuOyfPpVIJCwsLuOeee4IMPv/88/ja176G4XAY/IG8HTZ8RjnS5eL2/uFwiLm5ueDLw2U57r6S1zDwd6PRwKlTpyaWteQt1tzx1G63sbi4GECDJm9UK3nlshCXo65cuYLNzU2cOnUKjUYDDz74IHZ3d1GtVrGxsYHhcBhOXJakjZW8giLGl8WnTleXwQv7ep9ZpEGGlLc8ACfWl/VzXYeeMYwBAMmvxbOVxzT6ySMNxPLWr1WHWQY+VpdWml55LcAVM/6SP8sn0yu35surb11/WYNfmb7Hu5YtPdiYlm55ECOJlafPbrGWSvQWOA1a9DkE0tehVCpNnAMjQYulEDiylMsJusHlLAZ30HBGglOLOo7evp01+tN8WeFkWK9+GYb/5Wm98myQYrGI++67D29+85uDEyuPkOdInztc5PH5jUYDDz300MRuHsblLBgA80TcrHIlSTJxHcD8/DySJMHCwgLOnz+ParWKTqeDzc3NcGjd+fPn0e12J+4IYnqWsx7rmLueut0ums0mHnnkEVQqleDzNB6Pcdttt6Fer2Nzc3PCuMtly5ii4TOm++KLL+LFF18Mu6N4KB2wd6rw0tISZmdng2Pz5uYmPvvZzwJAOC+n0WiED8/PmZubCyAnSfaW8ORSoQV8NYCRu4/oVyRB/fr6egBz5XI5gC86s2cpVd5e7RlrL55uNwvUe4A4ppR1Ot4I2ePHe541Q6H5tkBD1mg9z/MYGNGDH49eD5CZFgjlCZsFCrUcWDzIupWDAoa39LiendH5aPnxdPG0ZdN56GfaxQHAxOCZZDnwy1UFCcCmpVsexHiOrJ6x9oTPAiwamMiwlkBTISfJ/qFFAAKgkg5aJP7nO06Z04jJG5glePLWwadRTK+HZD5ckpEn08pOwFNw6/V68O9gHcrRPpc8eHjb4uLihM8JT9blhYHHjx+fAHixEZZ8Vq1WA/ihn8XJkyfDrcylUgnvfOc78cwzz6DdbuPatWtotVoBNMn2iu1YkTM+jUYDJ06cCDMjo9Eo+ErNz88Hf5tCoYBz587h2LFj4c4UAj5d91L2q9UqkmRvFoczJ+12G1tbW0F+Op0OLl68iMFggHq9jocffjjUp2w7PQCgIy53SzWbTdRqNRw7dixscR8OhxPLVJ7RLxQKYTcUd2wRrDz55JPhTB7efs3tphKMWW1MEOONJGP9Q4eTMmeNKj1wHEs3L1nGyQMLMePF8NK45NlhlZWeF8bTtXni5B00We+sfDVgsHiwfGpi+WperZkOjzfyoQey8p0c/DGsLoO+l84jtrtlaySPeUBNqVSaOM07Jov6v/zotKehWx7EAJMVRlQrFSmXJzRIkUtBGph4WwhlXAko9JIQ40ifGM0XnSmZhgZASbK/RZujU7keOg048UYTUiD1aN/rMLrDchnEUpC7u7v4+te/Hvw45GifRpHg5vjx43j88ccnzllh2GazGS50XFhYCIDg6tWrEyCR5bDAKpUF7yUaDoe4dOlS2ArNPHjAHbC3JEQH1OXl5ZC+9E/iN4ERl5x2d3dx7do1DIdDNJvN4JvSaDTCtmhrR1yz2cTs7CwAhBmLGzduhHCc9eJOLqYJ7J94TLAll1jX19dRrVbDshwPwpPHADAPWT7u+tnc3Ax1eNttt+Fd73pXAJf0n+EsGz+caZM+TvV6HZVKBe94xzvCbBtn2jjb1m63wyWdSbJ/RgzJMi7WMpyUUw3QtKzHBj3Wf5m/JstgeIY3zzPLiGfxYuWnf1v93OI3NhNkxY+BFyuu/LbKbumqrLax6kL/z2OQNQ/8nxdUSjujZ8/lDAxtBf0gY+0f0/1a32XFkSArTdMJ1wW6MsRkV4Jl614/Ge8IxBjkKSBJ3shKdxw+1/GkcSZYkX4ucnlAghINbOisKff80+iQHw2S0jQNo1M5U5MHkVtlks9ioMVSdl66PJafvEmiYywBy40bN7Czs4NSqTRR1rvuugsXLlwIRo+Hzq2vr090cAIhLpUcP34cb33rW7GzsxOcWLkEIUGNNGCcIZEHwrH8HHlwhmFhYSG8k1vr5U4Zdn62KXdLDQYDvPbaa2EL9Fe/+lXMzs6iXC6j3W4f2NFlzfrNzMxgdnY2bLPmVQh6lMMTfimXnOWR8kofozTdm7n5xje+YToRWjMOUtERVMzMzODmzZsYjUaoVqtYWlrC8vLyhELjNnU6KBOwbG5uhgMJ5YySjNPtdkP4+fl5U/bIN7dkx0gPVDR5BjJGnrGe1gDJtorx7uWVRxd4fTi2JGrx6OXp1dlhDJeXRh7gJnW7Bld5ZmBiYNnKR4bTsqMBsxVX7+bRZ8BYIFIPfiTQkEBdnpYeK3OapsHHUPYj62gBrXuYBs/i4s5SL69p6ZYHMSQqav6WipGkwYxXqVLwpHKRhlDO3uhlJ93QWgCHwyGSJAmn0wKTDczpe+5akTuQCHg4zd/tdg/MJE0rLHomRpeB/FmjA+5MYifQIziO5M+cOYPz58/jySefRKFQwN13341+v4/NzU28/PLLAbRZl0XSuBH8DQYDtFotbGxs4PTp0+G0Wh4gJw+S63Q6E341rPf5+Xn86T/9p8NSFg2m9L+Ra79a6ZAocwRY0vFbh9ne3g67n/TBbWxTrw3lurkemaZpim63i2984xvh3BoNSikjdMCmUy2djXd2doJcyvLKvCSQoV/LV7/6VWxuboayWzNttVoN8/PzOHHiBHq9Hn7/938f3W4XlUolfKQPDmfDjh07FkCR9GGz6oftHQsjn3kG3ZoNsMDcYQcQVtpZRsbLX//Oa6ysd3n0hmfILf3hlU2DDP1M148umwZSmrJ40MAjqx2z6iQ2COZ/ya9uT/ZraXPkib1y9saSTcaVM53FYhHHjx/HaDTC9evXo2cccXNKr9cL9kYP2rWMWcCQccrlMgqFwsSFvFn2Nou+Y0CM7hSegGqlLp/xv5whkADFMuAcxcoL9+SZGDIPnuRKMCLXOBke2BOEubk5bG5uThg2idjpAKtP7dWdNNbBdLxYncr/OizPdpEzFAzLMvPW4uFwGI70L5fLaDabuHjxIiqVSvCr8BRymqahg9II83Zr/qdBrNfrWFpampi5GI1GYealWCyGQ+fouHr69OkJvtfW1nD58uUDSlMCDq2U5QhJtkeS7DnDUp7oF8JRi1wG1VPOMk9Zt5S5mzdv4sUXX8T6+vqB/KVMAvv3biVJEpa+yuUylpaWwnKX9AnTskFZ5IF0PFl4dnY2XL64tbWFa9euBVnnoX+PPPJI2AnGGTeesCvriYqYd1cR4Jw6dQpLS0sHjDiAcLhfTJ7Jv2U0Y+G8dzofC5h4YEqGm0bB636RBT6stD1wIb/zABIvvgVqPP3CcFZ9eYArlmaMPEOs31t5ennF5IC/2W81WNFAhP/ldTWaV+0KoTcbcGBSq9WCo3uMP7nLVftwWmW3wCX5Y3+XQCbmI5eXvmNAjNW5gYMK2AIi8r9l+HXDSXDCkbfcbs289X+59KTTtG46lrzILds6foxXPs9SKJJ0+a2dUfzN5RkCM80HBVluh758+TL6/T4eeOCBMGvBW6Wzpj+LxSKOHTsWpi5v3ryJNE3x5je/GTdv3gy3OstzeXhM/qlTp1Cv13Hp0iXs7OyYy1PSN0ZeZyA7LNuVbU7QI0cgJNbdcDjE1tZWADLnz58318el458lrxIs7e7u4uLFi3jppZdMMCt5kIA7TdOwtATszWLw5F8qP569Y8kKZ2LoC3Ps2DG87W1vm/AL2tnZwebmJlqtFtbX14MPUK/XOwBUteFj2Xq9HjY3NwO/pVIp3OckKU1TtNvt6C41a9SfZZS9GQ4vblaaMpzmX7/LO9Mj41hl8wxwnjrQPOq+b7WfTl++13xqQCnLIn9b+XjhqUtj7RYDJJZ+lTxaBjnGu3QrsACBDCefJcn+LInlnEte9FIUdans37rcOg2CfwvgefZTh+V/qe+p02Nykoe+Y0AMDZJ0fLXOUZHhZQNJ59ss5Ol1Ov6XHYnPGE+iXim40ptcC52c0pOAyAI9mj8NzDxgo9PRZfKISyO1Ws2sO3Ykgg7OyrCsPAiuUqmEpTHJh1ZccsmCzweDAWZnZ9HpdMLSFkfzNIavvfYaKpUKFhcXA09yFq3f72N7e3uifmq12oSPh1624xIW+aJvjK4zhh2NRtje3sbq6ipuu+22MGLRS0WybrXSZf0Mh0O88MILuHjx4sTUrd7+KEGXlh0Zlof91et1LC4uhnNh9I4zfrO9CODH43EAs8DenVO33347dnd3wy6vWq2Gra2tA+VlmnKZVvJLea7X6weUMX/v7OxMOCRmzVAwXW+EaBmxWH+ZVkFbOiYGjjTPnv7R4WKDmRhfnr6wjLBl1D0dksWvl26Mb/nOaousuo7pOwtkyHdyICIBhSyHnDWRgEN/9IwMsOdvyINNdV+2lp85Q5+m8YNG5YBGzsBI/Rurx1h9jsd7B5ByMwqwv9v2MHTLgxjZOFSAltIH9gVRGgct9GxIObUvp+TkVDsFjrMxXLrQt2WTLwIlnu6rO5FUmJyZ0UBMC5UnaFrpeOW1BFMrhZhBoE+M7MwyjX6/j0JhbycNwcO9996Lubk5PPfcc2i32wD2/IA2NzcnOpTkm0TnaF5sSB8hLosQLHDkLkdCcmRgzWoxP+l0LOuVskGjrUdBlA85Rct4lUoFp0+fxrFjx7CwsDBx7gllw1P0us1Go1FYamw2m9je3jZnN0ieMyPjSCf1nZ2dcMEod2ZxizxnEvnZ2trC7u5uOKzu/vvvx4kTJybqDACazSYWFhZC/fDwQ3kAou63Mj7Lb50VQ/mU26tjQMCTZe95FoCx0pFx88bLCzAkkM7iI1anVt6W8c5LXnidbmyUb6Vj8SbfxfiMgSEL7OiZUanvdd+U8eTgRetBzaMFXuTp2zJ9Lg1ZulXWi+StWCxO3IWm05TxZXryuQ6vB0FWm0gdwzobDAbBv5Oz1Nbmjyy65UGM9soG7KkuTbLC9ZKSRqp8LhuKwiGNmWxYCrYUODnbIvOSvBM0yalAGdaKo8GJpzR0+fMo2KxwcleSVc+9Xm/ifp83velNweisra2FM1SKxeLEjddeeqVSCf1+Hzdu3AgzK8ViEcPhMMwejMfjsJuHAGd3dzcYZQlirA7O2RXO7mlFIOtapkUwwCUVqQSZbqlUQrPZDPUmFVis7DKddruN0WiE+fn5MPK6efNmqD9LPkjymSfPPL6/0Whgbm4O9Xo97BIajUYol8sT1ynQcVeCO+bF5UECkEajgQsXLoRZuNXV1XDOjAYh7IMEhXQc1zQej6O3V+uyZ1HMcOaJq789cKr50n1Np2EZLfkdiwsc3Gli5emVIVZ3sfcWKNHlOUy6Hr9WXWbxJ+vPmk2xHMu9d3rGQfZtb6aGukPzJI/X6Pf7E32Waeq6qtVqKBaLE7Oosr7lIN2qG11vLK9nc1hG2WelneNBptzEUqlUJu50y0O3PIixnCABWyBlGE+x6Gl93eGsKX4uXcjRsFQ4uuGlcubJpORXghjNuyybR55SiAE5TXod1orH8nE7shWG04pMr9Fo4Ny5c3jiiSdw8+bNANRozPVlkVaazIszHVyeGgwG2NnZwWuvvRa2cM/OzuItb3lLMKTlchnr6+uucddAhQpK17eUBelYR574TH73+31sbGzgxo0bmJ2dDTdSz8/PY3l5OezIIVkj7vF4jHa7jddeew1bW1s4c+YMZmdnww3Pa2traLVaUZ8T6z9nWWR/IRhrNBqoVCpYWlpCr9fD+vp6WDrsdrsoFAq477770Gw28eKLL+LGjRu46667Jg7rajQaYVpZEh2rR6MRdnZ2sL29jU6nE+53YvumaTpxd5Jur9FoFM6I8QyyBw4tQ5o1K+ENGmTf1+np+LH0NWUZe83DNO+lrvLIkh8P4FjAK6tsOi+ZrmVUPWAE7Osuma6ni608dJrsh3JGXC8L6UGszI/vAUwMiPR7C+BwEFCpVCZmLK12YTrNZhNJkoSleem+YLWZN0sr61mDZ9luMm3+lv8JxHju12GWlW55EEPSHTXm2yJHilroGJYNotOWSFOOXqXQlUqlA6NqxpdGUfu/pGk6YRSZdx6KdUSrY+VNU6ZjKSteOWDV03i8d0bMzs4OnnjiCTSbTVSrVWxtbU04k3G6kTt1YqMmzuicOnUqGDmO8iuVCk6ePBlGLuSBy0vFYnHilmPL/8gDe3LUIWWAz4D9i9vk4X9Mm5c9pmmKO++8M9xz1Ol08NJLL6FQKOD06dO47bbbJuJKIHT58mVcunQpjLJ6vR5OnTqFY8eOhfKtr6+H4/uZt6WoLFCjlz3H4zG2trbC7iNefEmwpW/x3tzcRLfbxfnz50Pao9EoKDD2Fy77SQMxPz+PpaUljEYj3Lx5Ezdv3pzwkeIIU8sfgHAYn9d+VhzyrGVc/tYzQ1bdWWl7fUX+zgMcdHgvnCW7lkxbsm4Zdv3bK5M1Ytd15vHnldd75w0ksvSFpxd1f5bhCSr4noNUSyas8NqFgTrfAjCMT9Cvj3XgEq7OU+tzDtIajUaYlZY+Zppv6kZrloW/dR3rdrbqlrOx1F10JeBuzKwLWi265UGM5YnO/9ZoCcBE40klrBuOz0nSKDAdGY6OxVo4rGl7GkF5wiF5ldtutQAyH82bLrv1XBtWWa68pJUNpwn1c+a3tLQUdrHcuHHDPAGSzrhcDokRDWCxWAz1TRBUq9UwOzuLM2fOBCNeqVRw9erVMNujd/E0Go1w9oxUZhLs0qiTeGKuVSfWyFy+LxaL4f4iqfyYn6xLWRe85fnYsWP4wz/8Q2xsbITt5e12G6dPn0az2UShsLdbbG1tbeIMIc9hlv9lXlLxFQqFcPAcb/2mUzUde5966ikcP34cb3vb29BqtfDqq6/i+PHjmJubw/r6Oq5cuYILFy6gWq3i+vXrAfgyD3nvFpXewsICrly5gvX1dezu7oYRpqxz8szD9LJIt0XMAGojEQvLtCzDrt9bv3U45i/D5uHbmrHIAgeS76yBTuydV67YwEr3Eys9rx48/jy+rLxlntbyjOw3cpZcDwokCNE8y3fcgizBS5Ls+8RYPOtrCpi2tgFJsuf4XiwWsb29HY5S4LEV2pYA9iBOlkHbTw/My3d6NpfHaHBTwzS2hnTLgxgtoHKtzxN0vSTEsPruIynYGkl7My000jKsdtLlrhlO2fOYeOYrR6jA5G4S8iK/PZLhsjq/BmteWjI9AhCrA9IP5c4778Sdd94ZzkThYXI8hK7b7WJubg5puu+LYo1imefMzEyYEajVauG0WF62yHbhbBh9PDh7IJes6GxmjSzkCEoqMl1f8jfbTj5n2zEfeZM0gRgBFpdLtD8U65p+PnK6fDQaYW1tDZ1OB2fOnMHc3FzYwbW+vj6x3VwCM9apLIecBpZKb3d3F+12O8gvr23gsg/B0tzcHNrtNl544QUkSYK5ubmwxXpmZgaDwQCXL18O+TUajXBqLy+/BBDWzhuNRjjrQvo46aVO3rnlkQZpnm6w+ot+p9vcy8MKq2XM4snKxwMwHgCyeNDlkzzHAFUWWLJ41PnGgJTOK09YnXbeetDv+FvrL76T/Z1Ag0cpyHyl34vmx5rV0TzE7Bbrmm4H3uzxzMwM5ubmgnO+7MvWIEUOyiRZMiLzs/qFtg2si3K5jNnZWaRpGnwVj0CMQdzaqhsN8DuGbhBLMegOLJW6NGwyD65BSgGhgFnoXRokCjJnYTR/eTu3LKPkQYMBTRpxx4CSjC9BjHy+traG5557buLuIx4st7CwEEbiNEjc4it5kfwy7OXLl8O23lOnToX7k+gJT+IU7Gg0Qq/Xw+LiItI0nXB+5X+r7thuuu7ZTnoKOgYyKS+SL2DyDBmmy91XEkhIcNZqtYKTn5zFabfbeOmll7C8vIxTp06hWq2iVCqhXq8HB2rPKFnGRs7g0GmWO8EIYuSM2vr6Op588sngyMe7lnZ3dzE7OxtuB+fuMPpLJUmC1dXVcAs3Z9MIflivnGXSypt1Io9W0G0ZU5w6PbaTBczzDAZkujp9HdYyMLE0Pd5jwCaWXl5wkZcnzYsul1V3edPP0kNWul7+1iApSZIDetvSwXLGkKRBjCQ9SyMBkOzfHDBpHrkEww0Nso6lXuCFqfJkcoahX5kexGiZ1oBFPpM8WeH5nwP4YrEY7nPb2dkJ50QdgRiD2DgSSOiRhh5x6MaSF0TqLXUyH5JMRzrlAvtropzJkaNGvazEMLLz6PVU+a3zjilAqSAtgKbDWc91eto5LEmSMAMi6xUA2u021tbWQnz6bHCUXavVArDh3UI8B4QGUiN8AOHMhCRJsLCwgOFwiIsXL+K1114LTsac1eA9Rby7Sa7JxkbO/I4dda8BLOVI1zm/2a7kT7eR/C+Brb7VXBps6YxLQHb58mW0Wq3g9MvTO3kTt+bPGoXyeaFQwObmZtipRGXLNj99+nQAVTx4kOV49dVXcfny5aBgeRcVlaqcXZTLQYVCAdeuXQs80mGeZwtJYpg8J5N6/cQCK1nGXesTmX4eQBJLXxsRK289aIjpAC9tq295cS2DFgMJecCKBRAskCgHeMDkLj2PZ9mfLB4snRf7ptxr8EHSIETrEHk1ieUXI8+YskAED6PUoFUuNc3NzSFJEmxvb09srdZgWuoWPUC06t6qawmg9HO2EWeZO51OWGWQg55p6JYHMcAkKLHAigQGMpwGGYxneXLTYEinXjkbI4WLo2mZpjYcGqxo3xgd11qzzUNeR5Z1Z/22nmmlTRBjxen1egeAQpqmwSGVh74VCgU0Gg2cOXMGjz76aLjIT96ELC915KiCW26r1Sre8pa3TFwA2W63ww3SnAauVqsT1z5kjVypXKx6152Row9tEGU8Ag+epmkpGC0XbPdisRi2N+t1ZW3wxuMxNjc30ev1cPr0aaysrGBmZgYnT57E1tYWNjY2Jpzr9FIZgDDzQX6bzSZKpdLEjFGtVsODDz4Y2nQwGKDf74erC/jhFvpyuRzKoHdz6bqQgwrWNeXMMsDyjBiPYqAgZphlXOs77wBBPstjXGNp6m/9Xr+LybgHhHS7kCzwZuUR0zkW3144j7dYGlll9gYkVlqyfN6MiwdugIPLSZJ/7RMjTxmXg3E5Qy3DkA/OXlKv6naV9cV0rTLo8PxP2yfJAsOSOLBhX5aD+2npOwLEkKQAyIaM+cnIZ9oXRb7XgEW+08CJDSUPtmN4hqFwcxkkSZKJpSSdvxYyyz9Dl8ubbciqwzyKne/kKa3kjyBGl4Hv5XPuYCJy58mu8/PzYcmJMyj0pWm32+Gix2aziTe/+c2BT96ATABEv5vZ2dngwyHbwiu7bFtZH3oaWV68xq3HcrSjZUtuNbZ40PVDh2POyHQ6nQMjTckj64tbsdvtNm677TYsLS0Fp9ybN2+G83L4kfzV6/Uwq9VsNnHnnXeiXq8Heb1y5QqSJAkgkp/5+XkcO3YsKGdeQwAgnAMkFbAcTMh60rLN3U2WTMrlrpis6jqzAIIFDmNyYhkCC9ToNPLMImjeLF2jy5W3r1sgR5dX56P5yAIJsbw946l5swakMf0dK6s3qxCLI3/LpR/tlyLBjZWWjGvNxMiZHtkXpO3Rg2L5vbCwgJmZGWxvb4dBGstKHjlIlvFkP9TtZs3WyPfaHur6lRs49CrFtHTLgxhr2gywL7bSykeOQmV8qUh1I3LUbRkA2Wj0jZFCKZW2/M81T2tWiGQpTQlSvBFTTNnIOrGeeSMg/uZsiH5HfwcZ1jPYzIuzDU899VQ4G0H609TrddRqNRw7dgy33XYber0evvCFL4SwDDc7O4tGo4HFxUWsrKxMdKArV64c6Mi67qQBlcrHqj85nVsoFA4AN6YtZUQf2CbDcTQmZZfAjCBCHghoGTmZ5nA4DE6/Z8+exYkTJ8IJmnT6laO8Wq0WADVHTdVqNThl06dpZWUlOAfu7u5ie3s7nLZMueB9VbyXqt/vYzgchuUhbXw9A00e9BkxJDodWwYvD1mAMC8dJnxWGpYBsvpqLE0NxN8oXr1wTNeb2bbS88CM1TetfqrjW+Aglp8lf1IG5G/pByNnViTAsQbPUjdIEKN5kktOupyUA/qScdmJuoeX1/b7/XALNXDw/BetM7Td0DbAepYlQ1ZcLceynHnplgcxPO4fOCiY8qOfSdIgRz6TQqdHjTKcdCykwFLRF4vFA4pVAhx5sJ2H6CUPuqNbvy0hiqVl/Zd1ar3nLhgt7Gka3y4t+aex5nLPaDSacAjjzczswOVyGffddx8ajQba7faBZSumyRkFAptms4nd3d1wLxAdUOUIRStJCSQk+JT1Sr6440y3BQ0xlY4+9E13bvJDR1e5I4FbnS2AagEa5ttqtfDiiy/i1KlTuP322wO4qFaruHHjBobDYTiHRZ4gzJ1DBFJSBnhejwWeuetuMBig1+sFR+xz587htttuQ7fbDbNqsi2kA6JcXtV+VywjlXvs8kuPPKPp1afMW7/XfVLKkpWPNpgWbzFjHCuLBWZj5fby1c+sfDxdlJc//cwD5jE9YoGGGMXKHctDzqLIgbN8Z/kuWvEk4OKAVzq9WgDW2lFUKBSwtLSEmZkZXL9+/cAt7rp9LJ9Gqd9ifUEPvCWvnu0hef0mL93yIIYKz7vsURoIKXQkjZ71qFC+s3wZOIsgpxhlvtJISuc06exFI8GRuL6DSfIh+bWm5ixjnFcRWvFiSqRYLB4wyiyL3vLqCTvrj/calctlvPWtbw1Ou71eD51OJ1xTwB0rXHOl0ZQdi/G2t7eDkqhUKnjooYfwJ//kn5xYmmq1Wmi1WsHvhunK0ZZMXzuOsz05WyFn6WTZObvH+tIA25r5I+/Mo9VqHahX3b5S/sgLZ8YuXbqE7e1tnDt3DsePHw/3smxsbAQAw3jFYhELCwthZkZuMdWyMRqNsLW1hV6vh16vF4CXBEVzc3NYWFg4MKgYj/dOpyYYoS8N/Zs6nc7EziQtT91ud6JOtAxr0KPrzpJtCyQyfCys1Yd0PG9AkWVIvfLF4lijayvd2KDJI90HYiT1pxzQ5eXLAlH8ttKRM0JZMqDz9ABRnuUkPcCVfVjyIPu3BkgyP/mbA3XmWygUwinW29vb2NnZMe0Yw0sbJfmwZplJ9J3TM0/A5AXEFmDSZE0e5KVbHsRY6FgbGi3ISZJMOGNayo3PNbrWo35g0mNeCpH0mdANLTtAkuzNxlDhz8zMYGVlBfPz82EEzF0tktfV1dVM5DsNecbQU7wcjeuOK+8O4jP9WyuAUqmEVquFJNnzsOd226WlJRw/fhxpmuKVV17B1tYWarXahLEkv5ZTLLDf4arVKobDYUjjxIkTOHXqVFBMEjStra1NbCOWIJnpyzLJ0ZVVb/LU4Fhd85v+Ozw/JU3T4OmvedDASbcH62A8HmNjYyM4/Z44cQI3b96caEfK3+zsbDhzhs9YRsoh8+OJu81mE51OB6urq9jY2JgwJMvLy7jrrrvw0ksvYW1tLcyS8Qb0RqOB+fn5CX7pBK5PS5X9kE7clgzLsNOQVX+yvi19IimWn9cn8vKUBa6kkbT6nebRAl46rFX3fC8dTL38pP6TM+dZZdd1nVU3mr8YGM0CNjKeBCn6OA+pw7SOkO80L57uSJIknAmj0+HSPSlNU1y/fj2kLZf2NeC36ofvPNCRJMkBXxzJj3YStgC8fncY2/QtATGXL1/GT/3UT+F//s//iU6ngwsXLuCTn/wkHn74YQB7jP7sz/4sfv3Xfx2bm5t417vehV/91V/Fm970ppDG+vo6PvzhD+O///f/jkKhgPe97334p//0n4ZdEdMQK1T6ocR2++hTdZPkoN+MHMUynHYKlYpcT3czDaYtRwcMU6vVgsB2Op0wek2SJDhJ0rGTAip5WV1dNetD8iefeYJmxdXPZF3KziYVEom7gLx0SEyTPhS8wPErX/kKZmZm0Gg08Mgjj6BareLixYvh0LRyuWzeeK1JA9lyuYwbN27g6aefDrMi1WoVjUYj+NTwxuV+v4/r16+HDitvKdeKmiMl6VSny0znXLkmLIGBPAVY+1yxDFxK0mBFKyJLQUul0+128eqrr2J1dTXInJzWrlarOHXqFGZnZ0N7erOQUh7oB9NsNrGxsYHNzc2JCzkBYGtrCzdu3Aj1BuyDYbZHvV5HpVLBwsIC5ufnTTkk5TlES8uvJyt6NCzrWP+OpTuN4Y0ZVAuQZBloWR4PYOmRtQd0PPmy6iEPsNIDDS8dT09Zfc/Kx6oHiyeSNYPCb9aXnKWU4ZMkmZhh0nzIpSJv2Umn55XL0uveRgVPLmL1oJ9JvjRYId9pmk6sNsTaLtb/YvSGg5iNjQ28613vwp/9s38W//N//k+cOHECL774IhYXF0OYX/qlX8Kv/Mqv4N/+23+Lc+fO4e/+3b+L97znPXj++efDVskf/uEfxtWrV/HpT38aw+EQP/ZjP4a/9tf+Gn7jN35jKn6kE672S9GdXpKFLGUnsxCkVLwS4EgkrZ/RAMqOwLSWlpbCEe68KJFCcenSJfT7/eArwrh0ePUUTkwpxigWToI6SeVy+YCjVpqmwcdB82V904jyXiN2CM6AbG5uAgCeeeYZjEajcPaJdHDVxl6nT5DEbb48PZdLTrIMBDfHjx/H0tIS2u02kmQfwMo2lKcz6x0EloHjjh+p+OTshu7gu7u7aLVaoe63traioNmTcy0PEszwygd5lcPCwkK4aZt1KAF5zBCxnCdOnMDx48cDoOX1Dkmyd/LwHXfcEbbT0y+m1WphY2Mj8Hj+/PkAYrQSZN1ub28fcGL0+PKUt/xtgZksI+2FsZ7HwlnfHnn8eIAkT5qxtC0DHQvnpecBFS+OJ9MesNHfUm5ieWa1H/unBCQSmOgDTS3wpJdurNlbTVrm9TOvLIeZ8SBZR4dYO2xp3zh5YLk3WLsNp6U3HMT8o3/0j3DmzBl88pOfDM/OnTsXfqdpil/+5V/Gz/zMz+D7v//7AQD/7t/9O6ysrOC//Jf/gh/6oR/CV7/6VXzqU5/Ck08+GWZv/tk/+2f483/+z+Mf/+N/jNOnTx/IV9+Psr29vVfA/38mQAqVFFpvdCUFRxsEHVYbRusZgAlDJ8/CsISbo00as7m5uXC67Gg0wsbGRlhWAfYAA3d6nD59euIYdln3mic5As9SoJ4B9OqPdxbpePSk9zqafs50CEyoKBYXF8M2afqdyDuQrE7jLa3wvh7tBFooFHD33XejWq3iueeew2AwwO7uLo4fP47FxUW8+OKLYSbsjjvuCJcUcmePnAGUW4j5zfpfWFgIoLVarQbAIGejyCfbi4qBSyssu94uqQEcyWsDrZjl83q9jmPHjgEAut1uGEnqJSRZtxK4M185q8St3QBw9uxZnDx5EidOnJhQfqPRCN/85jfx4osvBgdv7uTS+cry8QoKS/Y90n3GMiRW3WkwaBnGmEGKgRnKi/Y1kOG8gVgMAFn6QfNg1R35scqm60CGicXR+eYBIvqZZ/Slvo/NHHl8ezwxrDUTI/O0Zmh0ml4dECS02+2JHY9av+UBMdpGTQsadLqefxH/09+w3++HVQOrf8T6RRb5XjuHpP/23/4bHn74YfzFv/gXsby8jAcffBC//uu/Ht6/8soruHbtGt797neHZ/Pz83jHO96Bxx9/HADw+OOPY2FhIQAYAHj3u9+NQqGAL3zhC2a+H//4xzE/Px8+Z86cAQCzw+uOqRWVbly55dOakeF/7qDQ02c6nPxmGI7aSdxxwWUKiVaJbgEEp0eOUtfW1vDaa68d2MIsf8uy5VXullLN6uT6jBiS3JlkKUM5QyHXegeDAZaXl/GmN70J9XodKysrGA6HGI/HuOuuuwAg3BDd6/UmeJTfupOnaRrykOesAHsg89ixY1hZWQlTwuPxGLVaLfj2cMZmfX09LH1sbm7i6tWr4ZJCgk7ObvBm5STZO8fhgQcewCOPPIKTJ09icXERjUYjOOYxX/JJPxG5tbxer+Oee+7Bm9/85gnnYIIc7dxM0opQtiPP35GXaXJmkHIuz3uQs0+cYZF9h3XF55ovgqTjx4+H9mi329jZ2QmARSpMnuDsyS7Pw7EGGXkpb7/wAI3O1zOesTw9veSBGZ2nx2+sbFZ6lhH38o7la/GYx5Bn8ZVV1qz6kaAjFtfiXfqF6Y8E+lZ5LMddyjr1+87ODlZXV9HpdILDPcNJe+INWEgMowdyeeVCll2m48WXvqjSbkkeDwOmJL3hMzEvv/wyfvVXfxUf+chH8NM//dN48skn8df/+l9HuVzG+9//fly7dg0AsLKyMhFvZWUlvLt27RqWl5cnGf3/b/dlGE0f/ehH8ZGPfCT8397expkzZ0LlcMlFVph1zLEGHlJI5AyKDkOhkmHkexnGmnmRu47oO1AoFMKOFh7exp06pVIp+MkQQLFcEvEyD43SLSUrBTJmHGQ8D0UTxDBdOQrnjITeXWXxOB6PwzbtwWCAhYUFdDqd4FTLmYu3vOUtePHFF8OIXoI4q8yaaPj1XUm7u7t47rnnsLy8jAsXLuDq1avB8bfdbk+A2fX1dezs7GBhYSGMPjhLQsAyOzuLCxcu4A//8A+xtraGYrGIer2Oubm5AIAoB/SlSdM0LKvIWRzpq8LrA+655x4Ui0W8+OKL6HQ6blvpdtN1lKYpNjc3kaZpqNPV1VW0221885vfDO1ZLpdRKpVw//33o9fr4atf/WqQR/rPsAwEMRydFQoFnD9/HhcuXMD58+fRbDZx8+ZN1Ot1nDx5EteuXcOTTz6J1157DfPz82E3BJdfpR+YRQSK3iyNLLuUQ/1b1qH8LT8xRW6lHTMgWQbZA2WWgdR867LoMlmzFN5/z/jE8rXytPjPqntPP3lx87SxVa48PGkAI2cwJYix7IIFmPies8ntdjuAmd3dXZTL5QOH1nl15ZE1iLfqTOeh87Li6kEgByzSBmog9W0FYsbjMR5++GH84i/+IgDgwQcfxLPPPot/+S//Jd7//ve/0dkFqlQqB7yzAbuja+Chw1sGVTamRM3aCUsaZzn1p/MmSWBDogBbI+lyuYy5uTmcOXMGm5ubYZcMUXupVMKFCxcwNzcXAJ+nACzh1yM9q/Nq8oCP1R4sU6VSCcCLTq2y/hqNBpIkQavVmgi7vr4eZllk3bz88stIkj2fIPqfWLM7urz85vKb5XC8sbGBubk53HvvvcE/p1wu4+bNmxNl7vV6uHTpEprNJubm5kK78pCpJNk/P2U4HKJcLofzbFqtVrj8Ui598hA3yoN8J52mpQI9f/48qtUqXnjhhQk/GUthy2cSoCdJEm5Pv3HjRnDInZnZu22avkCFQiEctLezsxOcndM0nRh5allKkiQ46n7Xd30XLly4gNdeew3PPfccHn30USwvL2N2dhavvPIKXn31VSRJEvKUbRAD271eL4DSmDGOkWXUvTB5FHEMUFjPYsac/wnoeLFfjPcsYKPbK5ZvHrAi9aYFHmL8aR4sfWSlL0m7D+gyynRiSyM6P8mHBDF6WYn91NvCnAWoePcb8+HgQA44syhL13syo/utlUYM5PC9PrxS2kdt9w4DZt5wEHPq1Cnce++9E8/uuece/Kf/9J8AACdPngSwN6o7depUCLO6uooHHngghLl+/fpEGru7u1hfXw/xpyEL7crlHGDfwUg7AcrGk+t5Woj4rc8Q0Q2jEbB0iuRsEUeshUIBzWYzjOYlT1xKmJnZu0SLnWd+fh4LCwvRQ9pidJhO4SkPPVLm97lz57CyshLO/aDzrDTK3MqcJEk4rr/RaATgQOWwtbWF+fn5cD9OrVYLyyDebBO/Jd90kObVDjIs3xNU0UB3u92JWQYAuHnzJtbX1yfkQBJB1ng8xsrKCh544AG88MILePXVVwPw2d3dxaVLlzAej8PZNN1uF0tLS1heXg51S3mRdU7Znp2dxb333otXX30V169fnxi5WeXT7VosFnHs2LGgZI4fPx52Bm5tbU3shOJsDEeOMaVnGUfOXD3xxBN48sknUa1W8ba3vS1s5WYZ5S29nI202pLEEazX9p6RzEuWIc4yfNMo6Tx9NUmScK3DzZs3Q58BfP8TOcWv6yYPMCF5AwOtdzwwFANqFtDRzy2+8gAvC8xkpWk5n8q4chChl4fkcpLHY9ZAgzMag8FgYnCYB2x6FCuzJxuHARlyuZkfWUe0gzKvaegNBzHvete78MILL0w8+/rXv46zZ88C2DNgJ0+exGc+85kAWra3t/GFL3wBH/jABwAAjz32GDY3N/HUU0/hoYceAgD83u/9HsbjMd7xjndMxQ8rhQjWWg7S/6XHtAxDku8tBCpHzHyuHWitqTQaAPIod/EwnPRDKBT2Dwnj9Dw7pp5R8DqffG+BLuabpdw0SRCjlRB3T1Wr1WCUgb1RBy8J5EVlTKdcLmN2dhY3b97EaDTC/Px8MKCNRgOdTidcN89pV10GXR+SV54RMxqNcPbsWTQaDbz88sthdFsqlcIFi1z66/V6E6cpsw2vXbsWth9z1ESZoS9Nv9/H7OxsAAM8EXdzcxOdTif4NdFw9/t9bGxs4JVXXgEALC8vB9AnwTX9UcbjMe6//3488MADeOaZZ7C2tnZgiU22rTa0rBPK2PLyMsbjvUPx6vU6lpeXw64hCaatmTz5n2mTb/oVsY4GgwE2NzfRbrextbUVwOFwOJwAJNVq1TQKsmy80dsawVuGVv7OYxStZ56RlEbQCmeloZ20PQPHvs+lVi8fXTbth2HphywwIfUV/2sHVksOYnVqzW5b/Mv/0jUgq+xZbaFBrtVOlnxIXa/tiWUn9Hs5AGA63NRB3dhoNA4MomOg0aNYG3gDPRnWGpjrXYnWIE7bP6ZH22zFyaI3HMT85E/+JN75znfiF3/xF/GX/tJfwhNPPIFf+7Vfw6/92q8B2CvcT/zET+Dv//2/jze96U1hi/Xp06fxAz/wAwD2Zm7+3J/7c/irf/Wv4l/+y3+J4XCID33oQ/ihH/ohc2dSjKRwyOUdKhbZCYG9ESiVqpy61xWvO7HV6fUzOdMjBV7PCGmeZcNKhc84dLxk/TJvTTGFq59L3mOdg+XXCoHntchnkqdXXnkF3/zmN8M5LLzTqNFoBEda1tcLL7yAixcvhqURLiN83/d9H1588UUMBgPMzMzg7NmzmJ+fx40bNw4cwS3bQz8jUOJhcQsLCzhx4gReffXVcG39yy+/jI2NDWxsbOD48eMA9mcGZP2Mx2PcuHFj4vRcWTfVajXcE3T58mWsr6/jwQcfxLlz59DpdPClL30pbPPmEiGNPR1ikyQJxko6DMq65pZxYG/mhDvdeOKwpYQl8YTdZrOJkydPhrah43Sa7t84zjyt5QwJXOQ3qVwu45VXXkG73Q73Jm1ubuL69evodrvh0EL6eZH0HVNSHpn3zs7OgUGLDjet8pd1rNPy0ogZPCuM1AVWfppKpRLOnTuH559/3g0j9Zgus6UrOFLWPFqgRueTVS6r/uSMhpWXlabsz3oW3eLR0sUyHflO+0tqXqRsj8fjcL3JYDBAtVoN/WM8Hk/4Llo7iuTRH5rP0WgU7nTjQIrpaJ5idafbOSZPOqyelZG/rTRl3ehy6TSlTdWHtealNxzEPPLII/jP//k/46Mf/Sg+9rGP4dy5c/jlX/5l/PAP/3AI87f/9t9Gu93GX/trfw2bm5v4E3/iT+BTn/pUcAQFgH//7/89PvShD+F7v/d7USjsHXb3K7/yK1PzIx1uPeXtGXfrnUa9fGY1tFQYerpMvycI4QzCaDSauHeIHUuiVXmNOdNmPnq5zBIOa9bFqocs5W7VFZ1NLQCTpmm4KoDXBUgHON5+fOrUKdx5553Y3t7G9evXJ/xAePDd/Px8OCBtZWUlLL3QIGqAaSlZgoCXX34ZOzs7+MpXvhJ2GNHP6ObNm9jY2EChUMCpU6fClmlr9ErFRZJObQRL4/EYx44dC3cgAXtLqnQGllPQbHv6hAB7u6g4W8L89RR2tVpFp9MJwKVWq2F2dvbAKbZ6CXU0GoW2oUPxyy+/jEqlgmazOQE6eRQAR4oWL/pbgplerxfudaH8ckceL2/s9/totVoT5ynJZSbZllJxbm9vmzNOMVARk/cYeMlLniGPhfP4YL+/efMmrl69euAcIqv+Y8AgZvh1fNmfPKAQyyOrDqy8gMnLFGU4ayDF+vGcZyX/Vpk8mZE+isDejPnW1taEruEMI33iut3ugdl4DxiRZw487rzzznDL+8WLF8PJ5RJoHsb4y7qm/53eFCJ50zqU8axZIasNrbQYTrbHtPQtObH3L/yFv4C/8Bf+gvs+SRJ87GMfw8c+9jE3zNLS0tQH23lEhMdvVpa8ZFHOfEiSI0k9O2IpNL0kJGdIPEQqf2uwoxErgGCo6XjJqwv01eaSLAGzAFkeyhK6JEmCn4TFA5clJD/1eh1nz55Fr9dDu93GxsYGGo1GmI0AEIDJ7u4ums0mTp8+jRdeeAGFQgFf//rX8eKLL6JSqeCee+7Bn/pTfyoYYt4dwnt0pEMx2+r555/H2tpaOOOFiop1zN90KB0MBubyjGwzSZQ/njxcLpdx7733olar4Qtf+EI4R0E6u9Fgp2kaLmW8cePGxEhMlkO2Kc8MYvg03d/tsLCwEHyOrNkR9gGWlb45nU4Hm5ubIS+Wh6fn3nnnnTh16lS404gzTnJ3AsvFOuEWas543X333cEx+ebNm9je3sarr746sW2c/lG6nnUZWq2WKb9W+2hZzgIruiz6nZW+BxK0sdXhY4MRhpPOk1aZPENtgRGLB6s8MoxVp1ll12XT4b08rXSteLrMGsxYAEwT+5fUxwTabDfuPtTARt7iTpBgXYFh+WEC+1fJLC0tBYDBYxrSNEWj0cDZs2fRarVw9erVifytcljEern99ttx9uxZvPLKK7h8+fKBpSEvTe0iwfBSJ1kARqdzWAADfAfcnQTsV7a1Rz2rc8lGkCDIikvFrmdEdMex8pFr3zKuXMpi+PX1dQwGg7BbhJ9isRhuZNZl1FN7Vpl1WXTYmLBppWCd1stwEpgkyd4Sy8LCAi5cuBAcZh9//HGUy+Xg/1Ov1wNwKJfLWFxcxNzcHHq9Hra2toJDLg9BW1xcxOzsbNiuzJmTbreLnZ0dbG5u4uLFi+h2u+GKBi7VccaOOwqosDhjwhOBGT7WySXxRF7ObqyurmJubm5i1AbsT43L9ufdWZxSLpfLYfZDX7TJGZpisRgcPev1ekhzZmYGd911Fy5fvozt7e2JizJZ3iRJwrUXnn8Yl7f6/X44ZFEaYjpYE9TIz3A4DI7U1WoV586dw9mzZ/Gud70rLFvdfffdGAwG+L//9/+i2+2Gafper3fghmzdBpzFsQYOHvCR72MG0QILOoz8H8vXAzZWvJjR9t7FysNvS09YaVsfK3wsfc2bXgKxZk2kTHH2wUtT86B3J+n/Md7YTnq3IweU0ohT1glc2J/l+V8chGmfHE83Sx6tmcJarYaVlRXUarVwRYini3Rb6LS4U1BurogNEmQ96Gf6d6FQMO90s8Iehr4jQIzceSQ7iV6Dk0KlG08qcgvRW+urOk0ZV/vnSOKone8lwmY5Wq0WdnZ2JrzeOSq+/fbbsbCwcCBvq4Pyo8vuCWcWauZzToVanYpKAdjrPA899BDG4zG+/vWv48477wxlBhC2LHP5iOW47bbbwmzC9evXJ9q2XC7j2rVr+PKXv4xKpYK5uTnMzc0FgMfLC7lrh8pG1jn5BPZnJObn58Olkzy0Lc+onkQHTPq8PPvsszh16lQAY8ybSlDOZCXJ3gF+PJ8mTdMA3CTfnL0h+Oh0OqhUKnjnO9+JYrGIVquFmZkZLC8vY25uDi+//HLYtk4FT5BEx96YgmH5Gfbll19Gp9MJdV2v1zE/P4/jx49P9BV52B9Hss8//3wAeK1WC+12G5ubm1haWgo7txiv0WhEnQAHgwF6vd6BdrHaKkuerTjym78tY5SHPGAi040Bolh6+jlBgHWnjh6gxYCRjCt1hwcQLJ48wKNBiBVH+xJ6BlrrNQ2KdHzddhKI6PSknwsdz7mzFNh3VmWaTEvvyrHAh+ZfLmHxs7m5iS996UvmRgZJ1mBUPk/TFK+99hquXr0afHvykFe/Ml/LWVeD5thAMA/d8iBGAhCtECgU2ptcVyzjWTuSpOBbToSMy50GfMaPdCCTvMkr6WWeAMJ9NkT+NDy7u7thZsFzfJRkKWP9Xr/THd1SBkmSmPkzHkfwp06dwsLCAhYWFrCxsYFXX30VJ0+eDNOuly5dCsfxc2s1HVYXFxfx8ssvYzQaYW3t/2PvX2Nkza7ycPx5q7qquute1ffu0+c6Z65nbh6Px2M7JAgrBqLIEL5Y4QMxEY4IJkJIIaBwCRYIQRA/Yz4AQYqAxEaKFAUFRCwsQOZiM7ZnPDP2zJmZc+/u0/dL3a9dVf8PrWf3qtVrv1U9P//+io7ZUqmq3su+rr3Ws9dea+3doQlDexaelLy1teXGmKdfR6NR1Ot1c8wZOI5nE1E7dHR0hGvXriEejzuXbt1fPlAMwNmvkMkRzElbEraH50Axbgzz6XQ6TkOkgzdKmqJnGLeu6MFWLBZdfisrK4jH43jnnXecVxQ1ZKlUamhVZhkkSvpgJN+dnR1sb287MMW4ODTgTiaTKBaLzqZmZ2cHX/nKV07NQY4d50cmk8H8/LwDS9a8kHVrNBoOKI8CYbpN1lhaQF+OsczLJ5DkPR8Q8n1b9fKBMqsOvnpZbR1VvtUeqz6SbuV7+qPz1Vv8+jnLJkaXofPke2EGvWH9KOcb35WaS+kVSD7ExaVPUPM/gY2cy7oczms5DzudDg4PD13bfPQnk7U4DYLAATD5jOxX+d+X5PNsCxdEjI1ljZmkk3ejkfm2ADFMnBh6H08StUTJ2hDL6mTes1SGknjlFhHrIr81EKIKTqP0wWDgBP/e3p6zc5BbG+l02gEdWYb+HbZyCXt2nBQGoqhqXVlZwcrKCiKRiNueuXv3riN4bgvt7u7i6OgIyWQSsVgM2WwWBwcH2NnZQSqVwuHhoaujPCySdV5aWsL58+dd8Dd5RpI1noPBwB1KKL2AgiDAzZs3sb29jfPnz7sotdoGRO+ha0EvQS0DyZHpkSECcIBFe0sMBgMXqfjw8NC5fEt64/YRt3omJiawv7+P1dVVXLp0CcVi0e23EzBduXIF169fHxo/tkMzf5mC4Fj71Wq1nH0Lr3NMeBAjAFy8eNEFs+PRAdxaots6jXLJ5DY2Nhzo5FlLMzMzLpqwTvQ0swS+BmB6nviEt2yvbn/Y89ZzPiBkPauvWUJ8FBgK+8j+8Al2CS58ddVl+VbYVv/Jtln5aO23fs/6tram9HXf9lJYXYETxwnKDwpswL/ItfLUQJnfFoiyFpC6jzWN6+v6vl4EyetW++Vzss76Gv/TSF96VllgjW3Ui6Vx0gMPYiTRyg7Se6t6ADTBMy+dt5x8/E+BZxlHWQbBFnFKGx5ZJ2oaCoUCkskkNjY23NEEvV4PhUIBV65cMQ2Kz9pvPhRuEbF+V5+bJJ+j0RuN0dLpNNrtNmKxGFZXV917+Xwe5XIZ9Xod2WwW8/PzePrpp5HL5XD79m2sra052wcmGt9KEJPP57GysoK1tTUEwTHI29zcdO2gUZ48sJJggrYn9MCp1+vodDp4+OGHcenSJTfeDETVbDadsar07qF3UCwWw7PPPotareYMjrmdI1dwBCaShni/1+s54EQAod2sk8mkAwbpdBqRSAQHBwe4efMm5ufnUSwWHZ01m01nk3R4eIiNjQ0EwYmdll5JakZEAEVtDmmX4QpmZ2cxOzvr+oVReN///vej2+26E8hlwEPmzXbVajUHTDg2zz//PJaXl006ZzyfMPrmt2+OjGLgllC1vq1nZRq1mNB5hoEuDdAswe1rY5jg9z2v623Vl20c9YwGLLrOVp1893z8m9e0Xcqo7Sl5T84JS37Ib91Puu91XnqRawFBHz8fZ5x0nmHyQYOaMJ6v6ya/GZ2c4SWkEboGfL6+CksPPIjRhrFSdeVTScpERq8nj9bQ8FtHJ5STQ3qUMFneSEStWiXJ8rk65+pZrnx5cCC9f3wMMgxpW8kCNVa+ZBK+wx8BuD3ctbU13L9/H/F4HI888ggWFhZwdHSEXC7nntnc3EShUECj0cB73/teXLx4EdevX3fh7Xd2doaM5WiLI+0hVldX0Wg0sL+/j8nJSXfqdRAE6HQ6zkuHhrP8PRgMnDGpVBdPTU0hkUjg4OAAd+7cQSqVQjabdTYg+XzeCWMavtbrdVQqFdy7d889z9Ogr1+/jrt37zqQMjs7i16vN+Q+zTpouiJDZt2ZBw2haRQNnBxuubW1hWg06lzSu92uC1jHvXVJ37IOwAmwkP83NjawubmJZDLpNGbJZBI7Oztot9vO5mdyctL9jkQizqVTMnFJR8yfkawZKJAu5EyasTKysqZhi6bPAmB81/S8Psu8ChO4ugwfcPA9q8fQ+oQJMd+7mq9a71httMqw6j2qzfo9aXfhyyMITp/XNqqvNbhg0kCWv6UwlvfCBL+UARZYkHYlPk2JbxwtMCZ/W20Pe99H1xavYN7kKdSgSsNnqbX20cio9MCDGBKgRRC8rxmZ1jhowrFc2XwuafIdSdCSyPUBWZFIxEUn1eXwXaqU5Um+kvHLsn3gbBST15oqzUzC8tHu1TJ1u11MTU25Q82SySTW19cRBAFmZ2edV8z+/j5SqRR2d3eRyWTQ6/Xwp3/6pxgMjj20Jicnncsvy5UHZ/J6t9vFxsaG807SrvayvdIYj1t08nnmSZuazc1NByakyzGDw2UyGRQKBRc8b3V11WnTJicnkUwmTwFfBpST/czyGWCPdjvUNmSzWSQSCVSrVQciW62Wu1+v151B8I0bN1zQPsalGQyO7Vl4hAXrQa0QAQTbR1dxAgpqoXq9HrLZrGtLPp9HLpfD8vIyarUaNjY2nJ1Or9czjyogkKfWiVqsbDY7FFeIAFPTLQ3ffUzdYsxhq0CfYD7LbysfH+DQz5C+wvL1leMT+mH3fGDHAiPkOZoHaQGu+dOoeujfWkPEfOTC0Kon+SkBjAT/sh7kp1Y/WcJe0pAEdD6DaTmWFr3Kd3XifS30E4kEFhYW3ALJonfNv+Uc4/VIJIJcLof5+Xlsb28PnbfmAzCyTZYtqLZzoXcX7WOoZaYtjm8xMU76tgAxWmOiJ6Imah3CWk4GHQbcp9GQHgAsgwLSKl8+wwknk6wPBaaMLGzVQb+n+0UStO85XtPPSW2AbAPbLgWxTBSai4uLqFQqKJfLiESOQ6bv7+/j1q1bWF5eRqFQwEMPPYRXX30VjUYDhUIBa2trTvgDcBNOtp92J5w0sVgM73vf+xCNRvF3f/d3Qxoa9iWNYOXRDdTuUJsRiZycKJ1IJJBMJt02FvuHJ8z2ej1sbW05Orpw4QKeeeYZJ+TpGk1BOxgca3wouKUWhHlL981oNOrAWzKZdNtnTAwYyIi1d+7cwdbWlgN2ZIiHh4colUro9/s4ODjA3t4eWq2W0yJJps/6cEus3+87RkQ3duYzNTXljKePjo6cTU48HneaH4JYAivSDMvjERJsr5yXQXBydpJFY/1+f2g70QIhei5ImgibB2H/reuWYA7L3/euvGYJ7LDn+S23abSTgS9vXTdfe3R52tBU8lRL48SyNQDybW/p560+kQBG/me+uh6yzrpsa6HGdwn2SXcWjweGDVd5X3osWVoYghttBwUAs7OzeO6557C3t4evfe1rQ2AgTD7p69FoFJcuXcLDDz+Md955B6+//rq5UNdJy1ZZZ71IlG1l38pDXOXzlUoltFydHngQI4lWaku08PcRq3xOGt3KiSPdey3AxDIkcTMvHR+GAoErYB9job2B5cLGejNZjMeqq3zeQsY+xqXtfCYmJk5thbB9m5ubSKfTODw8RCaTGWJw1KL0+33s7++7s4X6/T7u37+PRqOBlZUVZDIZF6VXhweXBzmyXB7LoL0S+J1KpZzAZnsGg4FzSabhMY3UuGXFMPsy76tXr2J5eRl///d/j4ODAwAnhz7SW0aOD9tLDyXmLfuOHkzUSNBQDjjxIup0Ouh0OojH4261k81mcfXqVdRqNQeguJorl8v40pe+5GgylUphe3vbAWkCLIIFBh4E4LYvI5GIM6wl0GJEX47rYDBw512tra05A18ZMJD75alUCsBJtGGejk0a7/f7jsERJGkal/0lr8lndOJ1a0VpPSf/jxL28j15fdw6Wf+teTgu4LDAg5WXZQsYVodRv61yJKjSdZLjrvPQAEleDwMyXPxZz0oNrLWok/VnfenEwTlHey0Cb80Xdd8Aw2YN/K0BgHUfgAsEeXh4eMqIfZwk6X59fR29Xs8FutN0OippACjLIE/hmXkEM61Wyy24qJ2WHpHjpgcexBCESEDA6/xI1Zd8TwINa8JLVSYNEznxLA0F39GgQ9vK8L9012OSqluGopaTXWoS9D6uNYE0gOFzWusigZcmcA0IpZCVzzQaDeeyvL6+junpacTjcccMstkslpaWnIs1jcD4fqVSwRtvvDEEsnQdeZAjt+M6nQ7+/u//3oE+3UbdLxxD2sbwN7UOQRC4oHU0mn3qqafQ7XZRq9WcATAZZq/XG9o68/U/QQ7rJM864tlQ3MKhcXCxWHS2LPV6HY1Gw2k2ut0ustksnnnmGUef9KSq1Wru02g00Ov1kM/nndGyttOi9kUGVpSHe+7t7aFeryOdTg/1JcOjR6NRrK2t4c6dO87oV/aJpHUa95KeksmkGysel8A+1UCcidtcProfN4UJYJ+g1s+EvesDFBbAGAeEyHIsYBD2YT/6NB+av+hyfQsembevzmHgRgIHq3xf3hLAyO0kAl95Dzix8wJOxxXTQU75HoELtQpaA6PraY2/3CLS9iHkD4xTo713qtUqXnvtNZP2gPEBCBeNe3t7Y71jyTRZZw3eyC/kYo9bSeTv42h+fOnbAsTI33IFLK+RoEiglpZCEqilaZH56j1bPuM71sBiRJIoZD7cBohEItja2gIAZDIZJ2i4apYAwmIW4/SZrIvFOLSKFBg+kFK2sdPpoNvtYm9vz4WNp/aA2yyHh4fIZrPu3B/mJ/tDTgSZaCdBYMeyZeRWWR8CFZ+AqFQqzh2b4xCPx5HNZp32g7FX6NL+1a9+Fffu3cMzzzyDjY0NvPXWW0PASoJnMiiWSSATBIEDCQQvXGkRjN2/fx+dTgfT09NIJpPIZDKYnZ1FLBbDzs4OXnrpJXdUQTqdHjrzaHp62vUltVY8JZs2VqS5bDaLQqHgAA7V5QQTbBfH++joCKVSaYguksnk0DETQRA44EptDUHgYDBAOp1Gv993p5kTSJJ+mKeVgiAYinej72l6lnT1bpKVhwVwmLRti8VPfMBH07CVj6+d44Ak3++wfGXdfMBItntUXSTo4OLRB6yYry5fluXTxEhNCf/rMQwDI3qrSpYr6xWWOEfkolXOG7mo5VzVwl4Gz9MpjK4l3bEMq37ytwZfsq0SyFhyQUcCJj/juzLe2VnTAw9i5ABLjYEEKkwcAGv/VhO0NuSVRCxBkUXcfEerDlmWtomQZQBw0U1pFwPAGTtGIscHFFJQ6fZZTFQCJCbdN6OYvCRa7hFrRtvtdrG+vu5C5FOIceXcarWwvr7uVIvT09N47rnn0Ol0hrQHrVbLeS/JvuI2hDxJ1jpThn197tw5B6okqAiCAPV63TE9eSRANBp1xzrQs+mll17CE088genpaWSzWTSbTadhIrBisDzJuKwx5v1Go4HJyUk8/vjjWF1dxcHBAaanpxGJRNz5UvV63dm6xGIx5PN5vO9970O73UalUnFbNZubm27FSK8hAh96VAFwmhGqxGnjUqvVHJ1RQwacBOvTwFL2dyQScbY53CqlUXI6nXb2NRLY8XRsgpd6ve5WotR4csvKokN6n8k+HQewWADCJxz0O/qedV3XxZdnWN6aV1iC1gcOfPnpRZLF+3Q+YX1n9UEYGPG1wwcUpP1IWF7WuxaIAU62hjSI8bVF1sXqW504RzSY03NHLr54z9JWaMDsoys5Pr7FqV5Iy/y0Zki+53vWWlzKdgMnuxtSO/Nu0wMPYpikwNZ7qhReWjuj3ZslAUkhBGCIEOV7vCYJ1orkyLggllAjEfM9GRRNns3BtLW1hVwuZzJXazVnTUDN0CxC9z3vO2+HK2l5+rN+n5OWbnkLCwtOcAEnLsvyFOxKpeJOeGY03+/6ru9ysVj4qdfrLmZLNHp8OiwA7O3tOQ2IHGcauVIbQnsYehRRC3FwcOAAALdD/u7v/s7F74lGo+68Ky2wfYlRd8+dO+e2fd7znvcgk8m40OYEMvTYYR9w24XxbUh37Ptms4n9/X3XXtaZ4EG6WWs1Nn/7hBtXXHoFzH5lXzIgowYbTOy3WCyGTCbj6lapVBAEgdtmkvTDPtWnV1u0ehag4kuj5sI49/R8H1VHa9U7Kk+fbcYoIOF71npG520J61F10PX1aZtG/ZZ9pbeTtDG6vC7L1DLC107ZVvms5ptyW1wDAGmKIMvXIIfPM38f8BiVfO2Sc0g+p9sm26XLlHKRfajtXOTWsa7XOPXX6YEHMRRYAIYQvAQmchXCztWEo1V+wGlVssxTgwVNfHIvlPWytqD4XzIF2kJUq1WUy+WhczNisRguXLjgVtfWBLeYppVkf/G/njxW/owRo9usY9doTYROk5OT6PV6ePnll11UWZ59lEqlUCwWnccQg97du3cPU1NT7nyk2dlZx/i73a47ALLRaCCXy2F/f99pKAC4fVqeP8R2c09X2nNwNdHv9/H22287g1nmMxgM3FlHW1tbzthUx6rQAJP/JycnnV0MARTtcdLptDuPiIcksn40hgbgypSnQGvaJFC5cuWKC/fPraj9/X3cv3/fMSVqlwjquMXGepGOuQ9Ow0e6U7fbbed1RHscTYvtdtv1OQVOLBZDs9l0Qkm7V0ua4hbUOKtT4LRNmkwW0B8FGsIE/ijgo+lB8xjfuz5BHlY3Czxo+z/5kStq/VtrcsL4g+wDDXj0ql0DYfktx0ZqQyQ/lddJR+TxUi5IECOFsG8sfOMn28o+4aJTtksviCUt6nHWNmq+Mn33fHXVvNzS7oQlizZ12+T48r6Ue3xuVFlh6YEHMVQfWhOMSQoWAKc6WE4E6SLG+5IQpIDSg6UZkSZOTj6JUimkpZFvIpHA+fPn0W63cf/+fScoJiYmkE6nUSwWh86N0WDBNyFHgRpZV6s9/C2PHGC5g8FgyHBVf1uMLZFIOBuLer2O7e1tpwaWcVYymQzOnTuHZDKJ27dvo16vu1W8BDQMSEfbEQZpI4OT42f1B+vFLSuqn4+OjrCxseHylCuvXq+HcrmMWq1mCmzdB7LfqG2g7Q1dsHm/3+9je3vb2aoUi0UAcJF85dhwO4j9JzVCQXBsg3Px4kXkcjncunXLXZuZmXH5E7gwj8nJSQd4eG9yctJpvAhA+J1MJlGr1VAoFNDr9Zznl6ZtAjaecxWJRFAqlZwbOc/P0kwYOJ5T1WrVR74jaTxM0PvyGcX0fXNNa0jCAMZZngu7poU9vyWvCgMwVrvD+sx6Rv7XecrywzR+kq/Idui8CagJVGhfJuWCjhQttfVaQDONEvjyXS4ydZ/oRbXW0Fp5Ml+5sNVpXI2GNomwAM04yZIJ0r5U5i23xyQQle07a3rgQYwkfLni0tf4LIChySMZrKWB0ZoZaQejJ6xc5ejB4zWpmZGTUaqQqVWYnJzEpUuXhrabGDvDstnxMUXdX7J+mrB9Ali+S02MJlIJrCzGbuXDbbMgOI5R8sgjjzgj0Hq9joODA2xtbSGZTGJmZsbZnXB7p16vO5sXMisGo8vlcshmszh//jzq9Tqazaa55aPHJhaLubD23AakJ1K/f+w+nEgkUCqVXJRKBp4jPRGQ6BWMHCt5/hHjwdBomYHurl+/7oQ2Y+RQ2EtaZyKtSA3WYHAcl4UB8nh4JnAMSOlGzbbLrSMZW4dHLkhDPemtRQNi2hjJU8BZV7qK06ibdjPcsuv3+86I3aIfHoLqo23fb5/A8L0v6VRrKq1n9XuWYJR5hoEF/Vu/a71jvRsGPMb9+Oqun5E8d9TzYfY78hlL26LfkWVKsKK3O6UN39HRkVkPq+8toavL1doVzV/Jq8kfLFrkt49urGdHJWv8xk2j6Jt1kKErpEOAlL+yvmepA9MDD2IAW9NB9Z4W0Bp9M7Gz5apFPsfVJpMkeDlQcrLxv1Q1as0O85aCVO6fUoBag++bYFYdfczJ6gtdf52XPHKAz1EYW0xBv89+plcP2z85OYlz5865GDTdbhfb29t49dVXkUgkhtyDrX6grQ0DzZVKJfyjf/SPcPXqVfR6w2cf1Wo1t+1EwSvP96HdD4V6JBJxEW0zmYzbcmIMCWm7JGnP1x+RSMTZ3jA+TCQSwY0bN3D//n28//3vx8TEhDubSZ68LYNnWeMkV4ekO8ZkIeAgQ+XWThCctimg0bPOk++yDtwSo9Ch4TDBpASOQRAMgfJIJOKOKSD9EzhZ9NlqtU4FNLTmgW+e+2gnLPmEQRgwsBZEFj/yzRU+r/MOK9eqhwYFGmzI+9ZWzzh1G9UXujxru1WDCvJVrSHSAEc+L+lXa6QsDZU1zhY9SN4tr8k8pXaC9yVNWzTBe9IeTZZjLTB1nuPUX6aw+7qe8lsvyDg+WlZycUPQqBfu7yY98CBGqrEsBsL/kunL94AT4CIP5ON9rTXhOzIgGDDsS68FrQVuJFNhPrQRoOqerq3WhLM0JBZDlM9aoEITrO4/XQ7rqYVnv3/iyaLvyfL4TZsUuQXVaDTw2muv4ZFHHkE+n3dGqEEQuPOifJbusp6yT2n3sbe352xt5ubmsLy87AQqjWGr1SqazSYODw9x9+5dV99oNIrHHnvMHV4ZiRyfukwgdHBwgEqlMqQRobZCbhHpMSAQIogZDAYucJ0MrEWa4qGPzFfSpzwdm30g5wVBGTUpUoPDSMRybPgu+5IaFMlECULoLs5ggXznoYceGtquYqwbtpF9JON0DAYDF4/GYsr0XBu1IrXmgCWMfe9ac0rPY998C8s/DLT4BJP+9gloXxvI3zQPtECE1F5YvE8/zxRmMyPrp4U/k94y4jUJZHzjI0GMtIXR93VbfNtnwLBzB//L60xy28raApI83hL6XHBz8cU5xAWD1tKElROWxrkfJmcsmRUEgdOMkh8RxLBtcpwtLdQ46YEHMbQvkUKZTFFv08iBks9YSFkzUTmAcmDkfdZHvmehb504+FLYJBIJx+gtK2+f9iSMMfuuWyDHSuxbGpHK57kFpvO1mD0ZSDwed2HxmX+5XHZC72tf+5rbdojFYkNnfsj+tIQd34nH49je3sb169cRBIG7RlsbAptUKoVCoYCNjQ18/etfH+pjHmZJV+rt7e0hGxiGwCdTpO2Mjrgsx4j2JbS94ZYONQ1f+cpXsLi4iGvXrmFtbQ2Hh4eYmppyWzZy31n3rxZYQRC4/Kkts7a6tAaTjFVqbyTgksckSProdDou4i7rOT09jbm5OTcnqRUjqMnlcjg8PES9XneRnq2xrVar6Ha7p7abxhH+mlFrgO5Lvrz1mPqe9c15/U0BJf9bz2jBLZ+1tBaSHsLuS+2BRVca7GhtmeZxFmDQgESDKkmX8h1dB4u3yLx1HXTfa8Ckn5UgRo/fYDBwRuiyXOnCLQGHBDFShjD8BN/j4kWCGODEXVmOha63bLNOvkWplYd1XWpUZJ6RSMQttrntTlqSPMGSsWdJDzyIAYaZttSoSOEot3T4DjAcrVcKHI24ZefLyW7VQ05uXqN6Xk9afgdB4Fx6pVDkgX9yr1duN/mYHe/p1ZdMPkFhJZZLuxyZB/tRByCzgJUEkbQB4fXl5WU8/PDDLngaPZSYL7cddH113Tk+NIaWXlPUKDDwnhyfbDaLw8PDIaYxMTGBfD6PVCqFSqWCra0tNBqNoe1H1p/ALB6Pu9PG19bWTCBDEMOjFTY2NtDtdp032sHBAebm5nD58mWUy2WUSiUkEgkXI0UySZkvgQQFHEP+Z7NZp62hJo3Mh7ZJch5RC0YvIUnbHDvSK40ppU3QYHBst3T79m1sbm5iamoKyWTSBeabmprC5OQkZmZmsLCwAAAuMJ8OnCV/MyaNJVh8AMInxKx5I7910u+MA5z0u2EaBU1PvjlrgQJrzstnNJCRgk1rYHxCXj+vaV8DCgto6ff4zWdkXZmo8dOG8z7eIulD8nMJTCxQxKQBmZxnTKRTej3JvmUbWKbUUMqP1MKwv6jV1H1u1U1e8yVrsWvxfJ+MsJLkYTwmhYsdYNi5ADiRsRYgHCc98CCGg6+3c9hhJGL9HGCvAviMJG6Zt0TdOg8meSSA1MxY20zyGarXNzY2XBvkWTK01ZibmzOJ09IWxePxoXKlYbJUGWuGZamSp6amnFcJy2Q9rHg2fEY+xzypraBL7uLiorM1IeB77rnnUK1WUavVsL+/7zQxo1YeTHSbbrVaQ4x+YmICzzzzDPr9Pl577TWnFeBZH9LboVgsIhaL4f79+zg8PHRbWloARSIRJ6inpqYwMzODXq/ntAuSkXNcODaTk5PY2dlxh0qyXVtbW3jttdews7PjAEmj0XDaj1wuh0wmg8nJSddHADAzM4NCoeCAAu14IpEIHnvsMdd/jMvCeDscL22/Qnpnf3ILiC7WOogYv1utljtVvNfroVqtol6vO/omIKZmbH5+HtPT0yYNcbyr1erQXJJ0pQWUBFRMUoBIGpbtlgLfAkvMV85rayFgaWUlX5BzVdtFyN9WHaw2yXJ0W/X9UQJFaw/kGMiPTzhZAlHzSvkO6UH2kbSrsOrRbrcdDQ4GA7eQIjCQ8Vl4TfeHBnOSr+vtJA2OeLihBHwSiMmxl7Sl291oNNyCS9rz+AB4WPKBFM0zw0DbOGURxJCXUXsktxVlP4xbfys98CAGsNVkciUgB0hqbPgck56cvO9jGvytJ6aeJHI1oFdpmqiC4HiFvLq6OqRRKpVKAODU9UtLS6GrNX5TIDN/rX7WZctJagkmggz5HoAhm4wwoMGyuYfabreRyWTwxBNPuIBxFO4U2DTq3dvbOzUpwhI1Dp1OB4VCARcvXnTuwclkcihSL1dArG8ul0M+n0elUsHa2hpqtZrzvpK0BcBpXpLJpAvjPzExgf39fczMzDiQxtgpzWbTHUp58eJFzM7OuhD8BBV87saNG+j1ei5PBrrLZDJ48cUX3TZRp9PBxsYGbt26NbSylvUdDAZDR1YkEgmk02ncv3/fHSUgV8/yvBgATijI7SUKYOnxwXFut9uYmZnBzMwMisWi254aDE40PZVKxWmjeMSDb6XZ7/fdeU00OGZinBrgxI1ertzlljO3s7htK8vhdq4URgCGPLg45gCGtrUGg2MtmIzhJGlEjkm/3x8qm9dl1GM5h3hchORpuVzuFC0STEvhqoWIHmf5HwDy+fyQbRMN7WWbNOjis5xzsl9oiD01NYV+v++AqIzqyoWf3jKWiyS5gGRoA9Y/Go1ic3PT3ad9idzypZDl1rf8Zj3lApTbpRLgyrrFYjEXL0om2e/yPQlg5OJHLgx18gEZHw+05ImUaRbIGRdk6HuW1k4uaPRC4d2mBx7ESKGrjaaA4U5lICTg9IBoBqGDQmlUbqk9JSNhkkZmemvHAlhBEKBQKGAwOA5NL92WgWPCkFswsmzJdMMIXbdZ5qXBh66rPDdJPjsYDFxcEd/KXCYyOW7vlMtl5PN55+nDaL0UptlsFsvLy7hx48apVaCvXdwK6Xa7yOfzuHTpEoLg2FX45ZdfdhF75dlOExMTzsV3f3/fBbGThtySLuiBw7OLcrkcYrEYqtWqE9ixWAyFQgHnz5932zD7+/t48803XXA/nnnEvqV2iFoS0hSN6JaWltxKkEx4YWHBBa/b2dlxGph8Pu88tOjpJIWcnDvsA6ltYNA7CVLJ3HX0aqnFarfbLmYP3ck5ZvRwI6ja29tzhz5a9Mcy6/X60H055ppxS6ZtXZd0Leeu3NqQAEg/a83jsO0iXx5aGOjfFt3pj96i0fXyXdfJet7X1/oZvbjT40JalcBH/tZjb5Wtr1t8YDAYIJvNDpVPvsRI15zTBFB8hrSrAZ6v/ZrG6JShF8zym3mz7fLcNHl9HHBh8UDfHPL1ox5Hn3zSv7Ut6qgxfLdg5tsCxFhbNz4hKgGJ7FAL7TJ/Mja+Iw0W9fM6fw125GS3iGxiYsKp1Tc2NrC5uTlkTzM1NYVLly6dMvYFTgcX0nvJVt/52q/bxvsybohM+XweL7744tBxAfRGoYutrAdXbDwp9ubNm1hZWcHc3JxzK7516xZisRguXbqEeDyOc+fOoVKpuBWXb6KQqdPWgzF3OAa0hxkMBjh//jwqlQrK5TImJiZQKBScMXClUhnS1mgBMzU15Q5dZEwaAKhUKs4omzTw9ttvY3t7GwsLC7h8+TKCIMBbb72Ffr/vgBSNjRm479KlS1hZWXGMv9VqYWlpCdlsFrFYDKurq4jFYsjlcm4bbGFhAZubm0NHQHQ6HTzyyCPY29vD4eEh0um022YieJOqfO7Rk7Hq/W2fZlHu+8diMdRqNWxsbKBQKCCTyaDVaqFWqzlbAmp1qFWRJ1drmgyCY+1Lo9EwAY6PgVq07mOkFsMGcGr+aqBiMWlrPvnolbQly7b4Q9gKOmxBIuuq7+m2+uxgrHL0Nc1TLWDDuWMtRHztspJ13RL6sg4E3vwtPYC0US4wbFujF7FhddDgRgM6DQ4AOC0RjzYBjnnk/Py8CwZqgfFRAIYpnU4jn89jb29vKESBrw+tLTdZvpYBWpZ8q7QwwLcBiGGHSlsPKXTkCoXMKBKJOILWqFN2uMwLwNA2ijVZJYiSzMlHCGEMO5lMYmVlxR2ER6afSqUwOTnpAqDJ+utJQsFEIRmWdL1kXeR/2lZoZkabjW63i7m5OZw7dw5BcOLhwtgsdK9NpVKIxWJ45plnnItzMpl0LsCRSASpVAqbm5tIJBK4cOECYrEYzp8/j1Kp5LYOtIpW1lsG06NKvNVq4e2338ZgcLydwjoPBgO3lbG9vY1SqTTE2OSknZiYcNqXbDaLXC7nou9yu4d9yv7rdru4ceMGdnd3sby87LbKSDc8BHN3dxfRaBTz8/N4/PHHhzx1qtUqVldXUa1WnTt+NBpFNpsd0uKl02ns7u46micQOTg4wL179xCLxdx2FgEjwblso1wx8joZLMuWNM55RcBar9edFo1ATLqcU9skj13Q4yjpk+7VPtr1gRuLxq1FTlhIfmt1Og5IsASr7zmrnFH/w/KwyvX1r3427N64K2uf0Ld4p843LD9f3XzvS/5MbQxBvtxiks9z7mitkf6vF6Xa1km3W/ITLrS4VcntVc63y5cv40Mf+hBWV1fx13/91+bJ7boPrBSPx/H888/j8ccfx0svvYRXX331VJRh/pb9oOWpfJ4LZB9wlGMiv33bZmHpgQcxEilbVu8WQfmAhMxPCmq5otb3ZdKGaTI/nae8zzpog7B4PO48N3wMSiZNcIPBwLnwhsVwkcligFJIWasIpkqlgq9+9asOgFCrQPflxcVFJyw2Nzdx48YNrKysIJlMOs0MGUU0GsXS0hIODw9x//59JJNJRKNRZ7vBmCW63qwfGQS3dOLxONrtNt555x0cHBw4LcTt27cRjUYxPT2NwWCAnZ0dt2evQWEQBA4AcJumWCy6eDf1et1pFWi0SsbDyLisp/Rw0u3I5/M4f/68Y3BcNRIoEtgQfMhx4uptf3/f5U97DJ4szai9EqTxZHLSEftHMmUG+yM45TsEgQQBpEPOyXq9jjt37mBmZsYd9kgtUaVSGTp2IYwmm82mK0sDFJlGrdJ9aRR4sejMKsNaCFjP6HIt3uBrm6yfT3ifBaxoHjmqv7Thqq6v9TsMvFgAVNbFB0h1H1n/tUbF+lhlyucJfDgnpMCXdefzFsiRvFnKFLZPas6DIEC1WsXa2ho2NzeH3vUl33zo9/vY3d3F3bt3sbu769WSaPlg0QGf5yLnLCkIbO+rUemBBzHAsHGZHBRL9a23NSw7Gg1++F8aL+qytIGw77eepDowk0S3FjO18rWYGevHqLgSfMn3NaH6frNeUqOj28IYJul0GrVaDXt7e26Pma6+xWLR2WfUajXcu3fPxQtZWVnBwsKCG5PJyUmcP38e169fx9tvv+0EnXRB133A3xTE0r26Xq/j/v37zniYof7j8TgqlQr29/edOpfxVGSanJx0BryFQsHZ8NALh4BFGz7TxZLCnVGDrTGIxWJ49tlnUSwWEYlEsL297bbn6D2lgzKyL8gEL1y4gImJCXeydyaTQb/fx+XLl3HhwgUX9p8nZJdKpSENB/tVGieyTzOZjNuCYj9K4DIYDFz94vE4ZmZmEI/HnVZGRnvmFtzLL788dDK6pm1eq1arp4KAMY0SvNbqUL5nzVP50avRcYCJdU0KRwskaWEeBpr0osJXB53kmJ4FCOp744AT3zX9vFWXcUCLFrpWub6PBBxWOdJModVquZAEkj50Oyw+q4GYlB9Sy6ll0cbGBnZ3d4fqchawKfnP9evX8c477wxtj4/KzzfHyGvCxso35mcFPsC3AYiRngQkEMkgNAjQBC7va3AiGYgEHXI7inlo4pb5yzw0M5QaJKrbg8A2KtNt4DWLYTI/7Z3kIyJ532o3+1pGdpV1GAyOtSOxWAzPPfccarUaXn/9dVy4cAGRSAS1Wg2Hh4fodru4cuWKW2WwjSsrK1haWnIAgMDg8PBwKPw9bUA0U9F9RK1FtVpFr9fDG2+84bQx3GKZnp5Gq9XCxsaG255jjBrJTKLRqLN/4RYS7VLomk3PHXleEMeS45xOp1Eul7G6ujrk0iz7X/Zxv9/H2tqaO4CyXq/jypUrbkuNB0Fq+k4mk7h69aqjgVu3buFv/uZvhupOT6pz586hXq/j1VdfRbvdxuTkpBsDgl8yLL36oq0Tvcm4HUXjRsaRmJ+fBwCUSiWUSiXk83kHbllnHrvgY3hcmVr3JN1a9B12XX/7+Ibvt5VPWJ1G5TNO2fy21Pmj2upLFm8Z575PYOlrYc/JvPgdBmqksNd1k7xXA5bB4OREdykjfEBG5mvxGvmMBIZSLmhgouWCrw8lT7VsEGXZ8r81fmy39NbzyQNrfGR7ADj7Owm+wurFaxb4GSc98CBGgxAZydTSjvA5iYD1M9YAa0FhaWP4HK9LY2CdjwVQLNCg6+djRgQ+OiCU7gffu6OuMf9EImES9mAwcBqScrkMAE5IMtDaN7/5TdTrdUQikSENSTKZxOLiorONodp2Z2cHvV7PbYGk02lcuXIF6XTaBQWs1Wool8tO60CwQzAyOTmJK1euoNFouHgM3M6o1WrY2tpCpVJBLBZzmp7BYOAMc3lmUTKZHDLinZiYcG6bZJJSS8Lxl3TGqJarq6uo1Wqn6CgIjt2SNzc3nZ3NwsICtra2kM/nXf0WFxedgSvfZz0IWg8ODrC/v4+rV6+iVqthc3PTlSGD4NHIV2on2+32UHwN1n0wGLjxY1mTk5MOvJHe2+22C4C3urqKdDqNRCKBRqNxasuI9aZdgKYp0t5gcBIjRs4HeV9fk8li7tZctACNtU2t58kokOBLcuFj1XUcgeO7FvauTBYICMtD0msYaNF1GgVmwoCL/m+Nua8sq61Wfax6S+87nbcee/JaSTPkDToP7dUnwZbFw33gNCzJ8sahUwlGJOiR28MaPFvyzQI15Dv/AGKMJPf3+FtHtAWGmQU7Xe7hW4NMLYkcJF6TyBs4vU1jvasnKYWQ3AeVaL7b7bpgYQwoJPPit2S2mtjlWRb6XWsiWvnLvqOWQDMDqlzb7TZee+01PP7443jmmWdw9+5dFyuEdhQAhs6/aTabePPNNxGJHB+KOD09jUql4rZPaBsyMzODtbU1ZLNZ5PN5ZLNZF8p+MDh2ST88PMTR0RGSySTi8ThyuRzm5+cRBCfnfNDIuF6vO8DDIwDkVgr7nW7UjMLLWD1yP53aOTkG1PjIgHBsv4z7owHynTt3sLKygmg0iitXrmBubs4ZR6+trTm7Hi1cZSLIYv9KJklwQk0XcGywLWOD0CBaeiqRpmWUUpalaYcamqOjI+zs7DiQAsCBGfbV5OTkUGwVnbiSrNVq7r9Fx9Z8l8/LpL2N5BySAQ/1vJJjpeegbL8uy1qw+LaOw8q1rkkepEFR2AJlFDgKA02WwNc0JueBfF5v4/jAgVWuvm4BGKtOEiBwK5TaFRr36ufJm/UikPZ2jF8lE2lnMBgMaRp1e32gr98/fXwLAUA+n0ev13NHsVj9L2lC8h1qk2WdJJ30+33U6/Uhvuxb/GrZymc51tJoWNZRnyM3bnrgQQxwetJZ+4d6wluInvekr75MUsBJIrAIlBNGAwsSl6yTLJvf0g04nU47Rq6BGBOvWWDF11/6mhQEFnOQWwD63mAwcBOA8UharRa2trac63Kn00EqlcJgMBia0O3cpQABAABJREFUqIPBcWTiYrGITCbj7FbYd41GA9euXXNh/OU5U1NTU85DiFsljPMCANvb27h3755zX06n08jlcpidnQUAXLp0yZW1s7ODO3fuIBqNIp1OO+0CAREAd2aKdE/2AT4p+OWWWDweRzqdRqlUGoq7w/HjltMjjzzi6EWqYnlcgo+2gyBAJpPBuXPnHLi0VqXyP2meW4LSwJpMVII2eV0ybL7P/Mm4JG3Q7og03mq1hoyedV8OBgMXKFDek8/qb2te+57XfWiBdN//sDlsjY1+R/7XYGoUeBmnTVayhL/miz5a0QLYescnpDWg8AGhsKSBjjau9bWTtKsjTI9KnOtSYxqPx5HNZp1no+x7qY0Z1adW38jnyDs497lFW6vVhgzqdb9IY2GmIAiGjkngNVmWrIemJ7kgp6OBto+05g2/5eLnrOmBBzFBELgtACat9pKDqztWG/4ySaL1ARGtldHaGB02m4l5yrpqps2DEHlmTz6fd+XSnkL2gRaekvlLex3NsEYxPdlmaiFkGyWI46qk3+/j9u3bzp5jZ2fHCaFCoeCEvxyLWCyGfD6PtbU1NBqNU4ai2WzWefSwPYwbQm1CEAQuXk0ul0MQBCgWi1hbW8Pdu3fdioTgR9q4TE1NOWZFxgXAbbkweFskEnEaIgkkKNQ5ttKDgROYWyfAse1Ns9lENpt10UXluOzs7GBvbw+JRGKIUXIFKceH+UsNCT2uCCb1+GoQT8AiV8k0tmVwRTJS2Ub2qXRnp33QxMQE0uk05ufnXRn08iAjZPA6blPKOsk2ttttB8YskG21Td+36N4CIZr+fc9a4FF/hwFN/ZHeYRaI8WlhfOXKj09o++oeBmYsrxwtAC2AIkGwFtyWZkaXy/kk6+gDPlq4k/bkQpD3yYvllqrsd7kNIgUyjfU1D+U7uh+07PHVVd5j6IGpqSnk83nUajUkEokh2z7Zd5aZhBwj3Zf6nu5bS7YAJ2dHyfysxHvajuYs6YEHMcDw1o1ksBpkyOf1oJDQ+SwBCJ+X75N4gZNBl8aQFiqVAMfaZ5XeAgRm+hTkarXq4qtY2hxNiPyt7S58THsUiOHEtfKRatCJiQmcP38eAFCr1dBqtRyg4eqFz7LO3W4Xd+7ccf0sgReBB7dgWAeet5RKpZzR6NzcHJLJpBv3RCKBp556ColEAqurq054NhoNHBwcAIBzh37sscfw/ve/30XJrdVqiEQiLly6dFOXBrxkIuxnCY7ZLxxTvsdDGZPJJJaWlnBwcOCMW8vlMmq1Gm7fvu0AAIEfaXNqagrdbhfJZNJ5WWmmGQTBKdBj0Yjc1pHGuvK8GdkutpsRkcmgaEPEejLgHW2TGLmX9ej1epiZmXExgiwaZH25VWklHx1LOrUAjAXsredkXfS8tvrTAlq+usnrWoDpsjWw85VvvavrputkAUcrH8lPwgCS5DnyWWuL3Sd4eV/W2eofX135zd/k6aPAoOwTCTBlXQnS5UJZ24rQTED3v26HBhcyP2o7yDe5KOHRH1KDImWSzIfPxGIxt+AnCOn1ekNnssn+tMabdSVvsICM7kf2vR77cdMDD2LIDKly1wBFo3UL7WtC1+BGTzo5SBJk8L7MV16TqwKfHQ6JIZVKAThGvDS8pDDK5XJOoMt3NLDSyHcUkwpjJEEQDJ3/otumQczFixddkLN2u+3C7FM4ylUA68ZVkqyrFJI88+jpp592pycTXMViMXQ6HczMzLiYM0yJRAKPPvqoM5rVNMDJS9fpbDaLlZUVDAYDlEolvPPOO6fGTB52xntyr5hbKOwPyVDYX8lkErVaDXNzc3j22Wcdw6K2gp5PBGWxWMyd/CyFghQYOjG8uizbYqCsJ7UobAf/A3AaFzlWev6RwQPHUZw3NjZcXB6uYGnfRZfrdDo9tGiw6LBer4d6h1jXfPc0ANcgR85T+Qzv+0CTTxBaZUuhLK+F2cHI7Ww918OEchhP0t9SYFnJ4p+jrlsfX19r7YAGStb4WyBB5i+f9dVN9qEEJNIuRsoW9pUU8jo/XQe5UJVeUvI5LtAYoJM8QcZ3YvgH9pvFM3VbJfgiAOFCiAsdq65SvmlgJO3lpLzV7dYg8KzpgQcxvkkInAgPubcoJw0FpGZY+lsSixYA/EgmrglB/pbPy+inzE8yqkwm44grEomYAcEsZsfrVPHLdup3NXPQTEPmz9D9FtOQqtqjoyPcunUL09PTyGQy7mTnIAjc9ojeXpO/NdOl3QjPOuIJzY1GAxsbG86G4/z58+4UZM284vE4Ll++7LQsuhxqezY3N3Hz5k1MTU25uicSCXd+kdwH59lFLIP2J4zNoq3xuWqR22SJRAIbGxvOViiRSCCZTLotrIODA6TTaZNR6TGUjJ+JXlYa2FsMnfUlI5U0zzOOCBalTczExIQDZMDxFlyr1XLaq3a77eidHmiyHYyxc/nyZRQKBVipVquZoeEt4Ma+8CWf4JfzVzJmCwxYQCYMxFjfvo9PUwDYW00+4BJWtgVmrD4L60fA1nhoQS4Br+S9uhx5XfJaSbt6HvjaI+th0YxlXCz7YpRdi+5zzV+tMi1ApesoT+TmtiuDWzLCMDAMfvmulieav5fL5aGt5mg0ilqt5k6Sp8eoflfWV44jjyuxFkVyHKQ9jG++hqVvGxAjO1JvnzBpoKInCTCsCpbvy0PxNOETZMjJEcbkiOplHj5DTa1V4Du6bLZHtoPaAGls6es/33+ZtzxZl2UwUfvCcz52dnawtbXl3mObY7GYO1pATxjd97wvT7yuVCr40pe+5FydqR0qFovuDCELnAFAsVjE008/jddee23otFzgZEupXq87d+2dnR1nxJdMJt05UGQEFOZkHvR0GAwGQ8Jeaxm4FciTm3mcAJkKNUHNZhOlUgnz8/NDJwnrcbJol9e0AaWlNSTwZD+QifJ5MizarXA7MwgCkzmx/Gq16iIHM8gd6V/2Cd3dV1ZWvEKT7tWyfaMYYhi4089ImvMBAh8o8F0bdd0HOsK0LMBpDasvH18d5X/dXktw+f77QIW8r+e0vK7f0+NrgRMfuPSVz2/OAwIBLrokT7fykIsQCW7ILyigNf342qjrJfOW9aRWNAgCx3tqtdqpBbmkGeYh68x68BojfgPA7u4uut3uqTPLZH8DwzamWhFAE4EwOpPvvZv0wIMYdjA1IRoU6P9aiHBwOCjSYFMSCd/hM3KSMQ/5nCQKGfTMAl3yfQmILFTLZ/Q9+b4sn3YYVlA4X/4ySRBjuQMDcNFneUo0t5AYEZaHQdJ4VsaI0W3R6ka5L8ytkZ2dHezs7KBQKOA7v/M73UpC75dbzCKfz+PKlSt4/fXXh4xpub/darWQSCTw9NNPo9FoOG+mg4MD3LlzZ8jI1toSI3ih8S8ZJ/uPDFSC2WQy6WxiuPXGPu33+zg4OMDS0pKXCWhBIBkpV0pa26fHnFtY/M12cUzIrAmMZV/z1HFt4A0cC4Fmszlk46THhvFzuFVl0Tu1Z2HCIUygavBjCcJRwGAUUBgFbqz8x70mf+sFm+9Zqy66f7WwkklrS33CmL8lT/F963d9YMZKPjrXz8i6aKNXCV70sRr6XfYPj9egjQvnciwWc98yP+Ypw1v4QIIGPBxfLpKKxSKy2Sw2NzcRBMFQTCe2T/IjXXfWn7GuIpGIW6QxH4azsBbfup+llnYwGDi7GEvTzyS1NWGyxpe+5SCm1+vhP/2n/4T//t//O7a2trC0tIR/9a/+FX72Z392iIn8wi/8An7v934PpVIJH/zgB/Hbv/3bLoooABwcHODHf/zH8Sd/8ieIRCL4gR/4Afzmb/4m0un0meukJ6/c+x93dUFwIhGjBj/AsFEvB9OySdAMQwsSmXzI3KqnXqX4mBKFibbZGSdpYMSJJUPG83qv18OtW7ewvr7utkHy+bzTlMzOzrq284wcRrjVDEaWJcclHo+j1+sNgR/geFURi8Wwv7/vzmli28NQvzzRmomriXa77eLLFAoFp8Ganp5GtVrF9vb2KaNqqbUIgmOPKD0uUliT4QFw0W2DIHDu6NyOAY4ZwP7+PhYWFobGVoJdzTRlP8rTomUdLTonOKGGSArVSCSCZrPp+pxbgiyXK70gCJxrOrdApTCUjJ0Mv9frOSBqJenFpJm/THLO+QS2dZ19Kn9bAF/TjAUUZDk6jQIWowAMv/VWh74fVrZ1fRSAkPxJLgplP1pbGOPkOeqavu4DTnJsrTrLeeLTvFg0Qfqk8JYLENI4QYz2BA1Lci5bdiKDwXEAyEKh4GI6NZvNoXmi5Yssk3aCBGBHR0eoVqtoNptDxvr9ft9pxq25xTKkNkV63UpvRA0amd+79UpybXnXb3rSr/7qr+K3f/u38Qd/8Ad44okn8LWvfQ0f//jHkcvl8O/+3b8DAPzar/0aPvOZz+AP/uAPcOnSJfzcz/0cPvKRj+DNN990avEf/MEfxObmJr7whS+g2+3i4x//OD7xiU/gc5/73JnqIzUFAE4NBu9Jgo9Go0M2LHzGx0SkwNCTg0n+lkaglqCxkkTVEiRxUug9UK0FAIbPYZITTgfJ8wEgnZ8WYhLEyH5gALl6vT50XhJdA9PptIvRwu0TasGomZFAQI8dhSa9g1g2AcbW1hZu376N559//pSrrh6fXq+Hg4MDF+hKglPatFDwy224aPT4ZGl6DgVBMDSZCYI5qWXgOFkHbYBOg+jBYOBWXfPz8+5ASQbmI6Oo1WrY2dlBuVzG9PS0Y0IrKyuo1+suCBfHOJPJYHFxEXfv3jXHVF7jqoqGt+xv/o5EIqfcqFl/6W0WiUSG9u+lS7oErXL/n6taa+x41pNOo8DDuCDD0mb4eIEFQHzaEF9dR9XLAi66rnLspBAfpx4+IavnSxggCQMW1m/fu9Z9WU95z8d7fXnJOa/zlnOf1+RvKSsIBGQ/U6jzA8AtAIAT+tbmB/yWgErKCzk+jAy+s7MzpM1kHaVGiAszyZ8JfuSCg3WTNGDNOQsUk2fJc+BYDoGVpGGWIbecz7qgBv4/ADFf+tKX8NGPfhT/7J/9MwDAxYsX8Ud/9Ef4yle+AuC4Qz796U/jZ3/2Z/HRj34UAPCHf/iHmJ+fxx//8R/jYx/7GK5fv47Pf/7z+OpXv4r3vve9AIDf+q3fwvd+7/fi13/917G0tHSq3Ha7PeRiyVgp7BitSuM94GSVIJ/T2y+WTYue8MxH5mGtAnjP8rbxMQa5wmEZ5XLZRTsl6ufBhaOMHFknKUzD0LDVBiZOVGulPBicnCgt36fAOzg4wO7uLoCTAzk5lul0GnNzc9jb23Ph7CcnJ922AhnBwcGBs+2RfXh4eIjXX38dOzs7KJVKKBQKeOihhxzy12PabDbx1ltvYW1tbegQSU7+o6MjtFotF2OG4I/lpVIpLC0t4d69e87eRTIfliNpRGop9PYlBTjj3RAoXLlyBfPz8w4MNJtNrK6uotFoYH9/H+12G41GA5VKBXt7ew48VSoVnDt3zgXqy2QyiEQiWFhYwP3790/RvmTAQRA4IEJgwnrK1WW32x0KbKejnRLgBEHgjHgJDjUzlFoZHexOJhkjRn7k2Ghatv6PAuxy4SHH1WLo8r/kEaPK8P0e539YXWRfWIIEsIWVTBK46Hf0b+D09pNvYRcGODQwkeX68hoHKOk2AScaR01HPlBHOpCaC9l2gnT9DtPExMRQPCyrTlZd5PYtQ0HIRQ8wbLsm55ccY/1faj6lJlZr62XdtOzTphTAiS2gjlkj+5Btk/Y646ZvOYj5wAc+gP/yX/4L3nnnHTz88MN47bXX8Ld/+7f4jd/4DQDAnTt3sLW1hQ9/+MPunVwuhxdeeAFf/vKX8bGPfQxf/vKXkc/nHYABgA9/+MOIRCJ46aWX8P3f//2nyv2VX/kV/OIv/uKp67Iz5bdGfhJ98p41WeU7mmjlBLeYqI9JkDH61IbyOpn/9vY27ty54zQDZPiJRAIXL14cIgxLiLJM2mCQmKS2AIBThWrmJ4OzcULpaL2sPwVeMpl0Nh7T09PY2dlxAEQ+L/uUq3Su/hmATp9qPBgcx4XhOFAjsL+/7wDGzZs3cXBwgFQqhWw2i1QqhampKVfHN998E2tra26bLQhOvGtk/BoeFy8jz7IuMzMzaLVa2NvbcyscSWtSEPIEcam54NgQEEgXRwrqmzdvuud6vR6SySR2dnZcH6fTaXc6NEHh7du3Ua1WUavV0Gw2MT8/jyeffNJt6WjhpNXPQRCg2Wyi3W67lZZcWUqGTVpj+8lIOaYcLwIcvWKVApjPJpNJr3BlDAs9/0YBGAtYWHOT4yVBqQaooz5hZeo2jwIpYR9JY753Zb6WLYIP/PjAC5MPMISBDUtAh92XdRmnXE0H1rfUepAfUlMotSWaFsgbpJ0Ln2HMIz3e5JOc71JDKeskeTAwHOyPY9Zut3FwcOBcqiXdkw4k//EBPbmYk55ifFa2Qb8r+5J01+/3h6J6k7/yWS2bJED6vwLE/PRP/zQqlQoeffRRJxB/+Zd/GT/4gz8I4Fi1D8CdXMs0Pz/v7m1tbWFubm64ohMTKBaL7hmdfuZnfgY/+ZM/6f5XKhWsrKy4/9ZqQ05g6X7GTiaxSsYuk5wgkjkTHGiUKiejTFK7I5+TWhLJ+G/evDlkAwKcGAw3m01sbGxgaWnpFOPS/7mCBTBk48AYJJHIsRs33WCDIBjygmk0Gi5eAZG2bhsFcRCceFJxK6ZarZorETkmnOw8PXlychKpVMpNFhmgSpYvx6rX67ktrZ2dHZf39PQ0zp8/P7RCYKRLzfDp+k1tTLlcxtTU1Cn7mVgshpmZGXS7Xed5I7eVpMCmoKdA5JYKD7gkMyWIobaLGoxUKoWNjQ3ndk2mSCYSjUbdGUvf+MY3ABzbmtVqNXQ6HczOziKfzzuGLIGAHhMGAORYagBprc6sfKTxslz1tdvtofOTtF2CdCOX5QwGA9RqtaFox7JM/U6YIA5bfMg5LPkDGbcWcr45FwZU9HX9rgZ5GrxY4IPv+cCM7A/rXfnfV18NKKxrur81OAFOnyfnAzQ6nQU8Wc9IEKPbabVR3qddiQRh5FuWh5MGv746WvWVZRwdHbkjOfQ7UibJd3WbmPictOWT18OAq55fEjTRiQGAWyjTiYVlSfkq5fBZ0rccxPyP//E/8NnPfhaf+9zn8MQTT+DVV1/FT/zET2BpaQk/9EM/9K0uzqVEIuFWyDLJSSWZj5wo1mSXYdq1vYpkWMBJuHUAQ9oJqQ4n+gZsWwxZTylINJOUQkrmxe+pqSksLy+PdQ6FnpjWJJJ9ovtUJmnQLN+X3jaMbMt2WePEusj85fYG+1qOGQEU6yq30o6OjrC9vT2kGeDk4oGJBFV7e3vmXjhBFN/f2NhwxsLPPffcUDTZIAictoiBp4CTs4dk3gQw7BMZbp9bQrQHosaENF4ul5FKpZDL5Vx+7XbbMQ15mNrk5CSefPJJ3Lt3DxsbG4hEIjg8PMQ777yDxx57DLOzs3jmmWecV0Kj0XBaF8mMqTWS88f3rVdYQRA4ACY1VHyHGi75jtzmpcbMmjvS88kCKXJlStDMOS5tFDjWrIN0hw+CwNFxKpVyx0wQdEaj0aFjN8g3GPmU2j0KMoJygvlUKuV4hgz2SOCq+RbpnAKS80LHtZKxhwgSaAgv+aKce9L7jABf96nmoWyXHG/9W2uUrfHU4FHyCp8Q1dct+pQ8VQMI2Q5qQKRtl94CkWCLNCOBgpWvfJf8SQN12Q/afpGJiylG5ZXjxn7i2I5zj+XpU+45z2W9rTHTdMH20O5Pamtp2yb7U74vtbFnSd9yEPPv//2/x0//9E/jYx/7GAA45vkrv/Ir+KEf+iEsLCwAOD54b3Fx0b23vb2NZ555BgCwsLCAnZ2doXxpP8H3x01y8ksC1rYt/M9tCzIJK3osv0kQHHAaUFFgWoShhTW/pWGY1QYyl0gkgocffhiZTGbIoIsTa2lpCfl8HuVy+ZSaUwo2TeTWhNMMjvetetKLRtebzCASiTh36unp6SG3cl0f3W62T+630jWck53bW7pNBAfpdBpLS0tOUL711ltotVo4OjpCpVJBpVIZim0iARPHlZovCiepxpWJWsPd3d0hDzDZb1rNyzbS2K5Wqw0JN56WTQ1OtVpFuVxGMplEt9t1YEmCboK7iYkJTE9PYzAY4NatW04A7+3tuboVCgVMT0+7Mbxz5w6+9rWvOYFG+mB/kzZ0wD7ZF1LlzpDmGxsbDtRL9365CpMAleMutTSyLPaF3iqV9MO89PlUzF+DeRkVVQMebtnR/ot58NBObuFRK8YjFVjHo6Mjdw5YPB53YQay2awD09JIMxqNugNepfsqcHJOFu2QGACx3+87Q2cGIaSLbCwWQ6FQcMbn0oaJ86tarbp6Tk1NodlsOv5Ez0Ha3XEeHB0dOe8/tl07DFDze3R05CKOy77udrtO89vr9ZyWgRGb2Qb2OwB3ThABJU88l5Go2+226we5mCEg5byW/IPt4MIiCI7jsZBeaecn7Q+1cNYCXi9itPmAplcL4MhFoOwLzh+WwXmt+aHkQ1ImSl4vea0uw9LoWCCM3zJGDumffIHjIRcQoxbeVvqWgxgezicTKwwcnwq8sLCAv/iLv3CgpVKp4KWXXsKP/uiPAgBefPFFlEolvPzyy3juuecAAH/5l3+Jfr+PF1544Uz14UDIgZVghh1HJkvmKt3jpNpYgx5OChlbADhxYeNqVmoHpJcFwY0GD0xSPTkYDJx7byqVckxf2hZQy8DJp/O0VjO8LpE2P766SYHB1aoESHyODIkaE360tbxG9xwbuRJk/jJKsRxbyQDkipT1oU0NmRjHDoALsc3xkUyI9eSWDpOMkKxXd9SI3bx5c6gecoUs+5bbRu12G+VyGY1Gw7kr88P+p1Da2dlxAeC0cAOAQqGATCbjDrNMJpNYXl7G9va2a+/t27cRBAE2NjbQbrfdid+0sYnH40PbPlojpFdnFEYcr2g06iIyT0xMYG9vD41GY6gvZOgCqZliX9KTTSYJBmu12ki61kkzX9/iQf6WgFPPaS545FYhP9Lwk+NEWpNzVC46dPkSpOn6yGtya0TmJ5+T53rJcrSQBYZ5FftMapS1sNbbJbJ/5Ye8SwtrSQuca1xUyLPFeI+BFWn0Ho/Hh44u0SCUi0HWYWpqakijJec37eA4pslk0vFWma82WJXjLOlMyhur73S/W1oOygvprclnJe9m+Vo7znnHvpB0pfvd0pwx8Z6mDT1P9AKeCyvJswmY381WEvD/AYj55//8n+OXf/mXcf78eTzxxBP4+te/jt/4jd/AD//wDwM4buxP/MRP4Jd+6Zdw9epV52K9tLSE7/u+7wMAPPbYY/ju7/5u/MiP/Ah+53d+B91uF5/85CfxsY99zPRMCkskBGl3QNUvGatUwzOUvGYE8uwIMmiu7rhCll4mBC90g+VKWTNOCYq0VkISBFdkXFE3m023EuNqn+7DtDvQhGb1DT8SEFjMW04Sa/WqDW3ZFiteCF2xdT5yvIBh7xgpLJrNplPdy7KsFTjHdWpqCvV6fYhx9PvHRsbFYhFTU1NotVou4B61RjQsbjabKBQKeP/7349ms4lqtXoK7MgUiURc7BhuU0lmTUZCrQ5tTsrlsju3hAwykUiYgoeTnyvQweA4dPjNmzcRiUTw5JNP4vHHH3dHFqRSKbzwwgv4sz/7M6dFYup0Otjf38f29jYikYhb0dJeRbaTxsOsN8dJruQYHI9G0ZFIBNls1m2jUC3ONpEx6+1TAM4t3BKOvV5vyL1aAwD5rCX45b1Rq0DOJwI1Ob/YZk2zBDcEKxLYMC8pcLT2lHlIYSNpSF/XQEiCGn5rQ2sNNmQfyvrJPtT5SpqUddGaAN2XFJw+8GVpDOQYWmMq8xo17hZ98LrFP3XZ3PbVAtzilbpP9PPyGoESr/EdukTLLXbZbvJyX966/znGUhvDfK3+k3OT+XEbU8o4+YzctpVyQPJhOffPmr7lIOa3fuu38HM/93P4t//232JnZwdLS0v4N//m3+Dnf/7n3TM/9VM/hXq9jk984hMolUr40Ic+hM9//vNDRqOf/exn8clPfhLf9V3fhUjkONjdZz7zmTPXh1biU1NTmJqacttFAJzalYQYBIFTWcoVjBbYGq2ScIgmCZLS6TSy2SwmJiZcVFqCKa1KlPuqGlwEwbGmaHl5GblczjH1bDbrQrdzFc9Vt56QFBS+iawnkXyPydqb5n0dWpqJk5GrJLpIy3d1XrL9EjTK32TEUluimZ+0geh2u1hbW8Pk5CQuX76MTqeDg4MDTExMIJ/PY2ZmBr1eD1NTU5iensbi4qIrs1qtYnNzE61WCzMzM5idncXy8vKQ/YQWBMDx5F5YWHAeQXI1ApwIQwaaKpfLCIJj42kemUANTLPZBAAHbghcMpmMs6GJRCJYW1tzTG5zc9OpylutlnPF5nkomUwG+XweyWQS586dw8MPP4zr169jf3/fuTTzuWq16rYruJKSLuacC6RrtieRSDgbklgs5tT3BGacd3JVK1eLHBNq+vTqlNGPZd9bq1s9j8PAjBZe1ruS/vgttTFyNSy3VOX7pAH2nRTmUghYYEUDCFl361nrPS3crOcs+yXZjjBBzHZZgMB6j2PuAyZao6U1Cbruerx0233j60sa8Mhruj0E8RrEaPrUC1ZrzHT+cqtHAha9ywAM80Vdjqy/XIjwvjbD0H3ApLeG9fgwVg1lgQb77COp9TtL+paDmEwmg09/+tP49Kc/7X0mCAJ86lOfwqc+9SnvM8Vi8cyB7ax0/vx5pxLvdDqoVCqO4Ucix2Hk6WUDnCY0CVL0KlhukwFwRMtn9vf3HWOmpobq/Z2dnaEVlnSnkxOE9xkEbnt724GkpaUlzM3NYTAYYHd3F61Wyx2yp701JONk3vzWzMJKlpZI3vOd4SQD0NFVejAYnIquK5m2bDsnp1Ztsj81s5J1DIITQ00CTMYlkVsC+/v77qyk9fV13L59251YfenSJUxNTWFzcxP37993IboZAXhlZQWFQuEUQ+LvVCqFhYUFrK+vu3KpPeNvAlx6XvGwRNoN0SX93r177v7m5iaq1SoKhYLT+HU6HeTzeQRB4ADC6uqqs6tptVruVGhqmNrtNu7fv498Po9iseg0MJcuXUKlUnHeWJ1OB4VCAeVy2Z2nIpkntX8cAxqPAnCAHgCWlpYwOzuLaDSKcrmMSqXiBCUXAKQVzj16XskxZh+zbhZA0desFAZm9LclUKRQtDQIUnhojQkwDF4IhCTdy/LkilmXLcvgt9xS1ZoOyb806JNtlfckyNBASAtjDSTYVj0WWvjrfK16Wf95TWtyJI1KLRGflws7q1zWWQNK2SZuDeo26bwBmLxMvqP5ctj7Unuu8+NvPQYSdOs2aw2o7mMLjErZqDUukqblNiH5rlQmUB5LGTVueuDPTiqVSk5LwcEgU6/X6y72hm+lAZw+oEr+5n/gNEESmbZarVPvA8daIkuIS2FOAqlWqyiVSkPI9vDwEJ1OB3Nzc6hWqw6McWWqCVyr431Ahtfkh9donyMnN0GMtoUiWJH9yj1s6R6u264nkTRcZj7SG0o/L0EO2x2Px7G8vIx+vz90ujSAU3Y/3Eo6PDzE3NycqzNBGcPc7+3tIZ1OnzpZWfYDt5XK5bIzQKTQpgaGoCCZTGJxcdG5gvd6PVy6dAmpVAqHh4eo1Wq4ePEi8vk87t6967bH4vE4arWas2lZXFxErVbDwsLCkAv9YHByUi01k4yVQ03alStX0Gg0kEqlnPaH2kvSGA0l6ZWVTqcxOTk5ZIuVyWRc4EUuIljXbDaLeDyO+fl5Jxj5XqvVcuXI+appk2Ndr9fNoGJW0jSumbcWYNaqU963wA/baQkjOZ8oZLV3lAWepODUz1ht8oEC3zWZN+tiAQVL4Fv5aMGn22T1axgw8QEaSxBL4anBjc4L8BvQ6qR5ph5PDQYkUJR115omX7mal1p1Z7usZyknrOvWItQ3Jr7tL9kX0naJ9qF8Vy4+ZHA/9g3L1ocHnyU98CBmf3/fERVP6CRDlkZiGt1ae41yokgULAWgNOy1UD0ZL/MIggAzMzPOpZV1ILFwdcqVu6xPr9fD3bt3nVCQRr0sU9Zben/4CMaarLJ9crUt39GnKLP+2siT+Upi99VDM1A9+aii56qeWyBcFUhAA8BFuaUmbmJiAlNTU6f6TI4NjbP1ikZPZtmnWgDG43EsLCygXC67shhpl9qayclJzMzMOMAl63/z5k1sbGyg3+9jc3PTnV5NL7RI5PjcIm7NXLx40XmHSKPHXq+HjY0NVKtVF7I8Ho+jUCi4fW2eZVWtVp3nE+2P2K/UAlFlXiqV3NEPBBTsc2oGU6kU3vve9+Lg4AB3795FMplENptFNptFPp8fAlL9/skBoZVKxWmXZN8z8YgKfV3SsJUsAanH1RIyeu5ozYslHCzGrwW/5CW6bnI7QNddfrSmQc8hfU+3UZehBa61jSSTBR7G6V+dlwYqYcDGys/SxPjq7QNaugzddxLwafkgQYzml773dRmWFkc+b9kwco5bNCpBHOcx68g6676QtCnrIfOW3omM49VsNof6mmVxASw9MQlerBhj46YHHsSw8yYmJlyY9Wq16owVSTDAacGtJ7RcRTFpQakZjSQKGqVSzctBnJycRD6fR6VSGQJXUkVHLQjBS7/fd0bJ1WrVCT66kUripECW+WrBK+vqY9yaOcr89ZEDvL60tIRoNIpSqeQIns9aEX5lv8r+1XnLfh8MjjU++XzeqybX7u3UClGLxSB4yWQSs7OzKJVKAI4BCN3V5cqBIEhqCaxJzpTJZDA9PY319XW3BcKxZ7wXCTIvXboEAFhbW8Ph4aHLhy7SkUgEs7OzqFariEQiTtsoPeVkXQaDY2NwAhcaWDOR+bzzzjsuiB9tyd566y2USiU8//zzaDQa2NraQr/fd56I1DiyX/XKkP2azWaxu7vrDqvjAoOaRW7T8RytbDaLubk5L4Dp94/dq8MEpU6+MdJ0Fib8fAKaddL8QL4v55LOSwpgqz1as6rrpLerLC2MBCJ660Z/h9XT+ljPWe3Q/+V1ue2l226V4xOwo8CLrIMsU5sNWB/fNpnMU8bs8eU1MTHhbDB9zwHDoQA0jWg6Y33I72WdJO8CThakVogEi8/rNgKnDzzWsWFkn3MLie3gIpR99W7TAw9iKKwymQyC4HhbRrrIaVQMnHYrlIZ6vM//0piKxEMgIfOUgEnGgOj1etjZ2XEr0nw+j83NTbeClbYMNHqkJod1pzsuBQhX+nJfnKCJdZHt1u1nskCOpW1gH1tgg3Y7q6ur2Nvbc4aq9J7SZfgAFOsnJzz7IwiOA+k1Gg23raWZkaUK7/V6zj2XwexmZ2fx7LPP4s0333TnDskYLARi0WgU+XzeGVHrNlgrcMbR6Ha7zsC5WCwOxfegUS6BaCqVQiaTOcX8er0eFhYWEI/H3flZcvWntyAqlQpu3ryJdruNubk5TE1NYX5+HoVCAf3+8SGR3W7XGTBHo1G8//3vRz6fd4A5m826+CK6jy0mKvuC9MktKiZZR3pHMT3yyCN44oknhmhUpzD3apksAaqTNWaW0JXuqdruxDL4twSwFr4+MGDdC2vDqPflYswqMwyg8BkfkPK1zaqbL399Pwy0sZ/1olKDAksA63ws2rBkhAahcsEok9xKCmuftSAGYIJNXwqjDUmDzFvaXek4T755IX/LsSHfl+8xqCXHhuBFyldp7C5P3R5nLuv0wIMYMl/gmOFJt06pVeE1be/CJIGKTLwmtRx660MOjiQmKZzL5TKazSZmZ2dd0DHaBVhCQ9ZrampqCGhJIaoRvE4+QKNXI5aQ5nNcSVt59/t93Lt3D1tbW+6gSgIqOVGldkW+L/tMlsn2crzS6bTLT/YTf9MYWo5Jp9PB7u4ulpeXkc1mcf/+fWegzHgGDLIl+2piYgJzc3NIpVIuoF0mkxmKlaMZ1fb2Nl599VU0Gg3nolwsFp3RKrdQKpUKtra20Gg0XBwL2q7kcjksLi4OHRVQq9Vw7949xONxzMzMDBnLcXumXC47DV80GkU2m0Umk3HHFdTrdbzyyitYXl7Ghz70Ibz11lvY2NhwUY1brRYSiQTi8fiQJxCTnDOSXuR4MWwBt9AkrRYKBTz11FNot9uuPTs7O0OxaKxE9+pRzE/e0wxXPzeu0NSAxwIEcltYC2b9npybPo2LTxhyDHRbffXy2WXouln9Z5XNfvXxPCsfq+ywelt8z8eTdHmyXePSie+arpMFdPntM1IdZ2yt7TAAp2hKglLJQ605SDrjO5Z3IXm2b2dB1sfyzpQLPUnbPJpGn/Okgb8lo0alBx7EZDIZAHARWX1ghMmaXFo1DwzbpRDZag2NzNMqBzgmShpRNptN3L9/38WqoR2MXu1LAMZtMraNII1aClk2gzfJpLUrFpAIY/wEEr6toX6/j93dXZTLZedu3uv1hlzdfUkzdwnoOFGkZbxPgPAaVwDai0zayNAIlW7AQRAMhfceDAbufCRqvfb3952dCd2J2WcMKPfGG2+g2+2iWCxifn4eyWTS2TCxTltbWzg4OHDG5rRnqVarzn5nb2/Pae1yuZyjvWq1CgAuciu1K71eD1tbW4hEjiM9x+NxTE9Pu5O44/G4sythNFeCh2984xuIx+PodDpYWFhwWkENfKXWQQtr0giDinU6HSSTSTz22GPuDCrSAoHm4eGh284jwwWG1er9ft95Fkpa1EJR07amYx9t+wST/JABW4DAWkTI93zCwbJD0DyIZTJvDQqt9/WYaG2J1RZtSyHnoMUn5XuWsNf9avWBT+sg8/SNqa98a3ys/xoEWHnquksHAplk/1k0Z/3W78r/8ro0f7D61QIcml647aN5Jtuk66P7h89Z4JkaGrafvIgLNmpgdJwsAKZWa1R64EHMYDBAqVQait+g9+vkxCYA4P0wdCv/U6BKQtBuzhqB83tychLRaNQBkHK5jHK57OqRTCad8NDofmlpCZlMBqVSaeiwRWlfwSRRM5mhrpvczwybbPIdvacpmQC3SPgcQQw/1jgwSbWnZs5S46AjIEtBofuc9ZHMmcBgZWUFjUYDN27cQK1Ww+zsLIDjM4nkqoNbQSyLGgsG4WPslX6/j9dffx13795FNHp84OTy8vKprZ9arebOK9KTWK5sjo6OHG3cv38fExMTuHr1Kj7wgQ+g0Wig0Wg4g9zd3V1Eo1GkUik8/PDDSCQSuHDhgmu3dIknMNvY2ECpVEK1WnWec9xK44m8OiaLZpSSviRjm5qacmAtl8s5I2sCGxkz59y5cy4OT6fTcUeQ0PiX85jeXnJ8z5J8z48CNXqL2BKcfEcumCwQZfEF3Z86Ty2kLPAm7/ve538LqIQ97yvD137fPd/7Vj/pPvP99/WH1a/AaY2HBWQ0H9FlS3qwaMLSAoX1GevlK9eyOZF0FEZjUkZpECx3EMLkHpO0h5HlkmdJzS3ntzTyZR/RiD+VSv0DiLFSrVY7FWiKwsfanuEzkpi0TYzWzGhGxf964uizRAaD49V2pVJx6n3aDNA2IggCpNNpRwRsy+TkJJaXl3H+/HmHbqlhYEwUCVaCIDhlE6PbAOAUSJJ9Jp+T1zUx63fZFgIxnXSemhHIwGK+cZPh6qW3Ge9xu0sb+HI8+e7h4SGq1SqCIHDbT9RS5PN5pxWRfcKxjUQi2NvbQ7fbxcLCghPA8Xgcs7OzKBQKKBQKiMfj2NraQrfbxcHBAQaDAVZWVtxJ23J8CBC1/QlBEA1wAQx58SwuLg55FQGnQwHwO5FIYH5+HrVazWlv6IZOIJ5KpYaCuUlG6WPw8n4ikXArLtotHR4eIhaL4ZFHHnEan06n47Qx169fx71791zkXx6bQE0Ro5f6hJpeOVrPhamvNZ3wmk8AaxrV7/hADHD6zDJL22AJO9nHlkeLft/a7vSBF9mH+rr1vq5z2PNh/RhWRhhI8dXBes43HrI/rWTVw7LB0dctrYmvbF1HmeRiWcodmbR80rJPgxW9uJUy0gJLfNaK3cTftJOkTCK/okmB1PxRU/N/zbED/7clMkBN0NLIiJ0vBbkU/hLIAKdP9NR2JxLtSpQrhasEPUSrdPdtNpvuWW34xGvT09NDB/a1Wi0HEnjWDoW3XiEwWeiabbJW0zrxXdqCaAbDviSI6Xa7uHz5svNQ8eUt92bldp3sE4nkAZiTjgKcE0oGHmQ/cezq9Tru3Lkz9A4PHeQELJVKiEQizsakUCi4FUcmk0G328XW1pbzOKD24/DwELOzs4jFYm5sO52OE+LUzMgTryWjkh+2jf1Pm5YvfelLiEajyOVyeOKJJ1xwOT2OFmDM5/N47rnnXNA8Hn9weHiIZrPpwDKf1bFZNBPT5XGbqF6vO5Dd6/WwubmJWCyGxx57bMjVksCYgJ4ASALQIDg2aLdc/nW9rGsSyFlM2HfN+q/zt+aN5g+8prccfIDC6lvNt/T7lnYoDNDJ/Hlf2gD5+mXU/zBAYAl/fvRWhX5W942ub1hddT3C6uUDHaP+W2XLNlkBSS3gYeUrj1zRPFsu0nU/yv+W/ad8P6x9nKv8LcdatkMa+VITw2CekmdTI63PSBsnPfAghsSivVWsySz39nlPDrp+l/+l6k9PKsB2s9T1IRKlN4gOsZ1IJJxwpIDjM4xE3O12nTEoV786KJxk3pY2Rv6WGigL7DDRZVcLBhJor9dz2xe0TdLRdq282Q/S5kgyZ9ZR9onMW25dyXEicCgUCu4dbnXINvOQRBnThquF7e1tJJNJdwjk1NSUc/eld1AsFsPKyopzVe50OqjVau58pAsXLriDF6mpAuAA6Kh+p0aOBrHUxlngVNKnvHd0dIS3334bAJwnFI8juHDhwpBWr9/v49KlS277SwaKDBNYBI+Mlry+vu6iFC8uLrptpMPDwyGtIW2UgiAYisrL+uhI2/z2aU8sQTaO4NHP+QCHBilMGqTIVSj7zBJmo4SpFnxh9eY9CzRZ4MPXRl9dfO/6wIbFf+U11lPOR8u+xOK1vj6y8ggbS0vAW+MjDaVlvlouAP6ge7psK4aW7Du5eNNJa1TkIlrPjzB+L781sLG0QRZdyEM7ATgeOzk5ORQElnzz3aQHHsRoAW7tXwInROcLuqMFgKW1kc9RiyDVd5LhyslGQVutVjE3N4d0Ou1ik/R6Pezt7Q0JWyLgo6Mj7O/vo1QqYXd31xmj8r724dcrWQvIWBPXt2rlNX3kgGRYBADc+uD5QBIQWCtJAM7oiwBGnsPDfpDt0lonGYOh2+3izp07QwcqMjJtJpNBLpfD0dGRM2xtNpvI5XJotVpDHm0s7/DwEOl0GsvLy27rhQcmHh0dodvtIhaLIZFIOPf+IAicgdvy8jK63S7eeOMNd6jpYDA4BTpl0mMTiRyfKM1IzgAcsPIlrdHp9/vY39/HwcEB6vU6IpEI5ufn8fzzzzuvPll2JpNBsVh04NJaTeryGB6A2jcasQ8GA3dsxssvv4y1tTXX/lQqhVgs5uaCFB5kngxDoOuohbvVj+Nc8yWfELTy0HYRvvd8YEV/9LthdZPX5HUtIH2/9btasI1TVtgzYX1g5aV5sK8fdH5h4zpq/Hxt03JjFBiSz8kyrC0n+azVf3q7XPaNzEfmrQGVBA8anGmDX1kGr+lFq7Ug5bt0luCHdpvcQmY9ZXyrs6QHHsRQAPI3kzU55dYSn9HfWqsitzk0wmW5BDN8zlqZSeFIgcytGEZl5bPNZhNra2vOnqJcLg8BJJ7GnEwmTx1JL5NlW8LnrNU82yV/B0EwZOuj86JRLwOYxWIx504uo7tSeyJXX5wojGlCN2oJjmSQNY6J9GghAGm1WlhbW0MQBFheXnaHfh4eHmJqagrZbNYBkPn5eeTzeUxOTjrtigZ+jE5JQ7VWq+UOQut2u+4spEgkgnQ6jd3dXZRKJRSLRRcA8MaNG+h2u84TTdOf1f/yntRwAHD9a71jrbCC4Njzqt1uO+CVz+dx5coVZLNZU1hFo1GcO3fOqYYZX0bTv6RvnlouI3nK9sn5QYZ4dHSEbDaLCxcu4Pr1686VWtpIVSoVk8mPAiOjNFWj0jigxHpH/7cERdg1+Z4GaJZxrl4987dPSGnhrwGYZeenP1b7fNesMbD6keVqLYpFb76tJJ/diu5PLRPGaY/Vdp182g+frJH10eMtAYmUafoda95TBum+0EBG5mPRCz0wwxLvc8udR80wyCu384ETPv0PIMZIckCtPUA5iJogtN2MtRLRk0qDBeZj2YxQOzMYnBj9ttttJyC1Rbcukx4kskyCHHo8aWbAcvmt62xtPfG6b3L6YsQEQeC2N6anp52wo4aCWxW0PyEq5z1qSBgnJQgCHBwcYH19HYPBALOzs5icnHTbENxvjcfjLiaKdDmni7lmVK1Wy4E9hsVn9Ni5uTk8/PDD7mwiGvk+9thjWF5exmAwQKVScdFrZ2dncf/+fZRKJXfERTQaRaFQQD6fd2CJZU1MTGB2dhZ7e3sm7cr+1N90TSaAovZEx3/QNCLT3t4eGo0GJiYmsLi4iGeffRYLCwuntvvk+7lcDk8++aQDPjyxmhGn2Z8EqQRzuVzOGS9LD7FIJIInn3wSV69edePQbDaxtbWF3d1dB+xJwwSvDHSnx9Nixlrt7hNK1ty2hLaVj49HWPnr9625pW1RLAEv3/dtW1ltsMbVJ0B82zi+ZI2B1X6rn3WfA35NWljf+fKw8vfV15eXD7RY1y2bIkmvFjgB7CjAYWNn1ZNjr206NZCReVmLVN03jIDvG0trMcB3OO9lqAYuVuV5emdJDzyI0cZNAE4NJnAyCNJCWgIPiwj5jMUgrW0mfmumZ3kMyHcIBKTB5+TkpNPW8F2ZJ9V2MmnwIvtBCgmrr3Td5TXaLejElX6v10OpVHLh7BmrJJPJYG1tDYPBANeuXcPt27exv7+PaDSKZDKJK1euOFfldruNVqvlTlYeDAZYWFhANpvF3bt3nSZjaWnJHTwotVdBcBLKnwGXCAIJgGgITfBXq9Vw+fJlXL582eXDYG35fN71UzKZRKPRcAc5cjunVqs51+R0Oj0UR4gANRaLIZVKYWNjw6Qtjrl1Bgu9fOTe8jgHqfF+t9vF6uqqo3tqpEgHevxZbhCcxOXpdDooFouYnZ1FEBzbshCoVCoVVCoVFyPm8uXL7lynZrM55OnFlRm9kyqVCm7fvo3Nzc1TzJ51l+7VrJv131pc6OfC7uu8Rwlw2X/8bQGccfKXPMG67tMeSboPE/K+fH0AIQx4hLXF0hKcpZ0+Aa753rgAw0oacI1q6yjwpOtj8U59XT7r26KRtisW7Vr5WrSnA9Jp0CKTlBHkS742y2tSxnAxTk0qg2iyXIKcs6YHHsQA4TYXJAqLIWjwYv3WWhzmIYEJgQqfoT2H1NDIVakmzE6n4/YVtRDR+9u0k5AaINbD2kcNY6gWMrcAmKWJYaJ2ZHd312lHpqam3Nk48XgciUQC09PTWF1ddYKethJ37951Z13RBoMTjgAHALLZLI6OjrC5uYmtrS1Xrgx3TXuanZ0dBy7khGZ7OAaMs9JoNLC2toa5uTl3QKLsF57MzFPFU6mUqy+NmjXQLZVKODg4cCeqc0tIM6SHH34Y8/PzWF9fx+bmpttWAeC8p5rNJjqdDv7+7/8ejz76KB577LFTAtkCAgxCmEqlMDs7iytXruADH/iAixxcLpeHDHjleTCDwXEMmTfeeAOtVgvxeBypVArZbBbpdBr5fN6de/Tmm2/ib//2b5HJZFy04Ewmg8XFRQcWSc+0tZGumexv6VXR6XRcvbTQ0O220iigN84zYcJ71PM+wTbOM7JuFmiyyh2Vb5jg92nkfOBJC8IwoOW77ntO5u/TrvgAmNVO/Y7mcVafMGmhb9XbAhfAcHwaeT+s7lYbpLzRconlSPkEnNaqy2flHNMLGVkvzk1et2KL6fpyW0nObXoPy+3ks6YHHsRYqFgSahgaZrJUbPI5aYOh86W3DJNlBMbnCQikS6l8jvWXofG5ciXSTaVS7jwcrZ6zJqBumyRkawLqa5HI6cMfWdbR0REWFhYwPT2NVqvlth2q1SoqlYo72JBnA0k32s3NTXdNThz2AfuKB2q+733vc657pVIJ+/v7WF9fdyBCe+4QFMoktWpcbSQSCVSrVezt7Q0Z4LL9pCcZJTeTyWBychLT09OnIvgyTU5O4uGHH3Z9KM9nYh2mp6dx5coVpNNpzM3N4caNG7h+/brrk6mpKddH1LxRAyWTRe/UwgRBgOeeew79fh9ra2t47LHHsLS05MaPBzVubGxgfX0dOzs7KJVKLhjjysoKbty4gd3dXXfuETWF58+fx9WrV92YMAovaYbHHqTTaecSTs0UAeDR0ZE7PJKHVnK78KyrNktgyXuW1kkKCC1grP8+Jh4mdEfVTV7X78l7mrdYAlHf95Whnx/nOR+Q0jyXSQtHDSDC2jAKPPjqIb/DwNgoLYmsxzhl6TKtcvW2U1iybNAsYKLpTr4j7QmlvabVBvmujMkln/HZWsk6MfYWeRgBEHnCqHZb6YEHMcBpNzStoaBgI+CQ/7WgB06EvXzXGkDmod8LgmDIzobvT0xMuG2K3d1dx6Tle1NTU7h69SqC4NjTJZ/Pu60KCt6JiQmUy2Wzzvwtv63kAy2ayOX+qEztdhuvvfYagiBALpdDLpfDzMyMAwKdTgeNRgP1et0RNq3WpVDXE0naKcViMZRKJQwGA+dpNDMzg4WFBdRqNezt7aFerwM4Dv5WrVaxurrq2sKJG7YC2NzcxM7ODlZWVpDP54faLid2EATIZDJD50NRc6EZE13+r1y54rzO5BbXI488glgs5qIxA8carYceegjFYhGvv/469vf3h0L5UzOlT33W40k6rVQq2N/fR6FQwEc/+lF88YtfdOck7e7uYmNjA0EQuAB9586dw4svvoh2u439/X3cunUL77zzDtbW1jA5OelOpGb7GBOGqy22nzRED6VKpeLuyf6KxWI4d+4cLl26hFwuh3g8jmaz6SL2MihfGNOUZfqAgyXQrOu+5BOgYULQJ6x9z+v7PgFm8R9L0Fu2M2epi69uvv7Q9dBl+PpCJz3nfCBM8uew9G4Aig9kWfXQdZIf/b4GeT6aBU4DGH1Nlm8tSCVw4D35vq6L7hPa4kkQQp4nNdnyI/n25OSkW3TJOcwQIWdNDzyI0ShUAxC91yfVaD5jXPmbg6cBj3yGNg18lnYLFKC5XA7ZbNZtMci4JpqQKXAZW4MaGWlARoNWmaxJqcGd7C9L3WnlRxCjmQvdvxmBlsSbSqXcad3pdBpLS0tDMUTkfq8sSxs20xOIQf5eeuklTE5OIp/P473vfe/QqiIej2NxcRG9Xs/ZtMg+YJksl+81m028/vrrCILj7Tu6U0ugK9+TWil5XfbtYDDA2toa3nrrLSQSCbe9ks1m0ev13FECjPUj+zUWi2F+fh4LCwvOq4qMgICDnkC+ceXY3rt3D61WC9PT03j88ccxPz+PVquF/f19/Nqv/Zo7WZ0HTy4vL+Ppp5/Ge9/7Xpw7d86Bmvv37+PP/uzP8N/+238bOm+KoGowGDiNoGwL6V96nkgPhcFggMuXL2N+ft69Q1f1fv/4rKhRngxhAMQnpMbNxwde9DUtqOT1UfmFAQfdDikYrTb6wMSoNA6oGCcPiy/qFCa4ZV5nfd4HuMLqaeXp6/9x+0e33fdtlWnVJ6xMKd902waDExdpi6/quvIeF8mS//kMkIHhgH18R3qc0jaGckvK3rOkBx7EaItvObC8BpwMmjSG43tyu4jPWFs90r5FHnRFTw1qLKQLMbccePaOPOUTGHZTowp9f3/fBUwjiGA9AbiD/PSE56rfYqi+1Y3WxOjndCwaJoIsOUHYRkaqpReWPKG5Uqk4Gw89NnqyRaNRJyAZlI59RNsK1pFGo5YwtQAY+4oAbWdnB2tra+7gxnw+P+RdZfURk+6X1dVV5x68s7ODQqGA7/iO73BlE4j4BCNBFT3Y6P30+OOPhwbJ47Vqteo0LdzuWl5ext7eHn7/938fN2/eRL/fd1E1o9Eo3nzzTbzyyiuIRqN4//vfj36/jy9+8Yv44he/iBdffBEf+MAH8IUvfMEBdBpnc1zkGMq2+VbMkUjE2QtpWhwMBi52zKjVo37X9z8sjRKouk0WoAjLOwzQjBK6Og9dFx9o0HUMA1hWPcIEuQWSRoE0X5lW23zJB3DkPR+/s/pLv2/RWthc533Z15YWxbdo8z1jgR49nlIeAacPaZXlkIexjfroGb2wt7Q7vnbIJO0OucXPA1zfDXhheuBBjCRaTYRywusJoFfrVl5y4OXASpRKrYlc7UsbiaOjI2xvbztDUBIJAQDfSaVSqFarLkibpRKWQt8Kxx42CWQ+UnD4GIxui86PIMLqV/YNBRy9VJ566im3PSK9W6rVKhqNhgtfHQSBO+qAdkDsv06ng1deecVFipTP6i0qPWbA8WSioXK/33eRknu9Ht588013dAK1AnKF4gOCkuba7baLPcPnqJ7d3993wlu+o5l3u912h1Cm02m8733vQyQScWcn8b1+vz8EniWYpN3L7u4uPvOZz6Berzu7FwIj9g+3u8rlMv7n//yf2N3dxWAwwJ07d3D37l138rY2HqTbvHUel6QXDVZJVzRc1vcIsOS9MHsCvbr2AUT5jNXvui5W/tb9UdflvTCbBH4szxALNFjAxMfzfHlojaz2tLKe14saa1wsMBEGpDTI0PlqXhjW12Fttp4Jq4P1nHXP4pFh71g2J8DwglprWvS4SMcVbfPi6yO+K7eEWNZgMDhlOqDnU1ibJL+VQUe5uBx33HR64EGMRo5yEurEwbOSnmTMWw+yfJ7qssHgZAuJBwBWKhXU6/UhY1PfBAWODUHpmkpjVmnLIQ1SI5EIKpXKkAeOxZCYP9G4LFfel3XS9/W5SUwyDohOFlPjEQGrq6vOe0keC0CXXBqIUlvxxBNP4NKlS6hUKu5etVp1RsNBcBwxFoADMXrcEokElpaWkEgkcOvWLbc91+12XWh71uPo6Ah7e3u4fv068vk8FhcXnYs5NRDj9CNw4uJIm6Dr16+j3W7jH//jf+wEuPYkiEajuHbtGlZWVrC5uYlMJjMUk4WMQX6Yjo6OnNFsJpNBs9nE5uYm/s//+T9DB00SDDKRpmKxGO7evYt+v+80UbOzs2g2m7hx44ajeWrZ4vG4i+Wima3uD80E4/H4kNebnCMMJmj1qfXfN9/lfS1wrTnpE64+Yex7zwJRFh8JE+RhmgZ9TddDbyH4yg3jbaP62+KX+p6vH3zgyso3TBjrb1/fWG07C6iy+lFfHwwGLvSDr45WP1jgR2+7W7QlF4xMlh2ozFO3QS/GCT50HfU7uh1aRnFLigb68miYd5MeeBAjB0AmMlYpaDUj0oDHR8jyNwmMAIZq/8HgxAiWsU54X9ZNE6asSyaTQavVGnJLszQylqDQ9/QE9U0ECY7khOJ9fW4Sk3W6sOxjfY0eWXfv3nUGXnKrieHuFxYWEAQB9vb28PLLLyObzTobm4WFBUQiEQc+yuUySqWS02xIwcy2zc7O4vLly0gmkzg4OEAul3MxXLhd1W630el00G63EYvFsLOzg8PDQzzzzDNubAgEfQKT1zl+8hkeAEmXYcabsRhYEATOFuaVV15BPp/HzMwMjo6OsLW1hXv37mEwGDj7mJmZGZRKJfR6PRfWn3RADcm5c+ecTcz8/Dzu378/tJXETywWc8catNttlEol1Go1HBwcIAiOtYUShMhowuwDSXOWcOR85Ancsu94j4ek+vpY/x4FYKz/GtSMk/TYW8JPl+ET2j5Q4GP0su+s/NiWceqkr4c9H/auvK/70afR0XzJp7kIE/xh748CI+NoSvRYWEDJ6pNReetnw8Zb5qf70bcQ94FKCYh884fP+eLD6Gt67pHfMh/p/UkvyH8AMZ4kGYUGM7rTJOqUk8qagFY+csB6vR7S6TRmZ2cRjUaxtbWFZrPpDvmT9jFWmRxs1r3Vag1pJmhzw4i+9L+n8NVxaEZ9dH/JPpF9oIncF1xNukbzfcsLiNeJyuWZPNx62dracoAvnU7j6aefxtHREe7du+f6gucq5fN5FItFpyW5cOECIpHjuCLcXmL9L1y4gMceewyRSATvvPOOcwFmf3Kbqlwuu0MWOS69Xg/7+/sOZE1NTXmZh6StjY0NZ3TN/pmcnHS2PHNzcw50yXflNw1bB4MBstksBoNjNW+tVsPs7Cyy2SympqbwzjvvYHt7G5FIBNlsFo8++ihqtZrbMpLG3+fPn8df/uVfotPpOJqV6mjWt1qtukjD9XodBwcHuHbtGh5//HG0Wi0cHBzgxo0bzu1fbn1aNK5BOD+M8msJDNqO+ZiupGXJnM/KJGWZ1nwPE/xh933PjBLYowS39XzYHPcBFCv/ccu27umtCauNo67p+octwoDxbHwsmrHy0Pn56qm1ML40arveAkX6ut4+pTwAcGphbkXfls/JfpD5ciEuNSm67jKFyUUZ6FMGbZVetf9wdpInya0kLZwtoEKmrV3O5MDqJImXK/JcLoeFhQUAx6HdpXGvdE/TdZHaIQp4bqfkcjl3No98NxI5PrRvf38f+/v77mwKPQm0lkbXge3XCFyWo/vFFyOGWg++JwlWC2WCGG5BSQ3TQw89hFwuh3K5jM3NTRweHqLf7w95vNC2plarOaHNk6Wz2SyKxaLzAALgbGsWFhbcWU6tVstNJBr2MojdxMQEtre3ncaE8QzW1tYQjUadG7TuP514pIGmw0Qi4YyRuU1GTx+LaTACsgSwfJfjTyBQLBaxuLiIYrGI1dVVzM7O4ty5cy5Oz+HhIUqlEtbX17GwsIBOp4Otra1T8Xl46CMBUDqdHvKQm5ycdODpxo0bji60JsZiyhbASCaTXndLHlug85D9P0r7ogXUOABH1t8SZrou+rrvv5V/WH4y+YSrxV9GaWI0T9CCT78f1o6z/A973wJf42jWwoShL/9R12QfyGthND1Kk+ejJx/wZN4++tW0rA15ZX5WPlIDpLVBlsbGarfVRqmF0Vvuko+MMw91euBBDDsOOAEI8j/tHfRqkc/I1aicIBwIvksNAjUsuVzO2WdwkHTQMx0Ez1L3MX/aw8TjcRe1lXWS7t16BaK9razVRViyjF81iBmlidHv60SNTrvdRjQaxSOPPIJGo4F79+4hm81ifn7exT/hOT9hgfwIchh5dnNzE8vLy3jhhRdwdHTktkBoi1KpVFz9pDEst0/y+Tza7bYDo8DJGVc8bdnqa9m+TqeDGzduDAl10hG3ZwhE+v0+Xn/9dWQyGVy5cuUUUGR+7XYbt27dwvLyMqampjA1NYXl5WWXLzU8lUoFrVYLm5ubuHbtGmZmZjA3N+eMnVn21NSUO9GaHmKs0zPPPOO2cWq1GprNpqPrV199FclkEqlUyrn3MxAfNTGaQVlMVwJl2g5YNMM8dbKYtPV/FMiw8joLc5VlacHnKzNMsPoEmBZ4Pns/SzBaAthXX0vAWituy92Wddb/LSFtaY19/aH5nDXvRoGVMM2Jj1f5nvMJYF+5vnbp37J9elGt85GLTLlAlPNPgxPdd1JrIxfT3DLnNUtrosdb5ysXtLJcyk5LUz9OeuBBDJM1kS1LbTIDvQLWKjqpmZGeONFoFJ1OB9vb2+j3+0MB9GgzwTy1ViIMYPT7fTQaDReRVRp36Wd9E0pPdAuFy/8yf/lfThorLspgMKyJkfd9ZXM7hFsf8Xgcq6uriMViaDQauH37NkqlkjNA1SHnZbKYWSKRcEEAE4kEVlZW3HhIUFqpVLC+vo5YLIZHH30Us7OzyOVybguHWh/gGKitrq4iHo+7/Kw0GAywt7eHra2tU/VlH3JPOJFIOCB148YNlEolPPPMM844GTgGvw8//DCq1Sq2t7dx9+5dzM7OYn5+fihODM8j2trawtramvPm2tzcxM2bN509US6Xc2CMGhcaUlcqFQdIc7kc3ve+9+Ho6AitVgvlctl96vX6EMhLJpPo9XqnvJY0nVlCJBKJDBlAyncHg4Hb1pN5yPv6Ou/pMbH++4SSD3RZwth61weCRoEb3/NSAOkyfGBjVH0sgGNtQ4yqv2+17itbAxeLJ/r62crDBxqtPHxpFICyBLjvHpOsl66L1npY9ZFts2hb0qUGKRLcyL5jXbV9pb4mF9NyAc88KB8skCe3ofSWuzXvzpq+LUCMBC0WwwSG7Q3Y6TIsMwef92l4WavVhlx5+Yx0cSYRMAXBSTRg3teDKZ/ltzQIle8zb3p00N2Y71lErj10fGVa/STva2t19qH2btHPaGZF8NfpdPDaa6+5NlYqFbTbbXduEE+BliDJN/HlPW5XfeMb30Cv13MHTM7MzLitp8Hg+LgD1qNarSIIjo1xGZOFQlpa2FMDoYEa63B0dIT79++7MZN0yDwODg4AnBi0vuc978H9+/fxjW98wx2UJscklUrhqaeewp07d9y5Sp1OB0tLS4jFYi7KbbVaRT6fH9qCOjw8xPb2tjs5m9qgdDrtAi9ms1nMzs5iZWUF5XIZX/nKV1xwPp7GPTs76yIw05iaLvGFQsHZaMmkha0cO/YLz9eSDFfSsAYxMm/NqC1hKp+TzF0LRTmG+t3/N8knyMOAlp6r44AdC/D4+l/e8wEAWW4Y+Br1bNjzEjiNAhzjXgt71gJG4wKksPZZ1y1+a42F1d9W3j4bFABDHpVSw6Wv6e0mC7RKYMbI6HJBrnc6tEZQyk9tHzUYDIYOM/6HAyCNpAWyFuA+2xcLTcfjcecmx4i03AKhIJaCCsDQAYTWhJECTbuhyudYP2piGLFUanNisRji8bgzHvb1h8W0ZR/JftL9JoUEbU90IoiRKFyicd0fBAQEX/LIhDfeeAPnz5/H888/j9dff93VQZ41JPtUJtYxCAIHAs6dO+fc3Km1kOcN8YwexkahLYwsj/VnsEGpNdB9Oxgcx2IhiOEzHFcZebjX6+HWrVuo1+vI5XJDfWjlnc/n8dRTT2F5eRlvvPGGO2bh/PnzyGQyaDQaKJVKzh260+m4KMoTExO4du0aBoMBSqUSDg8PsbOzg52dHVevTCaD559/Hp1OB5VKBb1eD1tbW47WGacml8u5CMzz8/M4d+4cotGoiz6thaemMz1uMkaMJdjpXu0DFOMIWv1fC3Ne04BIXtcaW5mXBQrGrRev+RYZZxF4uo1h4Ml3Xz931rpYACqs7aOuW0AirF4Wr+N1mUfYgmhUvcLus86WLLLeseqhr0leIOeC5K2UbdbiXTp9+EC8fIfPMrzB5OSk6ZUqn5X5yHuyDH1+U5hzhC898CBGG7nKgQaGJ4Xeu6dwnZycRCaTwcTEhFOjV6tVZwMjgYpe2TE/aiykh4xWx8mypfBiPflsNpt15UtBTQ0Ro8HK9smkiTbsv/xoIpPRgmU5NGLW21GyfNkmbhHpyK78xONxZDIZ5+bLySTzCEuRyPGBhIlEAg899NCQ6y7zbzQa6Ha7LsT+0dERstms27biWUBy/AA4GxLWT7et1Wrh7bffHrIRYr8cHR25s4C47bK6uupcodnX1WrVGQ9rRjYxMYH5+Xmk02m89dZbuH//Pu7evYter4epqSm8/vrrKBaLmJmZQa/Xw9zcHEqlEmKxGJaXl5FOp9Hr9bC5uYmXXnrJnV9F2o5EIu5ICMkgGUumUqlgY2NjyJiap1gzsnGtVjsV9E7TpWwXwbhm0qQtRvm0+sMnbN9NCgP5Vv21MLRAkk5hIEQLJUu4+UDDqLIlP9Rgycc7xm2Tr52j6nPWexZQHFWHUeWE0c6oMfTdH0UPGpRRFlnPa+0Lr8lntAEu6YjxWOS1wWAwZAdj0YPOu9froV6vD3kWSRlk0aLV7752nDU98CDGB1qkwNaIPQiOY3FkMhnnJdHpdHBwcODC/UciEec9IsER86NA08cTWIgaOEGimoBINKxXp9NBJpNxLtUUZACcUOR/ElUYiNGrAuu+D6kT5On3I5EIVlZWsL+/j2az6bbWfMbLrK8EJpLo6Q3U6XSQTqeHQIwcXz1xWZdUKoV0Oo0gOAlGJ9sq3fui0SgWFhaGVi9S68b6ckxoU0Kjbd1Ha2trQ4H3gOMzgDKZDMrlMoLgeEuSgpn9JDV5UrMm2yiZbiqVwjPPPIMrV65ga2sLGxsb2N/fR6vVwurqKvb29nD58mXkcjm0Wi2nAWo0Gmg0Grh48SJu376Nu3fvOgBBcEkQI8vTDJsMstPpoFQqYXNzE4899hg++MEPDp1gzu0mxsthWyVj5SpPl0P6lzElfEDoLMlqlxTy8jn9rA+gh5VltcsCKfqaD/iFCWdLEGrBYoEjJr1il20cBQTOAkysevv60uoX2S7reauPfKBiVH7jPqPLD8uDSS8kfXQInBjX6rmgDXL1u7wmj6thWXq7R9Zf06kMpMnypCeqBlByK4vPp1KpoVPppRnCuOnbAsQAw1ba8p78T2+UVCrlznaoVqsu7D0FJw0mdaIRrzSWIuABhg2JNcHxfdZT1pmESk0AABccjvXp94+DpVFjpNvGsiRxSSasGY8GQBagoZCT77FuDz/8sCNMerRQg0Vgw3bK83boQi77pVwuY2dnB51OB/F4fGhFISeVZgrxeBwXLlzAo48+OnQCtRz/weA4mnI6nXbuyQSfHKdKpeJsn2RMnnQ6jYcffhi5XO6U6yDrsLCwgMnJSRd4r1qtIp1Ou33gbreL69evY39/H4PBYEirx7bcvXsXS0tL5haLBACRyMlhosvLy3j77bfRaDRQq9Ucw+BWH4MUVqtVRKPRIRdrbgXRPoegQfevj7Gy/Yyd0263XbBCuVVHYMOgeTxaQrtXy3IJfvjfEhDvBsz4AJolRPR7lpDxgQ0LMIwj7JkkrfvKCXvXV46vLoPBSeTxs9TZ6jMtrCWt6Hd97Rk1Fvq9UXlY7RrVx+MAMOv5cZKPpmW+kjdJ+ca60l5FewHxOh1ReByOPAxSb3mHgTtJT7JufJ/1Ij+nGYaUQdTiyIXqxsbG2P0FfBuAGOD0SkeqvrjVkEqlnAcIDynU5/VoAACcJmq52tcomQNLsMN3CC74O2wSktASiYQLeCZBTD6fd8hWJtlu2S+6n3y/2Vca9PmszXu9Hu7cuYNEIoFcLoelpSWcP3/eeazIlTltUp588klnHEqBX6/Xcf/+fWdTwpgqltuurNvExAQuXbqEa9euOZBEy3qtbo1Go+4UaboVA3CCloKTti/RaBQXL17E3NwcpqennbCX5R8eHmJvbw+ZTAaFQsGdxlytVnHnzh236uh0OshmsygUCkgmk5iYmHBnINXrdXeG1Jtvvoknn3xyaJtF9rfsh0gkgnQ67TQzt27dws7OjjuQkdq8SCSCpaUl3L9/322XnT9/fggwAnCaIEt4+IQscByJuF6v46WXXkIQHNseZTIZB7Ty+TxmZ2edQOPY+46yGAyOt/U4PhYg0FoSyXDH0SRY7ZDPjiNALS2BfjdMGMtnpDCy6qjLt/77hL9P80OXfyYt6MO2uEYJbKufNC8NE5I6L6t/R/XrOHXSDh86Sf6h8/E9y99h8ygM+AFwi1qp8ZDbQuTVGuTIepAHWq7NfMbatvLVWbdRjyf5qtSk6xhYzOMfbGLGSER9k5OTmJqaQiKRQBAEbruoXq87A1MSCzUO1uSSlt4yDoxUnUnCkACKgEQaCEpVoDWRWCYFM8ul1qPX6zlthSZiPZF8RCif8WligOETrGX+wPHW1tramgvVr71f8vk85ufnAQDb29t47bXXnIHo3NwclpeXMRgMhlbslUoFqVRq6IRqSxsTjUZx7tw5TE5O4t69e5iYmMDly5fdOPX7/SGvKuAYmExPTzvPp4ODg6FtINqk0ANsYWEBy8vL5rELnU4HN2/exPr6OgaDgfNgymaz7uwiGTqfY00PqWq1imw2i8uXLyObzbpAfvv7+5idncXExMQQINM0KduUz+fxzDPPoNFoIBqNuq09ahzX19dRLpcd3fV6PRQKBezs7Ditic+dXdKqTjL+DbU7PNNKCstkMunGPZ/PY2VlZciWSqdareY9UJLt94EU2QZpRO/Lx9c2nSwwpfP2gRarjpYg00BMXgsDTBaYkR/rGXnauxbwo9popbB6jnrXl59so2ybvH+WMRxn/JiotdBpFKDTQEHWOUzzoX+T38igqVpeaA05ZZLm3e12G91u19nmSe2/LFvSnwVipUxhObTZ5PVWq4Vut+vsEycnJ125GjyeJX1bgBgaoBK4cIXZ6XRweHjoVtpc4bPjaTcAnKg/Jdjg4BLVUjDquBjACTHJrSG5V8gy9eSwVt3dbteBLxIvDScJcORKygdm+FtPLus5AKeEi6WJYSLQoPEoD7ykEGOguGvXrqHT6WB9fR3r6+un3H0JemZmZtzKY3Nz0wRgsm5bW1solUp49NFHsbi46CLzyvGXfcIxmJ6eRr/fd+AklUphbW0NhUJhqD085BA4OfuIQfNu376NVCqF9773vTg8PMT+/j729vaws7PjYrGw32goznFkGwjc0uk0lpaWMDs762xFtre38fbbb+Ohhx5yoEYDTM30aBPU6/Xw2GOPYWFhAbu7u9jd3XXtlyDvoYcecm7m1rkmo4QXt92oLbx69SqWlpYckLlz5w5qtRqq1arziFpaWsILL7xwKi9Jl7VabUhb6QMjvlXyuKv0UeDGumYBqLB+GnXPBxrCBLYvb/mcD5zwuuVma73rq/uoe2EalrBrUpiGtVUCgnEA5rjJAonj5Om7bi0m9TuaN0t611oZ+a7uJy1bmD8XRNqUgQsY3Z/yfb3AtcrU73BRxMWpPCzWd4TNqPTAg5hcLueEDtEgDR7pRUOCGAxOjFX1KleCEPnfssYG4AhAurlRiFCrY60o5TUf8UjPkX6/PxRdlQCnXq+7PPieJDqZn1WG5ZanhaS0C9ITkTYv09PTOHfuHIDhWCKbm5tOwMkjBAaD4z3ber3ujhCYmJhAMpl02y79ft+5EEvgyTpwFZLP59FoNLC2toZHHnnErVD0RJHqV9JAsVhEoVBAEASYm5sbWqmwH+LxuANht2/fxuXLl/FXf/VXePvtt/Hcc89heXkZS0tL2NzcdHFg9vf3XcA97aXEazKYVK1Ww9tvv+00iNls1oHUV199FalUym2Fzs7Ourbs7u6i2+06jUs+n3ceVNeuXcP29ja2traGxo/9w36Mx+O4ffv2qROjZbI0A3yXB0AOBgO3jZTJZDAzM4ONjQ23TUV6Iyi2aJb/q9XqqbLHWQFb4MIShr48rRQmtMI0JKNAQBhAseakr17j1j9M2IflMwooyTqeBfiQhi2hGFYHn/bJ+rbqGdY2/U5Ye335WO3w5WGVEwaQ9XUJenQeUmaR38i4Y755EQYMff99WlEZioR8wHJCGSc98CAmnU6j0+m483EkcNGDrt2lmSTq5bNSdScP3pJgRaJmTSBykDXDY/6WXYC+JjUAg8GxKlhqaXQ7wpi9lSTh6jylezXbwmdpOHbz5k0Xq2RhYQHT09MuKizPK5KGmrpu/f5xlEiCz9XVVTz99NP4R//oH7mTqrnVVKvVhgDRzs4O9vb28OijjzowRPWp3JO1GKEc32QyOdT2IAiQzWbxwQ9+EFNTU/ijP/ojdy7R7u6u20IrlUq4ffs21tfXHahkxF/pIRSLxRwApXsxQTTLo9Zue3sb+Xwe73nPexCNRl0smFKp5A4ZZRly3IrFIr7jO74DyWQSW1tb2NvbOwU8rPGdmJhw4yNp0mJmcvzZv/V6HUEQuCMQ3n77bUSjUQcQG42G276g4bLMS+ZP106L2Y8rhC3wYq0ix0lhK+mweowDMsapj/X+WcCCVV6YUNVjIoXsuxE+vnpKTZsub9w8rH4Y1d/j/A4bb9k/YWX5NEkW8JV9GwYMpBeZzFfPD2vsaPogt9k18JGgR16Tc8DXDs07+a3rrO1Bx01nBjF//dd/jf/8n/8zXn75ZWxubuJ//a//he/7vu9z9weDAX7hF34Bv/d7v4dSqYQPfvCD+O3f/m1cvXrVPXNwcIAf//Efx5/8yZ8gEongB37gB/Cbv/mbSKfT7pnXX38dP/ZjP4avfvWrmJ2dxY//+I/jp37qp87cwM3NzSFXNAk0iEL1YBARSm8iOUhyD08Sgn5O2rro1T6flXlZWpgw1Mu8G42G04rQ4pzlaaHD9zVhyw/fk3ujWlgR9FnECcC5z9JAl6dMM+gcDy+k9XrY5NV9QJA2GAywsLCAlZUV9Pt9d9I0hTrtJ6SBarPZdIc61ut1lEolBEGAmZkZZ9ity5ZjCADT09P48Ic/7PaU5+bmsLGxga2tLezu7qLf7+Pu3bvY29tzrtmS9pgvtWcEnoPBYAjMSK1gv39y7ESz2cSbb77pzkC6cuWKW001m02Uy2UcHh46bzB5ztTOzo47RFPShtQwSaA8NTWFCxcu4M6dO6EaD82sOM4EqvSmu3v3LtLpNF544QUMBscnUt+4cQP37t1zNkE+Js8YMRaNjErvRmNhMWXf+z7tke8dXx66H339Yc2XsLyt+a/LPQtIGDf5wJCun/WO1ljo5338YRSACAMDYUmXZ7XDB3rGXVT6xtGnHZF10C7XrKv+WHKF8s4yhdB1CIJhja00Ipb1knJWt0HWXQKZ/7+AmHq9jqeffho//MM/jH/xL/7Fqfu/9mu/hs985jP4gz/4A1y6dAk/93M/h4985CN48803nZD4wR/8QWxubuILX/gCut0uPv7xj+MTn/gEPve5zwE4Pr/mn/7Tf4oPf/jD+J3f+R184xvfwA//8A8jn8/jE5/4xJnqS6NEmWgjIztSAhC53WMxC62pkUKOQITbFhJwWPmRcKgB0mDAshKXYIdGvRJ4yLKtiWEBF1kn2U5N6DJZRw7wGQITfo6OjrCzswPg2Iak2Wy6MSDw0t5c1uQNguPjHfb39/Hyyy+7QITcakqn087jhdtX3JpifBbGeaGnDkHPI488MgTMZPkEhgsLC/jIRz6CVCrlznriVhHdhCcmJrC+vj6kJpUu5WwHJzr7hydXUzPD/WLuW5N5MKbM2toa7t27h16vh5mZGRSLRWcwvbKygsHgWDPHYHNsJ+uk6YzXJZMaDAZOc3bv3j1zy4dJevHxmIZms+naQS1Yu93G7u6uO/WaHlfyfChNd4PB8bajPEDTelYnn4AmcBv17rc6hdVn3He1MAgDfmHlWqDA8o60QJBVjuYpZwFzo5IPMMpyrTaN8z5ga1HGue8DMKPa6/s/DrDSc86yo5T1kfe17ZsPLGtZ5FsAW/WS18MM6OUCXtb9rOnMIOZ7vud78D3f8z3mvcFggE9/+tP42Z/9WXz0ox8FAPzhH/4h5ufn8cd//Mf42Mc+huvXr+Pzn/88vvrVr+K9730vAOC3fuu38L3f+7349V//dSwtLeGzn/0sOp0O/ut//a+Ix+N44okn8Oqrr+I3fuM3zgxiyKx8oEATj/TB9xG1ft8SAJZhlNRqsF7y/yhGxCRRbi6Xc+HdWaaMTTPO5JGARf7XmiWN6PXpyjJPGaGWLsi3bt3CnTt3MDk5iW63i+npaQA4tfURNsEZS4YnGTNC8ebmpqtTKpVCLpfD9PQ0lpeXUa1W8dWvftXFg5Hn/ywtLTlNju4zrY1ZXFzEhz/8YUSjUfzu7/4uXn/9dXz84x93Gh+Ow9LSEiYmJlyIf4sZSjdH0oU87ZweXfQ045jQjojvHhwcYDAY4PDw0LQfKhaLmJubc/FYNG1LrSPLoOaHMXP6/T7m5+fdGVDSoNu3suP2UavVQhAEKJfLDoiVy2W89NJLiMfjDtDyvCSr/5no3WDNB99c0aDYujcugOCzFrAOSxag8OXve88nJEfV3eJd4wC3UULZKtsCST7tj/XOu2ljWD0lT7HKtb6B4bEYBSrOAl60NiasrWGCXS4y9DlI0jiXz0p+TucPaQys62WZQWhtidV/Ft/UdDfq3jhzUadvqU3MnTt3sLW1hQ9/+MPuWi6XwwsvvIAvf/nL+NjHPoYvf/nLyOfzDsAAcKr5l156Cd///d+PL3/5y/iO7/iOISH5kY98BL/6q7+Kw8NDFAqFU2W32+0hj5xKpXLcQKFxkUACCEfr+qOTtAaXefsGndsrfJ4f7VrNdy1CkoNOYZPJZFAqlVzsDGmx7ksSofvaqcGNXqHJIwBkm2R7ef3y5cuYm5vDyy+/jHq97iIN6yMEfOMiCZ/lUmvx7LPPYn9/H6+88ooDoP1+H+vr69je3sbMzIzzPut0OqjX684bhsENM5kM8vk8isUi0un0kL0Mt6kuX76M7/iO7wAA/O7v/i7+/M//HJ1OB5/73OeclqHdbrtAePF4HJcuXUKz2cTe3p6z/aBmkNogi7HSbovbTDSSZfReghUJejhGnU4HExMTODg4wM7OjnNV53lJMh6RjlUkXSMlrXIeXrx4EQAckJFbsaRbviNj0rTbbfzd3/0drly5gve///14++23sbGx4QBMo9EYCnfgSzTk1gzTJzh9NCUXEDIPa67pPKzy5PVxFiK+Oup3fBoD3ztWWb7+kbwpDLiEgQzfosPX1jBQ5gM4Ye21+pq8Maw+o0CPvuZ7FvDbt8j/rI8MDcGkx2JU2ZyzcrEtQQxwInt0vnK8eE3OA+5QyPOM9LzwgXlLScB3tcZF5yXr9m7StxTE0NuB8T+Y5ufn3b2trS3Mzc0NV2JiAsViceiZS5cuncqD9ywQ8yu/8iv4xV/8RW/d6IYcxqh8BmUWaiSalYHt+J7UwMhyNAFI4KNBjIVSGe2QaJtbDxrdcgUt26dX4No+R7ZdGifzng/EWAIAODlCIAgCt40gnyOIoOZoFAJn3rSjoT0JaSaZTKJer2NmZgbPPvss3njjDRwcHLiDJfU4M492u41SqYT19XUHai5fvoyLFy/i6OgIGxsbWFpawosvvohoNIrPfvaz+PM//3N3/MQrr7yCy5cvo9frIZ1Ou+09RvhNJBK4dOkSer0e9vf3nXZGau984JpMr9VqOe8l9jlpbGFhAYPBwBn0AsATTzyB2dlZbG1tIZ1Oo1arDY2rpUaWEXIl7dDwmC7xg8EA6+vrDrBZTJi0Qa85CdYymYzL8z3veQ8mJibw5S9/2ZURJrToXq1pwidIfUJQr/z06li2x9LO+bQ6o1bZVv3CnrP4jpW/BmVWO/Q9q86+Olr5amEYVv9xgYlVJt/X7fHxcN3msGdH1SFMPozTfmozuXBpt9tj1SUMzDCSvOTxVp2kt5HMg/RLrYwcSz5naX589606jLOIkM9aMvcs6YHxTvqZn/kZ/ORP/qT7X6lUnF2ARJ0aFXJQpAbDMnCSDFODAZmnBAbSWFILeolSJaiQ96UxsMyD20ZTU1NuO0K2h1oZizgsEKOBjL4m+4Hf8sgBJllGOp1GoVBAs9nE2toa9vf3EY1G3fYIABdZWNbFt7piufTcoWblm9/8Jq5du4YXX3wRX/rSl9BqtXB4eOg8hGh8K5OvLMYK2t7exqVLlxCPx/H444/jQx/6EADgr/7qr/Cnf/qnODg4cKeXJ5NJxzQWFxexurrqwvxT0NMg9+LFi7h8+TJu3Ljh6ijHQK/U2J8EMtSyMK5CMpnEe97zHiSTSRd3oVKpODuhfr/vDI1pc6UFkHSvpC0OtyYnJiacVmp6ehqbm5t46623hs5WYowJzXyi0ajzTGJ5d+7cwcHBgdOSMmAftwW1Zkf3A7fSxk1hwtIHbvTvcYSuTlow6Hu+d3R5mmdY988ioDUYsOqq83k3q2Mrr3HHjZoFyxbQKmdcwRfW7+PUMwxY6LEj76SdG+eK1hDp+mvQqmkoCAIXLoRekExSbsk8qXGlTSbzlAtleVivpZH1AXmprdX9YLVTyzer/95N+paCmIWFBQDHEVgXFxfd9e3tbTzzzDPuGRp4MnFvn+8vLCxge3t76Bn+5zM6JRIJ5xGhk2TamnCkoRMZKglBq5wtNaUkGjkBpcDQAAUYHjC9DSWfI2Nn3TghBoPj0M2NRmMIdWsDWV2WBDCSWWgAo/tP95UEMbK+jEZ7/vx5nDt3Ds1m03nIsG2MVptOp4dsQqyVtTa+pNFyp9PB9PQ0Ll++jHw+76JA7u3t4W/+5m8AwNEgI9XKbTrZ98CwuzjP8bh58yauXbuGW7duIZfL4X//7//tNDCDwcBFnGy1Wuj1eshkMm6LR27bcBupVCphamoK2WwWc3NzaDQa2NnZQbVaRafTGbKR4bua/hgBl+CBW2apVAqLi4suLk25XHYGx3osWQ7pitd8ZSYSCdy6dQtvvvkmWq0Wpqenkc1msbCwgCAIHHiqVCpoNBpIJBJOu/q+973PHfpYq9VQqVTc1irtlNrtNqampkKF1mBwrIkZxehG5SF/W3PSWoXq31qwWPmOk8KA1LsBT1YdfEAqrM7WoiesDH1d3rf6WT+ry7UAHTCePY8FCKz7VlnymVF0EZaYF2NYcSGgj4IJa5cGI8y30WhgZmYGhUIBu7u7btEjDxqWfS75p9xu0jxfL8Ilz7fqqsfUaoMPmPN5tlP+DzOD8KVvKYi5dOkSFhYW8Bd/8RcOtFQqFbz00kv40R/9UQDAiy++iFKphJdffhnPPfccAOAv//Iv0e/3XbTOF198Ef/xP/5H52oKAF/4whfwyCOPmFtJYYlqN+D0ykZuzUihw8FnkqBA2kpIcGTlrwdM3iMyt4BRGDFxVc1VLD1VJNFKRC3bIPc49eTVjJllMl+CFv1fE3S1WsXXv/51TE5OunDyhUIBk5OTDt3LdnBSVioVJ8B9B8MBGHIln5qawsLCgrPZeOGFF3D9+nW888477tl2u41r164hEongxo0bbqx1/7JPIpHj08n7/WOX5m9+85v44he/iHg87iL+UlNBwVutVlEqldxhl5p+pDEdhT2Nb69cueKi+jabTXfkBevEbTiupNjvtO959dVXnVYml8thfn7eMTi+x20a6SHHj9xb17TPc5voql2r1Vy0TWqbFhcXnZs7x6VSqWBtbQ3ZbBaZTAbT09OO3glqeT5WrVZzW1VhgsIXI8aaN9ZvTdu+ZAmwUffHFXK++ll5+YS+xXN89RwXiGhB5mu79b4Ev2H97BNwo67J66OAl/wdJlBZv1EgzGqL1a+azwMnZ9wBJwsxOa9Htdt3rV6vIx6PO49MGutLutHeqayb9kgETg4dlvzKoocwWSb7yQJE2sXatzDwKQpGpTODmFqthps3b7r/d+7cwauvvopisYjz58/jJ37iJ/BLv/RLuHr1qnOxXlpacrFkHnvsMXz3d383fuRHfgS/8zu/g263i09+8pP42Mc+hqWlJQDAv/yX/xK/+Iu/iH/9r/81/sN/+A/45je/id/8zd/E//P//D9nbqBvYnDfnqtmYBjw8F0LkJDZS82NLk8TkZWnft73nM6TBNdqtYZcyDlxtJW6JlAJwKwySfAUmNwGkrEBdIhoXm+32yiXy9jf38fGxoY77oHGs4VCwUWYpYYklUq59jG4GgWmPCNJAoxut4tyuYyXX34ZTz31FNLpNJLJpDtEkaCNxxw88sgjiMfjePPNNx0IsTQ/wMk2FyMHl8tlzMzMOBueTCaDeDzu+pvGtfv7+462JECWNkbsY4IZnh+0srKCZrOJg4MDp7kKgmAodk2/33dHZ+zs7LhrBFO7u7uo1+u4evXq0EqL7+vxJc1oBnt0dOSCCDJuDYNFBsHxNtnOzg729/dx69Ytdy5UsVh023Avv/wyAJw6QoJeY9SSMfaL1qJq2uRzYSs+iznq32GLDF/5ctXoy8sHYsJAjm6HFhphQMZ65izA4N0m3S+kQV3/s9RnFMDifx9gGFXfMFBjlWk9o8fRApH6eW0KYGm7R33rPu33+27LOJvNotVquYWsBqX8yMUoFzI+MwZZjg9sWJ6vGsDxmgV0ZB21pn3ccZXpzCDma1/7Gr7zO7/T/acdyg/90A/h93//9/FTP/VTqNfr+MQnPoFSqYQPfehD+PznPz8UA+Kzn/0sPvnJT+K7vuu7EIkcB7v7zGc+4+7ncjn8+Z//OX7sx34Mzz33HGZmZvDzP//zZ3avthI7kSc/c2Dp/klCkCp3vie3enTIfR+z0ESoCVret+JWyPJpOd5sNhGJHIekZ735LLcvgGFDTq2ZsbQtzIP9Qw+UWCzmVsrMS4MYtkMbclL4tNttZ2jLAzjT6TRSqZTTPsiVtrUXy/gjBDGMN8LtiSAIcPHiRSQSCbz++utOY9Xv97G1teXOPXr55ZddsD1LKNKOZWdnB/F4HAsLC0OHlcXjcXe6ebPZdFukjUZjyM2eNMQy5OnlrC+3hur1OqampjA3N4dMJoPDw8OhbaOjoyM0m02kUim3nchxe/jhh7GwsDAUE4ZHGfgEAYGWvtbpdLC2tubGkNolguVMJoOVlRW3308tVKVSweHhIc6fP+/GYzA4Vn+3Wi3s7e05Gk4kEshms84NXIJCCZRlohG2dS9M+Mt+kn2gvQetJBk2y7EY+7haGPntW91bz4blN+69sDZac0DXzWqrZQsoy7L4mdVXWvDK53yLQV0//V/WWwpO3RejQF4Y+Bk1TuSXPHpDa6usPrDK0P3LCPSzs7PI5XKOF2lwpMdM9gu1w5yTUlZIbb7uP7ZDluGrt7UwB2wPJt0HZ0lnBjH/5J/8k9CCgiDApz71KXzqU5/yPlMsFl1gO1966qmnnG3DtzINBienTff7xwf9UdDpJDuVIMYiFo1qNXK2mKoeZM0MJPHID7UD2Wx26BRkWZ9ms2nGcJHMhVokCWIksNFxBBKJxFA/yaMNZNnyBFzZbxS8NFJttVqoVqu4du0aLl++7AQ5jxDgwYOy3dxOkdqZbreLb3zjG5ienkahUBgSjtls1tWr3+9jf38f2WwW73vf+/DKK6+4SMd6HFqtFjY3N52GIQgCXL16FW+//Tamp6cdU2q325icnMTMzAy2traGtm4082d/SuYh+4geTZVKBYPBcQj+dDqNRqOB+/fvO88jeXIt6a5arSKfzyOdTqPf7+PVV19FEAQ4f/488vm8KSC0MCJAPDw8dK7M7HP2Uy6Xc15S/L22tuZs3Ah8aXBordRpkF2tVrGxseFA8uTkJM6fP49HHnnE3Bf3uVeHpbAFwTjJYqoWc9bXRzFi32rfSmHCwNIKWCtfyVd8v/m+DyRa9dKu+Lo9GlBYdfT1p27ru026fNZV1yks+cZV04ev7yhjfIDRWuha7eC9fr/vFj1cCNJYngsorQHmHGcdyE+0h63FI2TdrNAE1nj6AKwl83x9N256YLyTRiV2GEOgcyUOnGwjhTEhCiEOftiqQW7lWPlJcKCfk4ShtRGRSMR5JDHirMU0uIrWRsIsm9tE0tjVAjIWQyShS/dqmaRrNYl5cnISc3NziEajQ94s8Xgc6XQazWYTh4eHyGQyWF5edu7N9G6JRqPY39/H1taW24p66qmncHh46LZl9vf3Xb2mpqYwOzt7auUTiUTc4YvPP/88Xn/9dRwcHAwFbxsMBrh79y5mZmbcFlosFsPt27cdaOl2u07jsbi46LafJA2QruT23mAwcJozqQliolG0XAXxvC85dhMTE8jlcg783LhxA3fv3nVaGgLJjY0NpFIpZ6hMOmdbmS+9nUqlEra3tx3QZ9A9aoBWVlZQKpVw69YtPPTQQ1hcXMT9+/fdWHOLUEbVtQS2njfUMhWLxVOMkc/SgF0mn1CQ96zfYcl6R+apt2ateug8LLAwLpDytSfsOeu/XljJesqkeZJ81xL+PiE0ThvD3vU9F9YPowSij0584M3Kz9cX+j3ttSeDnzKFbev7yuNc6/V6qNVqmJqacofhardruci0tpYZrkLbNVEjyjozWfJAtkPKQTlGsi7A6RAivMa8zpoeeBCjGQhtIWhYKg8fDFuFSC2MDC4mLb6Zh28gJAFKtbkeZF6T3j8U+slkEjs7Oy5KrCZWgpher+eEs/Z+0ocL+gAL6yGNbXmNglETK48QkPXiCpzqT04e4Diy68HBAe7evesMuS9evIhz585henratSubzeL8+fNu+2Jubg4LCwtua6ler+Pw8BCHh4fO/VxPII4Ljzx4+umn8dZbb2Fzc9PZZESjUaTTaWQyGdy5c2fIXT2fzzuD3lgshs3NzaF+lGXILTzNzKSRNUEO+5jjzrrHYjHMzc05zQjBwrVr11AoFNBqtZwGh8a3khaLxeLQiebaTioSiSAWi6FSqWBra2voPCtunwFwHkd0veZZUwRdAJybNI9bYL9JetL0JedKMpk058xgcGzQKDVwPsGmv638rGQBEStfLZwt8KLzteoZJmh1/vweZ+Uv7/lAo68s6/1xy9NpVP/7+K2vbrINlgbg3SZfOfq+BULD2lCv1x3v0MazGlDo8uR32Jh1Oh00Gg13nMrh4aErTy5WuGCVYyq3kyVPkADXsnWS9dLgRdOclmlS86/brXcszpIeeBADnHQ8GTC1BRT28jlN0CQGqZ4DhoPbAbbBUlhd5CDqZ+Ukkd/Sg0NqKWRevBYEx4f3RSIRF+WV93310kQoPzRe5fvSJkjmQVdhlkWBuL+/j0ajgbm5ORSLRSSTSQemkskk5ubmMBgch8+n0NX9OzExgUwmg3a77epCTUkymUShUHBxXuSxB1ZetBt57LHHkMlkcHBw4Fx4+/3j8P7VanXIborHCnCbp1arIZFIOK8qua+s+4+MjHRCuxpJXxoI8T0enjkYHJ/MTY+ESCSCXC7njI4Hg4GzUzk8PMTR0RHy+Tzq9bqLm8M+4MGYsVgM1WoVt27dcvOh3W6j1Wq5OEsAnFZqYWEBjz76KPL5vDstm21gzByCGNl+C6RL5kcto3UfwJB7tR5XDQzCQIZP6GjBOI5w812XwEODI9necUGB1V7rXV9Z+v6odoW1yXp2VLn6eQsY+sq1eKN8l0nyXi1Idd9Y9626juoTqx/kPbk4sYCAzissT31dgvtkMumCWkqNsPzWiylZHw0grG0l8kULTMvnLHk6zoLCoqFx0wMPYmQnUjMhY3JooMCkgYF0l7NWhLoseV269VqIU5YtVYxS00Nj2FqtNuRVJesDwG2XcYWty9BMQBpx6TrxQ22HrL80GpVt5nOTk5NIJpNOw0H34Y2NDezt7bkw/4lEwoGs8+fPOzuL6elpzM3NnZqM3OLQfTwYDFCpVJwBKftOCgupAWm1Wk6zEIvFMDMz46z8r1y5gvv37zv6mJ+fx/LyMiKRExfpSqXiBHe1WnUgRWqstPG2ZCI0jpbjLMeGYILlzM7OupOg2+027t69i/X1dWcgzROteQDkuXPnXPvL5TL29vawtLTkQC8NlLvdLjY2NlCv1522hyCRcYiAY41ZLBbD9PQ0MpkMADiVOel+ampqyLDcmheWsLNAjEz9ft+BmHGAyKjkE8jWdU1/o1aLFg+xBMe4zDoMPPju+0Ac3/UZmIbVM0zgy+f0b31NAhMrzzDBbfWnVabFz2Xb9XPy/ihwNKpuwInpgdR+SCeRUX2p2xOWWq0W6vU6MpkM0um0O+BVJhlEUwYolXKJ42KBHWv+6vuy7RqcWu/Lby6C3m164EEMB4cCn8apPsEtk1wl87/FQOR1zQDkAGlNjjV4csAp2CKRiFuJcxvMR1wAnLDjxJHCVNpVMNYM+8JiFrwutVdBEDhwohE8y+TKn4HRuM3QbrdRr9fdWULz8/PY3d3F/fv3Ua/XkUqlkE6ncffuXcRiMRSLRXNsdKIQlXvPEjAwEfwFwbFLMk+dbjabzkuGZxRJAUuNRxCc2KHcvn0bsVgMh4eHQ+NgGX/rk9Hl+PE6k1S5Si2NHEeOBY2ML1++jGw264AWPYTo9dNut7G/v4+5uTlnp9Tr9XDv3j1Xb3p8DQYDBxa5hZVIJFAsFpHJZNBqtfCNb3xjiC6C4Pi8JJ755BOyFoBhmxlLSN5ju6Ub6ajkE6iWEPGBAL2Cl3mMWr37wJQlaEe1wxLUPmBzlt9Wfj5BLZ+X42LVc9x2jwJzFmANa7f8DutXq/xRbZZlh409P+Srcsv4LLYeWshbcknyjGq1ekobIwGbXIDLoJ7UHkuzBQsAW2CE+cn+0V5Hug/1b+anF3FnTQ88iAFOiIKDxmu+iaQj0VruvhxgEitXz0xa62ERYhCcWJJbBEshRlsYClZtPKsJj4dCyngorD+FkzZQ1HVlX/E3y2RfhG0n8b1arYbV1VUXmKlQKCCXyzmgQe0FXawPDg4wPz/vvJV4VEEul/OOK3Citq1UKkPB21hfJm4TXbt2DWtrawCOt+U4fouLi8jlctjb20O1WnV9u7u7i1arhUKhgHw+j3w+744d6Ha7KJVKp8ZX2saQNqhaZn3lUQCyTXyXRswSjNBORdIKNTX5fB6RSATZbBaDwbGX2tbWFq5evYqHH37YaVs43m+//TaazabbBpJni/EsI9L09va2i8I7GAywu7s7ZHBM7ya62OtFgsUcJZOMxWLOzsqiqUajcaqfRq1m5XM+QWUBG18+1u9RZfvyPivD9tXP6i8NwKx+Gxdk6iSBd9i1sHprnhVWnrweBmgssOd711eepEv9ru+3VV4QDB+PIuc0f2t6HAXWfNep8WHQ0Gw2i4ODg6EyrMW25CHaFs/qJ903+tsCg5ouWK7V3ncLYIBvAxAjO1KDBY0u+Vsb6WrtCXBay0KDSBkwTyJNa2XH9zVRS3ASiUTcOTj0SGIZFIi6rbShSCQSzqhLuvgx6Ylp/ec1GWnXd1gf45nI9rbbbRwdHaFUKmFnZ2cIzNCQMx6PY3FxEZcvX8be3h729vZc3JO33noLjz/+ONLptHe1x5U6D1aUYyw1XtQSfP3rX3daOfZXJpNxFv+5XA5zc3PunBIGvtvb23MHTtJ4uFKpDLmCW8yDruHcnmF95FYM26U9AkhX3Pq6ePGiA1HVatW5HlcqFRQKhaH8qGIGjrfzcrmcO5phdXXVncdE8CBVwdRIsd87nQ7u3r2Lu3fvnrLhCYLABTXUBrhW0kxxMBg4w2E5J9mPPCF8FGjxCV35HSb05bVxQNKoZ87CnC3AE1ZnWYYvL/4OE5gazFiATwv2UW0Ka4OvPmFjZ12XY6SBWVgdRwHZsPdlX/me5X/SMhfEFv/1AUJfPXXZUi7V63Wn9eYClotkmSSYknJO9qmWRb4+0v2g62mNjTTutfr13aQHHsTIzrM8gvhb2k9YA2kNFoUWhQzPB2J5shxrkMcx/k0kEpiamhqKTWIdEsg8+WHMDk0sFOysI/9rQ2X5/GAwHNHVOnJgMBg4sKTbwgnFbaS9vT1ks1lcuHABi4uLOHfuHIBjzcO9e/dQr9edHUo8Hscbb7yBxx57bEgjIydIr9dDqVQ6ZdTG35zIVKVSK8KtK6li7XQ6KJVKmJ6eRiKRQLvdxsWLF1EsFp2NSqlUcltIjAjM+khQKumAdZHgTxqM6z7XtEH6TKfTmJ6eHtrLbrfbTpsimZJcba2traFcLuPChQvY3Nx0Km8CrKWlJadxazabzl7n6tWr7kwoGceHEZXZVtKbvGbRpzUfguBkK1C/A8CBLSvpRYLvGR940fNDziF53ZdvWDm+3/L/KGFp1Zm/LV7mez4syXEZJ48wkBTWz757vmQJPP3eOMBF3x8FwkalsDKtMad80HGONIAIq6sll6Q9Y7PZxM7OzlB4DT6jk15oST5kjYkFnKxnrP+sH39rLfK4NBqWHngQw6QJAPAbwEkBbRnBkUh4v9/vn/IAsgZJgyb5vlzJ8/14PO4iMgInWg0ZOE4mGe7eN2nHmejWhJH1ki7SMj9pTCuTXll3u10cHh4il8thdnYWW1tbmJ+fRxAcR59lIDzu8zIeTDKZdNGKU6mUa2e9XnfC05pwg8GJSyG9avhfr+rYjlqthnQ6jWg0itdeew0LCwsoFouuzsDxVtRXvvKVU/Ys2jCO4IBMTI6RLFP2ufbwIr3Q44zXqQGJRCK4f/8+stks2u22O4aBzy4sLCCZTLpD46jd4HYYA2fl83kXgfjWrVsol8soFosoFAooFovOOJhxfEqlEkqlkgPOMkaM7lfrP5O0h9FMXWp3LJr1CUn9jA8wWKDFN299yQcq9NwbBXzGAWPjACzf3A8DJT4g4AOeYQJc5qfzDeNNvjxksvponDHS9Q4ToL46y+sS8PrylHlIAa75g7yuBb2st2ynfu/w8BDxeByFQsHFsZKLd8lXmKQ23ycfrXmr56rFd319LpMlR86aHngQIztJr3ilr3wQBC5Uu3zXmoRa2yLz1wTKe/y2VlLyngQKMqAZgCGjSw1+JCP2aVVkYjmWQa+sn7aNGQwG7jgCTZypVArPPfccSqWSOzSQdjwaYABwZxGtr6+jXC5jaWkJuVwO5XIZq6urePbZZ53g5/Hzr776KqLRKJ588kksLS25/WBtdS9XAMCJQTLtgXwMU/ZPo9HA6uoq9vf3Ua1Wh+yTaNvDowasfW6dpI2SBK0WU5TjKQELz0nSDKXb7eKdd97B8vIyMpkMtra23HYSaZr9xIi77XbbHQnRbDZRKpWQz+fx6KOPOnuY7e1t7OzsuO0wnoFVLBaRzWaxsrKCy5cvAzieF7Rd8S0QLPqXoFS/MxgMhrYJ5TiFlWOlMEYblnyCX5c9qsxxro8SrOMmnyA9Sx9Z9dI87d30adiY+UCdxcvChKN81hLAo97TZY3zjA8o6HbIj2Xwa/FifmstiqaXdDqN+fl5TExMuENg5aLKl69V/3HqxmvWQkKDHd0P0tZU/j5reuBBjLa8lsKfgc0YKTUSOT5gUG6dSEKTgyJV2DJfWaZ8Jwzc6N9BECCdTmNqagrVahWJRMIha76rXaNlfSj4NVL3MSKLacj2M38mGa1X9gENP1OpFC5cuOBC1vMkZNqPSEABHG8jlUold7YSJ97W1pYbi1gshnK5jGaziX6/j7feessdLJlKpU5FUtbuzOwzfiwNm1yxRKNRHB4eYm9vz9WT3kDNZhP7+/unPIdk3WV/Si8zCWI0CJYAVr5PzU0ikXB9rz80fq3ValhcXHR2VPQIY0Rj2rnQZuro6AjFYhFHR0e4f/8+4vE4pqensb29PaQtol0Qz5Ti+PFoBto5HR0dOTsin6CX6uUgONa+8fBXmfi8jBHD6z6mPA4ICFtc6LKtPMZZ9Y8LOKzFxbtJlrCWeYa11VcfS1hZ/e17/920xQegdFlW3j6wOM5zZ6mfBaLOUhe9yBw1JpLXWnxX1oNnlWWzWWcnSN7D5yzth9W/YWDFVz8NiHRekk71ouXdpm8LEKPtBziQk5OTToACcKvNyclJ52bLoGHAMPPTzMIK9a9X01ayiIdqfXrOUHhIApTEK/OgbQ6FpAZQskx5XROZBeAo+BkjRvaFnESvvPIK4vE4isUi8vk85ubmcO7cOQcCCGoolJ999lkngKnFqdfreOutt5xHDtvFcsrlMr75zW/iypUrmJ+fd/FNaFwstR7sNwleZX/rLSBul2xsbLj7lgaB78u9ew0WWbYcO9KLzo8eSzJv+ZFbSfIDHEfLLRaLaDabDkhEIscxkQ4PD12kYebR7/dd7BnpNUU3atKSdAlnIk1wW+rg4ACrq6tIpVJ44YUXcPXqVWc3xLGk1xLbxTyTySSuXLmCbDY7BG54n8bWYcJV9pU1Ljpp5hqmTdB5WvNZM2x9z8rrrMJej4Gvvtazvvxk+b78Rwl8DcZ9ZWhec1YgEQZG9HzzASFfnjqfUXn46hIG6KxnR9H0qDIkLdLLtd1uu4jmhULBbRmTf/uMamknqBdaYWXyff3cqHml71t88SzpgQcx2m1TrsLj8fjQScGDwcAdTshnfKo4YJhh+VCqbzAlSJCEIYVRu912h3vRI0kGuJMrZSk82+32qXZrYtFgRTMZuVdKLRPLkwbDmjjpKdPpdLC9vY1YLDa0DZHP5zEzM4OlpSXcunUL6+vryOVybjU/Pz8PAEORZzudDmKxGDqdjvO8SiaT6Ha7uH37tosh0mg0hgQhz4aSAp9tk+MnwRonME+lZjwcPX7sG+uoANnHfEbmrcdM1tlK7HueBO9jDBcuXMD6+rqz+Wm1WiiXy0gmk0ilUkOGs0EQuFgxjUYD8XjcHb7YaDSwvLyM6elpp0ljGHV6n+k5Qe0avZmojaM2iKdc88RrauQYs0gmmXe32zW3qGS7re1T3tPgwjcfrZWnrpNVBwu8+IRdGI8YVZewRZD13/f9rUpWvrpvfXUEhrcLwwS6VU4Y0NR1GacN1vPj0IUPpFnJB2DDytDAQL4b1s+0J0wmk85bSYbIkOBextSSAEaPpewfH03pMdLv+OTl/1vafOBBDLcCKPiJOHnaLo2fJIMmqgWGV8y+yWNNOmkdrrcN9HU+m0wmMT097bxgUqmUs1fQ5VE4E9xQsDAuAQmSwEO3wWcLY93Tz0m7Id0v7E8AyOfzWF5extraGvb29lw8lUKhgGeeecZtTWxvbyMajSIejyOVSiGXy6FQKCCTyWBmZgaDwcB5fi0tLTkNA8taW1vDzMyM8yaSQlZG0JUTVIMOjnkQHHtR8RyhMEYojxtgf2t6kH2k+1ECIoIZxpfgddJtp9Nxh0HywEZqxPhJp9OYm5tDv38cD6fb7SKXyzkbJml0HQQnbtEEy4z03O/3kUwm0W63USwW3fED9XrdRSuu1WrO0JzjTZf+GzduOHDKbaZCoYC5uTkAcAdolstllMtl1Go189wkjl+r1Qpl9EyScY4jTC3wwOucQz5AYpVp1e0sTFrPRR+Y8YGCUfXjvLAMR2X5FmDwAbhxAYu1JR/2Tth9LRzP2s9WCuuPUXXTQl1rJnwgTYMma3EwarxlpHCmo6MjVCoV58nY7XbdESqarqUMCtPAyAVtGAjXNn7yPt+XMvBbAbIfeBDDLSIZeAg47rRms3kq2Jz+Habe0pNdeo3IrZwwBkfiSaVSKBQKaDQaKJVKzluDnhkkMPk+BZMESYx90m63kUgkTgEYCkt+S+aimZhvpWEFumPejIgMHBuZnTt3DoeHh0in07h8+TLefvttd1+ewk0QWa/XnSHp5OQkCoWC0xiwHJ56TEB6dHSEzc1NzM3Nub1gPdZsLw1keV1OSgKbzc1Nd2bRqMlFGiFz0PvNki6s1Y3uZyZug5J+p6en3TlH5XLZ2cgwIF4ikXBu1gTi3IZjHegaz3oHwXFQrmw2e0obMjk5idnZWZTLZayvr7s6TE9Pu7GTW4PVahWZTAZBELhrtVoN6+vrCILjWDzpdNoBGrrYhwXYAuDAW9g4WIw3TFBqwSDHRQOgMA2IT5CGrbp99Zf1knmMK5wtIekrfxSY0/d85fs0Y7o/wgCQBOH6/ihNipU0/xoH/Fr/ZVuYrMXoWYSvT5MxTtlWf3Juy4jAlBPNZhPlchnz8/Nuu73VajleJeWcXOBQ7hFgaPkXBpatbw16NKjWY/9uwMwDD2Jo3MSkkafeKpE2AHqiASf7j3KbiflJwqJQ4zvaoJT5xuNx5PN5JJNJZxMCwGmKaN/CcpmvRNXAicYoFouh3+8PRV+Vk4U2I/ytQYxO1kSSIEZPNlnu7u4u3njjDRdzpVAoDNnTyGc1IWcyGSQSCWdPwW0HBvyTalBOyq2tLXS7XczMzLjtFDmJrJWOtjE6ODjA4eGhGWNHTkBJJ5KOZJ6S3qy85G9tGMz6ycM8ZewfbvGQXoIgcAdrUpMi+5ZbNyxT0qGOn8TE06rz+TwqlQo2NzcxGByfjD01NeWMepeXl4e2H7vdLlKpFJ5++mlnV0ZX7J2dHQwGx1uSuVwOzz33HLLZ7Cm6Y2o2m0MRqiXdWf2pv+XzWjCOEpJhwEj/DhPo46SwtvnqK8fsrJ4dYcBr1Du63nIe6nqNC0SsFb4uW5dp9VMYGNL1tNo0apEhy9H3JR3Isnz9E1aGNTacq3JBJzXKcnEaBIFbDGezWRSLRWxtbQ2d4cSFjTawtUC8BBwamFr9IPPT13whQN4NaAW+DUCMDgwnD+XiNeC0sNEdK7Ur8uA+36pNDwhBjjz+PJlMolgsIhKJDJ2iTKZOy3ISmhb4UpDyPmOpcLuF4epZR2pqWIdxQIxm5FrAy2clMInH4+j1elhaWnLaFAo4alD4HvuGY9FsNlEsFh2Yk5NUnn+lJ93+/j6Ojo4wOzuLWCyGWq02BFItZkmtR6fTwdbW1pBGRY+nLCsSOQ7332g0UC6XzdWHXL1pozpe0/clDbLusVjMaecymQw6nY5zYWfgOWqoaEsUiRwfUCkPiGPsGBleQAM6ls1EMFMsFlGr1VCv14cMiGdnZ90BpUEQYGlpCZVKBXNzcw5U07bljTfewPr6utsi8iX2MzVuehx8c24chjhKWFp0JfO35gmBqK8MKZB8+fqAkQ8IaZr0ARJfn2gB45v/41yT98YFLxrIW3lrga6fP0vdrHL53wdk5PM+nuejB51fGJjR9KHvyzkqt7Hl+3p7hnaJ9Xrd2bTJ/K1QEz7PKfmeHjdNh1a7yBPlYt4qZ5zx0+mBBzGyw7llwSTVZxYD0UJPDqYeWJkfcNrvXU68WCyGdDqNdDqNdruNcrnswtEnk0kXzl4eVijbwjpJo1Lep3srXYHZbq70ZZ4+5hE2WYMgGAoPr4mOICkIAly6dMlF3a1Wq07Y5vN5pxFi4qSktqDdbmN1dRVLS0uYmppyQEaeyMpJwfqxX7j/u7i46ECdBnuyD5nX7u4ums3mkLZItl+naDSKq1evAgBeeumloS0sDQ6lzY3sc9mvsi1ywieTySEtDN2kp6en0el0sLGxgbW1NXfoZ61Wc5oSAh0eDikPBiUwYp+zbpbanNcymQwymYzrw3K5jFKp5OLRRKNRzM7OYnZ2dsi2h/Y+UlPJNrFsSwBaR0n40iga1tdlWfq6FiCjhLIGJ1JAau+dMIEdljQY8b2vr1t14HOa74Xd18+MI8DDxk0LQSuFgRVtszROfjrvs4y1bIvVx1b/62s+ECOf99GBBDLyOWvO8j+jpCcSCczPz7s5KzUyUlZZeerx0/LSR4cWPVnydFSfjEoPPIihHUC32z21fWGtPHmPajsJViQRSbdZKdx9HjESwNDYkuCF6vJYLIZ8Pu/sALTbrizLt2oOgsAZ3jK6rWybNPjV7ZL5acJiubTT8K2iZHj4UqnkbCA2Nzdx8+ZNJ8ykITXrNhgMHAgCjgHR2tqacwVmX/kmuuyTRqOBe/fuYX5+Hvl8HtVq1UU+ZpJgrlaroVqtDglZ1stKvJ9IJNxY8ZBOeX4Wk9b0SUCqVbq8T5piSH72LzUgBCEXLlxw9isU+vT46nQ6zki6UCi4yMGkbXnCrdxak22UbdbXstms20qS23wEumw3g+bNz89jamoKt27dcrZqYf1NTZrV97qe44ANXYYF5K1Fi1W3cYWNrqN+15qDGuyGtcO3GNH/fYJCA1Wd/6h8ffUZRyBZNDUKIOhFgG6b9b5VblidfNtzPp6pQYTV71wcaVmj6xNGZ5xbnGtyoeADZPRsnJ2ddfKQx7RIXu+Th/q/nh+yDyTt6/b4QLOvT8dN3xYgJpPJOG8Ki6DlNW2EqTtdggq9paHfk67QkchxtFeeL3NwcOC0Iqzn7OwsIpEIyuUyBoNhDYusg+UqLZEzV9bylGHmIePm+JAw22ZNRLkNoetGIct7q6urODo6wrPPPus8WYLgWFskBR7ftZhmt9vF1tYWlpaWUCwWsbu764CSFi6a2bXbbdy/fx9LS0vIZrPOY4dtpE0QwVOhUMD29vZpIsLwRJR9kUgkUKlUAADFYhFBcBzp9/Dw8FRIb/kt+77b7Tr3aTnGpBkyP7nnzf7jczTu7XQ6bguJAEaeXE4NDetBjz0JxAmqtdG4pjkfAAiCE+Nr1m96etp5ns3MzGB7e9tFH/Yxr37/+PgHPQ4WCAkDG75kgadRQGUUoLDy0EzcJ7i0B8+4IODd3LOSrsMooGVdt4CSXhSF1U2Ox7jASfM//c4o8Mbfmq4sMOTTTvjoRvOLsEWjr90y/36/j0QiceqMOm1rKWUQcLyo29zcxPLyMhYWFhAEgZNBbBc1MpqfnoXmWaY11j5+ret91vTAg5hWq+WC2nGVbBHYqNWZFOqMGsvr1G7owdbghUxZnmhKrUgqlXKhojudzqmovz7Ni1V3ghga+HJVbD0bxkwsQ1itNZD5SsNW5t1sNrG7u4t6ve4IPB6POyH8/2vv34MkO6o7cfxUdderX9Wv6enpeWlGSCMskABhxrM2GC8TSIIw2LBhHgqMDQuLV8JswGIF6zUP//FFNg5YzLLgjTCwEWbN7kYYvMuyYIHEChuBkWCQhMSsRhrNSJp3v1/VXd11f3/M75PzuafPyXtrJCymVSeioqruzcfJzJPnfDLzZKYmr5N3d3fL9u3bReS83wtmVbTx5I4BPk+cOCGjo6MyOjoalrQQDv43OLkWW9pZTjyFhdu8sdR19uxZ2bp1qxw4cEAefvhhOX36tMzPz4dZD0/ZFwqFUJ5KpRJAS1dXl/T09GzwiUIdg1h549RktD2+cRggdjVhJqdUKoWZO6TPu8YshWTNKrEsszzweUaYeSmXy3LNNdeENrJkU+TCCcm63mPUjuG29IDua081H88ocVtq45gV1xrN69GsBexj5fBmAjEQs3Si/s356fJZ/FntruNb4FIDDo7rPdP55yGLXw3IGEBx/fAAxiuDpX8ZwMbqm0EN8uPBj0j6mI8kSWRmZka6urpkfHxcxsbGJEkSmZqa2gBcLb9B1B8P3PUyFOtUXe4sUMv5tEubHsSsra2FEwxx+R9mC7iytZDCcOhzYjRyBOmwIJzR0Wg0gkMqxwWy7u7ulnPnzgXfGKSjlYulvBhBazTdaDSkVqul4seESOelw1re7Awanvvc58r4+LhMTU3J3NycLC0tyaFDh1KKsFQqBVCTNfJF+jjD5OzZs+GCM951puNoOnv2rDSbzeCrgW27zWZTJicnZX19Xa666iq58sorw/ZEXDzJy1jMI3xAMMMH52BsN96+fXtY1jp79mzKB0grV8hks9kMp+riWgzszNLyyh+uP5xcvLS0JOvr6zIwMJBaQsQ0NGZn0Kb6nButqC1DwGCcrxuoVqvS1dUV/JEYlIucP2G4XC67fmOoCxx0p8kzZO2QZ0i1EfVAFvMRAyuxgYImdkL38tLpxoy6NTuRxYsF7vi5N0KP6RUvrSyeLGPK3x6vOk6sHDq9i5ErnRafT8VhNECxysJk2Rn0J55JBnhhUAEfSN6AMj09LUmSyPj4eDhY1AIyIht9XzRQs2yhpc89uYn1q3boWQFi1tfXZX5+XoaGhmRwcFAmJyc3OFmKbBQia2lJK3TMcjD65gblQ9nwDASDXi6XZWlpKXWoHc9mIF+krZUcl0PzAAdanm7MUlIxRQTDp0dMMzMzcurUqbBcMDExEWY5cKjZ7OystFqtcPouk9cOKBvutFpYWJClpSUZHR2Vvr4+F8gw8ZbDZrMp27dvl+7u7nCf09LSUtjCDLA5NjYm27dvl1arFU6+xTH6OIcGdTE0NCSLi4vhDJcf/OAHsnXrVhkdHZXe3l4ZHh6W06dPy4kTJ2RmZibM3LAsiZyfboajbZIkYQszrsJgp2muK1Ysa2trwYm6UChIo9GQ1dXVkA4+3Ibsh4SZGj5PQpOl/Dk91DnPHmG5DIbVMxZcJ6urqxv8mLRcaFnx5NYiz7Blkdf3dJ/wKAaOtDHQ7ywDHOMvqwx6EIJnHvixdIgVhvnT9WyBH493Dxh54DGLr6xnFkCzDLGVBn97zuja+MfqxJMhbNLg5WS2FzH+kyQJenjr1q1hJ+zMzEwYhLOfHPjXDrl4b9WjtpPW4IvTuVjgCNr0IAYNjgvy+vv7w7ZU7gS6EZg8pAxAwcsovNMDwAYKgtE5/BNwyizvoEFeegkBvzVq1miZpxGxlRkABMQdzSqbJuTDSw9czrm5OfnpT38qhcL5M01wzQBOagUggM+GdfZHrP55CQpnh+zcuVPK5bLMzMyYoNRKb3FxUY4dOyY7duyQer0uR44cCXVdLBblxIkTcuTIkXC55NDQkAwNDcno6Khs27ZNRCScqNxoNGRxcVEmJiZk+/btsrq6Go7XB8BBm2/dulV27Nghk5OT8uijj8r09HSY3eFZEchpT09POOiPndNZ5rjtMPpbWloK1zBAFtbX12Vubi7MQrHSs5QyXwUAOeEPyyaIR4EiEq7zADWbzRDGW0pkWRORsPyr29Di2xrhctgYeXKj65fzifHt5Zc1ePCAvGcMrbT5vxePZ21jfFr/rb4Zcwi2yDNeFiix4lj/s/LNqkMPzPB7z9dDE8LpHao458qimFxxfrApOIhTA88kSYKfi76rDGlAH6yvr0u9Xpf+/v5w4S2uLOFBL/Oo+eG+EwOJeds8qw4s2vQgBpWCrWWlUim1S4PDsVDoqTMRuwPr6Ttt3NlAMiIdHByUarUabma2hAU86LVHVkS6U0HpgxfuOIzStROhV3daqPT2Y4TBbFOSJGGph0/e7evrCzNhMMhcJzHhZRCD8OiIW7dulVKpJOfOndvgjGx1OpHzPk1Hjx6VarWaupMKy0wi56+AmJ2dDVciMKgZHh6Wbdu2yZkzZ+S73/2u1Go1GRwclOHhYanX6zI8PByc+PhMh3K5LFu2bJFarSazs7Py5JNPytTUVFAsWDoplUrhIkYevUAOsDzYarXCNQsi58EV33YOGQEvk5OTATBZ694iF0ZXfFEqdqSxcsaHQXqSJGGJjbfCYzYLt2kvLS1JsViUK6+8MnWFBbdbkiShX3jKMe/oDfxlzS5kpWvxkhdMeEtEOqwns3mUu5Z9Nmx5jK/33BroxeLqcnh1lAXM+LlXr168GCiIlcd75w1s9WwDv2PAUiwWw/liVlk8EB0DStYgHP3aWvJHmpBF6AocXpkk5/1mMEuubWKsfaz25udWm3v2tl3a9CCGkeLKyopMT0+H4+mxg8RbWrIEEwJSq9Wkr69PisViaHiekQHx0g7i42IunOHh8W2BJ2tPP4cDiIG/gXUqr7fzyCIGPoVC+rReJi4/CAaOrxOAs+oVV1wh9Xrd9TthgjMw32aMO3VEzt/RBCPNSyO6fAAL7BeFLdU9PT3hcL56vS6/+Iu/KE888YQ88MADsr6+Hs5bwK6aX/7lXw4zaIuLizI5OSlHjx4NZwBdeeWV4eRg5FEonHeaLZVK4fC4mZkZeeKJJ+T06dOB756eHtmyZUtYgtHOr/qDdsZSG79jBVcoFILMw09Gv0c98fZ38ACgx8AG4VGn+h2ADk9Vnz59WiqVimsYwBNf/JgHZGSN4nU/zAsMdDw8t/57PFoG96mCs6w4F1O+rMFEDBjE4ucBgHl4awe46t8W8IkBPA846XQswFMsXjiUjvuvTqMdkIpwDFQAoHh3kZ4BAk9WHqurq3Lu3DkZGhoKgzHMyFizJ7FnPNBim8GDcgAXSz9lAWWPNj2IEbmwSwejXTheYllJr+2h8nldE0IDPwXMDMzNzaWm2a2D4LjjwdF3eno6bB+NKV/diWEkLKUOIylyYZs1BApgBh/EZbI6tFZOADFaoehZLRZcrt+1tbXgo1Gr1YLvDE69BajBklGSJMEoJkmSOi2ZZ5jm5uakXq+H82CYb5QZPhbgj51da7VaOOen2WzK6dOnZX19PVyEqK+ZYOdgBofwQcJt6LxmDedWvj4Cx/rv3r1bTpw4IWfOnDFnYUC4yRszHZj1wKWMFpDWbT01NSXr6+syNDRk7oLToBX1x4dF8uwVbkxnGWDQg+WpSqUie/fuldHRUWk0GuasHtJOkiScWM1kGUurj1jhLQOFvNoxjlmAhfP2DAC/t/SElZfmvV3ydIzm0TKyVvx2jU4e4JgFoqzf+r/VPvwdqwdOKwsUW7zjow8Ujc1UWMBKp6vDW063GCx5g1Q9QwNaW1sLDr+1Wi3cfB3zXdNlQr6ss9ihGP1SD3TzzsbHaNODGFQg3xc0OzsrtVpN6vV6uMnaEhwo6mq1Kj09PalRL3YSgfQOIcs4YBcS3wcDo2wpMK8Tac93jg+wwifEwg8BRkhPa8ZGcTqcNf2PZQ3Nt2egYNQfe+wxmZqaksHBQRkcHJSxsTHZsWNHAJu4bwcj/PHxcenp6ZGZmRmZnp6WxcXFYChxLkp/f790d3eHQ9/W19fDHVT6YDfmD+VaXV2VhYUFuf/+++WFL3yh/Mqv/Eo4xn9qakqmpqbC0goAkdVufGgegwD4QfEMRaFQCEtRu3fvDgbfUr4AWmhnzFBhjRs8QB61czrCYEfW8PBw6vBCljmcg4MlK6zDA1zyoX7aODDIRJsjbq1Wk6GhoQ0+X1yHrVYr5bTtKVJLvpgXfHtOiJ7hsMJ5hiGLJ/6fZWytdNqhvIYgBgL5mTda9trNoqxZonbicRwLHOpyeXrSAwp58rP+a55hO9BPteOtVz4NwnU47scaKPABrZoX3TfxDHk1m02ZmZmRQqEQ7N3c3NyGtJgPBiZ6xsWrV65/1kvWsmde2vQghglKfX19PWy7HhgYCH4JHA6+A7gpOEnOjwwXFhZSU+2lUimlrC2DwUKrnRVjHRjpsKCw4FkKp9VqpUbEMHQw4sy7BVQ0aWXBo2fEQZ6e0taADDMgCwsLcu7cuYD6y+Vy8J2p1+syPj4uu3btkuXlZTly5Ij09PRIvV6XiYkJ2bFjhxw9elS6u7tlcHAwnNeytrYmO3bskGazGZZO9MnAWrmJSNiRg7rDqKS3t1dqtZqMj49Ls9mU733ve6E8vMuI2w3LX3rUxCMhOIGzYltbW9vgOI2wXIfweWm1WjIzMyNTU1Ph7CLkoWWLeRGRsM0d2855yYsVFNoXaejb4MGr5W/CYbDcdPLkybCcNDg4KFdeeWXqCADEYx8hPMsLvDUYyKscY0BCyzynq0fT3oCE/3tG3ANTemBgta0X1+JRh0E6Vrny8hrTZ08HecCBebGMLsfz/KLy5tMOsZ7JA5IRxwrD6TBYAa8YzOnLY/W9YxpwcJthZWFwcFBqtVq4g8+SIfDCp9pbcmX1F21TtK5rlzY9iMHZFahojKJxu3V/f3843IynwFHJOEeE75wBaYHTaJKVf5IkKSdMJm5YNnRsSCxHLa/Dsp9NoXDeT6a3t1eWlpbCcgmWm/IQ+AOwY17xjg/w07zjP96hfldXV2VkZESuuOIKmZ+fl5mZGZmdnZVHH300jPyvuOIKGRoaklOnToXlGex+Qhjc2yMiMjY2Jt3d3WEbtAUyNIgpFC5s4eZTjru7u2Vubk4efPDBsIzYaDTCUg9mn3RH5OUvbh+WKTg2QyaxLMPOs3xTd1dXV3C+A+BZXV0NVw14homXObFOz2vS6+vrcvbsWRkeHg4XOOppZOQJ8KSJl1OZUC8YNLRarbA8hZm0vXv3mnw3m80NcmyBT/0+D2CxAAC3D5e5HQDkhWvXuF+MAfWMNj+z/mujpP/rdGJGph1g5qWvw8dAgJeX1RaWHuBvi7x2y6pbHQ7XrORpVy8/fs6DFdRhT09PsE+Yhde7GHUfQp3wygFO2u7p6ZFqtZpy8uUysX7XdW7ZB6ucPCiPLV1l0aYHMbxsAIMBwkV5OOEUvgzYAopnHnqPKSeNOPU7T+lww+olKS2M2OUDQUR5l5aWgp8CC2m1WpVGoxG28MZI5ydywTjrMJjBsEbAltKDXw2cXMvlsuzcuVN27twZnIDPnj0rR44cCaCLfXrm5uZkbm5OyuVyML64ygC+L9buMq8NCoXzM0zwm0KZjh8/Hk56xvLL+vq6jIyMBKOOMrIsWAcC8jcDEQAJ8ANAA1AjIsGY4znkAj5dDJa5bAyArO2hpVJJBgYG5IknnpD5+XkZHx8P/jaQeyxdaVCE/CwQoEG5yPllOuwIEzm/7MlbuTVh+VO3GZfNI93nPD5jACYGljhOloKO8RQzkHnKqfO00vDISlv3Xyt8FqjzAKLFT2zWIQ8IysNDVpi8baBl2+MXehsDBsTDUqwFFHXeMTBp5Y0PBiheP4EuAGknYNiK1dXV1MWwmjfuT7pvIV0RSc36xNqa/+eZKdO06UEMjCvuj0Gl1et1abXOH8aG235xtw4rzywD6AmlFnBP2aOh2eh6yhXPOQ6/w384nLIDMKNr3sFikVc+LMfpMOwZD148JQ7jmSTnZ2+wFPLiF79Y+vr6QidrtVpht4++Z4n5xDIUti0fP35cKpWKjI+PB9+jxcXF1FURuuxY/uEt3Gtra3LkyBG5/PLL5Zd+6ZdkeXlZzpw5I4cPH5ZqtRqW0PRoQkRStzUzWcYUv9FWfDkjfKgAHFhJ4RBBvf5srafz8iFmmODvMjU1Fc67mZ2dld7eXpmampJmsykTExOBJ9QR6pyXD61RFJexWCzKwMCAVKvV4LsDEINya3lhv7GswULM6MUGDHnACN7rZxqkWcTp6rz4eYxfr8y6HHlAWJZx1+A3b7yYHtHh9Ox0nnixfNppDyt9C5R5MuXVDbctrvTgZRwM1uBDaaWj9YK2BVaeyAOnhvNBp9o/k+2GVb9Ii4GL1ldWXWEQ5oFiz6Z5vy+GNj2I4bthWq1WuDcHo8zl5WUpl8tSqVRSRsFSEhYwgcBoQIF4/N8iT6lZClC/Z+K8cLEkyol32NlSrVZlZWUldddNTImDYHR0mTBq5xtVvbIUCoWwlRkGG2eIYLnkoYcektnZWVlfXw/gwlNsMOz9/f1y6tSpAJD6+vrCgXGYnZqbm5PZ2dlwfxUrVPgN6YOeFhcXZW5uTgYGBmTLli3y8MMPp2Yr9AgCaWUBOcgN8sFSUqPRSMkSACLHxdUGS0tLG3YAAKhYssqjxELh/EWVU1NTsrKyEu5RarVacuLECZmampLh4WFZW1uTqakpGRgYSF1TgLJrkMRKi+ulVCrJrl275NSpU3LmzBkRkQ3brDnO8vKyeY2HBd4tsgYMGuR5cWMGVudvGTwrXW4H/dz77+kPS7ZiaTJZ4MTKU//WececN7OIZZX/W7KQlU6Mf4+4v2TVZSxvb6dPoVAIJ2wjHHZMFgqFDVdwWHxbcgXeeRDLfp5M2o9OpyEiG0ANeMO1J3lWIXQ9WDaLw+syPtWdSSLPEhCDkR/fI4FRNHYrjY6OpjyyPdQpYgMcC7XyzAS+vY7HaeCd7iR6tkMrzqzRTbFYDEAGIIJ30XgCiGe8TMJ1wCPm3t5eGRkZkVqtJk8++WTqFmIY0nK5HIyzyPnze374wx/KVVddJdu2bQvARURSO42YF9QH/CyGhoYkSS5sbVxfX5cnn3wynIFQr9dl586dcvnll4edL7z7qVQqSX9/v7z4xS+W6elpmZmZkfn5eZmcnJSpqakwvbq2thYUFDt0cxtUq9UNba/rFMt76Mi8jKXjsbLgawW4vTSY8QjTxcvLy+FKAvg69fT0SJIkwfl8fn5ekiSR++67TwYGBmT79u0yNDQUpsZ5G6kH1kBo92q1GtoXPji6XUUkLJN5yp2BnUUXO6rTfHCenLZn4C3gHksrz3vWD/gfG8honkQ2TtNrkMthNZDJ08Z5AUHedvEAXCys/u0NBPm9Z6TzGGHNX6Fwfsa/Xq9LqVQKp8KjX/O5SggfG8Rm5cNL+NphF7qK4+hZT27DYrEYLkrGLdm4hZ53V2n7ZNkgCyTFZOpiQCTTpgcxGNnqaUxuQDj49vf3h0PX9NoiExstvY2U84iN/LQwWV7jOk/LMLLgWIZe5wlDiB0huqwgnRaDGOYZhG2ze/fulVKpJPPz8+6IGXcnsSFCO3V1dcm+ffukXC7LI488It3d3cG5jOsLPCGPer0uSZKEaw2S5PxhadPT03Ly5Enp7u6Wnp4eGRwclNHRURkaGpI9e/ZId3e3NBoNOX78eHh/2WWXBX+jhYUFmZqaCuf6YMcaH+6nOzKMPCspbj+EX1tbC7Nbers2wnMdYYZifn5+wy3bLGesdFg5Yrt5o9FIbf3EjiOcOYOR4w9/+MMwO7SysiJTU1PS29sbgMzExERwsOZ+YhkG9JVz586FLfO1Ws00zEmSpHx9LNJ9IAbeIPech9YDOm1+5xlDC+x4+XvGWOsIL52sNLRB1PWal7LAqE5T143Hu8dDFiBkPvLEtUCBNcD0yszp6DBa5jgunqEPwdle6+isfPOWkZeL0P94KQm7NZn09msMXrASgRlk6D2ebdV1EqsPDm+BFX6vXRBi15F4tOlBDO9MYmLB4Ltlent7U2duWEKk0aM2/nxGi5cn/mvwgudJcsHLnPPk2Rit5PVMDb75wzMVfDYKo2qvs8OnSJerr69Prr76aikUznvJt1qtcJ0CiPlhPxcuGxx+Ybz5ziCdDggAgP1psCyC2Q1cP7+6uiqTk5Nh2am3t1euvPJKGR4elieffFKWlpZSYGd4eFgGBwdlz549snfv3tT9WydPnnSVHy+TxEaGfJ+QN6Lh57ibCYDHam+uT/xOkiRsl8SsJJQeDl8ESBWRcNowX9oJB1+cCoywo6Oj4aCs3t5ec3s3CIpydnY2yArzzSAo64wYS7m2axC93zoPnVdsit3Ll8kbIHkDD6/MeJdnx5AH2iwwEksnBmg8PttJzwqnw7Jh1CDCq0NP91ppe/zl5dUaNHd1dcnAwMAGcJ63ffR/XtZFery5AzKG3wAMWDaG7w6Wl6EvFxcXw9EL1kwo17UeYCE/a3DngU3+f7G06UGMJgt8YIQLYwhfA4TRAs7bYBlpM3kC7wkqCwMMhLXHX8fjjzaGmn9OCyfOAshY24X1t+WtLiJhOQedt9VqycDAgIyNjcn8/HzYxQOji/uctMDjzqVSqSQrKyupQ+U4T67zoaEhWVtbk76+vrAlsFKphPA9PT1Sq9XkyiuvDDMoDzzwgExOTobdaLwbBwfjzczMyPHjxwPYqdfr4e6nQqEQ1rh1nWPZxGt3rkuOj/JYPi4AnTgFGHLCClf7ZfEIjc+zYUULUMGKbHZ2Vnp6emT79u1y2WWXhb6B05Tn5+eDkzR4f/TRR8OS7I4dO2RgYCDVR1AvlUpFduzYEQ4mhKOwNjhQpjHSxtwzAjpOLIwGflbfyaIsICJiz3Iyfx7wRfgswGGFjYW3Rs76vU5Hj7BjYIWBLYdvpywWr/iOLS1mgZnYIDUrLc0LgAD727EO5eVXHoBY5Bl4bSd4sI0ZYGwGGBgYkN7e3tSOR657uFgsLS0F3cIbKLiMsT6m9XgegIK2YKfgdvsaqG0Qc9ddd8nHPvYxuffee+XkyZPy5S9/WX7jN35DRM4vTfz7f//v5Wtf+5o8+uijUq/X5eDBg3LbbbfJxMRESGNqakre/e53y//6X/9LisWivP71r5dPfvKT0tfXF8Lcd999cvPNN8sPfvAD2bJli7z73e+WP/iDP7ioQopsRO/W1P7CwoKMjIyEiwG5kpliSkU3hhZ2bYTzNDgrCU/ReHG1geVnMHAwJjiHxeJd5MKptvo5lwVTlGNjYzI+Pi4rKysyPz8fRvXIr6enR66++mqZmZkJdycdOXJEHn/88XCmDfLDEgePbkDoiJOTk2EGBr4mXV1dcsUVV8jo6GjY6dPd3R0Md6GQ3lqt2xNLLlhKOXbsWLgbafv27XLFFVfI1NRUuEgNs3B8Rgzzqx1u4SjLZ9PASU+3MwAPpqtZJpIkffEn6oVHYLxVGnXDypSPIoBjb19fn4yMjMjg4GAK1DQaDZmbmwuO2Lg5+4knnpCzZ8/K+Ph4OEiSR3mFQkEGBgZkx44dcurUqeATo/sQzojxRnKW7GcBGZ1PFtjw+lkegBHjMU86ujxah1jAz8qTBz46LQ1GdL1YvHo6Ry8ntkM6H6sN+L9VdiuM5lG/07xaQCEPMNLPcUQHBnyaZ92eTJ5cgtBPMYvNu0+xI3NycjKkNTw8nDodHMdV4OBJPr6CdZCWDeYPbQL9YvGs5TVrWbhdIKupbRCzuLgo1157rbztbW+T173udal3S0tL8sMf/lD+6I/+SK699lqZnp6W97znPfKa17xG7rnnnhDupptukpMnT8rtt98uzWZTfvd3f1fe+c53yn/9r/9VRETm5ubkla98pRw8eFA++9nPyv333y9ve9vbZHBwUN75zndedGHZ2LJHNwzW4uJiQK9LS0sbZgtAPLpoB7GDWFD0iFor45hyQ168Por0ERYEg6lHLTDSOL8DxsMCWXo3iaW08R+zITD64+PjIa9KpSJdXV2yc+dO2b59e1hDnpubC6N9zIqJyIZD6zjPqampoDxQdmxnbLVasrCwIIODgwEQdXV1ydVXXy31el2OHj264XwYTRqcosP/wi/8goyOjgaDu7CwINPT0wF8WW3IMofRGjvbYTmNlxi53KiP6enpDXXCSo2BCnyNAMiQFwM5yFClUpH+/v6wXf3EiROpW7xxQ/fQ0JCMjY1JpVIJzsEAREtLS3L06FE5ceKEDA8Py9jYmIyMjARgxzvZ0CbagPAFnfw8puhihidm5L20YuAlRl5/9cBSHsrSJ145YuDDCmMN1rLS9vK3DLdVFzoNLfNenllAzgJmnL7+nQXCstosryHWeeryxNphYGBARkdH5bHHHktd7It4ACh8n1qr1UoBFeiAPOBPv4Pds+qE2xa/2dZYdgI6wwKQ7VDbIObGG2+UG2+80XxXr9fl9ttvTz37j//xP8pLXvISOX78uOzatUseeugh+frXvy4/+MEP5MUvfrGIiHzqU5+SV73qVfJnf/ZnMjExIV/84hdldXVVPve5z0m5XJarr75aDh06JB//+MfbBjGVSiU1yhRJC7rez7+wsCD1el0qlYosLy9vOMpZJA2G+Bl/c17a7yVL2C1FaCkF/Z554VG3yIWpTu4sDOKWl5elWq2GzmF1cH34HpNXNg0ceYQicmFtt1KpyNDQkOzcuTP4noB/zIpZ068nT56URx99VEZHR2VsbEyOHTsmIueXx5rNphw9elRWVlbk+c9/fhjJ9Pf3hxvI4aPBdWWBJV0P5XJZzp49K6dPnw67n8bGxkI50e5adlBeLPdwm7CTtR454z98f6anpwNAsZQLluQYvOAbznwAtTgzpq+vL4CpiYkJaTQa4TTrlZUVmZubkyeeeEJKpVK4KG5oaEi2bNkS+MGOiZWVFTl16pScPn1a6vW61Ot16enpkdnZWZmbmwszY1pmkiQJ6TDl7TOWEbP6pmco8hj+dkaOedPU77OAi5deHuDnAbQYYIkBBsTNUy8MaPRzaznc4zGPsctKy9JVHsDJkwfqCQNCvSFBf7y8rTx4oIFT6Dkt+PtBp+MKFtYhVvrWcz3rjTCWczzaAfl6u+A4L8z+ah0Xa/8Y/cx9YuDENzg4KCIid999twwODgYAIyJy8OBBKRaL8v3vf19+8zd/U+6++2552ctelvItuP766+VP/uRPZHp6WoaGhjbkA18G0NzcXPgN5W2NtnXnXFpaknq9LrVaLXUbMpOFZPV7/e2tf1pgSAs9z6x4Da1H/hAWLJ+IyIZlE54JwCxJrVZLHWOPb5xgi0sDsdbqOXJmKTRLKTHQwVJFo9EI26d5Vw2o1WrJ/fffH66PWF9fl+XlZVlcXJRCoSB79+6Vcrksy8vLAdCiLrD0wzdbW0pM84315ZmZGXnkkUcCGOrr65Nt27bJ7t27pVQqhdkHlj8oIvDOsyT4zfVi1SvOcZicnExtg0S7wyEcS0j4QB74zqZCoRDun+I8MOvGs3s84sNZNd3d3XLdddeF5Vhckjk7OxtmM6enp4MeQH0ARKKM3AcWFxdDXVl1ERtBayOXVzb1szygQRswnZ5+7s1A6L7rhY3xEiM9+NG6KAsY6Od6tN0uPx55AMwC9Vk8WulmATrL4GeVy5IlgAz9PsteeM+RBpbdMVuuZ1Wg03A+DcdnO8MgwhsYM5CxHJGtsmkg6/UznU5WH8minymIaTQacuutt8qb3vQmGRgYEBGRU6dOydjYWJqJ7m4ZHh6WU6dOhTB79uxJhdm6dWt4Z4GYj370o/KRj3xkw3O++0GPzEDcQVZXV8OSh94yzeFBnJ5Ol50f2bdG88D/NVJnQfM6hGfwuCNqoYHRZIWGjoflNF2WEydOyMmTJ6VcLsvg4KCMjIzI6OhoOI2VT6m1lIBV/5YRYqSPWYHh4eFwONvs7GxoV8yuLC8vS6FQCFOmcPDdtm2bTE9Py6FDh2T//v1hJ9rc3FwAI3orolWXeAaQhW3J5XJZ9uzZE5xfJycnZevWrSkfFYBI7NYCz/BxQb1hyhdABO2keQDI2LJli0xNTaVmrUAYiTFAAoBhIFwsFqVer4crKQAk+ZoKtL+WTfgfFYtFmZmZkUqlIrt375Y9e/ZIs9kMN5GjzbBDSkQCEEObc/mwgyNLoVnGH2lkGa5Yv81Lll6I6RmLdLtZOiIGLqyy6LJbfc7jJcanx0PM58GjrEEO8oQeyJOHZTAtB1tdr56u0rooqyz41pfsWgNTj2fNI/+HDwv0NvdJ5gUDAGvgDB2m5UxvKEBc7RNq2Sr+zUBJ56vbE7rmYrZVM/3MQEyz2ZTf+q3fkiRJ5DOf+czPKptAH/jAB+S9731v+D83Nyc7d+4M/3maXkQ2CCePEpeWlmRoaChsBdZkNaS1Rdkaseh0mKwRp7VdzxJyT2nzle0sLLzEwLzjJmWeieLOB6C3vLwsZ8+eDbMm9XpdRkZGZGhoSPr6+oLfi2douB51fTKVSiXZsmVLUA7btm0LyxQ4mBDXRmCGAVvBm82mPPjgg5IkF2YPFhYW5Mc//rGsr69LvV6XQsG+jdpqa3xz+v39/bJ79+5wQNTZs2dD/eKbZ2T4HeRPAxr8j43mAPCGh4elWCyGcx2g7MA75AdLRiIXgFWSJNLT0yP9/f0iIqnRWb1el+c85zly5MiRsIVay2qhcH5WaHV1Ve677z5pNBrhpnFsT9++fbvs2rUrnI6NXU7Dw8MbDAPagG+vjpFnpL2Rep6BiE43lkbe9LlsOo4OkweEaD6zAEoewNAOef05lo/V9/FtGXWvTTl8rA11vKw6YKDBfOQF0iIXfN7gl+LFtwZtsbR1/+erWcAr9AjzAkCD/yBOK4s/PdOuw+uVgTwya8mNdhJuh34mIAYA5tixY3LHHXeEWRgRkfHx8XDsOAhHm4+Pj4cwp0+fToXBf4TRVKlUUltrQdzgujGskQ6WVUQkddCa1XEtA8fPWQi9RvIUGb+Pjc40yudnvHTE+fJuEQZF6BRYVkL5tSMy4sBprNlsyvz8vJw4cUK6u7uDE+jIyIgMDw+HW6b1cdtZZddGAUs0WLrATMHKyor09vbKwsKCVCoV6enpkT179sj8/HyYgl1fX5dDhw4F736AHZSX68ECV0y42gCXad5///3ynOc8J+wGsOLqsuglMSg/BjQiG48P1+liKahQKMjCwkLq2gJudygxzCRh9qe/v3/DVk0ornq9Lvv27ZPDhw/L1NTUBtktFovh9GLM4mDZETu5AHCHhoZkeHhYJiYm5LLLLjPbHPF5FtBSkFY8Jl3XHijRYfTgQ6cXU7B5FHAWYPcATFaaVv78Pzar5QEvL12dfladxECAV99ZeXn1p8NxeL0kottcp2eVzasHTfoyXOYrr5x4bYGdmtiQ4Ol8nVYM6HI7af9RnVbevsV56o0K/D5J0ptkLmZW72kHMQAwDz/8sNx5550yMjKSen/gwAGZmZmRe++9V6677joREbnjjjuk1WrJ/v37Q5g//MM/DBdniYjcfvvtsm/fPnMpKUZJsvF49Ng6bqFQCN7c+gwP3YlYQCyjpePoLcIxJWzlB/41qvaUAfLkTqXLr8EdnmN3EJ8Mm9WpsXzRaDRkZmZGHnvsMalWq8EBFKPznp6esFtK161O2+p8cKKdmpoKS0dXXHGFLC0tydraWnAkLRQK4RyE2dnZsKyBNWu+iJLrL0vJ8JZ0XC65bds2GR0dldHR0XDuDNKMgV9dj2ib9fV1KZVK4bJJvcOIqbu7WwYGBmR+fn7DfUOtVit1qBWDJIDLmBz29vbKjh07wjlKnD9ADByIL7vsMrnyyivlgQcekOPHj8vAwIA0m005deqUPPHEE9Ld3S27d++Wa665xt3lgJlQS6YtcJ/VVnkBiMcP5+sZTw6XBUy8kW9sl4YGr5axjfWjGJDIA27yEpczdgbKxeajyxJrf60zPTmy0rbqOi9AxXUAmBXVRpvJmmFnnnW+/NySVx6cMmHnorZbiGPtwPX4sOpJb1zRaWFgxHG9urgYahvELCwsyJEjR8L/o0ePyqFDh2R4eFi2bdsm/+Jf/Av54Q9/KF/96ldlfX09+LkMDw9LuVyW5z73uXLDDTfIO97xDvnsZz8rzWZTbrnlFnnjG98YzpJ585vfLB/5yEfk7W9/u9x6663ywAMPyCc/+Un5xCc+0XYBWVAwutWe3YwGRdLGnZU2CxvHsRA2KxbEtZacOCzzzEpJN7AeaccEzJru4/Q89AsH2b6+Ppmfn0/lp4VWK2qEgdFdXV2VqakpOXr0aLhbBLM0cKLW2309Qjtu375dBgYGZGZmRgYHB2VgYCCsGWPnS5IkYYaOdz4tLCzI7OxsOByKl1/yEM6hYfDD5zEw/zFlqN/FRpiYQUH9ssNukiRhFgbhuS55fRzPe3t7gz+Lp+DxDst5jz76aGqWBMtSuGMJ964kyflZrmuuuUZqtZosLCzIyZMn5eGHH95QD1pesVRpURaIjoEIS1b5vzZ0Oqzua1mG0OLRMmRWPGswxP/1EQoxcJUX6Ol2yAsW8qTJzyz++B0bxax6aSf/vPxb5Y+FBV9arnGliJW3LivIArM6Pvoyn6PEOhnp6EEzwlgbMbjMsHcxQKftpLZrlp30dBqXXdvQvNQ2iLnnnnvk137t18J/+KG89a1vlQ9/+MPyP//n/xQRkRe84AWpeHfeeae8/OUvFxGRL37xi3LLLbfIK17xCikWzx929+d//uchbL1el7/7u7+Tm2++Wa677joZHR2VD37wgxd1Rgw3cJIkKUSK9/ytDb93k6cWNK+zWcrAU6oQJI2SrQ7toXWLJ68z67y1wGImrFKpyMrKygZhtQwyv2NAg2+cKzI5OSmFwvnzSnC67+joqPT29m44w8Sq60Lh/I433lWDI7WtOgH4wSm1mCHEUpOltCyjJnJ+JgaAiRUADn2r1+shDMganYBPqx3wG3WGPOAzgyWhVqsVZpl0m2A7JtcZ0sDSqwaj/AxKBs7VtVpNDh8+HHb+ob7BH3aU4VZs5D8wMBAcr3HAnWfMV1dXU2fgtGN8YmG9d1qBW3EuRrHqPi4iqQFRVlwLBHBaGnjxoMeLm4dHDxDFgIwHurRes9LV4SyevLzygFYrH++3lZbHs04XYdjW6MFeO3Kk21TzpNsdz3nJXoMH3ixggWF91Y7WDVxGXp62+p5lc3TbaT7arSNQ2yDm5S9/+UUpC6bh4eFwsJ1H11xzjXznO99pl70NpAWBTztlD2+Q18ks9OptPdMCrAUyBoCYZ42uOS6EB8sEGglbZRDZuF3OU3Z43mg0pLe3NywVeeW1yFMUqB9cSDgzMyOnTp0K/izY+TQ8PJxaehKRsHsM7ce7onR5PCXIz4vFYnAy5bMVYkobtzCjPtbW1uTBBx+U/v5+2blzZwBM8HHhcmtlp3nSCovlE0aeeV9bW5MzZ86kZoUKhQszLfqOqq6uLhkcHAzghrfJ8wcjPq6L4eFhufrqq+XYsWNy7tw5qVar0tXVFQAKZmGKxaL09PRId3e3nDt3Tn7605+GGSNcPOrJnnVGjKXILcXI70Exw5vXaHn9Ke9zbWwgB1kzjl6auuycphU3izxZ18bdAtp50moHiHI6elYi1uY6b0/P6jQ0ENR6KotH/Zt1t97KHEvfys/KH32TAQf6sD5PSwNdPcNlhfNkVLtCWPVo8Yq41gyQtone8SFZtOnvTuLK03dXoMJ0Y4qcr2Dv4De8Z4G1iNPTyNYLL5Jew+TOyP+9JSGdhxUnpiD0f/go9Pb2yuzsbKos/Fs/0x82yFwn6OxwqF5dXZXZ2dlwqFpfX1/wNYETKnianp4OswE9PT1hWzWMaLlcDh2c64OpVqvJtddeG65HwJbghYWF4OvBcTGbo2+xnpqakkajIbt37w5HevOIBeXGcsnS0pLMz89LuVyW3t5e6e3tDc7UnB9mifisH+S7trYmZ8+elaWlpdSsSb1eD5fN6esH6vW69Pf3b1Ae+HgAG9/1el2uvvpqOXv2rCwuLoapbSwtdXV1yQte8IKwQ2NpaUnOnTsXdkjhIDBL1pIk2TAzZvUvrZx1/9D8e2DakgcvrgdOYnFjPHEaVn+1wmaR7pNe/u2krQ2/Z9xBFu8xHnS8LBDiAUd8W8vnlm7LC/Q8sMo8MeH4BfgGilyYAcmTX548ORxfc8LAIyZXrJPYhvGFwwBK1uDBqmMte2xf9HurDrRNboc2PYhhIdbOVkwMSth3JqZU0PBZqB8NpBvfE1StNCwUzICADzMDMueThvW9QNbIwOvUSZKEswn4IDw2JBzWM4Kaf9Qf/us0kW+j0ZAzZ84EAzg0NCSjo6NSr9dl+/btYQfV8vJyOGwN5/xgJqS/vz/43uDaeQY22PXU29srW7duDQoI1yBMTU2FSwuxM6DVOn/BGm58hdygjbUzNb5XVlbk5MmTcvbs2dRZN93d3TI0NCQTExMyMjKywWBYdTc3NyczMzMBPNRqNenv7w87pPToh0/7xBk5eheUJYe6fbu7u8O5TSISZnsee+wx2bJli/T39weghMsiRSS1zduTF1wXoY1ZXsMZI0shWwpYh21XscaMfrvlsIxwTG9ocBcDcHmMpce7HoxYRi0LTOQBNx5Z4MoCTVrPcXwdVoMhSxZ0Xp5DNvof82FtKGlXZ+rfmFHl86W8PFgeETbLB8cibdf4mdUW+plHHkjNok0PYrLQPIMBvNOC4XV6DwDo0aulMK3wTOyIqTsZOgh+YyaDy6TPCdAG0ULrMVpaWpL+/v4wW6LrQJdJKzIPOHo7xZAGbz8GUDl58mRqK/fo6KgMDg7Kzp07JUkuXEiI02NxdgsupxwcHAwzHz09PdLX15c6n6VYPH//Un9/v0xMTEiz2Qx3O83Pz0u1WpVarSYHDhyQxcXFMHuDPCwFCpCJaV/s9BE5DwJWVlZkeXlZpqenZd++fbJ161YTyJTLZVlfX5eFhQU5e/asJEkSZl9GR0fDkpiW8+7ubunp6Ql1niRJCIfRHOqa24CVqFbmWObbvn27zM/Py5EjR+SRRx6RSqUiAwMDMjg4KEmSSL1eD+f46JurdbvrQxY9BZil6LPIC6uVMf/OA0A8w+fxrtvXM/TWqFr3sVhf9ow8v9MjaAvc6TK0U+cse7rsHq8cTteT549hHTFg5aH5t+TS0kuctjWjzUs9rVYrbIvmnZ5cJ7H/+h3vQEK/5etHMHDFjK5eymEXCuYFszmoUz0romVCZOMOKQu4ePWoQSDLX7u06UEMN4xVuawkYMAw0ubZE0uYudNbSiY2IrKADr/Xy0UaaGnQwmHZGMX8djzBskYerVZLFhcXpaenJ3WRmFdGD9hZCixL0FlhMCBYXl6Wc+fOyZNPPhmO/cc9RvV6XbZs2RIuQpuZmZGZmRlpNBpy4sSJVJv39vbKwMCA1Go1qVarAdhg9065XA5AAbNvhUIhtfNp9+7dYemGdw0xyET5WMFw+ZIkkcXFRXnooYekq6tLenp6UtO7IhdA0tTUlKytrcng4GA4YLC7u1uWl5dTZUOc3t5e8+4rhO3p6UnVM9oYH5YDlvckOX+f0+7du+Wxxx6T+fl5WVxclMXFRTl37pwcOHBAdu/eLf/wD/8QlK6WCRCWCb0Rq9eHNHmGi/u/9z7LKMcAQCxvK741oNF9yTOi+lmM96w09bOYoWUeY2XjfK1wsTLFwBOH1zokT/t5ZOUVqxPwqfU+H1kAx3/0J+woioG4GB8MUrB8C2CkQYc3w8JLy+AZ4UHsB+PpeN2XNAjWtsADa0/VqVfkWQBiQIz22LDwOzQyHDe1I5RGjyL+7IxWVLFRkEW8VAUhZZSsZ1isDmwBHavjMC8xBQI/h1qttuEsD50O+OTpTSttr77As4XodWfl+5JOnz4tSZKEk2PhT7N3714ROX+w3ezsrMzMzMj8/Hw4RRb+PiBccAiQABDg+dfw0ow21NxWCwsLMjMzY/oHgbCEtmPHjhTAQX7wR9m2bZvU6/WUE67mr1A4v2uIHWoxkkPdQs6wXMbLPmhH1DPfXQWFWC6XZWhoSJrNpjz22GNhi3Sr1ZKpqSnp7++XtbU16enp2aA8dT3pmZgY4NDp5InnhY2NgGPpemBdk2dg9aAkZsA8HZI1erf45mdeXVrlsdKLgaOsOFnAo922yQKqnu7Nm4eXBi8jsz5GHwFA1z5vMVCq8xKRMGOKNPl4CF12vcoQq3/O07MVedKKyTnHzfLJbIc2PYjROyxE4ss/mCKHQtfhCoVCWEvUBiYLzYNiQMFSKEDOWrj1riRMFWrBjCkwi2LvGo1G8C3BWQgxEGd1Al3nVt5ZxonTAvFMDe7pOXfuXFhK6e/vl+HhYRkeHg73d2EJB/cp4dbmhYWFAIqwXZidVwGSBgYGZGBgwDQwkB8Yf4zGePrXqqtW6/wBda1WSyYnJ8PBfa1WK/j/7Ny5U4aHh8NSG49sEBZbqbGMhPrS2/YBYpAOp8VbupEuAxkAxmq1Gj7wI1paWgqzSs1mMwWkLNlH/VjkKUivD+UdlXN/ioW30ozxkwVodHwPmHj6QfMeM8IxcPF0GZJYnh5Y0GGs8njAzSLP8OfRde2AJS0z7CjLd6bxrCUPSDku0tOz6bF8kR7vUgIfGIRYcm2tKvDSsx7Uc51re6N50gNrz45ZYD1mp/LQswLEWLdXW6MQ7GrBPS98aq/IBYSKUbcGMkjXEgLOE2QpJPzWwsZ8Il+AFk06vuYtBhRiowA8W15eDssPXjgdB/m1qzit+tMKR/OsP1hSmpubkxMnTkihUAhOsOPj4zI6Oio7d+6UVqsVnINnZ2fDDiU+gZNnl7DbZ//+/e6VCkmSpNauMRrT959owvbow4cPy9jYmFx11VUBDI2NjQWwrZebuNzd3d1hRxd4ss4dss7/KRQKqW31UJy8JIRZEyjOarUqu3btkh07dgQ/opmZmXCLdV9fn9l+eKZvoke+npzp0S6n1S5wfyphLcoDpGL9x+qHXr/Ma3w5vM5b90srrPfc6s9ZgxArvVi+edqDdW+eevfyiwFHL13k7W3gKBQK4ZoUT6/nAcAMSOBro10GNIBgoMVhdD6W3WHg4y1RWXaFgRH3UeaX+y//b5c2PYhhY68bCcoX4arVqpRKJVlcXAw+AdzY7O/AxoAbUpPXMDo8hB/v9H98J0l6t5GVpkb2HJaFkju8pTQ83tfX12V1dTXcV4R0dbnbVWTcCTwFZoHPLNLpYtfT5OSknDx5Umq1mvT19YWZmq1btwYfF+xQgvPt6upqUESWf4jOE/LX29sbtovjdufJyUmZnp4OO5+4c1er1VDPx44dk9nZWenp6ZGJiQnp6+vboKwtZShirzlbBkzLBNb2cZAdOwOLSKrf8DULKysrYet7f3+/7Nq1K9xqzcrNajcARqvtmHS/476SRVaf4frjPCzSACDWd6x8rHe6zaz2yVseLw8vTgwkes/wPAsMcN1YxtWjizFmHpDygK/135MBbWh1Hky85K93OC0tLUXL5gEZ6zdmPuCQy30KR3RYu1oRTm+ztsqkdYIGPDH+9XO2S5w+992YzYjRpgcxaGiRjZ0KlYhlAhzqxttCtZJiIYUwWArKGzXFBNhyEtZxtKOl7ryxrbKWsFromuNZ6FxEwvblSqUijUYjdJx2KK/SZFDmKXoP0Fm8wyDjG/cCnT17Vh5++GHp7e2V/v7+sJ17165dqZ09AB0LCwspPw89EmF+8ZvPccGMBYOa2dlZWVlZCUszV111lZw7dy44Jk9PT8vll18uW7Zs2XCXEZcVl6rimgfcE8X86B1uqBt+HjtzwhoBdnV1ydramjz22GNh6XFwcDBcMYG+ZrU7zoiJtal+fjFK72LIA/V5gbrVX2MAIg8Q0QDa4i9rwKCpXQBh5ZcFfCzDye+tcuetjxhvoDzp63J44MdqU4sP9DF9YrzHb0xOMJhuB9xa4EgDCj3Y51kUDWisNC3w6tWn9S7vQETTpgcxvMvIUrpokFqtJsViUebm5lJbT5Pkgke4NTNizZiwV3cMTWvQoTuAFnp2RuatcojLZBk2fq558pyVNejjfBjIYGsvbzH2Or0lwFnKT5dFx7M6tAd8OD7LBu8iaDQacurUqWB0h4eHg5NwvV6XwcFBGR8fT9Wf1Q5cfkvZYav4yMiI7NmzR1ZWVmRxcTFczrh3717ZtWuXrKysBKfgqakpWV5eDv448FvBbE6rdf6qgmPHjsni4qKMjIzIrl27ZGBgYMM5EtbyEuJ3d3eHc2VYpkUkNRvYarXCtRTFYjHs6uJt48eOHUtdAGnVlT4jxjLyWlFquYgRjwZjYbLksB1Db4HuPMYWvOY17Fa5rHSz+PZAlUecr6eLPKPupWeBHM+o50nT03/WOw1kOD8up+Vf4rWtl39WOTgfXc/wm7PKqv1x2BZpGwU+rHZkvWiBM6scWmbZTnk7fZGXrp+8tOlBjIgEJ0fu8Awi4JiIETnCiFw4CE0fu8+U1UDWc2srGsLyb8uL21sS4rSt9FjA9HSip2h0fE0w/KVSKTiveunF/meFzasssgyaDuOBItQ9Dr4DqMGheIODgzI6OirDw8PS19e34QC9PKSBYrFYTO0kwjv40PT09MjY2Fi4rgFOyFj6Ab8rKyty6tSpsCx6+vRpmZqakqGhIdm3b1+4wBLpFwqFANTxnHdaIV0tb7xLYnZ2Vo4fPx4u88T9R1Bi6D+xXTi84y1Guu0sufSABsu1Jfux/muB4Ty8cr3q9f8YqInlw/Hb5ckCh1YY5l+DC82rR1l90TKAHr9Z9RF75+VhlcsDz5D1LKCr88uTtk4DZA0uuQ9B3+J9kqR3H4psBAhaFnU9sMxqHxZdTl1WTs8DN2x/tA68GHpWgBgoXEx1AyEmSRLOBcEImKfpsBukWCyG4+1XVlZCekhbT8PheVbHQjjrHfJA+mxodGfCd2yq0kK67Yz0rDDMJ+oXV9FzGl45PeOgO4NFeQXeUlBMesTCfPF/tPfq6qo0m02ZnZ2Vxx9/XEqlktRqtbD0NDIyIn19fan7nqw6jPFlhdNhAaaYR/ijTE1NyeTkZNjuCcV2+vRp6e/vl23btqWAkjYQmE2B7xCPqJC/dST58vJyOPTPMzq1Ws2VtcXFxVR6On3vnUVZcqfTzJLPLKOVBfg9Zc//88h0zGjEyhHTRbEwulxWfWSl7VFefRkDqzo9zSfHZ5mMheX/aFtLBrQBtmTUA34WbxzXawueGcHggn1v0E9xYrueGbcAPJdND5otX1JdDksmvfpg26pdH7KAqkfPKhDDyrhQOO882d/fH3ZSWKgXH4AXGBDeUobwWVNiFjL1OiY3tsgF4bQcaK30mY+8SkZ3Li6P5yCqBR6Aj8/Zsc4GYZ6zlt80j9Zv6z/St5QUv9dl8gyg1eHW19dlcXFRJicn5ciRI6mt3KOjo+GMmVKp5CozyyhY4MXiGWHW19fl1KlT4SRfzGpwuq1WSx555BE5efKkjI2Nyfbt22VgYCCl1HDLNMI3m02pVqtSLpfDRY8MsBFvaGhInvvc58qRI0dS5+CwosdMk0XYFq/rRrcF5IXrSZNVl7r+Ys884NgO5THK1v8s3REDU7G4Oh39P6sus8Cc5iEPaGr3vVWnHk+WrvXy0DogxrsFfHk2RGTjFR7Wb933Pd2t8+UdsqjvGDCL9SOtG6x8PcAFna4HgVbfgb6w6kDriA6IMYgbEQgVfg61Wi0AmNXV1dSli4iLb24I3ouvHREtZWM1qsUjwjIx+k6SJIWs8R5CxTyygGsh5HwsFG3xoMviKQWMDOAnwz5JbPy89PModK3MLOWmO7cuu8VHXkVtPUPZlpaWZHl5OTgJl0olGR4eli1btgRQU6lUUs7mzJdn4LLAbrFYlL1798ri4qIMDw/L1NSUzMzMyNLSUgCUIhJ2XC0sLMjp06flmmuukfHx8ZQcgw8sD66uroZdecgPS0N4jpvFtdMutw2DGJ0Xn2iq+5DVn/R7D3BYsqYBllenuv94aXrPvOdWf2wHWOShLEMcA1JeWjFfoqw82+HFSjNP/Xq6zgIrsfb38onxyXXDAzdtH/RRCFnl4GcWEPCWsMED4llltQbE1iYFfOtBtO5nGrxpgKP9PzmdmF3Jok0PYriCCoULtxDD8ZBvKgbIEdk4zccVzCPB7u5uc2odcXUDacQaM6QMfiwHYo7DF1HqDsEHoMUUsgfCuA60AbCEHUCmWq0GAwge86LudgRag0TmR5NVtiyFpg2m5o/lgtPT/jQ47XhoaEjGxsbCZYnlctlUKFl86Xe4F2pwcFB27dolzWZT5ufnwxLT3NycLC0thaPKFxcXU0ATAEdEwgF1ACiQIfjTrK2thffnzp2TqakpWV1dlfn5eRNAI00GOVyHq6ur4U4uC3jEgK5VR1476T4SG71a+ek0+Znlv6Z5zALpeiDhAbY8MhurF/6vDUzMwOpy5ck7DwCx2oT/a53qxdVgol0do42rx5NXDvxnwG/VV5Z8ZRH0qUh6FkX7ZrIdsWRfD+byAEpP32blr/PT7drZneQQ/DTK5XK4fI5PdNXKwvLqthqa/2OvPgwBT/VB+XODeee8MB8WGNFo3AJGWhHx7hNOS+TCEhU/YwWU1Zk8ACRyYSdVuVwOQEYDMc1znnw0jxYfXrqxODrdLL6sNtLP0V6QH8w2TE5OymOPPSbValX6+vpkZGRExsbGZGRkRHp7e1NO6FnGyqofkQsHMmLnE3YQzc/Py+TkZLiZG8tH2OmwtLQUTjAWkXByMM552bt3r1Sr1eAsD5qZmUntjgLfrJhKpZK5vVrk/EnQOGsmi7QR94yRlgPdr7n+uH9mgQTLGMXCa8Oo41lGznsWM34x4BKT1TyAI0axMrDx0gM4j3fdh2Jgw+M3CzR68T196vGKnUDaFaEdo5wHbPE382E53VoDLI4DwmBTZONGE7SZly8/92wjp4F8rIMAuc7zADhNmx7EDA4OBgWNm5DheMqdBDMqeA6KKQDQ+vp6uF1YI1+RjcjVUqD6v+5IOj1+ZqHfLAXH4MxTxPqdJotXDo/6rFQq0mw2w8gdW3E5nVj96tGo1x66c2mytqBaoMNTPjEeY8TADnlglmZlZUUmJyfl0UcfDbc/j4yMyJYtW2RoaEhqtVoKLICyFB8T6hszQaOjo2GGBduhq9WqVCqVcPDf7OxsuAV8bm4uLPVs3bpVarWa/PSnP5UtW7ZIoVCQubm54DPGeep24GU0XX84IyarjFoGsuogy5hxGP2sXeNu9TER/zI+K08vH6sv5ukXzIfOxyqfTsP6b6VvDags3q0yZdVtFpDQ/MW2mmvj7vFr5deuDMWeM68WANE2RMfzgAOTtkc6PG/f1ks9nB4P4rlfY+WC9SZ+67bR59pYZbaW+PPQpgcxhUIhbJ3Wzqb67BhuVE9gvc6zuroq1WpVRDZucdajPN1xLCWhG1Nvq+YwMUWgO3UepJsFKLhcmg/rf7PZDEsmOE9GX4SmQZrO3wNhXnj921O+WWX38o/Fs9rVApj8G1cbLCwsyKlTpwKw4FmagYGBADrA/8V0esg+nI1ZcWIXXl9fn4yPj4fDH3HQ3sDAgKysrMjJkyflxIkTwYmbd/1ZsloonF+Ksg5ETJIkONbnUc54164sW/3Fko8s46rj65FyuwbNMqSxwUOMH2tAkZV/LKylC7LKkYffPO+13myHT35v6fO87avzYV3Ks47MAxtlzwXAkxVLr3q8e3aGAYe3JZzz1bMlMRDqAS/+r8+G0WlY/2OgLYs2PYiZm5tLCRs3EDvy8tSaiD16AXkN3Gw2U1uMLUWuGysPQIBhYDSLNCwE7Rn3rI6TNWqI/eeyWEpjZWUlXKLYaDTCQXlWPXlA0euIHvDw/mcp5pii9oyeVw7mVSsdC/xBFrH0NDc3F7Zy9/X1BX8a3srNcuXxnCULVtxisRiWYQcGBmTnzp1SKBTkxIkTkiTp6wa4DDhXSeeFm7E1X0ly4YyYPMDEqkPrnVVe63m7ijMrvKWg81AeXmLyF3tvAaUYf3nqxOo3FnkDlJiPn37ugcYYn9YATvfJLN0R0zFW37HKmaddPd6s55pX/UzvxEU8fSwHp6k3hDAP2j5o3jy+rbaO1Uc7fZ9p04MYq0EZJReLxdRBXIVCIfgk6MbW090aaWKZAEtTugNZQu8hVc6DgY/mSwuSNqScBoMeT7Ho8NY7b+SRZShWVlakXC5LT09PuGDTIk+Ydf3lNT6WMsgTR+cR+w0Zs9KxlEJWugy6IUu4lfvRRx8NxwNg59PIyIj09PSE03uZdFvnVagaSCNesViUgYGBcJieLo+eOkZ8nBFjge7FxUXTyGYZHk9ONcWAcSyuBwA942cBcX7v5eEZcs7Typ/1ijYsltHRdWCV3Sqf90z/twZQXl1lAZCn8t7j0eIhBni9NvXaV/N1MSApS+bZ7sQAjH6P9tebV7w8rRkaTjNWt9reMojSeeJd1u43jzY9iOEG5+k1BhzWqYTc6fHRjocWItWzJSIXTgzOEmgtJGxIrZGtZzRjyktTTInFUL+F5i3wpMvcaDSkXC5LX1+fNBqNcLKrnnrNYzhinSgG0ixqJ6wXPwZ8LOCSBQK1kkU9Jcn5mYulpSU5ffq0HD58WGq1Wmord71e3+CD0k5ZmLQ8b926VQYHB2V+fj74zczOzoY7tLz4+owYBmyNRsOMo+u0HSCKdGIg1gL73nvNdyytdilmlC2AE+trlizFymUBVi8d/VznrfnkcmUZTavMMTDolclrh3b7eAysev+zwGeMX13mmJzxkg3vUkI8nGXGfGlbxWngf8weWPorq928/ss2ydo63g5tehADAMEVyo2tt8Jx4/CHiVElwvJIdWVlJXUOiOewlKdTIS9LcWhD7eXBW6yt/PIoUKtO8ih8i+BUip04uDMHQMZTnF7ZPdDAZQO/McVgddTYe68Dt0toI22IYoaJZRnbnZeWluTEiRPS1dUVrkbALM3AwIC5ldsDUjoM1x+uQKjVajI2NiZra2uysrIic3Nz4Wbu+fn51PEFsYPums1mOKcJ/cWSr3bAg24vr2xWOrquY3Vj8ZaXdw1k85CWYQsMx3jTcSy6mPNgLL70O9ZBMSBgEfcR/ZzbOgu0clpZ+VmAJNb/Y2Ari3eLWI4QhgfiuHwXrgbgGQNzPaDmerJAiLe87/Hm1QHrJk4HdaBndvL0zRhtehCD8y4AZBjFiqSNvIciWZisb1byiM/vPKHQhlWDFE6D4/Bvy/BpvnR6+I5tr2byOpv13AIgFu9YQqhWq9Lb2xvuAcKWeCtODLTE+IgpWAscchpWnroOLKOn+c2SBcswefE1X1pp4IC6mZkZOX78eLihnXc9aX+amAx4bYrnuCiyt7c3OAM3Gg2ZnZ0NoAYn/1p5YOcap6uBcpahttrE4jUPeUaaf1tT48xnTEbzlIHjavm0ZCiWbyxty7jqMjHvsbTaeW6lh/ys83a8/GNtlQfI5G0HxMkTBnIb23jAsm1tb9Z5amCMO9NqtVoAMLhMNkmScHyI1hsx0KGBkiW3MZnQaVv+mlp+LZ7apU0PYrq7u8M5JXz+BjcUZgC0869nmGJGyJuSszqsVrZenghvGWYWQO2DwzMbeYSQ+fCUMNJlHiwlG6sD3CuE+6pKpVLKTyZJkrAtPquOvPr1ePDCxPj20rOUTt78vXoHWUZcKzpPVjXAWVpaksXFRTlz5oz8v//3/6Snp0fq9Xq4lXtwcNDdys38xN4xX3AG7u/vl4mJiTBT09vbG+Lw8iGfEaPbmw2S9dtStB5vFq95ymrJhgdws2ROG1gNIvOQBZ7AB6dvvdf91ioDk5Ylb0CQB+R4QI/b3DNsumxaznWYGA95y2yV0drto2WKP14ZPZ5E0iDOkg/wsbS0FPqayAXQgCV6rif2hdQ6xdKBWr/wFmpPD1l1Ztk9Te3Kv6ZnBYjBbg+M9LXC8zoEk2W0Gbjwt0VeI1vhrJGEzjeWlwV+OB1re3kWXxZ/HqDQ76w0cCggRu2VSiVcxImROYxqluFBmTzl4ikPjzfvmdUh8ypxTXk7czsGMia7/FlcXJSlpaVwK3elUpGRkZEwU8NLTyw/WYbF4hc7nCqVitt/lpeXzTOLvHrTylYrV0vxewY4q045T13PXnm8NCzy+LX+W3nE5FifxWSlG0u/HV1gUZ7+lrcOY2l5ZcrzrF29hW9Lnvgd1z0Pjj2ydFwWyMYBljxrUiqVNuhDTlsfOgiAYgFHBm1W/eu6suQXaTDfHE/boDz9UdOmBzF8sBoqjGdkuKFAPDMDpynP6Oi8vE5g5eMpEysNFqSYwmHBsUY+3rN2FLCF4DVfsfTgJ8HpYpcL2gZAxruLxwMrsbyzlLLVkZnPWD15Rt76n1dJsrKMxecye3lyGJYNHLq3sLAgx48fD8tCw8PDMjY2JsPDw9LX1yflcjl1xounyCz51u+5DZMkCT5RntHOStNKNy/F6tQzrBoc6/5gpe9RDJzkAaV5eM9bH1k6IKZ/vPQ8fcbpQF95W649fjhsXiDoyWsMvGQBNUuX5imDB7JifOj3bNswQPcOsUP6Wl7hP2P5qui0GJBoHxqr/bh9PB3JgxFvx2qMNj2IKRTSW8qwKwlXlSOM1fGh7C0g4wmvpcy0QsqrVLztinlGdSKyYXYipuyyDHVMQevDzTyUrfPVlw62Wq1wfgwfombl6RErM92BYrzgt9XhvTbzDHlMOcXS5PrTbZaVvn4eK7uWXc53eXlZlpeX5dy5c3L06NFwNcKWLVtky5YtMjg4GLZye4Zb56XLx/wlyfmZIc23VRZP9q2yZg068vQjLy6H0cBJ14k2CFY9tFOHWWE1j3nDxdK12iLPOxD6dt78PYBqpWsBpRgfIumlzKw8vPeW7Fu6lN9Zh9Pp+FlyB/5ZnrB5BasOHEfbEOu8Fl1vepMB6y2tO/De2zBglcv6bdVZXnpWgJhSqZTawsmOULETFxE2q5Gs5ywguF9DCwfzyN9Z77zTezXy1jNDMWPm5ZuHrOlCTe0oa9Q5gIw1c4M0s0BhTOl6vFlGKYt3KJUsZchp6rytuHnqLVYHFnj2ABQ/R5uur68Hf5ru7m6p1WpSr9cDqKnX66lThL1yaH4QTkTClQZMug0sgBdL28vP62N5DF9WOAuAaV4upi/qNNqhvPKoQXaevHWZvbyy6gMGXsf30rSAsGVkrT7tgc8syiqDZUM0v97gxJIbSz/o9/iPe9KazaYkSWK6TGjeeJlI6wbdR/R5L/jmNLy2897rlYKs/hWjTQ9isG1TJN1A2NLL70FaqDwvczzj8OxHgPcAMeBH5IKh1mBEp635soSb78hgAKPLw3HzGH9LqelOYXWSWJqW4tH54f/6+nrYQcO7V6x8Ywo4T/3qDmbxbSkmr2Pqd1npcbpZyozTttLJAj4aHMTkXysY3Iz9xBNPSHd3t/T09Mjo6Khs3bpVhoeHpb+/Pzhue8T54jJWj78YWW2i5Uo/8wYMVnvGDJIVzuJNy7RnlLJA1MVSu6ACeVo6w0vDAhWxAY1+/nSX3eInFjYL0Hh1mKV3dDj+L7JxxgPvY32e48K2FIvFMNjT13pYs056Jgf1YPGo68oDZ3o3kwX+PbCGsJ3lJIewbGShQRh/fdeDB2I8JyTL8FiIlU/zRXpaeDhdTZo/jqPDWQqUUXEWecqey5tXyei6yYO2EY53la2trblbW638vPq0OqnHl1fX7ZQ9S6l7Ss56p5/hf9bBh7qM2rDqMmowYL2DU/b09LQ89thjUiqVZGBgQEZHR4M/DXY9eSNPpKHz8xSpB0hi4Zn0wMEDnRbIsZS0R7E+rY1mrD/nBXQWefrCGzXr37o8Vp1zHK8PefpCP7fqJQ8Yt55rHazLmzWznqev83PrnKesQQq3rZZHSybwnoFBbOCkZT1JktSAWu84go0qFNL+L7q8nk7R5dW7f7Xs6HNjLpY2PYjR6I6FhNEjHyJkXVGQR0GysLBAAyEzUEI+FjFPzDN+63zwnIXEAlaW8eN3OqzFV0ypWorby9Mqr6eUms1mmJHRW3Gz4lr5xMrohdflySMPsfeWktTvLcPsKUcLnOi0rLLodGP16fGHdsFlkWfOnJGHH35Yent7pV6vy8jISDhFmC+BTJIkdWqzVRarbiyArssW41uH0cbZSsdrd62EY+3j+WFcjCxZ6eR9npXWxfLB8mClod/rOrX4jZXBaw9O2ytLDCBZ+iqrL8bqyWsbqyyeLIIYGAB0aJDAdoyfaVvGeeo0mG9t0zTY4ueef6QujzWAaFdWQZsexFiggpVgkiSpU0Wtjpi3cj2jagEaDmOBCA1I9LSg1zFiTmuWEOURnhj40eE4vKf42wE1CAsgUyqVwlb5WBoW/1mjujyAyBo9WmG8bYs6TyvtrPrmes2rSMGTDuf9tt7lAU/43Ww2ZWFhQRYXF8PSU61WC7dyY9fT8vJyaqmQy+mVw5OZPPLGvFrGQvdJT/FabWLloZ95oEy/98qr41n5ev0zD3nGX/MYG/hkpaF50zzHZDLGp8WH9y5GMZ3Ybppem1t9nPn2HHDxwYwJrzSIbLzUWG9K0XWD59rpPOsqAG1LrXLoZ9qm5am/PLTpQYyIv8tHhwGS1Mc1a+dfr/NYhAbko//1fU1Z6XiAiMFZ3m11sdkfja69EYHOj3nkjpalCLKON9fhV1dXw9H11l1SFgDA+xgwbacTaYCWlZ42rlkKz1IyMQPBgEnzyL/z1rXHk1XWLNAjcsH3bGVlRRqNhszPz8vjjz8eThEWubC9vh1+LEMda48sEMZpWM7+Hi8eUM16ltUmWl4sI2/xqGXz6TASVhk0YLRAGcgDNRYgystruzpY/84CeLr+YwNc1oceefJn5esNlHSdQ5/rK1v4+g72+4yBDUun4TnbT84fx5doHafz0PH4vwZsFyOrT20x6hIgoFZQHkHTx97r9TwvrpUWPrz+KLJxqYjDeoYrzyglxmdMmVvpWQKNbwtZ5wUwujxZ4InfA8hw59JGLWbA8iqxWD1YZeK8LMOjy6HLnFVfmh/PkHlhrfRjfFvybIWx+NcnX/On1WqFZafJyUmZnJx079Px+PV45DCxNmun7XXanqx5YMri3Us7RnlGr167xcpgyWiWrMR4toxVO8ab//O3VQbNvzbKVhoe+LHCWLpSz5C0SxbvWfWo8/NOlkda+uBI2D/LnuTRi9bMPvOiT4rHRy9PsRsFf3vuGnlp04MYER8R6vciF3YzcQWLXGiAmILSyjqm9LmBtWLEM23oPSXOfMGJUguWdx0C58n/PaBixdGKJCaQHhiLKUdddzgEzzKiXjqecs9jhDyg4SkZ650X7mIopvy8sHmBGOipGHqtGPOCojx8MX8xHvPU0cUoTp1v1rEJsfyyZE7/9wyX/h3Tcd7AJK+MZ5Fu/3ZBldYhngzl7aPMU5ZuiA3aPP6ZZytuLK3YYNKqJ21TGCzoePjGOw1kdF746OMsrHLF2hekgRTCc/vG9HM79KwBMVx5jBYt48zbob1pRKSLOExew4G8c084PN5bnVmTVTadphb8WMe0AIYldHnBh5WmVxYrLpdT5PyyEk9l8juPlzz5ef/zjiY1n1Z9ZfGSl+88z2IgQofx6s/LJ0sZxvKyKI9itOJkAaFYX7D489Ly6jIPr1YdZxndWDoWP1Zf8QCCJmsWQ/9+KsA7Bj50OK/s1sDK4jUGDHTYWP+MlcHiy0svJqPtyDrysviHHwwDGp494fOrYnrVs3Ee+NAzLvzeKrf+WEdZXAxtehCDbblZhtlqPGtrmAUAuKGsqTWd1/r6etiRwYJj8ZMkG+/BEJEN+SCcjhtTIBejmLyZGaaLNWTtEM6QSZIkdfJvVp5Z+XsG2vrvGbS88a2887ZROzMr7RhyK42Lac8YqLHStZQj8+4ZeyjwvGSlbaWfJ02r/1rp5QHY1jtdP+2kkyffvODC4qtduth+r+NlXU3gyaxVPkuvZ+m3vAMaK5z137IlXlzdn6xt4tqfE2T57Oj3enVA54t3vCqB99YsEaejHYf5UywWw4TBxVDbse666y759V//dZmYmJBCoSBf+cpX3LDvete7pFAoyH/4D/8h9XxqakpuuukmGRgYkMHBQXn7298uCwsLqTD33XefvPSlL5VqtSo7d+6UP/3TP22XVRGRcC9LTIhF4oYKFaxPKLRATaFQ2LBmieeMQNF4ejbBUojezInVwS0gY5HXgb1O6uXpjdq8tGIKQI9mOD8NzNbX18OlgtaaamzU5VFepW4pCSsNj5d2DUZWW2al5bWdDtMOwIvJoxfG40UbaAvcZAHmpwpOPV3QrtzE3mfVv0UxWbOee/WRpf8sfj2KAUwPOOXl3wITnFasbTw+LF1ggZxYXppi8iziL6dY6Vh8tgPSeEALHxVOS1+fw+fM6Db06pfBR8yHjdOz2lHbKNjDp+IX0zaIWVxclGuvvVY+/elPR8N9+ctflu9973syMTGx4d1NN90kP/nJT+T222+Xr371q3LXXXfJO9/5zvB+bm5OXvnKV8ru3bvl3nvvlY997GPy4Q9/WP7zf/7P7bIrjUbD3f1gKV0LiTOQ0Y2jFTsbTEtAEV9fqKfDxQ61s/KLHXnvjU6stC1+LKBmKTDuFO0aXhbsGDjgMpdKJalUKqEjeGWKGYg8dea9i5Fn0LOMaZ62igG9PAY9T/poy6wwTFpWLqaePP68tGKnaXO8GEjS5AHzdg0y0td55zHcecCuxz/zG5NxxM8y1vp3lmxrGYrxqXWkZ0w9/afLHOMbv736baffWKDHA1+xtKz/Xr2xntODN/5eX18P58doXgqF9FkuDIQ0ONHyqPPT7aLljOuZebHiomwXA2Ta3mJ94403yo033hgN8+STT8q73/1u+cY3viGvfvWrU+8eeugh+frXvy4/+MEP5MUvfrGIiHzqU5+SV73qVfJnf/ZnMjExIV/84hdldXVVPve5z0m5XJarr75aDh06JB//+MdTYCcP6WkuJqvSPeHHVjVGulqILS9uzYtOF/Es5SZigxmtUPFMH9KneWi341ojGB2Xw2DruMiF6xX4Xpx2lSX/t4zq6uqqVKtVaTQa5pHbmkePjzwGyjJGFmmjFwMvWaSBL55lGewsymP4OF/9nZVuO+WMAYJ2RsbMf568LgZsewMX5tFKV+sYjycPJOh086Tn6QkvngdWLbLq0crDi+OR1S9jvzVQ4nSseoi1sTdbr/nSRjgmR16/QZjY1uIYUOOZfK9OmFfmhe1hTD48XqzVCG9mxqoL5lEDv4u9duBp94lptVrylre8Rd7//vfL1VdfveH93XffLYODgwHAiIgcPHhQisWifP/73w9hXvayl0m5XA5hrr/+ejl8+LBMT0+b+a6srMjc3FzqA/IQsnd+TEzY+aA63bG8e5i4wbS3OPMXUzQewtXvNFrXvFi/rbLr+so6Z6RUKsnExIRcdtllsnv3btm5c6eMjY1t2NWlFXQeI6I7DZ41m02pVqthR1ZW21n1kUd5ZwGsrP9ZACmPgtfpZRldHSaPAfPCaCCThyzZjbV1HoCSJ2xWn7EMUoxiwNfjxXtmKXH98fjKaqOsvHV4DwBYZYzlwXxn8ZZFnlzGAK1VV1pf4GOBhqx0dP4xvRkDAZpnDTBixKDHkmPdvvpuJJ6lx3JObNt07LnW5xaoAU+xCQSvbi6GnnYQ8yd/8ifS3d0tv//7v2++P3XqlIyNjaWedXd3y/DwsJw6dSqE2bp1ayoM/iOMpo9+9KNSr9fDZ+fOnWY4q6OxkGQhwSRJOxPyAUPY9msRdyI0sPaJyepQWhFqQAYHV0/J6HLEeNUUc7rq6uqS/v5+qdVqUqlUpFKphKWeUqlk8st8xP7H+IPTdrlczgViPLLitGtI2gGIlkOeZVS8OmvXQFjpZqXnyZ/13ko7BuYs8kbdHn9WHrE0rfxjRjxWx55xZaOXB0DpuDFZytvmeQdCVjxN7cgJ16VloPP2cw9Y8rPYjLdljPP0YysdTy9ocGOBHU5Pf8d44PeWPWB7Yel71oN4bp3lYvGhQbZuR7Z5Vn76GA/PVsF+Zt1flZeeVhBz7733yic/+Un5whe+cFFI/KnQBz7wAZmdnQ2fxx9/PLyLnZHClOXBzUCCly7wH+uQWce7478+8RBpaWVgCbSFfq01y9goRv/3RhveiASCODQ0JFu2bJHu7u7wvFQqBb8fzaeuV51XltHi76WlJalWq6bhy1JmMT60Us7i3XtvhY2tPWcpXE+5WnH07xhPHD7L2OUxhu0oJA1ALfmMlUPLfN68Y8A3Txq6Tay+lSetGPC4WD1qASfNV540PLLAldX2lgHP+p8XUHnhvLbIw3Peds8CoFmyaOll/V4/T5Jkw8yHTitPm7FtELkAcjQPsGc6nr6HKVZ3+hkPtjXvPxcg5jvf+Y6cOXNGdu3aJd3d3dLd3S3Hjh2T973vfXLZZZeJiMj4+LicOXMmFW9tbU2mpqZkfHw8hDl9+nQqDP4jjKZKpSIDAwOpD8gyDB5i1qfAeopJJO2M2mq1pNlsbkCwTFpwvWm9rN1UGpx4HdEDR1rJxIQoSxkMDAzIyMhIWNLRDsaestJ8xUYtVj0gfqPRMOvRy1+nE4vndfqszprHaF1M543JouY9L3+6L3D8vAAqywDkBaV5ysLG2VP8MR64bLofWflbaVtpeXKcxZcVJqtceQ0Vh7faOUYxXWDVWVbaWXIb6wOa9zwOoO22hx5IZIEPfHhrsqVruXyeHs3iKQuMxUCUlz7rak7bAkexdKxt2ZpifVXr1ouhpxXEvOUtb5H77rtPDh06FD4TExPy/ve/X77xjW+IiMiBAwdkZmZG7r333hDvjjvukFarJfv37w9h7rrrrtTFcLfffrvs27dPhoaG2uLJE14GCkmSpJZ6dHhd4bx9Tnt6WwhdG0AdXjegZzR03pyeLrMl6FYeWXXmGe8kOb87aHh4OFzTAP5wrPzCwkLqjiMPPGUBLK+DIz+cGaNJ16+uKz3NasXV9WGVJS9ZbRADF1Ycrx3bAUJeXh5f+pllcC3AF1PcHt9a9vPIvwWuvP8Wf/rD+Xu8Z7VdVru2Q7H0suJpeqpAJmawsu6CwvfF8pBl9D1DaelgLy/PoGbxHQN21qAlpvPzEuJYjr5WXWjAYflO6nuSPP51PE+fW/9jPF4Mtb07aWFhQY4cORL+Hz16VA4dOiTDw8Oya9cuGRkZSYUvlUoyPj4u+/btExGR5z73uXLDDTfIO97xDvnsZz8rzWZTbrnlFnnjG98YtmO/+c1vlo985CPy9re/XW699VZ54IEH5JOf/KR84hOfaLuAXkfUAlsoFFIXaVnxEXZ9fV26u7tTh+FZh8/FePE6DAuIpRTYMFvGxvOxwbc2zBaC13l5nX5tbU2mp6elWCwGJ2zU4eLiokxOTqYO9dPLdVrQmQeteHR4TmdlZUUqlYo0Go3wzmpHS8nEDII2Vl6bxp57RtO6bdzKQ++Es+pGA3IuW1YZOd+suvaMQJaByFLQlkx47aXJ4zOPQvTkL8ZfHrCUZSxj6cd49QysxaM1EMnSSV66Xr/L06esvqPDxOrLkvFY2DxhYno3RlZfho6ObfXP0xe9clrhuL6wNINNJVzfGtzwAIBtiC6bNaDXes6qP6sdPb0Oe6DTuBhqG8Tcc8898mu/9mvh/3vf+14REXnrW98qX/jCF3Kl8cUvflFuueUWecUrXiHFYlFe//rXy5//+Z+H9/V6Xf7u7/5Obr75ZrnuuutkdHRUPvjBD7a9vVrkQiVat0ZzBTNo8BCiFkTvJmeLuOF4LVGfCGw1LqcN4+eVRZfJG414daXrxSsL6mtubk5WV1dlYGBA+vr6ZG1tTWZmZmRubk7W1taiDsEWkNTl9cAe09ramlQqFenq6gpnAlngIgZGOC8mSylze3tGzIrnKYGYsuXf/K1BbJbRRnt5dWx9Z5HmxQJTsXieMbGUnpUGysVltOrcGwV6IOlilakFBHWdWmAzK02PpyyDmDeddknLvx4IWjxoEO+lG3vvhde/dR/NA2qzdB7r26dShzFe2gVSHIeBCQMiHAti2QHW43gnIqlTfzkvHlDFgKqnt2JlfEp1mjwdUv1zSHNzc2GnEs4QgaOSpUAxswICItXhkiSRtbU1KRTOO69q5yfEtYwFAAvil8tlKZVK0mg0UnzpI5pF0uvA3rXvhcKFG7iRB+IjDhxt4cPDPHp5W3WgPdS7urqkr69PGo2GrKyspMLxJZoM3LgM+qRhlFnzpt+jTNgZtrS0FMJal3bC2KJM+t6PVqsVHJRZoVpXP4B4Rxtvs0d6vC0ffOszbbRh0GXWQEErBj1VrHnluHq2zmoT8OgZCq5/a5ddV1dXSha5fizQinpMkiTMdLKMra+vB5nW/U0fbbC+vr6hPFxGz9By/9d9zJoRYxmyQIkHYHSe1llQemRtGQQ8Z/llsGqBMk93xPhlHaTz5P+x5aQYDx7Y9ACoVRexmU3WaegHMV8OLoul+/AefoB4rm0L1w+AAUCF5Y+i9Y0uB/JAfPQxlncM4tAmMX9B/s1tqOuDdYRVV7rtLYDl/UZZWb9NT0/L7Oxsyq81Rpv+7iQ902Ehbm8EYHU0hBfZCCwso6mJBZyNuzdS4XCct8enDod3bGg9o4RnlhB6dYDnmIFZWVnZwDOHsz7eeQIMMnQ5dVviUsgs8urPM6xePObfMxgcluNY9Z4nv7xl0eFjecXKHVPyMfL6F95llT0rTUsRaorxGFOysTR03461uRVfl8H6tn5b4NnTVe3WbZ7wMZnOS3l4z9KdFki3wmTJO3+y8rUAndWOsTDWO6+9Yv1EPyuVSqkjLKxdRqyLPZ5iu3e9PuLpZMuWaR3pDR6y9IpHmx7EgLiCszoLwvN3sViU7u5uKZfLUqlUwswNN6J3iVWWULEQedcHaGFiBBwTQOSHo6gx2uX8mP9SqbShfjR44VF2TIHHlLgGbFlG1wM0nA+fGWN1JI9PTi8WR/Oat9NZMueNtPg/6joPZRmCdoyOp+wsMBYLr9Nrx2DmMUax+LE2t9rVaqOYDGhes/LQ4bLkUofLAlR5KdZWHrBqF7RmhfEGSVn8We88WbOMppYpXa/6dx7+rPyyBjJe+Ty+8btSqUi1Wg3pNJvNMIuvAYz3rWe3mWf8zwNqYs84nZizMVNsk0UWte0Tc6mRnn63FIc+qwWN2N3dLaVSSbq7u1MH9Kyvr8vs7KwkyYWp9EqlIuVyWRYWFlL3VoB0vhqRa551XP0fvOi1WvYJ0XF4uhM8WJ0VaSMfq77ygEEsyzDStsoLp2Dr8CbNFwM3TLnzKciYXuV6tJQm4jFPnqK1frdDOl2ASmu5UsfLmoWz4njGh+ssD+npccsIxPjy4lm8xkBYuwY7j9Hx+M4yMhbvXtg89azlz5tN9PqslQ7zx+2Xt909feOF9ZaoYu/Ac7vkxYmBEXzrvmEZcq1r2gHSWWFjfUC/1/wWi0Xp6emRtbW1MNstcl7HYhYaS1awC3r5D2nx0hvXgQfCYn0T8a0ZGKsf8rfWU3nlU9OmBzEgS4D1e5HzU3S1Wk3K5XIQhvX1dVlZWQmCsba2FvxJkO7a2loYNVvgAOEsgGL5g3jxtUJiIJMkyYbtzogPQCGy8Vhqzluvp3vKAXl5YIY7hrf9GZ9SqbRhDdoSaF2/KD+e45weAM7V1VUzzTyd1gN3FsWUvK6bLMMfSzvWya0Rz8WALs/Ix/jQwFPLrZUe//fS4/ce4Pfyz2tgLCPm1Z+lQ7zwHqiI5e/JnJWPp790/CzwaP2PyUysDFY6F2uY8uSLvHU45seqD5Yv3XZePXr8aDDk6S9dL1ZeFh/8PEmSAFTwX9shawcS8vR8gaw2i4E7Ty96O3Q9WYYNsnbUtkPPChDjGSHdeL29vdLb2ysi530sVlZWpNlsbgAe7PXNnWJ+fj4YZQgN+71oT3FuSPAQa0S8Z3AFYsFl4QEyZ3TuKRuvU3vCjGf6nVW37DOk28QCcZwubiJnr3kuH4dHGK5nr34B7FCPXkdlB1XLQCMeO02zXFh1EzMiut5iBpi/dVwLWHg7D7z0Pb6y5FT/9oy6p7Tzpqv/e3URSxNtZsW3QBi3aRZItAyFBQi5Lrx+8HSQlbaXVx7AEmsT1lVWPhZfPCueRy6sfsUfS4Y9PaTbIgu0WvrRKl/etsvqjysrKykneZ2npZ+1LrTa2NLhvItWzxh7+gNxPLCi+dKbSC5Wxp8VIEYkLeCWIxGOyF9cXAzrjBwXBK90/IYR5AbRv9E4vBOB0St8adDgTBbKx5KA3k0BkMRGGfnoo5410OGychmsOoyRJYgog+6AliLXnQp1ZO3e4pkx3WFQB8ViUfr6+gKw1MAC9Yi2ZGOmOzHvWrLKystnMSMFWcO5Nh5QwK4rTB+zAtCKRCsUzZtWQMwTpxMDojo9xNWzQJ4MxNLLk4/133vWznMPqOTlkdOxyui1BX5n9SmdB8fNWxeWXonJi0WWAcfvvPxyXEv+YnWln2lDyfohZhS93UH6d55+5T3T/Hr1pnWft40ZPOhdp5ZO0/xa9Y13lh0AHx6YtPLneojlpcGMfn4x9KwCMSJpZV2pVFLLQ3wwm8hGr23ELRaLqSsGYAQBInBuCQQOca2zBjzQovNk0MOzQzwdh6lFbew8FM1rqLqeLGUMw4rfMQVqKRqrjPwNYdajYqTF4BB1gXJw2gx4kiSR5eXlVOfnD9ehzseqO604QRb44TpmsITlNSyjsRwxYZsxt4Vey0Y61WpVlpeXTXniuOVy2Zxd1HEs4+Epa88AaT69tLzfsfDaELRjXCyQCeJyZznNW3Hy8OWRB6Bi7aEB7VOhGOjz+IiBMM2bZTB1WNa9Xh1bBtTSW7H6uFjg6L3zwLel56w0Y8AJ7+E72Gw2Q/3w2VgcltPRdQcQp2e9OL7WbbodLf3ObWiV3QNwWXWcRZsexPCoHJVcLBalv79fKpVKcNDVa4m6sdCQcBzVhnZ9fV3K5XJq1qBQKGyYpQEf+M886WUN3YnZeKNsyMPyZbE6KvOHPHRZLYHWndJSKl5cywB5Cj7myMpgxVpHZSBULBbDzA870lrpcn4eaLH+c1r6GdrG2u2FdHp7e8160Et+mPlDW6MeILeYydM75vR3sViU3t7esOypy85129vbG3zBrDoC4ayj1dVVc6mS09br31Z6XH8oH8gz5lrmrOVDAEI9sNB9TfctizxjbMmC5vOpAg2PrDbPa6h1/47Fi0pblRYAACSsSURBVBlf/d4Cclm6jdPltuSBlp611mF1XhxGl1kD1RhQ84yxVy5+zoMzpBeTB0snLC8vp+LjMFGr/FoO0JfYZ5LLb5XJ49uiGJDWfjgsF9bMTLu06UEME5ArtkgvLi5ucIwC6VEEAIyIpMAGCws7mMJoAiUDpCA8RuMs5Pivt8uxEIK0kueOYSkPnZ6IhF1Xuo4s0vlosFEopHe+ADjERvyeAsj61qDQ4tUaFWivfE+JWAqVy2zFydu5s4ywZQj1LFGpVNpQDpHzl6BqoAXZY2PObc5b7gGSkuT8qA/O6hrk85ZOHDnAwEoDv0Lh/KznyMiILCwsyMLCglvXIhLShNL26qlQKEitVpNms5k6uJGXbZGHtUNOA3kQH06oZU8rcsxsecZdP/MMtlU2yyB6fGjyAHeW7HpyY8mvJ9PWf5YxrVdjAxeWI+SnZ16tgYDWzdZHlyP23yq3lYb3Lk/dc9/ROhz1xFfesN7lOmGydCGei2zUpR5Q83StBkK6/VhnWwOsp4OeNSAGnbJSqUiSJDI7OxtG6lpwdDwRSQEYyzAnyYWTRldXV2VtbS11s7POA2G58XnrsiX4LHCaBxYeXk7R5dBhY0JloWPdqcA3gyrt2Mr8W/no0ballDSvHm/sX4RnfMIyQIDOz8pHP8saLWj+uI68tL12yjJQ7F+kjQI/09/sKwQFiG+e8RG5cG4QlGapVJKBgQFZW1uTxcXF8B7yjBOs4RCPU3a5n3V1dUlPT09qG7wGC6VSKXVasFaOeI4jENDGug5A8BdD37TCaKPvgQhN+tRrLz76iaU7PHBiPdNGXx+FwO9jQCgmy57RtsJYoNVKIwbekY43ONH6zssDZDnWI/xT7dvWM11XeYCYlg0PWHHd9Pb2pmwLyqrlI8anZxOQhwX8rPJpOeS80V4W8NR8YsDBAKdd2vQghmdAoOzW1tZcZWL95ik7q7ERXq81MjBhYUODQRh5hI/32vmYR5f6HQuPBj8WCOKwWhlxGC1gGuhpo42ysoMrzz55HZXJA1PgDfnosPiNZRVeNkBZ2DjrI/U1D96IQteXpXA9UGS1SawerNFQDBjpd1o5s7LT9YP4mh/dJ7Rs8wfHEoAPzHr29PSE0WKtVgu+Y3iGNX69VFatVsN/ACLIFZcTh1Ci7nkHFmS4u7tburu7pdlsugYaYA6O1wsLC65ihZzD8do6EZsJIEo7aVv1jMGNHrl6xlCHzWpTy/hagETzqXWmBlmW3LDcav7YQV/zYJWZ80I8S794fdIDNhxG14sFxixdoOvDW6ax6iFmvPGO3RkwQEiSJMh1bJZYt72uQw8EWT4zlo205MUCTJZO5YE72712aNODmEKhEEZrcLplg+cpbA6jlYTu8DCKcPhF5+QDzbQBYoWrT7/VvgUMwDSfMSXF72MjK/3fWi7z8oEBwxQ8KxYPKOm8YwqdgYcn7Mwj6h6GAEsirExXV1dTHVfXE3cu/d7a+ZDVHlxOr52yfut61O80eUqN5c8zNJ4RQ3yv7NZ/j3Q9eUqSP1n+RaAYsMRMLI/W+TeDcQZGlh8Z+j3AmKXURS44VJdKpUynas9vS+eNZ9opP9a3LSOiyQuTt111Omy4uW9Z/MYGlqw/EC424xIrkyYt515f0HE4bQYHXv+18o3xqdNsNBrhxF4AGl4O1rYlT7p4r/sVy7sHTHT6ni6P6UsvrXZo04MYdBjLg5u/rWdoAEb7PBuC5SkIEBytWCjQ0Tzl5AEWy8Do+F4H0+9ZkBh4IX8ObxlKjqeVksiFUbKux9h/q4N4ilvPwOiZK04X0/tsWPRUZqzO8Jx3FbXTCa0wsbbj/1kKUYfVcuNNx7OC4/d4rsvvrV3HFLFXTx7/FqDVYXSeXnpW2lmKUht/q40qlYqbN7cRdjlqg8r1yMBIG2ErTc+AMn+Y3eElXKs/JcmFEbs+FduqJ64f7Wuh68ED+5wG5EzPIGpAzWUHn9bSdEwmrfr0ZM0LmwUELF3l1Y+XH4dFWlYbcrzV1dXQ3ljKhd2JybuWJauPxMJ4YEvXk7ZhOm1P5thX8WJo04OYVqsVppBjnddS8Dx1zu9EJAAYOO8yIsYzOFFiSUYDGvYLgAMlKxkWJI2Mdce2QI/X8bJGB1an1/Gz0rDiaQXtdVjdNvoAu1iHZQOhn7P/ggcoLGOm/8cMgKYsgxqLhzysurHaW2TjQV6QNZ7JwswZGw42OHzjLn9rBz6eVWBDxc7y8CFjuWaD5hmYPA7hsXqLyaXV13X9em2s20XLsq5jTqNQuLA5gNNjBc/1AX3BoJ0/2oeO4yE/1km6nT3CxYJY3rP6gk4XxOlzXbIjKeLrsDxABPDSs7oW6Xph0k7eVtljcsh5ePXG70ql0oYdfVZ4a1eOBc4073plQLsvYLnJKp+n/9nG8HMR2dBPLT2o07IADPNh5R/rt1n0rAAxekueiD8KxTur4bgBsW4OJc0CwD4vWI+HIWDHSk5TKy48s0CJVRbm2zNwVkfMC+osgdXo2jK8sbT1SE2nz2SN2jg9qz50Z0Tb5DGCnG5eEOIBsTx1EeMny8h75dffDGjwnw0bn0yMHXxdXV1SrValWq0GYF6tVqWnpyfINvNQLBZlbm4u+CXBkRZXQKyvrwfnxCS5MHOmLygFH9yf+Ldl2HW9xerU+2+9s9qI5Sgmu3kAE4flmRU8075sun/DwZnT57BaB+C8EcvgsGEUkbCkJmKfUaRly9NrDOwwyGN/O/5gUCdyXla18zSXXYNH3ce9/qYNqtXXvb4JvvXSMkAMfFaYYgCA84vJZqvVCv1JH6uB39YuJdbh3vIjtxfKZ+lRbZusMmoXCstGcHgLiLdDzxoQI2J3PvzG2jYEBAKJ6Tr4WMDPBSMVjDKRjhZuvMOOCxFJKWtWUpYzGDc0Ax094tZl0hRT9J4AWUKn82E+YoAoyxBb/PJvy1BboImf8egP7Yi603x74M/q6Jo0uIzVm9dJs8CMpVAsRazDcPnYSVYDXs1Dq9WSpaUlEZGUUcMSBvuDwMGV/cJwmzjvguLLOeGnBgPMh03q2TQ2bvwfYfg33nuzFxw361msX3B+XM+avGeewcLSAG9I8NKxyDIc+jnLuadD0GbwQ2JaWVkJ29/ZEdvjk/sRD+IYNHlgR+QCmPXaAeG7urpkZWXF7NccF+Cbt8ZbAEaDt5jzLM9aQt9o8vjXfVrnz4QDLVFmBoFwpG80Gu6Waw+0enWl682qK+bb4p2BjQ6L/wzE2qVND2L0rgGefgN1d3dLf39/uHYAhq6np0eWlpakUChIX19fUDIiFxxYtbMtK1wtGFrwmBAuthatw2iKKTqt3Jg/L54WxCyQ5L2POex5fMfS89IBj/qOKm4bPiMFYQBSPaWq89F8cxtrQ8HveXStw3lKJAZeNGkl0C540iBG82/N3MDY6isXkiQx1+zRt9j4WYY2CzhyG1mGWfcRLw8LkFjAhuPyYAR+Cnq2QIMi67cmBl95KI+sWHlz+WM6wwND7C8G4hlv3WfwHKAM4Cimxzg/7WDNeXK/Eblw9YcuMwaoGlBbOlD3TR6wegYe8XgWzQun69MDnlwuKw0NrrTdstqJeeIBPrs0aNlAuWKyy/1RL2dyHG17UfYYQMyiTQ9iRNIVgxEi33Asch7hQuGiAefm5kLjLS4uhgpHp+JOANJLVwBNEG442GGHjDWTwWvDoEIhfRGh59xqCT4Ll9VZ8Dv23DLmXj667j3l6hkqq0z6G2CDiY0XOiV3mtjlh3yir+V7o42eLr9WnDwlqw0ul0FETNDk5aUpTxvG0vKUcpYx1fVjtZEG8Fa6XAdakeVVaJ6RiJXb6zOad0vu0WdxNg5mabUBBQ+xcnHftOpfA41YnWSFs3jS4JvrwFpSiAEfnvlk3x/eHo+8Af44npYdrXf4GcCIbh+0hVc25p11NS9TakNuGWWUi0EwO1pboFkPAphv/Zt5tfp+q9WSlZWVMFDQMmzxy+lqHRDTUVwnzI8GOShbDPBoHYX0wb83gxSjTQ9iADgwY6INWZKcX69fXV3d4KTGgg3QgbVnAAlL+UFI0Jn5/AsRCUCGOyTzox0C+R1PtXroNfZfCzCTp9g9kBFTaFb+WYYZ5UPH9JbNGHDwcyiRRqMRlBny5usieHlEZKNvDp88rPlkg6wVPYfRI0YtVxow4TfKwWXFb2uams8eYbnQS6hcJkvhcTn1t5ZTbnvm3ZOrp5s85Qxe84a3+oIH5Pm3lj3r7Kms+uU65fbhWSqrPLrPWb5R3mCByxzr0xa/Fj8WUER+2OzAzsd4D565H/MxCHqJypNXz8hbfHll4r4VAzzspyNyYYmL65xPzrVkTueFPHSenkzqZ9Zp60tLS+4Ml1cH+jfrGQ3AOLzFl57RjNUDh4PcZ/Fu0aYHMXq3DxsFoGZvhM7AAUoa4IP9CiwjgTVa9sUAYfTGp4xyg0NRssJhowTyfoMH63eedx6KZtI863carWs+rXwBLnDmh3XYmNV5EbdcLkuj0UgBDV2H2tiyt7/HF7czl43fcV6cj1bAWg45batu+T1AtZ7V0IYP5dFARitKDYi0cUUY/cwjz0B49erFYx7x23pukTdafCqgR9ebt6U1NorU/TtmZK2+nAcw6rJ7+bQDNjUvmAFgvWgBKZELvn+6v7Ch4q3UvNTPPFrgMFaHeQCBp5d0GTgdnZb2k8wCfR5wscIiHQ8ssI2wZAODm1j6lg7nsurZFYufPLPIzDO+GezBJ05ENuj2PLTpQUyz2dxw8zKWdSqVSmr7NU+DcqXyez0a1gYTja23TIMAmngLI6/XIqxeO0T6mljosoSH68BTZJYx9wQ9ZiBiypMVgtUxVlZWwgWdlqMex8N/qz4t5109u6Pbhjsv3mslxuDBMsYsE54hZ/8ofsb5W+kxKMczC8SwImK51fEscGPVjSUPenkSaeNmXc8gI6z2o7DkWAMrix9LzmIAP0ZapvOAoixglZW2xWcWz7qOMHvpzeJ4oNEDnl59J0kSdt9Yd0zpvCxDKXJhyYnlMkmS1F1zDHgQ39IBVt1wHKsMmrQce0AD9gD2gXkAzzHAzO81P1nARtdzsVhMDYRBsC/cD3Wa2r7oQQ/z48k++NHhYzyDEFbzyOeN5aVND2K4onVDNxoNEbmwbIRdFnwUOu+oENnYUFheYuNidU4W4LW1tXBEOzsKI00+6TemPC0l5AmMVS/6t2eovPBZ+XnG3OMJz7BTBTcpc/swUOS0C4VCADxWnVkdTCtGa6aGga9Om5UB16EOg+e8VMVTzwwuOI7FDx9SaIEPXa/srMfl0YCB5bXVagWjCD70iEvXo57V4dG2li+ALs7fUnZcJ7xMpuN4xlYrYaYsQ8hhrDJ47a3LZKVpPdfpx4yhRRh8rayshEEaZoBj/MTKrP8ziOdyYjCo9aSWKZYJXqJHWHaY1j4qWCrl09FZdrkOmTxQANnJ8v3ScRm8WbMTWhZi9RiTTS27WkfoWRjWGTybYfVbq1/ofqABDvPryajnQ2XVI3jjtNtpC6ZND2K07wEbZ1aKSZKE8ytELjQYtn1yAyIufrMSZ+cu66AoNBYMGh8kZjnzaSHQyttyItP5aSMWS1fH5W/PKGggZb3Tz9hA6/oFIBkYGJAkScKOMTaCPOopFospvxDL0KMc6OSWIcY3KwGtpJC/N+pAvladQNbYsDCPPEtkKTEGQVqhWGQ5/FltqYFBlsJlJaTrxKorS9ni/A/UNwwE3rFC491mvLtPgz5dPt1GXB96mcwCaFw3Vhm0QmeyptljQEKnY8mm10at1oXt8Ahn+fhlARnP4Fj5Ib1WqxUOPczqJ1Y74Vv7z3AfhX8NwvDJ6CIS7uPicuq2537E5WDerA/ziDTYt063C8+wenoT4WJ1znXPaWg59OoUG1dwt5dFWf1dk7XUZ/HOaVjhrT5l9eW8tOlBjFUxloCyorOERCss7jQ8AtEK2eoI2JpZqVRCeA16tJJl3pGOVqr8Hr89UIM0rHiatCHmZ56StAwEh7H8Mfizvr4uy8vLYbugTpdnCaBIWfF54VnZABjomR0rDvOo09ZlsGZQOLx2DOVycDhdb9ohUCtCreBYmWhQwIRZRF7O0ru1OB8uD/PNzo9WHWlZhsLnWRauD6t+rX6r64XLovPjtta8cd1ZS7wgnh3g/HTftHw5dDsxqNKyYxkLy+DocNqIWGE8g2G1sf6NsulBgo7HdWoZXd2/dJ9BG7KTvtaVhUJBFhYWwsAGH6uOND86jFUvlj4FD5AB1iXgFwCd9Tinb+lES3fG6lODSXzjOZ/xY+lj7re67bL40zzqesKHNynodtfE7dYOPStAjGUQ+L3lI6EFjxUOGxBewwO40VP9lhDri7s0n/jWU/kWWcrOM2xZdWOlx4Y3htQtvqyyo1wagWuFvrS0JD09PVKtVlNOvpw3H3ClR57Ihzs2d36uA84bxtgyEli/tRS0rlv+rdfLGbxyWt66OhtKNkwsjzqull8N/FiZsd+YBnRePcUUPrej1ad0HWhfDj7Hw+JZL01Yy68sXxokWLNPHvjTMs/ADf91H7XqEM+5LXl2TSS9LM3LK3p5Ttclt7cnk1x+DbhiZOkX1Lv3jkm3BddjDDBoOecDFqFD9WBPA2OWWZ0PG2ePD/6veQIPeuYXS3lZwETXrSV/lr712hd1Cn74CgQPtGmAqfP2bIaeTbHalvWEVX8xO9wObXoQA2IB8RrM+i2SPrGUG12PUNEI+mAkqwNxGPzW+XsGwOp4TJbysgwbvwf/nFceRWcBL11P2gBqBa/rhxUEZmNwjo9I2gOfT+G1jDbyRxhr2pJH7drwaMViec9bStGTJ+aNDSn+6xkjlBfxMZ2u5Zn553LreudZFvjlsGwjD82DHiVZy7MaSOg4uq358DOdl3bws+Sd25KNFssJ52mVFb/1rICuO86D8+Z61QBFgyDdj4rFYqrvIwye67rndPRytmUUuFx4prcA67rhdtS8WksoMeLZaEs3eEaS65XrkIEoL9sjDOtSrWs8UGcBGAtoMF/Mn96lCqdaTkvr3qzlaI8H5KUHfphxwRJSoXBhMC1ywW9TH8gY0/dapvRAW9cn1zm3mQYuetb06aBNC2K4oqzRlA6rAQ6/06MX7ixamLB2q5WHlSemHDWIYUXJwsj5x4CMVmaeUbWMTcwAa2Wt82LypsURz/NCZwQvcn6bdblclu7ublleXk4ZTCzLIV1W0CBWcnx/FeIjPNcjjLs2/lq5amLDo42fpQTBl57V0GCCy8tXB0A+1tbWUqNBLjvXP8qE8BZwKhaLUq1WQ12jbnkHgQYvum2tWQk2NAir+xxP0fPx9FrutJJkWdOnA+v+w7vXdH0xra2tpc6E0uVAelxWDSCt6XEuJ8syy5SelbLkzdJpqDstS5wOh8d7ri89UEAYgN92rkLQO2M4H523BlUcTuelz93SS0ha1vR7zs8i77kHQnk2BnLDsoyyeICFn2mZsnQ+8kFYzLjgvaWHrHwtHe7Jm+ZTgxruF5wv60zOSw+CdPx2AE4hebrg0M8ZPfroo3L55Zc/02x0qEMd6lCHOtShNujxxx+XHTt25Aq7aWdihoeHRUTk+PHjUq/Xn2FufvY0NzcnO3fulMcff1wGBgaeaXZ+5tQp7+alZ1NZRTrl3ezUKW9+SpJE5ufnZWJiInecTQtiMH1Yr9efFYIDGhgY6JR3E9OzqbzPprKKdMq72alT3nzU7qTDxe1p6lCHOtShDnWoQx16hqkDYjrUoQ51qEMd6tAlSZsWxFQqFfnQhz4klUrlmWbln4Q65d3c9Gwq77OprCKd8m526pT3Z0ubdndShzrUoQ51qEMd2ty0aWdiOtShDnWoQx3q0OamDojpUIc61KEOdahDlyR1QEyHOtShDnWoQx26JKkDYjrUoQ51qEMd6tAlSR0Q06EOdahDHepQhy5J2pQg5tOf/rRcdtllUq1WZf/+/fKP//iPzzRLF0Uf/ehH5Rd/8Relv79fxsbG5Dd+4zfk8OHDqTAvf/nLN9w6+q53vSsV5vjx4/LqV79aenp6ZGxsTN7//ve7FzA+k/ThD394Q1muuuqq8L7RaMjNN98sIyMj0tfXJ69//evl9OnTqTQulbKKiFx22WUbylsoFOTmm28WkUu7be+66y759V//dZmYmJBCoSBf+cpXUu+TJJEPfvCDsm3bNqnVanLw4EF5+OGHU2GmpqbkpptukoGBARkcHJS3v/3tsrCwkApz3333yUtf+lKpVquyc+dO+dM//dOfddFMipW32WzKrbfeKs9//vOlt7dXJiYm5Ld/+7flxIkTqTQsebjttttSYS6F8oqI/M7v/M6Gstxwww2pMJulfUU23vyMz8c+9rEQ5lJp3zx25+nSxd/+9rflRS96kVQqFXnOc54jX/jCF9pnONlk9KUvfSkpl8vJ5z73ueQnP/lJ8o53vCMZHBxMTp8+/Uyz1jZdf/31yec///nkgQceSA4dOpS86lWvSnbt2pUsLCyEML/6q7+avOMd70hOnjwZPrOzs+H92tpa8rznPS85ePBg8qMf/Sj52te+loyOjiYf+MAHnokiRelDH/pQcvXVV6fKcvbs2fD+Xe96V7Jz587kW9/6VnLPPfckv/RLv5T8s3/2z8L7S6msSZIkZ86cSZX19ttvT0QkufPOO5MkubTb9mtf+1ryh3/4h8nf/M3fJCKSfPnLX069v+2225J6vZ585StfSX784x8nr3nNa5I9e/Yky8vLIcwNN9yQXHvttcn3vve95Dvf+U7ynOc8J3nTm94U3s/OziZbt25NbrrppuSBBx5I/vqv/zqp1WrJX/zFX/xTFTNQrLwzMzPJwYMHk//23/5b8tOf/jS5++67k5e85CXJddddl0pj9+7dyR//8R+n2pv7+qVS3iRJkre+9a3JDTfckCrL1NRUKsxmad8kSVLlPHnyZPK5z30uKRQKySOPPBLCXCrtm8fuPB26+NFHH016enqS9773vcmDDz6YfOpTn0q6urqSr3/9623xu+lAzEte8pLk5ptvDv/X19eTiYmJ5KMf/egzyNXTQ2fOnElEJPm///f/hme/+qu/mrznPe9x43zta19LisVicurUqfDsM5/5TDIwMJCsrKz8LNltmz70oQ8l1157rfluZmYmKZVKyf/4H/8jPHvooYcSEUnuvvvuJEkurbJa9J73vCe5/PLLk1arlSTJ5mlbrfRbrVYyPj6efOxjHwvPZmZmkkqlkvz1X/91kiRJ8uCDDyYikvzgBz8IYf7P//k/SaFQSJ588skkSZLkP/2n/5QMDQ2lynrrrbcm+/bt+xmXKE6WkdP0j//4j4mIJMeOHQvPdu/enXziE59w41xK5X3rW9+avPa1r3XjbPb2fe1rX5v883/+z1PPLtX21Xbn6dLFf/AHf5BcffXVqbze8IY3JNdff31b/G2q5aTV1VW599575eDBg+FZsViUgwcPyt133/0Mcvb00OzsrIhcuKEb9MUvflFGR0flec97nnzgAx+QpaWl8O7uu++W5z//+bJ169bw7Prrr5e5uTn5yU9+8k/DeBv08MMPy8TEhOzdu1duuukmOX78uIiI3HvvvdJsNlNte9VVV8muXbtC215qZWVaXV2Vv/qrv5K3ve1tUigUwvPN1Lago0ePyqlTp1JtWa/XZf/+/am2HBwclBe/+MUhzMGDB6VYLMr3v//9EOZlL3uZlMvlEOb666+Xw4cPy/T09D9RaS6OZmdnpVAoyODgYOr5bbfdJiMjI/LCF75QPvaxj6Wm3y+18n7729+WsbEx2bdvn/ze7/2eTE5OhnebuX1Pnz4t//t//295+9vfvuHdpdi+2u48Xbr47rvvTqWBMO3a6k11i/W5c+dkfX09VXEiIlu3bpWf/vSnzxBXTw+1Wi35N//m38gv//Ivy/Oe97zw/M1vfrPs3r1bJiYm5L777pNbb71VDh8+LH/zN38jIiKnTp0y6wPvfp5o//798oUvfEH27dsnJ0+elI985CPy0pe+VB544AE5deqUlMvlDUp/69atoRyXUlk1feUrX5GZmRn5nd/5nfBsM7UtE3izeOe2HBsbS73v7u6W4eHhVJg9e/ZsSAPvhoaGfib8P1VqNBpy6623ypve9KbULb+///u/Ly960YtkeHhYvvvd78oHPvABOXnypHz84x8XkUurvDfccIO87nWvkz179sgjjzwi/+7f/Tu58cYb5e6775aurq5N3b7/5b/8F+nv75fXve51qeeXYvtadufp0sVemLm5OVleXpZarZaLx00FYjYz3XzzzfLAAw/I3//936eev/Od7wy/n//858u2bdvkFa94hTzyyCNy+eWX/1Oz+ZToxhtvDL+vueYa2b9/v+zevVv++3//77kF+lKlv/zLv5Qbb7xRJiYmwrPN1LYdOk/NZlN+67d+S5Ikkc985jOpd+9973vD72uuuUbK5bL8q3/1r+SjH/3oJXfvzhvf+Mbw+/nPf75cc801cvnll8u3v/1tecUrXvEMcvazp8997nNy0003SbVaTT2/FNvXszs/T7SplpNGR0elq6trg5f06dOnZXx8/Bni6qnTLbfcIl/96lflzjvvlB07dkTD7t+/X0REjhw5IiIi4+PjZn3g3c8zDQ4OypVXXilHjhyR8fFxWV1dlZmZmVQYbttLtazHjh2Tb37zm/Iv/+W/jIbbLG0L3mL9dHx8XM6cOZN6v7a2JlNTU5dsewPAHDt2TG6//fbULIxF+/fvl7W1NXnsscdE5NIrL9PevXtldHQ0JbubrX1FRL7zne/I4cOHM/uyyM9/+3p25+nSxV6YgYGBtgatmwrElMtlue666+Rb3/pWeNZqteRb3/qWHDhw4Bnk7OIoSRK55ZZb5Mtf/rLccccdG6YaLTp06JCIiGzbtk1ERA4cOCD3339/SmFAgf7CL/zCz4Tvp4sWFhbkkUcekW3btsl1110npVIp1baHDx+W48ePh7a9VMv6+c9/XsbGxuTVr351NNxmads9e/bI+Ph4qi3n5ubk+9//fqotZ2Zm5N577w1h7rjjDmm1WgHMHThwQO666y5pNpshzO233y779u37uVtqAIB5+OGH5Zvf/KaMjIxkxjl06JAUi8Ww7HIplVfTE088IZOTkynZ3UztC/rLv/xLue666+Taa6/NDPvz2r5Zdufp0sUHDhxIpYEwbdvq9n2Vf77pS1/6UlKpVJIvfOELyYMPPpi8853vTAYHB1Ne0pcK/d7v/V5Sr9eTb3/726lteUtLS0mSJMmRI0eSP/7jP07uueee5OjRo8nf/u3fJnv37k1e9rKXhTSw1e2Vr3xlcujQoeTrX/96smXLlp+Lbbia3ve+9yXf/va3k6NHjyb/8A//kBw8eDAZHR1Nzpw5kyTJ+W19u3btSu64447knnvuSQ4cOJAcOHAgxL+UygpaX19Pdu3aldx6662p55d6287Pzyc/+tGPkh/96EeJiCQf//jHkx/96EdhN85tt92WDA4OJn/7t3+b3HfffclrX/tac4v1C1/4wuT73/9+8vd///fJFVdckdqCOzMzk2zdujV5y1vekjzwwAPJl770paSnp+cZ2YIbK+/q6mrymte8JtmxY0dy6NChVF/GTo3vfve7ySc+8Ynk0KFDySOPPJL81V/9VbJly5bkt3/7ty+58s7Pzyf/9t/+2+Tuu+9Ojh49mnzzm99MXvSiFyVXXHFF0mg0QhqbpX1Bs7OzSU9PT/KZz3xmQ/xLqX2z7E6SPD26GFus3//+9ycPPfRQ8ulPf7qzxRr0qU99Ktm1a1dSLpeTl7zkJcn3vve9Z5qliyIRMT+f//znkyRJkuPHjycve9nLkuHh4aRSqSTPec5zkve///2ps0SSJEkee+yx5MYbb0xqtVoyOjqavO9970uazeYzUKI4veENb0i2bduWlMvlZPv27ckb3vCG5MiRI+H98vJy8q//9b9OhoaGkp6enuQ3f/M3k5MnT6bSuFTKCvrGN76RiEhy+PDh1PNLvW3vvPNOU3bf+ta3Jklyfpv1H/3RHyVbt25NKpVK8opXvGJDHUxOTiZvetObkr6+vmRgYCD53d/93WR+fj4V5sc//nHyK7/yK0mlUkm2b9+e3Hbbbf9URUxRrLxHjx51+zLOBLr33nuT/fv3J/V6PalWq8lzn/vc5P/7//6/lNFPkkujvEtLS8krX/nKZMuWLUmpVEp2796dvOMd79gwkNws7Qv6i7/4i6RWqyUzMzMb4l9K7Ztld5Lk6dPFd955Z/KCF7wgKZfLyd69e1N55KXC/5/pDnWoQx3qUIc61KFLijaVT0yHOtShDnWoQx169lAHxHSoQx3qUIc61KFLkjogpkMd6lCHOtShDl2S1AExHepQhzrUoQ516JKkDojpUIc61KEOdahDlyR1QEyHOtShDnWoQx26JKkDYjrUoQ51qEMd6tAlSR0Q06EOdahDHepQhy5J6oCYDnWoQx3qUIc6dElSB8R0qEMd6lCHOtShS5I6IKZDHepQhzrUoQ5dkvT/A+4p+ED8QFUvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "part = 'corner_bracket0'\n",
    "depth_img = ipd_reader.get_depth(scene=30) #ONLY FOR PHOTONEO CAMERA\n",
    "rbg_img = ipd_reader.get_img(scene=30)\n",
    "mask_img = ipd_reader.get_mask(scene=30, part=part, instance=0)\n",
    "plt.imshow(rbg_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-21 00:09:24 - root - INFO - reader.py - get_img - Opening image from ./datasets/dataset_texturedbg_1/test/000030/000/rgb/000000.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-21 00:09:24 - root - INFO - reader.py - get_img - Opening image from ./datasets/dataset_texturedbg_1/test/000030/000/depth/000000.png\n"
     ]
    }
   ],
   "source": [
    "# Flir and baslet cameras have multiple exposures which can be retrieved by specifying image_type\n",
    "all_imgs = [ipd_reader.get_img(scene=30, image_type=image_type) for image_type in ipd_reader.camera.images]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2320.26 ,    0.   , 1053.13 ],\n",
       "       [   0.   , 2320.15 ,  805.064],\n",
       "       [   0.   ,    0.   ,    1.   ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd_reader.cam_K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd_reader.cam_c2w #The camera coordinate system is as in OpenCV with the camera looking along the Z axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0., -0.,  0.],\n",
       "       [-0., -1.,  0., -0.],\n",
       "       [ 0.,  0., -1., -0.],\n",
       "       [ 0.,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Util to convert to a different available framework\n",
    "intrinsic_ipd.constants.CameraFramework.convert(\n",
    "    ipd_reader.cam_c2w, \n",
    "    from_camera=intrinsic_ipd.constants.CameraFramework.OPENCV,\n",
    "    to_camera=intrinsic_ipd.constants.CameraFramework.OPENGL,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Utilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Removing symmetries from poses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'discrete_symm_rots': [array([[1., 0., 0.],\n",
       "         [0., 1., 0.],\n",
       "         [0., 0., 1.]])],\n",
       " 'proper_symms': array([[[1., 0., 0.],\n",
       "         [0., 1., 0.],\n",
       "         [0., 0., 1.]]]),\n",
       " 'symmetry_mode': 'full_discrete',\n",
       " 'continuous_symm_axis': -1,\n",
       " 'fix_continuous_symm_angles': True,\n",
       " 'ref_pose': array([[ 1.,  0.,  0.],\n",
       "        [ 0.,  0., -1.],\n",
       "        [ 0.,  1.,  0.]])}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part = 'corner_bracket0'\n",
    "ipd_reader._get_symm_params(part) #NOTE: used for symmetry removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "        [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "        [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "        [   0.    ,    0.    ,    0.    ,    1.    ]])]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove symmetries by specifying part\n",
    "part = 'corner_bracket0'\n",
    "pose = ipd_reader.o2c.sel(scene=30, part=part, instance=0).data #returns a numpy array\n",
    "ipd_reader.remove_symmetry(part=part, poses=[pose])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (scene: 30, object: 23, transform_major: 4, transform_minor: 4)&gt;\n",
       "array([[[[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "         [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "         [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3249,   -0.6633,   -0.6741, -166.4593],\n",
       "         [  -0.1151,    0.6797,   -0.7244,  220.2536],\n",
       "         [   0.9387,    0.3129,    0.1445, 1750.2346],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.5102,    0.7794,   -0.3637, -372.5465],\n",
       "         [   0.7988,    0.5861,    0.1355,  162.0151],\n",
       "         [   0.3188,   -0.2214,   -0.9216, 1967.5482],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[   0.3161,   -0.8828,   -0.3474, -289.677 ],\n",
       "         [  -0.9216,   -0.3727,    0.1084,  202.8125],\n",
       "         [  -0.2252,    0.2859,   -0.9314, 1889.6468],\n",
       "...\n",
       "         [   0.7155,    0.697 ,    0.0468,  180.2697],\n",
       "         [   0.1061,   -0.0423,   -0.9934, 2039.3486],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[   0.498 ,   -0.8633,   -0.0821,  -64.1376],\n",
       "         [  -0.865 ,   -0.5012,    0.0223,  231.4823],\n",
       "         [  -0.0605,    0.0599,   -0.9964, 1984.3297],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.9744,   -0.2089,    0.0832, -212.1853],\n",
       "         [   0.2112,    0.9772,   -0.0196,   39.54  ],\n",
       "         [  -0.0772,    0.0366,    0.9963, 1991.4798],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.0714,    0.9957,    0.0587,  170.0804],\n",
       "         [  -0.9974,    0.0714,    0.0008,   13.2525],\n",
       "         [  -0.0033,   -0.0586,    0.9983, 1964.314 ],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]]]])\n",
       "Coordinates:\n",
       "  * scene            (scene) int64 30 31 32 33 34 35 36 ... 53 54 55 56 57 58 59\n",
       "  * object           (object) MultiIndex\n",
       "  - part             (object) object &#x27;corner_bracket0&#x27; ... &#x27;wraparound_bracket&#x27;\n",
       "  - instance         (object) int64 0 1 2 3 4 0 1 2 3 4 ... 3 0 1 2 3 4 0 1 2 3\n",
       "  * transform_major  (transform_major) int64 0 1 2 3\n",
       "  * transform_minor  (transform_minor) int64 0 1 2 3</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>scene</span>: 30</li><li><span class='xr-has-index'>object</span>: 23</li><li><span class='xr-has-index'>transform_major</span>: 4</li><li><span class='xr-has-index'>transform_minor</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7c598abe-dcfb-49ed-9057-6c7442dc327e' class='xr-array-in' type='checkbox' checked><label for='section-7c598abe-dcfb-49ed-9057-6c7442dc327e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.1998 0.7808 0.5919 -209.3 -0.5991 ... 1.964e+03 0.0 0.0 0.0 1.0</span></div><div class='xr-array-data'><pre>array([[[[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "         [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "         [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3249,   -0.6633,   -0.6741, -166.4593],\n",
       "         [  -0.1151,    0.6797,   -0.7244,  220.2536],\n",
       "         [   0.9387,    0.3129,    0.1445, 1750.2346],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.5102,    0.7794,   -0.3637, -372.5465],\n",
       "         [   0.7988,    0.5861,    0.1355,  162.0151],\n",
       "         [   0.3188,   -0.2214,   -0.9216, 1967.5482],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[   0.3161,   -0.8828,   -0.3474, -289.677 ],\n",
       "         [  -0.9216,   -0.3727,    0.1084,  202.8125],\n",
       "         [  -0.2252,    0.2859,   -0.9314, 1889.6468],\n",
       "...\n",
       "         [   0.7155,    0.697 ,    0.0468,  180.2697],\n",
       "         [   0.1061,   -0.0423,   -0.9934, 2039.3486],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[   0.498 ,   -0.8633,   -0.0821,  -64.1376],\n",
       "         [  -0.865 ,   -0.5012,    0.0223,  231.4823],\n",
       "         [  -0.0605,    0.0599,   -0.9964, 1984.3297],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.9744,   -0.2089,    0.0832, -212.1853],\n",
       "         [   0.2112,    0.9772,   -0.0196,   39.54  ],\n",
       "         [  -0.0772,    0.0366,    0.9963, 1991.4798],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.0714,    0.9957,    0.0587,  170.0804],\n",
       "         [  -0.9974,    0.0714,    0.0008,   13.2525],\n",
       "         [  -0.0033,   -0.0586,    0.9983, 1964.314 ],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-677ada50-f49d-4214-a435-b1930ce06a8f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-677ada50-f49d-4214-a435-b1930ce06a8f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scene</span></div><div class='xr-var-dims'>(scene)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>30 31 32 33 34 ... 55 56 57 58 59</div><input id='attrs-b9563737-5e7e-4c78-b2b5-dea61d7f9453' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9563737-5e7e-4c78-b2b5-dea61d7f9453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1404f2ef-7072-4072-9b79-7e23d96fa4fa' class='xr-var-data-in' type='checkbox'><label for='data-1404f2ef-7072-4072-9b79-7e23d96fa4fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,\n",
       "       48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>object</span></div><div class='xr-var-dims'>(object)</div><div class='xr-var-dtype'>MultiIndex</div><div class='xr-var-preview xr-preview'>(part, instance)</div><input id='attrs-a4efb8d8-fdf1-4a17-8c74-b7f069b1fb07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4efb8d8-fdf1-4a17-8c74-b7f069b1fb07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9d9f67a-15c4-4070-8a1b-83737ead4263' class='xr-var-data-in' type='checkbox'><label for='data-f9d9f67a-15c4-4070-8a1b-83737ead4263' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(&#x27;corner_bracket0&#x27;, 0), (&#x27;corner_bracket0&#x27;, 1), (&#x27;corner_bracket0&#x27;, 2),\n",
       "       (&#x27;corner_bracket0&#x27;, 3), (&#x27;corner_bracket0&#x27;, 4), (&#x27;corner_bracket6&#x27;, 0),\n",
       "       (&#x27;corner_bracket6&#x27;, 1), (&#x27;corner_bracket6&#x27;, 2), (&#x27;corner_bracket6&#x27;, 3),\n",
       "       (&#x27;corner_bracket6&#x27;, 4), (&#x27;gear1&#x27;, 0), (&#x27;gear1&#x27;, 1), (&#x27;gear1&#x27;, 2),\n",
       "       (&#x27;gear1&#x27;, 3), (&#x27;handrail_bracket&#x27;, 0), (&#x27;handrail_bracket&#x27;, 1),\n",
       "       (&#x27;handrail_bracket&#x27;, 2), (&#x27;handrail_bracket&#x27;, 3),\n",
       "       (&#x27;handrail_bracket&#x27;, 4), (&#x27;wraparound_bracket&#x27;, 0),\n",
       "       (&#x27;wraparound_bracket&#x27;, 1), (&#x27;wraparound_bracket&#x27;, 2),\n",
       "       (&#x27;wraparound_bracket&#x27;, 3)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>part</span></div><div class='xr-var-dims'>(object)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;corner_bracket0&#x27; ... &#x27;wraparoun...</div><input id='attrs-4a72595a-319a-41fd-97aa-55f0bf56411f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a72595a-319a-41fd-97aa-55f0bf56411f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c5ef2e8-899d-4f31-8757-b9affc11974b' class='xr-var-data-in' type='checkbox'><label for='data-6c5ef2e8-899d-4f31-8757-b9affc11974b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;corner_bracket0&#x27;, &#x27;corner_bracket0&#x27;, &#x27;corner_bracket0&#x27;,\n",
       "       &#x27;corner_bracket0&#x27;, &#x27;corner_bracket0&#x27;, &#x27;corner_bracket6&#x27;,\n",
       "       &#x27;corner_bracket6&#x27;, &#x27;corner_bracket6&#x27;, &#x27;corner_bracket6&#x27;,\n",
       "       &#x27;corner_bracket6&#x27;, &#x27;gear1&#x27;, &#x27;gear1&#x27;, &#x27;gear1&#x27;, &#x27;gear1&#x27;,\n",
       "       &#x27;handrail_bracket&#x27;, &#x27;handrail_bracket&#x27;, &#x27;handrail_bracket&#x27;,\n",
       "       &#x27;handrail_bracket&#x27;, &#x27;handrail_bracket&#x27;, &#x27;wraparound_bracket&#x27;,\n",
       "       &#x27;wraparound_bracket&#x27;, &#x27;wraparound_bracket&#x27;, &#x27;wraparound_bracket&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instance</span></div><div class='xr-var-dims'>(object)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 0 1 2 ... 1 2 3 4 0 1 2 3</div><input id='attrs-03a0f94e-5a6c-46d4-b72e-eb8bb10b2725' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03a0f94e-5a6c-46d4-b72e-eb8bb10b2725' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94b401a9-12fb-4169-a036-ad11592c2ef8' class='xr-var-data-in' type='checkbox'><label for='data-94b401a9-12fb-4169-a036-ad11592c2ef8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>transform_major</span></div><div class='xr-var-dims'>(transform_major)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-56788cd7-8cfb-42b3-ae22-5803047ec860' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56788cd7-8cfb-42b3-ae22-5803047ec860' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-350b2f0d-6125-4c5e-8ffd-7dda8dd846d2' class='xr-var-data-in' type='checkbox'><label for='data-350b2f0d-6125-4c5e-8ffd-7dda8dd846d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>transform_minor</span></div><div class='xr-var-dims'>(transform_minor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-acc21f81-8335-45a1-9d56-ad30fa6f7a5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acc21f81-8335-45a1-9d56-ad30fa6f7a5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e40faa3-4b42-4187-82ef-e2460c08b981' class='xr-var-data-in' type='checkbox'><label for='data-2e40faa3-4b42-4187-82ef-e2460c08b981' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09ce99f5-f721-4816-97d3-73817c84b415' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-09ce99f5-f721-4816-97d3-73817c84b415' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (scene: 30, object: 23, transform_major: 4, transform_minor: 4)>\n",
       "array([[[[   0.1998,    0.7808,    0.5919, -209.3239],\n",
       "         [  -0.5991,    0.5754,   -0.5568, -105.5557],\n",
       "         [  -0.7753,   -0.2434,    0.5828, 1853.1825],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.3249,   -0.6633,   -0.6741, -166.4593],\n",
       "         [  -0.1151,    0.6797,   -0.7244,  220.2536],\n",
       "         [   0.9387,    0.3129,    0.1445, 1750.2346],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[  -0.5102,    0.7794,   -0.3637, -372.5465],\n",
       "         [   0.7988,    0.5861,    0.1355,  162.0151],\n",
       "         [   0.3188,   -0.2214,   -0.9216, 1967.5482],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[   0.3161,   -0.8828,   -0.3474, -289.677 ],\n",
       "         [  -0.9216,   -0.3727,    0.1084,  202.8125],\n",
       "         [  -0.2252,    0.2859,   -0.9314, 1889.6468],\n",
       "...\n",
       "         [   0.7155,    0.697 ,    0.0468,  180.2697],\n",
       "         [   0.1061,   -0.0423,   -0.9934, 2039.3486],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[   0.498 ,   -0.8633,   -0.0821,  -64.1376],\n",
       "         [  -0.865 ,   -0.5012,    0.0223,  231.4823],\n",
       "         [  -0.0605,    0.0599,   -0.9964, 1984.3297],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.9744,   -0.2089,    0.0832, -212.1853],\n",
       "         [   0.2112,    0.9772,   -0.0196,   39.54  ],\n",
       "         [  -0.0772,    0.0366,    0.9963, 1991.4798],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]],\n",
       "\n",
       "        [[   0.0714,    0.9957,    0.0587,  170.0804],\n",
       "         [  -0.9974,    0.0714,    0.0008,   13.2525],\n",
       "         [  -0.0033,   -0.0586,    0.9983, 1964.314 ],\n",
       "         [   0.    ,    0.    ,    0.    ,    1.    ]]]])\n",
       "Coordinates:\n",
       "  * scene            (scene) int64 30 31 32 33 34 35 36 ... 53 54 55 56 57 58 59\n",
       "  * object           (object) MultiIndex\n",
       "  - part             (object) object 'corner_bracket0' ... 'wraparound_bracket'\n",
       "  - instance         (object) int64 0 1 2 3 4 0 1 2 3 4 ... 3 0 1 2 3 4 0 1 2 3\n",
       "  * transform_major  (transform_major) int64 0 1 2 3\n",
       "  * transform_minor  (transform_minor) int64 0 1 2 3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove symmetries using the implicit part names specified in xr.DataArray\n",
    "ipd_reader.remove_symmetry_xarray(ipd_reader.o2c)"
   ]
  }
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