tutorial/Tutorial1_EIIE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial1\n",
    "We show how to train a profitable RL agent on portfolio managemnt with EIIE algorithm on US stock market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Import Packages\n",
    "Modify the system path and load the corresponding packages and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-05 15:20:49,305\tINFO services.py:1476 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265\u001b[39m\u001b[22m\n",
      "2023-03-05 15:20:51,267\tINFO worker.py:973 -- Calling ray.init() again after it has already been called.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "from pathlib import Path\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "ROOT = os.path.dirname(os.path.abspath(\".\"))\n",
    "sys.path.append(ROOT)\n",
    "import torch\n",
    "import argparse\n",
    "import os.path as osp\n",
    "from mmcv import Config\n",
    "from trademaster.utils import replace_cfg_vals\n",
    "from trademaster.nets.builder import build_net\n",
    "from trademaster.environments.builder import build_environment\n",
    "from trademaster.datasets.builder import build_dataset\n",
    "from trademaster.agents.builder import build_agent\n",
    "from trademaster.optimizers.builder import build_optimizer\n",
    "from trademaster.losses.builder import build_loss\n",
    "from trademaster.trainers.builder import build_trainer\n",
    "from trademaster.transition.builder import build_transition\n",
    "from trademaster.utils import plot\n",
    "from trademaster.utils import set_seed\n",
    "set_seed(2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Load Configs\n",
    "Load default config from the file `configs/portfolio_management/portfolio_management_dj30_eiie_eiie_adam_mse.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "parser = argparse.ArgumentParser(description='Download Alpaca Datasets')\n",
    "parser.add_argument(\"--config\", default=osp.join(ROOT, \"configs\", \"portfolio_management\", \"portfolio_management_dj30_eiie_eiie_adam_mse.py\"),\n",
    "                    help=\"download datasets config file path\")\n",
    "parser.add_argument(\"--task_name\", type=str, default=\"train\")\n",
    "\n",
    "args, _= parser.parse_known_args()\n",
    "cfg = Config.fromfile(args.config)\n",
    "task_name = args.task_name\n",
    "cfg = replace_cfg_vals(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Config (path: /data/sunshuo/qml/TradeMaster/configs/portfolio_management/portfolio_management_dj30_eiie_eiie_adam_mse.py): {'data': {'type': 'PortfolioManagementDataset', 'data_path': 'data/portfolio_management/dj30', 'train_path': 'data/portfolio_management/dj30/train.csv', 'valid_path': 'data/portfolio_management/dj30/valid.csv', 'test_path': 'data/portfolio_management/dj30/test.csv', 'tech_indicator_list': ['zopen', 'zhigh', 'zlow', 'zadjcp', 'zclose', 'zd_5', 'zd_10', 'zd_15', 'zd_20', 'zd_25', 'zd_30'], 'length_day': 10, 'initial_amount': 100000, 'transaction_cost_pct': 0.001, 'test_dynamic_path': 'data/portfolio_management/dj30/test_with_label.csv'}, 'environment': {'type': 'PortfolioManagementEIIEEnvironment'}, 'agent': {'type': 'PortfolioManagementEIIE', 'memory_capacity': 1000, 'gamma': 0.99, 'policy_update_frequency': 500}, 'trainer': {'type': 'PortfolioManagementEIIETrainer', 'epochs': 2, 'work_dir': 'work_dir/portfolio_management_dj30_eiie_eiie_adam_mse', 'if_remove': False}, 'loss': {'type': 'MSELoss'}, 'optimizer': {'type': 'Adam', 'lr': 0.001}, 'act': {'type': 'EIIEConv', 'input_dim': None, 'output_dim': 1, 'time_steps': 10, 'kernel_size': 3, 'dims': [32]}, 'cri': {'type': 'EIIECritic', 'input_dim': None, 'action_dim': None, 'output_dim': 1, 'time_steps': None, 'num_layers': 1, 'hidden_size': 32}, 'transition': {'type': 'Transition'}, 'task_name': 'portfolio_management', 'dataset_name': 'dj30', 'net_name': 'eiie', 'agent_name': 'eiie', 'optimizer_name': 'adam', 'loss_name': 'mse', 'work_dir': 'work_dir/portfolio_management_dj30_eiie_eiie_adam_mse'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Build Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = build_dataset(cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Build Reinforcement Learning Environments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_environment = build_environment(cfg, default_args=dict(dataset=dataset, task=\"train\"))\n",
    "valid_environment = build_environment(cfg, default_args=dict(dataset=dataset, task=\"valid\"))\n",
    "test_environment = build_environment(cfg, default_args=dict(dataset=dataset, task=\"test\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjcp</th>\n",
       "      <th>tic</th>\n",
       "      <th>zopen</th>\n",
       "      <th>zhigh</th>\n",
       "      <th>zlow</th>\n",
       "      <th>zadjcp</th>\n",
       "      <th>zclose</th>\n",
       "      <th>zd_5</th>\n",
       "      <th>zd_10</th>\n",
       "      <th>zd_15</th>\n",
       "      <th>zd_20</th>\n",
       "      <th>zd_25</th>\n",
       "      <th>zd_30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>14.642857</td>\n",
       "      <td>14.810000</td>\n",
       "      <td>14.617143</td>\n",
       "      <td>14.765714</td>\n",
       "      <td>12.643501</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>-0.008320</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>-0.010062</td>\n",
       "      <td>-0.143726</td>\n",
       "      <td>0.005374</td>\n",
       "      <td>-0.010348</td>\n",
       "      <td>-0.011415</td>\n",
