examples/quickstart/online-bandit-with-delay-simulation.ipynb

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    "### Delayed rewards for bandit algorithms\n",
    "Literature on bandit algorithms frequently does not take into account that rewards are not observed instantly. In this notebook, we demonstrate the impact of this delayed feedback on known bandit algorithms using a simulation.\n",
    "\n",
    "The simulation works by sampling delay from an exponential distribution as described by *Ktena, Sofia Ira, et al.*[1]. In future experiments it would also be interesting to prior the distribution with the values in context $c$.\n",
    "This delay is expressed in $d_i$ representing the amount of rounds the algorithm has to wait before being allowed to observe the reward.\n",
    "\n",
    "Key assumptions\n",
    "- Delay is independent of the context, action and reward. In real-world applications there might be a connection between these.\n",
    "- Feedback is never received in the same round as the prediction that is being made. In high volume traffic, it's highly unlikely that a user is able to respond to an action before the next user requests a prediction.\n",
    "\n",
    "Open questions:\n",
    "- Should the model be aware of the delay it took for getting the reward?\n",
    "- What is the default behaviour when actions are taken but rewards are not available yet?\n",
    "\n",
    "[1] [Ktena, Sofia Ira, et al. \"Addressing delayed feedback for continuous training with neural networks in CTR prediction.\" Proceedings of the 13th ACM conference on recommender systems. 2019.](https://arxiv.org/pdf/1907.06558.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
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   "outputs": [],
   "source": [
    "import copy\n",
    "from functools import partial\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from obp.simulator.coefficient_drifter import CoefficientDrifter\n",
    "from obp.simulator.delay_sampler import ExponentialDelaySampler\n",
    "from obp.simulator.simulator import BanditEnvironmentSimulator, BanditPolicySimulator\n",
    "\n",
    "from obp.policy.offline import IPWLearner\n",
    "from obp.dataset import logistic_sparse_reward_function\n",
    "from obp.policy import Random, EpsilonGreedy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "experiment_round_size = np.arange(start=1000, stop=20001, step=100).tolist()\n",
    "\n",
    "n_runs_per_round_size = 1000\n",
    "max_scale = 1000.0\n",
    "min_scale = 900.0\n",
    "n_actions = 4\n",
    "dim_context = 5\n",
    "reward_func = partial(logistic_sparse_reward_function, z_score=False)\n",
    "\n",
    "bandit_policies = [\n",
    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
    "]\n",
    "\n",
    "dummy_policies = [policy_class(**args) for policy_class, args in bandit_policies]\n",
    "train_rewards = [{policy.policy_name + f\" {type} delay\": [] for policy in dummy_policies} for type in [\"no\", \"RI\", \"RD\"]]\n",
    "train_rewards = {k: v for d in train_rewards for k, v in d.items()}\n",
    "eval_rewards = {**copy.deepcopy(train_rewards), **{IPWLearner.__name__: []}}\n",
    "train_rewards[\"n_rounds\"] = []\n",
    "eval_rewards[\"n_rounds\"] = []\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get reward dependent delays & no delay data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 27min 19s, sys: 21min 27s, total: 1h 48min 46s\n",
      "Wall time: 1h 18min 41s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "delay_function = ExponentialDelaySampler(min_scale=min_scale, max_scale=max_scale, random_state=12345).exponential_delay_function_expected_reward_weighted\n",
    "\n",
    "\n",
    "for experiment in range(n_runs_per_round_size):\n",
    "    env = BanditEnvironmentSimulator(\n",
    "        n_actions=n_actions,\n",
    "        dim_context=dim_context,\n",
    "        reward_type=\"binary\", # \"binary\" or \"continuous\"\n",
    "        reward_function=reward_func,\n",
    "        delay_function=delay_function,\n",
    "        random_state=12345 + experiment,\n",
    "    )\n",
    "    sample_n_rounds = max(experiment_round_size)\n",
    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
    "\n",
    "    # print((np.arange(0, len(training_bandit_dataset[\"round_delays\"])) + training_bandit_dataset[\"round_delays\"] < sample_n_rounds).sum())\n",
    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
    "    # Take into account all the delays during training\n",
    "    for policy_class, args in bandit_policies:\n",
    "        policy = policy_class(**args)\n",
    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
    "\n",
    "        for n_rounds in experiment_round_size:\n",
    "            train_rewards[policy.policy_name + \" RD delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
    "\n",
    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
    "    # Ignore delays during training\n",
    "    training_bandit_batch.round_delays = None\n",
    "    for policy_class, args in bandit_policies:\n",
    "        policy = policy_class(**args)\n",
    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
    "\n",
    "        for n_rounds in experiment_round_size:\n",
    "            train_rewards[policy.policy_name + \" no delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
    "\n",
    "    for n_rounds in experiment_round_size:\n",
    "        train_rewards[\"n_rounds\"].append(n_rounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 12min 4s, sys: 22min 2s, total: 1h 34min 6s\n",
      "Wall time: 1h 3min 41s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "for experiment in range(n_runs_per_round_size):\n",
    "    delay_function = ExponentialDelaySampler(max_scale=max_scale, random_state=12345).exponential_delay_function\n",
    "\n",
    "    env = BanditEnvironmentSimulator(\n",
    "        n_actions=n_actions,\n",
    "        dim_context=dim_context,\n",
    "        reward_type=\"binary\", # \"binary\" or \"continuous\"\n",
    "        reward_function=reward_func,\n",
    "        delay_function=delay_function,\n",
    "        random_state=12345 + experiment,\n",
    "    )\n",
    "\n",
    "    sample_n_rounds = max(experiment_round_size)\n",
    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
    "\n",
    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
    "    # Take into account all the delays during training\n",
    "    for policy_class, args in bandit_policies:\n",
    "        policy = policy_class(**args)\n",
    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
    "\n",
    "        for n_rounds in experiment_round_size:\n",
    "            train_rewards[policy.policy_name + \" RI delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_average_reward_per_n_rounds(rewards):\n",
    "    rewards_pd = pd.DataFrame(rewards)\n",
    "    rewards_pd = pd.melt(rewards_pd, ['n_rounds'])\n",
    "    rewards_pd[\"average reward\"] = rewards_pd[\"value\"] / rewards_pd[\"n_rounds\"]\n",
    "\n",
    "    plot = sns.lineplot(data=rewards_pd, x=\"n_rounds\", y=\"average reward\", style=\"variable\", hue=\"variable\", markers=True, dashes=False)\n",
    "    plot.legend(bbox_to_anchor=(1.1, 1.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "del train_rewards[\"egreedy_1.0 RD delay\"]\n",
