rl_experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "from collections import deque\n",
    "\n",
    "from tda.nn import TDA, TDAClip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from collections import namedtuple\n",
    "\n",
    "Experience = namedtuple('Experience', ('state', 'action', 'reward', 'next_state', 'done'))\n",
    "\n",
    "class ReplayBuffer:\n",
    "    def __init__(self, capacity):\n",
    "        self.capacity = capacity\n",
    "        self.buffer = []\n",
    "        self.position = 0\n",
    "\n",
    "    def push(self, *args):\n",
    "        if len(self.buffer) < self.capacity:\n",
    "            self.buffer.append(None)\n",
    "        self.buffer[self.position] = Experience(*args)\n",
    "        self.position = (self.position + 1) % self.capacity\n",
    "\n",
    "    def sample(self, batch_size):\n",
    "        return random.sample(self.buffer, batch_size)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.buffer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a custom neural network for the Pendulum environment\n",
    "class TDANetwork(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size, time_window, scaling_factor):\n",
    "        super(TDANetwork, self).__init__()\n",
    "        self.time_window = time_window\n",
    "        self.fc1 = nn.Linear(input_size * time_window, hidden_size)\n",
    "        self.tda = TDA(time_window, scaling_factor)\n",
    "        self.fc2 = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = torch.relu(self.fc1(x.T))\n",
    "        x = x.unsqueeze(-1)\n",
    "        x = self.tda(x)\n",
    "        x = x.sum(dim=2)\n",
    "        x = torch.tanh(self.fc2(x))  # Use tanh activation for the output layer\n",
    "        return x\n",
    "\n",
    "# Define the agent\n",
    "class TDAAgent:\n",
    "    \n",
    "    def __init__(self, input_size, hidden_size, output_size, time_window, scaling_factor, learning_rate, buffer_size=10000, gamma=0.99, batch_size=64):\n",
    "        self.model = TDANetwork(input_size, hidden_size,\n",
    "                                output_size, time_window, scaling_factor)\n",
    "        self.target_model = TDANetwork(input_size, hidden_size, output_size, time_window, scaling_factor)\n",
    "        self.target_model.load_state_dict(self.model.state_dict())\n",
    "        self.buffer = ReplayBuffer(buffer_size)\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)\n",
    "        self.time_window = time_window\n",
    "        self.gamma = gamma\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    def update(self, experiences):\n",
    "        states, actions, rewards, next_states, dones = zip(*experiences)\n",
    "        \n",
    "        states = [np.concatenate(state_window) for state_window in states]\n",
    "        next_states = [np.concatenate(state_window) for state_window in next_states]\n",
    "\n",
    "        states = torch.FloatTensor(states)\n",
    "        actions = torch.FloatTensor(actions).view(-1)\n",
    "        rewards = torch.FloatTensor(rewards).view(-1)\n",
    "        next_states = torch.FloatTensor(next_states)\n",
    "        dones = torch.FloatTensor(dones).view(-1)\n",
    "\n",
    "        # Update the model\n",
    "        target = rewards + self.gamma * self.target_model(next_states) * (1 - dones)\n",
    "        self.train(states, target)\n",
    "    \n",
    "    def update_target_network(self):\n",
    "        self.target_model.load_state_dict(self.model.state_dict())\n",
    "\n",
    "    def predict(self, state_window):\n",
    "        state_tensor = torch.FloatTensor(state_window).view(-1)\n",
    "        action = self.model(state_tensor)\n",
    "        return action.detach().numpy()\n",
    "\n",
    "    def train(self, state_window, target):\n",
    "        state_tensor = torch.FloatTensor(state_window).view(-1)\n",
    "        target_tensor = torch.FloatTensor(target).unsqueeze(0)\n",
    "        self.optimizer.zero_grad()\n",
    "        prediction = self.model(state_tensor)\n",
    "        loss = torch.mean((prediction - target_tensor) ** 2)\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1)  # Add gradient clipping\n",
    "        self.optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_agent(agent, episodes, gamma, epsilon_start, epsilon_end, epsilon_decay, update_target_every):\n",
    "    env = gym.make('Pendulum-v1')\n",
    "    epsilon = epsilon_start\n",
    "\n",
    "    total_rewards = []\n",
    "    for episode in range(episodes):\n",
    "        state, _ = env.reset()\n",
    "        state_window = deque([state] * agent.time_window, maxlen=agent.time_window)\n",
    "        done = False\n",
    "        episode_reward = 0\n",
    "        steps = 0\n",
    "        while not done:\n",
    "            action = agent.predict(state_window)\n",
    "            # Exploration\n",
    "            if np.random.rand() < epsilon:\n",
    "                action = env.action_space.sample()\n",
    "\n",
    "            next_state, reward, terminated, truncated, _ = env.step(action)\n",
    "            done = terminated or truncated\n",
    "            reward = reward if isinstance(reward, np.float64) else sum(reward)\n",
    "            agent.buffer.push(state, action, reward, next_state, done)\n",
    "\n",
    "            if len(agent.buffer) >= agent.batch_size:\n",
    "                experiences = agent.buffer.sample(agent.batch_size)\n",
    "                agent.update(experiences)\n",
    "\n",
    "            episode_reward += reward\n",
    "            state_window.append(next_state)\n",
    "\n",
    "            # Update the agent\n",
    "            target = reward + gamma * agent.predict(state_window)\n",
    "            agent.train(state_window, target)\n",
    "\n",
    "            state = next_state\n",
    "            steps += 1\n",
    "            epsilon = max(epsilon_end, epsilon * epsilon_decay)  # Decay epsilon\n",
    "\n",
    "        total_rewards.append(episode_reward)\n",
    "        if (episode + 1) % 100 == 0:\n",
    "            print(f\"Episode: {episode}, Reward: {episode_reward}\")\n",
    "\n",
    "        if episode % update_target_every == 0:\n",
    "            agent.update_target_network()\n",
    "    \n",
    "    return total_rewards\n",
    "\n",
    "# Set up the agent and training parameters\n",
    "input_size = 3\n",
    "hidden_size = 64\n",
    "output_size = 1\n",
    "time_window = 10\n",
    "scaling_factor = 1.0\n",
    "learning_rate = 0.001\n",
    "episodes = 1000\n",
    "gamma = 0.99\n",
    "epsilon_start = 1.0\n",
    "epsilon_end = 0.01\n",
    "epsilon_decay = 0.995\n",
    "\n",
    "# Initialize the agent\n",
    "agent = TDAAgent(input_size, hidden_size, output_size, time_window, scaling_factor, learning_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andrei/miniforge3/envs/rdl/lib/python3.9/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
      "  if not isinstance(terminated, (bool, np.bool8)):\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "zero-dimensional arrays cannot be concatenated",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[159], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Train the agent\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m rewards \u001b[39m=\u001b[39m train_agent(agent, episodes, gamma, epsilon_start, epsilon_end, epsilon_decay, \u001b[39m10\u001b[39;49m)\n",
      "Cell \u001b[0;32mIn[158], line 25\u001b[0m, in \u001b[0;36mtrain_agent\u001b[0;34m(agent, episodes, gamma, epsilon_start, epsilon_end, epsilon_decay, update_target_every)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(agent\u001b[39m.\u001b[39mbuffer) \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m agent\u001b[39m.\u001b[39mbatch_size:\n\u001b[1;32m     24\u001b[0m     experiences \u001b[39m=\u001b[39m agent\u001b[39m.\u001b[39mbuffer\u001b[39m.\u001b[39msample(agent\u001b[39m.\u001b[39mbatch_size)\n\u001b[0;32m---> 25\u001b[0m     agent\u001b[39m.\u001b[39;49mupdate(experiences)\n\u001b[1;32m     27\u001b[0m episode_reward \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m reward\n\u001b[1;32m     28\u001b[0m state_window\u001b[39m.\u001b[39mappend(next_state)\n",
      "Cell \u001b[0;32mIn[157], line 36\u001b[0m, in \u001b[0;36mTDAAgent.update\u001b[0;34m(self, experiences)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate\u001b[39m(\u001b[39mself\u001b[39m, experiences):\n\u001b[1;32m     34\u001b[0m     states, actions, rewards, next_states, dones \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mexperiences)\n\u001b[0;32m---> 36\u001b[0m     states \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39mconcatenate(state_window) \u001b[39mfor\u001b[39;00m state_window \u001b[39min\u001b[39;00m states]\n\u001b[1;32m     37\u001b[0m     next_states \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39mconcatenate(state_window) \u001b[39mfor\u001b[39;00m state_window \u001b[39min\u001b[39;00m next_states]\n\u001b[1;32m     39\u001b[0m     states \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mFloatTensor(states)\n",
