clip_quant.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_42675/3384911348.py:16: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(model_weight_path))\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoModel, AutoTokenizer,AutoProcessor,set_seed\n",
    "\n",
    "set_seed(42)\n",
    "# torch.manual_seed(0)\n",
    "# model_path = \"/home/workspace/model/MiniCPM-V-1B-sft-v2-1B\"\n",
    "# model_path = \"/home/workspace/model/minicpm-vit-1b-w8-lenovo-llama-w8-pergroup128\"\n",
    "# model_path = \"/home/workspace/model/minicpm-vit-1b-w8-lenovo\"\n",
    "model_path = \"/home/workspace/model/minicpm-v-25-lenovo\"\n",
    "model = AutoModel.from_pretrained(model_path, \n",
    "                                  trust_remote_code=True,\n",
    "                                  attn_implementation='sdpa', \n",
    "                                  torch_dtype=torch.float16,        \n",
    "                                  device_map=None) \n",
    "model_weight_path = \"/home/workspace/model/minicpm_v_navit_250_0927.pt\"\n",
    "model.load_state_dict(torch.load(model_weight_path))\n",
    "model = model.cuda().eval()\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/lmquant/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "2024-10-23 06:19:55,049 - ChatAPI - INFO - Environment variable OPENAI_API_BASE is set. Will use it as api_base. \n",
      "2024-10-23 06:19:55,051 - ChatAPI - INFO - Using API Base: http://15.204.101.64:4000/v1/chat/completions; API Key: sk-5DjKHIAjndzkjCtb85D57d2c3e13403e95B5D2F02c61561b\n"
     ]
    },
    {
     "ename": "ConnectTimeout",
     "evalue": "HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7fb84b43c450>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)'))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connection.py:199\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 199\u001b[0m     sock \u001b[38;5;241m=\u001b[39m connection\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[1;32m    200\u001b[0m         (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dns_host, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport),\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout,\n\u001b[1;32m    202\u001b[0m         source_address\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource_address,\n\u001b[1;32m    203\u001b[0m         socket_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msocket_options,\n\u001b[1;32m    204\u001b[0m     )\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 85\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/util/connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 73\u001b[0m sock\u001b[38;5;241m.\u001b[39mconnect(sa)\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
      "\u001b[0;31mTimeoutError\u001b[0m: [Errno 110] Connection timed out",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:789\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    788\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 789\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[1;32m    790\u001b[0m     conn,\n\u001b[1;32m    791\u001b[0m     method,\n\u001b[1;32m    792\u001b[0m     url,\n\u001b[1;32m    793\u001b[0m     timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[1;32m    794\u001b[0m     body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[1;32m    795\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m    796\u001b[0m     chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[1;32m    797\u001b[0m     retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[1;32m    798\u001b[0m     response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[1;32m    799\u001b[0m     preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[1;32m    800\u001b[0m     decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[1;32m    801\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[1;32m    802\u001b[0m )\n\u001b[1;32m    804\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:490\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    489\u001b[0m         new_e \u001b[38;5;241m=\u001b[39m _wrap_proxy_error(new_e, conn\u001b[38;5;241m.\u001b[39mproxy\u001b[38;5;241m.\u001b[39mscheme)\n\u001b[0;32m--> 490\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m new_e\n\u001b[1;32m    492\u001b[0m \u001b[38;5;66;03m# conn.request() calls http.client.*.request, not the method in\u001b[39;00m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;66;03m# urllib3.request. It also calls makefile (recv) on the socket.\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:466\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 466\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_conn(conn)\n\u001b[1;32m    467\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:1095\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1094\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_closed:\n\u001b[0;32m-> 1095\u001b[0m     conn\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[1;32m   1097\u001b[0m \u001b[38;5;66;03m# TODO revise this, see https://github.com/urllib3/urllib3/issues/2791\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connection.py:693\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    692\u001b[0m sock: socket\u001b[38;5;241m.\u001b[39msocket \u001b[38;5;241m|\u001b[39m ssl\u001b[38;5;241m.\u001b[39mSSLSocket\n\u001b[0;32m--> 693\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m sock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_conn()\n\u001b[1;32m    694\u001b[0m server_hostname: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connection.py:208\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 208\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[1;32m    209\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    210\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    211\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[0;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPSConnection object at 0x7fb84b42e310>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[1;32m    668\u001b[0m         method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m    669\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[1;32m    670\u001b[0m         body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[1;32m    671\u001b[0m         headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[1;32m    672\u001b[0m         redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    673\u001b[0m         assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    674\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    675\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    676\u001b[0m         retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[1;32m    677\u001b[0m         timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[1;32m    678\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[1;32m    679\u001b[0m     )\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:843\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    841\u001b[0m     new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[0;32m--> 843\u001b[0m retries \u001b[38;5;241m=\u001b[39m retries\u001b[38;5;241m.\u001b[39mincrement(\n\u001b[1;32m    844\u001b[0m     method, url, error\u001b[38;5;241m=\u001b[39mnew_e, _pool\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, _stacktrace\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]\n\u001b[1;32m    845\u001b[0m )\n\u001b[1;32m    846\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/util/retry.py:519\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m    518\u001b[0m     reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[0;32m--> 519\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m    521\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
      "\u001b[0;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7fb84b42e310>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[0;32m/home/workspace/code/git/eval_mm/vlmevalkit/vlmeval/api/gpt.py:230\u001b[0m, in \u001b[0;36mOpenAIWrapper.get_token_len\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 230\u001b[0m     enc \u001b[38;5;241m=\u001b[39m tiktoken\u001b[38;5;241m.\u001b[39mencoding_for_model(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel)\n\u001b[1;32m    231\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/model.py:103\u001b[0m, in \u001b[0;36mencoding_for_model\u001b[0;34m(model_name)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns the encoding used by a model.\u001b[39;00m\n\u001b[1;32m    100\u001b[0m \n\u001b[1;32m    101\u001b[0m \u001b[38;5;124;03mRaises a KeyError if the model name is not recognised.\u001b[39;00m\n\u001b[1;32m    102\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_encoding(encoding_name_for_model(model_name))\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/registry.py:73\u001b[0m, in \u001b[0;36mget_encoding\u001b[0;34m(encoding_name)\u001b[0m\n\u001b[1;32m     72\u001b[0m constructor \u001b[38;5;241m=\u001b[39m ENCODING_CONSTRUCTORS[encoding_name]\n\u001b[0;32m---> 73\u001b[0m enc \u001b[38;5;241m=\u001b[39m Encoding(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconstructor())\n\u001b[1;32m     74\u001b[0m ENCODINGS[encoding_name] \u001b[38;5;241m=\u001b[39m enc\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken_ext/openai_public.py:72\u001b[0m, in \u001b[0;36mcl100k_base\u001b[0;34m()\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcl100k_base\u001b[39m():\n\u001b[0;32m---> 72\u001b[0m     mergeable_ranks \u001b[38;5;241m=\u001b[39m load_tiktoken_bpe(\n\u001b[1;32m     73\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     74\u001b[0m         expected_hash\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m223921b76ee99bde995b7ff738513eef100fb51d18c93597a113bcffe865b2a7\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     75\u001b[0m     )\n\u001b[1;32m     76\u001b[0m     special_tokens \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     77\u001b[0m         ENDOFTEXT: \u001b[38;5;241m100257\u001b[39m,\n\u001b[1;32m     78\u001b[0m         FIM_PREFIX: \u001b[38;5;241m100258\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     81\u001b[0m         ENDOFPROMPT: \u001b[38;5;241m100276\u001b[39m,\n\u001b[1;32m     82\u001b[0m     }\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/load.py:147\u001b[0m, in \u001b[0;36mload_tiktoken_bpe\u001b[0;34m(tiktoken_bpe_file, expected_hash)\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_tiktoken_bpe\u001b[39m(\n\u001b[1;32m    144\u001b[0m     tiktoken_bpe_file: \u001b[38;5;28mstr\u001b[39m, expected_hash: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    145\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mint\u001b[39m]:\n\u001b[1;32m    146\u001b[0m     \u001b[38;5;66;03m# NB: do not add caching to this function\u001b[39;00m\n\u001b[0;32m--> 147\u001b[0m     contents \u001b[38;5;241m=\u001b[39m read_file_cached(tiktoken_bpe_file, expected_hash)\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    149\u001b[0m         base64\u001b[38;5;241m.\u001b[39mb64decode(token): \u001b[38;5;28mint\u001b[39m(rank)\n\u001b[1;32m    150\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m token, rank \u001b[38;5;129;01min\u001b[39;00m (line\u001b[38;5;241m.\u001b[39msplit() \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m contents\u001b[38;5;241m.\u001b[39msplitlines() \u001b[38;5;28;01mif\u001b[39;00m line)\n\u001b[1;32m    151\u001b[0m     }\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/load.py:64\u001b[0m, in \u001b[0;36mread_file_cached\u001b[0;34m(blobpath, expected_hash)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m contents \u001b[38;5;241m=\u001b[39m read_file(blobpath)\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m expected_hash \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m check_hash(contents, expected_hash):\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/load.py:25\u001b[0m, in \u001b[0;36mread_file\u001b[0;34m(blobpath)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;66;03m# avoiding blobfile for public files helps avoid auth issues, like MFA prompts\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m resp \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(blobpath)\n\u001b[1;32m     26\u001b[0m resp\u001b[38;5;241m.\u001b[39mraise_for_status()\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;124;03m:rtype: requests.Response\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, params\u001b[38;5;241m=\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/adapters.py:688\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[0;32m--> 688\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m    690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n",
      "\u001b[0;31mConnectTimeout\u001b[0m: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7fb84b42e310>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connection.py:199\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 199\u001b[0m     sock \u001b[38;5;241m=\u001b[39m connection\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[1;32m    200\u001b[0m         (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dns_host, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport),\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout,\n\u001b[1;32m    202\u001b[0m         source_address\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource_address,\n\u001b[1;32m    203\u001b[0m         socket_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msocket_options,\n\u001b[1;32m    204\u001b[0m     )\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 85\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/util/connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 73\u001b[0m sock\u001b[38;5;241m.\u001b[39mconnect(sa)\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
      "\u001b[0;31mTimeoutError\u001b[0m: [Errno 110] Connection timed out",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:789\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    788\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 789\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[1;32m    790\u001b[0m     conn,\n\u001b[1;32m    791\u001b[0m     method,\n\u001b[1;32m    792\u001b[0m     url,\n\u001b[1;32m    793\u001b[0m     timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[1;32m    794\u001b[0m     body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[1;32m    795\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m    796\u001b[0m     chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[1;32m    797\u001b[0m     retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[1;32m    798\u001b[0m     response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[1;32m    799\u001b[0m     preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[1;32m    800\u001b[0m     decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[1;32m    801\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[1;32m    802\u001b[0m )\n\u001b[1;32m    804\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:490\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    489\u001b[0m         new_e \u001b[38;5;241m=\u001b[39m _wrap_proxy_error(new_e, conn\u001b[38;5;241m.\u001b[39mproxy\u001b[38;5;241m.\u001b[39mscheme)\n\u001b[0;32m--> 490\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m new_e\n\u001b[1;32m    492\u001b[0m \u001b[38;5;66;03m# conn.request() calls http.client.*.request, not the method in\u001b[39;00m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;66;03m# urllib3.request. It also calls makefile (recv) on the socket.\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:466\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 466\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_conn(conn)\n\u001b[1;32m    467\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:1095\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1094\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_closed:\n\u001b[0;32m-> 1095\u001b[0m     conn\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[1;32m   1097\u001b[0m \u001b[38;5;66;03m# TODO revise this, see https://github.com/urllib3/urllib3/issues/2791\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connection.py:693\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    692\u001b[0m sock: socket\u001b[38;5;241m.\u001b[39msocket \u001b[38;5;241m|\u001b[39m ssl\u001b[38;5;241m.\u001b[39mSSLSocket\n\u001b[0;32m--> 693\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m sock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_conn()\n\u001b[1;32m    694\u001b[0m server_hostname: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connection.py:208\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 208\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[1;32m    209\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    210\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    211\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[0;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPSConnection object at 0x7fb84b43c450>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[1;32m    668\u001b[0m         method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m    669\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[1;32m    670\u001b[0m         body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[1;32m    671\u001b[0m         headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[1;32m    672\u001b[0m         redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    673\u001b[0m         assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    674\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    675\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    676\u001b[0m         retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[1;32m    677\u001b[0m         timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[1;32m    678\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[1;32m    679\u001b[0m     )\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/connectionpool.py:843\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    841\u001b[0m     new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[0;32m--> 843\u001b[0m retries \u001b[38;5;241m=\u001b[39m retries\u001b[38;5;241m.\u001b[39mincrement(\n\u001b[1;32m    844\u001b[0m     method, url, error\u001b[38;5;241m=\u001b[39mnew_e, _pool\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, _stacktrace\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]\n\u001b[1;32m    845\u001b[0m )\n\u001b[1;32m    846\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/urllib3/util/retry.py:519\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m    518\u001b[0m     reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[0;32m--> 519\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m    521\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
      "\u001b[0;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7fb84b43c450>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m model \u001b[38;5;241m=\u001b[39m OpenAIWrapper(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgpt-4-1106-preview\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m msgs \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHello!\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n\u001b[0;32m----> 4\u001b[0m code, answer, resp \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mgenerate_inner(msgs)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(code, answer, resp)\n",
      "File \u001b[0;32m/home/workspace/code/git/eval_mm/vlmevalkit/vlmeval/api/gpt.py:173\u001b[0m, in \u001b[0;36mOpenAIWrapper.generate_inner\u001b[0;34m(self, inputs, **kwargs)\u001b[0m\n\u001b[1;32m    170\u001b[0m max_tokens \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_tokens\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_tokens)\n\u001b[1;32m    172\u001b[0m context_window \u001b[38;5;241m=\u001b[39m GPT_context_window(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel)\n\u001b[0;32m--> 173\u001b[0m max_tokens \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(max_tokens, context_window \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_token_len(inputs))\n\u001b[1;32m    174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m max_tokens \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m:\n\u001b[1;32m    175\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    176\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLess than 100 tokens left, \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    177\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmay exceed the context window with some additional meta symbols. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    178\u001b[0m     )\n",
      "File \u001b[0;32m/home/workspace/code/git/eval_mm/vlmevalkit/vlmeval/api/gpt.py:232\u001b[0m, in \u001b[0;36mOpenAIWrapper.get_token_len\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    230\u001b[0m     enc \u001b[38;5;241m=\u001b[39m tiktoken\u001b[38;5;241m.\u001b[39mencoding_for_model(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel)\n\u001b[1;32m    231\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m--> 232\u001b[0m     enc \u001b[38;5;241m=\u001b[39m tiktoken\u001b[38;5;241m.\u001b[39mencoding_for_model(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgpt-4\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    233\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(inputs, \u001b[38;5;28mlist\u001b[39m)\n\u001b[1;32m    234\u001b[0m tot \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/model.py:103\u001b[0m, in \u001b[0;36mencoding_for_model\u001b[0;34m(model_name)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mencoding_for_model\u001b[39m(model_name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Encoding:\n\u001b[1;32m     99\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Returns the encoding used by a model.\u001b[39;00m\n\u001b[1;32m    100\u001b[0m \n\u001b[1;32m    101\u001b[0m \u001b[38;5;124;03m    Raises a KeyError if the model name is not recognised.\u001b[39;00m\n\u001b[1;32m    102\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m get_encoding(encoding_name_for_model(model_name))\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/registry.py:73\u001b[0m, in \u001b[0;36mget_encoding\u001b[0;34m(encoding_name)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     69\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown encoding \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mencoding_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Plugins found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_available_plugin_modules()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     70\u001b[0m     )\n\u001b[1;32m     72\u001b[0m constructor \u001b[38;5;241m=\u001b[39m ENCODING_CONSTRUCTORS[encoding_name]\n\u001b[0;32m---> 73\u001b[0m enc \u001b[38;5;241m=\u001b[39m Encoding(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconstructor())\n\u001b[1;32m     74\u001b[0m ENCODINGS[encoding_name] \u001b[38;5;241m=\u001b[39m enc\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m enc\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken_ext/openai_public.py:72\u001b[0m, in \u001b[0;36mcl100k_base\u001b[0;34m()\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcl100k_base\u001b[39m():\n\u001b[0;32m---> 72\u001b[0m     mergeable_ranks \u001b[38;5;241m=\u001b[39m load_tiktoken_bpe(\n\u001b[1;32m     73\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     74\u001b[0m         expected_hash\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m223921b76ee99bde995b7ff738513eef100fb51d18c93597a113bcffe865b2a7\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     75\u001b[0m     )\n\u001b[1;32m     76\u001b[0m     special_tokens \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     77\u001b[0m         ENDOFTEXT: \u001b[38;5;241m100257\u001b[39m,\n\u001b[1;32m     78\u001b[0m         FIM_PREFIX: \u001b[38;5;241m100258\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     81\u001b[0m         ENDOFPROMPT: \u001b[38;5;241m100276\u001b[39m,\n\u001b[1;32m     82\u001b[0m     }\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     84\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcl100k_base\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     85\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpat_str\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(?i:[sdmt]|ll|ve|re)|[^\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;132;01m{L}\u001b[39;00m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;132;01m{N}\u001b[39;00m\u001b[38;5;124m]?+\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;132;01m{L}\u001b[39;00m\u001b[38;5;124m+|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;132;01m{N}\u001b[39;00m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m1,3}| ?[^\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;132;01m{L}\u001b[39;00m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;132;01m{N}\u001b[39;00m\u001b[38;5;124m]++[\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mn]*|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*[\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mn]|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms+(?!\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mS)|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms+\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m,\n\u001b[1;32m     86\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmergeable_ranks\u001b[39m\u001b[38;5;124m\"\u001b[39m: mergeable_ranks,\n\u001b[1;32m     87\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspecial_tokens\u001b[39m\u001b[38;5;124m\"\u001b[39m: special_tokens,\n\u001b[1;32m     88\u001b[0m     }\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/load.py:147\u001b[0m, in \u001b[0;36mload_tiktoken_bpe\u001b[0;34m(tiktoken_bpe_file, expected_hash)\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_tiktoken_bpe\u001b[39m(\n\u001b[1;32m    144\u001b[0m     tiktoken_bpe_file: \u001b[38;5;28mstr\u001b[39m, expected_hash: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    145\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mint\u001b[39m]:\n\u001b[1;32m    146\u001b[0m     \u001b[38;5;66;03m# NB: do not add caching to this function\u001b[39;00m\n\u001b[0;32m--> 147\u001b[0m     contents \u001b[38;5;241m=\u001b[39m read_file_cached(tiktoken_bpe_file, expected_hash)\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    149\u001b[0m         base64\u001b[38;5;241m.\u001b[39mb64decode(token): \u001b[38;5;28mint\u001b[39m(rank)\n\u001b[1;32m    150\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m token, rank \u001b[38;5;129;01min\u001b[39;00m (line\u001b[38;5;241m.\u001b[39msplit() \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m contents\u001b[38;5;241m.\u001b[39msplitlines() \u001b[38;5;28;01mif\u001b[39;00m line)\n\u001b[1;32m    151\u001b[0m     }\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/load.py:64\u001b[0m, in \u001b[0;36mread_file_cached\u001b[0;34m(blobpath, expected_hash)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m     62\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m contents \u001b[38;5;241m=\u001b[39m read_file(blobpath)\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m expected_hash \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m check_hash(contents, expected_hash):\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     67\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHash mismatch for data downloaded from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mblobpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexpected_hash\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m). \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     68\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis may indicate a corrupted download. Please try again.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     69\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/tiktoken/load.py:25\u001b[0m, in \u001b[0;36mread_file\u001b[0;34m(blobpath)\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m f\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m     24\u001b[0m \u001b[38;5;66;03m# avoiding blobfile for public files helps avoid auth issues, like MFA prompts\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m resp \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(blobpath)\n\u001b[1;32m     26\u001b[0m resp\u001b[38;5;241m.\u001b[39mraise_for_status()\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\u001b[38;5;241m.\u001b[39mcontent\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(url, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     63\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, params\u001b[38;5;241m=\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m/opt/conda/envs/lmquant/lib/python3.11/site-packages/requests/adapters.py:688\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    685\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ConnectTimeoutError):\n\u001b[1;32m    686\u001b[0m     \u001b[38;5;66;03m# TODO: Remove this in 3.0.0: see #2811\u001b[39;00m\n\u001b[1;32m    687\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[0;32m--> 688\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m    690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n\u001b[1;32m    691\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m RetryError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n",
      "\u001b[0;31mConnectTimeout\u001b[0m: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7fb84b43c450>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)'))"
     ]
    }
   ],
   "source": [
    "from vlmeval.api import OpenAIWrapper\n",
    "model = OpenAIWrapper('gpt-4-1106-preview', verbose=True)\n",
    "msgs = [dict(type='text', value='Hello!')]\n",
    "code, answer, resp = model.generate_inner(msgs)\n",
    "print(code, answer, resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "# image = Image.open('/home/workspace/code/llm-awq/awq/airplane.jpeg').convert('RGB')\n",
    "for file_name in os.listdir(\"/home/workspace/dataset/demo_samples\"):\n",
    "    image_file = os.path.join(\"/home/workspace/dataset/demo_samples\", file_name)\n",
    "    image = Image.open(image_file).convert('RGB')\n",
    "\n",
    "    # First round chat \n",
    "    question = \"Please analyze the image in detail base on the following requirements:\\n1. Give a brief overview.\\n2. Introduce all visible elements in the image. Including axis, numerical value and all text.\\n3. Do not estimate values not written on the image. Use markdown format only on tables. Do not output x-axis and y-axis if there is no coordinate axis.\\n\"\n",
    "\n",
    "    msgs = [{'role': 'user', 'content': question}]\n",
    "    answer = model.chat(\n",
    "        image=image,\n",
    "        msgs=msgs,\n",
    "        tokenizer=tokenizer,\n",
    "        sampling=True,\n",
    "        temperature=0.3,\n",
    "        repetition_penalty=1.1,\n",
    "        top_k=20,\n",
    "        filename = str(file_name)\n",
    "    )\n",
    "    print(answer)\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/179.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/218.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/189.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/122.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/150.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/180.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/96.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/80.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/128.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/182.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/39.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/132.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/100.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/212.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/149.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/124.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/34.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/162.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/15.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/185.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/97.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/114.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/65.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/129.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/81.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/123.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'top_p': 0.8, 'top_k': 20, 'temperature': 0.3, 'do_sample': True, 'repetition_penalty': 1.1}\n",
      "Saved answer to /home/workspace/dataset/lenovo_result_pince/194.txt\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "# image = Image.open('/home/workspace/code/llm-awq/awq/airplane.jpeg').convert('RGB')\n",
    "for file_name in os.listdir(\"/home/workspace/dataset/lenovo_pince\"):\n",
    "    image_file = os.path.join(\"/home/workspace/dataset/lenovo_pince\", file_name)\n",
    "    image = Image.open(image_file).convert('RGB')\n",
    "    # First round chat \n",
    "    question = \"Please analyze the image in detail base on the following requirements:\\n1. Give a brief overview.\\n2. Introduce all visible elements in the image. Including axis, numerical value and all text.\\n3. Do not estimate values not written on the image. Use markdown format only on tables. Do not output x-axis and y-axis if there is no coordinate axis.\\n\"\n",
    "\n",
    "    msgs = [{'role': 'user', 'content': question}]\n",
    "    answer = model.chat(\n",
    "        image=image,\n",
    "        msgs=msgs,\n",
    "        tokenizer=tokenizer,\n",
    "        sampling=True,\n",
    "        temperature=0.3,\n",
    "        repetition_penalty=1.1,\n",
    "        top_k=20\n",
    "    )\n",
    "    output_dir = \"/home/workspace/dataset/lenovo_result_pince\"\n",
    "        # 保存文件名与路径\n",
    "    save_file_name = f\"{os.path.splitext(file_name)[0]}.txt\"  # 去掉文件扩展名并添加 .txt\n",
    "    save_path = os.path.join(output_dir, save_file_name)\n",
    "    \n",
    "    # 保存答案到 .txt 文件\n",
    "    with open(save_path, \"w\") as file:\n",
    "        file.write(answer)\n",
    "\n",
    "    print(f\"Saved answer to {save_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Title]\n",
      "Model 1 (current)\n",
      "\n",
      "[Footer]\n",
      "Lenovo 2023 Lenovo Internal. All rights reserved.\n",
      "\n",
      "[Overview]\n",
      "The image illustrates the current model of a product delivery process involving Lenovo and its customer, highlighting various stages such as sales list entry, governance compliance, security review, and service delivery. It also points out disadvantages like slow progress into the market and high costs for each version.\n",
      "\n",
      "[Section 1]\n",
      "Detail in section 1:\n",
      "\n",
      "**Product Entry:**\n",
      "- **Description:** The first step where the product enters the sales list.\n",
      "- **Importance:** This is crucial because it initiates the entire sales process and ensures that the product is available for potential buyers.\n",
      "\n",
      "**Governance, Compliance, Privacy Protection, Risk Management, Certificate:**\n",
      "- **Description:** These are critical steps ensuring adherence to regulatory standards and protocols.\n",
      "- **Importance:** They ensure legal compliance, protect user privacy, manage risks effectively, and provide necessary certifications, which are essential for maintaining trust and credibility with customers.\n",
      "\n",
      "**Security Review (SSRB):**\n",
      "- **Description:** A thorough security assessment conducted on the product before delivery.\n",
      "- **Importance:** This step is vital for identifying vulnerabilities and mitigating any potential security threats, thereby protecting both the company and its customers from cyber attacks.\n",
      "\n",
      "**Delivery:**\n",
      "- **Description:** The actual transfer of the product from Lenovo to the customer.\n",
      "- **Importance:** This is the final stage where the product reaches the end-user, completing the transaction cycle initiated by the product entry.\n",
      "\n",
      "**Service Delivery to Customer:**\n",
      "- **Description:** Post-delivery support services provided to the customer.\n",
      "- **Importance:** Ensuring customer satisfaction through ongoing support and assistance, enhancing overall customer experience and loyalty.\n",
      "\n",
      "**Disadvantages:**\n",
      "- **Description:** Issues faced during this process include slow progression into the market and high costs associated with each version.\n",
      "- **Importance:** Understanding these challenges helps in strategizing improvements and cost optimizations to enhance efficiency and profitability.\n",
      "\n",
      "**Security Development:**\n",
      "- **Description:** Ongoing development and improvement of security measures.\n",
      "- **Importance:** Continuous enhancement of security features and processes to stay ahead of emerging threats and maintain competitive advantage.\n",
      "\n",
      "**Security Operation:**\n",
      "- **Description:** Operational aspects of security management.\n",
      "- **Importance:** Efficient operation of security systems and processes to ensure smooth and secure operations within the organization.\n",
      "\n",
      "This comprehensive overview outlines all key stages and considerations involved in the current model of product delivery and security management at Lenovo.\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "# image = Image.open('/home/workspace/code/llm-awq/awq/airplane.jpeg').convert('RGB')\n",
    "image = Image.open('/home/workspace/code/I-ViT/0.jpg').convert('RGB')\n",
    "\n",
    "# First round chat \n",
    "question = \"Please analyze the image in detail base on the following requirements:\\n1. Give a brief overview.\\n2. Introduce all visible elements in the image. Including axis, numerical value and all text.\\n3. Do not estimate values not written on the image. Use markdown format only on tables. Do not output x-axis and y-axis if there is no coordinate axis.\\n\"\n",
    "\n",
    "msgs = [{'role': 'user', 'content': question}]\n",
    "answer = model.chat(\n",
    "    image=image,\n",
    "    msgs=msgs,\n",
    "    tokenizer=tokenizer,\n",
    "    sampling=True,\n",
    "    temperature=0.3,\n",
    "    repetition_penalty=1.1,\n",
    "    top_k=20\n",
    ")\n",
    "print(answer)\n",
    "\n",
    "# Second round chat \n",
    "# pass history context of multi-turn conversation\n",
    "# msgs.append({\"role\": \"assistant\", \"content\": [answer]})\n",
    "# msgs.append({\"role\": \"user\", \"content\": [\"Introduce something about Airbus A380.\"]})\n",
    "\n",
    "# answer = model_fp.chat(\n",
    "#     image=None,\n",
    "#     msgs=msgs,\n",
    "#     tokenizer=tokenizer\n",
    "# )\n",
    "# print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoModel, AutoTokenizer,AutoProcessor\n",
    "from transformers import set_seed\n",
    "set_seed(42)\n",
    "model_path = \"/home/workspace/model/minicpm-vit-1b\"\n",
    "model_fp = AutoModel.from_pretrained(model_path,         \n",
    "        trust_remote_code=True,\n",
    "        torch_dtype=torch.float16,\n",
    "        device_map=None,\n",
    "        attn_implementation=\"sdpa\") # sdpa or flash_attention_2, no eager\n",
    "model_fp = model_fp.cuda().eval()\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Title]\n",
      "“Top-down” Compliance Mechanism\n",
      "\n",
      "[Header]\n",
      "Data Security and Privacy Protection Working Committee (China-site)\n",
      "\n",
      "[Footer]\n",
      "Lenovo 2022 Lenovo Internal. All rights reserved.\n",
      "\n",
      "[Overview]\n",
      "The image depicts a hierarchical structure of the Data Security and Privacy Protection Working Committee, detailing various departments within Lenovo China, their respective functions, and the specific roles assigned to each department for compliance purposes.\n",
      "\n",
      "[Section 1]\n",
      "Detail in section 1:\n",
      "\n",
      "**China Geo**\n",
      "- **China Commercial**\n",
      "- **SMEs**\n",
      "- **China Consumer**\n",
      "- **China Services**\n",
      "- **China Ops**\n",
      "- **Global Account Operations**\n",
      "- **ISG (China)**\n",
      "\n",
      "**BU**\n",
      "- **IDG**\n",
      "- **GSL**\n",
      "- **PSO**\n",
      "- **Smart Device / Commercial Product Solutions**\n",
      "- **SMB**\n",
      "\n",
      "**Functional Departments**\n",
      "- **GR**\n",
      "- **HR**\n",
      "- **Finance**\n",
      "- **Marketing**\n",
      "- **Legal**\n",
      "- **ICAC**\n",
      "- **IT**\n",
      "- **DT**\n",
      "- **IT Cyber-Security**\n",
      "\n",
      "**GSC**\n",
      "- **LFC**\n",
      "\n",
      "**Lenovo Research**\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "image = Image.open('/home/workspace/code/I-ViT/1.jpg').convert('RGB')\n",
    "\n",
    "# First round chat \n",
    "question = \"Please analyze the image in detail base on the following requirements:\\n1. Give a brief overview.\\n2. Introduce all visible elements in the image. Including axis, numerical value and all text.\\n3. Do not estimate values not written on the image. Use markdown format only on tables. Do not output x-axis and y-axis if there is no coordinate axis.\\n\"\n",
    "\n",
    "# msgs = [{'role': 'user', 'content': [image, question]}]\n",
    "# answer = model_fp.chat(\n",
    "#     image=None,\n",
    "#     msgs=msgs,\n",
    "#     tokenizer=tokenizer,\n",
    "#     sampling=True,\n",
    "#     temperature=0.3,\n",
    "#     repetition_penalty=1.1,\n",
    "#     top_k=20\n",
    "# )\n",
    "\n",
    "msgs = [{'role': 'user', 'content': question}]\n",
    "answer = model_fp.chat(\n",
    "    image=image,\n",
    "    msgs=msgs,\n",
    "    tokenizer=tokenizer,\n",
    "    sampling=True,\n",
    "    temperature=0.3,\n",
    "    repetition_penalty=1.1,\n",
    "    top_k=20\n",
    ")\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.]], device='cuda:0', dtype=torch.float16,\n",
       "       grad_fn=<SubBackward0>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fp.llm.lm_head.weight-model.llm.lm_head.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.6403e-04, -6.1035e-05,  4.5776e-05,  ...,  4.5776e-05,\n",
       "         -4.5776e-05,  1.6785e-04],\n",
       "        [ 1.6022e-04, -7.2479e-05,  7.6294e-06,  ...,  1.8311e-04,\n",
       "         -4.9591e-05, -1.0681e-04],\n",
       "        [ 6.8665e-05, -4.5776e-05, -1.6785e-04,  ...,  4.3392e-05,\n",
       "         -6.1035e-05, -6.1035e-05],\n",
       "        ...,\n",
       "        [ 2.7084e-04,  6.8665e-05,  7.6294e-05,  ..., -2.0599e-04,\n",
       "          4.5776e-05,  2.2888e-05],\n",
       "        [ 6.8665e-05, -9.1553e-05, -1.6260e-04,  ...,  7.6294e-06,\n",
       "         -2.4414e-04, -9.1553e-05],\n",
       "        [ 1.5259e-04,  2.8992e-04, -3.3569e-04,  ..., -1.2207e-04,\n",
       "          3.0518e-05,  6.1035e-05]], device='cuda:0', dtype=torch.float16,\n",
       "       grad_fn=<SubBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fp.llm.model.layers[24].self_attn.q_proj.weight-model.llm.model.layers[24].self_attn.q_proj.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.]], device='cuda:0', dtype=torch.float16,\n",
       "       grad_fn=<SubBackward0>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fp.llm.model.layers[0].self_attn.q_proj.weight-model.llm.model.layers[0].self_attn.q_proj.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0986e-03,  1.8311e-04, -1.2970e-04,  ..., -3.0518e-05,\n",
       "         -2.7466e-04,  1.3733e-04],\n",
       "        [-6.4087e-04, -1.0300e-04, -6.4087e-04,  ..., -1.5259e-04,\n",
       "          1.3161e-04, -1.5259e-04],\n",
       "        [ 5.6458e-04,  8.2779e-04,  9.1553e-04,  ..., -1.9836e-04,\n",
       "          1.9073e-04,  3.0518e-04],\n",
       "        ...,\n",
       "        [-8.0872e-04,  0.0000e+00,  7.0190e-04,  ..., -8.7619e-06,\n",
       "         -2.1362e-04, -3.2043e-04],\n",
       "        [-3.4332e-04,  6.3658e-04, -3.0518e-05,  ...,  4.8065e-04,\n",
       "         -1.2207e-04,  5.3406e-05],\n",
       "        [ 5.7983e-04,  4.5776e-05,  6.7139e-04,  ...,  2.2316e-04,\n",
       "         -3.1662e-04, -2.4414e-04]], device='cuda:0', dtype=torch.float16,\n",
       "       grad_fn=<SubBackward0>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fp.vpm.encoder.layers[0].mlp.fc1.weight-model.vpm.encoder.layers[0].mlp.fc1.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_ids\n",
      "attention_mask\n",
      "pixel_values\n",
      "image_sizes\n",
      "image_bound\n",
      "tgt_sizes\n",
      "The aircraft in the image is a Boeing 747, specifically one of its variants known as an \"A300.\" The model number 'B-6125' indicates that this particular airplane belongs to the B-series fleet which includes various models and configurations based on their design features such as engines, size, and passenger capacity. To determine the exact variant (such as A300 or other), more information would be needed about specific characteristics like wing configuration, cabin layout, etc.\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "image = Image.open('/home/workspace/code/llm-awq/awq/airplane.jpeg').convert('RGB')\n",
    "\n",
    "# First round chat \n",
    "question = \"Tell me the model of this aircraft.\"\n",
    "msgs = [{'role': 'user', 'content': [image, question]}]\n",
    "\n",
    "answer = model.chat(\n",
    "    image=None,\n",
    "    msgs=msgs,\n",
    "    tokenizer=tokenizer\n",
    ")\n",
    "print(answer)\n",
    "\n",
    "# Second round chat \n",
    "# pass history context of multi-turn conversation\n",
    "# msgs.append({\"role\": \"assistant\", \"content\": [answer]})\n",
    "# msgs.append({\"role\": \"user\", \"content\": [\"Introduce something about Airbus A380.\"]})\n",
    "\n",
    "# answer = model_fp.chat(\n",
    "#     image=None,\n",
    "#     msgs=msgs,\n",
    "#     tokenizer=tokenizer\n",
    "# )\n",
    "# print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_from_disk\n",
    "dataset = load_from_disk(\"/home/workspace/dataset/MME\")[\"test\"]\n",
    "processor = AutoProcessor.from_pretrained(\"/home/workspace/model/MiniCPM-V-1B-sft-v2-1B\", trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts_lists = []\n",
    "input_images_lists = []\n",
    "for index, _data in enumerate(dataset):\n",
    "    promt = _data[\"question\"]\n",
    "    image = _data[\"image\"]\n",
    "    msgs = [{'role': 'user', 'content': \"(<image>./</image>)\\n\"+ promt}]\n",
    "    prompts_lists.append(processor.tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True))\n",
    "    input_images_lists.append([image])\n",
    "    if index >= 2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = processor(\n",
    "    prompts_lists,\n",
    "    input_images_lists,\n",
    "    max_slice_nums=9,\n",
    "    use_image_id=True,\n",
    "    return_tensors=\"pt\",\n",
    "    max_length=8192\n",
    ").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 100])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs[\"input_ids\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 94, 73454])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(inputs)[\"logits\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'input_ids' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m model_inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m: input_ids,\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage_bound\u001b[39m\u001b[38;5;124m\"\u001b[39m: image_bound,\n\u001b[1;32m      4\u001b[0m }\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m vision_hidden_states \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m      7\u001b[0m     model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pixel_values\n",
      "\u001b[0;31mNameError\u001b[0m: name 'input_ids' is not defined"
     ]
    }
   ],
   "source": [
    "model_inputs = {\n",
    "    \"input_ids\": input_ids,\n",
    "    \"image_bound\": image_bound,\n",
    "}\n",
    "\n",
    "if vision_hidden_states is None:\n",
    "    model_inputs[\"pixel_values\"] = pixel_values\n",
    "    model_inputs['tgt_sizes'] = tgt_sizes\n",
    "else:\n",
    "    model_inputs[\"vision_hidden_states\"] = vision_hidden_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the latest cached version of the dataset since wikitext couldn't be found on the Hugging Face Hub\n",
      "Found the latest cached dataset configuration 'wikitext-2-raw-v1' at /root/.cache/huggingface/datasets/wikitext/wikitext-2-raw-v1/0.0.0/b08601e04326c79dfdd32d625aee71d232d685c3 (last modified on Sat Sep 14 07:29:56 2024).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text'],\n",
       "    num_rows: 4358\n",
       "})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset,load_from_disk\n",
    "dataset = load_from_disk(\"/home/workspace/dataset/MME\")[\"test\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, _data in enumerate(dataset):\n",
    "    a = _data[\"image\"]\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_lenovo(root_folder):\n",
    "    import os\n",
    "    import json\n",
    "    from PIL import Image\n",
    "    import re\n",
    "\n",
    "    result_list = []\n",
    "    image_tag_pattern = r\"\\u003Cimage\\u003E.*?\\u003C\\/image\\u003E\"\n",
    "\n",
    "    for folder_name in os.listdir(root_folder):\n",
    "        sub_folder_path = os.path.join(root_folder, folder_name)\n",
    "        if os.path.isdir(sub_folder_path):\n",
    "            json_file = None\n",
    "            image_file = None\n",
    "            for file_name in os.listdir(sub_folder_path):\n",
    "                if file_name.endswith(\".json\"):\n",
    "                    json_file = os.path.join(sub_folder_path, file_name)\n",
    "                elif file_name.endswith(\".png\"):\n",
    "                    image_file = os.path.join(sub_folder_path, file_name)\n",
    "            \n",
    "            if json_file and image_file:\n",
    "                with open(json_file, 'r', encoding='utf-8') as f:\n",
    "                    data = json.load(f)\n",
    "                \n",
    "                task_description = None\n",
    "                for item in data:\n",
    "                    if item.get('from') == 'human':\n",
    "                        task_description = item.get('value')\n",
    "                        \n",
    "                        # 去除以 <image> 开头的部分\n",
    "                        task_description = re.sub(image_tag_pattern, \"\", task_description)\n",
    "                        break\n",
    "                \n",
    "                image = Image.open(image_file)\n",
    "\n",
    "                if task_description:\n",
    "                    result_list.append({\n",
    "                        \"question\": task_description,\n",
    "                        \"image\": image\n",
    "                    })\n",
    "\n",
    "    return result_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## Task Overview\n",
      "You are an expert in interpreting complex PowerPoint slides. Please thoroughly analyze the provided slide screenshot by following these steps:\n",
      "## Task Instructions\n",
      "Identify and extract the title, slide header, slide footer, and page number from the slide.\n",
      "Provide a concise summary of the content displayed in the slide screenshot.\n",
      "Apart from the title and footer, identify all charts in the screenshot. For each chart, specify its type, position on the slide, and provide a brief introduction.\n",
      "Conduct a detailed analysis of each section in the slide, offering a comprehensive description of all visible elements.\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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www39uvGG6wNYN43CwmefFT8vedhtN98MRUuWLHn+2WelNzOCMmzZc/6fMxWTniYYSDCQYCDBQIKBBAMJBhIMJBhIMPC/gIEp7we23ZdySwETdSzsmN5CVdt88825NClgVLt77rlnr732EgAstpkeSC+iWJaVl6f6+y+77DLaI7X2A4cf3tDNLZkpZLq/q6ukqqqhtm769Onf+c53cnOzd9xtN+mgLrzoYjrYHu98p3OG7TiVkVhOpsMPP9y+2Z///Ofz5s2TV0lI7YYbbvy3m/5GRfREIC610DlAeYWF1LnCCkmWs5zZQ6Hy0MnFFGO+TfBTy++++24a6YqXXrr66quffubZP1z4+0MOO2yot5euqxLa9SOPPMIjq+mczAx6mjRaFEuqpp26f/rTn8RvU3f1msJP6c0bdspvv4Z4dH3S7mIdmNIej6LXt9122/U32kguqH0PPvjxxx//85//LEPVjjvu+OKLL955552HHXaYAhBCu6b3PvDAAyqH27PPPpuGrPCsWbPYCC654orddtrp3HPPpWTSkAMA2VmXX375xhtvXFpTIzPZ73//+2uuuebLp5xSlZ8/OjS0miq+kqT0ce9dd7XxuNqxTIOD4LRH98Ybb5SsW20VpSW0Xx3Z813vkqZLJRqybzk3iy89y/5kABt0Fo3+/vQXTjnld7/7XXV1tUhpcQG14pzzCxTwRA3gF3nOcACxXzvzzBv+ej1jhN3g1fX1LUuXMT0sWdHIs03PZ0SgMOu+tNWVzpfq6wMkv/JKmJObBAMJBhIMJBhIMJBgIMFAgoEEAwkGEgz8ixiYsh/YBmAKcFlVFY+f7b58hlyX2+68M31sl113nTd7Dr2O57Bp6VJZguVG4vrjj1XSJx2yf4BOmqL3UkrpQny2QZns66ODqaGqvp4+2dvRISxWx+Qx5n6kFFGkh8eCx9XWViW5DXt6eijAUi5rurCgEBjV06dTR/1KcXJIElVZqi36aHyqra/pocEjjjhij1122XrrrSl49tyeddZZM+bO5fUtKS465AMfGOzpoekVV1XRtOm3119/PWCam5tHJzI++NGP2knLq0zNfsc73tE/MEjVByH4Napr7rVbVVXlhqvTJ0iCr9gBUkKax8dLqqvX33TTns5Oz+HQbmQP05HXFCZFa4MzLikAWOvdfb0Q0jB7No307XvtPXvBApD4HEqlvvjFLza3tQPbi3PnzoUZvaMAL3vhBQDQ0nlTww7kqB4ArPUCm/rZMSjAujlnzpzUYNiY7Z663t3X/+53v/vRRx8diJz5VPriykr1AJ5q6tMuZWTgRg3wZsTr6+uLiovbli+n0gOj2ghmh1xXnvf09qxYscLrg319S1csZ1ZACW1NTfWzZvEtMxIwMdDb1VNaWwuNsIoaIFYTawU+eZhgIMFAgoEEAwkGEgwkGEgwkGAgwUCCgTeGgSn7geWyGhyxIXRYQuYHH3zw4X8+Vl5VeeY3vk7boVPlFRbc+8D9tuBWVFelB9I0vabmZrogLZD+JH9VdmZWT28vhTA3Ly8/NxxN1DBzpiRS9g/zKttjXDV9Wndrq127VZXlza0tnJ8ON+p9fiFvoFYlW6YfUskuv/IKDk/qVldPOKGnqro6FeWdyqNB0YT5KsM5QkEXpex5Qv/Ozc6pqKysa6i1T5Uey+VIr/MW/dOW4FR/H6WrvL6ur6Ojtr7OBlrN2UXsp9KSovvvulP5vKz8gaFBm10rykvtED6wvs62WDuQldRKbl6uY37Bab80AIIDkyecShxt69Wphx9+mCec7kqdFoDdN5CmcJbX1mby5Dr6aGKcD5ldYHwow9ZfyPSktalxYHRkvfUXNC5bWlJWOjE6kl9SbIO0fjW1NHuybMXy/LwcBwt3t7XWNdTbkNzR1RnveR4dSNu7a3euP1d8s/KzJ5XWER2kh8OnjdA52Zk2GAvj/sBHj3j0sX/88Y9/3HOftzfU15533nnvePe7x4eHNEffLqwoZ8Kw+5eybYfwvu/d/y9/+QuN97377kuHr6mtrauvf+HFF2nFvlYyCuTmVlRUdvf0yOwFG9z1NG1aLuV5eCC95TZbX/j7C04++WSB2VJ5C6o/67vfHezu6e/oEB3AY5/rCOLkSjCQYCDBQIKBBAMJBhIMJBhIMJBgIMHAOsLAlP3AWXkCjQu5ZOW+oiYBg0Z333338Yvef//9M2bMaGxuFhlbWFpK1eGt5dHlzaMhe5EfleZD8VuxaBGlkS7NPTg+MuK5oF8uVt7F1uXBT8ix2dHVw4WooX4H0hYU0J65jnk+1WkPsE3CnIe2sw6m0lTekBfa+UNjY6KIVQJCrdu2CjY6s7fsd1Xt+b/73c/PP/+b3/zmLrvsormulhY1T5s2TVg1BVhmaZuHqWeA6e7tV6Ebr/tJYecb+eqhkOCenj7bdzOGh6l5fKHhvFw+Uo05yzfSuj1cbYDEbzsEeKONNrryyit5re2VLS8p9boDnHQ/9nnqNeDhypWOclz7Wl9bS9V3I1WYknb5cgiXFBfytcKk50DyHD7zS0t9Alj34TknP5/mvxoYK78WF+YbFy9SNZ2QbF/xyBhbxKht3qwA9m/r5lVXXi4F9P4HHKRR1Ybg89Kw6RrYBpp5QrSzOGe+XOHuO++8M/zwwitAAa6oq4Nw9wZUKxy/lGHPRbl7bqQYU3xq8QMf/rDt3E89/k9h4WK8b77hBm2VcDtnZvp1JcDJTYKBBAMJBhIMJBhIMJBgIMFAgoEEAwkG/nUMTNkP3N7cXNNQTwFbvnSp+NVD3vc+uZFmzJkzbivs0BBV84Mf/OAFF1xgb7B9odSzoItGsdDOkgWur9QhP1FlqYnUISG5xQWKBK2VUlfX0OCIICoZpZfSxanIk6sY9dKvzrnl/qVr2dArQJquaIusd2llFF1aopJUZSHNQekaHaVC03KB4XVHB4vjHRxKx3qmrarc1Bqletmt+o9//GOTTTbxk9psi83LybKr2V5ZNVOIg7I6MKByLVK8OXrlptJZIcTONOpqbfWiM37ojVydCq+8dJk+7KIl2i776U9/ml4HJOpfT38flRXYwBAIrTYI9CtLAD08LzrbyVcNgQ0qdMFz4eL2RUNF2DxcXKyhnr6UGoIm3N2tACDVAwNw6j9Nr5VKBgaCOz1g1alUGRl05rKScFiRc4xhprS6emJoSCy6zGSymsFSKFBWpt3WFSvkGPvt975Hm/3GN75RXlapEup3Z3NzVU0NRRlCRGgDBgyAQSoubVGta6ZPhyjD55Xuri7A2z3c39EpHABiz/nRD7fZdnvDV95Qb592cWGRU4gnIvDW2oXkYYKBBAMJBhIMJBhIMJBgIMFAgoEEAwkGpoqBKfuBKUKOgh0ZDDuBufVsBp4xe05HS8vwwCCFbfa89Wh6TctXPPXUU54oSRHiAKTpcdVS1UbGxyh+VDXP83IEumZ3NDXx7VICPaHIpXp6x0fHSotLRscmCvMLUr19w6l0d2dXemBo7uw5o+kB4cWCqG+/9TbFnn36mS984Qt96RStj9rW2d7OoQpCn1rp7e7mv+UNpnQFR66zkTIyKmtq+3v7KN05WdkjQ8Oa+MrJp1RVVB5/7HHXXn2NCs/71a9P+tKX99j9bfvvu19fW7uw5rzsrM9++jN33vH3RS++dNYZZ159zXWnnvLV2uoaZ0HtstPOTz7x1K9+8csH7rv/huv+9PGPHRmCn1+56HhapFgChmJJD+TC9UQCamYCZwJzkBYXFDY0NFAyqdZ33fF3mbrSvX1UTR5SneIr/vGPf2zztFOFJIV++oknT/7iFy++5LIvfelL0izzkCtZV1NFb4RAyCyqqDAK2ocByPdkMoKoqCgDEl00VpLVk0oBJz02Mnr9n/78qaOPaWlqhuerr7xqw/U3qCgrHx0e8TcwOKzj9jFvveVWff3pDdbfkOGAMeI9++zDNsF4ocv03vZoIAw3AHQttkS4YSaYPWPmU48/ccvfbrr4D390gtadN98Ct889/Yxh/c1559dWVW611Vbty5bPmDnLODJsTAZ/8jzBQIKBBAMJBhIMJBhIMJBgIMFAgoEEA28AA1P2A0+Mj2Y6Xzcn95c//9kWm2x8wH7vHezpqq6pypjIaFuyuLSw4Phjj/79b87/9S9//svf/BZAtTVV3S0hPpk6tGTxS9RQW3qzMif82YDa3tZSXV1pt7DTa2279ROd08Zd6rGSFFRRyM1LltA2fU319UslfejBhzz1xJMnnXyKv0033JDzsKO91ebhjNFhZwCr1kk+blKpvrKaGimaioYGqhrqenu6bIz1fGJwqKSoeEgEtT7k5lFY+7u677/3PicVff1rpzW3t1eVlR3ziaM/KglW2LJcMDw4BJ6ddtiRftsmFjo7+7Mnnnjs0cdkZOdMjA0cd8yxzY1Np595Fl1zrz33pD9/7r8+D5780mLprAvyczMmxPsCLAdKxEI7J7m+pnrXXXe1CVYG5sGBlFON99/3Pe995zscDXX9tdd8/gv/tfP22xVwQ8sgle7PrKqcN2fWjX+54TOf++wHP3BoX3qgKD/vm2ecLpR6aGBQHiyfbe2dRQWF9HkK7XBvX1dHZ0NdPX2Vyl1QWjaaSuXkF/R1dkJdMHiMT/hzz4Lgz72ShmYwPVBbXa0SP7E1/Pxn586bv0BxOah/ce7Py0rLjBGtOGjMefmD3d333n3Ptlts4ZAqe7aZGHjOT/nqKTYPf/jwwxtXLCsvK7Hbu7uzs6KKeaF8aDCdl5tdXVWh+ZO+8KUH773/oIMO3nrzzTecv76w9qYVjVtts622dt1xx29/81t1NbVV06eHc6dGR9kOYhXdr8mVYCDBQIKBBAMJBhIMJBhIMJBgIMFAgoF/HQOZ493dr78WOaY4+qSAiqN5ORK960lJWVlQeOrrnS3ED0mn4lekocnMzCdpn60tufzAVdOmyXWkPK8sX3BHe3t1QwPfoF89LCgvlwW6KC+kBRYgLVuVsGSKXIj4jU7iXakRaeLla3w8lJkYLyovT/f0+ExFvl+ACR4WvptXWiqzMQ+zrbY+KVQFefmUK1CpLSsnmABGoyjulT1SM3d0odjm3Fwa8jnf+c7XTj/9sYcftrEZkPTMEPZcVDTS06OPRTU1Y3193Kde5+10ok/wwRYXBdW6p8c9GBgMnHOrNf2ys9ceWjeSWrcsXVo/ezbwnOHkLOI4YFt/JQxrj7Io18yY0dHYSFe3R7eXEpuVJYCc0zg0VF/PCaw7slhLjl1aUbHyKF1ZwaRphhb9lb47il1vEIgu0/LS55+fPW+ekvoYY6AoJKYagCV7m5cuWTJ7/vzxVNrO7dbGRmPEtwyTJbW1Y1y4kmCLWI6cuoCUVdtpyXu//e06mJ2XKwd1bUUFrZ6Dev4mm3Q2NekO2Lqam+GkRQro+npVQRSX8tLFi/UCJp3mzEvesnix2HiR1WDm+3X+8NhIyHymAAuIQ5leP30mJRMMJBhIMJBgIMFAgoEEAwkGEgwkGEgwMBkGxjIzckrKpuwHpsrarukYG1ofXU6QM52NGuYrpys1huIUuVhzPbQpl+YmFrZEjquODhpyXJgyaZdvtb2gXV1epNrVzZrF9Uepyy0vp14O9fQU19b2Sv5E4Rwelm4qe2yMvhQU7ujoXdVqK5O2TMMcHqL6Uuok11KbG/U4KokCFhfzSQMstAs3nYYOXwMALomjKcJ0rdxcpyhJ9hW0QQqiNFrR5mS3+ujQYVqfTpVK8pSf39vSUgZOp++Ojva1tFDVhGFTAqtycvpTKV+lpRL3C4ay6uqxwUH7YCmKAVFlZQs23lijg44s7uujADctXuwnGiMl0FnJ9lRTXLtbWmqYBqTmam72q3bTvb00UvDCBk1Sx2279UkZzujsZGtQg06xQaQ6O/MgvagIEqjZLA724mqRRSA7nZ69/voZIyNBN66t1U195x+mrHJZg9avsQ0CwsM2bNmtsrJswqYAAx7G0u3t9fPmFQwPF0SptiS4fue73w0Ji5cusduZj9qRxRRgejsAwKldlRt9PTW4OiIv98TwCE17NKpwPJWCWCckKTlz/nyfYVBgXB7vrCwmBlhCTuFhciUYSDCQYCDBQIKBBAMJBhIMJBhIMJBgYF1gYGo6sGBaahgFWNOxmkex8ZWqwz1aQE/LyaH60pc4dTPSaYqQAvQ3CaWmz5tH1xqJd67m58c5tChyjsypmzGD4kfbKaup7WlqkqaYatT80kuOTVKM1q2YqhRQmLamfhcYwpFCNGSnLsnqXFZGZVLMuzRt95VChdNpHsXi0lJQAXjanLmg8iI9Sw3BFxp98V29PKIKUyapcDmSY/lpbIzq7onb0vp6SY1tdi1raAAk7bSQ6mvDqr2+/ZTflOakjaKlO6DIiUc0diooRc75QFoC0uLnnps9ezYwVFhSXi5KmW85t6iIQqgDzoXynEKb09en1zpF85QyKsA2FjZR012BrUL+VegCSSVIZOTia40wZiAUozDrlxHhYfbJqTvY38+/qjYlqawc0ZDjJnTKKb7j434KOvbYmODz3LIymcQgNhXBwx8e8FlRoTuQrEzI+ZyRIYvY+9///gsvvri0uLg7ldpz991uv+VmG4PpzwarsLiYS7ezpQWWwAwPmpMpur+9ozg6SxkSlBIgAGA183LDua6BXCvsCLZQ+/QVkMmVYCDBQIKBBAMJBhIMJBhIMJBgIMFAgoF1hYGpxUJrlbuyraWllp+QRkSBGRtrb2qiVtGj+GBjxZi/VHgzlabEwUj9/fQfka7rbbIJ76572g5VM+hyIqLz81uWLw+eQFmjKUvd3fIh04skthp274xcWrRzgynGtbVO8qEH0pEKOIdF0spgLOfW8HBZZSW3redar5w2baSvzw2tjwalKpdG7e31Cke0zFGeBMgjJdgHbTMc5+svvhws7IXR0ehxVk5h4YrFix0FxOnKrzsenU7MCdwkPXJdnY4UOcXHUUYisauqxinYUYvg1Lqe+oQZeqAwYKHggsPBqe/azY9MA5R2bwTnOb/r+LhMzvFXNbgBBqBog2rg9I4RLgE3vTTkrxob4+bNKSgYcjRUYWFrc7Os2tAY/kZHRUH7VS9orZRkgcd9bW3BlZ2X1758uZ84h0FIe1e/oOi+7m46PJS6yo3v0FBwvIvxbm6mS2vLp9pADiSu2v4oVfXipUtp40AFoVOaHfSkgAp1U9/hJwAv5jkjY6C311lN6veTcQ+V63wUTm+gw4By9XM7j40FzOtC7PZP1OCYLJPPBAMJBhIMJBhIMJBgIMFAgoEEAwkG/jUMxLHQU9aBh22XpV5G5/TSfOg/gmbDptbiYlt+g+OxqGigq4tuQ4/qbW3l7qPzBP0tL49rsbiigmIUNuJSpTIzqcp64S2f9D0xyb70t7YGFTE/nwYV9OSCAomjqVh2uvo16K4UxMHBWFkSMk3ppaBWz5gRpxGm1IXTZemlfeHwoQJ7ZXt76XW0YlpfSRSe/bLGS92NIqu17kbhACrYIjXYQ5qeFmm2sUpmnyq9TrQ2h3AFD21ubqqtjY6nJFA1qmu9/f1+mhgOumQR5+pEOHSXV7lt2bLaWbOCFlpR4ZHmXN5VIRWRfuhez0KXAT883NnZGVJD0QBZB7KyUrT3KCoYYr0Y66hhQzVHbkYGrThgTHOjo85YgnP2CI2mI/+qOuGE0gtLftKio4yglKu2t6OjLBpBKFKt7ufSqPv780tKBJarObekJGTVYnTQhDdBG7nQaeAw37JoEaXaNmwQooGgyUcj2NPSoiru98GoktjewaMOAxCSioLkARywyjrQ20sD7+7oiD3DLw8NL73XRkZi9IIkuRIMJBhIMJBgIMFAgoEEAwkGEgwkGEgw8K9g4A3qwFQUXlPuPhqU5oP/M9odSsOJtVDaKXdfgEz2rMjJSbmiXtLEaFCyNHW3t/uRjmR/LB1PmaCalpQowwHY0dYmQpiiFRImyY0UBTMLP84YHIyTV6mErhV8kpRPsdDptML0TA9peuHFjAxH4zauWDF9vfUokzRzP6mccu5r2Acb5fHSVnxDGXNlFxfTdQGjcHakkwf9MyMjOL3nzBEeTKflJuWnVUYIcTrayexFFWvdQ2qn8hJH0Sc91EGoAI+GbHiGnK6mJpWH+u14LSnpWLHCw2AXsAV6YEANXslXycTEihUr7BD2VcplVcURxeqheRaBIRXOgvK1NNrr29vVRY+loMIzeLyVWVhIC3UyLzXSw1znJ0c5sVgutEKxLK2tlduZdSDo5I4gGh93qG/9tGmaHhtm5RhViWK6A1o1BEVURq7oiKmc0tKVSmzAYVYWrRsN8PnHHYQHMHNtAwzkdmJrN7aMUMLr2SxGRmjaDAFuFDSg8Tbs2H1tS3SAOdpVHg+Qz+RKMJBgIMFAgoEEAwkGEgwkGEgwkGAgwcC/iIE3qANLKExHKqTppdO0FwoPxSyoPQ4Bjna60ovoeJQx6hZHKBelLE2pSFX2QyqKvxVQPSbvVJQminOVzqk2KalovJTbcChOdjbfLsWYOiTWN/wabVilpAU/IUWXthapo+rUuuaohRQqbdGq6VrSLwlppkPWcNg6KzjSx0JtsZs3DriNQ6DV6f14ezA9PCogRZYnNDH1AxRUGpVOivKmmzyWqtKcjgSQ/Ll8RoVjHVJwMke3V9QTYn0zM6XCcljxeDg1eYDSCFGUdk14VYGA1ug0oNBuURH0BV8rLdeL3d3aglWfuhlCwaPmVE6NDN7mcOIUx226rK6O7x3eVKWeoGwbl2iAABxGhCEgCopWxtdQINLVg02BB5g7OnbaqxFyWAFeCUUGs/zVcZZm92HEIYEBQm4w2rs6Iw0ZhF6kx6pZi3l8721tbmKQQD4mMjwvT9YxJUM4AC0aYlMpDYZ4denKIsj9iLpAGDCfXAkGEgwkGEgwkGAgwUCCgQQDCQYSDCQY+Jcx8AZ14H+53aSCBAMJBhIMJBhIMJBgIMFAgoEEAwkGEgwkGEgw8L+NgVgHfiUR1P9260l7CQYSDCQYSDCQYCDBQIKBBAMJBhIMJBhIMJBg4H8bA1M7Gwl0mVHY75sH5kTYpTuFa6rwTLX+KYASFZ0qPFOt/80uP1X8TLW/U63/ze5vUn+CgQQDCQYSDCQYSDCQYCDBQIKBBAP/URhI/MD/UcOddDbBQIKBBAMJBhIMJBhIMJBgIMFAgoEEA//RGEh04P/o4U86n2AgwUCCgQQDCQYSDCQYSDCQYCDBQIKB/ygMJDrwf9RwJ51NMJBgIMFAgoEEAwkGEgwkGEgwkGAgwcB/NAYSHfg/eviTzicYSDCQYCDBQIKBBAMJBhIMJBhIMJBg4D8KA4kO/B813ElnEwwkGEgwkGAgwUCCgQQDCQYSDCQYSDDwH42BKeeF/nfL65vA8/+Xfv/d8P//FxtJ6wkGEgwkGEgwkGAgwUCCgQQDCQYSDPybYyDxA/+bD1ACXoKBBAMJBhIMJBhIMJBgIMFAgoEEAwkGEgysMwwkOvA6Q2VSUYKBBAMJBhIMJBhIMJBgIMFAgoEEAwkGEgz8m2Mg0YH/zQcoAS/BQIKBBAMJBhIMJBhIMJBgIMFAgoEEAwkG1hkGEh14naEyqSjBQIKBBAMJBhIMJBhIMJBgIMFAgoEEAwkG/s0xkOjA/+YDlICXYCDBQIKBBAMJBhIMJBhIMJBgIMFAgoEEA+sMA4kOvM5QmVSUYCDBQIKBBAMJBhIMJBhIMJBgIMFAgoEEA//mGEh04H/zAUrASzCQYCDBQIKBBAMJBhIMJBhIMJBgIMFAgoF1hoFEB15nqEwqSjCQYCDBQIKBBAMJBhIMJBhIMJBgIMFAgoF/cwwkOvC/+QAl4CUYSDCQYCDBQIKBBAMJBhIMJBhIMJBgIMHAOsNAogOvM1QmFSUYSDCQYCDBQIKBBAMJBhIMJBhIMJBgIMHAvzkGEh3433yAEvASDCQYSDCQYCDBQIKBBAMJBhIMJBhIMJBgYJ1hINGB1xkqk4oSDCQYSDCQYCDBQIKBBAMJBhIMJBhIMJBg4N8cA4kO/G8+QAl4CQYSDCQYSDCQYCDBQIKBBAMJBhIMJBhIMLDOMJDowOsMlUlFCQYSDCQYSDCQYCDBQIKBBAMJBhIMJBhIMPBvjoFEB/43H6B1A17mxDqoZzwjY82/N1CvSlzJ55oYeAPIfAOvrNnuZE/eQOWrvaLm1f5WK/C/8DUGYLWG4hmxruaFyifD4arPV4Ph/8rXVUfwfwHmVTH22vdvDJjXrnPVX99Y/au9tVZK08pqf6/nrZVl1kq3q1UYf135yuu/8aLr9Xy+/jpXK7lW+Fcrs/Lr64EkLrPylX/9ZkoQ/uvNrasa4GG1vzVr/v+CzzXBeMMY/r8L/+uHPC65JtL+rzxZFX4DvfJvMviVj/8mK7Cunq+EZNWbdVV5Us//RQwkOvD/xVGbAszxVM9Ygw1l5uRkZmWNTUyMjo/7zMjKCn8ZGRNj4xnjE5kZmf7GxsaGh4f9mpmfj0N5Gv+NZ2XEf766VuUmr32vcFxP8rlWDHiYkZUzODw6kZntpi814D4zvzAzO9eT+G9oZGxsItOTzJy8gaGRzMzM0dHRkZERI2VA/YWbvLwwMGu71tqucVzr8wDPFK9R9DKWkVlQPDKMkrImMrPaO7uy8/Izs/VrZBRpZeeOj2X4VbFxHV2FMtf5PdhjGIDhJmMi/GVGf4OpwZGh0Yyi0gBDZo7PGKopwRDVHybFWrG35nPFxsfHw9TLz/duZkGBsWtubnYzRTRPWjw0MZW/GCdrfmbm5GdmZKM9xJZdXNKfHoBDI6jyKV2ZWdnjo2P9vX2Bq+TmBfRmZ09gOKtcqwG8Jt4me+LFwMTGxgYGBtC8at1nFhb29fWtUv2rbr0yWW1rPo87OzWSWIXGYkqLSS7DpIDPnPzRkfEwBTIyh0ZGs3PzNBr/xW2BNW4uo6h4MD0AaUMDg4gEzaT0MScnYxy1hT9Em5lbkJlXqNqhQeMS5tqqf+PRV9VO9W9NPEz25A3g5+XerTLr4+ExfD09PZnFxUMjI0awp69P5enBQZ+Ttb7mc4WHh8xki1d2ZlYuFKVTg/196czsPKOA58C/m8GB4VAGArPzcFMLn0/fw0zMyc3My0exK1fMGDw1x38rB2hKVPEmFs7IDAzfVVhoKU8NpLPz88YzJkbGRoeRGuQVFWFwYYJkZ2fmIryp4TPu/pqfYaLhY4WF4SY3183Q0FD4OpG52h9U+zMugVaz8zKLSo1Oun8gIyd/MD20Zs2v/WRdwT9ZK6PEn4ys1MBQZkFRZiFqRBg5AI45ZDyjV53XKwlj5U1cM7TDDAKG9kEDVFxs/mbnTRn/kIw4h0eN5mgYQTi3uEeSm2E14ioP1JtNYMgiBmj9TSS2wL2tlWPDg0OZRcVh6LOzjbt2SYmr/iG8+CtgxoZHMrKyM3LzBvpTo8MjYYr5Gl0rkWYux39vNvwZpJI1/gi9MdJWgwp4yfWWx0DmeHf3W76T/8kdNLct5y9fkcqK17jIi9ZN/1bKo6Rz18jAYGFhYTY+q5jfIiaLzZWUlYa3onfdEMNcWRMZOaS5lfWHZ691eR1bWVnJaxX9T/1tLNIyrC65rpISQzCSSpFysrOzicL5+fm5paUDXV15eXnZubkZOTljqb4sqm9Ozni0/mW5GR2FPIO7VhROCf9GdqpGMoLOcF9fXn5+d18vmEvrajNGR6kmSAucY6OjpDGml6KiIjDTCOnBa4VznTxEaWM0AdiIJAN3ASkxuRYXZwwMdLW1VU5rGE6l8oqKUr29xR5SzV/3NVV6hs9smGltraiqysjN7e/oKKmoyCD9NzWVlZW97mZfq+BUl+0sOtvaLuM1PDaKDscmxouKizs6OhqmTQPzWH/f2l9YWyWepfr6EXK+/o6OjqXTaJhoDs+r0ueqME+JOcDn8OBgYWnpYH8/XuUmIzu7t62trKpqIpoOawL1Buj/9fM3zcUz5uW5typjzMpiZYHVgZHhkpoaRqBYdswvyPWWJvzFiM2M0JHuD3jLhbeB9MjoSG5h4UAqlZ+bm5VXCJPjQ2yT0TTPyjLLcGOfMepWbVPNWl0TCa/95PUPQQzzlPDz34tR6HYA5GWwo/WITI9CiNRltYFvDKZSOXkBP6/zCvCwcEV8BnoDAs1oq9XgYLzeQZba0V52YUAjxObkZGUW5GdQepmACccepgcgEyMNjUaieaghgvON9DfU8iZeKCozO0tn6UgZWZllNTUDfb36nRNZ2fRFgRzdDsx2ItXfVxBU4tcLT9zftZamiXW1toZJV1ioOSgNGlFmZlbO6ubXwH8hkpKWkdHV3h4GJVKB8isrM4YGJ2JyX2sba3v4Bubv2qqZ9FlmUUnGyEh3Z2eOtXVsTO9i+JGEd1af12F9iTwGq8y6eBb39vaWlJQEOSo/v7+93esDQ4Pl5eUwNmnba/uB0ggMy711P9XRAaqw3EeLewyS8Q2ARU9Unj216tfW5Gs/05CZYnUYwXoHy8rLrV8wBsj4vVWpC/2wRU3QgScmcKwC3CziWmZZXCzmTau+An5vvVmXmqErrn+1WZAdxm1VqGIYprTYvVlgJ/W+ORgYy8zIKSmLGP2b00BS678LBlab7RFYWTQoHuBIOQnraPAs5uTn5fkjT2WMjfE/4LZkiMKiosKREbKB91ayp5fFtTfErVZW8u+Cn38nOKx2nIQDPT0WUYZWQ0MoDCtN5OMiHeaOjvb395eWlpJ7jFpOVhB//cuyLA0N5bnJzx/q71fPZN16c/E/MgLymrq6ioaGkb6+VGcnRQqclmeajyW8lLKXnWvVpHl6SKmfDM518jx0VkjDmoQ6MGAtz8rNCUt4Oq0M6s/IzeFAWUvh1wRlSuWJ4xXTp4/3948MDECITwJseWUlM8FrNvJ6f1xXaza1qrCycizFIzJIAaulk+Tmdre0lBYXvV5QonIl9fUj3d3jfX2INq8gv6C6Kn9wEM4n4H9t15SQqQLcibpbUFw8MTra0dJSXVcXxMFILFtb9eHZVJuYrJ7XeB4R3at/58bJzTENgsw6MjKQToMcyY2PDr+63MvfikpLg1VraBCPNmtMcDOaLjM2GNhyVn5eQVZBCNZxEUlzstkX4jfXZPavnyRikfTNxc+a8L3c44wQHDEwwMyH45GSx8dGKcBTBSZUMjoKQ7hibDp0Ly6AQpKVlzc2NKRyeqA2Q8zA2FhfX09ZRTlNQ+Gy8gpsFv1goVbAV+D6d/+fOlqQlSUKIkj3mZnYb319fU9nJ0zSe9FbbnaO/8aGhguieJOponTt/R8bq8QTUObExGBPj7ayS0u7VqyoqKqhSKzaRHw/OjycU1BgFHLB4A9tj40GhXByelh7u2/y/GWXLCgqpKwGn3YqlS0wobMLnLHC+TJIr8C85qqiQDyJyiEnI6O7vT1gJju7sKSksKI86IojU7b5qiHwNBRruWTTQbXsiRF9qpl1Iwzu2FigeUIdhfPNvPr6+yxbWYWFedlZLN2R5ShEC7JsvKzKvoIcUBj6kaHhXMVEKwykC7IyB7r79CW/8FVxT6tSy5sJ+yt1rwLhK4+S//9zMZD4gd/iY78mf4lZFW0KS7XgY50xCujA5KzRSPX10I+xAkZowGGLSoJBfbVL5V5es4nViq36VevxIrHqw+R+JQYMSQ4JPpZuDUGE/LC8UXQLCoZ6eqi+FiFrcwZdIkhyYc0zWBwXDLNK5pSWjvb1hfJru6aK/NcvQMetCUEcTKetiELyRiNHYi7ZvaAg9mbT0slhhQWFA339yKuIoJDqWxuY6+ZZRGwv9+BlJ7CKI8mlp6urvKoyONKHh+l7SsIYWbm8uOhNpWdaEIkBFIziE6lUMGeUl48MDQU95//LtTY/MISY+0Rq4al1dXVcGc1Llxq4ipqasXRqSiTB7c+XIgp6ImgZIWgzZiyxOB73eKo0uSqesiOhn5jIrdS5fHnVtGnjAwPmyKr1r1p+Sm1Nlblp6BVvbmgzkltfaRw6c3MIr/0DaSIgk0BRaQnMjI0MobeY5GLEhnt/gkt7ur3McABjAikNCMtXGJXIlYRcY/6MOXuiKoVj3v5Kk6FaLpuVX1/nzet/4Q3gZ00YYpitMniXprELgnVbc3OwmBQU8GGu+cprPRnLQGaF2CNFK52GHJEXAWVsEPhnb69WqGGjkQExxKaOj2YVF431h1gbelopNXg8YhB0jEhWjnhIaNBNhM+prXevBeq6+C1YQrPD6pBdUDA4IPaWZzWjsr6eIcByIGxV1wvy8kPvBoeK6+om0qnXP74AnGyyd3V1VVRUQK8yAcnZ2bn4WB8NJ8QjxPQc9y/2owIDhCyACmMC6XTaE9FAOXlTG98pAf8a8MewrfapclpqVk42+C1h7e3tjLkDvX2F1dV4tcKraU8IZXwNP3Bcp4Eo5ujG6gUzDwwQt8hXel1WUrpao6/9Nai1eXnDvb1GMEyHvDwRIl4BHkwGYlYgw2iPGgJWnozBKYeXvzYAq//KUszq0d9P3gidys72WVxaAhjjvvKygsTjPjo4LHqFoo71+RSFEVY6ensUp7PqaP4391tZy5txE8/uV9ccKDa2MkRdWBOqVxdPvr1FMBD7gRMd+C0ynJN1Y9XVaFUmZb9KCAsx8+nAoyP0FmwUI8BncVs3pEoyliUWz8orKRlLvexnWLWhNyADrQrDqlUl9zCA+Wbn5HV1dwWZPp/DbNBwEB3SPT0GRQAtKc3DkupqHjkK26xZs+PYXUGDXGGEkaBycBozcBjc/x+XvXYkBtJnT39faXkZ6czCPzQ6EpwGBLIoEJGIP0Q+Gwmh3bbnvqlgrtRJXl6gV7bGpjM0lO7ryy3Ip5ZUNtQP9PUVlpRO9PdNCaKp0rMBKqJJ8pCnUmUcrXZzcWNG+vA6wcOUgNfiSvy4J7jEl06ht6LKSmHtIkCKxRiPc+X2FVG0xscmE4tffvnV/+lmSXn5QH9/Pk04MzNop+XloxwssZXn1YXfwLdQYVXVUFcXaUz9hMLQInwOrRtZ8A3gc6X89yqSy84ydUmB2RzpOUHu72pvEwDPD6yJuJVVpUDWB3OfFpjBZzIyMjgwwD3FGJExOj4yGHYIY8suioQus9+MrU32DWJoZtjq9jqvqRKzal9/5WvCsGpzgfUVFy97/vmGhgZTMnjymf+oEONrM9KsWdcrT+zqdyEGn2jYJ6qALsjkLx3s6wt8MjMz1dMTqJoffjDd3dtDV6yZORMV5ZeWWuksfLFq4XVAxpzUjc6+gSVPJW/iRQUaE0IuIjfTHmDToby+rsd2gLIy3c/PLxBCLx5VEAGzo80CE3Ew6r8MUEhhMDHRH3mbc8vKhrq78bGS2tqJweGVw7oqbQwPBIstrIYg8wDzWPgrLJxI9/3LsKyzCgw0G3LYRz0+bhlFCQgpBGTxfK4yiV5hk8FURQfW/MourwTFMq2zaM8TyFdhVnbO8MBA7iS26ZUvrnZjuy/SjSk5v7y8bdkyNc9af32LF31StTHfwwe8GMheVP+bdummkPv8stLetvayyoqg0AplsmuDDsxItxIvYVl52V6Qm5M31NcPD2TIPnt/Skrcs/wWRza7VSFdlVpWfb6O71+tA68cOFCtvI/ne9zulBa7dQxqUt2bjIEkFvpNRvC/d/U4F+WE9Q6DZqHEAngSsslbzHUT4zFL9dAV1tdXC5Qr+cL/Etv698bkOoQuIDYz8+WAMfb1VMoYlbxi6x2z5OTnF2dn/+P++//4xz+2trbutNNOh3/wAxXTpmXFkaWC/RiJRRKuIwXjDXQNRZE9R8bHxPdmZGd1trdn5mQLLWttbCSyFxcUCtsSeRgiosvyh7u783Kn5gd4AyAFpMavrSLHNL/4YsOsWXxxbR3tW223bX86hf7/9tcbN9tggzcVoKKqqnR7ux22999//xVXXLHVVlsdcsghNVSjKcpGbwwPa74FM6tg5b+FOePIdd/a1nbzzTc//vjjPDYCLI/82MeKXx3GtmaFqz2hjhLNhMNR1UjJTAB4C5ZSRIZeFxcFuH3p0ppZs556+OGf//znKO2II47YwC6+V8Jb1kUjr7cOUtSq8tOqr5m8eWbE2NhFF/7+hBNOGBob3WWXXa677rrCSfYClNsznJc30tsz2JFCG7ncL1mZnS3NJcVldgLbFJgT+X8EVhgpnc3LWX1PQSy1T4lFx4VXyoKrwv9m36ON1kWL9tprL/oqXe7Pf/7zjBkzampqVq41rxMAqGDeHWcISaUCkyFqy0sUrWLZkQcyKGB2lPjMy2tdvvxX5/1y0aJFM6fPOPbYY2fOmBHMDTx4kdFHiytREd+Ez1Vny+uE6U0tht8WFUmEKJaESVG08QtPP/2HP/xB97fddtsPHPp+Ubh9nV3CVnV5lBXG7o91cbWvWGF0SurqbOuAbU1bqpjM5ExaWf2qtCdMY6CjI/zE897fj6ThmV2tqCTEL/wbXVlhrWDvw6OKi0tglaVyKJ3OW6Vf/yO06KSksjLV3Y2QUn0hYQeqHh7sI2thhv/j66sVwDOhV4zewscfv+SSS8yUAw44wOAGDkBzizZMoXB8lapMx14V7atV9a9/Deu7rRiFBenIMZ5TkM+EJJhLiEpc+WrzYzDV7ZWSouIQBzRqSUmVlJb6E5exEpg3FeCVrbx8s1I0imSC6CP8snKmr14++f5Wx8B/M6y3ek//Q/u35tyOpTTxYCEvjTCqeHPU4GBvf99ob8/zzz9fWMLuWVxSyA9UWVhZkTEy2tfdXVQQ1qpYIlmNZ63ZxGvgerV3X6Pkf+BPMMkhX1Bc1NvVZUkrF4Y6MdHb3s4SXFRSEvxI0YL3k5/85KqrriIK33777evPm/vOffcl39g9KC4qqFKv6QGeKv6nNLiGjI96oLsbbOVi81JBsbQPqrO1tW769HhzFNFzoLPLClqUkWk5z51iLNxUqeLl/q62MmdkNMyezVjQN5DiOmyhlJYUtXR0TpsxXX/XKPtabU4JnyrvaWmxHfGuu+8+6eSTn33hhT9df/19Dzxw/vnnxzlj1mxpqjpAqOH1d+AVOWC1N3wtra4+8qNHELmwCzAoWFFatvdee2284QZrAvlaT8bGmlYs59wbjKJPK2uqO9vaq6ZPZ9/x1pq9myo+ly9cOHODDVa8+OIHP/xhVqG+gYGrrrmG0k5LXBOq+NFUm1iznv/xyUpXcCgZITnIwb29QhmjeOZx3LWlpeU1DB8j6bSMq/SEUkaEnOx0X2/ORG5xWSnlV6gmz54mOP1CCoC8PD6r0Wg/cNy1l7XfGMrVhva1QY/pYSqvTJU/rNl+vB7BD/vFcjhh+8vImDdvngBL+LEb5PWPF2BgrDA3hw2upDwowNIH8qg3zJ0rmjQ7i09c7dxXw3nFRRPDQ3/729++973vDY2NV5SEJLdnnn56WBKxUP72Sfarg/9f7/KaSHjDT+z7rcioDKYlen7GhAXiq1/96p//fP3YRMaWm2+6YL352++yix6JRLWghDjzKeZkmqyzNbNnN7344jSIHR7m1vO1QgMBhjBcER29/N/Lwzc02N7VOWv2HPx/sLU1rAuR1jQxtvb98JMh5PUTQ1zDZPBPVr/kfzUzZtRFKr1udHR1Nsyeg6hy5KfEr6KpMdn8WNlWILOszPbODhM2TgFo4sulXFBRIab69XdBhSGqa3gYars7Ok466aQbb7kFGNdee+1f//pXu1Tyy8r4KJAu+7IZFBTUN5M+w1TNykz3dKMolioJ2DpaW0Rt2CMzJLuHX18Z+oCBCF2lNdWB5EZGly5ZMnvBglRH58DgQEjaHtkCXoWKV+hmJRpDFev6epkyX6l2rW2tZeV4pXzy/1sPA8lwv/XG9HX1SNSUrCN4qy1S/3zwwbPOOmuvvfaaPnP27nvtw0Gx2Wabb7rF5h/72Mduuv56cYw8k2upNF4PVmMqaymXPJoCBgrKyqwW3/nOd0455ZRnHnuMnkbIyCsspAATdGLX/Z133mnNy87IJFU89MgjkktR5EK4VLQvi5mWDXsKTa7TonZj2mVHnCVqpgVmV5Q/++STG264YXF+fm19/Yknnti2YkVhaUROUdbNddr4666MoiuqfHio1OI9OoKERfx6eWB43QTQvgYcPAOElSeffJICXODAm3T61ltvDf6B/y/XKtIcqYVA4IE/9x98/6GXX345wPiP+ECmN0ybPn36JptsMqUFAxVef8P1u+22W3FJ6axZs/hpScBcIqS6ddXdmeuv37p0qXnx4pIlFOC8rKwVjY104LXWP9VZsVYJaa01xw9XrT/gc5Wi4YwTW+CyBOTm2ajg7B0BCChhlSKvug1CbW4OapEa6sILfrvhJhvnFZXMnjOnqqa6qqqqvLKiurZmgw02ePe+7z355JMv+PWvX/Xy/9aXqeLnNeCifFqPnEIDablZmRxx0BXL9K/x1po/5RcV0vdcDuxhbzr33HPPOOOMG6+9NpSMouXDZpIoK5smbr39Nn5RY4B5ujd83oL5icigEPfOw/jGp79Vh3jN1v/3n4QM81GorUiNoL2bcddfL95TfME/n3jKStHf1RV+krKIKhIVWCdAti1ZsuOOOxYXFODtCxYsmFVdLcU9U5dokbVexaXF73nPe2647jq4Dl7NyIfJ575OgFmHlbD7p7q6WEaCFaaoaJ999nn+iSdef9J+s96f6/Zbb9n/oAN5E9Zff/3f/OY3nrBT2wIzVVAHe3ut5uYCUw4FmM/KSorXLVu2DAHHFo14mgTjzbob38ngLKqo4Mu1ZUB3KMAHHXRQbd00INWtMuq1r1CBGxSSm411FW+z/XbLX3rJDLJX31b/yeZRPNcma/1ffx7PYvWsvPnX60xq+D+NgUnX4P/TvUqAX4kBDFSUM7YlQsxeDlpvrCDx+tqYZ+3/znfPfvs733HWd85evny5tzBw4ggNi8Z1zXV/3v/A94ktXLFiBS7sPAl8Noiwth65BEg74cahCJEB2Du2JcndEg6yC+cETjg2LuhjEjYIf4rOWPKVFNiX6pds1j43x66EG2b7sVEPw863nOxwU1KcHhzokhImbAzLVSZwxvw85YXXKuZreLGk2I0oVvZvJYlQoUBONsurv7DpLi/Xw6GR4ZBHRg3ezQkZO14uKepJYFhUbfg1KqxdwACSghRqkMdicCCjqFAlSnryMgzRW+EnZfLznMqohrhkCA1SMwRGXYtbD8WyIIrVIRyl4B4MXtQKUAP8JY4iHLYJ23GWz7/wwjk//tH5v7vAcQJGRMiTQEpKmq9hBUqnDzjwQLtIRzMm8nNyLdJhrOPElSz9uSElFYewN4w4T5GTCQ2ZZTJQQoT/0HTIhxwSOXZ2d2maOP4y0qAo6ntcRgEgqcHrgYSig23DaE8uu5MASPakTKRFWOf75azu6u1VLen2ymuuJgD19QQUwXNWgCF4XtTsBsF4DfGgFg99RUXuycKIKvwUJc1GUeFGT0VXKZmLQoaU7O7tBWqvIH/Rj47z6epSFTwEEQFd9feFEHEF+kJ+EdgIznMbtEiHIRVKSC2G5snEXoC6UFt+fqgkOuARMKFd/gGVFxb61HSgZxjLy0WEYVhzwDwcSCXbUbpDnhtZ9BxoxmZODwclE+nPLSudOXtWHgqR/DYjY8ONN6LtZEc0j5CEZkjK4kWf4t+8Enrqs6gIeLDla5iDk12SpkSn6WTYd5qXG/Ldon9UZ+O4cUeZ5hRKDsrGhAJwojbYsBvTyLr40O65796rrr0WACD/2te+tnjx4mefffaOO+4IOVcmubxoEKE6EF5xcXd3d6CczMzzf/sbvpSs7Mze9MCNN/0t1d1VN33a8PBQLK4ZQQNnbyGK8q5XoJH/BG2ESZGTHeiwsCAgE57zneYajlaKGUWYL3b/dnbWzZwRUrmWlhhHw1dVXbXJZptKyaawF+3vUJXn+gI2Qx+GAx54BTNQ15hicXOhRc+jKaCMP/CHGVSAU02gq0AAJiOAo09Y84du/XnoV5QPmWxAGgqDFQXcBn4rxSCLT