DL-Assignment10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group Members:\n",
    "\n",
    "- Name, matriculation number\n",
    "- Name, matriculation number\n",
    "- Name, matriculation number"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 10: Predict Stock Price\n",
    "\n",
    "In this assignment we will construct a simple recurrent network with a single hidden layer: a long short-term memory network (LSTM).\n",
    "This network will be trained on historical stock market datasets of two distinct companies, and our objective is to leverage them for predicting their future stock prices.\n",
    "\n",
    "Given that our dataset is non-textual, we will skip any text-based data transformations during the network training process, focusing only on normalization of the data.\n",
    "Basically, the dataset comprises the daily closing prices of a stock in the market spanning from early 2000 to 2021.\n",
    "Since we will be working with a single time series feature, it is important to select the appropriate dimension $D$ to represent the stock price at each time step.\n",
    "\n",
    "Price sequences of length $S$ will, hence, be turned into matrices of size $\\mathbf X = \\{\\vec x^{\\{s\\}}, 1 \\leq s\\leq S\\} \\in \\mathbb R^{S\\times D}$.\n",
    "For each input, you should provide the target value $\\mathbf T$ based on a sequence length $S$, where the target for each sample corresponds to the price of the next time step: $\\vec t^{\\{s\\}} = \\vec x ^{\\{s+1\\}}$.\n",
    "\n",
    "To speed up processing, these sequences will be put into batches, i.e., $\\mathcal X \\in \\mathbb R^{B\\times S\\times D}$ and $\\mathcal T \\in \\mathbb R^{B\\times D}$.\n",
    "This will automatically be achieved using the default PyTorch `DataLoader`.\n",
    "\n",
    "The datasets that we will use are provided here:\n",
    "\n",
    "GAIL stock: https://raw.githubusercontent.com/Pranavd0828/NIFTY50-StockMarket/main/Dataset/GAIL.csv\n",
    "\n",
    "NTPC stock: https://raw.githubusercontent.com/Pranavd0828/NIFTY50-StockMarket/main/Dataset/NTPC.csv\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Targets Preprocessing\n",
    "\n",
    "\n",
    "Initially, we will load the datasets for GAIL and NTPC, selecting only the \"Date\" and \"Close\" features to be able to obtain $\\mathbf X$ and $\\mathbf T$. \n",
    "Then, we will normalize the value of prices since neural networks are sensitive to the scale of input features.\n",
    "Finally, we will reserve data from '2018-01-01' onwards from both datasets for testing purposes, where our objective is to predict future stock prices.\n",
    "\n",
    "For a given index $n$ into our data and a given sequence length $S$, we provide the input $\\mathbf X ^{[n]}$ and the target $\\mathbf T^{[n]}$ as follows:\n",
    "\n",
    "  $$\\mathbf X^{[n]} = \\{ \\vec x[t] \\ | \\ n \\leq t < n+S \\ | \\ 0 \\leq n < N-S \\}$$\n",
    "  $$\\mathbf T^{[n]} = \\vec x[n+S] $$\n",
    "\n",
    "where $\\mathbf N$ is the length of the data. \n",
    "\n",
    "\n",
    "For example, for the original data $[10,20,30,40,50,30,40,25,60,70,...]$, sequence length $S=5$ and index $n=2$, we would have the representations for $x = $ `[30,40,50,30,40]` and $t=$ `25`.\n",
    "\n",
    "Finally, we implement our own `Dataset` that returns the input and target."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the data file\n",
    "\n",
    "Please run the code block below to download the data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "import torch\n",
    "import numpy\n",
    "\n",
    "# URLs for GAIL and NTPC stock CSV files\n",
    "urls = [\n",
    "    \"https://raw.githubusercontent.com/Pranavd0828/NIFTY50-StockMarket/main/Dataset/GAIL.csv\",\n",
    "    \"https://raw.githubusercontent.com/Pranavd0828/NIFTY50-StockMarket/main/Dataset/NTPC.csv\"\n",
    "]\n",
    "for url in urls:\n",
    "  filename = url.split(\"/\")[-1]\n",
    "\n",
    "  if not os.path.exists(filename):\n",
    "    urllib.request.urlretrieve(url, filename)\n",
    "    print (\"Downloaded datafile: \", filename)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1: Data Loading\n",
    "\n",
    "Implement a function that:\n",
    "1. Loads/read all data from the .csv files `GAIL.csv` and `NTPC.csv` \n",
    "2. Choose only the `Date` and `Close` features to retrieve the date and price variables\n",
    "3. The `date` variable should be returned as a 1D NumPy array with the data type `numpy.datetime64()`\n",
    "4. The `price`  price should be returned as a 1D `Torch.Tensor`.\n",
    "\n",
    "Note:\n",
    "\n",
    "* Please make sure that you convert each date to specified dtype above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# load all data from the text file\n",
    "def get_data(datafile):\n",
    "    # Read/open datafile CSV file into a pandas DataFrame\n",
    "    data = pd.read_csv(datafile)\n",
    "\n",
    "    # Extract date and convert to numpy array\n",
    "    date = data[\"Date\"].to_numpy(dtype=np.datetime64)\n",
    "\n",
    "    # Extract closing prices and convert to torch Tensor\n",
    "    price = torch.tensor(data[\"Close\"], dtype=torch.float32)\n",
    "\n",
    "    return date, price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['2004-11-05', '2004-11-08', '2004-11-09', ..., '2020-11-25',\n",
       "        '2020-11-26', '2020-11-27'], dtype='datetime64[D]'),\n",
       " tensor([75.5500, 75.2000, 75.3000,  ..., 93.9500, 94.1000, 94.7000]))"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read each datafile\n",
    "gail_data = get_data(\"data/A10/GAIL.csv\")\n",
    "ntpc_data = get_data(\"data/A10/NTPC.csv\")\n",
    "ntpc_data[:5]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2: Train Test Split\n",
    "\n",
    "Implement a function that splits the entire dataset into training and test sets:\n",
    "\n",
    "1. This function takes a tuple containing dates and corresponding prices as input.\n",
    "2. Prices corresponding to dates before `2018-01-01` should be allocated to the training set, while prices from `2018-01-01` onwards should be assigned to the test set.\n",
    "3. The function returns two tensors: one for the training prices and another for the test prices based on the split criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(stock_data):\n",
    "    dates, prices = stock_data\n",
    "    idx, = np.where(dates.astype(str) == \"2018-01-01\")[0]\n",
    "    train_data = prices[:idx]\n",
    "    test_data = prices[idx:]\n",
    "\n",
    "    return train_data, test_data\n",
    "\n",
    "\n",
    "# split the datas for both dataset\n",
    "gail_train, gail_test = train_test_split(gail_data)\n",
    "ntpc_train, ntpc_test = train_test_split(ntpc_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3: Data Normalization (Min Max Scaler) \n",
    "\n",
    "Implement two functions: one for scaling data, and the other for reversing this scaling.\n",
    "\n",
    "1. Scaling function will normalize the data with correct statistics `min` and `max`.\n",
    "2. Reverse scaling function will convert the scaled data back to its original scale \n",
    "3. With `min` and `max`, here is the formula for the scaling process:\n",
    "\n",
    "    $$X_{\\text{scaled}} = \\frac{X - X_{\\text{min}}}{X_{\\text{max}} - X_{\\text{min}}}$$\n",
    "4. Normalize train and test sets of both datasets.\n",
    "\n",
    "Note:\n",
    "\n",
    "* When returning to the original values, ensure that you use the correct minimum and maximum values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "def min_max_scaler(train_data, test_data):\n",
    "\n",
    "    # Compute the correct statistics\n",
    "    min_val = torch.min(train_data)\n",
    "    max_val = torch.max(train_data)\n",
    "\n",
    "    # Scale the training data\n",
    "    train_data_scaled = (train_data - min_val) / (max_val - min_val)\n",
    "\n",
    "    # Scale the test data using the same min and max values\n",
    "    test_data_scaled = (test_data - min_val) / (max_val - min_val)\n",
    "\n",
    "    return train_data_scaled, test_data_scaled, min_val, max_val\n",
    "\n",
    "\n",
    "def inverse_min_max_scaler(scaled_data, min_val, max_val):\n",
    "\n",
    "    # Revert the scaling\n",
    "    original_data = scaled_data * (max_val - min_val) + min_val\n",
    "\n",
    "    return original_data\n",
    "\n",
    "\n",
    "# scale the data for both datasets\n",
    "train_gail_scaled, test_gail_scaled, min_gail, max_gail = min_max_scaler(\n",
    "    gail_train, gail_test\n",
    ")\n",
    "train_ntpc_scaled, test_ntpc_scaled, min_ntpc, max_ntpc = min_max_scaler(\n",
    "    ntpc_train, ntpc_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test 1: Scaling\n",
    "This test ensures that both scaling and its inverse operation are implemented correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_train_data = torch.ones(50) * 20.0\n",
    "random_train_data[:15] = -10.0\n",
    "random_train_data[15:25] = 5.0\n",
    "\n",
    "random_test_data = torch.ones(30) * 25\n",
    "\n",
    "# scaling\n",
    "scaled_train_data, scaled_test_data, min_val, max_val = min_max_scaler(\n",
    "    random_train_data, random_test_data\n",
    ")\n",
    "assert torch.sum(scaled_train_data) - 30 < 1e-5\n",
    "assert torch.sum(scaled_test_data) - 35 < 1e-5\n",
    "\n",
    "# reverse scaling\n",
    "reversed_train_data = inverse_min_max_scaler(scaled_train_data, min_val, max_val)\n",
    "reversed_test_data = inverse_min_max_scaler(scaled_test_data, min_val, max_val)\n",
    "assert all(reversed_train_data == random_train_data)\n",
    "assert all(reversed_test_data == random_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4: Plot the Stock Prices\n",
    "Plot the scaled stock prices of two datasets, distinguishing between train and test data points on the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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OMVlZWcz06dOd/r05OTnMnDlzmDvuuINJS0tjIiIimNTUVGbs2LHMtm3brPbV6XTMrFmzmNTUVEYikTDsn6TKykrm2WefZTIzMxm5XM60bNmS+fDDDxmDweBxnmVlZTFjx461es+BAweYuLg4ZuDAgYxKpXL49wwaNIhp376907+ZYRgGAPPUU085fG3u3LlW2/Ly8pgHHniASU1NZSIjI5lmzZoxTz31FKNWq63y4NVXX2VatGjBKBQKJiUlhenbty/z0UcfMRqNhmEYhlmxYgUzYsQIJi0tjVEoFEzjxo2Zxx9/nLl586bLNBNCCCGEkOBD7QtqX1D7ghBCGEbCMG50wRNCCCGEEEIIIYQQQgghhAiMYmoQQgghhBBCCCGEEEIIISQoUKcGIYQQQgghhBBCCCGEEEKCAnVqEEIIIYQQQgghhBBCCCEkKFCnBiGEEEIIIYQQQgghhBBCggJ1ahBCCCGEEEIIIYQQQgghJChQpwYhhBBCCCGEEEIIIYQQQoJChNAJCDSDwYAbN24gLi4OEolE6OQQQgghhBASlBiGQWVlJTIzMyGVhvdYKWpjEEIIIYQQ4jt32xhh16lx48YNNGrUSOhkEEIIIYQQEhKuXbuGhg0bCp0MQVEbgxBCCCGEEP64amOEXadGXFwcAGPGxMfHC5aO9evXY8yYMYJ9PxEGlXv4orIPT1Tu4Sscy766uhqZmZkAjDd4Y2JiBE5R4IVbuVdUVKBRo0bm6+twJpY2RjALt/rjL5SP/KG85AflIz8oH/lB+eg7uubnFx2T9txtY4Rdp4ZpOnh8fLygDQ6GYajBE4ao3MMXlX14onIPX+FY9jKZzPw4Pj4+LBs44VjuAGi5JYinjRHMwrX+8I3ykT+Ul/ygfOQH5SM/KB99R9f8/KJj0jFXbYzwXvxWQA0aNBA6CUQAVO7hi8o+PFG5hy8q+/BE5U6I96j+8IPykT+Ul/ygfOQH5SM/KB+J2NAx6T3q1BBIhw4dhE4CEQCVe/iisg9PVO7hi8o+PFG5E+I9qj/8oHzkD+UlPygf+UH5yA/KRyI2dEx6jzo1BLJx40ahk0AEQOUevqjswxOVe/iisg9PVO6EeI/qDz8oH/lDeckPykd+UD7yg/KRiA0dk94Lu5gahBBCCCGEfxKJBFlZWebHnjIYDNBoNHwnK6Dkcjlqa2uFTgav5HK51drJhBBCCCEkfJmu+aurq4Mirpper4dWqxU6GQ6FYvvBFb7aF9SpIZDevXsLnQQiACr38EVlH56o3MNXOJa9UqnElStXvHqvRqNBbm4uDAYDv4kKsNatWyM3N1foZPAuMTERGRkZQdFwJcErHM+b/kD5yB/KS35QPvKD8pEflI++M13z5+TkQKlUCp0chxiGQUFBAcrKyoROilOh2n5whY/2BXVqCKSqqkroJBABULmHLyr78ETlHr6o7N3HMAxu3rwJmUyGRo0aQSoN3tVRa2pqEB0dLXQyeMMwDFQqFQoLCwEA9evXFzhFJJTReZMflI/8obzkB+UjPygf+UH5yB+x56WpQyMtLQ1KpVK0g3NCrf3gCp/tC+rUEMjp06fRqVMnoZNBAozKPXxR2YcnKvfwRWXvPp1OB5VKhczMTFGP9nJHdXU1oqKihE4Gr0yNrMLCQqSlpdFSVMRv6LzJD8pH/lBe8oPykR+Uj/ygfOSPmPNSr9ebOzTq1asndHKcCsX2gyt8tS8EHQq3a9cujB8/HpmZmZBIJFi1apXL9+zYsQPdunVDZGQkWrRogUWLFvk9nYQQQgghxLmamhr07NkTPXv2RE1Njdvv0+v1AACFQuGvpBEfmTqbxLweMRu1MQghhBBC/MN0zf/66697dM0fSKZr1mAfMBXK+GhfCNqpUV1djc6dO+Orr75ya//c3FyMHTsWQ4YMQXZ2Np555hk88sgjQRkpfsqUKUIngQiAyj18UdmHJyr38BWOZW8wGHD48GEcPnzYq9gYYp0S7omkpCShk+AXwVY24dzGCGbheN70B8pH/lBe8oPykR+Uj/ygfPSd6Zo/JydH9PHwguEaNlTbD67wUTaCdmqMHj0a77zzDiZNmuTW/t988w2aNm2Kjz/+GG3btsXMmTMxZcoUfPrpp35OKf82bdokdBKIAKjcwxeVfXiicg9fVPbhqaKiQugkEIR3GyOY0XmTH5SP/KG85AflIz8oH/lB+UjEhtoP3guqSIz79u3DsGHDrLaNHDkS+/btc/getVqNiooKq39iIJZ0kMCicg8/t6vUmPrtPmzJFee0TOJfVOfDF5V9eDItpeWtJk2a4LPPPnN7/x07dkAikaCsrMyn7w13odTGCGaUh/woK6/AE78cwUcbzwudlKBHxyQ/KB/5QfnID8pHIja+th/cEaptjKAKFF5QUID09HSrbenp6aioqHAYLX7+/PmYN2+e3fbly5dDqVRi8uTJ2Lp1K8rLy5GWloZevXph7dq1AIBu3brBYDAgOzsbADBhwgTs3r0bt2/fRnJyMgYOHGheo7dTp06Qy+U4cuQIAGDs2LE4fPgwbt26hfj4eIwYMQIrVqwAALRv3x4KhQJLliwBYGw0nTp1Cvn5+YiJicG4ceOwdOlSAEDr1q2RkpKCPXv2AACGDRuGCxcu4OrVq4iMjMTkyZOxdOlSGAwGNG/eHA0aNMCuXbsAAIMHD8bVq1eRk5ODiIgI3HPPPfjf//4HjUaDrKwsNG/eHNu2bQMA9O/fH4WFhbhw4QIA4L777sPq1auhUqnQsGFDtGvXztyj3adPH5SXl+PMmTMAgHvuuQd//vknKisrkZGRgW7dumH9+vUAgJ49e6K2thYnT54EAEyaNAk7duxAaWkpUlJS0KdPH/zxxx8AgK5duwIAjh07BgAYP3489u3bh+LiYiQlJWHw4MH4/fffAQAdO3ZEVFQUDh06BAAYM2YMjh49ioKCAsTFxWHUqFFYvnw5AKBdu3ZISEgwN0xHjBiBM2fO4Pr161AqlZgwYYK5LFq1aoW0tDTs3r0bADB06FBcvnwZeXl5UCgUuPvuu7F8+XLodDo0a9YMjRs3xo4dOwAAAwcORH5+Pi5fvgypVIpp06Zh5cqVUKvVaNy4MVq1aoXi4mIsWbIE/fr1Q3FxMc6fNzY2pk2bhrVr16K6uhoNGjRAhw4dzEse9O7dG1VVVTh9+jQA43TJTZs2oaKiAunp6ejRowfWrVsHAOjevTu0Wi1OnDgBAJg4cSJ27dqFkpIS1KtXD/3798fq1asBAF26dIFUKsXRo0cBAOPGjcPBgwdRWFiIhIQE3HnnnVi5ciUAoEOHDlAqlTh48CAA4wjI48eP48aNG4iNjcWYMWOwbNkyAECbNm2QnJyMvXv3AgCGDx+Oc+fO4dq1a4iOjsbEiRPx22+/gWEYtGzZEhkZGfjrr78AAEOGDMGVK1eQm5sLuVyOKVOmYMWKFdBqtWjatCmaNGmC7du3AwAGDBiAgoICXLx4ERKJBPfeey9WrVqFmpoaNGrUCG3atMHmzZsBAH379kVJSQnOnTsHAJg6dSrWr1+PqqoqZGZmonPnztiwYQMAoFevXlCpVDh16hQA+HSO6NOvP178fiMO3ooEEI0nL1xw+xwRGxuLAwcO0DkiyM8RMTEx5ve6c47YsmULANA5IgTOEaby89d1hBjPEXl5eWBz9xwxfvx41NTUoLS0FDExMVAqlSgvLwcAxMTEgGEYqFQqAMbp2ZWVldDpdJDL5YiJiTFfbJvWZTXtm5iYiOrqami1WkRERCAuLg6lpaXmfSUSCaqrqwEACQkJUKlU0Gq1kMlkSEhIQElJCQBjIDuZTIaqqirzvnK5HM68+OKLePnllxEfHw+1Wg21Wg2pVIqkpCSUlJSAYRhERkZCoVCgsrISABAXF4cdO3ZAJpOhpKQEycnJKC0thcFgQGRkJCIjI80N8djYWOh0OrRu3RqnT59GfHw8ysrKoNfroVAoEB0dbc7D2NhY6PV685rHSUlJqKiogF6vt8tDR/mtUqlQU1ODmpoa83m2S5cu5v2CXSi1McR4bnD3+iEyMhLHjx+n6wcv2hjs64ecKin+zCkATgPPDW9J1w/w/vrh9u3bACC6NkawnSPS09OpjQHfzxElJSVYsmSJz+eIcG9jFBcX4/Dhw0HbxgCEP0fU1tbCZNmyZYiKihLFdQT7HCGXy9GuXTtUVFSguroacrlctG0MnU6H27dvh2Ubo7q6Grt370bfvn2tzhHutjEkDMMwbu3pZxKJBL///jsmTpzocJ9WrVrhwQcfxKuvvmretn79eowdOxYqlYqzwWEqZJOKigo0atQI5eXliI+P5/Vv8ERFRYWg30+EQeUeXvq9vw35ZZYZGlfeHytgaogQqM6Hr3As++rqasTGxgIAqqqqEBMT49b7amtrkZubi6ZNmyIqKsqfSeRNQUGB+fHSpUsxZ84cnD9/Hnq9HjKZDLGxsea8YBgGer0eERFBNZbIiqMyqqioQEJCguDX1c6EWxsjmIXjedMf/jiSi1nLjTd96drTN3RM8oPykR+Uj/ygfPSdt9f8gRRM7QtT+wFw3MYwCaU2hrMycreNEVTLT2VkZODWrVtW20y9i1yNDcA44ic+Pt7qnxiYerRJeKFyDy/sDg0AUGl0AqWECIXqfPiisg9tGRkZ5n8JCQmQSCTIyMhAVFQUzp07h7i4OGzYsAHdu3dHZGQkdu/ejcuXL2PChAlIT09HbGwsevbsaR45aWI7NVwikeCHH37ApEmToFQq0bJlS6xZs8b8uu3U8EWLFiExMREbN25E27ZtERsbi1GjRuHmzZvm9+h0OsyePRuJiYmoV68eXn75ZUyfPt3pTf9QF0ptjGBG501+HDx0WOgkhAw6JvlB+cgPykd+UD4SsWEv8eSojZGRkUFtDA5B1anRp08fbN261Wrb5s2b0adPH4FSRAgh7jtXUCl0EgghxK9SUlKQkpLi02cwDAOVRifIPz4nML/yyit4//33cfbsWXTq1AlVVVUYM2YMtm7dimPHjmHUqFEYP348rl696vRz5s2bh6lTp+LEiRMYM2YM/va3v5mnrXNRqVT46KOP8Msvv2DXrl24evUqXnjhBfPr//73v7F48WL8+OOP2LNnDyoqKsxLFIQramOQUGIQxToMhBBCQllKSgri4uKEToZHhGpj8L1AErUxLASdo1JVVYVLly6Zn+fm5iI7OxvJyclo3LgxXn31VeTn5+Pnn38GADzxxBP48ssv8dJLL+Ghhx7Ctm3bsGzZsqDsae3evbvQSSACoHIPb3JpUPUjEx5QnQ9f4Vj2MTExKCoq8vlzarR6tJuzkYcUee7MWyOhVHh/ecyefv/WW29h+PDh5ufJycno3Lmz+fnbb7+N33//HWvWrMHMmTMdfuaMGTNw3333AQDee+89fPHFFzh48CBGjRrFub9Wq8U333yD5s2bAwBmzpyJt956y/z6ggUL8Oqrr2LSpEkAgC+//NK8BnmoCOc2RjALx/OmPzRp2gzIK3C9I3GJjkl+UD7yg/KRH5SPvjNd81+4cEGUS085IlQbw1X7wtM8pDaGhaB32A4fPoyuXbuaAzM999xz6Nq1K+bMmQMAuHnzplXPUtOmTbFu3Tps3rwZnTt3xscff4wffvgBI0eOFCT9vtBqtUIngQiAyj28SSRCp4AEGtX58EVlH57YI7F69Ohh9VpVVRVeeOEFtG3bFomJiYiNjcXZs2ddjqLq1KmT+XFMTAzi4+NRWFjocH+lUmlubABA/fr1zfuXl5fj1q1b6NWrl/l1mUwWcg38cG5jBDM6b/JDo6PlTvlCxyQ/KB/5QfnID8pH/lBe8sPTmRzUxrAQdKbG4MGDnRbeokWLON9z7NgxP6YqME6cOIH27dsLnQwSYFTu4U2rNwidBBJgVOfDF5W996LlMpx5S5ibydFymU/vV6lU5se2o65eeOEFbN68GR999BFatGiB6OhoTJkyBRqNxulnyuVyq+cSiQQGg+PfE679+Z72Lnbh3MYIZnTe5Efe1WsAuGPBEM/QMckPykd+UD7yg/KRP8GWl0K1MVy1L1QqlcMYblyojWERnCHSCSFE5LhO7hoddWoE2v/tuIwkpRz39mosdFKIHxRW1mLB1kv42x2N0SaDgvQKraamBqNHjwYAbNiwwaOLczaJROLTElBitWfPHsyYMcM8JbuqqgpXrlwJaBoSEhKQnp6OQ4cOYeDAgQAAvV6Po0ePokuXLgFNCyHEP9gxNRiGgYSmChNCCOGR6Zq/sLAQEydO9PqaP9CojeE/QrUxQq80g4Q/o78T8aJyDx9cHdYamqkRUNdLVfj3n+cAAFO6N0SELPArLlKd968Xl5/AzgtF+GV/Hq68P1bo5FgJx7I3GAzYuXOn+XE4SkpKcvhay5YtsXLlSowfPx4SiQRvvPGGIPk0a9YszJ8/Hy1atECbNm2wYMEClJaW0o1PIrhwPG/6YsuZW1Bp9birc6bV9s5dumLFNeP1D8PQ8qe+oGOSH5SP/KB85Aflo+/omp9fztoP7gjnNgZFrRXIrl27hE4CEQCVe/gwcPRq3K5yPv2P8EvPGqpYqhJmvU+q8/51+ka50ElwSExlr9Ub8NvBq6LOr1BRWVnp8LVPPvkESUlJ6Nu3L8aPH4+RI0eiW7duAUyd0csvv4z77rsPDzzwAPr06YPY2FiMHDkSUVFRAU8LIWxiOm+KHcMweOTnw5i95BgKK2qtXvt66znzY67rUeI+Oib5QfnID8pHflA+ErFx1n5wRzi3MWimhkBKSkqETgIRAJV7+DBwtCFf+/0kJnZtEPjEhCl2p0ZlrRapcZEBTwPV+fAllrL/4/gNzFpiiRMgthktoWDGjBmYMWMGAECn0zmM59CkSRNs27bNattTTz1l9dx2qjjX55SVlZkf234XOy0mEydOtNonIiICCxYswIIFCwAYR9i1bdsWU6dOdfg3EhIIYjlvBgP2qaGsRou0eOMNg8paLW5aQvtwXo8S99ExyQ/KR35QPvKD8pGIjU6n49xue11PbQx71KkhkHr16gmdBCIAKvfwwTUyTqXRC5CS8KXVW8pALVA8E6rz4UssZf/NzstCJyGsRESI/9I6Ly8PmzZtwqBBg6BWq/Hll18iNzcX999/v9BJI2FOLOfNYMC+zmQ//mjjeYf7Ec/RMckPykd+UD7yg/KRiE0wtB/cIUQbg5afEkj//v2FTgIRAJV7+KA2pPC0rBgmQgVppzrvb+JdKFwsZX/6RoXQSQgrsbGxQifBJalUikWLFqFnz57o168fTp48iS1btqBt27ZCJ42EObGcN4OBdTBwy+OLhVVW+9H1qG/omOQH5SM/KB/5QflIxCYY2g/uEKKNQZ0aAlm9erXQSSACoHIPH1wj46b3yRIgJeGLHZhdK1CQdqrz4UssZS+TirfjJxSxp22LVaNGjbBnzx6Ul5ejoqICe/fuxcCBA4VOFiGiOW8GA/Z1JvuS0/acTzM1fEPHJD8oH/lB+cgPykciNsHQfnCHEG2M0JjjQgghIqPnaETKZdSPHEhanfAzNQgRmlwmsYov429KpTJg30UIIeGKfZnJ7rigTg1CCCGBoFQqodfT8tpEWNSpIZAuXboInQQiACr38KHX2zciKVhjYB2/XmZ+rBZopgbV+fAllrJvlR6HE9fLA/JdMTExqK6uDsh3iRV16hDiPbGcN4OBw5kaEttOjUClKDTRMckPykd+UD7yg/LRd6Zr/rNnzyImJkbo5AQ9aj94j4YNC0QqpawPR1Tu4YNrpgYDalkGSmFlLd5bf878XCvQTA2q8+FLLGUfo6DxK4EkkdByX4R4SyznzWDAvqJ0NlODoZkaPqFjkh+Uj/ygfOQH5SN/KC/5Qe0H79ERKJCjR48KnQQiACr38GHgGBpH7Ur/sW2055fWWD3XCDRTg+p8+BJL2at1gZsW/sfxGxj04XacL6gM2HeKTbjPVCHEF2I5bwYDq5karO3yCKnNfgFKUIiiY5IflI/8oHzkB+Ujfygv+UHtB+9RpwYhhPiBjqMVufbETQFSEvqeXZqNAR9sR7lKa95Wrba+kRvON1lDGQ1qcU0dwFlKM385gEPfvIQ7Bg9DbW1twL6XEELCDcM6tbMHdtSLUVjtRzE1CCGE8K22thZjx47FBx98QNf8RFDUqSGQcePGCZ0EIgAq9/DBFZi3uEotQEpC255Lxfj9WD6ul9Zg8cE8AMC5ggr8/T8HrPZbsO2SEMmjOu9nYu7TEEvZawM4S4kxGFCTcxhl5w+GbeDAxMREoZNASNASy3kzGLA7K9iXnCmxkQ73I56jY5IflI/8oHzkB+Wj7/R6PdavX4/s7OywvebnE7UfvEedGgI5ePCg0EkgAqByDx+OGpG0tjG/vv8rx/zYNBvj7z+Ip55RnQ9fYil7nT4w5xw6txnR9HFCvCeW82YwsL7OtDyOj7KOo0SnZt/QMckPykd+UD7yg/KRiA21H7xHnRoCKSwsFDoJRABU7uGDa/kpANAG6AZjuIhgBcRcnX0DgLhmxFCdD19iKXutITAzNcprtK53CiESiYTzX0JCAiQSCd58802fPnvVqlW8pZWQYCGW82YwYF9mOpuQRzM1fEPHJD8oH/lB+cgPykciNlqtpR3lqI1h+kdtDGsRrnch/pCQkCB0EogAqNzDB1egcADQGQxQUH8ybyKk4s5LqvPhSyxlH6iZGnadidW3gZiYgHy3EG7etMRIWrp0KebMmYPz58+joqIC8fHxiI2NFTB1hAQnsZw3gwF7dpyO1Xlte8anQOG+oWOSH5SP/KB85AflIxGbiAjLrXlHbQwTamNYE/fdoBB25513Cp0EIgAq9/BBMzUCwzafn/ntmEAp4UZ13r/EHChcLGUfqHNOUaXGesNnHQLyvULJyMgw/zPNzsjIyECLFi2QkZGB3377DW3btkVUVBTatGmDr7/+2vxejUaDmTNnon79+oiKikJWVhbmz58PAGjSpAkAYNKkSZBIJObnhIQDsZw3gwH7zG6wChpuvZ+jQTbEPXRM8oPykR+Uj/ygfCRiExcXZ37sqI1h+kdtDGvUqSGQlStXCp0EIgAq9/DBFSgcAHQBDNobDlJiFVbPV9UtQWUSF2kc9ZAWZx04M1CozvuXRMShwsVS9uwRvFFy/132FVepMVx6mJ8PYxhAUy3MPx+XaiktLcXixYsxZ84cvPvuuzh79izee+89vPHGG/jpp58AAF988QXWrFmDZcuW4fz581i8eLG5YXHo0CEAwI8//oibN2+anxMSDsRy3gwG7GWl9KzHtmcwWn3KN3RM8oPykR+Uj/ygfAxjQrUxXPwYl5aWupV8amPYo+WnCCHEDxytYXzqRgUGtUoNcGpCV3KMwunrL49ug9dXnUK3xkkBShEJpMzEKBRU1AIAqtQ6xEbSZY2tMpVljVa5H5drq6jVQgsZPx+mVQHvZfLzWZ567Qag8G3ZrLlz5+Ljjz/G5MmTAQBNmzbFmTNn8O2332L69Om4evUqWrZsif79+0MikSArK8v83tRU4+9DYmIiMjIyfEoHISR0scfOrD6Wb762ZGyuPymmBiGEEFJHqDYGD+0LgNoYXGimhkA6dAjtZRkINyr38OFo+altZ28FOCWhzVVwYlldIHFH5eFvVOf9S6mwdGLUavUCpsSeGMperbPOE3/WgqpaHeIVADM3HszceMQoJEBtuR+/UZwMBgMuX76Mhx9+GLGxseZ/77zzDi5fvgwAmDFjBrKzs9G6dWvMnj0bmzZtEjjVhIiDGM6bwYK9rNTKY/nmx3bLT1Gnhk/omOQH5SM/KB/5Qfnou5iYGDAMgxMnTiAmhGPoBUp0dLTLfaqrq6mNwYGGNApEqVQKnQQiACr38OFoDWOJmIMABKH8shqnr5s6NfQGYZb9ojrvX+xl3rQiW9pNDGVfrnLe6ceX21VqzN9wDsOlcusXPusEvJLn+QfKlcYRTUKQ+1ZuKpUKAPD999+jd+/eVq/JZMaZLN26dUNubi42bNiALVu2YOrUqRg2bBhWrFjh03cTEuzEcN4MFuy+iv4tUizbYTtTI1ApCk10TPKD8pEflI/8oHzkT9DlpVBtDBftC6kbs+mrqqoAUBvDFnVqCOTgwYNo3ry50MkgAUblHj4cxdTo3TQ5wCkJXRqdATvOFzndJ8LUqSFQo57qvH+x69lLK07gxZGt0alhonAJYhFD2at11h09tsuS8OXZZccBAHrbCcC1Zd59oETCyxRtIcTGxiIzMxM5OTn429/+5nC/+Ph4TJs2DdOmTcOUKVMwatQolJSUIDk5GXK5HHq9uGYeERIIYjhvBgv2DAylwrL0n+1p3l/n/XBBxyQ/KB/5QfnID8pH/gRdXoq0jVFdXY2oqCin+6Snp1MbgwN1ahBCiB846tSIjaLTLl+KqtROX58zrp3gMzWIf7GDo/51sRh/XSzGlffHCpgicXlzzWmr5/66tbX7orFzsUwrxz2rjTMVfpkUjaiI8JyZNm/ePMyePRsJCQkYNWoU1Go1Dh8+jNLSUjz33HP45JNPUL9+fXTt2hVSqRTLly9HRkYGEhMTAQBNmjTB1q1b0a9fP0RGRiIpiWICEUKssc/n7CU2bc/zNFODEEII32pra/GPf/wDV69exaRJk1zekCf8oDaGPYqpIZDRo0cLnQQiACr38KF3MDKOGpf8UbuIodAwKdoSU0OgqRpU5/2LK1aKRieODiwxlP3Wc4UB+R5TKRzWt8CKMzqsOKODyFYDC5iEhAQ88sgj+OGHH/Djjz+iY8eOGDRoEBYtWoSmTZsCAOLi4vDBBx+gR48e6NmzJ65cuYL169ebp55//PHH2Lx5Mxo1aoSuXbsK+ecQElBiOG8GC/ZMDatODYqpwSs6JvlB+cgPykd+UD76Tq/XY8WKFTh48GDIjfwXQkJCglv7URvDHnVqCOT48eNCJ4EIgMo9fDgKTE2NS/7cLK91+roiQmpefkqoQOFU5/1Ly9GBUVkbmDgSroix7P11+rF8LsfMjLNr/fOlIjJjxgyUlZUBAGpqjHF+7r//fhw7dgxqtRolJSXYuXMnJk2aBAB49NFHcezYMVRVVaG8vBxbtmyxaliMHz8eFy9ehFarxZUrVwL95xAiGDGeN8WKvayUjtWLbB9Tg647fUHHJD8oH/lB+cgPykciNqb2gy12G8OE2hjWqFNDIDduCBQAkwiKyj182AYKbxit59xOHHtn7RmM+mwXqtU6u9dOXC/DG6tPmZ+P7pBht0/XxkmIjzIGLq6ocX6ju6Ra42NquVGd968zNyvstqk04hgtJHTZ77rgPN5MwKx9RugUBJRG459zCSHhQOjzZjBhX046m6lBfRq+oWOSH5SP/KB85AflIxEbaj94jxZ3F0hsbKzQSSACoHIPH2Uq65voERHGPmTq03DfD7tzAQCrsvPxt95ZVq/d9eUeq+fPDGuFj+7pjINXSlCm0mBwqzQkRMuRoDR2apSqHHdqLDt8DS+tOIGXRrXGPwe34PVvoDrvP4eulHBur9bYd4IJQeiyf2DhQbtttiN4A8IgjvIIFJlM5nonQggnoc+bwYQ9A4Nx8Nh2P+I5Oib5QfnID8pHflA+ErGh9oP3aKaGQMaMGSN0EogAqNzDx/PLrae1JtcFYaLGpedqtc4X589MiELrjDjEREZgSOs0TOraEEkxCgBAktL4f5lKY9fQN3lpxQkAwAd/nucx1UZU5/3nwq1Kzu1cM3uEEE5l3yLNSeNQL47yCBR318QlhNgLp/OmrwysSyP25Y3t4BkaTOMbOib5QfnID8pHflA+ErGh9oP3qFNDIMuWLRM6CUQAVO7hqVFyNEpuFwOwH0FHXHO1ZJezWRimTg2dgUE1x7JE/i4PqvP+06ReDOf2arU4lp8SY9n763A3Lf8WGcFxWRlmMzVKSrhnEBFCXBPjeVOs2DPv2ANmvtuVY7UfDabxDR2T/KB85AflIz8oH4nYUPvBe9SpQQghfrbpmUHm8Ll655MOSB2rAJguOjVqtI5vYkfJpZDLjLnPFVdDzRFomgQHvYPjwtnxEK5MnQ183tqqUutw5oZ1TJO7uzew31FXA6joQp0QQvjE7qtgn9urbGYr0mAaQgghhIQq6tQQSJs2bYROAhEAlXt4ilbIEBOjBEAj5ty14VSB+fGNshp8suk8CsprPf4ciUQCrd6Y54fzSq1eq9Ho0eaNP31LqAtU5/3HUaeGWG7giKnsR9XNpODTpK/2YMwXf+Gvi0Xmm2uKyGhUVVWh6tU41IWzMVo0zq3PFEvZ+SI6OlroJPiFwUAdwMT/xHTeFDvrmBqW7ZO6NrDZL1ApCk10TPKD8pEflI/8oHz0nVKpRFVVFfbs2QOlUil0cpwKhmvYUG0/uMJH2VCgcIEkJycLnQQiACr38BUplwPQUKeGm/65+Kj58S/78wAAW84WYv3TA+z2faR/U7c+c/aSY7irc6b5+R/Hb/iYSteozvuPqVOjc8MEHL9ebt7uKgZLoIip7J++syVWZ9/gdarGxcIqAMDG0wVIiDb2YEilUsTExAAKifXOhaedfpZcLodEIkFRURFSU1MhkUic7i9mOp0OtbWed8CKFcMw0Gg0KCoqglQqhUKhEDpJJISJ6bwpduzOCmcdwq6W8CTO0THJD8pHflA+8oPy0XcSiQQxMTFo0KCBaK/bFQoFpFIpbty4gdTUVCgUCtGmNdTaD67w2b6gTg2B7N27F1lZWUIngwQYlXv4Ki8vBxDhtzXtw8GZm8albmptlhd6aZT7o230BgYyqfFixvS/SVpcpI8ptEd13n/0dZVJJpXg0L+Goee7WwAA76w7i4ldOZZBCjAxlX2UXOa3z96fU4JLdR0c3pLJZGjYsCGuX7+OK1eu8JMwgVRXVxs7dkKMUqlE48aNIZXSJG/iP2I6b4qd1UwN1nbbWYzUp+EbOib5QfnID8pHflA+8kfMeSmVStG0aVPcvHkTN274fzCjL0K1/eAKH+0L6tQghBA/GNw6FTvOF6F1ehwAwDQogGZquKdpSgxyi6vttucUVWHoxzuttim4ghM7sPbEDUzoYrzhHRdl/RNI8TWCi2n0qUwqQSqrQ6q4Si1UkkTLdP5heJqqoWHVFXaHhl6nwYwZM4DsGnw7LgqREe6PhoqNjUXLli2h1drHvgkma9euxbhx7i23FSxkMhkiIiJEO7qNkHDEOFh+yrZTIxSW9SOEECIuarUajz/+OHJzczF58mRERvI/OJAPCoUCjRs3hk6ng14v3riLodh+cIWv9gV1aghk+PDhQieBCIDKPfStzs7H6RsVqJ9gXBdxTMf6AIDUlHq4WFnuMA4AsdYjK4mzU8O2Q+OpIc09+tzrpTXmx2U2gcPVOusLHYZh8NfFYiTHKHD0aikSouW4q3OmRz+8VOc9d6mwCp9uvoBZd7ZAm4x4h/vpWJ0aYiRk2fv7Jpaj85hBr8NPP/0EAPhqTBQ8bd7IZDLIZP6bVRIIgwcPRlRUlNDJICQo0W+m+/SscRjsATM0U4NfdEzyg/KRH5SP/KB89J1OZ7nm1+l0ou3UAIxLZcnlcsjlctc7C4TaD96jOeQCOXfunNBJIAKgcg99T/+Wje925WD7uUIAlhHSNdXGG/Q0YM49ejczKiPBu6Baq7Pz8dKKE1bb1DqD1c3gnReK8MDCgxi3YDfmrD6Np3/Lxp5Ltz36Hqrznpvx40GsO3kToz77y6o8arV67L1cbJ4lYGDE3akhZNnXsJZomzW0BSQw5hFf5x+tg6BuTj+/qpCfLxc5qvOEeI/qj/u0rF4Nq5kajG2nBl14+oKOSX5QPvKD8pEflI9EbOiY9B51agjk2rVrQieBCIDKPXwUVFgHeqqtNc4QoMale9yd0WIKUOwu0+yPD/48b/cawwAa1k2C/TkldvucK6jw6PuoznuOPZtm0d4r5sev/X4S939/AG+tNQadNh0jUpEuiSNk2VepdQCMnarPDW9l3s7X2UevdzBTw9n5LXsxT98ublTnCfEe1R/3WXVqAPh403mM+mwXKmxmodJ1p2/omOQH5SM/KB/5QflIxIaOSe9Rp4ZAoqO9G11MghuVe/gx3W6NqFtShZYBcO3CrUqsznYvmNfYuuW93LXiyHWUqTTIL6vhfH3l0XzzY7nM/ma5pzfQqc77Zt4fZ8yPTWXz6/6rOJVfbl5+KqJupkZKrGXas0EEFU3Isjfd1GIY45Rrvvt9HM3UWJN90/GbtrzJbyJEiuo8Id6j+uM+HatzmWEYLNh2CecKKnEg13pABvVp+IaOSX5QPvKD8pEflI9EbOiY9B51aghk4sSJQieBCIDKPfyYbiY2yMwE4P6ySuFs9Od/ub2vN0sPrTqW7/C1V1eeND8uU9kHLM5M9GytS6rzvqvV2gd1e2XlCatA4QCw8sm+5tfFUM+ELPtf9uVxbucr1oajmVSmGSLhjOo8Id6j+uM+R8tP2aKZGr6hY5IflI/8oHzkB+UjERs6Jr1HnRoC+e2334ROAhEAlXv4MQWVzr9unFLo7wC+ocDdpadeGd3Gq88/dq3M6eummA2/7Le/MRwZ4VkQY6rzvlu4J9duW3GlxtxxYZo9kxRjWYrM3WPIn4Qse9tg9nwv0KVzsPyUS7m7+E2ICFGdJ8R7VH/cp2X9zjFOFhcUwc9hUKNjkh+Uj/ygfOQH5SMRGzomvUedGgKhG5vhico9fJnuMYphWZxQ8fjAZl69z9XSVmUqDcpr7GdpAJ7fLKc677trJfZLhRVU1EKtNXY+KSKMlzLspcHEMDJVyLLv2SQZANAgsW4qc13W8JUinbfnsZ/Gh/xaKFTnCfEe1R/36VgzNZydksXwexjM6JjkB+UjPygf+UH5SMSGjknvUaeGQFq2bCl0EogAqNzDj+k+a3xcHADuhifDMJi95BheXH48gCkTJ/ZyCgDw5ODmDve1HY3urUldG1g9L6vR4vAV+yDhAKBzEEvAEarznmudHmf13DRzxlZN3bJUSoVx9gx7KTIxzNQQsuxNN7EaJyv98vk6PXeZREZFo7CwEIUntkIp59wFMIT2ElVU5wnxHtUf91kvP+X4N49ulPiGjkl+UD7yg/KRH5SPvlMqlSgsLMSmTZugVPqnvRFO6Jj0HnVqCCQjI0PoJBABULmHH0ndEGlltDEWA9eIuYKKWqw5fgPLj1xHjcY+fkA4mfHjQavnYzp4FgjcGzOHtrB6/tmWCzhXUMm5b2WtZzdkqc57Lq+k2up5lJz7UuXDjecBAEpFBACbmRqe9T35hZBlX1KtAQBI67LOdB7i497WkbxSfLsrh/O1nS8NQWpqKlI7DoVk4tfcHxDinRpU5wnxHtUf92nZgcI5Xjf184ugjz+o0THJD8pHflA+8oPy0XcSiQSpqalo27YtbwMNwxkdk96jTg2B/PWX+4FwSeigcg8/pt/4oqJCANydGuz16cN9mYA9l25bPY+Q+X6R1LlhgtPX5VLrn8L1JwvMN8xtOVqWyhGq8565XFSFWq11j0SDpGin74nmmqkhgnokZNnPXXMaAHDsahnvn333/+3FiiPX7bZfeGc0MhNZZZXUhPsD9J7VoWBDdZ4Q71H9cR97xlxOUbXd61Fy429juF9X+oqOSX5QPvKD8pEflI/8obzkB+Wj9wTv1Pjqq6/QpEkTREVFoXfv3jh48KDT/T/77DO0bt0a0dHRaNSoEZ599lnU1tYGKLWEEOIZ021W08mWe/kpy2Ov16oPURFS3zs1Fj96B0a2T3f4uummuDuOXy/3OT3EsZtl9r/nroJSV6uNI//Zh4oYlp8KhMpaLd764wyyr5Vxvq6qm/kViAFUcpkEarUaTz31FJ566imo6/cExn0KTFlovWOIz9Qg4kFtDBLKtC5+Gy2dGoFIDSGEkHBiuub/8ccfoVarhU4OCWOCdmosXboUzz33HObOnYujR4+ic+fOGDlyJAoLCzn3/+9//4tXXnkFc+fOxdmzZ/Gf//wHS5cuxWuvvRbglPtuyJAhQieBCIDKPfyYbiZmZhqXUeIaMcfetv7kTbu4EuHq2WGtECHz/WcqNjICfZrV43wtIz4KqXGRbn/WH8edBxm3RXXeM9wzmZzXh5/35QEwToM2dWx8veOS4OuIB6LsP9p4Hgv35GLiV3uc7sfu0/BXvkgkEuh0Onz99df4+uuvodPpgB4PAS1HWu8Y4jM1qM6LQzi3MYIZ1R/3aV2ssxgVYbx+Evq3MNjRMckPykd+UD7yg/LRd6Zr/s2bNxuv+YlP6Jj0nqCdGp988gkeffRRPPjgg2jXrh2++eYbKJVKLFy4kHP/vXv3ol+/frj//vvRpEkTjBgxAvfdd5/LkVdidOXKFaGTQARA5R5+TGvZl1dUAQAu3qqy24cdp+HVlSfxw1+5gUmcyD09rCUvMzUAQBHBPRvjf//sy8vnO0J13jNct15Ms5ccHQpvjGtnfmxagurHPVfQ9NX1KKwUbpR1IMr+rIPYL6KiiLF+bgjtTg2q8+IQzm2MYEb1x32uZjHS8lP8oGOSH5SP/KB85AflIxEbOia9J1inhkajwZEjRzBs2DBLYqRSDBs2DPv27eN8T9++fXHkyBFzAyMnJwfr16/HmDFjApJmPuXm0k3LcETlHn5MMzV2XTNOy/z9WL7dPvllKqvnq7Pt9wk3/767IwDHMTX++2hvjz5PzvE5cZERaFAXAyDGgyWoDB6s5UB13jPsEaWmDgrTzCXTDRpbYztagslLbdZZWpPt2cwaPvm77JcfvoaDuSV227lG5bID+AX8/pbt2lchvvwU1XnhhXsbI5hR/XGfq1m9kaZODZr86xM6JvlB+cgPykd+UD4SsaFj0nsRQn1xcXEx9Ho90tOt1zlPT0/HuXPnON9z//33o7i4GP379wfDMNDpdHjiiSecTg1Xq9VWa7xVVFTw8wf4SC6XC50EIgAqd2KrpFpjXj7HhAbVAUNapwEAIqT2fe//e7IPumcle/R5igj7z4lk3STf+vxgjPp8F8pU9qPIP7qnMzafKcDG07cAADVaPWIi3fv5pDrvPaVchkq1DlVqHRiGQa1Wz7lfotKSxzKb6RyxbpaTP/i77F9ccYJz+66LxX79XrZW6bFIjlHgDgfLu3GqLnYcRDwEUJ0XXri3MYIZ1R/3uY6pYbzuoZkavqFjkh+Uj/ygfOQH5SMRGzomvSdca98LO3bswHvvvYevv/4avXv3xqVLl/D000/j7bffxhtvvMH5nvnz52PevHl225cvXw6lUonJkydj69atKC8vR1paGnr16oW1a9cCALp16waDwYDs7GwAwIQJE7B7927cvn0bycnJGDhwIFatWgUA6NSpE+RyOY4cOQIAGDt2LA4fPoxbt24hPj4eI0aMwIoVKwAA7du3R7du3bBkyRIAwMiRI3Hq1Cnk5+cjJiYG48aNw9KlSwEArVu3RkpKCvbsMa6XPWzYMFy4cAFXr15FZGQkJk+ejKVLl8JgMKB58+Zo0KABdu3aBQAYPHgwrl69ipycHEREROCee+7B//73P2g0GmRlZaF58+bYtm0bAKB///4oLCzEhQsXAAD33XcfVq9eDZVKhYYNG6Jdu3bYtGkTAKBPnz4oLy/HmTNnAAD33HMP/vzzT1RWViIjIwPdunXD+vXrAQA9e/ZEbW0tTp48CQCYNGkSduzYgdLSUqSkpKBPnz74448/AABdu3YFABw7dgwAMH78eOzbtw/FxcVISkrC4MGD8fvvvwMAOnbsiKioKBw6dAgAMGbMGBw9ehQFBQWIi4vDqFGjsHz5cgBAu3btkJCQYB6dN2LECJw5cwbXr1+HUqnEhAkTzGXRqlUrpKWlYffu3QCAoUOH4vLly8jLy4NCocDdd9+N5cuXQ6fToVmzZmjcuDF27NgBABg4cCDy8/Nx+fJlSKVSTJs2DStXroRarUbjxo3RqlUraLVaLFmyBP369UNxcTHOnz8PAJg2bRrWrl2L6upqNGjQAB06dMDGjRsBAL1790ZVVRVOnz4NAJgyZQo2bdqEiooKpKeno0ePHli3bh0AoHv37tBqtThxwniza+LEidi1axdKSkpQr1499O/fH6tXrwYAdOnSBVKpFEePHgUAjBs3DgcPHkRhYSESEhJw5513YuXKlQCADh06QKlUmkcwjh49GsePH8eNGzcQGxuLMWPGYNmyZQCANm3aIDk5GXv37gUADB8+HOfOncO1a9cQHR2NiRMn4rfffgPDMGjZsiUyMjLw119/ATCuJXjlyhXk5uZCLpdjypQpWLFiBbRaLZo2bYomTZpg+/btAIABAwagoKAAFy9ehEQiwb333otVq1ahpqYGjRo1Qps2bbB582YAxhGYJSUl5psZU6dOxfr161FVVYXMzEx07twZGzZsAAD06tULKpUKp06dAgCPzhF33XUXbB07dgxLrh8AEG/etmDRUkyfPBqHDx/GCzuqUaKxvuGuqq1BTk4ODhw4ACB8zhHGmMbGfFq9ahWa1K+Hlh272+Xpzq2bcSHa4NE5IqNjP7vPketUWLJkifkckSZToQz2FxTp8ZG4deMaAAUA4PKVazh9dD8A1+eIfv36mc8v7pwjtmzZAgBhe47IUythujTR6bQAJFhy8Bra156GgTEeG30bKLA3X2Mun7WrfzefI2xHrr6y8iSuntyHxjGMIOeI8+fP++U6ok1by5JbJt/8vg0xqlt442S81fajR49CHptofl5YVITLly56fY5gk0sYTIi7AhRdwbVr0cjLs+6gNZ0j7mNty1/yDLT3/BrS1xHXrl3z23WE2M4RKpX1TMNgFUptjNjY2KC+fjh+/HjInhv4bGMcPX0BgONZpuUlxQAioDcYsGbNmpC/fvBnGwOA4G0MOkfQfYi7774bDMNgyZIlIXH9IPQ54vDhwyF1HyLQ54jaWssSv8uWLUNUVBSdI3w4R5juEwp5r1Js5wh32xgSRqDoYRqNBkqlEitWrMDEiRPN26dPn46ysjLzH8M2YMAA3HHHHfjwww/N23799Vc89thjqKqqgpRjRC/XKKpGjRqhvLwc8fHxdvsHyooVKzBlyhTBvp8Ig8o9dD23NBvXSlU4dKXUavucce3wUP+maPLKOvO2+ZM74r5ejQHAartJi7RYbHlukH8THEC5xdX4evslPDm4OZqlxjrcb83xG5i9xHihkD1nOBKVCmj1BrR+fQPYKz5tf2EwmqbEOPgUbgzD4POtF/HZlovmbetnD0C7TMvvwMOLDmHrOfsgspfeHY2XVpzAyrqlwzY8PQBt67v3+0F13jM7zhdixo+H7LafnjcS7ecaL54OvHYner+3FQAwc0gLvDCytXk/rvoUGSHF+XdG+ynFjvmz7K+VqDDgg+122+/r1RhLDl612nbl/bEordag69vGxtXl98bYzWjxBDuPe2QlYcWTlrg01dXViI011vGqqirExNTV0zcTrD/kzXKvv1/swq3OV1RUICEhQfDrarZwb2MEs3CrP77g+r0zkYLB0Lbp2HK2EO9P7oh76645iefomOQH5SM/KB/5QfnoO4fX/MQrdEzac7eNIVhMDYVCge7du2Pr1q3mbQaDAVu3bkWfPn0436NSqewaFTKZcYSKo76ZyMhIxMfHW/0TA602tANlEm5U7qFr5bF8uw4NgDu48f/tuOz0szS60FoA+W/f78fyI9fxwELnAVf1rIWfTTdd5TIpsueOsNrPmyWFJBIJnhnWymobu0PDtI+tbo0TESGTmoNVA8Cc1adw+kY5Fu7Ohd5FfA2q855hBz4d0DLF/Lii1pKPyTEKZNVTIiM+Ck8Pa+nyM9UC1Sd/lv3tag3ndtsOjZ8f6gXAOqwFn2NZShykI5xRnRdeuLcxghnVH34oIxhzjCkPwoARDnRM8oPykR+Uj/ygfCRiQ8ek9wRdfuq5557D9OnT0aNHD/Tq1QufffYZqqur8eCDDwIAHnjgATRo0ADz588HYJzi88knn6Br167mqeFvvPEGxo8fb254BIumTZsKnQQiACr38MM1IvpqiQpqnR6REdznLaUHQauDwY1y4/TU66U1Tvdj39Bmx9KIj5KjX4t62HPpNgAgNS7SD6nkvnGlVBh/Jp8Z1hJrjhuDTh+6UoqxX+yue13mdAQk1XnP6Oo6trpnJeHDKZ1xx3zjTcl//MfYIRYhlUAuk2LjMwMhlRgfu6OyVou4qMCuVerPslc7iC9ia2CrVACABKxA4T58r8Hm7lhOcbXV8+joaHOgu+joaMsLne8Hjv/Xh28OHlTnxSGc2xjBjOqPe/Zech47KS1WzurUoF4NX9AxyQ/KR35QPvKD8tF3pmv+Y8eOWV/zE6/QMek9QTs1pk2bhqKiIsyZMwcFBQXo0qUL/vzzT3Ngv6tXr1qNmnr99dchkUjw+uuvIz8/H6mpqRg/fjzeffddof4ErzVp0kToJBABULmHJtsbfWxSB8u8jPh0F3a8MJjzNV2YDqtjz3qwXekjJdY/HRlsf3HcJIiQGcvP0bJZp284DwxLdd4zmrqOLYVMivhoyyXKpcIqAJa6ESX37CbjiE93Yd+rd/KUSvf4s+w1etezT4a2SeP9e2tsOlNsO22lUin33z3inbDp1KA6Lw7h3MYIZlR/3HP/Dwecvp4UqzRfRwm00nTIoGOSH5SP/KB85Aflo+9M1/xRUVGcS3QSz9Ax6T3Bj76ZM2ciLy8ParUaBw4cQO/evc2v7dixA4sWLTI/j4iIwNy5c3Hp0iXU1NTg6tWr+Oqrr5CYmBj4hPvIFGSIhBcq99DkrBMiwkGnRt5tlcOli1wtaRRMrpU4D/D0vyPXMeqzXbhWosKKI9fN22U2S0HxlSVymeNYAlz5bpsOu9ddxCagOu8ZU0yVfTm3oXBzFoY7bpbXut6JZ/4s+2q165kaC2f0tDyxWn7Kh+/V6Kyefzati3tvjKkHPG4MxAhlPe8TEASozotHuLYxghnVH36oKkrMS2qG0CWlIOiY5AflIz8oH/lB+cgfykt+UD56T/BODUIICXbOpvZLndwU1zt4nzudGgXltbjry91Ydvia6wQG2Mnr5Zj09R4culKCA7klTvd9fvlxnCuoxIwfD+JwniUmSYTNDW0nfREeiXKw5BfA3UHRKiPO/HhG3yYArGN6yKQSaHQGrM7OR3kNrYXprWWHrmHUZ7usttkeA8TipRXHff6Moko1xi/Yje3nCt1+j4rVmfLV/d0wvnOm1esajQYvvvgiXnzxRWg0NvE25HVT0w3WHSOEEEL4pZCClp8ihBDiN6Zr/sWLF9tf8xMSQHTHQCADBgwQOglEAFTuocnZTI1aJ2vfbzp9i3O7O50aH2w8hxPXy/HSihOuExhg93+/H8euluGeb/ahTOXeRc7lomqnrz8/ojUy4qPw0qjWPqUt0smyRewA7T8/1Asz+jbBrKEtzNsUEcafzCq15aZshFSCZ5dl4+nfstF53ia7z6Q6756X/ncC5woqhU4Gr/xZ9hW1nnUMWAUKr4uqMeHL3TiZX44HFx1y+3PYMzXGdqpv97pWq8VHH32Ejz76yD7gXUSU8X+d2v2EByGq84R4j+oPPxpnpsM0ToNmanintFqD+evPomH7nq53Ji5R3eYH5SM/KB99Z7rmX7duHQW55gEdk96jTg2BFBQUCJ0EIgAq99DkrBPiYl0sAC6z6pba8eTzTMpU4r14qFTzPxK7UbIS+14din8ObuF6ZycSot0LJTWwVSrevKu9OVA4AERzdIh8uysH607cND8/b3Njnup8+BJ72d/wYkmuGo2xk7ZJPaXnX2ju1Kj1bQ0skRN7uRMiZlR/+MFoa80zNSimhnfeXnsG3+7KwQP/PS90UkIC1W1+UD7yg/KRiA0dk96jTg2BXLx4UegkEAFQuYcmZ50Q9/Vq7PbnjGqfYfw8Nxqgap3rNfXFwJu29JiOGZzbJS7iW7hjwX3d0DApGp9O6+zxe5UK18GpK2utO5uozvNr9VP9vH6vzo3g2nzyZ9n3be5ZXAp2zfEtpobxvMPu7HNbRKTlcQjP1qA6T4j3qP7wQ1VRap6hR8tPeefUjXIAAOUeP6hu84PykR+Uj0Rs6Jj0HnVqCISPm3Mk+FC5hyZnnRqNkt0f0fzM8JYAAIMbMzVqtYG9QestDetG8oh26VavORo9+OLINn5LT7vMeOx+eSgmdW3ocB9H1dSdTo3iKuubtVTnfdMwKdr8WCGTonOjRK8/a93Jm6534pE/y97VTaoIm/gwXGkxLacGuD+SV1U3Cysm0nVdsCNlvYcJjvOXN6jOE+I9qj/8iJQxrJgaAicmSMVFyc2PabaL76hu84PykR+Uj0Rs6Jj0HnVqCOTee+8VOglEAFTuoclZp4btzUVnTPs6m6mh0uhQXKXGEVZQbTHLu22JlWFgGHNe7bpQhB7vbOF8T0Z8VEDSZistzjiS/I6m3KPgo90YnX70apnVc6rzvtn2/GDzY40bMy0SouUOX3v6t2weUuQ+f5a96ZTz6ug2GNI61e71Pa8Mdf0hrNPMn6dcT3kuqlTjycVHAbhXF8IV1XlCvEf1hx99e/VgxdSgG/LeiIuy/M5N+26/gCkJDVS3+UH5yA/KRyI2dEx6jzo1BLJq1Sqhk0AEQOUempx1Qkjd7HX/76O9zfs66ySZ9u1+9J2/zWrbtRKVW98RCFqbG8/LDl83P95ythD93t+GylotHlh4ELeruYOIR8mF+Wla/kQfPDGoOb64ryvn6zFuzNT4bleO1XOq875hzyZwh1wmnssaf5W9wcDgYG4JAKBxshJD26TZ7ZNu0zHItfwU+7yVfb3M5fd+s/Oy+bE7dcG50L3JRnWeEO9R/XGtqNL18n3nTmazYmr4O0WhSc2aEX0wtwRH8koETE3wo7rND8pHflA+ErGhY9J74mn9h5mamhqhk0AEQOUemvR6xy1GmZszNbo2SkKE1HhKVuscj0g/mV9uN2J9wAfb3fqOQGAHzeZSUFGLDSedjwoXavplVr0YvDK6DVLjIjlfj3bzRi47rgbVeeGcf2cUujVOFOz7/VX2BRWWAN8VtVqvO3LYo1Bj3Jh5wb4x5mlnk1F4TKumOk+I96j+OHeuoAI93+We5com0WvM11LuLGlK7N2qrLV6/q/fTwmUktBAdZsflI/8oHwkYkPHpPdo/QCBNGrUSOgkEAFQuYcmrpka03o0QoJS7nanRmSEFHV9GtDoDCgor0VGgvVoa2eBji8VVqJFWpz7ifaTyro1953ROWlgvzGuHZ/J4ZW7wZF1rE4uqvOBZsn7yAgZFj9yB9rO+VOQlPir7G9XWWY4lam0bp1j2P2ETF0eKeUylMHYAdcgMRpVah1iIx0f4/IIy4fIHHQ8RkdH49SpU+bHDoXw0GGq84R4j+qPc/87ct31TgAapKfAtCf1aXhOrdMj77b1LGjbmGnEM1S3+UH5yA/KR9+ZrvmPHj3q/JqfuIWOSe/RTA2BtGnjv0C4RLyo3EOT3mDf2fDvKZ3w2pi2bn+GVCqxujn53wN5dvvUOpnBce93B9z+Ln9ypwunVqt3+NrfejfmLzE8cydQOGDdaUN13jufTuvs1ftsZznZzq75lrWEkiMnrpdh54Uir76fzV9lv+mMZaZTWY3WLm5PSqz9TCMJR828UW4Zhfr88uPoMHcjbju5acNeSs/RbCqpVIr27dujffv2kEptLjHDJAAe1XlCvEf1xzlng0Ka1FOaHzfNymIFCg/NXg2d3oCdF4pQXqN1vbOHzhdU2i0FW1zFvWQqcQ/VbX5QPvKD8tF3pmv+0aNH21/zE4/RMek9OvoEsnnzZqGTQARA5R6a3Ihf7Bb26OfYKPsR0zUax50BYhlB5mzpLJO31p7h3P7CiFaIkvu6Vr//uHtjgN0QpjrvuUSlHJO6NvTqvXPqZvo8PqgZ5+vzN5yDWqcHwzAoU3HfoLjryz2YvvCgz7Fq/FX27K6Bv/VuDBlr+akNTw/A1ucGOX0/w8Bh58X28447c2RWnRrupTUcUZ0nxHtUf5zT2Sx3+tm0LubHA1qmmh8fOnzYHCicCdFOjR9252L6woO4/3v+g3hfuS2eWHWhguo2Pygf+UH5yB/KS35QPnqPlp8ihBAf6ThmaniD3eyMj5Lbve5shoNY+JJGqZtLdQmlWWqs09ej5FLUag12wdKJc7brfSt96Ni6p0cjDGqV6jAuCmBcsumjjeex/Mh1LH6kN/q1SOHcL7+sBo2SlZyvCSkpRgEAGN0hAw2TlFYzNdrWj+d8j/XyU8YZHlycLXG38qhl2RNHVVWj0eC9994DALz22mtQKBQOPi00b7IRQog/sWdqxEVGoDnruoR9nk+NMqDAPFMjYMkLKNNv0ukbFbx/dqh2BBFCCF9M1/wnT57E3Xff7eSanxD/opkaAunbt6/QSSACoHIPTb72acTVrWMfw1rPPoZjbfuaIOjUSIi274wZ0jqVY097+y7f5js5vIqNjMDzw1txvjasbbo50Dt7pgbVeddsA9/7Gig+LT7K6WeUqjRYXrcu+edbL/r0Xc74q+xNI3VNs5ocxbdw5zM8wV6uSurgO7VaLebNm4d58+ZBq7XtOBF3pyVfqM4T4j2qP86xBwHIZBJzjCQAGN4u3fx49KA+Ib/8lKPfIT6EaJYJiuo2Pygf+UH56DvTNf/KlSs5rvmJp+iY9B51agikpKRE6CQQAVC5hyZfZ2okKI0dAbGREYh2Mkrd2fJTYnG+oNJuW7fGSW6991ZFreudBDbrzpb4cUZPu+1f3NcFETJjA5s9kpLqvGu2nRr+VlptufD25212f5W96fgyxeBxJ1A4G8MwDjtI3b1H5GvHUyijOk+I96j+OFZarcHvx/LNzyOkEmQmWoKzDmiZilVP9UP2nOEoKSkxz6gL1Zka/uzUMHUE9W1eD6/1T/bb94QTqtv8oHzkB+UjERs6Jr1HnRoCOXfunNBJIAKgcg9N7oyCe6Cp4/V5nx1mGf3fPcvYAWAboBAIjpkav+y3D3BuG6zZkcpaHd/J8YshbdJw+PVh6NXU2NCNi4yAUhFhXgaI3clFdd41jU0cFn/HmitlxdKwvSdiuxSWL/gu+1qtHpW1Wujrji95XSeaqTPN088CgJRY66niXAHFufh8LymEh8FSnSfEe1R/HHtg4UGrQQBSiQQpsZH4Y2Z/bH3eGEupS6NEJCoVOHfunHlJz1BdSol9rZB9rYzXzzZlmVwmBVNivK4V+Qqpokd1mx+Uj/ygfCRiQ8ek96hTgxBCfOTOUi5t43VY/VQ/ztfu7m4JiiyT2o/2NwmGTg0u7gb//vsdWX5OCX9SYiPxxb1d8UCfLPz+lHG6qKnsft6Xx9m5Q7jZdmq4e2PdW1adGjbfdaHQMtNITPeBGIZBj3e2oMtbm1FdN2PLk5katjE1qtXGDsTICOu6uYIVN8MZr27u0OwOQgjx2sn8cqvnpnN/x4YJVrE1TCTmmRoi+jHjEXumxsSv9vA629eUZxIJIGPNeOEacEQIIYQQ4VCnhkCmTp0qdBKIAKjcQ5M7jZypU6eic6NEl/uV1wXwfWH5cbz1xxmr145cKfUqfYF0f+/GdtucLallEhcZgScHNfdHkvwmIyEKb03ogBZpcQBgjqnx3wNX8caqU6hW66jOu8GuU8PP976rnMwIOsRjHeOz7Ks1elSpddAbGFwvrQFgOd56NjHOGFJEOL6ks+28+XTLBQDGYOhsB3Pdm/rs+z2y0L0xRHWeEO9R/XGfsw7tqVOnsmJqBCpFgWW7/NR768/y9tmmLJNKJLjn7knm7bbXK8R9VLf5QfnID8pHIjZ0THqPOjUEsn79eqGTQARA5R6ajl51fSPU3bJnT6FfuCfXvEwMAHy5/ZLD93VrnOjW5/tbelyU3bZIufOfmmk9GuHEmyPMSyUEK9tlgNQ6A9V5NwQ6poaKFZtmX451cPo3Vp0yP9b6mC4+y/52ldpum+mGVnp8FPa+MhRHXh/m1mcxDHAqv8Kn9Cw9dM2LdwV3/XYX1XlCvEf1x33OOjXWr1/PiqkRmr0atn++swELnjIt2SUBsG3zJvP2becKefuOcEN1mx+Uj/ygfCRiQ8ek96hTQyBVVVVCJ4EIgMo99FwqrMRHmy643M/bsv+K1ZExhbVMla16sZFefT7fNHrjDeOxneoDAFqmxWJEuwzOfT+Z2hnf/L07XhzVOiQCD9veYFDr9Fbl/tPeK/hks+tjJdzYjnzk+/7Lutn9Mb2PZWmzWo5l3H7ck4tnfjtmta2kWmO3nye8rfM3ymowd/Up7DhfaL6xUlxlSUtNXadMBOt4y0yMRlyU3OFnWlUvBpjao6H9dg9wLY9HjOh3nhDvUf1xn7NOjaqqKvNMhh/3XMHwT3biUmFo5a3tTA0+B8aYrkMkEglqqi359vbaMw7eQVyhus0Pykd+UD4SsaFj0nsRQicgXGVmZgqdBCIAKvfQk32t3PVO8L7sF2y7hMndGqJpSgwaJysd7ieWdX5NN6gbJEbjyvtjne5rYIBRHbg7PIJRhE2Dus/8bRjYMBn3MgwkEgnmrjkNAJjYJRPNONa/Dle2MzUYnpcmap+ZgHkTEpAQLccX2y5ZzdQAAJVGh3l/2N+ouFai8ul7va3zE7/ag8JKNX7aZ4zLcuX9sVYzNUwxQdyJpeFITKTx8u/eno2x5OBVt94jlViWMUmI5u5AiYqKwsGDB82PHQrRkcMA/c4T4guqP+6TSx2PTczMzMRNjeU34mJhFZ5blo01M/sHImkBYTubUsbj4BiDuVMDaNAgEzhhvNmUGieOAUTBiOo2Pygf+UH56DvTNf/Ro0edX/MTt9Ax6T2aqSGQzp07C50EIgAq99Dj7n1FX8retCSOsyUExNapEelkfX+TMpVvI+HFRsZxg2HXdR1++CvXPOIeAKrV/AR8P5JXgv/stv7sYORqpoZcxs+NiiiFMbaLbRD3tcdvcu6v4pjR4Qlv63xhpfVSU7nF1ajWWJbVOJJnXO4uQub+JZz1RA0GP+65AgB2HRrNUmI4388wjPkmT/8WKVjxRB/O/WQyGXr27ImePXtCJrOJpePNDaejvwCfdgAK+Vsr3d/od54Q71H9cV9avOMb7J07d0ZlrdZqG5/LM4lBI5uBPr509NsyDa6QSqyPyT7N6/H2HeGG6jY/KB/5QfnoO9M1/7Rp0+yv+YnH6Jj0HnVqCGTDhg1CJ4EIgMo99HDdp+vUMMFum7tl//NDvey2ldUYb/7XcNxk/WSq8QdQLGsmm0bdKxzccGXPNlEqQmuyoKOb79/svIwnfz1qfs5XWd39f/vw9tozWHzAvZH2YmXbqWHbQRfhZDSqJ2o03J0Uf5y44dH+7nKnztdq9Xh91Ulsd7JO96n8cmh19seM7cwgb/SqCzJuYlpWSqc3WHWWqVll9PXfu6FlepzP3+2WNTOB8mvA6pmB+T4e0O88Id6j+uM+Z78BGzZswLmblVbbxHKdyJcYm2tIPmdRmGdqQIINGzbg8UHNjNtFMoAoGFHd5gflIz8oH/lDeckPykfvUacGIYT4QMIR/PbXR3q7/f5v/9Hd6nlmYrTdPqYbmt/uzLH//rqvF0tbVa2t69RwMFNjYtcGWHBfV0zu1gCTuzUIZNL8ztEoQQbAn6cLrJ7zacvZWzx/YmDZdmrcLK+1eh4bxU/nl6OOtm6Nkzi3V6v9P6r11/15+HX/VTy46JDDfUqqNVBzBC33ZFQqO2YNwwADWqYAAO7t1chqP72BgVZvwIjPdmHat/vN29mdGlERjkdjaTQafPjhh/jwww+h0TibieVhLdCpgYtbgM86AVd2e/ZeQggJQbYxJexet/nJE8llIm9s//xmqdwzDb1Sd1FtysPIuusH2