      "Cell \u001b[0;32mIn[157], line 36\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate\u001b[39m(\u001b[39mself\u001b[39m, experiences):\n\u001b[1;32m     34\u001b[0m     states, actions, rewards, next_states, dones \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mexperiences)\n\u001b[0;32m---> 36\u001b[0m     states \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39;49mconcatenate(state_window) \u001b[39mfor\u001b[39;00m state_window \u001b[39min\u001b[39;00m states]\n\u001b[1;32m     37\u001b[0m     next_states \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39mconcatenate(state_window) \u001b[39mfor\u001b[39;00m state_window \u001b[39min\u001b[39;00m next_states]\n\u001b[1;32m     39\u001b[0m     states \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mFloatTensor(states)\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: zero-dimensional arrays cannot be concatenated"
     ]
    }
   ],
   "source": [
    "# Train the agent\n",
    "rewards = train_agent(agent, episodes, gamma, epsilon_start, epsilon_end, epsilon_decay, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iOGbWml0AgNr6KPP87kM1zOMEQRDpBgk8BEFwqW8gnYYBehwEkbaQwEMQxzFO+4Q2RGmE10OrtAgifSGBhyAIAxGd13IdCTwGyGmZINIXEngIguDS0MD27SEIgkg3SOAhCIJLPWl4DNDTIIj0hQQegggATdNCsQIq4rBVqBsfnuVbD2Ded3vcVinUaGTTIoi0hQQeggiAp6etRe/HZ+CfCzYHXRVb3Gh4rvrTHNzw1y+xq6rahxoRBEG4gwQeggiAP81aBwB49N2VAdfEHi/L0ndWBa/BUg3pdwgifSGBhyAIA/ql6g1RclrWQxYtgkhfSOAhCIILOS0TBJEpkMBDEMcxFHhQDnoaBJG+kMBDEIQBvQzkRcOTkVGJyaZFEGkLCTwEQXCpp8CDBkjcIdKRFdsOYMveI0FXI3Bygq4AQRDB4WDRIh8eE6TgIdKNbfuP4srn5gAANj5xRcC1CRbS8BBEkDhJHAFDPjwEkd6srTwYdBVCAwk8BEFwIQ2PkYz0SyKI4wQSeAjiOCbisExLVsNDWy8QRLggIT0JCTwEQRjQy0B15LRsgOQ5It2gNpskUIFn7dq1uPrqq1FSUoKmTZuiX79+mDVrliHN5s2bcdVVV6Fx48YoKSnBvffei9raWkOa5cuXo3///igsLETbtm0xduxYmmkShALkNTw+VSQkZML97TlUg+q6hqCrQRApJ1CB54orrkB9fT0++eQTLF68GGeeeSauvPJKVFZWAgAaGhpwxRVX4PDhw5gzZw5ef/11vPXWW3jwwQcTeVRVVWHQoEEoKyvDwoUL8dxzz+Gpp57CxIkTg7otgsgYyIcns9h1sBq9Hp+B8ybMDLoqRIrIBCFdFYEtS9+9ezfWrVuHl156CaeffjoA4IknnsCf//xnrFy5EqWlpZg2bRpWrVqFLVu2oKysDADw9NNPY9iwYRg3bhyaNm2KqVOnorq6GlOmTEF+fj7Ky8uxdu1aTJw4ERUVFY4+CgRBGInolo5Ja3hUVyZkpPv9zV+/FwCw/0hdwDUhUkW6t1mVBKbhadGiBbp164ZXX30Vhw8fRn19PV588UW0bt0avXr1AgDMmzcP5eXlCWEHAIYMGYKamhosXrw4kaZ///7Iz883pNm+fTs2btzILb+mpgZVVVWGfwRBGPEUaTkDe1oylRNE+hKYwBOJRDB9+nQsXboUTZo0QUFBAf7whz/g448/RrNmzQAAlZWVaN26teG64uJi5OXlJcxerDTxv+NpWEyYMAFFRUWJf+3bt1d4dwQhRtj1j7KRlkkgCDf0do4/6JtMolzgGTNmDCKRiO2/RYsWQdM03H333WjVqhU+//xzLFiwAFdffTWuvPJK7NixI5EfyySlaZrhuDlN/AXbmbNGjRqFAwcOJP5t2bLF660ThDRBd0W0eagcNHYQ6QY12STKfXhGjBiBoUOH2qbp1KkTPvnkE3zwwQfYt28fmjZtCgD485//jOnTp+OVV17BQw89hNLSUsyfP99w7b59+1BXV5fQ4pSWllo0Obt27QIAi+ZHT35+vsEMRhCEd6hzDTdh1ygShJ8oF3hKSkpQUlLimO7IkdhGZllZRiVTVlYWotGYGr1Pnz4YN24cduzYgTZt2gAApk2bhvz8/ISfT58+fTB69GjU1tYiLy8vkaasrAydOnVSdVsE4Qs0AKUX6R7ELb1rTxDeCMyHp0+fPiguLsatt96Kr776CmvXrsUvfvELbNiwAVdcEdvgbPDgwejevTtuueUWLF26FDNnzsTIkSNx5513JrRCN954I/Lz8zFs2DCsWLEC77zzDsaPH08rtAjCJfrPRnaAzHSTT6bfH5F5UJtNEpjAU1JSgo8//hiHDh3CxRdfjN69e2POnDl49913ccYZZwAAsrOz8eGHH6KgoAD9+vXDj370I1xzzTV46qmnEvkUFRVh+vTp2Lp1K3r37o27774bFRUVqKioCOrWCILIUGjsINIParVxAovDAwC9e/fG//73P9s0HTp0wAcffGCbpkePHpg9e7bKqhHEcUFEsVEt3U0+BJHJmBf8HG/QXloEQRBEWjPho9W4ftJc1NaHb++3o7UNmL9+T2ArHvUmrePdvEUCD0EQysj0DpVimoSTF2evx8KN+zBj9c6gq2LhzlcX4cd/+RIvfLoukPI1zu/jERJ4COI4hqXdPo413gaijBn58T5ghJ06yUCZdmzecwRTvtjgeaPVOet2AwCmzt+solqeON4FdhJ4jjN+858VuHvq4sAa/t/nbcRvP1h13H94acNx+po27j6MM8dOwx+mrzWeSPPnQd+dOBc//SnGvL8Kz8z4NuiqeMJg0gquGqGABJ6AqKlvwPOz1mHFtgMpLffvX27CR8sr8e2uQyktN85v3l2JyXM2YNmW/YGUT6SOdO5cf/+/b1BVXY8/zjQOdul8T4Qc8X3kvly/J+CaeEO/kOB4l3dJ4AmIv32+AU/+bw2ufG5OIOWrVP264WB1ve35r7fux09eWoBvKjN7U9egzUeqiz/eO9Swczyv0CFoFWWgy9KPZ1Kt2QGM6uywD0z/9+e5aIhqWLHtAJb8ZlDQ1SEIAOlvEkr3+qczQYmatEorCWl4AiIaQMtLp8YeX8K593BtwDUhZMj0GWRm3x2RiVCbTUICT0CIhGSYv34Ptu47oqzMMDX8n7y0AEs37wu6GgST5Fw00wUYHryAjOk0aSDUkEmv/HhvvyTwBISTann51gP48V++xPm/m+VT+b5kK8WtLy0IugrHPapdOsLQrgiCSGJwZcgo8U0eEngCwinq5tIt6rUfYWv4R2q9xbcgiFQThu+GSDEZJMVn0K24ggSegGhwaHhuJt5OK68METdD0PBpwUg48fJeQtCsfCUM3w1BuOV4b74k8ASE42oJyVHn0zW7cPKv/4u/z9toU6but1TuRKZCy5QJwp507yuNq7TS/W68QQJPQKhepfXzfywFEAvsx8MYgOr4bvhhQfVu5aqRbSaZ3q4y/PaIDMTQ7wdYjzBAAk9ARFXH/RMYN0nDEz7IJySkhFsOJQhXHO8COwk8AdHg0PKOh/427NoNQp7jvD8liFCT6RpYJ0jgCQjVDU9WdDjO231oyGShLxM710y8J8KedH/lqiMtz123Gxt2H/aeUQDQ1hIB4RR40A9fUmNjT/OvmFACq5l5aXrpPjg4keG3R2Q4Xtvvqu1VuPFv8wEAG5+4wnuFUgxpeAIikK0ldM1dJNIzQVAzMZJpAt2KbQdw+5SFGb9JrxdU+dkFtSJS5SqtVTvSu52Qhicgog4Sh6ypQ+RjCt0mcplrzTl+CUO7UsDx0jSve2EuauqjWLZlP23Sm6FonN/HI6ThCYggNCyiRc77bg/6TpiJT77Z6Wt97MjOOl6GnIBhPGYKzcMn01bV1dTHlovSJr2Zi96aEIqJboCQwBMQTiYt2UFHJL1hawmb8m/465fYfqAat09ZJFcJhWTTqJuWBCkQ1NZHfXcqPt4HDCINMYQj8daA071XJoEnIJz20pJtWCLp00m1SfIOIcP+I7Uof/R/+AltSHvc8MaiLXjvq+2+l5PuQm5UYQC2NH8U5MMTFEF/REGXD9gLaWTSSg1OvmKyGpOg2tX/VlaitiGKz7/d7Ws5IfhsCAC7D9XgF29+DQC4rLw04NqEm6g6eSftIQ1PQDgFHvQDTaFq02+ySMUTOsIYg0bTNKzaXoU6p914lZWXkmIIBw5V1yd++73iNd3fuXFLIW95pXuvTBqegFDvwyPixMP5HUKOFwVPpsl1qW5Wk+dswOMfrlaeL/97CvmH44DsgPfxikos37YfIwefGtqNZjM5eKcKomk00fUb0vAERBCzBjebyI15byWmr/JntZZd/3m8mLTCOHtMpwFk0mfrg65CRjP8tcV4ftZ3+OSbXUFXJTBC+IlKodEqrQQk8ASEo4bHh0HHTRyeKXM34s5XU79ai0xaqUHmMW/acwR3vLIQizbu5aZJvdkrteWl+4Dh9rPadbBGbUU8kuavIaXQptFJSOAJCKdVWrLyjuwqrbCTdZxoeMIu1+nbzIh/LsGM1bvww0nzpK9NNzLToJX+AhuL491M40RUMByJCGHvr5wggScgAjFp6Rt+yDsJisMTPrbsPeqYJuX6nXA341ATRif0MJLuzynqQrOfqZDAExCqVxbIygdhaPh2ZrvjRMGT0aSijaVewArBh0MYON5fydT5m9D/yVnYtIe9gzm12SQk8ASEsw+PLAJ7aXF+h5Gwrgg5HvDy6FPdt6a6Mw/7dyMDjYOZwa/fWYFNe45gzHsrmedDt4digJDAExANUfvzfgz4+sYexG7tIsQHsONllVbQyDxleeHC/zYWzlYcQ9M0TJy2Bh9+vSPoqjAJ8tnN/W43tuw94vp60lpYie+LZiaaRq4MfkNxeAIiCJOWobGHsN1/f7AGlz/7Oa7t2dY3gUfTNKzecRAntz4BudnBy/uZJtapDHImVF7KNUriaees241nP1kHALji9Ct8qlH6sWTzPtz41/kAgI1PeH8ufreBdJGtePXUHw5i0+owEXyPf5wiI/Aom80ErOEx34dZSPvL7O/w/cEavDh7vW+rAV76YiMuf/Zz3PvPpUry8/puUvEWVLWfMPaVKu5N0zTMXbcb3wssvZYpTyS/IAlKS7Jk0z6hdJqm4fNvv8euqmrrOYdr65xU6BkIT3ujcpVWukMCT0BEJTYP9SKVHzhal/idTpK+X6u0/jL7OwDAf1dUes5r7ne7ccZj03DGY9Pw19nhDIA35r2VGPDUpzhUU888z3rM+kNerFhumtiCDXtxxysLhc0dKprx9FU7cePf5uP8330iXN4bi7Zg5mr7gJx+uqEdOFqHj5bvQHVdg3+FKGDdroPYvMf4LvXm+jWVB7nXTl+1E7dMXoBzJ8y0LcM80E/4aDW6/uZjfLuTnzcgEBokzeBqeBQq9mXa9LvLtuHuqYtxpJbd9wQBCTwBITOQiEjlrHb474VbcMZj0zDps+8s54LR8IinZQUeXLvzINbtsu/EUsmtLy1AVXU9Dhytw7iP3G1voHJMfOp/a3DJ058ahNwpczdi454jeHPRFoUl+cePXpyHGat34d7XBTVwCprxrDXfAzD6QNjtLLFpz2H84s2v8dNXUh+QM87tUxbi7qlLMN5luwPkHp2b7uLA0ToMnDgbFz45i5tmyDOzsXpHFfNcfCNYp7LNcsuLs9ejIaph4vS13Gt2VlXjzMem4ZF3V9hnnkbwHpN+cp3Kbv++15fho+WVeGnOhtQV6gAJPAHhtHmovsN120Z/+VZsN+En/vtNLB+Dt364ZzfmwINHaxsw+A+zMXDibNTUu5/VqoxgHbZH+KdZ6/Dd94fx6tyNlnNKqiqQiWEloIdCt+93jvkjy5a9R/DHGd9i/5FaT/nsPiR2vZ9bdCw+ZhZ6Z8k2qetS6bTKe4fmpzLvuz3Cec5e+71FY+Rm8jZ5zgYcrKnHq/M2OaZNG0dfAR+eIAzTew/XOSdKESTwBITM5qEi37Os03IqBuuvt+7H+b/7BB8t33GsfCN2VTb7E+u1FtW1fPu8pmkY+/4q/O3zcJqYUoGTMK1H9aCccidiibT/9+cv8IcZa/GLN7/2VF6mREyQeVdhuOcFG/biJy8tsGiMwjbxcIOmaRjz3kpMne8sgHHzEPLhcZ19RkCrtAJCr4b9aPkOXN6jjU1aEZOWQBweg9OyY3LP3PX3xdhxoBp3T12CX156qkW1abf03mzSEp1lrdhWhZe+iJXz47Pbo0lBrqlMoWwIBtLuPB56V9FLZcqIa2a+XO+sUeBatGSEyZC3Nb81F7xHJfpczOmWbk46OxvydnEbMu8xFULC3O/2YMoxzexN53Z0lQevnsbd0r2RThsLsyANT0DoP7i7py7BNh9U+JYydb9T4cOj94n4/cdrhE0BgMPmoTan9M655463d3b0SlgHNFanJFNVvSAq77Osj/nhP6mesGoIWSiBUFWGj52AIfoOed9b2picbKg66t3sw30KDA3Pp2t24cv1e7Bu1yEc5ixoyERIwxMQ5hUCew7VoG2zwsTf+kFL2ap0XUZhCTz470VbsHTzfjx+TbnhuFthQt/5HakN9wqWMBBWoU2UMMfhSQVeXl9Q92Kus75fmrVmF77deRB3XtDFIRdvZpqwvUcV8IRKo4ZHw55DNRj28sLEsTZFBZg36hK/qxcKSOAJCLNJyazR0P8pZNIS8eFJ8Ucu0hn/8pg/xQUnl4hn7OE+0nx8F8IvIUbEDJDqMPauZvde2k/k+Nz2xOu71DSxdnnbsYG4R9tmlnM8c0q6yC5+NxvuKi2Thsesad9xwBrnKFMhk1ZIsDPh+PFBh0HDo7/jvYdrDQOJ+WkI245TeFvpZM8O/m3LITo4hKAZc0mFYBRm4YsnjIrUubLqqLUP4Fzmpi+TWpIvnXswiERa1rRw9P1BQQJPSLDbSsGPJeTRkAUijWqaoYPzqyMP8wChCr/uUKQVGn1J/e9Y3ZTg3XFTl1caDx6prLpsURFEbK+hDTHFMe+llWqBZ9GmvZi99vuUlsmDBJ6QYLetk8iKKpFBLsybh2Za1FNRghbAHEv30k7S+JWKvhe7x5NJorWbZupllZZMeW4Ea9WBX8MAr5Zm4TDVt/P11gP4yUsLsPtQ8FutkMATEmw7WFVOyyleQSODk7wj2qmF7b6cSJfOVJSU308ATssqgoKGgVSublLRLnh9ZIZ9Qq7hPWPzcS+TXS/zs4wXeMaNG4e+ffuiUaNGaNasGTPN5s2bcdVVV6Fx48YoKSnBvffei9pao1PV8uXL0b9/fxQWFqJt27YYO3as5SV+9tln6NWrFwoKCtClSxdMmjTJr9vyBfPeUcalwSJOy3JxeFIxMDlWyRBcUeOdCq9QENIpvEqlkdn+7/Zavwh6SbJd20yF8k62DH11g9KqilZZNJ2rVVoS7SakvY8FoTg8WnD7KIahG/d1lVZtbS2uv/569OnTB5MnT7acb2howBVXXIGWLVtizpw52LNnD2699VZomobnnnsOAFBVVYVBgwZhwIABWLhwIdauXYthw4ahcePGePDBBwEAGzZswOWXX44777wTr732Gr744gvcfffdaNmyJa677jo/b1EZ5o7L0waOHIxxeNTkqQpz52tcpWZMa9dZqfyoNE3zXfMWtEnLCWkh5zhbJh6yz0iKHmOmCaf1vEpLMj3ru+B9KWEzzweFUKRlaIEJumF4Tb4KPI899hgAYMqUKczz06ZNw6pVq7BlyxaUlZUBAJ5++mkMGzYM48aNQ9OmTTF16lRUV1djypQpyM/PR3l5OdauXYuJEyeioqICkUgEkyZNQocOHfDMM88AALp164ZFixbhqaeeShuBx64xqPqgwxiHJ47ddgjmWbSXqovKF3f9fRG27D2K90b0Q46dg1UI8SpEGU027h92apalu7jGY8VEY2SFcRVfKuVr7rMRqIRTCo3z+3hGaLd0LTiNeRjGnEB78nnz5qG8vDwh7ADAkCFDUFNTg8WLFyfS9O/fH/n5+YY027dvx8aNGxNpBg8ebMh7yJAhWLRoEerq2BEsa2pqUFVVZfgXJLYrEgSu96LaDgN29TGfsn9Wam7sfyt3YtWOKny19QA/UfjGMy4ig8+6XYfs85B8tilZpeVTQ06jVyuFH0FM/SiXFe+IG2mZ67siV2amohk0POHT7qeSQAWeyspKtG7d2nCsuLgYeXl5qKys5KaJ/+2Upr6+Hrt372aWPWHCBBQVFSX+tW/fXsk9ucWixTCcU1SGTXlBoO+/omaTls0sOpV1D7nFSSkDJ36GSlMQshA0E+WovCU7oS4lPjz+F2Hhl29+hUuf+Rw19faRzLlxeMzpGMmctGNel6VLXZMm34CID09US/2ydH3ZQSMt8IwZMwaRSMT236JFi4TzY6nfzb4T5jTxAU82jZ5Ro0bhwIEDiX9btmwRrrMf2GotlJm0kr9TI+WLd8cWk5Yp0rSqb+V4EmDcsGrHAX5EW4F3kPpIy2yWbdmPYS8vwNqdBxWXp868qoJU+YDpi/n3oq1Ys/MgPlm9S/h6WW0f67bo07WH94QNq3O14ASPoL8VwIUPz4gRIzB06FDbNJ06dRLKq7S0FPPnzzcc27dvH+rq6hIam9LS0oQmJ86uXbEPzSlNTk4OWrRowSw7Pz/fYCYLGjsthjqTVnh9eKJRTbhHS2XN/e5kg+7EzeWbA1KGrJlY4NXvmue/AACsrTyIuaZ9grzek/Bu396KCT12fnd2CMXhcTivpbAvC/knkEBkLy1A3eRRljA8R2mBp6SkBCUlEvse2dCnTx+MGzcOO3bsQJs2bQDEHJnz8/PRq1evRJrRo0ejtrYWeXl5iTRlZWUJwapPnz54//33DXlPmzYNvXv3Rm5urpK6+o//K49Sr+GxRz87jck7fM2CqInP6VmpdCRVkZMfr8HLpN9cH9nBJGyxnrYr3ifI3H7CLhCqgnWfcsH75Mpjanj0oTpC1peJ4PteWoJOy6pWaTmuYjURhkm2rz48mzdvxrJly7B582Y0NDRg2bJlWLZsGQ4dijlHDh48GN27d8ctt9yCpUuXYubMmRg5ciTuvPNONG3aFABw4403Ij8/H8OGDcOKFSvwzjvvYPz48YkVWgAwfPhwbNq0CRUVFVi9ejVeeuklTJ48GSNHjvTz9pRi1fAkfwttHiow/IbNh0eP+R4Ny/LNZoRQDKWZiV1by5SnrrL9pIsPzx+mr8UPX5iLmnp1e8o49UvcSMvciY1mSCWKm74sbP2fCvht0ey0TCYtX3jkkUfwyiuvJP7u2bMnAGDWrFm46KKLkJ2djQ8//BB33303+vXrh8LCQtx444146qmnEtcUFRVh+vTpuOeee9C7d28UFxejoqICFRUViTSdO3fGRx99hAceeADPP/88ysrK8Oyzz6bNknSAtRLJD5OWLv8QND49DZpm6AjNcXgMHZTEii6v+O0j4UfuMlos8+15HQhUBbcMvH3yVgSFzIdHlD/O/BaA/Z59QaN/lqxqGsIliHUHxxXiy9LVlSe1BUgIPhZfBZ4pU6ZwY/DE6dChAz744APbND169MDs2bNt0/Tv3x9LliyRrWJosDfT+OG07H/jk/sY7JedqurgROok+rwz0QHa9tYlH3zw3Zs/iG8tEb4GUtuQOg2PHn1Si5B97Cnq83MKPGh2xJVF5pIwDNReME9ClMV18zm9H6RXRLUMxm7mqG5Zut7RT02eXtD3aXZ2ZS+dkzt1d/J3+IYsZ1iCmOhTsES1zkAhR+X4Jdq+0n3QZOF0S6J3HM8n6vTdcXx40qPV+Q/foGU2abkvw+hHJZeROfRIEJDAExLsfHhE2pXsbulh84OxbC2hj8MTdS8A2qx2518jnn3aYzZ/mWd/ntpJhj5IQ9u0S8cxwSitS4okclY5MuOXvh2JbBEhqx12TqPhaG2D7m+J/MWTBopIAMZAl6UHUqoREnhCgkXgMfx2cg7UhJwRUx0jRQbL5qGm7Q2MsxRxbZCbj1u047Wu2AnZQ3WBBjtfCef7y4RnIIPo7ab7U2Gv0nLul9yW4RSHR3aV1gP/WoZuj3yMdbvUxmXygz998q2r6zQAR2rrmceTvzVlmhZpk1YIPgISeEKC1aQlbqO+5x9LsENg+a3BpBUC9aIeu+qYHe1kPhw3t2k0afElnrA6sHqZ9KsUWMKgRXSrAbF77xGjM4lNHrpkIWkcctpRJ4HGXbm8d2KYaDhFWhacAMX5z7LtAIDJczY4prWUleJX99S0ta6uW//9YXR/5H94bqZRYNKMEo8ydwZpc3cIvgESeEKCXVtw0lJ8tLzS9jyrjFTIO05jjf687eahMM9SbLBoysyaI5Hl+8F/mEFhNWnpfouYDiTTB4H8zJQ/+RBtK/49ilh7nvPtblz05CzM+26PbyWZcdQ8S+YXNc40LIIRV/MY0nYWFE9PNwpMVh8e9w/MLlyIE2F4TSTwhBThAd51/mFofkmiUc3QoRmXpWumQUe87q5WcAjMRpnXyRflCzJ1NqeNRsOpmQgSix+YoG8OL1CeSuJF3Dx5PjbuOYKfvDTfNr1TNb4/WINNew7H0jokdusHYzEFH/u/o9MyJz/eAG5v+j6O2nVIfHgyPvAg4QGfZzAhs2ghqmncTu7fi7aIr/gwpbQLaOgVlVGbw4L5OXux04egf/Mdt+3SL+oa3JejaRrOHjcD/Z/8FPuP1DrW2KkP8WLyYC9L10dmFzf5e6lTmNE0DcP/vljuGqjbWkL+/aop1wsk8IQEO1W5H3F4wjZzt1sO/fuP1wTmwyN3nYsLfZCZWIKYaN0yLdIy8/HKdtSWPMWW5vKcbP2kcV624e+PV1Ri0ca9QhXZfag28XtnVY2zD4/EgzTkxfXhcUzCzM+pb/j7vI2W83LaqXC2/C17j+Ljlc7uDGYz8/G8SsvXwIOEOLZxeCTzOlrbgLEfrLItIwzqRcNeWg6SSa1gSHzzbenvc9mW/dh7pBZm9HvC7DpYjbxsd/MAV09U4qJV26sw9oOV+MWQU9GrY3M3pTlXx265oNC1Rn+BTMBu4A/bPTbOT3bp678/hOGvGTUAdvXV7yyfl5NlNEM5LEvfuPsw2hUXIsfltxPLz6yNNRZqjr4eh+fA/L+VO3H9pLlYuHGf6zqpREYjXFsfRV6O/bOsN+/0y0Hffmvqowr30uKX9+T/1liOh2HMIYEnJNi1Bdl28sJn3+GfCzYbjr325SaM+3B14u+UOC1LaC/MTsvme7782c+55+zQosC3Ow9iyeZ9+NVbyy3nJ/x3Nf67vBLvjzgfe4/UYsBTn6Jlk3yhvFMdafknLy3A7kM1uO6Fedj4xBW+lGHRtOl/2zz4/Udqcekzn+OkVif4Ui+ViMzY7aIpi0b5TUkcHtPfJ+gEnq37jkrl9cW63Ya/nTQn8fbwztKteOBfX2FQ99b46096M/M2+vCwMZipHOqqH7Ttni1P2JF5HSLvrrquAfk5WUKLIp6ftQ71DRruG3gyN80pD/8XD1/RDXdc0EWipmz01b/z1UUozM3mprWj8kA1np5mFWTMLNy4D3/+9DtrPUjgIeLY+U688Ok6FORmY8K1PYQ+qK37jliOPfyfFcb8fW57B47WYWdVjXB660DrzuHQfF+Hausx6A/8bUle/Gw9AODVeRsTs9PvDybrrToAmgWJ/Hcf4j/P52etS2Yp47Rs+tuySkvwpl77chMqq6pRWZUMjxCGDs4PZGMTyaTzSqP85GDGKtHulcxcvUuXzhj7av6GPfjvih347dXliWNxreykT2Pf0PRVO82lCdWZZWbSNA1LtySFlb/P24hcnfaIJxw5Olr78B6+3XkQg/4wG9f3aocnrz/DcM6sTTlSW5/Qftx8Xgc0LczFzNW7sP9onSXfxz9cbSvwiN6J+ZkcrWtgJ3TgtikLsXFPcmzhPcu9h9n9lKBCylfIhyckWLdESP7+z7LteH3hFizZvF8oLxHVqd/qxSf/941jmr2Hk+Yl60DLv06m6rsP8oUEfeTVKMe2nS6OySwVstttNVRprsIg7lg2noViZ0vTueVbD+DFz75DfUM0JRoeM43z7OewdgN+Tb0uErHp3LvLtuPzb3fjnn8ssaQR6UuMKx/ZDUyfz5rKg1iq6+9+8+5KY1rd4CkTs8wunaZp+OSbnagUiGmm54XPYtqMNxZvtZz7rcm1QO9UXteg4U+frMPw1xZj1NtW7bOe774/hNunLMSSzUkhUPhexZI5snpHlTFfyYzJpEUksFObx9EP0J7L87nxbdxt1TLZIRMIUWL8sf3Iev52muFvr7btsDk3uluSbxIOOL/9RqW58I3FW/Gj3u3lytf9lvGvu+pPcwDEfGnKmhVw0zmhaRreXbYdp7crQpeW4mZCvQ8P6xu3nUiY0rHS7tRp8OKfCy9L+QEx+XvZlv2W8+ZQFazrnLCr0/9W7sTw1xYjNzuCb8ddLp6pDVPmbrQ9//7X24Xy+ekx7con3+ySNmeHRdMaBoGHNDwhweIrymgbKhuM321Ptq5mYcNt9czl2tWjui45TdSgMQUe+60ljLh5pjX1UTwzYy2Wbz2A33/8DV5g2L7dIlIdSxwegXbILIs5Yxa7FgC27D2C26csdHWtE1O+2Kgsr5r6KKrrnfdkWlN5UHg1F4v3vtqO+/+1DBc//ZltukjEmHdhnr1Ja+X2KsbRGMZvhVdf6z0Jfet6DQ+vfAfJRf889T5/Bg2Pc024KedviAVtNC/tV9kWLf2JYN56U5IsotV/etoanP+7TyyaHNl8ec8rDKFQSMMTGgTUwgpL87vxyQo8lvrYmrT4J82nGiTsxkHMQGrro3hmxrd4ZkYyHLwGDf9dXompd56LpgW5jnkcOGK0/8dNBu5MWpoxYF6K9DoP/vsrLNAvn/YRWw2h6f5jx4xpbvlbMrgf7/mYBzbZp7hk0z7nRPG8dZln2XlbS+TD0/Cw0nNX68gV7yxYGEyEbCHHeSk9nyYC35oXYsKpr0WwEShz96EaPPdJzA9w/vo96NamqXO2Hie1QUACT0gQWQ2cTvscyftJaKaVMTZCjUM+emQ+sjB8kEAs7hAAvPLFRvz8Ev5KDiDmBHnG2GnMc27uxk7DI9/8xC/YUSW3osgvRHyYDgvsuh1J/Mc+HbcegukiiPBn2pItwGyqY13P+kaFfHh0eYnspeVEg8GHR1+Oe5oWJIdDERP7zNU7USczozIRiXidxIpdLdIODtckNx0V7QZl604mLSKBpSmw7O+CeYn4P/jd9mTzb9A0YQdhUT+EWFqJTpTltBygz3KtQGdq5yslcutmbUZQZtMsHx+0n9uDCKe3Sfjpml2489VFhtWBos/ObNJy48DLQkbDw93aQXjgFBOc9K8xyrlnx7Jskuq1qVXVSa0pK//a+ih++soiDH9tiUXDmo7U66QcN32AiIAYAnmHBJ6wIKLhUamU8duRzbMPj+3l/JPmcu02JTWX5/WRqH6kIvllMb7g+MDgRosn4kvGvE66JCPZQUqWOljt1lbA5pyMRCK2js96hr28ENNX7cRj76/kprGDq+FxoWVN5ul8ccJpWaAc4yot9jk552P9AK0/4XDdsQSsOmdlJSu2z0GI0Wt2DtfW26S0VMD4ZwqkAJEi6nV+S6Ka7ni+B47Woe8TnziuNAuDBp0EnpAgsnRWpRnK76bn1X5vb7YSPyejng3DBymLnVbMTV+qVMMjkVY/2PiNiA+YqHlVvEznNLt0catkylT1zgzRizX2+2MJcbzZvexg7pTeEJndYMaSE9R4NOjWuu9nRGTXoy9FJnSFJfyG8JWMOoj2bSICj+7ehbvBY+neWLQFlVXViWC3vMvJpEUkEGkKwmpugTR+Nz55p2WzwOeufuarZJa7swQeu2pYnFsVi5EiSg+WnOBFWRJUl+Snhkcma9l2y/XhiZh2SxfJzKXPj1F7IlmmPh/L8nt7E6+MZkZEQJCZbzQYJR5pWM9Xb0Gu92nyY3wO6vKyT+ecUq/hEf0G4vmKBMOVyddPSOAJCVZTgtzg67U81Uj78EgsS7fX/phMWsIqHs3zBxmEScuur3Gr4TFueun+pmQuVSnveKlzTV0U89fvMQx4Kt6rSJ3sHsHyrQcw5r2VFs0D65ov1+/B87PWeRLeeBoeY/pj5iFFYrLTDuhGM5Zeq6NP4778Bk75rCyNO7uL5R8xXafB/34YgmUYfHi8xiPjXB4GBTqt0goJltkVM40YIh+g343Pq21axmxld05u5Ye/QqY/WF82y4fHrfpbeBbJGqAkBsKc7BSatGzO3f+vpZi15nvh9FwND8w+PM7YfbfxgIYHjtbhDz8+07YOQ//yZeyaM8oEStXlY/itObaZ+OcipOEREBCiDmk0Tlon4YSVCattNjTozTr2ORk0NVIaRF0eKepbRIqp1927jN+jDGFwGSANT0i4fcpC/GH62sTfrMYkrGoUSPbPBZsx7kPrjuqqkG3b5uT2O1Tzz8kEHrReK1eWNW3qUa3h8boNg1tUmrTMdV6xrQpb9ooFbjMLO7H85NsizzHXjiwBc9S3uw5ajhkF2+TvTXsOOxeqz8csRDAqoRfj4t8W7/nINh3HZ6Q7r99awkkzJFonfbxBs7bLko/L78Lsb+RFO6bSh0cvjHhdls67pzBEfCaBJyRU10Xxx5n64HNWVLeXv36+QW2GOryque0+OhkNj1TgQUkpLQzriuyWc7vRKshs4uqETHv122n5gt/PSvxOlWnYrDFxQmTvLZZgyEsre5+yJlQnDY+U5gUiWpXkeYP5SSBvS16Mi/ROy47vS3daKqSCSZDy0hbFJ3PO6eo8mLRE7z4ECh4SeNILsRZj/v6CWPErvTOuhCnK7jt3q+HRwFbl6g/VNUSxdPM+g/rXmDb1XzTr1XqJtCyziatKVMbhUV1lW5MW57jlbhRVKtskGEYiCgMP6n9rIoN+7Dw3Do9k+U4DolHrotdIGLUmbuEHM1TXojTOb1d5KdXwiJvzkvnG0ol+umTSIriwBqvxH32DkW98hYnT10pF+LRrj14d1Lj5yjpMQhOa4Trn464emub8LB59byX+789z8fiHq4XKTgW2Ji0X+WkaDA1GXL3NEBYlyg1LHB4W9hpFvklLdo8nEaGP7WempuWZ4/CImoe43w3HLCSyW7pDdtzYO451Tjha25fvfO/unrk5YGJqNDzO6PcPE/bhkSwjDKu0yGk5jdi89wg2H/NFaN4oF8P6dWamMy/7jNhs4nK0rsGww3KQ8CKpmrHddsJ0ndTWEg4f5D/mx+JMTJm7EWN+cFoobFqsKsfHE5H+xeKkHJva64+4rZoUrACKbkmlpk3YqCBdJfYFX209gHUmPx4Rk5IIsnF4kj48nPIlyq6ua8CdryyyTaMvx61Wxy6lzNJsp25l8aa9mLtuj7V8CeFMFSLF6O+dV6+siDenaxJ4CC5ObWP9bnGHRLtx+XBtvS8Cj9d4JjJ+OnbnhKOGgr1busxtqP6exaLd2j0M+TLd7pbOLF7iYrOpJlS4aItmU5OYD49+J3J+ukmfrReqn7wPj1EjxY7DY62jyNYShr20GOW+9uUm7DlsH+xPj16rJOsrxKoDIOcXZFx1Zs3tuhfmsa/T/a6qrsO2/e73kJM1PdmhDzzI6zPNE+dt+49i/EerkWP6dnnFhcCiRQJPWHHqIO3U3zI+PEd1GyGqxOsqLbezAXO5cqu0PFY6AOxm4a62ljBdk6pbDLcPDz/HbfuP4qRWJzjnIVAp0SdgNA/x6yf7HAwCmubcehJbS3DzE5dEDhx13o9Knx9X0+BkFrOp86Hqel06e42H2zamF9Se1S1ScZWXYCVkNTy8fjArAuhHi3umLpES2MKg4SEfnjRlytyNXOdZGQ7X+CPweI7DY5u33XXGkzIRU5kaHpmuLYDvWVTbJXofUc068HmthwjBanjcV/7WlxYwj0dM2YqU4NaHjb9Ky5sAL+rHwtMI8LVf4ml5aYzLqMU1Mzy+P1iDl77YIFwfFYP3Lt1msW5Q+W0anidPw2MSyWW1U375i8pAAk9IEWmk/1u5Uygvu71ejtZJbHwngRd1OmD/cdjH4TH9LSXwCCeNYY61EojKx1rm59/uxpHaele1CWKvH8DfODxe07sa2yxxeJwzMcbhES9UVavTOL+56TXj/73m51ie7rdXIcdc5/+trDSVZTXvGSaYBg2TxLvSa+eEr0qinxeo1PDURQUCDwpWmFdeCOQdEnjCikjbqK4T1M7YNFS/NDzyq7S8XZ/MyHidzM6/fm/n4Qes+k1btRN3T10i1BE7BRpM1f2ncvNQMzKrg0SJIGIcNBWatMxwA/9JVty8SslJ6HK7tcTWfS79VnT140ZadtRKscnNtvdD+b8/z8XFT3+WWB3rdvD2qhnSa0LFNTzO6UQCD3r9RGlZOsFFpC2LmgHskh3xyYdHtmlbBlqJtHrM35SoSUsDJw6P0NXO9fILXpGfrvneZJoSzE/TuDFOZOshMxCq1PCs2lElld5ZwyP/Yv1cZW9wAI6o06QYb1NzzMw58KBVKNlzqAZP/PcbS7kibcWo4eGV405Iy802DoX6/KNRDcu27MfmvUewbtch5vUimB3ZRTfd1CMSjdsN+mXp1XUNeGPRFuysqjakEd0Vni+ABy/wkNNySBH5oHizYovTsk1DrakPh4ZH5np7YchkGpOoh+wMJAzrikQDNIreWVQzCcgeXqNME1C5LP2a57+QSu/XxFNG8wCIr9KyK8foNySrZTVreMTK5QcetLJ25yGpOrHKA4zfqorXl2MSePT9SE190tyTn5NlqYsMhtVdLq7XT3LtzPXRqJYYH0TqqrcWvL1kG95esg1tigowb9QlieNeNTwhUPCQhiedMS8HdINfQrd0pGWJ6+1mCq41PBq745ayzwunFMxPIENRB26+Y6vxb1EfnqO1DVi9o0rZrE3lKi1ZHE03KSgDMDktu8xbJnieJR+TgCzquMtNJyh8MbWDjIM8TY4rfx5Twlzz0mrdb70wkHdM4HH7nI0aHvHr4ui/E7uuzbjE3rmCT/5vjeXYjgNGDY/IN2rXH4gGNPQTEnhCikqTll07VbHaQMWgpwGGitpv2OiQj46GBvG6eRXSgsD20buY7cd8mfR/s6+75vkvcNkfP8fM1bvc1c2ECuHdLX44LUdgfo5i17hBthzRfLw64bOu5/VFQsK97jdvWbrb+7fT8OgnTcltW/T1kpgUqfThsSnXF38ZgQZqVywtSyc8wfd7iNj8ZcRrG5w6fxPOHjcTq01+E/IxbTSIWlJstRpmp2UZk5ZnHx61H7TIDFB05szV8Jj+FtXwrNkZi/b7ztJt9gUIEqTTsh++BZbd0iWvERVkI4hwB1/Zu+Ltus7D0dmbIYiIPmqnpes8rY5T9prp/3FyHJyWk8c9CoEem5rRaZmfzmDyU9S83W59EoeWpRNcRD6s7Gzvg4RXqfvX76zA7kM1+NVbX3vK12ng1fPal5tw3QtzceCINViZ+TKZmQ7rg7S7DbPToerP2bNJS0BT45SfJ42BRNog99Jy3LTS5Zs1CpzOeeh97eSWpbMHN9nB2a1JSyQ/x7IFrjVoeAwDuoeCjpGbxXdaZtVLhUlLBHOflCXo59Xgsn52iHyidm0iBPIOCTxhRcikJThIyGhL3ErhZsHC60dmV413lm7D4k378Nwn1kil5g9OfJWWhrowfJEKYQ2EW/cdwfRVO7mDRFTTTDNm52eyaONeLNq0z0tVA/bh8ZrAinmhgKyGR7iciFVQkSlTj1kj6KgtcRQUxerCyudjU1ycWLpkQpGVYTJYNXLsfGQ1VWb0/ZPIqiez1tkYh0dMm6IqPpiYDw//XBhMWrRKK40R9eGRaYT6aKNmGqIat0yr86tQ1bjXi3RclaZlkwBDUyRRkbp6lhOPxEw7gO9ZdpXW+b+bBQD4yy29MPi0Usf35vT4jtY14IeTOPsGSTwQs0lr18EabNx9GJ1KGgvn4RY/OmLLbukCRfDGEyctpawmiZuP7trH3l+J/qe0tE3vqBljmoitx9w8/waD8ONOG2ZXB74JWLOkl9LwSN5qQ1RDbnbyb8MqLUFtijINj0CaqMbfAZ5MWgQXoRkh5/gn3xgjMMss8f7Xwi3MdB9+vQNdf/NffLxih0DNWDk7pdYsM0wnDtVYo0S71fBAA2oZoZa9dBYfr9iBh9762rel/4CDScsm3fwNezn5mSVP+/L1+w/Zle9ENqMnuuipTyVy8ICEpkIU67fpnIthxq9LXmdql/qcYs7RaswX+k/lm8qDeHH2en5iS03sz7pdhGBIp0vIEzj+s2ybUB5OATedNkSV1YKyylmwkf0N6jELu6JxeFRst2GGTFqEb4h0XLwGtLPKuEeL6IcB8NWW9/xjCeoaNAx/bQnzvEWz4qZx6+oi4mzMGmxFfXgsO/wCqGVqeMQxd3zDX1uC1xduwT/mb/aUr32ZNucMnR67gzcfN8/QUrVdRqiXpQs+gve/2m7KVzIPzrJ0J6Gda9Ly+dU5rWoULt9FRfXaAv3jWbFNLuhkMg+xOjguxVdUThyLSUs3YtsKkUaJTAkigRKjUfbEEQjHsnQyaYUUkYFG+OOxSWbuS1WNO7IfdjTKd0rkwdLwmDuBek6vnJMdsQwkLIFH6i44ib/3uEmgbZGiJi2L4oZ93aodVYZBw1Mf5aOqXyWOA7fgjfz8n0uTf7hwaOcJfTKhFWSiDrvBTohmpNZdZznESOWUG1vIcXOf5issf3OyTNyGD07BLMzvXq8FFI1VpmrSIjI0vDj7O/z50+8c6xQUpOEJGYePDeIibcNNR2E5d6wgr/ZVmc0/WazaUYXpq5KmOJH7P+hBw2NelQHwZyY8AlRKJBBvA6a/OReaZ8ip6qKC2XjVv7JVxuGpc5DI3C7R9kpUA9ZUHuSeF56PaYL9Hc+kJVaMbZ1EI7SzTFoyyI75d/19MbbrdiXP4mgBzdj58Fzfqx2aNcqVqwjEtLA8YQegvbQIBgOe+hSAmDQcMz84p7P189CAvYdrMXDiZ7jjlUXMNG5CpLgR5r/RdZ4il7MEFPM3VVvPMWkxlvSznJalHBI5x92aa0SKdnq3yd/2HTg/D/edlFvfhlTj7HzrvQzpSMu6QutNs3zD9gQRYxweP+Kv8NA04C+Ofj4mWPF1BNsJ11zn4j4tPjtR+/PJ41aTlmjxEchrvhds3IsH//1V4m9jpGV+XnYCYX6uu2Hf6wQvBPIOCTxhY9fBGny8ohLPfrLOMa1m4xFvSGdzLqpp+N1/v8H63YcxY/VOpp3WTTv12rZFOgbWsnxz58k3aZnibkQ1w545ifx09dALft99fwj7GXGAWPi6kaStMyh/8BOOy+OqVuwy/SrHK45Cn4s8Y+9cTvjgBd7ktWFWWuMeU/4+Vc2y1MB8Xuyg8LoCnmAnI1hz0jqZuMzHo24kHrmkCTbvPZL4zROKZcqJCG4DmmfqI7362YVhWToJPCFk+GuLhdJpmqgWwG4mAOw5nPQxURHePpavt8YtJPAwVE+WvbQ4/g9mp+W/zdngaNLSf/Cj3l5uOR/E92xXpJ3zsYbYxrF/nGmNZcTLg12+mpvmtdH13x/Cw/9Zjq37jjDP+1m26HkWljg8IgIPZ0AxyzszTNt56OuXSrOBpsG2AYqa9GL9mICm2uDDo//teCkjLyPm/obX/3h1Wnblb2TS6CWP86+xC8wYiYg5IDfKzzb87VnDEwIVj68Cz7hx49C3b180atQIzZo1s5z/6quvcMMNN6B9+/YoLCxEt27d8Mc//tGSbvny5ejfvz8KCwvRtm1bjB071vISP/vsM/Tq1QsFBQXo0qULJk2a5NdteWLf4Vr8exF76bcsUU3sA7IfFDXoxRynkO78MsQ6DFFEvg3mDtumcs1LeuOwTFos9LnpBR6mNojzpP109bEdSGzSaRrwwVeiIQa8l+82nx9OmofXvtyM//eq2CTADU71dNuU9SEehExanDLN35LZWV9/1iAI+Lw3nKZZ72rOt7txz9Ql2H2oRsJUJZiO80ykNIlcU5WlUrbpjCYjGQ2TPPrVTcbAgzbl2CigRDU1jfOMa5oywaTl6yqt2tpaXH/99ejTpw8mT55sOb948WK0bNkSr732Gtq3b4+5c+fi//2//4fs7GyMGDECAFBVVYVBgwZhwIABWLhwIdauXYthw4ahcePGePDBBwEAGzZswOWXX44777wTr732Gr744gvcfffdaNmyJa677jo/b1Ga26YsxLIt+5XkpWmaUCOynQlomqEhqzK/eNZ2CFzPMmmJ7pbOclpmceCozmzl9tn4aNOyX6Wl75RN56Ch2sf4QLLwbmPv4VoAMad2v3ASznm+bXbsPFiNWWu+T/wt9D0cayZrdx7Ee7ol7nb1MztHN9jM7FUT1YCIqWXdPHk+gFggycHdWyfrciwd18wl+YnohTk3WkZrHB6xCZvXR+rmnejnbMY4PHaae3uBUORxN8ozani8mrQyfln6Y489BgCYMmUK8/ztt99u+LtLly6YN28e3n777YTAM3XqVFRXV2PKlCnIz89HeXk51q5di4kTJ6KiogKRSASTJk1Chw4d8MwzzwAAunXrhkWLFuGpp54KncCjStgBjml4PJoUNM3Y+MWsu+x8DH+7rxIAMQ0Ra8NJ8/PgaXhEo1Tf9ffF+G785cjOihidtxn141XZVw2P4DlW3VLh1CqcNshVWg5FxzdKlaHqqJh/l574tzfkmdmG47aTGpOvkN6C68eM2qxN4vUX2/cflVpBGBGauOnv05tJi1UHu7+TZVlNWio0THYYtqMwOC3blMP5HctDbA5WaBJ4vPZjtCydwYEDB9C8efPE3/PmzUP//v2Rn5+fODZkyBBs374dGzduTKQZPHiwIZ8hQ4Zg0aJFqKtjdzw1NTWoqqoy/Es3RJ2W7Yhq/gR989q4RQQesx9OrFzj33yTlnjTj+fhVhj012lZ7Jw5Qrao/5dj+bbnNOw9XIuGqIZ6B/8op9ft52bq/mwtIe/DE79HWQdzg6knlU7LGv/ZyUSAFvXt4Jr5JN6fkyAjmqXrZfESaePotXbGOZeYhsd8M6L9mDmVZ6dln02sIoRK4Jk3bx7+/e9/46677kocq6ysROvWrQ3p4n9XVlbapqmvr8fu3buZZU2YMAFFRUWJf+3bt1d5KylBRXdmNmmp2Bk6lq/7Oolez/oARZ2WcyV2mo/3F06DLq/KbgUlEewHw+S5jXuOYItutUdUg+8qnm93HsJZv52OE0d/hPMmzEzEmGLh9L5FNXJhwVzb+Rv24O0lW+2viYC5DYnds4mYzusHRz80PMaVZBqO1no3i2oQ68sM2iWX98lr8tY95OxNWrwiG6Ia7nhlIbd8N467+muMkZb519iZs2PdpkDUZMuFjpfYEgaTlrTAM2bMGEQiEdt/ixbJ27xXrlyJq6++Go888ggGDRpkOGedLWmW4yJp9IwaNQoHDhxI/NuyRY0jcSqx26hNFM0k8KgiFepL9hJ6k0mL08G4GUAjLp2d4kk/W/s9Rr7xFTNCtFtsxR3TyV0H9Zut+m9EekPnnL/7UC0+//Z7blqn2oisKnGLX5uH6nn8w9Wo+PdXWLXdXpP8m/+ssBxzqh9v9ZLvPjxRdrRzwH4XdzOi1TRqePTH5e/TYsIya3g41yVNWuznPGfdbssqOpF87dALCvHJ098+X4/7/7WMe41d4EHRrs/8TWaCSUvah2fEiBEYOnSobZpOnTpJ5blq1SpcfPHFuPPOO/Hwww8bzpWWliY0OXF27Yo1qLhWh5cmJycHLVq0YJaZn59vMJOlCxee0hKrtldh96EadT48gksd7fMxXpgKj3zmbsxmkxZnfyyWOcwJwyDGLJuv3geAW19aAABo0ThPumweMquk3JhZvGDWwNkKLU4aHl8dv9XnyavtroPV6I6mzHNZkQheZ2ze6yjwcDQ8ftyX0TdEs10Gr++bfvTiPIwYcBJOanUCJ53z++UKdo5XOmNdpcXR8CTKZ+fjtB+fm3fCesaPf7hauByL4BKJiG0EaroVz07LIVimJS3wlJSUoKSkRFkFVq5ciYsvvhi33norxo0bZznfp08fjB49GrW1tcjLiw0U06ZNQ1lZWUKw6tOnD95//33DddOmTUPv3r2RmysfQjvMZEWAU0tPwO51Ncp8eBzGcQuLN+1Dr47F3gpWANsR13iQF7TNlYZH93vvkVqh+gDW2f5WXah4O0Tehf22IaZ6mM75PeMy37ddh+lUE9b7UlX/VAZEK8zN5p7jPR6n6hkEHo0tFPhBVDOW0bW0SSJaegTGCNDfH6zBo++txGs/PdeSj5u26NZpONHSNPNRsQlbsp5sgcupV3HTZt0sgXda2SfS+5nzyIRl6b768GzevBnLli3D5s2b0dDQgGXLlmHZsmU4dOgQgJiwM2DAAAwaNAgVFRWorKxEZWUlvv8+qfq+8cYbkZ+fj2HDhmHFihV45513MH78+MQKLQAYPnw4Nm3ahIqKCqxevRovvfQSJk+ejJEjR/p5e4GQFUnGyVTheBrz4RELVx7nXv0miccIoi2z6ioeeFC+6etXhW3ZKya0MFH4sOw1PMaTWSZNnt/vzLyKzk7GdGp3rM5W1YwxlZr2AhuBhzcM2Q5ekYhR86F7Jn7PqGNOy8m/DQJtRP1z5WqyXOahx7q1BDthvFieychJKHBl0tL7Kwk6/touS3cpuGRCpGVfl6U/8sgjeOWVVxJ/9+zZEwAwa9YsXHTRRXjjjTfw/fffY+rUqZg6dWoiXceOHRMrsIqKijB9+nTcc8896N27N4qLi1FRUYGKiopE+s6dO+Ojjz7CAw88gOeffx5lZWV49tlnQ7ckXQVZuiWFGsT20sqK2M9Y0ssdNAnLCc5i0rLZLV2U+IDiPgyPyZSkUNSwy8lquzfG8FDR/9i1P3MHaavhcTJpCUTVdosvJi3OvdppFnmPR2avr3qfBR5zS45KCh7sSYqGSMR5AqK/0quvEivyuN3f5jOi2lzL1S5eiUG4ErzGTgOWJWrSUvxhZLzAM2XKFG4MHiDmAD1mzBjHfHr06IHZs2fbpunfvz+WLFkiWcP0IxKJJAaOaFSs08/JyuJum6BBfskvs4MJoC2bq7GzqhoNJgFHdGsJkXLcznDMl6n87u06EYtJK2I8p6IadnmYn5YHFx5OkElFGh4fGu87S7cxjzuZGmSvMcMzb6nCKHTwBY8I2O/0YLXVyVnYpMUx16loB9atJeyrwFsFZbciU2OUI4vwHng2QlLMpCW/SsvrwoEwLEv3VeAh1KPX8EQFR63srAjAWT1qNmmJ+Y2IHfMbfecx65tduG2KdTloHUfgkfHhiefgdvZm7lyUznQkTFrG8ANqfGDssjB3kPYaHieTln8CjwpFyCff7PRcFt+Hx76CPFOPHxoeo+bAwWmZUe+9un37EukEew99KrfO2fGk5mucIi/HYZm0DNgJ9VrqwmvaacBE5RbzdV4jQ6TlsnQiWLJ0Gh4NYp2Fve+ENbaGEyLOwqlA/1G/8Nl3zDTc3dIlfHiS92b/xYt2Zw4x+KSQcVp27+hpVz4fc7vzYtJitWF1Ji3vGd0+RSwUh52AwHs+TnF49G1AP6j47cMT28uPU68Iu23sPWwNBCv6+HmmOyWaSptvxXj8mEkL7I/JrodQMckQFfLtNYliJi07k7gb0nJZOhEsMYEn9lt0ldZhm+BgUc0o8YhpeDTsPlSDl+ZscE7sI3pZhvcp8palu9HwuJ3hfL11P4b+ZV4yP4UfvtTs1qDmVuPDY5eJxYfHRsZ0EhZZ70vVgJ7K1SN2755r0rKpYMTkn+c2IJ8oZi2hrXMso/zaBnZgxb9+7tyXGAQ7txqeuMBiusb8rHjtkaUhEi1ehRlZ9F7t4vBEIu5WaXnV8IRhlRYJPGmGPkpmVMEHBM1ochHJT9OA+15fii/W7ZG6TjUGnwHOx8gLPCjltHwsC6cZDq8z+u8KY4wo0VmaW20b71yDqZdWoWCX0fDY+jc4anj8W5aeyrZrGVgF7sHZaZmt4fEbTdMM5enbUwQRZvtiVW/Bhj3Wg8zykr+Nq7TE75mX0uLEzEnI2ktLvGyxRSb2ecinZMXhcVVWBsThIZNWmmHU8KhXkYrkpwH4cv1eT+WqQP/98AZT3h5OUoEHEwKP+CUC2XkivgWBXV7W2CJinboMMj489ju725fD0g6pW5aeuo7YzjmWNxA5R1pO0sDxWVOF2SxqZyZlVZv1yvYctsa0YpZtyMedhoeHdWsJ+0rovy1RbY/5eblBtK3aanggJvSo1vCEwaRFAk+aYXZa9tqEzNGaRTU85sEmiLYsoinhdVzZMj488WXpThoewfxUjNOnPvwxlm7eJ7VKy7yyRMUrs2uB5scVv29N07B250HDxq6OJi2BfdPcksq2azZPGXfC5lzj4I9h0HykWHjjbh7K8eFhvWc3m4cafHh8kNx57dHRadmhCO8Cj1g6u3ABoooas/uj1/keOS0T0liclj22IUuHJZCfeel3UBhnx3LXulmW7rxKS7TjVvPhj/twtb1Jy/S3XtmlYh82wL79mf1u4h3em4u3YvAfZmP43xfr6mNfjjmIYaxsNc8xlfFB7Dap5GkpnQfXZAK/70X/DUQ1zTYAILMqglofFmp8eKx5serAdVo+dp1x8iBeAc/L0oXL4Z9zu5lxJixLJ4EnzdDH4Yktc/T4AZmm+iK57TvCWGkRgBePiA8PDz+WpYuidlCy0/DwtQmapuad2d2K2e8mXv5fZq8HAMz8JrnJotMjYWl4VM0YU6rhsTErutHwmPNI5aBiNWk5a0lYdyL8Peg1WW4jLXOOi5r2kwKT9Zhj2Qo08sKTKoPJzXhNJCLWl6l3WiYNDyFJdhYSusVoVEPVUW87b5s/wjDYWUXx8gHJaXhi5Tg6LQvmJzwoOWTopOEznzKruZVoeGzOmZ9W/Dmyg2DaV4b17FWZtFKr4bEReDjXOMbh0f3222xg9tmxNWkxTrHuRdQXy3CfhtVo8vfs5G/Ey/K2KQvx3MxvXfWTKr450cvt/IqyXAo8bjVDvPyCgASeNMNs0rr6T3M85ReLpeFutqQniLas7/RkP8Zsqa0l4mXYs2zzfsFVN2IPyynd4k37mJuYxjFfXq9KQjCUwc/T4sNzTM5h7Sjt7LTMEHhCsOpDlqimYd2ug9h/7L3p3zHrHgF7AdksWKRyJYx5t3R9ybFVWoxrmCYtQYFHl07flpds3i90vR3mOjw3ax0zXUNUw9PT1/Lbq90ERHMWXp2ImaIl+xhTcuFVWhrw8hcbMGPVsaCaGbAsnQSeNCOiW6UV1exj7IigauVOEAKPiDmAhxsfHicefOMrfLrme8d04ipw5zSTOAEXj+Vg+KvB5OjptzbPrJWJax/qGBoep0Evm9FTqYu0nLrGu3bnIQycOBvn/26WpWy3W0sENXOObS2hOyDwPbJNWi7K1l00e63zNydSDz0sodxQvknTpapcx+s1sfKc9t8SmSDuOliDx95fhTteXXTsGm+EYYJCAo/P1DdEUfGvZXjty01K8svSBY1SsjUATPboQCLquMNLR+9mlRZvmwo9/1sZi7lj927E4/A4Y9cxm4sxropSZNKyycMs8CRMWiwNj0M5fpq0UikvfP5tbHA+VBMzRYsULbN5aCoxC82WaijSZLKyc6ut5G8ZIZefG614g+Z9oYCmiZktDfVz6cOjGjJpHQf8d0Ul3l66DQ//Z4WS/AwmLSUDlvEjDEGbFEYkhgkPlhMsl2PlsDQTlqTH0tr1x6pMWgCws8q6N1HyeuPfDaZpn4pX7WZZOsuHx+lWWe9XXaTl1DV6/cz6s7XfQ9NHC+e0SWcfnuA0PHaDr6hJy4XPsud3L+qzI1IX/fuxa0vRKH8Zvwwiedj58Hj1xXELLUs/Dqiqtq5o8kJWBEmnZRUfT9TdigMzQe+lJUNWhG0i4REvRWRWmYzEaqfhESzX82zQmEGdwd/C/2Xp5gE8Pki50fCwXK7SMdKy/pHc+tICwTg8/PyyeAFvUoCmaUZHeAHzuJc+S3+paiFVVn7itT07QaxBUxE3Tey7tQvMqLcSyOBVK0TL0o8zdh/iz8ZFiZiclr0SBjWjWww+PBLXZUUiUhqheDkntTzBOe2x/9t1oKmK12O+Wh8/SdPUaAbscjC7ScXbGuvZON0rcy8tVW1XsWApg1cfngDlHZg3D9XXIxKJKJ8E6Z+De5NW4pfxuORT5N2aXbUaogq2ltDkA65at5bwHlPHDWFYAUwCTwq57I+fe84jy+C0rGbAsrP3yuSTakRmxywiEbmdf+MdRl6O8+eSHNQVaHjEkvGvN2VQ36AZzqkyifKw+vDY5WNfDquDVjVj9PodyfjnVdcZFxmIlGxXv5Xbq3DT3+YLl+8V/eAZ1Ux7aZnNRKzrFQmHKlejiW7CbLjGJi8eKuqsQRPqPzSb9