examples/quickstart_ja/rtb/rtb_synthetic_discrete_zoo_ja.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart: 様々なオフ方策推定量によるオフ方策評価 (離散行動空間)\n",
    "\n",
    "このノートブックでは入札オークション (Real-Time Bidding; RTB)の人工データ上で様々なOPE推定量 (モデルベース，重点サンプリング，周辺化重点サンプリング，double reinforcement learningなど) を紹介します．\n",
    "\n",
    "特に、以下の2つの順番で解説していきます: \n",
    "1. セットアップ, 人工データの生成とオフライン強化学習\n",
    "2. 様々なオフ方策推定量によるオフ方策評価\n",
    "\n",
    "このライブラリは，オンラインとオフラインの方策学習およびモデルベースのオフ方策評価の一部で [d3rlpy](https://github.com/takuseno/d3rlpy)のアルゴリズムを利用しています．\n",
    "また、実装のワークフローは[Open Bandit Pipeline](https://github.com/st-tech/zr-obp)を参考にしています．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCOPE-RL モジュールのインポート\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",
    "# d3rlpy アルゴリズムのインポート\n",
    "from d3rlpy.algos import DiscreteRandomPolicy as RandomPolicy\n",
    "from d3rlpy.preprocessing import MinMaxObservationScaler\n",
    "\n",
    "# その他のライブラリのインポート\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": [
    "# バージョン\n",
    "print(scope_rl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random stateの設定\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ディレクトリ\n",
    "from pathlib import Path\n",
    "Path(\"logs/\").mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# warnings\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. セットアップ，人工データ生成，オフライン強化学習\n",
    "まず初めに，今回用いるRTB環境を紹介します．\n",
    "\n",
    "#### 強化学習でのRTB環境のセットアップ\n",
    "RTBにおいて強化学習エージェントの目的は，予算制約が与えられもとで，一つのエピソード内でのKPI(クリックやコンバージョン)を最大化することです. KPIを最大にするために，エージェントは入札価格関数パラメータ $\\alpha$ を選択します．\n",
    "入札価格関数パラメータ $\\alpha$ を利用することで以下の入札価格を決定します．\\\n",
    "$bid_{t，i} = \\alpha \\cdot r^{\\ast}$，\n",
    "ここで$r^{\\ast}$はKPIの予測値または期待値を表します.\n",
    "\n",
    "この問題を制約付きマルコフ決定過程(CMDP)として定式化します.\n",
    "\n",
    "- `タイムステップ`: 一つのエピソード(一日，一週間)に含まれるタイムステップ(24時間，数日など).\n",
    "- `状態`: それぞれのタイムステップで，環境からフィードバックが返ってきます.\n",
    "  - タイムステップ\n",
    "  - 残予算\n",
    "  - 一つ前のタイムステップでのインプレッション単位の特徴(予算消費率，一つのインプレッションあたりのコスト，オークション落札率，報酬)\n",
    "  - 一つ前のタイムステップでの入札関数パラメータ(強化学習エージェントが選ぶ行動) \n",
    "- `行動`: エージェントはKPIを最大化するために入札価格パラメータ$\\alpha$を選択します.\n",
    "- `報酬`: 一つのタイムステップあたりに得られたクリックまたはコンバージョンの合計数.\n",
    "- `制約`: あらかじめ決められた、一つのエピソードあたりの予算．\n",
    "\n",
    "より詳細に環境の引数を確認したい場合は，次のノートブックを参照してください． [examples/quickstart_ja/rtb/rtb_synthetic_customize_env_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_customize_env_ja.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCOPE-RLで実装されている離散行動空間に対する標準的な環境\n",