+Uy/WXv5WK8uvIE1Dp+ZvYSQgjfTNf8f/zxh4trfkL8izo1BNKrl/1obBL6qNxDD1ebMp4jYC9X2T99Z0uMbG8dU4Jr+RdnNy9NnSp8xyHw1spj+QAAuc0N5MWP9MY/7sjCE4OaYXznTHwytQui5KE1VdXRqEnbmQe+jpasVuswa4klqLWzdaRzi6vx+C+HcfK6e7Ff/G3ZoWtYesh6ZomrmwQ/PNADDRKj8dX93Xz6bk+WawJ8X37KnfN9flmN1XOuOB4GhrHr+AE8m6lhu2dVXYeNKbaGid7A4FqJCjlF1Th4pQSVtVro9AbzjRWJxPlyYFqtFi+99BJeeuklaLVam1c9nFly8HvWEwZYfDdQlgf8Mtmzzwkw+p0nxHtUf9wncfLb36tXL7tOD7EMfuGL3bUVj7Mo2DM1evXqZb6m5fot9of7vz+AJQev4eFFhwPyfYFAdZsflI/8oHz0nemaf8mSJRzX/MRTdEx6L7TW/ggiKpVvAUhJcKJyDz3OGpVsXGX/5ODmdtukHDcq5U7iU4htpoaJ7Sj3fi1S0K9FikCpCQxHsTLKa6wv9Hwtq293XsYfxy1LJkU4ucn89x8OIL+sBvtzSnB87gjfvthHKo0OL/3vBABgZPsMJCoVAFzfJOjcKBF7Xhnq8/c76tO4Vsp9Xl534ia+ut/773PnfC+1mkHBYMJXe+w/R6O3Km8Tb9cP33zmFo5dLQMAxNp0augMjFWH5KjP/kJFrRbPD28FwBgrx91znlPuVIL1L1geG1jnE73afl8Rod95QrxH9cd9zn4CVCqVXadGqC0/ZdupoWc9PXG9DLVaA3o1TYY3TMsvSiTGvFREGJdODdRMDZPdl4oD+n3+RHWbH5SP/KB8JGJDx6T3aKaGQE6dOiV0EogAqNxDj7tBmm3L/t6ejRzOVGhXP97qeaQbI8yFbqsaDIxVXozskOFk79B05maFm3sa80mrN6C4yvMbtDdslmdyFnPCNBPAtmNFCOzOi1KVMT1Hr5Zi85nALJ/laKmOy4VVDt9zq6LW4WuuuHO+Z8fJqVLrUFJtP327Wq3DuYJKu+22s6GcYf/ppo4lwL5TQ28wmONqAMbjp7JWhzf/OAPA8RJeHifCU0XnvH9vgNHvPCHeo/rjPmfLT506dcqu41vo60S+6Ww6NUzXoAzD4K4v92Dqt/s4f1NNarV6rDhyHX/7Yb/dfuaZGhIJTp06ZV5SNVAzNUIR1W1+UD7yg/KRiA0dk96jmRqEEOIDtU0D5zubGBm2Njw9AGuO3+CcpWHy00O90PPdLebntvEpnr6zJW6W12BG36a4VGS8ISvU8lM6vQEt/mUMbNWZFSC9fkKUIOkRUr0YBW47aUCbmBrLE7/ag9M3KrD1+UFonhrr9vfU2iyLpFS4XsbLp5vRPGGPcKyqNY68n/z13oB9v6Mgosdtluaa0r0hVhy5DsDYGZQe779jOe92tfnxKytPcu7z9Y7LnNujeVi+zXb5qVKVFnqD45s2FbX+jzPiFr0OkNElLCEkfDkZz2B83abPw91BOMHgWokKa2xmMJpmbmhZUza+3XkZr45pa/f+Wq0ed8zfirK6ARb/2Z2DF0e2Mb9u+gRTFgayUyOUyokQQgjxN+HvcoSpyZPFvSY08Q8q99CjZd2oTY5RYER77hkKprJvWz8eL49qwxl3wyQlVmH13NSYyqi7uTq8XTo+mNIZ7TLjPV2lnnfrTt40P2bfHI5RhN8NR3dHzpsCe5++YZzZse7ETWe727Ft7po6tpyJlAv/c69iLc81/svdeGNVYEekjO1Y3639ouUycweI1oelJtw537NH2rpzHLA7sKLd6MwycbRkVBxHEPZ31p11+3MFs+czoVPgEP3OE+I9qj/uc7YU4OTJk+1e5zHkhOAe/dk+1oTp72P/bn+7K4fz/WdvVpg7NABj7IzDV0rw5prTKFdpzR0LUokxL03Xd75cE7jLtpwqa4WfacsHqtv8oHzkB+UjERs6Jr0n/F2OMLV161ahk0AEQOUeemq1lgZOyzTHo+09KXvbhqhptJ1pqj3XWvpCDexytKwRV2yQUCePcO9vzi2qtnruTmyEjacL8J/duQDsR/Edu1qGcpXzRq+zZSoCxZR+k1/25wX0+90NFG5gGMjrykSn975iuVPnIyM8m23BPse4M0PHlZjICLvz1o7zRT5/LjcPjkGti2W/9nzuW1L8iH7nCfEe1R/3Oftd37p1K2S2gcIFmtHrD1xLMppihrgzm8L2uuvL7Zcw5Zt9WLT3Cr7cftF8TS2RSLB161ZEmmZqBKBTQ2czW7KoUtxxpNxFdZsflI/8oHwkYkPHpPeoU0Mg5eXlrnciIYfKPfTcZsVEcDaCy5eyP3+rEm+uOW2OvyBnBYY2Bwr3+tN9k+ZgSZ9wpJS7NzvF9kaEOx0Oj/9yBG+vPYNT+eWcHViOgl2beBtUmk8XbtnfhODy2pg2rnfyEjsbOjSI59yHASCvu4Fhe3PBE+7UeU9n0LA7ZnhZfkohw6+P9MYb49r5/FkecdULe/YP56836s1fWnhGv/OEeI/qj/uc/aqXl5eHdEwNrssmg3n5Kde/27ZBxtlulNeaO4AkMOalaabGnku3PU+sh2wHUxRXuV7WNBhQ3eYH5SM/KB+J2NAx6T3q1BBIWlqa0EkgAqByDz3s0ebORnD5UvaVtTos2nvF/FzGWkhZAmF7NUJpOQNfcS0HJONoeP95ugDP/HbM/DxR6XgpMlubz9zibLCrdXqOvS1E0KdhHkXpiicBsD315zMDES2X4YE+WRjSmrtOGgwMIuoyTOvFTI2coirM33AWUYmpLvf1dO3sYW3TzY89WX6Ky4dTOkEikSA9PgoP92/q1nuOzx3h9PWoqChs374d27dvR1SUTSwST2YLXT/o/PWoBKD4IvDHM0DxJfc/NwDod54Q71H94UdaWppHp9xgo+To1Dddj6o01tdDth0Yn2+5iElO4nlFy2UwXWbJpBKkpaWhsMLF7EEe2cbqu1Jc7WDP4EJ1mx+Uj/ygfPSd6Zr/o48+sr/mJx6jY9J71KkhkF69egmdBCIAKvfQU8kKnKvWOu7U8KXstTYNnAgp10wNYXoXArG+cLDgWiKgQSL3Rd6qbEuAy3oxCs59uHy+9aI5FgebRue8/GMjhY9x0io9zq39/Nmp0So9DsfnjsBbEzogJZZ7llGETGLuzPhmJ3eQbmcmfb0X3+7MwbM7nS8ZUaXWYf3JAoevc82CYi+r4Wmnhmn5DJN7ejSyev5/f+vm9P3PD2+FhGjnHXAymQyDBw/G4MGDIZP50OkS46pDiAG+7AEc+RFYPMX77/ED+p0nxHtUf/jRq1cvu1mgEWIY3cATrt8/fd0gAdtlUbV6AzQ6AxiGwdmbFfh0ywWnn63VG6Cvm6Upk0rQq1cvjOxgiZen8/N1b7VaZ/X8vJuzXMWO6jY/KB/5QfnoO9M1/2OPPebbNT8BQMekL6hTQyBr164VOglEAFTuoa3WyWh5X8peazPKLIK9/FTd/0ItK+BsCn+4yS+rsdtWUeV8WSjA/RkMJjfLLSMG4+sCPXPNEjpxvcyyn4ub0YHQtXGSW/spIvx7aWL6fK7lm5qlxGD20Ja4WmIstx3nizyeTcG+oXIkr9Thfu9vcByQe93s/tj54hC77Y2So82PI6Se5dPGZwaaH3ON4G3hJCYQAPyjT5ZH3+ecizyVuPjbCs9ZHpfmOt5PAPQ7T4j3qP7wY+3atXYzNEMp1hlXpwbjoFPj7M0K9H1/Gx79+TD2XCp2+dnGTg3jY5lUgrVr1yItLsr8u1nqIIbZtRIVarXOZ826o1pj3anhKHZdsKG6zQ/KR35QPvKH8pIflI/eo04NQgjhSa2TmRqeSo+3jNK2HRXGvpkp9PICOurUMNvzylAAQEqsceZFvRgFqnSuC0jjZTDqLo0S0TTVeCOaKzDmXV/uMT92NcI+ED7f6nx0pEnzVOc31/liG9j9jmbJ2PbCYKTFW8+u+Xmf9wHNT1wv4+wUuV6qwq/7rzp8X/vMBEQrZFDYzFrp1yLF67RkJFj+rp8etB8N5GqGTKLS9YwirVaLr776Cl999RW0WpsbMZ6crPShcROHEEL8xdWVQyjP1GAHQU+um+1qGmRj2wkw6eu9KK5SY8vZQrdmgmp01jM1TP8n1/0GFlfZz8L84a8cDPhgO9q88acXf421arV1xwjNiCaEiJHpmn/Tpk321/yEBBB1agikWzfnyzyQ0ETlHtqcjdDytOy3vzAYvzxsvPFo229g3TA1Phaqa8E2oCFgHcg8nDRIjMaV98fi4GvDsHBGD/z5zEA0TLBeQqhnE/vZCquO5Ts9dhzNFKgXo0BkXQPdttFr+x53gpH727US+5ksbElKOf4zvQe6Z7k3o8NXtjMd0uK4lwqbu+a0y886kHMbYz7/C4evlFhtn/fHGUz+v712nU5//+GAw89iLzt1at5Iq9eiIryf3s1efiqJo4NCzsMMGY1Gg5kzZ2LmzJnQaHwIbmpw0Thiz+RwuVRVYNHvPCHeo/rDj27dutnNzAilmRrseFeJdYM2TNfKzmY22Mbb4LLlbKF5sIlUIjEfkwl18c+4Pv+ddY5nXnpKZTNTI1Q6Nahu84PykR+Uj74zXfMvWrTIt2t+AoCOSV9Qp4ZADIbQuEAhnqFyD23OYmp4WvZKRQSaORixLuPoNPB0iRy+6Dn+ru8e6CFASsRDKpVgaJt0pMZFgn2fOC0uEkPa2AcB23auEK/9ftLh5zmaDNOmfpx5tsFvh67hh79yzK/ZBpr0dIkrPhkMDMprtKif4DyIXIu0WNzJCoTtb7YxJlylz5nHfjmCMzcrMOWbfXavHbtahuOspcAA4Mpt62XJnhnWEj/O6IleTZLx6yO9zdsVEVI0TYmxeu4tiUSC54e3wpTuDdGhQbzd67adkaalzQBgRDuey8XV8VjqYHZMVILxf3YnnUHHva9A6HeeEO9R/eGHwWCwW35KJoLBDXzRsY4T0+wL03WwbUwKtn//ec7ha2ymeFoRUon5mMwpMgbsvlhY5XmCPWA7U8NVzLRgQXWbH5SP/KB8JGJDx6T3qFNDINnZ2UIngQiAyj20TeiS6fA1b8reUQNUzrH8VKCbPBqdAWqdHn+cuGn3WkoMdwDmcFStssxO+OieznbLCZmsPJrPuV2jMzgcpffk4Bbmz9t1oQjvrDuLvNvGRrftzA8hY5889NMhdJ63ySoWCBdXMR34FhdlvSTXU0NbeP1Zzm6iAJby2HLmFj7ZdN7u9WslNRjSJg3LnuhjF1A9t7ja/Fjm40jbWXe2xEf3dIaE49xie2xW1Fr+piiO+CN+c+sMcGoF92vN7OOMoKYU0LiOXRMo9DtPiPeo/vAjOzvb7vfC198PMWHPEjYN7jBd53g7s+Hh/k3ttsmkErtj8o1Vp+z2a1JPaX689sQNr77fxHamBlfMtGBEdZsflI/8oHwkYkPHpPeoU4MQQngyb0J7Xj/PURxg9nYhAoXrDQz6vr8NrV//EwdzS+xe9zB+cUib2NB4I79fi3oY0DLFrfWcTVYcuY5Wr2/A8E93cr4eGxlhd3O6su5GtO1MDSE7NXacL3L6+h8z+2NG3yZ4eVSbAKXIKD46wvp5FHfcEXeWw3IVW8Z0k+WRnw/ji22X7F6/t1cjh+99sF8T82P2TSm+10fnOjafHdYK9WIUeHFka16/y2k37P/1cfxaZF3Hl8FmCZHqQuP/pXnA8aWAnqOT6dohYMubwIaXARoNRQgJcbbXB6HUqcG+0W/qkDf9DFepvQvWzTU4wd0lu9hLTK7ONnZq2MbDc9fmM7esnms5YqYRQgghxCjC9S7EHyZMmCB0EogAqNxD1/+e7AOlwvEp1Zuy55qpMbxdOiJZ6+qbGq2BvGVdVKnmDJRoEkoNZ1/Nun8cnlVEmZcN8qRT44XlxwE4j0Wh1nE33m1nagi5/JQzg1unomPDBHRsmBDw72Z3YnRy8v3s5Z+85Wr5iJ5Nkh2+9mDfpmAY4G+9G0MmleDvdzRGeY0OWayRoXzgOjafHtYSs+9swTmzwzsSeHy2kscA2rrZKvK6v9k25gZTd9Pniy7Gx3oN0O0flterbwP/GWZ53vgOoP0k4+PjS4ETvwFTFgLRvsdzod95QrxH9cd9zpYdnTBhAs5syrHaFkrXZlYzNcydGsZtpqWjGiZF43op9/XTqPYZGNomDd/sumxeVqphUrTdfvViFJgw0P6YVGl0WHv8Joa2TYOBYXCDNRN185lb6Pf+NhRXqfH2hA6Y2tPxoAVbO84XYsOpAqtt+3Ju45GfDuPbf3QP6jKkus0Pykd+UD4SsaFj0ns0nlYgu3fvFjoJRABU7qHHtC5/erzz9fi9KXuuxst3/+hu9VyI5k2Vi6V2QmndZl/t3r3bKg4CX0HU4+riHTi6p1FrE9/F1dJPQpBIgM+ndRXs+511agxjxfZwFsTdXb4E+mxcT4k372qPlnXLUr0zsSMW3NeVx44GI9tjs1dTY0cL39/jkTvnAs+ylvqQ1910Kr5gvZ9eBxRdsHRuXLMJxG77vLrY8vj3x4DL24BNr/OSZPqdJ8R7VH/c56x7ePfu3fYxNYL4hritGtbvsukay8AwVh09zgbffPOP7pjasxG+Z8WAG9k+A72bWg8w6NQwkfOY/Hr7Zbz0vxOY+u0+zo6T/LIaqHUGvPS/E+7/UQBm/HiIc/uWs7ew80KhR58lNlS3+UH5yA/KRyI2dEx6jzo1BHL79m2hk0AEQOUeekzLzrgage9N2dtOe5dI7G8wmp8GcCT+/hz7vyWTFWTZ3en64cC23NkdHMN8CIptWmaqWar1LALTkgy2Mziul9agQGQdG7OHtkSCknvJp0CIZQXCntajsdVrX/2tKx7okwXAdbwMdzjr1Pjons4+fz4fbM8tE7s08N+XuXu+yugERCUCKa2BpCZAXH3u/Qxa4KuelufXDlq/btsJcnm7fRqO/epemlyg33lCvEf1hx+3b9+2O6dLQ3TAiWlwkd7AoLzGMosvM8F+5gUATK/7bQeA+qxrV52BwQ/TLZ0c703qiF5NkzmPyd2XjB3jOUXVqNX4PvCBy+gOGVbPNUG+DBXVbX5QPvKD8pGIDR2T3qPlpwSSnOx4qQkSuqjcQ4vBwJhjFbha396bsred8cDVIBUiUPiyw9fstiXHKszT72mmhoVtubM7vyIjHHeEcc0OkEosa0ZbPsM6gLNGZ8DeS8XYdbEYto7klWJsJwc3hf3E2fIYQh8mMqkEk7s2wI3yGrTLjLd6LTJChjua1cPP+/Kw/XwRGIbxacaCo5sRX9zXFXd1zvT6c/3pXg+WzDCJjIzE2rVrzY/tSCSedcC2uNP4nif3Gp8f+ZF7v4WjrZ8X2wRjZ2zq0/l1wLxE4K4F7qfFTfQ7T4j3qP5wc/ZbyiU5ORlSjfVvVplKg1qtHlFymYN3BQeDzYWQnBVT48T1cvP25BgFcoqr7d4fE2m5/RHNygu1zoC4KDnOvT0KcpnUPLOF65jMvlZmflyi0gAAEqLlVp0qJt5eP7DTCQCRQV5uVLf5QfnID8pH35mu+Y8cOcJ9zU88Qsek92imhkAGDhwodBKIAKjcQ4uWFWxW7uQGNeBd2dsuFeAs2LO/J2rsu3wbK49eBwBM6mo/gjuCFR08lJY48JVtuVt1asgdHzNzV5+228ZV/B0aWN+Mv/e7/bj/hwPmNaXZkmMUrpLrlW92XsbnWy4CMM5q2Hu52Hys2i6DJTafTOuC3x7rw3nMxrJuKCzae8Wn73E0U0PB03JkfBvaJs2rGVcREREYO3Ysxo4di4gIH8fNNBts6fmSRRj/OaIud/waAGQv4d6+ZpblcWIW9z4eot95QrxH9YebVs9xAeDkum/gwIF2y09dLqrG+AWW5S0OXynBgq0XnV5bipHe5oLXNAN214UizPzvUfP2+GjumaDsayGJRIIR7dLRJiMO7esGN0TJZVbXBKZj0lF8rY2njYG9W6XHcr7++7F8u22XCqvwxqpTuFluvXRVSqzlxuCAlilWr9l25gQbqtv8oHzkB+Wj70zX/C+++KLv1/yEjkkfUKeGQFatWiV0EogAqNxDi1WgQqnz06k3Ze/OUgESmAKF+7exc9/3+/HcsuM4faPcLl1tMuKsZqrQ8lMWtuXOjlswol0GUuMi7bYDwFKO2TBsreviK0zo7P4SQY46m7aevYUDHEuKuWP7uUK8v+EcPt1yAQXltXji1yO4//sD+HFPLnR6A95Zd8Zq/0cHNDU/Pna1zKvvDBT28lRbzzpfy9rVTC1N3bkiRmE90jLCxXkj0CZ0yURWPSUW3OfvWCc256uSXEBnHO2KyLr4JmM+tn+bt7Nlbl90vU+E87hI7qLfeUK8R/WHm87g2QCBVatWcZ4uLxZWYfLXewAAU77Zh483X8Bvh67ykcSAse2EMQ0WyS+rQUWtZblI25/lTc8OxNsT2uPeXtbLTX73QA9seHqAw2VkTcdkMwedGn8cvwHA2Bny8qg2dq8/t+y43bbJX+/BL/vz8Mxv2VbbNXVLhy5+pDcaJimtXtMFeacG1W1+UD7yg/KRP5SX/KB89J7gremvvvoKTZo0QVRUFHr37o2DBw863b+srAxPPfUU6tevj8jISLRq1Qrr168PUGoJIcSC3cCI8MOIa1c3SgGYI4UHKqTGtRKVXYNyao9GVjfMafkpx9gxNTITo7B+9gAAxlGYniwvYVriwJMOJK6bIhdvVeLhnw5j2nf7UVTpOKimIw8usgS1zL5Wir/qlr36ZX8elh6+hsUHrG+WjO1kWWqpV1NxT7ONY83UcD5LinF5s0GrNyDvdjWqbdbeFtsyIJ/f2xU7Xhhst+yFu7RaLRYtWoRFixZBq7VfhsN8wmK7tBX4ogvw62Tjc9NSUVznEYkfL1sj/DOTiQiH2hgkVHDO1HAhwsFN+qNXy6xmD567Wel1uoRg+3uscDBT2nYATqv0OPyjTxOrWZgm7iwP9fyI1k5fP3SlBHd3cz7QhGEY1Gr15s6XA7kl5tf0Bsa8vXVGnN2ylcE2o4YQEtpM1/w7d+50cM1PSGAIOk9o6dKleO655/DNN9+gd+/e+OyzzzBy5EicP38eaWlpdvtrNBoMHz4caWlpWLFiBRo0aIC8vDwkJiYGPvE+6tSpk9BJIAKgcg8t7AaGqxv53pS9OzesA919oDMwMNjcfC+sVKOgwhKEWmSDzwVlW+7sOBopsZFWHVcGBnDWN/bFfV3x4cZzuFZSgz7N63mcFq4G8dUSlfnx9VKVeeaIN0pVlgtamVSCbI6ZGJ0bJuDxQc1w4lo5Hh3QzOvvCgT2TA3b5S7Y3LnP8P6Gc9h2zn62R3q8+Nag9Sl2iEaDBx98EABwzz33QC53EAienZ+HfjD+f+UvoPw6oKmqSwjXicSLtGlrXe8DADJ+yoJ+58UhnNsYwYzqDzdPb2h36tQJN/Idny+r1ZYZDcEWgNpu+SkHnTdD26Rh05lbPn+f6ZhMinHwe1anW+MkpMZFIi4qApWsGSMA0OSVdZjaoyFKqrXYc8k+5hkAVNZarqESouV2M3i5joELtyoxfeFBdGqYgM/v7Sq6gRJsVLf5QfnID8pH37Gv+T/++GPH1/zELXRMek/QW0+ffPIJHn30UTz44INo164dvvnmGyiVSixcuJBz/4ULF6KkpASrVq1Cv3790KRJEwwaNAidO3cOcMp9R5U+PFG5hxbTyHepxHUHhLdl36Se0unrphuQgZqpoTcwOJlvWb8+PioC9/RoiLzblpvjNFPDwrbc2csNpcdHWc3wcRR3waRejALLHu+DV0e3wZvj27v1/aPaZ5gf6/QMDAYGb689Yw72zv7OMo4Al55g3ySRSSRYfuS63T4SiQSvjm6LJY/d4XB0pViwZyuondz0cXdZkIOsEZkmaXH8LHkUNLjODQbWzZ8/X7U8lnLcnPHm3KLivoFkJzLO88/mQL/z4hDObYxgRvWHm67ut9rdU6BcLnfaBVzF7tRwce0hNnqbWSvRCu4b+dN6NrJbgsobpmMy3cXv9auj20IikeDE3BH4xx32MZqWHb6OLWdvoUar53i3JQZZhFQCuUyK7llJVjHsuDo1Zv33GG6W12Lj6Vv4fKsbyywKiOo2Pygf+UH5SMSGjknvCXZHQaPR4MiRIxg2bJglMVIphg0bhn379nG+Z82aNejTpw+eeuoppKeno0OHDnjvvfeg13NfHIjZkSNHhE4CEQCVe2gxNTDcWRff27J3NWra9GqgJqVr9QxWZxvXD25bPx7Zc0ageap1cEQKFG5hW+6dGibgqSHN8fm9XSCra7iauFrCKFohQ/2EaDw+qDkSlJYLn/oJjhva707qgDYZxpu1760/i63nCvGf3bl4acUJAJZYDwCgUvv2W1rB6hThOmzXzurv0+cHWqzC0qlx/FoZFu3J5dzPlyUh2LNBwg7DAIcXAhc3WbadXWN5HJvO8SYPzi2mnt4t89x9g/uf7QT9zgsv3NsYwYzqDzdt3e8MO35bRa3jgQhHjhxx2gPCXs7KNFPjy20Xce93+1Dr4Ka7WLBnanRtnIjUWPtZdpO7NoBEIsFPD/VCSqwC3z/Qw+vvMx2TrgYvmQapSCQSvD2xg9ufb5qhYSoH03WhRCLBp9O6YHDrVADc14i3qy3Lhu73MjZaoFDd5gflIz8oH4nY0DHpPcE6NYqLi6HX65Gebt1oTU9PR0FBAed7cnJysGLFCuj1eqxfvx5vvPEGPv74Y7zzzjsOv0etVqOiosLqHyGEeONIXimeWnwU+WU1ACyBwv15E9/dUXmexGPwFPuzX1huCXh4ubCKs5FHgcIdk0gkeHFkG0zoYhx9xz522KMPh7YxLo/CzsqMeO7Oi58e6oXODRPstg9slYp6sZE4V2BcL/tiYRUe/fmw1T5a1gyEf/95zsO/xhp7pseFW1V2r3doYJ9GMZNKJXh9bFvz8zf/OMO5ny/BO8O3A5ABsv8LrH3W8S4RHMtB1djPdnGosu5asiTHsq3JAMf7G8R9I4+4j9oYJNSYZmqwZ3fml9Y4fY+znxcda3aGui449UebLmB/Tol5JqdYGVi/uSuf7Mv5O2rKpwEtU3HoX8MwvB1XJzm/MhOirZ5P7uo8vobJgm2XwDAMNHUdqLYzOUzLlOptZoVeK1FZ/e2+DkwhhBBCglFQDRE0GAxIS0vDd999B5lMhu7duyM/Px8ffvgh5s6dy/me+fPnY948+1F6y5cvh1KpxOTJk7F161aUl5cjLS0NvXr1wtq1awEA3bp1g8FgQHZ2NgBgwoQJ2L17N27fvo3k5GQMHDjQHKW+U6dOkMvl5h62sWPH4vDhw7h16xbi4+MxYsQIrFixAgDQvn17dOzYEUuWLAEAjBw5EqdOnUJ+fj5iYmIwbtw4LF26FADQunVrpKSkYM+ePQCAYcOG4cKFC7h69SoiIyMxefJkLF26FAaDAc2bN0eDBg2wa9cuAMDgwYNx9epV5OTkICIiAvfccw/+97//QaPRICsrC82bN8e2bdsAAP3790dhYSEuXLgAALjvvvuwevVqqFQqNGzYEO3atcOmTcbRlH369EF5eTnOnDHe4Lnnnnvw559/orKyEhkZGejWrZs5sGLPnj1RW1uLkydPAgAmTZqEHTt2oLS0FCkpKejTpw/++OMPAEDXrl0BAMeOHQMAjB8/Hvv27UNxcTGSkpIwePBg/P777wCAjh07IioqCocOGYPUjhkzBkePHkVBQQHi4uIwatQoLF++HADQrl07JCQkmEfnjRgxAmfOnMH169ehVCoxYcIEc1m0atUKaWlp2L17NwBg6NChuHz5MvLy8qBQKHD33Xdj+fLl0Ol0aNasGRo3bowdO3YAAAYOHIj8/HxcvnwZUqkU06ZNw8qVK6FWq9G4cWO0atUKOp0OS5YsQb9+/VBcXIzz588DAKZNm4a1a9eiuroaDRo0QIcOHbBx40YAQO/evVFVVYXTp08DAKZMmYJNmzahoqIC6enp6NGjB9atWwcA6N69O7RaLU6cMI7CnjhxInbt2oWSkhLUq1cP/fv3x+rVqwEAXbp0gVQqxdGjRwEA48aNw8GDB1FYWIiEhATceeedWLlyJQCgQ4cOUCqV5iCbo0ePxvHjx3Hjxg3ExsZizJgxWLZsGQCgTZs2SE5Oxt69ewEAw4cPx7lz53Dt2jVER0dj4sSJ+O2338AwDFq2bImMjAz89ddfAIAhQ4bgypUryM3NhVwux5QpU7BixQpotVo0bdoUTZo0wfbt2wEAAwYMQEFBAS5evAiJRIJ7770Xq1atQk1NDRo1aoQ2bdpg8+bNAIC+ffuipKQE584Zb9pOnToV69evR1VVFTIzM9G5c2ds2LABANCrVy+oVCqcOnUKAKzOEa8ejwcAnMm5hsdaqJDewjgSy6A3lquzc0TLli1x4cIFt88RsbGxOHDgAKoqYgBYptavWbPG6hxRk9AEgDHA6ZIlS/xyjjDea4+HLYbRY8mSJWjYsKHV9sL868itqqBzRLNm6NGjh/m9XOeINatWATDevC0oKsL6vTsBALdKjQ3wxkodrlQbf6oP79rs8Bzx1oA4TFhiWRZsfBbQISIH27bZdy6YLFmyBLrGvczPr5aoUFZRhQ3rjPnt6Bxx61YhDFHxmDLmTkTLGNTojY3qP7Ltl5sy2f/KEHM+BNM54sK1WgCWJeCWLFlid44YPHKcw7/blZqaGq+vI0znCEA81xF5eXlWf5/tdUSigYEMwLVr15C67d9wNMdI16A3ltcdL+xzRI+CrWjpJD9rE1sgquwSAGDz8h/Qd9zfUVUrQzqAwqTuSL2Z7XCuh06nxV/bt/t8jmjUqBGuXbvmt+uILVu2AIBoriNUKsvSg8EslNoYYjw3uNvG6NSpE44fP07XDzbnhqwOxt9qg14HKQADJFBoK7BkyRLOc0NWVha2HjgJODjLfr18IwDjTfjbpRV1aTRe5+09ehryK/tE28bQKYyzg2USBrdv30ZuzmW7v+9Kbg4OH9bycv1gmq21atUqPN1KjYrIFGzLB/JKjPGa7u5YD0MaybB+tbEOmq4f8q7oACg485/tu105uH7xDNKTLbOelyxZYj5H3LyRD0AOVa0amzZtwu3bt1ERkYT5R6w7Mc7fqjQfa2I8R4wdO5buQ8D3cwTDMFiyZElIXD8IeR9Cp9Ph8OHDQXUfQmzXEbW1lph1y5YtQ1RUlCiuI4L1HGG6TyjkvUqxnSPcbWNIGH8O73VCo9FAqVRixYoVmDhxonn79OnTUVZWZv5j2AYNGgS5XG7ObADYsGEDxowZA7VaDYXC/sJBrVZDrbZMzayoqECjRo1QXl6O+Hj7m3SBsm3bNgwdOlSw7yfCoHIPXnoDg+avGX8AU2Ijcfj1YbhUWIVhn+xEolKO7DkjnL7f27If+8VfOH3DMvrzyvtjrV7ffbEYf//PAbROj8PGZwd6/PnuKKyoRa/3ttptj5bLcPbtUQCMQRAdpTGcuVPuLV5bD52Bwf5X70RGQhT0Bgad521ClVqHyd0aYOXRfACu89VRGbC3s+W8NwaLD17FG6tOmbfNHd8OD/Zr6vR7vtp+CR9uPI9XR7fB1nOFnLEi2C68M1r08TMcUev0aP36n+bnXGVQWFmLXu/a1w93hFpdqa6uRmys8cZMVVUVYmJirHd4KwUwaIFnzwArHgSuHeD+oJHvAX2est+++ing2K+OE9BsMJCzw/i4833A8SWW1yb+H1BTCmx8jfu9jfsAD/3J/ZoHwu13vqKiAgkJCYJfV7OFexsjmIVb/XHX+YJKjPxsF5JjFFj62B1YuOcKZg5tgQaJ0Zz7b9u2DaeYRvhk8wXO118c2RofbjTerOjdNBlLH+9jvlZ4oE8W3prg/vJJgXatRIUBH2w3X4MuPpCHf/1+ymqf6X2yMI+nv4HrmDQYGEilEvP/XF5acRzLDjse7OEM+9rgqcVHse7kTbw1oT0e6NMEAPDO2jP4Ybf1kphTujfER/eINwYQ1W1+UD7yg/LRdy6v+YlH6Ji0524bQ7C7DAqFAt27d8fWrZYbAQaDAVu3bkWfPn0439OvXz9cunQJBtb0ywsXLqB+/fqcjQ0AiIyMRHx8vNU/Mbh165bQSSACoHIPXs8vyzY/1tRN1bfE1HC9hIy3ZR+jcD6hzrQ8FePHqBpcHRqA86UNiJE75W5aPqBKrcMHf57Di8uPm4N4/q23fbBJRzo6WN4pNpL7GNLoDVbLTwHAPAdLLLGZboTM33AOt6vULvZG0HZoAEBkBHcAUjZvY2qM7pDheqdQYzph5e503KEBAGltubfrddzbzZ8vBWLr8pXdoQEA8mhAWc/xe3lafop+54UX7m2MYEb1h5vWtPyUVIKW6XGYP7mjww4NwJiP7l6iqXUGczwHAKgW4TJGq7PzMeGrPbheqjL/5pqunWQc67RGyPi77uA6Jk0dGc6WW9142rtjeUDLFKvnpr9Tx1qilGvZy0wnx4MYUN3mB+UjPygfidjQMek9Qe80PPfcc/j+++/x008/4ezZs3jyySdRXV2NBx98EADwwAMP4NVXXzXv/+STT6KkpARPP/00Lly4gHXr1uG9997DU09xjOYTOWr4hCcq9+C1qi44NgBU1BpvrOnqbn64sy6+t2Xv6oawkP0K7Mbc+tkDcH/vxjj42p0Cpkh83Cl3U1DIr3dcwtc7LmPlsXzza92zkvC/J/u4la9jO9UHYN+Jsf2FwZz7a/QGHL9e5vJznblcVO3T+0MB+0aDK3KZBLnzx+Dc26Pw9d+6+TFVwoiMjMSyZcuwbNkyREZyxMQwWfWkd19gYAXGfWQbIOO42RwZx/1euRJoWTejLp1jBC/Dz408+p0Xh3BuYwQzqj/cTHEW5G7erI+Pj3cak429UEO1WocajeX8589BMt44X1CJp3/LxvFrZXhzzRlzoHDTtTfXdbI7g43c5e0xWc6KM2bry/u74riDGd62sVJMf4uGFQelqNL1gBKxobrND8pHflA++s50zf/KK684v+YnbqFj0nuCxtSYNm0aioqKMGfOHBQUFKBLly74888/zYH9rl69CqnUcqHSqFEjbNy4Ec8++yw6deqEBg0a4Omnn8bLL78s1J/gtREjnC9VQ0ITlXtosczUcN3I9Lbsd18qdr6DaaaGAG3QqT0amR+3y4zHe5M6Bj4RIudOuZsa5ufrAnrb6p6V7NZ3PdK/KaLlMvS3GeWXHMM9ylitNWA1q7POhGEYqDR6vL7qFIa0ScOwtmlQupgxxEUuk+CJQc09fl+wYc/UGNgqFafyy/HUkBZ4e+0ZjO6QgQ2nLIGJU2MjIZFIECV3PQMkGJnWxHXI3dkQjIF7u17D+jIFkNoGKDhh/flyB5E65NGAMhl47QYQEQV82gGoZB3/jr7TQ/Q7Lw7h3MYIZlR/rDEMg082X8CCbcZYQfllzoODm4wYMQK/HHC89JGE1eMRHy3H3suWa02tBx31gfDYL4fNj8tUGruZGtEcv6fsgOq+4uOY/PmhXnhgoTEeQL0YBcZ1ygQANE2JQW6x9eAQ29w3/Z3vbziHXk2T0a1xEro2TsS6kzet9jtzoxzP/HYMs+5sieapsRAbqtv8oHzkB+Wj70zX/BMnTkRERFCFahYlOia9J/iaEDNnzkReXh7UajUOHDiA3r17m1/bsWMHFi1aZLV/nz59sH//ftTW1uLy5ct47bXXIJMF380BUyAeEl6o3INX2/r2vec6m4aVM/4qe0ldr4YQTdBnhjkL2UsA98rddPz4ulxChEyK6X2b2DVmHR2f7FF/bDoDgy+3X8Lvx/Ixe8kxtJuzEafyLUHIezd13clyR7NknH97NJ4f0dqDvyA4mc4DiUo5fn6oF46+MRwP92+KOe0r7GZjOFuqIiy4OxvC0QmN3SkSlwlM/QloNcqyrewqUJ5v/z7AOFMDABQxgFQGxNgsRcXT8lP0Oy8e4drGCGZUf6xtOVto7tDwxIoVK3Bfr8aIc7D85HrWDXGlQoYnFx81P1epXSzzF2CFFZZZCQaGMXdqSOs6ZrgGCbgz2Mhd3h6Tf7+jsfkxuw3RMt1yjfZQf/sYZiPap1s9v8WalfHi8uMArDulTLacLcSq7BuY9u0+r9Lrb1S3+UH5yA/KR/5QXvKD8tF7gndqEEJIMLC931yr1XsUU8PfGAGmanCNjiOeK6k2jj4/fq0soN+r0XF3atRq9bhaorLaNm7BbvNjpcJ1uf/2WJ+wuIFfXqO1WuucLTrC/saDGM4V/qTT6bB8+XIsX74cOp0PN8YczZroNt3yOKYekNwMuH8pMOB547ahrwM1DgLXR9jM4Oj5qHvfSQghAikod29mBpdohQwn543Ewhk97F47faPC/Ji99BQAqDTiiqnB/t00MEDe7Wqr7VydGnIeZ2p4a1oPS6dGpNzSiGBfO9do7H8nZw5pYfV814Ui82PTklYVTpa2Kq7SOHyNEEL4Yrrm379/v2/X/IT4iDo1BNK+fXuhk0AEQOUevGzXzf/9WL55mzszNfxV9pZA4YHnzt8d7sRc59U6y42LHllJrO32AcTT4y1rpbqK8zKwVSpPKRSHj+7pzLn9xPUydJ63Ca/9fhKAfX3gKvtQ7+hRq9WYOnUqpk6dCrXawzW/x35ieeyog6H1KGDmYeD1Iuvtg14BZh4BOk5x/PlymyCqLYZZP+epU0PMdZ4QsaP6ww92PnZrnORkT+BwXqnVcxXHjXYhsZeSyr5Whid+Nc4qkZk7NThiavAYKNzbY1LPGmwUGSE1Xxs92M8yO8N2cNA7EzsgLkru8DNNHRYVtY47NcSK6jY/KB/5QfnoO9M1/xdffOH5NT+xQ8ek96hTQyCxseJb65L4H5V78EqNsw6ApWbN1HDn5r63ZX9vT0vciiWP3mH3uvmbA9yr0blRIuf0d2JNLHV+fGfj+s2Nk5XmbezRmF+xlkk6kFNinn1gMrClpaPC1bIOr45u41NaxaZ1Onfg6VlLjgEAjl0tA2CfL1xlH+ozNXwSwT7HOjmhpbQ0xtOweq8CSGnBvb9JVIL184QGQPcZQP0uxuc8LT8lljpPSDCi+mPN20s7dj56OgClWmQzNdLjueMkmWNqcMwe5XO2ibfHpN5guY5SyKT44YEe2PniYKuBH1O6N7J6T6LSvkNj9lDr37ZqtQ4VNZ51PNVq9Xhg4UH8Z3euR+/jE9VtflA+8oPykYgNHZPeo04NgRw4cEDoJBABULkHL9uZGvIIKXR1DRZ3AhJ6W/bsoM8psfYBn/3dsXD1topzu+1IfsLNlzo/fzJ/gdfnT+6ID6Z0wpqZ/dCknrFjg73kBHtJqaf+exQGmzsp7MPMUSwOE674M8FMHmH849n1T6XRIc+mbtieB7jKXkodgY5JWTdz/LEUVEyK/bbxnwOj5td9Jz83weh3nhDvUf2xZrD9MXYTOx/lHs5auFRYZTWTU2jN07hv9JhnakTYd2pcL+G+dvWGt8dktNwSz0QikUARIUVWvRjrfRQy7H1lKADjNcbYjvXtPuefNstRFVepzctQuWvpoWvYdaEIb68949H7+ER1mx+Uj/ygfCRiQ8ek96hTgxBC3GA71TtCKmHN1PDfqZQ9sptr3WB/Lz818MPtnNttR/IT/t3Xq7HrndwUGxmBqT0aIVGpwJW6m/H/O3Ld/LrtLAPb9ajZxW0bi+OJQc15S6cYyeoqmY51c+lyYbX9fm6MhqUl25xhncX47tToONXxaxKpf76TEEJEwJvfne925vghJdx0egPWnbiJWxW1nK8nK+0H9ADGoOGAg4FFIvipbZcZj8cHNcNbE5wvKZKZGI0r74/F4deHcw5UipLLcPHd0ebnxVVqbDl7y6O0sGNw/Lo/z6P3EkIIIWJGnRoCGTlypNBJIAKgcnesuErtMHCxGBSUWze2dpwvwspj+QDcW1LG+7K3fDZXgGbTq4EOFC5EDI9g5E65j+1kPzIvEEzHL2B/08O2k4O9jIJpBOfMIS2QO38MXgmx5aZsmfJGz+rUqNHaj2K1PQ9wlT1N1Kgz4l2gcR/rbQxPnRoj59tvs12uik1Sd17lafkp+p0nxHtUf6ypba6L72iW7Nb72Pko8+KH5+PNFzi3V9Rqsf1cIa8DW5Ycuoan/nsUY7/4i/N1vYPrW9NsSXacr4/u6YzeTZPx3PBWvKXPl2Py1dFt8UCfJj6nQS6TolvjRADAuYJKq9eeHeb6b2Xn4eurTjndl2EYPPLTYcz48SCvbQuq2/ygfOQH5SMRGzomvUedGgI5dcr5BQUJTVTu3M7cqECPd7bg7/8R37S7ayUq3PXlbtyuNgbnM8XW2HCqAOtO3ATg3ig4b8ue3XDkWjdYqEDh/767U4C/MTi5U+6vjBK+U0ARIUVmgnHd6hHt0u2O6Z0XivD6qpOo0ejNnY8dGyaERVwVrk6NG2U1HPtZX1KZyv7ga3eat/G5zndQaz8RmL4WmPh/lm1S1vnNlxspPR4C2oyz3iaL5N4XAKT8ztSg33lCvEf1x5ptp8b7k9279mLno5THGYIP/ngIDy46hAVbL/L2mbsuFAGwBMG25WoJrtTYSEzskonJ3RpgSveGWPp4HzRMUjp9jyfEckya2h+3KiwBeV8Z3Qa9mtp3dNkGe/dkGbPrpTXYcvYWdpwvwrTv9nuZWntiycdgR/nID8pHIjZ0THrPp04NjUaD8+fPQ6fzLFgVAfLz813vREIOlTu3MXWjsw7mljiM4SCU134/iRPXy83Pm6fG2O3jzkwNb8uePXuFa91gkwBP1ED3rKTAfmGQcqfcGyZF220b0jqVY0//emKwcRmpCJnEbj3tUpUWv+6/im92XjbH1GCPjvzg7k4Y0joVR14fFrgEBwhXp8YzS7Pt9rM9D5jKPo0V5NQ2Nk/Yeeog8NAmIKEhIIsAOt9neU