+LGpKVpmmdTGJm0jgP0jeT7g941PDF1pDofHhXbUwSFyOyYhX6lmwxCT0pS3WyblVfNg90qLUWeHzJ52HX4TrVhvS9V2kmvufzm3ZXCaQ/XGAUeg+mBc00IxgkmUQdtA9Nfx1N5Og2PgD8dC+6WEZLCCM9kZDeoNyjw4dE0McHJNtKyC5OWCmEtBIu0SOBJN7KyIomNFJWs0hKYlbnJJxW4dVrOktXwaMb/29dJnYbH84Bu1vCYw/ErUfHwT1k3ypQbHJ1Ix2Xph2uNgUJFFgyEyeysn8BpmlHbYNwtXdxpWRTj1iju8zETM+/KX5P4bdB68a+JmbQkCzKXC7F+31bD40LiqY9qCnx4gm/HJPCkGbFGF2t5KtpP1PS1h6hvFcKN0BdBROrjjZcgUlQ8iRIfHqFU4tebg8T5reERXQlzqKYec7/bI112Oi5LP1xjFHi8bIAbBGaTVtQsROvTMu7Ni17RGHjQpYZHkVDJS283qKtZli5m0rLfS0ueqKZA4AnB4EICj8+o9g1T7sOjWTsxV/kEZBiLD+JyTsuyGh7xe9PvCM5PI2rS8q7+1mOIw6P578NjPsO77z/OWCtQDlBT34C3Fm/FrqpqAOr8OFLZDx/SCTzNG+dxNQV6wjBQsDCbaAyLCDifl5d3ptKHx+xs7eUR66910mJ69WMRdVp23EtLslwVUdrDILiTwJNm6AdrZT48po/fDUH1yfGPSM5pWc6HJ2HSEng68c7FPg6PXLlusfXh0VR58diUb7oBXme/Yfdh57wAPDvzWzz4xle4+vkvmPnLUNcQTYSMSOXqkeq6pNBZ1qzAdguAxOEQCTxmE5y9JtN67J8LtrguW1+W6i00ZJ8xz2/HqVqeBTW4WaVlJAL5VVrRqHenZTJpHQeo7qtiS6rjeavR8KggqKbsZvYbAX/fIjuETFoiGh7RzRI9q7+Nf+tnaSrU64CcSct82/HtPYTqoQEzVsV2V99xwLuGZ8gfZuP0MdOw+1BNoDNPMb8w/+shin6ctJv1x3x41FZc/924fSbJyYv+mHxNeZotp2/brbN1sixNaHXijv1HddcYz7lZkq5GwxN8QyaBJ83wIw6PiOOkDKmckSaLEv+IZeNQsDpJHnaxZuKIqrU9r+gw/a1flh7V1LUffvnGc+Z2kZMt3o41WH0IvPTB649pleZ8uzswc6zZPMGrRRgGChZmgVPVvnw81IThUGM2NPaZ+u/KPh+R7WnscFoZF2fCf79J/Dbfs5vAg1EFTsu0LP04QLUPT7ZOwyP7kbZqkm85ZvbhcSus6Jfcp7Jdx5+BzHPOypI0acWfj9BsPJZIRTwOr4/RzqQUVbBiJFaG+DnzfccFd9d+YwpuICroBOoHmgbsPsTf7T5OmDQ8esyOw/r360dgO7Urs4yCpmzeUYOQkzzuNKh79z0SX9p+9NjG0pbkEblFGwD58BCCqJ6dRXRxeGQb0J0XdMFfbullOGaZlXmo27It+wGkVpJPCDwS10Tgclm6wNOJvxNZ3wZ2Xt6e4yHTiiC9Oj2aAh8e67J04/nkJrjOeWkaY6sKBe1MxVJht6zaUYXrXpib+JsnwIXVh6e+wfx+dQIP1Ju5/dR0yWZtMIkJLksHgDqP0TI1iNd175HaxDV63IiiSoImhqAdk8DjM6odtXKyk9oJ2fZTkJuFU1o3MRyzhIf30Ci37jtyLM9UCjyx//vqtBz/v+DAHPs/P7Hw8/H4GEf8Y6nh7waDD4Qam5athseS1n2BGqwdtYpPS9PC0RED/GcZBmfPOHY+POZBUfVj/eDrHZ7zYPvwuGgDuuR6GcYpH7OQKI2gSQsA9h1maw/1k2ZRGqKaZ62dakdzN5DA4zMqVIF6crOyXDst5+VkWQQDs8Oel9rmHtuRM5Xjh5vBKkvSh2fbvpgDoEhJ8frYpRVeli6USpw6H3x4bJ+/g0mLk4yLxYdHwbf16pcbQx9pPATjBBPz+6w3mLTUOy2rgFcj2ZoaTVrJ306Dulen5aimCQsO++IaHotmVd6kpUKbGobWQAKPz6iWamMaHne+D9lZWRbJ3qwi9dKu49emUpJPaHiknJbl5jc3T56Pmat3ikU4TdTLTsMjVq5qTZlx81DvGxk6Yc6dd9+ikWOtTsve679iWxX2cmbCqYZ3N2FyWtZXpc40eJsF0BBV2xYNmrTwbNYQxXHKxusEWBMoI86+I3W6q5K42VanIap5XJQejvZAAo/PqNbw5GRnJbQTsg0optkwHlPZmSZj0KRS4ImVJWXSgpwPDwBMnrNBaIYyf/0efLxiB257eSE3jfhu6YKVE6TeYNJStCLP5pzo1hJCpkJYhdqwaj7cwjVphWGkYGCe2Bi1AGqclvuf0lJJPk7IPmGjkKP7rpw0PAoCJor2H/t5Gh6XAo/vGuEUQAKPzyjX8GTpV2nJXZudZVVlxvJIZuTVz8JNvbzgapVWJLkfmQwij+ZwbQOGv7YE31Qe5KYJTsOTzG/bvqPKfGB4WAQeDwXGnJaNx8LgE5AKPFpBlKJ/4ubB2+yPG/TgxiTuw2PSanvZWkL/GJyXpXt1WhZfVVhzLMilOXnMpCXvw6OCoJsECTw+49lJzUSObkm1rMytj+ETJ2ba0P3toW7xfFLpZBnvZOVMWvIaHkCdDTpVy9LNvL4wGeW2sqoaL32xwXOedm3Q3LlZVmkl4kmJmQrNbyyUA6oH0mFrCbuvxrxMPUTVTsB6xks278PLX2yUzEf3W+/D4/OydNE4PABQe0y4snwnLjQ8Vz43xxB6xC1BNwkSeHzG7SZ3PHKzs5I+PJIfjz6GTxzzYh1PPjxInUnLbSwiwF1odQDKevBU7aWVCmx9li0Cj3uTFgCLiicMgcxUkg6rtOwwVlOdy7IPIX0Mdbvxr/MTv0X9W3jBBp2apNfAg9DEhabaep6Gx53BcfWOKhdXGQm6TyOBx2fU+/AkdRmyebM2zVTrwxPPU1mWXOLbEiTq7+Oy9Ng16mYnqdpaIhXIVJEfZ0boYl+WpacD6SjYqfQRc6ONdUu2YMfA04r7vUpLg3jcqLqEhsd4PJXP00zQ3ywJPD6j3ocn6bQsay7LyrIafqKm1TpeJPDktgr+t+psmX2YTGRlyX/0EUSUCSCp2jw0FcjEG+IN3MImLd0ri0Y1w6qzTCZMGh7RmqgMbKlyeE7E4eG0RdF+wRDTyhzfSvA6N8j4G9VxTFoRxuKVVBF0qAISeHzGDx+eeGOVjdqZFbFuomKOx6KitikReExL82UEmAjkNTwqEX0+YfLdcIOTDw8vHYuoScNT2xDFoZoG13ULI7xB+HjW8ACpHZxFNTzczUOdTFqefXjEt5aIm88sJq0gBR7S8GQ2qmeh+jg8ssJUNkuzYcpCRRyeVExIsxImrdjfMt+vbODBOKpmJ6LPWAOwbtdBbNftfBw2bH14TM/L3FEntpYQLEf/zmoboqg6WmdzReYQIgWP8HemyldD0zTvfi/6/BzOi06c9Ldn3qPODu8mLfH2UMspSzbKciaRE3QFMh3lkZazsxLaCVmHaFbAPfNu6V60Cgmn5RT00F42nmStVhNBnUlLLKNdB2swcOJsAMDGJ65QU7hi7LQP5mbAbRdCGh7j37X1UVRVZ5bAw3sM6WvSCh9Ogpio5lff7o/UJjWNvG87OyuChqh34U3KpHXMadn8ItxsLaEK0vBkOP7E4XGp4WEM9OaOyUttkxqeVAg88TJjZZnll/K2TXFO5+bMa2PL0uXKi0TUfayiJgoVqyL8xq59C5u0RHx4NM0wO66tj+Jgdb3NFekHr1mkY7whTUPwo5sN+qrl5ySHQVGTFu+d8L7teL4qVu0Kr9Jq4KzScrFbuiqCNtOTwOMz/kRadpc3L/CgKvVzvDqp6KCTGp7Y3+ZbeO6Gs1DatIB5rZtIy4C6VVqijzu+rDTM2N+LvUlLLI9kGv3s+Eht/XFj0vrbnA1BV0GasGp4WLgReHhaN15bzj2Wr4q+UVTjx3Va9lwD9wTdJsik5TN+aHjig7Vs1M4Iw3fFXDsvg2xyawnXWQgT0Zm0GqIa3vtqu+F8diTC7byyXM5wgo4hEUbsZmzmUy9/sREFudnWdILlRHUCz/RVu1CVaRqewIcDdShblp6CR5Kfmw0ca0uiEyFeH3eoht0mc7KzADR4jrQMiGuIa+ttnJY918IdQfehJPD4jK9xeFyYtFi7pasinlMqGnVclolGgbcWb7Wct4um7DbSMmHFTqBnCUMvfPpd8g+TWdIO8y7Rv/v4G7QpYmvwiODRFC5LVwmrubrR8Pxtznrm8X/M38w8nputTsMjmgcvDk8EwS3TCrpFkEnLZ0YMOElpfvpIy7L2YFYcntr6qLJZ1C/f/BrXPP8Fd3WASvROy2t2Wvetys7iLz2PLUsPzmk5k7B7JiplfQ3Wme2OA9XqCggBmdS+1AUeVPtQ9sU31NTlqxd4RPsFWf+xuCC1/4h3M6y0wKNgt3RVBN3GSeDxmVNLm6DfSS2U5WeIwyMbeJDhtLz/SJ3STmXZlv34eusBZfnxSGh4NC0Rddl4nm/ScuO0rGmZZXJQhb1JS+x5iaTSNOvmlJnGVI52IB1R6cPzgzPKFOUEbN131LLUPT8naWZ1s6mwCDurYvtQfbvrkOe8RK0Gc7/bgzteWYgak5tCJAKs3OZ/H80iaJMWCTwpQKX5JDc7KzEjWbZlv2Q9rJrMyqpqrN3p/SPUkxKnZV0cniyWwJPFPg7EVynIvZPaBnWasEzCzp9A9HGJ+I2p3pOOMKLXcqggqgFTv9zkmO7JH57umGbij85QUSUAsb5p054jWLBhb+JYSZP8xG8VG2T6jUyYghmrdzH6rYhSV4urzigTtmSsqbRq41MJCTxpRnZWBEPKS11fyxK+eB/5lae3cVVOapalJ01a2Yx7yrLZLyu2SkuuvLqGKOl3GNi+asEHtvdwrWOaLXuPYluIAzCmO605KxrNCAfN1DRHp/JzOjXH9b3bO5an2t/uoqc+Nfydl53Mv7ou/IK118jbqt137r34JPywVzuhtD/+y5dqC5eEBJ4U4Gpnbg7ZWRG0alLANOM4Idtx3Navk3QZQKqWpcf+f+2f5+JPs9ZZzmdHIkxBCDhm0pJ8fnUN0UDVsarKfnDQKUryEeEgZ8VKnAhi97XnkLPAk8mIOsr6CS+Eg1tUbqnjt3+tvrvq1bHYlzJUvuIR/1jq6XrVjzPIrSpkIYEnBahsC/GG5aaBsXx47MtyV/MJH33j6joZnO6jQdNsTFouNDz1wXrwfPD1DiX5NGucpyQfFVRV12P2t7ulndx/d10PjLmqOwDghPz0X2iqb4q/v87ZxKOah6/ohtYeVryxPkUVy6+T+fs7msY10pf3KMWfbuypPP8bzmmPv9zSW3m+buGF5Xjkyu6u8mvRON85UUjwVeAZN24c+vbti0aNGqFZs2a2affs2YN27dohEolg//79hnPLly9H//79UVhYiLZt22Ls2LGWGe9nn32GXr16oaCgAF26dMGkSZMU3417VEr3ca2Fm9DgWVlygtKZ7ZpJlwHw93CRpbxtU/z2mnLmOaf7OCE/h78sHRHIiqEatEDXVP78n+5ndXnZyc/8hHxrHJwgufWlBdLXnFZWhFv7dsKkm8/CX27p5UOtUou+nZY1K8S34y7DyseGpKTsey85GXdc0AWjLuuKkhOchWGWLxXrO1PlI+JGsVlygtwAHK9q747N0aaoUL5AB8ZeXY78XOehtlmjXPRoW6S8fDOssBzXndUOt5/fGb++vBs6NG+EDs0bCeV1RrsiFDfOS5v9uXwVeGpra3H99dfjZz/7mWPan/70pzj9dOvspqqqCoMGDUJZWRkWLlyI5557Dk899RQmTpyYSLNhwwZcfvnluOCCC7B06VKMHj0a9957L9566y2l9+MWlTOUxvEZrYsssxl7afEYfXlXabOPat6753zccl5HrB57KcZefZrhHE+YuX/gyfh05EUoyM3m3qsbDY95V/l0opFOyGmUlwEakWMBNC8tb4OTWp0QdHW8o2uLkUhsYUJutnPXfMHJJWha4PF9HpMoypoVYuGvB6JjC+tAd/5JJYnfrGXVrE9JxBE9rjP9zz39BCsrityXGp88+9Xdxfpd58wjUGMaGty9te35rEjEojDIy4kVfOeFXTD7lwNwamkTobK6tWkKgExaAIDHHnsMDzzwAHr06GGb7oUXXsD+/fsxcuRIy7mpU6eiuroaU6ZMQXl5Oa699lqMHj0aEydOTLy0SZMmoUOHDnjmmWfQrVs33HHHHbj99tvx1FNP+XJfsqhoC6e3K8K0By5MRKp1k2cWx2k5FXRidKR2/PrybgmBqzAvGz/p0ykRvAvgCzydWjRGp5LGAPizTDdbS8Q2WXXuSFMxQ5OlsU7IyQwTkF5CCK4eonQtbYLRl3flns8yCTyinNK6CQY6DG5OdD02YMXKjjA1Kjef19E2D7aGR1zLe2b7ZsJpRZCNkRM3afllOpPxcVFRB6dJQFZWhCESGsttWpArVJbI6r5Xbj9HKK9UELgPz6pVqzB27Fi8+uqryGIEQZg3bx769++P/PykmnLIkCHYvn07Nm7cmEgzePBgw3VDhgzBokWLUFfHDvRUU1ODqqoqwz+/UPEdndq6CU5pnZS63frwCH94ikcSGQHj2Rt64s4Lu1iO62Nn8LLTH+f6EbjZLV1w0hi0VoxFQa7epJX+Ao9fsVL8xK696b+1+G+R5hmB9+/0MtOKT5anWmFeNiZcy5+0sn14FJm0XOhVZa+Iy2Ze+um2zfimsIigZl2DO/n9NybfG9b2LXqyOYKtnqJCQYEnPgHnVLx54zz0P6WlUF6pINCuo6amBjfccAOefPJJdOjQgZmmsrISrVsbZzHxvysrK23T1NfXY/fu3cx8J0yYgKKiosS/9u3tl0d6QYXUbs7CjaYmWyL+jOrJTk62eIYiMgPv/vVL4nkCjxvVsWggNYnbTBn6DrBxyHx44nQtbYI/Dj1TKC1LQAg7dt8dS8MjcldeV8c0Lcix1IulmInAXlBmfYuqnJZFfXgGdtP1/5ISj98aHtG8o1HN1ftsZhJOnLQuIhMGUV8/1fGb/Ea6tmPGjEkEbuP9W7RokVBeo0aNQrdu3XDzzTfbprNseMlooCJpzGUfOHAg8W/Lli1CdXaDis/I3Km4yTNIO6vTrEOPyCDG+2j1HTZvlulmoNAg1vmGZY+uYX07JX63KSrErX064q4Lu6AwpD48cqsH2b/d8H8923rLQBC7aurvXeZ2IjaxpkQQ1UY6fS+sc6qWpYvmoq+DrFYo/l17aUpO7VCknWqauzqYwxqIaHic8xQTDeICD2+cZR2VCZyoGuneb8SIERg6dKhtmk6dOgnl9cknn2D58uV48803ASSFlJKSEvz617/GY489htLS0oQmJ86uXbsAJDU9vDQ5OTlo0YK9rUN+fr7BTOYnKsZAcx5uZiMy8T5Uz3YKciQEHg8aHn1QLrvVYm58eEQIi0lLPyOPRIDHro6tdttxwD5434ODTsHT09f6WjcWMmYqlU84VSY+22ahOxdvPyLfXyTiTcPFGvhYfmpZDk63fmp4RNHXQHZlV1xAit9H747FWLRpn6KaxRB5S1FNc9XvmvscJ60LaxwwFyuqkY9vyyFT6/qohryA+knpr72kpAQlJSXOCQV46623cPRosgNeuHAhbr/9dnz++ec48cQTAQB9+vTB6NGjUVtbi7y82LLJadOmoaysLCFY9enTB++//74h72nTpqF3797IzRWzRfqJGrW7dw2P1CzaRf52iCzLlCmbK/DoZg91nJUibkxaop1oSOQdoxZEd9ypDQQlsLk1t3qtbapu1+65sjQ8QiYtRDz5M7HqxJp8O30vTA2PwCxeZRxPL/OzeFXjefz55rNwzriZSssXMmm51PCYX2NWJIK8nCzuSjmRb1x0rIj36yI+lXFSEZiWh68GuM2bN2PZsmXYvHkzGhoasGzZMixbtgyHDsX2bjrxxBNRXl6e+Ne5c2cAQLdu3dCqVSsAwI033oj8/HwMGzYMK1aswDvvvIPx48ejoqIi0YiGDx+OTZs2oaKiAqtXr8ZLL72EyZMnM1d9BYEKJ0tLG3XxZQTp7Clj6xXpHHjfrP5j4q7ScuG0rAnu/hwWk5bR3Avm7yZelzQrRNSx81hqw3Vey00F9iYtXbqI8f+2eUaccraHqeFhrd9xEpL9fIaCY6O+9Ug7LcfdH4793apJAVooDtAp8ojcbsnDev4FNv2tiElLNJK/Gx+eIPfF83UIfOSRR9CzZ088+uijOHToEHr27ImePXsK+/gAQFFREaZPn46tW7eid+/euPvuu1FRUYGKiopEms6dO+Ojjz7Cp59+ijPPPBO//e1v8eyzz+K6667z47akUaHhsZi0XOTh1k9CBfkyPjweTFrCTsvCtbHma0cYtgkATIOo7m71z2305d0s1wUlr8k8NpV1TJWAaic0GM+lTgvLeuasZh4znYnlE1T7MfjwSAoO8XmRn21BJGdNc/f8LJdE7PtbkT5KtB9LhkkRr3iQGh5fp3hTpkzBlClThNNfdNFFzMbao0cPzJ492/ba/v37Y8mSJbJVTA0KviNzg3JjehCR7JPlqUXKh8fpfETMpGUX/Ex2Zq9pYs6QqdIYOGEwk0TYx0MimwGQDZnA/u2uXI8ZCOA0kLE1PM4V89rUWH0Iq4U7mbT0bSo3m29KSRWyw6nG8FpW/Rn7qeFhtZUCGxcCEcFOVOBJOi3zUlhPqNypXZb0WlOWpqj4dsztz38Nj9ov3u4DFC375GMBtYZ0L+V+YAYfHp6GJyLfoaXbsnTerJun+bE7lgqcHGP18Mx1rspVLPGc07k5uxxBDY+cn10EXuJ/swY11pgbe0Zi9c8TiBAtg+iKK6OGR66MuKDhRcPj3HaT5wdxgkW6N2lZS8q3mWCq1PC4cVrOWB8eIoYKValZCHDnzS9TnnT2tth9gJayOcdfu+NcPHxFN/zuh6cLmrR4kZZd+PAgvXx49AO5IW6NQ/2CNEmIlq1SRlF9vzwtql2deRorp7plRYxt8id97CMim2