Transformer - CALCE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import math\n",
    "import time\n",
    "import os\n",
    "import matplotlib.cm as cm\n",
    "import pandas as pd\n",
    "import glob\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_outlier(array,count,bins):\n",
    "    index = []\n",
    "    range_ = np.arange(1,count,bins)\n",
    "    for i in range_[:-1]:\n",
    "        array_lim = array[i:i+bins]\n",
    "        sigma = np.std(array_lim)\n",
    "        mean = np.mean(array_lim)\n",
    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
    "        idx = idx[0] + i\n",
    "        index.extend(list(idx))\n",
    "    return np.array(index)\n",
    "\n",
    "\n",
    "def build_sequences(text, window_size):\n",
    "    #text:list of capacity\n",
    "    x, y = [],[]\n",
    "    for i in range(len(text) - window_size):\n",
    "        sequence = text[i:i+window_size]\n",
    "        target = text[i+window_size]\n",
    "\n",
    "        x.append(sequence)\n",
    "        y.append(target)\n",
    "        \n",
    "    return np.array(x), np.array(y)\n",
    "\n",
    "\n",
    "# leave-one-out evaluation: one battery is sampled randomly; the remainder are used for training.\n",
    "def get_train_test(data_dict, name, window_size=8):\n",
    "    data_sequence=data_dict[name]['capacity']\n",
    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
    "    for k, v in data_dict.items():\n",
    "        if k != name:\n",
    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
    "            \n",
    "    return train_x, train_y, list(train_data), list(test_data)\n",
    "\n",
    "\n",
    "def relative_error(y_test, y_predict, threshold):\n",
    "    true_re, pred_re = len(y_test), 0\n",
    "    for i in range(len(y_test)-1):\n",
    "        if y_test[i] <= threshold >= y_test[i+1]:\n",
    "            true_re = i - 1\n",
    "            break\n",
    "    for i in range(len(y_predict)-1):\n",
    "        if y_predict[i] <= threshold:\n",
    "            pred_re = i - 1\n",
    "            break\n",
    "    return abs(true_re - pred_re)/true_re if abs(true_re - pred_re)/true_re<=1 else 1\n",
    "\n",
    "\n",
    "def evaluation(y_test, y_predict):\n",
    "    mse = mean_squared_error(y_test, y_predict)\n",
    "    rmse = sqrt(mean_squared_error(y_test, y_predict))\n",
    "    return rmse\n",
    "    \n",
    "    \n",
    "def setup_seed(seed):\n",
    "    np.random.seed(seed)  # Numpy module.\n",
    "    random.seed(seed)  # Python random module.\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    torch.manual_seed(seed) \n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed(seed)\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "        torch.backends.cudnn.benchmark = False\n",
    "        torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Extract raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load Dataset CS2_35 ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:188: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
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      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
      "Load Dataset CS2_36 ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_05_10.xlsx ...\n",
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      "Load datasets/CALCE/CS2_36\\CS2_36_11_15_10.xlsx ...\n",
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      "Load datasets/CALCE/CS2_36\\CS2_36_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_06_10.xlsx ...\n",
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      "Load datasets/CALCE/CS2_36\\CS2_36_2_3_11.xlsx ...\n",
      "Load Dataset CS2_37 ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_04_10.xlsx ...\n",
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      "Load Dataset CS2_38 ...\n",
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      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n"
     ]
    }
   ],
   "source": [
    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
    "\n",
    "dir_path = 'datasets/CALCE/'\n",
    "Battery = {}\n",
    "for name in Battery_list:\n",
    "    print('Load Dataset ' + name + ' ...')\n",
    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
    "    dates = []\n",
    "    for p in path:\n",
    "        df = pd.read_excel(p, sheetname=1)\n",
    "        print('Load ' + str(p) + ' ...')\n",
    "        dates.append(df['Date_Time'][0])\n",
    "    idx = np.argsort(dates)\n",
    "    path_sorted = np.array(path)[idx]\n",
    "    \n",
    "    count = 0\n",
    "    discharge_capacities = []\n",
    "    health_indicator = []\n",
    "    internal_resistance = []\n",
    "    CCCT = []\n",
    "    CVCT = []\n",
    "    for p in path_sorted:\n",
    "        df = pd.read_excel(p,sheetname=1)\n",
    "        print('Load ' + str(p) + ' ...')\n",
    "        cycles = list(set(df['Cycle_Index']))\n",
    "        for c in cycles:\n",
    "            df_lim = df[df['Cycle_Index'] == c]\n",
    "            #Charging\n",
    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
    "            c_v = df_c['Voltage(V)']\n",
    "            c_c = df_c['Current(A)']\n",
    "            c_t = df_c['Test_Time(s)']\n",
    "            #CC or CV\n",
    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
    "\n",
    "            #Discharging\n",
    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
    "            d_v = df_d['Voltage(V)']\n",
    "            d_c = df_d['Current(A)']\n",
    "            d_t = df_d['Test_Time(s)']\n",
    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
    "\n",
    "            if(len(list(d_c)) != 0):\n",
    "                time_diff = np.diff(list(d_t))\n",
    "                d_c = np.array(list(d_c))[1:]\n",
    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
    "\n",
    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
    "                health_indicator.append(-1 * (end - start))\n",
    "\n",
    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
    "                count += 1\n",
    "\n",
    "    discharge_capacities = np.array(discharge_capacities)\n",
    "    health_indicator = np.array(health_indicator)\n",
    "    internal_resistance = np.array(internal_resistance)\n",
    "    CCCT = np.array(CCCT)\n",
    "    CVCT = np.array(CVCT)\n",
    "    \n",
    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
    "                              'capacity':discharge_capacities[idx],\n",
    "                              'SoH':health_indicator[idx],\n",
    "                              'resistance':internal_resistance[idx],\n",
    "                              'CCCT':CCCT[idx],\n",
    "                              'CVCT':CVCT[idx]})\n",
    "    Battery[name] = df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## or 1.1 load the extracted data\n",
    "\n",
    "**note: If the above loading fails, please load the .npy file.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
    "Battery = np.load('datasets/CALCE/CALCE.npy', allow_pickle=True)\n",
    "Battery = Battery.item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 plot figures for capacity degradation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22bae8af9e8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rated_Capacity = 1.1\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "color_list = ['b:', 'g--', 'r-.', 'c.']\n",
    "for name,color in zip(Battery_list, color_list):\n",
    "    df_result = Battery[name]\n",
    "    ax.plot(df_result['cycle'], df_result['capacity'], color, label='Battery_'+name)\n",
    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. build model and train\n",
    "\n",
    "## 2.1 build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Autoencoder(nn.Module):\n",
    "    def __init__(self, input_size=16, hidden_dim=8, noise_level=0.01):\n",
    "        '''\n",
    "        Args:\n",
    "            input_size: the feature size of input data (required).\n",
    "            hidden_dim: the hidden size of AutoEncoder (required).\n",
    "            noise_level: the noise level added in Autoencoder (optional).\n",
    "        '''\n",
    "        super(Autoencoder, self).__init__()\n",
    "        self.noise_level = noise_level\n",
    "        self.fc1 = nn.Linear(input_size, hidden_dim)\n",
    "        self.fc2 = nn.Linear(hidden_dim, input_size)\n",
    "        \n",
    "    def encoder(self, x):\n",
    "        x = self.fc1(x)\n",
    "        h1 = F.relu(x)\n",
    "        return h1\n",
    "    \n",
    "    def mask(self, x):\n",
    "        corrupted_x = x + self.noise_level * torch.randn_like(x)\n",
    "        return corrupted_x\n",
    "    \n",
    "    def decoder(self, x):\n",
    "        h2 = self.fc2(x)\n",
    "        return h2\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.mask(x)\n",
    "        encode = self.encoder(out)\n",
    "        decode = self.decoder(encode)\n",
    "        return encode, decode\n",
    "    \n",
    "    \n",
    "class PositionalEncoding(nn.Module):\n",
    "    def __init__(self, feature_len, feature_size, dropout=0.0):\n",
    "        '''\n",
    "        Args:\n",
    "            feature_len: the feature length of input data (required).\n",
    "            feature_size: the feature size of input data (required).\n",
    "            dropout: the dropout rate (optional).\n",
    "        '''\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "\n",
    "        pe = torch.zeros(feature_len, feature_size)\n",
    "        position = torch.arange(0, feature_len, dtype=torch.float).unsqueeze(1)\n",
    "\n",
    "        div_term = torch.exp(torch.arange(0, feature_size, 2).float() * (-math.log(10000.0) / feature_size))\n",
    "\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "\n",
    "        pe = pe.unsqueeze(0)\n",
    "\n",
    "        self.register_buffer('pe', pe)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x + self.pe\n",
    "        \n",
    "        return x\n",
    "    \n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self, feature_size=16, hidden_dim=32, feature_num=1, num_layers=1, nhead=1, dropout=0.0, noise_level=0.01):\n",
    "        '''\n",
    "        Args:\n",
    "            feature_size: the feature size of input data (required).\n",
    "            hidden_dim: the hidden size of Transformer block (required).\n",
    "            feature_num: the number of features, such as capacity, voltage, and current; set 1 for only sigle feature (optional).\n",
    "            num_layers: the number of layers of Transformer block (optional).\n",
    "            nhead: the number of heads of multi-attention in Transformer block (optional).\n",
    "            dropout: the dropout rate of Transformer block (optional).\n",
    "            noise_level: the noise level added in Autoencoder (optional).\n",
    "        '''\n",
    "        super(Net, self).__init__()\n",
    "        self.auto_hidden = int(feature_size / 2)\n",
    "        input_size = self.auto_hidden \n",
    "        \n",
    "        if feature_num == 1:\n",
    "            # Transformer treated as an Encoder when modeling for a sigle feature like only capacity data\n",
    "            self.pos = PositionalEncoding(feature_len=feature_num, feature_size=input_size)\n",
    "            encoder_layers = nn.TransformerEncoderLayer(d_model=input_size, nhead=nhead, dim_feedforward=hidden_dim, dropout=dropout, batch_first=True)\n",
    "        elif feature_num > 1:\n",
    "            # Transformer treated as a sequence model when modeling for multi-features like capacity, voltage, and current data\n",
    "            self.pos = PositionalEncoding(feature_len=input_size, feature_size=feature_num)\n",
    "            encoder_layers = nn.TransformerEncoderLayer(d_model=feature_num, nhead=nhead, dim_feedforward=hidden_dim, dropout=dropout, batch_first=True)\n",
    "        self.cell = nn.TransformerEncoder(encoder_layers, num_layers=num_layers)\n",
    "        \n",
    "        self.linear = nn.Linear(feature_num*self.auto_hidden, 1)\n",
    "        self.autoencoder = Autoencoder(input_size=feature_size, hidden_dim=self.auto_hidden, noise_level=noise_level)\n",
    " \n",
    "    def forward(self, x): \n",
    "        batch_size, feature_num, feature_size  = x.shape\n",
    "        out, decode = self.autoencoder(x)\n",
    "        if feature_num > 1:\n",
    "            out = out.reshape(batch_size, -1, feature_num)\n",
    "        out = self.pos(out)\n",
    "        out = self.cell(out)              # sigle feature: (batch_size, feature_num, auto_hidden) or multi-features: (batch_size, auto_hidden, feature_num)\n",
    "        out = out.reshape(batch_size, -1) # (batch_size, feature_num*auto_hidden)\n",
    "        out = self.linear(out)            # out shape: (batch_size, 1)\n",
    "        \n",
    "        return out, decode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 train for model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(lr=0.01, feature_size=8, feature_num=1, hidden_dim=32, num_layers=1, nhead=1, dropout=0.0, epochs=1000, \n",
    "          weight_decay=0.0, seed=0, alpha=0.0, noise_level=0.0, metric='re', device='cpu'):\n",
    "    '''\n",
    "        Args:\n",
    "            lr: learning rate for training (required).\n",
    "            feature_size: the feature size of input data (required).\n",
    "            feature_num: the number of features, such as capacity, voltage, and current; set 1 for only sigle feature (optional).\n",
    "            hidden_dim: the hidden size of Transformer block (required).\n",
    "            num_layers: the number of layers of Transformer block (optional).\n",
    "            nhead: the number of heads of multi-attention in Transformer block (optional).\n",
    "            dropout: the dropout rate of Transformer block (optional).\n",
    "            epochs: \n",
    "            weight_decay: \n",
    "            seed: (optional).\n",
    "            alpha: (optional).\n",
    "            noise_level: the noise level added in Autoencoder (optional).\n",
    "            metric: (optional).\n",
    "            device: (optional).\n",
    "        '''\n",
    "    score_list, fixed_result_list, moving_result_list = [], [], []\n",
    "    setup_seed(seed)\n",
    "    for i in range(4):\n",
    "        name = Battery_list[i]\n",
    "        train_x, train_y, train_data, test_data = get_train_test(Battery, name, feature_size)\n",
    "        test_sequence = train_data + test_data\n",
    "        # print('sample size: {}'.format(len(train_x)))\n",
    "\n",
    "        model = Net(feature_size=feature_size, hidden_dim=hidden_dim, feature_num=K, num_layers=num_layers, \n",
    "                    nhead=nhead, dropout=dropout, noise_level=noise_level)\n",
    "        model = model.to(device)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "        criterion = nn.MSELoss()\n",
    "\n",
    "        test_x = train_data.copy()\n",
    "        loss_list, y_fixed_slice, y_moving_slice = [0], [], []\n",
    "        rmse, re = 1, 1\n",
    "        score_, score = [1],[1]\n",
    "        for epoch in range(epochs):\n",
    "            x = np.reshape(train_x/Rated_Capacity,(-1, feature_num, feature_size)).astype(np.float32)\n",
    "            y = np.reshape(train_y/Rated_Capacity,(-1,1)).astype(np.float32) \n",
    "\n",
    "            x, y = torch.from_numpy(x).to(device), torch.from_numpy(y).to(device)\n",
    "            x = x.repeat(1, K, 1)\n",
    "            output, decode = model(x)\n",
    "            output = output.reshape(-1, 1)\n",
    "            loss = criterion(output, y) + alpha * criterion(decode, x)\n",
    "            optimizer.zero_grad() \n",
    "            loss.backward()  \n",
    "            optimizer.step()                   \n",
    "\n",
    "            if (epoch + 1)%10 == 0:\n",
    "                test_x = train_data.copy() \n",
    "                fixed_point_list, moving_point_list = [], []\n",
    "                t = 0\n",
    "                while (len(test_x) - len(train_data)) < len(test_data):\n",
    "                    x = np.reshape(np.array(test_x[-feature_size:])/Rated_Capacity,(-1, feature_num, feature_size)).astype(np.float32)\n",
    "                    x = torch.from_numpy(x).to(device) \n",
    "                    x = x.repeat(1, K, 1)\n",
    "                    pred, _ = model(x) \n",
    "                    next_point = pred.data.cpu().numpy()[0,0] * Rated_Capacity\n",
    "                    test_x.append(next_point)      # The test values are added to the original sequence to continue to predict the next point\n",
    "                    fixed_point_list.append(next_point) # Saves the predicted value of the last point in the output sequence\n",
    "                    \n",
    "                    x = np.reshape(np.array(test_sequence[t:t+feature_size])/Rated_Capacity,(-1, 1, feature_size)).astype(np.float32)\n",
    "                    x = torch.from_numpy(x).to(device) \n",
    "                    x = x.repeat(1, K, 1)\n",
    "                    pred, _ = model(x) \n",
    "                    next_point = pred.data.cpu().numpy()[0,0] * Rated_Capacity\n",
    "                    moving_point_list.append(next_point) # Saves the predicted value of the last point in the output sequence\n",
    "                    t += 1\n",
    "                    \n",
    "                y_fixed_slice.append(fixed_point_list)             # Save all the predicted values\n",
    "                y_moving_slice.append(moving_point_list)\n",
    "\n",
    "                loss_list.append(loss)\n",
    "                rmse = evaluation(y_test=test_data, y_predict=y_fixed_slice[-1])\n",
    "                re = relative_error(y_test=test_data, y_predict=y_fixed_slice[-1], threshold=Rated_Capacity*0.7)\n",
    "                #print('epoch:{:<2d} | loss:{:<6.4f} | RMSE:{:<6.4f} | RE:{:<6.4f}'.format(epoch, loss, rmse, re))\n",
    "                \n",
    "            if metric == 're':\n",
    "                score = [re]\n",
    "            elif metric == 'rmse':\n",
    "                score = [rmse]\n",
    "            else:\n",
    "                score = [re, rmse]\n",
    "            if (loss < 1e-3) and (score_[0] < score[0]):\n",
    "                break\n",
    "            score_ = score.copy()\n",
    "            \n",
    "        score_list.append(score_)\n",
    "        fixed_result_list.append(train_data.copy() + y_fixed_slice[-1])\n",
    "        moving_result_list.append(train_data.copy() + y_moving_slice[-1])\n",
    "        \n",
    "    return score_list, fixed_result_list, moving_result_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 setting and training for overall performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76cb24fb69e64417a82f49789955ab16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed:0\n",
      "[[0.00174216]\n",
      " [0.01036269]\n",
      " [0.22153846]\n",
      " [0.01447178]]\n",
      "refor this seed: 0.0620\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.00522648]\n",
      " [0.00863558]\n",
      " [0.05692308]\n",
      " [0.00289436]]\n",
      "refor this seed: 0.0184\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00174216]\n",
      " [0.01208981]\n",
      " [0.03538462]\n",
      " [0.13024602]]\n",
      "refor this seed: 0.0449\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.04181185]\n",
      " [0.07772021]\n",
      " [0.01230769]\n",
      " [0.06657019]]\n",
      "refor this seed: 0.0496\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.2369338 ]\n",
      " [0.        ]\n",
      " [0.03846154]\n",
      " [0.01013025]]\n",
      "refor this seed: 0.0714\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0493\n"
     ]
    }
   ],
   "source": [
    "Rated_Capacity = 1.1\n",
    "feature_size = 64\n",
    "feature_num = 1\n",
    "dropout = 0.0\n",
    "epochs = 500\n",
    "nhead = 1\n",
    "hidden_dim = 16\n",
    "num_layers = 3\n",
    "lr = 0.001  \n",
    "weight_decay = 0.0\n",
    "noise_level = 0.0\n",
    "alpha = 0.1\n",
    "metric = 're'\n",
    "seed = 0\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "K = 16\n",
    "\n",
    "SCORE = []\n",
    "for seed in tqdm(range(5)):\n",
    "    print('seed:{}'.format(seed))\n",
    "    score_list, _, _ = train(lr=lr, feature_size=feature_size, feature_num=feature_num, hidden_dim=hidden_dim, num_layers=num_layers, \n",
    "                             nhead=nhead, weight_decay=weight_decay, epochs=epochs, seed=seed, dropout=dropout, alpha=alpha, \n",
    "                             noise_level=noise_level, metric=metric, device=device)\n",
    "    print(np.array(score_list))\n",
    "    print(metric + 'for this seed: {:<6.4f}'.format(np.mean(np.array(score_list))))\n",
    "    for s in score_list:\n",
    "        SCORE.append(s)\n",
    "    print('------------------------------------------------------------------')\n",
    "print(metric + ' mean: {:<6.4f}'.format(np.mean(np.array(SCORE))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 prediction for figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed:1\n",
      "[[0.00522648]\n",
      " [0.00863558]\n",
      " [0.05692308]\n",
      " [0.00289436]]\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0184\n"
     ]
    }
   ],
   "source": [
    "Rated_Capacity = 1.1\n",
    "feature_size = 64\n",
    "feature_num = 1\n",
    "dropout = 0.0\n",
    "epochs = 500\n",
    "nhead = 1\n",
    "hidden_dim = 16\n",
    "num_layers = 3\n",
    "lr = 0.001  \n",
    "weight_decay = 0.0\n",
    "noise_level = 0.0\n",
    "alpha = 0.1\n",
    "metric = 're'\n",
    "seed = 1\n",
    "\n",
    "K = 16\n",
    "\n",
    "SCORE = []\n",
    "print('seed:{}'.format(seed))\n",
    "score_list, fixed_result_list, moving_result_list = train(lr=lr, feature_size=feature_size, feature_num=feature_num, \n",
    "                                                          hidden_dim=hidden_dim, num_layers=num_layers,  nhead=nhead, \n",
    "                                                          weight_decay=weight_decay, epochs=epochs, seed=seed, dropout=dropout, \n",
    "                                                          alpha=alpha, noise_level=noise_level, metric=metric, device=device)\n",
    "print(np.array(score_list))\n",
    "for s in score_list:\n",
    "    SCORE.append(s)\n",
    "print('------------------------------------------------------------------')\n",
    "print(metric + ' mean: {:<6.4f}'.format(np.mean(np.array(SCORE))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oQ1tLnaDcGl+F/RO83H4MwzD2G4bxGfpKIugZgYvDJUOtLFeDa6IL5idc5X62Wu47XuX6uUO2OhQR1ARfzU4MXScrDKhiCeKv5Hrez9y2XtImpZQHEIgeHnejtZQud+wOSqnCRlN0ttwXNtvs9bBBTwBRUHCB9oCeJRn0xB4FFTWEzwR5M7kWtfxyv+dw9Z+xazETcED3Hsid+GPWNWw/Hz3rd9CVvqQWUf+tKLn1+1SBfSil1OfomYr/RfcWSLuG/V6P3C9y1zKrb1vLfXHMEi6EEOLmkVhbYu1CSawtsXYxkVj7yoqKtVda7psVsk3uc4XVLxblhCR0RWkXj/6jX6uI5R9b7r9WSnkXXKiUcrYMqcp9fIdSqrCrWLlXsPP/sY613Bd17CuZY7l/CLgXPWvokgLtq6aU8lVK5R9i01QpVdgV9cLaiGV73+to3zNKqdr59mMFfID+u/DdNeznO/RVw8lKqUuGfCilrJRSwbmPDcM4jf7nWIcCw7eVUgO5ippe+Xxquf8q/2uY77jV8j11Pe/nT+ihe+OUUvULLPsfesbTn4pz+E2ufK+TDzqgyKOUaoueqTWeqx+udDXeyR8IKaUq8t/V6/yfiUjLfXCBdvVEF+kvzJVe/1h0Db7Cfj+v6TN2jX5B90qYgP7s/WsYxlUnIQ09cUrul9R5ltfgEpa/Q1vyPe6llBqslLItZF0X/nvP1+d7XqED4LHoK/kDDMO4ZEKX66GUalHYFwBLWz6xPFxaYNklE45Y3otX0LXYYoDlxdE+IYQQN43E2pdvo8TamsTaxUNibYm1C2tLobE2egI8M7red6V82zigS86ALtUgyimpoStKNcMwUpRS24COSqncmSlNwF+GYew1DGOVUupl9MyPEUrXbTyOru9TG/1PYyPQy7LLCUAXpdQGy3opQFOgN/ofdv6rhWvQf0DfUUo1syzHMIypV9n8BUAS+p+FLfBZIUNL3kH3KHiQ/4LS7sAHSqktlvONBmqgezSY0YFgfrlXrBXXZhMQqpSah/4n2xMIAHZSYKbXyzF0wfuh6PPdqpRaBexHfzmoiU7seKKvzOZ6Ev0Pd7pSqge6xmZ9dFH4xeiZSK/GN+gryfej3/9F6KHe3ujhM7OBNyzrbkEH6M8qpTz5r97TZ0aBWXvznVukUupZ9D/TXUoX9r+A/lwFAYfQhehvlsfR79MHltdpB/o1HYb+LDxoFN9MzGfRtbjClFJ/oT+zQ9HDzWYahrE+37oz0Z/Z35We8CQKfZW4F/AbMLyQ/a+ytPtPy+9pOnDCMIwf8y1vDSxXSq0HMoE9hmEsvs7P2FUxDCNNKfU9/wWKX13HPn62BMczLO0PRU96EW9pVxD6dyv/EFBf9JfkeMvfowj0VfkaQF90vbZtXDw79SR0EJ+OnlX8ZR13XiTUMIyF13oOln23V0ptRtfzSkO/tr0tbdmM/nuVX4hSKgz9+3sGXfutPfqzkAaMMAwj6TraIoQQ4haRWFti7SuQWFtibYm1L3bLYm3DMHYqpd5E1/vO/dxkoP+WNLBsc9V/S8RtyDAMucmtVN/Qwcdi9FVFM/ofy+gC63RA/3OLArLQgUAo8BHQKt96PdBXIA+gA6tU4DD66nPtQo490rKfdMtxjWts+ze52wEtC1k+p+D5oGcP/QgdUFxA/7ONRA83uaOQfVxTu/Idsy7wAjpQykAnZKYDboVsE4keGna5/fqg/yFGWPaXZNn3j8BdRbyv89FXg1PRQWBfYHQR73GRbUBPvrTO8p5moL9A/Ay0KLBeL8txUvK9Lz6WZW9YHgcXsv8e6MkI4i3vxxH0P0+PQtZdW9T7UdS5XeF1rY6e7OOE5bMdAywEWhexvgGsvcbPaaTl5o4OqM9YzvMgOvhShWxzB3qoYzx6yNJG9BDFYEsb3iiwvjXwNnoIfnbBdqJrrX4BnEYHWwYw53o/Y/z3Ofe5ivMPsKwbBdhcy2tXYD81gffQQ/MSLOd5Af2F9Vny/W4BXsAY9FX1A5bXMf/6YwG7Is7pcrc519n2vuheMuHo36Ns9BfclejhcZe8LugvvOssr1sGOjA9ZHmP6l7v6yg3uclNbnK7tTck1pZY+wptQGLtgp+Htdf4OY1EYm2Jta8x1s637WB0T+Iky/uyHz3xpP31vpZyuz1uyjAMhBDlh1JqDrqnQh3DMCJLtjVClDyl1Gj0l8+phmFMLOHmCCGEEKIMk1hbiItJrC3EzSE1dIUQQpRbSk+C8Ty6p8I1DwETQgghhBBCFE5ibSFuHqmhK4QQotxRSnVA12cLBvyAGYaeHEMIIYQQQghxAyTWFuLmk4SuEEKI8qgbMBmIA75GT+Jy27DMRBx8FasmGIYx/Wa2RQghhBBClDsSa2sSa4ubRmroCiGEELcZpdQb6CD6Sk4YhuFzc1sjhBBCCCHE7UNibVEa3BYJXS8vL8PHx6ekmyGEEEIIUa7s3LkzxjCMSiXdDnFzSawthBBCCHFrXSnOvi1KLvj4+LBjx46SboYQQgghRLmilDpR0m0QN5/E2kIIIYQQt9aV4myrW9UQIYQQQgghhBBCCCGEEDdGErpCCCGEEEIIIYQQQghRRkhCVwghhBBCCCGEEEIIIcqI26KGrhBCCCEulp2dzenTp8nIyCjppojbgIODAzVq1MDW1rakmyKEEEIIUaIkzhbF6XrjbEnoCiGEELeh06dP4+rqio+PD0qpkm6OKMMMwyA2NpbTp09Tp06dkm6OEEIIIUSJkjhbFJcbibOl5IIQQghxG8rIyMDT01OCTHHDlFJ4enpKLxQhhBBCCCTOFsXnRuJsSegKIYQQtykJMkVxkc+SEEIIIcR/JDYSxeV6P0uS0BVCCCGEEEIIIYQQQogyQhK6QgghhCh2CQkJzJw587q3nz59OmlpaVdcb+3atfTr1++y64SGhrJs2bLrbosQQgghhBClhcTZAiShK4QQQoib4FYFmldDAk0hhBBCCHG7kDhbgCR0hRBCCHETvPzyyxw9epTAwEDGjx8PwAcffEDr1q3x9/dn8uTJAKSmptK3b18CAgJo1qwZ8+bN49NPPyUqKorOnTvTuXPnS/a9fPlyfH19adGiBX/++Wfe89u3bycoKIjmzZtzxx13cPjwYbKyspg0aRLz5s0jMDCQefPmFbqeEEIIIYQQZYHE2QLApqQbIIQQQoib69mICEJTUop1n4EuLkxv0KDI5e+++y5hYWGEhoYCsGLFCiIiIti+fTuGYTBgwADWr1/PhQsX8Pb2ZunSpQAkJibi7u7ORx99xJo1a/Dy8rpovxkZGTzyyCOsXr2a+vXrM3z48Lxlvr6+bNiwARsbG1auXMmrr77KH3/8wZQpU9ixYwczZswAICkpqdD1hBBCCCGEuBYSZ0ucXVIkoSuEEEKIm27FihWsWLGC5s2bA5CSkkJERAQdO3bkhRde4KWXXqJfv3507Njxsvs5dOgQderUoYElyB05ciSzZs0CdJA6atQoIiIiUEqRnZ1d6D6udj0hhBBCCCFKO4mzyydJ6AohhBC3uctd4b9VDMPglVde4bHHHrtk2a5du1i2bBmvv/46Xbt2ZdKkSdd1jIkTJ9K5c2cWLFhAZGQkwcHBN7SeEEIIIYQQlyNx9vWtJ26c1NAVQgghRLFzdXUlOTk573HPnj2ZPXs2KZYhaWfOnCE6OpqoqCicnJwYOXIk48ePZ9euXYVun8vX15fIyEiOHj0KwNy5c/OWJSYmUr16dQDmzJlTZFuKWk8IIYQQQojSTuJsAZLQFUIIIcRN4OnpSfv27WnWrBnjx4+nR48e3HfffQQFBeHn58fQoUNJTk5m3759tGnThsDAQN58801ef/11AB599FF69ep1yWQNDg4OzJo1i759+9KiRQsqV66ct2zChAm88sorNG/enJycnLznO3fuzIEDB/ImayhqPSGEEEIIIUo7ibMFgDIMo6TbcMNatWpl7Nixo6SbIYQQQpQaBw8epHHjxiXdDHEbKewzpZTaaRhGqxJqkrhFJNYWQggh/iNxtihu1xNnSw/da7Tgo38J3XAEs8lc0k0RQgghhBDithGy6jCrf9spcbYQQgghxBVIQvcaJCWkM+CFXgR2asCi2veWdHOEEEIIIYS4bYQ//Qldhrdij3NLTh2PLenmCCGEEEKUWpLQvQY2Nlb8MWURa92CCYpaV9LNEUIIIYQQ4rbh/+14Znd8neaZoax+d3FJN0cIIYQQotSShO41cHKx5+6J/TjavhdVjfMcCDlR0k0SQgghhBDituDXrg73LZ9IAu4EzX6b44fOl3SThBBCCCFKJUnoXgf3Tv4A7PhOeukKIYQQQghRXByc7FjY6mEa5kSwt8tTJd0cIYQQQohSSRK61yH4wQ4ct65Nvy+e5UxkXEk3RwghhBBCiNvG6JBp/NLoUXqfXURaSmZJN0cIIYQQotSRhO518Kriyt6PZlOReJY/811JN0cIIYQolT799FMaN27MiBEj+Ouvv3j33XdveJ9r166lX79+xdC6y3vjjTeYNm0aAJMmTWLlypVFrhsaGsqyZcuu+RjBwcHs2LHjsutczfle7/GFKM0y27bAjmy2Lgsr6aYIIYQQpY7E2ZdXHuJsm5JuQFnV/8lgjj3vQ+VtG4EXSro5QgghRKkzc+ZMVq5cSY0aNQAYMGBAibbHMAwMw8DK6tquZ0+ZMuWyy0NDQ9mxYwd9+vS5keZdt5I+vhA3g3dnf/gBTq3aB3e3LOnmCCGEEKWKxNm3Rkkf/3Kkh+51srK2IsqxBhVToku6KUIIIUSp8/jjj3Ps2DF69+7Nxx9/zJw5c3jqKV0Pc+DAgfzwww8AfPXVV4wYMQKAFStWEBQURIsWLRg2bBgpKSkALF++HF9fX1q0aMGff/5Z6PHmzJnDwIEDCQ4OpkGDBrz55psAREZG0qhRIx544AGaNWvGqVOn+OCDD2jdujX+/v5Mnjw5bx9vvfUWDRs2pEOHDhw+fDjv+dGjRzN//nwAQkJCuOOOOwgICKBNmzYkJiYyadIk5s2bR2BgIPPmzSM1NZUxY8bQpk0bmjdvzqJFiwBIT0/nnnvuoXHjxgwaNIj09PRCz6Wo892+fTtBQUE0b96cO+64g8OHD5OVlXXJ8QtbT4iypm1/f3Kwhh37AFg8cy0/vTiP5bM3E3chpYRbJ4QQQpQcibMlzgbpoXtD4lwq0zhmb0k3QwghhLisZ5+F0NDi3WdgIEyfXvTyL7/8kuXLl7NmzRq8vLyYM2dO3rJZs2bRvn176tSpw4cffsjWrVuJiYlh6tSprFy5EmdnZ9577z0++ugjJkyYwCOPPMLq1aupX78+w4cPL/KY27dvJywsDCcnJ1q3bk3fvn3x8vIiIiKC77//nnbt2rFixQoiIiLYvn07hmEwYMAA1q9fj7OzM7/++iuhoaHk5OTQokULWra8uFdgVlYWw4cPZ968ebRu3ZqkpCScnJyYMmUKO3bsYMaMGQC8+uqrdOnShdmzZ5OQkECbNm3o1q0bX331FU5OThw8eJC9e/fSokWLS84hIyOjyPP19fVlw4YN2NjYsHLlSl599VX++OOPS46flJRU6HpClCUens6EODan4aHN7Nt6nN5PdsMGEwBZ2PLjcz9w/0f3lHArhRBClHcSZ0ucXVJxtiR0b0ByhUpUOXe+pJshhBBClClVqlRhypQpdO7cmQULFlCxYkWWLFnCgQMHaN++PaCDuqCgIA4dOkSdOnVo0KABACNHjmTWrFmF7rd79+54enoCMHjwYDZu3Mhdd91F7dq1adeuHaB7J6xYsYLmzZsDkJKSQkREBMnJyQwaNAgnJyeg8GFrhw8fplq1arRu3RoANze3QtuxYsUK/vrrr7zaYBkZGZw8eZL169fz9NNPA+Dv74+/v/8l217ufBMTExk1ahQREREopcjOzi70+Fe7nhCl3UHfDty7ewZ/PPAWfpj4cdxPmLNyaP/NFPxnvsPqNvUJHtYCK2sZdCiEEEKAxNlQfuJsSejegMxKVXA7mEz02SQqVyv8wyaEEEKUtMtd4S8p+/btw9PTk6ioKEDX3erevTtz5869aL3Qa+jyoJQq9LGzs3Pec4Zh8Morr/DYY49dtO70YnyRDMPgjz/+oFGjRsW2T4CJEyfmBeeRkZEEBwff0HpClHb13hxDwsCfuCfiW3Y6tOD+T/Ww0dlJqYyZ+yTc25oL93mx1z2QUw0CqfHc3XS7t/V1HWvn2nDiTscTG36WXs90x8PT+cobCSGEKPckzpY4u6TI5ewbUb0KAMf2ninhhgghhBBlx/bt2/n777/ZvXs306ZN4/jx47Rr145NmzZx5MgRAFJTUwkPD8fX15fIyEiOHj0KcEkgmt+///5LXFwc6enpLFy4MK8XQn49e/Zk9uzZeXXDzpw5Q3R0NJ06dWLhwoWkp6eTnJzM4sWLL9m2UaNGnD17lpCQEACSk5PJycnB1dWV5OTki47x2WefYRgGALt37wagU6dO/PLLLwCEhYWxd++lZZsud76JiYlUr14d4KKhdQWPX9R6QpQ17fv7sWvOYja5tOfC55/lPT/ml7GErDzE7GGfsq5WTyqnneX+kI9wf2jsdR+rZedGdL+/Hff8bxALe79RDK0XQgghbj2Js8tPnC0J3Rvg4FMVgNO7I0u2IUIIIUQZkZmZySOPPMLs2bPx9vbmww8/ZMyYMXn1v+699178/f3zhoE5ODgwa9Ys+vbtS4sWLahcuXKR+27Tpg1DhgzB39+fIUOG0KpVq0vW6dGjB/fddx9BQUH4+fkxdOhQkpOTadGiBcOHDycgIIDevXvnDffKz87Ojnnz5jFu3DgCAgLo3r07GRkZdO7cmQMHDuRNljBx4kSys7Px9/enadOmTJw4EYAnnniClJQUGjduzKRJky6pHQZc9nwnTJjAK6+8QvPmzcnJycl7vuDxi1pPiLKo5wPtaJ+8kV5j7rjo+dZdGzHmt3EMjfwJv8wwfm3yGE3SD5CTbbrhY9aMCL3hfQghhBC3msTZ5SvOVrlZ7bKsVatWxo4dO275cY8eOEflpg1YW6UbPU/8zu+TFmJtZ02r4W2p38z7lrdHCCGEyHXw4EEaN25c0s24ZebMmXPRhAWi+BX2mVJK7TQM49KIXtxWSirWvhazh37CmD+eJWTlIVp3vY5hmPmGkkapanibo4qxdUIIIW4nEmeL4nY9cbb00L0B9ZpU5a/GI+hxfhl/NRrNiPeHcc/UwdTxq8lG14582/c9zp9OKOlmCiGEEEIIcVur2L4pAIf/DgXg96lL+fH5X696+zNW3pyx8mZ25zfwNs5yIOTEzWimEEIIIUSxkITuDTJ1aY89WQw98TObXNoz/62l/Nj6eTyy4nho2ctENO5f0k0UQgghbnujR4+WXgNClGNBw1qRgzU563ew7o/dDJvYj/s/vveqSzA4m1NZ17Afnnd1BGDHd+tuZnOFEEKIMkPi7NJJEro3qPnIDnk/HwrqxdBX+zB6+wc0y9zPnDYT6JCykYWfrCrBFgohhBBCCHF7q1LDg70O/jQ4uIWjv23Kez5k5eGr2t6ZVLIdnek6+g7ScMT1r6U3q6lCCCGEEDdMEro3yK9dHdJxAMCtU/OLlrX67AmScOWuZ7sRYduAn5uOZf67f9/Q8c6fTmD28Bn89OI8Fn688ob2JYQQQgghxO1iX+Ng2qdsYsxv4/KeO/LPHgB+fHYu3w76qNDtUpIysCUHs7MTLm4O/FO9P4PO/MYPY38gK1MmFhRCCCFE6SMJ3WIQsmArv9V5gO5PBF/0fLM2Phxdt5Nv+73PCdc6DDkwm6Gv9OGfH7Ze97H+Hvw2Y34bx8gP7+Gu57tz8siFG2y9EEIIIYQQZd/Af//HT03HArDPTtfUzdm+l0Ohp7n/k/t4aOEL7F4fccl2seeSADA7OQHQcq0eVvrAF6P4reUzt6LpQgghhBDXRBK6xaDTXQHcfex7PDydL1nWvFMDHlo8nm5xK9j4ywYAolbtue5jmR0dL3oc+s/+696XEEIIIYQQtwsPT2dGhn3Opr/2Ylq5kH12TfE5sI1dv27LW+fg8n2XbBd/Xid0lauO5WvVr5S3bOT+mTe51UIIIYQQ104SurdQu75+5GCNOnBpz4CrZbi7XvQ4ZsuhG22WEEIIUWZ8+eWX/PDDDyXdjEK5uLgAEBUVxdChQy+77vTp00lLS7um/a9du5Z+/fpdcb3g4GB27NhR7McXoqxo39+PwI712VfnDjonrqH1hy/lLUsPOQDA961e5KR1TfZsPkZSbCoAyu2/zhlhdk3yfl73x+5b1HIhhBCi5EicXbbibEno3kIubg4ct6mD16kj17+TzEwAfm3wENnYYH3w+pPDQgghRFnz+OOP88ADD9yy4+XkXHv9TG9vb+bPn3/ZdUo6oVrSxxfiVnAeew/HrH1okHOUPfb+RKlq9Fz9JT8+O5dROz+klvk0od+vJyUmBQCbfAldh53/svCjf0nGhdjn3i+pUxBCCCFuGYmzi4ckdG9Tx9wbUDv++hO6Kl0ndHtt+YRjNnWpfDK8uJomhBBCFJvIyEh8fX0ZPXo0DRs2ZMSIEaxcuZL27dvToEEDtm/fDkBcXBx33XUX/v7+tGvXjr1792I2m/Hx8SEhISFvfw0aNOD8+fO88cYbTJs2DdBXyF966SXatGlDw4YN2bBBlzZKS0vj7rvvpkmTJgwaNIi2bdsWeiXdx8eHCRMm4OfnR5s2bThyRP9/Hj16NI8//jht27ZlwoQJHD16lF69etGyZUs6duzIoUN6dMzx48cJCgrCz8+P119//aJzb9asGQAmk4kXX3yRZs2a4e/vz2effcann35KVFQUnTt3pnPnzgCsWLGCoKAgWrRowbBhw0hJ0Qmm5cuX4+vrS4sWLfjzzz8Lfa3T09O55557aNy4MYMGDSI9PT1v2RNPPEGrVq1o2rQpkydPBij0+IWtJ0RZN/DpLvhkHmXDoj1UO76BNS9+gpc5hvs/ue+/lXaGkR6nf9/s3J3ynq7fzJu7nuvGvzX7cefpFTI5mhBCiFJD4myJswFsim1P4qpEe9fjzn2rycrMwc7+2l9+K0sPXTcPRzZ4NaXZhV3F3UQhhBC3m2efhdDQ4t1nYCBMn37ZVY4cOcLvv//O7Nmzad26Nb/88gsbN27kr7/+4u2332bhwoVMnjyZ5s2bs3DhQlavXs0DDzxAaGgoAwcOZMGCBTz44INs27aN2rVrU6VKlUuOkZOTw/bt21m2bBlvvvkmK1euZObMmVSoUIEDBw4QFhZGYGBgkW10d3dn3759/PDDDzz77LMsWbIEgNOnT7N582asra3p2rUrX375JQ0aNGDbtm2MHTuW1atX88wzz/DEE0/wwAMP8Pnnnxe6/1mzZhEZGUloaCg2NjbExcVRsWJFPvroI9asWYOXlxcxMTFMnTqVlStX4uzszHvvvcdHH33EhAkTeOSRR1i9ejX169dn+PDhhR7jiy++wMnJiYMHD7J3715atGiRt+ytt96iYsWKmEwmunbtyt69e3n66acvOn5R6/n7+1/2/RWiLLCytqLjAP1ZHvH+MH6xsea+d4YAYMIKv/2r2b2pGgD2FV0u2T65Ty88v/qV2c3H0Xr7B/g4OOBqI1+hhBBCWEicXWQbJc6+uXG29NC9xbIbNcCBTELXX18vXavMTDKxw8raigsN/ahjOsGJ8OhibqUQQghx4+rUqYOfnx9WVlY0bdqUrl27opTCz8+PyMhIADZu3Mj9998PQJcuXYiNjSUpKYnhw4czb948AH799dcig6zBgwcD0LJly4v2ec899wDkXbEvyr333pt3v2XLlrznhw0bhrW1NSkpKWzevJlhw4YRGBjIY489xtmzZwHYtGlT3va551DQypUreeyxx7CxJIAqVqx4yTpbt27lwIEDtG/fnsDAQL7//ntOnDjBoUOHqFOnDg0aNEApxciRIws9xvr16/OW+fv7X3S+v/32Gy1atKB58+bs37+fAwcOFLqPq11PiLLurlf7sqTKQEIcWzGnxxT8Mvbx0F8vAuDseWlC956P72NlpX6MOfglkx79lnsnzOTIyZO3utlCCCHERSTOljhbLi/fYh5tfWE+HFm1n/qBNVjZ9iky+nbjgc8K//AUZJ2dRQYO2ANOnQJhPaya/Dtj5j55U9sthBCiDLvCFf6bxd7ePu9nKyurvMdWVlZXrJkVFBTEkSNHuHDhAgsXLrxoqFVhx7C2tr6uOlxKqUJ/dnbWtTTNZjMeHh6EFtHzIv8218swDLp3787cuXMver6oY16t48ePM23aNEJCQqhQoQKjR48mIyPjutcT4nbg5GJPv3MLAWgNhG4Yzp7nvqRu+Hb8gxtdsr69oy31fv4Yc4+lLJj7LABZn04ga8EiTN16YuSYcHK1vnUnIIQQonSROLtIEmff3DhbeujeYv69dK2P9J0HWTTwLe4+/j2+33561dtbZ2WQqfQvVZ8XerHDoSUD503CbDLflPYKIYQQN1PHjh35+eefAT27rJeXF25ubiilGDRoEM8//zyNGzfG09PzqvfZvn17fvvtNwAOHDjAvn37ilw3t3fCvHnzCAoKumS5m5sbderU4ffffwd0ULhnz5684/z6668AeedQUPfu3fnqq6/yguC4uDgAXF1dSU5OBqBdu3Zs2rQpr7ZYamoq4eHh+Pr6EhkZydGjRwEuCURzderUiV9++QWAsLAw9u7dC0BSUhLOzs64u7tz/vx5/v7777xt8h//cusJcbsL7FifUTum0TFpPVVqeBS6Tp3u9Tn43TY2vbqEiUzBzpSJ3YBehHl0wMrNmZCAh9n75FcYjz4Gll5LF7HU6hNCCCFuJYmzb+84W3ro3mL1m3lzwqomlcJ2Y2PKAqBCdtxVb2+TnUWGJaHr5uHInm7DabVkAnu2RRJwR92b0mYhhBDiZnnjjTcYM2YM/v7+ODk58f333+ctGz58OK1bt2bOnDnXtM+xY8cyatQomjRpgq+vL02bNsXd3b3QdePj4/H398fe3r7IQO7nn3/miSeeYOrUqWRnZ3PPPfcQEBDAJ598wn333cd7773HwIEDC9324YcfJjw8HH9/f2xtbXnkkUd46qmnePTRR+nVqxfe3t6sWbOGOXPmcO+995JpqZU/depUGjZsyKxZs+jbty9OTk507NgxLzjM74knnuDBBx+kcePGNG7cmJYtWwIQEBBA8+bN8fX1pWbNmrRv3z5vm4LHL2o9IYTWdHRrAD72a8aY19y5/9Qf1M8+xkY60G3vt7D327x1z7/9NpXr1NE9ixYvhgEDYOdOyFd3TwghhLjZJM6+veNsZRhGse2spLRq1coobFa90mpx1UH4XdhFmrUzTbIPYsKKxOhEKla6tG5XQQur303T6F00yNZXF3773xLuntSfeZMWMvzNwj/kQgghyp+DBw/SuHHjkm5GiTCZTGRnZ+Pg4MDRo0fp1q0bhw8fxs7O7qL1fHx82LFjR96EBeLyCvtMKaV2GobRqoSaJG6RshZr30yGYXAkPZ2lsbH8HRdHg3R3cr77F++fjjMp7R0AHnjqdbbe2xdvR3t+f3Y8ldavgp9/hvvuIz0mhr6nT5OYk0NIy5ZYKYVhGJzPyqJqvuGzQgghSi+JsyXOLm7XE2dLyYUSEN2iHT7mkzTKPkykVS2sMRPydxgAy77eyLcDPyxyW9vsDDKt/gv2mvTUJRyGT7mL1b9KoC2EEEKkpaXRoUMHAgICGDRoEDNnzrwkyBRCiOuhlKKBkxPP1qzJPwEBzGjnQ6cpA5j8c1+GP/YHSbjyw4ypzGg/ifUtAshcfwgAIyMT5s7FsVIlYnbuZFdKCuFpaQC8dOwY1bZsISYrqyRPTQghhLgiibNLDym5UAIGff8kqxusoEviajY16o3Pwa+IWhvGkRa16PNoRwDmv92Yoa/2uWRbO1MWWfkTui1rccS6LvVNx3B78HG4R5K6QgghyjdXV1eupjdh7my9QghxI6yVAo9sIh+pzbGltQg8vZ8e/MsDdt9RKesCANNeiWFM3eV4As2OH+dg7dqc+OsvfO+9lw9OnQIgxWTiWvoxxWVnczYri4ScHFJNJpysrGjv7l4sk8gIIYQQhZE4u/SQhG4JqFjJhcCIRXw76nPavz2CnObfYLX3EDv/rEJ9yzrWX/wAloTu4plrSZz5O/ft+QzbnMyLeuhaWVtRNW4/P3YYz7B9X5ORloWDk1wdEUIIIYQQ4lawsSRQM81mHO3S856f5vAC9pZetznRcZyJvoAn4HcyjYrTQuj5z6uMMhzAu4Jex1IKLzY7m2ciIvisQQMq2Nrm7S8kKYkfz58nJDmZ8LQ04gqZcfwff396VKx40XMmw+CVY8ewU4qpdWXODSGEEOJ2ICUXSkjFSi48tOwlfANrcNSmHpVOHiZlZzgAh2wbUTPmSN66/Z/szMj9Mzl9PBY7UyZZNhfX13JxcyC7WWMcyCRk5SGSkuDIT1vh3Llbek5CCCGEEEKUN9aW+0zD4NNHHyXbxoZO06fjlZSYt06Ljsepa3UMAI8f02nxTwQASSOApdUAMFnWfeHIEX6OjmZhTEze9vOjo2m7axdfnz2Lo5UVd1euzEf16jGvSRP+8fdnTUAACtiSlHRJ+3YkJ/PBqVO8dfIkmWZzcZ++EEIIIUqA9NAtBcI9m9Dh/Fqi/j5KKk6E1m5PryN/YDaZOXsqgeqW9Y7vOY2LKZNUO9dL9lGpQxOYC0cX7+SfJ3cz9fRozE4uWMVEg6PjrT0hIYQQQgghyon8PXRXd+5M1+3b2ZCYeNE6Vc7twsWcCkBXn2NUzjoFUVDf/jC+03x59asvebpLS4bem8WmanqWbe98k6RNP32aeo6O7GzZEjebwr/CNXJyYme+GbrPZGZS1c6ObfmSvGczM/FxdCTLbOZcVha1HByK50UQQgghxC0lCd1SoOLXU4gbNJCm2QeJVpVIbdAQjyOJHNoXxY6R7zDSst6Fw2epaM4k2+bSkgptB7Ug/kkPRn8zJu85q7QU0u9+AMfFvxdbW89lZtJtzx6au7ryYzmd1VEIIYQQQohc1vkSus7W1nmPc2W6uhIYoXvkmitXpqHpIETpurl39w9j2NLfaZO8iy8WPc2ji+6gQ8B57MfFsKBaNUyGQX1HRzYlJTG1Tp0ik7kAbV1d+Ss2lnSTiWVxcdy9fz9Bbm7knDtHtbQ0znp5cSYri9oODtTfto1TmZmcCQrC296eqMxMFsbE8Ei1atha6UGchmFwJjOTA2lpHM/IIM1kItswyDYMUkwmdiUnM7JKFe6vWvVmvKzXxGwYzDhzhgo2NqWiPUIIIcTNJiUXSoH2/f2oFLOf2R0nsuyh93Bq0RCAvYtDaXP4XyJs6gGQcvQsDuZMsm0uvZJeuZobix6alvd44RP/8DHPYvf3IrDMoFscFsbEsD8tjZ/Ony+2fQohhCg/pk+fTlox/V964403mDZN/++bNGkSK1euLJb9CiHEtchN4GYZBtb812P3mKVerX3r1nnrWnXqBJZJ0LC1pXXCeZpWiQPgRf/X6e67hg17BhD28Bj+3rWbvvv20X73bgDau7npkmpKwYoVl7RjVNWqxOfk8PuFC0w/fRozsCkpia19+xI1bBgAUZmZnMjI4FRmJgChKSkAtNu1iycjIlgSGwtASk4OLXbupObWrfTcu5fHw8N5/uhRXjp2jNePH+fdkydZER/PzKioQl+TpJwcuoSGXtRj+GZaEhvLM0eO8MChQxiWWsRCCFHeSJxdvkhCt5Rw83BkzPopjP76QTo81IlsbMj5ZTH1c46wvUE3AEynz2FnziTHtvBJz7q+dlfezwNndGdnxR5Ym7Jhy5Zia2dYqh4qZquUBEtCCCGuWXEGmvlNmTKFbt26Fft+hRDiSvKXXLBRKq+m7vhPP4XPPoMvvoDatWHKFKinO2qgFAQHw+nT2FiSqoP3rmHFoS55++16Xxp8UY+Yn6tAgi1+Li7/xfUzZlzSjmAPD3wcHPjp/HkOxsWx9vXXaXXo0EXrRKSns+f0aRpYksr7U1PJMJkuSfCuiI8nNCWF12vXZm1gIKfatSOhQwdSO3Yks1MnTHfeyVhvbw6kphb6nWBBTAxrEhJ4+dix63pNr9W6hIS8nw/fhP8xQghRFkicXb5IQrcUqlnHk20u7bjv0CysMDB1aU8mdtiej8bByCTbzr7I7eLxYKtDKx6PCCerbyvSceDUpFnF0q647Gy+tFyFzzYMEvLNrCvJXSGEEPmlpqbSt29fAgICaNasGfPmzePTTz8lKiqKzp0707lzZwDmzp2Ln58fzZo146WXXsrb3sXFheeee46mTZvStWtXLly4cNnjjR49mvnz5wPg4+PD5MmTadGiBX5+fhyyJDRSU1MZM2YMbdq0oXnz5ixatOgmnb0QojzJX3LBWqm8BG+Mtzc89RQ0bAiRkTBxInh56Y3c3MDXFw4dwj7f5GcAJldXkm2caW61C36ryQNfbOK5QdsY3tuWJfMzADDbXTpiTynFsEqV+Dc+nvqHD3Pnpk2sGD8+b3mF7GxeO36cRl27Ev7AA9ROSaHTsGF8bUkSO2Zk8GbduvDttyyPjeWJf/5hkqsrd3p4UMPBAXcbG5ysrbGzssJKKZo4O5NkMhGVlYXJMJh4/DhvnTgBwL9xutdxmklP9XYyI4MJR48SaumxaxgGIUlJRGVmYjYMFly4wIenTjElMpIXjhyh+549dAsNpffevQzYt48hYWEM37+fF48cwVTI944tSUlUtdOdXhZbehkLIcTtSuJsAZLQLbUSp73FUes6APgPDyLKuhq99s6lihGN2dq2yO3Sok/y6N+fMOvsWX4feZCvq7xEzc2/McTtX/6Ym0V29vW36d2TJzEB7ta638H5rCwAuu/Zg8uGDRxLT7/+nQshhLipgoODmTNnDgDZ2dkEBwfz008/AZCWlkZwcDDz5s0DIDExkeDgYP78808AYmJiCA4OZvHixQCcO3fuisdbvnw53t7e7Nmzh7CwMHr16sXTTz+Nt7c3a9asYc2aNURFRfHSSy+xevVqQkNDCQkJYeHChYAOClu1asX+/fu58847efPNN6/pfL28vNi1axdPPPFE3nCxt956iy5durB9+3bWrFnD+PHjSbWMPBFCiOtlk6/kgo1SeQlemwK1dIH/ErrOzlBdT32c7eHBo88/z3p/fwAMW1v2+NaheZN1fLz7NN8zmo94gYyNO+j3y30ALF9jzy+/QMHv4G/4+PBJWhqDDh4EoIKlxy3AfHd3ptWrh6+ld+4P//5L2+3bGdexI02OH+cuy9/DM6+/zo7165n57rvYjhtX5Hn7OzsDMDgsjNGHDjH1xAleP36cA6mpbLckbjcnJfHG8eN0CQ3lg1On8ko0/H7hAm0sZR6+iIpi8P79vHj0KJMjI5kZFUVMdjbpZjOx2dmcyszkcFoam5OS+PD0aQ5Y2plhMvHJ6dNkmc3sS03l7kqVCPbwYMKxY/xo+T/1+rFjbCkwQZ0QQhQ3ibMlzi4JktAtpfo+1ona6REc2XeGwI712T7pM/a5B+iFhcSGuapXcmVJuxa8X7cu2Jk5+/kYwmnAH8k96HpfZSaOuP5hT9GWBO7MhrrG7/nsbNJMJlbGx5NmNhNyi2pkCSGEKP38/Pz4999/eemll9iwYQPu7u6XrBMSEkJwcDCVKlXCxsaGESNGsH79egCsrKwYPnw4ACNHjmTjxo3XdPzBgwcD0LJlSyIjIwFYsWIF7777LoGBgQQHB5ORkcHJkydv4CyFEIK8Eguge+vmJnQLTo4GgKenvndyAm9vADKrVuXr/v35wPI3T5nNHKpVi7pnz9IkfHPephty2uX93CfuJ94asZ+H66xk0rNJHFhzHhITcQoN5emBA3mpkJIMXRYs4AUXl7zHnfLVv935zDN8aVlW/dw5Jv3wg15w8iSEh8OhQzB4MPz2W942HdzdmVy7NocKzK/xzdmzRKSn42dJ+L554gRHM3TP4v2WL/crLD14tyUlsTYhAS9bWxI7dCC7UyfSO3Vid6tWbGrRgu0tW7K7VSvC2rThbz8/APZZ9vHBqVM8e+QI006dIsVkoq6jI183bIiNUsyMiuJIWhpvnTxJ7717L30fhBCiDJM4WwAUPU2qKHE2ttbUb6YDveGT+mN+rS8/vjAPvyGtL7tdLQcHXqxZk/dOnuRC0zSY8Tk81QMPEpn4ux/Rq7Zi19KPp56Cp5+GNm2urj0pJhNNnJzyrsZHZ2VdVKPqyG3aQzfdZCLHMHC0ssLGSq6BCCHKprVr1+b9bGtre9FjJyenix67u7tf9NjLy+uix1WvYgbxhg0bsmvXLpYtW8brr79O165dmTRp0nW3XxWWGLkMe3tdnsja2pocS4kgwzD4448/aNSo0XW3QwghCsqfuM1fcqHQhG7FivrexQUqVAAgx/LcycqVAVA5OZz19MQ7NhZvyxdubG1RBYba7acZpEL8Jx6kfuIMnOGUVyA185VFu8i778KZM/89tnyxB3BITsYhX4L3rk2b9A+bN0P+v5kLFkCnTlC5MurgQd5wdmZo8+b47dgBQKCLCx+fPg3AE97ejI2IyNv04WrV+D06moTsbHZZeg6fzcpiRVwc3SpUwM3m8l9NGzk5YasUO5KTua9KFXZYOpP8bUkO13VwoL6TE6/Xrs2bkZHMsfRyS7SUfci1NDaWv2NjGV+rFrUdLi1dIYQQ10ribImzS4Jkp8oQK2sr7p9+L4Ed619xXaUUzV1d+Sc+ntiRbTCiojizaAdWmNnU7x169ICff4auXSEp6eqOn2Iy4WptTRVLfaq7Dxxg8P79ecsjbrMJCAxLLTCPjRtx27gR2/XrsVu3Dr+QECYdP05ocjKZZnNJN1MIIUqlqKgonJycGDlyJOPHj2fXrl0AuLq6kmz5Et6mTRvWrVtHTEwMJpOJuXPncueddwJgNpvzanX98ssvdOjQ4Ybb1LNnTz777LO8uu+7LTPHCyHEjchfWuGKJRdye8gGBECTJgCcf+ghAE5ZEromb2/O5iZ+c+UmdgtRgQRqoBO1NWNCOau8+aj/GozcjghOTrBypb7/8cf/NoyOvnhHVzuDebVqYG0NzZpB06Y03bCBNgcOMPn335nyyy/UPneOD3/6idG//87LYWEsmzKFzz09aWNvT6LJRIVNm9idkkKg5bVIMplo6ep6xcPaWlnlJYyDdu3iL0ut3I2Wkgp1LMnZEZUrYwW8la9nWKwlGX4oNZUB+/bxeVQUs8+eBSA8LY3++/axMF/9CpNhEJGWxoq4OH4+f56PT53iI8vt3RMnePXYMaZbSlcIIcStJnG2AOmhe1ubXLs2/cPCuGP3buo4OFDR24YX7xjDsM1fMvPws3zRah/tdnzGN+Pn8fxXV76Kkmwy4WJtTSU7O96pU4fVCQmsTUjACmjt6kqEpYduUk4Ov0ZH09HdncaW3rxl0dqEBKaeOMHQSpVo6+pKutlMqslESHIyU0+c4H8nTmANNHN25tMGDejk4VHSTRZCiFJj3759jB8/HisrK2xtbfniiy8AePTRR+nVq1deja93332Xzp07YxgGffv2ZeDAgQA4Ozuzfft2pk6dSuXKlfPqjt2IiRMn8uyzz+Lv74/ZbKZOnTosWbLkhvcrhCjfLuqhy38lGKwLWzkgAH7/HXr31nV0MzJIyMyEXbuId3PjzCefYNOjB+fylTbg448hOBgs9RgvYmtL3iQZVavCuXOYXVyZuCoYB/NjjOULzpkqsS2lKwNefgU1aSJ88QU88cSl+/rll/9+/uwzyF8/t08f3Vs3IQH69tUTvZ09C1u2oPr0YVu+3fTP9/M7uT/4+2O4u9O9SxeyMjKwT0rCbdgw5g8dSprJxKhq1fR6hgGX6Sm2xM+PX86f57mjRwFwsLIiw9LBwseS0K3v5MRdXl78ERODk5UVaWYz25KS6OPpyZqEBMzoZHtu+Yf3Tp5kSWws1sBdlSoBui7wX1cxudpdXl74ODpecT0hhChOEmcLAGUUMktoWdOqVStjh2WYj7hYUk4Oww8cYLllKFLtCxc4dP/9OGRm5q2zzeYOApM3Yu9w+W72ASEh1HV0ZEGzZnnP7UlJ4Xh6Oivi4/np/HkSOnTgjchI/nfiBMMrVeLXpk1vzondApOPH2fqiRMkdOiAa4EhYMfS0wlJTiYsNZW3T5yga4UKrAgIKKGWCiHEpQ4ePEjjxo1LuhnXzcXFhZR8k/mIklfYZ0optdMwjFYl1CRxi0isfXkHUlNpGhICQN+KFfGyteX78+cZ4uXF/Hxxc1F2JifTaudOAA62bk1FW1sGfv01W556CpODA9a5Zc1CQ3US2DKfBQAdO8KWLZCTA2PHwsyZ4OND2v7jnHpsKo1+mshR20bUzz6En282z7bbSp37OxD82RBU3z7QtCns3w+PPHJxoy5cgDVr4IUXYOhQePttnWxds0Yno3OTrikp8M47YGMDAwboHshvvaXbFROjhwImJOhktIeH7hVsmZcDgA4dYPdu6NZNn8OKFRAYCC++CA0a6Mdr18KwYdC/v046N2yIlb09vidP8mJmJk/6+JBhb4/RrFnepHMz9u/H9fnnOT5wIP9r3JgHq1XjnsqVmXX2LKvi4+no7s7GxEROBwXRYudODqal0cDRkfC2bYnPzqbSpk3cXbkyj3t7U8XOjkq2tiggw2zGzcaGY+np+O/YwXeNGjE6Nxl9GbHZ2ThYWeFsXWiaXwhRxkicLYrb9cTZ0kP3NudmY0PPChVYHhdHZw8PetSpQ/OvvmLThQtU3LGDkykVabv0CzZ+uoUOE+647L5ye+jmF+DiQoCLCwk5OXwRFcWh5GROWILOiDJeU3dLUhL+Li6XJHMB6jo6UtfRkeFAZEYG6xISbnn7hBBCCCFEyStYQ/eyk6IVtn2+n20sNXjPW+rrZru5/bc8MPDSjf39wd0dliyBXr10Qjc9HScnaDSqHfwE9bIP88038O23tjw0pyPMAXf3PxlcAYZ6Q88Hg7A+e1ZP2DZ8OOzYoROjw4bpW359+lz8ODeBm1/uhGr5WXqPEROj25pbW3fWLEhNhUWL9OMBA+DAgYtLTFSqBMuX6/ZZes1GBgZSZf9+7LOzaV2nDrG9ekHnztCuHTg48PCpUzgcPQorVvC6tTVRnp6ENGrEyhdf5IPVq7H18+NcZiafffMNDnfcQcXq1TkK7E1JYVV8PCZgXPXqBBUy0RBAU2dnPG1s2JiYyOhq1cgym5kXHY2vkxOt3dzy1ovOyuKHc+cYf+wYrVxdCWnZstD9CSGEENdKErrlQFNL2YOGjo4M9PLildq1qV+vHl+PGkVf5UhypR+x/f5ruIqErmsRV5XburmhzGZc+/ZlWng4pocf5q+ePTEM45oLbJcW+1NT6VGwflkhGjs58dP586Tk5OByhckchBBCXB3pNSCEKCsKS8jC1Sd0C6vBe6JKFX7o3p36L73EJRG6nd1/vVwDAnTv2X//he7d9XOTJ+v7oKC8TR56SN/OnoWNG+G33+C77/TNxwc++WQiXbvqDsD07HnV537NvLxg9Oj/Hr/1FmRmwp49ui5vixa6d/DXX+t1g4Ohbl1ddmLJEn0SO3ZQ86uvyO7VC/r2pdnYsajchHFWFsTF4XD0KGnt2uFYrRo2CxZQsVIlhmzYwJANGwAw7OwYYTZjnZPD+O+/x2Rnx8T77+ex/fuplJBAzzvuoF2+xGxBVkrRzNmZg5Y5ROZGRzP60CGaODmx3zLjdEpODlU2b87bZkdyMjlmMzZWVvwWHc3TERGkmc15JSM8bW2pZGuLl+U2pmpVenl6Ft9rL4QQFhJn3x4k+1QOdK1QgS8bNuS+ypVxsbZmQs2a/BUby9D9+2nj6spLlQfR7eCfzJ/3DXUbWLNwoY6fHnsMTpyAMeNyiO9+kphW2Zf00M3VyMmJN9eto+bGjQD89PbbvBIdTXS7dlSxs8NsGGSYzTiVoWFGqSYTblfR3sZOTgAcSkuj1WUCPyGEuNXK8kU1UbrcDiW6hLhZbIrooVvopGiFKNjD10YpzNbWjHr1Vf719790g507dYmErVt1D103NxgyRC/L/7vq7AyzZ+uyChbVqv3X8TY9Hf78E954AwYOBEdHvbshQ+Cpp/TjW8LeHixJUAAqV4bXXrt4nQkT9A1g1CjUZ59hZ1mkOnWCo0f1bM+Ojjqpu2kTTkFBujTE1q24dOoEr7wCH34IzzyD+vBDrNu0gblzYckSrJYv5+1vv807XHadOqjnn4f163X5iKFD9YuXj6+jI7/HxACwwTJaLzIjI+9/7+Z8M093cndnfWIikRkZ1Hdy4puzZ7FWioerVcPBygqzYRCbk0NMdjYx2dksi40lPidHErpClGISZ4vicr1xttTQLaeyzWa+iIpiVlQU/p+t4pefXqUFO9lNC7y4QHXOEFcjgLPnFDk5lo2+3MmUnp5M9PG5dIfh4RgtWrCrQQMm3303b/z6K3WPH+fAwYM0q1yZxiEhnMvKwsPGhpr29tS0t6eFqyuv1KqVl+Q9lp5Outmc16O4pNmvW8dzNWrwbr16l13vYGoqTUJC+MHXl/urVmVVfDxTT5xgub8/9rmzCwshxC12/PhxXF1d8fT0lGBT3BDDMIiNjSU5OZk6depctExq6JYPEmtf3qmMDGpt3QrAPZUr42Vry4wzZxhdtSrf+fpecfuItDQabt8OQFRQEBVtbXFYvx6ANQEBBFvKL1zk/HldxuD113X92huQmqpL1a5fr3PEW7fqvGi7dnDvvbrMbRkuFfkfw9A1f11ddT3iBg0sXZIt/v5bl3SwtYVRo3TPYaX0do6OOqGbkqKXRUdj/vFHtvn6Ym7alENmM5XOniW0Xj0e/Oorqjs40HPvXlbGxwPwdcOGPBIeztcNG3KXlxfVt2xhXPXqTKtfv9CmDgkL41BaWl5vXyFE6SJxtiguNxJnSw/dcsrWyoqna9RgXPXqfDEO+Ak+qvU4R1o/yJg1E7GKi2VR2mjWP/oFx1vupMG4tax+qivbXvAg+TX46y/o0UOXtMIwYMwYlIMDq9//kqW26TStXp33Ro3C9P33bH3sMc5lZTHQ05Ma9vaczMzkZEYGy+LiUMAUy4e23jY9P64RHFxSL0sek2GQZRg4XkUP3fqOjtgoxSHLkKvBYWEkmUzsSE6mfRF1t4QQ4marUaMGp0+f5sKFCyXdFHEbcHBwoEaNGiXdDHEFSqnZQD8g2jCMS2bjUvpb5ydAHyANGG0Yxq5b28rbT8GSCTY30EPXRqlLSjgUqkoV3bW2GDg7w6BB+gY6sfvbb7BwITz6qH6ucWPo1w9GjtS9eMskpXQyFwqvR9y7938/V6igk7c9esDKlXoytt9/18s++ACAhMGDqb5xI56LF+NnZYVbWhoDNm8m7fff2dSyJbWCgvjI0xPvjh3p//XXvNSiBY+Eh/NIeDg2SvHgZSZT87a3Z3W+OTrOZWaSajZT75Z1mxZCXI7E2aI4XW+cLQndck4pRV9/f6aOHMnzv/9O8B9jMZycYPhwBs6bw8CZc/LWPWL/DY3eO4Lbe/px376weDGoRYtg0yZeqfwN7/ZoCz9tJScoiB2NGxMwfTrbMzLo7urKD889h1u+HgRdQ0OZffYsFWxsSDGZbvGZX15uLSvHq+hha2tlRX1HRw6mpWFYSksAbE5MlISuEKLE2NraXnKVVwhx25sDzAAKmZUKgN5AA8utLfCF5V7cgItKJvBfTd2rLTR2SUK3wONbrVMnffvsM9i3T89HtnKlLmP7wQc6oTtypO69e9te5+nR47+f77pL3w4ehHr1dMbb2ZmKQUFUNAx2JyZyLDOT3obB3OnTcdm/n+7r1vHtpk0X7fJspUqcat6cyCpVCIiIwOt//4Nu3S49tmFQ3c6OhJwc0kwmnKytqbNtGxlmc6no+CKEkDhblA6S0BXUdnDA/Z136D92LA5r13LK15cdAweyqUsXfCZPplZ0NP+0bk2fbdtYSzAv8R6NOlbhwaUPcsDXi/oOpzhvVZsPokfpHY5sR8pYMx+P+x+fvjyGHv/7Hz1Az7q7cqUOhIBna9Rg1KFDPH/0aImde1HSLQnmq0noAjRydGRBTAyDwsLIspQx2ZqvbpYQQgghxM1mGMZ6pZTPZVYZCPxg6JprW5VSHkqpaoZhnL01Lbw9FZWQvdpkbMEevqqEE7q5lNLJW39/Xb72wgXdc/enn/Tjl16Czp11rrNfP7jtcxu5dSfyJ2GVormHB80tDx96911is7NpsGwZ7Q4cYGnNmvqFCw/HbtYs6m3dSr3sbF3AeOBAnTmvX1+X0EhJ0etmZBDw9tucefBBbNzdMVeujN8DDxDSuDHx2dlUsLW95acuhBCi9JGErgBgXI0aPFW9Os95ebHszBmOpqezq3dvujRsyJPVqnFvlSrMeOwxnvrtNzbTHmOTFQozhOvtZ9h/wtJFNixYoCfZnT3LCmU9mG0v2DJ+79eMWL8el8hIPdPaypUA9PfyIrZ9e+Jzckg3m/nizBnePnkSs2FgVcJ1aNKvoYcuwJhq1QhLTWVRbCwA7tbWhKen37T2CSGEEEJch+rAqXyPT1ueuyShq5R6FHgUoFatWrekcWVVwUnRch9bX23JhSL2VdjjklSpEjz5pL4dOQI//6yTu08/rW9t2kCvXtC/P7RsqRPC5ZGnrS1x7u6E3nkn3HHHfwsmTtSlMqyt4eRJmDwZfvhBd4EuoPeAAQDsadMG35AQto8dy+KgIMLfe4+27u4kHznC0fPnqT5iBJVkUmYhhCiXZMYmkUcpxX1VqgAQnp5OTHY2NkrxWcOGtPfw4Kl582DMGL2unx8nNpxggtuXrKtwF/euf4KePeHLL/Uks+HhUNdHceLL3jz+wKt8HRYGU6fCqlWwZs1Fx6xoa0t1e3s8bW0xgKS8WdhKTl5C9ypq6AIM8PJiW8uWeY8HenlxND0d820w6aAQQgghyh/DMGYZhtHKMIxWlSpVKunmlGr5o0XrfD10rzahW7CHblHLSpP69XU+MjxcJ3fffVd3MJ06FVq3hiZN9HMbN+rOqOVNTPv27G/d+uInvb11MhegVi347jvIydE17Hbs0D8bBnz9NTmjRzPyl18InDCBJj//zDd9+tB/yxbaduoEAQG4DhlC4NixeHh56TdBCCFEuSMJXXGRRk5OABxOSyM2OxsvW9uLZ218+mlo2hQWLKB2h1q8n/gYd8YtoHmbi4f+1KkD8+eDOc0KRrfmhxFV8Zv5BGfdGmH07w933w3PPXfRNh6W+roJNzmhm2M2k5STQ2x2NucyM4nJyrpknWvtoQv6anyudm5upJvNnM3KYmNCAp1272ZdvokNhBBCCCFKwBmgZr7HNSzPiRtQ1KRoVxtFWhfo4VvUvksjpXQ1tZdegv37deWAb7/Vc4+98gp07AheXjBuHCxdChkZJd3iW8PT1haPqymNoJSuV9Gy5X/J3ocfxua77/jhnnto6OjIMTs7xr/yCgNmz2b5vfdy8p57SHJy4p+2bbHNzobhw+H11+HXX2HePNiyRSeGhRBC3NZuackFmXm39HO3saGKrS2r4+NxtLbG06bARyQgAMLCrmpfzZrB8i3ZPPZyNjZxLvi0qUiXhQvYT1NU7iyxfn55vX4rJyVR/cIFElNTwdaWtTNn8mu1arzbrx/rUlNZFhvLGz4+VLO3ByDNZMIAnAvpRWsYBtHZ2RxPT+doRgZLY2M5nJZGXE4OpzIyKDgF26qAALpUqJD3+Fpr6JKTAzY2fF6jBhdsbKhvmYH2h3PnWBkfz4bERBbFxHCnh0de+4CLk+VCCCGEEDfXX8BTSqlf0ZOhJUr93BtXMCF7tT1zc+VP2hYsO3at+yppXl46tB8zRpeD3bIFfvkFvv4aZsyAChV0v47evaFrV3BxKekWl15WSnGXlxfvnzpFFVtb7Nu0oXedOnT28GDdo48ysmpVls+ezUdLl8K776LyTTId9dxzvPP440ytWxf3gt/nLAzDIDEnh7icHDLMZtLNZho4Ol40ibUQQojS61b/tZ6DzLxb6j1bowavHD8OwJ3u7je0r+6BDhxb7pD3+OWXG9P8vd0Mbn2aR5Km4f3QQ3D8ODz/PJ379OHk8eMYDg4Yrq4ER0cTDKTVq8cr06Zx0MODtm5ujKlWjYOpqTQJCWGIlxfzm+lrA89GRLAuMZH1gYEM27+ff+Lj845b0caGNm5uNHF2plblylSwscHOkqx95sgRdiQn5yV0000m4i29hK8qobt8ub4yfs89jJ01Cz76iJSxY6nn4MDkyEhcLAnniHzjzYbu38/25GROBQXdyMsrhBBCCJFHKTUXCAa8lFKngcmALYBhGF8Cy9AdJ46gO088WDItvb1cMinaDWxfUGnvoXs5lSrBgAH6lp4O69bBnDm69u5XX4Gjo07q3nsv3HknVK9e0i0ufZ6tUYMdycn09/TEwcqK+RcusCYhgUBXV1q7ujKub19enDqVB3fvJtrS6Wbqr7/S7+OPeenHHzn88su0cXbGVKcOz9SuzcGMDC5kZxNjuWUX6MlroxQhLVoQ6OpaEqcrhBDiGtzShK7MvFs2PF+zJv87cYI0s/miMgLFYepUyMgIYMbPAbwV052l1gPo+OEM7LZtx+nYMTY1bUoNPz+qnDyJQ3Q0AE5HjzL71Ve5c/p0LmRnA/DHhQsALI2LA+BURgafnNEjBg+mpbEmIYH+np48Wq0adRwdqe/oiH0RydmpJ05clGxtGhLCcct4sCvW0I2KghEjICkJZs3Szz3/PC6bNzPr/ff5eeZMhq5fz5gJEwi39No1DIM/Y2IA3cvY6Srr9AohhBBCXI5hGPdeYbkBPHmLmlNuWCmFAgx0Pd0b6aF7LcvKEkdHPWFar16QlQWbNukKAUuXwpIluvJA374wZAj06KHLzQqoZm/PqsDAvMcDvLzYlpREgIsLMZbvRb+cP8+/GRmo+vUxgCETJjCiRQuemz+fNi++COjP5TuOjmzq0IFv3nmHNhUrUsnODi9bWzxtbHCwsiIhJ4cnIiIIS02VhK4QQpQBpa2GblEz715CKfWoUmqHUmrHBUtyTxQPOysrOlp65hb3kBsbG5g+Hc6cgZBQO5Y1eh6H9ASs/l3Byv5v0aHqKgJc32PNosWoNWtQa9Yw+7PPaHfwIJ9+9hnRlnq3+9PSAHCyJGm3JCXlHWNzYiJZhsHQSpXo5+VFU2fnIpO5AA0cHYmw7C8pJycvmQtX6KGbkQEjR+r7+fPBx0fP/tC/P8yfT+c2bfh22jR6b9/O/HffJT46miyzmch8+9+Xmlrorg3DIMtSx1cIIYQQQpRuuUlcG0ty93q2LcztktDNz84OOnfWvXSPH4ft2+G112DXLnjwQd1Tt3Zt/Z1h7Vo92ZrQvO3tGVSpEnUdHWnh4oKbtTUTjh0DYKwlC55lZ8d3ffrQ8quvGDd/Puzbx7K338YuJ4de//7L/Dlz+KZBA96xs+OFr79mdGYm91Spkjc59rlC5hcRQghR+pTZAjmGYcwCZgG0atVKqr4Xs1dq12ZrUhJ3uLndlP3b2elyvB/t6845b3+qnt/LoMXjwMGJxFXW9Ek5CQ8DVvB5u3acv+8+XvnlF/6qVg3Cw0l45hmoUIG4nBwyzWa2XbiA/5EjHKlenbWWyceaOjtfeuBTp6BmzYueaujkxNLYWFJyctiQmHjRsssmdO+/H9as0WPHhgyBwYN194KEBHjlFdSXXwIwfcgQnvnzT7744ANe8vUlKF8Zi13JybR1c8NkGPx8/jzV7Oxo5+ZGi507OZKejr1SeNjY4GFjQwVbWyrY2FDZ1hZPW1tcrK2pZmvLiKpVcZVaV0IIIYQQJcYayEEnZ6/1i8nletjcjgnd/GxtoXVrfZsyBfbu1UncefP+mz/Zw0MPiBs4UJdmsLMryRaXHjZWVjxUrRofnz5NPQcHHq5Wjc+jovKWt69UiZm2tpxTilUdO+K9cSNh336rixnPn69XOncO5s6Fv/7C1ccHJysrzloSuhFpaXxy+jTP1KhBA8vE2UIIIUqP0pYFkpl3S4k7PTxI6Njxph9HWSmqngwh/Uwcu02uONTIZMgDGWyfWwvibfGccJxdKSnsGTOGUatWMeDrrwF4OT6e8Hfe4biDA7RvzwfbtmFlGJz28mLKI4+gunenSXi47j07adJ/Y7oefBD69IGZM/Wl/08/5cWMDL5v3RrvLVsYVqnSRe1zKCqhu2ePDoQmTYJRoywnYwm4PTzgiy/grrvg/HkevO8+eP99hrz2Gjvef5/JjzyiV0eXhwBYHhfHqEOHsFWKFf7+HElPp2/FijR1diYhJ4eEnBzic3I4n5XFvtRU4rKz6bB1Kz+8+y4H7r+fttOn/3d8IYQQQghxS1kpBYZxXZOYXW6S3Ns9oZufUrrDR0AAPP00HD0KERG678S338Lnn4O9PXTpovtSdOumw/ny7KP69XmxZk0q2tjgYG1NUocOmIG1CQkEubkx+tAh5ltGs/b39IRPPoG2beHPP+HkSbjvPvjoI2jSBGVnx4aGDYnx84OJE3khNZXFKSlUtLVlSp06JXuiQgghLlHaEroy8255ZGeHY52q1AfAnq3z7HmqXhYz361G4nYvuOs07gFp/PDQw/T+dS5LO3Xi5e+/52jfvhyrVg37s2dZ27o1Z3r0wHf+fL58913GLlmC4759/x3jo4/gu+/0z8uWQffuOsn73HM0NZs5fPfdrE1N5c49e2h15528OGIEaY6OhffQTU+HRx4BT0945pmiz6tnTwDcAV5+GWPnTib++COze/emetWqVLazy6vdu9nSMzjbMAi1jCv7ulEjqtnbF77viAiMKVNQqalU+fRT2LkThg27fHuEEEIIIcRNkdsrt7gTsOUpoZufUlC/vr717g1pabBqlb799hv8/TdYWUFwsE7wDh+u1y2PvPN9X8gdtTfQywuAZf7+3L1/P79fuKBHLzo4wJgx+pbrwQd1Z5WQEFp88gmEhcHcuXxcsyaNe/fGNiAAIiNh+3ayp0zBJigIVczzrAghhLh2yjBuXbWC/DPvAucpMPOu0penZwC9sMy8axjGjivtt1WrVsaOHVdcTZQxq1bBkPEpJO52AcCqVhrmpok82rICLyR8TcMZTxPv4kJUx44EvPACHzRsyBcREWwfMACP1FQ9Za6bG/zxB1gmIePTT3WQkjuBmaOjToT+8MNFx14bEEDnjz8mpVMnnPNPWhYbq2vkbt2qe+gOHnz1J3T4MPj6MuOuu6hgb8/x7t35rmlTjrZrR/cdO7D791+67dpF9MCBJIaH8/nSpahp06BTJ8htw7ZtMG4chIQA0HrhQl5dtYpBK1fCwYOwfj1UrgyNGl3Xay4ulmU2M+fcOVq7utJcJocQQohLKKV2GobRqqTbIW4uibWvzHH9ejLMZt6tWxcrYMKxY7xQowbTrjLLqNauBcAIDr7ocVrHjleepLecMQw4dEiH78uXQ2ioft7XV/fa7dlT3yTnqH106hQvHD3Kn02bMqjAaMSCHtiyhVeHD8fGzw9jzx4anLl0sOyarl0J+uUXHC5cYHO1asw4e5Yna9TgDje3y/Y2F0IIcW2uFGff0oTuzSJB5u1r4vHjTN17hu6H6rPzq4rEnbOCdH3lubJrGkntUsl4Mhzcs1kdEECXPXtodegQP4SG0vjTT3WRrdhYeOwxcHLSvXStreGFF3Sv3UGDdML388+ZkZDAD56ePHPuHCOmTAHAaNwY9f77sHs3vPQS3HOPLt3w0086EXyN4rt1o8KqVQDk2NriP2sWD7u50f7112l78GDhG3XrpqPVpCSdqM3KgsREeOghWj7+ONXs7JhbuTKOtWtjkzuJwSuvwFtvSRmGG/T+yZO8dOwY3StUYEVAAAA/njtHeHo642vWLPZJA4UQoqyRhG75ILH2leUmdKfVq4fZMIotoZvVqRO2l5vTQXDyJCxYAIsW6QnWUlN1n4727fWUFz17QsWKJd3KkmMyDP6Ji6N3xYpXTLiuiY/n/gMHOJOdDYZBXxsb7n3zTWIqVeLY0KEM+t//CN6z56Jtwnx8GDh1Ks8HB/Nk9f/mM081megaGsqJzEzSTSZGVqnCjIYNb8o5CiHE7UgSuqJMMxkGX0VF0d/Tkxr29mSbDFKTrVi/HhYuhF9+MTApg0p1chjazZasrmcx+SYxq1EjXcusKDk58NlnMHRo3iRpJzIyeDI8nJerV8f0+ecE/e9/2OXk/LfNiBHw88/wxhswefL1ndCBA2Q9/DBWrVvD7NlYpaZiZRhk2tqSGBjI797eDNq4EScrKzwmTNC9bmfPhpEj4cQJ2LRJl1fw9ITKlel9+DCx2dl08fDgxJw5vP/LL9R0d4fduznXvz9vTZ7MZy1bXl9bBXft28ei2FjqOjhwtF07jqSl0WD7dgAeqlqVb3x9S7iFQghRsiShWz5IrH1luQndj+vVI7sYE7qmO++8fEwrLpKVBStW6P4XS5bA6dO6NEOrVro8wwMPQNOmJd3K0s0wDCLS0zmekYFhGPTOV8auuq0tHZcvZ9KxYzQMD8fa8nchw9aWf+6+m4FvvqnLOlSrxrL4ePru28cQLy/2paZipRQH27QpqdMSQogyRxK64ra2b5+eKOHAAfjnHz0Ea8AAGD8eatXSt+t28qSeiWHBAj2mKzlZByjHj0PVqjfe+FOn4LvvyIyKIu6554ipXh3/HTtwzMjgLR8fnvP11SfUq5eOTO3tdRLaMqkawKiDB1mXkEBDJyf+jY+nmbMz+1q0gGnTML36KtOHDOGpX3/FXnp2XJe6W7dyPCMDBaR07Miy2FiGHThAJVtbqtrZsbd165JuohBClChJ6JYPEmtfWW5C99P69ckwm4stoZv7WFw7k0n32P3nH13KbdMmHVrXrq3r8g4dCs2bl+/eu1eSkJ1NhU2b8h4/XK0ac8+fp2fFivSrUIFJmzbxg6srdwwZgr1lbhAAo18/lnl48HaHDqwcPZqXIyP5Pjr6lky6LYQQt4srxdmS5RFlmp8ffPihnhghPBxefRU2bICOHXWw1r49zJ2r6/hnZ1/jzmvVgq5dYcYMiIrSk6jt3Fk8yVzQPYMnTcL+yy+p1qgRle3sAEh3cKCWp6deRyldJmLSJN2GfMlcgEq2tkRnZ3MwLQ2Aw2lp5CiFacIEvu/RgycXLiTu2LHiaW85sis5mYbbtnE8I4MWLi4YwN379zP60CEA7qlcWb/WZnPJNlQIIYQQpUp5ncSsNLK2hqAgPbhuwwY4elR/b2jZUvfV6NZND3obNAi+/houXCjpFpc+Hra2PFatWt5jP2dnmjg782dMDGMiIoiqXJk6wcGs2rePuj//zJx77mFjr16oJUvo+9NPrHruORwrVeKejz/GnJRExubNeqRkRkYJnpUQQtwepACkuG3Ur6/Lxj7zjO7QevKkzoXed59e7uQEd9wBTz4JAwdeY3lZFxc9fe5N5JVv5oZ6Dg7/LfD2hjffLHSbKnZ2pJvNnM7MxNfJiUNpaRxJT+e3Cxf4dtQoRq5ciWfHjjBvHiGBgXx65gxv+vhQ19Hxiu05kpbGzpQUojIzic3OJtFkIjEnh5MZGThYWfFizZp0u8ldGjLNZuZfuEB7Nzd8HB0xDIPPzpzRPV/ydb8+m5mJyTCokf91uwEr4+OJSE+niq0t3zZqxNiICP6NjyfLMHC1tqalqytZhsGYw4eZ4uPDkthYDqSlMaNBAxkWKYQQQpRj1vniAJkgqnSpUweef17f0tJ0r901a+C333Qptyef1D12e/SAfv2gdWtdrqG8+7JRIz5t0IA/LlxgoJcXd7i5sTkpCQU0c3bGx9ERn3r1eNvFhdlNm7I2Pp6hLVuSaWvLbz/9BJmZtP36a2K/+QZbk0ln2q2s9BwnU6ZAhQolfYpCCFEmSckFcVvLzoZ163QP3d279dxix47pWXAHD4bq1WHUKHB2LumWatuSklibkMD4mjWvKjF4JjOT9rt2cSIzk3fr1uXlY8eY37QpLx49Skx2Nu23bOGPWbNwPn+e7j/+yEoPDz6oW5cXLcnQ148dI9Vs5mPLcMDdyckMCgsj0WQiIV/9YCvA3cYGdxsbvO3s2JOSQj9PT369yUXIXj12jHdOnuT+KlX4oXFjdiUn03LnTgDOBgVR1d6eWVFRPBYejrOVFRfat7+qmaBDkpL44NQpzmdlYaUUdkphZ2WFnVJ42NiwLC6OFJOJ5HzDwgzDYG1CAg5WVlS0taXJ9u2Ygd4VK/J3XBwAR9u2vapkuRBC3C6k5EL5ILH2leWWXPi2USNis7OZcOwYL9asyQf16l3V9lJyoeQYBuzdqwfjbdgAW7aA2QyVKkGfPtC3r07yuruXdEvLhi6hoaxJSMDHwYHj7drBgQOk9+iB45kzRA8ZglGjBp4hIdhs3gyBgbB+PTg6gkw2LIQQF7lSnC1/NcVtzdZWD6fKlZMDP/2k6+6+/bZ+7v339RxnI0aApepBiWnr5kZbN7erXr+6vT1H2rYlJjsbNxsbXjl2jO1JSURmZDC6alXmtGnD8s6dGdytG9888QRHqlVjz7hx8OCDALx18iQA79eti62VFfOiozmTlcUT3t5UtbOjn6cnteztcbexuaiXSc89ezh6C4ZKrYyPB2B/aioA25OS8paFJCfTz86Ot06cACDVbOZwWhqBrq5X3O87J0/yT1wcrV1dMRsGSWYzWdnZZFp6OyeaTDQqkJhVStE5Xw+CxA4d6LZnT14yFyAiPV0SukIIIUQ5Zi29csscpSAgQN8A4uJ0J5ClS2HxYvj+e51r7NBBJ3f79tWdQ+StLlwXDw/WJCTQ1MlJP9GkCYfCwnh2zhw2N2tGjo0NA0ePZmFICDz+OLi5Ya5SBavly3WCVwghxFWRQSSiXLGxgdGjYe1aiI/Xw6wqVIAxY3Rlg969dU0tSx6xTLCxsqKqvT1O1tb4ODjw+ZkzAAR7eABwvEoV4n77jRwrK7ru3s3jTz0FcXFExcVxZ2goDpmZ7EhOBmB1QgLt3Nz4tEEDXq1dG38XFzxsbS8ZMljP0ZEj+SY+uBlSTSZ2p6QAujZwZHo6Tx85gpOVFVbo3syLY2M5mZnJWG9vAA5YagkbhsHWxEQy89W4NRsGB1NTWRwTw+r4eO6rUoW1zZuzrnlztrRowc5WrQhr04apdeoAVx4m6WJjQ4CLy0XPRViOn9uG0xkZhKelkWYy3fDrIYQQQojSL38N3dthJGR5VLGiLtn2889w/jxs3AgvvgixsXri5SZNoF49GDdOJ36lHOzFXqxZk0/q18+LqQGauLlh6tSJCo6OuFhbsys9HR55hDMffgiA1fnzusNJUJCerU4IIcQVSUJXlFseHhAcDLt26UnV+vWDQ4d0CYbGjfWkCVu3XsdkaiUowMWFVEsSs62rK/ZKsT4xkS99fan/88/0/PFHnNLS4P33cW3blrXPPcf6Z55hZ2wsWWYzR8+e5c4CScqLZGXB+PE8/eKLDFm0iDhLIvi1iAgCQkLyvrhEpqdz74EDFyU496emMvrgQcLzPVeYbLOZUxkZjD96lBzDYETlyqSazdTZto1sw+DeypXp7OHBWydPMjAsDAU8Wb06NkqxJDaWXcnJvHj0KEG7d/PY4cMAxGRl0WfvXpqEhDDAUlKif+7EcwX4Wc4/7ire+CaWWh1OVla4WFsTkZ7OnpQUuu/Zg9vGjdTcupVG27dTZfNmRh44wNsnTjA/OpotiYmcyMiQL3pCCCHEbebKhZ9EWWJjoydZfucdXZbhxAn44gto2hS+/VZ3BqleXc/PMWsWWELPcs3B2pqna9S4aNScvZUVG5o350xQEBNq1uRUZiZpJhPL7r6bSgsW8PqYMRAaCsePwx9/6O7PHTvqOUyk3IsQQhRKSi6Ick8p6NVL38xmfRX+hRf0lXjQ86F16qQvGpf2C8af1q9P74oVSTebaeTkhL+LC4tjY1kcG4sC/IOC+P3OOxn23nu4Aj927879//7LqTlzOHH//Ry5916sq1SB117TJ7xqFTzwANSuDR9/rDPe06bhC3yzeDGZv/1GWqNGvLZ+PdmjRnHW3x9ve3sePnyYVQkJNHJ05A3L1flHDh9mS1IS9R0ded3Hp9D299+3j+VxceRYEp3P16jB6KpV+SMmhlr29vzl50cjJyci09MZGxGBl60t42vWpImzM61cXZkbHc3c6Oi8/X1//jz1HR2ZGBkJwBQfH3pUrEhFGxvqF1Eawc+SpK1pb3/F17tvxYos8vBgoJcX3587x8G0NPrs3UuG2czoqlXxdXLCzdqapbGxrE9M5Od8bQN4tVYt3qpb94rHEUIIIUTZIJOi3d5q1dJVAh5/HNLTYfVqXc5tzRr46y+9TqNGMGQIdO2qO4/IxGqaUgpbpWhoKcXwa3Q0GxITifHw4J377qPP2LHc4etLztChnLG2pvbSpXrDv//W30EsI/KEEEJoMimaEEWIioLNm3WA9s8/cPQo1K2r85y9ekGrMjAFTIbJxJqEBAygroMD0dnZ9NmyhX2LFrE/J4exDz3EX48+StPDh8msXh0XS+ITW1sID9cneuaMzmpnZOjZIJycOLZtG8989hk/ff01NmYzzpZavEfffJN6r7xC4927OZSWxvBKlfi1aVMMw8Bj40aSTCZGVanCnMaNL2nriYwMfLZuZZCXF70rVqShkxOd3N1RSmEyDBRcdqK4DJOJI+np3LF7N8kmEz0rVOAfS+2Mpk5OfN+4MS2vor4uwO/R0QS5uVHDweHShSEh8M038MQT4OYGixbBhx+yq1EjHh4+nN0NGzKjQQOerF79kk2Tc3I4mp5OVFYWY8PDCXRxYaGf31W1SQghSiOZFK18kFj7yvxCQghLTWWZnx9hqakyKVo5YzbrSZgXLYIFC/R3CJMJataE1q11T94ePXRCuLw7kJpK05CQvMcNHR0JT0/Pm7j5f5GRTIqM5IWUFKZlZsLIkZhr12bKu+/yUK9e1CwsPhdCiNuQTIomxHXy9tY9cocO1WUXZs2C+fNh4kR9GzFCX31v3x4qVy7p1hbOwdqa3vlKC1TPySHV0ZGm991HutlMdycnFj7xBIFPPkn22bP0eu89fhsyBLdmzSC37tW8edCsmR5blpAAM2dSx8WFDR078vLQoXjb2THlyBF+f+MN7po8GX78EfOHH4KbG2GWycxOZGSQZKkjW1Tt3TWW5OubPj55ZQ9yXc0EIw7W1jRzcSHAxYWNiYkMr1w5L6E7uFKlq07mAgwr7A2Njta9A+67Tye5Z826aHGLM2dYGhpKy6++omNODlStCtYXD7x0tbEh0NWVQHQd4piyVM9DCCGEEEVaGxhISFIS3SpUILe7TIBl1M/VeK9uXSrYyFezssrKSnf8eO45fUtO1j12Fy6EnTvhzz/1eq1bw6BB0Lmz/tm6HNboaOLszLrAQE5kZHA0PZ327u6MOXw4b+6MnZaSbssrV2Zamzbg5UX68OG8fPfdbB86lJopKVClCtx7L3TpUpKnIoQQJUp66Apxjc6cgc8/1xUIcidBqF0bunWDhx/WPXdLczzefc8eVloSnY9Vq4atlRUb164l3tUVax8fjrZrp8ePde0K9vaQmqqjzblz9TS/8+eDiwvBu3ezIzmZVLMZN2tryMxk0pIlPPvll+SYzSS4uDD61Vf58emn+e7cOSYcO0ZTJyfOZWUR06ED8dnZfHbmDM/XqMGBtDS+iopiQUwMMe3bX7YnbqHMZl3YrHZtNijF4P37CWvdmkFhYWxNSmJf69Y0vYYvVRftd+VKCAuDl1/Wmf1q1XQP3b/+0o+fegoaNWL/99/TdOzY/7b18gJPT3jjDbjnHv2cYeRNiTw8LIzwCxfY3aoVODqW7g+NEEIUQXrolg8Sa1+7A6mpNHZyuu6yC2vi4zmVmckDVasWc8vErWYYOqm7erUOo3M7p9apA3feCR06wODBeqLm8qr/vn0cTU9nc/PmNN+5k8iMDGyUIrVjR+ysrHh12zbuePZZ+m3dquPm9HT9PeXsWTLc3Eg0mahgY4Od1LcQQtxGrhRnS0JXiOuUmamDs82bYft2WLxYJ3hdXeHJJ3UP3qZN8/J3pUqqycSsqCj6e3qyLzWVx8LDebJ6dV6oUQOX3MTili06svT1LXQfy2NjmXPuHCHJyfSqWBF7Kys+Pn2aZseO8eEXX9Bjxw62Nm7MHTNmYFhZ4efszEPVqvHskSN42tjg6+TEpqQkOrm7sz4xEYD2bm5sbNGi6IabTHp64Q0b4O674f77dXmIAQP0G9G+Pfz7r06O2tqSbjJhAE5X0/3h1ClYuhQaNoSAAD2t8Ysv6rpdoN/IoUNhxoxCu2SbsrJY+sYb1Ny7l+YVKug2hITo0hXe3vrxyZMweTK8+CKRbdrgs3ev3rhpUxg7Vk8E8dZbYGd35fYKIUQpIAnd8kFibSGKz5kzur/AvHl6vq8LF3To16aNnpy5WzfdWaQ0foe4WSYfP86UEyfyHjd1cmJ/WhprAwM5kp7OuIgI0k0m3kpN5eUOHfh2xQoeGT6csHr16P3OO5yuVAkAF2trPG1sqGhri6etLcMrVcLOyorZZ88yvHJlniikJJoQQpRWktAV4haJjdVX3ufN05Ozgp71dtAgPSFCjx462Xs7i83OxmvTJgAWh4TQb8IEZvXty4lPPmGMtze17e159+TJvEnKCnqoalW+KSKBDMCbb+oer7kcHHS0azZDUBBY6s1hba0j4YYNYcIEPa6tMDk5OitvGHrIVm45CKV0othkgkmTdIR95526R8C1iImBxx7T7czM1GUbNmzQCd6oKH7s3p0RlStjtWABpKXpbZ54QncBz22HEEKUYpLQLR8k1hbi5jAM2LVLD4T791894AygSRPo31/33PXzu/YQtKw5l5nJp2fO4GJtjaOVFQEuLnTds+eS9YZWqsRYb2+67NnDW0uXMv6TT7DNzibD3Z1TLVow77XXsI2MZEeTJmwyDEyGQWx2NiagrasrW1u2vPUnJ4QQ10kSukKUgNOn9URqS5fqW1aWHhXUpQv06wd9+oCPT0m38ubw3ryZs1lZrA0MZMO4cbz+008wbhw88AC89hrY2tL38cdZa23NmL//ZviaNaxu3pzJDz7Ic46OfLRunc6Ov/WWTqoahp5d4s03dZQ7ciTMmaN7vs6cqbs0DB0K7drp3rQ7d+pEau6sdgC//qqz6XZ2OslrYwPbtul95vbAdXXVb9r+/XDihN7/tGl6Novikpysa3KcOsWGl16i0513cv6OO6h85AjmffvIWrMGh6++wlCKiIkTafjUU7BxI9x1lyR3hRClkiR0yweJtYW4+cxmWLcODhyA2bN1ohfA2RkeekgPROvaVVf0ut1lms04r1+PKd9zwR4e5BgGQytV4tkjRzgbFETV0FD9wphMOs7Pyspbf+u4cTzQujURNWviaGVFFTs7jrdrd8vPRQghrpckdIUoYdnZunrBwoW6LMORI/r5Ro2gVy9dmqF16xJtYrGacfo0444cISooiMFhYbz1+ed0+e47vdDVVSc1AbNSWBkGZytWpFpcHEe8val37hzKbNbrtm+vg7KICD0Zm6+v7u361FNXX292zRo9bi13n6ALlvXqBV98oR9366a7Ptx5JwwcWDwvwuUkJ4Nh8FtGBsMPHCCsdWuaODkRtGsXh2NiOL5kCR4zZpDi4ICdtzd2x47BJ5/A00/f/LYJIcQ1koRu+SCxthC33unTsH49LFmiRwCazToE7t1bjwC88049IO12nVit1pYtnMrMzHt8T+XK/BodjY1S2Frq6yqldEeM6Gg9f0Xduhft44K7O2+OGoVL69Z8WqtW3jbhaWkk5eTQys3tVp+WEEJcNUnoClHKHD4My5fr29q1uu5urVo6sdu5M9xxh74SX5almkw450aXhqF71Kal6ZOMjMRYuJCEmBiOdezIq3XrsmDUKJwOHYJHH9X1KY4ehYkT9bbOzvDRRzBmzPVNHBYergO95GQd7D3zjE4Ut2mj9/3jj7o2xi22LiGB4NBQlvv7U8/BgQbbtwO690HGpk2seuEFHLOzdYLbzQ2OHSsfXTKEEGWKJHTLB4m1hShZaWmwbx/8+Sf88otO9oJO6D7xhO6T0KjR7TWg643jx1kWF0dcdjbja9XidGYmUy11djt7eLA6MPDSjc6f1yXUPD3JmTIF8yefYJedDcDGZs24IzqahCee4Ne9e/myb19e69+f4YXMjSGEEKWBJHSFKMWSk+G77+Cvv3Ry12TSNbLuvx/69tXlGcrFZK2pqbqerbv7f88lJuqEa3p68RYfXr5cd2Xo3r349nkdErKzqbBpE1Pr1CHYw4MOu3fnLWvm7Mzpc+d4v3ZtHlEK/P11yYoXX9S9iYUQopSQhG75ILG2EKWHYcCePXoE4G+//TeFRJUqes6Ou+7S9y4uJdnK4pdtNrM5KQmTYVDf0ZFaDg5X3MbIzibj/HlOvvoqTsuXUyM+HpWTk7f8pL8/tQIC4NVXdQ8bJ6ebeQpCCHFNJKErRBmRkqJLvv74oy7PkJKik7uDB8OwYbr3rowKur003r6d+o6OjKlalcH79+c9/2LNmnx+5gxPeHvzYf360KyZru0LZPTti0NSki7CfNdd+gMihBAlRBK65YPE2kKUXseO6Spja9boqSHi4nQ52c6d9cRqvXvrimO3U+/da7U8Npbe+/axrXJlqo4bxxazmb5bt+KSkZG3jmFtTcqiRbj27KlHBRpG+X7RhBAlThK6QpRBOTl6SNXKlfrKe2Kijidq1oQJE3TdLG/vkm6luFGPHT7MD+fPM8DTk98uXMBeKTINg1+bNOHNyEh8nZz4s1kz2LSJxIUL+fL0aZ5esADHfPXEUqdPx/mZZ/SD6Gg9KVz37tdXnkIIIa6RJHTLB4m1hSgbcnJg0yY9b8fixbryGOjeuw8/rJO77drdvnV3ixKWkoJfgb9hdyqFvZMT/9jakjh0KO5Hj+oF9erpF2nJEl3E2N+/BFoshBCS0BWizMvK0j13167VPXf37NG5us6d4Z579IQIdevKBeSy6HRGBg22byfDMmnb6aAgFsXE8HC1agwOC+NEZib7LDPmzTxzhicjIqgUH895GxtUjx7EDByI26pVTF66lHfat9cFmPfuhfr14d9/dS9eIYS4iSShWz5IrC1E2RQeDqtWwaJFsGKF7nTq6Ql9+ujybj17godHSbfy5jMMg98vXGBBTAy/RkcDMNjLi0Npaexv04ZRBw+yf/16+p06xRvz5uluz46O0KIFrFsnX7SEECXiSnF2eajOKUSZZmen5wl74w3YvVuPvH/uOT1v2EMP6dxdtWowcqS+In8bXKMpN2o4OLA831X/6vb2jK1eHTsrK/xdXDiUlkamJdl7IDUVgAsVKnC2Rw+wt+fbSZOwNpvp8/LLuuByWBhMnQrnzsHw4bB0qXwghBBCCCHKqYYN9aRpy5dDTAzMm6d76S5bpjuGeHlBx44waVJeda/bklKKuytX5uN69fKeq2pnx9msLAAOpKWxs1Ej3uvRA9PevRAZCR9/DBs26NnmNm4soZYLIUTRJKErRBmiFDRpAu+/DxEROn/35ZfQrZvO3XXooMutPv+8fpycXNItFlfSvojCyIEuLuQYRl4id39aWt6yI+npAPzr5MQ/rVvTcfdujJ074Ztv4LXX9H1YmE7yPvLIzT8JIYQQQghRqlWsCHffrefrOH9edwQZPx4yM+Gtt/R3CG9vePRR3av3dvweUdXePu/nanZ2xOfk4BcSwt6UFFytrckwmzlmGFCzJsaDD5JUv77+0tWxo655N326roUnhBClgCR0hSijrKygaVN47DH46Sc4fRq++EIHYjNn6lxehQp6FP7EiXD4cEm3WBTGxkr/GXa2uvjPcQvL1MSPhoezOj6evSkpdLaMiZt/4QKx2dnsSE5m5Kuv0mn6dGIiI+HBBwlLSWFSmzZkREXBk0/Ct99Cr14YKSm38rSEEEIIIUQpZW2tvyO88w5s364TvO+9pzuH/Pyz7ixSowYMGADffw/x8SXd4uIzt3FjVvj7M6xyZUZWqUJ1OztslOKp6tUBCLN0pvg7MZHGb7/N7x98oLs5r16th0n26aOLFQshRAmTGrpC3IYyMnTd3dWr9RX2kBAwmcDXF7p2hR49dCwi82aVDok5ORiGgYet7UXPv3fyJC8fO5b3eFVAAB+eOsWyuLi859q7ubEpKYm9rVrh5+KCWrsWgMXNmtHP3h6GDoUVK5g5eDBdf/yRRsnJYDbrOh1CCHGDpIZu+SCxthDlR1KSLhubW3f31Cn9fFAQPPWU7qjq6FiybSxuhmGQZjbjsmED//Px4XUfH94/eZKXjh3jSW9vZjRsqFecPVvXvPvjD+jVS2e6LYlgIYQoblJDV4hyyMEBunTR5VS3bIEzZ2DaNKhTB777DgYO1BMiDBwI//wD0nmzZLnb2FySzAV4qVYtxlStCugeu10qVGBRs2Ys8/PLW+dOS6/dc1lZxGdn5z0fnp4Orq6kLlvGN3368OCSJZxZswb8/KBxY2jeXHfLEEIIIYQQwsLNDfr31xW8TpzQPXgnTYKTJ2HECD0CsEsXePttPRevZbqHMk0phbO1NXUcHFgWF8fa+Pi8EmeO1tb/rThqFKY6dTDfdx9md3dy6tXj182b+fbsWWafPcucs2eZFx1NhslUQmcihChPJKErRDlQpQq88IKeACE+Hv78U8+ZtXmzvrjs4QEtW8LLL+tJEyQGKT2aOTsDUNfSFcLGyorenp55y3MTuvtTUzmcr85uuOXnXcnJTBs+HBuTiS79+sGFC1C1qh4q9tprEBqq3/Sff741JySEEEIIIcoEpaB1a3jzTd1Td+VKXdErPl6HkQEBULu2/nnHjrL/HaK9uztbkpLouXcv+y2lF6ItE6cB/BkXR6fnniPJxgarnBxsMjOxf+klgoKCWPDddzx4+DD3HDjA/AsXSuoUhBDliCR0hShn7Oz0UKlZs/QErv/8A6++Cu7u8MEHeubbwEDdo3f7dsgXw4gS0MCSyG3t6nrR802cnABoa5lU7bmjR3nQUii5kq0t6xIT2Z6UxJLYWA7XqsVzTz5JspcXfP45cfv28eS335JlY8M/L76o3/SRIzkeGMi6995jXUICJks5nlSTiRMZGbfqdIUQQgghRCmklC7d9uGHsHu3HgE4ezb4+8O77+rEb6VKcP/9sG1b2ey5O8fXl//5+JBlGByydI44n+/L0Mr4ePYFBrJt/36OnT5N6uOPM2jjRpqcOMHi114jvFIlap4/T5R8gRJC3AJSQ1cIkefMGZ3g/eILfZUd9LCqAQOgUyc9UULdulJ791YyDIO/4+LoWbEi1krlPR+Xnc3JjAwCXV1ZGx/PlBMnWJOQgKu1NY9Uq8ZHp0/nrTvA05OtSUnc5eXFV40aMen4cf534gSb3nyTOyw1dxf16kW3tWuxNpnw/eEH4qtXJ6x1a4bt38+25GT+9vOjV76ewUIIAVJDt7yQWFsIcTnnz+t5O1auhPnzITlZJ3d794a779aTrNnbl3Qrr86v589z78GDeY+bu7iwq5X+Nxe0axd2SrGueXO98MgRaNDgkn28GhrK2wEBkJ0Nmf9n776jo6q2OI5/T3ojlEDondB7k05ElKKACvpAsaCCCnaxYAcs2MUCKgqoqGBBxQYKUkSkCii9914SIAmknffHGULoAZJMQn6ftbKSW+bePb4s3smeffc+Ap5hxyIi50I9dEUkw0qWhNtuc0PUtmyBr792LRl+/tn1/69SxQ1We/RR+Ocfb0ebNxhj6BgRcVwyF6CQvz91PVW70QULMqVOHZY1asQ/DRrwcoUKjKtenbHVq/N2pUqMqlqVogEBaRUGS+LiqBoSQrOPPoL69eHtt+ny668ErVhBYHIyn8yZw2VTpxJerRov9u5N4ZgYdsyYAQ0bwpgx2f7fQERERERyrqJF4YYbXMXu5s0werQbwjxhAlx1lWvv1r276/Ll6WSQY0UGBBy3fXT9nGot/x46RN30ydlKlVxz4dWr4d5703Y/1bQpk9u0YWrjxnDCU3YiIplFFboiclapqbBsGcyY4QYkLFvmPmwuVcot1tq3d5+8Fyzo7UjldC5fvJjY5GSm1KlDgwULqBUayrc1a558YnS0G20MbCxblmLbthGYbtgaAF9+6VblIpLnqUI3b9BaW0TOR2Kiq9z95ReX7I2PB39/93fDDTdAu3aukjcnWXLoELU8/94V9PPjYEoKia1asS85mcJ//cVblSpxf6lSJ78wNRWWLmXwN9/w9KBBxx+LiXH97UREzoEqdEXkgvn4QM2a0Levq8zdtg1efhmaNIFvv3WPUkVGuuTuyJFuUILkLCUCAph38CDhM2eyOiGBqp4evCd5/HEA1kVFUe3994l+801WlS9Por+/a4jWsCH20UfZsH8/F8MHgiIiIiKSNQICXNuFd95xc3knTYL774d//3W9dkuWhG7d3MDmXbu8Ha2TvkK3VmgoydYSk5ycNhwt0t//1C/08YFatZjbrRt1R4xgQfpWDAUKwNNPZ2HUIpIXKaErIuesUCHXduHrr2HPHpg5Ex5+GNavd60ZIiPd0ITBg+G//0B5P+97qmxZXqlQgYHlytEgLIzOhQuf+sT27eHff0n44w86lipFz6uv5t3vv6fBl19iGzVi7bPPYjZv5q2nn2bC3r3uNfv3587JFyIiIiKSLUJCXPHHq6+6wczz5rkuBdOnQ9euULw4NG3qunvFxnovzoh0CdsaoaGAa7uw2/PE2oktGU4U6e/P4kqVGHHVVccfeP55Dv77L/98+imMGMH6gwdJTk1lb1ISo7dv52Bycua+ERG56CmhKyIXxM8Pmjd3021XrYK//nKVvPv3wzPPuMm3lSpB//7w999uNoBkv6iQEB4pU4ZnypVjfsOGXBIefvqTa9WiRqlSfFOzJv1KlqRaRARLIiLYePgwo6pXZ3rt2vT94Qf2TJ0KEREuw3/PPdn3ZkREREQk1/Lzc6MZXn/dDWWeNQuefNL9/XDTTVC4sCsOGTrUdSvITr7GUDUkBF+guadNwq6kpLNX6HqU8Ex/++jKK3m8d+/jr926NfVvuQX69GFtdDSDp06lz6xZ9Fq5krE5pURZRHINJXRFJNMYA82aucXX0dYMH3zghqm9/bY7FhYGjRvD2LFw6JC3I5aMaOQZ5jDv4EEWx8UxrUMHKm/Zwu3XX+/GGJcqBcOHuz4cIiIiIiIZFBDgKnMHDYKlS+HPP+Ghh2DHDnjggWMD1yZOdDM8ssOSRo2Ia9WK2ukqdHd5qlKKnCWhW93T1izF15eXb7iBzs8/77LWTz1FSLrsdNt//mFg27Z8Gx1NlU2bqPrCC+5NiohkkBK6IpJliheHPn3cIIRdu9wsrfvugwMHoEcPN0StVSvXmmH2bNCTRjlTrbAwfIDrly1jWkwM2zp1YnuJEmwvVgxmzSJ57Vp2tW1L8iuvuBLsbdtg4UJvhy0iIiIiuYivL7Ro4WoEli51BSK9e8OECa4Xb+HCbvunn7K2MMTXGAJ9fNLaK6Sv0C18loTu0TYNR/3YvDmUKAH9+pHi48Onl19+0mvqrllDy2HD3JsUEckgJXRFJFsUKADdu7u+Wf/9B5Mnu767cXHw7LPuk/kSJeDaa91ghJ07vR2xHBXo48OIKlXoHBFBfEoK9cqV4/mpU6k2bhy2QQO+2LePPq1b47dvH5vefx+ioqB+ffjkE2+HLiIiIiK5VL168O67bmbHzz/DddfB559Dp05uZkePHi65m1Ut3Qr7++ML3Lt6NQM3bqSwvz9+PmdOoVQ5zeDhxMhIWgwbxl0PPZS2b2PRogA8OWZMpsUsInmHuRimlDds2NDOnz/f22GIyHnaswemTHGJ3GnTXDWvj4/7hL5ZM7j6aqhVyw1TEO86mJxMqK8vH2zbRt/Vq1l/ySW8uWULH65bx8qbb6bMif2/pkyBNm28E6yIZDljzAJrbUNvxyFZS2ttEckpDh92PXfHj3ct3PbudZW7118PN97oikSMybz7fbFzJ8vj4wFoEBbG1UWKnPU1961ezTtbt6Zt72/enC1HjlDL8++ovfRSAAJ++43EK644/sV79rgZFSKS551tna0KXRHxusKF4X//g3HjYONGmDkTHn3U9cl69VVo0gRKl4bbbnOf0m/Z4u2I8658fn74GJPWV3f+wYP8GxdHncKF6T9oEGurV3cT8uLj3WL03XfdCz/9FHr1Ul8NERERETlvQUGuVuDdd12XrwkT3PaoUW5Qc1QUDBwIa9dmzv1uKFqUweXLM7h8+QwlcwGuLlz4uO2l8fEs9SSF00s6VfuGRx454aQkWLw4w/GKSN6hhK6I5ChBQW4x9tJLrq/u5s3uE/hWrdwjVffeC2XLQrt2bg5Xug+/JRvVCgvDzxiu8/TVrR0WRmKzZlw1ahQ89hibjeHrq68m9YcfYMQIuOUWGD0aihU7lpH/6it44QWstSSlpnr1/YiIiIhI7hIQ4NovjBvn2rWNHg3lyrmEbqVK7m+K99+HffuyNy7fE0qEh2zaxEfbt+ML5Pf1pcN773HtwIEAPN2r17ETu3SB335jZ9OmTHr9day1rqKlbl1Yv/7YeT//7IolRCRPU8sFEck1rHWfto8cCV9/DWvWuP2NG7u2DFdfDVWrZu5jVnJ6L23cyE979xKTnMzbUVFMi4nh+Y0b8QF6Fy/OL4sWsal7d3dy7drEtG5NgXfe4dMbb2RBtWoMfeopAK547TV+b9CA8vHxjHrmGUz+/LS66y63Qg8K8t4bFJGzUsuFvEFrbRHJTbZscb12P/0Uli0Df3/o2BFuuMEtL4ODs/b+M2NiaLloUdq2ASxQNSSEQGNYHBeXdizS35/+W7eS8NNPPFa9OoEDBqQd2/z225S+7z638fbbrrIFjv2xcxHkckTk9M62zlZCV0RyJWth+XL4/nv3NW+e21+5sluwNW7sPuRW393s89WuXfxv2bLj9t3x008MmjiR4pMn81ZSEhG3384NU6bgm5rKlsqVyRcTQ0hMDL89/TRHDh7k2ldeYUvhwpTas8eVVowaBYmJEBjoyixEJEdRQjdv0FpbRHIja2HhQhgzxvXb3b4d8uWDrl3hppsgOtrN7chsf8fG0mzhQgAWNWzIe1u3MmL7droWLkygjw9feGZO7GrWjCIBAXy3ezfXLl3Kyr17qdyt26kvesUVMGmS+1kJXZE8QT10ReSiZAxUrw5PPAFz57rWDO+959oxDBvmPoEvXBguu8x9oH20mleyTq3Q0OO2G4SF8Ue3btw/bhwUK8bCQ4d46+678fW0Vyj17rvkX7wY/5YtufLpp7n2lVfY0agRFT//nJiXXoJ166BlS/c/YosWbkUuIiIiIpIBxkD9+vDGG+5vhcmToVs3+PZbt7wsVw4GDHBFIpkpfcsFX6CipyQ4MiCAmunWy6G+vgBp+/6qVo2YSpX4q0YNuj733LELXnYZdto0UocOPb45sFqWieRpSuiKyEWhVCno2xd++w0SEmDaNOjd2w1LuP9+NyChYkV3zsSJkJLi7YgvPlEnPL9WOSSEWqGhfL17N69u2sT4PXsoWrEiLF3qGiC3bet66r7xhntBRASLP/qIxIAA1vbrB3PmuP67w4e747fd5kYbi4iIiIicA19fl8QdOdL12x07FmrVcgOYq1eHRo3gnXdg9+4Lv5dfuoSunzGU87QQqxQcfFxCN9hTHlwhOBh/Y7ht1y7u+eEHLnvvPZa0b5923ta+fTGJifg88IB7gu2omJgLD1ZEci0ldEXkouPjA61bw9Chrm/WunUuZ1izJnzyCXTo4Cp5j87pyu5BCRcrPx8fHipVinYFC+JvDK3y56devnwAPLpuHYdSUrgyIsKtmu+669jjYrVrw5QpsHIl4WXLArAzMZFOgYHUrVOHQ3fc4UYYL1rkRhqLiIiIiJyn4GD43//cbLGtW+HNN12xx333QYkScN11sGrV+V//uApdY7i+SBG+rl6d+0qWpEa6hK7xnOdrDM+WKwfA57t2US00lHJBQQzr1w/atGFhgwanvlFUlOs1p4IHkTxJPXRFJE85fBh++sl9Kj99OuzZ4/KKdetCz55uUEJUlLejzP2stRhjOJiczJwDB6gSEsL+5GRqhYamLV5PZU18PFFz5/Js2bIM3LgRgL/q1aNZ/vxuwTp1KtSoAaGh8MILrhWDiHiNeujmDVpri0hesGSJG6T27rvuib9GjVwbt//9D4oXP4frHDpELc+/mWsvuYQK6Z5iS7UW3+nTAbDR0Wn7DyYnEz5zJgA3RkZijOGv2FjWNWnCiG3b6F2y5Olv+NxzrndEQIDrs9umjZsEJyK5mnroioikExTkemd984173Gr+fHj6aVfV+/DDbqhaVBQ88AD8/jscOeLtiHOno0nbfH5+tC1UiNJBQdQOCztjMhdcbzGA3/fvT9u3PD7e/fDJJ26BunWrq9a99FI1RxYRERGRTFGzJrzyiltevv66q9p98EHX2q19exg/HpKSzn6dEyt00/M5zVo4n58fEX5+Lo7QUCL9/dmVmAjA7qQkhnXufOzkb745/sXPPQcDB5L066/Qvj3jH3uM3itX0nvlSu5YsSLt666VK1mfkMCq+Hg+2bGDOPWgE8nV/LwdgIiIt/j4QIMG7mvgQFi/3j169csv8P77rmVDgQKudWunTtCkiUsIS9bJ5+tLoDHMOnAgbd+yuDgAEiMieHv4cI5Yy4CAAHwqVYJnnoEvvvBWuCIiIiJykSlRAh56yH2tWOGWmqNGQdeurlL3ttugTx8oU+bUrz9xKNqJni5bli2nqBq5o3hxvti1i8sLFWLy/v3EpaYSl5LCrsREnnzwQV7s2ZO/d+2idK1aAMSGhpLfs07m33/ZGBZGJeDg2rX8sndv2nWPRrM1MZFiAQFMi4lhemwsydZy+7mUHotIjqIKXRERj/Ll4Z57XEJ33z748Ue44gqX2L30UoiIgB494OWX3SNZF0HHmhzHGEPT/PkBaJE/P3XDwvhg2zauX7qUanPn8si6dTy1fj3/hoW5aXdffkni66/z5c6dXAwthEREREQk56haFQYNcoUfEyZA/frw4ovuib4rr4Qffjj5b4IzVegCDCpfnpFVq560f0jFimxq2pQG+fJRxNMyYXdiIrs8ZcFbixRhRY8eUL48c9u3p/Vbbx178ebNHPS0K+tasiRbmzVL+9ri+YoKDmZpfDy7j15PjyKK5GpK6IqInEJICFx1FYwb5/rsTpgAN93kZnc9/ribilu7NgwZAhs2eDvai8svtWoxsXZtfq5Vi6GVKlE7LIwFBw8S6uvLs56haSsTEtz/EFdcQUD//hRv147EsmWhShVYutTL70BERERELiZ+fu6JvZ9+csndu+5yS86rr4aSJeHVVyE21p2bvir3VAndjIj0JHT/jYtj85EjFPK0Y1geH8/GlBSeHDKELVWr0nLoUPZXrgyLF1Pvgw8ACN2921WnXHstbNvmLmgtT40bx961a9mfnAyQ1tJBRHInDUUTETlHO3bAd9/BmDEwa5bb17ix+5T+0kvdAAW1ZsgaCSkphP75J8+VK8cz5cpBSgqf3nADN3/11bGTGjaEuXPdtDsRyVIaipY3aK0tInKyI0fc3wNjx8LkyRAc7AaodbnlCNekzAZfy57mzYk4jwFlCw8epP6CBWnblxcsyOT9+0mfvWmVPz8zYmN5K39+7m/ZEo62X6hTxzX+/fln90TbW2/BP/9AgwZMbNyYDi+/DEC3IkX4ukaNC/gvICJZ6WzrbCV0RUQuwIYN8NVX7uuff9wjV76+bnBC8+bQuzcULuztKC8u5f7+m6ohIbwTFUWStdSYNw+sZcqOHbTZvh0efpjEoUPZ2rs35YODITmZVF9f/oyNZX1CAjGeqoQwX18K+ftTyM+P4oGBJFtLkI8PvrjhEw3z5TvrELejDiUnk2QtBTVRWPIYJXTzBq21RUROz1pYsABGjIDPP/fkVUvHQ4ftrB9chnKR574+tNby3Z49xCQnY4E2BQqwNiGBzUeOMH7PHn7au5cbIyP5fs8e+pQowev9+mF++41UHx98UlOPv1jFirB2LQC/NWxIu1dfBaBl/vzMqFfvAt+9iGQVJXRFRLJJbCx8/z1Mneoqd1evdp/Ut2wJ3bpBixauD5cKRy/MjcuW8cWuXSftf71iRR4qWpS9nTpR8PffaffKK4wBIp98kp+vvpqHrrqK1aVLg7WEx8VxICzsuNcHHz5MPl9fdnmSsjPq1qVlgQKAW1R/v2cPnSIi8PM5uVtRhdmzWX/4MDY6OrPfrkiOpoRu3qC1tohIxuzcCZ9+ncyj78XDinBCQy233WZ44AGoUCFz7jFq+3ZuW7mSzhER/BcXR7PwcN5MSGDRnXdy5O67ueqOO874+i/btKHnE08QFRbGiksuyZygRCTTnW2drR66IiKZJH9+uOUWGD0aVq1yfbV693bTcfv0gerV3dNPvXvDjBmgtlXnZ0SVKoyoXJmPqlThmbJlWdm4MQHGuD5gAQE8/MILLCtblt8feYSijzyCSUyk49dfs+yOO9gKHFq8mNhOnTg0YgRLy5Rhap06/BQXx97rr2dR9+7cPHEiWMvalSvh5pvh/ff5ctcurl26lGFH+5CdYP3hwwAazCYiIiKShxUtCrfdaWH4PzBiHldfa3n/fTdErWvXY+3aLkQ5T2+3PUlJRPr780dMDDf4+XHFa68RerRv7iuvuMrco268Me3HHn/8wb5rr6Xy7Nkwfz68+iqHb72Vj776ij9jYi48QBHJFqrQFRHJYta6at0ZM+C332DiRDh40PXZbd/eLe46dXIJYTk/pf/+m8sLFmRk1arUnz+fkvv303T0aFpGRrLsttt4fdEilj37LH4HD7rn4BISjr346afh229h2bK0XbOrVaPJ8uVp22PffpsetWrxQKlSvFmp0kn3N9OmAbCjWTOKBgRk2fsUyWlUoZs3aK0tIpJxMUlJFPzrLwCOtGrFnh0+vPsuvP8+7N/vkrvXXQePPAKeh8HOyeGUFP63bBnPlivHlP37+Xj7dowxRPj58Wvt2uTzDFADoHhxNwBk8mS4/XbYuJFvunWj2zffnHTdXxs35vlhw/hrxgzo0ME9WigiXqMKXRERLzMGKleGO+5wvXa3boWvv3bb8+bBTTdBkSLQsSN88IE7LuemqL8/S+PiOJCczMr4eCqVL8/bffsyvHdvhh8+TGyFCvh9/jkUKuSmVaxbBy++CDVqwODBLpn7/vsETZrEoJtuIiglhfWVKrlmaE2b0uXxxyl44ABbjxw5Yxzr0ieKRURERCTP8U3XX83XGEqUcMvOTZtg2DCoVMltly4NDzwAp+gkdkZBvr78UKsW9fPl45EyZVhxySUsb9yYmfXrH5/MBahWzX2vVAmaNAGg24gRrHz8cQASKlWCl15iRrduXPbPPzT/5Rd46CHXL05EcjRV6IqIeFFqKsyZA998A999B+vXu/3160Pnzq5yt1499d09m25LlvDtnj34AKnA+5Ur89mOHfx14AAAVxYqxE+1a5/6xWvWwKFDULcu/VatYti2bfSIjGRGTAxbmjWDxYuhbl0eufNOfr7tNpY1bgzAFzt38taWLdxRvDh3rloFwJhq1bixaNFseMciOYMqdPMGrbVFRDIuPiWF0D//BCC1detTDtmdP98ldX/8Efz83Lr/scfc3wCZatcumDTJVZAcOuSGo9Wpw8b4eKr++SePVK7Mw6VL0+e77xh3/fXHv7ZfP7jsMrjmmkwOSkQyQkPRRERyCWtdoeiPP7qvv/92+6pUcdW8jRtD8+bg6+vtSHOe2ORkftizh6VxcfwVG8un1arxz8GDTImJoW3BgjQLD6d4YOBZr5NiLYdSUhi+dSsD1q/nQIsW+BnDjCZNaL14MdcNGsT4okXxDwykdPnybDlyhCAfH1KtJdFahlSowGNlymTDOxbJGZTQzRu01hYRybgjqakEzZgBcNaBuUuXwvDh8OWXsG+fG6Lcv78r6jjFHN5MY60l4q+/2J+cTNuCBZkVG8sTH37Ibb/+SuTw4fi+/76b9AyuV8Sdd0Jysss+i0i2UEJXRCSX2rULfvoJ3nsP/vnH7StVyn1IftVVEB0NateaNb7fvZtrli5lbv36BPv4ED1lCqt696bQ7t1p50R8/z2pERHEJCen7XuyTBmez6wRxiK5gBK6eYPW2iIiGZdiLX7TpwNnT+getXevW/OPGgUbNkCdOm6o8u23QwZqEs7Lsrg4Hlu3jp/27gWgXlgYCw8dcjMhjhzhsf79uebPP91ciUqVXMuyO+90gerxQZEspx66IiK5VGQk3HabeyRr2zbXf7duXfjoI2jXzk3Rvflm+OGH42d8yYWrGhICwM979/Lxjh3szZ+fvz77jCdvv521PXoAMOvee2l3Qrn0gZSUbI9VRERERHKO80myRETAM8+4QcqffOL29esHFSq4cQ+eLmKZqnpoKO0KFkzbvszz867ERFLCwnilRw9ueuIJd3DNGve44PDhsHJl5gcjIudMCV0RkRzOGDeg9rrrXCuGvXtdErdLF1fBe/XVULiwOz5ypBu4IBemYnAwoT4+DNy4kbe2bMEAzdq04fWbbqJSnz7c8OSTVNm8mRHXXEP9lSv58LXX2HnNNZSdNg3eegu2bPHyOxARERERbzhVz9yM8vNzBRsLF8Lvv0Pt2i7RW6EC3H8/7NiRiYECNUJD036OLlAAgJ2JiWmDfteUKsXabt1cw99x49y+oxln4L2tW2mzaNEpvzr/9x97EhMzN2ARSaOWCyIiuVhSEkybBuPHw/ffH1vkXXIJ3HgjdO0KJUp4M8Lca3lcHFuPHAEg3M+PxuHh/LJ3L9/s3s2WI0fo+MUX3DN8OH4HD5784qJFYdEil2lXrzG5iKnlQs5ijGkPDAV8gY+stUNOOF4G+AQo4DnncWvtL2e7rtbaIiLnxkybBmS85cKZzJ8Pr77q1vu+vtC7N7zwAoSHX/CliUtJoceyZZQNCqJ/6dKUmz2bDypXprC/P12XLgXguXLleLZcOY6kpjKleXPaz52Lz8KFULs2FWfPJj41lajg4OOueyglhYWHDvFzrVp0jIi48EBF8iD10BURySOshSVL4OefYexYWLzY7e/SxX21bQulS3s3xovN1pUr+eqppzgcEEBsaChDRoxw44mPNj0uVw6mTHFlFSIXISV0cw5jjC+wCrgc2ALMA3pYa5elO+dDYKG1drgxpjrwi7W23NmurbW2iMi5ycyE7lFr1sArr7j2a+HhcMMN8NBDrr1tZrDWEj5zJr2KFSPC35+BGzYQ6OPD7cWK8W7lyvx36BCtpk5lb5cu+FhLatWqbNq/nx/Gj+f+Zs2Ou9b6hAQqzJnDqCpVuLV48cwJUCSPUQ9dEZE8whioVQsef9wVhy5ZAgMGwN9/u168Zcq41lcvvHAs2SsXplClSjzUrx9P9O7Nyz16cNXEibBgAYwZ4xqexcZCjx6g3roikvUaA2usteustYnAWKDLCedY4GhNV35gWzbGJyIiF6BSJfjwQ5g3Dzp1cgPUqleH7t0zpxWDMYYaISHMio1lekwMFYODKRsYyM6kJJbHxbE0Lo6YfPl444EHoEAB7MaNlN69m1tvvx22bz/uWkX8/QHYnZR04YGJyCkpoSsicpGqUcO1u9qxA/79132if+QIPPWUG65Wty689pr7tF/OT3C6oWjFAgNZf3SwxI03uv/Qb74Jc+fC5MleilBE8pCSwOZ021s8+9J7DuhpjNkC/ALce7qLGWP6GGPmG2Pm7969O7NjFRGR89SgAXz2mVvD3323a7sWFQUPPAA7d17YtRvmy8eCQ4eYGhNDw3z5iAwI4Jvdu6k+bx6ve2ZEvNmtG+zbxxdLlvBW167kX7ECrrgCVq1Ku06ory/BPj7sUkJXJMsooSsicpE7Wrn7yCOuMnf3bnj3XQgIcPuioqBmTRg0yE3WlXMzskoVKgUHUy8sjIMnVuJ27+7GFo8c6Z3gRESO1wMYba0tBXQEPjPGnPLvAWvth9bahtbahkWKFMnWIEVE5OxKloShQ936/ppr4L333Lr+zjvPv2DjlYoV+btePf6qV48PKlcm0lNpCzDfMzdid1ISFlicmsqT/fqRMm4crFgBLVu6Pm+PPopJSiLS359dGoomkmWU0BURyWMKF4Z+/Vzh6Lp18NZbLuf43HNQubKr3L3vPjddV50Czq5X8eKsvuQSooKDOZCcfPzBwEDo1s01NvZMCxYRySJbgfSd0kt59qV3O/AVgLX2byAIKJwt0YmISJaoUgU+/dS1W+vc2VXv1qoFN98MnqLaDAvx9aVJ/vw0y5+fcD8/IgMCTjonyVpik5NZGh9PtdBQfK+/Ht5+G3btgt9+cxPcAgO5Z/x4VeiKZCEldEVE8rDy5eH++2H6dNi0ya2/Chd2/bnq14fixd0k3YkTlY88m3y+vhxMSeGkYaPXXgtxcfDTT94JTETyinlAlDGmvDEmAOgOTDjhnE3AZQDGmGq4hK76KYiIXASqVHFjHFatct2/vvkG6tSBgQNh797zu2aRdBW6AFHBwQC0WLiQaTEx1AgJcQfuvts96rd3LzzxBAD9X3uNCQ0asLZRI/p+9hn7lNwVyVRK6IqICAClSkH//q7d6+rVMHo0tGkDY8dChw5QqJA7PmuWKndPJdzPj1RgV1ISCen/A7VpA1Wrup66nkfVREQym7U2GbgHmAQsB76y1i41xgwyxnT2nPYw0NsYsxj4ErjVnvQplIiI5GalSsFHH7nhaS1auKfwypZ17dXOtSV6tyJF6Fq4MLcXK0a3IkX4sHJl/lekCBWCg7miYEH6lChx7ORKlSAkBJ5/HrZt45/27fFPTqbi/Pk89sgjzNp64kMjInIhzMWwhmvYsKGdP3++t8MQEbkoHT4Mf/wBn3wC337rkrmFC8Ptt8OVV8Ill7h+vHndB9u2cZdnGESEnx+7mjfHxxh38Oef4aqr3M8//njsZ5FczhizwFrb0NtxSNbSWltE5NyYadMAsNHRXo0DXCuGxx6DX36B8HB48knXXi0oKGvv+/6mTWx4/nm2FyrEyFdfBR8ffPv3dxlmY/QHhMhZnG2drQpdERE5o6Ag6NgRxo1zn+qPHQvNm8Mrr0CrVlC6NNxyC8yfn7crd9M/krY3OZk16XtUdOwIDz7ofu7XL2//hxIRERGRbFOzpqstWLrUrd0fe8w9PDZ2LGRlfV/18HBevuEGPm3fnuuefZbVtWvDkCEQFMThcuX4bepU9p9nG4b4lBR2HDnCxsOHWZuQwMr4eJbGxTFt/36SUlOZFRvL6vj4TH5HIjmLEroiIpJhBQvC//4H33/vWmR98w00agRffum+ly/vJuxu3uztSLNf4RN6jP2Tvr2CMfDGG67EedMm+PXXbI5ORERERPKy6tXdg2KTJ0OBAtCjBzRtCtOmZU1it3ZoKAAGiO3cmZ5Dh7pMMhC0fTtXtGnDpi5dXG+IiRPdizKQhN10+DCFZs6k+N9/U272bCrNmUPVuXOpOW8ely5eTLelS2m+cCGV585lRVxc5r8xkRxCCV0RETkvBQtC165u1tfKlTBqFBQpAvfcA2XKQN26bgjDmjXejjR7nDg04p9Dh04+qVMn11ts8uRsikpERERE5JjLLoMFC2DkSFdncOml0KSJ25eZCvj7s61pU7Y3a0ajfPn4NyWFpH/+4ZuVK4n67DOGX3stNSdNgsaN3cCON96A0FCYM+eM1/37wAGOWMtz5crxcZUqfFq1Kl9Uq8bQSpUAmLhvX9q56w4fztw3JZKDKKErIiIXrHx5uPVW13Zh+XLXjiFfPpfQjYpyn/4PHQrbt3s70qyTPqFbNjCQrUeOnHySvz9UruzGD4uIiIiIeIGvL/Tq5QovPvjAJXYbN4Y+fWDHjsy7T/HAQIoGBFAjNJQka1mdmspCPz82lC7Nn4MHc9tLL4Gfnzv54Yfd97M8ybb40CH8jOHxMmW4rXhxbipWjB5Fi3JfqVJE+vuTaC1+njkWuxITM+/NiOQwSuiKiEimMcY9SfXII/Dnn7BxI7z8MsTFwQMPQLlycNddMGEC7Nrl7WgzV8F0Cd2iAQHsOV1PsCpVXEmziIicJDo6mtGjRwOQlJREdHQ0Y8aMASA+Pp7o6GjGjRsHQGxsLNHR0YwfPx6APXv2EB0dzY8//gjAjh07iI6OZqLnUd7NmzcTHR3NZM9TEuvWrSM6Oprp06cDsHLlSqKjo5k1axYAS5YsITo6mnnz5gGwaNEioqOjWbRoEQDz5s0jOjqaJUuWADBr1iyio6NZ6fk3fvr06URHR7Nu3ToAJk+eTHR0NJs9fYkmTpxIdHQ0OzzZkx9//JHo6Gj27NkDwPjx44mOjiY2NhaAcePGER0dTbznkeQxY8YQHR1Nkuf/b0aPHk10ugFMI0aMoG3btmnbw4YNo0OHDmnbQ4cOpXPnzmnbr732Gl27dk3bHjJkCN27d0/bHjx4MD179kzbfuaZZ+jVq1fa9oABA+jTp0/adv/+/enXr1/a9gMPPMADDzyQtt2vXz/69++ftt2nTx8GDBiQtt2rVy+eeeaZtO2ePXsyePDgtO3u3bszZMiQtO2uXbvy2muvpW137tyZoUOHpm136NCBYcOGpW23bduWESNGpG3rd0+/e0dlxu8e33yTtp2Tf/c2b17JF19EM3r0LO6/H0aOXELJktH07TuPhITM+90rvH8/AC0++IAhXbtSIT6e+mFhfJqcTIumTdlTowbJ/v6M9fOj8RtvEBsTA8CTH39MZKNGxHlaJzw/YgQvXXstVQMCCPTxOel3r+CkSfDww9T0tHv4/uOP03731iYk0OnZZ7k83XDii/F3T//uZe2/ezmJEroiIpJlSpeGRx+Ff/+FFStcFe+oUdClC5QsCddc43p5JSd7O9IL52tM2s8R/v7sPV1Ct3Jl2LAB9AiYiIiIiOQA+fK5jgc//ODaqg0f7oo0MmvsQ7XQUB4uVYp6YWEU8fenf+nS1PAkXeNSU7G//061r77ipyZNCDl40D3i178/iw4dYndSEls8T77960nsPlyq1CnvUzIgAIDyQUGE+vhwMN0g4hc3buSnvXtZl35wsUguZmxWjjXMJg0bNrTz58/3dhgiIpIBBw7AsmUwfjx88omr1C1QAG67Ddq3d329fHLpx41m2jQAbipalBkxMWxo2vTkk376yfXS7dvX9aE4+piZSC5kjFlgrW3o7Tgka2mtLSJybo6uCW0Oq+jLqGnT4KGHYOFCuOQSl+xt1ixz77H58GHKzJ7NsKgoroqIoMzs2bTcvJkZN9+cds7cyy/nuTZtuKd3bzpGRHDjsmXMjI1l46nW2MCH27Zx56pV3Fm8OL/t30/z/Pn5rFo1ABotWMD8gwe5ITKSz6tXz9w3I5IFzrbOzqV/MouISG4VHu4GL7zyCmzZAt9/D61bu4XiFVe4/l3vvJM7++0Oj4ri82rVXIXu6cqOr7zSrZCHDYNnn83eAEVEREREziI62s3GGD0aNm+G5s1dLUJm9tctFRhIuK8v3+7ezZtbtgBQvH59rh48mDjPGrnx77/zy4ABTFyxguFbtzLrwIG0dgqncvRYZEAAkf7+7PT00E2xliWe6t6d6qsrFwkldEVExGv8/V37he+/h/374eOP4cgRuO8+15IhOho+/BA8rYxyvLtKluSGokUp7O/PoZQUjqSmnnySMfD663DLLS6r7ekpJiIiIiIXD/907bhyIx8ft1xdtQpuvNG1YWjUyK3NT7XEPVfGGFrkz8+UmBje3LKFMF9f2hYsyA8tWrChf38YNoyvrr0WgPCRI+m7ejUbDh+mef78p71mrdBQIvz8qBMWRonAQDZ7WjWsTUjgsCfoXadriyaSyyihKyIiOcLRtgv//edaMjzzDOzcCXfeCcWKQc+esHgx5IZOQRGeNgqn7aMLboxwcjJ4BgWIiIiIyMXhYIsW7G/RwtthZIrQUBgzBv76C4oXd2vzZs3cmv1C/VCzJtubNk37igoOBjxJ17vv5sGHH2ZG7do8P3Iki59+mt0FCjCgTJnTXi+fnx+7mjena5EiVA8JYU1CAkdSU/n30CEAGubLpwpduWgooSsiIjlOtWrw3HMusTt3LvTqBRMmQN26UK4cDBwIS5dmTnVAVijs7w+cJaHbuLHLYnsmwYqIiIjIxSHMz49QX19vh5GpmjWDOXPgs89g3TqoXx8eeeTCnqTz8/GhWGAgxQIDCfPzo4hnqNmupCSstexKSuKPevUAqD1zJoXr1cN07gyzZ5/2mj6eyugaoaEkW8uq+Hj+jYvDB4guUIA9SUmk5IYKEZGzyPaErjGmvTFmpTFmjTHm8VMcL2OMmWqMWWiM+dcY0zG7YxQRkZzBGPdo17BhsGaNe8SrWDGX7K1ZE+rVg7FjXZuGnCTCk9Ddc6aErp+faxo8erTLXIuIiIiI5GDGuKfmli+Hm292XcQqVXJr9dONjzgXkZ41dK8VKyg4cybJ1jLsmmsYdNNN/DJvHtx9txsw3LQptGzpHuc7KiXluGsd7afb5J9/GLxxI5WCgykbGEgqx9bod6xYQdiMGVyzZMmFBy+SzbI1oWuM8QXeAzoA1YEexpgTxws+BXxlra0HdAeGZWeMIiKSM0VGQu/erjJg0yY3RO3gQejRA0qVgkcfdcMbckJbrAxV6AK0b+/KjGvUgF27siEyEREREZELExHhZl/Mn++Wsf36Qdu2sHLlhV23SEAAr1esSO/ixbmlWDEeLlWKmZdfTr4XXqBdgwYuczxlijt55kxX6VGwIOTLB0WLwvr17pi11AwN5cXy5elTogR3lSjB4PLlKe9p6bA6Ph6ASfv3E5eayoyYmAsLXMQLsrtCtzGwxlq7zlqbCIwFupxwjgXCPT/nB7ZlY3wiIpILlC4NDz7oqnYnTnQf0L/xhqvmrVXL9fk6fNh78WWoQhfguuugXTv3c9Gironw5s1ZHJ2IiIiIyIWrXx+mToVRo2DBAqhTx63JL6Qt2kOlSzM0KoqhUVG8VqkSlUNCeLB0aXyPDplr08b1eahd220HBEBQEOzd6yp4Bw6ESpUw+/czoHRp3ixXjuF+flw/bx61AwIISUggcNAg7JYtlFiyhOv/+IPYI0dIzqm93EROwy+b71cSSP+X6hbgkhPOeQ74zRhzLxAKtD3VhYwxfYA+AGXO0BRbREQuXj4+Lh/arp174urXX+HFF+Gmm+D++91k3htugAYN3CNi2eVoQnfv2Z49CwtzGenPPoPx491qODQU3nknG6IUEREREbkwxsCtt7oHz+68Ex5+2C1rP/oIqlbNopuGh7tpyYcOuTZmQUFu/XzffTBpkjvnrrvcJLfYWEhMhKQkSrVpw9jkZBrNmEHyN98wx1NIcWVwMNuaN2fSvn3cVrz4seSxSA6WE4ei9QBGW2tLAR2Bz4wxJ8Vprf3QWtvQWtuwSJEi2R6kiIjkLEWLusXkihUwebL78P6dd1zVbu3a8OOPJ7XWyjKBPj6E+fqmVeiO2bGD+vPnY083gOGmm+C771zF7tixmdOETEREREQkmxQrBt9/D5984sZD1K0Lr7ySxevvsDCXzAXo29dNVg4NhU6d4OuvYd8+aNUKrroKHn4Ys2ABnWbMAMBv82Z2FCwIQL01a9jyv/+x4vnn+XCbHhKX3CG7K3S3AqXTbZfy7EvvdqA9gLX2b2NMEFAYUHNBERE5Kx8fuOwy97VnD0yYAC+9BJ07Q0iIq9i97z7XmiErRfj5pfXQvWnFCgA2HzlCmaOLzlO59lq3+Fy40GWiRURERERyCWPcsLR27Vz3g8ceg2++cdW6RzskZBlfX/jnH4iPhwMHXGL3oYeOX1M//TTJs2dTyBju//dfRpcpw6JHHuH5kSMBaAb8DvD446AnwSWHy+4K3XlAlDGmvDEmADf0bMIJ52wCLgMwxlQDgoDd2RqliIhcFAoXdm1ply2Dr76C7t3h889df6/rr4cvv3SD1bLk3v7+Jw1FWxIXd+YXtW7tvk+cmDVBiYiIiIhksaJF4dtv3YNnGza49mfPPJMNw4uDgqBQIShXzi30TyyQyJ8fv3btKFewIM83bMiWyEjiPVUeS6pV44dmzbh8+HCoUAGWL8/iYEUuTLYmdK21ycA9wCRgOfCVtXapMWaQMaaz57SHgd7GmMXAl8Ct9rTPqIqIiJydv7/rZvDxx7BlixuoNnWqq9atWNFV8G498XmRCxTh78/8gwdJSjdgYenZErrFi7ts8zPPwJ9/Zm5AIiIiIiLZxBj43/9cXrRHDxg8GC6/3LW+9bZaoaFpPycMGULPJ56gyeuvc83gwXzw4IOuT8TTT8PcuZptITmWuRhypQ0bNrTz58/3dhgiIpKLpKS4OQkDB8Iff7gBubfdBo884j6Uv1D9Vq1i2LZt3FOyJO96ssW9ihVj5NmmQ2zZ4soYoqJg+nT3+JhIDmWMWWCtbejtOCRraa0tIiIXatQoNzDt0CG3/n70Ue8tc9fExzN+zx5KBARwY9GiFP7rL/Z5ZlhUCwlh2ddfw2uvHXvBggVQv753gpU862zr7Jw4FE1ERCTL+fq6GQlTpsCaNdCrF4wc6Sp2mzaFDz+Ew4fP//pvVapEiI8PX+zcmbZvd0aeMytVypUM//WXmyQRH3/+QYiIiIiI5AC9esHq1XD11fDEE3DJJfDff96JpVJICI+WKUPPYsUwxlAjfcVuaircf/+xk0NCYMQI+OmnC/vjQCSTKaErIiJ5XsWK8P77sHYtvPwyxMbCnXe6HrwPPgibN5/7Nf19fGieP3/ap/31w8JO6ql7Wr16waWXutVuvnzwyy/nHoCIiIiISA4SEQHjxrmvLVugSRP45BPw9oPjNdMndFNSXIHFypUu49yxo/tDoVMn1ztCJIfIUELXGFPNGHOvMeYzY8zvxpg/jTE/GGNeNcZ0McYEZ3WgIiIiWa1UKff419KlMGkSXHstvPuua8Fw661uuNq5qBYSAkCkvz9RwcHsyWhC1xiXzM2fH1JT4cUXz+3GIpKraK0tIiJ5hTFuOPHChVCvnltjd++edYOKM6JjoUJE+PkBcPjo/IvKlaFmTeja9diJ33/v+raJ5ACnTega52ZjzDxgKfAcUAqIATYCvkAH4FtghzHmQ2NM+SyPWEREJIsZA1dcAZ9+6qp2+/WDr7+GGjWgSxf4/XeXZz2bemFhgBu8UNjfP+MJXYC2bWHfvmPtF7Ztg+HDXV9dEcn1tNYWEZG8rHhxmDEDhgxx6+yKFcFb7dqvKlyYPS1a8Fjp0q7lQnpXX3389oYN2RWWyBmdqUJ3OfAE8D1Q3VobYa291Fp7nbW2p7X2KmttTSAcuBUIA/4zxvTM6qBFRESyS5ky8NZbsHEjPPsszJrlkr2VK8Orr7r2DKfTs2hRZtWrx7gaNSjs78/+5GSSM5IJPsrHB1q0cD/ffDP07QvR0bBixYW8JRHJGbTWFhGRPM3HBx57DP7+G4KDXQuGAQPA07Es2wX7+pJoLSnpe0AEBbmhG6+84raXLvVOcCInOFNC90mgmrX2BWvtaf9ytNbGW2u/s9beAFQF1mR2kCIiIt5WuDA895zr9/XFF1CypGvPUK6cm9QbE3Pya/x8fGiaPz8R/v4U9vcHYP+5rlDr1nUlw1OmQJEibt+vv17AOxGRHEJrbREREdyAtH/+gVtucRW77drBjh3ZH0ewj0uRHT6xAKNNGzdgA869B5tIFjltQtda+62159aa2lq7xVo7+8LDEhERyZkCA908hOnT3cIzOtolesuWdW1vd+069esiPAndjZ7puCnWsjI+nsSzVeyGhbln0ABuuAEqVYKJE9OmRySkpJC0f38mvDMRyU5aa4uIiBwTEQEffwyjRrluY+XKuVYM2eloQjfhVH1yw8OhdGlXofvUU67xr4gXZWgomhwvOjqa0aNHA5CUlER0dDRjxowBID4+nujoaMaNGwdAbGws0dHRjB8/HoA9e/YQHR3Njz/+CMCOHTuIjo5m4sSJAGzevJno6GgmT54MwLp164iOjma6p2fiypUriY6OZtasWQAsWbKE6Oho5s2bB8CiRYuIjo5m0aJFAMybN4/o6GiWLFkCwKxZs4iOjmblypUATJ8+nejoaNatWwfA5MmTiY6OZrNnpPvEiROJjo5mh+fjsR9//JHo6Gj27NkDwPjx44mOjibW88zxuHHjiI6OJj4+HoAxY8YQHR1Nkqdv5OjRo4mOjk77bzlixAjatm2btj1s2DA6dOiQtj106FA6d+6ctv3aa6/RNV1T8iFDhtA93T+kgwcPpmfPY08iPvPMM/Tq1Stte8CAAfTp0ydtu3///vTr1y9t+4EHHuCBBx5I2+7Xrx/9+/dP2+7Tpw8DBgxI2+7VqxfPPPNM2nbPnj0ZPHhw2nb37t0ZMmRI2nbXrl157bXX0rY7d+7M0KFD07Y7dOjAsGHD0rbbtm3LiBEj0rb1u6ffvaP0u5czfvfq1YPu3cfRoEE0bdvGM2QIlCo1hlKlolm69PjfvRKBgQC0eeEFLmvblraLF1N17lwuHzjw7L97RYtCQABcey1Dypal+2+/uRUv0KplS3oVKgT//gvody+v/O7Bqf/dExEREcnNbr0VFi+G+vVdzvTJJ+FcxlBciLSE7ukKLqpXh88/hxdegHHjNCBNvCpDCV1jjL8xpr8xZpYxZpMxZteJX1kdqIiISE4VFgaffQbLl0PTprB1qxuKe+edsH27O6dF/vw0CQ/nYEoKcSkpTPP0aNiTkRYMRYtCfDy0agWXXQYFC7rn0T76iM5//+3O+fPPrHlzIpLltNYWERE5pkoVN4T4xhvhxRfhqqtg796sv2+wry8Aw7Zto9eKFYw78dG7GjXSnpID3KJfxEtMRp70Msa8C9wJ/AQsAxJPPMdaOzDTo8ughg0b2vneGocoIiJygm3bXBuG0aPdB/c33AAPPwzLiu/kxuXL+aByZe5ctQofoEG+fMxt0ODcbvDll+6iwKwaNWi2dCncdRcMH57Zb0XkjIwxC6y1Db0dR26ntbaIiMipffwx3H23q28YNw6aNcu6e43fvZuu6YaeNQgLY37DdMuc8eMh3VOb/PEHXHpp