examples/quickstart/rtb/rtb_synthetic_discrete_zoo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart Example of Off-Policy Evaluation with Various Estimators (Discrete)\n",
    "This notebook provides an example of conducting **Off-policy Evaluation (OPE)** with a synthetic Real-Time Bidding (RTB) dataset and various OPE estimators (e.g., model-based vs importance sampling-based, marginal importance sampling, and double reinforcement learning).\n",
    "\n",
    "This example consists of the following 3 steps: \n",
    "1. Setup, Synthetic Data Generation, and Offline Policy Learning\n",
    "2. Off-Policy Evaluation (OPE) with various estimators\n",
    "\n",
    "\\* This library uses [d3rlpy](https://github.com/takuseno/d3rlpy)'s algorithm implementations of offline rl policies and model-based evaluation.  \n",
    "\\* Also, our implementations of OPE are highly inspired by [Open Bandit Pipeline](https://github.com/st-tech/zr-obp)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import SCOPE-RL modules\n",
    "import scope_rl\n",
    "from rtbgym import RTBEnv, CustomizedRTBEnv\n",
    "from scope_rl.dataset import SyntheticDataset\n",
    "from scope_rl.policy import OnlineHead\n",
    "from scope_rl.policy import EpsilonGreedyHead\n",
    "from scope_rl.policy import SoftmaxHead\n",
    "from scope_rl.ope.online import (\n",
    "    calc_on_policy_policy_value,\n",
    "    visualize_on_policy_policy_value,\n",
    ")\n",
    "\n",
    "# import d3rlpy algorithms\n",
    "from d3rlpy.algos import DiscreteRandomPolicy as RandomPolicy\n",
    "from d3rlpy.preprocessing import MinMaxObservationScaler\n",
    "\n",
    "# import from other libraries\n",
    "import gym\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2.1\n"
     ]
    }
   ],
   "source": [
    "# version\n",
    "print(scope_rl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set random state\n",
    "random_state = 12345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log directory\n",
    "from pathlib import Path\n",
    "Path(\"logs/\").mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore warnings\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Setup, Synthetic Data Generation, and Offline Policy Learning\n",
    "To begin with, we briefly describe how the RTB environment works.\n",
    "\n",
    "#### RL setup for RTB\n",
    "In RTB, the objective of the RL agent is to maximize some KPIs (number of clicks or conversions) within an episode under given budget constraints.  \n",
    "\n",
    "We often try to achieve this by adjusting bidding price function parameter $\\alpha$. By using $\\alpha$, we can adjust the bid price as follows.  \n",
    "$bid_{t,i} = \\alpha \\cdot r^{\\ast}$, \n",
    "where $r^{\\ast}$ denotes a predicted or expected reward (KPIs).\n",
    "\n",
    "We often formulate this RTB problem as the following Constrained Markov Decision Process (CMDP):\n",
    "- `timestep`: One episode (a day or a week) consists of several timesteps (24 hours or seven days, for instance).\n",
    "- `state`: We observe some feedback from the environment at each timestep, which includes the following.\n",
    "  - timestep\n",
    "  - remaining budget\n",
    "  - impression level features (budget consumption rate, cost per mille of impressions, auction winning rate, reward) at the previous timestep\n",
    "  - adjust rate (RL agent's decision making) at the previous timestep\n",
    "- `action`: Agent chooses adjust rate parameter $\\alpha$ to maximize KPIs.\n",
    "- `reward`: Total number of clicks or conversions obtained during the timestep.\n",
    "- `constraints`: The pre-determined episodic budget should not be exceeded.\n",
    "\n",
    "For more about the environmental configuration and its customization, please refer to [examples/quickstart/rtb/rtb_synthetic_customize_env.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart/rtb/rtb_synthetic_customize_env.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardized environment for discrete action\n",
    "env = gym.make(\"RTBEnv-discrete-v0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Synthetic Data Generation\n",
    "Next, we collect logged data with a behavior policy $\\pi_0$:\n",
    "\n",
    "$$\\mathcal{D}_0 := \\{ \\{ s_t, a_t, s_{t+1}, r_t \\}_{t=1}^T \\}_{i=1}^n \\sim \\prod_{i=1}^n d_{s_0}(s_0) \\prod_{t=1}^T \\pi_0(a_t | s_t) p(s_{t+1} | s_t, a_t) p(r_t | s_t, a_t),$$\n",
    "\n",
    "where $s_t \\in \\mathcal{S}$ is the state observed at timestep $t$, $a_t$ is the action chosen by the behavior policy $\\pi_0$, and $r_t$ is the reward. \n",
    "\n",
    "`SyntheticDataset` has the following arguments:\n",
    "- `env`: RTB environment for RL defined in the previous section.\n",
    "- `action_meaning`: Dictionary which maps discrete action index into specific actions.\n",
    "- `state_keys`: Name of the state variable at each dimension.\n",
    "- `max_episode_steps`: Maximum number of timesteps in an episode.\n",
    "- `info_keys`: Dictionary containing of key and the type of info components.\n",
    "\n",
    "For more about the data collection and visualization, please refer to [examples/quickstart/rtb/rtb_synthetic_data_collection.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart/rtb/rtb_synthetic_data_collection.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# behavior policy\n",
    "from d3rlpy.algos import DoubleDQNConfig\n",
    "from d3rlpy.models.encoders import VectorEncoderFactory\n",
    "from d3rlpy.models.q_functions import MeanQFunctionFactory\n",
    "from d3rlpy.dataset import create_fifo_replay_buffer\n",
    "from d3rlpy.algos import LinearDecayEpsilonGreedy\n",
    "\n",
    "# model\n",
    "ddqn = DoubleDQNConfig(\n",
    "    encoder_factory=VectorEncoderFactory(hidden_units=[30, 30]),\n",
    "    q_func_factory=MeanQFunctionFactory(),\n",
    "    target_update_interval=100,\n",
    ").create(device=device)\n",
    "\n",
    "# replay buffer\n",
    "buffer = create_fifo_replay_buffer(\n",
    "    limit=10000,\n",
    "    env=env,\n",
    ")\n",
    "\n",
    "# explorers\n",
    "explorer = LinearDecayEpsilonGreedy(\n",
    "    start_epsilon=1.0,\n",
    "    end_epsilon=0.1,\n",
    "    duration=1000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# start training\n",
    "# skip if there is a pre-trained model\n",
    "ddqn.fit_online(\n",
    "    env,\n",
    "    buffer,\n",
    "    explorer=explorer,\n",
    "    eval_env=env,\n",
    "    n_steps=100000,\n",
    "    n_steps_per_epoch=1000,\n",
    "    update_start_step=1000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "ddqn.save_model(\"d3rlpy_logs/ddqn.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload model\n",
    "ddqn.build_with_env(env)\n",
    "ddqn.load_model(\"d3rlpy_logs/ddqn.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "behavior_policy = EpsilonGreedyHead(\n",
    "    ddqn, \n",
    "    n_actions=env.action_space.n,\n",
    "    epsilon=0.3,\n",
    "    name=\"ddqn_epsilon_0.3\",\n",
    "    random_state=random_state,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize dataset class\n",
    "dataset = SyntheticDataset(\n",
    "    env=env,\n",
    "    action_meaning=env.action_meaning,\n",
    "    state_keys=env.obs_keys,\n",
    "    max_episode_steps=env.step_per_episode,\n",
    "    info_keys={\n",
    "        \"search_volume\": int,\n",
    "        \"impression\": int,\n",
    "        \"click\": int,\n",
    "        \"conversion\": int,\n",
    "        \"average_bid_price\": float,\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# collect logged data by a behavior policy\n",
    "# skip if there is a preserved logged dataset\n",
    "train_logged_dataset = dataset.obtain_episodes(\n",
    "    behavior_policies=behavior_policy,\n",
    "    n_trajectories=10000, \n",
    "    obtain_info=True,\n",
    "    random_state=random_state,\n",
    ")\n",
    "test_logged_dataset = dataset.obtain_episodes(\n",
    "    behavior_policies=behavior_policy,\n",
    "    n_trajectories=10000, \n",
    "    obtain_info=True,\n",
    "    random_state=random_state,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/train_dataset_discrete_ddpn.pkl\", \"wb\") as f:\n",
    "    pickle.dump(train_logged_dataset, f)\n",
    "with open(\"logs/test_dataset_discrete_ddpn.pkl\", \"wb\") as f:\n",
    "    pickle.dump(test_logged_dataset, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/train_dataset_discrete_ddqn.pkl\", \"rb\") as f:\n",
    "    train_logged_dataset = pickle.load(f)\n",
    "with open(\"logs/test_dataset_discrete_ddqn.pkl\", \"rb\") as f:\n",
    "    test_logged_dataset = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Offline Policy Learning\n",
    "Here, we learn several \"candidate\" policies to be evaluated and selected using [d3rlpy](https://github.com/takuseno/d3rlpy)'s algorithm implementation.\n",
    "\n",
    "For more about the offline RL procedure, please refer to [examples/quickstart/rtb/rtb_synthetic_discrete_basic.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart/rtb/rtb_synthetic_discrete_basic.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules from d3rlpy\n",
    "from d3rlpy.dataset import MDPDataset\n",
    "from d3rlpy.algos import DiscreteCQLConfig as CQLConfig\n",
    "# transform offline dataset for d3rlpy\n",
    "offlinerl_dataset = MDPDataset(\n",
    "    observations=train_logged_dataset[\"state\"],\n",
    "    actions=train_logged_dataset[\"action\"],\n",
    "    rewards=train_logged_dataset[\"reward\"],\n",
    "    terminals=train_logged_dataset[\"done\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Conservative Q-Learning policy\n",
    "cql = CQLConfig(\n",
    "    encoder_factory=VectorEncoderFactory(hidden_units=[30, 30]),\n",
    "    q_func_factory=MeanQFunctionFactory(),\n",
    ").create(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cql.fit(\n",
    "    offlinerl_dataset,\n",
    "    n_steps=10000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "cql.save_model(\"d3rlpy_logs/cql_discrete.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload model\n",
    "cql.build_with_env(env)\n",
    "cql.load_model(\"d3rlpy_logs/cql_discrete.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Off-Policy Evaluation\n",
    "In this section, we aim at evaluating the various performance statistics of policies in an offline manner using (basic) model-based and importance sampling-based estimators.\n",
    "\n",
    "#### Estimation Target\n",
    "\n",
    "The goal to estimate the estimators' average policy performance, the *policy value*.\n",
    "\n",
    "$$ V(\\pi) := \\mathbb{E}\\left[\\sum_{t=1}^T \\gamma^{t-1} r_t \\mid \\pi \\right] $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import ope modules from SCOPE-RL\n",
    "from scope_rl.ope import CreateOPEInput\n",
    "from scope_rl.ope import OffPolicyEvaluation as OPE\n",
    "# basic estimators\n",
    "from scope_rl.ope.discrete import DirectMethod as DM\n",
    "from scope_rl.ope.discrete import TrajectoryWiseImportanceSampling as TIS\n",
    "from scope_rl.ope.discrete import PerDecisionImportanceSampling as PDIS\n",
    "from scope_rl.ope.discrete import DoublyRobust as DR\n",
    "# self normalized estimators\n",
    "from scope_rl.ope.discrete import SelfNormalizedTIS as SNTIS\n",
    "from scope_rl.ope.discrete import SelfNormalizedPDIS as SNPDIS\n",
    "from scope_rl.ope.discrete import SelfNormalizedDR as SNDR\n",
    "# marginal estimators\n",
    "from scope_rl.ope.discrete import StateActionMarginalIS as SAMIS\n",
    "from scope_rl.ope.discrete import StateActionMarginalDR as SAMDR\n",
    "from scope_rl.ope.discrete import StateMarginalIS as SMIS\n",
    "from scope_rl.ope.discrete import StateMarginalDR as SMDR\n",
    "from scope_rl.ope.discrete import StateActionMarginalSNIS as SAMSNIS\n",
    "from scope_rl.ope.discrete import StateActionMarginalSNDR as SAMSNDR\n",
    "from scope_rl.ope.discrete import StateMarginalSNIS as SMSNIS\n",
    "from scope_rl.ope.discrete import StateMarginalSNDR as SMSNDR\n",
    "# double reinforcement learning estimators\n",