       "      <td>0.002751</td>\n",
       "      <td>-0.061124</td>\n",
       "      <td>-0.072128</td>\n",
       "      <td>-0.126481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>64.099998</td>\n",
       "      <td>64.129997</td>\n",
       "      <td>63.400002</td>\n",
       "      <td>63.759998</td>\n",
       "      <td>49.393219</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>0.005332</td>\n",
       "      <td>0.005803</td>\n",
       "      <td>-0.005646</td>\n",
       "      <td>-0.225326</td>\n",
       "      <td>-0.005459</td>\n",
       "      <td>0.007794</td>\n",
       "      <td>-0.008521</td>\n",
       "      <td>-0.008222</td>\n",
       "      <td>-0.017393</td>\n",
       "      <td>-0.031253</td>\n",
       "      <td>-0.042012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>48.020000</td>\n",
       "      <td>48.380001</td>\n",
       "      <td>47.400002</td>\n",
       "      <td>48.240002</td>\n",
       "      <td>41.672607</td>\n",
       "      <td>AXP</td>\n",
       "      <td>-0.004561</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>-0.017413</td>\n",
       "      <td>-0.136140</td>\n",
       "      <td>-0.003100</td>\n",
       "      <td>0.004561</td>\n",
       "      <td>-0.004885</td>\n",
       "      <td>-0.017185</td>\n",
       "      <td>-0.029824</td>\n",
       "      <td>-0.045096</td>\n",
       "      <td>-0.052918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>74.099998</td>\n",
       "      <td>74.599998</td>\n",
       "      <td>73.589996</td>\n",
       "      <td>74.330002</td>\n",
       "      <td>60.821106</td>\n",
       "      <td>BA</td>\n",
       "      <td>-0.003094</td>\n",
       "      <td>0.003632</td>\n",
       "      <td>-0.009956</td>\n",
       "      <td>-0.181742</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.002922</td>\n",
       "      <td>-0.007421</td>\n",
       "      <td>-0.004776</td>\n",
       "      <td>-0.002812</td>\n",
       "      <td>-0.013054</td>\n",
       "      <td>-0.001687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>93.410004</td>\n",
       "      <td>95.510002</td>\n",
       "      <td>93.410004</td>\n",
       "      <td>94.849998</td>\n",
       "      <td>70.945084</td>\n",
       "      <td>CAT</td>\n",
       "      <td>-0.015182</td>\n",
       "      <td>0.006958</td>\n",
       "      <td>-0.015182</td>\n",
       "      <td>-0.252029</td>\n",
       "      <td>0.009257</td>\n",
       "      <td>0.010322</td>\n",
       "      <td>-0.004803</td>\n",
       "      <td>-0.010627</td>\n",
       "      <td>-0.001714</td>\n",
       "      <td>-0.005438</td>\n",
       "      <td>0.006133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open       high        low      close      adjcp   tic  \\\n",
       "0  2012-01-04  14.642857  14.810000  14.617143  14.765714  12.643501  AAPL   \n",
       "0  2012-01-04  64.099998  64.129997  63.400002  63.759998  49.393219  AMGN   \n",
       "0  2012-01-04  48.020000  48.380001  47.400002  48.240002  41.672607   AXP   \n",
       "0  2012-01-04  74.099998  74.599998  73.589996  74.330002  60.821106    BA   \n",
       "0  2012-01-04  93.410004  95.510002  93.410004  94.849998  70.945084   CAT   \n",
       "\n",
       "      zopen     zhigh      zlow    zadjcp    zclose      zd_5     zd_10  \\\n",
       "0 -0.008320  0.002999 -0.010062 -0.143726  0.005374 -0.010348 -0.011415   \n",
       "0  0.005332  0.005803 -0.005646 -0.225326 -0.005459  0.007794 -0.008521   \n",
       "0 -0.004561  0.002902 -0.017413 -0.136140 -0.003100  0.004561 -0.004885   \n",
       "0 -0.003094  0.003632 -0.009956 -0.181742  0.001482  0.002922 -0.007421   \n",
       "0 -0.015182  0.006958 -0.015182 -0.252029  0.009257  0.010322 -0.004803   \n",
       "\n",
       "      zd_15     zd_20     zd_25     zd_30  \n",
       "0  0.002751 -0.061124 -0.072128 -0.126481  \n",
       "0 -0.008222 -0.017393 -0.031253 -0.042012  \n",
       "0 -0.017185 -0.029824 -0.045096 -0.052918  \n",
       "0 -0.004776 -0.002812 -0.013054 -0.001687  \n",
       "0 -0.010627 -0.001714 -0.005438  0.006133  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_environment.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Build Net \n",
    "Update information on the state and action dimension. Crreate networks and optimizer for EIIE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "action_dim = train_environment.action_dim # 29\n",
    "state_dim = train_environment.state_dim # 11\n",
    "input_dim = len(train_environment.tech_indicator_list)\n",
    "time_steps = train_environment.time_steps\n",
    "\n",
    "cfg.act.update(dict(input_dim=input_dim, time_steps=time_steps))\n",
    "cfg.cri.update(dict(input_dim=input_dim, action_dim= action_dim, time_steps=time_steps))\n",
    "\n",
    "act = build_net(cfg.act)\n",
    "cri = build_net(cfg.cri)\n",
    "act_optimizer = build_optimizer(cfg, default_args=dict(params=act.parameters()))\n",
    "cri_optimizer = build_optimizer(cfg, default_args=dict(params=cri.parameters()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Build Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = build_loss(cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Build Transition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "transition = build_transition(cfg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8: Build Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "agent = build_agent(cfg, default_args=dict(action_dim=action_dim,state_dim=state_dim,time_steps = time_steps,act=act,cri=cri,act_optimizer=act_optimizer,cri_optimizer = cri_optimizer,criterion=criterion,transition = transition,device = device))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 9: Build Trainer\n",
