training.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I tried training an agent for 100 vehicles instead of 10, but it didn't really learn.\n",
    "Some of the cell outputs might say 100 vehicles instead of 10, but the paper only uses 10 vehicles."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/ray-project/ray/blob/master/rllib/examples/sb2rllib_rllib_example.py\n",
    "import ray\n",
    "from ray import tune, air\n",
    "import ray.rllib.algorithms.ppo as ppo\n",
    "from IPython.display import display, clear_output\n",
    "from time import sleep\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "from itertools import count\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage stats collection is enabled by default for nightly wheels. To disable this, run the following command: `ray disable-usage-stats` before starting Ray. See https://docs.ray.io/en/master/cluster/usage-stats.html for more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-27 00:41:54,742\tINFO worker.py:1536 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n"
     ]
    },
    {
     "data": {
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       "        <h3 style=\"color: var(--jp-ui-font-color0)\">Ray</h3>\n",
       "        <svg version=\"1.1\" id=\"ray\" width=\"3em\" viewBox=\"0 0 144.5 144.6\" style=\"margin-left: 3em;margin-right: 3em\">\n",
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       "            </g>\n",
       "        </svg>\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left\"><b>Python version:</b></td>\n",
       "                <td style=\"text-align: left\"><b>3.8.15</b></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left\"><b>Ray version:</b></td>\n",
       "                <td style=\"text-align: left\"><b> 3.0.0.dev0</b></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "    <td style=\"text-align: left\"><b>Dashboard:</b></td>\n",
       "    <td style=\"text-align: left\"><b><a href=\"http://127.0.0.1:8265\" target=\"_blank\">http://127.0.0.1:8265</a></b></td>\n",
       "</tr>\n",
       "\n",
       "        </table>\n",
       "    </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "RayContext(dashboard_url='127.0.0.1:8265', python_version='3.8.15', ray_version='3.0.0.dev0', ray_commit='3e18487d7427a3ffcea9fecd5c79ca3e81fb92d0', address_info={'node_ip_address': '127.0.0.1', 'raylet_ip_address': '127.0.0.1', 'redis_address': None, 'object_store_address': 'tcp://127.0.0.1:47994', 'raylet_socket_name': 'tcp://127.0.0.1:22624', 'webui_url': '127.0.0.1:8265', 'session_dir': 'C:\\\\Users\\\\TeamD\\\\AppData\\\\Local\\\\Temp\\\\ray\\\\session_2023-02-27_00-41-50_737710_23660', 'metrics_export_port': 29876, 'gcs_address': '127.0.0.1:32085', 'address': '127.0.0.1:32085', 'dashboard_agent_listen_port': 52365, 'node_id': 'aa484c8c5dc1fd377b96b8e07e5007698e5ffa11ed3e61bea1e54421'})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if ray.is_initialized():\n",
    "    ray.shutdown()\n",
    "ray.init(num_gpus=1, local_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platoon-v6 \n",
      "------------------\n",
      "reward_threshold None\n",
      "max_episode_steps None\n"
     ]
    }
   ],
   "source": [
    "# env config\n",
    "import gymnasium as gym\n",
    "import platoonenv\n",
    "\n",
    "env_name = \"Platoon-v6\"\n",
    "env_config=dict(\n",
    "    num_vehicles=10,\n",
    "    steps_before_truncation=200,\n",
    "    attack_interval=2,\n",
    "    cycle_interval=2,\n",
    "    cycle_num=1,\n",
    ")\n",
    "env = gym.make(\n",
    "    id=env_name,\n",
    "    render_mode=\"canvas\",\n",
    "    env_config=env_config,\n",
    ")\n",
    "\n",
    "print(env_name,\"\\n------------------\")\n",
    "print(\"reward_threshold\", env.spec.reward_threshold)\n",
    "print(\"max_episode_steps\", env.spec.max_episode_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-27 00:42:00,079\tWARNING env.py:166 -- Your env reset() method appears to take 'seed' or 'return_info' arguments. Note that these are not yet supported in RLlib. Seeding will take place using 'env.seed()' and the info dict will not be returned from reset.\n"
     ]
    }
   ],
   "source": [
    "from ray.rllib.utils import check_env\n",
    "\n",
    "check_env(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray.rllib.algorithms.ppo import PPOConfig\n",
    "from ray.rllib.algorithms.dqn import DQNConfig\n",
    "\n",
    "config = (\n",
    "    PPOConfig()\n",
    "    .environment(env=env_name, env_config=env_config)\n",
    "    .framework(\"torch\")\n",
    "    .rollouts(num_rollout_workers=10)\n",
    "    # .rollouts(num_rollout_workers=0) # curiosity doesn't support parallelism\n",
    "    .resources(num_gpus=1)\n",
    "    # .exploration( # https://docs.ray.io/en/latest/rllib/rllib-algorithms.html#curiosity-icm-intrinsic-curiosity-module\n",
    "    #     exploration_config={\n",
    "    #         \"type\": \"Curiosity\",  # <- Use the Curiosity module for exploring.\n",
    "    #         \"eta\": 1.0,  # Weight for intrinsic rewards before being added to extrinsic ones.\n",
    "    #         \"lr\": 0.001,  # Learning rate of the curiosity (ICM) module.\n",
    "    #         \"feature_dim\": 288,  # Dimensionality of the generated feature vectors.\n",
    "    #         # Setup of the feature net (used to encode observations into feature (latent) vectors).\n",
    "    #         \"feature_net_config\": {\n",
    "    #             \"fcnet_hiddens\": [],\n",
    "    #             \"fcnet_activation\": \"relu\",\n",
    "    #         },\n",
    "    #         \"inverse_net_hiddens\": [256],  # Hidden layers of the \"inverse\" model.\n",
    "    #         \"inverse_net_activation\": \"relu\",  # Activation of the \"inverse\" model.\n",
    "    #         \"forward_net_hiddens\": [256],  # Hidden layers of the \"forward\" model.\n",
    "    #         \"forward_net_activation\": \"relu\",  # Activation of the \"forward\" model.\n",
    "    #         \"beta\": 0.2,  # Weight for the \"forward\" loss (beta) over the \"inverse\" loss (1.0 - beta).\n",
    "    #         # Specify, which exploration sub-type to use (usually, the algo's \"default\"\n",
    "    #         # exploration, e.g. EpsilonGreedy for DQN, StochasticSampling for PG/SAC).\n",
    "    #         \"sub_exploration\": {\n",
    "    #             \"type\": \"StochasticSampling\",\n",
    "    #         }\n",
    "    #     }\n",
    "    # )\n",
    ")\n",
    "\n",
    "## comment and uncomment this based on which policy you want to train!\n",
    "\n",
    "# config = (\n",
    "#     DQNConfig()\n",
    "#     .environment(env=env_name, env_config=env_config)\n",
    "#     .framework(\"torch\")\n",
    "#     .rollouts(num_rollout_workers=10)\n",
    "#     .resources(num_gpus=1)\n",
    "# )\n",
    "\n",
    "config.horizon = env.spec.max_episode_steps\n",
    "config.lr = 0.0001\n",
    "config.create_env_on_local_worker=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[2m\u001b[36m(RolloutWorker pid=16188)\u001b[0m 2023-02-27 00:42:20,056\tINFO policy.py:1196 -- Policy (worker=3) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=16188)\u001b[0m 2023-02-27 00:42:20,056\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=21220)\u001b[0m 2023-02-27 00:42:20,191\tWARNING env.py:166 -- Your env reset() method appears to take 'seed' or 'return_info' arguments. Note that these are not yet supported in RLlib. Seeding will take place using 'env.seed()' and the info dict will not be returned from reset.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=22656)\u001b[0m 2023-02-27 00:42:20,211\tINFO policy.py:1196 -- Policy (worker=9) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=22656)\u001b[0m 2023-02-27 00:42:20,212\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=16204)\u001b[0m 2023-02-27 00:42:20,218\tINFO policy.py:1196 -- Policy (worker=5) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=16204)\u001b[0m 2023-02-27 00:42:20,218\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=21220)\u001b[0m 2023-02-27 00:42:20,258\tINFO policy.py:1196 -- Policy (worker=1) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=21220)\u001b[0m 2023-02-27 00:42:20,258\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=13196)\u001b[0m 2023-02-27 00:42:20,314\tINFO policy.py:1196 -- Policy (worker=7) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=13196)\u001b[0m 2023-02-27 00:42:20,315\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=9852)\u001b[0m 2023-02-27 00:42:20,359\tINFO policy.py:1196 -- Policy (worker=6) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=9852)\u001b[0m 2023-02-27 00:42:20,359\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=27164)\u001b[0m 2023-02-27 00:42:20,334\tINFO policy.py:1196 -- Policy (worker=10) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=27164)\u001b[0m 2023-02-27 00:42:20,334\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "2023-02-27 00:42:20,725\tINFO policy.py:1196 -- Policy (worker=local) running on 1 GPUs.\n",
      "2023-02-27 00:42:20,726\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=28432)\u001b[0m 2023-02-27 00:42:20,625\tINFO policy.py:1196 -- Policy (worker=8) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=28432)\u001b[0m 2023-02-27 00:42:20,625\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=3768)\u001b[0m 2023-02-27 00:42:20,640\tINFO policy.py:1196 -- Policy (worker=2) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=3768)\u001b[0m 2023-02-27 00:42:20,640\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=7788)\u001b[0m 2023-02-27 00:42:20,638\tINFO policy.py:1196 -- Policy (worker=4) running on CPU.\n",
      "\u001b[2m\u001b[36m(RolloutWorker pid=7788)\u001b[0m 2023-02-27 00:42:20,638\tINFO torch_policy_v2.py:110 -- Found 1 visible cuda devices.\n",
      "2023-02-27 00:42:23,288\tINFO rollout_worker.py:2037 -- Built policy map: <PolicyMap lru-caching-capacity=100 policy-IDs=['default_policy']>\n",
      "2023-02-27 00:42:23,289\tINFO rollout_worker.py:2038 -- Built preprocessor map: {'default_policy': <ray.rllib.models.preprocessors.NoPreprocessor object at 0x00000183FCAF7760>}\n",
      "2023-02-27 00:42:23,290\tINFO rollout_worker.py:757 -- Built filter map: defaultdict(<class 'ray.rllib.utils.filter.NoFilter'>, {'default_policy': <ray.rllib.utils.filter.NoFilter object at 0x00000183FC80ACD0>})\n",
      "2023-02-27 00:42:23,318\tINFO trainable.py:172 -- Trainable.setup took 22.725 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.\n"
     ]
    }
   ],
   "source": [
    "algo = config.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "suffix = \"many\"\n",
    "save_dir = Path(\"saved_models\",f\"{env_name}-{str(algo)}-{env_config['num_vehicles']}vehicles-{suffix}\")\n",
    "save_dir.mkdir(parents=True, exist_ok=True)\n",
    "print(save_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e7332d5e4564fabbec2119053643b6c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Checkpoint:', layout=Layout(width='initial'), options=(), value=None)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "checkpoints = sorted(glob(str(save_dir / \"checkpoint_*\")))\n",
    "try:\n",
    "    checkpoint_path = checkpoints[-1]\n",
    "except IndexError:\n",
    "    checkpoint_path = None\n",
    "\n",
    "w = widgets.Dropdown(\n",
    "    layout={'width': 'initial'},\n",
    "    options=checkpoints,\n",
    "    value=checkpoint_path,\n",
    "    description='Checkpoint:'\n",
    ")\n",
    "\n",
    "def on_change(change):\n",
    "    global checkpoint_path\n",
    "    if change['type'] == 'change' and change['name'] == 'value':\n",
    "        print(\"changed to %s\" % change['new'])\n",
    "        checkpoint_path = change['new']\n",
    "\n",
    "w.observe(on_change)\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if checkpoint_path is not None:\n",
    "    print(f\"Restoring from checkpoint: {checkpoint_path}\")\n",
    "    algo.restore(checkpoint_path)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(rewards_history):\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.title(f\"{str(algo)} episode reward mean during training\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Rewards\")\n",
    "    plt.plot([x[\"episode_reward_mean\"] for x in rewards_history])\n",
    "    plt.grid(alpha=0.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_history = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "reward mean=3.82 iteration=280: : 3610.694157600403it [58:58,  1.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved_models\\Platoon-v6-PPO-100vehicles-many\\checkpoint_000280\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "train_time = 60*60*1\n",