    "env = gym.make(\"RTBEnv-discrete-v0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 人工データの生成\n",
    "次にデータ収集方策$\\pi_0$からデータを集めます：\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",
    "ここで $s_t \\in \\mathcal{S}$ は時刻$t$で観測された状態，$a_t$ はデータ収集方策 $\\pi_0$によって選択された行動，$r_t$は状態と行動に応じて観測された報酬を表します．\n",
    "\n",
    "`SyntheticDataset` は以下の引数を持っています:\n",
    "- `env`: 前のセクションで紹介したRTB環境を定義.\n",
    "- `max_episode_steps`: エージェントの行動インデックスと実際の\"離散\"行動のマッピング関数.\n",
    "- `state_keys`: 状態変数の名前.\n",
    "- `max_episode_steps`: 一つのエピソードにおける連続意思決定の数（上限）.\n",
    "\n",
    "より詳細にデータ収集と可視化について知りたい場合は，以下を参照してください．[examples/quickstart_ja/rtb/rtb_synthetic_data_collection_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_data_collection_ja.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# データ収集方策\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",
    "# モデル\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",
    "# 再生バッファの設定\n",
    "buffer = create_fifo_replay_buffer(\n",
    "    limit=10000,\n",
    "    env=env,\n",
    ")\n",
    "\n",
    "# 探索\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": [
    "# 学習の開始\n",
    "# 事前学習したモデルを利用する場合はスキップ\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": [
    "# モデルの保存\n",
    "ddqn.save_model(\"d3rlpy_logs/ddqn.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルのリロード\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": [
    "# データセットクラスの初期化\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": [
    "# データ収集方策によってログデータを集める\n",
    "# ログデータがある存在する場合はスキップ\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": [
    "#### オフライン強化学習\n",
    "この章では実環境と相互作用せずに，ログデータのみから新しい方策を学習する方法を紹介します．\n",
    "オフライン強化学習のアルゴリズムは [d3rlpy](https://github.com/takuseno/d3rlpy)に実装されているものを利用します．\n",
    "\n",
    "オフライン強化学習の手順について詳細に知りたい方は，以下を参照してください[examples/quickstart_ja/rtb/rtb_synthetic_discrete_basic_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_discrete_basic_ja.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d3rlpyのモジュールのインポート\n",
    "from d3rlpy.dataset import MDPDataset\n",
    "from d3rlpy.algos import DiscreteCQLConfig as CQLConfig\n",
    "# d3rlpy用にオフラインデータセットを変形\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": [
    "# Conservative Q-Learning 方策を利用\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": [
    "# モデルの保存\n",
    "cql.save_model(\"d3rlpy_logs/cql_discrete.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルのリロード\n",
    "cql.build_with_env(env)\n",