1gUkedrZ1tl+GbzOdcDj1trXMycsERGRi1eJEvDhh+5prCFD4IMPYMwYuOTSCLiyAH9Gukfmq4aEsOd8niHr0QOqVCFl2zaiAwOZ3L8/zRYuzPD/qYtIjqO1toiIyCncfrubEXz99dC6NbzxBtxzj5sZnNmOtlw4Ku7E1gvXXAPff+96QFx3Haxdq4SueE1Ge+ga4N+sDERERORiU6QIvP46bNkCL70E65f5Qv+6jLmmFPxdiGohIew936Zg9esT3749Sf7+TKtTB785c+DRR11J8KpV8O236uslkntorS0iInIaDRrA/PnQvj3cdx/07AlxcZl/n5MSuieupY2BLl1cYjcw0PVbE/GSjCZ0RwA9sjIQERGRi1WBAvD447Bxg6HloB0Q7wtP1GbRPRU4MDM/SacbvHAW8Z7XDe/SBevjA6++6p5Dq1IFunVzjX1FJDfQWltEROQMChaEH36A55933ceaNIHVqzP3Hkd76AL4coqEbtpBX9dPd8oUeOCBtOHEItnptE9nGmP6ptvcAdxojJkK/A7EnHC6tdaqcZ+IiMgZBAXBmEcKULbpHBhbml0TysL02rSdnMqAR6Bdu3N7fCzBs8jcERHBN/Pnc93Ika63V8+eMGECvPsu3HJL1jyTJiIXRGttERGRc+PjA08+CY0auXESDRvCJ5/A1VdnzvXTV+iWCAxkV+JJLe2PqVwZxo6FxYvht99g2bLMCUIkg87Ubu/dU+wrA7Q+xX4LaJEpIiJyFqUCA8HPQs9NDB8YRs/BB1n5Y3k6dHAtuF5+2S1SMyI+XWXvlsKF4Z133Be4AWl9+8LcuXDJJVnwTkTkAmmtLSIich6uuAIWLHAPpF1zjXsSbvBg8LvAgRLpE7rFAwLYfOQIKdbie6riiFatXEK3TBlXKpyUBP7+FxaAyDk4bcsFa63POXz5nu46IiIicoxPugVhsRB/6L6Zz/6J5e234b//oHFjN2Nh5cqzXys+3WNgJw1Xu+kmCA93VboikuNorS0iInL+ypaFmTPhzjvdEOLrr4czFdRmxIkVunCGtgu9e8PmzTBwICQnw8aNF3ZzkXOU0R66p2WMCTLGXJcZwYiIiOQFv9aqxc+1alHGs1DcmnqYe+91g3KfeQZ+/dW15erb98yzFhLSVeielNANC4Nbb4Vx42DQoKyZHCEiWU5rbRERkVMLDIT334ehQ+G776BFC9i06fyvF5GuwrZ4QABwfAHFcfz8oFQpqFTJba9Zc/43FjkP55XQNcb4GmM6GmM+A3YBYzM3LBERkYtX+4gIOkZEUD4oCH9jWJmQALiC2oEDYd06V23wwQdQqxb06AE7dpx8nfQtF/aemNAFeOIJuPxyePZZ6NQJDh/OqrckIplIa20REZGMu+8+V8OwapUbljZz5vldJ/1QtBKehG7c2YYXH03oZuTxOpFMdE4JXWNMa2PM+7jBDT8ClwOjgCZZEJuIiMhFzc/Hh4rBwayMjz9uf2QkvPcebN/uBud+/72buzB48PGFtkcrBsJ9fU+u0AUoWhR+/hnGjIGpU91qV0RyLK21RUREzs/118OMGe4htbZtXcXuhSh+tpYLRxUt6r4WLLiwG4qco7MmdI0xDY0xrxtjNgN/AD2AqZ7D11tr77fWzsvKIEVERC5WVYKD+X3/flr88w+XLlrE4kOH0o79mLKdWTf8Q8NxywluFMMzz0DBsklUenojly9cTN/VqwEoHRjIgkOH6LpkCb/v23fyTW680SVzR46E9euz662JSAZorS0iIpI5ateGWbOgbl3o2tUVSJyrzhERABQ7WqF7toSuMW6i8Tz9X7Vkr9MmdI0xg4wxq4A5wN2e79cDRYE+wCnG/ImIiMi5uKN4cRrny0egjw/TY2L4fs+etGMfbtvG3wcOsCUylsDnl9Ng1CpCSyey9vmy/NWjEjt+KAQW7ilZkqbh4Yzfs4fH16079Y369oWUFFepKyJep7W2iIhI5itcGKZMgauugnvugccfh7N1TUjvqxo12NikCeGe9gtnTegCNG3qWi6sWnWeUYucuzNV6D4FVASmAJWstd2std9aaw8DNluiExERuchdVbgwU+rWZUrdupQNCkprv2CtZWV8PHeXKMH6Jk3Y1LQp82+tzL5/Qhk9Gsr5hMArVeHBulTfHslvderwQKlSrIiPJ9We4v+mK1WCkBD477/sfYMicjpaa4uIiGSB0FAYPx7uvhtefhl69oQjRzL22kAfH8oEBRFyNKGbkWzwHXe4dXb37pDuaTuRrHSmhO4gYDXQFlhtjPnaGNPVGBOUPaGJiIjkLVXS9dPdnZREbEoKVUJCjjvHGLjlFliyxMCDq2BjCJc19eOZZ6C8DSU+NZVNpxp+5usLNWoooSuSc2itLSIikkX8/FzLhZdegi+/hPbtYdu2jL8+1MelyzJUoRsZ6aYZL1zo5leIZIPTJnSttc9Za6sCDYBhQGPga9yk3Y9xlQOqHhAREckkVUJCWBYfz92rVvH3gQNuX3DwKc/18QE6b4PRc7n+ejcw7bnmReGbktz030omnaqXbq1a8O+/WfgORCSjtNYWERHJWsa4lgtjxrjeujVrwrJlGXttQX9/APYnJ2fsBf/7H4SHu34PItngrEPRrLULrbWPWGvLAq2BMUArXF+v8caYd40xl2RxnCIiIhe9ThERFA8I4P1t23hj82aAkyp00/u5Vi16Vy3C558b/v4balY38F4Us26pwLPfxJ78glq1YPdu2Lkzq96CiJwjrbVFRESy1o03wuLFEBDgKnXnzz/7ayL8/DDArsTEjN3Ezw+io5XQlWxz1oRuetbaP621fYHiQAfgF6AnMCsLYhMREclT2hYqxIrGjfEFZsTGEmgMZYJO//R1x4gIPqxSBYAmTWDGNMOXX0Lg/mDm3FmeVq3Akxd2atVy39V2QSRH0lpbREQka1StCr/+CtZCq1bwyy9nPt/Px4cIf392JSWl7fv30CHmeJ6iO6W2bWHdOg0hlmxxTgndo6y1KdbaSdbaW3CTeK/L3LBERETypgAfHyp42ixEhYTga85t0H337nD/tK2Ye1ezaJGlRg24/36IiwNq13Ynqe2CSI6mtbaIiEjmq1cPFiyAatWgc2f47LMzn1/E3z+tQvdIaip15s+nyT//nL5q9/LL3fc2beDgwUyMXORkp03oGmPCMnIBa+0Ra+34dK/LlxmBiYiI5FVH2yycrn/u2dQoEIS9ditXfrWBapcm8M47lrI1kxj5XwhUqKBHwURyAK21RUREsl9kpCugbd0abr4Z3njjDOf6+7PbU6F7dHAxkLbvJFWrwsCB7mcNR5MsdqYK3U3GmOeNMRXPdhFjTKBnKu8M4IFMi05ERCQPuqJgQXyAtgULntfrm+fPT8mAAMYGbWTug3NoNXIDexNSuP3yEP4Ivxr7++8QE5OpMYvIOdNaW0RExAvCw13LhW7d4OGH3eA0e4oxpJEBAWnVuEvi4tL2HzjToLSnnoLSpeHNNyE1NbNDF0njd4ZjlwODgSeMMYtxvbuWAHuAI0ABoDxuMm9rIAF4DXg3C+MVERG56N1bqhT3lip13q8vHxzMlmbNWHjwIPUXLGB6uY3w0RbCR0fx0Lc3sYg3WHr1k1SZ9DZ+gb6ZGLmInAOttUVERLwkMBDGjoV+/eDll12tw7Bh4JOu7DHS35+VCQmM3737uIRu7JkSuj4+Lql7551u+lrjxln3JiRPO21C11q7AOhojIkCbgYuA24DAtOdtgn4y7N/grX2NHXnIiIikt2Otm4AICyFQ/es4LV7WjHxup60nz6MqcV3UGTqV9Sso6SuSHbTWltERMS7fH1h+HAoUMAldVNT4f33jyV1by5WjPe2bWPsrl0Epsv0HkhJOfOFO3Rw3+fMUUJXssxZh6JZa1dba5+21jaz1gYDEUBJINhaW85ae6O19lstMEVERHKWEF9fSgW63FDJgABSgeKNEmi34xMW3fgKl+4fz4y693JXn1RiY70bq0hepbW2iIiI9xgDL70ETz4JI0ZAnz7HOiU0Dg+nS0QES+Pi2J2YSLGAAOAsFboApUpB8eLw999ZHL3kZWdN6J7IWrvfWrvdWnskKwISERGRzNMsPByAW4sVA9xAB+PrQ90xjxDfrz99Gc6ujyZQowb89JM3IxUR0FpbREQkuxkDgwfD00/Dxx+7YWkHDrhjNUNDWZWQwOYjR4jyDCw+MaF7KDmZeUdfcPSCHTvC11/D4sXZ9TYkjznnhK6IiIjkHl9Ur87e5s15tEwZAFalm9Ab8uaLEBLCsOv+oGBB6NQJuneHDRu8FKyIiIiIiBcYA4MGua8vvoArroCEBJfQTbaWZfHxVAgKwnByy4WO//1H43/+4Uj6IWivvgoBAa4xr0gWUEJXRETkIuZrDIX8/Qn386N4QAArExKOHfT3hyZNKPbXeBZM3s+zz8IPP0D9+vD556ee9isiIiIicrF6+mn45huYOxeuvx4qmNC0Y8UCAsjn63tShe6fnt5lh9InegsWhGuvhS+/hO3bsyV2yVuU0BUREckjqoSEMO/AAf5K3zC3d2/YupWA/vfx3HOwdClUqgQ9e0L79rBzp9fCFRERERHJdtde6wprf/4Z7ukUCvv9AYgMCCC/n99ph6LFn7j/mWfg8GF47bWsDlnyICV0RURE8oh6YWEsjY+nxcKFLI+Lczu7d4d+/eCrr2D8eCr88Cazf9nHsGEwYwbUqQOffqpqXRERERHJO+66C8aPhyX/GXwfqwMJvhTx9yfc15eYdBW6r23alPZz3IkJ3agouPRS+PXX7Apb8pAMJXSNMZ2MMUr+ioiI5GJDKlTg6+rVAVhyNKELcPfdkJgIXbvCQw/h0/cu7r4b5s2DEiXglltcte6KFV4KXOQip7W2iIhIznP11fDtt2DXhVL2pfpcFlaIskFBrE3Xwmzy/v1pP8el76F7VLt2sHw5bNuWDRFLXpLRheP3wBZjzMvGmGpZGI+IiIhkkQAfHzpERACwIt1wNGrUgMqV3c9t2sCcOQDUrAkLFsC778Ls2VCrFgwZompdkSzwPVpri4iI5DgdOsCHHxg2/hnK/b0CqB4Yysr4eJI8ydtdSUkE+bjU2kkVuuCGU4DrayaSiTKa0K0IjACuB5YYY/42xvQ2xoRnXWgiIiKS2UJ9fSkdGMi8gwdZdPAgiw8dYk9iIsycCZs2Qdu27vuBA4Cb+NuvH6xe7fqJDRgAzZpbli3z8hsRubhorS0iIpJD3X47vPGGG5Y2+5kSJKZYRu7YAcCuxETKBwUBp0noVvN8Trt8+UmH5h04wIb0A4tFzkGGErrW2g3W2metteWBy4E1wJvAdmPMZ8aYS7MySBEREck8tUJD+XHvXuotWEDd+fOpO38+tnBhKF3aleUCLFly3GsiI92Q3qpPb2L28iSaN4c334TTzIQQkXOgtbaIiEjO9uCDMHAgzPwqGN6J4tE1a7HWsisp6cwJ3chIKFDglL3LGv/zD+U9T8aJnKtz7tVlrf3DWnsTUBlYANwITDbGrDPGPGiM8cvsIEVERCTzjKhShe9q1GB8jRr0Ll6crYmJ7E5KcgcbNQJ/f/jkk5Ne5+MDK9qsg3cX0qABPPQQXHYZbNyYzW9A5CKmtbaIiEjO9PTT0L8/8H1JDnxQxrVesPZYQvdUPXSNce3NFi3K1ljl4nfOCV1jTGtjzGhgJVATeA+4AvgGGAh8mpkBioiISOYqERjI1UWKcE2RIlxTuDAAK4/21C1WzI31/fBDuOGGtNYLxymZwC+TUhk1yvXYrV0bxoxRb12RzKC1toiISM5kDLzyCnTqdQS+KMtjg5MBKB8cDJymQhegWTOYPx8mTYING7IpWrnYZSiha4wpa4x5xhizFvgDKA30AYpba++11k6x1j4K3AJ0ybpwRUREJDNVCQkB4N+4uGM7X30VbrrJ9Vh4991Tvm5/SjK33gqLF7thaTfdBD16QLpBvyKSQVpri4iI5A7GwIfDDFy+gwkv54fxJc/ccgGgZUtISoL27aFePThwgFRVQsgFymiF7jqgN/AFUMlae5m19ktr7ZETzlsKzM3MAEVERCTrlA0Kwhe4Z/VqNh0+7HYGBsKnn0K7dvDkky6pu2/fca/b42nRUKECTJ8OL7wA337rkrtTpmTzmxDJ/bTWFhERySWKBQXQ5ZU90HI3vBPFqm/dDNPTJnSvuAIefRRat4aYGFi5kvh05yq5K+cjowndq4Cy1tqnrbXrT3eStXaVtVZDG0RERHIJX2MYXL48APMPHjz+4JAhrnHuvfe6ZrnpFp57j/bcBXx94YknYPZsyJcP2rZ1/XWP5odF5Ky01hYREclFhkSVh6eWQcN9PNkvgIDpkafuoQuuWOLll+G999z2mjXHnbsv3bpaJKMymtC9Dih7qgOeR8RGZl5IIiIikp3uKVkSSNdH96i6dV1Phddfd4McPvoo7dCeUyw8GzRwPXX79YM334QmTWDr1iwMXOTikSlrbWNMe2PMSmPMGmPM46c553pjzDJjzFJjzBcXELOIiEieVSk4GAIsDF5Cs2aGxMFVWTYl6MwvqlDBfV+79rhq3l1K6Mp5yGhC9xagyGmOFfYcFxERkVwon58fJQICWJWQcPLBmjXhwQfdI2JPPkkBTxXv3tMsPENCXIeGn3+GdeugaVM3A0JEzuiC19rGGF/cALUOQHWghzGm+gnnRAEDgObW2hrAAxcQs4iISJ7l5+NDi/z5ubdicX76CQKj4pl4b3GmTj3Di4KDoUQJWL36uJYLG/VYm5yHjCZ0DXC6ph41gd2ZE46IiIh4Q5WQEMbs3EmhmTMpOHMmly5adOygMfDWW7BvH3P79qXR8uXsOcvCs2NH11vXGDfYd8AADUwTOYPMWGs3BtZYa9dZaxOBsZw8QK038J61dj+AtXbXecYrIiKS5/1Zrx5vR0WRPz9cNmIzfqUO06mTa0N2WpdcAr//TlxyctqupemHE4tk0GkTusaY+40x64wx63ALzO+Pbqf72gaMBH7OroBFREQk8z1brhx3lShBz6JFqRcWxrSYmOP7edWtC598QumdO5nbty8tH3nkrNesVw8WLoTrr3dtwxo1gg0bsuwtiOQqWbDWLglsTre9xbMvvcpAZWPMX8aY2caY9meIr48xZr4xZv7u3ardEBEROZP6JYNIfmURRYtbOnSwLF58mhOvuw62b8d3zpy0XUtPbHsmkgF+Zzi2DPgWVzHwEDAV2H7COYnACuCrLIlOREREskXrAgVoXaAAAD/t2cPUmBhWxsfTNH/+tHOSb7yRDnv28OHrr9Ni/HjXT2HTJleO+/jjULHiSdctVAjGjIG+feGqq1y17siR0P60aSSRPMMba20/IAqIBkoBM4wxtay1MSeeaK39EPgQoGHDhhq/LSIicga1QkNJjUhk3eDZ+N5fnyuvDGDuXEOJEiec2KYNAMGzZxNRty6pEREsUYWunIfTJnSttb8DvwMYYw4CH1lrNdpERETkIlclJATgpIRuQmoq0+rVo+onnzDx6ae5/O+/3YGPPnJf777rJqKdQrNmMGMGXH01dOgAffrA+++7lgwieVEWrLW3AqXTbZfy7EtvCzDHWpsErDfGrMIleOddwH1FRETyvM4REbwbFcVfkbF8+eK/xDzQkM6d3frXs7R2ihaFIkWo8dxzbPX3576xY/mscGFSrcVHC2M5BxnqoWutHahkroiISN5QLigIP2N4cv16Gsyfz5X//suR1FQSUlMBKBsayhUvvMALK1bAnDnwyy8QFQXPPgsHD0JyMnzyCaTvw4ubr7ZsGTz6KHz4Idx8Mxw65IU3KJLDZNJaex4QZYwpb4wJALoDE04453tcdS7GmMK4FgzrLvC+IiIieV6Qry/9Spbk3pIloWIcD31wgIULoWdPtzQ+zmWXARCYlMS9H39MQmoq6zUYTc7RaSt0jTFfAQOstWs9P5+Jtdb+L3NDExEREW/w9/HhmbJlmXvwILsTE/ll3z6WxcVR0M8tG3oWLcrgjRv5NCaGJy+5xL2ocGE35OGuu1xS98cf3STfAwfA79hyIyAAhgyBsDB47jmYOxe+/dYle0Xyksxea1trk40x9wCTAF9gpLV2qTFmEDDfWjvBc+wKY8wyIAV4xFq798LfjYiIiABUDw0F4LMKy3n9zcY8eL8PPW+yVBy8nthUl9kNvf9+tnfrRtNPP6X35MkE3XcfS+PiqBgc7M3QJZc5Uw/dIoC/5+dITj95V0RERC4yT5crB8B/hw5Re/58VsbHUzssDICaoaEMKFOGVzdvJik1FX8fHzfx7O67YdiwYxdJSHBVug0bHndtY+Dpp6FlS+jRw7XiHTMGunTJpjcnkjNk+lrbWvsL8MsJ+55J97PF9et96ELvJSIiIifL7+dH5eBgViUk0PDmA7wYV4AnnjBQMJXwHjvxP9pWoWhR8l92GX4TJtDy339ZUrUqnQsX9m7wkqucqYfupel+js6WaERERCRHiQoOxgArExKo5KkaCPHxoUpICMnWsv7wYSofbQz21FMwebJ7tqxHD9eGYdq0kxK6R0VHw/z5rq/u1VfDE0/AwIHHFfSKXLS01hYREbk4/V6nDmVnz2ZpfDyPP16ACX8dYfb7FXm0YUmevC1dFW6tWnD//fz26KPMmzQJJk0CX1/vBS65SoZ66IqIiEjeFOTrS7mgIEZt385Da9cCEOLrSxVPcrfXihV0+e8/rlmyhNkhIbBihSu/rVQJqlRxCd0zKFnSDYu4/XZ48UVo1w727MnqdyUiIiIikjVKBwaSz9eXJXFxGAM9h+6F2rEMvCuI2bPTnZhu+HCjKVNInTkTay1bDh9mf1LSSde11pLkmWkhkqGErjFmpDFm7GmOfWmMGZG5YYmIiEhO0bt4cQr4+RGbnEyz8HBqhIZSJyyMywsWJC4lhY2HD/Pz3r2M2rHD9VM4Kjoa/vwTUlLOeP3gYPjoIxg1Cv76C1q1gu3bs/Y9ieQkWmuLiIhcPIwx1AgNZUlcHACxfkkwaAmlSkPXrrA1/RjUQYNYGR1NQkAAf48axcubNlF69myKzprF1iNHjrvuy5s2ETBjBgdOmrImeVFGK3QvB749zbFvgXaZE46IiIjkNAPKlmVRo0YsbtSIv+rXp2hAAMG+vvxWpw6LGjViUaNGNMqXj5Xx8ce/sHVrNxRt0aIM3efWW2HiRNiwAWrXdrlgkTxCa20REZGLSM10Cd1diYmEF7R8/53hwAHo1AkOHfKc+PTTFPntN6bVrUup6dOZdeAAPkCStSw4ePC4a76/bRsAu09RvSt5T0YTukWAfac5th83yEFERETyqCohIaxMSDh+Z+vW7vtZ2i6kFx0NCxZAoUJw2WUwenRmRSiSo2mtLSIichGpGRrKnqQkdiUmsispiciAAGrXhnHjYPFiuOGGYw+xFfL359Cll1J2wwZ2r19P+0KFANISwieKP8vTb5I3ZDShuxFodZpjrYAtmROOiIiI5EZVQkLYkZh4/CNgJUq4wWg//QTn0O+rWjWYPdvlg3v1gscfP6eXi+RGWmuLiIhcRGp4hgbfvHw502JiiPT3B6BjRxg6FH78ER599Nj5qc2bA1Bs8WKahodTNjDwtAndA0roChlP6I4GHjPG9DPGhAEYY8KMMX2BR4GPsig+ERERyQWODkk7qe3CnXe6Ct333jun6xUsCL/8AnfdBS+/7PqNnWZNK3IxGI3W2iIiIheNxuHhNM6Xj7WHDxPq68vVhQunHbvnHrj3XnjjDXjrLbevXrNmJPv60m79ejpGRFAzNJSlp1n8xqqHrgB+GTzvZaAi8A7wtjEmDggFDPCh57iIiIjkUZU9VQgr4+NpFB5+7MBDD8EPP8Brr7ly27CwDF/T3x+GDXMVuw8+CC1bwoQJUKpUZkcv4nVaa4uIiFxEwv38mNOgwWmPv/mmG47Wvz80bAgtWkRAtWrctX075MtHzdBQft+/n6TUVPx9jq/F1FA0gQxW6FprU621dwDVgHtwi8p+QFVr7V3WWpuFMYqIiEgOVzE4GB84uY+uMTBoEGzZ4pK758gYuO8+17VhzRpo3Bjmz8+cmEVyCq21RURE8hZfXxg1CsqXh//9D3bswPUbmzIF9u+nRmgoidYy58CBk14bq5YLQsZbLgBgrV1prR1urX3BWvu+tXZVVgUmIiIiuUegjw/lg4J4Y/NmYk6cvBsdDbfcAiNGwNSp53X9Dh1g1iwICHBr3d9+u/CYRXIarbVFRETyjvBw+PZb2L/fJXWTr+4GSUlQqBDtX3qJbtOmcfnixZz4ua5aLgicY0LXGFPKGNPGGNPxxK+sClBERERyhybh4cSnpjJ6x46TD3br5r63aQOHD5/X9WvWdMPSoqLgqqvgq68uIFiRHEhrbRERkbyldm1X8zBjBjzyU2t4912IiqLI22/z9cCB3PXVV+xMTAQg2ZPYVcsFgQz20DXG5AO+Aq44usvzPf3HBL6ZGJeIiIjkMp9Vq8b4PXtYfuJgNHAltj16wJdfwoIF4Jnke66KFXMz1jp1gu7dXUXDnXdeWNwi3qa1toiISN51440wdy68NdTQ+It+9JjSGUaMIGbSJJ759FMWPvssxSIjOeBptaCWCwIZr9B9CSgDtMQtMK8BooGPgfVAk6wITkRERHIPYwx1w8JYeaqErjHHxvjOmnVB9ylQACZNgo4d4a674MUXQR1GJZfTWltERCQPe+01aNEC7rgD/osp7WZQPPwwBQ8d4pPvvqPzf/9x8GhCVxW6QsYTuh2BF4A5nu1t1toZ1to+wA/AI1kRnIiIiOQuVUJCmB4bm/ZoGMCSQ4c4kpoKkZFQqdIFJ3QBQkLgu++gZ0948kk3ITg19YIvK+ItWmuLiIjkYf7+rp1YeDhcey0cOAD527Uj2c+PgS++SHy6ARKbzrN9mVxcMprQLQpsttamAHFAoXTHfuHY42EiIiKShzUICwPgvtWrAdiZmEit+fO537NNs2bw/fewaNEF38vfHz75BO67D954A267DVSwILmU1toiIiJ5XPHi8PXXsH69Gz+RGJQfv9q1KbdpE5P798cnJYVqISEsiYs7aVCa5D0ZTehuBgp7fl4NXJXu2CWAPh4QERER+pYsSe3QUJbExQGktV+YuG+fO6FZM/e9Xj3YvfuC7+fj4zo5DBrkkrvdup33zDURb9JaW0RERGjRAj76CH7/HR57DLj00rRj77z9Nt8NGEDHn39m0a5d3gtScoSMJnR/B9p6fn4T6GeMmWWMmQoMBj7NiuBEREQkd/Exhg6FCrEmIYEUa0/up3vjjXDTTe7nHj0ypaTWGHj6aTcUeMIEaN8eDh684MuKZCettUVERASAW291T6ANHQoTGj8PU6fyT/369J0wgSp//MGnQ4ZwuEMHb4cpXpbRhO5jwLMA1trPgK64AQ37gXuAx7MkOhEREcl1qoSEkGgtv+zdy1+xsQDEH21wGxYGn34KI0fClCnw+uuZdt9+/WDMGJg5E1q2hE2bMu3SIllNa20RERFJ88or0KAB3HJnEBvKRVO9f/+0Y1O6dKHpwoUkbNvmxQjF2zKU0LXWxltr96Tb/s5ae6O19lpr7XBrrcaQiIiICAC1QkMB6LxkCZ/s3AnA7qSk4yfy9uoFnTvDiy/C/v2Zdu8bboAff4QNG1ylbiZeWiTLaK0tIiIi6QUGuiFpqanQvTv4XNrOHejXD9u3LwA7fvnFixGKt/mdy8nGmCpAI6A4sA2Yb61dmRWBiYiISO7UIF8+ptetywFPAnft4cM8sGYNK+PjaRwefuzEQYOgbl348ENPk7DM0aGDm7vWrh106gTjxkHJkpl2eZEso7W2iIiIHFWhAnz8MVx3HTz+SiHe2LQJihWjZEICWwoXJnDMGDhwAMqXh2uu8Xa4ks0ylNA1xoQDI3CPf/kAh4AwINUYMx64w1p7IMuiFBERkVzDGEOrAgXStpenG5B2XEK3Th2Ijob33oM774R0r7lQ0dFuSFrPntCwISxeDJGRmXZ5kUyltbaIiIicSrducM898Oab0Lhxabp3h0q+vrzRrh2Pff45TJ/uTty4EcqU8W6wkq0y2kN3GHAFcDMQaq0NB0KBW4DLPcdFRERETlIxOBhf4L+4OJJTT3hyfPBg2LEDnnwy0+/bvTvMnu3aLrRuDevXZ/otRDKL1toiIiJySq+/Ds2bw+23w5Il4O/jw19XX33sBD8/eOcdr8Un3pHRhG4X4BFr7RfW2gQAa22CtfZz4FHPcREREZGTBPj4UDE4mFc3b6b2/PkkpKRgpk3jrc2boUUL10v3++/B2ky/d8OGMGkS7NwJl18Oe/ac/TUiXqC1toiIiJxSQAB8842bLdy7N6SkQFj16gzv0YP/PvwQe9VVbuhwUpK3Q5VslNGE7iFg+2mObQPiMiccERERuRiNrlqVroULszw+ntkH3JPjgzdudAc7dYJt26BVK1i0KNPv3bq1G5S2aZNr2btmTabfQuRCaa0tIiIip1WsmKvUnT0bnn0WGufLR98+fagdFcWq7t1h1y6X+d23z9uhSjbJaEL3PaC/MSY4/U5jTAjQHz0GJiIiImfQNH9+ehUvDsCPe/cef/CGG+CJJ2DmTHjjjSy5f/PmbgF8+DBcdplrMyaSg2itLSIiImfUsyfccQe88AJUXFyKqXXqADC9aVPw8aT3fvrJixFKdsrQUDQgPxAFbDbG/A7sAiJxPb0SgPnGmFc851prbeaNqhYREZGLQpVgl6ua4Ol7kNZN19/frUw3bYJffnHPkfn6Zvr969eH33+HNm3c14wZULJkpt9G5HxorS0iIiJn9c47MHcu3H6bYfHiAoT5+vLfkSNw5Ihb2H7+Odx4Y5aspSVnyWiFbjcgCTgINAE6e74fBJI9x69L93Vaxpj2xpiVxpg1xpjHT3PO9caYZcaYpcaYLzIYo4iIiORg5YKCCDCGtYcPAxCTnMwja9ceO+Gqq2DvXpgzh6v+/Zeg6dMJnD6dAM9XtblzTx6qdo7q1YOJE91TaW3bwu7dF3Q5kcySaWttERERuXgFBcGXX8LBg3DbbYYawaGM3L6divPn81bnzvDbb+4EuehlqELXWls+M25mjPHFPVJ2ObAFmGeMmWCtXZbunChgANDcWrvfGBOZGfcWERER7/Lz8eGDypVZdOgQUSEh3LN6Nb/u3curFSu6E9q1A19fUn74gUkdO9I0PJxm4eEYY1geF8cPe/ey4fBhKoWEXFAcl1wCP//sbte+PUydCuHhmfAGRc5TZq21RURE5OJXvbrrp9uvH/RuUZmoazcD8N7tt3PDd98ROWECNG0KR9fYclHKaIVuZmkMrLHWrrPWJgJjOXlqb2/gPWvtfgBr7a5sjlFERESyyK3Fi/NWVBT9SpbkkdKlWZOQQIq17mCBAtC+PT6vv86d48dzR/HiDKlYkZcqVOCRMmUAWJmQkClxtGrlpgX/+y9066ahwCIiIiKSe9x9t5sr/MmgMB5JqsZn1arRq3hxfmzcGL7+GipVgrfe0iL3InZOCV1jTAtjzG3GmL4nfmXwEiWBzem2t3j2pVcZqGyM+csYM9sY0/40sfQxxsw3xszfreclRUREcp3KwcEcsZaNnhYMAHzxBTvbtuXdt9+m/rK0B3jS+u+ujI/PtPtfeSV88IHrq3vrrZCcnGmXFjkvmbDWFhERkTzAGPj4YyhUyLXMPXwYaoSG8tkVVxw76cEH1X7hIpahlgvGmKLAFKA6YAHjOWTTnZZZ03f9cEMhooFSwAxjTC1rbUz6k6y1HwIfAjRs2NAiIiIiuUoVT+uEa5YsYXGjRm5neDjj3niD7i1aUOWVV9z0MqBwQACF/PwyNaELcNttrp/ugAEuoTt2rFsgi2SnbF5ri4iIyEWgSBEYNQo6dHBr2f5D8jGrbl0+6tiRO375xZ20fLl3g5Qsk9EK3deBWKA0boF5CVAOeBpYjauqzYitnmscVcqzL70twARrbZK1dj2wCpfgFRERkYtI4/BwIvz8+Dcujv3pHgfbGBDAx1264D9pErRsCbNmAS4BnNkJXYDHH4cXX4SvvoLnngOrj4kl+2XWWltERETykPbt4d57XXeFpdMD2d2iBYOeeoqbli51zXa/+w4SE70dpmSBjCZ0W+MWmts928Zau8la+yIwhoxXDMwDoowx5Y0xAUB3YMIJ53yPq87FGFMYt4Bdl8Hri4iISC4R6OPDyKpVgeNbKexNSuLbbt3A3x9mzoTmzWH5cqr7+1Pj+++zZFH62GNw000waBAMHpzplxc5m8xaa4uIiEge8/LLLnd7662QFONH8YAAdiUlQb16sHIlPPqot0OULJDRhG4BYLe1NhU4AESmOzYLaJaRi1hrk4F7gEnAcuAra+1SY8wgY0xnz2mTgL3GmGXAVOARa+3eDMYpIiIiucjRtgvph53tSUrCFC0KS5fCZZe5nTVr8lHt2rw3aBApdepk+uNjPj7wySduIfzsszB8eKZeXuRsCpAJa20RERHJe4KD4fPPYc8e6NMHCvv5szspyVUplC4NQ4fCzTdDTAyd/vuPgRs2eDtkyQQZTeiuB4p7fl4K3JjuWCdgX0ZvaK39xVpb2Vpb0Vr7gmffM9baCZ6frbX2IWttdWttLWvt2IxeW0RERHKXCkFB+BnDSxs3cvV//7Hg4EH2JiUR4e8PUVHw00/wzjvQqlXaa3xXrIArroADBzI1FmNgxAjo3Bn69XMDgkWySaattUVERCTvqVvXtRD77juI/bEIuxIToXx5WLAAypSBzz6D997jp717eU4J3YtCRhO6PwNHR+U9D3Q1xmwxxqwH7gPeyYrgRERE5OLm7+PDXSVKEOrry8/79jFm5072JCVR2N/fnRAUBPfcA1OnsnLtWno//LDbv2ULFCwIH36YqfH4+bnBaM2bu4nBM2dm6uVFTkdrbREREbkgDz3k5gnPG1KUnTvAWusmp61eDa1bw3PPccXcud4OUzKJsecx+cMY0xC4BggGfrfW/prZgZ2Lhg0b2vnz53szBBEREblAdefNo0RgIH8fOMBNRYvydtTxM1GPpKYSMmMGT5Qpw+C1a13DsD//hG3bICIiU2OJiYHGjeHQIfjnHyhWLFMvf9Ewxiyw1jb0dhwXG621RURE5HysXg1Vq1tSm+9mynh/2hQq6A5s2YK99FJWHz5MjVGjSGrb1ruBylmdbZ2d0Qrd41hr51trn/S0RvDqAlNEREQuDlVCQlgaF0dMcvKxCt10An18KB8U5PrtXn45DBniBqT9+GOmx1KgAHz7rUvsXnop7N6d6bcQOS2ttUVEROR8REVBrwEJMD2S219L156sVCkOvvwylbdsodevWlpcDE6b0DXGFDfGfGuMaXeGc9p5zok83TkiIiIiGVElJIRNR44AuB66pzlnZXy822jQwPUE+/TTLImnVi349VdYvx66dnXJXZHMorW2iIiIZIUPnwuhRLM4NrxVinXrjj2Vv6d9exZXqMCNkycTl5LixQglM5ypQrc/UAH47Qzn/AaUBx7OzKBEREQk77m8YEGKBwRQPCCARvnynfKcKiEhrE5IINVaN8Xs/vth6lQ3PC0LtG4No0fD7Nnwv/+B1r6SibTWFhERkUzn4wN3vXkQjOWWO1I52mn1QEoKvzdsyCXLl7Mnk4cLS/Y7U0L3KuB9e4Ymu55jHwBdMjswERERyVtaFijAtmbN2NasGY3Dw095TpWQEBJSU9nsqeTl7rvdWN+ePWHnziyJq3t3GDYMfvsNHnssS24heZPW2iIiIpIlWlUOgjvXMnOqLz6PrsRn2jSaL1zI1Lp1CUpKIvG777wdolygMyV0ywLLMnCN5UC5TIlGRERE5AyqBAcDHGu7EBwMY8dCfDy88EKW3feOO+Cee+D112H48Cy7jeQtWmuLiIhIlmiRPz+v3h9C2SYJBL4fxSVHihCfmsrExo1ZVLEiRV5+2dshygU6U0I3ATh1eczxwjznioiIiGSpKiEhQLqELkCVKq7J7ZgxcPhwlt37zTfhqqtcYve3Mz0kL5IxWmuLiIhIlvA1hv5lSzNlTDA+KT4ceb0SWEj19WVU+/YUWLUK1q3zdphyAc6U0P0H6JyBa3TxnCsiIiKSpYoFBJDP1/f4hC5Anz6wf7/rjZBF/Pzgyy+hRg3XT3fVqiy7leQNWmuLiIhIlqpYEV58ERb+HgiT3YzVeS1buoO1asH06V6MTi7EmRK6w4DbjTG3nO4EY8zNQC/g3cwOTERERORExhiqhISwMuGEgsVLL4X27WHQINi9O8vuHxYGEya45G6nTi6HLHKetNYWERGRLHfvvdCkqcXn3SgqxuejVM2aTG7e3LUs69QJDh70dohyHk6b0LXWfgsMBUYZY+YZYwYbY3obY+4wxgwyxswBRgFvW2vVTVlERESyRZXg4JMrdME1uI2NhVGjsvT+5crB+PGwfr0bmJacnKW3k4uU1toiIiKSHXx9YdRIg/8Rf+qNakC9sDCuHTCAw4MGuaRu/vxZvn6WzHemCl2stQ/jHvM6APTHTdn9EHgEOAh0sdb2z+ogRURERI6qEhLC5iNHWHBiNUH16u65sjlzsjyGli3dcLTffoP+WgnJedJaW0RERLJD1arw3HPwzTcQ+0dBDoaGsvC++2DqVKhZ05XxqkohVzljQhfAWvujtfYyIB9Q3POVz1rb1lr7U1YHKCIiIpJeo3z5ALh1xYqTDzZuDHPnZksct98ODzwAQ4fCRx9lyy3lIqS1toiIiGSH/v2hfn0YOSAfHPJlSVycq1J46CGIi3OPn0mucdaE7lHW2mRr7U7Pl9L2IiIi4hXtIyLoHhnJ2oQEUq09/uAll8CWLbBxY7bE8uqr0K4d3H23K3AQOV9aa4uIiEhW8vODDz+EvbvBb2QFlsbFuQPVq7vvS5d6Lzg5ZxlO6IqIiIjkFNEFCpCQmsqWI0eOP9Chg/v+U/YUNvr5wbhxULkyXHstrFqVLbcVERERETlnDRrAPfcYkr8vwV+zPYUR1aq570ro5ipK6IqIiEiuUzUkBIDxu3czbf9+Nh4+7A5UrgxVqsCXX2ZbLPnzw88/u+Tu1VfDgQPZdmsRERERkXMyeDCERCYzf2AJ/tp3APLlI6ViRVbMnHny02+SYymhKyIiIrlO9ZAQfIEH167l0sWLuWTBAuzRBeidd8Jff8G8edkWT7ly8NVXrkL35pshNTXbbi0iIiIikmHh4XDvkHhYG0bfl+IBmFmlClUnTmTpxIlejk4ySgldERERyXWKBATwb6NGTK1ThwdKlWJnUhK7k5LcwdtvdyvVN97I1pguvdTd8ocfYNCgbL21iIiIiEiGvXRLfopFH+C/d4uwcSNMi4oCoFbHjl6OTDJKCV0RERHJlaqHhhJdsCDtCxUCYEW8qzAgPBx694avv4ZNm7I1pnvvhVtugYED4fvvs/XWIiIiIiIZYgx0G7wfC9zZN5WX27VndcmS7mBMjDdDkwxSQldERERytSrBwQDMio0lISXF7bzvPgBSBw/m8NEJvtnAGHj/fWjUCG66SbMlRERERCRnalU5GHqtZ9IvPiTMK8WdDz3kDrz4oncDkwxRQldERERytTJBQYT5+jJg/Xra//uvZ2cZ6N0bn48+Yk3Nmtna1DYoCL77DsLCoEsX2Lcv224tIiIiIpIhVxcuTOUb90LFQ/BOFNMr1Wdr8+auh9jBg94OT85CCV0RERHJ1XyMYXKdOnQsVIgFBw8eG4720kssiIqi5oYNpPz8c7bGVLIkjB8PmzdD9+6QnJyttxcREREROSN/Hx8eKFcKHl4JewNI/aQSf/XtCykpMG2at8OTs1BCV0RERHK9S8LDuTIigrjUVLYeOQJAav78NBk2jM1FipCYzQPSAJo2heHD4fffYfDgbL+9iIiIiMgZlQ8KgmoHKX7dHvi+JFP9Grt5FF26wA03wKFD3g5RTkMJXREREbkoVAkJAWBlQgIp1rLx8GGS/fx495prCJ42DVatyvaYbrsNbrwRXnoJ5s3L9tuLiIiIiJxWqwIF6BQRwfg3QzAFE/l2UCmS/5oD/fvDuHEusSs5khK6IiIiclE4OhxtWkwMBWfOpMKcOQB8cdllABz5+muvxDV0KJQoAddeC7t2eSUEEREREZGThPj6MqFWLZqUCCXiwQ3sXhrEu5OrwiuvwOOPw9SpqtLNoZTQFRERkYtCycBAQn18+GLnTg6mpHBPyZK8XakSrWrWZGbNmqSOHOmVZrYREW5I2p49cP316qcrIiIiIjlP0csPENo0hqeftmzfDjRpAtbC4sXeDk1OQQldERERuSgYY6gSEsK6w4cBGFyuHPeWKsWz5crx+vXXE7xuncusekG9evDBBzB9umu/ICIiIiKSkxT09yOu30oOJ8KAAUD9+u7A3397NS45NSV0RURE5KJxtI9upL8/Bfz9ATfs4ecWLdhfsiS8/bab3OsFN9/sZksMHAgTJ3olBBERERGRU/qkalUomUDjXrF88gnM3VLCTfl9+21ISvJ2eHICJXRFRETkonE0oVvJ008XwN/HhwohIbzcpQvMnIkdMMBb4TFsGNSu7Vov7NjhtTBERERERI5TITiYysHBRPTaRrFicN/9htQHH4bNm+Huu5XUzWGU0BUREZGLxk1Fi3JXiRI8W67ccftfrFCBWbffzi+XXIL1Ui9dgPz53cDgw4fh1lvVT1dEREREco6aoaH8mLCLXs/EMWcOjNt3uTvw8cd6xCyHUUJXRERELhoVgoMZXrkyVxQqdNz+a4sU4YmyZfmoY0d89u6Fyy93Qx68ICrKVepOmgTvvuuVEERERERETnJb8eIAxF+2ncaN4eGB4ST2e9Ad/O8/L0YmJ1JCV0RERPKEKiEhfNeyJf/17g3TpsGsWV6L5fbb4cor4bHHYN48r4UhIiIiIpLmyogIKgQFsTs5kbffhu3b4dl8b0D58vDvv94OT9JRQldERETyhDJBQQT6+DD2nnugSBHXyHbBAq/EYgx8+ikULgx33gmxsV4JQy4yxpj2xpiVxpg1xpjHz3BeV2OMNcY0zM74REREJOeLDAhgV1ISl1zihvq+8QbEVawN//xzXtfbfuQIseozlumU0BUREZE8wdcYokJC+M8Y+OMP13KhYUP4/HOvxFOoELzzjit2uPZar3WAkIuEMcYXeA/oAFQHehhjqp/ivHzA/cCc7I1QREREcoNIf392JSYCMGQIBATA2B2tYfVq2LjxnK9X4u+/qTxHy47MpoSuiIiI5BlVgoNZGR8PNWvC4sUuofvAA7Bvn1fiufZa10f3jz9gzBivhCAXj8bAGmvtOmttIjAW6HKK8wYDLwOHszM4ERERyR0iAwLYnZQEQPHi8OST8NqS9u7g00+fVxXCLs/1JPMooSsiIiJ5RpWQENYdPkxSaqpru/DRRy6Z27kz/PmnV2Lq0weaNIEHH4Rt27wSglwcSgKb021v8exLY4ypD5S21v58tosZY/oYY+YbY+bv3r07cyMVERGRHCvS35/dSUmkehK3Dz4ISRWr8VWBPvDZZ7BypZcjFFBCV0RERPKQKiEhJFvLusOe4sQ6dVxS959/oF078ELiyscHRo2ChATo2RNSUrI9BMkDjDE+wBvAwxk531r7obW2obW2YZEiRbI2OBEREckxIgMCSLaWlzdtAmAfRwi4Zy39Y54EYOXrr3szPPFQQldERETyjCohIQCu7cJRvXq54WiHD8Nzz3klrqpVXeuFqVPh+ee9EoLkfluB0um2S3n2HZUPqAlMM8ZsAJoAEzQYTURERNK7KiICgK89hQ5T9u9neZ3NxDUpyK++7ajy0Ufw11/nfN1UDYzIVEroioiISJ5RJTgYOCGhC1CtGtx3HwwbBvfe64XI4NZb4aabXE75q6+8EoLkbvOAKGNMeWNMANAdmHD0oLU21lpb2FpbzlpbDpgNdLbWzvdOuCIiIpITVQwO5uFSpVgWF0eKtSyNj8ffx/DHiFC627EkGT/XMywu7qzXSp/EjUlOzsqw8xwldEVERCTPKODvT6S/PytOTOgCvPoq9OgBw4fDli3ZHpsxrvtDo0ZuTtvBg9keguRi1tpk4B5gErAc+Mpau9QYM8gY09m70YmIiEhuUjM0lCPW8sG2bcyKjaVKSAh1avpQpHsKk21bWLYMRow463WOpKam/bwrMTErQ85zlNAVERGRPKVKSAhT9u/n4TVrGLBuHduOHOHtLVtI9PV1/Q5SUmDsWK/EFhAA77wD27fDoEFeCUFyMWvtL9baytbaitbaFzz7nrHWTjjFudGqzhUREZFTaRweDkC/1auZERtLzdBQAFrdG0P3kM9JMb4wYwacpY1C+oTuliNHsi7gPEgJXREREclTOhYqxO6kJN7fto0hmzZxyT//cP+aNYzcvh0qVIDKlV0zWy+55BLo3RveeANmzvRaGCIiIiKSR1UPDWVr06ZE+vsDpCV0yxbz5cCNMXxpu8N33511/sSRdAnfZad6Qk7OmxK6IiIikqc8XrYsca1acahlS8J9fdOqBfyMcSe0aQN//gle7PP1+utQtizcfDMcOuS1MEREREQkjyoRGIi/Z318NKEb6e8PXbfweeT9ANjRo89YpXs4XYXu0gz03JWMU0JXRERE8iRjDFVDQtK20xK6l17qGtguWOClyCBfPvjkE9iwAZ54wmthiIiIiEgeVjooCIBqnjVzZEAABKYy/66C3MbHmE2b+GHcOEr//TelZs1i4IYNx70+fcuFJUroZioldEVERCTPqpIuoXsgJcX9EB3tvnux7QJAy5Zwzz2up+6ff3o1FBERERHJg8ZWr87LFSoQFRwMQJsCBehbogT7Wm/hi4qdWOdbjshBg0lISSHY15exu3Yd9/qjCd1QHx+2qoduplJCV0RERPKs5vnzp/188GhCNzISqld3gx687KWXoHx5uO02UNsxEREREclOZYOCeLRMGYznSbaC/v68V7kyUaHBHOm7leEpfWm6fBk9d++me2Qkq+Pjj6vKPdpyoXRQELuSkrBnGaImGaeEroiIiORZd5YoQWyLFvgbw8H0PXObNoXZs886uTerhYbCxx/DmjXw9NNeDUVEREREBPD01K0fw5d12xNHCE/d9yA1g4NJAUZs25Z23tHkbunAQBJSU4k7WkAhF0wJXREREcnTwv38CPf1PVahC9CkCezfD6tWeS8wj0svhbvugjffhL//9nY0IiIiIpLXtSxQAICt9yXwsHmNwls20mLTJgAeW7curRL3iOd7mcBAAHYlJWV/sBcpJXRFREQkz8vn53d8QrdVK/d94kTvBHSCV16B0qVd64XDh70djYiIiIjkZfeXKsXe5s1JubURIdd3ACDgh1m8UqEC8ampbPH0yz2SruUCwK7ERO8EfBFSQldERETyvHwnVuhWrgx168LYsV6LKb18+WDECFixAgYO9HY0IiIiIpLXFfL3x8cYHnmzHGtMJTaOmkKT8HAAlsTFAel66KpCN9MpoSsiIiJ5Xj5f3+N76AJ06QJz58KBA94J6gRXXAG33w6vvgrz53s7GhERERERKF4cYupfRuXt00lY5CpxO/73H3EpKWkVukdbLnRZsoRUDUbLFEroioiISJ53UoUuQLNmkJrqkro5xGuvQdGi0KsXeJ5kExERERHxqhoPtSOcg0x5YDrdihQB4L9Dh9ISupWCg2nuqd5dr/5hmUIJXREREcnzTuqhC3DJJWBMlkwi25+URJJngXsuChSADz+EJUvghRcyPSwRERERkXMW3LUjCSGFaLz4QzpvqAS4tgtHWy4E+vjweqVj++XCKaErIiIieV5+X1/2n9hyIX9+qFEDZs3K9PsV+usvrlu69Lxee+WVcNNN8NJLsGhR5sYlIiIiInLOAgPxv68vXRnPzP6zCTY+fLpzJxP27nWHfXyoHhICwFIldDOFEroiIiKS55UPDmZHYiKHTkzqNmvmKnTPo5r2bH7wLHDPx1tvQUQE3HVXloQmIiIiInJO/B7vT2zRKJ5a0ZOaC8rwZ2wsv+7bR5nAQPL5+pLPz4+ygYGq0M0kSuiKiIhInlclOBiAVQkJxx9o2hRiY2HFCi9EdXqFCrnhaHPmwMiR3o5GRERERPK8/PkJe34ApdlCsXcPktiiNSmtW7OhSRP8fVz6sWZoqBK6mUQJXREREcnzqngeAVsZH3/8gWbN3PdMbLtg0032TbmAKb89e0LLlvD443ABxb4iIiIiIpnC9/I2ANRd/x2fjzH4GIMxJu14zdBQVsTHE5eSQuoFrINFCV0RERERKgUHYzhFQjcqyvU2yMTBaOmXrhsuYMqvMfDeexATA088ccFhiYiIiIhcmLJlsVddxSCeJfXe+zlxqVsrLIwkawn7809qzJvHpsOHMdOmMXnfPu/Em4spoSsiIiJ5XrCvL2WDglh5YssFY1zbhUys0E1fjXBSAvkc1aoF998PI0bA3LkXGpmIiIiIyIUxn33GrlbduO3Q20x4cOpxx64pXJg3Klbk2sKFWREfz5idOwF4d+tWb4SaqymhKyIiIoLro3vKBGvTpq6HbiZVDqSfYXahCV2AZ5+FYsWgb19ISbngy4mIiIiInL8CBYic9Bm7g0px/fttSHhjeNqhEF9fHixdmvtKlQLgV8/6WjN+z50SuiIiIiK4Prqr4uOP63ELHOujO3t2ptwnMyt0AcLD4fXXYcECV6krIiIiIuJVQUHEDP2U+TQg8OF+sHz5cYdrhoYCMDM2FoCNhw/z057/s3ff4VFUbR/Hvye9EkhCBwGl9640BRFFsWHDgoD6WF97bxTbY1fsioroY0OwoSICCigq0qX33kNL7znvHzMJm7AJSQipv8915drdmTMz92Y3yZ17T9nPo5s2EZeRUerhVkQq6IqIiIjgFHQTs7LYlZaWe0e3buDrW2LTLnj2QFiXd4qHYrrySujXD0aN4qi5ykRERERESluzm/rxzgU/k0ogSR69dAGi/P3pV7064b6+AKxJSmL4mjU8u20bP2i130JRQVdEREQEqB8YCMCevAXd0FDo0KHEFkbz7KF71LWKyRh47z347TcICiqRU4qIiIiIHJf7n6/Jn/Tm4JQ/j9r3W8eOxPXpwyctW5JmLQfdnrkrExNLO8wKSQVdERERESDa3x+A/enpR+/s0QP++Qe87Ssizx66Xq9VTM2aQdu2JXY6EREREZHj0qoVpLbtTIN9i9m9aJfXNtnTL2Rb4aWgm2ktu1NT2ZOaevT0aFWUCroiIiIiQJSfHwAHvBVZzzoLEhNh9uzjvk52D90QHx8Opqfn6rErIiIiIlKZdLu5CwAhfTp73d8yJCRXcdJbD93/rF1Lvb//pu7ffzNmy5YTEGXFo4KuiIiICMfooXvOORAWBpMmHfd1snvo1goIIBOI1cIPIiIiIlJJ1brxIvZWb05E8l52/e/Xo/YH+/pySnAwACcFBnqdkmxubCzdw8NpGhzMX3FxJzzmpgS//gABAABJREFUikAFXRERERGghr8/hnx66AYHwwUXwDffHPe0C9k9cmu5BWSv1xMRERERqQwCA2HqzwDUG3YW7NgBu3eDxyi17GkX2oaGkpSVRWJmZs6+pMxMNiYnc15UFL0jIrxOyVAVqaArIiIiAvgaQw0/v/zntb3iCjhwAH49umdBUWT30K1ZUI9gEREREZFKonaPk/n0gonOg4YNoV49GDEiZ392QbeNe7svLY0ZBw8ybPVqrlm9Guu2aRsayp60NG5Ys4Zdqaml/CzKFxV0RURERFzR/v7595g991yIioIPPjiua+T00A0IAOCAplwQERERkUru3I+uYEjw92QYp1MDn3wCixcDcHF0NANq1ODUatUA2JeezvPbtjEpJoalCQm0Cw2ld0QE59SoQcuQEMbv2cPkmJiyeirlggq6IiIiIq4of39i8ivoBgbCNdfADz+Al7m9CitvD92Y4ziXiIiIiEhFEBUFLe6/kDp2F+u+X+2sT3HnnbBrF53Dw5neoQMnBQYCTg/dFYmJXFWrFptPO41l3bpROyCAtmFhrOrWjSg/P6+Lp1UlKuiKiIiIuOoHBrK9oOFbffo4xdxly4p9jeweuvXdhLXA64mIiIiIVBJ33w3p1aJ59JOWcMMN8OefcN55OfuzR7CtSkpib3p6zlQMnowxtAkNZVF8fJWedkEFXRERERFXi+BgNiUnk5aV5b1B9+7O7fz5xb5G9pnDfH05KTCQtUlJxT6XiIiIiEhFERkJd90FX38NKy4ZBQ0awL//wpo1wJFFg2ceOgTgtaAL0DEsjEUJCZz099/sraKj3VTQFREREXG1CAkhE9iUnOy9QfYiDrNnF/samW4PXR/3emtU0BURERGRKuKeeyAiAka+Gul0kggJgfvvByDY15eGgYE5Bd02+RR0RzduzGMnnUQmsDUlpbRCL1dU0BURERFxtQgJAeCejRu9NzDGGRY2aRJ8+mmxrpHdQ9fHGFqEhLAoIYE5hw8X61wiIiIiIhVJjRpw773w3XewcGdd58HUqeDmw9m9cqv7+VHPnYIhr0h/fy6Mjgac+XarIhV0RURERFzZCeS0gweJzcjw3uiqq5zbm24q1jWy59D1NYZzIyMBeHrr1mKdS0RERESkorn7bmf6hZEjgX79wFp44w1YtSonH28VEoIxJt9zZE/PkO+CxpWcCroiIiIirhBfXz5q0QKAA/klh2eeCY88AsnJUIzVdXN66ALnRUVxTa1amkdXRERERKqMatXgwQdh2jSYl+WuUTFqFLRpw5VZWZxWrRo31a1b4Dlqur1396mgKyIiIiLR7qf9+wtKDjt1cm43bCjy+bN76Pq4PQ5ahISwPTWVxMzMIp9LRERERKQiuv12qFkTRr0QBm+/nbO981df8Xfnzow4RkE31NeXEB8fTbkgIiIiIhDlFnTz7aEL0Ly5c7tuXZHP79lDF47M27shv4XYREREREQqmdBQp5fujBnwZ/tbnWkXzjgDnn4aBg3KVeTNT62AAPXQFREREZFC9tBt2tS5LU5B10sPXUDTLoiIiIhIlXLrrVCrFowZ424YPBhSUpxF0u6/H1audAq9+agTEMCu1NRSibW8UUFXRERExEOheuiGhkKDBrB2bZHPn7eHbrPgYEAFXRERERGpWrJ76c6cCXPnAuecc2RncjK0bQsvvpjv8S2Cg1ldRXNoFXRFREREPFT388MHOJCRUXDD5s1LpIduiK8vJwUGqqArIiIiIlXOLbd49NJt0QJeftnpNDFxotPghRcgn3ly24aGsjstreCOGJVUqRd0jTEDjTFrjTEbjDEPF9DuUmOMNcZ0Lc34REREpGrzMYZIf/+Cp1wAp6C7dm2Bw8C8ydtDF5xpFz7bt69KJqMiIiIiUnWFhsJDD8Gvv8Ifcw3ce6+TZ19xBfzwAxw4ALNnez22bWgoAGcuXUrPxYvpuXgxj23aVIrRl51SLegaY3yBt4BzgdbAVcaY1l7ahQN3Af+UZnwiIiIiALX8/dlzrBVz27WDw4dh3LginTtvD12AruHhADy9dWuRziUiIiIiUtHdcgvUru0xl262/v2diu9333k9rndEBJfXrEntgADCfH3Zm5bGqzt25OTblVlp99DtDmyw1m6y1qYBXwIXeWn3FPA8kFKawYmIiIiAM6/tumNNgXDddTBgANx3H+zbV+hze+uh+1STJtQLCGBlYmKRYxURERERqchCQpxeur/9Br//7rEjOBgGDoR33oGrr4Zdu3IdF+bnx1dt2jC9Qwemd+jAIyedRHJWFptTKn85sbQLuvWB7R6Pd7jbchhjOgMNrbU/FXQiY8xNxpiFxpiFMTExJR+piIiIVFktQkLYkJxMZkGf7gcHwxtvOCvxPvtsoc/trYeurzH0q15d8+iKiIiISJV088359NIdPNi5/eILePrpAs+RPQVDVegkUa4WRTPG+ACvAPcdq621dpy1tqu1tmvNmjVPfHAiIiJSZbQICSHNWr7fv58/Y2OZHxdHRlaWl4Yt4Npr4d13ITW1UOf21kM3+5rbUlOZc/gwf8bGsig+vkoMFxMRERERCQmBhx+GWbNgzhyPHVdfDX/84dx++ins2AFffgn/939HnaNFSAgA65OTSynqslPaBd2dQEOPxw3cbdnCgbbAbGPMFuA0YIoWRhMREZHS1N79dP/SlSvpvWQJpy5ezPg9e7w3vvhip5fuggWFOre3HroA7cPCAOi7dCm9lyyh66JF/HzwYPGegIiIiIhIBZPdS/eZZzw2GgO9e8Po0ZCeDg0bwlVXwdtvw86duY4P8/UFIMVbR4xKprQLuguAZsaYJsaYAOBKYEr2TmttrLU22lrb2FrbGJgHXGitXVjKcYqIiEgV1iU8nL87dWJ6+/ZMb9+eCF9fliYkeG/cu7dzO3Nmoc6dXw/d86Oi+L1jR6a3b89P7doB5H9NEREREZFKJjgY7rkHZsyAhXkrgc2bQ69eubd9/nmuh35uh4lUFXRLlrU2A7gd+AVYDXxlrV1pjHnSGHNhacYiIiIikh9jDKdFRDAgMpIBkZG0CAnJf37bqCg4+2x4/nnYtu2Y586vh66vMfSpXp0BkZGcFxVF/YAAzakrIiIiIlXKrbdCREQ+S1Q0a+bcfvEF1K0Ljz2Wq5euMYYAY0hTQbfkWWunWmubW2tPsdY+424bZa2d4qVtX/XOFRERkbJWYEEX4NVXnWkXCtFLN78eukW+poiIiIhIJVOtGtx+O3z7LaxZk2fnM8/Ak0/CJZfA+PHOFAyzZsH27TlNAnx8SKsC61CUq0XRRERERMqjFiEh7ExLIyEjw3uDli2hRg34++9jnisznx663q65NikJWwUSUhERERGRbHfdBUFBzgC4XCIjYeRICAiA005ztl17LbRv73SuAALVQ1dEREREAFoEBwOwLr8Vc318nKSyEAXd7CkXfI91zZAQYjMz2ZeeXpRQRUREREQqtJo14cYb4dNPC5jRrHr1I/cPH4YpzsB/9dAVEREREQBahoQAFDwFwmmnwapVEBtb4Llyplw4Rg/dQl1TRERERKQSuv9+MAZeeKGARk8+CU8/7fTcnTYNgABjtCiaiIiIiEDT4GAMxyiu9ugB1sI//xR4rpxF0Y5xzexewSroioiIiEhV07AhjBgBH3wAu3bl02jkSGdhtN69Ye5cAALVQ1dEREREAIJ8fTkpMJAntm5lUXy890bdu4O/v9ONoIBeAYXtoXtSUBC+wE3r1rHNnRNMRERERKSqeOQRyMg4Ri9dgNNPh/XrYdkyZ8oF9dAVEREREYD/nnwyAHPzm1IhIgL++1/49VdYsSLf8xS2h66PMTzWqBEA8+PiihyviIiIiEhF1qQJDB0K48bBvn0FNLzuOoiKgg4dmNe3Lw/efjtU8nUoVNAVERERKYSratUiwte34CkQzj3XuV2+PN8mhe2hC/BAw4YArM1vMTYRERERkUrskUcgJQXGji2gUWQkjB8PwcFsbdyYU+fMgenTSyvEMqGCroiIiEghGGNoERLCuoIKus2bQ0AALFuWb5PC9tAFCPPzo35AQMHXFBERERGppFq0gMsug7fegsOHC2h44YUQF8edH31EbPXq8PnnpRRh2VBBV0RERKSQWoSEFNxb1t8f2rSB2bOdBdK8KEoP3ZxrqqArIiIiIlXUo49CXBy8+eYxGvr54RMYyJ+9esHUqZCZWSrxlQUVdEVEREQKKcrfn9iMjIIb3XgjzJ8Ps2Z53V2UHro516zEyaiIiIiISEE6doRBg5xpFxISCm4bYAxze/Z0uvP6+cHAgZCaWgpRli4VdEVEREQKyRfIzKfnbY6rrnJuFy2CGTOc8WEeitpD19eYY19TRERERKQSe+wxOHDAWSCtIIE+Pvzcty/cdpuz4Zdf4NNPT3h8pU0FXREREZFCKlRxtXp152vzZjj7bLj99lzTLxS1h26hisgiIiIiIpVYjx7Qrx+89JKzSFp+Anx8SPL1hTfegA8/hLp1nYOysvI/qAJSQVdERESkkPyMIdVaBv77L32XLGF+XBwA92/YwLnLlnEwPd1p2KQJbNx45MCYmJy7Re2h62cMm1JS6L90Kf2XLmV9UhLWWoavXs2lK1aQWsmSUxERERERbx57DHbvhgkT8m8TYAxp1oKPD1x/PTz3HKxZAwsXllqcpUEFXREREZFC8nWLsL8cOsSc2Fgmx8SQnpXFyzt2MO3gQebGxjoNmzSBuXOPHLh+fc7dIvfQda/52+HD/Hb4MFMPHmRXWhqf7N3LN/v3syIx8bifl4iIiIhIeXfmmXDaafDss/lPixvg40OaZ4eHnj2d25UrT3yApUgFXREREZFC8s3Tq3ZtUhKbPMZ8rU1Kcu40awbZ9wE2bMi5W5w5dPNec63HuT3vi4iIiIhUVsbAE0/Atm3wwQfe2wRm99DN1qQJBAbCqlWlE2QpUUFXREREpJD8PIqrrUNC8i+u3nsvfP21swyvr2/ugm4Re+gW+poiIiIiIpXcgAHQpw88/XTu/hPZAnx82J+ezoqEBGeDry+0bAl//w1paaUb7Amkgq6IiIhIIfl63G8TGsra5GSuX7MGgHbuYwBq1YJLLoHQUGjUKPeUC+5tcXrotgkN5Z+4OG5fv54QHx+aBAWpoCsiIiIiVYYxTjF3zx54++2j9/u7uXM7zzlzhwyBP/+EBg3gn39KKdITSwVdERERkULyzdNbFuBARgatQkI4tVo178XVZs1y9dDNLOocuh73W4eEkJiVRRYwoEYNWoaEHCkiS5VnjBlojFlrjNlgjHnYy/57jTGrjDHLjDG/GmMalUWcIiIiIsfj9NPh7LOd9c7i43PvO5yRcfQBDz8MkyY5PXRfeql0gjzBVNAVERERKaRcBd3Q0Jz7P7ZrR4vgYGLS0zmUnp77oGbNYNEiGDkS/ve/4+qh63nNL1q3pkVICOuSknKmcZCqyxjjC7wFnAu0Bq4yxrTO02wJ0NVa2x6YDLxQulGKiIiIlIynnoIDB+Ctt3JvX+/R2WFf9hQLxsBll8GFF8KPP1aKqRdU0BUREREppLzz2WZrFBREC/fxUb1069Vzbp9+GoYNw9edzyvvYmeFuWYrj2sG+/rSIjiYpKwsdua3zK9UJd2BDdbaTdbaNOBL4CLPBtbaWdba7DfoPKBBKccoIiIiUiK6d4eBA50Ot9nT5QJE+/vn3K/91198HRNzZOeZZ0JKCgweXIqRnhgq6IqIiIgUkmcRtmZAQK7tOQXdvFMgDB+e62GQm1QWesoF95q+QNPg4Fz78i0iS1VUH9ju8XiHuy0/NwA/57fTGHOTMWahMWZhjOc/QiIiIiLlxKhRTi/dd945sm1c8+b81K4d7zVvToiPD78fPnxk5zXXQO/eMHUqvPkm7NpV6jGXFBV0RURERArJcz5bP2O4ODqam+rWBaBJUBB+xnjvofv88zkPcwq6RZxywc8Ygn196RYezhONGwPkX0QWKYAxZijQFXgxvzbW2nHW2q7W2q41a9YsveBERERECqlHDxgwAF58ERITnW3V/f05LyqKm+rVo01oKCuzdwD4+8Pttzv377gDHn+89IMuISroioiIiBSSZw9dX+Dbtm15r0ULAPx9fDglKMh7b9mGDXPunjxjBlhb+B662Yuoudee36ULo9yCbt2AAMJ8fdVDVwB2Ag09Hjdwt+VijDkLeAy40FqruTpERESkQhs9GmJi4L33jt7XNjSUXw8fZkFc3JGNAwY4leDISJgyBbwtolYBqKArIiIiUkie89n6eelh2yIkxHtxtU6dnLvd33mHq3799dg9dBMT4ZVXGHPKKTTcu9drE2MMLYKDVdAVgAVAM2NME2NMAHAlMMWzgTGmE/AeTjF3XxnEKCIiIlKievVypsZ94QXImxJfGBUFwPPbth3ZGBkJf/0FH3/szNfw6aelGG3JUUFXREREpJBy9dDNp6C7MimJFZ4rMwA0bZrrYctt246dhD33HNx3HwDtN20iv/Jvi5AQph86xNaUlGOdUSoxa20GcDvwC7Aa+Mpau9IY86Qx5kK32YtAGDDJGLPUGDMln9OJiIiIVBijR8PevfD++7m3X1yzJhdHR7PSW+eHQYOgTRunsFsBqaArIiIiUki+x+ihe1q1agDcsm5d7h0NGzo9AFwn7dt37B66K1bk3K3puZhDHj3caz6yaVPB55NKz1o71Vrb3Fp7irX2GXfbKGvtFPf+Wdba2tbaju7XhQWfUURERKT8O/10OOMMZ9mKvH0c2oaGsj4pifVJSWxOTuZAerqzwxine++yZWAtaVlZpGZlkZaVVfpPoBhU0BUREREpJM9F0bz10L2kZk0uiY5mnbdFyiIj4ZlnAGi9dav3JCwlBW64AZYsgdhY6NwZgLoHDuTbQ/f/6tfn1PBwVmvaBRERERGpokaPht274YMPcm/vEBpKJtB8/nxO/ucf6vz1Fzuyq77t28PBg7BiBdXnziXo99+p9scfpR57caigKyIiIlJInr1y8+th26NaNWLS0zmY/em/p0cfZeE113DKrl3ejx87FsaPh//+1xk31qQJqdWqUefgwXxjMsbQIyKCdUlJZLkLqImIiIiIVCV9+0KfPs6sZakey75eGB3Nl61bM6FlS55q3JgMa1mUPT2a23nC9u5NcmYmAKnWkloBeumqoCsiIiJSSN565ebVIiQEgG/37/e6P6FmTaLi4vDxVvCdNs253bYN9uyB2rVJrFWLugUUdAFaBAeTlJXFp/ksniYiIiIiUpkZA6NGwc6dTv+IbAE+PgypVYvhdepwd4MGAKxITHR2nnYaXHEFJi6OZjt25ByzrgKMfFNBV0RERKSQClPQbR8WBsB/1q71OgdXvLvaromJOfrg7dud2/nzneFftWuTXLOmM+VCAdfu6F7zzvXrjxmfiIiIiEhl1L8/9OwJzz6bu5dutjA/PxoHBTF+925uX7eOa9es4e5rr3WOXbw4p91t69fznzVrSMjIKK3Qi0wFXREREZFCKkxBt1FQEI+ddBIAm7zMpRsfHe3cydubNisLduyAyy8/ss3fn5SaNal96FCB1zy1WjWWd+3KrI4djxmfiIiIiEhllN1Ld/t2mDDBe5sb6tThcEYGb+3axad79/JTdDSbTzqJG+bMoVe1arQNDWVnaiof7tnDnNjYUo2/KFTQFRERESkkv0IUdAEucIu2a70VdN0euuzbl3tHTAykpTmTf/3zj7OtZ0/SatSgRnx8gdczxtA2LIxO4eGFik9EREREpDI6+2w49VRnSYq0tKP3P964MbfUq5fzeHHXrjS55Ra6LlnC3MBAlnfrxuIuXQCPqRnKIRV0RURERArJt5DtWgQHAzA3NpbD6elsSEpiQ1IS1tojBV3PHroff+ys5ADQsCF07w7p6XDGGaRHRFAjIQFTARZnEBEREREpS9m9dLdtg08+8d4m1PdIVh/i6ws33QShoc4CxUB1f38aBAbyZ2wsi+Ljc768LnpcRvzKOgARERGRiqIwUy6AkwTWDwjgpe3beSl7XlxgYuvWxGYXdEeMgEsucSb4GjHiyMEnn+zc+jlpWnqNGvhmZVGtAizOICIiIiJS1s49F7p2dXrpDh8O/v6593sWdH2Ngagop2vvr7/mbO8UFsYPBw7ww4EDOds+bdWKa2rXPuHxF4YKuiIiIiKFVNiCLsC09u3577ZtfOFOreBvDIvj48kMDT3SaMUKWLDAud+tm1Pgbdcu13kyIiIAqH6MaRdERERERORIL90LL4RPP4Xrrsu9P9THy4QFffrAt9/Czp1Qvz7jmjdnQZ78u5O7EHF5oCkXRERERAqpsHPoArQNC+OC7N64QNPgYNYmJ5MJ9Bg/3tm4ZQu89x506gTz58PDDzsZqIeMGjUAqBEXd7zhi4iIiIhUCeefD507wzPPQEZG7n2ePXRznHOOc/vBBwDUCQzkgujoXF8NgoJOcNSFp4KuiIiISCEVpYcuQIuQkFz3VycmkpyVxT530TSefhpWrYIxY/I9R6Zb0H3h9ddBvXRFRERERI4pu5fuxo3w+ee593kt6LZuDYMHwyuvwMGDpRPkcVBBV0RERKSQCrsoWrbm7uJoXcLCaBUSwtrkZN7dtYvNoaHOZF6rVkF4uDMeLB+Z1asD0GP5crjiimJGLiIiIiJStVx4IXTo4PShyMw8st1rQRfgiSecDhRPPVU6AR4HFXRFRERECqkoUy4AhPn58Uv79kxt354769fnicaNAbA+PlC3rtOoYcMCz5EVGXnkwbRp8OefRYpBRERERKQqyu6lu349fPnlke1e59AFZy2LG2+EN96AmJjSCbKYVNAVERERKaSiTrkAcHZkJLUCAqgTGMjjjRod2VGvnnN7jIJuplvQndm9u7Nh/foixyAiIiIiUhVdfDG0bet0us3upZtvD11wCrqZmU5HinJMBV0RERGRQipOQdeTj+fxbdo4t9k9dfNhgoOJ/u47bn30UWdDQsJxxSAiIiIiUlX4+Di9dNeuhUmTnG0FFnQ7d3Y6XrzzDmRllU6QxaCCroiIiEghHW9BN5f+/Z3b7duPec0DERHEh4Y6GxITSy4GEREREZFK7tJLnTXPnnrKqdEWWND18YH//hf+/hsef7zcFnVV0BUREREppKLOoevNv1278nWbNnDeedC8OYwcWahrpgQEOAnmpk0q6oqIiIiIFJKPj5Nyr1oFX39dwBy62YYNg0GD4Nln4aWXSifIIlJBV0RERKSQCvgsv9Dah4VxSc2aEBHhjP0644yCr5ldRDYGQkNh3Djo06cEIhERERERqRouvxxatoQnn4Qgc4ys3hj47jvo2RPGji2XvXRV0BUREREppBKdcqGw13RvTXZBF2DJklKPQ0RERESkovL1dWZQWLECpnxveKZJExZ16ZL/AX5+cOutsHs3LF1aanEWlgq6IiIiIoVUJgVdz2sea3iYiIiIiIh4deWVzoxnTz4JDzdsROfw8IIPOPNM53bmzBMfXBHpvwIRERGRQiqJOXSP65ppaaV+fRERERGRysDXFx57DP79F374oRAH1KsHXbrAQw/B6tUnPL6iUEFXREREpJDKvIeuZ0F37VoYMMCZ42vy5FKPS0RERESkorn6ajjlFHjiCbC2EAf83/85t+eeW67m0lVBV0RERKSQSmJRtCJf0y3oGshd0L3vviPDv8aNK/W4REREREQqGj8/p5fukiXw00+FOGDECLj/fti6Ff7440SHV2gq6IqIiIgUUlkuigbkLugGBBy5HxxcWuGIiIiIiFRoQ4dCkyaF7KVrjNPwnHOc++WECroiIiIihVSWc+gayD3Ma/v2I/e3bi3VmEREREREKip/f6eX7sKFMGVKIQ4ICYFp0+D00094bIWlgq6IiIhIIZX5HLqeFi50JgDr0cOZT7dQk4CJiIiIiMjw4dC8OTz6KGRmlnU0RaeCroiIiEghlXlBN2+vgNtugyFDICUFDh4s3cBERERERCooPz945hlYtQo+/bSsoyk6FXRFRERECqksEqdcBd0ff4RJk5z7r7wCt98Odes6j3fvLv3gREREREQqqEsvha5dYdQop39ERaKCroiIiEghmTKcQxeA8HC47DJncbR77nEWRssu6O7aVeqxiYiIiIhUVMbAc8/Btm3w7rtlHU3RqKArIiIiUo5lJ2u5Ssn+/kfuq4euiIiIiEix9O8PZ53lTL8QF1fW0RSeCroiIiIiFZl66IqIiIiIFNuzz8L+/fDyy2UdSeGpoCsiIiJSkYWGQrVq6qErIiIiIlIMXbvC5Zc7Bd19+8o6msJRQVdERESkiG6pV6/UrlXNzw+Ah046Kf9GTZrA+vWlFJGIiIiISOXy1FPOwmjPPFPWkRSOX1kHICIiIlKR2L59S/V6gT4+x75mx44wbVpphCMiIiIiUum0aAHXXw/vvAN33+30lyjP1ENXREREpKLr1An27tW0CyIiIiIixTR6NPj6OrflnQq6IiIiIhVd27bO7erVZRuHiIiIiEgFVb8+3HknfPopLF9e1tEUTAVdERERkYouOtq5PXy4TMMQEREREanIHn4YIiJg5MiyjqRgKuiKiIiIVHQREc6tCroiIiIiIsVWo4Yzh+7338OyZWUdTf5U0BURERGp6KpXd25jY8s0DBERERGRiu7OOyE8HJ5+uqwjyZ8KuiIiIiIVXXi4c6uCroiIiIjIcalRA+64AyZNKr+9dFXQFREREanofH2doq6mXBAREREROW733QfVqsGYMWUdiXcq6IqIiIhUBtWrq4euiIiIiEgJiIyEe++Fb7+FxYvLOpqjqaArIiIiUhlERKigKyIiIiJSQu6+25l+YdSoso7kaCroioiIiFQG1atrygURERERkRISEQEPPgg//QS//17W0eSmgq6IiIhIZaAeuiIiIiIiJerOO+Gkk+D++8Haso7mCBV0RURERCoDFXRFREREREpUSAg89hgsWACzZpV1NEf4lXUAIiIiIlIC+veHunXLOgoRERERkUpl2DCYMQPCwso6kiNU0BURERGpDK6/vqwjEBERERGpdIKCYNKkso4iN025ICIiIiIiIiIiIlJBqKArIiIiIiIiIiIiUkGooCsiIiIiIiIiIiJSQaigKyIiIiIiIiIiIlJBqKArIiIiIiIiIiIiUkGooCsiIiIiIiIiIiJSQaigKyIiIiIiIiIiIlJBlHpB1xgz0Biz1hizwRjzsJf99xpjVhljlhljfjXGNCrtGEVERERERERERETKo1It6BpjfIG3gHOB1sBVxpjWeZotAbpaa9sDk4EXSjNGERERERERERERkfKqtHvodgc2WGs3WWvTgC+BizwbWGtnWWuT3IfzgAalHKOIiIiIiIiIiIhIuVTaBd36wHaPxzvcbfm5AfjZ2w5jzE3GmIXGmIUxMTElGKKIiIiIiIiIiIhI+VRuF0UzxgwFugIvettvrR1nre1qre1as2bN0g1OREREREREREREpAz4lfL1dgINPR43cLflYow5C3gMOMNam1pKsYmIiIiIiIiIiIiUa6XdQ3cB0MwY08QYEwBcCUzxbGCM6QS8B1xord1XyvGJiIiIiIiIiIiIlFulWtC11mYAtwO/AKuBr6y1K40xTxpjLnSbvQiEAZOMMUuNMVPyOZ2IiIiIiIiIiIhIlVLaUy5grZ0KTM2zbZTH/bNKOyYRERERERERERGRiqDcLoomIiIiIiIiIiIiIrmpoCsiIiIiIiIiIiJSQRhrbVnHcNyMMTHA1lK8ZDSwvxSvJ2VPr3nVpNe9atLrXvXoNS++RtbammUdhJxYpZxr6+ex8tJrWznpda2c9LpWXnptK44C8+xKUdAtbcaYhdbarmUdh5QeveZVk173qkmve9Wj11yk/NDPY+Wl17Zy0utaOel1rbz02lYemnJBREREREREREREpIJQQVdERERERERERESkglBBt3jGlXUAUur0mldNet2rJr3uVY9ec5HyQz+PlZde28pJr2vlpNe18tJrW0loDl0RERERERERERGRCkI9dEVEREREREREREQqCBV0RURERERERERERCoIFXSLwBgz0Biz1hizwRjzcFnHIyXHGNPQGDPLGLPKGLPSGHOXuz3SGDPDGLPeva3hbjfGmNfd98IyY0znsn0GUlzGGF9jzBJjzI/u4ybGmH/c13aiMSbA3R7oPt7g7m9cpoFLsRljqhtjJhtj1hhjVhtjeuhnvfIzxtzj/n5fYYz5whgTpJ93kfJFuXbFpVy6clO+XDkpJ66clPNWHSroFpIxxhd4CzgXaA1cZYxpXbZRSQnKAO6z1rYGTgP+z319HwZ+tdY2A351H4PzPmjmft0EvFP6IUsJuQtY7fH4eeBVa21T4BBwg7v9BuCQu/1Vt51UTK8B06y1LYEOOK+/ftYrMWNMfeBOoKu1ti3gC1yJft5Fyg3l2hWecunKTfly5aScuJJRzlu1qKBbeN2BDdbaTdbaNOBL4KIyjklKiLV2t7V2sXs/HuePWX2c1/hjt9nHwMXu/YuAT6xjHlDdGFO3dKOW42WMaQAMAj5wHxvgTGCy2yTva579XpgM9HfbSwVijIkATgc+BLDWpllrD6Of9arADwg2xvgBIcBu9PMuUp4o167AlEtXXsqXKyflxJWact4qQgXdwqsPbPd4vMPdJpWMO8ygE/APUNtau9vdtQeo7d7X+6FyGAs8CGS5j6OAw9baDPex5+ua85q7+2Pd9lKxNAFigI/coYMfGGNC0c96pWat3Qm8BGzDSWpjgUXo512kPNHv20pCuXSlMxbly5WRcuJKSDlv1aKCrogHY0wY8DVwt7U2znOftdYCtkwCkxJnjDkf2GetXVTWsUip8gM6A+9YazsBiRwZSgboZ70ycud/uwjnn5d6QCgwsEyDEhGphJRLVy7Klys15cSVkHLeqkUF3cLbCTT0eNzA3SaVhDHGHycB/cxa+427eW/2UBL3dp+7Xe+Hiq8XcKExZgvOsM4zceaRqu4OT4Hcr2vOa+7ujwAOlGbAUiJ2ADustf+4jyfjJLP6Wa/czgI2W2tjrLXpwDc4vwP08y5Sfuj3bQWnXLpSUr5ceSknrpyU81YhKugW3gKgmbs6YADOxNJTyjgmKSHuPDEfAqutta947JoCDHfvDwe+99g+zF3t8zQg1mNoilQA1tpHrLUNrLWNcX6ef7PWXgPMAi5zm+V9zbPfC5e57fWJdQVjrd0DbDfGtHA39QdWoZ/1ym4bcJoxJsT9fZ/9uuvnXaT8UK5dgSmXrpyUL1deyokrLeW8VYjRa1V4xpjzcOYQ8gXGW2ufKduIpKQYY3oDfwDLOTI/1KM4c399BZwEbAWusNYedH85vokzfCEJuM5au7DUA5cSYYzpC9xvrT3fGHMyTg+ESGAJMNRam2qMCQL+hzMn3EHgSmvtpjIKWY6DMaYjzsIeAcAm4DqcDzj1s16JGWOeAIbgrMS+BPgPzrxh+nkXKSeUa1dcyqUrP+XLlY9y4spJOW/VoYKuiIiIiIiIiIiISAWhKRdEREREREREREREKggVdEVEREREREREREQqCBV0RURERERERERERCoIFXRFREREREREREREKggVdEVEREREREREREQqCBV0RaRcMMaMMcZY9yvLGHPIGLPAGPOMMaZOnraN3Xbnl+D1ZxtjJpfU+SorY8wWY8xLZR2HiIiIiBSO8uyKQXm2iBSFX1kHICLiIRYY6N6PADoDtwI3GWMGWmsXuft2Az2ANaUfooiIiIhIhaM8W0SkElFBV0TKkwxr7TyPx78YY94Bfge+NMa0tNZmWmtTgXneT1H+GGOCrbXJZR2HiIiIiFRZyrNFRCoRTbkgIuWatfYw8CDQFBgA3oeCGWMuNMYsMsYkusPI/jHGnOGx39cY84gxZp0xJtUYs8MYMyHv9YwxVxtjNhhj4owxPxtjGuTZ/5wxZrkxJsE9x2dehqptMca8bIwZaYzZAcS52wONMe8YYw4bYw4YY140xtxtjLF5jo80xowzxuw1xqQYY/4yxpx6rO+VMSbKGPOeMWa3e9xaY8zd7r6vjDGzvRwzxr2Ov/s42BjzgjFmq/t92myMefYY1+1jjJljjElyn9f7xphwj/3VjTEfGGN2uXFtM8a8f6znIyIiIiInjvJs5dkiUnGph66IVASzgQzgNGBa3p3GmFOAycBrwANAENAFiPRo9h4wDHgBmOPuuzTPqU4F6gH3AcHu+cYB53m0qQX8F9gF1HTb/maMaWutzfJodzWwEriNI79rXwBGAI8Cq4HrgCvzPJdAYCZQ3X0u+3CGw800xjSz1u7x8v3BGBOM832qBTyBM0yuqfsF8CHwszGmibV2s3uMAYYDn1pr093H3+MMs3sKWATUB/p4u6Z7jl5uvN8BlwFRwHNADfcxwCtAT+AeYA/QEDg9v3OKiIiISKmZjfJs5dkiUuGooCsi5Z61NsUYsx+onU+TTkC8tfYBj21Ts+8YY1oCNwB3WWtf92gzMc95qgGDrLWH3OPqAK8aj6Fc1trrPc7rC/wN7AB64wxZ83S+tTbFbRsF3ASMsta+6m77BViR55ihQFugjbV2vdtuJrAWJ6l9AO+GAW2Aztbape623zz2z3DjHAGMdrf1AxoDH7mPz8bpnXGRtXaKx7Gf5HNNcJLKv6y1Q7I3GGN2Ar+6yfcKoDvwlrXW8/v9aQHnFBEREZFSoDxbebaIVEyackFEKgpTwL7lQIQx5mNjzNnGmNA8+/u5txOOcY0F2Umma5V7Wz8nCGPOdYdmxeL0Ztjh7mqe51y/ZieZrnY4PRpyEjhrrQV+yHPcWTif2G82xvgZY7I/eJsDdC0g9jOBJR5JZi5ur4aPgGFuDwFwks6FbjKYfY6DeZLMfBljQnB6GXyVHasb71wgHaf3BsBS4AFjzG3GmLzfJxEREREpW8qzlWeLSAWjgq6IlHvGmCCcIUZ7ve231q4FLgJOxukxsN8Y87kxpqbbJApItNbGHeNSh/M8TnNvg9w4uuEkijuAa3GSrNM823jIG2v2/F8xebbnfRztnjM9z9d1OEOo8hOFsypxQT4CGgH93Lm3LgXGF/EcnmoAvsDbeWJNBfw94r0dZ6jYKGCtMWa9MebKo84mIiIiIqVKebbybBGpmDTlgohUBP1wfl/9nV8Da+1PwE/GmAhgEDAWeANn7qwDQKgxplohks2CDMZJDIe4n/pjjGmUX0h5HmfPyVUTOOixvWaedgeBhTjzeeWVWkBsBzgyj5f3gKzd4g4rGwE0wflQ74s856hb0DnyOIzzPMfgMfTOwy73uoeBO4E7jTHtcRbf+MwYs8xau8rLcSIiIiJSOpRnO5Rni0iFoh66IlKuGWOqA88DG3AWBSiQtTbWWvs58C3Q2t2cPcfVsOMMJxhIz04yXdcU8tjlQApODwcgZ7GEC/K0+xUnYdxmrV2Y52t5Aef/FejkJnIF+RCnx8BtwHduEuh5jkjjsapxQay1icA8oIWXWBdaa3d5OWYZzvxkPkDLwlxHREREREqe8mzl2SJScamHroiUJ37GmOyhVeE4c0PdCoQAA621md4OMsbcjDMsaxrOp9XNgMtxFxmw1q41xowDXjbG1MJZVKE6cJm1tihDkmYAdxtjxuLMydUTZ3GFY7LWHjDGvA88YYxJ58jqu9XI3cvgE+AWYLYx5iVgE84Qre7AnuyFHrz4BPg/YLoxZgzO4g5NgObW2oc92n2HM3SrM/CIl+f3C/C5MeZJYDFOT4LTrbU353PdB3EWZsjCWQE5HjgJp/fGY9badcaYuTiJ/wr3ud4IJALz8zmniIiIiJQs5dnKs0WkElFBV0TKkwic4V4WiMPpLfAp8Ia1dk8Bxy0DLgReASJx5qd6H2cuqWy3AVuB/wAPA/uA6UUJzlo71RjzEHAHTrL0N3A+sK6Qp3gQZ86rMUAW8D+cT/Lv9rhGijGmH/Ak8ATOisP7cJKyfBdRcI87E2c13CdxEtgtOEmlZ7tUY8zPwOnk6YlhrbXGmMHAU25MNXES988LuO5cY8zpbqz/w5nraytO0p89v9nfOMPPGgOZwBLgXGvtjrznExEREZETQnm28mwRqURM7hENIiJSmty5tvyttWeU0vX8cBLB8dbakaVxTRERERGR0qY8W0QqM/XQFREpJW6PgFNxhlj5A0OA/jjD1k70tQOADsDVOEPL3jvR1xQRERERKQ3Ks0WkqlFBV0Sk9CQAF+PMqRUErAdGWGsnl8K16+EMJ9sH3KxhWCIiIiJSiSjPFpEqRVMuiIiIiIiIiIiIiFQQPmUdgIiIiIiIiIiIiIgUjgq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFYQKuiIiIiIiIiIiIiIVhAq6IlKuGGNGGGOsMWZEnu1bjDFbTuB1x7jX7XuirlEajDHVjDGvu9+vDPc5dTzGMSf0eysiIiIi5YNy7eOjXFtEygsVdKVcM8Y0dv9ITiiDa1eKpEMc+SWvldALwB3AcuBZ4AlgT5lGBBhjZhtjbAH7K3yia4xpaIx5zhizyBhzyBiTbozZZ4yZaYy5yxgT4eWYy40x09x26caYA8aYVcaYT40xw/O0rW+MucMY87P7/Up1288wxlxSArG/bYz5xxizxz33LmPMH8aY64wx/gUcG2GMedIYs8wYk2CMiTPGrDDGvFfQcSIiUvaUa0tJUa5dtpRrV75c243BHuNr5PHEJRWbX1kHICJSSP1P8PnfBL4Etp3g65xo5wPrrLUXlHUgVYkx5j8476FA4F/gC+AQEAX0BsYCI4Foj2PGATcCycBPwGbAAC2BC4C+wMcel7kDeMhtNwvnn4dGwCXAWcaYV6219xbzKZwCXAP8A3wHHHRjPxcYD1xrjDnbWpuR53m3BKYD9YGZwM+AP9AYuAK4D0gvZkwiIiJSepRrF45y7TJQRXPtsUB1L+cywKM49byfixmPVAIq6IpIhWCt3XiCz78f2H8ir1FK6gG/l3UQVYkx5hrgfZyk8lJr7U9e2vQC3vJ43BsnwdwB9LDW7sjT3h8nyfQ0H+hrrZ2Tp20rYB5wjzHmM2vtomI8jb+AGtbaLC9xTAf64SSzX3nsCwGmAOFAL2vtvDzH+gGZxYhFRERESply7UJTrl3Kqmquba0d6+1ExphzcGp5S6y1C4sRi1QSmnJByi1jzBicT8cAhucZWjAiT9tzjDFTjTH73eELG40xLxpjqns5b3tjzBcewyhijDGLjTFjs4c6uMNRRruHzPK89jFivtJt92o++wPd4SG73WJHfuepbYx5yRiz1hiTaIw57N6fYIw5uaAYjsU9hzXGnGyMudcYs8YYk2KM2WGMedUYU83LMVvcr2rGmFfc++nua5TdpqV77u3GmDRjzF5jzOfGmBb5xNHUGDPJ/X4kGmP+MsYMKiDufIcJGWOGGGN+NcYcdJ/LFvc17urunw185Db/KM97qbHbJt9hf8aY/sYZqnPQfc+sM85wH2/Dema75/EzxjxqjFnvHrPdGPO8MSYgv+eYz3Ora4x5y31Oae779RtjTBdv18X5xPYMj+c3uwjXijDGvGmM2