HX1SkGL7secXIDkvi9COqJwIOK6sJCxNHerRzAGhvsJpgsmdRcJ1GBJ8/Bh8etQ7tfkMCTAvwYRNxkGaR/idQqLU41WzctwB0XdEFRYS5XeNMrdZo14qzQc/BJYKFpWtqatPQ369QGgqq9vGbD+tvvckXgrlYRXNYtVZ2IMWLury7lb1/BLFdQwxNxMDdGDAKP+HeudNjz8BqTgQc9FO9wrYwgKwvrk7abYIr0UaJjRSKdxD01KIrR5AYSeFKAGpOWWYr3nocdqge+lMXh0a0AGP9/PXBOJ6uJIWJzPQ/RVVqKNfqu4WkNsngnAiYrS7w6Ln18mcj4tYkgsjzXuoxYl84gzCXJzY7gJ3064o7zOxvO64Vw2TbNGtNYZhVnp+XkWRmBRwRRK4/6Pla+f7DDrdlSBNaCFrtVVkKBBwXrGEn8n52edTRjV2kRMdSYtCxH/K2H4o9SLg6Pc9m8WYpew9O+eSP8e3gfxrXG22tXzN8HJ46oD09oTFoGrYF4ZxvcKi1xp2W15arNj9d29c89x6Se5IcQSP6RnRXB2KvLceUZZYa0+oFW9vmx2gLfaVlMQ6XapCUeadmDScuFhqddcaFU2xFdyeZGY8m6d7vvXER7Ixp4MBkoUyg5APe+SioggScVSLbhRnlWbYg5CzcdtdTSX8X52sWFsJQtpOFhHy8qdF54GEHENLA4l5duPjw8nwEnXwI/nJZFHkms0xacVUq+O+dy1SGk4cnin3MyiUUYxxL5St4La+Bjhf2XEZJVa3hSwdqdsbhwMs7wkYhRcHU2aUWYv1XA0v7b+WuqDDwYMf1fBPLhyXBkGzhL42BupG76aXMH17W0CSelXP4iH4fUXloC+fHKHHoOexNaY/6mzk3g/cQGAucPNSxbS4gsS2fhh7wm0j7k4vCwzT5uUG9eYGlNNEM5ZnOBUQhl31s8jVk40rdI2abHXKXFSOdk0tLXKUe5SUtwlZaCsmTykO3TVZphrXWxYqvhEWjzooEH3SyCULXPmhtI4EkBsn0qL7y78W/vqs+mBbkYfXnS0bGsqMBV/iKDRlGh+BYfIiWzyrz0tFIhwSoSkQ+WJh5p2TmNmUHdW+PLUZfgrgu7yF8sUI+I4bheEEqNcCbyTNzG4fGKavmUl5/+3syChnEVHfsa9qBm/juCaQ9ciIevsAaUZNeVkStrWbrDu9HnkxfUKi0Fxcpa/PVGbjmNuP8aHlsTpMCoLzpxS/gsS9wTLUvPcGQ7Vdasxrr00EuNjpUDzdDZ/vhsZ+0IC6cPqLxtU7QV8JOJ48WkJVwGZybNQ/QTdeMEq2lAaVGBUgHEsCzd4MOjrAgmTHOsoIZHtGo8Pxc3qNbI8XIz++MY6sAVcqzv0HzM7MNzSusmuOOCLkJ+GsxIy9z62+WTPCmj4ZGNiGyHirfo56IOJ+HVC9Y4PBHb75zVR5kPyWp4uO2ecYZMWhmOrDaGF97dmKd3zBFg3YaId+ooRgw4SeoZiKRlO1yKqr/tOwQWok7LbgZgTcGyWLt6RDjHna5zVS7jmMizlilXVli1L9djBpb82Bnqn0G2ndOyw2oXc/YGLYMCHx72ZMs+H4NJS7EAmQqn5UQenN8qyuNp8ZTAyM/cP950bnIyKyQMS2t4hJIDIA1PxiNv0nIO/qVKG2CY/We5G0hEtBqy6mInvGyEavaPFXmWMZOWgA+Pi9cSz1VlP+h2+bkfCiBRHx7RNq1yTJWZ1f/gjDLccE572zSs3DTNOOCZlSC8SYfTCGxXdbG2ypo0sNOJxhEKymlZRZOQE7rlIjoLa3hc3AjTadmUj34zVBFhRljD41Bhpg8PLUvPbGRlE16n4yVPXjn6bNwGQBNJK5WfUJkeTRmSjq/CW0u4GI3jg5NK277x/iQ6csGkF5xcwrnemoGYwCNRS5eaSHa54mkvLS/FhGtPt03Dq49Bw2NKxPvunGTWCCKeovcxBR6edtnmOYUh0rIKiYfnPyVSnozzrt8+PMdKNKbRvSSlcXjiGh6JF0AangxHdnDmBf+y+9sNmqbxTVoyTssKlzkeK1xtfu6KMCC6LN2NIBb//lX2gyLmSS/FXXRqK+E8xXyyZJyW3QlzvHJF8SKIG5yWTUKB3V/mfM2DMt/nhl/bbm2aAgCu69XOco7V9zgJo4YVaF5UrwzM1VFuDtLnLZlWZtg2PEHF9yDyTPSaN9akzPyGRSduWUmJR5ggfXh83S2diCHbvluekI8te4/a5qEqloNx92x3eYpcp9InKJZGPD9rXYx7aYl8fpqmCZkJ3Gj0/TBpuV26LZqWO5FnmV5EylWsURTPS632i/tY9EKBqIaHMRnRf/duv4E3h/fBmp0H0bN9M8s5XuBBUfzW8GRFImhgmvy9lyurFdX3B44KIZeTSRFE2kG+Lg6ayt3SE/KOxKSKFespVZDAkwJkO+h2xY1wW7/O0ADc+8+lSvL8v55tLcc0Uz5ZRhWPMEICj5TTspoy7fKXNjNqYoKRm3olOk6/TFq8zoglnAjWgdchso6KagDFNTy63x4fmZzQ4E6w12A/seCZO5irtEwDp5uVTo3zc3BWh2L2SY5JSzTSsoxJ182wlxUBGhjHVXw6ckK3XIEq26x97rG6mcvQCzwqd0t309+RhifDcSPRX3VGGfYdrtVlIqL45jO8/4mWYzFnyiQu5R1HrYamuQ8sx08jnh/rWn0ZIoNGVFDicSfwHKuX9JV29Uj+9sOHhyfEuPfhEa+nyuX7MgO0mIbHWRC0xOHhaOOcHF3tTFpuYZvTHUxauu9f+V5ajLq42dFdBFnzppRJyyDwSlwogHVZupU8g4bHOU/z9ic8nCxarG+VfHgyHNkG7qQmjJ2TnGFwZp68fZZk8ldt0hL1+TAjs9Gg/nKRy6J+anggvyzdqU253oFbEJnlx6KBB0VHLZW3I7syxwneOKE/LhqHx1C2QJ+gAv5eWvxr3Gp43BABuz0peS6K+yheejvx0c1tsPocu3xYbd58SNQVy03gQYrDk+G4nZHafhiyH5xAPm41PGLbFagdWLzscm3x4RH8/kQ0QW6qldTwyGgbRJ65fZ2YJi3B8nnv3K2ZzK0vjdeBjuXHwi/X/X3YbeDKNWMZc7Cej0SklkaLwGrjjt+37reKODxDz9Yt/Resjwq/GJ52jYfUsnTJvGUw58fKX1bGENfwxNulOGe2ayZXGYWQwBNCIpYf1gYl+83wncp0HbEhOq/3vOPIm7Sc03iKwwN3MymRTsPNAORmt2an52kceNVPXaV8eIQ0POK1VOn0Wd62CK//v/NQ3rapQLnOsNJommYUCsyrtDgCHGszSyczl1f4e2nZTb7Uanj05hdzfTSIB8WTRc6kZZYyHNL7qHFlCoCmQ7I7lItaJuPFiN5TUWEuOrRoJFUXlZDAkwJkHQtZDoq8NBK5Wg9pmmEw8neVlkzeqvMzX+vuXlmrQ8yIRns2XOPCh8fp/oWiGzNKFK0DV+Bx7cMTEX6nEV2vpUL4Oa9LC7Rr5twJC/nwcBLZOy2zB0MnkTUrImZm7XdSC4FUMc7t3JxRjtNeWsnfqiMtsxDZGsENMsKkNw278a8z2jdDxxaN8Mrt58hlyqkL65uQnYiJhhdQHVPIb0jgCTF2jUmFhseySsulqcDtvj1e0nrrV40duKiQIiK4npAvvw4gsSxd4TMSMfm1by6+v5klfykNj0D7kHEe1v9OYX8rJPAIXGsWCoz3wxMk2WlV7kcFAM/dcBa7fJtr3MbhEak6K41fPjx+Dt68fhYAzulUjM9+MQD9T2npLm/mxMV4zKmdmHMQdRmIPzOus37I5CESeFKAex8euzzl8uI5+bKWvsbKlhiAXGoT+PmJaQREObnVCYwy+OkfHHQK87jI6oL2zeXVtcm9tDyo1M3nbfJ6/f+dhwnX9kCvjtbZvGgVeB0i24fHOT+5zUPV96IiQq+X1YMGDY+d07IhM/1Pq9Y3IqjhkaFlk3zLMafbNm4e6vMIp/GEY+/l+llzu7207GSR2/t1li+LcSOyPjzm4JhOZYVNsOFBAk8KcDsLszVpSX6eIjNPtyt7nGYDZk2SEyJJZQSef9x5Hh6/pjyZf8T4/Myv5+eXnOy60+jUorFwvWTyNeMU4M04JhgLOK9LC9xwTgewEF8azj0jkTaJ6zg8YpcowW1Z5vbvamuJiPVoJJ65A0qiUdv1RTbaKz9gRgpWUaxMH2U2Szqm16flp9anm1FxIX4x5FTxSunq4tmHR9S87JBMxKE6lZDAE0Iiif+LfRhCeTLSa9BMq0fk8pSpi0onaNn8WjbJx83ndUxeC+O9svoCt5aCM9s3w0kMjZIdbnZL/82V3VFygnU2Hsetel78MnENj/o4PPrfanpQv7YNSVyruzdbp2Xec2VXyJXPmCzmCYIZo4ZHdRwe6/0xfXgAXHNmGQDgR72tW2aIIBOWw0urs2h4OOlOatVEbLWYQBuQnVSJjgWs5/SDM8rkCkshJPCEEYaa0Copy2p4rOnNgQdFfHGG9e0klLfxvFpzDcAx0YnmH7HWJ64x6VIir6HRk5eThWn3Xyh1Td8TYxtxyggpHVs0xpejLuaed7uKTbQGcv5GzolVtw8/8FKq3eIA3qQjwhiAZRxrVeHotKxra6nQ8PDayhPXnY7Xfnouxl5dzjzvmK9UHcTqxEpvfv92wraQwOOw31jX0ibKfb3iJOPwJI91tulDA1bwkMCTrsg2HK5vgX7Fi8AMx42PRiy0vkMFJfKDZH6W/BGxXP/uPefjytPb4KVhZwvXgVs3ico9fk05Rlx80rF6SZTBENr0+C0U8HJnHVdu0vJh0BcZDtwMPnEMu1Xb+PAY/HZY35rhOvVxeFg4mmvAvzc/YCmRIhGgIDcb559cYthGQQYpx3lpDTv/AjsNjQpN7b/u6iNt0hLVCLGclkXNd0FAAk+IsWs4KjT5ZqdlkcCDbhsw77o/Dj0T/7zzPOn8PO2lZRIWNE1D97Km+NONZ6HTsdmJaP8ga74yc/N5HVGQm52olyix4Il2593VR1zokBCIBfKTcVp2Ks8NQiYtD523WVDh5csLPJh0DjUOLGKmOJmasjKwfzdmTbSo3CC2aa/1GNuk5TxZc4J3VaO8bNvyRLBbAWur4RHI23q98aqiwlxHAcb8zJo3zhMoOVlS0L45opDAE0ISKzI8+vC00DVaXnpZp2XVA2m74kYWB1wxHx51X5jbvAZ1b43pD8iZr2zrIdGJOml4XM8MRf1oPF5vJktC4vGnc/UninbyWr0WRL4Mt4KkCpxiJBmWpUciniYjgIBGiSXwKHgYvDzmj74Eb/2sr6fyeEKtc52c07I0ROYyZDU8eTlZ+OqRwbhF5/vopn5eXTFUQwJPCHhgIHsZtF3bEPloLu7aKvFbxOeFF+LemMaxWCZck1rEek6kw/TiG2n9CL3kpVLwki+Xt2zV9aDjUehgmzydM3XrE5RSp2WRfDgH9dW0bh7KLoMVJsKs9UmJ0zLs34/ZP8nPeDYaNPYqLQV589pS47wc9OpYjN9efZrr8owTS5t0Zk2+QN5OPjysNCIUNcpFkwL7uGIJzaP+WMjMWHpI4AkBJ5gaVbwR2XUcQpF0bdSoQMyUY4wAK5KnWs1BLD95DY8nk5YP5sFUE39Xj1zVHY9e1d163ucvm7uXluAxVn5utUsjB5+CO85nC35K8dBO7ILzcbWsjDRB+EfE3g0fs5lN5ffENGkxl6V7L5T1vPXc0qeT0LWyeds5FLu+LdN1PTs0c5eNQ/kJHx4B60AYkA8LS/hOxPR/diK5ViUSCdOwoSZn5uj6++NpBJjHRDQCXtQy8uWlArc71LOucm/SUp1QDJamj1u0KeGIi08GAPxtzgbX5Qs5LXu4af0YbRdCiTdJSfYJxoMpcVp2HPSSv7Oz1Gp4eHt7+YHMhrjSGN6reIZiJi1uUQn6nliCl287GyeWyIbMsD+fWKXFOR+OnjUJCTwhxt6kpSYv4+ahIhlJFhzP26YzcWNiYmmj3Hb+qqw/XjtGmctFZ17SdRC8jtvBuSxXbuPGYPAmY/M1PLx0xrKtx7MikRQYtGJ1su2LDFpi8dV2bhENiieLk0acm1ggPc8ZHTAKLO724jNeE+Fo5Aac2opx1BusCOD26YOFTFohxk5NKO80x4YXH4S7vNZFZ2OO92POzyI4COTpZRZpXSWjBs/OmhKXO61K0R+SEQRFqyA3SxXLT7xs4aKFEYlTIlIsKx9LpGW7XjfC/iOh4WFoffwmtlkrv7Rsg8Aj8R2IbMbLSONXpGX+pIxRnk0+LEdfQxami2UdivXk52ShW5umrq/3SsRBggjaSdkMCTxpinQz4mhEeCYtZeUK5G0J0+5SwyOKVTOj5qP0GoJE5nInbZzf4VB42Q89u72r/JxWnRnKFkzXu2Oxq7p4LZeF0YfHLHAbBYZkeYZEjPr4HyE6XrS9hsdYlt9jHKsvSbVZ2u4eG+Unl7HHF6QYX6Wauv6wVzt89ejgRFiLZP7qcNZcxdPxNVhhggSeFCAiv780rHfit+iMWAZmpOXY3DPxt9GHh1euVLHJa3gmNeZxAcFLYa+qKifPA4tLHx6VdRH3o2Ef/9lFJ2LqHedKl+uHKeTGc9n7hbFQFXhQ5FrbVVo8LQMzLaB++1ArTm3NLMy50XQ+8+MzhdJpGlvYV9F25DSsdueSZ+8bePKxvPlX6IVWGWEoJytiEXZSjWM/lKJ6iEICTwiIALi4a2vHNHpEZvCGD4mj4TGatPTneE7LLk1aPIGHkaOYhsf9p+TVPMjDs4ZH4nqnsvzeS4vXDnKys9DvpBLpcv3QSMk8AhFNidA3x71WJxTYaTt5vwPUakQiDgOXqQ9x8y6v6dlWOC1vLy2viITlSJy3mMXZWrrkefXw2mxEQlvqFdl+JmgLFwk8aYq63dJ1H6pAT8VqsCIfF+/DyMqy5im62aRb/DNpeTcdqErtOl6SYC3c+hvx81M/fMvkKKYncVdDTdOMpmObl8NznGWaDgRNWl6JOS0LCmkRcQ2P26qznl/QGp4HB8dMVzec04HTR/LzSoXjuV8ELcDIQgJPCBHqqCVnm6wOy2jQEvThcas54B636nhESvCm4ZEvTwTvTssyJi1/6+KETO6iGruwd55e6qd/X3YbbPIi8ib3LJLH62N10vCkcpWWBv/atkiked75waeVYvHDAzH+/8qZ/beoSUsGrtbcSSMnk5ejpsvhmOV8sB85LUsPIUI+PAL56L3/Wek1TTPYw40mLfflslC+LN2Diseq4XGdldJ85Exa4p2zL3VQ3G/FXqfaTOVMWmpWafGm66KaVLtBzEzKlqU7Df6639kOK7rEykteL7yXloKPwGsOLU7IB8AxaXE0d17g99GpEyrCEsNMFNLwpClCH43khoiqtwAQuY51WOQj8uTzYRawFH203k1aMhoe+ZmXSiKI4JXbz1GWn98aKRV4GVQNQoFZw8gRXp1mz5GItyXNojhpbQzmOgUaHtvIw+A4LXsrMpYHw4TIrwc/xdBzYs7yl55WKpif+neYqs/JqR8O21dNGp40RWRwdPqMtFhGCQwaHm6kZdWzcNZszfk6lQNkOmp4ZDpnuTqI5RaJAKVNCxSWG35/ALfVM5th8nLEAg8aLAMJkxbb5GWH53bpUBarnn7C/PYVFCtVd5ukZc0K8c1vL0W+7j3b5R2N6usgXgU3dVNNOkxU9JDAkyZYHHsFdHP6mRKvXRr30rJXJcfSOJfrVI4etwMdux6irqf+fKSpdFrWF8UMv+/yHmUsWqK3K+rUrvqtOJXb76QWid9i8Wzc10VY4DFocKzCj1nDI6vgEV3+bayTTKRl6ext8zNPvDRwAg8qaD1S35/DeT9j44ggW57r/kLysqDlIzJppQCnTsmND4sKDQ9MEZD1HYndkkdZNGjc61izR6H79+S07D4vu6SpXZYesEkrElG6lNzvQIksXrlNziQn9s1xNKO6S/NMoZYjnN+86/VpL+kW2y6gqXkDYk4+Msu/4ziaLXTnsyUEV14fo5+osdIwAw8qaD9Gvzi135edRtbtjvcybgJm2jYrdFWmtQ6sdyH+HFMNaXhCiX0jiv3tnIuToGUOee9W0BBp0rw07M5LTCPgFvOVMoNtViSCBl6MooB8eFhXqfa1YqdT15llZ9kvfXaDU245OsFDZNDxUj2DwJMjtls6K03EdOymczuiVZMCnOVyN2wRHAd/ST9Ar/glHPtZdf0zUuWx48V9662f9cXHK3ZgzPurFNUmPSANTwoISsg1LEtndP+apnGDbal0o4uAH5uDZdIS6dBUdnoy7yfbMHsxnvNcJ4nrners3qQldp2MSUsov5DNBFUSC7yZvL9cm820eBOQxHsxmbyysyK4tLwUrUz+VMZv3zviTssqyuL3Q+Y+K3GN92K5/lNOaYXytkmu2u9c5FsqLSrAsH6d1RbMqovvJchBAk8KkG3Qqkw6hhUckupPbqRl5SYtq3+A2Cot95+SdfNQVSYtrxoeibQSZgapOkhoeESLEEnnh/OjTJaqfHhEfN/MAo8xIjq7kO8P1sTOSwzKKhF1Wmb516iG6bOmRNDyJ60TjfLcbQ9h6yYgWcFUzTeCFoBI4AkBbhoB75o/33RW8g+nrSVMGTk5wgJetiyw0fC48uFxVQ3mtVIaHpsOPZWBByO8F5c47zdqTVB+jJMyEw0hgcfDU9W3jdxsQS2aLlllVTXjfNDDRwwRk5xbmLvPM44pcVr2UeCx8+G595KT5TI7RkqibLt4rLJuEqnEV4Fn3Lhx6Nu3Lxo1aoRmzZpx002ZMgWnn346CgoKUFpaihEjRhjOL1++HP3790dhYSHatm2LsWPHWhr9Z599hl69eqGgoABdunTBpEmT/Lil0MAbIC7v0SbxW79SgBXdVdPM9nfnclW3X5ZJK9UaAZmc7Hd9T109/OpIhLU2EhqeIPID5Myyvvvw6H5bnJY5+dY3MAZ2m4HTT+wEfdk+xCtRxqtS7bSsGp5A1rmkcSJgIaBOUxUyOSM0+Oq0XFtbi+uvvx59+vTB5MmTmWkmTpyIp59+Gk8++STOPfdcVFdXY/369YnzVVVVGDRoEAYMGICFCxdi7dq1GDZsGBo3bowHH3wQALBhwwZcfvnluPPOO/Haa6/hiy++wN13342WLVviuuuu8/MWA0NkdlfcKBdjruqOnOwsNMpjv2puP6ZwlZYdzPsQKIO1LF90xmMpUuKm7DQR3jU8EmkV5uW6DhLmLyf8GGxEoifL4LaKMZNu8uIcm1Vaeg7X1Cd+x7VC+rSpnD03zuebXdxqeNyuTmIFW1T+KJxMxpIlpnxzXJJ4mPgq8Dz22GMAYhocFvv27cPDDz+M999/H5dcckni+GmnnZb4PXXqVFRXV2PKlCnIz89HeXk51q5di4kTJ6KiogKRSASTJk1Chw4d8MwzzwAAunXrhkWLFuGpp55KS4FHpK2KpNEAW8c0c0cstMkjcwWZQGW4+VnvxW8fHnOJUqu0bE1abusTQ07YcBR53NZCOJXKwHep8P2wQ5VJS8SHJ0fQpHWktkF3/TGBx0W7V2H6apKfa5N/8ndstZ3n4hKkcmNNmbLkTVock75cNgb0ba0wNxtH6xqO5RkeaceNu4KfBOrDM336dESjUWzbtg3dunVDu3bt8KMf/QhbtmxJpJk3bx769++P/Pyk2m/IkCHYvn07Nm7cmEgzePBgQ95DhgzBokWLUFdXxyy7pqYGVVVVhn9hwW8fljix1SPsPEXiiaiAadJyqRFwq3GQuaVsGwExtVtLOOSVgo5FZRmBCzwCabxU0bBKSyRqKIB6ne1G1lFX9dM8ocBubpwsrbXC6NssNHC201Dc4JWb7vW/I5wTHpj2wIWm8oL7nsIkcJkJVOBZv349otEoxo8fj2eeeQZvvvkm9u7di0GDBqG2thYAUFlZidatWxuui/9dWVlpm6a+vh67d+9mlj1hwgQUFRUl/rVv31717flMslGVFbE7Gcc4PJqxE4+YzjFLVdyxxIQE0yxA+DojwiYt898yzsKGJbOa6ZxwNpy8JdI6rNYx+4moroNT9F35co+j3dJNGh6R9hc3zxnHSnFtnFdsfXgiwKu3n4Onrz8DJ7U6QUFp9jDlHSX5iut4wrAKSp9noW6llytHYwX1YeZrmVwG+5FL94pjxow51jnx/y1atEgor2g0irq6Ojz77LMYMmQIzjvvPPzzn//Et99+i1mzZiXSmRtX4uPXz7YF0ugZNWoUDhw4kPin1yqFEfNt1Os2YDmltAnzGjEbOfv58K5001zt+hFWfiKaEm+zbec6uCnXc+BBhb1ifq5LgUcmrWB9RTo51g7YXpFy4RFKKyCYMDIyLw5gLSBwzPdYtqKPKZWmoAiAC09piet6tTv2t782LaYPj6nIuQ9djN4di+WKknhosnfo94o6kcmqKs7r0tzfAnxE2odnxIgRGDp0qG2aTp06CeXVpk1sRVH37t0Tx1q2bImSkhJs3rwZAFBaWprQ5MTZtWsXgKSmh5cmJycHLVq0AIv8/HyDmcxPZJ3zRDqMg9VJh0YvmzjKLiFU/t26NGmp7EBksrJflu6xHjJpHeSZ/OzkjE+qIxcWYtTOCl0qpGxx6xTLw5Ovmu7+zG1IJNtoQsMTPjWYJa6Vj/KOpnFWaZmeS1mzQpzXpQUWbdqnrjL68nx6DW7fb7pt4hkU0gJPSUkJSkpKlBTer18/AMCaNWvQrl1sdrB3717s3r0bHTt2BAD06dMHo0ePRm1tLfLy8gAA06ZNQ1lZWUKw6tOnD95//31D3tOmTUPv3r2Rm8t3tktnmHZsEyIDnfFD0ZlreCYt5yyt19j6Glg/cTGnZRcV4eQvIzzZdSxBrdJiXWe3QaUqVPax/qzSkkgrsizdQ13095cj6MOjJ5UaG1nMr070XbrWRAi68LCO6Z177erj1Cd40Uj6ETzS7H+p6nNiZePUP4dZ9vK1V9y8eTOWLVuGzZs3o6GhAcuWLcOyZctw6NAhAMApp5yCq6++Gvfddx/mzp2LFStW4NZbb0XXrl0xYMAAAMCNN96I/Px8DBs2DCtWrMA777yD8ePHJ1ZoAcDw4cOxadMmVFRUYPXq1XjppZcwefJkjBw50s/bE8a5gcjPkKJ6h0aXHYemadIfnN0qJX45NvlFGJ2LQBGsTvWirq2E6iNbXFFhUmi2G6tSuZdWfo59dFa3Ao9oDSIRtdqG7CyW6OsN5YEHPQ1ySdw4aCfqx56f2Jet6LHyIgL7Ob6Z/WoiEbZwyh6YrfzrrvO4ZYmsnnv4im4obVqA31zZ3TGtCKo01V6/HbkVoqkpxw98XZb+yCOP4JVXXkn83bNnTwDArFmzcNFFFwEAXn31VTzwwAO44oorkJWVhf79++Pjjz9OaGaKioowffp03HPPPejduzeKi4tRUVGBioqKRL6dO3fGRx99hAceeADPP/88ysrK8Oyzz4ZmSbq8ScsZvVrXrerebvNQ7iotVyXxYe+263ydXvD449AzUZCbjYHdWvMv0OcvWV6LE/Jw4GhstZ+9hkeoeH69BK9/c3gfR4HG7aonYadliDsZu111l0rce7uZ8mFkFFscoNPwWJyWBTJ2kVY1jfNzDEvl47iZsHnBi4/K6e2aMY8P69sJZbodxHm3cMcFXfDT8zt7ElRUm1oBGCoc8xkLB2GJBh7HV4FnypQp3Bg8cZo2bYrJkydzAxMCQI8ePTB79mzbfPr3748lS5a4qWZa0uBBw1PcKBf7jtShz4ktuLtuc/P0sBycmR0YAojIdbpCWzUpQJ8T2b5aTtfGynPQwOl+2y1L10dMdYPoY+zdyT+nQRkhRlgbJJDGjeYw1aia2VqFUfGMRRU8fjzNE/JzEnt62eHnGBfz4XF2WpapyDVnlmHMD05zTpjINhxt1WiCS125TmWF4+mwob20QoCQk66pGek/etn5wnsjzsdDl3XFY6aP3G6X4mQ91BKJsJyWnUvRC2p+a1YMQiEj7V9/0hvndm6O8f9X7ms9UoGUalzG/uWAL6u0ZNIKzBpEnk2rps5Cr+gqrXfu7iuULlXwoi279eERgfVW2FtLMCZigmW41TKHCVmttXjG8hkZrAaKqqEKXzU8hH8YBB5OX83rxNs3b4Th/U8EIN+eVXcEkYjVd0NkPDDED/LZWdh+dg4M6t4ag7qLmdMcSnJ5VTDdisqBzYUfryMqtpY4qdUJWLcr5nMocrsjB5+K7w/W4H8rdxqO63dIFw3O17NDseWYXSgOv2nM2Z7GsgjA53qoNgil+uuxc1p2a94Umaza58UpWPFGrUELQKThCSEiMw4RHx6x6LGcj0/BYDG4e2u0Ky609a1hangk47bI9vvWZcH2GRi1Sf59sqGYVfqg4BHBl1VaCtK21fl1iNCsUR5evKW35Xh2VgSLHh6IBaMvMWzqK4tbkVgFJ+RzBB4XGlrA22ILpzrwjjEJ8Lvz0uwNJi1zvj7eVCj6KZeQhidNMdixuRoe53x4TstxWjfNx86qpN2epU7m8fxNZyE7ErH1z3Breo/wBDUbfn7xSXhz8daEdkumPFa5qglDPyJjpZKJ2eOE6j2YZOFHFmf/dkMJx8fLD6dlP5axN+YJPOa/Fb5HnhO4tQ7WQv0wk4YVw4ITvyMPShC2V0AanjQl6rDPjihG9apVLTrzwYsM6Vkf07U92zHzzsmyF3YA9zP7LBcD0YODT8Xchy5GyybGgccx5oauMD8C5InWIxVIbbOhsFxfNGeK+32/3o/bXBW6UAnBFXgsGlp/EYlBBgDZgpu0MjdEDsX0wx6jr4xP9VXc5ts3b6Q0P1lI4EkTzM3OYNLyINHz5JF4lifk56C1zhGzgaHi4ZmsRAYIttOy42UmQUpikHbh3Kh3MnVyYI7z7A09heskWg+3+LEMVmZZugh+bB4qc9+qlqW7QWqXboi1Pz/oy1kJaf6m/A4xwHZath4TdQ5P9XP0Q1AxhhQBTuVsN5QKWG30zeF9cFl5KSb++MxgKnUMMmmFENYHaP7GGwQ0PELRYzmaEp4QxetsyooKsP1AtWN5lmsRkV4mDrjT8HDr4HB9NkfgseMHZ5ThrcVb8dna74Xr4dZxV2WHLZOV0sCDIdBuOeG2iipNDEE+pitPb4Of/3Op5bi/q7RYe5OJBR7MNn1QvGq5NauHmVv6dMTRugacf5KaXRH0uNF09u7U3NdwGqKQhicFqOjv8k2B5jSBVVpisBtvK85KEtX2YbfOhm58eLh5MY5NuvksAMDT159h2AogyyAUeizYUo9ge9kLTi6RCyYok1ZBGlnkNg91DrTpl+bCj1x9yTMSQXnbpoyyTBMWn314WIhoeHjvL+zCzcf3X4A//PgMnN6uiJvGcA9abFXgPQNOwhntmymvT8gfly0k8IQAuwb0iyGnonfHYgw9u4PhuHGVFhtZp2UAmHRzL/y/C7vg0tNKmfmwTFoyDqxO5QNiH5TRtOTtE2Rdf2l5G3w77jJc16udyYfHx89dUda39eskfU1526Z4adjZUkKXsHAkkMYfk5batGHr6IMWkOO41ny5LI8ZeJDltGwReNj5sa5Nld+vyLPrWtoU/9eznUOgSb3/pXzleXl7n0yGo43GIYEnBXgZj+8ZcBLe/FlfFJr2sTHG4VG0LD0CXFpeitGXd+M6G/OCfrnV/ERgvVZkJq2vnl+7lMdjp+i3ApARrmSfiNvb0F+3auwQPHqVeNTYOF1KTjDEinEsMyKu8RB5Zr4KkgLce/HJAID/69mWnygEfqFu+hKV1WY7+Jr+VmnSYnxEbn14ZDQ8DT5KPP5o33zI1GVZYdaYkcCTAmS/HRGpWCTSspCGR/9boKWyZ1fucWvSMsYP8qrhsT9v0PDICDySL17FQNGIExzOicTelDKDr6uS2Pi9W7qTan9g99ZY+OuBmPijM7hp3LYzlUNn0DNmke/Vb9lV9LsyT9qkBB6Z+BuS6HM+pbW4c7FdlQwWLYVVZ2Ul5ecXMuGHnJZDiEgjMTR+Dw3cuGJAoFzOV+fapAXrByq6uov12x32Gehniut3H/JaWAKzTT4MfUNQnZnfg+R/7u6LQzX1+MP0b/HSFxuYaczhCsyErfO2q49fwzWrSD99eMxkRcTvzarhES/HT4EHAN4fcT7eXLwF9w88Rfia2voo91zQm++mC6ThSVP0H6RoXAoWsh+KqDpZpnw31VdpAjmzPd8Z0FyWPgij1z5mym3nGP4ORZ8l7Jdj3RLES5Z+L0uPRCJoUpCL/Fz3XZ5fr8et31QYmgtgbbd+aKEm3dwLJSfk4bU7zk3sAdijbfK7ZU2SrD484p4qfgs8PdoV4bGry1HcOE/4mroGvsBjXGHrpWamfBnH5MIohAvS8IQBy7JsZ0T20pKd54lpltSv0nLjZCcaD8eOj++/AJ+t+R7DHJx8cxRFG1w99lJc+8JcrN5RBQBobursgjZXyNZBrYbHX5OW6zx0v30LPOizGdFvQdqcvQrZtWWTfHx/sAYXnhJbVn1peSmGnNY68Q5Wj70UCzfuxU9eWsCsAwDD6kqA/xxYx+2EC6+4fTw1NhqeVAYtDVEgZ2lI4ElTjDtLuG+BskGw1As87jQ8WZL1ZtG1tCm6llqX2ZoRDWDmRGFetu1g4LbPUtHXyfsbeS9Tj9PWEtlZEelZt4qWardfkZs8vKJyOw8RZv9igLkCjMLMKh6x0nltLhIB3r2nH6atrMT1vdvrjifzLczLNnyXrCLNGh6eFpF11G8NjxvsBB49rmoe/FwrJZBJKwXYrvxwicgHKdLRypu0xD4n3r5BLNx8oBEFGh5RUrWCKAx9jt8rhng4PeM8RVq2M2ximTjhVzvze8bsdkKQn5OFDi2MWwGwfXjs/3ZDWbNCDOvXmbudBWB0SmZreNw7LdeHUOCprW8IugoA4Lia0yCUh8JOn4QEnhTQu1NzNGuUK5xe1rTkpcOUbZu8fkB/bZeWjfHysLOF6+BGa6TCpCVKykxNIRhQRasQgfhz0b+fRnnsncKd3mGu4L5IBhg3PuS0Ujzz4zMxo+JCsSx0v/1qBzIa2gj3DzX5O5YvoOARnR94NcM4aV7Ne2nFy5t6x7k4p3My6i/rvYZRw1NrMrMFJUv87KIuOKX1CfjlpacGUwEPkEkrRXRs3gj7jxwQSiu7PJz3bYotS7efJVnKZQUeNF35iWnDUSeCMmmJ4jbGkB/hCPwiuSxdvA5uFF/lZUVYsHGv5bjTcv+8HPm5GXNJbSSCa9xqXMMgkKay3QumM2tORNuQ16jtBg0Py2nZdCyevN9JJeh3Ugk6PfThsWuteddH/fPhcYt+lVbLJvm495KTmen83sqkqDAP0x7oz7+G8zsMkMCTphjlDl7gQeeGL7sHpxcfnrzsLMssBQAKXKycSaWGx+0dy86sQ6b9tUUmurbIwJydZb/qSyYookr0g4df70dGmeBGK6JSMEqVSUsEJx8eYZMW45ivGh6XDUlfpQWjL1HqqByGBROpgExaKaJd80bOiSRQZdKSbeesxQuxwc/5Wl6ck3bFjdCmiL13Fw99eUEFOlM9ALpdqaSys5J5lsJJBYRqXmTvOG4EHvV7nfmD6v3p/IQ1yLr0WfaMMfioFeGtJRgVDsqHR3gSYZMu6NbUmrMPYxgggSdFjLnqNFx1RhnuGXCikvyMq7Sc0/AwmrTkTGmyXN+7HffcA4PEA3ABZuHA3x5WZd9n158FquE5do/5OWwfGyv2q6r4V7FxEvZETFq/urSr4W8VgoRhlZbLF+Sk6XNbS9HAg67bFSvmFrMe7kxaXjEINIwyczg+PCKkkQzqKzef1xFNC8yGIPbDefGWXrirfxdcVp7chzFsWmsSeFJEyyb5eO6GnujTpcRyzqtK2EvHLqsd4Wo7BGp9z4CT8NwNPZnh+2XvOQwmLafHrj9fLOC0Hoa+Qca8aDeAXNGjDfM4L5S+kw+PiIbnZxedaLujtBsMwQuV5qwrI8NG11Rti6ZvE2wNT5bp7zB8Yf6jsjmVnJCPpY8MFsp/yGmlGHUZfx/GMEACT4pRuWIimad79IOWSN28aDtys7Nw1RllaMFYsi47KzQ6LfuLXqslIxDceUGXxO/3RpwPwL4zci24KXwABbliGh6nuj4z9Ezm8WH9OuH+gVZny6ws+zxFnZb1WaiOw+NX+P6Lu7YCAMZM2h672qRyyElFpGUW+g2VxXx42PmkWgsRRnHAKQaWnnSOtEwCTxiR1rpwjnuviYV456wnlRqpOE4rNJSiq+77xwQXEQZ0bYV5oy7Gd+MvR3shH67kfTSxiT/iJ6JbL7BNG8nfvNl3XnYWc/8gx1VaosvSFbcFo0nLen7EgJM8l1HetggzKi7EFw9dLHWd3+2eNQFiLks3tYasFI0qhTrhnDURE91aItUOu5mlz0svSOAJAV5nSKn8gC46tSX+fVcfLBh9SeJYs0Z5UuMMc7mwZD1SuVmeXsNzssTuxgDQpqhQWJWuv6U/3XQW7rqwiyVNIUMDc0WPNmhXXIjrzuL7SDkRH9xENTyqn7/TMwrKaZk16J96rA2c3akYI4c4xyJh1WNGRXJZrwbgpFZN0KRAPFaXDCpfFatvCkzDo2urNXXWoHzmNiWztUQYift//ubK7g4pwyNSpXLLCxFoWXoGwNOYiDkt6y/g5K9PH4kkgnYte2QQNE0+RorsiiwW+r7Mbx8IXvaqv2V9dm2bFWLU5d3w4uz1AGLmjvsHnoJB3Vtbrmucn4PZvxjgyXYev8cCQaflji3Urjp06hhFBR4/TVrxKr5y+zl4Y9EWDD2ng+t8T2zZWFdGeAYoR5gaHtPfKRrj8nX9TjVD4DF/DjLL0oPCri4jB5+Km87tiLJmhbZ5+N2c0qm5miGBJ8WwGkunFo2tB1WUJdDl6/sA2XbcrJH4Tr96upY2xe+u64E2RfYfrh1G3yN/8TtSbfJc8qTZV6hxfg5uP78z91o7YUem9iIRjZ+7oacvMzcVPjyq6dWxGPM3xAIlxjUXpUUF+Dkn6JsohvabxgMIEJyGRN/mq+us8TLMbZS7l1aa+PBEIhFHYQdwuVWPi2vSETJpBcgbw/vg8WvK0ffEFtLXnlYW2/TyxJaN07LD/PHZHXDhKS1dX2/U8CiokA08R+1uAhuPusW8PDxVHZKIINO0kG16EVly7vZdie6lpU+nQnNy7VnJqMy+7aXl8rogBA2m75bZh0c40rKCCh2DpeEx+4Xx2naYzC5ndSgOugqOuN4KJQSQhidAzu7UHGd3Su7pcmb7Zli2Zb+hk+Xxt1t745W5m3BLn4745ZtfsRNJxuEJUnCS7XOMnWpqTVof/Px8zPpmF+5k+Ng40e/EEqzYVsX0xanTRXU0a3iC7pSfu6EnqqrrsLbyIC482RpaQQUdbBy7z+ncHB8u3+GYx/hre+DWlxbgngEn4Uhtvaf6nN2p2LhJrct8nFqn08o9N99lKj9l1YEH3fgAVTM21hQOPChdmn8Mv6gLGudn46JT3U8G03ECnCpI4Ekxdm3xzeF9cOBoHXPZtpk2RYV46LJYoDVVkZa9mG68dhreBB5/MWsKytsWobytu3gvDww6Be2KC3HRqdbVbvq9cszOw6la+cLjqjPKHNPwhDJDLBub19YoLwfLHhmEM8dOt5y76dwOyMqKoE+X5owrk5zU6oTEaqe/HvN/cov5u/JL6LTTREXA7zOcNs9M5qGu3vdecjJu+tt8nNWhGZZs3h/L36xJ0f2+68IuCT80PzlaazVpmc28dgJ1WMjPycYdF8hPpFKJ273fwgCZtEJETnaWkLBjJpXL0sOCwffIbyc9hXkV5Gbjlj6dmMvUa3SzVPNg5v8Gqd7z4NVQJm+eX1hOdhZuOa8jTmolt0rOC1FNQ3NdfXj3d8Mx5+X+OhPtCzedJVyO3eNhCVm39euEgd1a44x2zfjXCZfOh/Xe+p1UgqW/GYQ//PhMbln6ychDl3XF0t8MwrmdrYKqykCATiati05tibFXl7MvtvWr81qz1OSZStJZg0QangygvG1TzFu/J/F3caNc7DtSh0u6WbUIZvSdjlMsFDu8zn5lB3SDX4inkp3xsp2GDHoNj/l5+hW8tHFeNg7XNnhSocfhRVE+u1PzhOOvG3q41KZ5JaoBxY3zMPWOc5Gfk8V1DH/0qu4YcGpL9