    "cql.load_model(\"d3rlpy_logs/cql_discrete.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 様々なオフ方策推定量によるオフ方策評価\n",
    "評価対象の方策が学習できたところで，方策の性能指標となる様々な統計量を，オフ方策評価を用いて（オフラインで）推定していきます．\n",
    "\n",
    "#### 推定目標\n",
    "ここでは，方策の期待性能 (*方策価値*) を推定します．\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": [
    "# SCOPE-RLからのopeモジュールのインポート\n",
    "from scope_rl.ope import CreateOPEInput\n",
    "from scope_rl.ope import OffPolicyEvaluation as OPE\n",
    "# 標準的な推定量\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推定量\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推定量\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推定量\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": [
    "# 評価方策を定義\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": [
    "# まずOPEのインプットを準備\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": [
    "# 計算に時間がかかります\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": [
    "# opeによる性能評価\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",
    "# 性能推定結果（辞書）\n",
    "policy_value_df_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\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",
    "# データ収集方策に対する相対方策価値\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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dx2az8f0CdcTPjGfw+wmuxpgy1mUH4+zsbKWmpurUqVNq0aKFioqKFBISoieeeEKxsbEuKPHiCgoKtGfPHg0ePLjW0OqoEydOSJICAwNdURoAAAAakMteru0f//iHevTooQULFujvf/+7/v73v6t169b6+9//7or6LmnDhg2y2WxKTk6usb2iokLZ2dkqKCiw7ysuLpbVaq3W75NPPpGPj4+uuuoqt9YMAACA+sfhO8ZvvfWWbr/9drVo0aLK/pycHN15553y9fWVdOFua9++ffXhhx86XVRGRoZKSkrsYTYzM1OnTp2SJI0aNUoBAQH2vuvXr1dwcLDi4+NrPFd+fr4effRRDRo0SNOmTbOf7+OPP1b//v0VERGh4uJirV+/Xj///LNuu+02p9ZRBgAAQMPmcDAuKCjQQw89pJtvvlkjR46Ul9eFm83x8fFatGiRJk2apODgYGVnZ+uzzz6rNag6YsWKFcrNzbVvb9myRVu2bJEkJScn24PxsWPHdPjwYaWkpNjrcURMTIzatm2rdevWqaioSD4+PoqNjdWjjz6qa6+91um6AQAA0HCZbHWY4b1jxw4tXLhQNptNd999t3r06KHS0lL94x//0KZNm3T+/Hl5eXmpT58+uu+++5rMXN3c3Nwq6xvDNUwmk6KiopSTk8ODCG50w/v7jS4BbrDsjm5Gl9Co8fsJrsaYch+z2eyeVSl69Oih7t2764svvtDcuXPVpUsXTZkyRQ8//LAefPBBnTlzRi1btqzT3VsAAACgPqhzgvXy8tKYMWM0d+5c++oTCxcu1Llz59SqVStCMQAAABqkOqfYiooKlZaWKjAwUL/5zW80a9YsHTp0SA8//LBWrVrF7X8AAAA0SHV6+O5vf/ubdu3aJZvNpsjISP3mN79RfHy8Zs6cqY0bN+q9997TypUrNWXKlMt6+A4AAADwNIfvGL/11lvKzc3Vc889p5dfflmxsbF69dVXde7cOUlSYmKiXn/9dfXu3VsvvfSS/vznP7utaAAAAMDVHA7G3333nUaPHq34+Hi1b99ed9xxh4qLi5WVlWXv4+vrq0mTJum1116TyWRyS8EAAACAOzgcjIODg3XgwAH7duWfa3oZRlhYmB599NHLrw4AAADwEIfnGN92222aO3eu9u/fr+bNm+vw4cMaNWqUQkND3VkfAAAA4BEOB+O+ffvqtdde086dO1VeXq67775b3bqxgDwAAAAahzq94CMiIkLDhg1zVy0AAACAYeoUjGuTn5+v4uJiBQYG1jjnGAAAAKjvHA7GGzduVJcuXRQWFmbft337di1cuFDHjh2z74uNjdWUKVN0xRVXuLZSAAAAwI0cXpWi8sG7Srt27VJqaqpKSkp0ww036N5779WYMWOUm5urWbNm6ciRI+6oFwAAAHALp6dS/Pvf/1ZoaKhSU1PVokUL+/6UlBQ98cQTWrp0qR577DGXFAkAAAC4m8N3jP9XRUWFDh8+rJSUlCqhWJJCQkI0dOhQfffddy4pEAAAAPAEp4Lx+fPnJUmtW7eusT0iIkIlJSXOVwUAAAB4WJ2mUmzbtk2FhYWSpGbNmik/P7/GfgUFBWrevPllFwcAAAB4Sp2C8YYNG7Rhwwb79pYtWzR06NBq/Xbv3q22bdtefnUAAACAhzgcjN94441q+0wmU7V9RUVFat26tXr06HFZhQEAAACe5HAwDg8Pd6hfYGCgpk6d6nRBAAAAgBGcevgOAAAAaGzqvI7xtm3btGHDBh0+fFj5+fkqLy9Xs2bNFB0drauvvlrDhg1TaGioO2oFAAAA3MbhYHzu3Dm9+uqr2rlzZ5X93t7eio6OVl5entLT0/X555/r17/+tZKSklxeLAAAAOAuDgfjtLQ07d69W/fcc48GDBggPz8/7du3T2+//bbi4+N1xx13KCsrS++9957eeOMNhYSEKD4+3p21AwAAAC7j8BzjjRs3asSIERo5cqRatmwpX19f9ejRQ/fdd58+/fRTnT59Wm3bttWTTz6pjh07aunSpe6sGwAAAHAph+8Ynzlzpsa1idu2bSur1aqcnBy1atVKJpNJSUlJSktLc6qgsrIyLV++XAcOHNDBgwdVUlKiqVOnavDgwVX6zZs3T2vWrKl2fHR0tF5//XWHrpWZmamPPvpIWVlZCgwM1JAhQzRhwgR5e3s7VTsAAAAaLoeDcUREhPbs2VPthR579+6VJAUFBdn3eXt7y2azOVVQUVGRlixZorCwMMXGxtrPXxOz2azf/OY3VfYFBAQ4dJ3t27frlVdeUXx8vO655x4dPXpUH3/8sU6fPq3777/fqdoBAADQcDkcjEeMGKEFCxbI19dXiYmJ8vX11f79+7Vs2TJ169ZNkZGR9r5Hjhypsl0XwcHBeuuttxQUFKRDhw7pqaeeqrWvl5eXBg4c6NR1Fi1apJiYGD3zzDP2O8QBAQFKT0/X6NGj1aZNG6fOCwAAgIbJ4WA8cuRI5eXl6bPPPqsyhaFTp0565JFHqvT19fXVqFGjnCrIbDZXuft8KVarVWVlZQ7fKZakrKwsZWVl6d57760ybWL48OFaunSpNm3apAkTJtSlbAAAADRwdVrHePLkyUpJSdEPP/wgi8Wi6OhoxcXFVes3ZcoUV9V3UeXl5br77rt17tw5NW/eXAMGDNDkyZPVrFmzix73448/SpI6duxYZX9ISIhCQ0Pt7b9ksVhksVjs2yaTSf7+/vY/w7Uqv1O+W6Du+LlxL34/wdUYU/VDnV/wERQUpL59+7qjljoJDg7WuHHjFBcXJ5vNph07dmjlypX66aefNGPGjIs+QFdQUCBJNd6ZDgoKsrf/Unp6upYsWWLfjouLU2pqqsOvy4ZznJ2WA0d9Z3QBcIOoqCijS2gS+P0EV2NMGavOwbi+uP3226tsDxgwQFFRUfrwww+1adMmDRgwoNZjy8vLJV2YtvFLvr6+Ki0trfG48ePHKyUlxb5d+a+63NxcVVRU1Pkz4OJMJpMiIyN1/Phxpx/mBJqqnJwco0to1Pj9BFdjTLmPj4+PwzcxG2wwrklKSor9RSQXC8a+vr6SVGVaRKXy8nJ7+y+ZzeYaw7QkBrEb2Ww2vl+gjviZ8Qx+P8HVGFPGcvgFHw2Br6+vWrZsqeLi4ov2Cw4OliQVFhZWayssLLS3AwAAoOloVMH47NmzOnPmjAIDAy/aLzY2VpJ06NChKvvz8/N16tQpezsAAACajgYZjMvLy3X27Nlq+z/++GPZbDb16NHDvq+iokLZ2dlVHqhr166d2rRpo1WrVslqtdr3r1y5UiaTSf3793dr/QAAAKh/6uUc44yMDJWUlNjDbGZmpk6dOiVJGjVqlIqLi/Xkk09qwIABio6OliTt3LlT27dvV48ePdS7d2/7ufLz8/Xoo49q0KBBmjZtmn3/5MmT9fLLL2vWrFlKTEzUzz//rIyMDF133XU1vvoaAAAAjZvTwbisrEz/+te/dMMNN1R7S9wPP/ygVatWaerUqU6de8WKFcrNzbVvb9myRVu2bJEkJScnq3nz5urZs6d27dqlNWvWyGq1KjIyUrfddpvGjh0rL69L3wjv1auXfv/732vJkiVasGCBAgMDNX78eE2cONGpmgEAANCwOR2MLRaL1qxZo4EDB1YLxidOnNCaNWucDsbz5s27ZJ+HHnrIoXNFRERo8eLFNbb17du3XqzJDAAAAOM1yDnGAAAAgKsRjAEAAAA5MZUiLy9PkuxrBZ8+fdq+T5LCwsJcVBoAAADgOXUOxv+7soMk/eUvf6mynZaWdnkVNUB7TpTodOk5o8todEwmk4JLzCooOMNbgIA62pJ1xugSGjV+P8HVGFPu0