el+H1cZY+40xhgvbUcYY742xmwyxiQbZ5j9n8aYoXnaNXbjOsN97Pm9n22M6evubwQ0yrN/Qp5zFfo9lud9fodxpgNIdq95jrvvo7zHuccGGud3yX5jTOAxvmfhwOvuwyu9JZgA1to/gVM9NvV0b7/Om2C67dOttTPybPsmb4Lpbl8NTHQf9i0o3vxYa9PyJpjZceD0IgBolmf3Le62R/IWc91jM6y1Bf7OFBGRsmOUayvX9hJDPvuUayvXzhufcu0iKGaunZ+b3Nv3ihOLVB7qoSvl2WycIQZ34Qyr+M5j39LsO8aY0cAYnGELPwL7gPbA/cB5xpge1to4t217nGEOFqdn2WagGtAUuA14HGd48FjgYpw/jB8DWwoZ83dALHC1MeaBvMOTgYvc5/Syl33ZzycE+BNnWMYM4AecxKGRe/xkYFMh4ynIq8DpOJ8Cfg+cA9wN9DHG9LbWpuRpHwD8BkTifIoYh/tPgDFmIPANzlDrH4ANQAOcTxkHGWP6WWsXezzHZsDfOMNMfsZ5PZvifP8KPWzETX4+AobjfOL/DRDjXrsfsBZYCEwADuN8/77H4/3jbi/oGjcD7wCJwCSc91dfnOE4FxhjellrvZ3jc6CP+3zigPOAB4FawHWFfH5NgLk4PQF+wxla1BC4HOf7eqm19ke3+QScn5nRwFb3MRT+vRuAM2S+Os5wuADgUuA1oAXwf3navwOsxOmhsBvntTwP+J8xpoW1Nns+p8M4c4uNwHkPP+Fxji3u1xM47z1wfvayLc2+U9T3mIfXcF6Hn4CpOD1GpwMbgSuMMXdba2PzHHOp+3xettamejmnp8twfibmWWunF9Qwz7kOuLeFTdyOJXtaA6+/V4rLGOOL87oCLMuz+2qc36Vfuv+snYvz/tkGTLPWHkBERMqz2SjXVq5dAOXayrVRrp2tLHJtb+1r40wXkYDzMyBVmbVWX/oqt1848zBaYEI++/u5+/8CqufZN8Ld96rHtpfdbRd5OVcNwMfj8Ri3bd8ixvyee9z5Xvb95O5rV8DxF+SN22NfABB+nN/TCe759wONPLb7AF+7+0bmOWaLu30mEOrl+3bIPV/rPPva4vyxWZxn+3T3fHfl2X6Ru90CI7zEsCXPtpvctvOBiDz7fIG6Xt4Puc5b0OuNkxSl4iSJLfO0f9ttPy7P9tnu9kVApMf2UJzEKBOoU8jX6hf3XI/l2d4TJ5k4AITl2WeB2UV8T2S/vnOBQI/tkTjJmAVOz3PMKfm8P3/FSXjqe/u+HCOGLfnsK857LPt9vhNo4uWc97v7b/eyL/s1bF6I792Hbtuni/g9r4+TgGf/w3s1TsJpinIe91zVcOb4ygJaFfX4POeKdn8WnnDf4+vdGD/L087ffZ33Ag+4963HVwJw/fHEoi996Utf+jrxXyjXzrtPuXbubcq1lWsr1y6DXLuA4x/x9nOhr6r5pSkXpKK707290eb55NZaOwHnU8drvByXnHeDtfaQ9TIMohiyJ1Yf7rnRGFMH55P5Jdba5YU4j7cY06y18ccfIgCvWWu3epw7C6cwkwVcn88x91lrE/NsG4bzSfNoa+2qPPGuwJnvqJMxpjWAMaYBMACnx8Gbedp/Dxw1zKUAd7i3N9s8n/xaazOttbuLcC5vhuIkTm9aa9fk2fcYEI8zgb23oUIPWWsPesSTCHyGk8x3PdaF3e/T2Ti9HV/w3Get/QunB0EkzqfmJeUR6/HJthv/U+7DXD0drJd51qy1aThzV/lRsgtrFOk9lscL1trNXrZ/BKQAN3tudIeUnQHMstauK0Rsdd3bo4ZyFcRauxMYjJPEX4Dz3lgHxLpDDoe6n9gXyO058wFQG3jHOkPCjkc0Ts+TUcCtOL2XXsL5J81TJM7rHIWzwvNTOD1aooH/4CSaHxhjzjzOeEREpGwp1y4+5drHplxbufaxVNVcO79Y/uM+HHeccUgloCkXpKLrgfMJ5eXGmMu97A8Aahpjoqwz/HcizrCy74wxk3E+Bf/T2x/M4rLW/mWMWYczRKiGtfaQu+sanE+yJxzjFHNwPul82BjTGWfoyp/AUmttSS4w5G1+oE3GmO1AY2NM9TyJewreh4H0cG87GI95vjw0d29bAauATu7jufk8n9m4c0AVxBgTivOJ8V5r7ZJjtS+mzu7tb3l3WGsPGWOW4Ayla4kzVNGTtwnqt7u3NQpx7ezv0x/WmVspr99wkuBOwCeFON+xZOD0vslrdp54ADDGnIQzFK4/cBIQnOe4+iUQU7aivsc8zfd2QmvtAWPMV8AwY0xPN3GHI3NSvXsc8RaKtXaWMaY50AvnPd/JvX+O+zXcGHO+LXgo2ss4wwL/AIq76q5nTGtw8kVfnNdwMPAk0NsYM8jjH6fsD4R9gfestU96nOZDdzjr6zjvkaN+fkREpMJQrl18yrWPTbm2cu0TpoLn2t6cBZyM01tai6GJCrpS4UXhvI9HH6NdGHDAWjvfGNMH5xPfy4BrAYwxa4EnrLVflFBcHwPPAFfizH8ETi+CdI4x1421Ns4YcxrOMIwLcf7YAOw3xryNM9zEW9JRVHvz2b4HZ/hTBLnnvNpnrbVe2ke5tzce43ph7m1EIa5fGNXd252FbF8c2bHm1/sge3v1vDvy9mJxZc+5dMxPg4/n2sW0P5+kP/v1yI4H4ywWMh8nWf4DZ1hfLM4Qt8Y47/UCFzgooqK+xzwV9H56G6dHws3AX27vj+E4c7d9W8jYsl+HYiXVbm+dP9yv7E/eB+D8DjkL55P7sd6ONca8ANyDM7faoGMko0WNKxOnx8prxpi9OL1UngRud5t49tLx9r36Fqeg272kYhIRkTKhXLv4lGsfm3Jt5drHUlVzbW+yi+HqnSsAmnJBKrxY4JC11hzjy3O409/W2vNx/kD2whnmUhv43BhzVgnF9T+c4VTDAYwxnYB2wFRr7f5jHWyt3WGtvQFnUv+2OMPdDuAMzRhVQjHWzmd7Hfc27+T13hJMz3YdjvEafJyn/bGufyyH3duS/HQ6r+xY84upbp52Ffna0fkMO/L2frgXJ/G7wVrb11p7p7V2pLV2DM5cZCWtqO8xT/m9b7HW/gMswVmwoQZHFmj4qAj/yM11b0tk2Jt1TMdZNAbA65QFxlnd+wFgFnCutTahJK6fj+zFU/p6xJnEkV4wh70ck91bKm9vEhERqViUaxefcu1jU66tXPtYqmSu7SWeWjhzYGsxNMmhgq6Ud9mfYub3Kes8oIYxpk1RT2ytTbXW/mWtHcWR+cEuKsK1Czr3dpxhOqe68wRlz/Hl7Y9gQeex1tqV1to3cD5JBGdF4JJw1FAr99PghjgT5h8u5Hnmubd9Ctk+e8hW73ySmr6FOYl15slaAdR2k/hjKc7rmR3rUTEZY6oDHXGGxx3vXEoFXbu3McbbaIp+7q231WaLww9nAYi8+uaJB5xVksFZ2COv/IbwZULOSq757S/o5xwK/x4rireBIJzeA9kLfxTlU+/JOKt+9zjWP6n5zP+Wn+z5+0yecxhjzFs4KxXPwOktkFSE8xZH9j9yeVf1nenetvVyTPY2b3OqiYhI+aFcW7m2V8q1lWuXEOXax5Zfru3pOpxFib+wJTfPt1RwKuhKeXcI55f+Sfnsf9W9fd8YUy/vTmNMqDukKvtxT2OMtx5j2Z9ge/6yPuDe5nftY5ng3t4AXIWzauiPeeKra4xpaYzxHGLTxhjj7RN1bzHiHt+yGPHdZYxp5HEeH+BFnN8LHxXhPB/hfII/2hhz1PBqY4yPMaZv9mNr7Q6cP45NyDOkxBhzEYWY08vD6+7te57fQ4/r1vXYVJzX81OcoXt3GGOa5tn3FM6Kp5+W5PCbbB7fp8Y4CUUOY8ypOCu1HqLww5UK41nPRMgYE8mRT6893xNb3Nu+eeI6hyMT9ed1rO//AZw5+Lz9fBbpPVZEn+P0SngQ5703w1q7qbAHuwlV9j+pE93vwVHc30N/ezweaIy5xBjj76VtGEde8989thucBPg2nE/yL7TWHrWgS3EYYzp7+wfAjeU19+FPeXa/hdM76mFjTE2PY4JwhsGCM3xMRETKL+XaBceoXNuhXLtkKNdWru0tlvxybc+4sl/390oiHqkcNIeulGvW2gRjzD9AH2NM9sqUmcAUa+0ya+2vxpiHcVZZX2+MmYrTIywMZ26qM3CGaQx0T/kgcKYx5g+3XQLQBjgX5w+256eFs3CKFc8aY9q6+7HWPl3I8L8F4nD+WPgDb3gZWvIsTo+C6ziSlA4AXjTG/O0+331AA5weDVk4iaCn7E+sDUXzJ7DUGDMR54/sOUAHYBF5VnotiHUmvL8M5/nOM8b8CqzE+eegIc4k+1E4n8xm+z+cP7hjjTFn4yxy0BRnUvgfcFYiLYwPcD5Jvhbn9f8eiAHq4QyfGQ+Mcdv+jZOg322MieLIfE9v2Dyr9no8ty3GmLtxCleLjTOxfwzO+6oHsAZnsYIT5Rac1+lF9/u0EOd7ejnOe+G6EvyEdjfOXFwrjDFTcN6zl+EMN3vbWvu7R9u3cd6zk4yz4MkunB6ZA4GvgCFezv+rG/c37s9pMrDVWvs/j/3dgGnGmN+BVOBfa+0PxXyPFYq1NskY8zFHEsUiJ0nW2s/c5PhNN/6lOIteHHLj6oHzs+U5BLQlzj/Jh9zfR+txPpVvAAzCma/tH3KvTj0KJ5lLxllV/GEnv8tlqbX2u6I+B/fcvYwxf+HM55WE8709143lL5zfV57Pe5Ex5gmcOQiz3zcpOL9LmrnHFPp3iYiIlD7l2sq1j0G5tnJt5dq5lVqu7eFMnJ/fxdbaRcW4tlRW1lp96atcf+H88voB51PFLJw/LCPytOmN88dtF5CGkwgsBV4Bunq0OxvnE8hVOIlVIrAW59PnRl6uPdQ9T7J7XVvE2D/IPg7o4mX/hLzPB2f10FdwEooYnD+2W3CGm/T0co4ixeVxzZOB+3ASpRScBQ/GAtW8HLMFZ2hYQedtjPMHcb17vjj33P8DLs7ndZ2M82lwIk4SOAgYkc9rnG8MOKsaz3Ff0xScfyA+AzrnaTfQvU6Cx+vS2N03xn3c18v5z8ZZjOCQ+3pswEnEq3tpOzu/1yO/53aM72t9nMU+trrv7f3Ad0C3fNpbYHYR36db3K8InIR6p/s8V+MkX8bLMT1xhjoewhmyNBdniGJfN4Yxedr7Av8FNuH0xMgVJxDqPs8dOMmWBSYU9z3Gkfd540I8/w5u212AX1G+d3nO0xB4Hmdo3mH3ecbg/MN6Nx4/W0A0cD1OD9ZV7vfRs/1tQEA+z6mgrwnFjH0QTi+ZdTg/R+k4/+DOxBkel+/3BbgEp3dDnPu6rMRZDCewuN9LfelLX/rSV+l9oVxbufYxYkC5dt73w+wivk+3oFxbuXbxc+2J7rVvLu73Tl+V88tYaxGRqsMYMwGnp0ITa+2Wso1GpOwZY0bg/PP5tLV2ZBmHIyIiIhWYcm2R3JRri5wYmkNXRESqLOMsgnEvTk8FzUklIiIiIlJClGuLnDiaQ1dERKocY0xvnPnZ+gLtgDetsziGiIiIiIgcB+XaIieeCroiIlIVnQWMBg4C7+Ms4lJpuCsR9y1E08PW2rEnMhYRERERqXKUazuUa8sJozl0RUREKhljzBicJPpYtlprG5/YaEREREREKg/l2lIeVIqCbnR0tG3cuHFZhyEiIiJSpSxatGi/tbZmWcchJ5ZybREREZHSdaw8u1JMudC4cWMWLlxY1mGIiIiIVCnGmK1lHYOceMq1RURERErXsfJsn9IKRERERERERERERESOjwq6IiIiIiIiIiIiIhWECroiIiIiIiIiIiIiFUSlmENXREREcktPT2fHjh2kpKSUdShSCQQFBdGgQQP8/f3LOhQRERGRMqU8W0pScfNsFXRFREQqoR07dhAeHk7jxo0xxpR1OFKBWWs5cOAAO3bsoEmTJmUdjoiIiEiZUp4tJeV48mxNuSAiIlIJpaSkEBUVpSRTjpsxhqioKPVCEREREUF5tpSc48mzVdAVERGppJRkSknRe0lERETkCOVGUlKK+15SQVdERERERERERESkglBBV0RERErc4cOHefvtt4t9/NixY0lKSjpmu9mzZ3P++ecX2Gbp0qVMnTq12LGIiIiIiJQXyrMFVNAVERGRE6C0Es3CUKIpIiIiIpWF8mwBFXRFRETkBHj44YfZuHEjHTt25IEHHgDgxRdfpFu3brRv357Ro0cDkJiYyKBBg+jQoQNt27Zl4sSJvP766+zatYt+/frRr1+/o849bdo0WrZsSefOnfnmm29yts+fP58ePXrQqVMnevbsydq1a0lLS2PUqFFMnDiRjh07MnHiRK/tREREREQqAuXZAuBX1gGIiIjIiXX3+vUsTUgo0XN2DAtjbLNm+e5/7rnnWLFiBUuXLgVg+vTprF+/nvnz52Ot5cILL+T3338nJiaGevXq8dNPPwEQGxtLREQEr7zyCrNmzSI6OjrXeVNSUrjxxhv57bffaNq0KUOGDMnZ17JlS/744w/8/PyYOXMmjz76KF9//TVPPvkkCxcu5M033wQgLi7OazsRERERkaJQnq08u6yooCsiIiIn3PTp05k+fTqdOnUCICEhgfXr19OnTx/uu+8+HnroIc4//3z69OlT4HnWrFlDkyZNaOYmuUOHDmXcuHGAk6QOHz6c9evXY4whPT3d6zkK205EREREpLxTnl01qaArIiJSyRX0CX9psdbyyCOPcPPNNx+1b/HixUydOpXHH3+c/v37M2rUqGJdY+TIkfTr149vv/2WLVu20Ldv3+NqJyIiIiJSEOXZxWsnx09z6IqIiEiJCw8PJz4+PufxOeecw/jx40lwh6Tt3LmTffv2sWvXLkJCQhg6dCgPPPAAixcv9np8tpYtW7JlyxY2btwIwBdffJGzLzY2lvr16wMwYcKEfGPJr52IiIiISHmnPFtABV0RERE5AaKioujVqxdt27blgQce4Oyzz+bqq6+mR48etGvXjssuu4z4+HiWL19O9+7d6dixI0888QSPP/44ADfddBMDBw48arGGoKAgxo0bx6BBg+jcuTO1atXK2ffggw/yyCOP0KlTJzIyMnK29+vXj1WrVuUs1pBfOxERERGR8k55tgAYa21Zx3DcunbtahcuXFjWYYiIiJQbq1evplWrVmUdhlQi3t5TxphF1tquZRSSlBLl2iIiIkcoz5aSVpw8Wz10i2hC9wf59L6JJMSllHUoIiIiIiKVxsTR3zP+zCf45qXpZR2KiIiISLmmRdGKYPOavVy8YBzVF8Qy6ZtruXzzJ2UdkoiIiIhIpZD6/W9c/+/rMAv+arWcnoPalnVIIiIiIuWSeugWQZOWtfGL3cMPdQbTf8uPpKVqPhARERERkZIwbOlr/PbFAgBWvz6ljKMRERERKb9U0C2isGpBHBg0iEgO8UvjK8jKzCrrkEREREREKoUzr+zKav+WXDt9DH/9tKKswxEREREpl1TQLYbzn7mCH2tfxAV7vuXTuz4r63BERERERCqNja+9SwDprH7tu7IORURERKRcUkG3GKJrh3P21snEE4bfzLllHY6IiIiISKVx/q1nsN7vFOov/rusQxEREREpl1TQLaaAQD9WB7ekwa41ZR2KiIhIufT666/TqlUrrrnmGqZMmcJzzz133OecPXs2559/fglEV7AxY8bw0ksvATBq1ChmzpyZb9ulS5cyderUIl+jb9++LFy4sMA2hXm+xb2+SHm2vG432h1aWtZhiIiIlEvKswtWFfJsv7IOoCLbWqslp2+bXtZhiIiIlEtvv/02M2fOpEGDBgBceOGFZRqPtRZrLT4+Rfs8+8knnyxw/9KlS1m4cCHnnXfe8YRXbGV9fZET4XDTltTf/iXbNsRwUtOaZR2OiIhIuaI8u3SU9fULoh66xyGuVRtq230s+X09M79YwJfNrud/7W7nw0vH8vfPq8o6PBERkTJzyy23sGnTJs4991xeffVVJkyYwO233w7ARRddxCeffALAe++9xzXXXAPA9OnT6dGjB507d+byyy8nISEBgGnTptGyZUs6d+7MN9984/V6EyZM4KKLLqJv3740a9aMJ554AoAtW7bQokULhg0bRtu2bdm+fTsvvvgi3bp1o3379owePTrnHM888wzNmzend+/erF27Nmf7iBEjmDx5MgALFiygZ8+edOjQge7duxMbG8uoUaOYOHEiHTt2ZOLEiSQmJnL99dfTvXt3OnXqxPfffw9AcnIyV155Ja1atWLw4MEkJyd7fS75Pd/58+fTo0cPOnXqRM+ePVm7di1paWlHXd9bO5GKJqBzSwAW//BvGUciIiJSvijPVp4N6qF7XBpc1RemwZJxv1Lz1+lctmcK6fgTvCKFuG/C2bZ+o3oUiIhImbv7bli6tGTP2bEjjB2b//53332XadOmMWvWLKKjo5kwYULOvnHjxtGrVy+aNGnCyy+/zLx589i/fz9PP/00M2fOJDQ0lOeff55XXnmFBx98kBtvvJHffvuNpk2bMmTIkHyvOX/+fFasWEFISAjdunVj0KBBREdHs379ej7++GNOO+00pk+fzvr165k/fz7WWi688EJ+//13QkND+fLLL1m6dCkZGRl07tyZLl265Dp/WloaQ4YMYeLEiXTr1o24uDhCQkJ48sknWbhwIW+++SYAjz76KGeeeSbjx4/n8OHDdO/enbPOOov33nuPkJAQVq9ezbJly+jcufNRzyElJSXf59uyZUv++OMP/Pz8mDlzJo8++ihff/31UdePi4vz2k6kImlzYWd4GVLe/oLN57bjt/s/hgB/os5oz9k39CYkLLCsQxQREVGerTy7zPJsFXSPQ/+rurFvRE3O/OJZGmdt48um13PZqnFMfHYqQ0ZfyJ89rmfWZZfTeXgf2p3WpKzDFRERKRdq167Nk08+Sb9+/fj222+JjIzkxx9/ZNWqVfTq1QtwkroePXqwZs0amjRpQrNmzQAYOnQo48aN83reAQMGEBUVBcAll1zC3Llzufjii2nUqBGnnXYa4PROmD59Op06dQIgISGB9evXEx8fz+DBgwkJCQG8D1tbu3YtdevWpVu3bgBUq1bNaxzTp09nypQpOXODpaSksG3bNn7//XfuvPNOANq3b0/79u2POrag5xsbG8vw4cNZv349xhjS09O9Xr+w7UTKs06nN+PLptdxxYaP2NDuD27IWO/s+Bbi7w5jas2+JN17G5c9fG7ZBioiIlKOKM+uOnm2CrrHwc/fl+n3vMHQV64EIGr0Tfj5+3L5Y4P4/PMbOX/tl1R790d4F3aZuiyJ7sop09+mZccGRb5W3OFkvjl7FD4ZGaTXq8sNPz5Y0k9HREQqqYI+4S8ry5cvJyoqil27dgHOvFsDBgzgiy++yNVuaRG6PBhjvD4ODQ3N2Wat5ZFHHuHmm2/O1XZsCX6TrLV8/fXXtGjRosTOCTBy5Mic5HzLli307dv3uNqJlHeh9wzD5/8+onnGej7pcCdtxv4fK7+aR+D0WZy+aRpBj1zNgm7z6Na/ZH/WRERECkt5tvLssqI5dI/T0JeH8OXIb/n4po8YMPRUAHx8fbh6zTj84mP4/rVf+fDc51hU+zQGxfzAXy/9UKzrfPfoJEYseIlhS8Zyw08PsfSPDSX5NERERErN/Pnz+fnnn1myZAkvvfQSmzdv5rTTTuPPP/9kwwbn71tiYiLr1q2jZcuWbNmyhY0bNwIclYh6mjFjBgcPHiQ5OZnvvvsupxeCp3POOYfx48fnzBu2c+dO9u3bx+mnn853331HcnIy8fHx/PDD0X+vW7Rowe7du1mwYAEA8fHxZGRkEB4eTnx8fK5rvPHGG1hrAViyZAkAp59+Op9//jkAK1asYNmyZUddo6DnGxsbS/369QFyDa3Le/382olUNOfe2CfnfrXrLqRL3+YMe3sYQzZ8xOJ3JhJOPN3OaslO3/qMv+qtYl1j8n+nEm/CWeffnA8HPV9SoYuIiJQJ5dlVJ89WQbcEXPnkxQx/b8RR20PCArnozjO5YepDDNj4JakE4LdiTbGu4RsWnOvxyh+WFOs8IiIiZSk1NZUbb7yR8ePHU69ePV5++WWuv/76nPm/rrrqKtq3b58zDCwoKIhx48YxaNAgOnfuTK1atfI9d/fu3bn00ktp3749l156KV27dj2qzdlnn83VV19Njx49aNeuHZdddhnx8fF07tyZIUOG0KFDB84999yc4V6eAgICmDhxInfccQcdOnRgwIABpKSk0K9fP1atWpWzWMLIkSNJT0+nffv2tGnThpEjRwJw6623kpCQQKtWrRg1atRRc4cBBT7fBx98kEceeYROnTqRkZGRsz3v9fNrJ1LR+Pn7MnvSIj7udA/n3XJGrn2Dbj6d2Z//zYfnPUeyCaLjdx8W6xqpn04hnASaZ6zn9Onvl0TYIiIiZUJ5dtXKs012Vbsi69q1q124cGFZh3FMywPbsjusIWcf+LnIx3588wSGj7su5/FHvR/juj+eLsnwRESkElm9ejWtWrUq6zBKzYQJE3ItWCAlz9t7yhizyFp7dEYvlUp5z7UndH+Qaxe8zJ7NMdRvHFmkYz/peDfD/n2Nz1vexJVr3mfv1gPUPanGCYpUREQqA+XZUtKKk2erh24p2hTVkuaHVwEw49N/mBE1kB/fmVOoYzPjEgH4c8oy1vudQu21/56wOEVEREREKoqgc3rgSxZ//u+vIh/rl5LMQWqQdd6Z+GCZN7n8Fq5FREREsqmgW4r2t+tM46xt/PvXJnzueIwBB3/h0DuTC3WsjXcKujUbRbKsbne67Z9HVmbWiQxXRESkwhgxYoR6DYhUUe0v7AxA/N8rAfiy2Q18OPiVQh3rn5pMkk8IXa7pCcChH/48MUGKiIhUUMqzyycVdEtR3ctOB2Dhc9/QLtaZoLnujrWFOtYkJgEQWbsasT16UtPu53+3fXJiAhURERERqSBadm5IjIkmfM1qlv6xgSs3jOeG7+4rVOeHgNRkkn2CadW5IcsD2tD9r0nEHU4uhahFREREik8F3VJ05jXdWenfiht+eIBaNgaA5rGrAZjy1mzmhvfh45sneD3WJzGRLAyR0aEMeO4qNvo24fJxt/HpfRNLK3wRERERkXLHx9eHleFtOWvzFJbd9VbO9hn/+weAbRti+OHt2V6PDUxPIsnHWXx4/plDaZu2iklXvHzCYxYRERE5HirolqKgkACqrf2DL5r/h0RC+LrhVZyUtYN1y3aS+uKH9E6YS+1vJnk91i8piSRC8PH1oWGTKOacPpwQkhn6ypUs/WNDKT8TEREREZHyI+XVZ6jOYYYtGZuzbef0JWRlZhHWrAUX/F8/Zn6x4KjjAtOTSfFzCro3/Pww23waEL1iaSlFLSIiIlI8KuiWsoZNorhq7fsEZ8Tje8/1AMx791ea73YWOTvl8JEpGBbNXseGFbsA8E9JItGE5uwL7dv5SLtXfyiN0EVEREREyqWB1/fk79AeAPwR3ockgglYtpKfP5hLJIcA2P33uqOOC0pPJtnvSI69vlpLLtr9NR+e+1zpBC4iIiJSDCrolhEfXx/OuqE3iYQw7J3hdEhbDkCzjI3s2x3Ht6/MoEu/Fmw+/ToAAlKSSPQJyTm+57U9c+53/ekjMtIzS/cJiIiIlIF3332XTz4pn3PIh4WFAbBr1y4uu+yyAtuOHTuWpKSkIp1/9uzZnH/++cds17dvXxYuXFji1xcp7zb953YA1nU7i1VBrem0bhb735+Ssz9t4w4APrzwJeaHdGPjqj0EZyaT4h+c02Znk7YA3DDtEWZ8+k8pRi8iIlK2lGdXrDxbBd0yFFYtiEnXv87san1Z59eMj3o+CkBqg9YEPzsWgDMPzSQrM4vAtCSSfI8UdBs2iQJr+WjoO3RIW863z08ri6cgIiJSqm655RaGDRtWatfLyMgo8jH16tVj8uTJBbYp64JqWV9f5ES4duxVbFi+k2unPsqq6+6kTfpqhi96mfV+TUkhkIAdOzl8IJEbfniA7skL+Xv8HIIzk0kNOFLQ7f/NSD667n0Atn37d1k9FRERkVKnPLtkqKBbRYz48Ab6xs6iefo6hv/+FB8NH0fDrJ0M3D8VAF+yWLN4O0F5CrrZznv2SlIJIOPT70s7dBERkXxt2bKFli1bMmLECJo3b84111zDzJkz6dWrF82aNWP+/PkAHDx4kIsvvpj27dtz2mmnsWzZMrKysmjcuDGHDx/OOV+zZs3Yu3cvY8aM4aWXXgKcT8gfeughunfvTvPmzfnjjz8ASEpK4oorrqB169YMHjyYU0891esn6Y0bN+bBBx+kXbt2dO/enQ0bnDnpR4wYwS233MKpp57Kgw8+yMaNGxk4cCBdunShT58+rFmzBoDNmzfTo0cP2rVrx+OPP57rubdt6/Tyy8zM5P7776dt27a0b9+eN954g9dff51du3bRr18/+vXrB8D06dPp0aMHnTt35vLLLychIQGAadOm0bJlSzp37sw333zj9XudnJzMlVdeSatWrRg8eDDJyck5+2699Va6du1KmzZtGD16NIDX63trJ1IRNW1bj4BAP4a9PYxFQc4UZesiW7PDtz4R+3by64dzc9qmLl5DcFYSaR4F3fqNIxn+/vUcMJHU/30GB2MSSv05iIiIFER5tvJsAL8SO5McNx9fH66bcCNTfpnGhXu+YZtPA07K2sHm8+/knNg5/Fmt91HH1G5QnWlRZ3HV2ve56ebutBs5kAuiomgcHOzlCiIiUiXdfTcsXVqy5+zYEcaOLbDJhg0bmDRpEuPHj6dbt258/vnnzJ07lylTpvDf//6X7777jtGjR9OpUye+++47fvvtN4YNG8bSpUu56KKL+Pbbb7nuuuv4559/aNSoEbVr1z7qGhkZGcyfP5+pU6fyxBNPMHPmTN5++21q1KjBqlWrWLFiBR07dsw3xoiICJYvX84nn3zC3XffzY8//gjAjh07+Ouvv/D19aV///68++67NGvWjH/++YfbbruN3377jbvuuotbb72VYcOG8dZbb3k9/7hx49iyZQtLly7Fz8+PgwcPEhkZySuvvMKsWbOIjo5m//79PP3008ycOZPQ0FCef/55XnnlFR588EFuvPFGfvvtN5o2bcqQIUO8XuOdd94hJCSE1atXs2zZMjp3PjLP/jPPPENkZCSZmZn079+fZcuWceedd+a6fn7t2rdvX+DrK1LebXhwFK2evIq9AwYS8mM8F+75Bh76hiwMGfgRsWk9ITaZ9KDcebOPrw/Tm13CVes+4J/G/Tk1UVMviIhIPpRn5xuj8uwTm2erh2451OTbl/i85U0sfmUC/2t3O6fv+w0/MvGxWV7bt/3nY/b41OHZcQ+RfO5TTLvifoiNLeWoRUREcmvSpAnt2rXDx8eHNm3a0L9/f4wxtGvXji1btgAwd+5crr32WgDOPPNMDhw4QFxcHEOGDGHixIkAfPnll/kmWZdccgkAXbp0yXXOK6+8EiDnE/v8XHXVVTm3f/99ZHj15Zdfjq+vLwkJCfz1119cfvnldOzYkZtvvpndu3cD8Oeff+Ycn/0c8po5cyY333wzfn7OZ+iRkZFHtZk3bx6rVq2iV69edOzYkY8//pitW7eyZs0amjRpQrNmzTDGMHToUK/X+P3333P2tW/fPtfz/eqrr+jcuTOdOnVi5cqVrFq1yus5CttOpCIZ8sRFBGUkMOLjm6nxwzjG9xnJFp+T+GjAk8yN6EP7nfMJtYlkBB3dEWLIqvcY3+dxTk2az5yvl5RB9CIiIvlTnq08Wz10y6F2pzWh3er3nAd39Wfvjqf46rYPiOrX0Wv7BqdEM+u8B+j3433cveIjAlakQ/W3Sfv5V35e05j+3eIJ69XBaRwTA1FR4KNavohIlXGMT/hPlMDAwJz7Pj4+OY99fHyOOWdWjx492LBhAzExMXz33Xe5hlp5u4avr2+x5uEyxni9HxrqrHqflZVF9erVWZpPzwvPY4rLWsuAAQP44osvcm3P75qFtXnzZl566SUWLFhAjRo1GDFiBCkpKcVuJ1IR+fg6OW/HPk3p+PuTwJPcAIy/vBpnTr4LgKzAIK/Hdf7vCOjzND+9/AU3NkhjZocOnBR0dFsREanClGfnS3n2ic2zVdWrAGo3qM4NU+7n4nvOyrdNvx/uJSsljecn7WMCwwEIOLc/F91zCsG9O/NvjTM43KI71KoFzz6b++DVq2HlyhP5FERERLzq06cPn332GeCsLhsdHU21atUwxjB48GDuvfdeWrVqRVRUVKHP2atXL7766isAVq1axfLly/Ntm907YeLEifTo0eOo/dWqVaNJkyZMmjQJcJLCf//9N+c6X375JUDOc8hrwIABvPfeezlJ8MGDBwEIDw8nPj4egNNOO40///wzZ26xxMRE1q1bR8uWLdmyZQsbN24EOCoRzXb66afz+eefA7BixQqWLVsGQFxcHKGhoURERLB3715+/vnnnGM8r19QO5HK6vzXR/BrdSe3zmrc0GubDr1OYY1PSwZsXsj65GQe2bSpNEMUERE5LsqzK3eerR66lYhPoD/3XBzGJd/cx9iJQ7j27++JCwuh09qNXHx4Chx22h14732W3HorZ1SvDoB/69bODmsByLKWZQkJtAgJIdjXt/SfiIiIVBljxozh+uuvp3379oSEhPDxxx/n7BsyZAjdunVjwoQJRTrnbbfdxvDhw2ndujUtW7akTZs2REREeG176NAh2rdvT2BgYL6J3Geffcatt97K008/TXp6OldeeSUdOnTgtdde4+qrr+b555/noosu8nrsf/7zH9atW0f79u3x9/fnxhtv5Pbbb+emm25i4MCB1KtXj1mzZjFhwgSuuuoqUlNTAXj66adp3rw548aNY9CgQYSEhNCnT5+c5NDTrbfeynXXXUerVq1o1aoVXbp0AaBDhw506tSJli1b0rBhQ3r16pVzTN7r59dOpLKqVbca/Q/NYOOqPQw9JdprG2Pg3xr9uCDmf9xesyZv7tvHi6ecQrCPD74pKVT7+GO47DKoU6eUoxcRETk25dmVO8821i3iVWRdu3a13lbVq8pSs7L4NiYGX2PwT/Ph/m9W8ej9E7g+/SMSfUI4b+SL/N68EyftzGDrg6c7B1kLS5bwTkwMtwUEMKZxY0Y3bgzA7EOHqBMQQEu3a7yIiJRvq1evplWrVmUdRpnIzMwkPT2doKAgNm7cyFlnncXatWsJCAjI1a5x48YsXLgwZ8ECKZi395QxZpG1tmsZhSSlpCrn2i90m8SDC69g0S+/0NX9HWIys3j55s+5Z+OHxNWqzZTZszjz5JOp5zH8VUREKi/l2cqzS1px8mz10K2kAn18uNJjlcJRPYL54K/bmTWqBf/7+WHmPPF/PMOj/M2Rbu+ZmeDbuTO3ArfNmsWcw4cBSM7MpN+//1LT35996rUjIiLlXFJSEv369SM9PR1rLW+//fZRSaaISGHEduoLC6HpqJeYX60x6SkbsKvj6bV/IfuJInrfXoa2bs0Vo0dzaPBgvmjVimgvv2/2p6Vx9rJlnBwUxOS2bUv9eYiIiJQE5dnlhwq6VYSvMaQby9LLaoI7ZccDvMizHW4FZ4oSzuq4h1kex7T44gvit27l1549AcgsZG/uTGvZmpLCuqQkNqWkcCgjg9iMDNKt5czq1blAn9CIiMgJFB4eTmF6E2av1isikp+B19Zk/Pgbuf6f9+nmbjtIDT7p+yEvNbuA/75/HefzE1898QTd6tbl2dBQnjv5ZPzzLED89f79LElIYElCAvEZGYT7+UF8vNOjwp0GTUREpLxTnl1+qKBbRfgZQ2pWFmtOOilnm/HNYvS/r+c8brhies79i2c345mXLmFhxFwGvzwWmkAfj3lReixeTIAxzOnUKWfb8oQERm3Zwi8HD5KclZXr+kE+PmRayzcxMV4Luh/u3s29GzbwbvPmXOXRs1hEREREpKz06QOnbBvH4NPvYNjG0Sy/7AlaXtSCa68JYM7aNdxb8z+c/9+fALj7o3kMbdGC13bsYEitWgyvU4ezIyO5ae1afnEXagH4Ky6OcyIjSW3UiMBDh9iYlMQpwcFFjs1aS4a1RxWPAQ6np7MzLY02mi5NRESkUlJBt4rwxZlXN8PPj0n33ceEqCg6btzIMx9+mNPmqn7fkN1FN+OJxURyiFMObYdbOsMbSzA9DeAsmjYvLi7X+ZMyMzlj6VIMcF2dOnQOD6d5cDCnBAcT5e9PoI8PL23bxgObNhGTlkbNPF3y/7dnD3GZmfwZG6uCroiIiIiUG/XqwVer22HMNwz2+O/Jzxg29K8G/3UeX/PPBOKu8ueB8YP5fO9eAubN4/Rhw3h/924ARtSpw6d79zL78GHCfH3pdegQAJNjYnjopJPIspYPdu/mipo1qe7vnyuGJfHxfLN/P8sTEticksL+9HQOpKeTbi2/duhA3xo1ctpaa2k0bx5xmZms7d6d5iEhJfJ9eGnbNmYeOsTHrVpRW8NrRUREypQKulWEr9tDF2DWTTcxddcu0vIkiq1Wzcu5P/6sz2EmNDQ7CAxMYsvN5/ML51CvxQd0H5IAfQFz5NgfDhzgUEYGMzt0oL9HQumpS3g4AIvi4xkYFcXapCQWx8dzWc2aLHBXE9yZlgZARlYWn+/bR+ewMNqGhRXrOVtryXKfu4iIiIhIceVJmwGnoGt9fGj2448sWrCWak/cx6173qfvjdOJSEmg3oEDxKSnE9ikCW+1a8cNdeuyNimJmdu3kzJ1KtkrU6xMTATgy337uHndOrakpPDfk0/Ouc6huDj6LllCgrU0DwmheXAwXcPDifL357UdO/j54MFcBd3tqanEZWYCsD45meYhIWRZS0x6erEKselZWWxKSeGBTZsAmBsby6U1axb5PCIiIlJyVNCtIvyMIdWdAzfALXCubtQoV5vGe/dy4JRTiNq4kZp/fg+AsZaHn5pDnbv3MpxPGLH2Y1Y9GcPHX77GyCeGMHTVKlqHhrI2KYlof3/6Vq8OsbHOnGANGuQ6f9fwcIJ9fPhm/35OrVaNbosWEZ+ZybDly7l30iTevugidqWmAvDurl3csWED59SowbQOHUjNyuK+DRtoHBTE/e60ERlZWcw8dIjFCQmsS0piT1oa6e7Qs3RrWZeURGJWFiu7deNkL8PY0rOySM3KIsyvdH4MJu/bxyObNzOpdWs6usVtEREREamY/NycOr56daqNuZfYv+cRMX0SrXZuzWlT85ZbSAF+iYkB4PyoKGo//jg3/PxzTptVbseGb/fvByDBLcZmWcu3+/ZxaZ06PD14MD0//jing0S2ubGx/Bkbm/P465gY3t+1K+fx9pQUAM5ZtoyZhw6xvnt3moaEkJGVxTu7dtE2NJR+bjE401pmHjrE5uRkZh8+zLrkZHalprIvPR3PlTR2uPn6vrQ0An18WJWYyJWrVvFYo0bcVK8eKZmZTI6J4cwaNagXGOjEefgw7cLCCPf1ZXJMDGvc3H1vWhr/JiRgjMEA6daSnpVFhpvTj2zcmLvy/E8hIiIicPSES1Ip+RpDmttDN9CdZ2tn9ly2p5yS0y5y0CDnTnIyuMndmIMLcva/PeQGJlUbyrB13zH+gdf4bP0hHtu8mU/27qVTWJjTG7ZTJ2jY8KgYwv38uLpWLT7bu5dfDx0iMS2NgLQ0rp45k+fHjeOz779np5sg/uVO6bAuORlwktO3du3K6RkA8OjmzZy7fDmPbd7MjEOHOJCRQUpWFgYI9fWlbmAgKVlZR00Pke3ylSsJnzsXW8jF3o7XbevXsyE5ma/dZF1EpCoaO3YsSUlJJXKuMWPG8NJLLwEwatQoZs6cWSLnFREpjOyCbvZtxA+fsq5aW69tG+3dC8Aj9esz9PDhXPsWnnoq9ebMYYY7z+5Wtwj76KZN3LDAycPv+PZbuuzcedR5T6tWjcUJCWRay4K4OC5buZJfDh2iq1v43ZGaSlJmJjPd6R3+dvPij/bs4c4NG7h7wwYA9qal0XXRIgYuW8at69fze2wsdQMCuDA6mtGNGzOueXP+6NiRIB8fdqSmYq2l9l9/0WXhQl7fuZNtqal87j7HMVu2cO2aNTy7bRsA7+zcSZ+lS3l882ae3rqVIatWMXrLFr7at481SUm0DQ2lW3g4XcPD6RMRwTmRkVwcHU2wry/fu3mztZYuCxfy3NatiIiId8qzqxb10K0i/DymXMgu6GIMsxcsoG+zZvDnnzBjBuaZZ+B1d6G088+H//0PJk3KOc+tE8fn3D91x3J8Bp+K7RiPbZjEKQ+nkZUFPps35xvH0Nq1+XDPHoavWcOE55/n2hkzGDN8OAA1srLYk5bGxL17afTeezTq0YNtNWuSmJzMvwkJAIQlJbF/xQqi2rRhUkwMZ9eowaQ2bajmpZdtalYWob//zmqPX2jWWowxZFnL9wcOALAlJYUmRVyIIsvtNeBnDD6FmNJhb1oaMenpAMzJk8SLiFQlY8eOZejQoYSU0JyO2Z588skSPZ9IRWSMGQ+cD+yz1h5VWTTGGOA14DwgCRhhrV1culFWHnkLugQEMGvq+3wy+Ceejnk6V9u6GzaSHlUH/7rRBHo5V/t//uGX7t0BZ5oEgBmHDtHXc6HhVq1gxgw466ycTe1CQ0nOymKT26sWYHW3bjQPCaHxvHl8tm8fe90cFGBxQgLXAj+5eXBsRgbgzOO7NCGBj1q0oF+NGpwUGIjxkuM2CAxke0pKzjQRG1NSOOSeY0F8PKlZWfziFo8Xuj2Pv3GLsnMOH+ZgejrnRUbybdu2BHhZzM3TjWvX5hR0F8bHszghgcUJCTycZ5ShiIg4lGdXLeqhW0X4eky5EOiRPKWefDJERMB558Grr4LnD37v3s7t6tVHnW9zi1aEkUjLFktpu383yVNOYU3PLbRpcqR4mpWeedRxfapXp1lwMElZWVw7YwYAHTZuBKBWejqZwKhffuHZ119n0osv8uftt2Oio3lh+3YAfnrkEaLbtWNJfDztfv2VF95802sxN/t5nhIczGI3mZywezcRc+fyw/79LHeTUIB5cXGkZmVx7erV9Fi8OKcn85+xsTyyaROpWVl8GxPDBcuXc8aSJXRZuJBaf/5J4O+/4ztnDr6zZxM0Zw7hf/xBjblzeW3HjqNiye4l3Ld6df6IjWV+Pr2GRUQqi8TERAYNGkSHDh1o27YtEydO5PXXX2fXrl3069ePfv36AfDFF1/Qrl072rZty0MPPZRzfFhYGPfccw9t2rShf//+xLjDlfMzYsQIJk+eDEDjxo0ZPXo0nTt3pl27dqxZsyYnpuuvv57u3bvTqVMnvv/++xP07EXKzARgYAH7zwWauV83Ae+UQkyV1lEFXSC1UQPGXX32UW0jLr6I3xoMy/dc/ZYsAaBLWBirk5I4a+lSFick0M2zoAuwYEGuh21DQwF4ZccO5sbG0iw4mJahofgYQ8uQELakpPCBuyhbw8BAPtu7l0HLluV0bNiZlkaWtWxKTibIx4fhderQKCjIazEXnILu33Fx3LhuXc62gxkZnFOjBklZWQT9/jtL3Y4Y8+LieGTTppy8d3liIjvT0ri0Zs1jFnOzn1tMejpdFi7kQ/c5AKRlZbEuKYkeixfznsf0EtbanEKziEhlpjxbQAXdKsMXyMgu6HokaH4F9S71mIrhv888w8VPPZXzeHm/MwDoddVUllz9CUGk8nrd5/h3+5EFGXq1j2fkSHA/pHfiMIaFkZEs+OKLnG1nLF8OQPP9+1ndrRs/uT1qOwQEcOqaNYQkJTH/llu4OCKC05ctA2Dgr78y5fHH6fDBB+D2CsBacIeq5cQQEcHUgwc5ed48rlu7lvjMTEZt2ZJT5AW4evVqoubO5dO9e5kXF8fapCSyrKX3kiU8t20bMw4e5MpVq/j10CEMUCcggAuio3mqcWPGNG7MIyedxF0NGnBj3bpU8/XlW49fhi9v387z27ax1n1ObzVrhi9w6uLF7E5N5bO9e6n3118kZx5d/BYRKUl9+/ZlwoQJAKSnp9O3b18+/fRTAJKSkujbty8TJ04EIDY2lr59+/LNN98AsH//fvr27csPP/wAwJ49e455vWnTplGvXj3+/fdfVqxYwcCBA7nzzjupV68es2bNYtasWezatYuHHnqI3377jaVLl7JgwQK+++47wEkKu3btysqVKznjjDN44oknivR8o6OjWbx4MbfeemvOcLFnnnmGM888k/nz5zNr1iweeOABEvXPv1Qi1trfgYMFNLkI+MQ65gHVjTF1Sye6ysffS0HXzxgOnJ+W83hNVHN2BTUB4JzMqfmeq9b+WC6NjubHli250d+fUz75hLPnz+dkd/qFHIcPH8l9gTZuQffdXbuYcuAAfSIicvZ92KIFz518Ms+dfDK31KvHj+3a0SgoiKluvtw9PJwMa9mXlsbmlBROLqCQm+3CqCgOZWQcNaXZo40acVf9+gypWZP/q1ePz1q1ItLPj+e2bSMuM5Nb6tXLaXtOZGSB18h2bmQkrUNCWJyQwHseBd2F8fG8tXMn8+LicorVAI9v3kzbBQty8u4Z7v8Ahzx6KJ+7bBnXu8UHEZGSojxbeXZZ0JQLVYRnounZQ9e3oIM8hjNtatWK7z1Wz93YpQsADfbtw/ePPwBol7oQ7JEE9uf1pzD06Y957ZnF7L7k/7g96nPqjfwPUUOG0NXthQAQ6S7kYH76iZaLFsHChQAEePQW7rZ2LeM9KsP7LrnkSJzvvQfr10NqKrz7LuzfD1FRgFNAbRcaygNuL+AgHx+WJiQw9eBBQn18aB4SwpKEBBKzsmgQGMiO1FRWJiYS4nvkO/P5vn2kWcuXrVoxpFatgr5jJGVm8lVMDNZasoD73eveXLcuUX5+tA4NZWKbNly2ciVTDx7kP2vXAs7wt14eCbiISEXXrl077rvvPh566CHOP/98+vTpc1SbBQsW0LdvX2q6q6Vfc801/P7771x88cX4+PgwZMgQAIYOHcolnr/3CyG7fZcuXXIS5unTpzNlypScxDMlJYVt27bRqlWrYj9PkQqmPrDd4/EOd9tu782lINn5tclT0M3yO5JHDv/lc+aERZPZsTW+KbnnNTxYvx2RO52ODTVnGfwfa0TQry14L2k3xu2IMfeVV3Jf9IUXnC93f4ivL3907EifpUsB6O2RTzYMCuIhdzHhbAvq1sW4HRsuq1mT+fHx3LdxI9/u3895hSi03tOwIX0iIui2OPdMHV3Cwzm9evVc266qVYsMa9mckkKDwEDG795Ni5AQ6gd6m3TiaM1DQljZvTvdFi1iYXw8gyIj+engQXp5/B/xb0ICyZmZBPn45MzZuzE5mRYhIVyxahWHMzJYnJDAmdWrsz01lWluMfvtZs0I8vUlKTOTnw4c4PTq1akdEFCouEREyprybAEVdKsMX49E03OIk29Bn8J7LGy2p1Ej8Pgkft/JJ5MYFMSot95yNrRtCytW5Dq8euZBvgsbil9CLBu++R9N7QYYdycAGc1a4rfey6fjF18M7jAtZs3KtauGO0XDUW6//ejHl18OcXEEr1jBPffeyzcREcyNjeXGunV5Y+dOJsfE0LNaNcJ8fTHuULYb6tTh6a1bmRwTw78enyR9sW8fAJ3Cwrxf30O7sDDe272br2NiqOuRrM48dChnnt5LoqM5KTCQTzw+efsnLi6noLsyMZGrV63i5OBgvm3rTH9nrSXTWvwKMTxNRMSb2bNn59z39/fP9TgkJCTX44iIiFyPo6Ojcz2uU6fOMa/XvHlzFi9ezNSpU3n88cfp378/o0aNKnb8x+o1lleg+zvY19eXDLc3m7WWr7/+mhYtWhQ7DpGqwhhzE860DJyUpygoDm8j3fJu8zeGoBaN4PNPwfMf5vBwImd+5cyLC5yfOZXzp1Q76ny9773X+8UnTgT3n/HePj68tH07L51yCgM8OmAcZd486NGDle+/zwu9enFJzZo8unkz37nz1BYm1wXoHB5Oj2rVuLlePcbv3s2/CQmE+h7dTcQYg78xNHc7aYxu3JhTirhuBcADDRsyfM0arqpdmzRrmeF28qgfEMDOtDRq/fUXXcPDyV7meGtKChlZWRx2f/dvS0lhzJYtPOmxoNofsbEMiIxkyKpV/HjgAKeGhzPP7bAiIlJUyrOVZ5cFFXSriFw9dI815cKAAc6CC4GBzqJoy5fj4+8PwLwzzuC0OXNIrFWLxKAgQrOHgT31FAwefPR1E5zet03thlzbb9t4P/U612LM4gsByHrueXx69oDTT3canHIKuL1bc/z1V+Ge7JdfOl/ZvviCN++6i+UzZnDZ/v10bNSIWH9/bvn7b1KuvprMiRM50KULTf7v/1hgLV+7SW24jw9n1qjB9wcOUCcggKaFSED7Va9OhK8vl69alWv7xpQUrnBXGzbGcFeDBtzn8fz+8SiW37thA8sSE1mWmEhMWhrR/v6cvnQp/yYksOW004j09yfLWr7dv58ViYnsTk0lJj2dA+npVPPz40B6Oj7GcCg9ndGNG3P5MXoVi4icCLt27SIyMpKhQ4dSvXp1PvjgAwDCw8OJj48nOjqa7t27c+edd7J//35q1KjBF198wR133AFAVlYWkydP5sorr+Tzzz+nd/a87sfhnHPO4Y033uCNN97AGMOSJUvo1KnTcZ9XpALZCTT0eNzA3XYUa+04YBxA165drbc2VZ23PDp7GoZTPv2UlIAAmmW36ds3d8OAAGjZks8mTybgjTe4fM6col38yiud9S7mzIFrruE+4L6DByEoCDZtcr48Fk9j0iS44goAWv/xBxP+8x8A4nr3JsjHJyePLAwfY/irc2cArqlVi6xjtM/2aDEXM7uiVi2ucPPZi6OjybCWMVu2cF2dOozbtYu3du1i9uHD3FCnDp/s3cuWlBTWeCyKvCklhac9irngjI47vXp1prs9dhfGx5OSmUmQl8K0iEh5ozxbQAXdKsM3vykXvBV0f/wRsgu1Q4c67dzety+98AKTIyLAx4fw7ESpeXNnUbVnnoHHHjv6fJ07Q55hWade2oAX55/BGPfxY69E075hb4b06o1PndrOdA+vvAIjRoA7Fw2//577vFu2QOPGRx5PmwY33wxhYfDOO5A9T+6tt9LhoYfoANC0KddNmZIzjC34xRfB15fobdvg22/50ccHGx6OT2wsWdWqkfXOO6zp3p1GBw/iYwysXQsHDsCpp0J2wpe9WIWPD61DQ9nbqxePb97MS9s9RzSS0zsBYHidOty3wSlytwgJySnoWmuZHx9P46AgtqSksCopiboBAcx1p6VYmpDAmTVqMPXAAS5buRID1PT3p6a/PzX8/dmYnEyEnx9+xrAnLY0Pdu9WQVdEysTy5ct54IEH8PHxwd/fn3fecdZeuummmxg4cGDOHF/PPfcc/fr1w1rLoEGDuOiiiwAIDQ1l/vz5PP3009SqVStn3rHjMXLkSO6++27at29PVlYWTZo04ccffzzu84pUIFOA240xXwKnArHWWk23UEwF9dDdVL8+AK2z8+4aNaBWLYiNheefzynw7u3enZM95sT11Cd4IX8kd80/gK1b4cUXjzzeuxd27oR27ZzHO3bAvn1ObnzDDUfapaVBejrMmUOwW/SNLsx0A3Fx8OGH8J//gNtRIWf0WGKis7hy9vdk1y7n+t27H/u8+fnjD6hfH04+OWdTdk/gV5s2BeDN5s15y10Y7eWmTZkTG8sL27fnLKgM5OTR2RoFBvLTgQMsjI8nzVqurFWLL/ftY1VSEq1DQrhzwwZODgriYY8CtLWWgxkZ7E5NZXdaGpnWEu+ugbEvPZ3OYWH0KMT0aZnWFjxCUkSkEJRnC4CxtuJ/4N61a1e70J13Vbwbtno1/9u7F4Cv27Th0pUrAVjUpQud3YSsIJevXMnkmBgGR0fzTdu23LthA680a+bsXLkSWrd27mdkOD1rW7Y8cvCECU5h1tPKldhWrTE+TkJzR6MpvLn1AqqFWwaeY7ms+zYGxE6m+hP3OHPqLlzoTKWQXRy+9FKYPNkp+jZo4CSLjRs78+j6+oJnD4MDB+DPP6FPHyeZ3rTJmc7h8sth9mwn9pdfdlZvq1kT3nzT+zeheXPIXtE3KgquucZJWj//3Hne48Y519m6lX2DB3Oqnx/3T5zIljp1eP2SS1js60ubsDDo2hWs5eWrr2bQvHksHz2am2rV4gE/P9Y3bsyEvXsZ1agRT27dSrfwcIbVrs0dbvH39aZNuaNBA25cu5aJ+/axu2dPr0PcAO5Yv57xu3dzuHdv/I8xVcPapCTmxsZyRc2ahBeyd4aIlG+rV6+u0HNWhYWFkZA9BY+UC97eU8aYRdb+P3v3HR/z/Qdw/PW9y132TiQitgSx9+bMolZtrSqt0halrZYOqtSPqlKt0VKdOoyqVUVVS+29916JDERkj+/vj88lQo0gyZG8n4/HPS7fu+9dPhfB+973/rzf+l0yTiI3aZr2M2ABfIBLwPuACUDX9S80tadyKtASiAP66Lp+zyBaYu3bm3r+PIOOH6eMkxOHrInL+eHhdM20U6ullxd/VKyoDmJjVSI1U6/Zz86fp2aLFtQ+dOg/z9+0cRpz//bFh6isLWjoUIiIgO++U8eZW6J5eKiBarfq21ftkGvYEEwmVTm8YoWaSxERAXXqqCT08eM3nuupp+Dtt6FUKRgwQN3/zz/wyiuqv+/SpdCjB8TFQcuWUK8eDB9+c3ye7tIlFT+HhKjH+fmpdVSocCMxPXo0VKum4vejR2HQIPDxUWv19WWHvT3hR4/SqmJFRl+8yLdhYcSkpuJvNtPC05NJ589nfLv3ihblcFwcCzINMU7vQWynaXxTujTPWoem6RYLZxMS6LB/P4fi4khIu3Mtcoi13+/dLIyIoNOBA7xftCijihe/67lCiEebxNkiuz1InC2Zm3zivip0b/d463V61YGdpnHaz49ily6p9gjp7Ozg1k+nO3RQ1y1bqgARoHBh9QG+kxPExTHlRx+aR8H332us36gxb0ExDIah1FsHvXvXonu3CjjZ20OvXqqqwMFBPc+tfcVuN2TB2xvatbtxXKLEjU/602+3fqIFwGefwW+/qSrcL76As2dh8WI1bO2JJ1TV8muvqfPs7KBNGzh8WCWIAfz8KPDbbxyzs8POWnHx3NatlMs0wAGjkTesn+qX6dsX6yP5qHt33nN0ZNj+/YS2a8dTS5eSZGfHW3XqsKpaNea6uaEB88LDaePtfcdkLqihGFMvXOBAbCyVXV35LiyMhRERfBkcjL/15xSamMjs0FBGnD4NQHJaGi9ZK0pCExM5m5hIQloaiWlpuBqN+JhM+JhMuNvZqYplIYQQQjwydF3vcY/7dWBALi0nz0uPizMXyNyuh24GZ+fbPseo3r35ftw4CmROuA4ezF+falw8cY4lKyJpNzALfYytg2ho0kQlXzPPtxg9WiVPv/pKXaf76it1uZP0Yga3TP19f/tNXW73/ZcuVTvaPDxUde2KFeoyf75K6lasCNu2qWTspk3q9szruZ30vpAGg9oZt2TJjfsKFKBaUJBKCletysi5cxlZu3bG3bqu09zTkyRdp623N5qmEZaYSAtPT66npjKoUCGMmkaPAgX4OTyc/1kHqwFEJSczLzycXdevM7hQIYo5OFDQ3p6CZjNGTUMDHA0Gvg4L44uLF0lOS7trEcUc6+vcnKnVmhBCCPGgJKGbT2RO+5kzBZZZ7RKVOZELKhFs+fRTnrpwgcm3JlEzT7gtVkwleKOjVbJ1+3YVAKZXBffuDdOnY/DzpV09lV/Vddi1SxW8rlihdoiND3Ji4MC+NDsOwcHm237An2007cbQijFjbn9O48aq6sDXV73euDgVXIaHQ58+8MYbKpk7fDgsXEj5995T53XrBgEBqro4NlYlinv0UPf5+DAsU+/fmdu2keLsTIqm0X7jRsbY2/Nts2bE29vztpMTT33yyV1fRoi1xcOR+HgqubjQ21pt0OnKFXpZG6132L+fremtKSCj31h8aiplt24l2pp0vpURKOLgwPoqVQjI4qRiIYS4H1I1IIR41GVlKNpt51Xccv/KmjXx++039AEDVCFBZKSqUgUCSjrSbkBhOPEaRxfuJ/jMHYYEZxYSApMnq9h0xAh1W506apfY6NGqIGLKlLs/R/XqKmZPTla72MqWVQF67dqqb+/336tZG5MmQb9+qtXZ4MFw7hy89ZaKpd3c4Ngx2LIFxo2Dp5/+7/dxdFS77WJiYPx4tfaLF1U17sCBqvgjKkq1eyhfXrWZOHYMpk1ThRUHDqhkbvPmKlEcFKR25MXHw+efo4WE0PL8eVi/Hl5+Gezt8TcYeHHZMlWMERsLf/3Fly1b8ltkJIcy9d5dFBnJ7LAwKjg782n6zsTbqBUby1Rd53h8PMFOTow+fRoXo5E3rcME41JTefvkSX6zzuk4k5gIQJqusyQykiPx8USnpBCdkkJCWhpORiNOBgPO1ut2Pj43tW4TQoiHJXF23iAJ3XzC7g4VuvcKMtMZb0no2mkaZ/z92XC74MbBQVUEtG8PlSur29I/1a9XT13STZmiqm6tfbBA5VOrVlXFsWlpaj7bSy+pGBFU7nPIEBUnli9/o1VXrrJWsWZwclLDKdLNnHnj6+HDVeBsMt1YbHpwrWmwe7eqOEhOVj+L4sXVi5w6Fbs//sCuWDHYswfz+PH0W7YM3cEBLSFBVUxERKh2D507w6uvquczmSAggOCTJ6m/bx+HixThuHVqsTkp6aZANXMyt4qLC0fj4wFYe/Uq0ampTChRgmqurtgbDMSkpBCZnExkcjIH4uL4yjrVWBK6Qjy6dF2/76m1QtxOXmjRJUR2y46E7k275XbvVpUNt+tnO2kSwXXmQ1eV0L3mXJA+5h85ccWTNkFH+fBYtxvnFi2qKmErVrwRc6a3RzMYVN/dWxO6nTurVgrPPKOOV6++sesuvT1A3brqunFjdZk2TcXAoOL/n37677pLl1aXHj3ULjdnZ7XT7d9/VSBfpozaTRcfr3r9psscV2feDejvr3bRWed8cP06hIWp9xIXLqgev3v3qkTviy/evJbPP7/5ePZs9X2PHMEVuFS3LjO/+ILKKSkM2LePvtbTZtwlmQsQYq28Dtm2jd7+/nwbFgZATTc3Gnl48N6pU3x24QLOBgP13d35NzoaXdeZefEiLx87BqjfE3ejEXuDgfi0NOJSU0m0/rs75swZTtaujbc1yS+EeDRInC2yy4PG2ZLQzScetuXCrRW66dd33FRUrhz88cfNgdltn9hOBWV3YDCoLgcnTsCpU7BxI/z6K4wapS5lykCrVtC0qYorH9kPr28NzDP/3NPbJpjNkKlClzffVD8AUEnwJUvg5Em0okXhyy9V1ULlyqqKY/JkdUl/viZNsF+9mn91nfCRI0kymTgLBERFsbBPHxg5ktmZypzrubkRaG/P3IgIfo+K4vPz53E0GBhYqBCOt2nrcCYhga9CQ7mYlPTwPxshRI5wcHAgKioKb+sWUyEelK7rREVF4ZDe7kgIAXDb7fV3bblwGzedf6+EXbVq6nrwYNxGjWI2Hnz6KcyaVZkP6cZJhxCcK5XEp91TN3bhvfuuiskzB8kmk0q+7twJNWqogLpMGRWL9umj2ptlYcDXfQXeJhNkHrqT3h833b3eM9yJi8uNwpBChW60Z4iJUQUWUVEqjv7rrxuxcp8+6mc5apSKo63cNm5kqLXf8REgslYtTrZrR41PP1WPvbXvbVoaGAyUz9RKIz2ZC/Dn5cs0dHfPaJX2fZkyfBcWxsorV7iYlMTqK1fwM5k4Ubs2TgbDf/6vTklL49fISLofPMiB2FgaZt4FKYSwKYmzRXZ5mDg7VxO6mqZ9DbQBwnVdL3+b+zVgCtAaNaiht67rO3NzjXnVTRW6mb5+0Ard9CDxrv943ePT7PthMKgP50uWhGefhYMHVVy2cKEqMJg8We3Yat9efWDfosW9Y+JH3q1vEjTtRoXCK6+oLHZwsLo9fXvbtm1qgvKff0K3bqypVg3n337jiIsL1aOiMEZE0GX2bJg9m4qlS7PG05PyTz6Jx+HDHDEYmNunD2327sWQlsYHBQveNpkLUNCaoL5g3TKWkpbG/86e5aWAAApkZUqyECLHBQYGcv78eSIyDX4R4kE5ODgQGBho62UI8UhJj4u1u8TWWWm5kGUlSqidWdb4zAOVk3zvPXh9UBxffGVH/BYTpZ5Ut/XqBdqHH8KHH/73uXr0UJdbnTqlWqU97lxd4Y03bhw/+aQa2Ja5b1ufPmrYW1CQqtRt21YNWHZxgbVr8dm+HZ8tW9S5ixer5xg6VFUE//ADLF8Or7+O+ZNPMGkaydYKq1KOjjgbDKy+coVAe3suJCXxtpcXniYTpa1J8GKbN5Oi63Tx9b3jTAw7g4Eq1kT32YQEABZFRFDGyYkyt+nHLITIPRJni+z0oHF2blfofouarPv9He5vBQRZL7WAGdZr8ZAeeihapt65kCmAza4F3qeQEHUZNEjFXcuWwZo16oP/X35Rg2+7d1fJ3Zo1bdSWISdpmqqkSFekyM3VvdeugZsbTQDefJMiiYkUMJs5duUKa1es4Or69Tz300/4HjkCmzcDUB7Qf/iBpAIF0LH2Wt64UVUjaBokJKjtdNu3Y65QgaD4eLS9eyE5meWurrx/+jRnExL4KvO6hBA2YzKZKC5TtIUQIsdkpeXCfVXoZsVtkn92djBphiPvfggrV6pCh969VUeB5s2hf3/1eX+WBASoS1506xAOJyfVlgJUZciGDTfuu3pV/azHjVPD2/75B37/XV0ymzQJKlUibMUKVpQrx/jChWlWty4JaWnMuHiRLTExmDWN5p6eALR0d2d1xYr8eeUKMy5epIOPz12XHGhtbXY2MZGktDSeOnAAs6aR2KgRAL9HRdHj4EFO166N12NfzSLE40PibPEoyNWErq7r6zRNK3aXU9oD31sn8G7WNM1D07SCuq6H5s4K867MiVtzNrRcyOrjcoO9PXTqpC6TJ6tAds4cmDULpk5Vu7CaN1c9d5s2zYPJ3dvJPIkYKGgNBoO8vAh6+mmer1oV/06dGF+iBG+mpaktdp9+CmvWYC5fHs6fV4FryZKq1DkkBPbsUeclJcFTT/H73r0EnTgBQLWyZak5cCCR3t65/EKFEEIIIWwju4aiZRdvbzV3rHt3VYw6bx68/z588IHqMNCwITRqBC1b/je3KW6R3t7gf/9T11euqJh4xQrVt7dNG9XOoUULeO45vICnrRd9xAgSk5N5/8QJSEoirUsXCjo4wDffoP32G00rVaLpgAGMb936nlsKnYxGfEwmziUmsi82FoCkTL0W+x45QkxqKvtv05IhLjWV6JSUjPcBQggh8pZH7b/yQsC5TMfnrbf9J6GraVo/oB9AEesEUXFndnfYCpbllgu3nG/rCt07MZvVbqm2bdVusYULVTD73XcwY4aqTmjaVCV327W7/cyJ/OD1wED+uHyZzv7+qiIB4JNPbj7p1ClV2vHddyqZa2engteFC+G33wgCvmjfntLly1Phiy/YMmAAR0qWhFdfJW7XLsKPH+dIrVo0f/VVDPJ3VAghhBB5TJYqdG/TZ/dez/GwDAY1k3f4cNWm7McfYd06+OwzmDhRtZpt00ZdmjR5hGdQPEqsFbZ07nzjNh8ftZttwQKoXVvtkJs0CW3MGBwABz8/VQixePHNz/Xnn+ri5aXmjowdCw0a3HxOVJTqfXzoEKOjohjx1FN8l6k/7/KoKArZ2xNmnWdx0doGbfXly/wWGUklFxfGnjlDZHIye2vUoKSjI7Gpqayx9u8NTUwkNCmJsKQkQq2X6JQUktLS8DebOVCz5h1bQQghhHg0PGoJ3SzTdX0mMBOgevXqMnr5HoxZ+Ppu7jQU7VHm7q5aY/Xpoz5I//VXFTutXAk//wx+fqpyt04dlQAuXNjWK8495V1cCE2fVHwnxYurnmtjxqjsePqn/ikp8PvvnPL2ZrTBQGhSEp41azJp+nR6r1wJgwfjBBQDiq1fT/yaNTju2JFPSqOFEEIIkV/cLh6+tcXCvWLtnI6pQ0JUvhBUm9gVK9ROth9/VDN2nZzgtddUGzM/vxxdSt5UqBAMHnzjuFYt1YahcmXo0AFSU9Wbj8WL1cDjUqVUv7g//lBx9r//qtJpUOXVgweDr6/q13voEAAvA0/9+Sebg4M56+9P4IkTrClXjk+6dcv4tuktGdrt3098WtpNS/z0/Hk+Dwpi5KlTTDp/PuN2X5OJgmYzBc1myjk742lnx4XEROZFRLA/NpZat+z4E0II8Wh51BK6F4DMabVA623iIT10he4j3HIhK1xc4Lnn1CU1FVatgq+/htWrVVD7+uvQuLHagvbMM+oDd2GlaTeSuaAqddu3pzjwe0wMVXfs4IqbG79+/DEz2rXjyyJFmLd1KwXPnuVaSgrv/vgjFC6MXqECyYULY05NVQFu27Y2ekFCCCGEEA8vPS7WM22BvzW2vlfMnJtFEo6O8NRT6pKYqKp2J0xQCd+PPoIuXVQhROPG0pLhgbm5qUl16YxGaN1aXdLZ26tYuEMHOH1abRsMC1PzMH766cZ5kyer7YVmM/69etHh5MmMuzr++y8DFi1i0Rtv4LVuHZUSEoioVYuipUrx7hNP8OzhwwBUcnZmo3XI3Z9XrtDA3Z2fypbFz2y+bfX48bg45kVEcDBTQjdV1x+7935CCJEfPGr/VS8BBmqa9gtqGFq09M/NHpn/E77T13dzpwrdx/G/dqMRWrVSF12Ho0dVO4ZVq2DIEDW8tn59FXd17qwKVcXtVbJO3gV41s+PbiEhVAPSmjTh7SJF+PjUKSoGB/Pk8eMc37oV3/XrwWDA/PXXTHj3XS517MgnVatmPIeu60SnpBCRnExkcnLGdUxqKrGpqbgajbT08iJI9gYKIYQQwsay0nLhXrGyrXa92durnWrNmsHu3Wr2xDffqF1spUpB166qRVnVqrLJKkcVKwa7dqk+GWfOqCz7X39B+fLqjUm6du1UH9/Nm+mXksLbAwdSPCyM195888Y5f/7JbpOJ6yNG4FmlCldcXGjj7c3Ys2epuWMH+2JjGVu8OIEODndcTnFHRxwMBg5Y+/W+cvQoX1y8yAfFijGiWDGup6Tw4ZkzNPX0pLmXFwBnEhJwNxrxuEs/4KjkZDTA1WgkMjk5o6/vxcREZoeG0sHHhwrW9xWhiYlcTUmhrLPzA/1IhRAiv8jVhK6maT8DFsBH07TzwPuACUDX9S+A5UBr4DgQB/TJzfXlZcaHrNA1PIYtF7JC06B0aTUPDODAAfj+e7UL6q231KVcOfUBevv2UKOGLVf76DFoGrurV2d+eDgdfHzo7e/PkshIrqWm0sbbm4UREXz11FNE+/pmVAo4xcez5ZVXeGvsWM5/+SV6mzZoYWH82bo1nYODuWY23/WdQzUXF7ZXr55bL1EIIYQQ4rayktB9kOfITZoGVarA9OlqgNrKlSoW/ugjNQ+sWDF44QUYOPDmDVsiG6X3qi1WTF169frvOWaz6onRvj0zgevt23P6/HmKLVzIWT8/PqlShRMnTvDhhx9SeeRIouztiWnblojXX2e3lxcnEhJo7OFBv4IF1XbFHTvUG5v0379Dh+C77zA+9xwVnJzYEhNDWGIiMy5eBOD3qChGFCvGzNBQPjp3jn+uXqW5lxe6rlNs82ZKOzpyuFatO75Enw0b8Deb6ejjw/SLF7lavz7udnaMP3uWzy9cYG9sLPPLlSMlLY1CmzahA2dq16bIXZLPQgiR3+VqQlfX9R73uF8HBuTScvIVu4es0E2XvjEnr267KVdOBbAffaR2QC1YAHPnwvjxajtaxYqqpVWDBqqK19XV1iu2vUouLhmVut+UKUOqrpOcloaD0Uh9d3d+jYzEpGkEmM1cTEoiztGRKrNm0XDPHj7+8ksKLluGMTqa5itWEA1s79ePQx98gI/JhK/JhI/JhJudHY4GA4OOHWNJVJRtX7AQQgghBDf65Wp3KZbQ7hEzP0pjp/z8VC6xVy+IjIQlS1QXgBEj1KVBA1Uo2qcPeHvberX5m4udHS7FisHrr1MEmAKqYXKTJvD772jff4/bggW4LVjAMm9viI2FhAQIClJ/0OvXq6FsEyeqZHH//uqcjz7iq9at+aVQIRa/8Qagiin2xcaSpuscj48HYF9sLMlpaZywHh+JjycsMRF/a+UtQFJaGuujo9kWEwNAWFIS060J4jVXrtDa25v5EREArI+ORtd1Nl27RnoDk6NxcVlK6MamphKWlERkcjJmTcPRYMDJaMTDzg43OzviUlNJ03VcpI+IECKPkX/V8omHbbmQuTcY2L6aIDcUK6baLwwdqobWfv+9SvBOmADjxql5BR07qrjo2WdViyuhfqeM1kqDJp6ezA4L49fISHr5+fH9pUsApNjZsaZaNarNnMmyChWwuLgw8IMPmDVxItVnzqR6sWIqsHzlFUhOhpkzwd2dQHt7IpOTSU5Lu+fUaCGEEEKInJQXKnTvxMcHnn9eXf79V82dWLRIzfUaMUK1JevRQ83zytSBS9ias7Pql9G1K1y6pN64hIaqXhoAx47BqVPq68uX1R8wqGrdAQOgd28qLl9OReDqTz/hU60azp070750abzWryc6NRWAuLQ0ZoeG8oM1tgcouGkTbxcpwv9KlCAmJYV6u3axz9q64VYvHjmCnaZxKTmZUo6OHI+PJ3jrVi4lJWWccyYxEYBBx46xKDKSySVL0rlAAY7FxTHk+HGOx8cTmpREjHVNtzIAbby9+ePyZewNBs7Vro2HycRfV65wKDaWtj4+FJUKYCHEY0wSuvnEndos3G9KLP2Rj1I1QW5wc1NbzQYOVMndLVtgyhSYP1/FQu+8A02bQps20KiR+oD8EY3Pc1V7Hx+6+vpyJC6OvgULUsvNjVMJCUw8d45G7u4cjIujzb596uQWLejy8su0btNG/UDfeefGE8XFQceOlClVCvvERMKTk/Exmeh64ADXU1P5q3Jlm7w+IYQQQuRfj3MP3fvRoIG6fPAB7NsHU6fCvHlqsLCzs5pL0bmzmncrYw4eIX5+8Mkn6usff4Tr19UfWGKiartw5YqahNe8ueq3YWcHTz8NP/xA/Dff4Lh7N53WrYN160gEjpcsyZZixdhWty4bAgLYvGIF3q6uDO7alQ4+Pow7e5YJZ8/yVuHC/H31KvtiY/mkZEl6+fmxICKCl48dA6CPvz9zw8Np6OHB8/7+NPf0ZNjJkxyLj6ewvT0vBQTw9MGDnE5IIDIpiakX1Iz0/509S+cCBZhz6RIrLl+mo68vrcxmCtrb4282421nR4quE5+WRnxaGjMuXszY2ZecmsrO69ep4erKE3v2kIqqCv6lXDkb/MEIIUT2kIRuPmG8w9f32gaWTr/l2O42W8zyCzc3Ffc0b66ODx9WgySWLFEfbINqzdC0qYqJqlXLv8ldZ6ORuZkCpQbW5msjihbFwWDgWHw8I0+dYmFkJACVChSArVth+3b49Vc1iePoUdXvYtkyugPdgSsdOnC2USNWhISQZDYTnpREgatXVTmJVO4KIYQQIhfcbqdbXqnQvZMKFeDLL9X8ib/+gqVLVQy8YIHKFbZvr5K7zZpJa7JHiqbd+ANxdFTXLi6wefPN55lM8PzzOD7/vJoeHR8PX38NI0ZQ6sQJSp04wTN//XXzYw4cgAEDsCtXjlVXrtDj0CFWXL4MwIBChbA3GCiR/j2Bz0qV4usyZW56ii9Ll77p+M0TJ/g1IoJ54eEAWDw8+OfqVX6NiGD0mTNUcHZm/j2SsfYGAz0PHSLY0ZGj8fGsu3qVS0lJpNfznkxIACA6JYVjcXFUdnHBTt5HCCEeI/IvVj7xMH1zM0tP4D5uwWdOKlNGffh99KhK7k6ZovqKTZ+udi/5+0O/fmrIhDVuyPfc7OwwGwyUc3bm55CQjNsDzGY1drlePZg0CXr2hNGj1Q/uyBHOfPQRAJ6LFhH02msc6t2b34cPJ3HQIPD3J61UKfZNm0ZaWpqtXpoQQggh8onbvZHKixW6t+PoqHamffklnD8Pa9aoQoY//lAtyQoWVL14585V3bPEY0jTVMn1wIFw+TJpGzfy9bBhHProI/587z2e/fJLIl98UbVzqF+fevXrc+SVVyi8fDmlz57l9ZMnsZ8/H3Sdaq6u1DaZeMXRERdNg9694cUXYfly1bD5Fp19fbmcnMzR+HjsNY1XCxVStx84AEBTT897Lr+ttzeNPTz4KSSEQHt7PjhzhqcPHcp4/uPx8Xxw+jSFNm6kxs6dDDl+HICp58/zzMGD/Hv1avb8HIUQIodIhW4+kTlYfJCq2lsrdPPqULSHoWlQurS6vPoqREer+GbdOvjpJ1XF6+KiKna7dVMVviVL5t/q3XRmg4HSjo44GAx3/t00mSA4GPPgwdR1dqb3339zqFo1mi9eTMUTJwjcsgVd0zCcOkWFgQP5a/9+mhYuTHTdurx1+DA7KlViXOnSNPfyuulpU9LSOBIfT3xqKkZNo7KLS76sOhdCCCFE9sgvCd3MjEZo3Fhdpk1T87a+/15V7/7wA3h6QosWKuFbv76axSUeM5qGoU4dnq9TB4CyQHNQVSsTJkD37mjbthF8+TIz33775sd+9hneUVFsOn1afRJQp47K/AN89RUEBsKhQ2oi9Zw5sHQpn7z7Lp+0bk2MkxNRqakUcXBgbkgIdppGAZMpYyDz3bjZ2bHG2pbtp7JlWXH5MuklH65GIwsiIhh1+nTG+f9GRxOamMgga2I3IS0tY3ehEEI8iiShm09kVwI2/VkyWi5ky7PmTe7u8NJL6hIfD3//rT6E/ucfNesLVGuGTp3giSegenUVEOdH+2rUyNLvUkF7ewZ27sxX9eqx5do1zrZty7ETJ2i1fDkz27TBJzqaaVOm0OKLLwBwB74ENoWE0GLiRM40a4aXyZTxfO+cOsXH585lHK+uVClLn/gLIYQQQtxOXm+5cC8m043kbmoqrFgBCxeqlgxz56rOWDVqqH67vXpB4cK2XrF4aB4e6g8a1HbF2bPVFsYDB+DiRfUHX6MG1KqlWjysWAFvvaUGj3z7rRpKEhAAMTHqORwd4ZlnAHAtVQrXl18Gi4WumzerPsAvvaQGvoWFqT7AzZqpxyUkwMiR6rGVKt20xAbOzjTYuVNV15w8ya/ly2fcV+bMGepXqsRXV6+y0toqwtlg4Jx1KFtKWho/hodzMj6e+LQ0Eqw9epPT0nA2GnGzs8PFaKS9tze7r19n+eXLvFu0KOWcnXPsRy6EECAJ3XzjYYPFO/XQFVnj6AitW6uLrsP+/bB2rfoQetQoNYfA0VHNJejSRXUcyE95RdN99Kt62s+Pp/38SElLw6hpNE5OZkKPHgBcdXXlnWnTmLx+PcuqVOH4iBG4HD1K7cOHiWndmrAnn1RTfz08oF07lkVFUcvVlZcLFaL34cOcjI+XhK4QQgghsiQ9Ptb1G5HyrTHNvXb+5OWY2miEJ59Ul88/hx07VN/dFSvgvffUpXp1dX+bNvl77kSeUaYMfPzxzbd9+y04OKivdV0NZUs/btVK/aIkJKjti+XKqcsnn6hzv/sO3njj5ucbNuzm46AgVfa9ZYs6/vhjNYfD01NNry5aVE21XrMm4yGdgEt+frhcvYpTYiLXypXDyWJhq50dbX19cXvqKVZfuQLA1AsXeO3ECQAcDAYcDAYcDQbsNI3Y1FRiUlNJ1nXeO3Uq4/mLOTjwvxIlSEpLo92+fZgNBhaXLy87AYUQ2UoSuvlEdlfoSsuFB6dpaqBEhQqqJVVkJPz5p+qxO3++2qJmZ6c+bG7ZUk0VrlrV1qt+9KQPLSjq4KD6W1h18fdneNWq9Pf3Z/64cQQ5OrJhzx6OjR1L8OrV8PvvGefOqFiRc6NH0y06mt4GA6FJSbn+OoQQQgjxeHK2bq0qlWng0/0OKMnLCd3MnJxUTNuggSpmOHkSfvlFhWWjR8MHH6hWZH36qMFqISEy5zbPSE/egnojdOvxzz//9zHjxqnr8eNVovbAATh3TiWDw8NVpa69Pfj5wYkT6g1Vq1aQkgKbNqnjxET1uF271HMVKgTPPae2RnbqRIFr11QfEIMB1z//ZMq0aRnf/vKsWXxXowZJoaEct7OjsJ8fp2vXxnCHv69br12j1s6dAPiaTOyPjQVUG4eV1sTw5ZQUvDPtFBRCiIclCd184mF38meuPIAbwWr+CEFzlo8P9OihLl9+qSoX1q2DefNu7F6qUEHFKN27q6/t5G9uhkmlSlHcwYFPzp/Hz2SilZcXw0+eZHZYGADtfHywf+EFpjdowPKdOylx8SJjFiyg3qZNNNq7Fzp0AKDl9OmErFwJwcHg6wulSqkqAyGEEEKI2yjq4MDi8uVplKnP5q0VePmhh+6DKFEC3nlHXSIiVGL3u+9uVO4WLAjt2qkwrXFjlbsT+VStWuqSVVFR4Oam3jCtXg2uriqx26fPjU8J9u5Vv1TWhs5aYiJs3KhaP3TujPO5c7y2YAEsWMBUoGfLlhj8/dVzN22qEsGff67evIWHU2PmTPofOsQ/lStTpXp1NkRHcyYhgd8iIjKWdTYhARejkV8jIjAA3f38su9nJITIl7RbE3WPo+rVq+vbt2+39TIeaQvCw+ly8CAAusWC9s8/GV9nxbATJ5hw7hzjihdneNGi/HPlCo337KGhuztrq1TJoVXnb7quPoieP1/NDVi7Vn3o7OGhPlhu00ZV8Pr42Hqlj4Zkaz8rNzs7TsfH42g0EpuaSnEHBzRNI03XWRYVxc/h4cwLDaVYWBjvpKbywo4daivYtWv/fdLXXlOVAWZzrr8eIYR4HGiatkPX9eq2XofIWRJr35/0OBvgnSJFGFuixB3PjUhKosDGjUDW4/K87NQpFfMuW6YKG2JjVT6uVStVudu2rToWIieFhoez8uOP8TtzBpezZ6l98CCmmBjVJDo5+Y6PS/DxYdezz9KtVi3c4uKoc+AAnu7uTK5Th18rV2bl5ctMv3gRgOj69XGTKh0hxF3cK86Wf0HyCWmR8PjRNChSRLWNeuMNCA1VrZ/WrFFVDHPnqnNq177Rn6xSpfzbe8xkMGT0rSuWaetjOoOm0c7HhzJOTvwSHs7JQoVwKltWTeRo1owTQ4eSlprK4WnTSLh8mU4TJmCYPFkNW3juOahSRU26E0IIIYTIJvm1QvdOihdXl969VVvVNWtg0SJYskTtXrO3V/O1OnVSFxmqJnJCwQIF6J25F3BamnqTlZameoYcOaLmclSurKZff/stFC6Mw8cfU2fyZM7e8nwjHRxwSUigVFAQBWvV4pcmTThQpQp15L2FEOIhSGeifCK7hqJJI3fbKVhQDW2dPVsNjN2yBUaMgKQktTWtShWVAO7fXwW91tZN4hZBjo74Wytuq6WXeLRty5hFiwieNYt29vZ0LViQ5X/+qQK2b7+Fxo1JrVJFvbM4dEiVTwshhBBC3EZiw4Z8UKwYIC0XHoaDgxooPHOmin3Xr4cBA9SmqtdeU3Fv9erw4YeqjaoQOcZgUAldoxHGjFGfLnTurFq0VaighrgNGQIXLqj2Dq+9Bm+9Bdu3k/bbb2wNCQEg5Ngx3pszh10vvkhAvXqkVK7MtS1bGHXqFPuuX//Pt03TdUITE7mekpLLL1gI8TiQhG4+kd1D0YRtGQxQs6YaILF9uwpyZ89Wt/30k9qS5u2tWjJMnarulxykomkaJ2rV4kCNGgQ7OWXc/k7RorT18WFKqVIAHImPh5EjWdCjB0l2dhhPnVJlICEh8Oab6gcvP1QhhBBC3MJsMGCyxt73isEloZs1BgPUq6fyZnv2qALJcePUDvgRI1RerXZtVeSQ3qZMCJsICYFJk+Cjj6BaNQwdOnBg8WKeWreOVjNncvjAAZY1akTRAwdIPXwYl7p1adW6NfOtrVcy63fkCAGbNhG0dSspaWk2eDFCiEeZJHTziYdN6N4pbSUh6KOhYEF4/nn49VfVq3/1anjlFTVBeNAgNdS1cmV4+WXVjywhwdYrti0no5EQZ+ebbgt2cmJJhQq8GhiIj8nEkbg4dOClV17BYeVK9tStCxUrqiDtk0/Ufj+DAapWVZ/GCyGEEEJYvRoYyICAAN68R08ASeg+mOBgGD4cNm1SMyfGjVOfs48fDxYL+PlB8+YwebJU7wrbGxQYyG8NGvDHiy9SJiSEikuXUvnXX6n/9dfM7NOHkhcvMqplS3jhBYiJgcREUnWdhZGRFDCZCEtKYntMjK1fhhDiESMJ3XxCgsX8w2xWw1cnTYKjR2HnThg7Fjw94ccf1VCJgADo2FFNE7561dYrfvQEOzryfVgYI06dIiolBd1goNukSfDXXzBvHutGjGBZ377q5F27oGdPSEy07aKFEEII8chwNhqZGhyMyz2GHsmci4cXGKiSu1u2wOXLsGABdOig5k+8/rqq3i1fHt55BzZvVm1QhbClkk5OvFK7NtsDAni5Z0+qf/klf7ZoAV9/DW5uJLu68tLYscQkJPCR2UzHdet4YssWJj//PF/26sWze/cCcCQuzsavRAhhSzIULZ8o4eCAAWjs4fFAj/c1mQDwsgalstH88VGlirq8847qq/vPP6qSd80a+O03dU5QkBos8cQT0KCBag+VnzX39GTjtWuMPatGGrTw9OTPK1e4npKCoUwZGjVpAsDGsWOp8/vv8PzznK5WjYGjR7OsY0c1/db6d0YIIYQQ4k4M1oSuvSR2s4Wb242BaaB2qy1dCosXw4QJqpLXzw/atlUtyho3hls2bQmRK2qmz/IAYgsXpv/773P6tde42qsXHuHhzBoxglnW+3sDyWYzpqQkdX58PD8MG0av69dZW7kyDR/wPb4Q4vGm6XmgB2T16tX17du323oZj7xUXceA6iH61P79nE1IYEf16ll6bEpaGt9fukRvf38Mmsa+69epuH07AwsV4vOgoJxduMgRuq6Su5s3w8qVatBEaioUKABPPgmNGqnhrY6Otl6pbUQlJ5Oi6xyKjSU6NZUO+/ezoUoV7DSNWjt3AvBlcDD9AgJg8WIuP/ss9snJOPv7w5kz6gc4ebLqdSGEEHmUpmk7dF3PWjAhHlsSa+esGRcuYPHwoKxkFnPUlSvwxx8qufvHH2pnu50d1K+vEryVKkGTJmr2lRA5LTktDfO6dQA8XaAACyIiSGjYkApbt1I2OZn5e/aofiITJqjdgFeuQI8eXB49Gq8jRwCY2qEDAZMn09E6hFEIkbfcK86WhK54YOuuXqW2mxtmg3TuyAuuX4fly1X17qpVqhWDnZ0a3NqmjUruli2bP6t3zyYkUHTzZjr5+OBrNvPFxYsADCpUiM+CgjgZH49lyRK+nDSJJ0JDMdSoAQsXcr1pU3bOm8eZpCRMmoa3yYS3yYSXnR3eJhMuRiOappGq6ySnpeGQH3+4QojHmiR08weJtUVek5SkhqetWaMSvIcOqduLFlWFDW3aqF68+bWwQeQO7Z9/AJhUsiSvnzjBqVq1KL5lC6OLFWNEepI2JgYyVfNu/P13jkydSp8VKwA43rUrpX7+WW3FXLcOqlUDf/9cfiVCiJwgCV0hxH3TddUu9q+/VAXvunWq31hAAPTrpyoYnnwy/3QV0HWdwE2buGjd5uRrMlHcwQEno5FG7u58ev480ampAJysVYt/o6O5NHQob/74I6uqV+fFN94gxsmJK66uN5V9uBiNfFaqFIOPHychLY1d1atTTqpzhBCPEUno5g8Sa4u8LixM7VhbuFANF46LU8ncZs1U9e7TT0trBpH9VkRFcT4xET+zmXb79zMtKIgBx44xPySEzgUK3PYxu2JiqLpjB5WPHWPo3Lk889df8Oyz6hOKs2fV4JQpU8DFBerUkeSuEI8xSegKIR5aaKjamvbNN6o1A4CPj+oq0KKFSu4WKmTbNea0C4mJHI6LI8Bsxstk4r1Tp/gqNDTj/iBHR47Fx7OxShVeP3GC0zExrFy6lIqffJJxztX69TnWuzd2hw6xatAghp86ddP3mBsSQtc7BG9CCPEokoRu/iCxtshPEhJUW7Lff1eXU6fA3h5q1IDmzaFdO1XcIK0ZRHaJTErCd+NGCprNhCYlsb5KFeq5u9/23EtJSfhv3KgOdB3dOtsDHx9Cx43Dd+pU7PbsUbc5OUGPHjBoEFSsKL+0Qjxm7hVny155IcQ9FSwIzz8P//4L167BsmXQtKlK8vbvDyVKqIFqo0bB/v22Xm3OKGRvT1NPT8o6O+NnNlP+ljKNNt7eAIQmJbE/NpYuAQFUnDhR9a9wcoImTfDYto0afftS5ZNPGFasGH+OGIF/VBRF7O0BFaAJIYQQQgjbcXCAli3h88/hxAlVzDBgACQmqli3ShUoVgz69FHFDidP2nrF4nHnYzZTwsGBUOt7gQCz+Y7n+mbeIqlpLBk8GAoV4pO//iKgVCnazpihfjknTEBv1Ahmz1YzPQICVPsGIUSeIRW6QogHlpSkgtjPPlPB7v79ql1Det/dypXVtZOTrVea/f66coVm6Z9+A0vLl6dtpmz2zOBgXgwIUAfx8Wrf3rp10LEjeHnBsWMAHCtUiIhKlXiveXOeqFWLYT4+nA4MZPrFi7T08qKJp2euvi4hhLgfUqGbP0isLYRy6ZKq2l22TIV1UVHq9po1oW5d6NxZ7XKXESPifu2OiaHKjh0AxDdocNfZGhuio9kUHc2s0FAqu7gwt2xZAjZvJjQpiUB7e87VqQPArsuX+XbkSJ5avx7Lnj1qWErHjrnyeoQQD09aLgghck1EBPz8s+o/tn49pKaqNk7du6uK3kaNVKuGvCAxLY33Tp2ivLMzx+PjGVm0KPV27WJbTAwacLBGDcrcrtlaWpqK8mNiCJs3j8hp0wg+dw5zZKS6386O/VWqcDkxkT7vv8/hWrUw5fV+FkKIx5YkdPMHibWF+K+0NDVMbcUKmDsX9u1T7Rrc3KBhQ+jQQbVn8PW19UrF4yJ9SJpusWTp/Lo7d6IDi8qXp+DGjeiAnaZxrX59dl2/TkRyMh3278cuJYW4Ll0w+fnBq6+q5tDBwTn