    "del train_rewards[\"egreedy_1.0 RI delay\"]\n",
    "\n",
    "plot_average_reward_per_n_rounds(train_rewards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Delay under constant drift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "experiment_round_size = np.arange(start=1000, stop=20001, step=100).tolist()\n",
    "\n",
    "n_runs_per_round_size = 10000\n",
    "max_scale = 5000.0\n",
    "n_actions = 4\n",
    "dim_context = 5\n",
    "reward_func = partial(logistic_sparse_reward_function, z_score=False)\n",
    "\n",
    "bandit_policies = [\n",
    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
    "]\n",
    "\n",
    "dummy_policies = [policy_class(**args) for policy_class, args in bandit_policies]\n",
    "train_rewards = [{policy.policy_name + f\" {type} delay\": [] for policy in dummy_policies} for type in [\"drift + no\", \"drift +\", \"no drift +\"]]\n",
    "train_rewards = {k: v for d in train_rewards for k, v in d.items()}\n",
    "eval_rewards = {**copy.deepcopy(train_rewards), **{IPWLearner.__name__: []}}\n",
    "train_rewards[\"n_rounds\"] = []\n",
    "eval_rewards[\"n_rounds\"] = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7h 49min 13s, sys: 7h 53min 49s, total: 15h 43min 2s\n",
      "Wall time: 5h 54min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%%capture\n",
    "\n",
    "delay_function = ExponentialDelaySampler(min_scale=min_scale, max_scale=max_scale, random_state=12345).exponential_delay_function\n",
    "\n",
    "\n",
    "for experiment in range(n_runs_per_round_size):\n",
    "    drifter = CoefficientDrifter(\n",
    "        drift_interval=5000,\n",
    "        transition_period=100,\n",
    "        transition_type=\"weighted_sampled\",  # linear or weighted_sampled\n",
    "        seasonal=True,\n",
    "        base_coefficient_weight=.3,\n",
    "        random_state=1234 + experiment,\n",
    "    )\n",
    "\n",
    "    env = BanditEnvironmentSimulator(\n",
    "        n_actions=n_actions,\n",
    "        dim_context=dim_context,\n",
    "        coef_function=drifter.get_coefficients,\n",
    "        reward_function=reward_func,\n",
    "        delay_function=delay_function,\n",
    "        random_state=12345 + experiment,\n",
    "    )\n",
    "\n",
    "    sample_n_rounds = max(experiment_round_size)\n",
    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
    "\n",
    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
    "    # Take into account all the delays during training\n",
    "    for policy_class, args in bandit_policies:\n",
    "        policy = policy_class(**args)\n",
    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
    "\n",
    "        for n_rounds in experiment_round_size:\n",
    "            train_rewards[policy.policy_name + \" drift + delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
    "\n",
    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
    "    # Ignore delays during training\n",
    "    training_bandit_batch.round_delays = None\n",
    "    for policy_class, args in bandit_policies:\n",
    "        policy = policy_class(**args)\n",
    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
    "\n",
    "        for n_rounds in experiment_round_size:\n",
    "            train_rewards[policy.policy_name + \" drift + no delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
    "\n",
    "    env = BanditEnvironmentSimulator(\n",
    "        n_actions=n_actions,\n",
    "        dim_context=dim_context,\n",
    "        reward_function=reward_func,\n",
    "        delay_function=delay_function,\n",
    "        random_state=12345 + experiment,\n",
    "    )\n",
    "\n",
    "    sample_n_rounds = max(experiment_round_size)\n",
    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
    "\n",
    "    for policy_class, args in bandit_policies:\n",
    "        policy = policy_class(**args)\n",
    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
    "\n",
    "        for n_rounds in experiment_round_size:\n",
    "            train_rewards[policy.policy_name + \" no drift + delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
    "\n",
    "\n",
    "    for n_rounds in experiment_round_size:\n",
    "        train_rewards[\"n_rounds\"].append(n_rounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NgH6jEsTJSlcKsUwAsQn49gMY22KdXABgjK2GcF/+gXO+rI1tM1segDF2G4DbAKBPnz6dNnCCINoP5xwa16JixyJZoqKpLiRmzNsVO3SuQ+c6GBga1Uaougqv3YuAGkBduA6J9kSE9TAONR6CXbEj3haP2lAtAMAiW+AL+xA7uci0BEmSBKtsRUO4AQYXs9UNiFerZIXb6m5mKdIMLTomU+wZ3Ii2t1y/M3FamlfoseDI+Ya8Vi80Q4NNFi7IsBGGVbI2Ww4bYbgtbpT5yuC0OJFg75JJaQRBdBE9ndBVATAQwHkAsgCsYowNa+/GnPOXAbwMiFmTXTFAgjgV4ZxHXWlAkxAJ62GohgpZkqPuNZNDjYcQ1IKwKTb4Vb9Yj8nQuS5cZWDNhBKHcOtxxsENYVliEfOSIimQmISqYBUkSYJNtqHUVwrGGOLt8VANFVXBKthkGxhj0LmOOFtcpwgkRTr8a09iUtRl6bV6ITO5mdBpKX5il7uyX+c6Ll18KeZOngsAuHn5zYct37z85uh5LJu+DAkgIUYQJxNdKcRKAfSOeZ8VaYtlP4A1nHMVwB7G2HYIYVYKIc5it13RZSMliJMU1VChMOHG0w1hcTK4gYpABcobyxEyQnBZXIi3xUMzNHDOwRhDhb9CbBsRJboRSYHUSo5q0/Jkk0VcVEgXQeJO5owGl7d0I7aXWAuRafUBAKtsjQojAFCgtGkd6mwhpHMdZY1lzYTO0YRQV/YTBHFq05WRnWsBDGSM9WOMWQHMAPBhi3UWIyK4GGPJEK7K3QCWA7iQMZbAGEsAcGGkjSBOOQxuIKyHEdACqAvVocJfEbVEhfUwDjQcwMaKjdhXvw+lDaXYWrUV3x36Dl8f+BprDqzBmrI12Fy5GWsOrsG3Zd+i8GAhiuuKIUkSPFYPNEPDAd8BVAYqURWsQmWgEg6LA4mORLitbritbiQ4EsSf/fC/eHs8EuwJcFqcsMgW2BV7NIZLluTDRJjX6kWiLRFui7vZa1vL7e03rUP7ffux37cfU96f0uy1reVj7S/3l7f2MREnA9s+9uDZkXmo2680Wz6JyczMHFZWVnbM5/Dcc88l9e3bd2jfvn2HPvfcc0mtrTN37tyEnJycfEmSCswkr0dj+vTp2a+++mqrZtd77rknY/HixR4AWLZsmTsnJyd/8ODBeZ9++qlr4cKF3ta26QhLly71TJw4Maej6/Q0XXaDcs41xthdEAJKBjCXc76ZMfYIgELO+YdoElxFAHQAD3DOqwCAMfYohJgDgEfMwH2COBlRDRV+1Q+d65CZjKAWRG2oFg3hBgS1oHDbcSbceBGrlV22I6AFIDEJdsWOA74DMGDAKlmhSEo0lkkzNAT0ADxWT6uuO7tih12xtzIq4ZKLtTSd6Bal0426UB3mTp6LLHcWAGDptKXRa20uL522FACgMEVMODhd2faxB+/8LAe6xvD2z/rj0A8u6BrDZ49k4IqXi3tiSKqq4ljrDnYGhw4dkp944omMdevWFUmShJEjR+bNmDGjNiUlpVn2/zPOOCPw3nvv7bz11luzO3pMTdPwt7/97YD5fv78+Yn33Xdf2c9//vPqZ599NqmwsNB19dVXH7HMUntrTZ5qdOkvBc75RwA+atH2cMwyB3Bf5K/ltnMBnJ7fvsQJj6qr0Vgq3dAR0ALCkmWEYJEsCKgBqIYKi2SBaqjCLRiJowIAMOF+s8k2OCyOw/ZvcAO6oSPRkRhta0tMJdmTjiikYpfbajPjkDrTtXayUheqQ25CbjOh01L8xC53ZT8gXMM611EXbvsZVhOowfjM8ad2+orFv+iN8qK2lWZ5kRNaSFyA0nUeRGa24od3k1G54/D/ZACQmufH5S+UtNoX4cUXX0z8xz/+kaaqKhs1alTj/Pnz9ymKgmeeeSb573//ey+Px6Pn5+f7rVYrnz9/fvH06dOzbTabsWnTJueYMWN89957b8Udd9zRp7q6WrHb7cacOXP2jRw5MnjgwAFl5syZfUtLS60A8PTTTxdfeOGFjQcPHpSnT5/e/9ChQ9aCggKfGXd5zz33ZCQmJmoPP/xwOQD88pe/zExNTVVnzZp1mAl38eLF3nPOOafeLGt0zjnn1C9atMh7++23NzNotKccj2EYuOmmm/qsWrUqLiMjI2yxWKL1GzMzM4dNnTq1euXKlXH33HPPweXLl3unTJlSV1NTI//nP/9JXLlypffjjz/2FhYWuoPBoDR48GD3r371q7Jbb731uKf5vvvuu3EPPPBAb4fDYYwZMyZaZqi+vl76n//5nz5bt251aJrGfvvb3x5omWT3888/d9577719QqGQZLfbjXnz5u0ZMWJEaPTo0YOeffbZ4vHjxwcAoKCgYNDzzz9fPG7cuMDxjvNYOKlNtgTR2YT1MMJ6uFl6grARhqqr8Fg9COpB7Kvbh0a1sWmjSO4nqyIsVaomYq+sihW6ocOqWI/5AZlgS2h3PNTRhFTs8sksmkzrUJozDRbJ0mVCyERmMqpDJ64h3pwhSkRIygmgZq8d4UY5KsLAgPg+x52Ffv369fZ33303sbCwcKvNZuPXX399n5deeilpypQp9bNnz05fv359UXx8vDF+/Pjc/Pz86EO7rKzMun79+q2KomDcuHG5L7/88r5hw4aF/vvf/7ruvPPOPt98883222+/vfd99913aPLkyb4dO3ZYJ0+ePHD37t2bH3rooYxx48b5Zs+eXbZgwQLv22+/nQwAd955Z+W0adMGPPzww+W6rmPx4sUJa9eubTWRaGlpqSUrKytaozIzMzNcWlp6XKa5119/PX7nzp22nTt3btq/f79l2LBh+TfddFPUYpWUlKQVFRVtAYDly5d7AeC+++6rXL16tXvKlCl1M2fOrDEtYvPnz++QZdLv97O77ror+5NPPtmWn58fmjJlSn+z7ze/+U36xIkT69955529lZWV8ujRo4dMnTq1Pnb7ESNGBNeuXbvVYrFg8eLFngcffDBr+fLlu372s59VzpkzJ3n8+PElGzdutIVCIam7RBhAQuy0xxfS4LDIkKXjC7Y+mTC4IdJhMoagFkRAC0CWZATVIGpCNagJ1kDjmrBacYCzyJd5zHxcDi5SBDjaNzNNko/u+otdbk1cASeXkOoqi1KqMxUMTFgLuQ6fesy1hzsN0yJqcAOKpMAm26KZ8CUmRSdBBLQAOOeQmISQJvQAk4Tb2SpboRoqVF2FxrWmeyHyQ4CxyCzUGE0RhaNZ3jG31d1kbT1VOYrlKuqajP0PKykcvX/UgCteOS4BsGzZMs+mTZucI0aMGAIAwWBQSk1N1b744gvX2LFjG0yL07Rp02q2b98eNVlfccUVNYqioK6uTvruu+/cV1555QCzLxwOMwBYvXp13I4dTZY6n88n19XVSd98841n0aJFOwFgxowZdbfffrsOAIMGDQrHx8drq1evdpSVlVny8/P9vXr1arXQeGeycuVKz1VXXVWtKAqys7PVcePGNcT233jjjR1KYhdbHaCystJisViMF198MQ0AVqxYsS32HL///nt7VlZWyCzxdN1111XNmTMnJbJu3PLly+OfffbZXgAQCoXYzp07rbHHqq6ulq+++up+e/futTPGuKqqDABuuummmieffDI9FArtf+mll5Kvvfbabq1vSkLsFEY3OEKajtKaAMobQnBYZciRwGqXTUZDUEOtX4VFZshJ9aCXt3XX18mGZmgIaAEY3IBFsqA+XI9D/kPwhX2QmYw4axyqQlVClHExI9CqWI8rqWZ7Uh0cr8Wqu4i1NJ3MFiXN0KJWIr/qh8ENWGVhjVQNNTqpQDf0w7LXN8toz9EkgmLazXsFDLDLdngsHlhkCxrUBtQGa+FQHLApNqi6Cl9YiMQEW0I0oWt2XDZsig11oTpUB6vhC/vgtDgRZ4+DQ3GgISyeb4meRHCI9CJuqzs6e1Q3RJoQi2SJnhcRw/Lf9IauiU9VthngBoOhMmz/vwQAxyXEOOfsyiuvrHrhhReazfh//fXX44+0ndvtNgBRpNvj8WitZb/nnGP9+vVbnE5nu1MvzZw5s3LOnDnJ5eXllpkzZ7YZR5WZmamuXLnSY74vLS21nnvuuQ1trd8RPB5Ph0yzsdUBOhIjxjnHu+++u3PEiBHNLKAHDhyIWgJ//etfZ5577rkNn3zyya5t27ZZzz///EHmOZx99tn1b775ZvyHH36Y+N1333VrtQISYicp/rAGf1gH54DEEH3IaLoBX0hDZUMIflUHAyBJDG6rAk3n0CO/FjnniHdYEO+0gIGhpNqPOIcCp/XkuCXMsjV+zY+GcANCWggGDATUAAJ6xKIcY0lwKA54bV7oXEej1oh4W/wRH2TtTZXQ3sD0ruRoQip2ubW2WEuTIikdFk9mSgvzMzL3KzEJOtejmfOtshUMDBrXoOoqDBjREj6m1QuAsE6a8XUthRET7QYMWJgFNsUGwzDQJ64P3BY3KgOV0A0dLqsLhiGeF+asT3M8FskCh+KICjmLbAHnvJmQM7gB1VDBOYdVth6Wi0w39MNEvDnpoiVx1jj09vQ+rJ3oID9bsh2fPZKB7csTMPlPxdiz0oPt/5eAaS/tPt5dXnTRRfVXXHFFzm9+85tDmZmZ2qFDh+S6ujp5woQJjQ899FDviooKOT4+Xv/ggw8ShgwZcpgrKzEx0cjKygrPnTs34eabb64xDANr1qxxjBs3LjBhwoT6v/zlL6mPPvroIQD46quvHOPHjw+ceeaZDfPmzUv661//Wvb222/H1dfXR2+sG264ofZPf/pTpqZpbPr06W2e1+WXX173yCOPZFZUVMgAsHLlyrhnnnnmuGpjnnvuuQ2vvPJKyl133VVVWlpq+eabbzzXXHPNMX1JxMXF6T6fr8O/HM4444xgaWmpdfPmzbb8/PzQggULokG0EydOrH/qqafS5s2bVyxJElavXu0466yzmn0m9fX1sumy/ec//5kc23fHHXdUTp8+PedHP/qRr+Wkhq7m5HjqnsYEwjpkicGqiHu4MaShrC6Ikmo/WFPcd6wxHookIdFlQZrXDlkS7zWdQ5IAgwMWiaHSF8YNc79FituGuycNRHayE1W+EOzxMqQTxE2pG7oQWboQWeaMML/mR2lDqZhtCECRFchMlJmRJRkJlrbdhgpToEhHz0nV067BY4mH6mwhZQoSBhZN+KoZGkJ6CKqhAgaiMXTNbr7IsiQJ4cI5R4I9AXbZjqAehGqocMgOpDhS0Kg2whf2weAGHBYHvA4vFKZELUFWxQqH7BCCDmImqcviiiZ4Nd2ApquZgTXLO2ZyLFnmW9veJNbd2BqtWVKPN7cacZx4s7TI7Ehh/Rp5fQ2O0xJmUlBQEPzd735XOmnSpFzDMGCxWPizzz5bPGnSpMZ77723bPTo0UO8Xq+Wk5MT9Hq9rT6833rrrd233npr3yeeeCJd0zQ2bdq06nHjxgVefvnlkltuuaVPbm5unq7rbOzYsQ3jx48vfvzxxw9Mnz69f05OTv7o0aN96enp0Vgvu93Ox48fXx8fH68rStv3Y1pamv7AAw8cKCgoGAIADz744AHTjXr11Vf3/cUvflFxzjnn+OfPnx//wAMP9KmpqVGmTZs2cMiQIf4vv/xyR+y+brjhhtrPPvssLicnZ2hGRkZo5MiRxxwTcPHFFzfMnj07ffDgwXkdCdZ3Op38ueee2zdlypQch8NhjB071ufz+WQAePzxxw/cdtttfQYPHpxnGAbr3bt36PPPP98Zu/2vf/3rg7fccku/J554IuPHP/5xbWzf2Wef7Xe5XPrMmTO71S0JACw2E/bJzOjRo3lhYWFPD+OYCWsGwroBVTOg6gb8YR2aYcBtU1DdqOJQvUhtoEgiAkTTOWSZIdVjg90iw+AcNkWCZgASE6YCU2j9/bPt+PnEHATCOl5dvQc/G98Pr321Bw9dPAQ3RkTY/ZMH4dfvbcT+mgCyEhz45/UFGJIe121iTDM0kX0dHFWBKoT1MKyyFbWhWlQGKqEZ2mEPNMYZnFZnmw/Olu5CoHWhNeX9Kd2SnLMuVAevzYs0Z5pwg7YzRixWXB1pxlwsphXGtODoRlPMkpnqIqyLyQdARCzwGGGFpmSquqEL6yIXViSP1QO3xQ2bYgMDgyIpcFqcUVewBJGd3qE4hLXQ0A8r6UOceDDG1nHOR/f0OFpjw4YNe0eMGNHtD8b2UFdXJ3m9XkNVVUyePDnnpptuqrzxxhtru/KYuq4jPz8/75133tllxkkRncPevXst55133qBdu3ZtkuXjr/vaFhs2bEgeMWJEdmt9ZBHrAVTdQEV9CAfqAmgIaWBoMiwokgSJAaV6AIokIclljTxYOewWCXaLDJvCUOVTcd1razC+fxKuH9cXz/93RzOhdfu/12HWlDzUNKqY9cEmzJqSh1+/txGzpuShujGM/TWBaFuK24ZZU/IQ77CgvCGEZHcQad7WZ3t3Bpxz1IfrcbDxICr8FVAkYdEK6CJ4nhscFtkCl8XVZszWkSxaLd2FQNfFYLUnML2jFivOOcJGOJoZH0CzLPnNfkxFNKvMZDgtTjgtTqiGGnUR2mQbkh3J8Nq80QBy001out0ciiMqflVDhQTpiLFzpmsvFptsE9kDCeIU5YEHHshYtWpVXCgUYueee259y1QJnc26devsl1122cCLL764hkRY5/L8888nPfbYY5l//vOfS7pChB0NEmLdgKobqA+o8Id1VDeGURsIg3PAbVOQ7Goq62K3SLAq0mHuRCnyUKz0hfHU/xVFhVaK24Y7zhuAG+d+26rQincIt5K5bL5WNYaRleBAvMPSqlXspesLYFEYEl0dD95XDRW1wVpUBaqiQcv1oXqohgqrbIXX7o3G4zith1tP2gqGb4/rsCO01zVo0p7AdDM3mOluM7ghimPrwejsOAYR9xTr7uPg8Fg9Iq4tUgwjzhoHq2xFo9ooyg0pTlHEGsJtZ2a+7ygWqfuTURLEycDLL798XDFXx0tBQUFw//79P8S2xc44NLFarcbGjRu3dufYTnbuuuuuqrvuuqvHksiSEOtCdIOjtMaPfdV+6AaHxBjsioxecXZ47Ap0A5BbCK0nl29t5k40l4OqEbVsxVq0YkVXS6FVG1BhlaXosvn63roSPDF9OPxhHXdPGhgVYSN7x+OO8wYgqOpoDBmwyRpc9mO7RXRDpBU44DuAmlBNNFbIrtjBIabxOywOeKTohB54bW2ndzhSMHxHOJrQOpZUCbEFtDnnIo4KTXFVUfcf53AoDgSNoLACSnL0WKawMq+hOZFANVTYFXuzOoyxxNvjD2sz47oIgji1iZ1xSJy8kBDrAnwhDapmYE+lD3UBDeleu0gdEbF0+UIa9lX52xRase5Ec/mpK0e0atGKFV0thdZrX4n9P/nT4Xh19Z5om+nCnHlWP/Ty2qMirKVl7MXrRiE3zQ275ci3SX24HlWBqmitRDOJaVsld1qzch0tvcOx0NJdCHQsJ5XBjWipIVN06YYugsQZg6ZrcCpOsQ8OuKwuSEyCy+JCb09v6IYOjWvo5erVpqAiCIIgTk9IiHUiQVXH7gofDtWHEGdXkOCyIiPejiqfij/9pwg/G98PYU1MoT+S0AJw2HJbFq1Y0dVSaE0v6A2LLKFXsh0PX5oPiQEPX5oPBvGq6RwWmSErwYE7zhtwmGUsrBmoaAgj1cNgszT5zQ1uoMJfgapgFfyqH37ND4tsgUWyROsfmrQmulqL4TpWjmTROpZ4rNigdTO43YABXW+aAMUZR4o9BYCYEWiTbXBZXACARrURCfYExFnjENbDYIyRO48gCIJoNyTEOoimG6huDKO0Voglr0PBoDQ3qv0qHv94S7PA+V+/txFPXTkCwJGFVqw70Vx+acWuVi1avzx/IJ777w5ML+iNeKelmeDSdA7GgFq/ioagBlli4BzQDAMhzYDBOTw2BS9eNwphzWjTMvaP60YhJ8UNm1VCdaAae+v3IqAF4LA4oEgKkhxJza5JrPjqqGuxrWD4Y7Fo6YaOkB5CSAuBgcFmsYmUGIYhAtMjtfzcVjdssg0KU6JWLbO0UVuz/5IdTalojpT+gCAIgiBag4TYccI5R0VDCNvLG6DpHC6rgv7JLjQENWw75DssnsuM4WqP0Ip1J5rLD7y7EbOXb8Pdkwaib5IzKrSsCsPvpuRHYtBE3rFAWEdYNxAI69A5hyIxOG0KwpoBRWJw2xRkxFvgsMqQGENNYxgJTqmZZSx2JmWlLwyXvQHF/s2QJQ6X1RUtRt0ZFq8jJSQ1aS0YnnMuBBU3oukXwrpIucPQNJtQkRTE2+LRz9sPqqGiKlCFNGcaEmwJcFlclO+JIAiC6DGoRsZxYBgcW8rqsam0HkkuK3LT3MhKFNP37/j3OjitcquB86bgSnBZ8ORPh+O9dSXR5QpfCLOXb8M1Y/oi2W1Dv2QXHp6Sj1SPWF5w65n424wzMCDVjUBYR3l9CGW1QWw/5MPuCh+Kqxqxt6oxYpkTOaiyEh0Y2TsB4wYkY1SfBJzZPwmjsxORn+lF70Qnkt02JLqsGJDqhsyAF68bhSSXNTqT8tGlRXj8YzH5hsGCLEceUhzpSHWkItGWCLfFDZ3r2O/bjynvT4m+lvvLj3j96kJ1UdGV5c7CwISBSHOmweBGVHBVh6rhU33wqT40qA3RPFphPYzaYC1qA7WoDdbCIlngtXmRaEtEoi0RA7wDMCJlBIanDMfItJEYmzEWZ2acicFJg5HsSEa6Kx1Dk4eit6e3qM9HIowgiBOYzMzMYWVlZcdsNHnuueeS+vbtO7Rv375Dn3vuuaTW1pk7d25CTk5OviRJBatWrerypH/btm2zDhw4ML+1vr1791ouuuiiaBHvSy+9tF9ubm7eH//4x9RHHnkktaGhodP1ypgxYwYd7bzbs05HIYvYMVDTGEZYN1DlC6ExpGNwuhv+sI59VX4EVQMeu9JmPJcZw/Xr9zbijx8W4Tc/GYJZU/KjsyYX3Hom9EheKH9Igz9sQDc4NMNAUNVhgMMmywjrBjgHHFYZiW4L4h1OOGwKLJK4R2WJwSKzYxIYQS0I2dKARq0WGZ6M6ExKU5CpvAHlgXqkxzlhYZZjsni15lo8WgxXWA/DH/ZH6/q1rPGXE58Dh8UBh+ygGYIEQUSZsGDCiLpQXfS55rV5tS9nfLmhp8ajqioslu7/jjp06JD8xBNPZKxbt65IkiSMHDkyb8aMGbUtS/ecccYZgffee2/nrbfemt3tg4xBVVVkZ2ery5Yt2w0AxcXFyoYNG1zFxcWbACFGb7311uqj1bXMzMwcVlpa+sOR1jkRISHWDgyDY29VI/ZUNiLeYUFqnA0GB7YdFHFJZrC9af1qLZ7LjOFacNuZ0A3hMvOHRdHtQKQmJCDSR0kMUGQJVlmC26agd4ITFkVCdWMILpuCZLfIqt9RgloQe+v3osJfAQYGSVGwoyaIAckDMGtqHwxIs8Iqh8GZhCnvT213jFes+DJpzbXoV/0IasEm0RjJnWVX7Ojn7Qev3QurZIXOdehch8zkaIkbgiBOL2atntV7Z83OI1omYkWY+f6apdcMamv9nIQc/6NnPVpypH2++OKLif/4xz/SVFVlo0aNapw/f/4+RVHwzDPPJP/973/v5fF49Pz8fL/VauXz588vnj59erbNZjM2bdrkHDNmjO/ee++tuOOOO/pUV1crdrvdmDNnzr6RI0cGDxw4oMycObNvaWmpFQCefvrp4gsvvLDx4MGD8vTp0/sfOnTIWlBQ4DNDLO65556MxMRE7eGHHy4HgF/+8peZqamp6qxZsw5zQSxevNh7zjnn1Jtljc4555z6RYsWeW+//fZmX8KjRo0KHuncAWDp0qWeRx55JCMxMVHdtm2bY9iwYf7FixfvkSQJH3zwgeehhx7qres6RowY4Z8/f/4+h8PRrFzPF1984bzllluyAeC8886rN9ufffbZpMWLFyf4/X5J13X273//e8+UKVMG7tixY/MFF1yQW15ebh08eHDeJZdcUlNeXm4599xzcxMSErQ1a9ZsP9qY28Ln87EZM2b0KyoqcgwYMCAYDAajD5NFixbFPfLIIxnhcJj17ds3tGDBgr1er7eZ8Lvuuuv6bNiwwRUMBqVLL7205plnnjnw4Ycfep599tnUTz/9dBcAvP/++3EvvvhiyieffLKrveMi1+RRUHUDW8rqsa/Kj/4pLtgsMnaWN0ZdkKYbMtb6FetmNOO5BqV54LYpqPOr2FMpRF15Qwh2i4S89DiM7Z+EMf2SMH5AMs7JTcX4AclRN2JGggMpHhsG9YpDVoKzQyLM4AZqgjXYW7cX6w6tQ02oBgn2BCQ4EuB1eJASJ8Nm8yM33YJpSy7BoUDpEV2NLd2MS6ctRW5CbtTNaLoWa0O1aFQbUROoQW2gFjXBGthkm3Ahpo7