3Exty0AUArzUMhsN7nMKok0WFXodSbIlpZ2vWvr7OkfGBwcCemjILytre0f//gHCfuET55IH7700ksRDO2CcKxpabdCXijdicdaq+4xovjPfV4ukujp7UEwgSqKCtUZSCs/nFocsBGX0TPswn3EteA/IFAlUVQt8ODHcb4mSDxxYMOwwoa1wENTVffNUOThoQts3jLcflUy7JHGAbIDAYzaGp2VMTIejrc1MXVTneAET5hWWjSPsjLBYMa9vGQUFvjqV0OpZq9kFxUa7iF5aIsKv3b613//hwtfeOlFxIxLEr4Bz8PsHGGM+rDDDtM0qIaGR4466ihqRshOl5eLk+u+2nRNAT0zAWEGPcjOoLN4ghEPKx3/nk+MsajIFn9sQWf9Gh7aKmk66JTOohm+5fggYtMk+uqnmI2srM2T8CJsoBxDHNoL574q/3IlWPHEBGR6CHV+ZyK1UwktMQPZnEk12m+//Qw11JeXFm+99dYlZWUmo2I2ZPZ2dsKh+RgmVLRihmHN1lBYE40pTMI8FLmJZ8ck052LvbADG8/IsF0WS8ctqWdoHqI4+qw3/YMD8V/aSAwNoJ4XlyzKs3ZbkcFvj2s6Df4wHYqLvQXJLmjs7OzU64DtnJww+6JTiMO0jXK/BTg57aPpGeihAIvuRBKBJqPJC+y4C+Gr5R6qA3WFCkNb0TmCbibrlxnkJ5vBLLgo84XFi8761jfV6ZUAjHUfYbhyc8OCKMuaJaNAWu6w0cOOjwA8hpCR8YNzznn62eethn2pcNSW3JYgIUGF+ZVrItg3HQkYkTCDeiej5wJrjUjssrKZroaGmO8j5erq6pqGhqG+PhUaC+1a90GCzJBKvFohFQQWz0FEHugclNhsOEctrLbuAykWF3di15LwRfh/eZ5iZdHUVoMXkaVXdJNOzpbt3IrCMifQVQlmgS57nyAnlQ5/sn8olkoNmePufcIGCunpTbGMi4K2QqATLUd1BrmLaSYAEtajvLBwmHf5+X4NAESCgRugovm4C+F5dNJ1mIny2zlwLnh0AptVIO5UmB1RCoAYcnTllbge3YT8IEIIScOQo1U4ppCYxjTqJjDwKEZyMlJJnr81MJDEQr81xnHSXpjMOGZwNL2iUIV0RBkTVfX1zUuWfPvs7/z2t79lvu3tT/f0pWZMq58+ayaHj6yPd/797uJCu3gqr//rjc89957r//TnmrExa20UT4XbZAUeKoi0XFqX6NDXrCz25nAe4/AwS2pslsY1heayrGNqDNGgbFu+vHbOHDpHZ1NTVXW11SOcy8qsWFbW39amgE1cjksVBlyUldXJZ+gqKSGaWYX82tfbqy8uDy25+GTwUTOERwknmSe1zmuqUfZ+q46kMiGKifIjTKu/HzYEgZO5ZArxq7otMMVlZXIjDXR2VtfU5NCaOAFk2bSTR0zv8HDljBkdy5Y5c6VHZyOeKNGoE/+8Xlpfr06BviLB3BQ5/S8SbcMmNLlJGR0GBoJXwbFGkYplLQyW+GhHinUAYNAoODLszW5thdvm5cvLK6tYHOLhBC1R0VsWZtiQZ1KyEz9RS9Zbbz2+xPcddNDGG2+cV1GhdaBagy3AtoSNRzoet7Cv8Xa4nKIi0ZIhNyMbvIGI5AmyclU4qnSk1tGv9hJbUG05jjwhmiaeaCvd0QHJUoDa+iXTlQMeBWaHrFdRLGsM56qfwHCsC12uvKLCEcF+uvraa3xSxXwSmP5w8UXHfuLoQEXOq7QfrKggtGV9zckhb4ZUXpG0AW86Al32mFlHDQc4hYgbenTX3dg4a/ZsiFHMWY4VYLNJSdu9vRZab8lQCu3osLiqFnI0zeUrFdNAS7NBDylMUxJ1BItPfK28AWF5abkzZkMSrwxGniIGfH4hkXtp0h4JPjPTKJRHmVftryM0B/Ci5RbVmTB9LS35RUXglHfH2o9IBK3yVXriVKTCzIwXn376He94x69+9asf/vCH22+//ec+9zku/lR7O/ILoxCJhoaSXaOjva26pla16MROb7UF4okC3l4BfI3/bbWNjtUZ7OwMhBPhEEpDjIDM4UNDiA20NF52LD6iEufjsV6R1Pv75fHmone+6BVXX0UsTg8Nf+j9h550ysl93T32E5pWve0dpcVr378n+RlFIQYbBswanovljY133HFHamgkLzerOC/vn//859NPP73JRhtDRQy3fhkmIxtkbxF9YQB7zSbvojrCMkprXbyYkwE24hO/yGpImoBCRzLQxVWVzimdtd569taKAoWiM88806T2etiRrmvpNLnK5NLroMPn5DLnkNiU9JXOCgBCDySvRGV4giCj2YoscR6YDHRFLoxkcVPaznyhNCgONaoKDt1ot7SyCj8MiZcU7o8a0krE+oIuGtTR//lSDEECKJSPPEveiuE75phPYM5iMgG5fOkye1l7u7r92tjSzHnV2Nj45a+eapc773FpXV2v0OKczOKycnxYvA+cwEOYUzFfisya5TbNEpqpSaOjVMQcekWUChF6A7uABBcTQETMIUeXIbNlN8oQYfiCPC2dcmkp+RvDTLe0eBFCgBc4s8SKA5LN9Zl0IQf70BAVHXdyKUa+D/QcLKdpr4R2Xo0bfUcVnhl0FGOlcA/VoArsKCOjv7UVJWi9wFhnZnbgnFkUjZB9TRnYgEZKi5KByUQ0Zra6nH40bdo0vO5b3/rWoqVLyopLDj744OrpM0xVnS2WZU1bykcDR5fOkp2b8pYe6O7sKswPW9mBoR4iv7a8IklhWBzl5O/vp9H5KT9qTsk4Uh0W9NFzc5BKI0gbiiDQK40vvRTiWrEaykAU7BNej7bQI1TF9EVJBDYYs18d9Dc0xC5TVl3T3dIMA8DAKudttNFxxx0HNjiPd40aKf5Z8URdTc2V06bhe9WzZoUZRI3BxgcHA/+ECgYL+07BECWoz4zAkM1u1X2b+rLygvZddtgBVFRBuAUbZymN4pnnFjoBe3BkeKvNt/DEdDZXdUHWMTOIaAFOilBI4W4EWdsz81NtbcWC+cUbi123CkvCHG1xN2aIJCz9Uf7hsP0VxtCbLF+MvGZff3+hlckR65QfW4EialQgTDd/tC/1FBYaGo0Gjm0WRGgXbLVaipOV/dIcnm8EU4MDhXn5NoBcduUVH/jAB/Y96H09ra1xK86VthaIBGaAK66uJBhgNUze49mZ02bNsgPoqmuvuemmW9QZFsGurqCTj40JqImlFBv3w9lCqPd/omdnTdk4A/iWJUvq58y5+OKLkavpf84553C7GlMDHRas8nIZ3WicGJCGvMIUxMQA7cXmIE24j9zUi0gQNqwyxGKGArNxQuUHu7vh0N74umnTLHymrTENE5Y+GZE3bMOtryrEumEVo0NgnmyzzTZEEc+Vt9slJgYAT5s5A2syre66+75ZMxqWr2g+/LAPllZUWMEliVbYGVHWIxwG0SLvPHzJOiA5pT35DOiIIaJDe6Y0rUfadoiCFgMHY2Vj8cnJQdjOTDZeOI5XYq6uNsNHRR9Ae04wLitzLIXpjESFKhg45d3b3wdOkARDc5SjUYX0ZGQDRXHrr7EhAiTJ9dbAQKIDvzXGcdJehFVc4iuH00S7Sgj0YwSOnHyM+yunnHz33XfTsxYvb9xv33d/6lOf2nXXXQtyo7QNTGv9KYFkX/v6N1SNe5IYsBgrX1ibeRQKC2i/+EXQB6qqLOr4KTs63oGt4DIKBxEnEj5wInvb8DJilpNgKJB4UDgByKJVGHLsEFKxKhyKDpDu7CyfNq2vrc36p7AWCx14OyAiu18SjqBRWDwidVQTyngl6Dy6weEcmZNjiSFUHsW4Erm0aw2YNnMmB2mvlWx8vEI9mZla0QS3JL4vW69VxPKZW1jY3NioLYJw4MiyYlRUMBzAId0pMOgoya3uREdBZFZWVzuyCJwVJfQHSl3RxKATE3sqKiu9EraWkeYjpRTHdw9+l8VbmkSF3VtjSqKSDU4GTqUXLlzoNAtAAtuiAnv5vOt0++AMDlJpZU3NkUceqQkxR5nB/xRyeBgFrBxbD9mH6LFYeWR9sDZ4QLJRodGBSQuYGgiJZUTVkZGXFi60oJZQwCKMERjJ/UYxFsggHO6slA520EfLrRVFLquA+bVd4blFi3U2ysX6xBNP3H7H3/PzcoWW8VEMpAd+/etff/KEE0GVVxBE1bDqRPseVeYV1GWVtakYBgl9zMy+Wv6h3dofUE3kra01XrGAjn6c/knL8SJkwpJhJeIjsFqi3shIqrPDw9KiYqdTxhnFiL+sLWIm1wZ+pPSGPZfh8AYEFlBHKorMCroGD1oxKN5ta2pCtNAYVuWCgiXPPccuDsnMN2k2oLKygsiL2NfU5MU6wNBpV6yorK6aL6tndvaBBx7IYxNoXp5MCWmFANDrRkdtaoVkTets9fTpI9096lQDOSObQMM+Hc2ytQLv4djIsEWdYznf9kciddhSGggg2IxIMEWFeRMFQfLo79PHkLBqbAK6DHoOx3QmJXTYobWPP/FEUbFupvd6+z5GM+BhIoOpyLBO1q5euDThBvLRHrnkL3/5i53hKt9s880fffSfnFM33nwT35S+8NuwxAHMRajyrlNwPIfS9uXLIdmEtY2TiON4kv5UCpKxMob8iUjHUAzO/cov5Cdq2I677y4KFLUERRcYnB5pPoyQndW4kLdi2JgDjK7daDhGEK+juBiKR9y1qBPB3wE6IhWChBAMza9uDAS7jPyB8rSbhgiOXN6P/Ki+ZWW5zj5ljPOK9FeMPgPDBDjCZVZh0WTzhY7Hc8FtFATMVy4PV7t0JgRYhj5lEIJzMrOcuokU6+k22dlXXnrp6aefvuSlRc++8PxPfvrT9773vexi/HEiOb1VRs8RwjA0yL0ZjuVEz1hcfp6e6nuaE1gCAge3VlY6xpQJT5dhDOXgfsaR6Ue6baJ/GT6AF0XbjwN7wSsMN5PNAFGzN6i7nEiZHCdFjHruw+RhaIjGV4V5GF2kE3IAGSMSapDFowOfAMYGCtErO46jgTx0GZ+srOxqalIzO5cegcE9Qx4iMawWkVqq1NiYFE3IwJPMvHw4QYSmLYNLhDNg5qF2LwLHjXFEMKCieX784x9v7Win7VTVUxTHOtrbFYUQ8xEvQMamGyoqgrHoMCoDmlNUrC8WJDSgEmjEfsUks+iNcLRGORECn4+0d/Bb0dRmUWRHgB+16xHj8ssE3N8PDPZQxBwQ6CDZyPkPVC0GtyRw6XJsl8XFyiA59fAn6kiwElod/MSI3N/f0tY6o7R073e/e/fdd8ejKljxRkapuIVBBQuJ04KaZG729flqfA23uulvDsXF/1tXrAAJXHkXXyrJy8NsYSMMwxoXQzbxQBS0GtSs2vySkqbly+dusIHhk/X3yyd/ZZ8994If4LHAqoDVO5x0FVyyQ2G3triYoIzloZNiFAWk2HTLo94ctPpAP2g4it1YKUvoKcuO03epZVQxr2ALltdg+I6vaN1ExqGPZrehmT4dlqQMMHbmstGki0L1Gn0KD0ycosoKq0MgwvFxZlxmoTPPOmuXXXbRTWZ+a73Ky+pqMW02IwOtFUWNlcrZAVc0N3Ed43qmWlt7Vz7zn4GjVebmyY1vyDqZZogWdXUllLGIni1Ma6VnRBX2J8+cWVxRYX3Zfrfdrrnuut7ubkMvt6LgnLkbbljJi9vYWBAxeWOn14DREcCG/kTzHWx4aXdzM3OAOn0FTyxOIHX+Uopo3ezZCJVvl03fCXMonMzAiKywCvUx1OwSBMb6X1wCBpr8L352rt5ZblRYXl3d09Eh3gxtk9aQ+s9+9rO7776Pka6hpurE444PE9OhgBYRlcK/T5ZVZJZO9yNpbpLSUkDqBZqBfBd7rrncR3PO5V4pDmJMJM55FUPubWxkxKcPIz/VCmyMJ74aAmE4wjoyfEim7Qma4TYvKisNUelRaAlgAJAxFuJ9rCwwFqgCXbG5hNlVgD5BEnqdXG9dDKy6+L51e/kf3DPz3SpiYmMoYYZHn1bWxx7/59///vdlK5rEoHzxC58TUEc2xSlwmY7mFgZv5b/45S/ffustn/v0pzisqHNex2csA8y9VlbmQ7oHeZ0/xNpsaQl6bHU1h0DgXhgx/isnfmBOQXSOWRvro7C0yoYGqwhtbDSdAkxZRblqGSspCYzunGakNNwnltWUsRzWzJpJn2xcvIgFXRn1E54sHgNiTXluadNBSh4ntEVhbxlB3C8IUdNYm9QU0+bNG3J+YmdnSVkp7XRIAjBHs9YFX58ngB/s7dHBXvJFV1fD3DkYaIgFTaVWNK7AmoMEmZ/f1dKMcbP4+iJ4FVTqD6LJQDoI5XQLog9VIqSwKmZ0CCFP1ON0KizJ/AghNC4E1AkJI4mqx9Ii242+cIRZyVJ9veCXlszGVNFoSlLRg9SVo6P5lpnqadPIgg7PJCLMXG89T4K02dXlBnLg2SobiD3yU4WF2YDGV+xrVYtcaNF23M7WFsUkbaadltTWwCpoLS3ChCxg/EhkODd88prwHKhGkAhp1QfYy9Wu8R83+3A4AbWQXcN6f8lllyqigyLq2Yy5M55+lqz+fI9tTqMjBL6OlhaLmfVSBjaLHHJ1KqOsWsRKhhWHQBDLmDxAEihNiEEqxYoBGDBYqCyZXrFSemIMwzodCfduCHwhxKCq0tjRyQZ7ugeHogyxQ0PO8ABVnJCDdqczKz/Bz0VPwlAbyoHS4MeILN/IAKjqL66uBlIQ0crLYUbkpJ2HcxbMp5zbYgCr6FMxEW6kosrp02gIRpmBxmZOlgv6P4+ZF0P+ML73gQEaiHttkal1EyrCcDs/qbNTowRB3dEcMwBUuhc0sQbiV3lg0KNQSZCQy7gC2NGJ7IE4SSHRaY2xQD8qmD+yjGhOZ5EZejbKKJACTCTYdLPNli9ZAiQ/8bCFGia71ENAFDIqBr6+ntihIo4LL4pCpaSxIHmVBQQGiDNhgCLBUbue6zi+4UZ8Gm4gb3bDvLkqoeIqUCKxCgZSkE/JzJMZmV4nXJyPIjvL9MApAEZcgyKqgmAKHI9cpWXyPWRqiDqKojAcwf+BJ+TkoHPqH9rgpiupCB2k/KMosyAAlpvDWUQxkL4L/bP+oEZDRkBEguoM407nGR5mRwihK8gdJY8MMxQhxUA5JSXks9C7gQGfsUanFy4i6stqbfx9kk9w+otfjO99VpVXCBURBokttLW2drW1iSmQIMcsLsovaOvsOP83v+EKo+1RMXG29sbG4EsBbsQKICoYH0CVLyp4wrjLAGQ60CII94yFOBtCA1FOKSUoL/DeiLty5wZPF0kxCoEWh2yeStcHY4icCqdmYwqLTFrtra0sAmYoeV1VWkGKJpSS+JthMjHVR7Ex0bzCcKBra7162lptfubw4SPqaGwMPCo3t6ujnZydVSiKKEsB8m7NjOmsPLhZ4FqdnZQ6yaJeeumlyvIKJgORokqCJwwyF1y0Kd1cNlIC12fMnFnV0NDf1dna1IiZSEKmmO5YLKgoheVl/OoAti7EffectVEZzit1Yk2lDjmLpHChQIxfMACfTAMapduY12rjD+Q5xMcNGf3HLIBw/k/1wCBzKu7nXWonKPURfhhn6VRCJBDq8kWLlEeKAZtRvA/ggwJmR0x+vrgJQzNj1izKjKO8ESMFuLejI/h4MZPSUl0ub2iA5BAkkjFhFWbCVq2AcGZEK4I1d9YG6yMTijR2hCfqbz6tY5JLmMncuXOzolEOkLgidZfGSJkRBs9oTt2Cz7BC9fUZEWwkqDGReuYV/NCv+A+HsMzSyBJOgt8yEhXwAeSEiwbSxbXQc0Rg0BrCoRk3U/0wl2mVV6fweOQlCpuhdiiEVespWtMRC6XcXQhsxvrrx3Aydut4AHhtlyHDatXMTBb4QEamGv7x+D+F7UjVTh5QCWRipNojYBB+pLjXomKqFXRwwe9/Z4kLU7Wm0kyPDjCil7/sWrRSoGcOeaukuWksGDU6J6Pn7GxmnaULF6KAOfPnC+FxzgIqQlHqZSNl0SN66YdNUsjA5gh6IMAwO11gITJ5MWTrVPPixd5CDAhVH0PkXZRBU2E+UsyNq9kbOF1Yrbq6BMqp3BOTNAyW0A+0FwY5jAv+4kVjKlWqrhEX7dRCjXBusuKKyrS3tf3ohz8sLcy3N4l/RSJrMCMA0hHOaaXgM8G07UTJL8dpq+3tam9uQiTAw6wsfCaXWcy5DCQUa0bYWhXwX1wcdS4Pg0WlyIOHGeOCH2JMyM5FPLC88kmQ5gQVuijDY2O21+FzbY0rKNXeMo5Mddgj840KDTc8I0jdDEzylaOt1kYmybO3DgYSHfitM5Zr7Qk+GJhXJG5aKkaHg++U1e3www9vbG6tqq7YfItNxRASKYJ8k50z0CMWlIN3oriyyvaOjTbY8Dvf/4FPCgW5kNDJg0Tod/RrQWWFJ+q0lcUT90SEUZ4+6qXsJjZDjo12d3dhOuQM2qxtMEFbILI4ZS4lhLglqLjZWU2NK2gLy196sbC4qHrmDLpf3ZzZ1oaw1GVlllZWBK0snepqbenr7Jg+c0ZhVSVBVgH6pKWapkodDduBSAN2CEc7CVVojcS3/erdF196kYobQxXkmGgHlIaAwcps/2EQC+yfzMoMyUhzc5598glvFQmz7OnWYm97G5mPeiOrbW9Pd3lVpZp7O9qJzk7GGx8ciHT4wu6uTnW2tDSrVvfBQEpTpwrZlQtKit3oS09fb3dPd6+NkQ31BE1fVa4eqCguL7NotbS36Qzhsi+dCns42cvtdpsYZzu3cI4ODQKDsNvSuIICgJVbKqbPnx9OnqB3SWdqfAVZRVvFiA5hwYYaAoeta+TY4SHWgZ7uLivx8NCg+qHILk0KBiRoS1Zw9+CEHrocH7iQOYsKKopTiJMzciO7/lrpzWpprbJgIjOLnD2K9rqSDA75wPvlCHGgIY7D4GJ9Qm+aq57WwD1FFINPA5pVXGQIyJ210xo6m5s6mhrJtQADpJ+aWpqrZkwvoxHJTjk40NTcVNFQjx66Ojvg1h8SMu5+LauvQ2ZsE4uef55szuuuvEHJLCmWSKy/M8gNq10xK+xsa9NWy7KlhkN5W08Lysta29sCAVNIIJKGEG04n7n+ghWLXsotLgK/V6zlrcuXzVp/wVCq3xa13q5OxAPgnjCaEyTOnGgDfOW0BoWDGUKYghliphQX9fd0t65YDmCbo5BZd1trIIbKCjh86dlnjZoboptRAHP4nFwXJT1n2DnJ8VtepjQys4E8s7CAPwqdv7hwYbe+iyF3JhXfi6j1/v7gOI3UyOjEyRC3BxLvjo1PME0gXUKJwSL/hSGb7GIWicALAquac3Kc5PHo449zis6eN/dte+25w847UYObW9pvu+02ERCINkgqrqjysA1UfEd/v/kF1R2dHfDGZ+6wU7NDeDZMwhtTEf3HFAgHbI2OGO54q7MyjB1enBgdaZg1E1YhUIEuWx/7eon1fDultTVGylRFZma9IfDnxe72NjQTWgn7GEe1wjmAn3S1tb707DP4lZEyI2iDqkV+eeVlLcuXGdNAEjjq4IBJpFq1mSP8NmacriF+ppOgSkXhfJOh7X98biBW1Z9NrvYVjd0trVS72uoa4rSpRMbbattt4tCGq6+7Np7FTr0ez8pkOjSavbYGhNkdmKFEyTDTJ1Q+8PNsiW0EeYrrya+o0EE9gjpHgCLywEUHgplAeTel1VXI2KRbvHgRLNXMnGGWKa9+sUXml4e4q7dopC8883RexEvVSS3BagJ5FwYDUxDBq6u5gvGivo725ubmlToJ8lq1s+4RgNe7W1uMSEVVpSb49twg5peeX6jO8ppqe6fZmKwjIcMCC5fDV1iUCgsXL1mC+AMn5HMeGhJN4Kuxj4/+CzaY8XGaqjhRsjiKpcYYR9QbXIuRgSDQJHucnNKRy5f2TnNGP9Y+UJm2dLDAJdBMnNWim/IQ/LGWJMwTGmsa6q1W+RXlFdMaGpcuoe1gvMqXN9Tr0WA6ZVlUj9fd+Iqo4IoZQk8NBKwqVlJVOXP+eioJnnzb4LGmaGUJnHZ0lN2H5Q6odGbg2dlUO2MG7zylSLiT/toOwApAB3BhYkbEIt7W2lJcU41o8VhDZhwtW8Bg/wVPC22kvk5QyWQTHkeyBycwo2gWh5qjQCE8xBPAgMSEwvmhzhPAYDggb1yxnFpLK2MO8Bz8yBJi7ZbisGVwFDQRdr5EwS8xozM0vgZWwfeYk23JiKkOMWOVfd1dHjIrQ515Cu0+Ic2M9pNwj2nrrSdygdJOAVODLT+xKr7WqYdsYJWCR50bHh/78EcOt17UVFb99Kc/bVq6VJ1agW0qJfcsBYx8hZ5VpS+Ig2Hoxz/+cVm5eN2MjTbaqKqqDAIhX2082FRT445yGNNX0jMO9hr0zDcwe+7cAGoImcplXhSGhpA7WluZXHFv7XLbxtEKAqawOxgApDWrtbUFwuHKStowYzqiReuWWsQJXn3BypQMctTYKMqPC3sCNmKJGgIfQJ/pFJghmV5KwEj3p9iyiwqLuDjyi4rFrFm0K8ocAp+iCWfm5C5fsrSiuvrbZ32zcUWzbNjbbrnFsUcfk52Ta08NuzNrWqCHaNO+kcJssfflixdpsSaa7OCng1qjgQFdhDoTShcKeS+mNaBew4r9MlmyeoTTsPt6ly5ZzIMShoYdkyTgZA1pWXAbDHh8TDdx4RXLlzUIKHPAQXWVbNVmZVjWC/LBQKyyYiIzRGsgiV5BZh4bX/PAuTAQyfXWwkAs+L21+pT0ZlUMRLJmWCkjQ7hV3819993nJjc7s7Oz+0tf+pJ7Jli+BWa2wqJiRlMRUI0vvMBnyAC/6Lnnwu7c4mLRXMHEGAX44TKNSxZfevlle++993bbbTdn7ryK6hpZEEVTy/66dOnSsGJFp3RqmlHNGsmkJ4PLHnvt+e5933vbHbdXNdQvXrrkG2eesdvbdvfutttvJ/fpHy/4LX1P8v2JSLX+9nfPnjdvri3KG2y04fwN1n//Bz9wyeWXUYZxsbyyUmVCxTKsNq447fRvvG3PPfbaa098jY6Ha//gRz/0VmFp+YIN1t/nne/42MeP/OtNf8MQ/dHn8T4CNCUTA62dOaO4ovz6v9zw4cMP08rsuXO23m7b6bPnlBYV/vinP7Go46pYNnVr6fJljoa3Hlxx1ZXE+u233+76664l9i1rXHHIgQfMmjtnxpy5G226yUWXXOwhDbm9tYWUdusdtx973LHrzV/PXsGGmbMl7Nl9zz2kIrO4OvBz2pzZBJGu9jYw//aC3579/e/JP1QmrjI31/mxp5566mmnnfbd7373G9/4xo9+9CP3xsvNL37xC5uCzjvvPMsS3HY3NQms3WH77eVpvPf2222Wo7MG8cJfFARoiIkaDzzwwO57vG3b7bY95rhjg8JDGYhyAtF7LY2/+d0F73nve2bNmV1eXVNeXbvp5psdfsRHbrzxRvu12PvVEPbIRZtzZMBYlcRW3lvvLR7EHWtJeV0t91RrWzvUbbHFFpwG73rXu4jBOksHRh5kI8QktMnr7qlbFiExjZ/97Gc32WCDDxx6qDKCgR995BFShfDOgpxcnmSfRx955J133knMhSLtsUZrUXVoWOvqeeyxxz70vvfZBrnZFptvuc3WdTOmb7HpJr8+77zlK1YM9HSTt0Lg9ysXkDBBa7Mbn+zuenrMMceAdvfdd//GyScTv+pnzABbvFeKkMTEEKSooaGvfe1rM6dP227HHSyoQuDqZs0kPv7iV7885vjjoJFaggI32Hij40884bbbbmUi0aMlzz8vyErA84c+9KEdt9vO9Lnkwgv5K+pmzgSYjCMObWKw1wXdLKusMrlqamv5iz5y8MEOTYUu0ekrFi9+BfxX/R8E1sjEQ/xbsWwp0Za94Jbbbt1qs00Fd+64805mX/2MmVtvvdUvf/2rRUsWywig5qC7Rtscbr/99lNOOUW0LcRm59sclSWYDbHx4p599tlIMTiIJrvYX0SOxfuiBwY4On7/+99D6ehYxn4H7A+l8g8NDI7wudpsrA4j7hNCtG5vGAEdUfFj8GruuMvOYDZDSTnnfO+7H/jQB/GH9dbfIL+4dPsdd/jzX24whXtT/Wa6GuicXEYI4Kyvf33nnXcWzHLiccfRQrUYnBK1tfQr9r6tNt30W9/4BtmLzPqLX/9qi622VFtJRSXN/DcX/NZsJW/JYW5G/PGSi3fbbdeCkrKGmbP2fvs+O+20k7O4NSThEAJrWbqUDFc/bZrg3lgDMftuuummfffdt7a2FpHLcMZtuMMOOzh3h9BqInDKEa/UEJTAV+t44fsUL2KZ4HCHkSB1zmdqdnZukMLf//73y99GTm1saUGNwxNScBXpVEdzE7nwqWeexjx5So0+mqyoqoaBr5/+jSXLllbX1YZAmHRqJJ0K6hyFv6jwofvv22qbrXfdfbdr//wnrL6rt+fue+95+z575xcUouedd91lwYYbfObEEywB4ma0DpgyVFpVde899+y5556brL/+ySefzAzJ3QThxtpAm0EcNe6//e1vbzR37sYLFlgpDNx0cSg8WpNc5F18Xl/Ywp57fuEpXzt1/Q03KCguAd6Gm24+f/0Fn/zkiffeew+c109r4FgT8tDU1HTNZZc5I9cyZzXB4uQJx0NEMyFjJyB885vfdPODH/zguz/4Pv+e9EVyEAoTgB8LXPA02pQh6sRFXRwettUcty+Tiru2Yb0F8+unz3j/AQdgQXrNtmhBpIG4R3WSDuiH8xS22XRTvq/Qp5ychx566IMHHYRxoeE50XXckUc6i0E0AeK0L9F0s1Yu46mzdzE7Wz52bFw+4Zr6Bm1tueWWn/jYxx594IFlL7wgZiFs6cfbozQEVmT8X1CJr0LQ8Q0GEbN4wezZWCWSaF22LGyjzcgQpmQHrCm8+eabH3Hkx6x6YqMee+jBs779LUReWlk9Z956VtWPfPSIB++7F6HWRXrmS4sXhS6s7WI7EH80GvkbrQ4c4ErpCNOMMXVvAro8CYFggthHRx+47z4z1B6Qjxx+OJ+2w35gOwdgFM4oXYWD06Fujz32eNvOO7PW8eyZsJYptfmkx7r52c/Pfdvuu02bPg0fAHBFfR1zPCM706T1/RPHHD13/npFZeWVtfXWfezi61//+vOPP24UtcXcBiyGtsB2JrkoTUgUyIpNZGRYuQ5938FW6o7urjPPPJOxSUc45/EWFejgyMAgfq5C9CNpyCmnftVejd6e1Kc/feKGG22EnzB1miCIH4tzI7RnbfRcunZ6Zp6QUyPSfscGB9Ht1ptttummmxIGDLRANhWaO9ZlXIidVwrrP/3pT1YuoSJy+LusIPDpiLsllowoVMErEB7r+Z6YFJa5LTbdVHfEbqBVHLi2Ydrc+Qve9ra33X/XXQQJ5+QJqNEEUwqXKeHESIdeCR3vtSe9fzCVpuvaJ9/X0wsp682ff/P1N1x00UW4nrXlpC99ubK+gSXXG0jXhVGQfcBw/fXXv22Pt5VV1Wy25Rabb7VlbkHRPu94O9lGflYYM/oWdy/SXbl8EbmvUM3rawH94Tnn7LXXXvbuIemtttoKzG9/+9uJHOZjV0tLzG0wJblO+trbjZTdbc8//ZRzJaElPydP4IzBhc0vfvGLiLlNcq9ox0Sgi2jTE9kpEExyveUxMN7dnfy9hTEwZvtKd/dQV6ezfwc7O3wO93Z/4vAPcw7WlZXMqKtOdXcM9feMDkiK1TnS15Nubp7o658YGISTnmXLJngzpCBavnyCG6S3R4GJ0eGBro6rLr3oHXvurhJLgRXPXwlJJbqpKsjfYOaMi88/L8XlOzLsGEbvjvf39be2XH7h70ryQ4LOH3//7Jv+8ufdd9rePUa58q+0IPePv/tN64qlzzzx2G47bufX+qpyn4U8FlGxgpzMj3zwUBlee9pbWhuXjQ2mbAYC/Ifff7BK8rIylrzw3Hm/+Nnbdt4hLl9RUhi36Nf5c2ae8bVTGpcu8rp3teJmoLfrHw/dv+8791F+em2V8kqWFuaBRFvanVFf89yT/1TSp01gsNTV1nzx739bVVas5C9++qMn//HwwQfsWyYmKz+nrrKsMDfrnLO/Fbfy+KMPvWvvPebMaFBS/bV8UZVltlO7ryguAM9Tjz0iLnDx88+qf+/dd6ksKawpK1M47I+S+5EEGfXaRkzrv4eFPHgxHhynWVhYXVRsuynjszE6/rDDbLryd8SBB452dnrIJzUmBYhoW+suMujsPOoDH6gqKtD6sR//6MTIINRNhIWl58F779p5u631V+U+wVlWFLZ9G1/+hY8fcsiL//iHJnqXL+9bscIuMjWvdcqgtJGunpGe3uGeXtL5e/bZJ1cm5qKCn/z4h6zHMrm+fc89ytjpMzKuu/Sysb7+gY5OREKW72lqHOvrJe/4/PCBB8SEtOy5Z6+88PfIqSIv1ycaA8ys6io3NUWFP/nOtyc4l1L9zS+96EU1+Lzrbze+a/fdKtl2I8rUUF5WZl1VpS7r2tu23+HBO/4ufmDRE08Nd3aPdveO9/a3L12mmGRiCix88kmzY7i76wsnHF/O8CHnx0Ybrnh+YaB8rtrFiwIxkx862tHzZRf81otwVVdV3tvRKiCsr7Ptffu/dyWtogR1xk0X5WV/+vhjYNv0GU/3j/b37rDFZl4vy8v54/m/7m9v7Wlp6m1tnuB76+v5widPmNtQ51dzyoA2FBWV2yiILDMyDtxrr87Fiwkxk+Ff5QhV/SjqhquuMKwl9m3lZM6qrQaMOhuqK4pt/M3I2GfXna/6w4XDrR3jnT1jnT2dy1d8+/TTuYeDMcDS78/gUVNh0p7nrGwonehPr7Xd+KFjPwGG0tBef2PjhtOmGQgN/eOxh4QRtLc1lTgmI8KYzuIJ6fY2xBn29za3TPT2Dbe2PXrHHZwLysCVufPed+ydL7m7sMbcLPMRTZoFfjVT8AcdXPriQvhsfOmFieHBzx1/rN7NqK486rAPxRiGz+7mxs7G5TDgpw8euP/Cxx/baP7ceETMa9PNPQ7zkQ+9X203//X6A9/7rngWVJeX+DXGGDJ4+y4733vLzQgspjcUEsh1eOj2G67fY/vtFCjOzKgVEYAyi0sqCwrd+HvfO9617Jnn0FvrS4sDsfWnBju7rhYZEVGj5IOSwKMrs2ZNrMJMurN9JN030Nd96SV/CG5yI8FiMDpmpPpaWt3g0q1LlnauaLS/8bknntAXR+MqteilF7q6Ovhxe7o7G5e8dNQRh+m+PDnzptWjBPe6Nqe+Vu8qCvK++Y2vdaxYhhtIjqQ55aHi6X8+GmPpy//1WfztxGM/oTD+hsvFz2EJr4O6xx+8H1q6G1f4s75ACDrxp/t9Lc16FyYpBbunG8Z6m5v06/iPfCTGzxlfOQkNGKNlzz9n3gFDK9gT/Cx86nGcGTwvPfc0eFYsfvGYj390zvR6MCgTwzC9LpC0+/mzZ/z2179QrGnpIpznF+f8UP1gqMgv8Bf6nplVmpPrzxYFX+M/Zbwe1wA5j9xzFzgtUlzDeB2+Korgz5ddigMoX5yXHTfnPh5or++4+WY3XXuN7i9f+Jx3O5Yt1WWcYVp5mV+322RjX49iweE4jOZyWU52/K55scWC+ZgVEoITr1igWxa9hLo+sN++0/l7I04+s6oy5kK+1peWnPCxj8ZoxIK8Engm3bujw1+YdzYoNa7Amv58xWWALM/Pbago81UT/dZ0AaLDIztvummAv7IMVi++8AL4hD3zy2dVaZFVDzasa1Y3Pu7nn34iWKfWRpzIFYrUaU2xIoRFoafHWpNy+PkrvPSGa64R7mG3lNPgR3r7OEm7Gpvm1TeErQsZGX//643dyxtNDXavmKHhA8888MAsweRR38/+6ldDv2jabW0+A29JpRoXLsQYQWu8Fr/w3HCqt2nZ4sG+bvd4ghkdjyae3FBjJQ4RUJYSFX731FMDwIIV1GNrQ0vLmjPOE53tb2sXZv2zc86JaeOs005rXrS4TnwA815Gxj0332Lqpds7zL50a7vlQzCSBVGuslRH2wP33GmGllUU182oXdG6/Atf+pyveNcFvzh3OFqR9XHRM08h1OWLXjj6yCNmT6uzLsQw+1wrPRs+0hdSNL5fPvHEhpKS6vz8g9/5Tl/VOdTR0fLiiyuew2RGHr/33i3nzwck6lpJNtbNUmEdhL2S4gt/+YvAwYYGLWToxz0S+uTHj/QrsnzkrjuvvfgiJAddakCrKnnqoQdRdfvSJSgZiYZG29tH2zv8hXERzx+NDrz1RIJBJxOhmlP9e++0o3og/6MHHxzkk95eWT/NfYjyK0bxj/vuOfTA/fTaIqPj+LxhLedUjljHRrNn/eanPwnQjo6E9Z2/u7WlS6gUZjIybNnadN5cYOsaScCNtubU1lgu3ZMf/nHP3V4EOZj9mRe+Pv3wQ3vvslMgiZzM9WZN17R74pBX9Pe6iy4Kc4T9i7AkoqElLEkTXWvhzGulnOTh/0UMhOVpbBwlJNdbGQN2g3BZ2DEV8v1H259Eo91y+22lZcWdvf1fOvYYFk3ur7BbqazMkQiiJgd7egvKbMbICvvuxsftyhO+xRArHI6JN2di/J7772NLf/yJp+tqKkUzM+7KGMHwxs17zz33LFmyZPHyFTxpLHb777+/V1i7tRLiKnkI+XwyMi697LIXX3ihu6v3gPe8i6dF4hwGyBv+elN6cOQzn/mMej74wQ8+/fRz682dtddee/GuMLJefMklfD6SXl1y2ZUL1l//S1/8ImMty6JMxbHJcAz3zM1xsi5boDyEO++4HYOoAmrjWeWNWbxk+c/OPVcyanlQ7KVRJxMjg+KhhxyyZOmKObOmL13WuNuuO/n1hRdekNVQQ8JyRW8e+fGP8+rMX7BAkAy3Blbe1t7OLEpLtlPo4Ycfvu5PNxA9Kfd6pHcMjTXV1U8+9pgTESFW/hqiKXMsJ7lB4Ix95JFHurq6+5Ys/68vfOEvN9wAGOkKdRkwwn6hS+WEXgHqM6dNN2qsmLDHDaU77PehL0uXijleb9Zs9mBjJNDrfe9735VXXsngLBER2yxHZdjaaseyuox7djYR9No/Xdc7OFRfV61dyDQcKpdfeo899nKCAeV0660323uvvbbffnu4lS/t+uv+JPvOJVddJaLsl7/8JQ8k+z2ztLdWmzPBAxld4qkEHJaVlzctXnLzrbdK+6E7Bx940NjgsC4csN/+d995lxMSGImNDp/q0GA40sO+OnVy0HNdhvA/q1pB3rHHHnvvvfc6CWaHbbfZdeddbAx+8cUXz4+8i2zJfBpcCh5y0jKuq/zBu+769Kc//czC5znwwbf3nnswKtv7eP/9999www0Qde9DD5701VNuuO5PXEYvQ8v9+8p+YI26ZK3sT/V98b++YOeqMX3q2eeeeuopjrJwlZeNUIBTafHhcM7inu2MroGBL335yyEZ0vj4aV//+g03/EUlArKOP/5452eaNdB46623Ll60dNniJT2EJ65IyagqKviXykqK5GPn/JTxm3jhRXsF0cbvfvc7J2rMmjFt8003e/8hwR8uVThbvu4//Oij+m7XHwdLAHeNCxgS1WTl5F531VVf+dKXX1i8tLK0OD00iPb4pe0mfeDhh+649bZljU333HPffffcd+kfLuYHqJgxvbIw3yz+xCc+cf8DDzhLJjg5h8fmzplVWVbubBYBvbZ+2cQlGlGb4d+rL90JuwcjN4ve3Xv//UskA8vO2mH7bTbbcOPO1raiwsL3H3oo4uzo7BHg8ImjPvEyFaFpM4fnOYpcEK8A7bjBXXfd9eRTz5aXFUvKGrtfrr32WjnSuMHuuuve35x/PgIQ9tnf0Tlt9hwObf6Z2urKlo4umxpMbUgurQrpoMQrOjanIC/n73ffte8B+y9avGyLzTbGVexXDDznL39p7+wxu6+87DL133LrrcJa933vuzhyxeiinIXPPGssHnjowfN+cz4C2GzrbbgWDIHtAOecfTZPZmNTc3GII67YYbvtudlt4v7jRRc54lsZ467Al7/85ZAs4JXLznOXPoYt6KtPo1cKRf+v9VcuR6Sud0IiBcE4p4RLPd3fh2V5yfOyQpGkhMPARkQW7LTzzrBhesK2oHR+NtwGAzEEGENreyeGqaecIbPXW8/ZORLbSvYmepMbXlIwexlkV77k0iuwenSyw7bb8ZkIbHnooUc6unpp/R876uNXX3El76baDAHnuj7lhdS8EzxU0iAVj4zagjuYHjABS0tKkRASjTEARYqZ/QD2uocxXb38GSFn3oIFzzzxhNiESy+7UgEMeYP113cgnF4sWrz4sksvta4sXrrijDPOQOFz58xxGCn2u9UWW+qjiwfbDlWjo3VrEPwAEj91ozYx0o8/+0zc0xDZAUtjIi2GBfKWVFd971vf+tFPftze2S0X+owZM0RkmCACQa1x1/75+tqqyseeeNKJSldddZW9jjrPVRWwHE0Bhr8nnn4GE8AlhDi8953v5OjDhB988MEbb75FbPmzL7x4+pln3LjrLiayGBZePmnzTjjqqOuuv0G/586cwR3How4kVIQ+G1ssDq2+Sg0YUn9FHiqcAbR8sEJU+OWEhYOeX66kqMD2JBQuP3lHc7NN0aJwsycy5s2f39LbtWhZ4xlnnnnF5ZdbmPbf990bb7ChGFQ+8+tvuGHatLrGptZvfutb22y7rYSLwlIAp9E1rzhzu4B2Oynt/7T7drirC8llZWYNTwgiDReE0xWHRsL2K+mFSktKkJA0y0YE0iBTShGe66AUC5fo6vrT9dc7b8n2WmP05xtuOPrYY6tCWq+QnAleqXnY4PLm1prqCk48eRy7ZYWcMUMiLgsB3ijMBEG+bffdUYKl7bnnnnvhuYVPPvZ4Typ12llnOVGX+1QAs6z16E19a3bKE2OEFHVE6IJFUD0+iQQ//MmPubjhTVCABA0ckooVZhcX1FTbkSs011aLj370owV52Rz1NpcV2ygrB0fYfxCyNwklkyvP0jl3wYKnH38cPV92+VWaEzIf0zMv5Vrp2blDIFGJjTMmV3fknVYbPgAwMJgIMpJc9ocLrX3AVqeAPSIZeUOULw5mAWpsburpT5162tdEe51wwgn2mJgamBKat22nrra6pa3j8Sef+PUvf9XW2cWKZseU6UFQmzl9BiEw5JeS2Ht4RKO5NmjbNYYLD45klBVIc4p1c0SX2eLe1iqPo9XpsssukwvTfC4vKjrlK1/hXuYDR6tKSs4n0uTRJ/7JZztqsw0ttLJ8t113RRhGRB6Ki3//B+RttcQ2ue7NLwONj+mpwP6u5mZB6eeff35rR2d5iUxhFQcf9D7EYELJbiM0Qzdv+Otf/nbzTb5uttVW9pxzC4uXfvzhh0Uz3X3v/VhPQ22tAeU9lqzLK5dcdLETEKwFBBKcynbiongnsDDDsCsIq06utzQG/i+q7wnMrxMD7FgTbd0TXf0Tnb0j7V3D3by4Yza7WtbsNjK5/3TllYNd3bwTLJpDrR0TA8NKhr+u8Dce/fFg8OyNsoqlBzqbm/u7uzacv553Cwvydtpu23/cc69zEu2SjF0TLzzx5CcOO5zHsiQ7p6G84pG77/EWu2n0+uClF/yOo8m73G4bzpp969XXgWrCsXItbcD44mc+U2pXT2bG5htvxET39l13W77w+aEOnswUX0fbihXvfU8IphUAVV1e1t3EydDLkci8zcD8oUMPIZ2HmtlTyyquv/Ty4H5ZvER/WXaXPrdwzx13wtYbqqpmNzR0tDT3dXX2dnbQD7tWNO69w06sp5/9xDFPPfRwb3MLSNitx/pTt914o1fqQ8bUrEv/+AeHWkiCne7o5Hu56IILgIExbL75phpV53WXXNrX1DLY0v7kPfe3L16qXRBuHdndP/Hhw5548CFOG+/K16Ij9/3972qWA4oSf+0ll7KUa5SbSLvu/cRxoSOwZ8igzmc8BEYKYBf/5rd+pQOtN206gPXU8HUsWbbxrDklkb/u+suvUF6LoXxf/1B3j7/rr7gSVlW+87bb2rPatGypOOiW5sbp0+q565DE5z/9KUluxlPp7sYmjYbudHbtu89eXuFL/9F3vsVdOTGQbnXSvUi8yD/AABz/saj548wZ7etXw4oXXvzNz86tzA9G1oPe+S61oSKQNL7wYlFGJpv6tIrK5x77J6j0eggFdnX36MjgkOV279120zVoB+1799zrb9dc65RDjsqYVB698+4ZlcEV7O8LJ5wIQqj2mWrjm03tts22nn/4wIMevfuejmXLHfKItBzFeOv1N6iz2Iax3Gw0r11+YOQ90dPfvWS50Ydw3Xzh6adZ9ydSgxO9qQP3eYfn8Lnv3vtIfgswY0cICC7ukdGXnnq6urAIhEV5uU8/9QQJoa21mUkaMZSWFF17zVUMzyKyYMMoLH362XO+fsaix54wv2Lvt8/tNtk0tJuRecG5P4cBZBzIdWDwXbu/TRfqSkq/+dVTDUTw5IyONL34AjX03O99946/3MD5Ewd3wPxqfCA86U8hs7YlS7ffbHP1lOXmHfzu9yz85+OaCN6YwSHwm6qfPOoTfuWTqSsqefrBh8GDXHUQkUv+01Bfix640C+98EJDY+wmuvv8jXYY5Ygb8GpGzCFmFOFTgd7UcFunwmblofvuVyXRSEbG904/Y6CtI3RwZNQcKbZDIiPDMIFToxG2B7zYtXgZtD94821wAqv+MJCvf/mkxhdfwkBMCuCB5NNHH+Mnvla/dixfoVogaX2sp++zxx6HqwD7qI8eYZgC/TsHakVzf3Nr6KkjRmC1tvqXP/xRmBfgGRjEN275y1/MQXBuu/nmPmfV1z/18CMtBiI9AFdYh/BZz72OIB+6J3AzI+U5kn7mkUfn108D8BePP7FxIV/0mCCICehfttyYatTgIpKmF1+Kkaa/CBUSvOLXeQ3T4nkRYzJQ4yt/MWXCm1fQ6uUXXxRt6wviGJdvqr0DE3DT29JqyOz2NG1P+8pXwCk2BHO2w9k4YnEfOPh90s14a2ZD/a1//auJaWggDT/BN379ox8bDpDw8//xdxdgBfJOoQ3M5L7bbg+1FdtZGDjDp449Nsym1IBRNhPNqT/8+jzPETye+5WTvsQ5E3zaqdQ9t99ugHjC9VHvjEsYoJ5+bwk3cGPeffTQ9/tV05879jhkEHh4Kg1F/NsBaZlZfrWOANKvEL702ec+f9zxHm65wYZ/vfoa4wInMNDR1ISxz6gNDm2fnz7uOPqfGadCvxri3Xfc0U8m/ve/9S1pmbzlVw2BIUQ0dPU2v7QIkeMJir3w+BM6roN4uHSFjz/wYHg3MyxVR334MNEixjfV2GIxQuHPPPTIDptsFjOirTfcKCxwff1mlqExy7gNDQQ8gPkD793vibvvmxgaxU71paep+Y/nna/mmOquuOgiUHnevmw5dJXlOJUhtIhXx1NVR1Tr1++c9nVDYHRgCcMHw2B7Z9yLMPXMze5eGEMwl//+Qmj0t/7MWTob5oifdLm7D0+z7ts+bOz22mnneJW0/vY2NiPpRx98oLamCrX49Uuf/WxYBPv612QyMc952WMmTCx2nUV+4I4lS3QZ/D5vvOZaOEHw+gUMPByWbrjiSoCZvJvNX6B+sGkaAFZ5cwfr8yLklNI8s7JE5ei+eYre2hYtmRgcMdHgB2P54uc+i8hZTS1h737XO9Chcayvrvrbn/8c1hFOWtbGvpQ6EbkWY+fzL8/5oZ/UZmbBib+VUy++CYgiVPT0nH3mmcDw58ZXtoD158zx1bBe/LvfKQOqvtY2zm3M1vAZxN+fd54CJQX5JqCpZ4/rp084HlTI7/yf/1xnEWTozlToGQJj/mb4vG6OI0itHPjOdwVW05sKa2J3H+LcdL35jnz0086bb9ny3Aumm7mmTMBwS+uhBxwgwZ1fa8vLFy9cqEdYH0hA9YGDDgIzbG+2wQZwu8f2O1hhjUjLwhc7Fy1NN7WGqnpTpgYUBWA6eoJ8iOR6+v15HpiYlWV0DEKswtasBdNn2A8t48WZJ51syevG2/vSuEeEsSGCx8wZ02L5c5cdtl/+0ktksECfw2OIQcjMJz9+lCErzc7BP819NG9Go2209Ni991VFUTZ+Pe6Ijyrft7wpZrzmhcI7b7U1hBumjddf4EwEbZFXiXl33HST7mN0MGBzH0HO6mz4zLJn//HYmad8VeWo3V/oKct0a3uQHJxvvMYimzx5y2Ag9gOvadB/S2v8/4GdwxjtyePxj4ypElaxEzOGcWlSgiVjYJW3kYm5i+uA0XE1DK3MUCIPA73Z/jp7U5euWK7Y9BkzbBfcascd7VNi52P1b1y8eP7GGztQlCXeLqz2nm4bVhnwGFPlw+B0BQPjJeOfs2p+c975u/Iel5ZZropKy5hbv/CFLzAYy3TZ0dau/rPOPLO+tk6sUH9Pr7N82Ds5K7JzsrhD+SJ4m9XMCy3VC0+S+5DdPjuztqr6lr/dtO/+B3QuWy5PA++3w0tmzZljM8+Muvr2zs7G5maWdb4UtlU2P2UuuvAPi5974Tvf/NZ6c+eVFBWz8rLl81Fsv822Rxx+uF12DJbxDmrOBCljZBxhRrWV2jaoJ554yql7bMP77LW3HV8S12663faVpWVNLy1iZwXJg/fe98uf/2LDDTaQIpXVvDg6VMCuxcM+9CHWbshnprUNDPI72ztgZ1X8E0dcwVH5yl/4Gj1c+RH8gXKHOg21vPzA9x0E7dDLkcjFEba1yIgL7XS/osJzf/kLA830YZ+YkyQMBLOxEYRMr++26y4f+chHpMz20G6i7AIaHkmk8Kc/P3fG7OkDQ6Pf++E5zMayBzlzqDvySKyEId7oGH9y6aMZ7llDz/0I2uM/cQwriSBuKTSgl7XVczuxbTUEGAgNX6AQwxEScY3YvpiZlQnDvD027/GHSJhkOGCeLmqbnJ20mi4pLGLEdRN2WEW5Otntbd287+57HHm99Q47yBTStGix1B2If8cddvjYRz8KNglt7rjz75IGvQx85ARe2RE3AZ9cDkNDPKK+8reII7DDUH6XsCnRgTFycnR28mf6FRnbhs0b2dLY5PBb5uqa2mp263e+850OWZEWVXlJRGY0TPv8V78K/mAOX7v7IaOzpaV2+nRpP8UsOKmlp7/PcNiFVVBRbktnyKOTn3fipz61wQYbyA3GnL8qzKveh31ZRUUXXHDB0089Jay5uqrqpJNOmjd3ru2CHBRxKmNDf9KXv7zLjjvBak+6/8I//gGRmBGeS/KZKzOeAYgIj+/ITey6XLUV90bZT+HSI1m67Jjo7DQNJVwRJXHHXUzy/dMbGj74/g8UcEN194qd2GmHHc2g7KzsBx99JEaXLKI8VPw8wQsXJSxVX15WdklB4Q++//1PHHWUac+dIqeeky3cGMTpdfWyuUDmjX/9q6NWQBgAiC4UGDyM0QUPzjnhndMXPRKbgAC/8pWv2GlpUKVwMsoGCBKMIGrg7ZcJ9pSTvrLJFltUVVZybQ2n0g7jOeSQQ7bddhs5ndq7uu644w77kB28GWev3WjLLbmOFz797Pd+8tNpDdPsZrT3Urvy3DqadbONN5GqQDXoJ/bPvAJawFv858lacfsyYqMhUAbNrCxmqvJjOrzUJQ+Q7dxwjhl+6zvfqaqokI/30IMPiZO1vvTCi1dffY39etiUXXCiEhCzIRbIkJtfwEn+kcMON5Ugz9Qz0ThV1Ekf5OmpKC+HDeYUURhnnnH6GV//hkSJ41K/OkeEmlpcIlrkI4cfhuC5cu6+5x7kKpW9IA4OMQDrddzZlWCH/r4yTPFPk33GfV+JASlnZ81f8NWTT7nztjvuvuuud++3n8AfxGZmmX3Ohca3DZ9gCi5WfGDliyvr12z8dCXFBqgiwgaSBFmxTzU4BscEIYTMQ0KlTv36adIzw7D0DVxPfGvp5hbrlERP3G7z588XFwNdmhYxZAev5oCEj/k0OvGE+sjhH/npj3682Wab2VuIy2nUBkUur1122gnrLykplm3B7lBvQr54ohgSzHPfgw7st6c/OoJVhWHCfvWrwZ3oYDwn0LS0cJHxWQU8rOzn67iBB1UNCYreaUfbofd993vG6WY9PRJVmIAOekXtUfB4Bl83hzPSeoVRvo7a1yjy8lBGixfWh7fYpYy5EQOk7JZEgxDCVww5LnmPHnjwQb7HY489hlIse+JNt9wsr5taxYnIicDdetddd9n/SYCx/R7AEHTLLbdYmqV65FO04ryTvdXJS909OEPYqZuZ5djnk0/6StoelowMq0Pb8uX8kCH/5RQvYo83sC/hJ0gdB1aJ/FBSCQBSuIG8CdidsCnNmYBIK/6L20F7YeZkZJi4r5+e8ajw0isTxxeV+OYzxKOFXCiayUR+i5ctHRwdmzF7pqCMulmzRVuEqAzZ8h3bVl1NHth4000khLEPXBYAVUprwkEqHaMEVGqBbT7Yd+y9D//8enPmUpKld+a35weP6HYiM7hsI4aPjIP3BFQh5IF/2GURZ4bzwBb3b5x+uuWSPs6z/YkjP27cwxDb1+2YDO+xq15xeQfL3djEnDmz7NK3ekpPYOqJI5CTrrahQcYTbBmpA4xMSLDUmr7kl5Vh4CjfsNqrrJjt9yVlZUISvG5eWHm/d84PZkyf3j8w8OzzL/z297+rnz5NntG+nu6rr71GunIQ4sxaRDxSKkjIJoO9uXzqGWeoHzezIOqR1AXS15vLkhGEnibXWxoD/wqLe0sj5i3TOTwr5pUhitHRqazoQ0EaF8TCpFpVZdpb9t1bkHCa/5bOXo0B6pNXHBsgoRG2KHTx7fvss+0OO7zM44L+HI6a8NL6G28snln8rXuHo4jg0gQ5C5fxIrnBc+3uus8+oYXodHWhNRJxNcycST7wTHLF7bbeRjhi0Anp55HyAOy99t4bYxXaq8wzzzyD+ZKBAYBdurc4uKE4bSwLP/urk1ScMNHdLaJY+d123536JEbL/V9v+EsskIGHeBrF004n7vgKVIpxwElmppDOPffcU/naqqp/PPIoXRH/VaczKmEDOvv70zKF0cy32m67kP0lOk8yiPLZ2fFxyhb7LbfayqewZOHBjhWVkTsOZdRTR0yRzoX76j7Ira9EZs1N6VI5zMC8MzMpt5YHf6LBQyLQaESA6lcbakQma5qqbBnQhOe6YzT9qrMf/vCHaeZeUZuAOqcjKGOT4KxZswyEGDODIlET+QPCgToZkPAjJc9zLzz/j38+pneU3l12341qKw+ZoXeKjAhnTMd4iZiCZ1QRxs55UVE4MalLDc4jFDQOnq132okAKJWOoZE2RklrnhNQ1QDmx596MrzLqRKdTkEZdoAESU7ZFrLOxIQDsfwG7c62tWoqjG5FPVn+J4PfQBgFNex/4IGzp8/0VUIUSY9iLIHNE79effXVRB8I/NAHPig3b1DGMhwyUtje1uEYZLC5gAiZ+khDkBlp9sZCglsnaxdKB/v6BOm12iAtv052EBPJDXphwVaPzXXmiGVeoB4AJqsnIGR8nFREU4IWFocddt8dkg0o4FciedaCBU6rCkBmZEheRYXwm2o15OFr1B9PnFe1rg4ymUO8pk3TusESed4tfVq0KYDpShYa7aI9R1C++93vDnHjGRmXXX45s5eAArY3jm5ZbWDY6HtLYfHwAgsB6YkrToeDfrbdZRciizIgRI3x81cB88qX0IWovzQH0gyWgdAOPfTQ+lmzQu4u4ZeOj7JHo6RE5CTMoEIPbSgI3JILSP5YUvv4+Kx58yQlwvlkdsJzDLFBV4CGYKZvtvXWomSFKXbZIexFcQZR2Cf7C80HSTC7UCf06BW4/tX/0YYzPM0jI6UuU9UOCIr97JkzmY0qysrEhTrh3Ih///vf12tOPxZJBfROQDsgdcHOCztI2Uq+9rWv0UkUfv6FF0UPshiGYjZhRidjB+LIyDj66KMrG+qRfeh4dMY7FElkJUwRrTkN4N577hWkClfxhXTZiaJXX/URK6KvevQ6voCN8sOeGLiTLM0OCa+qImoLDw3UOzJCrbKaoCiJr1azNbyO6l8u4rhj5OcyZ01l4d+0U0NpA6mkSg6ekdXp5V+lNeroEN659c47Y6QMOjQiWXxUpACqC8UitQUJIae6uXOhXb439M9IxB09c+ZMm00Ugi4bH/SCcccZZoZPVQ64MsQdbW0YnUhgOMesLC7WNixC3iM2NS+qLf2auZ0m63tMvVafrXfcEZUbaBtSYi2UxQ1rNdDeNbl0xFSdrJ6pPjeOECK5ka1P7nEnFiVNhIkWnb2H/NhGVSuAvKK8zLmHwsjRKnXOQKBjmtWTTz3pFTqM4HMvqsYiwspjru+w/Xb7M29F5I2G9cKvkCarosBXWKU8P/ToIwwlwQoQTeEpdQHjOmC//Tikly5fbvuAZdS4AE8rwJNZ7YmnnrJTA0kY9NfgS8qvG3qOFhddQDx0e8sMkYQJQ3C4XGWmNnlAT9EJ7dQKYtn1qfwfL7rYIo7IkZlxD+vO4HBdbQ2BTYQ2dCkGe/Z6kL7MMmStCSTn3WiVC+taaDc6NDimdl+JN2jpmX/+kxYdNhtkTNgGQgrSRDi6eXxcejnygDLss2wWzoW2U2w724U4Y8dGw6LgJTXbsjF7dtB1hwN/u/6G66UhNNZmFl/L7Xf+nWLsK93YQk9c8QqexiJgccTqd91jDxIgsxJsCJkmtIixV54QaJh0AwBMSzCwYonNOk0WXGYYaeGZNZFNTFQsxQjJ0Unx+giM5HoLY2BSWeot3Of/rK5FIl3MtvCReJ5bpmmSxFlZRmMuZh0i36yKmbCSR1csu2D39GdOqpaWVmITvmNrWfOKFa2NjTX19aRXBs7p06fjJi0rVnAvqEv+QGk57QwJoglnTnTiH0mUHgEM50vSKqUeySwrs5zgyLiSTRraxG4lP7RhlYRH4icBUOEwL84ZKhnhR0eIKWG1JjdH1vGYW/l0IEHXihWNS5eyEVpvvMUDJiU1m7HK8ffK8vJHo02VccdxXscP6DsJ2+sK50c7x8Dj7Fa9Bg/wiLyWE2zUhluAWTniPX4EGjufHXIbzJzkC4urJiNh0T1pFeYtjXJgWhfU79wO2oAKwalm9UCCr0CVifQNyHCxyAIwuNpwww1JhFYgDbGOq1kf80pKQMT1rTn12ylkjUl198Ch9F5NTS0KIwZSsvKkELvaLD+6H9AS+YjoALYWCiawuYgORkWMc436dbUrsk/nqtA6p4wl04ZweDPclG+7mMSubbLppjYxDo2OPrvwOaJzLKwEQSeP3zk/XmvRnm2ZYLDtivJmbVMnTOqmcwutrMzMUmxpHczx0u4nii4bjeYMIrvGshdfdJwPI7GeIpJ4KH2SNeOxWw34+CsAjAWRVHM8sfHDCy+8EACw51KAWccmaj/NnjXrfQcc2LxkWXVV9bw5c0f4HsXbNzToNXKSYYfDmRnbPq5lzY0UxaDpxYpFXO8qn1Zf4QCQMGf2HCOoO+iK0iUFNLRwE1mtjaNzjFCLnVGrvPqqW9LP4sWL7Y4rKy7hjwre7Gh7m4Wf4E6sgXB9sYGQuKZRO4Q7e7qVR58QDtXxELyq0km+rGQRfm9dtEiSWEK8SoRF2AeenZXJH05/QFRygyMqJ9wc+v739w0OFBcW8gbQlDpNq7KysAMcm8rOiqMYgKdmGLDtGTxBLrEzNT5hKErvSaKN4TSsk4BmQgduRr6HLnhjD/NVKyKHPXGWONZhjBybTEs34pzgWoRnCUsDvaHerCw3FEIU5V2U40XqpXtocqxu4Jk2U0SHNvNCoEMJSIl6Jr4Nct4irimsgJF1869fukQ6/PgRR5xw/PEEVloo/RadSKNKNNcYh8/2O++88OmnQY540BqLmFEAUuxUFELjpBAiYJACG1fMEVewwQb0/9qa6qsuvwKlURu8q7xRoNETPvGoJS++6GFAadjckBG86OPj2KlfIc1qoi3D5BKdulIsVjJ4fv61C/PXtFYcQQwA/BNlLH7uOfYLQOoFBZi9j4wMYLT3BloDsHdXkj1TrzXLk0CT4k4PPlgudwsc9iLixok+nvOdSpPBOqCYMspDr+cqURs0mmNQTVOikAAyHD9WUAA/KM4aDOdeE5keUymwGYMMJc+22gQrGdN/PPII6jI7XMajcdEiSIhtlKjU2Hgrfn1KXcbqBUSAkK/P2h1GnFknmiPBDN3QAELDHk6KNrKR5jOl+icrDDMCrORUlx+eYch22WuuuUYXIM2sBwazHZY1d/bs9ecv4ObVNY5xyMRy1en8ZEg2oZhTWbIwCKOvEn7g8vJSBfh79UKEmtTZeKwZzbyFoxkpG/jRqoYUY5BiS/XuZHBO9hwP+epXv4qNlJeWiqqw/d5oeoidCgTgz2Qc9S7npL5od7J6EPC6oedInAADNY/1R3MWCJix7ITIgmA7GNCQHA1waILIjW8IYnIlxUnnHsse0QJa2NbW/p53vQuW2JHxKwsoDPpDEvCmBvWHdyNqxzn9+Wp2+wzSGHviwEDN9On23DrZUQoJlrjgz3DuXYR2NSiDoohtQufURj+XAIVLwE8i+5TEJLFOJh7pCd4ZzNwhD2BuTi5bCSS7jDX7monj3b3e+U52W4xIdGE4SSs7S0dAQxJzjoCGEIBuxgSmC7vttpskKdjZpZdfzuYLjBlz5kx3wDWTVne3IQsOHnovhEYnJFkksLhYZphsKJPnbw0MBEEhud7KGHD4mxzDDlmNDi4nFEphb9T9Oc2RPhB8FNahmGu/EiMaeN6rL/wUu6O0+MnmTXqqRFh4DREkKJpUTWYzAa85vKFFOAtFS3mrFIsyFkNF9BC/I4HjxRY2X50l4kVphYLeEnmKcEMFtEzcJEBgZJZkX/3qq7eU1APByeDRlgIuVWlLMTV3dYfze2njhGZrIfXDugsAfJPDB+vv6+khMirMdyfQrqS2xlFMTttTrUN6HUHcvGwZqHzFdgkl8k5x2Kqf6s0KQBhiUwQJyYYtk+ijmEMzFBBoqKEYDKKkJ6Bk0Xdqn8AbAJO8cW02ApXz8nEl6yackK70IghYkZtaDa//0kq4Io1RtZY678KVFEpqA5vB9UR4cDBJDA8f9bEjw8NoSZM1RJZuSRB9JSgzrFJ3t9hMLomteE233GgjGjVdXZSXAjFpGCBtxQubh2teOiJITOgRZYgF5ONHHeUAQNpvSlrySPIgkXBOGi0heT8992e5xcWQZpTVaZGMJRW/WpFiknAol0EPDYnvUib4gwshDR0y7UOgh4ZDuyQPSLYW+g4lJEVRi0i0oKrKcxRO2+E0YMJQyZqQx0/UE0ZhfJzZgqcU2QCGr16oWEy3sOfYFeOuPCFY+ERQyZyXU1P7yRNOVC915PZbb581d94nP/lJeGhavqwn1T9/k00WL3xO0prJ2oUcEeY64LAKZShUcLL9TjtSYgVTuGe01ilKPplYByerh4bwtF3NQ4PxjJbIhynHJK1vaCC4iOOFVR2ENCZ/URWBdjIyRQJDkQ5Cl894FCZrYlVX8EpGwcdLPahuaHAuGkGzP5qqzGQ2ZcEYZy9DO+/lfoccvMGCBWLVljU23nnnnaUV5VCHaygAYJQA2yDBN3w1yDoLQshRiV+Z8UMZeJAgObJwTQakLvAZ+tVYx0K2+6CNyCpXXIzfUaJ4FG1PgBBSHo3KWKvcdg/4MXO16CdxsyIbvYvS0nbn9vYpoxLsBfPEQ20nQS2C2En2Tux0oAsLhfEiYgrXD07RkREpACaDc6rPf/GrX1FWf//bCwjiPMASrZFTIWp6bd2tf7tpnz33al22fIONNxkfGSUZF+Tlift526674V24FoUK6jAEofXye2GSthNzqRHfCb7kQr+aRLiHweLJGRgeQQ/6Ys7qMvwgIYMlZMapuWalbIVGgU3z2aee1lMnhSKe2OGzsl+xGrySTlY+f503QuWdsxVwuGyZEQFA7EolMYe5mZkZO4GNFLUtzhr1OmuOi+EESAXYcKiPYY4MDwvURxIa3WwzmBzh6oeEoABEfEP6Hz+hTGtceCWy1zA8qSQgMNIbkZAuW0dELIcl0naV/n6MKxjseMaCiym0r0fK+1RSxAGGEwxGubk333wzLYLv8arLLkN4Lo53bTnyVUI+IdeIj84Td2Gqn3arYJhArWtooA3LpwiTBlr90fQPCFEnPIRer6MrICo31/JhoWFDV2sUwzxgjQaJqeSrFvUaNj5w6PuHhnDyvrv/fifY0Jgd0Dfc+Ncc8V0ZGcHCniUIq99zzFZVUM5o7isTOWtCWAJ0gZlgfIzAo1OI3D2jLGnEEBuIybqlflM+/EUy0sv3E2Hrjd0NX/jc53F+gcFnnX6GXpAHTLRzf/JTwHj4yeNP2HqLLU0Evs2VL4Z64gqjOcAWv27oOT6/OiuLNq5rumNMZUN0DrxEaEPd3bDqEtSGTuQBRXlh4RgbLy8p+cfDj4CcoAYhWKuJDLQgsEUCgy05jI/C7OHQpIjr8ek+8u9G3YieGoX4MmQU72uvulL0hP6aHYLj4iVSwdhSqaR7eilCgOSe7l7CBrIPll+LTiR/Mprjrgwxljnw5OXmMXlY+IysaYJB4bHelYFPXJnTBI2jag0ofq6SkqoqMBNaiBZIgrt3xdJlhAR8aZuttt5p2+3U4/XTTjuNePPpE06485ZbwpNoziJ1qNBrqxhEqVZEnB5PRifJ87cMBgIHT663MAbCNCY6ZGaa7a5gb+MLyiJzBq9aHN1n5gdJ5RWj78p5v6oVH6ewrmMTCvorLysdSqWDmkHvZaSPLNz0QromnouhYGE9kWmcF1TomqZxscCwQnhevkWdHOCeZIZNA0X9hEbFMN54OLhccDR2U9Kq+v3kk5SzUsjAMT30oks3bQaOfwoROwXBU+ShPlobtOgn/kNVczLjvH4KkLP5OZ0lErU1+uIzz7BGM/fy4+GzfIkMq7HCjNt6yyf+7sBDeBiWQztKNKoeWI2bZhOFao2qDR4sA/SuvjanJtwjMufYo4+m/tmYBzkUJMBDSJCkMzO578AJnrjvr/8T8ArTzDWq48zh1mZCwG233QZU1RJkfOUHVr8NS/Ixclfa3kaTEbXrLRVUVpY/9fQzCrCtPvPC80zLtlwuXPKS40OdqdvV1aMJIcAw4NJWvLytFUgmZ00vXbbcjmMSichVRgc9RXWGklaBqJzkPHPOLJunQyRbezunKxwGect4R+IXgkJj5EVedy5ifbQyQTK0IxswW/V1U2CAe5iHt4LycpgMrdhxmp1trRXA/P7993cG4CZz53Jfy4fZ1tYB5uBFMWSTX0RMuqKq5m60kQjMmGbYFFSLXH3yctPBeFDfd+BBUrCU1dYNDQwKOyMYHXzggaaGWD5631/+duNRxxy9x157nv297y556cU589eTo3iyZvlLXVQROx5PPfkUN2KwG2rrOEOOOvpY8NON6eHG1NwJge6TXNICBYqKRFh+FbMAlYayHMhR67CNHmh6REUyk8B1eEbkJpGuvWxuCC+s5TIo5Lk1L8+o5Z2OXhsK0fUEF9G6u+4ccsUHIqwop2EwiEBaW0uzY4rjGn513q/FRPAbENoEs3EU59ulTrIcC0d0GlaDu3LQgacq9g/v8m+DBMEUTR6TH09VM4tA4xVCqhGJ+UB+aamq0JLKyU+Ik71KGY2aAuFhFNcKeOQXP2c2IA+ard6i1ShpIqA0FIuklbEj19b6/d/xjg3XX39mba1tF+JjRcuPCvPLzUU2yqyTCyQjNIKMCaI2S4Ga99t3v1//4pc4+e777IPZ1s2YgUSCUI7XjYzqTCzSAZhiT+1vWrYMBvTWeJtuNGG4Ihx7RWIbxaDJjIMWAFMhaGWUZ9hQGM0YFyQEFcrQ7ojKaIZdSWFj5BMxhIFbR5dpKJpDZc75JJxSNQVlfO5zn9tuiy2cGY6AZYU1fAZUGdLzVJs1lFYc9BmA16kogS1pm2MZY7QKBCdzpEeF9RFtCCqJFi9hOwgATjAitM30oyqVwAzKjOHBrBh/ve6rGQ1jwDM28a+lJcUq99ACGsh7YOC//uu/jjzio8x1Vpemlha7Ng474iNcWMJKH3/oIXEczBNSFSBaNRggS/JU+6t8bh5uGoZYLLSq8BMDFr5GOjATGV8+ssE61uE4QgvdSLtY2U477WQK2y6xcOFCEx9Id91zN/KDxo98+DCxMLgud6vnXHasVMBAdUxmIxnj0+rqF8xbz8ZsqR1iImfWNnSIHA4tva0d7bBkC3Gqu8tUDWM6MWEVFhBh1bBycYC/gX5JaGJqHHnkkbOmzzDowlgk67bVVjqPX//mfIqWfp144olh+sRhvZMMDA/EOqFno2O+oUkqooXfeCEJBEnY8BCEYaEsCDELtFPxwF0dHWFfeiT8IO+wKHCVFwqur7AslpWWhPU3YDBkb1YVYg5XxAPD2AXxMFRLD46meDTbFX7lOSr6+te/Lg7IwSKiwUU62IjU0dkRmAwDkxzL0evNjU24lrdsImM1kGXAggUMp387Thw9a8Ig6p0IO68oiacF4hRqFO0k8oggF2YQ3TVjQmiVgCu2kSDq9PfHC3RBvug3mxcyeBpUYn4hD776vd62h4oo92I07B5/x7vfJR7byq60qVVaVaUhkUHYfrxSxB3XdHK9hTHwRnjoWxgdb8GusZyGxTsIuCIBrXnYwfx56xEmrCL33nX3R4/4KIayclUIzCa6VlWAPaCRWqSxeE4DhfEIywmGPjo4VF5blzMc/CQU4OCJGxtdKUlb1QgxJEU1YK8RDKEFD/FcB9tLveQ54WwsO3PolRdxXDWIQmQ5JLtzDmq3f3CgtKrCwh9gEAvL0B4Z7/F6LcZce5ReGlLytOGqasBPPdeWT8khMFPGAF8DSw0xexn0FsCo85bbbqVj3HnP3b7WVde0dbTLlYih+wqYmvIK5VO9fdlleHW2cFwYsC7AAE7NyNre2FxdWi57JLaeX1OTM5AWwjpjvXnl1dWXXHjh7357wd333kNs1XNhQqoQ7aZmF2DgJARdOyIi2rgbP3/9n2HNiAwcwWQwMixfl0CyK6+5GjLFDh1y6KE6SwEmVKnTpt8imrngn6jjOD/MwDEt6M7b77AcIgkKUkVJKWB6u3v4bzv7uyuqq4AN7dxD4RSf1EC5iOhJttRW1db84Y9/tFCxqzqOYv+DDmxrbjEc4HRBtSQilq4VzS3Bi55OsRw7MsdgEWgUCGtwFLPqBmz90dbE8qKQisxzaLekgSTGD3wiA7TIFWbjt02YnLwqpKM6voV7dmBosCy/qH8oHehvMHw4ywMwdNGinCB7rXkZjrDaj48XlASPDU+ms6woM86SEaC43oL1/37rrY0tzSQq+45YScKKTmHOybW0AvG3553/8D8ePeXUrz74yMOxyv3CosW/+d0FoBL7N2fW7JXza7WmZVy3fpPVqN+nnnaaiEGCL+99amiwoa6mpbX9Jz89948X/uHss88+4uijdUHQ6Wo1xF8hJ+g86DZLMHtRbDWA23x2LtnvTMZItzHjTECzGDzw7F2YJCNCuysgYfJrNTV4ZY9sX+zsaOfCghyVi5xnkudwUKGm0T9qz8kL4j7Q7VP92y03v/j8wuAZywhbznAPzAO5Ap4JRvuBOKP5Xs6T/2qXlNRNiIEiNymYjooplxLPNA/h58Y+yG9RaKX8e97Sd/u+AIPqULsuw5V73nhWFnH5FELeiSBrSgQ1PpGbkQlUWIJZAXteoZM8G+1/Y8pZ+OILeAvCpsPjG+71Vyt6qgZakICWSUGdyg889tL+Qa8x5cYnusVhOAAT5h1vwGPJQgOaD7MjJ9u5RLpGvXcmjdRirIxogJdDDVUNDXASKCHsMghx/mCxPTvoRcEoEAYOIXV3dlQUl0KOr1Ip2vSBAlNjpNZQTDRNnH3Q7I67svIm/moArCbIJszhKV4gFw5tdO667bbv/eD7N916iwpMOr1AABiIr1RxXMvNa9PtWltGAPHz8G5EDNR+T4x1b38Ib0ZFbY2NtXX1kjDHJk6KqxaFpfiJ07uytHKgI5zdgqJA6xOW4FEOHv97UuJcd1tgCgssnGwiKEcZQ+MVyIdeq5fWGfuYDLn3d95tVwkdZN1TwFx4ZuFzZ3zzrFtvuUVapu122EFuRV59TRtQVa21U6/9UIuGD2BOO7RbJyyI0epAXYcNIHldGffUUefTvnZtr/NXtTE7aQuSWZZt7GTNkVXB2V2EE9xVPdRL4bghv1FZ+dv32tt+4Ntvv72rtZXhhilQ0ixlcN0w8aN9v5zDLLOoUgAHgMO6MN4TcA+94tXFNwkhEWkVcRJv6QvM62CYklmTs461dcmi713zyxZ65lRFDIeAqXgruIEQE8RiEmCIhnVtdYRn64qe0cY4coTWiF07EyPL+W+YQBQILeHn4GDwsVsCJFMUgh9Yk+3BsmNE+6tF08n5F3NjUHGZ0uHFxtfV1nY3NlasarOL6CGmCiXdaDH0JLrHEcxEm3ekiBPDjOwbZkz/6c9+Bg+FmcENYAjQttEZGRoMdJ9vIRqBehF2YZqMjzOfOYVLIWZSqPPiYF8vmUpD+qgYJmPs3CP4YG8aDnMtiDHRoZiykCpJHPSQ7b66rladJq9DniwxAlj0mgnAMrTNTjuJaLvl9tsY0YRKwYyohAcefOC+Bx/gn7joj39cb+ON5W2RcM7iYnXRQXERkk5HfU0+3rIYwGOT662MAQxLygH8ir8Ci9TVitIy5nPGMJ5ZQV/0B1xJMt6wjSS6yCurKcDxcysHDm75x0ZtIKmpqlZhuf1+Un1QkGpqsBv8KGZY+FFF5IFRvq+rK3DqKMcAxmQnF3GfAkYF5QQObtuIn/ITcmGxPGJhOKPaFKDbZJeW4muqJSXTOS0xIzanRppSACxSHYNyFVkrWTylFnwZkkgKiZ9TL7g9gyIaXWG1ILGN02x7LWnvO/QQ2TKm1zfsuN32FrNrr77mnnvuWbpo8WUXX0IZtmHSpSOcjLhjvOpogm/bc1tHSCTkyILqaobnXnGPWVkz5s61ohxy4IHHHXfcnfferd1dd9r5m2ecSbGxW5LD/C/XXw92WjQ/jAqJ4+DUzdCjqVxYvzUmvJiZSaglvcpzS9hlGxZDK/JKtUQNbg1OCptXeRCo8YRgisrc2XNgklDrRU4VPbJobb711mJWVbv+5psTsmWt8NBgaYUCzIgOb+L6JoPR5qgrr7xajmFxVZjL408+ydG3dNlStufmlmZGepe1R/wkqkulBoh6cMizBAku42J8GCiIh+iNrCa81lZABhFIy4soChFWV1TSNNArtJP0rY7As8dVIiUOdgowKYq//bRTTqUPP/bQo90dnc77tQ1ALDRatRJPBj90WYcVIJKyTUgzJhKSFLV4yVJuWDGNKtQvuHJurWK8OhmFQdWkLxUUFJZUVppc9z7wgG6eeebpm2y6cUVlyJ7y9HMLP//FLziMcbJ2LckcCAZC9/WIDnPWWWcxY//ut+dzpztvCQwQIkjhqUcfjafMWquCMbYMZEShlV4rNnOoHIWUlpeb7PR/wbqGgKBja0MsVQS1OfJtagIA5tpaK5/sYWAXRnt83KwJWJoY5woWbmDGwUxzWyu3j+FeuoJpaPHzLy2KlSujzzTQwHLR30+Z4Zx0IBermRxjOsv3aEwJlz7VrGlRoGwcvqJtZq+gpUTux7VCxXqCj2gddZGTKCQaRSdh6BE3Gbq0VDeFCBpKvWYjU5hjBxGGFiPNkElIID4YIFMr3grMI1aMBXPecQe/kNRTFGD0JiHNd79zNn0Y3+CyOPrjR8EtQ4xW1pUCDAaiKnjAjCzF/uEbhpKWCK6YSbIzMc1o1CnckBkmSKTReRj6PjzshmavAEETH9ApawFksr+oRN+J1gHJEQ10d/fGrpWgX+bl2myZXVBA4YQjiILbksICR77TDcLEiWxYZofRWeugEMqDQWIql8TX1A9H9ZpuFGAOt8032RSL5nUP8SaLFjs1gEkR/GrFTqdSdygbs9xAv9Ggw4C9lEE+juwjCEOnYAZ3RTwYO4zxSOu7Fz1BqFRiqDaDrESeYGHwDAkQYmjkjWB+YNgKhGNbu6CDyNoCvUwTZHQcD3/wq1hVhjDT5Mijj/72t78txvWMM86YN3ce1ZzP/94H7mf/Wrp4Ma1VN+XfK45uptpfnHloMOx6CBsBaPvRQGvdxlE99Zxjn93EE1/xwKnWP1n50P3ogsm3ve1tQot1/E83XI9PeOXvfw+5juSkgLR4zdpzzz0hijlbSD+uZZnwE6yy8EKpg6wFuUi4yE5VVlykYjjUHdRiIOIcxcw9/X29sd4F7cEyFem9GMtkQL7Gc2MaZl2UZdDJxqwwRuTcc88V7CNaB8HYtAJgBVQCdWutCpmtK3rGBAI+osgd01xUAJo02cE51GdnbIbDgUkBZmKqp8dDgImNMr5w6LR7hUGIvL3i1n4jnFlsPFZcMX26zb0mBgqA+eD8jfieV3QwftEgBsOWhNKjo1q3pFoWO21DKy2VrYoVSXOW47yyUtktWb1NBpUYcVNJBIM4r9a24K5HFaYASJCdX7HZohLjWkKmMne0JesHkS/YnphIZs0yiBRUPzH8GW4TVQ00eQzNDQUYuMSVjs4u5T1xMe0htrrp01e89NJGm2/+qU9/Bi3x3rNuex6OjYj289vf5JB5Ky/EggTAaMZA+jW53toYCEpRcr2VMYCREH3Gx3A7R9GY1S3NzSced7xIHizg0X8+xtTKtorTDQ4N4i/YHLEeGxIgZp3GRKzx1bIxDw2RM9719neQPxqqa5o72imNO+y4k013JVXVnc1Nln8nGHmdNIy3ig2LxS8il/RUYYeJLT6RpRlDI3yTFXrb2oMbLTLQ5pWW9XR2aBHXTY2G9YxIWlRWjn2PyuMS8v6NdHZ0WO3wKP5eDfGucL6l7UwrI7tWW9pp8WEJjDY522dCYVMPwII8NzHx0osv0rYxbNoRpolPe3j2d797+ZVXWBA23mQTcpXQWRxQr1lYCbKOZuFLrCorhxdoKVdPFN6JYMjM9HmXThWVlmeNjouxtmbAkjjCzPy8U089lYgPjbvtuPPPf/kLui7zIvGxfvYsRgFKvrd1x/ph4ZFiJ0M+iFQ/do/fh+i8yHMevtGJQk9DSc/VoFN+BSE4AUBkpS+Ssy08vtKBAxL6+q7/61+84smfrr9eW4IG7Q7VYlinQdLRYQOwE0ClZmElXd7UaCGPtuqQ5fLCAmCREykXNcdvXGS9jPDpdEqLIeNFfmVlX3NzsBlDn61HhIy83L/95a++2RAl3vuIwz/ixikLOghjuhZqnhhnv6c1nXHGWcQXexp/+pOf2OOnUzAJWotlsMbw0kf7/RBY1ljYwRXI2C7NqI8d3V1EQ2uwnnK/84ZZRFnoSSTk0Q0XrC/SST5kh17ahcoVoColdaqurkb9RAFdLisplekSQpzcNTA2Qg5Acn4V2U+2yM/L5Rghbn76058+8bOfARFr8dt2251BwT4lo7y/HMLBgpDNtl9aUYGWyA326QV3OpNQeflHP/axz33+8+ecc85pp31j+vSGv/z1xiXLl1WVliPdgF7Wm5zQWbCBQYX2OPlJwKeq4FsB01BcGeWKuC9BF5XM8k/PNJT0OgXWvJBEiA4NB3NMVFVWkSDtlNOKKwS5DQwQDeUEBh4BVwCtdgkWtmZpFBhl9rp3dQaciP2LXMdaDDCXlo719kI+OtQo8nOttJSRh7wynE6JKyPLFubm7bHHHlw6tXV1ht6gE24UcLiUcVzR3ERPvuaa64jjJCeZUUqLS/yqCr+CPNKsQhiIJpBEDDmU0fYUMD0Nn0B0xdRspHC2vPLSUCzYJvLQPCSEr9FmM3qJ8gMjIkSC2B1ej7ognxJZLZSLhDMjQnhy+BaEYIa+ugyNNPq8GLkOWOIpxUm8zgsBzsJCe92XLF1COz/8Qx9msHBWnNek1HKOCyHSu2rm93PZekdId8NGBhiv4L2+htk9iZilnsraGnotgHUTO8jLzx0dGmG6Qoo2LutsSFDPZY1nstoIhxkeZqxDfkH9ysykDfBkKvz8s89tvMGGtvkxTGSN5Rg1rD7dlZ42c6aQkHvuv89ZZIJc5s+dp3fB/mRfqzQBQ8MhKU2k4wlA5XcuLCvvXtFot3/FtIb2lua84sIVK5pwVE2YcWPDYfjw88C4cKcMLvxR3NskdSILs4vznFGd3oNOGXxAAauTUYBS3Qw0EBhJZpZ0jXGMujQH6bQ0418/4xv1NXVV2VkygR35iU9gSngptAQMR1MJVySeq8HrZrGdEZr3UIUzZ85QxvD51VpAg0KpijKRpJcvs9ygmVFxMlFwqaASZCOUXW2asEkVYIOpgaLikOcCOTplp6iqUohFd3PzM889W1NZZc9CX39IuMjnK6Fgb2TwNcRg0308geUUyWkBSdA3NAQSXDcgN+qv5zyuclKw95mEKN9yJvZBLl+XnMNfPvkr8GxzBwtvztKl0+fOG3IC9siI/efGUot6JwCKMoKBgBP+8Sh4DsQm+qOkeJyaHeX4gVjDak0PxCyAKDK64cvsfZzOgf/k544MhSOXoBH8Tn6O4Vzt05CpWfkwYHoV6VQqN7IeQrhfxZTa95yZkyszPwjtXGBvZ5zaAEvadNP7Hn6IGGCRZSlrlzNSmNKHPgQq081qe8D++3/+vz7v4c033bTHXnvd+Le/GQ6nXtM/oTRwS4b7vNwNN1j/uYXP29j11LPP7LnP3j1dnZiJfNaYQAAgcpRiQWz9ZlxqIG0VZsc0Dccc9LW2C/CCp4N/NfrVDWBCD3Nzg+JdW8tp+aUvfvHAg99XWlj0g3N+IDaKg/raq652nJhZD2PmVGnkR0WaiF81cIW7xihlYZoSPa8NxvAMdWXbVs3aTnIIw53b15diN9lmq20dpU7lhR+mLHo7Bm6PSW9X9/Kly4g9rHWWFXSXV4AXQYZUC2Ge4xsYrM6K8LI5ha0n9Doe1tDgyxdbA+RzhuQ7X2psHCHNnL/eu/bZZ8ny5dMcK19by8mvaDwr/QcGEhcyAI/aeM79avEaGxl99ulnMBZclzhBVhRBwxzvxZDEdPlyu0vC6RAZGfFBAKYficK7hhXBYP5EJqRj64eZ6Fgk1sCiivKJgXTw6wgZwybHxhesN9+IeNcmjqqKygDWyEiN6K3i4tNPP/0bXztNEvLLr7oSnfDqf/3003lczHfEbA9zoO3k+g/AQKIDv5UHOfBdy5MtHJEjJXC0vHxME19Yb/acxqYmnccLtt1mGzM/CKCmfW7eYHs73l1RXS1rn6VL7hzuF/orRuNFh52SYguyc6TG3WD+goqqqvbly6We4n7qaGrCTYrLSu+44w5+lRIL1eAQBVt2e+4LeyY1YSMtj2SIJZNxKrC/HJFLI6l+p2uyXFq/+YELc8ONphl0y4qK8VCHFhbb45EfFE7qHgbnPxyNyT0nK1heYy2LN9s6p1q/0o0xVv2y01KeDOGL8mFgjvU1NezQgUfn5y969tmzv/89cozX7WPcZKutKFTkJM5tGmfdrFmSIfHkOP2PRzSs77m5FhgZZfBQTkUqIvFF63xrEv7mZQV4iPJ8K07IYW4k5zkrWJjQvPnred2vvE9dbW1s8MaCoMMeYYnCzXtaW/t6emc62D2ELWUURxGVILHwD6bSNrrEDdlZjU2DlovJ4hyQwOMXZackEmYKSoqO0pFZ5PcXXsh7cOmllxoy2bD0NyhRkRQ+kh7obm+vnTmzXXbEadO7egKiBJ7RRlgxeru7KELwxm46Y948uVd5mJa89NKc9dbra+9QsjCvgAbPyU/GrGho4A0h43IWBdEzO+uiiy6yojLPn3j8CccdfYwYbwPd2dRU1TCNoUS4sl2pRAzK/A1/+vM/HvsnBsRffYwDk8JutBwSkh5BDtzqvoUzLNjiloGeSjnTgFFZSR57jlYuQhgAqvUPtfzq/POsqYwLYr/BA1EiC2jeyhRkhhBoLo7W1nZnzwZXQElZyMrV1w8/Q2MjYJbziIyu9UCQtlMKIqUrVleTupi3+YU4KxCJNRhUJ594Yk9HB3kra2ycMqkSYgiBoKq+jg1o0aKXnEzIbi0q1Sp7yy233Hnn3VVVFabM1htvJuCKCINK6SRaC1KF+8hfJ4n0S888E4cGkON1me7evHTpu/fd12az737vB8qHOC4mqklkOLIRp5w5TnsnnQtIdnAXozovk+Rw5oECiASGhRcaBec2EcCDaEKMNj0jdwrlAb4hGtnTnTKGRph8ECqPFVMCGPCVINxFV2AycWhxOi1u3D3PAG/8O9/zns62NnTLHaFFnaW0mKEqb2ptQW+AeeyfjwdL00bVHBXtLS0kRdKiGZceSMuFFoSn0lKJwdGMd/EEAr6BVgbRGgjaBSuYH5ht9Ij6RtUAuUYL85mNRk1n5BG7JZ1khpaCSl9Ty94BG8YXtMCg7dviAUgkBwkqZPJz2geJvKg06OdBYeOCEIoSxMcMQcA3XHWV0IaK8gpJ42gpwQOJObS3S46K2SI5qKMAcz2BExhCWgiO/rwO4aLrYTIMhO9ruyTOceaymS6GVhcUw5wLStZu+FCBVhTTwag3PLtjPGbIleGJYeITJ5zw9GOPbbL1Vkq2NTXxZCJs2Z75WCTJ17vsicx4u6AAb61GAmtokTOorr5++bLl7CC97W0WBRkWu1taSMx/vOwSR6ObqDwtNnDat4lEYB5hxMEFgNdBU8MVpNWZs2huBcRxdsCaWr96jp8YODeEWuj13ACpBAC+BkaXXwC9RfmFze2t1//pz/u8971iIBGDfrlUZfqYvO712ayHeSPIWqenemej+7LlKyAHWuAknDXKLGjDv8CNzs7IYR54SEkIOSqi59MBnAojGVXZGd/A1kaGJ3jP5kyf6cyCGbPn2k5aVFPT09oCYC2i9s6uTuYY0rnp7AmXdZmtp2JgqOKRRQ81ogDAgEqgB5USVg0Tc1CAeWTEDh3B7YySamCX0R3bSUDLrgF7+OenPvUpduobb/qbGWQDbTBXxcd9IdfOzqzxCbVJ3m1OjqZT6tQd7cXeOV85QmU+E5/C3gddeACa05bxCvNaG+Lqu7vHnZyYK4dgscnQUFPN3IYZ4qVVZaWAX/Myaq7/fq6eoP+KAC8wPRk5VIWPG0G5J0SuDmHmMC+iSGas9va99trr2ecXGherpEU2TNjCQhTIAijmVVXTZ8/ebZdd7Yi+6667Xnz+eYftsf7s99596+sbuto7SspK7eHsamnZaostKUsgYW5mrNRfo2aXafZoNjul83Ykq0Orwc8cBZbLp4UdeajMfwO/yp2OD0dsma4YP0aiSJGVGdURhKwp79pvPyff2uFiIjsw/KD9DwiQ294VZRoLczAK39ULCAoJQqJd4sZdPeqfEj2vAtqrbgFjzmM4m++4IwvXkwufs81H6uz5M2ZX1NYwEuGfBoJkUlNXa3KtTGTd09vziY8fJajKJMX/62vrqOr9o8FQGCy5jErpQUEOLBGvau+VL2EGTUz09/aQKnIyc2aut94lf/jDY4//047ipuYWnhUHDnPeUjUr6uqCSpnlkG1+jhyjoA7sQlISazdaMX0+eOj7ETxzDzrJLs0i9mWNhW1NzimAWOVNLtYlKywxw4liIoDAafmwaqBPEpRkJwTUxsWLGSe6uzoxqKuuvUaEQLo/vf1224hOxwEaagJ7FPLT1tKCGB1h4k0cfjxr1NYDRu0wa6QAjEbNhGW8MGSB+7EGvtLx5P+3KgbWTuhv1d7+B/aLrBB6HXFhywDrLrGMuc4ZG1IKFecXPPjwQ5/+zGdYx9l16Rgj6ZRMp8z2LGH4nUVo37e/3Xm/TNSUN0cIckxh/cTKr37l5CASTUzgbvhLV3Mz1lZZX28Nln4AD+1LpZy/Qi5x4SZhCbEiFhcSwsmaQdmL/GZAEkVMBLEQYog4spNk3RDJwzpKy41cBLieNUYNemMJx6p8tcUIewsG10jOUNLzoByOhRCd6mnTABMOVh0fF4F8/0MPUtqF5h750Y9xwXU1Nlml9EXSF6mkp82cgZNSOdRA+vMKuY0QwGdSW1FlJcPNI79iiS3F7PGECTItIQbYLvAHISB6EZ7B39bZQbvu7esNfrnIvrB86VJ2Vwqw9J5qbmxu1hesPJbqqC5wqELyk9rYrUlaBGtrsChQcjnp0EkPWgEb353Uu9ZUNXhFx4mBGHfofm6u/KIwACp5RGxDJVEJ9JToK3QtmFaDgEJM5LCiq7S1B/ycdtppYOYW0woghQzMWG89fukgOJaWUueeeOwx3nYgsXZXNjRAgmqDsyCOl7P9srLy6SeefOThR8Bt5dhp+x00RLoa608RgwY7OkPT0epi9fXk7Xvvoxho7M8xTGqTA9YnsIFX6h8hJsovMtbfb6nTOyo6CUOvm1qadRASIMpaRaflHeUBhhzrGYSgE/X0OdK5rw/MlmQIJ7PxA1sXWfSNECU2DBlyKmS3KXNHRvQVSQeUhpCEkENu0623ZseB3c6ubu5lP1mJ7awGD7zx1ZJ+VG6kAEzOcwrovAULAiVHfi3g8YQ7rLqrs3uLTTfzCtMSwdcN2qPvqUfHxSKaFDy0diXpP5ibFi8GS9OSJQ2zZzvsiE5VI0q4TERzeWtEOQHUNa6Cigq9EKPLcCNinN8MzqFRbIL9WvwYuhw2dU9MfP7zn+eUQGbveMc7CBOEPI0aCB2UW4gNwqSi1dP3KMagtd23jGIWELb65RkDxpVXXbW8uUmdc2bOctCLjNnuTTR6BfpUuZ4iJ0M2e8bMbbfaWvcRgN3yTctX8GWJOsYoIEQZQjzsKc9+///YuwtAu45qfeDnumtc29TdWyjUedCixf5I0QIFHjysSHF4uLvLo7gWl7oANeql3kYbT26uu/1/+0x6SG+SNmkj96ZrE073nT2zZs03+s0aEZkFhKlO4cMyxSK/poZGAjNbsauPHSC7Zo2Sw3Rv9GMwrQwbhsqIlL9kKk7kQAZ6WgzDLO9kQgYXNaFDmne6KT/Kv4RreZS0HqcAFBVXlZZXl2XbO9vWrMH0QGRfLk5il4RiZqzZPHXK8EBm/DcPmGllXqy9XSrkvuk/VdgcRyrk9M8KbS4jfhlz3sxjzOeLEkUfVQzCFp50dnVvxnuOzY3kVCYh4DERxvxuQP+va6+97sorTfA5kd6lcZNQrN4eo9gp06Zaq8InmSYlz3rjGwU37WKFiUTBw2Se+UpWl7l7zLPuWuHRKaxdscLsALHf/d73OjTx3T3/77nPk0wVTTVktJE0+Mhcg0tyCDc9KiHDvT0MfGaFWP+Ab3k8/H31y79cAIjGWbttYTytyBRRWV3dlddcBT5byXHaO7VCTU3seIIAVwcEW7FoFjSbqqEcV5VknBfKwVwUpoT4V4rWittelbxWOh3VELwqMR1krl8FSO7rH7V4plSE/dSnPsXPrD33tE+yRDns7JTL5qe0yYz/slgUmtasqGTHwq1D8IiCpH6XDkq+d3Kyopi/5iCpxA4sN5ETn/iBHrIqIv2sVEgOd4DouMnUmqmngM0yy9rp/DmOxAqYWbxVK7eIpZvwKmwpqLPdSZnXL9NELwUicwCj+SkJm1DkKWXU66wB1Jpl87kYsZnS2kxJsyRrW2SQF/XR7yYfkXqo52sKlV50pmbaNM5ZMvNHEtAhm1wwt41p6L7zq4EgBm0Ti85NuOHmm+S1wxoJzHrA/gF7TNtWr3n6U5+mMJiqM4co+WZqTjrpJD0UrRR180p0xmmF6uvrt7oVp1ISdK/04dmaFzOqJGu06WNaf9bMWU6l1imkYrnJdNFTWSVB++mfF39yVH5ArYmwm0dAK5/NXFBv3tzdTFLMZqLMmw0lSsX2C3OhLDVS2SGgKMpEjltbnjepJEfwWjTeaK5z1SpDMu2bbT7WIQPZdIOSk2KkMGUgpkcQyrS4bpdi1dU1co5iaUDlnU+jKTL5N1zZXLzMCcoSTGSwdsnpCQ5Ssfa4o7Prla965QEHHyxqotLGOhMbtu+2rmuxzguSMDTrZGZc9qkd737nu0iwh05OsTc4hMU8Dj+yzF4A09l0sC5vn4MP5qh5P/CII+zY0tgaVn384x/31VYZ/ZQpHrccqSZag/sXLfrMZz4nCY7Fwpk1sLN3392voaRlJ1NmzDQZZKBy75130oEEJV8GAdMMS5Zl2aq6bPWEX2p42RwO4b7LIKAoxrOLI6DbkEJ2wqx9dORAb6/JSEtWnpxvDfWs+OFpp55qJ6e+QfuoCWOW1C784Ve/Srsr3Xv+mY9/XEtqWvGjH/2opt+aNw3NM047rWNdqwBaUyODfQ84YNX993/2s5+95LLLDKB1esizZi67bi6j4dncvVDejRjMOZdUVNj8YUxpmaUm0ice2BYmNzd7MWLQBtlV4pOpRIrlJ5TrdKZSofHSSAniXXOW9WpFmUmH0dXWDtuzmqdOTWe38Mn+hgzgSHbu2ae3+5570iobNuUlIJM4LSFYhkvejSFs36KhWVU8waKytW3rtI+1kydr+inGgkQxnH369Km8cdFGG5pkOrvBNe9CuJE6mSxFZiJ1iaIwaQpVBJh1jvk9w6EkO2qbGoaJZOrUdRtkWusrF3T/7WvWiNTqbiYCy35gKDkGrOqtfkt/wyVhq4dJ2BrDMcugoGZSRWQenZnrlJNOOuCQQ6DNvCDS6obsHlTZwDi815572JmGFJgWsUgVFaePVKxeupRK1P70pz7F0UVKOhtjI3RfpuDtWd4xlDU3o6aWURnJfvWrX6WNVZHHHnW0wz+z3YNFxQ6nrTCn7hTZGTM717Vab2k+3DUzjgB1erD1RnJHSuWypFlrIBU6VKNrXVfKXAmEBiKlmHnnaD5CbhoYKRUgZXfD4pg0bT1VOHlumDrVanyk3cVIZpdZG1ymOnnyJHZg5TxLoLtGnUPm1G5Pb3dbZ4f9y7IenvKXhwxcLCs/SLX+3/YoFOsOW6pyuZNPPtkomcFH1J2rVpqTmDRvd2ztk5/+FHMEDS2HMwAFHW+U+eMf/+xFATECsBBAN6ykCUtVQ0ax6IYRp6b8nSvf/8Y3vvLFL8KZZ5goDAQipVZit7r4q6PTFV8+cdz0kzdKG/Hssdvuznw2ns4GjvkdDa5XYZAXF54mp5gxlTfF7y1veQt6gLNR2BIA+Hu3tI98aKh6cjyDxXrOtP82Gyevf1g60l8KgK0E2daGXM4UDLrCvmQ9RVZfrAowrmKRt1p8aNgqbTrIDiNX815/+v0fsESaVKkpS+4HSzZEq6g0TENIjOREbZCE5QCcAdxX5JOG3MmRTRZ6yDgw4u+MvfLOn3DLZE6eTAKQFaeSkiJtFCgyk5R1pBoalrc+aWpBiDUCZtloS6woSKO/JzO52ycyNNA7mO0SV2YwdQFxJCXSpsTsamVj7rxBz14ARgnBHWb2xa98GQ+Eho12DiOglSlFu0K4YM5Gch7jv/U4bvQfNYubq2uy4s2IgZNWZCVhs09+8TCZIlK/5Noxxxxzyokn8S+ljou/9h//mDZr1szd5hr1kgk6yyV+8pOfrM6P6d/1rneZF0g1zrrcTEh+TO9iZ59uc/+tfShmSEeGJ8+erXH40Ic+dNVVbgufor6fc845YjF7yCqlbmarbEZHlG1GISWKTDsK1AI4awmRFu2q2SuDTu1YdliUJ09K5QVpIFWKZAQlLd1EK6c0T2bVlwVaif0POywbSuevATc1c+N11z3taU/TbrRml+qVqBfKqtIiLKnSSB8LZa00wZzvuPXWyTNmqIkoLw9D7e16NB2HFlSmS4KM838Y+or6GkZz1Dna8k2NxjlzVt53H4ESgqo9+UlPUjyM1BUz7YN3alg4k0euRNeVugYC0yIFqTPPJ108yFCNCVOwtl3T6k/a2unjeucffu97VJJEBUBcOkH63HDDDRYOqCZud9dQ0FaZ1LlIobSIOktvFSzrdO4OCtaaKfPECsszQi/LCORB7ByllB01K+R5Zk6UuPgXr+UqZq3ooJwkWICwiSePWOaep82ZnHxzAUdTR3Iw00qlM4Wdn4+gsB5fs2bngn7B6gnJ4dlFDDodTfeZZ54JBOqhmjzbYsOlLFespnz4wx/u6M0uc7KHM6sX9tiXlNoySrhzfY877jhxg9dgwwEKOKpipkFT8WsbG80Cu6eHbpbaOvgjYaIKZ5pv9GigyBE7Tejtnxd/ZiUzPz2U7YFxWMnKlUcce6xScfZb3uqAhuNOPlmBVCyBAEk0DrBpnlG60rFtzhzWGbHNbm153kjH9Q60qp061V4kxcyCea72iFl899r/fr0bfrPBXlmpTFQmKK+Fz2qiVUUVFdCwsox/7s4250fDok+W9QonWwjlna63uXh5E9D8ipQqXj//1S/vue9eVyHssdcehljz77lHFqf2U4thOl6Ll0WRv1wdhvC3tUfHbXZAAYOeM89UKFcdzN5zT0NA5fDzn/+84xIZey05NCbRZ2k5WWVUOrVSq2vxBWMvb1pywmsb6t1HwCSxesUK8xFaaBEpXYZYXlYuXqKaL7pv/kc//L/aQFvAcPdsqi5/naTRkWGS+RHT3Fx05YLAQfLlpvfN4RDuuwwCpbtMSiIhm0QgtctZ15uRtFITyxbA+FMT5pjZ40443hDNmNLm3kMOP4xxRuugGdKvG8E4wYjJTjNw1NFHv/IVr8g2FNk7VVl5xSWXnvSkU0Rn5tXeWmtoH/+EY7UXi5csscTxgksv0YRodxxQ5F4+LTHKpPcd6B/IOmlroTOq2K8zcKZ0/fQZVdn2s1KH6pqK17Lb1tJf1ONFc0mmQXnGfPJkIJvQRuCNL4aydWL6Jyc/2CdsRJUlsMQBm8PTp0458zWvseXGARs01EP8++ZbjM67e7LzIffabd573/0e0vgvq60rK1lj4ZntqWvXtmiazWgeecwxuvKrLr/8O9/+tsMqJ+WXSZOj52tduoz+jMwj2UGUlcwyBGbDpvwv2mwfkRkG50IXdw4ZK0PGQdPQM2f8vg+83xm51m5pc2+57d96C6xGH+AigZRBIEIPbJiUbINIM9/6EnYGgxudNyOtlY3O0yq32NuY3hZHWubPgBFcGtPwxYVXcgd3xE6Netm9wUU9D1JEQ7v+GqtrM5S7uoy9dHUwdLAHDQ2bfvWr38DNu4sQuTvz7KabbvrRT3646P4lw4OjRr1HHHIotRFyxYOl3iSq6XF9qdW/Ok5a4clMEFajOerZTuAi94syOoiejc7JyWxZnV0Z8tU1OOic2bMnNTcz5ut7LrroInuVDZfFS2cHUZumQSPBq6gYsLnRy3Aw2xFaW6PoyghEgky2TZeUStDBhxxy4H77W7hIEwDKSlP+S+67b+F987HHX//p9wcdfPAtd9ym/GYDFDs/u7O1BhV92cjPfmB27y7bsSVq0hTSmHbpbM5Fz2oago3CxcnG7hmUuZyOFuxKkbBKQlF93epFi/559VWYzyc/+xmeP/jhD+1/0IG6dsv8DFAqy0qXL13+nnefM33qNFvB7d2SLnHpzskhwbsBdGZj7OlBOZjIkBNdOBojx/956aWmvTE0s0cH7rev3jqLfTMP5Y2sgWwf8rOf91ysj8XSUme81CQ6hQ2GLMbD3MRosfQ73na2QaQZFmgbDVi9XO9mp/r6BfMXiIFixhlGojCXNVTdpB3YirH5ixYuWLyI5Zny8BeREb8MkvvZaMmMWHk2W6SSm6qhv2WNb8z9j0KycPEiZ+A+6eRT2Dtpiz456FOxN/vAvw2WCArKYkGBck6UsBmdzg9TsvzqzxZijOave8HWu9zYmbe6OPfbohEMkn84297dNYA0dQ/UDaCmMlzbYsglI1RVVhRJVtI4KoQMGax87n4j2cYBkzjlxdkm5Gzk3dVdmV/qrGXLjF3dXRofDd3ZZ5997AnHz7/rbiftf/Mb33DOAnbE5il2OBt1SZGVwGIRI0zmzJpNGXv4N7fnzLDPkUSGhKqDqOWFRapp5sskwsYPJKlXan2j8wXa2mVWTVPzFz//heOPP96EmhBOd3vu/3u+Bl8yae7srgsvdMRUqUXgZ9q5/opX0Gcy24s5SiBIV359B8/nnfe766+77nWves0+e+zpquQbb77pZz//+d0LF7hMZNWqNZ/+6EcPOvBAhcpMBJOjHHzrW95y+d+v4PNPf/vryaec4jR1WCmEVmr87Bc/p/kxRx51/Q3X2+yHrVmYTdUGi2jyU3hGxmlxvtKebd1kM29sPOmkk8777Xn11bVmJKHB6gVDA332PQRV1Ib7zjyTd6a9KOmr3Sj29ij2F158sQSK/ew3v1ltevkZLzF7ctO/rseOMDRB3KwDHNlDMQHNiDgYsKGx4cmnnooyfeXrXzPoN2H0x9/9/sPveZ+ZnXsXLrCIl8u9C+db92vpk90f02bPVj61525AQryVKKHURJmOkFtcbWFlkm+/j3bDJ/swfFTYVHl7NafPno3hv+mtb9Hh2qDIoGcJaGdvn8XP733vexFaZUaPZopQVbUlG1vVjmVrbouy499lGbEeSVDecAwZh6v4Uz9iIlIsIPKuJJq3AlriOTRRlUztDDi3N/+sW9emSxOEfPg/zABRQ5Q/lkLqgOkBpilXNU6hFWmqNeZJLX2x4oKqWnusnXDDBhPWJgZgYhc6ek+E9TiaCLOowyXGCcU2Zt+/bCm1yd93333zwUvbV6xw0XrztGlrV66cPGvme97zHhXtxltuveGGm554wvEaZ2uDL7joQidmWWH0zyv+buLG8cJu69EUA8RyBr2thUhkbvwgjVYJta1rTUdjeLEDxzlk4EWxNJKVrkjo7bMN+4VnvOSZT3+GRklrxIX5WCpwZ82FNHatbSHBjQbKgL2v3UwF+RUBW1ue952358ZKcrFI3joHdccE6vSmBgcrvPZ/3mCG9ze//e2tN91sWbgDoujW1tGurDr3wX4OcxOmCWztsaXDuircd7SrWyeVGhaFR6E1l1dhSYllCA+MHMbEzp5RZllK3sp93/z5TprsSox6cEAbIoPWrlpti5CnZfUav1Y6aEWzKeP6+k998pPK274HH/yVL3/51a89y1liP/jhubfecstTn/Y09l4KmLz45S9+oczrR9Qm86S6ElXe5gtFi/BTTjlFJjJykJNR5QsueN7znz97t7nartVr1xjJLFi8ZPbsmUuXLvcuRmsebMez9IBh+Rc/+5kR1GnPesaHPvK/6gix/7j8CqMavYk0asON8RR479lse76SKqXydAwC8ecuhsDDNHG7WGofg8mxB0keZ3U7v2tUN6NryRqU2tqVy5ebYDZHaO9ob1+vuVurkvxL53xgLzZjYBq2uxg0W6moW82GnkVFjz/22B/94FydjUEzbolvVH77W0x3xuipQdH4am3ZkLNONH9YqO6wpjo77XnSpCYcTrPoyeFFw8MWsUyaPWt4oA8NNoSSR1pDL7pPLV1pRWV2r+lASX1TY99gP/Jj2lIfrxc3gKirrddi205Mt5S5tkvpA8773e+0j5ow3brGjs3B7R1WxP3y57/QLlsVk90p0tGhadZWPu//Pd+GT+Okxz/hCfoJ9kzNnrMurOe87JJLDSLtOKK/K1XhaD7ejhd2PNERrl0WtW6DYdteFyctSZFU20aIdWBo8+ff51wih5oCymDbCI/wJzz+8UgOkmbeWreqi82uR5o8xfImqbYbTeucUZR1LTp4g0UHL/30lz8H+AEHHUh/XazY5QU1xKUjNJ7KslXu5K3QNLBYl8VepvA5Z9YsDB+qGYcvLnXKlAW11rwKW1xe9qQn/denP/3pd777HL0O4V/5ytdstzbaW7LkfgMaT2NzvZKgDLB9ZV1+fWPH6tVGEoxxegsZUddQb4L8l+f+6vobb9af2NR3xotezFTrTgL7v9xiM9Kb3fGba22bNmMGZpLr6TVA14/a4/qOc95lRG7LogO0Z+6+m41MKd87uljmsrl/QwfFwHjKi17Kom4lR7FUPs3dONaLZOMMybcA4WnPfAaF8TGcX4+eDUocI9nTidWbVpg9bzfb2sm3Rq7WAtjqmq7lK2bl7yGUiXlrZX6RgiWIxcXsCZOYbRsaupzfll9e/vZ3vBPuSpGemBCbbJUim06ViqbJk+x0RRtk7qWXX3bBcRd7mTy5mVbwGRkaefrTnvrfr3u9csiemS2Yzy/0lfCs+83fBJZdV5JfrsyWZSivNNpIbGmo9cTSq6QNDo/OmjbVJIV3Lvlwm/gxHJw+b3ebDU47/fRLL7rYAWkG0P1L+1XSru6uyZMmq4OCmVXq6ev94f/94IUve5l1dYgBA5pyuHLpUkWZiVjWT5lkjqJZJcoVlYx2dqqN6lrOokJK+5xXPZUQApmw7MSl8AlPPE6VYVqDfFYgGXby0/9Z2GxRtKkeNGvUqPTpTzn1issvh+RXv/yVpz/nuT1r1gowtWmSPRp0NljMKr5174sWmdTPtnZbcGtvAkON+7BHsgOHYFZclW2XcBGKwuwTTZQTNaK0srq+qrp95cpsrmd4qN/3/K4BdccJQI7v8lhzYTZEGcsWHXR3WxGtyCnhVEJTFIl61Xxw/QJjuwgo7mF8Y3Pbp7r6kx/7+Dnvf5/gjP9//vOf3T013fC6tY2///lvRfsNOJgsNhto8sDYscT55+mk66JiHC9T0qLBvox9bfzY9QAERzc3V2SzNn1GckrUpOaNfSYXaqgIFkT4M1UZK0/2PfRQLYyFAwsWLdTMfv2b3/SvrrFeTeeZUgN9A2601lYbEDdMnrJq8WJQZw1g1vaO1tRVm3B7xZkv/+mPfvzBD3949rTpzlc30SbTmTGxxze/+X/e/ta3EZWNtletmpY/tMYg+79f+7rvfPe7Cog1F1ow9VRhyAhMUfFHP/IRmxHQS82y0uVUAquvGdYUabmpeUQC8aLsVKr8Eg9rT7Rj1nE4Fx7J/5+3vNmEoKxxVZ6UWon6+9//Pt09pmDrNdR6UFtzW5W/gwfVX3T//SqF5ksj093eIUcsa3rFq19N5zWt6xzr0NuX3WUla5zGnlV2hbavt3uwX3tiguO73/q2UqFKvuLMV5j3crtgNkx2qlllNT2t937yM57hyPi5e++t31q1dKmNDDJXXeDHKFyxVJ4UXdHJo8zSmrekVVfZ9N4nyRaQK/bKrkTNnjHT2YR2aLu3FHXP8iAfl8ryzGc8g00sO4PA7db5K9NRyq6VK7MlNg4V69KktZtCxWZlh06KTP2j/fNN1dXda1s0+E76UObNXJixApRCYgsyo6WqYG7J1bvl1ZXp2CF2O+pQmKq2MuRV2OhHNPkntWCSxofmDntZ09YqoK7EjlCzwmtWra7Kb7NXIJ36y17pmA8JtwXjY5/4RErgq17xCvhj4fbLIMBZZWe5HSl/yUte8r4PfxBftWvita9/nbxzVgjFRvoH9Fyi1AifcNLJH/nIR/Rf5vVg+JGPfKyhNlvd7eoKy0yk0sXkpg/wH5sytGzWLat6G6VnvYNqr6ACWeI4efEnR+o5NtksqXkLMle7iDt/0LqEW0Ol9HrxaFt0B7rjrLTnLY39Q1mV5EEJ0KpsbXnenJ5tjuSYNt2xKAYzxWUlNj70jQzhh87mvG/hgjed/Tblx9aDvgeOjXAQlhZJZdQvm/0xvatl6xnokwrlXZXWyEgXu3Xf0EidxcD5DN049mxHdFPjunUt5iDM2tx2xx1Z6irU79ILLrjIViPV0KUO9TU1ClvWBTj0zrlcXZ2qau13vqOVsOjOaMQMl0GR6UirwG7997/50bRqdsSr3SPzPe+yvuQcZVhBcgS6ysUWYtbjc5//PFU//6Uv8nP7HXdc9+53swPbgSylVTW6axNzqy6//FITbVZKd6xey7Nxpl7bDvCWzjazZr/+7XnaZLNXJKhZCgNNDMZ0fOZtOXpS2TbWTX/G7y6MgJnleHZtBEaLMrNTNi+bTUkypGRDwgFbCqfvvruqbu/u5Zdf/oynPyObTc9frLK6dV1ajeakh2uvuvqXv/qVvs26REMBgycjrLVr1tjP42CCd7zlbXvN21MznI1dKqvQEk3bWa9+tVV2RvAOHoBsR3u7dWiGAoazxgTWkdkcp2sxJmPcM5eZNVKGhnmXjJUVZ82xFx6s1dEa6U7or/8wEEmdTWtbO/KjeUUdtYzU04CKi1GDzcHypzPOeLEJ9hYmAuLyRmkKW3a15377aVKtBRq16Yhhp6oKnzF+nTl7Vrtjnx3dYQhVVu4udTvlrrv1VoBk+5TcDupahfJyg2z74ihGE4Mnwg0Z/WlYjCZJRZaW/GIqwwHdklnGPffY0wE/FkQhRXoF68+//MUvwkffYxWfJliK0GbPuuXLCTTn/aa3vc3cvzGiThgBrqmsdormj3/wQ5YBC9j49MlKYMY1zX3WVefP4cxOWvLk796ADGqdzaznH6NSLnxKMgcS9MmyUieNktk2+cIzzlAGDC658ADtxUvu99LU2HDSiceZQ3UOEzu2nWMGN/ZeyhH3CCfmmQ2n7D5qa3PkmCBMfyedcKK1rEZm1tOWeEHLDTHlDYgsYyst1bEZDwHz+c99nhNl9TMGqdmJZYPWlPWbmGhr70BIKMwPk6yyR7Ip6myGOr//TaYqSxxlhE43Oyqst9dZxBf+7fyst6vNrMQwz4aVs2f/9NwfX3/TTbLmrW99K5MkIg0HqRDccGrZ0qXAUTF2mzvHC0d4WtOWQZGRtbwdL7961mJpj6kNv3xmHnTYRkV5rv7M00+3J+2j//u/7kay/NKcyNq169auXmvv62c++cmf/vgnM+fMlRwqKbGqUlaA84vhTZRA0kIvMvc74ADHWpqSkNf4pISIq7O7V5/94hf+P3wGOCRkmb6Zx1VD1pVZmHr3rbcSsq6z881v/B8TQzJd3kNeOKk68xWvXHb/UrPsxqM+mZtwXoiVAuxRJo0UniTeQIF6FJZYSmbzF5t53LBtcGr6w3Azy7j8+kwlyeaxVOqUHGN39Vop1ARZGfiiF75QQbKz1KTVqgULQQ2BltYWjUlmT06VN79Cj00qGx/nB0nyMc+tMz0yKLC+PKkQNqt9+dIrKhvV1EHjG7YFhFza58zJthIIwjQnOYkGWFlAJYVNQHbybC7AfgTnh+XXkdozv35hLUtyfvNYhrwxbh+K2m728HOf+QyfFpKwck+ZPGnVmjWWM1x66aWqjJxisbecz0IbsVhIzHQjN82JaFUsFXZMgFtzsmRs6mGNzHIhf1iUIKlgetmU38xN0jwKmNy0UlECpdSJqaC44oorPvCBD1iWwht6o+IPDpmJGLVU5Je/+PmvfvtblhlheUZinSloKJo1Svntgk5BsB7hy1/5yiEHHeRabPUFyAapms3f/v48yxPEYgLQisesi+nttUVCQIes+jR18hTJlL/Kufp14CEHO13i7Pe8h0VOHWSdwxVtujERwLypJGTtUp51KNxZvcaU8mvp5+6zjyORTzruBB7Mfqra1ho4Fe8rX/ySvR4sSBj1mpaWRGxqnEhkf7j9h/39hx99tDbtyCMOl2tIqSuUKG/mgsGKzcvscJYRfWpT1phktjKzWlYI59fVW4DAhCUhpon5lxcmQWbPmF1WXGZn8p67zTPJqPl98lOeorrN3Wsv+/bZ1rD61rVrs05KQvJXc6XmXZnJurBRU0mdoNZLwk1i7eBw2pYpEp8s67Vs5G1vfouSIzg7sikDm061kNaGeOyZ5E15E9av6kB/SwzMYSlXulRRaIeVAec5ySkg33/ffVklUebNGqxaZfbBzAUY9UTYoIpjWwJMlBa40U07b8uAB3skpzo/JU3PjR+aFJ7sqzCUcJV0fvMIB82yKowAwURRlkbxasPpI6foT5mjLRMrKVZxdLJcbOSmCQKq79AalDVk99uZE0HpCTQlDUwU1LFk2iJbTFVh7gjq057xDG0vw2B2tl92QKaYs5piUlIneNnFlzj6SLGXIgtuFy1YAChfN/nopDQIehMHJfrnxZ8cM1t0fqm2CkUNfFjnQQJ3qSaQStkxVLI139UCR0C4K8ySBh4baOnA81aV500qyVHal917b3l+ZGXlF9jtblOGlVVVIyPADwyKdOIvf9lL1RSUkvLC+iQtWjDzjFp4RcnOdxIkwZLjBodB5McJm4wa4RRW6XL1o1VXFhfwpk23fM++coZonxzJ7Kg6Ta6OhmU5TcQrny3LlwNK32e5u826TAtIuwkyxZg1hRnGlJO2RRVwlP2HPvGJbMpm6lRZiXibO8hOHMyfvW99nOVdb33Tm517rxkxzBMpNRQepmMb/gmXHMsw1GuZsvDOO80fmal85UsyY6+aZQMIz/pvi8i0VN8591z9kbYqFQwvKctUtk2CEI67EgJFbjjdldITaRmDQG9/1sRkxy9joXm+pI3WfRqaaAq11CxCWoPsZXTUtL0hL+uiCwA09B5+9ByaGC2X9mi0p9eUW11Tc7rqQ2emcbekTU/g7lOdN1Mh86L7V/SvK5Yvd/2GHiBbnesqPwzHRuKmBn2D/XmuVBxw3oynprZ1xfKGSc3mp22o87WsvMLJItnVNcMZbzco6evu8smCVefBOpvHPU7tLescNdyxYiX7sEvhXvyyl573xz9K2Wte8cpv2znpdK5sc++tGsT999sP4UR33Udhfw8CP3nW7N6W7B6m0qpsF7El34Y4WuQ1K1dpIjE3zBY5bDeRbLoUUcmfRqjPyEzBuVGr2Cy8Lq2sYIzSTJul7u/pNZNa4tzo6hoJ10Gbz6ysr8suECqvuOP223lzP4F+C7bMOlpYwFIGqWAH4M2YFJIidZlzZifPr1vDDNtb2xiyDJV0V0bksom9kU8rzDXxwLPUx2BHn+9+T8eiWOhu5GR1me42s1Jmq567rKu0BVRaLPSy7M9ktuWyuSoG9nUGLnLZmFLCYTtpylSWQBPqwDH4QOcqa9ezZX8aJFniaCLZaMbYq8pm2jwflkesQxyJWre2VSFoX75C9wOIzMBiahzJtO6aRcIBxY0NRvY4sFXNqadhkFREAWLIAm3UmWdZz4XxqMPaquoawvFOxtJ+F8nWVLesWt08eVJWql1l2dsn1bpxPITNwYlu2N38hQtmz5wlUdiUyWHXbuXXEQxAD6SWuA9aNGvRWj6Kzva2usmTenu6kSvzHdlMuYFadqPJgNKejar7+w8+7NAlS5ciQosXLpI7Htm5btlym5ydVmQQYIWhuXBZg3urRAqe8bSM22OffdpXr8nwzM87yAUlvKKqOjta0+UebpgYsO7AucOWPJSbnaGhHFQ9LcNbfP8SFkrbEzKbg5PY3JfT3GwCCzJKAqw2nsJ0LjIAh3t6S2pr2leuUhKyolVSLNVWFZLMWHTAYYe7Iqyro7N2UvNgV3dZdZVykg2U8yN1wxFqu3jDseGukVDXsomMvMFB6vDJrIXJbwNWYQv7gZPpCeCqRmdrmyHFQHuH8mkYYYpEAVBcjVfgwFF+Sd2kKVOMC2WxhYWWDLAaZecnNzX2dLRnJ2nV1XauXkN+fZNbcDtgpZpXWGm/xomgjZorxxFnw/fR7I7oquamrva22uYmY31/2lOaX5GRnW7KFNbRuq5+UnNPf3ZKTdGgSy8zG7Lyj7BRxgSKAZZ8UYwN7jR3WfpcE5s/F4ApI5PvbHkHZakgLs/M3zXCj0bPuQP8X3/TjSR7Oe7YJ+TnSbIt0DRXJulcWVPLhOwa21olfyS/XaKu3gFRVvuLXQXJ8Mw//3lDgRxPigWVlykJzCBrVmaHOasXzgHe5FrobGiORmaNz4ACbzOei3OsdJQjiK+VqE6zX7lqFTOjdc7oxx577rl82TKrY1Q09kb322WLO/J2S/XohltufpyJM3SouHjxomyJOxLpIIO1q5HNlic97akdFmJUV2bmo6KSgb7sdrfmWTOXz18wc97umerZLEGf0u8IK1MACrCWgaXUEQMWhmh8+hzuVUx8cVl9nVxWSuWmK5eW3Td/xqyZWsZex5WXVyiTrMOmmQRX4LWiN9x0I2vw7nvMS9tojXRRrEQzVFj366plosfJZZMbdxj9XEvO26rlK6Ta/ouSdD6f6QA5riWvLFcah/sG8L3Rrp7MCGzLRl+vQ31qmpssi80I28AgZdasWIkiar1322OeDkLrbULZ9EdlbV3LqpXOX8zqS/6iGqd+TZk1S5Pb3NRk3Y3pD32rNauy2loMsCDMmiNZY2+I6HQBtilqA6VRrtMfu9b4W5OvDJhV8av/Wp6/FSlZHYXSGlu0jRBrDdAaR+9qXZU6s7QmtjQ+yIPiqsvWd9fU1KqAElXaYC67uzybkyzvXJXtUfJZjtjuPmpLS1WljON58X3zd9t7bwbk6spNm0wzVfMThWKnHhuaOp5U0rw7ewwUqIuKTFWrhEwQsD2ayYKtGT11x0qirJQ2Nkgs0g5YbbIsZt7szx/PYX9Ca+u67IB0C0Oc81RdpVJrO9DpfLtR1dnRnlEX35xqqTVua5VNWhhbcqyjMZFNSbcbUi/rSvDqoaF777ln7wMPSt0WxRTgMU82CGEPdIhJnt1lHYGu1kFN5nnb21Uu+sgCsXhXLP2aqecCN7ckmEgCqWrNXemlnsm4xsmTjWOy8YO02AW2xeXZxGhDVbaxZVAzWpT7xKc/5cgJDb8VNL/6+S/Ka2pXLFw4fdZM+7MwyZJKh1o5sCo7+82ZT6bmOVLGTCiEVPOsdjPsF5foQVJ+GQmMOiPNzmGjndVrjE7WLV9h4lUzr7ctILMhSKD2FbG0JsIaLVZoLb+FYNZ7Z6M7leuB4WKdHb/WW+Uv+zV60YPICK3xYEenMmB0Z7ot64+am/59/fVZeXB8Q1XVXvvua/BmHTsjttPR7EVqmjK1u73NwEMvpl3VokqC/VZcFAnzaLLbmaaGcMafVXW1RoZSLet1pjJCMvS21qJnLaHJuNFhZmfZp1XfY948obQbqqoyqezVNTYpTiln0yDNrfcFHOJlF0PAytPS2vrgwLtYtm51craqihsg8L/xyFusDy0nBeEnGxrmh5gFIYZ0yXGMhIKHlKT0tRBcY2qhY0ZFUJTe3jPOOIMlykj0dWe9luFCK4YTGvlp+HR+2jUdyRiBG4pN72N+N+mfn4KeBWU4Js9peLphcgpCCqHGxJL+3Jy3gnsh1Bg5Yzz4ymWMn0JYLxv6H+ON2pscXm8YfIyEwqeCqA3l+7qxwAROIaCXQtgNHQvvYwQm/ymND/tbEJJC+S2kcYzYgg7cGYiMlqxfsCk060r7+iygOvOssxxWxnT/k9/8pmXZMl2mPjIZSFMsBQkbRrq594fVnLTkZ2MJYzTf2EMh7KOJpSB2THQblnB+NpnqMUEKogovScPCn2Netlz/DQOO0WSMDr6mgkf/9GmM/w1FeR8TfMzXhw6bgvNDyEP/jhG7yT83juuhddukEI4FORtWwEL1LMhU+J1axEi438EHM+gMjoyaQVALjDsJSd6SqI3x3DC9m1MjuW/o8yHeNxZSwHPjT1vokpJQ8EzgJtuEAlzJ55hQGwZP+hdcxryMCThG7BjPD/vnGGkb+99Y/pgghn2elO8bfkoBC3maJG/oYeO4NukyRoGNJYzxsKGQjT2nrxsG2dDPhu58FvJxQ5kbv28oYeOvO9hFEugzJiEb6pDR0exU6gFzLu945zu++vVvzpg25fgnHmcD8IZNcco4AYsfQlZe7pjkj/E+5uuGmox53zBgQn7L8Rc2pXpzad/w60O/j9FqkzqQUHg2VLvgWHjZ0GfBMV52VQQSB97smpBdNdmRrjEIbG2135z/zblvGF3mZ8NJxfy3/zTfG3rd6H29/AeC9zi8JDvSsIIJyzLfWquRh0f6hvqtQLMq2MQte6w54Wy+M7+7bzMUeP2obqPYHsrhPyl9QJmC70JauPzHW/7zmD8LQca8PKy3h/aQvj60n0KMY71tlJyCz4d9GSvqgQAbAvKA29j/bi7sWH8P/J38b8nvAyGy/66PZTNpLOjAnmbGpKax0QS2dRNmyh3o8t1vf3tyfb1DsZ//3Oda666MWZ3L/MEDa0iKpSBhw0gf4n1L9N9amRtqsuXytyqWMRm6VWELaDx0qC3XvCDQy8PLfHDWP7T/DSVv/L4lYbckFRtL3thlS+LaONTGLv+R82AcxvpUop1dv2pVc/6yLuco2kNrTU2hnPO/XtSD5Wyc3rGSH/z3xv43dnlwiOyvDf1s/PURuGQCH5yQJCRF9LACt1afLRT7sPFuzsPDyrduYpPP+oCb+brJIJt0fFgFHtbDxmI3F2Ss+6NWfuOot7dLSsLYhDwQK8LGtN4wfZpbcy3wWDh/QUNN9cpVa9hLedmwKd54MusBGQ/z381F/TDBHqiJ670l5LcY/w1TvTkFNvazOZcHqfpwOmwuugcJiT8eSwgEB34s5fYulFarcay5QoD9w2+tWPNrkW5+2U+25BL1zX6xlPya4V0o6ZGU7YiABdvWybMDK05WjplncXHTNddf31BTs//eez/7Oc/BEJQr1mHrPy21SsOR7ahQiA4EdiQC+QWu5hYtc3DZiz2s1tTsyPgjrkAgECggYGmbQ+NcH/WP/NORP4TZNtqCh3gJBAKBR4NAcOBHg16E3XkIONYifzINEmK1qv0k/cPD1eXl5k2dNONwTpqhMQzCdqE4/GRDO8bOUzpiHvcI2CCa37em8GRLQCsr3QOplWzv7v7QWWcpUnZ52XBlhsW0S3YKazCEcZ+loeBWIOBosdpaB8t15u9tYlaJWZ6tQC+8BgLbDgFGS8dnvvyVr+gbHKqtyrbEk/24o450nN4mIylYgzf5NRwDgUBgYwRiacDGmITLBEDAAZi6BAtWyxuzw0WcNYWouObRLYLJCJxdP4PH5I/wZQ2eAEkKFccBAs6uzI6+ra4uq6szdbLovvsuvfRS59WecsIJ7hnqzdt+s8UF+atlo1yNgxwLFbYlApZcuqfa4YiEug/P4YhmFZ1wtS3jCFmBQCCwZQioiS4xnFRf393rOLH+FzznOX/+458cXYnubvxvy0SGr0AgEPgPAnEm1n+wiLcJhECRLTHOrF63zpo9tymsWbkS3XWerWNIdz/gAHf/2tfpMpLswMzsmNLsCNYJlLpQdWchgN4W19ZaDm1xQdOkSbguDpCtCx0cNNXiVm0uzilJC0Szo9fyKw52lrYRbyCwbRHIzvh1enxRUeuyZSzATiees/fe7vJS5rdtRCEtEAgEHhaBrp6eCy64wGHsJvdd0+AUdOfYZ7ffbeaK+DBqPSyk4SEQSAjEudBREiYwAm4acK49ZuK8RBfDIbqurXPxSHaory00ed6Srjfw1cUvGPIETm2ovqMQsJ+8BgdIpaW01OVXJXV1plRK6+vXLlmiyCED2XyKYmZxwfAwSryjVIt4AoHtjoDpHnOGJoCyy1fd49XRYVbIpsTtHnFEEAgEAhsh4CbbabvttjZ/ib0pfmMbF0rP2mMPB59s5DdziFHOJmEJx0BgYwQSB44qszEy4TIBEHAZHGOF8ZmTsVy+54AiF9D50/X2buIzaHO4kZsWu7mPjrrcbwIkKVQcBwi4DdgeciRAoXK9qpKzYv58d9iuXrRo8ty5NgPTMTMCDw258TI7fjyeQGAXQkCBt9HdBZtZi5pzm2m/ttSszy6UxEhKIDBhEJg2Y4bbjM29uupZlRwdHGQKdufFhElAKBoIjG8EYi30+M6f0C4QCAQCgUAgEAgEAoFAIBAIBAKBQGBbIBB24G2BYsgIBAKBQCAQCAQCgUAgEAgEAoFAIBCYOAjEPp+Jk1ehaSAQCAQCgUAgEAgEAoHAYwCBrb3uaDSOrnsMlIpI4jZEIPYDb0MwQ1QgEAgEAoFAIBAIBAKBQCAQCAQCgcC4RiA48LjOnlAuEAgEAoFAIBAIBAKBQCAQCAQCgUBgGyIQHHgbghmiAoFAIBAIBAKBQCAQCAQCgUAgEAgExjUCwYHHdfaEcoFAIBAIBAKBQCAQCAQCgUAgEAgEAtsQgeDA2xDMEBUIBAKBQCAQCAQCgUAgEAgEAoFAIDCuEYhzocd19oRygUAgEAgEAoFAIBAIBAKPNQTinOfHWo5HencwAmEH3sGAR3SBQCAQCAQCgUAgEAgEAoFAIBAIBAI7DYHgwDsN+og4EAgEAoFAIBAIBAKBQCAQCAQCgUBgByMQHHgHAx7RBQKBQCAQCAQCgUAgEAgEAoFAIBAI7DQEggPvNOgj4kAgEAgEAoFAIBAIBAKBQCAQCAQCgR2MQHDgHQx4RBcIBAKBQCAQCAQCgUAgEAgEAoFAILDTEAgOvNOgj4gDgUAgEAgEAoFAIBAIBAKBQCAQCAR2MALBgXcw4BFdIBAIBAKBQCAQCAQCgUAgEAgEAoHATkMgOPBOgz4iDgQCgUAgEAgEAoFAIBAIBAKBQCAQ2MEIBAfewYBHdIFAIBAIBAKBQCAQCAQCgUAgEAgEAjsNgeDAOw36iDgQCAQCgUAgEAgEAoFAIBAIBAKBQGAHIxAceAcDHtEFAoFAIBAIBAKBQCAQCAQCgUAgEAjsNASCA+806CPiQCAQCAQCgUAgEAgEAoFAIBAIBAKBHYxAcOAdDHhEFwgEAoFAIBAIBAKBQCAQCAQCgUAgsNMQCA6806CPiAOBQCAQCAQCgUAgEAgEAoFAIBAIBHYwAsGBdzDgEV0gEAgEAoFAIBAIBAKBQCAQCAQCgcBOQyA48E6DPiIOBAKBQCAQCAQCgUAgEAgEAoFAIBDYwQgEB97BgEd0gUAgEAgEAoFAIBAIBAKBQCAQCAQCOw2B4MA7DfqIOBAIBAKBQCAQCAQCgUAgEAgEAoFAYAcjEBx4BwMe0QUCgUAgEAgEAoFAIBAIBAKBQCAQCOw0BIID7zToI+JAIBAIBAKBQCAQCAQCgUAgEAgEAoEdjEBw4B0MeEQXCAQCgUAgEAgEAoFAIBAIBAKBQCCw0xAIDrzToI+IA4FAIBAIBAKBQCAQCAQCgUAgEAgEdjACwYF3MOARXSAQCAQCgUAgEAgEAoFAIBAIBAKBwE5DIDjwToM+Ig4EAoFAIBAIBAKBQCAQCAQCgUAgENjBCAQH3sGAR3SBQCAQCAQCgUAgEAgEAoFAIBAIBAI7DYHgwDsN+og4EAgEAoFAIBAIBAKBQCAQCAQCgUBgByMQHHgHAx7RBQKBQCAQCAQCgUAgEAgEAoFAIBAI7DQEggPvNOgj4kAgEAgEAoFAIBAIBAKBQCAQCAQCgR2MQHDgHQx4RBcIBAKBQCAQCAQCgUAgEAgEAoFAILDTEAgOvNOgj4gDgUAgEAgEAoFAIBAIBAKBQCAQCAR2MALBgXcw4BFdIBAIBAKBQCAQCAQCgUAgEAgEAoHATkOgdKfFHBEHAoFAIBAIBAKPAoGi0UcROIIGAoFAIBAIBAKBwLhEYLRou6sVduDtDnFEEAgEAoFAIBAIBAKBQCAQCAQCgUAgME4QCA48TjIi1AgEAoFAIBAIBAKBQCAQCAQCgUAgENjuCAQH3u4QRwSBQCAQCAQCgUAgEAgEAoFAIBAIBALjBIHgwOMkI0KNQCAQCAQCgUAgEAgEAoFAIBAIBAKB7Y5AcODtDnFEEAgEAoFAIBAIBAKBQCAQCAQCgUAgME4QiHOhx0lGhBqBQCAQCAQCW4fADjg3cusUCt+BQCAQCAQCgUAgMBEQCDvwRMil0DEQCAQCgUAgEAgEAoFAIBAIBAKBQGBbIBB24G2BYsgIBAKBQCAQ2OEIDI/GBcE7HPSIMBAIBAKBQCAQ2M4IlBRt9wuCgwNv5zwM8YFAIBAIBALbB4GSkpLtIzikBgKBQCAQCAQCgcDOQ2BkZHvHHRx4eyMc8gOBQCAQCAS2CwIj27+P3C56h9BAIBAIBAKBQCAQ2DwCO2CzbnDgzcMfXwKBQCAQCATGMQKjsRZ6HOdOqBYIBAKBQCAQCDxCBGIt9CMELoIFAoFAIBAI7OoIFBcXx9HQu3omR/oCgUAgEAgEHlsIFDnrY/vPcYcd+LFVqiK1gUAgEAjsGgjYKjQyPBwceNfIzUhFIBAIBAKBQCBQQKCsaLuvhg4OXEA7XgKBQCAQCAQmEgJhB55IuRW6BgKBQCAQCAQCW4jA9r/2ITjwFmZFeAsEAoFAIBAYXwiU1FTn3J4wWpwrGonfQCAQCAQCgUAgENgVELDMq7tnexuCgwOPryFdaBMIBAKBQCCwpQgUl+R0khkHLorfQCAQCAQCgUAgEJh4COjGR4r040X6dL2595HtbwXOmTxva9vS0Ub4CwQCgUAgEAgExgcCw0W50rqGzA4cTyAQCAQCgUAgEAhMTARc8ZBuebC/SQq8F40MD3X3loxuryuCs/FDbf32tjNPzNwIrQOBQCAQCAQCgUAgEAgEAoFAIBAIBHZFBIID74q5GmkKBAKBQCAQCAQCgUAgEAgEAoFAIBDYFALBgTeFSrgFAoFAIBAIBAKBQCAQCAQCgUAgEAjsiggEB94VczXSFAgEAoFAIBAIBAKBQCAQCAQCgUAgsCkEggNvCpVwCwQCgUAgEAgEAoFAIBAIBAKBQCAQ2BURCA68K+ZqpCkQCAQCgUAgEAgEAoFAIBAIBAKBQGBTCMT9wJtCJdwCgUAgEAgEJiwC6ZaF8aO+Ww83qUzouUlYHtYx8HxYiLbKQ+C5VXA9rOfA82Eh2ioPgedWJZx0eQABAABJREFUwfWwnscnnkmr//SJm+k0HzZ1W+Uh7MBbBVd4DgQCgUAgEAgEAoFAIBAIBAKBQCAQmMAIBAeewJkXqgcCgUAgEAgEAoFAIBAIBAKBQCAQCGwVAsGBtwqu8BwIBAKBQCAQCAQCgUAgEAgEAoFAIDCBEQgOPIEzL1QPBAKBQCAQCAQCgUAgEAgEAoFAIBDYKgSCA28VXOE5EAgEAoFAIBAIBAKBQCAQCAQCgUBgAiMQHHgCZ16oHggEAoFAIBAIBAKBQCAQCAQCgUAgsFUIBAfeKrjCcyAQCAQCgUAgEAgEAoFAIBAIBAKBwARGIO4HnsCZF6oHAoFAIBAIbIzA5u4/3NjnDnZJlx8W1Cu87GA1Hja60PNhIdoqD4HnVsH1sJ4Dz4eFaKs8BJ5bBdfDeg48HxaijT2MAS03urGXbe8SduBtj2lIDAQCgUAgEAgEAoFAIBAIBAKBQCAQGJ8IBAcen/kSWgUCgUAgEAgEAoFAIBAIBAKBQCAQCGx7BIIDb3tMQ2IgEAgEAoFAIBAIBAKBQCAQCAQCgcD4RCA48PjMl9AqEAgEAoFAIBAIBAKBQCAQCAQCgUBg2yMQHHjbYxoSA4FAIBAIBAKBQCAQCAQCgUAgEAgExicCwYHHZ76EVoFAIBAIBAKBQCAQCAQCgUAgEAgEAtsegeDA2x7TkBgIBAKBQCAQCAQCgUAgEAgEAoFAIDA+EYj7gcdnvoRWgUAgEAgEAo8QgXTT4CMMvB2CFe4BLrykSELPRwZ2AcbCS+D5yJBMoQowFl4Cz8Dz0SCwbcMWimXhJcrno0G4AGPhZfzgWegTix5NCrc4bNiBtxiq8BgIBAKBQCAQCAQCgUAgEAgEAoFAIDDBEQgOPMEzMNQPBAKBQCAQCAQCgUAgEAgEAoFAIBDYYgSCA28xVOExEAgEAoFAIBAIBAKBQCAQCAQCgUBggiMQHHiCZ2CoHwgEAoFAIBAIBAKBQCAQCAQCgUAgsMUIBAfeYqjCYyAQCAQCgUAgEAgEAoFAIBAIBAKBwARHIDjwBM/AUD8QCAQCgUAgEAgEAoFAIBAIBAKBQGCLEQgOvMVQhcdAIBAIBAKBQCAQCAQCgUAgEAgEAoEJjkBw4AmegaF+IBAIBAKBQCAQCAQCgUAgEAgEAoHAFiMQHHiLoQqPgUAgEAgEAoFAIBAIBAKBQCAQCAQCExyB4MATPAND/UAgEAgEAoFAIBAIBAKBQCAQCAQCgS1GIDjwFkMVHgOBQCAQCAQCgUAgEAgEAoFAIBAIBCY4AsGBJ3gGhvqBQCAQCAQCgUAgEAgEAoFAIBAIBAJbjEBw4C2GKjwGAoFAIBAIBAKBQCAQCAQCgUAgEAhMcASCA0/wDAz1A4FAIBAIBAKBQCAQCAQCgUAgEAgEthiB4MBbDFV4DAQCgUAgEAgEAoFAIBAIBAKBQCAQmOAIlE5w/UP9QCAQCAQCgUDgQQgUFRU96O9x88fo6ChdCuoVXsaNgusVCT0fQY4MDg7CrbS0tLg4sy709/dXVFQkOY8ez4GBAaLKy8sLkoeHh0tKSpL8Mb99fX2VlZXJcWRkJOnjz66uLiqVlZUVPHR2dtbV1fnEGyUJHBoakoTW1tampqbNlc+enp7q6mpChPJCE2kvxJjiJa2g8+bkCEWZFHtSsre315/UkFJiE57kpIQnyX7H4OnPJIoQL35JSAmXC8JSgBBPVVWVTyR7ksw1a9ZMnjxZvBLilwfyASU5cOju7vZCFLE8+JMf/js6Ourr6wv6jHkhXCxJT1hJY4qLAvD3m/5MJaQAwhghD/FnyqOUQN4KaUzx+k0eEkpS8RCi0qcxeCaZEk6yWDw1NTUFtR9W2vbzMEbPzZWr7afAFkoOPbcQqA29jQEtl3WV2/0pGmlr2+6RRASBQCAQCAQCgcA2RWC4KFda15Abp2x300kd281v2tfOdw09tyoPsA7+E+3xUqBhBSGPEs8k329ijF5SXA/NAUSKC/lN9BKTQb0SqRPcn/TkAZWdNGkSb8lnok8ccafN0adN0jZaiYhKJHgXV1IvEeYCFGNeqCFUYoMpXYJzQTXxLi4eongjLQkkgap+C3/66kmYJP198uI3JSohIIiIJK2hocF78pl+OfIsvW1tbaJL8wL8FJ4kLSWQI0Loqa2tLXgY8+IrgQWGL16aYNG8UVUuCMuDPx8anzFi058JrsInEEm7Xy5kFhIrxpTFKd6C/41fxuCZMBEL/ZM0+PCTJgg2Dr7DXMboucPi3dqIQs+tRYz/saCNDA919ZSMjjwCUVsSJBs/1NbHWugtwSr8BAKBQCAQCAQCgUAgsAkEMBADOLQnfcNDvKMQm/D6iJwSyRFLolKoGh6VGNTm5AmCyQiC0fHvBZ9BvbxT1cMDmySeyQMhpBUEUp4NMHGqTconqpA6ZDX59MuqjCwJIhZ/ekT00AQs+edNKITNL1ULVNO7lHKkksfLJh/BxUh/MRbC8ikIdy6gSFHQHAFGO4ltb29PsfMJBGiITsK9cEGGW1paqEQIz4TkE5SlSJL5fwgCLDoeSEvxCi5eOBAlLFGEcEzpfWh8NpneAixUSkJIk/wUIxd/ipoLNR4BcYWPeOmcQEsJfwRyNql8OAYC4weB4MDjJy9Ck0AgEAgEAoFAIBCYYAgkBpK4DdXxLoQhUaltkhKSyUnMp0BLHkIyffAWFCgF5FMof3qhFfUSE0PJMCVUJ4lKHvwKzqUQNn0d85vYY2KDBWZueTAlBfQrFn6w7qTwmODpTxH56sE8MbekgD8phhkK7h2L49kLgZsUwjEpnF5Enbx5SUlLaHCEifR6oaGnsbFRvElPn0RBh8KkAFOw1eAkUCD598IPCQR6Uiyb/E0TBEIhk95FQRlBSEifEt+24hoPTyR2k3I250igUHROaiTNRcc/DBMCMExxedmcnM25y0fyk/78mAsgM0G3uSDhHghMRASCA0/EXAudA4FAIBAIBAKBQGBcIIAeIAmYCdaxbt06m0WRH/RvWylXYCBeEB5iH5qDYS/Im1/e6EarxNySKZJiKHoS5RcPTDLTL+GJTRW48SZTkRQoJDyRahHhdfYSJ4aMT4roITge/zwk6ovRoYgEig6AflliORaUoeomNeFYoIJJFBdyvCeuSDdp8SdRlJE1XEQtvSnJvhIurhQ7Xoo3cuFfQhi3BbFtmFjvPEtX+rQ5fRJbFhdsPbwRCBYvPtE2xYtjM0o/NM6bi4JAOgjrScnkk56Ec/fI6zQL88jmYkBE/7QmfOrUqVJB5uaUCfdAYIIiEGV6gmZcqB0IBAKBQCAQCAQCOx8BxIYSiew1Nzczo2EgifxsE+XwN4SQqAJLLJC9zcnnAdHClFA7LEhAHIYjjoTCeUHJuHtZvnx58swnaX7RKo6bk5zc+fFCFJaYku9PvBGvs7uYi6iTy0NwVx5oRU/MOalHE44stH5hiN3hwwXSyHGTTwpFTnpB8pHV5JNjoqyJ+nonlpLU5plwL4nuiitlmeiEFXDFihV+8UBBJIpjSik+DKWHyF/giI5A/pUKqQM1CTKRKPI5rlq1ynQJOYnwbzJdD+FIeQ8mn0pdUphKIiITAkgsyUy4Xh5CziY/EWuldyK9q1evlpWSsw3ndDYZaTgGAjsegeDAOx7ziDEQCAQCgUAgEAgEdhEEMCUMAVXAQCTJiz+TUXGbpBBrQkVwG7QncZ7ETzYnnE+f8iypKKlEQ8ztZz/72d133+0TaoQNelm6dOnMmTOTnBSKfAHvu+++5LjJX9ISG6TGH//4R36wL0pifcgS6kWIT6iUZ5MSkmOix/z8+9//FhZiXKjhfcGCBUnDO+64Q3T8F8jexgKTHN5o7he9l0AvRKWwgtDKVy8rV65MeArFDJuyiU+5lqIQr9ShjlgrM/WyZct84o3MtAf46quvlsyN1djQRfJTdJRJL2vXrk0vMgKvZlwljQ7J1rph2Id9l6iUrffmH/4T9U1o01ZyaG4uRlq8PKzAMR4kNumveNATGqj7I9BzjNj4MxAYbwhsdn/FeFM09AkEAoFAIBAIBAKBQGC8IYDbJMKG+l500UUXXnghNrX33nu/613v2iaq4iRMiGQmiogCoSgPIRkF4pMfZDJ5u//++5FJTGnOnDlcfEVsfvKTn/zf//3fX/7yl0STMEOfEidkAn0I+dJLAgsq+b///e9PP/10kqn385///OKLLwbC8573vGc84xkwwcf43Jwon0SKbd55553HHnusgIgWBTyXXHLJi170IhJuv/32xz3ucXwmgrpJUQkNsFBM2tldyeRfGr0LS8+0+5d9+2tf+5rVzuR85jOfIT/xUmGTnqL27sFy3YGEwb7kJS/ZZ599mPfJfOMb3+id/RYnJD8Zwx9CJUL4EQs5X/jCF0hgoL711lu/+MUvHnnkkSk44voQJuVNCqezsAk3aaeSPyH5hCc84dBDD8VaDz744Gc/+9nApICsSWncpKhNOr7zne8Eo+fmm29WmKFhOkB0j4BOb1J+OAYC4wSB4MDjJCNCjUAgEAgEAoFtg4BB27YRtI2kFMaghZckOPR8ZAAXYCy87Fw80Q9UhDLY0e9+97tPf/rTU6ZMSTtI0a0f/OAHM2bMeOELX3jjjTciPAsXLjzssMOQTEa2Zz3rWagUS+O//vWv/fbb75hjjsHBzj//fHL4ZyfEKnfbbTcMCp/BlG677bZ//OMf06dPx6DYb3/84x+jdqeeeqq4sFzG27322uu4447D/USEw8yfPx8nf85znjNv3rz3vve9hx9+OPlUEkrZu+qqq175yleyDNNHRKgmC+cTn/hE1tHEKsXFPz0RKvZeFAsXwid/85vf0IfyYOcBy8J1RUrDZz7zmc997nPpQH/W3UsvvfS1r32tT1deeaV4AcVDivr444+nHs3FTght8V7mbuQZHcXP0cUnP/nJkkm4KBix99xzT5Jx78svv9xa4sc//vHA4RnCc+fO9ck7nuYXqvT829/+Rj5MaE43jiYC5MXHPvYx5BA1FcWPfvQjwl/60pfS6le/+pUsuOWWW17wghcgwLQFlCR/61vfAgjwzzvvvPe85z3ke+Sa1HF/+ctfTjJM5JcXOhP1i1/8QkBzAYRQWFyf+MQnpFHmotNi4RMbP+SQQ2Ql3bBN0lJOQRXh5Pmf//wn0p7kyxpQ8POGN7yBB5ny5z//2csBBxyA59NTLCY4zj33XAHRe4r99re/VVREJPd//etfH3TQQYoKPXFm0R199NGzZs0yZyEfJUfqDjzwQJoL+KlPfYoN+Yc//OG0adMUBiVWdkBbCRGjWY9FixaZoeAH2oq9UNy331Oo5oWXFNf2jndrU1RQr/ASem4thoU8zdZsbP/noaYSt3/sEUMgEAgEAoFAIBAIBAITGAE8x6jX6A3hfNnLXoaQIBXoB9b3qle9CrHxCYe86aab/vrXvx511FFIMj6DMv30pz/Fb1kF/+u//uu6667zglviTqeccsoHP/hBLO7zn/88USeffDLGhep89atfZS9FBVG+17zmNWiMuLCaG2644YILLkAC0UjsDmmkElulT7jKJz/5SdQL78J20FGs5p577kEOycF4cWBEixp4DuHIMP6MHvPwpz/9CXmjwFvf+lbB3/3ud9OZOy7EHa2SZ+ir30S50S1J/sY3vsEGTs73v/99RlQPPzRk5uXhLW95C4ESi7OJevHixRigpFmrPHv2bCnCM7E4KCGQSNp3vvMdHPX5z38+C6eZgu9973vYL0eqMlcKC6gjjjiCPtLLp7gQXdZjAj/3uc+deOKJBKK1WKVYpEIufOlLXzIf4U8cGPvdf//9v/71r8ujX/7ylxY8SwvCKVGC+GVi5dML6ghVBvlrr70WyBJIOKhRWVpBhhwSxPv2t79dQqj0P//zPwoGJSWZBAu8ZRAYxfXtb39bAunJFC9SdF3ukEO46HBLAmWuSD/72c8CBCO9/vrrsdnvfve7CsMHPvABExNIMvkSnrKbN3Td6gM8X+yKE0AUp6985StPecpTzJKgzeirbAUvhozEikuhesc73iFbsV+/BHI3HWDx9tlnny29PMhx5usvf/nLpl3kowmOD33oQ/DhWaSSFk8gMBERCA48EXMtdA4EAoFAIBAIBAKBcYEAhoMLIT9suaiddzzBn5gk7sFWiYheccUVCAnmtvvuuzP94TY4DOsoPsPChuzhIfjJ3//+d6QIk0FvCOHTg+EQixQJbkkt5syCShohiR7j24jWHnvsgbBhuSgWEsVmyPCL8TK9UokH1BS9ARkqhSBhNejQZZddhpBbjcx+y10sJONCTNm0sqqWwRbLYhJkZkSVETmkCIOiIbHYOOOtUPgtEoW2Pe1pT2OYFV1ajktJ7IvCJ5xwAjskWLAm1IsHekqaP2mFB2K2iCXjs7iwdFohcj7ZwSs5DJ7mF3Bp70yp0kI39sz3ve99+CR7JkpGBxMHgvNAGWkXnE9aQSYdhQVe2WEFOLG4rkkKfA91l0b6mEdAthlFoSQiHM8nQuAvlwkhSpb5U+bi0mykfErFaaedBj1RpwRC3rwGGikUfPzKTRxS9tFNHmHaFi0z6ioY8h1rBbjcXLJkiVQTiMlj1HS75ppr5Ii0nHTSSTywrsMKGpQxvSIKoiRWvJLw+te/3qQAwGllPgUt55klHFaIq2yFvFCCKzOmAGS65QMy0bSLqDnKX8iYrHnxi18s4QoGz4roK17xCvxcWkxq0FZhsBnZjIPYxRVPIDAREQgOPBFzLXQOBAKBQCAQCAQCgXGBAC6R7Ku4gZWiaZ0tGoNNpf2x2MW+++6bCAOfWB/G4kHYUGL2PYQQK8NAcD92VOZfrAYHw458wruQT2QD/bB2F9dlzETYiLKmGqciP7E+K3jJRJmwU/wH50GoMD0cDD9EjYilmEfAj370o+gQaWgP+sSnrzgS9QQn3y8dePaODt111110YLFkHvT45KEhLseDSAXHA1FNfM8nVnGOaCq+RD1cCyuzzJsySJcXdI6Vkl2XWFbls846i0qWRvOAtqGaaB7FJF82i4JABklf+cdOhaWbd0t/2ZCZlPE9PgnE4nijGGXQNgmUfJ+kQmZh10gd47CH6RU1xSQTn+cHAsJ6QXQlhAQEXi4wHSfGKy0exmcBrTBPxt4UI5+0lQWkSaAXSZAppMENiUXUiRUdTiv7pJF/syQcuSg85h2wbkiivpZw48zkECKlfNLHr3QpV5JvTTjhUOUuaSL1Qo40ihH40JNYJc2fdvaaqhAp4YBSSgWUBfJdCbS4WjZBSXnA7T/+8Y8LKF7lU77DRD76xLMJAsFBpLRLYMKK/HgCgQmHQOmE0zgUDgQCgUAgEAgEAoFAYJwggHVYMuoXE2N0/chHPoIkIAwWIdvd+upXvxqrQYEsSUWoEBVqe8FqsBGkhYuFzegHCx4OzCCM7wnuK6aHqyDPCAli8/73v58Ld5KTRQ51+cMf/sDky5EHwQlHYlEdFAUls4rYRtCnP/3p2BSSgzEiNmyMDJ60FUpE1iQjM95xM9FhNR6sDPEjCpWSNGIZVP2JyFmIa++or9KFR6FYNEQLJQSpQ2KlhV2UkdleVqZOknmwJtni5De/+c0k82b5cWJitBI7Nk4skyMqywMh//u//3vOOecISBrLLTlifNvb3mY6AN8DuOgohjcyHYMlbR6mJ+XpLCBA6IYHQhsnRyZNFphiEJ1kUgCBtCMamcT0IIYqI3tYKBYtFqH8ieTz6U8y6U8+lPzC7cwzzySK3Rsmchkm4uICWwrTzS9laCJepcLaaYvAyWRilWpRS6n5DskxJfGkJz1J4cF7FQnycWwFhrUZ7QS7hPBDNzlorkSGsmArQsnky48U8YOQy1+TL4TQREEyK2Gtu3XjCgxyi/aTY8ECNi6ZomB7x3JNlLAJyynAWrAtRnpSQ7pIMOVBuOSL8U1vehMFGJaVSbFLiFj4jCcQmHAIFNncMOGUDoUDgUAgEAgEHuMIDBflSusacps6OmO8jckM0DeZWaHnJmF5WMfxhie+wcJGbaTCaljEBvNBRRBUC1y9YyOoS6KCyBhCxZqHtKAubJuo1+te9zqEDZ8kBOVTMITCRtAzXEWQhAnJPnERECVLjgUPSYJfdM6T2CNWhr0kloUVo0OoCznpK55GK5rwRhp3PDOJ9ZtWRIuRTFpRRkpF7ROLaOKumFJKe+FFdFzQYxyyINkmVTQMGogr3QAlIrHzJqUS6LcgnG4QQ8OQZIqRRgH+5Tt3sUuy5ONj/kxTCemXMZZLsrsWUlF4IRAPBAIX2KYskHySRZ0QEAWKSx9KihSxly6fkHbphQ/5VMVpGW9TkqVI5so7auPDzK0pRmknJ00TCJI8SxEYeUjYUhtcNBEXCTxLLM8pm3hL/rnTQYmScEGIUszoKaX+FC93YFIDLImXpijkO2l0FotPnpQjiL2k+Zoi8lW28imvvdMELCLlKKzoQEe+5JOQah8XEZk78MJzSvJ2+h1v9X1zyQw9N4fMI3AvGh0Z6uopGc0Oq98eTzZ+qK3fvgV3e+gdMgOBQCAQCAQCgUAgEBgnCOAtmAB2gZngJDgVOoFlMbQiSFgEcoKeJQ7DBTnEbbBK+vODdXhBupANxIY3fxKYyBJp/kSnCfSOVmEvqAiB3JE3cfGMn6AuYhecKO4iQpwYP4USF+7HhQUvxUUCNXAntJA3jokVk0MaOVgZyiogmoQs8ZCYUjI1M0hKF0fjfmJ9AgINuZDmT8L96Z0EzMov+RJINypJAk7Fs1+awIcQfyYTa+JUdLPo11eEObEyivFJFBDI5D8FwdZMK/iVOnySB9J44N978uYdUDxwkbSkfEo4mSCSfZ7EunE/MHrISZmLALOWc0mcEAFOn0TK0SeW/DPOOMN2XHiKEQJSARyiQMqzKKSIekmfRNR5SC68iT0p6Zee9JcoZYBKoqA8tSWcT8WMEMoLQgJIaSXLRMpd1oCRsVf22bKbwJSDhKS8Jt+fHv5NiAhCODmkcZEjgtCZwsRyhNiGBFgmiksQBFieJvkpUfEbCEwsBMIOPLHyK7QNBAKBQCAQyBAIO/A2LwfGwZuUWeAbm/y64x3Hm55IFLaQtPKOGCAYeAIqgsUlfLijc2ghzpP8YxFIEQrBHaFFe5JPjqiOd97wlsTl8BwuPhGLUGGY/kyUKb0gKkhLoi4pLLGYMAnCCuKFhl4ITO+8UdU7md4TmSGBf++Ep19J8xXzwb4ogHoRwo9QHKW0EGnyhjQWXjAoPhOVElAQjz8lLalBAn7lHcEjSkDET+r8yT8FxEhtgGCbPKQkkJl04Ci4tEsF/saDgAgzOcmDVBBODuQRb+6FhwufciFlTXKnsHjRTtkkLknzp0+kiQUCEINq0plLQRPpEouvUlfIFzMX5jhScF8TkklPfgQXO81T1CktCXZRp68J4aShhHD066snBUzxJgl+M3wfQJjnROALaUwliib8SIWkFQoAR49kEiJRii6WDg3Ii4uoFJ1QQOAnTWQkuKhUUGB7vFBgk2K3d7ybjPQhHEPPhwBnaz/tGDtwcOCtzZfwHwgEAoFAILDzEXgMcmBjLGPiAjcwPDWWNeg3Zt35+REaBAKBwGMSARy7QP8Sl9YuYdcFx0eJyua47raS/yjVKwTfWXrCwQyFrsH0hOkb/YKXNMdR0G3Dl83htjn9Nwy7w953DAfOpvfiCQQCgUAgEAgEAoFxjoCRZTLs2EzoVlLv7DNGPGxH41zzUC8QCAR2VQSS7VrqNFCJRyWWlezeu2qqx0+60uoA4GPCVn9YvGBNflqqMH6UHJ+aBAcen/kSWgUCgUAgEAgEAg9CIK1EZWCx08/2TqfOWv7qCQ78IJjij0AgENiBCLADbxgbGpw48LayK04IuyUEdpae4k2Y+7Xc/Zvf/KaJUcQ45iA2LJabfA8OvElYwjEQCAQCgUAgEBhfCFjhZrLfIjeLn9P2SIMeS+DSDszxpWtoEwgEAo8NBOwiThwskS5tlHRbFL05TvjYQGXHpdKubNOgsiBNiaad8Glr945TYmLGFBx4YuZbaB0IBAKBQCDwGEOABdjIxtE+RjxemIWxYuPOGO48xgpCJDcQGEcImJIr8F5qYb+MkBqrbWWHTMI3TvB449g7S890bJuz1hjkdQoJdrmwMWLhMgaB4MBjAIk/A4FAIBAIBAKB8Y6A1W5plGPEkwY9413j0C8QCAR2UQSQXilDhlkjmSWtyPXnttqjsTluOd5o3s7SE/juuLITGAemg6mBNEmqj9hFi9s2S1Zw4G0GZQgKBAKBQCAQCAS2KwJMvha8WQ4tlnQalkEnk8t2jTSEBwKBQCCwOQTMwTmNKTFS7CtZgLVO481Ouzn9J7q7Q7BgnvDHh2UB5BmHdQ0TPWnbW//gwNsb4ZAfCAQCgUAgEAhsAwSMNW39NdYx4jTuMdZhcnHHaYx1tgG4WyZitChXNJr+l8u/F35HikYL7/wkcbEc8UGwQmzD5wGUNnSL94mHQDICJw5mkg4fQ8DiwrYdlpHwd6u2rkEH4ThoGaF3YBZOObLD1JiIEQUHnoi5FjoHAoFAIBAIbBaBcWt/SGS1oF7hZbMpefAHwdNqNzaWdBw0Amzx2zYf64zR88FajKO/drCeI6M5p/3gtSO5ovxvrtgtllyKnAI0UlyU//qAS97jtsEK725rb2tsaCRu6bKls2fNXr1m9dQpU5P0vv4+g+C62rr+gf6K8oqCY2VFpVCKR3NT89BwdllrSXF2fm9rWyuKwmdvX7Z1kAcweiktKeWigJWXlaPzyMw2pzHZWUn5xxQBOlxSVPJgUvzA54f77w7O94dTZ7PfHyN6mpIrLEVRZtra2iZPnizt995777/+9a/ly5fz4HF4wWGHHXb44YfDy7ZVjZgl0470mzRpEhfTedq01JQJq7gqmatXr54+fbpPP/rRj0499VQ0r7GxkSj+HQqI75GA7Cn///znP0844QQLZAihw9q1a+mwatUqv+YKU0lOkfKDIorUr+evf/3rGWecQZqoNadEffe7321qauLyxCc+cY899qA2BcSo1iQTK5/iVUH4Twr4zSpQ/nxsyqc2+Xvf+97/+3//j8LCikiKqOpdFJKQVBXwxz/+8ete9zqSBQcjF0kghLbkC8izT3Tw4iGcI2XgLJSE8I8DcyGZbh7p9fuw/YtY6FPwVnjhuOOfMcrkdogNOyYpd3xGR4yBQCAQCAQCgUAgMJEQKAzJRvK8bf1vPgWJ2uV/k68C1yu8PKqUrm1ZiwAPDg1iuQjw3//x95NPPrm4uMS/15z1GmPidDA4WvuVr35l5qyZJaUlj3/847/9nW8LVV9fj9kacCPAK1auoEdTY9N73vOe2rra2bNnP/3pT7/77ruxZQTYpzSOR4CNxb0/KqXzgTdp6S0g+ejlh4TxjABW4wq3Y4899qijjlLY3v3ud9u2iqcpsYkA44QoHLaJTyYKh911dnaidsuWLcPiEGDs7g9/+MOnPvUpXNGdcD4hh8ozAiztiXa2tLSsWLGCT6Ud/SMN9V23bt20adN48OfKlSsVadF55yJS7Bp1XLBgwe9//3tyfEI4aULyN77xDVGIHTW9/vrrnTVFW+r5RHnsWro8/PtVTdzWniSLQtIwSe5+//GPfwhOOJ0TMyeKZJ+e9rSnmRogjViacOeI0/KMctOQWJHSNvFhkvkkxwvh6Z1PCSFQqHgeGQLboI17ZBFHqEAgEAgEAoFAIBAIBCYiAsWJyY21YxZxKGYYzpJUlBtlZtgGlob62oYickaKKsurDLIvu+Ty3u6+2ppaNrSBvsGaqlqDaYPj888//3Of+Xzbuvb/OuXJV1555TvOfuduc3ZnHDNWrq6sodDM6bPwhB/+8Idf+sKXGbg8F1948fve8/7zzjvPIN7Yury0gvzhweGR4dHyyjIujzJrxnDghEuaGIDLWPAeZWQRfJwhsO+++86ZM0fRQupYRD/0oQ+xr7Jkoqy//OUvGUgPOuggp9yjx0jgL37xCwUVYcaTFeZZs2ZxRAvnzp178cUXv+td7/r+97//vve9T/n0YMUHHnig6RskmbUW1503bx6BaOrvfve744477rrrrtttt91w72uvvXbp0qViT9hcc801ixcv5v8pT3kK+V6wbtUHBeUB86SDasU/ikvaueeee+KJJ95xxx2XXXaZ5Bx66KFTp07FbAVBUO+///6TTjqJ/ojx/PnzfZU68m+88cbnPOc5qg9pxNLztttuI/apT32qSQGsG1H/zne+s/fee5uEklLxJrOtmqiGPuEJT9h9992JEvvpp5+OS2PCz3jGM/xS6Y9//COdTQE897nPhQ89x1m2TyR1tkHrPJGSG7oGAoFAIBAIBAKBQCCwlQgUCBv2u54AbyChaNSy6DzFy35ZgkoQYC682D38KH9Li8uYgCxRxg36ewde+cpXXnzhJSy9uLGBcndnjzFxVWWVAfTypSuM8n//2z+87W1v6+nuveqqq4TNqOxozhrLkaFRQ+2vfvlrhuBX/fPqn//858c94fgLL7zw7jvvIT/pOTSQHahTUpQZSB6x5qivsHkCnP0W5KT3ktFcyfqvGyAYr7siAsoVAvzBD35QwXvDG96gACOQL3vZy/yJ+FktjEBK9/Of/3w0ku1X2UZflWeO2B2Ky8CLfwq71157/fSnP2UCVZI/+clPKu0kf+UrX/nb3/6mxCKWVj7jnCzGX/ziF5FDhPmjH/3oBRdccNddd7HoMi+T6Z1KokA+BWc3poZKhKjzkCirPwXHkFWTZHNmwcY5L7roore85S0+4bTnnHPODTfcQGefCMROv/rVr/olk23Z+mpE3Tvh1jCjxNLi3aJrYZFedukjjzwSH5YcSeCZWKJQa7VVYoXyggN/7GMfI18qLPZGgM8++2yAUMYvEk5hSO6KBWcHpSnswDsI6IgmEAgEAoFAIBAIBCYuArgs9lsgw1lC1htK0d0HkpXnvRkBzr7mrUtZEDbhR/5bUlLU0dFZX19nxTJCvftuu5N2/+Kl3V3dzY2TWHmHh0dGB3N//fPfRHToIYf6+rijH88cff2/bqioKB8cGCorK0Vr29e1t7S1LF60eK+995o2bSqFjjj0CEP566+9nkmqqgpvzw0yLDdU+5QR5sw69kg0f8DEm6DKfv8jJ8PpAff038wlnl0QAUQO98Pfbr755p/85CdpRysuxwb7kpe8xCfv7LRSzoRr1kZxu+mmmxYtWnT00Udz9CcWesUVVyB+yKHf9773vS9/+ctxYO7oKCqINjPA4szYNZOyUGizncMWG1uifM8996CRflN0yOQLXvACHJWV1UblhQsX2kRAmlAIJ8rqBelFhtlaafuDH/zAAmxE/cwzz2TX9dWqCkZsHkigD5eTTz7ZLBIDL/M1jkoIey931JRP71juM5/5TKk4+OCD7YW2QONVr3qVdc5HHHHEPvvswydpyDM6LQnPfvaz055nhm6hJAGfT2s0brnlFmZtRFqM7OeIesKBhGySK55HhEDMHzwi2CJQIBAIBAKBQCAQCDzGENgka9uYAGeopEOQ0+j0Uf+WMQUPjOCoxtYL713U1d69+5zdRdK6tnVkcLSkqDjbVdjeMXf2XH5yw7nmhowP3PHvO/yWFWfWjtLikqbmxmuvyijHlOYpJKDNtk0O9A9gHYJkAUfzd7p6kU4unkemeT5ogeo+SE4+lhTXeuHJc/zucggwb6KUWCh7LItr+hPvZftFgCXXdlbl+c4778QJMV4eDjjgADTYvlxLgnFCflhu2UgJscbYC3c8EOtDgPFAZJgjDskoihP6FSMhAorR4mREFNUkEMn06YUvfCHKyjDLEksOeiksbszqi6zivTSh1Z577omyXnLJJay1S5YsIZZuvj7ucY9LzBkLTeT5tNNOs7OXDqIQljRaeaEAIy0dvLzzne/EYJmR6UwIxi4u7tKC5VpirfISghWLhTsyj07b/Jy2Q3NBuW1vJtAi7c9+9rOvec1rvva1r4nI1ICv8TxiBIIDP2LoImAgEAgEAoFAIBAIPFYQcNbNRv8s9C0kH3Hc4N8Grxs6P4L3trbOqrrKkgqn0Q75nbf37rWNNb0D/WXlFR3dXcOoZEmusqba8LlvcMCpt10O3a2tmTZ9xqy5c5iIegcyR36GRkZ5qK2rr29qzJUUF5XlyqsqS0rLikpLyiorBoccd503YCfNndm8tUlISDwQKgMmsyJvgErykGhweo/fXRcBJs3/+7//O+SQQ3BRZA8/xAmxR9tosUrEz6/1wJhesseiwaggyooresdRYYMz+8qPU6aJYlX2Z9pXzMpqGzDeiH8yw/KPYXoRlmQbd/FehNZXn0z3/PnPf2aDtbX49a9/PZpKAdwVi06LlsWVKCVajgA/+clPphsKjaNirRxFRHMrmQm/9dZbMWf+aeUTO7aHB7ydHxSddRfR9UKChH/gAx8QL2YrXuqRzIMgiK7V0ZJDLPJPMjXQXe5E8S/hDN3czRpwsf34Zz/72Re+8AW7o639FvWuW3x2RMpiLfSOQDniCAQCgUAgEAgEAoFdD4Fk7s2nK7Fhu1/Ty/p9sI8+yY3NdW5dNXSuqCrt7u41ajcaLirJ9Q/21dbXlFUUt7Zm98TUN9R19XQWlxYxR61dt2b5ymWz587qHxyoqinPFksW5UrKivY7YN/2zrYVq5bX1ldx7Onrtry7sbmhtLyop6e/rKiypKyYT+YoxuOtXWKJ/G5o/JbwBMR6XPJcOHPiL57HAALoq5OfbHP9y1/+onxipMccc4xFxeyi+CHbJtMuyoqdfv3rX2fYxJCdPvX2t78du3NalV9LnZ2S9da3vhXhtDbYyVL2/WKGyUpMCJnJlovochcKBVVBkF4uyQyLgvqEjlpi7UR0X7FNhl9+EsvFXYnCMynp3YvoEGPvKKujpyx7JgQXJXP//fdHgD2//e1vCUHarZTGvWliizLGy85s67JYkl0aCbd3F3W//fbbxYvEkswifemllwprWTjJkgAElZojXv25z33OOVgYPrH+ZJSWcF8x3ne84x2CQIZuglA12ZwfA6VpuyQxOPB2gTWEBgKBQCAQCOwsBAx3dlbUm4wXe0nuhZf050PoaehjFMWbUZeRGcsAW4GRmXEbRwMg4yF/GktxGSM2CX8EvwU5hZck5CH0fASxPPogBfUKLztGT0fBlldUDA0OGsVmFDS/L9G2RbFnxtjs8V5kyXJ2CFUuu/827/igHzcJe4xo5a8tu75193bLzaqKbP1k/2B/RVn+EtHhQbt/mWJ7+7O7T5SH+tr6ZWtWyYvpNdPbOtaVVJWVVpcPl4z2Dg+W11aOFOdOesopfzjvvNvvvfOwQw47/9ILc8Uj+x1ywGhZ0f2rVnzrW9/ab7/9XvzCFx9w2EG1zfX8dA/2Q++6W24YGOp7xnOf1TM0UF5d2T86PFKaG3DXcXHxkB3B6VbhkpKkp0T5l96HR7Ojs7xLjoG4QlhaXDo4NMAxe1zblH2CwujQSMbey4pK3OsiteWlZSKBATPf1m5kJCdBWXhJf0b5TDhs7W8BxsLLI8NzTPCCGsq5Xb54LIurEoXpIb2WE3/zm9+8/PLL8diPf/zjKhEWZ+etg68E9ILHKlHyFDm03NcWWe7ZpE9RERsykoz7YdEoIorrdGh2Y7UDQ7aEmF3XaVV8epyuLDphMckPf/jDmkpk8s1vfrPtwdYSs+5is6I466yzBKeb+pjaVcI1tgJ66IYes+Li8/bMO7KLH9X/SU96EqIrXU5Z56L4S4vTuZhtmb5t+pWuF73oRSqdF5/susf5kdtkLv7Sl77kFHd7EHBm9mEtuSR/5jOfEZCef/rTn+hDK2eJSQj9pSWtHreWGwIs4eYCgONB7zdHgzdXL8hMqSu8pD835z993TG/BR3Wq7idYy1yGNl2jiLEBwKBQCAQCAQC2xiB4aJcaV3DJm1KhX50G0f5SMWNGWoUxGxOT/6N8Oz+MvJLfozPjHW4+NMgySAMq3EqTBK1OTmFiLbwZWv13EKx29zbztKzv7evsqqqD/5lzpItGRweKi4pHTE+Hxl2zO1QbpjR1RrjyuIKJK9noKeilAE2O4q5uKgYnzTSrSyvLMmV9A71VpVWDeeG2zuzVZr1VdmmwY7eDi/dAxkfriitGBzJrjsyxi0tKsUn17Zn9q66yrrB3KDbXxzpzGRkB6TtjM7jcesMexrbkTG6oTN71z///nfk/Fe//vUzT3vm9bdcf8oppxiC33jDjX39fQxu//veD+y+7164xPX/vObo4481dqeGC4Qzlpu/2pQ+iQ8UGK8/wb7+yRUNDGb3rFYUZ3Sdzn6ry6tHcyNuVSqCRa60LzfQ29fHT01ZzcBof3kRgjE4MjxcU1ZpPTkMRVRT3SDglj9i36RnojbpvrMcH2t6Sq+yqggB3AtmqDSmgqTMZ8U/f2WuFcXaMUWCN0ujsUHFm39/CsumquiymvpTE5d4HWYoCBdh8VXS0jphRlellzuGyRFNRSZT2PRCgoB+0UXekmEZQxapncCYqri4k69R9eKhktlGoTBn9yolHXijpMJPPWF50zK72MlXRFqbzJECQkmv+UqaeOdHvPxkdbmigpJWPuPV0s6PGKWXi+A0sR0ajfeJBAEFkXa/4kpqky/55IDRu9+EYaqS3r0wNVtNTVsBHTT9pje9KfkhZOOHhI0duYyrelQ0OjLU1VMymm2Q3h5PNn6orQ878PbANmQGAoFAIBAIBAKPEAGjMfvBfv3rXxvP2dXmF6s57LDDiDMEZD0wurIYz6o/wz6jrkcYTQTbSgTS2DGNFLGu7PKj3EhrTwdT0dBo/1DRcFFZNnTtY83NuWDXuuDS/lyfI5vLc4b+pSMlI125Xhy4qLSoJ5edRlteV5VZenMDyGdRVamv5eWV3rkYy46WF/fnhhyD1TXUUd1Qt65z3WhlNjIeqSwqri077blPN7Y2rM9Vl6zra9+jau8nP+W0H/zyRwboeMVr3vrfJ5988qlPepqIqibV/tezTnXizhCVK8rOfvs7a6bUO6FHGXvCfx1v6WlZeeVAbkBcZbkyC7mHcoOob3lx+WBuqCRXhKv3j/abbKoszgj8MMNvrr+4LLMA9+e6+SzNs5RM+VzZSLHgkiN1RSWV5VLXlesbLRpx4PSgULnRyly5EXh5NfKcoZeuj9rKfAjvEwOBNJGn1iBpyBvip8Sicwy2GGyqTf7k7mHUTXyYHw/GmwJKqubOL4LHXaFFIwXnKCxiqT56F1ws/CQmzMiMhyOiaQWNVpS3tDSaBNIwW3IS0xbcCxf0lUwEmAcqCSJG7/hk8umdi2vJqEpDD5fCk0gycpv27gri3Vf0leSUXr+UJJyfFNwFyBTQjIs6GXsxc8pgyEziIvInD5hzCpjoMXf+vSduXNAhXrYKgeDAWwVXeA4EAoFAIBAIBLYvAkZpRlrWvLHvGQkZitlWlziw4aMBmYGUs08dr+rF1+2rTUh/AAEDU6+MwP6hgiO5YeuQl61avqZtXW9/T1l1eX1zE2bY0rrWiN+Ytbe7r6aqxuhWNsnQ9Bi8piG1EbkXo2QmJmNZ/g3K58yZIywzV0YLSkuNfY3sBT/y8COXtixfc9ctRsm777/Hc1/yfGN9I3t/smu1drRfv+BG9remqU1nvuHVdjniAEbYF15/sbCsWy9/3Ss5LuteoVCRf/Axh+5/xIE+IQnE3rruNqXIn9TwVRopQz3GJS4U5kgaOfxYmUlnAQ3NEQypS0Y/alQ4XiuldGSIepMmT3Zkl5mauuqa0ZGR0YGhmorKxtr6sqLiqvKKyU2TJ1Uz+mWsPp5dEgGFRLoULcVYQUo8U4HB5ZQTFE65UtKUc978qTqk1owjD96VPbVG2fNnMq5y9KdK4RNvCTdlUnHjwhuZ5CeiS2YSpcSqKdypIboUixLuJSu3FRV0SwxWHSSfQHoSnoKLTjuswEuRWpDi4keDQELSnwcvFCDKOyEp9lSDuIudHBKSC2n+hAw1vCTF+KF/auGTklJNMZKlTnQ8pIiQ6gQvJQVPOMTv1iIQHHhrEQv/gUAgEAgEAoHAdkQAbcCFDLZwYNGwA1u/im8YkBkMGZz5NQZKoz0DoDRc244Khej1CGSELc+Es2OvcNP+4aG17a1dfR2lNZUVDVVDZSPdfd19xQODI8buQ+0d7XtM3qN6Si2W29LRKr9KKw26inv6+yZNnWL/rfF3e3d7eVX5aPHois5V9Y31PcV9NgD7tZfYVb2DQ4Myvba2ZmHLYh5GykeWd6ysHKgsqiupmlLTletx0NWa3haLq7tae0oqS3w1VyIify5duRwht2q7p3PVjN1nlFSW/Xvh7SW1Jb1dPTUVw91DPe5S2m/Gfi5N6ljblejHyNBIGlsXVZR0jLjntGd0eHRkdKS0vLSqrmSkmhGsj/tg+VDbUMfg8CA1yuorBsozojJQItXZsL6opKiysaasoby3qK9zqHOwdNDL8Ci7WVllU5Ut0p1dnSVVRXXVtUbu2brYeHZRBLA4RVGRSFbQRPCyEpIniigcXqcpk3reVCuF0LsXHpIjdurPFCR9xTzVCAQymUwF5I5w8i86EfkkeL6SZguzE1FMfBIpRTj59IKpelFurUZOYYXiLe0E9i5SvJSLdy8mH3lLjyhE5JNYJMpDz6Sq98RXaYXfJsXEQmcuSWYSkhptmviTfC/Jc9KcY0qgiLz7JY0EXwsepM6nBKCXeB4BAsGBHwFoESQQCAQCgUAgENheCCDARjxOQzXg8zA7XHXVVY5FSfEZkxn9GDMZ/6WRXBrhbS9tQu4DCIC9zNnKbE05l/LmN+aVFQ+MDPSNDs1sri+uLlu1dlVbd0dZZdlwGW7cMm/eHkUVxStaVrLKGjfLrIHRzIxja273YM+aFWsZeRwZhelmY+6Skckzp5jv6OjsKCsvGxod7ujrNLBunj7Jrz2K/cP9SsJw+0iv46CbakurM03+ffdt5kqKR0uqazObWHVJzdqOlptvvtnmwJ6hXr9OJDL+Hioebu1uK6kqzThJ6eh9S+YrY4fuexirV297m+XJ/UPZ9arG4s4AKq+tsMyUJnRLo/ayinIS2ns7KN/V3UWfzo5MNxZgA/eWdevY2YptWx4ato25rqG+aVqzzdILl87v7e/LiH1vX3lZSXlNRUl1KYrf0tEynBsamD5YksvG9/Hsqggon0oU5uZFGlUfLVWiiCnJXApUk7fkQRBfkUll1XtWYkfMx3ThhIpfEsWFH5+4K/8oonZSk4ippkjNOimZBZMs6quI+lXgyfGJZ2SSfMVeSfZLMWET50zvYvHQRFwbuoiXC4ULv14SNeU/ifJbYLnC8pA+iTElIbFrQhJVTr/iSnKSVkk+zSlMsUK8JHjnmRBik3ye49laBGIVytYiFv4DgUAgEAgEAoHtiADeYvTGSJi2/jrI1KGphjs2fxr3pIGaAZaXjCL39GxHVUL0gxGwpDc5GKQ6C9rW3lxp0bRZ04orSnoGunuGegZzA4PFg8VVxTXNNTPmzBgoGljZurJzoLPIUVBMR+WjZbVlFfUVazrWrFi3gqP3vtG+kbIRplqee0d6vftHCAmTZk4qqS5ZuHzh2s61XFa1reK5qrFq6pyp/ly6Zik5w6XDbb1tXYNd3UPdzTOavRAlFh58rZ1Uu/eBe4tldfvq9r528qfMntI0vYkLDwuWLeAuuqGSIRHxLCAJBPbn+rlTuLyuXIzeBZcQLj5xrJvs/+X06ejvIHaU2iXD1Q1VtU0o92BrTyvPw8VDo04Nq8hVN1Vnkge61nWvyySXDPflsk2e8ezaCGi1sDW/Hu1V4nLaLnQXC01NGQpXIJBc0EJEEeXTBmKniRaax0meE7EkJ+GW/hScT/45eicQMyTHI5SouSdyiwMTjjnz7yvPF198semeRE25iE7VTn/6JYpPLwQW2t4UEZds6qe4mJCURrHkE5rxfC8+cUkSvEgyZXimiYc0jz893DF5flJEXkRKfnrhOSWHKB6wZRNqVKWG5PvKWzyPDIHgwI8MtwgVCAQCgUAgEAhsFwSY7wwQXTvp4CJXa3oMdwyqbAwzYEoWAB4MjPxZGA5uF1VC6AYIZDuBSzKLljXGxq9+s2OTS4pmzpnd2de1Ys3q0ZJi1s6+gV702G7t5WuXt3W0CuUWX2dHr2trKS0vO3C/Axbfv8Re4pq62qGRQUdJV9dWTZoyuaaueuHiRfYV89/Z3dHR1VnfWNfQ1Lh67Sph+WlsbrKQmL3XEVOd3V3LViz1677fltZ102dOyxUX7b/vfuvaWpcsXVxcWrJw8QJhS8tLZs6etXTF/YuWLBZK7CSvXL2qrqG2srrKBcIDQ4P8dPf20IQ+Lgp2kNealtVir6yusA5ajOWuL7Vauyi7TJgm3ll3myc30bmjq33ZiuUDQ/38OPS5b2SotqmhpKJ85ZqV69atra2tlnDXERNis7SDWFe3ttg77SwxlKYMM45nl0YA60PVEiHUamF6nsRI/Wq4MD31SFPmF6/DDhUzKwi4oJcOBna5Vokz1Qey28ha2zrQzV5HrlnMPJrrcu5ccQlO2T2Qa1fy1Myqqta+0Z78gWyltQ19w9mGBYfMDfZncirKynu7exxAZdLQHUXOFBQjuot8esEk/coNiiV6jGr6ypF/jlpa7l4kKjlKl+SkIFx8Si2zdHnnnp1Xlzf/Sg5Roih84u4hyi8hWnLkFjP360nB/UkgySleEHkE8WfivUL5M3nOy4ufrUYg1kJvNWQRIBAIBAKBQCAQ2H4IGDAZ2bjnxm2QltHaoub2msLYKD9MysZJXozMjIS2nyYheUMELGO0QtO+xdJc8aS6xq7h7uKS8ilTpt56x50suhYmOve4tKyimh1rZLS2smrZouVGq6xNhs2G0JZiVlXVrG1fZwWoUbF3I3tGsqlTp2GZd999L1OZEfnates6Ojrnzduzrq5h1ao17e0d2WHRo0WtrW5eqaqvd0BOsXtVBCShpaXV78DA0AH7HLTw/vn+rK6u7esb4E25sHa6u7u3q8syUQrYjphRAv6bmibxSeCUKdNaW1vYnxoa6gRRqJBvg386s4HRuampgf+uro5169rEwkW8lZXVNTV1bW0d+QULDv1yDUx22/CkadOHc0Xcuzt7RoZHS7KBfRHusfce+65cvXLt6pbK4vKGxsmjvYNmCZx3bfy/IbzxvoshoHgnhqZseJe69KffRIy5ePF4MfVjzfFISanrcKy9rygvdTuOO6rLSrR1ub7eIYvqUcb713YuW92irpUWl+WGBhsbGpDsdR2dXf39KHSuuKS/v7eqvGz65OZRR9K1tz3u0H1Kyyut4y+pKK3Ks9NEIBmENbPWRbt/yE2/FLjuuutuueWW0047zT3ACxcuRML//ve/u7XoKU95ChLrNjKk9Nhjj3UjsbRoeKXCVE5KUZqIVNzTJl7p5ZIcRVdItVj4SX+SkIRw4Z6kpZf0m0Ql/3gvx8KTJBRQLbjHy9YiEHbgrUUs/AcCgUAgEAgEAtsRAYNCht8TTzzRrrZ0RsuRRx65HeML0VuIAMqWt/w4JiobPOXXRbMXGaGualnLjjqSYwsara2u2WveXgvuW2iQiu7axIiCytDJk6fK2dWr17Le1NbWc7eH0ZZaA2OL3v3yv2LFKtIOPvhQhBkZNhAngTnIVwPuxsZmEnBaD47KRuRwNGPl/fY64Jbbb0E0vHPhjqaSYKxOPQTVoDlPpF0P07jPHvusXLna/Vt47PLly7mQPHXqdCP7pUuXM17R04oD9Nt5QuSgCggzGoCTc+nvH7SfGdGlgztuSPZOqwYltWnSwNBIV48rjl03XJKpUVExb968FatWDAwPUUBSensG3NBUzi6cyw7ZjicQKCCQJvNGizYkJqpUrig7ArCUadVFYT0Dw529g529Ix19Q+U1jfYerOns7egf7uwfXdPVv84yYbMutdxHWzp6Orp7B/FmazWc6G66cHgY9UVlkU9rbVQWpfqmm25Skm+77ba//vWvJ5xwwhvf+EYm4vPPP9/qm5NPPvm888674oorfvGLXyC0GuQFCxaopAWF42WiI7BhUZvoaQn9A4FAIBAIBAKBXQQBB5YiSxKDZrgFJ+y94ydfC3kxmMvOuDKqdmEpVpmZfiorZ8+YzUAq77zT2aAZybRzDyfEMI3C85w2hxWz+uY5bRchDD68kYxnNjU0GZfzabDO0UhdcLwUTeXuSasiSRYqi7p1DZkU4N8nkbobhnDR2etIDTKFEuP+u+1/9/y7ReerT+meVTzcV8EJZLbyFe/1CamlA3fxCotMi8UdS2IRljfuohPKWUQ8czRrI+E88J+MV1LtSHNJkDTBpRc5lxzXDo+fDA1NxgMCxdYGYLzZOuL0FGcLBfI0JV0AZyFDkaPLi92lXVpTXl5dZVXCYF9/j6mn8rListJip7M3W7lQUd4/0NvT051tRGZ+zpZnFPm1bEYl8ijtzL/KqmhSnXLmAoq7//77v/nNb77hhhuUTwTYXM9znvMc80RK8j//+c9///vfz3rWs1SNB9SL/054BIIDT/gsjAQEAoFAIBAI7EoIIBWYA8qBWvhFMwy8OO5KaZygaTGoTg/98U+LNVPWIIT5hcfde8/ee8HiBey3xtZpCgPlmzZtGs+u27X+GKf1SbZinoxRRtioo3F5ynTclYc1LWs4ckEmReQFiTUQJ5Y7sZgkNfBYTNWA/t577xU7n4b1vBHrK+bpk3g9JAgixvuW3yc4nf0qXdQ2vcIzsegu4oowo/RcEAAvYuRZWNw46YPr0plMfnBskdJBQJ4lMOmG60oj9fxyFJAQNFhyROrx4phrYeMJBAoIlCDAqlWeBmd0uCiX/g0NWwyRGYlHLLXPjVSVlNRXVNRXVXa2t3b3tGO7gwN9ZUW5ppqqBheJlZd2tq5rW9cymG1TL7M/wUb0gaJRh5x393Qrn8qhR3lWMZVDd3ErvSqCORqO/lSSVSI1Tk1UyO+6664Xv/jF7373u1Wul770pT4VFI6XiY5A7Aee6DkY+gcCgUAgEAjsUgggGAZkaIPBmUFYRhjyHNj7LpXOCZuYlBEIXkl+RyuOl5Jy+AGH/+vf/6qtyc505dTXy9zahAryYJBt/G2QLU9RRHlqMG0sbsDNcyHHcWDCDcQTcUUy/YlCk28IjjkTwj87FS6KORN49913i8JXYfFev40Njffcew9Oyw8dCcFsnQaUjLqWgHqQW0J4FtYKT/pQks9ElY3+eUBfMQGfKEltp3yRhgAnyQg2dxL4QaE7uroIFNweTqh4EXBkYP2ZPXTmIl5+sjmd4eJcdohvPIHAegQy868TsPw+MNeXVaH844w3Wwy88lNhVYU7r8tKRousdm4rLi+uLC02o1OeG6lz1HORY8rbBro7RlxFVlFeVV1pscGQnQvlzmUrqi6tIVI5NGuzZMmStCpB6TXhyOr75S9/WRRWQZ9zzjmWQyuxqc6aOUKDrYZQ/hX41atXK/NJsfid6AgEB57oORj6BwKBQCAQCOxqCOAPaSUt7oGceEd+Ms4Vz85DIFuKmX+SCkU5OxczE6vBdG1j7azdZt218C4cb82alTOmTrMW0/CaedaYG6c1npaVAiKrHNlC8Ubu2KbRNvKZGClibJCN66ahOckG3IbsmCdSqhjwj3yKlBBFwuE9qagQpXi49VcUi5cs9k4COXyydyGfXkgWHE8mUEQU22vmXncuvpMc7+LyIhTd0FQx8uOFo5fEt+kgIrScZxHhz0SJRaKcekQIOlFZXmpltAuBO/juarcHeHBopLKmuq6yvqqiEg59bZ1T65orJoU9LZWj+F2PwHrOO7re/Js4MO6bLVgtzj5Wluea6mrqqqqryquc9VxeUVRZp04V93T3WalQW2t2KdfWNjKjuWFoeKCizN3CDm/P9Q8PVpSWEeLwaYfVqS977rmnC7cRXadhWQKt3OK3TsOynuIjH/mIFRD8HHTQQWLkU3VQmyyWNi31ne98J62bWK9x/GeCIxAceIJnYKgfCAQCgUAgsGshgGAkusv8i36kxCXb466V0ImXGuNgSssLx2JZCG2tpocxds7cuUtXLMUA5Z1RMn47Y/r0mtp6PllNsVZMUp4KbngtT/FG66LlL3bKkQs+6eGIJeKryCf/RBmCsz6RTIKHT0HEyAPm7LFvXBQ4Khck2bXSaCrTsYi88E+IKBh1kXDbIH1K/vfcbU8EmCaIMRJL1WTUJVOMEoIPI8zCkiAsCs2bqH0SigsPkoDqUtWxQ8za/ovMi9TyVEIk3H1RxSVlfNZU1Az29nW2dY/2DozWQm495Zl4hSA03g4IJDswU3AqF6lmWRGdPyhLZRvNylhRrrG+0rLnbOFFcVF9c3NmHVYfmyo4ZOVpNNdU0ViRbcPP5pucnpU2fJqrcnF3VVm5HSbKs/P2fVV0leS0MEGZf9KTnvTEJz5RDVIH99tvv1TU99hjDy6q5Ktf/WpzVaqhks9F8Hh2AQSCA+8CmRhJCAQCgUAgENh1EDAyM95LBBjt8W4QhkUEDd7peZxWPcsIeTRkj2FZti3WBSp3LrpncHQQ7aShUXI6z0zGLVu2Qt5hrd5xQi9G1Tw4CJr1Cbc01PZiB6+BNdMrzolkIqX4JM8WM2OYyKeROs5JgoE44UgyfuvTbrvthvSSwANpxCo2aDM5fLIVe2hoJaewdLOwU4y4Lvd7FtxDN9EJQiAJqYzxQEM8lhCOzMiURwySBHFRlUy6oRNLly7FvelmLbRDpIllB86WRre3YiqZQHfb5IrJcQYWCcP9A/WV2XFZA6MDVUWxHnqnl+jxqQDqmj91fb12xaPF2emAeLL7kjKyW5wb6GEXZuMd8q2srNin4SGXCefqKnJdncMl5SUmD0tHRqycqSwtKc4h0aP9g/2Wbai5yq0yrxgr56mxVde0tKqMWExF8aAOqhRIL0d/KuRqt4rgV+Efn6iFVluLQHDgrUUs/AcCgUAgEAiMawSMXcanfmnwVFCv8DJGW7SEC57gF+Xwi0uM8bPhn5uTs6GfrXrfQj23Sub28LyD9bQe0/3AVvnKFKPhIrf+5rJLVu64446RqiIj7NHS7MrQ/r5sL/fcOXPuvc9NKiWG2slCy6cBNB6I6/JGeSRWzqKsBuLIpAE3DolAoqauFMIbcVfD9DRYl8tYLv88EyIKQrxQhhHYJ56ZsziKjmc+PWSiskmILCjEyCDMc+LY/OPV1EC8efZLpnG/eBmNUQL7IUVKYVSBBy78C45+p1gSl+ZZRN2d7VY/k2AVNcklxeV77rXnspUrOlo67fZsqqsf6HRfcV/xg67AeVDpeNjyvIPz/UHKbc0fj3E9HzYfx2K53pibmXmFLcohsGlB9IjSYiVzSU7Vs+c3o8HlVTl7hN3F7Tho5l/HZQ1b6qxWjpZUV5RYNTHQM1pXlTWhJTkEeqQMby5CXjN7snxRLxRvbax4lF7FmE/F1SeLF/ypwCvMinqaIVJzuXj3ghUTwrNHJSWKYyGx6oX6yCfhgvBJcvpKuCD+3NC/Px/lk4rZxkIKKm38KbmkgAVvhZfN+d+u7mOUyVYEbP8nOPD2xzhiCAQCgUAgEAgEAoGJj0BhoJaW8hpSJ/q3qn1tdX11SWUJkjl92qTGuno7dfFAFivjYINm8xoG1kbVjLoGx4bIHI3CjaG9oLLkgMdXPBOZNHS2Ypk3DzpqeMrW6pN3UZCTbFOEu6wI77W/0fDa7twkh4vohEqSvYhUKBL2nLvnfYvvS1QWE8bMfTXWN5THorFTYSVT7HZLkn/nnXdi5ng7gzBHUXi8iM5Yn38vdLPCW1rELhYsxZm+kt/QWNc8dQrdikqzU7KrqqoxhCl1jThARW79Iv+JXygiBdsFAYw48SBnqClLpWVqU9HosNUXIzn/Hx0qr67I2fKb3zVQXFJaXZtNHQpTUparKM74c37jwshgZivOuGhJWfmqdatM3JjBUZgFVPIJVlOU28RXufhTpVNf1FmVwotCTnAmocTpXJkoFUQFTARYEI+qpF74pFLw7KtfSypIE0Qs/HDx4pPgqoN3LvHsRATSUvmdqEBEHQgEAoFAIBAIBAKBwHhHIC2BTDQ46TqQG8BIDZGNdA2X0T/D62Qs9SefacTMJY2kMVIjb+4eI2wDcb/sTom4GlIbFrPNkoZkkowkexLJ5NkwmmfRGUB7CEE+PYbsuKVP6LTHQByLNha3gBmtFQVaKwo+95u7390L7uZTLILwJiwWTTGDeAKxU1+pzZF/ahCIMIudBL/Ysq9cMq6bv/RIEHJ8QuA5EgIckUqFT0JhAuLigYnbMmyh/Jo+GO/5HfrtSASSETj/mzYDi9xfWIqz2hKBHMnWOw+MlI2YPxkuYQYezpUo7iP9I/0Dub7hosHhoqHhooFhFLl4aDQ7YzqjygKVFhWXFBW7tVtBdeQVFqrYK6iqm9qkWKogXhIpVWKVefWLAsq/iqwOKvM8qBHcVUz6+KRUq5sePklOLyTzqSJwVMv4VBcSJRZjSohfAnmIZ+ciEBx45+IfsQcCgUAgEAgEAoHABEAgLbE3kDVEpi6q6dfA15/GuMa1RtIoosGxA6iQXqNhI11UMBE/BiifUEGjaj45Ir3Gyigib95JTo4G5R6OZHoIx6LxSZzWNmDREe5XXGl/r3XUxBqpk08yHu4rmpqG9fQkxPi+cD8wd0P5dNqWX6HowDHFRQ3KzJo5Syp4E7WxPoEUYOniB1uWcN4wZ8IN9AnHnOmcvPnERUAviId3IAjl2GoC7YjOTNkjGUmIJxAoIID6pn9cLCXAT/xTxyrLyrOalhvpHXSc2iA/rtbKVZR0DHUO5QZ5GigZbB/oaB/q6M719hQNDBWNDhYPD+YG0V+LqBW/bOH9aE6RViVVWOVWDUJNFXu15g9/+APpFjv4pIpxx3VT7VYR/IkV/+1vf+OZN5VRxfSi4phmUt2UeYWfHzJVAbNOZKqPqqrostjzTxKYwgrO3W88OxcB5SieQCAQCAQCgUAgEAgEAoGHQcBoFq9LA9lseJ1bv2/WqBp9ZTt172jaJYvokoX7oYJplJxcvBsiczRiRjsFRAuJ5c6DwTdWjJTyIzge62ui0HijobZhutgNoI2/jcITEeWHSxq7cyQc4bR6mTTeCBTK7S8G8QbofPLgXXQ8G+XTGb+lKmU4osq4bsu6lsQHEGD0wCcMNjF2avNsTE9t5i8viK7Rv3F/SghNkv5U9dU75Ymlv33OiRWjBw+DdXx+LCHAaOvfMBr8QKodCV1scmk0V56/2ciGXyexDxaN9OUGOnI9a3pbh0pHe3JDPbmBQRt9K8pGSsv67U3IDa7Lda0daGvpae8a6rUTuMg+fvbgvLVW4VfUv/GNb7z4xS9+yUte8tGPflQVO++885RJZVupNqGjslCBuyKK4noX6rLLLlN6VVI1Tp1SZVRSnxKLVomScVgQFe26667Dq1WilJQk3HuqHYSk9/Q1fnciAjEPsRPBj6gDgUAgEAgEAoFAYMIgYBRrQG0cTGNbgh3S47EIGvutq8/Oi8qGwg6vHRkxbq6uyCgowynmaYSNIgpo0IxMkuOFZwNujJEfo2d8klhCEFGe0U7+M8l1dQgt/3walPvKfzJbsQATxYNQGC/PJDgdmgcDemTVV+Zi70kNwZFVo3O8lObzZs274747BKQGIs1RvMi5qOksFA3xWLTBtmF+uHMRkGe6odD4Ob4t9vr8NkjJdC60hDg5bG1LS1d3h22c2ELjpOahwaG5c+faJl1dXEb4bnvNmjC5HoruEAQQYI9TsLKTxAtU2IvqVpytjy4rK+0Z6l/X1bquw31jXX29XT193UNOxqp3fHkDZtnV093b3ddQW5cbGK4prpjeNHVG09T6skqFX3ElQUFVidhsX/e615100kmqjzLMRW1S7L/+9a+rXK961av8XnzxxfbDY7NPeMIT9t57b+v8PZdeeunjHvc4LFe9/uc//6kuHHPMMarMJZdcYvHFFVdcIYiwl19+ucuHiX3KU57y85//XB1xz7DCr+KojwnLTJ+0sGSHYBuRbBKBsANvEpZwDAQCgUAgEAgEAoFA4EEIGLUaxWZD6mxg7qIWbHcEsTTMxQ+xQRahZOBNNiVfMUDMMA15DYuNmFFKLjhtYrOkcWeAIoc7R57RV45pdzF7LP5JD9IMo3kTBReLLfnnmMytqCxRouPOA29JFN1olczCyKdBv6jR5gP2OuCeRfckoy4bFxKOCRjcM/xKiOikNDkSKxRHEdHNJ+6iEwU6Iax3iRI1nfnE/zl6KOBJ437EmIWcqIRS7Ad+UMGKPxTv/L8HyG9+YfR6p2xSyZSTa367B3qWrVq+dNWytZ2tvbmBXnuAK4tL6iqHKorWDXb511s6vHD18uVtLW19Pb0j/UM5+3ozkY6M9quEmxLCXf/0pz996lOf+slPfqL8K8Nmf97xjncceeSROO1LX/pSHr761a/efPPNOO3b3/52xd4qhp/97GeHHnroj3/8Y1V7/vz5SrLq+bWvfU1dsGvgRz/6EaJLJp9HH3005nzUUUede+65mLOqmumfZ7zplyapHkW271wExpEdOOtS0iSQwpqvCQ/7W8CuELbgsg1f1MGH1WTX8LMNQduJorY8L3aikhF1IBAIBAKBwMRCwBrGZLlB6gxhEeDhoox/7rP3/ncuuadnoAe/NZ4uKR7BA416UcT7719mPO3FnygiD47kwVF5Y0rFVLl74T5z+sx759+LNxqOE47cuu4IIzW8RhqN3QlJ+3INsimQhBiFc8RpbbI1NOdTQGyTI8XwagG9MH+JkViGYp5J9sy/fz5HfLWwB1J2JMUwbZ5FwWKc+Pn999/PkXooBForFMn8OPPZr6gZtWnFxF1bXblk0eI1q1dWVVdj2n1DgyWlmS0a2ZDS7tYOLEREAyMD1cXVE6sAhLY7AQG8IDPiGtmVWNjM0tvS1to10FdTX9U30FdaWdE0qbm2saG1q2Ptuhb7BJobmsuaymtKKibVNDU0NTmAHAMedJz08EhHW6fyrMYprscffzy6q26qbqqh+qJIX3TRRcqwP5Vzczovf/nL/aK7//73v/l8y1vesu+++9oYjPQefvjh+PMNN9xw5ZVX8m/+6/TTTz/ooINwZqRXKLXDxBCLMc583HHHqYkkQ09lVI+8FMjwToA0onwAge3Oga3nfyCu9f/9zwqHbCnRBk92mL7NACOYsLuxt+RX4OzgN08+bP5tPZFO7yrNmGesNmM+b/QnCVuiya7hR+qtzrCpaDB/kkdlTU27mwDdtdDWpkprhFRatdcFbfyZ2fK4i00H6cgO03S+2TZhpnqory9V8o3gzM452H5Plgtbk180UR62q0rbL7FJsiRv8pE1TnWwTC83MOBd457ljvn7sjIuBiKyUotcaIVl7iblbK3j1oK5Of23Nt7wHwhsiMC2Ks8bynw074WKVnhJ0raVnttKTkG9wsu21fPRYLhh2IJ6hZcdpWfR0ND6+4FFPdA/UFaRLT++9957B4uz0567u7q1q7o/rPKgAw9M9wPrIvFSjTB7bDKcphXFbLMctc/JfcGiBSTwjCsSgqMixmizpcuYMNpp7I5JciQfjzUoT4N1o2oSDLKN43nWWZMAEHGRIxaDeO4WM2v5kWH8nCPuypthugE93otLiwU34E49Pg3iqUcskiCUJIgoqUc4cisgd1rx4JdWkyY1C9vRlq3l5sibl1xx0f777L9szYq1K1oqSkrpP9Q/xL2suCzl2sa/myvPhewuvKSwm/O/seQd41JQr/Cyy+upAEijjEjPI8MZNTQQedCgJj9kHxoeGik1uCgayVt0FcuKEbcCl/UP90ybMa26rnbNmrXrWttrSytGS4t71nVOamyeOXl664rV965pn3To0QZ6rZ1tTfVZ3VHwlGHF22QNPqz8J80VfoX5hS984RFHHHH77bcr/OqOqqQ8q0fqmhSpfVIniLL9xje+8TnPec4JJ5zwm9/8xic1UUUWhDR1U75TUm1iEEaAf/jDH1pE/c53vpNPNZqcVJW2FiWxbzKISFOiiBW1X7FL42bH4Q+swR6H5bOQxgcVg00me1s4bi0l3BZxbixDtj44Z9PIeEt+M2Ep7IMlbBzJo3HZEk12AT+S4LgAzZgK79ZC95epVyP9/fpLbUFFZWV5ftLafiBdZta5NjTwgEsJotYN9PW5Uxw5Vv0eDdqPPuyW58Wjj2t8SpBf2SEQ+SMQtY9aQ/9nsOjPbz+TQSWuhuenqMhXeTc+UxFaBQKBQCAwfhB4YOiYjfWtzEwjSGNfbBBF9OgQjYa1qE845An33Xef1tXDAxfdKD4pCDMRsoo5e0eVDcSNiXFU3S7PeCl3Llppg3JDcGyTuyi4JOOSYbQu2AhbdPzgtPN2m0eCo3eM1z0kiM5w31XA99xzj9jFQr7R+W677YbuikWnwJ29i2df0QORWvwpLmKpZ+00Id55ZhDmk+TEmdFpA4CMx5aVGWdThnok4O0800SWIRWCYwtSvWJ1dr50osrkiFdAx4mNn5wNTR4lAkrRo5QgOBE4CSacCUt/eCsuMi4tKcpKS2YdKyoye1JqY+1obnJjU3mueLCjd6R7oGw4V11UXldWPbmmoaqovHjQFcHlVWVVRj5kKbqlJcpyuYJqRYNCfvfdd9s2b2tAKskq77HHHuvPv//977asK9Vq2ec///nbbrsNg7WtV7lVzsWuzgrC3uvTtddeq5wr8KlqkK+001NVcmjWNddc4/evf/2r0s6bYu+TapsV/kc0SBb7Jp8MmTw99rXw7iWeh0VgR7RBWV/x0AQ1y7X/PA/j+T8eH3h7cPAHXMf+9+HVGBti/d9brc9m5EwI5+6uLv2WXlyXXqG7y9fntpUrVVodGOLkPxKiHclocHaMXrXKp8VSpdVwLYvWIRkbd1Z6H1P5tTmQNYhm2n2VL2mSIvNZVFSR573Zu9zKX38nv1wdYA4jc4wnEAgEAoFAYDMIpHG+1tXDi7bTWbVYnxdrLfWbo0XZxsWj9j/q6n9f7eqVzMNwdjiWvtQoGWk0sMZF9afILZ8WLaOOGKbBNw8G4j4ZQCeDlX4Wf9bzisLwGhn2lR8SuPNPCFbsaetoc7qPETBvumbRieu4g4675q5rCPEnIYg39ks4HYzadfTUI4p6FlfrEO644w4k1tJrWvHg14A+KcwPVoAGkMOz5FMpsVk4cKSbM7HQA/FmaOTXchsQ1NZVS8v8BYsqa6qpx93XmqqMS1tJvhmYw3lXQEBeJ0q25YnJKtUD28TzA7nEHAjimq32rMiVNtU15Gbm+odZaErrqm0lGFJLqoormIhLyiosU+S7msWmpKKvtKZ0ODtLqyzbC1yBKKgCuKiy+qxnPcthVziw+SPvr371qxVIRPfGG29EgBV7ddmeBXt6VYrPfOYzSvvLXvYyC5tN5Tz96U+n5lOf+lQLpFWoM888k+cDDjiAKF+ZkbFcDzuzKScvcDj44IP5SVbZrOTnbebrR8tbjs7mfYrCR2hvLeCbF/lY+bJ9OfAjaOFMAm15qK21YmfmwVSnHiv5u9XpVC3VoppJkzrXrFGv2pYvN0Os/9Pd6nQH8gtCdMO19fXZsmeZ1dOl71S3i8vLrZEuHszWg1kuttURb4sA2snsyM4tFqX87KqEObWJMs6YKMum/OL2LJsM4vxj+2UWzq9e+w9D3mLcwmMgEAgEAo9lBFIDi87ZoGh8PDQ6VFVdNTg6ODA8cPh+h99w13XaXu7FJfYQFRlzGx/rQG3WRRFxQmNlw3FDauZWxluWYSNj0vBVhNYwnbdkdOKYODP3xGMF1HTrhXnGeA21+UyXG3E07Dba9uno/Y6+dcmt1PAgq36N7NFanNbBVHptSfBLJt18tQSaWAFRXCpx9CJqD80RZv6lSIw0WbBggXECJRFynwwSROrr8uUr/CktTiPSzVTX1FCJwsnz6GBO8Oaa5oaKap1R/0h/VXHVY7kU7TJpL1Cv9KKoeKROAdvyNBq+DedHZGmHY34DMLOwAd1IUWnJqEuRinOlRaVN5Y1Vk0yg5MpzlhK4LWlopHKkqEEgu4WHEeXiXEl5rlT0xc0NRSSaibEquLhsZGCosaFRKVXIbQY+6aST6KYMK5PPfOYzVRwTQKeeeqqSzJ03EzoswzYAqwtqzSmnnMJRTeFIwn777ZcmiR7/+Mcr5Pvssw85AiLSfj3Pe97z+PcJPU7VhCNYUl3grmpvFT6CC5KJ3uhRc7mR7PGSwN/IVzhsAoHty4ELEWbk8wG+4XA3T9oVnAhp3iFzTM+mM/mBrw/x3xRL8rAhEdrQ/SGCb/LTI1Zmk9LGuSPQ6qZM6Vi7JtfV6T6DyrraSZac2NVQN1nvV2wi2EF9Os5RO36zsyidMVBTVaE7147o6vVqvnJXsbUUOyuxj6ks2xzIck1mZHRX9hjC5M8U1T4y32ups6/+aU/9GxzUUqd2f3PSwj0QCAQCgUAAAhuOMr2P5gywh1DfkZKi4aLhQw869IY7bsi2KhZn5y8MDFoCXYsWaoHZbPk09vX403JiZBIrZr9FkplnMWGjbe2zEbMRebbS6oHHV6E01MbiviKZHrHbBswbIRpw1NqoXf+LWuuIb114a2r7CSQfST7x+BNXrl7JwKt3FlwUxBvHM1UhwLpvPvFVjiRw9I4tEytqyvuKsQvLUCwWsfNpcpxPYXlwmzA1RG2RtZfGhjrfOjrb+K+orBakrLScU1VF1WD/YH+uv6h+zNAvytcERiDViw0ToCRs7Lihh43e8wROFct/MFvvv/lfL1gwmy5KPWKAV57R3dHi3BB+W2Yqv6y0KFfC4putfLP2LVeWHVbXN2RbQnlJ2fDQQIlAiivakZ88Mu/Dm2GquSEFVTVRHVL5567Y+1Xm3ZOksqiklmmoQdIiCG++Ksx+07JnLiix2sFFWC9cDIlVDZVFLeONo+Cmk4j1ruIkOVuJz/rGR0RjHlBzIW1rBY6R8xj8c7sTlUfDP7dHfii5wZE2Byxk2l3tUJvtJrIBw/EajVOmrFu1So1VmVVdj7AaAlWOY/6s+VE1PtvwlD/8XQ3niARvLopw34YIPETl0tbLRBkhX7xo4rMF6rJV2216QhNu83Z+NxcanLJ1GyoWogKBQCAQ2CUR0Jx60qBTArN1NfkGtn2o66jDj7r+rut1jNrd4YFBp81aH9zQ0KTHxG8xWGRYEENqZNLY2vpnJNMQ2YDYtl7jbEZdL4iucTmffjHM5CGxYo7G1vxrtLFTQ20S9M6G8sT6JVmbb3Dv0cyLmgQCn3L8U+65/x6jcBQao059OgotUvt+EWn0mwSf6Ln3HnvbwetPHbo/yRFdCkUNaouafF9J8Il87hka+QMmqisrGusbTASgBDgwbalUVesc3/rKsvKe7p7e1o7ShskVRZnBLZ5dEoFUQRSzLU9dMon5zVimspTGN6P+8s9KZ+fODBdnNS8bfDqXtXhkuHSkuHTU5FPxaHGRY7McSFVSVl5qp/rQSMlocUZ9GYVLyzNbMrnFJSruQH5rgBKrPKsaSqy4lHBiVQ2FWSX1q6Yw7YrIdnov/KeqlFctZ0Bl0kdwBTsr2/nlzURZwWEeSoVVE9UpVdU74R4BSePuhXzS1IutwidFvcnfJJ8CngygPCXepM9wHIPAdufA4lP20rxOKtJp9enG1uCk2dauTd2YBmRl7YFnw6/e10f9wNeH/W+qvlur0sOKHbceQKRd6OnrdXZHV0uLGj7U26tWq7ijA5oOBypllx+owGqv7nmgp2dooD/rGs2ipXUvJsnyZ0Sn5mzjlFqrvF2frc2sDUvIdlVs+wnfsMAXYpE7Big++VeO/VZVseQbXWkr5Vd5ZaWV0A6M9lWjLEdx5ULYR/OyvfP30egWYQOBQCAQeDQIGFs6TT9JGMkuazHG1iWa+C06/uDjr7z9ypLyEm2sw/iryiu450fAOXTRQJnnNPA1yMY8tcbIpM7UuxG2UPYW8obW6mqFzVrm4mK9sJE0OkpItt4qfzCHX7xXI4++GvUSkozMKDTPToEWkehI4NPC5lOOPWVVxyrdN88cyffVys8UNWMXKotLo8Fk7r333vcuuFcXnzoLX8lnLhYjP4Qks7Y/xeWTIIlvuwnJcZn6l6amhpKi4paWNWtbWkpKi2ob6rt7+niuLK/s7+nt6+g2nMgG67EzTTbsKo8MHZOUrSVjBmNuhcn4Qn74mNaK2l+fiR21phkVLh4pyk6ccQ4WVyNN0/yYbTYRZe3haGaqcZazL1Wl2HFeThqJqg1YaFl2aHPSU/Em1bvyrAyb6FFuC/rz5l1EfufOnZsqgsKv5HtXDVmSlWdfU00kh3uSyVEtFlYd5OjFn7xxR5X5FJfghIg3ReHTo3wKpJf8AuzeH6XYx0LwHcGBdxaOG9Ob5LLlNCmrZjtL+50Ur874xJNP0je7bPAf//iHyV0HXbgbScepPqu0Fe45HBhwK9qvf/3riy+++Lvf/hafTVOnqv/qPPOjtiZV8p2Ugq2IduMSshWBx4HXrJ3ezKP50yE4mUSueR/p7bXa7a677pKz++cfO7rlpq9yzZkSOo/NSArnQCAQCAQCgQyBPAdeD0U27nRmbXbcTsme++1z7e3XGun2DWVWU8QVB7Z5yEh35crVfhlOuWOqWPF+e+13/4r70dpkcbKS2Xjacc0GxI6SJsSLIH5NQPuTNVW7bQCtF+auxebulwQ6iIsHf9q4aGyNFYsoa/+Li3XEGvYTjj1h/vL5/HChOv8G5WkQTywDNZVYwOhAiBXaHL0YCWDIBgB5Tptdj0QCbwTSBHOwB5gHLgxfIgICsfoUKeVn3doWYSWwrLzE/ksvma1scETA0YGhhvx50QOjA1VFsR94fXHalf5ToGQK0pany3hsOBuTsfdmQ5tky7Qf2Ig9o3PZlyLc10kmOdvwzShhzANDJWWZ3SUf0tJrs/nD/YN2rWc3bA8NZFy0oqx8yLY913pVlA8Pcc5oMXe6KbSqpKUTqWYly60y7JPiaqpIkVZfVDol3BRVmu5RwpMRmH8FO/0q7VZ2qDVqk19RqDhC0ZxwclJlVJ35VJuoIXYC+dzyJym/5f7D58MisM0GvjJYocmWWQ5lpSy7jFTmu4tlYIBR8R1vfrNCIMudqHb0UUfVKyVrW5QkoRS4wb5+kyr9PT3es13ueJQSb1dMa2uVZTyuqJ061dSKAxyy83scEdGWra5xDoPjiZkiK6wvGhlxbLnrfMTZk1+iUFRZ8f5zzjGFWddQrygr09pl7bslOvas77bPPq3WIE2f7pAnouyNtGdS0WyeMcMWAf6VbB3M5KnTzHCqAKZtRaooWw/Uu25dlRmjZDeTTKnNn2+hxLOn9XV1VdbXuyKoxIxRb68M0EmkeiJ1pnz5J1MN9JBMKwKyGPWRuj0bh/KTtQJa3pTd7woNXWlnpySoOVlboN62t2fSamv7VCewe1LnzDMM8ydVZJ5t0AVaaamTK6zw6G5vlwQ9WKZ8ZeWa+++fMmMGPOmjKjoZo8cJ73W1d957j8y7z1zyyIgV0YC1raLUxJULHsin9uDguT/8ofvB9bUvfPGLXBHe39NtN5LVKd29PbWTJvW3t5VVlJsb1kdKXQagObOiIjdI9Pb21UKvuLhng7Ul+kU+6WCrhNsbpCw1SeQLnrCSSy5ru+bKKx03f9xxxx166KHZCdVFRauWLdMZCysIz8BxaKZio8mBaq2SAw0uTukw/SbtwFTSamszYK0Tdl6IhoxKNod0dWmhympqWlas0JBl/isrZZ+w5uRbV69WYkUBikHJ6Ouzd9rS8Qbnl/T3yxfJtP2p395apSVvRYf/2tWrJ8+effVll5lc9zKSz3qDicZJk2SiRFUpEsPDfe6Uym+ekQppyfK6upo+Xpym0tXaCjTL5qbMnq18ihomWbnNT9Wz2zc0N1vYnB6VpX31agMa+4AzbcuzxWzf//73v/SlLy1buUpJec2Zr/zyl7+M/KYqWVxSXFFdt2b5csLZitUC6ZWcDMz8iREuEnANQBlIu7rkKfm9+Q0zVY2NnWvXpswiSk9gZNbf22eWpMfop6Fh2aJFs+bOVfYoADp+JE2Bl61+nTEORrlQJlOM81asEFyGYu0yDg5ZqY4nEHhECGTt5Lh8UrUqqFd42UJlU/CNPW+tnI0ljHF5lHqOkbb9/tzBerIBG4KXWlOZL2AatOKybPmxW1LKq8rbu9tLKkuygVAuO4Rh7uw5t995V1FRtm5Zy6lNY3r1u6plFZuS5o7yhssG2SRo6m0M1jb6M7FKjgYVOCqLsQaf2IxAjo6m5cckiIJkDS+ZOq7muuaFSxfqXPzJv1ZUEBJWtqzkh6o8+/WexkLMxall1seJlz4G8dLFjyF+lsL8dQ8cyTEcooYeR0T8y1CSeTO16qvmXQKzDrc2m1ptbV1n6FVdVaE9zxJeUz1v3rw161rWrWotHhmtRYaHsolyjGZzBUPUm/uU3Hdwvj+0Mg/x9TGip9KryCUcFBJDMgVb+fQ8BDhjPj3AgdefFpTswNkv22/GgLOH5ddvYRlpsTOwMuMw9+yr4amXbODygPWqYPHKVla3GWMJPtZ2kA5UzwLlsiUb6aXwmwitP9W+5JhekrvqxjG9W3DhXaVI3gov6c8Nf5NP1XxDx0fz7vi6/JAvG1/B3C9p+Ro8kerRmMqyY5aJbDMOjHWgYVicBlRKVAMZYL0l+nfD1Vf/9Kc/XdfeVltVrau44PzzjdeN2lvzG81LbG6prhnVjmNZdpZb528aprd3xZIl+gCDYy2sgyA0rEb9CJImuG7aNOy3t6VFrROLzah+s3Y/f8WLqJX2tcuXM1Sua2vlv62t3XjezXad3b3V5WXnnHPOySef/IMf/GDN0qVT3Brv1i+3YDc2mgVduXixHoj+iu/kmTO71q2rbZ7kUEUKI2mWFnXn5265i7Fm8uQVCxbM2HNPI/6mPJmUisqGhv6ODkUQV0GtcZuaKVNM/yJjXatXu1sg9UxuK7MytRJo5snKynDsYWy8tpb8Gtdn9/frNk0KUIP+9c7JqK7mKNKMLtbXZzQSScCU8qROt6y8u6kAIBiG9gg/4bIyf/K7hctZ7Sovr2lulsDpM2fiOfX5OWZctLe9nbZ4O0JYI42l2ZoNcrQFWIoOE7AgNQVQN2lSRnTa2jDDVFgHhof0i7pktQ59QpZ233ff1fffP3XOHH82TpsGBJ2rJGCGqXLWTp7ctXYtR3cRYjtaAe5uFexZt85cyYpFi2bsttu6lSv1so3Nzfiqf8P5/ps3syG43AUXXODrkUce6ZOOVM+tCLUuXUptZU9DQGFp56d++nSFREA3PCGfihBoTFhIXcfatVkOsmkPDVUbc6xeLVE88Iy+Tpo+HQ/jYkrCcEZ3ro/PsthZnZ2dkKEwBn7/vffO2X13C9oExPx716yRKRaoyRfC7cF1trbVZZ/72Mc+/elPX3/99fUdHYCCRqMJRUXCXH5/f8e6dRR2swTQ1aDsguXR7I4NpYLYrAvJbwmD/5Q5c1YvWaJCUczchEGW4tEweVJtaYnyac5l/bFk+dmorAJWVvbkG1lX2J3z7vc01Nf99U9/fOITn6jVlo0KiTkgKVqZvwmDnqqDZKK45NKhHDj9/d/97nc/+clP/ulPfzrssMMk2X3QZPqqVspclYIcuclFfimBWYubn2F1qumsPfZYa4Zi9uzyqipTEmXV1bIjgUyB2tJSv8Zuo7ImP4aToXJHxpneUpVIiicQCAQCgXGFgLbO8FIn4snlrVzaYc3ykpZlTVOauvrzE5e1VbOmz7jn3ntork9JjaQ2WdOtk9Lr6XqE0nIKiEyavvSnX38yw2okra7in4lYe+hP/YlxtsGJhloofsjhAZPUWZA2b/a8W+64hSh+fCJKXNpzfkgmhCbkCMvl6H2PvuaOa/REHI0okkyrnX11dBaZeK9kotl77rmnPkhDrdejA//8SDj16CMWbMdXfQrNnes1efJUbLm3p0cHwXimI+ahvrmJddqOzWwkYDPwcG9ttn+5oiyXKRDPLoaA4iFzFXtFSJHbqtSpWhhqItNZafO3f55E5bKal/+z4Cu5FNzzBHgMx91womV96CRjF/rNN0q7UHp2YFK2GQfW2OGiGl8VQCObmkuklEnrV+f9Zm17m0T19Pb861//WrZ8udZ58vTp5ZX5+7L6+1vyS3q0leU11a0ta5qmTMFqZs2ZYxBvV6o2t16L7LJT7Bef7OtjHxaL6/a4uJuPqVkJ0ARnOmBcFVmHMQX/6e/r6MomdXafN1fjq1ree9fdNBwcGf3L+RewJRrZY7OZ/ClTkD0yTdOabs0o5QMPbkZbbLCT1c4xQiaSHISo8ygtbVu1CgHuYB5sbsYkyW9rb3f+o9/pu+2W2ZmnTGG3ZW7VM7UtWaJfERfGRVuhoNSbpyJWpS68805crkkaWNvye3Wc31vd2FiByuqC8nZsiSrBOaqr+/JmcAY7rUziVHo4Caxjuuzq6u7tZTlkZdVpTd9jD8ERJGtEutl4a2unz5qFp7FPZlx6yhTHPuomMT32TNIEqa1vkHQtF33uX7jQ9BI2braC5iYLUq/p/f3vf7+70XS3b/qfN2SsvrERg0KAwSgfpV2SZZNYpiFa5sna2sx6YG9oIeGwklnceTNTkJlkPfkFYxgUGyyIdON6VmpIY1aiELCSknvvvXdNayuzZJaElhaYYJKY4dS5c1HlCobxzk4zC+ZilBk80xXHgq/KZwHlaa4cKDb1Cg/ze3c3yV0dHcYQZhlEDVXeFDYm/ba1a1H8ydOmMZCyfNLcUMZUuXIuCyTB0IGo5kmTpMXEh8xhwxccV/fOD/WUt0svvZQOlrrJJhERIqVZevOGVnI8inEaZAAn8+AG5q4uurGf47pcQI3GZ+nNRye/RMQEbaKEo5kIQIlLWcLSa9y90d+/xoTC3LlOR/nIRz4C31/96ldurhOLDDIWEyMrLj0FJEFcyqf8MqypmzwZv126cKHqcMkll6xrbTv8mGMMc+gPDdUBjFQyBpqsqpaXI65ZE5xfDF9VUdm+Zo2CTCBzNJlwMOcyddYsKRIRn7LSThrjpEZLOTzQyK+v82oqy40EVUGAM1ziCQQCgXGHQNbW5Z90GtZAziKtzu6Rfq3lyrUrm6Y2aeVmzph5/5Il2lLn8Wj8edckavy1nFrg1BH4qrXX2Wn/NeCYqiZds8mdqBlTZziVimRfjUy0tx7dpf4aTdX2aoeTZNFp0te0Zd1TOllaFGI0N80PHsszRkoyskqNg/Y86IZ7b/Cnh4TUw9r06CufUmckkJShBgl6YX8SLiJi6azX8GtqmGLefSKKDnpbQuhQWV5aX1vXOTRgHlfU9JFA9wPb1Jl1wfrfunqaxP3A+XK0K/woAEqOzJUY5UTB8HhPLlueQqubeU7rpxNfTYbcVPa857//R55Ik8sDlXK9iZiPgs/1Z2vlA9kdtsnbM5P8/8idaG8TXf+diPeGUySPSo1kAVbuTfA5ooEspV/51KD/9a9/1bQfevDBHP1pDS3yYIOLkbo/1Rv5x1jk3fA9s02NDK9ZtqyorBTxcBicNrSPobarq21dCwKsCGekoTa7br7T4py87c7oX0PM/pa1sPnYOzsyUlpWUtTUUHfuuedeccUVV1999drWdRdcdOHJJxw/d9bMe+YvePKTnywUBptZbvObcDAf/K27o8OwHnEiwVLSbJNrxi7qqpub66dOWbFwofpFk87uLpeIVVRVdrW1VdXVZkupZ82sbGycPmc2Ksg/CPoG+rUO5Q0iaR4cHrKgdOXq7GiK+mlTYVXVnNFeZxa5jNsiZCyipXVdmVXBI8PIcG54CM3u7OzQZdoIIY0WWCGWmRwHtZdkcw3kMHJW1VQjtB1trRbQ1k2dwgQHJT1ur6kH7LfXYqQaMw7Lli7t6+/De/nRhw31dOuNisvLpMJXmkyZO4c1XjZZXuthh88astFR230nzZihe6MD5Tnquf9x7bXf/va3n/b0p8sglkPKGAuYAzbxgbCZS2bX1S9aaSxdulJEWgYxJutiM+nd2Hr+YWZn+mMEXr7cvcTg1RMrEmIRPCPY1dWCYGUW7+rRtYw2PuGr5sJZlS1snjptGoYv6zH/rM3NMkdCM4Nq/+CAtE+bO4d6JTXVOmPLtND1YUsO8kSrNH8GIALcaVNTR4eAluUrFetWr2atVSwRWqmjP1i49HZ1meRMowRrB5bcc4+1CYqHWOisDJDQkJ9QV2KpZ3fUTTfdZNacu5ySYH6k3SnbihltK9jDR1k9M3t75p99NT/aMLYwCQI65TyNLSwFt2xBkWBIhznzgUklYQ1JzLnISnmazQpZWZ2Xr8h1tLSI15ipqb4OARZKVorU4mor9nwCSBYqXzG9MwLLIFEQMnvePMLVmrlzZqtowk6eNYs5l2fL6UWX5Q4ReRMBIfyYZCEcMze0IqHByrqyMkMiMxSyhl+l1K9lHYzk3OVdT36AJTeVZiXErgAJt0BdLPEEAoFAIDAOEdAweiimVzEs10R71+Cb8vO7z9x9Fi5aqKP0lbuvmjf0VSei8eeutdQRaOgSydQKav/51MZ6J0QH2trRyjMJnqzrz+W051pdvZ7+Md9eZgdwoMT8C2522AutfCXfti9BME8CefbongTnvmTNErH7KpQtjigutqzd1neL0TCAGj4RZVjCRWOeumOaZJ2U5c719ey6JAgluBdR+ySUa5B49oiCGnT2lTVbjwkHbT9HnhFvKtk77Ws8uwYCMl1CFBvF0pMqyKNPmqGsf5nk/MsD7xlz8Z6tgvaSj8Z7WiOd/ys7ECv9y39PXjiNMRInv/H72EVgm401VQDNXFYNUN/8jaRZHSguvuqqq+++5x4F8LnPfa7WWRP5wx/+8N3ve5/2lPEHvcTomqdOvfuWW37+85+7eP3ft9+6x157nnDc8UccdWRvdw8Kesqpp/W2rK2fMnn5ksW/+c1v/vzXv3R3dgl4xGGHf+ITn2AHsxNVW5w9uVHs0YESGSdhW7MbeXi0vz07xlD3QzeRWttz3u9+e8ghh1RVlt+/bPmnP/uZj33sYyzGBFK4a13LV7/61euuu44wyXnLW97yuMc9zr3ya+5finNefullV/zj73YUv/+DH7joggsvuuTi227998zZs77+1a/ZxoxqfvLst1184UUY6Z7z9njb28/eY/d5OPDUWbPvu/OOvfY/YKS76x+XXnLhxRf944q/r1675rBDDpXGt7zpzcO9PWtXr5kxb/eutS3NkyddcdGF3/u/71979TVzdpt7yEEHP/P0Z7lST7rs7D/iccd841vf/Nf11x33hCe+/g3//d1vf+eb3/6Wen7l1Vf1tLUODA3+5S9//unPf7Zw/gKtw1577PnSl7/sicc+AW8f6OiE5P+d+wMce9+99znrda+9+NJLvvaNrw8PDknX6c981hvOfltva1t7y5q62gaNRCNDomMJSorf+e5zHHwFNxeCv+1tb3PZd/PUKeYEbvjXtUzBwDxgv31e85rX6PCwYmRs9T1309CMg25Vn/ff//3fj3/84ztXLJ82Zw46+ulPf+qPv/+DXtnm2Be96EWnnnpq7eRJCLCcWnjnHXK/oalx2ozpqmNXfkzAsl05WmWOYLCrP8udrq6ly5dRr2lSswmRPQ484OfnnivU2WefbSZCf2+ltD77mMc/7qmnngYBi28vv+D8u++8C2KnPeXU//fCFzQ0N+Uqykd7+66+9pqrr7zq1a9+NdA8v/3Jj+1r0p2/733vs9hGQTKFsW7lCvvDqUTmorsX/f73v+fBMMIQQUAJNFkzd689FXjk/Pe/Pe+WW25R6hQzRV3fP2nK5BtFcuWV7V1d+zc3/e4Pvzfg0PGfeNJJw8Y0JcWQ92+krxdfr5+cGZPvn38f/1ZNGycddNBBJ598MrYv4zo6son8G2+4Xvn/4Ac/qHAqD3/85S/gbEzzuGMff/KppxLlwl/zJiZTIN/SstaK7ot/f4mRzdKVq/baY/fb7rgd7TQUO/744y+74vK7775bLpgGQq3bmJGnTl2zcOFXv/41ef2qV72qvKryztv+babf1RYHHnzQNf+61pSKx7YuQFmObhAj34HMjJ8lpLzsO9/7rox71jOeaeRUPan5Hxdf7OA0oubuu8/vf/lLx3EpQqKGD//dretu+fetTNOtLS2w+q//+q/DjjlGRbPWQL1jxM7ah3gCgUAgEBhnCGjDdUbZcMOiTScP5hxF67xZZ+xnQ4gjDjzi+tuur6lebwbghOt6+NGq6/t0dho3rRwqqFU3KNKtaBT1LCiil9R1mu0ljWMisWgnITynldKYJGLMUTtsbOP6Fu++Cqsf9DupedK9991LMg+i84k3xl49tSlRs9v6BUL0CK4C1m0tWrTIn0ZKOju6cfQuFJ0lMyWWBATYV34s59bjk0Y9k6E8ZxQ6mwo3P1/X05XNBc+Ynm1+djcSyex7/UPZPqCG+gapNrVdMjDaXFE/zvI21HmECCirQiq9Kbw/Ew2W41slcUOjXKKtKXiSa6xreLbhr5Nr1MMUagtGDPTbAl9bpfH48JzwHx+6TDAtiszjbSuVWe7yFtP8cmiWnPzK5Be/9GW//dMf7bO888477S38yle+onG84tLLjjn6aL2CeVTGn/sXL3n3u9992T+uUMIrK8q6+wcbaqrsNe3vG3Ts+bVXXb3/QQcuXrDwqc94+pLFS5snNa5rYR/OTW6sV+V+8qMfn/JfTzb7lFmoitefS5TtK66tabIeddAy6dwFF5x/8MEHW7Pa6ngtS3Q6O7Hcpz/zdPeIWevL0qU118pfeOGFr3zlK9s6upob663frK2ttpT6icc+7tvf++7cWbMr6us+8r73f/STny4vzj3lqadddvEl9PQ+MJI7cN+9v/L1rz339GcPm5kyAzA6QvPauur/++73Tjzl5N6u7mkzZ3R3dL7/Qx/8wfe+3zs4rMZmx7k7+au0ePc5c/96wfmzZ8xs7Wivrqj85ne+/d73vL+hsc4VAqQNDgw3NtW3tXbU1de88Pn/76Of+PirXvHKiy69BNd/xznvJE0oflavWblm5apXv/asa6/+V5FTtCwl0WaMZnsmPvLB97/nwx9qW7W6rLLipONPuPm2Ow476IAjjzn6pz/68aCtQWapR3JTmhpe+epXfeKjHxOjleKp13Qi3hGHHea0AB1enysZrJsaHl60YAHGovcF2tOf+cz62tonP+nkH//4x86Ral216lvf+tbXvva1teva8pFnDZPJh9e97nWf+Nzn7rnttjPOOOPft9/pU3ND/br2DqXumU976he+8IU5c+deftllp59+Olv+vvvuc/nll1uazlSo585KSP4+CR2t3cXLFizYa599G+pqzaTonn364he/6B7z3/3ud473ePOb3zzgoEAddi730x/+4AUve+l/v+rV3zv3R0RUVpX39Q58+jOfPOOFL6pvaqysqHz5K17+y1/8+pabbrB290lPetKKFSthVldtV3utvet4O2t2R1sbTc7/wx/QTqVXK6PzVuQUFSdynX/++dQz862Df8ELXkDtIasQ8nWptqrSMnsjhuc973k8tHdlc/yNdbVtnV1PedIpP/vZzwwjhEI+1x/cld8YbG7oc5/73ILFS6rKy3oHsuVze+6+2zvf+c5XnnUWQ4P1w+d++9vveMc73vrWt5555pm29a5eszYl9sD99wWCaR2I0c0KApycmXf5okVsv/hwd2+/FRH9g6ZRSp7whCf88Y9//MhHPvLZz3/xYx/58DtxflvCWlr8QvXgQw+fO3vmvYsX337TTaeddtrqtesa6mraO7urKsp6+wcff8xRlkYjwM99/guOOeoIa7xTjKnDQ9dXrl47/+67dHumPN72xjd+8zvfXX7/EhvvTTNRjOZvfOMb7RX/029+QwFFC8Ge5hauri5F7n/+53/e+q53mRCxD8J6AUmmUjyBwEMgMFyUK61ryNq6ifOkwUphsLi1iqfgfjU+mguNXkYw8ls0t1bUQ/t/lHo+tPBt+HUH66nJ1TiZbOwb6NWP9OcGB3Kjl93y99aB7vKmqjnz5iy4f4HsaFm7cvqUqVZT4cgzZ87WF7CFYqfaRm2mNhlNNaVoLMTdJCwGq0joas1pckdNkUydka42eTZ36QV95Zn12K8/MVVxJZNsYqGCkCwKopIfvyTruUgg2Zkdvuo9/emTUnTIPofcdt9t+ikS8BYqaY2RZPI10TxwpAbNdf3kk0xnjkZNqCyWaxClDaeG87IsBJTYgb6emqrqmTOmuRtp+Yql1fV1ff2DRiC1jousqi7qHeluaZtS23TYHgfV5GpKRzdhiXnYCrKD8/0Rl9jHiJ5KiCxTWgoZJ+Hp2Sro1q9szodR1woPsWy82V1I2W/B2arpbMxVcEldQUaSsycNx9L7emPy+j82+k9B7Y2+TAyHhLxcMLZUK1nyjBW3pF+QR1I4TpI/VpmR4aGunpJ0OdZ2yIds/FBbn3GMbfKAmxxp8KKRdeywiSBzluxaWn23sZgsPOWUU5xpW1FahgbYE+N4YYZiefbt737n0n9cMamh6aMf/t8f/+ynb3zj69sZVfsGDz/i0LPf/vZDDj/M+qEnn3bqwsVL6xtqTz3ttB//5If/+78fHLRkurXdctxlixezqjFJKQeZAqMjmXktvwCJJmqlKLTgOg2/xfYi1tSceOKJ06dOdo/2kiVLE+5/+ctfWCb1BLvPnf3yl7/8T3/6A2o0Y9qUq/917Te/9S2LqK35FIubcYdGc3/6y/n7HrD/2We/BRlG0+YvWvjiM84oLivdY8893/r2s5Ht0rLijs6eD3z4Q7V1dTN23621re0b3/7W17/5HRV4t91mP+/5z/nABz84c86sgcGRexYskiJc3YrfW2+/7ROf/CTCRsIvfv2rz3/hC4cednBLawdkyX/+C1/Q7PSpETxvdHA096tf/3rl2nUDg8PItiJ8/U033nnXXYjcoYcd9trXvea5z3sOd8n90Ec+dvU//4n4Oe+qorpKeUeDf/LTnx5w8EFnvfasZ5z+rMbGOjB+7etfv/nft6o8escEY2119Y0333zCCSfgrsgwAqx9ecMb3uAcY1kMtCpTvPkl0OYU2lavZh502jACXFKUe8XLX/q+95xz+unP7OrJFjs5UoOR/7bb70SoXnbGiz/1qU+995x37b3HPB2/uXDLATBYkVL+7rvv0bkaZQiVZZkLbPPFyVeOTJf0N81MT5TYCxt1V2/fwoUL3/SmN332s5+97967v/LVL0nRj376k9eceealV1z+wx/+379vu+VtZ58Nwz/9+c+2JNk93j/Qv2DhQi4rVq/a/8ADn//8599xx+0tq1dRZsXqNVbOi51WCs//vve9Z511lg6ecMhgiR/60IfEiIJSL5X2Zz/72RdecqkSdc3VV3V3tF928UWMpUYSjORoLcvn5KbGFzzvuU5ocyTVe97zHhuYDVvNoItCAo0YVBvTQyQboPz43B/4XbV82ec+/SmQcrzmH//QQq1bscKaancrKr1knnTSSVdd+c9rrrry0IMPvP3Ou6+66iruFAaXhdbwuX/+/Jm77+52K6Z4ldwUw0UX/O288877+Mc/7kxs/iWRYdZJLFYjO2UNmHg+R6b+7tZWYyDc9Q2vfy3kn/ec09Fm8wWf+cxncPebb76ZwAMPPNDEBwTkvuiMihiNcezZe+45bcYM+wjs/DfDYl5JjC9+8YuVHKR66vTpf/jVr7wzO6DBvQP9sv63v/0tHH70ox9lqwZtjrAuIL/iOsuFeAKBQCAQGDcIGDFqjdNDKQuh/dPqasatf2G81YJpD9FUdlGMUZ+ikdR3ZA1+/jEa0VYjnLoVRNdXXYleT0vLM0rJeKv7IJCLniIxT8F5JiAxVZ8IFy9G6kFxfUVrufAgOg0ysSLStNLWeVfc0VTytdh+xes5ep+jb73nVn4y423+xgHRkUMHPFx0wvqlhuhEmo8t28KTujBK8i+4tEiXP6Xdo2fHkDmSI2kki4uSHvgQwg9pTOh5SOJnF0FALhdSouTIYmOSrXqsZcv+lf7nX7FlFqVWzJFmBZlBfs5v4V+5MbkLQ0rX/0vBS0qL8/9KLbd44P0BsdkKjE08W6XkOPRcgD1ethaBbdoGKfL5OqAmFPX3O/779jvvdCC+tvipT32qu3/wBMwKBzbqRYQ0q3b8ao7tEKb30Y87xtrjksrSU570JIP1lSvXTJo8mR3MzSpOpl25KrvW5V3nnGMZqjZaw6o9/fCHPvSHP//FkNqCZO2yop3Sr4xqf7X7S5YtHRwaRb951is4Z8ueT5Ml+hiTl4KwdLmRz7gff6Nn38AQNuK4LGznF7/+9bOe9Uws6ytf/cZ73v1ukvVDbJU6BcbP3/z61/ZfOhroqKOPvvvu+5zXfNihh/79yitbVq9+57veNXXKlKqq0rvuYlFbPGf2bMC8/wMfnjlj6ooVq0X09Gc9a+WyZe94+9utRHWy1Pz5i775zW++493v/vFPftLekdkMcadstXZ1NTVuu/12wZ/z7GejPW7ToaQUVZQV689+/7vfnHDiiUsd+NTcjIm5C2ifvfc+3LbP2lrHCH/yE5/4zOe+WFZadMUVVxz7hCe0tbaad3C1g0YEqh/68IdXr1ql03rhC1+IEXX39P/wRz9iWGDBdrQ3HSB28YUXsogCU1Y6FtjRkETZKOsTeHVmzPte5GBNU9NHX/1qK6cQ4Je97KUmJnRyVOXfMl3rty666CIWfhbF933g/Xp6Et55zrtIdjpWR+s6gA8MDNZUVnih0mj/wMgQm3TO9uZkjK1G+/M33LKROhCLn1VLlkybO1d3LhRjiEXCrryysPklL3nJG9/0Vichy53z//Y3e1N3mzePjfG73/nOFX+/Us9sXZflAGlEorx94hMff8Pr/1tRXLZwoakZQpS973z3uw5Ls5T3k5/57JGHHfqHP/zBEEGxKSkrXbRksUGBBtVqZH9+4EMftBD9aac+RVjFXrFELK+74Uadv1HC8SeeaP35mta205/zbCu0rZWzs7enLZtlGOxnP8Drhw0cFt133yc+9cnV61pvvvH67JbI7GSoCvZzqxV++ZvzLI1WPg1BeMbb2VTZgZ1tTiX4vP71r//vN75Jen0l1pyL1WmiNjSRiaisUYgT0YHGAsyDx5HpcGusR8YbZZNDsZQWypuxEoRtwbvZlqOOOko5bGvvfM5znuM9hbXLwAiGGpbV9ecP0DLcYXM2PTE4PDpv9kyH2MlcVc8QykwG2mwNuVJU7ny7jg6Lrj/wgQ+sWdd6LfZ+1FEtK1dOmjL1hFNOkd675883vmNnT2tJJIEy8QQCgUAgMH4QMP1brBPVBehHWZpGR4aLNIpD+x980J1L7nFih4ZLz6hbZI/Vus6dO2/hosUIquZaU8ldQ21TjLFHooKJypp816ntPmf3hUsWIs9GMnpPnjXj5OitNM6oJs8aZ026IY3Wm2FZS457GwzQR8Nr+tUUKp8C+iouLzyn5l0sJBs+UczLvnvte9OCm3xCVySBBO0wquwdi+aHStLCneb0F5waNMdvpV0sXvhhLqah2FMqvHDv6epetHA+xqEv6OjpLi7JDiUVVqLcD2xzGm9mu8dPzoYmjwYBYzkFyaPIJTkKlYfL1onNVvKx2WaVqxCS6YiogkvBnfnXtmPu2Te/+Q/uE87/lX42fM9cbHNjLdv4SfI3dp8oLhNd/52Ic1bUtupRzsb8S8GdltBheapjqCoqXCSXq6p0B0/aXtg3OPjqs17T3WnoPPqyV7y8paOd/eeKf/6Dsct5wqzBGsqmhsZ2s4NOTeh1yWpDd2cPsTVVtQ11jc4S/Muf/uroWScR7bXH3svuX75oweK777xn7z336ekdsCrpD3/6I/JTychpsX9ZZltTDzXZ2mXrhGsry60THh4YrK+pHejuKS4tZ/6tqqlzkxgrpSpiwTNuds2/rvcnYlxT13DbHXe1tLbfftsdRx/1OHdqu87uikuusG67eKS40tXbI7nHHf34ipKKkSGLeEoPP/QIzHugb/DMl5051DdYV1VXUlR65KFHDg+OuM1p8cIl5aWVf/3z38pyRatXrjn2qKOf/ozTu9a2TZsyPTeUe8Nr39Dd1dtQXfOzH/0sNzS6xx57SXV5Zfna1rYexsrBoYaGJrbiyvKqsqKywd7BqrKqklE009lYI9/42jdPfOKJ5aUV+++9f097V9Fw0Rkvesnjj3p80Whxz7qOirLKJz7uiZnPodFVK1bra7IjGYdG4ayNevpTn2Edtrmzqorq1531ehc2OaTilptuHRoY1vXSITvXu6jomGOO0f/BU59tYps0LFov6JOeL7V39XWNDO197V3XXP0vYJqve9tb345IM8ZzP+rIY3xqaJyUbTPOWG3u7ee86+5FCwaKRksqymVPW0c726wpjMWLF5lu+MPfzm9Zs9bKLZdF9TAwdvfokge6u5QcG7YX37+kZ2Bw3p57iEZ2a/nmL1zQ1ddva/Qp//WktetarLTK8sgt5O1dH3z/h+bNmdfU0DzU3S+lIwMjM6dNW7diTUdbpzy69dbb5cjkxslvPftsu8F7OzvoMMvhTzbnWt3GBlxWav+wJCtd9gYjvVnpKi9btPT+/lzusMcdPTAyvHTlip/98hdFpSVf+NIX3YkFqPKqaudhIofZ6VZm9/sH5i9eZFnznHm7m1tw3tjo4JBpzbKi4saGRpdjma90lLft38vXtLz45WfM2WsPC+ntbRYdBZ5y2qkQs3fXiWViv3f+ff7c/8ADzCNU19ZUNjbYJ5zIvDGKIUvWBY1kJzbLNXnErAo9AxSnj1mozMWApqe7r7GhecH8Re0dXfvte0ButLi/u88a8PrmKdf96wZZ39Q4ieOoUldRvXjR/WKcO2f3ivKqrk7Uuqq4pPyqK68pLymdPWvu6lVr65qn9Pb097bZ8b2qrLhkzz32dg5q/8CQSYIly1cIe+ppTzvwoEPKq5xube/5yK9+fd7d8xe87qzXHnTwobojp6vccO21r3jJS5atWnXGC17gejDamwDY6o6T3vEEAhsgkNWF8fQUVFMNPYU/t1bHQsAxL1srZ3P+C2IfpZ6bk7+t3HeWnmAxXSt2baxfja0LfswbOpbCnwge9siPr/iemcfbbr8tGXW1zzoI8784LRAwVX50o2yk+AMea8Ry74J7/ckPn+TodjFYk4n6XD75R5vF4oRFfjBhbJkj5oleCs4R7+UuRk09nzgzd+8MAMLyxj/arJdHTRcvXUxhkumWzSY7PDJ/7L8+hRpSgc9LIB18IpkaAnrngUyp9uugB78kc+QfM/fLZ7Ys6IHoREqygBQmB/uVZAlkQqfkJp/NlZOCZ+p5Cn9uzv/Oci8o9hjRU3qlVEYXnpQ7Kflb8Zs/JEXd8s/YPv0rLc6VWra60b+Ch+Q/GYezkP/5V2oosuE/S0Q3+WyFhg/pdZPCOT5koG3zMWVBoeB5eYjCX/CW4i78+RBBdsCnpEYhooJW2/VlveF0m8ShwXW5p/Z0yuxZDghmPLz+xhtMNzocSBOviZQ2OyTP/b8fWENrY/DfLrpo7bLlWkyLkL9/7g+uvOZqJqO583ZnRdQEuzjUeVQadDcM3bdggYlDBuEXvOhFLMld+aP/65zEi+NlI+nsECDtvuZedPoP1jYrQk1nSpdPGm6Pgsg2mCurKOt3kFYPEys5M6ZNE9BiWsZPMS5dvvzkk0+WhgRIA1NbWbZslf7WX+gPeG6sq/fOQ/7y7vVbEdKGBL9DTvrN7/mxh4ZBDzHo7+1dtyY7yLert8cK0kE83P31Pb26AZt/8GSH6+rGnM3kJCf3uPb3Ozvq0xb36kIYZkVkR/Gxxx6bbXLOM09LTC0Mqamskti8DlksdGtZtVo34xTu22+/XZd24403lhcVO3dbPwcEXZ3iruMhUDe8evlyv1y477PXXvfcd5+5ZBmUEr7lv2a4qeFXEoDiXW9HrNRRSbfckj8o0kZrq+K1P385/2/nX3IRzmYj91GHH2G6mym43KRwd498dP+Qjr9vbQuFXSCcs3S5s8OhULKSTCvNtIbmKXzF7lYsXgwiQxI2cNGZ3ma8vfuue23Jbm5oPPmEE/lhovz/7N0HgF1F9T/w7fu2991seiGhSq8iRcXeCyhixYZdQRQUGyiCYkXBrnQVK1JEUUCq9FBDes9udjfbe/t/5k6y8gsJEg0E/N/L8nLf3JkzZ86dd85855yZaVq5UtMcT71rw87VDQ0WmhoQ1FVWtXW0n+7QoOSURS538mehRcjrkHzFFirrSF/84ufJRy16tSp8Nre2gKM2yoLoLYrme99t111Jkv9TQZs++8zPzetpCqFoBcVF4uTlL6sMm4J6g1Cxp7ll5Q64NhUe+lFxkcXk+YW5bzj6jXJqdUdrm0ODxCqg0FAXxkNeh3uLwdyQZJWzuJLNtNVltCHRvDv23LBCXgFZ6WMKujEWMTdBqu5lEI6xbtUqzOiNBK4U+Vu3RsJ+ETpS8Dk7gnJgwBhOjXnZ4fQL3d4r06Wt+ELfWn1+4Glz5tx3xx1W2pv/evjhh0XpG4SFKkpLH37wQeEewyPD4swV92O0HM1u4D/5yU+qyoMXggfeT/vvf/tbe2eHRWannnzyaWee6Xx6FfH/RlaRSq9UAqkEUgk8cySQjK439xzQWkwndFfsxJ/CYqawob7eziPwYaKNgyqmTo0BaFGZadpoUyRSxeyUG2ZaQS11Q2eadFaWvZbfRWcyAfS8R6jR6lJQo8AZX/E+LmSlGwNIVMQNDU+XRspuVCqdoZwzY86SFUtoYBSUYh3wjwKTLXJNHq5jLDG4Jk8lYkylyMrmxicrDFS7RyE+VZ3jKl14NmRiT7VFEyoryytra5qaW3oH+s3m55WXs1n15VVTZkx55rzWlJNnsgSC1y0MXp7UZRCVXqkEnrwEthsGhiWMvq3CpR9Nw1CgV155pUBKnq7O9o7pU6dFnqAXCBNKvO7vf1+6cOHsefPo1E988qQ777mbwj3+/e+zylSG6spKWBQ2pkOhNXDXGB0+e++73x3gcWEhY4AUmG23Hjvo0rzoU9ay0dQmQORhcux6xesLD8RHiqxas2iXPfZoXbRoXRJczWYceeSRol79cBgJkFjIKGNjMadHrIshuxvmAX22yqdsVHxszhY/4Y9gLZJnbtgq+UGOWdNnMDbu4RxwyHMmCnKoqw44Bw7fbc89zz/v++//4IeAqwcffNC+XEJ/y0pLbrnhHzjXHPuHaUiMlYV/ok3i+gsyz8qCnC2/ZJDgNGAG5agNvAtPVeEG88JTyE0zpeANbgGAE2aDxSLAeP8kP00So4MZPGAMn3b3hXuZdqZOZK8XgWcwVcM/deoprWZ/h7mBu170ope99/h3APng3MjgUKagEBG+Qgcr2U5NKQz0tbY6IkszkfVSHP+A/Xnz5mFSBghNi0R/HXzooc5ANkGgCrPmODFT7vI04MKsLBi4tKhYTl0EALaiVc7aqmozCz093eW1tV3JhDogZ1cnW0zxRopR7xsYAvB0HpmRypSVPXj3XcoiOGvGTNxaQKvjHXDAAT67O7u4gjubmtUeToLEqOjlvt7mdS2Nk+sgRrId6O81KhnpH+heu1Zx53sh1bthA8dCXqbAKl/7iAz2hy1J8GngApM3t7R5oSRgXMKjq7f7UejeUkjVp4W1RmTk7B41F4ZDw5OzH30qJVHbMRZe/vCwIjr2tMlTsESq5C8baqrTZ5BCn4S12npp0z277b23mSTF9RYF77//fgQxac8tmznzIJC5BQXmHcgcTeFQ9ilFedq0afsdfPCYSR1XT48me4N9gwO2EzPG8tQqhpe95MV77Lab36owO/Sr+RasjzDXs3F/sdCi9EolkEoglcAzRAKJCaUOg811P5zlCPa+3sFeVkDwlrDNEE1WXLChtY3SzhSVGJXQyWy9DGyl6Wmq1VcUqEQY0megkABgYwyq2PAmolOJ0YAyB1JoXaUkutRObyvIBMsP0IZJzPwQcgydujEZ7ZOt9whZtSgFQu86fdcHlj5A2aJmbANs45Pq9jROkSulOprc3K5P9y71aoX2suwaonZlMYCsRkkxtuEAYPTZDk2QAScmt1HApFbbFzrXzGji/ZYfHQ472dIrlUAqgVQCO0oC2w0D04PWlBSUlAhk7EyWjNoEyGr1odHR0twMR6jhtUYCcj6j/eD1NQimfylrsZ1N69e/7jWvoS7hT/tRvfrVr4bJ+rq6KGIak8qGjd/3vvftvc8+dreif01nUta54FRXl6AiZAc6OqhpK1qcvEpZo5ysoAyoQE66GJFd5s4bHRy0nLK2ulpcjn16MGMNp0PxnFfLMtkWWDZjffRxOzY8kp/JWDBpGE9rc4vR5pT71l4YqwBBxQaqFxrBCaMoP0NiIbS1uCwnwM+k4bysuMSS6SMOO3zuLru2t7SwZ1MmNdhBEfpS8IVHPv+tbzkODmFp0AyHuwbzAiAkaDZZbkHybKpllpxsHj3vkOd+4xvf4GFG/DWvfx1XsBLKYgwPClYmcnMD8zROnkyS0idPmhQPTlDp1pq2xfREyOHNivWFM0F9AJihtcLW+c8O160IZwiNtaxbx4FpW68/Xn2F7aNvueWf5eXFv/rVr8797nd5uSvsyz00DJ02Tp3WtmaNSOzKmlq9xBG4Q4Nhih19/Is3s+gUSCNYTWbabVZMUPEsLi1Sr1braWavWWLZLP9m6UnbO/UUPCN2j1A7+uijScY7he1Y7jlz55oKYZPxqUa+dEHnZOKQ3vWrV8NscOl5553X0zNQVVlqTkRHNWLo6uziitclFLH1B+Lg+1ifg3v7RPYuWhYC5MSfD1hvrQnmIsKJ04VlmaIx0ygFBc7iau+2o0mmu3cA/udJyOTlFxYU9iVDEDjcy7BDm3ENwG82Z1pDAyfthqYmndMKAhEEpk5EA/jJaKx26R5qjFMGBCJFJJ2tquIcv5fr8spkJjcdu6WpyY/LmRiXXnihKpTVZA3THMyoPTgEwoG+nbIpVdvQQGikBP2Srd3OBOH5cVoy3dHd5dX4lXkFBKLqffxU+/r0TyMw9QLAurFfIhRtGsiv2Cse9Y9JMQdrJUvU2tes8b4cBx7nm/CTXqkEUgmkEnhGSYB6pF0ds0IlWpNI37qxgKV/uD+TH/y67e0tzJlFtH39FH9YTxsniyWyRL5SwjRqHMMYGNCZCCqIMlDqEfVrEDJhtRkvT1Ukke0zMpFTQaTEJDOFtDdbZmRC91LCECmCiKjIV3RoYDcMweJ1iylnbNDGwLP8MbQKbzJTvzQ8gnibM3vO8hXL5UQqWI4kOtoYSaViub2RSIdKN5OOuO0esIdP1TklQTq2B4f6wzxsdgjU6t7Qw8DtOnde3si4mdOaxopn1GtNmUklkErg/zcJbLd5OPqRUoZ2gi3Isx3UAihCCufnT3/yk6uuvPLPV151/V+v++Ull373G9+kOoUZ33zzzaLzLew8/ctn3Hv//LKy0o9+/GO8xw7peeXLXt7avL7L0lDnyuXlv/F14WzhmVOmOm1ouH8gP1NUU1lVVFLavHads4Irauuc+OpwXVGmmeKSscEhDslwVt+olbQFpZnCIWs8bUFk/4qxMXOlF/78Fzff+A/mobKk9FMnfRIG44ltqK0jCwT//tfrijNFUyY1lpaVDw8MtvAr2jXRessBKG+Is9LC1JrqavBgi3/0fogCShbeszwwCbu07z77NNTUWrBqybSnTsSZvtNOlXV13zv3XExaXWnPIa5INulNbz62qalZHO83v37OH377u/e9570SXRx27NNAdzfxqpe/W2PQ96cujDmdtbam5rkHHnTJxRcffcwxzz3kkOamJpTlH+ofgKL5GDmH9W9TsaQK2zdOahwZGDzn7K9VlZZBiXvuvof2MunkED4f/yf9sY+S+7bWVgHPRZnMfnvtbR5BBRdfdBEALDaYA9+8sQ2NVVoHQyaR26955av+cf0Nbz769T1dgOKAs3YqamsNAXQYGKmjpaVmyhQm1rlYJqU1TcvtjyX22xsMS4WhsuoaTmNya1q7zlcu2Z6ubl9lc1rjksWLJc6YPh1LbsjH5MejCxa4nz1rltch5f7583v6enEb6TuGes68nZcvXvLt75xbU1XptGQEVWd3BlEMC+6bXz95imicO2697be/vjyTn6OrkOT4yOic2bPtUqVnkoAdruNsAmToKmkQ7VUVBjHZwdtvdFJdVx+gvgVja9d6fzmZQgMoU++OhhrsG8jLzTLsqK2t88pMBxRXVDrW+Oe/uNBxTa999WsGenpXr1xFtlMaJzvGGVfa63wt/XzFsuU+p02ZGnYRS/p82EDRLMt4CJjni9CKmqpqC5VlkNhqnn7YytzR0Pyx0JM5rdcsX/7tb31L/ywpzIhwjh141cqV4u3n7rSTns+3750qa0c0mDX8vhJJymnNw6dOPrmzu8si57323DMsAcjOtgGbZdXK8m6QOY0gnQNf0LW+qmc68UOKIR1VUFZd3bN+vZ9FQMIFBdnc6MLg0yuVQCqBVALPPAkY1cQLaywOZ6ZxBcVF7ZuvhyfDxv7NzdQ+9AgNwoHuKTpoU0qCJYM3VTrIyqqDsp6y1OjIAN9KZFbiVLuBClAKu6rUPZPhRtVGWSiYskRWdQiCrzSqzBI9kigACjXpYDMFCy0zSdJjWQzLD77KLDgIq+AranKaQwfFOzo7IsMRQhvboIY++Co9zopimw8DQXVFz4QGykMgMmNV0xRXFs8+IWG1KG4S+QnWAz/zXnvKUSqBVAL/gxLYbhiYynP1J0tPS6qrnRlLWsNj4697zWtf88ZjnrPLbgcdcOBzjzhyv333dVjL5IZJDl//01VXiZmku7v7OH767C30kpe8hMtr7ty5VgI7SpRziRmwYe873vEOMck2eeZbti/un6+4wma5P/vhD4UxC2dd9OCD1C5VS0PDqxQ0rc+FiB8KmrblfeXBE3b79bO/5qQf55RaC2qhr0WJDm1ylimlf+KJJ4If3Jjvec97fvTDH3LB/fWaa+wn/PznP9+axojtaXZWSrvYoSfoC7IFO5fkkN8iZHtjOKlVsCgLZOPi22+//b5//vPUk04SFNo72F9WUvq2t76VFYGCRArV1tbYKNvKYZ4665AFLXOcPni/DauGM6VhwfO/qta8ZIrXv17k+rZWbSdPCAj/3/72t0VhgRpsVV5REfgdLFNunmxf+tKXrr/+ejSDS/bWWzp7urmj+dirJ036F/End2ci2dLWwtJSx5G1bQhLT20mfOGPfyxOWDPrJjX4Kg7bK3jx85/P0epQCK5LEcW11RVAsW23cCv2e78DDnzZy15uX2hLgsMkijCq5NAgT6MR1TTH/zp613iCU1jHMMmCR8MOKQTgdfd0dwvKBfiD2zYZlOQloWXWOHkRnKVGG7IhVV9Ty/cr0aW2lUuWOLUItdNOO80AAlg1CPDVOUm77LknbycKOsNRRx0FNiOuU1WUl3unmcI8ezLfesMNHJ4O/mHynZBMFA/deafwL2ZeP5gzcxZA3t/ZaZo8J5n7x/+Y0PS+XqMEfB533Fu4b8/99ndWr1jBJas33nnzzd6RLuRoYpt+Z8rLTaUb+HC0YljwNplY2OxqcUpw4jqQQgj6XVyyK9HXuFOLXqQDxy5ZW18vHbi1PNuQxNzH4oULxSZodRSmT2XJhJSs4DK+scqLz71t7drJM2aoxSs2jlm1bBmC3pEI/BtuuEFktfGNyQuSKauosE00OkZRFTU18gSng+D/goJXvOIV9sMTsKCNBkYllZU2p16yYIHNyc8888wNgr2LipyyJbBB8fRKJZBKIJXAM0oCCfxklJIZ/zAPbgdbCjWHZaEqoU03dJ0MQUWPj7NNTKQM1ClY66vL+EEiBQiFGhJQ46ySrwrSmUoBk2Awzele8xkjuJE1cUlk+yIShmkVMa2vrAwsmnppcjYCpnWvLnQAYBob1pUCAMsJSyuCjs85U+aIl1YEk+pSIyWsIVJiZJBhQzQuqBlLUN3aIltsoIYwwWpBM464MOkpaXiET61mTaQY3uBBOluGDaTG/s8Wvs+o95wyk0oglcD/FxJ4DKD6L9trMXCi+qnIvvZ2mIdKrcwUveKlL1u7aJHInGGrRLp76hont6xbK8758t/9ltL89a9//Zy99w5n2/z2tyYgbR47KfGnwai05J/+9CfG4OOf+AQbcMYZZ3z0Yx9lYM76+tfsNtHd020sHjbsKS1mSBglWh4AZk4oXIocM8Fi9Iedn4256fre/gH2JC6UtROVKFzRsE67Nb6n9N//kY/cfNNNN954Y3PL+k98/OOEoQjFLbCWUcEb/W6UPzQ8pFKcb01aPGMsjZzqYjyCXUkOAIQ2Lfi0M9NfbQT09795CvrKUFVWAX7vstdeo/39bIbTaM/74Q8Rr6woe2TBow21tWDMpz9z6p677X71lVch5RFLhTe1ADaRT8bskIMPsamY5k+aMpkTe03Tut132dUsNa91EMimNcxaOtTTrZl/+dt1NvcSv4oaeQLAJrCFMXMPgl5b+1PdZo8QNAHMcgtff8mLXwJZPfzoApiK6Lp6e+S/7rrrjj/+eK+YtT7qJS/WANuG2DrNlpAnfeJjMPmyRxZw1JuuGBobAzVZTcvFBdXb24CZ1UKkiDHCOUyyxNJJGMwuzMu1rFSMV3ay2ll17Kv2emXk6auwYIIySkDBfEcQRU4OnGwigAva2VQvetEL9TGTEStWr/nohz54woc+ZIMyFv1tb3sbcGsqx2wLDPn73//e6dannHLKlddcbROs4O3Mywf8XvuqV1/+m+Cuf/UrX6WT2IeMQ/vNxxwjut78f+hCY1k33nCDA72a1qw99JDnfuD9J9TU1uELP14N0GjhgEmZa/5y7fnn/cAxVwfut//K5Svws3pd06dOOvEjn/xky+rV+h7JEB1O3Hvv8fIbEfpcnCyPj8MU6c4n02RCUwsR5edkW+tFYtrut6m3Wz+vgfrM4QcddMQRR/iV+QU5nheUDTHe7e2Kq4UMBbdbYf7hD37QSukjjzzy81/8ooHLwQccePudd0Czyl5zzTVeq7JmjoBbYzIM6J0RAwfsrZ3J7lxcu00rVpx++ul/+OMf/B7NhphB0Hkemn+fvfHs9GL2Qbggnuutvcd9EtTta3qlEkglkErgGSIBVpdWT9AiC0bJBjVLYbJKoOCGrg1N65uAzNGRbPFlomxoeFaeUaN75aTY6GGJ7JfBTHS60pBR5dKuLqiYUqVIYVdamlY0tgmbNQwPx5wyQ8JUtDwxJtl9rIICp1QlGqIYdMnJuiGCN/csNd4gWI9UDeg6OmHB8gUq4grGDw7BVG2BV2FX3Eagi1uJODekYjvceGSwgVsFkVJ7BOdMCWbsg6UIstK1JWdcNNLGFdESsSQWmhifIe80ZSOVQCqB/28lEGb+ts81Pk4/ctJRbZyQa5qaee14+Q499NDJAjXtllxeTrEOdXbVTZn6yle8orerm9J0FuvSRYucd2pL/8MPP/wXv/jZJz7xCQ6xI553mI2drG4999xzeatoZzjqyj9duddee+E2KNxCGypkKSig2lG63RYAJ35Um+vUTpkCy9G8clZYkVNc1D/EsTQgKrW8pJh/9ZOfOBEKCj7n/PzB3l6qnEJ33uxFv/613Zj3fs6eCjIYPKg8h2d95UwrhKP5YYFAsmgMtiY0OUmAkZBh1BGyFsaMjJAMIwRTfeiED0xtnOzcbpYTRHRuKuInnXpqx7omsOr66/52/g9/WFtV9cMfnA8S/OynP4HEBIyCnRzmV111lYWaVo2ijBOfKKsumlXuxxe/8KhVa9eIEufcO/aYN5Et91pZUTGTY0ciDBA4l68MEC/HLwBsRmDuTnO/8IUvfPnss9Fka7fWrq2ls4iTE5cpy2fig5cbtDZDoXXC4D/3mc/+7Gc/48kEJq0OLSSY7KzBvsHJDfWfPfXTXJ2iiGeBaLNn482uHTZ8wkNo3fg4Y+ktaKOqRZyx39oMbLPxWq1HaZdHoJrl33HEUFpRYUrb3htCCeTRE3QV1IwJzGUo63UM9NlpqxUpLlY8O8D2xz/5KXv/ve98mxws/La1VWV1tWkap1K3dXSCf5AeJ/DlV1wh+tr5UvPm7CSY3Mhm6uzZ3//+99/4utcuW7b8W+ee64xiPZw/8/vnn68ia8pf8cpXHv2G17W2bPjlJb9cvnTZzOkzaurrR4cGOca1KzSzoEDg/W677PqrSy97yVEv/NMVV33+C18SBQDY//qyS/0WNJBkCFDzDRm8RK/bunGSIUvY3iwAgRihBCklQWg6kra7l5M0dPK4Nkx+KYZfDVOn2nrdb4oj/evf/MZ+++3nBwu09/b3Qa3yGzPZ11uEhd+IyIif/Pxn6Hh93Z2dhlBmc3beaS5M/uOf/dQm0l46eOwNCu4gT2WHeu3ztQE/NuIylYUZNH01xeNlNTc1C4u4+bZb7Yb9zXO+oeoPn3CC7n3I4YfLGV63JW3J7uWKpFcqgVQCqQSeURKIAJiCZXQsKoowePfddjcjGQEnHEiVMTdRlzIKUsBFipqh8YgaZDfZJuqOwoyDBHp+2pRpVDrlGRUpC2iwAQOjLJ0QEIywGXEEJTJDyMYaLTaRSFfT89yt6mXpAF1Tk7KBvmqUk0cBZRTC2uDlixFkifCjNXhATWaJxkXBECS7SVPdLinMELJqMcKRmT1SlrvYCEcim+Kr6oRKqS7s32EdWXJaB+IxiElmZLVLZl70Z9SbTZlJJZBK4P83CWSPdXRsU5stKdzsinuRA4cT18Tu5GFRot2CYxFuvSSHA69lgGqg3Mkzpn/tq1895bOn2dzqqquvPvKII/LGTKAGTWpovnjZUs5MTksjb2pdIpXKikStTSmLWR0dHJio98ndBPT4+IuOpu4nqmAkqHj2I6LZx+ff1hTWjgFTRbRDzIxGMQ8F+QWMKMtUUV83ddKk5g1tL3/pS+EK+WXu6+7hgfzwhz4kzwU//tnRb36Tglus2v6MjCq7Ei0Z5llWnkaVKkJ6CAqrXrJkiQy33nor0CKzR0AR6yXRpfmPf79brG4iMdpvdNyQlReErIspdbHlckbBYsBJvC0tIRZLpTj0SKVWsUaxoJCfEAlsmG9PZs09m6jryd1s+f2SgIBw4NM6qrKiopGssabVaysqK9esXklEmHHFF5RIYiyPPBn2JKzLozgasIwWk5uxESXG/xxeqD3hkv3SVBeaoMeOjpjXJxZNLnckkpmRZM5lol+Fd+MXkbhtPUVHgIC8MpAqZoJgvcRt3K879h8zKeeef/7VV1zx/Je9rLO5WXvHQn1hh2c9XHXeUWzaBAObtc5QJspHNizJFuUj7k5ZPcdTRcJ7TKZmtkYHP16rSwbSMORyGVRNnxq2i0uvVAL/gQRsfJ9XVrHFgfTW+uF/UMt2KRIV2uNJ/Wd8KuWnZHWP+Bo61q9va/QfX+MTp2yNzn/G5xPX9d883VF8MlbUsuPvRseDge4c7M4rLHpg7cNN3RuGnAc/OtjV30VjFxfl21xj3ty5jy5cXFAQTiWMCpmNiDoTrDWS8ZVxoQYBXemUrXtikdlnsBh21erro2ypX/W63JgmVoqRhZaVMqLQAaDK3efsvmTNEriXZsaDgvoGvy69zQbJJpG6Vi/savgEsbMCUqIqxkOMl0YBZaWMf2SQUy1uoF/s6XgesSAgNE0up5TIcziKoyiczDQ2MmRPjcGBPra/oDBvPDdn57m7rWlZt6G5XQheaU6RLTQbK+v2mrl7cVZx3vg2RCPuqPe+rX015XNbJfbE+VN5Phn50A9+fX6bfIdW5Pkaf5uPL/uskGf2+NhIT1+IpXxqrjB+KC3fBu2zHdkAG2y0M3nylMGeXq4nGnR4eNRmTvyEu83d2TZLf/3rXy1oUeNrXvMagMCAndr12uh0Gpm6Z5KlbMeASZYgmnlVsDHRUNnVAWLZLg2P/GsCK+EeTTWySB0trQwhcJI1OLTTnDn2iDZ7atmzXbLksZ/wd7/znbGs8VnTZojFBSe3ykyy8zPTGCnH6iy3YU359WCU6Crk3JaB6SJVPw8/ErXbNVtSHFeFzZa25fo/YyNB4AWimAMuQjw0kDncNLngK8L1tn0KiDJ4GuPrS6BiWM2riC28FCElZOPFiG4LO0+U1+G9iPd1dIDd0xqn6EU6ITOvXumuTXUmczWb5IkZnIfOJgVcNRuxJY50S00QoIZhu0DJjBUd2xyHigihqDDB/IkHW10TjMqz0ZWQJNm/inDUqF5vR1rgKkGYE0WezA2GFb/iiit4GQQ/Zzl319bi4V1obpB2JOJmouFbJBvlE8ok8DW2C2/SMTnBp3SJW6QQE2POmEeNvup4xPIERdJHqQRSCaQSeEZJYELJ0XiWGkWFxp4adw6MDA+MDBRmgl93dHT4kN0O+ecjt7un5TyVH8jkIAUjAWDz+DCkuW+A0xDIkEMimEohw66ULQXLEMgGoFKViivIvqNAt8O0svkKmqoCWp47Y+78R+czvi4SQ8rTOJeKCNwrER0FeaFB6+g6xhv6CKqXZ1hz+GnRVIsaBZRZfISOPPC5ViCIN2URQVCKsm6kSK8J0LqGr7i7s5290wSBPbl52ZnSknXr142Mjqhl1D7Rg/1F2aHGvKwdM/7EcHqlEkglkEqABJ5yHQQKTJiNx0q8uLR0Q0sLPbv/vvvtt/feDzz04CUXXvS7X11O21aWlgnZtbPRC498vmhnYLFt9Wozl/Q1PUvn2jAJMnDwC9sjwhmK2IYrOqO3UCCbq1QVCflMIe+sa2zc9sjb5dLSMYuEhLC6ioqDUACAkdFKxybxyGVnt2/YYHHqylWrFixcaA8wLWWTGKHy4pKyopLPfe5zbIZsW+XHybrgTUEIXlI2bNWRwDDNISgzuKysmm0txpTC4RoFh0gJVjzZ9ELb3cMxW61iS4KACb0pdQYkZ2vigK8KAb/QOmyEZieu3OyAkZIawzxxAPMOEUwEjlu5oMdQu/oDCwH5JR/j/wKLW6p9C2lbeV9WowtgLigtXrlsuVLhTOaBwYDFMwWhLo3HcASuCQ9hAa3OBqtrRTLPDZfH6qLjd7Oqy0UjB7EHYSKlRaGZCamIfnVcj/RYbYS5JxoY6SipiHuDjFAqYUnHDpn9b7y1WX3/7qu5DzM4z3vucw2w6qZNs2OJDbFNOgThcizjoKDQbWidakfHtka/SF9NmMFegNC6R9KofOmJWMKLHh3DoVVymNXLt8haeLNBKgH0usdAXl6+u/GRsGYhvVIJpBJIJfDMlwA15grKMFHXjNdo1qhBixtqnz0ZyQpbYe0/d79bH7o1nEpQ4NC3oPPhTxf9HwPZWHMw1TQlKw+RQsIuN0gx2aZNGQIE5aHJpfhEXGLEvdLhTwTlQUrkc2tXWDAMSEPakbIhEzripfl75XGBzRJBUzyoTnFsQ8geCWAGfWXAqq+czOxX9AwrLk9MRxkDcaWxzLGiOKFpUIEyjI0xIsKYtrCF5UWWKFUsXrLMiY8SNdkArqw4HPVEdM/8N55ymEoglcD/sASecgxMdsbYDMdGALFJlmYmaVjrIWuyGq/+05X2bbaBsOWsEQALgRbl9da3vpWOFlw7edYsh6/SnhOhONJZmgj1NpH87/41lE8uajqM8ilx9QV9vX1ExIQEs+lKoGagn2D4kWQjChvkVjU2fva006xtvvSyy26yV3NnJwC8+87Wae5y8kmfFKRU1tBgNwkWKPK52ScbSSaujRXFZbRmavsHMpUV1Y2NdtiwIEcpFFjToXB4cti/OpqleB8wXGLaNyP+RF8TB6B60cGDnAFXRzSYNDZpc/iQJ9AxdwEvcXIGSfzLDbjx6aYhRqwRe/Hmv//Mt8lWT0/H+vX2rJo5ZTrXKJOf0QP7exI2Az5TS2RDvWF6Jex9kvCc+HVDE5KejGk3/2I9YS4UT9y2GuUeBZcnHAQbyYa1Yxvj7VUDYyfl/vUR84fvod8lIdIw5aYX+q98T+5OwZGBgbPOOsu4xMpkZ1Z6PYpiL2nSRipY2pgYW/p44gknsVEeRn50E78OXSeWjaIL/D/h+5LNFV+6zBtJPb7GNCWVQCqBVALPbAlQZRgMajYrAXtZo4VFhcP20Bwb23vnve9aeJdHCQikzsf5ZllwWp1NBxFhXcVNTFuaC75awSvaWR5aUREpwYM67DiCXtkkQqTGIcZLcKNSxgZqYcTlQQowRtMqp0hKBtk4mTEAkSIIo4K1zJkbNcahBWMtMUJrQUzaIqoZHXjVKAtCRtZ+KKhhQzYQFzOoGT8A0qLV0NciZhRvkDx+oGJOYDnlMfBwdl+p1V4FBWrUCsRHBkaL8gtqqmqqisIOWwOjA0W5YTo+vVIJpBJIJbBDJLB9AN7WWBc1GqEC12JwIT3mKqusciop1dnd22Mv4ve/732fO+OMoe5uKrWivMK+OC2rVuUWZqhYu0CtfvgR++vk5Ab3URiOJ542WjVs8zMatlt88tf/5eIx5SAcZ/VIYNsS8yZnBMOPyfSf3yb+38T7x5IkuAItNqlQ5JJFwh0dTh8uLS+z3e6Rz3++Noa51bIyMCavpGSkuyevpLS/tVXRrXFAGi5PlXXJyUQxe5mySgtB3ZfV1V12yaUFmUzT2rWTZs4c7u4mTOkCukJ4b3KSMAmMDw1vVURbqltdoWJ0EuymXvbSN1W7d8Ub2aSHjAByUiCPB3+iOTBVBO2xCbGif4eptsTOVtMcTcQ227DylUcfbZ82Bl5Wc/LRsb6pBV5FaD22J24CaNskz6SVAQBzBYdjmh9zxVaHlrmCozv0fChR5oRA8JR6wvD7ZyMQTLC0nKHXbbr0jVidzOHlJJMUUohxU5Yn9W9uJuOw5dpp0+Rev2TJ5J12shIdtfHAVPxR/otObNe/vj/mTrt8i8zEmyCc5F1LnLg8CulbWawe6WmPgrEumSXiRABBeqUSSCWQSuDZIgFKD6tRg7k3tKEkh8aGRgS1ZI3svefe9zxyT35etk0c6HmwsKKiKpobUBYOpAAZd8ASTBVyzMsKGHPYQrO8uD6BSQRFF/O+qojmlx8FtsPoCChFM05keypdWXt8ugE1wWBQFtmGmoZHlzwKA8PeDJ9SiAhsRha+hUtRkB9xbIDfto1UL97k1ChQWSIG7E8Rq3Yvg80a0REXDf1GkycbUgrian1rqzwkMzwy7Kam2v6Y5V3dHUB+YSa4rDVBA/Gj7ODYYHbx/zWiz5YekPKZSiCVwP+KBJ5aDDwhpc0AsHSob8qcOZYp8j0WlZYV5OXbG7myvl4YrQOOTIHS3Rapshn05qxdd4XZ6OsAU7mSKOBNjrIEtk7U85/fqNRkKYNjYM50hcF9qC94m/9zoo8pGcJQE1CHIOY90S7VkYPjlEvEOI0KDg5L2FkUZob1CjbDPsZtbT3tnfX1IX9+ZeX4Vo4mNh0Qaku4DcxHWApBJRtjFFVV2WfZUl2tM+k71NWFmjyRwdjG8Dk8sq1GCc/kFACeyOHgRf6XuNwHPjxVTRL2HLgKVSYOQxkxIFOCMCMwiywlxUKh8PUxBEPR//RikhXtampCk8sdFOs19S7ebCSc5Yj5jYQTRyWmjGBCiruEAe/FFbJtWi9t7PPYhcFBCJvoYF0pLXJ5lWbL3SAWQGCCaY0A3AT6m15BcpukJIOejY88TQpGHkLik77E51Xa9SQZwQhdI38jMCOS8DpCmza65TEW2N60Nvvx5GO7/sXnpnZtbC8O/SUEkfKv9McTkWLEpB4yjKSiiLaYM01MJZBKIJXAM1YCQWE+xtIFDZiEOrcNdNjI894F99JyQPHQ8IjJfdivrq5BCk9vhJHaBfea/YdUwVF4koNXBtgVnhSTLBsgykbIydNrPMDvKgP4CjlLhCHVKAWe9HXZsmWsOVirlJsY7bVizYqYM/qQI/JkrNUISKtIcbYJZY/ES6vIVxnUC/RiSYp0+pzvl2JnQLmI0UcwboiIH5xoBWbwLJsiDj4MPDvSqbIqU5iPpc6udkT4lguLi8oy5cWFGa0Y7OypL6surAlGOb1SCaQSSCWwoySwaej/lNUPJ2z2F6uiVbtbWpyQVFld09/eYXWiU2ptdGhZrAxhZtGE4tgYXyiVyidshE1Bm3H1j7Ly+IzQYpt5V/rxf7CNv+ERc5jj1rVuzBAq2i4X5gP/m+AQmoH50VHnpsInGuNpNBXaxcxUNTSwRgybsxEmTZlCGjJ4sFVmNqEpBgkdl5wExCGrol5Wt61tUPEElUUiUYCBbCJP2Yg6PHq8cLaWkrj1Ih11hcFAAhT9G6FOpBxIJnh4gis18RdHUavXxSS7ImMx/8T9drlRhRmHOBSwlYf1sYFsUm/g9jFX0oiNUG2C7cib1/eYjMEb7C9e5BzobELskUikTCi5/oP8PU0yxHQFo3wCnfA0kXwUXRRFgipli3LbWNOT+8eIJIyVHJtkszHr0BIncOBflZGHCOkjD5vYfjxtncl8QXybE2VlC+2NTG697GOpJdWGTh5+AsmvIKY8Nk96n0oglUAqgWeyBGg7ijNyaLLcTdguIol7OnS/Q++efzdd7au5ezf0PJBJ0YGLkKfM7j2lluFM09wAMOTJkQsiesRPS0MqKzODJRvYyWbBn1IiyETE6Ei0s7GBIny5cKYM7LvZbcRRQ1lMMspqQVCR6A2OZylhDCnp5kYFXRtsyI+gG1VjGB1k8YYH6RJR4GHGiURea0jY4AQddWES89jTWIzJLAUFT7Eks0dSImOwt8wYlohmeqUSSCWQSmDHSuAp8QNHF9kWPydaS3uG+9Ex2x06YsURunRoaXUNLCAi2gjbLr5FxcUjg4PWylKghZmMobMrWhEalopHYFtjoUOlwQm1+afjcbLgFB67bP/mZuXnyRPA8P8FSKH4Vq5I1UMUHr+tVPArRrQQQY5mJOHBfKd9PT3FFRVjwwHx9vb0lVuc46mNfJPdlTHa2txcO306w0NEmzDX45jYhJfkiTxHceXlhJ2oJZYk2yCHbcSSqeVYXj1uiNfl3pLWkL4l+TxeYiElgWcspcs9EqH2aOkT7I1kTJFhI2MBBm68VApHGTMEQgmFjQ/C93D5Gshtj4v0rCbnLQ8vYmSEtbZLplhou1aqJdaAw8gSAYJrkZmY6F7f0woBC1tkh7Qn8m8UaBBqDn+AmPONb2STEKRHiT2eVPSXqjRSk0FX9//j1w8/vuxjU8JwxMiGz7+7u0tQdF2doZb7vNxQ9cZ3kUhYRWKYQ8oWr6RfyTPxMArEu0HHJV3KxDWRbbMbp0mGV4zQpo4qAzl48ZvlTL+mEkglkErgmSkBijBO/WEvaMWwJScTljt3l11unf9PY5LovzW8yeTbHDJsvy8SOImIrmDfYT+oct7ceWvXreWSda8IvCq/U9PpQ1HNqNGrzLREYJJqBSbBVHYcBfCVEZOudokcsHY9VIVsqE1rnLZs1TJ5Io5FEHYFpFFDBP5UHTq+gs3oS8QGhKxIpCCRpQPapTAiwDCcLINK3WuIbO41UyJvthbF45FkHurri+7i/LycDa1tfb3dAf3mZvUODCTn6JVkjWRB2raGLrd42qYk40NF2el64GdmT0+5SiXw/4UEthkDbw2U/MuFl8jNJsFheGvT4Lz84aFB0M34t8Beso4asvFDf39w8yYnhVoQ68BQapdKHkkCnvNzcnkImRCf0MO4XZ2Se4N0Wx8HhOYSd7lxB9rNak6ebv1jPDsZtTNgwfX2mE/YbCzsmiuiNxi30aEAN4Hi0RFo247Gw3hOpnv7kwMMkuH7v9yAoUIG0a7ASm9aBf1YLmyY62uguQlNhOaEuNSRopKM3XELM/nwfElRZnSgX/TwmBU1+Xmjw0M5w0M1tVXjfd25+WFV9WNpPvY+UE0kE26SKtgextkkQcgWWpdVEALHx3MdSTAW4qxcQXZBCqMQXrgNmwRvpLS5fOTaTGJJSixlS2A3EpK/IAftAi8T6oHnvPxQlUTVZW/KnRTYuJWU+3gxsVuOpt2U4d/8m7zfx+fBGYEEBpIYs5ChrztypcaJ/GFYswnvSY+PQmKCRY1Owt2WronMGx9CesndpiLR1Rsq8sp98hFszLlRjBsJx34VsiWXm1D7BM8TZf7dDTrBUZBsiGKwErz0yT169rsOZMeCz3/TO1L7RgY2IxxSN06qTDzZKCC95f+8yY3Pt0JnYv/n/0Nty5knakpvUgn8ZxKIv9z/rOxTWioqkwn2Jm6eZKWx+OMzbyudx1PYLOW/5HMzak/d16eZT4ZOLBrDHQUOCubkF1pSe//994Ognb2duZlcilcuOHZy4+SHHllg1BD9n1CfyGGfrW2tHKSwJeahTf5VFAyBLL5VCnwFbmFLfloFuV6BTNUhC/GSJOTpHgVOV5XCsRFLT2+cvmz1MplVEWcnZXNFmOoTTMVwRNFW9tpGy70qkJU/Yl2GA0sTWBeIFRetIKg8gXWx6mJWoOIVK1bIgwERzk5CKiwMQL2rYwNnb3FRGOP19fVnSopnz57t3EfnA+eMjZcWF9s8W9WmwLfWMf5tf36a3/vW+Py36Smf/1ZE25Qhlec2ievZ9Tva7OVuZUy6TQL495m3qoP+fdEnzBHphk9bK4sFLSjg5+xsaQlxNXl5To4LHrfSUrOSY0ND9G98VeYXn5DqdnsIzrmezGdPXx+eB3t78QvPgCa81tHA/IsbI/n/O5hHORL/V54nd0dim/09uXLbOdeTkcx/1sDtzGhKLpVAKoFUAqkEUgk8XRKIAzXjAVesk28TCAQRxR57Ch+ChdyzCxctlMGoJuY0fgAmPYVaIU+lgE8F41bMcC8/ra9W28oPNCpolyxwNADZZHdlTy33NfyQB5pFEJL0yXm7y5xd5j883zjKvU+k1IUfT6FfF04QQRnZA3Y+4JFHHokNwYNsLh5jT50PjD34FhSHe4VGcxq7iagYEbVH9gBgZKF6iRAvskrFfadlJgE8uGSQyNftPvrJlVI1oJ6fteWgKgTTK5VAKoFUAk+DBDYq8aeupsGBgYB7bfVktwRXVZXderrWr29ualIpWMkkiFClqf8DZ9dTx/YEZbYqU1VJ77MEPlktMJgqn8gQbiDq5C+Fhf9HLOmXVAKpBFIJpBJIJfA/JIEIHTUoRLRAm2HxVjcvKPdsdMka5vAA22kZ6gMX48gBfDWWMISAD4FA6fEpqAkuouAyrjAQks4l21gfPMMSpYCXqvMVWgZW+WlhTmygHNjIzoYzWzpaZOA0lhkFT8FX+eXEFTysdkzizdFNdy8K65ZdKKjLJ5iKK8UVRAE+R0SLFDR+w4avKlIdII1nn8giKHMYwuXm4oHHWAoiCGqvNqpO6zTZBAH6cpKAZmqFDGNbj2tTUXqlEkglkErgqZbAU46BQ6izsOUEBgcdOjj4i1/84uMf//hDDz1kNWZcOemMIOrSSkj68alu8DbRxw2WRO386U9/uvbaa828mmS1MtPk6zbRSTOnEkglkEoglUAqgVQC/wMSMKRxaQjsZ/EMfOgebuQI9Tlv+rxly5dF+CrdU8MbcBSeBF+lA5AAoRDoCDJBRNBRzggXETHMaO9qlzng1NxcAw9PIUzokQeYAxZB6SAlMCm/4osWLXKDK0/Rh2kVgTwRl9ll9KW49JUtK9XuqVLCrYFkaNk4h7NXjaApNjxCiitbCkCrLApqhGnxplJ+XRSUUtyNqj1SyrZbMrtUgQ08e8qbHZG5gZNEmQFvBAdG0nEU8aRXKoFUAjtMAk85BoZyKdNgJOLhqCMjV1999a9+d7lJxIgkqcugSpOdcqjFHSaJrVRMd998881vf/e73/TWt/7lL3/RkKGRkfzkoB0lOH5T3+9WJJcmpxJIJZBKIJVAKoH/KQkYpRjSGLFolRWteVkh1DkZwoTD4fbdfd+7HrwrPvUJvsK6oKA80C9ECrgqaFwBCnokEVI1CgIOjYjcQKRGGWKSI1JVRH6wk/cVvrX9VcSiKEsEJjl4Jbr3VFk8+KyproFpIVVkodbo150+fTqaKPPZ8u6CxHy2TmMCX5cvX44sAAz0ql2i0GgEsedrbA4MrzoE5RG5jXmtgJAxpqDqYF1EIu7VFrAfZfwrIifG1CgRxla7dCn/Uz0jbUwqgVQCzzYJPOWYc7S/n1oMyi45FcD2+TR+UV6hCUj6ETD2iF+VQg8b9j7zMHBuaQnFXVwYNnikuHEbIn9scpteqQRSCaQSSCWQSiCVwP9PEgBTIwzWaOHQ8WwkAxhYlOP00eWPRkDoUwaDH4AT8AMmoURlIUmZYzgxx2l0BkCJcoKF4Cu4CLLClnKG0dHIiOIuNzIbfgCfEXWjjKDzgQ1RZFbcZbMrpFauWikP9Av0qkWRmJkbGY61nxZusQeOTq+fDkJHoIs4uIvzCNo5deWRiJmYWX4E5ceMS2aM4dk9wKxG/OAZAygA+fgBs5VFXxtlRlwitKyNmfx0HEU26ZVKIJXADpPAU74H1XhOdmFpCTU8MjxEF2f19jzw8EO9I4Oz5+5UUJSxFbMMI44gst9iQb699WnPHSaMLVUsEHrPvff6/e9/T8u/8IUvzHJS34Z25zZR6LLHKYTUFbwlyaVpqQRSCaQSSCWQSuB/RwIm88E8sWvh/7AVZpjdBwVB0OnOKGoKYBJoFOTc2tI6qaEhUxTWgsGH0amrCIgbZtWT83gBY099VcSIIkYUiy7mREUQpJQIx0Yoy/uquIIyR3yrXokuPluOWQgTWJUB7HRJRBno5Wxwrwi3sHs5farXdcC8A+5ceCeCQHJkBnZ1jwcYVXWxmdiAmVFIautSrxQtUhcwr1KZtcsBllZGKxJWLZeWSUQHcaCXvxx+LioMG2UNdvU58zKnPLjQVZFeqQRSCaQS2FESeMp1EJXnaFBqMa+gwOwlhdg/2J+XlWNWkn61BhjopYupV05gWpW631GyeHy9wO2ws5Fyc4944Qs5q6nygoThwO2WrmfWauYtcZimpRJIJZBKIJVAKoFUAv+BBKzZ4uxUMI4BxsKBiiHgedfn7PHIyoVO3gMOjXnAP95RgWPTp89atnxFjBwGDKUbAgmCi/BVBgMe99wDQO/MaTOXrVzGXRxxsswQKTrGRaAsqCmzwRJgafjEnSukDnDlfAZu8QNjzpo1y15cciroqbrcyKwUCmpBGVRWr5udd9r53qX3emR4pgkowMZgbXTqyoMlbZGOc8BYcURwC/eSgFrcyONIJxyqPbbCjfS+nt7ly5bYzgv87urrzcm1gCyc2KRR40MjxZmMbKNZycGN/8FrSIukEkglkEpge0hgu2Fg56aPJjtDOAapu6ODxqSCq6dOHehsuvavFtL+ha581ateNWfOHGpv3ry54OWKlSt++ctfzps377DDDqtlMHp7SsvLNIriPv/886n7d7/nPffPn3/TTTe9/OUvp0mdIfDj88579NFHac93vOMdFrewN2yAKJ+W1avrGhvB1AcffPCWW26xuIU6duj8Pvvs8/wXvnBDa6siDz/8sJW973nP8TS+mKJLLrlEQM5RRx31kpe8RI2F5eV33XILPq2WOeigg951/PF2tIZ+VXH77bejqbqjjztuQ1PT2Mho7aRJ7c3rUbv11lsZpEMPOeQNb3hDkbij7m7NzCssvPKKK2688UZmYO7cuZift8su9nDcHu8rpZFKIJVAKoFUAv9GAkb2/ybH0/uY0YkVTtzEr9uLz+1FZ4K9iZvty+f2kvoEexM3Tw+fqhO2VhD2b964G1Z+YZGNpuBADECMfcN9vK/GG0Ygu+6yy/0P3J/JhNOPuHYNM6Lj1KACUvXK3ECPnroQXLlmJYho7AReRoQJwUqJMclGRGgqBfSiD44G/2p2Nt8smMpPa4wks684UZGcYCdq8kS/rhspADlUDKYuWb4E0MUt3lSnCFztkkF+vKkFajWk4aCWk285QmhubTd4U4W46Oi1VqnM0DKCKHR1dKqroLAwEp86aXJJednaFesg/MaK+sLc0HAu9PjWHv+J8uMTpWhCTJ+4iV+3ln+LRJ6GxAn2Jm5SPv8bsU+IceImledj5bmZWCYebe13MZF/4uaZI88Jnjf+1Cca89TcbDcMDADn5ucP9vdTpmUNDf1tbQDwNb///Te/+e2bbr+V25RKu+CCC6jvuuoa+jpTWtr28MNnnnkmZeqsdjvuWxljBUlnR8dPf/rTz3zutJNPPMlBRMDnJz91ssiivfba6+hDDlm0eFH0tcoDr+6yyy707KrFi6fNmvXQ/Plnn322RGYATWbpZxf8Yua06Q6vr25sFLJ07rnn/vLyXx9wwH5U+fHHH983OETE5//oxz86/7xjjz32A8cfb/Pn1vaOooL8iy66WJ5PfvKTZM6i2ML661//+jHHHCMWmlUoKq+4/cYbz/7qWX++7rpCezaOjl548cWf/exnQeXGadOam5ve+ta33nTrbaVFmdLikua2NvWeddZZb3z965+aN5hSTSWQSiCVQCqBVAKpBJ5yCWyCYAYUVnGF5a+qNOYx6ujPHhwcGcwUhzWug4P9h+x+0L0L73HPrQpARnwITLqxHFcKdGp0ATGCsuArcAjWQp5Ao0Q53QOTvrpRHTrSgUygNIDMri6DH+hXQY5lu1i54XgwuJIZS4Yq7jEA0EKtiivo3mx+rFoezODEeAlZGFi6wZgaUVPEKAgklt/90qVLFZdH1cD85MmTFZQTAy7gHDMyyyDzYH9wOOfmZA2PjBQUBlcwhG/n6rzxfKz2tfWMjg9Mmdkg9g976ZVKIJVAKoEdJYHtpoPoQWaB4qMf3fhc8cgjZ5xxxs233/rZU05tWd/Sur7lxz/+MQVKTVOgJvT22GOPfffdt3egn0PVfhH0OymYEP3+979fXVH5gQ98ADX5izNFvLt8s/vtt9+tt9z6z1tvO+J5h23o7Pjb3/5GBVPi03ba6YbrrnvRi17017/+9S1vecvdd9/d0df3ox/9qLKs3LysDCPWIQ8PU+g1lVWiht583Fu/8pWvrFi65JvnfJ0Fu+aaa3z93e9+d/rpp992801f/vKXeaqvuuoqvmL7JOJh6YrlbZ3d9Y2TQHR25S9XXXnMm990zXXXnXzSiazOsqVLf/zDH06ZNs0WiqKlgeFbb73tvHO/y84tb2r627XXclnvvffeO+oFp/WmEkglkEoglUAqgVQC/70EIgYGMl2oGfYI6DUO8dVIAwKEiqHNvXfb+5b5t3gKGcZs4CVHrkdxCGQgAfHCqIY9QCaECQkbbKCjiFGQ/EihbAwT2ZYTBU95UIW/gaBGWTJw6gLMwK2xjRTZIFJFoE2uWulGPiirS0GZEVdc7XJiQxFPuYVlAIDxDOvyPEPFUtRokMPZixmZVYcyhjXWPRyObSkeyY8IXB2xsRtFojMZQUQUkTPmx4b8PYM9UtIrlUAqgVQCO0oC2w0DO3uut6fH+XElZjebm6lXAPjeBx74zCmnnnzyyZQyVfuGt7zlda97HYV78MEHOxCgoLgYsi3JFAkbLquqotxHhoeVWtO07v3vfz8boIi5QyD58ssvB26hU+HNex144KGHHgrN/v3vf6ejHf1+3z//6amJxiuvvPKcc87Zec89RSyrsau764ADDkBWjR3t7dR6W0f7W9/xzr9cc/V73vMeWlsteTnZAPbXvn7OP/7xj9e+9rXPec5zhC57GQsXLqxvbOxp31BcUc4PXFZaFHB7cqTe9773veaWtlM/dfIXvvAFtoSJesvxx6t6l913x/Af//jHPfd8zvHvfreJ4dbVq597xBEf/ehH5+6yy456wWm9qQRSCaQSSCWQSiCVwHaUwAQGHssKaJDpN6KA+tzsveve9z50ryEQzAn4yQlJQn2eGoTAwMYtgCKAKvPcOXNhV4+AUik4hFHlhxU5VH01sEHfp0RVgJ3WcCkIWBp7SIFIZbY7dAScMhjwGK6g6R4W9RWydcnc2Ngo0QS9sohE57B6ZbZYDE0Mu/DAV7HbzrthAGU5xeJhQ1u4iPEPWisrp0SUI6bVXqXkwa0GoskzbADGnWA4hKbMbmbMmGGRMzYU3I5vJCWVSiCVQCqBbZXA9tNB8cQ8h7knmNOqWmhw7qxZ73znOzN0ut38rZ8ZHGxqbh4YGa6prfWVK3jX3XbrGeh/4MEH3ff09nb39Pziggt2mbfz+084Id+Zcvn5ncniFjOuX/7KVxonT7aRghbWCxzqaEfENKPo6y+dfnpbZ8c3vvnN/Q44ABGrUsorKpYtX86dG4KrmYGRkeKSEkHL+bl5H/nwBw9/0VFjWeMFmcJ1zU25+XkLly774Y9+MHP2rKKS4rziomkzpmcK8weHh7LyLewttN/FggUL6HpTrfbHuvOeu2+44YbDDj3kxBNP5CVWfMq0qUNdXWZArYIGvLv7+uH2u+68s7KmpnbqVHYsKPpU129rx0zzpxJIJZBKIJVAKoFnmARAOxxFDOzeotaIBqE+114773X/o9YAZwwemH6ozz34Kg/UB3lG7ysKkOQuc3d58OEH3UOGVm9xlpr6VwqmldlwAkFjDylgagSWEl2K8Dm7AGAT8VCxWgBO1anI8UjoSAS54V7gE0FgOGZWCx5kgLHljx5gABi4xYbiKpJhr+fstbZ5LawrJ59tpAAJq9R+K1AxxuT0VQY3yqoRQY+gbk2WDpZrAiLYUB0e1AIwA/yusvyw/0t6pRJIJZBKYEdJYHti4LJE1WYNDNgv/ze/+U3f0BDP6rSZM3llqVQaE2SlvsuKS0wEUtnafNxxx/Ho3nbbbauWLaPBL7rootXr1r7iFa+YNH26pSeUJjwJCn/iE5+oamigXk00Nq9aZRYT3zvvvPO6VdYCL7YG+OADDrRY13oaZ8azTmqZP3++PLRtZV0dQyVl4ZLFI6Mjb3zjG+FtujhTXu4TD40NdeKoEWcrOlpbURsaGjaN2pPMaFL9Ku3rG9xtt91EDv35z38eHB498MADyyor1y1fTtHDt8yhKtwzVF/6/Of6+wcOO+LId7/znY/cey8LUV5fv3bFih31gtN6UwmkEkglkEoglUAqge0iAbbeFTEwgskxSdkMPfS47+77zn90PkAo3YgCFIQ/DULc8IUaz4CCLvjWUMGAYfGyxZCwWGV5ol807iYtJ9xrjBRxpvW0IXI42e8KqoSu1W5MBb4anAC6EXNCleoFdD0VXO1TXUY+CoKjapQCACuCQ/RRgGmxIdRZTo+wYcCDyE4zdlrXHPaviq5m/KBj2CbbihUrNA1jsDe4q4g2ohl5xptElKWjHHlWnURgGM6XjjdN1iLbaKsrvVIJpBJIJbCjJLDdMLAVwDzAbMOYm9HR6667Lj87e//99+eVZR44denfBQ88cNddd3X39Qaf6vAwBMvTarOrgeEhM4tsgL2jpjZOhnhH+/vXNzWxB8KEBkeG3/fhD/e2t0ft3zBtGgxsZYmvjVOm8DaPjI+99KUvrWlspHOtyB3o7a2oqVFRcVGxowLs+aAu8TxEfNjzDrM0dyQ5hRgyh5/7B4dFVjM2NHLTypUMA7tinjeaKNHaytL1HNF4NjdrsXF5abEiXZ0dFVWVoos629vtcd0/OGCyk8HA/E03/eODJ7zfkcL7HnjgCSec0LV+/eTZs3fUC07rTSWQSiCVQCqBVAKpBP57CVgFHM9GQsrBSD5DlFcyD/68vZ533yP3gaMSJ3AmkGlQBApCgG4iA/IYZsQdlT3iGoU2DUJgXUDakMNlvCSbRCAZyDQIcXkq0cgnXvIIuPPpK4xq+GHcYrgFYYLc4CjICq/KEP23anQPoGIGKFVdpCyAWUUoG26BqeKll65aKg82EMGhG9gVkManWG436oJs1SUDriBnqNhXDYw8e6qgQZGy0gnEcA576gLa8aBdPQPpeuDYI9LPVAKpBHaMBLYbBqZqzXwWFRebvezt7KQTaVve1OFkRwc7PAOZnKit7RvsC21Zr/Agezb0dnXx3wqVhpkvvvhi63U/+MEP1k6ezKgAtNRlb3/fjKnT4FWbM1C1BZkM96y4HYHU9tPq6+nhKKZzg67v7aGFc/LzGI3f/uby+Q8+ADw7iqm9q5OD1xpjApYz2qFYBP3K8lJqurKqyhwq5zPVzA88OpblUCVa29YTVhV39/TPnDq5ur6+v6e3vbWNTleR/R9o+cGuLnwyHg5G0sBibuGxcbtVn/O1rz/y0MPHHn30RZdc8sUvfhE+3jGvN601lUAqgVQCqQRSCaQS2B4SgIE3IdmN4dAwsJGAkcbtD91uXBEdnhF/Ght4JELY6MIQBXQELHExe/ZsYBJwNRox5AgjkMpK4wc40/y+cY48EC9YG+koBb5K9xV9NwCke24DX1F2Y1jimEk1xtXFoCwKkDDYyX+LoPsJpKpSxWWWKL/5fUMa1YHE0uFbV8S6+FeWLwFg5gGWiFUjIyM0KS71GkfFdinlkWZqi9q1RbpmyozhCICJAhiWSAKlmVLE0yuVQCqBVAI7SgLbDQNT8CKVbeAgJgZepfUocpouvzA/Lz9naHjg4Uce/MqZZ2hnWXnJ6Nhw1tjwmrWrSqrKX/6Kl3b2dN99z52nn/HFGdOmvOPtb80aGRro77Uo98EH5stfX1dTVlqclT3e29PV39NVWlG2YvnS/LyckuJMYSY/U5rpFSM93J8pK+4b6hsY6u/u6fjUKSerxZl3u++1e1VN5chQ30OPPFhUXDhr7iyu3Z4uGzj3O+Lo/vvmO8L3ec89tK+7p6aquqe1LVNQaFFyQW7OpPoGC32KCjP33HV3QY546UntTc01kxoryyssKl7f1DzY01uYX8ACudiJcDRUQf7I0BDkX1xaZr6XTTvzy1/mDJ9/771OE4wvmMd4s78d9eLTelMJpBJIJZBK4Jkpgeg8dLrBKaec8stf/tImjk6UBX7gijiNC7FAMqZfzT6zts/MVvzvcQUA2wpKu4xtcnNyIUCx0LCrTTRBSiBQChgJUrpiuiEBTOjdeeoGhpQHHAUC5fEGvUdlQVnRavJ4m0YUPiXCjQYYtmsOQ6nkIF/UkOVZBTI5WmVDDfZGR369QkXyeIQ4OArWygCHw8mqwJugZcXhXnBUonufLincEghGx68akdK7+JAVVAodfQ8/oe25uWoEcSMRKfJgHhHCQQ0Yjnnkd8/tgWEQWinsSXQjYvB/r4ekLUolkErgWSSB7Wc7kzWxw/39RVVVJipNi5KCE3f7e3upTupbuPK73vUuibQh1emIpKmzZ7c1NZlinLfTbFtDd3b3OpK3trFxLFG1cpqh9GlGk7rs6+6mZIMCHR6mSYdGRkUQ0cgqKi4quP7661ctX07/qsgaY6cueVSQH2YfaV4R1+xNb9+gugQusQoYcKKfxOGxECakFL1M+2dnMoKLhkbHnGOsape6RDzZL9oRTf0dHa9//evtenXZZZd5ZDhSnCyBdtrwpz/9aTFMDz78kD20+sT/DA05nN46ZyFNL3vZyxJK6UcqgVQCqQRSCaQS+PcSYDQBGODBqQoPPPAAMOzoAXZKnCrUcckll0hxlgFkIvHfk0tzbCcJbOYEjvMRXpbxg/diCAFkGnKAqbNnzYY8vR1YFDQFJqFNgw1jGCMHeeLWzdFVa0xipAEYy+8R8AmOipf2fnmJ9QSU4UZYlxMVHemy+Qp8qlrK/rvvb21XxMDaaoSDEwVxKBGsVRwoVdDAyUgJTMVShKOGScC5Kwy0ku24MKBGT+FqtcgpvhqRiKLlseoYwciDijDpafA5T51BGqKddV28RR7UqHjEvZ7ih0zC06xwcFR6pRJIJZBKYEdJIKxd2S4X4OpUO0G/+UNDNOaxxx5r/+Qvn3VWT19v30D/BRdcYCb7qKOOcvYv5W7trqPlLCKxt3NXZ+dzn/vchYuX7rPXc1796lcPJLOblCmuLBLOyw4YmPZkG/K5cNvbKVMYtawY1q7i0X3dq19z6mc+d9/d97zl2GNfcMSRP/7ZTyc3TPrjlX/aadZOM+fMLCop7Vizuii/YOWKFeD+zGnTO5qaqXWqGU1GSy0UtE/mgV7OGhqCgWMiKyKs2lezvkYeGqXUMcccY+Oum2+7/ZWvfOXhhx+OggBvGv/888+HqA8+9HlHHHyw85+EQ4PlWr3HLrsogjKLgmx6pRJIJZBKIJVAKoEnlgDzBEWYBWZHWBYHAbJ3TBL8AAW97W1vY1PYI4lPTCd9ut0lABmiGT6zw1JgZyN5U94XHDvAbzo6YIQDK65vWe9lJQByyMuSYuAhG/AJiHp3kKR04cTAp3BibxlkBT4Rl1mip/IYeCgYhx8SEVQdyjoD6AtSym+81NbX5iYs76qs5I8FfeFtmeFw1bnBKpgK/eITZJVTcaMqieCoURCCxjMoywkMGxFpIzAPnMsjMfKMN8MhT+WU7qvMHmFSz1y3fp10T1EmJO2NBdc1rRco56sGFuUWlJaUqt2W2nhOr1QCqQRSCewoCWw3YBYmRBNX6mjixX3JS14C9E5rbPzWd8+1O9SXvvSlz3zmMyY+GQ5aeN2KFTahMhvKhsSpyoK8HLtJUfHo1ArIKSqiux1KRDvT77aGYAOSvSdygNLiwoK4EKW8rm7O7DnX/OmPXd29D86//+yvnfPmo4/5xw03jo+MZgry62tqu1vbGqdOE9W8ZOEicH+yYOb6ejq6qLKSmrZ3NLnjKo4tSmtqhhIzgL7TBSpsutjTY25VHkq/or4eM9zU5uAP3G/fm2697Vvf+taPfvozjujf/va3MD8b8K2vfe2ee+456dOf5i6Gil/84hf/6le/mj5rVn5FxY56wWm9qQRSCaQSSCXw7JIA0MIasqHMyte+9jVb6TIxkAYbFBsC/AAnLr64CISeXQ18VnMLHAKommC4IqA3DE7i/eio8QzcCByKXZcNHPV24D2J0KAZfPiT39V7dGNsY6QBeYaYsuJi1EBEAyTp0C8XMQrA5ATGlkcpRNSuM6AMfxrAwJYGM57qNrEzWANshANvG9tEQKt2pIxk8IYyhqXI7FMpVUdw7kY2BJUC5iPelg3DakQT6IV1MYxVbOh+mJRTA2W2ZlhZnEh06b3oqA5ZUpIhDKJqa8OgTih1VlgdnV6pBFIJpBLYURLIHttO2zVl26GhtZVyd5S70B+KlWZf39pq8+Spc+aM9PbmFRXx4tKY4CuIKxRZnjj3ueeee9KJproVt0CXumQ5nK+bZcVIXx+VSvNKlD/cFxc3r1zJ0hQ7nWjFCjp9zbq1U2bNXvjgA9b01jTU93R0ltbVdjWvL6+pHujuaWvfMGXa9CzbVQ+HGVYMGFswJBQ0d3SWyJyenhKz6bS5DQ+rqzuSiCardlqdWDB1an97uy21hDdT3/Lbnau6rs6BgAyGi4o37cpoKO6kYiYBul69aBGGqfi6hgbLhhY/+OBOu+3mbOQd9Y7TelMJpBJIJfC/J4HR7Ky8sgruuMdf1PvjE3dgCvu1xdq3xic7xZpEnx7YAHK4ZwERYVk88nneeefZQtI92wR1bJH+tiZuK5/bSn975d9hfAaYOpaXmz00MliQX9A3PjCQPXrD3TcN5I4197ROmjqpbnKdUU1/X1d+Tq6hxcjoeEmJgxQrAUuT71yyOAcjAULjEK5aKeLdjHy4Z71liSCilwvoGmB49QoaDkn3io1/4EnwGGpFhDBlriivWPDoAogUUvVVBhh1xuQZC5ctNM6J3cZcCQTL2YuCgRke9KXIDB6MplSNmkTdSXVuDMnQEYevar1Uh1QprIsljHEjY0yNLhngdi1a29RUVFSCwvBgP8dDbU2V3Ub7+nvWrm/Oyc3vHxrMG89vqKmtL60Z7x8qySmcUTulIrsib3wbohF32Hvfxo6b8rmNAvs32VN5PrGAonwiVjLtde6551pY6ms0GY8v+6yQZ/b42EhPX26y/f7jm/Dfp4TxQ2n5dvMDA4HEmuvU38QPTE3XzZix+z772E0Kr5y3Jks5Wmlb2Tqam6lOM4r0vvVOq9c1fexjH6OjvTPavzg5Os+W0dbfApPhvSYnrbtBylbMUHSx4/I2bGicOZP5B3HFDDXU1ddAp8Mjsg2CwbZoHs+i5cFjDmo7XeUWhsUnlTaj7u2tnjRJhvbmZlpfvWMDA4LMyqqqRpJ5SkzKSa2P9PTQ+3b5ovdrpkyRP8SeWTZcUIBz3YvpovqBZ8uAqX7x2b2trVOnT8dh3aRJULF1OTvtuSfkjGB6pRJIJZBKIJVAKoF/KwG4Qh7AhrlksGAPhoYZYh+Dnc3N9QjWgkaiGfq3BNMM20UCIQI6ucDCGMprTyyUjTr4Y701sNP78tUIR7pxgsEAvJr47NvhRu8OgDTGMMDwBoW7G1QYhMCuEQmDoMYt3q8361M9MqNjqIMImqh55JKo4IMPPWjU615Otesq7petXhZz8vqCryjHsHkUjM2wpxYjFgAYbFYdCgiqLgJsaBll0Qe4Va9EnCPrEzUxesrGIsgiLgMimowHRCJaxrb+qUaJMisLkMuMIISPsYLsjUENWE2vVAKpBFIJPP0S2G4Y2Ano9DT7AExSeaz1eG/vqiVLNGn1kiWzd92VY3Td8uVgcJibTFaqWBIMG//0Fxc8//DDjnvb22hJF/sxZKnwyIhZT7bGpDdtO5LssE8dGwd45IwjkBtlXmKDAqjbVwraUUamWN0MDw7ahBFYra6szOrrG7IsRwjZ2Bi71LdhA4JQcWExHB3GGbh1GVtoAu0c6i0osOBGRWoMPt4sNPrG+/spcfS72tosSg7oevbsgvJyel+RnOJiZDubm03nWg/NTqArZUMSdMScIJJeqQRSCaQSSCWQSuDJSIANZUGYJLCBQYEfmD9ABXphKBksGAMc8hWieDIE0zz/vQQiBkbHmMHb8QkDRzgKA8d34dVI98qig9QwBt7zyuQHPmX2Qr07SBiYNMbwVUFPBbV5aghkdOErJBmviBu9/YirJRpyhCHW+DgK/LoGGIZVQHXkBAV7lGBDwVgEDncv0Vc1YkZZ1UGw+MQJmniQ7kZPUxbPYLmK1CsztIyC6hBBQYp2RU5IQ43GS9obfddwtcwo4M0nrmJ3RU0VEYejkJu18byM//69pBRSCaQSSCXwH0hgG6JQnph6wKtZWbb8cy478OmG1ps2dy7MOdVuzAm4pYihVmqXZxWepLhtkbXPns955zvfyaTQ6VaclIQzilrpR5pYZjQD8kysSEWyiJfaBXp7xCHDt/CwegsKTJzyF9tHonPdOtYlOIGFjY2M5EtparKyN4JzuDd3aKi4pGSwpwdZ+zYMDwxQ6M4iEEikLsTZHtxiPraoz+EB1dVFg4P2Z6yoq+ttaxPA5CTkMJPa3c08qI5d6WluDrUk6BrzFZMmjff1cQtr41BHh5Y9sfTSp6kEUgmkEkglkEogSoARgW/ZILYDSmGYWJxg+5LtG9ksN/AGgMGewifyp6J7GiRAzuLAYkXuuYJtjOV1TJk69ZGVCwdHB40HvK+s8WH+2KJkOCHMOI4TDCpgRThwzow5K9eu5AT2ir1frmCI0cYisCL0C0x61y6JRjIGKuoxzREzI4WOly4RmAR9HVqhCqUU32O3PZavXC5RGLOnBjPgKGwsM0BrPkWPQtZXlEU7w6jKSkEWBoZ+4/YoixYt8tWADRHIVnWxH3IGoBAxMGcDIurVCVGW6JLTlZU15kDjvt5uEdslpUX9w0MlxaXGXWNDYz3gff8wn0To0unZSE9Dl02rSCWQSmDrEtieGNgmgLSzT4FBNCY9a3oQCBzt68eAo3fHRxPn7dhIQV6+QOWxkdFvnvONyNvo0HBudo5PRwmXJAtduJHt8CCGJh6uW15aKsimkD1I6MsTV9hadpLVP4B4oDM4VFEWXLtZY6gLnB7NGhoOKSPusxzniwI74TN4j/FnSwlQ2Q2Xb1yv5TMZTIT0QGfMgXo4cRsY4CXGUvLVY6Yvv6REHhw64A+1UCQ5ORAAdiM/g8GGJcnpRyqBVAKpBFIJPB0SoHifjmq2vY5gFpP9k2LRJ8MnWDVRj+KQhk8FQReXR6AOuzaRZ7vc/Ad8bpd6t5XI08ynyQfLnrINVkDh7Oyx0THGH250PnB2UTYn50h2EsKWFVbnTp867aFHFiRLxDIeGQtBlT6b2/hTmwFOzJvI8H65ZN2vXr0aFgVZZQYsTW3AmdAvP626IgolH+nuYVfZlEVKlwApZ02dJQQaalUFBDvRQ9BEAUyVDrJGFI0Txw67B3E90oVAYlXD3nC4T2M5n8hC0eA3xI6IVkuR2SNYF5M2wfJUPwSPgf/S0nIDnq6ODR4VF4UpG4mFJcWCq1s2tG1obc8ZGy/NFOWNGC7ZoCVs0bLFC/NbTJ9IfJrf+0S923qT8rmtEnvi/Kk8n1g+mz19dv2ONnu5T8+28dsNA28m+mfmV4DZtG16pRJIJZBKIJVAKoFUAqkEtlUCyUAtYGBXdGQGvFdcvLJtTVVdVc9gOIm3rLRoyqTGhYsWIh7BJNwYkSoUCqYCjUpJVBDQBVl99ekr3yyIO3v2bPnhWwDVV2NZ0DQ6b5WSBx0ZgFjIFjUAeP7D8yd8s0gBq/CtPCgjghN0IhsH7HzA7Q/fHmfnYVToVzpU7NPyNDSj6xjM5p0GVrmvowcYEXk0HHv4UQuE7CnPM86thqutrYeWLSAGjJ2SKf5OhvLqKr7u8ZzgDxjs7esf7S8NpykV5mel7oFt7X1p/lQCqQS2pwS28+Tx41kL/tJt+QNTt+kPpt2mv20i/h9kfrwE0pRUAqkEUgmkEkglkErgf0AC0VmhIVYCu4ayLIrqhgO5Z+MCWuhucuNkZyOBiOAiDAk3goiQqhtgEiKVHp/ypoKLKLhARJBVOhdxY30jLC1RChStOl9B3LjmFubEBsqBjQRntnS0yMBpLDMKngqHll9OXMHDasck3vbeee+7F92NrAsFdfmcMmUKrhRXEIUIyLVIQd5mbKCpItUB0nj2iSyCEeEjBevC0lIQQVB7tVF1WqfJ0RGNQxLQTK2QwdHKCKZXKoFUAqkEdpQEnnIMvKMaltabSiCVQCqBVAKpBFIJpBLYvhIAFF1own5iy+BD93Aj96nPedPnLVu+LMJX6Z5Cg+AoPCnyWToACXAKY44gE0QEHeWMcBER/t72rnaZA05N9gD3FMKEHnmAOWARlA5SApPyK24Fb3Teeoo+TKsI5Im4zC4IVnHpK1tWRrewUhYtx8XDoCn/rRpBU2x4pIEzZ86UAtAqi4IaYVq8qZRfF32lFHejao+UsmOWzC5VYAPPnvJmR2TOwyxRZsAbwYGRAU/TK5VAKoFUAjtKAv8LGNhc4pP821FSTutNJZBKIJVAKoFUAqkEnu0SAN7gQ/BPQ3Kyc/KybB4SjtV1Sd93933vevAu9576FLEM64KC8kC/ECng6hFMCAp6JBFSBRqBQxDRDf8t2CwmOSJVReQHO3lf4Vt7TUUsirJEYJKDV6J7T5XFg8+a6o0HLyELtUa/7vTp09FEmc+Wdxck5rONxyA5oxhZABjoVbtEodEIYs/X2BwYXnUIyiOcG/NaASFjTEEQOq5MjrhXW8B+wBj/isiJMTVKhLHVLl2KpqVXKoFUAqkEdpQEnt0YWBR0MDVP+pI5XQ/8pKWVZkwlkEoglUAqgVQCqQT+JQEwNcJgSeFgJEdQJCfrwqIcp48ufzQCQp8yAJYAJ+AHTEKJykKS4GsMJ+Y4FVEcvaNygoXgK7gIssKWckKJMivuciMzCA18qhEuRRnBZcuWwZYyK+6aNm0aUitXrZQH+gV61aJIzMyNDMfGvaCxB45Or58OQkegiziyOI+gnVNXHomYiZnlR1B+zLhkxhie3QPMasQPnjGAApCPHzBbWfQhYZkRlwgta2MmP2yalV6pBFIJpBLYURJ4dmPgKLUnvx54R0k5rTeVQCqBVAKpBFIJpBJ4VkuA5xLMi5eGCIT2BwqCoAAwbAnscXIKcoZ4QVA4ECaED6NTVxEQF1oGBYFA3lRPIVg4E4CUmeNUdDFUjKAUuBGYjFBWZsUVjJklqpdL1gWaegphRgSrOiATWRWBr7jl1FVQADP60LhP9boOmHfA/QvvlwdIBk3Vqzp08BCxq7IqxYbq8JPU1qUWKVqESfkV1xbt8lXbXdFrLREd3KKsLky6yAcReVDjQkc8vVIJpBJIJbCjJPBUYWAe1/inYXFnqXjzb9sZAa1s0WH7xJ//ltpmGSaoxVo2+9ws82ZfN8u82dfNMj9zvj72RTwBV08y2xNQ+M8eqdc1Ufvjb/4zstux1GYsbUfKKamnRwITfezpqS6tJZVAKoH/VQmId46n9oBw2jg27gzGUV7QXXfdVYwxaAcc8oKCf7yjvtbX1UOnDj2SHzIECF1cshG+wpxgKugIT8owc9pMoDECacQRQQ0dRfhvfQVEpcgDcHLnugF3YW9VAKsQ5rx58xRXL2gqxFopNzNmzABB3XMIu0BcHKKwx6573Lv0Xo+U1QQMwMmu6NQFXOWJEDoGXUc2sMqJjRMY2w3m4W24GmOI+4oN6XhYvHixzOC3R1C3S6u1AgyO2dLzgf9XfyZpu1IJPFsksM0YOEKC2LzNt00WGQS7jo+bTszNzxseH/WZnZOflVsw0DsQbvIKh52fV1CUnZ0XborLenv6/WXnFY6OjPf0DWQXZPyNZ+f29Q+OqKmkbGhkrLOrJ8uMYU6e9HDAcE5eMEO5+VkFmY6unsFhujvPDKYHjz666Nhjjzv99C/39w+Ojo7nlZT19tqlfyAv3+nBOYODw3nFpZ29faPZOW867q1ve9fx/YPDvf2D2YVF6hoeHY+Uh4ZH0Y9/WQrmmqMd7+ntlwcDMg+xezm5/gZHnROYPzqePTA0kpXn1AHrf0wSB1Yl4nzMcqHcfJ+Dw6NulFVLn9xJde6z8wpGhsfIocdCISLKK8wazxnoH8ouLCau7Kzc8bHskCEr19/w0Gh2fiYyvLHsWBbKGzq6sjPFiKKGQzeBz7yC7OJS96OMNH7zcslsaGSEtcsuLGS6swtDnJVdMsRmeV9DI8O5pSWaqCyx+J5dUobztvZO3OI8NCG/0FOJUkIi5pPXEarOFMvQnUgptDS/UIq3423GFxeKF5VgNRT0lhNWO7t7HbKseG5BYWd3T26mqG9g0PPc4pLOnt7uvv7c0jI1xrK6h/w/v+Cif9x86wQDkZlIUDacB8lkipvWt+LfrRT8BIFkin2Nr8Y9DgN7OflDA8