1MoQQ2UQSukIIm7h+HbZvhxkzVBVDXJxq21Snjgpu27aFsmXzXiun9H9Ttft5YVev8s2rr+KXmEjrefP+e/9TT0GrVuoH5+eXTSsVQoiHJwnd/EFibSHu7do1WLQINm5USd4zZ9Rn+HXrQuvW6iJtTMXdBG/ZwrH4+CwndHscPMgv4eEZx+WdndkfG8s7RYrwv7Nn8bCz42pKCgDzLl6ky9ChEBpKqp0dx374gTLdu+fEyxBCZBNJ6AohbC45WSV3//xTBbq7dqnbS5RQiV2LRVUyeHnZcpW21WLPHnZfv86fdnasnDGD0AIFeGbxYirGxWE+fx6ABDc3zJ99hqFbNzWhw80t70+jE0I80iShmz9IrC3E/dF12L1b7Vpbvhx27lS3BwSoxG6rVqpI0s3NpssUj5j41FRSdB1XO7ssnR+bmsrG6Gha7N0LQB9/f74JC6OCszP7YmMBVbFbwGSikYcHPxUvztH9+0nt3JkiERE4//23agp9l569QgjbkYSuEOKRc/68qtpduhT++ksNmXB1VQHuE0/Ak09CgQK2XmXuGnr8OJ9YE7cAzTw9WX3lCssqVOCJ06fZ9cwz1DhyRN0ZGAgXL0JQEOzdK0GYEMJmJKGbP0isLcTDCQ1VVbvLl6t5udeugZ0dNGig2pI1bAj160v1rngwjuvWkZCWxvSgIF6xzulI52lnR0N3dzZdu0Zo3bp8cfEic+fPZ/XQoZhSU9VJL7wAY8aoSdgnT6qpf9IIWgibu1ecLX9LhRC5LjAQXnpJDZW4cgX+/Rc6dVLDJZ5/Hvz9VWuGsWNVdUNamq1XnPMmlCzJtqpVCXJ0BKCyiwsAYUlJnCxblppffIHr77+z6scfVWPiVq3gyBE1kU4IIYQQQjyyChaEPn1g/nyIjIR//oHXX1dfv/eeSuiWKgV9+8LMmWouhRBZ9VGJEgC08PLCxTpMzeLhAUBDd3dqurkRnpzMa8ePs+naNdZVrkyziRNJTd/pN3s2MSEhpPXpAyVLQvPmYG3VIIR4dEmFrhDikZG+PW3pUpXs3bpV3e7jo7alPfec2hWUl9vItt+3jyVRUcwKDubFo0d5IzCQKq6u9Dx0CIA3AgOZWKqUOrltW1Xq7O+vrqtVs+HKhRD5kVTo5g8SawuRcy5fhgULYPFi2LxZHRuNUL26in+7dJHeu+Le0nQdg6ZxKj6e9dHRtPLywtFoxMFgIDY1lZJbtuBnMqFpGkfj4kjSdf6qVIkm8fG8+ckn/G/KFFWx6+cHly5BzZpqgJrRCEWLqmobIUSukpYLQojHVlgYrFwJf/+tkryXL6vdP61aQZMm0LkzFCli61VmrynnzzPk+HH+qFCB5w4fJjw5OeO+Uo6OlHRwYEWlSgD8uW0bTWvXxpBewhwSopK8Y8eq4EsIIXKYJHTzB4m1hcgduq66ac2bB2vXwqZNaqdawYJquFq7dqpFmY+PrVcqHjdjTp9m5OnTAPQtWJCvQkMZXqQI40qUwH7tWiocPsznR45QZ+xY9A8/RJ84EUNSknqw0QhTpqjrvn1VvxAhRI6ThK4QIk+Ii1OJ3Q0b4IcfVB9egHLlVGK3WTMV6D7u7Z50XWdddDQN3d3ZHhPDwbg4dsTEYNY0wpOTWXPlCufr1gVA++cfXOLiiClfHr7/Xk2dW7cORo6EDz6w8SsRQuQHktDNHyTWFsI2IiLUQOF//1VzJy5eVJW6tWtDmzZq7oRU74qs2BETQ/UdOwD4vUIF+lgLR/6tXJkGu3cD0NXXl7nlyvHFhQsMOHyYVxcu5HVvbwovXAgHDgDwweDBHHjxRa6lpLA9JgZN02jj7c03ZcrY6qUJkWdJQlcIkScdOaIGSyxerHKYuq4KVHv3VsFt2bJ5L7idcPYsw06e5Eq9erjb2WFYuxaA5IYNsUvPZHfqBGvWwP79qg1D48YQHGzDVQsh8jJJ6OYPEmsLYXtpabBzp2pLtmwZpP+VDAxUsW+bNmoHm5OTbdcpHl26rnMxKYkAs5llUVG027+fTj4+/BoZiavRSKC9PQdr1mTQsWNMvXABgFnBwfT18oK//yZy6FBcjh2j8ldfcaRIEZp6eLAvNpbI5GSu1q+Pq1TuCpGtJKErhMjzLl1Sge1XX6neY6DayrZsqS6tW4Orq23XmB2WR0Xx5L59NPf0pH9AAJ2tn5SH1qmDv709AMf/+YcSTZtmtGE4Xq4c73/9Na4xMVzx98egadRzc2NgYKDNXocQIu+QhG7+ILG2EI+esDBV3PD777BqFVy/Dg4OKqnbqBE0aKAqefNagYPIHrqu47dxIxHW9m6vBATwxcWLTCxZkvFnz+JlMnEuIYEXChZkSlAQSyIjmbpjB6tatgRgea1aPHHwICkODrzfoQNHX32VZ/z82BQdTRVXV57Jy0NPhMglktAVQuQrJ0+q1gyrV8Mff0B0NDg7Q4cO8MQT0LQpBATYepUP5mpyMu3272dDdDSVXFzYdf06ALurV6eSiwu6rlNw40ZaLFvG23PnEuXvT/1Nm4h1csI5Lo5af/7JMQcHAC7Xr2/LlyKEyCMkoZs/SKwtxKMtMVG1ZVi2TCV4jx9Xt5cuDRaLGiwsyV1xq7dOnODjc+co6+TExyVL0mbfvoz7mnl6EpOSgr3BwOpKlXBfv540XeevL7+k3s8/A6CXL4+2fz8Av9euzXUHB2a0b8/eEiWIatsWTX7hhHgoktAVQuRbKSmqYve77+C33yAqSt3+xBOqNUPTpuDra9MlPpCimzYRlpREkvXf71UVK9Lcy4tzCQkU2byZSSVLMiQwEC0pCQoVuvHCn3uOf7y86NqgARfat8f0uDccFkLYnCR08weJtYV4vFy+DAsWqPh3/XpVvevvr6p3O3ZUO9icnW29SvEoSExLI1XXcTQYOB4fz68REbx96hT13d2p6uLCV6GhrKtSheo7dvBT2bL0SJ/IFxentkAuXqwqZzKJcnMjYcMGCpUvn/svSIg85F5xtrybF0LkWXZ2UL8+zJoF4eGq79gHH8Du3dCjB/j5QcOGMHUqHDtm69VmXQGzOSOZC3DJOoE2vWK3lpub+kTc3h6mT1fN1MqXh+++wzJ5Ml9PmMClhARiU1KIWboUrl61xcsQQgghhBA5wMsL+vVTu9VCQ1Us3LQprFyphgn7+qrE7g8/qLg4D9R4iQdkbzDgZDSiaRpBTk609PIC4EpyMnXd3YlLS6P34cMAVHd1BaNRXdL72TVtCqVLkzZ9Ot6LFtF72DCMqak4PfMMrFvHycOHuVS2LNe+/tpWL1GIPEu6Vgsh8gWDAapUUZd331WDJP74A+bPh0GD1DnNmql+u82bQ7lyj+62tAIm003H4dbeV/tjYwGokLnkomtXNShN16FVK1i9mjabN4OzM3/UrEmrrVuhfXs1QlkIIYQQQuQpLi7Qt6+6pKSoYcILF6rLb7+pc0qVgjp1VMjYqhWYzbZds7CdCi4uPOvnx4BChQhydKSRuzvRqal08vGhpKPjfx/g4gKHD2MA3jxzBqdq1XhD15k9YQI0akSJ9PNeeOHGY55+WjV8FkI8FGm5IITI944dg19+UVUK6ZW6lSuricHPPANly9p0ef/R5/Bhvg0Lo4DJxNWUFF4LDGR8yZK8euwY34eFcbVBg9s/UNdZf/UqG155hWG//HLjdnt7Vb4cEpI7L0AIkWdIy4X8QWJtIfKetDQV/m3eDGvWqB68kZEqP9e8uYqDW7eGggVtvVLxuKm4bRsdtm7lg3PnSP7kE9594QWGrViBz5kz6gRvbyheXP3SSWJXiDuSHrpCCHEfzp6FpUtVcnfHDlXJUKeO6jXWsiVUrapaOdjSsBMnmHDuHOWdnbmakkIzT0++KVOG7gcOsPP6dY7WqnXHxx6Ni6P01q0ERERwoWvXm+9ct06NRBZCiCyShG7+ILG2EHlfSgqsWgVLlqjBaufPq9urVlXJ3W7d1A42Ie6l96FDzI2IoIqLC7siIkiwt+f1hAQ+Wb+eVA8P9q1aReU//wRHR/jrL/VmSwjxH9JDVwgh7kORIjBggKpWOH8ePv5Y9fwfNQpq1YLgYPjf/2DfPtv1Gytg3QfnbDBQwGQi3NpDNzw5+T/tGP7zWOv9F319uZA+1CDdc8/x05w5XKlTh/Avv+TXiAhS0tJ4++RJRp06lf0vRAghhBBCPBLs7FRF7hdfqAKHPXtUzOvkBGPHqnEMQUHQpw98+y3ExNh6xeJR1S8ggOaenmiAh6sr3nZ27Pb3hw8/ZEe/flR55x1eHjKEVG9veOop1f/jwgVbL1uIx44kdIUQ4g78/GDoUDUsIjJSBa9+fqoHb8WKaqfQsGFw5EjurquFpyeNPTx4KSAAP7M5YyhaeFJSRrL3TtwzlReHfPMNX7VurQ5eeglOnaJL7954bt5MgZdewvOJJzi6di3L16zhg/QtUkIIIYQQIk/TNBXrvv222hV/6RJMnKgSusuXq6Sunx906QILFqjiByHS1XV3Z0mFCmyoWpXQunVp7OnJRev7lW3WTwK+aN+erT/9BKmpqnlzyZKwa5ctly3EY0daLgghxH0KDVVb0dK3pKWlQZMm6gPmDh0gMDD31tL70CHWXL3K2Tp1KLBhAx19fPiidOm7Pkb755+Mr42pqawqVIgmZcoQW7cuzps2/ef8a05O9HvjDX4ZMwYiIuDWyl4hRL4lLRfyB4m1hRDpdB02bYIff1TJ3PBwVcVbr54aPtynD5QpY+tVikfJ4GPH+PzCBQrZ23M+MRGzppGk64wrXpzh3t7sWr6cKt26AZDauTPGb75RzZyBfdevs+rKFZLS0kjWdZJ1HSMwODAQz3vsTBTicSctF4QQIpsVLKgmBS9ZAqdOwejRapfQoEFQtCi0aKGqGE6ezPm1pFforrp8majk5HtW6AK4GY0ZX6cajZz38ADgYNu2nPP15dtevW4+Py6OQQsXqgN5Qy+EEEIIkW9pGtStC9OmwcWLaqBar15w5QpMmqSGCdeuDW++CVu22K5FmXh0vFiwIP2tbRjaenvzVenSlHBwYMf16+DiwvRKlWg1fjwxjo4YFyxQU6mffx727aP/0aMMPXGCaVu3MvrECcaeOcMHZ87w46VLtn5ZQticJHSFEOIhFCkCI0bA4cPqMmyYSu6++abaOVSvHsyYAVFROfP9izs4kKTrPLF3L2lAsSxMiu17y7jiC4mJACzq1o0ic+eyu2jRjPv+bdYMgHoHDqgb0q+FEEIIIUS+ZjRC48Yq1t22Tc2f+OgjlfSdMkUldosXV3Hxtm2S3M2vyru4MCM4mK/LlGFJhQo86+9PNVdXdljbL1xMTGRFrVr4LlpEdM2aqmrmm2+gYkVmderElR49ON+1K8mTJpGamkqnffs4efgwHDtm41cmhG1JQlcIIbJJ6dJqeMSBA3D6NIwfD9HR8Mor4O8PbdvCvHkQH59937NvwYJsrlqV9VWqsKVqVXr5+d3zMRNKliTY0THj+JC18dn+2FjQNE5YtzitrViRBbVr3/zgP/+UaFwIIR5Rmqa11DTtiKZpxzVNG36b+4tomva3pmm7NE3bq2laa1usUwiRN/n5wVtvqZYM4eFq/kT58iq5W7MmlCih7t++XcLJ/K6aqyunEhIwr13L8suXKengQKLZzKoffkA/fZp+c+awqF49yp0+jUdYGADa8uVozZqx4NVXmdS8OQQHs6ZLF+LWrrXxqxHCNiShK4QQOaBoUVWtu2+f6u//2mvquls3ldzt2xfWrlX9dx+GncFALTc36rm7U9PNDTvDvf9ZN2oaRTNV8u6Pjb3p+pSvLwDrKlXioLv7zQ9euRJWrYJz527c9v33sHXrw70QIYQQD0XTNCMwDWgFhAA9NE0LueW094B5uq5XAboD03N3lUKI/MLDA557DpYtU0PVvvkGQkJg8mSoUUPtZBs4MHviYfH4ed7fn9HFilHT1RWAytaCkrOOjpzw9WVWoUJ8M2MG3y1bRsq6deqXZPhwGDKERBcX9pQowfry5WmyYAGGbt0gOdmWL0cIm5CErhBC5CBNg8qVYcIEOHMGVq9Ww9PmzgWLRVUqvPuuateQm4yalvH13uvXqbp9OycTEghydORA8eLUnDmTUc89x9pKlVjQsSMAYSEqL6C3agVFivDzrl1w5IiK1i2WG08eFgYmE2zYkJsvSQgh8ruawHFd10/qup4E/AK0v+UcHXCzfu0OXMzF9Qkh8ilPT+jdWw0TvnQJvv4aypVTSV6LBYoVg6FDYfNmSe7mF75mMyOKFaOPtRWcpmk4Ggwcj49nzdWrAHxQrBjPPfkkdg0aqDdV48bB5MmsO36cyrNn0+Dzz2k7diwOly7B4sVgbSMnRH4hCV0hhMglRiM0baq2n4WFwZw5agrw+PFqgETVqmqY2oULubAW63WA2UwLLy92Xb8OwJPe3gBsCwoizWjkyYIFSZk+nac++4zxs2ZB4cJo1j1yrZo0UeOMQfWRiI5WX69eDSkp8NlnOf9ChBBCpCsEZNo+wXnrbZmNAnpqmnYeWA4MutOTaZrWT9O07ZqmbY+IiMjutQoh8ikvL+jTB5YuhYgIFQ9XqqTCxjp1ICBAzcP67TcpuswPKjo7A1DU3p7C9vZ8cfEi/Y8exdFgoJz1vltV8/LK+Hp5rVpcCgiALl3AwUE1bv70UzW5Oi5OPiEQeZokdIUQwgacndUA1xUr1ACJTz9VRa1vvgmFC0OzZvDdd2CdFZDt0it0K7u4sKBcuYzbu1nbLQD0K1iQ38qXp7ufH9ENGrDZ3h4qVMi43+Pq1ZsbAq9apa7TF23dQiWEEOKR0QP4Vtf1QKA18IOmabd9P6Dr+kxd16vrul7dN9P/DUIIkV2cnFQ8vHSp6rk7Z44asvbbb9CxIxQsqNqVLV8uyd28qoabG6sqVmRM8eLMKVuWGUFBzAgKYnmFCpju0ErOy2TK+Nrf0ZEP+/RRB0FBqsDktdfUNkhnZ6heHawVv0LkNZLQFUIIGytYEAYPhi1b4OhRGDlSfajcu7fqt/vss2oWWWpq9n3P9ISuUdNwMhozbq/h5kZBsxkA50y3l3d2ZktMDOFDhjCvVSsqzZpFv9dfV3eGhJDq7U3iK6+Q+vTTah8dqChdCCFEbrkAFM50HGi9LbMXgHkAuq5vAhwAn1xZnRBC3IWHh0ru/vyzqtxdskQNFF6zBp58UsXEL74If/2VvTGxsL3mXl44Go3UcHPjpUKFeKlQISyenll6bDsfH6Y2a8bp/fvRDxzg2PbtnFu5Ej19XsiuXWoyX+3a6s1WLtN1nTSZAChyiCR0hRDiERIUBKNGwfHjsH69SuYuWwYtWkCRImoy8L59D/99Mid0QVXqOhgMGDUtY3tT5oRuLTfVcrG9tzfd3noLvxo12FamjLrTy4t11aphHxmJ8eef1ehiUJ+Q79kjY4yFECJ3bAOCNE0rrmmaGTX0bMkt55wFmgJomlYWldCVfgpCiEeKnZ1K5n7zjWpFtmgRtGypkr3NmkGhQvDqq7Bxo+yoz6+eKVAAgI4+6jPJmlevsiomhuBt2yhiNrNm2zb49Vf45RdISlLJ3C5dICHhvr/XB6dP02n//gdaZ6FNm2iye/cDPVaIe5GErhBCPII0DerVgy++gNBQWLBA7RiaPBkqVoT27R+uQsHOmshNv95ctSqX69UDoKT1E23HTNucni5QgIbu7my+dg2AZp6e7ClZkoODB8M337C1bNn/fpPvv1cT4YYPf7BFCiGEyDJd11OAgcBK4BAwT9f1A5qmjdY0rZ31tDeAFzVN2wP8DPTWdfnUTQjx6DKbVdz744+qLcP8+SpGnjlTXRcvDsOGwcmTtl6pyE2zy5Rhb/XqNPP0pLmnJxHJyfwaEYGGSnKtcXJSfTu6dYMDB2DKFDh3Dj766L6/16jTp1kYGUniA3x6EJqUxNr0OSNCZDNJ6AohxCPOwQE6dVLDWy9ehDFj1PazZs1Uv93XX1dFsffzltx4y7W9wYCjtSK3uKMjAOFJSRnna5pGq0wDCJp4eKAbDPz95ptQqhS7ihe/+Rv4ZNrBO2ECfPml+nrlShWBCyGEyHa6ri/XdT1Y1/WSuq6Ptd42Utf1JdavD+q6Xk/X9Uq6rlfWdX2VbVcshBBZ5+QEnTurwsvwcFU7UL48fPIJlCoF1aqpOPkBiynFY8TeYKCCiwuapjGiaFEA5ly6RDlnZyo4O7P6yhUik5LQdZ1t9vasefZZUrt1gw8/hHnzsvx9UjO9wdp9/foDt0+4lpLyQI8T4m4koSuEEI8RX1947z0VxM6bB7VqwbRpUKOGKoadPl11OriXW1suZBZsTejG3/IpdPlMk2Yru7igoZK+uq6ztFw5Zj75pLqzfv2M846kJ3pfeglSUtR+uf79KbxpE4EbNxK4cSOFMl1a791LQmoqfQ4fZsLZs1n/wQghhBBCiHzDzU21Jvv9dzh9Gt59Fxwd1SyKChVUbPzJJ3Dliq1XKnJaFRcXHA0G4tPSqOPmRiMPD7bGxOC7cSNt9+2j5s6dNN2zhxlvv61+Mbp140RQED3mziUl0/udw7GxWHbtovaOHRmXmjt2ZNxfe+dOXP79l/Fnztz3Gg/GxmbLaxUiM0noCiHEY8jRUbWB+u03CAuDGTPAaIQBAyAgAPr2hUOH7vz4uyV02/n4MKZYMT68peq2iacnAwsVYmZwMHYGAz4mE+HJyUSnpBBnZ0f/oUN5evly+OMP0qw9d79s2fLGExw9mvFlK2dnnvDy4gkvL1pZL8UdHPjj8mVWX7nCt2FhDJO9c0IIIYQQ4h4CA1Vl7vr1arDwJ5+o9mVDh6rdbB06qGre5GRbr1TkBBc7O/bXqMGGKlWYXKoU40uU4MeyZSnv7Mzvly8D4GQwsNbODhYtIqZePUoeP843zz7L0UzJ2V/Cw/k3Ohp3Ozvc7ezwsLPD12SiQ6adh/FpaSyLirrvNV6VCl2RAyShK4QQjzlPT1UAu2MHbN0KPXqooREhIWrI2o8//rc64W4JXaOm8V6xYviazTfd7mQ08nlQEC8GBABQwGQiPCmJiEzR8X4fH3BxIWzJEtqPGcOWTL119fHjM76e6e7O7DJlmF2mDF9ZLyOKFQNgzdWrGefFyhhjIYTIVywWC99++y0AycnJWCwW5syZA0BcXBwWi4W5c+cCEB0djcViYeHChQBERkZisVhYunQpAGFhYVgsFlasWAHAuXPnsFgsrF69GoCTJ09isVhYu3YtAEeOHMFisbBx40YA9u/fj8ViYdu2bQDs3r0bi8XCbuuAm23btmGxWNhv3d+9ceNGLBYLR44cAWDt2rVYLBZOWj+gXL16NRaLhXPnzgGwYsUKLBYLYWFhACxduhSLxUJkZCQACxcuxGKxEG3dejN37lwsFgtxcXEAzJkzB4vFQrL1/+Fvv/0Wi8WS8bOcNWsWzZo1yziePn06rVq1yjieMmUK7dq1yzieOHEinTp1yjgeP3483bt3zzgeM2YMPXv2zDgeOXIkffr0yTh+++236devX8bx0KFDGTBgQMbxkCFDGDJkSMbxgAEDGDp0aMZxv379ePvttzOO+/Tpw8iRIzOOe/bsyZgxYzKOu3fvzvhMsUWnTp2YOHFixnG7du2YMmVKxnGrVq2YPn16xnGzZs2YNWtWxrH87uWN371ixeDixaHUqDGA3bvhmWfg77+H8NxzQyhVSo126NJFfvfy2u9eCUdH6rq789PXX9P2iSd42s+PNt7esGgRhmHDaO3tzY6YGKb8/DP1nJxoO3YsDsnJ/F6iBJ0KFyapY0cSJ02i4Pz5eL71FisrVGBFpUrUW7IE50x/1r4//MCeTL8bd/t3T9d1mDoVpk7luvU9jfy79/j/7j1K7Gy9ACGEENlD09Quoho1brSHmjYNevYEFxd47TV44QUoWvS/PXQfRAGzmX2xscwODQWgqL09ZxMS+OLCBfabTCypX5+i9vY0+Ppr/u3XD+2HH248+MIFlYU+dgyOHweg5q+/sujbb3n/008zTruUlEQJawsIIYQQQgghsqpSJTXGwcEBzpyByMgbM7E8PdXIh0x5YZHHNPHwYDzgZTJRzcWFBRERxKWmciUlhdBGjfiyY0cur10L589jPn+ecYCTvT0HEhNVWXft2nD+PABDvb1ZtWULVbdu5Vdvb3RdR9M0olNSmBcejvnoUaYGB9/0/TMPUYuRIhWRA7S8MNi2evXq+vbt2229DCGEeOQkJ8OWLfDxx2D98JTWrSG+QRhrqh2hX2F/vixd+oGee9CxY0y9cAFQieGBhQoxxXqc7kkvL36/fJkksxlTvXo37mjQAP79V30dH68SuxUrAuC4YgUJ9vYAbKpShdru7g+0PiFEztM0bYeu69VtvQ6RsyTWFkLkFdHRMGcOzJ8Pa9eCnZ2KjXv2hCefVIPXRN5xMTERH5OJtVev0mLvXlZXqsSgY8co6ehIfGoqf129SuFLl/C7coVemzYx8MwZtKpVYfLkG09SpgwcPpxxuK10aYrt2YOvvT0zL16kv7WtnH5L9eaV5GS8NmwAYEqpUrwaGJjjr1fkLfeKs6VCVwgh8jCTSc0oq19fVSbMng2ffQbRv/uDmzdbu8Zw/n3Ve+x+TSlVineLFEFHtWNwMxp5p2hRUnSdQps2AWT0rgqvUoVCmR+cnswF1RA4k/YbNjC3SRMALkmzMyGEEEIIkU3c3dXMiQEDVI7um29Uf90lS1Qyt2NHdV/t2rZeqcgOAdYikSouLgBMOX+ew3FxPF2gALFpafx19Srn/Pw45+dH5caN0dILXbp0UVn/yEjYs0dN2uvUiSMREdSYNo1j772H7yuvUPDjj7Hr2JFUOztS0tKwM9zoapqQqUL3ulToihwgPXSFECKfKFoURo+GiAho98VFqHyVPV97Ury4qkrYufP+ns+gafjb21PQ3h53Ozs0TaOA2ZwROAGUc3YGIDwlhc+mTaPtF1/c/In3bfwyZgwfz5qF3+XLXEpKuu/XKYQQQgghxL2UKaNaMJw/D2vWQK9eKodXpw5UrQqffgoxMbZepcgOPmYzNVxdWRoVhVHTaObpSQtPz5vOaZ75uE4dmDRJZfv37IG9e+H999H/9z/mNGtG0MSJUKIEbWfMILl5c8qfOEGY9X3L5eRkSm7ezKpMQ0yk5YLICZLQFUKIfMZkguBm8fDBAXqvPs3AgbB4MVSrBo0bq69jY7Pne5W0Vt/W37WL10NCiK9VC4YMgcREVRKRudfUb79lfDn0p5/Y07cvCSdOwFNPQenSRJQoQZP16wlLTMyexQkhhBBCiHzPaFQx8IwZKrk7dapqxfDaa1C4sBr7sHUrZCq4FI+hzVWrEt+gAbENGlDb3Z3Gnp4kNGxIWqNGxDdoQNcCBe75HGXc3DgzcSJ/VqtGcpkyGbdveeUVLm/dCsCG6GhOJiSwJnNCNyUl+1+QyPckoSuEEPlQ+jA0r8KpTJ6sgtePP1bzyTp0AC8veOMNNbvsQRyqUYP5ISHUcHXl/aJF6RcQwKDAQEYXL65OMJuhd2+wTjUFINM05OOjRuF35QqvNmwIixbB0aP4njqFz59/cn7uXGjeXDUIFkIIIYQQIpv4+KiWC1u3qjkUTz6peu7WqgUlSsAnn6hd+OLxY9A0HIxGzJnaItgbDGjW27OqdpEitJg4kTa//ILH0qXM/eYbHJOSqNioEdjZUaZjR3a/8AJvd+pErYMHAWm5IHKGJHSFECIfMmraTdfu7jB0KJw8CX/8AZ07q84IJUuqvOvJk/f3/GWcnelcoAAmg4FRxYszuVQpJpcqRd1bB5x5e6vrevXAxQUMBnjtNUq99x7rmjdnRYsWsGgRp0JDueTpSd/ly6n+3HOwerVK9AohhBBCCJEDataEH39UBQ5ffAFFiqh4uVAhePpp+OcfyAMz5sV9quXqSpCjI2uvXsXRy4ta3bphmTyZ440bQ/36BP37L5VOnqTMoUNMnTIFdF1aLogcIQldIYTIh25N6KYzmaBlSxW8HjmieonNmaMSuw0bqmnA2b7dLCwMVq1SX6emqn5VRiNLpk+nwzvvMKlqVbZoGtPat6dF5inrXbuS5O9PSvpjhRBCCCGEyGbu7tC/P6xbp1qp9u+vCiAaN1azsv73PzWjQuQPLnZ2HK1Vi4RGjQitW5dijo6crV2bdz7/nLS//6bsb7/huGIFH/bsSfWjR0lq3pxGP/9s62WLPEgSukIIkQ+lJ3Lv9p9AUBDMnKmqc8ePh4MHwWKBihXhhx9U7jVb+PmpscK3aOzpSbKu88aJE4w/e5aJ3brxUffuLHrnHTh9mqSiRTFfugRt2sCmTSrbvHdvNi1KCCGEEEKIm1WoAJ99Bhcvwpdfqs1m776reu326XP/Q4ZF3lDN1ZU/r1yh/f79HPbwQHN0ZGSfPsx68klMqam8+r//8dNrr7Fg9Gj0efNsvVyRR0hCVwgh8qH7+ce/SBEYNgyOHYPvvlODI3r1gsBANRn42rWcWeOT3t5ca9AAA7AnNhZ/Dw9mDh7MgmeegaJFWblqFRW/+oooT0+oW1dlmytVUsPWhBBCCCGEyCGOjtCvn6onOHgQXngB5s9XQ4br14e5c2XcQ37yrJ8fhe3tORUfTw1XV/r4+6MbDLw6aBCf9esHwNOffkrn999H69YNNm+WKXvioUlCVwgh8qH0Rgv30/bL01MlcnfvhgULVO50+HDVR+zNN3NmQISz0UgpR0cAyjs7U8BsJtwaHe8ym9lXsiSrmzRRJ/fsqXpDzJ5903OkpKWReIeLLo3PhBBCCCHEQyhbFqZNU0OGJ09W3cS6d4dixeDDDyE83NYrFDmtnY8Pe2vUYH/NmmytVo021jkhCfb2rBwwANfff2dl9ep807Ilup0d1Kmjpuzd76ASITLJ9YSupmktNU07omnacU3Tht/m/iKapv2tadouTdP2aprWOrfXKIQQeZ12S+/c+3ssdOoEK1ao6b/t26uJv/7+aqvZ8ePZuFBUIhegorMzBUwmQpOSANgXGwvAyB490EeMgJkzOdWpE6mbNnF90yYAjsTF4bp+PQ7r1t328uyhQ9m7WCGEEEIIkS95eMCQIXD0KCxbptozjBih2jH06gXbttl6hSK3VHV1zfi6uIMD152caPnxxzw/bBgR7dqpO86cgQkTbLRCkRfkakJX0zQjMA1oBYQAPTRNC7nltPeAebquVwG6A9Nzc41CCCGyrmZNNTRt3z4YMAB++gmCg2HwYFWdkB3GFC/OuOLFGVCoEEGOjhyPjydV19l3/ToAJ93ciB05Ehwd+eWppzjn64u5Uyc4e5Y9+/fjePUqw4sU4X/Fi990qeTszA7rcwghhBBCCJEdDAZ48klV/HD4sGrN8NtvKm6uU0fFy9KOIW/zM5sZWKgQzxQokJHcdTUaAdg6aZKqhmnQAJYvB9kxKB5Qblfo1gSO67p+Utf1JOAXoP0t5+iAm/Vrd+BiLq5PCCHEAyhXDqZMgVOn4OWX4fPPVY/dTp0efk5ZiLMzw4sWpaC9PeWdnUlIS+NAbCzH4uMpbG8PkNGGYZOzM6N698YcGgpFi9K1Vi0WjhrFh8WL83bRojdd6ru7E26t9hVCCCGEECK7lS6t4uILF1SsHBUFzzyjeu0uWAApKbZeocgpnwcFMSckhACzGQAPOzsAIs1meP11ePFFOHcOFi605TLFYyy3E7qFgHOZjs9bb8tsFNBT07TzwHJg0O2eSNO0fpqmbdc0bXtEREROrFUIIfKsnOodGxCgeogdPAhvvAGrVqleuz16qED2YaW3Xxhy/DhpQFNPTwB+jYhg7dWr7I+N5cdmzdg1fDg0aMCeatWw7NqF8amnIDX1pufyNZu5nJJCsgwkEEIIIYQQOcjNDV59VVXsLlgAsbHQpQsEBcGnn0JMjK1XKHKKk7Uyt6R1LsjV9Cx+jx6qL8fQoRAaaqvlicfYozgUrQfwra7rgUBr4AdN0/6zTl3XZ+q6Xl3X9eq+vr65vkghhBB3VqYMfPQRHDkC778Pv/6qBkO8+CJcfIh9F+WcnSnr5MSOmBgC7e3p4OMDwFsnT2LZvZtTCQmk2Nnx9yuvwLp1dBk3jm3Nm8PixWpbU0JCxkTZAiYTAFGy500IIYQQQuQCg0HtYDt6VBVmBgbCa6+p65waMixsq767O6OKFeOnsmUBuJKe0LWzU9n806dVVYymwYwZ6j5pwyCyILcTuheAwpmOA623ZfYCMA9A1/VNgAPgkyurE0KIfObBR6NlTUAAjBqlErsvvQTffQdFikCHDqo9w/1yNBo5WLMm0Q0acK5OHSq7uNz2vPCkJC4nJ3PMZOKfWbNg9GjYtAkcHcHeHsaMwS/9XEnoCiGEEEKIXGQ0wlNPwb//qiHDrVvD5Mkqsdu9Oxw4YOsViuxi0DTeL1aMgvb2uBuNNxK6AE2aqDdJ6V55BQYOBGdnGD489xcrHiu5ndDdBgRpmlZc0zQzaujZklvOOQs0BdA0rSwqoSs9FYQQIgfk1me/xYur/mGHDqldRX/9BSVLQvv2D1eJ4Gutss3MCJxNTGRjdDQA5Vxc4L334Pnn1b62unVh5Eg6Bgby7MqVREVFEZ2QQJp8Ei6EEEIIIXJZzZrw889q7sSLL8LKlVC5MnTrBjt2SLFmXuJpMv13d+CMGZCYCOvXq+Np0yA+Xm13XL489xcpHhu5mtDVdT0FGAisBA4B83RdP6Bp2mhN09pZT3sDeFHTtD3Az0BvPaeaPQohhMhVJUvC+PEqsTtsGPz+OxQtqraaPUiP3fSeVJlVcnHh5/Bw2u7fD0AFZ2e1hWn2bLW/bfVqVRIBfD9+PI2LFSM0OJgX04MoIYQQQgghcllIiCqAOHwYBg1Sid3q1aFdO1i3ThK7eUGwoyOH4uL+e4fZDPXqgYeHOp4yBXx94ZdfcnV94vGS6z10dV1frut6sK7rJXVdH2u9baSu60usXx/Udb2eruuVdF2vrOv6qtxeoxBC5HWaltPNFu4uMBDGjVOVCJ06qeC1TBkYPBjCwu7vudZWrszckBDmhoTwT+XKzC5dmulBQUwPCuK3cuUo7OBw8wNMJli4kMSoKM74qcYLZc6do9vw4ff/zYUQQgghhMhGfn4waRKcOAEffqjaMjRqBHXqqIpd8fiq6urK/thYEu80lHnJEihRQhWfWCzw99+SyRd39CgORRNCCJFPhITA99+rHrutW8P06aorwocfwu0+vL6dhh4edC1QgK4FCtDIw4PKrq68XKgQLxcqRIe7DM209/Ki0y+/UO7rrxnRpw8tNm6EggXhm2/UlAoZNyyEEEIIIWzE2xvefVftYvviCzU7q0YN6NMHdu2y9erEg6jq4kKyrrM/Nvb2JzRooDL5hQvDk0/C+fMwb17uLlI8NiShK4QQwuZKloS5c+HgQWjeHEaMgNKl4Ycf4E4fYGeHEr6+HCxenE969eKJjz4itnx51Wu3UyfCKlbki8WLeZCuPxcTE/khLIxZFy8y/cIFPjt/nknnzvH+qVMsiohgbng47508yQ5JGgshhBBCiLtwdob+/VUBxJAhKmauWlUVcR48aOvViftRzdUVgJ1ZeQ/Qs6fK4PfvDxcv5vDKxOPIztYLEEIIIdIFBani2HXr4I03oFcv+PRTeOcd6NBBTQTOTh19fNhy7Rr9AwJ4F1hRuTKdRoxgf0oKwXv38lKHDsR064YrwJtvqog6KQkqVrzr8w45fpz5Ebef5xlob09YUhIpus6Ga9f4u3Ll7H1RQgghhBAiz3F3V60YRoyAqVNh9GhYuhTatlUzKkqXtvUKxb0Ud3DA3Whk5/XrXElOJg3wvs2QZ0C98fnxR9WXbto0GDs2V9cqHn1SoSuEEOKR07AhbNkCc+bAlSvQubPqG7ZzZ/Z+n+5+fpypU4fhRYrgbDCwtnhx2LKFttOm0Xj+fCZ37ozr3LmqFOL556FsWfVJ+T3siImhjbc352rX5lLdukTVq8fV+vV5PTCQ84mJpFirfsOSkrL3BQkhhBBCiDzN01Mldc+fh9dfhzVroEIFeOstOHXK1qsTd6NpGlVdXdl27Rp+Gzfis2ED11NS7vyAoCBo0QLmz8+9RYrHhiR0hRAiH3qQNgK5zWCAZ56BY8dU64UzZ9Sk3+efz/5dRwZNo5yzMztiYthz/TqnExKoX6ECr7/yCvNnzQJXVzXBDSApCT3TAuJTU0nN9PO8mJjIyYQEaru5EejgQAGzGS+TCXc7O2q7uWWc52FnxyVJ6AohhBBCiAfg5wcTJsDRoypm/vhjCA6GQYPg8mVbr07cSVUXF3Zev06y9f3DucTEuz+gSRP1hkiGN4tbSEL3AVgsFr799lsAkpOTsVgszJkzB4C4uDgsFgtz584FIDo6GovFwsKFCwGIjIzEYrGwdOlSAMLCwrBYLKxYsQKAc+fOYbFYWL16NQAnT57EYrGwdu1aAI4cOYLFYmHjxo0A7N+/H4vFwrZt2wDYvXs3FouF3bt3A7Bt2zYsFgv79+8HYOPGjVgsFo4cOQLA2rVrsVgsnDx5EoDVq1djsVg4d+4cACtWrMBisRBm/cdj6dKlWCwWIiMjAVi4cCEWi4Xo6GgA5s6di8ViIc46zWjOnDlYLBaSk5MB+Pbbb7FYLBk/y1mzZtGsWbOM4+nTp9OqVauM4ylTptCuXbuM44kTJ9KpU6eM4/Hjx9O9e/eM4zFjxtCzZ8+M45EjR9KnT5+M47fffpt+/fplHA8dOpQBAwZkHA8ZMoQhQ4ZkHA8YMIChQ4dmHPfr14+3334747hPnz6MHDky47hnz56MGTMm47h79+6MHz8+47hTp05MnDgx47hdu3ZMmTIl47hVq1ZMnz4947hZs2bMmjUr41h+9+R3L93D/u7N6dsXFizIOH6Uf/eOHz/CV19Z+P77jQwdCj/8sJ/ChS3077+N2Njs+90rGRPDxmvXqDx9OgwZQvmEBIo4ODA1MRFLmTKcrFKFn5s0YSFQvVEjov/8E13XKfy//xFct27G7161ceNgyBAq2dsDN//ulXN2hmXL4I03qODszJWUFD6bNo1WrVqRpuv0PHiQam+/Tds8/Lsn/+7l7L97QgghhMhf/PzUTN/Tp6FvX5gxQ3UH++wz1SlMPFqqurqSubQm9JY/pPjU1Jsf0LChul65MmcXJh47ktAVQoh8SNM0Wy/hvrm6qiqEJUvAywtmzlS9wqy5uof2ZuHC/Fi2LG8VLkw5Z2eaeHlR3tmZs4mJ4OTEt19/Tc933iHK1RXX48ehRQvi1q/ncmIi0datUslpaYQnJ+NqNNLcy+s/3yPI0RGj9Wdf3tkZgOvWoO1UQgI/hoezMyaGhJycBCeEEEIIIfKcokVVMnfTJihWDAYPhkqV4JdfcnbIsLg/6YPR0l3MVKG7IDwcp3//5VBs7I0TatRQfXQ/+wweg12WIvdoj8O223upXr26vn37dlsvQwghHhtjz5zhvVOneKdIEcaWKGHr5TyQ9etV37Bt29Sk3+nToVat7P0ew06c4NPz54lt0IA+R47wz9WrDJoxg7d++CHjnAs+PvT5/HNWde/OwdhYym3bxpyyZXnGz++2z1lx2zb2xcYyPSiIV44dY2e1alRxdeW3iAg6HjgAwPZq1f4T7AnxKNI0bYeu69VtvQ6RsyTWFkKIx8/SpfD223DggMoJTpgAmTYsChtJ03Xc16/PKOqYUKIEbxYpAkCjXbtYFx3N7xUq0Nrb+8aDZs6E/v3hr79UCwaRL9wrzpYKXSGEEI+l+vVh82Y1OC0iAurVgz59wLr7PFuUc3YmSddps28ff0RFUc7JiZn9+vHK+vXw4osAFIqMZPCnn9Jx/35esG6tL+fkdNfnzHyd3kd3b6ZP4sNlf5wQQgghhHgIbduqERDffguhodC4saraDQ219cryN4OmUdnFBQA7Tbup5cJh6xuZ/5RdPvssFCkCPXtCfHwurVQ86iShK4QQ+VCPAgVwNBh4zt/f1kt5KOmD03buhK5dVcBapIj6EDs7tpY18/SkrpsbYUlJBNrb08vfH29HR064uMD06Wz99lvGPf00T27ZQujatVxJSqK1lxch1mTt7Tzn58eAgACKOzgAqtUCwL7r1zP+U5ZhaUIIIYQQ4mEZDPDcc2pwWv/+8PnnanDaW2/J4DRbauLhQZCjIyUdHDhtfS8AEG6d/xJjbeeWwdERvvhCZeOtsx+EkISuEELkQyUcHYlr2JDgu1SSPk58fOCnn2DNGihfXgWsDRrAwYMP97wB9vZsqFqV3TVqsLtGDZ7286OA2UxEcjLY2bGneXO+tg7T2zRwIPvefpvfY2MxG+7832tLb2+mBgcTaG+Pm9HIfmtl7t7YWJp7egJwyRrMCSGEEEII8bDS84FHjsATT8CkSaot65w50pbVFkYULcru6tWp5OLCruvXAdWKId31WwejATRtCm5uaqCIEEhCVwghRB7SuDH8/Td89x0cPqwGQbz2WvZWIBQwmbiQmMjm6Gh2Xb/O8cBAUq0JXNPu3VC3LtSpoyZS3KVMWNM0yjs7s+XaNVZfvsyx+HjquLvjZDBktFzQdZ2916+zMTqaFJlmIYQQQgghHkJQECxYANu3Q4kSaid/w4awe7etV5a/2BkMOBmNVHN15XRCAisvXyYqU0FHzO0Sumaz6p/71VeS1BWAJHSFEELkMZoGvXrBoUOqp+5nn6ngddq07KlAKOLgQHhyMnV27WLGxYsUMJkoNWcOQfPnw9mz8Pzzqrlv3bpq8sShQ3DhAuzYAZGRNz1XVVdXdly/TvO9ewGo4uKCn9mc0Uvrn6tXqbR9O/V27WJuRMTDL14IIYQQQuR7lSvDxo0wa5YqgqhWDV59Fa5etfXK8pfGHh4ADDl+nIuZWq7dNqELN6batW8vPTOEJHSFEELkTQUKqF66u3ZB1aowcCC0bg0nTz7c8w4tXJiVFSvyR4UK/FGhAv9WqcLmTp3Y3L49uLrC7Nkwfbo6+d9/ISQEAgOhenVo2RJSU1XkHBvL2OLFWVmxIisqVmRR+fI86e1NaScnDlrbMJzK1FPrTKavhRBCCCGEeBgGA/Ttq/rrvvSSKn4oXRq+/17aMOSWGm5uvFW4MEfj4jiRadjZHRO6ffqo4hFQf1AiX5OErhBCiDytYkVYtQo+/VRVIlSsCDNmPPjQNGejkRZeXrT09qaltzfBTk74mc14m0w3Tnr5Zdi/H5o3hwoV1OSJkiVVlW6PHlC2LHTqhFtSEi02bOCJs2dpP2YMxlmzqOjkRNuZM0nr0oWUQ4f4/n//o/eKFRltGIQQQgghhMgunp4qmbttGxQvroaoNWwI1g1kIofVd3cnDXjxyJGM227bQxdUD93Zs1VJ9Q8/qEIRXYfdu/n7++9lsHI+o+l54KOX6tWr69u3b7f1MoQQQjzizp1TH2qvXq12LM2aBaVK5dI313VVobtq1Y3bnJwgLu6m06IqVsTbGkEnOjpiHx/PRT8/3lyzhsYGAwkuLgwMDMylRQtxd5qm7dB1vbqt1yFylsTaQgiRP6SlwTffwLBhqv3CwIEwZozahCZyRlRyMj4bNmQcB5jNNPbwYE5IyJ0fNGUKDBnyn5vrbNnCppo1c2CVwhbuFWdLha4QQoh8o3BhlU+dNQt27lTVup999uDVuvdF09R44cqV1TQKo1Elc996C8aNU9W7H32EZ3g4p/38WFelCoaUFNbUrUvApUs0mjWLvmXLsviXX3JhsUIIIYQQIr8xGOCFF1Qbhn79VJxcurQqCs0DtYCPJG+TiZ/Lls049jWZ7txyIV2PHre92XDgQHYuTTzipEJXCCFEvnThArz4IvzxB9SpowbG3u2D8Gy3dSskJkKDBjffrutMP3+egceOUSclhVqHDzNp8OCMuxNNJkzdumEYMQKCg3NxwUL8l1To5g8SawshRP60caOq1l2/Hpo2VUURxYvbelV5z9G4OEpv3YoG1HN3x6xp/FW58t0fFB2tqlL27uXM9u0UHTqU/z37LO/MmgX29rmxbJHDpEJXCCGEuI1CheD339U8gSNHoEoV+PjjXKw+qFnzv8lcAE2jnKsrusHARrOZyIoVSTabiXZxoeGnn7KlbFkMc+ZA586QkpJLixVCCCGEEPlN3bqwdq3aZLZ1K5Qvr3b736uAVNyfUo6OPOXjwx8VK+JvNmdtGLK7u2qA3KgRR3r35pyvL+/88INqgizvEfIFSegKIYTItzQNnn0WDh2Ctm1V94PGjdWxLVV0dsZe0wDwDgxkwpYtFPj1V/6tVIlmEyey78MPYd8++PZbaNQIFi607YKFEEIIIUSeZDBA//5w4ICaQTFkiKpJsHW8nJcYNI2F5cvzhJcXVV1cOJGQwNXk5Cw//mpqKm+8/LI6mDsXnn46h1YqHiWS0BVCCJHvFSgA8+erioP9+6FqVdUzzFbVB54mEydq12Z39ep8VKIExQsWJMlsBiDZZGLXc8+phsAvvgjr1sG770pjMyGEEEIIkWMKF4Zly2DOHNVjt3JlGDsW7iPvKLKgqnUCXcu9e3n+8GHSshDjX0lJYX7jxhRJn7WxYEEuDQkRtiQJXSGEEAJVrfvqqyqh27QpDB4M9erB2bO2WU8he3squbhgNhgo7+x8031xmgbTpqn+WE88AYcPw+jRMHGibRYrhBBCCCHyPE2DZ56BgwfhqafgvfegRg01bFhkj3pubrTx9iY6NZVvwsI4Ghd3z8dcsWbVz/n5MWX0aFXo8fXXktTN47KU0NU0raymaYM0TftB07Q/NU37V9O0xZqmfaxpWntN0xxzeqFCCCFEbvD3h6VL4aefVJ60WjVI/7DbVko7Od10HJ+WpnpEXL2qFmo0wqhR8Oab8NdfNlmjEOL2JI4WQgiR1xQooOLjRYsgPBxq14bx4yEpydYre/y52NmxtEIF5lmnNe+4fv2ej7mSkoJZ03i6QAHmlC8PDg5qJ5+npxrCLPKkOyZ0NaWXpmnbgAPAKCAQuAqcAYxAK+BXIEzTtJmapsm8QyGEEI89TYMePWDLFjXJt0cPGDgQLl+2zXrsDQY+LlGCvgULAtaELqhgzcsL6te/cfIff9hghUKIzCSOFkIIkR+0b69667ZqBW+/rap19+yx9aryhrJOTjgYDOyMibnp9vFnzlBzxw6OxsXx0pEjlN+6lVmhoXjY2VHN1ZXtnp58/O+/6uRr19A3bbLB6kVuuFuF7iHgHWAREKLrureu6411Xe+i63pPXdfb6LpeHnADegMuwD5N03rm9KKFEEKI3FC6NGzcqNovTJum8qYHDthmLUOLFGFmcDAamRK66fr3v/H1wYO5ui4hxG1JHC2EECJf8PSExYvV5dIlldR95x1ISbH1yh5vdgYDlZyd2XlLhe64s2fZFhPD3PBwZoWGYtQ0mnl68l7RonTx9QVgVmoqPkuXAqA1bgy//prr6xc5724J3XeBsrquj9V1/fCdTtJ1PU7X9d90XX8aKAMcz+5FCiGEELZiZweffqo6GVy6pAZATJhgmxlkmqbhYDAQf+u0th491Kjhp5+2XcZZCJGZxNFCCCHylXbt1CyKHj1g3Dho2VIdiwdX1dWVnTExGYPRIpOSuGZ9H/BNWBhpwJjixZlXrhyDAgMp7ODAh8WLcyw+nigXFwYPGMD1IkWgXz/IQi9e8Xi5Y0JX1/Vfdf3+3q7qun5e1/XND78sIYQQ4tHSpAkcOQIdOsCwYdC1K5w+nfvrcDQY/luhC1CmDJQrp6a4xcRAaCjcmvgVQuQKiaOFEELkRz4+8N13MGsWbNqkdrctXmzrVT2+qrq4cC01FZ8NG+iwbx8t9u4FwMVo5FRCQsY5tz4m3WedO9Nh2DDVN658eahbl/B//yV4yxaOS4L3sZeloWhCCCGEUEHqvHlq6MOiRWpg2rZtubuGOyZ0QSV0ASZPhoAA+P773FuYEEIIIYQQQN++avNYsWKqGKJvX4iNtfWqHj+dfH15s3Bhyjs7szgqil3Xr1PU3p4VFSvyckAAE0qUoJC9/U2PaeLpybDChRlUqBDvFinCherV+WjAAHB0hE2buDZ6NMfi45l28aKNXpXILllK6GqaZtI0baimaRs1TTuraVr4rZecXqgQQgjxKNA0VaF78CC4uanKg48+yr0WDI5G470Tuu+/r663bs2dRQkh7kjiaCGEEPlRkSIqFH37bfj6a9Vbd98+W6/q8eJpMjGhZEmGFymScdu04GDqubszPTiYN4sUQdO0mx5jbzAwvmRJPgsK4sMSJejl58fwzp25sns3vPACgVu3YpBdfHmCXRbPmwz0B5YBfwNJObYiIYQQ4jEQFKS2kg0aBMOHqx5hM2bALbuesp2jwcCG6GiCtmwhKS2NPypWJMTZWd1ZvPjNJ4eF5exihBBZIXG0EEKIfMlshv/9T7Uu69kTatZUsyn69VNFEiJrMrdRqHKfbzaquboC4LthA1uqVqXa7NkM/vVXXt2wAaZMAYslO5cqclFWE7pdgOG6rn+Sk4sRQgghHif+/qoFw4cfqqLYbdtg4UIICcm57+loMLAvMTHjeH9s7I2ErtEInTqpSbbe3nDiRM4tRAiRVRJHCyGEyNeaNYM9e+C55+Cll2D1atVn18PD1it7PPjb2zOnbFl0XSfglhYL99LEw4NJJUsy8vRpfg0JoRowacYMdefIkbBuXfYvWOSKrPbQ1YC9ObkQIYQQ4nGkaTBiBKxZA1evqsqDuXNz7vs5Gm7+rzv21i1T8+fDqVOqDOLEidzrBSGEuJNci6M1TWupadoRTdOOa5o2/A7ndNU07aCmaQc0TfspN9YlhBBC+PnB8uUwYYKaRVG9OuzebetVPT6e8fOjp7//fT/OzmDgtcKFqe7qyspbq3sPHIBw6fz0uMpqQncW0CMnFyKEEEI8ziwW2LkTKlWC7t3htdcgOTn7v4+j0XjT8X8SupqmJlCULQtxcXDmTPYvQghxP3IljtY0zQhMA1oBIUAPTdNCbjknCHgbqKfrejlgSE6vSwghhEhnMMCbb6qi0IQEqF0bZs+29aryh+quruy8fp0aM2ZQ8auvaD1uHKkxMdCyJURF2Xp54gHcseWCpmmvZDoMA57RNO1v4E/g6i2n67quz8j+5QkhhBCPj4AA+PtvFah++ils365aMhQsmH3fI71C19lgIDYtjdg7DUirWFFd16ihhqXNnw++vtm3ECHEHdkojq4JHNd1/aR1Db8A7YGDmc55EZim6/oV6zeWshwhhBC5rk4d2LULnn4a+vaFDRtg2jRwdLT1yvKuYYULU8bJCT04mOKOjjTbs4cfR46k14gRqmXbP//YeoniPt2th+7U29xWBGh0m9t1QBK6Qggh8j2zWc0XqF1bBahVq6qkboMG2fP86QldP7OZkwkJ/63QTVe+vLqOjIS1a+GXX9QENyFEbrBFHF0IOJfp+DxQ65ZzggE0TdsAGIFRuq6vuN2TaZrWD+gHUCTTdG0hhBAiO/j6wooVMGqUmkexcycsWAClStl6ZXmTj9nMC5mqTJp6ePB5q1b0io6GiRPh+HH54T9m7thyQdd1w31cjHd6HiGEECI/6tEDtmwBNzdo3FhV7GZHO9v0hK6HnR1OBsOdE7qurtC2LQwZAs7OKkgTQuSKRziOtgOCAAuqDcQsTdM8bneiruszdV2vrut6dV+p7hdCCJEDjEYYMwZ+/x3OnlV9dRctsvWq8oeqrq7svX6d5FdfVaXR77xj6yWJ+5TVHrp3pGmag6ZpXbJjMUIIIUReUr48bNsG7dqpnrrvvAN36pCQVek9dN3s7HA2Gu+c0AVYsgQmT4agIEnoCvEIyuY4+gJQONNxoPW2zM4DS3RdT9Z1/RRwFJXgFUIIIWymdWtVoRsUBE89BW+/LXN9c1pVFxeSdJ0DHh7qBz5/vmpuLB4bD5TQ1TTNqGlaa03TfgDCgV+yd1lCCCFE3uDmpraP9e8P48dD8+Zw7dqDP18xBwcAktPSVEI3KxniUqUkoSvEIyIH4+htQJCmacU1TTMD3YElt5yzCFWdi6ZpPqgWDCez6fsLIYQQD6xYMVi/Hl58UcXMXbvKrK6cVM3VFYAfLl0i7NVX1Y6+Ro1g82Ybr0xk1X0ldDVNa6Rp2heo4Q5LgebAN0DtHFibEEIIkScYDDBjBsycqdrZVq0Kx4492HOVd3YG4HxiohqMdrcK3XTBwXDiBMTHP9g3FUI8tJyOo3VdTwEGAiuBQ8A8XdcPaJo2WtO0dtbTVgJRmqYdBP4G3tR1Xd4uCyGEeCTY28OXX8JHH8HixVCrltQk5JSSjo4UMJmYdP48fc+cgeefV3e0awdxcbZdnMiSeyZ0NU2rrmnaJ5qmnQPWoPpt/W29u6uu64N1Xd+Wk4sUQgghHneapioO/vkHoqOhbl3YuvX+nyc9oXsuMfHeLRfSVasGqamwZ8/9f0MhxAPL7Tha1/Xluq4H67peUtf1sdbbRuq6vsT6ta7r+uu6rofoul5B13XZZSeEEOKRomnw1ltq9390NNSpo+ZSiOxl0DT21qhBRx8ftsbEoH/0EcyaBRERqhJFPPLumNC1fpp/FNgCvGy97gr4oSbearmyQiGEECIPqV8fNmxQM8saN4Zly+7v8QFmM34mE58FBeFsNBKXlZYLNWqo623y+asQuUHiaCGEEOLh1K4NGzeCu7uKmWVYWvbzM5tp4ulJRHIykyMjmdm6NZcaN4Zhw2DHDlsvT9zD3Sp03wNKAn8BpXRd76zr+q+6ricA0p5aCCGEeEDBwSpALVsW2rdXW8uyStM0wurVY0ChQlmv0A0MVH10v/kGUlIefOFCiKySOFoIIYR4SEFBKmauUAE6dlStGGRYWvZq6O6OBrxx4gT9jx6l9Ouvk+riAhMn2npp4h7ultAdDRwDmgHHNE2br2laJ03THHJnaUIIIUTe5e+v2i+0bAkvvQTvvXf/AWqWe+hqGoweDbt2waBBD7ReIcR9kThaCCGEyAYFCqiYuWtXGD4c+vSBxERbryrvqODiwuV69bhYpw7/VK5MtIsLR3r0gF9+Ue8hfvrJ1ksUd3DHhK6u66N0XS8DVAOmAzWB+ahpvLNR1QXy2YgQQgjxgFxc1MCHF1+EsWOhSxe4dCnrj3c2GrmelYQuQI8e0K2bCs6y+hghxAOROFoIIYTIPo6O8PPP8MEH8N130LSpavUqsoeHyURBe3vqubnhYDDQp0ULdC8vdee4cVIW/Yi651A0Xdd36br+pq7rRYFGwBygIar310JN06ZqmlYrh9cphBBC5El2dqrlwvjxsHQptGgB165l7bHeJhNRycnoWQ2y2rWDq1dVpa4QIsdJHC2EEEJkD02DkSNh7lzV3rVWLTh2zNarylvsDAZquLqy1dOTxfv3w1dfwf79Kqm7e7etlyducc+Ebma6rv+r6/orQEGgFbAc6AlszIG1CSGEEPmCpqnZA0uXwsGD0KABnDx578cVMJlI1HVislpx26SJuv7rrwdfrBDigUgcLYQQQjy8rl1h7VqIiVHDhqVOIXutrFgRI7A9Ph5694aqVeHdd6FKFdi0ydbLE5ncV0I3na7rqbqur9R1/TnUtN4u2bssIYQQIv9p0QKWLIHz56FePdiz5+7nFzCbAQhPSgLgZHw8g48dY3N09O0f4O8P5curBmQnTmTn0oUQWSRxtBBCCPFwataE9evBwQEsFli3ztYryjscjUZCnJ0Ze/Ys55OT1bCPdFOn2m5h4j/umNDVNM0lK0+g63qirusLMz3ONTsWJoQQQuRHrVqpANXODho2VBUId1LAZAIgPDkZgFmhoXx24QJTLly484M6dlTXo0Zl04qFELeSOFoIIYTIWaVLw4YNUKgQPPGE2ukmskcXX18Avrt0CV54Qe3u69dPDUibM8fGqxPp7lahe1bTtA81TSt5ryfRNM3eOrl3HTAk21YnhBBC5ENly8LGjRAQoALUxYtvf96tFbr7Y2MBiE5JufOTjxoFPXuqUuD4+OxcthDiBomjhRBCiBwWGKiqcytUgKeeUoPTxMMbUawYpRwd2RkTAwaDatv28cdqp9+HH0Jamq2XKLh7Qrc5UBU4pmnaLk3Tpmma9rKmaV00TWunaVovTdPe1zRtCWpi7zRgCfBxLqxbCCGEyNMKF1aVupUrq6La2bP/e46vtUL3mDUxm57QvXa3hK6mqU/Yr12DKVOye9lCCEXiaCGEECIX+PioAtI6deDpp2HyZFuvKG+o6uLCwshIlkdFMfDoUaKdnOC99+DIEejfHxITbb3EfM/uTnfour4DaK1pWhDQC2gKPA/YZzrtLLDBevsSXdeTc3CtQgghRL7i7Q2rV0PnztC3LyQkwIABN+5Pr9B96+RJnvHz43RCAgDR9xqS1qCBusyfr/rpCiGylcTRQgghRO5xdVVJ3aefhtdfV/ULQ4bYelWPt17+/syLiODJffsAKOzgwLCuXWHHDlWtGxEBr72mqk/c3W272HzqnkPRdF0/puv6CF3X6+q67gh4A4UAR13Xi+m6/oyu679KECqEEEJkPxcX1XKhXTsYOBA+//zGffYGA8OLFAHgzytXMm6/a8uFdPXrw9690nZBiBwkcbQQQgiRO8xm1XKhUyeVZ/z0U1uv6PH2pLc3bb29M44vJSWpTPmECSqhu3ixmkg3eLCqOhG57p4J3Vvpun5F1/VQXdelvloIIYTIBfb2qpi2Qwd49dWbA9RXAgIA+Nua0C3p4HD3lgvpataElBQZCyxELpI4WgghhMg5JtPNSV3pLvZwqrrcmPH6TVgYfQ8fBiBxyBCmjR+v7vjuO9WCQeS6+07oCiGEECL3mc0wb57qp/vaa+qSkgKB9va4GY2suXoVgFKOjlxLTSVN1296/Ofnz6vBBumaNYOiRdV+tFvOFUIIIYQQ4nGUntTt2FGFuZLUfXA9/Pxo7+1NF19fCtvbMzssjMikJDbExDCwVi2GzJqlTvz+e3k/YQOS0BVCCCEeEyYT/PILvPyyqtIdNAhAo7yzM+esgwmCnJzQgdhMfXRjU1N59fhxLLt333gyFxf44AM4fBg2bszFVyGEEEIIIUTOSY+ZJan7cEo7ObGoQgXmlSvH5FKlANh1/ToJaWnq62rVbkxu3rDBVsvMtyShK4QQQjxGTCaYPh3eegu++EJV6oY4OGfcX8rREbi5j+6h2FgAjJp285N17KiGGLz/vnyqLoQQQggh8oxbk7qffWbrFT3e0tsvdD5wgDdOnLhxR4cO4OOjBi4vWmSTteVXktAVQgghHkPjxqkZBFOmwNY3ikCyStb6m80AXMtUobv88mUAijs43Pwkrq4wcqQaC3zoUO4sXAghhBBCiFyQntR96ikVN0tS98F5mkx8UrIk3iYTh+PiANABvLxgzRp10uTJNltffpSlhK6maW01TZPkrxBCCPGIMBhU24VPP4W9KxxhUjAF7Ex42NkBEJWcDEBoYiLvnz4N3Ej23qRzZ3W9cmXOL1qIfEjiaCGEEMJ2TCaYO1eSutnh9cKF6V6gQMbxlfQdgRUqwEcfqWHLmVu8iRyV1eByEXBe07SPNE0rm4PrEUIIIcR9GDwYRo7UYUVBOiyqSbC15UL6J+dnEhIyzk209ru6SZEiEBSkAjAhRE5YhMTRQgghhM3cmtT9/HNbr+jxld56AeBCYiJ6etu2Pn3AyUkNXj51ykary1+ymtAtCcwCugL7NU3bpGnai5qmueXc0oQQQgiRFaNGabz0Esz8xMRPkxxwNhjYb+2bG2Gt1PWys7tpUNpNKlSQlgtC5ByJo4UQQggby9x+4dVXJan7oNp4ezOhRAk6+PhwJSWF0KQkdYevryoQSU6GN9+07SLziSwldHVdP63r+vu6rhcHmgPHgclAqKZpP2ia1jgnFymEEEKIO9O0/7d339FVVWkfx787HRKKIk0pFmyADbGOY++9jmVm7G3sotix917G3nvvY3utY3cEVFRsKKI0QZBeQ/b7x7lAgASDJvfcG76ftbJy7zk75/7CXYGHJ/vsDTfeCP/4B5x1VqDV88ty/bBhfDl5MqMyDd3lysqYXNMMXYBVVoHvv08KsGpGTJ/Okd98U/PMXkl1Yh0tSVJuKCmZt6l7221pJ8o/ZYWF9O7UiZM6dACg/8SJc0+uvXYyU/fJJ+H551NKuPhY5PW8YoxvxBj/CawE9AP+DrwWQvghhHBiCKGovkNKkqSFKyiAu+9OCtSfruwEL7Xj8VGjGJX5rflyTZrUPkN31VWhshIGDZrn8Kk//MCtI0bwzK+/NnR8abFgHS1JUrpmN3W32w6OPhoeeyztRPlpzYoKAtB/0qR5Txx6aPL5qKNg9nIMahCL3NANIWwSQrgH+AboDtwIbA08AZwH3FefASVJUt0UFcHDD8PWWwNXrswrTxcxauZMmhUW0mphSy6svnryuW/fea8XAjAMC3EAAFm4SURBVAATZ294IOlPsY6WJCl9JSVJzbzBBrDPPvD442knyj8VRUWs3LTpvDN0Abp3h1tvhaFDXdKtgdWpoRtC6BxCODuE8D3wBtAROBxoH2M8Nsb4eozxFOAAYJeGiytJkhamtBSefhparTmFD09ZhvdfKaJ1cTHlhYW1N3S7dYMWLZJdIk4+GYYNA6C8sBCg9qUaJP0u62hJknJPixbwyiuw4YZwwAHQr1/aifJPj4oK+k6cyOCpU+c9sf32Sdf8yCPB/0c0mLrO0P0BOAx4COgSY9wixvhwjHH6fOO+BP5XnwElSdKiadoUjrz7V1hhMv87sRNlny1JeWEhU6qq5u5EW11hIWyyCfz2G1x1FfTpA0B5QVIm1NoIllQX1tGSJOWgJk3gqaeS/bx23hl+/DHtRPll3ebNGTZjBst/9BGfVJ+p26ED3HwzvPNOMhVaDaKuDd0dgc4xxj4xxsG1DYoxfhtjdGMHSZJS1qd7R5a6ZiAsM5Vve3VhzGdNiMDU2n5Lfuut8NprsNFG8PXX85waO99maZIWiXW0JEk5qk0b+M9/YMoU2GorGDky7UT54/D27bl/lVUAeH/ChHlPHnhgchfgVVc5S7eB1LWhuxfQuaYTmdvI7qq/SJIk6c8qLShg884VcOVntGwzi7sPbAODKmqfbduuHWyxBayyypzN0WYvtTDKhq70Z1hHS5KUw1ZbDV58EYYPh222SW5a0+9rUljI39u2pXVxMXeNGMGXkyfz+exN0goKoHdv+OQTuOiidIM2UnVt6B4AtK7l3FKZ83USQtg2hPBNCGFQCOG0Wsb8LYQwMITwZQjhobpeW5IkzdW8qAhazeCsx8fRpDxC79UZMPB3fkPepQuMHg0TJ85p/v4yY0YW0kqNVr3V0ZIkqWFssAE880xyo9pOO8H0+RdGUo1CCPSoqKD/pEl0//hjVu/bl1Gz/++w//6w775wwQWuZ9EA6trQDUANi+4ByQ69o+t0kRAKSXbz3Q7oCuwbQug635gVgdOBv8QYuwEn1DGjJEmq5sLlluPIpZfm8LWW5PKnJ0KAfXYoYXCtN32TNHQBBg6c09D9Ydq0hg8rNV71UkdLkqSGtdVW8MAD8N57cPjhUNPWE1rQQ13naevRb/Z6uiHA5ZfDzJmw3HIwZEgK6RqvWhu6IYTjQwg/hBB+IClCn5n9vNrHcOAu4IU6vt66wKAY4w8xxhnAIyy4m+9hwI0xxt8AYoyjFvWbkiRJ0LakhJtXWokmhYVsu0YTuOIzpkyGLbdMbimr0SabJJukPfssUzJLLnw/dSpT3BhNqrMGqqMlSVID22svOO88uO8+OP54m7p1sWRx8TzPL//557kbMXfoAHvvnTx+9NEsJ2vcFjZDdyDwJPAUycyCNzPPq3/cDRwIHFXH11sG+Lna86GZY9WtBKwUQngvhPBhCGHbmi4UQjg8hNA3hNB39GgnNkiStDBLl5TQcuXprHnj9wz/pYrNt4yMHVvDwKWWgk03heefnzNDNwJfTZmSzbhSvmuIOlqSJGVBnz7QqxfccAPcckvaafLDAW3bAtCxtJS3xo3jnfHj55585JFkhu5rr7lBWj0qqu1EjPFV4FWAEMJE4I4Y47AsZVoR2BToALwdQlgtxjhuvny3AbcB9OzZ09+ZSJK0ECEE1m3WjP9bZhhcOJlBp67BnnvCK6/AfL9Uh402ggsuYIOXXmLmyivz7pJL8sXkyazdrFkq2aV8k2IdLUmS/qQQ4Ior4Msv4cQTYd11Ye21006V2+5cZRVuXmklfpkxg+U++oi+EyeyccuWcwesu24yQ/f66+GEE9KK2ajUaQ3dGON59VSEDgM6VnveIXOsuqHAczHGmTHGwcC3JA1eSZL0JzzVvTuf9+zJqhvOoPvZP/Pmm3DUUTXcSrbOOlBVxUVnnMGzRx5J+axZfDl5ciqZpXxXj3W0JEnKkoKCZNmFtm2TTdKGDk07UW4rDIEmhYUs26QJHUpL6T97Hd3Zzjsv+fz449kP10jVOkM3hPAYcHqM8fvM44WJMca96/B6HwMrhhCWI2nk7gPsN9+YZ4B9gbtDCEuRLMHwQx2uLUmSFqK8sJDuFRWsWVHB+5sP44wzOnHxxdC5M5x1VrWBG2wASy/NrJEjWXL0aA764gu+aN06tdxSvmmgOlqSJGVRmzbwwguw4Yaw447w7rtQUZF2qtzXo6KCfpMmzXtw5ZXhnHPgggtg2DBYZv7VV7WoFjZDtzUw+ybMNpnntX20qcuLxRgrgWOAV4CvgMdijF+GEM4PIeycGfYKMCaEMJBkvbHeMcYxi/RdSZKkWnUvL2fI9Ok0PXQI//xnsk7YnueNY4UPP0w+vv2WFZ58kmYvvcTU8nJ2f+cdvnCGrrQo6r2OliRJ2de9Ozz2GHz+Oey/v0vA1kWPZs34ZsoUJlVWznvin/9M/gDvuCOdYI3MwtbQ3aza403r6wVjjC8CL8537OxqjyPQK/MhSZLq2X5t2nDm4ME8MWY0fe/uzMSJ8NSFzWnepik7bjK3NAgtWjB1s81Yo18/xo4bx4Tp02leWppicik/NFQdLUmSsm/bbeGqq5L1dM89F84/P+1Eua1HRQUR+GzyZP7SosXcEyuskPxh3nYbnHFGDRt5aFHUaQ1dSZLUeCzbpAknd+zIV5MnQ0HkjjugZOkZTD61G71nduWBrsnH/auuypIbbcSSP/7IpO23Z/zsta8kSZKkxcjxx8PBBycrBjz6aNppctvsjZQXWEcX4MgjYfhweP31LKdqfOrU0A0h3BVCeKSWcw+HEG6v31iSJKkhdS8vZ3qMfD91Kq1aQctrv6CkaRW77gpjqi901LPnnIfF990HMfLq2LHcOnw4g6ZMWeC6n0ycyMjp0xv+G5DyhHW0JEn5LwS46SbYaCM46CD45JO0E+Wu9iUltC0upl9NDd1ttkkWIr777hp2ZtaiqOsM3a2AJ2s59ySwTf3EkSRJ2dC9vByALyZPJsbI2CUns8dNvzB8OOy1F8yYkRn4178STz6Z8/bfn3bDhvHlhx+y7YABHPntt5wwaNAC1+3Rrx9dP/44i9+JlPOsoyVJagRKS+GJJ6BVK9htN6ipXykIIdCjWTP6z78xGkBZGRxySLIw8S23ZD9cI1LXhm5rYGwt537DzRwkScorqzZtSiBp6E6YNYuZMbLmOpE774Q334R//SvzS/OSEsIVV7Bd794AfP/881QBLQoL+Xy+jdJi5rfsv82/AYK0eLOOliSpkWjbNlly4aef4NBD3SStNms3a8bAyZOZOmvWgievvhq22gpOOgn69ct+uEairg3dIcDGtZzbGBhaP3EkSVI2NC0sZPmyMr6YPJlRmem4bYqL+cc/4Oyz4a674LLL5o5fbdVV+bpjR5r+978A/K1NG36aPp0J1Zq306xopZpYR0uS1IhsuCFcemkyyfTCC9NOk5t6VFQwCxaYAAJAQQHcfz8stRTsuy/UNEa/q64N3XuAU0MIR4cQKgBCCBUhhKOAU4A7GiifJElqIN3Ly3lq9Gh6Zn4z3qakBEh27913Xzj99KRQBWhSWMhXq61Gt6+/pmlBATu0agXAl9UKsAk1/QZe0j1YR0uS1Kj07g3775/Uza+8knaa3NMjszHaAV9/zT0jRrDr55+zySefcMBXXzErxmSq8333waBBcMopKafNT3Vt6F4GPADcAIwPIUwAxgP/Bu7NnJckSXnkjM6dOWqZZTigXTtO79SJv7ZoASSbPtx1F/zlL/CPf8AHHyTjV9xwQ9qPHcu9LVqwerU1eGcb71ILUk2soyVJamRCgJtvhu7dYb/9YMiQtBPllk6lpRzevj2jZszgoG++4dkxY/hp+nTu++UXvpm9sfKmm8KJJya7zfXvn2refFSnhm6MsSrGeCiwKnAMSeF5NLBKjPHIGN2aTpKkfLNu8+Zcv+KKXL/iily8/PI0KSycc66sDP7zH+jYEf72N/j1V+i+cXLX+J4//kjnsjLKCwrmmaFrQ1dakHW0JEmNU9Om8OSTUFkJe+4J06alnSh3hBC4deWV+VubuVsF3LfKKgD0r76bXJ8+0KQJ3H57tiPmvbrO0AUgxvhNjPHmGONFMcZbYozfNlQwSZKUrpYt4fHHYdSoZObBrJ7rQYsWcPzxFIweTbfycq4bNozRmTV4XXJBqp11tCRJjc+KK8K990LfvnD88WmnyT1rV1QAyYbKGzRvTpOCAvpNmjR3QMuWsNde8OCDta6le/bgwZz3448NHzbPLFJDN4TQIYSweQhh+/k/GiqgJElKT48ecOON8OqrcPaFJcn0gyFDoG1bntt3X/Z6801uGj4cmHeG7iwnHUrzsI6WJKlx2nXXZO+J225Lli3TXLsstRQHt2vHTSutRFFBAWtUVMw7QxfgkENg4kR47rkar3HBkCGca0N3AUV1GRRCaAY8Bmw9+1Dmc/X/rRUiSZIanUMPhf/9Dy6+GNZ/5Hp22n132GEH2n73HQ9ddBG9114bll2WCdUaupNmzaJFUZ3KDKlRs46WJKnxu+AC+PhjOOYYWHfdZG1dQeuSEu7MLLUA0KOigvt/+YWqGCkImZLoL3+BVq3gpZeSnZlVJ3WdoXsJ0An4K0kRuhuwKXAnMBhYvyHCSZKk3HDDDbDOOvD3w5ryzQrbw7ffwoABzCoqYq0HH6QqRsZXW3LB9XSlOayjJUlq5AoL4YEHoHlz2HvvWlcPWOyt3awZE2fN4p9ffTX3YGEhbLstPP00fP11rV9b5R2A86hrQ3d74CLgo8zz4THGt2OMhwPPAr0bIpwkScoNpaXJpg+lpbD77jCp/Yqw2moMWX991n//fQr/+19OGDRozvjfbOhKs1lHS5K0GGjbNlkK9quv4Ljj0k6Tm/Zo3ZomBQW8+ttvzLMv7MUXQ4xwzTW1fu2vM2dmIWH+qGtDty3wc4xxFjAZWLLauReZewuZJElqpDp2hEcfTX5xfthhSc3VdpddWGnoUMbuvjudRo6cM3bQ1KkpJpVyinW0JEmLiS22gLPOStbSfeCBtNPknhZFRVy+/PKMnjmT76ZOZVJlZfKx9NJUbr89PPMMZDZcnt+IWo4vrura0P0ZWCrz+Dtgx2rn1gOm1WcoSZKUmzbfHC68EB55JNn8ocVOOwGwxG+/0evxx1m+sJAAfOF9ZtJs1tGSJC1Gzj4b/vpXOPLIZJUyzatns2YArPy//9Hs3XfnfOzYsyeMGgX33DNnbPVZvEOnT8921JxW191KXgW2BJ4GrgHuDSGsDUwHNgauaph4kiQp15x2Gvz4I1x2GWy4YRd2Xmkl+PZbDn3xRf71/POMa96ca3bbjV+vvZalysvTjiulzTpakqTFSFERPPQQrLkm/O1v8OGHUFaWdqrcsV7z5ty20kqMyyzRNnu32EsLCxm0xhp0ueAC2GcfaN6c6VVVc77u80mT2KFVqxQS56YQ67CocAihKdA0xvhr5vluwJ5AE5Ii9dYYY9VCLtGgevbsGfv27ZvWy0uStNiZNg023DBp7H7Sr4qlCn6mfNll5xnzxsUXs/npp6eST9kRQugXY+yZdo5clut1dF1Ya0uStOheeAF23BGOOgpuvDHtNLlvxwEDWOK997jv2GMJrVvDiy8ytnt3Wr33HgB7tW7NY926pZwye36vzq7Tkgsxximzi9DM86djjH+PMe4eY7w514tQSZJUv8rK4LHHoLIS9tmvgJKlO885Fz/5hCHt27PEG2+kmFDKDdbRkiQtnnbYAXr1gptuSpq7Wrh1mzfngVVXZZOrr2ZWZSUcfDBTqi2z0H/ixBTT5Z66rqELQAhh5RDCP0IIvUMIfw8hrNxQwSRJUm7r0gVuvz25jezMM0l2flh3XcJqq/HZBhuw0gcfJFN5JWWljg4hbBtC+CaEMCiEcNpCxu0RQoghBGdXS5LUgC6+GFZbDQ45BEaPTjtNbjuhQweu79KFd1ZfnXcvuQQ+/ZTi228HoHt5Od9Pm8a4mTNTTpk76tTQDSE0DyE8CnwJ3Af0Ae4HvgwhPBZCaN6AGSVJUo7ae+9kw4crroBnK/4OH30EhYX8uNNOlE+eTNx+e+jZE/r3TzuqlIps1dEhhELgRmA7oCuwbwihaw3jmgHHAx/Vx+tKkqTalZbCgw/Cb7/B4YdDHVY9XWw1LyriX0svTZOCAm7ZYAPGr78+bXv14pSHH+avLVoA8MmkSSmnzB11naF7E7A1sD9QHmNsDpQDBwBbZc5LkqTF0DXXwNprwz//Cd98kxwr2mILflliCcKbb0K/fnDCCalmlFKUrTp6XWBQjPGHGOMM4BFglxrGXQBcBjh9XpKkLFhttWSm7jPPwN13p50mtxUVFLBOs2Y8Mno0mx94IADHPvUUf2nWDID+NnTnqGtDdxegd4zxoRjjVIAY49QY44PAKdRcLEqSpMVAWRk89VQyA2H33WH6dOjerBkvr7NOMmCrreCdd2DAgHSDSunIVh29DPBztedDM8fmCCH0ADrGGF3JT5KkLDrxRNhsMzj+ePjhh7TT5LbHu3XjrTXXZPTqq3PoOefQ4ddf2eSSS+hYWuo6utXUtaE7CRhRy7nhwOT6iSNJkvJRp05w330wcGAyA6FbeTnHHXssl595Jv93/fVQUgK33JJ0e6XFS07U0SGEAuBq4KQ6jj88hNA3hNB3tIv+SZL0pxQUwL33QmFhclfbrFlpJ8pdbUpK2KRlS9Zt1ow7N9mEZzfckGVuvpktJ0/moVGj+HXGjLQj5oS6NnRvBE4OITSpfjCE0BQ4GZdckCRpsbfddkmBesEF8PHrxWzcuTPnbL01p06aBDvvDDffDAcdlHZMKduyVUcPAzpWe94hc2y2ZkB34K0Qwo/A+sBztW2MFmO8LcbYM8bYs3Xr1vUUUZKkxVfHjnDTTfD++3DZZWmnyX0bt2wJIfDIFlsQKiu5a6utKJ0xgzMHD047Wk4IsQ4rMocQrgD2BcqAV4FRQBuSdb+mkqzRNftCMcZ4aoOkrUXPnj1j3759s/mSkiSpBlOmwAYbwLBhyQoL10z9nhuGDmVyixYUrrVWMmj69GTGrvJeCKFfjLHGhqAS2aqjQwhFwLfAFiSN3I+B/WKMX9Yy/i3g5Bjj7xbR1tqSJNWfffeFJ56Ad9+F9dZLO03uijEyeNo0KmbOpM0OO8C773LmWWfx0i670L9n4y8/f6/OrusM3T2BmcBEkt/m75z5PBGozJzfq9qHJElaDDVtCo8+mjR2DzgAujYpZ3qMvNW5M8MffTQZdMMN6YaUsisrdXSMsRI4BngF+Ap4LMb4ZQjh/BDCzn/qO5AkSfXm5puhfXs44ghw9YDahRBYvkkT2jRvDm+/DautxvEPPcSwESO4bfhwpldVpR0xVXWaoZvrnDUgSVJuue22pEg98aJpXLPhhwAUVVby6skn89fvvqNwzJhkNzXlNWfoLh6stSVJql9PP51sJnz88XDttWmnyRPPPUfVHnswobSUE48+mu1OOom/tWmTdqoGU18zdCVJkurssMNgt93g3+eWcn1ckwdWXZVbu3Xjir33pnDKFDjzTOjfP+2YkiRJUtbtthsceyxcdx28/nraafLEzjtT0L8/FT16cNtVV/HzJ5+knShVizRDN4SwEbASyRpg84gxprYxmrMGJEnKPWPGwOqrQ/Pm0K9fshzD8m+8wQ9bbDF30G23Jd1f5SVn6NZdrtbRdWGtLUlS/ZsyBXr0gMmT4fPPoWXLtBPlidGjmbHMMjyy664MvOQSANqVlDCmspKjl16adqWlKQesH79XZxfV8SJtgdeBriSbNoTMqerd4JwuRCVJUna1agX33gtbbQW9e8ONN8IKrVrx3ppr8pdPP00GHX44HHoohLDQa0n5yjpakiTVpGlTuO8+2HBDOPFEuPvutBPlidat+Wb77dnn6afp1bEjN+2005yiaomiInp17JhqvGyp65ILVwHjgY4kReh6wLJAH+A7ktkGkiRJ89hyS+jVC266CV54Af7Zti3HXnABp1WflTtiRHoBpYZnHS1Jkmq07rpw2mlwzz3wn/+knSZ/rHbLLZS0bs2/r76aK3/7bc7x4dOnp5gqu+ra0N2EpBid/T+uEGP8KcZ4MfAAziqQJEm1uPjiZOmFgw+GbQvacdZ663HZfvsx6MUXkwGnnJJuQKlhWUdLkqRa9ekDq62WrEI2dmzaafJEu3bw9dfQvDm7XH89ZJaTHTFjxjzDfp42jcO/+YaXx4xJI2WDqmtDtyUwOsZYBUwAqm8j9z6wYT3nkiRJjURpKTz0EIwfD4ccAq2LigH4eeWVkwEPPgj//S8AMUZGLka/WddioSXW0ZIkqRalpckyZb/+Cscdl3aaPNK8OZx7Liu89Ranf/IJy5SULNDQfeCXX7h9xAjOHDw4pZANp64N3cFA+8zjL4G/Vzu3E+DvECRJUq26dYPLL09uJXvj/qYADC8vh59+gjZtYNNN4ZhjuHPECNp/8AEDJk1KN7BUf6yjJUnSQq21Fpx1VjLP4ckn006TR445BlZZhYsfeYQNWrRYoKHbP/N/iq+mTKGyqiqNhA2mrg3dF4CtM48vBPYIIQwNIQwGjgNuaIhwkiSp8Tj2WNhmG7jstGL4qSmjZsyAjh3nLhh2440M+vBDABu6akysoyVJ0u864wxYe2044ggYOTLtNHmiuBgOPBA+/piuo0fz9ZQp7Pr552zz2Wds89lnvJJZw2JqVRXfTJ2abtZ6VqeGbozx9BjjoZnHL5HcGnYv8DSwY4zxyoaLKEmSGoMQkt17y8ogXL8iv8yYmZxYZx34+WeoqODMQw5hyfHjmdbIfoOuxZd1tCRJqoviYrj/fpg0CQ4/fM6ysPo9e+wBwD/eew+AZ8eMYcDkyUyYNYvu5eVcucIKAPSfODG1iA2hrjN05xFj7BtjPDPG2CtTmEqSJP2u9u3hvPMCsd8SXHbTTH6dfVtUhw7wxhuUjx/P6Q89xBQbumqkrKMlSVJtVl0VLr0Unn8+mQihOujSBdZYgxX/8x82aN4cgDM7deKDHj14v0cPTujQgSYFBXOWX2gsam3ohhDahxCeDCFss5Ax22TGtKltjCRJUnVHHw1LbzgZ/t2Fez6s9pvyddbh41135Zinn2bq8OHpBZT+JOtoSZL0Rx13XLK9xPHHu/RCne2/P3z4Ic/usAP/+L//o2t5+ZxThSGwZkUFd40Ywd8HDkwxZP1a2Azdk4Hlgf9byJj/A5YDTqrPUJIkqfEqKID3Hy+DJrO45rhyZs2ae+6lv/+dspkzaf3OO+kFlP4862hJkvSHFBTAbbfBtGnQu3faafLEoYfCWmvReuRIbrvhBv4632zcUzt1okuTJjw0ahQjp09PKWT9WlhDd0fglhhrX7Ujc+5WYJf6DiZJkhqvzksX0v7kIQz/rIx/XTyFV8aO5Y3ffuOTFVZgfHk5HTKbo0l5yjpakiT9YSuumGyS9sAD8OCDaafJA82bQ//+cN55NJk0ieJOnZK13jJ/eLsstRTXdOkC0GiWXlhYQ7czUJe5yF8By9ZLGkmStNjYbLeZsOGv3H5hKdu++B1bfPYZz40bxys9e7LhSy/BqFFpR5T+KOtoSZL0p/TpAxttBMceC6NHp50mTxx7LKy9dvJ45Eg48ED49VcA1qyoAGCHzz9nUmVlSgHrz8IaulOB5nW4RkVmrCRJUp3duvJKPHtnCRVlBax589pUhEIAzjnoIComTybee2/KCaU/zDpakiT9KUVFcOutMHEinHpq2mnyxBJLwMcfwyefJDN2KyvhiScAaF5UxL5tkq0L+k6cuLCr5IWFNXT7AzvX4Rq7ZMZKkiTVWUVRETuv0pzrrw18+n4RS73UGYCvO3fm8+WWY/qLL6acUPrDrKMlSdKf1rUrnHwy3H03/Oc/aafJEyHAmmsmH6usAo88MufUtY1o2YWFNXRvAg4JIRxQ24AQwv7AQcC/6zuYJElaPBx4IGy9NQy9oQOMLGOVpk15ZqONKHvrLWjWDEaMSDuitKisoyVJUr0491xYbTU48kiYMCHtNHkkBNh3X3j7bRiYrITVpqSEDqWl9G/MM3RjjE8C1wF3hxA+DiFcEEI4LIRwaAjh/BDCR8DdwPUxxqezFViSJDUuISQ7+ZYUBNpc351Ll1uea/bck5llZTBpEhxxBAwenHZMqc6soyVJUn0pLYXbb4fhw+HMM9NOk2eOPDJZhuGkk6CqCoAeFRX0a+QzdIkxnkRyK9gE4GSSnXhvA3oDE4FdYownN3RISZLUuHXuDFdeHhj1QQVjnl+KsqWW4oiPPoKLL4bnn4du3WDAgLRjSnVmHS1JkurLeuvBMcfAjTfChx+mnSaPtGkDvXrByy8nm6XFyNrNmvHNlCkMmjKFYdOnMyFPN0gLMca6DQyhCGiVeTomxpgz33HPnj1j3759044hSZL+hKoq2Hxz+PRTWP3xL5myxFT69uyZVK2bbgpHHQVXX512TFUTQugXY+yZdo5cl8t1dF1Ya0uSlL6JE5M1dVu2hH79oKQk7UR5YsIEWHdd+OYb2HFH3j7tNDaZOXPO6SYFBfy8wQa0Ki5OMeSCfq/OXugM3epijJUxxl8yH3lVhEqSpNxXUAB33AHTpsEv13Zi4JQpzIoR1l8/maGbWftKyjfW0ZIk6c9q1gxuugm++AKuuCLtNHmkefNkxsgaa8B//sNft9+e5woLuW2llTi9UyemVlXRNw/X1K1zQ1eSJKmhdekCZ50F377YjKkftGTw1KnJia5d4csv0w0nSZIkpWinnWCvveCCC+Dbb9NOk0fKyuC//4WnniKUlbHTQQdxWIyc0rEjAO+MG8enEydSVcdVDHKBDV1JkpRTeveGZVeeBdeuSP9fpyQHu3WDoUNh3LhUs0mSJElpuv76pD/5r39BHvUf09eiBey2GzzzDIwcCZtuSsuZM1m2rIyLfvqJtfr14+FRo9JOWWc2dCVJUk4pLYWbb44wsgl7nzaFSZWV0KMHAE+8+GLK6SRJkqT0tGsHl14Kb7wBDzyQdpo8tMEG8OyzMGgQnHsuzQoL55z6YPz4FIMtGhu6kiQp52y7WRHL7vYbPNaBxz+aQlxnHQD2/Pvfk5m6kiRJ0mLq8MOTbSZ69YIxY9JOk4e22AIOPRSuuoo1q+3T0W/SpBRDLRobupIkKSc9d0MTqKjkguNLGVLahJFLLJGceOaZVHNJkiRJaSoogFtvhd9+g1NPTTtNnrrqKmjXjpOuvnrO2hUDJ09OOVTd2dCVJEk5qfvSpTQ5ejCD+5Wy/5XjWOW++/i1eXN47720o0mSJEmpWn11OOEEuPNO+OCDtNPkoebN4bzzWGPAAM66/34AJsyalTcbo9nQlSRJOSmEwNp7TIG1fuOdK1oxfsYSvL7eevDII/Daa2nHkyRJklJ1zjmw9NJw1FFQWZl2mjx0yCG8v/HGHPPMMxTOmgXAlMznXGdDV5Ik5axX1lgdTvgWphdSdstKnH7ssVBeDvfdl3Y0SZIkKVXNmsE118Cnn8Itt6SdJg+FwKu77ELb337j9V69eO+YY5iYJ51xG7qSJClnNS0shE5TYb+fmPZqawZ/3Ym43XbJDN2+fdOOJ0mSJKVqr71gyy3hrLPgl1/STpN/Pt5sMz5bfnk2GTCADb/8kiXXWgtmzEg71u+yoStJknLffj+x1PIz4dqVGLfXP5JqdZ114OijYebMtNNJkiRJqQgB/v1vmDIFevdOO03+KSgq4to995zzvPTrr2HgwBQT1Y0NXUmSlNO+WGcdnunRlf0vHQ/Dm3BO/x1g1Cjo1QtuugnuuSftiJIkSVJqVl45aebefz+8/XbaafJLcQg8tMUW3LL33kxo2jQ5+M036YaqAxu6kiQpp3UrL2eXpZZivU2qYOuR3Hx1IQN/aQVXXgmtW7utryRJkhZ7Z5wBnTp5A9uiKgqBGSUlXNerF+2efDI5uM8+cPnl6Qb7HTZ0JUlSXmheWAj/+p6iplUcdxxEAvToAf37px1NkiRJSlV5OVx3HXzxBdxwQ9pp8kdxCAAsUVTE1LIyJnfokJw4/XT44YcUky2cDV1JkpQXVmraFFrOhAMH8/rr8OyzQM+eSdU6bFja8SRJkqRU7bILbL89nHOO5XFdFRckrdEliooAeOHhh+Gdd6CoCK66Ks1oC2VDV5Ik5YXlmzThqhVWYNpOw1ilaxW9esH0fx6a7ARxzTVpx5MkSZJSFUIyO3fmTDjttLTT5IeizAzdJYuLARjWuTNstBH87W/w0ENpRlsoG7qSJClvdC8vh8LIwRdNZvBguOrJZWHrrZPfnv/732nHkyRJklK1/PLJ3sEPPAAffZR2mtxXfckFgEmzZiUnVl0Vxo2DqVNTSrZwNnQlSVLe6F5eDsApLfuxy66Riy+GcetunZw89tgUk0mSJEm54fTToV07OPFEiDHtNLlt9gzdsoICmhYUML6yMjnRpk3yefTolJItnA1dSZKUN5YuLWWrJZYAoPfFM6ishJO/PhSWWy4ZMH58iukkSZKk9DVrBhddBB98AI8+mnaa3DZ7hm5RCCxRVMRv8zd0r746pWQLl/WGbghh2xDCNyGEQSGEWlf0CCHsEUKIIYSe2cwnSZJy29HLLANAkw4zOOkkuPORcr46OrPcwuefp5hMkiRJyg0HHABrrgmnnpqzqwbkhNkzdItDoGVNDd3rrks2Yc4xWW3ohhAKgRuB7YCuwL4hhK41jGsGHA+42ockSZpH68yGBaNmzOD002HppaHXfWsmJ//AQmGTKiu5YehQqrwfTZIkSY1EYWGyb/BPP+XsJNOcUFB9hm5xMePmb+gCvPNOCskWLtszdNcFBsUYf4gxzgAeAXapYdwFwGXAtGyGkyRJua/N7IbuzJlUVMDll8PLA5ZmXPtV4eWXF/l6Zw0ezHGDBvHsr7/Wd1RJkiQpNZtuCrvtBpdcAiNGpJ0mN8XMpI7C2pZcADjvPPi//0shXe2y3dBdBvi52vOhmWNzhBB6AB1jjC9kM5gkScoPbUpKgGSGLsB++8EGG8D943eG116D++9fpOtNqaoCYGTmepIkSVJjcfnlMGMGnHVW2kly3xJFRfw2c2bypLwcDjkErroKKirg5JPTDTefnNoULYRQAFwNnFSHsYeHEPqGEPqOztEd5yRJUv1rVlhIcQh8m1kMLAS45rrIqVPOZuQSKzPr5psX6XotiooAmDBrVr1nlSRJktLUpQscdxzcfTf07592mtwVYN41dEOAO+6AXr1gl11g0CDITATJBdlu6A4DOlZ73iFzbLZmQHfgrRDCj8D6wHM1bYwWY7wtxtgzxtizdevWDRhZkiTlkhACrYuLuX3ECAZOngzADeVfMXW78dwybm8KP/iAcc8+W+frtSgsBGD87OJNkiRJakTOOgtatUp6k24bUbslioqYMGsWs+b/Q+rSJdlZLofWrch2Q/djYMUQwnIhhBJgH+C52SdjjONjjEvFGJeNMS4LfAjsHGPsm+WckiQph12+wgoAfDRhAgDvjB/P2ieO5s6ywxlS0oGyE0+sc7VaUpCUQzZ0JUmS1Bi1bAnnnw///S8swryHxULIbIoG0CqzV8dpP/ww76DM/z34/vtsxfpdWW3oxhgrgWOAV4CvgMdijF+GEM4PIeyczSySJCl/7dOmDWUFBXwxeTITKiv5afp09lipBbuf0YrLZpxO2eDBcOyxdbrW9MytUy65IEmSpMbqsMNglVXg1FNh9jKxmtffMhuhvTp27LwnunRJPn/3XZYT1S7ra+jGGF+MMa4UY1whxnhR5tjZMcbnahi7qbNzJUnS/ApDoGvTpjwxejT7DRwIQPfyco45Fp5qs10y6MYbYeLE373W7IbuWCtbSZIkNVJFRXDFFfDtt3DrrWmnyU1tS0o4o1MnPps8mcnVJ3t07gw//ggHHphWtAXk1KZokiRJdfX3tm2ZUlXFC5nfoHcvL6dDRTG/HDGFrXklGXTVVb97nemZpRkGT5vWYFklSZKktO2wA2y2GZx7Lowfn3aa3BAz/xeYvVhbz2bNANjys8/mDiosTJq6mb03coENXUmSlJd6dezIg6uuOud557IymhQWUrHlWH7svkZy8Lzz4IsvFnqd2TN0v5s6dc5jSZIkqbEJAa68EsaOhUsuSTtNbtqxVStWbNKEzyZNWnBztBxiQ1eSJOWt7uXlcx4XZDY0aFtSTOuTJ7EHTwBQ+eST/DJjBl9MmsTgqVMXuMa0TBO3Mka+nTIlC6klSZKkdPToAf/8J1x7LQwZknaa9M3eFG321mjFBQX06dyZqVVVvJfD05ht6EqSpLzVvqQEgJWbNJlzrENpKe93/pmXNtmUNws2Yex1N7DGG2+wWt++LP/RR3w8YcI816g+K/crG7qSJElq5C68MJmte+aZaSfJTes2bw7AFp99NmdJhlxjQ1eSJOWtEAJfr7su7/foMefYXauswiHt2jH1kEGcwUW0+W0Mu73yCkctvTQAH8+3Udr0qipaFRUBMHLGjOyFlyRJklLQsSP06gUPPggff5x2mtyzctOm/KNtWypj5Mcc3WfDhq4kScprKzdtypLFxXOeL9+kCfu0aQMdp/Lhzp34kq4c/fhzHF9RQYvCQr6YPHmer59WVUX70lIKgFE2dCVJkrQYOPVUaN0aTj4ZcnQSaqqOX2YZAO4ZOTLlJDWzoStJkhqdbrPX1j3gJ84vPZPuw36gy0030b28nJuHD+eLSZPmjJ0eI00KCmhdXMyomTNTSixJkiRlT/Pmyf7Bb78Nzz2Xdpr0rNesGQA9Mp9nm71Xx/lDhvBLDk76sKErSZIanXYlJezbpg3rd2rCh4dtwvtswKSnXuOg9u0BeHjUqDljp1dVUVZQQJuSEmfoKq+FELYNIXwTQhgUQjithvO9QggDQwgDQgivhxA6p5FTkiTlhkMPhZVWgjPOgMrKtNOkY9fWrfl5/fXZZskl5zleVljIvausAkC/+ZZsywU2dCVJUqMTQuChrl35oEcPvr58GT5uvgXlX33MQWVN6dq06TzLLkyvqqK0oIA2ztBVHgshFAI3AtsBXYF9Qwhd5xv2CdAzxrg68ARweXZTSpKkXFJcDBdfDAMHwn33pZ0mPR3Kymo8vttSSwFw/y+/cP/IkQyeOjWbsRbKhq4kSWrUmjSBVY/anEKqePuid+hWXs5zY8YwKTMNYVpVFaUh0KakhA8mTGDarFkpJ5b+kHWBQTHGH2KMM4BHgF2qD4gxvhljnJJ5+iHQIcsZJUlSjtl9d1h3XTj7bMihfmVOaFZURI+KCh4ZNYr9v/6a9ydMSDvSHDZ0JUlSo7dlnw2YFsoYfPtrrFGarI910vffA3OXXOic+c38RT/9lFpO6U9YBvi52vOhmWO1OQR4qbaTIYTDQwh9Qwh9R48eXU8RJUlSrgkBLr8chg2DG25IO03ueWettRi03np8t+667NyqVdpx5rChK0mSGr2CpmWMX29rtpnwGM1eaEe7khL6ZzZGmx4jpQUFnNW5MyUh5OQaWVJ9CiH8A+gJXFHbmBjjbTHGnjHGnq1bt85eOEmSlHWbbALbbAOXXQY5NAk1JzQtLGSFJk3o0rQpzYqK0o4zhw1dSZK0WGhzykEszQi+POdp9qhoy5eTJ1MVY7LkQkEB5YWF7NW69Tzr60p5ZBjQsdrzDplj8wghbAmcCewcY5yepWySJCnHXXghjB0L11yTdhLVhQ1dSZK0WAg778Tk5bpx5vjeTL1jFlOrqlivf39GzphBaUFSEnUvL+fn6dMZv7hu86t89jGwYghhuRBCCbAP8Fz1ASGEtYBbSZq5o1LIKEmSclTPnrDbbnDVVTBmTNpp9Hts6EqSpMVDYSHlT9zHMmE4q9x2H9sWt2aJoiI2b9mSPTO3lHcvLwfgS2fpKs/EGCuBY4BXgK+Ax2KMX4YQzg8h7JwZdgVQATweQvg0hPBcLZeTJEmLofPPh0mT4IpaF2VSrsidxR8kSZIaWo8eTFttHf4y4A1+e+FCLr543tPVG7obtmiRQkDpj4sxvgi8ON+xs6s93jLroSRJUt7o3h323Reuvx5OOAHatUs7kWrjDF1JkrRYKd95S9YL/+Pbq55n5Mh5z3UqK6O8oIAXx47lhTFjmDxrFmNmzuTHqVPTCStJkiRl0XnnwYwZLDDxQbnFhq4kSVq8HH88sU07npixM//556PznCoIgZ7NmvHMr7+y4+efc+XPP7PGxx+z3EcfpRRWkiRJyp4uXeCgg+DWW+Gnn9JOo9rY0JUkSYuXpZaiaOAAhrZagz1fO4Lv3xgyz+lnV1uNfmuvzQplZXw6aRLDZswAYIIbpUmSJGkx0KdP8vmCC9LNodrZ0JUkSYufJZek/M7racl4lttiOXjrrTmnWhQV0aNZM9Zq1ox+EyfOOe5GaZIkSVocdOoERxwBd98N332XdhrVxIauJElaLC2xy8bcvd+rDKEzU/7VC2Kc53z38nJ+nj59zvOXxo5l7b596f3999mOKkmSJGXVGWdASUmypq5yjw1dSZK02Nrtxi25ocmpNP36ExgwYJ5z+7Vpwz/atuWAtm0BuGvECPpPmsSVP/9MnK/5K0mSJDUm7drBscfCQw/BF1+knUbzs6ErSZIWWy1bwsr/2hyAQY/1m+fcik2bcv+qq3LPqquyTrNmc9bSBRhR7bEkSZLUGJ1yCjRrBmefnXYSzc+GriRJWqz949wuVFJIl4sPIY6fUOOY7uXl8zz/Yr71dGOMPDN6NHeNGMELY8Y0WFZJkiQpW1q1gl694OmnoV+/3x+v7LGhK0mSFmvlzQoYseImAAw6+ZYax8zf0P12ypR5nn8yaRK7ffklh3zzDTt+/jmDp05tmLCSJElSFp14Iiy5JJx1VtpJVJ0NXUmStNhr886TjCpoy4p3nEr8dsGtfGc3dFdq0oQCYNTMmfOcHzBpEgA3dOmSPJ9vBq8kSZKUj5o3h1NPhZdfhnffTTuNZrOhK0mSFnulbVvy4269ABi34z+SDdKefXbO+W6Zhm77khKWKi5m1Hxr6H4xeTKlIfCPzAZq8y/JIEmSJOWrY46Btm3hzDPBvYFzgw1dSZIkoMcjp3BB6+tY4rv/wRprwK67wsMPA7B0SQkti4poU1JCm5ISRs2cyYTKSrr973+0ee89/j1sGKuWl9OyuJhly8q4cMgQ9v7yy3S/IUmSJKkeNG2aNHPffhteey3tNAIbupIkSQAUFUGXa49lXx5iast2ycHzzoOqKkII3LHyyvTu2JHWmRm6/SZOZOCUKfy1RQsOat+ei5dbDoCrVliBNcrLeerXX5leVZXidyRJkiTVj8MPh06dnKWbK2zoSpIkZfxt78CArvvSc+kRVN33AHzzDay3HkycyB6tW7NO8+a0KS5m1MyZc5ZV+PeKK3LzSiuxXatWAOzeujXHd+hAZYwLbJ4mSZIk5aPSUjj7bPj4Y3j++bTTyIauJElSRmFhUqgOHAhPFPwNWrWCvn3hrrvmjGlTUsKoGTP4YvJkliwqol1JyQLXmb2J2tVDh/K16+lKkiSpEdh/f+jSBfr0AW9ES5cNXUmSpGr22gu6doVzLypm1qefwzLLwL33wqxZQLKe7vhZs3h/wgS6l5cTQljgGis3bUq7khLuGTmSPj/+mOXvQJIkSap/xcXJimQDBsATT6SdZvFmQ1eSJKmagoKkUP3qK3j4rfZw8cXwySfwwAMAdM3Mvv1i8uQ5M3HnV1JQwE/rr89fW7Rg5IwZWcsuSZIkNaS994ZVVoGLLnIt3TTZ0JUkSZrP7rvDGmskjd2Z+/wTll4a/u//AOhWrYlbW0MXoLiggPaZ5RkkSZKkxqCwEE47LZml+8ILaadZfNnQlSRJmk9BAZx/PgwaBPfeF2DDDeHhh+Gww1huzJg54xbW0IXMerszZzZ0XEmSJClr9tsPOnd2lm6abOhKkiTVYKedYJ114MILoXLzrZNq9Y47KDjsMJ7o1o0rll+eDVu0WOg12hQXM66ykhnuGiFJkqRGorgYTjkFPvwQ3nor7TSLJxu6kiRJNQghWXJhyBC4KxySTNlde214+WX2mDKFk1u3prCGDdGqa1NSAsCvztKVJElSI3LwwdC2bbLdhLLPhq4kSVIttt0W1lsPLrqkgBmn9pm7ne+yy0KLFtCv30K/vnVxMYDr6EqSJKlRKSuDk06C116D//0v7TSLHxu6kiRJtQgBzj0XfvoJ7r6bpJE728yZsPPO8NJLtX79MqWlAPw0fXqD5pQkSZKy7cgjYYklnKWbBhu6kiRJC7HNNplZuhfBjBnAHnskJ849F4YPh+23h6+/rvFrV23aFIAvJ0/OTlhJkiQpS5o1g+OOg2efhc8/TzvN4sWGriRJ0kLMXkv355/hrruAe+6BAQPg7LMzB4Abb6zxa5sXFdG5tJRPJk1ifGUl4ysrmTxrVtayS5IkSQ3puOOgvBwuvTTtJIsXG7qSJEm/Y+utYf31k9vJphdXwGqrJZ3egw6C3XaDp5+GGGv82tUqKnh89GhavvsuLd99l4p33uH5X3/N8ncgSZIk1b8ll4R//QseeQQGDUo7zeLDhq4kSdLvWGCWbnW77grDhtW6QdoVyy/PVSuswNWZj5IQeGf8+AbPLEmSJGVDr15QXAyXX552ksWHDV1JkqQ62Gor2GCDzCzd6nuc7bBD8nmddWCXXRaYqbtKeTm9OnbkxMzHqk2b8oVr6kqSJKmRaN8eDj44WZls6NC00ywebOhKkiTVwexZukOHwp13VjvRqtXcjdKeew4++CB5XMtaud3Ky90kTZIkSY3KKadAVRVcdVXaSRYPNnQlSZLqaMstYcMNa5il++ijMGECNG8OZ50FN90ERUVwxx0LXGO5sjJ+nj6dWMuau5IkSVK+WXZZ2GefpPx1dbGGZ0NXkiSpjkKAc85Jlsy9//5qJwoLoVkzOP98ePNNOPro5PjNNy9wjdKCAiIwy4auJEmSGpFevWDSpBrnNKie2dCVJElaBFttBT16JJs+LLCqwuylFwB69oT+/eGrr+YZUlqQlF8zbOhKkiSpEenRAzbdFK67DmbOTDtN42ZDV5IkaRGEAGecAd99B08+Od/JDh2Sz6Wlc2fn9u49z5CSEACYXlXVwEklSZKk7OrVC37+GR57LO0kjZsNXUmSpEW0226w8spwySWwwETbH39MPnr2hH33hQ8/hKuvhuHDAShxhq4kSZIaqR12gNVWS1Yiq6xMO03jZUNXkiRpERUUwKmnwqefwssvz3eyc2do1y553LUrjBkDJ50E554LzJ2hO8MZupIkSWpkCgqSPSe+/TbZN1gNw4auJEnSH/D3v0PHjsks3VqtsMLcx48/DjNnOkNXkiRJjdpuu8Eqq8ANN6SdpPGyoStJkvQHlJQky+O+8w7897+1DNptN7jySrj7bhg3Dj75ZO6maM7QlSRJUiNUUABHHQUffbSQOll/ig1dSZKkP+jQQ5PVFS64oJYBZWXJcgvbbJM8f+cdN0WTJElSo3fIIUmdfPbZNew5oT/Nhq4kSdIf1KRJMkv39dfh/fcXMrB9e+jWDe64g5Y//kiPb75xhq4kSZIaraZN4Ywz4O234Y030k7T+NjQlSRJ+hOOOAKWWgouuuh3Bl52GXz9NZutuy79jjySdgtdfFeSJEnKb4cdBh06JJukOUu3ftnQlSRJ+hPKy+GEE+DFF+GTTxYycIcd4PLLGX7wwfxvlVVod9ddMHNmtmJKkiRJWVVWBqedBu+9l+w7ofpjQ1eSJOlPOvpoaN4cLr74dwb27s1PV1/NJfvtR8no0Va2kiRJatQOPhjatKlDnaxFYkNXkiTpT2rZEo45Bp58Er76auFjSwoKeG3ttakqLoZrr4Vx48D1dCVJktQINWmS7BH8yivw7rtpp2k8bOhKkiTVgxNOSArWSy9d+LiSEJjUtCm/bLIJPP88LLEEHHdcVjJKkiRJ2XbMMckewaef7lq69cWGriRJUj1o3RoOPxwefBAGD659XGlBUn799/bbYcMNk4M33gijR2chpSRJkpRdTZtCnz7JDN2XXko7TeNgQ1eSJKmenHwyFBTA5ZfXPqYk09CdWloKb70Fb7yRnLjxxoYPKEmSJKXgkENg+eXhjDNcbaw+2NCVJEmqJ8ssAwceCHfdBcOH1zymJAQApldVQXExbLYZbLwxvPhi9oJKkiRJWVRSAuedB599Bo89lnaa/GdDV5IkqR6ddhrMmgWXXFLz+dkzdGdUX0BsjTWS3dRcVEySJEmN1L77wmqrwVlnwYwZaafJbzZ0JUmS6tHyy8PBB8Ott8KQIQueL83M0J1R/V6zbt1g0iT4+ecspZQkSZKyq7AQLrsMvv8ebrkl7TT5zYauJElSPevTB0KACy5Y8NzsGbq9f/iBiZWVycFu3ZLP110H48ZlJ6QkSZKUZdtuC1tuCeefDxMmpJ0mf9nQlSRJqmcdO8KRR8I998B33817rjgzQxfghTFjkgcbbADbbw9XXw1rrw1TpmQvrCRJkpQlIcCll8KYMUnpqz8m6w3dEMK2IYRvQgiDQgin1XC+VwhhYAhhQAjh9RBC52xnlCRJ+rNOPx1KS5PNH6oL1Rq6I2YvHlZYCM8/n9x79sMP8P77WUwqSZIkZc/aa8Mee8BVV8Gvv6adJj9ltaEbQigEbgS2A7oC+4YQus437BOgZ4xxdeAJ4PJsZpQkSaoP7drB0UfDww/Dt9/WPObFsWPpN3Fi8qSgAPbbL/l80UUwc2b2wkqSJElZdMEFyU1ptW0krIXL9gzddYFBMcYfYowzgEeAXaoPiDG+GWOcfZ/hh0CHLGeUJEmqFyedlMzSveiims+/9ttvbPbpp8yKMTnQrFmy/MJbbyUzdiVJkqRGaNVV4Z//hJtuguHD006Tf7Ld0F0GqL5989DMsdocArxU04kQwuEhhL4hhL6jR4+ux4iSJEn1o21b+Ne/4MEHYdCgucen/PWvTNxoIy5ZbjkmzprF4KlT5558/fXk8x57wGGHwaxZ2Q2tvFWHpc1KQwiPZs5/FEJYNoWYkiRJAJx9NlRWwsUXp50k/+TspmghhH8APYErajofY7wtxtgzxtizdevW2Q0nSZJUR717Q3HxvIVqk8JCKoqK2HyJJQD4svomaKWl0L598viOO+CDD7KYVvmqjkubHQL8FmPsAlwDXJbdlJIkSXMtvzwcfDDcdhsMGZJ2mvyS7YbuMKBjtecdMsfmEULYEjgT2DnGOD1L2SRJkupdu3ZwxBFw333JfmfVdW3aFIBdv/iCSZWVc088/jhcdx2UlCSPpd/3u0ubZZ7fm3n8BLBFqL5LnyRJUpaddRaEABdemHaS/JLthu7HwIohhOVCCCXAPsBz1QeEENYCbiVp5o7Kcj5JkqR6d8opUFS04KYPFUVF7L7UUgD0nzRp7om//AWOOw722iuZpTvKkki/qy5Lm80ZE2OsBMYDrWq6mMubSZKkbOjYEY48Eu6+e94lyrRwWW3oZgrHY4BXgK+Ax2KMX4YQzg8h7JwZdgVQATweQvg0hPBcLZeTJEnKC0svnSyHe8898OOP8567rksXAL6YPHnBLzz22GT733feafCMUnUubyZJkrLl9NOTG9POOy/tJPkj62voxhhfjDGuFGNcIcZ4UebY2THG5zKPt4wxto0xrpn52HnhV5QkScp9p54KBQULztJdprSUFoWFPDl6NDcPG8bNw4bx3ew1dVdfPfmiAQOorKrixTFjeHnMGKa5UZoWVJelzeaMCSEUAS2AMVlJJ0mSVIt27ZJ5DA8+CAMHpp0mP+TspmiSJEmNSYcOcMghye1kP/0093gIgY1atOCNceM46rvvOOq77zhh9v1mTZrASivBRRdxw4AB7PD552z3+eecMv9ivFIdljbLPD8g83hP4I0YY8xiRkmSpBqdcgpUVMC556adJD/Y0JUkScqS005LPl966bzHn+nenZEbbsjIDTdkr9atGVB9+YVNN4VZs+hwxx1zDvWfOLHhwyqv1HFpszuBViGEQUAv4LR00kqSJM2rVSs44YRkP+BPP007Te6zoStJkpQlnTrBQQfBnXfC0KFzjxcVFNC2pIS2JSWs3awZQ6dPZ3xlZXLypptg1VVZ+X//mzO+0kmVqkEdljabFmPcK8bYJca4bozRqd6SJCln9OoFLVvC2WennST32dCVJEnKotNPh6oquOyyms93Ly8HoPMHH9D8nXdo/u67XL/66qz86ac0nzQJgP9NnEjzd96hl1sBS5IkqZFo2RJ694bnn4ePPko7TW6zoStJkpRFyy4LBxwAt98Ow4cveH6Lli3p07kz42fNYuKsWSxbVkbVnntSOnMmT3/1Ff9ecUVO7NCBzmVlPP3rr1nPL0mSJDWU446DpZaCPn3STpLbbOhKkiRl2RlnQGUlXH75gufKCgs5f7nlKAoBgM1atuSEPfaAbt3Y/P77OXrppbmqSxf2adOGH6dNY9LspRkkSZKkPFdRAaeeCq++Ch9+mHaa3GVDV5IkKcuWXx7++U+49VYYMaLmMeUFSZlWXlgIIcDJJ8Pnn8+5/6xb06YAnDF4MBcNGcJFQ4ZwQ/WFeSVJkqQ8dOSRySZpF1yQdpLcZUNXkiQpBWeeCTNmwJVX1ny+uHpDF2D77ZPPb78NwHrNm9OssJAbhg3jrMGDOWvwYK74+eeGji1JkiQ1qIoKOPFEePFF6N8/7TS5yYauJElSCrp0gb//HW6+GX75pfZxs2fq0qYNrLwyvPkmAO1LS/lto42YsfHGcz6+X2+9LCSXJEmSGtYxxySbpJ19dtpJcpMNXUmSpJSceSZMnw5XXbXguZD5PGeGLsBee8HLLydLLwCFIVBcUDDPhyRJkpTvWrSA00+HF16A555LO03useqXJElKycorwz77wI03wujRNY+Zp6F74onQvLlTFSRJktTonXACrLEGHH44jBmTdprcYkNXkiQpRWedBVOnwtVX13x+nobukktC797wzDPw+utZySdJkiSloaQE7r03aeaeemraaXKLDV1JkqQUrboq/O1v8O9/1zzzoOn8yyicfDK0b59M65UkSZIasTXWSG5Su/NOeO+9tNPkDhu6kiRJKevTByZPhmuuWfDcPDN0AcrKYOed4dVXkwV4JUmSpEbs7LOhY0c48kiYMSPtNLnBhq4kSVLKunWDPfeE66+HsWPnPbdAQxdg771h0iS48srsBJQkSZJSUlGR3Jz2xRdwwQVpp8kNNnQlSZJyQJ8+MHEiXHvtvMfL519yAWCzzZJ1Gs45B+66Kyv5JEmSpLTstBMccABccgn8739pp0mfDV1JkqQcsNpqsPvucN11MG7c3ONNa5qhC8l03uWXh0MPhe+/z0pGSZIkKS3XXptsJXHAAcmmwoszG7qSJEk5ok8fmDAhaer2qKgAoElNM3QB2raF//4XCgvhjjuymFKSJEnKvpYtk5vTvv4azjgj7TTpsqErSZKUI9ZcE3bZJZl9cHuHbry15posUVxc+xe0bw8bbJBskCZJkiQ1clttBUcfndTLb76Zdpr02NCVJEnKIX36JEsu3HtzEZu0bPn7X7DFFtC/P4wa1dDRJEmSpNRdfjmstFKy9ML48WmnSYcNXUmSpByy9tqw445w9dXJJmm/a6+9IEbo3t2mriRJkhq9pk3hvvtg+HA47ri006TDhq4kSVKOOfts+O03+Pe/6zC4a9dkN7XRo+HGGxs8myRJkpS29dZL1tG97z546qm002SfDV1JkqQcs846sN12cNVVMGlSHb7gySdh663hnnugqqqh40mSJEmp69MHevSAI46AkSPTTpNdNnQlSZJy0Nlnw5gxizDptk+fZHCMDZpLkiRJygXFxXD//ckyZYcfvniVwTZ0JUmSctD668O228JllyWbpP2ujTZKFt8tLGzoaJIkSVJO6NoVLr0Unn8e7ror7TTZY0NXkiQpR118cbKW7hVXpJ1EkiRJyk3HHQebbQbHHw9ffpl2muywoStJkpSj1loL9tkHrr0WRoxIO40kSZKUewoKkqUXmjWDXXdNJkQ0djZ0JUmSctgFF8CMGXDhhWknkSRJknLTMssk+wQPGQL77guzZqWdqGHZ0JUkScphXbrAYYfBbbfBoEFpp5EkSZJy04YbJnsEv/IKnHlm2mkalg1dSZKkHNenD5SUwNlnp51EkiRJyl2HHQZHHplsLPzoo2mnaTg2dCVJknJc+/Zwwgnw8MPwySdpp5EkSZJy13XXwUYbwUEHwWefpZ2mYdjQlSRJygO9e8MSS8AZZ6SdRJIkScpdJSXwxBOw5JKwyy4walTaieqfDV1JkqQ80LJl0sx9+WV4662000iSJEm5q21beOaZpJm7004wZUraieqXDV1JkqQ8cfTR0KEDnHYaxJh2GkmSJCl39eyZLFnWty/suSfMnJl2ovpjQ1eSJClPNGkC554LH30Ezz6bdhpJkiQpt+2yC9xyC7z0Euy4I4wfn3ai+mFDV5IkKY8ccACsvHKy/MKsWWmnkSRJknLbYYfBnXfCG2/AX/4CP/6YdqI/z4auJElSHikqggsvhK++gkcfTTuNJEmSlPsOPhj+7/9g2DBYbz348MO0E/05NnQlSZLyzO67Q/fucNFFUFWVdhpJkiQp9222GXzwAVRUJI+feirtRH+cDV1JkqQ8U1AAZ54JAwfCk0+mnUaSJEnKD6uskuxHseaasNdecNttaSf6Y2zoSpIk5aG99oJVV002SXMtXUmSJKlulloKXnsNtt0WjjgCzjsPKivTTrVobOhKkiTlocLCpJk7cKBr6UqSJEmLorwcnnkG9t8/qam7doXPPks7Vd3Z0JUkScpTe+4Jq6+eFKH5NqtAkiRJSlNxMdx9NzzxBEydCn/5C1x9dX7U1TZ0JUmS8lRBAZx/Pnz3HTzwQNppJEmSpPxSUAB77AHvvw+bbgonnQTt2sHJJ8NPP6WdrnY2dCVJkvLYzjvDOutAnz4weXLaaSRJkqT807EjPP88/Oc/8Ne/wnXXJftVXH45zJyZdroF2dCVJEnKYyEkt4YNHQo335x2GkmSJCk/hQA77ABPP53cAbfVVnDqqdC9Ozz5JMSYdsK5bOhKkiTluY02gs03h2uugRkz0k4jSZIk5bdll002TfvPf6CoKNm74pZb0k41V1HaASRJkvTnXXZZsplDcXHaSSRJkqTGYYcdYNttk/0qdt897TRz2dCVJElqBHr2TDuBJEmS1PgUFsIBB6SdYl4uuSBJkiRJkiRJecKGriRJkiRJkiTlCRu6kiRJkiRJkpQnbOhKkiRJkiRJUp6woStJkiRJkiRJecKGriRJkiRJkiTlCRu6kiRJkiRJkpQnbOhKkiRJkiRJUp6woStJkiRJkiRJecKGriRJkpTHQghLhhBeDSF8l/m8RA1j1gwhfBBC+DKEMCCEsHcaWSVJkvTn2dCVJEmS8ttpwOsxxhWB1zPP5zcF2D/G2A3YFrg2hNAyexElSZJUX2zoSpIkSfltF+DezON7gV3nHxBj/DbG+F3m8XBgFNA6WwElSZJUf2zoSpIkSfmtbYxxRObxSKDtwgaHENYFSoDvGzqYJEmS6l9R2gEkSZIkLVwI4TWgXQ2nzqz+JMYYQwhxIddpD9wPHBBjrFrIuMOBwwE6der0hzJLkiSpYdjQlSRJknJcjHHL2s6FEH4JIbSPMY7INGxH1TKuOfACcGaM8cPfeb3bgNsAevbsWWuDWJIkSdnnkguSJElSfnsOOCDz+ADg2fkHhBBKgKeB+2KMT2QxmyRJkuqZDV1JkiQpv10KbBVC+A7YMvOcEELPEMIdmTF/AzYGDgwhfJr5WDOVtJIkSfpTXHJBkiRJymMxxjHAFjUc7wscmnn8APBAlqNJkiSpAYQY839JrBDCaGBIFl9yKeDXLL6essP3tXHyfW28fG8bJ9/X/NI5xtg67RBqWFmutf07oHHwfWwcfB8bB9/HxsH3sXFYlPdxoXV2o2joZlsIoW+MsWfaOVS/fF8bJ9/Xxsv3tnHyfZUWb/4d0Dj4PjYOvo+Ng+9j4+D72DjU5/voGrqSJEmSJEmSlCds6EqSJEmSJElSnrCh+8fclnYANQjf18bJ97Xx8r1tnHxfpcWbfwc0Dr6PjYPvY+Pg+9g4+D42DvX2PrqGriRJkiRJkiTlCWfoSpIkSZIkSVKesKErSZIkSZIkSXnChu4iCCFsG0L4JoQwKIRwWtp5VHchhI4hhDdDCANDCF+GEI7PHF8yhPBqCOG7zOclMsdDCOH6zHs9IITQI93vQL8nhFAYQvgkhPCfzPPlQggfZd7DR0MIJZnjpZnngzLnl001uGoVQmgZQngihPB1COGrEMIG/szmvxDCiZm/h78IITwcQijz51USWGvnE2vrxsMaOv9ZMzcO1sj5K4RwVwhhVAjhi2rHFvlnMIRwQGb8dyGEA37vdW3o1lEIoRC4EdgO6ArsG0Lomm4qLYJK4KQYY1dgfeDozPt3GvB6jHFF4PXMc0je5xUzH4cDN2c/shbR8cBX1Z5fBlwTY+wC/AYckjl+CPBb5vg1mXHKTdcBL8cYVwHWIHl//ZnNYyGEZYDjgJ4xxu5AIbAP/rxKiz1r7bxjbd14WEPnP2vmPGeNnPfuAbad79gi/QyGEJYEzgHWA9YFzpndBK6NDd26WxcYFGP8IcY4A3gE2CXlTKqjGOOIGGP/zOOJJP/ILUPyHt6bGXYvsGvm8S7AfTHxIdAyhNA+u6lVVyGEDsAOwB2Z5wHYHHgiM2T+93b2e/4EsEVmvHJICKEFsDFwJ0CMcUaMcRz+zDYGRUCTEEIR0BQYgT+vkqy184q1deNgDZ3/rJkbFWvkPBVjfBsYO9/hRf0Z3AZ4NcY4Nsb4G/AqCzaJ52FDt+6WAX6u9nxo5pjyTOZ2hLWAj4C2McYRmVMjgbaZx77f+eVa4BSgKvO8FTAuxliZeV79/Zvz3mbOj8+MV25ZDhgN3J25DfCOEEI5/szmtRjjMOBK4CeSInU80A9/XiX593jesrbOa9diDZ3vrJkbAWvkRmlRfwYX+WfThq4WKyGECuBJ4IQY44Tq52KMEYipBNMfFkLYERgVY+yXdhbVqyKgB3BzjHEtYDJzb1MB/JnNR5nbhnYh+c/H0kA5v/ObZ0lS7rK2zl/W0I2GNXMjYI3cuDXUz6AN3bobBnSs9rxD5pjyRAihmKTgfDDG+FTm8C+zbzHJfB6VOe77nT/+AuwcQviR5PbMzUnWkWqZuV0F5n3/5ry3mfMtgDHZDKw6GQoMjTF+lHn+BEmx6s9sftsSGBxjHB1jnAk8RfIz7M+rJP8ezzPW1nnPGrpxsGZuHKyRG59F/Rlc5J9NG7p19zGwYmaXwRKSBaqfSzmT6iiznsydwFcxxqurnXoOmL174AHAs9WO75/ZgXB9YHy16fLKITHG02OMHWKMy5L8XL4RY/w78CawZ2bY/O/t7Pd8z8x4f2OdY2KMI4GfQwgrZw5tAQzEn9l89xOwfgihaebv5dnvqz+vkqy184i1df6zhm4crJkbDWvkxmdRfwZfAbYOISyRmbG9deZYrYLved2FELYnWWeoELgrxnhRuolUVyGEjYB3gM+Zu0bUGSRrfT0GdAKGAH+LMY7N/CX6b5LbHKYAB8UY+2Y9uBZJCGFT4OQY444hhOVJZhssCXwC/CPGOD2EUAbcT7LW21hgnxjjDylF1kKEENYk2aSjBPgBOIjkF5H+zOaxEMJ5wN4kO6R/AhxKsj6UP6/SYs5aO39YWzcu1tD5zZq5cbBGzl8hhIeBTYGlgF+Ac4BnWMSfwRDCwST/lgJcFGO8e6Gva0NXkiRJkiRJkvKDSy5IkiRJkiRJUp6woStJkiRJkiRJecKGriRJkiRJkiTlCRu6kiRJkiRJkpQnbOhKkiRJkiRJUp6woSspJ4QQzg0hxMxHVQjhtxDCxyGEi0II7eYbu2xm3I71+PpvhRCeqK/rNVYhhB9DCFemnUOSJEl1Y52dH6yzJS2KorQDSFI144FtM49bAD2AfwGHhxC2jTH2y5wbAWwAfJ39iJIkSVLesc6WpEbEhq6kXFIZY/yw2vNXQgg3A28Dj4QQVokxzooxTgc+rPkSuSeE0CTGODXtHJIkSVpsWWdLUiPikguSclqMcRxwCtAF2ApqvhUshLBzCKFfCGFy5jayj0IIm1Q7XxhCOD2E8G0IYXoIYWgI4Z75Xy+EsF8IYVAIYUII4aUQQof5zl8aQvg8hDApc40Ha7hV7ccQwlUhhD4hhKHAhMzx0hDCzSGEcSGEMSGEK0IIJ4QQ4nxfv2QI4bYQwi8hhGkhhPdDCOv93p9VCKFVCOHWEMKIzNd9E0I4IXPusRDCWzV8zbmZ1ynOPG8SQrg8hDAk8+c0OIRwye+87l9DCP8NIUzJfF+3hxCaVTvfMoRwRwhheCbXTyGE23/v+5EkSVLDsc62zpaUv5yhKykfvAVUAusDL89/MoSwAvAEcB3QGygD1gaWrDbsVmB/4HLgv5lze8x3qfWApYGTgCaZ690GbF9tTBvgYmA40Doz9o0QQvcYY1W1cfsBXwJHMffv2suBA4EzgK+Ag4B95vteSoHXgJaZ72UUye1wr4UQVowxjqzhz4cQQhOSP6c2wHkkt8l1yXwA3Am8FEJYLsY4OPM1ATgAeCDGODPz/FmS2+wuAPoBywB/rek1M9f4SybvM8CeQCvgUmCJzHOAq4ENgROBkUBHYOParilJkqSseQvrbOtsSXnHhq6knBdjnBZC+BVoW8uQtYCJMcbe1Y69OPtBCGEV4BDg+Bjj9dXGPDrfdZoDO8QYf8t8XTvgmlDtVq4Y48HVrlsIfAAMBTYiuWWtuh1jjNMyY1sBhwNnxxivyRx7Bfhivq/5B9Ad6BZj/C4z7jXgG5Kitjc12x/oBvSIMX6aOfZGtfOvZnIeCJyTObYZsCxwd+b51iSzM3aJMT5X7Wvvq+U1ISkq348x7j37QAhhGPB6pvj+AlgXuDHGWP3P+4GFXFOSJElZYJ1tnS0pP7nkgqR8ERZy7nOgRQjh3hDC1iGE8vnOb5b5fM/vvMbHs4vMjIGZz8vMCRHCdplbs8aTzGYYmjm10nzXen12kZmxGsmMhjkFXIwxAs/P93VbkvzGfnAIoSiEMPsXb/8Fei4k++bAJ9WKzHlkZjXcDeyfmSEASdHZN1MMzr7G2PmKzFqFEJqSzDJ4bHbWTN53gZkkszcAPgV6hxCOCiHM/+ckSZKkdFlnW2dLyjM2dCXlvBBCGcktRr/UdD7G+A2wC7A8yYyBX0MID4UQWmeGtAImxxgn/M5LjZvv+YzM57JMjnVICsWhwD9Jiqz1q4+pZv6ss9f/Gj3f8fmfL5W55sz5Pg4iuYWqNq1IdiVemLuBzsBmmbW39gDuWsRrVLcEUAjcNF/W6UBxtbzHkNwqdjbwTQjhuxDCPgtcTZIkSVllnW2dLSk/ueSCpHywGcnfVx/UNiDG+ALwQgihBbADcC1wA8naWWOA8hBC8zoUmwuzG0lhuHfmt/6EEDrXFmm+57PX5GoNjK12vPV848YCfUnW85rf9IVkG8PcdbxqDhTjj5nbyg4EliP5pd7D812j/cKuMZ9xJN/nuVS79a6a4ZnXHQccBxwXQlidZPONB0MIA2KMA2v4OkmSJGWHdXbCOltSXnGGrqScFkJoCVwGDCLZFGChYozjY4wPAU8DXTOHZ69xtf+fjNMEmDm7yMz4ex2/9nNgGskMB2DOZgk7zTfudZKC8acYY9/5Pj5fyPVfB9bKFHILcyfJjIGjgGcyRWD1aywZqu1qvDAxxsnAh8DKNWTtG2McXsPXDCBZn6wAWKUuryNJkqT6Z51tnS0pfzlDV1IuKQohzL61qhnJ2lD/ApoC28YYZ9X0RSGEI0huy3qZ5LfVKwJ7kdlkIMb4TQjhNuCqEEIbkk0VWgJ7xhgX5ZakV4ETQgjXkqzJtSHJ5gq/K8Y4JoRwO3BeCGEmc3ffbc68swzuA44E3gohXAn8QHKL1rrAyNkbPdTgPuBo4P9CCOeSbO6wHLBSjPG0auOeIbl1qwdweg3f3yvAQyGE84H+JDMJNo4xHlHL655CsjFDFckOyBOBTiSzN86MMX4bQniXpPD/IvO9HgZMBv5XyzUlSZJUv6yzrbMlNSI2dCXlkhYkt3tFYALJbIEHgBtijCMX8nUDgJ2Bq4ElSdanup1kLanZjgKGAIcCpwGjgP9blHAxxhdDCKcCx5IUSx8AOwLf1vESp5CseXUuUAXcT/Kb/BOqvca0EMJmwPnAeSQ7Do8iKcpq3UQh83Wbk+yGez5JAfsjSVFZfdz0EMJLwMbMNxMjxhhDCLsBF2QytSYp3B9ayOu+G0LYOJP1fpK1voaQFP2z1zf7gOT2s2WBWcAnwHYxxqHzX0+SJEkNwjrbOltSIxLmvaNBkpRNmbW2imOMm2Tp9YpICsG7Yox9svGakiRJUrZZZ0tqzJyhK0lZkpkRsB7JLVbFwN7AFiS3rTX0a5cAawD7kdxadmtDv6YkSZKUDdbZkhY3NnQlKXsmAbuSrKlVBnwHHBhjfCILr700ye1ko4AjvA1LkiRJjYh1tqTFiksuSJIkSZIkSVKeKEg7gCRJkiRJkiSpbmzoSpIkSZIkSVKesKErSZIkSZIkSXnChq4kSZIkSZIk5QkbupIkSZIkSZKUJ/4fQDfxc38zQYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2, 2, figsize=(24, 18))\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        t = i + j\n",
    "        battery_name = Battery_list[t]\n",
    "        test_data = Battery[battery_name]['capacity']\n",
    "        fixed_predict_data = fixed_result_list[t]\n",
    "        moving_predict_data = moving_result_list[t]\n",
    "        x = [t for t in range(len(test_data))]\n",
    "        threshold = [Rated_Capacity*0.7] * len(test_data)\n",
    "        ax[i][j].plot(x, test_data, 'c', label='test data')\n",
    "        ax[i][j].plot(x, fixed_predict_data, 'b', label='fixed predicted data')\n",
    "        ax[i][j].plot(x, moving_predict_data, 'r', label='moving predicted data')\n",
    "        ax[i][j].plot(x, threshold, 'black', ls=':', label='stop line')\n",
    "        ax[i][j].legend()\n",
    "        ax[i][j].set_xlabel('Discharge cycles', fontsize=15)\n",
    "        ax[i][j].set_ylabel('Capacity (Ah)', fontsize=15)\n",
    "        ax[i][j].set_title('test v.s. prediction of battery ' + battery_name, fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. use grid-search to determ parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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      "K=4, lr=0.0001, num_layers=1, hidden_dim=8\n",
      "seed:0\n",
      "[[0.34146341]\n",
      " [1.        ]\n",
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      "------------------------------------------------------------------\n",
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      "------------------------------------------------------------------\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "re: 0.4428\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
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      "re: 0.6463\n",
      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=1, hidden_dim=32\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=1, hidden_dim=64\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=2, hidden_dim=8\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=2, hidden_dim=16\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
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      "------------------------------------------------------------------\n",
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      "------------------------------------------------------------------\n",
      "seed:2\n",
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      "------------------------------------------------------------------\n",
      "re mean: 0.5654\n",
      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.35365854]\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=3, hidden_dim=16\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=4, lr=0.0001, num_layers=3, hidden_dim=64\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.0087108 ]\n",
      " [0.00863558]\n",
      " [0.06923077]\n",
      " [0.10998553]]\n",
      "re: 0.0491\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.07142857]\n",
      " [0.07944732]\n",
      " [0.01230769]\n",
      " [0.00289436]]\n",
      "re: 0.0415\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0778\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.02264808]\n",
      " [0.13126079]\n",
      " [0.10153846]\n",
      " [0.0853835 ]]\n",
      "re: 0.0852\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.09407666]\n",
      " [0.08117444]\n",
      " [0.19384615]\n",
      " [0.0130246 ]]\n",
      "re: 0.0955\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.24390244]\n",
      " [0.13989637]\n",
      " [0.12769231]\n",
      " [0.06078148]]\n",
      "re: 0.1431\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.30139373]\n",
      " [0.00172712]\n",
      " [0.02      ]\n",
      " [0.11577424]]\n",
      "re: 0.1097\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01393728]\n",
      " [0.36960276]\n",
      " [1.        ]\n",
      " [0.01013025]]\n",
      "re: 0.3484\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1564\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.16202091]\n",
      " [0.01727116]\n",
      " [0.12615385]\n",
      " [0.23878437]]\n",
      "re: 0.1361\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.08885017]\n",
      " [0.39378238]\n",
      " [0.07846154]\n",
      " [0.06078148]]\n",
      "re: 0.1555\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00348432]\n",
      " [0.44214162]\n",
      " [0.11692308]\n",
      " [0.00578871]]\n",
      "re: 0.1421\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.10627178]\n",
      " [0.02072539]\n",
      " [0.11230769]\n",
      " [0.10564399]]\n",
      "re: 0.0862\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.1358885 ]\n",
      " [0.03454231]\n",
      " [0.24      ]\n",
      " [0.12156295]]\n",
      "re: 0.1330\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1306\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.0261324 ]\n",
      " [0.10189983]\n",
      " [0.04      ]\n",
      " [0.08972504]]\n",
      "re: 0.0644\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.06097561]\n",
      " [0.33506045]\n",
      " [0.32461538]\n",
      " [0.11143271]]\n",
      "re: 0.2080\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.14634146]\n",
      " [0.15544041]\n",
      " [0.13692308]\n",
      " [0.3748191 ]]\n",
      "re: 0.2034\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.20557491]\n",
      " [0.00172712]\n",
      " [0.04307692]\n",
      " [0.0593343 ]]\n",
      "re: 0.0774\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01393728]\n",
      " [0.46977547]\n",
      " [0.18461538]\n",
      " [0.03039074]]\n",
      "re: 0.1747\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1456\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.02439024]\n",
      " [0.12262522]\n",
      " [0.01692308]\n",
      " [0.07959479]]\n",
      "re: 0.0609\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.02787456]\n",
      " [0.09326425]\n",
      " [1.        ]\n",
      " [0.13603473]]\n",
      "re: 0.3143\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.02264808]\n",
      " [0.08635579]\n",
      " [0.14153846]\n",
      " [0.27206946]]\n",
      "re: 0.1307\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.01219512]\n",
      " [0.00345423]\n",
      " [0.00153846]\n",
      " [0.02894356]]\n",
      "re: 0.0115\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.14111498]\n",
      " [0.05699482]\n",
      " [0.18307692]\n",
      " [0.0463097 ]]\n",
      "re: 0.1069\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1248\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.00522648]\n",
      " [0.39723661]\n",
      " [0.01384615]\n",
      " [0.01591896]]\n",
      "re: 0.1081\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.00348432]\n",
      " [0.01381693]\n",
      " [0.17538462]\n",
      " [0.30535456]]\n",
      "re: 0.1245\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.14634146]\n",
      " [0.21243523]\n",
      " [0.16615385]\n",
      " [0.07814761]]\n",
      "re: 0.1508\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.34494774]\n",
      " [0.02072539]\n",
      " [0.77538462]\n",
      " [0.01157742]]\n",
      "re: 0.2882\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.02787456]\n",
      " [0.13471503]\n",
      " [0.32615385]\n",
      " [0.10853835]]\n",
      "re: 0.1493\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1642\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.08536585]\n",
      " [0.11053541]\n",
      " [0.11384615]\n",
      " [0.00434153]]\n",
      "re: 0.0785\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.06968641]\n",
      " [0.32297064]\n",
      " [0.01384615]\n",
      " [0.00868307]]\n",
      "re: 0.1038\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.05400697]\n",
      " [0.23488774]\n",
      " [0.01230769]\n",
      " [0.03183792]]\n",
      "re: 0.0833\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.05749129]\n",
      " [0.05872193]\n",
      " [0.00769231]\n",
      " [0.10853835]]\n",
      "re: 0.0581\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.18118467]\n",
      " [0.00345423]\n",
      " [0.22769231]\n",
      " [0.0130246 ]]\n",
      "re: 0.1063\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0860\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [0.07081174]\n",
      " [0.55384615]\n",
      " [0.02170767]]\n",
      "re: 0.2400\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.00522648]\n",
      " [0.41968912]\n",
      " [0.01846154]\n",
      " [0.22286541]]\n",
      "re: 0.1666\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.06794425]\n",
      " [0.06735751]\n",
      " [0.14461538]\n",
      " [0.09696093]]\n",
      "re: 0.0942\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.03658537]\n",
      " [0.16580311]\n",
      " [0.08923077]\n",
      " [0.1910275 ]]\n",
      "re: 0.1207\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.21080139]\n",
      " [0.32642487]\n",
      " [0.22153846]\n",
      " [0.25470333]]\n",
      "re: 0.2534\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1750\n",
      "===================================================================\n",
      "K=4, lr=0.001, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.07839721]\n",
      " [0.15544041]\n",
      " [0.11846154]\n",
      " [0.05065123]]\n",
      "re: 0.1007\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.17421603]\n",
      " [0.09671848]\n",
      " [0.24615385]\n",
      " [0.07091172]]\n",
      "re: 0.1470\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00174216]\n",
      " [0.15544041]\n",
      " [0.16307692]\n",
      " [0.27496382]]\n",
      "re: 0.1488\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.33449477]\n",
      " [0.00172712]\n",
      " [0.04461538]\n",
      " [0.48335745]]\n",
      "re: 0.2160\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.03484321]\n",
      " [0.11226252]\n",
      " [0.01692308]\n",
      " [0.1910275 ]]\n",
      "re: 0.0888\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1403\n",
      "===================================================================\n"
     ]
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      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
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      "K=4, lr=0.01, num_layers=1, hidden_dim=8\n",
      "seed:0\n",
      "[[0.6445993 ]\n",
      " [0.17271157]\n",
      " [0.61076923]\n",
      " [0.11432706]]\n",
      "re: 0.3856\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.28919861]\n",
      " [0.00863558]\n",
      " [0.48615385]\n",
      " [0.02315485]]\n",
      "re: 0.2018\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.06794425]\n",
      " [0.05872193]\n",
      " [0.03230769]\n",
      " [0.        ]]\n",
      "re: 0.0397\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
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      " [0.35076923]\n",
      " [0.17366136]]\n",
      "re: 0.1799\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.30487805]\n",
      " [0.49740933]\n",
      " [0.06153846]\n",
      " [0.34442836]]\n",
      "re: 0.3021\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2218\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
      "[[0.33797909]\n",
      " [0.47668394]\n",
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      " [0.18379161]]\n",
      "re: 0.2681\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
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      " [0.10274964]]\n",
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      "------------------------------------------------------------------\n",
      "seed:2\n",
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      " [0.00923077]\n",
      " [0.13024602]]\n",
      "re: 0.1402\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.41637631]\n",
      " [0.01208981]\n",
      " [0.06461538]\n",
      " [0.08683068]]\n",
      "re: 0.1450\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.0174216 ]\n",
      " [0.14507772]\n",
      " [0.16923077]\n",
      " [0.04920405]]\n",
      "re: 0.0952\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1598\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
      "[[0.62543554]\n",
      " [0.10362694]\n",
      " [0.15692308]\n",
      " [0.84370478]]\n",
      "re: 0.4324\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.54006969]\n",
      " [0.27115717]\n",
      " [0.23384615]\n",
      " [0.05354559]]\n",
      "re: 0.2747\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.22125436]\n",
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      " [0.01538462]\n",
      " [0.00289436]]\n",
      "re: 0.0633\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.07142857]\n",
      " [0.08462867]\n",
      " [0.06307692]\n",
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      "re: 0.1854\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
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      " [0.04317789]\n",
      " [0.02      ]\n",
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      "re: 0.1281\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2168\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
      "[[0.1010453 ]\n",
      " [0.3955095 ]\n",
      " [0.02461538]\n",
      " [0.74529667]]\n",
      "re: 0.3166\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.40069686]\n",
      " [0.00690846]\n",
      " [0.49846154]\n",
      " [0.        ]]\n",
      "re: 0.2265\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.04355401]\n",
      " [0.09326425]\n",
      " [0.05230769]\n",
      " [0.27930535]]\n",
      "re: 0.1171\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.20731707]\n",
      " [0.02936097]\n",
      " [0.18      ]\n",
      " [0.4211288 ]]\n",
      "re: 0.2095\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.07317073]\n",
      " [0.23143351]\n",
      " [0.69076923]\n",
      " [0.0723589 ]]\n",
      "re: 0.2669\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2273\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.27874564]\n",
      " [0.00518135]\n",
      " [0.11384615]\n",
      " [0.18379161]]\n",
      "re: 0.1454\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.18118467]\n",
      " [0.10362694]\n",
      " [0.20153846]\n",
      " [0.3487699 ]]\n",
      "re: 0.2088\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.14111498]\n",
      " [0.07081174]\n",
      " [0.13384615]\n",
      " [0.49638205]]\n",
      "re: 0.2105\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.01567944]\n",
      " [0.15025907]\n",
      " [0.78461538]\n",
      " [0.02460203]]\n",
      "re: 0.2438\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.29965157]\n",
      " [0.6044905 ]\n",
      " [0.06461538]\n",
      " [0.63386397]]\n",
      "re: 0.4007\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2418\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.13066202]\n",
      " [0.00863558]\n",
      " [0.03384615]\n",
      " [0.04196816]]\n",
      "re: 0.0538\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.41463415]\n",
      " [0.14507772]\n",
      " [0.06153846]\n",
      " [0.06512301]]\n",
      "re: 0.1716\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.0261324 ]\n",
      " [0.4611399 ]\n",
      " [0.78923077]\n",
      " [0.44573082]]\n",
      "re: 0.4306\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.02961672]\n",
      " [0.03108808]\n",
      " [0.14      ]\n",
      " [0.10419682]]\n",
      "re: 0.0762\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01045296]\n",
      " [0.05008636]\n",
      " [0.14461538]\n",
      " [0.07959479]]\n",
      "re: 0.0712\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1607\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.19163763]\n",
      " [0.17271157]\n",
      " [0.41076923]\n",
      " [0.00868307]]\n",
      "re: 0.1960\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.22125436]\n",
      " [0.34369603]\n",
      " [0.35538462]\n",
      " [0.12301013]]\n",
      "re: 0.2608\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.03310105]\n",
      " [0.25043178]\n",
      " [0.23538462]\n",
      " [0.03328509]]\n",
      "re: 0.1381\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.01045296]\n",
      " [0.25734024]\n",
      " [0.24615385]\n",
      " [0.34008683]]\n",
      "re: 0.2135\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01393728]\n",
      " [0.06217617]\n",
      " [0.02769231]\n",
      " [0.04196816]]\n",
      "re: 0.0364\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1690\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.27003484]\n",
      " [0.02936097]\n",
      " [0.08307692]\n",
      " [0.00868307]]\n",
      "re: 0.0978\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.2456446 ]\n",
      " [0.10708117]\n",
      " [0.32307692]\n",
      " [0.48625181]]\n",
      "re: 0.2905\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.62891986]\n",
      " [0.19689119]\n",
      " [0.00153846]\n",
      " [0.62083936]]\n",
      "re: 0.3620\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.60627178]\n",
      " [0.0984456 ]\n",
      " [0.28923077]\n",
      " [0.75542692]]\n",
      "re: 0.4373\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00174216]\n",
      " [0.18825561]\n",
      " [0.38153846]\n",
      " [0.03907381]]\n",
      "re: 0.1527\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2681\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.2630662 ]\n",
      " [0.16580311]\n",
      " [0.24307692]\n",
      " [0.08104197]]\n",
      "re: 0.1882\n",
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      "seed:1\n",
      "[[0.31184669]\n",
      " [0.45595855]\n",
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      "re: 0.3864\n",
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      "seed:2\n",
      "[[0.19860627]\n",
      " [0.18825561]\n",
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      " [0.10853835]]\n",
      "re: 0.1304\n",
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      "seed:3\n",
      "[[0.02787456]\n",
      " [0.06390328]\n",
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      "re: 0.2490\n",
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      "seed:4\n",
      "[[0.02787456]\n",
      " [0.11053541]\n",
      " [0.36153846]\n",
      " [0.68885673]]\n",
      "re: 0.2972\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2502\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.11672474]\n",
      " [0.56822107]\n",
      " [0.28923077]\n",
      " [0.60057887]]\n",
      "re: 0.3937\n",
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      "seed:1\n",
      "[[0.1533101 ]\n",
      " [0.45423143]\n",
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      "re: 0.2242\n",
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      "seed:2\n",
      "[[0.46167247]\n",
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      "re: 0.1342\n",
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      "seed:3\n",
      "[[0.2543554 ]\n",
      " [0.00518135]\n",
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      "re: 0.2839\n",
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      "seed:4\n",
      "[[0.28397213]\n",
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      " [0.00144718]]\n",
      "re: 0.2127\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2497\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
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      " [0.58610709]]\n",
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      "[[0.        ]\n",
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      "seed:2\n",
      "[[0.04703833]\n",
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      "re: 0.3926\n",
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      "seed:3\n",
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      "re: 0.1868\n",
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      "seed:4\n",
      "[[0.33623693]\n",
      " [0.69948187]\n",
      " [0.67230769]\n",
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      "re: 0.4621\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2549\n",
      "===================================================================\n",
      "K=4, lr=0.01, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.30836237]\n",
      " [0.20552677]\n",
      " [0.66      ]\n",
      " [0.32561505]]\n",
      "re: 0.3749\n",
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      "seed:1\n",
      "[[0.80139373]\n",
      " [0.09499136]\n",
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      "re: 0.3779\n",
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      "seed:2\n",
      "[[0.36236934]\n",
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      "re: 0.4887\n",
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      "seed:3\n",
      "[[0.05400697]\n",
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      "re: 0.2208\n",
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      "seed:4\n",
      "[[0.14808362]\n",
      " [0.06735751]\n",
      " [0.41538462]\n",
      " [0.23589001]]\n",
      "re: 0.2167\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3358\n",
      "===================================================================\n"
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      "seed:0\n",
      "[[0.02961672]\n",
      " [1.        ]\n",
      " [0.19692308]\n",
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      "re: 0.3743\n",
      "------------------------------------------------------------------\n",
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      " [1.        ]\n",
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      "[[1.]\n",
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      "[[1.        ]\n",
      " [1.        ]\n",
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      " [1.        ]]\n",
      "re: 0.8312\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.6423\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
      "[[0.61498258]\n",
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      "seed:3\n",
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      "seed:4\n",
      "[[1.        ]\n",
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      "re: 0.7580\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5667\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
      "[[0.1010453 ]\n",
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      " [1.        ]]\n",
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      "seed:1\n",