    "plot_every = 1\n",
    "save_every = 100\n",
    "\n",
    "current = algo.get_state()[\"time_total\"]\n",
    "with tqdm(total=train_time) as pbar:\n",
    "    pbar.update(current)\n",
    "    for i in count():\n",
    "        try:\n",
    "            start = time.time()\n",
    "            results = algo.train()\n",
    "            end = time.time()\n",
    "            delta = end - start\n",
    "            pbar.update(delta)\n",
    "            results_history.append(results)\n",
    "        except KeyboardInterrupt:\n",
    "            print(\"Interrupting training\")\n",
    "            checkpoint_path = algo.save(save_dir)\n",
    "            print(f\"Checkpoint saved at: {checkpoint_path}\")\n",
    "            break\n",
    "\n",
    "        if i % plot_every == 0:\n",
    "            plot(results_history)\n",
    "            \n",
    "        pbar.set_description(f\"reward mean={results['episode_reward_mean']:.02f} iteration={algo.get_state()['iteration']}\")\n",
    "        pbar.display()\n",
    "\n",
    "        if algo.get_state()[\"iteration\"] % save_every == 0:\n",
    "            algo.save(save_dir)\n",
    "        if algo.get_state()[\"time_total\"] >= train_time:\n",
    "            print(algo.save(save_dir))\n",
    "            break\n",
    "\n",
    "# training done, play sound to notify\n",
    "from winsound import Beep\n",
    "Beep(300, 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(algo.save(save_dir))\n",
    "# checkpoint path is grabbed by glob when restoring earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "json.dump([x[\"episode_reward_mean\"] for x in results_history], open(save_dir / f\"results-{str(algo)}-step{i+1}.json\", \"w\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "figure_save_dir = Path(\"figures\", f\"{env_name}-{env_config['num_vehicles']}vehicles-{suffix}\")\n",
    "figure_save_dir.mkdir(parents=True, exist_ok=True)\n",
    "print(figure_save_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZ+Cee+7BfffdhwsuuADLli3zy2u4IpPJUF1djSuvvNLj47W1td1uo6Ojgxtv9Wa7a9euxeLFi5Gfn48ZM2ZwMb527VrMmDEDFRUVkMvlTiL9yiuvxHvvvYcvv/wSzzzzDGpqamC1WvHAAw9gyJAhbq/najToL6xWa4+WR0VFAXDsU++//z7ee+89j+vu2rULQM/fa3evHUjkcrnH5T35/IM5foan8fb0e/BEX95bKHwuBEEQ/QWJdIIgiBCEuTmz1GOTyYQVK1Zgzpw5yM3N9Rhtu+SSS6BSqfC///2vy20/9thj+O2333DDDTf4NJYjR44AAGpqarBixYpu15fL5Rg8eLBTVHL48OEAgGPHjnX53KqqKrz66qt49dVXkZKSgm3btuGhhx7CsmXLUFpaCkDso7xq1Sqn5xUUFPDH2V+WAeC6nut7mzNnDtatW+fX1nQ92S4T33PnzsXkyZPx9NNPAwBWr16Nm266CRUVFWhtbXVq1XfRRRfhyJEjuOCCC5y25Wvf69raWrS1tfn0GQWC2tpatLS0QC6Xd7tP9fW9+kppaanPn0dDQ4Ob+79SqfRaquFvSktLeSmEFE/LPOE6qeQL/fU99BZ23A8dOtTpPJOYmIjExMQgjYogCKJ3ULo7QRBEiDF79mw88sgjOHr0qFON6RNPPIGoqCi8++67bjXlgwYNwj/+8Q8cP34c77//fpfbX716NVatWoX77rvPp9r05cuXo7m5GQ8++CAUCve5Xdc2SAB4iyfp/c7OTq+CTCaTQa/XOy2rra1FRUUFYmJiAABbtmxBdXU1brzxRkRHR/P15s+fj8LCQnz//fcARKG/fft2XHXVVU7bnDNnDkaNGuX0Gp9++ikUCoXHFnFyudznNF5XerLdY8eOoaysDH/5y1+gVCqxbt06AKJ4Hzp0KC666CJs3LjRKZLI/pdGEadMmYJp06b5ND6bzYbly5dj4cKFyMnJ4ctHjBiBefPm9ezN9gKbzYbPP/8cF154odt3AjjvU319r77yww8/YNq0aZg8ebLTODxlQxw5coT7CDAWL17s8fgIBMuXL8e0adMwbtw4viwhIcFr5oYrRqMRAHq0f/fX99BbVqxYAbPZ7NZm0vVcRBAEEQ5QJJ0gCCKILFiwACNGjIBCoUBaWhpOO+00zJ07F6WlpTj33HOdDNLWrVuHv/zlL3jhhRewa9cuvPvuu6isrMSIESNw/fXXw2azYeHChWhubu72dR9//HH8+uuvPo3RYDDgpptuwvvvv49t27bh448/Rm1tLXJzc3HWWWdh3bp1Tj29TSYT5s+fj3fffRebNm3CggULcPbZZ+P//u//3MyoGDqdDmVlZfjss8+wc+dOtLa2Ys6cOZgyZQruvPNOAGJa/X333Yd3330Xv/32Gz766COkpaXh9ttvR0lJCZ5//nm+vQceeADff/891q5di3feeQeJiYm47bbbsGfPHsTGxvL1Vq9ejddeew0PPvggioqK8NNPP8FsNmPYsGG4+OKLcfvtt+Pzzz/36XOS0tPtrlmzBpdffjl27drFa6i3bduG1tZWFBQU4L///a/T9r/77jtceOGF+PLLL/H9998jPz8fN954I/bt2+f0/rpiyZIlmD9/PtasWYNXXnkFCoUCt912G/bu3esk/gLF/fffj9mzZ2PTpk148803sW/fPiQmJmLChAmYM2cOkpKSAPjnvfrCP/7xD/zhD3/AsmXL8OKLL8JoNGLx4sUoLS11i5q/9dZbeP311/HZZ5/h559/xrhx4zBv3jyfyiP8NdZFixbh559/xksvvQSj0YjrrrsOx48fR1JSUreRcpaV8a9//QvLly+H1WrFJ5980uVz+ut76C01NTV48cUXcffdd+Prr7/GsmXLMG7cOCxYsAC1tbW9yh4gCIIIJkG3mKcb3ehGt4F2Y22FGO3t7UJFRYWwfPly4bbbbnPqJ+16O+WUU4Qvv/xSqKmpEaxWqyAIglBVVSWkpaW5rSttweb6GGuh5UufdADCqaeeKvz4449CY2Oj0NbWJhw6dEh45513hAkTJvB1li5dKhgMBiE/P19YtmyZ0NraKlRWVgpLliwRoqKinLYnbdOkVCqFv//978L27duF5uZmwWAwCNu3b+etyaS3iy++WNi6datgMpmEuro64f333xcyMzPd1jv//POFvXv3CiaTSdizZ4+wcOFCYenSpW590gEI1113nbB582bBaDQKzc3Nws6dO4Wnn35aSE9P7/IzYe/X2+O+bvemm24SBEEQXn75ZaflP/30kyAIgjB79my3bd9///1CSUmJYDKZhK1btwpnnnmm2/tjLbXuuusuj+ObMWOGsHnzZqG9vV04fPiwsHjxYr7PdLc/rFq1Sti9e7fbcm9t/QRBcGuxl5KSIrz00ktCaWmp0NHRIVRUVAg///yzcN111/n1vXprmeZ6Gz16tLBq1Sqhra1NOHHihPDQQw8Jf/rTn9zamkVFRQlPPfWUUFNTI7S2tgo//vijMHjwYK/twzy1WvTWgs3TZ7dq1Sph1apVTsvGjRsn/Pbbb4LJZBKOHz8u3HfffcKtt94qCIIgpKamdvk+ZTKZ8OKLLwrV1dX8HOLL/uLL9+Dp8/Z2HvL2GfjyGZ566qmCIAjCqaee6vS+Hn/8caGiokIwGo3CL7/8IhQUFAi1tbXCK6+80u33Tze60Y1uIXQL+gDoRje60Y1ufbg9/PDDgiAIwt/+9regj6U70Uo3utEtcLfnn39eaGtrc+vJPpBvcXFxgiAIwoMPPhj0sdCNbnSjm683SncnCIIIc5544glkZmbi4YcfxvHjx/Hmm28Ge0gEQQQYlUrlZEqYmJiIP/zhD1i7dq3PPdkjDdfPBADuuOMOAPC5vIcgCCIUIJFOEAQRAdx88824+eabgz0MgiD6iQ0bNuDXX39FcXEx0tLScO2110Kv1+Nvf/tbsIcWNC699FJcffXV+OGHH9Da2orp06fjiiuuwPLly7F+/fpgD48gCMJnSKQTBEEQBEGEGT/88AMuuugiLF68GIIgYNu2bbj22mt5S7+ByK5du2CxWHDvvfdCr9ejuroaL7zwAh5++OFgD40gCKJHREHMeycIgiAIgiAIgiAIIshQn3SCIAiCIAiCIAiCCBFIpBMEQRAEQRAEQRBEiDAga9IzMzNhMBiCPQyCIAiCIAiCIAhigKDT6VBRUdHtegNOpGdmZqK8vDzYwyAIgiAIgiAIgiAGGFlZWd0K9QEn0lkEPSsrK+Sj6TKZDGPHjsWuXbsGbM9TIvKh/ZwYKNC+TgwEaD8nBgq0rxM9RafToby83CcNOuBEOsNgMISFSDcajTAYDHTwExEL7efEQIH2dWIgQPs5MVCgfZ0IJGQcRxAEQRAEQRAEQRAhAol0giAIgiAIgiAIgggRSKQTBEEQBEEQBEEQRIhAIp0gCIIgCIIgCIIgQgQS6QRBEARBEARBEAQRIpBIJwiCIAiCIAiCIIgQIagifcmSJRAEwelWXFzc5XMuuugiFBcXw2QyYdeuXViwYEE/jZYgCIIgCIIgCIIgAkvQI+l79uxBeno6v02fPt3rutOmTcNHH32Et99+G+PHj8dXX32Fr776CqNGjerHERMEQRAEQRAEQRBEYAi6SLdYLKiurua3+vp6r+vefvvtWLZsGZ599lns378fjz76KLZt24Zbb721H0dMEARBEARBEARBEIFBEewBDBs2DOXl5Whvb8eGDRvwwAMP4MSJEx7XnTZtGp577jmnZcuXL8fChQu9bj86OhoxMTH8vk6nAwDIZDLIZEGfo+gSNsZQHydB9AXaz4mBAu3rxECA9nNioED7OtFTerKvBFWkb9q0CVdffTUOHDiAjIwMLFmyBGvWrMHo0aPR2trqtn56ejqqq6udllVXVyM9Pd3razzwwAN47LHH3JaPHTsWRqOxz+8hkMhkMgwfPhwAYLPZgjwagggMtJ8TAwXa14mBAO3nxECB9nWip2i1Wp/XDapIX7ZsGf9/9+7d2LRpE0pLS3HJJZfgnXfe8ctrPPXUU07Rd51Oh/LycuzatQsGg8EvrxEo2GzLjh076OAnIhbaz4mBAu3rxECA9nNioED7OtFTWEa3LwQ93V1Kc3MzDh48iKFDh3p8vKqqCmlpaU7L0tLSUFVV5XWbnZ2d6OzsdFtus9nC4oBi4wyHsRJEb6H9nBgo0L5ODARoPycGCrSvEz2hJ/tJSBVRaLVaDBkyBJWVlR4f37BhA04//XSnZXPnzsWGDRv6Y3gEQRAEQRAEQRBEF5xz1204+y+3BHsYYU1QRfozzzyDmTNnIi8vD9OmTcOXX34Jq9WKjz76CADw3nvv4cknn+Trv/jii5g/fz7uvPNOFBQUYMmSJZg0aRL+/e9/B+stEARBEARBEARBEADyJ4zDrKuvwOxrFiE+LTXYwwlbgirSs7Oz8dFHH+HAgQP49NNPUV9fj6lTp6Kurg4AkJubi4yMDL7+hg0bcMUVV2Dx4sXYuXMnLrroIixcuBB79+4N1lsgCIIgCIIgCIIgAEy//CL+f+rgQcEbSJgT1Jr0yy+/vMvHZ8+e7bbss88+w2effRaoIREEQRAEQRAEQRA9RJ+SjDGnz+L30wYPwsENvwdvQGFMSNWkEwRBEARBEARBEOHH1IvOg1zpiAGnDckP4mjCGxLpBEEQBEEQBEEQRK+JTUrgqe57Vq0GIEbSid5BIp0gCIIgCIIgCILoNRc+fC+0CfEo338Qv7zxHgCKpPcFEukEQRAEQRAEQRBEryiaPwdj58yC1WzBxw8/garDR2Cz2aCNj0NsYkKwhxeWkEgnCIIgCIIgCIIgesXp1/0RAPDLm++i4sAhmNs70FhRCYAc3nsLiXSCIAiCIAiCIAiix8QmJSCzYBgAYN3Hn/Pl1UeOAaC69N5CIp0gCIIgCIIgCILoMcOmTAIAlBcfhLGxiS+vPnoMgCjSYzQaREVFBWF04QuJdIIgCIIgCIIgCKLHDJs6GQBwcONmp+XVR0sAAJPPOwtPbPgZFz56b7+PLZxRdL8KQRAEQRAEQRAEQTgzfJoo0g+5iPSqw6JIV8VqAQB5Y0b178DCHIqkEwRBEARBEARBED0iOTcbCRnpsHR2omT7TqfHTuzZh5Vv/we7fl4FAFDFxgZjiGELRdIJgiAIgiAIgiCIHsFS3Y/t2I1OU7vb49+/8CpSBuVi7NzZUOm0/T28sIYi6QRBEARBEARBEESPyBsrprAf2bzN6zrtrUYAgEpLIr0nkEgnCIIgCIIgCIIgeoRarwMANNfWeV2nvbUVACCTyxGj0fTLuCIBEukEQRAEQRAEQRBEj2DR8Q57tNwT5vYOWM0WcX1KefcZEukEQRAEQRAEQRBEj2BmcO1G7yIdcETTKeXdd0ikEwRBEARBEARBED2CtVdrb23rcj0TE+k6cnj3FRLpBEEQBEEQBEEQRI+I0Yo15ixS7o0Ou4inNmy+QyKdIAiCIAiCIAiC6BGOSHrX6e4skq6OpXR3XyGRThAEQRAEQRAEQfiMXKmEMiYGQA9q0ind3WdIpBMEQRAEQRAEQRA+o5JExTuMXdektxuoV3pPIZFOEARBEARBEARB+AwT3O1GIwSbrct1KZLec0ikEwRBEARBDFDGL5iL4dOmBHsYBEGEGSyS3tGNszvgqFlXUU26zyiCPQCCIAiCIAii/0nITMeif/wVnaZ2PDJjPiwdHcEeEkEQYUJMrCOS3h08kk7u7j5DkXSCIAiCIIgBSPrQIQCAaLUKuaNHBnk0BEGEE2ofnd0BwGRfR62jSLqvkEgnCIIgCIIYgKQOyuX/508YF8SREAQRbvBIejc90gGg3UCR9J5CIp0gCIIgCGIAkpqfx/8nkU4QRE/gxnE+RNKZkI+hmnSfIZFOEARBEAQxAJGK9EHjxiBKRpeFBEH4BjeO66b9GuBowaamSLrP0NmYIAiCIAhiACIV6WpdLDKHDw3iaAiCCCdY6rpPkXQjubv3FBLpBEEQBEEQAwxtfBxiExMAAEe2bgcA5E8YG8whEQQRRqh64u5uoD7pPYVEOkEQBEEQxAAjZZAYRW+oqMSBdZsAAPkTioI4IoIgwokYrQaAQ4B3hclek65QKqGIiQnouCIFEukEQRAEQRADDJbqXltSiuO79wEApbsTBOEzPYmkd7aZYLPZ7M/TBHRckQKJdIIgCIIgiAFCcm42Rs2ajrTBgwAA1SWlaG1oAACo9bogjowgiHCC1aR3+FCTLggCX4/asPmGItgDIAiCIAiCIPqHK//+OHJHF6LT1A4AqCkphanZAADQ6PXBHBpBEGEEb8HmQySdrafW68jh3Ucokk4QBEEQBDFASM7NBgBEq1UARJHe1iKKdLlSwZcTBEF0BU93N/go0lkknczjfIJEOkEQBEEQxABAppA7RcutFguqj5Sg02SC1WwBQCnvBEH4BjeO8zWSzhzeqQ2bT1C6O0EQBEEQxABAGx8PALBZrXj12luhiI5Ga0MjAMBkMCA2MQFqvR7N1bVBHCVBEOGAWud7n3TA4fBO6e6+QSKdIAiCIAhiABCbGA8AMDY14+jWHU6PmVpEka6RRNJjExNQcPJJ2P7jz7BZrRg6ZSKGnjQRjeWVUMTEIHVQLorXrMf+tRv78V0QBBFs5EolFNHRAHoQSbeL+RiKpPsEiXSCIAiCIIgBQGxCAgDA2Njk9hirS5emu591x82Ycv7ZiFarsfGzr/DHf/4ftPFxTs8rPHU6/m/+BYEbNEEQIYc0Zb3D2ObTc5hIV9ufK1cqkZiVgdpjx/0/wAiAatIJgiAIgiAGALGJokhv9SDSTUyk6xwiPWf0SADAoPFjkJSTBW18HCydnShesx6HNm4BAGgT4ty2RRBEZCN1dhfs/c+7o92e7s6M4xbe/xfc/+0nGDZ1cmAGGeaQSCcIgiAIghgAaBPiAXiOpJtaWgA4IulyhQKpg/IAANmFI5BdOAIAUF58EG/dfBc+uO9RAECMRoOoqKgAj5wgiFBCpRNFekerb1F0wOECz/qkD7eL8+ScbD+PLjIIGZF+3333QRAEPP/8817XueqqqyAIgtPNZDL14ygJgiAIgiDCEx5Jt5vFSWHp7qwmPTkvB3KlWBWZmp+HwROLAABlxQcAAO2SFNdojTpgYyYIIvToaY90ADDU1QMAkrIzEaPV8HaQSlWM/wcYAYRETfqkSZNwww03YOfOnd2u29zcjIKCAn5fEIRADo0gCIIgCCIiYJF0TyLdZG+PxCLpGUMH88dkMhnGnzkXAFC2TxTplo4OWC0WyBUKxGi1PtelEgQR/jh6pLf6/Jzje/YBEMtoskc6tBwzoCOcCXokXavV4sMPP8T111+Pxkb3Hw1XBEFAdXU1v9XU1PTDKMOTKJkMCZnpwR4GQRAEQRAhQGyX6e7OxnFpEpEOgPdXL7dH0gGgo00U5iqtmPKeNWI45IqQiP8QBBFAmEN7TyLp1UePob3ViBiNBhPPns+XUyTdM0E/k7788sv4/vvvsWLFCjz88MPdrh8bG4tjx45BJpNh27ZtePDBB7Fv3z6v60dHRyMmxvHl6+yGKDKZDDJZ0OcouoSNsbfjPOfuP2PmHy7Fa9ffhiO/b/Pz6AjCP/R1PyeIcIH2dSLYsHR3Y1Oz237IImKaOD1kMhmPpLc2NPLnWTo7UXP0GH9uh7ENGr0eqlgtxi+Yiyv//jhWvv0+qtf+Tvs5EfEM5HO6xq6nOoxtPXr/x3fvw/BpkzH+zDP4smiVasB8hj15n0EV6ZdeeikmTJiAyZN9c/U7cOAArrnmGuzatQtxcXG4++67sX79eowaNQrl5eUen/PAAw/gsccec1s+duxYGHsw+xMMZDIZhg8fDgCw+eicKGXouDEAgCmnz4aus+fPJ4j+oK/7OUGEC7SvE8EmKUPMrstITIKsqMjpsbSkJABAcno6ioqKkDdKdHYv27wdI+adBgBoLq/EmNGj+XOibGLJ4aixY5E6YhgAYOSUiYiraQJA+zkR2QzEc7oqTo8Zt1yLBHs9uTZGhSKXc0lXdNTUAQCi1Sq+LCMrs0fbCGe0Wt97xAdNpGdnZ+PFF1/E3Llz0dHR4dNzNm7ciI0bN/L769evR3FxMW644QY8+uijHp/z1FNP4bnnnuP3dTodysvLsWvXLhgMhr69iQDDZlt27NjRq4P/pHbRVK+lvR07duzw59AIwm/0dT8niHCB9nUi2Cy0G7zt2LIF1YdLnB4zRMswE4BNLsOefftweWoyAGDZO+9zkX5o6w6n64np9Q2Iz8pAWWUFYnIyAABRahUOHjxI+zkR8QzEc/rkhWchtWAoAMBqsWDjD8t7pDHaY9UYc94Cp2VNBsOA0Sk6SYvL7giaSJ84cSLS0tKwbZsjDVuhUGDmzJm49dZbERMT0+0Ob7FYsH37dgwdOtTrOp2dnejs7HRbbrPZwuKAYuPszVjlSiUA0SgmHN4rMXDpy35OEOEE7etEsJDJ5dDGiz3NDfUNbvtgW7O9BZsuFsl5OZDJ5WhracGJffvRXFOLuNQUnNi33+l5HfaMRKVGzdsqxaWl0n5ODBgG2r6uiRO9KXb9vAofP/wE96XwlWM7d7stU0QrB8zn15P3GbQCgBUrVmD06NEoKirit82bN+PDDz9EUVGRT29CJpNhzJgxqKys7IcRhx+KaFGkxyYlBHkkBEEQBEEEE028eHFts9nQ1tTi9riJt2DTI31oPgCg6tBRAMDa/36GykNHsHflaqfnsDZsMRoN1HpRpKt1sVCoVCAIIvJgIr2xqrrHAh0QJwNrSkqdliljyDjOE0GLpLe2tmLv3r1Oy4xGI+rr6/ny9957D+Xl5XjwwQcBAI888gg2btyIw4cPIz4+Hvfccw/y8vLw1ltv9fv4wwHW0kCXmNjj54494zRMPOsMfPzI//EfboIgCIIgwpPYBHHC3tTcApvV6vY465OuVMUgu3AEAKDqiJgSv/Lt/2Dl2/9xew5ru6bSangkHQC0FBwgiIhEbRfpLPOmNxzbuRup+XkwNjVDGx9H7u5eCLq7e1fk5uY6RdQTEhLw5ptvIj09HY2Njdi6dStOPvlkFBcXB3GUoQtrg9KbSPpp1yxCzqiRGPrtMuz+5Vc/j4wgCIIgiP6E90j30H4NADpajbDZbJDJZBgyaQIAoPLg4S63ySJpMVot1DqHSNckkkgniEhEGyeWzLQ1Nfd6G8tffgsdxjbUnyjHwvv/Qn3SvRBSIn327Nld3r/zzjtx55139ueQwhq208f24scyPj0NABCtVvt1TARBEARB9D/sWqC1sdHj44IgoN3QCk2cHpl2p/aKA92IdGm6u8QQSZMY74cREwQRamj8EElvqqrGV08/j1GzZwCgPuneGBhN6QYorCZdoVRCrffdTVCuVEKXJKbIR1NdGUEQBEGEPbxHekOT13XaWsQLb+Za3W0knYl0rQYqnaO1EEXSCSIy8YdIZ1g6RGNvZTSJdE+QSI9gpOkjPYmm61OS+P9KNR04BEEQBBGuqHSxGH/mGUjKzgTgPd0dAEyGVv5/3fGybo2hOtpEd3e1Lta5Jp1EOkFEJBp7h4i25t6nuzPM9hbcFEn3TEiluxP+Ra50fL26pETUHjvu0/Pi01L5/+S4SBAEQRDhyxk3XoNT/3g5v9/a4DndHYCTUWzFgUPdbptF0vWpyTz6DlC6O0FEKhq9/yLp5nZRpCtiqCbdExRJj2AUSs+R9NT8PCRkpHt9XpxEpFO6O0EQBEGEL/njxzndN3qpSQdcRHo3qe6AowWbdHIfoHR3gohEFDExiFaLusAvIr3Tnu5OAUGPkEiPUKKiotwi6YBYN3bHx0tx2wdvIErm+et3iqSrSaQTBEEQRDgiVyiQWTAUAFBbegIAUL7fe4Sc1aQDPYykpyQ7LSeRThCRB6tHt1osaG819nl7LJJOIt0zlO4eochd2hmwSHpKXg5iNGrEaNRIzs32mAKvT0vh/1MknSAIgiDCk/Rhg6GIjkZbSwv+sfByxKWmoLGiyuv6TpH0LsQ8g9Wsy+RyAEBjZRUSMtKhVMVArdPB6Ie6VYIg+pe0wYOQOWI4tv/wk9Nyf5rGAYClk9Ldu4Ii6RGKQuk8/xJrj6QnZGbwZVkFwzw+l2rSCYIgCCL8yS4cAQAo27sfNou1S4EOAKYW0TiuraUFjZVdrws4IumMltp6GO3GdPHpqR6eQRBEqHPpXx/Cor8/jpxRI52WM5EunczrC7wmXan0mt07kKFPJEJReImkJ2Q6atGzRg73+Nw4SSSdHBcJgiAIIjxhF9kn9u73aX1jUxMAoPLgEZ/WdxXp7QYDmqprADj72xAEET4wHZBo7wjB0NpFurHJPxkyzN0doKCgJyjdPUJRKJVO93V2kZ4oiaRnFngW6dJIejTVpBMEQRBEWJI9Soykn9hb7NP6e1etwfYpE7Hpy+98Wr/dRaSbDK2wmC3IGjGcIukEEabEaLUAAF2Ss7eE39Pd7X3SAUAZE41Ok8kv240USKRHKG416Tzd3RFJzxzhnu4eJZNBlyzpk04zWwRBEAQRdiiio5ExbAgAMd3dF1obGvHBfUt8fg3WJ51ham3