N9eqBzGzKOes721SvwItBs7+xO+sY49traO5pb25YuX9Fhj5DR0fbOzuziYoul3Dumk2BJQkzc6Pg4Fnx1n23PkiDiwH9stdqJyB+2SQBvIT1je8wciYHz3HxcBZ6LSqTIM/+Bh953wgfPPOtr8Y17GsqWlMXGyu9rLCh/eBGFZDgkcWOGkjKVrm/dgPbGUn2DfoBDgyOhmfpYpiT0N+3VtXr6Deiyi0o72ruycwsGB4ZDhtx89EPxwiKkvAX3ukfoh8nLVelGsecXqlQiJokulgoF9YfkFbi/8+573/r2d571tXMwiZ/4Wwhs6665+bG4fou4zP7cxIbHp6Ht+YUhf6IlwmstLMIMMYbEohLZQo26X4FePYRheXQtBXOLit3LFvp6bJF+WFzWTyB5heFTYwuKni2KO+XziSXgl/eMuia4DRr4MWefbiuTE3Q2u9lWOlvLP0H2v+Rza/S3V/qO4pNYRkbCgl4wMu6G5YAfG00JSAbwokfUJ7wHEM6bO++hhx8CHWFIwE8KxAuUKgupSgcjJcrsfGCocuWalRK5ZCMFNy4IUwg0gCozXOqRxbr8zOAllAuyArfAKrJq4WKNOFNOWBTUhD9d0LJsqsCAe+nyL1u5DNxFFk31YgNjfNHaGL3ZyKpXEdVJxIlPAB6+dQHhSoH3OFGWTFQts1o0KgZ+K+4pso5QtkQZVMYGfuTH4ROcD7y1frKj3vvW+Nlaesrn1iTzn6Wn8nxiuU3IZ7ObrZWayOYX7Zr4urX8T096ZGOirgmuntKb7RGLkuxrMDKa4KvccIZ6kX3/R4Z///vft7dteOkLXjx75qygIseAzHHqb3xgwATh3Xff3T8w8JrXvKZ66tSsgYG84qKy4iL7JFDQIW6nrna0t7e7taWsqqqwomK4u5tcCsrLR5KT7mhwipVcqurrswzbBeok+pc6vuaaayxWOe2001TUuX59RX39I/fdRyObTKWI29atq5k8eemCBX+86srcrOyLf/1ri11obtzm5+b1Dw4UFRQyCfS7kwRYue62trzc3KLy8tLi4rb1zRVVlaODA3+68sqbb75Ztr333vuQgw9mkIoqKvo7O4vyC7IymUUPPjht5oyMgKWEQ7zZvRHzwcwMDERLJkQp4KXeXifN5xcW9qxfX9bQsGj+/MpNy4Ham5oYP5kt6HGEQmAyO5uBwSrDpuGabwrZLhnYkM66oq/JHhEXyoMDA4QG+sIfkK2nOjp6bF3+wCB4NtjZCZpZseRUh6KSYkapZe2aRx555M7b7kScxTr44IN33nlnlpKtUpY5tL8H+rlJi4jI7LdE4VVT5szJGhxkTcttyDEQdnrsFJdVXV1VU2MiunrSpN4NG0prwwutsVnl4GBPZyfLTSbkCYFffPGPmMaKykqfqjaFXFFdddBBB02dOXOou6e3p6eyvKJnw4ayurqzvvjFL3/ly6NZ448+9IgNLcMwRIs0trNTBzP/DMIzvbXJkELzGfIgrrw870K0Fm6JgDC9CC3yyDqnwurqwr7+ocGB8cFBGQrLymTo6uxgyHECcxEvruqnTfvyl754xhlnXH7Zpa9+49HVcG93t0GAVrevW1fV0NDV2qpRTj3ElFejiF6aZ0nYyFjGbHrSajWW67FZWRiODIg3M1YyMe/d6d5d69dXNTbiXKPsdjLY33/jjTdefvnlr3rVqwqShniaNTzsoAnZRdQNdnVpuJfrHQXKAwOCz0rL4Lo+U/Fq8XvBkr4kTK0/8RUUl5eTEiZDn9Qh44kXIzL2lFZW9nd3F42Pawgm9eENra2VNdXq8rptaRLn70kpr7QUD96jEY9vOqE9QwcSv8GalSvDlqSZjBfdkQTvKY6atpPkQw899Psr/qB3uceedGXJUPigbH50/clCOAyEzp/JeAui+uL4zH4sGuiVyT+ebFuqCdaTFfk1GbSReGcnmUvU/TCGW+PN7PGx6uJJHhUMmrIbLywuam9tI3CeE1WXl1eQnuLFZWVjQ0N9WlQW2p5eqQRSCaQSeLwEJoaL9IxJRGpcHjqEQhsZHxkaHirMBDRIlxy0+wH3PHw3o015BpU1Ps5eAJC0GRwohVoGWek99oJKZLlgXWVpVApKftoPZYowAloKTWYYlZajvphLNxLlhH5RRhNlKfIY87iQRcopTdS7PCpyGbHEquVUxGdQ6Xl5EmdPmn3T3TdRv8yK2tXinj7HJ5CvOl/Vq7F8y9rlimVxmKjTwAaj1t3Znpsd/N52CYOjC4qD8l+0eFHeeD6039XcMTCWPWl2bRoL/fgOlqakEkgl8HRKYHtg4IRfQ+0AJoeHCkZHacb27s5vfOMbCxcunHfZXLE6OZlCQ8zssbD7IUCyaPHi93zgfXN3mvemtxzLc9zU3FRZXWUEPDg81DBjRtuaNRSr09ZL7LDf3e2eAmUJjNNVRRHnwCrJVg3gitU3cBdVHnXxyPiY6KCCsjLwoKKq6r5//vPgQ5979plf/diJJzIJNfX1YC1sU11RuaGzY/3q1RQ9dc+KKDJgUA4mZWe7UV1BZaVHsMdAcpZ9DZj66IIXvOAFAX0mW/z3DvQ31NR++MMf/tAHPghU4GzJww/vfcC+X/rClz7ykY/Aokb53evXsw0lVVVDPT2BZnm5RPUyZmxhzaRJIz29pdXVD91554GHHvz1M8/+4IknArRVAB5g09NTDFok06hG6079I2GsDvX1VTQ0yACEgBb2rwBvIB/CCUWys7WU+cFSXlnpaF9PXl7u6HCYocgtyCev4aFhwJuJKxwbhT2wp+1//PWvv/6Nc+677z6A3exAfl4+a/exj33sU5/9LPxv4w6gAsoIghrzFsO+F8yzdwEAe19x8tiLkOjCXihSClVV9uKtstIaIFzl9PR4NNrRQRSoZY/mgKaf//znmX84xLsrLYLRylc3N1VXVh5zzDEfOuEDu+y5Z09L2EoE6GLODTuKCgvxprFtzc0YgHPcw6naQqS1U6aY+9AZ1J5vDNHT442TUmTYxpde8Mrly2F7sLx3fUs4jXFoiDwC+h0fH0hmWzSW+S+rKC8Yz0cKwNOKe+68a8bUaXvssQekahxgeOGNj3d3403B8upq76U7aRpioT/gioN9aMRX0iRzm5M4aVF+Mo+jB719oKODxHzNAl9LSxHX2+WpKCsrzM42+BgaHd5pp528+r72dsx4Gt6suY/+fnRIVYqCfgvmjzQTrCxy9GJy3kZ1ff2G9eurGxstyVKWQDasXkMa5KaUiQ/zCFSAX2wpCO2V9PcX6RhWmiXCND/Vu6GtpLIyN5n+8Nbyi4qIXS21U6dGVgXVeXegqR9FZV3dlKlTTY70NDdrvrfvB4XzjdMQw8OkasYsOj1MxPjhdXd1md3IGhnpTY70wGS4bGTa2jrY3l5TV6c/kFdHa6uhosbWNTSUJIeFyOV1qwV9Iz8v1JI1vyyzFQ4hMcckXA+HPX299mYpzGT4TEw5yebH5QduOsakTJYfeEHBSH8/akPOtASnLfFLr1QCqQRSCWxJAhEDU4Auz6lEAb1UX9Dho4N5BYLUgvY+aPeD7nroDnpWqI/ZbylsCpBJZdHS1CBbRmuxm+gAmUuXLg16Lxl+UMKeshr0p+2aKWp6Hn2aCgXpjAW7oFLpLBF1Kr9hj8SYWaIa+RKoO3qPeVWQ0bTHlRQaVQZ4O5pR8BhlRmHapGn3LbwPtSVLlhgjqUhOHMbVxQjKgze1y6Mi99H6kAODwgY5NAk1rEqRP+jnEHZWWF5RsWZt8HsXiLjp71e2qqoWS31DfcUF6ZzjlvpZmpZKIJXA0yKB7YaBbV84ofXoyqLyMIkokHFgeMhB6VQh940WGXIaNEOPBfmF3IBUZ29HexVfkzxJ1M1ofx/VzzBAI0HtGpXaVX9Q4GWBaJ7KmhrZuhJIZidEILC0vIxWRUdmBqm8JGDa/o4OLLQ0N3ukUkq8LzlrfmRwsKymZvHDDwPADbXBM1wMPPC2FhZy65nLzBodo8vD2N2VuDTBA5OffFw8bG9+85sZnuc85zmf+MQn2IZbbrnlnK9//Ze//OUH3n8C+2bA7aoo27jzIXvmePhCm1IwG3ahyGRaW1qqEv+buVIrb7j7wuC7sLC9uVnV5pPxCStqBTBp3K8hYTOKZLcMA30mJG/TGQPjGpYEIBGUIgyPpyxiSeLlY25hm5GBgTFVjA0X5wczwzLBO5o8nj3Co9u1YUPl1CmcyFYvLX3kEZ7z9a0txx9//JuOfvPYyOjf/va3b37zm7feemvL2rV19fVYYreUDd7IxPZ7icMweU7OhrVrzSyA1gTOSNXlnMEAAQAASURBVMJOLOv6lSsZxJFhwefFgx0dwVeYlxd8mPx4vb3ahB+tE5sc7Gtri4j8a/98LQ7ZZq244+67+PN/8KMfLVrw6MUXXTQ+OlY3aVLz2rXvfe9758yZ44AHAtSjajQ2K4vY2f6w6Ei8WVeXdlbU1Ji2AIlZ6QwznpdHViolvbXLlnm50+fNkxPn7ocHBssxNjq6ITnYkO86Kz8Pmq2Z1ADVEbsxRMOUKXCX/mzcMHunuVoN8ZqdsXLLaKBy0iSktBfnYJinvABcuFFieiaMTSykp8nQqXeKed4BWFFB4sJ5GBVxJq9f793len3WTbW2eq3333+/oZZVXqZCAkIzF2CaI3nLHW1tIK6oABCdNFtXr1aW+9dkgd8CYRpk+O2olByCoLq6SoDAqVM5UbEU3kImY45GzyGHrqYmTaieMqVj3To8C5VrX7WqeGRElyEczPuteYn5ubkym0owx4E+HOunoaXhF19ezo/qRmYE9EyU+XXNT3mkUd7uPffcgxN5vGhRDMZQaOoV8uDZvRGTUJA1CxbM3GWXsfXre5PtW/jiFTEiM93TvGKFt68K9JUic60wsBOeYFgXmjw62tvVhZrfVGFRZvKsWWuXLxfLV2vOIitLB5g8bbr+oLhfa2N9gxv8E1RQIOZKBvtkS69UAqkEUgk8gQToDU9ZxpEw1WlBSX9+ST5XsGv/3fa/d9HdFCw1a2UPxRJUa3W1R8KmZKbzaSeKVAbBVjAnFSTS2JbOUCVtqSzlCS0ropaoo+BSapDFZBekyOMpZYgammwTaspG1ccgesR40XLGKoigJoUmpHujI1oVzAQDIQMO75p/lwweyaNdapk9e7YMPMA0s0QWRL0mfxVBFk01qlpZBfHT29/f1tbuqWuwfyA/L4dCZnhVrV019XWdrV1Dff27zJpbmSlBv6o6TNynVyqBVAKpBHaUBLYbBqZPDbfp3zDydV8cdjWsKAvwJysXKhjOyQ91jY5YZOhMvdy+4cHuHqt5h3iZWBKRxmXFJRSuME76lEkQxAg3iuntaUu2gggrKPOZg6H+MLTNFTht/lLckYnYBCUajgMSVC2NLAXSAAlWrFhRkimiiIvh6kFQtNsmD75m8gvcG0kbwcvMllSUlVcbJQ8Nw960uQ0Z+aaMsLs6OyuEbvb0XPuXa03KwmDX33CDAkzFc5/73LcceyyrViZsta9PvcKwOcD7IPxMobXQ3d1dqmAP8guKGMfAdmlJzgAvVGdmJIR0CsDOKwpH0qNcUgjjlIBD0BHnGBShdsicGK3I4aML5opAOC2Li8wkE3VxTfWIBvf21U2fRrYdzc0qNU2LzjAIODyUCWAkAw8TEpGyV2hiSdW4ghbccOledNFFi5YuO/1LX/jUqaf2d/UWVVYe9uIXveHoN6JTN22qKF8RTtAXD/vgAFDTgybv63ioY6CkDOrL1i6tygwNZirK25qa6mdMx4/J6uYVy01yb2haxzZ3t6xnrYcHh0C4vq4usw/qF6Yl55zZc/bZZx8CZGURf/lrXv3ud7/705/+9G9/+9tTTz3129/+tvfeMHUKZHj4kUdkKip4RLWiY32zz1quwoZ6MI/UdK3165vrp04V3C6WuJUTvjDsUBIkrx+y39Omgu7rV69SS/WkhoGubiIYTY6sgCf1mcGuzv7OjsrqaqheD+FCbJg8GabSjckK4m1vaVHWhayvxAJO++p1GA24J2f9xzsNXcjPYWS0oKRY1Vy9Vmfn5WSPWvk9MFDmtIzOnuopk3E+3NtjPkhUf3llBfHKgIJXOWnqFBMThloNjZPCmx0aNKVSXF42NBBWWMk86LdgxgfCHxsVSeHVI1VsXTfQWFbql7h0wSOiMPTVhunTw9vJZJqWLvX7QscUyVBbm+LqChHJ4pl1FVP1yTUqOr2iIk9vNE/REpA5SUKh/MmW2JcXWIdsaYMm92dwPjzckXjp5SElskKDQCBzQQQmdAyVeteuJWHDqfLi0gMOOCBEbo+YkhoUay3cXccozhR2i2kXn19TPbOyYnyQiMo1RBcO02E52R0t6xFv0NVzckTXK6vDZOfmDIXF9H15g3llNdWdbW35Q4MlVZUt69bV1ddhYNWSJcLm/caNzvySwhxEbk5VbY3ZrjBv4rhLvHV0mpUhUr+m3Lx/rY3RM9MrlUAqgVQCj5VA0GwJLvXpfjwrHALsnrIaz07OB374LvtvGOZQO9zCiQO4zD3rRmsFe5QsYKFUpzROeeChB6hlah+2hGMZDnqJgQBfjWeix9XTCDKlI+JTETlVSr2zoUAmmi62wKNZs2a5B1PharUY8LgBX2WWiKynNK4bKlEVFCnNbL2YVcpSfMWn4CPDJzdMA5qsoSaYgEaQ11pj1Z6MTQJ9Oh+TLcGghJ2fR4cHC/LynVIsPG1DeysO6+onRc6nT51KITctX5XVP7Jr/VxE0iuVQCqBVAI7SgLbDQMHwwBfDVn9avzcnT8SIIRNjGzIZHAPj0FuGgniUpciei3ypMTzzR0mQ1j3oMIDd975l2v+3LJ+Pc1roGy1sMDFMEbPygrgtqLC+lLLjIXsshAmKU855ZRk9J5PvUJZKFss6oaa5hSi97kT+ZxB0+uuvpp54MLl1UQNkxzUlQ0NCx944C9/+YtlilMnTxHnfMC++wnJ5gAUsI0yrxTvXOe6deyHcTZShx12WGzCpKlTxWfS/vvtt5+GY/jRxUv+/Oc/c2lCxVY7M3UoCJ3lNINOoVwVzZ8/H3ua9uIXv3jqnDkidbvaWluamq+++uqewT7rmf+e8KmUKeFFixZZC8rqHHvssZCARgFRsMd3v/MdlN/ylre0rF6tOVV1dSsWLvz1r39tlbWp4rlz5x511FEz5s0TSY5tO2mAcAxScUUFhM+bHNyGIyNFtbVDnR3eGmN2/fXXF+blYolrsaSoFMJk89hLtVhI6T1ye3YnsNNr0gReYkYXP/vuu+8rXvEKkpGOeZDVylU7ZC5bsOBPf/oT6EU+L33pS/fcd18IIz9ZehreZp49zEIRxtIKZK+DTNRlJBEjittaWqbNmgUDc0RfcuklX/3qVz3VFnzecccdph4OOeQQ1rchWRLMZ2vJN9kyzJpPLMBbArbDUiXCb1u71kQ73ryC5z3veS9/+csNMtQ+2NsLQRWZlYD9+gfWrF6F+LXXBnc0+i9/5Su4EB3pWKY7lZfrEvoYEfFmWwDM3VqQk3PLzTd742y/efTXve510H6l3uWHYPof6E3WbJfV1glbCG2sqbFs97bbbhM+YAdRkvdmMbP7nnsSJoaJqGn16gULFshgdGI9Nm4RFzhhBZdhyLfOOUcffuc732mwYhhkGqK/tVXKTTfddOedd77xjW/c84ADehKkR5LLFy1CRxfymojXsOMNb3iDH+OkObMvPP8HMhz/oQ9xfa5euFCTyRkn+rbMRjQ8qxBjUU1N88oVN/zjRnQk61dY2u05zwnhwmNjjpsERJVVOx70BMIJgq2oJJ8wRKuqEg7wj3/8Q4fxS9RPXv3qV5NhV18P4fNE+MmYv0DKVxNYXBLesl/NqsWLvQh9zFDMb9yvct5znqPHuoSBkKFh3MMPP3zXXXeREiIGfOIX9AcjMoMtb8TXuqlT77jppj/+8Y81tbUnnnJKa3IoiL4n/ze++lX9wZyX9fyaPHn2bGWt1ADc+diLwjKL9EolkEoglcCWJUB1u9ij+NgRwS6osmu4d/999r9rwV1Bn4wNG2CIhS7MFFdVhYBnOjBaPZkpfAqZcVyybIlPIcpsRxg15YflsnRUBMPGPO7lD3uOFBUZ8zAHvrpXtVp4kulABstXnx4xeUyJC5qVn96WwjSziTC24vKzHXGO0idozToYsRgRRTOEB3n232P/h5Y8pAoM0MMeqQIq1kzAmOGm4bEnM/+we9P6rsiVpzisq6ktyoS9uFxKGWYghQc1MsR9GzpnN063J1ZBVhjdpVcqgVQCqQR2iAS2GwYOik9coh0dklUr2fm5FHf/0JIRC0yzbAw0WpDYDO5II28rP9u6OsvbWgdtRlVYUFdZIdL1s5877Re/+MVAX38mLyzC5Jy8+OKLv/SlLx36gheECMZZsz77yU9eeOGFLRvaoAJqt6u3R37ZjnzJS2h/ZoaixwBLQ9d7dO6551LuUi644ILvnX9eTWUVcHjBL3/JGsnPFXz6Zz/7wx/+sK2jHUEbRP3kJz855eRPvf9jHxOFPNzZSWuL3ixIDtlDnAYvLsw8+OCD7gGSZYsWzdp557Aup6urKi//kksu+fpXzzJzyyL+6Ec/Ou+HP0BT7C73piDbL3zhCz/+2U+lDI2OqFcrDj300LPPPnufgw/+wXnn45MVNK16wcUXnfuj86vLKwOfF164ZPmys8/5upwve+UraspKLdI1k8yp/fWvf51523///Zkfrsv5d9xx4oknQt0MUndfr/bOnjFTk7ms7Ud16YU/NwWw7977vPClL+UWG4GHgeDc3L6WFk22cxVHKIkROMu011572SPX2iNvk4E0VwyvNjpR0OLY2tpvnHmmZj786AJbS6l6bXOTlcNW8376859fs3TpP//5T+HT3ouCb3vb2+69f77pAI36+c9/jpk999xTOtEB4c58kM2oIX98jB2tKA3zCNagwoqCtnHi7XDomQVgL5vWrbMDGYRmWGCm4NJLL4WLmHbcztx550fvv/+jH/3oTTffVFIsWjms7f32d7597JuP/enPfmZkUllcLMz7M5/5zB/+dMXUxskYAMlOPuXTeoKXLu5A9xjgXM3OuuSSiz//xS80tay3KRomL/n1Lws+eZL3+Oa3v72zpYWD3mjg/e9/P4Qv/jnEsHV1gejaZejg6/oNG75y9tkf/eAHTzrppPrp082SgIWGDnZ00yjol/2/5oorvv/97193/d+9oMkNk4wzjH5M6ICR1uvaBc0YAqu/+93vZDB++sMf/mDM8dCCR3A7b9ddrSqD+n77h9+TyftOOEFL+R20SC89+s1v2n2XXT/4wQ+aE0HT1IOF3FdeeaX5oMqy8s7uLj9ANULpppx06VM++xkjrdcf/cavfe1r+n/fUNjG7NLf/voznzoFA8HbHDbJLr76978766yzHnz4ob7BkEHvJXlTLbjy1u656SaSf3TRwsmTwtqwX1x0IT516Ve/8lVGPADwyoULTz/99N/85jeDo8P8JhdfdsnBBxy0ePnS0kzx7qaNbENqOa53PRpcKJasGTYJp77xuuvOOeecv//97/oALWGvrV122cUPRMSgsZoVDd/5zneuuOKK++6fr1H6OTzc0x/2bPvYpz5lPkuvOO0Ln9cTTDN5X35BBx940Fve9lYzVoICv3P22UgRyPQpU1etWV2cKXruwYd861vf2mnevLDfm0AVESvplUoglUAqga1IwCjGlv/xIR+vmzD3loDeg/c5+PYHbs/NJNsNDo1mCgvZMvOVtq2Hb2lsakp+nzSk6VpgkuZ0sXT0mNFLnAs2GKBgA4ysq2Oa2UqZWXw2xSCEznchgjhTZTpVKZmNQwwAWAdPAWBrgBUEhiNsjtO+ZpzpRgXRxwMjrqyCbKva5ZQow+67737nA3ea2GWkUICZsY0Cs2uSEasK4kROT1UHfuNTQ3w18euzpqoC+u/o2NAsZDp7jIl0ugfeamtCePa6NWuqi8tQ6x/ujwu1ojzTz1QCqQRSCTzNEthuYz6qkHbnqzGapA3pRArahruUNW3N8csG0JUS82J4qgCb4iLhzZTp+nXrwIlzzzufN2nhIwsgE+P4z33uczffdutXvvKVlYsWAcDrV62Shxb97a8v593iXDr1U59u29BmaS5nXUFyqB1TRHxUs0DTj3z0o9a48iQbu7/pTW/63eW/sXCX35h/mI5mLcCMn/70p+h3d3TS7Ked8pmm1vUQ9TKeyYEBXHEPhkYlUU+2AnrRi14EDNx4802wxJ+vugoA3tDUJIMVpJ0bNvDO/eAHPzj88MPlf/vb337lH68Ab6wfVhCrQO+LXvBCrtH+nl5YUTP/ccvNBvpNK1Z85GMf09LXvva1mbwC+X9/+W/BDFAEDMXkUAizGmHhxKCa6eUEttKVhxa88XTG7Nn8fueff/6tt9/GVyycaWBk9Oo/XYkNCFkz7eT08Y9//LTPfzHMHZgOGAsrlxCEgrmFQ8hTXx9vtcy9A4NAHbTZ39UVMti+i4u+q4ts7UCGgWNe85ovf/nLaF70iwvsemEu4A2vfR024CKO1imzZ/MWMqIMGz+tWe3bb7n1vnvvO/iAA5etXMFJm7FL2aY9JPUES64xw8Qy4Z093Tjp7eiA3/Qcll7QrNZV19XttttuOVnZgCt+TCHffvvtAI+5FcgTAL71+uvf9a53AcCfOfUz2u4lsvEH7H/Ar371q4cefNCK3Pb1680+qP2zp5wKQD7wwANXXXVVVXnYPuTCn/8i+uqFZP/4xz8+8ZMn6bE/+eGPsMSnet6538PJBz7wgUfmz8eJfbw9/eBHP1pbU8Occ1p61z/4yU/e9773Ga8Yyoi8/cqXvqSb6Z+6vj4giFo8tDXDQrKtkv3ud7973HHHWdz7i5/+bKC3D5+Q3vq2VtiebDlUxTscffTRl/3ql5yT9957r72LvTICMWgKGZIl3y972ctMOvC72srTYMVaAKxpoETebymcnzqbxcN8od7m6hWku/Leu++RwSjKr8+i4jgUw7a5iTjH1LR67bnf+o6K4lxMmPgYH8fDySeffOe9d0OSD93/AFJmXryFuKoN/69//ev9Tn956WVcskR/w9/+rm/A3tpu1a7FyV/84hcv+dVlL3nJS+6/5751q9fe+PcbZNDJjbqsCfdq/GYFOesV1vHrNvbiuveOO0444YS//v1vfqf84fffex/ES2OQA/kYPPmx22lPq//4+z/4jXIXE6Aa9Q0boXEXi/uor6mVErzie+555pe/8rZ3vsOIzYt4z3veI6DALE9H24aFixb1j4xSDtfd8HdErBEwKLTJW/y9K55eqQRSCaQSeLwEYOAEyYYnVJNPGNiQg2K59+F7g6VOFtnGuWDphj+UrWl3+pkeo34lmkaUAouyMr6CoB4xgrSraCY0IVLTecEUVpsgbYRp5ZeZ6kNBFTJTVuArsmwTLUqvyknHKhVCt5LQKnhVLcYbMj/66KMo4BmTNDlSMoPKpsgZNY/klK5qdUnxKY/BAMNhF2gYmKXj1GXjMCCdTcGeqvEf1HiyCQUKlLlPlBlTFIgCSxiQmeWVrqDMeCvLT9cDh46UXqkEUgnsKAlsNz+w2cWwMVJxMaRLAxYXFhtWGlhfdtllN9xwg+ZRhZbI0JW0LbhiI0UqmCYFiVcvXfLlr545ZUoj9FJVUTk6NMy2vP+EE5YsXfrzCy9YuWoVXSts8r7581Hm27THMn39iRNP/MEPfyjOB7Q49PDDPWIM1AjmwT9qBAzgTPpaGCf8oN4AAOw4NT4uqlkGA322REFW4ZOf/CTX2b0PzqfoZ82dK8X0Lu1vpG7VI3ernN865xtw4A03/UNEK8eUjYvfetxxNLuGgElHHnnkb3/3O1sgNk6efNjhh8MSfHfg8fMOO+yBBx9kOaxJtm/tnJ12+t73v3/QIQfzVX7s4x8fHBw49ri3/O36v1vCOnunOS952Usjn/lFGYjXguGWttbisrC/VJY1RslOGHzp5AOmW+9qp5/r/vY309GnfuYzwTiPje23//5qd2/lMDevegmERaxrbOTSZOEGe/us3OaIM4tr1ZEopc9//vMQ7ILFS1784pce9cKjrMV97etfD2ipiD0DnKB0p0m9/bi3chui5qQiphfCIkP7ANvd2lk+Xfbfyhr/yplnvurVr+ZB9cj1pje/+Y4777z7nnuYZQu/TRfbcdp+vpA864iZMDOdlc1lHS16RV3daGcnSKm3aELAS4LbLUzNZNaagXbCYXFJfUMDSy5I67zzz797/n3f+NrX+dvZVMvLG6ZN89RKqVWrV0+bPh2F3/zutw5p/PQppxCd9s6cNWvX3XYDFOfOmzd95kwrUc22fPa008j8L3/9K5cjz+TsOXNEWd91990//cXPL/vlL08/4wxh/aLaADatNiTQaXmkte4sC5VHRtavXVs/adKHPvQhq7X7enu9ZUMMIb6QFae9TxL49Gc/I8yey1eksZ21nOn1yIIFwnHtMW6HZ33yop/+9J9333X4oc8zdaLthLzPvvt+9GMfu+HGG7VIHr8sb9bNP++4Q6gF+oIO8PP9887zCo5/97v1T+/3hUcd5acBCh5y2GEiq20kumbtWu+FNKB9Eyt+lW1dHYDiGV/5sl/H3gcdJE7hPe9/32mf/5xHq9eusfbY9tGf+8Lnl61c/tlTPwOOEqMmv8M0z7ve5ceCt7ccd1x3f9+tN92MJd3DYHDPvfaiATzyIkiGq/ayy3/1/MOOMPEUBmSVlQcceCAUesONN3B65No7TXCdbdUscc8LjcWzr+gvX7ni86d9zo8RHXXNmDnz7e94h9ot5f/6OedcetllQO+vfvMbQfvy+xU8unCh+rxWG6GF7dYLC5vbWi+86KIvnX66GSU7ww8MDni5piz+dNVVr3jZy3jXOze0m5ER/IB5kw6LliyOY0o14ie3YLupxPADSK8dJIHYD3dQ5U9ULUXk8QR7EzdPVOYxz2LxxyRsvN1WOo+nsFnKf8nnZtSeuq9PM598wPZ5LgiLNgK+TTRGhpGFMGnjrr6ukewRRnN4qG88J5euW/DoIgcyeETLUexcu4YWMKGLXqK04VjC4XQFOxHRHHrPQIVxlB82ppbhRhSky2mQYxZYfpl9pTlRVmO0pzAqlsI4J7lkMziBNlUnIfIsj2lBQBTKRVyGiGCRgqJVpDopjC/KqjDDGPlE2VeJkRkN0QTEEUENA9ZN2RtaG1cuX9rT1Z0pzBcQVZQbtomhos1fq5qytTqupCjs0jKQNbC1WGhF1PUE19P83p+Akyd+lPL5xPLZ1qepPLdJYs+u39FmL/fpOaMjaNWn9PrdH//AsXn+D87/xre/dc43v8lpCeheceWVKqVAqV16GQAzzOW3YQZoUq+NurcPk7lV2ThOqWlK3FO6uHnVqqiyQSMuL+qearbjFGoTEowvXgo9C0nyT6Ivm92eEERcjKUbNkZ1vsqPDcQlUuiATSCVuEODxRO+68y9KVNgrZ/97GfHvflYT/mpRF0KjnXDjIHlxt+IhB2nGxpUx7WErGNv0Qe38Iw424NhK2ZtkyVQ8wn4tFmXaVTyYVHED7N8+AYUA/R1klAyigKA7XlrkbN9v8444wzWCMpXI0AebZjYUf5PUEeQKv84aUgXdcyvaA2z3bPRIQUW1DzF6V/8/KxZM6zD5Fw98vDDrS72Cojd6uvvfv97z9ltd35CzNvRKgiwoCCEhWdlMf8q5aNj3rjcVSGAlgyJyBVNL2saKnKCIvSbbMnLiPI/E5e2OLXVvLj48yC9piavCVzHp92nzEcIHZcOsUdbTrBMshRc8auLAQY+oSxTFKr70Pved91115ES96Nth7WlLAm0lt9LXLFokQkU9BljqBsAlm7DLXCOD5ZDG3zlGPc2NdwyVL8NvmKbooWOWkLGRcqGKPyyMo1lor93zjlO9BH8bM7FHuB6mGNmrRvWasyAxDavMmIwpzCprl7PwQNIjAA4HebUu7s4uiWa5ud0rauu4eTkC9BGjfXqeaQF/Mcwcr4GS8iMaUzbLwL8RLJNnSr4XBfiffVGCFgAgj4suuGQ5z63ec2aSVOmgI7OJxNtbiJfQ/R2cpPTfDxHNCHYsMorIwqDGIvFdTYy+dvVV//thut323U3XPlRWWHmF2SHbW3hO7CyeuWa1R/90Id1Y52KE+Ceu+4Sic3hf8IHPqD4umXLQlR8XoEO43flXeu67Rs2qN2MRphoSKbJOMxt38Vbjnn96vprr+W1nj5tOve7iScp0r1KtROIea7vfO9cXV2kRtx/Ts+3xzvR5ebkmoeCiuunTDGrJcSd9Cw/9gqCikl29haF7uBvMzjGjH5K4qK/973vYbKoMGMmS05r7NTCF0046ZVKIJVAKoEnkACNEZRGcskWlRWjQNdRPiwIdQoAU/Ky0OQu2ehGGegZVo/aZ3a5iD0yYKDoBNS4BywpZzqKTkMEKbO0iChFf7Is9CcLYu44TGcPmugeMi5CmYanDKlo+T1SXTBbzggsCVskqpEOxxvTLCc9z2iiFkxSPBqwsBAARkdOdWmdp74ihWdlEccVYxGJMNkKUv44j2gZZcwjApzL5p65ZLBQi6udZVAdlqRoF/pbA8BPIPn0USqBVAKpBLajBJ5yDCwq2I5TG9o29HX3DPb0CkSEnX7/299WJniMwh0bHmEqBgaGrrnmWqoZFop7D9ql6eMnnQhp0LNUsAFxUJqlpXx9hvXCOC+84AImhPvURatSrxQ30TAYPmGSMP5NXKa+wmwMhkDbKDvKHWCjvo3RpSiIPluiJO2MWkgMhwnlBneTg5ocTZwc3wJcGUljW3TrlEmNIpfAYPkBYEVUZ60sBmxDBCmxfhva2pglPlU7V61cvFg0L8c4OCQS21m4DnTZGp8MHn5EXzNpeGM5gKtAmZvR+a754dwFCAcEFT7KrY2sVcQiou+84w4beuFZ7Yylizz5AJtXrsQnOnzarnhgLD4dcoM+O4fOw48+KoyWxRJtfsQRRzStXCmdU9dCaK5gLGmLtcFgsJ2rHchrualpCBzaPJlVttoZfq6bMiW+AjwQFLGwi0GeyQuyRAkdryycLdjb61VyYpvYbl63ToA0w0+G5MiskpjRgJxc7jhHCoIyLCAW1ETJkoNsFrXaA2n21Kn2fCYEzn9uXhARztedBFrjWX48G5Tc8Je/OHjpsOceykhrO2lYoJuTm3P8e98jHqFzQ5sVoQVFmfXr1qKMMcMatQSWnG80Go55fOSBB4xlOCpnTpt28qmnco7e9Ne/BqlumreOQ4dQJDH2Aq1xLpBe07hD9a6iZOxCaCZi1MIpKpahvasTe8Y3cRMyiFcMmTwOslbQnBDHr8GHqHXSI2rZHKmlvYCxPsl/bs7iz3/9i0mWNxx9NMDpPCdbeHuzhlZY8k4xSYxC7HRRb4TrWNMMa3h9/XY4YKXLRuaCz8Fme2gRnV9c27qwrTcZ6nKaqed77ygLs3/NK16BJXt3mUM544tfEpitp4lLX7JimXXsStkv2lyAPmMsJdIeESMw+4lrizer/2OSrPzQhKzr7fpwVUOD7ewcqY1tcyvC92UwiaNSZ3HLGX4O1iBUVZEA5QC6z5k3TyJoHRZ4DwwISp86fbraMSz/nXfftXL1qtr6Ot7gl73oRe9+33vtMY5VstLb9S47UXe1ttAkplpwmF6pBFIJpBJ4AgkwbZ7a+4PaYa5oKrYM2qRw6B+ztJSPKWxfZaB1aTBPXTStwYzM0mUIQ47s7Cn1U5gA+hDZiBJpS5admZbTuIURp/bpOsqT5mRQ5AzzsKK9HDdgs0AhZskpdx4x2cH6ZGUxf9Lpc1oUBQXhXmQRxwNqMVHxmFlBRBidMMBwsvHwsE9KEgUDLTVKD4OosbCjviJ4QxDDeGNctE46g4Kg/KjFPEHhDw0hoqzMLjrZXG0wrGHDkPRKJZBKIJXADpPAUx74R9tSea7YRKrf0J/qFDYzN1M0ZRofWl/z2nVGsODTa1/16pEh4ZFhvpNipX9pTOpVoCNX0pJHHrE6kVcTYIDKZIBwoFZKOdqkWEW850f1ldqF8oODN885L+HoPJkTPRwAc7iSL/FfShw1ml22aLogZ05OHjAo2Ta/Dqqx8JXj2CD+Xe95Dw/S/vvtx/1lW93XveENPKvoUP08w5zALAEOVcqRdcv111ucDOqvaVoHe8ONeJNzxdKlDck+1Y/nk9nAhmwMoU8ORp/2zhXMysqC0DBAtF4g0I1/vx5I4E63+dMf/3TFG1//Bm4uAFFj8/JzBNNm5eUHu8X7LWaY6yx5HcX1dWKhibp22lRLUgWOdtkQcv/9uU/RBIPhJQDM3tTArVW+GAj7Ho+OtjY1WZWqIWZ58RcQYG7u6nVrUecP5KADzIhC7TCJvYihO19dSYuSIxQT4diWmfj1B2aSCQXp2V2m0Q5J5M9jeds//1lamLG8WTareeUkOnlwQra6Aado3HDL+k+bOb/whS/UNJlb1qwBxQ866KBd5+38uz/8/rAf/QhO49S10BoRuFQPJH+7Li1YtEh42z777UuoBgf6SW6yZFrz1aK6sI+UkU0yZY63XffZx6yB6uBVsc0A4Ytf/vIXHH74Zz/72ecedhjgjTG4UVwASEYyt952m5dlS2ROe7sT1yZx4+bUeSzltKexVuvS8KdJBwWFi+Ec/4YsGBZWYJNwMw4yo8a9qVKzSG5s4WbkZEF7eAsjI6J8NU3Mv76q13lN5K8UIl6fNeQI6jAGSVodltE61SmeEjwyYjhlTGdewEDJO+J/9ivwUgRgWz/vd4ExgDMMa4aHgW1b2ZkJsnRZHl5uzLjx0knMa4dmK0vLbaxSJyACbE7cCGV1dToS57BBmGymfkJnFhDoJ5OXhxOVSidYEd01jY1mfLjlvWv9k1v+yquvsi7AHuM8KU4wUtb4TMQ1ACwWQJeyo7jYbyMwKbZkc8ySTQHGRke6uru8SosHzD7YoIsQ9IpXvvKVLzkqzEqYZ7Fdme3ZBfDZUQyA9xvDRnqlEkglkEpgaxKI5sxTdsrMGdvB6GRnWeUzWlxqP4TqjvYWuzla4zs07GnYJ5lxocDBUaqVTafZfKVRd5m5y90P3U2b0dUIwoosNZUuQ0TR8kSjRnchIoAZEdZfTrVLQc2N9WUUuJwRfsPh9DblKRu1H0xbMqUbMS2sqwmRrOIKUvtQMSWJDfehIcXFUDQKrvhVLRR1BMa21vcVbxHZqjTWvmpVsFxtLc2OonDxDWBj9ZJ15GByGQ+sUnlV+dTahoqCdE8s8kivVAKpBHakBJ5aDAx/CkwsLS93EiggQbdycNGV4koLsnOMvHs6OliCA/c/wHDWlrwwTKa4xGk3VC2164oa2dD6umv/zMlJ53IOczEd9vznC0N9/Wte+4+b/kGVuyLEghrd0LMurj/a32QtJd66bh1d7CulLIN9qkkd/Sh72ty5tT5ljrYBQnDjKZ5BF3q/tL6u3vOsLEc3qc4qHMVhm/POOw+ucPprf3ePpvFHl1dVZsrLRGy62J7vfPMbn/zUp2ZOn/6JT54kwpZXlnkTBGvLiPrGSaPDIxySHNmqsnjGUtjIp09f83JynX7Um8ytqlRDfJY6N3VVli2gS6qrnM+0au2ag484/JJ99mbt7Plk36Bf/e63xxz7ZpgQkaHgIx9hkALgyeNlhz6K8ioqRrhYFWBxZ82yIpRNBf80lnfXEaz5mcKh8bF165ttlQxB8dOqlLd5pCdstuF41U9++lO5Bfn4d7BzzeTGB+6914zuzJkzCsvLcro6w/66YXphZPmqlUQ8beYMLtwxY4S8cHwxR16w6wUFa5YtFZbMH06SPevXEzKGuXCz88JSK2CPuF/+qldOmjG9a8OGFatXOWJ66ozpIJyn9kvzvn52wS/sABxfJTG2IWKHaD7G/v7VK5ZPnTnziquu/MhHPvLRk06UZ9+993ZA1zGveHkN8G8NVXkZMB9ecV6uNgZ5lpS0N7eK2OZE5Qv1yGlA4oTt8BkkkyypGuzsdK/4Hvvv7yWalDHdcOaZZ/Ir/vWmmzREH2Dn5dF/9N577rnHFEDj1CnLFi9y4yQsQwSbhPcND2ndHvvu07RmTWv7BgKfMn0aR7RSvU3r6qZOufBnP7v+5ptsy1bbUK9R5OagjcOff6SCVqR/5ktf/MnPf9YwufGd7z4ewZ7eHvt1c1Xvte8+YU1vQ4OjnqbbxvnSS+c/+IBXs9PO84BDkw62g9Kt991vP+3SE2yNYuJj0eLFudk5Vqp7a36hNvP0aX2v4ZEmeHGLH11gWFZeV7tq2bIg4dxcERDepv5job4ZED9kod3VlVX2ZDd06+jpEiwnRJxA1i5cOH3evN9ecomBl/eu/6Opc3h9NhENPzCveyxXx8OnV2xii0/e0462VoceG2befcc/8ewdVTdOohzEh3tTestZX/8aNnVCDuSS/Dz9c/2Gtvq6uorqqvDb6w/nb9WXlRqueZVc9zayxkBvd08lHC4IcHBQv9JGUeI1OTl2FAOGn1qFiI/0SiWQSuBZLoGo3jXCIcBgHQtL1TR3ttY11lU3VJv57e/rMvNupjUrO7ekxAgozL/zBzAiNLx7isjN7JmzF69e7D76eBWk1pCiNqVQpFSlp4nyz1itY/5RXShTtyYTKTlYFzWToYA3F66JSFWojrqWSD8jiIKqfZqpVARlatNX9N0ogqBpWQUZr6B4k6BuRsRT8+ASsQEeU5Uy+1QRlihh6fhhCCRKaW5pIRN0xBl1bGhvnFRvZ42FixbYA2Q4bJY6giDr4TIEGhgbKEvOy3yW94WU/VQCqQSexRKgwZ/ay6xnlqPjnUVUZPFdJmDUsTEamdLktoqrPWEDI9abbvwHq2AVKGVKKfP9UsfhvrJy1cqVViGyKFa9CtmF7kRXshPGzfYNdskZr9gYdBKXTti9EPpiVGrr6y0G5hqir+UP6DZByz59laiIzNamUtOG3cnzkEOwtICiUlvLdjgWp0kIMZNQ3dCAQztC8+DJaVivuPXJTI6v8KSFuBqLIP6FbVv5euONNwrT3X2PPZyXY9Ng9g0PmlASXE//4hPi5f5Cio0KPs+xUatrxD8LjYK7eAiBpWDkSktNBkN6RvOsHQ8Ytlk+mBwewwPGwG9CQE0tmYoK5sorUJZfmpmy8EjDAeDmFSu8C/yD+hZY1tTVQYZhq6rsbM5ts8uao+G2sEABTUaUD3Dl6tW9/f1AIAAWAOfq1drIQAIVZppZOHMQms9PixmmV11uguStL01egRN3o+uPQ7UwmUVmrVlfjlAVEZflmtCsxa5gD2phADE4aGZaNu+ad5QpRQFBZDW/tbm5pqGBqbb/FrGThpBaOR2nZEm3P+vSTaKrjs9QET1zyrSpU6ZNseLbi5s6bZpSBhYA8NVXXMGjPmPqNOfHoixcOTIfVrcmc+045BElCs0/5bTTdICbb78d9NUrdCYMyBAbSwg2dSNhQkBJX7I0QHh5aXHxJJ758XE1ap13RIZ4diGyZsUK0NorMMTRe+0ujoJZJBXV19Zy7f7u179+eMECu0zZLk7HcHQ2DvUosQaQP4IA8H133cU7Ld1S2Di4wbBAZStmDYBwCOjatk0GoWtmFjildQ95sIpP7wLzeiCxALRcpl40+bcnwXUkKSc6fs4rlizZGMeup21yaChF+GrB8+rFi8XS61w9A31qUaOyPuMoSttVJAxbilgGnEvRhYgCD6Kp9asoOoucFbRc3Djq9NNP99MgFkS8Ba5yLJleMfxSdRyWocPVrN6C/DxPMaOlyOqlllCgKUXO+mnTPLU02m8KDk+vVAKpBFIJbE0Cjx1pxDxsEJ1D87CPNBZTFc1fNEw0vHSjF3Pl8tOZ5qMpn53n7bxsxTLpbBYlyVbSZm5oLdOFFJ2vtJMiPoPyTCA0bU/HMgrslxppTtZZomyxFjqN1aDQKHOMUaSqwzMlGfRkYhylS/TJWLPv9B460QwphW3amJ40e+heigtZRgTb2NAQPODQxVrhTTatcLnBG+IwuXQ51Yg36arQdukaaAqVKi7OdRhieqUSSCWQSmCHSWAT2HvKGKArqfBBewP19oW/gQEjbzOT9kOmK8MwNC//oP0PeOmLX/zQww9/4XOfp5Hp6+BZ7e83XrevrJtpM2aYARW+HBECjUyb0/JWftryKl6btUAQZk93N3QNezFLxtTOsPUpm/w+patooiwVT0d7wPDIAZTEnBb6iKJuXbmSF9pOs9Y9ymmDfzxc6DTjgQEUnv/85xdoZhKYxN/NcnjK8hiso2bJKwDGVLhHdvnixZdecgkIUlVRwVTIxkhM8GlovnrpUp+AHwwMrth/6I5bbnG+DvZsyCyg1NRsV08PW8IK8gOznVGSZAIPyIZzMMbGQiuWLfvJeeeZOLjmD39ghPjrNBl447WWx/WPZD2kXcowzGIJfF29ciXw6ZHloG0tLaYbwHWv6dILL0QBMrcn9qc+9al3veMd8oiwhY3NK+OEYcOSNiIlp0pw4pFs3MhR1KysS7qGSwewG+sb7NptcZIURKoSqO/YHgdQ9fT1OeSZm5eQkdUZFLGNE+JkLmZbOD3IpBTimUS8K5cuxZt4WrYZ0FUXQCi/dIDQMMWLsCrVlkjGJYQm24EHHuili5Ll3hfi62/Bww99+cyvqMsiYYtFvW4GHA9icbNGxwQ563VaZ5VVaVUVtk0ZaMihBx1kvax123F7ttDnEyGLIUffPtJRGmp0DpZgXb59m131dHUZ33hrA0NDgr21yzBCjZbakq0+s3T58pAt2b0MQW0Rge/tC33fafZsC6H1KLxFyfgxX3/99Y5WlrLokUfEaSOiV8QxU5SbkYdavBFfBXjjX+26kNAMuNdXlb74xS9ubm2xQ3Wc7zdw0eXe8853QtRIHXfssapzVBI6JnS0CDC+87bbtIvw40laYqGJFHT3dmQWgu4tex1+dACtrqKsdPMFdmjTu8gz+vPF2AO6fubeKQAN6CqLvhfX3tFB8sZha1eu/NMVV9jMzIs2ESMqJIraGgT9xPgPw6TNN+HT7MYbjjla1d8/73y/RE9ttKV2PVM/t/xYYLl3OnnGjA4xEfYST69UAqkEUglsRQL0Fs3pYfx0E89Gonni5G/Eh7QXrEiPSZRnAnlGvUdBUbatbWFnLPhTHi5ZipcBpQxps2hDZXNR4NIVNL8J5cpAqSqurEoBYICZEUHHU1x5hILRETuirHtlOXXRl0glosBASKQMUcansihIV690+hZlGtIjVlJ17AKLqTlyMgoyq51SxZ5EmRGUh3WIFsRTlBWEdXHlBtk4PvGINCIDwu62IuY0OZVAKoFUAk+HBLZH6N//1WMRVUdPqydijPk5A0rhfhQnQxuOBM+SR1bLmAKlHBtnzPjcqZ+56593fPc737nphhutaTSw5i6zIzQt/IIjjjSUf+XLXn7n7f88+cSThvoDIrLm8He/+92eu+9hwxu6eHw0LAneWGlipaDuTFHm4AMPMta++MKLqOY7bv/nC1901EknnST/hGiV8pXlQDOo9TAuz4UrBF7ywslmxhW2YVeo8mv/9lfYzEJHbjEWwsmiQnn//Ker2JWx7hBrZKdcCzsvu/gSiMJ2O/bptcj5qOe/ADJ5xUtf5pgZaFyILxg2uWESv58w4EEn6GQyB0NQ+LzoIpBYqw3NxW1i6XWvfS2ACnxbcsnz6SAcaGS//fYLh5riqqp69aqV5CMK9L3Hv3uf/fdb/OjCM88+a9qkRnC9q6V1xsxZNuJ+6OEFy5YsfdlLXzZiCrmwsLOjo6KqGuIsSEJGNf/jH/3Yt771LStmc/Jy771n/l3z79trt90tmrXil7l6//HvFrv76U+ebAem2qrqX/3m8tO/8EVopixTpJl97R3lJaVLFy22GFijMoUZSzELiksG+/rtdmbWQw+rqQxYMTu8pbHiTBGZEzULzSSz33/961/tdcT6iv0WenvNn//Mdb/bzrvAP3hGQS3ichc89HAmN2+XufOkeF8ffP8JTkJ27bXHcxz0qofwXdsp2uJbu2QxwAIKyNmGwOTpBZnl5mA173DNtdfak8xu0kw+Zk76+CcI7ZMnfvLPV139ype/QuiyaQ6MffgDHzzl5E+Z12h3nqHum5VtkyobOIlmgDxx6+VaEAv9qoJ1d2iWSkIntG9ZSYkhEsNfXVgIGV7zl2vP+sqZ+oDu+p3zvv/ut7/D27Sa1xDEsnZd/eMf+Si8ahm2937kYYf/8vJfk9VNt94ytXHyLItyC0Jw+PDAoCBvnw579BMDmL9w2ueMbEYGh8DC/s4uG0E5Jor//FUvf8Uee+3pfGZREo4vqp8yWX9w9XR2OSvLmEnkMOQfuTVqwbAVs7qQuX+dHBwVsKA/XP+PGx0CbDG2yORLL7rY6mv0deDTTv0MfIt/iHT/ffYtKi0hwKv+fA2ez7j9didvWRMuUuCfd9/54he8QEVY0ou0TizfjKnTxTiY4tG3dbzh7h7vy9qB6rp6W3P/4PvnkdIRzztMJL8t9JyLpr3f/8H5flNvf+tbxVO89di3wL1g/NXX/tlhzsEPnJ2jT/Jo5xfmrl/XZGhFyDSMPQX6Orv8JB1AVVNV/bbjQvHDDn2elcC6lpGc46MWL1v67ne8c+8DD3TA9big7ilTHEJeU1U5oRzSm1QCqQRSCTxWAgkG3pggasxd3BnLOqAVTavHcsZMJVMvI8MDxgyeNtY3Ll2xnNoEQcFFU3JsBL1N8ZrWp3tNDtLJcgKZrrhwA8ikh2kzpsSlrAk+Rdgd2j6GmykodpqVMatIwXqqXvPmnkLLsimOiOLIslDSXbSunAwaVQ/rKhL8sYl/WLp6MakI7Go2llOagfYUtYi9EZTHpV7uCo2VjgjK2mWMl5MzqrG77bKrfRmXL1sasbo81oi5L8gu3LC+Zbx3RNV1NXX94/0F2WG7k/RKJZBKIJXADpHANmPgCHEjr+OPQb+2Q+4fHCgtygg15ku1FcNwf3A2FhXmd7e1lDU0WH0avlZVdDc3V1dWuB8bGe7p6qQMs0aG58yaec+dd/Aj/fYPf3j04Yf6HRyam2vLZaGwu+26i80W3/m2ty585GEg5/h3H6/sW4455vJfXkb1AyTrm9blFhXadMtODHbuHxkasBo1U1m+ZvnyF73w+e9++9t/ceGF53//ew11dZPCQtC+qspyjOfnmqntqpk8eaCzM684ExgeHcoUFqxvbc6x2HBslLKudpSRPb3GR/JKir733W+/4MjDz/vBD+67f/4D98/nJjbEtyHQgfvvW1xeKhaKg/Pd7z3+ymuu/MfNN33z29+aO3tWTvZ4RXnp1792FsTFwfWGo9+IyVe96pV/ufYark4LiTs7NtQ1Tl6zbNkLjjzinccdd8Ell3z/3O+aFW5sEBFdAXt/4mMfbairdR7Mj3/6ExtlAF2XXXrxn/9yrc2fB/ud4TQ0dfKks88683vnff8nP/5hz7mD+dn8t69//3vf11BXk5OX39uxobGuYWne4mlTpgrLFgVtyleQOdc6fzXQ/9KXv/yKP/wBnrnqmmsuuOhivaG6tuaUT5740Q9/JFOQ19neUdU46UMfPGH5iqU33fgP8w47zZzx4x+ef9w73/XpEz8xNNi/6y7ziguD49fiW69+Ul1tb0e7qY2xwYGC3JxHFj5qlHDw/vsP9vXaGQMsHBseMm3uKOHS2lrxseHFZWd1tG/41eW/9k69FLuFH3H48+BG8yB842FWYngot7ysu6Wlee0addXXVOdlh+Bx1V3ws5+CjvbE+vkvfq54VVnZUS98vmNvLaAdzxr91re/8dWzvv68Qw++8KJfmJuAig012PWjjnrR988794QPvA/pvJws7J3/ne/wal7952tv/Nvfde8D9tvv7DPOAP8sGEW2vLRkdHioMD93fGiATHixz/jSl+T/2jnnCHe2lecLjzjCcVDhoKDQhvCTAPGxCG/j/9g3H3Pb7bdcdsmloOCD9937jbO/+pY3H7t0+TKinrfT7LbW9XXTpur/Z3zpC+d+57vc2vffc/dzn3foFX/444aO9vq6Gu+XnA0diivLm1attMDq0EMOUtYBlR/+0AdGh80ljedkjWVKS6ZPnfydb39Tx7vjrjvn33/vO9/+jk+feooeWFdVUVtTZYF2fl7OwoceGB0ZKiks6O7qqKyqEiixvrmd8/ORhx/UM6dOaRzoDxvClVeU/ebyX331q1/93R+vuHf+fZp02KHP/fBHP/LqV74KlcHenrvv/OcnPvZx/fwuseg2IK2q/MTHPvKhD3wQ0raaurGh7uyvfoVMbrntlocemG86xlrc4kxBeWnxlMaG8ZGhvKKMXcobZs7M6hvLFJe1tq43GTFz1oxLL7nonG9+45qrrublVukLX3DkiR//xMHPPSQnP/fkkz4x1Nd7+e9+713vNm/eby691HyBX5BuVltVaWm62A36ocZgzRqz0bAdXU9nR3GmsKejs6Kh4azTT59aXy+s4LeXX/7H8J6yYPvTv/B5b1lm2WDyrOEhAPix+i3JmH48KyVgRP6M4tvPKvIzcRO/bi8+txedCfYmbrYvn9vrpUywN3Hz9PCpWwnecvJtmBe1ScHwUE5+IZMNK+bm57ZtaBvNCdO7Zt88te5j/oPzfQMjwyxwdjYzBGHKAOvCnN4aqxRhqvxmVJkMiFFmfgKZaVTm1d7+CMZXDIWaPIWKzcJHaB2qzs2VM/ppIVI5XWY5lUIEq9Ayd4Js4CvK1CT4KpFTFxiGYOMjiYqgYGaWSsQG3jwCd1Eza4+s/AAwLA1aIwVvyymbRFWUlVWQxqrVq1rXh50v5B8cGiwsKd5pxiz7O7a1bsgdzyouLNQotjiralJ8a4//VPDxiVImXvfETcy2tfxbJPI0JE6wN3GT8vnfiH1CjBM3qTwfK8/NxDLxaGu/i4n8EzfPHHlO8LzRZE405qm5yR6Lh8T+19SzhYzakrevjyqk4otqapY88MCcnXe2NpKut2CX80clNgvkeDVDSdVSoLKFjQL7+uJ9x9q1XgkKUflSsnxq9rxVIIfSX7WKdChc6ticopT+tjaxOuJOnV2EoMxDHUIaO+smTxYhylpkqqulsBYxpGfK9Ok8z2W1NYKZnaPL3ytyCQiPkUi4Qh8dSl8V7jUkvzATardIOFkUasmgIjQ4S4AmLS+zFrhYF7sVY2bJww9Lb7TFTkKQfeB01SJmTygv60VQDJTTUGfMmzcSN0OuqUHZFfmcOmNGgKxqFCbaLUK5TVvU6Mxb+/eUN9SvXbZs8qxZoqgjk4QmnFUezLN5weWesCQwlYeQlWLDGmbMcLw9OVuozNtsu2NOzoDZHGVsYiB5BUMjI0xpR1dnmN91lmDCT46Y4fZ23GrCnvvuizJnfjhpWTi7LZQHBkvtHpmcD9w4eTIpEThD7jVZRuu8X/MIijj6yOQ3CqJg2WxmssjhN+3t2LTa2d68IlR1DxECZEukJOurCsTKdjQ3V9bVDfb0KK4veVn1M2YMixm2OLm1ldDYfo80v3HKFCtObXbFTbrf/gc9Z49d77n/fjSZW2+nTEx7S4vwZplFVtuiecO6JucUm2a3LxTJ6FRmVbz6+pkzx5Mg3sf/LGTTqYRzEYiRhGZqrEW5evXjM0vKzWRam5s005sKHDqD0QGSLS1E5zxbS4rdlNmiaWho5YoVspF8GdkKV3Po9FDYUJQ04gsKDK9fH4cjxi6Tp093ljGByIYNr8mKVrJVkT5geOTnI9E8P/mItxY0z9XZ3tSE7bALd1dXmJhIYv7Fgas6RAKPj9uWWU/TFhR0J+Ok2KmK7ZrmBSXreL1Zy7ajAyGOftQoKs500vjwiB+1HyZOsOoHFF5xXR15egtIeRHkUFRdre85SZgTw1Sajhp+FMm6AF3RK9DqUG9dXfgheFpYqBugUF0elvQjS4yYX718+dR589YtWYKOVxNHkMpi1Y/CjRRB0WbldFcHlWmUEaHfIMHaRM2vNZ6XlmMtw8iIEIbw80yvZ4kEwla8ZRVbDKj0K3hGNWKzocYEb/8Zn0rpxo74juFCfmJboz9R0ZO82Rqd/4zPJ1npf5Bth/EZttMfMQU5Ygo1N69ruCc7v+DuZfd1jw2ubFtTXV/tBEhqp6S4wDKfxYsW2YZSbFBUTewLPeme9vb6qEHaiQIPDtLkGKToTaXPjR+YS4n0lcSgxGiqsTFai1JVViJDSQi0JQ1Mw9OWCvrqU7paFJeOvgEVK+kNyqkUFTd35twlK5dIZAsoalYJcUZZHgMJZbHk3lAHwyajsQGf4wo1FDBgsRVtzDBpBeiLQ6R4ujUxDJB6uwV9DQ+FLQ9raqvKq6vWt7SJZhrpHx3q688fzinJLZheN3m3xnmZrEze+DZ4YnbYe9/GPpryuY0C+zfZU3k+sYCifPwMKR+/33PPPZfjwVc/1S0WfFbIM3t8bKSnL3d8C0PrLTZqWxPD+KG0fBu0zxNXYMmi5SyETu0GKNjXN4dnzJnvNTWGvwCwcwBQMKL1kgL07esrcuDKmjVUJzXqVbmvsi+OTYyTvf4LkzG3wTSdS/PCyXY8gqasJUWB8nVEClRj1K6Iw3jhzK6VK9Vex3+YABhhzIqzDaAR11xlQwOsVVZd3dXWFg5bMonb31+fHF2Lh8A5j3QyyjcELwIP7NzT0wPjBbbh8I4ObKhoKoadqgonJO4+Wh7zOWyIA+sB/mobZjWEHpbAHgYJt5bmWjAsXtfWQSBZd0cHYzhjp500AXGGLfI5ecoUsNz5qFA3Y6YvE4Up5Uk8ZgiqLjlSCA4BgOEocmPP8MZA2tJJ2dDjAfhBgDGYJcXLyit2AVxHRvqSLXBJQ7Z8s7YJ/pRfikZhO9A3T5HJVISqRr0gVhAbmQTe77LXXkJGEXdYFKAYYEmydjpTUgqOsszhnQ4MaAghhdXRmYxTBaunTiWHdgB42rSANwR+m9vOFWaeLTPJBNPuJwtOd3ay0Bh2qd0uSeHwZNMH3d32/YKOgGGTExpSX1o60N4eKCTHzOJfThx7A5BSETA6PCIS209fEPX4wGBLc/Mk/Wp0tGtD+68v+6Wnxx7zJlHoHc3rq+sbuta3mAswuWDCwryAfTzMwItZiA3Uxs0ugtWvbKZk3KClAVxtOjp4s5zxq87mVCRCMDyB3LTM1ijaGH4mXoq94ESAa2lxsTW6mhB6mlFFe7viljSH3tId9uJWlvB1FT0K2rRJlKkLX+E8B1DiKiuZBjKhg4iOoYhpBQutNQTNnuZm3NrXTTpwKegX0DU/RYzmFBoT9Ks3qstLwaQ3okYzHTPy8x0z7VdmHgSrngZOBOlVVNjfGXG9H+XQ8ZKjIyVplykMzgrL0fRbkRSmKuSp8FuzRtp4Tqft7katcdq0sDN8Qb48BmryWBBuS9MBv6OSkrBflwmXTSdzaCzf9Uj/gHA9L0hPKy0rA30x4xdHITh+RB/z1Y/dvqvKinfwTs1aaWbVpEl6oyEd4TgSOfRklXZ0aBH5O/QDq2YfTE5t8T2miakEUgmkEiABGiNYn+Si90ayRumojv4u05frW9bXTKqRYdrkxhUrllG8BYVFlsrSyQAkAxdNqvy0mUTGix6mf6TQ4TEFcTnlp4ThTNoYIqWlFaG9FZFTOuLyIBgpMyWQKuWmuAupMNFcVMTPLJ1qpavVIrPTmB5e+jA7Ak7Dq5Sniris0XQhKKcqcMWaYE8inKyliKCAMlPlXry0eyMQn4yCKVeT79S4zAKmykvLukfCVDseIm9WzWSNhJ2xMrmGfjaFLI7B5GmnSiWQSiCVwI6SwHbDwJSaeGD62sVFYw8nHtJ1K1YEBxHnT4JSNDLcQIvJ8fECpOlH2JjOzUq0OQzGfys/Rbwxdrq4OLqMDFgNcDlFaWF10bkUPaCliD2iFeFurfXV2Hc0rEip4Vuzm+7ICBeTrbCoZjDCZkroq9TIm1EJA+isrOa1axsaG83uQgiYR1btNLUqiktK5Xeak2wYUDWkIZ094LaVMwz9k8u9Vhi4dzY3O2hXGjwhxbZJ8AP8g6C9nY255aycNGnI9sWcyfZtggogZPCVxyyZUi1PZnydiUpQ4IT9q0y0IoiaC9rsWN9cmZxGEEC7o3SSLbKjHcW/1jE7lUQBTY2MtjU1xTldGWCPccfRDAyYzmVlo9c6ZEuqYLnR37B2bXVdLTvPtR4MJxhDwl1dBEikEAmYZB9dpVbbgnj2HEwiyIgq6wbZbK8StEvO+OUvNWPN2uHeS9d2m2sHeOY1JdAdwyyoGQdWUaUBIOXlkaqcZijCDIK3nOwvMiEBZy4rBYOF6Q/eZpsqJ+hLKUOG1g1tDhlivA879JCbbrnN8tEjjzxSjdi+7rrrrrn2r3NmzbDjlDOfuccdCGTiGmUXNsQwYNWEhfcVEx//WTt9ulcv3cSBMkLo1Qurc/I+PrPOoWdqqc5AOAZJ8GTh2Jh3YVhAvOLGBEwPOvM52dlLu+TXM/1qsjMZ0xy4qjS5YNqiqIgjN4g3O9tJtmZtbAPGD6yvKuKrG48CJE78n76SjwTrb+MN5Oz16Wb2UQtTP35NPT2yNc6cCd/q1Wo34gkeUeOqZDaR0xiTgVTcm5RLP4G4JnHw7E3p1fGRT9mKS8uMB3Gr2fl+AonTFQXt9aY22Dzc5qKmgZItUkxgmQMQFS8IWRPCi0jmOIjCe4+/rAJHqVVUwLTxsidZYCbxdQDAOoZOa+e2mmnTinJy/BA8VR1mvCPshXeUn29SKiiW5DeIDpmMd3fHG3ILOsQmbX19agy9PcXAxJdeqQRSCWxdAvSMy3P6x/nA9BXNRqVDlcYe+z1nv0XLH7ZxCV1E7TOwRixUOmUOkbKV7BF7KpFppvoipqWR2G6kJDJDrACbRVlJ9Ih2oqwoUulAqXQFZTaGoehQcwHAkWX5TdG6xwwGZI7pitv0YenapfS2dDzjSi0SFRFuZkyidvdcxJ5y9qJgCxL3jFd0U8usXayn5hBCNGSeIo5Ce3un6lxISREhpWr7a2ASe6MDY3YDra6oriwOC4wHxwaLcooib+lnKoFUAqkEnn4JbL9YaOreYXQVFT7pPto5uBZhTqtQxPRCSgmQM3A3cjVQptNpcfiJSaAuq/n6Ro2NAwgRwKkg2EoXU/T0rAxufKVJ+aGCU9Rfosdp25CYRD/KgJp7mID7bGKArmzAe3ajdZxSOGc146AAsgZEWQLcqgI/fHF4Awgp94mYbZwjKzMidkWSQb3BS5lQ8JSLUkUosBMCwi1dDkcQx+C0/HweYywxY2jyvgZ4YIVM0nxespa1a0uSR1qhuIIBYyQ7KjMtGHOpOjLAIQxIkBjZomafCijRpfklYnGTuGuZcYIfV5RMQYJ73WsAzrmI5VHELbLoEFeoRb2MusYmMb3IxiZjXjY2Gz8hdjQnh2NTfu+Rk00eRcCz+DoChYRbPHiVwe8nRbZkfThRV1nd1NkJ8ZqVcHGoEpScqvDelcJPFEUIjfY18QRK9DoC/64k+ArPimuCUgTHNe31qcXrzy0t0QMRkcHGvz/4wQ9k67KNVlh/O8dGVu9973vNL4g0NmmtPwz09WNmY6+Df9BRNgF4obrHXZhxhZ6gy3EeJnKDCQ1oHpc3JIh1Dm82AWYq0kaJeNOdYl/Fqhfqq+khHYO7m0g5BdQiSJtEjJCU8sq0kcSCtz/pe73Ji0A8/hwQ99aQkgIqy2yopF3CnsE/dUlRl0j4wFZOjnOJOQpiHvLXReVREXF5U5oDQOrbWA1V85raKC45tZvwQ6XGf0h5nGTwyOWXpQnxbequnNogt5zCoEW8WwEWQuizs82zGM/FUHatxjbG8BxfKz5pDBNqOEFTXZ6iSUTeNZlPpCtYPmmSk6V1znpA1xvx+nRgHTvp22Sod6nLRFv8VWiLiavw8yRDrzsi3mSMiFu1yBDkk17PBgmksdBpLPRm/TQq2M0St+fXcMbPmO0V7B5SkF8wmDU8kDX693tuHMwb780aKCgusF/m3fffXVpSaD8/45LBoRFLZGMAM/jqirqOqoE/6WdKTGLUz1EBUsssLBRKd0VNS79BnlQTzSwzBaigR4jYtgo0hWMlxrYH65zJ8O7y00qHaTUfNQoWpkVTtDOVSDd6ig5VD8FaHETBIogfFWHYIwQteJHIOlDFmBTLZp7UOiBsoCyzRDgccQB4Q3A4K1fc19M1MjTsfGABQV3dHT0D/bYbdcxe7lheVVl5bXFV9uBIaW5mes3kqtyqNBZ6e/bPbaSlN26xxFP+O9pirVtPTPncumzCkygfP1U6xE8yjYV+YnHFp9s5Fto4m0KEeynQMIr1JqJbBkBKDsIxfA8VJ4NODkcwsre1Fbji5hUtBPBwfNmuwZDayFtxKj6gPqo6CRkyihWrKR0NkMNDMMxnGNkny4Apd5OO9G9Y4AdjVFXZ1FjYZ/TB8r+NUO40dHLAneBMWl6PYS2EpQ4kDiuH+jIkqoA9IC7n4cjAPojbZADC4Fjt+Euaw8NJTagdkwERQfvaDws5L6ffgtleFsUSSlGXCOqgKMeTb0UHufcUNabIrAH6GAt8ghOOgRWrmck4cCCIi4bibfYJOxnW9/VZT6uIRZ7FyfkxPMkycOipAjMuXGHVFWSF52SKgV0UrY1B8jGxzOoKqzIxi5Pw44n0AWDNTzAVk2anbBy6/KLkIRP42Q0KxILbUNDWYgk0FbzqNQn2crys/KKXK+TnlU3kbEmozCHQPYmw9U5RkD+89ORkIE8jlMI/lkhbn1G1VnBshpyFhd6RR7x/ypKhCYvCBPbDvfHtxF1K1i5ZWlNXyzGb099/8sknn3baaVpt/ZUxAWo6DFJWlpISMXa0tILl+ETT8iwwUhXyuLZmA0wY5HoXyRwNnjUZ9jZECC9rS5fpiejGJ2SceBEFZWV4Q1+rsSROweXlhRdtoZeV6smcuuEORCcJcf7JANoTjy43qcVeSTccKqmqisuGycp7sRwrwE7v3UsZGQmoUtB7MrtfK0Y6cSZw6cupuJFWTnFxZU6OkZAFAjo2lnRLtZTEeRawWS8i/eT1BT6TUG3/IisiQ/ywi8QUxICxlK7lEMnwe0/mmzwCKPGj0XqIPKYnfPrpeSTQo9i2YUNDoQ+bF0hmN0heBhQ03Fv2XsLEEOe2vpeEf1sk4DdbzDcOS1sKbjlAba1oPEXMsuFHXYIcBBp4oX6JgqvNpADA+nPoq0ngOpb86FSKvkuL1KKBW3vpW3qxaVoqgVQC/99JIJnDpHtCsFhsfDCf4fC8/pKakumzpy9dFU4Dalm/tq66xiZRJaVWtFTTaWyQ2Uy6Vykq1yJb2t4UpHtai96T7gampZwl+kSWdvII2kSEN5VXViKL4BNNwycUIGSZ8eOSTofLb/ku+qrzVDqWXKpgkSlqmhPulROR2VNmP7T4oag55VRjRMXsOKzrq4Jgthsb+6taouGNimRmGlQRp8jx7B4L0gNv1WHbrdZkY4ji8rJAvyhTXlRhgZITIHpa27PKqgvrk3ntKMf0M5VAKoFUAk+7BLab0wNeMfoEJ4ITiZOHrk/0MlxHe4Zxc4ITAEXDXyoyD/5LAE+e+cUEzBjmUsqGqjyx8lCvQkl9ygxm8OuGm00IDU3eP9qWdjaOh0KIDh3wQCLEKbwTPyAKyBGAdwIb5EFXWJJH7p1ty3c2mCz+hLHRZzYMuMGM7vXrqfsQqJkESbqXn3swMh/cev6gL6HFyTpPzVdcNilAtT92TCyrFqlLK6CyiNbQ4VDlGTOTWjdpkp2FGDnOOg5bEkNTFVqBH2N3l/yRMe3S6m4xyckiIum+8iG6UQQwkDmUTc55irMMcEWB8GkmM3FXhhsrhZKgUCuGvRQX3kzwRrvlKzuNlERLUmX2lpQC3pi9Wot7h4Y6NmwIU8tCZJM5YOs/hcWqOlj3TEZIKm6DPzbxqkGeZijIEJNcuF0dHZzAUYaIqytIxpWc0CPFcAIP8kr3LgIydBxiRwfTGsIKwPj2dh1MJLO3piKg1jRHWLwNdCV4bDImlTLtPDZeVFwCtzsla+ZOc70725s5y1dtldU1Ok/W4BDjrceqCylVIxVuXHjYFFcWvj72wiRnPr+9KRhg3kRJ3LTpsXkec289MxbIR71AqSemcoxL3Eshdv3H+MANsQP2dlrrjfHVNs/cFHjfsX69cUlnsijLjcy6LiYJwdSS/kN0wUeayI1UVecdGR6JkC6pqVFrCLGwHKCysqyuTviD7LC1JeLa68X5xXltsTPwsHtKJl46Dv2qzUl4Zepwz4mM24HW1igoL07/Ds1VpZ9DW5sRkFJQbwCxSbLJF+DTHFmBbjM83LpunZVjnMBmkfQxyJNHQ1siQZ+x6iArYRTDw75GfshLPX68XMS8IfoALM23rEj4OTt13OV3ajyaSFi7Qp8xpZIEVweuQF9zBNSFSYfkdYf4AqEcMH8yvxP7XmhOeqUSSCWQSmBLEgiGKUxKh6nQYGaSE5JYyekzZqxdt5a2p2Qsxmlb39JgqUtltYxUcZjTTNw1tFBU/kCjdCoujDqSQDO6ndYCUz1i3KXT7UwwFBopUIZhtBDVF0K5uYAx+tLll44NpZgAkNsspxTU6EWh0TQeD7AMXLseqYL+3GnaTgtXLMSzqil8pCSqEdhGxFdlpaOvRdJtkahGeTySjhreFMderD3GQlewrKVlKKCDsYDDs8JkrmtkeKS/uyfo2/JsRytvScBpWiqBVAKpBJ4mCWw3DEypWaIJD1BzhqFigIOmtsQuGTpDb0CsNhmtFsoAYrW0QJijDkxKYo+N6S1uhC3DcFzsKPfjwADNGwGkPX+CPQDzOEI5b8FnLinYw8JXM53JBrawLl3rqcGuT67ICJnYicCJ2dPycjDJ9KkNeEJFPFFJ2A+LJQMnMOtCs3MZqbQu2X8LxuCWiiYKM7JFt6fmjCTIOYDh5CwcHlTZ1KRSB/molPFgMwKKA3uSrRpZO6teOb5wXhJmAHLc2PEYprJvY2hgAmYCvAEgE0doCM92GaZHX1xOTk1jI0nKDFxFhjEm4shcg69RXBoRnX5EgVVth8wxzwoxVtgAhECZAKmTp6EVATeGCFLmmUDQMfE8YZ41p7axMYaOBwBs7eWmiGix2Z6y0trYvnYtU1pgAWey3bHaNd/KTIwBJ6LEoRZjBxZREfAJS5h3r16tNEqIb1wzdCEEB2zClJ0tkkyEbVyAjTEy9AajZLSLl1VFBKKZIdrc7MmIlxM2+DWJALAJsweAPeUnZLDliSuoQ0R0d7e9R3RK0gCccCKby00Q+5YugF+kPVmRJP65zaPnXLfZUvYwVArbPoPosKVencwBcQ1wpMtP/oiEZaugmsGTLp28RI8ATvck4N5eUJ46z5G0iRTa9GPRu6LoQEDV+OmpC7WwsDYZEnFxg75mDcgHk3HfL9BWKT8cXY5vP/STwsKNEftJL7J0OWBJSDuZxfAWoliwil30XSoyVxE8tzp2MvMi0VsjOjfend4bmDEBZOEDySROA2/Q66ttaPB0sLVVrxA04R2pgvy9QRXpewrKZvzkKWVCDlqn+VAuHgTZh+3oyAqodpOEkEg3qRR/broZHsIa4NFRYdJIaU5YgpF0b1+D2IeH/VS9SnV5SKSx/5hH2OK6bkXSK5VAKoFUAsk87UYxUDtjTkVPcNyez9nzweWP9A/3g8SUGO8s2MlqA4OrVq2JPlJ6CVZkmDh7zUiyg3JSg6yeGzmnNE55dNGj7lGmS+ltkJjKAjIjfKUP3TNqdJ2CKMiJLApUmcystqd2pTLPyNmLlBuIF8cRWtOxGMMGTGuGesnqJYggJScdGGt0L4OKUGaUVWETLDVaGAz38u4qIqcaUaY/eYapazcQb5iGTDav7uvpXb5sCTWrlq6+3rFx3o2w6ExO+j9kss45K9j99EolkEoglcCOksB2w8DUqOEjNaolwBXtGW7EKLoShBNHn/GpT1jLRGJMpJeVpRMjYItwYiK/zIiLlI5AMUBEM7Cq4Bcy1neQgAGu2NQkHjg4hOEBQ+rEj0cvKx4vANgN/oy1AyABM3AL2YoftuIl8TKFnHGONynOXySB4k6SQ+ivnD5dGxu46evGhiftZR5kYIqCGUt4Y042FjGIT4AKOhtvEq5Yr0A0ueIoHMFQ7+OQVRBawlXABskVpMe1jm2GEPvJXxBRwrYsDDCM6qtdglVkDoKPOvI/8b7iO8LwhEAUDPLBhHAsVVjXKkDdW0uwkF21ZOCRUyQwkKxDCPBYYoLuosFjxkMUa3Z2OJKqvt7qTECfwQ4iApkSJElQtn3auFdZ8hK97zivgUntQvyxnUFj9YeAnWB1y6KGYbGwj1rkh5/fPAWWfAKc4rTMdKgCnrcVGehuJkVxnU0UsRtTLXbpMIOgocBVb0uLPhP6T9KT0dzs8kLNQeBHo8ILSryO8etmOeNXLysKxFc/B19DipjwBGbriiE9cRHEFK0LYpeY9EC8hXtNJmTrZjfdV/gVuGJf3dSxA9RM3o4nSICLPm1IFV5iQi1QjsSTfdGQDQSBRnNVBihJL9r4ZhOaoVO5kn4VeJJ5U4f3a4p8SgyzAAlj8WW539ivkt4benISpyDd5V4K+Yd7vxHFYy1Jdw33yVcoHWOBYMJ5aD7QG3+PyUKDUHzT1mUIGqKFlCR0MFL2GZzJElFOiITunVzh3SXzaxufJsF74T7JFvOkn6kEUgmkEthMAiw8TOhspKjzhwf78gqLwLyFixba85hpG3Rme3Y2JAhGPmf3PeY/8GCySKU8Ylcm0jwsmgCqzG5YMWoqzN9lZ69tWisl2NNEGbJE7DXYaamt6uSMiBfQBZjRh3VVzTeLlOBq7lmTv5EyjK2uOK2MpszSEYTAoyNXjeKl5Z8YI6mXWsaMGqPFZB/ViweUkQJfaVop7tlTS5TxDBiDxPhx35vsV4qIIUd7W5jWZ8FldjNj+szuPua3zeF5JQWZ3LGwOmxk3PQo7tIrlUAqgVQCO0YC2w0DP+XsJ5HSAfcKsEyCdujiZGyfYCGL/cBI8ZiJ/1koMsDwlLO0pQrYBoYruBbtPyWmOtlDmHWBnbi42bDIM4MRUOimQfmWKG23NPtvg0MsTmlFBbyIgQBlE1i+3ep4HCFmjyn1htw4j9WeYHZHC8IJgc8ZcuAVJBY7e/0/9u4DQLKi2hv4hO6Znp6cZzYH2CVHMYAgKD5FBbMIRswBFRAMiIIYwKeCiqigItFAUlEEySIoSs4Lm9PkHHtmema+X90L+yG7y4P3dlGgr+tw+966p06dqjqn/nVOVYlzhjkNEaBTFppBZYAD+o02vnoKESFMyIqjJZCncYAnbuKwWB/KziFaErDrYcARheWLQWfUpWTgA3hLJPi33XO6mv2GpcM24BpYNEDZqFh5YshDLrLUFI1UOBLBObA2Ao0bp889yUkgJ4GcBHISeH5IYIPFZlas1QnmJlrxwb5AdOO2uE/FM+AjL9lxr7seulMCZogplAD65UT1oUBl5pit8ZbhY1Bi5AmdskQeesICxt/63E187xMA2F8LqeBMljRe3wvQMmphuUsU8eS5XLxyQB2QzIcsR1mwWgKhOXVBVnYWZRmxtp5z4e64447lZeUrVq6AVLEaw2z8SA9C22ELWbnIV5EBYJxDxcw03qBxXPkEe/Ia7O+1jyKuuKcF0qVLw7Jkl1IrlQ9HR8aqistK8sNMaO7KSSAngZwE/l0SeM5gYJqatg1erGjjB6oZ8BDDaf8bmAcWsuqPLgawhHcOdXXR+JuUaX7w6T6Da/oZzlMG88O92dcnD1YBn8yMhbJcrwwhw2aJMnzl8gryi71/T5+hZ8q/iKuyZNJ0b/C8RW40eXGB4m2TmT5T+puTjwKyrECmowzLbIEmbGxqinDAXfmSA7GwiMH/LF46coSKRo4lFr6NDigK096Pu7s35hZqDbG4ZhOUJZpcMHYI0dGT0Xw573XSjlOFxiZ5BfYsGUtOT1XUVAs7M5UtcnhgaDCdKsGHXByspV050AsDlpjHHG6coycKZfbC/mry9aFhhCfichVtk+mfqTw3SeQ59HBz7WFzRXim8nmm9DeXb+55TgI5CeQk8EwlEGNgCNPlW1ZMQG/sFx2bHBPkY9kS0/CSHV9yx4P/LDEYyBMIFTY1iF2svmKLIdgYsrKDMCqjA3aCr0yJUQGQ6S34ijj46jkwGb91w9DIDiiFJKV3D3l6zhIha4whd7mwTaKdBUKDqUCpsVPsnpWYU9dDCNkYCQP4YYgXLVrkK+cbx5DY5ygjJdpZXsA5ZmSHN6+gYvdgM64w7FJAlJ0xKQFOAjDOC55eGNgBkzzSrW0d2bBCy3qrqZI8m5+Elc/PVPi59DkJ5CSQk8CWlUAc4bhlaW4Vatxu9CzFDO5SxwH3RoreEkqLJGERD+liGMVmiByeW4WJp0GUVWCcsIoZGziyKywTg8MqBPZcUZAzYwBKefs0SP6fkrBzljqb1g3nRcX7P0XbdP2fiD6Nj9m/sE5VmePgZxuAV1YKmyYZNhIBhxUzwIwxgQgFszhcDTLh0rOvvLimjQ0ONpcVOoj7G2QYiTRMkdi2ujZseIYyE+4Ju40Cu24E4C9A7rmdOqxTCm8TYVbFQnEQmtOeFbelpgTxqu9NZh3yFfAWLb610pR4OdVDg8xdOQnkJJCTQE4CLwwJxLab5mcPmIzYcnnI8O267a53L72bGWJxPHExE+CoxNyw4C675pWfLE4MXyFSIwew0HOJ43hpNANojA54Q8RDCUBWmNZzhtJfZGXEVsaoWDLi91cWXLUQKf8t2ycXptYnliIzXuKiZcQQ49bnEGltdS0eREcziyCrTL3i1PXTcl8/IWq5yBE4x4ZSuI95A5WtMcYJmg5AxoAcvZJAWdDHm7J4LqXnmMFGWFc8NfLCaCy5UuYkkJPAf6gEnjN+YK5UfkPKnQ4NE43W8UYbApvw9DMEpvJzjo31trXRs04S+HfJW+7BJWh6GFvRGfemXTEPuoedq6INIZiKYHtsHwWhbSbmdkvxb5sfZyBB45hh/FhBC4MqREc/vpZyS2X0JDoy4tdVQIdU9bW0ZHt6bIYUNliKfNEkYEes2ABzCretWgW1SmDOXP2ytbET33SGKO4nUY5/SkbIpB3wZ7SLuDkGJn9qcIDX13E7Q/39HMLJyawjmBDPTzpmKezPVFZT3dvenldcVFFXOzlqxWkBpy5qWEIK/i63WXQcdLCpjPGsCAYBXhomhDFHtM1VqMrclZNATgI5CeQk8LyWAKOjfDHadD8d7EDYEJERcTDQHjvuccdDdyQKQ6Q0EGgX5Hg9sARQaGxl4ilaK3hBRIA22koqBW3Ct5LxskqwevVqqBgKBX2lATKZVBYcnmQfjTHgTAZLAj9dKPvWh54zT/76CiL1BAVZGI9AnrAoCqyVb+N8Y0f0w4887AkwjI7iuHFJxhGNguxiD3C86hhUjjG/V0rtL97w0NbRYaofhfGprN2rq6tqRL31D/Sua2stToUjFSVzWENZusy9iYPydEluZ2i1lrtyEshJ4N8lgeeO/yo+vITujjx41K5JRTgz4CgRR45y6e622LW6sdGO0M6b+XcJ1CZMMWOMBN4ceuTmnzfffM8dd8B2DCdLw0YF/vket/KiXEKA7eRlc2AmsKKpqcTuU8KPo9jjrSqigLqjSy4/+9nPjj/++CUPPsjMBy9rdBKSDasMIAQht6xY8c1vfvNrX/ualboMOPkwqzzG7KVtjZ+KySgqHk3INoxIpqeXLVt29NFHX3bZZVqCiXAU4pEKEz0Y7ePNrjtn6KKLLvrNBRf0d3fLiGkPTSg6NIhk2teuNZlilLC5fANN7NlS6/E9QqR0Tu/m0uee5ySQk0BOAjkJPG8kwGy5YsuiUPH5wMwNcwcA37PkntiEMRAwJJ9n7KrlegVxJXMBn3GosxtAFzU22l8fgqnMkNixYKbz8rxl4ObPnw/rIui5ZPCtV57HWzR7HgPXYDejKXjuZU8AYNYKA75yM3fuXDT5b8NMcbR3BvaamprM0UvACMYVhILnULQ0EhuiuI8dufPmzZM1cG5II7EssM0/HG++pXToe+KvMgqihvB96LncpfccllYQNytXrsReSSK3HjiWeu5vTgI5Cfx7JPCc8QM7DyZoUlOw8Y7QBQVLH3roxhtvZIooVlqVJaDNd9ppp2233dbZoVt7z6fNVVcWHzbdNfdrayUuyomJK6644phjjjnxxBMXLlzIumAV0nMqsqgpZmZzdLbU8xAMHG3a7CCZ1Q8/zNrt9tKXQuNM3ZbKYtN0oj0/lHdqaOiss85q6ej45Cc/ySntdFzWm7HHgL/k88ADD/zsF7+wd8dXvvIVO0upUBY0TGg7vcZRW5sJhw7fxgI0FT0xIbG1wXffffdFv7k4L1n4jsPeqVUoISydN1lgEWmiuGjSRsLDQxf88qLPHvd5u3T97W9/227R4qnspK08Vj30kF09Xrz//sYcisN4b7pQ0QIwMqyORiGGF04/Xvfww8YEixcv3twnuec5CeQkkJNATgLPAwlEK28eM52W/ipR2Go/zNtOvHT3l952/22FqbCr88T4pN0fWSUAmJEHO40KmHuXxIYHfLNcrDzD7llJFoTVE70MZK5bt44ZMl3rPrZHTfVNbZ1tsbPXKCjYx8e3jwZT5RJT9opJQp+FivE24pA5fmJEKi7avW+R9QoOlx4OlxiURUpiuBqExq3n/kqAN/RRcM8DbG0w17FXLDgOJYaf8eave6ciolxTXYnJvl5nJHfm5U9VVFUODY+yqulUmk0f6QvScD5wYd5mzyB8HjSVXBFyEshJ4D9fAs8ZDBwAsAW0dn4eG3Oeqq2P/nzttSeccAIt7DR37uyaqipa22Tm4W972/EnnLBg3jwGyvMn/lUf8Z46ApjcPJ2/z6gK5cUgmaC1NBkww6Sdjv3sGRhwRE25GdBoNwvHCCiFsFu7IodjeJ5JHs+Y/xCRPRjOjM1mP/GJT5h8vfOee5g6dneT2T5j+v8q4Q3ShnV5csFdRle92DvS9ITKYyAFjTGBbkwHsJ1EgRNT3bUzZ1o4Lb26nhoPu2Xa4SwcvLwpRr2F7dnmMC0SYV2bb/ID19dWG0mUVVaNDg1Ojk8UlaQKgod4WoCYRVuVZeVmr4sTBYNDo4119cOGIEUprB577LEPPPTQkmXLCApoF2uA5oayPLEVFabTGaHUso/SdK5de/LJJ99w000rli3bOD0isTyfWIJnugvUE7/N3eckkJNATgI5Cfy7JBBh4McyD3gyP2BgJn7XnXa446G7jUZGs2ERrxnYosKEuV6vWlrWx5iWzXUDKJoNtzAYFZaOKXTjlRl8lxlh9y4eVG9BUzTbuwDPsGW00QX7BXyC0O5j16vEOPEQRoVUmXso2lexGxYDvALe8t/6FhG8wave4gQbQqYBdVPAbJonsKvhAaPskziln7zTckQWVxKjw/JKLEeFZd9lKr0NLGUXTHx0HPHwUBR0XZjHyjPRSlc4lWhZs3ZycLwpOrXYufBFeZsehzwm4tx/chLISSAnga0pgU3ii/9NhnQiTcct5khYs5s0o0lCqjY/le4bsO9Rws3YhN178/OTxflFqfzS8tb2Tn8t6xwdmwgJCpPuAYkN/2I+YIYAGyLMgwBklZ0Yz+ZNdQ70OmvosHe9++o/XXXDdTdcctnlJ5741RlNzb+89NITv3qyjNgmmY5nJ9EPUKmkdGBoRBbymshOeTjpaWHSz8BAYXJi0u5Kien8wqyPi0vCbsKJIgmsKA032PY3UdTS1oHtUISiVP/gsLcoDY1kPFS8zPhEYbKor7dPvqmyimxm/N677i0pSO60w85DXb2FJaXVjc19A4PFJem8VMmExDzD09Ojjk2anna8nqConr6+IMbCwhFzpYWFYxMTQyZZhTkBjUxdSemKVWvyksWyzkulsTeaGQ9F87Mg4cnw6Nj4BOGXmnkdty+jydYC88QBJt7/4MPpsoq84hJpQkVYouNvoigwn0qHhbP5hf556xOl8zGZPCalotTg8Cj6EqNAkh2d3fnpMlkUFhWHv2XlEoxkxoiuIJEsTpfJXLWtWrN2dHyisroG2WRxieOEM/1D5RXV+VP5yXR5Ij+xdOlyFT13/oK88YlEUXFpeUWqsgq3akNJUCDY9s7uzHgWbz19A1h146dMizSqkYyzgPOKUo64BWO7u3t33n7n6dHx/Emh8lPZTLaotKK7vXtiZGxidCK/qOSjH/zoueec99C991VX1+VPFwyN2Ed66u//uH3GrNkTOKyoVGSLlmSkRAqLDSJSm/4ODA3njWcrqqrzgmB9m0mlS6+9/saddtmVJDUTMlT72lhIXxo+DxVXXDwo4M1+4OC6ZmytWH7AxhvDY3LY8Oo5evNMdcczLeYzpZ9L/8KUgBH5f9S1oRbAFdeGn8+UyQ0fPunmmdLZXPoNZP+PfG6O/pZ6/u/ik/52xJ7cgT1lgQ/NpYOFjzzyCPTIZWrME0/vEqDVs47PNTQCU2NUGe8dZYDk8iQOOJISduWARQQQRTluId5Cnj53AjDs6pUL/pSdGGZuW4ga1kXK8xgAg6N+YgwF4zHOZGQBV3PfhmRgs1fwqsSuGNOCsujEABtlOcaYVmKfKA6e0UEEw8j6G5cdBXKID16SII6Ixo+ycESL/ZYXhO8T7G2zzTZkBXL7G5faK9uJbajHJ934dpPXhmTIujb83GTif+PDDYzl+NwitZCT51OLcYN8nnSzua82JPuPap8xVxt43sDkVr3ZYhg4zCyWlYUZv74+Dk9aEt8Ua2ZwsLq+XhBsD+8ZNyn3Y2HhYF9f++rVMxYs6F6/XoEFBplyXL58eWHACZvGBpb+xss+k8VO3ElMTE2uXLWK13fR4sV77bXX7nvu4e+xX/j8ueeea5ejS397+UgmTMdyBpZUVaEpfqi3s6OyqZGFYFSSVZVBj9sWOJEIZ+RUVo5mRmlnKfn3EOerzE+XtLW2IuLk4bVr1nhiISjVPnPhQt4/YKa7o6OqqYk7N2U1aUUFH2aiKCnA12ZXVTadsiN0Z6fdjzs7OiansvycdqueHBoeI5Bo+ehQT49NjYnC5l5YEiBd5nlBQW1d3frly1mR0tpaEmNWywkwP79l5Uo897S1Ldxxx4nxMcYmb3rK25KqSjtFVtSGZTZdba2IFFVXjfb3l9WGWmB+GDmFuvL3v+/q7d5ll13sy1VTV4egaQHmkFTLKysFIZtUlh1OympqxibGh62JLQzHFxWWl7GReVNhbnt8JOx6nRkeqmxsbJg5o9+pgJUVgpCBX/cVjQ3kMDJk7W2/MxBkzQVrt8ls3jQ7WlyS4hrPnwoWkWcWt1YUYSOcuxANI0yGO3/QEMAWzdqRTT/cZ6cmNZUmK5HSaXXHV+wQrHicYS2xAhoTyI4ECFwNKkJzUxPpyaKyoWEsk5kYGjJfjW37hIwPDjp94q2HH77tokUt69alqwU1lzz44IPKu91222kzfML9gwOmwWsbG4Z7evAsi0GtqKJibHysuqkpm53QqlvXryOo8oZ6nueO3u6wi0l52OcjbDTtsMSuTmmyI8OpdInmSiAk09PWSoy4VQpzGfjMXTkJ5CSQk0BOAs85CcQDR8bLhXlanVEDHcFgABhcZOIBYK5XCYBJl2RMiQSMF5vIWICCMKFXQouB5Iceesg9e8SaMX/WzSKCFD8tIr5iU5h7doohfvjhh4FVRk3WwCfKlvsCuoCl9F7JLgwqEjYkKXUjR3YHb2yQlNzLTD9q2EABVy42Gp1gr6P9Nbz1EymvfIs4ruDemAhM60M4HOcGGPJFGfOIQPKSuWeOIXDU4h2qJZAdljxRLvRzTuDnXMvPMZyTwPNMAlssFpqihCcdmk73UfZ//OMfban/spe9bOddd7ctE6nRfbDiVb/97T//+c9Xv/rVL33pSz2kiL/1rW/Ria43velNpiFLy0MgzcZXbHVocxek4QI/JNt+++1Rti2WvXxBF2gk/lYC+1EAZuecdpqJyYMPPhiA+dmZZ65bs/ad73zndttvTx2jOdDVdfPNN//1r39lZpB6+ctfvuPuu7Nnne3t9QHwZL9/xg/e/e53NzY38ZL+4Y9/+Mtf/kLpf+ADH6ifMxsbve1tP/zhD6X/4Ac/2DRzpqwtDHro3nvP+fnPX/KSl+y/3ysEIrMHu+y0iwjd9cuWKaCFqQzGT37yExgeqYNfdxBzMmubbbjRmR/mraKhgSHBXm9rq5W0ZLWN04NKS2HF0047jQ+ZoF71qlcp4PhYhpeU7xC2F1Xc29/38JKH/3LWT2ID+brXve5FL3pRWZpjtWLt6tU45xEmvSuvvNIsss9ftPfeFtDee++9pg9evt9+tmVmD80CtK9fd8EFFzBs73nPe4pSxUO9PT/68Y/233//F7/4xTj8221/99XhYOROOwGoAclHtvacc8654447zAeD2TvssMPee+9NpF5pBsQSr4VW8HAekfkRL5ydEB0kuHTpUnCc8OF2k8oXXnjho0uXvv71r3/LW96iZhn+2vq64d4wqWxZUX9nx6233nrLLbfgxChBRhibs+22Ir3NJtxz373EQZ5eLVmy5Mc//jGhvfWtb33ta19rZ5IQnl1YeOmll5r/JkAreI0vjEJ+//vfD40G4SNL8gw24ZOnxNqVwxyvvfba2267zWS8TUFe8YpXvOa1r22eNcuJU3f+7W+XXHKJ0qnHm6+9VunMdBheNM+bh59Lfv1rXzkMUkYHHnjg7ho85Oscr2hBu0mHuKHm/uYkkJNATgI5CTyHJGDkgFtmi+nnzATnhkeH2ejMeMbh9NbZVpSXmIeNsHGhUYT03roof1DQxa5JAL6iM7Nh5oOPPggxsiPBOkTnDhgqMHmsEqBrTIUCwLlgwQIQ1KJcP0FuCbz1FXsnmNm3aBqJsa3IxnHUYYK4v9+4AquGW7Gn1xPUkMUGZBsnds8aegV7YwbuZc2NndhQD+UlfZwd0O4TpUAQZR/ix9tqxx1FC4PRqaqoHB8blQbkHg3z1ANTYS8X0VlTqcJkWXHYufop/MDo566cBHISyElga0tgi2HgwGh0cJHIz+nRUfv+feMb34DWzvr5L7gF6WWYCsz73ve+9/e///2AAw6gkX/4ne989atfZSfMmAIYP/3Fz199wKsu/91lmyyz9C77/bI3vHPUrolJXrdYj0PYtmXwoXwp5T333JMhcYwAT+D3v/99H77xjW98/YEH3nXXXeOZMXjMZGq6pua8s86yXzE1PT45ETvmmusbP//5z3/0yCPry8t5XCH54798wq677orDQw899OFHH+E3LywoPOOMMyD5HffYA8Z+9NFHL7v8MmzYkZjFqq6pOemkk6744x/22GMP9oZtkDWc1rVmzUxQbWTknPPO/fRRn/HceU6MInj5kj33lBF7Q0SmCYa7u5UO/4zQV7/xDZL57ne/+7a3ve2a66/HZKKg8E9/+hN0d9BBB0nDqPC2w3Ic4GTLBI6Ojy2cN99k8Lnnn/fNr3/j6M9/4azvfx9LfUMD5APvnX/h+Szk6d/+LjmAcOdeeP7Rn/qMMkKJigxw3nfffaeccorlu+94xzsc6rtqyZJvf/vbRGf3ZtMH9z34ADoW0I5G096MomS33347G6k4KvrcC84vK0kznHhTTIBTeoaQYY7qsMBDzmcP/TSI4P/ndrfi6Ac/+MF3vvOdwZHh6sqqy373WzSVSH3HYwLtxIzAqaeeytiTFbC6ZOmj1lzZUNqUhCcS9PT1cilD+Mcdd9yvf/3rzp7u5oZGmBloJwHmmX+e9H5z6SVM+2577XXVb37zrve8m1Rrq6rvueeeK678I66+/tWTbd8lfKBj7VqNwXTAyFhGvb9ozxdd/vvfnfb97115xR9eddBBv/vd7z760Y8qixiAs372U/8wY4H6Zz7zmbbVq82b3PL3v9VUVjkd47rrrvvySV+97OLf7LXni1SxIcvo0JD14fLKXTkJ5CSQk0BOAs8tCVD7LjyHMUle2I8zoLu8YI7TZUYWNX29nYLIylmlCW/D6fQcqgyZCGHjDSYAyPSTudxu3nZ3PnhnjCQRhDYhTO5TCWIULY3Z2EA52kmLYUUETJVS7sii5gYwNtsuJRPjQzicOTZYkgwIjz3DYDPD5wkbiv+YrM99yHxDxdyzxh7u4+ygaBRc8U+5MLJYRYTV9hNvBgDeyjTOfe3a9fjs7mwvCtPXyXiQs255KznYm0MBDQMqqitm1TVWFpWOToymk5v2eTy32kOO25wEchJ4jkrA2H7LXBSlKFCXWF/qld91KJP5wx/+4Aic8upqz8HU3/zmNzf/7RbAGOK6//77v/DFL3CW0t22IzJxePnFl77yla/cHDdhajMo1STbgD5dTGVnJ/Ne/NKXetLV0mL3KaDxc5/7HJ8eXBSnhNPWrF8nvSdCXj/2sY/ZohkilcuPTz8dkGOHQK/ujq62dS0/OO17rAtYfv3VV1PVbAObUVdd88/bb995993mzJ171x133n33PXu9+MVV1dUXXHihhUHCvz/8kY9YwvPTn/2su6enfubMs88++/d//MMXjvvcm9/8ZkzaApKX9fWHHMJCTA4OXnPNNaYGeCZ5CHuHh22A8fOzzuJuLaurY0ukj/d5wrww4LA6KPIx7rvvvozW2T/+cVdb2xFHHNHd3/ff3/624wtEODM0w729nz322OO++IXGpqZLLr20s73jgQcffNWBBwK6po0FEr/3ve89//zzF8yZl0oUKd2N19/45yuv4s8Uow5gFxcm+eSxRyZc3+yZbSKHMqMYQJw/09aOAyPDiH/6M5+xCdVXTvjyT370Y1HcUOIjjz5qNoE5/PjHP068XL6geENtHcRr2bYqI3lZoMy5Gk8hGzVoDHJhgEXIT01Omsswmrj66qtPPPmrHN0P3HsfJutrap2oFNjj4Y9Y+sxRR33mmKN33Gmnc887b9369bffccfrD3od+dfV1+OT7No7OhSZExgQNadgmmCgt8/EBCTMR83DHMo4Pg7hFyeS4gW0IW7b66+9btHCbVQ0wV71xytvu/VvXPo4f/iee8jtvPPO40Ne8uBDI2PjN//tb+CuGhHdYMJlt912cwiTocbA4MBPf3LWzTfe9Mtf/pLvWt199rOfhbrP+tGPW7q727u6VPonPvoRcyIGLgYQDlvUtMgkd+UkkJNATgI5CTznJMCEMW3YNonrL5AJ5cKWNLzJWWbL4MS0LDcpqweL8vf6xPjEMMmH7qFHlmLBvAXL1i1zLz3cG7tVkWIfPXGhLDEiTP2iRYvEvsG3KDNY0vuQAYJUpQzAu69PRrKASL01I48TuWBA1oZJ7LKJYIkZX0gVKejdTD3LaNIc/24kRkehQHHJTGGzWQj60PgkPktJYizBwGGgEnaHtMQtLHhmZH2IAULwEzMsLJ4lUAplQRAalwVmZO1b6XNXTgI5CeQk8O+SwBbzA1OUTAFVSNmBEPMXLpw7cyasYtpyzqJFVJ51s0CCcp544olUKlTGfgAedHEMnv/rv/7roNe/PpsNuwRvfFGgdnZCxI5YVCflPjwyJvD12GOOmTd3Lu180003ccnS0df9+ZoX77uvWFanEz380ENIsUwXX3zxnXfeSSmPDA1bXrt26dKvf/3rtDAYs88++0jD8AAwnIHnnH8uHy+ISF+vWbe2s7eH1/ewdxx67i9/uerRR+fNny+M9tbb/g7DW2tK0e+3334iXa+65s8usbJHH/vZg/7rNV/4whdg8szAINDFAEC2xdXVjA+g2NLR9va3v33nXXaZtB6mqOjw97zHubhCpovjwCR2yDqfyFKyVUAdzy07hzg6lt0e+alP/eznP1dSAee9XV1KxEl7/kUXfuKjH/v0pz89f9ttRZ6zoFYiladLAdfGeXOnR0YVh4gy2XH4zSRuw4wZznBSX+hkJ7N8sKyv7R2tXwVb2U4oUZhxMGyZjEkKP6FKqE/QeLqqKjMwQHRrli0zZ1GSSvG4vvRlLzPHYeKAJDu6uwQ2w7ru2fvYD0wyuOL1jdN4FcSSl8cMM65u+HhvvO56wdvMKiF/6qjPeIhnM9OV9fUnn3zyL84/733vfg8/cE1T08TwsM8Z157+PjjfYt2psbHgHy5JM+S/+MUvQFAVqs18+rjjYGk8mFAgnJGBAZEChg4c+xC4+GQB0sri2CTOZMLUeplwrALPN/315u+fdvqHP/zhgpKS3rY2dY3V8rLyeBBDPgqlKVZWVIo70MbIB/OaOif2fvu8nHu8p7W1pr5WMi50DE9OZGPioc3nlgSr4NyVk0BOAjkJPKckQOebyH0iyywC6wD4lVYGAAlw5udNxfDVVDKQyRgxT14xEKyPxEzAwgULV65eyaYwCkwwAOmVGz8l9pf9NQiREdsB08YQmm2CVA2x+JMNDORinMCEIQhesuPy8ty4CwWMsUeMGo4lcHE2yB1lttVbYwGWS2J00ETcW+nNWUOqFlVtMIg4ly9+sGGEYLyHcxQ8dPmW5WWsHQqJAcRh8hiHY6miphodWZSngpN8MjO5Ys2K+rLquYvC8rHclZNATgI5Cfy7JLDF5uFMbI5HU4NWjdCMA319IIGln1ClFTBU8A3XX88F94bXvg4koD0p04npSSsw7QKxfs0a+0hRkcDw5gTBDGQdoiOEODoJwCJSKWc0N1hYe9znP8fNCwOvWrvGFCkkA3uwCk6+8Vc86j333wdE1c6YwVpAmDbiEiDdMxBcfwAw9W2rCqaCyuaXRtYSWQ/dCKN1mk6qNP2DM3842Nc7c85sq4IXLtpWcarraotL0yG8J5n42Cc/wZB8/ZRvvuu97xF8i7i9nQZ6egQ2WxBjxpVp6V2/3klIfJ5KCE9aQooZNg8mtACYzeDlJrcghMgFLXde4mqbN7a1nXnmmRIoS6WTexxEmDfd0NyUN5kdy07Aad874wf86p8//ovzt1vc0901Hj2/+757+0eGw+royL3MHK5rayktLtnpRS8ybTzc32/mVnYgKPiHeQaP6AhZvg8ueTiTnVBYYd/ePvTIEpuE9Q70CwPOOPdwfCyZKq5qbHjvEe9XiSYIXrr33rYKG+zpwT2QjwKkrRTMHm7j9cCQqsJm7VkVCZYA1TW4Hs8cc1CbNXjZK16hjMx/Ip1OJYOnFHuVdbW3/OWmb3/vdDBeSQl8qL9PvLSyc/BKM2vunMzIcP/QoCBtIQBtnR2ahDKaZwHXhSVzwApm5qxWavzIWl2HMHVT0f39TL5hgZ2szBd4RSb4NFkD7ZtW+MhHPiILu5FZ6eRbsjXQUZvkGXbwSqU0OZLhfLYGWHnFERgHmH0wcWAmxd5j/T09Eqei0YkEJZErWDGRzV05CeQkkJNATgLPIQmYuqTGMRz/dROfjWRYAk+yLDE+ZEpgRTbIQ2ngQIaGcXH5ycQYpXR1h52xmAxpWDr2Fz5kUqVkMtCXzAW4eu5D89pQrgTQr899K1MjimAxoyhrb33lFQpgLdTq2zhfTl30Y6yLAgOEIKiMMj7xgILn8vWc9UTZ+MQrgDYsFos2o1YcKTmBJZY7g4g9DyVGUBoG0V8ZeYuyD51IjCs3yGLGMMMr0ogZsKCaNHJXTgI5CeQk8O+SwBbDwFxz9C8FN0Kf2iqwrs6CVStF/vGPfwTlWFkpNrWjp+uLX/yin7b8Fz275667/+RnZ++5yy52pfJ5sePauwOq2eQlgpSBcbEugGKIki0qFB3EeDh6h0uQ7bnwvPOhVs5Jup5Cp3BBZa7CXXfa+dDDD1+/YkXDrFm2zmIkhKfSvhy/hSVO68kfenx7CdOfibzgZJYR7yJNDVzxGMe62xQuvQ8dsS7ma/lIcdLT1fXaN7wB4F+2YgX8Ywkre2OzCCfNjkUrdnhEMVNdV2fnRN7F+po62z7tuM02lomiZlNlGzVLYA8K2ZEbM8NgeKJovYODRx155E677GKqVby0xbHAVVk6jWcvOb25T3v7+61BlWB0cFDRcG5GFsOOboh/xpaSVNlai40ZRYYtE8VEDQwPAsboxHXnL/HGwckSM3We+OmhJdx8p4wcu+jnHbfdZr3ugm23EfRuOCCL8tpa/PO4potTPrTvs2QKol5kDTpaGoQ3LcRPr3yFE2+VlCkVxC5yy5pknHe3tkpgea0VyyYROLrFjdmFSzLPTXKgoIwuGUGzHioRxowv9n7JS+2nZZKFbNvXrmXOmV4QV1CWWuMEhoc9rGpokB7bGIbw7eM1FJ1wiJTAry996UsSkyoiqnjGvHl9PT1WREPmAV1XV9t8225qcHUiv2D7RYtJabCrS+lqGxqMCZRl5ZrVhxxyyKePPFIBPbEfdXVjI/lkbAMenQutCLkrJ4GcBHISyEngOSSBCAM/xq9TD9zFO2OZ2Wco2R0z6ZAkTc/4etvc0GxUYETBsgRDEF0mnVlS87/AocN7WXy2w0OGWEpmkV1jehBBgUH0oalkn6AcDP3oKOvvQzmi4CtW1Vt0jIjY3BAFVl7uc299LgEj6CsW0xOmjSXFHlvmiRGFt3ApQ8yM4h+IhV1dBjmRazctpbe+xSfeXJhEwXgp/hYdPCDLpHJ3b7/d9lKiII3nvvXXfcwkOgi6RqdHFTB35SSQk0BOAv8uCWwxDEzN0a1Ou4UHQljs5KQIYaUSxGvh6P133HHl1X9616GHgSKwpShc2vOWW2895WvfoBY//JEP77jjjn+8/HL6Gh1mojBCX/SmgwVCVC28ZOf96AwAmARK4aoV8+sr2Tl1yVyjxPAYkDkwPGRVKjVNF4uAhXXDHtSJBBgmYglxFFasXMF8Aa7W9PI6xjod/ywNPyz451yi0YGB1evWAjnAvLLR4LYIBmBgJ/aJqWBsXEzX2pUrubiLEgnHwLIlnMCMAbBtjpRRtD4mQKkoxKhpzhybJ9sKm1k64kMftA319ddfH3bGsDoo2l/RyTkMnrIwIYwTAb7hDW8YkeXQkHuyQt9xwUqkyGZkQ4x0tOOXQjFLBbBoVdVFF13E4NmO2JO4YTml0LrfEOBUXq6wsSVzGENDbb3JYDQlK458lbLguoefJVY64uUmxb/4bVnEaNNzK73JUVi4wtoh2VcstsJa1ou+0rkXuI5tMuQbr2lsHBwYKCktRYQHFQMkpUL5jRG3hXUcMyYyOa640bEMKx7ga2HhnyKa5OBbM8kTo6OMNLlhgANWhSoslgwp1HWorMjxbjBhMsJzWN1gBUuYj53SwpgNB3wIOksGbMuIBFSENP/4618dlLX77rvH6DrIcHyctMVUq5rgWjdfPjBAMmbKs9NTbLkiWPQeipZQGwVHHXXU9ddcC7SriB132dXWWYjDzNgWGtDb3W0tVFwvub85CeQksDUkoOP/R10bykhvuDb8pIXcmynzN1bXdKP7zTG/4cMn3Wwu/TN9voHsk/h8pnSeaXr5xqY5FkJskuhVz2Pb54a+jZ/HTDIxrphP2UkM9T3TfJ9pelWXtQ2JrTHtZ5GXPz4xzg/MBMTYEoeqkgkDBZkD62/vf+h+XMGWSidNjEiNGeLNO9nNMGucTrPC7mMAjHg8AGAvDFoU0JIlIwQF1DYYOw9RNrwBUD0nH2lkEea4h0W/hZOHEfEXk/IysMEVhMw2uSTw13iGq4AP2Y0nKCOCgvQxBfzgGcNujHb8VTq5yzE2uNhDx/AAZUVwQeb+mitf37LekMM98SLiE/aX9QSVJcYV3lCTY1yVG//dXL1sSOnbJ36+ufT/ruc5Pres5DcnT881XV1DdrHm9EST8zfWD2689WTD23hcHX+oJcfptxS3qMXXs9w+H8/2yf/dXLk2pHuW+dwcP/HzmKsNaTYwuVVvthgGxiXW/Z9aDBxPTVGa82bNoqaHursFl8KcNje2ZcSEU3yASSHQ69Yd9dnP3nrbbT8680c04yc+8QnzmhyRcA6nnNYJmgQ4TVFGrlQASVNmURxB7JKJvYs9qeGxtNWQXRajfYk9R0eL5+uj+uXLy4efDRs5uEkVp/gGq+rqRkdGBqMz7kqt1x0Zueqqq6rKKgRId61bF3ck9kZBeANpbQV0/JJJ1rGJCejLT8gHNRGznd3d9lKWtZ2N21tbA/N2q+rpgZ1M5CKViCKCWOn6WbPe//732534it/+DkS3gfDdd9yhl+KQbcC2C3pEKoQz5eezjgyqy27bhMmDPbO5mXVB01fwME4krqypARqdSMyX/pe//AU+ZJhrm5oIlnlkaMcmJ0QFxwURX+XgYjXV3t0J1NXNmmVs4dBjVgpjq9euZTXNAZuJYL241v3deeedZYcNcnCRMMy/x667Kaz60pHsEWmrZFbcHITYbNuAWF3ExOKNsWQJ0SQ98wJUFILQuNEEYWanwwwxhIwsOoSgcm1/zZSSsLECWImxuMgaRjLafeT0009vrLeHV5M0hEwaVmjLS90tfeAB+4HPWrgQQc7b+x96cMH8BTAt045ta4ZxEuy62PiWFgpRhLPJFKc3h206MhmVki5RplIcqguuaYXi8oWxLXXmmo7Hp4gIVpcjHgxZOOF94ZQMUjL62eeVr7QG2GzCq/Z/ha3gtG1jFIkt/FYW2bvPXTkJ5CTwApcAdUfnmG5jR2LtSvvFI7MXlGQgIsVnxKlc+tMNlc4c8CUyH0TEkEFibJyUfgJ7jAijQFa0OvvFnNHqW1toBiMszoZccOgez2otZpVdUKdMkjloxgWT0iiLNLiNo5o9V0B0YEKYU3rJFJCF8rmieWUCV2EVDcj0UC6sTAx0FZMQ5OjGK2aO6MxZ+2mS1+CK6fRQYmwQC+EYOeBBRphhbbdbvB3TjAjZoi+ZK54mZpE9J2pPpMFJPEvOLOKZ8Qpjm0SCM0M1SSmBC/NyxIMEvjKUkkssGVl4iDcUsGFY4kOXLBJ5CWlyV04C/2sJ6Cx6lkauYRt6aZx+xlNImpxLk/NWi9X4vdUXNMg4O31HH5FYA/5fM5D78LkugS2GgeMmKAJWUwtWYmoKJABI1rauFzz8vR/+4C2HvOm1b3oToGVrBS2P83bm7Nn0OsxmY2SwsLO3mxUxjSk8OCAQ2yTaVtfpslabRCuKNe5USYkWrO1S3JotfWqRralRGyZ7aMcjmyGpEqDXT1s3IeinEcZQb28jI2FSs6pKl2BgrHTtaGlhJVgazEssoPoPV13JVNjNSC6iuOFkhiF/anpkcGhsZFQorP2a+7p7qkrLtl2w0M+21WvO/MEZtjI+9C1vfdc7DzvwFfs7sMeHVh1DgCyBDuZIWJx3rlpVM3OmG2uVxTax3g7+5SocHhvDHpPQ1dlJdHBX4Hx01AYXdqSwBTYLFMMqHbiipkYf7mrv2GHxduOjmezYOBtikrisvGKkrx+fAN6vL/ql82xLEkknJBFyaKBTU5DkjIZwJrCdHPHgbJ7VjzzCgHGyg9CDnZ0yrZ8925SBOF67cCgm5+1gb59SW906kRnbbttFw7b4mpwqmM6DEN3g2Rm8jXPmsNIcqq1r1gha7hsccM4Qk2x5MItuMriitMyktSIQPmw/nSHpATzAwxhzX1NZzaCCmnYFI3xCQ1Z8MrQZBDg+oVy9Xd0c8uVV1X72trVffeWfON6NGufMnIWfvMJEZU3tQ/c/UJoqAd233XHHvvZ2GZGViABN3FbkCutgJLHx1gyHs4t51Lu6ahsbVXp1RSUh2/BKZbmMIZhzlWJVlq9Mp8+YP98p0LC0lOZEzOcH5erUqFWrLBcP52xVVRFgHLxgWKN5k7q/RjyOVkKT+Yd7NTNC0zm051AvuSsngZwEXtgSoOJincCW0Q/Gam5iBfKCEgxVqbxsHHzLQJMAa0Ug4JznFDJMRZFSuTRtUL8mpqNLSjYaypIsnnJ9FuQWZy0jrE7mhe0hqH1s+GvkwvwZYGDVc2nYOjrf83ikYdTOMiqav+oaKR/Cum6kjNuAlASCDmm4tAqNBE6GS5WXNGQUQ2XtB5h0saQGRYhIzPDJl6lixTxnKAnTh3JEeccFOy5dthRvqMGu0iASw+bgM4jWYaHAFmMPZXmZjldSDASLPBkOQJYLajH0RVylGDJxI/scOPdtzLMiqDiFwgOWPJQvCpC/Kstkc/FQpJK7/vcSoAc0vw3fa6WaoqYedzo/9Ttv9QKtznPtX9vzRKvWyPUXDVIr3UAhd/NCk8AWw8AER/1pUqFFRjdFpaX777+/EFyhv+UlpUceeSTNrckyaNQ6PQ7cmrakiH1ocywbIwX/YXm5V5YNn/SFL9CYjfPmTQwMIG5bQ5SdK0u9glKcco0NtdRodzRVWWfnieLiK6+8Uozu7BkzrQjlwdToQ8RRsijebdhOyOtWr+5tbZ0xZw6/X0my+OSTT3bIjc5AlfvrSJt0UcrZRVS5vJilmqrq5qYAHXUkq0DFYEv2yKOPcHUyMIizGRy/3lriy+n6vve9D2y2fljUtDJyU1p5A3CWNzTUNzQMdnTAV8ob4Na4JaKDELv5AKYFJGZsSG8oCrKV49oVK/h5HRSoFOyHGYG0yOq+flBZx3bhh5msKK+wafO1V13FzOj21hjbHuxTn/ikLaN4Pp3Z1zh7TsuqVcyV/ai5fDFjGVBJOj138WIc4pzEaA1awClKH/nQh8tKS4FKHNl+WWUpoOpQEdjAlZl2f20uxWwrKfjN1900Y4bCcGhbjLTLjjvJa9e99oJpnc/AUxrLKjSPbBa2Bz4RJxb7dWgnNo7q7u+1j3TX+vX2UfNKuVhc6a2wwpXBhNw9F9Jsfa89ok0xOL/3yI9/Qho4WRqV6IAoG2Vh2DiAZebh16jES//ivHPrauus762bOdNghDvXV6hhWKnNnkDFxE4UDmHySUV9vXW8ELi2ZBDTPHu2t986+WT7ir/+tQcZAfCHEzUiCs4/bLk4+QhM8FBMAS82gsYr5m4wVlpTY617djpPlL5GJheSV01KhELuykkgJ4EXuASMyahW2oNycBOjJiO2F5pYFN+EI+PCprNuVLEnTCQtHQ8qPCQTU5wSmB0OxqigwCsPaWBy8wnD/SzITWXJyyWvwEOeQwnCAUjUPjxphDB/9nyGxo0ETIbn8CE8iWcoXeVyCLOSTJsndovkFlZMYwPJDG+MEJTRh1IGIxLFkcnUQwYOwoyRqldxvkYO6DB8KEijRbnhksVbDGW9de+CATxf0bqC3HyLMXgAJ9hWHKMg6aVhH2OAbfLat9hTEPxjSTKjCPwriM9xhWGcQMXyRSFMrEcL2ZB1Y4iCAp5d8TQHIgouF6XI2cFnobk+v7OI23asBzQnTVd5tUlNlALRCN17q09JqUvqU5ouTSulFqtT6FOa8fNbSrnSPYUEtuRYXEvS2njDYgyszQm+1drEo8KcFt/SlR6KbYY2f3nxr4868tM2EHZkEWcdh+1JJ50U8OHoKPjx/TPPxHRTQ4NTdhvnzU/asr+/n/4NGTB709MrVq5hguwIVVdVx+PKAKDwyLKl/HKWBANjIAe8BD3aypjDmdeRFhYfmzeasTjg+OOPF1Ts2CHI6rDDDtNVkOrq6zn2qGOckeOnmG00PWEG7HcNe8sd83ye6RQgk7bFkegKwdhFyeRvfv1rOx7z8e378pdT9NbZij3m2xyMopWU2vw2t+CHDj30j3/+03GfPe7l++0LHF795z9zZp7wxS9IIKU9pWyLFQRYUuLcAxRIgMUSPsVAChvu6ejAAOCdLinZc489rH4Aut70xjeed+EFb3vrW/mu9fArrvzjd77138ybPl1bU0ML9Le2gevmYrl87dclR4KyYNU2y051Uqi2rg5uUrXDYym7X1/8G7W2/XbbwXgMYb/AvLHMy178ErEmjkEyMCFtDBxy8MG/vOiiy357+ZrVqx1fbMMzVWwxcHlVpfOBO6NzAk0BMMYguqiwwmTS7IWSouCvWpY1NaTNJPMLWdA6QDpayEQI8ardeNBQ29z81re85VcX/+bAVx/4znccqmr+8KcrzzsnnH5UU1E5d84cu3BoY6tWrmyqC8ckWjZsu2/akKCcUcShLb6dt5/7nd0lIvuEax50H+FAvI41+u0Vv9fkFPbGG2/81Kc+ZeXz7rvsamNtwNXqYuMtFer8J1MbZhYMOIBYM42WrZuyEed8xRVXxN+aPTE+cPizqhfaAJ9DzpdeevEOixdx+FO1UxaSWShuXbSFUhYP566cBHISeGFLIEYdgAHlY5cH6gW0gGSoxxeUYChnapxpgGbZLzoWIGQNSYOuDsOGaAmut1LaDsrQltDoebaYfZRGAg+DrdzKF5tlzBxXULyzMSYnC/LHs+PThdO77rjrPQ/cU1FeInwJIwpSXc0U1/iG8QqmORomGX97O3vW7JWrViopk8feMUkkwCz6KnaxKqyye86IKywKTJhiei4liZkUkNLI3hMmRkqfe2goBYd7zvR7whATFMPkc4MiObq89VCT095M+EomLwn8jYGuIRPAgKxLQ1VME9NVlVWr16yWEVHHPHgrd0ZwXdj6yyqrsomx0emp6camBoXp7umUWBaB89EJtr6hrqF15ZrERN6OTYu3cl3lyD/PJaDVaa5xr9cd6AH9Sy/zxORRXHht2OyY53qQh/SGT/z0Nu44ca95nksqV7zNSCBYji11aViPkWIfmPCpKbqYQtQcHVcjiDm2Z7Z7Ou644w4+6PXnnXfeoe867F3vfTd/GgD83ve+11caMQvXZGayqgpCbpw/v2ft2qmREYGyqeh4OlbQxKRNoeUFfvz0nJ+fc9653Mh+vvuwwx079Imjj5aRtr7Shlj5+eJXIS6ROsEStLVp7qIlQD4pRbFa/fLFL3/pG986RSzQ7y69XCQwre1zkNs8JV8xpe8nXU/R62OAMZuhRP0dHR/72Mfauzq//OUv8w2GrlhcPHP+/K985Su8iGCYjBxEzFy5AftB8BNOOOGNrzvYQtY3vPGQr371q+j/6Ec/+uJXv1oVRThDuYrPVJhbVkabflWV2Qq6ThwSIgw+/isbG9esX9c/OACsKm9NfT06r3zF/hPZCSDttttuszP2kUcdRYYlRcUMHgs3c9YsLnT88LVi/vyLLpALsqg1z5nz3e9+tyyV/sutf7Xhk9hsbmRkGWBl5MPEDFeqJyyiyiUEKoM0HNqw5957C1/ncocPnZl06KGHOmkZmwKh7SDN6V3f3CzKXVSz9Mywk5/UBVFY782Q4yFVU8N4P7pymSOytJO2NWuMeIBV+stzUc3BzDtWce3aX5x3niOXqyoquazF1V/8q18f+s53QpgOJcIYmnYgYa3VxWmnnWZ2w3TGd04/Dax1qrP5FxPtdh23aFymhgX2CQ/jiUgVKqoZkMXbbCvO2VfaKkhsKbDTrbaZv4DVP+tnPyUoRdvvVa9SfGHhTLgnZKIN2NIMDH740Ud8S6SGNQLL+ajNMjgm2vSNhdkQtRiBWXPncvVjwNCJIjargkLuykkgJ4EXuATOPvtsikXwDqVB2RqfuYKieIFdbCtLoeAQGtVKFXsSdHsi4S9bQB7ig9gmaWhm1gTMk4yxYzhiifnwWRCbrF0bMophMCYNOVglJ9+ydzG89BCHjKlSKBcQKA2j7C3bKhkAjGfjGXFV6l1BIGF/mUjSkAWT6ifLorASgLKyRsFfFhllD5E1YCAHF/n467kpYDIxaEHKhUKMw8nQ5yyszxkyf7eZtY0BmIykJ3MUfIWCVwYMzDGeCdwIh5n2CZ4RlIvSSa+AagcDEiim4gQ+CgowrIAempiOM3LvCc9zXI9gyXhe2Lsod+Uk8H+RAP3pcy3QFXfMeISmkRsWeqWl6XEuLVlv8kS71bDdaLH+avD+5q4XpgTyp6JWskUKb5rFrkLBDomQiWZi6Om8VHrVkiUwGw1bJFqmtZUupj01UyCQwtUcIRn7NjlUiXbOKyrUPNevXSf9bPE8Wifvmc2ih8MSGvhwOj+vqKJ81fJldfX19jHKy2R5JkMyK0wjk4mgzqC523bLMmCt3N7Fg/39onOZlDyLeyNlXVRRIfqXNpeyCaTMzxd7hHnLlQFg/2UV8IkNPmFPaHCcY8BhSHY5FksESwf/bX09YGMBKjMzng0rln0iJEjwbfuqcERBurpmyo4dmIlO4YOQW9paWYLaujrFHx7oVy7J5Ovsefn6nAkRApUdHWWHsKE4VU0OBJ602eLMhdvaV0mhfMVM6tLuzSKTJ7MqZHr98uX4tO6hbd06uaAmjdXFMPb9990HwhGyrbktavUhCswqIXjuE7HBGAhCjnbkBmLtK+ZnX1sb+hIEMD80FPiprVUpFmz39vfJYu68eV7ZGJrSwQb+aZyGaL13Z7TlBm4VxEZZmZ4+6JcvVO03+cpAZyScqlVcUW7LL6TS1dUcwky7qQHR0YYMqszFHwtnNvOud3ay5xKbVcFDqNO8PPqOKixTBRNh9xEDDp9oVwZT1v3aK9sSXCt4DSLoO24FUdki28ciScYW3SdsPLfCiiVLFmy/vWBpxtvQBG+haNHKLnqTyVcQWVQQaVGREG4CVH3xRKPZAY2hu61NSs8xbJ8XR16YfCFtBSF8dUrs5iGxnb/RWFfzzl05CeQk8HQkMOkIgvLKTR4yqvs/HQrPWpqgRjZ1mR3+4Ac/aOJMFIkpXfEj9Nj/yLwEdKxlOJ/+9Kelp+E3R39TeT7Vs83R+R9ZeiqiT+Md80q1WhpjWtb0Jctr936TngwTZUtt0sbIwGaOQjDvLIDLrh8f/vCHTZ2zOPQtDun2rc0nW6BlFeTnjY6FWelMMD/TN95981hiev5O27R0tPQM9lDvpemijta2mmDlratKKh0LZWTCUtD/Smcelq1UccqFfyaVsSZ80BGmZZIUxE+1rOy+YnSkV0wGOpiSaODuOfwcNwA0Pfc5rmRkcl/IMQrsLLEIZ0MK1vU5ZOsVWCvHhbMXLl29FHuI4EGmmEEWY3jwBE3UECd5PPvWZYChjC7oN2Ybz4G34MGe9Ek6VSRcbmpyostZjOOjqbLS4ZFMRXVVQ3XjyMBg15r2+vKqRbMXVCZKG1INielQnKd5KdEmU271et9krpt/mONz87L537x5CnnGrzRXNzqLyUS+h5/+9KcaqqYr2tHxHJqHJmoKxiJKYMSrU089lQtKO3/Na15z5pln8nX9b9ja6Jun4HOjtFvyQZyv8lIveuIZZ5xx7LHH+qnXbzKbfxefm2Rmcw/zp6eyQyOF02HaYmtcYfxQVvEMtM//zMQTxE3ETBfFCtnOi7bSZTz4Tq0yBTyoaW9pVcloUqpWWI/mSxeXFpROTmTnzJsnCsqcpInN6eykc4cpVolBZecbta1aPW/hNkP9faN9/UWJQGpqZHRyesqeSdPZif7evpqZMyaHhi0nsllUXqJwqLunOFUMS9vhSaNnUuw+MT4wYD8nJ8QrF+iuk7hAppYVK9hdwM/eTZCnk/isN9KSwlxRURF4RukDkNpZebRZsQXGbIYFqJbp+DsyNMiRCJ9nB8OCH9tW2dHSdlCZ/n4IDWYrmswu3nmX7tYWYEkcb0oajuKpqXByr1N/amowRm5Cgrk3YVFcIest2yuoG04mPQ+VGuemFaA1Tmxi9BV8iHly6m5vb5o7V+CxNPnJJMRIgDvvtpv1wKBjAJBm1p0ouGoVIGodj1OLwFEf6jbOOkJfeZWLBzXAUY7c6BRlWfNn2n8bsnWqksSN9Q3F6RK7UvV2dlSWV8i0Z31LTX2dHaLgw2IH6nLAZidNVJTaSWts3ByBnaYw0zR7tpUZBGuefHIsoyqDhPPyMr19mfExRSYry5g0BswUF6fEWtuIS0RAeWmZmi0pLTMBwd2dKEkNqXEzEeMhOJyNt+VYoW2qS1KjwyMFBSN4UN0hDtl+1MkiMxfjwyOanJB25Z0YG7el9tjQMMDsDC6VbhNpwodXWfrB/oGK2vA2VZJSRuwVFhTiE1dmXsZ6eutsx1JWniwukr69o72hrn5sYry2pnZsfKy5sSk/UahBOph6RlOz4Yz2Y490IpQvUrkrJ4GcBF7gEjDLBvc6R5ADkDKM1TiN929RESbgKCaTBxv/VU3hrUCWrfMXAL7173879O3vaG1v233X3Rqbm+745+0dXZ3A28RklqEpMRuaKrFU533vea/VUBaq/OGPf7DRQ0Gi8MMf/BANTNvH0x5bicN4vjI/z3RDaLNBFNOTJOVsJJZq0aJ5y9euGpsO1mp8bJRFmzVjNtd2VUX1upb1ZoRZZ68gTyVlr92w4MwQ1BrDV7U/Z+aclWtWeq68WgIryUwbF/nW3o0+9NPAwxjGvYdMc6iXaJo7WNKyMpYaNXjb2MOgHx3zsJqWZMCw8ZVkMIBXJm09X7ZmGfAgI5lqckybS9Yxe4wp7O0n4xjjbQz7ShYIQsuYl0Ziz1EwJa/lemKMNDaaMTVPNFXVlQMjw9LLMTtq9nsAnzKF3udu/1LPc9dzXQL/OpUfuseGPhipk61YPn1B75OBbqJtmyeCeE888URPhOkZa1n95z5u23pK5BPu+9CHPnTdddcAIC95yV5XXPHHgw56zcMPP2LzWAv8//XvVuQ8Jj2NteguTK094SJAV/z3Mc0TvY+fPCFh7vb/KoEt6Qf+v/LytL/feFrgSS1347YcP3naOfzvE27gbdPTL48TjpNtSBMM6tMbWzxO4D/ov5uT9sbPY6b/VWOGZ/9jx35G8tk43/+EJ0+zwjYWzlN/+D+K7qk/z73NSeC5K4HnkB/Y4AxgMEoDS+AKOAHwMCYzOIMHoAK1AJlAGm4M7Dw3tosTgDpGeJ67AT9iz5tXXKb2L0AH2c1VYv5GCmL6CSrGBhtP/NCPwrzCf4u2nMxOvPu97/3d5Zd/45RTPvGxj5mDNgluAZQNHWJ+rGERO/PJT33qlxdccOq3v/3Zo4/+wvHHf/e//7txxozWdevt+mHzy+HMqEU0W49/ADhvyqT8RH5SvI8Bc37/xIhJ1QfaH1nf3zJZlNefGRRNnC5Oj2cmypKlluE8smpZXgirCpWuQlW0uDO1DwHCln6qa3XKxap58NOCqapeevXrLZwJW2o8Ptc2YliLLKRqz06JjfI91DzciGOCMD20ABJaliZM9aZSQC+YirimIpl7SMAMLxe0tocr7UoybJhA17p4ydDBpEwx45M4jo+HDWNYkkUMjOFwiX2OeXQcWFxTU+fD7HhmoK+fH9jKsvyC6eGxzPwF22Snp4b77PQ1WDiWZ0Z5dl3zdrO3qciryPmBn9gHn+V79bvJHDW/TT9/LPlUrEUe+yVtrGemH1NEfsVjNo0/QpVPJra5fJ+c7n/6vUF5xjcO4Nhuux2qqipsxfKqV72KYox7h5tYnaK3bPmju++2Jz/I7f/8xx577vbOQw+/5NLLzvjB94888hO63fhEJpkoplMFWeov/1P+T/f9JuU5rW/k5RNZECa1FcTox5SZtWnbAgZ1Ey6zbBLEAvdzI40e0sTy1D0VM+cHDhJ5GtdW8AM/jVy3SJJNGvz44VP/3SK5PzWRTfK28Scbkm1o2fHN//h3kx1gY/rP5pOnlvkT3+JqQ3mfyOEmHz4xwYYPn458npjjf879k4qT+5mTQE4CLxwJgBaGQcADsAGfwBt2ELT/gvEKNMJxJwEMY2tDPy2YhH9gHkgDRjKAk8z4BtRxERpc5KFxD7zhRjJDn00L06DKtemB7r98EY+3jMUinQlVByz5rP11fuC1f77KsYijw4NOVl+3bs0pp3wLM6NTdpMsHp+crqooK8hP/PLCC2pqa+trq8XjvP6g13z/9NPbW1o6Ojsa6utGRkdKReuEIePW4jweqIahaRithuFqPPRUHYbLPQOdRWWCsQv7+/pnNs6eUdO0du368cxYYXGhulOP6k6d+sS43F/3wK3qi+MwIUwtJADIaCWwdqINIGtwr9lAzhKjwK/rEwttEFTpKHNnYYDDXJynV/y90njiBrQWaACmxhHR0iOimaGMB9h1A2Oy4J323JogABgPSPmp4cVea6A9brr4Qcf2Iigg6x5XSEnvBhj2ua808qnJhLOeU8VFs+rD6Y9OiBjqHy6Ymi4qLMqbDG2v2P9y13NWAv8fACtCrGceL0t4BQZ7DCxr65vw8YTZuU09f7reoPjbstKwDkJ8n/kikSAXXHCRTMHgO2+/65e//PWuO+/yzsMPq6yoFqNXJj6zPN3R3v7wQ4+MDNtar3j33XajUpoaGyvKy/52661WVdjttST12E5a4nBdiYLkFuEz6IyNyxsgcJiGfEx3B63yuAQf/+9GDx5/kfvvFpLAFpvn2EL85MjkJJCTQE4COQnkJPC8kgDsAUK44JwwtEok9t9/f4f2gQ2wBPwAqwAkHGuwTbwNAVzhJy9cLAgfxkQkQwf24KYDSFDwxM+nIa+NB1TxJOFj+NePaFwb/3xW//Z2d/X19s+ZPev0077T1ztUUZm++Ne/ueHG61720n0gz2ku4PzplpZ1xoi2/dhl5x0nxp1QMGG1VCqV6Opor6osLUoknLBbWGBbrK3HeZDxFPeN/4TZaPXGTZ3P3TqcGVYRw2NhsRXkWVFWAaaqIDU4Phm2m4IngV43IKJ6VF+agb+eq01tAAqNq5uf1hNENBLOW+kB4JDv1JSJEhdMKw20DOjKAhHxyWgiDpRqMFJ6JRyavxdZsFYaw3ukAGaO3Bi+Iut5TIoH2OSLlNzLUnoeoEVxsaYYU1AB2HOvsQmNxptS+9ZD9zEAromwtw9N8ViOxHvlOSIItnd0OVXBlT85VVqcsrukK0JJQaS567klgdgV8TjP8aRZ9IuCiTTI4/8NoE4riNM/8a9etAlMuDFK3PBE+5MDak/6K8PpgrGRsWIRIMXppY8s4zW945933vWPOwuLkhece/6NN/7l52f/tLGpcXpiamJsymatQwPDDiupra4rmC5ob+2oKq9CYf2a9elUaaLAwa4FNuCxBW4iP5Hv5+SmsOsGrjbG9pvjM6iLgPmf+NdPrsjwJMzcxddjqhhL0RePPc39Z+tJ4HHJb70ccpRzEshJICeBnARyEngBS8BCzRi4AgwgipES8ANgACSAh1MALr300r///e8kFHBCFA0LOUgMZkBT0oBMMRG4Ih5oeQsI+bsBJ//fBWxUFjycVro+u38HB/qMCDvaWy0JttnFzjvuBOKCwbCuTR0B4HD6et7U/LmzhgaH21paIS/bN9ibanpyyre2dUgmC8OmD1ub84LCaW6b6fxscAZjoTCbF7aMUjuq1d/S0hKIFD6EVFVTfuFjezXHdRcDYNUESapZDUCYMdgJwYKgalYCUBl29anoAGncqF/peXfBbA+1Bz/j7DSqGDmLHYCBY6wrAYTJM4wNm2O518zcSwzrwrGI4AFljS3+hP8WfsaJ57LzUGKcaF2OaURZ7j6UKbcwgs5SwoOUQDjQKyVw7ith2ISAiOwklq80+IknemJ0jWGJEcnkhXXFuev5IAEQ1b+CgBjjf1O6a/QsAn8RdvVTUR/7ayIpvH7afyMhRYgxohIBx7CVj31wnDlSOG17nII8R4kU5iedy/Xb3/7+uj9fl59XeNUfr7r99jsnx6a0QcrS6Vw9nT0T2cmK0or8wkRjXWNdbcPQ4BB/NW0aSEznZScAUSQ5CJ8Rh3FZNs3n46WOeH+CHOQkJMK/gI7/dWVKRCj3Z6tLIOcH3uoizmWQk0BOAjkJ5CTwQpYAbBNjVzgBKgA/SAOKcGY7MOxUpBe/+MWcfo5tByG8hRlAKdhDMoDBX6AC4nWDDvcvIjAGap74JL5x/+Qrdj08+emTfm+YCo+W+UXOiiel2No/nUWHCYe3v+bAV3e1tb/6la+65867RoeGRQvaVzKZSlkY3Nw8Y82qdSVFhU7gy2bGHMAb3o5PzZ0liLfcjonjo9byBRFtrUulBf9McjovMZWXDVVo8J1n/6rgwO/o7ayfaQvIBjhwdHC0pKxUJU4lp4tL7FwZlnlz7MOcUKXKUvUArar3RNtQ0XGr8JMH2Cuh0UBpjJwhYRS4cFFAXL1rIYA3hAmahqw7OtxDsH5K454T2OdwuOx8C2NrJKKgfQWRakggrht0pJRASsAYKSzFLQ0bYCo2PJQYD3iGq2F1qFgLlBIdZHEuATDc0dUFD0sv06KyZHkIPy3NjNlkcxDPilZUWFxWXV2ZqkQqgORkGdCRu55TEogdlVG1bawoAGDhzyES47F3RZup4EixPYNyh762qWs8O5nMSxQkkvmJvPGxqXRZ+cT01A7b7bjXS15aV1cdRdxPL1ux8qBkQXVtNQBakEzs/qK98guSrR1CHjqbmutb2jsKE8WVNRYUhG6FUrq8NEDVaZuoTiYTTye+5v9ztjk+I+z7/5M9dlcA/eIxmssDjMlss99v9G3uwRaSQE4DbSFB5sjkJJCTQE4COQnkJLApCYAZMANIADDAD+AHDAALwbcOWre60vFI9jryKTgBEQHAwIwbH8I2kAYfGpQSB7vG7jVgA65ADaLYVJ7/i2dWa4bgXg7FZ/lvMlW2aPvFrR3dnb19dY0zl69eMzKWbZw5q7Ao/d3v/+CrJ339oUeXOs5z7333GR2fvPGvtySKS2/9xz+TxSU77Lxjoig9ODSc5zzByqqty/l04eR04bRw68K87HQBd2fYGDra9iczNgY0qhdoc3g4HKwQVwo0CBWrZdUEqXoY15rGIKIYOOSnVbPApJo1BLc2WMOwslfNei59HJPMT6sNaACe+1bVeqWRoOZDdHylacld+0FE44mXB4PTgghQ5r/1iQQBfE452zGcJgUAg81AuIdoouCvt7JDB9b1HBsSIIisewxzCEvpr0v7BM5jD7ZkCgIMIwIqeyWBHGXkQznGlH1rO64AuQvCya656/khgQj9CsOYFBmRzZtw+POE2bnIGxz7hJ/4d1osxTP6x0G7qX9T9o4qyBubtAndZLKk4MDXvrqsovzBJQ960mNqKW86XVZaVlVOn5z6ne98/ZunCIhuntWcnZro7e9p72rnsr77vrvx/IY3viFRXKBfj2TGQkb5OriOXvDMmAyR1Jv+98Syb7iPFMiU3P0LswabAsCbevb8aC//KaXY6n7geNbo6Rd3a4Py/zR+dLYX1PVCK+8zrdycfJ6pxHLpcxL4z5cAlAIJAL0wCWyAYYDEQ7GpYAzYIBaaH/iQQw6RBl6CE2AGeEP6P/3pTzx4ixcv3mabbeJjcsEh0CJ2wcEVKCC+GSFEFu9frMwTbOwT1U3Y68ngjzdCiLGbZ/Uvd+6lv//Tm9/45qM/96VTv/2DlraWhhlzDzzokL6hzA/POqero2unPffafqfdTv/hWR/6wIe+d+bZl1x+5T133bH9jjt/8StfrmlsEJlsZd1IZjJVZA+brcd5iBC3aRj5ZGMpBQCsxpLponRwvQ50iUkWZmkGwamCVTXVJRVhbS0AGcdLqyPYEiAMDtJMRiWqZVWs+tQ45AnfCjPWAMBmz7UQn8C68WSHdiKZxIhAm27gahhYSpASqEbQh4CreRMJPJeLFoKCHB1WhBq86iFwjmF+ZlloVFLiE0GJUdCiUOYcjql5HruLNVEFhG8lQwozyPqQxxtvyhXtv1uYLimWSwyAJ7J2BSuSRtFShSWSTYxmcFhZFBY5567niwQm6RJLEQDgyTy4LnSUCNXRKE9QOP/b0sbhwxt/PVk8OWYxQkGmIPT69AGvP+CITx7hcOA9991TC8xLZN/23re9/u2vv+WWW47/8nHzFm/72S8d1zy/+ZOfO/Kcc87Z59X76Bc2e3v7+95+8KEHj+vThXm9Y73TJVVUq7xSiZTTvzfO9CmebI7PJ+7Gv+FzM5fE5Sd9gnuqxCoXtxEYDi71xxYQxxscBKUs7RYQ5gYGcjdB+FN9fVtVEP9pmPM/jZ+tKvwc8ZwEchLISeD5KoHn0NlIHHFqIcYY0K8bQzTAwHMXRBHfSANjwBtQCpxzyimnWC96wAEHuP/jH/9o51KfAFRXXHGFods999zzs5/9zCfQjuebrOX8EJkYDbIefx1WsT5+PdH1wDthiGXo98wGfY+T+j/+12n3ExPTZen8s392UVdX97x5c9/61jcK/eZT/Pa3TxsZGf3Up45sbKwcGspWlifOOPMch9Fut93i/fZ7RVk6b2gkrzSdl3HiTnRi0f+Rk6f+HPQztCZr2RUm88bUan725gdvnrN4xvKW5aOToxYAD/cN5U8nasrDBs61TbWr1qyEM018+KnuYg8wEAjrxoHKsfvUbIhaFnsMWBqCA5DeQpvSaxvAp08kwB7YzLcMMHPwxqAXZkZc+m0WbmOX7HgNMHSKsvT8t94CtIC0C9zVAr3lv4WfoeXYYcuJLTEcizIADFTHAFjj5EOWRpPDPx40TgSll1JLlhIbnojtdrYxx3I6VdTf29ff12Pno0SyQD/NTk7PnDM7MZ3saGmd6M9Ul5TNa5w1r35W7mykp25vW/utprXJLFTrpp9Ha1bt2OTtE5dZmIOCgced6pU3FfmBJyMMzI3Kzfb/Fc4GmpvDihsSPM0bO2ANZ4dLEiWJvET/eL8uUFJQ8ujqR2+66SbzMkJsqiuqk3nJ5euX/+hHPzKH+PEjPto93F1XWtfS2/KLX/xCq160aNGrXvEqaXozvVWpqt7R3pqSmpHpsGyhsqgy+9gGe0+Tnc0m45He+J1HNLAikJGg8aKpvMLgd44wcH4BheyyOsVG90T9WD1tZq+suB71a4aAfjjjjDOOPfZYP/XKjfP15JnW+yaJbO2HjFd2aKQwNmFbIbMwfiir2GIYmMqmVQndse7Z0VGbdVglQ9CjY2OldbWtq1bR5sUihdrbNTsqXmNVW/F85GOTLuXlQ909NL6dJfTAQnshmjPMZOJpcn81SnJg71Xg1OQk4nYqFNMTEovhmZ7ORGfrFTs9QoLx8VD9RgYmtoPmD9/K1Gl+DlGg1stqa8cHB/HATlQ3NXnL7Mgwzig2NpMT2URRUWZoyMyt+dvxaLKTCQmU/aM+zJtG3MZNLRTfpH4mgzVzsCHN2Jis5SIBQ8XA2GsCV+PR8rBEKmVny0K7pESjIgVJlJUNdnaSZChUMjkmwCmdniCmTKa8piZjN0i0olVh8sUzu+dvIBKVNPATnaWhqCGkyQkHJnenkA9XoO8iKxR8ng0bNcZisSFAXJxRTOLcT6OMqAYD/ahrFfI2RDt/uEHcx2rZhhi2LlVSy30kC7UTjcbwTAITIyPhSXSRbZB/lHvXunV1DQ0jQ0PsuqnowHxhIQox8+jEQrOhJK7G7IYSXUGM6XQ2OivCQCGM/BIJDEhfolFlMqGA0TkiUsrLT9WNN5uW8L9MjY4GDjFfUKAg7lVKICxBJExbZ4ZyRdUXV2jM+ZP+Sk+gRgOqKb+01LabWkv4VtxadESEbwstwWppqaqr0/aopMCqADYRaAMDysgLgObw4KC9ShwcSc7FVVValw81ib6eHgQV0Amh7hGZGB2NBYKOG1dohPxFg4NlVVXTkQSUl6weK4j7oqLBri59zeHxgzpdVZXc7SJqHVg26jIb5BCkRPikEf3FLVJJ3LKCmodyjY7a6HODSPEfyaxAhQaGyXN6WgIPiyRGLh7iq9O46qWLulUsRu/dxH/d+Cp+nvubk8AzlcBTYOBnSupZSx+3/A3N/o477vjJT34C4YATBxxwwNve9jYWkA3Cz6OPPnr33XevXLmSbYV5LBjeY489aAAUXLred77zHecDUxR+biD45IJwyRjGPdbJwngoxsCPaT+qLAJ1hq75hXnLVrWs7xq0O4weTBHa0LesLJ1KOY1pat26FrsvScwkW8daU1Mlz/5+ex0NVlY674fvesC60draat92d3eyBk7pHBpy+s7EnDnzLBFtb++0fTJq/jJcFRX2Sa6kHQEoOyU7t4ShjrYQtli6UBp6kSqxsFQujkOyjLS7uzeTGZFmLGy/THVMz5gxix1Yv74VV3yrvsJVfX0tyvjp6uqJ0+MctYaGOvm2tbW4x797fDY0NKEsZUdHm+NVTEpkMuPSL1o0v6UFwhzwhJKTYzJZXF5eWlNd3dvVDeAVsjQJI+WhHfbYtmO4dVX7solktn+kT0xjRUm5HWiLE6XA55KlD/HvkLY6otUpZGMAtQCRUqQsiOGHV0AmO+h0UylVKIVJD3ui3pl+MckeqnGfgNPz588HXyVGh1o2rkBHm4GiYVEmlV83mI/8fDmyFG48RMfniEgfh2cDupqWlAyKNC4U/IR15Y5Pn8hCYnBXSpAbHdZBGhxKjJqIa6+gZS2znz0qqxCzsGbViqGBwVSxsdZoSbpYP503f+HQ6EhvR9/4yGh5Ybo8mWquql/QOGdzGBgzsn6Ki9C8/R+TPQWFZ+fV84xPWDeIPcbAMUqLoXDB9Gg2M5zN3P/IgxW15fmpRP8Q3dBXlExpbxqh5mE6T/vUqLRPzVXdaZ+aLoIb2qdkWjj3rMavfcZzLjFe0Eo990m8NkQjRFn7hywQkRJxzRIFl08kJvx46kca2ZmsqaoO7dMkkVyQwkzcH7fddltcuVCQEgX0XeVllVKal3SvkUugh1q6oo/43CucxxR8YjWEUAuf62guZYw7JjaUxRPd3yd+Ctmg8JcseXTS4pO8wvJEyT47vQT8nR6bTBYkw3ZcsEXUA0xhBgwcGmt0GyPk8HMTF94IROfdgIH93ES6Jzz6j2qfT2ZmavJZwMABQW2Ry1g8wJjBwbEI2FCaYWRfUSHCBu5tnjevp7VVA5oxf74FTIqqqclXKwFOmNwAYAyUw0TutHbp+US0I6Ib9WpPCbZXI6Pz5CINvIE+FEcL6xsIxqoZTehLf6hsaAiEhoa0SEQ0xoBIAQ/HEiQTaa25paW2oQHkA4C7ooP1DD60mERp6Xi0WEtLqm5oyJvIyrc/muYsr6hAB8LHvOy0aekDIEQ55sE9ow1D2rYxQiZxpYYOAfkAwHB+RweWcFJaX9+9dm2VwyG7u2tmzOhcu9YEavvq1Y1z5w50dIROCP6NjYUsQiZhVCRTABhqCuWNAEY26rFee0hQcRp5OTTDT8KRqcQoAFcB2WazY4K0ImiqV0NEQaTAcAQgfV6iHp24EO0AKdMCCSA6lEkbWYFM8HaUqVLgx3YZqiMGwHJRO+DlY5olEpF7GeEhFEH/jFRV45w5A52d+KydOVOmSk074Fap/d8/pPAPPsqxNMJjRCHBcHc3pQM/Dw0MMO9QtMhCNHGGGcaY9goTFjiJ2NDYfOWVuMP4BrexrDwsKCrCayJsAZjQnsYi9RRKjeGofeLtSVdQcNGAhmBx2L92be3s2STmWz+VFBge6umxW0hIoPoSiVRlZWCmt5cQDCrj+QVSVS5w1IBC9XkbtxBt102qqkr7d/QngsSOBwcvyiVkAR6XlJhnwTlxyVdBQuVG9ShlqAVzQ/b3R1bpstnyujo/SQFPqEkuC6VQa1giFl+Fv8oW1WmgMDbGeNDp/a2tflL3IQsVHVW9CnIf5j5k6h8+NTPMhTmrEGXnE93TpBLKihB6RDTL4GfuykkgJ4ENErjzzju///3v633UmoGa57qkrqcTcVAYNf7973/nrDPqEgsdd9UN3z6NG6ojaI+NLz1URp7TBHQAvV5UkuwbGJ6YMnVVkp2aHJ809iqaTpQOjYkV6ysoLh8Mux9TcenCVEVmMsE3OzCcTZXVdPT2GxXX1dXbmKp/2LzcELNMDbd2DZaUpGsamobGpod7wFcqtnB4gAsxwYNYXFYxNDbR3T1g5rO0qhEDNFIyzaMDOQ+3dA6Ym2xoqO/s7Jo1a0E6XRqOTho2LZuHTFFROj9RUF1dlZks6GjrHR1XimRmyOA1WV5ekZcs6xkca293NFG2srKO+eoe6J87d97g6Agom19UPj01tr6DyyhZVl6XTFetbeuRsqSifhQP4xykVbazWr6mF2OTBanM+LjpgKBhU+X5Rellq9oMSUlpapRuzyMtswcinfMKE319XanyYieq9PUPzGyaXVfZaKhNoxYmgrc/2M1oOzR6kTwpS+MNFW0orN7p6oceeiioyqIia8VVjRG8VxAyd5bPfWU87cPtttvOz4cfftiI3MBaO4EWVKLdqozLVSik6hU97KG3vgIVNCGvDDCMwaSUdbwiPdLZMH8KTbgah1LKCwMapM+lxBUKYAATbFwnJa40SMwwuBj2lexYaiaPyQDOG+pq+A/Gx0bRHBjsmzVvrqH/yFhGdmhmJ7IDowNVxWVFvF+567kpgSc6gZWAKjH7U5YqnTF7xkRBdnXb6u6BnkSqaHgsU1KVHp0ca543c+ma5VJqnxr86Hjsu0rml4dJnPW9670KA+DuztlzZwsoaOvpEJZs+e7IeEb7RKFpVlN+UWFLd1tPpkdvKposSpelE8VFeWUFveP97T2Ptc/Jwun+oR7TT33D4Zzq6dL8idHJNT3rNNTymvLS+vLbH7pT55oYmxwa7dHFugd79YjVXWu16rHJscKiQh1neGK0oqSivLpyXWeLnpIqTU0UTj7askx3aJjZsLZ3/XD+aGtnu86FfscQHTVLoZauXi6cJVGYmMqnIibSibziVEkmO6SneF5WUba8baW+6cqmpu585O7iouL8qcLsWHYoMzyUHSozzjKmgnKzNt0Li1IeX6pihBar8Xg2MwzYcteWksAWw8BhuJ+0R3kAnGHg7qy8dev4iOjHoLvb22lqgMywOAaoYTwdzTLap1+DNpr3bQBs2aAlE1bIjI3BM4bXI93d5Sa/I+8TMwk2KLwkPqGp4TAf0q0Ut4deeF4JQg8N+ck9GMboXmcycoEhbOGfV1gA9P7Xf/0X3vSuK6+8sm7GjO7W1sMPP5we//jHP/7qV7/aaANO5kmbGp+oFIMVeRFj9CIjk1fI8s65j8clPnTjnIrR/n4ScC837IX7CKz26PmzZgHAZSKOUik30EWAT5lMTW0tG1U/c6aJLFqAi5ilISV+OZ9j0nQZnyZcKheHJCivh6FcEeBRuOB0BeCjPSfi7MIksHzBzsgziRMVZFLAh2azzWiFBsSpa2ASQeXgFaTIuAoj0BiQrSyAmqEhBYkzIlirf7hfQ9YGIxHqBnvUN6xWABmCytiLxD4yMMBd7kNF4Cwd6ewM8okolCM7OGhHy+He3rH+fgOFRTvs4MOYgbh+8enCGwnjBN6jmLQkiC4IPGoqqkOCgPkKCzFpwsVgxBMtAFTzJG4kwVGpODAtErEfG0jTRLPZcSdwcixHPkwCi2QWELj5C/f42cSVn68WQgPIy3OOHB06HTVpgFbjDOkLgoNU8+baNb1JpEYT5K/sODdBwDPsJ7VIaQLAnsQSTtfVaQz33nHHXXfd9drXvtbsu7JISYK6z71/+YtxjzNUKNA3velNL3nJS+y5KccQCCBNVC48x0IbjxY4lNTWmsBkBn7985+LAtprr70qmppwq4J8hQ3yQD+SSjjYA3uuEL9QWPjQXXedfvrpbMMJJ50UCkUa0UyH6pCL9u1Dl5+xnGFg7U2jN7NDzqF5mLoiZFfEXriJxvfxDSI+j+9zf3MSeMFKgAKhAHfYYQe2z1jQGEvXACSCDjFPWlrK/UsPsAi69v9SSv9/uLoBD4f4F8YTQZ01ViET49Mjw6OThVWZccnsRWXCt3xsKn9oONM/At6Ujk2CcyXFZeXTiZK+YMbB5ETBdGJ8mhlNpytrhfSN9E0Pj7N6qbxkanxkrLayLl1Z0toKLNkuODWaoXPy+Y2T6crR7HT/YGZozERtEgXbJUHRhMD3yn9UUGyVabKrf7isuiFVXtbTN9rR3c9XnEgKAxpxjmdlZbVco1hgXqayzAR8O9HU1FBZK463B4UQCVRSMpgxhV1YWdc0WZBUChEtJn7zkiap81JlFbWNNWxmZ99QZJdSw0NhtW1pVf3U9GRH2DzZaDh/fML0elG6tKygOD0ynu0fHhXoO81pXpAsTpvdK00migcGB50PrLJGJ4YtiOQgrQAy+7rHJ0LMMCVJ13nrUpVwo4s5MLY2KFIFMxtmPvjogxQpWxnsVxj+sAZ16ggipcDDuGiad73brmlewbp+siASeOsrpgQe9i2aikCZI2ug70MQ2mWcgxNAFFlNzhPUvMWGiowTu4fJvWJiMBO3RmYI+vVQXtLH2YnV94lSIIiyD/HjbXW0c7XyolNVUQkDS8OIcBr09w9wZuHQ+cCpwmRZcVr6qc1M0CCeu54zEqBGIg0TsFve5FBmcHhyZDAzOFk4xb3VPdDHi1lTW7O+a/1Y3phGW1RaNFEw4b6+IbTPjv6O0LpSoi0Kuoe6y2rLUpWp7v7u9t52bxNFkWMjL7+mvkaade3rtHBKchKCzQyVpkor64WHdIdgisJpCXpHen1V3VSdLcwOjIWN3ML4M8zw5BeXFdc01QyNDsl6bcdavUBD7c/0z5w3c3hiuLu3O+6nBVPxMLXYVyj0DPdMFk5OGRIK7UhOVdRXJMuS61eFLesKUgU+p5YbZjckS5Mt3S2jU6PQb2YiRE+k0ikp5YVPybDR3teerk6P54/XN9U/sOSBkYmRvGT+eGayNFmSTBRNOd1parLYuD4aJm2pWPHnTCv69zG6xTCwBgQDuwRAioDtXrHihz/8oUDogkTAJyy6GUGThQcccMCBBx4oNJr213oET3IqarXgsejSIIeCAg9BL9o2YOkI7vpr4M5WG3ono0hO+NDYWqZgjM9jCGekDukxAIEPyMfAndKNRu1446CzmKavp5vLEUsPPfJocSJgYJZpYnj4lFNO+cc//tHZ0ys4LQ4/K8/Lk9J0JqslIzyI3qDrdSdB19q0TqsIfgY/GOQQ3YRAYiBtgwcvgoW+rRFuHXk1Q3R35G1btmzZGccc0xoFh++999677rrrfvvtJ9ZXcJhczIQp7/TICAVRHDlpZSd6LOSiaOPjw/39yJZWVprpDXA0kh5mYicnIcBqnAue+DC+CENFmJH2MC5RqAJCjmUVJrynhIDwLWOSJfZVEKQSScAJAFjaUnJ4OPT/yLkaizqw5AoxsOFC3EU+nqHv71hvr3Vr7KLnmoQRHkVmPnv//fd3QOKiHXcUO4Cgtz4PDcn4xZyIyOdstrK2lhy80nhS0ekgNc3NmX4TCIGa2T515ysG21eh1OoognZB9+GcoOjHAXF6IfZGArJ9jLGo7kBBn4eWpoVElasgChjzL+WTrpi4gmWiPTANtL53+ula0cEHHzxz4ULO24JoyBLaQOSI5pTWaAIRDdhDHHLATk6a/rj71ltNHzpMI/Bs3r27W5DP/gceKK2lYt867TTpTdZoAJ/+9KevvPrqiijgR9DOnnvuqcEQOzHWVFXF5VIKVcbrKuy+ZGJCBITYtfLGxu+cfPKJX/uaAx9Nz3tCzgqrXnWoUEafGDCZ5nBP+HYHFQM/OqoXXHzxxa9//eu9FZiuUuIah3VDWQiK9EzxaD/mF+JL0RKJTNR6PYgnIDBJ5j4PmuFfL2z/64Pcr5wEXnASACe++MUvgjo6iLlXsc1ADm2gbxrY6dQACaG42WAQt4SMAsrVMcMSnXBRG7aSEg7t4J/8sfEpwcBVNfW6uEDl4ZHRolTp0MiooaeBI8YAm4GhsLcw6GslaG1tHWs+NjHV3cuzO1WUCioiOzVdW9+USpf09k8MDFnaWgRAT04XVFRWlFdWmtvr7elTolSamc2TgFFIpYtGM9nefm7kSRnRD0bMcxuq+wcnunp6Yc7CZFF2bDpRVFxaXkXtdPUM9fQZgxZ5nj+RLS0vqaqpFHw9PDou9+ISc7AFcfqauvJ163qCKZ6W16gBQ11Dc0lJ8Xg2r7O7J1FUwkYOjRirJCur6woSBc7lVYp43MKhXVlVpVIYIKHRJWWVfF5mlFMlxbIrLJ6g3EHByYJJ8Yuqyba0Aq37ewZbWztJjDc0wxUWLbKlEkVjkh4ZIu4nVbzdvO3ufPDOGEkSBW2vSRgsSUC9M8XSMG0o44FYINJ4pa6UChjXiBvA2HBFStMlPoTDDWz4ciWjfiVDnDxhWk8AZm0sJosZH5J2jCW0Q/dxdlA0Cq74p1zwg1VEli9f7ifeVKK3Mo1zX7t2PT67O9uLgjMiaXSBjXXLW8cnJovC+Is9maqorphV12hPrNGJ0bT5iNz1nJFAPIkWrVH9V55FCVpnOJg3vLZl7WTRlD3iuIUHxgfLayo0MGBvzZq1YYKvqKC0qkw7NPArTqdGMqPcxdnJ0D4f6+9Ndb1DfZ09ndOGS8WFE2PZRIkZs9JUeUl7V0dbd3toVYhbOJtKeD40NjwwOpiZHNPFRGg7LtjbqvpqMcmGWdn8ycyoaJSS2qY6f8emxtu6O8uqy7X/5ESRlC78GIqLKU2keFiyE5MCLiyUrNXs17QKrCiorAlzRhr5/G3ma/BrW9bJDjg3tjEyqqiqrGmsNXJb37LeVNTkeDYT1mWUVNRWPtbfR4dlJKBR2ZVFf78j6u9KN1kwZbKguqKypqIulV8yOe2Y8zERd0Txr9L1K5b8Ro9zD/7PEthiGJg+DcyovAiaWiVy7rnndgsuNRouzNf66cqRzPiPfvJj7cNCpre//e2JwoKxzKiwqyS34ujI4NBgSXFKK7B2kaIMKxiFzTAYQkZHRmLLof1rH5RvnJH70igZYMZEhc4WWMiP/X6G3gwJXU9rAy18ntarVDfU5yUthA6mV9Y6KtufGR/zb3h0BLdAe3FlhYdumK+isnLIAWVhwxY4hByZE15ZsabwQIy6ozAn/QeTOnO8LFmmcAIOZMQcOA6PiGqE/mYyArDf+c533nzzzUCnxg5eX3v9dToMmXzjG99QkKYZM8TEyrSkskLELQqxVIEip2rohxKHQnkcIRaylTsjJLjXcAngD5AGepGp2VYUIw+wIuBEAoG+JBmWa4JARhwjIz4n1fhh6NnRz4CUsMeFPjgoU8L0FxFrUkNG/pkakDgCz1gyfQ03ox+eRzeKTAiDPT2/+tWv7n/4YdMGftptoDxdMjgymkomrM246KKLdt5lFwVUR5qjSexwHxY5M7jjmf4wlV5ZX18etv0cdZ+