    "Build trainer from config and create work directionary to save the result, model and config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Arguments Keep work_dir: /data/sunshuo/qml/TradeMaster/work_dir/portfolio_management_dj30_eiie_eiie_adam_mse\n"
     ]
    }
   ],
   "source": [
    "trainer = build_trainer(cfg, default_args=dict(train_environment=train_environment,valid_environment=valid_environment,test_environment=test_environment,agent=agent,device=device))\n",
    "work_dir = os.path.join(ROOT, cfg.trainer.work_dir)\n",
    "\n",
    "if not os.path.exists(work_dir):\n",
    "    os.makedirs(work_dir)\n",
    "cfg.dump(osp.join(work_dir, osp.basename(args.config)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 10: RL Agent Training\n",
    "Train the EIIE agent based on the config and save results in workdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Episode: [1/2]\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "| 180.462766%  |   1.141238  | 0.756975%  |  15.019211%  |\n",
      "+--------------+-------------+------------+--------------+\n",
      "Valid Episode: [1/2]\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "|  9.596335%   |   0.450412  | 2.141257%  |  31.037195%  |\n",
      "+--------------+-------------+------------+--------------+\n",
      "Train Episode: [2/2]\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "| 180.460853%  |   1.141237  | 0.756970%  |  15.019067%  |\n",
      "+--------------+-------------+------------+--------------+\n",
      "Valid Episode: [2/2]\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "|  9.595491%   |   0.450386  | 2.141303%  |  31.038387%  |\n",
      "+--------------+-------------+------------+--------------+\n"
     ]
    }
   ],
   "source": [
    "trainer.train_and_valid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 11: RL Agent Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resume checkpoint /data/sunshuo/qml/TradeMaster/work_dir/portfolio_management_dj30_eiie_eiie_adam_mse/checkpoints/best.pth\n",
      "Test Best Episode\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "|  14.641799%  |   1.304315  | 0.718882%  |  6.743468%   |\n",
      "+--------------+-------------+------------+--------------+\n"
     ]
    }
   ],
   "source": [
    "trainer.test();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F1r50eLOnB1tsLCUeK7TXZGQAzjF8F566D3Z3c/8B94j1DHYZ0UhrMjL4tuENRBpKKQ50d/PWVasQ4MJf/pLTQ0OUZWfzsWeeYcDh4N4DB1iXmUlNYeGC+7xi+XLSEhLY3twclHYfNYYX93V2Mj41RcfIiNehsIM9PZSkpc25oNNfzD7vER1LKCL/CUwCv/ZxSI1SqlVE8oBnReSw4QF5u9btwO0ABQUF1NbWAs700Kmpqe6ka9nZ2WzcuNG9QCguLo6amhrq6+sZHBxkeHiY5cuX09HRQbPx4Kxbtw6bzcYB402Xl5fH+vXrqaurA8Bms1FdXc2ePXsYNmolVFVV0dLSQmtrKwAbNmwgNjaWQ4cOAZCfn09paSm7du0CICkpiaqqKnbv3o3deJiqq6s5efIk7YaLXFZWxtTUFEeOHAGgsLCQoqIidu/eDUBKSgqVlZXs2rULh8Ph/AfW1NDY2EinsX5h06ZNOBwOjhrfOouLi1m+fLm77QMDA1RUVFBXV8ekMU69ZcsWDh48SI/xbbC8vJyhoSFOnDgBQElJCVlZWe4JwczMTMrLy9mxYwdKKUSErVu30tDQQJ8xDFNRUUFvb6+74FCg/QTOb13B6qcjR47Q3NwcFf0EkJaWNmc/1R09ytr0dFpbWmb007K4OPYeOULt6CiZmZn09fXR0tJC3/g4tx86RPfEBOeOj3Oss5O+yUnejIvjcHc3PcPDlCYlUZmZSW17u/v9tdj9NNf7KTEvj76xMc6dnuaadev410OHqM7M5Jr0dPZ3d3Owo4OXW1u5aflyduzYwcTEBMuWLVtQP21KSuLPhw/TfcEFpvoJ4MDAAAAvNzXx6HPPoYDsuDgcfX20Dw7S0NBAeXk5r546xYqEBHbs2GH6/TQ8PMyaNWtm9FMgSCSMh4pICfAnpdQmj223Ah8DrlRKjfpxjbuAYaXUN+Y7trKyUrk6NlBqa2u5/PLLF3RutKO1Xx7uZgSN1ffcw8X5+fz27/5uxvbSn/6UmqIifnn99QA8u307d54+zbH+fveE9ysf+ABX/O532CcniRFh2vgMuf388ylOTeXzL72E/dOfJjHC1sA8d+oUVz38MM+/5z38zcqVvNbWxrrMTI7391P5q1/x7Suu4DMvvMA9V1/NR88/31Sff2fvXj7zwgucvv12itPSOD04yIf/8hcybDa+fOmllOXk+H2tyx58kLrWVmJE+Mu73sXVjzzC0zfdxI7mZr726quM33knU9PTJH/3u9xZWclXt2xZUJs98aZdRPb6O5cdkUNhInIt8K/A230ZFRFJFpFU1+/A1YB3Pz2IrFu3LtS3iFi09qWBfWKCpoEB9ypxT/KWLaNz1OMtl5fHge5urlq1im9s3QrA7xsbsU9O8oXqaho/8hHeu2ED4FztXZKeDsDpCIyicy3+3GjovmjFCjISE1llDAduN+YwVhtDgmb63LViv9bwwp44doztp0/z6NGjPBrgHNTRvj5yk5KYVoqnTp4EoCglheykJKaUYsDh4Fh/PxPT025tZjH7vIfdsIjIg8AuYIOItIjIR4AfAKk4h7f2icjdxrEFIvKkcepyoE5EGoBXgT8rpf4S6vbabLZQ3yJi0dqXBkf6+lDAuVlZZ+1bnpw8w7AcHRkB4KuXXcYd5eUAPGwMDX1w40bnnMoVV3DLxo383Zo17g/ppkg0LD095CQlnVWnJDspieT4eHYYRmC1YRzN9PnG7GziYmLcQRJ7OjpYvmwZcTExjExM+H2dQYeDjtFRbjQ+6P94/DgAxWlp5BjFx3rsdrfR3BSAJzQXZp/3gAyLiKwXkRtF5GMicrvxuynTppR6n1JqhVIqXilVpJT6uVJqrVKq2Agj3qyUusM49oxS6nrj9xNKqXLjtVEp9b9m2uEvB3xMXloBrX1p4FoV7o/HsrOxkYTYWNZnZZFsrCpvGhwkOT6eUuMDeEVKCvdfdx05y5a5gwGajHmBSOLNnh7Ozc521613ISKUpKUxOD5OXEyMu/KimT6PjYmhJC3NnTF6T3s7lfn5JMfHB2RYXKHfV5eUsHzZMo7195OWkEBqQgLZRn6ynrExd537VSbT5bsw+7zPa1hE5FwR+a6ItAJvAo8APwbuNn4/LCJnROQ7InKuqdZoNJqQ80ZXF/ExMWzw4rG4DItr7vWE3e7+9g24zzkvJ8drIamClBTiYmIi0mM5MTDAWmOYazauIbxVaWlurWZZnZHBiYEBhsfHebO3l8rlywMyLMf6+ni4sRFw5iB77B3vIMNmcxv0bA+PpdduR4AMw9iEG5+zayKyBvgacCNgB14EfgIcB3oAAbKAtcAlwEeB/ycijwL/ppQ6Edqmh4e8vLxwNyFsaO1Lg32dnZRlZ5PgJWVJ3rJlTE5P0+9wkJmYSJPDwfXFxe79GzIzee7UKcp9/D9iY2JYmZoacR7L2OQkZ4aH3R/Ks3F901/tsd9sn69OT+fhjg72dXYyrRSV+fn85vBhvwzL2//wB/ewV7rNxtqMDJLi43n9Qx/CbgRReBqWPqO/glU10qz2ucI2DgH7gVuBR5VSI3NdyJhAvwn4lHFuZJjOILN+/fpwNyFsaO3RzeT0NDEi7Ovq4pqSEq/HuOYfOkZGmJyepsvhmLGS2+WxnD/HWP6qtLQZa2EiAZehW+3DsLiG8FZ7eDRm+3x1ejo9drs7KMDfoTD7xAR/PH6c92zYwH9UVVGSnk6SkV6mxKP9rqGwbrud3rExd+r+YGBW+1w+37uVUpVKqV/OZ1QAlFIjSqkHlFIVwHtNtSqCccXSWxGtPXqZVooLfvEL3vPEE7SPjLDZxzdSl2HpHB1lv5cUIZX5+QhQXVDg814l6ekRNxR2wmVY5hkK8zQ8Zvvcda97DxygJC2N/ORkvwxLhzHHdXVJCeV5eaT7mEjPMDyUnrExeu12soJoWMxq92lYlFJPLPSiSqnHF3quRqPxn7nWoR3u6eElY8Hg86dOcaC7m98boa7lPvJJeRqWN7q6AOd8iovqggLaPv5xLjDSlnhjbUYGZ4aH+cZrr0VM3jDXJLovj8XliZ3jZd5pobjudWpwkLetWQNASgCGJX9W9NpsYkTISkx0eyzBNCxmCXu4cbSxlMJOA0VrjyyO9fWR8f3vu4daZvMfdXV88ElndP5P33iDdJvNPQbvy7Asn2VYMuPjWZ6cPPOYWX/P5uObN/P2NWv4lx073OsuwsXx/n7W3HMPfzpxgmVxcWeFGrs4PzeXXe9/v9sAgPk+9/SO/s64rj8eS7sR4j3f/xlgRXIybcPD9DkcQTUsixpu7EKcfFZE3hSRYRFpFJH/EpHIWmobAqqrq8PdhLChtUcWjx07xuD4OF946SWv+4/19dE8NESP3c5jx47xkU2beO+GDZyblUWWMfE7m+ykJIS/GpYL5xjy8kVmYiIPvu1txIoELRHjQnmptZUTAwM83dREaXr6WaHGnlxSUDBj8ttsn6fbbGQlJpISH8/WImceXb+GwlyGZR6PBZyh3m0jI0GfYzGrfaEeyxeAf8MZbnwn8AzwX8BXTLUmClhoKpilgNYeWTzd1IQAda2tvNjSwnOnTnHxr37F8Pg4SilODgwwOT3NzpYWJqenuXLVKu699lpefv/7fV4zLiaG7KQkzoyMcLCnh7ypqQW1bVl8PGXZ2TNS0ocDzxIAvuZXfBGMPr+0sJCbzznHXYkyOT6eYT+Hwnx5V54UJCfTOjxMX5CHwsxqn9PDEJFUpZS3ogLvB/5eKfW0x7HdOJM8/oupFkU4rqR3VkRrjxxGJyZ4saWFj5WX89CRI/z0jTcQnCnWHz92jGtKStwfYHUtLYAz8ikxLm7eHF75yck809TE2OQkxSbyfVXm5/PH48fdiUbDwdG+PmyxsTimpnyGGvsiGH3+xI03zphn8ncoLDMx0a+yyAUpKZwx2hlMw2JW+3wey5sicoOX7ZExI6fRLFGmleJQd7fPSoc7mptxTE3xjrVredvq1Tx54gTPnjoFwG/efJOTHutIXjKGo/xdlX3bpk3ucOE1fnxr9kXl8uV02+00B6ng1UI42t/P5cXF/G9NDbdt2jT/CSHA06gmx8djn5x0J+70RsfIyLwT9y5WeMzDBHMozCzzGZZvAb8SkUdEJN9j+2+AX4rIF0XkH0TkeziHxnylt18yVFVVhbsJYUNrDy2tQ0O0j4xwrK+Pkp/+lI333893fdQ2ebG1lbiYGLYUFfG2NWvoHRujfWSElampPN3UxGseQ1D1HR3kJiWR7Gedjk9ccAHrMjOJFeGmyy5bsJ4L850fGeEaDlNGNcx1mZn8xyWX+FzU6YtQ9HmysR5ldA6vpWN01K+Je3B6LC6C6bGY1T6nYVFKfQs4D0jG6b24iml9GfgmcDPwHeA64KvAf5hqTRTQYgwrWBGtPXQopbjy4Ye58qGH+Pprr9E5Okp2UpJ7LclsXu/ooCw7m6T4eK4pKXGnIfn+lVcypRTfNMbIY0SYmJ6esbBuPhJiY/nt297Gj6+6im4TRqE8N5dYEfaGqUha5+goQ+PjrMvMXND5oehzl2GZazisfWTEr4l7cE7euwimYTGrfd7Je6VUk1LqOuCTwJdFZAewTin1NaXUBqVUslJqnVLqi0op/7OrRSmuQkJWRGsPHS80N3Okt5dDPT3c88YbvP/ccynPzeW4sf5iNvu6urjA+AaeZrNx5cqVnJOVxd+tWUNZdjYnBgbITkqi0PjgCTQ5YcXy5fzD+eeb0p0YF8eajAyO9PbSa7fziJEVebE4Zvzv1gU4ae8iFH3uj2HpGB0l31+PxeO4YBoWs9r9jgpTSv0aKAOagNdF5PNWCC/WaBaDnzQ0kJmY6F4R/8kLLmBNRoZXw9I+MkL7yIjbsAD88vrreeammxAR3n+uMxdsaVqaO1NvSZCy3gbKusxMjvb388N9+3j3H//oDqVdDFwRYQv1WELBfIZldGKCofFxv4fC8qN0jmUGSqkepdQtwNuBW4B9IhJ5Af4hZINR1MiKaO2hYWxykj8cPcoHy8p44Lrr+M4VV1CxfDmr09PpstsZGh+fcfzrxtCSp2HJXbaMYsN43HzOOQCUpqe7PZZAhsI8Mat7XWYmx/r62GeUVD41OMjTJ0/69MSCyYn+fmJEFpxKPhR9Pp9hCWQNC4AtLs6djDKYhsWs9jkNi4jEiMhnRORFEakXke+LSI5S6jlgE/AkUCsiP3BVc1zqxHrJCGsVlrp2pRSPHT3K5PT0WftCqb1jZISJ6WnOz83l/NxcPnXhhQCsMYZwPD+E97S384SR9dbXZPSajAz+65JLuO2880x7LGZ1r8vIYHRy0p0doGlggHc98QS3/WX+mnxtw8NnGdVA6DEWDcYvUEMo+nxew2KsYfHXsIAzMmyZH2HkgWBW+3weyxdxps4/DtQC7wb+CKCUGlNK/SvOlPlVwCERebup1kQBhw4dCncTwsZS117b3MyNjz/Og2++eda+YGmfVoovvPQSxzwW7vn6MHEZFleeq8npabb89rfc3dDA6vR0n8kJAb5cU8O1paULnmNxYVa3axiq3+EAYHdbGyMTE+xsaeG1tjaf571y5gwFd9/NPz3//Lz3GHA4GPZigMzmzwrF8z6fYXH9n3xlRvBGQUpKQMf7g1nt8xmWW4GvK6VuVUrdiTMt/sUi4vaTlFKv4zQs38EC4caa8DExNcWnt2/nmaamkFzfNVzz5xOhKyX0Wns7X9q1i/s8KvR1+lhpPdtjaR4cxD45yU3r1/PDt77Vr/vduG4dn9i8mXOCVAs9UGbPb7xglP8F+JaXUOpppfh+fT1XPfwwgF8RZTc+9hil99zDE8eOzdgeaYkZwZmEEnwblgHDsKT7GRoO8J4NG/jAuZFVY3E+w7IM8OzZTpwFvma8A5RS00qpb+IMTV7S5Ofnz3/QEiXc2vd0dPDd+nqueeQRfvbGG0G/viu09+mmprOGw4Kl/S9GUkZX5mDw7bG4ck0dNxY7ulK//+PmzVxbWurX/dZkZPCDt751wVURzeouTk11FxRbFhfnNt5XrVrFkydOnLVQ8PFjx/in7du5pKCA83JysPkxJLO/u5tuu52b//SnGf3WNzZGpolkiqF43ufzWNyGJYB2f+S88/jqli3mG+eBWe3zPW3PAf8uIh8UkbcBPwXagIPeDlZKNZlqTRRQ6ucbeikSbu31xrfX7KQkd3W9YLK/qwtbbCz9Dgcvzwq3DJb2p7wZFmPC1ltuqDUZGe6hsBPzFKsKBWZ1x8bEsCY9naS4ON5SWIjCuU7mPRs2MDg+PiOXF/zVO3v0hhs4PzeXnrGxOa8/OjFBt93OusxM7JOTMzIO9I6NmRoiCsXzHgrDEgrMap/PsHwSeB24D3gCyANuUkotfEbNCyJyr4h0isgBj21ZIvKsiBw1fnqNGRSRW4xjjorILcFslzd27doV6ltELOHW/npnJzlJSWzOzaXLbg/qtaempznY08Pfl5URHxPD/Qdnfnd6YscOn+lV/KV7dJRX29rITkri9NAQn6+ro/yBB+gYHSU1IcFdJdCT3KQk94frif5+4mNi3BPyi0Ew+ry6oICawkJ3rq7V6enuQmGvzppn6bHbiY+JISU+nuykJHrnMSwtRrqYq1atAuBwb697n9mhsFA87y7DMjQ+7nX1/cD4ODEi7uPChVnt862871JK/S2QAmQqpcqUUqH4dLkfuHbWts8Bzyul1gHPG3/PQESycGZargIuBr7gywBpop/6jg4uyMsjd9kyuozho2BxYmAA++QklxYU8E8VFdx34AAvGJFMfzp+nA/s388n/ZhInottr72GAv7pggsA+Oqrr/JGVxcHu7t9RgGl22zub7EnBgYoSU8ndoHDWuHiJ1dfzZ/f+U6KDYO4NiODc7KySI6Pn5F6BpxldnOSkhARshMTGXA4vEbpuThtGJa3GobliGFYppWiPwLnWBJiY4kV4Qsvv8z6n//8rP0DDgdpCQlhS9oZLPx6Qo0IsIH5j1wYSqmdQO+szTcADxi/PwC8w8up1wDPKqV6lVJ9wLOcbaCCSlKQoy+iiXBqd0xOcqC7m4rly8lNSgqqx/J6R4c7J9d5ubl86dJLWZORwb/s2MHx/n7e+fjjTAMPvvkmg8aHfKA8fuwYX3/tNe4oL+e285xTka4PzF1nzvhMkZ5us7nveby/f1GHwSA4fR4XE0N8bCwrDcOyLjOT2JgYKpcv51UvhsW1LsP1s3eOvm42kmVuzs0lb9kyt8cy4HCgMLe2IxTPuxjeyLRStA4P45jlBQ84HGEfBgPz2n0GPovIeqVU40IuKiIblFJm8zcsV0q5/OR2wFst1EKg2ePvFmObtzbdjjOtPwUFBdTW1gKwevVqUlNTaWhoACA7O5uNGzeyc+dOAOLi4qipqaG+vp5B4yEeHh6mo6ODZiPCZd26ddhsNg4YkT55eXmsX7/eXTfaZrNRXV3Nnj173Omoq6qqaGlpcadO2LBhA7Gxse4wv/z8fEpLS90uaVJSElVVVezevRu78Uarrq7m5MmTtBtvzrKyMqampjhipM4oLCykqKiI3bt3A5CSkkJlZSW7du3CYXxY1dTU0NjYSKcxqbpp0yYcDgdHjRK2xcXFLF++3F2fob6+noqKCurq6pg03hRbtmzh4MGD9PT0AFBeXs7Q0BAnjOiqkpISsrKyqK+vByAzM5Py8nJ27NjhTqm+detWGhoa6DPG3CsqKujt7aXJiAAbycxkYnoaW3c3nWNjDDgcjE1M8IpR5MpbP1VWVs7bT6vXruW63/2OjvFxkmJiKMvO5lBDA1cmJ/PT1la++9prTExP89+rV/OlEyf46auv8qENGwLup5+8+io58fG8MzaW+NFRMhISGJ6YYFIpRicnyUlMdD+Tnv3U39FB/9gYk5OTHO3tpTAjg9ra2nn7KS0tLSj9VFVVFVA/zfV+6jX6ZW1GBvX19eRPTPBoZye9AwP0dXfT3NzMifZ2Upcto7u7mw5jPmr3wYNcZ2iZ/X7a2diIAFlxcaxKSmL3yZPU1tZiM4baOk6epHZoaMHvp7a2tqC/n+wexuTFPXt46yWXuPvp+JkzpCcksH///pC+n/z53ANmvJ8CQinl9QVMAPcCm3wd4+WcC4BfAhP+nuNxbglwwOPv/ln7+7yc81ngvzz+/jzw2fnudeGFF6qF8sorryz43GgnnNrv279fsW2bOtLTo378+uuKbdtU69CQ6es+2tio2LZN3dPQMON6DZ2dim3bVNw3v6nK779f7dq1S5Xde6+6+Je/9PvaI+Pj7t83P/CAuu6RR9x//0ttrfrfXbtU4re/rdi2Td3xzDNer/G/u3Yptm1TbcPDim3b1LZXX12AyoUTzD7vHxtTb33oIXWsr08ppdTP33hDsW2bOtnf7z7m3J//XL3rsceUUko9ffKkYts29WJzs89r3vbUU2rFj36klFLq9qefVtk/+IFSSqlXz5xRbNum/njs2ILbG6rnnW3b3K+jvb0z9m198EF12YMPhuS+geBNO7BH+fl5PtdQ2Ntxzl00iMjrIvI/InKDiGwSkQIRKRSR80TknSLydWPifQ+wGXhb4CbuLDpEZAWA8bPTyzGtQLHH30XGtpBhD/KkcTQRTu0nBwYQnKlJco1hI7PzLA2dnXxl925WpqZy66ZNM1KQn5eTQ2FKCpPT07xtzRrGxsb4WHk5r7a3nzXh7I1nmprI/uEPOdnfz7RSNPb2siEry73/61u38h+XXMIGY53HXENhrrYCARerMksw+zzdZuPZd7/bvT7HNUzV7zG82DM2Ro7xv8g29s8VGdY8NOSeuzknK4seu53u0VH3pL+ZOZbFeN4HZg2tDoyPB7SGJVSY1e7TsCilngLOB/4eGMaZEv9RoAHn8NNpYB/O8sSfxfnBfzNQrjwqS5rgCZz5yDB+Pu7lmKeBq0Uk05i0v9rYpllinBocpNBYE5FrjP/6O89in5jg23v2zBjP/klDA5t/8Qtea2/n89XVZ63zEBGuX70agL9bswaAWzduJDUhge+//vq896zv6GBscpI/nzhB69AQo5OTbiPiyTmGsZlr8h7giDGkUehh/KId1xqTPsMIKKXoMSbv4a9zLD1z9PPpoSFWGlkFzjUWge7v7qbP+MCOpMSMLj594YVsNNo6OCtjwGCEzLGYZc7kMkqpKeBB4EERWQ5sxZnhOBdnFcku4ACwQynlvXCEH4jIg8DlQI6ItOCM9Poq8JCIfAQ4BbzHOLYSuEMp9VGlVK+IfBl4zbjUl5RSs4MAgkp1taVybs4gnNqbBgbcaUkC9Vj+cOwYd9bWkpWYyC2bNvFSayuffP55rist5d5rr/WZovzTFRVk2mxclJ/PRFYWNpuND2/axI/37eOHV15J2hwfAE3GuPTTTU3uVe+eHosLt2Hx0YY049ura31HromKjgshlH2eMctjGXA4mFLK7anMZ1iUUjQPDvK3xpqLqhUrAHj5zBm30TLjsYRK+7evuILXOzqo+OUvvXssEWBYzGoPJG1+h1LqIaXUXUqpTyilPqmcNVh+b8aoGNd+n1JqhVIqXilVpJT6uXJmUr5SOWu9vNVlMJRSe5RSH/U4916l1FrjdZ+ZdvjDSWNC0YqEU3vT4KA7kaI3j+Xbe/Zwj4/V+K7V3r82coB9a88eshMTefBtb5uz7kVZTg5f27qVGBG39q1FRUxMT7trffhsr7FQ74XmZvYbiyG9GhbD6MznsbgMS84iR+aFss9dH/79hsfSbfSnS2NKfDzxMTF0jo6etWAVnP06OjnpThuTmZjIppwcXmxpcQ+FmfFYQqnd9aXE02NRSkVMVJhZ7dEVEB8BtIepzGokEC7tk9PTtAwNuVO/ZyUlESMyw2P59t693P7MM/zEiHLxxGVYnj99mmN9fTx58iQ3rV8f0BvYpd3VhqaBuaPvTw0Okm6zMTIxwc/37yclPn5GfXIX71i7lh9eeaV7weBsXG08ZiyOTFvk8fdQ9rnLY3ENW7kMi8tTERGyk5K4u6GBSx988KxU+9tee43UhAR3mQCAywoLefnMGbrsdlLi493pZBZCKLW75lE8PRb75CST09MRMcdiVnvAhkVE1onI9Uaalw/NfplqjUbjhdahIaaUcg+FxRiL51wey7RStBlpUT5bWzsj/5RSin2dnVxaWMi0Urz7j39kzEjkuBBcXlPTHCGYSimaBge5+ZxzKExJ4WBPDxuysrwuekuMi+MfL7jA56JHlyE5OTDgXji4VEhNSED4q8fSM8tjAecE/rCxQv3M8DC3PPkkX3z5ZZoGBnjoyBE+dv75bgMFUFNUxND4ODuamyNyfsWFN48lUtK5BAO/E/gbcywPAFe5Nnk5TAG/CEK7IpaysrJwNyFsLFT786dOsSknx++qeLM5ZXyIe9YU8Vx93zU6yuT0NJvz8tjX2UnTwACrjcijtpERuux2Pr9hA1X5+Xxr715yk5K4rKgooDa4tGcmJpKWkDCnx9I5OsrY5CSbsrP5t4su4h2PP+5OORIorg+Z8ampRR8Gg9A+7zEiZCQm+vRYZv/eOzbG86dPc7i3l+LUVKaU4iPnzcx7W1PoXMbW0NVFeW6uqfaFUntCbCyJcXEzPJZIMixmtQdSGeYHOI3Kj4HtQI+pO0cpU1NT4W5C2FiI9kGHg2seeYQPlpVx33XX0TI0xCW//jVP33QTG3Ny/LqGyzvwrCniufr+jLHo9KpVq9jX2cmhnh63YXENg23Oy+OTF1xASXo6OUlJAadFcWkXEUrS0+f0WFyGcFV6OqUZGTTccotrnVXAeA59hcOwhPp5z7DZ3JP3rrDiHB+Gpcdup9tuZ3Ryksa+PuJjYlg7K9JuZVoa/3XJJfzvK6/4XTfeF6HWnpaQMNNjMX6PBMNiVnsg766rgLuNSftHlVI7vL1MtSYKcK3CtSIL0f5aeztTSvHYsWOMT01R39FB6/CwX2tBXLg+qFf68FhaDcPiyhd1sPuvsSSu+5yfm4uI8P8qKnjfAmpXeGovSUub02Nx7fP0sBY6hBUfG8syYxX0YkeEQeif98zERHe4cbfdTtyseaTStDS3oTk9OIhjaoq+sTFeOXOGtRkZXssBfLmmhoZbbuFHftas8UWotXvmgYO/eiyLPY/mDbPaAzEsMTjXsGg0frPrzBnAGVK6/fRpt5FwGQN/ONLby4rk5BmlV3OSktxj8q5rlWVnu+c0wJlj6vuvv85bV60K6rfAkvR0Tg4M+PRCvHlYZnCNx4fDYwk1MzwWu53sxMQZRvhLl17K/ltvJS4mxr2WB6CutZX1XqLsXJyXm+v2WiOVtISEGbnnImkozCyBGJYXgfJQNSRaKCz0morMEixE+y7jm2VqQgKPNDZyOgDDcrinh+HxcZ44fpyrS0pm7Eu32dxDB2eGh4kRIT85mY05ORwyDMv/vPIK/Q4H37z88oDbPRtP7aXp6QxPTPhM6X5qcNA5FxOkD4j0MBqWUD/vmTab22M5Mzx81jxcckIC+cnJZCUm0uhhWKaUYr2XBafBJNTaPZ9hiCzDYlZ7IIblTuBGEXmXqTtGOUUBTvouJQLVrpTilbY2thYX89ZVq9jR3Oy3x3Kyv59z77uPSx98kKHxcW7duHHG/rSEBManphibnKR1eJjly5YRZySRfLOnh2mleKSxkRvXruV8k5O4MFO7OzLMx3BY08DAjGEws7jCT3PDYFhC/bxnJCa6PZbTQ0M+vbzsxEQae2eufQ61YQm19tkey2AEzbGY1R6IYfkxztQuD4lIs4jsEJHts17mClZEAa7MplYkUO1H+/roHRujuqCA8txcjvf386bx4TCfYXF5HW90dVGans6W4uIZ+11vvkGHgzPDw+48XxuzsxmdnKS+o4PmoSH3amyzeGp3rWWZva7CRdPgoPuYYOD2WMIwxxLq5z3DZnOHG58eHHSn1p9NVlKSO+zYNa8SasMSau2ecyzTxjxkVmIiqREwx2JWeyCGZTUQjzNH2CSwEiid9VptqjWaJUWDseK8Ii+P83NzUcABY2K91SjQ5AtXnfdbNm7k61u2EDNr8tv1YTswPk7r8LA7h1alUav7bmOhZHleXnDEeHBOVhbxMTHsNUole6KU4tTgYNDmV+Cvk7lLcY4lMzGR0clJeux2+h2OOT0WF64wYm+ZDKIJz6iwH73+Oi+2tPDNyy8/61mPRvwON1ZKlYSwHVFDyhJKAhgo82kfn5qasdL5zZ4eBOcHgOe3sMS4ODpHR7l3/356x8b47EUXufftOnOGjpERjvf3kxIfz33XXus1osr1YTvocNA6PMxbjJXr5+fmkpmYyK+Meilm1zK48NSeGBfH5ry8s4pUgXMCemRiIrhDYYYRDcdQWKif9wxD2xvGl5CVPv5vrpxfsSJsKSqiZWjIZ0boYBFq7ek2G4Pj4yilePDwYS5cvpxbZg35hguz2v3yWEQkxRjq+oipuy0BKisrw92EsDGX9tc7Okj93vd4zSOM+M3eXkrS01kWH8/qjAx32Gzl8uUo4DMvvMB/1tW5h0IA7nr5ZW57+mmO9fWxJiPDZ5iu68O2c3SUHrvd7bHEiLC1qAjH1BT5yckLXpQ5m9naq1asYE97O1Ozyua6IsJCMhQWBsMS6ufdtTq+YR7D4llV8ouXXsruD3wg5FkIQq09LSGBaaUYmZigb2yM0vT0iMmsYFa7v6WJh4GL5j3QArgq0FmRubS/0tbG+NTUjJTyb/b0cK4xXBEj4l4Q6fIuBsfHGZ+a4lGjuh44Q4v7xsaobW521+3whstjcUUKeS6Gu9yYjwmWtwJna784P5/hiQn3XBA4o5pcE/rBHArLW7aMhNjYsBiWUD/vZ3ksvuZYDAOUm5REakICqxahLk2otbuHcx0O+hwO9/8iEjCrPZA5ln1A4CvLlhiOWWmurcRc2l21xh86coQeu52p6WmO9PW5a2QA7ugsz4SLmYmJ7qzD9okJdzjy6OTknIbFMzkjzCyUFQrDMlu7Kyhgt+Gh1bW0UPyTn/Al4w0ZzKGwj2/ezIs334wtLpBEGcEh1M+7p8cSFxPjc7W8y2NZTOMaau2e+cL6xsYiKreZWe2BGJYvAP8gIleYuqNmSXK4t5fcpCQcU1Pcd+AATYODjE1OzjAsf7t6NeW5uVxiGJbi1FQ+VVHB9tOnuePZZznQ3Y3nkkN/DIu3OiXn5ebyherqs/JIBZN1mZmk22zUd3QwMTXFx597jmml2N/dTbrNNiMxolnSbTYuDlJ0W6ThmpQ/0N1NUUqKz1Q7Lo9lKQUwuMLIO0ZGcExNRZRhMUsgX4H+HmdE2HMi0gA0ArMrLSml1JKeh6mpqQl3E8LGXNoP9/RwTWkpzYODfLe+3m0UyjwMy43r1nHjunUopUhLSODa0lL+9aKL6Bsb47v19e51CqvS0jg1OOjXUJjLY/Gc2I4R4a5LL12gSu/M1i7GgsyesTH+cPQoB7q7+ZuVK9l++nRQh8HCTaif93WZmWwtKmJHS4vP+RXw8FgWMeQ61Npdmo4az3BmBA2FmdUeiMdyK7ARZ1bjzTgrOt7q5bWkaWxsDHcTwoYv7SPj45weGuKcrCz+9eKLaRka4l93ONPGneMlJFREqH3ve/nqZZeRFB/Pt6+4grUZGbzQ3AzAhzdtAuZepxAfG0tSXBwnjTmNUEcIedPuSkfSbIRO//DKK4kRCeowWLgJ9fMuIvz4qquIj4mhdI55k3B4LKHW7npmjxhfqILp5ZrFrPZAKkjG+PFaeFWdKKHTyJZrRVzaD3Z3s/qeezhmTJy7JtDPycriutJSzs/NpXloiK9edplP9/6C5cvJ8ijodMPatYBzeOyzlZU8+c53zvkNFpxey+T0NAmxsSFfVOat312L+/ocDmJEWJ+VxTe2buXjmzeHtC2LyWI87+dmZ/P8e97DXW95i89jXB7pYoZch1q7S4vr/RNJHotZ7Ys/G6iJevZ2dHByYIAf7dvH17ZscU9gn2MUs3r+3e9mSqmAQn1vWLuWb+7Zw4asLJITErhu9fxrbdNtNjpGR8kNUwGsDJuNkwMD9I2NkWGzESPCZywcjm6G+erjFKamcu811/B3a9YsUotCT3JCAsnx8W6PxapzLIuKiGwAfuexaTXw30qp73gccznwOOAq0PyoUupLoWzXJmOYxoq4tLvS1d9/8CB/OnGCo319