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      "seed:2\n",
      "[[1.        ]\n",
      " [0.03108808]\n",
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      "seed:3\n",
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      "re: 0.2947\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.05749129]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.2170767 ]]\n",
      "re: 0.5686\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4780\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
      "[[0.44773519]\n",
      " [0.25388601]\n",
      " [0.07230769]\n",
      " [0.12590449]]\n",
      "re: 0.2250\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.16202091]\n",
      " [0.06217617]\n",
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      "re: 0.2127\n",
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      "seed:2\n",
      "[[1.        ]\n",
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      "re: 0.7688\n",
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      "seed:3\n",
      "[[0.16898955]\n",
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      "re: 0.2534\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.06445993]\n",
      " [0.09326425]\n",
      " [0.16615385]\n",
      " [0.2894356 ]]\n",
      "re: 0.1533\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3226\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [1.        ]\n",
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      " [0.24891462]]\n",
      "re: 0.5876\n",
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      "seed:1\n",
      "[[0.22299652]\n",
      " [0.07081174]\n",
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      "re: 0.2041\n",
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      "seed:2\n",
      "[[1.        ]\n",
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      "re: 0.8300\n",
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      "[[1.        ]\n",
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      "re: 0.6100\n",
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      "seed:4\n",
      "[[0.04181185]\n",
      " [1.        ]\n",
      " [0.43230769]\n",
      " [0.01736614]]\n",
      "re: 0.3729\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5209\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.54124457]]\n",
      "re: 0.8853\n",
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      "seed:1\n",
      "[[1.        ]\n",
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      "seed:2\n",
      "[[1.        ]\n",
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      "re: 0.3879\n",
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      "seed:3\n",
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      "re: 0.3777\n",
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      "seed:4\n",
      "[[1.        ]\n",
      " [0.24525043]\n",
      " [1.        ]\n",
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      "re: 0.6326\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5357\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.22648084]\n",
      " [0.06044905]\n",
      " [0.31846154]\n",
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      "re: 0.4013\n",
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      "seed:1\n",
      "[[1.        ]\n",
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      "re: 0.5450\n",
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      "seed:2\n",
      "[[1.        ]\n",
      " [0.02072539]\n",
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      "re: 0.5144\n",
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      "seed:3\n",
      "[[0.04878049]\n",
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      "re: 0.5530\n",
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      "seed:4\n",
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      " [1.        ]\n",
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      "re: 0.5610\n",
      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.51916376]\n",
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      "re: 0.2148\n",
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      "seed:1\n",
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      "re: 0.2293\n",
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      "seed:2\n",
      "[[0.25783972]\n",
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      "re: 0.5956\n",
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      "seed:3\n",
      "[[1.        ]\n",
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      "re: 0.3649\n",
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      "seed:4\n",
      "[[0.11149826]\n",
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      " [0.36468886]]\n",
      "re: 0.2123\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3234\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.03658537]\n",
      " [1.        ]\n",
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      " [0.18958032]]\n",
      "re: 0.3365\n",
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      "seed:1\n",
      "[[0.09930314]\n",
      " [0.04490501]\n",
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      "re: 0.3153\n",
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      "seed:2\n",
      "[[1.        ]\n",
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      "re: 0.5382\n",
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      "seed:3\n",
      "[[1.        ]\n",
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      "re: 0.3341\n",
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      "seed:4\n",
      "[[0.02090592]\n",
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      " [0.01736614]]\n",
      "re: 0.1343\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3317\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[1.        ]\n",
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      "re: 0.9114\n",
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      "seed:1\n",
      "[[0.19163763]\n",
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      "re: 0.1700\n",
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      "seed:2\n",
      "[[0.30139373]\n",
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      "re: 0.2264\n",
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      "seed:3\n",
      "[[0.05226481]\n",
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      "re: 0.5453\n",
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      "seed:4\n",
      "[[1.        ]\n",
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      " [1.        ]\n",
      " [0.02460203]]\n",
      "re: 0.5269\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4760\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
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     "output_type": "stream",
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      " [1.        ]]\n",
      "re: 0.3815\n",
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      "seed:2\n",
      "[[0.31184669]\n",
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      "re: 0.4395\n",
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      "seed:3\n",
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      "re: 0.5826\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3963\n",
      "===================================================================\n",
      "K=8, lr=0.0001, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.44076655]\n",
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      "re: 0.1874\n",
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      "seed:1\n",
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      "seed:2\n",
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      "re: 0.6387\n",
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      "seed:3\n",
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      "re: 0.1879\n",
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      "seed:4\n",
      "[[0.02439024]\n",
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      " [0.36468886]]\n",
      "re: 0.2527\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2904\n",
      "===================================================================\n"
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      "------------------------------------------------------------------\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=8, lr=0.001, num_layers=1, hidden_dim=16\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=8, lr=0.001, num_layers=1, hidden_dim=32\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=8, lr=0.001, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
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      "------------------------------------------------------------------\n",
      "seed:2\n",
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      "------------------------------------------------------------------\n",
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      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00348432]\n",
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      " [0.20769231]\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=8, lr=0.001, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.08536585]\n",
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      "re: 0.1073\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.0087108 ]\n",
      " [0.01727116]\n",
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      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.06271777]\n",
      " [0.44214162]\n",
      " [0.        ]\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.1533101 ]\n",
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      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.0261324 ]\n",
      " [0.32297064]\n",
      " [0.16923077]\n",
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      "------------------------------------------------------------------\n",
      "re mean: 0.1325\n",
      "===================================================================\n",
      "K=8, lr=0.001, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.07142857]\n",
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      "re: 0.1289\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.00696864]\n",
      " [0.00518135]\n",
      " [0.25846154]\n",
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      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.05923345]\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "re: 0.0559\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01567944]\n",
      " [0.20034542]\n",
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      "------------------------------------------------------------------\n",
      "re mean: 0.0945\n",
      "===================================================================\n",
      "K=8, lr=0.001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.02961672]\n",
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      " [0.19076923]\n",
      " [0.05499276]]\n",
      "re: 0.0693\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.0174216 ]\n",
      " [0.00518135]\n",
      " [0.03076923]\n",
      " [0.19536903]]\n",
      "re: 0.0622\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.04529617]\n",
      " [0.01036269]\n",
      " [0.22307692]\n",
      " [0.21562952]]\n",
      "re: 0.1236\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.07839721]\n",
      " [0.14680484]\n",
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      " [0.00434153]]\n",
      "re: 0.0635\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.0261324 ]\n",
      " [0.44214162]\n",
      " [0.10461538]\n",
      " [0.22720695]]\n",
      "re: 0.2000\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1037\n",
      "===================================================================\n",
      "K=8, lr=0.001, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.00696864]\n",
      " [0.00172712]\n",
      " [0.13692308]\n",
      " [0.0984081 ]]\n",
      "re: 0.0610\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.04006969]\n",
      " [0.04317789]\n",
      " [0.12769231]\n",
      " [0.16497829]]\n",
      "re: 0.0940\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.03484321]\n",
      " [0.04835924]\n",
      " [0.13076923]\n",
      " [0.08972504]]\n",
      "re: 0.0759\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.01916376]\n",
      " [0.01381693]\n",
      " [0.14      ]\n",
      " [0.11287988]]\n",
      "re: 0.0715\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01219512]\n",
      " [0.12780656]\n",
      " [0.21538462]\n",
      " [0.11287988]]\n",
      "re: 0.1171\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0839\n",
      "===================================================================\n",
      "K=8, lr=0.001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.00348432]\n",
      " [0.2970639 ]\n",
      " [0.20769231]\n",
      " [0.12735166]]\n",
      "re: 0.1589\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.03135889]\n",
      " [0.00518135]\n",
      " [0.02153846]\n",
      " [0.06078148]]\n",
      "re: 0.0297\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.10627178]\n",
      " [0.40587219]\n",
      " [0.09076923]\n",
      " [0.03907381]]\n",
      "re: 0.1605\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.17770035]\n",
      " [0.01036269]\n",
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      " [0.12735166]]\n",
      "re: 0.1112\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.02090592]\n",
      " [0.24870466]\n",
      " [0.05230769]\n",
      " [0.20984081]]\n",
      "re: 0.1329\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1186\n",
      "===================================================================\n",
      "K=8, lr=0.001, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.03658537]\n",
      " [0.00345423]\n",
      " [0.01230769]\n",
      " [0.17945007]]\n",
      "re: 0.0579\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.01045296]\n",
      " [0.00690846]\n",
      " [0.24615385]\n",
      " [0.19536903]]\n",
      "re: 0.1147\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.01567944]\n",
      " [0.15025907]\n",
      " [0.16615385]\n",
      " [0.05209841]]\n",
      "re: 0.0960\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.00348432]\n",
      " [0.02590674]\n",
      " [0.22615385]\n",
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      "re: 0.0700\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.07317073]\n",
      " [0.07944732]\n",
      " [0.02461538]\n",
      " [0.12445731]]\n",
      "re: 0.0754\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0828\n",
      "===================================================================\n",
      "K=8, lr=0.001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
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     "output_type": "stream",
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      "re: 0.0650\n",
      "------------------------------------------------------------------\n",
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      "[[0.12891986]\n",
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      "------------------------------------------------------------------\n",
      "seed:2\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "------------------------------------------------------------------\n",
      "seed:4\n",
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      " [0.05538462]\n",
      " [0.1577424 ]]\n",
      "re: 0.1047\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1003\n",
      "===================================================================\n",
      "K=8, lr=0.001, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.32229965]\n",
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      " [0.07525326]]\n",
      "re: 0.1480\n",
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      "seed:1\n",
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      "re: 0.1417\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.02439024]\n",
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      "re: 0.1016\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "re: 0.1001\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.04529617]\n",
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      " [0.10130246]]\n",
      "re: 0.0922\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1167\n",
      "===================================================================\n"
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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      "seed:0\n",
      "[[0.19686411]\n",
      " [0.15544041]\n",
      " [0.11692308]\n",
      " [0.29377713]]\n",
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      "------------------------------------------------------------------\n",
      "seed:1\n",
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      "re: 0.2489\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "re: 0.1133\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2076\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=1, hidden_dim=16\n",
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      "------------------------------------------------------------------\n",
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      "seed:2\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
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      " [0.42141623]\n",
      " [0.20153846]\n",
      " [0.01013025]]\n",
      "re: 0.1635\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
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      " [0.08923077]\n",
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      "------------------------------------------------------------------\n",
      "re mean: 0.1414\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
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      "re: 0.1434\n",
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      "seed:2\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "------------------------------------------------------------------\n",
      "seed:4\n",
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      "re: 0.1768\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1930\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
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      "------------------------------------------------------------------\n",
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      "re: 0.1746\n",
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      "seed:2\n",
      "[[0.1358885 ]\n",
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      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "re: 0.1741\n",
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      "seed:4\n",
      "[[0.37979094]\n",
      " [0.13989637]\n",
      " [0.16615385]\n",
      " [0.01591896]]\n",
      "re: 0.1754\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1485\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.01567944]\n",
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      "re: 0.0812\n",
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      "seed:1\n",
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      "re: 0.2194\n",
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      "seed:2\n",
      "[[0.71777003]\n",
      " [0.01381693]\n",
      " [0.13384615]\n",
      " [0.29232996]]\n",
      "re: 0.2894\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.02787456]\n",
      " [0.        ]\n",
      " [0.28769231]\n",
      " [0.11722142]]\n",
      "re: 0.1082\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01916376]\n",
      " [0.18307427]\n",
      " [0.09230769]\n",
      " [0.04196816]]\n",
      "re: 0.0841\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1565\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.01393728]\n",
      " [0.0552677 ]\n",
      " [0.14769231]\n",
      " [0.73227207]]\n",
      "re: 0.2373\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.04006969]\n",
      " [0.04663212]\n",
      " [0.17384615]\n",
      " [0.11432706]]\n",
      "re: 0.0937\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.0174216 ]\n",
      " [0.13126079]\n",
      " [0.02769231]\n",
      " [0.0593343 ]]\n",
      "re: 0.0589\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.01045296]\n",
      " [0.24525043]\n",
      " [0.00153846]\n",
      " [0.12301013]]\n",
      "re: 0.0951\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.05052265]\n",
      " [0.01554404]\n",
      " [0.54      ]\n",
      " [0.36324168]]\n",
      "re: 0.2423\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1455\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.60452962]\n",
      " [0.02763385]\n",
      " [0.31076923]\n",
      " [0.10419682]]\n",
      "re: 0.2618\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.08885017]\n",
      " [0.07944732]\n",
      " [0.05538462]\n",
      " [0.12301013]]\n",
      "re: 0.0867\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.40940767]\n",
      " [0.06735751]\n",
      " [0.63692308]\n",
      " [0.17655572]]\n",
      "re: 0.3226\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.64285714]\n",
      " [0.12435233]\n",
      " [0.12923077]\n",
      " [0.10709117]]\n",
      "re: 0.2509\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.16202091]\n",
      " [0.06044905]\n",
      " [0.04153846]\n",
      " [0.0723589 ]]\n",
      "re: 0.0841\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2012\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.02787456]\n",
      " [0.00863558]\n",
      " [0.18923077]\n",
      " [0.46743849]]\n",
      "re: 0.1733\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.11672474]\n",
      " [0.0328152 ]\n",
      " [0.04461538]\n",
      " [0.70767004]]\n",
      "re: 0.2255\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.02090592]\n",
      " [0.582038  ]\n",
      " [0.04923077]\n",
      " [0.15484805]]\n",
      "re: 0.2018\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.05574913]\n",
      " [0.0552677 ]\n",
      " [0.12615385]\n",
      " [0.0853835 ]]\n",
      "re: 0.0806\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.04181185]\n",
      " [0.05872193]\n",
      " [0.00307692]\n",
      " [0.0130246 ]]\n",
      "re: 0.0292\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1421\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.05400697]\n",
      " [0.417962  ]\n",
      " [0.14153846]\n",
      " [0.47467438]]\n",
      "re: 0.2720\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.01916376]\n",
      " [0.41450777]\n",
      " [0.07076923]\n",
      " [0.00868307]]\n",
      "re: 0.1283\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.10452962]\n",
      " [0.10017271]\n",
      " [0.31692308]\n",
      " [0.38350217]]\n",
      "re: 0.2263\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.02961672]\n",
      " [0.00518135]\n",
      " [0.10307692]\n",
      " [0.67583213]]\n",
      "re: 0.2034\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.14982578]\n",
      " [0.05008636]\n",
      " [0.06923077]\n",
      " [0.3748191 ]]\n",
      "re: 0.1610\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1982\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.41811847]\n",
      " [0.01036269]\n",
      " [0.30461538]\n",
      " [0.06078148]]\n",
      "re: 0.1985\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.15505226]\n",
      " [0.00863558]\n",
      " [0.48153846]\n",
      " [0.03039074]]\n",
      "re: 0.1689\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.40243902]\n",
      " [0.10362694]\n",
      " [0.2       ]\n",
      " [0.21128799]]\n",
      "re: 0.2293\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.35714286]\n",
      " [0.19170984]\n",
      " [0.02307692]\n",
      " [0.48480463]]\n",
      "re: 0.2642\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.05226481]\n",
      " [0.12607945]\n",
      " [0.07076923]\n",
      " [0.12879884]]\n",
      "re: 0.0945\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1911\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.21602787]\n",
      " [0.01208981]\n",
      " [0.17230769]\n",
      " [0.74384949]]\n",
      "re: 0.2861\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.16898955]\n",
      " [0.01208981]\n",
      " [0.14923077]\n",
      " [0.00434153]]\n",
      "re: 0.0837\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.02787456]\n",
      " [0.03108808]\n",
      " [0.06461538]\n",
      " [0.41823444]]\n",
      "re: 0.1355\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.11498258]\n",
      " [0.02936097]\n",
      " [0.81692308]\n",
      " [0.65267728]]\n",
      "re: 0.4035\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.10627178]\n",
      " [0.34196891]\n",
      " [0.01692308]\n",
      " [0.10709117]]\n",
      "re: 0.1431\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2103\n",
      "===================================================================\n",
      "K=8, lr=0.01, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.11324042]\n",
      " [0.53367876]\n",
      " [0.32153846]\n",
      " [0.17221418]]\n",
      "re: 0.2852\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.1010453 ]\n",
      " [0.31778929]\n",
      " [0.70153846]\n",
      " [0.33285094]]\n",
      "re: 0.3633\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00348432]\n",
      " [0.49913644]\n",
      " [0.04461538]\n",
      " [0.16787265]]\n",
      "re: 0.1788\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.09233449]\n",
      " [1.        ]\n",
      " [0.06153846]\n",
      " [0.01447178]]\n",
      "re: 0.2921\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.08885017]\n",
      " [0.26770294]\n",
      " [0.00615385]\n",
      " [0.03617945]]\n",
      "re: 0.0997\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2438\n",
      "===================================================================\n"
     ]
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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      "K=16, lr=0.0001, num_layers=1, hidden_dim=8\n",
      "seed:0\n",
      "[[0.29616725]\n",
      " [1.        ]\n",
      " [0.01384615]\n",
      " [0.0853835 ]]\n",
      "re: 0.3488\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.05923345]\n",
      " [0.10017271]\n",
      " [0.16      ]\n",
      " [0.07380608]]\n",
      "re: 0.0983\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.11149826]\n",
      " [1.        ]\n",
      " [0.19846154]\n",
      " [1.        ]]\n",
      "re: 0.5775\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.08362369]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.10853835]]\n",
      "re: 0.5480\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [0.07599309]\n",
      " [0.24      ]\n",
      " [0.07091172]]\n",
      "re: 0.3467\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3839\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [0.26252159]\n",
      " [0.57538462]\n",
      " [0.17945007]]\n",
      "re: 0.5043\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.07491289]\n",
      " [0.08981002]\n",
      " [0.11538462]\n",
      " [1.        ]]\n",
      "re: 0.3200\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.07081174]\n",
      " [0.02923077]\n",
      " [0.25615051]]\n",
      "re: 0.3390\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.20307692]\n",
      " [0.31982634]]\n",
      "re: 0.6307\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01045296]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.7526\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5094\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
      "[[0.01219512]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.08827786]]\n",
      "re: 0.5251\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.        ]\n",
      " [0.17789292]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.7945\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
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      " [1.        ]\n",
      " [0.18958032]]\n",
      "re: 0.6342\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.02787456]\n",
      " [0.03454231]\n",
      " [0.14      ]\n",
      " [0.14761216]]\n",
      "re: 0.0875\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [0.02072539]\n",
      " [0.09538462]\n",
      " [0.05209841]]\n",
      "re: 0.2921\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4667\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
      "[[0.13937282]\n",
      " [0.15025907]\n",
      " [0.09692308]\n",
      " [0.2170767 ]]\n",
      "re: 0.1509\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.        ]\n",
      " [0.41623489]\n",
      " [1.        ]\n",
      " [0.18958032]]\n",
      "re: 0.6515\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.31881533]\n",
      " [1.        ]\n",
      " [0.17538462]\n",
      " [0.24023155]]\n",
      "re: 0.4336\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.        ]\n",
      " [0.17962003]\n",
      " [0.06      ]\n",
      " [0.170767  ]]\n",
      "re: 0.3526\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.04181185]\n",
      " [0.25388601]\n",
      " [0.13384615]\n",
      " [1.        ]]\n",
      "re: 0.3574\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3892\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.02961672]\n",
      " [0.27288428]\n",
      " [0.21076923]\n",
      " [0.24167873]]\n",
      "re: 0.1887\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.21602787]\n",
      " [0.04663212]\n",
      " [0.19384615]\n",
      " [0.17800289]]\n",
      "re: 0.1586\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.67770035]\n",
      " [0.44214162]\n",
      " [0.24307692]\n",
      " [0.23878437]]\n",
      "re: 0.4004\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.11498258]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.7787\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.13240418]\n",
      " [0.03108808]\n",
      " [0.19076923]\n",
      " [0.20694645]]\n",
      "re: 0.1403\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3334\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.38615385]\n",
      " [0.21997106]]\n",
      "re: 0.6515\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.10452962]\n",
      " [0.02763385]\n",
      " [0.17230769]\n",
      " [0.05065123]]\n",
      "re: 0.0888\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.13644214]\n",
      " [0.18153846]\n",
      " [0.2633864 ]]\n",
      "re: 0.3953\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.10615385]\n",
      " [0.19536903]]\n",
      "re: 0.5754\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.06271777]\n",
      " [1.        ]\n",
      " [0.05692308]\n",
      " [0.22286541]]\n",
      "re: 0.3356\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4093\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.03658537]\n",
      " [1.        ]\n",
      " [0.30461538]\n",
      " [0.01881331]]\n",
      "re: 0.3400\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.        ]\n",
      " [0.34542314]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.8364\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.09233449]\n",
      " [0.05181347]\n",
      " [1.        ]\n",
      " [0.18813314]]\n",
      "re: 0.3331\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.08710801]\n",
      " [0.15889465]\n",
      " [0.16615385]\n",
      " [0.14037627]]\n",
      "re: 0.1381\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [0.25734024]\n",
      " [0.22769231]\n",
      " [0.34442836]]\n",
      "re: 0.4574\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4210\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.04703833]\n",
      " [0.28670121]\n",
      " [0.24153846]\n",
      " [0.32561505]]\n",
      "re: 0.2252\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.08362369]\n",
      " [1.        ]\n",
      " [0.31230769]\n",
      " [0.17800289]]\n",
      "re: 0.3935\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.06620209]\n",
      " [0.27633851]\n",
      " [0.18153846]\n",
      " [0.19247467]]\n",
      "re: 0.1791\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.06794425]\n",
      " [1.        ]\n",
      " [0.25384615]\n",
      " [0.18668596]]\n",
      "re: 0.3771\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.09581882]\n",
      " [0.42659758]\n",
      " [1.        ]\n",
      " [0.3357453 ]]\n",
      "re: 0.4645\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3279\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.03658537]\n",
      " [0.12262522]\n",
      " [0.18461538]\n",
      " [0.170767  ]]\n",
      "re: 0.1286\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.24390244]\n",
      " [0.08635579]\n",
      " [0.20615385]\n",
      " [0.08972504]]\n",
      "re: 0.1565\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.77700348]\n",
      " [0.10880829]\n",
      " [0.16615385]\n",
      " [0.18523878]]\n",
      "re: 0.3093\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.06794425]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.29377713]]\n",
      "re: 0.5904\n",
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      "seed:4\n",
      "[[0.63240418]\n",
      " [0.04317789]\n",
      " [0.20923077]\n",
      " [0.02026049]]\n",
      "re: 0.2263\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2822\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [0.03626943]\n",
      " [0.37230769]\n",
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      "re: 0.4010\n",
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      "seed:1\n",
      "[[1.        ]\n",
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      "seed:3\n",
      "[[1.        ]\n",
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      "[[0.01045296]\n",
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      "re: 0.1541\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3400\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
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      "------------------------------------------------------------------\n",
      "re mean: 0.4026\n",
      "===================================================================\n",
      "K=16, lr=0.0001, num_layers=3, hidden_dim=64\n",
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      "[[0.03484321]\n",
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      " [0.22797927]\n",
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      "seed:2\n",
      "[[0.00522648]\n",
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      "re: 0.1303\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
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      "seed:4\n",
      "[[0.01567944]\n",
      " [0.3851468 ]\n",
      " [0.02307692]\n",
      " [1.        ]]\n",
      "re: 0.3560\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2111\n",
      "===================================================================\n"
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=16, lr=0.001, num_layers=1, hidden_dim=16\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=16, lr=0.001, num_layers=1, hidden_dim=32\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=16, lr=0.001, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
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      "seed:4\n",
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      "------------------------------------------------------------------\n",
      "re mean: 0.1156\n",
      "===================================================================\n",
      "K=16, lr=0.001, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.19337979]\n",
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      "seed:4\n",
      "[[0.00174216]\n",
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      "re: 0.0842\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1059\n",
      "===================================================================\n",
      "K=16, lr=0.001, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.07317073]\n",
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      "[[0.13763066]\n",
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      "seed:4\n",
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      "re: 0.0402\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0805\n",
      "===================================================================\n",
      "K=16, lr=0.001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.1010453 ]\n",
      " [0.20207254]\n",
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      "seed:1\n",
      "[[0.20905923]\n",
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      "seed:2\n",
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      "seed:3\n",
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      "------------------------------------------------------------------\n",
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      "===================================================================\n",
      "K=16, lr=0.001, num_layers=2, hidden_dim=64\n",
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      "re: 0.1954\n",
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      "re: 0.0706\n",
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      "seed:3\n",
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      "re: 0.0215\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1139\n",
      "===================================================================\n",
      "K=16, lr=0.001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.06097561]\n",
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      "re: 0.0981\n",
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      "[[0.10452962]\n",
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      "seed:2\n",
      "[[0.06097561]\n",
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      "re: 0.1003\n",
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      "seed:3\n",
      "[[0.19163763]\n",
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      "re: 0.1445\n",
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      "seed:4\n",
      "[[0.02264808]\n",
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      "re: 0.0635\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0947\n",
      "===================================================================\n",
      "K=16, lr=0.001, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.00174216]\n",
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      "re: 0.0620\n",
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      "[[0.00522648]\n",
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      "re: 0.0184\n",
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      "seed:2\n",
      "[[0.00174216]\n",
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      "re: 0.0449\n",
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      "seed:3\n",
      "[[0.04181185]\n",
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      "seed:4\n",
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      " [0.        ]\n",
      " [0.03846154]\n",
      " [0.01013025]]\n",
      "re: 0.0714\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0493\n",
      "===================================================================\n",
      "K=16, lr=0.001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
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      "------------------------------------------------------------------\n",
      "re mean: 0.0904\n",
      "===================================================================\n",
      "K=16, lr=0.001, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
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      "re: 0.1247\n",
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      "seed:3\n",
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      "seed:4\n",
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      " [0.19230769]\n",
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      "re: 0.1650\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1299\n",
      "===================================================================\n"