D0paeq7rEcbXN+rHd5YvBUVDtum2C1aYJX46FWnOda9vG0Rlm3ZHzOdSradAae2xKw13+Pm8zoyzxc3zsPB6qS50Pzptysu1J2LoLhxnuUbLSsqwMQfWyOuu4W5Sks3nX/mx2dyhWm7vicdtEJm/Ahuq0cf8DHdIIEnxTTyobE8MOgUNMrLwaXH9jCZ+eBFWFN5EOc5qP8B4IT8HNzerzPqGqJoxdn07VeXdsXIN77CLZwOUAUXHPML0S/XtSOVTst+xUcxU1PP2Jn1GF4ESjtHyFkjL8JXWw/gEl1AyZIT8rH7UE3i75NaneBYRnGjXLx4Sy/DsU9HXoTPv/0e1/Vqhz/NWgcg1t7MdCkxvvOP7r0An3yzEzed2xGz1uzCAIb5TwSvHX/c1NTvJHufpYLcbAw+rdRy/Pc/PB33DTwZ7YrZg+askReh6mgdSm3CNMQEe/n7eOHms/D8rHUY1rcT/jDjW6lrn7/pLCzYsBd9bBZT6JujuWU+OORUHKyux9U9k76IerPd/NGXKN9gsk0za3752fpIzNbvJz8nCzX1UYNmLs5bP+uLZ2d+KxDz5viiR9sidC4RX1Uctl3YSeBJMb07FuP6Xu3QWXBgF6FRXo5h883mjfNsOyszj1xl/1H/sFc7XHByCVpxdjtXQYsT8rF8zGCmMy8LvYbHbw3M6Mu7YvPeI7i1j38CHwAM6t4a7ZsXondHq6DqRt755MH+WLRxH67rxQ9I2KppAQZ1Nw4WMyv6Y/3uQ2hbXIipX27G0HPac6/v0LwRNu89gk9HDkCRab+wTiWN0elY5/jysLNRUx9FcePYLPs3V3bHrG92oX3zQtx3iVED0r2sKbofW4V4rYdgiq2auBtU2zcvxJa9RzGk3CrEyBCJRLjCDgChgaNdcSHW7z4sXXabokI8fk0PAMDdF52ID7/egett2oGeRnk5TB8zHjmmFXRNC3IxUWfyAoBeHZtj4cZ9APi7abcrLsTWfUdxeQ/x5/76/zsPby/Zil8M7mo5V9QoF3dfdCI0AEWMlYXzRl2CTXsOoydjQtCrYzFeuf0c27J/fXk3jPtoNZ64todQXTuXNMaG3YdxWTl7nzlVuOkOze/QzJTbzsY3lQeZwVDTiYiWVlGv/KOqqgpFRUU4cOAAmjb1b7lxpjLq7eX454LNKMzNxurfXpqSMjs99CGAmEYgPkCmO9GoZjCdxO/xxJaNMfPBiwKqFZ/a+ihq6htSpgWToSGqYfxHq3F2p+YJ7acIew7VYOHGfbikWytXEZ5V8t33hzD2/VX4+cUnoXcnZ40tjyO19SjMzVbmeL1l7xFc8PtZAIC37+7ruJy6uq4Br87biEu6tcaJLdkawyO19dhxoJp7PoxUVdehqWDbr6lvwN7DtZ7ij9kR7yvevacfzmjfTOiaP874Fh98vR1vDu9rmbB44YLff4Ite4/i0au647Z+/PhhqhAdv0ngOQYJPN44VFOPqV9uwmXlbdBBcRReHvEPfNHDA1Hiwtk7Heg7YSa2H6jGXf27YNRl3YKuDkEAAL4/WIOzx80AAKx5/FJL3Cgi9SzcuBeb9xyx1eimin2Ha7Fs635ceHLLlOxSTwKPJCTwpB8rtx/AkdoGQyyjTGNXVTU+/3Y3rji9jfA+VwSRCj5avgPNG+fhvC7ygVMJQiUk8EhCAg9BEARBpB+i4zfF4SEIgiAIIuMhgYcgCIIgiIyHBB6CIAiCIDIeEngIgiAIgsh4SOAhCIIgCCLjIYGHIAiCIIiMhwQegiAIgiAyHhJ4CIIgCILIeEjgIQiCIAgi4yGBhyAIgiCIjMdXgWfcuHHo27cvGjVqhGbNmjHTLFy4EJdccgmaNWuG4uJiDB48GMuWLTOkWb58Ofr374/CwkK0bdsWY8eOhXlHjM8++wy9evVCQUEBunTpgkmTJvl0VwRBEARBpBu+Cjy1tbW4/vrr8bOf/Yx5/uDBgxgyZAg6dOiA+fPnY86cOWjatCmGDBmCuro6ALE9MgYNGoSysjIsXLgQzz33HJ566ilMnDgxkc+GDRtw+eWX44ILLsDSpUsxevRo3HvvvXjrrbf8vD2CIAiCINKElGweOmXKFNx///3Yv3+/4fiiRYtw9tlnY/PmzWjfvj2AmDbn9NNPx7p163DiiSfihRdewKhRo7Bz507k5+cDAJ544gk899xz2Lp1KyKRCH71q1/hvffew+rVqxN5Dx8+HF999RXmzZvHrFNNTQ1qamoSf1dVVaF9+/a0eShBEARBpBGim4fmpLBOFk499VSUlJRg8uTJGD16NBoaGjB58mScdtpp6NixIwBg3rx56N+/f0LYAYAhQ4Zg1KhR2LhxIzp37ox58+Zh8ODBhryHDBmCyZMno66uDrm5uZayJ0yYgMcee8xyvKqqSvFdEgRBEAThF/Fx20l/E6jA06RJE3z66ae4+uqr8dvf/hYAcMopp+B///sfcnJiVausrESnTp0M17Vu3TpxrnPnzqisrEwc06epr6/H7t270aZNG0vZo0aNQkVFReLvbdu2oXv37glNE0EQBEEQ6cPBgwdRVFTEPS8t8IwZM4apGdGzcOFC9O7d2zGvo0eP4vbbb0e/fv3wz3/+Ew0NDXjqqadw+eWXY+HChSgsLAQARCIRw3VxKU5/XCSNnvz8fIPW6IQTTsCWLVvQpEkT7jVuiJvKtmzZQqYyn6FnnRroOacGes6pgZ5z6vDrWWuahoMHD6KsrMw2nbTAM2LECAwdOtQ2jVkjw+Mf//gHNm7ciHnz5iErKytxrLi4GO+++y6GDh2K0tJSVFZWGq7btWsXgKSmh5cmJycHLVq0EKpLVlYW2rVrJ5TWDU2bNqWPKUXQs04N9JxTAz3n1EDPOXX48aztNDtxpAWekpISlJSUuKqQmSNHjiArK8ugUYn/HY1GAQB9+vTB6NGjUVtbi7y8PADAtGnTUFZWlhCs+vTpg/fff9+Q97Rp09C7d2+m/w5BEARBEMcXvi5L37x5M5YtW4bNmzejoaEBy5Ytw7Jly3Do0CEAwKBBg7Bv3z7cc889WL16NVauXInbbrsNOTk5GDBgAADgxhtvRH5+PoYNG4YVK1bgnXfewfjx41FRUZEQlIYPH45NmzahoqICq1evxksvvYTJkydj5MiRft4eQRAEQRDpguYjt956qwbA8m/WrFmJNNOmTdP69eunFRUVacXFxdrFF1+szZs3z5DP119/rV1wwQVafn6+Vlpaqo0ZM0aLRqOGNJ9++qnWs2dPLS8vT+vUqZP2wgsv+HlrwlRXV2uPPvqoVl1dHXRVMh561qmBnnNqoOecGug5p46gn3VK4vAQBEEQBEEECe2lRRAEQRBExkMCD0EQBEEQGQ8JPARBEARBZDwk8BAEQRAEkfGQwEMQBEEQRMZDAo/P/PnPf0bnzp1RUFCAXr164fPPPw+6SmnDhAkTcPbZZ6NJkyZo1aoVrrnmGqxZs8aQRtM0jBkzBmVlZSgsLMRFF12ElStXGtLU1NTg5z//OUpKStC4cWP84Ac/wNatW1N5K2nFhAkTEIlEcP/99yeO0XNWx7Zt23DzzTejRYsWaNSoEc4880wsXrw4cZ6etXfq6+vx8MMPo3PnzigsLESXLl0wduzYREBbgJ6zW2bPno2rrroKZWVliEQi+M9//mM4r+q57tu3D7fccguKiopQVFSEW265Bfv37/dW+UAWwx8nvP7661pubq7217/+VVu1apV23333aY0bN9Y2bdoUdNXSgiFDhmgvv/yytmLFCm3ZsmXaFVdcoXXo0EE7dOhQIs0TTzyhNWnSRHvrrbe05cuXaz/+8Y+1Nm3aaFVVVYk0w4cP19q2batNnz5dW7JkiTZgwADtjDPO0Orr64O4rVCzYMECrVOnTtrpp5+u3XfffYnj9JzVsHfvXq1jx47asGHDtPnz52sbNmzQZsyYoa1bty6Rhp61dx5//HGtRYsW2gcffKBt2LBBe+ONN7QTTjhBe+aZZxJp6Dm746OPPtJ+/etfa2+99ZYGQHvnnXcM51U910svvVQrLy/X5s6dq82dO1crLy/XrrzySk91J4HHR8455xxt+PDhhmNdu3bVHnrooYBqlN7s2rVLA6B99tlnmqZpWjQa1UpLS7Unnngikaa6ulorKirSJk2apGmapu3fv1/Lzc3VXn/99USabdu2aVlZWdrHH3+c2hsIOQcPHtROPvlkbfr06Vr//v0TAg89Z3X86le/0s4//3zueXrWarjiiiu022+/3XDs2muv1W6++WZN0+g5q8Is8Kh6rqtWrdIAaF9++WUizbx58zQA2jfffOO6vmTS8ona2losXrwYgwcPNhwfPHgw5s6dG1Ct0psDBw4AAJo3bw4A2LBhAyorKw3POD8/H/37908848WLF6Ours6QpqysDOXl5fQeTNxzzz244oorMHDgQMNxes7qeO+999C7d29cf/31aNWqFXr27Im//vWvifP0rNVw/vnnY+bMmVi7di0A4KuvvsKcOXNw+eWXA6Dn7Beqnuu8efNQVFSEc889N5HmvPPOQ1FRkadnL715KCHG7t270dDQkNjRPU7r1q0tO7sTzmiahoqKCpx//vkoLy8HgMRzZD3jTZs2JdLk5eWhuLjYkobeQ5LXX38dS5YswcKFCy3n6DmrY/369XjhhRdQUVGB0aNHY8GCBbj33nuRn5+Pn/zkJ/SsFfGrX/0KBw4cQNeuXZGdnY2GhgaMGzcON9xwAwBq036h6rlWVlaiVatWlvxbtWrl6dmTwOMz+p3ggdjAbT5GODNixAh8/fXXmDNnjuWcm2dM7yHJli1bcN9992HatGkoKCjgpqPn7J1oNIrevXtj/PjxAICePXti5cqVeOGFF/CTn/wkkY6etTf+9a9/4bXXXsM//vEPnHbaaVi2bBnuv/9+lJWV4dZbb02ko+fsDyqeKyu912dPJi2fKCkpQXZ2tkUa3bVrl0X6Jez5+c9/jvfeew+zZs1Cu3btEsdLS0sBwPYZl5aWora2Fvv27eOmOd5ZvHgxdu3ahV69eiEnJwc5OTn47LPP8OyzzyInJyfxnOg5e6dNmzbo3r274Vi3bt2wefNmANSmVfGLX/wCDz30EIYOHYoePXrglltuwQMPPIAJEyYAoOfsF6qea2lpKXbu3GnJ//vvv/f07Eng8Ym8vDz06tUL06dPNxyfPn06+vbtG1Ct0gtN0zBixAi8/fbb+OSTT9C5c2fD+c6dO6O0tNTwjGtra/HZZ58lnnGvXr2Qm5trSLNjxw6sWLGC3sMxLrnkEixfvhzLli1L/OvduzduuukmLFu2DF26dKHnrIh+/fpZQiusXbsWHTt2BEBtWhVHjhxBVpZxeMvOzk4sS6fn7A+qnmufPn1w4MABLFiwIJFm/vz5OHDggLdn79rdmXAkvix98uTJ2qpVq7T7779fa9y4sbZx48agq5YW/OxnP9OKioq0Tz/9VNuxY0fi35EjRxJpnnjiCa2oqEh7++23teXLl2s33HADcwlku3bttBkzZmhLlizRLr744uN+aakT+lVamkbPWRULFizQcnJytHHjxmnffvutNnXqVK1Ro0baa6+9lkhDz9o7t956q9a2bdvEsvS3335bKykp0X75y18m0tBzdsfBgwe1pUuXakuXLtUAaBMnTtSWLl2aCLei6rleeuml2umnn67NmzdPmzdvntajRw9alh52nn/+ea1jx45aXl6edtZZZyWWVBPOAGD+e/nllxNpotGo9uijj2qlpaVafn6+duGFF2rLly835HP06FFtxIgRWvPmzbXCwkLtyiuv1DZv3pziu0kvzAIPPWd1vP/++1p5ebmWn5+vde3aVfvLX/5iOE/P2jtVVVXafffdp3Xo0EErKCjQunTpov3617/WampqEmnoObtj1qxZzH751ltv1TRN3XPds2ePdtNNN2lNmjTRmjRpot10003avn37PNU9omma5l4/RBAEQRAEEX7Ih4cgCIIgiIyHBB6CIAiCIDIeEngIgiAIgsh4SOAhCIIgCCLjIYGHIAiCIIiMhwQegiAIgiAyHhJ4CIIgCILIeEjgIQiCIAgi4yGBhyAIgiCIjIcEHoIgCIIgMh4SeAiCIAiCyHj+PykeZ6uNRIT8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the rewards\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.title(\"Rewards\")\n",
    "plt.plot(rewards)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-1266.7164], dtype=float32),\n",
       " array([-1449.1514], dtype=float32),\n",
       " array([-1079.915], dtype=float32),\n",
       " array([-1566.1954], dtype=float32),\n",
       " array([-1383.953], dtype=float32),\n",
       " array([-1195.3547], dtype=float32),\n",
       " array([-1350.0673], dtype=float32),\n",
       " array([-1304.0881], dtype=float32),\n",
       " array([-1383.3112], dtype=float32),\n",
       " array([-1458.5315], dtype=float32),\n",
       " array([-949.8648], dtype=float32),\n",
       " array([-1377.4651], dtype=float32),\n",
       " array([-1391.4587], dtype=float32),\n",
       " array([-1361.6381], dtype=float32),\n",
       " array([-1283.8612], dtype=float32),\n",
       " array([-1170.7483], dtype=float32),\n",
       " array([-1572.3121], dtype=float32),\n",
       " array([-1436.8456], dtype=float32),\n",
       " array([-1268.324], dtype=float32),\n",
       " array([-1633.9093], dtype=float32),\n",
       " array([-1189.4419], dtype=float32),\n",
       " array([-1379.5791], dtype=float32),\n",
       " array([-1561.0482], dtype=float32),\n",
       " array([-1145.2428], dtype=float32),\n",
       " array([-1694.7274], dtype=float32),\n",
       " array([-1475.4373], dtype=float32),\n",
       " array([-1359.9235], dtype=float32),\n",
       " array([-1638.1277], dtype=float32),\n",
       " array([-1328.7804], dtype=float32),\n",
       " array([-944.8103], dtype=float32),\n",
       " array([-1400.7831], dtype=float32),\n",
       " array([-1034.6903], dtype=float32),\n",
       " array([-1191.6315], dtype=float32),\n",
       " array([-1374.069], dtype=float32),\n",
       " array([-1159.7784], dtype=float32),\n",
       " array([-1374.2853], dtype=float32),\n",
       " array([-1579.9633], dtype=float32),\n",
       " array([-835.4543], dtype=float32),\n",
       " array([-1377.6083], dtype=float32),\n",
       " array([-1682.4266], dtype=float32),\n",
       " array([-1587.6165], dtype=float32),\n",
       " array([-1380.3907], dtype=float32),\n",
       " array([-1506.7631], dtype=float32),\n",
       " array([-1306.3829], dtype=float32),\n",
       " array([-1320.0455], dtype=float32),\n",
       " array([-1403.0656], dtype=float32),\n",
       " array([-1364.6025], dtype=float32),\n",
       " array([-1287.3079], dtype=float32),\n",
       " array([-1390.3209], dtype=float32),\n",
       " array([-1279.2894], dtype=float32),\n",
       " array([-1383.4531], dtype=float32),\n",
       " array([-1386.2344], dtype=float32),\n",
       " array([-1381.1526], dtype=float32),\n",
       " array([-1738.2275], dtype=float32),\n",
       " array([-1371.8367], dtype=float32),\n",
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