lUTZpeR1BEOFBVFQUYrQaAEBsYqLTY/4W6YIgwNLZCUV0NOkND1C6e4TiVpPO0t0lrdf0yUlOghwQXeDlCsdzlWQcRxAEQRBhR8awIVAolTA2NvlUX94bbBarU8pqe0sr6k+UAwCScrIC8poEQQSOaLUaMnt9eGyit0i6/wwhzfZoOpnHuUMiPUJhNemdpnYAgFoXC0V0NE93Z8uz7NH09KGDceNbL2HCWfOctkPp7gRBEAQRfmQVFgAAyvb5FkXvLdK6dJOhFXXHxVZvKbk5AX1dgiD8j0qn5f+7i/Q4AP6LpAOOunTywHKHRHqEIo8Wa9JbGxphMZsBACmDcqDW6wAABzf+DsBRlz5n8dUYdtIknPWXmwGIPRABSncnCIIgiHCE+cvU2SPbgYK1YQMAk8GAutIyAEBSLkXSCSLcUGndRXru2FHIGD7E7+nuAPVK7woS6REKi6RbOjvR2tAIAMgdXQhAFO7Htu8CIDq8R6tVKDx1OgDwFBfWpoVmtgiCCHUU0dE4+y+3YPi0ycEeCkGEDNqEeABAa31DQF9HGklvb21FQ3kFbDYbYjQa6FOSA/raBEH4l5hYZ5GuidPj5rdfxs1LX0Fcquj63uYnd3dA1CkAoCCR7gaJ9AiF1aRbOjtRVyqmnhUtmAsAaKioRFnxQQDA0MkTMOHs+YjRqPlsFgDUlh4HAERTTTpBECHOtIsXYvY1i7DgzzcGeygEETLEMpFu71seKJzS3VtaYbVYYKyrBwAk5+Vg4jkLcNGj90Emlwd0HARB9B1pJF2XlIiUQblQqmKg0euRM0YM9rW1UCS9PyCRHqHIlSySbsaeVWsAAMOnilGmxooqHN2yHdVHjyE2MQHnP3AnAOC39z/G0a07AADl+w8BAJRUk04QRIgz9eKFAABtfHxQx0EQoYQ2MR5A4EV6e5tzTToAGKprAQCp+Xk4/4E7Me3ihRgyaXxAx0EQRN9RSSLpqlgt0vIH8fss29af6e4skq4k4zg3SKRHKAp7TbrVbMaeFb85PdZYWQWrxYIv/u9ZcV17T/XtP/6Md++4H58ueRLrP/mcP0az3wRBhCqDJxYhfUg+ANEgkyAIEZ29fZLRXvIWKFzT3QGHSB+/YC4/LhOzMwM6DoIg+o40kg6I9eiuGP2Y7k6RdO9Qn/QIxVGTbkZjZRVO7C1GzqiRAIDGikoAwOHft2Lrd8sw8ez5qDpSgqpDRwAAm7741qk2RBkT42QMQxAEEWxuefdVxKWlOqXdsd6uBEFIatL7M93dYAAAGKpqAIgldYzELBLpBBHqqFwmu3PtKe5SAuLuTiLdDRLpEQqvSTeLaSS7f/lNItId/VK/evp5tLcasWPZL07Pt0j6nirVJNIJgggd4tNSMXhiEQAgCY4Lf7lCgWi1Gp0mU5BGRhChgUwuhzZebJdk7OeadMARSZeSlJUR0HEQBNF3VC6T3RnDhjjdt5otTsd8X2Einfqku0Pp7hGKXBJJB4DdK37ljzVIRHpbcwu++L9neS26FNZLnWa3CIIIJdKGDgYgdqqoKSnFtu+X87aRrlEAghiIaOLFVkk2m82vqameYJP4VouFT5AZqmvc1ksgkU4QIY/U3R0AL3k9sH4TAP+axgEUSe8KEukRCqszt9p7pNeUlGLLtz/i0MYtqD5a4tM2zO2iSCeHd4IgQom0IYMAAEe2bMffz70MH97/GJ/ZV7tcYBADk8yCYTj1j5cPWE+V2ASxv7GpuQWCzRbQ1+owGgEA7a1Gvqy1rp5PnLG/lO5OEKGPa00647f/fAyr2YJKe2msv7B0sBZsFEl3hdLdIxSFSyQdAD568K892kZnezu0AJQk0gmCCCHSh4iR9OojjglHk6EVmjg9RdIJAMB5992BoZMnoLrkGPav2RDs4fQ7rB7dEGDTOABot0+QsXp0ABCsNjSUVyIlLwf7fluHMaefCn1yEpSqGKd2rwRBhBbM3d3c3gGlSoxuWzo7cXD9Jjx74SK/l8/wSLqKIumuUCQ9QpFL+qT3Fu64SAcOQRAhBHNzr5KIdOYqrYolkU4AcSnJAID49LQgjyQ4xCaKkfRA16MDjnR31n6NcWLPPgDA5q+/548lZlLKO0GEMuw3tO5EGV/WWFEFQRBQU1Lq9/IZSnf3Don0CIVH0s3mbtb0DhPplO5OEEQokcZE+uGjfBlLtaV0dwIANHFiTbYuKTHIIwkOsczZvR8i6eX7DsBiNuP4rr1Oy796+nm8fPVN2LtqDRrKKwBQXTpBhDoskl533CHS2fEbCFi6O4l0dyjdPUKR9knvLZ32mnRKdycIIlSIT0uFKlYLq9mCutITfHm7PVLnanpDDDyioqKg1usAOCLKAw2W7t4fkfTqo8fw6Iz5bo7PphYDN6VtKK9E1ojhSKK6dIIIaVgrU6lIry+vDNjrUSTdOxRJj1AUSvea9J7CjePUdOAQBBEaMGf32uMnuCEVAJjs6e5qSncf8Kh0sdwwbqCKdPa+A90jndFdS6aGCvEin8zjCCK0cUTSHZPggYykm8k4zisk0iMUv9ak0+wWQRAhAnN2l5rGAY50dzKOIzR6Pf9/oIt0Y2Pg0919oaFMvMhPpHR3gghpmLt7rSRTrZEi6UGBRHqE4sndvadQujtBEKEGc3aX1qMDjnR3FaW7D3hYPTowcEW6ltekNwV1HIyGcoqkD0Qu/etDuO2DNyBTDMxWiOFGVFQULxmrP1HOlwc03Z0Cgl6hmvQIxR816dw4Tk0inSCI0MCTsztA7u6EA018HP9/oBvH9UdNui840t0pkj5QUMVqMeX8swEAKbk5qD56LLgDIrolWq2GTCbGb41NTTi+Zx8SMtLdMtf8Ccv4pXR3d0ikRyhypSjS++LuziPpNLtFEESIkJqfBwCocbngMzF3dx1F0gc60ki6Nj4OMoUcNos1iCPqf/qzT7ovsHR3TZweKl0sz3whIpfswhH8/2i1OogjIXyFRdGtFgvM7R349x9ugFypRKfJFLDXpEi6dyjdPUJhkXT/9EmnSDpBEMFHGx/HXbvrJKl4gDTdnSLpAx2pSAcAbXx8cAYSJKKioqC1ZxMYQ0Skd5pMMNQ3AACSc7KCPBqiP8gZJRHpGhLp4YDK7uzOjCCtFktABToAWDqZSKdIuisk0iMU/7q7k0gnCCL4JOflAAAaK6tgsZvNMLpKdz/v3jvwwPf/49FFIrLRuoj0SKxL16ckY8SMaR4f08Tpubu9sbm5P4fVJaxlYnJuTpBHQvQH2aNG8v9jNJogjoTwFebpYurHTBeKpHuHRHqE4t8+6XTgEAQRfNjFvbR/K6PdwNzdndPdc8eOwsw/XIrk3GwMnlgU8DEOZFS6WETJgn9ZoXYR6bqkyBPplzz+AK5/5TnkTxjn9hibjGprbgmpNP9ae0snNtlGRDY5EpFOwZ7wgIn0DqOx316Tu7uT1nAj+L+mREDgNel+iKTT7BZBEKFAcm42AC8i3X5R4don/ew7b+H/x6WmBHB0Axt9agoeW/kdrn7+yWAPxS3dPTYCzeMSMtIBAGl2I0UpvEd6iKS6M+pKxeM2hSLpEY82Pg5J2Q4n/5ggprsnZKTjkZ+/wqyrrwzaGMIFlonGWpr2B7xPejSlu7tCIj1C4TXp5r7XpEdTTTpBECFAChPpkv6tDE816YWnTseQieP5/bg0EumBIrNgKJSqGAwqGhvsofB6bJvNBiAy091VOnE/j09LdXuMTUqEmkh3RNKzgzwSItBITeMAIDqI6e7DTpqE+PQ0zP7TlSGR6RNootUqjJt3Ok6+9AJMOOuMHj2X9UhvD1Ikfcr552DODX/qt9cOdcjdPUJhM1LWPvVJtx84lKZEEEQIkMRE+gn3SLrJXpMuVyqgVMXA3N6BGYsuAQAY6hugS0r0KGgI/8DM2WITEyBXKGC1WACIbvyDJ43Htu+WodPU3i9j0ejFSHpjRSWSsrOgi0SRbk9L9TTxFJ8u7ufN1TX9OqbuYJNrFEmPfLJHOYv0YEbSmWN5bGICBhWNQcm2nUEbS39wxk3XYfafHFkDbS0G7F+zwafnss+qox8j6RZ7JF2pUuGiR++FXKHA5q++Q3N1bb+NIVSJ/CmlAYrCDy3YKN2dIIhQgl3c13qIpHe2mXjkVBUbixithkfR1370GQAxJZsIDLESUz5dchJUulhc+teHcM+XH+LiR+/DpHPP7LexsHT3mpJScWyJkZXuHiWT8YiXp4mn+PQ0AEBjVXW/jqs76u0dGbQJ8bxLAxGZ5I4W69FZlDSYIl0d6/ApGX3azKCNo79IsWeq2KyiH0X2yAKfn8uN4/o13V3cR2QyGeQKMXasiYvrt9cPZUikRyj+qElnUQ9KdycIItiwC3ubzcYv9qUIgsBn/1WxWgyfNgVypQK1x47jyOZtADwLGsI/xCbG8//1KUmYfvlFmHL+2dxlnAnHniCTyxHbC9M3jT3dvfroMfvYIiuSHqN1pA57mnhKsH/WTVWhFUnvaGtDS20dAHJ4j2SiZDLkjxcNDY/t2A0guH3SWWkIAIyeHfkiXW3PJKo8eAQAkDZ4kM/PZZN//RlJZ6W1TuOI1XpYc+BBIj1C8UufdHJcJAgiRGCmcc3VNV7PayZJG7bCmacAAPatWc/Tfsk4LnBoExxCWJ+SzEVYR5vYY5fVifeEs++6FY//+gNyx47y+TlRUVE8SlvDRHqEubtLzRG7iqQ3hVgkHXDUpadQXXrEkjViOLQJ8WhvNeLw71sBBLdPOhOegPg74slsMZJg579jO8UJkpT8PJ+fy8Rxf9ake/o999RKdSBCIj1C4TXp/kh3J5FOEESQ4e3XSt3r0RnMkVati8WI6VMBAMWr16O5RozeKVUxbs7fhH/QJjhEuD4lGQmZovt4efEB++PxPdqeTCHH5PPEFPnBE4p8fp5KFwuZ3RzKke4eWSI9RhJlUsVqnSLrABCfYRfplaEn0tnxS5H0yKXglJMAAIc2bYGpxQAguH3SpZF0IPKj6Rom0nfsAgCkDspDVFSUT8/lIr0fI+mAezRdraNIOhBCIv2+++6DIAh4/vnnu1zvoosuQnFxMUwmE3bt2oUFCxb00wjDC7kfatIp3Z0giFAhxd5bmUXiPMEc3odMngB9SjLajUYc3boDVrMZhvoGAOTwHihiXSLprEVY2T67SO9hJD2/aCw3gOtJBgSrZWw3GtFkz6DQRVhNutolFVQaTZcrldAnJwEIzUh6HY+kk0iPVJhIP7BuE8+kCWok3X68lGwXRSsbX6TCIull+w7AYjYjRqP2udxIlyKeO4xNzQEbnyfMnc4iPUZLIh0IEZE+adIk3HDDDdi5s2vHxWnTpuGjjz7C22+/jfHjx+Orr77CV199hVGjfE+FGygo/NEnnae7k0gnCCI4xCYmYO6N16Bo3ukAgHoPPdIZbPaftZ05tHELzyZqsUfT46guPSBII+Xx6ancYbxs3363x32hcNZ0/n9PJlZYpkRbcwuMjU0AxAyKYEby/I1rKqh0n2aflbm9o98vtH2BmT5SJD0yidFqMGjsGADAgQ2b0NHWJi4PZk26XfDt/uVXAEDeuNGIjtCuRTKFnJ/rWhsaeUeFVB9T3tOHDAbgKBXqL9wj6ZTuDoRACzatVosPP/wQ119/PR5++OEu17399tuxbNkyPPvsswCARx99FHPnzsWtt96Km266yeNzoqOjESNxJ9fpxBkmmUzGU+JCFTbGno4zKioKcqX41dosll6/T1YnEq1WhfxnRYQvvd3PiYHB3BuvwfTLL+L3y/bt97qvMJGemJkBANj18yq+bnNNDbJGDkd8WmrQ9rVI3tel7u7ZhSMgVyhgMZtRfaQEgBhJ78n7LjzVIdJ78p3F2iP2bc0tsHR0oqOtDTEaDXJGj8TRLdt9fv1QxtUZPSEjjX8+bN9vrKoOyf28wW76mJyXHZHHwUBn+EmTRMPO0hNoqqhC6qBcAGIkPVjfN0t3L99/EI0VVUjITMfgiUU4uP73Pm871M7p0oylDmMbao8dR/rQwUgbko9DGzd3+VxdUiK08XGw2WyoLT3er++JtWGzmi2QKxVQ63Qh85n6m568r6CL9Jdffhnff/89VqxY0a1InzZtGp577jmnZcuXL8fChQu9PueBBx7AY4895rZ87NixMPajMUJvkMlkGD58OADw1kK+wFLdAaBwxEhY2nvXm1YhmdyYOHlyn+rbCcIbvd3PiYFB/kix3+7RtRtxaOVa6K1AUVGRx3VjJVk/puYWyGob+bpKqwAAKBg3Fp1HvafMB5JI3dej5DIn4ZhuN2YyNTYhN10UjZr4OIwfPx6CIHS7PV1aKr+4B4Dk7Eyv37lcqUTe1ImoLj4IY10DBo0bKy63CSgqKkL13gPInTwe17z4d6z4+7/Q2EUmRrgwbIRzS6WCcWNhPiaK3/wpkwEAVmOb188s0HS1n8vtfjkavR5TTp6GTns6NBEZTDpH9JFoOHQURUVFSMkSMyb0CfFB2x919gnEvMwsNBwuQUJmOk455yxo2npvrMwItXO6zp7B1NlmwrixYxFlP74KJ09E696DXT43baT4Poy19Rg9sjCwA3XBah9n1d79yCoajey83KDtL4FG24NU/qCK9EsvvRQTJkzA5MmTfVo/PT0d1dXONVbV1dVIT0/3+pynnnrKSdjrdDqUl5dj165dMBgMvRt4P8FmW3bs2NGjg19qkrF969Zei+somQyX2v8vPngAbc0tvdoOQXRFb/dzYmAw054V9OunX6D4t3VdrpsxcyqG2/9f98kX2LZlC38safI4DDttBkw2K3bs2BGg0XZNpO7rOnsNNCPK/j6rjx3H5nXrcSHE937g6JEuf0eUahVmXXUF72VcfaQEaUPyoYrTY+euXRBcPjOZQo6rnn8KhTNPQUttHV76w2JomxoBADVl5dixYweK/3wvrnv1OQwqGoOTb7kGT515Me8fHK7Ej3cu8ZPu04mTxEmKE4cOh+x+flZ9A3RJiShvqEf5/q6FAxFeTLx+EQBgx+q12LFjBzJNRpwBQJDJg7Y/XhQjTgzt2rYdjeYODJk5Dfr8HL+MJ9TO6bljxHNDa2MTduzYgajMVIw+dwHk+thu3+8pI4cCAI7v29/v39XRW+9Ccm4OskcOR1bRaLR2tAdtfwk0LKPbF4Im0rOzs/Hiiy9i7ty56Ohw75HnLzo7O9Hpwd7fZrOFxAHVHWycPRkr60sLOOrKe/nisHR2QhEdDUV0dFh8XkR40pv9nBgY6Ozts1pq67vdP0z2iVeb1YoNn37ptH6TpA1bMPezSNzXNfFiHThLVWQ0lFfC3NkJk6EVal0s1HF6tNrrxD0x4ax5OOOma/n9le98gEv/+iDkCgU08XEw1NU7rX/535bwVnv6lGRc++9ncXjzNgCi8ZHNZoOptRVv3vQX/HXNMsSnp0ETH8d7dYcrzFSp3WiESqtFXJpjn2b16U2V1SG7n9eXlUOXlIiErAycsHsWEJEB69Hd2tBkP/7EjNUYjToo+6NM7qjRbmtp4Snf2SMLoNbr/OLbEErndJXdFd3UYoDNZkPVkaMAgJRBud2OL9XeT73y8NF+fy9NVTVoqqpBYlYmAPEcFwqfZyDokZ4L4Di6ZOLEiUhLS8O2bdtgNpthNpsxa9Ys/PnPf4bZbPaYs19VVYW0NGeHwrS0NFRVVfXXsMMCh2lc31N5Ou2p8gpqw0YQRBBg7bNa7e7sXVFTchwAsHP5Ci7KGbxXOrm7+x3m7F5fVg6rxcKXN1ZUAgCMTU0AAG18fJfbSc4Re2fv+uVX/N/8C7Dlmx9gqLO78rs4vGcXFmDCmWfAarbgk0efRHN1LdKHDub+BdKIfXurEa0NYoRdlxT+Tu/Mrbr6yDEAojCPkskgVyi4YV8oOrszGsoqAABJ2ZlBHgnhb6TGjQDQaTeOC5a7u7Q9YXurEYb6BlQeOgIAGDJlYlDGFEjYJElbi/j51x4TfxN1SYndth9lZUrVdmEfDFh/dhW1YAMQRJG+YsUKjB49GkVFRfy2efNmfPjhhygqKvI407BhwwacfvrpTsvmzp2LDRs29NewwwJW89WX9msMs0mMxAfTmZMgiIGJWq+Dwn4+M/gg0veuWo1X/nQzPn70SbfHmqpIpPcVVawWQ6dMdOu5y8yKDPUNaK1v5MsbKsQJdGOjGK2S9lL3RFxqMgCgZPtONJSLAr+5phYAuPh0rCt+j2XFB/D7l9/ijRvvcIrKtrhE3ZlIj40kkX5UNOVLys7Cgz9+hru/+ABpg8UL7cYQ7JHOqLeL9MTsrCCPhPA3DpEuHvOsBZtMJnPyOeov2LFibu/gE4jHd+0FAKTZI8eRBOuRzvrTd5ra+Tk0MSujy+emDxWd3asOlwRwhF3D2qiqqAUbgCCmu7e2tmLv3r1Oy4xGI+rr6/ny9957D+Xl5XjwwQcBAC+++CJ+++033Hnnnfj+++9x2WWXYdKkSVi8eHG/jz+UUUSLkXRrH9qvMVj7jEhtV0EQROjCop6mFoNPmUGCIOCIFwdvdqGi0esRrVah09Q7Q82BzLn33I6TLjgH79x2D/b+upYv17Jsh4ZGRKtVfCKkp5F0lqrdUl3LlzVV1yB3TKFbJJ1FjNjFaNXho3jh0j9h6JSJyBs3Glu//dFpfZaJEQmRdLW9BVu1/WI6RqNGjEukMpQj6fVlosldUjeigQgvoqKiuIEki+SaJcbFMRo1LAEsb/UEa1doam3ly1iUnwn4SELtItIB0eUdAKK7CLaxSLvNakWNPfoeDNrt35Nrm8mBSkj72+fm5iIjw3ES37BhA6644gosXrwYO3fuxEUXXYSFCxe6if2Bjj96pDM6TOIsaHQE9ZglCCI8YFFPX6Lo3dFhbOOTjrqkpG7WJjzBIjHpQ4c4LWft14yNTbwfPQA0VvYwkm4X92xCBQBa7P/HpaXi2n8/i9s/ehsyhZz30TW5GMAe/n0rVrz5Hm/HxzDUs3T3hG7eZegTYxcXTVXV/H1WHSlxMsRjmSOhCEXSIxOVLpaXqpqaxeNSEAQeTXedSOqXMdmPlQ7J+YAJdnUECkHHJIlEpPtQcsCi6PVlFf0+kSKFnc+oT7pI0FuwSZk9e3aX9wHgs88+w2effdZfQwpL5PZIusXsh5r0IJ5cCYIY2LCop6Gh7yIdAEyGVsRoNBEZQekP2OfmWjKgtYv01sYm7uxus1q5L4CvkXR9ipjuLhXp7P9hUychd7TYFigpO8tjxKgr2ERPJKS7q3l00Igf/vUasgsL8PU/XsTp1/4Bp137RxibmtFpCt3WZqxXemJmBqJkMjfXfiI80dizW9qNRidvik6TCTEadVCCPZ4i6TylOgKFoMYlwwiAZJLE++efFgL16IBDpEu9BAYyISXSCf/AajitZks3a3YP+6EnkU4QRH/Dop7SOue+0N5qBNIckUiiZ7CLPG8i3djYxAVXc00tbBarfXn3kXRtfByU9prVZkk0ngl9JtABQBsX5zFi1BUs3Z0ZEYYzzFSpvbUV6z5yBC2Wv/I2lCoVyosPBGtoPtFcW8c7x8SnpfKMCyK0mXD2PChjYrDp8288Pu5qGsfoaGuDLikxKNeRan6seIikR6BIdy03ACTBti7S3VPyxH72NSWlARxd97B0d7lCQWVpIJEekfjT3Z3NwFG6O0EQ/Y0/090BR8ojmdL0Dh5Jd6kPl6a7s9+dxgqH8PIlkq63b9NQ3wCrxPS0WVKfztDEx0Ft7zXbbuhZJD0SatLZ/uua0m/p7MRXTz8fjCH1CMFmQ2NFFVIG5SIxO5NEehggU8hx6V8fgkKpxJ6Vq2H00EqRiXSW6s5gIrGrmui+kpqfh8yCYdix7Ben5TEejhVTS+RG0j1lGHXyslXv3lJs8rKltt7rOv1Bp6kdVosFcoUCqtjYAS/SQ7omnegdcj/WpDtOrmQcRxBE/6K31477S6RTe5e+wVIQXUW6NN1976o12L92I377z8f8cV8i6Sw6L61pB5xT3/nrxeu5i7HPkfSIasEmiot2SQpvuMHq0pOyqA1bOBCj0fIAUGKmZ8M/75H0wGdkXv3C0/jDM39D/vixTstZtFx6rLTzSLouYOMJFq7u7oAk2Kb2HmyTnsODDTO6o7I0EukRCXN3908kXTxYuqplIQiCCAQ8kl7nn9n9doqk95oomYz/DsQmJUKmkPPH+AVeQyMM9Q1486a/YM/K3/jjvkTS4+3O7q6ivMWDSNdI0t0HWk26UhUDuVJMgmw3GLtZO3RhDu+JOSTSwwGVpEbYtR0ig4v0FmeRHuhe6WlD8nk7NVdTSz6hJTlWTKwmPQJFYFfGcV1NkrBIurHRP6VlfSGSv5+eQiI9AvFnTboj3Z1q0gmC6F9Y1LPVX+nuNEPfa6QXeDKZDPpk0eQtKiqK90k3NjV7fC5Lje0ykp7q7uwOiOmP7KLfZq9318RLRLrBt2gyF+kJ8dzcLhxhE0w2my2kzeG6o4FF0snhPSyIcRLpaR7XUXuLpNtTlmO6iOT2hdGzZ/L/k3Kc9yd2rmdZVADQbhew7BwSSTgmL91r0ru6jmci3eAn/5e+0MEy3iLQfb+nhO8vFeEVRSDc3QNYS0QQBOGJ2CR24eCndHfuHEsivae4Zh+w9PSknCzIFWJk11OdKuAQ7xq93ikC77Q9LyIdACr2H4LNZsP+tRsAiCZzni5Gu8LY2ASbzQaZXM4nFcIRVkfb0WqEIAhBHk3vcfRKp0h6OOAk0jM8i3Rv6e6B7hI0avYM/r9XkS6ZzGPGcQqlEgq7WWWoMu2S87Fk5bdIHzak23VlCjk/TzvXpNsnSbxkxEZFRfHvzts5vD9h308kegb0FBLpEYhfa9LJ3Z0giCCh87NxHK9Jp0h6j3FtiROXlgqlKgZ/eOYJAMDRrTucDN+kmFoMjii4/WLQFT3rke7BKO7D+x/DS4uux/61G/k2WD2przXpNqsVbfbJgtikROhTknnWmTfUep3X8QYLTy2lwpEm+/esT00O8kgIX5AKvPj0NGjj43DXZ//Badf+gS9n7b88ubsDgfE20qckI2/sKH4/Kdt50seTf0Nnmwk2q9h5ItQd3sfOmQV9SjLGzpnV7brSGntphlF3n79ar+t2orU/YaUJVJZG7u4RCbm7EwQR7qhitbwll79S8Hh/XPrx7zGubeviUlNwwUN3I7uwAIb6Bnx4/2Nen2uzWmFqMUAbHwdtfLzHlno8km5vuSalpbYOLbV1PDVan5LMJ46ZU7MvGOobEJuYgBGnTMWZf74Ru35eiQ/uW+K2XsbwIZiz+E8YfdpMdLaZ8OyFizxOHnRFVFQUAPg92s0mmFjpRrjS6sVtPz4tFVMvWYjEzAzEaDUw1DWgZNtObP1uWTCGSdiRTmzGp6dixPRpyCwYBrlSiZVvvw+g+0h6IK4jR80So+gtdfXQJyd1ke7uOF4EQUC70QiNXg+1LtZvnieBgPl4ZI0c3u26DpM8I5+EAKSZDJ4/f5bq3tbS4tTfPlh0kMErhyLpEYhf+6QHcAaUIAjCG8zgq73VCEtHh1+26Yik06RjT1G5RNIzhw/FpHPPBAC8f/fDaKqq7vL5jrr0eADAnMVX45y7buNtmbpKd+fbsEfCpRfiPXE4Zw7vp1x2IeRKBcbMne2WIQAAVzy5BEXzTodCqYQmTo/5ty52W2dQ0Vjc983HGDZ1sttjUTIZbv/4Hdz+0dtcrPsLT+m74QjLjlFERzultZ577+2Yu/hPmHj2fIyePRPTLl6IK55agoTM9GANlYB7TXqGPf1aWtft1d09gBmZw6ZOAgBs/N9XsNlsUGm1XHQC3o+XcGnDpokXP9OsEd5Fui4pEUXzTkdq/iAA7sZ9Haaua9K5aVxDUx9H6x9YFoCaatJJpEci/nV373oGjiAIIhD4O9UdcPRJp5r0nuP6mY2dOxsymQyVh47gyJbt3T6fiXRdUiLUeh0W3HYDZl19BW7/6G1kFxbwOvGuRHpbsyjS9cliaz5TiwGCPY3eF1j0NjFLbCGlUCpRcPJJTutEyWRItTtFf/nUPwEAk849E4WnTsesq65Arj219owb/4TU/DwUzTvd7XUSMtKQUzgCOaNG8kkJf6GOkHR3S2cnvxhnx7pMLsfwaVMAACve+g8+++s/HPuN/TsngoP0GlCfnMQju9JyEHYMu4nEALq7s8m9igOHeBZOoiTl3Vt5CG/DFuJCkEXSEzLS3bw0FDEx+OM//w+PrvwWf3j2CSz6x18BuHe86M4TIJTarwEO7xh9SjKuev4pnHLZhUEeUfAgkR6B8Jp0L/WBPaG7GTiCIIhAoPNz+zVA2ic9tC/MQhHXSDqLUB3c8LtPz2+0R9oT0tOc+iynD8nHnz94C4BocNRVSzVX93hfnd0ZnsomCk+d7nQ/ISMNCqUS5o4OrPvoc+xY9gtkMhmu/fczOOfu23Ddv59FUnYWj6B7coiWOpb7W6SzsgM24RTOuKa8544uhFoXi7bmFvz40uvY8L8v0Vgp7jeR2NM6nJBG0mVyOfInjAMgTnSxbBje/ss13b0b47K+4DAXbUS9vWNAsiTTRuXleOHR2hD+LVCqYpyyWF1T3ieceQbGnXEaZDIZrGaLpATI+RzqyGTwku6eEDrt1wDHBErRgjkYO2cWTrvuj0EeUfAgkR6B+DOS3ulDf0WCIAh/E4hIenurvQUbRdJ7DIukN7nUjPsq0hvKKwEACVkZSLCL9KojJTiwfhPv+91VFB0A2pqcL/597ZHOkO5LTDiMnDHNqSVbcm4OAKD+RDkEQcAPL77GI4EdbSZoE+Lxp3/9HTK56FLvSaRLI3mxfo+k292bI0Cku/auHz5NnPg4tGkLz5BwRDzpmA0mrpN0SokrOoume69JD9x1pPR3ov6EvWNATjZOu/aPmHHlJVykux4vbL9ShXAbNm2cc+TcNeV94jnzAQA/vPgavn7mRb7c1Uyzu7JVPtHRECoi3dk4Tp+S7LUrSKRDIj0CYTXp/nB3p3R3giCCARM6PTXs6gpyd+89LJJWU1LKl1k6O3F06w6fnt9YIYr0xMwMXl9cdfgo3rzpTvz47zdgs1pRsn1nl9voaGtzyhBzTavtjlaJSP/9y2/R1tKC2MQE5I0dzZen5Ikive74CQBiq7B/nHcF/jZ3Ib566jkA4PW4gGeRLo3kaSX1sf5ApXN3qw5XmEcAE1osO0E68cMinqEspgYCXZUIaeL0iNFquEO4u7u7PSPTz618o9Uqfm3aKhHpk85dgLPuuAkL7/8LH5NbJL0l9Cd/XLNwpCI9ITMdQydPgM1mw5Zvf8CGT79ExcHDAACTt8/fW026/XVCpSbd9dwmk8kQl5ISpNEEFxLpEYjajz/inZTuThBEEMguHAEAqDhw0G/b7GjtfWuXYSdNQsEpU/02lnCDTWxIRfqxHbt5RLo7eCQ9M52L9MbySgg2G355fSkenbkAnz76ZLfbaZOkvPc83d0h0ovXbuAt3UbNOoUvZ5H02tIyvqypqhpNVdXY+t0yNFZWOW3TU7psoiTd3d+RdLbvtkdQJF2XlIgYjYZPljiLdDEqGMppyQOBrgI1mjg9j6Kb2zvcjD47uxGJvYVlYHSa2tHR1ob6MlGkJ+dkO61ns1p5NgyD7VeqEC6j0Ca4RNJHFvD/J54tRtEP/74VzdW1sFmt+O8Dj6N4zXps/Pxrp+fxYJva83cYajXpJoP7uS0+Iy0IIwk+JNIjEPZj1tMLGE+wg1uhVPIZSYIgiEASFRWFbPsFyYl9B/y2XRZJV6pienQ+kyuVuOalZ3DtS89ArQ+tvtn9BbtINzY2cXF1cMNmn5/PxG1CZjqvSZcKXlOLwad2ZdK69N6mu1vMZhzdsgMH128CAOQVjeHrJOeJF/gski7FarFgxZv/AeCYrNB42B+k7vP+Fukau3lUT997KNLK090TMGTyBMiVCtQdL+MTOoCkZ3KIG3xFOiyTxtUXAnAW6a5RdMBhHOfvdHfXkigWSQfEY5VdA7PzvhQ2yRXKkz8ae7p71ZESAEByXg7PRmAifeu3jtaElQcP462b78Lx3fuctsOCbXKlgntWSeHu7iFSk97h4ftKIJFORApsZrDdDz/i7OAGKJpOEET/kJSTBVWsFub2DtQcPea37Up7S3tqveWN+PQ0RKtVkCsVyBg+pPsnRCDS/twl23fB3N6B3St+9fn5zABMpdVyAySpGPMVqQjoqVAtLz6ATZ9/g++ffwWdJhNO7N0PAMgeWcDr0lN4JN1dpAPAhv99idcX3453//IAADH93LXNWpKkJt3f6e66ZFGYtNSGbm9nX2FGfrqkRAy2G5Ed2rTFaR2KpIcGLIOj6shRvoz14hZFumdndyBw6e5MpLPJnjqJSN/+w8/46bW3AQDGRveJhXDYr1iEu/pICZpraiGTyZA1YhhS8/OQmp8nnoN/+bXb7Uiv4z1NlDCR3hoiNenSAKPZnpURnz4wRTqFRiMQDXPY9INIt1msMHd0QBkTgxi1OiJm7wmCCG0cqe6H+IWgP2BpjzEaDVSxWo9RH09IZ/Ezhw/BUR9ajkUabFKj3WjEh/c/BnWstkemfpaODrTU1kGfkuwxku4r0kheT2vSBUHAp489xe/XlJSio82EGI0GKXk5qDtehsQsUWB7iqQzDm74nXu/yGQyxGg1jsicXucUXfd3JF2XJLYiM9RHgki3p7snJnIjvvJi5/IW7sJNNelBhR3/1UeOYcjE8QCAsuIDyB1dCE2cnn9Pns6pnd24i/cWlu5uaBD3I1NLC+rLyhGfloaV77yP2tLjUGk0fDJOSjhkaLCWa8amZhzbsRvjzjgNgyeN5+fAYzt3u6Xxe0J6HR+tVrt9RzzdPQRr0nf9vAoTz55PIp2IHFR+THcHxHoiZUwMRdIJgugXWKp7WbH/Ut0Z7a1Gu0j3/eJMKtLThw3MSDq7wO4wtsHS0QGDS92pLzRUVEKfkszvN1b0XKRLa9Lb+/gbZ7NaUXHgEPLHj0X2qBGwWqyQKxUwt3egpaauy+daOjthbu+AUhUDtV7HRbq0/Rrg/xZs+hS7SPdja8Jg0Spxd2d90KWRWkDiwh3CBl8DASbSqw6L34/VbEHpzj2iSNfrYdJ7F+ns2IhWqxCtVvnsY9EdnjqAvHbdbYjRalFtTxH/6bV3PD43LCLpXKQ3oerQEYw74zQMnTKRHze+mnYCjut410h6VFQUf51QqUlvqqxG8doNMNTVo3TnHkw8ez4SMtKDPaygQCI9AmH9RNlJqK+wtjPk8E4QRH/AIullfqxHZ7CU955c9MdLLhAyBqhIZ5+Xp/pOX2ksr8SgcWL9t7Gp2acokCttzdJIet9/48r27RdFeuEIPgFQd6LMp/r4tpYWxKlSoNHr+YQDq0e3Wa2QyeU8ldQfqHSxvPVVS53/WhMGCyau4lKTeWYCE1cM7sIdwgZfAwEm0kt37samL75F7bFSyOSihOgukm5qMfAsmvShg91qpnsLF+mSY8HXEhre5isMRHpbUwsvA8kvGssziHoi0jtM4nV8tMt1vFqv4/4sxhBJdxcEAW/ddCcAcLPW+PTUYA4paFBNeoQRJZM5jOP8lJpODu8EQfQnWYVizXJ5gCLpQNcthVxJkKTapQ8d7FaDPBCQRtJ7izS9vTdRdMC1Jr1n6e6eYBNBOYUjJM7u3lPdpfD2YJILfRZJZxFHf4p0vT3abGoxuDlohyNMpDOBbqhvcBN5vE96CIupgQA7/ttbjfh0yZNYtfRD/l2p4/TQJsYD8CzSAaD8wCEAQKZLr28A0CUnYfoVF/U4EMRrqXtQdsNg18ehvF+xLBxjYxNqSkrRUlsHpSoGcakpYibDrj0+b6uTt1N2vo5nn6GpxQCrxeKfgfuRpirRy4Tc3YmIQJrC2ddUQAb1SieIyCA2MQHnP3gXj1SHIonZmdDo9bB0dnKh40960ytdOouv0mp5C7GBhNQ4rrc0lDuEeUNFz03jAMDYJBHpfviNK9sn1qtmjRyO1Pw8AF3Xo0thF/oavQ4Zw4di5h8vQ6bdWLB0117xsTi93yZ1WEp4SwSkugNiAIBdXwDweLw7WmWFrpiKVApOmYrLn3wUqlgtF3fS459FdDVxeofPhJfjumK/KNKzCoa5PTbn+qtw/gN3Ydol5/dofGzSqifeGAxeRhHC+xUz4zM2NQEAjmzexh87sa8Y5nbfJ+q8mfeFWvs1V5hI1+j1A1KDkEiPMNR68YTT0Wby26wY73GpVvllewRBBIcLH74H0y+/CPNvWxzsoXglc7h4EVd5+GhAZvbbe9ErnaW722w2AEDG8KF+H1eoIzWO6y1SYd4b0zjAOd3dH9liUvO4yeedBUA0x/IFHo3T63HBg3fhvHtux/gzzwAAntIrVyj8ZnrG6vkjoR6dIXWUrvbQyYEbx4WwmIpUTr/uj5h0zgKMP/MMbuwnLVFhUXNNnJ53NGC9yl2p2C8aAmaOcBfpcWniJCjr+uArsR5q0n2FZ8Fotfy9hRqsTzqbmDz0+1b+WE9S3YEuIumSaH0o0mFs45NBAzHlnUR6hMF+yPwVRQeADlNgelwSBNF/FJwyFWPnzgYQ2nXVrM6wyd6yy9908Eh6z1qwAcBxe3TU18/v8icfxa3vveaxN204ESWT+SfdXSrSe5nu3hd3d08w8zhAnIg+tmM3dv60wqfnSs2nknOznR6rKSnlIt5fKe+8/VokiXSJwPIUSWfXMjEaDWSK0BRTkQo7F7Pznc1qdTJ9YxNm2rg4JHKRXuFxWyzdPWPYUN7ukMF6rKcPyXd7XldZKJ6M43xFeo3ck9Kn/sQ1kn74d0ck/eiWHT3alrey1dhEexu7EOmR7ommqhoAzt4wAwUS6REGa/3ij4sXBo+kD8BUE4KIBORKJc5/4E5+Pz4tNWRbGrELNl/bo/UUXpPuY7q7Jk7PJyiL124A4JtIV+v1mHTOAuRPGIecUSN7OdrgoktOwmVPPILhUyfzZezz6w0NFdKa9N6lu0vd3ZmpWF9hUakjW7fjjRvu8DmNlIlwXVIij+rt/GklDm74HSf2FvMUUn85vOuTIsfZncHaZwGeI+nS/U0dwu2yIhE2uZQ2eBAA9wk6do7WpSRBpdXCZrN5nXyrO15mz1hRu01osd+i1Pw8p6j2rf95HXf+7z2PkW5FTAwvwelNTbrVYuETDmpd6Il0pcrhxM6i3PUnynBky3bUl1X0OJLe4aUNHusW0VIbuucUNmE/ECPp5O4eYagCEUn3kiZDEER4MGRSEVLycmCob4AgCNAnJyF9SD5Ktu8K9tDc4CJdIsb8Ca9J9zF6wlq/tNTW4cSeYgC+tWHLLizg/2eNGIZjO0Lvs+6OiWfPx+TzzuTvxWI2w2o293p7lo4ONFZWISEjHbXHjvdqG42VVTC1GNDa2NSnsUj55Y13cWzHbhzcuLlHhmwsZTZ92GDIZDKY2zvwn7se4o8bG5uQkpfjt17pughqv8aQRkFdnd0BMXrbbjRCpdVCFRvrlElBBA6ZQs7PxWn2CLdrNwYm0mX2yHhLTS0snZ0etyfYbKg8dBiDxo1BVsEwp+OfvY4iOhpJOVmoPXYc+pRk5I8fCwCIS01xK4/RJYkTCOaOjl5PHJoMBkSrVfbOAb2bNAwUWnsU3Wq2OE2OvPKnmxEVFeVT9wkp7LtzjaTHpaYAAJpravsy3IDCvvuB2IaNIukRhqP9mv9EeqeJRDoATD7vTNz56XtIzMoI9lAIokfoU8Qf4vLig9wxPX1oaKa8a+L9nw0kpd3A0t19i8oxV9mmqhrUlBwDACTnZHXxDJFsSX1lpgezpHCA1ZmyzIGOPkTRGR/etwQfP/KEx6ipL3Sa2vH3cy/Di1dc2+exOLZpwr7f1vbYMZ3to8yjoKm6xulxlkKq9VO6OzPKCuWoV09prRc/I0N9g9e6WCbCmOcOEXiYSAQcaeXtLpH0DmObk2+It1R3BjOPc3V4ZxmggCPlPTkvx/H69jIPKX1JdWdwf5IemIj2F5p451R3KT0V6ICkJt3FW0qfJl4btFSHrkjn6e7pA8/hnUR6hMFq0v15gdtB6e4AgJMvvRBZI4djzOmzulwv3OtPiciDt6ppaETVIbHuM33Y4GAOySsBT3fvobt7gl2kN1ZWcXGkiI7m4/RG1khHJD1cRXpiVqbTfdeL9N5Qsn0XNn/1fZ+2Yahv8FuL0b7AxhBvN75y9VEwNjQBgP8i6dzNOnJEOssK6GrSxtEuKzRLdCIRTxNLnvwopOdpb6ZxDEcbNsf5UBEd7WRKnDZU/F1KkYr0JO8inU3y9AbmKeFp+8GGt1/zU+aIt+v4OLsZZXNtnV9eJxA0VYmR9IGY7k4iPcJQx4k/Yv5Md+9kaTID3N09bcgg8a+9PssTOaML8eSmFTjjJv9FeQiir/ALmoZGVB46AkDs9x2KcF+NAIn0jh66u8eniyl2TVXVsJrNPNrHnLa9IW1zlzFsSMg6CHeFa9ZQRx+c3SMR15r4pmpnke73mvQIjKTvXvEb9v66Fqveed/rOux6JhQjnpGKp4kl13R3wLnDQneR9PJ9YhZXftFY3grM1RuFRdJT8nL5MjY55TQ+P0TSWQeGCWfNA9C1SV1/o+WRdP+IdG8ZseGR7i6eVyndnQh7ApHuTn3SxTQb9v5T7SL97L/cgiuffszpxD7hzDOgUCoxZNL4YAyTIDziiKQ3cAfljFAV6f1mHOfb+cwRSRcvFJizdlciXRWr5ZEgc0cHlKoYp8hQOBAVFeXWD74vzu6RiMklY62x0lWki1E+f7i7yxUKLvYjqSa9pbYO79x2D/av3eh1HVMra8MWHpH04dOmOE3ShSMeRXo3kfSGbiLpZfv2o7b0BFSxWow/cy4AuGUkpfNIusNcLrbLSHrvRfr6T74AAIw89RRkFxbg9o/fwe0fve3mPh8MuEj3U2u0Tg990hXR0fyc0hzS6e4O47hQmkjpD4K/JxJ+haW7+zMV0JEmM3Br0tOHOlqDpA0eBG18HGZfswgTzpqHlEGOGd9hUycBcJ8dJohgwkSCob4R1SWlsFmt0CbEh2SaX7+lu/saSWc16XbzmhZ7xKErkZ5lr7lsKK9EmT165FqHGerEJiVCGRPjtKwvPdIjkTaX31l2McnwZ7o7O1atZkvAjo1QhUfSw6AmPW3wIFz/2vO49uVngz2UPuEp3d2TQZtzunvXkXRBELDxf18BAE6+5AIAgMZ+rcSc1lMG5UKmkCNZEknXe4iks0i76zHXE2qPHceBdRshk8lw89JXkVM4ArmjC7njeTDxdyTdU7CNvU9zR4fbhGMo0VxTC5vNJk4qJMYHezj9Con0CIOJQ3+KdJbuHqMeuCI9bbBDpGvi9CicNZ3fZyk4uuQkbrDUXb0qQfQnjnT3Blg6OlB3vAyAby7l/U1/RdJ9SZ2NksmQOigPANBgbxnGave6FOl2N/Syfft5D+6sMKtLT7LXo5slZmoUSXfGtayMGRwxWhvsxnH+EOmSevTeGEeFMywzMBxasI0+/VTIZDLok5PCerLe13T3ntSkA8DvX30Hc0cHsgsLkDO6kJ/vq44cRUdbGxRKJVIH5TmZc3rKRMmfIDq/H9u5p9vX7Io1//0MgHMaeGyCf4we+0KG/feixU+14h0eylbDIdUdAGwWK/8cEtIHVso7ifQIw5Hu7keRbp/hHMjp7q71u5MXnsX/ZymhLIoOUCSdCC2kxnEAHCnvISbSo9UqKKKjAQSwJt3ou7t7zuiR0MTp0dbSgsqDYi0/qwfuKtqSbTeNKys+gPL9BwE4myWFAwn2evTju/dxI1IS6c64GrQ2ubSJ8me6O+9nHEGp7r7CRDprMRvKjD5tJv8/nI2uPE0sdZXu3tFm8snEra25BTuXrwQATLvoPKiZB0lTC6oOiy34xs6dzX8HAPdIuj41BUnZWbBZrSjd1TeRvn/tBlQdPgqbzcYzhbQJcd08K7Co9XoUnnoKANGzwR94uo4PF5EOSFLeMwaWwzuJ9AiDp7sHoCY9GOnuMrkcY+fOxqK/P47CU6d3/4QAwcziLPa+vEMmOmrOEzLFi9nhUyfzZSqtFjJF+BlFEZEJF+n2i6iKg4cBOPfyDgWYaZyls5Mb3fgbQ30DzB0diFarUHDK1C7XHTl9GgDg4IbNsFmtAByRja4i6awlV8X+Q462Q2EWSWemcfVl5ThhN1iidHdnOoxtfL8A3CPphnqHSO9rLSWPpEeQaZyvtBvCw909Li0FuaML+f34MI76sd8MqYDzKNLtE1UN5V2nukvZ9bMo0rNGFjgyp1pasH/tBgDAzEWXOq3vahw32N4/vfzAoT5PHAo2G1750834+7mX4cSeYgCANsiR9PEL5kChVKK8+CCq7EavfaXTQ5/0cGi/xmCdMwZaGzYS6RFGICLpLE2mv/ukR6tVuOvz93HVc09i/JlnYMGfb+jX15eSZnccPbjhd7fHElkk/aRJTsulvT8JIlCo9ToUnHyS10khtV4HuVIBADDYI+mlO3cDAAYVje2fQfqIOsCp7gBgbu/Auo8+BwCcdcdNXYqnETNEkc4uHgGHSI9LTUGMRoMzbrzGyZcCcETQGsoreGspXVIiYrThk42UaJ98bCivxN7f1gEAyosPBnNIIQmbEDe1GNzSgVsbGmCz2ZxM33oLmwxuiaD2a77C0911oe3uPmrWDKf7kRBJlx7zHW3uk3TMxLC29ITP264vF0uHEjLTncqbtn67DIAjE5F1IolNchbN+ROLAAAl23b6/JpdYWxqRl3pCW7S5q+WiYOKxuLBHz93yq7whUnnLAAAbPn2R7+MA5DWpDuu4+NS7JH0EG6/xnA4vJNIJ8KYwNSku7tC9gdZIwuQPiQfVrMFAJCckx0UZ8f4tFSoYrWwmi3Y++tat8cTMtKRMigX8elpsHR28pMhpbwTgSQqKgrTLjkfD3z3KRa//gKmnH+Ox/VYPbqpxQCrPROkdNde2KxWJGVndttKrD8JdD06Y8Vb78FkaEXWiOHcZdiV2MQEHhWTOk8zka5LTsKUC87BvFuud2q5qFTF8Am65ppadJpMPNoUTlGAxGyxJr2hvBLrPvoMS2adia3fLQvyqEIP9lvb6MHAymax8gt/XXIioqKikDNqpFMqryc8/c4VzTsdAHBs++4+jjj84MZxIR5JZ2KMpRaH0/HuChOqZcUH+DJPUesdy1dg5Tvv46dX3/J52412fw9tfByfyDC1GFBfVo6jW3fw9dj/Kq3WqZZ68IRxAPwn0hnMpM1f5mRj5pyKpOxMjDvjNJ+fkzIoF3njRsNqsWDbD8v9Mg5AkhEruY6PS7X3SA+ndPcwPqZ6A4n0CEKuVPITmV/T3U3uaTL9QVK2aBxSsmMXrGYLotUq6FP7X1CwKHrt8RPcBApwXJwlZGUgd8woAGL9ZmuD2BKERDoRSCafdxYueuReHvFI8PLj5VqPDogXWyxKMahoDC546G489uv3XJgFi/4S6W3NLVj1zgcAgNnX/MHjOgUnnwQAKNt3wKnlVUsNi6QnI2+seNzHpzkiZqzOr6OtjZvUsTRo6XqhjiOSLqax+lJvOhBhWWveXKbZvqNPTkbRgrm44+N3MP+W671uT65U4p6v/ovrX32eL8sfPxbJudloNxp5qvBAwhFJD92adJlcjiGTJwAAtv/4M4CeRdIV0dHdTt70J45IukOke3J3bze04vvnX+GeHb7QYWzjE5esDIid87d88wNfr3z/QS4uWRs2lS6WG576W6Rzo8f4eL9sj51D47yc97UJ8dzFncHKJg//vtWv51xWPha26e4k0olwR/oD5ulk2ltYJF0mkznNZgaaJLtgqC09zi8Uk3P7v9dwmr39WvWREtTYU1cBYM+q1QDE2tRB40YDAE7s28/b8pDDOxFIUvKdU6y9pVI72q8595M9tkOMyE04ax6mXXI+dEmJmH75RR63kTYkv18yaaT1iYFm05ffABBNIT2d1zylugMO4y5FdDQvcZG2suNmPJILH3aBERcm6a9RMhk36Gm0p6YSnmGTtU2VXYt0XXISNxRMHzbY47oAkJybjbTBgzBi+lRewjL5PNGodNdPq3iUdiDBJkJ8MXsMFvqUZCiUSljMZhz+fQsA3wWFXKnEPV9+iDs+fickenRHRUVx8eic7u4/40h2vLAgCBPpO39aCXO72FGipqSUBz0S0tNw3n134IY3XoRMJkNt6Qm337S+4u9090R7h4w4uxiWEq1W487/vYe7PnsfcoWCL2f7uLfzSW+RXscz8zj+WxUW6e6iKSeluxNhi0rSI12w2fy23U5TO2z27fWnw3uSvQVH/Yly1J0QW0Yl52b32+szRs44GQBQdegI2luN/OJ7769rYW7vgEwm47VoZfv284s2DUXSiQDCZvu587GXlmKeIukAcGzHLgDAGHvLIACYdO6ZbtGcETOm4d6v/osLHrrLb2P3Brsw7I8+0K31jWiuqYVMJuNGb1JYR4cS++fEsJrNjos5+2cbKxHpevuFj7R1Do+kh0kUIE4iOMLhAi6YsH210ctFNZvU0ack8U4guiTvnQGkEz7a+HhEq1UYN19Mdd/89fd+GXO4wYIO6hDuk86O7ebqWjRWdC8ockaNxKl/vBxRUVFIGzwIybnZyBg2hJvUBhO1XgeZXJwgaq6t5enQphb/ZWiyz0ihVNq3LR5H7a1GfLLkSaxa+iGObd8FQ50oxE+5/CLMXHQpL0Ha95t76WFfabWf1/2V7s7MNz1lUBXNOx3xaamIS0uBRhJNZ5FulkHgLzra2vhk8WB7TT+vSQ+LdHfxN1Sfkgy5fZ8ZCJBIjyCYKPRnqjujw/4jGeNDb2F/wdLd68squClJSj9H0rMLCzB86mRYLRZs/lpMw/r2ny9hw/++wr5f1/LZPTZTWr7vAL9oo0g6EUhYmxiWZeJtAs3RI91VpDvXtlo6O6GNj8OY0091Wj79iosBAHljR/d90N3Aarn7Q6QD4OUrnpzXWfmAp0iyq3BV62L55IantjaOVL3wiKQn2idImyqr/TrhG4ms/+QL7PttHbb/+JPHx3kkPSkJCRl2kZ6c6HFdcT3HY7GJ8RgxfRpUWi3qTpQ51esOJNjEtyo2Nii+NL4Qb78GaKqu5oIiLi3V43ijoqJw1fNP4tx7/owRM07m0WQAvIQmmLDJx7aWFtgsVnz33L+x9qPPnOrT+4qrh4P0nL/9h5/w3XP/hiAIPFo+apbY3Wf7jz/jX4uux7f//LffxsJgk6/+SHdXxWr5NaAiOtrNOPKkC891Wpf/b8+IY2Wm/mTPqjUARO8EtV4PpSoGgKOEK5QxNjbxDAtPmQmRSlBF+o033oidO3eiubkZzc3NWL9+PebPn+91/auuugqCIDjdTAFq0xOOBMLZncFa76i0/SfSWX1s/Yky1B23R9Lz+leks3rV7T/+zAX59h9/xmd//TusFgs3QAHEmcqaY8f5BYWa3N2JAMIuJBrsIrK7dHdXkd5QXsmjvXUnyrDSXqN96lWXY8aVlyB3TCHi09N4bTYTGIGkv2rSGSyVM2vkcKflqlgt95TwFCE1eIguM3HlWaSHV036kElii8lKP7X/iWSObNmOt2+9m0cGXWmpdY+kxyYmOKU1j18wFxcvuR8yhdwpKyM2IYGnzLpOqg0kWAmZTCYL2V7pPJJeVYPm2lrYbDYolEp+/pUyZPIEfj7NHT0SaUMG8ceYv00wYYLS2CgaqW37/id8+eQ//Tph53q8eDvnGyTlRQCw6YtvUbpzT0AmD1v9mO7OjnWG9NyfNiQfg4rG8PvSCXYWSe/0cyQdAPasFEs0R82ewSeMjY1NsHR2+v21AgGb7PbmvxOJBFWkl5WV4f7778fEiRMxadIkrFy5El9//TUKCwu9Pqe5uRnp6en8lpeX148jDm0C0SOdwdLNvKXU+ptotRp6e2/M+rIKh0jvx3T35NxsjJ0zCwCwaumHHtdpkPzQVOw/BMFm42lbZBxHBBKWGs4j6T2sSQeAA+vFloJrPvgUmz7/BjabDTmjRmLh/X/BbR+8icuffJSnwitVMW6tcPxNf7Rgk1Juj6RnjXAW6eyC29jU7LFfu6cUcCau2Cy/55r08Li4KDz1FADAPg/dLIiewURGYlYmn8iRKxROhlHn3vNnTL3oPAyZON453T0hnkfdWdrvQMRqNvNrEFejrVCB+U00VdfAZrHyCVBPJS6Tzj2T/581YjjSBjsi6bmhEEnnIr0pYK/Bgh6MNi8diaSmneb2Dr+bxUkx2ieyNfFxffYGYKZxDKl5nDSKDjhfVzPB7u90dwA4smUbTC0G6JISMfeGPwEIj1R3Bsu+iLdPcOWMGok5i68OCR+HQKHofpXA8d133zndf/jhh3HTTTdh6tSp2Ldvn8fnCIKA6mr/GipECixy2x6ASDprvdFfIp1F0duaW9BuaEWdPd2dtWETBCGgr6+IicGVf38cMrkc+35bhyovESXpbPCJffv5mAHqk04EFhbt6C7d3VskHQC+/seL2LH8F+xfI5qjffvsSxhxyklQqlQYPLEIQ+1uxYzEzIyAunwHK5KeMWwIZHI5bFYrAHDTNG/mPdL0QJvNBplMFjGRdH1qCnJHF8Jms2HfmnXBHk7Yw2rSMwucfQ90yYlobWiELjmJt0GMz0hzF+msXMXPJlnhhrGpCapYLbQJ8XzSPpRgxzY71puqqhGflor49FSc2FvM14tWqzF27ix+P3PEMJ7GCwDpdpNOT5OD/mD2NYswevZMtNTVY8/K1djqoRe3lv1mNAbuXO8q0r21DTZIfm+Obt0e0KivsVnMHJDJZNDodbwlW29IcBPpjhRt1pLNYjZDoVQ6XVfzmnQP7e76is1ixb7V6zDx7Pl8DBs/+9rvrxMo2O8xywK47ImHkT50MGpLT2Dn8hXBHFrACKpIlyKTyXDxxRdDq9Viw4YNXteLjY3FsWPHIJPJsG3bNjz44INeBT0AREdHIyYmht/X2VPCZTIZjxCFKmyMvo6T17K0mfz+3tpb7S1QYmP75XNLkZjGyWQyNFfXwGq2QKmKQXx6GpqrawL6+pc8dj9yRxfC2NSMr/7+vNf3LP2hqSg+CJlMxjMZNHH6kN/HQoGe7ueE2O6Hp2PbJ4pUWq3Hz5BF4oyNTW6Pd7S24uC6TXz52g8/xdoPP0VUVBQu/dtDmHjOArS1tKD+eDlyRo9EUnYWyvbuD9j70tpFenuLoV/2h6aKSrS3GqGK1SJtyCBUHy4B4IiCNFVVexyHNCvhxO59yBs3GvrkJMhkMi7SDbX1/LksPT5arYJK1z/n0N4y2l77eWL3PrQ1Nof0WMMBNjnmasgYl5KC6sMlyCkcwZclZKQ7iXRdYgLPKGttaAib7yIQ53RjUzOSsrMQm5gQkp8Dm9hrqa4Rr1mqaoBxoliTjnfs3FmI0WjQWFGFhMx0JGSk88lBdi7KHVOIo1u2+32MUVFRmH/L9XxfLDz1FOxc