Ynrrwlxd95jNH7brrLnz4mlOq2pAoeL7LUiGQ27cBdcFdUaAvk6zFwK75gK5WCsnzfo+M4A3LJBOajVcRho/HHJBe8Glv6kI2SB60Hh6unTXrwp/+1OwJHsw4HvO5z+EBiC+pqAhHT0STNRpA7OwVhB8aj6mNMDIby7S1HXrooYcddpitCyBV61dEARgIWvfGBfGWt711qL+PwOtmNO/94hffde99n/rEx9/znvfssuuugwMDmOzpC81AJLnGqYDBDz9hgmKUz1m5XCkLy3t7OaJJzGy/MipseG5eIPLo0un48dYNVgNv3OwRUPfQCjTNcrfddtMO/Ywv8iAWEvAJgmEUpQlF0hiNQsE9DMJR9f6ZbojQvq9Cu8pdOQnkJLCRBB544IHzzz8/hihe6to6KfNHn8AVnlACHupB+vtGXz+NB8FFY/z05PA5KlYuBdG6PlToRYnEkVAFpaXVsqaqh4fDItLobYhIYl/SacsrGIQQlUNj4M1zekN3t1jXSNFPfFIRNAmjz2cL3isRXUE7eVJbW8NimLJUqJh7RZNdbW3RunUIBI8ldUq7osM6ME0gnzQIuqGIQETxuYOD4ZxbPHiFHxQsMKZxDTwIExEzCJihUc2gdnYOy91Pn+PEV6jxCUN92JBeqZUI5JNpT08/gm7i0qFLwbrHkr+0qZQgfZKNyCvoGxhKlDoPJvhIu/rb62bU1DXUW92dGR5XWNjYjjfkg4hMlTqo6IgB9N0smLdg2bpl7nHCX+rDuPgUrCcKGNc79jznBA6D9WwWZSiCR1cjgXVRpq6Nv0WZghaykJ1BvIexPNGXtb+y8AnKasRP9N34BEGDeB+GIURBAeLekrO3ZmM9xEYsT4mJUUZROyn1PK5rDz1p7+xUp+gQe19Pb3NTQ2NT06NLlySSiYlssMkIloYzg0tJMjOVMQyI20Du73NdAlqs+EoDyvyixFTh+NR0FlAUeDFn/gJaxQRNRWUI3dcwhkRSlJbX1jV4+Fj7TBV4qMnNmTt/eCTT3WOxfZGm1ds3oDWacNE+Ozo5ewfLK6r03P6BId2h4fH26W1+QUJf0wdRkKC9o2tkNGzC4rlRiLk9BD3p6urQAgeHRhoamykTLVx7XvLIIz4pMKWfl58022ehRWmZKRssjY0b7KVHBcZM51dW1RQVl3TZQqCrR0apklKzUSg0z5jZ2mYDuT68+QTDbnQZWQ8MChHMwBhWQ0ivv8+ft83SdSvQ6e7ppdxa17eUV1WWlHF3O2ace4YQn6yon/z7ud5Q/vP432IYWNEouWAerCktLdVwqTwPhQvt8aI9KXfNHQbWdjWUL3zhC9aT7LTTTpqpFgyJ8WqyNOLji5NF8VJMQMWlSfFzIlvZ3BykxyOqt2kqkdcL6OKOQ1PKYNgMGqxZ5fezOcbwMK8pMAOP8Reb2ATbzKk7GLu4JIW3omThmCPFIlAtQPr444+3BwlS9t5k7sJMleBkizPDgD/a6qm4GKtx1nYACpSB7QhmY5IfO/jEotCmtMFKjKCigYKXChiHngIqwNgnPvEJi38AYBFTO++88wMPPchssAoXXHCB2YGqxsaOtWvZY3aLRfLcDcZ0IfeOmOemw4ayk4fZAQX3MKDZSD5mC/xXes8Dt0ARwEzIhBgBZs9dgJn4cHJwz4DFm4phm4vVGCQNyXgXhZ3TGnQE+lgJkbfW9HK6EmA2S++YqkAqiCIKnGYIpUcQWdwSLxDOPKsOXPE8ahiz5sxmy/00/hAHKLL3hhtuYLMx6Cuf++stIkLZrU8e6u4G24gxoFlbOg0PX3fddUZsBGItd29bm2qKh4m1TU1j5gRNmfON4zPaI81UhQbgIXe0T6jax1ZBRwFj8CrmiVjuosTlKwjf381d6FhzZhBHzZl5+da3vkUhdvb2Xnvttcd8/vOi7wRBmA2SLEQEaCRqNnKql1lbziMaZYQlDWB9eztp8IoQRKhagcTl5YrGeeJ5qLJs9szvfx8Afs/hh5kcgS/552Vnfa/CO9Giv7OjsrwiaE59zdhRju7jhegmGhob7ct1zHHHibTkcNYkUlFhJVBYcg61CQab1DBxE/lvsR3sQVmZcRIJaPAaQ5jsUCOiGKLKDZMgMK1cRGHodxHUR+2x8kYB9nF/lGHcrsif5BGMazaWbXwfP9+ctHPPcxJ4fksALKHewbAY4MXBsXH3jAtODVN9lKGftCv1tUUEokM/oQMKCwprz3R/E1a11VXCszo7bYLRD3ta/GKqur6+wf2IhWvDQ2KjipP2FLC2ZnTBgvmZzFhvd+/EWCYVQGaIafG2knrMTvRTuZNZ+2iM2ocym22aNYMaGBrot0NTOmW2czozOlKUqJjZXN3Zbt3fqAGf9JPCkQoLyhwiVVpg4OB5cXFqPDuBQkNTY3VlsYVERpdO4qTnTRFkRjPNjQ2lJXlLl7aY6vMbZTxwTSYL80eGM73dXbC59amW6lXU1qCGWxRwKL3AITxX8d9WwaLD3Z2dnJoGEMprhbPhLr3X19fLhlBoQ8PjAcOVFCWLCvuHBrp7u8prwuxA72ib8X26MmXkoELZOkFMJFxQWACsUtoAgFc0pxpUj2zZwgULV65eyXix/gbE0LtXbvyU2N9glyPXAp3MPiLroToKpseSxeFhAFjzoKvpWwS1ohgJeG6ALjHtyqwENiLrKY34KbmjzGJ6y9AYFEmMDpqIeyu9hehKafWN+zhTnMsXP9gwr0EUOEfBQ5dvDfDgZHHpCos4o49nKbFUUVONjizKU+ngK8tMrlizor6seu6imVukMeeI/HslQHUUFhSO5U0YlxelkiPjIwMj/cXl6QVzt9FCLOnnzi0pTddW15lWKy1Jz5g1s6Ot0xJynlvtTke28KG0RI8vAwtNTqWKikezY5MTU/Uz9Peato72wf4hGNXQbWxiPDs+Wd1cK/2yFcs5n6WnkUy/S+mvzdg6O7vhCIv8bZoq9h81CyWGwz4FU4X5vEdppwnX1NTV1zaYaBoeDGeYGeebpTGa0mz54Wyz529VlWXtdkPon9k8g+ZpWbe+s7urLF1uRyH8wL/29bOd3kDfoHGrsgArdtCzgR9nHp5NA0lZlJD7uJTz5y9YvXoVT9vo8IhtApUUJ80zZ+RPTHf3dc+oaiwpSKYK7bpCG9FIIRoaLvdfl8FT9N9/bz0/P3PfYrMMHIxhkSGdKy438sXZMQ11ytHxhv/85z/Xd3baLVC4r+rs6x/83ve+R4nz8frb3d7uQwjE/g90JTWdqqkBjt2H5YWRLTGfbEBvAYqJX/BPesoaeAiDA6o9zFuHTZgBYCpbGs8CLIQ6PKTuWfcoEri+uVlKalp9JgrCKJ/9gMBnzJnjhMZf/vKXTmhMCoYoK+OtrYpCkqyiRKpn3TrmxLdAJnuAIIhFrdvqEaQZ6e0VnsspJ5lMARJCiIEf+wO1ujeQkRGBXHXVVS0dHQLI/va3v11yySUt3d3iTvfZZx+CUhxbB4dp4wjYA3KYYRpBQSbOt/yPLKJLuLh/0W0ibGFlbNLZaUMSQdTWEnEMylcYM4ZhlRDDbCwVFjalGeFgWSN3sRBuyIooLKAFVIJMIrirCIpJMj6HShXck/hvgEMhYt2GKcG16CtbUgU3IFRs2xB8QsXR6jVSwnC8txlB+QrB8847j5Bvv/12e72MZy0GC9tU8hJbJu0qr621DNvhDIyx59gmDfkqYBwZ6Cebig4mgj3u7maM1XVTvM9HR4elziG9thHFI4RI7+JiTQJLHPiC+TgdFAHD9tOSpt9mIVROXDpln5gwGOXZxu0mr1DwaF8oAx9zFivWrDnyyCMXL1wI1du1G6mQIN6mO5m05xk/MNCoQdqUSyyddhtYys/X2PgXRDVrSAM9PfLi6zbds3DhwjkLFiiaIH9caR5efeQjH3HPJRuzFITQ3U28lcBwFCanvOZ9JiyD6+oiHPvMaW9yNDoZGRx8zWteY77Jdlw20IL242o1o4ANb/mfAXXUzIWgT7zWDAt1SyUSIdQ8CrO3HCDUr5ajteDEni4m9c37RJuOarRh9ieZtMpd7J1uq62GyYUoiEALUfyYc8WPb6SJb3J/cxJ4IUuAs84BHsKCrHf4+c9/rvdRffRqjJT81Ldia0WF/u97TXSiyZPkTFNt6IXxgZ7CqxosQ82bGuQKtcdyiOMImr+4KGFWdCwzMuA828yI59LAxrU1VUICAwg125g3BQPbCljK6qoK47ihwX7fOipWejPOVm0kCvPsqGyZqOeT2XGTnaUW14a9/afabQNWmB/nIn1jQ115aWl7Ww8z6rnzEXDSBLBVlQ8PTvT1dnuOK7l4W2V3/ex4V8dwdmLMOHp0ZMhminhDoaeb76dD7lLisKS4uKqyHJ94YBDLSkvsvuhoB5PR5WXpoYEJPHsSgLig3+Kk9GgO9vcro8GMf6omXVaRKhUpnbV8mu/GEzZSNcGTgLrjek2yx3aNRfOQ2NWj4QrtFytAlWuyo6u7S10zN9JwyRpywIc0v5RhDGMFY3Sxbp770HwxlCsB3e5z38qU/xZgxgM63voqxKVzybaHqFHfxvlyWKEfY10UmAAEQWWU2VnfouC5fD03/44yaO2Vtic7IyhIXnGk5ASWWO5MEgY9lBhBadhif2XkLco+dNQTrtwgixm42ivSiBkIw/zc9byQgNAveG0sxEeOTkyOFxYlyirLqkorO9u7erp6U0Ul/b0DNVW1ICskDMS2t3Yk7TmQKh0bNTjPa6xvKi+tAIxhTmkyI2NT2enmxhk+gVF7u/s8DHB3mA8sD3YFhpEFREuK0x5K3FDXCJ3KLqZsEbJTYdAHjKUBwqUpS1dmx6cryioRqaupl8uaVWvxAxLLVzL/3CPrr8/9w6QEPpEY1gWhYW9w1w320MQzlOufBBjAm6yl9NNNnDJmo7u7R/ruzh6FVSI04XCKYiQzMjg8NDHtDJct6ZJ8XjSrZ6MQW0zoxsrAlZhLMMC4nMePp85crPB6WpgmNdSmlCFMO2HSpGx/w6xZYKJlw6YbX/ayl73hDW+wIvf0b3+XHoeU5i9cGI4Vam8788wzHYRojS5f1itf+cpD3nDw/MWLRSIxFJZyElL7ypW///3vf/vb39La5mJtdvWOd7yD0jfTa9xA85rOXbNs2a9//etLL7/MNIy8PvOZz1gHikNanioHvW665hp4A9CyLvfoo4+m1jED5IhQ+NIXj1eEs84667777sPR3nvv/dnPfpaKN3GqyHlFydWPPgrScGaa9QQbXr73PjxvSgGB2MxTWJIp0Eq7T9v5ec6cu265hZqQ9f6vfKUTEcwcwS1oHnjggUCIUGdSkheUiAfg56X77eeGZ/jGG28MIDmRdH7GAQccEJbpcqVmMp7L/f7771fSl770pcq+2+67gyjMDBN19dVXY+wvf/mL+9e+9rVvfvOb8Ra8c4JaJyaWLl3qqEkRxSZxHbxBMh/4wAdCqDD7KnDaGKK1FXEQ3Ry2KWpZA1SsLNewXa8eXb7stNNOs/SCnPd+6cvEcu/1ohfBx2HrL3t0QT6c2PbohjxLOA8TYxNZ8q9tapzMjJWWl53+3e8cc+yxduNctWY1gOc8DsukzPPdc9fdV/zxD7f/45+9/X3Cqd745jcd/s7DqhrqJ0cz1994w5KHHr7q2uvSRclHlj76+c9/vrK6akZT8/uOeL8dQbGx9P77xRaqOzWLT/HGuFIWG0rHQ8kld9+torlbSc/a2re85S0aDORsflsa2M/4KUDTx9Hak3qh2tFISkWYj42deuqphllvfOMb/3bbbX++9toVq1bNnzs33oZNLENve/sll122++67v+TlL+/r7MSVVmSwO3vu3Afuu++vt95qx4ZHly17bOYoml5RZcRiVnLBvPkPP7JksH/gjrvuTOTntXd2jP7zn+QzPTklaN/s5uy5c2bPnMUhL4jHxKZRoyiGa6655sEHH9SoNAMtxGp1k0rmGnSfV73qVTixNgvutajL0E1hJb711lvj3fwF2u2///677LZbucHu6OjDjzwiZFBMPgBv8kN7CM5zaxQ7O8nWamHNAFwPq7VNshDW0JD9um6++ebdd91VBHXVjBlda9b84Q9/0Nk1S53iYx/72JMkmfuZk0BOAhSUsCD9y0XPEwh7xHZQZX5S2p6495bmgT2eucTADI6WJ3+3Qb/Jyzsx0clEXrK4qKm5aunqcPYPRVRZUdY30M8XwQcCFNJOACGDRdvwq5RVl86cXbFyeZclRQX50zYhpJ3SZaVgbUlp0fq1bU5U4w/xtrenq76xobamoke4dF8PypbQsDi2TZo5e4YBMXQHjgazmA2rXutr61IlRQN9jGEvvwoP0lRi0lf1tRVdPYOd7R3llRU8P9bMjAwPVtfW1NdWtrTBup219XWDA30olKVLlQugBcmAZHLj1UmXpivKSxXWV/JCIS+/yPSnEJbamnJriFvWrVE64JndKTFst8Iwb4odAYChO3IQUV1eWYv29BhYOwDHmy3n2WUR5m+/YF3bSjvx1NfW9nT3GmpzIsmrubF5xeplrCeDCC4qNckbnCBo2hSTbBAcKyWQ6WKA3AeGRQszB9Hl2yVLlvgE2wYViEjjQ+F14KV4TqjSW8Mqe4l7S56S+RwRBJAlE89dWpeURt6MMqzrEy4Hb2MK8sWkT2BXhWLxAWxvUYuxN4LSuOTLgczT6zkiKCuX2WjWQGF32G773p6eVStXxFhdGi3YfVF+cU9H5/RwOLemvrbe0p2i/NDmc9dzRAJmgQy57RrwL4cDY57jK6zXzw+buhluVdRU2R9rxdqVg/1hb/DB/kFtpqOjc9F2iwb6BlavXWPSSWPOjofxuZagr0mj5fB/FpcU27TTLspiT3r6ejraOkJjHsnAo9qYVlRTVbN2/dqerp7G5kYRKN5WV1YniwzMBB8PG+DIyziytFxcXZiIF/HhmgwD/jBjJW55+8XbW022asXKiooqzmFeXH4QzdnIqrW9bWw0Y8s9E4I9vb08w/MXLnAG3PKVy9Mp01/lqNn9dfG2i7t7unGrdDixiUBVVTUk3dTYvHzV8r4e+pPPpWpgaMDS5cZGoL146fKl0tudVIn4ehvqwv9Wr145mcmm8hPp/LTViYZS+XZNgN+j2aHYCRy1DZIHGvz1L3dtSQk8Y5uqHlxxPbAKGy6txznoeQWPbTvE8nmZETQlcrK4xNxp30CbaeK6hqbWzm5Trdss2m56wmm6qRtuuvmGm276/R+udE7hUUcdlRb1JFJIhGde/p333//+Dxyh+Q4NjxQW5N+75JErr7v+15de9pMzfySUeiQzpvXf/Ne/+orKptwn86aXrlh5zU03/eL8C8DIvfbem2tuaDTTsXrNi170Ip1zfDKsU3p0xa/Ou+hXppw9mZicxt74SGZoJPPTc85VHDef+oyDqxNtHV3nXXBRiaiMzPjFl11aUVpmtkahVrW0XHTJJTbvra2rgzEyQ4OvOuggM6yjmRBDe+cDD/7x6j878jWdLMpMjN/1z9sXlZQmS8utOS4uKule36oflVdWG2Xc8re/r1qzdpvFi6enR0pr6jj3isNJjLbXKrv44kvvvOduW8S9731HTFqV4GzDxua77rrnN5deBgO/dJ+Xw43+MWIf++CHgNjBkRBpTHS33n77D878EbT/ile8oryu/n2HHfarSy42dGKERsfG7luy5NTTTvvpT3/6rne/G4C/+He/M/xi15sbGxm8hy699Iqrr/7tlVdC/mwk77OI8Le8853QuOKYO7/3kUf+cO21+7zsZaA4x6x5hw9+5CM6vxlhLeGBJY/84Mc/vviiX/3Xqw40H2YNRnbMdGBRWUXS6jH/Bib4MfJ6B4dMrjlWw7zezFlz7FyifsmEwIuSic629uO+8PnfXXa5ddKxtX74kaU3/vWWSy+5/Pq/3GR5xldOOnnZI49qhPSZxWY/O/c89285+JDD3vNeLe43F19q/sLAws77ifwC8xTXH3nkn6+73jyLe2MCQPHlL3/5WHaiprKK2b7jnnt+dcklxx5z9DHHHFNdU/Pb3//uPe8/wsl0PDMnnXQSkW6sbxxmWWtJeWHi52f9REv42Ec+uu0OO+23/wF//eut555/wYlf/ort8Mf6+o2lLBX73Be+eMgb32hJDAiqhTgg8eNHfuojH/3YeRdeENfXkZ85CjOa1sDwEIapeG1YfPWhb3v7ER/8cOu69WMUYl7eO955uLej2Qnd1UhKZdmt6tyfn1OaLjMyNbwTQv+1b34jWVBoa8G66pqTTz31uGM++7Wvfa2iukZ7MHFQ39T8on1ebpN+A+ui4tR1116r1xjf9A8NzmqeYe5GWz3k9W8A1C1kyE8k17e1awMzZs0OLnLxkVbHZSeEXrAB61rWf/Wb37z/wQf48x1DRtqqtag0/ZFPfOzhh5Zcc9VVVc3NN19zDff40pUrZzY69WScLfnGKaeA6JB2SXX1aLQOTe8LvuJowSFp5K6cBLagBHSlLUhti5CKelKIXkFNj4N2hL/qg270Ee4183EmGSGKGFpIFpfCJ/FXGxcq9L3Hk22KSYOux0x8iPiNUsR/BUgZ+YmwE7YSbLptHPLyHNR3z32POk1BN2d1hMZWGAwK7SlNrl3TZpcIas3fKgtuxYDYRrhthHH0sMDwt1DUtJ2k0pbSDXT0ZRnWohSNbUjQ1DzTiHZgcBQGthYuMzbBXtTVi3KsUkkGrj4pSdFwU0Vho+Vyq+96+4aHBoYYkdKycjOAdTW1NtPq7jESGC0tq7DKziDRANGqwpq60qHBrMgy8+7+FadsUpCorbPtVt6qjm7jEFGIDLe5QpRZGcv5REVy1drhw3xrZUWV+wExOgP94eTk/ELE3cyaPTMzPmlUrYCk4wnhE/b8BaXr14/1CTgqssaveGh40HORjf39Fi2XdHevL0wWcApFwVfjixYtvufeu4tLwpJp1kcDgDBZIpjWnL5JDYJnFwzrYQA/NYYY99K9Jj5iL6uvOAxUtIw0AygUfkTBBIHEPoTSvXUD1sIVYIDJVpRl6q8Bj1dMBqRN97uXC1QsU43Qc0+YYLn7lpsBspWdXPAZv/VKg5Q7Vv016IpzdCNfbViO7vFsikMgKIzd3tE+0NcvO8wbz1P19fWNVZVVHS2dmndjedjyGuqermrcnCdYRpjf+PJh/HDDTfxzc+k3pvDsPNnA3oab5wmfj4n/CVK0BVBYcZsUaTmRN2npa1V1WbqmrKu/s6WjHT4dGhk0SrSmoKqienRM8MRwZnzU96Lmhd3rtSWlJQND/W0iF4rpkFR7ZxtvanVtlb2a4WKQeHB4gD92JDM8e+YcG9GtX9uCJijsiW/1OOrFwcW963v9pbKk5zCwLFcLbGlbPzI0WlZRWphO9A300iT6uyz0d9llp/h78zmu582f67n4ZN/SS5PT2cGh4dLy9KJtt121fnVfT395ZZnJNG9NjjXWN/QP9k/nTwm6ljsOxyaA9JFtF29z1713hf5eXqqMBnMocCMr6aoVq6XkFu7oaufkSKWLLRJ2VrmQFnvolxVVAsBkMjVu9197EYXo5xATbRT8+CjUKUrUtYdPEP3/v31SM9vwYnP9YkP6DTfxJ5tLv4Hgs3CzgYeN29rWyP0ZY+BNMBFXyuP8qjO38QSG+5HIqegE+rqmpgfvvdcOQLZNosE/+clP0rzaKP3Iq2amhnOVtnWIXHNDoynoFWtXn/CVL7d1diF/zDFHAb1U9rdOPfUfd931oY9+RBRxdWOjQNZPH33UI8uW4uqE44+n2c2MBqfokoc/+JEPC8BOVVYMt7cf/p53W3ouZmiXnXYy787xZdhx3Q03+IrGp7INQTZRrujR8PjYn6+71qotp9Hg9vrrr1/X2lpdVXneBed/9KMfnU4UfPqTRy1fvYYL/Fvf+W+utssvu4wT29T9Ntsvfudb377d9tsXFqecRQygMjYmsIN90hcYv46Ol+2995vf9KYTTzyRwWPALG2VhYGPgpAhoxJfcTP110NACFwJwReJxOnf+faVV1/VPzK8YN48BzuxWGAGmwQIpcvLzjvn57++5GJVYHZfqc0WCzlmOOdZwTWWkfJ9RxzhPLLKyoo3v/UtSmdFK1Rz7Q03fP6LX/jud79bXFb6heO/eMvf/54qLnrzwW/iV7z77rtFce+w0458+0YVn/rMp4sg45LUV77yFTDshmuuve/Ou4Ec5Qxx19FQjwiVVGMQPOevfzNnz2qNjhcvKyr6wZk/5CWuq6nhewfGGmbMEH5y8aWXaiFmyvfdd19ygOe7enpWrll92ne/K1zQxa190a9+Za5MMJ42QyqKVtvYePnFFx9z3LEjE+OC7j5//BdZZbX8m9/85pLf/RaT4gw1rY8f+UlnPwrlPfTww0xyg8S/vPB8PtXq+jph9kIJ7NLBfXHZby+PMfCTWgVh1s6a6WAns+ecwBwekN7Q4MD+++//hS8dz63KDWKRnGJqyZSsJbMa3pve+pbtdtzhsHe/S1Mvq62R0hMO4YbmJqt8ldRAQW1qAyAoCRthNM5o5kTlVn3v+98vUs6sRJgp4JKdmrr88svJRLxDMlWsHk1/HvWpT/305z/fbtGik08+mSffSuNzzjmnqrZmbct6xHsH+s1DLVy07bSd99Mlplp/9dOzLcg33Pnv737H6ncDKcL57LHH1tTXkapVv9wg2empqvIKS3wNIrU6g0Vh84LeCcTyfn//dtvfFRNB1s+qv2tvuP6Bh5e85U2H7P3yfcSK275L0735hhvs/o3tm266yZwRgYehfLTejIfLonozKSHyMHflJPB8l4CeG5t2f6ksAIM5EElB4evanrBEsARgQOnF3rb/u0j0UDoX9g1xivnmp9lm/w+X7jkVUHEBY2F0RVOVFCYGhoI70SFpGX5ee09E0bk4X7euw+5YNCoTE2y0adMZ1d3dI0CRJXDJ6UAKTeDTW6WA55kzlk7pYC0PrRWNzT1FJ4AIqZkza6yPWRWdMmL1Kb1EPTqHU6Y+RIE+JJPBoSHGl6z6+4dQZqnJCpO0llAY1nLdui4KU3ae+wrl2TOAt3FAjovJag2feRVc2ZPmk6PTy5MJn+BNXpzM7pGlmhhKN4g31TYVJvO6W8NZ9CjjBHIDX7ffac66NYEHeymgVjAe9LYV0waq1gOt6VhV3eBkqWGalvto3pyFbDFqBq5KRJ54U+8agN2A8L9BnnIRliyKjTwRRJ/M8aPUqgl7rLZWYdKWkN340OfqAk30iQVvwn/wyRkgL9YhliH97x7oRVxKckBNVA7KBO5DyTz00w0PlUyB85g3PEgcU5Y1niUjNPlig/nwCQo+wSTJB8Re6RDJIE9BA55LbCaFS588q6pr73/g/ryJfCWd6Lf7RE/trErHgSCVu56LEqBbwiiWhokusdCZ6UxxfkldXf1EYbZ1Xdvg+KC+b3HYM2qfQObTb5+WHT+pfaaK0mAzjgSYbGif5vri9qkvb66/G5kLpdQXNGxAA88aqqGXraF1h4JE2FsHyKfKwFcNfnC0H9bQGeUiqgUbc+fMtTU0AIwIALyhvwve03eA/w39HXRvntmkS3a0t1aWleeNWeeRHcvLFOWVwxE09NTEdEHYjjbW1f7CxP7FsvaX3HPXFpPAlsDAT8lMZXk5+KpBQDK11dUMoZ///d//LfKWHjRVqV2yqWpVExH2/JY3vonl0Mjc33X3PRa3flrg8mc+o/ExFSKtbAh8z333waL77r0P95d9C+V/1o9//Na3vhU1DdXA4qMf/zj8Ayo4bUKCu++9VxogAZYQQaRNe3vv617X2d3NbGjuoXlv5irKL9hm/gJoTZSRhs75+c7DD1McwbQClXF1ySWX1tVWi559//vex7zttuuukIziAGYf/vCH5VWaSNZa1Zwssg7GusoZs2fzVTp8tbO3R8DTeRdd6N9ee+wJdB342tfy1GVGR5ltDd+gSeSqiXrUcOinGAkbHBMOaySvH/3oR46msJbJt3aWEoXbtm6d3r7z7rtbYsqdiIijKOT1rne9i0UiIh1vn333tdjp+9//PrJ81wKh99t/fwHAgqjZWuU6//wLvLV5mEjppsaGtvaOn/zkJzX19f09PYBfje0oh4bgPS4AHfNNb34zXGr1/9Gf+vRgTx/gZCMo4VD2lN5YopSmMNr8PYMbRL4mMqTRJDDGgna2tprh/udtt6kdobY2Klu7cqX4bcHGa9ev53wmN5hQ3DigqDGoEUeJhFGI3SkzGTzzaQPAEOBBBx1E8opjTHn+BRf87ne/+/rXvz5r3rx/3nGHHLUEBYHV7bH8ta+eKFOz5SSNDbkIXjj5yCM3Zj5+sm758lkLF1554YXcoR/+0IcXbrONsYLtms0m/vWWWxxtaVylltTgMqdH5OXBsUZ4Gry/IKUtSrRzupVb3o0cBepZeW6AYuigyrRJ5SI97Ue5VK4JAkf7yl0aQex//vOfYwXNYWu6xJwLALx42221ut332oscNEWY3LiHBYJ+xcWZh0INfWW07uCzxx0nYtmUB4JGQvg3K2Qgw/ZIYAQmOk5jw6SyiIFk7ELcYEEAq1r+rnvsWVdd1dreSbPPmjtHYT3UFLVWDd6i91+cdTax22Rur3337VizxhJxbnBc2f3L5Ei8U7qGHY/2chh4cy0t9/z5JAFKOy6O7kZ1QwislYt5slxFX6AGqQjqjmbzc6uXnV8x+BhCuI0wITe6uKxtawqueQjM6P5YpW3wxuKwLLE/cNashhUrWmPwA+fQGP5Cd9gGkPR9ylxHp74U0H08GPVXMtQQQbm/394d4fCn+CvpKRwayecu3/pQYkqMH7q319KNbtRQiJmh0AQxhjOKo/PtaXuvjCWk7u8PEJrAYwq0E3womQ/xoKReKZ2LBlZS/HuFDSXyiZI6NqmlpUfZ6UOjFK9inm34gA1pEPFWjiXFVvwkMTwwOABwdvS219Y7myBEO6tZhQoadSJgWkWWna/oYde/ynMW7BrL03OAUxZhljOZhIoRV3CiIBwU3OMWG9iWDCmSV3DCdMkIHRTwhgdsexgDAOX1lVG7mQWF8hBLsTy9QgGrHiLrueyIFAyI+cGGtxJ7Tp5mM93LBSdu8BnzZptdmx6hYGWTwEBR6JE8w+y/rJVdwACGCyamDQXV1HjeuO06CDN3PT8kAAnrLCMTo1rdyOQIT+x/VPukNDTgTfX3ElNm+o6WbESk5es42qdS6CnUhaargjRmPUX/0uy90r/iDkW3hP6++rH+roM8/f5ORZRQdYXF8YLquBmgIKbm+dEk/vNL8Zhh3nqMOo5IgwCAZQGTGPprZFbCqGa6WxuiW7UvPj3uxIMPPlibYLG0xRuvv8EnWtviRYs0XJMumqlheqnVLHl5sKimDNv4XLIdd9zR4N5KRZQpXE5SpsK9LPjN4vkTkWY77LILUgyJNbEo+xBvWrDG7X6TF/pssxG8pq9v2MZZMrHZXIjxVzQ9Ui6dv65eIG1VmLfmPI1Wc3nI5Ibp/ciKhL40Pf3Z448/+eSTX7T7HkJ2xcEK4eK7e/0hB5/69a/bFgPZeMBkct3lExcmXTGH1ApbDqCuWrOG3KB6cgvMiFKrqrICWRYILl2+3AQEXyVwyx51tLZa7el0ZvuawEW33HILJhvq62RheaojOizdtCY55JiXZ20nq2ZIBACLQrdI+NqrryYK9G1eEpvMivIygygLSn929k9trgI4kbbdmCz1Vn1PEmaogqBG8r70pS+9bJ99uE9/cvbZQqz33H13eakyS1XR1ypUMTZUbn939+z5ln4vlp0PhXKx0H09PZJB/hoS2bLcLnWt9i3KtaGLxGy8KT0SUF/w7SwHCI2OysX8gsVa3M5LHn0Ubm9Zv14d1Tc2anLWpcvxrYce6sOlSx62hDjwuqlr1uzZFvqadKirrTMJEiy61dodHdCsqGZS9ZHiE53zePlF4HngfJvttlOJ4iBD+oICC8slC5izoEC9KJFmKQE6MKFJIiMU0wTaGDoUtwTKSO1ah6fuDF19q02MjoyY6CFbxdEFbCEmpZagNTbNmqV0YUeYri7dweymOQVdz4J8wyiAWUVrUTUNZFZn7t8OJ/Pnz/e55yCxuvIJVl1Y9Zd/WxcgJT9MYFWUpVU99rQ6a9WuufaGt7zljaYnHMgUJkcnJviWl9xzT8OcOVpjMIQzZugFogNCj7DEMXK8xD3Iz9yVk8DzWwJ6t84VX7qYfkTtePjHP/5RkM6HPvQhXZgOoToow7jTbW2ByJ1OkhcIbK2xv37TmZQwbUNz6u/AD6jDYnpOveN80aKG1tZgtWlj5oAlkliX1691c5dXscY2ZawsdA5V5kPqKKYMssmUQWf9Y1BnoGnQabdlMqHlJEPBtyQgsf2kjBwiPsOCQCKSo70dMcDo+IlILMwZMwLP5uYkwz+lSsUBctZjoUDmKOMQKTYdCvPTc4k9UUDZKRHD1x2OJepVIgoKZU9mzqyVUVDptsO0djAqTsy8+xhzoo8s3bvdwu0AYBQIh4aUC1TpFYI0PIHg///Lc+EipdggT+nJLRZ+DDLxhg0FwZtX0VCiHWXUcIIU0fmc54CcZYQrWRCgcYhcKHP3HmLMTYyK8YasKy6Ir9CJt9dSTcoru2AczQ5Hh9X7qYpRkCMALL0q89AVy5OQJSYBlKVB+V/laXfItVA9ynjTkOLEZle3diPP0X92JGCJrGMwnbKrYWiiGqdL23iutE/gAo7FM6WkoRpfUUQaLX2iPbv0Qb1PcXRnafSOLdLf5SILXdtkkM7o3FLjZN3n2am1XC4ksNXnm1XmH373e5qRDbA3j8Ngb/jLTccefczaVasFglrBaNVoqjDBOvGFcoBqZExCRXWVmdGCfGsGyjiBnU2PV0BJYv5QETnUqFYIYHguiwMPeKW1vvF9Q304lon9QNxqXstHKdrykvQOi7frbWvXlIcHwiaNjE3SnhjxKqLCBGdr3O7CDf8rstETvUIHGOzrx5hlt+Hn5JTt/+Wl7ert7z7scMuPb7vttjPOOAO05qB+8MGHRf5bkcsxa7ADMrE9xaVlnLkFxUXLH3lk7vx5H/joRw995zuBnLN//jM7Tg0MDVVXVn7/h2e85e1vY7qEBAMhoE5YwhV2p3OOYwhp89eyrWmHNCSLlAvDxSLRLL2y48T4hKX9UxNZ4FABSbKsOMQ+BadiXv64Vf7lFWOWVTt5aDrPGWf2i+cetCvJQa95bSTbEvsJyC3+Z1eqPXbb/R1ve/tDDzxoJHH1n6666so/WVN93HHH+SvVTjvs6Fs6bu2adSroxK985Yh3v/d9737PtgsW1syaZSAT+xZQdoVJjuhC3AJtrvWWteswv+tOO99w7XUUimq1wFd9OZnNKmgXyMqDalLDoEEllqYcopZ22mCIvhoYLEmEHad9NZEZKygOXmX31WGf+i5EXnXAAemSsB+mMD/UCIwo1GBmeOSI977vO6efJut//v02kG//fff78le+JIJdY4CcA+peuDBwahMaLG3yikK427q73nTIIdssXqQ9EJko4h132bn06quuuubPr3ndQQUOzihJrVq7RuD629/05u23276ntU3D0/zCcKShes3KVYqw/aLFcuE5LyotszWnkzz6e3qNKqwFKLcCr7h4oDfEB86bPUdxrEBWNFJav2Yt/n3rnhhNBjXW1r3n8Hd5q81rq4Y7VuXJy02ceOG8+QQoruD3l/9Wy+FX327HnTrXr6+urRvu6fVJd0enqpk7a3ZYk1yYWL1ipSzmz5lL3VvrZ4oA8+paJF+hRe9jY4C0WGht3mSBKjj77LM1Uj55ydT1wW972+vOP/+PV1/9wQ9+8HWve90RRxxhhMSDzGY47NqIW/3KVA26Me7epJhzD3MSeD5JQGvX5gPsePygI6XzhCGDKNxTQZSJ/u6e7ooR4FaVQNCzUEg0iRwGXWF61ibulm9UUYb41OVxxWTEXRWfM2emH3ggxPcyfDo1fSuCWGJn4a1b1w7eSEMJAEuwFgMNCEkZF8dz1+zZtd3dwU2kgC4JlBqsqqlxbE8I2/Ewpu+v2XAsIOse+HSDWwMJR7a1tQUc7tv4Ew+j7CaBdokRkYWyoGA7p1WrOiRWOhceKGGFEr3M9HiCiJ/sO4XprVIbrphaZWq8MhrmLrWhvocoo0My/J62/vLV8OiAs3vd8EePT47nJad33W7Xex68J9p7NtrfwZrqKtOP1T6M5enm/8uzceYDSx5AdoM8vSUNhTUvSZ9Hsg3hx2YkNQYCJwQ8YxUbpGfxUfzQJySgvvz10Kha6Qyl/FQpSGlapstlFBcEz0j5a+gvAQBMP7tBExETr0b8KMeJydOH5Ik3kjEPK5lvXbEA5aLSW9ratIdYntZAc1oHeQ4PIELm/k5kQo00NzR3rxcrO72wau5WbeE54s+aBLRbeYkW1orG88dBYg3Sdj/z5/8HtU+NXIt9iv4ehlvz5s2aMcteXBq/1qtQiqaH6nE+1IV1BOMidHQHf3fdIfR3b2MNj4IO8vT7uw8RR0dGulKoLwdNRT/Dfe7a+hLY6hjYDj3WmdChDCGrKcZVrLLmAooAVIytVkVfd3R3UdP25NDizBHa6hZCc3aDjTHe+/73koPmqG0tXfKI+Ui7R0JisSUDfS21tbpSFhprPDGsMbHBsbOXJTPup9b1SRkJ6DEcd8/o+8TzMNDffOwZVy1uXVbnFE+G+C5bt/Er1kbDBTxYzfvAww/dfvudAlkFAmFV/PYbDnodltwjzlpUNzdPD4U56URp2COXsxTmEWG2+0te8uOXvkTUqGWZt0SBwWKMmRnmhCXRx+SrUNhzsXkIAnXsjcNmdEWwR0n9NGpxdpAb5xLxmTo4x1DA8te8sbDDMyIGBQQrQgk1pWYdg3ijMxvs/6QK8AkTmsOLeeaJBeA/+slPUmjWjvqK6AQSH3bYYWrtpJNOQtZya0uCzWjYGKCns/u000//7aWXXfH73zfV1hMLOkB76NnRFU8ruL3id78zYWGCgJ/23gfuJ71vn3aaiS/7b9Ec/PJ8+2Keu/t6wbbhzOiMxqau3sCVCnWylBstIVRHNHnmhljwZhqPuMxouF657yvpIA81uVg++Oe6rJsxwxJun1x44YUPP/oIUjf99ea/vPrmCy84722HH259FQYcN6VJeGVnUX83vlpWrQI7rYPmhycNEJ0Wk7XWaxLBkulQTZGzwiQiCVunzf9pbGEf77TzNCKfc3gVsW1wY1n7cBSzLa8168PUgMScQeLzBDIApZox1z2Be455HgYpDYk8MfdvEiQWCLarGhqcfqTKEA+CiE6D1GAMO/QCorA62vJgG7CTc3CARydhKMLf/3EbC2b6E2XfmVryU7edpOXzQhvTBGVNN8tLa9E8hj5zFN+77d/vueuuX/36kkMOfp3DvUxPlKZLDVotOjCFccopp3z91FNV6Lvf/W4rhA2qNDMRAtjTj9wrgkAGmeaunASe3xLQrfQgDZ4u2jDEca8LC1DStXUKmzIILSEHmvxZkIbc5aJ3+8snHZY8BCVTaXdAazxFolAaGPOWmgKK2LVly7qpMS5cptxDBdlmmzkClTs7w5pbHZzyQZBKFJ1Dq9EV0rA4HsYaj7eV5WITYU6UqWgqhQ+4szM8jDgJ7vH4K5CzqyvYJuoiVuZ0FMS9fn1Yy+rDWI1I7N5XWJIYG9AdTRs2p0gVrlnTRTcqiLeYNLpQBJ/7KTuixnPEA9BeODDAyPRjVWVJjHOnB9pD2ugZQZss2PPZOMSpwQJ9h4YGega6awKELOseHiivKG+a3fjwiocVDd6rq2lkT4mC5VL7vNak4RU+SUBBfLVs1bInydPwgByMvyWTWI0okTQuVhvbioxtPHuCiMSoKaCfnseJlVHWmlYYEqTTsvZVbDIk9i1+pJQeADYLo0JR9hNLCBK1cblv5ehhLE/pjRAIinMiFn5Mh52VhU/IjYhKSko9LylOhs2HihKODI7lifLqdWsbqoVLOxiGeHpn1TaJhS7KezaaulLkrq0qAaG8Th/ScaLGMD4yOFJSVUJL/Oe1zxLt8/H+HmaX4vaJbQMhjdPV0taiA+r7iqO/aLq6gC7jnmKhGSTWMXVtq1ceXvZYf9fN/xf93dhsOjM5NDiUqc+kC9L8A3xf+n6AwrnrWZHAVsfAAIwWo9mFWcGKCgclaU+dPd3gCtOlAdKetK19eilWaMFOOdb08Mg11tfbwaJ/YFCc2N777NPGOVxREdyeAoN5O4eGDSVmzpixbv16YM/+xpqjBJpmsU01ohOP6HGBoXKxiZQGBXgwFQPRthNkK06VWpde7tJsTtolyaLyKL4IsMQq55sNpXXsnq5uDuFUOoRvAXj2cnv5y/aWIzPJ/cWnbV1Ry8rVM+bNay5O9Tr8ML+gvK7O6fLSwDxkApc6TBVQtPPm4u23u/f++wjAHpgpR0fY4rqAAzPsReTe8MQ/N9y9CkKYliPoq/yc2Qwa/TzMpVVVdmpiWggBcTO+ELJ+64ltRjJDQ6w4gxfKm04LMOZ2ZpJNn9v+ykP2njSoBjexQMJ0cW8vwX7qqKMuOv988OmqP/8ZhVO+9S3n3DhOuautTRS0Y8phQptpLV++gufTWtyvfeWkZGXlxvv9QnechNjDv9Oh7YBakZdnoTVI5iyfMCSywvnMM48+9rNSChQXGWgPZ8wAzIRM49A+SqR0Ch7+RRehOfWXtgKYeTKhX6DLJEJQx+XllBYVpqSaDfiqkZzy3e+qIKtqsfrgww85BFJ4NlCHmD23qbnaGTMsHeM13WST+O0Vv7/vwQe3WbjAWR3rW1uwVJ8OK+Uc0cGdsGLVyjXr1oY5+4L8pcuXGXkIUQ4iNRPf2anqcaUVaT+yAzL9BEdlRLCC1rh/OaUV1kBMmwwjuekphoQ69lAIoKkTHJr0IUZ0lEsAtsCB0urqrpaWeNjkL10swVQmI3iPGxwFNSstcYf6AAEAAElEQVQvBMmWVDuixI2O5hof1wA0qrLSMrNLvsVMDLONIHHuidkXTGr2Rlie2GGVGOfOmumQqu7OTk5gND/x0Y9xxcf9SC8T+azW/uuQQx666y4V/YUTThCPcNAhh4z396vBIFhnaEStLtznrpwEXgAS0AH1JhrD5V5XUmirQkz5ee5nPBrz0GgsnpDaqlKJO/uGv+Iz9E2IzcwrxUJHyR1XOjWVC6auWtWmz1JEuj8OgR9KhlYwjnQxJZLRPIjU19vfK0RaBa1lupkCicKmUFu3rsUT6ojedgOA+QvoUqGo+VbW3tKHvhIujT0PJfZTFgAwsCYXeVGAeMOG9BS+7AjWc5y7oc1KS+28GiKx5cLooECRUsLexol96N4T1gcp64UVBM+eYyNGy93dcNwANiTzFW4ltrrEvtW2YPYcWZ+gPH/+7JWtK2Q0kR3Hlel4xsjyKJviULw4jEWtOCgYMVOzG8uTtfKhHPEvLx8SncZAPih7Ijs3coyLw12MYCwBb5F1L7FSe46ruOAx5JY4rtDYPhKy9EoqMZbITQHZWWx7GFdlJJZwUj3hewUtB2NXXa0sSJGJWsMhhpXFPQPioZOu4g83yFMNkmddVR2yvS1dJQVBCJnxTFlRmGTJXc8DCWiQ0eLCoNx0w+JUcKIadf+Htc/QkbW9jdtnrNDM+3CoGHbqR8Za/uqSiqBHxHrAKwX0d5t526xpWaN/6Tgb+rtGrms8zf5eWR42XNDpssPiWyftWvg8aAbPuSIE47RVL10BFNNoQJbe6EAIjQk8o0DBAH8N0Ne1tvT198XK1F/ptdGDX/d6+2vU19XyLlrW2BRtdagVjg6PCL/UjkcGB42zbcggVlaINQMAHqEpkkn7prUpem6xAw44QAEN3J2Rm66oaJozR/+E7oAHWjv01dJSrXBzQoBC2SQbSqFf19Dgk2Bioy2vbDi0ft06hzBZRIrtk046yeJMEdEvfvGLtWw9Q3+AUMF+3DIs1lqRgx2cvvzlL9vNy73iy1d3glUGBodkYeFxe0vLTjvtJCOvdDAlcu9i9gAUhWXY9Mx99903hA3n5QlM4pGzRbbhS0NTUzwa4GzHg7NkrdVc8sADhguAGdcuuws/G8QgQkQrV6++8cYb8SYjh/15onQSy5RkmDo7fuAHHIWB33344etbW23izRDafEuRpXESoCDYn//0Z/VV1ZgxSWzOQJBZLM/gXIiiyqFfN/6J2aZQFBA0au/qFHArVla+QJEoE+tU4UDiFR9uaAgQYlsV2OleLVBGpuXVnYL7pzFU14eT5Uh49z32sHWZmeb77r/vpptu0n4I01R/eXQko3tbVhpVKCak7RP+FqG8NVU267cWeFgV1IO+kWf14gsu0H421x5+8IMfeAX2Ww0rozvuvBOW5vN05rD9bLp7ei2rRkeNm4ZXeQYfMp20v2h1tRrEiboT461EYhm4FOy64CGdihrhxwYDptVN1Ky8SMCHbjRmHlrt1thFaA4maV5dyezG+pUrQ2VFJ2cQpj3YeJJRoNDJ3EBHq8MGnW5bNYdd2YKbNHra2rQcZdGuSIx4fesyypGd1osZN8gKkRT+Nx4NavEvEGN/22519zhh7+e/OO8V++7jeDAVZLZF/YYRUuRhto8N6HvyyScLiSMiziYNXu+WhcXM2qemjH7uykngeS8BOkGz15X0KX91BBcTQBcZJtql32yjrSsoImqNTdzaAgku6dAHA8qVF8ev5QyYsYbXAA5Q0c39pDHYglmzmvkgPZES/zQwxUX/19WlHnponecUEaUh+Ejfb262W1UeG6G8ikPtUF9IxUWLs9P3lT2CW8EBLjsE2R2qmCIyvVtaGjYJ85xK9KFMPa+uTvf1TRihukeTGL1CEDWMKY7PcRsbOzh8zZoOOpC2xInnkDCdJpm5Bp/L10O50Hv2zfAt/n1LG6OJYeVyI72/9Ln0EuMTBEaQZLwiBDwgyH+7YnXYk9nn+PFWKdxI762CSIaI57Lm/KGZNyXPOuFFMlLAuBbwhmcMIIJbwlQiBVcoHGIYJ3Hx5SsvZZQYpo2JxF/ZYMIrzQx7hjob5CmxXDxnxxH3FymFUi/I4plUFdmlQWKD5ON7WUiJSSmllwuuUHAvQSTP8ifJUz0KcFOPcTwqytp5RVHAz7nreSCBEPycl7SlMeWg8Wj82gad8Fxpn9qzUbG+GU/xxB1QRzN2UhDP9RQtPB706kShv6/ZAv3dqAlluRcVFBksB2U5EbTx86BJPFeKsMVkra3bWEPtuaF/6Xd2yRpyP3929tkXXXjhBeeff+opp+yz996QH5fmOw89lDuMixUmYYQLhPjS4omE86nRodytMrWDUWdXt/Nsv/fd02667vqVy5af9eOfwEX77bcflSq5NJzA0CH6xxx99N233dbe1vbd73xn1112eelLXmKrW68Oeu1rHcVlluXuu+763NFHX3fllQ5H3W/ffdNFxXBIXW1td1tburTUakx1FoJpDVO44CxgZnvCIqmww7Pdhuw/xMzKF6ovLQrzx3YVtnsbhpW3r39AZKzDDCEW58HYhgoo0mfIgTGQwLcMGM/kT88++8dn/eQrXz3J3sXCp3/2s5998IgP3HLLrZzRu+2y647b72Cl7qJttjVk8uTzx33ur3+5+ZGHl3z6yE9devElOBSbLcxX5+xob3/ve94jMYj4ueOOW2pt8f33H/aOd2y7zTb33nOPsn/0Ix8pzC+Akd77rncTnTDyjx3xgT123e26P1/DnX7iCV/W8VT/B99/xI9O/56Fow/cfc+RH/+E1aexT29sZFTK1//Xax649z65WE+7Yuky0BDIWbzNtvlT0x//yEdFMrOLPNqW2jKE2BMuRVYlVZWjmVEWUUguIfCChuYhjDnaWNLEmglnS4gXLVi4as1qrH79a18LjkHjkvz8EYuu8/K7Ojv58NesXn3csceC/b5tamz00CqN7RYvFi3i39o1a6783e/OO/fc3p4eMw5fPuEE9aXIb3vrW88+66zVq1bddeedxxx11PbbbfeWN79ZHDjiPzzjjMPf9ja5Wzv1yJIlIwLqkmEPKqJQterC7mK8xMajqkxbUCi7UbHr9JT4cxMcbetb3vqmN82fOw8OzI5PhLXZGpJzsJNFL997HwzccvNf8fDg/Q80NTQGp4PtW2yUnc0OcYFaNiaUwNwESFxWbodkI1FvNSQBbTxE/mm60hhKSLx2/br66hrbv6SMRwUSj4+vXLGipqJyns1OBgZQdnidHiRE4tprrkFW91HGP1xxxZsOPvjeO+4A+5dH0dQkYFSLF+lJ+4brr+9pbze7QbxHvP/9r3/d6zQDzabf6cEgujOlli0jcEEWkHM83PEzwGCLvaIObijz4hftlS4ucsiwIv/3qd8a648G7tFgVJC8MWOofTM+JSXQr7GX6Sr9XbvVVl3Ggg4K7n18rgT93JWTwPNYAvqCgZQCxh1K+3djgwwqVFCMtQOOKLetugQ6l76ztUUhd50xwr8hq/jGyM9eivErY1nPef8M+7z1kw5kc31FW8I51NLSpa1YZROpd9/CVOzd0NBwmDUuLKRwmDyJZ82CoMKeTCiQgxtGAWwWU+w9+CQjKgIRn0NiaHZ1UYoh1po0fCVxU1NNa2sPytJICcghLlObTGOAopbYKw/xbFWyRU6SyUh2iMgCz/SZgsCfEvvWQ2DSfV9fCHuSHrCUWE0tXNiMVWRRcOOvxBD+zJllQrmx4afsGHcFNCCGGI0R4rKQEgpKIZTGZpNSoqnGZWQsAY4iqFCIS+keBQ9dQoHc+xAnEht8Ez5ScXZ4c+9VXCmylh0KROcrwnfhM+ZBMnQQwaQbpYsZQES+RKHghvvQckwWHR9GUxjNyuIePz7EHmyAE9nJyOVGccgZb6pYRIDRmuqQRu6R+70UWZMg3sb8Y8aKHp+oU98i6wo34VDq3PU8kYD9BIxgNCQtQS9W6ar7udI+Tdbrv7oG7efGpeNowHqBdktj6Hratn7h0q+3VH/XZfQF/TeRl7DuLEz3WeuwxWDZ86RpbdViBNuwRS6OrGlRAeNjlDSFqA0lC/P7B4ahJutdBxxSnx8OfR0aHXEzZ+asr371qzZfY7soYq6/2C5qXgCV06lnzp9PiRphA8Ar16zhNf32t79NaY5FOxVtO2/+PXffbVumHXbb/funnc4BK7jaeTmXXXaZE1NLonWk9lvm73KozK577GE5Ikdxa0e7KNkfnvlDaAyKo8r5nzVlMz1CPRkJcrC6WB8wOtCNFUFK/sZgq0wVGwg47dBJTtbZ5uXZvkhChx4Loz3n3F84MXjN2nU11VXerrJf86o1zp4Z/O/vvO9976s0Dw0/R2tE2WTfQkeY4Yd0sTEOenUS68DoyAXnnkcCOHd48mnf/o4waW7bN7/5zXiwu1JdZZVkcA7PLjgxY/58hy27v+32fzqdlTsXKSyNZydkvc/++3/84x+38NUhPXx9+++/P/DMi0qGDC1pf+BDH5LMrqQWiDo3yPrbwFh9gz7JIYkNflErgYU3E6PDe2E2f62sxg+Hs1jiC3/1SwD7zB+cwdW5dMVyUwML5s8XJq1LTwwMpGtqrBByihKWhFWF0HdnG1bXmFkwFGJ3iVeUuzhkx9ji0+aotq0WpsveEw6pDo0M23YLe3zFrKx2wnKXVFVZbipS2thC8LMyWklu3ew3Tz3VOVgfeN/7zznv3OxYRoMhHBAT2uc4ZY/pYps2f/Ob38TP5b//XVwcqoYR/tznPieS3FSIkrZ3dXvIsU8ORWHwl9+1Zo3RkrFP6/LlTgASV//5z3++qCTd39OtXowJ0ulS0c2C861119Sd66tN0v4mvF+1/wGj0b7o6Iut0QZI3ojNjPhd995jI3RKlmv3ur/+1dHQFkizInSuTNUFMWLeoIQuxkxXa6saJwQC8bkuJshPYrGUX/vmN/QUfmn9i1g4mQ0Z6WuRFwYooqmlJ3AAW0f4yy1//d4ZP6DTaXaREaIJPDzvwgvYAIoYdnWShopWU6rVplzlpZt21WoGRx511O133X34oe8QdIBnoLqvs+vySy/74he/6JhiC4BZQdLQxl75yldqgXqcHeO4+lW9/dVtrOXwSodFKWzuykng+S0B3U1HVkZQhHHRc+0dIILGNOguu+zi5HYKKlicCExKQ9dtVYHwQduaKc7CzJVhWGHC/GNw4nU/vrWVDgvDVFamVq5s0cGxzUq6Afn8FQzkLw1GUwVlFcHXuBQKQuGwrf5SRICbo1IMIkEyqowNQqqiojyTGQeWSMaI2XNlj1ATTDvuW+mBOsaXIqLuqCUJEPRcdu6j5akOKwoxih7Gn6Ns1lHAClWJcqxOJVi4cK5lsz094TAIREhYYd3E93Qy/jEWVxOe+/qCxxVXrKrs3EhZV1+9enUfCvjJToiWCs5kRfaERk2mktn8JGRaVlxqKrwoUUL9kmefvS2iM6KCcY8uMck+iV6FSQEFIUmzyR664gLiR46e4xNgJiJZ+Is3iprcsCQx3rCNflyQWG5+koZ7/luom0X1LdlKqQZ5oUmJYFH2OWpeoUaNu0FZdn5iI+bBQy2BCWbssI8l8oy310IE/7JWNDc4JvDAQzTHMT42asuJUrMlFWE01Tc4MNQ/nMwvKE4UT4dx4pRBf1Ti3J/nvAQ0mImCiXQi7EY+npjo6O7sHenVTf6j2qeGGrdPzBojac+apc6iO2jAGrk2iedQlujkbQMq/UUj1xfEQYjL05t0AVoi9Pco4sNXyOrvfj7e38PG7/rO/9jfHU9HJSbhjelCe43miziRf6Fx5XO+PTyHCrDFJhxs4aOhaA0Ca60IoSWNvAkC7hoaHgI5OF2hgp223wHseXTVKhrT8kLqMg71sWqRWTLuFk7pW34wpD4HUhx//IGveuVENhwMkBkbd7DNuw47jL9u/9e8xpO+tja7HIG+++3z8njnJFadTv/kxz7O+7TvfvuFXAoK4ATOPYtF7Tkk3sCGVcCwnUgUXpfAtksLjvfF1TGscsQ5NiA3mJyPerCjk9OPVdQlmusbvGWKIECBrKeddtroaMYuyheedx5uwdc3vvHgdEkKzyeccEJoCtPTaMYy4R//8Kc+BQa8461v0/oBKpc+dvAb3rB62XI+vYbmGeZO7ff7j9tuM1OgL4Cdws5O+tIJ1/z5zwAwQ0LLsEOkx5g5geZjH/6ITHRXAJ5l+sXPz+FSMDm/7eLFEJFiem5uYmZTs+krwj/ymGN0db3f/AKsaDoAAIZ+7UHlOQADUlIE5gVMLohIx7xpixgX+fzcX/6STTate8rXv0F3GD1YErxgztz9X/EKEJFYUlVV6FgCTTvgkwOTu3VGczNpt7a3haztelpVVdPYyN0KCNkYWVA03yD//4knnmhjGG3GFtwCla2Yvev2O0Bln8RDjZHo6IUf/vCHQJSpE+1nwdx52FAc63h9fsb3vr/jdtvD1QCw2QQi+uxRR8OH5sMBMwmAPeIaGBqsrqicM3vOVVf+gYty5aOP1jY3k5V13Y6M4g02DrCkOcRPNzUJwIZor7jiirWtre89/F177PVibJCJdcjaAL+3BeHVEtY32KK5o7Vt1cqVnW3t6k7B1bIi87OYttDMjELAWg4fzm3g8NJLL+WFXrlkSXVTE7FrvYIInNWIQ95U6N3wyE+NRP16aP4CMCaWqM1mxVV/6eSTv/KlE6jmC3550Wmnn8azZK/mh5csmblgga+Af18Z3PDuas9CFb70hS+qCIkNwRXzxz/+sX6EVfli1fgL/vdTv/B5RUNo6iGOPfrn3uVz/8i8OQqUOOzQd1roHhqkzpJOk6EwDdMNztN67wc+YD5F2LlwawxbJMyloFUY7aqFxrlzeadjmrm/OQk8vyUQd1hlhHyCQsjLE9DEEDgjnQmjoHQ3nUgP0k/9fRakgQ3oN2ZGdjZAoMwN/uRO33qut9K6vK8xUlIEKgL/0CJuDR+ZWkM9NxLTlLZVZh9pTmmCMgknG9VUVyd1ecPBWIHHaJNxB7I8lJ2ULsS5NyUfGAjLUyU2jgTS/K2uDojO8IB85Cg75snMtVmC3t4R2CxOLA06YjCNZj2UO/AmO5SNMXiCfShHFHwefyJHDCiLSxl9Qj97q1CMMiHEb+Vr4Ess6o3FkRgRiRFRUsE3yMoIA1LiQUr6072BssQKixT6EKm3ECmWJI7hAcoSSEZKnsvdPcosF+kRZizPMJKJVrWQs4JI7AkKRCQxCu499K18pUSKUVBAD00HSCyND31O4WM4FrKS4k1xYt+y0mGPEBQBQV8hQvJGbmiisEGePox59q17l4IooJ+IKx0iePNTdt5qGHInT4wRAvOBmtBZZHPX80ACqaIww6JC1a96p8Q0D435udI+tXys6gtauEZL7dAeyqL7aLp+6i/6gh5h/KZ0W6q/k5LuoINoA3qHjhMaQ9AuuetZkkD+VLTpwtPPLVZaQR9Hg+Pwn7jCykqZ0KHBAWfz0N0FRWHGkfqzeREVbCpXC1PZYgtoWX0jXAJsenq4yEwWUtx0Lu1ZURa2IBffq0E4LDczEiJwiktSnJka4qLtts8bn4AoMvDVdB69a40jPGPLX1BzxSOPsD2wUMfatQFa29iwrU2Dto7XbltaLf0OVCAIbPuQncNFZbRqV+PT3APDk5PWzdpEKpVOMzVy0Rn8PyxxzM8rsKQnOlABLpLykssv41A188wvCmGm7QVRUsJTZ08sedVVVq9eu3bpQw9vC3cVFFoUCpjJsaA0bf5WcKxMLYIlnMH+gYrGhvH+gWRxUVdHZ/2c2RMDg8mS1IpHl3Z0dS7edlFFVWWh2YSebsXBjx5rg8UQ0xtNPjFslqHyJwjq1omJJWzuYblsdTWxY1WgtOIbdXHXi/pigXRvhq5j3TrUpNQV0XzRi1/Mc+sniwiuW2NMOM52Ar0YxW133LFtzRo5qqyapiYCV3ECoVAm9jA2qqjgpDVl4ApuTzNqI8PSayNGA5iRu0N6y+zD0d1NepbImm4I6qay0i4lkqkaAkTggfvvN8pptvsxHSF8q7PTcw2mddWq5pkzzWsoBYaZVft7GXf0R8c/1qvWkRHrroArrU5DAh1xrin6nKLhtlj6wAN0GXBI7BI4Aitgzmx23dq1s+zjv369ZjBXfU1NiyrTzmOzHZqreYFUqre9UzNIwnsa/NR0YbpkqLtH+0wWp1rXr7MZpt2whgYGTTo0z50X+sbkpKMhKszXREvsEDFE0uyRdUrwbrvt5izIlpUrZ1j2LPzGlt1RSTEGNxocmyYwsaKOqOAGaaJmTG5kZd0b2ZKAcqkCy7pUAaG5r6qv7+vslFcQpn6aSqlC8lcpZui1TA91UpLxqrulpdZenR0dFleHZi9E0PmldqorKVmzfMXsmc26QHyFRQuPX9at2Kjdij0O/66W9XjApMYpMebxYNikUXEZPZastNRAGEsaJEOiBQYbaZfUaOudx6nm/puTwDOQgB0CE+WVm5w11xSfAaGtn5RKpFhiKOKGdtIBwQY568LWA1955ZW77rqrKVRWiTp9av69RcoMplOF9Sl2BP2NCxE67HQ4VC8ctZc/XcB458cGPMwUM7FGX+OCZYqS4w4CzM+79Y6HByeKC4oFtozhobm5sr19kKrEqm5LZQE5wGBra2esqHEOzWFAYgf6Ap90Msp6t1fUkSICoVRBMP2pFPtOUUiMVdAOkaCCIsnIory8DESK9YY0CsgSzZvnhLawGC8YrGhnGvSZp6qqUo5MiZHyLQkwKFScNAAwblFGQRaYUZDVqwMORIRqwgzJe2418dq1gTdFkxKH8vVTGhTc0FE+8dDqYjBWgJJFOOiIXrHpcX1tRUFRtn5G5a133zhVZBHXUH5RXkVVaXmqbGIsOxD2rh52VFCyWPxaLM9mDFO/GJaFTM0y4MRD4pK79ErBIFLjVLfiK0ssT+kjedYghQKhkSR0SvioKbXEXnmIgudEwQmhLXFeeeiKC278oxaQlQzBmA2JjZcIWV2TZCzP+IaJkRh7cU3JIpJnM681HhQBBQIhIpc52TVr1nkoBpDPgKu8pra2orKsZ6C/u6fPcVLTtuOYyNalqxsra+pKq+rT1RV5FQlxoE/72mQ79zUpPW0az0bC5yuf1Anx0SXhL80SaR3/9W8ib6Jveui2+/7JDzyRn81MZ4LuyQ/73v2HtE+9wFBW+4y7vBsdTQfUTXQKzfgJ/T0cdh13E2nEYxpJ6pgzmmY8tOQhxaEWXG40e11DYkT0X090yafZ30eGBsdHRpuqG+rTtbvN3E1QYml+ibnD6WxefoLmDrvhmlbgx+N3KCDioM7DH/9/0hW3N51a0eRu1Z5ASD8V6kkp45/PifbJT54dGimkc7fOFcYPZRXPQPs8NRsj0ZibVhVYAJPw/bqnRpPpNPdPCB4aDytzHN3Hv6fB2VQZhLCoUoVCgLSwfXK86mrvqJs7p7i4SAAorytXGP2rYdl7STsDgGGV2qhTJdNhAK2O6xoax6Mjf5sbmyTuWB2G+/29fY6Kqa0ONkM7s1K3tLqmtq4epiqtqGxZvoI5r585S6EGY9Rhb6yqaptQDYz0T0/2e57NjAmrhsc0PwtZlEW7SU5N83oBRlx8ymRxbHxW7bnnniseFYLVbS6+7NJVq9cYUgiEBii33XlnTlEOQwKJi1NalESTdQFibdZlNWlFfUNvS2u1LZpGRnkT8zJjTvqdTmQ5ORfssEPeyKgZ+6nBIQDD3iXJknTj7LK2VatAXzKEtUzq7rXni7R+/CA42tNLLNWVVdZzCmWDiu1OrAoA1CSEb5lWNM1vb6J6HjyLdqJBSXNTE1gORbEnPo9hJ2jKO73jDjs4raq/o4NHmgvXjSB22bF/qgxq6u7oKE6Xtq5Y2TxnzujQoIgTwb20g6JZHKtSVIS14hPDIyXFqbzsZDh5iFXu7zflwXBOj4jUfmw9WF6yCH2w37KSgY5wrmN5UbHzOsCznvUt0uclkt3rwxmVcxcs5FMOajiRtCjXAch27ba383D/wHhmbFsn6ForZZUyJy3XvZFBX19iZDQ8LyhYXFYOQ1cAk4n8trVr7ZSmNlVrXWOjTVe4eSF5BTRfXRDpNYrNkmOLNaqbmtctXzZr4TZjA/1Cheubm+yobGnw5Mho8+w57MJAV6c0lvyO9/apbpWujvAgoECNU7sTvX241Rot/66oqxdiMGPefI1QdKADnGvrG5SU0P552z/0kf33ewWJjY+NN8yaDXAGgQutj2Yr412vRVWqLwcIN9Y36Cbq8v+xdx+AlVbF4sCT3Jvkpve22c7CUkRAsTcUsHfs7T17+avPJ/byfPYuFuw+fXbFggqiIqDyFAUsdJbdZWuSTe+56cn/d76zGxfIwi7urgHv53r5cr5z5szMmTkzc2qmOOM64lpTuOZ5En+o0prqqmob18kPX4GaoIw8SyF1evbAIsI/PSPD1FjWInD7EejmSgukx0YWFN9XT4yEw2HRycHUGqJ+Was5CAdirzn66DyuoA3/MzNWB6kLuSYa/EnUWSAC6VJLVXuAsgva9SYL8HMvOQ7cXTnAeFE6KoBAGheUjiolh+tS21NOOcWt3cyirlK36SVmOHTc0K969PNuclPLQgQNw+Hd18Nm3CrEenJhxVf6LsGPAJjicvXor5z02lzpqlW2Wti20y2IYnnlNOao3+Ncmh8V7SPTIzAT17W0cC7DpC6GABLZokhDg/OQBxEOrD85lDLrQ+Bn/FRF+mf+g0/Gqf0ODjpxL6zvlS2EmsnpNVb7btmyA55YCgJQBjpdDeFGYnm4qpHniA7+yJSV2CFuBE06UHjuVwo05IGeTwjx2ModW6eyIiwc8xU0QMYmx/TJkZksUSZTDIibgg1ZTmSnQeMSjGbDMDes+N+hA0/4qTjMJYIGVXWBhksyCFMBsV4aEMIgHWO9eODAwZAZArpW2UD2gjpoc3m9QwZkwwSKi5YTIxZWPvtkAREB0zrogpsXmTn3CMRM+dWlLD5joKgYks7o0o635GexzDCREybwj00GcxuWiIr805Pjtt6I8I2M9g/02tYULY4bY529WFEcztzC/4LSxR30Qyf2OciHiAOTtgYUTAvWNDTnYnou3HDGwV+zZuXSkU99UZRPykICySctMGwXOxCcIdKEmTp4pylEVAadlY5IwaOPOHrz9s3o8omm0B1f6SAVoDsS/SkdtP3XdyqmFP1SI6UOJ0MfqojvELX8XR7sQeuD9KpakDQQEbbdn9rVnxblmlrk4pMY4qKTFpkEUeMEOIHQYbnZLMOplGOKyJwZ3VlHJPeF2+2sNa1rbFpuKklMHCe1CgtN4gnDQlyRHXcUk0pnk95fHqGX+cnGlhbmmhybNCOOemFlw3Scwxs6OogyNWVHDWTu3LSJIa1oaGAhTNaJ92b3nDYJf6orepwxQdrd7T4hBibYHtO8rgpMTrcC3O2vccn3l77yFaP4gux1Rx71kY98hCjbafme976XYZg2KFtbixb1ItPxuzwOlz95N7zsSafSMy6nLa/AKCufkTM9PoE/LpuhV1yU/j6bVPEv3wlMSolO2bEwupYYbxoZrRGEVedhwwy2O45osK9f6BtaQbskazwILEI0jcwmyem/+XBj4MrgKjjiT+dB9wh36+rYKxmSTi1l3hWckmTizicLtnFD81m7rrnrWlsds9SybJkVXQy7PaXWBhcky+HMJ0dmOqVZvaYDejo61G64Qf+REGgjebIOsKYmMDkZ8rASzriD3kE7EiGzxxpa+AeUsFZ1uIES5BhXF4CFP/U+yVAFDof5fEP4yaIsjTjsukW3KyXni0xmswgXgYNsNYHbt3BybmKisbW1ywRpQUHLqlXqgioCdYQYHvis4sJCdnta7cJF4eXMbENjY3Z4RLNaF43npu6FhYYh/PrKmzDwAQgGql3x0IKmX5J1d7u2brXSuD/ZqCyk99UhMH4RFRnOk+AZO2OcR6OUPKXmVVzhmwSWgGhsAbDDWJ3fhhyHxqhriIeUHOxpH7IpZU6uTna4rw8y5BxknPeLz8yAVeu0CRqWA3BZHDmGcF9pihcKNdrVHePe2/ZzfCAi2WjIg1PF08pkWBSyBLLFhTChg2Jp1ZG9yro66ajAK0NI5AcrPOq6LeRcSo4Ddz8OEHW9GZnXUTA9tIb58Ce9CFqfrJShqvpYs4LRCTu0TEiunRdh0nQV6Ur1drF3SiYnM319fL8RKXTZL9OwbFmTLbU7d7aL3PwZQykbc9wk0NkZxojhT9+RxuCGQe05i8wG/erKIqUGbVWkIMi44dELQSA5dzrMM3vHEEzAE95CaWmqtzesoVXcV4++uqEhTFNjI8iy+fXo8F0F0NnZi8++egCXaNlNNhuWOMEt8hxvxYe6d/z36OWABR9keMoDN0CQLN3XhoZqmTUKWgBkXn3FATTqA0WDuuJgpJJpVTiDgEB/yqAgIBxokOEAsopgi+EJP5dJERgrBVvwkWPfCuTNxyoIZ3C8yBz5GSGrHRwYIkQRLo2yUVqwRb1iXUAEwBCQGZJwtiRHETnlAVYGfwqAeSAI8US2S/TgBqJQrSL5PcCiAkA5AYRwZCD4IIAvHTkyRAh4JR1z4OwrWryrFxBcBQFDcpcDx1a7G/xGTSQtGlcrk0m6TOSWtHzW/10+4Uw+Cbk+igzTHfJJtimFTxrohHuc8Ndr/0pr6K8MEinIP67vdESldHwmbF9QVzIimXOLDqNKHLwY2I3SyT31QZiSsVXighBaoQckK+6w86de0gpbx+eIamymZWf0mOyw5herKBsCPMuowjbMZma5b9cuewuMsrA9yg52d+uObYIhr0oJt0ikUiXhyMdCq5QFM0p1bd8eqytLFpsBrr+GQ9Py5fruEE5UVi5ft27FunXAiiEZGBobUHXFux0+zKmIOlxxlpFORq3v9CsFPaDRgXAiUUODr5dccsmbznzD8UcfAxPbgIsK0yZdP/ShD9mpK3BCMvIxxEypQKWsqsr50iYkjekKI5Fj8Hiwrw90wYfzLhDDwDoT2Fi0QQEEGnUPBwsLGp3G6Q7GxFQHm6R4Mo7LqkEJG025B1YbODCXOzkp8qmuq7MoGn+gwURZ9Is07zIzqxaNM0joQoVHUEoVgzFOrrgQpVvqXN/aGlY9pdOYI795Rb3DQE+PkQvdXDgjcmjIojrL4IORZvgtiE2Mt3uwoBcMrZ7FcLha7CnNlAhoIQkBpxDjj9pDKRz2qCNZF+fVyLbmFrmBYPbEnAKp0OKOmFZnDBp5kUTOpVm8S8w35BFoEVa5MyM5FCSQaY9aqRnTCn2zqUjdlgXbQmhjH9DQjla8ezFAiClexhJngixpEcWtHLb6XVCHJ0ROinG64P91dBiV8UZogsgpa2LfMIeCEEiGgfAhk9xUwSrEstiLQLPuGNKyYoXQXUUeVxnx17gSwZWxrjsZs3/nu9/ttFgz1cJaokJ6yQDvg+DZ3BsjXqsMGpYvd/6VaN8sMeBwE76HLnVmBpk4RsKVdZFYd7IKCMkyoVQGpXT0xmgaV6zQxNwU6biNjfCxlSBKTpgoiv9CI+1+LJSEibEtmTV9aJp0oaljyJs6ccU3sAjEecru0HCIKUnrLUYw0a1ZqZg4eQ+83H9zHLg7c4BiBfuVHG4kKKIOVI9d03GJB5gDUQd1oy8UylzfYeOFPk1d0DCKmGi/wUGzrNZ19eg3dCnQ5hfqOa3ngL/+RH72Ap7MB3wlIkQ/Jr+eB0C2W0EGAkV6PynoZZXYWEcRM16h89mzkhBkAB0nE2uP8EHAiu7uIbVLlwFA/YxKHYDFFoDpXTeia5VuNmVoSNcSTqMBHxpwdjyW/hIzYas4INDAasGpgvIwdhL9KgV/iawkgPJHnH3ilSBEIgjxgbCuUnG4xXT5JUZ2MU8QkKIszP0iRK+oleUBQRVwgwlKxYd+ZfNAQE6ho8z4iW/e5QfKtJIGQh2mKSgFAgTGC3MMoHQZMAR1PmG78DW2CKLQokboYbLqwPQrDyarAhVEUZ5op4ACWS2qk77AT80HPVQAol5888gGAhrxwQOCSqVDA7GKI1BmNUqEmAd8/NSCEA7ro3LP3YIDBtOKC4OHr2VJAlEkLZp7ScvnwN/lk1jC2QN/Qht7GEpBtkmvSLWjq4Pv5BO6pB8sfacv2EWFrSSPanK3EIe7EhEHYy106N/y+LUsiZiGeRTyuVjGRfSWvcxOhH3ebsU1p8lL5q+b45WHmyzCMJHrHp2R3r7yqsrRoWEHDpeUlJqdY150nYY32Vim1dlU/iypLBaNsBbuOCWaKjXmTG7Shem5icn5gvyKsnL7MMVaYl0LbvX+xZkSS16dfsRgptKFYYNrY5Oyk6aax7POXrITcqyvv6yq0sHPbuZKZcdjunOk3HbjDiemzqwiX58CoC7MG5cFm11dUyuwZkgsD3bdy9ve8Q6zuA6FZjNsZnagsTyOWve1oDANlbqmxpmxcPScY5by5maHLQOurtZHiCrFM+Y8KcOk+Tqjy11d7ivkiYSVosmJI6UNDVaPBRuTjODCDdpiHnoIvZqmJhUFXUrwwVtWjaLChGsjlB8bGU3Vl+aNjvW0tTNvxeUVFiFT43RpWU9X97IVK1JF6QlDEoNDNk47IgRYxDJvWG3z9oq1awPfKqvGevvYTgt3zcSal26oq9eyxdXlQh1cU522MEOOJxaAWe4bpustgk0WjfOY1Dg/NjY8MuxsJKuO9SbCWdGdMwesn1cj3mKI2sVjMOdgmerUWNwHTAsWWpRbXGyLkbBZpKfbMn/h3fSvUv4UVikoLIc8/nsEojwSYNUCQnA4ktlyuIif7fg1no6KpuUrDBZYCG1+IY9bNTFppM8yuxjNRutebHN1b29ppoyrGKK+VKpvx446S44R7IqjqqoJo+POGKytHR8eDgjwxhLX0LnfQQWMjyZzFxrI2SwBNxvnKitVT3JUAUlgEVu/fLluuCDZ3AU471j7YqbZdWQqiBbsUtzQgB146kIj6pQ3229fMSkSw4fpWTvf6utdpCTFnC2ZkSKoBtP0rxhY+G01G9opi5xiYtMogGtTPlBdy7K8rPneJAZO1DzwNHk4Msb5hdYaHXV2w3jBMWjA2UAP9CDmk1UGQWc9iS8buwgHpJN8pcyV7AGZ+2+OA3dbDuioqQPNQiFtpe8OpdNlORbegfb2m+nAdc4yiEyYiUPNCJZU/KGfoKfJxGTYD6yXaF627LqbdujQxDw8M6gKcmBOr63OpcTCG103PAVmEnfu7PYV8iI3oJAgg3G2zs4eZQHUA4iFzDdWV5cbF9UD68SQLxBipFpbmx0Ec/PN2wCJsZwXAVtpKYC2pIQpxGAZkxOJm5trjKHxquWRwuLA07tVX729wZ1VO7DSgxEJQXhYW46TEPOiOrgB6KhXnZtExgU+ZlNNOY+M2AkUSECjxvKrNwZEdbDFBHBcP1Ff1+B2gryZcMI/HuIA9Lw3r24YnRnMToRzrZRCNVA+LV+54sabboj81KzQAEpXj4EaHTJwhowX7Y5dW7ZsUQoJEuGgFdhBAEWeyiIWWHjCGS0SUe1FQcCRAwhknGsoJyC4oay5ZaDiTkj0qh16uKHqGNNil+J+pfuqRkG4T1CNvoRPEJYHK4DyqAUELII5VKGhrLFdlOKJFpzIjjsbUp9fXVM5NDY6nzfT3LqsKL94oKd3emiioiiMO4zPjdt9fqhF/YDgx50+ivALksMf4+8BwbjDzHtDvv33OwR1WDNgju3wqtRX5EdO6UTCbjS798ZMOxXlFdr+likpra9rnEnNtrV1GAZZOvJJQRbkkzbp0EgvIdfzEG+BLmUxZKZvIdgUjRbQEbPZdME7xaRftI+Ey0yVaMGt9N1Rdu5q2U99Ly5Mj49lJ0ayqYngXTkvPRwJbT3ObNgPnHsODwcO+Eys/USLZscnDhiGjYhBbe74d0+58N8FIHsner/t5PX+QL5zeUi82VRuPYkn+gErq1vFrrY13gottjaJE+Kvj2q8wye1H3n2BmJ2znZQpsjwsilWypZ2knZ3N4O0d7Y7/e4MEASaVxe7otecIeD6jsIkgIlg96Zrgdi9a4z9+t4pS/b9to14W+lassgfNsSi7hy26nIV5TiwPxy4nTOx9qf44cwjrOJCqXHhxSF2vKVzzz3XuiFdrs7Wthr3q+nSY5ixKHo8M+l+5dn7TCx/3ja/rLI7S+W2Z2JBw9Sq68AnZ+eMKU/OzRel8zds69nWOWpwFD7BsiQraZOALX/btp2sgPQYfHINYzAmyhIgeecOyin44RSKzaIfGcNa66VtBOnq6uN3yuMXqjxjBaEhfAJEzMYBVYUMXuRhcFUHByGWUDAGbNEKAw6CbA71YKPa2roZxAhZEfGh9baGi01ly4/teCuDdKVMkKqUj+uda+tl5crabdt6BXtwUG/E5IgjmuwTEr0rqCK+sjjTaGZzU0VX15idQRnBb2puONt7/L3WD0x2be/aOGubTnZwLm+6sqTCmVjFhaW85w0bb8J9zMQi1DHT4KtaaA3VGKb6xHXmi990001yRmzRKAXOSLAUS6ImVgSeNgxjiMzgQAyTwYEebuAVEtAl/6L8BER+YBGumYS1cmKIzJ6En8Y1dqodnqpWRcLPSjnxE3AtEjGUGTR71nwCDZ/N2tsGTox3bNviYEgsyuJnaTE9Xb3mCLMLA92DDmepSJVWFGZaqhvWNq3c15lYkFH17TyY5usdZrsdCLf9BOLClcUFwf2kU3Px/Kc9mYOWWdC258/o5N3Wifj798XedkPeK8ambjRabJm2fbq2pn5iMmsxFsV02acDPmxm0/Saqay0rK+/r87Rm3l5/QP9RivCTrrZGb9mA2zhkq0g3w4s7LGuzfpKWy1sfQJ8L5wXw2l/0pJTmXZndMBeOGMvPpZNFMwPT4/mF6Yv+cvvMsZtWuq27do2Oj5WXLiE5JPu6HPIp+En78Q46jsiHK1K2SkOmSfVUchloGh0irZKIeQ6K5n12CCQPaqEz7RPL0EB6TutlL6f+r5xw42OiEvPFlSmyh92/MOKRcHTBelw5k9g66JnYi0S/IS8ux/16hAo78KZWP7c83Hx/x4KPVq8pv1IvTUyc7N3pTOxbkXggkFeiJfiyx3+7h1TLQC5FfC9/zxQ+MreIQ5756letmwiWSnkjh89roWv2smeVYK+CHocjr2Ru6P3BeTvKOPfv5vstR7YFmdTrPo7Dod1XZXNzaZY/55pr7cDqgLzLf+2ZhgAHYEOwnLYsJ3adGUCfze0YH2SZx/96iKc2VNiqf33LoTqP5F1e2vlPxGNXNU5DtxFOaA7hXmwHcnjTyuihW3uJBNL2P8pJoxRnF9+mHjpkFIalmMkT4wiICWeEswwa7aI8KU80RFkYmzg5Q7CyrsXuMkv6JWf/2eYGJC42pZTyLn0NaaLo8yc+NO5MwCiWim/fHchsCFcPiWYHE3psqnR3lhHg5l1gR2YHFMhtG1Jg4MjEACEh8ci8e24pPLEe4PBZK2gATIMLS00nyNm8KdEMfaaNauF1cpG9nJ2xWwgLFtWe9NN7WZ1ImSRNvxXrVre12dvTTjdADS/kESLDSs7dvR7145AzaXC2VTge/fiXuVMRbF70p2WuKy5ta6m0ZnMvqbSKb+QgTNoivOqweSO8621u0pR5H54X73gCfhQ8gnmkb1KYamCBAY012RAXhCLY0JlrMMlsWvCkB6f8GoPP8s0ygI/ES6nqtWCY9DgvmtQMLUCDOWMBPoKuJwJP8NZXMhc4Kcr/SBDAGJbqA4cLLI6QBjQWF9rPtD9wGAOjwwuX+1y5hGr5FQH5sz0zPD4cHVx+RLeDxz0IQmD+YuJT5echJzMy4gmxd+3cH3EyeEM5P36XXweyCmYs3NujcqrdXro/PwXv/jFd73zv61WzBQXuRO0qLBocHhgcnxKDFxeWhFO3B6fknN0ZOxd737zt7/5ne7e7lMffup3vvfdxvqG4J3ZtFVgZWSe3xCywy5M3P6j/wPYk3AmviZ/qS+ccjJByn0yQDORmtu2c+foRDYvbU3ozBKUzzhUR56DfA6H20bphRfySdopDhUgzPRIui6IFlBwKmAMy5IWif6kQVTSi4LEnhZHfSfh+6nvdk7qTefGZ5yiNzk/WZwfxtUwcXZqLlWU80wXZOzQvuRm3O+Av1PJffF0wFRwOLPKGW4VFS5xIuX7KnlAYee+gOwrnckxlWDvNJta7iwKW6y7u92QQ3X3VeTA0o2NDQ7EvgCxBsTMDE8ODIR6F559kr6QI/eS40COAzkO5DiwmwOiiwVe6Lp1pzHqMLf2zGc+U7QjnDjzzDMdrChaOGid+UKVt/sSp5Bd0SDscd6TYyFkFyvyArmDMdbl2HkXNzKFfmErHQmiNaTxBd1tpJSj62MsxDVkRGS2zSUe+IxknwBBLMgCYNGdGmNOZUV91ly7pkR6MD3Jph5uaFWVADhsnVULmKqGQDKhWuCcV5ljWAs497Suzq7XcEaU4oBwc9UrXdQiFFQd3kIYOUq5kNjOYjBjLCqzPCtWLLfzWXXSYetXdRBWEM4yxLbj+Essd4FFcVgHPpYNwwHjU9n5/BmDGpXllSBACRoxpPTVE/GPJMggPEB4a2Pr9Ruvj1IhQ8SNm469IlJYqQifed7h/sXSUrGuP9EoQySQ/y0eVhZMeEJ7Dz9L4OZZ4Cew+C8FNGCRph1jZu/44BOeQwalms8kMNdforrkj9XhpyKoIKsgKwgfX2uSsQz0gmM7mBhYHgGGHaJOHTWLCMP82blMqrC8OJGuA5syUOehffg1SeSRBL2C36S2Pe7c7sQ9GOwJUULEWZAsDN7P3wBg991C8Yah+JsfBkrGxybSRSlfJ7KT2dHxRz/q0S7CcOFCU0NzSXFpwVzaRaBiYFGSGJgkfOFzXzzr4588at36I4888pKLf/OkJzzxp+f+pLG5YX52JiCfnwqnvpsZFgOHafM4p33nf0P0L6AOXIoAKUEgx0MG4N07NaT1XfJBGjNVpfayWy5cVJxeIvIZx9qifNICwhzlk55SHOlRcdCCt7H3kE62oxZTTITQr3iFGHVWEO30XXck54Hqu7XQcChJF5ekMimz95bfJzIHjdTtT/gmPM/9HBQO7NHkgwLs7giEPjjJ1pHWxrrYTv2U46aiqvxzyDXYZrAqbvc1YTszQ1fD4cwH6bFllJK7oNAIMJUOXRjznxwtdpBqyIHJcSDHgRwH/uU4wJdiPvSu7AjXx8OOnHPOOV/96lcdoPj1r3/90ksvFV/pzv0eBu6IczjG0ZY5sF8k7N1jTk/tMVIVCHngI3qEmDBVXMQuCH64jGjxwi9sbq4XOPf2Bg9YNmR6ke7dJe4xgoqJJhL5i2IhwX+MoCKQBLIgM1wUpKxauKGyuZh3aGhSKKg4vuEhi+yrGVMX9PA7uafS/crvcGi4QxUVcJYTIeALrlUHrHSVJmBdCwjneUuj+bKKCAXlh7CDL1UnD5c3+r4JhHDzsDXJkTpoSBTBIke8CWf51eUXGgjHNHGgIpGf4OCnsmr3wA0yvgJywvoTrrr+KiREbIFS3NHcMggksFoeX5EPMiAiUhNT3slPbCZgwRcYyywnlHj2Cy11K36KaaWAjASZQYCMFgFfVGxGCxreIyGA4IzHn5GfPqkOEFNeSIMbNAiMFsENAPETbpi5wE/sBTkKleqUgqH1qMgcnw4rzpbSIz4MYUj8B7E9AfDeOAqGhfOCTP/kNw6UxJv7+yt/8Lpv9WtNhRS8LUyHGwQryiqx8bRHnPr9c8757SW/+8lPz+3vHQjCb34wlW/lc31DnV2j559/vo0O3/rWt37/f79/7nOee+Xlf/7Vhb8w9BAW04qDZyYFwflxj2m4zFeI9Q/+zrgzNBAeorXAh93/8i3ADn9Pz822d3b09fdYAE9mxyeyVdUVdwn5jPpIj4g6qSb5hJlOEVeCTQUIsClucm7xP0WTTSNGNflH9D32YKqwEBo012gA62TVvQUu935IOZCLge+AvUTfyudRO2dcXZiYCrPBhoX3VSwZJAtjOPv5b19w9pmerOxykByFYbFsz3KAhjt79pn/AD/QeVZw2vFdA/3Mm07BliOboSMY/XzS1R8g0Fz2HAdyHMhx4F+YA3wsMWf0nGLw4zeGRjH4WYhJ9MCHgU8GUlUU9w2qTjcfFzbp82OsJVEgF0MXkQ8k+X+GQxEivEEIhPmCHFwTgE4nFDcKdSKBSgmZOHZeBJ8o5VAiX0pDQ733GFZ58fA4pbuJTwhtgkVFoiZVy28KMd5sJBFWYj8FTeY4Jj+bHfcnJAFXC9xaWpqtlY5hqigLktCzErumpsI+VtDkhJUaQYO2rwI2pIFpKSPCobF2bd3mzSF2lS2EHIWFQCHQBDaUFJHokQEEL0ywdC/yiC3NoAKFCqyAknAXWOSArCI51SWzd1QDvnb12s1tm73Ljy75Iz+xUYoHt2UGRI1HHXXU5s2bOc0gmwSWX0FzUPx1OTniElWkCjj7qkUW42dWZtThJ1BYZyxADKyZ4O8FtuBgrMle2RbjZ43MUCK9SPAbGStFeKDg3vwUNsBZBlSgBUBhvCo0h6qDd7HEnhD9CoP3/AvznPHf398Wkiwzdn7RngwH8JJ4UdGX2vNbWuLCjnxrYuemZsFM56e5YJf+5tLHnP7Yz376sxtv2FRbXePGi5KSzMToZKh0Nq+zvfPKP13ZWNt43NHHiUaXtywXPf/i/J9PjmcNChnUckFLiOZd0zs7E1BV7B/7NZlsBjhMAofgPwmDRb4h+HWQ6OTI+Eh5JhwN4CFIxEC731XkkyhCO+o71aDptJVI6zHIKgJpFt2kMrRD4sHSd4wCCqNUAYHd2rDk1GI3XnfL/ywtZi+1+Ao+oa9ncxobdd8u7OEp0BDS+s+SBqfssnAmC9L+U11NV42ssp0HCx/UcXZAo5ZOY7bSayqbjUbxYFWRg5PjQI4DOQ78S3FAd8qCBPdwj+2IAcDjH//4Jz3pSX6f/vSnP+hBDxIYeBbyHDoWmTyFQLKbNcxiibv9Uy8khU8cMzZFsCSHQIuVkchBdFA0W8Ap5AX6BNWVK5cJ2MwrcnljhAkaYvl2Yh6ZxdnsJ8iyNTbWo54fCbjMnhhOC1S7uhyfFOJDwFUHOEvkeKyFyFMiCAK2mhpXE4fVzsKqCBkaqmOWzZEqCI4UwCX6053GYjaZJcJNmMpyyhMJUSMflJVHoFspensZ+XCyhnT4o72uLoSvgkkhYpi6TMiMDjHcxOGwEteBIPr1Aqx0VMfwFT/j8mMkC0pBjvyUvv6o9Vu3b5UukDYvKoCEs5fIT5zxZ3SO/UIO2EhajJPhqUa1iOQjaWoHTTMJPLBC5tvw0/HXISyXjp9+zehiCCrAgbOqlUJvckd0pUPLvUvxAIuf0IYGQuAAQ48mgJtsqPB4wU/A8Uq6nGpEtfTIT+lqcfy1efLSVKlPS+u5VSgbkDM+5Df8f88j6kv+xVh5T+r+/jdOLt/y1zHaczPT6eLCgqLU/MxcShBckP7jH/94ySUXvektb3jRi17U29NTmEkP9PaTitmpGRFoWVkJTtbV1ZRWhCkZQyGT0+Pbtm2RIQTxdDt690HzHDScuKz/8G+IfgPtySDBXgSniwrdh5IOqp927Zjrz+VZu3rNXUU+F/Sdtuo04qYAAkzgaQd9p2s01EO8JR4sfTfMp0OgOBN5YUjIinJMNcOfew4bBw55DLxnnCuMHd3hv0j2ng5mYaXF7b3cIcx/JAN8nInljhkrhDNVVcZvXWJtpYJzPxZFMuKvn9nPf3eCXsPOYnJG3U1CZmhdtsC8uKx1UXxCX7UfbF/II392AsA8hpwZy0unbHPSi7lpNsKPCC/k95J7chzIcSDHgRwH7pADPCcOYpwB8xs809lZ99ZceOGFX/nKV84777z73Oc+UkQyAoY7hPaPZ+ByqSfWFe5qDbPBBYIiL+LGGFZxDWXgVYtHWQS+GucvVi0K4iOayZPikY0zF3PyIxEiaGQ7xE4Kol0EpSCHUmZfffInh1IGi5fFbF7U4qtKTUK6cLerK5xSEz1OL8mkbrnbcEXRigdfP4m3eaj+7OkJ1wIDKDBTI9wsu1YdW4ntiovxFIGGPwV18iBBdaYlfRUaj43Nuy4F/32V4muSIdxnKzPmeJQFQfghRUUQgHZkEdZBIMaHcooVBdt7+Lk78kSaIhgF596+sOIahvKgSDApPozEQiDyU048ka6g5cdwk0H0q7iyyBEAG4CAFThRcnxC1GL8LJeIUSBoXACFyiDDU1kQpKtXumgKZCMgPuGn6hCrTZEj5978hJ5EmQGM/PSLELiBrKBYF1ZegEVIZLWoOCJA+nBjaT1JqJhMdvKNb/GPJuxG1X/DK5/Ii4lQDp+X/fyVLRa5xW+mvGRiegIciw7Hp8ZPf/Tpf7ryT3+56i83b7v5gQ964GVXXPbeD7zX1/x0fkGRRdPzo+PuIs0UFBa0d7Z3d/c4T0sihErKy4r3rFK04AGC05YS7HUYQcD5ID6BD2GMwP/oxWTSGUzPhF0eFNAYzl1FPhf0nQCTT79UmA6SWxJOpCm7Di1qZex8Doq+q4XSUTSHe1PA2DK4dxCbKAfq9jlwyGPg26/+Vl/3DrRu9emf8id8jHm+7GUve+UrX9m5c6fjkYOFTo6MWxSfOyG5B1qkrr6+c9u2l7/85W94wxtcV++iYJj0JAdCLorSgSayZ5f88pdvfOMbP/vZz7p7tkzkPz/v9uADhZPLn+NAjgM5DuQ4EDnAcMSXGGzoZuOL+Eo6f1FUwPcSJPC0DocPtGc6OmLFtxPNCudaWsqhZNqTc8apFdRxB00V8hGRwPmDtiLSxYerV1e5l0i6gswEZw7mSKiuDsdEcezESCAL8NBrQtUl9MIhsVCMrHxavtzpxxVmXdSFA74qLtYtLRWPucZ+iOsJiEdo19BQ4Shim/S4pACqURXQMI2Mde6/lQ558ON2Pud2wZNnGZ1LX8Vj0emEM4o84EDv6KNbXFMsYFMLEuQH3LuvIkwZ+MEwFAFCz55NIxhwAwQhMhsydi0Kdx+2mhIf0KI4RrU0tSzwExDMjPyEtjkl9DrNCFh4YjLCcR5YTIYGICCoUUHUKQIyNPxCSUE1gqAUQnwFx3ppmIuWb8PPcCUVnOXXWHv42SAFP3EMAxGCHPhjFFZ4TErfkp9j8ISbB5IINF0cy4IDB2BxDDeOOfoYOUGQRzrIfr1HJOGggTzj80tsP3CyUzdvLvGK49lPeSmh73xeai6csJz2kvy5kEH7xGkGY0Ve7vB3zvHRouzb/gqMCY3fktLSokwhN+/Ee52wYtXy+saGBzzogctaW669/rqe3p7q2prunm6TrhVV5eZdZ+dnijLFDsFKFaY7OttBlt+pVaNjY0LfVFoMnG/TgpeA9kH4l9AbSRb2JmME4QTqvILu/sHC/MzkTFisQSRq7W6orSYAdxX5pG7kkwAbJ6JHNI4WeEHwunXr/NKp2LHQSiq8T31vPDB9V13UncJUoXEEqzFmwsrr3HP4OHDQYmCiD2uGVBds85Cu0zlSZMW8ZXixG8qRgNY2WVhlANjW8ySbaUyDzBLHDSQnw5OpTGZsPOtAu9n5uVRZaTxyT/8yPDrSvqujraN9YmqS8kun9qnSkvx0OksojXU52cIpI27rdqGfIyhnZkYcM5AkWuXjRbqcPukLYDhgUMe1Byy3qm2phbwBXRv5LTVhXF3V4LKIdJoJvOCXv7zwol83NDXmTU3aZjEzN6vqgF5hOkyEFuhl5iAMKwNvdCkvU+yKr4Bk2ulu+XlFhf70b2RsNK8wPTA0mFde5j2USrbTjE9OhLLlZQhPFReFLSaoKy0xJZsqyVx1zdW+9vT1SkmBnJ/X3dtzwa9//acrLi8td1LXXHZstKGledpJgO4VKysNpcrL/Dk4PORPHEYviiLtahw0ls/g21GiCYzyGhHHPUubJibQy7bZAnT+BT+/8aYNJWWlUxPjfjMaIvF7gIrM1LXhVSA29+Q4kONAjgNLjAPM0JJ6eFeRQ2IwQcgCt0Qd3qWIGUQIcI5x2r6QXyh4q5fF84eNg4s/ollF4jem2ztTxgu8+uotTDlsuXrsJEdQNFVbW8goyAxDNt2LAEZs1tkZDlJmBRAVM5v9A9mWWkAQJbOvyXyjfaphgkUpvoFScppaFDX39oYIUE6sEFwlkVjKTIwAD0x+J08UPkIzBz6bhBRlxeBQZtPFQmgGTYAn3S9MpNfXl5WXh6lROUGAjActthDBQZCmFphEnAWf7e2jqlNQNtQh4YgjVvgKAoMIphQPDFescO1QuHgJxxa4gUDTv/IDbvoU4b7K7xjna6+/1ovqWEyQcRjhjfWNLhDCWI/431cQ0OgdZ5SVolJfZYaSWMKf0dpiFKoFn6gQckdPXaIXlwxLiRAQDj2YeHySDmdAcEP8HGNdJEtULwz9+sr1x5bIXsVVgasgCIylc9a1FAiRJ9DzCYuQjDQ5YyPK3NXdxXHCECl+kSOc9gsNVCAQNwKG+RkFF33kWfRZyAysZ+HPRTMfaGLY4wYmr8ZBUg74ns2fmc8byU6FI7CErSLU/NTE9Pyk2DPPlF2aL8QjmpqZnJiyek7Z+clpvqhduPOi05iSnRgbn8zGdy4f7zG+T7uUKsnpT+c8h1s1+c55c0AVpPNv2HB9Z/cufxZniq674dquns76xjrnYPn6yU+f5Z9PTpA++b73Fhj/5W9/9ufW7VsEzw992MP7B4cMm4xPTA2NcDLz0oXFHErO7PTMHEfTy8I77GcTdzMm7v0rc/xTBv/i+9Rc3tR8HsLQnldQxD/OS66zxaCyyhpaWJ6u4U+SnLUr11LVvLmZu4p8uh+YfFINEksRyDm9I+piXRKOFl9JLAWnO9ZFL67va9Zee8OB6TtFoH30yD1htmurgoLczsVIRGRRqV5QhFu9LJpZ4kK2Q6FH+6r0DtMjVgvZFpA8pC8HLQYmN9rP6YqBv8kSAoemea9qaOhta2NdrKZyHKHDyLW3nKiyp4e2uPm7t7NTcQZWtsG+3sqGBmJBFBy+AYJPacfuNzc/8pGPfN/73ie9tCaM+5JX50IynqQTTKswgvSkBNEZ48lSrEPq7+ggshKtHPbruGOf1D6TjG3r7BxtUWq8p6MjVVQEHzAh5khk/bsX5HR0OiEjZajVnxQDMuBEqY2mlAzRFrumIN/d3SW837V9u/NPit1eCBPxuaVZJgFKShiJ7MhITWvrTvbPqqfi4nC3wFhYbqTs5PBwYEJBAa1QhWOo0H75H/7w6Ec/+jvf+U5AYGZmGL3T09dff319TTWjgkYQUD0yNAQ4ywTJYKu6u4GqW7ZssLu7vL5eOlCcghkvFn3V12fKygz5QtvIMD23eEZ1irh3rryy4re//e3I2PhJJ53E5gEOJiuHTKvB4QvaYEeHDdIcN2B9zT05DuQ4kONAjgN3ggPf//73xYQMSowoYqSkZ74ToA6oiLNnWZlYJHH7Qz8vhhQLCaKYpBgs+W1srNi0qZMvy5b5xOgIGoMtS86JkIGN8IktANAvAwEOe8FUyRyjZWEvcxkMVmLdhEl1dbUKCp+UFfb7xNHEhBUr6ru7B017SvGoRXyoFtcg4ZIXVeCPdChVVmbGxsJ2Xyn+ZPsgtnx5/dDQ1I03blVcUMdmwUR9CtqJHLm9wCs18nohLLOyMWZjbVk2bi56FRS4xsDyqKOWme+MOMvMaisIK1VDQH4pMEQjqh0HZeAAP/0pMcaQfn3auHmjF4wCFpzIT6AYXDkX5ad0+aGHV/KrNBKCD9gOSY+CcABHHmD38HOFQNcsFgKhp0YXU6NaTgBlgznOxFZQELbC18hqrpQiQmuJgnYeAjTUi3uoiM2nWWX2yaPRxecQUDWYakE+CFqBRCkoyAdKoieQOR/8wCX1zEzZEJ614XZyejYMC4mFU0UCR+HfxGxedsYkcVGqqGQur1B0aRLDSoNUYaawuJQceE8XlfjT2vHJ2Tm/LkQuLikvypTN5hUIHecKUlNz8/Zh+7MgXSynd27rbH74mioOfxYUFWenpj/88U8ce88THv7IR65dv/7Sy/44MzP75re/wwQl+Gd95uxPfPozFieIbp/y9GeYLznjmc966KmnnnvuTxpalj/zuS+oqK6bnC0ozFSWVVTP56ezbp6dLchOzasx4DmdNzI+k5cK77N56YlpcT6sCiT65yW+z8wXTszke7f3b3ourbiCs/lIFlFjCIBzYxPz0/MO5/KvoFCoXVzu6K2VK1ZXVda0dbbZ4Uwy7yrySRqJvb6CcsVOg16YAZYiBibSdIEMy0bIdQiL63v7Aeu7roNi6qCm8qaceeY4/KLi3fdSLym9uBsjc/BiYFtkEsspBg3DaZ7EBE4kp+0bdA0Bp8sAkqHukuRqaV1k2ktiA3Sg1vfoGR0QKXgmdnpnAidGNWw+1Nt79RVXbNvZds973lMlDpfwSVQcbY/eVnGV+qRaM8NBpAxXJ4OjXsixvluem6+/nsDZXsMDYAycL+UIDgVrRd2Tk6ScDRAkj/T0gC8qBs0QLE02oEv0yX3s6L3QiuqaGvPeDEBJZeV0coJFY3OzyFZQrdcXV7dt3qyvKyLlDGFfX7EVXJnMcHf3iiOPHEvGrsMiJzt8rH5JtAs5tviyRjYMMS3GAn73u9+NZMftE2MdgZIB2p7egcGIiWsZoyWDDz4Iv52vLxusIC+nncww9y4Oj4/xx0l7G3p6hL4lyVJnwb8MbgQPHkBx8cDwkLP1SyvC7RRq0b3VNjeNZscmRwMbBdVcj5nR0YHubigpmHtyHMhxIMeBHAfuBAcYF1GBjleH75fdAUTinQB1oEXYC0US66kjt6AxbOZjfdhl5sOLDW9sIs9QumwSmRLGhd3wzruFsHc5pYtU6+qqxDkIiQaIqfWSyYRpRpaIhfLil5VXhSk0mSPt4AAirGpoqHLjAWcUPiDLDIgjuvzJAjK7EGbc+QYCM7gZklWddCnyANLS0mRvnRlOCKsoImnCubExzIUa45aTFZMTq1tbm/0qDibcFFEdsAzyrl3h/CoNAT40QOAcj47mxWlbkFGkFBZVVpSJJ6EH4UiISVqZFcQ3lUZu+CQ+BFxECiCOSVGFr/DxHgHekp91C/zkw6hUcM5lwDRA/KJFFYpLRAgcAJHiidVhqJQ4JatqyCBQol8AkYx2mUF2No+qZQZEugxwg6caVQQC5vDflAIZ1WiRWawrUTyPFfIjXBGYSESgRA8kcQPC3qWD40Vm6UX5wcVaOo+gN1xXVVaeErkXCUlCgJcqCr8mhl0KZBZ0NolQeFfjJkXnCuZTxd79m85LTwkmw0t4T5l9TWe8xK/Z2fkRC4VN9RYWWx84GULiAn9OzqdmCiz9S49MzWZKKy2hHBoXSJee9tgnLl971O8uu2JnZ++yVUec+8uLjjruhOra+s6BoblU8eRcQVVNXX5R5sWvePXr3vKO7oGR3192xUMf9bhrbryJbwbb+VTe5Hz+RJizzSsqKZqcz5vOT+3GM981w0VhZpu+i+OLhbVh9bds/sVzn8NLYcF8Otzf5J9NyJazzznGXlle5XRedgpbHJeXPziWNzSWx10coU95vqYqq2usKene1RVGquwMvovIp40JJJPQ6iKoiRGlRN/DDWfkk9zSAhnMAEsXnhwsfccflUZ1cy5h1IXZMAmfew4TB8LenoPyjCQHMxg9DgFwsvfDPKHZRfbE+AkbqLFNwwoF9YDV9fUsRG1jo2uH9JgQIF66YAtF0nMFA/39za2t5M7yHdLG/OiXfv/738um8w2Xp7EZLFDSX9fX1bNULDb4OlV5BOGsgs7abCewuuyqpqbJoSEpRxznEPm5HRs3MtjE2hEcZpIhwA4RRC/sqIdEmtdFhb0WdsswCSZdZYgPmPI4C8LMLZg0vam0FDkGh0CQuaqh3jFadm7ULlvW37kr4zrE+TkWyIgd7aqoqhzo6qppaZ4dyw4PDQaTloSpwMaGkNOoLCV0cuW1114rEXpVjY09bW0S1XLDDTdIfMADHsD0QolOhv09cy5ImmpZvdoAAT64KnHLjTeuPeaYbF9/xjGYQ0PMOf7jQ8ChsrK4qkqUri5sBx9F2gjfxMbbtm0D32gxV6W7rc24AKLUAEMc60g2+ShoDELzhebOPTkO5DiQ40COAwfOARbk1a9+tW5c8PC05DlwGHemxHxiqmJMony4gDa57pIfqJ9no9mC2lo74kZFL4yUgIcJYClikAbtGCkxfEIgNshyK5cHoSLaR79KyTw0NMziBNuRLI0W/caALfqaYKo9mKQwA1wr/orbeqUDxcqwd8nthGFOiall47zIbBzbnCL3wJ9QBZ/xVZ3RA7OvEkV6cFOErZfe2xvuc2IZ5fSAjBb+wq5dYYEY86c6X1UHT9PF6I14olRic7MrGPI2bdnq0hkPUJacqbeyOmMYmCGeL5oHZW56zgHVFjAP9A3u6uhOQv2w9Ff+hJ+1EBafS0cyGr3ARNU8bDyXshc/Q0Tqk+LQi5EnWywqFnnKHL0dSEY8QQYzJoIGZ04LT90MsAaS7qu64Cb+lwFMHIv8xB8tyN0HRLr8PqkXzojlD2ARN8xXhMQ9wEgmANKlKCInWtSyc+dW7wjJjo5Z+WtBnXc8JEW1DeESponRsVR+uMTYezhKaYk9M7P26xaIQXZ1D46MTxaWlJuVLUilm1qMoeRNWQmdN5ean5udCeM+VghWVFYMj2Xnpmdc2mvR8PjUpAuKzB8IHYfHDIGMzxeEc6zsNiiyK3cmn4Nola3ZFxvQLCQwD+E2cENQI0PZoZGuGuexWR1QOveIRz3x2c97fnZirm37zpVrVonHt7b3lBQVmzW5+rpNlTXVnf3jI9mxlsb6l7/mzP/32jfWN1bsau9v7xkigQXpIvNRYX7CPZeWuOfl2ffnGLe2cAXxfEVVVYnYdXDWsJMFAFaDWL3snq7K6gohWN/AoFIC6dHh4YgbbK2atCxTCAzmzFzYYJ8pKiuaNIgzNzkeFoAMjKbHJkcqaghB6UD/8MBEXxTaufm5qqDulcRpycjnziifVABWhDDKJ+2AZ6BlYIDuiHUFFM5Fjz1kVAdqIg+tjPsmogbpRugU6uzkBy3m3H99V7CpugEQ9wNbhklQigr+vlNmiSnH3ROdgxYDE4jQ/9rom6xJtq7omiuvNI35xCc/ecWqlYLAr3z1f8gZCXOek1i3vrW1e+dOgrjmyCOz7rnV507lOR7Tkhs7fo899lh97vNf8AKBdE97O8Nwzg9/YF+q/cCX/OYSEubsByORV/z5yuuvve6Zz3wmgyfCLMwUG8f+22V/sJoXBJPGArmt27ed96UvPuMZz2ALN99w/Q9/+EOS+v9e/3q2fcIZA8n1Qpf96Y9ibN00fbAB4KEPfejRRx8NNxQxDJa1LFveGvbo2gYcnnC85PbNm4h+/fLWj33gA5B54tOe5taIn/3sZ5dffrlI9WUvealFzga6RbyfP/szGzbeVFxY9KznPPte97v/2OAAOHmzM3hSVlEOvYsvvhjVulQ4P/axj21cvtwaZguu4r5cw8w3bdp4w4Ybof3gBz/YqDUmEEYoNa1a+Ztf/YruXXTRRQ984AOf/OQnN5SWAAvVvLnZlatXDfX2/O43vxVI33jjjZA85ZRTTjjhBOH0QGcnZbbFunZZi/H8Sy+9FMc4Hy7neMG//xvln5nNw4GRwYF0cRFDveP66y/+9UWXXXaZ0Xd5HvvYxxk5F2zr38wO3D01I0dVjgM5DuQ4cIg5IOw9/vjjxRUiFjep6uSFFmwBe3qIaw7jzp5YiwjYGyvMG+sdGjbWLAAWHzKCUBKJ8QWZDNGRd2GY2CliKKBiFCwY2rGjm5kWpLGhXlhkVpIp4TL6FX1JF16KtVQKLAjI5E9LV6ltvZbxRudSRSAI8444Yo3DP5hCRSCgVIziFNyxI+yGhQOY/pQBbjU1Zdu371IvNzfWCzcn3ezc2Wsmk18rs+BNBulIdkEP/CEADgc3OsdS1B6dXZBNkJaVWQAczsRWMFMczklWJXeirCTd2bujoHgGiybz53uHOutb6uoaqlnkieykREWqaqoHhvrNpvIKpMMq8hMmqlAj5ggR/WKF6vbws5Q53pufPBkZIC+s5YLgGIbEwFirRX5qyshPrFCdzJGfaoz8JGMySwdBXYpDQJNJdCC2lgUh8hNDEn7WcEKkYFEc9cAN+eEGiCbGNK3jV7qygPgNcfvsdLogVVvXYAfWyOhQe1dnpLRgdgbYpor60lRYdj6aGs0U7XNLMFCH/zEzbmGv81I6enq6+odLyqvmCgqLNPpUvvhWTDsz7VTU6VTBnIPdENUzNOWAFfGksRHxpFhR/Fk+VTDouqm5ObGus1dKM5nK6urSTP6sq8X6+x3gPD0xOTYxXlFaZlwglTcxNDoyNjwyMT01Nj4/bcNw97DjYwaGJnd1dxWni7f+4W/iZMfZVFdWHrm+csPN7ZnScDmYGHXLti5rKk1Z9m8dnJoIh++UZcraO7eUZUpWrlldWJDqbG8bNxhRkC8Cn5qdEUVXmMLNy7fWL8bnjujiNsNkIDsjRVw9PzObKhqQp6o2vzg9OzgyPDU+YTGglOzkhN3B9Cg7OdnT2WVEgDBoo/6B7uUrW3a2d685evng8OjYxFhpVdnY9EhNbVWmPMwALTX5JP80hY7QI4JNTbzTTUrB/4+JFI3Yk20xi8y6aO9yUmrplIJOiUHEKfSazEd9F1BI3199LyjAQBoBGjYu9MaHX+z/lWs8aDGwTjdMCU5Pa0gSg6e/+c1vPvSRjxCiU08/zRWIJMMK3sqyUoGukcWdmzevWLfOth459aeve93rLvjVhen8PDaDqH3jW98pL808+9nPHuzpec1rXnPuT89rrK8dzU588pOf7O7tLy5MnXvuubpdpXbsbBfaMUjEWv+dnpm58MIL3/P+DzzjjKc+9ilPad+yxazpO//73STV/hzR8tDo2MMe/KCnPvWpTEjD8uW/v/jiD3zgA5f87tLCgrDKH5LTc/Mv/vd/++AHP1hZW2tj8462nTBkPPzSEE+wHwUFq9asQW/njh1vf+e7XvTvL7Bz4FWvetUfL79StorKsi996Uubt27Z1dZ+wsn36trVXVVTOTgw/K3vf3fzTRvLamsGu7rFn3qo97zrXR//+FkUitYx+Yy6GzLOOussColjN99sR1MqOzH1vOc9z7kKqXyD1h1V1dW4x19B/jve9KaPffws74WpfLE9F8ph0cpaDm2H1pVXXvnWt771TwlKRfrsubmPfvSjMrzrAx/gSbC1q9ev721vD+z92XnIR/iPzv2xf5s2bxVEg89K0c8Pvec9X/va17bvaGuodRzIxMc/+akH3+9+trG5MImzoDNAcu7JcSDHgRwHchw4UA784he/YC6//vWv65xZTL0uf0g3y806UFAHlJ8JYwFD2BV2PVrzFKbk2Ajmm4/LBJsB5rn6k70TETErCzEtU6WsROG6s2T4+c6UgrNE76AJdO2qc4svCCwmWiIQqargU8omEeF8R/S6WFea5YiqYAr5AxKFqU4F4YkyiwyWgh4Q6uqKd+0K23plVhcjBVu223tv7zCAxqZ9hSRmOtFJAKw6BaOP66vgEyiQZYaGnCBIh7+CMZtP8sgpUDXfFZfyVrHdA0MwrE5WTvEW5G9YFjbfDg6HZcMlFRkx4Zhd06kihAMIK94z/EP6XvzkkCAf0yRCwLsHt/fws/1W/IQHvsEt5lQEi7jgikiXGQ5aBNpeuENww095WPDIzxgAQ1iNEFPQA40Y00qPECI/JcIfzgCqGjRtbUcYQqyM0yISIYM6EDAfr6AhvaSkDG6lmaKGuvriovQQuCND0ABcjFdcGO7TSs2m4FZdXL78yHDMytJ5nGRjTCVMoKYL3DVkQ3A6U5YqLk0XFnX2DMBzZnpqenKau1WRqSzKlMynCzu7ep0LNTVbNJmddK5LXV1YqDg0OtkzGJclZuwZni0omcsvHZlwjLk9cFM2kluhnCosKrCBdi5Dc3r7nSLeUJpK7XJizuTkcccdJyyyKz4/VZFfVFKWKsfq1WuPXLGi6Be/+CsJ4QE2Nq6gU2YRVx6x2qTvn/50jdYnaf3Dw5U1jZppOIwyTY2MTU3P2FpX1DUwRD5N8I5lTTmFK8eIp5O7iFNDXbPWMUiUzU6I3+cKg2rInJ8usQViarZgfDYfU8jDzHy6sbl1KDvu/l+7iE0jd/YMFmcKK6obLOTe2bmzZaLZgEnRbAbYgqkCgjExM7vE5LOEfOIPuUU1VnjIJw2K+g5nTYAbuKfTECHTIOoQM1ME2YS7lCjws7ycjmgdCnWn9H3uiLVrp0cnx4fHXStVkjZDX+jstWTPx9JRi7s5JgdtsNlKWmt3rekhCmaEqR3JcFs8CTN7ee973/snP/nJxhtvoN7Zyam3ve1t1NU8sOVEP//pT81h/uUvf/ng+967IXkcAbVqRTAboY9etuy9733vby7+tdBXGPyNb3zj95f+9tvf/vb97nc/G0oEwKJtM5b6HZ21BdJqV6NGk2jSuXXtWo5FcTqs2HnM4x7vfKlPfPQjglXi29DY+Lmzzjr9UY92xNRnP/0pQ5g698985jM1lRXqJeXOoLIP2X5g0MTPIAOio/GYjDWexw4bENUhUnKxpfTf/ebi7Tu2rlm7dldP34/PPfeRj3l0U3PzlX+54tvf+c7aI1Z39w585nOftUPCUhY24bkveP7Hzjrruc99NjeorbvbugtzAudf8MtLLrmEzn3hC1944xvPZJtPOuF4WF1y0YUXXfRr2uiYLnhqtm9961vyfP5zZ3d2tH384x+3WuWTnz6bYlNRA8t//vOfn/KUp4jJcXXbzZvxBNNo8gc/8tEvn3023noEwJbhCYBPe/gp//d//zc+OmJCWKuVl2WSAfgwMn3VVVeJ5/UF27bevHNXh87329/4+nOe99y6hvo8R4vlJoHv5v1DjrwcB3IcOIQc4Fo95CEPEcywOPpbJsajcz6EVS4Gmsnj+kOAO2hdM89P1AeN6B3yC1kiriE7woNMbCAjH/Z5spPMH0fQnJgwEhyG0r2zWXNGiYHwJwdR5uAWFxRwIsGJZEpn0aQ7b5jTKRzFBPABMcdYVuaMyRASKMXQ+I1zy86FZOvhI4WxU9y70W8BmAkZAL3IL9aqrLSq2Sh6DyqQoFLINDbWqBPCMGEQ4aB4RAPCzB8EJPJxUaeUddFxTyBuqFeNDWFNb0ZguYCzGn3lVPCbJaoOViAgB1HYgp+AYALIapGCXV4iPyNn9vCzYjF+hpgWK+QEAX/QgnyVxgADvR5fOeUyqA4aAPoTSvKb2PeOFbCKnoxs+OkXZHngEyEDgiHesUKkCn9Ug0BExb2RapCRhhUoNWICsurEYzJAAzPjFDE01AhtGRArHeFKScR/JDgLdzFh/GemTYoZ5/OmpvMmw427poAFsakRV2eEdYYmZcuLLfquqa9tbC4usz7C0JHl9xa011ZVWQOwwkr//Hw3VDua1DoIWmOOtrWy0hFrNreb5HdManV+fpn8jY2rq6oap6bIg3as1QiW6wNyv/ud6ACZv/51y9VXbyotrQfKfrumpjXz80V/+Us/gLKtXHl0krnZS0fH0KZNA/X1K/0Ly+HHZ1atXr6stX5kdGpwaDys4qusLymtbm5ZVVUtPi+ZnUub+k0XlpWW1ZRX1NXUNvtzaHhiLOs4MEdhp/ILMj4VFpUPj0z2GFSamJPoq1+nbZVXl2anZl1hXFRSUVZVl0ezi8taljUOj4wn8+e2G4Rz1AgMYbAf+K4in2SVssOZGOtG4E+56DUpJcw+UZOo2oSZSB8sfSfrVCNUND9ru3UUfar9z9SBf7G6D9o8sPMQU7Z3azyDkdb2TE+Tfi352c9/7lGPepTLZnWs+sfHPe5xwjNmxnvjihUbr732RS96EZ6b1xW16iUtumAttu9sf/xjH+14CuGc+FN3LA/hczqUX38Sza7kUlwDvSBbpSKdPoq5ddwyuz1P9y3M01lb2vvFL37xg+9/3yte8QrbgAaTrSx/+9vfXv/GN510z+MtJCZzJRUVfV1d4uHB4RHm09i1qk26QsYELPMGJrTVRT0g74oD88AiZ8cHmnk2OOc3eAzTU6effvpVV137zne+84R73vOH555r6O+o9esF7d09PTQnWLKCgi9/+cvnnf+LRz3y1M997nNFBol37XLT72mnnfad750jxIWh/NYwW1hy4oknPveFL6R/qoYng8SM2UJt3fX555+PaazXy1/xCrPZFMmS5oc/4hHwfO1rX+vPT5318aef8TQHRPd1dFhlffbZZz/nuc/75je/+dKXvYwVxfOfnHf+85/zbLPriiDwHve4x4c//OFTTnskw+acjbGhIXxr39X11a9+lfUS9lc3NBrRgAlLpki5hW3JEd/K5p4cB3IcyHEgx4ED4oCgQofPVv7yl79k1BgI3hibdUBA7kRmw7geMW8sG467yTdwXVzfWn/j5k5GijvI1vgaI08pzK5giY2LFpDBdY/vddftUIqPyJMTXIkqIW9o1KJBtAiWgr1Lhf2fCiKNVVKQ7fAiw7JlNayZ8V82RUEZ2Lj169eot62th50FXGbAQWhtrXCelNW5IECeDVJKjert7d19vjTcIMl+2Yzc3h5uNhK8sZiQR4hdqFZhm+aSR1mZARcfOgTL1bncXzAjgQJghlUGVasLGqgQ15ud5uMMD4fzvVBnbsyjlK1GO3u2Ts5Ohoi3fzhdUFhZEU5vNiB+0+YNOAl/JEM18hMCsAI8ogFPRLU0tlx93dWL8dMUQ3CBZAtmt9zBlPVAcU4A4TVJ9AKUJdNqMXAfw1T8lI1z4pdfBE9URKIQiEvceulJk4XzsSI/VSRMhbOK4CndHDL8cR5w+PsVEpht5ikhQUALMa0JMu6BphYiPToSVo06DhnyTn2RbgMwnGdHphQMZfMmlloY7CYoi+QtZCx0OlaJ06nKHe5s7tTh2WaILZUIh646DyqdGh2e2Lmru6bW2WBZUR+2ONJUK+EG/mDdxHjYxG4G0Q26uDQ+6QyswnRRGjfyUw7NCruOe/vDZTzUpwib5uZq66uzE3kbN7YJQ+/RuhwPS0rLqmpq6xsLt28fmZpxbpY/a7Zs205oTXtUVxcMj/o3QurAmZmbb1m23H1IPd3j9hXb+ksCrVXQNOvXtxiO0GrayECXdO6s5m5eVrJ9+zDJ11JOxbLiWmJLawsh70vWHZAuDmpo39LS5tbKG25slzKfye/c1W1/n6msstJ074Dhp/bq+lLLSOxznJyZzuSnLNjo6u0Yy467wnNpyiftCPKZnB2rCRCuCbQdbHFMR6TVCDmO6TEkxp7BiyJHHXnUrs5d3oNjnNy1FpVo7eq1Tn3fX31varl508bZ7HTpfKYw30njepup1Lw4JhcD4/Fheg6Y18ktaPGGs92/EVMjqEaA2aVRN2hZZ19Zcenvf2/kl65++KMfKauucvnt1My0XaxOh6cnVkw4Z/hFL3mxm2w/+elP3ecB97c51l1lLtnu6w3LTo468uieXd2OpUuXlN94w01Sjly3vryscnRE/+J+gPod29ssA16+ckW8MThdXtbX11tcktm2Y7v05mUtFnlVVlf99aq/+dM+nn974b9nKiumxicrq2oGh0ae8tSn0eHvn/PDsmrHRLvRN+z6GBkN1wLf++T7zk/NDNujYW9DdqIZDfUNzngoKc7kzYVz8vwbNcg2OtbX02sJmQG07377O87+0uWVFpUiAVudqfeZsz4zOjhCuC0B8js+NpF2nn2qKDuc/dAHPlxZUvr5z3yemulxmZmzPvIRy5KrK8vPOOOMaH11pqhmwMYGBnSpVNHYe3vbLl2d9I98+GPHHXu8AxeKit3iazv9nPeSTJlazv3xT2+4fsPqVWtf8Px/r3Nn+tysO36HRoYf87jHOjPw+htvGB4ZNqz5vg+8H2fe8KY3enejeWiUoqKNGzdC3ix5nlvwpqZXti5H4I9+9COk6SOAwkmdGp6HNXQOIoFK7slxIMeBHAeWEgc4/Uvq0cNH9sSQaeH9hS98oTU4/C1LoJ7//OcHRzZZXLMv5PfF48Xz3+6ZQxx30HTs9jBZBO2dlbnhhnAMjERuHK9OinTLj82NeOEywhC2BoW5wva3RtdQCi8ZDiJSdsqaKv6i/FJkEGu5jodDz+mU7heNYgZhmFceJMsiXWzglzNqHndgYAzHPCCbLJVYX1/R3h6mN2ElkZMKiF8nW7GhavEuvxe4cWiFrt5hhRDwYS48t0CNO+tRkZToqrq1sLc3rBxGNSBKgb96dYM/4SwPKhDlEVg2NhZaTSzCj9yIVFt0zVjLHKPoGJRCxuLhv131N3UpiGpmHX+A9WvgA/44AA78BZm87Q2bNmBv5H/kM8LVa4xAZi/RZce6xoZGNSqINHyTWaiPcPjgJ4TVyJv3qwovMkdWIE1+8ScgcNCswawnLasK79grp3d1YYVPwMJcK0hXUaxUTg/SQJACrIIqkqhGidYR+Apnn/yak0A4zmMLyFICn8O9tYs/SFj0WcgNgmfhz0UzH2hiGAZystWUw7zsq8ssa1k5PZU/MpydHJ/JpApnJsYtPKitrMqUGDEZGRgeCWtXC2YnJodLy9KVVbbFjnZ27ShIzVRUOhiLSk1U15TWNxRPTY+OTwwVFs1LGZ8YaW6pX7W6znlRvX3dM7MTxZmUs3Bm5yaXr2jgYHbs6vXugpyp6ay9hUpVVRe2dwz1D3SCDGxPb3ttXfnKVeXFmfz2jr7s+GBRMWdtxNey8uLW5cUW3fOEjWqNj2eF3lVVlU1NjQMDMxKlWDEhyJfOOW9trbz22p0uUUk2ETplzcp555kVucVlsw1xBdTZxP60/K2ty7x3to9OTmRxpyCVX1gUlMVy9+mJWSMmZJ4bacu0vQSNy5c1LW/u6Oscn53OlN415JMCElS6Q9+jxBItBMaVlYTZGJN3ekEFgr7370Pfrz4Qfd+4ge7QCHXRBdGBYcMw1LJvr1pbLCrVC4pwq5dFM0tcyHYo9Ghfld5hesRqIdsCkof05aDNA+vgIBqxd+CTARVBl9+XvvxlIX5LbvbTRcalxYJKJoHAWUssxnNglZvdY485NTll/tP+e91uQ2ur2wbNkfpUV+0c4mYC6j2cTZDNKltb5SqlOmcPOkTa1lxLQxjzK/78VxuG2V0LpF2RtGnTJg3+rGc9q8YArWCyLAzHmgLV9s95znOIte0RTlF22RFsHftUZYFIXZ0O2qcrrrhCJ61PpxWRtCgxfhFFfK0NRraJXwZMBkGju8X7e3p1gac9/BEgKBgtBOUh6wxVUXHm97+/wBEIPtlV293T+dOf/pRNlcFZXC5APuKII3CS0QK8psp5fmVY4atxOKhyAhwzaNT5Xve6F0QN/2I6tvQO9NtgbNJYTgvLDcXZ/IwiHGDz2Cc0OvO5oa6mt2/Anz//+c87u3tPP/XhqjOH37ZliyFkI5dmwq3uZrwtNUej2WPz9l/6ylfNMNt9jWM17pFKtn5pCGs4Ct2TnntyHMhxIMeBHAf2zYFoH333wovSSwt1YjBg+Y8TGZ3zzyCyceIWPqUQZd/ADsIX1ioGEGyEOWAWjfVmR7h9A8PDen4ZRCyMgvXCmzd3RnvHLMI5+Lt5lmi6ln7INIjoiGljJU0VIk16MIWzswyEsFDwY6/chg27ZGDm2DIcAESwpGoQOJdMofweicydSx5wwFdFAOeeAtXZOYBp0PCrFkAsECsuzrdvGRCJikMe9xy1a95OXWI2lg5wQLit4ha2GKXwRylavFj/aFk0gOryq4gqpDsYJBpuKEmUGea1DZU7doQAPmk4h1QLD8JKb6iKfiE5kx8i+fLiMtPFhakMKkBjyv1iDkrFtAJgy8fgCYgMCHEMD346zgO0MI08MgKgF0VU7c/IH0XUwvSr8cYNN0KYay4nCMj0cGwsK4A2yJD0YAhapNyKn0CZj0UvxEDw4hef5QcBPzETORBQFw6AfCt+8hDAN2IOT8MH4MhgipIA87i4GYPzQUJc/Tg8Muh4Tjg4+Wl0eCw1nwy7zIXl3OH2oSX2mM8oMk9RkMeFy45PDY2bCQjHiVsZbelEpjhTVh4uA7Omb2Yu390fhG/lyhV7yWcZtpCjRD5X75HPsJva+cohZgzy6cqu25fPUsGqJfl0obq6Yo98OpmcfFoFoEkX5DOftCfyOWWApb6+ctu23VdbYa9KIUYYIBPX8RJ+8qlByaeWNdmvxWmH5ts/fR9I9H1wfiY/bBQopzIzY8NDyqpusKd32eqGI45cO5E3NjDSMzltama8sGAJySeVifLJwyd35JMyxs7Brz5BIoZQdqyj77KRatmwkX4RbGGFIgQg6vtt+Hlg+i64ANnGicL5wum8aatICuaLw3703HMYOXDQ2K2bJhB+6RvrQV11/d4Fn1Qu9HdJkEyM0gVhZJEaOxyrq6fviU98ooLMgzEo0maK94ILLpBZj+xGHwXpsHOt+gYH2DxiaoOD/NWNjfrf/qFBi4G3b9tmnRaRNV/avn17bbUtGeHYRn0WNNo6Og1WOQRLnBy7ewAFnwA6d0ofAyY745dY681HRkeiTZLZvQKpgnx+ALo8C+0CAZEnJAM56ZSdt/bHUJuYR+wq5+Mf/3irtKUgQf4Y/MNKwWuuuQaG0LC9+Q9/+AMfSOhr6FRcajWyIrqnltZWBA4MjThtSx8HlAFtHaghYefs2VZt7EAtkLTBWAdXWRZ6MQpMp/74xz/6ZNm5U9sxtqa+3p+GEpw+LQAuLSlWCuYS7cQ2lID85WvWUGyompSALzwxX1m1WAj9tf/5MsivevVrrUW/5MILEY5YQ81+Ack9OQ7kOJDjQI4Dt8MBfWn0sWLPr/PXmcvvNKwf//jHBoWdxWBklsGSyCW9HVAH5ZNdS8GKJYeRmhXw7i4AoQ5bw9vTsXsxYpvJ2JS7G/NoxBluyDMWKJLClHjQIl4qLg6bXT0MBKMvlGKyy8qclzEsRf5YMAkDwsYo5h697F00KML+xMMe4TnIw6DjGHPW0BC2qhogiOZGEX9yZy2tdayVONCf0cfwYteqk4ckRj6jiEeLEKbYS2wC2dQIGdXhADQQokYZfOLj+ioUVFZd+IA5MnuseuIkSARcQUXwCjAONKplQ4tPkAHEO0LgjBBAvLCq4DP0saIIWU4ZRCaAKAtI5CfI4CfsDPzEdvwUeXqBgxRFYga/rDOsQADzNvwcRos8CiLfi6F5xfET1WqM/OTc4waYkXXwVDVMsG4hEQcW+OkdBNjCSkHUkWF+HWgRuOrA99Xog1pkBlA2TyQHzrPh5t2l9SA5IoQQxOIYhKX43SOfw0tZPuGGz5EEbUQrtThCJN5SPsNZ6DITmH9A38Mx5h4NmmhHuAGUwnJHiSgRUoWvdwn5hDAu4YYHo6gwigiwdPKvt0EUOUep21ijMN+Sn3dS32NXoC5N5n1B/GIL5n4PAwf+Htf9g5URdzIRI0NtqTu2/sOoJ3MuXSdIGWhFXHHEGEghVSVWCTOzycX0Dlyqa2n58Q9+sG3nDgukjR8TSl2PEVRBI/SUqm1u7u3oIDG7tm8noJnCIkM1q448Uo2V9fUE13nO/YPDglhgSbPhTxSSXf2XKx3YmyBzpaUGYiNAcgcaLVXW3lddnrUGET3FDQJZ0QJaIG3P2eUw954VtBcW2iEDjZNOOslOHZCDLSouFgObhnUzk040GhjMMZsKE/O0Y4ODapfuPCrzzL/+9a9tx33NmWdCA0NE+DJ7TLEaGDaeeswxx2RKSrqSK7wtlbB+CSrCcu825agUAswqDBkbbAeBtwFDls+WafbVJDylRWC8WPhlL3uZtkCaPLi39sgjUcoJAgo+5oEdFmqwGT7mmQG3r8PybDPkP3Cu9c1bH//4J9hKXZpMBQAOSO7JcSDHgRwHchy4HQ4ICfTA7AuPSi8dvVIej/R3v/vd1uy8/vWvZ1mYSCZPztsBdbA+RaMGGoPGt2PimAAosT4w4RlbUTw4OMq4wEegyLgxowuTk0yDdD4iq8pigmbCiilBQjSRSKuvL3S0MFebNQm2NT+f0eQVKIgJZonVDqyyIFdUhJnhGBhLlMefYrDkWKBQu+K4J3PiV5SYxRKwwZb59isD3FxFjLcKSlSXIuA0NZWaRkYjg8XyelDHr0V4d3ev9AgZEJDhD4cY+aBRZhAEeLLt2BF8Dzj7KhvC1WgcIAZFaIcbk8rnYXD5HorHnMrihvyoZnkB8QlDbKf0mJKVn5ciESjZRKRIYMH35iecha8y4DnqFFcdBDxepGMOAj3su5zQABAtmhUJHtnwk3sQ/ARzlPn53iWq0QMCDyTy0ycZQMbMxfjZFCN5KIlyYyMKgPEELSiFGxxiI0IDZDX6CjesiDIg29RMGPFZUg/GmgMgchDW9LDFEI2YyGc4nHyJyyeeQxLmkcnl5aVaH//RdUv5DEsDiIfMCNTcd0rfw51emlVxjUjkbKIlQuQWx2KzJuJ5F5DPiLBfrCOle/R9hz9pqIkihGDRmlVrbtp4E77dhp93Ut8pF0bRNRvjcZK8+XPOLdS553Bx4KDFwBDWmfp/VAldpBRzuaPDI3EPrd+STMnO7TumpmePPmp9f2+f+7VFjn5nJqcqauvstu3esfPtb3tbUUGQg+WtrUJi4Wl2ZKTPZXQ1tStXrBjt66tvbDTBayFGl8VO01OZouIpGzMKUh1btl579TU/+P45SGpubLKmxQl327duKykuckyes+2k2Mlb4sjNyUnmsaKktLurayBZp8RS/eH3v//WN79pQ7KDrMIuV+fCFRdvuflmo4JHrFnrgDv4x39xPzCA87NzXbs6nUBgt/D05JTqeBN6nJ7ubl1CQ329M5yT9TPFAmYpNVXVYWp6cvKhD3mICjbceCOjXVpVbVPxUFe3i9Sam5q9pDIlmIYtUxOTpm0ryyssrm5qWWb38uTIKJyBWmXVt/4rLM4JEXyHuymyYyeecIKUiaEh562XFWc6d+1qWrECzvkFqfLSstLKqre95a04+6AHPBDmAVdHX3R1j/QPVFZUDnQZj8i86Q1vRJZFcmtXr4lYTWTHZyenip1/X1Kq4FOf9ERn5wXEurrdCM/OHS5BzdWT40COAzkO3FU5wNfhYOmthQTRL2QpDEoa07z44outBvrhD38oPuFyoVAIcRjoZK+TUWt2QAwc/vGGYSiqEci1tlZ1dPRxlGHL55NV0GJLDacZLRFPiXxBFAlC4SxsQBSHWH7ErlvXsmVLOH4ZTKFRjMFESoowHHLKBo6c/HV3zdovLD70lQ8KlDyGejOZ1K5dnaI7zr2cUBUrlpYKzsOZwxKFshEfLw6ysXQLNCiJ7lQhOHf7T1/fFDT462r0cDQh46Wnp1e6/N4BF+cAKAVWXhK2pLCCc2/rdIzruDfS/UISqqqIoQVWIBxYXAItRn0xgoIeqnnVcMNb0IBShXgbzshUI5jQkBglBGSlFviJ5wk/1xk9l3mBn0px0P0Z2SgbfPxKQYshhsX4mdEWIEd+whmBaOGwEUUCoLmhJx3rfFqMnyEOBwGxkEGFmFxOzQEIbvgqUZOhzio2y9kgDzK2oFGi1pECZ4lIWFpPuJrb8VR50MNMfMAoCDtC2xFRt+Fn61KTT5IDZ4KnTUmpNY4aAiH7kk+iclD0ndQZRuEtEx6xooY2WACNu4p84gMW4RhJ9sA86jtayLMUgr1u+bot27YsqDw+/+P6rpkIGHaFw4TDE3rj3HM4OXCQ+yBdW9TAuNQ22bETFl0QL80sQjMPjDzTjPWtrTpKO8BdBaS737Fpky7yzW9+s2lPRs7W1jAs6hzFZFxE12O/K+viIGLFoxmgXa4C1+1akKQKQB75yEcqbggFfNDMyrIZRDZOC5NX4bRsuuwQnI9nTXvSW0ua/vrXv6r61FNPBVzXz3hQBrSwFlLiPDC6UOFPL37JLjR6BgZYQoihTu/jeIRgBmamq52KkJwYQZesbDaMZDsJyHBAdZg3zsv77W9/e8Pf/ta5fXvzqlU0zcnPH/voR52hffUVV1BIJjw7PlHilIKKitqWFkdtq53hiTPYTAurSHlsKZNugbRNwuDDCuE28TrBklPVsXVrwFaoXFLy2NNOQ/vpp5/2mMc8pryuDntdGcX9qqitdYsFEpzOInFZU6OLgr00NDXhgMewLTtvYzbMGez+oWGcd2eVzdWO+DjIAoQvuSfHgRwHchy4e3FAP8ymsFP6Z+612RJ7gH/wgx9wQFlA/bBQge3QpTsci5k45NQntmyhFiaN98VaMZcCNmfBWvPLC4SwPKwbi+DXakE2CP5oQVEMU9mInp5w5a/EGK2BI9IzwyEDgKwSilg0kE3NmiYSL8nD6oGvlIDNpC4I6pI/RkeKiEMHBx3KG+Z18c2vCMo8q7OZ4ObPWKMX6DHCHR1hUld+fr9PjLvbiEZd1trToy7w4eNFdbLx0dEiGwPqkbm2Nmz15B7ERO9ehNCO9+KASFdWvRK9MPcokghhf8LZr0cpjgGUGHHUSTepK4jiScuvXol+FWdG4cmga3TEKuWTjZoohTBsZQMHe9XIC1cc8MhPLEUmfsqsIDiAqFEpQKwII2AyoyvyEyhVxFrAUQpYX4HVUkot8DPwYn4en2W2zE3BW/KzSF3Q4zDgHjgIgTM8VUeYtSA0FNEixnSkeAcQNKyQLWmUItzgZZUUHHo5XxDx/XuZCXc124wQFrrD1oOxwnb7f3Fp6csnJBfkM7aUdonMX1Q+icdB0XeChDm2zwFIAAiGRueR3lXkE55Yp9GNVWGgOIVqk9XY4r6eeNyJV990tTxRHw+WvtMRrRPkzGxVeIKYFhSGECP3HB4OHMwDjRhF8qHT9Nh6aspRL0+eUBKNgVDW3Wv1NdXFmUz/rl1Wf33+S1++8sorRWUnn3zyRRddxFR84xvfcvuOgt4JYuuKFUTELlkd7plnnmmBtBOzHCLFgLlW8b/f+56vfeObemQy+qsLf/2aV/8/Jzxdd911RJkeGmnkVYjoRLzMuNFpImY5kZVM1p5Z3Pue978PNDhffvnlVim/9KUvdVWvSA+2Yu/ZiQk2gDAu7AeOEhoENRn0BRxpImTVSYk02vfLawhnSiU3jAHuRSI4TC9LY4/uPU888dWvfNXZn//cAx7wgOc851nSaZS9wYNDw6965Sv0ucaxS8fGSkvCspZHnHKKZdWOubJqumH58nByhqnpI46gjTgT/KmpKTG2YwbZLWPyhgGf8IQnuA34l7++0BrmU099uHSXCd+4cdOD7n8/6fpE8f3Tn/70d/73uwX/9z3ppEc84hGuWdJqpiPC6ZIN9VWNja5Tevvb3y7/k5/8ZBcX6xFccMVRe9OZr1+xbh2i8Nx25MMjprlachzIcSDHgbsuB/TVkNdX67f18DwtI5UPf/jD2SmJUkQygkyxgRMudK1640NNLHudDOeGembn8pwD5G+OsnnB3t4xkaopQR6tkImDK10w451FSwqGkB7a7v21C5U59iec2UEUyWxv8PbtbV5EQUgWBzLTJSX5HR394ACCRsVl9kDDqmbVeWfo8UF+v11d4UyKJENwLbikAAp+VQcHZjdGpNArL7e4ekJYxZjCHEWJC2FuGbywzxZuHqCSGMwBxaOwAoHVllnVgjoroIUNsjG7GsVXGE5OzsFBjdHo+ySnge/s2Mj42LDlZfKDLygtK8vMF8xlR7KOlixKO5czTFxzJ9SoWQWlakE1fFhk+SHAqsqDdcACIh2GOKY6YCNuUhSEjClZLwv8RCYgXBRwYnFF8Ad6KNqbnyAkow9hoyYCgZIh8hNAPISSRAhIRCZ+ElfwzQEouBc/y3E48hNu6iXGQPnl4aBCRS6KQvLQQF8yCJKFM2jD2bFUOkyalRSWhJg8bDAfnysuV3apPciPKGFmYV6hdfYo5eYEflaVonBwJOgp+UwVFnd1DbsPBdWRdUtBPuEf5DNZD6+tcTsqZiKfZWihfZiPBOmam8AciL4TE+eiD5J9xV1xorpi8zRVYYm7g7KLK8J0l4vI8grjeo3Sndval5h8BpUkxnvLJwWnQdoR6yg+FfNoU0RJxyshQHt3O9Kka3qM3Rc/D1DfqzF0asw9aRPOxMrk2+5hPe1S04m7OT4Hcxov6SzCYAbp0Xvqw3UK0eAZYfaiQzT3aNTQ/UmkbcWaNT/50Q/p0tVXX/3Fr/zPKaecct555zmEaSQ7Fs5GTjp0ZpnGvupVr2I8rvzrX5xlZaZXm9j9ay70/e95L0/h/F/88sYbb3zbW9/yrne9ywiofciObrKoK5HkIRQakpxLjpIuq6y0mtqRXS9+6UtF4OCc+7OfXnXVVbbI/uyCC/QIw2OjXBC9tnsVzNiaXwWfZUKaB11RHNAIKxbFn2r060/FaYvVxUWFRUZ3jGYrEozK3JycBDseaDHOJxgff//73/+WN74JYoJMseUnP3O2UYAf/uCcj33yk+gyvFzd1ARDA97Gib/w5a887GEPy1RVmTRmNZ1byDIFyPNmZ8O4FJ20jRl8g7EIVDtGPe/Zz8EWFxG//b/eBZOPf+TDInz5TQLrGbXCZz/9KZpstELtj370ow1GIFD/qQmyAwPGh1/+8pe7ptgRZc98znNf/erXAPL5z3/+PR/8oCqYUIMU4xa6554cB3IcyHEgx4Hb5YC+2nceD6eTa8Wc8a6EWNYiuRbByZFs0Pe+9z0ulK+e2wV2MD7q6xNzBjGoJegEsOvXNyRTIGHOkzsISSh5Eej5ZTjgxhDAHy3OUbIm2Tsj7pdx8VVYldyl1CUxWKs9y3rXr28VAPM+kwxhbSGXgBsgW3t7R7Am41Y7jQiUeJnl5YUyCLcgJwUaKm1oqIIh10JOD2srEQ8FsP39Q0bMWWowgeLg+mTelHH0opQoVEUywFAAzDcAmfkDWYozmGUTT6KR08In9qdwGigkwBk3Yroi5eUZPFARbqg9EshnAAfO+KAs4BHne6y+B7dHBMXWS5QByV7kD6dRJgePAcWCq06iuhTcm5/qlY46iahANdoxipmOrAAQGngFeTnhY6BcIrCyQZi3gBB1oRrwBX7GCNbMBICKh+bPy/NVi8sJ5xgn7MXPMAIS+SkDhNULOAJRbRBH1QIJnzhphtrjioYYhkEG8z2+Gg2RE9iJ+SW3lwp7Rbz2IkAbLbinsQgMbCPb98inKfdwCJmvC/xcCvKp3T3aCP81Fvy1kaYnGIvK5/r1VQei746DGlpM38N6TAIQayeH2KWtiRCmSVz68qkdybOuiQJSeXyjd5DHw2OPPRZFaPGua9I54OS++HmPVQem76DRCOpvHrjQ/G8SA8/v3kwdNTL3e2g5kD+X7EXZ/0p236iwV4FwT2xyd/hCWkyJf942f0wncx5S5aFUpEqPo2c0WbsAZ+8XvXm0LtSJMVCWnrtMurOjTbclUfGYrpf3xJ56bwh76s13BJfQ2qFTESYVZeEAv21mKbd3V9dtCiSzosHi7v/T1rYD/rpRjxd+CdLg70j7cfqRnG4VqfNJehidPJAHfPqMRvxRDnBwMMf/w8qw5OxH9Xoix9Qr/o+s1ihaR0dPRaNztndOyIRWOBBkcnlzHMhxIMeBg8WB2fy8dEXVopfg6poOVi0HBU4MOG8L6pvf/KabEbiMOlu9Ln8oBku3zbl3Suh4Cwo+9rGPvfa1r2U0dcWLwk+ucM+fy88rwAwX1rNP+btX7vDqGEEznRMzs1Y9mUl0KccNW7raeien5oN5BRBKTE/iRhdu27aThZLOL2RKIMnasgtcQ+4g3GIIxEeUx5hvRCn+8ibdNGxzj3QAGR3AGRTpkWRwQPPuly0GH3oS+e4sDp4Ixhob+d/hLDFlfWLRfOW2Qs9EZSwLBzgL7Zy1LESVDlswAfRiORgr194eLjvh7OIb84rz9fXVVhMjBIGAqxpMq6EMMquaQywRquDYYbRyhYt5s/YBmTYvTM2NTQ4cf6/1PWPtI9O9fWNdfcO9JWVFVaWVg/1DRekSoeCmmzfPzAV7ikUgIB/mKorzt9BAhXALgdhoR67qvKsRLdA2nI2fwlSchKpEoGwxA0EAjBtg4idhEHJEfnLiEagitttX/ESpWjBNTgUBUYXHywI/YYJwiTLgWywLFFZoAoEuBBz2CRk58Q0/oeErNFSHKDgMDA1Zq47/I0MDJgMyxYXkbnxirKi0ZMXK1bg30j86PT5RPJvO5KVaa5uOXX5UWV5Zev4AViPizN6KsPAOsYX3f+TFXMLk9Nz4TMGNW9oLK5qyM/ODA6NjE+PuB3Ztr1uGyiqDKzUxPRUOfZ2a5IwuHfnUvjaeEyiNogWpmJbScGvWrEouPxkhBtHNwy5NRmQ6OoJo+RNj90Pfh3d10/eagvk04cmkDVc180o7e+0ZdI/w6OqjWrd1bEiV5ZXXFg2P9fcOdBcWhDPkl4h8ailqQj696Iu0I1EhyWTeshFM4OsaZoq6Ruwppsz0QudA7/AzhgzWfvoECCZQAYpJU+Sn74af5PSAfIf63rZj+1R2vGA6rypdccrxpzgWaz47W1Tg5J8g5PP5zuZxVbCze3jZViPExPDF/2/1RL1Qr0bUyp/5zGfe8IY3+DM2960y+/NQ69Fta7wTKfmuoh7NpuYP1WrT4D+UVx5A73MnaLidItrAo3cmKJrKu8a7nfw0NgoWUZPNO5G1HpdcxrIaO6TcUVcYZEKeZPs72ZUfAndY6nYQ2/tTCAidnLV4L713xr+/s+JQQrtnQV4jkhCjnLJGLt05JOm84h5cikxbqFuNsRdQUaw6cDjpEGMeRXA7GtGIXswGk/joLBZwXgCbe8lxIMeBHAdyHNgfDlhGxF/Rx+qELeSxLUgpHpV+e3+K3+k8Juz04Yrr5P0GPzgvzG3atlyYDmdEeaKB8NLTM8JScBZ5foIr2PokpuICCob5hSAI4djoGLuCxnB498tA+x0aCtdvKisno8byWrTLWRduycaP5LLHT5WV5a5WjGEt4FxSHqeZpGSyZFQ2CMsPKwsUXX0q4gMZEAyEGDzBFwDzcWErLJSfa3vEEatHR8cHB0NIrPZYRE7XnPb3h6O81MV3BFYRcKamZjm+0jUEclCKuhWrmtra+uAJpiW9uAYaVqh9fCJMpcpjjFrztTQvq61uEDQqK4MiIMusRikxyFSRWEVIKfL06wgVjJKoLvnVC0/xA7CoxjovUuCp9sifCBlMCHvgb4p1gZ8qYvehhFjcgICygCf8DDctaW4EqgsEX4UBuCcIlx75CSt1GTuQWdAumwy+MvrHH388fATnCkJbrOUXfPuLNdns9CRCpibDNJrE0sqAGyBjI9lwC0hh4fxU8N/Sef80/xPViz72A1vkW1oUdjUL9IfHndkW1mvkzwRHHAfwRKPg//RsXk1d7WBv79KRz5Urm/r7x/AfnoRNW9MUHqYAWGNpSk8UD9KlEc1uSNEcGtFDnFDt5Y70fVgMTN8rSoybTGdHhkmauRv7AtRLK7NzI32DuwTAM/NThZmgj0tJPuvwJ/YMZDLKJ/RIY9R34iqRrErUIUuUnwDgEtWgTTEqjorpT3ym7waJsPRA9b0oHc4FmJ2xZnTKmoii/HC3eRip9C83ubSofh6CxMPRBy3ammQuhE9J+GdrgqnPQJ2Yau9J5L0IThUWFRaEftMsZpLsZOWwjckwdYhpmfDkn0+WLFNmOfcqvderjLPB3Ar70iXJ1T40OIyFLx65Lp66F7y9XyPuYdB9vx+dVIzJYeFJuqkEFV2Sc+eSrSbmbIPVjS7LgQCHRaXjHZLijDAuqUvADZh3/3EgSWgFRlqKzj7hZORvqE5+p3eYn+cwJT5KKOjxKfm3aMvuN+m5jDkO5DiQ48C/NAfsQBFOiG3YLO4Xf4tz6WWf9usQcCuaFx28YKmgoMq8pEp4rh5oiHk8XECuISS5hjxC7qOwR4oMHq6z2MDotHgYENnYshiA+RQz8/O88x3l9OJAaI5mzAyC/OY8S0qKhofD4kDVyaMKrHDes0OdOaDwARkyEADEkby9vUMcUJ4ohNUic319RU/PMAjsWsQTKG6rDIJ2DyDcVo6+F5OvvAY8l0cTgKBSkCHD9wVBOhzkF6b65D7jWDs7CBMTukWlZZmitDx9g32VNZWTc/YCZ5saGuOUEeryUwXTU+F4YYY+4hm4mcxm4xW/WaXLmpZt3bEVEzAHVjLD3Cf1QjiGo5FFEmtrane27UQgfLjy0iGM1cou8BOxSEA1aIKfvfkJAtyk4JuvaFSF4qROpUJo6cEn2cNPwFEn5INVbBGNLiqODREhe4cqBrog2PiFlipM5ZeVhOudHfalOmeiDAyGbeGwCrFWYaqsXOUl5rhUtKQe6NnyahYPmeMTc0ZX5udSRYVhwzb5LM8UDo6MRKoLi4NsLDX51NxR0XBVg2K+o9mcaC1RW5OWqJUWUxrR0vTabmEcRIvIEOWT5N+OvgNCMIpT9J3Aj+cnZzgB5VHRcHZ4dGwUAvJkR4K+LxH5FKlSE/IJT0IOcyRjEaqNRkHYi4EDtNN33PNOSfFHSuxPqI90TAYEXUFIxseNMgASuz7ZcGD/9d0lOMXWgRaVpPPTvOrgX/u35NRiSenoQUbmkMfA+wqT9ClICV81u4AqkT+JBfmLo+TUfwK6u8ie0Wt/6qukK+iX5MVnAWbIcMsn5pQWcgLoR9Kewdpb5j3gvyz8PsAQNc9UNszjE7GiRVCjXfEloJlQd8DYJAVADtFvYhS9B6qTOJYtCp8StsdwN3Im5lG1FziYO9ZfiKLZ/NhMoZ9ICi6AunOI5UrlOJDjQI4D/+IccC7D4x73OP0t11MUEbnByeZgHVrOJN27WUydOYPghevlXQxgNpSfBwE9v/gQYlxYv7xGk3s+cft4gayDFG4if1G6P60oFufwFyMciaImOYUKUqT7Kg70yMyJ5Cx6Z0cAF2C4bEjVAMqmLJ5wKy28HBqagoY/4cMfjeFWaWmxC3rEbP4EGdp+XeGZzc6qTjaQFVGRdKf+bNmyy5/w4QSDgzrBPJsW7weGHjxl4Mv6VR3c+LvgiAklNjc7Oypvw6ZN1VU12sVmbpuvhJMFRWF+UFmJ0bCCLJ4cHBjc1d7Nw5YTORgFSOQn9OAMvl/4HHfUcX+77m+R26pG8l78DLc0YX6o0X2QyZKunt4eLJJTdagDltj4CufIXrVEvzxG9XADJPLTO3+ddMkcEVYQfF81K2aaLr4lP8M8sNmthIe79ypb7g5taz5VhC4sAgoaGO4dBEXwEyEywBmqA329Q8OjJeVlqZJ03kxoqerK6tKS0sm5yaV2NDTcuEtTs6K7kan5MOYynx8GdMS6LhuamtgtnxXVVU7vnRxdWvK5ceMmTYPnkCcMa9euNLzV3h7OGEdX/EWIPMlR4qNaX9tJP0B9r62prCN7w/3jEeZsvlmlfDJsVafhku7hznRp2HauR6FlS18+4Uxl4E9ujQtQQCda01Pn6UTNjUrklCLyEM7ZTWT+Fvo+OCgdyUQFT/ZT34W7YyOj1fUtDRUNFkKHshbapAvzbm9FrBbLPQeTA4sHnAezhn3A0jP6EpUwCtM+Mu5OllM2D0GJL36JLHH0ItPeibujtcUgxmy+KCuujlkiMotlPxxpu2tPqAj1JS8SI13o9cCW+kk80LXxuqoICjQQdtOT8BBMfwIeYuDkkROf/XoCf5J6A+eNTcSyUvcEwAFPTZB0o7vB5v6T40COAzkO5Diw3xxwntBrXvOa4447jhNmGeGzn/1sHSyHdb8B3MmMagl2xQro5AlTwcmLELGsrJrn6q/oSXuRWWjER3T8spkxHqFsUnzi9vFxvVqTLLiSKKZiMiQyIrJxJf1yuxElZdkylyCEAEnd0cCxSsInRxHv2hXOQ5YTEFXwI803jo5mhXxgShSOwiRMx5WX9PT0g5zEsc64CpOipj3ZqG3b2oIvXlQk0a+ZHyfg9vSEA3LVoi4GUTqnX4ppZA4rIDCEs8zmx+LSaEEdHGCYnC9d1tfnLMtQi2xwYIlB8CKxMJyaNI/enr5dTcsbm1rqu9u77Ad24RGwlqXBxxP5qZWDPU1uFoXJmlVrdnaGjdZWGltSKzSFWxI8hAXVgEd+4hX4IlWskAcnPfAHlswgQU44yIzDfmUWh8edupHzGAushfeGG4w+RLBAJS2yDLEy8/4jP9WCuoSfKVuUI7tAgDkI0HCaF0JkBgduqpAfGg6SNr0vfXpy3B70ZS1NVpD1D/T6hGT0Fqdthy4vKy6D/ET+RH5JNbYsqQdDqIQJGgMHM/nOhXbpZcZEcF1delenCc7hIvP+iXxmSitWrlyx1OQTbkjQZBqFUtNUDRelxS/B8IlYkm2NIo93eqHJtIKcEr1EWSJdt6/vI4OjqssUpp3hI3OqZEaPQMU8WAjUzMTUcUcf72WJyCch16yIhRIa42gOdfAnFUA+KbX+GS34QztItRT89EJ9Yrv7FDkpg1BZpwEsbZUHM+VX3BP5efv63t/bo0W0QsKxwPagC7EjDm+553Bw4J8XA1v/vGcKNMpZJHdfMR75i1ORJjDlVMSv/pf0eA9/+keI9kRuUapuy0ICHdYG03M9egJKTgEej+C2mQ9Dym7ME3JCdbwSVKErukFJFCoZ2rsx3+O17Cduu/mTkByK7HZ95qxewjgVJTwL88MBvm1OPJQY5Rp3T7Dyp6978+fvOC+gvZ/Y5LLlOJDjQI4DOQ7s4YDr60QUJmeicyZO0CcL/2InvCfXwf+v7p5pYDxnHAwSzMLuKlgB0yD8566ufp4rx47DJ3Ayf8h7Fs9G71lJOeG8atWKbDaswuVHygltVpWx5gVK5AIyFtxHdHlJyJyPsy4go5TXyCVdscKFRmEPKpiKS1cX79Aa554eN4eEKVO/0gXANTVl3d2DIMNYNumKwFnQvWVLp5wwkRN8OAiA3QXDWwUNFdJVJx2e0ENLDDjhJrSrrnbw8iT0ZBakyeNFdRxmc6RSaurrBvrDxuBK09Dh5uHRnoGeyjo3NlT1jrZjkYbjDasomMgEPdujpHNzbZ0VW3qBmOhdTmdKbdm2BUrgcKaJgZwI9DXyE3VJ04T1q6rAQ/46NPjrkUZhqhe44QbIQOGnzOJ2a7klIkF+6dpLjR6swDHoSfcVBI0CgkrVpQUjP4XQGOVeDH/GplS7eTDsEq6rBT7IxPkFfqJCjdawg6O6yqoKE5LdydxypjycDOxkqXRR2PBcNFfY27ZrrmRqXe1qmZfUg0wRr33rWDExHTa3YWxJebH9v1hxW/ns715C8qnVyAOpFpiJrdraurURGUaUJtPW3jV0DPY0JcE4UH1fsdowyujI4JiK/q7vRWlbFroH25yxpjWDVs5PFRUXNdQGfJaYfIbhRfJJbskn3KxcoNHwpA4SKRfVENbiWHzHLupJC0g+IcdDkuDrovoO+P7re2N9XVNdvRPYaOVE7UQmz+KSwtxC6MPcIYRo8J/76I7pYYhgk5h2X8gEoyLbntnRmJ/UsuTKxwBYWSn7gvD3dHmSbNGQB8j7U+rv5Q/mG4SFl2E4gO1PjsiOTkAkUE3Qi3iGWg88UA/82ZvAhNtSYkyL856QYQ/r4p86x5BBum97fQ04xAfHZMhNAu/hR+6/OQ7kOJDjwIFyQFQjYhF38U05qbzVGAIdKJw7kV9Xr1Ts3qNhkQIHKXGbXzSmEjm13ERunyiOs8geCTXFBuIZMPjZ3G6ZZeMWx3TZeIrIUYX8PsWvPE4E8jJ9VRGHMomQxbphVbNISS2KJJO9ZZ2dI/JIV4X8EmtrhbIWXe+eGQaKuYRzMn8bQnFhXoQvKjaP5WKjuL5XNg/01CgwFq3BUHXqgoxEBA4MTGsOKXJydnnAybLSEBVjAjwVAYGj7F0pQGRGFwKlc5TR6xIjf4IsM2Q4xEjAH1ixqvLHr1xq0bIUsTFUfVUXHBCLA/5UChDMBESYChqqJfpTLfCBsz+twATcV48/QYCP9wV+RiQFRdCOR1gpKxGNBE/UhFIueKRCvdITfmYkgoxSmeGJEMAxExBIIiS2uEQUyYMhcPMus0QF8Q0QOaXI7KsH8oIKNErhXvi6BB/eDWbiiSbALsMvC/zcSz5LOzvDmVJLSj7hg/OJDISxFa0JPYmEWWIUWr9SSKxPB0/fp8gAPY3SQm5JOOm6q8hn1Hdc0gNY+4BF2KXPwaKoU+iiDqSXhBOJg6XvoNHWoHdzs+nUninJ/QhilqDW3EVR2sP0w45+CPmSnpE8xcr9GTR2Yb3uLVHyKU5Lyrb7S4zNEiD604Xs1HsB5kLiwgs48X0hW6yXLC7kudMvYdjw74gcABj4eyJKfnXAIcJM5mz9GVISQwLVVMGBLZOL+37/Dhy3ccdGqWRQMwJPKg8/KmXMw0h+nGzfs2Tap8ifiAlkForcDqsPgP5c1v3mAOmnMPF3vwvdrTL+K9N+t2rIHDF5Ifb73e9+J5659tpr//KXv7gTntsduuLD+7AwBflh7qulpXLz9rBflCctaoqxivCM6eEFcgrl0ef7E55NTWU7dw7wFyXCVyLHUanKyvTGjW3ePUrxI0XL3GLvLAto3D7xqlJNTXXmI7ds2cHRjIGuernOFh46tzjGZgoCLoMA2NSyMEwV0Y/HKO/SOzoEXL0ceoFlxJypcuwt/GHr3Qsc+LjGhIeGRsSTyiIBJvKbRXZXFN9XZgFnjP2grZaIFfMnPyCVFWHrLOspz/zslLI+gbZm/Zrewc7puRBODw0MlZWE1cgc67Vrjtiy82aEoMIn1UFG0IszlsGDab03GQAH8kJEE1AxD2/bV6UQrqApWSmI9as6EOCJh1oEXfCED4Qt71TcEdNywlY6mHHCSoZINZjIFKJIxy4xrTzgQEwLggYyNPDToAMSABEtw8HGYC3oK8haBARNqVJoCGuVNf7BidOs0JscnzBvxhXDh8HRkenJ6eWrVpYWlg309E4OZC0AVXwqb8o2yMMr6XdQm83lU9PzI+PzGJKprNVmE+MzwwPhxO9sdsxe+UxpBkWZkqKBobC+oCQZ9Fk68kk4W1ubnExnIYAm09CaRossW+ZI8DC2IiWKPenS7i0tmW3bDkDft7eFQ5X31vfK8rLy6tSmTVsLMmHdBHlIzVl0QGdnSdGSkk+SH6Q0kU8yTD5xQ2tikZ4BN/CEhMOZUiNEs3qhUyQ8bvf1J40AwSIO2kQZkeyrslHfj1h1xNadW/dT38tKMtRhesINW0WlBaWpPDs15gr27Du8A0nNfT5IHDho88CEiW6wcH6NnJAwMkRE+vsG8zNlefMsbGpuNm96ajbfTdCpIimOvxLRSZ+3I3XWkGDKUeHhAsPF/hWEI5lYhRkvC++pdAgO9d1qX3j8eTvM2TsbiffIT7L3VcSNEYv+m3I0fllFXn5qZDSbV5TxPjwyNjw0OpYdGRwZzC8tzi9KjWRH8o3B2UM1NT41OzVhC0w6PyQWpfzpd2Z+BisQPjE+hRUFqaJJ62/C3UopDPHiE0YF/iTvIQBejDkS55xgBWaJ89VT+Y55TudPz0zOzk2DEPkcDhsDCtjAxfl0acXI8Fh+cenU5ExBYWZmei6VKdNATnrIjk2kyyrThZnx7KR0pRb+RUz8SomQFz7t78u+GL2PdG0j5iFY/lkmaDOWf97dW2Cc1hisxIU8hsEJGIrySyvgg6v56WLYomt2Zh45/uUXZjBZengnbwf67MWNvUnGKCPCasFSNYaXhJ/UAZL0gkYQNt2rCm9PRDUkI5yIJfXh0ARKMxlTKpEPs6DbhZU3H45vNaKxD3z2mX6g9B5gfm0BZ3ZCW3jxJwBxSOUAIS2eHROMNPm91b+Y26F0t/q3OJRcao4Dh50DZhj4oLFarhgd0RucdtppZ5555tOf/vSzzz5bREHlg+7crgk7KIjPJiHWHmTY1vDKEbzuui362rBGekZPZRuq2y9LHdi8c+d2i4dKS22jdYGQG4OqTPVZwCyIUpBTGJ1LEZSXm2/eFa0/h5LBRVd1dYVOgF8uc5xs5DJKFwCjVyLC+ZHgyF9SUuhuYLGuQFG3qWD0UPv75Q0OqES1yCnWAjlh5O5Dp2UWXDkdmk0XvuqCZOP7qoK3ymLEABgQ1QESIbsMpq2tA9sXauT4mhtWPLYaICIHMJcvr9ZqolYVyezXJ7OsEOMKywymgFbVev4jjzzyxo03qjpyw4tQKgm567ds2QIfj1DTV9wADQSkqVQeDyuAn142btyoajhz1n014YxwNUIjJkJJvCqKVlysCxR+qgvhHi8KmqCGm7q8Y5EaY6CLLi2CdgiDDA6UvGsdrjwChdPeIaBqf3pBcuSn2pHsUVxFSqFIdXDQfLKpjgzIbLdzrBqG0Q7KXJh3YAP64Byep6goCm01ulDtuaV8Flv3vTTl88gjm6yZIBsw11iYTMI1n4lG7R4VBy0o0nbNzVXXXdce21Qi3iYiWZ7o+04Q6AgIMoCgWRfT90pCdfPN7YprVjk9ShFFQkh+7irySd/JJ7nFJSQQXXKOA/FiMIrpHaW4QSWxdJ/6vunA9J2uaQtVm/iyPFXVoSu+vQjm8GjAv1AtBy0G5peTDyemE/2uHTs0qo0UFKBuxQrHC5AYSmKFENmatLRmz2FUd11O05D5sbAVpKKqChUzw8NMRRXD2djIwDBEEitdkWe30tiYzAxRIDaxSePJyZAWUVGA4eScj9L6+tAHWf5UU8N8GmYOmQ/kMWylD+KSTDilwa0VzpM0Gp9ccrAomKmRkar6+jDxK8iMG4Rc8ZesDdNqA06KT84DKKqu1m+61nBRIIchMfatkCRRfj2xe4KkxTcQiDh7IX4dW7ZAWD/i/HvjIiVVVbZD4WconDhYHDorWoiokU899VjSTAeFCviYctdR8uZidzmX7I0JKWq3fK60FOZ2g0UPYF+VolcRbqDu2LB6praW7RFAu2WPE+SmjaBZGbeoFwkFpw0LLLXu0uxKaWlFXd3U4CCbwbkO8hOFf18073e6uDc+aA9rLnJPjgN3EQ6IBBgIugxf1pBqcDd1RGbhbAT1e84553C/fJLOKzrUZOlIQ9eUTN7yuoL1SRATcQlruby+QkM2Ud/GjR0w96dgSUfnKzumX+UaxkgyBjYy69xkQ6wXHbJsYiplR0aySJM59JNJJMaVlF8UJ4KSqG+MM1crVza6oMcUjbLSFZENbvCJPafuXR+oFnZWhBijEfirRThnljJ07GMzWApbieDAU7p6XRKDyV4i7QACwkZDGEzN4R0mQkG97K5dnVgBf/Ol5nzUeOyxy+N6admAlVMVyASEuw8ChJEJW8d9qxF1KoKJpvcSGbJ25VoTqviMgaGTT65QVhZiMebkYQOravVKF7su0A5D0QWiFIz8xBwQyEzkJyDKxvxmrrDCoVnQiO/+9En06xdP4A9VaAALGqrBEdoBkvji82AiEy0yOyNXRVLAx0BBOO5pO5/QFRsIQOzCTE0JQpTwWErVwIq6fdVMMcI3D6zgknoYcfEIPwiGWjy2u3ek7ZHP5p6eviUrn+7bJhsahZJGpUaFdRVaiix5tAKGR/m0AwNpt9H3yo0bO8mYgtpa892uvodjpbWp9o16TWjBJHIe1d1V5JMwQ5gWUNuogMgxhhVc9EX4GS4ZRh3m/IP6TkfA0X9m57NmavR5joWeD51x7jlMHDhoMTAbRYwEIrpXOqCbC/GYUUmmaGTE6qjwdWZGNCUaNKZ6mOg7ZNUIqyh/IIq1TIbKbLBo27hx56ZNjBIDYAjOJ4okGyk3F8bY4AmM2JXQPc3M6C+iWRKzyTbLtCYrxO5EzOBud+EfncxUVLjCIpgl/98z7XBbNkAsJE5P69xtLfJnQIlRBKe0tMYNCrt2acS8eP5nYixvC+QwpEQ84UiuVOdPD7YXV1R4mdR3JK6DT44WNZiHk8iH+RQ/kjQm4/feR3lajmmxUq6mJppny1zKDuIFJMkYkB7f+I5KvajFoA8k3eBhxB4OgcOJP+foz32xrrCyYnxygjbJQITGenqif0mJDBIinBC5ksF99Cm95aE/P3ZfeO4rPbCd2CSriexjs+1P22mIfeXPpec48K/AgegwUV4+KL+HUvBK/WkJ9Gc/+9lzzz1XR/H85z9fjKHfiH7qoWVL0sOoQpfiVe86O6ezD7OIwhseHiOue2lqqt6+vQ9KssVQB/I+JbYlnIvjXSnFRVB1dVWKCxvkiVZPcGhVpOI8RdlUJx3tSvmVggMIF4Nxo9Xrok0TVvxOX4MLkYRnvFIeOZ6AjI2sJyAOKDYpbQaYu69fjT0hILDguMcJZxCkQJWdtdpZOp6rK/r3KFq+fJlf6DG8ssEEfLgJnMXhwPqqXp+EAWhBgWBS3w4yaBFyVYVTo/tUFLnkK/K50UiQjgRWKfJE1TDZvG2zzOpSKb4BK7+6sAhdIEcWiV0Fq5GfEWE0Eg8Ow978lEGNEkHwjqVwBhaQhOp6+McpWXkkqtfUlhqFoz6pLpqqGBRhJtbBXF1AyWwVNMRk9h6bSUVxXAAHEChnhKxxAYmkgQkN1WGRKmACLHqxVzoIMuBzcV7xoRXyOwU9agY8cSA2Cn6ia498Ok586cqnAReoYrihE6KF+cuWNRJPtET5RIv2cjCZ2QTRL5m5pb5Xbt8+EHNqO/wjKlrKu+bWoN6BAiTR9/Ion/JI1Kzal5AQFXzT3FT1riKfJByxJB9PYlvrdlCxGD+XHUR9x1iV4humYXsUWLJ3pyQ3V+jOcOCgxcB6dKplwwFlsNEkqkRZdTU91N8ZVQtdrTUYOnqDtMkU2Z3Bd+mUSRb/kN3ZZP+AkAxd70se07xlyQywKUgZopkh4HX19VhkJrAgGY4dHxnxLTAnWbVS1tDAlAWr72C6ZCz5gGjV95iX/slPfvLzn/xkamzMrKNwK3oGi8IRKg8m50lSePG5X72bpbrUXui+ddOm733ve3/4wx+4SBAeHxxcFMhhSNRBeFSkj9DJ6jJiBz2Z3KKB7aVhNiA4jnaYQV42VCDB5QA/OOecZz7jGd/4xjewtLyy0qDu97///UsuuEDmlJ0huvg9juBBIQR65vYhoCs1RGKqVhO4ra+ytjbYBgN+UpIzb3YvPVy01sQ3ki0ojhsJ+WQ1Nf/3f/+nZXe07QSECMHfo3S0UouC+WclBpTm5lgUHLC3Rj/gUBdzQP8sfHL15jiwFDgQOzG2gEaLxPQVfCw+4sMe9rAPf/jDb3/725/85CfzLD06EF7pYcA5elqxfy0stL4mxMP+5LbqfPSlxxzTsmNHF5PEjvgTtqIy/Q9jIVji4/J3RXciNIuBWXiLMH2KQFChW/bJaVAmWKJ/rAi6xJPSBWD4oDp2x1e/K1fWc9RtyuVSQ4DPrV4veML51uOBGSEo7t5ZC55Ed9KhBI56vYgB4SARblituiQGq+jrC6c9AejXI9HcrME6OACrilhKdTAZGgpn58bGwg2QW1vDZt3Nm3eoKJjs5IEnhjBOArwYHwIlBRClUO1POACFZOQcs+4Ye4D9qTpcBV9DQ0OfKb9fKSiFiQB4WfMy/je2kxOZgdrDzwB5gZ/gS9c0GkJshjSYQBgObnkBzSoDmCcNMQx56OGhbtm7Gr2AgBBREwgIgRsI6JOuIGLDtt78fBlgrl64wTnigJ/qghumJfzMLETLagFfvT7BH2Q5/akIyNCTP7JxSf1qXevzDXZokShs0MbVRD7rlr58QhVvSQKVxPZ162qMPonZonz6SgBwnrg5sAxpxACliiT63rRjR49S2mj/9D34jRFIZJHAWkFhpD+1cpTSu4R8RqpJOz0l7catKKMNC7fm5+p1CFzg5z+u75ROEwTVTpWBlux4W1IKcfdHJhiJg/JoP0LvIUM8eIJS39rav2vXtk1bL7jgAlph7PAxj3mM1QWWK+XRnDgPeVDq/qcAMdrKcUiiMjrDYjAn3/ned+y3/dCHPiRuIdn0R9djkEcnKvrSWZxwwgnHHnussz4omHhmqKenvKTcBDq+/fLHP7bt5/TTT69etoxC6pUOiCxzhr/97W///SUvUeoH3/3uox71KGvdYBWue1/sESgynI4fMfj/1z/+8cQTT8wrLbXx3wC20Ootb3nL+b+6oLGmHkrRaC0G43CkYaNqSJfHi05KiocTZkHBcG/vNddcY5kWS89K3eMe93jCY5+g53VkJzm89NJL8QRpvDBU/O///u/r3/yGdF7B5k2bmTehs2GLouKDpAJxfV0yzm1MQVsTj85t266/4doHPehB2ldgbIE6/GFCHvbFO+3CYUIw4eH6OZPD1Mm//du/dQ8M/Nfb3/bKV76yqqYakOimTGTHhZr7AvVPSQ/NND3dsnat2YfNmzef9KAHhUh9elrr/FPwyVWa48BS4ACdFbDxdSgCdaC/ooJXvepVunpeaZype8ADHvDWt77VJ7qvizvUaOujzDeE/yeP/6rX7tGxqbDt7aST1lx11TZBi4/Rl2WS/MnMxfAAhsr60+yfIHTHjm7+3IK/GyN8MZjASVeMKOYGEwCxSmx62jB42KwrkfUEx0bU/v4JPNFVyiM09VUUZ8VxR0e4+4fjrrhuX/8pIt22LUzXKAgyCB6BXG1tyU037VBcHjmB4staXmjGnYEQFUjBapWaRrbkdceOcBy3zGjULqiLUaiywj8ISI8rhAU/Ev0JuBdngi1raa6pKunub09lQoA0VTCTNxtWeAEyNzsnCJnIhqXg4eCGZOr7xONOvPqGqyEZOvbkVF4U+dNXLEJLQkSI9nlKPrV1tFkyLUgGUMAJDZTGzBFnwDFEZg2hFbSRvjdpQaPWjitr4nSZtUYyaPjJ5LncyDtzKZuYlpOGFuJnfbV4Wy0AakHQvGAXJgja1etFZhDMIcsv0IUb3iIcpTCRroiYAYu07Oz0ZLogVV9XY+ukE1I6urvkBDZ/psCsYmN1Y6lTORCeP1JXWKfIUnvYZ9hOJqeUos7TVOuM8cne5ExsMtA70D8xObPmiLUOYFlS8klgjFloIyw97riWDRvCanxq4ldKbEpyIopDIDmRriESfV911VXhaDrZ7kjfW8tLCun7cP9I6NCKyvqH+lpbm+fSYW8asJ75wum8FDmftw98CconeokrLsVlz7odbUrCcYMk0xEqqZfTM0j/Oz+PPm7D5g2Rn7F/JhhB3+eCvlNhf2I1fvq6P/pu368egHKFTsyEjMWOkpJ+uKBwafl1Wvbu+hw0RhtLtDLG4lJyYDaYGO3YtOk973nPt7/7vZKCQj3g4Njw5z//eTHeO97xDn2ubHdpnprTDst95ufDflRdZrgZrzCcY5UcEKInCGIt7DEtliypuuKKK77wla885xnP+J9vftOELX0zSM4+UUUqxHt4xSte0TPY99CHPlTMo286UOawanouswwK0kCKKgqEQzgKa7FHhpLKys4tW37wgx+89Z1vu/SS393rfvfTagoLNJk3XaY+Alh3QYjmF4NxmNLwxwNh7DJMGyZv5+erm5svOv/8T33qU3/84x8haZ0xV66ooGD9uvVvfvObz3j+8239ZeyZ7fvf//4O1sRxjcWvrKyo1ANy0aza5SxYmXWwyNCUQKkFfAgbS//FL37x/g+89+c//zlL4PoIvktVTU2mutoQUcx826pFzs6yEC1b8xf2dSf9cuxwZXYgVmzowApPeveMxG3h/LNSKqyi7woez7Of/Wwu0e9//3uY1C5fTuYPCkq5bcAHhY05IIeZA3p4Vk8XrV6Ky8cSpfzwhz/UrV1yySV//vOfqcwjHvEIPXZ0oRZU/tDhqWrA9Vd+BVBOhta3pIvSFlA2N9fedFMX946vzAqwVkKmiJv36CIrLv/69esh3N4eljrrlLyDZhTPiVdOnxIO+ZM5U0T+uEJ4YMA1oiGd/+cXWF6gM4wFbN5jyAeU0Br8Xbv6FWRbcYzPwDDJ4yAOHqoXvagaFY+Q7W8EkDOq+1WEOQNnbCyc36vD1/1KRw6/1osTtmIwyUaAHIN21HkAgaEmAAGSkI8xpGzq1VI2Gfs6OBjOQ65tCvu6J6bCzcOFpeEIqKmxMGstUR6znqXlJeLJDTdvgKcIHALq9ZvgNqYiJKDRoxSqsUuESWDkUTUH3dQrlopXFYTb3vxUi4g0MgeqaMETtKBC2I8JKuWge2GDlBXoRnr9slOEUC0J0eH85+iVEQnVaQhVQ08G8CEpPFC1qBhWGC4RgYIu/FQEk6GXyYRV2e50qq+tKy0J7BoZHYJSYVG4H7g4XQQ3h3Zq68rC0pZ1DVBaUg+voDBTXObs0fz8mWl3BZeUlTrk29BPWAZve0BJWdjsKkTZI5+Dci4d+SQ2VJUgtbTUbNwY1mxrF62pUYiH9k1C1LDKPcqnppHY3Fx90009+6Hv04m+j7f3h+O1or4XFIUNw4D3DA1SeNwgJFN5WFdQWRNUY4nJZ+iLkEyqKXKQz5ERTCDeeEKFRfUUQRNL8b4XP1s23rxxb37KjFL67lnQdzzBxv3W95nVK1fmT82PjY5N5k2WphwNHQ78hMmSUoq7NzKLB0h3huak2WaS3jxoVzr9sY997Fvf/d5xRx39mte85j73uY8tT9/5zncu/u1vXvWaV6885mgnRd2ZWv7xMsHuH4RnZt5pUuEYJBOTVgNRg0xZaVHaUthwvqUOgnroR9mG0JnOzDztaU8TA3N3HHwhTnDolLW7ck5Ohf0Af7ryiq7BvpOOP+HIo9dzSwBPTrw+ADxdSXH/Bz7wu8ls80Me8hBbfAs5WyHAXhzI4OBAnbG7lSsGhocEuHWNDbPTUyPZ4CXUtrR84lOffNJTn3LMMcdk6mrN47FeFHtxQIc4VXPhoQeXQgBsAXBy8+SPv/ENawhv3r79gfe973Of+1yz6/wG7P3md7/73XO+f8w9jjv66KOztgvPz1TV1oyNZ8saG174khc3NDeZAWDSUpUVroFrKC2ZO9AQeB/yk5KeKsibMfyZ3FbtwKq52R/95Fyj66vNZpgFtZMtuRWTdmQnJvbFT7LCk0Cr2RAL0aasja+q/Ol5P2OA73e/+9UuawmTNXnzU07DchL13Lw75ZbUsVhW9dF9nuymm28+6qijRL997e3Tibd0iCUlBz7HgaXLAb4R5EQjFJ/zJLTgXZmpM0xs3NMAKFuglxOZsBd8ssNAieqSKFgHG2oz+MmVd9pyc2vtzp29MJHIU+RDm4TkL8KZmeMghq44uTRYV2bUtLMz7CoU26BRnAZ/F7EMDYU1vfxIeXicfj1KySDiApx9RLJExHo4yjFgi86o8QLHa27f3qUzDFGTnjCJ7qqrS+BlryOfm6mKiUAZTObNmsyUGVdhwg1VbwzM1CJOE9GpoqWlXlNY88n3lRgCtgQTn1StuhgKeoGDxkKad6wIGdQ3NSX4KSnM62jvHB8bzJQXyCMRqILSual5ZzWMzoxPF6WLC1PhIhZ8W9basn3Hdnn8KXSEBn6iHQIgJ60QjAokJYIjToY2sDKHcKugQAwcJ5oAgYac8dcnJMMQ5t7xRHVoly3yM+KmCrEuh174KrMXmVWN8DguAwiZVJxt8QhpVKFqmERZxRORFdyAhb93QHyNfPYbIZeHC5mmVFpbXVlSnBkdGerp7XVpRXl11ehYmMmvyIRoubev15mUZQ2mQZbcRIg20hYeeGKyeYLCdJiyHu4b8KcGmpgMAWRzY2N5ZdG2Hd12DC8d+Ywyph1d3nTzzZ3wjA1H0rQpddC4UfvQQqdkIAmtrdU7d4ZlAqi+I33XdxG5gdGhLKqLysNpc4m+1zsnLDsRLvFWoxQDB0UltjkULyn5JIA886hZhB9bqBWqCbMXozlYZ7QLiyRSJT0YFiX8rHM52d78xCg6gp+AEA/8pAUyKNW6rHU/9b2spBL/xyfGZsamZniie5b+aBoI5J7Dw4GDFgM7c8hSJBvnCYShs8H+/q9+9au1VZUXXnihFiVw73jf+974xjdedNFFJ973vtn+fgp5eCg8RLXoIo3Humxe2B/e+RCWQ4f7JMKLM4EYV+lE3BFhwkgBzP1PPvlPf/7zj3/84xe84AUOnerr6qpraCguKx7u7//yl79M/p2JEtlyJ5jDe6KTj3z84w0i6ZcNMeiJqCiHalEOOD/KWLpPNv0afKLJUK2uq7NDaMpG5XTaCl5HN2kpPULjypX/rDELVGCjJ1CRHKfBeEPpDW94gx7nQ+9733+88Y12pfDP7v+Qh7ho5DGPeRy7fuzxxztBxTWJliALj3HG5CSiznja06xXE5UhR4d4EBfkQwaCbA5pD+uTk9lgF362cMUqKpz9GidS8LkoORUzkLPYoxX00aCN9vZC28FX+ukT7nvfY7Xp/Fx/ZydWlFdVxv7XbVozLhtLeLMYsH9CGseWpf3bn/+8o6PjiU98os15jCU5/Cegkqsyx4ElwwHeM4sgMIORQMJ8Jm/sSU96kvHKrVu3OhYrcSWXWT3BnQojqge+FOiAaNX1xU41WKiwaabAKB4ka5c137CpncLqYSCsp4WqPLxbHS8HF3oK+sQ1bGmpuO66HayVaA05SOBN6lfNdvN9QdDlKhjT9YsxiuYm+iQ/IMIqMIVVoVsrL9c9yrZ+/VoBeUdHmAHWbS7EgRZOuV6KkypdKcVVrTqEd3eHeRvvIIDjBRzIq92LR9jMLtTWVprv6eoalK4sIB5VlJaWOJObQw8NBCI/dte+qkV18QFHC3JQmX05/WlUUx4vYA9O9A6PDCU2N2zqNkKJb/LfuOFGQJAMTy0bbwny3buyuOEd8lp8RcuKGzfdKJ2HLXNchClgFrjGAD7yE+b4plIt4hPmg4BwVaMRKHuq5Yz8hLm5O78SZVCLXw2hlPOujBdIRzJQMJSN9dGIRFQcjgmg4RUcsBQPtZpEmbWCukRWQOGbPCBDAyi4qd0QT3YsTN1b2gnJVDoMQ1gLTTDyx8OOd092LltUENiydB6YQ8ZqhtAiVY4Idj9w3kBvj4kOtz5ZIOvWyfXr17mzsr1jALtwY0nJJ3zwfGAg3FytXTQHcdI0DQ0VTpvTWNqIWPqFvJeVK00X7yISWp+U3pG+l11z/TabC6K+T5tg8JSX0XfyUFbj6I9wWIBmbaqrSRXNkwEjU0tWPolxkM/5efJJkqPfBX/cwD38kcHgUcLPcOv43vwk/Hglg8xy4qfngPV92YrNm26an5gtmStOF4Tj4qamZwqtdyxe3GlfOmpyd8LkoMXABMUWnygQfvWtFqfaz1PX0jw6aLi0bCY7VlySecjDHjo5NlpaVWk6KxoPHoCNmoZhaJNA8YlPfDxJIlsiann6urtJ589+9rPLLrsMTBrOsT7ppJNUZ+DUWHqod2bm29/+9vXXX08PLSczrG6uj4DqznQBApJrrr76vPPOi0uDHvCAB4mUwNRrlycbL6/6y19+/etfu6GR4dmwYcPXvvY1anD88ce/8IUvBNxj8yVTQwf0IEJ6i28pA0sGjmyI1cUUpRxKMVCULpyamTYArt6/K0ZyR6Jsj33sY8XAzjeyWRcCpmFdoufsJkMGl/zuNwKZM57+NEs9QWDPfvOb36CanTOfaZYAc0Iv4/7b6clf/vKXut1XvuY1/d3dn/jEJ7DlYQ972DOe/ezi/LwPvOfdqBYf4i02pgsd81/6k+9//6qrrhIQMopOXhHcskazkxNF5WV/+N3v+gb6LQm+8KJfC7EYOVVENwLhzN497nnP0tqa7radiuglr776amunka+WU0899ZGPfGSmrGxXW1vLihVmLHV5lruL5DHnd7/73a9+9SuGhAtl21vkBh7qKZS1Svn8888HzVXjOI9GlMqsCjMLzhxwKMro8DAczPrKbypBmBs8hsrK6uLixz3uccPZ7Fkf/eiLX/ziueT4SpD5aLrgJz3lybqSudmZ/sGBianJ8sqKcMFxWJBWppWdwmo1Pvkxhq06m7Sf8YynESrVfvzjH+dnWO71ute/3qrl6LVgL0eEOL3+9a/X+uBbosxXgpgx4aHeXosYL774YsKGQLL3wAc+ECiboDbcuIFADowOt7Q2X/TLX+gyMP++971vVXOTs8EJjPDYeJDVwpykRz/60SeffHLkDO9SFXwiVeQVhKNTrM556xvONBVAIMGx3MApX74+5znP8XXjhk1f+MIXiK49wyYooIGTePunP/3Ji/UXT3nGM0CTbonSZz/xCaeQPv8lLxnq7sbMn/70p6QRVjzvGuqWrGcjABkXYCTCpq0vv/xyoqs6oo66NUcfbZDFaTbIkW7S/brrriONFpw/9alPda6rZjIhf8Evf0GeR8ZGr/jzlWg85ZRTWlaunBl3L9VwbWOjxfYDneH2BW1tQ6FRGNrN5dIidEQtMNfQfCtNaVM3rfzPM890kIsMiOVFsUzErGXV6rbNm5evWzdiX/2+rwG7O/XXOVr2kwPkfT9zHp5sJFk/5pfekV6V6ihYJZ0tVENfnQQtvhJ+/YNEyqLXZYx0bvILP6RHCPJIof6+gqP4vqjY1/gYOBGIsgYYnWCQSocDY2+4YXNhaSXVVp3+X40gm/S69tqbg8ImsaveIAkFrWgNJznpCvRddBOe7JSyW7eGsApkVMAwWcpbsGHDLpVKhDZihUAK6mbZeulgyqk6lSYDyyH0AhOSXnicAuDe3nCMlpzASgdHdc6+7esL4Z/aQZDO53YzAGdEBmUjJr46oBPb9DPsF8ZK12/LbPa0r2+QRYAPxCKfTY65REl1gKguAkkX5C9fXtbWNsbyup8OuywBRxEb5wysXX27xmey5ZUlbvocHR4rrixl3a659tqqGgdK2x8WRl3xELFqZwHBhL9P0GZifBUAkwe+AZ5DEj4GECHJLqNLQZigSOxaW1O74aYNUgAkJ9LxM442giBdS8HcLyBe1IKZHn8iigR6mDbcwHNUgw8OPjMlnDH8BA0o6doawoBoLM0NN4T7VTWuCqEjYvDv7e+vrq6F/9abNzmrorqqQq3kSgYETlholuxIMosNDmT4/eAs+qBo0fSFRDC9L2RbeFnIcOdeuIyTU4aHwiVP6fL67OTs4GC4TMu0dpTPssoyq93Hp4J8cjDo3tKRT0xwllOQz+S0OQIW9YKQ28HAvYGqZpKoZcne6tX1N9ywUyNKISE+7dH30muv3bpvfS/Nmy3QfGXFBLhifoa+b1dQZA0stnOiiivyO7p2TM6E9YNLRz6xBSvIp0ldrYkDqIiapY+iaHxpfMDGhX6YRlAHQ0LooghxtI4wI+q2/KTvPExVALJf+r7xxtnJqYLpvExBUTovXZhviYe1hPa8WVS4uPwmanEHqrF3yTvUi0OkR3vjsP/vt0LG1ujD8OTbinpA1dx20inu0GOUyBN7oBld3OKkIoGZzvqcc354zxNPdCeNflN7WCzv9uCmlhYHL+pS3/ve9579+c811YdtIQTU8tHVK5Y7Q0vwTCJ1PY7V4aBvb2uXQcvjibnld7/73c973vNkaGhu7mxv5+jT4f6hMKtZWBCuvxPbiHN42Hx9p4x885vf9Glmbr6irHR4LHvUEevsSX7G859vwpPQc8FNzP6///f/SO1XvvZVk6KsrGW0ddXhPF5xRegd6usvPO+8//iP/9i6Y3umsEhvDmBDbR3gr/rP/3ScIrnn2Wcy4tA85zMyKoiFv0b1Dj0P/jS1trosgv2TnxbpIKxXtqn1He/6r6c/9QxjAY4C/vqXvvTyV70S/IrSMouT4ePgkl/+4pcPPf10Vj1Vmjn9oQ+97LI/XnDBz1/0ohcJP6Zm5978hjOdLApPI76DwyO9e46gYORe/OKXin6RhrdgVpVXPOEJT1AjGhUXBU3OTBelwtmSPf19PKl4Vbdrd6RD6f3vfz8D7/5BQYv4+fI/X1lSVMwAKII6nYJrLe/1gAeI69hI7+985ztf9rKXWRn145/+ZP26I2/avAlDKsrKxXv3vM99MHxmZsoow5ve9GbY1NeEixDHp6Zf9G8v+OQnP6m9cPLss8+W+JKXvOSIY4+dtLA2L/gfbJ3tzlGftZeF5fosAx86HV9jhwUf3dlcXtg27P1vf/vbQx/20PVHrRdtYr5P//7v/y44dGLZy17+cgB/9IMfCKHf9KY3POMZzyCrNneVJeLx/Oc8GzI28cIBbzGtKFVwz3ve00JrcHhfHAvwNZbF2Pivdv6HT87J+PAHPvgfZ5551Z//jFedPd0kZGCgb3Y+r7K8zHQ0mIRWTtL7xz9dXldboxXadnWq4o2v/0+soz55QWTCY2FcXjpl5fyNN20444wzGKq//vWv5Zam5+Udf8wxlOWmm25629ve9tUvfdWUjtXR0v98+RVqEYobV4JV/9Dgsqbmz3zmM8J+PIGqSaehoQEFDeU4XmtsYrKuuqpvcOikex6PM8EvLCuzoAOfv/KVrzjjHD7gMAPbdrZZnG/dJl5xDpBPhs1i9fT2NTbUd/aEBUXNDfU019iH/dj4VmZXVVlZd18/hX35y19OU+QJdCXzIXSBt3vlZZcRRVGubL5WJvx/+lOfgv+alcGub2n5z1e/+vNf+vJfrrhcaG3R+8T0TE1lxcDwyMMf+pBvfeOb5BYyFixYBwHCvnonn3LP3Y8DsyYwK6qCYVjyDymlTeSf1YvWAcpcRn5nxF23IEPs4qQYTww2JXkoi//qK3SMdIpe+JMeffrTn37ta18LpnhpIXMsEn91CiKFsNPXW/58gc4sP3RoHqWkCb0nZ+cK8gsmZueKCwv+esP2gfHUwOiESAkmqtPNukbl5ps79J/si94AGuri8urw1WtlN0MGeYk6EC+IkhN4eZhXp2Ayhm1tu7ieRq+4AfD3yZ+qkEEtCPfgjHrr6mrFa5xOHJMHcGOUegM9pwwxGwjwYZiEwxYq4ZVP8sANW8TFMzOz6sI37FUEEG6riiJikARBK+hURY79/WFaGDJ+fcJhzwK9UsBBjq6mtqGytyf4Lc5czJufTdkxOj92wsnHdA47IWjTTHpCDDw9O1FeXFZTVVuYygBoHHBweABdAELAhCo4+mdgYQtz6XGnLpcaDqjAwEg7fmK4P/nrUmCr27fBBOYsO+Yo4l1rIhP/ZeavozH2sZGfQlkpttIs8JPVlgFPoKesdDn98tZik2lW6EV+gg9hGW7FTx5aTARfiI5AAJ12sWLFKgFGQ13NYP/A1OS4gyeHRwaXr17FkA5ZJj4wlprPKy8oKZrLX1bTePyKY0ryStLzi0TCWBHEdN9PIr9/j4H3nfHAvsxMTeenC2cL8q7f3JedywyNT2nUeRoyK9abcIZLTX0NfrZ37rKDXrw/NjS0dOTT1uyeniHtQhK0oDbViJrGY4gHI7S4rwSJtGhZf1IrAiYDhu/R97R11Avyf0t9H7CPzPVn8zNhb391eY1axoaHslNUeG5ksveE+xw7Nt03kTc2NN7TP9Q9MZ0tLAjrPZeIfFIZ/jz5FCPoeeg12Y6dA7bQMgxBEe6RLtonG13AH4lI4NbqcxTBK5q4L34qgp/7o+9O4i+Ymy+cS5XOZ+5/3P0zs5mSed3w7gHN+fxZsZXdwQVhkTQGJxqx++PiUg1P9aKL12cmLP65eNY9qYdIj/aAP7D/3hoZc1mj2VQ4ZOmQPMF/KK+8XY4eSL34rmNlhRzkQ0R06xxurrPA40tf+EJVYyNThMKetramlSvdlUIPP/CBD3zxi1986IMebDjcVI9u/etf/Zr+VNBLGw2BVlZX//d///fOtvZPfPQjWzdvyo6O/PgH54h1P/KRjxBBOsiUAdLTP/D4xz++t6tzYmZarGUq6V73uhdpFkGJUr769W+Ih50+smXzJlNSZ33s4xtv3iwAuPTXvxZmsHawtf/2S1/6kmnJX5z/87GpKROGjzz1NL2DCVhdCEO1dcMGU17MgCCHAdu5bfv3vv0dQaO4ZefmzcSOQ+BeiJrKqn3xjDhWNjaedsopJjBN8PqTvcEutQvRhabmVPFny403mjU98fh7XvSrC0Xg/T29ZiCJgIOaw+qcycmJ5HRKAaSggj7jz3ve9V/ItwOTVsMZAvpo+JTW1AjbrvzrX575zGcKn8aGR351wS9o9be++53f/va36sVGUZk4TV9pwvPiC3/9rW9+S1uc8/1zBC3gaCNDelA1MStsEwA/91nPVnZiLKu9tCyLKyzRpvrT+qYmBt7OYbOUTpO+9De/FWhde9XVZzzlqdriXe96F2OLkzoItNhEvWnDjW1dXcZKPvvpTwlB4WPRrz9hYmWgdjSYKRFvDS6Eo8WSReaQgSEemr08+sQTQZYuG+mKmWXwgiLj0/jGVBcki3zwBM4YyIeYk2l6Wjsay8BzgoqBl1/+p69//evCcg1tyQAJMbF5/TVXX3Dez7TUX/92FeRlttZXn/jSl770Nf/xWswROhJFoJzpqovC3pmJiRNPPlmEfNrDH0FChNkXnH+e0QEBJC7pHHFMAPxf73yHqVqRJBNy//uc/NFPnMVKITZdXDRD80W1c7NOGFQEId29/Ucfd2xpRbk7q3h8W7dtszX6DW98o/ZVkcZ9yQtfVF5SaoGA2JsGCV9dbaWxOro6LVvQv7ttE25I0Imb3SXYxgK2b7k5TCmnU0aaTNoHM5msPDeeor0oEeCa2GiCuJQtZOwFwDTCFPeznvWsrt6+N5z5evv86ea3v/F1XiaVNFltBOroI9dh2plnnvmjc77vVieRsxbxWHGgyeCTKSnZunGjtv7huT8xonHZ/13a07mLwJx6ysN+8ONz8YpiOlh+uK/PYXKZwjQqznjGM//rv/7rT3/4PWYesXrV7y79P8G8tkZU8PRzT44DS54DxBWOFCFiyq9aQFnfolvy6ysdoUqcM24Zh4zq6b7kpBR0R5/g8RL9MzBlXoCzny96THXJ7BdSfmdmw8SyHhJWgHuh7FY4dXeHGExO6Omg4BDxYaOlBK1OztfhMhYXh0lUj2xsB/SMUTKwbiGVIn8syE/wVRU6UpTqun31p+5FQDU8bNyvTx7Rsno58ZYqo5eaywMNRfzJna2oyB8aGo9xIICQkd7U5HzXcD8wtqgORRiIEBcSe4mEyKZGX1UHYPRx1SiDT3xcX/V7waYnc9GYI7NHu+ntJcZ2VASvkq3Iuy/mjWYIMoB4RwicEYKZXthH8IWjsSKJIMspA8OtuYGVEvkJMvgL/MR2/GTOvMABhorEDH51trACAczb8NPlCWFbjYLI92LEVnH8xDE1Rn6yC7gBZmQdPFUNE6xbSMSBBX56BwG2sFIQdYZKRdegReCqA99XdkEtMgMom0cGX+E8Gya8ltaD5IgQQhCLYxCW4nePfDJKS1c+4YbPkQRtRCu1OEIkaqy95DMc6iYzgbmlvjs4fSyqieaLLQWO4ovpe750j2zyYJ02pbAcCSJKhFTh611CPiGMS7jhwSgqjCIckE7+9TaIIucora+rj8J8S37eSX2PXYG6NJn3BfGLLZj7PQwcOGgxsD6CTPChNaQX5yqJo+538r1vuGnDf/7nf1aXlBiWEBrpR1xvhzCzOkIdoibwuOe97x1sT22tONCUl1jlhhtusIVyoK+PF76ro93UouCBZsrwuEc/amfHLpEYcaRjJpFAEwrSNFGTy4c41iTJnyT1U5/6tK8f/ehHH/WEJ7Q4mzeVeuXrXveh939AqHbWWWdZ0WQ5NITNbQraefPCoYmRkXvf+96f+9znTITCRPgHZ0tAHfEl2EAUVFkRk2CPPv2R4MCE2juFWHdPi/bVZvDp2LIFKxwDIvJnFcg9r8cqZRN01Mz0rHHptcccY95SdGTikcl0l7nJ55Wty604FaKXJqPaOprGulqjWbYWO29MtHafBz8Y2owN3Pg1oh0oiZldRrVj6zZTbS5FUKMFqxbQ8sJEbpjDc7IEFxqGANatWyeKe+ozn/mIU0+10th8MkJ0AU4CwUznG1utesxR6zHhhBNP1E0weMIk1w6ZGMc3BsOKaEG4Hao+mQ/EQ5PeRx59tE3g2P6rC3+1a8eOxhUrgDIhj9gVa9aYRzDPIEQXI5GfiuTEznGLjGbnAHEFJASCUCXHzWsmziOsrM4ltTD3DjcZYm+LQECIhBSP0FEGPEGGT/wM1h0mWKGUbOHPvHxLoIncF7/yFeuxLW43jwoaMUDp/37jG6QiLCjgtGWKRVx4ZfjGem/Hbr3kRS/G/8BAF4No3I6OVH4BHqpUmCpdj2m23LliWPGw00+3zp/HZNDkssuv+Pr/fg1beD+W01mrr73MmgosiQFi4aYiSHLOnPzsN1OUjm5uXO3sVgbNR/IdMidQ56Y85SlPQbLBP2uzN23dasDCqgHtCJkQZo+NuYXL1MrwWFjtI5JU8KWvfrVBiwc/+MF0ZyQ7/qMf/Si4MmNjlOULX/6KEPdXF1/8mMc+FjKesJRgYpKL491c/ete9zrDW//9zne86U1vOun+92doz3jOczQ6Qvhbj3nyk+FsellA/rinPpWQhMPVTNUmTRktq2GI//mf/zn3Z+c955nPwJN73ec+lloQQgtDNO6XvvI/lN0iDuYZQMJsPcIXPnu2xRpOEwBNTqKgPyEVlNG6OpTmnhwHliwHdEELuFEB2u1PGkHOdZ7ResrjoWKh906mgtnHmFM6UY+P4vLryigsBZGBgi8A3/+XoI/JAzXQ/AU+aBBQHcjV1WU29OnZoBF6obCrMByMpP/3JzSk8xH1Y0wVaFYU+6T/VBxgcU59fWFfXziWWYeGUulI05UpKGDTF8kGrLIgV1QEYkHGEImRdp0boxprV1wPKTMIFjC73l4fDlv9s19F4JbNzrPC4EhUlyLgNDWVOsgHjawA4B7U8Wuh2d3dKz1CBgRk+KsOh+GGRplBYI9k27GjAyHSfZUN4WrUmDEoit6zPpCvon9m1mNLyaksbsiPaiPpgPiEIaZePXpm+bWmRKBkE5EiQde3Nz/hrK+TAc9RpzhWQMCTML8PcxDoEXjICQ0A0aJZkeCRDT9JCzTggGrvEtXoAZmjH/npkwwgY+Zi/GyKkTyUuD2xEVkHlKIFpXCDQ2xEaICsRl/hhhVRBmSbSs5PUXzpPBjL6SByENb0sMUQzZ3IZ9jwvMTlE88hCfPIZDfvaH38R9ct5XMiyqfMCNTcib6XDA6OHoi+h+OgNKviWpDIWQ9NhMhtVB+JiXjeBeQzIuwX60jpHn3f4U8aalIEIVi0ZtWamzbeRK1uw887qe+UC5foWlFeOD2BvPlzbnp3z+w99xxqDnA4D86jh9XrCUqshyFJ44ODXHCTq29981uOPOqo8empr/3v/973fvf74Ic+VGnXfibz43PP5f0/8lGPsoeQL+C919bf5ubTHnn65Mzsb3732+ZlLTUtzdOuiZmZNjlmY2dXjxGq7P0f+IDiovTV115jnYCFRitXr2psqHvFq14Zzv6tqx2fnHCMUHFF+Wh27PeX/YEde/KTnnC/Bz9oZKB/pL8vnir0lKc+1e4OexDdS8DKtXd0zMzOHnePe1Q4yyFZdsvILD/iiNrqmq6+Xgcb2h5pqzOpXCWKts7WwLk1ojMzxx53XHVl1eabb7b8O6xHtSfKscD7eGzypFqiUMZGaK2j0cmCY7LXJpmnnnGG86iyVpl0dx99wglSLCJyo49JRXlWrlo1OTvjEHezl4KiYWuKsmMvfPGLnBKMIvSODQ6EYGlsNGsljxMdpqfSRYXyrFp/lJ20Yec+t8Bgdnn5vU8+uaqy6oorr6zTYdn+UVLiE9JUYTS+yxG+Qk1XxiXzq9BwXpc/v/Xtb2enJp/3/OdrIB0c24j8huXLX/sf/6HsZ84+u7y6Ogwx6A0LChqbmhqXLwdhMpvd1d5+wn3u451LA9r40NB9738/3t+f//qXv1x5hcaFs12v+SUZm6JtFD/x/vfr6Gi79pqrnvK0M8aGBt3YGPqmZB8UvnpnsE26ZoqKzAO7iwtLpUvUles+ZND1awj/2trb4da6fDmx1EBGdDt7e6CHq5BH1M1btgC9fOWKs7/y5WmLBkZHMJOMwee44+/xzGc/y1JkGBLCxuamUcuGG+rzUwXXXX/92Z/97EknnPje973P6obtO3bYp2qBI/jT2kI35uaM8vIwz7ltm1rudfK9bUXu7+7SKH+74vKzv/DFRz3ytKc94+n+LK+vu+hXv3zzW97864svUsWDHvLgirpaRgVF1luHk9UKwjS4+NMC4HDMtS1uc3NXX3ONk6eRbLZWxB7GMpqayPDoxHh3b88lv/nNgBtCstnutrYSDkd+2Jtgy4AFD/JYWG539BOf/KRHP/5xedNTw0OD2YnxVWtWYxQtK6mppjIf/vgn7nXSCZ/45Fn4umPnDmpVXFrS0bkLViff9z6p4qKrrrn68r/8FVfe8o63O/BzqLfHPv+JkeGymmoMtDwsOzTY1tnV1Fi/at0RJBPkvEzY2xw2TVhU5j6ngvz2XR3/+42vp1P5//Gfr6tpamxv2zmYcP5eD3zAkUeF+PYPf7ysqqFezm1t7aPZcecI/PtLXmz/le3fDAZXtaK0BEqsEbkNYwe5J8eBJcwBHVTEjkZzGf2psyK9wftJzo7ScUUHSDbazdW2zITzKgMboYgMHkX8KqK48MMj3uD43gnS1SLujf5rstomXNqkOjAFcq2tNS4+9XD+VCqroMXhwTDXEXENI54wEeTorqDBojEfAMoPvXXrWrZs6dNBgSk0ijFYXIirT5ZTNnBip93QUDIwEA52BlCvDpQ8RkgzGUdGd1JwNMqpUrGiI6wGBx0C0CNRKBvx8WJPovWK+AMlRk8VgvP6etHdFDR0rWr04DNkvPT09EqPXAVcnAOgFFhFlsqJFZx7nXGM63TwcPALSaiqIoYW+INwYHEJ5Bj1KY6l0EM1rxpueAsaUKrQicEZmWoEExoS8SrhZ5FSC/yM0mLgj+2TeYGfSnHQ/RnZKBt8/EpBiyGGxfiZ0RYgR37CGYFo4ZAYNYat5oaedKzzaTF+hjgcBMRCBhVicjk1ByC44atETYY6OBupxFuQsQWNEhEoBc4SkbC0njAIRT3DcgPMxAeMgnBDQ9oyu9vw0+rxpSWfZBLOBE+bklJHs2sIhOxLPonKHn2v6ujou42+l+6nvsvGs9XHER6xoobWg0HjriKf+IBFOEaSPTCP+o4W8iyFYK9bvm7Lti0LKo/P/7i+ayYChl0WPCdPGE3IPYeTA6FDP1gP9TOezKgSJjBt/bXu1CTPq1/9atNT1l5edvmfzP2aK7ODl4myEtUKTN2lecjS4oxD55Wyq0hhahm612RxlMuFRWI0yiJPq5St/5yYCt6D5RcG5awcftJTzjA3KEhw1pQ9nKbd2JXapibACZTlWIY3yRn0TLvNT87ELtiOXzAtsmW0fDJ8RdaXrV5tZN6Zybu2b6cD5vECnskQJtMLPZD1+Lak+nX36eDwkETdJXwYj9JMybhjBBd7BITV9fXCOEdJfet737Pg9s1vfatgzYyce25MvrEJAhUqIVDUoURz4mQs2JqBrCwL+23U7oQJCpkdn3R0kFOCwwlDTlcfGuKJsG0uZDDQG5VWQcd0Vzc2hrOqBgdNM1rUairbZUhCcTjayhK7S+8iW/mbli83N4sWLVhWEvpQj7sXWUd7tnkkxv/rGxvbtmyhtNZ7KWIKHevEXTislFEA/ATElPW4M0VXrTLcbVR7R3sbtNFlYvZRp5/6q19f/MAHP/T+9z3ZzJ6F0PoXhEMGfD0pfhIkPcL4eJYwcQn1NZCMiOEDdoPJjhpax1Js8VXYKWRiwXiXKrK0XqLqvANlGbw/w2099fWiVlXwJyBvVTMIujy0AAgHtZiNDLRo+8SR0hZFaVcItBJLW1WtcLZMAGONjKw/9ljjPju2b7fewYJ288bGYEkOhLkguKFq7pJRZZ2pTbYVZSWqtibCyKK2iAvPbEe3pN84wo4tW1a0LocGaYEtJhs6sXR/bj4PvTicTiac7a067qijCJITR5wRrTmC6uXlPfyUh3O29OYOlEIFBGgWIA5p86eRF8hYVv3Fr32tt6MjtF2J+w4L0Wiemcdjb5BBK8fDxqn7HieErVtHtGwY6u3to5UkfHhwkFqVlRSbvWcwiUdVff1Y4l+ahcFqWxi2bdwIGe8EADJplxD09akIkzmP8ZNFDT19A08/4ykn3OteONa6YgUkZR7o6kJmlY37FGFkBJdWtLbsbN9lul51/Cf7tXBSa5q75lYCa0q5pq7OWUMg554cB5YmB6iDvoUCxieaSKhSdileaKhHnviuF2VfbFvQRTCRlmlY36RU6BuThzZJ11vGPw/4N7HRC6XUqmYwnXOsD7FStb8/nNUUcaNl9NGvxaG6CzhQfGV1vMuXr9R79/SEQwQlIsELOCI9fZgMUvQbHEcFLWMxNdvZ2Rv6nOT4K2WVktmkrncw5VREvXonob1LZ0WD+jeJeKgHMM9qqhhu/lTWr0fIV1ZW2NERJnXll+IT5hheGB2dZN10GrGIl8g0NpEpkQ1XPRJrax2O5SaEfgiDAHmtIISG1a5dAecAJDuOFZaDFxWG1d0DI/2VdQaTK/qzuyrqKjIVYdXo+HBAQG+GCsN2La0tOltGWX48lAhJvwyK+IRXEzr2ZLF3TEz42YMPsoETGcv9UBxwf0Z+IpNFxhaEIA0QRWTAK3xmlL2jK/ITKPz0YEVEAxxfGWXMx4cFfgZeJPcDy8yG3oafRXAGBGeUUnusLrLL1snk/MqwZBrCXBF7n7yXVlaA5hgRcwyhUWbTfZ098yXTa6tXLkjgEnnhEKaKjKGE/faT4fbBubLSTGlFcU9P2B1wW/kcDAPOWZmXiHxGhSKiUNJSsY0wn87ivFYjFdIX5JN4JPpeZI0/GSCBaNxL34MTdAt9X7F8cnK8p2cgiiWq+RipwpRt/DM8ysRfKi4pHpkM6zU4FUtKPjn8FI1iLsgnj847/YUn1uEPpwLmtFI6jsUWV+rE4068+qar/YlwEPbFT0BQvZ/6rhdoamzK5Be55zLs+50P5zZ4CgqDFcg9h4cDBy0GpksGMIOAJH23kMzOVV54y+qwg0Xc+5s//OGMJzzh/F9cwK6fctppAjba5lgmh+ISRNQSPi9j2RE9OxkSY1gOrazNjVafDo2Mye9UW1oqIFEd7RQkP/oJT7jowl9auSrM/v4PfmRLoRWt4RCs+XnaS5R04qA54ziEgqZ8p+cEZmIVoYUxThtsmM/R8Sy7IgDesmGDF1XIowp9h95EP2Jis33rVqs37XXcvHULsDVV1aFrKAimRZ9io6SFzZZGhy5ksSf2TeYJbRD99ve+5zwnRwfFHaHHH3tcc2urgIFpYeMZSys/7dTt6u1x/hb9RIiIXRQEsDFJPKYq+jJmxnJuiZwhYcaqI4+cnJ7d0dYhXS+mm6PGX/3M52zIdP+wbLYrh7XBSdCuO4C5d/yBs1roeXdygYRSUclB8IhLIQAZteCMAFJLaV8+Au4xbNirB8EljWUIJPYRojhdibDKqD6KVIQ6EeNEduRn5533mU9/GjOdoff8f3uhIMdAwH0f9CD7bHCpobXVGt3e5NzgEsSOBV8nTIomy5717BqoZ2DAUnDrjYVPVsKpPfTdyfCniMi7qoVPKoWhIppSp+ZPZW2V5i6AKQXylmELzJyNFvr96uobb9okG3EN8pzsiwveydCIGUsErli9+tJLL7Xe2yw0n04erYBdIjqEB0FK9miZBx7o748SfvJ97iOuwxnZQBgZGxf62n97j3vcwz5Yi67Vhfmaw5CEmNOASio/bfMNDOEJGYSUlhQxVFqBTNtEUJCfZ/12fjpV7iSzjq7la9de94UvQAAovTZuOORRC9IIgmp3gHcMEcnPzs2++13vJMmmx8eSg9PBtwfboBLBgN5FF11UW1sDDhgaomtnOA+cwnJHqQxZx0ocGBufxHxDRWTDcdxWrdvxJq4mvYhFnbEY9RolGRsYwCUrLBwTEMnBMYJkC4CqzWObCSIYJJY2YbgYQCdA2QmV1he3c+bue/K9ZFZdGI0qCIF0so49kEa6mHRMK06Wj8qWe3IcWIIciJ0StdLfQi/Gunu/SI9BskR9kZ7QqK4xO9sNnFxom70UPTZNMRll5z+tESTrvmioPip0gAf4qC6JuEMxw2rJveZhSaQg0wnM4INMeXU7IiXpejDvuvekYDh6kEa7vs0u1OhcIkE/o7eR2djX9u1tXmgzzBkvU0wuHHHjETiAYIXiMnugQaNV510vhHz5/XZ1carDLmI1KqJzA1DwqzopOnZeOzSgp7cwRMzf0AnDHEUiTIm2IntkhpsHKEDgbGQPViBEvqlaF21IXAcum75Xd+QrDN2lBAc1Ig0cn+QUYbj4Z3xs2PGf8ksPgXFZxhE32RFra8aT+4HDxLW+To0soH5MLaiGj35SfgiIt+VBArCASIdh6P+SObSImxQFIcNd8bLAT2QCIkwFJxaHCf5AD/l78xME5gM/4RAtlAyRnwDiIZTAh4BEZOInSQOfCVNwL36W43DkJ9zUyxMAyq9BSVSoyPgzkocG+pJBkCycQRt2tGc6TJqVFJaQqKmw4XR8rrhc2aX2ID+ihJmFjlhNJESAEvhZFW6FGBwZjPKZKizu6hpmfVAdWbcU5BP+QT6T9fDaGre1C1oS+SzTarQP85EgXXMTGOsADIxoZcJwR/pOTAqtViT7ihe6/oi+FxWWVYUl7g6qLK4IrhfXIq8wrtco3bmtfYnJZ1BJYry3fFJwGqQdsY7iUzGPNkWUdLwSkrR3tyNNuq4JY/fFzwPU92oMnTLIYEIwbzqT7zoAq4CWmk7czfE5YMO5L34QINcjmDKiG/zUYDlHRq2PFQmTIV+l2B/orGNOsJCDLS8vC13qwx/1KNtWHV1rw+QjHvEIzj3bL0LgPeszTRJ+9KMfszvx0t9eYpWsAMBxPkTTJwI70N3d1db2kFNPFVKaCv7Mp84inT/40bkOnbIn005OazOYHxgJEUkzt169prYEwMXpQgESQdcjmO9VUMC29sgjI2RTrHr20bFgKQXAPe3t9li+/0MffPjDH37+T3/W39vHO7ev0mHLCFUEW1gjy5f3JcB8eipkkBxWRx95pJlV+345N0bRbOYUS/navGyZKV/bHQXA5rcvv+yP3B3BnhTz5HHAGF0IKS+18yIYIUZazyUFAjhcmMo3QOC+In2ZDs65WVbMQu9bX/9G967OiDNQRgOEJWrUISoYuQRINGnR/JudpucmJ5ltA7qmTMU25uT9qaDjmvCNTS3LhC1h9cucQNwcoqy8cPAp3Og23MA0S+vdPLZ3+5Aghgl2sV51/fUOKnv8Yx9tls/xWu4Zqgxj/HWGKgxIwkoE1dfZZVF3qDGZXgDByqSATGGh4QMejS5JEK8R1WjUQymZvUOQy+VPJpzdQpHW0cqWZkGGBIZJgNGR+hrz6GGe3G1GCm645hr6cMz6I7Vp8AaSOQr8B9fI+oq1a6+1ebu/b+Wa1fB0Sh9aHJNIPM580xutMV62YrmLKa3bnzbJvGN7djx75LojaQQzQ1apgF3ugkP7ZoWdJnbe8Y53PP6pT9XtgiPAgzaZ52GYJpDooTiGEuzoNm99xOo1xNjiufa2ttKSYteGOLecY9XQ0KiU7eL8J1ghGbQgGJWVmtuf1kFoKSQzchqatOjEjRSQCvzUdlqBzDhJjjKKxPGBefBVwKlNDQKZ9B7NTvApwyRtcg+KjjrIfHI2jPzSAwlJ7eCTEy0SwtokzA6RcOIoGMgwKwEZY+zk32Ry9GW5DhZ3mp2hdB5y3txYf9K97z2057Z6COMeMh37DLLxkbZ2u6/DjD1wpB0hKM09OQ4sWQ6wWXAjqDoWvwvPAsKE2Se/oXdNIkka7SAMS58uvvjiWEpmCqJPs3LEMRCMkTxAhd7vQB+KlETASW26TWACiPXrG9vb3XHTr9fSbUrl1XmBv19q7kV18NSRtrTUW5PsXdfkV7fjqx6grq64o6NLIjiysTK6tfXrWwXAOpkkQ1hbKF0vJ1t7e1iWomfQIehY6Ht5eRgFUAp6sadSaUNDFQwFZnJ6dI8SEwvomKgh/QZugAmUHswn86b45kWp6AbIAEMBMMMBMgOHQCnO35SNjUCj7kj/70/hNFBIgDNuxPRgE8szeKAi3FB7JFDvBw6c8UFZwGO73GP1PcSBIij9oUQZkOxFfoOb/mTOgOJ+qE6iuhTcm5/qlY46iahANdoxypkdkRUAQgOvIC8nfPTAEoGVDcIEBiHqQjXgC/yM5p5EAah4aP6kYw9B7NAQnGOcsBc/g1mM/JQBwuoFHIGoZlJVraP2icfC4jBA6sIlmEMG8/8/e/cBoFdVJQ582vdN75Op6YQQIIDS7N2111VX7A12/7trXV12197buhawgGBZ194roqCCnRJqSC+TmcxMpvfe/r/7LhkjJJjogAN+z/jx5r5zzz3n3HPuPedWj6+6ZpDQTsyHBVxL6iFehqCPQjZeSE9lURjURrEf1M/8yclwCJmvC/JcCvqp3j3qiPxVFvrVkaqnGIfVzxNOKG9ro+N3tvdwWNrh7D2sEzxUPxNzLuT7UYBYuozEpa6pEKFJXPr6qR7pM14YIGmQG7tDPBkaqccRXrxrmjQOJHkkeW5cdWz2DhuLYP7mgVPmf5MYeD7s+cg895AEjrnvtKnvDv8ipaIUrrcYYHhwkBPvWt4BW0mnhAM26FpfOZcqLmzb32KTqo2CqbycRz/qEXOz05uuv3agsyO/tNhNAy7WnZ8zxpzjKp3hwaHiktJvfeObu3fueuLjH3/BJz5pB+bs9ExhWfnI0PDE5LQwe7C7p7JmWd3yFUNd3RbuVldWPe+55/zHv59vavuXV10N4KQNJ9Koa39/zdDAoD9d3muIRaHX/P63YGwqPuGE4/2ODA8q1yi41U3T46MCjNmJscqKsvGxEfcBInVmdPi73/kWUl/0vHM+9N8fePjDHlJYkC4vK+nt6SqSJyerpKqivXXfiuWNYmb36KQL8m16FBRZB+UlO50KYd/cbJ7NVCjIyX75ua9gWz/40Q/NsJHeeee+PJWfsvE5a27mvz/4fhsn3/mOt/3nf55/6iknS4Sgvm6Zfc4lxYXozE+lbWYeHZsg4enJMBHtNp2w7VMYPzQ8M+ts9fT40LDErbdt+fpXv3bcujVf/8ZXn/q0J2fnzBcW5ecXpKZnLEnKcSJJqqTQ6Gy8LcMniVPT+qQ5MpmemiAiDDqVncQe8fCHqqBf/fKqmfHRvt7u/OJClUVQV1/1c2u/7bh2HdPM1ERVZXlJgd2j+b6qcUiIR3Z/Tk4YHR8tLikqLa9A57Jqy8JnzzrjzG9993v5uTl79u7DwnCf+3SHi8or1FdNbV1/xwF+gwbXhlJqk+dYqtKS3gMdL3rJi3kfX/zylw60t1XWVNuJWuTGoJxsxWiFOTwqTnGtrS1lRYXLaqrGhgaItLWl2Zh9U2O9CxuIsWXfXpInWCM1REdcJUXF+1taVacF7eWlZcQo4iLeLZtvszKlqaFxfiK4L+n8vNa2lqk5O67VdM589tx73/32lv37CKppReOYxQnjI7PZs/tam3PTudV1YavV/OzcBKUdGz9uzdq5mdnWfS1E4l+pm5M6DihCiWN9/XQ7XO0zNz8zOSWLeW2bZyfHJ8jEEhlrpMXwvu7ctn1ibPL+p542Z1VwYVFfT2dxVbnKdTPTmWed7qZPFVpQmM6amdzbvNuMcXVNJZoB7Ny1Hcvr1x2PEpjHR8fSRcVMzCUWp5y88dSNp5CA8kmjt7snmw2DGRm94CMf3bVjp2Zi9cpVAMYHh2qqQqS9+ZZbs1LpsZFR/ypr64LuTU2HLKNjg/0Dk1MzEJKt9/nxCTyyaNeAYT+UMj5x9plnWc8PAIO+Dvb1Y6eqZtmFH7tgembuvFecOzk6xqib9+ydmp5VdJkT1+fCdp3cVLrrQCd6amtq/LPA2qS3MxyR5LlD0xR2IGeev0kJ8GmW1KMSeE48bDrMQ114JEY6pcSgbqG6fPI4Rt4yGU6klsQnIRN4L9oiHqrm0SeBzeGZPYo5hZQOJjETHZmgaPPm5BrC7GxBixgAVcpau7bW6LMXNHAHFcoF5CO6BSlpcsMuYpC+8iMxuH//ANrAR9qS2KfSrT0SAQjMeJmyr1ix3FplBYVGPtmCi19CcLGINE4nnIIlDiKckFh5IxEkzPjl5ROCgM2hy7x8MH6hgqS+vsLAo1hLlBJpICK9iXFFQ4GiPjCo5eNy1j1DQ2MwGxRQIrQJBhOn4WxkxckLrRLRdvzxywDv29cCWEYYAPCMBUIRMzCUYAoq44837boJJN+ap8uHxg6A9avWG+yWBW2IgTaunwTjASBvDFaJy7sgU95EMmEoOUbFZIg8NOA6RulxhFecDINEyFEilwCeqy1KkdefaFN0jG+VDgkKpYAhZJQQWpSMmlJlpIFBclaiugAZeYS8YVmDgQND4YRgoVCUNppJ3oNZxHt8dRQleNx5R4PEwGb2Ef1PXw/7qPr4oNlz8K+gD4vyQEjlLSqi0mrNLWBKUbPIJk+iSGqBDIMq4mKp6efKlWGHuWpVcVEPqa5Gg4HiQk1hAWtgEL96dd3mzWHRBB7/2N5rmptbpB+1vffBRj7UhuLRFiUqgireW/TTOrion9Fy1TL2aRStjowwQJrvK3nGSYLD2/vuY7N3IqJaaiQ/K18YPGn9fbjeU+GHf7T9h9Xzw0MnLNw1fLCiu8GODlvon0yMXCyAHYmpxU0/Yht0rMVQlHwH+SaHqmk/3v/e99qv+KUvfEHAZmGnQb+brrnGjiblmfZ0ypQVy0YQb9u23T5h18ayHGpg8mrrbbd99rOfZTnGMLW5yLB52Gm2bMxloa4OMn1amA5tKI20evbG3/+eDlmYZbrJQZbe9SwaXABWWR+/do0iLrnkksKKCkOUribWrJvXciS/s231bHHwEjBrp9w6Cechxt4CDdNz4TpTfYMGRXcrxTxYftJbGMR1VIm7bVmFs6MbV65UtJF0ZxrrJ7Cj87E3MthPV5dVna6D6G1vN4+KTbw7B9gdP+2dXc946lMUwT9yTqXJPfQgIwy9J5ccuC3RPt7LfvJTJwBJVBZKEEks2NfoANbusE+PrpF4vSgUTj3fyPgEGE05k/ZrjZqoWyMYpW0eD5gGS0/Cnh26oouaTJbRKsKiF1lmkiWsbqtyLJMJTLOvltGas1WuweZwwHVWltXdSlGuhs/pwWgjz2DYB6+yJNvp2TC2bc/zQE+PXjOwkJw20bpnDzVYs3KFFKdeyvjG17/eftTbbr65Mhkgl15syUBJiflB28KrGxutF3A1zq1btloA35vsQ27fu5cZX3311ZYPWBGgismEjHTG/Cnj9mSl9ec5nnzyyUg1t+lPdBpKV0fyWuTn17C66uO44IXEiJE1opbEjAXSMVXgTGZV4LBlXaAqU4mO3bY5VjNi4WLSQZZAhRF5MW7yk6xIg7Nmv7pV7O985zvjV+ceO2MMtdxcKx3cvwVeXhvprZmnCUriScDT1BCO3BQY660E6ihftWKlgFnMSZ3c6BvWSxeEs2ToKkNAucEnHgk3DlXYQQCWjUVBiB1jMuj55ZVXvua1r4PfLVnR6XG9dndvn/lqU/HAbEdHrWUa9ueqXHWBF0szjJxYjdnV2qpE3rDbod/3vvdZcaAUCwe4dyiUHXckxhwoHkj6piwEkyQhuw34ks98lveGXwfpwXzxJz7xmc//7/1O2WiTNkgYCN/FSOrRqgMYwFjQ0d/bW+igr8Q7tC6arAArMfNkJHDfk4AGIT6LyxpL1iywm5lZ8XOYoIZfu6Fp0gswUm2mJkgX0NRU3dzc40+9jyaFVepoNEfsWgcNXm/ika5pRaomSLoWO6bDUFUVVtlo6LQPfhUEg5bBtLdDZOFh2noifRDgFSuq3cZknYi2VCngQWp8UBv9RZDSIRGx19ZWaLi022iTiBhkrFhhT8rMjh3NUpABP6pcU5y0gQMA9Iw4hTn2kt7BSPenth1mbYt9PyaAxRJEAT50KHNzK1c27t8/Ag/a0CMjzHLJqzuIGKACadWMeFJji3EwRIrliPyU40/ZvHNzxIAj6TDwLggNGGD4QQKQiCOsaSS9o0GJiTyrFKEgNJC2LBphfrk/NfLAEKwuIPdiARPadBBK8YBHGMEpgsCVhUGC8sLdN0wgUb0rSPWpdJ9gUJZJXQwuyFNxgG/deqvSgSnOWiQpSozylFFxC/IkVQKHGeULOpOXdWRnX4a/yhMWhOtVskhJ+XHlgne8HNTPZV1dg0tWPw8cCNt31RTFI2pVo8roUnd30HwvakG6kQ5MWQKtLu5k75XNzX0yHp29Z0V7pwYUBjZoKYOKVhAk9xb9RCorQzZDi++sie9EmPwZrGmCogFScrq9WPauOKoVGpP5CeFtvuuBzWplzoW+B21/0dogIZrTolDuaCLNMy2xadO6ZecAlVcaqZ3UhtrWeu4rXq43mh0b02Ha5etqnwsv/Li1XvY12VLimKLrrrtOHCWIdUmPpdFvftvbHYejRxEpOdLJmT0UVOSp4WbkfsWTeixnUHPN7S91z2r9shpX7wgILLx01pQTud76jncaSheqMUhrUIVPj3joQwRa2gJNtsdLiA1s0XFbQ7KVH3mAXZ0q3ZnJXHa7HWwzFkRp/dGJElZRXVHO+FGiLPbjdlm5IOSUC8/0Sw5t8m59sEW8zImvQRQ6m4c85CE//dnPicumUA2TdadaHBltEP35z3/hzCQ9ogjtoosuIpBHPuyhtn6JzJUuC67FulpkWUgDAUHszsS0rchb4sogSWcptty8eYsV0e56dbywuEV7Z/OnFpCPc6C9neg8AghLr8WWdpB++5vfWrFqlfaLkBEOuWFD1ZHcTPPKxz7+Cf/4ipdbrG6FsLORDnT3+PMxT3oS14a7gJ5ICU7JoSYJ0SHRMioCqXpF24Bd2mytlLgahdSjpa39Na/8Vw0BraADTk1zMLgidL2Y0jqkkrCfcDBliZK8wi3V9/FPXaQ2sQbSJttrN91w6sknka0l5Vf9PMjWu9q0ziw2ZFLs69CdE6A+zNpEzRnJ66epq7Ff683ABP1MHBfpJl/CkOrMrNL7ururli176Quf/9VvfP3hD3nYc59/zr49e2+85WY3Tm/dsd2FvnW1tSZOyyorXLXpXg47z2/adMP/O/c8YkHhu971LqM/VukbxCFPa/4RQ2NVChH56v4tp6mTg+0DxM43opAcC2Ez+JTVbskyOV6O+ubEUCT7gfPmgo+lmcYCLcWpJfG40KBzWCl2DB0Jn6c4Ox+Gfq644goIMWtzu3oRvj70MY+RRbmM5SMXXMgqbQmGUy3bje/Tt7/3fQZIIAZlUG6U6pprr7N8mt0h3tgWIj954QU02UY6h9J9+WtfV9EqBYMGuRipTwhGahB1VpYN1bY4XvjxT7g5yS4D7Dhn7oqf/2L9cWuVWGfrbzLcY6O1LPYsUCEWxD0pTfaeOZfWqblkZVcFbKUmx/6s62HkzTwZCSxBCbBcmu8Jmn/IOP0ikspyzR8ryOJmRTh4T0s1NhamN1m6r6xSu9HdPSyqYbaaYmC+SowtvF9nRUcfWvvAst0Mqu/Q5vjVzGrhbU40ObQw3SGLtlQ6RjTFMMfG1p/609raciGkJk5xSgldak0NPMB0KEhl8iCRB1iTYg+w0mEAEymULjbUayNVIjKQra1wZQEMSkQbSHg82n84YYNfhEDUEpWr33HQfqwCDak/BZmwjY6GGVcvuhWDCOn8XCOsBothDqMDxaGngwcGPE5NhFMJ7eVBIS5IwO+K5Stu2XELFohXlkgA8nTuCCZ9zT6ukcHn8UsUskOIEWSQG/pRC4YkSQBHaoQnDSGfAU7pAPypRtSgjPx1kDLKpbuHNsoz9K3JfJcXoQuaEam/gw0XYHCKHkWQJ5oB4xRwPFVEcUSnVVeQcnHncc53IMDJ0oZow4Rq2AeHfkhGx8KZXmkSShUU2qFkMDQruC5L7SE6Y81+UYuvudkca9vweVA/w0BDTiroZ//gwLIlpp+0hRKyCE/UQ1Wp1qI9xsaE5A2S2EVOPwn/j+29wPl2GD+Svdv85ZyfwnSxSk/nsPfB0aFBqkURZPEyPJkeHR8amRgZnxxPFQQtWmL6ST2N8IfddqSxoJ/Unr1HI8IIM8GL2mcRIMmQAgMwY8QiuFJESoyH2jtDIE8Ij8ne51PpYksccwusiVBl85b/xQ19qUULzZaafS01ehZP0HNzWth65zYlJyu84c1vNn/LOf7pVVdVlJUKICvLSj/84f/h79IwUzeOX3KFKWf6bW97mwNy3vL2d4jrapfViHvf+MY3unR0bnz89DPO+NTHLxTwnPf//pngTtt4si24gkbDM7RN1O3gDhObbtA10+ulq7fvCX/3WJOT2mKDtNT0CU9+8uXr1jlWRHRnRstZslXlZe96+9vsszJPuv2WW0449VR6z4PXB4hAaLmtUchr37fPu/On9Qe2L7ry9L3veqeJx1e99nUmwU455RREmuhev/4EXZc7clCim+zpH0CnyOG4k04yem1O1Z+h3WEYXV1SDDA6OcwE93Oe8xxOv0bK3KB5ToWyN5OWAnVzjE6xQrNZXNGvkQKMXPWrXzM5W2whZGNiXWJ0yxR3wK4pfaNjSexz9nVyOmwCMbdWNjdnxtuU2oc/eoH9w7pPW8iEjsjWpVmyXr9yJXjDBFue/GRL3cSfwj/W7gYmyM08K1HTQJjusjJIwf5J4NLPfFYuBKhQtWMrrwtjK6qqVq9ZY5ZYJWo1gidhTGRwEGtGql1BIYsuHGY72QQ2V111lTgfflSd//p/e+cHPjBtJD4ZticTN69rg4KfpDWfnkaq7GaJqYQF9tog866GA9x1rC2zt/ZHl/9keUP9W9/0RlI9/qSTnJ0qdHSFskEEW0yVYh5Bv2X5gHdRHInZPY4FADi1KgF+s/Qkb4Ez/EJfBcmlGySZdCqPZNCj7fzI/3zYaM5tt27+zje/dfxx6ywVfvzfPU7dpXOyT9t4Sll1zUh/n1X3j3vMY1/8/Of935e/YqKY06ASzdhijUVccMEFF1/6mUs/93kCHBgafubTnyYA1qMgoLq+Hssu0EZnUEWXS3V0LKuq5A85Ms18iplwTW1ZSVgBaFkCFbcbtnnvnulZu5LCxmnEW5OUmp9HNsWj1VCZGd5t4rq711bn/zz/P8z6ilGxbMjAeITBI0VTY2rz+te/3vjOd3/wQ5WI5be//e0iVapuztYhWGCoMVLND1PUq6++evPWbVY0GLj5z//8z0c87nFBEyorBcb2GLM4vhcnDLyyPERNFHogC/XUgruyfTXK88mLPw0/Q3vBOc8VADtEgASCbmfpYruMxRgQsYDCXEZdXb0DakwO2zdjWMFSLWfv0JywfyvzZCRwH5KAXom5xcd7fBaRvxCrHQyzhdj6ZMiFNFlZYf2q4rQGbrFxCY4uQKsiPNOAC5P8aoJibOldCywRACROtPSgWZsDXsfhyl+JgiVNE/PXpmm4+MTwa2E0BaFTSKaINfgrVgj/5qz71RRIjyXqBxzQxQFFL/9SFvhhsM+xpyfE29AiFfFeUIIRY+k6IKUrV0GGGp1p3dYWFv5IR7BfkHJBxZEA44WzLkVQ6p0ng2sxDzAAMDsUzAj27ua9hQVhg6g5dEcBuaAtJ213dwg+c/OsrhrNyQ8nIxalcD04OjyB3ygHSLgHWsLmfQ53CKu0UA4SKgGwtprEdMq6GJzijpTwC4k/I9lyETJ6JC7Ik+hASoSHPNULzDKqBU0rxkFCji9dD8a9SFeQDkst40uNg4fBo7FdiHVRDlj34St/JpHA7XuVNbwqSNsOmJDJKrguqdRxa47r7DXxMUiA6JmanHKiMvLMa4FxnaNDHIHZloOYyjA9aWuYTnhpPcaErCcYnw5zDOPzwQUSA6vEiuLSFSuLhvvn9rUE/UwXFowkFrGk9FPfrdbUqYcFqVNmMjQUDlvFCPUg/+DOOdlrIJhqFP0h9u740nD/05+098qy6sTeg4+tuiEcHh4rqyxzNBZl6BnsyS0MWxIo7ZLSz+6gn/2362eyZDXoZ04O/WQprM870XlhlVjjEJIYkwnmPDpq5gB8DIBxF+XJirEfTfVY7b3c5r7OrtLa4oqyioKs0BK6MtPlH8xtaVnFfZqabJHrojCoc7L2VSdjNlgLWOiAHOcke8/O1lbSJMZZJdGYblKi1tEhjNQo6lwcB6VhGnH7V9r37IkdoXHI8WSJAntm1XRUQQimxxRFqy3RWUB7du2io+bBbEjWioXmvrQUkP3msdeMTTnbFk4oFB6Ka7kyYNrvMkRUaTPQbITWpvhKwbyxcD57sgRL6e5GNL7KwhGsb0Cntpz/jTUmhGVrdwUwrlayNkgXGlkW/VqdTLk9Fjw5egI9TAX+jr17WZdODkKMowrLIkWHUWg7UKU/rnOMs5M5HblscXhy+JbmOFwIZKhyft7tuwjTl0AiLwlgivRITBe1esOGrv2tQpBbbropjpqTG7JLkhtuympqnE0R5YBUrSdUSuzv7XP7kd3dmKoUnOfnw6bTwrgYzCyfdkGJLF8uZcGpshotkE4aF+daWfgrUbOr0BhCW5GWxUFIiFQplul6AGg4jCiHvtA+pcJCZ1xXNTY6uCN6P0ExHDtpZZLDtIaHlabGuW7GliPlCEakVV6a9cY1axQK0sAdURCIInhV0qYTPwlOpZiAcKYUsaO831VSyUgeYFpEc4jUQ/ihrJISlQiVFWk8PtUX+HWIH+XJy7VF1oXGtTVhn3ZVfcP44EDYVJyTbXNsaXlZdn7B3MS4HbCwmXDWdBK7gW+HwARXpqbGOjyBNGcCRx6mYbqHKqpBhdAEjAeFDLc95XR1dAS7EJb39DgI3UC96uCsHWhurl+10r1EjmV2rJcl34Cbd+3i+qisqoaGnkQ9jF8YiyElE+aU3/ZmjbgicGT/GF0lBxWNL46h0QGRuluIdBXrjj8eScCMp7AotKkRFeQUOGrDxPACDCpDCU7x0tMizJ/YwbhpZGDrTz3Vmmwv2KRLpvrl8pVAHPA9lJyVakyE4ul48I4Ly561JJiFTfUZMyIutUkmqlIMrW1hHhDAgwBgJCCuDgqQef6WJDAreCstP+xkEt1YUpLQYR2WniPRqbFiNQu5/Cn7wp9y+WpE8tWvfjVD8HXh06GlhP3wTizIzgrrcLIdWuz3YNSRLF8SV07apZKbEw9h+dW1t83nV/cMjmq6BcDxQiCNJGNnv4zUjCO7j1avS5XIoI87bo1ebu/edu157Bm9WGqEJK2udyRpSP3J9lesaDJVqIHS6kpnvJCg32W0rJwbgC/holxam7VrV7lUXi69MJZjq6UxtFQZbdoEGDQasnt0BzU1Jc3NBwDHhggAX9ao4L59YessRrRmXoLPkGy+0MkqJTRNyVSPJkhwPTIyxmMhVZ9AJjFDjp3PolZHYLqyHRJ7uMtKiyvLi0wMV9WVXL/lt/P5E9O540XlheWVJbMTM10Hus0D49oJINOzU4oTAOsLYMYd9pHtq+5PzK8svwpSGqHA8gABAABJREFUIolplpUrdiXn6BHp5vg2JKARJhkvGIzytNYJm9KR50Xb6xMANagUw4UL8kS2BTXwq02QJEaeftEGmMOgOCJSNGwqVy+pa0YYqqQDlktxlRWV4d54lxSE9W1hAlmtGab03tbRQU/ldYqIDtGZKYY23fRxoKc7KzuXHubnFFSXV1QVlGdNTJelitbWrCzOKs6bPwaP/7B6rvoQ4/cvf0xf26Y8OpXFFiayirLzi/JyC0TvTbUlwyPTnVaK5WapQRdM2rm55riV48NLSD9VMQlElabMqlKtUR6VHlQxsZHy8jJrxVQ3MIn0EEBi70UdHX2q9Y/t3dDJ1CH2nuMKFfaen5fD3of7wxXQNiu5IUwHni6ZX7Wm9rrbftc9fKBAOJmam5mfWtm0hPST50MI9JOUWD0RkQCWWaUXFuQTUQgT2IhBJZ/IUBaNBo/XJBAMFtDhmvSonDaHhbIL8iRqSBaM4mjsfWZq0l6Uhqq6msKqDdUbZqdmC+ed2JOKajyfPasBD3cmOWyGb8d+QnMefiLAob/RLkLTlATnF154oUkmfzL/Q8EW3u9uO1oo6C95yXZyz8hY7vzBPusvwXW4vMF/KCk7htbncEj+kBY6EpWXbBlXDT0tLVTHUKS6oxxhgis3V1AhA82jKF0tLV7UUEV9vWhQlwkMEnVj7JQi8qpHe3u7+fTJrnQTR/YfBsPmGTuCX9/o4llbN0dGBGwUVOtsU6/IIXZyFo7qDIqrqwd2725as8YC0fudcQZi9IHUWkSuc3a3g2lGPVAIAxz4loRPyEaGQNSOJcotJIgTWZbUKhEB9jZrZkIUimJDa+m0CUPBg91RMIOZHx5mDLo4kQnmEUwgqHKlg52okPvT1qsG0fj4eLltls7PSJZ4MUWfIFG0I3mF4oogsaCvri4YC1cXMjndICSmUElJx+NRHHWXSJ5CER3h6rVroa1tWj4+NHjqWWfZMYtyc7NGJVi/IpyyG292lUV14BEei29rUmnruoVkRhacYWoXF7Q+iUC6W1uXNTaefOqpSHIhMN7nhoaUBbPIBDaEEkJoGtC2apXgOYjOSEQiWHIO4kp2UuHITU4hcrNLKtnK5TApDYraxAixcxnSjrVMRk+MTcirXjyQhwYscVmMF3gxXVnocNSka0cqYpToqkf6JvqK8a1cBhHopxKb1q4ViSFJWy+SlJHzQSamWBWtXLksZRPpcbU4Pva7BGmbakil+trbEWkZg7DWnLJKmTNwmlzFYd97LMuxT+PDPaSqZknglFNPNWmgUge6u+mJMZ3dt93GF7GUQDMapqAT+RCLIRJVrCDpgnmsheX01geuXIkYJ4FWGEJKdn0bu6E/euKp4RGSMSIQ+J2dNQxBJYSsFRq+yUlNeVRC40Gm6G1utwKfCjUdd5yvgl5ytr0WmCAZHtF/kGyyiQj9BGg8X02FWVkr5ZLliypILfDDXP5IXMcli/OFso6joQmUnNHRFmsNiYgTBpu+xNSu5QBB75NNd8Y7+pOdcioLs6edfbYd9XTYsFaY2XZdx+AgPQ/1a1uaEYrSUpuze9vaeOFuPLQQMzQCGoqZmZqmJvE561ZHgfjMk5HAfUIC7M6zwEp896spW0hclJfQceTmWAjt/DzItQkabbZsBpgLGNqBuLc/GfYVALN9zbh0D+Ndt26Nwwe0gsCETLBJZMuOpOrtHeZ8a9l8kkXLADPy+dx6Sc2v4gBoBJYvr9bwx5MXFB1DUGN5Il/AscsAqdvSvlkO5cpSf8IAc+ij8x1FUkk4XV1DCtJE89pRIph0IENLSz9GkORXoq/aFkVzeWM6DDBzIZx5FGNdjQl4nMLv/CaRBYQat9KK8uGhMNxc4jjoVFgeyfsvqQy7eQcnR0srSgtLggMwNhjiQziRZBgjPy9/ZePK5v3Nhn21YFpg1KJZwysq1vAaoBRwogenmk304FozSxTa0ihYZBvxR3BoqJOB2ihPRSzMqMtCLIiJ2GIA7E/yRAwPHjalK4UcoIqJfhWNbNkx7hd+FCqdu49r1RrlqauVaCJUXpAacy9KdAyHSseLy2ScDA2VwynI09Gh7gceGh4CnM4vdNF8dm44Hiw1nzrQdmAmPbGuZjXultajWwlhfKLMk7mjhoZLimrrzECEiz+skSgssjug2+0PiX4GZV46+kmj9LkUwwsFUzuqW4VSHkolUVXqUVU3mS+YT2LvbsYeAc+ofVKzFICmCYDvZO/HuQC7vye4Crfbe1a4jQl830iHvQ3qGgGzc+OO2quuMl28hPQzuh/0k84n9l6AOxJDMzr9Um+8AKDMVJqLRSb+NHmAQRNFgCkwYZJqlKev5Llg7/AQ7FHaezovd83q1dlToS2arJ7Mt+3QhVxcP/8OH7ouLVu5b1CzaJJmPK4YoxCWafJia5Yv57vzsGfckF5U5BQl8QBTpEC0hA75qvUUE4pahbLBdA3Ymi9KRp19CoFTtQnaJhZpnoojXtvUpO0eGhxs2bHDNB0kYldGbi6rdNky2iyLpaFKYb1O9FOQFB5/VzIaajVpX3JwBX0VS2i5xF26MbOflW52Ta59hz80GZpvRwIYEhP5JHeCARMvoQZOHPG/QzuSbCva58BM3bDdm8m4uA62TOxh92ZPDysiCmA6ZmQpV7ChCC8Q8uBZnXKDFSWTqC4ZNs+moAE7P5PLJ8aFdgRYWtq2Z49ID6SxdzZGUFZfkxIuwmy2syjxfEigqFBfhRY+iW0c2SUA3rdjR1icPGY3pX1cYVJaZRGgCW143GjhTlsXXAiA8W51CLIxIsgnW72rLIaORb8CG0PHGoVqhxVZ1xp2AU05t8xwmcaF/MPsYbJIRuHeUav3FTQSkRSVjiMX54QOPlk5L7uQnmz1KBZgaXqsS5dLIwtmJjn0y9wgTgGQofBS8IykoDB5eWpZJx3cruSaKDGnujZlz0TpmxjdHi8FGbkIJaI4OYBNFcuoIkjVFTsUhhBUN/ESQoBM1omFuV+a5hiwOERqBZHJ9pISp4tagiu0HhoYIAH42ZJ239iN6zL4qgYS83kbAmBLZXp6gljGx1V3jBvpBvJ4lPQ/9kYGZYppS+JEIj4sI7NWwhxIcqYL2owN0Fc9jKEcZOOdPCHRFhuQDypq5RPFS/bCqQEAgkOPHW/k47wrHiQnzPCTyXDTsMb8oTUlrlLQw8ciQIpNkQwJdSfTxYZFQiWiNjd3ONm1HjLm57uWDJhRmThOoWjjOJCRGwvat3ev6mYvCDDEoDZVrvgW5bBRTvAoJ1h1QT99tWICABpoUWFykaYlFQCA0Ter6IIAkwUUjJdRSESzvJoOQ1oBLPNkJHBfkQCDWGCFyXgOTVn49Ge/WOIREcLM6Y+RtRRGXV0dwkUzGxoTFq151Ip6Z6psjdFpWDyhlQibSEMYIAzTnPoTNiQ1NtY6QpjxhuYl6Z40C7IYDXbQFAwK8vikhfdrK4MVQ150MUrRdvFQnQ6tNZALvEgMvPaQZ66LNhujCdIaAFacXA7VAiY40bAgGAbNTklJjqgYIzD7KhHNmhHNJgalyAgtyiWK6RxBoKeTDkwziGuPcxU6OjrxpURkoAewUhQdOqPkMLCYCBswIoJcrwTen56N6zZu27UNcAyA/SIAL1KwgJfoapMeaUhHg05ZEVIAQG7FkPg59P7JgnAs48U7SjTsivOnR7pcioYBWi8QYkc6eSKPKPArBSOyo0QRqiZOv0cGfUWPjJAoVL0rFxniasWRsJqSFx5ICNl7dVG1dC9RnrIcrFYNfxARzEqRiDxuSQza8bUE10JzV1ieSFDFRf1EvF7owIGuO+kn7Vpy+qnS1UKsSjqs4qTEqiF8TElUoXih5HTGr9ugDWX8ZfZer1pVdFQ2xUX8yLi36Cez5UdFqzT0QzJJvYcz8NmOdIxQ8qgSOGULi2LvZAVbMOH5YNFM1XO3TXxG9JnfP5LAok2eCJzCVJtHzBXXLiYHNWtTxDxUh+9uDlZTGHxo482Wa9qZmbTF5pHMcXGX9ZpOY9C8ikKFHNJNP4JkukIUbjdd1GWF/jiZiRLFCcb49KE1d/Gv9a4H+1dkcLQVZ0a3trHRiZaiJptQNP/MXoxnwEfcwmiFWErUQAAODZ9hUeN83HQBWGLVYYbTIqVk3ZSiBcYWtYp1A87KSte2gBeBiOTB4BRO9uNFcU5t1pc4S0mUoq/VADlMWGAjajIrHrsKU81OADJViBKLmKPJaafEq2YsYxhgrA4jpsRFa4gMQV0yP4xmVqo7yUlmDhUtAse7QBepYcJQj9gX5vrMc65av16nxOQqcJSE4gRYu2JF665d5uHVjhqZGB0LDViyKtU8p5ELwgnBTLJQxEy7HUKDor5kTxejtRMYTFiI62zJZGV46LaTi231hIbrgxySMwYpgKL9iUhDAygnJXQGeiorw27YZOhBVIMjSFS0I6RrrDA3lmGEMhmSUC5eBXyKju2Umgquk2NOzUsnM5mE4+AUGIRGmDK+QBSIUF+0yMC+VocM8SWd2AmBz2HiWhZjpIZRhNBoQxgYUZoSxeWCQHqLC46STwijS9hpPO44qx54S0VUQgyfnFNS0dhoMJ/TJ84Ts2kxic47rimJIYACgxRJtIl+VRl1T5TrxYiMRlGErxRaauk4AhLDCkcl8/sIULkox50hEApGe93HZ100gs3Z0k9ogYG3gscKdk0827GRXtVwcUS24n9IXD0ZFkHYP+/MD+NNiU9J5RxiQwJGdgTtDm02zy/WpPNYlhFy4bosxMgEzPHqIeAnQCWidpWd2O3ttA49YChA0CWubhK9W/4h/KYwFN6xXk4LtYqRZip35fr1GDdWpaKhkt1x7owaZusv0OBsEru1NSkaFvAmgNxTQZnJJ/NkJHCfkQDNX+CFBS28L+JLbFUWClIeu25cVb9rX4+Wh/Hya0PTlxyQq11iv5oUvad3j8azqal0166w5oW1amG8aAa1dYj0J/sFxkhB6tR8td5WUAQzc9ZjyuWGYc22CRZo/akpQ8/69cfxBltbw+0DkPiNYapfkSDMBCIxEqPZQbbDjEOPWVwcSxQ02orc0hIYEadhBLxy+QmaUntWtVfoiWTrW134YHxPL4AwrWLE7wXlSpFXilLUSXGRTTPpualwyvH05ER+cT7KRYMNa5aFBWCjYYwACxEDOZx0wkkOwZIiO+7QIBHmIMGZsCacuCIjODVV61QOkBIBgyRP8vcJwXDKIlFrbzRTomCSGBGJFzTIaHISgLskyCpyB8xeFQCmtmT3p19fUeIXhhiKk54/4yeFSufxky3a1CB5SoQhxr0xyI8CrK+pv/6262VUul+ri/Qv5aVhGKW/r2fCPGp+OLefuFatXZPOzu9sa58dnqot469VzbrJb4k9oU8pKnTCJfnnleTXNVRNT2W1tXfrgMlzxK63nPn16493e01ray/F0Nv5JTpV5vevq59kiUhkqDX044Wq0D36v3r1SlqsUtTRgn6ql1WrwinQR23vxTt2hbA/qlZeVhgdKy8pVq4Q2lpoik2RJmcnK8qL0oVhBejS089wVbLKIgr2jkL2Sw60l2lgRG1ikO8TLYX1UVTW52F0RMoucK1BI+TQCNzB3tcfg7031je2t7TOT8wW2ByW7YgxumTHisUjS8wq7tPkLNp+4Pu0lO6tzIUFtcf4hGAr8xxZAuEK36N+7u77aTP1e9RVkQG8D0rgPrwf+K5ri9PGUftL9gNz9YwtmrCZmtNKWY7nYvCs7ft6drcNpovDUcAcZc4019DL8uWVt9yym+OrUD6uRK4h39G7pYK8yYUwTMApl4kU3rAiOIiycCLtvHNqjilEYSq/E04xGE8UI7IHvzlZLC3EgqqpqcYouqWnCiIHGBQtI2wCNr9g4OeAmpysqHDvXTg7CrDsEIrc3MYko0OzPCBRqxSeK9dWPCDyhAQ8NxdmjPgTNuEBABjQs3atM/YNy4e1vtJBwmNf6JrV1c7rcHRFKi+nMN/hSYMbTlnbPtA8mxpr6903PDFUWJzOm8+dHJ8qLa4Q0+7eu6eoxDxbWB0Ng0IVt3r56htvvVF8IuRGs0LJzVdlCV3wEiXP4eZqAzNPiwYSwD6p2htMAoQJniQRLy8hk6d3GBSHfTAYgcoAN+4gUXqUJ1Qe+CGMXBOyvKSBZtOb0dHn92Oc9JAno0SxsRR1p2jCV4QU8ozCNDpbXb0MwNTEmLvfs+YdnZ0/Mzs1ODpy/PoN9jOOD7mHajRnYj49l728uv7E5ceXZJUstf3ADGNyLuvGLa3psnp1NtA/3uOcy+zcwqKUGYh0oSO7awdHJlr2d+S6XDuxxCWinyS/bl21C0DpAH1TO8yK/iCPnrAFVSzRbzQHizX37NkP4E72Xn7LLXvlupO9jw6bPhodLsoPn6bGpimGQzFbO1pMwuQVza44rn7zjutziubrVlQOj/W3tu/LzwsB+RLRT5SECYBkZyKWiYjp0X/vLr9kLMzEuxT6TFDIXrNyTUtbC3kyVSmslUGRjHdhPysgPbKVLjGOYbGso7T38dGRob5+jW9JdtGjTntUUVZR3lxONv2bD1cEZ/YD38v2A2siM89Sk0AMaP+MSGmpMXIvpSccsHF3Dull6vdeqhgZsjMS+OtKgCNo3yca+HbOeeL2OcSI/8fD6+7rE94IbDh2op3GxvKdO8M5CPxpTiSvUTDJO4wuo60PYiQ+IlQOoPJrFS7n0guc4j1fZRHZ8Rd5h1xn/qVSYgDML+cvSoRQ6GXrk8tHOjrCKlww0uEB74FNzAazRL/CM5OTAtj+fojDeYcehKFZAKz8lpZWBMfgDUkw81alyAiVOBkePMoSOVWEFEWgVtBofRW0sElEGzKkl5SX7d8f1pdKmTELbM1irgUudonm7O/oSJekS1Olw6MD6WxrbWqKC8uwptDe3h6hrFKUSwIo2bJji3QCRA9nmihUB2BiJGHRLF9crOvELO414r0nMhwEzIlHDC4IARkepfuKDNRKhzYRWJ6QlVPugUGkKq9SFnLhAj1QeeBHCdrWrFizt3WvwJh8VIe8cJIhYIdjxXcYeP9o8wKSzviKBqRChS/EEyN6ZmdcpDDukB+KgaTuvt7+roG8rOzKfFuCQ80uwbXQBoZMAhsrIAHrUzs7eyYnZr1Pj00QyMrGxrpG+tnf1dtTUFiSnZtDoFHaS0E/KbMAWHWoUOZDnWLIZ/e6Y7ypik8SaYL1AirIOnpgbPCP7b10584DR7L3qdlwCtrwQBhkWZ3Y+4H2AzQBZhqem+vsnTUTWTS5u2+wR4mFBcGKl6Z+qrKgn7bvrlxJY9k7K2AjUZmla/327d+HO5AL8vTVuxGlaFD0XxZypvkH7b33KO3dNrqUHZSuC5vLncqayrWqYD4/5US5zHMPSuBuF3cY0sj8+ytJICqSOj76f5nKumsJiGmP6d9dY/sLv/4N1u892DZmispI4L4rgYNrrXl4XgUws3NOoAu76zlwQiYuHS/ZIaktLb1cQAB+iUM650+oxmWUws3l+UFioaB1voIiYRgYPqVPnHLgwMDwLyHhMkLLw/YOAzA4vXuBYdkyU9BzgjduJUczZlm2rMaBzTCjimMqVJMRcFVV2q5eiYIQZXFnUdXY2OBg587OLjjRGRNFcc60FpsJrUEiD6e8drEuygUMSpdXClTKtaFScWBQ7hcAmj2ApQMGGYFN/qRys5WCVCmJMMP8Oe8ZC0SBX6XjSKHSlSiYBIxryPnT2EEnnETkNzLiqxXaFaVhF6IQBQDk4AVjIhCQqgmDUqI8kaHoiEE6tNghIrENAKVLiVz7qn7Vyx3kqUY2rt24c+9OSLzLEkKd4WHBkhRzzvCTQKx0aDElHW1qROkoR7NRCSQtPOSmOOWShrBZepRhFL5398HItaQewVwy8BLGBVQrfkkS7xg8qJ/h9uklq5+qTJSrfmkOgsm2stJYzB/0Uwp1cv+jWA/kney91BlyaipqF2DVB54QYtXTT/VOIJAn9m6rWbB3ahDHAmgauVEMtUy37y36ieDYVqhotoYdnNIB8mEp5AmASRKIIapFtPfYxBEdkUaxKyLz3JMSuNtj4HuSmUxZd5CAECvz3IclkKnf+3DlZljLSOBulQCvC34+K6ff8QJ+pfiD28fn47yeeGL9vn1hvR93MPqycbqDd8t75ixy3biJ3DgThqZ79+7tky7E4jHz5yCxhFlEKkwFJmyQkW8txOIwK4X3HGMkn7jLq1dXi0wdkuxdXuXyREV9QksxJscUtTHUhMGxTYODs2JaGJDhq18Z7enjs6JWYOZP3qpcMLssTyLCgHlwiimHCXBt5YUEJCKVK1JNVhPfPjsdv8KGSMWJ3IDB40WUCH52PgsSAPALDxAsRMQ+YAGJgnAq0acT15woAPYS5Sm0ACk+hA1kxKk4PAqVudp7W/aSJ5lL8XjBEe4kAvPIoiCJaEY/AhAZA2b0rFm9Rq4oTxJTLnlCi6QYz8CgRsgHeWan9xzYI0UWOKUgSYmAcecdQk+sQXN96hQwRvx6YtgvFop1HYvwS0QSEQwJISM1yp/ckGSZlNKX1KNyYwwsaCdqfHmwj/LVq6uWvn5GU0J8cmz1jKusHOpG/mpKpePO4AWVGxqa7OnpTZg91N5r9+3rPhZ7h/h2e491be+EAQLrDqgEA4m6dK/QT1Ws6UMzbaSiVN2fC4NHxoCM7zA3NkUrWDSARbH32EAxTPuBlesIFARMT9y+QGNJmcZ9lZhMDHxfrdkMX3eXBPhiR/nv7qIggzcjgYwEMhL4yyTAbRUFh/8nj//yiXljUkRB97vf6htvbOZSxz8FMIIWoYs/RVNxtgS8lyQATlvA7MykZIqpnEMJktsdt9QKnKK3zcnjSirCie+cPz7lwqyL+4FtV4x7gGPAJpeJF/EyB9QGY560gIqDLsuKFcvsAQaMKs6o4lAlinP4cVtbJ/wSeeeAYeD020ULAwcXkhgJCzJd4NDeHi7P46kTgFBHueiHX17A/Hj4BY2ierwAQ3AUCEYAyyLFfVEgEeCRHeNyCfkg8QJSOvxnbDxj022bFiB9Aika8RUNUUSkRPJow6lAgjzxAgaDMEsEFv1y2QkQO3FRq3efFuQJWKX09fctyJMoojwFtwIk6WBgVpVyWdgskYjQGUWEKvIUojuuDMvR4yeZmIg1obV0SIQESjcrCEaiUjy4IED4JcJvOEAkjAYZI22kShQj0yOJ6i25HysecIFycsYImhsby/r6ZhbkSW4A6uvrlpp+IlWERu0plfOuWluHCNd7VDxfaZ3ozvBT1M9D7H3ljTeGOlX1qg/jib3fvmM/2uld2LtcBGW7OaEBgxYq5d5b9BPxsY061N5xTWIEdVCeq1r3t5JnbHCwHOVJVn+2vTO322fRs8JejGgJKmvJmcR9l6BMDHzfrdsMZ4stAaugj6lxAny37gdebP4y+DISyEjgb0UCXDes8uT82tbL7+cFitl4yatWNezY0SWAEb2I9PjEXEN+rTiQ9+9PYHxcLyJMi3n37+83ASjugk0oBdgUoiCBqw05T1G64kRxtr7yKUVc0vl/fqULzJxmZYsdnDxCGy+l8575046S5hHyDqMfL9ZSoslLcVf0WcEAUCLMXV0hmFSWFLQJXF3HAwxJiAfJqfUiNrOL1gS1eDIJ1MPB+9AiA1ohhNKl4xQ84OLitERkS4Q/BsDwCOpwDSaGiOQmWlY0oaEWL0TnF4bVq1dvb96ONSVKBEMU3G7YYCZVQvCgGQZiEf2CxCxIYom0YYSrDUZxePRCyBjnkYNBBoTSkSd2RVscI4iF4sjoAwwkD2ZBnpz7WFMwi3kEtLLDg5jVtavFtASCBo+80sF4J0/0KxfxXtS7r0RBYsSCEtkjzZHBSFVkMyqSKgZfmArXWyypR33FSwZwihfsI89FkFTucPo5s9T0U43QN1WwYkXdnj09KouoKQzlp1de1AIY6ZQZa6o7sfe6HTt6fL2TvYcl+jQNGMl4Ua3wHNbefXXWHE0AqcbhB3xv0U+6He2dNJgMEbF3iQyZhpMe83Fme7QggpK+WPauOBVhbzzk2mFrI2LVLCm7uA8TE84JzDwZCWQkcPQSOPqwNrNW+eilmoHMSCAjgXtSAvzUuCk4zgQnK6JTLjerb6pyLxE3GjECJG6xmUmOmnjAw9OVLojiqIkTOMSmPMF44XyL3LjXtbXVZnp52x5gfGJhMIfSu1vto68JA5yeJOqYlZGf7c8YxAoFxSDNze1iOaGpaCTOhdbUlLnvdv/+NpFkDBr98rP92d09KkrhmHJPoRJAymW6S3FgYBbgKUs6QoRgESEy0OYr2lALmL8rIzaleyEHaYCl5KYDLzxgN9tNToxNjA1OToWb8yTivbA05ewTOyzHR9wplZufKlBuXjrV1NQYg0kCx5fYz2wq5IojT/hJDGQsLpFnXwyViZdg5YrTUOgHjDWUECaa5RLh4BfNyJBXFp8ALARs5Ak/rlFoJTZg8oTEC7LRAyfyfIUKGNcfg4L2m3fejDzvfgkHJGmgh4gogIIiBgGwr+JtIlK8GSxkFBeCzZcRtqnpSULwLkt5UUWkebi7L7+mIS9rKfqfzMG/qBg5WblkJSru7kj00w2Lk2OxHktK03v3ddCYpaOfKsJDwnaCHzjQhwWKROxS/LIRlKs1Na7qoyJ5aWqqaG3tk3gU9l7sskbDJU5cV8mH2PuycCvW2O0DZPB4KEBpRfGS0k9CYAtBPQuDgt6un8km/GjvbJAciCjae5QnMTJAq1EW5EmMeI+CpeHgfWJ35CwL6z5Key8oC9fIsdCZyRz3hKWyU/PJQYU5eWF0MvPcMxK4u9qghemyeNWVYEDksBR+jyTWuwhXjp7yIyHPpN/HJHCUmnwf4zrDTkYCGQncNyRgqpQnhxfem0jYoch2ogmrKhvqt+5q4ynGUIp7JwAGY45COCps4zrLxVMUHTU2lpguFoAJkPwKscxArlu33OZWwRJIGeXibppL4R3KLgIUpkrhOwKoqamcmpoVQfHF+aZcT36k+4GR1NbWrSABhkR4hGcmdYeHbz/YGWGoBcCdtS23p6efu88x5dcCVoR1uFazyhtJNSPNZ43hn5OABHLA8Age8c6rcisKksgEncmEzAxfNj8/58CBcIMugpEnrq+sMOFcODMxg4CZqYl0QQjsebFrNqzpHu7o7usanxxNO6IrN21mB57jV6/fsv02NMAAueKgRbZHLunIiMVhZEXDih17dkg0gUZWzc3NnG/z4ag1MUWSCIYEPaSBF0IWY5At391XojNmoYiFBcxywe9+YOIVFQNQaxJJXrV6xwUkKIly8CnOLZvykoJmtCmU6DzAiEhFE6+ZanJDg0Q0oBmwTyYC0VxWUjQ0MNjX2z0ju/uBk9l+mFNZ6e6OAzNDk5XKRsnUUE26JkpjifzS/LxUzvhEWO+aX1FUt6xqYjzrwIHu7LmZYurnburkfmDHWre19aCZBJaOftIZJlBeXmYPgopDGPIQqXZWrqwzoStFDVEhbFIMqrtiReXOnbdv3xUH/il7L9q2sx2GQ+298biGwdHJrq72uTxjJeG6LBvV84uzs3LCSXhtbR1LTD+n6Sfdo8naoqjeRISpxN7DzosFeydPAqTtXkCyDsIlPcBsSpNiOID+syaPdDDr163fun1rNAcpd23vKxuadu7Y5n7govkCEVJebl72vKvFs0IEnImCCeEeeY55LbTgdiG+FQks/JudnslOh0bQiOvM3FyuPd6p3Isv+fQLXvSSyy7/aVZhcXZuamR03LhoVkHRwNBIVnbu5PRslhGP3FRWXnp8cjqruLR/cHhicjq7sBhMSEkXBMjc1NjElHS/3i0WmJ6Zyy4q8XVqZm58YipgyC/0tbdvYM4RyIXFEz7MZ3t31iV4ubILipQiWYnTs/P/cM7z/9+/vFJx8c9vfed73/zWd7xnpfLDb3YuYO9hWX5uSp6x8Un4YUNzoFCiMXTA+YU9fQO+ZheX+upfVjolZh53b0J21tTMdFZhQd9Af1YO3T6284QXC/5YtWixyr1P4iFMBkPnj/I3WtfdKoq/tfo9Vn4z8H+bEuDuL6lnoRY4Rp6FP4+VyIWMd3g5PJ4je1JoEBdBwhVGzqyTnZIzYLdu3ZFK50oYnxgtKEyXloXlynV1pcJUWfLz7YC11jHV0OCqoZL9+8OEieCN98xB5FnyEcWG/Ei5wHOy/ZoqbGgoEmvxO6X45TiKlgVyDmDyJ0qkx2hZpJQkjnIogzuRnc0BFYA5XGlwcDhi5mcLgJXo18zwwMAwGBnBQwWzk4+F1nHlsNgMjwI5hxk7kkmoLGaDIQZ1fNySksL+/oEYIiIDKvAbNqzAQkdHmPaUl4+LEqiWLy92YpYIEF+iYiUCE6YqC21CCNJAA9ogOf7442+86UZlwQCPdIXCI8SFgX8Mgz9xakcuaWzduTX635D7ijbxA/kYXEAVJIBJySRtU2MTDN4B+PWYDCdlsQ0agCFDFr/EQtrcfb9oQBUHHRk8e3KIjCTirVQ0JOABq1B5ZYFQIoJxyulHKiFg2a/qRj8kSEWwP5UILbIRbD8zDOSpROnHHWdcIxsGeDACHuYCJ0Mf4QF82GcBHAbPwp+HBf4zEnNT5DPDboR5GDdFJ9JB8x/p5wSlXaL6uWqVdRDhHG+1TP4kT/6iPgGwZfCqGCOUOWrCmjXLtm7dZ8BG+Oaqb3pqYQcHv67OaucWiU5Hnp52XVB2Q0Od86H37x+6k71bDjCrODgpA4WhfuSWiC6MmNxb9HP9+vXUidKin4iYKtWi5IaKMKVxYGLM3LvHmXBHtPebj8Xed2xVQWxN0blZijQ5H0Yk7+KoOF8Pq9ULhnCHl8MCS1wAC1Z0N9jRkcq96/RI1QLMApF368tizAMnDVEQa9Jp5SUbSCYnw4kCN9x04+U//clTn/rU0YF+RlJSWaFbc44ka8kqKpzqHtE9sBM6R9uGe7qdPjE5Ntbf2VlZVeXT/Gw481AYWWw22ZAkwx0btd5m2i1/Bw5oc5l3fnmZ8VuaVG2jgkFQV57199fU17PFdG46p7go1+GHzoYf6NfyBiudmjRK+oPLLzvz/qcXFBWOjo9tvuXWl73sZTNZc+6ve9mLX+LoidFkDFsbj7PBobAroGhZjS1TRsuNBVmERA1DX5IY/LLly9VQx969+uPyZcsONO+pX7t21F6X6mqDzOisqq11CSPh3K0VmUF+j0kgRrZH83uPkZQpKCOBjAQyEjh6CeivYwARZoSyw427BvcEM+Le/qGBkpJwsJOumdtXXlawa1e73pbzp/fWU4dOORwMEyZOObviPaERZ1f8w/PmJcsrRbcu5hEHih+3bQvTQXxlvST/EhIOuqJhiO44eI9EZ8RMTEyKVH2VBXK3d0J14EA/ZxEZfpUCCU80P99pNIEMibLHaLOw0KRo8Mt5BaI7PTUkgkwhNBZwin6c8k+8mObp7Q1rjJXlVxZFSB8YmAYsbwwkAKO8alm4f1VZ6BchmG3DKccGqXxiRM5khwO0S/KL7R5M5RbgAjYhpV/Cwan40DABD4RTAQkAjIhpyXPXrl2wkScBQuhFFvT4Exm4k0UpnHUlbt22FcGiGpAwYNPDTY+TujAj0kMgeMHIHeQJFXcfvwiDwYtfcgYfhyoUhx0EKIsEYL6DPNeuXQv/jh070Gl7MDwAyisrhVs7d+5cVl05MB+mUk3HDw0PrFy9Cg2DI8MjQ6PGVxBszaciOP1Hr7H3DORMODk8PzuclJ43ZnN4uBopBO2o9aSiPMenyNNETEVV+cjgEtJP40HNzcEW1B1q4wtlIHBDFRIpP/1UofRTzXZ3j6tx1qH6/mDv5elD7D3sC6BChK82F+x9fiab+lWUMJmZ0aFBeRU30N3TuHrZunXrwv3AQ93BmqYn56wmXzL6ieaonwZ6IkeMMTYOfrUJEglEjRMdewdGqzFOdOyLYhtmkoUyR3u/kzyPzd75kDALuFPzqems6cnZyXA/8MGTse4Zhc+UEj35RZCDEQxDytSdMTAbrbbnhhtuGJucOO2009x1QG8EwAyDwThob8SaKMGhjTTJ7dJ0rtR1Y8kYFYMMEW9y5qSBq66WFqPNfiGkl3EYROtMKymrjigoa12dcUWRJ40yZmmVA0ro9JDhz1Hj2YWVtbWxM4DEDQaGQYTZRaWlBq4Vpzkgggc96EHIsF6keJntDTNoo+IKYhXWiMB5oLkZVU7b0OuOJAuK4Bzr79ftB9swIjswIADOGhtjMMZjbXKvbGx093aIgTNPRgIZCWQkkJFARgJLQAIGcfVuCPFr+NrvzKyOK6x21nnp/ryYmdT3dXUNR+9Q36dX1eXp3/XmIrrQGyaPIEFH6cYj6R5g/GweszDVTKfbWaSAjxkB+5r0tGGhsh7ZV3/GmbehoWFBIxjdunJ16JYq66B5AqFTThZ2+pM7a0p1cHBcHOhPCBHjpa4uzINJ5GYoDkecBIwUFoYrcyMjwJToq/gNQj6uvEoE4BMf11ehoLzBj5+cJBzAHt043zf6MDLKQlaQcaBxDQwvykUMJN7jhCpGIEG8OSX4zSnFgiJmkABEJpDICwmJRsx+E3EGeRI7eYo8vaBBiiwRwK9qQhUMcN5JnkN4ASMj9r1wkGQnT1wrUUZUcWBIA84oOimKRgnRLSSSwII8vcOA2hgX4c70uwADtohccfD7avRBKYAhBOaJ7KDZHkhiXFIPliM9GMEsiSFYit+D+smpXLr6iTZyjiyoI1apxjEi8Y/1M7jogCnMH9t7+ljsPRtajwpNrGNOnTJY8TYVpUKK8PVeoZ8IJiXS8BAUE8YRBZZO/7U2mGIgOK2pds58UOY/luefae+xKVCWKvO+oH6xBjO/94AEFi0GDrQma5/CS3LBNNU3QFhcUMjGzIIKEZ2MQYec1egsiBKLfDo7daSmhdU9Jejs6Bg4cMCiKLpoJRBbMhNbu3Klpodp1SbXc2mYoKIoNFWTz8bkDWqb9Mf+pK+0s7yuTvZwyWByUYHNLjII0fMLC+yCuuqqqwryCyh0T0KAgdVf/OIXV17+01NOOQXt9vJQRqjQoNuGkLugRDhxZO9RTjKGLXLWdzpiEuUaSk9vd7egmh+hZbH/SaOpT0KtTwaXglgyT0YCGQlkJJCRQEYCS0ACfLhIhRhYv+yv6ITp+wQteu2KinBQsJiNR6iz0wvri3Wp+tsYN0rnI4riRHewmVD1ie8rO8yioJqaVG/veOgQk15Sun7ZXKiMOkezxMCglRdm+2z1szDr5SWC8acYzBhyLF123TFgGCxgNoslYEOtftmvLGjjO/AfZJSoLFngqasrMo2MR9Em5B7c8WuR2dXVIz1ihgRm9CsuRj6cYMAwCPCAtbS0R2/VV2AYVyJ/BIOKwzvazB3xDcjNVKHsEVJe0gCPazNLkPjEMbAF0WNKFjyHRCJUwOKqZrHrofJEs/AVAJkrTnbFIcDjRTrhYNDDBQKJDAjxolqx4AFGnrwaZKAB194lKtEDA0c/ytMnADAT5uHkWRcjeSSJcmMlCoDJBC8x2EZDrERkwKxEX9FGFFEHgE3NhHOYltRDsIQ2Np6FYFWPWgJRiYl+2um61PWTzBGJ8ihkW+jVPvnfST/D0gCcAsag6k7s3fKHoWOx97AHQbXKrhKpXEFODhWityQWqzVRz3uBfkaC/RIdLT1o7y3+ZKHOuMIIEa1ZtWb7ju3kdid5/pn2zrgIiq2ls+L9wGFlxNz07S1zlGHm926VwGKshU4IDGtIknFQJmcPwez8rBbZcqmcrBB5Do+GRfae/sGBsvm5QocNjgyXV1U6Y6CguGjGOpnhobrVq7LCTX3z6YL87Py0jeedbfv1STrDyYnxmeEwCJ12ikNvL70REgsvwz+PE8x7e2GQqNukjDkjoR+l0ErU1Jo6FgZnz87k29NQULB7925EPvCBD6xpbBzu69NNbTz1lLxUWkCeLiyYmZudGR4XuO/bvXvViSdmzc+NDw/pJ9LZWeWVFUM9oeHQrEsPETXC5mYde1hYXlZYVTk3GqZ8WYuDOwTwhrJ1dBZId7e2aiDu1orMIM9IICOBjAQyEshI4CgloFMT90b/NWx/S87CzU+F06qWL19pmdS+fZ0iAd2ZnhSonrS01JTmjP5X9+pkWYl8Qb2hINR8CWB9NJ84OpTr1jXs2ROuDuLhCY1EEd5FSkqEQResJ5XiT930smWFHR3itWEIYYBKzGYlthHp1tYDPHV/CtiwJlZ0hJU9wEJovbknjFNnZ3NbCwpyduxo5jNAiAt4BOeFhXkuMBJkoh/BCkUPbF5gkC78QwaaYfBJij+hVSh2xLQALBQbHg538JaWCHrDhDNHGaljU2HuKMgxufypwL4t859Ts6KOibFpSBwFglmMkJJdsgJXnoAUWdApDodTiQpCMwYRkMgzHePGKE8lIs8qU4uoFXeoPDnoqI1BO4JJFW0ASJvMDyfPlD3AEKJfceSGQe8iH9LAkepGHmLE4Zz+W2+99U7yLIy0IR7lvorJbfcVUznjC3l4rKyoGOjrb6gPe76379ianQpTggpNp/JLC4tK0iXYQXZOwaLOwRyl3t81WNjmlu3AOORN56XdDux08Ow8+pnuODAyZitcfjovN9yVlV9Ysnx5U0He0tJP1UejSFud0oTx8UnWEbVCXVN7+kn4alA10U8rcRN7X2734b593Xeyd3u8w3w4aVDFO9n7sMR0bqhZYwXMxBw6tNRmNnfczVnpwrwlpZ+4iPpJaWm7tsiQ0Pbt26PJRCkRIMXGF0ZIMo4NsRHvG9dt3Na8DRg5MJDDylPi0dv72MiwakpqISVQyg7/sybnrhU083WRJbBoMXC0EL0Zg9C82QagqTWcsXZ5k3bQ+p7f/OY3l19+OQt8zGMeo7NhJ4XJePCll1zijLWHPeIRe7Zu/fa3v82czj333NDE5OfXr1plP+3//d//GYahsqeeeupTnvIUiqssd5xRaMLQ7l9zzTVbtmxxaAfLf9KTnvTIRz/acuWwa9eTmzs3Pr5p06Zf/vKXBqiU/qIXv9jv9FzY7iJa9ts/NPTFL36xoqz8hS9/OXSmlC+55BLros98+MP37dh+3abrb7rpJppq087zX/KS3IkJRmJKWUsvL7SQ6yd0GC9+8YulrFu7RsdQVV/vWgAUymhDsg4pdJOZJyOBjAQyEshIICOBv7oE/rg/Mo/D+9Kx8vD0sDblWhvIC9SlolS/piPza7WgwEm/xmuUg18ouuNWupeIYy1R9+0FniTSC0cfS9FjQitj2CWbyj5wIBw0xfPjA8grF2CTut6VBVIW5fLXhZAuneVTis0k6uu5p+ZZTRWjzZ/y+vXoYYuLU+3tYVIXvBSf+K8C6pGRSRNTyopZvEhXkG6amwFM1+yR6IolA+wiOgTDgHiuQk1NAI6HYwUkY+NEYTm4a5Jw0T/cV1YdGOkb6yitLi0oDavE7SKVnQODC+ePNDQ1CGtFHeDJUCIi/QoPxCfWyhEFMtATExN5dpMDMHiiYEX+skPuzyjP6GoTi4xYg0QWAGRFzrwd7/iK8oSKPD1EEcmAx1fOPeGTw4I8gyzm58kZcDyv+4/lGW5MhYRk5FJ6LC6Ka3B42O3CGJcFwQKMgcFB70Vl4WIep2pbZBcqZTav90D3fOH02oqVZLukHnM5uRYgFoeF7pO2/LpKp6igqNSigxA+ITXIcyYsescR/RwIS+bHAC8R/YwGRUWRpKZiHSGVzZK8WqMV0hf0EzuJvRc44ZgO4AuPh9h7WAt50N6DOS9fEe09bM6ntLiemZ/LTeW6qXrGNuB4tVhh/vBkWK9hBGpJ6achITQfqp/if9XHftFJdOQjNkE5q5ROYrHG5brfyfe7efvN/sQ4DEeSJyS4Pkp71wrU1dYVZKfnLILOmstx5rgCnFOYCvPqmeeekcCixcCWzswn642jYRgOFrg6ykroqFl56Utf+s3vfLusuGRodOQDH/jAD37wg4c99rGCXsCvfM2rX/6SlxpHFL62tLYYSfqXf/mX6sbGfTt2fPazn7300kv37Gte3tBIIy/93Gfl/cIXvvCQRz96PLnMnU2+9rWv/d73vseSneiwt2XfBZ/4+Gte+aq3vvWtE/39Ngm37dnz6U9/+nOf+1xXbw+BWpj9ne9+t6OrUyknnbKRihvn+/KXvvSG/zj/Vf/yr0/r6y0rDQdXnP+WN573sldsPO3U173udT/7xc+nZ6bBIx61T3va05yM1d3Roct53vOed9lPLpfOhLQ1n/jEJ7wMDPQft2rVFVdcUdfQoBmyBKq3rQ0l2s57pkYzpWQkkJFARgIZCWQkcNcSMGWRrGEMUG40yM0Jc8K6sOpql+6EK39vj11HxkRK0nXB9gZxAZOMzoJyQpATj+2GDUujdY4CLe4jBxGwvcH79u33wsvkFxojNk/oTt/29rDvDhJOp+yAPciwqllx3vWkXHDwfjs7w4lZCUC2LPprCAW/ikOD7pXXrlzkWRptT66BcsEwynEkwkwOfIYv7LNFmwcqSNDsQA9UwRCdfkVzf62AFh4AEyHwcX1F4eTkHBqUiDV4fAIpwhgbHR4fHZqZDQG/9BAYFxfM58yNDY8JJtJ5+c7EQrMZJyUKgAWlSsE1ekSY4BEg3gaDBWghkY5CElMctJE2KTIixjSylwV5YhMSYSo8Mbss5IM87B8qTxjEOeSJBgxCBSDKE0IyRJJEBEjEJnmKmuA3kyzjIfIsIeEoT7QpV8AAlV93KeFCQTMzc1ge7O9NBkHG0Azb0Nioa1/gLEw5V3x6alSZ43P5JfIutQf7kSTCdF+rdfY4FaAEeZaX4HBgeCCVl08/Tdx3dg5NjCwt/UR/0M9kPby6Ju1omIl+FuOF9RE+ZZCuuikM6+npGVbLlAE8bT/E3sPYFsXwgE/sPcyB031/ugBMcUaDisvDEvf+oeH80nDKgA2EWam4XqOotbltielnMElqfKh+YpkF0V6iY/hMzINlTEknKyN9bV1tWJOu6gn2SPI8RnuvYHdTBhlGJ5yJVZDtRvGwHifz3JMSWLy1KMkeXZEeRaE3eNBqaE8aGhtN/DpRbdf2HTfffPPzn3sOgP/3//5fR3Pz2hNPvOWWWwrT4di68847z9jJu9/17ve8810sraet7Sc/+cmb3/Jm79dfc62ztdq7ui7+5KfaDnR88pOf3L97t9XUVu884hGP+No3v/HGN74Rnq179jTv3rPxxJMuuugiyxuYscVSX/7yly+44AKYv/utb4+PjG664Yb7n3lGnzU/eXkad8ap+b/++utLrM9xcmNVlTO7rrzyypKCcPv84x//eB3M17/+9S23bXnh856v4RBOy2K0nKK/6lWvEgB/9H8+bLp7d0uLGWws9w70//Tyyy+++GIjoBZHSdGpVhvuHRjwnnkyEshIICOBjAQyEvjrS0APl0TAumM9VTLJGog64YTGtrYDYjPOMXdQV66z86KP9stF9sKTFsvx3hoarIYNB0pxiP3qHH3lSVdX57e3d0qEBxi3m998wglNAmDeZwLgAiRXQIxxOoG1tYVzp/WwYj+BEi+zpCTcUSQX4qQgQ6HLlpWjUGAG0sM3lchDEMD29Q2auhFDwgkVB9cn86Y8eC9yiUIVBACFAmC+LMwcWZilOIMZmO4ej0JWPrE/hdNQYQHNpBHTZSkpcaxJiCVII0YF3vkb8KCZHOSFPNK8cfVGcSBfyLyoRABY9gLeJIE/uQqy86oVJ1FZMh4qT+VKx51EXOAa7wRl9VwUBYTIICvEgyRwR0xLhBYYgrk6GFEWriFfkGeMYO1PhlD2UP3JfctqHCSaY5xwiDxDRBTlCQDByoUcg7g2Ha1ogYRPJ554ovmJbdu2KSuGYYghfI+vRkNAQjsxf/vpTbHopfBLvLxX9wMjGy+kp7IoDGqj2A/Rz3Dgi68L8lwK+qnePeqI/FUW+tWRqqcYh9XPE06odNHxUdt7OA7qcPZeyBv3KZZOD4lLXVMhQpO49PVTPdJnTRMDJA1yY3eIJ8OTTjoJR3jxrmnSOJDkkeS5cdWx2TtsLIL5mwdOmf9NYuD52zdTLwWDuO/TsGjzwNbcuLNvbm7WdgoqolINDYoChZSU6badO0eS66T/58Mf/urXvraree+y2loX6oUV91OTP7rsstNPP/1nP/85SG20vnnb9u2vfd3ramqW/fSKK1RCmc202dlnnX02hIOucU/+fM1rX7tn796PfeSjj3vc49asWwesuKRk3fHH796zp9OddRMTvr79He+gzVf+7GcwO+/cpPTHPvax73znOyPJJXuyUH1rrYbHx9Y4z9lAb26OS31HJsa+8Y1vyCJoz8nLNWT96te85v++8uUtW7fa7aR17Dhw4Ps/+MGqFSvPPe88g2Mdra0PeOADH/TgB//yV79kJLXIs78qGQggCitINKb3fVXKcJiRQEYCGQksDQlo2JcGIbdTEQNOfyy8xA+LRedh8biZXIG3U/DH/5njyYZdfKaDciOEeWDx2K23Ns+6+D45+Vnn5WVyYvLkkxtvuGEvX9afnDbBmOgo8WvDTlSJIMUMggHpymltDRfJcsSFOtLFVJzLuNpZryoLGE4kR1l3L5AAHMUCHqQ9ii434kLEdPBCUC718LBF190QokSJAkIENzaW9/SEEA4Zskj31NRU8Cabm9vgV4p0sQ0M1kvbt8zHRRsueB++8hCcpKmUJGvYB4gYZJjKdsmwP4UQkUdCPvnkZS0tEz3OtUqHC1GHx8OGZ3FgcXnejpa2nsHukrJCXIyPTqQKC6yKvGHHDWpAERG/F9hOWHfCzZtv9o48HKGE20B6YDy4Rq2yEEme0rlSIJFH5tKF08g2qYs2KWIPiTx4DBIsDP6EBM3xFy/8bAGzFJ/UIAwyyi58xax3xJAnRtDsBRIp5KNcBEiXERkQwiag9Sca1AsyACPYn+Z3rVWHTVw9NBAulZQd+1bSmUlT+vhouJJKXuEEVHnZR/Q/wfyxwt7+V9QTfyy8xA9Hgj8skrtINHTBf+XlIb7QnSClhQP9032dXdV2wCUngd9BP1kP9peOfq5cWekkNZO6QbzJaVW0CHnORW9p6WQI3lUQHcDg8uU1t97aiS9Vo64pmBf6eWR7D4MvYEAGMyxwO3e18Z7W1l6lUABIAFDa+qaqAZeddneXFVcsHf2k8Gwk6ieLuF0/k/Xt1B4LEkmGQGgXAdJeATBz8ER98xX7Hu0Yc4ijY/CwCPYS7H3nDbJ7JwdS8nLX9g5D3mxOYbqkIKvA1b/uac+13OSIZhFn1g5jGncwBzTE50h2sQC/8HLX8Afx3RP/XaD58F3XYpNwZGEfY0mhjU5O9kd3tCV26J3ViSTbm5sbV6920oUxW+0v+3Gz3Emnnmofr33gOrC3v/3t5RUVk8kOFvpkKhWST33qUzXLltmdMNDVddlll33oQx+qKC2zZJrOXfmTn1zx85896OwH2IXr/KqBnh4DUeZsv/P9793/1NNExbqLd7/73fYqfPSjH9UHhxOb3Ts/OqrRp5SN9fU0npri0jkT5qKFxwFgfNzmXvPA/cODV1999fjkRFlBudZaOkjdTElFRW9npx3CoxPjr3ja05yMoeAGB3QlA9V0U7+IPMBhHDhJZE/KOkZxZsAzEshIICOBjAQyErhbJMD1ia6G439yc25/58IqjPNnzyNPEYyO78wzVl5//f7o4ErXu+lP/ToyRgcKRhAoiyhI36en0+Nz7PiaHEo9oE7T+dJAdOvAYrmQQ2ge13Y84ZA/IQHMQXfslnuIBWzSIVGEqEMYxvvUcSuXY+CFC2HXa3FxYVdXOOVYXulwKmLVqjpnIZs45YCCR49CzXnK0t0dLonxZ2TTC182YpYdMDL8CvkcsLVvX7+8SoeWHw/bhg2rBcCRDEIaH58iQ/CCDQ5xbn6YA3cIUVZOCFOLCsLq8YPyDDPGigvyPOnM67dcH51jxUV/GhLA0SGBUzqcYFBFjB4I/cIWY11cRHnKTvJ+VQpgdGJHLUBo5gojgk8vIlUEwCmXgQPZAfCFhCv8NKJGmEuM1KC4hTylwIYAlEiH03o3GKRE/IJ2MDEARjD5404W8pR9amJMWY43tXKgIJVWonC5b3Cgv2sge3auvLikIDss/56em85aYp4R0U3PzDnDi2TGs3La2nrHx8Ip4uSAYGsA3Kc5MT7d1tkxNTPvfuCxoaWln6Zp6QArUFPqRXXT3traCgEwPaGfUf9ZVlKV4UxZwEzjD/p55vJD7D3cjXzQ3kMArJaJwv3AB+09y+J/9m5xhMkkXxnaTJ4zYfsHRwbgJM8lqJ+EQD/JJzJOP+tq6va27CU6JqC1Yfhe3G2OEQ8w4qIA5MnW6Eaw92TNS0TFPOXy9TDyPLK9jwyFc/vy81J5WXkO+J2am8oPqheOhs4895gEFi0GjhTHplC9eizIERP+/d//fXV9/djgoFiR4Y0mS30GhsNpk/oQq4+EqU94whM2nn66jQhMyF4ijezXv/VN3dTmzZt/+MMfCqEtdXaK1ckbThTQPvvZz1aKc7C0rSDf9KY3GaS58cYbm5ubJ2emz3nOP7z//e+n3Pay/PjHP7YBWDysUZZCdxmkBUgme5evXBHOc06e3c17R6cmrSwaGhx0pTtswxNj8Bx/6ilDyaizXLo9DDJpp2ExaSYjGkeeq/c0EPbW/O53v/v51Vf9/dOeji/rgYTTiJQFR+H/Sad7j1VqpqCMBDISyEggI4GMBI4kgdgl6aP0U7k5YUfr3Hy4H9jsI4fPyUYxXj3zzJXX/H6fjk8HKuYxnCse4O2B4SbqFktK3KcSFsE2NTUa++3qCgEbAH2ujlJEVF5eIgA29RrjLr+w8S/hF3cpkUutdMgFbGaah4aCSy0FmK88eNhCZJIc+CTde4y1TJE6fdmUI/cUMGxwcsHdkyiQA4kAXjv8SbSc7u8f4TAQiG7fL5oTzKFoVEHrkQgbfL291o2FuUqMxDAAsFM4RYzKgpxAwBcXGPIOi5NxXVCRl50byAPJDZifzQEMLJFnWCsulwD4mluvgYHYI+PkSRreyRMZyiZVDGIE77iL61QXyIAZwII8pScEF8FPXChRooLA8FIgIQGRaqQKMB8MPQJpQ/9cF+GxFBlBehCsRPRzliQSFCQIW5Cnd9mdvYJm9Q45KUHukcV6cUWjJMgzi58VhBzJg5l75qsH707/UTuFOYVH0s+/YjoBKh2dVkCQ2NTkLJfOLrn6htqyovTA8Eh/36CvNrMvQf009KP6PGijRaI19WIBhYqjOchOKA+nuGGTFt1RP89cfs01h9p7OCAANpUOA6UqKi1S7wWpIvbuTGwbzIcH+hOAcEItxS6aKRqcnBgaHhoZG8nLz1lS+qkxwn7UTy+UdkE/D3QfiJWu8cGFVSr0ljWRpz8XzIGNeA9t3OAg84xIsE+2tPrPsPe8rOzCAmtKwrQZDEgKL9PzmWOxyOGeeRYtBqYNmg1EhxZElzY9Iwb255vf/GYBcJGZWAuHklVPNs2Wl5QuX7FCzElpwJxzzjnDvb0ysjebRUwRBzxZWY5rpn+i05e97GUPf/jDDT1qeR1FYDrXTOzEdDi0/fe//70e7sEPfvDb3vY2U8TUkSYhBhLvGmuo6leudMij9Nx06rLLf8wGZIkKp7HWjoMxkBNeUikj0P60Y7l1164Vq1Y5BsSfugq/GhThLtV3kzCqrrvuOtPXz3nOc6699lpT1tUVle9617sYVZCDVj7pI8XMMnoP8sk8GQlkJJCRQEYCGQn81SVwcHVS6K8Tj188rDecnnPSlQA1RLynn75606ZWPZoIzZ+cP4dm6JG5hkksFK4e9KJDTCYhswYHw5GzsMnihR8pAB4ZcX5lmJjikfOkIdHVeud/i0hBCrSUKyoTAJtsA6w4ngCYGLMBkxFaDqIeHwFxFmtgIFx8Kh0G3at07oGLbfbu3YcwpShCudwGpzg7+ZgHHztieGThHnBz7faNEoDZn5E2k1jiRqVAyCuArbq60mlPLS37ZeQ5WEmOkcryouyUE13n+c2EMDIxMJ83V+FeoOJw2dLQQDjR2l4qhCEjyHPj6Zu2boryxJF0bCo0OiHIg1xxaPAJX2SId3n9SRTcD662FCKCjXziL88eTsyC9+KBHHecHISZv/WOWsT4VaKCODm4kwVrEMKMfn9GfwwZSpcruFu5ue6SBBbhlWioQhHoQTbRESYYKYg0TaxGiEd2SuJE5SDPvGyYO7t6SsqD8sy5cig7z1nL8ANbag+OsD85J7rrGs8KwU9e2M02UVdTY4Xf1GTUz+ni8tLs3PTg8ACxOChu6egn+tkCnfFC2gZY7DZU3epdjfgUBqVKShCsBqmEA92A3a6fp6/ctKlNVR5i7yW+HrT3aWpAx2SvKq+xyW9wMATGalAWpSjCVUsUuHe4dzp7imIoxbrypaOfbO1Q/Vywd/pJREkTVMw66LNqperm1XBHklGezEFDx16sOSUEWoFHKRgnMVFxIs9js3dnqpUXlFI5U8E5yWBk1lwWbAWpzPbJe6htWOQYmPaE9s/9BIPhjoEii4yLCp2M39N5gAKVVlVe8NGPAnj63z/TMuPSivJbt9wGvL6pUftI50z2ui54557d+q21a9d+61vf0lDCyXTZofZoLjtrsD803AJpeD5ywcfOOussGqMRl90LDFGJze7O52RXLatxC/HM5ERvXziokH2Kq2dm5xxDDe3Q6KhQ2csqC3vm58oqK5xZcf2mTWaYdTaab72rJmBwZBidtPTEjSe7xGtqdiYnladoh11/4Utf/MznP2exkHOnHRNd39DAYNxmgCk0wxzj4XuoMjPFZCSQkUBGAhkJZCRwFBLgwBmhzdanhh473EvJ1ctLFXKiOX5JALw36cLCBIXuT59YXOxkoAnes65W7KOz09uKtYRszc0HRFw8Qj2mIE0itD09YTmxrpC7CQn30dJcuTiLMHgBA55vUFdXbBcxzP6M/jrH2tnOsVvnbkKyAAxbCFGS3bDRK+UniM04kq2tHTEeg0peg+Dl5anm5i7vSegaJmMBwKB0LgpKghSSkE+5fAwp3FkA0eWwx09ivGdVXo+MPBD7iIWWIxPhSBNyy0mHuDpdnIJkbmYuYB5JVm4n8Tn8IQC+dVOQZzLhA4PYAOOyI5UYpcPshS/u3RSCEIKrjZJD5NnDh0GwxChPvgeSkBoZ9Is7uQxAcPc560pEUpSGSoGf5MHLpRRyQzYyxAASCV8tk4B0jPtk3RxSpZPJQXmWiw3QhgAP/OQD3rsQemwsBAaQo9Aq8iC6iRC0+1PRFt9VlZXXldakZsOhUyPTI+6jRsbSeUwOImZmOsTwE24LKygtKC7MCcu5C1r3Dzi/KF2YFuvZ+WzqbgnqJ6WieyqFOZi/7OnpozBqUI2oF9WhrqmZGsEgVQktQKIJp4cAeN+Cfh6092JglCexdzt/b7f3ovw89j7YOxTsvbi4b6C3sbF2NnfMqeLsnf7kFobjlC3Tr6tpgB+SJaOfwd4P6mc4iB5tUT+j1QsNJGLZE02DHcmiromOhgPGmhRfqX2w97lg7xo38pR4uzyPxt6zsmRnXHK5YXzBCmItLPyZeblbJRAMftGeZCqfcuhR43ALCxHN+tOLQdfWffve9773Ke4FL3gBdaFk+1pb6ZOOKvQryaYg1W9rbluHY6fC4gQjiyyKmnp0XBKN0Dgfgzoi3afKZcuokWZYk6o7SSVaGBXRDQOaXbkod11Tk17z/PPPZ8zafj0H5MCMkioakX5RDglVnpwO5ysWV1UN0f5UCIN1SID13459RzCzd1nxrVu3uhzYp6uuump4dNSVwnCGbmFhEljjopvPTAKTS+bJSCAjgYwEMhJYMhIIPXUSd/nVU/lLit5Np3n66ctvvrk1umL+5BQaQdYPCqD4uNxZvaGvXriMutQDBwb0euIcKf4Ug3HtvOhM4QSp+1aKhbX2CfEX47qqGMhJr6oqdWKW7hsSXbAHGb5yDjmdEiGBQR8tPuRLxLDTnzD79YjcYO7o6FYo34DfzzdYs2aNTcdtbf0SYeCbemBOAstCpEIOg+L8wmyGTC5uCTClRx7BIwDXCJDiFzAMHBjAHGLZucLeEWapJLRWDmNcRhLzG+S54fSbt94sI2b/WJ7hWink+QS5gmxBhIp80EDskEtEDAajPEFGaUAVp2RxIYIlhOAUJdLmvUASA2B50SOdlyVWJ3y8iJR89asIsS5qFeQr5Ik45+MnaKVjFpEYNy3B17I6FAvIQ0AkEm1KQd6d5FmODN5XBPNLIGjDKWcMJQWpMN291B5zqGwhKgBOycSBZe4HPpx+5mJ5SemnqkS5+RuKlywjCLf+eJeumii5Wkaz2vwj/Ty98eab23CqLu6gn0dp764Ao/lKUcsw0yLYkMFM7hX6uWA7bJkQKC1eoo37pPXDFCvQOJBnTF8Ue9diqC84Z8JtZcbpgjWk8hdtbjKgyzx3KYFFi4GpPr3XKKpOc/mGD0WCzEAnMZQ0u1Y+v/Od76RJT3rCE84++2xRq+hR8TSgNjmKUAirpYZkw4YNjqQQRZsHnp6cpJRaYTD/97nPPfWpT3W3sG7qAQ94AOSvfvWrW/fuZdX7m5s1r4z8vHPPfeITn6i1slw5lZtr08s1v/mNNpeCOTHLwVqKzknlGhCVC2a6PjE1pbOETbugXUZPQX5B3cqVVmXjJfRwRUXYQVjs4XRWw8l6LQdxKRTjeqy8nJyvfOUr4LEQH+lxFYWMUu6yFjIfMxLISCAjgYwEMhK4hyTgvIrQXycxcDJOG86P1gmKZM46a8211zbrs/ypl4yBnL7SJy6gDjF2cMInXmBFRbY+NPabhqdjD7hiRZ3Nt+B19x4FwaMXFhg7cUk/KyVC6m2lmyXSh/IsuZty+cpZF251d4fwVV6dqeIAW9WMABPO6EFYDIZ5qPLCzGeQLhEGnXVJSY5EESBINKMEzhgejI7efksqCcjCc7Ct10rwGJHChkjEeMQA0Eb6EUMmvBEIAWCc2yoFeSDhwRQRCfyU5U/IZTnr5LOu3XztHeQJ3ifAfmX3YJAAkSdmwKAoVEEQKssYPT8KJFl5ojwjO8JUSMQtxCJFLuWCjPLEr3dIjD7gUeSJNmLEkZAYa2a3YlSMHukL8oQKMNFJxCn21Qh60CYRqQBQIheavZCbvEjFsl9lgZES5QlYRIEYATA60UZ0WE65RnaJPfTSaIlgEHlRIRHvqLjD6afBhVApS0o/0UPI5olIWGVhgdiph7pTlTQn1hSmVBDtBX/WWSuuvbblDvoZtehw9p46nL03DA4GTYbfwyJkpwZw3lv0k11oy+gt0zA8hAv048hD84kU13QAa3SeDvvF419u70SkdpiYkxm8K3eJGcR9n5zFGG8IA4Jhs44tRF6tBHZHXkdb++x81szklKObn/70pxuwdFXvbVtuW3/8+ksv/nRRfsHk0LALe50sVV1ZlTU55Ybe2alpA7+0obah8X3vfs8r/vG8V/7rv172gx/e7373o6BXX331DTff9PjH/t1pG08pKih8w+v+7Uff/4HjsqQ8+WlPXd7Q6OtXv/pVinvBRz7K7JvqGwyp7t6x85/+6Z+e8dSnObCqtXnf1772tY9e8LHZ6dnG+obs+aypicnuzi60n3baaRq9nNw851Rb+XzW6WcYnqXuJVVVxr1t8pGXdh63ek257R8Dg1f+7MrPXnLpjbfcrD/QyrATDYpboBDwj694eZj79STdPCOBh3MhCA+JmScjgYwEMhLISCAjgb+qBILjfnAe2Kh1uEUpTAXnnLzhOEsidWf8PDD607zcsPBPHyeMsdlIwCM98QJL3OO7Z0+vFB0uHxFDnEhBke6Oqy2KkCv6ixwASHp7h/iRHEoeOfc6ic1qlH7gQCefki8IM1QwFBTYDBzOoZUulJKXh5rATMWYjfsuEXxSYnFnZz/n1ZQmMnwS2pWU5O/b1y2v4vyiBC8wmxnu7w9zpMjzSWwpwrQI2qKxGOsKD6In6lcR+ncswBCDooJ8qx8LZifn0DY3M1lYGrbaclqWr2samurr6e5xFJAshflF2fOB/ZNPOtke4D+SZ54B89vliWXuATmgGePoj1sQMUXaYlQUxkVqxBXlCSc5CPuJnSikkwxSvZCGyB+1pmrVRawUyNevX4935496j4kwALDgLkbFhMahRxUAkRKEFtyJoNQa1wuR5Ikk50grXdFokx3l8HhE7EqU3cVKnZ3duKgoKxkeHOrv63GwGDoNt6BKpVvvaToha2y2sTrEFUNTQzXpGqiWzoPOMAs3HxQ4r7TAecFTk1kueMYmOfDign5WVOcX5A4MhYvBHGg2MTGO9yWin8ZQVKJpe780mUp4QZ5rkGwtp+F0lTbSByrn/f73D3uAj6Sfqlv1QUIsJNDQ4EKgcO931KKcuXAQenVFGXunACVVYeFDsLX5KSdR56ati6aVS0g/aab2xy+yEcZ8CAE7zMdDXB61jGsKKZ3FkadPFD6KkRz86ROLYBr8f5/YO8ZlkfHkDcdi7w31bftaZsem82dSVuGkc9N2pswKFMLRRZlps3uoVTjmGPjQmkmuH7yd0KnJCYalF43WsmPbdgOS3/zq1y+66KLvfuubrR0dNRUVbzz//Be96EXLaqrnpgWN6V3btzk+7rGPeuTo0GBxebm8To2mT64VetYznl6Qyrvkkkt+fPmP/ZNXy3vJJz7xjGc8I/QZU5MFpSWf/tQnP/OZz1z0mc98+qJPTk/N5Bekz3n2s1756leddcaZo8ODhQXpz3/20he94IVbt27dvX3b6WeeccVPL29oapwaGy1O561ZtSJ7fta/vbt3Vpfrxwxq5kw4BW5/CwZrqiuz5mcdUj45FHZNVJQUdnV3iWtXrmiamRh785v+6zOf++wDzjr7ox/+0EMe9GAbm21mHxoZPvOsB1x80Sdf8dKXLIiICYUAWDh83w2ADSUc03OozhxTxrsJ+N5O/90klgzajATu1RLQ9i5N+rlQCFsgb+HlKKmN2e8MfCQ8R2qe81IpPbXeSYdLUjPTs3npcBbxrbfuFOWNjoZ1vxw7np8jfNesqbz55n1JXxbCJPGSSMnq3b6+MHEKLFIlnXPMiezsHAAskfPNveY9FxSEU3Oiv6hcjjLg5ctrLX8WhnE9odXVggdsirGvb1hgFtFCCNgB1LgWmIUe2V0Micdp2rO+vswuVGSIqeLEF7cVU5PONUpOZuLBR3i/paWp/fvDWc2oEpKhhzdsdtldEMhQCq4Vh7vly+uF5KJQD9cWYYiEcOVKs6lTfV1dLpQCNj4RQm5h6tTUmNJHp0aKS4uzpsN+y8ryGgdnbt6yuaAwXMcKs0KjTI5fdbyl0QiTV1gepcE1F6YqLgYkyMAd8qTARj4kA48XvpB3/jdRkDN5ekloXg4VRrwjVcCDbHWqUIkxr3R/emCQC3K/RCGFCsm4cvnK3Xt3w0OepKo4FAL2AlsMidUy4gkETIyWcRcCjO5uwMSItt7unrLSYmo2Ojack05ZMz+bNT86NCpXTn4eYkaLRgsrm9Bz2OdI+rwAjGDvC2ALLwsAf94LzXFAOty0KK+0anY2a0FuBGVbbWVxZdRPl/9EGpaUftq12t4ezuKhGFE3cIQX8Tv9VK2qktapINp10kkrb711P/vF4x/sff5Qew+b+1QoxYDN4e7R3rPnglsrPbH3qY7uXirEguGRDjNbcxYeE440LBH9xPWCfpIG4mkjmv2KaaPeEh2JxTaKoTXVN7kzySgPuRECe6FpbEqbEOWJcbbAQrEc7H3r5gV5/ml7b20x0FWUG45eMw88NTuVl2VvcPb8zEIMcUctjlsQ7ph65L//pF3cTXZ0ZIru6ssdiDlki/Rd5foLv2U75+4vRBGzo55aFFD9np4Sd7WnUns2b7bGWKJ2k+2xMeqiKzPAwrBsCapavrx73z5K6bSNnpYWGkb5dBQaNo3QzORkbHBpmCJ0BppgSBiVPyWqXSEm5RsZG2XVkFNZ9xIYq5MzNLWFhc3bt8sV0Obl1TQ1OcbOn8aAV6xbN9rXZx1P1/79PlXV1lrYTBGdhLhv797Qx1RVte/d27hmjbEdx+LDj5Hyqqpd27ff//73t534N7/5DVNHBpuJLyaT97e1O/8hCuRv5PfeHkPe2+n/G1GzDJsZCdxZArPZWXml5WEV773nuWM3f4yUx+x+dWQf+tCH7AbSu8X45M6YRAm8eYde5XjLns8RFWYnG84SUHN0VkSadSC/0anZgnTuzdv3D0xmdfQMLFtWp/PVwepVmxqrdu8+oER/6gll5e2ZT7VCVvxjCbGeEQE8P300YriJXEYpHtmtJHTyjgXCcYZWCoeYu6nfj74j3wByvz7pSXXH3jmmvurrAeuOYy6YdcQYB+8rJOvWVe/ZE07SQhIHVywKvr4+zC3v29cqOFE6gk3+OMtD/y4UhIoDwIuN3rwXLgS0uFMu3xcA5xgwANiUFR9OsJM7BweGIBR0zkxP5mbPzOVMnHL6Cfv79hwY2DedOz427TCRsZL84sryqvxUEWyj42NdPZ3IIE+UQ44Sl4sqEQuEZrGo+diVjStv2XILF0giefrqE2lEecYYWDrCrGomIrR5ECwFkTIKR+UleQWRvKIRrEYW5CnRO8xwImOhpjAODDZfzz7x7Gu3XutTpEHspAhSldck8KHyJDePvWYcLfVC/oRsHrisrEJ1zM1M6VgnxkctAyh1klp5WVFxaXdf70D3YCo7pyJdmp7Lri2tWt+0tiKnIm/+MDMxqEqU9Ig/KsW3Pwl2xPxH+jA/G3Q8lbtj3+DoXPpAb7gbqai0bKC7u6S0qIoXmjJZMjU8Pjo9m5WTlz01Rj/Duga18FfXT/WIBlWv+vyqNbVDQ9xp3NMTLl5Rgz5RLfXlk3frcBlO1E/CxEhTU8Uh9h5mRAFXVpa5DZu9u81WrqzZnICkpJL5G3/qHbToOmdkqvfk+68fm+mbzp3oGznQ03/AwWclhTIuFf1cmAFmBfhCGCmRTmy4NFCBtWRwLRoFaUiMJukTAWoWwLMsAfCCPDVERMRqYGN9C/L8k/ZeXFhAf/Jmc0qyix52ysMKswrTs3m5yZmE7s2ez541YxTWSIdLtC3SSCziiNFxUGikajPV8oUXXviGN7wh/hk+HPm5u+zoyCXexZc7EjM3OzMyljv/hz7rLvL+GZ+C/1BSdpjW58/AJYvWPbu/XyVQBftyyurr127caA2BKql0pVCY3Z2fT9oJFUPDqJFcGll6Yz9Qjb4qndYTDiV3A2qO2ViZjNZriYGprODWWJaGTwidXN0gu625wtf00CBtKxJ4u07g4GIPWm5v0+p165wT7avdNk5/o8f0VQA80NnpZWZsLIz2FRfbM8QMUKK4VevXw2N3P0a8aCQ8jMGQ12Bvr1Zgem5ep6XX0cRgaFlTE6o+9L73tbS1v/RFL5Ql82QkkJFARgIZCWQksGQloDf2LJCnR56eCes/R6dzOMTcODGVr42NVfv29egB9YbRo9Ive3cWcvT4DUrDI0UHykHkAuoTdfphDDqZ1HWE08REOCwHmE5TvwyP/tSsand3nz5XoqgJMAzuInUkZfTjIUGAr5xOwH19IcZQhHS5xIcbNpTefHOY2oJNcCuohqepqcE9u4qDEKno4UuIosvLXYMUTlTm1HJYpSuOD8C/gBaM98imr6RhEhWpCkKtr8iADT2CTO/h7BOLJ9N2/ZrpzFVcKLEo+GryQoW8qYlw+BAvVqyIPPL0iRceg0mYESy4lY6Szt6w1RAlJMmvUBYyFuQpo0JRIhGzwmb0g0ED9tGZyDOl1rCgdETCBq0HTo/icAFeuiha7Vi8qnRolYh4Qt64euNNu28CgIsoH/pguN9XQgv8JpNm4OPuYjBRnsQb4RUnRXEFLjzNS0kkHWzm5qfR7J4OHM1PB6la9ukpyFlyZ2IRMr+SBEh1fDJcYW1tajoZecEd/RweC3o4NTvtbiRgBLik9FOdqiaV5SFhtJG5iFgl+lMimhP9tG1+zvyNdbd/bO8VR7D3cA0SmRgCgCQ/2ntKFVs9Ge09L2cmzFHlzIXFAsxnbGKsorpsaeonUZASOv3ST3/S8DgDjAU0S2RrhIYR9c4oKG0EZnfSPXIR5oK9Mz32LuXY7D0nKBskNky6XTapIGOW0jLPPSSBRRP2ZH+/MytmkvPWyyoqbBAZ7OgQ6YY1VxMTbML6DO2LiVaXCVr5TIEG2ttpEkY1io4iYGRsLDQ0VVVGic3xujTArLLlF75CaGbYBLKTKHRxdvTD7HCsmYmJ2C8Kcf1j3oJS59+JsSkW5aLQrFeTRr0glzgxOBiahrKyKOMQAFdUOADEJ2uc+g8cMEXsXVguok4nna6j7pRVXlPz0Ic+9LjVq678xVVO3rJU+/LLL3/Tv//7Ex7zmDe/7e2nnHTiO9729nuo3jLFZCSQkUBGAhkJZCTwZ0lAB+eR1WJnv0IX7jE/T6JO2dCw3xNOqHMDit7TVx6e3lmv6sAd8EIaXqNIjwes/wXvVxaJkHATeY38SBHCwEC4azdOScHgkYujaW+wR3Eg/Zps5BK4MgkSwDGRw7B8eZ2iOzuD8xCjvuBFhJt7SvfsCff9Kg68shTNbcUFtxVmpCod8eBXrqwyacrlRaRxcHhk4QO4kdjlMbjDkSxe4LHSVeznT4nIQLAXJSoLIzDICwkWFMejMOesaP4xOkEKCGGWnUMMMspToBvkueYE232RJDGWiJINJ2zwaefOneJYid7JARk+IVhIg0GF+sS3xhHuOOuYggRJiojyFAAjAwsI88S1nWrHIy/y8OLFFl9ZYOAXAYty9mJ2urW/FTCcaBDlgkG2PzFCIORJCIiXfeWKcMS0OzXQ44EKkRb9RWA0gJEFchggRxjR+YopNKCciDyH3gcjfSk8+CKq2bmwdiBqEa69HFk/a5egfqqOqEWuM8MRnVELqomED+rnhETVFPXzoL0vc4vYney9Wq4j2Hs4Fk5ZqjXatYOwoA3jHcmAl6IVca/QT4wQEWmQEmNhjDhiU9QbF7HRoMyswLsxpsWyd/KhYIoTq8w4HJp1LFpMthTs6V5Aw6LJOzS1yUFzwdIcPlFdrWrD/G0YLk0XJafS0TDnPIta9SGiSlZqutg16uTE8ILyJQpnHFjAPJZ0vXSuoLDQ6R2VTU2aVM2NXB4H1cNcZKJY+15QmF9oaNZxXLNZc/MzI6PDXd2jwyNuPpsZHUvl5vk3mbw4BCuVXzA/O+cy6q59LSND1v+kCtL5Wa4MnqaBs07bqqyrLy4pBUkb81Pp4YFBK2EOtLS653hqeGRibPy2Wzef/7rXbb7l1tf+2+tf+pKXfvyCCx2s9ZEPfvA3v/o1I7kX1HmGxIwEMhLISCAjgb9lCeiMQxAVls8Rg/jIXKse1ju3zO/pp6+94YY9XoQ0fjm1HDVxi486ax2xFHn1xdL9cgCEChxEwNJjIMQ/5kqChNO7oE4XWVUVlkdyOoV2vgrz9PImZQFwLoMjkYRJvEzFiRaFZDDDGQM5uYSIHR0jYjAsiEKhlcUUK6ejra0dAITwQyV0Mc1lsbMYDEnCOR65T/FIGxegKB3xkU6QnBZBO+4gQXaM4qTHECL6x9Kh4hwHPySsJg2hrwewxBDazc9jRBAYvWp/+nrGKWe4H9hLIvZ5BSHbbUOjY6OoEk7HAPsO8kSYLHhXEOTKQnyMJGMkLFiN07DRLydP5ZIPsciCu+CPJZUIM9o8ojurwRGgUCJShABY9h07dkiJvMvoJC14HGEVSVLpEBKy4nr7eoUHGPFLnmqHPFWTXN5xDQmaJfoKs0SoJHqBDc3SAYxOhSmQJfVgCj2UHLMef5J2op9BSfCyxPWTEtI3VaCi3Q9sdILMF3Qg6icGMaJCgSXMRntffcMNzf68k73PHY29R0EZqqGBjJ35QE5W9xb9xDjh+GUXjkynpXSbJNU+NfBimIBIGZcgH48gF8XeyS3YZDIexEBuF35An3nuIQksWgxsx0Bc+Uz7pwcHnfZNjXR0oQPwT8ti0NGR+s6ZTFZJ6XLV97jDKDo7KZm5X/8MRFsj42wt6lhUXJwW/bJSe4NNFI+OsmQao5/R/4S8mu9kFMpOo8lk9FRTBUCJaKDKqWTMBiRrDD2ouV+3DQ8NhS5kfl5TXuHYQ6uPSkoYuXng2CLMujZheFh7F465ThTU7Xv1q1fPWYCddH54eeUrX3lgYGDn1q07d+zQ7v/iF78455xzXOyAkXuo3jLFZCSQkUBGAhkJZCTw50pAz6hbjhEvHHx/Kf4U065eXb99ezgLV6DlV8cnUYAEjGso2hFiSReP6UaFpjpQ/SDXMPq++sH6+nATKQAIOYs++eUQFxZaRz3Dj4RKXBRLrKurFlaLCWPIF4hJYg/zrG1t3fDosv36yi9QNKcABo6jQvXaCgW/enVha2svMNn9RmLsW1aEiSlESvHrU0Jz8HxEg6iKEsCdY0C4oTBLVCJ/A3BVlZO0wmrhGL8h0ldFA+CBkA9mCQdtEtEGQ+Q6UhVFZ/PU9r3bSQzyKE/pGzduhNbeYJD4wgJUJns5TuQpSoHTOwa9EB2CY3F8G9k9InzYIs2+YgGzXkSqqI2hCxYwCBjvyDMRrSAE4w79YlGjElLE4arDVi+UAMZdU2WTUQZF+xMGj08iAfito0aqvDLiyAUckTWFEk4s0VdjB7KjWZbINRok4pcWSS9IL7m10Ik+MIRwqjZqVRDim5rop51zd9ZPtyX34Hfp6CeC6UDUT/sSVKtaJnD1dah+qlOqpfpUa2Lvtdu3d6sjlRJr6tjtPRz8HjbxJwao3iGnLbDdK/QzxgiIZyOVZZVsh2JTeNKL2uudyRioEgYzQAweKk9iJGQsE2wiz6O1d5qj6GBidv6Gds/PH9rk8EfmuZslsGj7gY2KDPT1ud7OQIpzomiD2WDHyRkXmqM7yUgtq2MnuiIGaQS1oLQ0cGfMzb9kRRMD1gZRJpGwMNXgJ6WUJXYDsGmMjSX60wYdpNuuEdTREZAwJGPVXsDbjTQ6PGwDEzLA0qxgmnYaHbzbnR6X6tGnp+n0asuzS0oM54b+RtyehNMa+7mxcNokjmoaGrpaWhRUumzZUDKcXLty5YHm5uWrVomTo9ngSKgc5oETSu7mWltC6JfaOc/HKpp7O/3Hym8GPiOBjAQyEtC9hj46iXvDMLW35D5716asWlXf1tanr+SZiVi6u8KROVxDHbfzonXmATasYQ5zTTpr/qKO0lc9r0c3qj81oyy7btGvROEE4Kqqoh6XByWHNknhRyaQoqYs0exCJw4njxNAPEraexKZBC+TS610VwLFgA1hYkW0rVlTtXlzOCcZQpGYQgW63gcHQycuC6p8FWEqvbq6qL29H1W4kA4YbR6rsKMHgjBZEMBDdUDV6GjYDKmjN1kOGD3FBvqnHPkUpkZBergHFgs7fGx0eNRiMWd75mYHBvML8m2pdeAzeWIQwVwOv5CXFpbeuOtGCL0LmyWiUIkI9sgbpHlQngrFaawULMgi4iUNQQ6a0SZFFUjBEaoA30Ge0tUdeMuhMc7FJ0+FejctjP3o9KMHtjM2nHHtbdd6IUO8Y5Bvk8hzkHhlicWRZ6C8Ytne/XtlBCbdV7SVlYQF4b6qO+nei4qLyosq/KmgoZ7ewpqGdFbYUbmkHlwEa8BD4jqmXJgZlsgm+jkfpud8txu2oKQoNxVmWQm/qKhwiegn4at0qkhb6ACZ+0WbJ9HPYuokkf5gSmLQz3z6WWNv+B/sPTki66C9h5knXJMGeEoCnj6MDo9Ttjybgx38nsoryk8OVB8JO/mVSG2MpbkA1b6Jvt6+pamfeCGKBf2MzZdEbHZ0dWiO2AtGiIgoaD7bBEy80qOQ2dqCPEne+0F5Hq29T4yNGioi4emJybmacApAsKFE+5aUUdy3iVm0eWDLmzV8uhpXBDAYy5XCtQmdnfb00hiTupZDJ61J6HWc/0jVNCemcAW6RMwszcSW1tSwXn/aQixQhsd2XHGpcTndkZlYD22T1wl3QmuQJpylsExKzIylKE4AXJwsSfJJovjWCwBK5oU1h77NNPLExOrjjpOFFtLgkD1ZRqJcAbOnsr5+3P14IyM6VAFwb1ubpgQXw93dOhINA5zaRMSHI0HiqVrQZZ6MBDISyEggI4GMBJaqBPSSujnUxV8jt/ZA6s42bFizb98BDp+OUp/IqdX3+VNMyi/06AH1ntxHDt/Klct0nTpKGXXr4HnAMrqdd8+e/T55V5Aukr+o1+7tDRuGpXAVJJoMhMpU7b59LbAhRqIuPkkM8OZpeaVSIiq5zJooznSlCSvxm0hPYm2tk6t7FK0UkPCEMDW5zocPEvNiJDoJK1dW2+mpy1a6uA5yBVVXhxlUoWB0eUPfPzqK8ZqacM6W4kgJTl/9uqWJD0AmCx4FYKiiCwFh8B+SiWiUbDhug0OwIg2HynNl08prNl2DU04wLmRBv0WYnBC0KUgRsvuTYJc3Ljf1Knz17kmqo0hGieJJZATXK7k4Su1IN7dMyIqTSKRoxom6wLL5ZJUIlSyWQKNTTI7+hQlAuU4++eSbdt7khWRQBQm0HsCkgRc0QEukfk876bRtu7d59xU7flGuamTHOF7QA4kXZPikOARI5C+Nzi65tdCEaX+6QRmckqFqxZTJv3uLflJ7ARt1UqdoVmvUnhZF/VR3KshXrHlRsxs21DsE66jtvR4GmO9k72XWzLM1la6KlYgML8q9t+gnFaWfrANr0XzoMKWl6obb/Gq4/Kk5It67sve1x2bvBAWtFsDISyo7pUasszY65yXz3DMSWLR5YLov+pxNjitkCcJK+301u4EN520kG1q8UiZ/WqsExnEW1C4AzIftMWGxcXLSoE+ekO7LIdOqTFejHBKTEUd/enS8kEsUGZuLDnkM4BlQcYxb8qdxvJBo7YcXO3wsV9DBLOBPmvj8hE6Q5nUXPpWYdsZOcvhbKNJJIUlPo7jwSaGa+wQPo/c1FhfKOtyj0TG5bVOUjgWnTE5LAVCbgimhvmBba2vns4Xfmo/I4+EwLU4aUaovraRHcZadB+mxv0Rodm5pC1RKrDtUHVoRh1Jg/l1lxZ7SAnSfDJ2RJC4ObSjxKNGZ/QviBfmHeszJcQiJERDj7eqUrOgJwsCg7dDiFv3dkIzKUKJy0YNmLwpFr666sqHBxiA3vZeYBkmGVA2vuINLtYEEo/ocGE6SUQkXnbw/iRCtnCbicuamQWm9DqPzRAWLFYcvWkfckeY/ifPuANDjKt1pp0aXDEFRsChnq0UMloV3ysPYE3ufTNysu4OMDM6/EQnc3e3nsYqRhscsCy/xz8Wi87B4kkUuh3enwDNJhzZ7dKcaQDEvF3Dr1j3zeWELHDr5Z7ERPuGE5bfd1qw78Ih29FOiNWdH9fdPtLe38RG1hHxEX2NYtXlzq95NEUxeW5oEPCWOkOXqSedBcvvkWrGiSdEmBZUlXVtKJoC99/draEPgAYN0lNTVWVg7u3v3PommH1HIveanVleX9/aGS020gdE7l1he7priaQ24dLRBi1lBV2Vl/o4dbQhTIjAYoEKhLavoB+M9orKXVa7h4TF/go80oK2+rqy7e8wNEam8HL7B6GQ47xqS+dyJDhf2zowVlxbkpLOHB0fKSwstgd6yc4sjX8UYsPGeSVvRJ5544rad24jRn5FrkrdwVLAtTFWKT4jnHa1YsULGrdu3KgWASFULb2LZpxg5c8rVCOLFtCoFGAyyKAUk/OQMWETnIcyEqWEhsfXScolIQeovMO5PZAiWzFon+pBCNvYN90MCm8hZCnhfyUqJkOxs3gmzKpN39969ls0RLNp6urqLCgtMa3kvKCpcuaphZn5uaDAMnZhXhQ3mVG7iH6LpTg9G7pQWEpQe0xde4p9Hgo9fj/43aguVoS35FctSRfn9fcntQRUVE5NjNGlBPweHw1DFTGpp6Wey2X7aInYCUZV0g6hpkfqlXWROCVW3KsPg2rX1W7d2YFk6ef4pe0+xyraOYO+Gx2hLcX7J8uWN8zOzmzfvMzg1O59DK5QbAsWq9PBYf1xfsHT001VejCjqJzPRXlEM+ulFogZEm0PJkyYx3CNFREzbi80UbIfC4wWP69atw+aCPGWM8gz2vmuL7Edp750d7ePDI1mTcyVpy1qDoxu0O5sb7oqkJJa5k+ImZnEY07iDOSzkO5JdLMAvvMQsR4JfQHgPvCzQcPiua7EpWLwAI3FeVX+UqQ4WJwsPM4sB0hxVShYkR0gAOJIlPt5jymKz+dfHp5Up5GXYOJT0tYZ/9ausy8EU4c4nu5fjNQzWYzsto6cn2ufdR7fV6ZpFs+t2TTN3K8bYtmpSLwrVB6fKy9niZLKQzLBeDE7uTA9vRqtq7RoAlY5NeIwSq9CITYVK8ejwNb7+uDMSta7ddNNUANMwHTztM990QbKK/jBZFinJWgCYEInglFoQck9PiyoxoktwNL70WBfEFVq62lpRMa6lW6EQ7rt2TGgy47FIFB0bGsMY0bPJT9ZK2PIV2DGeYv4kWUyhgc4x2mLDSRLqq5RjK2CRoG1ecKKdcJfPZ71RjNK925/Hk0CVAxnJXGmk7cx21rFIJWfQZCSQkcAdJcDizM/G1INhRRiQ1Vx0DzhUmY3mau4ENhXlJbt2dYijfLUwWLSjrdb62XAKgE/pV7vNM+YyMmF+pK8aJXYNGwDGPjg4LEDVhIKEATbA8IlCdYJSAGuEYXYpyfh42HfnT0485H71RzCAlFEAFotwELFdU9ZFc8cVF/qO5N4m7baWho8rEUJ4xJNOnwKwa1e78M/0qT5LEZxawIrAGhpgRoYXXMvF35VORLH991taXrZ/f5izxdfcrBHdsFw5NLA5OZ09PSSWOx+EVpwuEkKncgvQgPfe/h6lRHmKJxXqFGhl4YX/DT/X2VcOOsy+wuArp5yI0IPZSBifWwAGWHFg9EEEonRFwC8j4ChPibFTkA4Jjzwe1asukERu2AeJwagAMIDn2eNdOnH5E0nwKBESciAuxckoC4GLt6Vv2bJFiuye2267beXq1ZWV1QL12elJkp+aHNc5eSkqKw2MjI2OD0/khtst87NmQ5+byjpiDBwJu+d/yZYjlpsKp5qPzWe1t3dPTszQQH4CgjFbQT/HJklDyFddUz06uIT00+Q6vYhT+qpPXdMxIyZqU2gnxUNzmAn9ZIy29qtiSqgq46dg7xVFh9h7mKpRg4m9y3q7vc/PZJOP+4HDToGhQV9pyPjsGGwwT2SNDox29Q/0m9ukM3QD/qWgn2wH2eRDGzGFNpLx4iETzQJL8U5oOFXdeGEOHnwBIEOMGPfBLKsM0ryjPIO9L8gfkru293AHMD/ZYvu57KmsqVwOeJbtwc5O+us4afe8uS2FEhdvLXQSA4fZxSSI0mp7qJE4QTUHVs21JtOt/vxD4oIMwMh47A6646CX1L8Fhu7wItYNm6LHx6NYTJizN9NiIq7QFSUtbHAgHCCRHCJyh+yL/qfjuPkE/a6esr3H9mpXUmn8jEAnF1cgUqSnAdNQsn9DnUcioFSo7LbxkRGeCC9H5WpBTIxrHXQn8EjhPWhBvMcg5w6oKEmUiYZJgxt04OD5ZGi4A/Ci/0kI4YBys5HT00b3dSACYIlEYX07wtw7nVtc3N/bC0AX6KjNeKxajH7RE0cTFp2wo0TIBbP4yTFyPDI33JlqEQBr3KMWSWeSFvNjDVPpZCHDUWJeXLARZwQYc0lWSZFkoNBQiz0RDsBzRUlhoRc76oKyOUEgmTZZXAIy2DISyEjgDxI4GPhqfr1qpd2RpEvy8ALZID/PHIjG3J073rWEErXq0V9kv7ot7bl0D7MVa9m3BNIDLLQ2SQSlcYIcPKtXOvzSoZVLfwGDr3oKuUR9tbWVJon5mvodMBL9KlEWGcGjFnK/fFl79/Q5ohHAuhgleuHDA5MIrd5EoIhsnijXg2/qnUcrroNNQCgelksi2rwgjwurqzIKx38FEx+otPywmbwUH0ZG0ICqQFsqF5hCYZAICWIUgU4lSpdXESSAjESe3ZEqiQgGKTHK0y+xQIIGMYPfiFkKGAgJGTa0Eb7SyQ3+KCule489LIIhV5yQRqJYCGSUBgzkjAUiiqTK6CEKnwS38ZwwwBJJAxIYJKLBnwvyFAwIAySKoNSFcqEFjKqoGGhQfbLEQRA0g0GVByO4IKW4Dx0ZS+dB9gIxqgOPfhF8iH6Gc7yjzJegfqpBclYvHkSqU6sNpKhEFSoRd4l+hj3M9JOC/QX2HqrYA5WHzkS1IbRoJv5U7r1CPw0whYYsuTZMjVNmBkiY9NafROcTe6TqflnBneT5Z9o7S4xtTrgbKdlZuaB+mZd7RgKLFgP/gdzE/DQlrMIR0HHeL7R8dhEkGwlAStTvhX9JixMsc+HfHxDdt96SI0MsITK36fZjvGkaBMAiKC0FH8QMnl07jM3Ab5wzv1v5Nx2tXBuedbaWrNmnZSp4RuyR3HxoxnZ6PNwFrxK5EmrvSMRYsgaMGZc5gyTZOKFN7OsOh3l6fNIgygsJVHpi74mChB+1HxVA+0JdcO2X84USI+3+jJO0Ryp6UdKRoVLQpPPWuvklFlu+OvfvFw/jK9RR4vcgNdIPgHyUjlO/2BSILgoxfw4Svmk8wcU0heFMMnSsjWn5ZCFi+ESwlZXldXXoH0n8nj+nlL84jyMAKPlwcoOl2/fCwgGXYfb1OTaHE+p4diOrRsGc+s4u6MNfXGAGQUYCGQnclQQ0fQufNQ5sjkOmqdfK+VOUUlERjo3hHXKateSaR1GxdE6hdO2eQIgbLYozNwKbGUSf2K/sMHMiHQNi5QcYX7WlvkIinGPgYlGupCbLI91Um7OFxIC8Zy57bIpjOqp0IroPD9fTn0kA7DioMYFcjO58ipHnyEg4pghtikOGF026WL6jo0dGZcGvdG14bW21zg1t/gQpGAAsZoDHnDPa4JQX5V5wiiOhoK/gUYhNwBAaO4BTcbE7wIhE8hEpgYzyRKREZC/I0ycidbCzdaq4EEkSo7K8kJt0ADDAGeUJA+ErXUBLdL6iGTy0HpRDwmUnNw/iRb9qCssS/ZK5vMBMbZGPUmItkzA8uAMMs7r2J66xLAuCAaMTv1J8goHoIIfB3DIAeCL7J594Mmwx3kZYrDIA6EcDJDCgjRil4DEo0nwYCV1qjwNn1CnhYyERZxiJSPSz4HD6WUW8S0c/6RvZUgAkmQF2fHtHRzC0O+lnOB1tQT/VrOpgrwv6eXT2HgYIon6qXOW6FpxB0SJ6QosQc2/RT/qMYIagNmkvvfWnmWEsGMTBJh2wgAKPGgF/3kmef6a9ww8V4RdkhbOK+MjMYWbycIsll5qd3FfoWbS10JrCIJNkFJMOhT+TOeEwl5hECHoD3xfeWV2Ie8N/bg+Kwp/33ccRXCzMNK+DAhI5zRUbPY2judZlNTfr7fRPFc6jNhp02AXDiyoczZ/HOWB+VUKxEEWVuc2ir09niVQLg31ClVPw9ATq9LDlazVGxkazwib++ZHBMEauLy+orBjo7Kyoq0uXl5nTm3PU3VSYB8jNDt4VVB4vC+8KogmGyjXBVcuXzw8OWoZtdlgDoYU9bLmLlWg6d3JifGYyrIXLLyoM8Zg/Z2bqVq9CAx3u2r9fg96werUSBw902gpuKmQyOaIzKHlyDx65LRY9x4wn2XyiHi02yrGMnBerxxsbK6uqsiues+bYOdLGnAEpq9aOtK/7mMs9xgwqN+ZAjMGD0EcieGiIj2V/dZEBY76ak3gSpzOMnWXC4GOUcAY8I4FjkECyRcXQa7DHcKaD9jvk1lBohJuaVlgV1NJygCvM7dN0a6s5ao4+toAZQOIFhhVePEXppiT7+sLSaK6wFlv7KT70blJXgBQ6kWTJLidPdOcrq4dTIyC40oom6eXt7eF2Wb2PPwGgxLwi/wANHFCJntiUCc7t4OW2xhAFACSmbgoKcrdv38d/lVfchRLFwecCIzRgBDEmPJVy3HHHictwEcODiFZfhk4kiR+UFaURo2LvaEB5QX64FgXjigYzNjHq6pkowxDjFYWFo3NTc2ibHA9ncOr7FCfQRZVzg7zrHyPXIIkONol6QDRDi33pMAtRUAund4LyIA8N0VlXYvxqA3bEACfy/OIaEgEzD54QoIVQRi8cDDAxTEWGcEVeIiITxcEMxkOekAh0lbht2zaQ/lScXxggF4R7ECZRhGD/sB3OPX09B8J66bBaanI8TEU2NNQbk+3r61U61hTtJGHYivOLvauR7Pywc2dJPehkBzofkpzm1jhvlVal8xtqC9s7BnVS+UUOQs63sj+VP78E9VP1kTy1UU3mVgwJ0S4VJ8VjXIMyEL66Jn/6yfABBM+zoqClpetO9h6WwQOIUR/h/LG9D8OZzs2hnG7LtrtCVYJRXE6e1e5hhcXqNWuXjn4aJ8II/aSQiX42kBIr8CfzjGpv0wQpecc1YQbXNNkbCJLCS2RTXjQ4h5UntGCO3t7DmpNkfiiKLtpCUmhmOXQUxt3+mwSui1JK6EXDnBglCLGBfzrXZJeODX+iGbbh8eLPAOYf9Uo6YOnyyhgVblHIWWpImA1pUHfLpMwDa30ISyKunad1wQUXvOENb/jgBz/ofim3H2s17m76FauF8kSx65EE5GZ+yxsazIe67X7WNq2k8+MlxEXsdyZJ8/Cxj33sMY95zP3vf/9PXXhhbB8/+clPrm5oeOxjH3vxBRe07NypRdDE6FE0GXmlYTeRR6HxRdVHAs4+44xHPvKRL37xi/v27/dpyvh0Tk6hu6buzic0b8kKOq22xtGctV9clFm4PjhoVNycQm1dnQB4+y23nPuiFz3uCY//3Oc+p75ouBoMdRr19q8YAyeLzzkrJlSd5zA0MIA2AbDe2qXZyNOgG+QPY0+sLDG0u1OiR8QdLjMrKHA7ChsnZD6us+Avv/xy2rJnyxZn5SFS5pm4vTlpTI6IK/MhI4GMBP4yCSy0wAto2JwmOgZFJSVFYlotv8YEpHTzYBpG51oJe2KgpelmyGIwnUh3d793rSjr9if4mpry6PsCi64kVMJULZAeAWaJ2k/tAPxNTQ2wxl7PLwzK0qZxrHt6wiYUMPBDhQxBFIeCa66sWKKvIjEBrMleGUH6Cj+/ViPt/gdurmaQ8xopjwd36ZVQggaJC5hlFAD7E2ZEclQcr+W8Hxgwzg+WC3cCCfgRLxRUHFIRKdEDg4LQBgAeGASZAECCJwQChxwq6SBd2OsdsLAEMQfl2Q0PMKzBIJ3oZIyhC2CJkHO1/UZ5QuLBC2ATVgJdpAKTgjaJaNBnAUaGFI+8ojiYsSwxVpwSpZOzUMGEHhrg8SiUkGHwHqcQfcI7KZ166qn4ipNmiMS7PwFDAhjjJCYjYPSoPqgkEukSvBtJF8k3MX2D/cggXkzAc4hIDHd+D9FPQxVhHGHp6CdRq2KDGo2NNQcO9Iv6MKJGEHlY/ZROD01D9vWpyTvbexjDilZDK9TgQf28o70bMSEWRycTBWVT0SiReG/RT2SjH7XMRL2ToTAYC2qcBCgzS3HSGCXXktyFPDF+TPZuYE4pBGs/MAKiX5zOD45l5rlnJLCYk2w0IzagyahyljFJVVtcWnr1L37x4x//2DkQWHKd+hOf+MRHPOpR1gBrXFS/J4wdJezKfs+wfc+Xwja0Q8bMd2zejOXjTjllNjnrgtBuueWWz3zmM/yCX/3qV494xCMenKyDursptB+4dfv2a6+99sYbb9yxY4caePCDH/zoRz+6wZ0QdXWqRr9ouZK+2qzdXcxz7t69e/Pm2yzhwKBlxLwPbYeuuqu7B86Vq1YJb/SKVllrYZ25WZS+fdQ8MKjaVXky9nHb9u3WroWO1qVTeXnWz03wNpJR7btVFLyQkrKw3wwZfoW2w729Ldu3G/5fYa7Aou7hYd32t7/97S9+5av5ebkf+MAHnvGMZ9Q4GcuB0hw4d0rq8m1R+yvNW9qWF1bO87eSyZZwellurrP2SZs6XXfddcjjKjnvxFD9KjeB/ZXC9V1btugaUYIw14wVTEw4A+af/umfBpI1li9btkzVUxgzwGFcLBMD361Kn0H+Ny8BtqaRZ2fZyeHRoeMNJ3LkaPfsDHWLr5WunGat4ujIGEdNujDJ/cCcZv0XSJ6iwMayWbNEOgt/SgfPkGW0J5HrrPGR6BN5i9BEpJ2dPbwC6QjwCVo+JTKEW3J5531qh3mccjnvSpH+BCmL4gTnhtBFULIrKAaKScBWaNG1fsf8p6/I44kWOdS3fyjSpnvSMcVIsqoqf+/eLji1SAiDR5wAOdYUF8NIv8r1GBxGsHSsBXklxzu7H3jSLcBJMIkSD8oVd/v9wKPj+amCVG642kDfoVBBYzwjFxINIHkqkcuLWchJBmbESEQVAKRi34Ns5JGSd744hMrCNWARPnhzy1GeEQOC0UFWsfq8Yx8GYpEFGYJSL7IoHWGCJSmWd4LxSPdVIsx6RvBxihjXpgolokE6GhSEGF8Jqqa4ZmvzVhTSCbPRvgYGi4pBenAkhfuDzaJUMY5Gx4OQ58NlqEvO3wsbo5JaRqc5TqMwBOJYkqCfZcVGcgeGB9KpgtrGesz29YWxA0sclo5+qnqVTtRopl3RapCncqVTEonRptSXGkzs3a3dY3+w99HRQ+w9DJfILiP4xN7DGcjuB/YnDQ/2nk4Vlxd5oVE0kzrNzM3k5ts+YA6lvL21Y+noJ5opXtRPse6h+slMUEtENBzBaj+x/bCARaJffEV5RmkcSZ7Hau/wTA1PTI3OzczP5OdygUyqhKY489xjEli8GJgRJA8NC42IZbQulRkdfdoznrF582ZDyq4m0oFcduWV//u//3vSxo3f/fa3wxZQ0AxMFi8Hb7Q7JuaTHjwci+VlKfwi3uwiTu/wayJOG7HlppvOftCDdOnXXHed4QBj7AacrSByWURxYaFfo82ilBmjyHfCELrfv/gJM5/J85VLL/3KV77y81/+0l/6fydgfv/yy0vf856XvvSl//Ef/1G9YkVOf79DS9SOYWEVu5DxDmTU1teR/NRsGP4Xu8KmHZycCWuDtZ4Dyb4jrUbo/+JWzxn3UN7e9UV796fFA9A6osT1Em6ucnGORKt2aJH1NFD5KuVP/oI81sdRn1r36M0UOEJzcPDTn/70e97zHhHjL3/5y/LaWtdHWEKkL4FZg7W/q8uv8Z0cDCfzFf4MwfwiPaGYIz93ED5AdhPWWSQ+TbilKSfnV4mJfetb3+KBGsa0ftFhZSAf/uAHP//5z3/hC194ZPSL8+WwNfWQhzzkZa94xXvf/W4HX1kQYQUhfegfGeH2NjQ1iYotPgsqZEO4HcLJoeWouTO/i0NiBktGAn/jEjh4ukEUg+HrsE0lO3v9CQ1bdvaaFxSR8gU13bw6TbpP3Edts5BAd8BUvbiESBjAX+Rc+o3tIa+6qiq9bdt+jiPXWUTnK69aOzk6Gi4XBRC6hoEBMVVTk43EWS0t7cnXMBXJzxbduazHqZEc01gcPJoLbqtmWCLnFXJ9CkrA2/TY1xcCtrigEZhPCHazERYwqPUWVZqVAmynTktLr0R48IgM8TYMThKENol8wmS4LDY5m6vu7Ozm+0KIwonpcLWMUD1nLkvwClg6SvBY1Vg1kzsxNhlYULRP2Q7Pzc7esGLD1v1bYJAxypMovAjRXUMKQBgpdCdnKYrABQwkFoUMOXHhy5pkVGEN5WQoVwgw2tvR7AW1ivAVEtW0detW4lIKMpQrO8ZJAMtceXkjkTJCJfiBUxXjRbqMSjRJawrRiyzSPUiFAU6EQYswVQN5TXXNpi2bfFVHsvuKeKMSUxOT4nOjw0oZGBmemp5dsXqVGLi3s2tmaLKiwORI6eTcZGFOodKXzkN6+YUutc6iGLklNXWNtbMzTofujfrJOQn6WVFNP0fGbl8zj+Ulop+IJ8mVKx1jNi1oR6o/KQbizWR6V32x6tWgP9XaCSdU7NgxGG3B1z9l71VWzMsb7X1uNlk0V1ZGHtu3783OD9v4iSI3O7ekrDAnFXYELFn9xCz9jPxinz4zH/QjmFEQDvUmN5wa/SFDZkLbceerT0eS54blG7bt33aU9l6QdrNWWLSSPenS1tx0djr4ka5qn3aRasb3IYt74jlmQXNwF/5FAuOxzDrA0AjGWMHu0ML85o79D3zog3977bVmOA1tVFbXCI3U8cjYxO+uvfb9H/zQ1MxsqrR81l7RqZmswqKs3NTUzNzsfPbYxFR2umDGFtF0wXx27uj4ZHZh8QTo+Wz/slP5UiDJLi4dn5z2z9fhkbGhkTHRlcTBoZHsgqKJyek5LnSCJyuVD0z+gN8VpFk5fl3WkFVUMjlt2C8vOzdl7klZWurs/MJxBOSmJpMS5ZXuq1LkWsBgZ3PfwFAoMb8QjPS+/kEHXiM+N5X8FpfI6DuA6Vld1EheOv9Xv/ldeUWlJUrLV67Szpr2sp/oqU9/xvn/8V9r1x3/X2988wMe9OCEnjxEjk9O4Xo+OweROXkpyAP7+YXQdvX04ZEogqyKSwPv89myEJqUILFUfnZRydjoRNZ8zszUbHZR6ciwLzN4mpieufrXv3nFP//zNTfeqCrLSstUTSlqw27tgk9dfPH1190gKoUzJ49V5qcLi6bnxOqpwRE3uxZCHqpGoYMjgpTRsQk1KG9uXnp8eNTJv2QYFcOkgaXfsBcXlY6NjDPsrOzcnKJi2NJl5aMTkyPjEzkJctfXpgoK27q7lzctV8rQ6FhRuU1oMyptbGIyt6hYiaSa8J4zPTufS1ty3EcXqmNmbl6KX/yiWbqapTm360ZRCckAIEM0eye9fm3+6Lg/jfHnFBSNDI0UFhkHKIJONd16622FNGp4NLeweH5iyntfR+c///O/Pufvn7Vhw0lf+t//oxt5JWVQEReR5qZNIMwqcYDi5aVppk+QI0O5oSJyU8AA+CuQkdCGI2R4DwDpAlJFGIDcktKu3r6cVDqMI4TexOC67NPxRRFR4RWEdxlVumpKFRbDoP6Q9+KXvvxL3/jG+g0nvf2tb//hj378zW9/9wPv+8Azn/7MX/72t//6qtfccNMtQT1yU/AozUvAQ9sRVlTS1tFJgNKhDSZG5/FSWIyXYEEqMDFJXymT3/gp4CkuDWgTQ0sIdtF7LhXtGxiU+LtrrnUn0voNJypIrbHK4orKdEHR9m3bf/STHz/nJS8aGxkam5rIC4fiTEzPzqQL8s2+5DpQxyiMEQeH0Di7lVuZzNvcE+1ipoyMBO7TErDpIHrM/GCMBj8+O0uUePPNe4VJXMPoEHMNuWgnndRoyW6MfMR7gjEuoNjG3hHvHD4YxE78RYnmhvfu7Y5esiKgEkoJeGQX4ImmIJRLYCYYk1Fk5U9k+ATYb1UVmHAnZ/AokhOV+aYweDdjybGWF2G8VZhdXmtnL6p4qCiBhw/illQgAEQyHFZ4oJWFU2uJDKrQ4E8kmQsFYBsQSCyD9EgXAGNhZCRc3+BPRXtQu2pVpTiivb0jSikmYgQlCEMzSUahocQFQpu2beI04wuPCsKIUk5ef7JlcRIhVC54QaPiwECICywgFQZcy7Jv3z5UYQGAB7B0cvNIxAucJC9dlihzL4qDTSmibjCCIkiIzvZdONUg1x/BwGL1oROkGWPVhAZFQIsdqITW0skzLGBORh9gJnBEdvd0S0GwFJDIkA5Scf5UuqK9JAMKYZ4Z/eSJSFnSOX+9syTxfLhHLYTp6fksoqAbFrEZi1FHWCBq7Phdyvp5/PENrvpCMPF6VArNTKK7XOsjjGtgISohTo87rv7mmw8ci70H27+Tvaf27u2kA0SkWqNisAgF0YF7i36qbqrLBNgUJUc2Y8GLbQX49eCO3lJmwozjRIe39+3HZu/2LChUWflZpr3Mfxlg4usec1x2OF3OpB2VBI5N1kImT5x6vQN6iy2YHL3PLbDZw77E/re+7W1tnd2ynLThxCuvuPKmm24yLbxn9571G044bu1xb3372xjh+HDYLJrKTztCaVq3PDuTl9xUZhCGIjbv3m34NxRkwLWkhAp6ZdYlVVUu8mnduZMxhzZrcrK0uqrc6Ob8nKP9C4qLjAkbrNOUu8PPYM7szHSRI4IKCywHmZyZ1gYY0NXYTw4PORXZkU4aeuluD0hXVoRdlLkmQfvzqyoh7+3pNpY5OjFuvVduWSllzbX0JeyKGatuahoYHjJz1dPdxVqqViyfm5/jvmsmRCVmFEfHx4yUdrTtt4SrrK6WdW3dvq03XJsmcMmxH7qvt1dUbnrx9f/+hquvvvpfXvmvy+rqRgb6i6uqBoeHSqqrC4st/ZrGi7yzc7N5+WnDs4RQt2pVd3u7CUAdmOOLy3FXUgImVVpanHSEWvH2vXsLS4q1SmhwoEd2XlhO5oomDdOLX/ZSohwYHrb/9oc/vmzHzp1t7e2XXHRxfWPDk5/81Ac99CEEvmzFcqdDdXd25qTypudme/r7qhrqbTdFktinZ/9+wrHeJdROHBb546nQdG6e7lZxPZ2dPAh6YYVcd1eXddIOAt6za2d57TL1NTI8VFRaMpedNTzh6KysvsGBirrayprq4ZHhVJGhxLzS6mq1OS40mp7u6er0my4r7Xf341xY25ZfEZqtdGlYQtbf1VleX4eYCruIXa2R7C+1KhhYOPHZ2V2uNEg2dFU1Nkpwugh9s9/FwP5wcm6EqtR/807UkcG/Ay0tFANmrV5VQ8OFF174y1/9ykJoMM6d0ixa5GdW0yEJasEtU5UNDfAARkNvW5uMxvDVNMigpTPWg49zAui2NlRjioz21lYitafKuVBULq8gn5o1rFrpKkW899uE5DLMkRHHp2GTY1HZ2GAYYcDlmXPOGht3bjZFUhyfrmb58it+9CNz+Ir75AUfp052mJ9yyilnnnnmK9/whi9+85s/vfynWNi4cSMKkaGV16xDG7sup7WS1fJ16zhGjA5at16RgPULHfv2VdTXg6c/plSUq0TeD7ns37NHF2sivWPvXlbpkHC1qY6MEhBd1jxnlKc19Jvf/CY3J9d7dqHxgunutv0OtCmrKG9avvzMs8/eu2tnUWUFXe3v7VGthZUVlN80gfMly6qrxkdHwnhsbAcUnHkyEshI4C+WgNYpNh0RU7DW5Ngnv/pZ7YM2DQxjP/30pltvbY1NVuhiklW4LNKnGBSJjuTiYYvBHHZhh6Q2REroelIpX2PAJlEurY2+UnuoNfDVdl8dfSRGfAvS6VFdXcO23gEGAFJijKMQGZGgUCunibZZ1zW33uHUOvFiwQgDRFi2+AAzx+sTsjVfEOIrOp3+DF3A+LjmC51CaAUpzjuxCPmcgKNtjBmh9ZWXjxfTYAiGBzaQYOCHJN7uq2tAKiRiAAz6KiNRhG7r4FVGp60/7cbNN8obverQKSd3KXkRkeoFlOJTlCfMCMYgGJjhiWMHgCH3i2YsI4bw/aIt9CPJunT+D8/e4cAEK4qOwoTEvhjI9QLe0UakKlcKWYFXp7iTTnpEhM4TTjgBRzfccANO0YYG9KgUJJEbmv2paEhkjPIkt9D9JeulYTC2QhrAbKeUXdG+AnZskoKW1IMwR9a4nxXBKO/o6COQ+H5QP6uXsn46/o3wiZo+qMFobnV15a2tYWs3/aTzJO+runa4O+EDk0I98O7rMdq7Hfv90d7lJTEaohRBOD2kIfcW/aT8VlWgmRq7RpgAScb5WAyKPDEY5YlBVkCfF8veySe2DNMaifmw/CRXg/HHvvSSMpD7HjGhgV6cx/YJ1wMISJLlSb+79prv/+AyVVlcUPjDH/5Qp6J2GZumc9MNNwgVWF2IJWZmhEldra1Ox/nCF77ASrU4j370ox/3uMc97GEPM/JpJSoz/vznPvud73yHQ//u97znsh/96POf/3zsIR7+8If/y7/8i967r6e7urExJy9XQPv+D37AHletNk195jOf+exnP3tKdN3TTdvQ8Ovf/Pqzn/2sZUgM/jGPeYyloUZ6BHsORr7h2ms//vGPs9tXvepVTOIJj36UcPG888578pOf/KEPfYglOEzy/PPPFyGPjwwLn7p27njTm94E53Of+9ynPe1pPe1tLOfLX/7y9Zs2WbnEWl7ykpe84hWvcKiSyOHqn//8iiuuuPqXv8zLCZum3vLWt0IY2wgdCWoNlOpyxC1mjgVmZeXle7Ztu+yyy775zW+C174QyIte9KJTNm606mh8cECYcfWvfvnFL35RA/2hj33s21/9yje+8Q2Ms1sEC27rGurNzzKw0APbiJss6NVAWiXb2d2t0h/1yEe8853vtBK1p+OAunjRuefaql2zrNYR1gIz+IXie1tbPvu/n//t73+3ffv2E45f7+yrf3zFuZUVFTVNjVnZOe3Ne4+kPAJdVdm2Zw9JzobhjemvXHTRj3582b6WFmHS2WefrV4e+NCH2j1siIHHU5Rsx1LX7S0tP/3pTymDLvx+97vfa1/16g0bNhQZqjA4Z6OLuGvv3p/85Cff+973yI1kaMVznvOchzzqUdpvdz4I/n/7298K9pT76c9+9rrf/e7973+/1g2ntGVybPTXv/3N73//exvUaSP/4AlPeMJDHvrQE087TR19++tfx+buPXvKS8t27d795re8RY9ORaPqkiT9PPuss577vOft3b3r+BNPVNaOrVsFeCpdEXyF008//dxzz+UzVRN+bm5Hc/MvfvELNYiRt7zlLTt27bTQ2hJrfgzNpGZWYpfaUjU83HegY/OW277+9rf9/pprUgX5VmK//vWv33jSycRLUVt27zajQbtOs6EgWRaIKnGpMJ5A9HjB2xgexmnvYP9b/utNL3jBC+QycM3AvFjkZ8/wgx70oHB4ie49xyxySrRpcKS3bf/3v/99rom2nto88pGPLC4tGenrpQ8/+9mVJn/odlVNdevuXXZE2/utJ6DSjcWNgvDp0ZGmFctRAqdBig+9/32sEpGPf/zjn/bMZ+pa9+zaxQZh/trXvkb9dDDX/vrXNEEW+NVdZ1fXhhPXl1VW7G9udtgGueH321/9qlpoad5n+TSFrK6rG+rpZRp4DDF2sr7rSFqXSc9IICOBo5GATkTLsACp4XUfjLDKfDALFfFFB/rMM1dd8/tm1qcN1EmZ2GO5bBlM7LYEP/om2UWkojC3+Wj2wUjUWuruLTC0ucGfIi4m7EW5Oj5hmvlLf8LsT9hMu7mVc2QkS9QXYXSRWmDIGxurm5s7APOwNTLIVlxNjXh73NQWesBEzLpRg9jCFTDIlohUEanw3O5iCNHgQYxCAQPDacJa2PKDZukmOC37BSw7YDxKj0HC/vY2aKVwiwvzwylf5WUl+5v34LqgIm8uJ7lkwZhpVdXsdLhfx0BgIs9wVrayzjz5zGtuuUb2hUIxSFxgIOQUKd2vRj6RZzUMwglBo1+5oAWPeOnAUEJuaCMB5En3J8yRcbLid6nTBWngCCQyzjj1jF/+7peaXAWRvxTvpKStXpCnlhwxTnAgBBgIhyjgJyuQulc0gFFTkIBBpHlvFYFTtIXlgclQKSQKRT8YSKSiMGs6nJsV576UvnQe9C8QQ54qZWZ6vryo2Jj3Qf2cW9DP4dGhylL6mbV09NOQBPpVEJ8qet21tVX9/eE0lgV7px66WvXOlNRI1E+Vgvczz1xxzTWH2ns4DV71gYlVTwnVI8hEP/N6esLlWyVh4DuMp8BcNFc0OBkaAbu+84vChdJLUz9jLWMn6ifnJChtdjb9ZzhYZmtU3Z+HypOUfAUcWzmfZKH5gKk9i1iQ51Hae15WdmFOfl62hZ8hPspZeisjoqDuw7+LFgM7OMJlJ5OONR4dNW0lhiG1osL0k574JH4z9YptJa0yT8rv95WhSPz2V77yjne8Q3ggRTQrZr7xlps/+tGPCm7f/OY3AzAdeqv1qbfd9pMrr9D6iCjMygI2/yWSEc988WtfqzbcOzpq4/GDH/qQIut1EwAwjgUSgX/hi1/U5bbt3fsP//APpqOlC0I0Af/z0Y98+KMf+fY3v/XEJz95ILktTWQlr/ZdKbt27dJMK/opT3nK17/+9aHREa3jP//zP1N6VAndX/7yl4dQKmteHCXR2c6XXHpp/+AALlY0Nmlf3vqOt3/3u98Vc4L/0Y9+9NWvfrWzpxsSVwRd8PELgZUVl0BbWlRs3g9VFaVlr371q23f0Utc+/vfi6YEJwzDutDC/IKbN98qgvr3N7zBXF+IBouKBNVf/pqDmlLKEgDrgZpbW/xzyNamTZv++7//u7q2LsuKYvdMJDcAOVuju6P94osvXrViRWvbfvE/0xVBGRHMs+I02allujJcaJQ1b/HZJy+84E1vefPwSKCtrLRk1+495Gk4wIzxgx/0IAGkjD4d9qkqr9ArN61d29PWdv211xk7EFBNzYeVsGT1u2uu+diFF37nW9/6u7/7u67OzlPuf/+xZExaFlL92S9+UV1Zqandtn278OmyH/xQoKvKHG15/W9/+9SnPtV0MaEBMJN8/fXXf+lLX7KN+b8/9jGTqKJtAxAiT3PXIknMEkhhOrgs6PzHf/xHEXJre5t3GG7ZctvPfv7zs846i7rSB78//ulPfDKJbYZfKG6hFwdhoSyfXjz4AtV9/Pr13lXtOS94Pj0EU1JYtLt5L+Tk86lPfeoBD3iAsRU0m4/99a9/feWVV1LO7//oh6mcXITxS+g8gXzi0ks7W1oYiKieao1PT2l8GcumG28UmpKzgQ+d0LOe9axdO3fSBFbgeKu50bDPrWv3bj6cZRHBp0unjYb89trfP+1JTwnDNMnMA5dWFpOodgvbQmdeF/KBoUGjRQ76guF973vfez/wfsIhdhj+94v/95AHPojwLaHjshgq+vZ3v+OQNiNB55xzjhTSrqmsogMGpJiGqVpz2lN9fbZPv/+/P6hQX3UPNvyTPI7WnnjiOc985nd/8H3CGRkfMwzRYa6+pJQ5cJ5gq66sesELn/fO97x7WUODuf19e/YazmjZ3w4VM/ncF/6vuCBfbS6rbwi3ByddrN/Mk5FARgKLJQHtnjUa2mShEKd/es56XQcXhR2/p5++ZtOmFi2DXltzwV8sLS3nMmp4RVOmy3wynckdFEE1NBS0tY3poLVXvF6+r2hNg5McwRNWWkmM3rNOB5LBwXBalXK9++p31SoHFM9aqSuXFkkjKQuAhobylpYejYaHi6+bE6pVVzvSZsYLGGSg3FcueHFx9q237tY6AdNkQcJnrawsFbt51x5CrjjcKUKXsm9fOEBLKWhLUuIqa4ur+qRDjne/safTz4JUUIga+SrFxRU1RQYSItdDY32zOTNVNeXV5dXwD/SHA5Ms20mlw+C7ok/fePqN225EpHcPDESkjxBg4EtBhgzgv12etQ37O/bDrBQEkL/xRGQQmkRZUAstJOIZTGl4NapYU01KJxMdNAJsevQVBjXiE+JX1K3YsmsLbKFJT07GQkxTY1P/QL9YF2Y4pcCAHrXJnUCVKlYEhDLqsGDzyI4dZCAvSq+5uUV2ZEyMTxQWpEOvnc6VsbOrp6Q86MP0+ITlcpXxauXQcy6tB+VB2nOhTsezwsBrXm42Th0ztWpV1djQbEtbkGdufsqqRpAk09BQtnT0U/WRuchNGExVTjllRXv7GL2laeoOd1SIwqjfaB0G1aNCqsrTT1+1adN+LB9i72G2/6C9B6XKys2inyubXP1R0NEaVlXQkJyUdcJhTou8hjuHe4d7TWmSTKJLOUtMP5spXNDP5GqooJ8W8fX3ExFD8wnv+DXewVjisgUSYxe3y/PkU9o62gBjllaAZwuQkF60Do1GNEwSPhp7HxsaLnTblicrrHlxPjCbmJ3K7Acm2nvo4cAvzkOrIKI36t7EIw+4pLjAsiDzqLHnkw6msq6OPrnEpa+jA7BW21Qqi5X52X//rH//t9c/95xzykpKBZY/vvzyiz/9aedg9HZ0iPc6DhwA8+3vfKe+oeH1r/u3Zzzt6foQc1lXXHnlr6+6qrOtTY/6H//5n7XLakcmxt/9znd9/IILX/j8F3T39/39s541YUvPxMQLXvjCm26+eSZr/hGPfOR73vve17z2tccft073f87znnfjDTdUWG5qqCwvr7K84t3vf197RwfI0rKy+59++oqVK59uBWxRcX46//s/+MGgaTeU9/Rs2bq1wCqm0rLnPf/5Zut++7vfhdisuORlL3vZ/c88o2nlCgRff/NNb3rrW7T+j3/SE1/4khdrRJBt6e8//tM/veY1r3nZubbl/vNLX/FyZqYlAlZQUjw2Mty6v/UfnnfOzj27h8bHTrnfaf/1xjc+89nPCl11Xu77PvD+/zW+kOx6MqEmnPb75a9/bfWaNQ97+MOJBQG4vvJnP7PCeai/z5BsaLmctCzITLbi7O9oVwUSLeuNda8Pc2Y148dXdkG+aXmD3pf/+DJxWsiVlfWK817xkpe/7NGPffSQ21xLSl5//r87HUVlM9qI4c6/RCHRhb9aFrPZO/funp6ffeHznq+KxXV8LhMEgnmF1tbXb7/tNgtxGg0t9/UZ1Pin884zTUo9+CYaI0VbKDs2ObF9y23Pfu4/WCmt5/SLDAcLW5pu2fBHP/Hx/37/+1zwa0Su1Tpt5xmk0h/92MfMY9dUVaOW2uxva6NULe1tp2485XWveS19aKpvgPYXv/6VBeHL6ute8GIT7S8KA+2zM5r+5z7/eS99+ctjBT3qsY9RKKFREgdlEct3vvnN577g+Sualjv3lOq+9GUve8yjHt1vEW9JyRv+PcjHDZLUiYb4nZydufa661YuX3HeP/7jOf/w3M5e8wNZl37+cz/6znfqmpooEn0QhjbU1hn6cUXW4//u78xZrF+/3tp43cwtt20enwqDlOacjRxZq8znML6gk9OxWXGtAi2O0IAaU/BO8tY2+4ROC+bNFROmaJm0NdlU1wayf3v969/zgfcjxgLvrsHBG2688T/+/fxf//53H/nwh1FCgD29vZUVlbdt2fKYxz/u3PPOM0G96brr8fWr3/325ltuoSqTExPWQv/7+ee/778/+NQnP2Xblq1cNoddvfa1r711y20ukRrp7TX7fenFn2b7WHMG27W/+72RIGME2odznvMPFtgL42mRUXaDTUY32tra3/Km/9q2ZXPH/taLPvHxM844Q09j/XxoXpLFh3fWtExKRgIZCfwZEmBTrCr837BsiILDe3TrpSQB8J74VZPCSePLCop4e3ptDYtPHg2RJrqsrGD//lGDekIsEQL3ml+ozfEVsIZIC+CTFBGUHgGS2HbFdJ/q6paZiREA6+aif6mxAmzf8ZYtAS1/2ifNlyG51aubrBqOwJrr6IxqSaqry1pbe7XS6FQoDKbCbJ41AywUxBoC0MYhERgrpaUlLHTEC+Y1+HIBUBAYLIPHC/wIRgBscIJBOb5gxjjInoEhfTcA8HACkJeXjHGJniBZ8tx4+qZbN5GY9yhPmMGTAIRokOhTlCckAmDyxLLSRR3mY+E30aQ7ID3UYtNLZGRBnpDEQqWjgYj8CRuW8UVEpu+uufEaJMGMI2CY8ttxoMPMoUS1HOWZiM70++11ikgYRMKAufvWggJGRkw3bYBOiQvyjKIjGaWAB4xIGCiSSjQOImVoYgjLS/Dhw6oID9rIGS800S6uw+ln2ZLST7IVudErlG/cuGLbtgPq13uscfUVP9Fb6eqCUt2unyEAbj5UP4/J3lWourbVG1r6Q6shVxxtUeLS10/UUk41zunCCzloZ8iNPC3xi0I7+eSTt27fGuXJrBbF3mGOtuw6Waf6KtETm4L4nvm9uyVwxBjmWAtmSFZFmBpyhLBtn/TJ6ifLq1SnmKH/wIHK+nprPke6u/nu/HhTwft37zZHurdln7L+4VnP/sQnPkERdRGmnmxYFQFy68//r//SAOlOjB021tWbsnM5zZoTTrAhU7RgktB8nZnABz7wgUq8+te/guphD36IGU4GbG7w0ksvNcKKNhNxN998sx7u4Q94gLOp25ub9QZPetKTznzA2fbZmq21SSYu+7GI1xwmGv7nf/6H6Ya2Iy9PJP+FL31xWVW1Va+WGTMVFmLezxDC8855HntgPej/ze9/B6dVxMvq6nu7u9avO35wZIS7X1RS/JjHP/60+9/PTGPuWGglzK8WpPMtSTVrbZPwRRddZK9Prb2debk6lU9fegmy4XnqE5940acvrq1vGB0esgb1kY9+tPW4pt1MDLIckbPJNJOWz3rGM8QVuMO+NbSXXHqJCWczkA98wAOyHWRkuUty8rvJQIvAzTb3DVs9G6b+9FjWmk4Njxi8sp7sQEcHm7d42xjEpZ/9TC9XICvry1/+oujaYjLtwoc//OG3vvmtRfn5r339v33ty1/BuHj+sE9BKq3WTBxYBmeNrvXtFmkff8J6pYrkSennV11lzP+aa66x5JWvo/U0zFFfW2s63UppO0WFgmrEsm0q8fOf//zpz/p7n/Z3dDhV+6lPfcq3v/u94cGBuZnZhz3i4c9//gvAmGk3Xckv0eWLbAWE3IUPvu/9Qn3dMy3S6VrQq4d41KMeZVRPf/aYx/2dJdnyCs9e9JIXm/Bfu+44VYwjq5ff9Z53W7huG/nsxOTXvvH1n1xxhZ1eZnGbd+ww4kA5K8vKW9r2//f7P2D2XqMv10c+8hFDHni3pl2wp5UMsxbJcACW3/ve99qrbL+AiqO6PBurEqzQVgu0ztJixJgs5TS88pWvpHvzM7Nm6QnnQWc/wDANFQVM5tY2O6+DEZXZncV/GhhQNdQ7kg2AR+VdnyQ8toyeAuAdWnu5J5M7P9BGn5/25Kd8/stfdh+IDcZIclC5XHSbzcqragTeWPvEBRca1rF+cd/OnU6W/tSnL7a7TK2h0OjGZz7/uWc+7elf/da3DDOZR8hPDjK1NgEeACefcgr3y4t9BOgPd0o5qSW5UyT0wTm5+pVkVqca4056+8/z32BQjNGNj46pTSaMGEeM2rdvhJaXtqKpCZGZJyOBjAT+EgnYyxCzR69XmMYF00rogocnJs86a60ZYC0JGIasNdCo6i+0IRorXa2WzWJqdq3dqKoqsSrWuKq4VzpzBix2HRhwlMEIDDFag1nn4jf63+xaF6YIX8Ow48QUX9NXZUGlL9P4a1Sbm9s1gPGTRPAnnbTGhkx+NjJgAIMFmMvKUh0dAwI/YZ6ux6PttT+5u1tDOwgtamOhirOcmTOCVP1CaBgLXUUfDvuVGGkGDzN6AGMBBiWSD+J9coIiShQBuHo+SGZwuKuyrrKk0uGdo/29feOjE2k9R0GBeeCxidEzTzzz+q3XywKn4gKG5PGnTkpzrYfS6CkCGR6JC/IUQJInOXtRHDBCVilJYFaHwkgGIqFEIeRo1upyIUjMn+YY/BKL4khSItHJpSCN8ANPeuCmnZtQBSevCZHw6x+RpItUqGAYMLfEPknhAdoIAcuQkDywRJ4FRCREd/IJVEUFaUPP+em8kG1kCFV6bRQiw+Jh1wKDnMkvqV8R1gMuqYd4Z52RnhMWuM5M5YyaQigprK0rnRgOonP8TLowrWpShQf1s21p6adaYC8qevnyZbt391APSq7WaA7zpEhqRwqV84kmOOSbqpx11kozwKpeXRzZ3sMoRkUle6/o6b6jvRsfa+/po04ESDcm58PCT+XmZQc/MNrU0tDPkmiAlB+d1NNDPz0o98vQEEwI0sknyhPjRn84z7JoYfDlKwzsBSRlZrnkSb1hIM+jtffRkbUrV+VMZ81PuHjF2fz+mWjnIi1aXLakjGtpErNo88CxXcYkPYraYMQzNyeL7+tYvWADyfocjabjr+jKWH+/UUmxX152TlF+gYAWBtsUGOdzX/RCWyuZ687mvVdeeYUzrgRaNtMMjgyf/aAHrtlwws5tW50e9OCHPXRs2g6l7Otu2KSkQkcrWbZaWvrba35/yWc/Y0mzk42EiHafOvLymuuvG5mcMEV84saTd+/YvmX7tus2XQ+z9l3cfvkVPwXpoCwTgJA4xP/iSy8x07hq/fErj183PjG+8bRTtSDdfb3brdm2/yWVFxYhm/TOynrqM56eY7FTdtYJG08WIdSvWA7P5PiYSG/VmtV1DXVdroO3m7Ovt7jMAc7DgihBFwwmsRFgX3F17TLhrnKtedYTOSvoB5f9CGbwLz/3FbWNDWaGsSOEXrNmtXP32ru7IFSi9aXYFz6dcNKJZkphRttx648376cKDC7MCMyS+8r1/UYCSVjvpW2yw0N75E9MudONDTvw0Txb/fLlYtT2zgPM+Lvf+34qz5GeJY981KMqq6ugcvuzQK68stxKXAF2SVWlnatHUuup6SnIeRkoVOnCS+tjUeVPyG3xlVEvbospevSmVRUVxEJPhKxNK1Y4UaG0stIOVYu+0wXpTTfe0NPb8/VvfoOU3MZk/ryzo12gaz/t3z/n2fX1dRVVFb/+3e86Og/A1uhwMouBs+ZNnv/Lq15pQr68qpKoCeqhj36UCfnaFcsdgSbR9l1yNmXc3duj1sIK8AA2W1LuVOewWNf28n5z6XOzZpsRrJ2kYKtPWL9l29bLrvgp+ev+X37euV6MGoB/3BOfoFmkloZjyEcuAyXYpGbIcKRZd3dXQXnZuhPW9wwOqDLzpf+fvfsA8OsoDgZ+vfd+J+mkU7Fly73RMTamJBBqSKODE3oz1aY3E8CEYno3xeAkOGAIJQQIBtx7VT9d773377dvpUNYspFt2Zz9/R/i73f7dmdnZmdnZ3Znd+ORYNhipVrYMD/fOzuD8vXCONNMZnB4pPbMm26HBhrFBkt38pb2MvDgG10MB0x2VBUOK+vhAHO2vyGq+dJLLcwKGveJkPzzq18F1Ecv+HhnWytRzMnP+8a3LnrN619HKkQrmEDBH+hh41lPebJ473D2m0moNasFERgGegf6iR+ZfPt55xpxP/SR80eGBuVRXAi3ySN5tHhZVZW5IaYYyRSYh3X2q9nhb0QxHS3d9JPEmoYG4eK///0fVtXXCo6AtiHcokxANT3dgER+HGtjFbsxCUG/O5FLpac4kOLAoXMgMftC9mQ1OLxIYSBygK+5Jqzy0TN6osGa3o6+IstP500KhmOH6TrHAFsVZlXL5g/5wWloqHLVkKFcZn/SVMZKD7VgAAIkdO3kxmDdX0EeuDllXzkeSgFCq5SWBnPT0EATUlYQowccyzw6Gvw30GgbX6UzSZ14NTAwyTmBJ30onYXq2CYn2vAAIRAzw5wqdnltcnRU8C2l0LG+sjtnZv7o4Udu+AoZBi4FCyaGwJx/CDasVCcl4uzXQ3ehTmYFYejPwM+jTr3m9mvkjNCkw0dmGhsQGaSrDvkIwUMsQji2MMfVDgj7W075vYODD/LjP7Rx3idMW+YncgA0pqvIO4Tlx0CQISx/REBZtZx4xIk7enaoJULzgkz1QsZXwxA8kS8R1SAoDgg0sE6iivAzpkdfPaJhWFQLhHEDgYCDDBmQ0Y5AkH3NS8uTf0U9LCWjfNIgezmMEFJ8MPkMJy1jAv6vHPnU4zRZU1Nta2sIa9cXrApoQf1CuoaT4k9SpIkRG+Tz1MZrrmnRlBI105/r76Vc2gP6e41EoEiaByht7fHyUJFPyEOYfGrT2KG0u0T8xDrrNwJJ8FPXjvzEzMjP+9nfsUhHCCqLxZoEaYfuEOYiUs+DxIHDNt9g+tFhCzOijpcWLXZRc451tL3IDsa/evrT15joTXb0OY1ZB9LqJMz66p7WFuuIhkualNU70tdHn9qMYsbxzm3b8ICupMRJoTAsN9ropcImqWPba0CITJJINOnWd5577oc/8hGXA37gQx/y75/+4R9sL3z6s541PjxskdDEtT7PJbjwc5+zmkpNi7wVqGQaGT7+dPJhmNxOHsA9Doig64Pc19eL8Hz3e98LH/shLTvbLOp8vaa1a5/9vOeJItJ5mPuc2Bt/f9P1N91o8yelubtlj8GJI0HpgC+iW7RzdzeYwUela0DW8fSisckpiZwTKoPreOuttxUU5g8MDAmEduwwtWJExwe+bk9XN2ztBLYcrRzOCIeFpBHIORX93d2CT1QX5sZN8RlB56bsl6LwOvY0rzIhsWFDPJjaOWEsiYoyJsVow9q1ZihG+8IJ2DRjQ2Pj//7PL/JycyZnZouSaxs46vxAKoCLRSnkZGdZtd6xdaux+e7kFNVqhx607Q93hKhFvOtuuF5IMBfrjjvuUBD/2ToQ0HbOQMZ8PMExesEILb79UY959A/+61JO2k233uJTEg+c1trRCQ0zFAg00dDW2nryaadedtlPGFK2TJ9w3PG0FSd/fHIyLmxqT1WgyzFjE8kZ9z+97DIuFwXX3LKHn+++BtMNKqXwKCMX/YyMjC02BotQujbiPFOLjDuRgywApyhG2849DyJncJtAYg5K+fZebO+wYow/ZnnkJEgdXV0EwCcthXDS4lc7an0615qzZdXLf/97iW97xzssm5911lmW3I1Ysafgid3FuCSDJzRoZqbWdzK5Y7Eq6+stTZtEzHKvUVbW4uRkhO+QcF9NMwlizna3UlbWKaed+vyXvvTHP/qhfTvIj2fL8a5vvvVWYB/zqEc5SwyeeEXOtY4WEX1QVFaGI8xADEE+KiCjcfEQk+W3qiwKzqWU115/Pe1teVkwwnGnnmq/AzxF09n3rqETkVwKHZzfPjQkv50IGNjX1cVFZ9fZAA8ahusdYyOj7k5xfDSNsTg5pbpgnUxO3k3YQcKX1E+KAykOHDIHdOeYN/jA3oTipadv2bJRSCSVpZvrdLRulps6MsJBWRSdOBhqRLo/C0OnLrL7gbslmz5roYxduG7dGgApCkAk0mCK6PX+pCEpWErPexgZk3gT0Fw1RDkoS1H7k04wtlvUhQ/VSkPSCcxQy5zt7WFNSWbIqDRCWL4f2CjpE9uDtqmpKenpCXcjUbmyQRIOnEwejtBoyhPFEIOPzA7BGh2lLIfklOgXGgZimhBK8khUnVlO3g4IzJxY3PFPFCn+OFpyfD74rhALg3tO8O58OvKozVfffrUUHIs0epcfQJOAmKAWdRlfpGxs3Li7fXfkp6r38XMdUJFq+cExOMIZEDmhZ5CNfDbWIBY067cUr2yRIvyEvAAodEnUInJixZZNW27dfStWSARZu0BYQa1D/2Od8Qs0NonqPKqTqJlkQyl+Qh5uEQ2NBWdDpwXn8tJiCtydGjZqZZnfTyK0AwPDSRMjaZMLagRnamnK7YsgrJwHhubE9YfQIsXV9Q1Vc7NpnV0DSdsViaSbWwyDfnY++RzbJ5/9K0Q+8VkXIEgWenEbCVozSu+qVdWEljzog5pJE8tMEo47brUVYH+SK22tlOJEy59Jf5/1p3R/ai8TUz39YbU/Py9YsPvkc7X+TuQKysKGfII0u2jLVXFmzhK2rDz5HCGfRBdiCNHcCMQKnQiZiCLtyMcopgi2oEgiafdC+GXex89VKF3u73KSYf1i85H3or9vatrU0rp7angiayaDDnYyFomyIpx+2JYmV07HWrmYHDYf2F5T678CfSxpWtW3kW/b9u009rXXXvvUpzyFeGldZzLZIkjaCFmQGHuKhMfkhDsGSA/n1g13o4ODJdVVpNMBsARUN6Z9SB4HWNcER+by6uopN66MjjLlDUF6GtXPexOAytFynBXofDAHXDnL6itf+coLXvISlRJ0ieJseQg6v0Ap/ZZke3fccezkgqVoDWoCemoHlqtqbFCdI2r5wOBwp3mSfHv+8zOf+UyunpHAQKI/vPilL7nhhhvD8BjCfhzUYZna0nQhDLmm1jYR4pNdnF6ivlCvRyI0QDDSmIRUCkMk6qtqhInuF8cbZ0fVJHfc4wOtxAGWDWXesQ7CoHGATRnoopSdK5g0imt5jGeAHuk0Y9u2BSRnZthc+oxnPBO9dGFGcr0brHAb5fJMz1B/YZYXwBlXTCVTufq/KvomJouLwpyieoMyZUwkTSMFFegSQsxXiW0HB5fcCGoVsm5x1Vor6FinCnuA2QGGQ8T6W9Xa1EyBaHENrZkCLQuLGdlpmgBu2gt2HjyhoG3hdp4Wtvgq0aiqFKoxLbagxIB8MsGhLA4IeueeDQwNQcCydnpGCHySjcWGD8DCVmxdeXkpZEycOk4RjWhXu/MRAWc6yM+egHD4l8yImztQU5QcqhM0u519kjO2shcAUYSBJv0AlMKFBhy2/nQgud3XFlGlDw4Pi593DNjvf3s5sVF1aNzkMkwIqEWtKJVOaMJvYtBkZdp1PCcDmAIB+JmcYZUK7WbQODDcJI45i9GBAacu4xgXVwdBDr/9la98pcOc1zQ1mfTRHJA0SQF7cz2mG0i4VpaIOUjDNxcs4b/TqspLSzu7u53HBg4j1Xlmf/3kp4BQ4mKGyckojc6W0wrWgW1RHk4O9+YAo8Iysng55JtiMP2EcBDswHMyj3dryCIUcE+oVuBjsu7BcnSKZfJX6ifFgXviQOx995TjL/RNl1TzMnrLL4eITix+YOa7g5PoywOzh3AtvTIcmyA2x2A3t5CVY1Nu7W237cjLt5l2imLR3ykThdevr7j55hZ9VrQgBOhJj5jZ4eE5ukUnlWiMo/roASvARquYaORSSrp4IqOhdIrLQ01RQfaWUldGPRWpzotBLYyA6dyqsJoqp3Q4JNUJMgyuIN0OHKyMGhRjQ0OZUERjjapZrkDRmTQPzS0D5Q83itdv4ndltreHs1uxy5+UmN+CAlcKjVkuhhJsFVHd6tV1AmtidaiIfDDl2thY0ts7S0+KcTOMTs2F+2AsDXEZHKA4MTteWJyfNrc0NjpWXlrFWaJIQaM8EYLVqob5hjUbbr7zZtRBwwM+5GnIXW27EIIon1gUqJCO3sg36RAGwbyAd5z3Se0Awjnh52qMxcZokTOiIj8VQR0cUKE67S6zp3uwW4o8sZkSecje0LRhV/MuADENDjKgzjtmeuIABDEYKqghzPVzG2QzvNq4BGGQVTfQ119irj+coDGWkZ7d0LBamNLUaAgNyFhcmreLSeOm3639CU903cMTObmcbfnlHoocyqcg82GTfLgbKau4wmhjDgThpARjhbHV1v1RPucX53IzV5B8kl7y6RAsLeKdGGhTFBlkEUIeNB/J0QQySNy8efVttwUbOMpnoP2u/T0E8RFgD4DDw9N7+/vi/v19dnA0HCi1KOosOeadVFRUlCdXbg+vKPmE/LJ84gYxRjLadcyYLoMuL1FvZYvqaI2rGptbmykWBHp8xVU6CmNlxk8KJPZWfST09zvvTX9v3TUxMlqQlVecU2gGcnZhNistj1HKRL67npF0iz/TNfaX8z/bLx6gfrQ/Dof+fhdkkqnZQy99H3MGBXpYHjFJBnYST54cfWAv3+DQaHVVuZ2c4TgK3hqvta+PfhQkRKSCqBUUOD+ZtLGGCZZPrr3R2ZwQ6093KViXY7WTSJ6tsVonBFyjWnHTV70TREOKPBa52vbscR7F1y66yDGGVtIeedppjueR5/Of//xATw/z2jsQbl36xje/6YDlD33oQ5babBj+9ve+52glni7kCRfI3Cq/NIWRwN2tRN87V+Fxj3kMn5MvZ9nKF66mgmL4qUij1LHHH3/7rbfZZ/uGN7zuV//zC4Sf/tjHADkxFq4QXJwL51j094ST1rPS3dc7NT0RQozSFsLey+KCMDVup64hcGxomOdpLygX9Labb8Go4YFwZ/fC7FxHaxsHuLe/n4OEG9joFkKGiTzcHqzDK+mqAB9wnVNEmtkBl/cFvgm6GBt7yllPImqTU9MWtEeGhsw7UIiWyu3QFq0FlOJr1zRaIHXMb0d7exgOS8scTF1WEg4F7ezsxqjx8Qn7tcCJSlOKd79qV6/YV/8oBYRLdEXtL3/zK5CdL3Xxdy++5aabnvOc56hFWYpYHngGtiRnkHD21OIoEixtad6jfWU4bssxuGSfrWsXK8tKTUByYQf6+2tqamW+9qqrTTTgc21V2AysnUHzIDkiRlVx8N7znvc44wp269et++AHP/izn/706iuvqqvlZDqcPtuwbU6fQtQlzFVzNM2yDPWHAwBNg4AzOT65OB9uZUSjqUQHRcKNP6k6+6hNGDp4OfCnO/CHnET+wNqfER9kRvUKH1pWItK0uwxHHX20KGjHSju5el1joxmf4ZER0cuGEyIhAK60rIwBJFiHanYXMY88kLm0ZE9Pf28vyNZm4fzb3/6WNAoAECaNvYAzTWzERTUP2cI4gNu3b5fngo99jAA44fk73/++GQrC45ZM+fVTOOuzLgBhTYYGcu+BmYXKSnjqiajGk4PCedmLX2IeuqS62r0iahSrR3gMNhbwiXRne3tZbW17W5tzyywCI99YAnPtLhvlrq4+0d0Mc3PYAwMqDTQmwoznHGDiGjmZ+k1xIMWB+8WBOJsYlGT4Fx/KjQbTYXVw2kMX9ufatVVO1omKSyKVpcMWFroDMXhW9IaUmJNOoCL0ZT0XKApf/6V/7EMcH5/kQsgpjy7vl26JC1P+NFpF388qqyI2nypOe0d1JCftIYSzJxk943CsFmqqqamso4O9EMLHwKdPJNqmx1s3TQmmFKM5PP0iobm5C0zjHQIpOnWxfQcGhkEzWqHIi0/JqBSit/yJWExQVjpora3hPDAFsYIWhaR3v5hGR8lMP/sU1kJtnkoMnv35CT2+7tbdW2X2qAiexqyN6zZSiVihrshPjPVVHtV51GX+0VfKHAmG/mBCJC6NTyDAzZ/StQXOe5GZ+wqCkRRWqItoYyD+w1k2fJYOoFLQPmHDCbffebsX8uATEmQGRGZnQOCbdxzzoojGMpRAAwQ5xftAGBtVhwQvEFMFvimFgd61URQVxZWypnq/ZPiBKcweMcGBFuDxnBRh70NFPtvbw64oDU2WvGh9gmT3e3//gHfySexxns2sHXt7JwnJ/vLpz7vp72Ge62D9PSx7EDwyo33BZ2kTRRLCSFOE/Dwk5BNz9FaoElftTlyJNxne07YHadiCRj1C/41a7nD1dwoz9ndnYlnGUHVqHfiB6dZ3C/Ww+cDaMvgcidehV9g4Wldb1dc/dOU1VztwqLO5WZdjHBOySy+55LzzzqME9VFHT7kWyBD87W9/25Czev360JdGRm++4UaJRbl5T33Sk3kmmzcdEYZZHu/8gnN0/LpCyQlV9hJXi+YVR9s/4Izf3s6u4Z7etavXvPKf/+Wy//rhhsa1ynIjFXnsox7Nz6EXnOQ0MzFZVFg0NT5RUx8uud15+x2FRcUjA4Mzk1PcR3mcMqWg36KSUjEK6RmZ/M/c/IJ//Lu/x69td9wJuO79tCc/palxLTjgf/7Cz4p1trL3lS99+T3vfNcjT3sENOYspfLC7I+qrVta4JDkpS0ucXdrq2smRsd8ra6ukYKcyckpLpyjp22Nzs7MetKZT3Sssfff/OrXqq6pqrab447bbu9oa+/j/1jNPvkU+KiXI22mrqayanJkdGRwCOs41fjmwhseo4guWkm7WAomAs7p07cdfQzPwoJ8UbLuv+H50JJF7jebmHCd0pe++EW4ufzWKN7b10/1O42Jj8chdFO4RUXX1aDl8Y99rEpgSDVE4cI3bUcAaD2HRQl/1V6cmXCtlAjktDRx6eYmzjjjjKOOPRZWQtapG/2fSFBARl1YeXyiQMeGhqgbAecuUdBwp558Ci49+awnwY2D+pMfXVZaXmFqYLivv72ldWRoGCtc+v7YRz/GtDR5wJPcsDib7p929HV8YPC6q68pyA4jsfXVl7zwRU988lOgyq7i2Rbmuxi9CV3kqrSoyN2G7jR2o49DwjXE9Ng4ropTKSsuxqu0zKxjj95C/BAF+K9/+b91q1abz5saHfv+dy8uzMmV+QmP2csf4qGNshNMvJAr3jupALyiuEQzkTr+7tTYOGk8+sjNjoYyw2JdnatpkdwYXF1X19bS8tP//m8nZmujaMwNd3cLdUaLYKOq1athJU7BMWdmfBAlWX9kqMZG0deYRJpJ7AMOO2GL4XLLjTe582l2anqsfwBvMVlxDeruIjcl9XR2WUMxRWXuBrYwxMC2PS34qVuZayCWB8JRnbocfUcSDMN2/pM3a7yNq1az/BqsM3d1BVtzeloYhbr47VpfV4LYwmJYzeBCBwd4eDjIpPPDpqc1KJONXIX5Gldtp54UB1IcuN8c0Ok8y2D0r7l5AUrB+eSxMAepX325oaGipaVfx5RT76aoqRed2qZdSob21tPBkcI0lJ9i8cjmXU6PySv7gqOvpVMrAg7Nb3aLngdZohT6CgQ7eIMBYPtJMmCp1FceiCglYKWrC3C/XJTjjqvcsaMfZNBY5xSLgaampspJz319YUkZCYwKoKznCKjyLtGIJqd31QFieI6EGHcg49dXv7y1WJYKgh5QHvhQpLJBwEPFJYRkwsGDNBlwksWcQA6xyoDw4ffjZwNnEgRglVV1mDQUOjQQApH8qS4cUBc0/MqGcDDxQQaJSsEBpTjskUG9CRohjNOfakdy5CfI+/MTzoBwUcABBFYaThtBHpPtW75p100yoALaaicPpjC8G4YivXIaQUBAoPTIT0MSJNXlkYIKlYIjA1C+QslYpiCEg6QlsTyqzssILF1RD/5HfHAVUR7M9B75ufLl0wQEhIN07uuV2tEWhiifElGXyOfezbrS/7S/l91Nfw8TK/igZZch2+tgHgaLtLW+gEU6jl/tHruPih4q8ok63QfrEAhnTNNH9FOERGEmtESXMOtrCIz8xMz72d+jsAGSKTgyLay6h5TD5pZF8Knfe+LAYWM20beupwk9egItee6556qZAW3NzbLwOeec8/7zzrPlz9qpA3jcg2JWSmK4LzQ9w7HGztS9+dprf/zjH1uVsjLMj3rxi19MTRtPTJG6MInLobORSKKpN3osNtrdSpnaNEiMrO46Ndr+RouiYl89olpEZeufEAi7ODIyXcHCGfv95ZeT409//OPHb9ny6Ec/+ne/+Q31Teizs8K6kw7gD4SMJb1CkLYUGtGFPQx3NwA7/5mHGSNCAbeTmaUOMX4XD6e8Ihyn/h+X/LsAVCO+ONpwUCRlOjGZjChT3b2937/4e2K2d9xxp1pWN9g/U+qgDHmmJ8OA51YnE7+TE5PY8qUvfNFU8S9+8t+OCxIliwmv+JdX6IFw41fYSs17pLgLysoqxYCVlysu0cyCFnEss0VgG1txDHoaCLb2mj79r/96YlJlM+Jv3ZnMGX7nW9/qyiI+MDf105/61PTI6Ete9GKXQqD3da9+zfXXXWdhkEfKvXdtjpHMLUf2hfCxZcAx235ANkWmgWiNodER9x7TFzSEwXVqJoSzok6TYfJll17qPGSr9HGDd0Vy14K5SgvakHrve9/LQTLoOibNiVlUD/GAs43Q73jHOyxHW0H9xMcv+OiHPxzuTL755rNf9nKz3G6btMJvOK9fvYaSwhOyQaPBB7YwpNcwzcFXDv02TsCqbdeuD77v/TnpGZS35hN3QQ8SM7yCrQ2u2shO153btmOv6QnCHbaRDY+E6NyMDPuxiS5BfdOb3vTdb3wj8seN1vigRqejDff3KwglMWD+aQ52jLPI+ObayAlScIOSNvInYl/5spf5kxzis1Jw1r71a9f2dHYSUZ3CYeCWYUOzJufB9LW1CXwIw0xyLrSqEfXbP/xO/1K2vL7ewdHaQpQ76i6//HKJW446WvS4+5N0BJ62KGUXU0FDu5gEecPrXif4WRiFaQYiRxnbOMBkNCnKiLTyKxFXsVeiBjooHLy1qZgs6blWfZHGXHN6NvFzOHxFElqPLaiYcqRZcTFdIR7E6dB4jgTdobOjQ3eD88ue/3zSSJM4nITFJtEZMqhIPSkOpDhwPzmgY3oACUeRJqdhsb6inUcLmbD2e+SRtXv2dNOKemscc+kl12HKz6WhBDhCtDp1JL9fRSQCoqvSKvQ/hWfKkoOq+1OMYcyen1cqcTIphlE4yOnXSq9xPN7iK3NMpChWr65VtbOcvANLP3ih9BoainfvDr6o6uRXl6oNLqig9ECGqtohL39jo3NcnccX4lwMCuAoQvm4M84tjahDkSJewLHxhZr3p8SoG72oUV0IAUFZQJCgOurauKpq1MFTTqoSZMU5wHIiDVs4uoGfTUdaLIVS5Lw8MLGB0KcdO3bQq4p7xwdo+ARh6hSBKvWJL40i1DHWEQUIlFQR+UnfQgMJEPOYwoaz1vEoCz20eLG6pUj0lmWLfPZiYbBtqE1mMOFA68oDbX8iBEPwE0DIK964phECBiP4eIytkDShGTPDQR5FAAcBcIhhna+IggPMscgT96FLWTkPurDKhKwBTgNFqr3cvXzWrED51BxRimyVRxGZ0QqaCZ/3yWeImddMUT739ffqPXt6D+jvlUrdTX8P4Rvq0qyxXzsJHViZtTXBULUqHhLyiRAswg1c0ll0RhTpU5GKqDQIM3nwzho5XP0df3Qr1XGAbRDA6tRm4AdZG4TF98PykBhaluh7hDU78ZUHu23r1m99/VtOiuJ5cmxil+OhcR54yPS3vmdN2G0oA0ODTuhxwKx4YO6BtcvTTj31vHPPJR9WjTZu2PATcR02YBri9C77dkw7iZWyxEdeCwu79jgVaPtF3/xmW2fH1776VcqX2jJE8ZBf8+pX86ItSf3bJz7BTZqYmvzNr3/9+9/9DhoIt1xJpm+95ZYjNm0yNwumgWJocFD0JyDhIMv8fO+IsjZlUH3imWf+3//9n35iyvpNb3yjC4QdA8YdfeMb3vCTH/+Yh+CyX5f68IhoRogZTrhq1voq128QHPZXT35KuBRnfOwbX/2aIYQX7cjf0YmwnQky/Gc+GJf1pONP+PqXvvy6172OQvnkBZ/4wPvfzw90IIex+oRjjn3fe9/Lu2jZvp0/g1GGHLNSztnGXjhbMC8tLBqfGM9xsFBfv7U+QcYGUdlcBO4UYlc0CwJ3zJUzjZTjcf3vL/4H5gh3IKIlUNfhGBvf8qZzNNl/XHqpE+Gf9Yxn5ecFCNarBW0/91nPfsHzn09PWN6nAQPmyc7beaf+JsOwdWnuMWXaumePsO2Ksor+4cHLfnzZ0UcfzcsdGB3Z6IATu6QmYT3ECiMG9jkzKHizotNJAg6bBbDf26hsxTIjO6fOBQUFhR/+wAdf/6Y3esEWsc1WL9Wen5X9mMc85jOf/BSHfKCzk0NMMGDrJC+coaCptrLS0uc8+9kO5TKFcdKJJzIFiIqLqXyVk2i5IKqqts6Uzfq162BO/eGAC2w/8bGPm7gBGau5sg319U5+cJnQO97+9ttvuw1RwsVf/s9nW9EFh0vsxVzJMn80B+ZIJLcW0uOoIOa8KC9/cHiolPyMjlp7v+SS74tNsGbuNGYCKZtrouw2t3WW2SSFOGlES8EUcUlVtR0/1WvWjPb22nkbXXoTEJqSE2ubt2VVswaikqBkHuHKK6/E/5OPP1F6xaoG660vfMELzEM96ayzOKvOYEfvd77zHW3hUrHjTjnFBdHQrhVcwHqrqxOl4LYzbST4HC2Na9bYrGvW6aBwQmTB7KyNwUSxv6OjapVbrlaj9KKLLpJuHwH0VEe0mN42G8tmssBcmEbXI/RQjrfu9j8//wU32B1ReriKNJPJAnZljCHX6KknxYEUB+47B+jE4EQlq7ui8GhAKxH+E2ZKQyjsSSetv+GG3UxAOsSfumT0W3zUf6kjkZXUlBGSAeeXw2bM1ceZktKVAsfowDBQ1rtEpcIukgIHTYWRgkLzlfpiWToy0qlUIEhRHdyM7DLw042h0r1L8dULF7Gra5x+iF4ofEAwv1xZmb9zZ2fMGY1argsTQnAuH8wLdy56y5QSsAZ8tfNjjTIgczKTzCNR2UIbQ+jbyIQk/7Qb6BGFOomonpoNmWMryJxXQEsVL80u0qXTk8E3WGKzJLMMJx97svuB4ZmwfSmpaNgGq4nJcKgYQmAYTOFko3KAPBW4oS5gsc4DE3WhC1h/8lExhLMKlMRol6OC84lGRElc5ieEwcQZ6Jn6lE6fg4x80HBDcQO91gFWNunWDGJmBbUpJsuPyQgfGAxHRkPVr5HU3KixBn9MaotjR8XM1ISgKtBsMRsaGhibmoQS5M17Fubm1VbUluYGB2lidiI/Jz9yb4X8IgomhJwkz2a4fsROqDyRX06KHB2bNsdj02YwlNyyIVwqzxazlSWfmE9mtJe2M2aandDWjCmNGIVWOs4H+Zye1tDmlPb193U33LAnijoO7NffQ1yGtotyCM5+/T0c+p2TGY7pwRCMMlVDAgHMLQh3pkxOja+ub1w58hnntuCvc0EyzhNhBd2FZLQjE7b0gD/JNiYgSinU6X06r3fdCo26HoYogp/+BATMICrJFj+/h9Lfi115MxvOSS3ODvNBao9LSvewHxjk1HN4ORCGt8PyBN2RzEHqD/adxmHvA0lg5xmPP53AhaCJcPfPrGNjGeXcP51G61tPc2XLaaec6toVHikHeMvmo+yKtBmS2DkUgszp1VbwSJhaQPBLB/MN9GpeBwkm1qc/+clWSo/YsFHftg7JXbEh88c//JG7dixAkddHnX66U3xe+qIXGzY4wOI5hbNagGJ8M9lrGxuJYHJBTtiYSqwtt0lxaZCyRo/KhgaQuRbYxR3Vf5zHa3utUUXfsOWVy1pWVLymvsHFSwYVEeA/+9nPaBkromrk/8jGvXziE5/I0drT1mo4cR+sXqT/BE81WX/u6e62fMnBsOhng+jfPvs5YxPjwYky811b9463vs3yHaY5JWPtxo3A4gD3VQqcnd9rRRr/Qw938FViNAQFNz0dWicxKXRdh3c1bdr0ta997YILLjjtlFO0FAhiejdt3GRNGLc57YP9A9rms5/+zFe++MVj7cV1019WFgf49Mc97kuf/8KnPvlJcAbaO0S3qosyUBdMbJxCI9YZR2le7dK4aRM8EXLCMcfV1tS6dQkP3/ee93D2nCtmhdwcoR1mKnWIFwf4k5/4xFvf+lYFoW114Pjjj7/id793SLhtonNOQCkqclHzf3z/Er60JiYtrvDR9M5C4zljArXFZyMnUfz8wgpjaS6/qHvxC17I2EMvB/iRp57261/96rMXXqiBQizuwkK4Jrey0iFqQcwW5geHBu2I9sAHOUZxXGKvMBsDP9PS+Hsf/dePEjMR1PhDFE9/7ONiRAP+8ADxx5ghZIDQwk1ieV0dPHFJ8eCxDwxgoGO6Xv+61/s6OT0FPmPCSdEa6KRHPlI2jqIpdpRC0oVDJeXlHGAMFw4tWsFwbf1f7VodSy1fWzRG9c9+/jPdSmj0j3/23xj43Yu+7cAt9ZoMAj9shv/4BbL9x6U/cJWRQABUEFfv9myhVAiDC5Ae+9jHWomg6RGCCWay4KxXmvS5Ozicagw0DVRdU6M6kxwaVKj5F7/y5a9f9E3CTzxwgGdutosJKD6fR40cYfbHHXfcNddc82+f+swnP32hRpRigoxVxSGXASY2EWJI6klxIMWB+88B3Z8aCwNE8tBoUvxJo65bV7dtW4/+S5P7pUgl8tNkpMcoEC6WdHrbshLFxUrmNena+ildRN3V1VXTqDIETZ5EmvhNHDZx1PP0HlD0TKyxtraSmU3DGOgTXIIPTANYZ+3o6APHu19fDZeqtr8HBGpEpapTqfzr1uW3tQ3IBoLfiIzQRVVYmIKkFL8+JTiHccFaH6wiB1DnqCkjPsgS1WioktltqGKxkWwIoMwh6auqZaC68QexmAM3iXALNkNCdcQqso4G3ta8DccAj/yUzgQClucpJ7rCoDM7y/5WC36yCsD0jkAvWAfhWB1jQHEPDx+0iLOvSECsF6YFbKPrggQEyox26LGaVARh1MHf+EufS4mzA6YVYCIz6laVrzIpqWp/guDxyWgIPqcXqsoqiCLHTETSVIo5sUZfBdkqDmdFItVwkIheUiQ9np7t68p5EnnQEdK0Jmw1ENzYegKBDyafxR0dIdpr5cgnhMlAlE8Gm2bVyhiuvfaXT21KtDSfZk36e822bX3aSKPElrr3/b1OvRN4kXRA7Q44aQHtISGfwYJKHn2kvKRc3yHYBB73CAAqvOsyJqoYkzogAvfnJzZiMpIxNuHnofZ3kqPq0MXCMb70XugKoIX/pJ4HhQPpi8PDh15RbBlTm1YLPUHdarbk3b0+Znr5jbOWU2dnXIXqYMfahob+9m636ekGrHOStGb9ejNF1pcEhRrTNLZhrHr1arYyUMYkS3OUskOTXfNT1dAgSF/AJweyqKpqIjmgXBFPrhMLFheZxXqygqSXUBI+U9liXkUWHXvKKbvuuGPDpk1STGr5l5xwFaZsiZ31KCJrZCJ/ILt9lFKub2qy4sR35F04c1hOo4RsBJRmoRyJeH55eX97e1USnznmMqEkMNWv2o1jC/PhLigjE0wMhHXuHHLAY3I6BWjgWDtFKX0EYaWikg3ut3nugoJBx9bV1/NvEajLiRIzm2D8lh9AVazZsGFpatoMuVPBdEi9sWH9+tlktthEuotkdEu3AFu4dpw1ws0saSsMcTyjRe3Qw3Jzultb3QdbVlFuJKssr8CNXdt3QKZp3ToYTo6Hw0gCt4eCNjQa9A8OqAh/OGPFJaVDvb0wqVm9ZmF0zIK2dcXhgX6t4LGH1Sdagx6xZlht1kBJ09Kl4XgMhDSuW2sWxK25fHVHbcuMLWTD1VMuyNEWCuIMxU0q0GIst4GWOYMhdoda7ScbOcXFY4mjqLh089AKCvnubGsLPvlgiI1Hgk8UGbsGQrbOBsymQzicdjS0m6QI2nlu1ilTlo4xQSmtw3FlSUQcIKD1bWsjae70MMAznkQehIoStmtiNJLn3bt2QRh/QsRBwh+LtHMJ7RpOpUGcxLPNz7tGy8AAPlaDjfwgV2Q4mTkSpGDdhC9thkUGcQHaBevoXK6gd3Rpd8EC4fzkjAyCCohKzRHANhRJbEfzHbu3bUMxKXIYmEmdcdvIk4NJZQiPk7RZJ8kFGHBAlyrAzzTTmcT/KIsneOgFAsrmJ2exIoEcwyEy+UA4JZUVA729JoyIsVaW34NRTLqNW7ZYYUZ+IFmXcZKcuY+yYu69ithidibbREAGNE1JWbmJB1ixhWdF1ieHNyqonSCcelIcwIGF9LSs4tIQhfLQeXRSyOqC9w3lWNyv3moS06Sqnqj7+PNAgGpSm6u8w+kC6UsZxu/0P1pXIS09c2pu3lwkRWw72u+uvWMmI3/txqaOjkHKUy0USF/vAL9Fr1SL86JpNRXpmNaBKYeammo5JXqHgxEtKNJw1pTrZ8OaSURVzwWhoqKgv999aoKBwnGVYYxLYqGLi4u6u3vApxgBp9N8ksE7hRCzgS89+l3RM+GwQUwVIDc1Vezc2QcsfUtRq5Se8W5wgRsNBoivhg+1r15ts9IQbIMblsQBgRyGznTXJg0CjgRVw0cp6aqW7te46f5DQAodIjU7PTUxPD49eOQxTXt6t4/PDxRX56dlLw2N9E+PTeVk5VLroUhebtOGdbZl4VIEaCSFNurWrlp74603QhVRcIYwDPm6ybhxV36aL6BC2eXyQw/ylCS9HedMEQWgFOm+YogaIz/lj/yUjkAutxHTC1DSzat6x2R8kAKIB7tO23KaO401Bx7CHDeW+QlVXJXNoKAU/h9/9PHN7c345rZIwxEc8nOzDVJOlhK3PT4xmpGTLbo4r7CgtKAs3/kagxOjvQNrqupP23BSTlpO1tJBohFhpdJ7eNDl65/Ndg8QDvppYX52KT2bA3fNTbvHF7JnFjOLCkvLKgu72/qdZm04W0xfzCUHRQWZ2Rb5F3s6yGc+NFaCfGoOTcOAIbQaUdMYqXU9TY9dREiTMVRIixfpcLaBd8MGK/nDWlkeAh/lc19/t8qz3N9D/qraKjknRsORq1khxD47z+mpuTYRFPcOt1U1lO5ouXVkZjAjb76oNK+ypnx4IIzdK0Q+MYfMk8/Y373o18hBBdqD3CarONH8w0DCH8wth9onodH4iZBY3C9+4iF+IhAEKVEGxEQcYn+fnpyoKitPI2TT6SdvPDlvKS9/KTeTxp5fyshOX3JtqznKNOGfdLvpyaRHHETN/1GQYQ4lZNqPZmUo/vnHzwd7e4D60cGq+vNpd0VmcWF+fDLT/MAD8wT7oajk3vnAMNkfnbv4wO7+KeMSmF+cmXb1Tl5pSdeeltpq80OZXIjQFTlyFOXUVBhvkghendMJt0NdXfqeUYjHG7txYVmZXb/kzxBkJba0psZBOxhEdiVGhoCjt4fJXQZ9crMrP5BZD5Q1zAkRVu7U3Re4r8+L0LbkSOLJNK/AWVA5HEURs054FtNlr0uyq0cVYBpvuDf2ANNu4SsPM7krmHjBECb6Q1ldHf8TCfCBVR5vx2V9YQweYcEvLczb3JvFHigonB4btY1W+DHbP6u4aMIpROlpRRWVUyPD+UXFA709ll5dGiQaVgyzWYBwgZCbGHOyQSiqrhru6i6rqabwxYKqq6SqyqIfHPb2OoO0Tmg7lqVmXod1XbfSJz7w5MCgaXMXvRkeuaM6UUFJsUldBFoLRakTm/Tb4mQnksS8wiLbYnnpBeVlrvBBuCg5LSUnjaAFBfHOTs/gRk9LS21tHY96cmQ4BAWxNuhKDVRYyOeEpBrF8RaWV3i3bllQXi54m0+LqNnk6jm3v5qVAFI7YrItERYCqBLv3Pj4icYxrlvod1D00NBgOX0Rju5On5qcyLdBd2F+amKysLTEIWe5BeyPHM2huoCnlQIC5v5kFzwkQzteAYxLrnewqm9pkUigHV15Bfk828mJccdfuTY5v7RsbmLcCW/jkxMGcn6yttBeFjOdsiJGim2hFNxMWNhzi2/hRLHEu8YrUct7+VNXZyftvFsxGHMZGWYoSKayfHj+ISmiOjn2HEoyqUcET1V6QCbsDCdIzn+Gs7h3nrkmUNyFujCGudYPklxdDT6ZxCWQtdOSKAnTQMnON8eq2q8DgoA0fclZU+Z0lMUK8EOP4N4zNZx/7mtBAQyDdLmfbGBAH0GXWswL2MsFvtUWHQQaQQqSkPuDwsGW8ob6oc6u8uoqsiRKw0VZ7ktzxphfwpNbVDhvAmd2hsxnFuTPTIb4cN28t6vLmd84rL30KYmhIvHnyWxR6KpLSyQh5QNro9QTOZDyge+zDxzUhdnhtIxpFxAwmxaDZr386ls2HXvc1t1hllPvpgE8hQXhmr2mptXt7UJIgpNJLbCMvTQ21rguxVBIH1LdYOqtTMNVq4puv72drpCirI7MzdOpQWMy+pOtSX3RFXwwd/Pu2tWqOukyKMLdEgHm1OjoAAMuncKUTv2IMeL9ggamYVqinRnd3f1xaPYrP/g8VXiycSEAE+nBMMjLE4jT1RUOdqADLbry6CTW1lbRdiaF4eBP3iABExrDKrFgiUAQKEyBbHm5DluuYqcKB56fnUjLmt14VONN26+paCicz5qemp8cmxien5rLsCbAIV5aOm7L8dtbwnQkPew38NNYPD19zFHHXHP9NdQsQlABuHRTn/DBIhmkYIUHIavqV23fuR27oAEmzHnRjB+rUtgoJ9KCCzo15QgJ7SLKRiKWqhGEhEVVvpqGlhM5sZmWWSQFej5FHoonsooQ2wJDIj8BwZNlfgIrMwhNq5u27toaK7IXWQNifkNdjSM/W1uaNWpxSeHAyPDM7PzqtY35WW796F8cnyvPL1pdWbemor48s3xF+cAz08zCgom5tN/84aa0/PKSyjp2st1qbjrIzEq3+3x+ab6ivKq8unhiKq2jq704v8BGpRUin1oE83VATa+ltDjp0rLEycKM5tOU5MSvRHk04jHHNOzc2a/5/IkKT5TPff194k/7e93gCCtgZH4mbO9Pmw+js3s66tcUbt3aMpc+vuGoxl1tt4/Pj+QUpWXnpecV5rS3dK4c+YQJArHINBAmOJ2OeDO99DjdioRTCN4t16EaG+kB2gDrsEvf0QGD5+Iwl+Fh62eRnzogtkv0AHL8UcfvaNmBn/70u8zPg/b31Q31Lufsa++ZH5l94qlPLEkryVrMWJpz1SgrNi3lA6etTB9Yw//Z50A/+c8WeThlQD4ZfiB+7z+X9m+aA6FB+y7PgfnvkufADPtDuEtmn+45//5l938/RH7uX+TQ36H0gMI/dEzkvGf+HMjPewX8wcl86Py8Z2Jh+5Cg98HhaqqWAzlwDz4wc+TA/H/BFCbRQWu/OzyjN8IrY5YFczM5qpdFy4xjWinl/SMf+YjtP1wXXw8Kx8wdS+qg68DyO6DZ/hi7b+WanFvMzc7Y0TbY2jtu/pK1xz5mLKrL78aN7gdui2iwHVXHUOa/2W3T0tIRjT+J7EXIRFeWX+dPJjL7WyL7kjlo0dJXiWr3laGJHHXxSP0ZK2V6Oh9aAC8jnkPoTxYnYvmHlrrdzCebqqHB+rRqWl3tVqRwnDXIPgGuiAc+CoIMvegDs18bG0vb2kZBBhBj+Yp+0QIflr3iaIwYghytWLUAHqmz/tdQX+qaVYcNZrtRLzvNOvCmo9d2DDUXV+e09OwanRrJzc9amJ4XIuV+YCtCLW2tmdnO0fhTfjZuvG37bWDCFp8hgxU4oEarcDjmPTqZZo19YqzLLBuqOTYQtgIsBZMVISrgYLJJAfyEcPRpZUYLViDcu8y44U+tABRvGVvMMkT2coGQydznXeMPIDEFZzjbfF0VcQl8ko7/mkb6usZ1re2tAOIbv2L3nj1lAsysj6UvuR/YQrmJV4dtjDvitKKqsEQ0VroLOOZGpvPTs5rq1mxpODIvLe+gPjDqDvpA46Dp8Dxo+r1NtPC2lJ45OZ929Y078srr80uKRobn2ru6Hd5tdyufr7SitKS4dG5psd/cyNiwdbzh5FjWlSCfxMZthjoBvUEkdDotTjxEvZnN6e4OokVO9EQSohGbmupaW/tiH/zT/l52880dB+vvmc2tLZogfdH0WVpBTogQdjDP7GI4FWc2bWxVU832PbdkFCyV1xZOzoz2DfYU5IZYjBUinzYCEFryiQJCqyvhGHn2ovkkUnf793dfST7O4KfeoaB+hBy9TzeJifJIjN1Bf3cS0EH4eTf9PSNtqbu9I2MurTij8MwTzizAUSaPy4HJ8t34wEGlW1s44IG8NHzW5WG4vA6MugPyhoSY/8BPh6sfHQj5PqSkLy0+COvAB4lCuQ+43qXI/lzfGyl9lxx3/2fSxnf/+aHwJZL/QPzef+r3b5pDgfZn8//ZDHep5d7mXy4eC97z73Lme/VyzzD3/3qvwN63zLG6+1Z2hZTan2OH8r5C0E6hkeLAyuEAa4x1xTLzAivnGLkY1ovtQlwapqd3GZgsLB7vvBq/h/5YBmIxyW+vaGZG2DlixilZOMrrGx4Bn/HEgObYiPy1VzDaTJCJKxtw8G4d0p8yQ4N5LY+4UJGPQgHZ36xJ6Rwzj2zMREWYaCqFOQjyR89TNp/gAFTisM1Z6ZWioFLMTencyOiL8gB9AtBypYhs/hogUkDGBJBlgAyXL1regCOET1tUVHDzzeEOW8Y998/hIBZzvKBacZarsgqCw8ZVCv7+hK38cJMBGo5clKKi+bmZ6YWw2qOUDPbXzDu20t0Bo6NFuYVOcMjLCfulfRocDhsL5fQJP3HG/cCASAEWkvLIoB05wP6MVEOJmxohR+pAgwNCZJCZRyrFO1DmKbjKakc1LgELuD+BxU+ZrWIBwgEgP4SKZY+fmOyTsmrBBALmnZfLK2bWEwBk+oUwgIx7idpCdQrCjRN12x23xYkMiDktYt369W4+wDRHwmAjsIq4p9FEQ25eQTiha2DMwldpXlFeeljlnlqYysvMA3AFPcERWsjKzkT1dHpGZ+fA7MwCvtmVBmEdL8pnZ2/3zNxiadmKk0/hzwRDu+M/cfKipXSW/v4hKR59gczzijVZX9+4JiYzsu3X3/O3bRsgPxqFzCirQ6Hae29vt3V+4RR8YK0sj4Phpsan2jvbCdFsWohTAHkmXTSEy0EGcYw8g7NC5JOPilKYxw61Vz7DxEHN3fV35FsWRpfZKIxVMM6a6e+oi/0ddfq7PLG/R35izp/t7/29PUF36ClL2XNpczMLMxlLpiIfBjbgCurQfxaVFLv/LItSGVIcSHEgxYEUB1IcePA4wOhkS3FIGGEcCTYWl4xByXthxjHFuDrMLOYpW5Zpex8wi2YuSy6WnV8IJ9zyhcBUI0vRqqDz+3p7w+lKrD2VMpR5PtweWEnkR0GGHwVU4llVuAmJpcgcBMpv4kKXQ5tbBSB3UTqrTxUqjc6nsh41Mi6T233H+Wm+yixDtJ75Yt4jGoiFMzfVTXwjI1P8cCkyQ8lLfX24q5b1DwLOqA7a/Eb3NE1NzcAfV5EgnffoxCx0qVoRv4rAGSiOgQwQjo9SMsNZvexaAL0gCnyscF4kDoAAFARkhrPmkwchyywCCjcAicfqwBYcEEDGQ7VzTqJH6hcQvkRVZZXqokkNmrq4oH7hZhVLEYTIAEjkp7rUAg38hJ7mQ7jMapSCLhnIT2y+2CLoRaxSIMOEEc+/Vbt0ELQUICDEFvGnd5hLVKPMsNLcXEQ4g6C62JQRE/xURL0YQpLRGxGLLaWK3MxwIsaKeuwUCwxMZigQpVHQAkPcfkjIp0bBZyRoGswnhw5dgnxsOOlo0SKETitrFHKrKWUgA0l/z0/i6w+xv5cOD4ezo7SkliVIXlShn8ZuIuWhIp/4gwPxwZnl/m7yyJ9RIegRJNzkCKk4XP09KgF80y7etdqK6g7/PyCT8oH/f2jlFI0pDqQ4kOJAigMPGQ6wSrkK7K0Yqsq5Ymv6ZZOhgXXL1pSHKRatqPtGGJM4msUgewcQNOYsaDwlR1iNjU2okQnI/mOfMZfthuV0QYaly/mJ/ozMVp6Uls5zYEFK97W0lB87Ex02VYAAlK9sPnYk0lQkBQ6Jb5ZnCY4RDw1ek0f+1avj/asDEJAiJ8heVNjXN8k3gy1ogEtUo4VbxYHFIsar/NLr6hASTubnOUtUr/xHHbXGQdSqk8fDCPYr3JFHKhF6IKMUDn5LS4vhKT2yQhUSVccLddpTbCOJikRnkumvOsyE7X78DGvjnExApKuOiyI/lLipENDcMAfHohNUW1pb1OglAoGb6iAGAmMdAiAjEz/hrDhvGba4ASBjHXAIgOBXBnzAQ0thGshCPQgSI4GKgAzhyE8w8cdXNcIHWMBB08rkTYo9wMBCAwfg4B2EY7YcA7J3OHhiThDUK5FgKBhrjDgDKGWlPTDHE/NBUcLR5UF+Ip+5B5PPalT39KwU+dTW5AHC5MQKMLm1VV5bIEqLEzmEJFMn4Uhzf8q/n3y6B2jqgP5eoezd9PcwAxL7O5FTr/PQ9AXCTNK0Mkl+qMjnAf09nF9AbmPTo1EviL0SyTL7elj6e5xhIWPhcLh9s5mLcweJdl5pPeVhg88DEgv9sOFOipAUB1IcSHEgxYEUBx5kDrCx+A+veMUrOC3sJJcFuv37ec97noOamJje2WS33HJL9IJYt6yo+4MhfyruMlOv09ot6nKAOzsHWXtMWzY0p4gD7OHWceTYf6xeNapaZiZcW1svk9ESE6uXB8heZGF3dQUIskn3C47wY+lemJXsby5HXCesqSlob1dzgAAHJHsBWam4rORPkNnWvjLik4jdYVRzsWSADGeysDB7585Wjpk8+ANnaNixaNGUw+ZP3jg/00Y+Z/g77VieZUKA4uPl53Paw20GGKsuVuk+qsN194h1JhaPVHUyKzI5O4wWOa2jzaXPuUJFuh19IJjE8O54S5fmJvwM9QICPUVAiF4lJsiMG6iT6AUhPnFTBWFae+Q3ikMGQU7444ycHphjl1t8lYKYPyGGn9F95QML14z8BE0pPLG2zKZXuyYAAdoK+qoWHhFuyIPPkAQEBPgLwkemzBJxAz8xvH8kXNwgs18+FVTdVugqwe4QG7/3iHJ8Kystltk2WmgApS77gUuKi+vKaguzwy7KifSJgtwQ1b9yHsy0JdMJ0Lg956z0EDaf435g0dztHSMOrczJz8kpznPScU5eiH2AOc4Q+BUin1pK42op+FAJxJB0aU2NhTSypCHIibbWHFpHotZJ5DPP3nCZD+jv4X6N5W6i1KpV9W511t/HhsZDLfl5yBcUHY6wtKEiuSHV3NHcYpgAWr92oxSlVoJ80pxQgg88yWdw2snnZLirXOKf9vewMRja8uCJlySkpZIaMUcgUYr8ioc+vrgoDwK9Y773Q+/vWiRKDrFLD//bq4dXTo942GOS8oEf9k2cIjDFgRQHUhxIceChxAEWOHOW03L22WfzNH7+85+zsbhP3nksf//3f8+TYaVx1VhvrCi2170lT9lkxTeUS4I/g/2qliQk0jn8Ab5P0V9SBc+TtWe1kJ/GglQ4sZ7XeBECzZRUnKnN4GYduqu+ry9cEY8KQJiMIIDMF25vH2A0R/MRFfLX1bnjR5awToVwcJRiHVpbbm3dGxcNpoqSRKuXYTti9Peke7FYWlSUzeWWB0wp3AAIc98s9sJZOtwg7Ouxxx4l5LC1tT0mSpGZL8ccFU0MNyhx/JAP58rKcjZzV1cf09aZWCiS6IZ3rpGCA8MDReU5sJqZGi2vKM8vCZfTTI1OMrXBhIaLBhJ+hsh2/mfgdbJGDT70IM8uB5P1rFLoyYwz3FcNzeHEDTa3FoceBxhKsoGgCAgW+mSOrnXAyo0DCS3sdSR7Bx8+iuCnJouZUS0l8pk1Hw13AGWWAc7gq066qqMcSkQRCHDGeeihDkwU+T3++ONl4AkryWcEyt1IwXvPz7UHeGR0RHWYPz41mZ9TwMPPTsuWuTgrf9Wm2sCOlfToThb28Rix87MZzvwtKsyrri3p75/7o3ymhxmERBQz2lp6dRHMxz2M/cvKJzT0HVJEAPh3uoO2QAhJ0K+1nXdUaDUdLWbe19/zhoYcff3n+3vD6lWUxv79PSsjPXMpU3MPTXS7i4kkYM78wmRWdlZtRe2Kkk+CBrfYdyAMN50du8gnbuzr75V4SPL1d4kYJT9+mjsj9hIpYX1KujlKfcS7gsv9XX+5V/19dV19XnqOCYNwB9JSuMPO42Kk5L+pnweDA2FG5AF9bK24V/8eUGRSwFMcSHEgxYEUB1IcWOEcYFSxzxisvBFug4fFxi9iyMKcJeeFrcaEZYTJdh/IUVwpxhz3eXGfB82qs+d0fHzKicG8LK4Li1nVqmPlMwEZeWx9T3QyXVck0SqiPIxCFrBPnDeXyCIBBOkqUof38vJcu4sBBCraysCqkVVtgUVdvDjwZUavdF50tNcZqYCAIBHO/f0hLNyfCEc+87Sw0EW109DghCiCLfhWWloiqDk6wNBQI8MXeiKO+/rCRbuASARHvX6tTOM5gOqCIQsYNAubo6PhQmP1SvfIHBykpIFUJAUr0O7RLmpnE0c++JrwsxqLeKqqkCGa2pzPCEF1gZtJVDPIGKKVxVtCmLUd4YRzhpIzmSAMDSyCHhzqa+pRB6CvoAEOrHRfGfGwgn8siJ+A86J98oAAsrqkq5pHioqEA2FxHusAgYb88OcDRDmED4eB04516kJdDN6uLKjEebhpQdDwTXEZlIr8hAkgkAFZRcB6ICz+Ezkr6wlXToYbA/EHRQjBlvn5g8qnWZ5wIHkUpMjAv7h8QgafCY8NAbG3osIjBS2QhLC28KKxZE7ks4AEHXJ/zz9of9fuGlSL6ylESI2qIzAPFfnUKfb196B4l/s7isyvkXOJdgrgW+xTh6u/awX9SBd2JhYeLk9KrqxO8bDGJrUO/LBu3hRxKQ6kOJDiQIoDDzUOcCGYreeddx7EeS9Pf/rTvbDA/LKZ/DKeYh4OBntdyr19lg0uR7cumKl2oc3S0pFH1m3dNcxKVilDUB6GrBrz83M6O3ukSFcdi5AHtW5dTXd3OKkrekTBq0mu+cnPT+/pCXtTFQSTccmOhKfjdjhLCkKVJR3tS+9uHmYxy6wI+Bw51/E6pIZp6E9enCpkSJxM7mswRpM84dZ6XLJTdWAgOGwSgWXWy8wKn5wMC6Fe/Kor2v2rVpVcf31YxoQV3OS3yMOrFSPMwUOCbIx4kHme+fnuBw7HL8usosmZaciXlBSIdsYi2SRG17GsvHhuYWpiKrjfwZRNHp82rdu0ozVc7RudScnKo4glzevgkCAQeiiFkkBxa03eUQoTVzfxUaGB7dxUjMIfEFQKmpeW9hbLy96lKCWFky+P2OloqcPQVxY8/GXw4IYM6lJEHrVoSo93KfJ7gSEnnPfiJTLQJyyN7II8uiADiEQte93t1wGr9qQF01RXUhTO8R4dGTJFkZkVagQHpbJpu/nRMG3hmUmbWWluMIZkZkM4xMlnFVfXrqqam3UjVx9KQ8smVJSWlOfkZo1NhHDZRD5DWKwMf3H5JOQ4vHZtA9k0qaTRNZPG1V5mHzSfDAiMggRnTXbkkeU7d4Z4DYT8aX/PQvWB/b2zZ1TB2N/F+e/r75k7duzKLgz7GgDXg0xtZWQvmkYRYr/C5DNclw0lJOvXhDn2CH8m/d28Rt9yf8cu3UGzSiEPeChFt6VnsAsf/HnX/r52087WnaDFKnD4Hvp709q1fV3d0yOTmdPp1oGz4/ovti6kpac8M5L0oDwP+DqwHSqOjzS7qGeYNBOFYw7TwYVkx0yyRKN3eBd6JBonuelBXw0RKQsLuisZUgQraGcdMqQ4gy7paTLIJsWLDHZu+OdFSkz0S/mGT8k9AcCG92ROevkl0dqLYMqpP4BMrAOScbY7GT4DDskNChD2EjIkmy4AVLUnZFB7MrqAfNAnZMsQ7x9mxD0BFPIT3ILqSebkApz8fPBl8Bu+JgRCMrwrEhFLrq2HsFNBI4SIs9JqQXWAY0Y2MzPywYuyYO5FNcFB8b1fE7sKveFrMpIFPJO6ZMCTgG1eCPHaWzzJE6GFupLhcBkysJ4AIRl0AyaFhYaKADChRUIsq4gXj/wBjkcpwP+4OhHaK5DmSQinWUJFWO0qxcxMWimQn0zJM2XEIfkzMDO5IU22oJ7iewxVAlzZ+CRMABzA+EADMrEu2EoMwKG07waL8NWfCb0QjpjjW6AX2IRA1TFrAjkZ4U5IrRmzRW7EnAHV3GC9KasKGYKwwS05ihOfFQ9Ve1SX4BlzSkw9KQ6kOPD/AwdYZjQDC8wveoM22DeKMUC5HxLpE8994wanJJYFKtG4tqOl8aBuuqmZ6lMp9cV0VhdMjjiivqWlXX4mLwUrg5zJquCcbPQw3BiFfmP+9vZ+mSOekQqhyv60hAiCIpQk0rhPVpppQnqYipOT9pOhosJVPWEHr/eYzgdjU8rGLqc/2aDsbJgAsmpVuRt8Gaks1DDQJDHPvGBOu7KscDAVl18pAB3VDBM61ldISmS4WwFGBfjeUQ0lYcY4YxkZTIgp7lHQMUMTEzM8VXlkjgMBqxr53HurrxBQnXTFGxsb79x5p4oUhLYUnpIMxx55LP9WHjmjwo8QuCv+VGPEGYcju3ikWkF1fpWSmLhb9jn3x9bHClUw4iGPFRK1EYRjk4EDJtxU5xNyZMY6oFj82oKZDrd9/FwFVUCg5EVBXy3sq12iB4ToAPhVHQigRZnxInMkCs74qdVgq5QdktJjW0MAEzwR+RX1iw/w0asQggP+EmpAYqMcahfkrGT53LKlYXh4Qt8hKgiBNrYTcpcfkfDYWIRQugzr1tXedFPnwfp7bUtL573p7+Fsdg1KokDGItyTQgZi4658+SThuERQ9anYcTQ66T1i/RG6AMWFG6gjurKRfM/B+/uue9ffTQlF5rgoOz0NAylmZueK6hMPc2Qe8NkG8iS+x50DuqPOJorLQCcOIz8ry30CsfvppfyAYpfyOe2woICs0dNe8H6RQ2gqNzs7p7DQWYr+FF5DEKmqbDcixl9/24STnGNhtpYaDrEsgpdkEPaTRDE5oZ+/7SRNCBAwjpc80HCEIr3mMkEqXu81yIQ+nHRjm0KcQWnXi/AOt9c7TCAcSZm4ZHtNEhDQxYJI0mGbxZVNfKcDpQaOKuXS7P0EgcQpVcRAAqu0JKTN0fJUSbhdQSLrx3CFD9TZ4qLbMPRGeIYLHBJN7aw6fAhjWF3dRF8f1RM1uKp0JmUxP6pCxb17YK6TwVfmTAsIDvNITuPAXnMAo8PhlBFF5IQGprG2rDtMmwgXQDYz4xxSukAtHhk0n9sDwh+wwvPEeQs15uQ4F1IVmitrfl7L+lPEmKUNc/tGSlzFDWjghkMnhLWJQgPQo3jwJ33glC4umuSPHLPpSqBV4AOdMTVV4pD6ujrCIKBHkZKaGvdFuMauwLwA0417mSgsh7EQm6nh4fzKSiJhd1QkDQsIFcPKuZnwCaKCMEN0Mt0AIGsFzkTUV0dkBuGZnBxPjpcAQWJseksM4ZpN/9LTfWVYFNXUQFjTmBaeFJtXXCyyrcD8cW1tkMnERBsbGAh/KsUTTiYXmIGB8CTC0D62CIFkAmjG3ooP2Yt8SP2mOJDiQIoD95MDyXBkUAiTfZkZYYaUo03h0P9DIyOsZFqOC8StqqjI37mzh45XI/Uov69sXO9MQycx2f/GdmReW8Z0AFVPz6AhCRzF6UmrrN6FQEebEtjoanKADTIDA4MGNaoPcMOBE78ovObmfpioBQQq0QuLEwL+BESKkcW7xPXry5qbVR6uyYUATGSorCxDTWtrBww5xhDm/kFDKQMHnFXELQRHQUeOASVn9DBhbuzlu7pRxqGwEQ0jQ+CSfctVlR0dIZAbMu4HnlpcSAtjXRh2W1paMrJDnOT4+FhuRk55WUVhfjjDGYScvOAAL7voR687+ro7rlMKyVgBGmS4W3LCEybcRYhFfqKdP4NFkeT9+PnHXcRwVq8VY6D4OeAEAyCZukWIT5aFVQQUsKiQgnWxfSEGDfjjDIt//Zr1ze3NJg7wk3WOn6qGOYCA4K1Wk19ctxOzIj9liySocXh0tLa2Hsz52bCANjsTrkjNL8jF5FJXE/f3DfYMZadnFOUXLc6Hg6MXMHCfQQTsiniCVbOUnZWOaoshttROT82FZflkAYZ85hUG+Zyai/I5mhUm4QM/V4J8wlkYAREK8pksDnnR7mS7ra1P05NtraOliJx0DaVvkgrv+/X33J07+xTUHEBpXF+t6+rp+s70XDgvYGyYvdm3vnED17qvO2x0Z8QRFUDc4jaXOT06rrG7dZySonAP2QqRT9JIbnEAXVDSU4J85ueTTzSiSAoe+oU5FtFybV1t/tTv+AVYSuYVVCT09yQuGtV4JbN0kGWOqg8cn7D6nvu7erMW3M+eLibCtur8jNxwsliY9kw9DxIHHnAfmKPFN5hLtotQsnyAb3/721dcccVf/dVfPe/5z+e70q2XXHLJU57yFCcrkEi6Eulk1CcCRM48/JOpgQHypxv7le4DN2Z2cjKkWBV0nmEywQn+YnLyITHl2SprbwqAzgEg1nSB68z8Bv/ZwJYs9hJZdwIKfvrZz35G9Z9xxhlgBqfIKqKyyfHuHEWybwWTuKvcbWs8V7PZYKqINyXRO6/Oy0GbjqcnnYesI3nB97CLSC2WxPnViSOkF5lDZpXMJ3c/RgUEu1CW35WTo6eZaXccpCkoY56RCYEf+chH9N7zzz/fe6wFGhGTwDpuOd91n2sHPQ8eQtZLRBUCXH+JJe5CYD7w95JgMA5eWCPAjsxM/IevRyn1enir8Gc9hSLJkrtPclKas8mlF9zOsZ4eSuH7X/vaxo0bTz31VL46cgIfkumAMDHBp4Xe2BiP0SwJPkMVnIB50qAW+mkf1VFDMMQuL9AYaG+3T05T4ic1r5lQFGZYTNsm6xKZ1iXMjAwMAMWRji/KQhEQrcXtdD6pr+CrWhVq9CfgSLa6EeQCZDnHxnxVu6iyIJZwc0VHcrpm5Eao2m0i3HUz4skkunS734wuv/jFL6hCyyZnnXWWPysqK80muGxhqKMD8thFkMIMkRmQ5DgTswnccjxUexRL6wKhARIzUS2pJ8WBFAdSHLifHAiKK0S+BDD0WXq4EjW4ZEKGmWX+Q6cxdp2jZMMp54c2ommVMogbp7xLpDnFCHvxJxVnP6yjrXhQINBgFCkj0mVDBp9odxq/DPHSDZpGEqu/HCEV+RMQv5IpY5kh4w/amOLldxnhOztDTHIwGRNn2Ei9ZUvlHXcMGi/UYgSUouq1a0UOh/11ytLYXmhOY6Up0/7+cONRolZDWJMXHj5rAoTo+qJOvUpFQiT6ZMgAChD10sGqUBDOSM7Nz8qzzpm19+7i7OK9IyPFDpmlhbDxVUFoI8GaGDTcKnTLzlsAVBcccAkzsRSZWIHtCPSyj5/lIHBrZdiPn+WwWuZnaLFky6I8Mbha7TIADhQeKogQ8KWjTr1aCgmCq9GidhiCxglnrN+x5w4FeXS8HY0Fc+vhmgYblQUBbpCXmS8BN62JXWDCQV1cxEjX3Mx0Xk4Y6DFKXdWV1d19ffIoK7QJevOzC2nFaQUZYZ1jpT14FQMvIp8X5tNy8sON3Jx58jk7F+LnFzPScvOLUGMn/UqTTw2K7ZHzuoYGir1VA0kkD0RF4yKTTEY50TQ+Jf3dwr4GP7C/L0pM+ns4Fm6ffJLwEGqRX+ws94wpvlxiueEbyQE5vyiI4gqUT7gRS4IXuVRdVT00vHeRHHNQ56tegFd6n44vW8ypX/uED3oEfuKhJ/Z3Ge5Df8/ODx0w07FiwShezDAZKCp6fil1LNaDphYecB+YB0PPGo7iWpnR6ec///lPf/GLpz71qaY6qen3vOc9/37ppRdddNG1115bWlvLhTBbZXFPlBUVw5Phb9guY/NKYEri9VHk/vGG88mxxTQ6Kwkr1QMJtfGqMDk5I4hWdPOS5bXQP0k2tZW4UpwlIk6+ExMg/Vvf+tbrzzlHDa27d8PKcKHrDnZ2ill63BOfKAIp1EKBc8ASh8fcKYR4zvqASiGjsoBggklA9U+f6clJdVn/5EsHUBaok8shYKU6F+dJ4fq6Xw/aYQ1cSBU85WSs6JQzM0rzkGXLSU4fwZmlZO30P/7jP7r6+njCgVj/km7pJww36elWjyESOryyvpriSvw9S80LcU7dqiM3Lx5tZxGe9YBvyVKnarUCI8D6rWinsBztX7LIHNzsxHWMtei9e5lpPpDPOTdnvddUodXOCz7wgfd+8IMWl7dt2yaFMEQEA7v8AxxX8vKs8MPTX1R2aJH4TxLvV9UefivdMzeHDySksqHBJJ56felvaTFmH3fKKUPd3eVVVYyaACSBhoTgZJoBxVJGANMtuRDSxKapE5Wy+IL/nLQa91epcGCIfSClpVi9kLRvUFKR9ohJGPlCvUE+E+ByWkPGYe4rg4Oj6/1H3/ve+9//ftPkw+Pj6xsb//d//7e2qYmgjiZxSuWWi005M3dQlEzoaKzQ7klofWhrq9PO80T13U+swCH1pDiQ4kCKA/eWA5Q8/zLRwWHcoG9AoC3DqnBQuuGwHOeeiqu0OOmd1SiRXcj/kY2+pSalWzQzISuSsKGhuKtrnBUY3Sqjp5zy2xrCU1IH3c56prElMh8HB4NHqtJomsw50ccAAQAASURBVANVVVWpOkYnw53pHF8swckzODgBICUMDjSox9WrK5ubwx0/cvozOmyQQIuCEqXAkJXPbN2woXp4OOx1kj+6gqB5EYvd3j4CPk3OqDVKwln60NAcpxrC0mNOQFTNIZXHsACyIaa8vDQ7f8lIwA2A/9Bk31xaWHmuLqtiyHID0JuRFTxw0ODjZVfrLmgo6xcblZIfTyz2SsEZNcIZIRwSnFcQP1Wtxn38nNifn5CJOGsRBbHCIzNQqoMYNLwD7t0LBxi9kUXQkBlwJJsO2NO7ByZy4jNs1QIN79o3tqav3uFs3Qy7JEqBHoBKBX5WVu1u3oPJHo2bk53JPywozBscHrR0XFQawqTnpoInXFFRGltW2RX1sGPYIzNL4X7gySWtnTufsYRYR7dVVZeRTyeBk8/p+SCfxaVFVWUrTj4Jf5RPjVhUxGXd25U0KGnBdgKD5wTDjIaW8u5Xs5quGh6euuf+Pre4QAZW1zfq790dU7G/Z2SaBAknBVgh3dM5MDDRm5YTJrZY7sybFSafu4N0JvKpgwf5LChwrZfuE3WLtibzuERKUUcAlvkpka2OabEXhKmQxUX9F4EAxv6u7L3q7xMjo3lFTNRch6WrMfYFNeZlBxc99TwIHHjAfWC9TtSvyCEaUxuTlf7BgeyszLVN6wqKCh1RNzQyrBdWVFWGP63acdg4q/RQ4qkG/ySZ1CywhydZzMzidYh9TuaPCTH4JIbkhSBYa3rRlbKY5spEKU6bSHawhDU6e2mSWUzzonQETEieqFb+DHeUZGO3wdmo4BOd7oUPY7r0lFNOETTrpgidRI3knk/IWXVIZXDekmVn6fJDZlmO79J4ecZyjjTXMaFCNkHOyige/L3JSfnVC4IXEwEyqA5d/s8j9e5TZjJDHNnIstAJ6Z0w4CXbpeCAFrNJIISC+8wFCAd6k7CWvVTzBvddz1igdoNhcv6nwytpLhX5M9sMN6somQsPQchWv5PzIdUuj6XvwLfgLYaoJ5MLllODSk3uulC7CfIx+3UThucnni3+SIdbdDVNDzqExJ/GztwkLkWloTnUxfdO5sgjIX6VxYdgt0GORCUnizq1M7i1aWluCqGt3JnJATZfoFmRaQ+HUmEhd2nJPi3jVpD1ZD3ZfwlGcFwXFyf7QoxQxA33FfRIcdwBSj2qQyZ5UMifITN/PFkullNrxgxAFeVa3w2hdMy6zp073/ve9+5obr7gX//1Oc95DpTKamrat29ngpQIJ9Puycy6UpoJH72AzyfHw7AkLrJ6YCC0rCArm+ISaQxop54UB1IcSHHgMHEgMYD3wnI0NFVmYtlFscJxKyuLOzvDZTzRI6WmWMlcQblpOfqQ9qOg7BfhrdXWOnN4xlgpD5XFoKQGKT0AaWb5KXk61mAhszt4WekgS5cnDFtptiJXUvxSgv1nVE1OZzAiOEtr+/Zw8zADNFirg4OKNDautjptejFapcZBn+Ds8Ozt2zsVp6W5fNQ1L87eYLrWDl64+cR18WsZEz5uIOIY+DMO617ieAp/1CFZOhwYvnB2wlYgJDcspoHvIC6VTs1xJELkJN7JX1gQzsJVr6OABG57AQHyzGKE8wNxg9GsUiRjLPzlkQFkxVWKLQk/a/lgEWdowwcOB+UnyExwHIg4RwgQE/vtHVgDChw0BPjSy0vLt+/crkal/Bm5IYhdcX445CWCpjr8NFrt2LFDcWXlxAr81LIyLPMTn/FNdfjZ2tYKf9nk9wCCQBuouFW+JsPobF5Wdk1lTXVxWU56zujsaFVO1V75W0n/YR2Q8FnhEVksneysDHf/lNmluVc+cwv2k8/MFSWfWke7k88o7c7AImzRmtUiGlcTs8LMQGkpsienlk36e6H7wCXec3+fmJ46sL9393fZOFicV2i7Oc4kXVhsScbE9ERtVf1Kl899exAQHs08wqyv6YAklmxHftIM3mXAT3oMn6OEx/4us05xH/q7fqGufCElaclqc7rdFcF6X0m94WGOywPrA7PruTfTU5PESEtbvrMsuWfPnpn5BSLFkzRIXnzxxVYyzzjjDIuV0wMDlXX1PBwOEuHQncgcHVSQbEYlc6E1jDfhjKrc4CzJ5I4E/onBnNwk41bIk2gB47OyYX9mspI8MzFBRjlFHDbOZyhuLjaJuObKvvaNbzzuuOOobPOTnJ4ghQUF3/n+90867jjiboQLvzwuWsOgl0RB86gjSmHPKo/dvK65z2QFMuDwp4/9rsiRH8q+yMuCCDoIzEUUTygokjkEM0tJHHJTkrSJUkELJ/QqAlVunrsazQ4YYwxFg8PDq5JLBVHEAY6jHSdNLf4MSo5NgFgeONUeeahz697QFmTuIoeJCTuuCqurC/FWQafbc9RxVb2GT3574plbf3bWQSArcea1KcQgHLZASxEVlyxjKggHqFoEHu7sPOetbz366KNNM9Mg0Da7ID+H1j0YYrtDa/LG6RRL9yDii+qS3cvBxWUeJcCJAZiBRVpBawpRSxqlY8cO1szvfve7k08+GTdsV8ZhFHOztZdxwJQdhlesXo0oV4gEJcUrNqEwGWRS4DHHlRQFXlFsyWZv3rvWtKILFDg4b6pcpQET/5Jd4njia3y0SLawGXMZS0twsB3dsCPgnwP8tS996R9e8ALQLICrZfW6dWSyr6OjOrmiA6WhuR0VNj0dZnzsOdEQ7sNIIhSKsDoRKkU0VhSbwPzUk+JAigMpDtxvDlCAtBply+ii2Ayu/p6ZnWECMqCHw+HQe2dmwwCUXP+jTl4cPSmDglyp0lITjyVWCvk5PGS6VwamNsOXfjOugRmdIhDYiyxJN4+wFylP2hJA6YwBCHARFZeHW6WIvay+upKT0pYzmqdyqpo+ZrizWf1yKaEHWlNT/rZtIQLTnwYLOlNOV6KC3NXVLdEnKIGsIIKc9wRVg5KcvoIT3+GmuOFDfpnhY9hJDocKPgOesD0Kw57AXO7l4OhgvB+4a6i3rK6kuKJIyNawu5UnpkPMmI1LiS3RtKapq68LN7isXE0IoBqL0Gull+MRWeEFgTFwGj+jec3t9xV6B+UnVjMS5IGz/HD2IrPWMSWBLRoluqmimv3Z3NLsE0oREptAjV5A8BL5GdtrXe267W3btbUqIj+hAXPsEnQNf5+ghBuqQCm+ocUB0hLdGYu6kuLwjihF8guKZhdC5qryitycXNXNZuZV1ZVp4hX1aOL5BTF0wV2cm81wdnlhQU5NbXmQz7bOnIyMIJ9Do1l5QT6X0hftv11R8kkmIz6amLxx2DBfg8ZZDy2lKbWybBo9NmLS312FJVTxz/b3oeKyUlcFHdjfLTkvZExaRyJdQY3ohnnpzOmVJ5/hvDosIp8Eflk+CTCGeBKl4Tro8HjHE52U5Mcug43L/NRPCQMIIWQg6U2A+Cr/Iff3sbqq6vyc/BD/nGZv5sJsWmZ+dk523gPrl62oHvcXR+aB53VyYr5eQeycKkQd64FiJo4+8cTO5mbKWjDQP/7jP+qTRmAadryvz0xyCBjmhrmWPVm/FUFq8619M7wXO3txLa7viWsltcE7TVYppQtw9WtVl+/tE4lcSvamql1vDD6Vo4NHRwkudUxYjR9+RTJzb7hqVXV1HBLdWIY927bJbBcrt8cGXHHa1n59cm2fdcYQvWzZk1vLgzLuTUwAYzSwEKrUgY+vcPAbNn9yrjiNicMZgo1FGnsi5oZY+CfXHjqX0FJzcCmxQo3JPLGBdVxQE4Rt6y0svOWWW5gwMA81Ijh59q/dOVoolYIVKgnRtsJxR0ZK3HiRGCiBe9bPk025SiOcaoAMv9RaqJ6POVxTniU0OGmaEhUeL7gBB+lWMq2PK2tcDKSUllrGNGEO1OTYmM3eIGOjA7SiJFhd5ekpjieBdlhxm5OFWX8FpxRw24MTzYKrWlNsugoimX59Yp6Av3XrViQceeSRoNHpVHxGEuQclD5CEtOH+QOgHR54bh8bHEIt+JxMiygYU4IPb19WcmkHyw4CMdwgNlwggbWY7B/2HnBOMqsRl7A3tKJ2SSyDK664QuYjjjjCKVxO/wgZkos38isqqsyIJ1JH9qJLb6AK5k4yv16WTAGCbxrIjjEUxZmRMNGQelIcSHEgxYHDwoFkvphKi2otUcth9jBxt8qGhkaZgIZjOooPw9qjY6l3ph4NLzMUGNZs3JISLuukVURlaWOOkE/WDylVOaNFTvupxTBkCB4cDEcNBxM5ufzJr0dZ7hPgMigYPU+HYIomttirdnmYDSDwIcF3S5OqOZMqks5UbWqqvPXWPjXCn+lPY8up3uHhcIwWmPLIKZ3Fz6eNZ3FJjOTLqWqaPC7yqC6OCDDxwgE2miA/Jz9vfi4cEVTgVs8ZJyaGFTYwPXhlGpnvNDo8OjE2nm3UzBZHFdh7zFFb7th+h3dmN8/Wr0TeiCFDddEbAUGltD04/EPckLLMTzjD7aD8xHAQfMIcRVCEmejVfPiAKHWhLnojqNBS+CkzSyzyUwbesnqx2oSy9ISfTbfuvBVYEMCRB6u9w82feIIJckLYqiAuqQ4mMiRhUmkFeTmWxHFsxKLZzKQMiBXoV5JfmpWZJfN4/9DqyjonYWDLinqQJoTOWJu86A55PHZ2x0j/IEGyipOxGPaZM59cvzU8OpvIZ5iFWQnyqaE1fZDPJLBOi5BPbRRlKUq7RB0Qwh5CJXNlpbmkMB90KP1dQPvw8Gh/T7gOelk+yYYzyIcmhoV20BgalMDgklOlxoZXmnyGJVaMQjj5jP0dqhCO3NAp9D5yLhsZ8Bv5GXsr6jDTk/T3sHNBu2MpJgMSCT/mqGMOsb9nZ1qPy52dmV00O5c2r2s4HAuQ1PNgcuCB8oE5ZjG23Vm6P7zsRzdcdz0N+PjHPm7Dpo16aX5hwUBnZ8P6ppGe3m9+66Keru7XvO61dgL9+7//+/Tk1JOe9KSNRx1F/wahpHa7u6+77jquzuMe97gTTzyRf2V1uL+vj5vxq1/9ilCefvrpj33sY+liGsoE1M0333zVVVc97WlPW7NuXXtr6w9/+EPd9e/+7u/q1q0zUfnLX/7y+uuvN3w6guvJT34yL4VjatPCT37yE4kbNmx40pOfbJWvubn53z71Kcunvf3911x9NSn36Te/+Y0XkAVyGxiB5W5NDA/bybx9+/ZNmzadceaZCD/oI+4WYlYs5dRzjjrqqMc//vHHn3iiW+2333LL1VdfbY/NIx7xCAuP3v/rv/4LEGvj6DILIEKXn6k6vpqjhr/81a86SIze+ad/+qe+JHq83Fl2iYsLso6Lw8Kq9GrOreJq/PWvf41q4y6Yj3rUoyqqq4f6+3//+99TkWeeeWZFQ8OurVvt0waWM4mfm48/3rSYeLXQjk7vmJu77sorb7jhBq1gBD3rrLMe/ehHm703xwsxq6k8NLWIRr799tsdjPHIRz5SRRjL2b7yyitvuummJz7xidpOdDo/lrPnxCm1w4oOOuGEExCuXgQGH9L1QiMjd9xxxx/+8Ae/onS0uwz2eFsgdnYXGmHl3QUatpR9+sILRZJppt/94Q+YZnZ2/aZNd27b5jCqZzzjGRu3bNnV3PzjL33JbAscGo84wpIH3YelmkMeU9rUkMMMH/OYx5yQzHf0tLbeeuutZA+NNY2NO2699fvf/z4r4QlPeMIzn/lMMfk4a3mcmu9sa0MFOKwBAfPESaiYWQl//uHKKyHphNXf/u53QGlHwQK2tSOccBJChFsbf9aznnXUccdZrWABjfb2yow/agH/exdfbC86nB/16Edbhw+tmXpSHEhxIMWBw8IB87Bhi40hIgzU1It9wMzidRtW3b69x+DLyPNrKDFqGGRN0vHfpLCkKTTeFLdq48bq9nYbmMIxk+AYQCWuXVvPDGYashoVZEdSsFxo6l0KU9u7/IYecOLCFKNTTp4ezcn6TM6XdolRiMykt6VAw1f3esIoeFDj4xw/Xq78XEorOrt29QEIMjjsUUjySZ3kGv1tLgEkJaoOGkNDjmgIy3fMVqWkywA3iQiM3PCnquGJUg/+eMcsdj9fd2kuHDS1OB/CtjENMk2bmsbmhtxSzFOCAMfJmRaAnLzl5Bu2Xh9G5PR0/GFPQyDWaEwBBDifvGA4wo1H6MUx6MVgb4mRb34P5KdxRDr88ZlxgktQcmitGjm0vmIRCDijdhkkapp9/KxAL+arXU4je+Qnwnfv3u0TUMv8xDoVySYFFdFlwnrE4g/W4RtWOO5KTvcDz83Mjtg+a0e0U0Sy3ZcRLvpy4K2C7gcWGS/z2NxYZXblYRHnwwWETCbHpKdhhfuB6+pr3A/c0dVjcQBvbe8K8rl2vT2c3T0jYxOjK0o+yQzRIlHalxxqKS0oUVNu3txolVfTk1ViT8Dk0aBHH123bVuYPDqgv5d0dQU9AOD+/b21YyTp77bQ7e3vTY010/OLlMN8xkTNUjl5IB65hDpLsN3kypPPWfKJKMzRxDBECIbgBoHUBaToPqgg2/oFWmQmXd4jP/VWeRjwOBkhhP7uxtZkye3ko0++ceuNoB1if+9qa58bn8mezbQOLAo0ezErHDAgKueB8swOV0d5+MBJd4/1vaImPcyM/MmTnCgZbq/lSXryBThlZfV0tFdXVt1+5x1vOuctv73iD5UlpQOjI7kZmSefduotN960ecvRv/31b9LttcjMqqmrpUND5E9xyQte8AK+6Pvf//43ve1tA93dlVVVZpLp6GOPPVZnpsT1RoOW8Olzzz0XEsSXtHlIoTOHjjnmGHL53e9+97VveP3nPnOh4B+uL5S4l7ds3drf2enPP1x1ZW1VtQFD51TwK1/5yj+88IX82Be/+MU//elPv/rVr/7T85//5S996Q1vfIM44Jn5uZyMTMcAqOsjHz6fa3TJf/7Hu9/97le95tWOPTStbZFzfmZWf7C6bUiLwxs/dsh1OC55EKmd7C6+7LLLnNu802XkU5MVZWU0i+n05z772V//+tfTF5f42yeecvIrzv7nD37wgy960Yv49qMT4wwTJyh+4QtfeNHZZ48PDho51h1xxBc/85l3v//d+mpf/1BxUdiYtGXLlp3btr/mNa9561vfqtfl5ZqoDrfORsWnZ37lq1//0Ic+1Dc4gGp9GPewhXupZ9fW1OADtL/3ve+97nWvA3Z8alLZk084kXcqpzO6nekN2ze+8Y0/uPQH+Xn5FCVMXGnw6le9+u1vfzuVwZ+ExgUXXPDB8z+cnfBKsFP/0OBrX/Vqicq+8IUv/MlP//urX/4Kjx1wSAIOoGbVNOwYCICg1TiK9A4k3/Wud332C58HzeCNOQtLi+e88U1veMMbatessYg9YEuVNfmJCceYvfnNb56dnwvHmqYt2S6M4a961avIj6b8+3/6xy9/4YucUggMj44cf+xx//d//wc4EsiY5rjh5pucD27AwBx18WB/8IMfkB/mAhZpSs7tRz/60QsvvBDOYcCenHjLm87BTNiq6X9/+csPfOADV193rWOmzb90dnU6XdAswGtf+9rfXfEHzaftRX/56tC20aFhkE0xkHBiAAe6ldhjyKc+9SliSbBVcdTmzbPTU9vuvPMd73jHV77xjYqSEgJprsQJ27Z2yZx6UhxIcWCZAwt2wxSXhijeh85D5UKWSrlvKMfifml4Cvb1r399NOD8eSBANanN0kKGt/SlMHeY7v/Jk1hstrLMCTBKS5ucW8jLydzZPrSzfTCrwEFWE3Sph66zD2fjxqqbb26hEpOBxcRvWDX1J63FZ/MnfKhWmpMCl8g0RKBEGl5ljE53ak5Pz7KeZZPfuCDz6tU1fFork1SfiozIqOCkWcdy2AX1CFTUkzKvWtUAVEdHJ0VKf3oHyteGhrL+/kmjEk3OxMcNLzKoXXUxhQOPIriVl+e7u9gnCDBt2bIg21FruRga3qFt4ENdQ0MNJ51r51ERxCDJYjliU60LoRyhY8rAOTb2Ax913Ibu0daF7Mn2/j0Ts+OFxXlpc0vjoxPlpVWmVu/ctjU3PyfyEwRV+920dtPNd96MOqOnWQPIGKkNOoZCVHv3KaIBZ0XwU07EosgLsN4NoFgR+RmHLZOqQEV+4g9Q0DZXLjPv159h5BKNlUwWcN4Qy6NTFtPU4k8tvmnDpl3NuyJK+KOIxoOGr5gsPwg4DEOEmO9mlUmXzVDb4xTM4pB5dHhwoK9fLDRBn5gcy8jJrm9YbRycGp2eGhvPmF7KS8tsrG44evURhWmFWUsHsfdVh4R7eCDs65/Ndg8QDv5pyfk1mc7EuuG2Vj7wUlb+6MjE4MioPVOjY8MWBqrrqgsLiobHx4ZGxp0LWlJgHB9eIfKpaY48sraz00JJaCatrwNqXC3o0JXu7uDTEgYiRB4krl9fs317u/74J/09O3u//h4syf37e3tXyJ++GCIoC3NdlFGxND83ODoQViWyplY11WzdfVNOSWbtmvLxqeH2rta0EJYXjhPHor+4fOoyRBfOOlTUGOQH7STZBAcMZcA6HMM3XGLfNjU2Nbc2UyP4hhW0ge5JR3kn+fgZ5wjwk5LRMa0SofcQ+3tHW6szsQoy84ozCk/bfFruQq4Tbuy8W5oPPvBS+gLfim9sNi0o3rhEfBA1/0dB1itjB2e1vuUtb4l//vHzwd4eqH50sLr+bNpdkVlcmB+fzAyzAg/IE+yHopJ75Oi9qVesMgVKPiwJ9nZ21DY2bt2+7fVvfAN/4EMf+MAvf/2r1j17vvO9ixE5PjNt0SwzJ+zP6Rvo15fIHFk0wJx06inOyb3tzjuC/5edxfmR7Ytf+fLE7Myb3vJmnuFSRro/33buO2rq637448v2tLW2d3V+5nOfHRwb/dJXv8Ll4K929faQy+CGveH1jz398R+94OPveOd54yPDbz/v3N9ddeU3v/nNbTt3tLS3XXnN1S89++Uy8DpM23b2dM+lLVXV1tg2+tSn/fVXv/71I47ajPe8oF/+/BeX/ucPnvO8v33+i14oZuEHl/2woLgoLTtzbGIsNz/vU5+7UF3vfNe7dJuS6mr7f4hrnvBmtxGaVM7M/LdPf+qFL3kxR+u9H3j/5OREz+DAu9/7HgpqemEuxwkbZaV3bAvRvIMjw+rt7uu94JP/ZiLqn1/xitmlxYsv+f7wQL/q1m3c8PWvfPljn7hAtMmb3/42Gwduve22D59//tXX3jA4Or7xyCPyiwrRbjeXzbtjXGjnTJUUX3/jDe9673uaNm4wrbt1x/bu/r5PfOqTr3rta8R0jYyNcurKqyr/49IfnPuud6r0pltv+e1vf6ur4/83v/2t0qpKPXB4bBRWl1z6gw+dfz5WQ/XGW24++phjPvfFL0zOTBvVNNA5b33L+8//sBX1b1/8XZj7fd7znrd2fROWahG3Ahqs/OlmOTX+6v9+8/RnPgPY8979LtCuu/GG//vd5dA4+xX/UlZZkVmQ/71/v+QzX/i8Dqwdf3P5b7XU69/whjPOeqIWnxwbnZmbraiu6u/rzc7L1SLw3HzUUSXlZR/+yEe++o2v/+d/XRrlxEyHMZ5LzxN+/BlPeO/73vf6N70xv6QY4Rd+/nPaEfKf+9zn7ty+rbm1hVsrxBmTSSCp05raAjKf/uyF4L/vgx/Aure8/W0c3Y998t8wgRCSW2zEKCu6kwvzl//h95f95CfAkp+LvvNt+9vJgKghC+DX3nA9hxyXEHLyIx+BcKwDELf/++c/gwbIKgVT7V3d3SwJIscb/8RHP/rSl77UPmeyEe+aujfdMZU3xYEUB1IcuHsOJC6Ez3yNZXeD6eYCmP7+XgMor8aIRvmsXVu1dWu3MTq6xExDLwKVAejt7eMuSok5eZ5MQ8M60xAo9iLjkkIrLy8WARP9N3lYZn75Zi6IZVz6k4nMQzP0sEQV4QArziJgTjAu5aR7JybERfdCOBrxamFrNjWVdXSES00Y9OCzTSVWVlrsTbe7GEwpnExuoV8kNDd3gcnMRSDI6mIQDwwMg8aEZZx48Qk0sYk8QH8iFhOUlQ5aa2s4EUdB/GG6QNK7XyY161PmOLcufEmNEFPWJ2UjP6HH1926e6vMHhXBk12+cd3GO++8Eyvkj/zEWF/lUZ1HXZaFfXXLIBLwDdOk44ZPIMDNn9K1Bc57kdkUMwgsflihLqKtCP7DWTZ8lg6gUjA8YcMJt995uxd89gkJMgMis5Ed37zjmBdFNJbgL2iAIKdzXiCMjdFj9wIxVaBdKQz0ro2iqCiu1NximCJZaY8oMRMcaIEYnpMi7H2oyGd7+96NBmSJ9Gp9gsQB7u8f8E4+iT3Oc0e1Y29vmAPaXz79eTf9Pcxz6UcH9PcwZ0TwyIz2Bd+CE1EkIWbHFHmoyCfm6K0klrhqd+JKvMnwnrY9SMMWNOoR+m/Ucoerv1OYsb9zdK2aqJrH5/S11POgceAgM3D3rW4NKcp+sL2d7jBoCYP413/91yuuuuat57z55S9/OdFxJdnTqqp+9KMfCcrV/cgcgTMA0KFGBaJAz4r8LPnIR9zTK+Ykdjb6VIC0tdNXvvKVZNERUG8/99zNRxxhTY9c6nLgmH1xmzV/Q+ba+npD1+j4OMfDAhocdEJV6JOCos8688x/fOELu9raOEj6qiU4vZSyAMfNPQi3sVYtjevXP3/t2ve9731SzjjjjKOOP95CMXdOcGxBft71199Ij6BITiR89rOfLczLf8UrXlFZVzfh+ArnMCWDawiwmJ//9je/+YlPfILG4SzpVNOzM7SqRW9cOuXEk8wZtO1uBgQoa8WWu637bTn+eOEm9kh/4UtfQpRxC5L8so997GNtHR2/+MVPzSDYp7pqzRpr1z/+8Y+vuvJKTECFJy0z3Fg4OT6R5rKeqSkx1Wi3cC2zIlZQn/vc55ZWVgaroa/PyGSk/Jd/+RdARC9rQfNellu5uwKY/+Ef/kGKssKbP/uZz8hGA2LUpqOOEtV8y223CVA30mvQr3zta0du2mRV9sRTT7UhmNv2hCc8gcrQphho2dOIKtS5wDR2f7+FCwvHVn0f/4QnOOhbHrHQWNWXzDHXNjS87W1vKy0utsiMKIdpUuTvfOc74WzqPbICr4y4mFZZUyNsmGAIOcYK8uCueluXo+0iSly8AF/6n//5n6ktYxhbTyu8+a1vPeG446BN2dlxbS+3UVmwt8V88Qvw0UD+pOM+8KEP3XbLLUdu2dLT0XHOOeeQB7fgMSZAY69Y1IUqGfMV34TQ2zpeVlWFt/IQZsH25gUA91UKfjbU1X35y19+yl//NW2KFmv44ufv2LpVGyGHkWEWw1KAVhPUcOTmzRZxonyKORDgjpmpJ8WBFAdSHLj/HEhUS1hJiw99NTcfLuadmAs7aY0OhkifGhoqWlr6mblUtPGFTjbwebdRiclrgDACAyWFVuTY0L20mWz0G11q8A2zvdN7D8eKYzo4dK/dJBZdqEeJ1KPMILg1yZnTIEsHBAK+8kBktmApXRXSfTWAbt5cfPPN/QYa0KKXC86qVfUimlmrAEIVPsYsGri0NGxFlmggYNpKV50hwxgVbXfvkUxfcSN67CqCLQjQAA0+RkzvYY2bS5ljT7OY3kzVhRoLgopWFijozU6HM7HNIzOHoIefPpkRoOdVBLJRjMUiHSY9Az3ogglOGuDUBY1lfiqoUphIRCz7Af7ywAH5WiHhZ7ZWA1ntkAQNWA+YHtWhQn7pXBStw/RSO7BqhDwmH7PumJt23SQDKiJ/yAPbzFdMC/RmZ3uXHwSQ5Yn8xN6YX6IU1eXlZOVkhauwcAeZmbk5cDbPG1zfuTDg5tgvnZOTlxFYuqIeTHboB5RwdWomzM6E+4GTU5RQRz7HJoMczi7MuZxTNgxcUfKpTTWTxvLgMNzwfDYc5BIaTiKcE/nk1C2yih1i9af9vexu+nuYWMGTjOxwt0tu7O/htrOw55/M2MyeMR+OELVfWrvrPpPTk2WVYT/FCpRPrMAlePoln/4k4bjhBQlwlqivYRpCtDsCCW3MrN9J98iMmcv9XdfT36Xcu/4ebgQOQOwYMBuZNJC4HWmp50HiwGFjdhghkkOJs0lGbi5ngyl/1KaNnBnjAenp6+qKY4w+Se9T9ATLXJFxg54leboKP0ROYhdzEgg+w57WVl4QgVNQoCnGCISm9L3IxtcVEGueU5Rp7apVNgAL9OUSGwY4n4JmeRcQ8HCMbei94vLLo6bmK1LxhI9wGzt9zUwOfoAAJ1YMsHoFb5t2NZkJE+FkEOZ/YhkXUTbqUqyyzUVuwfFOzRgA0pKj4YB18pY+9uEPf3hidMzCIAQUl8H4YRTU5UwEjA0PQyZMBCQzdrxcDrAtFOjCIg4ZMpkR+iRm2tf6khe9SLy3GxX0QAdfgs9BRaAds4pQbaELJdf2OO8ahxfnQ7TVN77xDbHZXnADsQOulKiokL+zu5v3KPDYblsIcwhhxVnFWJkpdyP3dy6+mGcujFmjYK/I5HNe//qLvv3tU046iTuNCfbKClri2fLonGAMjmaCWIHjkY0iU9gwymiiU7jxnNIdu3Z9/KMfPemkk7QUpnGkLRq3d3a+9CUvkUfVZEDB73znO2g0FeICXhBMW9jDLMRNEcH2KFM1pYUzSFAqkiY0XQakmSuRSGuf9+53A2s7Gm77yh8mG4BbWMAuY7ucJhqw2kyK+BgIYwJFSJ1d+OlPm7zArjCNnQx1ZuNBgx7I5Nbubp6wlsJVG9RxDAS//FhtzQcGXEQAxx6Xdu7ebX8yn7ltzx7SThTPPvts8+gvfP7zi0tL4X/jjTcW5OYNDA+fd955R554Ivy1JnLIjP3A/kw9KQ6kOJDiwGHhAEXtAcqBgH4pGUMHPSaR4qUt/Yqr3LOnm67zlVKl9wxGlZVBS3Np6EmDGoVJ4cvvVxGJgDAGKECqMqjE4XAOU9TYIHiUogAHBsKoqzo5/VLIhnh7GAGROSbSfqtX16ra3mDv0evzQnW7nnT37r33M8mvLlUzW1FhqAIZqmqHvPyNjW4PCsfeQtJwCY4i3AP7Fk29og5FingBhzXPMPCnRGhA2EscWxECgrKAIEF1hm+Tk6o2HsFTTg4hyIobQeSM/DRGBH42HWmxFEoSY40w2XzkZp9cQRRNGu/4AA2fIMylwU+Vys+2RhHqGOuIAsQAoYrITw4wNJAAMY/paThrHY+y0EOLFxPxioBg6JQt8tmLka5tqE1mMOEQl/Kg7U+EYAh+AhgGPosEaxohYHSDjwcoSBpAY2Y4yKMI4CAADjGs8xVRcIA5FnnsYPLninrQhVXioU2FRylCtZe7l08x8ytOPjVHlKLkXPQFMqMVNBNW75NPd7AMaKYon/v6e/WePb0H9PdKpe6mvwdjKfb32K9t9Ac2zHcshe3fmKmKh4R8IgSLcAOXdBadEUX6VKQiKg3CrBd451wcrv6OPwRMdRzg+cWgalKLwA+yQjhsPrADmW1b5SZZTHPeVYyqtcdS685Nz+QWFNomatflbTffQsMee/SWhdk5y6fdHZ0w2Ni0PiczS4rtrKedfIqNwVf+/g/9Ap8WFi+5+Ht1lVUvfsELs9Izuto7rvjd74/auKltT8vrXv2av3vu365pWMUTW5pfuPiib73khS/q2tNSXlKq4Pjo6Ktf8cqS0rK+9g5A7Lktrah8+zlvrigte/ITz7JV+Jorr5oZtSOizG5eWAFYmJNbV12zODfvT789nV2WyOkLHSN2CdtcjBBnnHGGgFlo8GyB/exnLqyqqBCzGvuMuU+rkN5pf3rz5z/7mVBbGG457niJhrT52Tm3/YpMBtaaMz1lhfOGa6/DhBf+0/Mf89jHIYGzXVPf0Nfdo3vYxDs5No45+CCP1U7aiudsUATfVFV3d18c0SGDyeDPjIyqXQd2eJiLc086/oTf/d9vn3TmE//9e9+fGh7Bxsqa2uH+gav+cAWSa8or3vT6N8xOTec7/HBisrqiMsBx5blrhufmv//di/Mys7AUx+zvrS0r/+unPPUbX/3aC/7+H378wx9NT0zeedvtFlRrK6sgj294YuOrKoryC8YHBgHpbGuHyer6Bon+/OEPLjVvfuN117/jrW974+tef9yWY574hDNuuv6Gj5//kQs++rGc/IL+zq43v/FNMgsfeMNrX3fd1dcMdfesaVwLmbmJycXZudysbOOmFrfPVstqa1Rs3rBxYXJKSkFefvjNzSNmRbl5tu92t7aNDDqwcMR905/55Kcw8x+e93cb1jWNj4wOD1i0L16zrmnbHXdCmCwBaIdwR2sbnNeuXnP2S18mQ5SH4Z5eDC/JL0A1Bp55+hP+5aUvMxXyuEc/5pMXfALhssF/ZtKB5IV+KcpVdfWA9Hf3TA6P/Ocl/64swcbtd517nvS/edrTCOG5b3s7wge6ezQN3Fhta1evtj3Y/nibC8LQVVJi24lTxB5kvZCqLsWBFAcezhxg7gUnynn8YX6Nf2St1cDh3bjj96ST1t9ww24vXBq/jFpDGL/FR1Yyu1+KsoYw6X4NjjQkvSez9OgIsY/pUjnB9G4EZEe6NEdmRqdRzFduHsvSoqwMjEufQICbYVd1vEUGA8hgSoGhUlzErq5xPphsvFBgFbHEWlmZb8OwDACCDxTXxQym7bR8MChx51jkPnERlXIvr9ohH/GUkz3KaUcdINBWHYSlG68Zx9E+lg4U41hB9EbXFwIySwyunbitfafFJkwOnt7Jx558/a3XR9JkUBEEBKNNTE7AijsdHey78BNiiqBdRYCrC/LRk4yeMGc1LsNGuxw/OZ/4gy2KoA7OIEAYZLh5eHfWHiCgUixSBQdYcROyUiLtCgpiAsckb0RJowOIyaobsNu3rw9pfvFT6+CnZlLKO14BAmeJvoIsESiJXkCDs3QZJmZX3CEXiMIuQo5Yjz9xO5HPICRoWeHySQhJlybQ0EKgzU7g+bIMRPlEIEI0qGwJsbG/r7vhhj3+JCp+AdnX30OQs7aTosnuIp9YJLP0yChTNSRQZ9d9AMerh4p8ogJz/OoXzlVFJtlGstZHoxeGN5bqXKwyNMp5WPo7voU+mcwHYfJe5gfwqedB4kAQ98PzJKEyxi4jITESLKrjWfUiQO40atu1Sz/R8aSrzmIjMeJcmhb1p5GAKOg57oOx5GjPpON2SaTVsx27dzk/iSC6IYbS92I372c+8xkLkmY03/Oe93DDHHH0t3/7t267pcFdyt7csse1BMGRGBhwESs4lJdFWidOWZ22Amm51dnOIrTDsGzCLC/PkCBKefPmzUZZ4g43W9vLSst0hq6OzvTMLNPC7grSsZ/0xLMgzMOnxK1qCk4Wiyu/q8fzi8PQjpCoLAwVFrGz0zNQpN/oPAoWl5eDHJgzM0s7OD1ypDfswudBOY2Z6jXc2nDQ3doqm/wxmFbHsLjN/eM0YlpUynqgk5zlUZH+GbtlVGr4H/6cm0OsEGsMhNWrX/NqAxiwLihCiOFqanbGucSKUPHye6jLa665RkSuhsMKrSAC3FLz5z//eRPVFntFjxspLSz76oAsQGAFmrZDe1nCbS3uTzhoC/4bDLWv1lcLIGhxJpazr2nYZz/72ZdeeimATgKjBTr37JHnWX/3d04Fe9Ob3oS6pz/96fJsdzp0UxPcjLKwDbci5eUBCIfbbrtNC5qBJmzQcG4mPhuyWBXSTcGwYMSoQ6a7vd2as3VxiSLbMKR61SphzLtxZEfY1OR4D20KZuS8NVsMEYBdUlXlK0JM4oC5/ogjDI/0oCiDy/7rh0p94AMfMJ3hcDXjhpHe9b+OhkY1aSQJyCQbV1x1pc3JX/ziFy32AkskfvTDHzlz6z0f/GC0BuREu63IjqdWXWl9vZSq2lqNiC6/AKaeFAdSHEhx4HBxgCpm8NPVESDbX4o/qax165wW20MjGZv8Rj1GocnJNKSHuVjS+WNGHzqTlWwsoNkoq6CiwyHP1VS0DAAaqnzya8iztVZ0JP0JFG0Za6ytrWRm8wkp2L3IJL6HddaOjj5wDHl+fTWaq5rpDoIhQ6WqUyl97vDjtrYB2UDwG5EpLy9ShYUpSErx61OCc7B8eIOwihxAXVVVuIUYZIlqNKbIXFHhBr4QLRz9N0j6qmoZKGr8QSyOwU0i3ECIVEesIusc7riteRuOAR75KV1IF7AmxOVEFxKAstjL/sZPxhKY3hHoBesgHKsznCnu4eGDFnH2FQmI9WKgh210XZCAQJnRDj0Wl4ogjLow0AhBSoK0jZiawzAHE5lRt6p8lVkGVfsTBI9PLA3wRa5BVVmgUGRHTyRNpZgTa/SVMaM4nBWJVMNBInpJkfS8nDyfVtSTyIOOkKY1YauBoLdqFfkMe6yQE7FFOBa5nLGjox+93v36+heXTwiTgSif9iVoVq2M4dprf/nUpkQLFZo16e81TofWRholtpTEe9nf69Q7gQtJB8QKwEkLaA8J+dTLEtzDUQXlJeX6DsHWmrinxVHhXZcxUcXC1AERuD8/sRGTkYyxCT8Ptb+THFWHLub0qzD3F+QLtPCf1POgcODw7QdmrLtKLgndKauvIyvTc/M8BGqxOvFOyYcY1KnpKf4ehW5AEuHJ/6GzuZrkL45PdociPM4+8i5KCote9rKX6a693d36ksOKnvusZ1/8n//Jq4zSFjubrkt28wsKeI8mXbmOpJbImtyGUuyHqhDYfPlVV113xRXve9/7nAVFlAUzU110fSxlklkgtOrEx9Im4nKNEFa2CaUnryBfgKsRlDcl3hWQIzZudCCztUexPlkZmS5koD4FhIFsopg3Nb+0aPyjWE0EzM3MiJW1f1iIcmVZWePqNU6TFo1kJOZ2hq3IySSuXVl1q1bhDEaZLNC1YCIDjaXX2UxsmDQHZZ2QR5qTnWH52srn2Ozc4vQ0zMMomJ4BmfHhEUd22VL7lnPPfcvb3va1r3wFP08/84zbb7l149FHw9/a72mnnQY3vVq/xU+X4ootx39bW6kAiYA7Yu4Zz3pWYEJyKoCZBnMHsBru64O5o7OFsnN3HbsNAlb71RzQcAyIyQVOsjBp3FOL87EAb7mtxfDY0dyMEJHzxECcM5hxmBd5zmcWof2hj33s1Wef/e1vf9tVTK5lqmpo8ElxNxKP2qQhWiw93e1BOOzaKoRTJRjl4r5d27c7a6qmsop+cYtvf1eX0d2MiY3oJh1MTnNiSSOXu2Hdug+++91gYqyGjuOHSPXSomIyKYwZhJHkXBOjPgf19Mc+zsSKiuyvxhAHWZtVwUn72N/1nnebTbc/mRwSVC2iIqwwo3HnzTdXCoIw49PeHsOt69euNV86OT7eE/fPi7ju6jLNAZMnPOEJToG+8/rrOfZupZRSsXp1X0sLqfOeelIcSHEgxYHDwIF4eV7i91oNDiuV6WHxJzfXoU11HR2DlJg/eSx9veHkCGrNkD0xEc5hCnlDDHNYazIQsBfpdl/pfA9VbOCwoqw4y9uvRNpP5oqKAhcb8SUMB1KM3UlOXlMab1YiyAkOwSeUwVFQdLL3xDMJVqbxRZ7BwbCuyGGDGF8Rbk1NFbfdFs5JBpDJAQiF6d0Nw3BTBFa+GnHUXllZ0Nk5BCtUSJcZbh5R2KpTF8QUgQD977Ifwy8a2Q8Wy2WGT2GYb52emghLo3J6jPiChR3APTE24X7HnKzczPRAYG7e3gOf8ROBEGYa+QW8OL/4xp03Auid2ywRhmqEsEfZwM19/FQpSmOjIEERLgpucHLgHM0nTSAFRbCS+S78lK7t5DcUItz4jp8qDUNhZyfyo9EPH9BO3nzyNbdf4wUP0Y5A42PCTzf+jioCAdXhZ8C8rLq5vVlB2aT7CreSohAQ7it+SvdeUFhQWlDmTxWN9g/kV9XnpIUdlSvqQUXoDWhITslmgxnueSZBPh3XG+XTTuaigszssMqayGe49wu3/+LyifnQIIqkhQzguV+4eRL5DCcbSyQ/iJIY5DMcSF5lb/gf+3tyUsy+/j6xX38PUz/yk4eJsSmtn2VzcEZGbnZWQW44mmdmPOzkVyOxMZfGBLJvYnAgXCi9AuUTLVixLJ9RfUnElq7eLs2tv0Abi7CC5OubMmOv9MhkfW2ZnzjvfR8/wwHvh9LfpycnTBXhsFDZxapwCkDoQ4n0rahO8fBGJswaHpaH6rAHVQ8hMb1tIZq0IDeHL2SscvGvdEt273zPu4sKi6RodX1Gb+FauNLGdBGpssIp0TG/TY1rndLkmpmu7i63/pTX1upFhjQwuW0u4x3q6SGslFRQWCa0SGhy7qL76Kw0Ioc4kicyaj+NnCpyVJVE8s3zOeWRj7zooos4rhaTqQNKzj20xQV2OlVOJxcPOtFC9+Ddbdq4EUp2f7rDyZ5SIa8i9485Onh0DltCheBkIay5LgSorNQNrGPzbQICNIUrfJJjRZyaJ78jqfQKIdYWUQ31RL8wuXfRGZROJ9YMPChIh8FDFFPS35CGb6AhXxgtIDgQR0obkpwcdvnvrnCaIz8ZJsiMukY/tGTNf/PwhMWfddscm5n58rPPtgRKMRnwLAVjlHVgwyGYOKy3e/nORRftbtnztKf+Fa81FM/Kck41eC4ZNAuKJ3gIFAXhxaovVA0SbBEOrWaNWhWeosdiKwddMD8HlE8y19fUopSi7GlrM4eNY2oFjZ2ijTyIlUjdINy90J//whdMW/CclbW+6quzF0SwaVzHnkA4aHPR3cmOXH8Sg4mxMfhoTS6oP4d6e6uSK6BgRR6QbHjAKIqsYc2aP/zmN+d/5HwoSbREj16hbKyEkfExk/Tt+JZMqZI0zjZnXjYNp6xge7MtPH9tBEOTEfm5eU7/AgEVRNTp4mbHVYrVGoj/3N3b07JrF8y5+nqE88CUdduT/CZ04GbWg0xqXwfLgZnnNsySEvmnhDM0NKA99aQ4kOJAigOHhQNUE7UGVPzlftoDSYVu3tzU0tJNA0erjq428vqTT0pReyhzSslYYFxobKw29FCnCtKKBgXaTEGqd/fudp+8q4iio98MAQMDYcOwFMOcRIuBQFmqbWlpBQ0yEo1ESWLIb47VuCYlglLKqonqaHimBVXJ05NYU1Oxa1e4YEItcoJDUXtXEWcylkWIMQWejY2VdnoaONRu4ABcRZWVZZA3MkLDWKMKD8KrqspRqjpcAtNXv87ooOfxBDSoolFmoEAwxAMos19fYbJ5w2aTqhEHGZb52biq8errr1bcgIIKReBv4hUEuKlIFYr7E2NXN6wOk7D9/d49SXOEkUgif1JFmCCzDFpHurVltKtOIpbCGSXwRLLRRyPKqQiTA558cvjjJ47JrJTR/6YdN3nBGVgBAqxHZtxACxzkRIvf448+fuuurd59RY5fmGsaxRGOFvgA4gUaPqkOAhIDnxdWXCw0ZtqfblIGpXioWRFl8e+hIp8akdlDnLQpnLUasSdFUT61nQbyFWletOzmzXUOwTrk/l4HAsi4RAY0sZZN+nuJVSt9TYomViM0vKj3oSKfCCGfcXEudh8yTGiRyZT1S3H5kzoKRuY99Pf1966/YxSwNICZl+z0bC1CbZid85J6HhwOBC15WB79itDrA7oW1cnun5yZFRRqu0yBe7d6e89++ctf8oIXir/ZfOSRnF0vlsg6OzosBx/rXt9kS4+CkDn1lFNkeNc731lVUfnWt7zFEquwDH3rxBNOqKutbeto/99f/lLUqwVJCt6/H15yyZOf9KQfX3aZ0+rAIT6nnHyyDNnmjJeWeoT3FBdfd801MMnKcTlOHilzlvJi2tKjHvlIIQ7Dg4OucVDQnw6tUrVYBEccFbsmIokhiaGzIqRpQ+iJH1btL37285qqapf6Ui7CJhampjljHEI+nOGEo2h4WG2LaVraJZdcAiysaH8x27bOSuTkOEEK/Jbdu30qLy2zEDlkrXt6mpvn0LrmJL2KCi4oQDX0UBeDnykgscTWn89+yYuULSspzcsvSFtc4oZZYV6Ymw8DarL3JsYyuQfYEq5F2107dxp51q1dm+uy5a6uorz8lj17cMlEN+A7tm9/4xveIHj7HW9/u+AnjHrmM54B/mcvvNCcoX9lXD63OPb1yfCsZz5T2PFjH/MYKgGoX/7P/2A11pla1xDP+pu/ueXmm1F08003gXDEpk1WvDHh0Y96lEXqj330o0SCw2/OwvykekH7yPnny0lOdu7YoSALQl0W/7dv2/bo0x5BWoQuaybzLGwQoFREMcnsAi37op01KRFjQd7mpOXJyWOc0SVey8o8U29pSQo25mVl26QNGnf2puuue/M557zkRS82U0ACxxw0Qv3MzxMYF1vh/GqLvcl6O7oGxR2Es0DWqMjkIU7WNTba9w6ylOsSp/eRj3iEPNrOvVM2GDvNzDqCDFB93rOfA7cvffGLvsKqsKxMdL2K/urMMyXCDfK4QSZ1HGKWbQOw4SqxhAxjJjIUTz0pDqQ4cIgcoH5X1LOMNuXsWf7z3iK5XPAuLweHk6zv3iVn/JMGNY54tyAEnTBqZ4j5XLVzZ5tEXxmFYLILZTv55NWtrV3Q9ie7VooFQDYiAFyFmB8EikuiQbylZVhiJNOvdIYjmKxk6R5mH2uBD1ZQ4GCLEG3II1KdzCbEnb8br01ixMsZzVNenAzt7d3sRVapoZM5LjPIY2NhUyus4OZhp7JlTeFK9IAJJYMFBOrri3l8IMgmYNhIDQ5v0NqV0RkEmRVh1iNQqeTA/rC1xye/UiyaCS6WmdsAIJT8wgcQrp2ga3xAmq8yi+XhH0oBFhXIpMxVfdxRx925/U5+oOLROfeeoDELpYiG6nDbJDVk2jraEOtPcHyVE7sABNnXaEYDZdYVHF6Hqj2KSGSfyO8dZFipDnpScAmlcMbhiBgrH1hz02au1Y5jiiuIw7gBczV6ZEayqjF/3ep1rV2tZAAQFfnFTzgohUVyQk8RaMdzT9EOMX/GuYnCzEJ5Dvqo+qDPcmZAPMt/HjTzfUiEP5gA4yqKHHaOFbj0UJHP1auFv4XJI0KuyTQTh4182kCACrM5yInyiczGxoadO/u9aFDNhF33tb8Pqktx1cUepwMSA47xQ0U+tXV0bnUQtOAJqcYW01Ik1uwYiSUDep8MeqUehJ8H6e+7711/t7gCIC5lpYU5r2SNyX/v9kk8noPI9d0VOEjWJGk5/wPUj+6u3ntOj1gt51lG8gF9CXw/LA/30ioohiLADkwbbq3lvv28d95xx1a60sU5TlS2qPut73ybVFGvuhzXkRip3cCpVHcSkEPsbIkUqGzO0oHPZM4S60h/P5Uk/FWIrEOhXbd71hlniuMF5PLLL7/m+useddoj7KfVt21eFcjqRW+c6utTqn7duqsvv9zOUgPSq171KuOuPZk2czqIy30/TrHImJiwScZVt7Ai7vARPi28dmJq8t/+7d/Gh0YsGn/qws+ceeaZUswIOUXZgZJuMLzQZTmuC09LKyoOK7c5uXmOJOb76v/TSWSX+42EQwNivhYh9sE+/vTTJboCl4JQkUjvrbfd7gUcez7jsASUzMYhzLQaqVukZ2Q8//nP/8a3Lvrns8/+u/99not27ZRGwkBvX2lxoXObnZAsM/0V8c8qKnI7m3o/8elPv/qVr8IZY4/dy1ddfdWrX/Xqxk2bzPpaA7coev7551tvZw3ohHYvi7j+7Kc/AyBFJhwNyU990pOhbXDVoC498n7ZT37MY3/HO94RY5Kd1P3B8z9sy7FQXoRb7eefUxA4H3ZU2LVVUMjhB5/Tq+1U5yZn2Z7ylKf4Kj7ZrVTGTpfias2/+Zu/QTg5Ec2uBdVi9CUGJZWVU8mh//Yui0vDHxRpL82Ee/bZUlLuE7KL2BVE3rUmjYYnYYBPZqALKype8pKX/P7KK1wWbWqGgtPohMFJ0RiLb5rAaYAcezf3OoRMA1n9xlJwZLZIiznC2lVnizWq3SZFfpDpz2999zsvev4L8AdKoeHS0nCsorLSudaKc6Rf/epX22jtpuhf/OIX5lAAEXrwX5f9qKG2TkBBzZo1WoSoCIQhWoCEQThZP5HTu04RXlJPigMpDqQ4cDg4YMBlDYNEzWTuW3aggWnd2cUlVwJxBuThNT3itHU33hgWQv2ZeJrzcTHEhSvd3Y6WDTmB4vZQoa4QEi/N/2F2s+0UMRbQpTQ2LQq+zAYXKk66YZoJIFtU1NKpUwozgdztTwV9lQIyG1QKRa0srU7/89hFU7kJibkPben0pN+qqkrqs6WlUx6mLRy4BF7q6iq3b++UQQr06P+4nxbmEbJBCobqMujYWWXhh/8AplEAfJhXlzN85/iKTJ2C/NyMtLnR8bDaxjhubt6RkRvugpqcHstOCwuhBXkhMgtn5pNzRyI/Yf6IYx9xw9YbYhtGhivuAQffIgIY4iVWjQNMbewCTanIT5nxE4ERZ2yR7hel0bHHcGTCXGPJBgKA+Okd2yEDOIAyyyYPHjLMvOCzRkcvzGM2sw/wsai7Pz8ZdYb7rTu3+gQr7MVPIVdC4vBzYS74unv5mZ+Dn9ATUzbUO5yTkenolMysEB0wkzaz4sKhwyTIQlZ2CE6cThcaMDA7E66k0nChoRN7gHx29nbPzC2WlpVOja0g+YSnOYrm5hacZ9Sxr+CM+cTSVnntrn0Roum1F09P39HQHtmifIb+/ojG/fp7mFpSKumVs1zo+aWw5z9tIUyOFOQQsOyp8TB/5IS42YVZnUu9k4us44mR8bDnX10Eb4XIJ1ag5W76e7g+E9rEkgbAE32HlsAZpnKkK/KT2OOnRER5wTE06u/4qbhesz8/77m/d/R0aykH/WUvZc+lzc0szGQs5Qqqjsoh9fvgcOCw+cCmgPQWHkOIB15aEnbL4XQnkD3AtKSbVN/z4Q/fcOWVrHy7Pbm+AphEooaOlJvnl4tSa4LQGmxmprKkgFLm+LlrWDwQaSNngjBe8PKX61EOteI7/eY3v+HqNK5a7dTi5zznOXZ82ufJBx4dHzvuuONIbVTBdvC4z5a7Inj4Va99jXBTpf7mr5/GLc+n+ru7XRTEw+GNqCWMBwKYFxe5fPw9LtOHPnr+qSecDOec/HzLdCbMiTtK3VEMPQcUK7Iwm5znlhx9OdYz5mwNznBeTe2LXvISrs6nP3fhxd//3rrGtTY2v/9DH/rNr36FhGOOOlp8teuCeD7r1jTaeOykRb0ODnxLQSQGLU4XVTXYE84mef/7389X5F5+85vftCf5Ex/7+POe81ye8MhYuMbA9lrHQWfl5bNEDI2MCwPUE884wzwc/n/ui18gTM7T+txnP/fSV77Su1AoHVXMOaeUL80vdfbVGY8/nc9pZsF5kRxO2dZsDFdbmfTCCm6ef+YXXvSCF8rWtH69EOu61avf+YEPaF/M/PbF39VqNdU17jr6zIUX2oA12NVFU4xNTlA9FIRgZi49V9lJZv/93//9h6uuDFU0rOKavu51r0OpFueThwuKP3K+E7wR4kJmX5/+3OdacQXBsaSWrI3E3rWdY1ji0VlXX3ftJz71SSeK80Lj5JzJBeO9iBy3SSriEcXlgG74fPrTn774ku/b5wzy+97//kt/8AMR2g4Pk4fms3Iu3p7woL2ntVVLVa1apUa4gclKMJA89alP5WnzZgmVSG9s/MiHPgw4CexrbydLhDyYfSY1zIhb8VhYePTpp2MRvl197TVb/3UrCSRvb3+Ltfy3hj3ks7NC00Ps/foNjCHTSXqQBsKf1JPiQIoDKQ48EBwwcvGCGW22I9HhtFZi6C/OzFu+y43655RT1l11VQt1a8iTU3gsBWWcYhryzdh/yZptWPPkTzKOR0bGotca1GkS6GuXrNtwqWW6VIqxXmY5KVJAortF36o6Gpd8MJofPjIn8MN+EH8q6PGCFYwB05fSjQYsV7gZ66O1zTGecuhIcpKNioCSjV2OBHPL8sCEUwc9KtqDTIjJjxuAgyO/zPw4hHjgBlu/EJaHG2A4luJTXk46UKahW3bvADavLMsNioAUFtglU744H45NdgrjzFxwOyGp+CnHnnL1rVeDb0yRAh/MBNA7lBDoT7/gw9kvsEhIkJ/yC2GZ43gEbRnwTboHhqiAFYpU593ALbPiWIRpMTP40VuOkOX0aAjVBWNjkLnRI49E+CiuWYHygHAXfqqLSIBvfIczsNDDRYjNTE0KTDO1ssxPkNWCOdDz4n5g+MS1rwdCtu8zTF3CUhshgyo+k0b3Awu0C/JZV030R1wpMRJmbcgJtugOjl/2Jw54vKj6LyifnZ09ECAMWgefNY1QCddmIUSzkkBfSYvGInLkk6Ehm2ba198b/7S/h1u7FZQHQPQWlYadsdkZuQQjPzsvBAiLeisqciUladGmc8NzM/MzYzMjE9MTKpqemA7SuYLkMxxSgASs0FjL8hnbGpdiSuz1BD6uRe3PT12b/az7oBozAUG4RAwM/X3eLrdwcZqXQ+nvuZlZIVo9I0QfYHJuRjbp8Z56HjQOpIvzuFeVhaHyTx+3wHtMKWt7ZwU7xUJ4M70uRJZMFBYWG5bsEt6zbVtYRjMscSccGOg+3sSzDSNrfn7bzp36CWGiR/Q60iBSGkQvhCz8mZfHD6morzdoC1gVm+p0Za5jRVXVYH+/dDH1tLa+bTUPcGDjqEA0i6uqxvr7QaDIyO5aR/vahpqc+SSbDM4Q6O3psRzndGM6XTfgk4vBHuwfsCS4atOmnuZmGr1uzWq957x3vvPCz3/O4rDTuYJrbptTb19lff3iRJgpFFklilXULrQLih0LFbYkqcLxVPDxpzjkqCu9mDYQDNy8dauhFJIRbRuP+cMujOVvYxfeshR0KhMEN99wQ3llma61dtOmkJ5szBACDf7s9Az2iWxbcpaJMwmS+7vN24+OTwrNxRY9Vjy5oC6OMU4+6rGPOfG446+87joqwf5VoUr4Dz1NELBNtk6pQsywVXErq3grg7V06kP3Nl76zTIMJKdMo4j3qArp3suqq5Vahiay10ZrO5+41pigCTCK0qFbFQHH5FyA5qDR3l6zHguTk+Y4SEvIYEpiYWHPzp3rnCU+Ea6RqKuv515iCOnyC2F6yqytzM6LNuqa3FYLW4ZFoC7vlQ0NeIuW+akptMCQNxvxMRqI7i5JluUFbGMUMYYMu6HeLlyGRXITFYRVvWr9en9yvwmhNgKcDJMr1pvI7MUZkezTwh/M/rziFa9wzS+6mB7EWJwzulBHGEDGRoLjce2wbdb4bMrRErSo7Aw9SaMTyyRS0WZ1X73L7CX1pDiQ4sAyBxbS07KKS4PFesATrb0Dkv9iCYaDg9Z93/BUikK44IILnNUfh5WDwg9D81L6YrqjmhhrSxkG6fSw9hsfgytvdNZpi9lZ0wtpPJYrrr9jaHopK7+YGW8UOPHEtTfd1JqZEexjylyp0lLGnnXFBZ6SNWHam7/jE+fHtt6JiVnaFW6miWUw7nMQxsZm+D8gUJWxXpmNsAYUwzEq4vhu0LdFdH4+QKaBaU7pxuING9axvelVZQ0ZRh/0glBTU9TTY/9KCIAyCvhqzIIPh6Szs4u6jla+pRsYH3lkVXd3OPnSOMuUVymAmzat4+gaCpUFVooRxPgFT4ipWjZo+woZ6KndsQ74acSZn56emZ6sqnAOSH5dfdEvr/5lVtHC6OzgQsZ8RVVpZUnF4vzS8GBQ9emZGTl54SQtKJ107Ek33X4TbHHMEAPzhG/l6LWSJgX+kVLpRjSoiiCDA7QRkvCzBGKolhmewHp8xU91QRu2kaUgQFulJtNhvsxPAVb+NAzhPzQwM7aL40tB0HyKIFal0NOOsI2Jf8LPDUf2DJgiboUkOIAgQbSXo6j57HCYmZqYmZouLMgTDJWZlT42NTk4NJJfVLg0y5iZLsspri2tqC+rbiisKUgryFq6FysxsAL/wAcfDky8DymLC3NpGTkzS2lX3bBjKi1vdimLD7yQlu5inNKywqW5BQcmzc7MF5YWL6VnjYwNH7F+7cqRT4JBflgjmpLwsKBKS/P6+ojMqN4RW1x30JSanuQnQp4EISZHqZ944pqbbmonD1pfBtxL5FPswFzS3+eCfM6F023KSyr196mxBbKRbqmmtmJoqK9mdWnj2qqrb7tifH4kPXduem48Oy9zaT7ME60Q+cQTNhu6DtrfidZyf0e4joBFmLY/P7FOV8U3/SJ2NxpDooIyY7veh9Wk8VD6e3FhwWBvX115TVV+xQlrTkifTy9Mz8+yW3tuKSM7fSl9gQI3S5mRxvZzZH+Q/MTbOoiox34BDc2ngzvIVoChP+Gj1IHPA92PDqzxPqSkLy3Oj09mOjP7gXmC/VBUci+0zz2jgdfEwvadqFhZ7TpbnfNvF9KcrGtS0EKoraoL0zOF+QV9rW3VrqTr7qZtac/ZkVEH5zp3n7eTX1rGZ3DP7dyY+4YHa0V0JIFVczOzFZVVA+0d1Le+XVFecdSRmyvq6ufGxrzPj43baFpdXcMFGunuoZSDXp7mDS5abnXchxcwvVdXVvXsaXGPcZmrbpzuUFjohC0rjDW1daM9vfwZd/xmZ2XPuT24usbZhvzboY4Ouj67pJiZYERxlPSpJ53smllCb+bTkp1SCCShpN/sMel33hVa3EwbhnZHd/T0xLmDoKmzsrj3TlHGLl2RwDogWlDFzHCIxAigCjOLC4scnChDHJ+Isj3CLrk9/sQTKSuO2Z7t283wjQ6P4PDU4FAc5KzAC6swtSVqjc9JyrnTUKrNq9EotgqPjYQZu3Vr1+3asdN0kxYZ7et3988Jxx3vu5yaWNzt5PiEDcZWt+mL6ozMwvKKTcUl0O5ra5sYGy83DWEO2zHUi4utO3c1mtcQc5VfUFtdA+E8m5Nz8wa7uumF4IfTFP39gfNun5yewQTNVKmZWCGJXoatgGHjJVSDVDgvql983eJpj36MY/g4qJMDg6HF129Im18YGRoOrZnNLx3RvtWrVgv5divv+nVN8rh4eWZiEodVJ6RucWZWSv3adeL5xvv6A+cXlxxD3biuydwdF72/rZ12Ji3BvnGvb9iRVd7vDC0nhQwN1TesEm/t2mGSA+3c7Bz7rhfGJwAZ7u0rM8WeX4ClLt/CNDt7gdIKpgmuu+ZaCDzh8afL4IDQoY5OrDBP0bFz16oNG0hURVm5jhCcXncXjDgts5BB1N28J3QWKzOT4yHoMBnRg/4y5GPHvgNO7rkPpr6mOJDiQIoDh8SBZIcFDzTMXoclPuZV0DQGGrO61khOPHH1dde1RBNKujzGDu6fu3KYgAZts7+GGGMW98k409XVzwQ0iBgFOGzWFRVxdhXN7CWMa4npabJVpTQfe5S9Lp0mN8w1NLgGKZw5zBGVCKwhAxBOqLqiZxjhyAANsZ0So9VBW8IEem4b3Lq1OwKEjBdznVDq6QlLXjL7ZdEy3M1CCm6FmyE40ggmw9eBX0ErJ2vL0FAXcnwSsDg5GY7mqq6qMSwyllc11JeX5vcMtGfmhbDJ+YwJOfMFQCf3AzNdxkfD8nhGVpgnh+Gpx5567a3XSoGqPyNYmQ3uqI60eEcsX1fVJnZVBHkPS0Niws9uOGsOGQBRii0ElKHHO6YZgHySgXkAAkL8iZPsddyweA4a8qUb9aQHCyS5s8r6tlYDChNiZswvKS7Zum3rQfg50GNGGD5xpsOvTUkaFCH787OstBg+mlp16vVpcWaprKi4tqK2ODtM3E/NTRVkF4Czcp4gDBlpToBmxc2lBWtKEEBaZlZDQ2FH56gj3fIKTbMUiG7LzA7xBStNPjVflGpRAA6T6enZG0gf+whBJXK4Hb07LUsYCYCvJ57YcN11bbEv/Gl/D/KZ9PcwXRX7e2Feof4+NhTOmbOkT3L04yDbSbCA5f2ljOBFY87qhhA6tzLlM+nvYYU/9ney6okd0y95xklezH78dJyei1nCxgSskEfnWu7vsuGA5171dy0SeyixSw//c3jNyukN/19gcvBJgvtAOkniYJCMIBzJgqcUd6JzgM3ZOjlZL6JVRMDw98iQNb1y52ckFySQRV1TVFNwoZ1jbD8nr6+oiETyQypXrVJQL+Xf2tOzzhKo4wpnZkjnQEeHxT3hK+SVQqeDeZ7GQit1+iqvxrEGQHFBiRqHSh6RQEFqg37LlIefXS6PCeFkK5QjpvVqJPgElHGlqrHRuUpu1jMUd+5udjUxKRcEq4e7ScivnLxEy4NBceyLr7BySwEE58oxj8PDwq15+HxL3aO3o0OlagkFYZWXL9HBBXiOD/qVG4AgAJoi6JIT8khTrznUtIxMLF13xBFyWhEVfJtfXq5g2HyTbGcyg22SWNfEc/jEDmn4sUnY3DKwAsWuuuoq0AR447BbeXJKSmix9CS+C5NBZg1ZUzX2B/yN5729wpqdSoWQcFaTChwMWFRkTHXUluVT3j+3EPCRnh6ERz3r9l2tCXmUhgElOSMURebiFpJYFEAccA1DQcvOc6aR+czyBwSSxVjUgUlHSB/o7tayJIdPi1LS1bl7t2lmVzRxFGWztRvAoJSTWGI8JDMyRwgBiPlphoLAMOEriQUApVJu8MgIMp11QCQCkxOjzemcWSy7BCaUzTuId8FGogVDmSFAAkM0YVqa452t56udkIsRcrewqOxAV25ufVOTCDa84iQ7KlwpFhz+uGPJIrC4CTpVPJwTtixHW8OHLYmFOPw90QEOL6knxYEUB1IcOHwcoKD21yvcYXqGyqKrTz6p4ZZbuihDiohOk2ikMJz5k7PEN/Onh3HMDQDHZbTGSvmp7qisysvDFZoUJm1MXfsKcbqRjTA8bAQThLkEgrK+ctgGBmZUJI9fDzhGH5E9cQGKVmRiUrAmow12oKla2aDqEwebdvVnS8uISulPELzQ+Q7PUtaKpT8VlNlIZ8WSV+sFELVT/srCwSkW0QFGiLEGhvJDw9fBwWEIeI/unMUfGI6OSgseKTSMcWHSPxkErRRJBwQCPqn6pCNOumX7LWFcSwjET6CMZf5EYOSnzPDh6yqoFiMFTiqidimQR5RECIOJA75ivsGIO6pGf+JnrBQ3cFie2HwQiPxUqU03AKIOwsY1bYqlMqga3+AAvgcESDbvafbpT/lZvsxP+KuON4WfMqsR1crux8+wjRYtssFZQfDxWebochdm3+2ZWDjzF3msARjSTQnh1TIttbUMpDC4Q8mvJ/LTbtienl4sQhraV4J8wgTPdTTi3N6uccIBcrF9iRD5JBJaSusjgcBoC19PPrnullt6NFMUGIk+3av+ThR1dnM5gBAwLNLi2vqhIp+xv+tEXpDA5tUjiCuGoCvhZ5hUwk9ij7rD1d9NyWG1VrPeq65E5aRZBP6LCP//n5UeNh9Y63GNorIT54qbUUXyu4SAGrLIjZt/GPh7tm6lVYNfYQROgumpHKcue2IHrkuCYMU2l9TUkD/Lj/JTrKO9vQ7H4hXILMUUHV/Cgh5xDHUlm2oEOXsRLeTTUFcXl0x/5uXq7iJsgzYfHw+blm0JGBlx0LEtphyhGIlEFgG3VwnyNCBQOrMrFLgos8mOGqPmpZdeetZZZ73opS+l8mLgPphK+Q1iLGQhWbz1rjsZlKjRIFjZ2ZAHP6e42CdbQO06pip8CSWTs/uwLgwUVpKTwx5R5BS/GFqsN3KhFOTw88wtM1rZtiIqIJxfyrPSM6cmJhx/Keac0oE2lNJm5xw4bKEb28HmyrqCSFiySqBGITq3CXtNMfS0tIRp5uSYe/zR851dDAJHEff4bFxN2hO9YWbM6J74ydpUk1nPt/85+IRJWBoacSAOnLVr1xqGaWQrq6LNhx2cUCGkKJyvoI1wBqWGd7rY2K4uKLkPiSBJQVGxSKw1axAu3fwInBkOqmYxmJ+3HC2dC8r3drK0d7MqYKoagYKlozoJYubsU4NVcjmHaRFstAhP5ETvTSeNiEaL4dKZewYM3HMDlpxGbzPYhNC9lmYKRvv7ueCcdujJgxDTCuDbvmaaw7yx2s072KJsyzGSSSycXdKnrLugAISb1jF9w6Uvra1tdo+XOfi1aycGBsKid34+kwdkj4IeKGERQqQEKUo9KQ6kOJDiwGHhQDLLBhL1wvDaa3slh0Wfdtq6a65t84kKorKoNcMHdUfbU3dSYv3UoESzu0YEJqNRgxIOKj0jw8qwAV9+qli6FHV4LyvLHUwihKObROPRh9SvMZNTBKBh15ABCN0POEPUSA2OGhX31RqcPw0BBhFgvUMSEL6bsUzksHf2ACR5cYx4mpXFD3+1yMwo985f7u8PdwgHJZts7Ewcniw1G2rhBhM1ItxDCaOaIwcTZaEHsrOr5WQ2wEFZRdTrAU1mCMQa/anUqUefes0d18gpEQ5gSoc/JLFOBn96pGOgcV9d+AmawUjtCpprlij/Mj+hhyIZsMJYaZCVTQpkcBVp+AmgP8H3J8ITboRTSKEBuEQvIMhmaUszQQl6auEVA8K1li7nn/LTHqDAT/k1Ewte8aqSKp58bHqE+JTwM0x8LPMThuBjnQwgeFSUBHlGaVoxv2G0DZYpziMTIYnkiGg4UD4LCDP2YhrsV4h8EkVMdsix2ALyGUmApHZMuky4aDNKi4ZDI+RPO231Ndd0IEHTaBTpSt19fy85aH/XhXVAtQMIPmgkmcg9VOQTwnq9B/6E1uNF00d+Yimq78JPLMWx+9nfiY3WCXo1Lez63H9SEkqp50HgwGGLhS6qqHDNqc5TUFKyODFrYT89N09AfVamw6QcM5TLQzZoIGnd5s1TZlPKy/kYJIAjMRY3dtpKmpz9yyXQD+WUnwcyTxGb+h0b48tZhDQc6cy8FJPMwIJurViv4zuCyeszdaMDkya/ghW4spP83sLCRMLSFbHkaCXOLw+D/LkJ22Kd+F4Yist1TyxFAGfrpcJ603LzTAQZYKbGJ449+WQnHlmgduIx397ysqXswsUly91uPba2aVziCxlGjEXw5xbqRXQP94kyBTZtZlZ3Gh8anp2fKyqAUsbs3ExYr04WLfmoMqsretfGEgrLUvZ8f7+g34qGBl49L4k/OuJE6IpyENLmwsRB2CYNOEMmMVAMb3hCk4MgpDw9CcfCQxtWzQ5g6TnnnON8Jtuw7ZLt3rPHpAPrRrevcLiFU0Awp68PZ/ilgFtO9QurSTuok7Vlu7I0nBQV2XyLV4H/KLUuaqNU8pUkWNymU+TkpeOG5uDK+tNwjhuxu2sCcDQ32kFgBPjTJ9rTfKZB1OMdNOQTDHrHY8Ozw0C9ADU/MLDp2GNJjlJRZxmfzYyAAzGQvbgNAysgqVXksbuLVAzZKuZi4eSuCG0HVZMIpm/kJ09IJsniBeAspVLMs7ju5CJHypEEQgmG3nnjEOMJm9dQkZ3kcf5C7cwoS7tuS1ZW25EWAEmgtXrVNW3aBDi2V2s+ldo3zsxifiU2Kd7Cwa/aWU/+wMPUk+JAigMpDtx/DgTFnsysJXomBERb+6JCj9my4frr22hpxt+yQSxdjK0lL2m0saGB1nKu8/r1le3t4RpVapaiBsqLDMXFGc3Nw5wiOX2le+PCabI+OkYxhoEpWWD0LkN3d48UNrQihgb6k18sjMzwR9PS2yCDYAQxurKq1aWgX1rUKIAbzpcG0ERtTAREZsrZABEGssQVBJ/yP+641du2hdFNKZXKz+VLagkh0KgOit297jMz/EZL0YlLG3xaalgpVXiZmgoTAYidmZ+W6GtNOA437NSFLe5lZzrXMKwZbjlmy3V3Xqde7/DxSWZlvfAbJe7Hz7IN6ze0trXKBrJPy/yEnjtUYBUToc2KQDs3FVFAeffVA5pSfFckI0EKPuCnIpjpERzOlMdnC3eASOGOxpwRSehhKUK41mbGZQD8QH6CoGqtcPyRx9++63ZGPLY4kcSp4PhZUlSAD6MjQ9P4mRX4iSiXzwj2hNvieFiTh6qov5yMsJK/ch54ZmZDOEQLZxVX166qmptN6+zsi/LJbMCKstIK8jk+adadfIodWEHyqe1sUB0ZCYela18ti+EEoKamSoezW5vALwsMcTrppMbrr++SIufB+vvM/vK5fn1Na8eg1oz9fWYhXA4sA0N79+626cUgD1JIoCS9nBRNT4QF4RUin9pOk+3r7yFCAdUEHkVezLvhHl/XI1F3QIgiNI+caMFP3MNP0mtlGPeW+zsCybCULUffi/6+ccOGno7O6ZHJzGnnNCxmx/Vf4aQ2oB82z2zl9K0Vism9PhPrEOkwpj4sl64OdETuLZmRM/eHP/cfwiE2YipbigMpDqQ4sGI5kDoT6z6ficXW5/KaopxZWOSZzNpplJm2vXVgT89weo6jiYMDxqJl1dn9uGFD5R13dPKE+QbsRUYtY1HMkjx9fSEWlKXIKeIMcBFtFuno6CYz0VDmKSUmYzV3OPg/ybGLXGgZ7A22hbC3N1xSwMdgtUu0tMh+FlvjT5BFa7I72aMsV1+DVZ38GT1S9XLlzEAK0WKLK8LThENlpcjhRZnVzpyFMLuW12dVz9oyL1pxZmusl4cAKx4pO5jHqBboQUPQ0tDQOBdRLYnfO5Wdl7txQ01Pz/Rwf79rld2ONDk7fMSWdR1Dzbmlac1dO0Ymh4tLHfKUOTE2WZhfwofs7u1xIpnaE1qCww/PI1Yfcf2d1+MnBnpQx8uNNjcHFRry+MV8ZrelWqd5QRXCaJfIKGeCexfjDVvocUFBS/ZaZyBEXUjGDYly4r8MqJaOG6FNk1kJZfENi7RUTIcGnLnWHu8YqFLFMR8omcGUUwogXkwTQGz37t1AhVrCIdUmgSvEofV290xPTYS1h4ylkYnx6pq6qtoadyP1dXVX5pcJeSrPKz5h3TH5afkr6kys4H9kZE0tpF1z0y77u3KLCgcHplo7OlfX1g4ND4gVrq6rLi4qHB6fHBgS6jDjEDaxbitEPqGxeXNNR8eUhiOxGkUzkQTi5EKf3bt7+XIkXHPrqnp3fX1VW1uIFyBL5HBvf8/L27Ch/I47urU4cfXs6+85Yj17+oN0FReEiI/p8Rl9rbQot62r0yFQ6bmzq9fX3r7j+uxi57BUjk4MtrQ3O0F65chnpDpqEt2E5MMNmXoHSdbXiLcFf71DItbRcps2bOrs7oz8jDoHN4i6R69UEG8jBJn1HWD9eYj9XRSo7pCXnlOUXvCoox6Vu5Sbt5iT5YxHmzBsSk+difXQOhNLf9v/Cf3v4fjcf7oihPsD5/5DeDi2TIqmFAdSHEhxIMWBQ+IAQ02Qiaxs32B0hfdwvwsLz9lWllEZfFxBNm5FefH27b0yJGsdQnDC0aPMRBuSopfFCoxepQMmAHQ7i8wsSAYiu9ChTdaBhoaYheFwLJ6VF6Yni1O2vr5Bzp50/pXMDHS1uKeTtQpDtTjsya8qpCvIbJUOyQhk/fqq5uYB70xS3iNseemqmJ4OAY3LbqcM0VsbGBgBIVLNuEcIDxOesIVS9A04EmKPnSfU22vRKJwyhS0qlbOkvMhpHrGgwx+c+ZGWGQKL4NbW1pydH9bEJiZH+cDwKSoI+5bR6AZEKKFORTAE57bdt8FBKaDCwuO+UG1sxF4ZTBYAlUwHFPMw5ZQN5oDATY18VJkVly3y2SqWP7ExVop2vn0kSjNFGlGB0shPlUoEE4b4iVKZ169b39zSjHXwZ81DQH6sk4e/raViq/ETOBIsfjhIxyIPUGByucPUw0TYGJydVWwdOCc3S2JFZXVnd/dI/2hestcMmb7PLs3mp+djxcp5BEcIrQvRdpWVU2mZXV0DU5OziA3yaVdZUZG2CPfITi/L58zKkU+3fzi3VJNBkgBgshfNqnWam/swnEhoJrJEbMiSV02vEeX5Y3+vyN++PZwhqqx22a+/zw8NOYbd7Wj5o0Nj8q9fv06Gzs7esJdiaclt2qpwOtpshuNx9OzgDZL8FSifpB2qCCTSOEA+CbbZruX+jjqNrie2dbThZOSnF6XwSotzgL1jY9RU+ov0vf19aOgQ+/tI4GfokrPpWfaFZqaHFekFexRSi8APoka4P77Yg4hmqqoUB1IcSHEgxYEUB1IcOBwcYOQBE3ddLG9CY88x4xhzTEO2MoeNMdffPxbtPKYag5jpLANbTWL0oLwzB1mBopKZiTGRmchVY3zn5Vlu3XuUdDQTYxW2K3FNAWGDJpiEsEORpaqQLk+kkm3Nu2NhstQ5VwBGq1p1xx5bu3Nnr5wsUfYr+94ylzOzFGTLIgEo5j4rE2T+IFBwk4h2DwwZvmKcQYAGKrhwMsPHjTI22XACFVejgoxgL0KgoYEPUnySH+TsrABBjVKkexIIpYrAR43yKAIfC9GQ56sjUFn5VRr5CSsQPBDjM3DApKyqCUuy0UoGEz+BggYmRzQQDqzapZs+ABYEORUBHIHSvQCoiBo9mJPwsxQEfp08UEJ+4OeGY3fu3ukdKEjiJ2cYzlKwN3IGfDXiG9lAmhaJXgECQbYPHP7eoYQ0rIiygRw44IbMaoztC7e89HBQ5cp6komhiBJKI6qIQguEV758ahFoY3vkvGaNAoP5JAEhHoKkiQmDxCg8imjNpL/bB753Bko7EsI/199dJxqmUbQsyGQeWDCh4ZdogRyrWOHyiRUmnmCLXUiIuou0mz/CBwR69Cw9LvLTJ/xcTsfn+9bfIwSdK5wL7ea60GtWVod42GOT8oEf9k2cIjDFgRQHUhxIcSDFgT9ywHoXwyv+7b/Ba1oIF/Ay7LyzzFj8rgLmA1j0YxFK8dUn1p4/pXtiuhcLhvX15R0dQ3w2xi7TWU4LKYzg4eFxDpU/ZQaBQSmdI9TX51SQEKwbHTO+lkTX9DIuFWdPw82LxUZVC2BmassZ0xncVmr37An7MAGEc3TYrNuwS3lu3LZlc1yRVatquNVy7qM3HECFQKvOAwNhKcajlAwgwJlr4AIhBaNBz/aVrizqUAErOWMim3hxKewd9QJzQJCPGzJzMnEGZECYtvInkJ0Q2qtqGQBRReSngh5YRUdF5pO2nHTLnbfE4miRLqeC6orxlpwTYAHZx8++yE+ZlbJ4CxqUPBCGg0f6Pn6Gfctq90hUhPva3NMMYJQB/IR8XPXlKvMQmP4aiCuL59b2FbQC7J1HDZQqLKZhjrNzAYQnOJwfSHpkkK4WEHjjXsCXHptjRf0mvHKxT2jrKOHaAtUPFfmELRIw2QvBKCkp0qYEg1iSBIR40awSyad2+dP+nm9W6t70dwfd7O3v5FAXcKq46RLOJMHQvjB5qMin4/TIIdEltKTUrI13XfXA/o6ow9jfI6NoBj7wvMuUQ2dcUR3i4Y9Mygd++LdxisIUB1IcSHEgxYEUB/bnAHsrer8SozvMIOM4cXhYyQ0NtR0dPfwfrheTmi3LsYmuIN+A9Syz8nwhVnVhodNN0xSMi0I+xYVHGaQoywGTyOxmXNrBy/JmX0phizPNGZruUvInG11m+HhXRI0VFTnsbB6pPCqKGdasWd3XN85sBV8eifLz7mwlVgXvi52qCr4Wu5x9aWmF2RoxielKqWVgwFGMY3CI/iG3QRVgoloKF0Ie0DhszGLF4YDemF9OVj5oToGUIqdHBjWqF0oe6cv8hB4q0CIDGvEHYvipushPmb2DgJa1a9a29rSqVMilbDKjCFgZ9ucnhif8bIAGxxgET+QnHxgJmkldACruF/DKisrIT8SCDEkZ4kFZ0Q3AGfjILOiaM893RQIHGGNVzRWUKAMHGHxApHMbJGKIRJ8ggxVQTZovbCSOTpHaVYef9i1zmOE2PjeesG1l/ZAWd30gCqr4gEYNXV6e4z2yC7rekZ8QGE4mW4HyqXHJj4OutAhCYKsh0AJn8x4IISdSluWzoaGyo4Pg38f+HhnlJFuySgBUpEZVPFTkE7bL/Z3Gu0t/1/X28XNvaEAU2fvf3+kTCsEDYNSZdqbEO9tXVq94+GKT8oEfvm2boizFgRQHUhxIcSDFgbvnADvYRz9WXhlkzGUhkfyZoaGwmuoT65YJ6BNztrg4BLIy+pViJjJ2eUc2042OTrW0tHEVJHLMmIZ8MP6YtU15ot/ILJaYeHThaCvpLD+QVcHlHh8P+10lxhr9AsL5bG0dZJ4q5SuTWhFo+BqXYvwCzo6HT1NTQXt7WN6UXyIfxouTrcBpb+/2HiGrEXXcdhYn30y6F/m5o4BzCNWCZADh71d6VVXYEmyBS7qvMsuJcCjF0FOEo5TpzLVTC/7IBrKqsYt/iBCIRRuXOxohJ/wsVjDyE8DIKzsqdzXvAsEnADmWPvFIIQMaIKoABHy44YxSXBfpqIr8tDarFfg5MbOCcIakGltaWxS8Cz9lQAiiOLQgaCn5m2qaOMCAo1p1XjQuP8EnS4X+VB2icMY+cABlxhAulhQvvmpuoOSBtlLq9WfEWcPhZ172iouFhqfW0RewC8JokVJba9N4CKRHdZQiv4l8lra2hrum7sJPX/+y8on5GgvniZZfqBIG8hl7ZX//gEQNLX1ffxdfIO0Q+3vdgf2doGJUCDlIZlv0I0x4aMknhDUlnAm53hejV/BEYuzv3gmtT8T4cPV3XZv6UrXTBv3qiX7DmVip58HiQMoHfrA4naonxYEUB1IcSHEgxYGVwQG+DUTir4VM1hezmOnvelEWMz+HfcZKZvN5YRfyDUZGwh7giL7MHB75XasbV18Zi+w5RVjbTDsOA6uRG6YKth0gNTXl3CF3LAESHVfOQ/RpeXH8DTnV4pN0hqYUaMSKwAGktramoCC/s7OLqcrNi84ec3/Dhppbbw37bOVRr1+4qdEiKGsVGgx0D2wVlN9VphJlkwhtOdXoT4RLl8KIVzsgpaWCSMNJ0Yx7X+WRExwZJPpNAIfdxQxltCsOMdiiAuYyQ1JBS4XyIMp75Cc84+IqfgIrM9cFEO4TJiDc1ABo0ZFQo/wI5LTID0MpIGsdOaGqOukQjvy0LOyTItAGGdgIQfr+/OQUKWIuQJNBBkBEyblxw8Zbd9/q3VdwkOkTTOQBAUx5YAJtzg8PH868aImREKU8qIgEIhwmGhdFMAEBN/yZnRZwXlEPrDSovgB5TEYF5C0L3418zqw0+YQ/nDUECYzyqUU8qCBdVmd1PRKIOmKA83JWVQmvCCHQxPLQ+vuMZtX6+EPqYn8n2Gokrn4B90ghig8V+YQ2IcciHQHfyDOJRQ4WRX4u9/dlfh6u/q5FQpdPCxzTfEt7Y0pWVLd4OCOTij1/OLduirYUB1IcSHEgxYEUB+7CAb4mc0tisLqSc13DwaSZmZuamrY3d7mCh4XHs+IPMArZu7b7Njd3jYyMSfcwbTlC9fXVXJs9e/YoyN7l+7GJk52TGS0tYU8g+NHJjOadTbm8OOYma5uVzNbkQRUU5La2dkiUB0zp7PLy8jKxqJZi+N6cK6C4T5WV5Xz1uMoKJV4ZW1wG9wft3t0H2wRsmiq81NXVqL+jo5OJiQQIQ5JHyoG1eBkdOYQwc/1Gh41vAGzgx9JS9FeLi4tkthLL0FfdyPhYQX44GCltPhxhtTg/Kx3Jqq5fVT+fOQ2C92glo8XLMZuP3dG2XWY58QcOEPYJOc3NzVICNxN+ItyKsU8tLS34Jj4ZpQjhpvqVyCj3gKlGKUABG71if/qEn5iPQhHUsnFyQMNY/JSID/ycyE91aS8PSi1tQRscKOGnRC+7m3cjBBMwBKgIQWZVgIMWVQMuwJvbgBCZecgWhU0uQLi6snx+dq63t2d0bCw7J7B9fCIsohIGxQd7+kpy8lU0NDVUmx/2Tq+cB1FZOdkuSMIK9wPXrapdmE/r7O5bcGR6bpb7gW3aLMgvKisvm5ye7+nby0+cWQnyqRFjS+GzBtL0WlDzaZ21axv1eJsYNCjBkAGlnqOPXrV7d5h+IoR+pe/r76XNzT37y6fM9fU1jsnas2e3G48039hQOP17VUNDTkE6+VzKdl93lV6cn5GfU+hqtHR1rSj5RCDxI5+QxxzqBXpIIJ+RV2jXF8izRP1dZuT4ii0UBTd4mZ+6J6E9sL8fe8SxO9t2HmJ/r6urzc3M6urtWBid4wNnpYWD4hfm0nLdU5d6HiwOpHzgB4vTqXpSHEhxIMWBFAceFA6w/B6Ueu51JcxlZZbRW345RECx+IGZ7w5OqOxgjwXQsOsxuaHHL9vL4iLT8M47d2TlF/uTUcjV8aLGzZvrbr55bwytzLwv8dJu8HBOcmdnBwdMHt4R+5uLpWBf37A8EhOw4bIiq6n86hiyy45kjCq1bl2D84NdJiyFrQkCW5MfaB1rdHSM/a24FHBkttgoytKeVYYpex0aDFA2K+e8vz9sbuSMWU7JyQmnPcvg0GXVyQMyo98DN7uO29v7AQRWHnYt3CoryxxfxSBWCzZycxDS0FBjW/HwcIgoZhCrThVemprKe3tnB3t7MzOYqtlTs5OgBd81fZJfPTE7Xlic5+ao8dGJgtLipqamm+64KTc/B0wQeNoyq+KoDUfdfOfNnApYYTJuQMMuWeHEqMYNnyAf+aksnxYrECjRnxxdoPiuWMEuh7YXv2KSgQIEyaoDCplxpZet70/4ax0IqM6jrPkFZdUIMR6LxMbVjSKxwZGIP+rFZKxTik+OG1oWfL+gSedXAy4b4bGGjvlaCp4Dff0lxYWYPDEZTvl25tZC2tLk6OTU2Diq5VdvUeUaeB70Ud1B05cTIex9Odvyy3KG+/aCKIecqRzf+MAqsXMWz4sKC0fHhsXcux+4sKBoeHRsaCSspq40+dy0qbqzc1KzxmbS3BqLfOIGCdcQJMcnTaZZN26su/POEJ6AkD/t7zU339wmG3lQ0EvS360hT7d3dYZuspipt+JD7O89bQP4YOcAEQqiWJJf21A+PjXc3tWatpCxcuSTPC/LJ2HWoYJ8uuhqehqB8NSnsA5ncIMIy9zU2NTc2kyx4Jv+SP51WMJM3ki+gliEgfipeOjvd97kz0Ps7x1trRMjowWZeWo0DzmzMFOQkZeVl77k7va7Ed+kW9zdx4OU+bP94gHqRwdB5RCS7oLMgxMTnvKBD6FlUllSHEhxIMWBFAdSHHjYcCBxIVDDqFo2qZiDLLmu/v7q6lr2IkOQhbeqoWLr1m6WNHPKGczMZT6MQ7BcM8SqZhdypSQqyKBkCivINJTiUdxyiu2l4pH5YExDeVjJfi0bgmC1jb8nPVqTbEHAOcAwUR1zk3HJGwFHCgMUwoxR+ZVKjM4Kq1jMcX4X+9WqsnXIyspSHn1LSzuLPFr/vFP7aZFgKRsogbhggsxaZcQPDAwDCAIc0CID45jTihAQ4MMHYJypt6i0pLV1FA48pfk5Z1zPp2eEVVmoMqllS5AfLcottECam10AJUj29vdgwjI/YbJ191aZIy28escsNzY03nLHLZgpPxzg4x03ZEN15KcXiKFILYxybJQuxYtfLqg/pWs1aPuFMD6DgMm4h4SA9vx8IGRf4Pf+fAb2hA0nXHPnNRgFN58wB0zIa0oLfcB69wk/MdCzfft2MEHWmsIB6hoaSkpMKAy79Tjwc2rC2V/FJUWFpSVg9g0ODA+MZKdnlOUX5yyGs8fnFufSVt5uPMKVmR1ueJ5YDJTOzizwldx3W1Rc6JzljGzCPDYxtRLlU3O3t4dN4BrFb3whSO4pswc4ymcUe0IoT2/vJHkjpfvL56pVJVu39u7r76FrE4ykv4dz7ECzSyBtIenvRfp7uk5CSPRx8pmV1WAiZi7T3Ujd/UP/r737gLf1qOrGv/c5e+9z9un19p7eCIQIUgREVESlimBDUF6w/S3wilQrisrra8eChdcuYEeKgqBIJxCSQHpyezu9973/39mTHC/JTbg3ubk5N2c9n8uT2fPMrFnzm5nD+s2achSvM3ewdvqnySMQ6Z95vFMMRMZOHiZwyMPfW/URYMNk74G9EICSOuqx/mJI76/ciePdV+M99fDGeL8Hnvcz3turrURRZqm45DSGknuBTRTZlrL2BoVR/0h9AuxHastGvQKBQCAQCAQCgZMgwJ7zrH7A4paW0ypizIdBzIxj7aGgW7b07ds3jPxIiXYyozEo1p49wExe1hsDkRwx7EjpmZUeyYSl9DQu4F3OHIwBLQs5jEu+VS4XkkWKYQuS0NmZ1kiTzAzNuvnK6Fzdx6gswr1RlEc9avDWW4+TTBrrHOlChjdsGMAZh4aSY0oVME+imLNuLxYWiYpLKaw4QrjDc0UY+pTx9tWbFyjnZftSjygPlZBMySjgwSIaFUlrkj2qJgEk2cQNyclQJgRXPAHPLZlMEiuvonFXmhwbOaZefioLAsqihrdk8CQTDhKIlIsOagphjwTKbahRhqefSlfljCfJJ+JJZ0KwaHIIoZWG00aUB/JXXfJV195+rQRqQW2l6w9ogzAqmOsrJdpAggqKz3g2pjPSvIZHjFoolBwJiPKVSrYcy0jh1NMax3opurVpzZ2JBX86e6Da6MvJ0y6c8Vz7/RPBo3DqnXePSu3oGPLcP0WqWqN/VtVIhxH/5eO9a9++NColA4VG1D3ue7yXzcPk8W4sEGjgeGv3PHwUdK70T50WGqDTdekMNGMEDiqSO7NOq+vqzMaaCmY8ofQgxzsJWUhzodlsZG6gNTgxlPV8RL6DAz8imzUqFQgEAoFAIBAInBwBppvHNxzWm+XKPM52HvOOA9D7oos27t17lHXoK3MQE2AQ25QrPUqDTCJCLGDERnpvWUQSwoJkPbMXMTfLiRFUdiQrmQSPXA2SicdN0kFKby5cpjbGSkg2OkWywrdt26zoY8eGhYllIwqwrbds6b7jjjk/FSe9sjK5VQtmK8lUVXpmlTt29NmKzHKlJDOXHFmwvvZ2/rFRtVMjWQTIcZQV7uenSLpRWECJylIREuQlJLNlrI/PWdFqR08p8W2SZUeApVQLNUJ0E567L+IspZLIXCJNLr7oYp9uvfVWtrVIYThQwycKozQqqFCfcGk1UjvGOhAIoZIiMp4ILTVUgWIejik6ZxYnL/XURcCaT1lIYOhLlsEX4K06MHZAejLpgOVKQ20/VQQg8CSQ8rLv2L6DAlZo08dDFCUtBM2J6SCNLISTQDjFQOerStGB5iDy8BP6uaYe9QKV+4HR/tyL1FrgvvvnxjXYPzVH7kW2uquRPqMVNBOo7+6f8yI1U+6fd493d26n5e5aUKX0/K803ue0rLI0ax7XrpAm1h8Hba1jKFoR50T/VBEQQQNKBovBqEbGVK5F/qOhMwNE2PzRmRrv8NHBFIcAL9cak2jByc7uX4TA++ziHaUFAoFAIBAIBAIPLwLMvUSikreHIvgRx2ymScwyMVddtedzn7tDgEHszahlqOEtPrKSmchi5GVqi/dGn1AFBqLE4jMRYh8zJaUkUxipY0f29aXl0IxO1M5XNI9l6bIhCRiXPpFAN1am4rBFlIxkMjORkwtFPHJkMp/8hIUSKwuPJRvVIVgSEEg+UagLN5fFjzgYldA5FrlPKKJcExMzSqd81lNK9ijSrnaEUDuzOPGZQmT7WDxRjGMZ1VdipXskFpmoXb2uIkhgtqr99PWxVzz2muuvEWjAnnY4U8DC0ZnZGVpxOmWCfQ88KSaLuiuIcGVRPjPJzISRVayGqGyXw1O58AGLLGqXmQ+FSaabB7uzGpwCCgWRIhBg2S1szr4vdZfxwgsvJMd5YFkljU4gkBU3MjqCHqiINzy1Djw1k1zCak1POov0lWSRRIkUIC27kSWYWZxRuzX1qBR9dHKV9fgJ7Ub/TJ1EXdZ4/9QJ9TdNoKGt5DA7AfPVPpD7pwqqiAaVrFHZPN53fO5z+/3UVbwJOa3xnoEyVaMHGuy6EOGwOlf6pyoDx9u4cN6VXqpvA0Hr6wYCpglAanAh+eoo5RkZ73BLY7IxH2SA3AV+Eh/PWUIgOPBZAjqKCQQCgUAgEAgE1ggC7C0Gf2a8VGL7i/GT7btr16abbz7G9Ee0vLNBjCBJxjTEdlCszDMxItSUPY3/MA2z7ctq3LRpEMlB2AhkLPrkzSCuVq2jTvfZEoUX5RI3bnQbU7qsKFO+pEyDe/T0dB06lG7iYYl6+8pOVbQVlyQwHBXK1Fao9Lt2tR84MCSZ7N5ZGUsXFcExRUkx3j41dE6WDzZIq4yA2g0MtDNDSRapRMxB4r6+HmuxMw9nGVPSV0VLwMSHj8pCjG4i6UZCrnXWKkNnx+/Nd94MMcIznuIvv/xyYu+44w4p1UsViOLsVQo8sRQyhVVQAHQUzsUx02X3YPikZZ19VQWVFcBUMR9CfFIFFZRY3anHEa0gCqsd/XFUsxJi8HDN4Zgfmkisdlt7t5plULSfJHh8wgTIt4eZqvLKqEYXXHBBrppCgZNL9NXcgex0liXXmg4i1VcvEt9aWXNroRv9wUAoaE3aaiDKb92qf6abclTHT4+Kg6inp/PQodTlhL19fdj7J4X1gdw/Z2fnNKtWBrj2OrF/alNdSy00a2O8D9x8c+qZGiW3lMjTHO+blJvOCm8MQFAQrreQdk70T6OsofuKMdLb5TroaR1ba0JPc6uFsCFjogoNNgBV8EQ8wQhkVQZsA89THe96jqLTELvrfuDUu0hL/4nnrCAQHPiswByFBAKBQCAQCAQCawSBxgHFdGG58lM2XJXJ+cOS27lz0+HDaU8gmw9jQVeYgAxiHBK7EykXa4/pxgqUXkp2oa9EecRgUxzLpLG8pWdcohMS9/W1TUykfXeMPGSDHclexEiZzdisxCTLxTpniZLjjCo2vRiJs2SaSDM6Oq4IyuBvuKL0e/YM3nBDuhFXAvarQoltpJlltsoiDTUwTKL6+9uGh8fooBbiyc/mrFXYJGTFVIrOLFS7QcfH05Wq2biXmDWsFD9FZp3JZxaLpCQJmSfLrogGnjv5XVfxpE+W0FnttDdYGinVJZNDJWY8M4vItcYlZGGO+yQ9+bKoixKBiedQhtqaQAxMaAWWE/HMLQUWiakkma/qolCSqScB+cIiSbvy/Cs/88XPaFYp1UhGYvUBeuoPGTfFZTwHewaJlREOFPNVSi2IEktPMfHChCvOT+xCetkrhXT41pp61CJXJPdw1dHPTRXdR/9M+2ylV6k10j91D10R1NoX5t4Uo949+qfI3PMb/bPv8OHJ1f75wMY7lEjII0L/yT1fnzlX+qfeSG2jAFZHjh/R3PkvRua6eXQYU+olPoNsaIDxQY53U0UGl+JwYGM/jaF0LHlajBDP2UEgOPDZwTlKCQQCgUAgEAgE1gQCzLhsaeU3x4M9kAzBiy/evW/fUXSI9c/CY9Qikw12VGaoeRjTbDXmNetwx45BBhwrMPEZJ8M2NbG5Zdy6teOOOw76JKwgJIG92CA/acOwGBxJJGubKK7affv2kwYXkSzRRmRKz0/L+hSTRcnlEiXFcVeyrREqVqlI52DdfvtxRStFSnJwAGEFYXE5r4ow9+m5Y0e/nZ5opNLZ6IQrCL+jPCpIDeRNER4Vx6PVVHFQItNXb/cDs/hhQhpV1VFiokhAAwiU2NtXmlx83sWIbtbhRDx3bN3xqWs+JTsjWC1kob9FmCRkf6MiZPcTsNu2bON6xRuFPZmsyigSn1QQECSWQOuI51tWd8WJBCmd1YSeqsyfrBGllMUSaHri5PSHJ8Qkluuyyy679tZrBSBDK0KI9UgMDXWhg5Qg9b7y0itvuv0mYV9Vx5vmmkZ2FVcX+hAiQA2fFEcBkQnnlTW3FhqY9qeblFFTGGpWleL8O1f6p0Y0r6Q7aVM6azXdXi/K/VPbaSBfVU1Ay1588QaHYJ1svKfjxD0kQODu8b5pfv6u46D0AU2sZRvj3Xnp+401MZo4T7IIKPdc6Z8qon8aHTpAHj76sE6rmqbSvP3h8tOfI/De33jfc3rjXXsR6y+AechysaxF/NmwQEcgnrODQNyNdHZwjlICgUAgEAgEzhICrPOzVNKpFcPuzAlXA/nnmdLzpHLqqcyTm1PSs1BbWqp4DtUYxOVKM1J06637a4W72Bcrlom2tLh05ZXbrr/+MAOXNcyMFs9AbG2tcpwy4PAf8dnkxbWcnXvnnaNimOAS04ARadWorYnsSNUXz+KUAOWzZdHFPSJRMiuWmeYiHSRsHytWJq8YuYhleipi7979VGWqEpJpMNnOtVIXtniDiaW1iKSpl5+SZbtcEQ21O3E6ln2mN6x2Jj7bd2oq1SKB1TgUR0pFyMhrjR6IFIaYZ9u2ARJGhkecmtVSLlRa0gnYDcu47Y7Dh8dnxjq6EutwP3B7tQWe+GGaXmhsh6aS4mBy+SWXX/fF6+QiGYBZB2qDRWXxDfxQiZTHFT137rtTOLVFAzq1EynMXs84y0VIvuNUmJ4qlacYlCIeFWG4qwuxqkmUgDS33367TzbuyoIYw9n66jwXAATqyZJXtMqS41NXKZepqln5gUcmR8CoarSamJpyxpm+IdexI0clAxx9EvIbNpGwMp8mRDyyw6TSfJ9+YGnoee+HSjlyNZB/3lf6e0u4/xiqSpDXQlf7NrR0tIyNpluUt27YcOToIeR4285tvT3to5Pzw6MTUq61/rlr14BF9BSGsCZuUPp0tnNfX+mOO4a1hf6gz+Qxu3XrlltvHWpAVzOC1N3BZ67WuvLKLXePdzsXZlPzDaa5ALQ6j/e2ljQYdUAdprvDlWPDmgNRBIhyW6otA4NdM/MTOsya6p86Kj1z/zSxpX+CKPdPPVnYEEBH6eyTPqyOdsUD02O4icwoGRFGa6b98DTeSTaa0ni/4yZ4nvp4P7Rvf2Gh1lXuyGsiDK5i3Qg9+R9t8DaGxUmGxj2Gg5T5ua9xsZp+NXD/6e+Wdzb+u6rzfaJwRrUIP/AZhTOEBQKBQCAQCAQCaxsBpIXNR8dVg0MYPfOT3czUY9FKI+aqqzYziIVZSz552HyuMZKeT4/JyHyUC+NCihxtNTSUjoNiMjIi5WJxMsGRSczTT3akxCQ0PGwto6MEpENWZcfiME9pRkfTZcKJIzV8nixL/G1gwALmdAevcglnwkpss66lx35m3RivzNZNmzbYwUs3D8OUkZpJ76ZNvRMTCzfeeJuKU4mSRFGD3amaMhIuIFKNOjrSDUN+UpWQTPzss/X98OGj1MNCCaESykoTBrHsYrALFScWPZB9FU91zPIfe8ljb7jxBmLpLIFSVJxVrVyzAFTFSOlGrHiMVKQw9UjWLsqFs1xSqrh4Rai44shEaDOeJIiktq3IsPKTU10ahaqO7OftOM8hWBKIzF/VTmMJKyXjSTgdlLh5YPNNN92kOI0CHI1F27zrWDw8JYMJaSToIZiSvJ6MpyIAIsHBgwcVDSVQKGXFdahr7KGYQ51KzaljqNeRI2NwBvi50j81gg4AZC2SOZtabNjQd+ed6Whocys6p9FkwkKXs/53tX9+pfHepaEMKMK/fLw7O2pcn4GbRifNV3ApQqclU6HnRP/UJ3VI3dWoMUD0c5obiTqtKvtkcIkBrJqaVDpT451YJZK8VFharKXtCaaeCumPXDxnCYHgwGcJ6CgmEAgEAoFAIBBYEwhgOQ2XGquLPsLuSGLFojHsPDaZgOfqq3d8+tMHGNOMQvESMxaZ16xnicWw4VjVJGBE9tlaMIgzSOMTC5g13N2dTqViRHpkl8tPViZKuLCQlsgqhXxvZJVpriDWc5ZJK0Jwqic9afd1192WKR+Tkc2NHLpYl2sT66MtCURJiTDjlW7zyXkzI2Xuo6mKyomVntkpGxcGSqSbGDoQpXadnTbfJt2ooSLiGb6KYAbjk/SnFck+YXcoIis5/5SSNJioiPT0FCOZ7AKer7rsqz59w6d9ol6WTBlwyZWLw1IoyctEyAU7LsinTylO3b2J9ZBGYdnhSUP6kECmSNwDCMKqQ7Hzzz+fwiRTz09ffaLGky9/8vU3XS+9vPCUAD7Yixg8J0NHsgfOMPnSrV9i9+MDtEX7pRQvF/8YrTKexMJTYor5lGvnq7pQD0QZc8Jp6ElFF1rXxFg4QQmQevKIUAUtCAQ6q9050T/NmABWK9Oc/lpK57dBXtvpQuqldhpFXSqVsv6gXpJpNQHP1Vdvu4/xnuY1pPzy8d5iUimPdw2aJWtr/RNo0uvVAudE/6RnftSR2nSGkocrG2gn4mlyBG5narwrTlnwtx8Y/npaMTjZCePxLAQD77MAchQRCAQCgUAgEAisIQSSsZ9Ow0oWPzvMfxlkDGWkKBOVRz1q2+c+d5D9J97DzGXgYn0ScyqyfdEb1psAO3vHju6hoWnxGJHsxGJrEjhQCf/xU0osSFmIsTRjYxMSMzcZmgrFwZSrFJYoW1BilrTEArt3991ww6hys+lJJgW2b7dbb4l3Tl7pZaQnAlmtpgWNJMvI0PeJEA5jyx7xSYphBdk6R9iqVcdcD5NADcK96SYXVy4hfkpPZwGR5FspiSUCgaoCiiCkvang/GRaEetR6+wLxQ+zoYw23IXnxY/63E2fy/Ullm5ySewrpprxBBScxVx9ydU33HqDeik9Ew+SKU9bNJVkaRqtlnCmkuyEkEk+CcQCmc4gIlNKZclL7asvuvq6/ddR3kNhRai1KQzIq7UEkpGgjmrt4fICjokJy9RRKXMEaIC84knWcBmiBEXjjC5yZPemuY6Be+NjPMASU8nbkyXns3A10Np56Kzr2RsPiuzJhKfqnyv9E5LgXW1BixXEGA5aPw8fzaFvqObY2LheJOUJ433L5z53+HTG+9zqeNfxtL6tw2SaxNGL9E/CKXNO9E/DRBUME5oDx1+SPHZE+qk/qwgMjRQBEw3CjeH+YMc7IeT7W1cqpL94+p72WrZCIp6zhUDsBz5bSEc5gUAgEAgEAoHAWkCAKZee5AVtXHXJdk7h5uYKTsx+fexjd19zzZ2ojr1pDEEq4y1MQ3kY0yxFedm4qE7DF9p29OhcpoJMamRMZHYHYU04EoMyUa5SSbxDdngx8SJyJEbwCM82JWnZbqaALCgT3/IXvrAXD5HXmw6o7OWXX2x/K1NbESQQJcAzaavjwYNDklESJ8TWUDtyHIyFh0vjk4BS0Dn8ff/+ITYoydJLTAfSKCAvq5dBTDcx1PBVSqQIAt4OrlFcb3eb46VK1a4GtZtPyrQlV3axULTEe2YqxXjULuHZuB9YWX7CU4lEISQ+EYg5KA7+sFLiRedfdN3t1wmDyB3C2CaxRNGBbuqbdSNNPN3w2BPxJBPOxH7pS8l/izMDQcW3b98OgevuvE7tVDPjSfhTv+apBw4doIMiMBmi1AinlUCbyiteuQLw9EkCzaoiFBCvM5CsOvsPOggteaeL9Ro5HR1tAFxaTq5CEpTYtFKrlMt96YDwTlWYmJtora45V3CjgdK+ADSuUE63QBeaS+oym1ybswvLaTlDsSn1z2p764a+6prqn0hanvXQPzngR0bs0Z5SBWh76KyZEC17EMSrqbb1Tv3zsTuvuWbfav8Uea/xnqj13PxJxvvh44fcw10rzfnTkYfJSvNcqaWptb21o2paaq30TxMxmlXzeRr9M622WB3vUPKISWT+7vHub4LZEL3XAhPx/ubo+eAiIQ+WNN6LxTzhJcZzF56nNN7LClIo2JsKbqrzpL/DhBMSz9lBIPzAZwfnKCUQCAQCgUAgEFgTCKBbd5lcd6vjLFJWMoOMUXjVVduuu+4gy4w1xmpkOrP/mNcSsAhZz0xDMVgWE9Byy9HRKYsGOf3wN+Yg69kOUERLSqWQKSzAAYUAT05OEaJYQnyShXuTKNazoimm0JzLrZxHjowiXT4hcrITeNVVF4+Opkt3ED8aSulNAgJ7+PBQLpEhS1X2KIqtIByMZMWhlAiwLbKOy92//zixbFCMVErC1cVX+kuccZBXrfEfGQnxM7uJqKSOktHKJ7xCubIjHtKoXeaThICOnlddfNV1NyXXq6qRIJ4EksWwqtXdT5rIRcnzzjvvtjtvo5WMBOKogEUyZcyeXmmIpbNyoSc7IXDQIuIznhgvxcjPatOKemDJOGtTwnPeb/iab9h3YJ94dSckMyWeXiopDhoCEouRBrWWjGTKiFdrP7F0YXoKk+mTcrFl6qkRBbS+KhOujiRQg8LiJabYmnqgyvNnp7waCWdMNm7sNk1xsv5ZtYNbyrXTPwEOZJ0ZyLbKayAdSXOIN+FiQkQPRIAprHYaxaMbX3XVluuuOyzZqY33Dfce73qXkQsurUmyFteR8h+Ec6V/wkEXpXP+Q6F/QgNuhpU/L/AEGjwlU0GBMzLerS8gH2j2xgPQ5JhwuRK+ybP3VyE48NnDOkoKBAKBQCAQCAQedgRYeyy5xo5gbof0zyOShfe4x+3+7GfTCU+ezF4EMuVjzqJPmaaynrE4twfZbYjSsBFxnmz7bt++xXJaoqRkCrOtsR1Uoa3NKdDp3GNCFMdYJ9YnamRjnQLsTqLY697T0+nOHpFSEo5l8VYdPTqJg1Ep0y2loGfY8vj4NA8Yg5LZiobhWtRzyjKaSg12rZSUwc16e93zNIklSkw4Ob5mMpB5jkjq4ZMi5eWxZhBnakdzkjMhtBCUQEIyIczSVIR64ulPTsLz8se5azeh2dgVTKYAA9onOihRWTm9OlKPwgSqBa6O6tMNE5bYg89ISTHCYaJEEk7EU2LVkQYPl5iqpIkkmRD4wFNxGWEe9cdd8bhjk8foLIZiOIz6YlCESEyIBrLyU3bxF1100W233UZhrSyGPgriLu7rNQkyqg9oYFWmnq/UUwuSJVZfbzI1q84AzFzHarFKmTX16H75tDj1pTaEqe26mvvon2lHtCqvnf6pdTS6Xer6jxaEdu6faiFgxIvXYWCuxX3ViI973PbPfvagyno0U+6f9z3eB0i413jfpq3l1eU85OhyMFHKudI/qZrHe+a9xp0aqQh8EOA83k0JaWswmhLS1QX8fJDjHdoQU5Z9AUZTGgv+FActS0CcpSfAPktARzGBQCAQCAQCgcBaQCD5+7LJle53Sf/yc8UV533ucwdYzCwzpl42lJl6AsxZj0/CbEQJzj9/wOJQprZIj3WzLMg9e3ZaD8gtzCZWBEKIWcnC2rM3WEYB5qOvHvHkHDqUDp5hkStIwNcNG5JLM/O3XBb6ZAn0jTcOkcZkZOsTjqhkK9/mRmogkCSIzAqTwITNPk9vGRE250vv25dcr5ieCpKjOBa/AD4jL5VkpxuaqlDxLH7xbHo2sUhMXjVJk0YR1CDHUkm5pMQQqESIikhw+aWXX3PjNcBRTcqIz9VkQ2feKIZkKCkC6SUWRZQRjUQ4JeMBVoQ7S0lALQihvDA5sgCHBFVYlS+A3AJZLq2gypREgKWkJyXVAhqq8JQnPOX2w7dbIJqrIJ5AGaEBTBa/XDShPPW4EJn7wvqAvERpBbXGEPbt30c4B5oYKelMeUVAI2cnn3B5/SQZRIqQbLaW+P+aeqCnD5oS0grQcMa4VQBHj6ZlC7nhBO7un+muYy2i7nKtkf6pV2udmZl0q7N2B7iWgr/m0/QTE8lvn5tDAimvuMIe4CO5f0q52j8FGsP9HuPdwXLpYm3pPXeP9x3GOyiIVWIeF4YJZHQY4JwT/dOwWh3vef7IqNTihgDc9GpICoDII/4+x/slpz3eDQeDxVpo9wO7GTj9Kb77r7E2iuehRiA48EONcMgPBAKBQCAQOKsIsFfW1LNaeXanZ/Xn6Sq5mvEegZPLue97JmVn1Xmz53BhRrwwg+/6628Xw9JlFDJz0QA/L7xwMJ9pxJwVLwazYjKOjKygCvKKEY8bcJjY8bpv3xExDGI1JQE92LatF3dGySRDh7xZzI11gBVlkUCaAFuQBAf5jI4mao2PyS49UZRxfhOd2aOEs1CFUSksjKnNXse7ZKEPCTt2bCHfTzLpLBnTXHHVanFoKJ3pKq+yVJnOlnUrBSMlR0EeiTE6C41Z8Pl4LaXL0qg4/22NxS+7nxlAieVSNHbKRCZTvLyXXXrZl276EgUkVkc6A0SlLt5z8d69exVKbIYC8hCQhdNVPIVlkZ5k2W+99VZvOKgO5dX0ggsukIaQ3O7ilWgKALGUQCkNuGalgQNepBZ0gxKdlUiyRjk6cjQb9/JKJp5kQqQUr2jVRGuRGRa/NCKhRA0VVC43u09kSuYnmeruzQtJVN57KSVlCFQ7agBBHengkbjcVCb2pM/J+3NjiiSnJ9azmve+0p9uPByQEIIb3tQBa1Otgqbqyfrn8Brsnzt3dgMZi9N/tJS+BCLNatW5KQkjAvLayFfobds26NozMfqPJtNnpPfzvsd7upGLfGmk17h3j/dD+r8YA4cEvQJckNc3Mv5rv38aKR49HEqa1XDT6ADcsmmLgOEDMXVUHXW/z/F+8WVfuvn0xrsBRSbkrYWeX9YohSYbitNM4MkfIyBDeo/3yVM3lvbcI2X+uZq+MYzO/Dg6aaFfMTJrtZpsVcmHNBDrzh9SeEN4IBAIBAKBQCCwxhBo2D50ahgcySMkzARk/A2NuwKng/2HuuBmfb2dt9wy5KsYriNUUIDRZuUjG9dPdnBeLos2uNPnttvSKUrEys5exDwltm2WfcxoFsMgFoNBMaYd8IRlsZtZ50xzZqgEja3BidQxRufnU2LxyAl2Jy/JYoTxvT17+u+8czRTLHYqZxc2p5T5+WVcK5v1qobcqghVjx2jRfKDEZ6rTA0rrpm5clGbfAigfBbzjoykvX8q5au8SGNbZ8fIyLw04ulpGoFlnI3+vftvrnRW6rN1AifGJpoKzY//qsfbZ0va6PgI4QqlJ4bJgL5l7y0UIEFeouApGTTgSSuV5YNVXCbAN998s690UJaK23wLKHCxnmWXDBRi+GP9RFEynhltMnfv3o2OAo1wdcngI0UKlVGyjLMwJffs2nPnvjvpSSw8tZ1ySaaVhegkkyBMPVATYlIDzgJqkXTu7dUZ9KKlhbQxuLkp9ahqW8vOnTv7+gcP2zs7lLyClIFnYk21pbW27FMzLS6vlMrJ4T9TKx47fnxxYUUFU/9sa+HczP1zev6u/on2qeka6Z/I58GD07lHeesPWlmz6h52vwNcRXKLq514s0+aQ8cwlKTU7mm897WfMN7TTJlP6iiQxvtKGu/HDg8R5QpseNx228HmFhMHK9Nz0+XyVrvZl5rnR8ePDo0e0bUgs3b6p7GZ+6eerEbaOvXPalX/9MnEAVXFG2i6qNEn0llxkNTo3h4Ywk0aw0FG6dN4b5yS9fjHPn7foTTeoSTNqYz38dEREBlilaLzoB0KXaqn/cBoruh4zhIC4Qc+S0BHMYFAIBAIBAKBwBpEgM231Nj0yERmSTPLBBjEws7AYilKIJJVzVxm4QmIlEa8QKZJmze3HDs2qnaol/hsf3MWOhNaGuyLBelT5nKsbtQ0c072onhis0tWfCZySiGNcJcMsywZqaxPKpHGtr7iig233ZaWRmeKwqxnjHLfyiJBlpA1IdlJxKSxWZnybE2aMGGZuc7IpY/0voqntk/d3VyadR4hVVYLomioRJKRzIwDybmC0tNBGjQJdWQoS8yqnppJuBGe8RSAJ00kEy+NlITcA0/SGrXoI3zb4La8pJxWpgM8EntUAQGmRi5O6SJJJlaYFU7PjD8SKyXWqi7i1VGYP1ZGFREJz5xRzBXnXXHbHWm7r5py7cIzk3YxdM46KEJNMWHpiVJcxkF14Cme/hJIJl4kTcTgCZBXIoQzc4BGqklTmwRr6gGRmlKJ/pT05GbyE1DnVv+EtmaiNuWNKd1Gk6maRhfvqxbRgl8+3ttPZ7yXjh2byFjprh5t7dEBjFbQaWHonRP9Ez4mcYwp4OSurqPqxioiEm4eA9CfGvEqdZLxPvdAxnsekmm0mmMsNCsl/c37H7/smhocj0xlggM/Mts1ahUIBAKBQCAQCJwcgYahzyRmhEnAfvXO5n7mACxjd+UyDfl4GX+MM7agT6xnbAo78kk8w1pG9vSjH73ZUdLkiGQHi0eimIy2JtqsK41INiUh4kmwczb7T3LR/Ioyzs3dRUcVpET64I00dPATo1MpObH39u08wOlqTQJzWVTCaakjkPm2ZAxx761bB51xQxqxnqwwboDrOiNXQSKRNAnoJt6hXbygmcKJF1ARuRjESqQnmQJMfCSz0lxchUi59OSqJeSGG24glm65xAaevRlPUChUSpJPxDPj5g2fK8+/8tobr1VlPxVNjpSEAI1usrPFxau+WkuPpuZ1lbKI5++SWF57g9WLwtkjZxG7r1JSVeke6klm1/Gdx+5UKVplPKmavb6oskIVhx5AQ9H2LRNCDVAoXUqFiiRH4oxnpsHYLyhwrbzrmJ6UkRjO0CNkrT3oos0BtXqqnY5EeRUXOFf6J4RVQa/QFo0B2KFN4a/zaJFcI5MgYBepNSU+oX+6Fmt6tTMT4tOJ/VNz6xvyasHGeD+iX4lUlsT6W2ulYqIKmZRG+2ZNzon+qdYq6/EXA4Z0VgVjahVP1dSZ/R1QKX8H/NTPz8h4N6xAx/mblkWkXQmxHxgMZ+8JDnz2sI6SAoFAIBAIBAKBtYAA/oMDM+kow/BCncSwbhEe5Gfr1g2HDx9n0TL7GLhsX4YaK1BinIfxx7YWxhBI4DC+4450GBLzl+UtPdsxm4/oFpNRGsK9Sdi82TbLFWSJxSnALm/Ylx1sSka5vBkc5rUSe3oqLFEWqoKyHY+kIXLHj09jdyRgKZmYWcC8cWOnsiQgNhM/ykugmoTwRynCJ7nYnUqZmJhTFzGZClJDYl8JkRhppINP+I9zp+lJZtYZSiSrL1jmEjbLCuWvxitA197W7ggrXwkhU8YGnls5dU/Ek3xoEJjxpAaVgEb4ru27bj54M2pBbWyWBF8pIwEbHUS0gr94X6HhK84mRgAaakc9iOUbm0GdG0Vx/X39dKAJxcCbAXdbMscvPMmHAH0A1cBzI+5KDaJUTXWs5Ub7JdCsfuaOQWGiVESkZNCgqlwQUEei1MJbiTLKYnk2SHWhNXgmFgQ8mgLO2jSrrSK9vWkv98n6Z2mt9U/dAP6Q1w10FpiriBgNpHdpKYNdpLqIOWG8DzS65wMc78Ah3GyNMaIDaH1dFOs+V/onHDLv1e7+zujwefDmgZZ7ODwhCbrcN87IeNcEWsrjbH5Dw08LoW0zyf0w3mcBgeDAZwHkKCIQCAQCgUAgEFhDCLC3WMOMVxSxYXolbzAjDz/BoMbH0+plaXwWzxZkzjpbhwmIRKkGizCznfPO23rrrUeZ1D7hSHbK+cSg9DXb2chPtstRQXTC0kIkk1ilZPlolT22LMvMA5NCDfm9vT379w8rneFOGSa1LNSQiwRhb4oxuMXs3t126FDirtncJ01g48bkIz148FguiGTKsDgxPcY64se6zcyWWLYvM1QtJCPT26Ponh77hOfUmkAaSkNItpiRSVMG8lJYZR1VRfJNN9+kdOqJhCdXtjByqApiQEFnDzTEr+JJT5qIv+D8C27fezt+KwsiiscqDtFVBYmhKpBTKheeYuBAsUxEFQFPmlM4J0OAZVRTmuw/sF8RUgIKnqCgBoHoa1ZSMsVJs3tD2khMOFGEC+Rm9clMh7zgUk16NrZP37U32FdV9iYW5aaAonPri4Qq3JSuOOBXm9bc3UiUXF62HDptZAWFNoXnli39+LrK3qt/du/fnw4JU6M10j+1iypoyszi9AExYNdLtZcOoDojI3fdHa3L3T3eO0xxnGy8pyug7zXed957vBv4cEinJzc6TKZz2vpc6Z9pqDf+0BlTHlCY2jAuBNQr45kj4WmAgPGMjHf45D81zuangBJJjudsIhAc+GyiHWUFAoFAIBAIBAIPPwJsZUqwulDOhk830TM22cBAH/MOz2FGs5KFvZmzvnKcenIuDAHfY1g7C5mZiNLgPExt9iJKIA3OwML2SfbMf1yYQpqU4ll+GEWWbLUvFsduZgUqhWQSMC4uJfY3i5NWpPlkGae7akjA61BEREuJUp5//oYbbhhir6sOsd4k0J9IkYxXMumgOAqTpiwVIZB64kmQXuDExKx5EnxySLWyyBSjXmpNQmZ32dbPxSl3Y99GnJDflTQ1lVdiSrKb74GnyAaeCVC1JjbjqV4jo+kqJkI4msCCjAGfHCWSqcSMP90kUBwPsE+qRo5IiVUKLD7J5S1G1VRcWRJDPkujNrasLpi8r+oIXunFnH/e+dffcX2WQHmF+qR9pSFBTQn0SElhRZPsIVAtPCruE31IEK9E0KkUdqF7aD4l+ro2D/9RC0xE7VRE1bwNlPvon+kE5jXVP4EMee0LcL1Fx0iVafSuxphq1pQaVAvqJOIlNhXjsLl79E99ptE/0w7z3N/gQGxjvCdaqBFPHO9aXFkGGvmKJh90OrkOcE70T39ePMaaCsJNc6uF6qi7WkNJvdTFu9E90p+XNN77H+x4hyGZhhUODLFMgXPTKDqes4BAcOCzAHIUEQgEAoFAIBAIrBUEGkevJGXY+t5Y2EotrYu+8MLdR44cY5PhKpgqu5ZRKLBhQ/LdHTy4H2VmKTbM3GVs2ZU9rlyRbNeuHYxdLkOGb18f3jjqSGfJFhbSeVEKYdi5aYb9LZ5NmXkFftHd7QSmEWlqNcmXHTeDVNh5x+YeHj6eeSDJ1MiLfnmxWKu4B1aG6THWN20avP32IZEYIIEzM1NqtHHjINv1yJFDjHV+X19JRsba2pL7mmSWrs2ewq2taQm00rNuuJlakGD1KN8wPRE216zibyuLaS/rxkF7hltIGJuc6Orp7urumBgdsfTz0osvuXPvLbOTU9u2bJ8cn6ovW5lcu+C8CyzkXsVTcRlPyrOzLT+GP6jFAx90qiCSJlzKMASsCnKy7du3jxDJmOBqIZmMbPRMMulPMQl8Ik2J8FGQBGLIueSSS+AvnjQ/+bgEHNylqsKK81Y6CarJW3jHnXdADLHRLkokn9EvseIkVrQnF0GIAMkSt5TLiBPd2quJIUxNzriUuV6z3IB2pdpSOm5qfnZ+6NhxG0cH+vpnVmbgvIYeC1IdCt1cLKxYLTxdrBcG+wda29tcaTy/mE4vX9QDlpYqLaU+/bNcHBke2rBhQDuukf6phzcIcO4biQCbxTKajDjjyMg1m6Enawu9Wl9y1viFF246cmRktX/K8uXj/aAmu3u8rwwMplUVZmcUtHvnrmPHjugSus1Ab5slA7MzaeVFYzN7urBax0OH11T/pFLun1BK/XMq3ZasCjqzennUXbe/5ZZbDByISZ97vgRikNXUyRvOczU1pm7bexsh/i75Q0Sg54I9pzXeN8CWDvD3B7ilqWQ2sr5cKNrRb21O3V9mDdWYqYxTsh6yPxPp+IR4AoFAIBAIBAKBRwwCLJs1VRemZNZnNZB/nik9TyqHoy3dtHEfD3uXweqcKSmWF5arLSWroG++aW+ptWNlaW6hVi83l1h1xULt8su2f/GL+5h9nR1ttZUlNuOGwX5Up1io33rL7exspYyODPX2dPX19SCT+/Zae9zcVKwvLjtK2uFYbhlxYPLK0SOHFheWKuVmQhS7batzktO+u7nZaa6WsbERNt+mTezC7ukpK4Htv10oO6BoZUm5GzekJbtMbckZjjxbM9OTXZ3tGwZ709rO6XSdD1OSjdvX2zMwsIHxmNjy3ExTsbleW26p8GJVBwd7cPB9e/fRWRWU2N3daxvj0uL88ARH9HKlgh40+7lx42Z7ikeGnR01DQFTBkRRY/u2ATuLx0ZGmkv1to7q7Pz4/NL8ps0bKm21/bfeOr84zQe3NO90Gzf0du7as/OmW28ptZTnFpYWl/mcS6AuNpevuPTSL97yRU1WbiEhGdaDGzd7uxhl7/6DK/V01+745HRTqaJFCPzSTbfgLc3lluXaAjkoJu8ri/zIsaH5xWXsdHI6cd0LLrpE9Q8fPQ78to6uiSlktbmzsSNaJDYyP7dYqGuWJmvBuzq6lxbS+dLwbK00WNP0XE9X74aBjTjt9GRaJo3daXRlIczAR35mptJJWpVSWkPb290n8fFjxyfGJleWa539XUePHevs7Ort6jancvTw4QUHVnd0NJdLU3OzW7altdwL0/OTU7PpZLOV5sW5xVJT+b5650n7c+rND+U4QnqLPP2NC5u6qu3FTi7uytjU/MjoqL49MTbcVF9u9E9nxelrk0vLSyvNhTXVP6nnmu0D+/cad319A9PTDpNb2rZtS7VaOXLksO3BRoH+3BgvvRecv/OWmw8hdSvLiwv1lXIpLfU/2Xgv5PEO/1tuvUnnNEGkM+gYA70D9ZXa3r3H3ZnkVOOFeU7mudau1g1b+qfmRg8cPLiy5LrgrjXSP01V6NU6ud519PBRPd/YMbIy4xUeGx3Trv29A7r36PCYbm8+CCs2LWXKoKurG+MVyMe/3XHbnT4hyYvzS6mabdVdu3fffOutpZbKKY53vl98u625taO9Wi8szy1Ntze1Fpsq9bl6sdJcaCo2OXIh3RfcmKQsrqRBUW/8TFFf9qyOiy+LTflO/v+Dq+lXAznjfaW/h9iH9OeqDvf1x+HMlh4c+MziGdICgUAgEAgEAoE1jQDfXDEvgD5BTSYdA/fI0DAOyZvqO4t569aBm28+VKk4RMqhwa7zQbq6eFYxRo4ghiOmysHLtcK+ZFHZt8u4lBeHdM/wwIADn+0inpiasgJzicnI/9TS4nYWiZMlzWjGa5ieDZdLl4uUGguS0z5MtHBycprLFCtmp5JAPpnMUEtr8dg9ezbeeedx8czxoaGRzZs3WtUpvbwcp7Tlo7Lk9ujRw5s3b+3q6nR108TEmMR0bizmTDfHqKm6YALclbzWra3l7ALiJVMMbsm5RXPKoNmHD04wS0UursyZOKgVHROd+OnQ0LHmYlO5qXlqcrK1pWPbth3lUvX4yGhrpeXY0JGB/g0zszzDhY7Otq1btt9y+5dKRQ6e2spivVhfaWtp7epoW5hfOj50FEft7e6cGJ8qNtV37dhdKNYOHzhYairMTk3zELW3dQ5u6K+UWyenxqVxu3BXe8K/p7Oru6dzaX5hemYyuSqXF5rpUqh1tXf193bPzS4MHz9aWyn09fcgsdq0s91O7Hrj8qYpHnqzAEhvtb11z67dd+y9fWpimrShY8ObtmyE4eBgv9bcv39vDsNTu+sh6Natt94KHjarOQ6Ab9q8Gbaab2lxUUfClvWEtuZ2G4ZbqmlpwNjwaLne1NPaWVlJMy9p7cEas0Bd0lpbrlVamhKBLJSPHhuZW1ru7us1XdLe0d7d3pr758xcWiVbbdE/J9dS/+ycmJgZHR3Wq61iMJdhjHDUG3FDQ2k3u4fCFqprEfNfQ0OO+24/fvzoKYz3TmPk+PE83pfqy3m8dxu5jg2Ym50vtTa7vJvPec955y2WrCU+PjR2RO/192FlzfRPfx/M5/hbpC6QafTPmfb2TnMELS2OiLd32mkC6bI3vZp3V6/WZ8X7arWIvxhWkThADp7+Xplla+A53sBzCzyHR477M2IUn+J4P3r8qL856Y9Zcdmfg1LZHAteXCsa8PlBYDOHLdYanmGcuLkxs3nX9/jPg0fgbqwfvKSQEAgEAoFAIBAIBALnAgLZA5D9BMLOIsVYcMiBwY3Hh0ZaWtsqLdXNWwb27R+ZX1iqF5qWML6mUntHV2u1nTtycmpmbn6RW9MnKbu6e71nZuf9E1ni4uQE7OwWkFgkvkMmUb5K2VyqLC6tkLC8Um9qRh7LnV09Xd3dtXqR5FyiMCHiy5Xm2TkEb06MjN4kX37F5ttuHyZBAgpTYGx8cvOWzco6emxYGkXLQu2Nm7ZITwFCvJWlInTo6e33UwIaeqtIudKqdv5lHcSTLw01KL+ybDFwWmLKFEX7WcDIeaXEl7swOT21tAJCTtxapb2lq7ezWGkaGRuenpnaMNA/PHSsrZUdXdm6eRMf3fzsDM8b3xuXeA9fdlvr4vwcz/fC3Kz42vISr7V4a4ul5CQv1mtStldbOaLLzU2LHKwz04X6ioWuy0sL0pPRWilPjI+S01ZtWVla5MretGGQ83x2empk+HipqSh+YXZGYNPGwb6ernG+7KFjCupsr87OTCHejzr/sjtuu0Vx3Z3tFEaeJyfG0rTC7NSxoaPmNEoVPEc1FzdsGuzt75lbmHWr6djEaFdPp2bv7u3q7eturpTnFhdcnAWNhu+6yVIDzB9thhsXWa1YmLO1u7hSaS23ltLp3GvrKZUdTERJ/nIHHE/Pzc4turGmyZxCV9or2lsrFCenXZKcRsRa65+G0vjElE5LMZ3WSDE0WlrLBloeCHT2T/fu6u4yBEZGx6dOZ7xbR1AslAt126Tburv6Wirl6dkFfwX89cB+Nbe5IUTO0muzHboHKo4Ar53+2dPXrQ/PL85NTk/oxo2/Oql/tne2DY8O6eTLtSUJdHVLPDq62scn/UUZk15in/RYElrbWlYlSGm8i9H5SUssenrydMb7vM6f3J5cvgWzQg7qT7vkC6sc+Ox4QtfWCDzb2gQHPtuIR3mBQCAQCAQCgcDDi0CyvRrHFHujc97cI86KRoP7+vq9L7poYN8+m0UdgVNeYFkvO27HCU+DVtgePz7s0CUOYV+5eXt7+7yFJyasFVzhXLLu10U80iONIyOjLLtKxbnHK7Wak5a7hF2jwzeFXVSrDk8qbtq0mbPFbTtcMbbRco/QhPtx27ZN+MaxY849trq42nAvU6Nj+/bevXvn1YDO0nPvuLFYiWphCbQw9w7JauHrjh193vY5s9KloTkX9La0OrfJ3cVkWmOby0Vy5FIL8bTN8iXDddVoeHQUc060eXEFW+7pG7DSeKVQH5+csOLX5lfOmt7B/k47hGcmjo8OMZHR15np6Q19vU7dvWzPhfv37ZudnHAn77LEKytdbdXBgQFrtY8eOTI9Ptbd1cU37HztTQP9mzdtOnbo4MjwcE9Hu0OZW5qbLCiXnp95bOi4tdnVckm89HJZUiwlCWzmSlOxbNNyf5/l5SsL8xN2Ec/P2cXYWmrmKN6yaQPvK2N9cW7etlBzDxzClebS9u1b9x/dayP0cmroxb7uHudS2xsshq9eGJ/hOrOJmkt8546dOo4Dq7nF+P9xHvx29+6d9aY6LpGIQVMdW16pL/fbKtrWOjRy/Ojw0eX6cku1Ynujtm3v7Kx2toEre7k0wNp5zJAsLReGRoYnZ2Z5s/n/3SO0YfOmSmvVHErqJ2mUVMyxmBTiQlw7/VPPtwZAHzYSLY7gAe7u7jp61Hp+bV22EBrcXL7Wq7vfx6jU89Xk1Md7W2cXLl2uVLt7+1HH6dklI0V3aWmrmvLo7OluaU33A9scrttU21qwXzM1a6d/4uomfhLNTFujZywX18P1TzqL9zeQE5hTVj9vnIYw5yAAvd3SBktFpN+zZ5f0VnxYL2PsmDFUTStNHAJo7cNdktM681Md744SsHmhta292t5ZLDRzryPDRX+KjYoG+02jI9Nge4PTj+BrZ/7vRGB65jENiYFAIBAIBAKBwNpFIHkcPFbeJQrI7rK0z29eUl4k3o3HPHbnZz93sG5JbpFjxF60YrW9wy5Ti/UaDmAEsYjS8qey4ZyEwx1kCytCiuLY1yqXd/KerNSkFObxnZ6d27BpS9UOuuUaZ9qMbXMIJUrUXOroallYWh6bmOJTFaVEkts7u3hFpMySxWQHc1dP79HjCwcOHVYKP7HdhuRv3rp9YEPbvgPHeHgbu2Gnid+0ZWvfwIbh0ZWRsQka0kEp5KgLyUMjY7bjznOTITTYbXtnuaWVbhNTaS+uutBEWb39gwxTKS1XRs4BZlLA5kAMH+Gfta0W2wNec1ML3tnduVxYGR4fmZidaMZH4VdLeyyvvvzRn7/+s031WtqPW681NxW6rGy2v3ppkQd4aX5OGvF8v+K5Z+dn0p7olcUF8dLIRU7y+i7Z07sizNMrDafuYF8v79P4yDBfMTm8wb1dnVu3bHJS16GD+yvNTUqZm57iPebUtYidS/bw4YMM/Z6eLg5lS6kROZEOPMNsUd3GoWI1kc4V23dgb6ml1NbZNjkzmXY+b93UN9g3NGah9LFCc8G7o7tjbHJsy/Yt03PTFoiOjI/YDM1vjyRgQaYDLBe32laA92xieoLTbOOWTfxmC7XF0cXxNTVAkgvOKCgWOPrTSW7uB+b3bBxL3tXVjOebPbEuAIVCTdwh1dHZK8Ha6Z9zC4t2mxsL3sZFuaUwO79sW7h4PdxYbm3rMPugV4+MjYvW54VPfbzb8+uo7LZql53Dc3MrkxPTqK+N/+YyTHxYUd+4H3ihsdnBxvrizOz0pg1WHayV/jlqSDT6Z7NRUW5qaWvRe/XPo0NH9XAb2XVdMzV6uCEqsQQ6uRiJJahUK3OLc+NT47MLs1Z8+NTa3tre1S5MrAFiBTV2fTrjvQB/f+VMsTmFz98ra0vQYKfpIbypN8bz0CMQHPihxzhKCAQCgUAgEAgE1gwCmfquqpOM/8Z9MA7F4afdtav35puT649nyY4+jhPuvp6eXpuIh4dHOT2k8ZWviWMEGeBKckQQFwpfrvWQPKibNw/y3DouFWO0SJIP2XuTNckdzm2ed9gSMkmmN+cL37KzZubmWM+OJMo2SbqvyF1Khw65KChtDCbNVx5gLppSKd1V09XVg3Sho9Zh8p7s2lU+cMAxWmnlqtpJz4nnUiGE5dChI/hMf/+gr1TdsWNLb2/70aMjwvLyJCP/vtqijCXbMEka3Rp+4Lpzcdrbm8bHeYbH1DSfisxmpVLaZzs6jhg7Oljizm6rZbswPVbycmHZSl/msAeAPHI333EzmXyqfoI9nc3T0yPgnB54qhQ8Fe3tGB642V4r7BFpk6FITZOPn4UYKOApkjT+KI4sogjk6VKic6Rpq7Lq5eisRru0KFHivXv3WuWLfM8uzHNc8931DvSLGRod6ejuGp0Yb+JEbqmI2bFp1/6DB+RNMyONh/75cCxHBMGTzgpVI0dY+0QHC0fTovrlZetCdYy0adwW5elpEiSwnxna/Rv6W9uqx0aHhkdHnFmcJmDW0pMIsKWppQK2aKV3OhGtaJGCQ7BqcFa17JhDXez67ulpdSX1muqf9rjyzQLfncZHj05ar6ELGb/aS6fVBxYWlvUiMTqVfn6a432qv2+wMd6Xx8fyeEd8jfdFXdFfkFlTXitLzYWiDeEOlxvodRd029rpnyqe+6ex0+if0Lirf+qDebyIH+wbNMydlK676rr+yDTw3KLb8wz7qwI0ifV/DwnEGh8SEyJwWuPdnxF/wTTHCjqcNv86l01IROLA6Z/Yu8hwkDVYnPknYD3zmIbEQCAQCAQCgUBgLSOQVv7d7WqwF9bDQGQZ79jRdejQtPV+HqdDsQVZdbZCWk7swhg2X2ZE7EiRzpttEMtp9ID9TQKbWDyamgkYIRKIZH/39zt4Zp7JSIjErEnUiBUoMUNTMtmpIbES2ZT2jTJGyVEWbenGoJdgdHQa3yPHcUQoIgnnndd7/fWZnKeLeZnd2FpPT/fERDoNKGtFiOJkbGmx87lOsQYCRdlzcYi6k7eyGt7UozMu7exkpeQKZjrX1lbJti8hGbfEVtNBRM0UJoRTnSN0qbbSWq1u37WT7Yxwzs7N9W8YPD40hILa+by4sjw+MTHPr8t55PTqUqnaYStyVYz9tAvLS84tK/Att7aiqYi+TaouXCbHoVeYqmXY4kfHxobHRmuc0JWyReEk9PX3y3v4yJFiqZk06cUrV4kj42PHRocrrfzk7U6qZmoPbBistrcdOnLYJU+j42N+2tgs5rzzz7/u9uupBzHHcVk4PdjXb4G0o7hGjg/ZVNpSKjt8a+PAoNXUG3sGUdp0wFUttZTma08Nm66f0azzs7POjpqZmu5IC+nT4d56lMfX1sKa2w+skzuIlzduxZ1ORcvvnQxdUrPJ8VH7tymPo5zQPwtrs39y8rtGWqc1A6JPevJYM+hy/xSTCd7pjve2aml5cUmXOGG8NztBzUbg5aVFyw0sVajXVizRr5ZbNgykC8DWTv8s1tKyf123w/5+WzCWV2z4dYCBQ+nmpmf0Z0eai3dsngo6tU6kXt3ZxtfbwUOs8y/Mzun8+rxI/wSkIUFiknVpOJ/WeLecxICGEgbsbC1/TOytSH8Q7v7LLOZ/nnRbUjxnGIHA9AwDGuICgUAgEAgEAoG1jABr2JM15Mha3Zd5yUWbD+wbW5yfbXEzkuOI0xEvfQtzM25UmZoYs+S2HaGplFACp92ct7uPQexr3bHJzsRpKvhhEeTWzdV9dx4SJsQnRx87eEn8+Ojs2MiQQEdbq/ja8mK1pezr8PER0pxjXKgtixfZ39tBmtOMnc8kmSKIcpJTW2uRSsePHnaeE8WoMdDXY2nt3jtGFdfWWllenCeHkv7ZjkhndSGwuVgnraerY+f2rqFjk0cOHZCYJr4qos+hygtzE2MjSpeUkFzxTRs6ZqYmho4dkd3RzTYQOsCqs70CuFm2r2O0UaRyeWF2YaBvoNxc4fZUAzSJea1+zOWL91y8f/9+Z3whh44U4/Lt3+Cw5YVKtQ0/HJucwhVb2zvQFSuqt2/f7lQu9z9Jid77isxv2roNn799r2OuF0ot/OFO5nJed7uUTOeR8QlsxGlU2Nnw2Dhpwpy95daquYCJ6RkMv3dgENWZXVicmpneuGGz6184Ybt7erZu20HNY8cd3DXj3e9+4IkpFHrT5q137L3Tol95LfQ2sUHFtmpV1exGXphb5L9VO/uWC7Xioy+78uZbbxEW45QrnAGLcB514gyjk/NTc021pkpTeWl2sbOts2In+ZHjXMdMf1u45woLa22MOAeLShamIkIrtWWHirV3tBw/NqQPpr0C7jheWXTWd39PB6o81Lh6eu30T8PHvx3bBrkpD+5PPdxAM2YNJcPEW7/Ww3XmPNAELrmo7zTG+66+5YW6wWglvqkB6/mdweb2ss2buw4euHN2atKFZy3lSkdrtae9u1qurCxgd2uof+q3bv62X8G56PbSu9NrdmbG/JMJNoPXiuTB/g16+A3XfXFxwc75rckdW2/avWuXt/0Dbr3ucIz81KylEgN9g95uRbLgxFdv/R9vvXjPpacx3rftNCisx5gYGecDrhSSJzlNLjTuPyIu/UvOYc9JOXH6EM+DRCA48IMEMLIHAoFAIBAIBAJnEgH8Kq/cS8bV0hLXIjqU3FBFN9McQ185JBEbPEep2XWZv37ZO3l2k/XUeAv9zz9iuXPlJcpyV/axnbxOLT580OnEc/7xETdYbcFix8sv3XTk0BBbQczUxASFttpaOjg4PVkfOnbM3UHSzE5Py7Jrx3ZHJ930pYPC0td5UVdWuHp7uzvknZ6c7OnqWl5cnJuZwR77e3vx0FS9hQVp7G+VZfPGjU4kdj3P6PCw5ZQWF4qX12mrZB7cf8R5xgN9fcJK3DAwgC27A5VkcggRSez2rRtlmbGA1fJsLqqmJkVs37q1o63l6OEpaUSSnLPQwR2nVJIm3Q5cLArs2rGDkT81sSAs8V3pF+d3bu+RcXR4aHZm0llWtYWlVidaVzsZ/bMT05MjE6V6U7nQ7CLcaqn1sgsvPXj8sBOJ0oG69miW0vVLLZXqZRddceTwsbSKu+zWqBknbG3dun3zpq2WLR/Yf8g681JzJa1Cb+/avfs8h+XcfvudzinWXItptXiNBOta7Wi06pUEMe5J4kTaves8a9FHhtPqXOlzShcdkyPl8NDo8lKtsfC7KPvg4EYW/uHDRy1i95Ni9mc7MMmdQFTyT3bUd356blPvht1bd5ZWmqZHJ5uWCh2VtkrdTbLFXZt3DHb3H9p/oL6wIqa9XBXZaQtwtbtpqWlyZGpxalHitqaW2bHpPdt2VeqV5bmVZq5i3W3FQmPu/5J1s6f13NcYOy0h95PYKcoGwsT4wp7duzYODuCKCy4JW5zvbGtZXpgtF2vbNg1u6OuemZxJG7Cbm7CftdM/jayLL9wyNTFr7Oj/unfDCd9iRURPV9nIHRlKo9jQ06sF7Pg+fDAN9lMd71P14WOH3VzbUmmanZkoNS3v3rXZmLrpxtsX5mcdb+5ANX9H7Nrv7+4trZTGjo+ZFllD/XNZ/2zvbu1sWi5MDU8sTs93lNuqzal/bt+4dUv/ppmxqcmh8U19Gzor7VMjE63FymMuu3JufGb06HBLwVFpdbv/SRjs6u9p6xIvTbnWXCmUFqfm20qtl15wycFjpzHeXa52YO/h2ck5nLxcKC/VFkqO37fKw9+9psbf7dXu7o+4zSpmHe6j70qYPfMC/q/B/1nkmFUBEbgvBNbY7Wz3pWbEBwKBQCAQCAQC6wMBa+o8TJlrrrnGwtErr7xyz5492Tzq7e2FAVsIT2YtWe7Ix8juuTcwaUFdOgun4eb135TkrmRW3ub0zc3ufUnehpWVAk/j6PAYN549anOz45VSedNgH4p4/MjU0uxEmRvFFbi4juW5lYIYfsXWZixhivevq90K2NLywjRfaLmJO7e2ND/Pl9zR65SlFqskrYZdWZxeWJl37Km1xB3taKiDiheWZiclrReXW0q1FofqrLgEaW5pfsZHx+3w25Dc1lpemp9aWlhsLiwqsbY001Z2RleLMO/T3NS0EtVubnp0sIfHubDsxtKF+drCdLG21IQ9Fhb7OtuaeLAm3dgyVV+ct064yRlW9ZV+Bzsv8eg6a2qOF3upjo1Xuzb1tZXd+jMyZ5VjkzuWGPaczNXNAxsdfjU2fATPH2M3b901szBx9OChi3btue3ADUtzC/XFdLByqbnc39nfVq7Ojs8MHxnmTS2X6ckrXtnQv4kX0QnVc5OzODGTNiFRVZsWMdYhN9eaFmaWLCruautpb2kThmfdInHboVdqrc0tPQM9NFlZcLvv9PK8w3MWSkVO5dZOJxUVSnNz8zyuviL4FZfglFrlXXDs1+yyfa7mJRZd0dzS2d6C3PJ1LRQcEFYoLc2tiOFDrtJ50klbM8111wvXZyfmBjr7WoutS9ML81PzCIBO5JQvh3v3dfY0LzfPT8wiuoWFmjPDXJLVXLcNuG9lfmVubrY4V8MZHOPl3KDODVt6qz3jluZOLxQXahiyI3AlKxVa0gbIk3Xde3fmsxPD6V1uLnS0t4zNzk3Nzs0v66GLtcU5Pn2X5rSlrrA4P7NsM3VtecXo8147/XPT4Map0RlX/xSX51Amo7XS2uQeXCNr+MjQyuKsEWe0Gtd0bq00Y/XjQ0eqbdVTHO9m3FpKdWPc7tWeNtMbLcvz7o5KUwPNNgNPTrRs7ml3KRpa3NQ8PO4IvNmCxRZrp3/Wmvv7enU8p8nV/clZ1AsdMdDWPbgNGZ4anpqZmNLbjYiVxZWuFnebD4weGfZHQ8/X1ibIyiV/Xtx8XRW/NOOvhaPpTAg1D3QOGDtz43NwdgfaKY73qanZkiPwi6WW5opCgYbpmsgzVXm3+zf1+sbf7TRO8hA4yR/6dKS/WTaj0B9H836Otq6mjI2tJTlXvO8LgeDA94VMxAcCgUAgEAgEAg8DAhbH2qT6mte85glPeML555//gQ98AA2+7LLL0F3rYFk8mAPvjodHl+lz2kTCaUnpzKRmBxq7TKe54tQcy4ET86wU02lAK8WV9kpLd5vVxYsTw0eZvo54Kjc5EKfc1oI416YXZhamx7tdlzI7g8s5XMkVtbPz027UrZacTNs0t4ADF3o7WtHIkbHR+alpfrPiivNjKx02C5YKXGyLszPF5YVW9Km2WHWDbku5ZgHl/ILbhyrJDkyrtHs6OvBwfHthZtYVQU0tzTTFw7uqeFqN5MXZufbWFjoguH193Y6oXeLaSpKXW5tZrqSsUIM+FjDXFxckZXi6aJenu6OleXR8cml2jm6lctG50SjDht7OqYlx5JyxrL7NxTRNQEn12r/vNjMRhdqCZdlT6R6g5UddetEtd940OzJVLjM5y4sLi0h/T+eAa1QnhqcKc/VKC/dvunIWl++p9mLy48eGSivKL5kjKDteuNzOO7o4tzg/sdDV4a7duXJzubejTx1huzi9VK5XLLx0LjU7e6BrsIRgjI4sTi1Vii3YMpu70/XFpdZlO4mnl4qLTdWmNlza0VMkFJaL8BTZUmhlPbvDuK+nr9pYKb04s1gtVlElNUpbvtu7dXSHPC/PLneU08Fd7n51FbCTahemF2rTK+XltNfXUdcOwh1sHzBBw0tcXCC3pZTAKzeVm2UcnRpdmllpKbRorGXbquutg50b52wkH51ZmrOGtlLioywUy/Uyu98szZp6LLt3rXQTUo/pLzkJe0k39LtpZVYfq1oFX7NIfLGwtFIuutC1Vqwv93Z0r53+6fqu4spyxfXAdaTdBV5Oa24yuKZGj+sPrWnsLJbqBY5HvWhudrKwNFfRA055vDsFyo5iO6bdwKSBXSiOBrdqxiKplZ62jnrfxrmF8Yljo/Njsz3dXS7Cbqs6U3lN9E/HQXeVOkcnR1dml6tFkwBO80r9c2OX/jk3NzJbXCpUy61Icrm5pbvS7dPY0Kit4S1827V0B7KB01IxpVAaHR2R3aHvfLnt7W29nWlecmpkujhfgPkpj/f53mqv0cThjLrWHBrf7Br2FX/k8dfGuDhhfOQtwpkQ32vMyOL/BUTn/yPA3oWRYQP2Xmkj4ssQCA78ZXDEj0AgEAgEAoFA4OFFgK2J6DqT6Tu/8ztN7W/duvUf//Efn/jEJ9LKAbwf/ehHb7vttg9/+MNcwR7GWSJm93oafmDbUAtcd1wL/KENB0NKx2ay9bGpqeLenqJf1RZnJx05drypuZKu6KinQ5U5MBVNPuHcIB4Z+RbYVewt6lmhjZCziUVi4+J5itK21caDI0mmCoRY180g43IhQaTvAlJ6/FSQAFEkKE6YQA96r1BQCKcdswsLUgoLqDJpJNtVK1naH9vURB9OcpESeBRBoLLEUM9eXAkkUyIh4i0ml0a8BCSrNZ1JJsQdp9mXQo7E5OdyOYgSLIV0eNjY+Ci69PGP9Rw6emetPlepIqRWKy85c6qzu8dZVyNjY5XW8iKHti21vGYtLVRVBOiULlKJ3krMFYckLkpJGtJBGrXOMCpUQAw85cpHiGUNJZZdRVJXaCwNUBC1pRSfgGhAIYHS0WBQkKNFJJYSIBKQr44SrLYUxdxpvLG330lCEkhMgvSKA5dGoRtVJct4KgtFWCoU4clvblMoG3xhblaWrv7ekYnxmVmu4aaOakeLDcK1YmdrR393z/LMYvNJHVsKO+tPvobKVurlQsv49MLY9Hw6IxovsQx1eSHtaS+X5k2lLNrq3VwrpjOQzHt0d3UAYY30T02jOfQQY1ZAD6GbsK612ofF62bSGK3CGlf4VMa7w58IaYz35jTeS5VJM1OL6bLcZpNktcV3/+3M5i3OaRuaX7C/fb5/4+DE3ILttmukf+Yxrn9qOPWFlTGlfxpr/qj6KezPlF6tdgas/faQMVi8U1uXyyKNF2k0t2Er3lgmynAAstO/Sq3ueEsu2VMa79NzbeV260d6W7s+tWFT2Z+VNBbSn9kFu/7TFEsKu5vOyQL+eRw1n/5zr4cOlMl/fpP7enSUhnS4V8KIuCcCRf8fcs+4+B0IBAKBQCAQCKxtBPC3Umf3SY8LYRCsKd1ZRSfV5770lN4S6He9610333wzA4tN8+Y3v5n55chlthp7izRvtmwmM+LvLf9+ODD5PEWlUqv1qstL9VKVt7MwObPc1V6amFxURFdXK847NZX4cHt7xVFBDQqM13CQWcDnSTUSaRG1B6vN4dUTttQ4V7qxKXhFiXwVkknf2ImcKBlRLMvWVpfQ1CwxJAdfE4maacCFBaZqcm7IolwB7NVb9qyMgMRiaNOIT+F0NlfKQnHXbaYvRCGJ8/MphmRf+dHp3+oC1bnEQqXJyXJdqG0FrFrzrPD5edx74tPMzGJ3V2V2tsC2HJ8EC/pX+OW3/vpP/uSPL61M192IXE83CVkyqWM6YJh93N7aNj7vKpp62iVbaJpaSvZ3W+MA2MaKR2tp0yFMqEj6Wa/xprpXSXpri8XwEalg+tlw8kgpnHETA04euYQB9t2I94nJ3tLcMrc0x2lMgoWfPvG9OqhrYXHBSmxqNA7tyqrehVuWn8tqAJKO+PbTZwQnaWLZdaoa2FIWQjhJJcAEpFRTElqbq+MraULE3UoVCdNJQY3O0VRMq6hxM9dW6XX1Zsu2HTxlN3XjtDLy1sSTTiBaXrIZ2wrxxZWmxdQI6Yxo/WFl0UFrqcfPzy0tc2K3lPzx0Vc7WhNvWTv9M81EVNKyWWNHQ+jtOodZF+e3q4V4PwX0dgE7ddvbmk5rvBtbRq21I3a9g0PFU+czNZb+Vkz+63v/+bnP+xYEvLeza8VajtpCvalVD1gj/bPaXJ1eTJdd6cW6ax4yep6Y3FGt76bqTDoCsN0iheHJYdyyUk5jRw+Xq7Vk/qtm/3Tu9iTk7IaDkw+wX1sbTmu8Oz9+YXrW/pJ2f7a0zlK6PywdvGa8JQ6cxoU1HQ0ObNxZn5Bi7v0g9ua8aGj4ezszYuPGjXmS696JxajySeNzjU766exHFv1/0/SsFRkPUdHJfnD4wkMkPcQGAoFAIBAIBAKBwANAgKPPdP4P/dAPyeuqWBfMZveFn6gvO4n3JvsMsd+TEmAp8zQACyKxFnaP33e7EdCTBgdualixXMI1K/uqLWltak+nhXmFus+1tBfXMmAP0wvfSk8ZHeIJZAWnI1qSyMZqO4axp9IIi5ydXWhvS15WYSenYmsCS0s1S44bnC9LQ4pYomkSo701MS1XNNl12ez44Eahlhqmxacs0PpKu2XNDbMtlZiKsrURpSqkTbWNMD5WssVxeaUlna7qQSlzwmSmS9TWYoH2XTFWbqqeGqAGuV6kKafamqTx87V3VbxlbEuHQKcYpLCvC+w1q4FXFovV5mJtsdBSLfCUUqWjXF0puOWIdxdM6ZgxgHW0ttnG29fSRULCs77cXWq3+jmTW/RKvFciuukmniJ6yZjmbbV1e3ElLXe3oZdFK4FGx2klXy4uW6buNC0psWWWuncSgzOXirVSQot8R15nepz2sXIUF1LDLBSbXEdEsgJJc6GLCQ0wobnpHK8aompaJFXYTkmrBljUEisiaZXaWo9I1aCqIppLbZmokwwcREcTtjfZ22xzY8Gads7elkryRM1au55IuM7XZHOzA7HayjpCwUlhVr9LsGaeWsHtto3TgNP+AFVOhxA1Bk1rUymR3WVdzK7stHhYDysVKo0bXNdO/zQM89gp6ewaTX+3lr3u1DptbiC4Qys1nlFfb6q3Nlj9aY13GDRZAtyYljLiG3fiJmptYqCnw4nprdz7iwszpUKzPuAIczsL7BzQvmukf7YZ8Q6x8hej1pjdK6X+uWAHRMWN2bN82a1Wh1QrdlsUSvVNXRuX7QExX+PPXmPhhnAaAsWaTQQCeSBkaQBvr7RJf5rjvTTfUoRbfaHR8yBrFNVNCPp70gAu/fW2Mj/99U5/vNJfo5M8XNxikV5ObHn9X4Of+PBJkkbUlyMQGH05HvErEAgEAoFAIBB4WBFg0zCwbrzxxh07diDA1h8iZFboYcLZBZGNHoQEO7qvfV/34wfmkHV2iiqiWDjSXaQTxVpabkWvmgt8hr46ytg7bVRLL2Z1om3JidTg0mzs9KnhGGwclYQ6JyInjTNjTbKjD4gTDkGigFuHkFZHUCchDctSgP8EP7SbTZjxZwsxr0fO6PjgRrJaxV7kxEeS5CQw0WCc73/kuJXJymJ6oV9MxpwsURhqpfTuL6EYze4yIakHOgg4H0hiD9Ypmw29JPuayq0lPyf6oCH4eJLzCDutzdvfiBu3lrnXUq1+4Ptf4pokW4ZRSnIQRKVVkhxcGaD1amPNpDkLX+3Q9U7e2qRJdkHzeKdF0bU8i1BIPnaKlAuVhPhyYiyJp6+oWjK5yzWzCU7zSmXBmGWekE6rvhuMmj+q4QGXJcms4b6JwRLpZ9GBzNZyJ296qb6S1HWKVaNshNb5TlieNEkyOP0H22luqqQGTFqkpiQwyfGLijh3Zoj1WqvtvakKRQSDHv6RRrhN0bqPg3NVS0fhgyxrYVAKmZWolt0oc1/LO5MaZ/fhcUqzENzdaTJDM6bZk6RouhI2Oel4tlP10qaCxAPN16RWLuhFa6V/JvVTExlrqfPnVquk/RA6XLrxOJ1qphV8bjz2h/NznuJ4T2zaoXBJrBkBx7RZHtCY80kIFVpbik9/ylOr/rA49m7FcQOL+CSff7GxgmMt9E+atNqz3lgHoUPqXJzZjS6GLBarTZWlRcQ47aH1p9Fim3KbMZc6tqfiCL2MZ90fzBaLA9LoaYwF7+bGRKCB5lS8Ux/vOo0/qCBSur9HTo43uZiGWy39VV/1AxvsIOQNTo7hNMOR5hTu8fjzYiG3gZlXa3vnJe7+tt8jZfy8BwKxFvoegMTPQCAQCAQCgXMAgbSW6RG6FtpuLt5dHAx9YWMl1lRy6m/avSYmB8z65x1f5v7va+tX4ouNh8nWeO62n+pWxi432G8p+VMbpiBvZ6v7evk3k52XzObk+bIyr5b2uC5bD7qSjDa38jCmk1GNIzhvytnLSFojvb2vwo3VyCmv9N6YRaLBd6ep8bas8BOxxTE7a7HxR0wikaUsM5eb+Yi7fByiJEYuW+RKnLCFZofb0lAu4Sw/h5WSY6QkmZ6rmiQGndYa03tFWav6Z8lZh5yXblJ6N6x2R9SksnIY4KYenLflaBwbdbu6qtZUQ7XSghwtJUe2C2SXXKhbSWu+664yqqWlx55cs0YDwIP31bLwtMbZPTzmFdi1wE7cpXGxcMFpUZkjY8vJT9uEeJHQSJPePFB0UnLCPKWBNHRJgXUDlRSfHOyKQP1h14hH8FyK41zvpEhj4aWy0VjdK63qbsgX1t8SafZPTJr8SKSdeU3PBvlt5G50LFXIXbTR+kQWFhNJTtMbyT3fSJOIM4ElraYmyRXvy+K8U6aKZV5USZOya+RxgrgdzahJWumL4y3X8hQCHVVMG8CUqvqH35I1ude6xdlea6R/Lswap6m5lg1g+t01OkwE6eF5hBoX4hvjvcloNcmld+hNpzjezXOkv0X84CaP6k1Li8vFtPw2Nd/MjC39LYtLy3ql/u+4LA5JfXQFiGujf1Iyb5FodOa7ul1infW6oe0oOwncZObGbgF/ebz98dGf9WBL+/ULYU8O5LFgBY3Bkv9+4q1lq0qkOeXxbmCiu22tbt5yjILLw+ZK7a3zszOtHZ3+DuUBlEpszCul3/c7WFSElvmd/1gJJ43v9aTKnOy5r/QnS/uQx52dtdDBgR/yhowCAoFAIBAIBM44AvfDgc94WWdKYDYy7ssEOVOlnJacE62khmWULDi82/tErni3ZZftu7XyPkU9GYcn1b/hxkp1mZ52ELd7dMcceJtTzsxMtbd3rsYwjnngzRpou2woM38bHLvg7Bwu+rm5mTwT4WpfgXT9b6W0OL/kWCz3GLV3djBhZ2Zn2tvaGbJHDx/ZtGXz1MSkc6cxajHHjhzduHnTXWZuNnbP+Pu0ukVOfHJT+T4F5b50V6bVjnW3kJN8vfvTfUq8jw8P1Ti6a/Nhg/83SMgJCuonq0+a2sjtc9J+pUfl+FPsn6vp71/aQ/f1pHpijPq73p7ngO725RZmLUupFy24xd9cKy2QB0IigWWHRafD8AwW8yO20aYVK/XC0WNHN23cxDlsFspEiNPRnZ+c1pUUC9OTUx1dne4hS+Mr8+lVmL9i4ITm+Yppz16C3DNO5b2qUx4bqz8ba+xXfwk8RBV9qMbRiaqfcvieyrha7aHfDxwc+JTbJxIGAoFAIBAIrBkEggM/dE1xT3PkoSvpwUk+RT1zsnsXZZE5e53DyqljfOyOvWXWSya9JZENupt87FabZ18P74r0UjLukwOnrc2b7S57X1+frxz4g4ODMsrisNlNmzb953/+5+233+4nqvzDP/zDeaueIixr58A/cODA7t27UWhiHeu6+vXeqkbMiQicYrufmOVhCZ+jeh4/fnzDhg1OJTAKDAq9XSd3WLrFKfq2B7M1dvR/RFfXha3+bFxIJkatPW9961utyHW23xVXXPEDP/AD0hssmTl/6EMfMl4cdM+rbGigzYbAw9JA67xQzQQBDboWcLinMsGB10KrhA6BQCAQCAQCaxCB4MAPXaPc0xx56Ep6cJJPUc+c7N5FMf7QV6s5Hb6drwXq7++XLBv9THZGfLbakYGcnU2PuyK3LH6klxGPJPgksUcyHAAT9pVMdj86IR55+Nd//dfv+77ve9Ob3oROP7bx/NIv/dKTn/xkoi655BJvibOoe+sZMfdA4BTb/R65zv7Pc1dP/RxNzVMzerWKJI/u3Y8ebtTo/+aPfPLOPMqcDk5rWsdoMnBk/4M/+IOv/uqvxp///d//3aD4/u///re97W1PetKT9u/fr/NfeumleY5JdoOusXz67jLivw89Amuqf95TmbPCgRsL+R96oKOEQCAQCAQCgUAgEAgE1g4CR48eRT6Z+9mRywjLlNhFI0x5Vn62/m+55Zb//u//ZqOjrpxXhw4dwlclFpPJwMc+9jFpHOX9bd/2bVguW//xj388Qx+nJZ9zDBOQ8Y477uBe+97v/d6/+7u/Q4lf+cpXcpEJM/3RiSDAa6djhCZ6IwbLl2t5gjmaF7/4xTp8Pp3eqMF1n/GMZ1jpwKP7hS984bnPfa4by62A8PO1r30tfuswPxj6JMuVV175Z3/2Z7/wC78g5i/+4i8Mq+c///nvfve7b7rpposuuihtMG4cdmCYBOyBwFlGIO1qiCcQCAQCgUAgEAgEAoF1hQCCes0113BYsb8RWs4ohj7/bXZqsf79BMjmzZvx2+/6ru9673vf+/d///fM/cOHD/9N43n729/uNs7/+I//eMlLXvKHf/iHmDCW+/rXv/5f/uVfMGHZsegbbrhBgMvLm+QsH0kgh0sZo+Altu50XSEflV3LCFiczD2LvlrCYE7n2muvdVH5O97xjt/8zd/86Ec/quOjx25u+9znPmcy6A1veMNf//VfI8nvf//7DRPd2xSS1RAW/8vyohe9SLfftWsXxmuiR1c30IT1fHfY5skmUFgcQeZaxiR0e0QiEBz4EdmsUalAIBAIBAKBQCAQuD8EfuZnfgZBtUqTJ/YTn/jEr/7qryK0WPFLX/rSP/qjP/rnf/5n1JRDmHXO32tpKHL7wQ9+kE1vjfRf/dVf+cqg/8hHPnLhhRfiDI95zGMQWmHxjH4UmqFvUeh73vOeZz/72TzMIm+99dbf+q3f4vj6uq/7ui9+8YtvfvObn/WsZ1kpio0jHvena3wLBM4WAoYD9yyCiuK++tWv1nW3bNmi/z/ucY+zItr76U9/ut6uG+u3me4aETqwlQ7uM+/t7cVpuXyltGjife97H0fxO9/5zp/7uZ+z/tnyByNOgic84QkqZKqIBG+U+2zVL8oJBO5CINZCR1cIBAKBQCAQCAQCgXWHAHfuC1/4wssvv1zNOWYt2vzzP/9zHl1kldXO5cWa94mtz2THCjBkMez+O++887LLLuMK40O2LvS3f/u3Wf/oLifw7/7u74rhEM77Ibm8EGPeYyzCxsg3vvGNzH30mH+Yh41nDHPIpcRmyHXX/9Zqhc376O2WOp9//vnoLn5r4sbafh37aU97mtX7+KqpIsv7P/OZz5j92bp1K6JrocS2bdve9a53vepVr8q0Vv04kE336NsijQ4yJZbMvJJIHFsaI8L4OnG/8VoFJvR6pCEQHPiR1qJRn0AgEAgEAoFAIBD4igg4qPnjH/+4FZ4///M/b4WnlZ/scmY6Ty+iy6OFoGZqykbn0frLv/xLbltfOXhf8IIX8Btz+TLu7W+0JXjPnj0+veUtb2HZv/zlL1c677EtxDb9Yrw8xlZcWwuNKuPDaDCaYWko098n8jHtr6hwJAgEzgICuuLIyIi1yrro5z//eYsXdu7caUeAE86tdt6+fbsh8z3f8z2WPFBGl/7BH/xBXwV0+J/4iZ/Qn3HgV7ziFbnP50GUebU0spge8katDS4DQXGrA+0s1C6KCARWEYi/uatQRCAQCAQCgUDgkYAAKrKmqpEtPyqtBrJ6oecDa6ZVGFcDDwxP7NRjTbKG8P7pn/5pDltMmHsWuWXuZwKMwbLgbfrl6bIilAV/3nnnsfIt5qSAT5y9GHI+1MdXfi1kwMZID8VyvMisJN6ruHwCVu4A5PuUwznNWX7fA8bV0s+CSicWbc0tlzg6lM8KNi+AIMEQwpJpF0hiZeL9FMi4iRfWHFyLZh80kFxaAcWSQEqPr9pUdnRLpYTzp7wHlXwJcjORJiyZUgTkFThdHOTKGK4G8s/TlbPaEA9RYFW91UAuCHdFgDXE1VdfrVGe+cxn4sAaIpNeMz6enNIaCpVCgP0UsHxaYLU/C4g0y+OdodY6wjlvBlyW1TQ5/t7ve6i3mmBV1GrMwxs45/S8h8JrAc9VHc7OfU1xP/DDO2Si9EAgEAgEAoEHgsD93I20+v+jD0TuQ5DnHqbGagmh5yoUpxU4U3j+xm/8BnOfV9bJPTYG33jjjUgv+95KZua+Bc+M++ynxZqwIzw2u7A0HN6FKVFbgH0vgcjMmQXwBwwqJ7531aLdMybakbMdv3I0N4oF3uw5XEUMUKgsFuqBNqhlEZYAcZVYZKZSOUtuC156ufIKc/HCcuVkPmkpTEzKTKqzNKIIxL1z0+QOJoGAIk63vXL21VqsBk5XzmrGhyhwX3qaSshzNDABl3ZZpbUPkSb3L/a+9DxX8Aw97799T/q1WK8tT88212sn/frgI5P90NEVHPjBIxkSAoFAIBAIBM42AsGBzzji683WRFOzewoFUnc/2frYUfZfieFRRGs5dRGq7BXMEEnPrkWr/MSUsKxMd4XRBrQ5S8v86t7NFDZxxsSycFh5/AQdnHt6emCOi0LbPIKfjhcGPlRBqrEEoK2BgK8VhPO8A3KbG1FisNuDbfcpmVJKI1fmwAgtIZpYZObPuSmFRXorxZPVk11AZP556u9VCffIcq60uyaA22rF4UnzjM89anR2fp7reJ4r7b6m9AwOfHYGV5QSCAQCgUAgcO4hEBz4jLfZerM1USOGPpbFMWjxM8bF64WSZV6UVy8jV2KkzAwZ5siAn6hUplWYEtxkkUDKzJ8luy8wfVpTtub9qPpQ65khctJSf38/3gtqbw1huTiEKQZMwGacc2OJhD/FMkMTzrMY4vMjhlgJMtcVzjMRWtYjlxhOzkyPZZFegqxJlryaRYNK4KfnLumn9p/7Sv9Q43lq2v1PqvvSUwqqAjzVvAEmiDwPl/50+B+lTwg9XPqcoMKXBUPPL4Pjwf04Oxw47kZ6cK0UuQOBQCAQCAQCgUDgHEQAxcKL8q5dBPj48eP5ghbxCDBXIRbEzmb9i8dvs43rnRMIeHCt/NPbk2HILsRzEJKzqjJsPc5YQn0V7J1dwVkJYGoCYZEaAtQCfgLcT+t1fZU9h71hjrYJZGLsEG8puZp5NXO8Juauz4yaNOxaFukJkYBA6T1+kpAL0vQSZH3WzxsCHvibMgAIrODjWT8IRE3XCQLBgddJQ0c1A4FAIBAIBAKBQOB/ELDaNnt9XZKELFk9Ozg4iBVn2sPodzoun2F2D8qWfGFNTVhBdkKKyYwLN8OmxDeYwl0eYyzif0qK0MkQMLOAX/ED45++47SZfGaCisTaLYyyOjYpw262IjcNqD3mKTSEvNdff72mFJPdvLl1+PY1paXReK/s5Gsj8lFfrFgakrWRT2QK5B2wivaJKIGsci7xZOo/YuPglrFSQ4jlmYJHbG2jYusYgeDA67jxo+qBQCAQCAQCgcA6RgDV4X50LBavF1vfQtxMw0CCHvMcCkiTA8LIUn5QIylzYltSPSRIKd5bGtKkj+d+EMBpcdc3v/nN//AP/yAZTouymowQhqel0RleeGLL2Q/p5ioBHNUblZUSd3Wmt0bEZs1luJ/5R37kR1xCS+wv/dIvYdFaBKmTQANx9UufXcGya+7MnNFvAs1lrJaYW5l8CbzX1QN8baHimf0aCJ48s7CucIjKPuIROO29/o94RKKCgUAgEAgEAoFAIPCIR4Ctjx0x910AY0sqDyGui/ygQ5l3+YmkIVfSZDTEC2SWK4AY5BgkSnbJMr/yCePKWeJ9XwhAzInQ+GrmtDy673//+20G5pB3ZxUktc5nP/tZZBX1fdSjHrV//35ZHv/4x2usT37ykxzFj3vc4y644AKRUhJiLuP7v//7OX6x2de+9rXuc3boNGf+Jz7xCdmPHDly7bXXPulJT3Kjj0iXXaHH3/qt36obKIXwJz7xiRYCUIDCPukJ2ScscF9VeETG51kGCHjyFICpgTyP8Iisb1Rq3SIQfuB12/RR8UAgEAgEAoFAYP0igNtkcpuvLcVaM9vJzDZf5MsDhoyJzw+wVmlwXmGb46VBvbKE1ZTrF9lTqznQ/uRP/sRlVFiuSQRc9Ld/+7e93/rWt/IG33zzzXjpf/3Xf/3d3/0dVsyvq11cxfyud72LeMkw3le96lXCOBuPJWnC+Y1Oa4vPfOYzyNttt9320Y9+VGu+7W1vw5l/8id/Ehkmk89Z5Ktf/WoNig8/+tGPdjqaNuUrJiczQAFs2XtdPZn3ggKYwvqzMEf6ugIhKrseEAgOvB5aOeoYCAQCgUAgEAgEAoHAGkLAameXGO3YsQMHRrdcyMwnzBl70UUX8ToisR7U68lPfvJTnvKU888//2lPe9rTn/70Q4cOqQNihiFjwhZCI7qWVedlzLl6vPcI8w/90A+94x3veN/73veKV7zi4MGD5DzmMY955Stf+W//9m/WSN9xxx248e23344wI7o80jSx95hMwhWNV2PmfM5rCLJQJRAIBM4cAsGBzxyWISkQCAQCgUAgEAgEAoFA4BQQuOWWW3DRN73pTZ///Of/9m//FvXFePHPgYGBTHRRWQ7hjRs3EsYV6dPu3btFoqwf/OAHseV8pjfCLAG+mmkwFy7PsMQXX3yxZdJI8nnnnWcbMFaM1spOIN77jGc844UvfOG//Mu/cPX/1E/9FJL8mte8RnF0yAJlkZ2cU6hKJAkEAoFzD4HgwOdem4XGgUAgEAgEAoFAIBAInNMI8Mf+2q/92s/+7M/+6Z/+6Xve8569e/dapcwhbIE6jsolK4y4Wqtsfy9GKmDnNqIrjOXyG9snLJlVzXCwahf7lcUnpJeTWRgBfuYznymXr+9+97t5fS2lvvLKK+03FrYW+u1vfzuBTpbOO5BlkZIE2cn0MzjwOd3HQvlA4H4QiDOx7gec+BQIBAKBQCAQCAQCgUAgcOYR4IC1/Ng+aqKf85znCP/gD/6g8KWXXspVa5O2TyiueEz4qU99KjpqD/DXfu3X+vmiF73ouuuue+lLX4owX3311Ty3pHHzyo7E4r0WM+PMmK3l00Rx8H7Lt3zLxz72sTe+8Y08vTYh/83f/I3V1F/3dV/n0GkxPv38z/+8hdB8vwrN1Jeo7H8+85UPiYFAIPBwI1CsjY8/3DpE+YFAIBAIBAKBwOkhsFIslDq7C3fd4vlledea64Yt/mX63f0j9LwbidP7b+B5enh9pdQPF54csNgsly8Ga1Uz1kpTPBbtpJLRYd0yiitemG/WtmEuX85e6TM79QlllZ7blgRvTFhip1tZUG3JND+zG5IUZN2106Ff/OIXy+LJkLhLyWlbdBDArhWtFA8OTBRiLJkl1rzBXwnCL/v+cOH5ZUqcwo/Q8xRAOo0kgedpgPWVkhbrteXp2eZ67SslfIDfk/3Q0RV+4AcIX2QLBAKBQCAQCAQCgUAgEHhgCKCX+Ke8+dypzGwR0Uw+cdFMgCVAYhFdBNiqZm8J8GFeYuda7dmzR4JV+oESy8XxK9LK6pe97GXkyOuSJDEyYsiuBZYMl8Z7RVpQLSAG1830mCYuXtq2bZufpMkiWTyBQCDwCEMg/MCPsAaN6gQCgUAgsC4QCD/wGW/mVSJxD8lrjQOEnvdooAf58+HCM5drlbJ9uWjnqrsVN8ZIOWNxV2HJfEJZM00VyXWMzXLV2hgsXoxP6LQAxiuASMuVCTZwchoB+4pFrnLsTI+z8EzIyRFJH1lI4E9Gp0+3/+d63btRTlfOvSWc2ZjQM/A8EYE11T/Pjh84OPCJHSDCgUAgEAgEAucGAvfDgddsBbKRcV+m59pRO/Q8s20ReAaeZxaBMyst+mfgeWYReADS7tkJaytnYS10nAv9AFoqsgQCgUAgEAgEAoFAIBAIBAKBQCAQCJyTCAQHPiebLZQOBAKBQCAQCAQCgUAgEAgEAoFAIBB4AAgEB34AoEWWQCAQCAQCgUAgEAgEAoFAIBAIBAKBcxKB4MDnZLOF0oFAIBAIBAKBQCAQCAQCgUAgEAgEAg8AgeDADwC0yBIIBAKBQCAQCAQCgUAgEAgEAoFAIHBOIhAc+JxstlA6EAgEAoFAIBAIBAKBQCAQCAQCgUDgASAQHPgBgBZZAoFAIBAIBAKBQCAQCAQCgUAgEAgEzkkESuek1qF0IBAIBAKBQCBwHwjkmwbv4+PDEL16IfBqICsRej6wxliFcTUQeD4wJHOuVRhXA4Fn4PlgEDizeVe75Wog+ueDQXgVxtXA2sFz9f8Tiw+mhqecN/zApwxVJAwEAoFAIBAIBAKBQCAQCAQCgUAgEDjHEQgOfI43YKgfCAQCgUAgEAgEAoFAIBAIBAKBQCBwyggEBz5lqCJhIBAIBAKBQCAQCAQCgUAgEAgEAoHAOY5AcOBzvAFD/UAgEAgEAoFAIBAIBAKBQCAQCAQCgVNGIDjwKUMVCQOBQCAQCAQCgUAgEAgEAoFAIBAIBM5xBIIDn+MNGOoHAoFAIBAIBAKBQCAQCAQCgUAgEAicMgLBgU8ZqkgYCAQCgUAgEAgEAoFAIBAIBAKBQCBwjiMQHPgcb8BQPxAIBAKBQCAQCAQCgUAgEAgEAoFA4JQRCA58ylBFwkAgEAgEAoFAIBAIBAKBQCAQCAQCgXMcgeDA53gDhvqBQCAQCAQCgUAgEAgEAoFAIBAIBAKnjEBw4FOGKhIGAoFAIBAIBAKBQCAQCAQCgUAgEAic4wgEBz7HGzDUDwQCgUAgEAgEAoFAIBAIBAKBQCAQOGUEggOfMlSRMBAIBAKBQCAQCAQCgUAgEAgEAoFA4BxHIDjwOd6AoX4gEAgEAoFAIBAIBAKBQCAQCAQCgcApI1A65ZSRMBAIBAKBQCAQWCsIFOuFQr3W0MZkrsD/vIv14j1iTvz6cISbT6pP6PlA2yLw/J/e/kAxPFFC4HkiGg8+HHg+eAxPlBB4nojGgw+fC3jWC8W7/v/9ITQ5ggM/hOCG6EAgEAgEAoGHCAGGwMrUVGK7J7DfCAcCgUAgEAgEAoHAOY0AAuz/4h/qp1gbH3+oywj5gUAgEAgEAoFAIBAIBAKBQCAQCAQCgcDDi8BKsVDq6DoLNPvhrWaUHggEAoFAIBAIBAKBQCAQCAQCgUAgEAjchUBw4OgKgUAgEAgEAoFAIBAIBAKBQCAQCAQC6wWB4MDrpaWjnoFAIBAIBAKBQCAQCAQCgUAgEAgEAsGBow8EAoFAIBAIBAKBQCAQCAQCgUAgEAisFwSCA6+Xlo56BgKBQCAQCAQCgUAgEAgEAoFAIBAIBAeOPhAIBAKBQCAQCAQCgUAgEAgEAoFAILBeEAgOvF5aOuoZCAQCgUAgEAgEAoFAIBAIBAKBQCAQHDj6QCAQCAQCgUAgEAgEAoFAIBAIBAKBwHpBIDjwemnpqGcgEAgEAoFAIBAIBAKBQCAQCAQCgUBw4OgDgUAgEAgEAoFAIBAIBAKBQCAQCAQC6wWB4MDrpaWjnoFAIBAIBAKBQCAQCAQCgUAgEAgEAsGBow8EAoFAIBAIBAKBQCAQCAQCgUAgEAisFwSCA6+Xlo56BgKBQCAQCAQCgUAgEAgEAoFAIBAIBAeOPhAIBAKBQCAQCAQCgUAgEAgEAoFAILBeEAgOvF5aOuoZCAQCgUAgEAgEAoFAIBAIBAKBQCAQHDj6QCAQCAQCgUAgEAgEAoFAIBAIBAKBwPpAoF4IDrw+WjpqGQgEAoFAIBAIBAKBQCAQCAQCgUAgUAgOHJ0gEAgEAoFAIBAIBAKBQCAQCAQCgUBgHSBQrKdKlvJ/1kF9o4qBQCAQCAQCgUAgEAgEAoFAIBAIBALrGIFiqntToUGF4x0IBAKBQCAQCAQCgUAgEAgEAoFAIBAIPIIRaOwErpUKd1VxHU8GRNUDgUAgEAgEAoFAIBAIBAKBQCAQCAQe+QgkR3CxPjr2yK9p1DAQCAQCgUAgEAgEAoFAIBAIBAKBQGCdI1AsFjo7Souz0+sch6h+IBAIBAKBQCAQCAQCgUAgEAgEAoHAIx6B5WJTW2dH3I30iG/oqGAgEAgEAoFAIBAIBAKBQCAQCAQCgUDjVqR6odSUz8QKQAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBRy4CtbvOhX7k1jBqFggEAoFAIBAIBAKBQCAQCAQCgUAgEAiciID7gWsn/o5wIBAIBAKBQCAQCAQCgUAgEAgEAoFAIPDIQ6DYWAQd+4EfeS0bNQoEAoFAIBAIBAKBQCAQCAQCgUAgEDg5AsGBT45LxAYCgUAgEAgEAoFAIBAIBAKBQCAQCDzyEAgO/Mhr06hRIBAIBAKBQCAQCAQCgUAgEAgEAusKgUxsv+I7JXAudNDgddU5orKBQCAQCAQCgUAgEAgEAoFAIBAIPMIQ+IrsV32b0n7ger30CKt6VCcQCAQCgUAgEAgEAoFAIBAIBAKBQGDdILDq0z0VGpwuR1rNsG4giooGAoFAIBAIBAKBQCAQCAQCgUAgEAisVwSCA6/Xlo96BwKBQCAQCAQCgUAgEAgEAoFAILD+EAgOvP7aPGocCAQCgUAgEAgEAoFAIBAIBAKBwHpFIDjwem35qHcgEAgEAoFAIBAIBAKBQCAQCAQC6w+B4MDrr82jxoFAIBAIBAKBQCAQCAQCgUAgEAisVwSCA6/Xlo96BwKBQCAQCAQCgUAgEAgEAoFAILD+EAgOvP7aPGocCAQCgUAgEAgEAoFAIBAIBAKBwHpFIDjwem35qHcgEAgEAoFAIBAIBAKBQCAQCAQC6w+B4MDrr82jxoFAIBAIBAKBQCAQCAQCgUAgEAisVwSCA6/Xlo96BwKBQCAQCAQCgUAgEAgEAoFAILD+EAgOvP7aPGocCAQCgUAgEAgEAoFAIBAIBAKBwHpFIDjwem35qHcgEAgEAoFAIBAIBAKBQCAQCAQC6w+B4MDrr82jxoFAIBAIBAKBQCAQCAQCgUAgEAisVwRK67XiUe9AIBAIBAKBQCAQCAQCgUAgEHhIEKgVC031wim+VzWQ5X4e0vJz/8nuR8ID+7Ra7ilmPyX1irVCvalwKu8TSr0vTU6pxBPk3CPYEFsrFDhH7/luEnMPPRuZZVHoqb/vUeLD/vOc9wODfqVQTy3X3FQsl/yrNxWX6zX/5pcWF1eWU3ypOccI569LtZWcS8Z648ktQcw9/j3sLRQKBAKBQCAQCAQCgUAgEAgEAucQAsVK8+z8TLGrfXJ6YnJmsrmn8+jQ0eZKc7HctFJfnl+aX64tSdPcWq431ReWFpvbqtMzc8sr9WJHZ7HYvLCwhF0VUS/vQnO9XlzxqaVVgqbmsvTs+ZnZqToSUKwVW8srtaXllcVitVIsFYWXlhd8Ek7/murF5oJPy/XlheWFWrHm3+LKonexrSXlLawsUaaRWMDPYktLrVYTW2wS2zy7sLi4tFIsV/xrbm9fqNdnF5eE5+YXm8stE5PTzZXWlKCpVKsXadhcSill9p5fWp7zjfLN5VqtMDe3kMJtbXOLc0qfmp3yptXswmyKaS1Dxk+YACchQ+X6snj1VdXpuflUaGd3AmGlRv7Sci2FF6VbIR+JpTYWNDUxXSy3FivVVIsC2FshuTi/JLy8CJkWgbmZ+aWF5fS1Wp1fnGtubylWmuZXFhJczQUN1Fxumpmb1oijE6OF5gJwvP0DUVKsAZo003PTzZ1tIsVANbVyqbiwNK8uCb9Kc62wMre4kJkaarawTA52VgJy7tL3IF9+noWnWNt34CwU89AVUayUC9p6eXkVx6amROxL1WphGfPVA5vQ3KWlJZHNzc0r9QR3s35pTBUSAW4qNhWamqTw896g3+9slBzxBAKBQCAQCAQCgUAgEAgEAoHAXQhgXVMzkz3btk0cOtTd318olebHxxneTHQWe2tra7Fcri0uzszMrKystLW1VRDL2cWWlpZCrT42OtpWrZaKTc1NzZxZ5RSJWhZwp5bW1tnZ2WpHe7G5uDA13TLYX1hYGDt6tFivt1SrLeXySp1fslZubWXxM+xXlpbqWHBTE9N/kahyuVSpFMpl1r/f8zja0lLWqlSSKtEHbCJzio6ugfo8op58ZtJU2qoSHx8empmbveiiizCGqYmJ9mpbU0vL9PAIzcutLQTKLsw7Ojc3p5RqtYpOF8hYqRXwDv9WVpapsjjf1tmGpU9OTnYPDKQElQqIFhYWuvv6qLGCdC8t0aqEjS8tzUzPoeLtHV3I6sL0lGQjY6Pd3d0dHR3T09N9O3ctj4yUWloXp6bKTc3Fzq6h228f3LOnMIvAzhSbmyql8uLyEu5T7u1ZHp9o5jIslWtLi01NzYW2amFmdnJ2smvz4OTokLqTWam0JCiWl9UIN+/dtKmAJRWLEyOpptK0t7drEqxKyy7NzZU3bDhw3XXbd+/GvNQtQ5q+qm+9LkaWarVdjZYXl+CsIbxXUhWXdAZlnQn+dRpu3aWmYmXj4DnPgVFY2GVfrjfcge5/qQvjwCju3X1aQF+aW5iXQDhlWal55Z95CcGZaAPqxBMIBAKBQCAQCAQCgUAgEAisRwSS57GrfeTIYTZ2Z2fn2NgY1oTwdPX3T4+NQYS5jgh50GL+Khb79NQ8xojCTU1N9WzdWhsbL9YLEuGHiQm3tvAcFqrVxYmJtMazWOtoax8fGyk1NWN01ZbWQmvLsX37N+7cUZ+bT4R5fmF+cQH3a+vsUNrywkImZjhqJmm4HD8kypuaB1vDCRYXUxBJbjDVyeFJOjd3dRaWl6YnJ5sq5baOjjpdC8k7Njs9ozqp9Obm9tZqYrzNTSRjdNh4bSHRO8nUq0Q4TrvCSVtrLVeSiw5tod/yQrPVqw2Gf/z4cSjRsJU/dm4OSRGWXkrMRRGFYqlQbJqdTp9Uv6urSx2LXV0LoyOKGzpydHx07IKLLp4dH0csO5HqlpaVsTGtYCrBCtlCs+ymAbjK6VNaWOSjXWhpq5bb2pfnZmVpaW9drKVYakBjfGiYAsLULlfb5sbHcXVTFa2ofnf3ytRUc6nEm91cbT1y8CAqjir37d49fegQVBErOFBe49Lcz8y/0qrdTMrAjn8tLycnpfTTM6p5JvjX+uPA/Om5f+TuknsMNDPuAqBPvTk/aaStYMjaIc3smMRofJIrOPBdEMV/AoFAIBAIBAKBQCAQCAQCgQeKAPY1NjE6cP55k4cOJSbZ4IEs8qOHD/f19fnpwQ/5M3HFBkeqtLa0Cbe0tE6OjXX19EyMjlYrLStLy9WenumxUbsaWex4JCcwsx93Gjq0f3Dr5tmR0bYNg8N79+G6bd09Q4cPdXd0co/hfk28nQ3uV8OH5+dlR6qVlQIN4x8lU7+sAH0SX2j4gRt8tdDWv6E2PmH1aKmlssx5izfYZTk/39ndJfHE2Hj/4GCiww02sbK83NxSSYtPi0VpFufmu7q7C21t9elpjCORPf+QjUXLlGsK4qwtlJvmZ6eQ3sWFpF5X9gbjvc3NPKspSWNBa6LfTU1t3b1zUzNLK/Wuvj5TAvS0Gtx7Ymqqu8FUOzdvKczNjxw42D8wwEGtCty51FYr7j6UpzFXUFpaXkZTEWsu+HKlghBNjo+XS6Vqb9fM9Hhra0VZ+Dygql1d2NPcxMRd/IgnnIYzM1zB0pDZ3t5ZMkEAw5WV4WPHent7M/tta2+nWJ7XoIaw9CnLUqq4nyI1q1ejHawEDz8wVB7Qo1Py8uferGNpAG+Scm8WLyY/4PYpTfwYRY1HGrMUvMEGYUupvFr+3Yw5RaTxEU8gEAgEAoFAIBAIBAKBQCAQCJwCAjgwfyn2eNNNN33kIx+54IILHv3oR7O6e/FGfNKa2MZSTTEIbaZG+CkqWO3onB+fwNnSKuK+/pVjx5pxNkSru6s+Pv7e972vsyd5HQtNxa/+6sdxuvKaVtvaytXqRz74wR/8gR/41be+9Zuf9Sy7d1OWYjHRy4UFK6s7e3r4UWtzc4hAiW+ZT7ThGU6rpkul5Znk1M1qSIAalNraVyZnZiy3rrZyQe8/cOD2vXdaF11tb0MZ0HiuzksvvZQvrVKpJD7c3486qg6WQQ6SkajH8kqqUbWqsnhmKqten5+d5QXlIk2bfouF7B6XNhMWKQUoQ0O1I1AMlUS29g3Mjo6L4d/m+ra8+POf/3zSZ26eDlu2bEGGe7q60Vc7o5vb2pQ1OzWZGGZHe21yamp2BtMxL2B3dt/mTUvTM/zJ+C3vLO/0yMSI463wZ7CoCP6cIJpfoJ6f3mPDI9zCSk9r0VtaP/7JT0xPz/IkHz161Mrw0dHRK664YuOOHdMjI2l1d3c35TPVAiZRapQWRheLud25lAuK0Au4lE/wAT84/nXafuBz+1xoY8xuXtRW79HnDKS0sMHsjqmLyck8rnLX0WwSgF4zpAmku5dPpxbxoTEzITKeQCAQCAQCgUAgEAgEAoFAIBB4MAgwvF//+tf/xV/8xeDgII5E1Kc//ekFO4BnZtjhaJtHJLMcn2SflyutVkHbL9o5OFibmsYemw4dYsOvNDhYcXr6M5/97Pf9r5dv3759dj5R2SMjw09/ypN/8tX/+wlPeuLs5GRvfx9X59JKWmHLYcu/hR9WOzu7uW/txS0UZibGkVIGf6YMuWq22uKppFFpla0lBjszXS6VuwYHbJc9evvtf/3Xf/1X7/rbiYmJwY0bEMJrv/jF5zzrWX/8x3+c3NRtbdX2jvnp6cx1M4Esd3Vhd06H6t6+XQ2bpqbmZmdVM9c6nUNUKrYV+GObsBWlt/f2To2MkAw0ybrMFKCO0wmEjBV8lqdnsV/stJtWhcJnP/uZb3/xi/BSuWwJluvbnv+C3/z1Xx8bH2+ttLQsLczPzqU6NjctYaUNXlrq6JgdHe3buEErjE+Mb9y40fdUxODAYEtzk9XmdcjV0Ncm+67HxzUNkpSaqRmnblMEH6/I//6vj77uDa+3vtpjRmDfkaN9He2f+9znju7bt2nr1g7TDVgVDBecHXaXJ1G9Gsy/bO33UoNmc5fbnyyBXd9Jz4fjObc5MMTAp/cIpOmFBuiaU5N05zYweBYWJEhfG2zXtEdrS0XzmA0y0mRMDdPkbLgEf/b4h+83YRFPIBAIBAKBQCAQCAQCgUAgcPoIXP+lL77tj//kT37/917wspfd/JnPIMAY1PDIyFa0kLOKlxSpWlxkxqORTn5amJ3ffNFF0wcP4WmscUugZ4aG2wcHlyYm8DALidEwpOu1r33t057+tQNbtnzpus//xm//1rd9+wuvufbzvJ27ztvzxTtuHz1+bHxyorW9zb7i2YX5+sxMc6VcrDncat4a6VJvGzkIc215iTRFT01Po75p+W7dsuVptUwuUOuZp2dwhMROC/VN55//ute//v971Y/7ynP7oQ9/+Kd+6qee8YxnVLq754ZHjh450t/bh723W4w9ODA/Ojo0Mtxf60NaJ6cme0vNs1PTjrlCVmsOAFteRtSVNUOHQr1zoM/sQCqRu/XgwbxSFRq8qZDBRcVQEu1E3SkjJUa6ODs71ZhHwLp//hff/JLv+77R48f/8i//8s0/9/PPfu5zvv4bvnF+agoI/YMbxDctNFfb26sObLawdcn51Nafzwt3WMbc2TF+6JBab+vuOnbsWHdvD8e3IpCg6aNH+XLb+vomjh1rKVeGDhzYsmeP8NTkZM/g4Dc+59lXXf1YHnt+XIpddtll3/093wPDxMJsAF5cHGno39PTA2SMTF1QLZWxq1gtbGauam4sjF+96tjqZcA+LPzrnOfAFkJbVO9BcXnwx8fHDx8+PDw8/NSnPhXQmlaMrp86rvHmXLKOdq2ix5vOmRqfEC8s7wXnnZ+kxBMIBAKBQCAQCAQCgUAgEAgEAg8CgWuuuYafF1dcHB7evXs3ryMjfOuuXXfecsu73/3u973vfQMDA9/1Xd/1zGc+UyG333LLb/3W7/zAD/zAB973/g984AOXXHjRm970pnSaFG8wz+3K8tz8PDMeMd2waSM+uTAzfemjrvjxH//xD33oQ7/8y7/80z/90ysLS9/94hf96I/+qCXKaORNN97IBY14W6n7whe+8Elf/YTa8srQoUP/9V//9Z73vAdlvfrqqy+//HIk8+u//usPHzr0r//6r//0T/9Ek+c85znf/M3fbGlxoerg4kVUbRFzKxaQRiuQcYdf+ZVfefzjH//iF794aP/+wcENMv79u/9ucnrqMY95zMtf/vKdu3ahrA7WOnToEAU+/vGPb96w8XnPe97Tn/50nNZRvYrASo4Pjbzll3/xda/7qZ07dzpZ+siXvvR7v/d7T37yk5/2tKe9/33vo4lTsuR6/vOf39bZOTc97ZIbDjwMNvnAG8ukgTAyNd3T2zty/DhX8Pd93/e95nVvsPKcGu/623cC/NiRI//54Y+o5g/9+I+D7jd+4zc+9alPmUTQIiQP7Nhx5LbbPvjBD8p75513/uEfvd2Jzt/8zd/03S972d5bb33b7/7uhRde+LKXvQwl/qd/+EeLrn/4h3/Yem++cdtH8axtF5w/PzXT3tHxmte8BtH9iZ/4Cb5ntOuTH/3oH/zBH3A7U4Dyj3vc47LjXbm/9mu/9pnPfAYfBu/znv2cgY0bwatLNDjcg+hnDyLraSyefhClPIRZIZ52q9drDsc6cPjQU772aT/+6lf9/tv/8DFXP/bI8WPWAPzwj/5/3/4dL7Z84pU/9IMv/f7ve/zXPGXj+ef/6Z/+6Vd/9Vf79L3f97Lv+I7veOtb32ovuxUAact7wWVc6S4tO8U11UOoeogOBAKBQCAQCAQCgUAgEAgEHnEIPPEJT2ZD/8zP/NyhQ0cqbR34Tk/fwMTw6I/+6I9/5jPXvPjF33nJJZe95OX/6xOfQMwqIyNjf/POv33JS793bGL8e7/3e//zP//zW7/1Wzu7u51g7F5Z1jg3owdIPIpci4x/l9wODmz8zu/47n/4+39qb+tcWlz5x/e8L90hVGi+5aZbn/61z9i398CP/ehPXHH5lc9+9vM+/enPWvX8uc9/4cd+/FXf8I3f9NKXff8//fN7Xvf6N46NT87Mzvv5a//3N17wbd/+3Oe94Fff+msv/1+vLDSVMTSlKBGBRPPowK/2rr/7u1tvvfUNb3iD9aT8q3/wB7//E69+1dbt21760pfi2y944bfdfNNNuO6+vXd+1eMfZxYAw7/8ykd958tfhg9TPTmBm5qmZ2Z2n3/+Rz7ykX99/weW+Khn5yamZ37r7X98wcWXfO4L1734Zd//5Kc+7Tu++3ve/+8fPHD4yNjYuEuPMEkbcS3SVn18EnVE4+mWY5DzD3/kI+j1s771W7CYf37Pv/zf//t/kepyS+XiSy8p1Fae/oyv+8u//qtv+uZnPfHJT3rb7//ey1/xvxYmxrH6//rvj/7U6177e3/w+7j3tm3bfvDHXv3Bf/v3XeedX23r+KnXvvH4seH5ucVfePMvtVZy0xm9AAAYC0lEQVTbu7t7yy3V2bn5odGxDc7fKmDu1U9+8tN/+Zd//da3/lpHR5dDpT/2sU9887c+2x1I3/Ed3zU0NPJt3/btGndycrprcONznvO8f/iHf1DKN3zDN/z2b//2y1/5CruA1YgPkmvdP7Rr9d9ZGwrnvh+Y+3dx0ZKJ+YkJGw9M9vzCW94yNjT0+7//+zfeeKPZnT/8wz/ccd55XPM47Y//6I+a0pg4eNBuBPMQf/Knf2rLuZ3ifs42lkPYVQ96i9S1is7qwPF640yzs9YeUVAgEAgEAoFAIBAIBAKBQCBwTiOwefPmX/mFN7/hTW/8f3/5F898xtf/5m/+5tzMjFXB7HM7hBFLFGjfvn1/8zd/s3Xr1p27d80tL//Mz/zM1z71aTgkW/0V3//y4ePHrdxssbcR6V1ZGZ+aRFpkLFVY69XR8fHBnTsRY3JwQutvO1pavIH2C7/wC0984hP//C//cujYMezxhhtu+P0//MO3/c7vvP/fPsAviqlW2tq/dNONH/7Qf3zd1z/jve9970233PzvH/g3HmbrcnHXb/3mb/mnf/yHZ339N9q8Ojsz09XbUyg1L85MYwuIxk/+5E/u2LGDs5e38ydf//p3vP3t3/j130Aroq666ipMz0ppNG/Tpk1/95730JxHl7ave9MbLFCV0bbjdIDW9PR3veR7/uiP/ojrWH3/7M/+7IlXPxZiPMCPvvSSZz3rWdY8f9uLXjQ/M9Pa23v49ttJqzgHa3FxanIK4+3ZvHnD3Kya2pP8//7f/7O2+d/+/T/+6e/fvWHDBtBxbv/Hxz9xyxe/KOC45r9717v279+PkPvqkiq+2e/8zu/82Mc+BiL0hzeetoprrjR//FOf/Pu//3ue8x/5kR/h3/71X/91mgPwDT/7s7YrNzadFjeed97KxMT48eOVlurP/uzPOgrrOc99rk9jIyOa73u/5yU/93M/17llyzd90ze96EUv+sVf/EXw/vHb3vaZz37mA+9775VXXonD80E+/Znf9IH3v/8bv/lb7EsF3cPVz895P3C6H7hxePcdd975Hx/+yCte+colh7C1tf3ka17zdc94hrO3N9jzvZwWUYD7vz/2sTe88Y1O0LKEffOWLda1H9u7d2Z+rnNwAOnVzA7zri8v5faw9MI9Wg9Xw0S5gUAgEAgEAoFAIBAIBAKBwLmIgBOVX/6/XrF/34GffsObPn/tF86/7NKPf+KTFiRv2LipVK586cabbrv9jltuvc1NsRdcetmXvnijNbHYF1Och9MqZacnj4yOppODh4YcKJVMdJflNpdsoEV65xYWHGJVmJ0bGRmddw9QZ1dvb9/MwsKWLVur1bbPf96pVV96wfOe//rXv+E1r/mp6667/tprvzA5PbNtx86h4ZHlWn34yNGZufme3r6Oru7PfPaa9s6uHTt39Q5uGBjcsHX7jpbW6sc+9nEuVmc4c7ryBu/fuxfZxujQyB/98R9zL1HHwMBHP/rRrmoVo2vv65OMhtZR/+v73nts6PinP/uZp37t0xamp4QtWrYq2PlD+w4e4FpzBrW10NJzdN95+Mix48e5uf/2ne/89he9iIf3a57ylM9/6Ubba//lPe85cuiQ64smjx/fsns3bu+0LUw1n/w8NTx84MCBclPBUuoXvOAFL3zBt33rs55pp/SHP/xhKbmCX/D85yHAEs/NzFoRvWPbdgdl2eEs5qpHP4bn+aP/+V82+jrX+lGXX9FedXJ2FRVHmIeGhrZfeKEN1a953Wt//0/fYY30//k//8e1zBrCLU34EQJM/56+Pr76666/4Zfe8stOYZqbnRufmLz5llsf9/ivdgnxyL79lZbW5z7v+Z+55pqZ2bkvfunGCy+66LFf8zUzGm5+/qrHP763vU32lYV5FCz37ewNzuHsED4Lff6c9wPrE27cnpuaMsnR09NtAsMkij3A+pa9BBA05WCqxqbfv/qrv3rlK1+JHvt35Ii191/ae+edxpItCm984xvP27XbmdL4syUWOko6XG4x3XDtdrKz0AxRRCAQCAQCgUAgEAgEAoFAIPDIQKBcrqRbW+p1W0mRQ4tgr7/++ic96Ul8njYkonNOeGZy33777S77xb4sO75j714OT2zzwL79LYV0wxBiicultZkVZy13oaOcwNZIc/xyisroZJ/zd+4aGxr2CYtG4dz9g8597VOe+pznP6+9tXp8ZHjntu0T01Obd+1CF+1DdksTZylqYFtyS1fX9NysQp2YuzA6qkRyNmze5Kys1ApORXZ21Mryjp07r7322nc4eOod76CzpdHDBw/i6tggpnDgttu279nt7lyUhGcVrXAskTTZDev04zvuuIMwPlhKS4AAlwcHLileesVFF7797W/n9SUERNZaP/axj/3v//gQ/zBvs+z//M//fOlVVzmSanDz5tnRybnpGVB0tnc0VVs62torzaWvf8YzLLd2FrT9ybBVOwtgDx482NvV3dvf7yhmhWJAtjd7t7a3OxiJ2vyx9gBrGkVgQ4nTtrZOz86g68KOVoLtrl27+jrbR6dm+K6H77iD71p6V9FSErwjw6P82C//vpc/9uqr3bgDTM78hfqKdnTBkjmL1FJYVaFOGbVT/fmxdGsxss0xCRxywEUUHp6gfjiec94PbEhk77wrqrQZ9qvreN75zne+7W1vQ4Ahro1tgv/iF7/47Gc/e9RUyuws0O0a5yv+pbe8RSPZHW6QuLFMWJamUjop2pPaJp5AIBAIBAKBQCAQCAQCgUAgEDhlBHhrnSCF61Y6OxEqRjUrnSnOQeX593//d4cnWUlrG6rLh0jt6ehAlpLxbXWni3xxo7rrdRYqjft4xoaHEz2r1xIrbsQMDAx+6sMf+et3v/Pbnvf83r4+fK9SKPb39LqJ9+ILLvz0pz71+Ku/6tJLLrFQecvmzY9/8tdwwH7u2s9jeu9577/aMWst9GVXXH5k/77zL7zguhuud8Iux7XrlCqtLTfefNNFl15SqabreUePHXO6lVuIX/3qV3/rM5/59d/4Deg3lSw//uonPsE5Xcc4TnftHDp6FMO8+dZbzzvvPOxOveRy2DLmubi85KTotuYyfuuS3jbHR3d2DN95Z6XSat/shz704X//9w894QlP6u0baG1tGx+fvOLKx/zm7/7epz71GRtufarPuaS3c3lusa2jg68WO1maX3C1ka2ac0vLeIoient6rGzt6uzkHIa3E8Vc7Xv04CEQbhzccN7uPR/+74/ywE+Njrk9GCf/5DWfteZ8dHjEidZbNm3u7uxCWZ2HtLxSby5V7JHu7up1jNJ3f/dLnvakJ/3Qj/x/5ZbWYrEZxz9y9HhHZ3ep0ophbd+63crwdOXs0pI5BT5FlFL7itFSGtR5YF915WNQaM5qfkrJ3KusTU0QHB8avvLRj9aUZcdlN3YC5251ojf4lDvaA094znNgcw8GmR7gAu7x2TmzTXuuvJKf/RWveMW73vUuBFhf1CHMOSG6G/fsMUVh8/DvvO1tv/RLv6RhHvPYx/7qr/6qaZKbb77Z1EiRoMZtzgvz83q5C7IeOLSRMxAIBAKBQCAQCAQCgUAgEFh/CNgd+pKXvOSzn/3sjddea4ssc50djv/MrSzZN4v/3HLLLc6FQpU37dzJUOd6TdtZnflspXFXJ9chv1TXhg08p5ge672zu6u1VHLWzzWf+tQXr7/+z/7kT77jxS9+3KOvet1rX+sMZE7gSrly5PBhtPllL32pnca/+iu/goSPj44ibH/ytrdxktldfNttt/3Yj/3Y6173OttW+YG5N22OVbojptN9MVNTuC4v5Xd/93dbEIqw9W3atHXnTvcDf+H6621wdSCWinz6Yx/D52n7+Kuu+t//+3/fesstg5s2OUv5s1/4ws///M/jEcjhP733vZb7YoNOUX7LW97C6c3L2rt5M7KJLg7w687O8v1yk9og7TAjG57xDvL/5q/+ampszCyAVc3cddaoOonaeddT4+OuN6p0doGlUiorvVwo3HbzLTd/6UbMk4Y8ugTafgxkolzVu2/v3uaWFq0AnN/6rd+iM4+0JdO9bR1O5DbXwAnPMSsLrkQ9Jarypj17rH+mpGO6FW039Z//+Z+7RAcs6UrhcvkL1177f37rN3metSA8JVC0T6951f+2hRj1JdkZ1B/4wAec0c2rryx0zJJblVWEQ7z7+/v4q9v6+0cOHXoYR8Y5vxa6q7tbo1paoB9rY1MRR2+9FeJ+enQ1N1zp94aZVncRlgme2bG02kFjL05NW5bQ10imDQxI94bpN5kGO4K8NQ7Eehj7ZhQdCAQCgUAgEAgEAoFAIHAOIoAcvvFNb3rms79lc/8gEvs7v/M7F1x+ucWbz3jK0y68/NLztu9EkJy9ZJ3mim2ic2lfKHrJi4iGMey3DmzAHjmrkl+4WGTnY1wCPFi/+Ja3JDO+2v4jP/TDeJrdrQN9/cjV7NICJ6fdrV/3Tc/6P7/6Vmcjv/U3/i+x3/oN38QHy+y3YnTPnj2OayKZk/a3f+d3vN0A9C//8i9caPas4hFuu7UCmcKTExM8rv3dXc7ZtX7bruMf/MEfnJx3uW56nv7kJ/3jP/6jZaf0ecpTnjKzuLhpcPBf//EfN23ZsrSyopR/eOc78eqX//APcTY+46lP+8AHP1RYWrKq2RlUyOSExdId7apjWTJe+rVf+7VN7e0zx4/zyQEKYbGCVdVUf3xkxP1PSwuLnYMblsfGrW6dmZ2xc/jwwUMtpRI+aa5hfnnl8ssuQbxf/MJvhx5aa8rAnuedVzzq6I03qvjHP/QRu0F//Xd+q9mm6z0X/Os//4uzpvt7mgb7+mFi4bQl30vLiw7lwp+/9NnPIr2vetWrutO8Q7dbnfgULaRN0xAdnbffdNO//du/QcAhZ7/ya2+FRk9bh+Zwf5J7knCo5z7/uQ5q6urs0rjODBs6fHjztm1OPjM78OirrlqqF7Zt2uDksB0XX6y+ZMrSQPR/XrzB/NVn4SnW9h04C8U8REVwoC87VbupqeLSrcaVUxYhvO0v/uLoLbfYD2ziwb1HG7ds+cwnP/mNz/rm40cOW9VgXCHGDoW2UIEHuF6rOVTN3Uif+MQnNu/cWZhzvPSEVkeIXaJtZqg5d/aHqAIhNhAIBAKBQCAQCAQCgUAgEHgEIcA+Xyk2VTo75ianjh07unnzlqamogOAvW2GPXr0iJOrFhbmrWeenp7q6u2bm55C3rigOCTxZJcIDQ8NOaGKa8odRfY8NpWT0866YpQJ40obIeuF2ckpR/f0DPQLj48MO+PKqVl28c6Mj8kh6+TklKOClpaWuwf7ZezdtOHdf/O33/jMb5yemqbJi1/8HZdfftnP/uzPzc/P2b08OjoiFxflwsJiK59yU4nYlaUllzMhq+39fXtvu9WibhSXJhNjYwMbNnAyo9x4I55J+fnFhWpLK0rirhlLfxORnpxss8K6q+vo/gObtm2bmZgQiYbgI25jpVJbVxe6wQnHfc2NJ4br2IyAsIBbfOszMxj7hv6BpOHRY32bNtZWlm2SdojvxNSk9dsK6uztnZqYAGBXR4e8mE59pdbazE9caJWyVFKus5DIdSrV4IYNaaKhVnM8lZPEFh211VC4qaXc2t1laW3mSgmu/oGFmZnkICw433oBtXZudsVW6sZu59HxsQ07dhQsuj56ZNP2HWPHj3V0dMK8Vqtr8Z6e3nq91tLS6hQma9jtNOVktFTbzmR3Sjndm8Illx638jYmPPOD+q4S4KbTpsGnsbR5qalY2Th4znPg5rbq6PAQEN0i/cmPfcy0hIPadERHkLsVST/wyR5xMyX/8R//YRWEnqd/WCMhhqe+o8oV3+8UdbM4dzfBl/03OPCXwRE/AoFAIBAIBAKBQCAQCAQCgftFAA1uPJgJb9KpvE8uDhe6W9RJE5yK5KZ6sVZq73z81Y/Zs+f85z//uW1tHTfccN2f/un/+7M/e8eVVz7m3hrao3pv+x89u/dzosdylb/dO9mDjzmRE64CclKVlLWqVUbvVN5y3UPaqpD7Vf6U8G8gfL9iGh9X9fzKSU+Sgian+jxSOHClbGN6k028pVJ9ft5SfjMK/LdosLDTyUaGh7FcsykmNsyRNBeKlhAU2tuP3nab1QhmNfbu3XvBYx6zbJ39yZ57j4GTpYq4QCAQCAQCgUAgEAgEAoFAIBBYcwggjcjC6MT47/3e79mVynN7ySWX2Chr0TIf773VRcYwQO94zh0E1h8Hnp2fa66U8V4sF++1+zdfcWTLOH+vNQb8+Po3Vmz3OZ/w4lw6rMxP6du448vlmbGx9s5OS6lP2szBgU8KS0QGAoFAIBAIBAKBQCAQCAQCax8BHBhf6Ny5Y/LAATeq8pghBX3bty+Nj3OP3Vv/4MD3xmTNx6w/DmwtdG25cZa6Je+FwvLsrH3zKLE+3b9t27G9ezdu3To9Ps4n7Povn8pNzQhwsdntR02Ls7OW0fMGy3jievQTmzk48IloRDgQCAQCgUAgEAgEAoFAIBA4hxDAgTnMRsZGHZrL8ucza+3pmTh2jG8s/MDnUDver6qnzYHP+XOhF2yfn5u1Y9t+9LyN21lqrR0dYHIPtHBtcVFf33zxxfPHjnEOW9vAA2yrd06v6/MRT42OWhR9v8jGx0AgEAgEAoFAIBAIBAKBQCAQOPcQcIOuDZJLi4u4gLOBxo8e7entdRPquVeT0PgMIXDOc2BO3d7WFl5f8zpOwEKDMV7nyE1PToJI/3b0GSdwS7Wa7k/atKlQKhecmTY3hwbz/abpn1otZYknEAgEAoFAIBAIBAKBQCAQCAQecQjwdbmDNy3/7OlROXcCcYa12CO5sPCIq2tU6JQQOOfPhXaLb6Xayg+cjgKv183uZD6M3GK2XL46febGGY/xkeTy7ejudtJ6oUGD86IIuU4KWKyFPiksERkIBAKBQCAQCAQCgUAgEAisfQTSQcpNxZVC3dU+I0eOuFqpXKlwkrkPNe+IvEcVYj/wPQA5F36e9lro08iwNuufjrlatLF3FunlBEZoOXgxYfEi2zs6XGlF83JbmyOypBnctMlmAL5f93ElbuyM6EKhORZCr83WDa0CgUAgEAgEAoFAIBAIBAKBB4cAbxl31+z4uKt3kYJjR4+Sd1IC/ODKidznDALnvB/YZVZpdueUn9M96Dz8wKcMbSQMBAKBQCAQCAQCgUAgEAgE1hYCmMJp8YXwA6+t9jslbU7DrZvvBz6NDKekQCQKBAKBQCAQCAQCgUAgEAgEAoFAIBAIBNYqAsGB12rLhF6BQCAQCAQCgUAgEAgEAoFAIBAIBAJnGoHgwGca0ZAXCAQCgUAgEAgEAoFAIBAIBAKBQCCwVhEIDrxWWyb0CgQCgUAgEAgEAoFAIBAIBAKBQCAQONMIBAc+04iGvEAgEAgEAoFAIBAIBAKBQCAQCAQCgbWKQHDgtdoyoVcgEAgEAoFAIBAIBAKBQCAQCAQCgcCZRiDdjnuuP6d73ZH6FuvpkPSv+D7XkQn9A4FAIBAIBAKBQCAQCAQCgUDgAfCFAO0RjMC5zYH15toDapyc8Su+H5DsyBQIBAKBQCAQCAQCgUAgEAgEAmsCgQfAF2SJ55GNwLnNgbXNA+6jOeP9vx/ZbR+1CwQCgUAgEAgEAoFAIBAIBB7xCDxgvvCIR2bdVjD2A6/bpo+KBwKBQCAQCAQCgUAgEAgEAoFAILDuEAgOvO6aPCocCAQCgUAgEAgEAoFAIBAIBAKBwLpFIDjwum36qHggEAgEAoFAIBAIBAKBQCAQCAQC6w6B4MDrrsmjwoFAIBAIBAKBQCAQCAQCgUAgEAisWwSCA6/bpo+KBwKBQCAQCAQCgUAgEAgEAoFAILDuEAgOvO6aPCocCAQCgUAgEAgEAoFAIBAIBAKBwLpFIDjwum36qHggEAgEAoFAIBAIBAKBQCAQCAQC6w6B4MDrrsmjwoFAIBAIBAKBQCAQCAQCgUAgEAisWwSCA6/bpo+KBwKBQCAQCAQCgUAgEAgEAoFAILDuEAgOvO6aPCocCAQCgUAgEAgEAoFAIBAIBAKBwLpFIDjwum36qHggEAgEAoFAIBAIBAKBQCAQCAQC6w6B4MDrrsmjwoFAIBAIBAKBQCAQCAQCgUAgEAisWwSCA6/bpo+KBwKBQCAQCAQCgUAgEAgEAoFAILDuEAgOvO6aPCocCAQCgUAgEAgEAoFAIBAIBAKBwLpF4P8Hkf/WcRuyITEAAAAASUVORK5CYII=",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1281x720>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用示例\n",
    "root_folder = \"/home/workspace/dataset/lenovo\"  # 替换为实际的文件夹路径\n",
    "data_list = load_lenovo(root_folder)\n",
    "\n",
    "# 输出结果\n",
    "for data in data_list:\n",
    "    print(data[\"task_description\"])\n",
    "    data[\"image\"].show()  # 显示图片\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModel, AutoTokenizer,AutoProcessor\n",
    "import random\n",
    "\n",
    "dataset = load_lenovo(\"/home/workspace/dataset/lenovo\" )\n",
    "processor = AutoProcessor.from_pretrained(\"/home/workspace/model/MiniCPM-V-1B-sft-v2-1B\", trust_remote_code=True)\n",
    "rng = random.Random(42)\n",
    "samples, num_tokens = [], 0\n",
    "prompts_lists = []\n",
    "input_images_lists = []\n",
    "for index, _data in enumerate(dataset):\n",
    "    promt = _data[\"question\"]\n",
    "    image = _data[\"image\"]\n",
    "    msgs = [{'role': 'user', 'content': \"(<image>./</image>)\\n\"+ promt}]\n",
    "    prompts_lists.append(processor.tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True))\n",
    "    input_images_lists.append([image])\n",
    "    if index >= 0:\n",
    "        break\n",
    "    \n",
    "inputs = processor(\n",
    "    prompts_lists,\n",
    "    input_images_lists,\n",
    "    max_slice_nums=9,\n",
    "    use_image_id=True,\n",
    "    return_tensors=\"pt\",\n",
    "    max_length=8192\n",
    ")\n",
    "sample = inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 824])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample[\"input_ids\"].shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlm_quant",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}