EsORhGJo8FKPSRmF0r9HI8GTAZXHBIltgV+xwWVwiYSmJMIIguon169fb33333cTCwsKtW7duLZIkib/00ktJe/futcyePTt9zZo1WwoLC7fu2LGjWfbrsrIy6/r167fOmTNn/y233NL3xRdfLN68efOWJ598cv+dd97ZBwBuv/323vfdd9+hTZs2bXn//fd33XHHHdkA8NBDD2WMGzfOt3Pnzs3Tpk2rLSsrswLAnXfeWblgwYIkQJQ6Wrx4ccKtt97aahLS0tJSi1ncGgAyMzPDpaWlx22a27Jli+OFF14o2blz5+bi4mLbJ5984vb7/ez222/vt3Dhwl3bt28v0jQNTz75ZErLbf/nf/4n+29/+1vxtm3bDst3tnnzZucHH3ywa+3atdti25csWbKzd+/eoa1btxY99dRTZampqerKlSu3d0SEAcDs2bNTHQ6HsXv37s2PPfbYgaKiIhcAlJWVKX/+85/TV61atb2oqGjLqFGj/I8++mhay+2ffvrp0k2bNm3ZunXr5tWrV3vWrFnjmDJlSsOuXbvsBw4cUABg7ty5Scdar5IsYkfAH9awp7IRcQ4FiW4rwpqBO/69LppqomWw/f2TB+G1r/Zg1pQ8EWvlsUE3OPZV+WFEMqe7bDLy0uPgsVtgt0jdJiw4F7Ucd9ftRkgPQZEVuK3uwwpgh40wVB5AZfDI4itq8YrUKWxp8TID6QOaqFXIGUeyPRkp3hRRRofcigRBHANHs1wBwLDXhhW0bHtrylvbWlu3PSxbtsyzadMm54gRI4YAQDAYlFJTU7UvvvjCNXbs2AbT4jRt2rSa7du3R8XYFVdcUaMoCurq6qTvvvvOfeWVVw4w+8LhMAOA1atXx+3YsSNaG8zn88l1dXXSN99841m0aNFOAJgxY0bd7bffrgPAoEGDwvHx8drq1asdZWVllvz8fH+vXr1aLTTe2QwbNqxxwIABKgDk5+f7d+3aZY2Li9OzsrJCw4cPDwHATTfdVPXCCy+kAog+PCorK+WGhgb54osv9gHAzTffXPXf//7Xa/afffbZUavd8XLDDTf0Wbt2rRsAysvLLYMHD84DgMsuu6z6iSeeOBi77pdffum+++67ywFg7NixgdzcXD8ArFixwrVr1y77mDFjBgOAqqqsoKDgsKn4r732WuK8efOSNU1jFRUVlg0bNtjHjh0buOqqq6peeeWVxF/84hdV69evdy9atGjPsZwDCbE2qGgIYl+VH3EOEffVWgzYSyt24fdT8w6b1Zid7IIsMTQEVBxqCKJXnB39U9wAAJvSfeLLzIvl1/wobShFTbAGHpsHLqvrMPHVWkJVk5azGnUdCGuAVXIAhhMbD+2Ey64j1WNH2AijUW0EB4fX6kVGfEa0/A4lMyUIoivx2rxayxixjuyPc86uvPLKqhdeeKE0tv3111+PP9J2brfbAITlyuPxaK1lv+ecY/369VucTic/fA+tM3PmzMo5c+Ykl5eXW2bOnNlmSZ7MzEx15cqV0fIlpaWl1nPPPbehvcdpic1mi45RlmVomtYpDzGn03nEmK/28Prrrxeby5mZmcOOp9IA5xwTJkyoX7JkSZsCauvWrdbnn38+bd26dVtSUlL06dOnZweDQQkA7rzzzqpLLrkkx26380svvbTmWOMCyTXZClW+EPZUNqJXnB2lNUE88O5GOK3yYTFgFb4Q/vhhERRJwlvmrMYUl9i+ohGBsI5hmV4MSY+D3SLDbpG7RYTpho7tNdux5sAaFB4qxObKzQjqQfSP748EWwISbYnQuY5LF1+K/b79rboeY8XXwISBSHWmoawujJJKjrIqF/669BC2lBoorw9jaEoONF1CUfkB6LqOnPgcjOk1BsNThiPdlQ6vzUsijCCILufLGV9u+OFnP6wz/zoaqH/RRRfVL126NKG0tFQBRBD89u3brRMmTGhcs2aNp6KiQlZVFR988EFCa9snJiYaWVlZ4blz5yYAgGEY+Prrrx0AMGHChPq//OUvqea6X331lQMAzjzzzIZ58+YlAcDbb78dV19fH41FueGGG2o///xz74YNG1zTp0+va2vcl19+ed3KlSvjKioq5IqKCnnlypVxl19+eZvrHw8jRowIlpaWWjdt2mQDgPnz5yedffbZzcRecnKy7vF49OXLl7sBYN68eYnHcyyXy6XX1dV1WK9MmDDB98YbbyQCwNq1a+3bt293AsB5553XWFhY6DbPpb6+Xtq4cWOzh1ZNTY3scDiMxMREvaSkRFmxYkXUspedna2mpaWpTz31VPptt912TG5JgITYYVQ3BtEY1uC0KjhYHzxiDNijlw3F01efgTSvHYGwhgO1Qeyp8qN3ggNj+yeiIDsBKZ7ujWsKaAFsrtqMCn8F4u3xSLAnoJ+3H1IcKUcVXyZ1oTp4bV6kOlOhcwMSd2D3ATt+9WYxGgP26CSER5cW4U//2YI9lX7kxA9EQeqZcGIgHFISCS+CIE56CgoKgr/73e9KJ02alJubm5t3/vnn55aUlFj69eun3nvvvWWjR48eUlBQMLh3794hr9fbqovtrbfe2v3qq68mDxo0KG/gwIH57733XjwAvPzyyyXr16935ebm5g0YMCD/+eefTwGAxx9//MDq1avdOTk5+YsWLUpIT0+PxnrZ7XY+fvz4+qlTp1YrStsOrbS0NP2BBx44UFBQMKSgoGDIgw8+eMB0AV599dV9zXQM8+fPj09LSxv+/fffu6ZNmzZwwoQJA9t7bZxOJ3/ppZf2XnnllQNyc3PzJEnC/fffX9FyvX/9619777777j6DBw/O45wf18PwZz/7WeVFF12UO3bs2Nzj2d7k/vvvL29sbJT79++f/9vf/jYzLy+vEQAyMjK0f/7zn3tnzJjRPzc3N2/06NGDf/jhh2Zxf+PGjQsMHTrUP2DAgKFXXXVV/5auyxkzZlSlp6eH2zMBoiXMnJFxsjN69GheWFjYoX0cqgviUEMQVb5wNCGrVZYw64NN0VQO5gxIMwbM4Bwl1QEYnCPBZcWAFDfctu73+PpVP6qD1dhbtxeKrMBjFVZpr9ULneuY8v6UNms5msIrzZkGDt4svURNqAYhlSNeyUAwrCCoGqhqDOPRpUVtXpOGoAqJSeif4uqU2Z0EQXQdjLF1nPPRPT2O1tiwYcPeESNGHLOFoTuoq6uTvF6voaoqJk+enHPTTTdV3njjjbVdeUxd15Gfn5/3zjvv7Bo2bFioK49FHBs33nhjn5EjR/rvvffeVu/XDRs2JI8YMSK7tT6yiEH4h/dVNsKvavj5G+ujVrDY5KuxMyFz0zxI8dhQ0RBCcZUfGfEOnNk/CSOy4rtdhKmGil21u7Du0Drsq98Hj82DLHdWs4Sr7XE9xiZUbVAb0Kg1osRXgvpQPVxWC5gchD8cRFqcHUkuK/bXBHDHeQMOs47trmiEy6bAaZVRdKAO5fVBGMapIfYJgiBMHnjggYzBgwfn5ebm5vfp0yd0/fXX13bl8datW2fv27fvsLPPPrueRNiJRX5+/pCioiLHHXfc0Wbc3pGgYH0AvpAGJgEwmtd9/K6kFn/8sAgPXjQIr//PGEhMJEqtaQyjMawhLz0OTqsCq9L9elY3dDELsn43dENHgj0B8bZ4aIbWZsJVU3xlubMitRoBw9CgSApqw/XwqT5ouiYsYswQmerjB8CuCAttL5eGneUNSPHYkJXgQLzDgukFvZslfzWXxaQFJxrDGkr3++GxW9E70QGbQhYygiBOfl5++eX93Xm8goKC4P79+5slK/32228dN954Y7/YNqvVamzcuHFrd47tdGfz5s1bOrL9aS/EDIOjpNqPQ/XiB0ZsMP6v39uI70pq8cC7G/GP60YhpBloDOmwKRJG9k6Aw9r9okIzNJT6SlHmK4NqiILUye7kqAAzZz/GYgowACLuSw/DV7oWCNXD4AYatCB0uwe9Mscg0ZkKi2xpNb2Ey6YgNy0OpbV+vHR9ASoaQlHrmFnUvC13pT+sYc3uKuSkeJAeT/nACIIgOsqYMWMCxzNLkDixOO2FWGVjCLe9LnKDPf7x1qgAM4Pxs5NdkJiYSZnhdSDRbYVVliBJ3S8k/KofW6u3wq/64bF54JE80Riw1gQY0Dzwnhs6ZDWE2r2rwB3xyMo6E0UH16FvYg7SPb0h+6sB7wBAsbY5BodVRv9kN6p8IcQ5FHCOqHXMFGSmu9IUZr/5yRD08trhtCoI6Tq2HaxHWpwDXoelR64jQRAEQZwonNZCzDA4/CE96o4048BmTclDvMMCf1iHRWbYVe7D6OxEuHogCB8QbsiDjQexp24PrIoV2d5syExG2Ai3GgPWssQQAyCpQdQVf4kGcLi8vZGfNBhs28c4M/9yyACwZSmQPQHwHQK8WcARLFaSxJASZ0dDUMWBGOtYa+7K30/NQyCs45pXvmnmsqwNhLG/xo+0ODu8Tgu5LE9hDINDNQwokgTd4AhqOjSdQzc4DM6h6QYMLqpYWGQJLpsCg3NIjCHBaSHrKUEQpzSntRCragxjT2XjYe7I219fh6wEB/5x3SgcqA1gaKa3R0QY5xzl/nLsqd8DTdfQ19sXuqEfNQYs1ZkKw1ChGDrq9n0JZnUhI2UYDjqSMLzXKDhkK9imRcCGtyAPvBAoXg1knwVYncDeL4EB5wOew6o7HIbHbkH/FA8q6oOIcyituitrGtVms05biyErrvYDDMhJccProAfviU5I06FIEhgAv6qL2qkcCKgaAmFDFGgHoBkc9QEVjSEdiLRyiB8GbpuCeKcVNoUBYFB1AxxARX0Qu0JaZG0Gj02B12GBbgjh5rTJSHTZ4LTKsMg014ggiJOfLlUXjLGLAPwdgAxgDuf88Rb9NwF4EoCZtfh5zvmcSN9fAVwCMbPzEwD/j3dyro2wpuPZz3a06o5kAKobQxiQ4kaSu/tzYnHOsbd+L4rri9E/vj90Q4fBjaPHgDlSIYf9qNu/Bn5mwOrJxLCkwcC+r1CQfRYQagDb+xXAJODKecC+L4HUPGD3SsDiANKHA0WLgdzJQFwWIB/5FrHIEjISnKgPqqhuDGNAqquZuxLAEWPIshIc6OW1Q9M5Kn1hlNb4keiyIcFlpdQXPURYa0p2bXBhtdINDk3nONQQRFldUFhZGYNh8Kjx1G1TEO+ywiozKLIETedw22QYXIgvWWLQdA6rwlDREMZfl21p1YXdSxL71nQOSQIMAwDj4FwItoqGEGr9YQRVHYoswSJJkCLbyBIDA2BRJCiSeG9TJNgUOXo/BVQdMmOwKlK3lhkjCEBkfy8sLNySnp7eoaz/R2LMmDGDZs+eXXLOOef4W/ZdffXVfR988MFDBQUFwblz5yY89thjGSkpKeqsWbPKbDab8eMf/7ixq8ZFtE6XCTHGmAzgBQA/BrAfwFrG2Iec85aBhQs553e12HY8gLMADI80fQngXAArOnOMVkVu1R2pSMChhhDy0709EpCvGzp21+1GWWMZBsQPaH8MmBaCrPpRUbkV/ftMgAwJLotTWL/iewNb/wOsegr4n2UAGLDgWmD6HGEF+/zPwE/nAr5y4Ps3gMGXAA0HAFcqYLEfPsgWxNktGJIehzq/ivqgGrWOAW3HkJmvf/90e1SUeZ1WBDXhLg5pOiyyhESXFXF2S4/MTj1V0HQDqs6jLj8ODs3gCKkGAmENvrCGQMiAX9WEhStCnE2B12mFVRHiymVVMCjN3VwoccAiMVT6wnji4y34+cQcBMI6Xl3d9DnHtj108RDc/u91zcS56cJ+dOnmVrc3X837JCvBAc3gkJg4PsAhS0L8yRFRpuocOucIqDpKawJoCGpgrMkqxwHIjMFpUxBnVyBLQug1BDW4bApckWoacXYLMhNESUAhTMUPJYkxKDKDYQBh3YDLKkMhK12Ps/3McSP02lpFjo/Xcr/5ukOZ9WMxDAOcc8jyyfsDUdM0LFy4cJ/5/tVXX03+xz/+sW/y5Mm+++67L8PtduskxLqfrrSIjQGwk3O+GwAYYwsAXAagPTM8OAA7ACvEd6YFwKHOHmCSy4pXbhyNW+cXRt2RL143CprBcUZWfI98qYb1MLZVb4PH6kEfT5/2xYBxQAr70VC1HRkp+UhLK4CiBQEjBLbzM2H9UhyAoQHT/gHIVqDoAyC+D2B1AxanEGGOeKD4G+Dat8WBipYA/c8DXImAM+moY7cpMlLjZCS4rNhd4cOAVBfCmoEnfzoc/rB+WAxZa6Ls9tfXNbOOGJyjIahib2UjLDJDitsOt0OB26pQoH8LdIPDH9bQENCgGgZ0Q4iFGn8Y9UE1UqC9yTXojbgGLYoED7Mg3gEY3Ba1XgEQ4mpZc3HVmlCKFVemO9oUWi3bqhvD2F8TaCbOY/tb2z72Pvn5xBzsqWxsU+iZyw+82+QGH5jmQmaCIyoeWwpJU8hZdAkeuxIVcokuKzjn2FRaB39YB+cRpykDTPu8KeokxuCyKjCf0wwMksRgVyQ4rTIawzoCYR0657BbJLitCuxWGcGwjtqAigSnFW6b0kwohjUdUsR6F9YNqJoBWWKwW2TYFBlhzRDiWmLwOo6tvt2pil5bq8S+doRt27ZZJ0+enDty5EjfDz/84Proo492/PGPf+y1YcMGVzAYlC699NKaZ5555gAgLF1XXXVV1fLly72aprGFCxfuHjlyZPDgwYPy9OnT+x86dMhaUFDgi3Xs/OEPf0h74403kgHghhtuqHj44YfLt23bZr3ooosGjho1qnHdunXu4cOHN958882VjzzySGZVVZUyb9683RMnTmxm6fL5fGzGjBn9ioqKHAMGDAgGg8Hol6PT6Rx53XXXVaxatSru2WefLZ41a1bm7NmzSz788EPvunXr3Lfffnv2kCFD/GvXrvVIksTffvvtpL/97W/FF1100WFFr4muoSuFWCaAkpj3+wGMbWW96YyxcwBsB3Av57yEc/41Y+xzAGUQ30nPc847lKejNSSJYVCaB+/eMQ6NIR0cHA6LhIx4Z4+4K3xhH7ZUb4Fu6PBYPYdZwZrFgHEdCgfq9n0JVVLQL+NHSEnOg1UNAgiC7foc+PLvTdavpfcBk34H+KtFYL4aAKY+D+z8VLgjfeVChG18Gxj0E2D7x4A9HrDYgI3vACOuARzetobeDIssISfVgxp/GI2hIDITHLDI0mExZC1FWVsB/rGiLKzr2H4wgKCqI9ltQ7zTAqsiQ5EZnJZTzyIRVPWoFSukGdB0IbDqAqoQBuAIqwaCmi4EBQc8dgWpcTZIsnDZpXvtSHHbDrNe/XXZka1XQdVoVRy1JpRixRWAZkKrZZtZs9XM19eyv7XtY++Towm9lnGJbQm1YxVySsTi1pqQM8Wr2sIiJ0dcqwbn8NgVOCxys35VMyAxhkSXBZwDNf4wDA7U+sPwhTTh/