/gtsFqvTejq7SDcG8PhvrnJcw3WaTLBZLB5fi7m7A8DBjZsD+93bBLS1tECj10OXlOh14kCKt309yaWdaUJ6mij+4+P4hE7ZnmIMGj8WqljHbzebbDeb2gPyXveuWoOJZ4tlxes//hwbPv0yJI8nT7CARPbIAiRmZXBz1+yRBdj986pgDq1H9OTzDrpIHz16NDZs2ACVSoXW1lacf/75KC4u9rjugQMHcM0112DXrl2Ii4vD3XffjfXr12PUqFEoLy/3+JwHHngAjz32mNvysWPHwmj0X5uyQCCTyTB8uJiGafOh/iZnkJj6r4+NRVFRkV/HolaKJ/UhIwpgLfNce+dPRpw0BQBgM7bx92Ksq4c+Iw1TT5uFaklbEH+TMmwIJp49HzarFRteexd5KWnIS/GcppoS60hpjxWiUFRUhLRE8aIqJTODj12uVEIVp4dRkrpFiPR0PyeAGInQS9WJ6Z8avc7jcR+XLEbpslPTENeD88KBL36A0NyKxuNlGGpPfx41cTyiqgO3D8fZBUl2ega0fj6HeaOlvBKqgqGYdsYcHIvdDAAoGDcWAKCwWD1+polasbSotbYOlmbxQm7Y6EJ0HivjIj07JRWJRQ4X2vYWA1R6HUaOL0KnsS1k9/Wp54ipuI0Hj/j9d2QgolB5bls6akIRNKZOjJo9ky8bMmokEuwT1ACQN2woUuw16alxCWHzfQTinC63itlzI8eOQUxz6F27pdiNblPjEqAsKkK0/W0PHzMarUWO65XpF4ipzsc3boH85CnQp6dCJpejs82E6n0HkDdlAqbOmwO9xf/Zgqo4PRTR0RBsNkTJZFAolZg8dSo3BmYMGi4aacZGRwdun4uKgtVshlyphMXU7vV1khOT+f+yxpaAHwOWNhOg16NoymTUxHVf3uVtXx80Upx8M9Y3QpuUgPyRBeL14QjxszXU1CLKbAEADC0ogK3MHiW2T9JlZ2RAHoD3Km82omrvARjrG3Bs+a9hc04BAKFWnLApnHkyOmsc1yEFkyagMozeh7YH3l5BF+kHDhxAUVER4uLicNFFF+G9997Dqaee6lGob9y4ERs3buT3169fj+LiYtxwww149NFHPW7/qaeewnPPPcfv63Q6lJeXY9euXTAEIC3cn7CL8B07dvj0Q5c+QzR4qiqvwI4dO/w6lqFl5cg7aSLqGhv9vm1PDFogpuIc3r2Xv96EQ0egz0hDU2d7QMcwKVe8KDq0aQuWf/Rpl+tqS4/h9PtuR0dbG9Ys+wk2qxWGaBlmArDJZXycf3j2CYydOxvPX3I1d5QmRHq6nxNAar44IdfW3ILtmzfjbIglGq7HhVypxJX2DJst6zb02FRy+7ZtAICo5ATknzIFJggBPfYutvdi37F5s1cjLn+Tt20HUguGwqJ2fH4jLxEvpA9Jzj9Sig8eRNLoAuxe8RvSBudjyMxpaO3swKHSEsgUcthsNmxcvdopQnVqWTmyC0eg1tASsvu6Uq3CJSPFi8hfPvofqo+UBHlEkcH5JhOi7fs2o9HYih07dmDMoov4MqtSAbnGsZ7JaoHCfvzu+H0zqg4f7Z8B95FAnNMLSo8ja9woNNiPn1BCrlDgcntm0+9r1sDY0ITYwmEYueB0dMrgNN6z7ZMwa7/5HojVYFy6eK1TeegIdq9Zj7wpE6BMTgjIe8wZPRKAWIqjio2FKlaLw6XHUH/COcg1855bAQB7N28N6Gc9r7IaybnZaK6r9/o6mpKjOPXOm9BcXYNfv/0hYGNhnFJVA316GirqarHHh/fubV8/VSsexwd/34LxC+bCqlRgx44dOKVQLHsp3bMPbS0G5GECahsb+Ps/w/784j17AmYWufWKzQHZbsDZsQMnLf4jErMyUXj2GXxxbEZqyJ0TuoJldPtC0EW62WzGkSNive+2bdswefJk3H777bjxxhu7fa7FYsH27dsxdKh7j1tGZ2cnOj3UsNhstpC8SHKFjdOXsSpixGi3ub3d7+/NZE93j4nV9MvnJnV2Z69XW3oCIwEk5WQHdAysLtZQ39Dt6xjqG/D64j+jvdUIi9kMAGhrshvH6WJhs9kQrVZxI6aU/Dy/tjGJFHqynxMOgzVjYxMvr5ArFZApFE41e2n2bghWs4XX2/WG+jIxzSwhMz0g39HwaZMRJZMjWi1GHVsbm/ptXzixR5wQnnbx+ag8dASbv/qem0A1VlR6HIeptRVLb78fADBj0aUAxNp/vT1robWhEZZOs9NzmqqqkV04AuqEuJDd15NzsqGMiYGhvoGc3f1IS109knNE01NWhxqblAibzYbMEY72f/HpaU5u4PrkJH6/ubYuJPcZb/j7nM7qozVxcSH3OcQlJ0Imk8Hc0cEN/lgpXMbwoYiSyWC1WKBPSUZcagpsVivK9u1HefFBXhtcdfgoju3aAwDIGlkQkPfIzMsaK6sRny5AFatFjFbr9Fr61BTkjB4Jm82Gvb+tDehn3VhRheTcbBibW7y+TmtjE545/0qYOzr65Xtn+1lCRjouf/JRFK9Zj23fe26vyPC0r7OSqZJtOzF+wVzxe7fZkDF0CACg4uAR3i0pWuO4rmYGsKZWY8jt56HAzuUrMfuaRU4tjuNSU6Duo4dAf9KT7zXkChFkMplTDXl3644ZMwaVle59bAciSvvnJjUh8RcsHaq/WrAl2VP8mDgAxDZRQOAd3jV291gmtrvjyJbtKN/vSGdz7ZM+ZNIEXgPWX8Z7RGTD2q8Zm5qdWrVIHd5lcjkufvQ+AMD+dRvRF9gFp6sZjj/QJsTjupefw+LXngcAWC2WgJjmeGP7jz+jeO0GRKtVuOxvD+PkSy9AQrroHuvay9cTzNArNikReg+mcQxmKKUNsEN+X2DnLE8mg0TvMdQ6UjNZJpUuKRGqWC0X74D426ZQOkok0oaKrt9WiwVtYXIBGihYOzDXftOhAG+/JunoULJ9F0wtBqTm5+Hce/4MwBHJrjpSgk5TO8r3Oybsq46WoKakFIAoEBVdXAenDx3M+4b3ZpxNVdV8cte1Cw3bbunOPQE1CQWAxirxd6W72u+mquqAOs1LMTaIrzPzj5dhwlnzcOHD93rtnOINaY/0ku27ADgmSNKHiSK98tARdLS5GzLHBLAFWySw86eV/P/WhkauETKGD8WfXnwa59x1W8C70PQnQRXpTz75JGbMmIG8vDyMHj0aTz75JGbNmoUPPxTbXb333nt48skn+fqPPPII5s6di/z8fIwfPx4ffPAB8vLy8NZbbwXrLYQUXKR3+F+kt/eju3uUTIbELFEMSNOwGlg0L8D9mlmLF2NTU6+e32ZvwaaMiYEiJgYFp5zEH1OHaI9XIrzQJtj30cYmCILAf+ylFxOzr1mEvHGjYWox4PMn/tGn12O92ANx7OWOGQW50pHU1V/O7gyr2Yy3b74Lv777XwDAyZdewJ14PfVId4WZyOmSEvnzWqo9iXRxWxoPfZNDBXbB7othEuE7LRIvkuO7RaNbXVISMu2t/9jjcoVzciPrENLa0BjwjiahDvs9DsUWbNzZXWJo29rQiA8feBwAMP2KizF+wVwu0ln2TrnEW6f6yDGnzKikLO8Toov+8Vdc89IzPW7XJu1awUqfXK9JxpwmeiTsXbW6R9vuDUxghdKkINvP2HeqitViyvnn9GgbbDK7taERNceOAxDFtyZOz83OKg8e5m0FmVlcVFSUpAVb/01UhxMn9hbz65GDG37nAbJJ556J0aedihlXXgKZPOhJ4n4jqCI9NTUV//nPf3DgwAGsWLECkydPxrx58/DLL78AAHJzc5GR4ThRJSQk4M0330RxcTF++OEH6PV6nHzyyV6N5gYaSlUARTo7mfRDJD0uNQWK6GhYzRanH72GCvHATOzix8sfaLhI713kosPYBqtFNATR6HUoONkh0lWxJNL7G11SImZfs6jHs+GhjDSSDjjatbAfeJlcjtOvuwoA8OVTzzlFeHpDU1U1bDYbYjRqv0eycscUAhAjC52mdpTuDEwdXlcIgoAVb70Hq8WCjGFDIFcoYDGbuSt7VziJdBZJ9/A8FkkPZZHOW+C19O9ESaQj7fV8fNdeAGLXhSy7SC/duccpUtjuYuQ1kHukM0I7ks7arzlP6hWvXoefX18KAFjw5xuRN3Y0AIdIN9Q3oLz4IEyGVpTZW7iywERSjueMQZlCjrTBgwAAhTNP6dE4E+yR9MaqarS32CPpEpGuitViyJSJAIA9q9b0aNu9YdPn32D1+59g9fsfB/y1fKXVHkmXMuPKi3vUi5tdozZUVMLS0cGP7cETxyNGo4a5vQN1x8vQbjevZsEvpcRkkkS6d1a//zFsVis2ffEtKg8eBgBMPk80PN3580q0hFHv9+7webrhn//8p88bveuuu3xa77rrruvy8dmzZzvdv/POO3HnnXf6PI6BRiDT3V1PJoGEta5oqKiEIKndYEZSmjg9VLFatwsZf8GjlH1ILzS1GBCbmICskQXc5AugSHowuPz/HkHBKVOhT07G1/94IdjD8QvSSDpgF+kpjjaM+uQkxGjUsJot2Pb98j6/ntVshqG2HnFpKUjMzPBr6mHuaFGkr/voc2z97keYOwLXB7cr2ppbcHTrDgw7aRIAoLm6xqfoJUt318Tp+cWZp3R3FpXXSlpshRr93ad+oMAi5RazGeUs3T05CdkjCwAA5fsPIjEzgwvQ6iMlyBs3mj/fUE9dQYz2WuGQjKTzNHL31rAr3noPJ196AZKyM/m1jbRv+qvX3QZlTDQ/p9adKEN2YYHXsr6E9HTI5HIAwPCpk7Hs32/4Ps4MD+nuEhOrEdOnQaFUovroMdTaI8CBxFBXH3K/ya2S37aqIyXQJSUiKTsLo2ZNx56VvmUX6FNEbxI2Od5UXQNtQjxGzphm3+5RCDYbn1xnZaQs1d1mswXkOj5SWPPhp1j73/9BEAS3wNfq9z8J0qgCg89TQ+PHj3e6XXvttbjhhhswa9YszJo1C4sXL8a1114bVnb+kQaLpHeGeU16UrajR7qUTpOJp0UlZAYu5V0bx2rS+ybSAWD8gjlOy1Uk0vuVQUVjUXDKVADAxLPnuaWThiuOSHoTAKDdJd2dGZ8119T6LU2WZbL4+9hjkfTje/ai09TuNDHX3+xZ+Rv/35dUd0AUtCxzhmXNNJS5twStO34CgFiTzkw+Qw2W7k4i3b+wmvSmqmoe5dHGx2HEdPHcVLpzt1MUtqm6hosowJGtMZBhk+baxPjgDsQDrN64udpdpJvbO7Dhf1/x+5bOTh79AwBTSwtaJJk3jki6oxWflERJ/+2c0SPdaspdGTZ1Mq556RnEpaU4JhMk6e4qveOaZPyZcwEAu1f85r6hAQKbDAKAHct+wabPvwYAjD/zDG9PcUNvbyXKMmiYWB81ewYAcFNOR4aq+Lsdbc+Eo3r07mHXNRUHHd2Sju3cjRN79gVrSAHBZ5F+2mmn8du3336L3377DdnZ2Zg4cSImTpyInJwcrFq1Ct9//30gx0t0QbQ9VSaQkfSY2MCnDLMfp3oPF7o85T0ABlYMFs3obU06ALTZRfqY02cBcJjekXFc/zLvFke2jjYhnrvsyyXmTOGII5LunO7OJtF42rWHi8be0hiAYy8pJxuaOD3MHR0h4Sa+Z4UjUtLko0gXBIFPHuqSEtFpase+39a5rdfa0Ii2lhZEyWRIyc3xz4D9DKuBppp0/8LqJk/s3gdTi4F3AolNTEBbcwsOb97mVNplqG9wylahdHdH1pBGr4dMIQ/uYFxwpLt7Pt+u/+RzPpFXceAw/98TdcftBrle0t2TJCJdJpdjqD093RvTL78Qo2ZNx4wrL+Xi0VMkXZsQj5HTTwYAbPtuWZfbjGTYbyoA7P7lVxzZsh0AeImBL8Tas6XY5BorZdDZl7PfOlfjOJYJR6nuvtNYXsn35TURFkUHelmTftddd+GBBx5Ak0TENDU14eGHH/Y51Z3wPwE1juvXSDprv+Yu0lnKeyBcphks5dPoo7u7J1hNZ7RaBYvZjDUfiP3W1T3oj0j0jfwJ4zB86mRYzGZs+0FsoXLqHy/HHZ8sxSM/f4XkvNAUSr7gGknnNeksku7ByKivNLBjz4+eECyKXl580KmneLBoqq7hqajMedgXpC7Iu35Z5bUUp/aYGE1PGZTbh1F655LHHsBNb/+bp8P2FEp3Dwzl+w/i/+ZfgI8efkKc1JFExvesXA2bxco7KABiBK5VEtGT1rQPVNpaDLx1kbT9UijQVSRdXF6LXXZX6lJ7mzVv1Nsn9L1G0rMyne4Pnzaly+2x34qJZ88DAHSa2mFsanYzjhu/YA7kSgWO79mH6qPHutxmJFN99Biaq2txaOMWVB0+ys/ZybnZiIqK8mkb+mS7SLdPrv302jv47wOPc1d/Nonb4WIcF03O7j1GEAR8uuRJLH/5Tez8eVWwh+N3epX7qdfrkZKS4rY8JSWlR03aCf8SSOM4nu7eD5HgRJbuLmm/xmCujoEyj1PpYnlKtD/S3QFg6zc/ovpIibh9iqT3G2PmzAIAbPt+OVa98wEmnHkG8ieM44+fe9dteOfP9wZpdH3DLZLO093F/Ys503qqje4t7AKU1dv5A1aPzhyvQ4Hlr7yNs+64Cdt/+Nnn50jTkTd/5T2brPbYceSNHRUQkR4lk2HywrMgk8uRkpfTqwttEumBg/12AWKNOks93vmzKN6kUVhDfQNvBcXuD3QEmw2m5hZoE+KhjY8PeHswX5Ep5I5e9l2cb7986jnUlJRi/f++7HJ7LOsuMTMDMrkcNqvz5CULYhzatAXDTpqE4dMmd7k9tf2YZudtVlbhMI4Tr9knnrMAALD12x+73F6k02ky4W9zz+NGcQ0VlbCYzVDGxCA+I40HirrCNZIu2GzY+t0ybHXJUGBdk1iGKqtJ7yCR3iN2/bwKuyJQoAO9jKR/+eWXWLp0Kc4//3xkZWUhKysLF1xwAd5++2188cUX/h4j4SOszjEQ6e6mVvGEroiODniqcHIX6e4s5TYhIx1RUVF8YsJfsFnndqMRls7eG1ixi1yb1YqV77wvSS2jmvT+YpDdSffQxs2oKSlFybadAICyfQdgNVswavYMbhIWbrD9tNVrJJ2lu/tPpLNoHkuZ9AeOevTQEenFq9fh2QsW8Yk1X2AXY/VlFTiyeZvX9ZgZUyBEuloXyyPosb00p6Oa9P6BRdhMLQYc2rAZgLMzeGt9g5OBFUXSRdhnEkoO7/qkJMhkMljM5i4NNY1NzfjptXe6nVxoqamDuaMDcqWCG71JYZH0zV99D6vZguSc7C5bY7oa7bH9TBpJT83PQ+7oQljNFmz/8ZcuxzcQEASBT44INhsvQUjJ8+28rUuy16R3Y/jYYXRkqEZFRfGadEp3Jxi9Euk33ngjfvzxR/z3v/9FaWkpSktL8d///hfLli3DzTff7O8xEj4SyHR3afpNIIWmKlbLf4AbPEXSJSm3Z95xE57ctBLZhQV+e31tvD2S1IdUd0CskwGAnT+tRN3xMi7SI9U4Lj4tlRu0hQJypRJZI1l7I7Hl0Yf3P4aPHvob/rXoeqz/VJxMPPeeP/ucwhYqyBUKfgy61qSztDlPfXv7ChMWumT/uJMrYmL4dxRKkfTewFLk1370vy6N+mqOlQIIjEjXSC7Gdb0U6SySTjXpgYUZhe1ZtYbXJ0tFuliT3uh0n3DUpcfarxFiNBrM/ONl0Ke6Z3b2F3r7hGhLbZ1fTDoFQeClfskeUt5ZFmHFwcPcNEvaCcAV5jPBYIaY0muSQePGAACObt3h184dkQKbXE3N91WkO0fSvSEti4rWqCWRdBLphEiP091lMhkmTZqEhx56CPfccw+GDBkCADhy5AjaaMcKKjzdPQCRdEEQ0N5qhCpWixitlhsl+Rvm7G6ob/B4omKR9OScbKTk5UAmk2HIpAko23fAL6/vWuvbW9Z/+iWMTU3Y+ZOYgtNuz0RQabWIksmC6mAdCK54+jEMmTQer1xzS5eRxP4iu7AAiuhoGOobeEZGY2UVtnzzAwDgp1ffxqRzFiCzYBgKpk/F/jUbgjncHsHEmM1qRbs9GsKNHV1q0v1pHNdSJwoLfbJ/0t0HjRsNZUwMmqpreB1muLLh0y95xkZX8Iu9QXldrtcbYu3nLgA8/banULp7/7D2v/9DjEaN5a+8yZc119TCarFArlCgpbbeqV8ziXQR7vBu39cX3v8XTDn/bAydPBHv3HZPUMbETDpbauq6WdN36k+UI33oYLFXuj3TAhAnJdix3VBegdKde5AzaiTyxo7GjmXuEfAYrQZypfNlfpPd+4BH0vU63g2kriy8z8OBopZNrvoQSY/RahCtFk2cW7s5bi2dnbCaLZArFeK1NdWkEy70OJJus9nw008/IT4+Hm1tbdi9ezd2795NAj0ECGQkHZD2Sg+cwztrL+Ip1R1wGMepdbEOJ2v7D4w/YFH8vtSjA2Jd0+avf0CnSTzZtkva6URaXXqMRoNBReJM/OCJRcEdjJ08e6q7N5OetuYWbPriWwDAjCsu6bdx+QNH94FmHrlxuLtrEBUVBX2qvU+rF7fh3sCEglIV45d9eOhJoivx4U1b+7ytYGOzWrsV6IDo3Gyz2aCK1ULnx7IBoO+RdLlCwTMxmPElERiqDh/Fh/c/5lTfarNY8fU/XsTPry9FU1U1F6SWzk7KbLBj5OnuccguLMCk884EAIycMY1nD/U3cexc60f/D1aX7urwnpgtRtGNjU3oMLbh2E7x981bJJ1NulnNFm66x7wP2D6l1ukCUh4VSfTE8JOde9uNRnSa2rtdn0+wazSI0Yi/q1STTjB6le6+Z88eDB482N9jIfqATCHnhmeBiKQD/ePwziLpnlLdATENyDUdK86PqW6aeObs3jeR7orVYuHfS6SJ9Lxxo/m+l21PXw4UyXk5mHfzdXymuqsxAY5Ud0+s+/gz2Gw2jJg+FSOmT8VlTzyCcfNO9+t4AwGrMZTuox2SH3ptYjwUSiVsVitauqmJ6wnm9g6eIukPgcn8AA7/vqXP2woXrGYzjPb64lQ/p7wzM0HAs0hX6WIx98ZrvE6kqePEenSbzeY0qUj0H+s++gzL/v0GAHB39xaqR+dIa9LPvfd2yOzmXjK5HFPOPzsoY+LtLv0o0nm6e56zSGfXR/Xl4vVR6c7dAICskcOhiI522w4T6a0NjTzDjbUDZOfyGI2ab9efk7qRRA3zEvEhkq7jPdJ9y37hE+yxWu7uTunuBKNXIv3hhx/Gs88+i7POOgvp6enQ6XRON6L/YVF0IHCR9P5weHf0SPcs0gFHr3SGVKQrYmJw5//ewy3vvopotbrHr+9Id/evSAekRi2RdYxIL/qzRvrPH8AT59x1K8646VrMuuoKp+XDp03B2X+5BZc98TCmXXw+BnGRvtvrthrKK7Hvt7UAgOtffR6TzzsTZ91xU+AG7wfGzTsdlz3xMAA4lZxIjeNYRMlQ3+D3tmb+Mo+L0WqQM2okAODw78Evj+hPWirFC+GUfP+mvGvjHCLdU7r7WbffhPm3XI9b3n0VNy99BeMXzHUy3mS1q+2GVr/U1hJ9o3TnHlQfPYZt3/8U7KGEDKwMrWj+HAyZOB6dpnZ89/zLAIApF5zDHbn7Ez1Pd/efSC/fL9aa548f59ROkZnGsSBGQ3klWurqoVAqkV04wm07fEK3uRnv3/MIXrjsGi7SpfXQ6UPFoJs/PUwiidpSUaTHZ6RBEdO1WTGbIO0u1Z3hyFCldHfCnV6d0X744QeMGzcO33zzDcrKytDY2IjGxkY0NTWhsTE02mIMNKQXW5YAp7vHBFKk8x7p3mujWCsbFplmqVoAMGzKRGSNGI7BE4tw5dNLuClY2uBBWPza85h74zVd1mt6ilL6C56JEGHmcdK2ZgkZ6QF13mVp7Ky9GiAKvmtffhazr1mEyeedhYsevRfx6WmwWa04sXd/l9tb++H/nO4nZGb4vWOAv8gYPhSL/vFXJGZmoKm6BivefI8/1t7mEOmOenT/py6yqF5fI+mDJxRBrlCg7niZU3/ogUCL/f0GMpLu6u6uVMVg/JlnABBT84dMGo9F//grHvn5a+SPHwsA0NhFPtWjhwamFgP+cd7l+PFfrwV7KCEDM8pkk4Qr3v4P1nzwKYxNzUjISEfByV33DA8EcfZ2xM21/qtJP757L4xNzdDGxyFv7Ci+nF0fNZQ7ghil9pT3QR5S3tnEW1tzC4yNTdzgErC3tLNH09nn6U8Pk0jC2NiEtuYWyGQyJOdmd7kuj6T7KNKlvdJjyN2dcKFXIn327Nn8dtppp/Ebu0/0P9EqMf3XlxqY3tIf6e7JuTkAuo6kl2zfBQBY/cEnAMQfSSbGR848ma83+rRTccbN1wEA5t7wJxScMhXzb7keDy//kruZMvInjEP+hHFcpPe1Jt0TvA1biKe765ISeZSzO+RKJfLGiBcRbBInO0DR9ITMdD5LnVkwTDTVgVi3p1AqYTK0YsVb/+HjqDhwmHsCeOPQpi347G//wCePPgljYxNkMllATL38QeHMUyCTyXBo0xY8ddYlOLjhd/6Y1N09EM7uDIOfRDqrRz80gFLdGS12d+UJZ83DkMkTul3/jJuuxYSz53W7nibOe7r7mNNPhVoXi/qyCvzf/Avx8+tL0VBeCW18HK59+Z/ILiyg9mtEyCM1dK08dASr3n4fls5O7PxJ7DU/bGrXPcMDQSDquW1WK/avFc1MC089hS/n10fljmxCli3mqS5d0831DMvuY1Ak3TvM9DN7ZAEv7/OEr87uDDbB7pzuTpF0QqRXIn316tVd3oj+J9CmcYBz7UwgSBs8CEnZmbCYzag4eNjreus++gxPn3Mplr38Bmw2G+RKBbSJ8QCAEdOnAQC2frcMADDrqisQm5SAkfYfuubqWihVMU5iXhWrxQ2vv4gb33qJp30Fog0Jc+JWhXi6+9UvPI07Pn4HyXk53a6bUzgCSlUMDPUNKP5tHYDApbznjhnldH/M6acCcLSkqSkpxQ8vvopnL1iEtR99hm+eedGn7W749Ev8/uW3qDoq9sVOGzLIbZ1TLrsQt/7ndZx86QXdprsFiuHTxAvQXT+vcsuWkaa7s/TLQERFWJ2dPqVvIj1/vJh9cWSApboDQOmmrag6UgJdUiJufOsljD5tptd1M4YPwbybr8Oljz/olOEx5vRT8fTmX3H9q8/z/UJqHOeaLTTl/HMAAJu//h5NVdVY9u838PfzLsfhzdug1sXi+lef51E6E5nGESEKmyS0Wa345NEnefu6Cnt6eDAmWPUp/jeOA4B99t/TkTPFaxelKoaXlh3f5fBa4eZxYz2I9G66NUi9J9pbjfx3hHCH1aVf/n+P4G/rfkLO6EKP6/VUpPNIulYjSXen74EQ6VMBj1qtRkFBAcaMGeN0I/of3n4tgCK9nZ9MAiPSx50hZmEcXP97l8ZFNqsVtceOw2ax8h/t+LRULvLNHR347K9/R+WhI4hWq3DZE49ApdWiqaoav/3nIwBwEqC5YwqhVMVAoVQi1V4nGpCadPvnp9aFdiQ9s2AYACDNh5rZ/Imi2CrZvou3wfNn33opuWPEH0XmPM1EeoJdpLMUwMaKKnz55D9xZMv2Hm2/+sgxAEDa4Hyn5TK5HPNvW4z88WNx4cP34N6v/sszLvqLaLWKO+gf3LjZ7XFPNemBEOmsDZsuqW8inY2xtvREn8cUbphN7fjXFddi508rIZPJuID2RFK2mC2iiI7mExsAUGSvJx8xfSpueONfGDnjZKd092i1iqdOJmVnYdhJk2Cz2bD5q+/5OpaODrxz6z2oL6tAbGICnyygSDoRqpQXH8SKt/6Djx9+Aif27OPLa0qOAQD//e4vYjQaHrTwZws2ADiwfhOsFgsyhg1BYlYGhk+djGi1Cg3llag4cIivx/6PS0vhxzyDi3QvE28myXUWRdG7Zseyn/m5MUajxkkXej5vc5Huo+Gj47ebIumEO70S6cnJyfj2229hMBiwd+9ebN++3elG9D88kh4gZ3fA2eAiEDBn7R3LV/j8HDZ7HZeagpEzxOj4kc3b0Wlqx6bPvwEAjLRH13f98isXBSm5UpHuHKEFAlSTbv9BVMWGbk16bGICd053rWv1xLApYtry0a07UFbctUjPGzcaT25agZMu8C5KuiLXPnO96p0PAQCDisZAn5KMJGamU17p9bm+UH2ERdKdRfqgojHQ6PUwGVrRXFOLpOxMXkbRX+RPKIIiOhoNFZWo8yBsWQ2bSqtFfEYaAP9HdgD/RdJZpNdXc51Iw9zegdX/+RgAkDPK3fCJwVouAcCwqZP4/yzjh3kE5Iwa4WQcBziO3wseuhuAOPnZVFXttE5HWxsO/y62wGPeEm3U7osIYX548VWeKcdg7Q8TszI8upwHCpbqbjK0dlta1VNMLQZe2ld46nSMmi1Oou39dY3Teh3GNm4iyox3Gdxnwod092aXcwPhzP61G/HI9Hl4ffGfAQCjZ8/0aFTY05p06XW1ilqwES70SqS/8MILiI+Px0knnQSTyYT58+fjqquuwqFDh3Duuef6e4yED/RLJJ2LTP+IdFYDCYjCKH3oYFjMZrcfoa5gjqr61BSewl68Zj0AYMu3y5w+j92//Iq646LAkbY28SzSm3x/Iz7SziPpoZvunpDpEAVdGewB9vS7SeMBAAfXb+IiPSk7C2q7YY2U4dOmIEajwew/LerxuGRyOXev3bPyNxzfLUZRhkye4HC8LffuY+ALLBqTNniQ0/JCe7rh3l/X4MP7HwMATLt4Yb9EbaZdfD4ueOhujF8wBwBwaKPnGu52SZpiBnfqDYRI73tNulqvg1wp1vS1BqCsJFwoP3AQVosF+pRkXqLgSmKmVKSLae1ypRIp9kyg3b/8Kq6XneVm2KhLTMDJl16AEdOnwtze4bX8g5lJKZRKABRJJ8IPQ30DTC0GyOTybo29/EmgUt0Ze1aK5aNzFl+N0bNnOC2Twvuqu7z37tLdTS3SSDr1SPeFw5u3oa2lBbqkRG7Wp9SoceEj92DBbTdAl9yzSHq7xDgumtLdCRd6JdJPO+003Hnnndi6dStsNhtKS0vx4Ycf4t5778UDDzzg7zESPhBukfRx807HE+t+wmnX/lG8b091P7BuU4969LIfltT8PJ4OWrxGNFwxtbRwQ5mWunqUbN+F+hPlsFmtiNFouNDgadSSHzJjk/8vVNmsdSj3SWf13YDnXstShk6eCGVMDBoqKlF99BjaDa3cqTs13925mr3vlEG5PBLoK2lD8hGtVsFkaEXtseNcWGQVDONj7qtIr7KnuyflZEFuFyyAw4ywePV6HNm8DXtW/ga5QoFz7rqtT6/nC2ffdQtOuexCTD7vLABwMouTYuno4PWZ2oR4mNs7UG4vP/AnjnT37rMsvMGeazK0wtLZ6ZdxhSPm9g6evZEzagRikxJ4fTmDTUABQNaI4dDGxyE1PxdyhQJtLS04unUHACA5J4tfkLNjMHPEML6Pfv/CK6g+eszjOMpcOiB4S40liFCGRdP7M+WdtX9t8aOzu5QNn36J8v0HoUtKhDYhXjzmt+1wW6/uuGeR7uhW4y3dXRJJp3R3n7BZrNwvYPTppyIxKxPzHr4LUy9aiDmLr0ZCRjqAHtSkS7yeYijdnXChVyJdq9WipkY8oBsbG5Fib0Gxe/duTJjQvVst4X/6xTjOj+7uLDp5xo3XILtwBE657EIAwM4epLoDDkfVovlzIFcqUHeizKl926p3PkBjZRVWvfMBBJsNVouFX8Sm5OUgMSsDuqREWMxmrFr6AQDxBBmINnbtrfZMhBBuwcZ+YIDuI+kjZohlBPvXbuTL6k+UA3C0ipEizSAYK2mh5gusHvvE3mIIgsB7vWaNHM6j/31Nd2+pqYXJ0Aq5QsEjlYnZmUgfOhhWiwUH1m8CAHz3/CsARNddVhoQCBQxMU7Hms1m46nJnpC2bSlesz4gbVxYursuKREyhbybtT0z0FPdpbAWgTmjRuKqfz6JG974F2b/6Ur+OJuAslmtkMlkGDJlItKHDgEAVB06yo+3jIKh3HGYeStMOf9sRP9/e3ceH1V59n/8m0nIQshKIGGXfRVQQERQFIvVllZxw1ofsdZHre0j1bZu7a/aTVu1LkVqtVrFaq2trbuioogswYUtbBKWhCX7vm8zc35/zJyTDNlDMnOS+bxfr/OCzJyZ3JPcSeY693VfV1Skju39Shv/4dtqsKns9INyNjRYH9ewko5eKD8zAEF6D1R2b8pZX68X7/il1bVn36eb5Xa6mp1X5A3SBw4/Md297T3ptexJ75LdH62XJJ1xyRLd9u/Vihs2pNkF54qijrWjtlobRzeupFPAD6YuBen79+/XxImefac7d+7UTTfdpKFDh+rmm29WTs7JvVFG1/gl3b0b+6QPmeB5o9kvMkI/Wv0XDUhMUPb+A9qxZm2nnsdMMzP7fKan+hbVyj14WL+9YKk+/fs/rdvMPb1JI0dYqe7ZXx3QF6+/o/LCohavVHeHmgoz3d2+QXpnVtInLThTkqxWMVJj67zEE94sSL4ZBKcuPq/DY4pPSdaFP/xfSdKhLzzVwLP2eYL00afNUET/KLndbpXknPyeurzDvvvSzWJaGdvTVOPdq1uQedRKUWstTbk7RMd73mC5nE699ccn9K9777f2Hrak6R/2ztR16IyqklJrxX5AYtdW060gvY3XEizMjJDTv/l1q3LzRf93s5XdkzDUc9Hsq02eC2ETzpxjZaHkHjysQm+Qbl7MqauuVkmu5yKk2UZx7ycbZRhGq2NwNTQop0k3DdLd0RsFYiXdTHfvqZV0yfO6Xv7Fb5Rz4JA+/fsrLZ5TeNzze+DElfT22io2LRzHSnrH7d/8mRpq69Q/LlaR0f2Vn35ID13yXSujoaaissMLPebf7aiYGIVHeYP0GoJ0eLTe7K8Njz/+uIYM8byZ/9WvfqU1a9bou9/9rurr63Xdddd15/jQQWH+SHevbCxOdTIcYaE+xbn6RUbI2dCgf9zzaysA6KgT/7C0lg7cVMHR45o4Xxo0arhCvUVmju7ao4qiYt1/0WVy1vVMCq7Vgs3GhePMoEBqu3Bc0qgRShoxXM6GBh38rHF1t+h4Wyvpja976IRxSho53Pqj1pqYgYm67rEHNCAxQcf37req8+cePCyX02ldnCrPL5CryWpgV+UfPqJTZpyqKQvna8SUSVq4/DuSmhfrKcsvUOSAaMUnD26xkFt3iI6Pl+QpYvjJ8y+1e7554aCuukb7Pt3UI2MyDEOVRSWKSx6k2KREqyZEZ5hBegVBuo7t9gTp5s+Ls75eYeHhuubBX+vP191i/a797D9vaco58zVt0TnK9maR5B7KUE15uWrKK6w341UlZao8YQXnwGft96I/tnufFdQTpKM3ys/wtMgKRLp7T+1JN6V98LHSvFv3WtJauru1kt6BwnHsSe+4+pparX1mtWYsPk+f/v0VuY7nqjgrW//93cO64clHlHvgUIefy1z8ihmYKIe3EF096e7w6lKQ/tJLjW8Yt23bplGjRmnSpEk6evSoioo6ViwB3csMVnoiTdvUXXvSB58ySmH9+qm2sko7P/hYcy/9ltY88bTPak5HNf3j6Ha52kwHNjX+QRthXQk/kubpNdqTFzkaW7DZN0hvWqgqpo1090nz50qSMrbu9EmrLvaupJ+Ydic1pvk31NapX2SEpixc4JPh0FRYeLgu+38/0+nf/LrC+vVTVUmpnv/xXdb3x1lfr/yMIxoy3pORUXSS+9FN5h7h2d+6yLpt69trtOnl//icV5aXr+Qxp1hv0nqCtZ+wg8XVzO/D3vUbrfTInlBeVKS45EGKSUqS1Pl972aGBunuUs6BQ3I2NFhF21746S902c9/poHDh+nMKy6R5Eml3bt+o8ryChSXPEgT53syWMw3goXHszTCW1SxqqxMlcWNX9e66hqfnsqtOdZkXzrV3dEbBaINm1lNvSQ712+fsyVmunt8SrLCIiLkrKtT5IBoawtMRwrHsZLeOWufek5rn3pODodDM2fOlORZYX/kiuWq6EQcZG4jNd+Lut3uHn0fit6lS+nuo0f7tiiqqanR9u3bCdADKDzSsze2J9+cm8FCdHxci60nOmrIhHGSpJz0g3r1N3/QQ5deo3V/e7FLz9V0L9ixPV9ZKcltMVc+x8yaqZHTPenuZquTntTd1fF7QtPq7tEJ8a3uOx46cYIkNdsaYK6kN02bN0V5Mwgyd+yS1PabqbFzTtcZlyxRWL9+ytiepr/+4HarloDJ3JcuSSVZ3fMmaes772vfhs06snO30rd8oedW3KV/3P2rZqv05v69OG+/755gBukdXdnMOXBIbrdbn7/2Vo+NSWqsWhubRLr7yWqaap53OFN71m3Qro+9+x2XLpEkFWfnyO1yKfXV130em+u9oGTuS5c8K2ZN90Ie3rajQ9lJx/Y09pxmJR29UdGxLLmcTkX072/tFe9JIQ6H9TfMvLgbKFWlZdZ7n6QRwzRl4QJrVb2uuqbVAp3mSnpddU2H3juhfTnpB5tlM7WltknhOEmqr6lpc3sSgkuXIq2DBw/qyJEjeuGFF3T99ddr7Nix3T0udJI/CseZQbojNNRKo+qKod796NnpB+V2ujqVGnSi+prGPy4dSXWXpAJvG7YBiQlyOBza+cHHfrkSbhaOs2sLtuj4OKu6qNvlKU4zIKHl1XSzsFpB5lGf283ibXHJg30qpEuNK+lmcN1SSrwpybtCseeTjXri2pusvbtNNQ3ST7ayu6misEjP3PIT/ema/9VT/3urdnsDphOZGRzxKT0XpPfv5Er6G394TH/49lXN6jJ0N/N7PG/ZpVYGT2cQpPsyCy+uX/0PSZ52hlJjKm1JtufrveXVN+Rq8ATc5YVF1rwoPt4YpFeVlvlUFT7YSsu+E+UdylThseMqzsrp8HwD7MTldFoXrAaPPqXHP1/isKHqFxGhhto6FWcHvhaTuS/90l/8VN9/4iFd+8f7JXm63LQmP+OInA0NOrZ7b6vnoGcVZB71qWlAqjua6lKQPmLECN19992qqanRHXfcofT0dB07dkwvvviivv/973f3GNEB/igc53a5rDdwJ9OCqXElvevBeVPZ6Qfldru1Z13H+qsXZ2X7rC59+NTfumUc7TGLtPSLjLDS0OzEXEUvyy+w+le3VuF90CmeFmtmRV1TZXGJ6qqr5XA4fPa3S41p/llfeVKkm7aXOpF5X+Gx1vesZ391wPp/dwXpHVWW6wnS4wYn9djnMPteV5W1vJ/wRA21dT22P76pT55/SZXFJRoxZZKW/frnCvPWdego83dHR1vU9HUf/uVvevDi7+iz/3oyIA5+sc3n95O5laOisEi7PvpEkqcmg6nwhJX0ptsIOrIfXfL8bn/oku/qoaVXWxfogN7G/HuReMLfnp6QMvYUSZ5A13C7e/zztcdMeR876zRJjRfB28qMqSgs0m8vWKq/3vKTnh8gWuSsr9d/fvuw9TGV3dFUl4L07Oxs/eMf/9BNN92kSZMmaeLEiVq7dq2uvPJKPfXUU909RnSAP/qkS43Fntprz9WWpunu3eHFO+/Vyv+5scXV1pa4nS5r5Txt7SfddrGgPWZhL8mebdjMFPWS7FzrjX5LF2MiYwZYtxceaR5EF7WwLz0sPNwK5szK7AlDUlrdNmH1Pj/eevCd1SRILzrJ9mud5c9096qSjgXp/lKSnavVt98jV4NTp120WPete1sX3/HjDm+BYSXdl8vp9OlhXldVrSM7d1sflzSZ2+//+Rkd2blbm15+1bqt6c9IVUmpirNyVJydo6yv0pW9v/FnpD3O+voe3S4F9DQzzbitoqfdxSx+m3vocDtn+kdrRVirWikaZ6ooLOrRWkZo3+6P11sdWWp7oHUqeq8uBelRUVFavHixfve732nTpk1KS0vTjBkz9MQTT+jSSy/t7jGiA/yxki6pzeCtI/rHxSreG9jkHOye4Lg8v6BDxZGa2vbuByrLK9Calf67qGS43VbxPbMas52YReOKs3OsVc6WLsYMGuVZRS/LL2ixF3dxCxXezVV0t9utgiPH5KyvV2i/MGsunMgM8IvaCNJryst1JG2Paiuruu2CT0eZVc39sie9nTdZgXB46w69eNe9KsnJVVRsjM75n2Ua5W1n2B6C9Pbtb7J1p7hJkJ6fcUR/uuZ/tfvjT63bmmabVJWVy1lfrz986yr96Zob2duIoFJxku9POsMM0vMOZfb45+qIprUpvnjjXev/1JjoHf77u4e17d0PulyfCX1Tl3JuS0tLVVJSopdeekm///3vtWHDBpWWlnbz0NAZ/lpJrzzJlXRzFb3oeFZA03reX/VXvb/qr37/vEVHszRs8gSNPHWKX1KTOyOhyUq6mb7X0pudQae0vB/d1NJKupk5UFdZJbfLk8kw6JSRShw+tMWqstZKejtp7Kuu+4HCoyL9XvTGXEkfkJig0LCwTrcO7AhrJd2GQbrkaQu068N1+sFzqzR21mkaOHK4MnfuavMxYeHh1gUq0t1bl775M130oxsl+QbpLSnLK5CrwanQfmHWdqTWCkUBfVlAgvTDgS0aZ8rYkSZXg1MZ23fqX/fdrwnz5ihu8CCC9F6iqqRUL915b6CHAZvp0kr6u+++q9DQUF111VW66qqrdMUVV2j8+PHdPTZ0gj8Kx0mNfwS7mk5mFgTLzzjSzpl9094Nnv7VUxcuCPBImmua7t7W99lcSS9o5SKDGaQnNllJN3vDm/vyrUC+hX3pUbExViBX0k5BHldDQ0Cq0laVlMpZXy+Hw2G1TulunS0cFwiGYVg/y20VAjQN8O6zdzU4qSbchmN7vtKRtD06tmdfs64GJ3K7XFZXBS58IJhVnuT7k44KCQlRsrc4XW6AK7ubCjKP6rdfX6q//uB2uZ0uffr3VyRJx/d1vk0mAHvoUpC+dOlSDRo0SBdeeKFSU1N1wQUXaMOGDTp+/LhefJFUjUDwW7q7dyW9rR7abTGLYQXrm8m96z1B+sT5Z7ba3ixQzJ7jeYczGt/sJCYoZmCizx76wd6ica2tpDf2Sm+e7m5WuDdXyBNHNA/szIsFFUXFtt4jW9bDKe/R8fGSOl44LlCs7/eIYQpxOPSd+3+p8/93eYvnDhhIqntHGG63/vTdG/TYVdd3qJDbGw8+pnXPvaSMbTv9MDrAnvy1kp4wNEXhUZFqqKtrs26Kv5UXFFpZNJ88/5Ie+OYV+uw/bwZ4VAC6quvNriXt2rVLmzZtUmpqqr744gsNHjxYy5Yt666xoRP8VjjuJK9Um4FHdWlwpmAd27VXFUXFiooZoNGnzQj0cCxRsbFKGOKpiJudftAKooZNnqA73/ynVrz0jFUYzFxJz2813d3sld5kJd0bpNecEKS3tJJuPq6oSWspO2osHtczPXn7x3vaHNqtcNyJzOriA4cP04ipkzT7Wxdp8Y3fa/Fc9qP3jK82btHbjzxBZXYEtbZqqXSn5DGeVPeCzKO2/pkrPHrcFpXnAXRNl4L02267TW+88YaKior02Wef6Tvf+Y7S09N12WWXadCgnnnDiraF+Xklvat/BKMTzH22pd01pF7FMAzt27BZkjTzwq9p6rkLlDRyeIBH1di7vjgrR7UVldabnaETxikqNkaDR4/SiGmTJUlJZo/0Iy0H6aW5eZI8q+cR0f09/x8QLUmqLfdNd2+pDZu5l729vbiBVp5nrqR3/++8sPBwRUZ7vmbVtl9J9wbpI4Zp6KQJkjyZPS11MKD9GoCeYmaARcfH9WimWmPROHukugPom7pUOO473/mO1q9fr6efflobNmxQeXlwroraib9X0ruaTmal8Np4n21P27t+k864ZInOunKpzrpyqQqOHNPvl1wZ0DENneipKZGd7mnZ1NJK59SFC1Sam6+I/lFyNThbLepWX1Orhto69YuMUHR8nOqqqhUV49ljfuJKemIL+5g70n7NDkq9QXprFepPhrkf3eV0qta7j9+uzJX02KSBOmXGqdbtsUkDm42dlXQAPaW6rFwup1OhYWEakJhodeHobkO8F7Vzm7ROBIDu1qUg/YwzzujuceAkhUdGSpIaant2D+/JrqRbKbw2rVjtD+mbP1dVaZlVvXvQqBGKSRqoisKigI3JCtL3e1qZNV3pdNbXKyw8XFPOXaADn30pyZOK7na2nuZXVVam+MjB6h8Xp+KsHGtVtfaEwnGxSQOtegomM3Bvq/2aHZhV6eMGd/9KerT356S6rNz2bbRqKypVXVau/nGxmnpuY0HEmKSBzQpEDkj0XNwjSAfQ3QzDUGVxieIGD1LMwIQeC9JHTpsiSTq+Z1+PPD8ASCexJ33BggX6+9//rs2bN2voUM+b6muuuUbz58/vtsGh4/zXJ93z5jo8KlLhUVGdfjwr6VJddbUeXXadHr7sGqsy7LDJEwI6piETPa3xsvd7V9KLGoOot/7o2es6dMI4Lb7Js9e4vYq2Zm9v80JEpDfd3VxJrymvsKp7n5jybu5Tb6/9WqD1ZOE46+ekl1zMMnt194+LtW6LTRrY7DyzcBzp7gB6glXctoeKx0XGDNDg0aMkScd2E6QD6DldCtIvvfRSvf/++6qpqdFpp52mCG+qdVxcnO65555uHSA6xl/p7vU1NaqrrpHUtT+CjXvSe0fw0VNKsnOVk35IWd72KMMmdW+QnjRyuOZc/I0O7ctzhIYqxbvHzlxJdzmd+vjZF/TlW+9p87/+q4wdaZKkcWfMkqvBqY+feaHN5zS/v2bQZqa711ZUWeeYe86bpryHhIQoYWiK9357B+nm3vuOtB7rLKv9Wi+p3dDS1oSYFoJ0s1hia/UMAOBkVPZwhfcRUyZJ8mSTBfv7GAA9q0tB+i9+8QvdfPPNuvHGG9XQ0GDdvmnTJp1++undNjh0nL/6pEtNUt4Hdi7l3REWqv6xZsXq0u4eVq+UtS9dkjR88sRue84BAxP0oxee0lW//X/6nwd/026gPuiUkeoXEaHaqiqrCJgkvfPYk3r5nl/L7XRp7yebrNvf/dNfdKydNL/qMk+dCjPYjIzxrqRXNPbGLsn19H+Oa9JnPH5IsvpFRMjldKo0N78jLzdgcg4ckrOhQbGDkqx99E1NOnuervnDrxSfktzp5zYzEHpLF4SWtiacuJI+ZMI4DRw+VPU1tUpP/dxfQwMQRCqKzPcnPRSke1PdWUUH0NO6FKRPnDhRn376abPby8rKFO9N04T/OEJDFdrPU16gp1fSpa5fqTZXVd1ut6rLK9o5OzgcN1fSJ09UyrgxuvWlZ3z29XbFsl//3PreTF98nq6+/942zzf3o+ekH2p1//OO9z5UVUmp0j5cp/Wr/9HuGMwVhugTV9IrG1fSzZT6pvUNzNd+NG2PrVvbSJ6fNfON2tjZpzW7/4Kbv6/TvnGBbnzqMZ808I4wg/TecjGryJvuLsn6vp24kn7q+QslSempn/nl9xSA4NPTvdLNLidHd+3tkecHAFOXgvTc3FyNGzeu2e0LFizQ4cOHT3pQ6BxzFV3y80p6J4vHmftsa8or6N3plfWVZyV94PChuvyXd2rU9Kn65m0/7PLznbH0W5pyznw11NXpjYcel7OhQaddtFgp48a0+hiz/Zq5H70lpXn5unfhN7T69ns6VMjM3JPe/4Q96U2rfZvzKLrJPJq+eJEkaeeH69r9HHZweOsOSdKYWc2D9OSxp3j+HXOKvv/Ew1af+Y4wf1bs3n7N1HQl/ehuz5vXmCTfN8lmkL7ro+YXeAGgO/R0uvvIU70r6RSNA9DDuhSk//Wvf9Xjjz+uM844Q4ZhaOjQobr66qv1xz/+UU8++WR3jxHtaFod21lX3+Ofz7xS3dl0st62OugPtRWVKvKmmI8+bbokT1A3cvrULj3f9MXnSpLWPv28Pn3hn8r+yhN4DxwxrNXHmIXP2mt51pkq41VlJxaO81R3b5rufuLFnpiBiRp9umfP8q61n3T4cwXS4a3bJUljZs30uT0+JVmR0dFyNThVW1WlU2aear256wirdkNJLwnSjzVuk9i/cYskKbbJNobE4UM1dOJ4uZxO7V2/0e/jAxAcrPcnXexA01R4VJRWvPysLv35TyV5fqfFJw+W2+XS8b37T/r5AaAtXQrSf//73+sf//iHPvroIw0YMECffvqpnnnmGT355JN65plnunuMaEfT/ej+aNdkVU/t9Eo6ReNa0vSPvdubYTDn4m926bmSRo6QJGVs2ylJKs72Fmcb2nzPtMlcta3sxosn5l7q/nGe73lUrLcFW9N09xOC9GmLzpHD4dCRnbutomx2l7E9TW6XS0kjhyu2SSu2ZG8hvoIjR5WTfkiSb9Dant5WOK40L1/lBYWqLi/Xfu9+86YrWad/4wJJnswDs14BAHS3ri4itGTMrBkaOW2K5l91mZJGjbBS3fMOZ6q+puaknx8A2tLlFmz333+/EhMTNW3aNJ155pkaNGiQysrKlJHRdmumpm6++Wbt3LlTZWVlKisr0+bNm3XhhRe2+ZjLL79c+/btU01NjdLS0nTRRRd19SX0Gf5qv2bq8kp6Qryk3hN4+ItZPE6S3vvTXyRJp134NYVFRLR4fnhUlEL79fP+P1Ln37BcyWNOkSMs1CpgZrbEKvFWUE/w3n72Ncs08ay5Ps8XnRgvqXszHKzq7t5+35HRZuG4xnT3E1c8Tl18niQprZekuktSXVW1tWVhbJPVdDPVPfdQhioKiyS13JKsNfHe7IaqXlI4znC79eiy7+mRy5er8Khn7g1ITPDMyeFDtej710qSvnjj3UAOE0Af15170pPHjLb+f+al37Yunmfu3HXSzw0A7elUkB4eHq77779fX3zxhTZu3KhvfOMb2rdvn6ZOnar9+/drxYoVevTRRzv8fMePH9ddd92lWbNmafbs2fr444/1xhtvaMqUltNC582bp5dfflnPPvusTjvtNL3++ut6/fXXNXVq11KD+wp/tV8zdbUPaWOPdFbSm0rf8oUk6auNW7Tuby+q6Hi2omJjdOqic5qdGx4VpXvee1W3vvhXSdKpXztP31hxs771s1uVOHSIQsPCVF9Tq/L8Qkm+K+nDJk/QJXf+WMt+/XOf54zugVXb6ibp7hH9+8sR6qkwX9PCnvQBCfFyhIZaKeO7Pu5de5Yb96XPtG5LGeupAZB3KKPTF7WSx47WkPFj5Wpw6uiuPd061p5UXlCokpxcVZeWydXglCTFDhyoK++9WxH9o3Tgsy+19a33AjxKAH2ZuSc9Oj6uU3VAWmJmREnSWVddplPPXyiX06lPX/jnST0vAHREp36D/frXv9YPfvADZWZmavTo0fr3v/+tp556Srfddpt+8pOfaPTo0XrwwQc7/Hxvv/223nvvPR08eFAHDhzQL37xC1VWVurMM89s8fwVK1ZozZo1evjhh/XVV1/pl7/8pbZt26Yf/ehHnXkZfU6/yEhJ/gvSy82VwU6k70pNe6SXdveQerVju/fq999aptW33y3DMKw9u0MnjW92bsq40YoZmKjhUyYqLDxc8SmeFdeRUycraZQn1b3w2HFr24MVpA8bomETPb3Y45IH+dQxGJDgWcnuznT3xj7pcYqK8aS6Oxsa5GyS7WEG6f3j4xSdlKjQsDA11NX5tIHrDQ5v9WwtOGXmqdZt5kp63uHMxpWdpI4F6XO+/Q1J0r4Nm6yvUW9iGIYqij2v+ayrLtP4M2ervqZW/77v9wEeGYC+rqq0TG63W47QUOs9R1eZv8clKaJ/lCRpy6tvKD/jyEk9LwB0RFhnTr7iiit07bXX6q233tLUqVOVlpamsLAwzZgx46QH4nA4dMUVVyg6OlqpqaktnjNv3jw98sgjPre9//77uuSSS1p93vDwcEU0SRuO8baCcjgccpzkVdaeZo6xvXFGRHmD9Lo6v7ym8rwCSZ6U3M58vgHedPeasnLbf+39rcibIuxwOBoro8fGNvs6mXvOJU/6tJnNEJ0Qr/FnzLaey3xcaY5nb3fi0CEa4q3ibn5ckHlU/SIjFO6dP935fan1ttiLihmgAQM9FwFqKyp9nr+mvMLzZsrh0MDRoyRJJTl5CgkJUUhISLeMwx+yvG30kseMVnhkpJz19VaaZP7hTKsNXWxSUrtfX0doqGZ9y7Pl58s33+u1PycVhcWKTx6seVdcIkna9M9XVZKd02tfT3fp6O90oDcL6Dw3DFWXlmlAYoLikwer+iQy98zf47vWfqJTv3auaiur9OFTf+PnFxZ+p6OzOjNXOhWkDx8+XFu3bpUk7dmzR3V1dZ1Kb2/JtGnTlJqaqsjISFVWVmrp0qXat6/l1hYpKSnKy/MtKJWXl6eUlJRWn//uu+/Wfffd1+z26dOnq6qqqvkDbMThcGjCBM/qp7uNlmXDvdsD+jlCNXPmzJ4fV5hn2vSLjNDcBfNVV9mxr2PKCE+AmRgT55dx9laJMZ6gbuiokc2+TtPmzLL+f/q8uRoxtrG12pwlnuDOUVtvPS40PFySFBUboynzGzNUZi+Yr5z4REV7g3xXQ4Mmj5/Qba8hJCREhtutEIdDs+afJUly1zc0ez31lVWKjI3ROG+quKuyqlfOjdryCkXGxuicb16omtIyRcUMkNvl0rD4RCXFeFZzkkcMb/e1DZ0+VbGDklRbXqGI8upe+bWQpJD6Bkmy+sPXHc3uta+lO3X0dzrQmwV6nldk52pAYoIuvO5/tO2f/+3Sc0QlxFm/x9PffF+xsbE6+vk2jR95ijSye8eL3ivQcx29T7S3RlNHdCpIDw0NVX19Y4svp9OpysrKNh7Rvv3792vmzJmKi4vT5ZdfrtWrV2vhwoWtBuqd9cADD/isvsfExCgrK0tpaWmqaNIOyo7Mqy07duxo84c/fIwn+M3PytKOHTv8MTQtKSpWzMBEHSvMt9p8tefsUM/q6Fdpu7TXT+PslVKSNEdSveFu9v2ceGlj1fecokKNdzSuOEd706n3frnN53FLiks0IDFBSWNOsW4rrqnSjh07NGzyREmeVPfunjsXl1coOj5O5Q2e3xllRcXNPsf5+QVKiY1RiLfA3NH9B/w2h7vT7F17NHH+maoOc6i41lP1t+DIMW3bulWF9bVaKCk0KrLN1xYSEqIzf/R9SdIXb76nbd4Lor3RuMMZGn6aJ/2/LL9AH7/2pl86T9hdR3+nA71ZoOd5zaq/6oYnH9GYhfP0zwce7lJHiQnzzpDk+T2+Zf2n2rK+d9VKgX8Eeq6j9zEzujuiU0F6SEiInn/+edV595VGRkbqL3/5S7MV6csuu6zDz9nQ0KBDhzwtirZt26Y5c+ZoxYoVuvnmm5udm5ubq+TkZJ/bkpOTlZub2+rz19fX+1xYMLnd7l7xA2WOs62xmtWxKwqL/faaSnPzFDMwUXGDB3W4X6jZVqqyuKRXfO0DxdzPHRUT0+zrlDh8qPX/6ISEFnvB5h856