xMXEsNaYE1nIOPhbCgpYlZbGxcbKeX9wLxxchHF3b/3uynMVaRE9wSSSnvcPhzAYwxuLoT1v2TJOGMO5kfQMmdUeSAXJ7X4cppRSV5poj+eFjuCcy0FEYoFW4A9eDn1RKfW2YNzTH3S4Me4CW31jY0xNT/PNyy9nQ2amqXDY2JgYDtx664zU+PORtojJGb31u2uOpW9sLKKGMYKJft5DS96yZe55wkh6hsxqD+STYDUwuwBFHLAC51xNNzBiqjW+uRI4rpQ6FaLr+83Ro0ctmzrfpb1rdJQVycl89qKL+JuVK9kcpHxcKQHOk7g8llBP3IP3fs+w2RifmqJtZGRR2hAO9PMeWu2ez00kZDV2YVa76VxhImLDmVL/w8DWBbdkbm4GHvSxr9oIfz4DfFYpdTBEbdAYdI6Osjw5mTvDPJ/g9ljC9KHuWil9cmAg6hMiasKDy7CkxMcTH7t0Yp9Mz7EopRzAV0SkDPgW8D7TrfJARBKAtwP/7mV3PbBKKTUsItcDjwHrfFznduB2gIKCAmprawFYvXo1qampNBjZcLOzs9m4cSM7d+4EIC4ujpqaGurr6xkcHMThcDA8PExHRwfNRnjsunXrsNlsHDhwAIC8vDzWr19PXV0dADabjerqavbs2cOwkS6+qqqKlpYWWltbAdiwYQOxsbEcMpIn5ufnU1pa6k6tkJSURFVVFbt378ZuDEVVV1dz8uRJ2o1kiGVlZUxNTbnLihYWFlJUVOROgZ2SkkJlZSW7du1yu7o1NTU0Nja6o0A2bdqEw+HgqJFmpbi4mOXLl7Nnzx4cDgf19fV02e3EOxzu/+GWLVs4ePAgPUZ6k/LycoaGhjhx4gQAJSUlZGVlUV9fD0BmZibl5eXs2LEDpRQiwtatW2loaKDPiJCpqKigt7eXpqYmn/3kMixD7e3U1dXN6Cdw5jsKVj85DL2e/XTGmHQdHB/H3ttLbW1tRPQTQFpaGhUVFdTV1TE5ObngfiouLjbdT3O9n4LdT8F8P2VkZNDW1hbSflLG/2CZSNjfT579NGGUKPDsp0AQX+VVA76QyMeAryilgvrVTURuAD6hlLraj2ObgEqlVPdcx1VWVipXxwbK8PCwZTMcu7SvueceLiko4Nd/+7dhbc8XXnqJL+3axU+vvpp/OP/8kN7LW7//5eRJrvv97wH4t4sv5qtbtoS0DeFAP++h1f7dvXv59AsvcH5uLg233BLSewWCN+0islcp5dcwRSALJOejFAjFYoL34WMYTETyxYgzFZGLcerp8XZssFioQVoKuLR32e1hSeE+m8WcvPfW755JAyNp4jWY6Oc9tLiGwiIpASWY1x5IVNhKH7uygLcC/wTUmmrN2fdMBq4CPuax7Q4ApdTdwE3Ax0VkErADN6tguWAar4xNTjI0Ph62eQ1PFnPy3hszDEsEhYpqogfXs7vUnp9A5liaODsqzIUAR3Aal6ChlBoBsmdtu9vj9x8APwjmPecjbQml6wiUtLQ09xqWSPBYNuXkkJ2UxLoQl6gF7/3umYJjqX0wuLD68x5q3IYlwjwWs9oDMSxf4mzDooBenAkpn1NKTZ911hKjoqIi3E0IGxUVFdQb5XgjwWO5pKCA7k98YlHu5a3freCxWP15DzWR6rGY1R5IrrC7lFJfnPX6klLqB0qpZ6xgVAB3ZIoVqaurc3ssS3Xdhi+89XtiXJx7QWekfeMMFlZ/3kNNdlISOUlJ7qwVkYJZ7X4bFhG5V0Sq5th/sYjca6o1UYArdNMqHO/v58bHHmPQ4WByctK96j4ShsIWE1/97vJaIu0bZ7Cw2vPuyWJoj4uJ4fhHP8rHystDfq9AMKs90LT5c2WAKwUiJ15OExQeOHCAx44dY/vp08Bf84RFwlBYJLDUDYsm9KTZbD4LnEUrwVSTDEwE8XoRyZYluFZhLp42FlS9fOYMW7ZsoXN0lLiYmIgLjww1vvo9w2ZDiKx0HMHEas+7J1r7wpnTsIjIShHZIiKuu5zj+nvW6x3Ax4FjploTBRw8aJ2MMb12O68Zq5Bfam3l4MGDvNHVxdqMjLCkqQ8nvvo9w2YjzUiZvxSx0vM+G6194cwXFfZhnLXulfH6T+M1GwGmjeOXNK40C1bguVOnUMClhYW81t7Omfx8Xj5zhpvWrw930xYdX/2en5zMigDqzkQbVnreZ6O1L5z5DMtjONevCHAv8FNg16xjFDAMvKaUajbVGk1E8WJrKynx8XyqooL3/PGPPNvTQ7/DQY1Fs9164ytbtjBg4dTyGo035jQsSqkGoAFARFYBv1dKHViMhkUq5REWvRFKOkdHKUxNZUtRETEi/KarC4Caear9LUV89Xt+cjL5S9hjsdLzPhutfeEEso7li1Y3KgBDQ0PhbsKi0Ts2RlZiIsuTk/nnykraRkfJT05mdXp6uJu26Fip3z2xqm7Q2s0QUFSYiBQb61laRGRcRP7G2J5rbL/IVGuiAFfqaivgMiwAX7r0UtYmJfG3q1dbbuIerNXvnlhVN2jtZghkgWQpsAd4F3AQcFelUUp1AZXAR021RrOo2Cfmjg7vtdvdhiUxLo67y8q45+p5qxdoNBqLE4jH8r84I782AR/AOaHvyZNATZDaFbGUlJSEuwmm6BsbwzE5ye8OHybnhz+kx1hJ7w1PjwVgTWmpJb0ViP5+XyhW1Q1auxkCSUL5VuD7SqlmEcn2sv8UsORndbOivATtRb/6FX+zciXtIyOMTk7yZk+P18n4iakpBsfHZxiWaNduBqtqt6pu0NrNEIjHkga0zbE/gSCUOo50XOVAo5H+sTGO9/fzq0OHeO7UKQCO9fd7P9YIofU0LNGs3SxW1W5V3aC1myEQQ9AMbJxj/yVYYOV9NNNo1L+2eySYO+7DsPSOjQGQZbFkkxqNxjyBeCyPAreJyCaPbQpARN4FvBt4KIhti0gyF6GoVKhwGZacpCRSExIoSk2d37B4FrOKYu1msap2q+oGrd0MgXgs/wu8DdgN7MRpVD4nIv8HXAzsA75pqjVRQDQvmjrS20uMCH9+5zvpGxvjG3v2+BwK6zUm9T0NSzRrN4tVtVtVN2jtZghkgeQgUA38DGdoseCsR78B+BFwhVJqzFRrooAdO3aEuwkLprGvj9L0dC5esYJrSktZm5ERkMcSzdrNYlXtVtUNWrsZAppsN4zLp4BPiUguTuPSpZRyDYmJ6/dgISJNwBAwBUwqpSpn7Rfgu8D1wChwq1IqZLNuQZa3qDT29bHBw8Vdk5FB79gYfWNjZ9UT8WZYolm7Wayq3aq6QWs3w4LrsSilupRSnUopJU7+HjhsqjW+uUIptXm2UTG4DlhnvG4HfhyiNgBE7TqOaaVo7O1lvUcY4RqjHKo3r6VvbOysOiPRqj0YWFW7VXWD1m4GvwyLiFwgIu8RkbeKSPysfTcDh4BfAPmmWrMwbgB+oZy8AmSIyIpQ3Wzr1q2hunRIOTM8zOjkJOs9PBZXne1j/f1MK8W0x7eU3rExMhITZ1S2i1btwcCq2q2qG7R2M8xX6CtJRP6CM5XLg8DTwFERWWvkDXsJ+DWwAufkfqmp1nhHAc+IyF4Rud3L/kKcodAuWoxtIaGhoSFUlw4pR3p7Adjg4bGsy8wkLiaG/V1d/MeLL3LJr3/t3jd71T1Er/ZgYFXtVtUNWrsZ5ptj+TfgapyGpRZYC7wDuBsoxvkB/mXg20qpAVMt8U2NUqpVRPKAZ0XksFJqZ6AXMYzS7QAFBQXU1tYCsHr1alJTU93/yOzsbDZu3MjOnc5bxMXFUVNTQ319PYODgwwPD7NmzRo6Ojpobnbas3Xr1mGz2ThwwJn8OS8vj/Xr11NXVweAzWajurqaPXv2MDw8DEBVVRUtLS20trYCsGHDBmJjYzl06BAA+fn5lJaWsmuXs/xNUlISVVVV7N69G7sRsVVdXc3JkydpN6o8lpWVMTU1xZEjRwAoLCykqKiI3bt386fOTgDWZ2aya9cuHMYCyA2Zmbx04gTNw8OctNtp7eiAyUmOtbYSPznJ8ePHWb58ubvtU1NTVFRUUFdXx6SxHmbLli0cPHjQXRyovLycoaEhdyK7kpISsrKy3IuuMjMzKS8vZ8eOHSilEBG2bt1KQ0MDfUZIdEVFBb29vTQZpZED7SeAysrKoPVTc3MzfX19Ie8ngJSUFCorK2f0U01NDY2NjXQa/bhp0yYcDgdHjx4FoLi42N1PAGlpaUHpp76+vqjqp2C+nyYmJmhra4uKfgr2+2l4eJjy8vIZ/RQQSimfL+AAztDiGI9t/40zZ1gTsHqu84P9Au4CPjtr20+A93n8fQRYMdd1LrzwQrVQXnjhhQWfG04+vX27Wvbtb6vp6ekZ2z/wpz+p3B/8QMV84xuKbdvUwa4upZRSVb/6lbrm4YdnHBut2oOBVbVbVbdSWvtsgD3Kz8/q+eZYVuMs7jXtse13xs+vK6VCmldaRJJFJNX1O07vaXZNmCeADxkBBJcAA0qpuVLPmKKioiJUlw4pR4yJ+9mTcuW5uXTZ7e75lSPGN5y24WFyZq26j1btwcCq2q2qG7R2M8xnWBKBrlnbuo2fR03d2T+WA3Ui0gC8CvxZKfUXEblDRO4wjnkSOIEzncw9wD+GskG9xlxFtNHY1zdj4t5FeV7ezON6exlwODg9NERZ9sxco9GqPRhYVbtVdYPWboYFhxvjXFcSUpRSJ5RS5cZro1Lqf43tdyul7jZ+V0qpTyil1iilzlNK7Qllm1xjlNGEY3KSkwMDM9awuCjPzQWgND2d/ORkGvv6eMMoQeza5yIatQcLq2q3qm7Q2s3gzwLJDxlDTC4ScUZqfVJE3jHrWKWU+pSpFmmCzomBAaaVmrGGxcXy5GSKU1O5tLCQ04ODNPb10WBMPM72ZjQajcYf/DEsVxuv2bzDyzaFc2X+kmX16tXhbkLAuEKNvQ2FAbzw3veSYbPxuZ07eeL4cRq6ushKTKQwJWXGcdGoPVhYVbtVdYPWbob5DEso1qVENampqeFuQsA0dHUhzFzD4olrBf76zEw6R0d5obmZ8tzcsyb6o1F7sLCqdqvqBq3dDHPOsSilTgX6MtWaKCAaF03taG5mc17ejPQs3rimtJTEuDiO9/d7HQaLRu3BwqraraobtHYzmJm810QBjslJdrW1cXlx8bzHnp+by59uvJH85GSuL9XOqkajWRhLvpRwsMmeFYIb6bza3s7Y5CRb/TAsAFeuWsWZO+7wmoQu2rQHE6tqt6pu0NrNoD2WANm4ca7qzJFHbXMzAlxW6H/6NF+ZTaNNezCxqnar6gat3QzasASIK5dOtLC3o4NzsrKCUrs+2rQHE6tqt6pu0NrNoA3LEqdrdJSCWWHDGo1GE0q0YQmQuLjompbqGRsjOwjeCkSf9mBiVe1W1Q1auxlEWbD8ZmVlpXKlrV7q5P7wh7x7/Xp+dNVV4W6KRqOJYkRkr/JexfcsfJolEbl3AfdWSqmPLOC8qKG+vj5qsp5OK0VvED2WaNIebKyq3aq6QWs3o30uf+fWBVxPAUvasCyo6E2Y6B8bY1qpoBmWaNIebKyq3aq6QWs3g0/DopTS8y9RTs/YGMBZdVU0Go0mlGjjESCVlX4NMUYEPUbJ1exZtesXSjRpDzZW1W5V3aC1m0EblgDp6OgIdxP8pttlWILksUST9mBjVe1W1Q1auxkCMiwiEiciN4nINhH5mYjcO+v1c1OtiQKam5vD3QS/6QmyYYkm7cHGqtqtqhu0djP4HawsIlnAC8AmQHBO1LtyfyiPbUt68j6acM2xBGsoTKPRaPwhEI/lf4BzgI8Ca3AakmuAc4EHgdeAJZ+1bd26dWG7t1KK506dwt+1R912O7Ei86bL95dwag83VtVuVd2gtZshEMPyt8AvlFL3Aa5YtCml1BGl1N8DduArplrjgYgUi8gLInJIRA6KyFmVKUXkchEZEJF9xuu/g3V/X9iC9CG9EGqbm7nq4Yd5+cwZv47vsdvJTkrymVQyUMKpPdxYVbtVdYPWboZADEs+Tq8EYNL46TnG8hjwdlOtmckk8M9KqTLgEuATIlLm5bgXlVKbjdeXgnh/rxw4cCDUt/BJ08AAAO0jI34d32O3B3UYLJzaw41VtVtVN2jtZgjEsPQCycbvQ8AE4FnkYwLwXlR9ASil2pRS9cbvQ8CbgP+535cgrcPDwF8n5ecjmHnCNBqNxl8CMSyNQBmAUmoaeB24VURsIrIM+BBwIvhNBBEpAS4AdnvZXS0iDSLylIiEvIBCnpeSvcFmcnqaQYfjrO1nDMPSa0zKz0e3MRQWLBZDe6RiVe1W1Q1auxkCSWH5DPBZEfmkUsoBfAv4LU5PRgFJwO2mWuMFEUkBfg98Wik1O89APbBKKTUsItfjHI7zOuskIre72ldQUEBtbS0Aq1evJjU11V3jOTs7m40bN7rrEcTFxVFTU0N9fb07zcHKlSvp6Ohwh+StW7cOm83mdh/z8vJYv349dXV1gHO8srq6mj179jBsGIeqqipaWlpobW0FYMOGDcTGxnLo0CHua23l6b4+mj76UV555RUAkpKSOGMMge1rbKTWbqe6upqTJ0/S3t4OQFlZGVNTUxw5cgSlFB1DQ1yYm+vWmpKSQmVlJbt27cJhGK6amhoaGxvp7OwEYNOmTTgcDo4ePQpAcXExy5cvx5W0c2xsjIqKCurq6picdI6IbtmyhYMHD9LT0wNAeXk5Q0NDnDjh/J5RUlJCVlYW9fX1AGRmZlJeXs6OHTtQSiEibN26lYaGBvr6+gCoqKigt7eXpqamBfdTZWVl0Pqps7OTzs7OGf0EkJ+fT2lpKbt27XL3U1VVFbt378ZueJZz9RNAYWEhRUVF7N69O2j9lJaWFpR+2rhxY1T1k7f300L7acOGDbS1tUVFPwX7/RQbGwswo58CQinl1wtnFJht1rZ3Ao8CDwPv9fdaAdwzHngauNPP45uAnPmOu/DCC9VCeeGFFxZ8rr+U33+/Yts2dXpgYMb2i375S8W2beq2p56a9xqPHDmi2LZN/ej114PWrsXQHqlYVbtVdSultc8G2KP8/Oz222MxLuyYte1Rw7AEHXGGMv0ceFMp9S0fx+QDHUopJSIX4xza6wlFexaL7tFRGrq6ADjY00NxWpp7n79DYcPj4/zT9u1szsvjH84/P3SN1Wg0Gi/4PcciIttF5Mo59l8hItuD0ywALgU+CPyNRzjx9SJyh4jcYRxzE3BARBqA7wE3GwYwZIQ6BPEFjxWvh3r+aiOnpqfd0WA98xiW19rbOTM8zJcvvZS4mOBl7dHhl9bDqrpBazdDIHMslwM/m2N/HrDVVGs8UErV8deV/b6O+QHwg2Dd0x+qq6tDev3nT58mNSEBW2zsDMPSOTrKlGEze+eJCjtphCWXZQd3vWqotUcyVtVuVd2gtZshmEkoM5g1VLYUCWXlybv37ePe/fu5etUqNuXkzDAsrmGw3KSkeYfCTg4MECNCcWpqUNtnlaqb3rCqdqvqBq3dDHN6LCJyPrDZY9NlIuLtnCzgH4FDploTBbiiUMwyMTVFvBF5AfD7xkY+/txzXF9ayk+vvpr/fuklfvXmm+4oD1dE2Hm5udS1trq3e+PkwADFqakzrh8MgqU9GrGqdqvqBq3dDPMNhd0IfMH4XQEfM17eGAL+yVRrLEL/2Birf/YzblizhqoVK3ihuZmnm5qoWrGCx97xDuJjYynLzmbA4eDkwACrMzJoHRoC4LycHLafPs3oxATJCQler39yYIDS9PTFlKTRaDRu5jMs9wO1OOc6tgP/Bzw76xgFDAOHlFL+rdyLYqqqqkxf42BPD31jY9x/8CD3HzzIqrQ0NmZn86vrr3d7GZcXF5MQG8vW3/2O1Ph43uztJSE2lnONeZOesbE5Dcu1paWm2zmbYGiPVqyq3aq6QWs3w5yGRSl1CjgFICIfBnYopZpM3THKaWlpMZ3580hvLwDfueIKNmRlcU1JyVnDWmU5Obz8vvfxieefJ8Nm473nnMNlRUUMGAuxesfGWOkRiuzCPjFB28hISDyWYGiPVqyq3aq6QWs3o93vyXul1AOeRkVEckQkZ8F3jlJcK3vN0NjXR3xMDJ+84AKuLS31OVdyYX4+r3zgA/zlppv4wlvewt+sXEmWkVSyd2yM8akp96S+i1PGKtlQGJZgaI9WrKrdqrpBazdDoBUkC0TkARHpBzqADhHpE5H7RcTSCSID4UhvL2szMohdwBoTV7biHrudnzQ0sOHnP2dkfNy93xVqrOdYNBpNuAhkgeRKYA/ORYsngN8YrxM4E1C+KiLFvq+wNNiwYYPpazT29bE+K2tB52YZSSV7x8Y4NTjI8MQErxt5iQCaQuixBEN7tGJV7VbVDVq7GQL5yvxlnGnx36aUqlBKfdB4XYizCFiWccySJtZkCO/U9DTH+vvZkLmwCgOeQ2F9xnqW14ykeeBcSAmQt2yZqXZ6w6z2aMaq2q2qG7R2MwRiWK4GfqSUenL2DqXUU8CPgWtNtSYKcGVLXSinBgcZn5pasMeSGBfHsrg4eux2+o2JfE/DMuBwkBIfH9RULi7Mao9mrKrdqrpBazdDIJ8+mcDROfYfxbn6fsnTPjLCd/bupdGI7gqERiON9UI9FnAOh/WOjfk0LMGqca/RaDQLIRDD0oIzX5gvthjHLGny8/P52Rtv8JkXXuDc++7jtba2gM7f096OABtzFh5Ql52YOMNjOdbf7x4W63c4yAiRYcnPzw/JdaMBq2q3qm7Q2s0wp2ERkZUi4ipB+DDwbhH5ioikexyTJiL/B7wH+J2p1kQBpaWlHO3rIzUhgWml2NESmC2ta21lU04OmSZq0WclJrrnWPKTndWidxsGLpQeS2kIFl1GC1bVblXdoLWbYT6P5STOtC7gnJjfBfwb0C0ip0TkFM76J58DXgb+x1RrooBdu3ZxfGCAirw8cpKS3Isd/WFqepqXz5yhptBcZLbLsPQ7HFxXWkpCbCzPnzoFhNawuKrvWRGrareqbtDazTCfYXGv3FNKjeIcCvsYzjLFI8braZwlf69QSs2dz32JcLy/nzUZGZyTlcURY87EH97o6mJofJyaoiJT989OSqLbbmfA4aAwJYWawkKeMQxLKIfCNBqNxh8CqceCUmoSuMd4WRKVkED7yAhrMjIQEZ44dszvc+uM1azB8FhcYcUZNhtXr1rF5158kbbh4ZB6LElJSfMftESxqnar6gat3QzBj0ld4mQb+XPWZmSwITOTLrvdPXE+H/UdHeQnJ3vN8RUIWR7zMxmJiVxdUgLAc6dO0R9Cw6KT8lkPq+oGrd0M/ngsvmqweEUp9QsT7Yl4nnr1VQDWZGSQFB8POFO0XFJQMO+5Z0ZGglJ8K9vj20SGzUZ5Xh4p8fG82NrKxPR0yAzL7t27Lftms6p2q+oGrd2Mdn8Mxu3Gaz4EZwr9JW1Ymoy6KGsyMkg10tb7a1jaR0YoMemtwEyPJTMx0V0t0lVxMlRzLPZ5SiIvZayq3aq6QWs3gz+G5afAK6busoRodTjITkoiIzGR5Ph44mNieKSxkZqiIpRSrJ1j4WPb8DDVfhig+ZjtsQAUpaa6F0rqBZIajSac+GNYXlRK/SbkLfGCiFwLfBeIBX6mlPrqrP02nB7ShTjDnt8bynoxSilOTE9TZhTbio+N5fPV1XzhpZf404kTxMXE0PmP/+h1jcrE1BRddjv5QcjhNWOOxTAihSkpPGssmEz3UQDMLNXV1SG5bjRgVe1W1Q1auxkidvJeRGKBHwLXAWXA+0SkbNZhHwH6lFJrgW8DXwtlm15tb+dgTw8fOPdc97bPV1fz0vvfz4fKypicnnZHa83GtX1FSorpdngzLEUeczcZJhZfzsXJkydDct1owKraraobtHYzRKxhAS4GjimlTiilxoHfAjfMOuYG4AHj90eAK8VX1awgcM8bb5AYE8P7zjlnxvbqggLea2xzpVmZTdvICIB7pbwZPA1LuofH4t4WIo+l3SMnmdWwqnar6gat3QyRbFgKgWaPv1uMbV6PMdbYDADZoWjM8Pg4vz18mCuyskjzMofh8hz6fYQeuwzLiiAYFltcHMnx8aQlJLiLhXl6LHqORaPRhJP5at5HsuEJCBFxR7cVFBRQW1sLwOrVq0lNTaWhoQGA7OxsNm7cyM6dOwGIi4ujpqaGI/v387U1a1gGDA8P09HRQXOz0+6tW7cOZURRvPT66xTb7axfv566ujoAbDYb7YZBadq/n9EjR6iqqqKlpcVdAnTDhg3Exsa601Xn5+dTWlrqTq2QlJREVVUVu3fvxm63kyyCzWbj8OHDtLe30+YxBLf/tdc4HhtLYWEhRUVF7N69G4CUlBQqKyvZtWsXDsOzqqmpobGxkU6jWNimTZtwOBwcPepMZF1cXMzy5cvZs2cPk5OT1NfXU1FRQV1dHZOTkwBs2bKFgwcP0mNEpZWXlzM0NMSJEycAKCkpISsri/r6egAyMzMpLy9nx44dKKUQEbZu3UpDQwN9RiaDiooKent7aWpqCqif6uvrGTSKnVVWVp7VTzabjQMHDgCQl5d3Vj9VV1ezZ88eho2Sz65+mpycpLa2NuB+Aud49cmTJ93fAsvKypiamuLIkSMAQe8ngLS0tKD0U1lZWVT100LfT976qaCggLa2tqjop2C/n6anpwFm9FMgiFIq4JMWAxGpBu5SSl1j/P3vAEqpr3gc87RxzC5jrU07kKvmEVVZWalcHRsobW1trFix4qztZ4aHKbz7bu6+6io+Vl5+1v4vvfwyX3j5ZRyf+QwJQSggdMEvfsG0UjTccgsAXaOj5P3oR8SIMHnnnYRiRNCXditgVe1W1Q1a+2ztIrJXKVXpz/mR7JG8BqwTkVIRSQBuBp6YdcwTwC3G7zcB2+czKmZxfXuZzXxDYe0jI2QnJQXFqACck5XFeo/Q5hzj2mkJCSExKuBbuxWwqnar6gat3QwB5QpbTJRSkyLySZxJLmOBe5VSB0XkS8AepdQTwM+BX4rIMaAXp/EJC0lxccTHxMw5eR+M+RUXD1x33Yy/RYTClBSmI9QD1Wg01iFiDQuAUQb5yVnb/tvj9zHg3YvZpkIfCSRFhAybbU7DEoyIMBfePJ+i1FQGfdw/GPjSbgWsqt2qukFrN0NEG5ZIpGiOlPcZiYn0OxycGhgg3WZzryf57eHD1Hd08NHzzgtp2/67uprRiYmQXX8u7Usdq2q3qm7Q2s0QyXMsEYkrIsQbGTYb/WNjbP3d77jr5ZcB6BgZ4e///GcuKSjg/y67LKRte+uqVbx97dqQXX8u7Usdq2q3qm7Q2s2gDUsQybDZODMywqnBQc4YYZCnBweZUop/ueiikK2I12g0mkhCG5YASZkjJUuGzcabRuz5wPg48NdULnlByBEWbubSvtSxqnar6gat3QzasARIZaXvMO6MxEQmjIVFA8YkutuwLIFqdHNpX+pYVbtVdYPWbgZtWALEtXLXG551UM4yLEvAY5lL+1LHqtqtqhu0djNowxIgjjnCeT0NS7+HYVkWF0dyiBJDLiZzaV/qWFW7VXWD1m4GbViCiFePxW5fEt6KRqPR+Is2LAFSU1Pjc59n1Jd9cpKJqSk6R0eXjGGZS/tSx6raraobtHYzaMMSII2NjT73uTwWV6auAYdjSRmWubQvdayq3aq6QWs3gzYsAeJKh+0Nl2FZk5EBOEOOl5JhmUv7Useq2q2qG7R2M2jDEkRcucAuys8HnJmOl5Jh0Wg0Gn/QucICZNOmTT73rc7I4KX3vY/RyUkePHyYU4ODTE5PLxnDMpf2pY5VtVtVN2jtZtAeS4DMF4b3lsJCd036o0b1tqViWHT4pfWwqm7Q2s2gDUuAuEqMzoWr5vyx/n5g6RgWf7QvVayq3aq6QWs3gzYsISDdWAy51DwWjUaj8QdtWAKkuLh43mNcHsv+7m4ACpdIMjt/tC9VrKrdqrpBazeDNiwBsnz58nmPiY+NJSkujt6xMQpSUshZIh6LP9qXKlbVblXdoLWbQRuWANmzZ49fx7m8lgvy8kLZnEXFX+1LEatqt6pu0NrNEJGGRUS2ichhEXlDRP4gIhk+jmsSkf0isk9EIuopcBmWzUvIsGg0Go0/RKRhAZ4FNimlzgcagX+f49grlFKblVKLUjwhLS3Nr+NcE/ibc3ND2ZxFxV/tSxGrareqbtDazRCRhkUp9YxSatL48xWgKJzt8aSiosKv45aix+Kv9qWIVbVbVTdo7WaISMMyi9uAp3zsU8AzIrJXRG5fjMbU1dX5dVy6zUZqQgKrjbxhSwF/tS9FrKrdqrpBazdD2FK6iMhzQL6XXf+plHrcOOY/gUng1z4uU6OUahWRPOBZETmslNrp4363A7cDFBQUUFtbC8Dq1atJTU2loaEBgOzsbDZu3MjOnc7LxMXFUVNTQ319PYODgwwPDzM8PExHRwfNzc0ArFu3DpvNxoEDBwDIy8vjY+edR4nDwc4dO7DZbFRXV7Nnzx6Gh4cBqKqqoqWlhdbWVgA2bNhAbGwshw4dAiA/P5/S0lJ3JbekpCSqqqrYvXs3drsdgOrqak6ePEl7ezsAZWVlTE1NceTIEQAKCwspKipi9+7dgLOOdWVlJbt27XKvrK2pqaGxsdGddG7Tpk04HA73Aqni4mKWL1/ubnt9fT0VFRXU1dUxOel0Krds2cLBgwfp6ekBoLy8nKGhIU6cOAFASUkJWVlZ1NfXA5CZmUl5eTk7duxAKYWIsHXrVhoaGugz1v5UVFTQ29tLU1PTgvoJnOVV5+un9evXu99Ec/VTf38/tbW1UdFP4BzKCEY/TU5ORlU/BfP9NDExQVtbW1T0U7DfT67/q2c/BYIopQI+aTEQkVuBjwFXKqVG/Tj+LmBYKfWN+Y6trKxUC416qK2t5fLLL1/QudGO1n55uJux6FhVN2jts7WLyF5/57Ij0rCIyLXAt4CtSqkuH8ckAzFKqSHj92eBLyml/jLf9c0YlunpaWJiomEEMfho7dbTblXdoLXP1h6IYYnU/9oPgFScw1v7RORuABEpEJEnjWOWA3Ui0gC8CvzZH6NiloMHD4b6FhGL1m49rKobtHYzRGTafKXUWh/bzwDXG7+fAMoXs12Ae9zTimjt1sOqukFrN0OkeiwajUajiVK0YQmQ8vJFd5IiBq3delhVN2jtZtCGJUCGhobC3YSwobVbD6vqBq3dDNqwBIgrltyKaO3Ww6q6QWs3gzYsGo1GowkqEbmOJdSISBdwaoGn5wDdQWxONKG1Ww+r6gatfbb2VUopv7LqWtKwmEFE9ixWJuVIQ2u3nnar6gat3Yx2PRSm0Wg0mqCiDYtGo9Fogoo2LIHz03A3IIxo7dbDqrpBa18weo5Fo9FoNEFFeywajUajCSrasPiJiFwrIkdE5JiIfC7c7Qk1ItIkIvuN7NJ7jG1ZIvKsiBw1fmaGu53BQETuFZFOETngsc2rVnHyPeM5eENEorp+rQ/td4lIq9H3+0Tkeo99/25oPyIi14Sn1cFBRIpF5AUROSQiB0XkU8b2Jd33c+gOXr8rpfRrnhcQCxwHVgMJQANQFu52hVhzE5Aza9vXgc8Zv38O+Fq42xkkrVuACuDAfFpxZtd+ChDgEmB3uNsfAu13AZ/1cmyZ8ezbgFLjPREbbg0mtK8AKozfU4FGQ+OS7vs5dAet37XH4h8XA8eUUieUUuPAb4EbwtymcHAD8IDx+wPAO8LXlOChnOWse2dt9qX1BuAXyskrQIaIrFiUhoYAH9p9cQPwW6WUQyl1EjiG870RlSil2pRS9cbvQ8CbQCFLvO/n0O2LgPtdGxb/KASaPf5uYe6OWAoo4BkR2Ssitxvbliul2ozf23EWW1uq+NJqlWfhk8Zwz70eQ55LVruIlAAXALuxUN/P0g1B6ndtWDS+qFFKVQDXAZ8QkS2eO5XTR7ZESKGVtBr8GFgDbAbagG+GtTUhRkRSgN8Dn1ZKDXruW8p970V30PpdGxb/aAWKPf4uMrYtWZRSrcbPTuAPOF3fDpfrb/zsDF8LQ44vrUv+WVBKdSilppRS08A9/HXYY8lpF5F4nB+uv1ZKPWpsXvJ97013MPtdGxb/eA1YJyKlIpIA3Aw8EeY2hQwRSRaRVNfvwNXAAZyabzEOuwV4PDwtXBR8aX0C+JARIXQJMOAxbLIkmDVvcCPOvgen9ptFxCYipcA64NXFbl+wEBEBfg68qZT6lseuJd33vnQHtd/DHaEQLS+cESGNOCMi/jPc7Qmx1tU4o0AagIMuvUA28DxwFHgOyAp3W4Ok90Gcrv8EzvHjj/jSijMi6IfGc7AfqAx3+0Og/ZeGtjeMD5UVHsf/p6H9CHBduNtvUnsNzmGuN4B9xuv6pd73c+gOWr/rlfcajUajCSp6KEyj0Wg0QUUbFo1Go9EEFW1YNBqNRhNUtGHRaDQaTVDRhkWj0Wg0QUUbFo3GD0SkVkSa5tsWSYhIiYgoEbkr3G3RWAttWDQRi/Gh6O+rJNztDQeG8bhLRDaHuy0ajYu4cDdAo5mDD876+zLgdpxlU1+cta9rUVo0k6txLpoLJyXAF3CWOdg3a98pIAmYXNQWaSyPNiyaiEUp9SvPv0UkDqdh2TV732xEJFU5U4KHsn3joby+WZRz9fNYuNuhsR56KEwT9Yiz2mWtiFwgIk+LyADOtBSISKqI/I+I7BaRbhFxGJXwvioiy7xcK1NE7jGOHTGue6GP+/qcdxGRAhF5UET6RGTUaNd6L9coEZHfi8ig8XrcyEnXJCK18+i+FXjB+PM+j2HBWo9rz5hj8dwmIu8RZ6VAu/E/+bBxzEoReUREekVkSER+5codN+v+K0TkxyJyWkTGReSMiPxURPJmHZclIt8WkeMiMiYiPeIsx/Avc+nTRC/aY9EsFVYC24GHcWZtTTG2FwIfNbb9Buew0FbgX3HWoXCXWTUyvj4NXIQzb9IrOFOIPwf0BNCWZGCncf5/4Ky69yngcRHZpJSaMu6XjXNIbzlwN86CS5fhNBbJftxnJ/B/xj08hwc7/Dj3bcAdwI9wFvr6CHCviIwb19xuXPci4Dacns9HXSeLyEpgF86Kqj/HmUdqLfBx4AoRqVRKDRiHP4yzUuXdOA1+EnAucDmwzY+2aqKNcCdE0y/98vcF3Iozed6ts7Y3Gds/6uWcBCDey/YvG+dc7LHtdmPbF2cd+2lje9Os7bU+tingX2dt/xdj+zUe275ubPvArGNd22v9+J9c7u1/YuwrMfbd5WXbCLDKY3suTuMxDdw56zqPAuNAise2x3Gmky+adWwlTuN9l/F3unG/H4X7+dGvxXvpoTDNUqEXuG/2RqXUuFJqApxzNMZQVw5OLwSgyuPwdwBTnF3g6MfAIP4zDXxv1rbtxs91Htv+Dmdm4QdnHfuNAO61UB5TSp1y/aGU6sKZuXYaZwZfT14E4nEaJUQkHafH8wQwJiI5rhdOI38MZ2ADgB1wAFVWjdyzItqwaJYKx5UxxDQbEflHEXkD5wdcL84Islpjd6bHoauBNnV2FUEHcCKAtpxRSs2eNHcNpWV7bCsFjilnYSXP+3UC/QHcbyF409OHU7/Dy3b4a9s34Pzs+AjO/+Xs1waMcr7KGeDwaWATcFJEDorI90XkyuBJ0UQaeo5Fs1QY9bZRRO7E6YE8g9OLOINzWKcQuJ/QfLnyauBcTQrB/RaCrzb603bXz18BD/g41u76RSl1t4g8Dvwtzvmtm3DWVv+dUupm/5usiRa0YdEsdT6Ic3jmOk/PQESu9XLsCeBqEUnz9FpExIbTm+nzco4ZmoC1IhIzq215QIaf1whHQaVjxn0TlFLPzXcwgHJWWvwZ8DMRicUZHPE+EfmmUuq10DVVEw70UJhmqTOF80PQ7SkY62E+5+XYx4FY4J9nbf84kBaCtv0RWAG8b9b2zwZwjWHjZ1ZQWuQHSqke4EngneIs0TsDcZJr/L5sdli3MWT5hvHnorVbs3hoj0Wz1HkE+ArwlIg8itNAvB9nKd7Z3IczMuy/xVnbexfOkOR34wynDfb75WtGW+4TkYuBwzjDjd8CdOOfN3IIGAL+UURGcc7NdCqlts95lnk+DtQBO0XkF8DrOL+orgZuAH4B3AWsB3aIyB9w1lDvwxlq/HHgJGdnUNAsAbRh0Sx1tuH0Vj4CfBdoB36H04gc8jxQKTUuIlcZ57wDeBfwGnAVzkitkmA2TCnVLSI1OOeAbsNpSF4ArjDua5/jdNc17CJyM/A/wHcAG7CDv0ahhQSlVLOxcPTfcBqSv8cZrtyM0xN7yDi0GbgXp6Z3GO1rBe4BvqaU8jo3poludM17jSbCMBZOdgM/UUrdEe72aDSBoudYNJowIiJJXja75n+eXcy2aDTBQnssGk0YEZEXcGYhrsf5Re9KnIsPXwa2+Fqbo9FEMtqwaDRhRET+GfgQzvmbJKAFZwqVL6oQZ2fWaEKFNiwajUajCSp6jkWj0Wg0QUUbFo1Go9EEFW1YNBqNRhNUtGHRaDQaTVDRhkWj0Wg0QUUbFo1Go9EElf8PsZlkomRK98UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(trainer.test_environment.save_asset_memory(),alg=\"EIIE\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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