uW8mdjjHJEKGAJTGJ6dk3JK/zg58Jvf9g7t2OFsqz9YVOSCrrOoSmYMWwYPKYAsc3uklE1LbAMH+jP+/KeS1vpMiouLbf/617/2TJo0aS8APP3006VpaWm6pmkYP378oDVr1jjGjh0bAIDk5GStqKhoy+OPP57y+OOPpy1cuHDfQw89lDFu3Djf7NmzyxYsWOB9++23kwHgiy++cL755ptJ69at28I5R0FBwZBJkyY1JCcn6yUlJfaFCxfuLigo2Dt8+PAhb7zxRlJhYeHWN998M/5Pf/pT+sSJE3fFjnH27NmpDofD2L179+Y1a9Y4zjrrrDyzLxAISGPHjm185ZVX9gPArFmzzG3KVq1aFWe6ME2L2COPPNLpRg/iyPT0U2sJgGzO+XCIOLDXAIAxlgNgCIAsCEF3PmPs7JYbM8ZuY4wVMsYKKyoOK3HVLiSJwWFRsKeyETZFRmaCq9tFWEgPYWv1VnxX/h0YY8jyZDXrNwWY6YKUDQONe1ejetdngCsFw7PGI04LwrrjM7DXLgeTbEDOBU3Wr20fAePvAvavAz7/EzBgonA9/t/vgIE/BkrWiPb8K4ArXxXrb1gI9BkPvHElsOszQAsAgdp2n5MsMSS7bRjcKw4Kk7D9YAPCuo7cNHc0IWxtQG0mysxs/TWNavQh+PupedAMA9e88g3uWfA99lT60ctrQ06qGy6bKLlUFwhj+8EGfLmzEl/vqsT3JTXYV9mIioYQqnwhNARVaLpx9EF3M+Yv46Cqo8oXQqUvhPL6IPZWNmJrWT2+3V2Fb3ZVYe2eaqzZXY3vi2vwQ2kdiqv9sFuEyzbVY0OfJCf6J7uRm+bG4HQPvE5xLR9duhkVDaKA/SNLN2NPpR+PLNmMxrCO2/+9DtMLekevtSl0YtvMChPxDkuby62JK9P6aQqtlm1mzVbz1R/Wm/W3tn3sfdLa8Vsb3x3nDTjsnI52zrHLppB769t9qPSFo9exvJVrWuETbY/G9D9q9i/djKrGMPa20r+3SvTvqxKf145yH0KaOP+cVDdyUl2RVzcG9/Kgf7ILA9NcGNzLgwEpbgxME329E5yobAyd3lUsdF18cZsWJ/PVbD9O0tPTw5MmTYoKuddeey0xLy9vSF5eXt6OHTvsGzZsiMZuXHvttTUAMGbMGH9JSYkNAL755hvPzTffXAUAM2bMqIuLi9MBYMWKFe6f/OQntXFxcYbX6zUuueSSms8//9wDAJmZmaExY8YEZFlGbm5u4Pzzz6+XJAmjRo3y79+//7Cg5S+//NJ9ww03VAHA2LFjA7m5uVGLmSzLuOmmm2o6cg2IrqUrLWKlAHrHvM9CU1A+AIBzHlsOYA6Av0aWpwH4hnPuAwDG2McAxgH4osX2LwN4GRC1Jo93oDaLhOFZXqTGHT0WqrNRDRVFVUUIaAH08/aDaqjQDA0VASEsm8WA6SrksB9VxasRsnswuPeZcCsusM3vg33xNHDzx0DyQEAPALoGcKPJ+pUyWHwxXfEK4EwE/DXA+b8FqvYAfcYBgy8VrssF1wHjfhERZP8RywMvFPvpOx7QQoA79YjpLWJhjCEjQeRfK6n2Y0e5DykeG166vgDPfrYdD108pE3ryNGsG+bD0rSYZUSsE6puQDM4iqsa0RBqioeVGGBXZLjtYiae06rAYZXFjDzOhVupCywKusHhC2nwBVXUBVUEQgYCqgadc8iMQYs8PGNnEnqdFrhsStR6EmuJ8YU07K8JtGrdMQPtO8N6FSuErLLU6nJLcWV+Pk/+dDheXb2n1bbYeK94pwVJLit8IS3a39r2sfdJa8dvbXxtWeSOdM6tTTBpj0Wwo/3H43o1+3/93kbsrwkgK8GBV24cjUFpnlPSMnY0y5UZGwaLhUNVmfkqx8dr/d55e9vxHtfpdEZ/xW3dutX6/PPPp61bt25LSkqKPn369OxgMBg1aNjtdg4AiqJwTdOO+0OwWq3R55kkSdH9yrIM/RiFpdVqNY5UIJzoebrSIrYWwEDGWD/GmBXADAAfxq7AGEuPeTsVgOl+LAZwLmNMYYxZIAL1O901aWK3yN0uwjjnqAvVYXPlZiTaEpHhyoDOdUxdPBUVgYrDrWBaCGrZ9/BwQPb2xY8yxsGjhiBtWQpmzoAM1goxBQbsWy2sXDkXCFfjJw8DvYYBih0o3waE6gFbHND7R00ibdtHIm5s4IXAto+Fm7LveGDTuyJg3+YGNr8P1OwTQu8YsFtkDEzzYPyAZCQ6rdANAw9eNAQOq4R/Xl/QqnXkaNaNWIuZaX2Y8fI3uG/hBlQ3hpEaZxdWol4e5KQIy0FGggMOq4ygamBXhQ/f7K5C4d5qrN9Xgy93VOLLHRVYtb0c35fUoKisDmt2V2FLWT3K64MormrEjkMNKCqrww+lddhZ3hCdnFDpC6GsNoCtZfX4ZncVvtxRgS93VOLb3VXYUFKDioaQcE/ZLEjzWpGT6saAZDf6pwjrxuBeHtgtMv66bEur1hPT+rKlrAEl1YE2rTtOq9xp1iszpct760qiNVdbLpv9seLKIkvol+zCw5fmo1+yEw9fmo8Utw39kl2YNSUf2clOzJqSj9xUN+yKjAM1AdRHjj9rSj5SPWLdh6c0be+yytH7pLXjtza+tqxsR7PYxd5/7bUIdrS/Letceyx6+2sCAIR4vHV+Iaoaw53+fXUykPvN1xuGbN2yDqoqhIqqsiFbt6zrzID9mpoa2eFwGImJiXpJSYmyYsWKo8ZrnHnmmQ3z5s1LAoC33347rr6+XgaAiRMn+j766KP4hoYGqb6+Xvroo48SJk6c2HA845owYYLvjTfeSASAtWvX2rdv396mC7ctPB6P3tDQcPLORDiJ6TKZzDnXGGN3AVgOkb5iLud8M2PsEQCFnPMPAdzNGJsKQANQDeCmyObvAjgfwA8QIRDLOOdLumqs3Q3nHLtqd+GA7wCyvdng4Lj0/ebxYPeuuDe6vGTqIng5Q0rqCFj2FyJ/wPlgm94DvngauOUzgGtA1U6g7Dvgh3eAK/4FDJ4CZJ8DOBIAQwV+/EegercQX+nDAHvM94dkA+LShWjLnyaC9B0JwJWvCavYxrfF8htXCqGWfzlw4DsgLV/kHjsGrIqE9HgHUuPs8IU0NARUhHQdv75oCGyRmXmmdcQf1gGgTetGa9aFo83Aa5kqIaONeB8pkmoh3mGJxmfJEoPDIsPBJHDOoOkG6oJhSAzgnEFiwqrlscvRGXwtUzW0Zd1obzzWU1eOiJ5/a9adzrRePfCuSOly96SBSHbboEgMD0/Jj1rmzOWHL80HAMyakh9Nzrq/JgB/SIP5s54DUCSGFI9ITWJE0lFYJQa71y4SvaoGGsMa6gMqWvOwuW0Kfn3REDGDM05qdnwzRit2fACiltfY8zyaxc5cjhVnR7umHe2Pdb22/EyPZtGLZX9NAGFNP6b/k6cacny8Zs6a7Ox9jxs3LjB06FD/gAEDhqanp4cLCgqOGtD++OOPH5g+fXr/nJyc/NGjR/vS09PDADBhwgT/tddeWzVq1KghgAjWP+usswLbtm1ru7RJG9x///3lM2bM6Ne/f//8nJycYF4bMXFHYvr06bU//elPB3z88cfxFKzfvbBOTs3VY4wePZoXFhb29DCOipkfrKShBIn2RLgtblQEKqLJWW9efjOeOe+ZqCUMugqPxYUEXxWw90swbyZQXybcjmlDgYQ+YgbkD+8Al/9DPPG+mA1M/rMQYDXFIj9YfG8hvpQj5ETTVeDA94A9TmzzxpXCNdl3vLCQ2b1Nbsr+5wKSImZdulM6dE3CmoGDdQEUV/uhGRxehwWpHhskiaExpKGiIdRMqFhlCbM+2BQVJVe//A0W3nYmrn75G/zzhoJof+y6s6bk4dGlRc2E2rEEbreWSmHmWf2Q7Lai0hc+YqqGG+d+Gz1+yzHFnot5HrHn1BXn1zIVRJLL2iwVRKwQNQPFqxrDqAuoUCSGJLcNdQEVYc1AfCTzfVg1YLdIsFkkOCwybBYZFkmKTv/j4PDYLe1y/fJIiggj8t0kMwYOoCGooiGooTGkIaQZUHUj+hoNXOeAIrfu7jVzmx0WTN8iz1mskKv0haNCriP3THtmnZr3ScvPtK17xuyPFWNZCQ68//OzkOJpf+5Dxtg6zvnodm/QjWzYsGHviBEjKnt6HATRUTZs2JA8YsSI7Nb6yHHczZQ0lKC4vhgD4gdANdRmfbGzIrmuwskUJMkWsB/MGLBlQMVW4SbsOx6o2tUUA3b1G8DmRYChCxFWvVsIq/ThIiasPcgWIH0E4Dsoco6Zbsof3olYxV4Vbkpvb8DmAbYsAfqcBWhBIdLsccd1TayKhD5JLmQmOOELaihvCGL7IR9YxMKUleCAEpl1GWvdiLWYtXcGXmfG8wRVA/trgseUqqGt8bXXevLSil34/dS8Nq07v35vI/74YRF+85MhmDUlPyo6WlqPZk3JF9nsI9arxkgsnbBcSXDahIeiIaDBqjCkeuzon+yG294UR2cYvEtikRhjkBkgo/m+451WxDsPNxboBkdYMxDWhdXSZZUR0gw0RixyDEJsNQRVBCPWIgYxm7EhpAGRlBQh1UDTITkUSUaC04L/vXgIFFlY4WKvqblsXlNJYke85i0tdrH9Fonhn9cX4O8trHdHstjFfuaxMWJJrmM2qBAE0YOQEOtGDjQcwN66vdEkrVMXT8XcyXObZ8Z3pkLRQkg2OKyhGmDf1zExYDXAt68Alz13uBXsk4eByX8C9DAQqAES+wv3YjuSsTZDsQJxmWJW5ZHclDs/EbUprS6REDZ7AhCsO2qdyiMhSyJI3eu0ICtBlD8CgOrGMMqr/LBbZDwYcWFa4kR4Y2yQtxljBqBNIXO8gdutxfO0dBO2J1VDW0LLjMdqz0O3NaFlLi+49UyougGdA4fqg/CFtKbkYRCpRWyKhHinBXEOC6yyhNQ4WzT1giIxOK1ydObwkcTWiRIQLksMDqsMB5rCW+wW+bDKDEezEkVzfCkSGkM6KnxB1Pk1aIYBj90CgxsIawYaQhrsFgl2RUZ9QEdjSENQ0+G2W6BIDPUB8QOLMRax1rFm6SYEwlQY89Ec5nqNCr0p+QA45Jg21uIzD+sG3HYFyS7bCfO5EATRPkiIdRMlDSXQDR2943o3S9LabFakoUEKNcLNZFh3fg62ajbwP8vFDswYsPF3AduWR6xg/xbliHRNWMF8h4QVKyG7Y4OVZLGfmn2iBqXNLdyUI68XiV9jyyK5U4UI274cyB4P1BaLCQHHMLOyNRxWOVrVINltQ26aB2HNQH1QRWmNH3UBDYYhcjOZQd6mKDnSDLyjCbVjiedpz/ax8VhHElpHi8cyz++tiNAyIkLLH9bgtCpw2mRwQ4gSp1WGyyKsOYokwaqIP4nhmFOzdPpD3dDFzFs9LO5hf6UwTVkcwqoqW4R/0eIQ8YeGIe6jZhlUu0ZoxFbR8DoleJ3tT5DKOW/12mqRGbzmzFxAxMqZ+cDMcks2iwSDc1ENwOBoDGnwh3XIEmCVJVhkCRJjsFskWGVJ7IvrUHVRTD3BaUUCWcII4qSEYsS6gdKGUuyq3YXecb2jVjAAuHn5zdF1lkx9H6HSdUhLHwUPUyD7q8QDK753C+sXA/Z/Cwy6SFg6AtXCTehMEPFanU3DIeHy3PVf4CdPAm9e3VQW6fs3hbuy+Gsgc3TEOrZaCLNALZCSe+SYtA5gGBwhzUBI0+EP6aj2h1HTGIbBOVw2BWlxNgCszfQPFQ2hDsfztIzHam37lvFYXocFEjt8TGb5IYNzVPlEMk/TkmKRRWb2ZLcNDqsMziEsMhYZisROzFqJugaofuG2DtaLGb3hRiHAAFHNwZMOBKoAd1rET3gAcPcCavYCXBcVIbghXmWb2BeThavdES8EPyDEm2IX6zEWWZ+yzLcXihEjiK7nSDFiJMS6mKpAFerD9QBEyRMzW75pCQNEoW6npCAu5IdVC4Ed/EHkA/NXiTiwsE9ktjdjwM64BqgrFVnykwaKgH2pC2cd+yrFzMzNi0Vy15/MBnavEHFqxV8L69j+QmEFy/pRxDp2NqBYxAM5vs+xu0iPA8PgCGqijEyNPwxNF6KswhdqchcBRxRqbQVut1ZX8fZ/Ny/H1Fbgt1mL0OBAXSRjuvm/TpEYLLIQVQlOCxyRXGaKzGCVhfXjhHY1RS1cIWHhCtaLv1AdACYsW3EZQpjHpYs2NQhIUsSlfZaYCNL3LCHIGssBW7y4XwwNCDWI2ENDEwILAOpLAVeKaKvZG6kK3uJ7TFLEDxRJET8GLE7xQ4UxIQgtDtFu6E3CTbI0/T86EcVtF0FCjCC6HgrW7yEawg3YUr0Fme7MZpawWHckdBUe2do0K3LVbOCOVcIKtuEtYQUDIjFgfxYPn+q9wlLQd5x42HQ17mQR+D94ikhdsetzMQlg4fWHW8e2fCCC+R1eEcyffY6wdEhW8SDuQsEoSUy46awKktxNlrjeiU6ENB2qzqPFr2v8YVQ3hhFU257qH/sobvaY54DbruC1mWOiTYfqg2gIac22sSnCpZTgtCLRZYVFkZDutYMxERBvkU9Qa1ZLOBefP9eF2PKVC8uWFhT56DgXQsfbWwgobgBJA0S72gjs+0rULA3WA6XrgAHnA5veaypE//2bQO7FwrpbtlGs668S62ZPAHZ+Jl5VH7D7v8Cwq8SM4MYGIGmQEPyGHhFPTIg3R7z4v9JYDTgSxfjDPiHaGg8CrrSI5c3M1dlCyCkOES+phYVr3pEoxgcuBKMcKYNruk2Z3FzMnQyfK0EQJwQkxLqIoBbE5qrN6OPpAyPyZd9sVqShwaKF0Etxx8yKjGTGrz8grF0XPga89z/AOQ8CFz4KVO8SDxxnEpCcKx4U3YVsAeKzhGWj3zlCZMX3EXnJzKLhZhLYK+fFBPM7mtyVJWuAxAHCmiF1b3UtmyLDFnO3m4HbZukjKZIiIajqaAyLGn8yY5AjlilFYvCrOsKaqPUoYnbE+mHNQLrXjqFuEfTuD+silYNyEuZGNAxRzirkE5M+wj4g2CAsoqY7MVQfmZQhAUGfcIvrIbFN6bomIWWEhUiPyxTvd38u4heLPmheiP6nc4UFbfcK0R+77tal4ljBOtG28W0g9ydijGUbWwi1QMzxq5uE3K7/An3OFLVUs88CvH2EiOyVL87NHte2kAvUiWVdFf/vuAHU7jtcyDEWI+ogfiDZvWIfugbAACwucd6qX1jnzFnGptUOiFjnrGIdNShEncURcb021VAUBScN0W8K5e78PiCOm8zMzGGFhYVb0tPTOz3PWWewdOlSz1NPPZX2+eef72zZt2rVKufcuXOT5s2bVxIIBNikSZMGVldXK7/61a/Kdu3aZXv88ccP9sSYT3ZIiHUBnHPsrBX3cFAPoi5