    "from scope_rl.ope.discrete import DoubleReinforcementLearning as DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from d3rlpy.algos import DiscreteRandomPolicyConfig\n",
    "random = DiscreteRandomPolicyConfig().create(device=device)\n",
    "random.build_with_env(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define evaluation policies\n",
    "cql_ = EpsilonGreedyHead(base_policy=cql, n_actions=env.action_space.n, name=\"cql\", epsilon=0.0, random_state=random_state)\n",
    "ddqn_ = EpsilonGreedyHead(base_policy=ddqn, n_actions=env.action_space.n, name=\"ddqn\", epsilon=0.0, random_state=random_state)\n",
    "random_ = EpsilonGreedyHead(base_policy=random, n_actions=env.action_space.n, name=\"random\", epsilon=1.0, random_state=random_state)\n",
    "evaluation_policies = [cql_, ddqn_, random_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first, prepare OPE inputs\n",
    "prep = CreateOPEInput(\n",
    "    env=env,\n",
    "    model_args={\n",
    "        \"fqe\": {\n",
    "            \"encoder_factory\": VectorEncoderFactory(hidden_units=[30, 30]),\n",
    "            \"q_func_factory\": MeanQFunctionFactory(),\n",
    "            \"learning_rate\": 1e-4,\n",
    "        },\n",
    "    },\n",
    "    state_scaler=MinMaxObservationScaler(\n",
    "        minimum=test_logged_dataset[\"state\"].min(axis=0),\n",
    "        maximum=test_logged_dataset[\"state\"].max(axis=0),\n",
    "    ),\n",
    "    gamma=0.95,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# takes time\n",
    "input_dict = prep.obtain_whole_inputs(\n",
    "    logged_dataset=test_logged_dataset,\n",
    "    evaluation_policies=evaluation_policies,\n",
    "    require_value_prediction=True,\n",
    "    require_weight_prediction=True,\n",
    "    n_trajectories_on_policy_evaluation=100,\n",
    "    random_state=random_state,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/ope_input_dict_discrete_zoo.pkl\", \"wb\") as f:\n",
    "    pickle.dump(input_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/ope_input_dict_discrete_zoo.pkl\", \"rb\") as f:\n",
    "    input_dict = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_estimators = [DM(), TIS(), PDIS(), DR(), SNTIS(), SNPDIS(), SNDR()]\n",
    "state_marginal_estimators = [SMIS(), SMDR(), SMSNIS(), SMSNDR()]\n",
    "state_action_marginal_estimators = [SAMIS(), SAMDR(), SAMSNIS(), SAMSNDR()]\n",
    "drl_estimators = [DRL()]\n",
    "all_estimators = basic_estimators + state_marginal_estimators + state_action_marginal_estimators + drl_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_estimators_name = [\"dm\", \"tis\", \"pdis\", \"dr\", \"sntis\", \"snpdis\", \"sndr\"]\n",
    "state_marginal_estimators_name = [\"sm_is\", \"sm_dr\", \"sm_snis\", \"sm_sndr\"]\n",
    "state_action_marginal_estimators_name = [\"sam_is\", \"sam_dr\", \"sam_snis\", \"sam_sndr\"]\n",
    "drl_estimators_name = [\"drl\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ope = OPE(\n",
    "    logged_dataset=test_logged_dataset,\n",
    "    ope_estimators=all_estimators,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cql':            policy_value  relative_policy_value\n",
       " on_policy     13.670803               1.000000\n",
       " dm            19.692582               1.440485\n",
       " tis            0.013220               0.000967\n",
       " pdis          10.308965               0.754086\n",
       " dr            13.913196               1.017731\n",
       " sntis         14.604998               1.068335\n",
       " snpdis        12.109122               0.885765\n",
       " sndr          13.480904               0.986109\n",
       " sm_is          1.424221               0.104180\n",
       " sm_dr         19.258980               1.408767\n",
       " sm_snis       10.870918               0.795192\n",
       " sm_sndr       10.705989               0.783128\n",
       " sam_is         7.883300               0.576652\n",
       " sam_dr        18.898415               1.382392\n",
       " sam_snis      15.068570               1.102245\n",
       " sam_sndr      18.351390               1.342378\n",
       " drl           16.713641               1.222579,\n",
       " 'ddqn':            policy_value  relative_policy_value\n",
       " on_policy     14.049627               1.000000\n",
       " dm            12.252217               0.872067\n",
       " tis            0.000000               0.000000\n",
       " pdis          12.185442               0.867314\n",
       " dr            15.890223               1.131007\n",
       " sntis          0.000000               0.000000\n",
       " snpdis        14.933411               1.062904\n",
       " sndr          16.517896               1.175682\n",
       " sm_is          1.301702               0.092650\n",
       " sm_dr         11.948073               0.850419\n",
       " sm_snis       11.004493               0.783259\n",
       " sm_sndr        7.601301               0.541032\n",
       " sam_is         5.377394               0.382743\n",
       " sam_dr        11.900864               0.847059\n",
       " sam_snis      14.317214               1.019046\n",
       " sam_sndr      11.977408               0.852507\n",
       " drl           10.401420               0.740334,\n",
       " 'random':            policy_value  relative_policy_value\n",
       " on_policy     10.817043               1.000000\n",
       " dm            12.144439               1.122713\n",
       " tis            4.378170               0.404747\n",
       " pdis           9.833942               0.909116\n",
       " dr            11.604740               1.072820\n",
       " sntis         10.573255               0.977463\n",
       " snpdis        10.827806               1.000995\n",
       " sndr          11.139489               1.029809\n",
       " sm_is          1.673970               0.154753\n",
       " sm_dr         11.338904               1.048244\n",
       " sm_snis       11.611709               1.073464\n",
       " sm_sndr        6.183237               0.571620\n",
       " sam_is        10.448646               0.965943\n",
       " sam_dr         9.792576               0.905291\n",
       " sam_snis      12.081314               1.116878\n",
       " sam_sndr       9.547222               0.882609\n",
       " drl           10.263190               0.948798}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# policy value estimation by ope\n",
    "policy_value_df_dict, policy_value_interval_df_dict = ope.summarize_off_policy_estimates(\n",
    "    input_dict, \n",
    "    random_state=random_state,\n",
    ")\n",
    "# dictionary of the estimation\n",
    "policy_value_df_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1400x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize and compare the result\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict, \n",
    "    compared_estimators=basic_estimators_name,\n",
    "    random_state=random_state, \n",
    "    sharey=False,\n",
    ")\n",
    "# relative policy value to the behavior policy\n",
    "# ope.visualize_off_policy_estimates(input_dict, random_state=random_state, is_relative=True, sharey=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize and compare the result\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict, \n",
    "    compared_estimators=state_marginal_estimators_name,\n",
    "    random_state=random_state, \n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAR0CAYAAAByutDxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADVi0lEQVR4nOzdeVyVdf7//+dhlaMiu4CKuKRGaeIegkvua5Fme9lU0yjtU9OnsUWnmrSmaZl0HJsZS1sGNUktY8wmzSUX3NdyqRREBQEREDnI+f3hj/ONAD0czjkXy+N+u83txnVd7+t6vw7zlp5cvK/3ZbJarVYBAAAAjZyH0QUAAAAAdQHBGAAAABDBGAAAAJBEMAYAAAAkEYwBAAAASQRjAAAAQBLBGAAAAJBEMAYAAAAkEYwBAAAASQRjAEAtTJ8+XSaTSdOnTze6FACoNYIxAAAAIIIxAAAAIIlgDAAAAEgiGANAg1ZUVKS33npL8fHxCgwMlK+vr9q2batx48bp448/rtA2JydHjz/+uNq2bStfX19FRUXp4YcfVk5OjiZPniyTyaT333/fmA8CAG7gZXQBAADXOH78uEaOHKn9+/fLbDarf//+Cg4OVkZGhtatW6c9e/bojjvukCSdOnVKCQkJOnTokAIDAzV27FiVlZXpo48+Umpqqq655hqDPw0AuB7BGAAaoLKyMt18883av3+/hg8frg8//FChoaG248XFxfrf//5n23744Yd16NAhJSQkaMWKFWrRooWkS3eRR48ereXLl7v9MwCAuzGVAgAaoBUrVigtLU0RERH69NNPK4RiSWrSpIlGjx4t6dKd5aVLl8pkMmnu3Lm2UCxJQUFBmjt3rltrBwCjEIwBoAFKTU2VJN1xxx1q1qzZZdt+++23KisrU48ePRQTE1PpePfu3dWtWzeX1AkAdQnBGAAaoJ9//lmS1KVLlyu2TU9PlyS1a9eu2jaXOwYADQXBGAAAABDBGAAapKioKEnSwYMHr9i2VatWkqSffvqp2jaXOwYADQXBGAAaoJEjR0qSPvnkExUWFl627YABA2QymbR9+/Yqg/SuXbu0e/dul9QJAHUJwRgAGqDx48crNjZWJ06c0C233KIzZ85UOF5cXKwvv/xS0qW7y4mJiSorK9OUKVOUn59va5ebm6upU6fKarW6tX4AMALrGANAA+Th4aGUlBSNGDFCX375paKiohQfH297wceuXbsUEBBgmyIxe/Zs7dq1S2vWrFG7du00aNAgWa1WffPNNwoODtb48eNZyxhAg8cdYwBooNq2bau0tDTNmjVL11xzjb777jstXbpUP//8swYOHKhZs2bZ2oaHh2vz5s165JFHZDab9fnnn2vr1q267bbbtGnTJgUGBhr4SQDAPUxW/j4GALiCyZMn64MPPtD8+fM1efJko8sBAJfgjjEAAAAggjEAAAAgqY49fHf48GGtXbtW+/btU1ZWlpo1a6arrrpKt912myIjIyu0TU9P1wcffKCDBw/Ky8tLPXr00L333it/f3+7+kpLS9PixYuVnp4uf39/DR48WBMmTJCnp6crPhoAAADquDo1x/iNN97Q999/r379+qlt27bKy8tTamqqiouL9corr9gWrD9z5oz+8Ic/yGw2a9SoUSouLtaKFSsUEhKiV199VV5el8/7O3bs0MyZMxUTE6P+/fvr2LFj+u9//6uhQ4fqwQcfdMdHBQAAQB1Tp+4Yjx07Vo899liFYBsXF6ennnpKn332mR599FFJUkpKii5cuKBZs2YpJCREktSxY0e9/PLLWrNmjYYOHXrZfhYuXKioqCg999xztjvEZrNZKSkpGj16tO0tUAAAAGg86tQc486dO1e62xsREaHWrVsrIyPDtm/z5s3q0aOHLRRLUrdu3RQREaHvvvvusn2kp6crPT1dQ4cOrTBtYvjw4bJardq0aZOTPg0AAADqkzoVjKtitVp19uxZ29zhnJwcnT17Vh06dKjUtmPHjvrxxx8ve73y478+PygoSMHBwVc8HwAAAA1TnQ/G69atU05Ojq6//npJl15PKqnKxeYDAwNVUFAgi8VS7fXKzw8ICKh0LCAgwHa8KhaLRUVFRbb/FRcX1+SjAAAAoA6rU3OMfy0jI0P/+te/1KlTJw0aNEiSVFJSIklVPmDn7e1ta1P+9a+Vn1/VcR8fHxUVFVVbT0pKipYsWWLb7tSpk15++WX7PgwAAADqtDobjPPy8jRz5kyZzWY9+eST8vC4dHPbx8dHklRaWlrpnPI7xeVtqlJ+rKq7yiUlJZc9NzExUWPHjrVtm0wmSVJWVlaV9aB2TCaTwsPDdfLkSdWhxVNQTzGe4EyMJzgbY8p1vLy8FBoaal9bF9fikKKiIv35z39WYWGh/vSnPykoKMh2rHwKRVVTHnJzc9WsWbNq7xb/8vy8vLwKD++V76tq7nI5b2/vaq/NIHYdq9XK9xdOw3iCMzGe4GyMKWPVuTnGJSUlmjVrljIzM/V///d/at26dYXjQUFB8vf315EjRyqde/jwYUVHR1/2+uXHf31+Tk6Ozpw5c8XzAQAA0DDVqWBcVlamt956Sz/88IOeeOIJderUqcp2ffv21fbt25WdnW3bt2fPHmVmZqpfv362faWlpcrIyKhwd7lNmzZq1aqVVq9erbKyMtv+VatWyWQyVTgfAAAAjUedmkqxYMECpaWlqWfPniooKNC3335b4fiAAQMkXZrru2nTJs2YMUOjR49WcXGxli9frqioKA0ePNjWPicnR0888YQGDhyopKQk2/677rpLr732ml5++WXFxcXp+PHjSk1N1Q033FDpDjUAAAAahzoVjH/66SdJ0rZt27Rt27ZKx8uDcUhIiKZPn64FCxbo448/lpeXl2JjY3XPPfdcdn5xuZ49e+r3v/+9lixZovnz58vf31+JiYmaOHGiUz8PAAAA6g+TlRnetZaVlXXZtZPhGJPJpIiICGVmZvIgAmqN8QRnYjzB2RhTruPt7W33qhR1ao4xAAAAYBSCMQAAACCCMQAAACCJYAwAAABIIhgDAAAAkgjGAAAAgCSCMYBGoKioSJGRkTKZTCoqKjK6HABAHUUwBgAAAEQwBgAAACQ58Ero06dPKy0tTQcPHlRGRoby8/NlMpnUvHlztWrVSl26dFGvXr0UFhbminoBONmNHx00ugSXu1hy3vb1Lf85KE8fPwOrcY9ld3YxugQAqHfsDsbbtm3TihUrdPDgQVmtVoWHhyssLExt2rSRJBUWFurnn3/W5s2b9cEHH6hLly4aP368evbs6bLiAQAAAGexKxhPmzZNP/30k3r37q0nnnhCXbt2ldlsrrJtUVGRdu/erU2bNunNN99U27Zt9corrzi1aAAAAMDZ7ArG11xzjZ5++mkFBARcsa3ZbFa/fv3Ur18/5eXlaeXKlbWtEQBqxdPHT71e+9roMgCgSkVFRbrqqqskSYcPH5afX8Of7lVX2RWM77jjDocuHhAQ4PC5AAAAgDuxKgUAAACgGjx8d/To0RpfvH379jU+BwAAADCC3cH42WefrfHFk5OTa3wOAAAAYAS7g/GUKVNcWQcAAEAF77zzjtEluEVJSYnt69mzZ8vHx8fAatzj0UcfNbqEKtkdjAcNGuTCMgAAAABj1ejhu5ycHOXk5FyxTW5ubq2KAgAAANzN7mB89OhRTZ06VRs3brxsu40bN2rq1Kk6duxYrYsDAAAA3MXuYJyamqqIiAiNGTPmsu3GjBmjyMhIXuwBAACAesXuYLxv3z5df/31MplMl21nMpnUr18/7d27t9bFAQAAAO5i98N3eXl5CgsLs6ttSEgI84wBAADs4OPjo2eeecboMqAa3DH29fVVQUGBXW0LCwsbxVIjAAAAaDjsvmPctm1bbdu2TWPHjr1i223btqlt27YOFVRcXKzly5fr0KFDOnz4sAoLCzV16tRKy8VNmjSp2mt07dpVzz///GX7SUpKUlZWVqX9Q4cO1W9/+1uHagcAAED9ZXcwHjBggObOnasvv/xSo0aNqrZdamqq9u/fr9/97ncOFZSfn68lS5YoJCRE0dHR2rdvX5XtHn744Ur7jh49qpUrV+q6666zq6/o6OhKQT8yMrLmRQMAAKDeszsYDxw4UN99953ef/997dixQwkJCYqKipKfn5/Onz+vY8eOad26ddq1a5e6devm8AtBAgMDNW/ePAUEBOjIkSPVvop6wIABlfbt379fJpNJ/fv3t6uvoKCgKq8DAACAxsfuYOzh4aGnnnpKCxcu1OrVq7Vr164q2wwbNkz33HPPFVevqI63t7cCAgJqfJ7FYtHmzZsVExOj4OBgu88rLS1VaWmpmjRpUuM+AQAA0HDYHYylS09N3n///UpMTNSOHTuUkZGhoqIimc1mRUZGKjY2tkah1Jl27NihwsJCxcfH233O3r17ddddd6msrEyhoaEaM2aMRo8e7cIqAQAAUFfVKBiXCwoK0pAhQ5xdS62sW7dO3t7e6tevn13to6Ki1KVLF0VGRurcuXNau3at3n//feXk5Oiuu+6q8hyLxSKLxWLbNplM8vPzs30N5yr/nvK9BWqOfzeuxc8noHbq6r8dh4JxXVNUVKTt27crNjZWTZs2teucX68XOHjwYP35z3/WF198oVGjRlV55zslJUVLliyxbbdr106zZs1SaGho7T4ALis8PNzoEhq4A0YXABeIiIgwuoRGgZ9PgGPq6s+oBhGMN2/eLIvFUqNpFL9mMpk0ZswY7dq1S/v27avyobzExMQKq1iU/7aTlZWl0tJSh/tG1Uwmk8LDw3Xy5ElZrVajywHqlczMTKNLaND4+QTUjjt/Rnl5edl9E7NBBOP169