yrAT0Pc9Uro559/XpJUUlKiP/3pT7rzzjvVoUOHup4GAAAAqFfqHIzj4+MlSWfOXLgbERsba98HAAAANFQ8fAcAAACIYAwAAABIuoxg7O3trfj4eDVv3rxaW6tWrZheAQAAgAbF6TffBQQE2B/E+6Xu3bure/fuThcFAAAAeJrTwRj/x8tSLq/yMqPLaHRMJpNM58rkVV7G0jVu5He+3OgS4Ab8TnIvfj/B1RhT7uNlNjnc12Rz4Ns/d+6c/Pz8nCrmco5tKI4/fIcsh743ugwAAAD8grljV0X+5X2H+jo0x/iBBx7QkiVLVFBQ4HAR+fn5SktL09SpUx0+BgAAADCKQ3eMN27cqI8++kg5OTnq2rWrrr76anXo0EERERFq3ry5bDabSkpKdPLkSR06dEi7d+/WgQMHFBUVpZtvvlmJiYme+CyGWbP9B50uOWt0GY2OyWRSUFCICgvz+c9KbvTy+hyjS4Ab/CEpyugSGjV+P8HVGFPu06q5vwYldHGor0NzjBMTE9W/f39lZmZq9erVSk9PV0VFRc0n9PFR9+7d9dhjj6l3797y8mr8K8JZzb6y+jKIXc1kMsnm10xW32b8knCjc96+RpcAN7D6NjO6hEaN309wNcaU+1jNjv895/DDd15eXurbt6/69u0ri8Wiw4cPKzs7W8XFxZKkFi1aqE2bNurQoYPMZnPdqwYAAAAM5NSqFGazWV27dlXXrl1dXQ8AAABgiMY/zwEAAABwAMEYAAAAEMEYAAAAkEQwBgAAACQRjAEAAABJBGMAAABAkpPLtUlSXl6eli5dqr1796qoqEhPPPGE4uPjVVRUpCVLlmjIkCGKi4tzZa0AAACA2zgVjLOysvTcc8/JZrOpU6dOOn78uKxWqyQpMDBQ33//vc6dO6cHHnigTuctKyvT8uXLdeDAAR08eFAlJSWaOnWqBg8ebO9jtVq1du1abd68WUeOHFFxcbEiIiKUmJiosWPHytf30m83mTFjhvbt21dt/zXXXKPp06fXqWZJuqp1c1ksvD3M1Uwmk6KiwpSTY+EtQEAd9W3b0ugSGjV+P8HVGFPuU5cXzzkVjN977z01b95cs2fPliTdf//9VdoTEhL0zTff1Pm8lXebw8LCFBsbq71791brU15ervnz56tz584aNmyYAgMD9cMPP2jx4sXas2ePnnvuOZlMpkteKzQ0VLfddluVfSEhIXWuGQAAAI2DU8H4u+++04QJExQYGKgzZ85Uaw8LC1N+fn6dzxscHKy33npLQUFBOnTokJ566qnqBfv46MUXX6zy1r2hQ4cqIiJCixcv1u7du9W9e/dLXisgIEADBw6sc40AAABonJx6+M5qtcrPz6/W9qKiIvn41D1zm81mBQUFXbSPj49Pja+i7tu3ryQpOzvb4eudP39eZWVldaoRAAAAjZNTd4w7dOigbdu2acSIEdXazp8/r40bN6pLly6XXVxdFBYWSpJatnRsXt2xY8d05513qqKiQq1atdL111+viRMnOhXoAQAA0PA5lQJvvPFGzZkzR2+//bYGDBgg6UIw3bVrl9LT05Wdna1f/epXLi30UpYtWyZ/f38lJCRcsm/r1q115ZVXKiYmRufOndOmTZu0dOlS5eTk6NFHH631OIvFIovFYt82mUzy9/e3/xmuVfmd8t0CdcfPjXvx+wmuxpiqH5wKxgkJCZo2bZoWLFigVatWSZL++te/SpL8/f01bdo0xcfHu67KS1i6dKl2796t++67T82bN79k/1+uljFw4ED9/e9/11dffaUxY8bUerc7PT1dS5YssW/HxcUpNTVV4eHhl/cBcFGRkZFGl9DIfWd0AXCDqKgoo0toEvj9BFdjTBnL6XkDAwcOVN++fbVr1y77cm2RkZG65ppr7HdRPWHjxo1KS0vTddddp+HDhzt9nrFjx+qrr77S7t27aw3G48ePV0pKin278l91ubm5qqiocPraqJnJZFJkZKSOHz/O0jVAHeXk5BhdQqPG7ye4GmPKfXx8fBy+iXlZE2qbNWtmf+jNCLt27dIbb7yhhISEakvG1VVoaKgkqbi4uNY+ZrO51rXwGMTuY7PZ+H6BOuJnxjP4/QRXY0wZy6lgnJeX51C/sLAwZ07vkAMHDuiVV15Rx44d9dhjj8nb2/uyznfixAlJF15QAgAAgKbHqWA8bdo0h/qlpaU5c/pLysrK0pw5cxQREaEnn3zyom+7y87Olp+fnz2kl5aWVrvza7PZtHTpUkkX3n4HAACApsepYFzTq56tVqtyc3O1du1aBQYG1riUmyMyMjJUUlKigoICSVJmZqZOnTolSRo1apRMJpNmz56t4uJijR07Vtu2batyfGRkZJU5wo8++qji4+M1Y8YMSdKPP/6ouXPnasCAAYqMjFR5ebm2bNmi77//XkOHDlWHDh2cqhsAAAANm1PBePDgwbW23XDDDZo+fbpKS0udKmjFihXKzc21b2/ZskVbtmyRJCUnJ0uSPSh/8MEH1Y4fNGjQRddQDg8PV7du3bRlyxYVFhbKy8tLbdq