UB6ApSeuSqe8jXLoemX3OAlrLjF/8NbB1CZA7GRgwCfjqWZGeImOkiMPqSRzx4oE88MLDrWPRYP7VzYP5+58nrAnuNOHGqisGDABxaeIh04O0LBLusilw2Vr/bxHXzoLiXkdPl3BtJ4YuREewTogu1S/EjGJvsm4ZBtCrNwAmxMve1SKHnCm6sieIdCecN4knU0h9+XfgpiXCvb5nlbiXq3Y2pV9Z+itRiH7h9cConzX1x65riPQS0bbci5qLtpZCrS0hV/y1+PGyd7Uo6WXowM7/do2QC0aEHNeAxkrhmjW0pvg1XRNCLdwINJRF4uL2RCYj8KZZrmY1b3CAKQCMiPhSxPHAxWela4CsiLGSJQ57NlZ6VryxNfu86wbv7Tc8+bgy1beGYRjgnEOWT8IfWJ2Aqqo455xz/Oecc44fAL766isnAGzdurUIAJxO50gSYsfHSfLEOLmoClTBbXEjxZECi2SJWsLmTp6LpZcvgYUDOZljoWz5CGzgBULUqI3ii/7zPwEDJwPT/wV8/QIw7ufAjLcAb2bPizATxQok9hOiKnsCULxGWMeGXCoeqhkjgf1rge/fADILhAVlzxciDYasADs+BfSgSExbvU88RLXTsyxLt6CFhDuxvgwo3yLE867PhZg5tFk8vB0J4i/9jMhnGwT2fQ3YPUKwcF3krLO5hUjZsVwsb10KvHaZEAc5FzQXTdP+ISxq374C9Jsg9rn2X0LobPu4qRC9ee+Y/bHr9jmzedvC68W9NGCSOFZLodZy2ezvNUzckza3GFPs+G1ucQ+abf6qw/uLvxbW6r2rhfXJFHKKTQg5QxOu113/BWwu8cNj1+fCKrbrv0JAhXyRNo9okxQgvq9w8/YaGvkbLq5/xijxAychW+T2Sx0sKln0Gg7EZYn/Y72Gix83vfKE2ORcWKxPY/ZsrPQsf3lTjr8ubF3+8qacPRsrOxS7sW3bNmt2dvbQadOmZefm5ubv2rXLet111/UZOnTokJycnPx77703w1w3MzNz2L333puRl5c3JDc3N++7776zA8DBgwfls846a2BOTk7+1Vdf3Tc2LvsPf/hD2sCBA/MHDhyY/8gjj6Sax+zXr1/+9OnTs7Ozs4dOnTq13+LFiz2jRo0a3Ldv36Gff/75YeVMnn322aQLL7xwwNlnnz2wb9++Q++4444ss++f//xnYm5ubt7AgQPz77zzzszWzvPdd9+N69evX35eXt6Qd999N95sv++++zIuv/zyfqNGjRp8xRVX9Fu6dKln4sSJOaWlpcrMmTP7/fDDD87BgwfnXXzxxf1DoZA0ePDgvKlTp/bryDU/HSGLWCcT1sPYWbsTqc5UXP7B5Zg7eW6zckUfXb4E6Wah7g1vAUOmiCSp5qzI/ucDK/4CTH4MuOAPgOwAXO1MyNqdMAYk9BVf/AMmCuvY5sWHuytD9eKBv/FtYOCPge0fR2LI4iMxZGcDjbqwkiX0E64vcrEcP7omUkIE64WFB0bETchFOoi4DOEadiULK4seEpaZtqxbpkWpaEmTO7GlxWraP4QbUteEaJr8qBBN3BA/LsbfBUASltLY9CspgwBwIG+qEC5mf+y6+9c1bzNFW2OFONak3wGfPdZ0zNjjx/ZPmiXuyVih1prFrS2LXK9hws3qzRJCri3X6dFcqwnZwN4vIta5L4Gs0aJc2GEWudZi5Nro37xYzFqWLcDWj4Rl+hhLj50MfDZ/S+/qUl+bJxYOaHJtRcBhhvvpmiF99I+NufEpjoDVobQaDJqY6fZPunHIEYuJFxcX2/71r3/tmTRp0l4AePrpp0vT0tJ0TdMwfvz4QWvWrHGMHTs2AADJyclaUVHRlscffzzl8ccfT1u4cOG+hx56KGPcuHG+2bNnly1YsMD79ttvJwPAF1984XzzzTeT1q1bt4VzjoKCgiGTJk1qSE5O1ktKSuwLFy7cXVBQsHf48OFD3njjjaTCwsKtb775Zvyf/vSn9IkTJ+5qOc6ioiLnhg0bihwOh5GTkzP0/vvvP6QoCv7whz9kRoqUa2effXbu66+/Hn/DDTfUmtv5/X521113ZX/yySfb8vPzQ1OmTOkfu98dO3bY16xZs9XtdvOlS5d6ACAzM1N78cUX98W6MJ1O50jTOkYcG2QR60Q456jwVyDVmRoNwjatYUunLcXHly+Biylgse7Ize+LL9gZbwI7PxW/xCf/GQj5hSg5EUVYLI548VABE26bqc+J4GzTXWlaL346VxQV37BQlGYyY8jsccIikJwrZsPV7RcWttr9wpJDtI0WAnwV4q8xMsN27xfi1eIQn41kBdJHCguKN0sII2eSEGqNB0WS4CNZt0whYrEDAy8AvnrucIsVNxAtNB8rmkzrricDKP1OBNn3GgYMvhgYdYOIhUroLyxMkIGhV4gC9HZv07p5l4pXV6pou+wF4ZpsS6i1JeQqd4h78mgWt7b6tdDhQu5YLHKxbbHWuZ2fts8id6T+/z4m/u+8dimwbq5wMZ+G1FcF7S3nXIBH2jtAenp6eNKkSdHaja+99lpiXl7ekLy8vLwdO3bYN2zYEN3/tddeWwMAY8aM8ZeUlNgA4JtvvvHcfPPNVQAwY8aMuri4OB0AVqxY4f7JT35SGxcXZ3i9XuOSSy6p+fzzz02hExozZkxAlmXk5uYGzj///HpJkjBq1Cj//v37W80JNGHChPqkpCTd6XTynJyc4K5du2xffvml68wzz2zIyMjQLBYLrr766uqVK1e6Y7f7/vvv7VlZWaFhw4aFJEnCddddVxXbf9FFF9W63e5TI73CCQpZxDqRQ/5DCOrBqCUMaIoLWzbtP0jf8hGkVU8Bt3wqXD0LrhViJO8yMYtsyGWRQn8ykDKwB8/kGGEM8KQJUXVwE9DvbOHGiu8jrB2+cuDdmYfHkB34vrlFIL6viFmKzxI1Mo2wmI3m6dVl+chOKnRVPGTDfvHgDdQ0zVbkkXivXsOEsG1P4PyXfwduWipEV+WO1q1bpkXJrObQmtDZ8CYw7WUxqzbYINxlw64Chl0pxmpzAQPOFRamcKO4VySLEDuGJiyozkTxXg0A4MJVzSMiJlgvzk+SATAhJIdeIeLXUgY1zWAcdqW4Thzi+OBA2jDxf4pJIv3L0SxuRxNyn/yhdYvb0SxyzdpirHPttcgdqX/qc8DbN4hzv/NrYe08BTma5cp0S+qaETUwyIpkTL5t6M6OxIo5nc7oLIytW7dan3/++bSIhUmfPn16djAYjB7PbrdzAFAUhWuadtwBe1arNSp8JEmK7leWZei63up+Y7eRZZmrqtopAYMul8s4+lpERyCLWCcR1sMIaSHITARyxlrCll2+FI6gTwTfX/2amDG47SMhYPIvF+6Eze83xeq4U3v2ZI4XiwPIHCUsJAPOFw+IQG2TS2nwlOYxZDa3eE0eKESC6gf2rAD8NSJOZudn4sFcVwqUrAXKt4o4p2BdU5b1Uxk1AFTuFIK1eA1Q8q2wfNXsEYIqfaRwdWnBJkuX71DzGC7TevLa5UC/cwGw5jFcRiTDfVvWLVOIxLoTfQeBIVNFjFLepcC1C4UVSw0AjjghEMqLxNgNXVjlrG7xOfceI2Ke0vLEvdJ7jIh/8qQJV3fqYCB1CJAxQsQXZo0WkwMyC4D4fqJ0V9IAQLYLK1XYDzQcFDOKK3eIGLjyTcDBjcDBH4BDm8SPg4ObgD7jheVVtgFDr4xY56aI2CxXihByvYYBzuQYi9zl4jVnkpj1e7xCLrYt1jrXXovckfo/+2PTPbPhLXFNTkP6DU9umHzb0J2yIhlA54iwltTU1MgOh8NITEzUS0pKlBUrVniPts2ZZ57ZMG/evCQAePvtt+Pq6+tlAJg4caLvo48+im9oaJDq6+uljz76KGHixImdNlYAOPvssxvXrFnjKSsrUzRNwzvvvJN43nnn+WLXOeOMM4KlpaXWzZs32wBgwYIFx+WGURSFh0Ihmi1yHJBFrJM44DsAneuoDlYDaG4JyyhaKuLBrpwnLBVmoe5r3xG/0t2pwDVvAY6kkz8+yiyPBAjrhxoS1othVwmxGRtD1jL2ZuenIqdUzo+buy6D9SK2qb5UXJ/yImEFicsSqRNkayQFwEl+7YJ1QriG6sW1CzWICRrxfQBIIneWmZMr1CBEFCACwM14pFVPNbdymaIrZXBT4HxsDFdL0dXSupV+hrBG9RourC+AEIHgQnBJFvEZKQ7xuVqdQO8+nZueRIpk048tXm91Aa6ktrcxdGFBjJ4nxA8dXRXX1x8UY7Y6hfUv3CjaQz5xvmIDRPOLSbKYbWmPF9dh6JWiOy0/kmj2KBY5SE1tsda54xFyLfsVG3DzMpGTbe8XgPr/TskYsfZgirGumDUJAOPGjQsMHTrUP2DAgKHp6enhgoIC39G2efzxxw9Mnz69f05OTv7o0aN96enpYQCYMGGC/9prr60aNWrUEAC44YYbKs4666zAtm3bOu2LrG/fvurvf//70nPPPTeXc84uuOCC2uuvv742dh2n08mfe+65fVOmTMlxOBzG2LFjfT6f75inhl533XUVQ4YMyRs6dKj/ww8/3NNZ53A6QJn1O4HKQCXqQnWQmYydtTujGfPTnKlwBxuRuHuVCFTf9pEIyv/pPJEHadVsEZTPZOHOOVWnnocbgard4gHn6SWEQ+pg4eaZNEvM3HMkiPxMuRc1Zeuv3CEsIvsLI1PzJSFAnAnigaqrYvq/pxdQvSeSxT1TXMtuyOTfITgXYjzcKP78FUK0ejNE4LonU4gcM5jeUMVDNi5TTGqIdS2Ci2tVu08ILk+aEBef/lGILl0Twe2V2yP96WL7H94BrviXEBkhn0i4a4qQsE8cv25/U24rd6p4DfuF+LJ5Tn7x2xJDF+cu2yLn2iAsb4YWcaeGIj8IbCJ1RKBGbKfYIiWcQm1ba5kEkZ6CiSTJ9nhhBbZ5xMQJ87+/rkVKNPHIsgzhvIjsl/OYfUVKQHEesUr6j9k1SZn1CaLroRJHXQjnHPvq90VLF8XWj1w25R2kMDusLePBDnwH5E0DYIgvY0d8t4+7RwjUiCB8Z4KwCiYNEHmWoqLsd8AHdzVl6/dmCeGVnNtclMXmdGJyUxkcX7l4rdojEsm608UDzyxt09MYurgGdSXC/cp1YRmM7yMepobWtsUrNidXbbFYp760ueia9DvhlozLEDNVDxNdc4R4LV0P5F4I8aBXhfXK0IHakkiyURcAFnEn5vR4rreTimblliJZ97WImA77xOfsSm6K94u13Bm6+Cwki/ihpoUjSWFV4ZI2RZ5sEfe9aXU0DCGIs350XPc5CTGC6HqOq8QRY2wJDqv70QTnfGrHh3byUxmohB6xIphxYWnOVMjhAJz1B2FJGiTSU5jxYFv+A5R8IwQZk04fEQY05aoK+0UMmWITrp0tHzbPKWX1tO26bJmcMzbY35UiBEz6UPEQMzQRJ+RJF+5Nm0fETVmcQmx0hzgL+0WcVn2pEF7udGHFSMtrnj4i1uLVWjD98boWYwPn1WDzwHnzR5gjXli70oeLa9OVdUtPdVpzyVrs4i/WlWpxNGXWPxbMzPoEQZwyHClGbHbk9QoAvQD8O/L+GgCHunJQJwuqocKv+qMB+tG4sCnvIFFxwR7XB8xMT3Hdu2JmpBkPZosXVpvTEatTBG4HakTwfZ+zxEMpsT8wdJpIlGnOUGtthlmsONu/9vCcTLEWs/i+QrR4eomZmJIiBE1pibBG2jziIWnWCFTs4iFpaACYSEB7LIT94rwCteKVMVFT0duneYxXsL5ti1dsXJc5g7G1nFyma3HQJcK1mH6GEPuci2M74oQ70nR/KQ5hYbHHiSB52dJ5nynRPZx+IswwDINJknRquG6I0xLDMBhEPZlWOaprkjFW2NJs3VpbT9MTrsni+mKohoo9dXua4sIcKfBU70V8XG+wlu7IwVPEg96T3u21Fk9oOBcutfIi4aZJyW3bdWkm5zTjyhL7NYmyuv2HuzP7jBPpHMxi0VoQ2PMlMOA8IW4aygFPqhDLDQeFBalmbyRWijXFRTFJzEpkTOxPsUViuCJiytBFjFagRgjN+L5NsTvtifGq2y8sU2aqAzOuy3QztuVa1MIihsh0LephIWitroh4VZvXNSSIFpzgrskPe/XqlZeSklJHYow4GTEMg1VUVHgPHjxYNGLEiFY9ie35ue9ijPXnnO8GAMZYPwAnSK2dniOkh1DSUAKvzdssc/7H0/6DeE8WmJmeouBnIonpvtVA/hUkwlqDMeG26TteWKvCjcCgiwAwEbS/dUnbOZ2OZjEzs6BvfFvMxiz+Cuh7trBYRWsJfh7JY9YnkosrP+K2Y0IMORJEzE7ltpi6gBG3IGNN4ppJ4thmORtDBcA7x+KVfY4Yh6E1uRbVgBBczkTAkSiObXWRa5E4ZdA07ZaDBw/OOXjw4FBQuiXi5MQAsEnTtFvaWqE9FrHJAF4BsBtiXk9fALdxzv+vEwfaYbrbIlbmK4NP9UGWZFy2+LJo+7LLlyDz31c3t4QNuUw8sN29AAslJm03uiYC8MONIhjZnK0nW8T0/8R2WMzq9gtXoGwBti8TaR5Mi1RCduuTAWLLyWRPEKKu5axNU6ipQSGs934pXKGhhqYSQZ1h8dLDQliZFi9AWNWcicLSRaKe6CAnskWMIE4HjijEGGMSgJ8C+ADA4EjzVs75CVd7pjuFmKqrKGkowWUfXIZnznsGXptXBOhzDmfIh4R3bwEueVrUgNv/rcgfdjrNjuwKtDAQrBUxZYFqYQFyJgiBVfSBEGVVuw53Z5qiZ/GdIrN/fN8mcVa1s3XX5rEItQHni9i/+N5iezOPlym+zFmNR00f0SBEmRFuEl2GLmLUnCmAO0XErlGFAaKTISFGED3LccWInYh0pxAraSiBX/Xjp0t+Gm1bNuUdpGkGFEMViTk/fhC4+g0hFKzu5skoiY6ha2JmYUOZiMlyJokgdD0ccc0pTRYzU/TsWQVc/FdgyT1N4uzTPx6ex+xYhVp9mXArJuUAFVua5/Fqj8Ur7BdWUkMXKQqsTpHZ/VTM0UWckJAQI4iepT1+jU8ZY/czxnozxhLNvy4f2QlKdaAaqq7CKrd4SIYboShW4J2bRPHd8i1A4b9EQDeJsM5FVoTYyTgD6HeOEEuSImYiVu0CDm0RQfrJuSJFxNArRbHoHcuPvdxMbLHn1go4xxbDblki6PM/iVI8gy4BLn1OWESd8SLGq3qnyAcGiP3HZQGZI0UJIdcpUGGBIAiCaBftsYi1VqqAc877d82Qjo/usogV1xfjkvcvibokU52pkMHgZAoSf3gP+P4N4IqXmwL0r3v3lC3Ce8LCuShXowbEcmOFSKDqjBcuYi0M2D1iXbPcTKxr07SipQxuPQYt1qJmFsNOGSy2N/N4KbZIaaZ0EbRv6EDDIcCbKax2VjcF1RMnBGQRI4iehTLrHwOcc+yt34upi5tmoH58+VJkhoNgb994eIC+zSWSjBI9j5lGQvWLVBmBavGeSeIzsnuFFczqFtY1M5fx0YRa9V4gWAOMmAFUbBWuTUeiEH9WJxBqFO5p2SZE2LHmJSOILoaEGEH0LO16KjDGhgLIAxAt4Mc5n99VgzpRqQxUgkULwgkYONiO/xOzIodObwrQH3ENibATCcZEVn2bW+QGAyLlZSJ5wPQwUHcgkgE/YlEDE0Ksz3gh1OL7NsWgmQWezaLPHMK9qNiFJc4VCa5PyO6BkyUIgiBOFtrjmvw9gPMghNhHAC4G8CXn/KdH2q676Q6L2N66vc2LejtS4GYKEhurmgfoW53CakKcvOiasJABkVqBPvGqq8LVaJYYkhQh3CRFzJx0JJLVizipIIsYQfQs7Xli/BTACADfcc5nMsbS0FTu6LTBr/qhGmqz5K3LLpyHRKtXBOhX7xaNhXOBib/tmUESnYesALJbLNvczesEEgRBEEQn0Z5ZkwHOuQFAY4zFASgH0Ls9O2eMXcQY28YY28kYe6iV/psYYxWMse8jf7fE9PVhjP0fY2wLY6yIMZbdznPqEg42HkTQtJCYOJOBbR+LwOw7vgDG3SUC9FV/zwySIAiCIIiTivZYxAoZY/EQ2fXXAfAB+PpoGzHGZAAvAPgxgP0A1jLGPuScF7VYdSHn/K5WdjEfwJ84558wxtw4QsHMrqYqUAWDG/DavFhy+YeQwSCBwVm1S9QlvPivoozNuF8CE+6lWZIEQRAEQbSLowoxzvnPI4svMcaWAYjjnG9sx77HANgZU6NyAYDLALQUYofBGMsDoHDOP4mMwdeO43UZjWojpn04Lfp+2aWLkLH9U7D1r4kM+ls/AvZ9CZxxHYkwgiAIgiDazVFdk4yx1xljtzLGBnPO97ZThAFAJoCSmPf7I20tmc4Y28gYe5cxZro8cwHUMsYWMca+Y4w9GbGwtRzbbYyxQsZYYUVFRTuHdexohtbywGBVO4UlzJkkBNi1C0mEEQRBEARxTLQnRmwugHQAzzHGdjPG3mOM/b9OOv4SANmc8+EAPgHwWqRdAXA2gPsB/AhAfwA3tdyYc/4y53w053x0SkrXpIqoDFRCYi0uU6AGWDtHZNBfP1/EiLnTuuT4BEEQBEGcurTHNfk5Y2wVhCCaCOAOAPkA/n6UTUvRPKg/K9IWu++qmLdzAPw1srwfwPcxbs3FAM4E8K+jjbezaVQbsbN2J+ZOngtApKxwVu0B7vgS+P5NEZx/1j3dPSyCIAiCIE4BjirEGGOfAXBBBOh/AeBHnPPydux7LYCBjLF+EAJsBoBrW+w7nXNeFnk7FcCWmG3jGWMpnPMKAOcD6J6K3jEY3EBICzVPWfGTt5AQnw189ADQewxwzQLATYlbCYIgCII4dtoza3IjgAIAQwHUQcRufc05DxxpI865xhi7C8ByADKAuZzzzYyxRwAUcs4/BHA3Y2wqAA1ANSLuR865zhi7H8BnjDEGMVvzleM6ww7QEG5AUG+RskK2Ac+eIZYnzRIZ1wmCIAiCII6DdteaZIx5IITS/QB6cc5tXTiuY6azM+vXBGtQF6qDxCQYXI+krACclbuQ8PoVYqVxd4nkrVZnpx2XIAiiO6HM+gTRs7THNXkXROB8AYC9EMH7X3TtsHoev+rHpYsvjb5fdukiZG7/FFj/GjDz46aUFaqfhBhBEARBEMdFe1yTdgBPA1jHOdeOtvKpgnFY/lgGxKasGHMrMOEeSllBEARBEMRxc9T0FZzz2QAsAG4AAMZYSiQA/5RG1dXmDcHYlBWvAbY4EmEEQRAEQXSI9rgmfw9gNIBBAF6FEGX/BnBW1w6t51ANFQ3hBsydPBcp9iRYtDCc9WUxKSu+AiboPT1MgiAIgiBOctqT0HUaRGqJRgDgnB8A4OnKQfUkNcEaHGw8CIMbUCQFceEgMte9jvj4bODTPwKOBJGygqxhBEEQBEF0kPbEiIU555wxxgGAMebq4jH1KH7Nj58s+kn0/UdT30di1o/AzJQVFzwMeHr1zOAIgiAIgjilaI9F7G3G2D8hEqzeCuBT9EBOr26jRTYPicnAZ39satj4NhD2d++YCIIgCII4JTmiRSySTHUhgMEA6iHixB7mnH/SDWPrEQ6bLamFRC3JWz8HNr0H7KWUFQRBEARBdA5HTejKGPuBcz6sm8Zz3HRWQtd9dftQHihHnGSDWw3C6e6FhLoDwFfPAlP+BjCJ4sMIgjhloISuBNGztCdGbD1j7Eec87VdPpoepjpYDZ/qAwBYrW44FAcSnolo0Nu/AFwpAGM9OEKCIAiCIE4l2iPExgK4jjG2D2LmJAPAOefDu3RkPYBf9WPGf2ZE3y/78dymzh/eBpJyyCVJEARBEESn0R4hNrnLR3GCoBktCwcwkTtswwKKDSMIgiAIotM5qhDjnO/rjoGcCIT0UPMGdyqw52tg3C+ACfdSbBhBEARBEJ1KeyxipwWqocIXrse8ya+il64DjkQ4K3YAq58BBkwkEUYQBEEQRKdDQixCQ6gBd/33bpyZ9iM8lX4BpFcuFB23fyEsYwRBEARBEJ1Mu4QYY6wvgIGc808ZYw4ACue8oWuH1n3UBGtQHazGs+c/iwRrHOpq9iPB7NxIQfoEQRAEQXQN7Sn6fSuA2wAkAhgAIAvASwAmde3Qug+/6se0D6dF3y/78atIiBb4piB9giAIgiC6hvaUOPoFgLMgMuuDc74DwCnlq+Mt6xq5UmIKfC+k+DCCIAiCILqE9gixEOc8bL5hjCk4rCLjyY3BW5Q1ajgA7PwEGHQx4EnrmUERBEEQBHHK054YsZWMsd8AcDDGfgzg5wCWdO2wuhfN0DB38lwkWr2wB+vgbCgXHRsXAokDyC1JEARBEESX0B6L2EMAKgD8AOB2AB8B+F1XDqq7WbhtIW77v9vQa8t/kLn4l0iIywTG3dWUxJUgCIIgCKILOGrR75OF4y36XROswb76fWAAkiQrnBY3EiEBnAEWG8WHEQRxSkNFvwmiZ2nPrMkfcHhMWB2AQgCPcc6rumJg3YVP9eGGj2+Ivl92+RJgy0fA8KsBZ8IRtiQIgiAIgugY7XFNfgzgPwCui/wtgRBhBwHM67KRdRctA/UbK4D18wCu98hwCIIgCII4fWhPsP4FnPNRMe9/YIyt55yPYoxd31UD6y4kqcUlMDTgilfIJUkQBEEQRJfTHiEmM8bGcM6/BQDG2I8AyJE+rctG1k04FSeWTVsK+MoBQ4OzsRrY9x+aLUkQBEEQRJfTHiF2C4C5jDE3AAaR2PUWxpgLwF+6cnDdQQJniC/6GGz9PPCpz4HtXQ3s+4qy6RMEQRAE0eW0e9YkY8wLAJzzui4d0XFyvLMmAcCo2g2poRTQwkDqEEC2kmuSIIjTApo1SRA9S3uC9cEYuwQih9j/Y4w9zBh7uJ3bXcQY28YY28kYe6iV/psYYxWMse8jf7e06I9jjO1njD3fnuMdF3WlYNuXgR/cDCRkA1uWABayhBEEQRAE0fUcVYgxxl4CcDWAX0K4Jq8E0Lcd28kAXgBwMYA8ANcwxvJaWXUh5/yMyN+cFn2PAlh1tGMdN4FaYOtSsBV/AfNmAm/8FFj3KiVxJQiCIAiiW2hPjNh4zvlwxthGzvkfGWNPQaS0OBpjAOzknO8GAMbYAgCXAShqz8AYYwUA0gAsA9A1ZnNHPDD0CsCdBiyMTAC982tySxIEQRAE0S20xzUZjLz6GWMZAFQA6e3YLhNAScz7/ZG2lkxnjG1kjL3LGOsNAIwxCcBTAO5vx3E6hhYGPvtj0/sNbwFhsogRBEEQBNH1tEeILWGMxQN4EsB6AHsBvNlJx18CIJtzPhzAJwBei7T/HMBHnPP9R9qYMXYbY6yQMVZYUVFx7EePuCah2IDbVgLjfgns/YJckwRBEARBdAtHnDUZsUydyTn/KvLeBsDenpmTjLFxAP7AOZ8cef+/AMA5bzXlRSSmrJpz7mWMvQHgbAAGADcAK4AXOeeHBfybHPesybAfCDcA5duBXvki0z65JgmCOE2gWZME0bMcMUaMc24wxl4AMDLyPgQg1M59rwUwkDHWD0ApgBkAro1dgTGWzjkvi7ydCmBL5DjXxaxzE4DRRxJhHcLqBPQQAAOwewFJPuomBEEQBEEQnUF7XJOfMcamM8bYseyYc64BuAvAcgiB9TbnfDNj7BHG2NTIanczxjYzxjYAuBvATcdyjE7FkUAijCAIgiCIbuWoCV0ZYw0AXAB0AAGIFBaccx7X9cNrPx1J6AotDGgBYREjCII4jSDXJEH0LEdNX8E593THQHoUxSr+CIIgCIIgupH2JHRljLHrGWOzIu97M8bGdP3QCIIgCIIgTm3aEyP2IoBxaAq090FkzCcIgiAIgiA6QHsy64/lnI9ijH0HAJzzGsYY+fEIgiAIgiA6SHssYmokxxcHAMZYCkR+L4IgCIIgCKIDtEeIPQvgfQCpjLE/AfgSwJ+7dFQEQRAEQRCnAe2ZNfkGY2wdgEkQqSsu55xv6fKREQRBEARBnOIcVYgxxp4FsIBzTgH6BEEQBEEQnUh7XJPrAPyOMbaLMTabMUaJ/wiCIAiCIDqBowoxzvlrnPOfAPgRgG0AnmCM7ejykREEQRAEQZzitMciZpIDYDCAvgC2ds1wCIIgCIIgTh/ak1n/rxEL2CMANgEYzTm/tMtHRhAEQRAEcYrTnoSuuwCM45xXdvVgCIIgCIIgTifak77in4yxhEh9SXtM+6ouHRlBEARBEMQpTnvSV9wC4P8ByALwPYAzAXwN4PwuHRlBEARBEMQpTnuC9f8fxIzJfZzziQBGAqjtykERBEEQBEGcDrRHiAU550EAYIzZOOdbAQzq2mERBEEQBEGc+rQnWH8/YywewGIAnzDGagDs68pBEQRBEARBnA60J1h/WmTxD4yxzwF4ASzr0lERBEEQBEGcBrTHIhaFc76yqwZCEARBEARxunEsmfUJgiAIgiCIToSEGEEQBEEQRA9BQowgCIIgCKKHICFGEARBEATRQ5AQIwiCIAiC6CFIiBEEQRAEQfQQJMQIgiAIgiB6CBJiBEEQBEEQPQQJMYIgCIIgiB6iS4UYY+wixtg2xthOxthDrfTfxBirYIx9H/m7JdJ+BmPsa8bYZsbYRsbY1V05ToIgCIIgiJ7gmEocHQuMMRnACwB+DGA/gLWMsQ8550UtVl3IOb+rRZsfwI2c8x2MsQwA6xhjyznntV01XoIgCIIgiO6mKy1iYwDs5Jzv5pyHASwAcFl7NuScb+ec74gsHwBQDiCly0ZKEARBEATRA3SlEMsEUBLzfn+krSXTI+7HdxljvVt2MsbGALAC2NVK322MsULGWGFFRUVnjZsgCIIgCKJb6Olg/SUAsjnnwwF8AuC12E7GWDqA1wHM5JwbLTfmnL/MOR/NOR+dkkIGM4IgCIIgTi66UoiVAoi1cGVF2qJwzqs456HI2zkACsw+xlgcgP8A+C3n/JsuHCdBEARBEESP0JVCbC2AgYyxfowxK4AZAD6MXSFi8TKZCmBLpN0K4H0A8znn73bhGAmCIAiCIHqMLps1yTnXGGN3AVgOQAYwl3O+mTH2CIBCzvmHAO5mjE0FoAGoBnBTZPOrAJwDIIkxZrbdxDn/vqvGSxAEQRAE0d0wznlPj6FTGD16NC8sLOzpYRAEQZxUMMbWcc5H9/Q4COJ0paeD9QmCIAiCIE5bSIgRBEEQBEH0ECTECIIgCIIgeggSYgRBEARBED0ECTGCIAiCIIgegoQYQRAEQRBED0FCjCAIgiAIoocgIUYQBEEQBNFDkBAjCIIgCILoIUiIEQRBEARB9BAkxAiCIAiCIHoIEmIEQRAEQRA9BAkxgiAIgiCIHoKEGEEQBEEQRA9BQowgCIIgCKKHICFGEARBEATRQ5AQIwiCIAiC6CFIiBEEQRAEQfQQJMQIgiAIgiB6CBJiBEEQBEEQPQQJMYIgCIIgiB6ChBhBEARBEEQPQUIMAOccWlgH5/zI6xkchm7AMI683okIPwnHfLy09/M53mvS2n3S2r6Odj+dinCDI9ioQleNnh7KKQM3ODRV7+lhEATRRSg9PYATgXBAw8HddZAUCYpFAmOArvHoA93mUCDJDP76MMSzlYMxBsUqQ7FK0MIGdM2AYpEgyRIYAE0zwBhgsSvgBo8+qDXVgKEbcLit4ACCDWHY3RZY7Qoa60IwdA4mMUgyAziHrnFYrDIUm9i3FtKhhnXIFgkSYzB0Dk0VxwcAMIAxQFbEeXAOqCHxJe70WGGxy+AcCPo16Kb45IAkS7C7FTDGoOtirLLEICkMalCHpuqwuSyAAXF8RYJhcAR9KmRFgmSRoIU0SJIExSr+DAMI+zVIMqBYZEBiUIMa1JAOxSKDSQDXAZtbgd1lQdCnQlUNcN0AkyVITJyPJEvRzwkAFGvk94MhrrNikWCxyuDgCAc0hPw6JIVBsUjgBiDJYh8GBxSZQbZJCNSqUFUNjDFIEgOTmFhWGGRFioooxRK5J2QJalBDsFGDFtZhdSiwWGXomoFQQAM3ODzJdlhtCsIBDYF6FZqmw2qXxVANQFIi5xT5nMDF/aAoEmSLBF01YHAOiTHxmeoGpMi9ICsSwJu2lRUJTAK0kFjPhEWul8Uuw2oX/72DPhUBXxiKTYHEAEM3IElS859hPNIe2VYPG9BUHUwS49I1A06vFXaXRXwWQQ1qQBfjt0qwuyzQVB3+unDk3mewuSyw2WVIMgNjgGJVIuuEICkSZFlCqFEDJETvFz2sQ9cMWO0yrA4Fhsah6+IHEItcO8MQ/yetTgVaUBf3vMQQ9IVhGIDVoUCWGWD+P1YNGIYBxSJDUhgMA9BCGgwN4v+VIoEbPHrfK5F7WQ2Le0uWJUgyg8UuQ7HKTecf0mGxyDAMDl0zYHMqsNiUaH84oMHQxXFtTgW6waGFDahBcR/Lsvh/Y3MosDmU6H7Cfg2hgAo1ZAAMMHQOcA6bU4HNbYEa1AEuzplJTPyINDh69feCmReJIIiTBnaq/GofPXo0LywsPK5tg40qDu2tg81pEYKJA0wCGGPgAHTVAOdCELHIk5RzDkMXf5IsvhC5zqMPcCax6MMNjEUfIuKBD2hh8fA0hZyhc/FQkBg4F/tnYGASosfhnEcezEIEgTeJNvN45thi9yFFHkpqSI8KQlkRDxcxKCYeRKoBzgEp8sznhtiHLDMwmUFXxQNWkhkMg4MBkK1yVGhKshi7oUeEZ0QwcM7BIw85FhEVYhsAkWthGOI44qHNoudgno85ZiDyYBLDBpNY9PowhogAEw9H81xNsQkGcF0IbMUqN42NA4i5ZjzGmMMNsb64DpK4boq4FuZnLymmUNQj1wFQrEKAmAKZRa5xdL+8afzC0sqjn5P5kBX3Ao9+DrEYhimgxfWC+fyNnIOuGWI7ALLMhNCJXrfm19e8lmDiMzcFmXl8MS4GLaQLscKFuBViUFx/XTUgRY4jyeK6CgHHATS/HxWrJN4bHLKl6TMVIlLcy7pmwNB49BrF3t9g4v+kbgrKyImb4zHPHRD3MpNY9LPnkftAksX11TUe/TCahF7kc5Wl6L0dFWqRW0mSIuJebxqjrhmRe41Fxb/4sSF+ULHIWOTIDwvOeUR88cjHLgYnSQyyJUZ8M/GZaRGRav4wMW8kJjGoIQN98hKj30/HAmNsHed89DFvSBBEp0AWMYiHUGK6C4D4srPYZXAD0MJ6VCjFtnW0vyv2Sf0nQL/3BB9fd9+z8Sf2OZ18/XKb/a54ijIhiJOV0/5/r2FwhALC5bR2yW5YrDICDSrWLtkNWZGwdsmeZm0d7ZctkTZbzDa24+i3dnD7ju6/q/q74Jp3a39PX7/O6u+Ke4r6u6x/1Vvb0Fgb6umvU4IgjoPTXoj560II+lR89d5ODJvYG/6GML56byfyzs7E5//e2qyto/15Z2fi89cjbfUx29QfR39DB7fv6P67qr+Tr3m39/f09eus/q64p6i/S/o//Pv3KC6qxuK/fQdfTbCnv1IJgjhGujRGjDF2EYC/A5ABzOGcP96i/yYATwIojTQ9zzmfE+n7GYDfRdof45y/dqRjHU+MWNCnouZQIwr/sxfDz8+C1aFElzf+d/9hbR3t74p9Uj/10z17evcXF1VHv9P65CXixzfnw+62tPt7kGLECKJn6TKLGGNMBvACgIsB5AG4hjGW18qqCznnZ0T+TBGWCOD3AMYCGAPg94yxhM4eo91tQVyyA2Mv64/vPy2BYpGjyz+a0u+wto72d8U+qZ/66Z49vfu9qQ4AgDfVgYk3DD4mEUYQxAmAmD3V+X8AxgFYHvP+fwH8b4t1boKwgrXc9hoA/4x5/08A1xzpeAUFBfx4aajy8/Liev7ZvM28oSoQXa6r8B/W1tH+rtjnCdVfGemvjumvpv6Tuf9EHBP1N+//8O/f8foK33F9/wEoPNJ3K/3RH/117V+XuSYZYz8FcBHn/JbI+xsAjOWc3xWzzk0A/gKgAsB2APdyzksYY/cDsHPOH4usNwtAgHM+u63jdSR9RbiiErrFCW5wKEYQuuKILquwwoJws7Zj6ddghdKiv7W2E6n/eM5Jl+0I7doNZ1YKGnfvh6t/FjTFgfDeYjizUpotd3t/Zgo0S2Q5OwOaZIMSrIfuiAPnDEqwDrojDoaqw2IEoFndJ02/oeqQKkvBUzObn3+/rKZzjj3/zBQ07mnR347re8J9pl3R3y8LusMDbuCE/sxb9kuVpQjW+mFzWaAkJUNyOqAkJrb7+49ckwTRs/R0sP4SANmc8+EAPgFwxDiwljDGbmOMFTLGCisqKo57ENzvh7F9E/aNHw3ua2i2XDxhzGFtx9K/r5X+1tpOpP5jOSet8Ctwnw9S9SFYjAB4KARnZgp4YyPkuio4UuKiyz3W74/pr6sF31mEXedPgrGjCHzn5uhy8TlnQi0uPqn69e+/heHzNZ1fMCjO39/G+YdDh/cf4fqdsJ9pV/T7GyFVlvX4Z3qs/XuvmI6DN9+AfVfPwK4LLoDhDxz3dyFBEN1PV1rExgH4A+d8cuT9/wIA5/wvbawvA6jmnHsZY9cAOI9zfnuk758AVnDO32rreMdjEdOqq8FDIRh+PwAGKDKgaQAYtKpKKGlpkRW1Tunvin32eD8Ddv/kEvSZ/xqKb/wZ+swXWrq15ROhP/PZZ2EblAuoqsjae9znHyk5c9z9J8f1PxE+s+7sb3l/nFj/51q/p6DrKJ55M/rMfw2WjEyyiBHESUZXWsTWAhjIGOvHGLMCmAHgw9gVGGPpMW+nAtgSWV4O4ELGWEIkSP/CSFunYvgDCJeUYPclU6BVVUI7dCi6XHzjz6AdOtSsraP9XbHPnu4P7dyFPvNfg5KULF5TU6GkpaH/f5ZCSUputtxn/mtQ0tIibf/pkX7Z48buyRcBknjI7p58ESDL0fMD583a2u4/2vYd3X/7+kM7d6H/smVddv1OhM+sO/uB/9/e3cdaUt91HH9/Z87DPffeXXYRut0u0GXbLYaqLQsaEgt/WAML9smHKEYFi4kp0bSNaRQlUTRpTDWatpFIbKSlikJRiSQGBCu08Q8oD13YpcDusl1d6Ja1xeXC3nPveZivf/x+5+ycu/fcpzN35xQ+r2R25/7m6Tu/mTu/75mZe34MnB/rccyKPucqW8NltPq2t1E7Z9uqkjARKd96f33F1cBnCV9fcZu7f9rM/oTwcui9ZvanhASsA7wC3ODuz8Vlrwf+IK7q0+7+xaW2tfY7Yi2y5mzoSmTd7n6E6euxzrKn9z6Rk6b8z7XXseP++zm0ezc77r+PzrFjsWxw/NDuq0qf/o7//GrYj3Ybq9Vxz/rj2VyTZGICd192urfbWK0WO/VcfHpSq+XWVSObm8MmJnLL1Aa2FZZv5La52PTB+LL5uf5+r0f9jcMxO53TB8+PcMwGj8li58Sw6TWyZhNrNAbOk6WWZ9FzKkxf9JyanSXdvHlNSZjuiImUS31NAq0jR2gfPXraGoBxa3SKmD6//wD1d+0kqU8s0ygNb7R6SY3PzmKNScDxVhurhT/HD+M1vLlwem1gfCXTSVJaLx7pd9HY74vSHUtTLEnxbif0QRnnMnK/K2lKUq9j9QmskpLNz5O9+mrot9MhmaiTbtwIZnSPHyebb4U+IRPDkhSbqEOa4vPzeLuNd7oD/UX2nOzbNLd9B3r9DWahf8PaeedBt4s1JldZPy2sVl+2/la2zsbAcVrd9CY2GRKVtR7TIqeD0/7OdzBLIDG82w0fSGKHlLluL/vnTp+Dm2FpEpdJSDdvwrMMn2/h83PhPDPD0gpZp0NvrU7o8zJpNKBWI5uZwbPs5LbMSM44A5+bCwNhO8nkJI33vGdNnX4rERMplxIxoH3sGN7prOGT7tqmr8c6T8f0gURpcjI0mu02Vo3JTZrSOvIieOgcnDQNDZj326/QSXG1hrdbIWnBMA8zJJMNkk2bSKc34K15snYbMyPduBGrT0CnjXc6ZJ2QIJEkJGmKVSpQqeDtdrg7506yYUOIdW4Oz0LHyFQqYblOB6tWw89Z6JSdbpes2QwJ1tRUv0Hz3nTA58K3llu9jiWnPtXPWq3Q8MYkjf4ux4SnWl10ufx8eflG1d3x+XnIsrC/1Ziczs5CpYJVq3izGZK6XMxWqYAl4ce4DN1uON/dQ4PvTtZqx22GfzxzyLqhTpMESxI8i8c1SWLP8OH4E+PuHWsIKUWSJCH56nbJ5udJarX+MfB2m6x3vNrtcEyBpF4P9Z+mYT29Y53Xq8Nul6zTIalWw/kRekeHJA37kSThHGy18GYz1FMldq9rSewM3EI8mZNM1MO+z8+HY1Ud/D6urNnEW62wjUoVq8Xj6T4QZ+/4mFmonzRd8rj39zPW7cLj3jsX+glZXJd3u/16GoUSMZFyqdNvwsV/dt++mHDEC19seMLF1bG0Ei7sWRYapF5jlCShgSFcqOl0Ylt0svHoN+oe7nB4lmHVWlhXt4slCVavh5/b7ZAcVCqQpmHc/WRjZNa/YJNloSGrVsPFv9uNDUUWXkSPDb/PNcPi4SM1loa7PeaEbfQbk5D4hE/rSZjfPcQdP3UnjQZJoxH2tRseTxpgU1PQblPf+U6Sep10airedYiNSdw3S9OQNLRaoeFO037jXkSjspBNTp5amEuS+o16mpLGeAeWzzWgvTs6wyS1WniktHAdZuH4LhfrEnczzCw8zlxYPjW16PhqLbbvRSr+yK5CrQbT0yuefdFzhniXasg5sDBpW678lPkqi1+K8+fEwmRuPX5fROT0UyIGJJOTTLz7R8JdmvgpmlYr3NloNPBWG59rknW7JNVquLPQDndnvNWiO/MaZF0qb91CZcOG/t0Zi3dXyLLwiTcmcd7p0H39dZI0Jdm4key11+jOzGCVCsnUNMlEne7rr4ekqlolSdOTCVi3S9Z7TFKpkNTrZCdOkDWbJI0Jkg1vJZmcJGs2yU6cwNsd0nO2kU5Ph7sQc3PhE//kJEmShJ87nZAMNhrh4m5GOj0d9mNuLmxnhIa6d3cs33BYrcbqH6KIiIi8sSgRI3xqrW55y2ndZvUtue1tPrX3psrZZ5/GaIYbdndARERERlf2F7qKiIiIvGkpERMREREpiRIxERERkZIoERMREREpiRIxERERkZIoERMREREpiRIxERERkZIoERMREREpyRumr0kz+1/gv8uOYwlnAd8rO4glKL7RKL7RKL7RjBLf2919PL5BWuRN6A2TiI07M3t8nDvWVXyjUXyjUXyjGff4RGQ4PZoUERERKYkSMREREZGSKBE7ff6m7ACWofhGo/hGo/hGM+7xicgQekdMREREpCS6IyYiIiJSEiViIiIiIiVRIrZGZnaumT1kZt8ys2fM7BOx/GYze8nM9sTh6twyv29mB83seTO7Mle+O5YdNLMbC4zxsJntjXE8HsvONLMHzexA/H9zLDcz+3yM4Wkz25Vbz3Vx/gNmdl1BsV2Qq6M9ZjZjZp8ss/7M7DYzO2Zm+3JlhdWXmV0cj8fBuKwVEN+fm9lzMYZ7zGxTLN9uZs1cPd66XBzD9nXE+Ao7nmZ2vpk9GsvvMrNaAfHdlYvtsJntKbH+hl1TxuYcFJF14O4a1jAAW4FdcXwDsB+4ELgZ+NQi818IPAXUgfOBF4A0Di8AO4BanOfCgmI8DJy1oOzPgBvj+I3AZ+L41cB9gAGXAo/G8jOBQ/H/zXF8c8F1mQLfBd5eZv0BlwO7gH3rUV/AN+K8Fpe9qoD4rgAqcfwzufi25+dbsJ5F4xi2ryPGV9jxBL4CXBPHbwVuGDW+BdP/AvjDEutv2DVlbM5BDRo0FD/ojtgauftRd38yjr8GPAtsW2KRDwN3uvu8u38bOAj8RBwOuvshd28Bd8Z518uHgdvj+O3AR3LlX/bgEWCTmW0FrgQedPdX3P3/gAeB3QXH9H7gBXdfqmeEda8/d/868Moi2x25vuK0je7+iLs78OXcutYcn7s/4O6d+OMjwDlLrWOZOIbt65rjW8Kqjme8c/NTwD+tR3xx/b8I/ONS61jn+ht2TRmbc1BEiqdErABmth24CHg0Fv12fFRwW+7xxDbgSG6xF2PZsPIiOPCAmT1hZr8Zy7a4+9E4/l1gS4nx9VzDYAM4LvUHxdXXtji+XnECXE+4y9Fzvpl908y+ZmaX5eIeFsewfR1VEcfzh4DjuaSz6Pq7DHjZ3Q/kykqrvwXXlB+kc1BEVkmJ2IjMbBr4Z+CT7j4D/DXwDuC9wFHC446yvM/ddwFXAb9lZpfnJ8ZPxaV+f0l8z+dDwN2xaJzqb8A41NcwZnYT0AHuiEVHgfPc/SLgd4B/MLONK11fgfs6tsdzgV9m8MNAafW3yDWlkPWKyHhSIjYCM6sSLph3uPu/ALj7y+7edfcM+ALhUQvAS8C5ucXPiWXDykfm7i/F/48B98RYXo6PKHqPWY6VFV90FfCku78cYx2b+ouKqq+XGHxsWFicZvbrwAeAX4kNNfGR3/fj+BOE967etUwcw/Z1zQo8nt8nPHqrLBL3SOI6fw64Kxd3KfW32DVlifWOzTkoImunRGyN4jslfws86+5/mSvfmpvtZ4HeX2jdC1xjZnUzOx/YSXhx9jFgZ/yLsBrhMd29BcQ3ZWYbeuOEl7r3xXX3/orqOuBfc/FdG/8S61Lg1fg45N+BK8xsc3ysdEUsK8rAnYhxqb+cQuorTpsxs0vjuXNtbl1rZma7gd8FPuTus7nys80sjeM7CPV1aJk4hu3rKPEVcjxjgvkQ8AtFxhf9NPCcu/cf25VRf8OuKUusdyzOQREZ0Vre8NfgAO8jPCJ4GtgTh6uBvwP2xvJ7ga25ZW4ifLJ+ntxfK8Xl9sdpNxUU3w7CX5w9BTzTWy/hXZuvAgeA/wDOjOUG3BJj2AtcklvX9YSXqQ8CHy2wDqcIdzrOyJWVVn+EhPAo0Ca8P/MbRdYXcAkhEXkB+CtizxYjxneQ8D5Q7xy8Nc778/G47wGeBD64XBzD9nXE+Ao7nvGc/kbc57uB+qjxxfIvAR9bMG8Z9TfsmjI256AGDRqKH9TFkYiIiEhJ9GhSREREpCRKxERERERKokRMREREpCRKxERERERKokRMREREpCRKxETGnJndbGafKjsOEREpnhIxkQLkvvFdRERkxZSIieSY2XYze9bMvmBmz5jZA2bWGDLvw2b2WTN7HPiEmb0/dhK9N3ZwXY/zHTazs+L4JWb2cBy/Oc73sJkdMrOP59Z9k5ntN7P/Ai7IlX/czL5loRPtO9exKkRE5DRQIiZyqp3ALe7+buA44VvWh6m5+yWEbzj/EvBL7v6jQAW4YQXb+mHgSkIfjH9kZlUzu5jQtc97Cd+s/uO5+W8ELnL3HwM+top9EhGRMaRETORU33b3PXH8CWD7EvP2Ooq+IC63P/58O3D5Crb1bx46mP4eoTPnLcBlwD3uPuvuMwz2nfk0cIeZ/SrQWcnOiIjI+FIiJnKq+dx4l3B3a5gTK1hfh5O/axMjbAvgZwh333YBj+ndNBGRH2xKxESK8Tyw3czeGX/+NeBrcfwwcHEcX+oxZ8/XgY+YWcPMNgAfBDCzBDjX3R8Cfg84A5guJnwRESmDEjGRArj7HPBR4G4z2wtkwK1x8h8Dn4sv9XdXsK4nCY88nwLuAx6Lk1Lg7+P6vwl83t2PF7kfIiJyepm7lx2DiIiIyJuS7oiJiIiIlEQv+oosw8xuAX5yQfHn3P2LZcQjIiJvHHo0KSIiIlISPZoUERERKYkSMREREZGSKBETERERKYkSMREREZGSKBETERERKcn/Az0//qt+wJxYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "del train_rewards[\"egreedy_1.0 drift + delay\"]\n",
    "train_rewards[\"random drift\"] = train_rewards.pop(\"egreedy_1.0 drift + no delay\")\n",
    "train_rewards[\"random no drift\"] = train_rewards.pop(\"egreedy_1.0 no drift + delay\")\n",
    "\n",
    "plot_average_reward_per_n_rounds(train_rewards)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}