vO44qycZufFpyTL7XZbK51VJaXd/j2pLi1TdHycRnj7g5fnFzT7HBVFxUoZN0aJo4ZLkoqysnvl3Di2d78mzj9TQyeNtyrv5x48LLfbrbICz/aQAQMT2nxtZ1+zTGPnnK666hpteOlfvfLrYDLrVkjSnnUb5HK5Ajgae+nI73SgtwvkPN+3MVXH9+7X8CkTddZVl+mDJ5/t9HMM8m7ByjuUwc8q2sTvdHRGZ+ZJpzZRrF69Wvn5+VbLtBdffFHZ2dnWx+ZxMhwOh88e8qZSU1N1/vnn+9y2ePHiVvewBwtzX7JZoMofSnPzJUlx3lZRHWH146ZwXJvMq/5Rcc2vtg0cMcz6f8zAxBaD9MIjx3w+Ls7KbnaO2arNbL/WnUXjTFXe3wXjz/TslT++96tm55iF0cx2cyU5rV9wszNzX/rwKZM05nRPjY68w5mSpIoiz2vsFxGhSG8RvRMNHj1K3/zxDyRJbz78JxX1suJ5J6poEqTvXrchgCMBEIw+evYFSdLZ11yphCGtb4lsTdPinwAQCJ1aSb/++uu79ZPff//9eu+993T06FHFxMTo6quv1rnnnquvf/3rkjwXBbKysnTPPfdIkh5//HGtX79et99+u9555x1dddVVmj17tm688cZuHVdvY7Z2qvRjkF6W5wnS41OS2znTIzQszKryTQu2tlV7i66Zq9ym8KhIxQ0eZH0cOzjJyk4w1VVXq7yg0Oc2s8K7JLkanArtF2a9aWlsi1faXcO3VHt7fPeP9byOYy1kXJxYvbwku3cG6eYFiKHjx2nYRE9V/p0ffCxJctbVqaaiUlExAxSbNFC1Fc23CM27Yqn6RUToq41btOXfr/tt3D3FnIO1lVU6+HnvTdsH0DvtWvuJtZp+w5OP6Ilrb1JNece3OKZ4i8blHcroqSECQJsCWo5w8ODBeuGFF7R//3599NFHmjNnjr7+9a9r7dq1kqSRI0dqyJAh1vmpqam6+uqrdeONN2rnzp26/PLLdckll2jPnt7TS7gnBGYl3VPAz2wB1h4zmHS7XKq1eS2AQDNX0iOjo+UIC7VuTxw+zOe8lLGj5XA45Ha5VHjMUx2+0FslvqnirMYg/fC2HZKkhKGeIN2suN8TQbqZtm9qayXdVNJkrL1JcVaOqsvKFdovTI7QUO3ftEW5Bw5Z95sry/Epg3XDn/+o2/+9Wpf94mca6g3oh04cJ0na/t6H/h98Dzjw2Zc6tHW73n/yGbkaGgI9HABBxnC79dytd6g0L18pY0fru7+/r1OPN3uk5xKkAwiQTq2kd7cbbrihzfvPO++8Zre9+uqrevXVV3tqSL2StSc9AOnuHV1Jj/YGg9Vl5RSQakdtZaXVmqx/bKwVyCaN8A3Sh0zwBHZVpWU6vucrJY0Y3mKQXtJkJX3fp5s1fu5sxQ5KkiMs1Pq+9MhKepOtL2V5BT4p0Kamc9bV4FTZCVkAvcnxffs14cw5kqT1L/zT576K4mINHj1K0xYt1OSzPdXuh02aoFNOm64/XvY/GjLe0x4v58BB/w66h9RWVunP190S6GEACGKlefl65paf6Kf/+bsmn32WIqL7q66qusVzo2JjFZ8yWDnpB5Uyboz6x8XK2dCgghO2jwGAv9DYrw+ICUC6e2meuSd9UDtnephp2v68kNBbGW63lRLdNOV9oHcl3fwaRjcpxLdvg6cuQ0upxU1X0g9+vlUNdXVyhIYqPnlw43P08Ep6S6voku9Kell+voxeXHgly5vOn3PgkPZv/sznvopCz/ds6nlnS5KOmenxE8Zp8OhRik6Il9vlUt7hI34cMQD0bTnpB633K0PGj2v1vKt+c48VzM/+9jckSXvXb5LTWygZAPwtoCvpOHmhYWFWIHdi6nBPstLdkwd7emK3szqe7G3/lZ9BENIR1eXl6h8Xa+3nlhqLxh3ZuUvTFi20bq8oKtaXb76rfZ9uapZiLnmC9JryChmGobzDmSrNydOgU0YqYUiKVTiupced9GsobWx705EgvbiX7kc3bXrlPxo85hR9/Ozfm91nXkCL9xZa3PHuh4qKGaCkEcM199JvS/JsVeANIQB0r+yvDig+ebCGThynzB1pLZ6TMs6TzXTRrTcpJmmgJOnLN97x2xgB4ESspPdyAwZ6Ut1dTmeXeoF2VZm3nVZYeLgV6A2fMknf/tmtCo+Kana+GaRThKVjGiu8NwbpZrp75o5dPueagW5rgbazvl6Pfed6PXbV9XLW11sV1BOHDenRwnFVTdLdWyoaJ0mVRY1BemkvD9JLsnP1t//7WYtvAstPSPU/krZbR9M8tTRmf/siSZ4VeABA98ref0CSNHTS+FbPMRc7hk2aoNikgaooKta+jcHdOQhAYBGk93JWqntxiV/3erudrsZiWN7VwW/cepMWXvsdzb/qUkmSIzRUjlBP4bOUcWMkEaR3VI03SPdJdx/h6SV+dPc+uZxO6/aObCEoPHrcautlpr8nDEmxCsf1RLp7dSfT3Xtr+7WOqDxh7/3xffuVuXO3pMaaEgTpAND9stM9tT6GTmg5SA9xOJq1x9z27gdyO109PjYAaA1Bei/X2H7Nf6nuprK8AkmNxeMGjR4lSZow7wyFhIToR6v/orvf/bci+ve3VtJz6TnaIeZKev9YT6/0iP79rYrsBZlHfVa+O/u9t1bShw+1LgL0xEq62fqt8OjxFovGSZ6WcQ21dT7n90XlhY1Benb6ATXU1umIN0g35aQTpANAdzNX0oeMH6sQR/O3vVExA+Tw3l5dXi63260vXn/br2MEgBOxJ72XC0T7NVNpbp5GnjpF8SmDFRYRocShnnZ5o0+foXFzZ2vUjGmSPOm8UbExcjmdKsg86vdx9kYn9kofN3eWQsPCVHj0uMoLClVRVKzYQUmSOl+LwOxFPmr6NCvToaq0tJtG3qj4eLaevunH7a6QVxQVKXHY0F7bI70jmq6kH/GmuWenH1B9Ta3CoyIlsZIOAD2h8Ohx1VXXKKJ/lJJGDm/2PsT8O1tbWaUnrr1Z0QnxXDQFEHCspPdygQ3SvW3YkpN92oP1i4jQxXessD4++7tXSpKKjmXRM7mDqst9090nnjVXkqyq4U2Dvs5+7w9v3SG3263B3syHmvKKHkvr27/5s3aLBb73p6eU/vEGZWzb2SNjsIOmmQRHdnpqCridLh3bu0+SVFddo2LvdgQAQPcx3G7lei+CDp3YPOW9v7fLSVVpmfIOZejwl9v9Oj4AaAlBei83IADt10xmhffE4UOVNHKEz31m32dJGnTKSElSLvvRO6y6rJUgfdMWSVJFkxT3Tq+k5+Rq36ebrY97orJ7Z+xYs1ZfrP6n3K6+u/+voqjYqiNwZOce6/aj3v/nHcrwa00JAAgm1r70loJ0799Z8+I4ANgBQXovF8iV9Kyv0iVJI6ZO1qBRnqJmtVVV1v1VJaVyNlk5zztMkN5RZuG4qNgYDRw+TEkjh8vV4NTBz7dJ8v1+d+UCzeZX/tP4+BL/1zMINi6nU6///lG9/cgTVgE/Sdr6zhpVFpfoy7feC+DoAKBvM/elD5s8odl90XGelfQaP3bIAYD2EKT3cmZl6Ipi/wfpx/bsk9vtVuKwIRp9+kxJ0ra337fu3/H+Rz4pzHmHMv08wt6rsXBcrCbO96yiZ+xIU111taQT0t07uZIuSfs3fWYFi1UlgV1JDxabX/mv1j33ks9tOemHdO/Cb2jTy68GaFQA0PdlbPe0xpx41lyrXo7JKqAa4KwyAGiKIL2XiwlguntdVbXVUm3S/DMlSRnbd3r2PLtc+uL1d6w91BLt1zqjaeE4M0hP3/y5db+5kl5bWSVnXV2nn98wDH36939KknIPHj7Z4QIAYFs56Qf1xRvvyOFwaNmvf66w8HDrPnNPejUr6QBshOruvZy1kh6AIF2Sjqbt0ZDxYxXazzOVCo4c1/M/vksxSQOVe/CwnA0NWnLbD+VqcCqfyu4dZr5ZGJCYoJhBAyVJ+zdvse4v9xYiO5nv+8Z/vKpje76y0gABAOir3njwT5p41lwljzlF511/jT78y98kNdmTTpAOwEZYSe/FHKGhik6IlxSYPumSdCTNt9dz4dFjqiots1Znc9IP6s2H/qR/3fdAl1Z8g1V1kz3pkdHRqigqVta+dOv+w1t36Ms339MHTz5zUp/nyM7dVp9yAAD6qprycr238mlJ0uQF86zbCdIB2BEr6b1YdHycHA6H3G53wPZSHdm11/p/ZXGJarxp2k2tf+Flfw6pTzjx63hgyxc+1b9dDQ16+ee/9vewAADotcwWmMnjRiskJESGYSiaPekAbIiV9F4sJsmTBl1VUhqw9lV5hzKsiu6FR48HZAx9kbO+XnXVNdbHX236rI2zAQBAewqOHpOzoUGR0dGKH5IsSYpiJR2ADRGk92JxyYMlSWV5BQEbg+F269iufZKkgiPHAjaOvqimonE1PX0zQToAACfD7XQpP+OIJCll3FhJnqxEiRZsAOyFIL0Xi0seJEkqy8sP6DjS1q6TJKVv+bydM9EZ5lX9rK/SA1YYEACAvsSsmTNk/BhJnlanklRVRro7APtgT3ovFu9dSS8NcJC++ZX/ave6DSrPD9yKfl9kBulNW68BAICuyz3gCdJTxo2RIzRUUbExkkh3B2AvBOm9WNxg70q6DYJjAvTut2PNWsWnDNZnr70V6KEAANAn5B48JMkTpJsButS8YCsABBJBei/WmO5OgNwXpf7rNaX+67VADwMAgD4j54AnSE8ec4oGJCZI8gTogSrACwAtYU96L9ZYOC6w6e4AAAC9QUl2ruqqqxUWHq5Rp06VJFWXk+oOwF4I0m0uJCRESaNGtHifndLdAQAA7M4wDOUezJAkjZk9UxI90gHYD0G6zU399oW6881/6tt3rPC5PaJ/f0XFDJBEujsAAEBHmRXex8+dLYn2awDshyDd5oZOnyJJWvg/V2ni/DOt28396DUVlaqrrg7I2AAAAHqb9M2fSZLiU5IlSVUE6QBshiDd5qoKG/tjf/f392nAQE+RE/ajAwAAdN7OD9cpP+OI9THt1wDYDUG6zYU4Gr9F0fFxmrxgniQp3qrsTpAOAADQUYbbrQ+ffs76uJo96QBshiDd5hyhnm+Ry+mUJCWN9BSRi7WKxhUGZmAAAAC91I731lqr6ZXFJQEeDQD4Iki3uRBHqCRZf0gGnTJSkhTvTXcvZSUdAACgU9wul16845fa9M//aNu7HwR6OADggyDd5syV9LxDnnYhSSOHS2JPOgAAwMnI+ipd//3dw6oprwj0UADAB0G6zZl70vMOZ0pqTHe3eqTTfg0AAAAA+gyCdJsLCfWkuxcePSaX06mI/lGKHTzIasFGujsAAAAA9B0E6Tbn8K6kN9TWqSQ7V5I0YspExQxMlES6OwAAAAD0JQTpNhfi3ZPudrlUcOSoJOmsZZdJkgoyj9LbEwAAAAD6EIJ0m3N4q7u7XC4VHDkmSZq04ExJ0t4NmwM2LgAAAABA9yNItzlrJd3pUuHR4z737fuUIB0AAAAA+hKCdJsz96QbbrcKMo9at9dWVenwl9sDNSwAAAAAQA8IC/QA0DazurvL5VLp8Szr9vTUL+RyOgM1LAAAAABAD2Al3ebMPulup0slOXly1tdLItUdAAAAAPoignSbs4J0l1OG263t761V4dHj2v3x+gCPDAAAAADQ3Uh3tzlHkxZskvTPX/wmkMMBAAAAAPSggK6k33XXXfr8889VXl6uvLw8vfbaa5owYUKbj1m+fLkMw/A5ampq/DRi/zNX0l1OV4BHAgAAAADoaQEN0hcuXKhVq1bpzDPP1OLFi9WvXz998MEH6t+/f5uPKysrU0pKinWMGjXKTyP2P4e3cJy5kg4AAAAA6LsCmu5+0UUX+Xx83XXXqaCgQLNmzdKGDRtafZxhGMrLy+vp4dlC4550gnQAAAAA6OtstSc9Li5OklRcXNzmeQMGDFBmZqYcDoe2bdume+65R3v37m3x3PDwcEVERFgfx8TESPL0Hzd7kNuVw+Gw9qTLbdh+vEBXmD+LzG/0dcx1BAPmOYIFcx2d1Zm5YpsgPSQkRI899pg2btyoPXv2tHre/v37df311ystLU1xcXH66U9/qs2bN2vq1KnKyspqdv7dd9+t++67r9nt06dPV1VVVXe+hG7ncDgUGub5Fk2aOFFVSYMCPCKg+zkcDqsWhdvtDvBogJ7DXEcwYJ4jWDDX0VnR0dEdPjdEktFzQ+m4P//5z7rooou0YMGCFoPt1oSFhWnfvn16+eWX9ctf/rLZ/S2tpGdlZSk+Pl4VFRXdMvae4nA4dP8X6xQaFqbfLL5Y5fmFgR4S0O0cDodmzpypHTt28EcOfRpzHcGAeY5gwVxHZ8XExKi0tFSxsbHtxqG2WElfuXKllixZonPOOadTAbokOZ1Obd++XePGjWvx/vr6etXX1ze73e1294ofKDMtwtnQ0CvGC3SF+fPIHEdfx1xHMGCeI1gw19EZnZknAd9EsXLlSi1dulSLFi1SZmZmpx/vcDh06qmnKicnp/sHF2AhISGNheNowQYAAAAAfV5AV9JXrVqlq6++WhdffLEqKiqUnJwsydNirba2VpK0evVqZWVl6Z577pEk/b//9/+0ZcsWHTx4UPHx8frZz36mUaNG6ZlnngnY6+gpZvs1ieruAAAAABAMAhqk33LLLZKk9evX+9x+3XXXafXq1ZKkkSNH+qQGJCQk6K9//atSUlJUUlKirVu36qyzztK+ffv8N3A/cYQRpAMAAABAMAlokB4SEtLuOeedd57Px7fffrtuv/32nhqSrfiupLPXBQAAAAD6uoDvSUfrSHcHAAAAgOBCkG5jpLsDAAAAQHAhSLcxh8MTpLuczgCPBAAAAADgDwTpNmamu7OKDgAAAADBgSDdxsx0d4J0AAAAAAgOBOk2FmqupDsJ0gEAAAAgGBCk2xjp7gAAAAAQXAjSbYwgHQAAAACCC0G6jZl70l0E6QAAAAAQFAjSbczBnnQAAAAACCoE6TYWSro7AAAAAAQVgnQbc4SGSSJIBwAAAIBgQZBuY/RJBwAAAIDgQpBuY+xJBwAAAIDgQpBuY2aQTnV3AAAAAAgOBOk2Zqa7G26CdAAAAAAIBgTpNhZKujsAAAAABBWCdBsj3R0AAAAAggtBuo2FhHq+PVR3BwAAAIDgQJBuY1a6O0E6AAAAAAQFgnQbc4SGSWJPOgAAAAAEC4J0G3Owkg4AAAAAQYUg3cbMFmwE6QAAAAAQHAjSbYzq7gAAAAAQXAjSbcxBn3QAAAAACCoE6TZGdXcAAAAACC4E6TbWuCfdGeCRAAAAAAD8gSDdxqjuDgAAAADBhSDdxtiTDgAAAADBhSDdxqjuDgAAAADBhSDdxsw96YbbHeCRAAAAAAD8gSDdxkJJdwcAAACAoEKQbmOO0DBJpLsDAAAAQLAgSLexkFDPt4fq7gAAAAAQHAjSbSyUFmwAAAAAEFQI0m2MFmwAAAAAEFwI0m3MrO7udjkDPBIAAAAAgD8QpNuYg3R3AAAAAAgqBOk2ZgbpVHcHAAAAgOBAkG5j7EkHAAAAgOAS0CD9rrvu0ueff67y8nLl5eXptdde04QJE9p93OWXX659+/appqZGaWlpuuiii/wwWv8LDSPdHQAAAACCSUCD9IULF2rVqlU688wztXjxYvXr108ffPCB+vfv3+pj5s2bp5dfflnPPvusTjvtNL3++ut6/fXXNXXqVD+O3D/Ykw4AAAAAwSUskJ/8xBXw6667TgUFBZo1a5Y2bNjQ4mNWrFihNWvW6OGHH5Yk/fKXv9TixYv1ox/9SD/4wQ96fMz+RLo7AAAAAASXgAbpJ4qLi5MkFRcXt3rOvHnz9Mgjj/jc9v777+uSSy5p8fzw8HBFRERYH8fExEiSHA6HHA57b8kPDfN8ewy32/ZjBbrK/FlkjqOvY64jGDDPESyY6+iszswV2wTpISEheuyxx7Rx40bt2bOn1fNSUlKUl5fnc1teXp5SUlJaPP/uu+/Wfffd1+z26dOnq6qq6qTG3NNivRctRowYIffMmYEdDNBDHA6HVYvC7XYHeDRAz2GuIxgwzxEsmOvorOjo6A6fa5sgfdWqVZo2bZoWLFjQrc/7wAMP+Ky8x8TEKCsrS2lpaaqoqOjWz9XdzqqpkSRlHD6stB07AjsYoIeYVxV37NjBHzn0acx1BAPmOYIFcx2dZWZ0d4QtgvSVK1dqyZIlOuecc5SVldXmubm5uUpOTva5LTk5Wbm5uS2eX19fr/r6+ma3u91u2/9AWX3SGxpsP1bgZJg/j8xz9HXMdQQD5jmCBXMdndGZeRLwTRQrV67U0qVLtWjRImVmZrZ7fmpqqs4//3yf2xYvXqzU1NQeGmHgWEE61d0BAAAAICgEdCV91apVuvrqq3XxxReroqLCWiEvKytTbW2tJGn16tXKysrSPffcI0l6/PHHtX79et1+++165513dNVVV2n27Nm68cYbA/Y6eoqDPukAAAAAEFQCupJ+yy23KD4+XuvXr1dubq51LFu2zDpn5MiRGjJkiPVxamqqrr76at14443auXOnLr/8cl1yySVtFpvrrcy9LrRgAwAAAIDgENCV9JCQkHbPOe+885rd9uqrr+rVV1/tiSHZitUnnZV0AAAAAAgKAd+TjtaR7g4AAAAAwYUg3cYoHAcAAAAAwYUg3casdHf2pAMAAABAUCBItzFHqKdkAOnuAAAAABAcCNJtLJQ96QAAAAAQVAjSbYx0dwAAAAAILgTpNkYLNgAAAAAILgTpNtZY3d0Z4JEAAAAAAPyBIN3G6JMOAAAAAMGFIN3G2JMOAAAAAMGFIN2mQkJC5HB4vj1utzvAowEAAAAA+ANBuk2Zq+gS6e4AAAAAECwI0m3K3I8uke4OAAAAAMGCIN2mHA5W0gEAAAAg2BCk25TPSjpBOgAAAAAEBYJ0m2JPOgAAAAAEH4J0m7LarxGgAwAAAEDQIEi3qVBvkG64aL8GAAAAAMGCIN2mzD3p9EgHAAAAgOBBkG5TDmslnXR3AAAAAAgWBOk2Ze1JZyUdAAAAAIIGQbpNOcLCJLEnHQAAAACCCUG6TVmF49ykuwMAAABAsCBIt6nGFmyspAMAAABAsCBItymzurvBnnQAAAAACBoE6TZlVXcnSAcAAACAoEGQblNUdwcAAACA4EOQblOhYfRJBwAAAIBgQ5BuUyEOVtIBAAAAINgQpNuUtSed6u4AAAAAEDQI0m3KSnenTzoAAAAABA2CdJuiTzoAAAAABB+CdJsi3R0AAAAAgg9Buk05wszCcaS7AwAAAECwIEi3qVBW0gEAAAAg6BCk25S1J50WbAAAAAAQNAjSbcoRGiZJMlykuwMAAABAsCBItymH1YKNlXQAAAAACBYE6TZFCzYAAAAACD4E6TZlFY6jujsAAAAABI2ABulnn3223nzzTWVlZckwDF188cVtnr9w4UIZhtHsSE5O9tOI/cdqwcZKOgAAAAAEjYAG6dHR0dq5c6d++MMfdupxEyZMUEpKinXk5+f30AgDx0x3NwyCdAAAAAAIFmGB/ORr1qzRmjVrOv24/Px8lZWV9cCI7MNBn3QAAAAACDoBDdK7aseOHYqIiNDu3bt13333afPmza2eGx4eroiICOvjmJgYSZLD4ZDDYd8t+VvfXqMjO3dr2MAkW48TOFnmzyLzHH0dcx3BgHmOYMFcR2d1Zq70qiA9JydHN910k7788ktFRETohhtu0CeffKK5c+dq+/btLT7m7rvv1n333dfs9unTp6uqqqqHR3xyHPUupcTEaebMmXLTig19lMPh0IQJEySJeY4+jbmOYMA8R7BgrqOzoqOjO3xurwrS09PTlZ6ebn2cmpqqsWPH6rbbbtO1117b4mMeeOABPfLII9bHMTExysrKUlpamioqKnp8zCfDvNqyY8cOfvjRZzHPESyY6wgGzHMEC+Y6OsvM6O6IXhWkt+Tzzz/XggULWr2/vr5e9fX1zW53u9294gfKHGdvGCvQVcxzBAvmOoIB8xzBgrmOzujMPOn1myhmzpypnJycQA8DAAAAAICTFtCV9OjoaI0bN876ePTo0ZoxY4aKi4t17Ngx3X///Ro2bJiWL18uSVqxYoUyMjK0Z88eRUZG6oYbbtCiRYt0wQUXBOolAAAAAADQbQIapM+ePVuffPKJ9fGjjz4qSXr++ef1ve99T0OGDNHIkSOt+8PDw/XHP/5Rw4YNU3V1tdLS0vS1r33N5zkAAAAAAOitAhqkr1+/XiEhIa3e/73vfc/n44ceekgPPfRQTw8LAAAAAICA6PV70gEAAAAA6CsI0gEAAAAAsAmCdAAAAAAAbIIgHQAAAAAAmyBIBwAAAADAJgjSAQAAAACwCYJ0AAAAAABsgiAdAAAAAACbCAv0AAIlJiYm0ENol8PhUHR0tGJiYuR2uwM9HKBHMM8RLJjrCAbMcwQL5jo6qzPxZ9AF6eYXJysrK8AjAQAAAAAEk5iYGFVUVLR5Togkwz/DsY+hQ4e2+4Wxg5iYGGVlZWnYsGG9YrxAVzDPESyY6wgGzHMEC+Y6uiImJkbZ2dntnhd0K+mSOvSFsZOKigp++NHnMc8RLJjrCAbMcwQL5jo6o6NzhcJxAAAAAADYBEE6AAAAAAA2QZBuY3V1dbrvvvtUV1cX6KEAPYZ5jmDBXEcwYJ4jWDDX0ZOCsnAcAAAAAAB2xEo6AAAAAAA2QZAOAAAAAIBNEKQDAAAAAGATBOkAAAAAANgEQbpN3XLLLcrIyFBNTY22bNmiOXPmBHpIQKecffbZevPNN5WVlSXDMHTxxRc3O+dXv/qVsrOzVV1drQ8//FDjxo3zuT8hIUEvvviiysrKVFJSomeeeUbR0dH+eglAu+666y59/vnnKi8vV15enl577TVNmDDB55yIiAg98cQTKiwsVEVFhV599VUNHjzY55wRI0bo7bffVlVVlfLy8vTggw8qNDTUny8FaNXNN9+snTt3qqysTGVlZdq8ebMuvPBC637mOPqqO++8U4Zh6NFHH7VuY77DXwwOex1XXnmlUVtba1x33XXG5MmTjaeeesooLi42Bg0aFPCxcXB09LjwwguN3/zmN8Yll1xiGIZhXHzxxT7333HHHUZJSYnx7W9/2zj11FON119/3Th06JARERFhnfPuu+8a27dvN8444wxj/vz5Rnp6uvHSSy8F/LVxcJjHe++9ZyxfvtyYMmWKMX36dOPtt982MjMzjf79+1vn/PnPfzaOHDlinHfeecbpp59ubN682di4caN1v8PhMNLS0owPPvjAmDFjhnHhhRca+fn5xu9+97uAvz4ODknGkiVLjIsuusgYN26cMX78eOO3v/2tUVdXZ0yZMsWQmOMcffOYPXu2cfjwYWPHjh3Go48+at3OfOfw0xHwAXCccGzZssVYuXKl9XFISIhx/Phx48477wz42Dg4unK0FKRnZ2cbP/nJT6yPY2NjjZqaGmPZsmWGJGPSpEmGYRjGrFmzrHO+/vWvGy6XyxgyZEjAXxMHR0tHUlKSYRiGcfbZZxuSZ17X1dUZl112mXXOxIkTDcMwjLlz5xqS54KW0+k0Bg8ebJ1z0003GaWlpUa/fv0C/po4OFo6ioqKjOuvv545ztEnj+joaGP//v3G+eefb6xbt84K0pnvHP46SHe3mX79+mnWrFlau3atdZthGFq7dq3mzZsXwJEB3Wf06NEaMmSIzzwvLy/XZ599Zs3zefPmqaSkRFu3brXOWbt2rdxut+bOnev3MQMdERcXJ0kqLi6WJM2aNUvh4eE+c33//v06cuSIz1zftWuX8vPzrXPef/99xcXFaerUqX4cPdA+h8OhZcuWKTo6Wqmpqcxx9EmrVq3SO++8o48++sjnduY7/CUs0AOAr6SkJIWFhSkvL8/n9ry8PE2aNClAowK6V0pKiiS1OM/N+1JSUnz+wEmSy+VScXGxdQ5gJyEhIXrssce0ceNG7dmzR5JnHtfV1amsrMzn3BPneks/C+Z9gB1MmzZNqampioyMVGVlpZYuXap9+/Zp5syZzHH0KcuWLdPpp5/eYj0ofqfDXwjSAQDoBqtWrdK0adO0YMGCQA8F6Hb79+/XzJkzFRcXp8svv1yrV6/WwoULAz0soFsNHz5cjz/+uBYvXqy6urpADwdBjHR3myksLJTT6VRycrLP7cnJycrNzQ3QqIDuZc7ltuZ5bm5us2qpoaGhSkxM5GcBtrNy5UotWbJE5513nrKysqzbc3NzFRERYaXBm06c6y39LJj3AXbQ0NCgQ4cOadu2bbrnnnu0c+dOrVixgjmOPmXWrFlKTk7Wtm3b1NDQoIaGBp177rm69dZb1dDQoLy8POY7/IIg3WYaGhq0detWnX/++dZtISEhOv/885WamhrAkQHdJyMjQzk5OT7zPCYmRnPnzrXmeWpqqhISEnT66adb5yxatEgOh0OfffaZ38cMtGblypVaunSpFi1apMzMTJ/7tm7dqvr6ep+5PmHCBI0aNcpnrp966qkaNGiQdc7ixYtVVlamvXv3+uU1AJ3lcDgUERHBHEef8tFHH2natGmaOXOmdXzxxRd66aWXNHPmTH355ZfMd/hNwKvXcfgeV155pVFTU2Nce+21xqRJk4y//OUvRnFxsU+VSA4Oux/R0dHGjBkzjBkzZhiGYRg//vGPjRkzZhgjRowwJE8LtuLiYuNb3/qWMW