0iBRLrllxr0t/775BHAfcuBp47a9mmsD9vW30vwFunBv3O84PaGOGJ6o+1uKgIq+fKSMfXgWze5uOwtmlOXz+SVwoAWM5avtJXidH2wbHZdC5uyEskEnx2b1d8MrULb2liE8sxmRZnvJa4yRpUwTBAapz9TMTbNh1ErvKQzcBqUBzMLfE0mQ6JJR+DHeUjPygfidjQMek9rzo1VCoVHn74YSiVSrRv3x5Xr14FAMyaNQvvv/8+rwkMVTExThrYJGRRubvmKIZDoG0+cwuP/XwYOUXWa+dLOBbqvV5qP2rblrdlzw7KzD3azriNoQWhRMmdcpdIJLine0OrbT8+2MtfSXLIFEz0QE6JwxkF10pVUGuNx2QkK/jo1J6N8OODvVAv1smI+CDF1anBxXZdb66yD/VYNAqFAj/++CN+/PFHKBQcSz6ltgYa97Y8Z8/0kcqAmUeAl/O4g3p7Ss4aqVtb5ng/npefot9531AbI7xR/bFmGxA6UuZep4Y3+RgXGYG5443xH9iDFrjwOZCGMyYGi6vfXn8TyzEZH23s4CpVWY4JA8OgRVqc3b4l1dyzXtxhOzuIr44N23xkGMajGSTESCzHY7CjfPSd6Zp/9uzZ3Nf8xCN0THrPq06NV199FcePH8eOHTsQFWUZgThs2DAsXbqUt8SFsnHjxrneiYQcKnduKTY3QsVwkfvoz4ex6cwt5NssM7Mv57bdvldLXM8u8bbsXd0EDYPVf4Kau+V+L49Bwb1l6tS4Xa3B3sv2xzkAgAEuFlYBACLl4bGCpblTw8VdHNuZGlxlH+qdGnK5HDNmzMCMGTMglzsffWtHIjPGvohO5CkxrBlQbSc43s+07BWjB3Qan+/W0e+8b6iNEd6o/lirqLGeqeSqs8HEUT42TbG+aZIcY7kRlRyrQFLdc43OgFqO2FEmBQ6CevuKK4aD0J0aYjkmTTM92UHCHcW8sL3WYJczYIxj6Ihp4IrJ0aulHqXTEdt8fGH5CfR9fxtKfeiACUdiOR6DHeWj70zX/J9++qnn1/zEDh2T3vPqjsSqVavw5Zdfon///lbrabdv3x6XL1/mLXGhjBpm4YnKnRs7CDFgP0ooFHhb9q6Cp1sChXv18R55YpBxeaLP7+3i/y8LEe6WeyTrRsXf7/BvB0fjZOu1pt+d1AEAIHcxWhIATuZblmILh3gagCXIKsM473C1jUPCVfbacF9+yhmpeyOQ3SZj3cRxdqya4m1UFwHvpAI/DPPphEq/876hNkZ4o/pjraLWeqaGu50ajvLRtlODPaK/ZVocYhQy829Zmcr6u23x1UnPPt1yXf8L3akhlmNybV0cP7a/9c7i3Nf2WsM2X3u8s4UzNhgAfLPT+jzLV/vCNh//d/Q6CipqsXBPLj9fECbEcjwGO8pH73B1pFJe8oPy0XtedWoUFRUhLc1+zc3q6uqwuclBCOGP7Trztuv5BwrDMPh8y0X8cfyGw30e7t/UbluUH0es392tIRKVckzsksn5uoR1w5UvZSoN3lt/FucLKq1udL88qjWuvD8WE7o04O/LCADr5ReeH97ar9+1/Ik+Vs/vr5slwhXw/v/+1s3qOXut5QQX62CHighWAHBnszUipK7PA6E+U0On02HdunVYt26d57EQpBGu9/GE1fWokxOk0ibIav5hQF3Bb1qI26iNQYiF7TJCXL/TnnB2vfre5A6QSCTmGBe2S1/ZavmvDXjt95M+pYdhGKw8allH3HaWAMDdqfHBlE7Y+eJgn7472Dw1pIXdtqQY7iVfdG4MFnv6t2N22/QGButOWneeVKmdHwe+Onuz0q+fTwjhx5kbFej57hb8uj8PgOWa/9ixYxT/jAjKqztxPXr0wLp168zPTY2MH374AX369HH0NsLSurV/b1oRcaJy52YbwK6Wo1ETCKdvVODTLRcwa4n9hT4AjOmYgft724+iXzvLdVBbb8s+KUaBQ/8ahs/u7cr5Ot+3eGq1etz58U58tysHY7/4C20y4gEA4zrVpxtKXnC33OvHW5bK8XdnQT1WI3jBfV3N5coVRyNKbj16nj36r3lqrJ9SKC4yVoeT3uB4DWjbm01cZR/qnRpqtRrjxo3DuHHjoFY7Xt6CU3SSfxIFOO3TQFSi/TaN6yUFHaHfed9QGyO8Uf2xtu1coflxbKT7Hb+O8jEywvKbPr1PFmb0bWJ+bgpEneBmpwYA/PfAVbfTxGXDqQKr2HG1Oj3KVVoUVVp+P7iCXE/t0QhZ9QKz/rhYjsk+zes5fG3eXe3RtXEiUuOMMw/tBotxLCV2vsC+M6FKbX9jMjmGn1hpjvLx9I1yzu2Em1iOx2BH+ei5V1eeQHGVBq+vOgXAcs3/4Ycfen7NT+zQMek9r4bFvffeexg9ejTOnDkDnU6Hzz//HGfOnMHevXuxc+dOvtMYklJSeAiCSYIOlTs32xt95TVaZCREOdhbOK+Mams3UrtVeixnkD5bvpS9XOa4/9nUz+BoXV1PvbbyJG7XjQzUGRjzyPwpNoGsiXvcLfcEpRzrZw9AbGSEg4Dw/GHffK8Xa+ngqOC4gWHbqWG6ydEmw/UxHypkEutODa2Eu2PCNqYGV9nT8lMcxn4ClOQADXv67zti7Uf+m3HNsLm0Bej2D6++in7nfUNtjPBG9cfiUl38KhOuG86OOMrHJKXlN/+h/k2RmRiN5mmx6Me6YW5a4srfs6ar1Tocv15mta1Wq0fv97YCAE7NG4nYyAjzErXT+2Thp315iPOgc4cPYjkmnS0ROr1vE0zv2wSTvt6Dokq1XbuKa9BKRa398VRZa38dyNdAG0f5eLPcP/FZQpVYjsdgR/nons+2XEBVrQ6vj2sXksuDiwkdk97zaqZG//79kZ2dDZ1Oh44dO2LTpk1IS0vDvn370L17d77TGJL27NkjdBKIAKjcuUltZgDMXXNKkHQ4m4gwd3w7NK6ntLrBCQC/Ptzbrc/2d9nzdat05bF8q+emaf++LnkQrjwp93aZ8WhcT+l6Rx+xZ9xIWHN9TCP82GyXqjB1akTKeY5/IGLsY19nYBx2TETY3HCg872bej4MjHzX+QnYW/cvA8Z+DNTv5Nn7zv7h9VdSufuG2hjhjeqPhW08DU/Y5uPc8e0wsn06XhrVGsPapuPJwc2RVS8GcpkU/7gjC81YMy9NMb64Yro14bhG8SaQdG5xNTq8uRHf7syx2s7uuMkpqsLC3bnYfr4IANCneQr+fGYAdr881OPv84VYjkmFkwFOJvK6Tnrb2S3uBnav5OjoOGnT8eQtseRjsKN85Aflo2tqnR6fbbmIH3bnOozBQ/hDx6T3vB7q0Lx5c3z//fd8poUQQgAA+3NKBPleg5MBCANbpQKwH9SbFi/sjBIJ7wtQWTPN1LDteCKhQRFhKddxnTLx9G/ZVq+zl6qw2u5G4zpUsDs1DAYGtaxlHMZ2qo91dcE7ncXUaJoSg9zianRulOi3dBIOrUa6t999S4El0yzPMzr6Jz3ELdTGIMRelg+DHh7s1xQP9jPGhPtheg+n+yqcdGpwxZWa/PVeXHl/LFYevY7kGAUGt3YyMw7AmuM38MxvxzhjwY39Yrf58ZKDV7Hk4DXz80GtUhGtCJ8BFbaSYhSIjYxwOmPHNLjCdqZGgYPZEFq9wWo2OFenxk/78jBvQgdvkuyQbZyUWq3ebmYwIURY7CUI9QYG5ziWrCNEDLy6K7F+/Xps3LjRbvvGjRuxYcMGnxMVDoYNGyZ0EogAqNzt6fQGq/V0/Y1hGMxecgz/4ghu6DwIsLGh4O2MBX+VvWX5Kb98PEztDurU8I5Y6/wDfbIwsFUqujayxDGQSSX4+J7OVvtFOggqWsOxPnOoYld5nYHBM6yOH3Z8EttzA7vsP7qnM+7r1Qg/zvDjEkvEe61HAW+WA/2eMT5Xe99wE2udDxbUxghvVH+4NUuJwQ8POO+MYPMlH82dGhzX5o4G/xRW1OK5Zccx48dDLpetmr3kGByEprJyINd6gJNQHRpiOSblMinaZ8abn6fH28+uNY2mtl26rLDS2Knx2MBmeHlUG/P2apsOEtP7OzVMsFtS01fsfFRprL+3VKXh9btCmViOx2BH+ehaDWvZuq93XBIwJeGBjknvedWp8corr0Cvt79gYRgGr7zyis+JCgcXLlwQOglEAOfPnxc6CaLDvjn67iTjSKCeTfwXMDa/rAZrjt/A4gNXrUZcA/Yjh9hMNyy9vbnv7zrP8LYAlTXTSD1afso7Yj3XvzWhA35+qJdd/I7MxGir53IHsw9apodHkHDAuFyX6fg3MAz25dw2v8ZeDsL2BgC77LtnJWH+5E5IZnWCEBFS1B3Xeu8DHoq1zgcLamOEN6o/3L64rytaprsfy8qXfDTN0ORaP93gYAQNe5bnmRsVXn83W7nK++W3+CSmY1LJ6tj5YEpnu9ev3FYBABZsu4Q5q09h5dHrqNHozdfy93RviCcHNzfHJbFdlmr3pWIAQONkJb57wLLc3y03l69yhp2PtvE8bldRp4a7xHQ8BjPKR9fYs/XYs+aIf9Ax6T2vOjUuXryIdu3a2W1v06YNLl2iXjx3XL16VegkkAArV2nx1MZSzF0tTLwIsTJ1akgkQLLS/zf82J0SttOsnXVqmOIQeDsjwl913t8zNUyjqcJotSFeBdu53jbgvEwmwZ5X7NevbpsRb7ctlJk6NWzXqTaNaGXvYxJsZU9YfDihUrn7htoY4Y3qjwX7NBTrYXBsX/LR1Fn//+ydd1gUV/fHv7uwhaUKCCiCKIqIvfdu7EZjNMZ0035pb4rpvb7R9PKmvXlTTLXGaKLGrlFj711URFRERKTDsu33x7aZ3dk+u7O7cz7P48P0vZ4z986999xzDlf4KdtvoBmmsb+4wvEEuN4dFw0TwbJ6P5jeSWaIpuQY52OmH7efw6yFB3HDF/9YxjfmhSx5Jo+Pj9eesuhkzdESLN57AQDQp1UiOjSPtzzLnLzdF5hyPHullnXul53nfH6+WAim9zGUITm6xlli8IIrNQ7PEd5B76T3eDVNFR8fj4KCArvjp0+fRnR0tM+FEgMKhb3LKBHe/LqrCJUaKX7YTh03Jg2Nxg9mlCzCEurG2UfUV5irkk6VWkOMGAwG7GQMymzRmZID2yYDdhd/1XlzTg0/2TRQZ3I9pfBT3hFqbX2kjfVKJpUgPSEK03q0YB0PRELzYMJs7BkwZwPrONOoYds2hJru+UAul+Ozzz7DZ599Brk8BL1SeGjmxKh3PqExhrih+mNFywj/lJXs2bvvixzN3zWuvjiXocMW29BCTBx5enBf6/alfiWY3kkFo8/B3HbGiZJqi2dEhKkvn5lo7MOtOlqCX3cZJ9Lu/2mv5R5ZhNStxOSewJTjuuOXWefm7TrPit9POCaY3sdQhuToGtvcPEyGf/A38l5bh7ff+wj33ntvaPb5gwx6J73Hq6/VpEmT8Pjjj+PMmTOWY6dPn8aTTz6J66+/nrfChTNTpkwRughEgPGkIy8mzJ4aUbIIq8u7xn9GjSlfbLNs3/K/nQCMCepaPb8SH6x17PZnnrBMjvHug+OvOu9/Tw0yavhCqLX1PVuyQ7+ZvQ9mT2EnTh6VlxqwMgUDGh13BWMm2LT11Ag13fOBTCbDww8/jIcffhgymUzo4viA9w2qGPXOJzTGEDdUf6yYvSLaeRB2yowvcnSWKNxVvgwAuHCt3uG5YDFUeEIwvZNMTw0Zh9EhPsr5d9fcT2E+Z/XREs7rElTuf8P1egPGf7oFWc+twMUKbv0z5VhUXmd3vsvra3Ce4zjBJpjex1CG5OgaV0ZsSUQk/roah/99+XmI9/mDA3onvccro8a7776L6Oho5ObmolWrVmjVqhXat2+PpKQkvP/++3yXMSxZsGCB0EUgAownLtfhzH//PoOs51ZYktiZV3RFySMsq45OXq4O6Iqd91e7znVim2vAU/xf5/37flFODe8ItbbeNseG2XPD1oNDQkYuAOycGmajrJlQ0z0B8OGqQXr3DRpjiBuqP1bMibplkZ63S77I0ZFRw2AwuOVJ/cn6U3ahLM2E4gKvYHonmX1xldw+JFmvrES37meG9tJyLNqIkEo86uf9cbAYR025VB7+ZR/nNUw5Vjdwj/H+OFjs9m+KlWB6H0MZkqNrGly090OlB7BZ8QRKPhrs3gMrLwDzbgHObuahdOEHvZPe41mAThPx8fHYtm0b1q5di4MHDyIqKgqdO3fG4MFuvtAE9Hr/rUQnghNdCHbk/cHsv04AAEZ++DcK54y3rOiqqtewJgVv/WYHlv9rEK+/bTtAa9/MGFP2m61nnd63+8WRPv+2v+p8oOaWyVPDO0KxrX9oaDa+2GRcJW2b/Jqw0jxeyQo/pZSxDT+hqHtf0el02LJlCwBg0KBBiIiIcHFHkOLD91qMeucTGmOIG6o/VsyTzZFSz9cg+iJHs7E+/3I167hGZ3C7aezz9nrMui4HN/fOZJcrBMdCwfROMhNsJ0Xbh3upcmAsMGM2ahRetea00OkNOHKxkvM6Jov3XsBUm1CkAHC6tBqbT12x7B84X8H520w57i68xnkN9TldE0zvYygTSnKsrNdg+5kyDMtNsVtA5QuXKuvRNEZht3DNzJ3f7XJ6/62S1dhUqAVwGE1ryxER7dyoipXPACdXGP+9Vun8WhESSu9ksOGVUQMwrtIcNWoURo0axWd5REN2drbQRSACDDlqcPOvefsBGDvqzAnCIxereP+tRpvYkG1SYlze88nNXdE0ljvklCeu2f6q85acGn5+v0JxIBoMhGJbzwxp4G0OGTHwxW09cKzY2k5FydgDjVDUva80NDRg2LBhAICamprQy4HAg/FWjHrnGxpjiBeqP1bMOTW8yW3AhxxXHL6ED7U6a2hYN0JPmSmtVuO5JYc5jBrelWXrs8O8u5EHgumd1DAWZ9l61gJAdYPjfCaAdYHSpC7plnGWVq/Hs78dYl3HZdR4atFBO6PGnsJyTP1qu1tlN8tx19lyh9eQV7hrgul9DGVCSY5PLjyIdccvY+aALLw6sQMvz9xZcBXTv96BprEKvDIhDxO7NGedr2903d5rtTqM+sEYMq7mngWIHvSg8xuqLnpdXjEQSu9ksOG2UePTTz/F/fffD6VSiU8//dTptY8++qjPBQt30tPThS4CEWhoUhil1Q2s/VM2q8CiFf5d1au1MWroTBbx1snRKCirtbu+U3o8JnV1XFdv7ZPp8Jwt/qrzlpwafnm6FR1Z5bwiFNt6Zkxmb1aIioWuGQk4YwqjB7BjVAOhqXvCjPftHendc2iMQZih+mPFvBDHm8UFvsiRGWKqtEqNDFNSaXeShLvC0QIZeYTUbuERk2bxvoWA9YVgeifHdEzDqqMl6JqRwHn+hm7NcfyS40VhZqNBUozVy0OnN6CizruQv8sPXXJ47vilKqTEKpBkykVoluPW02UO76EFVK4JpvcxlAklOa47fhkA8P0/hbwZNX7acQ4AcKVajX/N24+0eKUlfJ3BYED7V1a5fMZFQ7J1p/qyG79K9dsZofROBhtuGzU++ugj3HrrrVAqlfjoo48cXieRSGjA4QabN2/GjBkzhC4GEUDEPie8r+gaK0k3ANz4JXs/QWXvSs0XTy48iN/3X2AdM0/UJ0bLOY0aP97dm/NZy/81EOuOX8YDQ9y3qPurzpuHuo7iF/NF5xbxfn1+uBKKbT1zsEur5rhpFq8EYGw7zNgaNUJR94Tv7zvp3XNojEGYofpjxRx+iishtCt8kWOLJlYDAtPQ4E4+DVc4yi/48oT22F14jTOnwqczugnaFwmmd3JC52aIj5I57JPfPaAV3l55wuH9EaaVUEx5qrV6uzyGhaYxUW5aLE6UWBegldWokRxj9V53pJeTJdUY+8kWRMsjcPSNMQCMcmzSaRg+XX/Kct3H07vi8QUHLPtvrzyB+wfTamVnBNP7GMqEihw/WXfK9UVeYBtt4kRJtcWose3MVbeeUWBoZt1RuxFhg6/wSjotEOF1wKGgJVTeyWDE7V7S2bNnkZSUZNl29K+goMBvhSWIUCbcc2p8uDYfn21w/OF96Gf7xHFVNm7S0XL/eGo0aHT4bd8FO8PS6qOXcamyHuWMhHlMmnDEqwWAjunxeHxkjt0kphAEylODEkOLh8FtmyJGEYnerVzERhUx258fAQCswX0wtAcET4T35zrooDEGQdijMScKD3AYyBu6WVeLMr10+TBqaB0YNZJjFLizfxbnudbJIRbG0I9ERkgxLDfF4v3Add4ZMUr7icATJdWoUbPHY5lJRpl/fmt31vGxn2xh/x6HUSMpWo6NJ0sBALU2IWwe+Hkva//6Ls09CuXrLlqdHkcuVjo0ohFEqPDRuny/PLeJzUJS5uLIsho169wtjMgUqXHWtmfE7c9bL6p0I7SUgfENqbzg+Dpn7PwamN0CKNrh3f1EWOLx0g+NRoPs7GwcP37cH+URDUOHDhW6CESAYbrUhlsnq6xGjU/Xn8L7a/JR18gdz7WkqoHzuJlh7ZraTZzz5X3gzKW93+wNKLhi76XBN/6u82FuMwtZQrGtbxItx/bnh+OXe/sIXZSgpC0jFw/Tq8V2cB+Kuhc9PBhvSe/eQ2MMguqPFY3ee08NX+TYPCHK0hSavUUAz3JqOGLTySucxyUSicM+v9ALBsLpnTR7Vmw55TgEFACM72RcgZ2VxDYoXalWo5qRjJwrr0ejTo9atf1YcMCgwaz9O/u1hFQq4TXM6d5z17DqSAleXnYUE/6zFf/ZcJq3ZwcL4fQ+CkkoyJGrTeQjDCDADjUMsA3Y0XK28fOZ0e0s2/1aJyH/rbEonDMefbMZ4afy/wJmZwA6J6HsmEaNjzoAZzZ6XvC/nga09cCiuzy/N8gJhXcyWPH4KyKTydDQ4HxyknBNUVGR0EUgAg3ju3T4YqVw5fADzNVb3n5s7xnY2u5YeS23B4WnqDU8uTv6gP/qPHlQBDOh2tbHKmV2Eynfz+yFZvFK0Rs7mOEWmJ4aHdPZ4SBCVfcE4IurBunde2iMQVD9MVLfqLMkhXa1+p4LX+XYLM4YYpE50cXHZJojrxOpBOjcIgHZTe29MhSRwub2Csd3clLX5g7PrX1isKWfwxVdqq5Rh1VHSjDuky04y7EorLpBi73nrtkdLyhkyzHCZMywXRXuCzd+uQ0P/LwX83YZf8tfq9yFJBzfRyEIBTly5ax5Z5Xj8HKeILVZxPP6n8csuU61pjBRuWmxKJwzHgkqOV4Yl4v0hCg8Nbod5I7aZHUVcGyZ4x+NTWPv7/yv1+VHQ3jNpQGh8U4GK171Eh5++GG888470Gq5V2QTriEXevHBdLkOt1BUOp3vg564KOOqgOfG5lqOPfvbYd8KZsJ2dVlKLLfb9me3dLOWh8NF2xf8Vect4afC7J0KF8KprR/WLgXbnx+BAW2SXV8cxjCNrfJIKRY/0A8L7u+LNgwPDiC8dC8efDcSk959g8YY4obqD3D4QiXav7IKbyw/BoA7xI8rfJVjcaXRuMjMtWAbOsqbsFhpJmOJLRKJBPJIKdY+MQT3DGzFOie0p0aov5OxCuN4hjmHOahtU85rp3RPR9vUWMs+V+jZ6gYtHvh5L45dqsKqoyWsc2YDFDMmv9kwVnC2kHWtF7Y6pxwrto/p703dCXZC/X0MFkJBjtsL7HNbfLv1LIZ/sInlMeUNOo6oIc8vMc67NJrmdZghqu4fnI1/nhuOFk1Uzh+sqXN8Tmu7aMWHuQtnvxOihMI7Gax4NWu3e/durF+/HmvWrEGnTp0QHc1eVbFkyRJeChfOREaGX3Ibwjl8uQsGI406q9HANuZuaXUDiq66/vCYrf4PDMnGnL+MqxDWHb/s1u9XNWggk0oR5SAnx/nyetZ+cowCpdX2K4MSouR4c3JHvLz0CN6c3NGt33YXf9V5S6JwH55x/FIVkmLkSIllDza/v6sX3l55HM+OyXVwJ+EKauvDD9tVhT2zuHOPiFH3MpkM7777rmU7ZPHBSCxGvfMJjTHEDdUf4MO1J1n73iTJ5kuOH6/Lx8C2xoUMtotnjCt9PWsrzYu6clJjkH+5hvEs01+pBI8Ob4tvt561nFPKhPXUCPV38qnR7VBS1YCxHdmrpN+a3BEvLT3COvba9R1cPm/ZAcex8zumx9t5aai1OqjkkZDYJPZdsu8iXhyfZ/eMRq3e8UpwB1TWaTDu0y12x7V6A0qrG/Dz9nO4rW9LpDgwqoUSof4+BguhIEe5A8tfwZVaLNh9HvcOso9y4S4ajqTddaYcOFpzPicX9VAmk+HdN14ENr8Pi+1ZZmP00DYCkSbjSF05+1z+KuDSIaBZZ9cFNhiAPd+6vi6ECYV3MljxSnIJCQm48cYb+S6LqJg2bZrQRSACDNOoEW6L6hsY4Z2YXhF6vQHD3ttklyiOC0cfbkc8Nn8/lh0oxtyZvXDX97sRq4jE4ddHc17LHBwBVq8QWyIjJLi9b0tM6toccUp+J+T8Vectq6i8fKfOXKmxJP4rnDMeSpnUos+BbZOxdtYQPoopWqitDz9e4hiEcyFG3cvlcjz99NNCF8N7eMipIUa98wmNMcQN1R9go03eiQgv2iW+5Hip0rqy1jY9nW34Encwrw62vffIxSqMaJ8KAIhXyfDg0Gx8uekMAOE9NUL9nYySR3AuTrqtb0uWUaNdaqxbY5+qescrxJOi5XbHGjR6qOTAuAkT8fqh9ZbjegeD4QPnK9C7FfdiEUecKatxeG7oe5tQ16hDVYPWLaNNsBPq72OwEApydOZp5I2xm4lGa1//Kus1KKlsgMZs1HDxG3K5HE+//BZwfhLw7UjjwTMbgU5Tjdv7fwGWPcS4Icb+Id+OAl4qsT9uS8EmYMWTrq8LYULhnQxWPJpF1Ov1eOedd5Cfn48jR44gJSUFX3zxBb7//nvWP8I1v/32m9BFIAJMJMNNm4d5k6CC6Z1hnhA3GAxo/cJKtwwagOeDlmUHigEAd32/GwBQrdY6TMDeLs36EZ3ROxPZTTk+qrB2Hvg2aAD+q/O+emrs44h7CwCrHh/kVXJKgg219eHFDd3SMXNAllvXku5DGe9XHpDevYPGGARA9aeu0T7sGlcyZlfwJcfb+ra0bNtOQnszqWY2akRGSPDR9C6W41U2oVTOl1s9vIXui4b6O+mu8emkKZ6+K+qcjOvSm0TZHWvQGK//c/kK1vFHhrflfIY3OVRqGhyHKzSX1xye6o+DxVh+qNjj3wgWQv19DBZCQY7NE+zrk5lEDgOiJ2hsrdQALlbUo+/s9aiqN9Ynd9ve33YVAbcuNj1kj/UE06ABAI0cxkdtvf0xLqrt84vgykn7YyFMKLyTwYpHX41///vfeOGFFxATE4P09HR8+umnePjhh/1VtrCmsZGfBMhE6BDLmCj3ZtVVMMP0zvjO5BWx5VSZw+sHtknGN3f0ZB1zlOfCExo5PtAAEGUymNzcKwOzp3RCv+wkzuu8Scbodtn8VOd9zanBHOw0aHQWo5Q/DDtihNr68OC7u3rixu4t8NbkjpwxprkQo+51Oh12796N3bt3Q6dzz6AdXPj+bRaj3vmAxhgEQPVHo7Pvy3nTNfVVjjd0S7f7bebCocRoOX5/qL/n5TItgoqQSNClRYLluK2BZEbvTADAXf2zPP4Nvgm1d/LlCXlIjrGOqbzJfeKMRXsvODyXzjEJazZqNDRaDVdzZ/ay6HZoO3Z+j3qN532HGrXrHEy7CsvR8dXVeHTefjzy6367fIuhQqi9j8FKKMjRmeHCkaeTu3AZNcwcOF8BgL0glwtzn//48ePQxZsM4BVF/glJEtXE/tjnvYEr+fz/lkCEwjsZrHjUTfrxxx/xxRdfYPXq1Vi6dCn+/PNP/PLLL9BzxGQjnNOyZUvXFxFhBfPjYZtsL9Rhemos2W+MtbqviNsDYFDbZPx4d2+MzEvFmbfHISc1Bo8Ob8NpULBNvOsKW6PGrd/swHO/HbLI2zxoGt+pGef9/kwoF6x1XsoQ+2LGQMVXt1bCSLDqnfCM4bmp+OCmLohWuB+1U4y6b2hoQO/evdG7d280NNgmBAwhfBiQiVHvfEBjDAKg+nOFI9+bNwuhfJWjOSRsg0aPC9fqYDAY8NOOcwCA3LRY7H1pJNqmxuKXe/ugS4t4DGyT7NZz7/9pLwCgol6DSEYH1Lb/PaBNMva9fB1emeBeuEd/Emrv5D0DW2HPSyPx4rj2mNE7A8NzUxxe+9xY1znzBrV1T7cAoODwujcvlkprbjSUxSkjMbRdimWc8b87emLrs8PQMT0OAFDfqEOjVu9RLkpnnhqs6xjGj2X7iy25A0KJUHsfg5VQkKMzwwUzfFRdoxZHiyvdemZ1gwYvLT2MHRxJyM2sOGz0inDVFTb3+V9++WU0KJoCkBgTeNc5fjYA4NUKt8rKQu3Ak2zDm54/K0gJhXcyWPHIqFFUVIRx48ZZ9keOHAmJRILi4tB14ROK7OxsoYtABBhmx0kXZkYNrviqXP9FlTwCP93Tx+JKHyGVYM0TQzBrVDvWdf832Jj4yjam6sHzFZj+3+3Y78BgYu4AN2h0eGnpYfxz+irm7z6Pj9edAmAdNEkkEnTPTLC7358u7v6q8xLTymJv3yimpwazs++N+zdhD7X14oV0H4Lw4EVJevcOGmMQgLjrz8Ld5zHyw7/tjqs9mNw146sczYmaP1ybj4HvbMRrfxzFX0eMcc+lEonFY3FAm2Qse2Qg/ndHT0zu2tzt55+7WsdaVMNFYrTcq9BbfBOq7+R9g1tj9pTOrEgBtrRLjXX5nI+md8W/hrdxed3I9qmcY4cGk0dEenoGAPuxlixCihZNVFDJjItGHv51H8Z8vBk931qLSif5O5hUu+GpYcszvx3CqqNuxPIPMkL1fQw2QkGO5rmUoe2a4qd7erPOMRdyPvLrfoz/dCvWH7/s8plvLj+Gn3cU4eAF10YQs3HDLSLlQKTSVLha41+uHBqAfV97we3GhOLO+P3/uI/r3GsjQoFQeCeDFY9mrbRaLZRKJeuYTCaDRhM+L1Og2LBhg9BFIAKMhjHLH4orQxzxy85zeGz+Acv+2I5pALg7ys7isDKJizJ2wHU2bvg3/Xc7dp4txw1fbOO8z2zUmPG/Hfh5R5HdeaZLM5dnSLTCf8kI/VXnreGnvLtfEWn9P6sZCd/lZNTgBWrrxQvpPpTxfuEB6d07aIxBAOKuP8/8dojz+IpDHkwsmfBVjrZ9wB+2n7Ns2+a/AIzJqP99QyePfiMtzlrfmX3RYCOc30mFzKrnaT1acF6THKPAkzaLz7h4c3IHTqPG6VJjHP2VW4w5EK/Wck9eRsmN70Bdow4FZbWoatDi4V/2ufxdwH1PDVsOmsLshBLh/D4GkkDI8cK1Omw5dcXrENHmkH9SiQT9s5MxpkOa5RzTk2nDiVIAwD0/7MHbK487febCPezwcc3jlQ6uBO7o56HnQIQpXJbZ0KDnqJfj3rc/dvwP4KcbnD/b4GDuLIxCulPd9h734yjAGLP9rrvugkJhjdPY0NCABx54ANHR0ZZjS5Ys4a+EBBEmaMM0/NQ7f51g7dc16qDXG/DzjnMO7nCN2SVZYxN2wtVqtf1FFUiNU2J/UQXn+YV7LuDdqcbEhFwxZrliwYYKBi8n4ZgDEKabq5yShBMEITrCZ3AUatAYgyC4se0LBwJnYU8uXONO7OrKw5c5CSeRsBcXxUV5NCVB8AQz98bz49p7/Zyf7+mDZvFRnMapZxYfwk09M/BTocrpM1Ry+3u3nnacn5HJvF3GhWz3DmyFlybkwWAwoNXzK13elxYfuuM+Ivh5+Jd9OHihEp/f0h3jO3OHvnaGuR2WSiSIkErw1e09MGvhASzZd9ES1tw2N8bXmwtwa59MtEyKtnseYFx0evKyNZTT7f2y0EQlQ2aSCr/uLMJyhhH9tYkdPCuw2uT9UXYSSG4D6DgMmJl9jX+HvQhs/Lf1+Lmt7v/OkyeBD1wbWgnx4NGs1Z133omUlBTEx8db/t12221o3rw56xjhmoEDBwpdBCLAMJP/acMoRnSVzeqYukYt/jxUjO0csRrNYaVcYQ4TpeVImOiMebuK0Om11W7+Brv5e2RYG7cTAHuDv+q8r54azFBoBxgrlvyZNF1MUFsvXkj3IYwPOTVI795BYwwCoPrDRbTc8wl/X+Uo9aI/7KrfWMsIEZSoMq7onTOlE0blpVoSgwcj4fxO5qTG4sVx7fH17T2cJiUGgLedeOLEmzzsmZ4fTNwJuxzFkY/DXUqqjDm8Tpm8Qtwdz/GdRD0Q+Po+1qi1eHnpEfxxUNyhHQNRr80hntafcB0WyszuwnJ8tDYfdY1avL/mJACAGYXPbDzefOoKAODjdfaJstcec/x7KpuoFFEyKW7unYn+2cn47JburHNeh/+bfwuwaY69d0Xz7kCKKU/SoCe9ezYAxKYB6T2M2wa9fxKTC0A4f2v8jUe9pO+//95f5RAdpaWlyMjIELoYRABhJQr3cLI+lKhV6/Cng45S14wEt55hjrfqqfHH1YqeAW2SLNu2ibCfGu1fi7+/6ry5467W6nHmSg2ym3qWXH29yWUVAP7Ov8Jr2Qhq68UM6T4EsUyEeP+NJr17B40xCIDqDxc9s5p4fI+vcvQ2x9yqxwdhzMdb7I5rdHrc/9Mey755Yu3m3pm4OYgNGkD4v5P3ubng7JY+mXjh98Oc58yeNkoHYcTcMmpweGq4AzO0z8Qu3HldHh3RFkNyknHjl9sBGI0ZGp2Bs1zHL1Whsl6Dvq2T7M4FA76+j8/9dgjLD13CTzvOYWi7pohzknMlnAlkva6scy+M5tUaNaZ9ZXxHP1l/ynKcaWRWmox/e89dg1anx+cbz9g9Z/3xUtw7yM2FpDZtfevkaBSU1bp1r1M2zWbvv1AMyFTWfrbUw/oujTSGsxr1lnG/9/3GPBv5q4DXE4B+jwCj/+30EcFOuH9r/AktxRWI/Hx7qyoR3jA7TuGWKJzJsUtVWHfcOlHeKtnq/uju/zrCxlPjfHkd7vhul89l+3h6N8t2oNfmBKLOj//UfiDpCrPLNpPrHQwKCM+htl68kO7FCemdILwn3OuPTm/A7sJyNGjcyzEHwONcFYDvcoz0coVublocXptoXInLDGP6n/WnsLvwmmX/pfF5PpUvkIT7O+kJjkKMmSfGffHU4DKkuRMWmPnske1TOK+RAOjRMhHf39ULb03uiP7ZyQCMCeuZ1DVqMfaTLbj56x1YuPu8y98WAl/fR2Z4IXcn28ORQNbrynoNy1ONi+1nrqLHW+s4zzEXed7e15jnQqsz4K8j3InuuaJlmLEN0Z2gYhu15t/fF0nRctw9oJXT8nLS/nrH5+TRzvNf5E12fE5dY83PkTve+DfCxhi3/TO3ihjM0LfGe8ioQRABgplHQxPGRg1brstLtWzbxn10hNkd+JDJbXPQuxux2Ucvgty0WDSNtcaOZX5X180a7NOzhYQZo7hBw09Ys5cnhM5gkyCI4EEmk+HVV1/Fq6++CpksFFf/+RjPjyAIwgm/7DyHaV9tx+PzD7h9DzPvQaCI9CEsz6CcpgAApWmCu7S6AT/vZC+gGc1IeEuEDmkcSYWlEiBWafTUcGT0+Hpzgctncxk+3IkixRxfM1edPzAk27J9Z/8sAMCw3BTc1relxTN97rZC1rPqGq3Gxmd+O+T6x0OQFk2shqIaFxPtBD/sOXcNnV9fg0MXKhxe897qEw7PMfMYJZhC92n1Bpxi5Mbwlk7p7LCeKXFK7H5xJF6Z6HouwNznnzJlirHPP+JV7gufLeQ+ftNP1m2u/BtmKhnJzROyjH+loTjGIPwFZeUSiBkzZghdBCLAsD01wienhiuYHVJ3w25FmPJdlFQ1YHdhOS/l6JfNdiNmrv5pkxLLy284w1913td3KVIqYQ0IBuc0ZRl/CN+gtl68iFH3crkcr732mtDFEBQx6p0g+CIc609lnQbxptWwv+wwTu6vOsq9wpYvfJWjt+GnACDC1PHXG4CTJdUY/fFmn8oiNOH4TnpL0xgFy7shNy0WL0/IsxgTuBKFA8BHHHH/bdFyGDUuXKvHxYp6px4bzAVzTA+jZ8e0wy29M5GRGOU0x0ZpdQNSYo3GmlBYz+Dr+9izZRPLJLm7iw3DkUDXa53egPfX5OPHu3tznq+sd+w1c6LEaryQMwyHCid5aFYdKUGblBg88us+9G2dhNeu78AK1dajZRM8MCSbM6G4u7k07Pr8sRzG6pQOQJSDEIp51wMzFgDzpgMnVwKaekDGUdc1pnBYcS0Ac05U23wdYQB9a7yHPDUEYtmyZUIXgQgwLE+NIMip0aDRQRuAzsyN3VtYtj311ACMH2U+MCexMzNrVDtM6tocc2f24uX5rvBXnff1XbLNcyKnBOG8Qm29eCHdhyA85NQgvROE94Rb/Xl/9Ul0eWMNVh42hnxpm+pZ3jNv8VWOtnnnmDDDyjq7V28wYOmBiz6VIxgIt3fSFzoyVnVfl5eKVY8PxoA2yZZjSgfhp9yhsp57pfbPO87ZHdPrDfhgzUnsKSxnLZhjGuMkEgkyk1Quk4Y/8ut+63NDwKrh6/uoZnj4a/UG1DfqcLGi3skd4Yk/67XBYMA2jjyfUU7qR26zOIfn+rRKtGwzx+nvrT7p8J4Hft6LebuKcKKkGnO3FcJgMLB0P3dmL1Y0DV+wyFIRA9zwNfvk3auc3xyfbt1ech/3NY0mQ6pcZT2WM9qzQoYA9K3xHpq9Eoi6ujrXFxFhBXNFvRA5NXadLccbfx5Dg0aH+kYdBr6zEVNNyaj8xbd39kROqtULIsvFQMgMczBV7yTu8MA2yfjv7T0wpVs6Pr+lO2v1woTOzdCzpXVlgMQmi0Z8lAyf3NwNQ9txx1/lG3/V+RiFbw53tiuj9pzjxzOGMEJtvXgRo+71ej2OHj2Ko0ePQi8ij0QmYtQ7QfBFuNSfWrUW1334Nz7beBoA8MqyowCAOMYCm6s1ar/9vq9ydJZTw9FKYzPmOWSd3hASk8SuCJd3kg+eGt0O0aaE3s+Mbmd3PlruekwyvnMzzuPdM61jtqcZz+YaM8+cuxv/2XAaU7/aDo2pryGRODfGMRnIMMTsOlsOtVbn8LeCDV/fR6ZHgE5vwMgP/8aAORtQcKXG16KFFP6s15tOXsEt3+y0O65yUj8aGrnnO6Z0T8enM6w5QWUehAb8dutZy/Zj8w/gyMVKt8riLuY+f35+vrXP32W69YKoREDp2FgDAIhjGDWO/2l/vq4cOLbUuC1nzCNxeXRonYSwCgHoW+M9QWHU+Pzzz5GVlQWlUok+ffpg1y73EgLPnz8fEokEkydP9m8B/UCLFi1cX0SEFcyVJLvOBn7i+Kb/bsd3/5zFO6tOYH/RNZTVqHHgfAWeWHAAL/x+2KtnGlwMVsxGjKUPD8B7Uzujb+skp9ebiZRamyZHibWkEuA/M7phdIc0fDi9K8Z3bob8t8Zazrduyl4Nl5Wssn1EQPFXnc9I9O3/pbWZeKwQcdI4f0BtvXgRo+7r6+vRsWNHdOzYEfX1objyz/ecGmLUe7AixvFFqBMu9eePg8U4VWqdJCyrUaNBo2PlHLjhi22se5Ki5bz9vq9ydGTUGNAmyWW/k+mpoQ+BSWJXhMs7yQcxikgcfWMMCueMR9tU+9C9KoXjcDhmnrwuh/P47f1a4u0bOmHLM8Nw7yBrkmJm7kAzfzPyLB4trgIAyKTuT2uN7cQOk2OOCsA0ajANH464WqPGvT/sxrIDF7GnsNzluJgPfHkff9xeiG1nrEmki67WWbw0tnJ4FviLYGgX/Fmvt53hlqXSSbioy9UNnMdfGNceqXHWXDYSiYS1iBNwnMuGyR8Hi/HhWmMYOHmk1G0DoDPMff5nnnmG3eePMnmWZPZz/RBVInvftg7NnQDs/sa4LbNZHDvrBDD0eeu+OUxViELfGu8R3KixYMECzJo1C6+++ir27duHLl26YPTo0SgtLXV6X2FhIZ566ikMGjQoQCXll7w8SsQrNpgdpd/3C+eOnX+5Gg1a62qA3/dfxK87i1DmxYoxNUdHk4n5I9s1IwHTema4/VzmYKq8ltvqnhyjQBOOAeD3M3vhnoGtcPeALJs4q8I2d8Fa593Nc0J4R7DqnfA/pHtxQnoPDsQ6vgh1wrn+PPTLPtZYoKicvSqzhY+LVJj4KkdmwmXm5JfUjczNzJwaQTB36TPh/E7yjSIywuVK8rgo7gS/isgI3NInExmJKigiI/DUKKPxo4oj10BumtWgMvP73QA8S25vawAxJ8xm1k+11nGkADOfrD+FdcdL8dj8A5j61Xa8/ucxt8vgLb68j2aPMTNPLjpo2bYN0+wPXl12BFnPrcDwDzahUuBFdP6s1zsKuBevOgrDbTAYcOSi0TjHjDIBcBu7FTahotc8MditcpkNWnFKP6dVvncdMHAWcP2n7l3/r33W7Q9ygb/fs+6XMt5Zuc03Mq4ZMPQ56/6mOZ6XNYigb433CG7U+PDDD3Hfffdh5syZyMvLw1dffQWVSoXvvvvO4T06nQ633norXn/9dbRu3TqApeWPNWvWCF0EIsDogsQFWyqR4Eq1vQGjuoHbI8IZVQ3WDsmwdk3tzjtKGOcKZsd0yynu1Q6OBkrD2qXg5Ql5SFDJUcHoCI9oH5gwU44I1jpvG35qdAd+4msSRoJV74T/Id2HIDzk1CC9BwdiHV+EOuFSf7gSbW84UYoft9vnBzBjDlP72S3d8B9TuJGPp3f16vd9lSNzJXAEw5DhKj8B8xqd3hAS4XxcES7vZKBwlevP3clzs/GDKwzx5Sr7Ve3OQqbZkhavZO2rNXrM31WEoe9vshy76mBRHZOrNexr5m4rdLsM3uKv9zHWzxPdFyvq8YOp/Su8Woc/DxX79fdc4Q85/rLzHDq+uhqHGWGemNQ4mGthfhdS4hS4q38WAONCTa42V2bjmdEyKRqtTdEx3DFY2I79eScpGxj5KhDt2tsJAKBMsG7XlAAb3zJu24axlbsIY77zK7eLyGLP98DCOwQPX0XfGu8R1KjR2NiIvXv3YuTIkZZjUqkUI0eOxPbtjmP9v/HGG0hJScE999zj8jfUajWqqqpY/whCCNzt2H+8Lh9D39vot1i7EVIJLlfZP/tanXsNuVanx9srj2P98cuoqjd+nOOUkfh+pn2MXYWXCePc8apwx7Pk7Rs6AQCeGpXj1OVTzJiTxX8/sxe+uaMnPrm5m4s7CIIgCCJ4CcT4AqAxBuGYBC9WPZs9ZxOi5JjYpTnOvD0Ok7ulu7jLPzjqM18odx3zm+nZwZVT47WJtBpVzHAZ/LhQmhbGNTCMGpcq6/HP6TJOQ0eVB4vzBrVlT7ZKJMBzS9ihmIuu1nGGvmLia15DMfHpulOs/e0mr4Gv/j6De3/Y7VLWwY5eb8CLvx+xeP1wUdvIfW7OXycs2yp5JF67vgMK54zHMAd5P7miWMy7vy9ev74D/nluOFo3dT75H3Shprnybuz7CZhjE+Wj7JT9dQDQlpE03NNFxOoaYPnjwLFlwJkNnt1LBA2CtsRlZWXQ6XRITWWvDE5NTcWJEyc479m6dSu+/fZbHDhwwK3fmD17Nl5//XW744sWLYJKpcKUKVOwfv16VFZWIiUlBb1798by5csBAN27d4der7f81qRJk7B161ZcvXoViYmJGDx4MJYuXQoA6Ny5M2QyGfbu3QsAGD9+PPbs2YPLly8jLi4Oo0aNwuLFiwEAHTp0QFZWFubNmwcAGD16NI4cOYKLFy8iOjoaEyZMwIIFCwAA7dq1Q3JyMv755x8AwMiRI5Gfn4+ioiIoFApMmTIFCxYsgF6vR3Z2NtLT07F582YAwNChQ1FUVISCggJERkZi2rRp+O2339DY2IiWLVsiOzsbGzYYK+/AgQNRWlqK/HxjrL0ZM2Zg2bJlqKurQ4sWLZCXl2exHvbr1w+VlZU4dszoYjlt2jSsWrUK1dXVSEtLQ/fu3bFy5UoAQK9evdDQ0IDDh40dhRtuuAGbNm3CtWvXkJycjH79+uHPP41Jgbp1M06m7t+/HwAwceJEbN++HWVlZWjSpAmGDh2K33//HQDQqVMnKJVK7N5tdDkdN24c9u3bh5KSEsTGxmLMmDFYtGgRAKMrV3x8vGUgO2rUKBw7dgwXLlyASqXCpEmTLLrIyclBSkoKtm7dCgAYPnw4zpw5g3PnzkEul+PGG2/EokWLoNVq0bp1a2RmZmLTpk0AgMGDB+PixYs4c+YMpFIppk+fjiVLlkCtViMzMxMNavYHZN++fTh58iQAYPr06Vi+fDlqa2vx8UFjw/7Ef1dgbHM1+vTpg5qaGhw9anS/mzp1KtasWYOqqiqkpqaiZ8+eWLFiBQCgR48e0Gg0OHToEABg8uTJmLfyb3x/VA1zdb9UXIy91y4AULDKU1xWiWun9qG0tBTx8fEYMWIElixZAgDo2LEjVCoVdu3ahf3XIrGwSIWvNxfg3ta1AKIRHyUzyZD9UVr222KMHX0dTpw4gfPnzyMqKgqTJ0/G/PnzYTAY0LZtW6SlpWHLli0AgGHDhqGwsBB/Hy4C4PyDHCOXYs+ePTh16hQkEgluvvlmLF26FPX19cjIyEBubi6Kdq/F6x2Bodly7N+/39Ku3HTTTVi5ciVqamrQvHlzdOnSBX/99RcAoHfv3qirq8ORI0cAgLc2olmzZsjPz3e7jYiJicHOncYkY67aCCZXrlzxqI0ovGocoO7bvgVP3nUjtRHgt43Iycmx3OuqjcjJycG6desAAAMGDEBZWRlnG5Geno6OHTti9erVAOBzG7F582aUl5cjKSkJAwcOxLJlywAAXbt2hVQqxb59RrfgCRMmYNeuXS7bCAAYO3YsDh48iOLiYsTExGDcuHFYuHAhACA3NxeJiYnYts0YT/y66zxvI86ePQuZTIapU6di8eLF0Gg0aNWqFbKysrBx40YAwKBBg1BSUuK0jVi7di0AoH///igvL+e1jairq8PJkyf91o/wpI0IVD/i3Dn2KuRQayMunzqFHADFxcVI0Wq9aiOSkpJw/vx5v/QjgrGNCMbEhoEYXwDBO8YIxrbB3f5Dq1atcPDgwaBrGzztP2w7fgmu+rEAsG3bNhw5XYS3j1nD6WzetAGy8pY+tQ3JycmWNsmbtuFsaQ0AY246jU4Hc74hrabR8lxHbYNxvZNxPHDi5CkA7PApVwuPQd2zecj0H8yx4gPZfwBCt41wxbx589xqI44c2g8gAiVl5bh69SrWrFmD5w+yx5kyiQEag9WItnHjRrfbiOd7JmL2HuMk847d+wCwvTe0egM+X7wWaYZyh21E0dkzsH2/ly9f7tcxhlqtxrx587zqPzhj246duLzff/2HJYfZultx+BJ0327BqlPGxQC/7S5ASt25gI0x6urqsGfPHt7GGL1HTHAqX8AYfeKpzxdjzv9Nxl9//WVpI6LlUouhrqa6GocPH3baRjCZ1D7O8v5MmzQJWzdvxI1J5Xjvin3OGzP9ko0LMnxtIxoarB5TCxcuhFKp9LofMcO2kH88YlfuUy1vxR7T/5XZjxiojobZ/FH779Yon7IQ5Rq5W/2IAW0SkGm6d8OW7ejfcqhg/Yi6ujrMmzdPsLnKUB5jSAyByGjkgOLiYqSnp2Pbtm3o18+aSOaZZ57B33//balIZqqrq9G5c2d88cUXGDvWmBD4rrvuQkVFheWjbotarYZabV3RXVVVhYyMDFRWViIujsMqGCAOHjyILl26CPb7ROAZ/dFmnLxcbdkvnDPe7pr5u4osK0Xu6p+F167v4PPvjv1kC45fsq4eHNk+BcUVDTh2ib2i8L2pnd3Ke/G/zQX498rjAIweGlUNWozv3Ayf39IdX246g3dWWScMzs4e55arui17z5Xjxi8dr6YEgHapsVjtZgzJYMCfdT7ruRWW7b0vjURSjMLJ1Vb2FV3DFFOiyuX/GoiO6fF+KZ+YobZevIhR97W1tYiJMU6E1dTUIDra9aReULHza+Cvp4G8ycBNP3j1CLHpvaqqCvHx8YL3q5kEYnwBBO8YI5QJl/qz6WQp7jLF+neERAIUvD0OD/68D6uOlliOr5s1GG1SHE9IuQMfctx2pgwpsQqM/HCz5VhuWixWPe68713VoEHn14xGqmk9WmDR3gus85/f0h3jOzfzqWyBJFzeyUDBHJNwwTX+5WL10RL83097Wffw9Wwzb/x5DN/9cxY9WzbBnnPXPH7mU4sOYrHN+31rn0z82xQpwBcq6zS4XN2AHJuE7L68j87k9+Wt3TG2k//qpSvdvTYxD3cNaOX0Gj7hs14XV9Sj/xz7Vf7PjsnF+E7NsOLwJdb8yLd39sSI9qmoqGvEvT/sYb177siBKUtH4/dRH/2N/Ms1AIAZvTMwKi8NM+cav0m/PdgfPWxyd3gDr33+19yYg3i1ghEmlsGBX4GlD1r3e/8fMO5d9363+ADw9RDj9u1Lgexh7t3nB+hbY4+7YwxBw08lJycjIiICly9fZh2/fPky0tLS7K4/c+YMCgsLMXHiRERGRiIyMhI//vgj/vjjD0RGRuLMmTN29ygUCsTFxbH+BQNmyyEhHjS2cQFtMBgMLNdXvuyN567Wsvb3F1XYGTQA98NPRcmtLulmV99kUxKr2/pmsq71xqABeJffI9gJVJ3/aF2+29cePF9h2fYkwR7hPtTWixfSfQjCQ04N0rvwBGJ8AQTvGCOUCZf6Yw671DxeiTv6teS8xmAA1Fo9y6ABAInR7i1McQYfcuyfnWxnXIlwI28BMwcHV+z2bpkJPpctkITLOxkMfHaL+yFumSHQ9hSWo7Cs1u6agW2sYaSG5NjndnSFOb+HI4OGK06WVNsd+2VnkVfPsmXiZ1sx6qPNrIWJgP/eR3/nWXA1JdDoIIm2v+BTjrsL7RODL//XQDw4NBuZSSr0zGIbEE6Y3pvPNpy2e/cmdmnu0W87WpB4z0CrYeSxETkYlpuCh4ZmY0yHNHTLSPDoN4KCKd84fok63WRzwIN3Wc+YczIIGwKNvjXeI6hRQy6Xo0ePHli/fr3lmF6vx/r161krq8zk5ubi8OHDOHDggOXf9ddfj2HDhuHAgQPIyHC9ypwghEKt4W4odxZcxS3/24H1x0tZx/nqWgxuy+7kOUp8Vl7rXnzFaxz3nyo1rgRwJxeGOwRdrMcQ4ucdRdh2hju5ui3MRO6eJNgjCILgQiaT4amnnsJTTz0FmczzmPIEwQc0viCExjw/lxKnxBuTOuLp0e04r+PKD+dNPo5A4U5+Oqbhw3ai8vouzdE8IYr3chHBg8q0+O1OG2Pe7C5VmNDZ/QnbWkZugjNXarDuONtILY+UoldWomW/pxcrz+OjXEdib+DI3wEAlfUahwmh+aDIlL9mzdHLLq7kB3dzf3rLvQOdex+Eck4NrnwuzMWCUTbtpsbULpZzLCiNdCPnzJuTOqBnyybY/vxwh9dc3yUdo/JS8fzYXKTFG0OrPTMmF1/d3gNSnsb85j7/+PHjfe/zxzvpZ8WkAp2nOT4fYVOPpR5kWGAaNXibfSMCjeDZjWbNmoU777wTPXv2RO/evfHxxx+jtrYWM2fOBADccccdSE9Px+zZs6FUKtGxY0fW/QkJCQBgdzzYmTbNScUkwhJmUrNeDIv99K93AAC2mRJmmeFKrucNskj3DA01avcMCR+stfcEmHWdMU4nX6v93Rk0JaiCd9DHRSDr/F3f7Ub+v8e6vO5UqXWFkb9X6IgVauvFixh1L5fL8d577wldDN/x4fsrRr0HI2IdX4Q6wVZ/ymrU2HCiFBM6N4NK7v6w2TxBaJ7gj3NgqHhv9Um7Y3xMOPEpx/SEKFysMOaVaJmocnk9czGt1saokZEYegaNYHsng50Vjw7CqiMluKNfS6gUkfhy0xnc3rclpk3I9eg5zPA4sgipXf2LUUSiWYI1D0aM0vNpLUf1sn92kmVcfqVajaPFVRiS05QVreB8ueNY7yWVDZaJZG+oYySUtp0P8PZ9dBUBwt/jwASV3On5BgeLP/0FX/W6/curOBPXt2hibStVcva8htmAo9HZy9wdb7jb+2Xh9n5ZTq+Jkkfg6zt6unyWL5j7/FqtFpGRPk4r/2sv8BZ3YnSMfM2zZ9Vecf9alqeGsHMh9K3xHkE9NQBj0pH3338fr7zyCrp27YoDBw5g1apVluR+RUVFuHTpksCl5J9Vq1YJXQQiwNQ3Wj947qxG4KNd1esN+PNgsVvX1jVyr0SxpUsLezfH1k2N8RSZq/1fGt/eredxMTzXwUeNwTA3rgkmgrHOf/9PoWVby9GxInwnGPVOBAbSvTghvQcHYh1fhDrBVn/u+HYXnll8CG+tOO7RfXZGDcaE68L/s3oLLTvA7qP/+chAb4vKgk85pjM8KzKTXBs1mOGnbFd/R3gZllZIgu2dDHZaJUfjwaHZiFZE4ulR7bD8XwPx2vUdPJZjapwSHdONIf0atXpobcI4x0fJWO9mjMLzSdV4B0YNlTwCctOK+UHvbsQDP+/Fi0sPc17LxUO/7PW4LExKKq0JmE+VVuP6z7bin9NGL3xv30dXnhg6F2GyfUXv4ve5DAP+hI96XVajtiu3IlKKrc8OY72P0TbvplkXdWp7Dw+5G54awQYvbWSkk7CLXezSiDvn8CL3r9UxFvUKbNSgb433BEWteeSRR3Du3Dmo1Wrs3LkTffr0sZzbtGkT5s6d6/DeuXPnOk3iF6xUV9vHYCTCF4PBwProHbxQiX/N24/3OVZomeHDU6O4st7ta+vdNGr0bZ1kd8zsVsnModGnlf117iJ34V0Sq4h06cYabPizzs+/vy9r35sxY14zigXuD6itFy9i1L1er0dhYSEKCwuh9/MA2S/wkFNDjHoPVsQ4vgh1gq3+mHPQrTjkmQFMZ+rDmyfxpYyOWe9WichNs08E/vzYXHTiWDjkDf6SozuhsZgrjc+Xs8chSTG+5wsJNMH2ToYSUqkEHdPjESGVeCXHDNNqd41Ob7eqPS5KhmYMbwhvjBqOPDVaNFFBIWOPRZfsu8jaV5sWKGYkRkFmE6lgX1GFx2UxYzAYLFEcAGDl4RIculCJW7/ZCcD791EnsKeGq+d/u/WsxXATCPio11wibZMSw/LSAIyGDib/3VyAoqt1uHCN3T7e0C3d5RxIMGHu8xcUFPDT5394N/dxdyY22oy0bks9iOZRz8hpInBODfrWeE/o1JowgytRIRG+qDk8M/48WIzPNp52eI+7nhPOWLTngsNzHZrHYfaUTnhxnNGjotbF7xkMBlQ3aPDfzQV255SMjt+cKZ3wHI8DMy62vzDCrZiTwYQ/63zf1kno29oaV9Y8eP5mSwFe//OoW0nn+YqvSbChtl68iFH39fX1aNWqFVq1aoX6eveN6uGEGPVOEHwRrPWnsl6Dn3ecc/t6vY2nhtRmUua16zvY3XNLn0wfSsiGTzkyF2X14VjYZAtzgdPJy+xJmtS40DNqBOs7GWp4I0fzBK9aq7fkIbA8L07B8rSwXQ3vDkwD3H9v74GR7Y2efDMHZLkMhWyOuqCIjMDr19uHKVxztMTj8gDGcFdXqu1z7Zjx9n10Nefs75wa7jz/1m92Ov2/8wkf9Zor38pDQ9vYHbP1KgKASZ9vtWsfWydH+1ymQGLu8z/22GP89Pmb5gDJOd7de+ti63Z0U8fX2bJ4JmNHWE8N+tZ4T2jNCoYR3bt3F7oIRABx1wuCybIDxbhwzXG8Tnf4ZP0ph+dS45SY0TvTYnw4erGS5RpqMBhw+EIl6ht1KLhSg17/Xodxn27hfBZzAHNz70w8MCTbp3IDwPSe9gmj/npsEA68cp1Xq3GExt91ntkxN6vjrRXH8f0/hThaXOXX3yYcQ229eCHdhyKmxtMHT0nSO0F4TzDXn5eWHnH7WvMEnnnBiO1C05RY+8l9Pvu2fMrxPGMs0t5Hr97UOO/zDAhFML+ToYQ3cjSH4mnU6e3yswzPTYGCYXiQebHYjbnoa1i7FHx1W3ccfGUUWiZFsxbscdFoKo88QorxnZvZnb//J+9CULnKLeHt++jKU2Pb6atOz/uKq983U8GRPNsf8FGvn1l8iLXfuUU857sgkUjwz3PD8f60LpZj1+rsc5mG2oJNvxDhPPeKQyQS4MHtxm0dwzCm9eB9Ejj8FH1rvIdqjkCsXLlS6CIQAcS8yilC4rqxZOasePjX/byV4a/HBrH2zUmrzInYrtY24hqjI7H0wEVM/GwrHvxlL95eeRxlNY0sN/L2zeLw7JhcHHxlFG9ldMZtfTPRvlmcy0RjwYq/6zxzFaBUImGtiGnUhWAYmDCB2nrxQroXJ6R3gvCecKk/W04ZE5WaJ2K7Zxr72ub1J7arwHu2bMJaIOQrfMoxJ9UYKivTjSThrghFo0a4vJNC440cZSZPjUatHo024afS4qNYYX3kkZ7Xnwip9X5ZhASREVLEq4zeH8pINz01ZFJe8yCotc4XQnr7PjLHhf+Z0Q0398pAYrR1TL3qaAnyL1e75d1/urQGC/ecd+tart93RqBya/BRr7cXsA1BrkI5d8tMcHr+WoAMOkFNtU2ox0QPFsrKTN8Xrcmosfsb4K2mwNHf3btf4PBT9K3xHjJqEEQAMH+g5W7UOGa82aMXK3363bYpxgTeCSoZ0mwGEtFy44owWYTUsoKMuYri0/XG0FibTl6xi2MKABFS4MGh2ZbOH9/Yju1eGp/nl98JF5hGDYmE7RLrqLOdYNLd7X1b+rdwBEEQoUAIJrElCCL4WGpKAL7tjHHSKy1eiS3PDMN+00IgW0+Nx0a2DWwBPeDdGzvjzn4t7fK3eYM3IYII8WLx1NDae2rEKSMRyfBST4r2PLRZ5/R4DM5pilv7ZNoZFbnCT33/z1nLttn4II+QsrzlPeXjdfkY/v4mlNcaJ7RrOJJHA67zTbqCGY1hbMc0zLmxM/a9fB2GtbOG6hn10WbMWnjQ5bNGfvg3nll8CIv3Og5zbYsjo4at6LhCdocKrnTE9U51zUiwbGc3Da3wU34hZ6zxb3oP4JaFwL3r3L830jTXpTEtwl3xpPHvoruA0hP219sZ5YT11CC8h4waAtGrVy+hi0AEEHP4qWiFawNAnJK/Dr85tNQDQ7Itq13MqBTWD6u5U2jucDRq9ThbVms5/3f+FbtnPzsml7dycjG1RwvWvqvYpsGOv+s8K/wU2CtdHHW2K0yur4NzPIg9SXgEtfXihXQvTkjvBOE94Vx/MhJVlhwAzDAj2U2jMagtv/0wPuWYlRyN1yd1RHObmPDOsO3Dm/Fl8lcowvmdDCTeyNGcrLtBY59TIyNRBYlEgo+md8GrE/OQ5UU+AqlUgh/v7o1/39DJ7lxkhP27+vqfxyzbZk8NeaSvRo1TKCirxbdbC7Bk3wXc8MU2zuvM4em8fR+ZCxeZ5Z3cLZ113e/72QnRncE1P+Dw9x0YNZiT+gDs9Owv/FGvdxQ4D+Gl5DB6TOvZApueGoo3J3e004UoGfUmMPZd4JZFQM5oQJXo+h4zZqOGQQfobIyDX/SxenCYaaxh7wvsqUHfGu8ho4ZANDQ0CF0EIoCYV83L3ZiXj1VaDR++2outSczsXWPNnhoALJ4YD/68DwaDAWU1zpN03TOwFe8DMFt6ZnnwEQsB/F3nmZ4aClmEZcUPAOgZHVmDwQCDwcDK81JU7lvuFsIx1NaLF9J9KOJ7Tg3SO0F4j5jqz/onh+DdGztjzRNDeH+20HI0e4rbEoI2DcFlGS54I0ezEbCivtEuakCyKbLBDd1aYOaAVr4X0IY6tfMwSKWmhNaJ0XKH7/Xw9zfhwPkKVDXY508AgOWHii3bn28849RLwjwh7u37aPbUkEjYuTAzPAwrxzQ6eBIuyWzUsI0OMKBNss3zA7Na3h/1+sI158myuRZotmiiQlZyNG7v2xIKFyHPRIEqEejzf0B0kuf3RjKikmg5dPGFjbfh4UXsfYFzatC3xnvIqCEQhw8fFroIRAAxr5rXa5wbCwAgluGp4UmsSi6Yq0hkNitOYjk8Qg6cr8CRi1UY/dFmp89NignNvBZC4u86z+xQxygi8TQjeZm5I2kwGDD9vztwwxfb8OdBa0d6VF6qX8smZqitFy+ke3FCeicI7xFT/cluGoObemX4xXtBaDk6+j9JQzDEn9CyDBe8kWOrJKP3xc6CctZirQU8hEJzhYyRo+OegVajiXlS37wgrKXJY2TOlE5obRM+qKCsFpM//wedX1uD06XVdr/xiAe5MyNM43hv30ezp0aETR20zZXToolzj6yB72ywbF+taXQ7sbfWNBa1TegukUgw67oc63UB8tTwtV5vPVVmd+zeQa2d3hPFYdToacptSvAA06ih4TAQlBew9+uvsfcF9tSgb433kFGDIAKA+UPO4clqBzNpl6+Y41LKI6R2sUKTY7hjj87+6ziqHcTzNNMkQMm6d74wApO7Nsfyfw0MyO+FMswB5NmyWhw8X2HZv2JaTVSj1mJXYTkOnK/AiRJr5zoUEzcSBBF8REZG4qGHHsJDDz2EyMgQjJ1u+U5SXF2CINg0i6e+krs4Ml6EoE2DEJBerYxe+xcr6vGHaTHW06PboU9rL1Zxe8iHN3VFfJQMU7qn4/mx1pDL5vDMRVeNRg2zp8PNvTOx+vHBuH8w98T2t1vPch53F1tjhKeYF7hJpc4XObpK6H25yrpA80RJNbq+sRbHL1W5uKcB83YVAQCu1rIXeFbVa/DoiLboYZrcD4SnxpkrNThW6VsfdcOJUsv20ddHY9ED/fDo8DZO75FKJVj/JNszL5TzDJn7/Nddd11w9PmlUkBmMtLVl3Nf02gNrw61raGR+v6hChk1BOKGG24QughEAKmqN7qdJiW6tsYzvSBc9CtcYgk/xbEywJG3hTmpoS239c20bAfKqJEap8THN3dDx/T4gPyeP/F3nbftpDK554c9OHyhElpGR1Gnt65GsPXiIfiD2nrxIkbdKxQKfP755/j888+hUHietDMcEKPeCYIvgq3+MEO3XqpsYHlQ6/UGvPbHUSzZZ58s1xz//t0bO/u/kBwILcdw8tQQWpbhgjdy5FrZbhtO2V/kpMZi5wsj8MG0LqwcOL/uLMKOgqsW4wbT00EWIcXjI9tyPq/WRTgrWyZ3bQ6lzPq75nGet+9jnSnscKNNIm5beWp0BjRodLjqIhQ1kx+3Fzo9/8XG05bt5Ycusc49OcropWEeiwYip8aID/7GT4Uq/HPa3tvCXWJMuUlzUmMQrYhEr6xE1nviiOym3KH5QhFzn/+PP/4Inj6/3lTP/vmE+/z6NxnX2iziFTj8FH1rvIeMGgKxadMmoYtABJDH5h8AABy66HwlA8CPwcBgMECj01tyY3B1AJkrM+Qciavsr7fm+kj3IFkgYcTfdb6yjjteq5mJn23FhP9steyfvWrNo2HrxUPwB7X14oV0H4r4nlOD9E4Q3hNs9Udv0xYwJ+TWnyjF3G2FmLXwIPJeWWWJWX/wfAVqTB7Pec3jAldYBkLL0dFCm1DsbQoty3DBGznahioC3Buz8oVSFmEZIw005X6Yu60QN3+9AxcrjDH702w8uLjKDNi3Ja6Yc2NnrHncuqrf7Knh7fv48bp8zuO2Y0CtXo/rPvobPd5ah9Iq92L8x9h4G3y+8bTD3/v+LnYyZPP8gtkQ+uvOIrd+kw9u/WYnfttrb5R2B3NUi5HtKYRzULWROpMx7sAv3OevnLBuR9rMZwls1AgqOYYYZNQQiGvXrrm+iBAl5qTigPerUYa8twltX/wLp0prAFi9MkZ3sH545RHW1S8TOjezewZzdYgtbVPDZ5VBoPB3nd/qxmoTcwccADbnX/FncQgT1NaLFzHq3mAw4MqVK7hy5YrPOaFCFTHqnSD4Ipjqj1ant4SPNTN3W6Flu5wRRqWuUYflh40Gjxn/22E57qwv7U+ElqOjUDmh6KkhtCzDBW/kyOVJXlzhPBmzv5jWswXncdsJ/UgHBj2DAVhztATXf7YVZ67UuPw9pSwCTWOtq9/N+Tm9fR/3nnN839KHB+CVCXkAAK3OgPPlRhlvL2BHb3DUr4tRWBc+3vfjHry3+iQ+XncK5bWNWHO0BAdMIZFH5aVicE5Tzmf8c/oq52/yjW0OkCcXHcTt3+5ESaVzA47e5ltQ3WA0ajAXfbrLxC7NAQDdMxM8vjeYMPf5z507Fzx9fomLZOtNraHk7EJUCZxTg7413kNGDYFITk4WughEkCKLkOJ608fOmxVeGp3ekrzMTEYTo2vsmI5plmOuVrooZRFY9fggy3671FjWOcIzqM6LE9K7eBGj7uvq6pCSkoKUlBTU1dW5viHY4CGnhhj1ThB8EUz1p5EjDAozNIrExu/gTGkN9HqDJcwLIFx/WWg5OlqTFYI2DcFlGS54I0cuT3LbZNyBQiXnzhlgmxNBIpFwGjbWHb+M+3/ai0MXKjFr4UG3fjNKbm0/zHPG3r6PWie5KrpmJGCUaeGjWsvdfl2rbcSTi7jLvedcOU5drobBYMDaY5ctx7u/uRb3/7QXBy9UAgB6m3KkOArRFQjKauwTm285VYbX/zzq8B61VoeRH/2Ne3/Yg7XHLuN0aQ0Wmzw8PPXAAYBXJ+Zh1nU5+PbOXq4vDmLMff4HHnggePr8ty50fr6M4UFkmyjc3b7/mQ3A/FuBqmKPiuYK+tZ4TxBkdBEn/fr1E7oIRJAyPDcFAPDHwWKvPpTMwZSZFNNKD+YKKVerxxSRUuSmxWHV44Mgj5C6TBxGOCdY6/y0Htwrjwh+CFa9E/6HdB/C+LDijPROEN7jSf3ZV3QNn284jZcm5KFVMv8TnQ0ae6NGPaOPXVrNXtn7yfpTyEpWsY55s4qXD4RuhxwnCg89q4bQsgwX+JBjlxbxuKGbMOMWR4sBuQyXth5eAKBm5LIor3Wer6Jv60TLduvkaBSU1aJLhjG/pLdy5CoTE3PYLGai7v/7aS9G5aXi6zt64su/z2DJvouWc4Nzmlq8/recKsN1H23GdXnOQzH1NSV4f2BINhSRERjRPsWr/4svXKuzN2oAxpxJjjhysRIFV2pRcKUW645fZp07U+ra68aW5BgFHh0hnGEnrIm1iT7SrCtw6YB1/8JuY94NaYS9UcNdT42fTLkv4lsAY9/xtqR20LfGe8hTQyD+/PNPoYtABJDWpsHW9emuXWYlEoklFq03Rg3mCgsz5ucxw1mZvTcA4PERORzus8Zrc9Pi0LppDNqmxuKzW7ph8QPU4HqDv+u87YA+Jda9hF19TB1Mwj9QWy9eSPehiO8TbqR3gvAeT+rPlC+2Yf2JUjz4816/lIWrP32ZEWP+/TX2MeOfWGBdyTxnSifERwlj1BC6HXKUKDwUEVqW4QIfcnziupyA5tRgwmeCcr2ebSA10y0zAXOmdMLcmb0tx+4akMW6xls5ciVdZ+IobNaaY5dR16jFhWvWlfhNVDJwXc700uDCPPeglEXgwaHZyGFEgQgU5bXcRg2t3vGEtiLSsez+b0i2z2UieERuEyI9pT1w30bg0f3GfXUVUGcKO6W2MUi5M+/WyPBIcRXqykPoW+M9ZNQgiAAQaYoJmqJwzwJsjkV7xI3E4rbYdpJu7pVh2R7VIQ0PD8vGr/f1YSXxy0xS4cAr17HuM+fhYDKhc3P0zEq0O04Ij21ntLTa+SogM3FKctgjCIJgQ56JBBEqXLjmnxj7apOnBnMStcoURx0AOqXHO73/5t6ZfilXKBBORg0ieGjRJMr1RX6CT2OK3mBghYrulB6Pni2b4K3JHXFz70yW94fZu8nXlAU3MeYDuIh0YrQpqWywLHYEgNQ4JZ4ZnetxziBmOC1bmCGp/Bkd4poDo0ZlvcbhPYVXax2eSxfwnSQ4sDVqRCqB9O5AYmvrObVpfk1j6jtEmOa8yriT27O4sNu6neC8ThGBg4waAtGtWzehi0AEEPO3uW1bbldDicToajr//r4AgHOMj6e7iZc0Oj1u/3YnPlprbJCbxiqw5KH+eHViB8s1EVIJnh6di/7Z9jH7bDszL43Pc+t3Cffwd5331qM/yw8hGwgr1NaLF9J9CMJDaBTSO0F4jzf1x1/z5w0mTw2Vg4m49ATrZNaYDmmc1wiF0O1QKCYEd4TQsgwX+JBjZqJwYxYFz0aN4kqrMXZYu6ZY/GB/dGhubyiVMO4BvJej3LTAcnpP7olYZ54ofx0pQVyUdRHciZJq5DWPw5HXRntWBicyvGdgK8t2o1aPbafLcLS40qPnu0O5g/BTFbWOjRqnnYSYinZiqCEEQG7TRsgYISEVply1hxcBWjVw+TD7mn8+BiovOH/+tbPWbb29t5Uv0LfGe2iJLkEEAHNHxNHA67r2xniVZqrV1pVgegMQ4cbY4MGf92LLqTLLfnyUDN0zm3hXYFiTeRGhgW3CSndpmaRyfRFBEISY8HVJJEEQvFN0tQ57i8oxqUs6y9vYX14BZk8NlSwCFWBPeG08UYpVR0sAAG9O6oAWTVSWfYI8NQj+WP/kECzcfR5jOzUTLPQUwD0hf/eAVhxXukZvAGoZY/2WSa6NNauPOg/t5ApzTo1IB5MKzrwo3lt9kvO4M+8OT2HK98+DxXjmt0MAgE9u7orRHdI4c5d4gyNPDebciy3OuoShmCcorIm0Cb/NNHIo44DqYmDTbKD2ivV4BCM6SfF+Y64MR5QXWLcN/Bo1CO8hTw2B2L9/v9BFIAJIoyk52KlTpzjP234Pp3a3NqbueGro9QasO17KOlbiJOGVIz65uSui5RH44e7eri8mPCJY67yzOKGE7wSr3gn/Q7oPRXwfnJLeCcJ7nNWfIe9vxBMLDmLx3gu447tdluP+mkD/ZL2xz36lhh3Oc/RHmzFzrjUERWZSNAa2tfeAFhKh26Fw8tQQWpbhgrdyzG4ag+fHtUfXjAR+C+QhMsYE/g9398bsKZ3wwrhczmu7ZSZYttc8MZgVChoA1BodLlVYx+mTu6U7/N1dZ8tZ+97KUWtKAC5zYoj4+vYeHj/30xnOV5fb5ux0BNNT5J1VJyzbj80/gNyXV+H3/Reg0xug1bmZzNkB5Q48MhzlFAGA4gruEIfJHKG6CYGRSIChL1j3mUaNCEaOq6O/c9+vd2zcAmDNx+HOtR5C3xrvIaMGQfiZg+crLPF+mX38Kd2tHRjb0JHMiWZbk0bBlRpM/M9WLD9UbDn2rGk1A5MaJysOHDGpazoOvzYaQ3KaenwvISyOxo+39BFvTGeCIAJLZGQk7rzzTtx5552IjAxlZ2Dy1CCIYOJabaNltewzvx3C5nzrKkt/rJQ1GAzYcMK4WEijY7cHJy9Xs/YTomSQRUhZE1yvTBB3CFfy1CDCDeYr3Tk9HjN6Zzr0VGAaYHJSY+0MMlUNWvx75XHLvrP6whzPz/h6B745o7IslvQEjSkRtrPJe4UX3hDXd2mOJ0bmODw/sI17Bl9mO36Vw5viiQUHMeXLbRjy3ibUNXo/mXzNQfgpZ8aeEyXVnMcnd3VsjBID5j7/4MGDg6vP37KfdZtp1Ki7xti+at1membotICTpPGWPByA8+uIgEJGDYGYOHGi0EUgAsSkz/+xbA8aOMB6wgB0aWGMnTm1h42bG6O/Yeuo8dnG0zh8sRKP/Gq15i7a6yL+nwdIaSDiF/xd5x3FfH77hk448/Y4RPHktkt4BrX14kWMulcoFJg7dy7mzp0LhULh+oZgg4fJUTHqnSD4wlH9eW6J/eIdMwlRMofnvGVHQbnri0zEm36fuSDp7oHehaXhC6HbIR6j0giO0LIMF0JdjkyPgzgXbY55caA5pJKt0cJZ/gpb9IyJgO0FV3GmJtJicPUEs6dGhJOY1koX4b16tDSGtX5rckfWcduQVsyFmwoPk4k74+D5ClysqMeKQ5e8foYjo4ajpOdand7OkG1GyHBowYC5z79ixYrg6vMzk4Uzt/s9ZH9tek8gnmGcOrwQmJMJ5K/mfjbTqMFz+KlQbyOFRNw1UUC2b98udBEIAdi+74hlW28wYP79/fDXY4MwKi+VdR1zXsVgs2K03EEsSFtoEju48Hed790qye7Yz/f0AWDsTLdNtX7Ud74wAv+Z0Q1HX/cswRvhOdTWixfSfQjjQ04N0jtBeI+j+uMsnjyzf8MX58vr3L42QWUyavA4eecrQrdD4RR+SmhZhguhLsekGAW+uaMnfr2vj0tPpCE5TTHvvr7Y+uwwAPaT/syxvd3CRhu46pI3nlA6U1gImdRxO9UsPsrpMwa3bYoTb47BbX1bso4zvT/kkVK8MiEP4zs3w/cze6GbB2HDmGG7nPH04kNYdcR1DiOuEN7mnBotYtgyjHAglwvX6h16xqi98JgJR4KubjfJsm5LGHrt97D9tRf3sPdPrQEaq4EFt7GPH/0d+ONfQAMjeT3PicKDTo4hRPD0vkRGWVmZ64uIsCNBZ3V7UykiESWPQPtmcXau88w95ve4UavHppNX4A7rnhziS1EJnvF3nX9sRFtM7tqcdaxnljVRPNOTIzVOiYldmiPazTinhPdQWy9exKh7g8GA2tpa1NbWupUPKvjwfSJOjHonCL7wpv6sPFyChXvO81oOT+bkY5VGo4Ynq6/9jdDtUDiFnxJaluFCOMhxZF4q+me7DqckkUjQLzsJKbFKAPYT5hqdwWIMnebSqGF/zDymm7+rCOM+2eKWEVZjykXhKFE4AKTFKy3bH0/vinWz2HMJPbOacCbsZtb3JioZElRyfH5Ldwxrl4Lb+rbES+Pb46/HBrkso7MQULY88PNep+ev1qjR6vmVGP7BJsuxksoGFJlkNSalhnW91kEooau1xpxKZn0x2Xn2qt0xMWHu81+4cCG4+vyqROt2hM1cx00/svflsdxhpHSNwAWGwWPRXcC+H4FzW9nX8Eg4tJFCETy9L5HRpEkT1xcRYcGANsYV9E+PboeMpgmYM6UTumUmYNZ1juNPOooPvKOA/fHkWjmw7OEBKJwzHukJzldbEIHF33U+Sh6Bj2/uhhsYyeYUDLdY8twRBmrrxYsYdV9XV4eYmBjExMSgrs79lc7Bh/eDMzHqnSD4wtv688xix+GpvMF2dfT9g1s7vNY8oedssjDQCN0ORTDkN61HCyRFy0M2x5vQsgwXxCxHrjwWFXXGhNWJ0c6TTT8/rr3dMa3J6+K5JYdx7FIV7v1hj901dveYwk85y6khj5Rawmz1z05CdtNo1vkBDvJjmP8vAJAQxf7/REZIce+g1mjfLM5lGXtl8feOPPjLPgBAwZVaSw6OveeuQW8A2jeLQ+f0WNb1Wh13v++aKbF4y0QVdr4wArOndLKcG9MhjbfyhiLmPv/dd98dfH3+8R8CHW4Acm1COsXZ5EEZ/CSgbeB+xjcjgE1zgLObuc9r+P0/i7mN9BUyagjE0KFDhS4CESC2nzEaIlolR2Po0KG4uXcmfn9oAJJjHMcedOSpccd3u1jXldc2oriinnUsr7nrTgMReAJV55neGUzjWPdM+lAKAbX14oV0H4LwEDKF9E4Q3uNJ/Wmbwg47VcmYWPMVZlPw5HU5eGhoNud1zAlCR+FLhEDodoiZn69ZQhR2vTgSb9/QyckdwYvQsgwXxCxHZ55Lrowa2U3tw+vZLmo8ebna5Up5S6JwF94Q254fjq3PDkNKnJI1jvw/J4ZdHeO3lQ5yPLrDw8PasPb/eW44Xp2Y59WzWjSxLu58fslhlFQ2oLLe+I1IT4jCqBHDWNc78tQoN+XgSFDJkRqnxPSeGeiWmQBFpNFYQwQpve4Bps0FIm3qV4SNx01kFHBum+PnbJoN/OAg14W6hvu4l4i5jfSV4Ol9iYzff/9d6CIQAcBgMMC0mAINGp3beufKqVFwxb7hXLjnPPrP2cA65onrJhE4AlXnHfVp7x/SGo+PbIs/HhnAfQHhF6itZ8ABBwAAXqBJREFUFy+k+xDGBzd60jtBeA9X/XEUWqV/NjuXWEW996EgiivqsfFkqWVi8M+DxZZzU3u24EwIGymVYNeLI1n7wYLQ7RBzElceIQnpcFRCyzJcELMcnbUN8S6SjgPAwVdHYWqPFshINE7U3/fjHssEvZlvt551+gxzTg1X7VScUoYWTVR2x53VYabHgtwHjzWVnB0qKD0hCjMHtMLQdk3tru3cIt7pszo0t55fdqAYfWevxwu/HwZglLnt++jYU8P4XTEbn6RSCX5/aABOvjWWMxQXEeRIOeqbtt7+mDs0VrNzbPiImNtIX6HZT4LwIxrGB7K20f1kQhKGr4bBYhSxX0Hw4dp81v5tfUPTtZvgD0crdRSREXh8ZA46t0gIbIEIgiBChtCdeCOIcOUhUxgRAHh0uHUlb3ObMKsVPnhq9J+zATO/341/Tl9Fo1aPjYz8dc3iozhDeGY3jWGtsg7liXu+YYbv4jIIEYSYcNQ2yCOlLj0nAOMk/PvTuiAz0Wps6PL6GtY1b6047vQZ7oSfcoazcjaNtUafOF/u5QSxCa7iMYe23U3JxMuq1Q6fUXClBm8uP+bR72r1Bu7E4qbvShOVc48aIkSIsNFj55vY+695YKQ4/icwJxPY/a3v5SJ8gnoZAtGpU2i64BKe0aizGiJy02Ld1jvbU8P+WY54azK9V8FKoOp8ehPKpRJMUFsvXkj3oYz3nhqkd4LwHq76c/iidZKhQ3o8HhvRFl1axGOGTY4Gc5gQX7jt2504f83qGdKzpTF0p0QiwYL7++L9aV0sufJutVlIxJxwFBqh2yHmJG5kEIXl8gahZRkuiFmOjuqAp/kO8y97H+7Gmijcu/rorKypcdYE4yVVDvITuEnvVommZ1oNJRUMr5SPp3cDAFyuVlu8T2z5YE0+53Ez+4uucb6PXM+zemq49qghQgDbxOHMpOJmJn3u2TNXzPK+PAzE3Eb6Smj3MkIYpVLp+iIi5GHGvOyR2cQrvZtXDag1zj09QjUBn1gIVJ0f1i4FT49uhx/u7h2Q3yOcQ229eCHdhyA85NQgvROE97iqP2qtHk9cl4NljwxErII9OTHz+92oUWs9/k21lt2//nLTGcs20+OgT+skTO3RAt/c0Qu/Pdgft/VpybrvhXHtMbZjGr6f2cvjMvCN0O0QM1G4LIgSqHuD0LIMF8Qsx3oHY3iVh/knymoceye4Qutm+ClbxndqBnmkFDf2SHd4DZ9eap/c3A33DGyFBff3sxxjzqekN4lCpFQCnd6A0mpuA8rZslqnv9EvOwlKpRIfT+/KOq7lMGos2HMegDGnBhEGKNzIPdvtNuBf+7jPxaQBAx7ntUhmxNxG+goZNQRi9+7dQheBCADmj3CEVAKpVOK23l15anBNWIdqAj6xEKg6L5FI8PCwNhiSYx9/lAg81NaLF9J9CONDTg3SO0F4j6v60yop2rIt4TBC3j3X8/p3uZI9Ubh47wWn10fJI9CjZRNWMmzAGHP9y9t6YFi7FI/LwDdCt0PMhemhnutPaFmGC2KWY/7las7jlyo982qYNTLH6zKYPTU8DQf32S3dcOjVUUiJDcyEa2qcEi9PyENWsrWt1zDmQCKkEotnSHEFt/wcJf0289zYXOzevRuTu6Xj+BtjGPex+37/nC6zbHtqgCKCFC7PDM7rkuyP9XkAeOok0N5B4nAfEXMb6Suh3csgiCDGYDDgdKnRTdSRe6QjuHJqPPSz0WLcuUU8BrdN5qeQBEEQBMETERERmDp1KqZOnYqIiFAcAIb2imKCCEdy02IBAIPaJqOTi+Swu86We/Ts/MvVGPzeRscXUJPgFXKGIcPbcDcEES5M69mCl+d0NeWT8Aa1KTenItKzvplEIhE8IbbGJgR3g8nzZe857vbe1jgxiDFv0jUjAbFKaygppifZtK+244tNpy37d3y3y7JNOZPsMff5e/fuHZp9ftswUxKmNZ4jnPfQ54x/5dH25whBiXR9CeEPxo0bJ3QRCD9y6EIFrv/sH7vj7uqdufjshSWH8eH0Lqg2udSXVqntVqetfWKw94UlAgLVeXFCehcvYtS9UqnEokWLhC6G91i+rd57aohR7wTBF1z1x+yp/MiwNnbnfKFRq8eojzY7vSZUp7GEbodilNYphlAPPyW0LMMFMcuxaYwCrZtGo7pBiytOEly7IivJ+WSqXm+w8yAzo/bSUyMYmH1DJ9zyzU48PbodAOCqKc/F2ytP4P7B2XbXm5OiA8C8+/qiX3YSTpRU4atNZzCpqzGMlvl9ZBorjl+qwvFLVXhoqPFbw1yUSkYNe8x9/srKytAKnfTofuDCHqDjjezj0Qwvywi50chhYBjUoow5tuxCWEXzEx1DzG2kr4ReqxYm7NvnIE4bEdLsKSzH0Pc2cho0APf1zvxsrjh8CS8vPWLZN39TmeGF2qbGelxWIrBQnRcnpHfxQroXJ6R3gvAervpjnqDie8V/VYPG5TVCr1D2FqHboRhGvhO9D+H8ggGhZRkuiFmOEokEqx8fjK3PDkPPlk28fk5GogqPdJVj3n19Oc9rnIRdMufmVISgUaN/m2Qce2M0HnZg2K6oa8S+omsAgIIrNawcJmbDRG5aHD6+uRuG5Ronrs3vo0QiscszotcbsGQfOwyhp1E3xETI1e3E1kDnmwCp6ft+629Aaifg1oXWayQSIJLDWwMAYtPY+w1VPoWtNRNycgwiyFNDIEpKSoQuAuEHbv92F2cysP8b3BqA+3q39cRYuMf6YS02xd+MVVL1DSWozosT0rt4Id2HMD4MTkjvBOE9XPVHa1phzLXi/8Gh2ThWXIWUWAUW7b2AAW044mBzsLPgKn7ccc7pNU1UMrx2fQe3nhdsCN0ORcutY5QGjfP49sGO0LIMF8QuR3NumadGt8PNX+8AwI7M4C7phjL0y+Zu57Q6AxQOpgca/eypMb1nBhbsOY8Ruf7JKaSSO573uP3bXTh8sZLzXJNoGedx5vsYIZWwQla1fmGl3fVM7w+CTcjX7bYjjf9skSkBjSnp/JT/WY9LbRY76NRAzWWjscNgAKovAbHNPK7gIS9HAQk9U22YEBtLK+vDES6DBgDERRk/qO7q3Z0m8IEhRnfLoe0oIXQoQHVenJDexYsYdV9bWwuJRAKJRILa2lqhiyMIYtQ7QfAFV/1pNE0mcSWcfnZMLn64uze6m1Y/O5v4YjL96x1YceiSw/OpcQrse/k6tEoOzdjZQrdDzBA4agdjo1BBaFmGCyRHI8wV/2ufGOLx/c7k6Gzi3ZpTwz/Tf69P6oCvbuuOT2Z088vzmfxybx/Ldn2jzqFB44VxuejQnDsPE1OOXN8WW8hTwx5zn/+WW24Jzz6/TGXdTuvMPjfgMfb+xreNf0+uBD5sD6x+0eOfozbSe8ioIRBjxowRuggEz9Q1ah2eaxqjAOC+3t0x7HZMj8eWZ4bhq9t6uPVMQliozosT0rt4Id2HIN4sm7SB9E4Q3sNVf7R6x54aZszxzl1NPBkMBry5/BjnuV/vs06UXebIXxdKBFM71KANbU+NYJJlKENyNNLIqA9ZSSonV3JjluPYjml252zDT6m1OhhMnqdmTw1PE4W7i1IWgTEdm7FCz/mL/tlJlpBRFfWNDq/jyrdhhvk+OlqUykQho2lT0cFMFh6VwD438nWg133W/aNLjX/3/mD8u8MmCbkbUBvpPVQ7BSKkE2kSnJwtc2yhbhItB+C+3t0dSGUkqkI23q/YoDovTkjv4oV0L05I7wThPVz1x5JTQ+p42Bohcc+osXjvBXy79azd8cI549GnlTWkS0svJhuDiWBqh9qFeN6/YJJlKENyNNLAmED3Jk+QWY5PXJeDWGUkBrVNthh8NTqrUeNiRT0GvrMRD/1ijNNv9pgKxUThtkgkEsSbomBU1rvOjcQF83109N04O3sc7hnYCoPaJmMMhxGJCHPK8q3bygT2OYkEGP8+0OdB4745fFVChvUaTYNHP0dtpPdQUH6C4Am1k5VIfHYgPrypC2/PIgiCIAjCjGlBQYgntiWIcGHTyVLUqI2e0DInfenICPeMGmuPXXZ4LkIqwde398A3W87iy9u6e1FagsmqxwfhWHEVhcklCAYNWn7CseWkxuKf54YjWh6Jjq+uhkanY4WfenXZUVypVuOvIyVYdeQSw1Mj9I0aABAfJcPV2kZcreH21BjUNtmn569/cggkEglenpDn03OIMEGm5D4eY8ohc+Q3oN8j7JBVtaVAQqZnv6NtBCLl3pVRxJBRQyDy8qiBDDecrRQwr6DwRe/pCVH43x09kdc8zutnEMJBdV6ckN7FC+lenJDeCcJ7bOvPXd/vtmzLpI69mKUmTw2t3nmoo7IatdPzozqkYVSH0F+RGwztUG5aHHLTQn/MEgyyDAdIjkYGZBsn29MTolxcyQ1TjnFKo7dCZIQE0Nh7aph55Nf90JkWa4SLUcOcr7SY8f808/zYXEzvlWF3nImr91HuhRcNIUIiGAaIta8AEYzE9BvfBm74yu1HDcc24J1ZwL3rgVRqLz2BaqtAxMdzJy0iQpdyBysFAGsCKm/1nt00Gv88N5wMGiEM1XlxQnoXL6T7EMQS+tF7Tw3SO0F4j7P64yxUizm+ugubBuo1oZ3fwV2oHeIPkiU/kByNpMQpsfelkdjwlOdJwgFuOZon4DUmT401R0tw/FKV5bzeYLA4oPorp0agMYefKqm0D/Fz36DWSFA5X+3u6n10J3k4Eea0v971NUyjRuEW4MwG6/7Beexra8sAvWNPrdSTPwKaOmDDmx4WlKDaKhDbt28XuggEz5y+UuPwXEYToysa6V28kO7FCeldvJDuxQnpnSC8h1l/dp0tZ52LVjiejJNK3fPUaOQp9EuwQ+0Qf5As+YHkaCUpRuG1cYFLjpE2OTXu/2kv63x6E6tXSLgkvI4y5RS9cM3eU0PqxKvPDFOOXKG9zVE2CBEz5X/ALYuAFy45vkbpprG29ATwXjbw603c55nGDkl41NFAQuGnCIInNE5yaqTFO4jDRxAEQRBhQkREBMaNG2fZDj0opwZBBAuvLDvC2nc2CWj21NC5qLpnrtQCAJ68LgcnSqqx4rCTyQqCIIgQIFJqnATVOsgpVFVvzEuklEmhlIVi38wes3FmwZ7zPj+LKxSYsxxOhBFzn7+4uDhE+/wukCmBnFHOrzHn1HCEugZQVwFf9DHun15nf03FeeDLAdZ9aRjK0s+QUUMgRo1yUUGIkEPHMQny1Kgc3DuotWXfW72r5FRVQx2q8+KE9C5exKh7pVKJFStWCF0MHvDeqCFGvRMEXzDrD3Py7dWJzuNLR5iNGk48Nb76+4xl+4bu6fjf5gJvixn0UDvEHyRLfiA58gOXHGUMT40L1+rszpvzfjZxEZIplPA1NwhTjlzzLDIpGTVcYe7zX716FUqlSBfwukoEXn8N+P0B9rGCTUDrodb9E8sBdaV1Xx7LV+lEA9VWgTh27JjQRSB4Rs+xOuLhYW1YgzJv9Z4ap/C6XERwQHVenJDexQvpPgSR+B5ugPROEN7DrD8tk1SWbamLumk2amiduGrM+euEZbtFExUeGd4WGYlReGJkjrfFDVqoHeIPkiU/kBz5gUuOMktODT2OX6p2eK+rPBOhRIQDo8OC+/u6dT9Tjh3T43BbX/bkNIWfch9R1+2kbCCuhePzDZXAua3sY0U72Pv1Fex9poGDcAsyagjEhQsXhC4CwTNcnhoSm0GYt3q/tW9Lr+4jggeq8+KE9C5eSPchjA/hp0jvBOE9zPrD7EGX1aid3mc2apwoqUZ9o/O8GZmJRmNJ01gFtjwzHI+NbOtdYYMYaof4g2TJDyRHfuCSY6TJqKHVGZzmHmqikvmtXIHmWLH9xG/hnPHo0zrJrftZ3xqJBG9N7sQ6H+FGXg7CiOjr9nWvOz5XzRHisu4qe982h0atzXnCJUFh1Pj888+RlZUFpVKJPn36YNeuXQ6v/d///odBgwahSZMmaNKkCUaOHOn0+mBFpVK5vogIKXQO4lgy8UbvXTISMKydi3h9RNBDdV6ckN7Fixh1X1tbi+joaERHR6O2tlbo4niB74NYMeo9WBHj+CLUYdaf6gatZduVnTGCsYho9l/HWeeyX1iJrOdWoFO6MaHnc2NzeShpcEPtEH+QLPmB5MgPXHJkhp86WWL11LjdZlFkOIWfiotiG2jMxmp3cfU+2i5MJewx9/lnzpwZon1+nlA4CRd16YD9sV1fA0vuByovcN9fe4W3ookFwY0aCxYswKxZs/Dqq69i37596NKlC0aPHo3S0lLO6zdt2oQZM2Zg48aN2L59OzIyMjBq1ChcvHgxwCX3jUmTJgldBIJn3DFqeKP3vGYUVy8coDovTkjv4kWsuq+rq0NdnX1M55DAYIrHf34HoNM6v9YBYtV7sCHW8UWoY64/Wp0eu86WW47fNSDL6X3mlcoAsPJwiWX7yMVKS//88EXjyt4oefgn4aR2iD9IlvxAcuQHLjlGmrwK7vlhD17/0xgKqEuLeLx2fQckx1gNGbaGgFDG1ugwo7eL3AY2cMnx+7t6ITlGgbkze/lUNjFRV1cHtdq5J2XYI492fG7DW9zHDy0Atnxo3NbZyK+ujJ9yiQjBjRoffvgh7rvvPsycORN5eXn46quvoFKp8N1333Fe/8svv+Chhx5C165dkZubi2+++QZ6vR7r168PcMl9Y968eUIXgeCR06XVliRczvBG705yHhIhBNV5cUJ6Fy+k+xDk9Drr9r65Xj2C9B4ciHV8Eer8/Os8bDpZiqu1jahWGw2L+W+NRXKM89xySpl1SFtZ34jCslqM+2QLJvxnq/21keFv1KB2iD9IlvxAcuQHLjnKIuyn9CIjpIiQSjCyfarlWJQsfNo+g437HocInMIlx2G5Kdj94ggMpQgZhCfYho+KjAJkHJ5A173J3t/zrfHvutcAACUqkxdp/TXgwK/A532Bi/v4LWuYIqhRo7GxEXv37sXIkSMtx6RSKUaOHInt27e79Yy6ujpoNBokJib6q5gE4ZTPN57GyA83Y/XRywCsiQ1/vqcPL8/nytVBEARBEATPRCdbt2u4V/QTwQ+NL0KXPy8qcdf3u/HOKmtSb3mk6+Eqc7JOozPg5WVHcOxSFee1TAMIQRBEqMNl1JCbjqXEWg3C7rSloYLt9IijxOGeQmGnCI/J6AvIGZFVnikANBwe6/0eAca9zz529HfLpjoixnp86YPAlePA8id4Lmx4ImjLVlZWBp1Oh9TUVNbx1NRUlJSUOLiLzbPPPovmzZuzBi5M1Go1qqqqWP+CgZycHKGLQPDEe6tPsvYfHJKNk2+NwcC2yXbXeqN3PRk1wgKq8+KE9C5eSPchyEDG4CEunX1OXQNcPePyEaR34QnE+AII3jFGqFJZr8HOq8ZQKUv2eRb2yzaklDPv6VhlpOeFCzGoHeIPkiU/kBz5gUuOXCH16jU6AEBitDX81IHz1/xXsABjOz8S4aEtgt5HgjekUmDSf6z7chWgiGNf89Rp43W972MfX3SXZbO657/sn10p8iTsbhLSvbo5c+Zg/vz52LRpE5RKJec1s2fPxuuv22ekX7RoEVQqFaZMmYL169ejsrISKSkp6N27N5YvXw4A6N69O/R6PQ4cOADAGHtv69atuHr1KhITEzF48GAsXboUANC5c2fIZDLs3bsXADB+/Hjs2bMHly9fRlxcHEaNGoXFixcDADp06ACtVmtxexs9ejSOHDmCixcvIjo6GhMmTMCCBQsAAO3atUNycjL++ecfAMDIkSORn5+PoqIiKBQKTJkyBQsWLIBer0d2djbS09OxefNmAMDQoUNRVFSEgoICREZGYtq0afjtt9/Q2NiIli1bIjs7Gxs2bAAADBw4EKWlpcjPzwcAzJgxA8uWLUNdXR1atGiBvLw8rFmzBgDQr18/VFZW4tgxY8zGadOmYdWqVaiurkZaWhq6d++OlStXAgB69eqFhoYGHD58GABwww03YNOmTbh27RqSk5PRr18//PnnnwCAbt26AQD2798PAJg4cSK2b9+OsrIyNGnSBEOHDsXvvxutmZ06dYJSqcTu3bsBAOPGjcO+fftQUlKC2NhYjBkzBosWLQIA5OXlIT4+3rI6b9SoUTh27BguXLgAlUqFSZMmWXSRk5ODlJQUbN1qdFcfPnw4zpw5g3PnzkEul+PGG2/EokWLoNVq0bp1a8gS2ANmADi0dyckZ/WYPn06lixZArVajczMTOTk5ODgwYPIz8/HgAEDUFZWhpMnjQaR6dOnY/ny5aitrUV6ejo6duxoeV5VVTUOHTqEo0ePAgCmTp2KNWvWoKqqCqmpqejZsydWrFgBAOjRowc0Gg0OHToEAJg8eTI2b96M8vJyJCUlYeDAgVi2bBkAoGvXrpBKpdi3z+jWNmHCBOzatQulpaWIj4/HiBEjsGTJEgBAx44doVKpLEkzx44di4MHD6K4uBgxMTEYN24cFi5cCADIzc1FYmIitm3bBgC47rrrcOLECZw/fx5RUVGYPHky5s+fD4PBgLZt2yItLQ1btmwBAAwbNgyFhYU4e/YsZDIZpk6disWLF0Oj0aBVq1bIysrCxo0bAQCDBg1CSUkJTp06BYlEgptvvhlLly5FfX09MjIykJubi7Vr1wIA+vfvj/Lycpw4YVz5d9NNN2HlypWoqalB8+bN0aVLF/z1118AgN69e6Ourg5HjhwBAN7aiPT0dOTn57vdRsTExGDnzp0AqI0I5TZCLpdb7h08eDAuXryIM2fOQCqVcrYR69YZw9+400asXr0aANCnTx/U1NRQGxFkbUR9fT1iYmL81o8Ixjbi3LlzYBKKbUSr2F7Iqt4NXUMVFprqbl5eHtpvuAvyygL81fJF9L7+HodtRHJyMs6fP+92G5GZmYlNmzYBCM02ImTzpzjBnfEFELxjjGBsG9zpP/x+XgnAPpHt8uXLXbYNdVoAsE4iODIwyaUGbFu7HGmTgr//4EvbUFFRYfld6j/41n9oaGhAjx49gnqMEQptRN++fWmMAd/biCNHjiA/P5/VRtSWKgCwQ/QdOF+BJUuW4ESJDoAxikR8XTHmzZsXNP0HX9qIgYm12MZYZ7Jv314oina43UbU1dXBYDCE7BgDEL6NaGhosMh/4cKFUCqVgvcjAGHaiBwUoIdJFvPmzcOI1lOQcnwuqmVNsSX9AQzQybFv40aUlJTghsh4KLWVsGX36TJ0tDmmq69ABCBIPyIY2gh3xxgSg21AugDS2NgIlUqFxYsXY/LkyZbjd955JyoqKiz/IS7ef/99vPXWW1i3bh169uzp8Dq1Ws1KXlNVVYWMjAxUVlYiLi7O4X3+Zt68eZgxY4Zgv0/ww5ZTV3D7t7tYxxY/0A89s7jDFXii96znjJX/+i7N8emMbr4VlBAcqvPihPQuXsSo+9raWsTEGN2na2pqEB3tJHlesPL7g8DBX4GYVOCpfOvx1+KNfwc9CYx4BdCqgQg5YBOqQGx6r6qqQnx8vOD9aiaBGF8AwTvGCFWGv78JBWW1dscL54x3eW+DRofcl1dZ9kfkpmD9CfsQcpO7NsfHN4d/n1ps7ZA/IVnyA8mRH7jk+MGak/jPhtOsY9HyCBx9Ywz2FV3DlC+ME/zz7uuLftlJASurvzlysdKSN+nNyR1xe9+Wbt9L76PvhEWfny90GuCnG4DmXYFRbxn3T68HWvYDlPHsazUNwJf9gXK29/e8dl9hxskH7J/9mr0BRCy4O8YQNPyUXC5Hjx49WEn4zEn5+vXr5/C+d999F2+++SZWrVrlcsChUCgQFxfH+kcQfKDR6XHHd7vsjqfEOl7V5w0UfoogCIIIBaRSKYYMGYIhQ4ZAylN844CjNq3wrrnMfd6gNybweysFeD3BPrAzITiBGF8ANMbgky83neE0aLiLwiZWfElVA+d1cVEyr3+DIAgiGOEKqff7wwMAAN0yEtCnVSKaxyvRqUW83XWhTF4z6ze3sq5RwJKIE3Ofv3379qHb5+eLCBlw13KjQcO8326MvUEDAGRKYJRN0vBnC/1exHBG8PBTs2bNwp133omePXuid+/e+Pjjj1FbW4uZM2cCAO644w6kp6dj9uzZAIB33nkHr7zyCn799VdkZWVZYuPGxMRYLIWhwPDhw4UuAuEj83cVcc5lpMU7Nmp4o3eaLwkPqM6LE9K7eBGj7qOioixuziFLt9uBE0bXfuh1gDQCqGfEob6wB9j6kXW/oQKIamLZFaPegxGxji9CFWZicCbTe2a4db9tctejxY6ShNvHng9HqB3iD5IlP5Ac+YFLjjEKq7F27ROD0So5GpGmROESiQS/3tcXUkn4JcGWSq3/nxiFZ9Oa9D76jrnPf/nyZURFRQldnNAiuql1u/f/AVFNjO/kSY5rdVogQvBp+6BGcJPa9OnT8f777+OVV15B165dceDAAaxatcqS3K+oqAiXLl2yXP/ll1+isbERU6dORbNmzSz/3n//fUc/EZScOeM62SQR3Ly87CjncZmTTFXe6F2nJ6tGOEB1XpyQ3sUL6T5EaTPCul1Xbvy74S3rscItQEKmdV/DXhFOeg8OxDq+CDdemZjH6/OUkYIPfQMCtUP8QbLkB5IjP3DJMb2JdUI5LkpmMWiYiZBKws6gYebr23tgWo8WuLl3puuLGdD7yB8kSy9IZ3gDN9YAcCLHnV8FoEChTVD07B555BGcO3cOarUaO3fuRJ8+fSznNm3ahLlz51r2CwsLYTAY7P699tprgS+4D9gm0yTCB2edBm/0TuGnwgOq8+KE9C5eSPchSoQMiE4xbpflA1XFwO5v2NdUFFm3tWyjRsrOt4EfJxtXVlUVk7ulgIhxfBGKOErvqJJHINqD1beD2ia7vEYhEk8N+v7wB8mSH0iO/MAlx07p1hA3XKGowplRHdLw3rQuHnvh0fvIHyRLL2CG6zLNHZ47dw6Yddz+2tPrAlSo0CUojBpiRC6XC10Ewgdq1Fqv7vNG7+SoER5QnRcnpHfxIkbd19bWomnTpmjatClqa72Pjy84LU15F+aOAz5s7/zavd9btzUNaFO5BSjYCPw7zXjv5vf8V06CCAMadXrO48+NzfXoOT/e3dvlNWIJPyXG74+/IFnyA8mRH7jkmBgtx5e3dsenM7pBJReXUcNb6H30HXOf/4EHHgjtPr/QSIxT8nK5HIhrbn++YGOACxR6kFFDIG688Uahi0D4wPnyOq/u80bvjlawEaEF1XlxQnoXL2LVfVlZGcrKyoQuhm8ktXH/2n8+AdRG13FUXbQe12uMfzf+m79yEUSIcepyNSZ9/g82nix1eE2Dhm3UeHFce/RtnYhpPdzLp2FGIpGgR8smdsebqKzx5pUycQx9xfr98QckS34gOfKDIzmO7dQM13fhmBAlOKH3kR/KyspQVcWdw4pwQcepxr99HwLg4p28kh+AAoUu4ujZBSGLFi0SugiED9R66anhid6fG5uLpGg5XhzvYpUoERJQnRcnpHfxQroPYZp14T6uTOA+PjsdKDtNcW8Jwobxn27FwfMVmPn9bugduB6vOVrC2r9vcGvMv78fouSee1U8NqKt3bFrdRrLtjJSHJ4a9P3hD5IlP5Ac+YHkyA8kR0JwbvwGeO48kGKc67O8k73vB6KaAKPftl77eS8BChg6kFFDILRa7ybFieBgd+E1y/Ztfd1PTOWJ3h8Yko09L41E66YxHpWNCE6ozosT0rt4Id2HMInZ3MfbjXV8z/mdwK6v/VMegghBDl+oZIWW+mF7od01Or0BTy8+xNtvugovJZbwU/T94Q+SJT+QHPmB5MgPJEdCcCQSQBln2bW8k+PeA54+A/R7WKCChR5k1BCI1q1bC10EwgfeWXXCsv3W5E5omaRy6z5P9e4s6TgRWlCdFyekd/FCug9hmrS0PzZjATD+A6B5d+57SvibmCWIcGDzqSus/df/PMba/23vBXR4dRWvvymPtB/axjKSjYulW03fH/4gWfIDyZEfSI78QHIkgg3WOykVxwIMviCjhkBkZrq/up8Ifv57ew9IJcC4TmlOryO9ixfSvTghvYsX0n0Io4hl70tlQLsxgDwauG8D0GWG/T3M0FMpHfxbPoIIAaQuLAhPLjpol0/DV2QR7N9Mipbj+5m98MG0LhjbMQ3Dc1N4/b1ghb4//EGy5AeSIz+QHPmB5EgEGy7fSZ3G+Xl3OPArcGSJ788JMsioIRCbNm0SuggEj+SmxWHH8yPwyc3dnF5HehcvpHtxQnoXL6T7EGfGfOv2qDet2xIJcMNXwEulQNfbuO+Nb8Hev3qG//IRRJATwTHKdJRXw0yKQufTb8ptfnTPSyPRMysRN/ZogS9v6yGa8FP0/eEPkiU/kBz5geTIDyRHIthw+U7WXfXtB+orgKUPAotnGvMAhhGRri8hCKK8thG/7b2Ayd3S0TRWYTk+qG2yZTslTilE0QiCIAgiKJBKpejZs6dlO6RpNxZ45RpQUQg0aWV/PlIBTP4cSGoNrH/DcrhElYu0CBn72tPrgSQHeToIIkzhsl9crm5As/goVNQ12p2b1qMFWtcd9+k3ZTZGDQrjShAEQRD8Y+7zl5eXh36fPxSovQLEOo8K4xRNvXX74h4guY3vZQoSyKghEIMHDxa6CIQHPDpvP7aeLsOqoyX47cH+UERKodbq8fYNnTx6DuldvJDuxQnpXbyIUfdRUVHYvXu30MXgD6kUSHQRd9nmvH7aj8Duf7OvuXICBCEWjlysxKPz9qOgrNbu3OUqNfYXVeChX/bZnXtvWhdcvJhsd9wTuHJqiBExfn/8BcmSH0iO/EBy5AeSo++Y+/wXL15EVFSU0MUJeTjfyagmQP0143btFfvznqDXWrfrK3x7VpBBPT+BuHjxotBFIDxg6+kyAMDec9eg1uqg1hpjAKvknrmxk97FC+lenJDexQvpXiSkdrRuP30GF65UAmk2Cx4aKgJaJIIQkg/X5tsZNBQmY8PlqgYcLa50eK+v7aatp4ZYoe8Pf5As+YHkyA8kR34gOfIHyZIfOOU46Qvr9qFFvv0A06gRZuMS6vkJxJkzFF85VGn30irLdpSHRg3Su3gh3YsT0rt4Id2LBAMjxo4syqj3fv8CBs4Cesw0Hm9wPIlLEOFGZqLK7liG6VhJZQMW771gd/6RYcYwCL62m7Y5NcQKfX/4g2TJDyRHfiA58gPJkT9IlvzAKcfccYAizrhd7qOcDXrrdkWRb88KMqjnJxAUdy48UEZ6ZtQgvYsX0r04Ib2LFzHqvq6uDllZWcjKykJdXZ3QxQkMUQnW7cgoo95lSmDkq0Db64zHw8zNmyCc0aCxT/bdPzsJAHC2rBaXq9Ssc0+MzMET1+UA8L3dlEVSDg1AnN8ff0Gy5AeSIz+QHPmB5Og75j7/o48+Kp4+vx9x+E72fcj4Vxnv2w8wPTUq7ReXhDKUU0Mgpk+fLnQRCA+QRUig0bEzHo7v3AxSqWeDJ9K7eCHdixPSu3gRo+4NBgPOnTtn2RYFMSnAtLmALBqQStl6Nw9ALu4Byk6HVVI+gnBEdYNx4PzUqBxcl5eG5Bg51h8vBQCcuVLDurZwznjWvq/tJnlqGBHj98dfkCz5geTIDyRHfiA5+o4o+/x+xOE7GZNi/Bshd+9BJ1YC+34Arv8MiGlqPc7KqVHuXSGDFOr5CcSSJUuELgLhJu+sOmFn0AC8a7xJ7+KFdC9OSO/ihXQvIjrcAOSMAmCjd+aqqiX3Ob5frwcaqoCCTcC+nwCd1vG1BBHkVDVoAADN4qPQLi0WSTEKNI1TAADKaxud3utruxnBWGwUqxDv2j36/vAHyZIfSI78QHLkB5IjEWw4fCelpsgw7s49zp8B5K8C1r7MPs40apQcdv95IQAZNQRCrVa7vogQHJ3egC83WePXfXVbd0zs0hwRUgnu6Jfl8fNI7+KFdC9OSO/ihXQvTlh6VyZYt4v3cd9wYQ/wTktgTgbw4yTgj0eArwYCWueTvwQRTJwurcG6Y5dhMBiw5VQZACBWaTUqxJgMDEeLqyzHxndqZvccX9tNiUSCQW2TkaCSYfdLI316VihD3x/+IFnyA8mRH0iO/EByJIINh++kxDRlb2CE9iwvAM7vdv5A2xBTepsFU8eWelS+YEa8S1gEJjMzU+giEG5wtoztJj8sNwVD26Xg+bG5aJ4Q5fHzSO/ihXQvTkjv4oV0L05Yencn/u2S+wF1FfvYlePAlg+A9hMBdTXQsh/3vUd+Aza8BfR5EOhzv/eFJggfGfnh3wCA96Z2thxj9pNjOLwmHh/Z1u4YH+3mj3f3hsEAj0PEhhP0/eEPkiU/kBz5geTIDyRHIthw+E5KTJ4aDZXWY592M/599ACQ2Mq4XXIE+GqA9RqdzeIovU2+s4v7jJ7mYQB5aghETk6O0EUg3OBiRQNrXxEZAaUswiuDBkB6FzOke3FCehcvpHtxwtK7Itb1DdoG7uP7fzYOTr4fAxT+w33N4ruNq7UO/OJ5QQmCJzQ6vWX76cWHLNsd061GPRlHrgtFZITdMT7aTYlEImqDBkDfHz4hWfIDyZEfSI78QHIkgg2H72SFMW8JirYb/+qtfS4U7TD+zV/NNmgAgE7D3rc1ajRUeFXOYISMGgKxbt06oYtAuEGDRuf6Ig8gvYsX0r04Ib2LF9K9OGHpXeLGxKrtoMNMFcNtfOHt9ueZsXB7znSvcAThB2Z+7yIEAoBm8UrWfvtmcWjRxH6BELWb/EBy5A+SJT+QHPmB5MgPJEci2HD4Tp7dwt5nGiNOrjD+3flf+/tsw97ahp+qLfOofMEMGTUIwglMo8aiBxyEfyAIgiAIAhKJBHl5ecjLy4PEnQl9MdByoPFv0/bs42ajRG2p62fUXbWPnVtz2brd5Rbvy0cQPrL1tOuBcbQiEvcNamXZn9ilmei9KQiCIAgiVDH3+dPT06nP708iZOz9unLrdrOuxr96BwukmNgaNTR1PhUrmCCjhkAMGDDA9UWE4Kg1Rveu4bkp6JWV6PPzSO/ihXQvTkjv4kWMulepVDh69CiOHj0KlUoldHEEwU7vQ58zbTA8K1a/CLyeAJxe7/6Dz/7N3jevsIpuCkTKPS0mQQAA9HoD6hq1ri/0EK58Gf2zky3b/Voncd4nxnbTH5Ac+YNkyQ8kR34gOfIDydF3zH3+bdu2ibbPzycO38kImz5+PcOoUbzf+UNPMbw/ygvY59I6Ob+X6REe5JBRQyDKysLH3SecqTd5aihl/FQV0rt4Id2LE9K7eCHdixM7vZsHI8yEfds/M/79eQr72lkngFt/435wRRF7f9nDxr+1V7wrKEEAmDl3N/JeWY3SKge5XTzkr8cG4cSbY/D4SPvY0ApGXzo5RsF5P7Wb/EBy5A+SJT+QHPmB5MgPJEf+IFnyg0M5Mj019DpAXWXdP7Ec+HKg44f+cqN1e8WT7HO2OTZY5/TAm02BL/oBNcE/ziCjhkCcPHlS6CIELRcr6qFlJB0UkgW7zwMAUmKVLq50D9K7eCHdixPSu3gh3YsTO72bByOOcmeYeboAiGsGtB0JDH/JeKz99cC4943b+34ADsyzXn/pAC/lJcTN3/nGwWrvt9fju61nPb7fYLOSr32zOChl9gnAAaBRa+3bJ8VwexdRu8kPJEf+IFnyA8mRH0iO/EBy5A+SJT84lGNqB+u2ph7QqtnnLx82HndFYiv2fkOl42vPbDCGtCo9BkQ1cf1sgSGjBhFU7Ci4igFzNuDZ3w4LXRQAwLFLRksohQkkCIIgCOfU1dWhQ4cO6NChA+rqwidWq0/Yemrs+5H7uqgE63bfh4Ab/gtM/ASISbEeX/qA8a+62nrs3g28FZUQDwaDAXvPlbOOvbH8GMprGx3cwU2Dxmqo6JXlfODbKjnasq2SR3r0OwRBEARBBA/mPv/TTz9NfX5/MnCWdVvbYPxny4Xd9sdsadnf+Dch0/i38gL3dcUHgIW3W/cjgr+/FvwlDFOmT58udBEER6vT41RpDdqlxlqSBb7x5zEAwG/7LuCDm7oIWTzo9dbVZz1a8mOhJL2LF9K9OCG9ixcx6t5gMODYsWOWbTFip3eLp0ajMZ7tH/+yv6n1UEDKWN0ujwa63Gw6N4x97Wvx7P2EDJ/KS4iTD9bk47ONp+2Obzl1BZO6prv9nDNXaizbX9zaw+m1LZOisfiBfkiNc+z9LMZ20x+QHPmDZMkPJEd+IDnyA8nRd6jPzy8O30m5yrhAStdo8tRwsvhEGe/YA8PszZHayRjStqqY+7qvh1i3h77guuBBAHlqCMTy5cuFLoLgvLzsKMZ+sgW9315vMSCUVqtd3BU4qtXWxInD2qU4udJ9SO/ihXQvTkjv4oV0L07s9M4MP7X4Hu6bnCXrU8YBU79zcj7Bo/IRRH2jjtOgAQClVZ71w+/5wbg6MFoegaax3HkymPTMSkRGouOEotRu8gPJkT9IlvxAcuQHkiM/kByJYMPpOxkZZfyrVXN7apiJa2F/zGxw0pjuS2pt/Hv1tOtk4CntnZ8PEsioIRC1tbWWbb3egI0nS1FWEzwT+oFg3i5j0suyGjV+33/Rsm2mQeMkeU0A+HWnNSlnlIP4wJ7C1DshLkj34oT0Ll5I9+LETu/m8FONNUCJg9Cag57kPm6m9ITjc5HcuQkIwhEXK9hhItbNGgKlKYl3Wa37YxGDwYBrdcZcMaM7pvFSNmo3+YHkyB8kS34gOfIDyZEfSI5EsOH0nZSZPFy1HDk1mPS4y/i3CSN/xo4vjF4ZGlPfL6Gl6YQBeLe180LFNnN+Pkggo4ZApKdbXbsX77uAmd/vxrhPtghYImHZdbbc7ti5q45j8wXCxW3X2auWbXN4LF9h6p0QF6R7cUJ6Fy+ke3Fip/cIhtFB7yBZuCtvC0dJ+pLaul0ugjBTo7YuGtr94ki0SYnBoyOM71JZtfs5Nc6X11uSf781uSMvZaN2kx9IjvxBsuQHkiM/kBz5geRIBBtO38lIk1FD4yCnhpmedwO3LATuY+TbW/0C8N/B1vuYufrqy9kLp/Q2i8oVMe4VXmDIqCEQHTtaO/9rjpYACK7QS4Gmsl4DrU4Ppu3gvdUn8d3Ws9hw4jJ2FlgNDK/9cRQD39mIax4mM3SEXm+wM5L8nX8FG09eAQAMa9eUl98B2HonxAXpXpyQ3sUL6V6c2Old6kb6OomLhRO97+M+PuW/7hWKIABUNRiNajUNxvCquWmxlpBRzeONoQ0Kr7q/evXWb3dYtvlK/E3tJj+QHPmDZMkPJEd+IDnyA8mRCDacvpORDE+Nxhrua1TJxqTeOaMBVSLQ/U7rudorQKOpfyezCQF6cY/x76l1wBuJ7HNyMmoQTli9erVlW095dbC94CpKq9UsWaw7fhlvLD+Gu+fuwfSvd+CBn/aiQaPD3G2FuFhRj7+OlPj8uw0aHUZ8+DdmzjXGBF5ztAR7Csvx+p9HLdeM68Sf2xVT74S4IN2LE9K7eCHdixM7vTsyauSMMXpxtLnO9UMjZMAr5cCrFcAti4BHDwCvVQLpzhMzE4SZ3/ZeQOfX1uCXnedQozYaN2IU1nezfbM4AEB+SbXb3tDny41JJ82hq/iA2k1+IDnyB8mSH0iO/EBy5AeSIxFsOH0nZQxPjWuFxu2Rr7Gv6fsAe982H0bJIdOzoozjCDO1xoXc+OVG+9+VRzspcfDAz7Iawif0AQilFIxESiXQmqwYlfUazFp4wOn1q46W4PrPtlr2VXLf81wcLa7E2bJanC2rxc6Cq7j/p71214zl0ahBEARBEOGKRCJBy5YtLdsEjIm+mUQqgZcuG7frygFFnP09XEhNfZ6cUfyVjRANTy46CAB48fcjeG9qZwBAjNI6DMxKViFCKkG1WovX/zyG58bmQulmPrkuLRJ4Ly9BEARBEMGLuc9fW1tLfX5/Y0kUXg+UHDFuJ7djX9P7fvZ+xXnuZ0llQEKmdf/APGDA49zXKmI9LqoQkKeGQPTp08eyLVZPjRZNolj7OwqMeTXymjke4Odftrpbzd9d5PA6dymrsYawmv71Drvzqx4fxFrJ5itMvRPignQvTkjv4kWMulepVCgsLERhYSFUKpXrG8IQl3pnxsJVJRpdxQnCj2h0est2ekIUatXG8FPM/q0iMgJxJiPH3G2FeH/1Sc5nVTVoUKPWoq5RazkWq5TxVlYxtpv+gOTIHyRLfiA58gPJkR9Ijr5j7vPv3LlTtH1+PnH6TkaY+ll1V4ErphwYLXpZz7ccCCjj2ffUXOZ+VqTcuFBqyLPG/bKTQD6Hl8jU762/G+SQUUMgamqsk/OBSHodjFx1kBOjbWoM3pzUAckxCqf37ygoxwM/7YXeA6tQjVqLpxcdRI8312Lx3gvYX1Th9Pp2qfxaJ5l6J8QF6V6ckN7FC+lenJDeiWBj3THrwDY9IQo1HEYNAIhieGZ8s/Usft9/AUcuVlqONWr1uO7DvzH6o83YcqrMcvxfw9vwVlaqP/xAcuQPkiU/kBz5geTIDyRH/iBZ8oNTOZqNC+VnARgAVRIQw8j7m9rB/p7+/+J+VprRW5dlBDn3j83vKYCOU1yWOVggo4ZAHD1qzdmgE6GrhkanR3WDlvPcU6Pa4fZ+Wdjz0kjLAOu1iXmc1646WoKpX21zadioajAmIu/46mos2nsBV2sb8dSig/jq7zN21z47JhdvTe6Is7PH8e5Kx9Q7IS5I9+KE9C5eSPfihFPvkQzP1PhM+/ME4UcuVVq9g2SRxhBTgL1Ro5hxHQA8seAgJvxnKy5W1OPIxUoUXq3F5So1LlbU4/8Y4VpbNeUv5jK1m/xAcuQPkiU/kBz5geTIDyRH/iBZ8oNTOZrz85mThJuTfd+/Ceg41T70FAA07wqMeQfIHm491nKgNZxthNz++WbMOTxCBPJ5F4AatRYVjRLUNWqhkkdaVkwBRgNHhDT8Y9LVa3Scx+/qn4WMRKv72rE3RuNKtRopcUrcNaCV5fgt/9uBbWeuAgD2FVWg9QsrceLNMZzxf3cUXMXNHKGlmHx5a3coZREYlpvizX+HIAiCIERPfX09Bg8eDADYvHkzoqKiXNwhEuQqYxxcANBxe6kShL8w95cBIFIqRY1pUREzp4YzBszZAAC4pY+9QS5KFoE4HsNPEQRBEAQR/Jj7/OXl5Zg8eTL1+f2JlBF+CgAiTRFtmncDpn7r+L6+DxgNHm80Me5P+NB6rrHWul1hE9Y/hcPzI4ghTw0BuPWbnXjneCzWmtzBmYOBPYXlQhUrYOj0Btz3wx7L/rs3Gl2gnhnTDq9MYHtkSCQSpMTZWwq/n9kLc6Z0Yh2778c9rP2KukZodHqXBo3eWYkY0zEtIAaNqVOn+v03iOCEdC9OSO/iRYy61+v12LNnD/bs2QO9Xu/6hjCEU+9tRlq3u0wPXGEI0TN75XGsO24NP3WtrpEzp4Y7/LrTPpddapzzULGeIsZ20x+QHPmDZMkPJEd+IDnyA8nRd8x9/oKCAtH2+fnE6Ttp9q44tsz4N9IDTwqpFHj5KvDSFaApI7m4pt66fWSxdbvdeGDKf91/fhBARg0BMCfie2z+AXz19xnUMpLtPTb/gEClch+tTm8ZEHnD0v0XsfOs1XhzU68MFM4Zj4eGtoHUTS8VRWQEbu6diTNvj0PTWOOAameB9ZlvLT+Grm+sRdsX/2Ldl54QheNvjMGO50fgX8Pb4Kd7emPu3b14DzPliDVr1gTkd4jgg3QvTkjv4oV0L0449T7uPaDzdKD/o8CI1wJeJkK8/HdzAWv/0IVKnCipBmBv1JjROwMA8MCQbLefX3i1zscSsqF2kx9IjvxBsuQHkiM/kBz5geRIBBtO30nbhN2RHi4oiYg0JghnYuAwRCnigBm/AgmhFSqXwk8JwKEL1qR7c/46gfbN4iz7JVUN+GZLAe4d1Dpg5dHpDahp0KKqQYOqBg2qG7Smf8btqnoNqtXG/aPFVazyF84Z7/TZ+ZerMeqjzchNi8Ud/bIQIQWe/e2w5fyYDmk+lT1CKsGGJ4eg02tr0KjT494f9rBWpDFZ/q+BaJcWC1mEFFHyCDw5qh3ndf6kqqoq4L9JBAeke3FCehcvpHtxwql3ZTww5evAF4YgOLAYNWzCT70yoQPuHtAKbVNjMX93ESrqNAEvG7Wb/EBy5A+SJT+QHPmB5MgPJEci2HD6TtrmvPDEU8PdZwKAnL8caYGEjBoCUFnPHiRU1rHjK//PA6OGXm9ATaPVCFFVbzVGVDdoUGUyVnAaKkz7tY3c+S3c4WqNGqdLa9AiUYX0BHYcvaoGDUZ9tBmAcQD1wu+HWefbpcbiq9t7eP3bZmIZ4bu4DBpPj26H/xvcGpERwjsmpaamCl0EQiBI9+KE9C5eSPfihPROBBMJKhkq6jS4s19L/LD9HOtctI2nRpQ8Am1TYwEAyx4egCHvbbKcm9ajBRbtvWD3/F0vjOC1vFR/+IHkyB8kS34gOfIDyZEfSI5EsOH0nbQzavAQ+rP3fcDBX4FrhdZj1Zd8f64AkFFDAMZ2TMNfR0os+8WVDazzdWodft5xjm2EYBgqzAaKqnoNahq1MBj4KZciUopYpQxxykjEKiMRFyVDrDISsQrTX6UMcVHGv08tOggA6PHWOgBA+2Zx+OuxQaznTf+v81wWC/6vLz8F52BQ22T89/YeUMmD6xXv2bOn0EUgBIJ0L05I7+KFdC9OSO9EsFBWo7Z4W0ztkWFn1IiSRTi8t2VSNIbkNMXf+VcgkQDvTu2MG3u0QF2jFkNzUtwOF+spVH/4geTIHyRLfiA58gPJkR9IjkSw4fSdPLqUvS9T+f6DqkTgsYOAugaYne778wQkuGZ8RcLr13dgGTVsqVZr8dLSIx49Ux4hNRkejEaHWGUk4pQy1r7ZUBHHOmbdl0e678kw568TKKtRW/aPX6pCdYPG4jVx8HwFjl/idqFSySNw6NVRvHpOrHp8EMZ8vAUA8N1dPTE8Nzit