fLbDarZ8+etbpOSEiIJFX7IhNvb295e3tXeYwfjK5jtVr5/gI1xL8Z9+DnE+CYuvrvxu4339VVubm52rt3r/r27VttaLXXqVOnJEn+/v7OKA0AAAD1SL0Pxhs2bJDValVCQkKVx0tLS5WRkaHc3FzbvoKCApWVlVVq99lnn8nLy0vXXnutS2sGAABA3eOUqRRlZWU6dOiQcnJyFBAQoE6dOsnT09Ph66WmpqqwsNAWZtPS0nTmzBlJ0qhRo2Q2m21t169fr8DAQMXExFR5rZycHD3xxBMaOHCgkpKSbNf79NNP1a9fP4WFhamgoEDr16/X8ePHdfvttzu0jjIAAADqt1oH44yMDM2aNUtnzpxRs2bNlJ+fr6CgID399NOKjo526JorVqxQVlaWbXvLli3asmWLJCkhIcEWjE+cOKGjR49q7Nix8vCw/+Z3VFSUWrdurXXr1ik/P19eXl6Kjo7WE088oeuvv96hmgEAAFC/may1nP08Y8YMtWnTRnfddZd8fHyUn5+vt956S+fPn9err77qrDrrtKysrArrG8M5TCaTIiIilJmZWWcn6TcEN3500OgS4ALL7uxidAkNGj+f3OOdd94xugS4yKOPPuq2vry9ve1elcLu26zz5s2rcrWGzMxMDRo0SD4+PpIuPbjWp08flgoCAABAvWJ3MM7NzdUjjzyilStXVnhwLSYmRgsXLtSBAwd08uRJbdu2TV988UW1c34BAACAusjuOcbPPPOMdu7cqQULFuirr77Svffeq+7du+uBBx7QP//5T7300ku6ePGiPDw81Lt3bz3wwAOurBsAAABwqho9fNe9e3d169ZNX375pd5++2116tRJkydP1qOPPqqHH35Y586dU/PmzWv0IBwAAABQF9Q4wXp4eGjMmDF6++23batPLFiwQBcuXFCLFi0IxQAAAKiXapxiS0tLVVRUJH9/fz300EN6+eWXdeTIET366KNavXo1T+cCAACgXrJ7KkVubq7+/ve/a/fu3bJarQoPD9dDDz2kmJgYzZgxQxs3btSHH36oVatWafLkyTx8BwAAgHqlRsu1ZWVl6YUXXtBrr72m6OhovfHGG7pw4YIkKS4uTm+99ZZ69eqlV199VX/9619dVjQAAADgbHYH4wMHDmj06NGKiYlR27Ztdeedd6qgoEDp6em2Nj4+Ppo0aZLefPNNmUwmlxQMAAAAuILdwTgwMFCHDh2ybZd/HRAQUKltSEiInnjiidpXBwAAALiJ3XOMb7/9dr399ts6ePCgmjZtqqNHj2rUqFEKDg52ZX0AAACAW9gdjPv06aM333xTu3btUklJie6991516dLFlbUBAAAAblOjF3yEhYVp2LBhrqoFAAAAMEyNgnF1cnJyVFBQIH9//yrnHAMAAAB1nd3BeOPGjerUqZNCQkJs+3bs2KEFCxboxIkTtn3R0dGaPHmyrr76audWCgAAALiQ3atSlD94V2737t2aNWuWCgsLdeONN+r+++/XmDFjlJWVpZdfflk//fSTK+oFAAAAXMLhqRSffPKJgoODNWvWLDVr1sy2f+zYsXr66ae1dOlSPfnkk04pEgAAAHA1u+8Y/1JpaamOHj2qsWPHVgjFkhQUFKShQ4fqwIEDTikQAAAAcAeHgvHFixclSS1btqzyeFhYmAoLCx2vCgAAAHCzGk2l2L59u/Ly8iRJTZo0UU5OTpXtcnNz1bRp01oXBwAAALhLjYLxhg0btGHDBtv2li1bNHTo0Ert9uzZo9atW9e+OgAAAMBN7A7G7777bqV9JpOp0r78/Hy1bNlS3bt3r1VhAAAAgDvZHYxDQ0Ptaufv76+pU6c6XBAAAABgBIcevgMAAAAamhqvY7x9+3Zt2LBBR48eVU5OjkpKStSkSRNFRkaqa9euGjZsmIKDg11RKwAAAOAydgfjCxcu6I033tCuXbsq7Pf09FRkZKSys7OVkpKilStX6re//a3i4+MdKqi4uFjLly/XoUOHdPjwYRUWFmrq1KkaNGhQhXazZ8/W2rVrK50fGRmpt956y66+0tLStHjxYqWnp8vf31+DBw/WhAkT5Onp6VDtAICGr6ioSFdddZUk6fDhw/Lz8zO4IgDOYncwTk5O1p49e3Tfffepf//+8vX11f79+/Xee+8pJiZGd955p9LT0/Xhhx/q3XffVVBQkGJiYmpcUH5+vpYsWaKQkBBFR0dr37591bb19vbWQw89VGGf2Wy2q58dO3bo9ddfV0xMjO677z4dO3ZMn376qc6ePasHH3ywxnUDAACgfrM7GG/cuFEjRozQyJEjbfu6d++uBx54QK+99prGjh2r1q1b65lnntFzzz2npUuXOhSMAwMDNW/ePAUEBOjIkSN69tlnq23r4eGhAQMG1LgPSVq4cKGioqL03HPP2e4Qm81mpaSkaPTo0WrVqpVD1wUAAED9ZPfDd+fOnatybeLWrVurrKxMmZmZki4t4RYfH6/Dhw87VJC3t7cCAgLsbl9WVqaioqIa9ZGenq709HQNHTq0wrSJ4cOHy2q1atOmTTW6HgAAAOo/u+8Yh4WFae/evZVe6FE+1eGXYdbT01NWq9U5FV5GSUmJ7r33Xl24cEFNmzZV//79ddddd6lJkyaXPe/HH3+UJHXo0KHC/qCgIAUHB9uOAwAAoPGwOxiPGDFC8+fPl4+Pj+Li4uTj46ODBw9q2bJl6tKli8LDw21tf/rppwrbrhAYGKjx48erXbt2slqt2rlzp1atWqWff/5Z06dPv+wDdLm5uZJU5Z3pgIAA2/Ffs1gsslgstm2TyWR76KKql52gdsq/p3xvgZoz6t/N8v9U/fOzIblw4f/9lfKLxbny9b1gYDXuM/62QKNLQANSV//bbncwHjlypLKzs/XFF19UWA2iY8eOevzxxyu09fHx0ahRo5xWZFXuuOOOCtv9+/dXRESE/vOf/2jTpk3q379/teeWlJRIujRt49d8fHyqnZqRkpKiJUuW2LbbtWunWbNm2f3yEzjG1b9k4YDRBcAFIiIiDOq54Qfjxsq4MYWGqK6OpxqtY3zXXXdp7Nix+uGHH2SxWBQZGal27dpVajd58mRn1VcjY8eOta2ecblg7OPjI0kV7v6WKykpsR3/tcTERI0dO9a2Xf7bTlZWlkpLS2tTOqpgMpkUHh6ukydPumVqDtCQlD/3ATgLYwrO5M7x5OXlZfdNzBq/4CMgIEB9+vSpcVHu4OPjo+bNm6ugoOCy7QIDL/05KC8vTyEhIRWO5eXlVZp7XM7b27vKu8ySCG4uZLVa+f4CNcS/GTgbYwrOVFfHU4N6JfT58+d17tw5+fv7X7ZddHS0JOnIkSMV9ufk5OjMmTO24wAAAGg8anzHuC4oKSnRxYsXK71t6NNPP5XValX37t1t+0pLS3Xq1CmZzWbbneI2bdqoVatWWr16tYYNGyYPj0u/H6xatUomk0n9+vVz22cBANQvvr5mzf1r9S+fAlB/1clgnJqaqsLCQtvqEGlpaTpz5owkadSoUSooKNAzzzyj/v37KzIyUpK0a9cu7dixQ927d1evXr1s18rJydETTzyhgQMHKikpybb/rrvu0muvvaaXX35ZcXFxOn78uFJTU3XDDTdUuV4zAAAAGrY6GYxXrFihrKws2/aWLVu0ZcsWSVJCQoKaNm2qHj16aPfu3Vq7dq3KysoUHh6u22+/XePGjbPdAb6cnj176ve//72WLFmi+fPny9/fX4mJiZo4caLLPhcAAADqrjoZjGfPnn3FNo888ohd1woLC9OiRYuqPNanT586+yAhAAAA3KtBPXwHAAAAOMrhYFxcXKw5c+YoIyOj0rEffvhBc+bMqVVhAAAAgDs5HIwtFovWrl1b5euTT506VeHteAAAAEBdx1QKAAAAQHX04bv6Zu+pQp0tumB0GQ2OyWRSYKG3cnPP1dk35AB11Zb0c4b0+3NZsSH9wvWMGFNZ3va9xhf1jzvHUwuzrwa76pXQ2dnZkmR77fLZs2dt+yRVesUyAAAAUB/UOBj/8iUZkvTOO+9U2E5OTq5dRQAAAIABahyMX3zxRUlSYWGh/vKXv+juu+9W+/btnV4YAAAA4E41DsYxMTGSpHPnLs0NiY6Otu0DAAAA6itWpQAAAABEMAYAAAAk1WK5Nk9PT8XExKhp06aVjrVo0aJRTa/wsJTIo4QlipzNZDLJdKFYHiXFLNfmQr4XS4wuAS5g1M8kr4ssXdlQeZR4ur1P74ulbu8T7uHOn1Ee3ia725qsJI5aO/nonbIc+d7oMgAAAPAr3h06K/ydj+xqy1QKAAAAQHZOpbhw4YJ8fX0d6qA259YX39/3os4Wnje6jAbHZDIpICBIeXk5TKVwodfWZxpdAlzgD/ERhvSbtrHIkH7her3izG7vc+XKVLf3CfcYPXqk2/pq0dRP4Xa2tSsYT5kyRaNHj9aQIUMUGBho14VzcnL01VdfadWqVfrXv/5lZzn1U5m3j8p8CG7OZjKZZPVtojKfJgRjF7rg6WN0CXCBMp8mhvRb6nnRkH7hekaMKYunw49CoY5z53gq87b/v3N2jbgHHnhAixcv1pIlS9S5c2d17dpV7du3V1hYmJo2bSqr1arCwkKdPn1aR44c0Z49e3To0CFFRETo/vvvd/iDAAAAAO5iVzCOi4tTv379lJaWpjVr1iglJUWlpVU/Kerl5aVu3brpySefVK9eveThwTRmAAAA1H12/43Cw8NDffr0UZ8+fWSxWHT06FFlZGSooKBAktSsWTO1atVK7du3l7e3t8sKBgAAAFzBock73t7e6ty5szp37uzsegAAAABDMM8BAAAAEMEYAAAAkEQwBgAAACQRjAEAAABJBGMAAABAkoOrUqCia1s2lcXC28OczWQyKSIiRJmZFt58B9RQn9bNDen3lAdvvmuojBhTmyxZbu8T7uHO8VSTZYQdDsbZ2dlaunSp9u3bp/z8fD399NOKiYlRfn6+lixZosGDB6tdu3Y1umZxcbGWL1+uQ4cO6fDhwyosLNTUqVM1aNAgW5uysjJ9++232rx5s3766ScVFBQoLCxMcXFxGjdunHx8rhxQp0+frv3791faf91112natGk1qhkAAAANg0PBOD09XS+88IKsVqs6duyokydPqqysTJLk7++v77//XhcuXNCUKVNqdN3yUB0SEqLo6Gjt27evUpuSkhLNmTNHV111lYYNGyZ/f3/98MMPWrRokfbu3asXXnhBJpPpin0FBwfr9ttvr7AvKCioRvUCAACg4XAoGH/44Ydq2rSpXnnlFUnSgw8+WOF4bGysvvvuuxpfNzAwUPPmzVNAQICOHDmiZ599tnLBXl566aWXKrxcZOjQoQoLC9OiRYu0Z88edevW7Yp9mc1mDRgwoMY1AgAAoGFy6OG7AwcO2O7WVnV3NiQkRDk5OTW+rre3twICAi7bxsvLq8o37vXp00eSlJGRYXd/Fy9eVHFxcY1qBAAAQMPk0B3jsrIy+fr6Vns8Pz9fXl7ufa4vLy9PktS8uX2TuU+cOKG7775bpaWlatGihYYMGaKJEydetm6LxSKLxWLbNplM8vPzs30N5yr/nvK9BWqOfzdwNsYUnKmujieH0mv79u21fft2jRgxotKxixcvauPGjerUqVOti6uJZcuWyc/PT7GxsVds27JlS11zzTWKiorShQsXtGnTJi1dulSZmZl64oknqj0vJSVFS5YssW23a9dOs2bNUmhoqFM+A6oWHh5udAkN3AGjC4ALREREGNRzrkH9wtWMG1NoiOrqeHIoGN90002aOXOm3nvvPfXv31/SpTu2u3fvVkpKijIyMvSb3/zGqYVeztKlS7Vnzx498MADatq06RXb//qhwAEDBugf//iHvv76a40ZM6baUJ+YmKixY8fatst/28nKylJpaWktPgGqYjKZFB4erpMnT7JcG1BDmZmZRpeABoYxBWdy53jy8vKy+yamQ8E4NjZWSUlJmj9/vlavXi1J+tvf/iZJ8vPzU1JSkmJiYhy5dI1t3LhRycnJuuGGGzR8+HCHrzNu3Dh9/fXX2rNnT7XB2Nvbu9q18AhurmO1Wvn+AjXEvxk4G2MKzlRXx5PDE4EHDBigPn36aPfu3bbl2sLDw3XdddfZ5t262u7du/Xuu+8qNja20soYNRUcHCxJKigocEZpAAAAqGdq9YRckyZNbKtBuNuhQ4f0+uuvq0OHDnryySfl6elZq+udOnVK0qV1mAEAAND4OBSMs7Oz7WoXEhLiyOWvKD09XTNnzlRYWJieeeaZy77tLiMjQ76+vrZaioqKKk2JsFqtWrp0qaRLb78DAABA4+NQME5KSrKrXXJyco2vnZqaqsLCQuXmXnqyOS0tTWfOnJEkjRo1SiaTSa+88ooKCgo0btw4bd++vcL54eHhFeYIP/HEE4qJidH06dMlST/++KPefvtt9e/fX+Hh4SopKdGWLVv0/fffa+jQoWrfvn2NawYAAED951AwrupVz2VlZcrKytK3334rf3//Kpdys8eKFSuUlZVl296yZYu2bNkiSUpISJAkW1D++OOPK50/cODAyy4VFxoaqi5dumjLli3Ky8uTh4eHWrVqpQcffFBDhw51qGYAAADUfw4F40GDBlV77MYbb9S0adNUVFTkUEGzZ8++YptFixbZfb1ftw0LC9OTTz5Z47oAAADQsDn0SujLadKkiQYNGqQvvvjC2ZcGAAAAXMbpwVi69DBb+SuaAQAAgPqgVsu1/VpRUZEOHDig5cuXq127ds68NAAAAOBSDgXjW2+99bLHQ0JC9MADDzhUEAAAAGAEh4LxhAkTZDKZKuwzmUxq2rSpWrZsqeuuu67WL9wAAAAA3MmhYDxp0iRn1wEAAAAYyiUP3wEAAAD1jV13jOfMmVPjC5tMpipfBAIAAADURXYF43379tX4wr+egwwAAADUZXYFY3veRgcAAADUZ8wxBgAAAEQwBgAAACTV4s13O3bs0Oeff64ff/xRRUVFslqtldokJyfXqjgAAADAXRy6Y7xp0ybNnDlTZ8+eVVxcnKxWq/r376/+/fvLx8dHbdu21cSJE51dKwAAAOAyDt0x/uyzz9SxY0e99NJLKigo0FdffaUbbrhB1157rU6fPq1p06YpLCzM2bUCAAAALuPQHeP09HT1799fHh4etlc/l5aWSpLCwsI0YsQILVu2zHlVAgAAAC7mUDD29fWVl9elm81NmzaVl5eX8vLybMdbtGih06dPO6VAAAAAwB0cCsaRkZFKT0+3bUdHR+vbb7/VxYsXVVJSovXr1yskJMRpRQIAAACu5lAw7t27t7Zu3SqLxSJJuvnmm7Vv3z5NnjxZDzzwgA4ePKibbrrJmXUCAAAALmX3w3fbtm1TbGysPDw8NH78eI0fP952rGfPnpo+fbo2b94sDw8P9ejRQ9dee61LCgYAAABcwe5g/Nprr6l58+a6/vrrlZCQoE6dOlU4fvXVV+vqq692eoEAAACAO9gdjB988EFt2LBBX331lVatWqXQ0FDFx8crPj5erVu3dmWNAAAAgMvZHYyHDh2qoUOHKicnRxs2bNCGDRuUkpKilJQURUdHKyEhQXFxcQoKCnJlvQAAAIBL1PgFH0FBQRo3bpzGjRunEydOaP369dqwYYMWLlyoDz/8UDExMUpISFDfvn1lNptdUTMAAADgdA69+a5cZGSkJk2apEmTJunw4cPasGGDNm7cqLlz5+rf//63Fi5c6Kw6AQAAAJeqVTD+pbCwMLVs2VLBwcHKy8tTSUmJQ9cpLi7W8uXLdejQIR0+fFiFhYWaOnWqBg0aVKltenq6PvjgAx08eFBeXl7q0aOH7r33Xvn7+9vVV1pamhYvXqz09HT5+/tr8ODBmjBhgu1tfgAAAGg8ahWMi4uLtXnzZq1fv1579+5VWVmZQkNDlZiYqPj4eIeumZ+fryVLligkJETR0dHat29fle3OnDmjF198UWazWbfffruKi4u1YsUKHTt2TK+++qrtzXzV2bFjh15//XXFxMTovvvu07Fjx/Tpp5/q7NmzevDBBx2qHQAAAPVXjYNxaWmptm/frvXr12vHjh0qKSlRs2bNNGTIEMXHx6tLly61KigwMFDz5s1TQECAjhw5omeffbbKdikpKbpw4YJmzZple8tex44d9fLLL2vNmjUaOnToZftZuHChoqKi9Nxzz9nuEJvNZqWkpGj06NFq1apVrT4HAAAA6he7g/HevXu1fv16bd68WUVFRfL29lbPnj2VkJCg2NhYp00/8Pb2VkBAwBXbbd68WT169Kjw6ulu3bopIiJC33333WWDcXp6utLT03X//fdXqHv48OFaunSpNm3apAkTJtTqcwAAAKB+sTsYv/TSSzKZTLrmmmtsq074+fm5srZq5eTk6OzZs+rQoUOlYx07dtSOHTsue/6PP/4oSZXODwoKUnBwsO34r1ksFttrsCXJZDLZvgcmk6lGnwFXVv495XsL1Bz/buBsjCk4U10dT3YH47vvvlv9+/dXYGCgK+uxS25uriRVWUtgYKAKCgpksVjk7e192fOrujMdEBBgO/5rKSkpWrJkiW27Xbt2mjVrlkJDQ2v6EVAD4eHhRpfQwB0wugC4QEREhEE9V/3zE/WfcWMKDVFdHU92B+OxY8e6so4aKV/xoqoH7MrDcElJSbXBuPz8qo77+PioqKioyvMSExMrfB/Kf9vJyspSaWlpDT4B7GEymRQeHq6TJ0/KarUaXQ5Qr2RmZhpdAhoYxhScyZ3jycvLy+6bmE5brs2dfHx8JKnKMFo+1aG8zeXO/+W0iHIlJSXVnuvt7V1t2Ca4uY7VauX7C9QQ/2bgbIwpOFNdHU8eRhfgiPIpFFVNecjNzVWzZs2qDbC/PD8vL6/Ssby8vDoxXQQAAADuVS+DcVBQkPz9/XXkyJFKxw4fPqzo6OjLnl9+/Nfn5+Tk6MyZM1c8HwAAAA1PvQzGktS3b19t375d2dnZtn179uxRZmam+vXrZ9tXWlqqjIyMCneX27Rpo1atWmn16tUqKyuz7V+1apVMJlOF8wEAANA4ODTHeOPGjerVq9dl5/HWRmpqqgoLC21hNi0tTWfOnJEkjRo1SmazWYmJidq0aZNmzJih0aNH214lHRUVpcGDB9uulZOToyeeeEIDBw5UUlKSbf9dd92l1157TS+//LLi4uJ0/Phxpaam6oYbblDr1q1d8rkAAABQdzkUjN9++235+fmpb9++SkhI0LXXXuvUolasWKGsrCzb9pYtW7RlyxZJUkJCgsxms0JCQjR9+nQtWLBAH3/8sby8vBQbG6t77rnnsvOLy/Xs2VO///3vtWTJEs2fP1/+/v5KTEzUxIkTnfpZAAAAUD+YrA48Fvj9999r3bp12rRpk86dO6egoCDFx8crISFBUVFRrqizTsvKyqpyhQvUjslkUkREhDIzM+vs06sNwY0fHTS6BLjAsju7GNLviuQ8Q/qF6427NcDtfb7zzjtu7xPu8eijj7qtL29vb9cu19a5c2d17txZ9913n3bu3Kl169YpNTXVNpVhwIABio+PZ3UHAAAA1Bu1WsfY09NTPXv2VM+ePVVcXKwtW7ZozZo1+vDDD/Xxxx8rJiZGAwcOVFxcXJUv4wAAAADqCqetSnHs2DEdPnxYx44dkyRFRkaqoKBAs2fP1iOPPKKDB/lzLQAAAOquWt3GPXHihNavX6/169fr1KlT8vf3V3x8vAYMGKD27dtLurRW8Ny5c/Xee+/pjTfecErRAAAAgLM5FIxXrlypdevW6ejRo/L29lbPnj01efJkde/eXR4eFW9Cd+jQQWPGjNHcuXOdUjAAAADgCg4F4w8++ECdO3fWgw8+qLi4OJnN5su279ChgyZMmOBQgQAAAIA7OBSM//a3vyksLMzu9m3atFGbNm0c6QoAAABwC4cevgsODlZRUVG1x4uKinTx4kWHiwIAAADczaFgPH/+fD3//PPVHn/++ee1YMECh4sCAAAA3M2hYLxz50717du32uP9+vXTjh07HC4KAAAAcDeHgnFubq6CgoKqPR4YGKicnByHiwIAAADczaFg3KxZM504caLa4xkZGfLz83O4KAAAAMDdHArG3bt31+rVq/Xjjz9WOnb06FGtXr1asbGxtS4OAAAAcBeHlmu79dZbtXPnTv3xj39Uz549bUuxHT9+XNu2bZO/v79uvfVWpxYKAAAAuJJDwTgoKEgzZ87URx99pLS0NG3dulWS5Ofnp/j4eN1+++2XnYMMAAAA1DUOBWPp0gN2Dz/8sKxWq/Lz8yVJ/v7+MplMTisOAAAAcBeHg3E5k8mkFi1aOKMWAAAAwDB2BeMlS5ZIkm6++WZ5eHjYtq9k4sSJjlcGAAAAuJFdwXjx4sWSpJtuukkeHh627SshGAMAAKC+sCsYJycnX3YbAAAAqO8cWscYAAAAaGgIxgAAAIDsnEqRlJRU42XYTCaT/va3vzlUFFBUVKSrrrpKknT48GFeMQ4AAFzOrmAcExPD+sQAAABo0Oy+YwwAAAA0ZLV+wYdRZs+erbVr11Z7fO7cudW+lnrRokVVrsXs7e2tjz76yGk1usLFB8cbXYJbXCy9aPu6dOpEXfTyNLAa9/B8b7nRJQAA0Kg5HIzLysr07bffavv27crOzpYkhYSEqGfPnkpISJCHh2uf6xs2bJi6du1aaf97772n0NDQakPxLz3wwANq0qSJbdvVNQMAAKDucigYFxUV6ZVXXrE9FNWyZUtJ0p49e7R582atWrVK06ZNk9lsdmqxv9SpUyd16tSpwr6DBw/qwoULio+Pt+sa/fr1k7+/vyvKQy2ZvTx1bHRPo8sAAACNiEPB+JNPPtHRo0f1m9/8RkOGDJGX16XLlJaW6n//+5/mz5+v//znP/rNb37j1GKvZP369TKZTHYHY+lSyPfz8+PhQgAAgEbOoWC8ZcsWDR8+XCNGjKh4MS8vDR8+XOnp6dq8ebNbg3Fpaam+++47derUSWFhYXad8/DDD6u4uFi+vr7q3bu37rnnHgUEBLi2UAAAANRJDgXjgoICRUZGVnu8VatWKigocLgoR+zatUvnzp1TQkLCFds2a9ZMI0eOVKdOneTl5aWDBw/qv//9r44cOaJXX3212ikgFotFFovFtm0ymWzr63LHGbXFGIIzMZ7gbIwpOFNdHU8OBePw8HClpaVVumNcLi0tzTbv2F3Wr18vT09PXX/99VdsO3r06Arb/fr1U8eOHfXOO+9o1apVuummm6o8LyUlpcJqFu3atdOsWbMUGhpaq9pr4rjbeoK7RUREGNTzAYP6hSsZN55yDeoXrmbcmEJDVFfHk0PBePjw4fr3v/+tV199VaNHj7Z9uBMnTujLL7/U7t27df/99zu10MspLi5WWlqarrvuOjVv3tyha8THx2vBggXas2dPtcE4MTFRY8eOtW2X/7aTlZWl0tJSh/oFymVmZhpdAhoQxhOcjTEFZ3LnePLy8rL7JqZDwXjEiBE6e/asli1bpp07d1bqfOLEiRo+fLgjl3bIli1bdOHCBbumUVxOcHDwZaeAeHt7y9vbu8pjVqu1Vn0DjCE4E+MJzsaYgjPV1fHk8DrGkyZN0siRI7V7927bOsahoaHq2rWr25dAW79+vZo0aaJevXo5fA2r1aqsrCy1a9fOiZUBAACgvqhRMM7Ly9OaNWt0+vRp+fv7q2/fvjVaGs0V8vPztWfPHvXv31++vr6VjmdnZ+vChQtq1apVhXN+Hd5XrVql/Px8XXfddS6vGQAAAHWP3cH49OnTevbZZytMNfjss8/08MMPGxqON27cqIsXL1Zbw7vvvqv9+/dr0aJFtn1Tp05VXFycoqKi5O3trYMHD2rjxo2Kjo7WsGHD3FU6AAAA6hC7g/GiRYtUXFys++67T9dee61Onjyp+fPn64MPPlBcXJxhr1Net26dWrRooW7dutl9Tnx8vH744Qdt3rxZJSUlCg0N1fjx43XzzTdXedcZAAAADZ/dwfj777/X0KFDNXLkSElS69at5eHhoVmzZikjI0Nt2rRxWZGX88orr1z2+PTp0yvt+93vfueiagAAAFBf2X2bNzs7u9KDae3bt5cknTt3zrlVAQAAAG5mdzAuKyuTl1fFG8yenp62YwAAAEB9VqNVKY4cOVJhHd/z589Lkg4ePKjCwsJK7fv27VvL8gAAAAD3qFEwXrlypVauXFlp/+LFi6tsn5yc7FhVAAAAgJvZHYxffPFFV9YBAAAAGMruYBwTE+PKOgAAAABDGbP4MAAAAFDHEIwBAAAAEYwBAAAASQRjAAAAQBLBGAAAAJBEMAYAAAAk2blc29q1ax26+MCBAx06DwAAAHA3u4LxnDlzHLo4wRgAAAD1hV3B+N13362wXVhYqNmzZ8tsNmvUqFGKjIyUJGVkZCg1NVXnz59XUlKS86sFAAAAXMSuYBwaGlphe/HixfL399dzzz0nk8lk2x8VFaW+ffvqlVde0RdffKGpU6c6t1oAAADARRx6+G7r1q3q06dPhVBsu6CHh/r06aOtW7fWujgAAADAXRwKxlarVRkZGdUeT09Pd7ggAAAAwAgOBePevXvrq6++0ueff64LFy7Y9l+4cEErVqzQ6tWr1atXL6cVCQAAALiaXXOMf+2+++7T6dOntXDhQn388ccKDAyUJOXm5urixYvq3LmzJk+e7Mw6AQAAAJdyKBibzWbNmDFDW7du1Y4dO5SdnS1Juu6669SjRw/17NmzyvnHAAAAQF3lUDAu17t3b/Xu3dtZtQAAAACGqVUwzsnJ0f79+5Wfn6++ffsqODhYZWVlKioqktlslocHb5wGAABA/eBQMLZarVqwYIFSU1NVVlYm6dIaxsHBwSouLlZSUpImTZqkMWPGOLVYAAAAwFUcuqW7fPlyrVy5UuPGjdNzzz1X4ZjZbFafPn20efNmpxQIAAAAuINDd4y//vprDRw4UHfccYfOnTtX6Xjbtm21c+fO2tZ2Wfv27dOMGTOqPPbyyy+rU6dOlz0/JydH77//vnbv3i2r1aprrrlG9957r1q2bOmKcgEAAFDHORSMz5w5c9ng6evrq6KiIoeLqolRo0apQ4cOFfaFh4df9pzi4mLNmDFDRUVFSkxMlKenp7744gtNnz5dr732mpo3b+7KkgEAAFAHORSM/f39debMmWqPHz16VCEhIQ4XVRNXX321+vXrV6Nz/vvf/yozM1N//vOf1bFjR0lSbGysfv/732vFihW64447XFEqAAAA6jCH5hj37dtXX331lU6dOlXp2K5du7RmzRpdf/31tS7OXufPn9fFixftbr9p0yZ16NDBFoolqVWrVuratau+++47V5QIAACAOs6hO8aTJk3Svn379Ic//EFdunSRJC1btkzJycn64Ycf1K5dOyUmJjq10OrMmTNHxcXF8vDw0NVXX6277rqr0tSKXyorK9OxY8c0ePDgSsc6dOigXbt26fz58/Lz83Nl2QAAAKhjHH7z3SuvvKIVK1Zo06ZN8vHx0f79+xUeHq5bbrlF48ePl4+Pj7NrrcDLy0t9+/ZVbGys/P39lZ6erhUrVuiFF17Qyy+/rHbt2lV5XkFBgSwWiwICAiod++WrrasKxhaLRRaLxbZtMpls7XjTH2qLMQRnYjzB2RhTcKa6Op4cfsGHj4+PJkyYoAkTJjizHrt17txZnTt3tm336tVL/fr101NPPaWPP/5Y06ZNq/K8kpISSZK3t3elY+X7ytv8WkpKipYsWWLbbteunWbNmqXQ0FCHP0dNHXdbT3C3iIgIg3o+YFC/cCXjxlOuQf3C1YwbU2iI6up4cigYz5gxQzfffLO6du1a5fG9e/fq008/1Ysvvlir4moqPDxcvXr10pYtW1RWVlblm/fK72T/8s5vufJ91d3tTkxM1NixY23b5b/tZGVlqbS0tNb1o3HLzMw0ugQ0IIwnOBtjCs7kzvHk5eVl901Mh4Lx/v37NWTIkGqP5+fna//+/Y5cutZCQkJUWlqq4uJimc3mSsebNWsmb29v5eXlVTqWm3vpTkf5lIpf8/b2rvJOs3TpbYBAbTCG4EyMJzgbYwrOVFfHk0OrUlzJyZMnDXt47dSpU/L29laTJk2qPO7h4aGoqCgdOXKk0rHDhw+rZcuWPHgHAADQCNl9x3jNmjVau3atbXvp0qX6+uuvK7UrKirSzz//rNjYWOdUWI38/Hz5+/tX2PfTTz8pLS1NsbGxtmkU2dnZunDhglq1amVr17dvX3388cc6cuSIbQWLEydOaO/evRo3bpxL6wYAAEDdZHcwLikpUX5+vm37/PnzlZ4oNJlM8vX11bBhwzRx4kTnVVmFN998Uz4+PurcubNtVYqvv/5avr6+FV7Q8e6772r//v1atGiRbd+IESP09ddfa+bMmRo3bpw8PT31+eefq0WLFgRjAACARsruYDx8+HANHz5ckpSUlKT77rtPvXr1cllhV9K7d2+tX79en3/+uc6fPy9/f3/16dNHt9xyyxVfCe3n56fp06frgw8+0Keffiqr1aqYmBhNnjy50l1oAAAANA4OPXw3e/ZsZ9dRY6NHj9bo0aOv2G769OlV7g8ODtaTTz7p5KoAAABQXzm8jnG58+fPq6ioqMqnC0NCQmp7eQAAAMAtHA7Gq1at0ueff65Tp05V2yY5OdnRywMAAABu5dBybatWrdK//vUvhYeH67bbbpMkjRkzRjfddJMCAgIUHR2tKVOmOLVQAAAAwJUcCsapqam67rrr9Mc//lFDhw6VJPXo0UO333673nzzTZ0/f17nzp1zaqEAAACAKzkUjE+dOqWePXtKkjw9PSXJ9kpks9msG264QatWrXJSiQAAAIDrORSMzWazLl68aPvax8dH2dnZtuN+fn5VvnIZAAAAqKscCsZt2rTRzz//bNvu1KmTvvrqK+Xk5Cg7O1urV69WRESE04oEAAAAXM2hYJyQkKDjx4/LYrFIkm655Ralp6drypQpSkpK0okTJ2wP5QEAAAD1gUPLtQ0ePFiDBw+2bXfp0kV//etftW3bNnl4eKhbt26KjIx0WpEAAACAq9X6BR/lWrZsadeb6AAAAIC6qNbBuKysTEVFRVUea9asWW0vDwAAALiFQ8G4tLRUy5Yt0zfffKMzZ86orKysyna8+Q4AAAD1hUPBeN68eVq7dq06deqk3r17y2w2O7suAAAAwK0cCsabNm3SgAEDlJSU5Ox6AAAAAEM4tFybr6+vrrrqKmfXAgAAABjGoWDcv39/bd++3dm1AAAAAIZxaCrFXXfdpTlz5mjmzJkaPHiwgoOD5eFROWO3b9++1gUCAAAA7uBQMLZYLLJardqxY4d27NhRbTtWpQAAAEB94VAw/vvf/64tW7aof//+6tixI6tSAAAAoN5zKBjv2rVLI0eO1OTJk51cDgAAAGAMhx6+8/PzU3h4uLNrAQAAAAzjUDAeMmSINmzYUO0b7wAAAID6xqGpFK1bt1ZaWpqeeeYZDRw4sNpVKfr27VvrAgEAAAB3cCgYv/XWW7avFy5cWG07VqUAAABAfeFQMH7xxRedXQcAAABgKIeCcUxMjLPrqJHDhw9r7dq12rdvn7KystSsWTNdddVVuu222xQZGXnZc9esWaM5c+ZUeWzevHkKCAhwQcUAAACo6xwKxkZbtmyZvv/+e/Xr109t27ZVXl6eUlNT9cwzz+iVV15RVFTUFa8xadIkhYWFVdjHeswAAACNl13BeMaMGTKZTJo2bZo8PT01Y8aMK55jMpn0wgsv1LrAqowdO1aPPfaYvLz+X/lxcXF66qmn9Nlnn+nRRx+94jViY2PVoUMHl9QHAACA+seu5dqsVqusVmuFbXvOcZXOnTtXCMWSFBERodatWysjI8Pu65w/f54l5wAAACDJzjvG06dPv+x2XWC1WnX27Fm1adPGrvYzZsxQcXGxvLy8dN111+mee+5RRESEi6sEAABAXeXQHOP9+/erdevW8vf3r/J4fn6+0tPT3fqQ3rp165STk6NJkyZdtp2Pj48GDRqka665Rn5+fjp69Ki++OILPffcc5o1a5ZCQkKqPddischisdi2TSaT/Pz8bF8DtcEYgjMxnuBsjCk4U10dTw4F4xkzZuiRRx5RfHx8lcf37t2rt99+223rGGdkZOhf//qXOnXqpEGDBl22bVxcnOLi4mzbffr0Uffu3fXiiy9q6dKl+u1vf1vtuSkpKVqyZIltu127dpo1a5ZCQ0Nr/RnsddxtPcHdjPuLxQGD+oUrGTeecg3qF67GX1XhTHV1PLlkVQqLxVLlm/BcIS8vTzNnzpTZbNaTTz7pUL9dunRRx44dtWfPnsu2S0xM1NixY23b5b/tZGVlqbS0tMb9Ar+UmZlpdAloQBhPcDbGFJzJnePJy8vL7puYdgfj7OxsnT592radkZGh/fv3V2pXVFSk1atXu+UualFRkf785z+rsLBQf/rTnxQUFOTwtYKDg3XixInLtvH29pa3t3eVx1z5sCEaB8YQnInxBGdjTMGZ6up4sjsYf/PNNxWmESxdulRLly6tsq2Hh4cefPDB2ld3GSUlJZo1a5YyMzP1/PPPq3Xr1rW63unTp6udMw0AAICGz+5gfP3119tWfHjzzTc1atQodenSpUIbk8kkX19fRUdHu/QNcmVlZXrrrbf0ww8/6Omnn1anTp2qbJebm6uioiK1bNnStrxbfn5+pQC8fft2HT16VKNGjXJZzQAAAKjb7A7GrVu3tt2VnTJlimJiYiq9Oc5dFixYoLS0NPXs2VMFBQX69ttvKxwfMGCAJOnjjz/W2rVr9e6779pqfe655xQdHa0OHTrIbDbrxx9/1DfffKPg4GAlJia6/bMAAACgbnDo4bvqVn44deqULBZLrac1XMlPP/0kSdq2bZu2bdtW6Xh5MK5KXFyctm/frt27d+vChQsKDAzUkCFDNHHiRJfe5QYAAEDd5lAw/vLLL/X999/r8ccft+2bM2eO1q5dK+nSMmbPPvusWrRo4ZQif83eF4wkJSUpKSmpwr7bbrtNt912mwuqAgAAQH3m0JpqX3/9dYXQu3PnTq1du1ZDhw7Vb37zG506dUqLFy92WpEAAACAqzl0xzgrK0utWrWybX/33XcKCwuzrUSRl5dXad4vAAAAUJc55S0cu3fvVvfu3W3boaGhysvLc8alAQAAALdwKBhHRERo69atki5No8jJyVFsbKzteE5Ojpo2beqcCgEAAAA3cGgqxbhx4/TOO+/ovvvuU3FxsVq3bq3rrrvOdnzv3r2Kjo52Vo0AAACAyzkUjPv376/mzZtr+/btatq0qUaMGCFPT09JUkFBgZo1a3bZJdMAAACAusahYCxJ3bp1U7du3Srtb9asmZ566qlaFQUAAAC4m91zjA8fPqyCggK72p4+fdq2pjEAAABQH9gdjKdNm6adO3fatgsKCnTXXXdp//79ldp+//33mjNnjlMKBAAAANzB4eXarFarLBaLysrKnFkPAAAAYAinrGMMAAAA1HcEYwAAAEAEYwAAAEBSDZdrO336tI4ePSpJKioqkiRlZmbKbDZXagcAAADUJzUKxsnJyUpOTq6w75///KdTCwIAAACMYHcwnjJliivrAAAAAAxldzAeNGiQC8sAAAAAjMXDdwAAAIAIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASKrhK6HrEovFouTkZK1bt04FBQVq27atbrvtNnXr1u2K5+bk5Oj999/X7t27ZbVadc011+jee+9Vy5Yt3VA5AAAA6qJ6e8d49uzZ+uKLLxQfH6/77rtPHh4eevXVV3Xw4MHLnldcXKwZM2bowIEDSkxM1C233KIff/xR06dP17lz59xUPQAAAOqaehmMDx8+rI0bN+r222/X3XffraFDh+qFF15QSEiIPvzww8ue+9///leZmZl65plndOONN2rs2LF67rnnlJubqxUrVrjpEwAAAKCuqZfBeNOmTfLw8NDQoUNt+3x8fHTDDTfohx9+UHZ29mXP7dChgzp27Gjb16pVK3Xt2lXfffedS+sGAABA3VUvg/GPP/6oiIgImc3mCvvLw+5PP/1U5XllZWU6duyYOnToUOlYhw4ddOrUKZ0/f97p9QIAAKDuq5cP3+Xl5SkwMLDS/vJ9ubm5VZ5XUFAgi8WigICAy57r5+dX5fkWi0UWi8W2bTKZ5OfnJy8v930bPTp0dltfcC9Pb29D+u0c1syQfuFa3gaNp5CwJob0C9czYkxFRka6vU+4hzvHU01yWr0MxiUlJVV+Q8v3lZSUVHveL9vV5FxJSklJ0ZIlS2zb/fv312OPPVZlSHeZdz5yX19oFD68N9ToEtCATLiT8QTnefTRR40uAY1MvZxK4ePjU+HObbnyfT4+PtWe98t2NTlXkhITE/X+++/b/vfggw9WeS04x/nz5/XMM88wvQVOwXiCMzGe4GyMqbqhXt4xDggIUE5OTqX95VMoqruD26xZM3l7eysvL6/G50qX7iob9efJxshqterHH3+U1Wo1uhQ0AIwnOBPjCc7GmKob6uUd4+joaGVmZqqoqKjC/kOHDtmOV8XDw0NRUVE6cuRIpWOHDx9Wy5Ytq51fDAAAgIatXgbjfv36qaysTKtXr7bts1gsWrNmja666iqFhIRIkrKzs5WRkVHh3L59++rIkSMVwvGJEye0d+9e9evXzz0fAAAAAHVOvZxKcdVVV6lfv3765JNPlJ+fr/DwcK1du1ZZWVn63e9+Z2v37rvvav/+/Vq0aJFt34gRI/T1119r5syZGjdunDw9PfX555+rRYsWGjdunBEfB9Xw9vbWxIkTmb4Cp2A8wZkYT3A2xlTdYLLW08ksJSUlSk5O1rp161RYWKioqCjdeuut6t69u63N9OnTKwVjSTpz5ow++OAD7dq1S1arVTExMZo8ebLCw8Pd/CkAAABQV9TbYAwAAAA4U72cYwwAAAA4G8EYAAAAEMEYDVxSUpJmz55tdBlowCZNmlTpOQbAmWbPnq2kpCSjy0ADNn36dE2fPt3oMuoEgjEAAACgerpcG2Cvt956SyaTyegyAMBhDz30EG9DA9yEO8Zo0Ly9veXlxe9/MFZxcbHRJaAe8/LyYm1bGK6kpERlZWVGl+FyJAbU2Pnz55WcnKytW7cqNzdXZrNZbdu21Z133qn27dvrwIED+vLLL3Xo0CGdPXtWLVq0UN++fXXHHXfIx8fHdp3Zs2dr06ZNevPNN/XPf/5T+/btk9lsVmJiokaOHKljx45p/vz5Onz4sJo3b6477rhD8fHxNao1KSlJMTExtvl5paWlSklJ0bp163TmzBn5+vqqVatWuuWWW9StWzenfp9wZfVpLFksFn300Udat26dLBaLrrnmGj3wwAOV2i1atEhLlizRX//6V3366afauXOnQkND9dprr9X6+9XY1afxsnv3bi1evFjHjx/XxYsXFRQUZKtFkvbt26cZM2bo8ccf18mTJ7Vq1SqdO3dOnTt31m9/+9sK6+rPnj1b+/fvr/C8xIYNG7R8+XJlZmbKZDIpJCREQ4YM0ejRo2v5XW4cGutYkqTVq1dr2bJlysnJUVRUlO65555KfZZf87HHHtPx48f1zTffKC8vT//+97/VtGlTB77j9QfBGDX23nvvadOmTRo5cqRat26tc+fO6eDBg8rIyFD79u313Xff6cKFCxo+fLiaN2+uw4cPKzU1VTk5OXryyScrXKusrEx//vOfdfXVV+uuu+7S+vXr9e9//1tNmjTRJ598ooSEBPXt21dfffWV3n33XXXq1ElhYWEO17548WJ99tlnuuGGG9SxY0edP39eR44c0dGjRwnGBqhPY2nu3Llat26d4uPj1alTJ+3du1czZ86stv1f//pXhYeH6/bbb+fP4E5SX8bL8ePHNXPmTLVt21aTJk2St7e3Tp48qe+//75S22XLlslkMmncuHEqKirS8uXL9c477+jPf/5ztdffvXu33n77bXXt2lV33nmnJCkjI0MHDx4kGNupsY6l//3vf5o3b546d+6s0aNH69SpU3rttdfUrFkzBQcHV7rmp59+Ki8vL40bN06lpaWN4i+wDf8Twum2b9+uIUOGVPgt88Ybb7R9fdddd1X4jXro0KEKDw/XJ598ouzsbIWEhNiOWSwWJSQkKDExUZIUHx+vhx56SH//+9/12GOPKS4uTpLUrVs3Pf7441qzZo0mTZpUq9pjY2P10EMPOXwNOE99GUs//fST1q1bp+HDh9vuEo8cOVLvvPOOfv755yrPadu2rR577DE7vxOwR30ZL7t371ZpaameffZZ+fv7X7ZtSUmJXn/9dVvgaNq0qd5//30dO3ZMUVFR1X4f/Pz8NG3aNHl4MCPSEY1xLJWWluqTTz5RdHS0XnzxRVu71q1ba968eVUGY4vFopkzZ1b4XjR0/ItCjTVt2lSHDx9WTk5Olcd/+Q+ouLhY+fn56tSpk6xWq3788cdK7YcMGVLh2pGRkfL19dX1119v2x8ZGammTZvq9OnTta79+PHjyszMrNV14Bz1ZSzt2LFDkirdjbvc3blhw4bZfX3Yp76Ml/I/NaelpV1xTubgwYMr3IW7+uqrJemy/ZnNZl24cEG7d++2uyZU1BjH0tGjR3X27FkNGzasQrtBgwbJbDZXec2BAwc2qlAscccYDrjzzjs1e/ZsTZkyRe3bt1dsbKwGDhyoli1bSpKys7OVnJystLQ0FRYWVji3qKiowra3t3el34LNZrOCg4MrrSZhNptVUFBQq9onTZqk119/XY899pjatGmj7t27a8CAAWrbtm2trgvH1JexlJWVJZPJZKurXGRkZLXn1GbKD6pWX8ZLXFycvv76a82dO1cfffSRunbtqj59+qhfv36V7vD+8s6j9P+C0OX6GzFihL777jv9+c9/VlBQkLp166a4uDh1797d7hobu8Y4lrKysiRJERERFdp5eXlV+tlWrjH+HCMYo8bi4uJ09dVXa8uWLdq1a5dWrFihZcuW6amnntJ1112nl156SQUFBbrxxhvVqlUr+fr6KicnR3PmzKk017K6PwO66s+DMTEx+tvf/qatW7dq165d+t///qcvvvhCDz74YIXf+OEe9XksXUlju8viDvVlvPj4+GjGjBnat2+ftm/frp07d2rjxo269tpr9dxzz1Xow5H+WrRooddff107d+7Uzp07tWPHDq1Zs0YDBgzQww8/XOv6GwPGkv39NzYEYzgkMDBQI0aM0IgRI3T27Fk988wzWrp0qQIDA5WZmamkpCQNHDjQ1r4u/cmvWbNmGjx4sAYPHqzi4mK9+OKLWrx4McHYIPVhLIWGhspqterUqVMV7hKfOHHC7bU0dvVhvEiXQkrXrl3VtWtX3XvvvVq6dKn+85//aO/evU550NfLy0u9evVSr169VFZWpn/+859avXq1Jk6cWGkVAlStsY2l0NBQSVJmZqauvfZa2/7S0lKdPn2av5z+/5hjjBopKyur9GekFi1aKDAwUKWlpbbfWH/5G7XVatXKlSvdWmd1zp07V2G7SZMmatmypSwWi0EVNV71aSzFxsZKUqW+68q4bgzq03ip6k/l0dHRki6FkNr69c8xDw8PW6jhZ9mVNdax1L59e/n7++urr76qcO6aNWsqTRdpzLhjjBo5f/68fve736lfv35q27atmjRpoj179ujIkSO65557FBkZqZYtW2rhwoXKycmR2WzW5s2baz032FmefPJJxcTEqH379mrWrJmOHDmizZs3a8SIEUaX1ujUp7EUHR2t/v37a9WqVSoqKlLnzp21Z88enTp1yu21NFb1abwsWbJEBw4cUGxsrEJDQ3X27FmtWrVKwcHB6tKlS62vP3fuXBUUFOjaa69VcHCwsrKylJqaqujoaLVq1coJn6Bha6xjycvLS7fddpvmzZunGTNmKC4uTqdPn9aaNWuqnWPcGBGMUSO+vr4aMWKEdu3apS1btqisrEzh4eF64IEHNHz4cEnSM888o/nz5+uzzz6Tt7e3+vTpo5EjR+rpp582uHpp1KhRSktL0+7du2WxWBQaGqpbb71V48ePN7q0Rqe+jaUpU6bI399f69ev19atW3Xttdfq//7v/zRlyhS319IY1afx0qtXL50+fVrffPONzp07p+bNmysmJkaTJk2q9un/mkhISNDXX3+tVatWqbCwUAEBAbr++us1adIklm+zQ2MeS0OHDlVZWZmWL1+uDz/8UFFRUfrDH/6g5ORkF1RfP5msrDwPAAAAMMcYAAAAkJhKgXoqLy/vssd9fHyc8idLNHyMJdQE4wXOwliqm5hKgXrpSq/THDhwoJKSktxUDeozxhJqgvECZ2Es1U0EY9RLV1pPMigoSK1bt3ZTNajPGEuoCcYLnIWxVDcRjAEAAADx8B0AAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAA1GdHS0TCaTfvrpJ7vPGTRokEwmk9asWeOyugCgviAYAwAAACIYAwAAAJIIxgAAAIAkgjEA1Cv79+/XLbfcopCQEPn5+enaa6/VX/7yF128eLHac44fP67f/OY3ioiIUJMmTXTVVVdp2rRpOn/+/GX7ysnJ0eOPP662bdvK19dXUVFRevjhh5WTk6PJkyfLZDLp/fffr3DOL/f/+OOPuvvuuxUeHi5fX1916NBBzz33nC5cuOCMbwUAOJ2X0QUAAOyzfv16jRw5UoWFhWrfvr2GDRum7Oxs/fGPf9SmTZuqPOfgwYMaOHCgTp8+rYiICI0fP16FhYV688039c0331Tb16lTp5SQkKBDhw4pMDBQY8eOVVlZmT766COlpqbqmmuuuWytO3fu1GOPPabAwEANHDhQOTk52rBhg1555RXt27dPKSkptfpeAIArEIwBoB4oLi7WHXfcocLCQj3++OP6y1/+Ik9PT0nS7t27NWTIEGVnZ1c675577tHp06c1adIkffDBB2rSpIkk6dixY7rhhht05MiRKvt7+OGHdejQISUkJGjFihVq0aKFpEt3kUePHq3ly5dftt63335b06ZN04wZM2x17t27V/369dNnn32m7777Ttdff73D3w8AcAWmUgBAPfDpp5/q+PHjatOmjV577TVb2JSkbt26adq0aZXO2bBhg7Zu3aqmTZtqzpw5tlAsSVFRUfrLX/5SZV/Hjx/X0qVLZTKZNHfuXFsolqSgoCDNnTv3ivX27NlTL730UoU6r732Wt19992SpNWrV1/5QwOAmxGMAaAeKF9neNKkSfL29q50/N577632nJEjRyo4OLjS8RtvvLFC6C337bffqqysTD169FBMTEyl4927d1e3bt0uW+/YsWNlMpkq7b/66qslSRkZGZc9HwCMQDAGgHogPT1dktSuXbsqjwcGBlYKuVc6x2QyKTo6usZ9XemYdOmOdFX8/f0lXZoaAgB1DcEYAOB0Hh785wVA/cNPLgCoB1q1aiVJ1b7uOS8vT2fPnq3ROZL0888/17ivKx0DgPqKYAwA9cDAgQMlSYsWLZLFYql0fMGCBdWek5qaqpycnErHly9frry8vEr7BwwYIJPJpO3bt+vgwYOVju/atUu7d++u6UcAgDqPYAwA9cDEiRPVqlUrHTt2TM8++6zKyspsx/bu3auXX3650jkJCQnq0aOHCgoKlJSUVOHFGsePH9dTTz1VZV9RUVFKTExUWVmZpkyZovz8fNux3NxcTZ06VVar1YmfDgDqBoIxANQDfn5++uijj2Q2m/XGG2+oU6dOuv322zV8+HD16NFDCQkJatu2baXzFi5cqNDQUP3nP/9R+/btdeutt2rcuHHq0qWLgoODq11LePbs2erQoYPWrFmjdu3aacKECbr55pvVvn17nTp1SuPHj3f1RwYAtyMYA0A9MXDgQG3evFk333yzcnNzlZKSovT0dP3pT39ScnJylefExMQoLS1NkydP1sWLF/XZZ59p//79euSRR/T111/Lx8enyvPCw8O1efNmPfLIIzKbzfr888+1detW3Xbbbdq0aZMCAwNd+VEBwBAmK38PAwDU0OTJk/XBBx9o/vz5mjx5stHlAIBTcMcYAAAAEMEYAAAAkEQwBgAAACQxxxgAAACQxB1jAAAAQBLBGAAAAJBEMAYAAAAkEYwBAAAASZKX0QU0BLm5uSotLTW6jAYpNDRUWVlZRpeBBoLxBGdiPMHZGFOu4eXlZffbOgnGTlBaWiqLxWJ0GQ2OyWSSdOn7y+IpqC3GE5yJ8QRnY0zVDUylAAAAAEQwBgAAACQRjAEAAABJBGMAAABAEsEYAAAAkEQwBgAAACQRjAEAAABJBGMAAABAEsEYAAAAkEQwBgAAACQRjAEAAABJBGMAAABAEsEYAIAaKSoqUmRkpEwmk4qKiowuB4ATEYwBAAAASV5GF9AQ7D1VqLNFF4wuo8ExmUwKLPRWbu45Wa1Wo8tBPcd4grMUn/9/d4nTMs7Jt0mpgdWgoeBnlOu0MPtqcGioXW3dHoxPnz6ttLQ0HTx4UBkZGcrPz5fJZFLz5s3VqlUrdenSRb169VJYWJi7SwMAAEAj5rZgvG3bNq1YsUIHDx6U1WpVeHi4wsLC1KZNG0lSYWGhfv75Z23evFkffPCBunTpovHjx6tnz57uKhEAAACNmFuC8bRp0/TTTz+pd+/eeuKJJ9S1a1eZzeYq2xYVFWn37t3atGmT3nzzTbVt21avvPKKO8oEAABAI+aWYHzNNdfo6aefVkBAwBXbms1m9evXT/369VNeXp5Wrlzp+gIBAADQ6LklGN9xxx0OnRcQEODwuQAAAEBNsCoFAMBptqwrNLoElyu5cN729db1hfLxLTOwGvfpk9DU6BIAl3NbMD569GiNz2nfvn2N2hcXF2v58uU6dOiQDh8+rMLCQk2dOlWDBg2q9pzS0lI9/fTTysjI0F133aXx48fXuE4AAADUf24Lxs8++2yNz0lOTq5R+/z8fC1ZskQhISGKjo7Wvn37rnhOamqqsrOza1wbAAAAGha3BeMpU6a4vI/AwEDNmzdPAQEBOnLkyBXD+NmzZ7VkyRLdeOONWrRokcvrAwAAQN3ltmB8uekMzuLt7W3XyhflPvroI0VGRmrAgAG1CsYelhJ5lBQ7fD6qZjKZZLpQLI+SYt4ChFpjPLmH18WG/xbQsosX5OfpIenS5/W66GFwRe7hUeJpdAkNGj+jXMfD22R3W7c+fJeTkyNJCgoKumwbk8mkwMBAl9Zy+PBhrV27Vi+99JLd51gsFlksFtu2yWSSn5+fOs+fIcuR711RJgDUK7FGF+AmSSP+/0+66Q/GFuJOa40uAHCMd4fOUuxHdrV126+5R48e1dSpU7Vx48bLttu4caOmTp2qY8eOuawWq9Wqf//734qLi1OnTp3sPi8lJUWTJ0+2/W/69OkuqxEAAADu5bY7xqmpqYqIiNCYMWMu227MmDH65ptvtHLlSv3ud79zSS1r1qzRsWPH9OSTT9bovMTERI0dO9a2bTJdujX/w2+m62zh+WrOgqNMMqlFQKDO5uXKKv6shNphPLnH1g2NY7m2d2YMkyQ9+uJX8vH1M7gi9+jdn+XaXImfUa7Toqmfwu1s67ZgvG/fPg0cONAWJqtjMpnUr18/rV3rmr/ZFBUV6eOPP9b48eMVEhJSo3O9vb3l7e1daf9FL29d9G4c61i6k8lkktW3iS76+DLfCrXGeHKPUs9So0twOQ+zrx6ftdW23fA/8SUXvX2NLqFB42eU61z0qpzdquO2YJyXl6ewsDC72oaEhCg3N9cldaxYsUKlpaWKi4vT6dOnJf2/uc+FhYU6ffq0goKC5OXFu08AAAAaE7elP19fXxUUFNjVtrCwUD4+Pi6pIzs7W4WFhVVOo0hJSVFKSopee+01RUdHu6R/AAAA1E1uC8Zt27bVtm3bKszRrc62bdvUtm1bl9QxatQo9e7du8K+/Px8zZs3T4MGDVKvXr3svrMNAACAhsNtwXjAgAGaO3euvvzyS40aNaradqmpqdq/f7/DD96lpqaqsLDQNhUjLS1NZ86ckXQpFLdv377Sq6bLp1S0bt1affr0cahfAAAA1G9uC8YDBw7Ud999p/fff187duxQQkKCoqKi5Ofnp/Pnz+vYsWNat26ddu3apW7dujn8QpAVK1YoKyvLtr1lyxZt2bJFkpSQkCCz2eyMjwMAAIAGxmR146OPJSUlWrhwoVavXq2yssqrOHh4eGjIkCG65557XDbH2BW+2fuTzhY1/Lc9udulF70EKTc3hyd0UWuMJ/fYsq7hL9fWWPVJYLk2V+JnlOu0MPtq8LXRdrV169ILPj4+uv/++5WYmKgdO3YoIyNDRUVFMpvNioyMVGxsrIKDg91ZEgAAACDJzcG4XFBQkIYMGWJE1wAAAECV3PZKaAAAAKAu4y0WTnBty6ayWOrPnOj6wmQyKSIiRJmZFuZbodYYT+5xyuOi0SXARfq0bm50CQ0aP6Ncp6q3FleHO8YAAACACMYAAACAJIIxAAAAIKmOzDEuKyvToUOHlJOTo4CAAHXq1Emenp5GlwUAAIBGxPBgnJGRoVmzZunMmTNq1qyZ8vPzFRQUpKefflrR0dFGlwcAAIBGwvCpFP/85z/VvXt3zZ8/X//4xz/0j3/8Qy1bttQ//vEPo0sDAABAI+K2YDxv3jwVFBRU2p+ZmalBgwbZXgHt7++vPn36KDMz012lAQAAAO4Lxrm5uXrkkUe0cuVKlZWV2fbHxMRo4cKFOnDggE6ePKlt27bpiy++UExMjLtKAwAAAGSyunEV6Z07d2rBggWyWq2699571b17dxUVFemf//ynNm3apIsXL8rDw0O9e/fWAw88IH9/f3eVVitZWVmyWCxGl9HgXFrsPEKZmZksdo5aYzy5x4rkPKNLgIuMuzXA6BIaNH5GuY63t7dCQ0PtauvWh++6d++ubt266csvv9Tbb7+tTp06afLkyXr00Uf18MMP69y5c2revLk8PAyf+gwAAIBGxu0J1MPDQ2PGjNHbb79tW31iwYIFunDhglq0aEEoBgAAgCHcnkJLS0tVVFQkf39/PfTQQ3r55Zd15MgRPfroo1q9ejV/PgAAAIAh3DaVIjc3V3//+9+1e/duWa1WhYeH66GHHlJMTIxmzJihjRs36sMPP9SqVas0efJkHr4DAACAW7l1ubasrCy98MILeu211xQdHa033nhDFy5ckCTFxcXprbfeUq9evfTqq6/qr3/9q7tKAwAAANwXjA8cOKDRo0crJiZGbdu21Z133qmCggKlp6fb2vj4+GjSpEl68803ZTKZ3FUaAAAA4L5gHBgYqEOHDtm2y78OCAio1DYkJERPPPGEu0oDAAAA3DfH+Pbbb9fbb7+tgwcPqmnTpjp69KhGjRql4OBgd5UAAAAAVMttwbhPnz568803tWvXLpWUlOjee+9Vly5d3NU9AAAAcFlufcFHWFiYhg0b5s4uAQAAALu4NRhXJycnRwUFBfL3969yzjEAAADgam4Lxhs3blSnTp0UEhJi27djxw4tWLBAJ06csO2Ljo7W5MmTdfXVV7urNAAAAMB9q1KUP3hXbvfu3Zo1a5YKCwt144036v7779eYMWOUlZWll19+WT/99JO7SgMAAACMm0rxySefKDg4WLNmzVKzZs1s+8eOHaunn35aS5cu1ZNPPlmjaxYXF2v58uU6dOiQDh8+rMLCQk2dOlWDBg2ytSkrK9O3336rzZs366efflJBQYHCwsIUFxencePGycfHx1kfEQAAAPWI2+4Y/1JpaamOHj2qsWPHVgjFkhQUFKShQ4fqwIEDNb5ufn6+lixZooyMDEVHR1fZpqSkRHPmzFF+fr6GDRume++9Vx06dNCiRYv06quvymq1OvKRAAAAUM8Zcsf44sWLkqSWLVtWeTwsLEyFhYU1vm5gYKDmzZungIAAHTlyRM8++2ylNl5eXnrppZfUuXNn276hQ4cqLCxMixYt0p49e9StW7ca9w0AAID6za13jLdv367PP/9cX331lZo0aaKcnJwq2+Xm5qpp06Y1vr63t/cVV7Xw8vKqEIrL9enTR5KUkZFR434BAAAcVVRUpMjISJlMJhUVFRldTqPm1jvGGzZs0IYNG2zbW7Zs0dChQyu127Nnj1q3bu3O0pSXlydJat68uVv7BQAAQN3gtmD87rvvVtpnMpkq7cvPz1fLli3VvXt3N1T1/yxbtkx+fn6KjY2tto3FYpHFYrFtm0wm+fn52b6Gc5V/T/newhkYT0Dt8G/HdX75vTWZTHyvDeS2YBwaGmpXO39/f02dOtXF1VS0dOlS7dmzRw888MBlp3CkpKRoyZIltu127dpp1qxZdn82OCY8PNzoEtCAMJ5cLdfoAuAiERERRpfQYP3yuaqWLVs6NJ0UzlEn3nxnpI0bNyo5OVk33HCDhg8fftm2iYmJGjt2rG27/De6rKwslZaWurTOxshkMik8PFwnT55ktRDUSlFRkTp27ChJOnLkiO0vPQDsl5mZ6fY+3377bbf3aYSSkhLb188//3yjWDr2sccec1tfXl5edt/EdHsw3r59uzZs2KCjR48qJydHJSUlatKkiSIjI9W1a1cNGzZMwcHBbqll9+7devfddxUbG6sHH3zwiu29vb3l7e1d5TGCm+tYrVa+vy5040cHr9yonrtYct729cRPDsjTp+EH42V3djG6BDQw/ByGM9XV8eS2YHzhwgW98cYb2rVrV4X9np6eioyMVHZ2tlJSUrRy5Ur99re/VXx8vEvrOXTokF5//XV16NBBTz75pDw9PV3aHwAAAOo2twXj5ORk7dmzR/fdd5/69+8vX19f7d+/X++9955iYmJ05513Kj09XR9++KHeffddBQUFKSYmxiW1pKena+bMmQoLC9MzzzzTKP5kATRmnj5+6vXa10aXAQCo49wWjDdu3KgRI0Zo5MiRtn3du3fXAw88oNdee01jx45V69at9cwzz+i5557T0qVLHQrGqampKiwsVG7upQdA0tLSdObMGUnSqFGjZDKZ9Morr6igoEDjxo3T9u3bK5wfHh6uTp061eKTAgAA2M/Hx0fPPPOM0WVAbgzG586dq3Jt4tatW6usrEyZmZlq0aKFTCaT4uPjlZyc7FA/K1asUFZWlm17y5Yt2rJliyQpISFBkmxB+eOPP650/sCBAwnGAAAAjZDbgnFYWJj27t1b6YUe+/btk6QKb6zz9PR0eFL27Nmzr9hm0aJFDl0bAAAADZfbgvGIESM0f/58+fj4KC4uTj4+Pjp48KCWLVumLl26VFhb9KeffmKtUQAAALiV24LxyJEjlZ2drS+++EJr16617e/YsaMef/zxCm19fHw0atQod5UGAAAAuHcd47vuuktjx47VDz/8IIvFosjISLVr165Su8mTJ7uzLAAAAMD9L/gICAhQnz593N0tAAAAcFkeRhcAAAAA1AUEYwAAAEAEYwAAAEASwRgAAACQRDAGAAAAJBGMAQAAAEkGBuPi4mLNmTNHGRkZlY798MMPmjNnjgFVAQAAoLEyLBhbLBatXbtWubm5lY6dOnWqwtvxAAAAAFdjKgUAAAAggjEAAAAgyYBXQmdnZ0uSCgoKJElnz5617ZOkkJAQd5cEAAAAuD8YJyUlVdh+5513KmwnJye7sxwAAABAkgHB+MUXX5QkFRYW6i9/+YvuvvtutW/f3t1lAAAAABW4PRjHxMRIks6dOydJio6Otu0DAAAAjMLDdwAAAIAIxgAAAIAkA4Oxp6enYmJi1LRp00rHWrRowfQKAAAAuJXb5xiXM5vNtgfxfq1bt27q1q2bmysCAABAY8ZUCgAAAEBuCsYXLlww5FwAAADAXm4JxlOmTNGSJUuUm5tr9zk5OTlKTk7W1KlTXVgZAAAAcIlb5hg/8MADWrx4sZYsWaLOnTura9euat++vcLCwtS0aVNZrVYVFhbq9OnTOnLkiPbs2aNDhw4pIiJC999/vztKBAAAQCPnlmAcFxenfv36KS0tTWvWrFFKSopKS0urLsjLS926ddOTTz6pXr16ycODadAAAABwPbetSuHh4aE+ffqoT58+slgsOnr0qDIyMlRQUCBJatasmVq1aqX27dvL29vboT6Ki4u1fPlyHTp0SIcPH1ZhYaGmTp2qQYMGVWqbnp6uDz74QAcPHpSXl5d69Oihe++9V/7+/rX5mAAAAKinDFmuzdvbW507d1bnzp2det38/HwtWbJEISEhio6O1r59+6psd+bMGb344osym826/fbbVVxcrBUrVujYsWN69dVX5eVl2Cp2AAAAMEiDSoCBgYGaN2+eAgICdOTIET377LNVtktJSdGFCxc0a9YshYSESJI6duyol19+WWvWrNHQoUPdWTYAAADqgAY1gdfb21sBAQFXbLd582b16NHDFoqlSy8ViYiI0HfffefCCgEAAFBXNahgbI+cnBydPXtWHTp0qHSsY8eO+vHHHw2oCgAAAEZrUFMp7FG+lnJgYGClY4GBgSooKJDFYqnyAUCLxSKLxWLbNplM8vPzs30N5yr/nvK9BWqOfzdwNsYUnKmujqdGF4xLSkokqcoH7MrDcElJSZXBOCUlRUuWLLFtt2vXTrNmzVJoaKiLqoUkhYeHG11CA3fA6ALgAhEREQb1bP+LnFC/GDem0BDV1fHU6IKxj4+PJFW5jnL53eDyNr+WmJiosWPH2rbLf9vJysqqdl1mOM5kMik8PFwnT56U1Wo1uhygXsnMzDS6BDQwjCk4kzvHk5eXl903MQ0LxtnZ2Vq6dKn27dun/Px8Pf3004qJibEtuTZ48GC1a9fO6f2WT6Go6vXUubm5atasWbXrKHt7e1d7jODmOlarle8vUEP8m4GzMabgTHV1PBny8F16err+8Ic/6LvvvlNYWJiKiopUVlYmSfL399f333+v1NRUl/QdFBQkf39/HTlypNKxw4cPKzo62iX9AgAAoG4zJBh/+OGHatq0qd5++2098sgjlY7Hxsbq4MGDLuu/b9++2r59u7Kzs2379uzZo8zMTPXr189l/QIAAKDuMmQqxYEDBzRhwgT5+/vr3LlzlY6HhIQoJyfHoWunpqaqsLDQNlUiLS1NZ86ckSSNGjVKZrNZiYmJ2rRpk2bMmKHRo0fbXiUdFRWlwYMHO/7BAAAAUG8ZEozLysrk6+tb7fH8/HyHX8u8YsUKZWVl2ba3bNmiLVu2SJISEhJkNpsVEhKi6dOna8GCBfr444/l5eWl2NhY3XPPPdXOIQYAAEDDZkgwbt++vbZv364RI0ZUOnbx4kVt3LhRnTp1cujas2fPtqtdmzZtNG3aNIf6AAAAQMNjyBzjm266STt37tR7772n48ePS5Ly8vK0e/duvfzyy8rIyNCNN95oRGkAAABopAy5YxwbG6ukpCTNnz9fq1evliT97W9/kyT5+fkpKSlJMTExRpQGAACARsqwdYwHDBigPn36aPfu3Tp58qTKysoUHh6u6667zvaaZQAAAMBdDH3zXZMmTdSnTx8jSwAAAAAkGRSMf7l+8OWEhIS4uBIAAADgEkOCcVJSkl3tkpOTXVwJAAAAcIkhwXjKlCmV9pWVlSkrK0vffvut/P39q1zKDQAAAHAVQ4LxoEGDqj124403atq0aSoqKnJfQQAAAGj0DFnH+HKaNGmiQYMG6YsvvjC6FAAAADQidS4YS5LValVeXp7RZQAAAKARMXS5tl8rKirSgQMHtHz5crVr187ocgAAANCIGBKMb7311sseDwkJ0QMPPOCmagAAAACDgvGECRNkMpkq7DOZTGratKlatmyp6667Tp6enkaUBgAAgEbKkGA8adIkI7oFAAAAqlUnH74DAAAA3M0td4znzJlT43NMJlOVLwIBAAAAXMEtwXjfvn01PufXc5ABAAAAV3JLMJ49e7Y7ugEAAAAcxhxjAAAAQARjAAAAQJKBb77bsWOHPv/8c/34448qKiqS1Wqt1CY5OdmAygAAANAYGXLHeNOmTZo5c6bOnj2ruLg4Wa1W9e/fX/3795ePj4/atm2riRMnGlEaAAAAGilD7hh/9tln6tixo1566SUVFBToq6++0g033KBrr71Wp0+f1rRp0xQWFmZEaQAAAGikDLljnJ6erv79+8vDw8P26ufS0lJJUlhYmEaMGKFly5YZURoAAAAaKUOCsa+vr7y8Lt2sbtq0qby8vJSXl2c73qJFC50+fdqI0gAAANBIGRKMIyMjlZ6ebtuOjo7Wt99+q4sXL6qkpETr169XSEiIEaUBAACgkTIkGPfu3Vtbt26VxWKRJN18883at2+fJk+erAceeEAHDx7UTTfdZERpAAAAaKTc9vDdtm3bFBsbKw8PD40fP17jx4+3HevZs6emT5+uzZs3y8PDQz169NC1117r0noyMzOVnJysgwcPqqCgQCEhIYqPj9e4cePk6+vr0r4BAABQ97gtGL/22mtq3ry5rr/+eiUkJKhTp04Vjl999dW6+uqr3VJLdna2/vjHP8psNmvkyJFq1qyZfvjhBy1atEhHjx7VH/7wB7fUAQAAgLrDbcH4wQcf1IYNG/TVV19p1apVCg0NVXx8vOLj49W6dWt3lSFJ+vbbb1VYWKg//elPatOmjSRp6NChKisr07fffquCggI1a9bMrTUBAADAWG4LxkOHDtXQoUOVk5OjDRs2aMOGDUpJSVFKSoqio6OVkJCguLg4BQUFubyW8+fPS7q0+sUvBQYGymQy2VbMAAAAQOPh9gQYFBSkcePGady4cTpx4oTWr1+vDRs2aOHChfrwww8VExOjhIQE9e3bV2az2SU1XHPNNVq2bJnmzp2rSZMm2aZSrFq1SqNGjVKTJk2qPM9isdgeGJQkk8kkPz8/29dwrvLvKd9boOb4dwNnY0zBmerqeDJZrVar0UVI0uHDh7VhwwZt3LhReXl58vHx0cKFC13W36effqqUlBSVlJTY9t1888267bbbqj1n0aJFWrJkiW27Xbt2mjVrlstqBNyh9+v/M7oEuMDWp28wpN9/vLnfkH7heg89EeP2Pv/v//7P7X3CPWbOnGl0CVWqM3MGwsLC1LJlSwUHBysvL69CYHWF0NBQXX311erbt6+aN2+u7du3KyUlRQEBARo5cmSV5yQmJmrs2LG27fLfdrKysmxv7oPzmEwmhYeH6+TJk6ojv78B9UZmZqbRJaCBYUzBmdw5nry8vBQaGmpfWxfXclnFxcXavHmz1q9fr71796qsrEyhoaFKTExUfHy8y/rdsGGD5s2bp7ffflvBwcGSpL59+8pqteqjjz5S//791bx580rneXt7y9vbu8prEtxcx2q18v0Faoh/M3A2xhScqa6OJ7cH49LSUm3fvl3r16/Xjh07VFJSombNmmnIkCGKj49Xly5dXF7DqlWr1K5dO1soLterVy+tWbNGP/74o7p16+byOgAAAFB3uC0Y7927V+vXr9fmzZtVVFQkb29v9ezZUwkJCYqNjZWnp6e7SlFeXl6Vy7GVT4coKytzWy0AAACoG9wWjF966SWZTCZdc801tlUnyld0cLeIiAjt3r1bJ06cUGRkpG3/hg0bZDKZFBUVZUhdAAAAMI7bgvHdd9+t/v37KzAw0F1dVmv8+PHauXOnXnzxRY0YMcL28N2OHTt0ww03uGUtZQAAANQtbgvGv1zNwWgxMTF6+eWXtXjxYq1atUrnzp1TWFiYbrvtNt14441GlwcAAAAD1Jnl2tytY8eOevbZZ40uAwAAAHWEh9EFAAAAAHUBwRgAAAAQwRgAAACQZFAw3rhxo8tf+QwAAADUhCEP37399tvy8/NT3759lZCQoGuvvdaIMgAAAAAbQ4Lxn/70J61bt06bNm3SmjVrFBQUpPj4eCUkJPByDQAAABjCkGDcuXNnde7cWffdd5927typdevWKTU1VcuXL1dUVJQGDBig+Pj4OvEyEAAAADQOhq5j7OnpqZ49e6pnz54qLi7Wli1btGbNGn344Yf6+OOPFRMTo4EDByouLk5eXo12yWUAAAC4QZ1ZleLYsWM6fPiwjh07JkmKjIxUQUGBZs+erUceeUQHDx40uEIAAAA0ZIbehj1x4oTWr1+v9evX69SpU/L391d8fLwGDBig9u3bS5KOHDmiuXPn6r333tMbb7xhZLkAAABowAwJxitXrtS6det09OhReXt7q2fPnpo8ebK6d+8uD4+KN7E7dOigMWPGaO7cuUaUCgAAgEbCkGD8wQcfqHPnznrwwQcVFxcns9l82fYdOnTQhAkT3FQdAAAAGiNDgvHf/vY3hYWF2d2+TZs2atOmjQsrAgAAQGNnyMN3wcHBKioqqvZ4UVGRLl686MaKAAAA0NgZEoznz5+v559/vtrjzz//vBYsWODGigAAANDYGRKMd+7cqb59+1Z7vF+/ftqxY4cbKwIAAEBjZ0gwzs3NVVBQULXHAwMDlZOT48aKAAAA0NgZEoybNWumEydOVHs8IyNDfn5+bqwIAAAAjZ0hwbh79+5avXq1fvzxx0rHjh49qtWrVys2NtaAygAAANBYGbJc26233qqdO3fqj3/8o3r27Glbiu348ePatm2b/P39deuttxpRGgAAABopQ4JxUFCQZs6cqY8++khpaWnaunWrJMnPz0/x8fG6/fbbLzsHGQAAAHA2Q4KxdOkBu4cfflhWq1X5+fmSJH9/f5lMJqNKAgAAQCNmWDAuZzKZ1KJFC6PLAAAAQCPnlmC8ZMkSSdLNN98sDw8P2/aVTJw40ZVlAQAAADZuCcaLFy+WJN10003y8PCwbV8JwRgAAADu4pZgnJycfNltAAAAwGiGzzE20tGjR7V48WIdPHhQFotFLVu21JAhQzR69GijSwMAAICbNdpgvGvXLs2aNUvt2rXThAkT1KRJE506dYpXUQMAADRSbgnGSUlJNV6GzWQy6W9/+5tL6ikqKtK7776rHj166Mknn5SHhyEvAAQAAEAd4pZgHBMTU6fWJ16/fr3Onj2r2267TR4eHiouLpaPjw8BGQAAoBFz2x3jumTPnj3y8/NTTk6OXn/9dWVmZsrX11cDBgzQvffeKx8fH6NLBAAAgJs1yjnGJ0+eVFlZmV5//XUNHjxYd9xxh/bt26fU1FQVFhbq8ccfr/I8i8Uii8Vi2zaZTPLz87N9Decq/57yvQVqjn83cDbGFJypro4nw4JxWVmZvv32W23fvl3Z2dmSpJCQEPXs2VMJCQkundZQXFysCxcuaNiwYfrNb34jSerbt69KS0u1evVq3XrrrYqIiKh0XkpKSoWXk7Rr106zZs1SaGioy2qFFB4ebnQJDdwBowuAC1T1M8w9cg3qF65m3JhCQ1RXx5MhwbioqEivvPKKDh8+LD8/P7Vs2VLSpSkOmzdv1qpVqzRt2jSZzWaX9F8+VaJ///4V9sfHx2v16tX64Ycfqvw/LDExUWPHjrVtl/+2k5WVpdLSUpfU2piZTCaFh4fr5MmTslqtRpcD1CuZmZlGl4AGhjEFZ3LnePLy8rL7JqYhwfiTTz7R0aNH9Zvf/EZDhgyRl9elMkpLS/W///1P8+fP13/+8x/b3VxnCwwM1PHjxxUQEFBhf4sWLSRJhYWFVZ7n7e0tb2/vKo8R3FzHarXy/QVqiH8zcDbGFJypro4nQ5Zh2LJli4YPH64RI0bYQrF0KdEPHz5cw4YN0+bNm13Wf/v27SWp0prFubmX/gTo7+/vsr4BAABQNxkSjAsKChQZGVnt8VatWqmgoMBl/V9//fWSpP/9738V9n/99dfy9PRUTEyMy/oGAABA3WTIVIrw8HClpaVpxIgRVR5PS0uzzTt2hXbt2mnw4MH65ptvdPHiRcXExGjfvn3atGmTbrrpJgUFBbmsbwAAANRNhgTj4cOH69///rdeffVVjR492vag24kTJ/Tll19q9+7duv/++11aw4MPPqiQkBCtWbNGW7ZsUWhoqO69916NGTPGpf0CAACgbjIkGI8YMUJnz57VsmXLtHPnzooFeXlp4sSJGj58uEtr8PLy0i233KJbbrnFpf0AAACgfjBsHeNJkyZp5MiR2r17t20d49DQUHXt2pWH3wAAAOB2bg3GeXl5WrNmjU6fPi1/f3/17dtX8fHx7iwBAAAAqJLbgvHp06f17LPPVlht4rPPPtPDDz9MOEYlRUVFuuqqqyTJ9iIYAAAAV3JbMF60aJGKi4t133336dprr9XJkyc1f/58ffDBB4qLi3PpK6ABAACAK3FbMP7+++81dOhQjRw5UpLUunVreXh4aNasWcrIyFCbNm3cVQoAAABQiduCcXZ2ttq1a1dhX/kb6M6dO+euMuq9iw+ON7oEt7hYetH2denUibro5WlgNe7h+d5yo0sAAKBRc9v8hbKysgqvf5YkT09P2zEAAADASG5dleLIkSPy9va2bZ8/f16SdPDgQRUWFlZq37dvX7fVBgAAgMbNrcF45cqVWrlyZaX9ixcvrrJ9cnKyq0tCHWX28tSx0T2NLgMAADQibgvGL774oru6AgAAAGrMbcE4JibGXV0BAAAANcbiwQAAAIAIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkNy3XtnbtWofOGzhwoJMrAQAAAKrmlmA8Z84ch84jGAMAAMBd3BKM33333QrbhYWFmj17tsxms0aNGqXIyEhJUkZGhlJTU3X+/HklJSW5ozQAAABAkpuCcWhoaIXtxYsXy9/fX88995xMJpNtf1RUlPr27atXXnlFX3zxhaZOneqO8gAAAABjHr7bunWr+vTpUyEUl/Pw8FCfPn20detWAyoDAABAY2VIMLZarcrIyKj2eHp6uhurAQAAAAwKxr1799ZXX32lzz//XBcuXLDtv3DhglasWKHVq1erV69eRpQGAACARsotc4x/7b777tPp06e1cOFCffzxxwoMDJQk5ebm6uLFi+rcubMmT55sRGkAAABopAwJxmazWTNmzNDWrVu1Y8cOZWdnS5Kuu+469ejRQz179qxy/jEAAADgKoYE43K9e/dW7969jSwBAAAAkGTwK6FzcnK0fv16rVy5UmfOnJEklZWVqaCgQGVlZW6tZenSpZo0aZJ+//vfu7VfAAAA1A2G3DG2Wq1asGCBUlNTbQE4KipKwcHBKi4uVlJSkiZNmqQxY8a4pZ4zZ84oJSVFvr6+bukPAAAAdY8hd4yXL1+ulStXaty4cXruuecqHDObzerTp482b97stnoWLlyoq666Sh06dHBbnwAAAKhbDAnGX3/9tQYOHKg77rhD0dHRlY63bdtWmZmZbqll//792rRpE6tgAAAANHKGBOMzZ86oU6dO1R739fVVUVGRy+soKyvT/PnzdcMNNygqKsrl/QEAAKDuMmSOsb+/v+1hu6ocPXpUISEhLq9j1apVysrK0vPPP29Xe4vFIovFYts2mUzy8/OzfQ3UBmMIzsR4grMxpuBMdXU8GRKM+/btq6+++kqDBg2S2WyucGzXrl1as2aNbrzxRpfWcO7cOS1atEgTJkyQv7+/XeekpKRoyZIltu127dpp1qxZCg0NdVWZlRx3W09wt4iICIN6PmBQv3Al48ZTrkH9wtWMG1NoiOrqeDIkGE+aNEn79u3TH/7wB3Xp0kWStGzZMiUnJ+uHH35Qu3btlJiY6NIa/vOf/6hZs2YaNWqU3eckJiZq7Nixtu3y33aysrJUWlrq9BrRuLhrXj0aB8YTnI0xBWdy53jy8vKy+yamYW++e+WVV7RixQpt2rRJPj4+2r9/v8LDw3XLLbdo/Pjx8vHxcVn/mZmZWr16tSZPnqycnBzbfovFotLSUp0+fVpms1nNmjWrcJ63t7e8vb2rvKbVanVZvWgcGENwJsYTnI0xBWeqq+PJsDff+fj4aMKECZowYYLb+87JyZHVatX8+fM1f/78SscffvhhjR49mpUqAAAAGhFDgvGMGTN08803q2vXrlUe37t3rz799FO9+OKLLum/TZs2euqppyrtT05O1vnz5zV58mSFh4e7pG8AAADUTYYE4/3792vIkCHVHs/Pz9f+/ftd1r+/v7/69OlTaf/KlSslqcpjAAAAaNgMWcf4Sk6ePGlbBg0AAABwB7fdMV6zZo3Wrl1r2166dKm+/vrrSu2Kior0888/KzY21l2l2UyfPt3tfQIAAKBucFswLikpUX5+vm37/PnzlRZ3NplM8vX11bBhwzRx4kR3lQYAAAC4LxgPHz5cw4cPlyQlJSXpvvvuU69evdzVPQAAAHBZhjx8N3v2bCO6BQAAAKpl2DrG5c6fP6+ioqIqF3oOCQkxoCIAAAA0RoYF41WrVunzzz/XqVOnqm2TnJzsxooAAADQmBmyXNuqVav0r3/9S+Hh4brtttskSWPGjNFNN92kgIAARUdHa8qUKUaUBgAAgEbKkGCcmpqq6667Tn/84x81dOhQSVKPHj10++23680339T58+d17tw5I0oDAABAI2VIMD516pR69uwpSfL09JQklZaWSpLMZrNuuOEGrVq1yojSAAAA0EgZEozNZrMuXrxo+9rHx0fZ2dm2435+fsrLyzOiNAAAADRShgTjNm3a6Oeff7Ztd+rUSV999ZVycnKUnZ2t1atXKyIiwojSAAAA0EgZEowTEhJ0/PhxWSwWSdItt9yi9PR0TZkyRUlJSTpx4oTtoTwAAADAHQxZrm3w4MEaPHiwbbtLly7661//qm3btsnDw0PdunVTZGSkEaUBAACgkTL8BR/lWrZsqdGjRxtdBgAAABopw4NxWVmZioqKqjzWrFkzN1cDAACAxsqQYFxaWqply5bpm2++0ZkzZ1RWVlZlO958BwAAAHcxJBjPmzdPa9euVadOndS7d2+ZzWYjygAAAABsDAnGmzZt0oABA5SUlGRE9wAAAEAlhizX5uvrq6uuusqIrgEAAIAqGRKM+/fvr+3btxvRNQAAAFAlQ6ZS3HXXXZozZ45mzpypwYMHKzg4WB4elTN6+/btDagOAAAAjZEhwdhischqtWrHjh3asWNHte1YlQIAAADuYkgw/vvf/64tW7aof//+6tixI6tSAAAAwHCGBONdu3Zp5MiRmjx5shHdAwAAAJUY8vCdn5+fwsPDjegaAAAAqJIhwXjIkCHasGFDtW+8AwAAANzNkKkUrVu3Vlpamp555hkNHDiw2lUp+vbta0B1AAAAaIwMCcZvvfWW7euFCxdW245VKQAAAOAuhgTjF1980YhubQ4fPqy1a9dq3759ysrKUrNmzXTVVVfptttuU2RkpKG1AQAAwBiGBOOYmBgjurVZtmyZvv/+e/Xr109t27ZVXl6eUlNT9cwzz+iVV15RVFSUofUBAADA/QwJxkYbO3asHnvsMXl5/b+PHxcXp6eeekqfffaZHn30UQOrAwAAgBHcEoxnzJghk8mkadOmydPTUzNmzLjiOSaTSS+88IJL6uncuXOlfREREWrdurUyMjJc0icAAADqNrcEY6vVWmnbZDLV6BxXs1qt+v/au/e4qsq0/+PfjRxkgygIiKiAZKikJmoeEM/n0sqpSLMpbXTKsSebfjU+5cyUHbWeqZkmzXGanNIylJG0qXzMCsXzEfGcx1JCATnJQdm49++PHvdEgHlgrwXsz/v18jWste617ovdxZ5r3/te9yosLFSbNm1qbGOz2WSz2ZzbFotFvr6+zp+B60EOoTaRT6ht5BRqU13NJ0MK4+eee+6y23VBWlqa8vLylJiYWGOblJQUJScnO7fbtm2rOXPmKCQkxIgQJUknDesJRmvZsqVJPR8wqV+4knn5lG9Sv3A183IKDVFdzSdT5hjv379frVu3VkBAQLXHi4qKdOrUKcNu0svMzNQ//vEPxcTEaODAgTW2Gzt2rEaPHu3cvvRpJycnRxUVFa4OEw1cVlaW2SGgASGfUNvIKdQmI/PJ09PzigcxTXny3axZs5SRkVHj8b17917RPOTaUFBQoNmzZ8tqteqJJ56o9kEjl3h5eclqtTr/XZpGIf0wFcOIf2i4jMohcso9kE+obeQTalNdzSNTCuOfY7PZLlug1pbS0lK9/PLLKikp0cyZMxUUFOTyPgEAAFA3GTaVIjc3V9nZ2c7tzMxM7d+/v0q70tJSrVmzxuXzdsvLyzVnzhxlZWXpD3/4g1q3bu3S/gAAAFC3GVYYf/3115VuXFu+fLmWL19ebVsPDw9NmTLFZbHY7Xb9+c9/1jfffKOnnnpKMTExLusLAAAA9YNhhXGfPn2cS6G98cYbGjVqlDp06FCpjcVikY+Pj6KiotSsWTOXxfL+++9r+/bt6t69u4qLi7Vu3bpKx/v37++yvgEAAFA3GVYYt27d2jldYerUqYqNjVVoaKhR3Vdy4sQJSdKOHTu0Y8eOKscpjAEAANyPKcu11bQk2pkzZ2Sz2Vw+37curqMMAAAAc5lSGH/++ec6dOiQHn/8cee+efPmae3atZJ+eHDG008/raZNm5oRHgAAANyQKcu1ffnll5WK3vT0dK1du1ZDhw7VQw89pDNnzmjZsmVmhAYAAAA3ZcqIcU5Ojlq1auXc3rRpk0JDQ50rURQUFFS5IQ4AAABwpTrxgI+MjAx17drVuR0SEqKCggLT4gEAAID7MaUwbtmypbZt2ybph2kUeXl5iouLcx7Py8uTn5+fGaEBAADATZkylWLMmDF68803NWnSJJ0/f16tW7fWzTff7Dy+d+9eRUVFmREaAAAA3JQphXHfvn3VpEkT7dy5U35+fhoxYoQaNWokSSouLpa/vz9rCQMAAMBQphTGktSlSxd16dKlyn5/f389+eSTJkQEAAAAd2bYHOMjR46ouLj4itpmZ2c71zQGAAAAjGBYYTxz5kylp6c7t4uLi3X//fdr//79VdoeOnRI8+bNMyo0AAAAwLzl2hwOh2w2m+x2u1khAAAAAE51Yh1jAAAAwGwUxgAAAIAojAEAAABJBi/Xlp2drWPHjkmSSktLJUlZWVmyWq1V2gEAAABGMrQwTkpKUlJSUqV977zzjpEhAAAAANUyrDCeOnWqUV0BAAAAV82wwnjgwIFGdQUAAABcNW6+AwAAAERhDAAAAEiiMAYAAAAkURgDAAAAkiiMAQAAAEkUxgAAAIAkCmMAAABAEoUxAAAAIMngR0LXJTabTUlJSUpLS1NxcbEiIyM1btw4denSxezQAAAAYAK3HTGeO3euPv30UyUkJGjSpEny8PDQK6+8ooMHD5odGgAAAEzgloXxkSNHtHHjRo0fP16//OUvNXToUP3xj39UcHCwFi9ebHZ4AAAAMIFbFsabN2+Wh4eHhg4d6tzn7e2twYMH65tvvlFubq6J0QEAAMAMblkYHz9+XC1btpTVaq20v127dpKkEydOmBAVAAAAzOSWN98VFBQoMDCwyv5L+/Lz86s9z2azyWazObctFot8fX3l6Wncy+hxQ3vD+oKxGnl5mdJv+1B/U/qFa3mZlE/BoY1N6ReuZ0ZOhYeHG94njGFkPl1NneaWhXF5eXm1/0Eu7SsvL6/2vJSUFCUnJzu3+/btq+nTp1dbZLvMmx8Y1xfcwuIHQ8wOAQ3IXRPIJ9Sexx57zOwQ4GbcciqFt7d3pZHfSy7t8/b2rva8sWPH6p///Kfz35QpU6q9DmpHWVmZZsyYobKyMrNDQQNAPqE2kU+obeRU3eCWI8bNmjVTXl5elf2XplDUNALs5eVl2teT7sjhcOj48eNyOBxmh4IGgHxCbSKfUNvIqbrBLUeMo6KilJWVpdLS0kr7Dx8+7DwOAAAA9+KWhXHv3r1lt9u1Zs0a5z6bzabU1FTdeOONCg4ONjE6AAAAmMEtp1LceOON6t27t5YsWaKioiKFhYVp7dq1ysnJ0SOPPGJ2ePg/Xl5euvvuu5m+glpBPqE2kU+obeRU3WBxuOlklvLyciUlJSktLU0lJSWKiIjQvffeq65du5odGgAAAEzgtoUxAAAA8GNuOccYAAAA+CkKYwAAAEAUxmjgpk2bprlz55odBhqwxMRELV261Oww0IDNnTtX06ZNMzsMNGDPPfecnnvuObPDqBMojAEAAAC56XJtcB9//vOfZbFYzA4DAK7Zww8/zNPQAIMwYowGzcvLS56efP6Duc6fP292CKjHPD09WdsWpisvL5fdbjc7DJejYsBVKysrU1JSkrZt26b8/HxZrVZFRkZqwoQJio6O1oEDB/T555/r8OHDKiwsVNOmTdWrVy/dd9998vb2dl5n7ty52rx5s9544w2988472rdvn6xWq8aOHauRI0fqu+++08KFC3XkyBE1adJE9913nxISEq4q1mnTpik2NtY5P6+iokIpKSlKS0vT2bNn5ePjo1atWumee+5Rly5davV1ws+rT7lks9n0wQcfKC0tTTabTTfddJMmT55cpd3SpUuVnJys119/Xf/617+Unp6ukJAQvfrqq9f9erm7+pQvGRkZWrZsmU6ePKmLFy8qKCjIGYsk7du3T7NmzdLjjz+u06dPa/Xq1Tp37pzat2+vX//61woLC6sU7/79+yvdL7FhwwatXLlSWVlZslgsCg4O1pAhQ3Trrbde56vsHtw1lyRpzZo1WrFihfLy8hQREaEHHnigSp+Xrjl9+nSdPHlSX3/9tQoKCvTuu+/Kz8/vGl7x+oPCGFft73//uzZv3qyRI0eqdevWOnfunA4ePKjMzExFR0dr06ZNunDhgoYPH64mTZroyJEjWrVqlfLy8vTEE09UupbdbtfLL7+sjh076v7779f69ev17rvvqnHjxlqyZIn69eunXr166YsvvtBbb72lmJgYhYaGXnPsy5Yt08cff6zBgwerXbt2Kisr09GjR3Xs2DEKYxPUp1yaP3++0tLSlJCQoJiYGO3du1ezZ8+usf3rr7+usLAwjR8/nq/Ba0l9yZeTJ09q9uzZioyMVGJiory8vHT69GkdOnSoStsVK1bIYrFozJgxKi0t1cqVK/Xmm2/q5ZdfrvH6GRkZ+stf/qLOnTtrwoQJkqTMzEwdPHiQwvgKuWsuffXVV1qwYIHat2+vW2+9VWfOnNGrr74qf39/NW/evMo1//Wvf8nT01NjxoxRRUWFW3wD2/B/Q9S6nTt3asiQIZU+Zd5xxx3On++///5Kn6iHDh2qsLAwLVmyRLm5uQoODnYes9ls6tevn8aOHStJSkhI0MMPP6y3335b06dPV3x8vCSpS5cuevzxx5WamqrExMTrij0uLk4PP/zwNV8Dtae+5NKJEyeUlpam4cOHO0eJR44cqTfffFPffvtttedERkZq+vTpV/hK4ErUl3zJyMhQRUWFnn76aQUEBFy2bXl5uV577TVnweHn56d//vOf+u677xQREVHj6+Dr66uZM2fKw4MZkdfCHXOpoqJCS5YsUVRUlJ599llnu9atW2vBggXVFsY2m02zZ8+u9Fo0dPxF4ar5+fnpyJEjysvLq/b4j/+Azp8/r6KiIsXExMjhcOj48eNV2g8ZMqTStcPDw+Xj46M+ffo494eHh8vPz0/Z2dnXHfvJkyeVlZV1XddB7agvubRr1y5JqjIad7nRuWHDhl3x9XFl6ku+XPqqefv27T87J3PQoEGVRuE6duwoSZftz2q16sKFC8rIyLjimFCZO+bSsWPHVFhYqGHDhlVqN3DgQFmt1mqvOWDAALcqiiVGjHENJkyYoLlz52rq1KmKjo5WXFycBgwYoBYtWkiScnNzlZSUpO3bt6ukpKTSuaWlpZW2vby8qnwKtlqtat68eZXVJKxWq4qLi68r9sTERL322muaPn262rRpo65du6p///6KjIy8ruvi2tSXXMrJyZHFYnHGdUl4eHiN51zPlB9Ur77kS3x8vL788kvNnz9fH3zwgTp37qyePXuqd+/eVUZ4fzzyKP2nELpcfyNGjNCmTZv08ssvKygoSF26dFF8fLy6du16xTG6O3fMpZycHElSy5YtK7Xz9PSs8t52iTu+j1EY46rFx8erY8eO2rp1q3bv3q1PPvlEK1as0JNPPqmbb75ZL7zwgoqLi3XHHXeoVatW8vHxUV5enubNm1dlrmVNXwO66uvB2NhY/fWvf9W2bdu0e/duffXVV/r00081ZcqUSp/4YYz6nEs/x91GWYxQX/LF29tbs2bN0r59+7Rz506lp6dr48aN6tSpk37/+99X6uNa+mvatKlee+01paenKz09Xbt27VJqaqr69++vRx999Lrjdwfk0pX3724ojHFNAgMDNWLECI0YMUKFhYWaMWOGli9frsDAQGVlZWnatGkaMGCAs31d+srP399fgwYN0qBBg3T+/Hk9++yzWrZsGYWxSepDLoWEhMjhcOjMmTOVRom///57w2Nxd/UhX6QfipTOnTurc+fOevDBB7V8+XJ99NFH2rt3b63c6Ovp6akePXqoR48estvteuedd7RmzRrdfffdVVYhQPXcLZdCQkIkSVlZWerUqZNzf0VFhbKzs/nm9P8wxxhXxW63V/kaqWnTpgoMDFRFRYXzE+uPP1E7HA599tlnhsZZk3PnzlXabty4sVq0aCGbzWZSRO6rPuVSXFycJFXpu67ktTuoT/lS3VflUVFRkn4oQq7XT9/HPDw8nEUN72U/z11zKTo6WgEBAfriiy8qnZuamlpluog7Y8QYV6WsrEyPPPKIevfurcjISDVu3Fh79uzR0aNH9cADDyg8PFwtWrTQokWLlJeXJ6vVqi1btlz33ODa8sQTTyg2NlbR0dHy9/fX0aNHtWXLFo0YMcLs0NxOfcqlqKgo9e3bV6tXr1Zpaanat2+vPXv26MyZM4bH4q7qU74kJyfrwIEDiouLU0hIiAoLC7V69Wo1b95cHTp0uO7rz58/X8XFxerUqZOaN2+unJwcrVq1SlFRUWrVqlUt/AYNm7vmkqenp8aNG6cFCxZo1qxZio+PV3Z2tlJTU2ucY+yOKIxxVXx8fDRixAjt3r1bW7duld1uV1hYmCZPnqzhw4dLkmbMmKGFCxfq448/lpeXl3r27KmRI0fqqaeeMjl6adSoUdq+fbsyMjJks9kUEhKie++9V7fffrvZobmd+pZLU6dOVUBAgNavX69t27apU6dO+u///m9NnTrV8FjcUX3Klx49eig7O1tff/21zp07pyZNmig2NlaJiYk13v1/Nfr166cvv/xSq1evVklJiZo1a6Y+ffooMTGR5duugDvn0tChQ2W327Vy5UotXrxYERER+t3vfqekpCQXRF8/WRysPA8AAAAwxxgAAACQmEqBeqqgoOCyx729vWvlK0s0fOQSrgb5gtpCLtVNTKVAvfRzj9McMGCApk2bZlA0qM/IJVwN8gW1hVyqmyiMUS/93HqSQUFBat26tUHRoD4jl3A1yBfUFnKpbqIwBgAAAMTNdwAAAIAkCmMAAABAEoUxAAAAIInCGAAAAJBEYQwAAABIojAGAAAAJFEYAwAAAJIojAEAAABJFMYAAACAJApjAMDPiIqKksVi0YkTJ8wOBQBcisIYAAAAEIUxAAAAIInCGAAAAJBEYQwAhrJYLLJYLJKkhQsXqk+fPmratGmlObzffvut5syZo8GDBysiIkI+Pj5q1qyZEhIS9Le//U12u73KdU+cOCGLxaKoqCg5HA4tWLBA3bt3l5+fn5o2barhw4dr06ZNNca1f/9+3XPPPQoODpavr686deqk//mf/9HFixcv+/vk5eXpmWee0U033SSr1aomTZqoe/fuevXVV1VWVlalfWpqqiwWiwYOHKgLFy5o1qxZiomJUePGjRUREaEZM2bo/PnzkqTCwkI9+eSTio6OVuPGjRUVFaXnnntOFRUVV/pyA8BVsTgcDofZQQCAu7hUFD/66KOaN2+e4uPj1aZNGx07dkxJSUmKjIzUiy++qD/84Q9q27atoqOjFRISoqysLG3atEnl5eX6xS9+oeTkZOe1pB8K47Zt2yoyMlIDBw7Uhx9+qH79+ik4OFjp6en65ptv5OPjo7Vr16pXr16VYlq/fr1GjhypkpISRUdHq2fPnsrNzdXatWt1++23a/v27fr22291/PhxRUVFOc87duyYBg8erG+//VYhISHq37+/bDabvv76a507d07dunXTmjVrFBgY6DwnNTVVgwYNUp8+feTt7a1du3Zp4MCBstvtSktLU2FhoUaPHq333ntP8fHxysvLU//+/XXu3DmtW7dO58+f1yOPPKK3337btf+hALgnBwDAMJIckhwBAQGOTZs2Vdtm69atjj179lTZn5mZ6bj55psdkhxLly6tdOz48ePOa0dGRjoOHTrkPFZRUeF46KGHHJIcw4cPr3ReWVmZo02bNg5Jjscff9xRUVHhPLZ7925HcHCw87rHjx+vdG6vXr0ckhy33367o7i42Lk/Ozvb0a1bN4ckx3333VfpnK+//tp5vZ49ezpyc3Odx06cOOEIDAx0SHJ07tzZMWbMGEdJSYnz+LZt2xyenp4ODw8Px7ffflvtawcA14PCGAAMdKkofP7556/p/P/93/91SHLcc889lfb/uDBeuXJllfOysrIckhw+Pj6O8vJy5/7Fixc7JDnatGlTaf8lb7zxRrWFcVpamkOSw2q1Ok6fPl3lvO3btzskOTw8PBwnT5507r9UGFsslmqL/8cee8whyeHv7+84c+ZMleNjxoxxSHK899571b9AAHAdPI0ZlwYA/Njdd9992eMXLlzQ6tWrtW3bNmVnZ+vChQtyOBw6d+6cJOnQoUPVnufp6amRI0dW2R8WFqbAwEDl5+fr7NmzCgsLk/TD1AZJSkxMlJeXV5XzHnzwQf32t7+tsv/SeSNHjlSLFi2qHO/evbtuvvlm7d69W2vXrtWECRMqHY+IiFCnTp2qnHfjjTc6zw8NDa3x+Pfff1/lGABcLwpjADDBj+fq/tTmzZt177336rvvvquxTVFRUbX7W7ZsWW2BK0kBAQHKz8933twmSadOnZIktW3bttpzAgMD1bRpUxUWFlban5mZednzJOmGG27Q7t27nW1/LCIiotpz/P39L3u8SZMmklTpdwCA2sKqFABgAl9f32r3l5aW6s4779R3332nSZMmaevWrcrLy1NFRYUcDodzpNhRw33THh7142395+KsL78HgIaFEWMAqEPWrVunM2fOqFu3bnr33XerHD98+HCt9teqVStJqvFxzwUFBVVGi3983rFjx2q89qVjl9oCQF3HR3IAqEPy8vIk1TyVYPHixbXa34ABAyRJS5culc1mq3L8/fffr/a8gQMHSpJWrVqlM2fOVDm+a9cupaeny8PDQ/3796+9gAHAhSiMAaAO6dixoyTpyy+/1P79+ysdW7BggZKSkmq1v7vvvlutWrXSd999p6effrrSw0P27t2rF198sdrzEhIS1KtXL5WVlenhhx9WaWmp81hubq4efvhhSdK4cePUpk2bWo0ZAFyFwhgA6pC4uDjdcccdOnfunOLi4jRixAiNHz9eHTt21COPPKJnnnmmVvvz9fXVBx98IKvVqj/96U+KiYnR+PHjNXz4cHXr1k39+vVTZGRkted++OGHioyM1IoVK9S2bVvdc889uvPOO3XDDTdo27Zt6tatm956661ajRcAXInCGADqmGXLlum1115T+/bttX79eq1evVoRERH63//9X02ePLnW+xswYIC2bNmiX/ziF8rPz1dKSopOnTql559//rIj1NHR0dq5c6eefvppNW/eXP/+97/1xRdf6IYbbtDs2bO1fv36Sk+9A4C6jkdCAwAAAGLEGAAAAJBEYQwAAABIojAGAAAAJFEYAwAAAJIojAEAAABJFMYAAACAJApjAAAAQBKFMQAAACBJ8jQ7gIYgPz9fFRUVZofRIIWEhCgnJ8fsMNBAkE+oTeQTahs55Rqenp5X/BROCuNaUFFRIZvNZnYYDY7FYpH0w+vLAxpxvcgn1CbyCbWNnKobmEoBAAAAiMIYAAAAkERhDAAAAEiiMAYAAAAkURgDAAAAkiiMAQAAAEkUxgAAAIAkCmMAAABAEoUxAAAAIInCGAAAAJBEYQwAAABIojAGAAAAJFEYAwBwVUpLSxUeHi6LxaLS0lKzwwFQizzNDqA2nT9/XitXrtThw4d15MgRlZSU6De/+Y0GDhzobGO327Vu3Tpt2bJFJ06cUHFxsUJDQxUfH68xY8bI29vbvF8AAAAApmlQI8ZFRUVKTk5WZmamoqKiqm1TXl6uefPmqaioSMOGDdODDz6oG264QUuXLtUrr7wih8NhbNAAAACoEwwfMc7Oztb27dt18OBBZWZmqqioSBaLRU2aNFGrVq3UoUMH9ejRQ6GhoVd97cDAQC1YsEDNmjXT0aNH9fTTT1dp4+npqRdeeEHt27d37hs6dKhCQ0O1dOlS7dmzR126dLmu3xEAAAD1j2GF8Y4dO/TJJ5/o4MGDcjgcCgsLU2hoqNq0aSNJKikp0bfffqstW7bovffeU4cOHXT77bere/fuV9yHl5eXmjVrdtk2np6elYriS3r27KmlS5cqMzOTwhgAAMANGVIYz5w5UydOnNAtt9yi3/72t+rcubOsVmu1bUtLS5WRkaHNmzfrjTfeUGRkpF566SWXx1hQUCBJatKkicv7AoCG6pOkArNDcLkLF/5zw92ny/Ll43PBxGiMM+beZmaHALicIYXxTTfdpKeeeupnR3MlyWq1qnfv3urdu7cKCgr02WefuT5ASStWrJCvr6/i4uJqbGOz2WSz2ZzbFotFvr6+zp9Ruy69pry2qA3kE3B9+NtxLd6j6gZDCuP77rvvms5r1qzZNZ97NZYvX649e/Zo8uTJ8vPzq7FdSkqKkpOTndtt27bVnDlzFBIS4vIY3VlYWJjZIaABIZ9cLd/sAOAiLVu2NDsEt8B7lLka1HJt12Ljxo1KSkrS4MGDNXz48Mu2HTt2rEaPHu3cvvSpLicnRxUVFS6N0x1ZLBaFhYXp9OnTrBaC60Y+AdcnKyvL7BAaNN6jXMfT0/OKBzENK4yPHTt21edER0e7IJL/yMjI0FtvvaW4uDhNmTLlZ9t7eXnJy8ur2mMkses4HA5eX9Qa8gnXy8fHqvmv7zM7DMPxd2MM3qPMZVhhXN3SaT8nKSnJBZH84PDhw3rttdd0ww036IknnlCjRo1c1hcAAADqPsMK46lTpxrV1c86deqUZs+erdDQUM2YMYOn3QEAAMC4wvjHj2V2pVWrVqmkpET5+T/cALJ9+3adPXtWkjRq1ChZLBa99NJLKi4u1pgxY7Rz585K54eFhSkmJsaQWAEAAFB3GHrzXV5eniQpKCjosm0sFosCAwOvqY9PPvlEOTk5zu2tW7dq69atkqR+/fpJkrNQ/vDDD6ucP2DAAApjAAAAN2TozXfPPPOM7r///korO/zUxo0b9cEHH2jOnDmKiIi46n7mzp37s22WLl161dcFAABAw+ZhVEerVq1Sy5Ytddttt1223W233abw8HDDHuwBAAAASAYWxvv27VOfPn1+9okuFotFvXv31t69ew2KDAAAADBwKkVBQYFCQ0OvqG1wcLDz5rn6YO+ZEhWWXjA7jAbHYrEosMRL+fnnWNMR1418Msa39vNmhwAX2XrqnNkhNGi8R7lOU6uPBl3hAz4MGzH28fFRcXHxFbUtKSlhCTUAAAAYyrDCODIyUjt27Liitjt27FBkZKSLIwIAAAD+w7DCuH///tq/f78+//zzy7ZbtWqV9u/frwEDBhgUGQAAAGDgHOMBAwZo06ZN+uc//6ldu3apX79+ioiIkK+vr8rKyvTdd98pLS1Nu3fvVpcuXQx7IAgAAAAgGVgYe3h46Mknn9SiRYu0Zs0a7d69u9o2w4YN0wMPPPCzq1cAAAAAtcnQJ995e3vrV7/6lcaOHatdu3YpMzNTpaWlslqtCg8PV1xcnJo3b25kSAAAAIAkgwvjS4KCgjRkyBAzugYAAACqZdjNdwAAAEBdRmEMAAAAiMIYAAAAkERhDAAAAEiiMAYAAAAkmbQqxU/Z7XYdPnxYeXl5atasmWJiYtSoUSOzwwIAAIAbMb0wzszM1Jw5c3T27Fn5+/urqKhIQUFBeuqppxQVFWV2eAAAAHATpk+leOedd9S1a1ctXLhQf/vb3/S3v/1NLVq00N/+9jezQwMAAIAbMawwXrBggYqLi6vsz8rK0sCBA+Xt7S1JCggIUM+ePZWVlWVUaAAAAIBxhXF+fr7+67/+S5999pnsdrtzf2xsrBYtWqQDBw7o9OnT2rFjhz799FPFxsYaFRoAAAAgi8PhcBjVWXp6ut5//305HA49+OCD6tq1q0pLS/XOO+9o8+bNunjxojw8PHTLLbdo8uTJCggIMCq06/L13hMqLL1gdhgNjsViUWBgkPLz82RgmqKBIp+MsTWtxOwQ4CI9+/mZHUKDxnuU6zS1+mhQp6gramtoYSz9sALF559/ruTkZMXExGjixIlq2bKl7Ha7zp07pyZNmsjDw/Spz1dl7a5vVFhSZnYYDY7FYlGzZkEqKOBNAtePfDLG9o2lZocAF+kRbzU7hAaN9yjXaernqwFxMVfU1vDC+JKioiItWbJEaWlpGj58uO655x75+vqaEcp1O/3YBNmOHjI7DAAAAPyE1w3tFfbmB1fU1vCh2YqKCpWWliogIEAPP/ywXnzxRR09elSPPfaY1qxZw6ckAAAAmMKwEeP8/Hy9/fbbysjIkMPhUFhYmB5++GHnTXYbN27U4sWL5e/vr4kTJ9arm++YSuEafK2E2kQ+GYOpFA0XUylci/co17maqRSGPeBjwYIFysnJ0R//+Ef5+flp+fLl+tOf/qR58+bJx8dH8fHx6tGjhz7++GO98soriouL0xNPPGFUeNfF7uUtuzdJXJvOl5Vq/MBukqSP1u6ST+P6Oc0GdYfFYpHDp7Hs3o35Px0Xqmh00ewQ4CJ278Zmh9Cg8R7lOnYv7ytua9hUigMHDujWW29VbGysIiMjNWHCBBUXF+vUqVPONt7e3kpMTNQbb7whi8ViVGgAAACAcYVxYGCgDh8+7Ny+9HOzZs2qtA0ODtZvf/tbo0IDAAAAjJtKMX78eP3lL3/RwYMH5efnp2PHjmnUqFFq3ry5USEAAAAANTKsMO7Zs6feeOMN7d69W+Xl5XrwwQfVoUMHo7oHAAAALsuwwliSQkNDNWzYMCO7bHDsb71odgiGsNsqnD9ffHuO7F6GpqopPB79vdkhAADg1upEtZGXl6fi4mIFBARUO+cYAAAAcDXDCuONGzcqJiZGwcHBzn27du3S+++/r++//965LyoqShMnTlTHjh2NCg0AAAAwblWKSzfeXZKRkaE5c+aopKREd9xxh371q1/ptttuU05Ojl588UWdOHHCqNAAAAAA86ZSLFmyRM2bN9ecOXPk7+/v3D969Gg99dRTWr58eb15wAdqX2MvT/3rl/ebHQYAAHAjphTGFRUVOnbsmCZOnFipKJakoKAgDR06VF999dVVX/f8+fNauXKlDh8+rCNHjqikpES/+c1vNHDgwCptT506pffee08HDx6Up6enunXrpgcffFABAQHX+msBAACgHjNsKsWPXbz4wyNDW7RoUe3x0NBQlZSUXPV1i4qKlJycrMzMTEVFRdXY7uzZs3r22Wd1+vRpjR8/XmPGjNHOnTv1wgsvqKKiosbzAAAA0HAZOmK8c+dOFRQUSJIaN26svLy8atvl5+fLz8/vqq8fGBioBQsWqFmzZjp69KiefvrpatulpKTowoULmjNnjvNmwHbt2unFF19Uamqqhg4detV9AwAAoH4zdMR4w4YNWrRokRYtWqTz589r69at1bbbs2ePWrdufdXX9/LyuqLl3rZs2aJu3bpVWiGjS5cuatmypTZt2nTV/QIAAKD+M2zE+K233qqyz2KxVNlXVFSkFi1aqGvXri6JIy8vT4WFhbrhhhuqHGvXrp127drlkn4BAABQtxlWGIeEhFxRu4CAAP3mN79xWRz5+fmSfph28VOBgYEqLi6WzWaTl5dXleM2m002m825bbFY5OvrK4vFUm2RD1wNcsi1LLL85395qYGrxnuUa/Ee5TpXk7t14sl3RiovL5ckeXpW/dUvFcPl5eXVFsYpKSlKTk52brdt21Zz5sxRQECAPHwuuijiynIN6QVmCAwMMjsEt9Csmg/FqE3FZgcAF+E9yhi8R9U+f59GV9zW8MJ4586d2rBhg44dO6a8vDyVl5ercePGCg8PV+fOnTVs2DA1b97cZf17e3tLUrWrT1waDb7U5qfGjh2r0aNHO7cvfQJp07hCFZ7GrGbR9uwBQ/qB8Tz9bD/fCNektLRU7dq1kyQdPXpUvr6+JkfUcB3waGx2CHCRG3mPcimLxaKwsDCdPn1aDofD7HAaFE/PK389DSuML1y4oD/96U/avXt3pf2NGjVSeHi4cnNzlZKSos8++0y//vWvlZCQ4JI4Lk2huDSl4sfy8/Pl7+9f7Wix9MOIck3HSGJcL3LIdX782jocDl5r4Brwd2MM3qPMZVhhnJSUpD179mjSpEnq27evfHx8tH//fv39739XbGysJkyYoFOnTmnx4sV66623FBQUpNjY2FqPIygoSAEBATp69GiVY0eOHLns+scAAABouAxbrm3jxo0aMWKERo4cqSZNmsjb21tdu3bV5MmT9e9//1uFhYVq3bq1ZsyYoRtuuEHLly93WSy9evXSzp07lZv7nxm7e/bsUVZWlnr37u2yfgEAAFB3GTZifO7cuWrXJm7durXsdruysrLUtGlTWSwWJSQkKCkp6Zr6WbVqlUpKSpxTJbZv366zZ89KkkaNGiWr1aqxY8dq8+bNmjVrlm699Vbno6QjIiI0aNCga/8lgXrojg8Omh2Cy10sL3P+fM9HB9XIu+HPMV4xoYPZIQBAvWNYYRwaGqq9e/dWearcvn37JKnSgzkaNWp0zfNrPvnkE+Xk5Di3t27d6nyQSL9+/WS1WhUcHKznnntO77//vj788EN5enoqLi5ODzzwQI1ziAEAANCwGVYYjxgxQgsXLpS3t7fi4+Pl7e2tgwcPasWKFerQoYPCwsKcbU+cOFFp+2rMnTv3itq1adNGM2fOvKY+AAAAaktpaaluvPFGST/c78TKOeYxrDAeOXKkcnNz9emnn2rt2rXO/e3atdPjjz9eqa23t7dGjRplVGgAGrhG3r7q8eqXZocBAKjjDF3H+P7779fo0aP1zTffyGazKTw8XG3btq3SbuLEiUaGBQAAABj/gI9mzZqpZ8+eRncLAAAAXJbbPRIaAADUD2+++abZIRiivLzc+fPcuXNrfAJvQ/LYY4+ZHUK1DFvHGAAAAKjLKIwBAAAAMZUCAADAVN7e3poxY4bZYUCMGAMAAACSKIwBAAAASSYWxufPn9e8efOUmZlZ5dg333yjefPmmRAVAAAA3JVphbHNZtPatWuVn59f5diZM2cqPR0PAAAAcDWmUgAAAACiMAYAAAAkmbBcW25uriSpuLhYklRYWOjcJ0nBwcFGhwQAAAAYXxhPmzat0vZPH/eYlJRkZDgAAACAJBMK42effVaSVFJSov/5n//RL3/5S0VHRxsdBgAAAFCJ4YVxbGysJOncuXOSpKioKOc+AAAAwCzcfAcAAACIwhgAAACQZGJh3KhRI8XGxsrPz6/KsaZNmzK9AgAAAIYyfI7xJVar1Xkj3k916dJFXbp0MTgiAAAAuDOmUgAAAAAyqDC+cOGCKecCAAAAV8qQwnjq1KlKTk5Wfn7+FZ+Tl5enpKQk/eY3v3FhZAAAAMAPDJljPHnyZC1btkzJyclq3769OnfurOjoaIWGhsrPz08Oh0MlJSXKzs7W0aNHtWfPHh0+fFgtW7bUr371KyNCBAAAgJszpDCOj49X7969tX37dqWmpiolJUUVFRXVB+TpqS5duuiJJ55Qjx495OHBNGgAAAC4nmGrUnh4eKhnz57q2bOnbDabjh07pszMTBUXF0uS/P391apVK0VHR8vLy8uosAAAAABJJi3X5uXlpfbt26t9+/ZmdA8AAABUwTwFAAAAQBTGAAAAgCQKYwAAAEAShTEAAAAgicIYAAAAkERhDAAAAEgyabk2ScrNzdXy5cu1b98+FRUV6amnnlJsbKyKioqUnJysQYMGqW3btmaFBwAAADdjSmF86tQp/fGPf5TD4VC7du10+vRp2e12SVJAQIAOHTqkCxcuaOrUqS6LISsrS0lJSTp48KCKi4sVHByshIQEjRkzRj4+Pi7rFwAAAHWTKYXx4sWL5efnp5deekmSNGXKlErH4+LitGnTJpf1n5ubq2eeeUZWq1UjR46Uv7+/vvnmGy1dulTHjh3T7373O5f1DQAAgLrJlML4wIEDuuuuuxQQEKBz585VOR4cHKy8vDyX9b9u3TqVlJTo+eefV5s2bSRJQ4cOld1u17p161RcXCx/f3+X9Q8AAIC6x5Sb7+x2+2WnKxQVFcnT03U1e1lZmSSpadOmlfYHBgbKYrG4tG8AAADUTaYUxtHR0dq5c2e1xy5evKiNGzcqJibGZf3fdNNNkqT58+frxIkTys3N1caNG7V69WqNGjVKjRs3rvY8m82m0tJS579LBbYkWSwWQ/6h4TIqh8gp90A+obaRT6hNdTWPTBkavfPOOzV79mz9/e9/V9++fSVJBQUFysjIUEpKijIzM/XQQw+5rP+uXbvq3nvvVUpKirZv3+7c/4tf/ELjxo2r8byUlBQlJyc7t9u2bas5c+YoJCTEZbH+1EnDeoLRWrZsaVLPB0zqF65kXj7lm9QvXM28nEJDVFfzyZTCOC4uTtOmTdPChQu1Zs0aSdJf//pXSZKvr6+mTZum2NhYl8YQEhKijh07qlevXmrSpIl27typlJQUNWvWTCNHjqz2nLFjx2r06NHO7UufQnJyclRRUeHSeNHwZWVlmR0CGhDyCbWNnEJtMjKfPD09r3gQ07TJtP3791fPnj2VkZHhXK4tLCxMN998s3x9fV3a94YNG7RgwQL95S9/UfPmzSVJvXr1ksPh0AcffKC+ffuqSZMmVc7z8vKSl5dXtdd0OBwujRkNHzmE2kQ+obaRU6hNdTWfTL3LrHHjxurZs6fh/a5evVpt27Z1FsWX9OjRQ6mpqTp+/Li6dOlieFwAAAAwjymFcW5u7hW1Cw4Odkn/BQUF1S7Hdmk6xKWHjQAAAMB9mFIYT5s27YraJSUluaT/li1bKiMjQ99//73Cw8Od+zds2CCLxaKIiAiX9AsAAIC6y5TCuLpHPdvtduXk5GjdunUKCAjQiBEjXNb/7bffrvT0dD377LMaMWKE8+a7Xbt2afDgwQoKCnJZ3wAAAKibTCmMBw4cWOOxO+64QzNnzlRpaanL+o+NjdWLL76oZcuWafXq1Tp37pxCQ0M1btw43XHHHS7rFwAAAHVXnXvEW+PGjTVw4EB9+umnuvXWW13WT7t27fT000+77PoAAACoX0x58t3PcTgcKigoMDsMAAAAuJE6NWJcWlqqAwcOaOXKlWrbtq3Z4QAAAMCNmFIY33vvvZc9HhwcrMmTJxsUDQAAAGBSYXzXXXc5H6d8icVikZ+fn1q0aKGbb75ZjRo1MiM0AAAAuClTCuPExEQzugUAAABqVCdvvgMAAACMZsiI8bx58676HIvFUu2DQAAAAABXMKQw3rdv31Wf89M5yAAAAIArGVIYz50714huAAAAgGvGHGMAAABAFMYAAACAJBOffLdr1y79+9//1vHjx1VaWiqHw1GlTVJSkgmRAQAAwB2ZMmK8efNmzZ49W4WFhYqPj5fD4VDfvn3Vt29feXt7KzIyUnfffbcZoQEAAMBNmTJi/PHHH6tdu3Z64YUXVFxcrC+++EKDBw9Wp06dlJ2drZkzZyo0NNSM0AAAAOCmTBkxPnXqlPr27SsPDw/no58rKiokSaGhoRoxYoRWrFhhRmgAAABwU6YUxj4+PvL0/GGw2s/PT56eniooKHAeb9q0qbKzs80IDQAAAG7KlMI4PDxcp06dcm5HRUVp3bp1unjxosrLy7V+/XoFBwebERoAAADclCmF8S233KJt27bJZrNJkn7xi19o3759mjhxoiZPnqyDBw/qzjvvNCM0AAAAuCnDbr7bsWOH4uLi5OHhodtvv123336781j37t313HPPacuWLfLw8FC3bt3UqVMno0IDAAAAjCuMX331VTVp0kR9+vRRv379FBMTU+l4x44d1bFjR6PCAQAAACoxrDCeMmWKNmzYoC+++EKrV69WSEiIEhISlJCQoNatWxsVBgAAAFAtwwrjoUOHaujQocrLy9OGDRu0YcMGpaSkKCUlRVFRUerXr5/i4+MVFBRkVEgAAACAk+EP+AgKCtKYMWM0ZswYff/991q/fr02bNigRYsWafHixYqNjVW/fv3Uq1cvWa1Wo8MDAACAmzLlyXeXhIeHKzExUYmJiTpy5Ig2bNigjRs3av78+Xr33Xe1aNEiM8MDAACAGzFlubbqhIaGqkWLFmrevLkkqby83OSIAAAA4E5MHTE+f/68tmzZovXr12vv3r2y2+0KCQnR2LFjlZCQYGZoAAAAcDOGF8YVFRXauXOn1q9fr127dqm8vFz+/v4aMmSIEhIS1KFDB6NDAgAAAIwrjPfu3av169dry5YtKi0tlZeXl7p3765+/fopLi5OjRo1MioUAAAAoArDCuMXXnhBFotFN910k3PVCV9fX6O6BwAAAC7LsML4l7/8pfr27avAwECjugQAAACumGGF8ejRo43qCgAAALhqdWa5NgAAAMBMFMYAAACAKIwBAAAASSY/4MNsx44d07Jly3Tw4EHZbDa1aNFCQ4YM0a233mp2aAAAADCYKYXxxo0b1aNHD3l7e5vRvSRp9+7dmjNnjtq2bau77rpLjRs31pkzZ5SXl2daTAAAADCPKYXxX/7yF/n6+qpXr17q16+fOnXqZGj/paWleuutt9StWzc98cQT8vBgRgkAAIC7M6Uwfv7555WWlqbNmzcrNTVVQUFBSkhIUL9+/RQREeHy/tevX6/CwkKNGzdOHh4eOn/+vLy9vSmQAQAA3JgphXH79u3Vvn17TZo0Senp6UpLS9OqVau0cuVKRUREqH///kpISHDZw0D27NkjX19f5eXl6bXXXlNWVpZ8fHzUv39/Pfjgg6ZO8QAAAIA5TL35rlGjRurevbu6d++u8+fPa+vWrUpNTdXixYv14YcfKjY2VgMGDFB8fLw8PWsv1NOnT8tut+u1117ToEGDdN9992nfvn1atWqVSkpK9Pjjj1d7ns1mk81mc25bLBbnY60tFkutxQf3RA6hNpFPqG3kFGpTXc2nOrMqxXfffacjR47ou+++kySFh4eruLhYc+fO1ZIlSzR9+nR16NChVvo6f/68Lly4oGHDhumhhx6SJPXq1UsVFRVas2aN7r33XrVs2bLKeSkpKUpOTnZut23bVnPmzFFISEitxHUlThrWE4xWXc4Z44BJ/cKVzMunfJP6hauZl1NoiOpqPplaGH///fdav3691q9frzNnziggIEAJCQnq37+/oqOjJUlHjx7V/Pnz9fe//11/+tOfaqXfS1Ml+vbtW2l/QkKC1qxZo2+++aba/2Bjx46t9GjrS592cnJyVFFRUSuxwX1lZWWZHQIaEPIJtY2cQm0yMp88PT2veBDTlML4s88+U1pamo4dOyYvLy91795dEydOVNeuXavcAHfDDTfotttu0/z582ut/8DAQJ08eVLNmjWrtL9p06aSpJKSkmrP8/LykpeXV7XHHA5HrcUH90QOoTaRT6ht5BRqU13NJ1MK4/fee0/t27fXlClTFB8fL6vVetn2N9xwg+66665a6z86OloZGRnKy8tTeHi4c39+/g9fAQYEBNRaXwAAAKgfTCmM//rXvyo0NPSK27dp00Zt2rSptf779Omjjz/+WF999VWlNZS//PJLNWrUSLGxsbXWFwAAAOoHUwrj5s2bq7S0tMaR4tLSUvn4+KhRo0Yu6b9t27YaNGiQvv76a128eFGxsbHat2+fNm/erDvvvFNBQUEu6RcAAAB1lymF8cKFC3XgwIEab6b7wx/+oE6dOmnSpEkui2HKlCkKDg5Wamqqtm7dqpCQED344IO67bbbXNYnAAAA6i5TCuP09HT179+/xuO9e/dWWlqaSwtjT09P3XPPPbrnnntc1gcAAADqD1OegZyfn3/Z6QqBgYHKy8szMCIAAAC4O1MKY39/f33//fc1Hs/MzHQ+UQ4AAAAwgimFcdeuXbVmzRodP368yrFjx45pzZo1iouLMyEyAAAAuCtT5hjfe++9Sk9P1zPPPKPu3bs7l2I7efKkduzYoYCAAN17771mhAYAAAA3ZUphHBQUpNmzZ+uDDz7Q9u3btW3bNkmSr6+vEhISNH78eJZMAwAAgKFMKYylH26we/TRR+VwOFRUVCTphyfOWSwWs0ICAACAGzOtML7EYrGoadOmZocBAAAAN2dIYZycnCxJ+sUvfiEPDw/n9s+5++67XRkWAAAA4GRIYbxs2TJJ0p133ikPDw/n9s+hMAYAAIBRDCmMk5KSLrsNAAAAmM2UdYwBAACAuobCGAAAAJBBUymmTZt21cuwWSwW/fWvf3VRRAAAAEBlhhTGsbGxrE8MAACAOs2wEWMAAACgLmOOMQAAACATn3xnt9u1bt067dy5U7m5uZKk4OBgde/eXf369ZOHBzU7AAAAjGNKYVxaWqqXXnpJR44cka+vr1q0aCFJ2rNnj7Zs2aLVq1dr5syZslqtZoQHAAAAN2RKYbxkyRIdO3ZMDz30kIYMGSJPzx/CqKio0FdffaWFCxfqo48+0kMPPWRGeAAAAHBDpsxX2Lp1q4YPH64RI0Y4i2JJ8vT01PDhwzVs2DBt2bLFjNAAAADgpkwpjIuLixUeHl7j8VatWqm4uNjAiAAAAODuTCmMw8LCtH379hqPb9++3TnvGAAAADCCKYXx8OHDlZGRoVdeeUW7d+9Wdna2srOzlZ6erldeeUUZGRkaOXKkGaEBAADATZly892IESNUWFioFStWKD09vXJAnp66++67NXz4cDNCAwAAgJsybR3jxMREjRw5UhkZGc51jENCQtS5c2cFBASYFRYAAADclKGFcUFBgVJTU5Wdna2AgAD16tVLCQkJRoYAAAAAVMuwwjg7O1tPP/10pdUmPv74Yz366KMUxwAAADCdYYXx0qVLdf78eU2aNEmdOnXS6dOntXDhQr333nuKj4/nEdAAAAAwlWGF8aFDhzR06FDnahOtW7eWh4eH5syZo8zMTLVp08aoUAAAAIAqDBumzc3NVdu2bSvti46OliSdO3fOqDAAAACAahlWGNvt9kqPf5akRo0aOY8BAAAAZjJ0VYqjR4/Ky8vLuV1WViZJOnjwoEpKSqq079Wrl2GxAQAAwL0ZWhh/9tln+uyzz6rsX7ZsWbXtk5KSXB0SAAAAIMnAwvjZZ581qisAAADgqhlWGMfGxhrVFQAAAHDVWDz4/yxfvlyJiYn6f//v/5kdCgAAAExAYSzp7NmzSklJkY+Pj9mhAAAAwCSG3nxXVy1atEg33nij7HY7ayoDAAC4KbcfMd6/f782b96siRMnmh0KAAAATOTWhbHdbtfChQs1ePBgRUREmB0OAAAATOTWUylWr16tnJwc/eEPf7ii9jabTTabzbltsVjk6+vr/Bm4HuQQahP5hNpGTqE21dV8MqQwXrt27TWdN2DAgFqO5D/OnTunpUuX6q677lJAQMAVnZOSkqLk5GTndtu2bTVnzhyFhIS4KswqThrWE4zWsmVLk3o+YFK/cCXz8infpH7haublFBqiuppPhhTG8+bNu6bzXFkYf/TRR/L399eoUaOu+JyxY8dq9OjRzu1Ln3ZycnJUUVFR6zHCvWRlZZkdAhoQ8gm1jZxCbTIynzw9Pa94ENOQwvitt96qtF1SUqK5c+fKarVq1KhRCg8PlyRlZmZq1apVKisr07Rp01wWT1ZWltasWaOJEycqLy/Pud9ms6miokLZ2dmyWq3y9/evdJ6Xl5e8vLyqvabD4XBZvHAP5BBqE/mE2kZOoTbV1XwypDD+aZW+bNkyBQQE6Pe//32lOSYRERHq1auXXnrpJX366af6zW9+45J48vLy5HA4tHDhQi1cuLDK8UcffVS33norK1UAAAC4EVNuvtu2bZvGjRtX7cRrDw8P9ezZUx999JHL+m/Tpo2efPLJKvuTkpJUVlamiRMnKiwszGX9AwAAoO4xpTB2OBzKzMys8fipU6dc2n9AQIB69uxZZf9nn30mSdUeAwAAQMNmyjrGt9xyi7744gv9+9//1oULF5z7L1y4oE8++URr1qxRjx49zAgNAAAAbsqUEeNJkyYpOztbixYt0ocffqjAwEBJUn5+vi5evKj27dubMr/3ueeeM7xPAAAA1A2mFMZWq1WzZs3Stm3btGvXLuXm5kqSbr75ZnXr1k3du3evsws/AwAAoGEy9cl3t9xyi2655RYzQwAAAAAkmVwY5+Xlaf/+/SoqKlKvXr3UvHlz2e12lZaWymq1ysPDlCnQAAAAcEOmrUrx/vvva9WqVbLb7ZJ+WMO4efPmOn/+vKZNm6bExETddtttZoQHAAAAN2TKkOzKlSv12WefacyYMfr9739f6ZjValXPnj21ZcsWM0IDAACAmzKlMP7yyy81YMAA3XfffYqKiqpyPDIykmeyAwAAwFCmFMZnz55VTExMjcd9fHxUWlpqYEQAAABwd6YUxgEBATp79myNx48dO6bg4GADIwIAAIC7M6Uw7tWrl7744gudOXOmyrHdu3crNTVVffr0MSEyAAAAuCtTVqVITEzUvn379Lvf/U4dOnSQJK1YsUJJSUn65ptv1LZtW40dO9aM0AAAAOCmTBkxtlqteumll3T77bcrLy9P3t7e2r9/v0pLS3XPPffo+eefl4+PjxmhAQAAwE2Z9oAPb29v3XXXXbrrrrvMCgEAAABwMmXEeNasWdqzZ0+Nx/fu3atZs2YZGBEAAADcnSmF8f79+1VYWFjj8aKiIu3fv9/AiAAAAODuTCmMf87p06fl6+trdhgAAABwI4bNMU5NTdXatWud28uXL9eXX35ZpV1paam+/fZbxcXFGRUaAAAAYFxhXF5erqKiIud2WVmZLBZLpTYWi0U+Pj4aNmyY7r77bqNCAwAAAIwrjIcPH67hw4dLkqZNm6ZJkyapR48eRnUPAAAAXJYpy7XNnTvXjG4BAACAGpm2jvElZWVlKi0tlcPhqHIsODjYhIgAAADgjkwrjFevXq1///vfOnPmTI1tkpKSDIwIAAAA7syU5dpWr16tf/zjHwoLC9O4ceMkSbfddpvuvPNONWvWTFFRUZo6daoZoQEAAMBNmVIYr1q1SjfffLOeeeYZDR06VJLUrVs3jR8/Xm+88YbKysp07tw5M0IDAACAmzKlMD5z5oy6d+8uSWrUqJEkqaKiQpJktVo1ePBgrV692ozQAAAA4KZMKYytVqsuXrzo/Nnb21u5ubnO476+viooKDAjNAAAALgpUwrjNm3a6Ntvv3Vux8TE6IsvvlBeXp5yc3O1Zs0atWzZ0ozQAAAA4KZMKYz79eunkydPymazSZLuuecenTp1SlOnTtW0adP0/fffO2/KAwAAAIxgynJtgwYN0qBBg5zbHTp00Ouvv64dO3bIw8NDXbp0UXh4uBmhAQAAwE2Z/oCPS1q0aKFbb73V7DAAAADgpkwvjO12u0pLS6s95u/vb3A0AAAAcFemFMYVFRVasWKFvv76a509e1Z2u73adjz5DgAAAEYxpTBesGCB1q5dq5iYGN1yyy2yWq1mhAEAAAA4mVIYb968Wf3799e0adPM6B4AAACowpTl2nx8fHTjjTea0TUAAABQLVMK4759+2rnzp1mdA0AAABUy5SpFPfff7/mzZun2bNna9CgQWrevLk8PKrW6NHR0S7p/8iRI1q7dq327dunnJwc+fv768Ybb9S4ceNYPxkAAMBNmVIY22w2ORwO7dq1S7t27aqxnatWpVixYoUOHTqk3r17KzIyUgUFBVq1apVmzJihl156SRERES7pFwAAAHWXKYXx22+/ra1bt6pv375q166d4atSjB49WtOnT5en539+/fj4eD355JP6+OOP9dhjjxkaDwAAAMxnSmG8e/dujRw5UhMnTjSje7Vv377KvpYtW6p169bKzMw0ISIAAACYzZTC2NfXV2FhYWZ0XSOHw6HCwkK1adOmxjY2m002m825bbFY5Ovr6/wZuB7kEGoT+YTaRk6hNtXVfDKlMB4yZIg2bNig4cOHV3vTnRnS0tKUl5enxMTEGtukpKQoOTnZud22bVvNmTNHISEhRoQoSTppWE8wWsuWLU3q+YBJ/cKVzMunfJP6hauZl1NoiOpqPplSGLdu3Vrbt2/XjBkzNGDAgBpXpejVq5ch8WRmZuof//iHYmJiNHDgwBrbjR07VqNHj3ZuX/q0k5OTo4qKCleHiQYuKyvL7BDQgJBPqG3kFGqTkfnk6el5xYOYphTGf/7zn50/L1q0qMZ2rlqV4scKCgo0e/ZsWa1WPfHEE5cdwfby8pKXl1e1xxwOh6tChJsgh1CbyCfUNnIKtamu5pMphfGzzz5rRrdVlJaW6uWXX1ZJSYmef/55BQUFmR0SAAAATGJKYRwbG2tGt5WUl5drzpw5ysrK0h/+8Ae1bt3a7JAAAABgorpx55vB7Ha7/vznP+ubb77Rb3/7W8XExJgdEgAAAExmyIjxrFmzZLFYNHPmTDVq1EizZs362XMsFov++Mc/uiSe999/X9u3b1f37t1VXFysdevWVTrev39/l/QLAACAusuQwvinE6wdDsfPrl/nyknZJ06ckCTt2LFDO3bsqHKcwhgAAMD9GFIYP/fcc5fdNprZ/QMAAKDuMWWO8f79+1VUVFTj8aKiIu3fv9/AiAAAAODuTCmMZ82apYyMjBqP792794rmIQMAAAC1pU6uSmGz2erMo6IBAADgHgxbxzg3N1fZ2dnO7czMzGqnS5SWlmrNmjVX/Og+AAAAoDYYVhh//fXXSk5Odm4vX75cy5cvr7ath4eHpkyZYlRoAAAAgHGFcZ8+fdSmTRtJ0htvvKFRo0apQ4cOldpYLBb5+PgoKipKzZo1Myo0AAAAwLjCuHXr1s7HLk+dOlWxsbEKDQ01qnsAAADgsgwrjH9s4MCB1e4/c+aMbDabs4AGAAAAjGJKYfz555/r0KFDevzxx5375s2bp7Vr10qS2rZtq6efflpNmzY1IzwAAAC4IVPWRPvyyy8rFb3p6elau3athg4dqoceekhnzpzRsmXLzAgNAAAAbsqUEeOcnBy1atXKub1p0yaFhoY6V6IoKCjQunXrzAgNAAAAbqpOPEUjIyNDXbt2dW6HhISooKDAtHgAAADgfkwpjFu2bKlt27ZJ+mEaRV5enuLi4pzH8/Ly5OfnZ0ZoAAAAcFOmTKUYM2aM3nzzTU2aNEnnz59X69atdfPNNzuP7927V1FRUWaEBgAAADdlSmHct29fNWnSRDt37pSfn59GjBihRo0aSZKKi4vl7++v/v37mxEaAAAA3JQphbEkdenSRV26dKmy39/fX08++aQJEQEAAMCdGTbH+MiRIyouLr6ittnZ2c41jQEAAAAjGFYYz5w5U+np6c7t4uJi3X///dq/f3+VtocOHdK8efOMCg0AAAAwb7k2h8Mhm80mu91uVggAAACAU51YxxgAAAAwG4UxAAAAIApjAAAAQJLBy7VlZ2fr2LFjkqTS0lJJUlZWlqxWa5V2AAAAgJEMLYyTkpKUlJRUad8777xjZAgAAABAtQwrjKdOnWpUVwAAAMBVM6wwHjhwoFFdAQAAAFeNm+8AAAAAURgDAAAAkiiMAQAAAEkUxgAAAIAkCmMAAABAEoUxAAAAIInCGAAAAJBEYQwAAABIMviR0HWJzWZTUlKS0tLSVFxcrMjISI0bN05dunQxOzQAAACYwG1HjOfOnatPP/1UCQkJmjRpkjw8PPTKK6/o4MGDZocGAAAAE7hlYXzkyBFt3LhR48eP1y9/+UsNHTpUf/zjHxUcHKzFixebHR4AAABM4JaF8ebNm+Xh4aGhQ4c693l7e2vw4MH65ptvlJuba2J0AAAAMINbFsbHjx9Xy5YtZbVaK+1v166dJOnEiRMmRAUAAAAzueXNdwUFBQoMDKyy/9K+/Pz8as+z2Wyy2WzObYvFIl9fX3l6GvcyetzQ3rC+YKxGXl6m9Ns+1N+UfuFaXiblU3BoY1P6heuZkVPh4eGG9wljGJlPV1OnuWVhXF5eXu1/kEv7ysvLqz0vJSVFycnJzu2+fftq+vTp1RbZLvPmB8b1Bbew+MEQs0NAA3LXBPIJteexxx4zOwS4GbecSuHt7V1p5PeSS/u8vb2rPW/s2LH65z//6fw3ZcqUaq+D2lFWVqYZM2aorKzM7FDQAJBPqE3kE2obOVU3uOWIcbNmzZSXl1dl/6UpFDWNAHt5eZn29aQ7cjgcOn78uBwOh9mhoAEgn1CbyCfUNnKqbnDLEeOoqChlZWWptLS00v7Dhw87jwMAAMC9uGVh3Lt3b9ntdq1Zs8a5z2azKTU1VTfeeKOCg4NNjA4AAABmcMupFDfeeKN69+6tJUuWqKioSGFhYVq7dq1ycnL0yCOPmB0e/o+Xl5fuvvtupq+gVpBPqE3kE2obOVU3WBxuOpmlvLxcSUlJSktLU0lJiSIiInTvvfeqa9euZocGAAAAE7htYQwAAAD8mFvOMQYAAAB+isIYAAAAEIUxGrhp06Zp7ty5ZoeBBiwxMVFLly41Oww0YHPnztW0adPMDgMN2HPPPafnnnvO7DDqBApjAAAAQG66XBvcx5///GdZLBazwwCAa/bwww/zNDTAIIwYo0Hz8vKSpyef/2Cu8+fPmx0C6jFPT0/WtoXpysvLZbfbzQ7D5agYcNXKysqUlJSkbdu2KT8/X1arVZGRkZowYYKio6N14MABff755zp8+LAKCwvVtGlT9erVS/fdd5+8vb2d15k7d642b96sN954Q++884727dsnq9WqsWPHauTIkfruu++0cOFCHTlyRE2aNNF9992nhISEq4p12rRpio2Ndc7Pq6ioUEpKitLS0nT27Fn5+PioVatWuueee9SlS5dafZ3w8+pTLtlsNn3wwQdKS0uTzWbTTTfdpMmTJ1dpt3TpUiUnJ+v111/Xv/71L6WnpyskJESvvvrqdb9e7q4+5UtGRoaWLVumkydP6uLFiwoKCnLGIkn79u3TrFmz9Pjjj+v06dNavXq1zp07p/bt2+vXv/61wsLCKsW7f//+SvdLbNiwQStXrlRWVpYsFouCg4M1ZMgQ3Xrrrdf5KrsHd80lSVqzZo1WrFihvLw8RURE6IEHHqjS56VrTp8+XSdPntTXX3+tgoICvfvuu/Lz87uGV7z+oDDGVfv73/+uzZs3a+TIkWrdurXOnTungwcPKjMzU9HR0dq0aZMuXLig4cOHq0mTJjpy5IhWrVqlvLw8PfHEE5WuZbfb9fLLL6tjx466//77tX79er377rtq3LixlixZon79+qlXr1764osv9NZbbykmJkahoaHXHPuyZcv08ccfa/DgwWrXrp3Kysp09OhRHTt2jMLYBPUpl+bPn6+0tDQlJCQoJiZGe/fu1ezZs2ts//rrryssLEzjx4/na/BaUl/y5eTJk5o9e7YiIyOVmJgoLy8vnT59WocOHarSdsWKFbJYLBozZoxKS0u1cuVKvfnmm3r55ZdrvH5GRob+8pe/qHPnzpowYYIkKTMzUwcPHqQwvkLumktfffWVFixYoPbt2+vWW2/VmTNn9Oqrr8rf31/Nmzevcs1//etf8vT01JgxY1RRUeEW38A2/N8QtW7nzp0aMmRIpU+Zd9xxh/Pn+++/v9In6qFDhyosLExLlixRbm6ugoODncdsNpv69eunsWPHSpISEhL08MMP6+2339b06dMVHx8vSerSpYsef/xxpaamKjEx8bpij4uL08MPP3zN10DtqS+5dOLECaWlpWn48OHOUeKRI0fqzTff1LffflvtOZGRkZo+ffoVvhK4EvUlXzIyMlRRUaGnn35aAQEBl21bXl6u1157zVlw+Pn56Z///Ke+++47RURE1Pg6+Pr6aubMmfLwYEbktXDHXKqoqNCSJUsUFRWlZ5991tmudevWWrBgQbWFsc1m0+zZsyu9Fg0df1G4an5+fjpy5Ijy8vKqPf7jP6Dz58+rqKhIMTExcjgcOn78eJX2Q4YMqXTt8PBw+fj4qE+fPs794eHh8vPzU3Z29nXHfvLkSWVlZV3XdVA76ksu7dq1S5KqjMZdbnRu2LBhV3x9XJn6ki+Xvmrevn37z87JHDRoUKVRuI4dO0rSZfuzWq26cOGCMjIyrjgmVOaOuXTs2DEVFhZq2LBhldoNHDhQVqu12msOGDDArYpiiRFjXIMJEyZo7ty5mjp1qqKjoxUXF6cBAwaoRYsWkqTc3FwlJSVp+/btKikpqXRuaWlppW0vL68qn4KtVquaN29eZTUJq9Wq4uLi64o9MTFRr732mqZPn642bdqoa9eu6t+/vyIjI6/rurg29SWXcnJyZLFYnHFdEh4eXuM51zPlB9WrL/kSHx+vL7/8UvPnz9cHH3ygzp07q2fPnurdu3eVEd4fjzxK/ymELtffiBEjtGnTJr388ssKCgpSly5dFB8fr65du15xjO7OHXMpJydHktSyZctK7Tw9Pau8t13iju9jFMa4avHx8erYsaO2bt2q3bt365NPPtGKFSv05JNP6uabb9YLL7yg4uJi3XHHHWrVqpV8fHyUl5enefPmVZlrWdPXgK76ejA2NlZ//etftW3bNu3evVtfffWVPv30U02ZMqXSJ34Yoz7n0s9xt1EWI9SXfPH29tasWbO0b98+7dy5U+np6dq4caM6deqk3//+95X6uJb+mjZtqtdee03p6elKT0/Xrl27lJqaqv79++vRRx+97vjdAbl05f27GwpjXJPAwECNGDFCI0aMUGFhoWbMmKHly5crMDBQWVlZmjZtmgYMGOBsX5e+8vP399egQYM0aNAgnT9/Xs8++6yWLVtGYWyS+pBLISEhcjgcOnPmTKVR4u+//97wWNxdfcgX6YcipXPnzurcubMefPBBLV++XB999JH27t1bKzf6enp6qkePHurRo4fsdrveeecdrVmzRnfffXeVVQhQPXfLpZCQEElSVlaWOnXq5NxfUVGh7Oxsvjn9P8wxxlWx2+1VvkZq2rSpAgMDVVFR4fzE+uNP1A6HQ5999pmhcdbk3LlzlbYbN26sFi1ayGazmRSR+6pPuRQXFydJVfquK3ntDupTvlT3VXlUVJSkH4qQ6/XT9zEPDw9nUcN72c9z11yKjo5WQECAvvjii0rnpqamVpku4s4YMcZVKSsr0yOPPKLevXsrMjJSjRs31p49e3T06FE98MADCg8PV4sWLbRo0SLl5eXJarVqy5Yt1z03uLY88cQTio2NVXR0tPz9/XX06FFt2bJFI0aMMDs0t1OfcikqKkp9+/bV6tWrVVpaqvbt22vPnj06c+aM4bG4q/qUL8nJyTpw4IDi4uIUEhKiwsJCrV69Ws2bN1eHDh2u+/rz589XcXGxOnXqpObNmysnJ0erVq1SVFSUWrVqVQu/QcPmrrnk6empcePGacGCBZo1a5bi4+OVnZ2t1NTUGucYuyMKY1wVHx8fjRgxQrt379bWrVtlt9sVFhamyZMna/jw4ZKkGTNmaOHChfr444/l5eWlnj17auTIkXrqqadMjl4aNWqUtm/froyMDNlsNoWEhOjee+/V7bffbnZobqe+5dLUqVMVEBCg9evXa9u2berUqZP++7//W1OnTjU8FndUn/KlR48eys7O1tdff61z586pSZMmio2NVWJiYo13/1+Nfv366csvv9Tq1atVUlKiZs2aqU+fPkpMTGT5tivgzrk0dOhQ2e12rVy5UosXL1ZERIR+97vfKSkpyQXR108WByvPAwAAAMwxBgAAACSmUqCeKigouOxxb2/vWvnKEg0fuYSrQb6gtpBLdRNTKVAv/dzjNAcMGKBp06YZFA3qM3IJV4N8QW0hl+omCmPUSz+3nmRQUJBat25tUDSoz8glXA3yBbWFXKqbKIwBAAAAcfMdAAAAIInCGAAAAJBEYQwAAABIojAGAAAAJFEYAwAAAJIojAEAAABJFMYAAACAJApjAAAAQJL0/wHU7Fak1IfeQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize and compare the result\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict, \n",
    "    compared_estimators=state_action_marginal_estimators_name,\n",
    "    random_state=random_state, \n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize and compare the result\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict, \n",
    "    compared_estimators=[\"dm\", \"pdis\", \"dr\", \"sm_is\", \"sm_dr\", \"sam_is\", \"sam_dr\", \"drl\"],\n",
    "    random_state=random_state, \n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1400x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict, \n",
    "    compared_estimators=[\"dm\", \"snpdis\", \"sndr\", \"sm_snis\", \"sm_sndr\", \"sam_snis\", \"sam_sndr\"],\n",
    "    random_state=random_state, \n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more advanced topics in OPE (e.g., cumulative distribution function estimation) and OPS, please refer to [examples/quickstart/rtb/rtb_synthetic_discrete_advanced.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart/rtb/rtb_synthetic_discrete_advanced.ipynb). \n",
    "\n",
    "For the examples in the continuous action space, please refer to [examples/quickstart/rtb/rtb_synthetic_continuous_zoo.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart/rtb/rtb_synthetic_continuous_zoo.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n",
    "- Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita. \\\n",
    "\"Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation.\", 2021.\n",
    "\n",
    "- Takuma Seno and Michita Imai. \\\n",
    "\"d3rlpy: An Offline Deep Reinforcement Library.\", 2021.\n",
    "\n",
    "- Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, and Scott Niekum. \\\n",
    "\"SOPE: Spectrum of Off-Policy Estimators.\", 2021.\n",
    "\n",
    "- Nathan Kallus and Masatoshi Uehara. \\\n",
    "\"Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes.\", 2020.\n",
    "\n",
    "- Masatoshi Uehara, Jiawei Huang, and Nan Jiang. \\\n",
    "\"Minimax Weight and Q-Function Learning for Off-Policy Evaluation.\", 2020.\n",
    "\n",
    "- Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, and Dale Schuurmans. \\\n",
    "\"Off-Policy Evaluation via the Regularized Lagrangian.\", 2020.\n",
    "\n",
    "- Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. \\\n",
    "\"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.\" 2020.\n",
    "\n",
    "- Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. \\\n",
    "\"Conservative Q-Learning for Offline Reinforcement Learning.\", 2020.\n",
    "\n",
    "- Nathan Kallus and Masatoshi Uehara. \\\n",
    "\"Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning.\", 2019.\n",
    "\n",
    "- Hoang Le, Cameron Voloshin, and Yisong Yue. \\\n",
    "\"Batch Policy Learning under Constraints.\", 2019.\n",
    "\n",
    "- Qiang Liu, Lihong Li, Ziyang Tang, and Dengyong Zhou. \\\n",
    "\"Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation.\", 2018\n",
    "\n",
    "- Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, and Kun Gai. \\\n",
    "\"Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising.\", 2018.\n",
    "\n",
    "- Jun Zhao, Guang Qiu, Ziyu Guan, Wei Zhao, and Xiaofei He. \\\n",
    "\"Deep Reinforcement Learning for Sponsored Search Real-time Bidding.\", 2018.\n",
    "\n",
    "- Josiah P. Hanna, Peter Stone, and Scott Niekum. \\\n",
    "\"Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation.\", 2017.\n",
    "\n",
    "- Nan Jiang and Lihong Li. \\\n",
    "\"Doubly Robust Off-policy Value Evaluation for Reinforcement Learning.\", 2016.\n",
    "\n",
    "- Philip S. Thomas and Emma Brunskill. \\\n",
    "\"Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning.\", 2016.\n",
    "\n",
    "- Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. \\\n",
    "\"OpenAI Gym.\", 2016.\n",
    "\n",
    "- Philip S. Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. \\\n",
    "\"High Confidence Policy Improvement.\", 2015.\n",
    "\n",
    "- Philip S. Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. \\\n",
    "\"High Confidence Off-Policy Evaluation.\", 2015.\n",
    "\n",
    "- Adith Swaminathan and Thorsten Joachims. \\\n",
    "\"The Self-Normalized Estimator for Counterfactual Learning.\", 2015.\n",
    "\n",
    "- Hado van Hasselt, Arthur Guez, and David Silver. \\\n",
    "\"Deep Reinforcement Learning with Double Q-learning.\", 2015.\n",
    "\n",
    "- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. \\\n",
    "\"Doubly Robust Policy Evaluation and Optimization.\", 2014.\n",
    "\n",
    "- Alex Strehl, John Langford, Sham Kakade, and Lihong Li. \\\n",
    "\"Learning from Logged Implicit Exploration Data.\", 2010.\n",
    "\n",
    "- Alina Beygelzimer and John Langford. \\\n",
    "\"The Offset Tree for Learning with Partial Labels.\", 2009.\n",
    "\n",
    "- Doina Precup, Richard S. Sutton, and Satinder P. Singh. \\\n",
    "\"Eligibility Traces for Off-Policy Policy Evaluation.\", 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "70404ee114725fce8ed9e697d67827f8546c678889944e6d695790702cbfe1f5"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}