3aNOO1115rsQXb1q1bjTlz5hhnnXWWsX//flqwcdjqWLVqlVFSUmKcc845RnJysnVERkZa5/z5z382MjMzjXPPPdc4/fTTjU2bNhmbNm2y7jfb9axZs8aYPn26ccEFFxh5eXm06+GwzXH//fcbZ599tjFq1Chj2rRpxv3332+4XC7ja1/7miExxzn69tG0urvEfOfw2xHwAXC0cPzwhz80MjMzjdraWmPLli3GGWecEfAxcXB05li4cKHRkueee84651e/+pWRk5Nj1NTUGB9++KExfvx4n+dISEgwXnrpJaO8vNwoLS01nn32WSM6Ojrgr42Dwzxas3z5cuuciIgI44knnjCKioqMyspK4z//+Y+RnJzs8zwjR4403nnnHaOqqsrIz883HnroISM0NDTgr4+DQ5LxzDPPGBkZGUZtba2Rl5dnfPjhh1aALjHHOfr2cWKQznzn8McR4v0PAAAAAAAIMPakAwAAAABgEwTpAAAAAADYBEE6AAAAAAA2QZAOAAAAAIBNEKQDAAAAAGATBOkAAAAAANgEQToAAAAAADZBkA4AAAAAgE0QpAMAgG6VkZGhFStWBHoYAAD0SgTpAAD0Ys8995xee+01SdK6dev06KOP+u1zL1++XCUlJc1unzNnjp5++mm/jQMAgL4kLNADAAAA9tKvXz81NDR0+fGFhYXdOBoAAIILK+kAAPQBzz33nM4991z9+Mc/lmEYMgxDo0aNkiRNnTpV7777rioqKpSbm6sXXnhBAwcOtB67bt06rVy5Uo8++qgKCgr0/vvvS5Juu+02paWlqbKyUkePHtWqVasUHR0tSVq4cKGef/55xcfHW5/v3nvvldQ83X3EiBF6/fXXVVFRobKyMr3yyisaPHiwdf+9996r7du365prrlFGRoZKS0v18ssva8CAAT3+dQMAwG4I0gEA6ANWrFihzZs36+mnn1ZKSopSUlJ07NgxxcXF6eOPP9b27ds1e/ZsXXjhhUpOTta//vUvn8cvX75c9fX1mj9/vm6++WZJktvt1q233qqpU6dq+fLlWrRokR588EFJ0ubNm7VixQqVlZVZn+/hhx9uNq6QkBC98cYbSkxM1MKFC7V48WKNGTNGr7zyis95Y8eO1SWXXKIlS5ZoyZIlWrhwoe66664e+moBAGBvBgcHBwcHB0fvPJ577jnjtddeMyQZ69atMx599FGf+3/+858ba9as8blt2LBhhmEYxvjx463Hbd26td3PddlllxkFBQXWx8uXLzdKSkqanZeRkWGsWLHCkGR87WtfMxoaGozhw4db90+ePNkwDMOYPXu2Icm49957jcrKSmPAgAHWOX/4wx+M1NTUgH99OTg4ODg4/H2wJx0AgD5sxowZOu+881RRUdHsvrFjx+rAgQOSpK1btza7//zzz9fdd9+tSZMmKTY2VmFhYYqKilJUVJRqamo69PknT56sY8eO6fjx49Zt+/btU0lJiSZPnqwvv/xSkpSZmanKykrrnJycHJ+UeAAAggVBOgAAfdiAAQP01ltv6c4772x2X05OjvX/qqoqn/tGjRqlt99+W08++aR+/vOfq7i4WAsWLNDf/vY3hYeHdzhI76gTC9UZhiGHg115AIDgQ5AOAEAfUV9fr9DQUJ/btm3bpssuu0yZmZlyuVwdfq5Zs2bJ4XDoJz/5iQzDkCRdeeWV7X6+E+3bt08jRozQ8OHDrdX0yZMnKyEhQXv37u3weAAACBZcogYAoI/IzMzU3LlzNWrUKA0cOFAhISFatWqVEhMT9fLLL2v27NkaM2aMLrjgAv3tb39rc6X64MGDCg8P1//93/9p9OjRuuaaa6yCck0/X0xMjBYtWqSBAwcqKiqq2fOsXbtWu3bt0ksvvaTTTjtNc+bM0QsvvKBPPvmkxRR7AACCHUE6AAB9xMMPPyyXy6W9e/eqsLBQI0eOVE5OjubPn6/Q0FB98MEH2rVrlx577DGVlpbK7Xa3+lxpaWm67bbbdOedd2r37t367ne/q7vvvtvnnNTUVD355JN65ZVXVFhYqDvuuKPF57r44otVUlKiTz/9VGvXrtXhw4e1bNmybn3tAAD0FSHyVJADAAAAAAABxko6AAAAAAA2QZAOAAAAAIBNEKQDAAAAAGATBOkAAAAAANgEQToAAAAAADZBkA4AAAAAgE0QpAMAAAAAYBME6QAAAAAA2ARBOgAAAAAANkGQDgAAAACATRCkAwAAAABgE/8fzHk3Y44ZNN0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.title(f\"{str(algo)} episode reward mean during training\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Reward\")\n",
    "plt.grid(alpha=0.2)\n",
    "plt.plot([x[\"episode_reward_mean\"] for x in results_history])\n",
    "# plt.show()\n",
    "plt.savefig(figure_save_dir / f\"training-{str(algo)}-step{algo.get_state()['iteration']}.png\", format=\"png\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learner': {'default_policy': {'custom_metrics': {},\n",
       "   'learner_stats': {'mean_q': 6.4511237144470215,\n",
       "    'min_q': 2.7953126430511475,\n",
       "    'max_q': 11.732878684997559,\n",
       "    'cur_lr': 0.0001},\n",
       "   'model': {},\n",
       "   'num_grad_updates_lifetime': 45300.0,\n",
       "   'diff_num_grad_updates_vs_sampler_policy': 45299.0,\n",
       "   'td_error': array([-0.0441637 , -0.49767232, -0.35539436, -0.00836372,  0.06586075,\n",
       "          -0.02716875,  0.29449964,  4.391131  , -0.37357712, -0.4998498 ,\n",
       "           0.03519106, -0.51175594,  0.12311363,  0.21552753, -0.37927818,\n",
       "          -0.44405413, -0.863081  , -0.30643225, -0.13228798, -0.23849678,\n",
       "           0.22427464,  0.4519496 , -0.10166359, -0.24961376,  0.21788502,\n",
       "           0.1150279 , -0.27191353, -0.01780796,  0.04873657, -0.6020794 ,\n",
       "           0.23836851, -0.09158516], dtype=float32),\n",
       "   'mean_td_error': 0.012666456401348114}},\n",
       " 'num_env_steps_sampled': 454000,\n",
       " 'num_env_steps_trained': 1449600,\n",
       " 'num_agent_steps_sampled': 454000,\n",
       " 'num_agent_steps_trained': 1449600,\n",
       " 'last_target_update_ts': 453510,\n",
       " 'num_target_updates': 906}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"info\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-27 00:38:32,613\tINFO algorithm.py:882 -- Evaluating current policy for 10 episodes.\n",
      "2023-02-27 00:38:32,614\tINFO rollout_worker.py:905 -- Generating sample batch of size 1\n",
      "2023-02-27 00:38:32,618\tINFO sampler.py:610 -- Raw obs from env: { 0: { 'agent0': np.ndarray((1000,), dtype=float32, min=0.0, max=5.0, mean=0.521)}}\n",
      "2023-02-27 00:38:32,619\tINFO sampler.py:611 -- Info return from env: { 0: { 'agent0': { 'action': -1,\n",
      "                   'attacker_action': -1,\n",
      "                   'cycled': [],\n",
      "                   'members': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0),\n",
      "                   'modes': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0),\n",
      "                   'obs_probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295),\n",
      "                   'probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295),\n",
      "                   'reward': 0,\n",
      "                   'severities': np.ndarray((100, 4), dtype=float32, min=0.0, max=5.0, mean=1.008)}}}\n",
      "2023-02-27 00:38:32,620\tINFO sampler.py:853 -- Preprocessed obs: np.ndarray((1000,), dtype=float32, min=0.0, max=5.0, mean=0.521)\n",
      "2023-02-27 00:38:32,621\tINFO sampler.py:858 -- Filtered obs: np.ndarray((1000,), dtype=float32, min=0.0, max=5.0, mean=0.521)\n",
      "2023-02-27 00:38:32,624\tINFO sampler.py:1144 -- Inputs to compute_actions():\n",
      "\n",
      "{ 'default_policy': [ { 'data': { 'agent_id': 'agent0',\n",
      "                                  'env_id': 0,\n",
      "                                  'info': { 'action': -1,\n",
      "                                            'attacker_action': -1,\n",
      "                                            'cycled': [],\n",
      "                                            'members': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0),\n",
      "                                            'modes': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0),\n",
      "                                            'obs_probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295),\n",
      "                                            'probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295),\n",
      "                                            'reward': 0,\n",
      "                                            'severities': np.ndarray((100, 4), dtype=float32, min=0.0, max=5.0, mean=1.008)},\n",
      "                                  'obs': np.ndarray((1000,), dtype=float32, min=0.0, max=5.0, mean=0.521),\n",
      "                                  'prev_action': None,\n",
      "                                  'prev_reward': 0.0,\n",
      "                                  'rnn_state': None},\n",
      "                        'type': '_PolicyEvalData'}]}\n",
      "\n",
      "2023-02-27 00:38:32,645\tINFO sampler.py:1171 -- Outputs of compute_actions():\n",
      "\n",
      "{ 'default_policy': ( np.ndarray((1,), dtype=int64, min=80.0, max=80.0, mean=80.0),\n",
      "                      [],\n",
      "                      { 'action_dist_inputs': np.ndarray((1, 101), dtype=float32, min=4.701, max=5.158, mean=4.931),\n",
      "                        'action_logp': np.ndarray((1,), dtype=float32, min=0.0, max=0.0, mean=0.0),\n",
      "                        'action_prob': np.ndarray((1,), dtype=float32, min=1.0, max=1.0, mean=1.0),\n",
      "                        'q_values': np.ndarray((1, 101), dtype=float32, min=4.701, max=5.158, mean=4.931)})}\n",
      "\n",
      "2023-02-27 00:38:32,651\tINFO simple_list_collector.py:520 -- Trajectory fragment after postprocess_trajectory():\n",
      "\n",
      "{ 'agent0': { 'actions': np.ndarray((1,), dtype=int64, min=80.0, max=80.0, mean=80.0),\n",
      "              'agent_index': np.ndarray((1,), dtype=int32, min=0.0, max=0.0, mean=0.0),\n",
      "              'eps_id': np.ndarray((1,), dtype=int64, min=9.880627846983086e+17, max=9.880627846983086e+17, mean=9.880627846983086e+17),\n",
      "              'infos': np.ndarray((1,), dtype=object, head={'action': -1, 'attacker_action': -1, 'reward': 0, 'members': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0), 'severities': np.ndarray((100, 4), dtype=float32, min=0.0, max=5.0, mean=1.008), 'probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295), 'obs_probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295), 'modes': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0), 'cycled': []}),\n",
      "              'new_obs': np.ndarray((1, 1000), dtype=float32, min=0.0, max=5.0, mean=0.527),\n",
      "              'obs': np.ndarray((1, 1000), dtype=float32, min=0.0, max=5.0, mean=0.521),\n",
      "              'rewards': np.ndarray((1,), dtype=float32, min=0.01, max=0.01, mean=0.01),\n",
      "              't': np.ndarray((1,), dtype=int32, min=0.0, max=0.0, mean=0.0),\n",
      "              'terminateds': np.ndarray((1,), dtype=bool, min=0.0, max=0.0, mean=0.0),\n",
      "              'truncateds': np.ndarray((1,), dtype=bool, min=0.0, max=0.0, mean=0.0),\n",
      "              'unroll_id': np.ndarray((1,), dtype=int32, min=0.0, max=0.0, mean=0.0),\n",
      "              'weights': np.ndarray((1,), dtype=float32, min=1.0, max=1.0, mean=1.0)}}\n",
      "\n",
      "2023-02-27 00:38:32,654\tINFO rollout_worker.py:946 -- Completed sample batch:\n",
      "\n",
      "{ 'actions': np.ndarray((1,), dtype=int64, min=80.0, max=80.0, mean=80.0),\n",
      "  'agent_index': np.ndarray((1,), dtype=int32, min=0.0, max=0.0, mean=0.0),\n",
      "  'eps_id': np.ndarray((1,), dtype=int64, min=9.880627846983086e+17, max=9.880627846983086e+17, mean=9.880627846983086e+17),\n",
      "  'infos': np.ndarray((1,), dtype=object, head={'action': -1, 'attacker_action': -1, 'reward': 0, 'members': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0), 'severities': np.ndarray((100, 4), dtype=float32, min=0.0, max=5.0, mean=1.008), 'probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295), 'obs_probs': np.ndarray((100, 4), dtype=float32, min=0.0, max=0.988, mean=0.295), 'modes': np.ndarray((100,), dtype=float32, min=0.0, max=0.0, mean=0.0), 'cycled': []}),\n",
      "  'new_obs': np.ndarray((1, 1000), dtype=float32, min=0.0, max=5.0, mean=0.527),\n",
      "  'obs': np.ndarray((1, 1000), dtype=float32, min=0.0, max=5.0, mean=0.521),\n",
      "  'rewards': np.ndarray((1,), dtype=float32, min=0.01, max=0.01, mean=0.01),\n",
      "  't': np.ndarray((1,), dtype=int32, min=0.0, max=0.0, mean=0.0),\n",
      "  'terminateds': np.ndarray((1,), dtype=bool, min=0.0, max=0.0, mean=0.0),\n",
      "  'truncateds': np.ndarray((1,), dtype=bool, min=0.0, max=0.0, mean=0.0),\n",
      "  'unroll_id': np.ndarray((1,), dtype=int32, min=0.0, max=0.0, mean=0.0),\n",
      "  'weights': np.ndarray((1,), dtype=float32, min=1.0, max=1.0, mean=1.0)}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'evaluation': {'episode_reward_max': 0.18999999999999997,\n",
       "  'episode_reward_min': 0.18999999999999997,\n",
       "  'episode_reward_mean': 0.18999999999999997,\n",
       "  'episode_len_mean': 9.0,\n",
       "  'episode_media': {},\n",
       "  'episodes_this_iter': 1,\n",
       "  'policy_reward_min': {},\n",
       "  'policy_reward_max': {},\n",
       "  'policy_reward_mean': {},\n",
       "  'custom_metrics': {},\n",
       "  'hist_stats': {'episode_reward': [0.18999999999999997],\n",
       "   'episode_lengths': [9]},\n",
       "  'sampler_perf': {'mean_raw_obs_processing_ms': 1.4545484022660689,\n",
       "   'mean_inference_ms': 5.639206279407848,\n",
       "   'mean_action_processing_ms': 0.09085915305397727,\n",
       "   'mean_env_wait_ms': 0.6365776062011719,\n",
       "   'mean_env_render_ms': 0.0},\n",
       "  'num_faulty_episodes': 0,\n",
       "  'num_agent_steps_sampled_this_iter': 10,\n",
       "  'num_env_steps_sampled_this_iter': 10,\n",
       "  'timesteps_this_iter': 10}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {'num_env_steps_sampled': 454000,\n",
       "             'num_env_steps_trained': 1449600,\n",
       "             'num_agent_steps_sampled': 454000,\n",
       "             'num_agent_steps_trained': 1449600,\n",
       "             'last_target_update_ts': 453510,\n",
       "             'num_target_updates': 906})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo._counters"
   ]
  }
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