7ytWrMCMGTOELgYhAKR7cUJ6Fy+ke3FCeieCgYsV9RgwZ4NlP0pu3+eWRTg3TPznlm7YX1SBgW2SIZFI0Ld1Eu/ltIXqDz+QHPmDZMkPJEd+IDnyA8mRCDacvpOaWvZ+dFP+fpgPA4nAkFFDAJrGKtApXoPDlTLO831bJ5qMDWbjBMNQEcVhqFDKoHSy2sofDGyThKUHilnHFuw+j3sHtYZaq8Okz/+xHF83awjapMSgRq3F7sJydG2RwHsoqNy0OOx+cSRiFJGIkgdWFgRBEARBGElOTha6CAQhepYfZPfRFZH2fWNXY4c4pQxDcngcOBMEQRAEETYkJydDrVa7vpDgl2gex1pSxrxs19v4e24AIaOGAEgkEnxwYx4yW2Uj9+VVrHMRUgnm399PoJK5z7NjcyGVSDCuUzOcLavFv1cexzdbzuLWPi3x5KIDlut+e7A/2qTEAABiFJEY1i7Fb2VqGstDbDk/06OH7zlEiNCEdC9OSO/iRYy6j46OxpUrV4QuhqCIUe9E8MHMNwcAKXEKyCIk0OisMWs7NI8PdLFcQvWHH0iO/EGy5AeSIz+QHPmB5Og75j5/fn4+oqNDM8F0MOHRO9nrXv8UImeUf57rZ8ioIRAajQZKWQR2vTgCX20qwM29M1DfqEOzBB4y2QeAZvFR+HB6VwDAttNlAICSqgbM312ElYeNobUyE1Xo0bKJUEUMSjQajeuLiLCEdC9OSO/ihXQvTkjvRDDw6h/WMLZf3todisgI/PHIQIz9ZIuApXIN1R9+IDnyB8mSH0iO/EBy5AeSI3+QLPnBbTk+vAuITeP3x8d/CFzYA+RO4Pe5AYLfGECE2xw6dAgAkBKrxCsT85CTGosuGQlIiQ0NowaT7gzDxet/HrNsf3JzVwFKE9yY9U6ID9K9OCG9ixfSvTghvRNCc7q02uKRcUe/lhjbqRkAoH2zOHxzR08kRstxV/8sAUvoGKo//EBy5A+SJT+QHPmB5MgPJEf+IFnyg1M53vqbdVuZwP+P97oHuOFLQBqaYfyDwqjx+eefIysrC0qlEn369MGuXbucXr9o0SLk5uZCqVSiU6dOWLlyZYBKSnDBFZP369t7oFsmeWkQBEEQhFior6/H0KFDMXToUNTX1wtdHELkiHF8odbqUNeos+wPz2WHfR2Zl4q9L43Ea9d3CHTRCIIgCIIIE8x9/jfffJP6/P4mIcO6rYgRrhxBisRgMBhcX+Y/FixYgDvuuANfffUV+vTpg48//hiLFi3CyZMnkZJin39h27ZtGDx4MGbPno0JEybg119/xTvvvIN9+/ahY8eOLn+vqqoK8fHxqKysRFxcnD/+S25RX1+PqKgowX6fbyb8ZwuOXKyy7B98dRTio7gToYuZcNM74T6ke3FCehcvYtR9bW0tYmKMne2amhpRxtgVm96DpV9tS6DHF4Dwsigsq8X1n21FVYPWemzO+ICXwxfEVn/8BcmRP0iW/EBy5AeSIz+QHH2H+vz84vSd1DQA/04D5DHA8+cBiSSwhRMId/vVgntqfPjhh7jvvvswc+ZM5OXl4auvvoJKpcJ3333Hef0nn3yCMWPG4Omnn0b79u3x5ptvonv37vjss88CXHLf2Lx5s9BF4JU3J7EHfGTQ4Cbc9E64D+lenJDexQvpXpyQ3oMDMY4vjhZXsQwaHZoHj5HJXaj+8APJkT9IlvxAcuQHkiM/kByJYMPpOylTAs8VAU+dFI1BwxMENWo0NjZi7969GDlypOWYVCrFyJEjsX37ds57tm/fzroeAEaPHu3w+mClvLxc6CLwSmK03LLdr3WSgCUJbsJN74T7kO7FCeldvJDuxQnpXXjEOr4Y37kZaz9aESlQSbyH6g8/kBz5g2TJDyRHfiA58gPJkQg2XL6TyjhATt4wXAhq1CgrK4NOp0NqairreGpqKkpKSjjvKSkp8eh6tVqNqqoq1r9gICkpvCb+WzRRWbZv6ZMpYEmCm3DTO+E+pHtxQnoXL6R7cUJ6F55AjC+A4BxjfHtnT8t2u9RYAUviHVR/+IHkyB8kS34gOfIDyZEfSI5EsEHvpPeE3hIeD5k9ezZef/11u+OLFi2CSqXClClTsH79elRWViIlJQW9e/fG8uXLAQDdu3eHXq/HgQMHAACTJk3C1q1bcfXqVSQmJmLw4MFYunQpAKBz586QyWTYu3cvAGD8+PHYs2cPLl++jLi4OIwaNQqLFy8GAHTo0AEZGRmYN28eAONKsCNHjuDixYuIjo7GhAkTsGDBAgBAu3btkJycjH/++QcAMHLkSOTn56OoqAgKhQJTpkzBggULoNfrkZ2djfT0dIvr0tChQ1FUVISCggJERkZi2rRp+O2339DY2IiWLVsiOzsbGzZsAAAMHDgQpaWlyM/PBwDMmDEDy5YtQ11dHVq0aIG8vDysWbMGANCvXz9UVlbi2LFjAIBp06Zh1apVGJrSiPPqKPRsrrT833r16oWGhgYcPnwYAHDDDTdg06ZNuHbtGpKTk9GvXz/8+eefAIBu3boBAPbv3w8AmDhxIrZv346ysjI0adIEQ4cOxe+//w4A6NSpE5RKJXbv3g0AGDduHPbt24eSkhLExsZizJgxWLRoEQAgLy8P8fHxltV2o0aNwrFjx3DhwgWoVCpMmjTJUt6cnBykpKRg69atAIDhw4fjzJkzOHfuHORyOW688UYsWrQIWq0WrVu3RmZmJjZt2gQAGDx4MC5evIgzZ85AKpVi+vTpWLJkCdRqNTIzM5GTk4PS0lLMmzcPAwYMQFlZGU6ePAkAmD59OpYvX47a2lqkp6ejY8eOWL16NQCgT58+qKmpwdGjRwEAU6dOxZo1a1BVVYXU1FT07NkTK1asAAD06NEDGo0Ghw4dAgBMnjwZmzdvRnl5OZKSkjBw4EAsW7YMANC1a1dIpVLs27cPADBhwgTs2rULpaWliI+Px4gRI7BkyRIAQMeOHaFSqSxJNseOHYuDBw+iuLgYMTExGDduHBYuXAgAyM3NRWJiIrZt2wYAuO6663DixAmcP38eUVFRmDx5MubPnw+DwYC2bdsiLS0NW7ZsAQAMGzYMhYWFOHv2LGQyGaZOnYrFixdDo9GgVatWyMrKwsaNGwEAgwYNQklJCU6dOgWJRIKbb74ZS5cuRX19PTIyMpCbm4u1a9cCAPr374/y8nKcOHECAHDTTTdh5cqVqKmpQfPmzdGlSxf89ddfAIDevXujrq4OR44cAQDe2ojc3Fzk5+e73UbExMRg586dAMKnjaiurkZaWhq6d+9uScAa7m1E27ZtLfe600asW7cOAKiNCIM2QqfT4eTJk37rRwRjG3Hu3DkwEWMbkZWVhfPnz/utHxFsbURdXR3ESrCOMSblNcHKE+VIvnYU5eXNg6JtcLf/0K1bNxw8eDAs2wZ/jzGYbUNcXJzld6n/4Fv/Qa/XA0BQjzFCof8wfvx4GmPA9zbi6tWrmDdvXlj0H4RsI7RaLfbs2ROyYwxA+DaioaEBZhYuXAilUhkU/YhQbSPM84TB0o8IhjbC3TGGoInCGxsboVKpsHjxYkyePNly/M4770RFRYXlP8QkMzMTs2bNwuOPP2459uqrr2Lp0qU4ePCg3fVqtRpqtdqyX1VVhYyMDMETGs6bNw8zZswQ7PcJYSC9ixfSvTghvYsXMeqekgaKT+9CJ8fmIhDjCyB4xxihjNjqj78gOfIHyZIfSI78QHLkB5Kj71Cfn1/onbQnJBKFy+Vy9OjRA+vXr7cc0+v1WL9+Pfr168d5T79+/VjXA8DatWsdXq9QKBAXF8f6RxAEQRAEQfCPSqWCSqVyfSFB+IlAjC8AGmMQBEEQBCFeVCoVFAqF0MUgRI7g4admzZqFO++8Ez179kTv3r3x8ccfo7a2FjNnzgQA3HHHHUhPT8fs2bMBAI899hiGDBmCDz74AOPHj8f8+fOxZ88efP3110L+Nzyma9euQheBEADSu3gh3YsT0rt4EaPuo6OjUVtbK3QxBEWMeg9GxDq+CHWo/vADyZE/SJb8QHLkB5IjP5Acfcfc5z9+/Dh5afAAvZPeI7hRY/r06bhy5QpeeeUVlJSUoGvXrli1apUlWV9RURGkUqtDSf/+/fHrr7/ipZdewgsvvIC2bdti6dKl6Nixo1D/Ba9g/p8I8UB6Fy+ke3FCehcvpHtxQnoPDsQ6vgh1qP7wA8mRP0iW/EBy5AeSIz+QHPmDZMkPJEfvCQrJPfLIIzh37hzUajV27tyJPn36WM5t2rQJc+fOZV0/bdo0nDx5Emq1GkeOHMG4ceMCXGLfMSdFIsQF6V28kO7FCeldvJDuxQnpPXgQ4/gi1KH6ww8kR/4gWfIDyZEfSI78QHLkD5IlP5AcvScojBoEQRAEQRBEaNPQ0IDx48dj/PjxaGhoELo4BEEQBEEQBEHwjLnP/+6771KfnxAUicFgMAhdiEDibgZ1f1NdXY3Y2FjBfp8QBtK7eCHdixPSu3gRo+5ra2sRExMDAKipqRFljF2x6T1Y+tXBAMnCd8RWf/wFyZE/SJb8QHLkB5IjP5AcfYf6/PxC76Q97varyVNDIHbt2iV0EQgBIL2LF9K9OCG9ixfSvTghvROE91D94QeSI3+QLPmB5MgPJEd+IDkSwQa9k95DRg2BKC0tFboIhACQ3sUL6V6ckN7FC+lenJDeCcJ7qP7wA8mRP0iW/EBy5AeSIz+QHIlgg95J7yGjhkDEx8cLXQRCAEjv4oV0L05I7+KFdC9OSO8E4T1Uf/iB5MgfJEt+IDnyA8mRH0iORLBB76T3UE4NgVCr1VAoFIL9PiEMpHfxQroXJ6R38SJG3VN8XfHpPVj61cEAycJ3xFZ//AXJkT9IlvxAcuQHkiM/kBx9h/r8/ELvpD2UUyPIWbJkidBFIASA9C5eSPfihPQuXkj34oT0ThDeQ/WHH0iO/EGy5AeSIz+QHPmB5EgEG/ROek+k0AUINGbHlKqqKkHLUVdXJ3gZiMBDehcvpHtxQnoXL2LUfW1trWW7qqoKOp1OwNIIg9j0bv6/iszxm5NgGWOEMmKrP/6C5MgfJEt+IDnyA8mRH0iOvkN9fn6hd9Ied8cYogs/deHCBWRkZAhdDIIgCIIgCIIIC86fP48WLVoIXQxBoTEGQRAEQRAEQfCHqzGG6Iwaer0excXFiI2NhUQiEaQMVVVVyMjIwPnz5ynmroggvYsX0r04Ib2LF9K9OBGj3g0GA6qrq9G8eXNIpeKOahsMY4xQRoz1xx+QHPmDZMkPJEd+IDnyA8mRP0iW/EBy5MbdMYbowk9JpdKgWUkWFxdHL60IIb2LF9K9OCG9ixfSvTgRm97j4+OFLkJQEExjjFBGbPXHX5Ac+YNkyQ8kR34gOfIDyZE/SJb8QHK0x50xhriXVBEEQRAEQRAEQRAEQRAEQRAEETKQUYMgCIIgCIIgCIIgCIIgCIIgiJCAjBoCoFAo8Oqrr0KhUAhdFCKAkN7FC+lenJDexQvpXpyQ3gnCe6j+8APJkT9IlvxAcuQHkiM/kBz5g2TJDyRH3xBdonCCIAiCIAiCIAiCIAiCIAiCIEIT8tQgCIIgCIIgCIIgCIIgCIIgCCIkIKMGQRAEQRAEQRAEQRAEQRAEQRAhARk1CIIgCIIgCIIgCIIgCIIgCIIICcio4SWzZ89Gr169EBsbi5SUFEyePBknT55kXdPQ0ICHH34YSUlJiImJwY033ojLly+zrikqKsL48eOhUqmQkpKCp59+GlqtlnXNpk2b0L17dygUCrRp0wZz587193+PcEAg9W7mn3/+QWRkJLp27eqv/xbhgkDq/ZdffkGXLl2gUqnQrFkz3H333bh69arf/48EN3zp/tFHH0WPHj2gUCg46/KmTZswadIkNGvWDNHR0ejatSt++eUXf/7XCCcESu8AYDAY8P777yMnJwcKhQLp6en497//7a//GuEEPvR+8OBBzJgxAxkZGYiKikL79u3xySef2P0W9e2IcCSQ/SW1Wo0XX3wRLVu2hEKhQFZWFr777ju//x8DAfU7+SGQ3/JDhw5h0KBBUCqVyMjIwLvvvuuv/1bAob4wfwTynTRz+vRpxMbGIiEhgef/jXBQP50fAinH1atXo2/fvoiNjUXTpk1x4403orCw0E//s8BC4wdhIaOGl/z99994+OGHsWPHDqxdu/b/27v32JrvP47jr1NHTVctG2ut4pa5zHVFSNet5rI2iBnpCHPrqLlGd2HrJu1Itv1csotg2YxixCiRIC4TLQuaoRQtWsFmptVgdQ/Vfn5/SM9PNz9Dv9/v6emej+Qk+v1+8v183p+3c77vz/nknKPi4mJFR0fr+vXrnjbvvPOONmzYoNTUVO3cuVPnzp3TgAEDPOdLSkrUp08f3b59W3v27NHSpUu1ZMkSJSUledqcPn1affr0Ubdu3ZSVlaWEhASNHj1aW7dudTRe3OVU3ssUFRVp+PDh6tGjhyPx4f6cyvvu3bs1fPhwjRo1Sjk5OUpNTdXevXsVHx/vaLz4HytyX+att97SoEGD7tvPnj171K5dO61du1aHDx9WXFychg8fro0bN9oWG/4/p/IuSZMnT9b333+vOXPm6Pjx41q/fr06d+5sS1x4MCvynpmZqWeeeUbLly9XTk6OPv74YyUmJmrevHmeNtR2qKqcrJMHDhyo7du3a9GiRcrNzdXKlSvVokULx2K1E3WnNZy6l1+5ckXR0dFq1KiRMjMzNXv2bH3yySf67rvvbIvNSdTC1nGyvpSk4uJiDR48WC+//LLlsXgTdbo1nJrH06dPq1+/furevbuysrK0detWXbhw4b7X8UWsH7zMwBKFhYVGktm5c6cxxpiioiJTvXp1k5qa6mlz7NgxI8lkZGQYY4zZtGmT8fPzMwUFBZ4233zzjQkKCjK3bt0yxhgzdepU07p163J9DRo0yMTExNgdEh6CXXkvM2jQIDNt2jSTnJxs2rdvb39AeCh25X327NmmadOm5fqaO3euCQsLszskPKTHyf29HuW53Lt3bxMXF2fJuFExduX96NGjxu12m+PHj9s2djy+iua9zPjx4023bt08f1Pb4d/Crnpp8+bNJjg42Fy8eNHBaLyHutMadt3LFyxYYOrUqVNuHffBBx+YFi1aWB9EJUAtbB2753Lq1Klm6NChJiUlxQQHB1s9/EqDOt0ads1jamqqcbvdpqSkxHNs/fr1xuVymdu3b1sfiJexfnAWn9SwyOXLlyVJTz31lKS7O23FxcXq2bOnp03Lli3VsGFDZWRkSJIyMjLUtm1bhYSEeNrExMToypUrysnJ8bS59xplbcquAe+yK++SlJKSolOnTik5OdmJUPAI7Mp7RESEfv/9d23atEnGGJ0/f15r1qxR7969nQoN/+Bxcl+Rvsr6gXfZlfcNGzaoadOm2rhxo5o0aaLGjRtr9OjRunTpkrUB4LFYlfe/Ppep7fBvYVe9tH79enXq1EmzZs1SWFiYmjdvrvfff183b950KjRHUXdaw657eUZGhqKiouTv7+85FhMTo9zcXP35558Wjb7yoBa2jp1zmZaWptTUVM2fP9+6AVdS1OnWsGseO3bsKD8/P6WkpKikpESXL1/WDz/8oJ49e6p69erWBlEJsH5wFpsaFigtLVVCQoIiIyPVpk0bSVJBQYH8/f3/9t2FISEhKigo8LS5t9AsO1927kFtrly5UmULd19hZ95PnDihDz/8UMuXL5fb7bY5EjwKO/MeGRmpFStWaNCgQfL391doaKiCg4P/FcWoL3jc3D+O1atXa9++fYqLi6vIkGEBO/N+6tQp/fbbb0pNTdWyZcu0ZMkSZWZmKjY21soQ8BisyvuePXu0atUqjRkzxnOM2g7/BnbWS6dOndKuXbuUnZ2tdevW6auvvtKaNWs0fvx4m6NyHnWnNey8lz/MXFcV1MLWsXMuL168qJEjR2rJkiUKCgqyctiVDnW6NeycxyZNmuinn37SRx99pBo1aqh27do6e/asVq9ebWUIlQLrB+fxbqkFJkyYoOzsbO3atcvbQ4GD7Mp7SUmJhgwZounTp6t58+aWXhsVZ+fz/ejRo5o8ebKSkpIUExOj/Px8TZkyRWPHjtWiRYss7w+PxqnX+vT0dMXFxWnhwoVq3bq1rX3hn9mZ99LSUt26dUvLli3zvN4vWrRIHTt2VG5ubpX5fnhfZEXes7Oz1a9fPyUnJys6OtrC0QGVn92vnS6XSytWrFBwcLAk6YsvvlBsbKwWLFigmjVrWt6nt1B3WoP1ujWoha1j51zGx8dryJAhioqKsvzalQ11ujXsnMeCggLFx8drxIgRGjx4sK5evaqkpCTFxsZq27ZtcrlclvfpLawfnMcnNSpo4sSJ2rhxo9LT09WgQQPP8dDQUN2+fVtFRUXl2p8/f16hoaGeNvf+4n3Z+bJzD2oTFBRUpQp2X2Nn3q9evar9+/dr4sSJcrvdcrvdmjFjhg4dOiS32620tDR7g8P/Zffz/fPPP1dkZKSmTJmidu3aKSYmRgsWLNDixYuVn59vY2T4JxXJ/aPYuXOn+vbtqy+//FLDhw+v6LBRQXbnvX79+nK73eU2sJ9//nlJ0pkzZyo2eDw2K/J+9OhR9ejRQ2PGjNG0adPKnaO2Q1Vnd71Uv359hYWFeTY0pLuvncYYnT171o6QvIK60xp238sfZq6rAmph69g9l2lpaZozZ47nvYRRo0bp8uXLcrvdWrx4sVVheB11ujXsnsf58+crODhYs2bNUnh4uKKiorR8+XJt375dv/zyi1VheB3rB+9gU+MxGWM0ceJErVu3TmlpaWrSpEm58x07dlT16tW1fft2z7Hc3FydOXNGERERku5+j+mRI0dUWFjoabNt2zYFBQWpVatWnjb3XqOsTdk14Cwn8h4UFKQjR44oKyvL8xg7dqxatGihrKwsdenSxZlg4eHU8/3GjRvy8yv/slytWjXPGOA8K3L/sHbs2KE+ffpo5syZ5T5qCuc5lffIyEjduXNHJ0+e9BzLy8uTJDVq1KiCUeBRWZX3nJwcdevWTSNGjNCnn376t36o7VBVOVUvRUZG6ty5c7p27ZqnTV5envz8/Mq9keCrqDut4dS9PCIiQj///LOKi4s9x7Zt26YWLVqoTp06FQ/Ey6iFrePUXGZkZJR7L2HGjBmqVauWsrKy1L9/f8vi8RbqdGs4NY8PuteUlpZWIILKgfWDlzn8w+RVxrhx40xwcLDZsWOHyc/P9zxu3LjhaTN27FjTsGFDk5aWZvbv328iIiJMRESE5/ydO3dMmzZtTHR0tMnKyjJbtmwx9erVM4mJiZ42p06dMgEBAWbKlCnm2LFjZv78+aZatWpmy5YtjsaLu5zK+18lJyeb9u3b2xkaHsCpvKekpBi3220WLFhgTp48aXbt2mU6depkOnfu7Gi8+B8rcm+MMSdOnDAHDx40b7/9tmnevLk5ePCgOXjwoLl165Yxxpi0tDQTEBBgEhMTy/Vz8eJFR+PFXU7lvaSkxHTo0MFERUWZAwcOmP3795suXbqYV1991dF4cZcVeT9y5IipV6+eGTp0aLlrFBYWetpQ26Gqcqpeunr1qmnQoIGJjY01OTk5ZufOnaZZs2Zm9OjRjsZrF+pOazh1Ly8qKjIhISFm2LBhJjs72/z4448mICDAfPvtt47GaxdqYes4NZd/lZKSYoKDg+0MzVHU6dZwah63b99uXC6XmT59usnLyzOZmZkmJibGNGrUqFxfvor1g3exqfGYJN33kZKS4mlz8+ZNM378eFOnTh0TEBBg+vfvb/Lz88td59dffzW9evUyNWvWNHXr1jXvvfeeKS4uLtcmPT3dvPDCC8bf3980bdq0XB9wlpN5vxebGt7lZN7nzp1rWrVqZWrWrGnq169v3nzzTXP27FknwsR9WJX7rl273vc6p0+fNsYYM2LEiPue79q1q3PBwsOpvBtjzB9//GEGDBhgAgMDTUhIiBk5cmSVWsD7EivynpycfN9rNGrUqFxf1Haoipysl44dO2Z69uxpatasaRo0aGDefffdKvHmiDHUnVZx8l5+6NAh89JLL5kaNWqYsLAw85///MehKO1HLWwdJ/9P3quqbWpQp1vDyXlcuXKlCQ8PN08++aSpV6+eee2118yxY8ccitRerB+8y2VMFfhsKQAAAAAAAAAAqPL4TQ0AAAAAAAAAAOAT2NQAAAAAAAAAAAA+gU0NAAAAAAAAAADgE9jUAAAAAAAAAAAAPoFNDQAAAAAAAAAA4BPY1AAAAAAAAAAAAD6BTQ0AAAAAAAAAAOAT2NQAAAAAAAAAAAA+gU0NAIBXjBw5Uq+//rq3hwEAAACgimCNAQD/Dm5vDwAAUPW4XK4Hnk9OTtbXX38tY4xDIwIAAADgy1hjAADKuAyv9gAAixUUFHj+vWrVKiUlJSk3N9dzLDAwUIGBgd4YGgAAAAAfxBoDAFCGr58CAFguNDTU8wgODpbL5Sp3LDAw8G8fDX/llVc0adIkJSQkqE6dOgoJCdHChQt1/fp1xcXFqVatWnruuee0efPmcn1lZ2erV69eCgwMVEhIiIYNG6YLFy44HDEAAAAAO7HGAACUYVMDAFBpLF26VHXr1tXevXs1adIkjRs3Tm+88YZefPFFHThwQNHR0Ro2bJhu3LghSSoqKlL37t0VHh6u/fv3a8uWLTp//rwGDhzo5UgAAAAAVAasMQCg6mFTAwBQabRv317Tpk1Ts2bNlJiYqCeeeEJ169ZVfHy8mjVrpqSkJF28eFGHDx+WJM2bN0/h4eH67LPP1LJlS4WHh2vx4sVKT09XXl6el6MBAAAA4G2sMQCg6uGHwgEAlUa7du08/65WrZqefvpptW3b1nMsJCREklRYWChJOnTokNLT0+/73bknT55U8+bNbR4xAAAAgMqMNQYAVD1sagAAKo3q1auX+9vlcpU75nK5JEmlpaWSpGvXrqlv376aOXPm365Vv359G0cKAAAAwBewxgCAqodNDQCAz+rQoYPWrl2rxo0by+3mlgYAAACgYlhjAEDlx29qAAB81oQJE3Tp0iUNHjxY+/bt08mTJ7V161bFxcWppKTE28MDAAAA4GNYYwBA5cemBgDAZz377LPavXu3SkpKFB0drbZt2yohIUG1a9eWnx+3OAAAAACPhjUGAFR+LmOM8fYgAAAAAAAAAAAA/glbzAAAAAAAAAAAwCewqQEAAAAAAAAAAHwCmxoAAAAAAAAAAMAnsKkBAAAAAAAAAAB8ApsaAAAAAAAAAADAJ7CpAQAAAAAAAAAAfAKbGgAAAAAAAAAAwCewqQEAAAAAAAAAAHwCmxoAAAAAAAAAAMAnsKkBAAAAAAAAAAB8ApsaAAAAAAAAAADAJ7CpAQAAAAAAAAAAfMJ/Aa8QbV911YjLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "stock_data = {\n",
    "    \"GAIL\": (\n",
    "        gail_data[0],\n",
    "        train_gail_scaled,\n",
    "        test_gail_scaled,\n",
    "    ),  # gail_data[0] contains dates for GAIL\n",
    "    \"NTPC\": (\n",
    "        ntpc_data[0],\n",
    "        train_ntpc_scaled,\n",
    "        test_ntpc_scaled,\n",
    "    ),  # ntpc_data[0] contains dates for NTPC\n",
    "}\n",
    "\n",
    "# Create a figure with two subplots side by side (1 row, 2 columns)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "\n",
    "for idx, (stock, (date, train, test)) in enumerate(stock_data.items()):\n",
    "    (index_train,) = np.where(date.astype(str) == \"2018-01-01\")[0]\n",
    "\n",
    "    plt.subplot(1, 2, idx + 1)\n",
    "\n",
    "    # Plot the training data on the left side\n",
    "    plt.plot(date[:index_train], train, label=\"Training\")\n",
    "\n",
    "    # Plot the test data on the right side\n",
    "    plt.plot(date[index_train:], test, label=\"Test\")\n",
    "\n",
    "    # Add a vertical line at 2018-01-01 for reference\n",
    "    plt.axvline(date[index_train], color=\"black\", ls=\"--\")\n",
    "\n",
    "    # Set labels and title for the subplot\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Price\")\n",
    "    plt.title(\"Scaled Stock Prices\")\n",
    "\n",
    "    # Display legend for the plotted lines\n",
    "    plt.legend()\n",
    "    plt.grid(True, linestyle=\"--\", linewidth=0.5, color=\"gray\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Sequences\n",
    "\n",
    "Convert the stock prices into sequences based on the specified sequence length $S$:\n",
    "\n",
    "1. Start the sequence from the initial time step 0.\n",
    "2. Each sequence includes the number of $S$ prices.\n",
    "3. The target for each sequence should be the price at the following time step (after $S$).\n",
    "4. The function should return two tensors: $X$ for the input sequences and $T$ for the target price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sequences_targets(data: torch.Tensor, S):\n",
    "    # Initialize empty lists to hold the input sequences and the corresponding target values\n",
    "    X, T = [], []\n",
    "\n",
    "    # Go through the data to extract sequences based on S\n",
    "    for i in range(len(data) - S):\n",
    "        X.append(data[i : i + S])\n",
    "        T.append(data[i + S])\n",
    "    # Convert lists of sequences and targets into PyTorch tensors\n",
    "    return torch.stack(X), torch.stack(T)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test 2: Check Sequences and Corresponding Targets\n",
    "\n",
    "Get all sequences and targets for a given data. This test assures that the X and T vectors are as desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get sequence and targets\n",
    "data = torch.arange(100,143)\n",
    "S = 5\n",
    "X,T = create_sequences_targets(data,S)\n",
    "\n",
    "assert (X.size(0)==len(data)-S) and X.size(1)==S\n",
    "assert X.size(0) == T.size(0)\n",
    "assert torch.sum(T)-4693 < 1e-5\n",
    "assert X[0,0]-100 < 1e-5 and X[-1,-1] - 141 < 1e-5"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6: Dataset and Data Loader\n",
    "\n",
    "To enable batch processing, the sequence size must be the same for each element in the batch (otherwise it cannot be transformed as one large tensor). Thus, our dataset needs to have a fixed sequence size $S$. An exact value for $S$ can be selected by yourself.\n",
    "\n",
    "Implement a `Dataset` class derived from `torch.utils.data.Dataset` that provides $\\mathbf X^{[n]}$ and $\\mathbf T^{[n]}$. Implement three functions:\n",
    "\n",
    "1. The constructor `__init__(self, data, S)` that takes the `data` (stock prices) and (initial) sequence length $S$. It should create $X$ and $T$ here by utilizing the function in Task 5.\n",
    "2. The function `__len__(self)` that returns the number of samples in our dataset.\n",
    "3. Finally, the index function `__getitem__(self, index)` that returns the sequences $\\mathbf X^{[n]}$ and $\\mathbf T^{[n]}$ for a given `index`.\n",
    "\n",
    "After implementing the `Dataset`, initialize `DataLoader` for train and test sets of both datasets with batch size of $B=256$. For the test set, you can opt for the largest feasible batch size.\n",
    "\n",
    "Hint: When iterating through the dataloader, ensure that  $X$ is shaped as $\\mathbb R^{B\\times S\\times D}$.\n",
    "\n",
    "Note:\n",
    "* Be careful about `shuffle` parameter for test dataloaders and keep in mind that you should use scaled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, data, S):\n",
    "\n",
    "        # store the data and targets as required\n",
    "        X, T = create_sequences_targets(data, S)\n",
    "        self.data = X.view(X.size(0), -1, 1) # B x S x D\n",
    "        self.targets = T.view(T.size(0), 1) # B x D\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        # return input and target value for the given index\n",
    "        return self.data[index], self.targets[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        # return the length of this dataset\n",
    "        return self.data.size(0)\n",
    "\n",
    "\n",
    "# instantiate dataset and data loader for a reasonable sequence length S\n",
    "S = 10\n",
    "gail_train_dataset = Dataset(train_gail_scaled, S)\n",
    "gail_train_dataloader = torch.utils.data.DataLoader(\n",
    "    gail_train_dataset, batch_size=256, shuffle=True\n",
    ")\n",
    "\n",
    "gail_test_dataset = Dataset(test_gail_scaled, S)\n",
    "gail_test_dataloader = torch.utils.data.DataLoader(\n",
    "    gail_test_dataset, batch_size=len(gail_test_dataset), shuffle=False\n",
    ")\n",
    "\n",
    "ntpc_train_dataset = Dataset(train_ntpc_scaled, S)\n",
    "ntpc_train_dataloader = torch.utils.data.DataLoader(\n",
    "    ntpc_train_dataset, batch_size=256, shuffle=True\n",
    ")\n",
    "\n",
    "ntpc_test_dataset = Dataset(test_ntpc_scaled, S)\n",
    "ntpc_test_dataloader = torch.utils.data.DataLoader(\n",
    "    ntpc_test_dataset, batch_size=len(ntpc_test_dataset), shuffle=False\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple LSTM Network Implementation\n",
    "\n",
    "In this section, we will explain the implementation of a simple LSTM (Long Short-Term Memory) network for sequence processing in PyTorch. This network is designed to process batches of sequences and predict outputs based on the sequence information."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7: LSTM Network Implementation\n",
    "\n",
    "Implement an LSTM network derived from `torch.nn.Module` class using `torch.nn.LSTM`, `torch.nn.Linear` and `torch.nn.Dropout` layers.\n",
    "\n",
    "1. In the constructor, instantiate all required layers for the given values of $D$ (input size), $K$ (hidden size), $O$ (output size).\n",
    "2. Network structure:\n",
    "    * One LSTM layer with input size $D$ and hidden size $K$. Please be careful about `batch_first` parameter when you consider the shape of the $X$ $(B\\times S\\times D)$.\n",
    "    * Dropout layer with the probability of $0.2$ on the LSTM network output.\n",
    "    * A Linear layer with the output neurons of $O$.\n",
    "3. Implement the input processing within the `forward` function of this LSTM network design. \n",