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      "re: 0.3765\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00174216]\n",
      " [1.        ]\n",
      " [0.00153846]\n",
      " [0.1316932 ]]\n",
      "re: 0.2837\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4278\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [0.12780656]\n",
      " [0.54615385]\n",
      " [0.11287988]]\n",
      "re: 0.4467\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.24216028]\n",
      " [0.0880829 ]\n",
      " [1.        ]\n",
      " [0.01736614]]\n",
      "re: 0.3369\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.0224525 ]\n",
      " [1.        ]\n",
      " [0.03039074]]\n",
      "re: 0.5132\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.0087108]\n",
      " [1.       ]\n",
      " [1.       ]\n",
      " [0.170767 ]]\n",
      "re: 0.5449\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01045296]\n",
      " [1.        ]\n",
      " [0.07230769]\n",
      " [0.09551375]]\n",
      "re: 0.2946\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4273\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
      "[[0.        ]\n",
      " [0.1537133 ]\n",
      " [0.00461538]\n",
      " [0.0593343 ]]\n",
      "re: 0.0544\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.02264808]\n",
      " [0.14680484]\n",
      " [0.08153846]\n",
      " [0.12590449]]\n",
      "re: 0.0942\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.01045296]\n",
      " [0.28842832]\n",
      " [0.04307692]\n",
      " [0.06657019]]\n",
      "re: 0.1021\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.13240418]\n",
      " [0.02417962]\n",
      " [1.        ]\n",
      " [0.08683068]]\n",
      "re: 0.3109\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.0174216 ]\n",
      " [0.24525043]\n",
      " [0.40923077]\n",
      " [0.0853835 ]]\n",
      "re: 0.1893\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1502\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
      "[[0.01045296]\n",
      " [1.        ]\n",
      " [0.07692308]\n",
      " [0.5788712 ]]\n",
      "re: 0.4166\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.05052265]\n",
      " [0.30397237]\n",
      " [0.11846154]\n",
      " [0.00723589]]\n",
      "re: 0.1200\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.17073171]\n",
      " [1.        ]\n",
      " [0.06769231]\n",
      " [0.16063676]]\n",
      "re: 0.3498\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.11672474]\n",
      " [0.00345423]\n",
      " [0.03846154]\n",
      " [0.09696093]]\n",
      "re: 0.0639\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00348432]\n",
      " [1.        ]\n",
      " [0.01384615]\n",
      " [1.        ]]\n",
      "re: 0.5043\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2909\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.05923345]\n",
      " [0.2970639 ]\n",
      " [0.08769231]\n",
      " [0.00868307]]\n",
      "re: 0.1132\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.22473868]\n",
      " [0.26079447]\n",
      " [0.24461538]\n",
      " [1.        ]]\n",
      "re: 0.4325\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.16376307]\n",
      " [0.07081174]\n",
      " [0.00307692]\n",
      " [0.16063676]]\n",
      "re: 0.0996\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.07142857]\n",
      " [1.        ]\n",
      " [0.2       ]\n",
      " [0.00868307]]\n",
      "re: 0.3200\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.06620209]\n",
      " [0.08290155]\n",
      " [0.05692308]\n",
      " [0.18813314]]\n",
      "re: 0.0985\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2128\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.27351916]\n",
      " [0.39896373]\n",
      " [0.02461538]\n",
      " [0.10130246]]\n",
      "re: 0.1996\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.43902439]\n",
      " [0.35405872]\n",
      " [0.16153846]\n",
      " [0.08393632]]\n",
      "re: 0.2596\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.34843206]\n",
      " [0.23661485]\n",
      " [0.00307692]\n",
      " [0.03183792]]\n",
      "re: 0.1550\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.26829268]\n",
      " [0.46977547]\n",
      " [0.03692308]\n",
      " [0.10419682]]\n",
      "re: 0.2198\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00696864]\n",
      " [0.01381693]\n",
      " [0.05076923]\n",
      " [0.25325615]]\n",
      "re: 0.0812\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1830\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.04703833]\n",
      " [0.0656304 ]\n",
      " [0.02307692]\n",
      " [0.11287988]]\n",
      "re: 0.0622\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.32055749]\n",
      " [0.05699482]\n",
      " [0.33692308]\n",
      " [0.09696093]]\n",
      "re: 0.2029\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00174216]\n",
      " [0.26943005]\n",
      " [0.05846154]\n",
      " [0.12301013]]\n",
      "re: 0.1132\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.25609756]\n",
      " [0.21761658]\n",
      " [0.03846154]\n",
      " [0.15918958]]\n",
      "re: 0.1678\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.04181185]\n",
      " [0.04317789]\n",
      " [0.23538462]\n",
      " [0.04486252]]\n",
      "re: 0.0913\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1275\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.02264808]\n",
      " [0.47668394]\n",
      " [0.16461538]\n",
      " [0.77134588]]\n",
      "re: 0.3588\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.01045296]\n",
      " [0.68911917]\n",
      " [0.35384615]\n",
      " [0.25470333]]\n",
      "re: 0.3270\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.02961672]\n",
      " [1.        ]\n",
      " [0.50769231]\n",
      " [0.08827786]]\n",
      "re: 0.4064\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.04006969]\n",
      " [0.24525043]\n",
      " [0.13538462]\n",
      " [0.00868307]]\n",
      "re: 0.1073\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.06968641]\n",
      " [0.00690846]\n",
      " [0.09076923]\n",
      " [0.07814761]]\n",
      "re: 0.0614\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2522\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.63066202]\n",
      " [1.        ]\n",
      " [0.10153846]\n",
      " [0.13024602]]\n",
      "re: 0.4656\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.1010453 ]\n",
      " [0.00172712]\n",
      " [0.09692308]\n",
      " [0.00144718]]\n",
      "re: 0.0503\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.12020906]\n",
      " [0.20034542]\n",
      " [0.03846154]\n",
      " [0.25325615]]\n",
      "re: 0.1531\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.01916376]\n",
      " [1.        ]\n",
      " [0.05846154]\n",
      " [0.62228654]]\n",
      "re: 0.4250\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.03135889]\n",
      " [0.11571675]\n",
      " [0.32      ]\n",
      " [0.08683068]]\n",
      "re: 0.1385\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2465\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.28745645]\n",
      " [0.1537133 ]\n",
      " [0.06769231]\n",
      " [0.79305355]]\n",
      "re: 0.3255\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.09233449]\n",
      " [0.39896373]\n",
      " [0.57692308]\n",
      " [0.12590449]]\n",
      "re: 0.2985\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.02787456]\n",
      " [1.        ]\n",
      " [0.82307692]\n",
      " [0.18958032]]\n",
      "re: 0.5101\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.81707317]\n",
      " [0.04317789]\n",
      " [0.66307692]\n",
      " [0.25036179]]\n",
      "re: 0.4434\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.04703833]\n",
      " [0.0984456 ]\n",
      " [0.09692308]\n",
      " [1.        ]]\n",
      "re: 0.3106\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3776\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.15679443]\n",
      " [1.        ]\n",
      " [0.14461538]\n",
      " [0.02749638]]\n",
      "re: 0.3322\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.17770035]\n",
      " [0.02590674]\n",
      " [0.12      ]\n",
      " [0.56005789]]\n",
      "re: 0.2209\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.48954704]\n",
      " [1.        ]\n",
      " [0.09384615]\n",
      " [0.80463097]]\n",
      "re: 0.5970\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.38850174]\n",
      " [1.        ]\n",
      " [0.12615385]\n",
      " [0.26193922]]\n",
      "re: 0.4441\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.71428571]\n",
      " [0.37305699]\n",
      " [0.16461538]\n",
      " [0.15195369]]\n",
      "re: 0.3510\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3891\n",
      "===================================================================\n",
      "K=16, lr=0.01, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.02787456]\n",
      " [0.27633851]\n",
      " [0.00923077]\n",
      " [0.26917511]]\n",
      "re: 0.1457\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.33275261]\n",
      " [0.38687392]\n",
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      " [1.        ]]\n",
      "re: 0.4484\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.63076923]\n",
      " [0.64978292]]\n",
      "re: 0.8201\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.16376307]\n",
      " [1.        ]\n",
      " [0.72307692]\n",
      " [0.70332851]]\n",
      "re: 0.6475\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.7       ]\n",
      " [0.77424023]]\n",
      "re: 0.8686\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5861\n",
      "===================================================================\n"
     ]
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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      "K=32, lr=0.0001, num_layers=1, hidden_dim=8\n",
      "seed:0\n",
      "[[0.04703833]\n",
      " [1.        ]\n",
      " [0.25230769]\n",
      " [1.        ]]\n",
      "re: 0.5748\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.34542314]\n",
      " [1.        ]\n",
      " [0.02460203]]\n",
      "re: 0.5925\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.06445993]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.7661\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [0.1208981 ]\n",
      " [1.        ]\n",
      " [0.03473227]]\n",
      "re: 0.5389\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.6945\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.14616498]]\n",
      "re: 0.7865\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.       ]\n",
      " [1.       ]\n",
      " [1.       ]\n",
      " [0.0260492]]\n",
      "re: 0.7565\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.05643994]]\n",
      "re: 0.7641\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.28571429]\n",
      " [0.0880829 ]\n",
      " [1.        ]\n",
      " [0.52821997]]\n",
      "re: 0.4755\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.7565\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.18307692]\n",
      " [1.        ]]\n",
      "re: 0.7958\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.16027875]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.14037627]]\n",
      "re: 0.5752\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.26628075]]\n",
      "re: 0.8166\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.       ]\n",
      " [1.       ]\n",
      " [0.1      ]\n",
      " [0.0260492]]\n",
      "re: 0.5315\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.09930314]\n",
      " [0.22970639]\n",
      " [0.20153846]\n",
      " [0.01013025]]\n",
      "re: 0.1352\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5708\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
      "[[0.05400697]\n",
      " [0.33333333]\n",
      " [1.        ]\n",
      " [0.13603473]]\n",
      "re: 0.3808\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.11672474]\n",
      " [0.10535406]\n",
      " [1.        ]\n",
      " [0.19247467]]\n",
      "re: 0.3536\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.17692308]\n",
      " [0.16063676]]\n",
      "re: 0.5844\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.09059233]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.1910275 ]]\n",
      "re: 0.5704\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5779\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.18466899]\n",
      " [0.30915371]\n",
      " [0.30615385]\n",
      " [1.        ]]\n",
      "re: 0.4500\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.        ]\n",
      " [0.03626943]\n",
      " [1.        ]\n",
      " [0.00289436]]\n",
      "re: 0.5098\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.28151986]\n",
      " [0.00153846]\n",
      " [0.12590449]]\n",
      "re: 0.3522\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.08885017]\n",
      " [0.18480138]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.5684\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [0.10880829]\n",
      " [1.        ]\n",
      " [0.30390738]]\n",
      "re: 0.6032\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4967\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.04703833]\n",
      " [0.22452504]\n",
      " [0.15076923]\n",
      " [0.0723589 ]]\n",
      "re: 0.1237\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.49059334]]\n",
      "re: 0.8726\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.5174216 ]\n",
      " [0.31088083]\n",
      " [0.08153846]\n",
      " [1.        ]]\n",
      "re: 0.4775\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.06271777]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.19247467]]\n",
      "re: 0.5638\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.08013937]\n",
      " [0.01899827]\n",
      " [0.17692308]\n",
      " [0.63386397]]\n",
      "re: 0.2275\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4530\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.19846154]\n",
      " [0.30969609]]\n",
      "re: 0.6270\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.08362369]\n",
      " [0.22107081]\n",
      " [1.        ]\n",
      " [0.20839363]]\n",
      "re: 0.3783\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.07944732]\n",
      " [1.        ]\n",
      " [0.36468886]]\n",
      "re: 0.6110\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.15156794]\n",
      " [0.42659758]\n",
      " [0.14153846]\n",
      " [0.16353111]]\n",
      "re: 0.2208\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.04529617]\n",
      " [0.46459413]\n",
      " [1.        ]\n",
      " [0.18234443]]\n",
      "re: 0.4231\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4520\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.10627178]\n",
      " [0.06908463]\n",
      " [1.        ]\n",
      " [0.18234443]]\n",
      "re: 0.3394\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.00174216]\n",
      " [0.06390328]\n",
      " [0.14      ]\n",
      " [0.14327062]]\n",
      "re: 0.0872\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.07317073]\n",
      " [0.16753022]\n",
      " [0.12615385]\n",
      " [0.2431259 ]]\n",
      "re: 0.1525\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.09059233]\n",
      " [0.05181347]\n",
      " [0.13384615]\n",
      " [0.20549928]]\n",
      "re: 0.1204\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.28397213]\n",
      " [1.        ]\n",
      " [0.10769231]\n",
      " [0.45151954]]\n",
      "re: 0.4608\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2321\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.06794425]\n",
      " [0.07772021]\n",
      " [0.47076923]\n",
      " [1.        ]]\n",
      "re: 0.4041\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.        ]\n",
      " [0.01727116]\n",
      " [1.        ]\n",
      " [0.2301013 ]]\n",
      "re: 0.5618\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.07253886]\n",
      " [0.16307692]\n",
      " [0.16931983]]\n",
      "re: 0.3512\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.05052265]\n",
      " [0.02936097]\n",
      " [0.2       ]\n",
      " [0.20549928]]\n",
      "re: 0.1213\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [0.06735751]\n",
      " [0.24153846]\n",
      " [0.39363242]]\n",
      "re: 0.4256\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3728\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.22299652]\n",
      " [0.00863558]\n",
      " [0.2       ]\n",
      " [0.22141823]]\n",
      "re: 0.1633\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.04878049]\n",
      " [0.30051813]\n",
      " [0.12461538]\n",
      " [0.39652677]]\n",
      "re: 0.2176\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.74390244]\n",
      " [0.47841105]\n",
      " [0.29076923]\n",
      " [1.        ]]\n",
      "re: 0.6283\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.14982578]\n",
      " [0.00518135]\n",
      " [1.        ]\n",
      " [0.16787265]]\n",
      "re: 0.3307\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.03832753]\n",
      " [0.05181347]\n",
      " [0.62153846]\n",
      " [0.63386397]]\n",
      "re: 0.3364\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.3352\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.41289199]\n",
      " [1.        ]\n",
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      " [1.        ]]\n",
      "re: 0.6425\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.08362369]\n",
      " [0.09671848]\n",
      " [1.        ]\n",
      " [0.21997106]]\n",
      "re: 0.3501\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.08635579]\n",
      " [0.81230769]\n",
      " [0.32995658]]\n",
      "re: 0.5572\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.52090592]\n",
      " [0.32124352]\n",
      " [0.23230769]\n",
      " [0.16931983]]\n",
      "re: 0.3109\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.18989547]\n",
      " [0.4507772 ]\n",
      " [1.        ]\n",
      " [0.03617945]]\n",
      "re: 0.4192\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4560\n",
      "===================================================================\n",
      "K=32, lr=0.0001, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.05923345]\n",
      " [0.19861831]\n",
      " [0.51230769]\n",
      " [0.17800289]]\n",
      "re: 0.2370\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.0261324 ]\n",
      " [0.10189983]\n",
      " [0.10307692]\n",
      " [0.00868307]]\n",
      "re: 0.0599\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.13763066]\n",
      " [0.02936097]\n",
      " [0.08615385]\n",
      " [0.52387844]]\n",
      "re: 0.1943\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.        ]\n",
      " [0.01381693]\n",
      " [0.04769231]\n",
      " [0.14182344]]\n",
      "re: 0.3008\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01219512]\n",
      " [0.18307427]\n",
      " [0.15076923]\n",
      " [0.14182344]]\n",
      "re: 0.1220\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1828\n",
      "===================================================================\n"
     ]
    },
    {
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       "model_id": "2dc2982d1b8542139a099c37aa3e330d",
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      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     },
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     "text": [
      "K=32, lr=0.001, num_layers=1, hidden_dim=8\n",
      "seed:0\n",
      "[[0.3466899 ]\n",
      " [0.29015544]\n",
      " [0.04461538]\n",
      " [0.10419682]]\n",
      "re: 0.1964\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.14285714]\n",
      " [0.00172712]\n",
      " [0.08769231]\n",
      " [0.06946454]]\n",
      "re: 0.0754\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.02417962]\n",
      " [0.01076923]\n",
      " [0.10274964]]\n",
      "re: 0.2844\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
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      " [0.19861831]\n",
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      " [0.05065123]]\n",
      "re: 0.1624\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
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      " [0.01899827]\n",
      " [0.22307692]\n",
      " [0.00434153]]\n",
      "re: 0.0812\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1600\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
      "[[0.01219512]\n",
      " [0.00863558]\n",
      " [0.16461538]\n",
      " [0.1577424 ]]\n",
      "re: 0.0858\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.00348432]\n",
      " [0.34715026]\n",
      " [0.01846154]\n",
      " [0.0260492 ]]\n",
      "re: 0.0988\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
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      " [0.15230769]\n",
      " [0.08972504]]\n",
      "re: 0.1183\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.20383275]\n",
      " [0.00690846]\n",
      " [0.12153846]\n",
      " [0.2170767 ]]\n",
      "re: 0.1373\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.04878049]\n",
      " [0.02072539]\n",
      " [0.1       ]\n",
      " [0.        ]]\n",
      "re: 0.0424\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0965\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
      "[[0.27526132]\n",
      " [0.24525043]\n",
      " [0.13076923]\n",
      " [0.0130246 ]]\n",
      "re: 0.1661\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.03310105]\n",
      " [0.44386874]\n",
      " [0.12923077]\n",
      " [0.03617945]]\n",
      "re: 0.1606\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.30487805]\n",
      " [0.1865285 ]\n",
      " [0.00307692]\n",
      " [0.04920405]]\n",
      "re: 0.1359\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.        ]\n",
      " [0.16580311]\n",
      " [0.20307692]\n",
      " [0.2431259 ]]\n",
      "re: 0.1530\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.14808362]\n",
      " [0.01727116]\n",
      " [0.18923077]\n",
      " [0.15484805]]\n",
      "re: 0.1274\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1486\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
      "[[0.00522648]\n",
      " [0.05181347]\n",
      " [0.10923077]\n",
      " [0.02026049]]\n",
      "re: 0.0466\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.00348432]\n",
      " [0.1433506 ]\n",
      " [0.12153846]\n",
      " [0.19536903]]\n",
      "re: 0.1159\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.02439024]\n",
      " [0.05354059]\n",
      " [0.16769231]\n",
      " [0.15340087]]\n",
      "re: 0.0998\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.02439024]\n",
      " [0.01036269]\n",
      " [0.20615385]\n",
      " [0.04052098]]\n",
      "re: 0.0704\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.0174216 ]\n",
      " [0.12780656]\n",
      " [0.02      ]\n",
      " [0.04920405]]\n",
      "re: 0.0536\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0773\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.00174216]\n",
      " [0.36960276]\n",
      " [0.04      ]\n",
      " [0.05643994]]\n",
      "re: 0.1169\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.30836237]\n",
      " [0.26079447]\n",
      " [0.06769231]\n",
      " [0.18813314]]\n",
      "re: 0.2062\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00348432]\n",
      " [0.0880829 ]\n",
      " [0.00153846]\n",
      " [0.09696093]]\n",
      "re: 0.0475\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.05923345]\n",
      " [0.28324698]\n",
      " [0.        ]\n",
      " [0.18379161]]\n",
      "re: 0.1316\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.08362369]\n",
      " [0.00518135]\n",
      " [0.02307692]\n",
      " [0.14616498]]\n",
      "re: 0.0645\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1134\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.33275261]\n",
      " [0.00863558]\n",
      " [0.05846154]\n",
      " [0.05643994]]\n",
      "re: 0.1141\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.        ]\n",
      " [0.01899827]\n",
      " [0.01846154]\n",
      " [0.0130246 ]]\n",
      "re: 0.0126\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.0087108 ]\n",
      " [0.1208981 ]\n",
      " [0.06153846]\n",
      " [0.00578871]]\n",
      "re: 0.0492\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.41463415]\n",
      " [0.12435233]\n",
      " [0.12153846]\n",
      " [0.18523878]]\n",
      "re: 0.2114\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.0261324 ]\n",
      " [0.00863558]\n",
      " [0.04      ]\n",
      " [0.01736614]]\n",
      "re: 0.0230\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0821\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.24912892]\n",
      " [0.04835924]\n",
      " [0.16      ]\n",
      " [0.07959479]]\n",
      "re: 0.1343\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.07839721]\n",
      " [0.05008636]\n",
      " [0.14461538]\n",
      " [0.18668596]]\n",
      "re: 0.1149\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.01916376]\n",
      " [0.06735751]\n",
      " [0.04461538]\n",
      " [0.02460203]]\n",
      "re: 0.0389\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.04529617]\n",
      " [0.28497409]\n",
      " [0.20769231]\n",
      " [0.170767  ]]\n",
      "re: 0.1772\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00696864]\n",
      " [0.0224525 ]\n",
      " [0.09384615]\n",
      " [0.01736614]]\n",
      "re: 0.0352\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1001\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.08362369]\n",
      " [0.03799655]\n",
      " [0.13692308]\n",
      " [0.00289436]]\n",
      "re: 0.0654\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.0174216 ]\n",
      " [0.00172712]\n",
      " [0.15230769]\n",
      " [0.18958032]]\n",
      "re: 0.0903\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.06794425]\n",
      " [0.11398964]\n",
      " [0.00923077]\n",
      " [0.06946454]]\n",
      "re: 0.0652\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.18641115]\n",
      " [0.00345423]\n",
      " [0.21692308]\n",
      " [0.14327062]]\n",
      "re: 0.1375\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.06794425]\n",
      " [0.00172712]\n",
      " [0.06769231]\n",
      " [0.20115774]]\n",
      "re: 0.0846\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0886\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[0.04006969]\n",
      " [0.03108808]\n",
      " [0.19384615]\n",
      " [0.03328509]]\n",
      "re: 0.0746\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.04181185]\n",
      " [0.14680484]\n",
      " [0.16769231]\n",
      " [0.12879884]]\n",
      "re: 0.1213\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00696864]\n",
      " [0.16580311]\n",
      " [0.02615385]\n",
      " [0.09696093]]\n",
      "re: 0.0740\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.05923345]\n",
      " [0.3298791 ]\n",
      " [0.02615385]\n",
      " [0.22431259]]\n",
      "re: 0.1599\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00522648]\n",
      " [0.02936097]\n",
      " [0.        ]\n",
      " [0.05499276]]\n",
      "re: 0.0224\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0904\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.11149826]\n",
      " [0.01727116]\n",
      " [0.08      ]\n",
      " [0.02026049]]\n",
      "re: 0.0573\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.05923345]\n",
      " [0.11571675]\n",
      " [0.08      ]\n",
      " [0.19681621]]\n",
      "re: 0.1129\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.04529617]\n",
      " [0.16753022]\n",
      " [0.03076923]\n",
      " [0.00289436]]\n",
      "re: 0.0616\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.04703833]\n",
      " [0.03626943]\n",
      " [0.09692308]\n",
      " [0.18668596]]\n",
      "re: 0.0917\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00174216]\n",
      " [0.00690846]\n",
      " [0.11538462]\n",
      " [0.05643994]]\n",
      "re: 0.0451\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0737\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=3, hidden_dim=32\n",
      "seed:0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.08362369]\n",
      " [0.27288428]\n",
      " [0.18615385]\n",
      " [0.02170767]]\n",
      "re: 0.1411\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.03658537]\n",
      " [0.00345423]\n",
      " [0.01692308]\n",
      " [0.19971056]]\n",
      "re: 0.0642\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.01045296]\n",
      " [0.07944732]\n",
      " [0.22769231]\n",
      " [0.07814761]]\n",
      "re: 0.0989\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.11324042]\n",
      " [0.32124352]\n",
      " [0.08307692]\n",
      " [0.10709117]]\n",
      "re: 0.1562\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00696864]\n",
      " [0.39205527]\n",
      " [0.02769231]\n",
      " [0.04196816]]\n",
      "re: 0.1172\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1155\n",
      "===================================================================\n",
      "K=32, lr=0.001, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[0.1184669 ]\n",
      " [0.37823834]\n",
      " [0.22153846]\n",
      " [0.21562952]]\n",
      "re: 0.2335\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.0087108 ]\n",
      " [0.36960276]\n",
      " [0.05692308]\n",
      " [0.19392185]]\n",
      "re: 0.1573\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.08710801]\n",
      " [0.23316062]\n",
      " [0.00461538]\n",
      " [0.18523878]]\n",
      "re: 0.1275\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.02439024]\n",
      " [0.00518135]\n",
      " [0.09846154]\n",
      " [0.05788712]]\n",
      "re: 0.0465\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.1533101 ]\n",
      " [0.04835924]\n",
      " [0.15230769]\n",
      " [0.08683068]]\n",
      "re: 0.1102\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1350\n",
      "===================================================================\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6f684324acf4eab8203d48f9e48b889",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=32, lr=0.01, num_layers=1, hidden_dim=8\n",
      "seed:0\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "re mean: 1.0000\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=1, hidden_dim=16\n",
      "seed:0\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "re mean: 1.0000\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=1, hidden_dim=32\n",
      "seed:0\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.21428571]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.8036\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.03310105]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.7583\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.9124\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=1, hidden_dim=64\n",
      "seed:0\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.00615385]\n",
      " [1.        ]]\n",
      "re: 0.7515\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.        ]\n",
      " [0.25215889]\n",
      " [1.        ]\n",
      " [0.0130246 ]]\n",
      "re: 0.5663\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.01567944]\n",
      " [1.        ]\n",
      " [0.30923077]\n",
      " [0.01447178]]\n",
      "re: 0.3348\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.00615385]\n",
      " [0.01013025]]\n",
      "re: 0.5041\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.3554007 ]\n",
      " [0.00690846]\n",
      " [0.03230769]\n",
      " [1.        ]]\n",
      "re: 0.3487\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.5011\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=2, hidden_dim=8\n",
      "seed:0\n",
      "[[0.02090592]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.07525326]]\n",
      "re: 0.5240\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.04181185]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.7605\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.00348432]\n",
      " [0.23143351]\n",
      " [0.29384615]\n",
      " [1.        ]]\n",
      "re: 0.3822\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.06794425]\n",
      " [0.30224525]\n",
      " [0.15692308]\n",
      " [0.04920405]]\n",
      "re: 0.1441\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [0.24870466]\n",
      " [0.30615385]\n",
      " [1.        ]]\n",
      "re: 0.6387\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4899\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=2, hidden_dim=16\n",
      "seed:0\n",
      "[[0.03658537]\n",
      " [1.        ]\n",
      " [0.02307692]\n",
      " [1.        ]]\n",
      "re: 0.5149\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.0261324 ]\n",
      " [1.        ]\n",
      " [0.01692308]\n",
      " [1.        ]]\n",
      "re: 0.5108\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.17443869]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.7936\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.25261324]\n",
      " [0.21416235]\n",
      " [1.        ]\n",
      " [0.01447178]]\n",
      "re: 0.3703\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.21254355]\n",
      " [0.30915371]\n",
      " [0.09846154]\n",
      " [0.00868307]]\n",
      "re: 0.1572\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.4694\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=2, hidden_dim=32\n",
      "seed:0\n",
      "[[0.02264808]\n",
      " [0.34542314]\n",
      " [0.11846154]\n",
      " [0.09985528]]\n",
      "re: 0.1466\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.03135889]\n",
      " [0.37305699]\n",
      " [0.01076923]\n",
      " [0.00434153]]\n",
      "re: 0.1049\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.1533101 ]\n",
      " [1.        ]\n",
      " [0.02307692]\n",
      " [0.02170767]]\n",
      "re: 0.2995\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.1010453 ]\n",
      " [0.00863558]\n",
      " [0.15230769]\n",
      " [0.25904486]]\n",
      "re: 0.1303\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00696864]\n",
      " [0.07426598]\n",
      " [0.00307692]\n",
      " [0.69030391]]\n",
      "re: 0.1937\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.1750\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=2, hidden_dim=64\n",
      "seed:0\n",
      "[[0.08710801]\n",
      " [0.00172712]\n",
      " [0.02307692]\n",
      " [0.04775687]]\n",
      "re: 0.0399\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.03310105]\n",
      " [0.50777202]\n",
      " [0.07846154]\n",
      " [0.11577424]]\n",
      "re: 0.1838\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.25087108]\n",
      " [0.40587219]\n",
      " [0.06153846]\n",
      " [0.07091172]]\n",
      "re: 0.1973\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.30139373]\n",
      " [0.24525043]\n",
      " [1.        ]\n",
      " [0.19681621]]\n",
      "re: 0.4359\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.00174216]\n",
      " [0.13126079]\n",
      " [0.03538462]\n",
      " [1.        ]]\n",
      "re: 0.2921\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.2298\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=3, hidden_dim=8\n",
      "seed:0\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[0.03832753]\n",
      " [0.07944732]\n",
      " [0.00307692]\n",
      " [1.        ]]\n",
      "re: 0.2802\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [0.12953368]\n",
      " [0.82153846]\n",
      " [0.29956585]]\n",
      "re: 0.5627\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.03310105]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.02749638]]\n",
      "re: 0.5151\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.6716\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=3, hidden_dim=16\n",
      "seed:0\n",
      "[[0.1445993]\n",
      " [1.       ]\n",
      " [1.       ]\n",
      " [1.       ]]\n",
      "re: 0.7861\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[0.33797909]\n",
      " [0.43005181]\n",
      " [1.        ]\n",
      " [0.5918958 ]]\n",
      "re: 0.5900\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[0.31184669]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [1.        ]]\n",
      "re: 0.8280\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[0.01916376]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.00868307]]\n",
      "re: 0.5070\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.7422\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=3, hidden_dim=32\n",
      "seed:0\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.59846154]\n",
      " [1.        ]]\n",
      "re: 0.8996\n",
      "------------------------------------------------------------------\n",
      "seed:3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.        ]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.02170767]]\n",
      "re: 0.7554\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.46307692]\n",
      " [0.1186686 ]]\n",
      "re: 0.6454\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.8601\n",
      "===================================================================\n",
      "K=32, lr=0.01, num_layers=3, hidden_dim=64\n",
      "seed:0\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:1\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:2\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [0.30923077]\n",
      " [1.        ]]\n",
      "re: 0.8273\n",
      "------------------------------------------------------------------\n",
      "seed:3\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]]\n",
      "re: 1.0000\n",
      "------------------------------------------------------------------\n",
      "seed:4\n",
      "[[1.        ]\n",
      " [1.        ]\n",
      " [1.        ]\n",
      " [0.46599132]]\n",
      "re: 0.8665\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.9388\n",
      "===================================================================\n",
      "optimal parameters: K=16, lr=0.001, num_layers=3, hidden_dim=16, result: 0.049259635560624085\n"
     ]
    }
   ],
   "source": [
    "Rated_Capacity = 1.1\n",
    "feature_size = 64\n",
    "feature_num = 1\n",
    "dropout = 0.0\n",
    "epochs = 500\n",
    "nhead = 1\n",
    "alpha = 0.1\n",
    "\n",
    "weight_decay = 0.0\n",
    "noise_level = 0.0\n",
    "metric = 're'\n",
    "\n",
    "states = {}\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "for K in tqdm([4, 8, 16, 32], position=0):\n",
    "    for lr in tqdm([0.0001, 0.001, 0.01], position=1):\n",
    "        for num_layers in tqdm([1, 2, 3], position=2):\n",
    "            for hidden_dim in [8, 16, 32, 64]:\n",
    "                show_str = 'K={}, lr={}, num_layers={}, hidden_dim={}'.format(K, lr, num_layers, hidden_dim)\n",
    "                print(show_str)\n",
    "                SCORE = []\n",
    "                for seed in range(5):\n",
    "                    print('seed:{}'.format(seed))\n",
    "                    score_list, _, _ = train(lr=lr, feature_size=feature_size, feature_num=feature_num, hidden_dim=hidden_dim, \n",
    "                                             num_layers=num_layers, nhead=nhead, weight_decay=weight_decay, epochs=epochs, seed=seed, \n",
    "                                             dropout=dropout, alpha=alpha, noise_level=noise_level, metric=metric, device=device)\n",
    "                    print(np.array(score_list))\n",
    "                    print(metric + ': {:<6.4f}'.format(np.mean(np.array(score_list))))\n",
    "                    print('------------------------------------------------------------------')\n",
    "                    for s in score_list:\n",
    "                        SCORE.append(s)\n",
    "\n",
    "                print(metric + ' mean: {:<6.4f}'.format(np.mean(np.array(SCORE))))\n",
    "                states[show_str] = np.mean(np.array(SCORE))\n",
    "                print('===================================================================')\n",
    "\n",
    "min_key = min(states, key = states.get)\n",
    "print('optimal parameters: {}, result: {}'.format(min_key, states[min_key]))"
   ]
  }
 ],
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