    "The `torch.nn.LSTM` layer returns three distinct elements. Refer to the documentation at https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html to understand the behavior of this layer.\n",
    "Ensure you comprehend how this layer in PyTorch operates before passing the appropriate input from the LSTM layer to the linear layer.\n",
    "\n",
    "Note:\n",
    "\n",
    "* $D$ and $O$ should be passed correctly while you can select any suitable number for $K$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.1667, 0.8569, 0.5920, 0.8258, 0.2810, 0.0158, 0.8852, 0.0347, 0.8204,\n",
       "        0.2566, 0.9367, 0.3832, 0.4615, 0.3619, 0.7099, 0.8591, 0.3451, 0.9540,\n",
       "        0.1761, 0.0624, 0.7423, 0.6222, 0.6790, 0.3388, 0.5690, 0.8277, 0.3125,\n",
       "        0.3850, 0.9020, 0.9350, 0.7082, 0.1103])"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.rand((1, 32))\n",
    "x[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTMModel(torch.nn.Module):\n",
    "    def __init__(self, D, K, O):\n",
    "        super(LSTMModel, self).__init__()\n",
    "\n",
    "        self.lstm = torch.nn.LSTM(D, K, batch_first=True) # output: output, (h_n: final hidden state for each element in seq, c_n: final cell state for each element in seq)\n",
    "        self.dropout = torch.nn.Dropout(0.2)\n",
    "        self.linear = torch.nn.Linear(K, O)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # lstm layer\n",
    "        _, (H, _) = self.lstm(x) # we are only interested in h_n to make predictions\n",
    "        # apply dropout to the output of lstm layer\n",
    "        out = self.dropout(H[-1]) # h_n has shape (D * num_layers, H_out), so in our case: (1 * 1, K = 32)\n",
    "        # get correct element of the output of the lstm layer\n",
    "        Z = self.linear(out)\n",
    "        return Z"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 8: Training Loop\n",
    "\n",
    "Implement a function for the train loop. It takes network, dataloader, optimizer, loss function, device and the number epochs.\n",
    "\n",
    "If your dataset function from Task 5 is not fully compatible in terms of the shapes of logits and targets, you can use `squeeze()` or `unsqueeze()` operations to ensure smooth operation of the loss calculation. Alternatively, you may modify your dataset function to address this issue. Both works.\n",
    "\n",
    "Compute the average training loss per epoch and print it out.\n",
    "\n",
    "WARNING: Loss function will not complain when the index order for the output $\\mathcal Y$ and targets $\\mathcal T$ is incorrect, just the results will be wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "def train(network, train_dataloader, optimizer, loss, device, epochs=50):\n",
    "    network = network.to(device)\n",
    "    for epoch in range(epochs):\n",
    "        train_loss = 0\n",
    "        total_sample = 0\n",
    "        network.train()\n",
    "        for x, t in tqdm(train_dataloader):\n",
    "            optimizer.zero_grad()\n",
    "            x, t = x.to(device), t.to(device)\n",
    "            y = network(x)\n",
    "            J = loss(y, t)\n",
    "            J.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += J.item() * x.size(0)\n",
    "            total_sample += x.size(0)\n",
    "\n",
    "        # print average loss for training and validation\n",
    "        print(f\"\\rEpoch {epoch+1}; train loss: {train_loss/total_sample:1.5f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 9: Run Training on Both Datasets\n",
    "\n",
    "* To train the LSTM network, you will decide the loss function when you consider the context of the problem.\n",
    "* Instantiate the optimizer with an appropriate learning rate $\\eta$.\n",
    "* Implement the training loop for $50$ epochs.\n",
    "* Run the network on the train sets of both datasets.\n",
    "\n",
    "Note that 50 epochs will not take long times, if implemented in an optimized way. So you may run on either GPU or CPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 49.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1; train loss: 0.08119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 218.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2; train loss: 0.01957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 227.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3; train loss: 0.00661\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 224.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4; train loss: 0.00501\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 224.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5; train loss: 0.00431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 215.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6; train loss: 0.00381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 223.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7; train loss: 0.00375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 164.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8; train loss: 0.00333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 199.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9; train loss: 0.00296\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 208.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10; train loss: 0.00282\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 229.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11; train loss: 0.00261\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 230.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12; train loss: 0.00249\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 234.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13; train loss: 0.00232\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 228.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14; train loss: 0.00234\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 219.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15; train loss: 0.00219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 220.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16; train loss: 0.00207\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 214.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17; train loss: 0.00195\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 185.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18; train loss: 0.00190\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 214.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19; train loss: 0.00175\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 221.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20; train loss: 0.00180\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 199.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21; train loss: 0.00166\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 159.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22; train loss: 0.00159\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 230.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23; train loss: 0.00162\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 219.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24; train loss: 0.00157\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 224.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25; train loss: 0.00140\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 209.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26; train loss: 0.00139\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 207.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27; train loss: 0.00142\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 217.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28; train loss: 0.00141\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 216.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29; train loss: 0.00124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 221.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30; train loss: 0.00128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 220.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31; train loss: 0.00129\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 219.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32; train loss: 0.00129\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 222.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33; train loss: 0.00116\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 220.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34; train loss: 0.00115\n"
     ]
    },
    {
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     "text": [
      "Epoch 35; train loss: 0.00119\n"
     ]
    },
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      "Epoch 36; train loss: 0.00116\n"
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     "text": [
      "Epoch 37; train loss: 0.00115\n"
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      "Epoch 38; train loss: 0.00106\n"
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      "Epoch 39; train loss: 0.00102\n"
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      "Epoch 40; train loss: 0.00097\n"
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      "Epoch 41; train loss: 0.00099\n"
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      "Epoch 42; train loss: 0.00107\n"
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     "text": [
      "Epoch 43; train loss: 0.00096\n"
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      "Epoch 44; train loss: 0.00103\n"
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      "Epoch 45; train loss: 0.00099\n"
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      "Epoch 46; train loss: 0.00099\n"
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     "text": [
      "Epoch 47; train loss: 0.00111\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 48; train loss: 0.00112\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 17/17 [00:00<00:00, 198.80it/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49; train loss: 0.00092\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 194.57it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50; train loss: 0.00090\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"mps\")\n",
    "loss = torch.nn.MSELoss()\n",
    "epochs = 50\n",
    "\n",
    "# for gail dataset\n",
    "lstm_gail_network = LSTMModel(1, 32, 1)\n",
    "optimizer_gail = torch.optim.Adam(params=lstm_gail_network.parameters(), lr=0.01)\n",
    "train(lstm_gail_network, gail_train_dataloader, optimizer_gail, loss, device, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1; train loss: 0.03489\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 13/13 [00:00<00:00, 174.70it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2; train loss: 0.01300\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 13/13 [00:00<00:00, 184.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3; train loss: 0.00384\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 13/13 [00:00<00:00, 208.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4; train loss: 0.00257\n"
     ]
    },
    {
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     "output_type": "stream",
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      "100%|██████████| 13/13 [00:00<00:00, 209.72it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5; train loss: 0.00177\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 13/13 [00:00<00:00, 215.96it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6; train loss: 0.00171\n"
     ]
    },
    {
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     "output_type": "stream",
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      "100%|██████████| 13/13 [00:00<00:00, 172.36it/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7; train loss: 0.00162\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 8; train loss: 0.00146\n"
     ]
    },
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    {
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     "text": [
      "Epoch 9; train loss: 0.00139\n"
     ]
    },
    {
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    {
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     "text": [
      "Epoch 10; train loss: 0.00135\n"
     ]
    },
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     "text": [
      "Epoch 11; train loss: 0.00123\n"
     ]
    },
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    {
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     "text": [
      "Epoch 12; train loss: 0.00116\n"
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     "text": [
      "Epoch 13; train loss: 0.00115\n"
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     "text": [
      "Epoch 14; train loss: 0.00106\n"
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     "text": [
      "Epoch 15; train loss: 0.00111\n"
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     "text": [
      "Epoch 16; train loss: 0.00120\n"
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      "Epoch 17; train loss: 0.00102\n"
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     "text": [
      "Epoch 18; train loss: 0.00097\n"
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      "Epoch 19; train loss: 0.00097\n"
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      "Epoch 20; train loss: 0.00101\n"
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      "Epoch 21; train loss: 0.00090\n"
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     "text": [
      "Epoch 22; train loss: 0.00094\n"
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      "Epoch 23; train loss: 0.00092\n"
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      "Epoch 24; train loss: 0.00087\n"
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      "Epoch 25; train loss: 0.00085\n"
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      "Epoch 26; train loss: 0.00077\n"
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      "Epoch 27; train loss: 0.00080\n"
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      "Epoch 28; train loss: 0.00076\n"
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      "Epoch 29; train loss: 0.00073\n"
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      "Epoch 30; train loss: 0.00070\n"
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      "Epoch 31; train loss: 0.00069\n"
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      "Epoch 32; train loss: 0.00073\n"
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      "Epoch 33; train loss: 0.00074\n"
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      "Epoch 34; train loss: 0.00072\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:00<00:00, 154.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 46; train loss: 0.00062\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:00<00:00, 219.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47; train loss: 0.00060\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:00<00:00, 218.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48; train loss: 0.00065\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:00<00:00, 203.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49; train loss: 0.00066\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:00<00:00, 176.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50; train loss: 0.00057\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# for ntpc dataset, initialize the network again with the same D,K and O\n",
    "lstm_ntpc_network = LSTMModel(1, 32, 1)\n",
    "optimizer_ntpc = torch.optim.Adam(params=lstm_ntpc_network.parameters(), lr=0.01)\n",
    "train(lstm_ntpc_network, ntpc_train_dataloader, optimizer_ntpc, loss, device, epochs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 10: Future Price Prediction\n",
    "\n",
    "Write a function that predicts the next price of the sequence (test set) based on the values obtained from the network.\n",
    "\n",
    "This function executes the trained model on the test datasets from both datasets. It takes the trained model and the test data loader.\n",
    "\n",
    "Keep in mind that all predictions are also normalized, as the input data is normalized. These normalized predictions should be reversed to the original scale for visualization in the next task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(network, test_dataloader):\n",
    "    network.eval()\n",
    "    with torch.no_grad():\n",
    "      predictions = [network(x.to(device)).cpu() for x, _ in test_dataloader]\n",
    "\n",
    "    return torch.cat(predictions, dim=0)\n",
    "\n",
    "\n",
    "# run the test for both datasets\n",
    "scaled_lstm_gail_preds = predict(lstm_gail_network, gail_test_dataloader)\n",
    "scaled_lstm_ntpc_preds = predict(lstm_ntpc_network, ntpc_test_dataloader)\n",
    "\n",
    "# reverse the test input and its predictions to original scales\n",
    "# reverse its predictions to original scales\n",
    "original_lstm_gail_preds = inverse_min_max_scaler(\n",
    "    scaled_lstm_gail_preds,\n",
    "    min_gail,\n",
    "    max_gail\n",
    ")\n",
    "original_lstm_ntpc_preds = inverse_min_max_scaler(\n",
    "    scaled_lstm_ntpc_preds,\n",
    "    min_ntpc,\n",
    "    max_ntpc\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 11: Plotting Future Predictions\n",
    "\n",
    "Plot the actual prices and model predictions for only both test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "future_preds = {\n",
    "    \"GAIL\": (gail_test[S:], original_lstm_gail_preds),\n",
    "    \"NTPC\": (ntpc_test[S:], original_lstm_ntpc_preds),\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "for idx, (stock, (actual, preds)) in enumerate(future_preds.items()):\n",
    "    # Plot on the corresponding subplot (idx+1) within the 1x2 grid\n",
    "    plt.subplot(1, 2, idx + 1)\n",
    "\n",
    "    # Plot the training data on the left side of the split subplot\n",
    "    plt.plot(actual, label=\"Actual Price\")\n",
    "\n",
    "    # Plot the test data on the right side of the split subplot\n",
    "    plt.plot(preds.detach().cpu(), label=\"Model Prediction\")\n",
    "\n",
    "    # Set labels and title for the subplot\n",
    "    plt.xlabel(\"Time (days)\")\n",
    "    plt.ylabel(\"Price\")\n",
    "    plt.title(stock)\n",
    "\n",
    "    # Display legend for the plotted lines\n",
    "    plt.legend()\n",
    "\n",
    "    # Add gridlines with specified style, width, and color\n",
    "    plt.grid(True, linestyle=\"--\", linewidth=0.5, color=\"gray\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a29cabff5744fce69e08a959ab87b9e77a9f67b498d08783caa8c3bb16f23a00"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 ('DL')",
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