00f3336+hQ4c6VTMAAAAaPpPNDTO8V6xYoYyMDM2bN8/Vp66XcnNzq6xvDNcwmUyKiopSTk4ODyK40Q3v7ze6BLjBsju6GV1Co8bvJ7gaY8p9zGazw6tSOPVK6Eux2Wz2N9EBAAAADYFL339cWlqq7777TsuXL1dcXJwrTw0AAAC4lVPB+JZbbrloe1hYmO677z6nCgIAAACM4FQwnjBhQrV3eZtMJjVv3lytW7fWNddcc9nrCgMAAACe5FQwnjRpkqvrAAAAAAzllofvAAAAgIbGoTvG8+fPr/OJTSZTjS8CAQAAAOojh4Lx3r1763ziX85BBgAAAOozh4JxU3lRBwAAAJou5hgDAAAAIhgDAAAAki7jzXfbt2/Xp59+qh9//FGlpaU1vtc7LS3tsooDAAAAPMWpO8abNm3SnDlzdPr0aSUmJspms2nAgAEaMGCAfH191b59e02cONHVtQIAAABu49Qd408++USdOnXSiy++qOLiYn355Ze67rrrdNVVV+nkyZOaPn26IiIiXF0rAAAA4DZO3THOysrSgAED5OXlZX/1c0VFhSQpIiJCI0aM0LJly1xXJQAAAOBmTgVjPz8/+fhcuNncvHlz+fj4qLCw0N7eqlUrnTx50iUFAgBQn5SWlio6Olomk0mlpaVGlwPAhZwKxtHR0crKyrJvx8bGau3atTp//rzKy8u1fv16hYWFuaxIAAAAwN2cmmPcp08fffHFF7rzzjtlNpt100036eWXX9aUKVNkMpl07tw5XgcNAE3QirRCo0twu3Pn/u8u8WcfFcjP75yB1XjO2FuCjC4BcDuHg/HWrVuVkJAgLy8vjRs3TuPGjbO39erVSzNmzNDmzZvl5eWlnj176qqrrnJLwQAAAI1JaWmpOnfuLEk6ePCg/P39Da6o6XI4GL/88stq2bKlrr32WiUnJ6tLly5V2q+44gpdccUVLi8QAID6xM8vQG/+ea/RZQBwA4eD8f33368NGzboyy+/1MqVKxUeHq6kpCQlJSWpbdu27qwRAAA0QX/5y1+MLsEjysvL7X+eN2+efH19DazGMx5++GGjS6iRw8F46NChGjp0qPLz87VhwwZt2LBB6enpSk9PV2xsrJKTk5WYmKiQkBB31gsAAAC4RZ0fvgsJCdHYsWM1duxYHTt2TOvXr9eGDRu0aNEivffee4qPj1dycrL69eungIAAd9QMAADQaPj6+urJJ580ugzIyVUpKkVHR2vSpEmaNGmSDh48qA0bNmjjxo1688039a9//UuLFi1yVZ0AAACAWzm1jnFNIiIi1Lp1a4WGhkqqOl8GAAAAqO8u645xWVmZNm/erPXr12vPnj2yWq0KDw/X+PHjlZSU5PQ5ly9frgMHDujgwYMqKSnR1KlTNXjw4Gp9s7Ky9O6772r//v3y8fFRz549dffddyswMNCha2VmZuqjjz5SVlaWAgMDNWTIEE2YMMH+mmsAAAA0HXUOxhUVFdq2bZvWr1+v7du3q7y8XC1atND111+vpKQkdevW7bIKKioq0pIlSxQWFqbY2Fjt3VvzkjinTp3S888/r4CAAN12220qKyvTihUrdPToUb300kv2V1bXZvv27XrllVcUHx+ve+65R0ePHtXHH3+s06dP6/7777+szwAAAICGx+FgvGfPHq1fv16bN29WaWmpzGazevXqpeTkZCUkJLjsLmtwcLDeeustBQUF6dChQ3rqqadq7Jeenq5z584pNTXV/vrpTp06adasWVq9erWGDh160essWrRIMTExeuaZZ+y1BwQEKD09XaNHj1abNm1c8nkAAADQMDgcjF988UWZTCZdeeWV9lUn3PFmFrPZrKCgoEv227x5s3r27GkPxZLUvXt3RUVF6ZtvvrloMM7KylJWVpbuvffeKoF++PDhWrp0qTZt2qQJEyZc1ucAAABAw+JwML7zzjs1YMAABQcHu7Meh+Tn5+v06dPq2LFjtbZOnTpp+/btFz3+xx9/lKRqx4eEhCg0NNTeDgAAgKbD4WCckpLizjrqpKCgQJJqDOnBwcEqLi6WxWKR2Wy+6PE13ZkOCgqyt/+SxWKRxWKxb5tMJvtdc5PJVKfPgEur/E75boG64+cGrsaYgivV1/F0WatSGKVyKbiaHrCrDMPl5eW1BuPK42tq9/X1VWlpaY3Hpaena8mSJfbtuLg4paamKjw8vG4fAHUSGRlpdAmN3HdGFwA3iIqKMujKNd9YQMNn3JhCY1Rfx1ODDMaV7xCvqKio1lZ5R/di7xmvbPvfu7+VysvLaz12/PjxVe6cV/5rJzc3t8ZacHlMJpMiIyN1/Phx2Ww2o8sBGpScnByjS0Ajw5iCK3lyPPn4+Dh8E7NBBuPKKRQ1TXkoKChQixYtar1b/L/HFxYWVnl4r3JfTXOXpQt3mGs7L8HNfWw2G98vUEf8zMDVGFNwpfo6nlz25jtPCgkJUWBgoA4dOlSt7eDBg4qNjb3o8ZXtvzw+Pz9fp06duuTxAAAAaHycCsYbN240/JXP/fr107Zt25SXl2fft3v3buXk5Kh///72fRUVFcrOzq5yd7ldu3Zq06aNVq1aJavVat+/cuVKmUymKscDAACgaXBqKsXcuXPl7++vfv36KTk5WVdddZVLi8rIyFBJSYk9zGZmZurUqVOSpFGjRikgIEDjx4/Xpk2bNHPmTI0ePdr+KumYmBgNGTLEfq78/Hw9+uijGjRokKZNm2bfP3nyZL388suaNWuWEhMT9fPPPysjI0PXXXed2rZt69LPAwAAgPrPqWD8wgsvaN26ddq0aZNWr16tkJAQJSUlKTk5WTExMZdd1IoVK5Sbm2vf3rJli7Zs2SJJSk5OVkBAgMLCwjRjxgwtXLhQH3zwgXx8fJSQkKC77rrrovOLK/Xq1Uu///3vtWTJEi1YsECBgYEaP368Jk6ceNn1AwAAoOEx2S5j9vP58+e1Y8cOrVu3Tlu3blV5ebliYmI0cOBAJSUl1YuXgXhCbm5ujStc4PKYTCZFRUUpJyen3k7SbwxueH+/0SXADZbd0c2Q665IKzTkunC/sbcEefyaf/nLXzx+TXjGww8/7LFrmc1mz6xK4e3trV69eqlXr14qKyvTli1btHr1ar333nv64IMPFB8fr0GDBikxMbHGNYcBAACA+sJlq1IcPXpUBw8e1NGjRyVJ0dHRKi4u1rx58/TQQw9p/37uSgEAAKD+uqzbuMeOHdP69eu1fv16nThxQoGBgUpKStLAgQPVoUMHSReWRHvzzTf19ttv69VXX3VJ0QAAAICrORWMP//8c61bt06HDx+W2WxWr169NGXKFPXo0UNeXlVvQnfs2FFjxozRm2++6ZKCAQAAAHdwKhi/++676tq1q+6//34lJiYqICDgov07duyoCRMmOFUgAAAA4AlOBeO//vWvioiIcLh/u3bt1K5dO2cuBQAAAHiEUw/fhYaGqrS0tNb20tJSnT9/3umiAAAAAE9zKhgvWLBAzz77bK3tzz77rBYuXOh0UQAAAICnORWMd+zYoX79+tXa3r9/f23fvt3pogAAAABPcyoYFxQUKCQkpNb24OBg5efnO10UAAAA4GlOBeMWLVro2LFjtbZnZ2fL39/f6aIAAAAAT3MqGPfo0UOrVq3Sjz/+WK3t8OHDWrVqlRISEi67OAAAAMBTnFqu7ZZbbtGOHTv09NNPq1evXval2H7++Wdt3bpVgYGBuuWWW1xaKAAAAOBOTgXjkJAQzZkzR++//74yMzP17bffSpL8/f2VlJSk22677aJzkAEAAID6xqlgLF14wO7BBx+UzWZTUVGRJCkwMFAmk8llxQEAAACe4nQwrmQymdSqVStX1AIAAAAYxqFgvGTJEknSTTfdJC8vL/v2pUycONH5ygAAAAAPcigYf/TRR5KkG2+8UV5eXvbtSyEYAwAAoKFwKBinpaVddBsAAABo6JxaxxgAAABobAjGAAAAgBycSjFt2rQ6L8NmMpn017/+1amiAAAAAE9zKBjHx8ezPjEAAAAaNYfvGAMAAACNGXOMAQAAAF3Gm++sVqvWrl2rbdu2KS8vT5IUFhamXr16KTk5WV5e7s3c8+bN05o1a2ptf/PNNxUSElJj2+LFi2t8SYnZbNb777/vshoBAADQcDgVjEtLSzV79mwdPHhQ/v7+at26tSRp9+7d2rx5s1auXKnp06crICDApcX+r2HDhunqq6+utv/tt99WeHh4raH4f913331q1qyZfdvdYR4AAAD1l1PB+N///rcOHz6sX/3qV7r++uvl43PhNBUVFfrvf/+rBQsW6MMPP9SvfvUrlxb7v7p06aIuXbpU2bd//36dO3dOSUlJDp2jf//+CgwMdEd5AAAAaGCcukW6ZcsWDR8+XCNGjLCHYkny8fHR8OHDNWzYMG3evNllRTpq/fr1MplMDgdj6cLdb5vN5saqAAAA0BA4dce4uLhY0dHRtba3adNGxcXFThfljIqKCn3zzTfq0qWLIiIiHDrmwQcfVFlZmfz8/NSnTx/dddddCgoKcm+hAAAAqJecCsaRkZHKzMzUiBEjamzPzMy0zzv2lJ07d+rMmTNKTk6+ZN8WLVpo5MiR6tKli3x8fLR//3795z//0aFDh/TSSy/VOjfaYrHIYrHYt00mk/z9/e1/hmtVfqd8t0Dd8XMDV2NMwZXq63hyKhgPHz5c//rXv/TSSy9p9OjRioqKkiQdO3ZMX3zxhXbt2qV7773XpYVeyvr16+Xt7a1rr732kn1Hjx5dZbt///7q1KmT/vKXv2jlypW68cYbazwuPT29ymoWcXFxSk1NVXh4+GXVjouLjIw0uoRG7jujC4AbVP5e9rwCg64LdzNuTKExqq/jyalgPGLECJ0+fVrLli3Tjh07qp7Qx0cTJ07U8OHDXVGfQ8rKypSZmalrrrlGLVu2dOocSUlJWrhwoXbv3l1rMB4/frxSUlLs25X/2snNzVVFRYVT10XtTCaTIiMjdfz4ceaBA3WUk5NjdAloZBhTcCVPjicfHx+Hb2I6vY7xpEmTNHLkSO3atcu+jnF4eLiuvvpqj6/0sGXLFp07d86haRQXExoaetG50WazWWazucY2gpv72Gw2vl+gjviZgasxpuBK9XU81SkYFxYWavXq1Tp58qQCAwPVr1+/Oq0A4S7r169Xs2bN1Lt3b6fPYbPZlJubq7i4OBdWBgAAgIbC4WB88uRJPfXUU1XuqH7yySd68MEHDQ3HRUVF2r17twYMGCA/P79q7Xl5eTp37pzatGlT5Zhf3tVeuXKlioqKdM0117i9ZgAAANQ/DgfjxYsXq6ysTPfcc4+uuuoqHT9+XAsWLNC7776rxMREw94at3HjRp0/f77WcP7GG29o3759Wrx4sX3f1KlTlZiYqJiYGJnNZu3fv18bN25UbGyshg0b5qnSAQAAUI84HIy///57DR06VCNHjpQktW3bVl5eXkpNTVV2drbatWvntiIvZt26dWrVqpW6d+/u8DFJSUn64YcftHnzZpWXlys8PFzjxo3TTTfdVONdZwAAADR+DgfjvLy8avNvO3ToIEk6c+aMa6uqg9mzZ1+0fcaMGdX2/fa3v3VTNQAAAGioHJ7/YLVaq7z+WZK8vb3tbQAAAEBDVqdVKQ4dOlRlubKzZ89Kkvbv36+SkpJq/fv163eZ5QEAAACeUadg/Pnnn+vzzz+vtv+jjz6qsX9aWppzVQEAAAAe5nAwfv75591ZBwAAAGAoh4NxfHy8O+sAAAAADGXM4sMAAABAPUMwBgAAAEQwBgAAACQRjAEAAABJBGMAAABAEsEYAAAAkOTgcm1r1qxx6uSDBg1y6jgAAADA0xwKxvPnz3fq5ARjAAAANBQOBeM33nijynZJSYnmzZungIAAjRo1StHR0ZKk7OxsZWRk6OzZs5o2bZrrqwUAAADcxKFgHB4eXmX7o48+UmBgoJ555hmZTCb7/piYGPXr10+zZ8/WZ599pqlTp7q2WgAAAMBNnHr47ttvv1Xfvn2rhGL7Cb281LdvX3377beXXRwAAADgKU4FY5vNpuzs7Frbs7KynC4IAAAAMIJTwbhPnz768ssv9emnn+rcuXP2/efOndOKFSu0atUq9e7d22VFAgAAAO7m0BzjX7rnnnt08uRJLVq0SB988IGCg4MlSQUFBTp//ry6du2qKVOmuLJOAAAAwK2cCsYBAQGaOXOmvv32W23fvl15eXmSpGuuuUY9e/ZUr169apx/DAAAANRXTgXjSn369FGfPn1cVQsAAABgmMsKxvn5+dq3b5+KiorUr18/hYaGymq1qrS0VAEBAfLy4o3TAAAAaBicCsY2m00LFy5URkaGrFarpAtrGIeGhqqsrEzTpk3TpEmTNGbMGJcWCwAAALiLU7d0ly9frs8//1xjx47VM888U6UtICBAffv21ebNm11SIAAAAOAJTgXjr776SoMGDdLtt9+u2NjYau3t27dXTk7O5dYGAAAAeIxTUylOnTqlLl261Nru5+en0tJSp4tyxN69ezVz5swa22bNmnXR+qQL86Pfeecd7dq1SzabTVdeeaXuvvtutW7d2h3lAgAAoJ5zKhgHBgbq1KlTtbYfPnxYYWFhThdVF6NGjVLHjh2r7IuMjLzoMWVlZZo5c6ZKS0s1fvx4eXt767PPPtOMGTP08ssvq2XLlu4sGQAAAPWQU8G4X79++vLLLzV48GAFBARUadu5c6dWr16tG264wSUFXsoVV1yh/v371+mY//znP8rJydEf//hHderUSZKUkJCg3//+91qxYoVuv/12d5QKAACAesypYDxp0iTt3btXf/jDH9StWzdJ0rJly5SWlqYffvhBcXFxGj9+vEsLvZizZ8/K19dX3t7eDvXftGmTOnbsaA/FktSmTRtdffXV+uabbwjGAAAATZDTb76bPXu2VqxYoU2bNsnX11f79u1TZGSkbr75Zo0bN06+vr6urrVG8+fPV1lZmby8vHTFFVdo8uTJ1aZW/C+r1aqjR49qyJAh1do6duyonTt36uzZs/L393dn2QAAAKhnnH7Bh6+vryZMmKAJEya4sh6H+fj4qF+/fkpISFBgYKCysrK0YsUKPffcc5o1a5bi4uJqPK64uFgWi0VBQUHV2oKDgyVJBQUFNQZji8Uii8Vi3zaZTPZ+vALb9Sq/U75boO74uYGrMabgSvV1PDkVjGfOnKmbbrpJV199dY3te/bs0ccff6znn3/+soq7mK5du6pr16727d69e6t///56/PHH9cEHH2j69Ok1HldeXi5JMpvN1doq91X2+aX09HQtWbLEvh0XF6fU1FSFh4c7/TlwaZd6mBKX6zujC4AbREVFGXTlAoOuC3czbkyhMaqv48mpYLxv3z5df/31tbYXFRVp3759ThflrMjISPXu3VtbtmyR1Wqt8ZXUlVM8/vfOb6XKfbVNAxk/frxSUlLs25X/2snNzVVFRcVl14+qTCaTIiMjdfz4cdlsNqPLARoU1pKHqzGm4EqeHE8+Pj4O38R0eirFxRw/ftywObphYWGqqKhQWVlZtRUzJKlFixYym80qLCys1lZQcOFOR+WUil8ym8013mmWRHBzI5vNxvcL1BE/M3A1xhRcqb6OJ4eD8erVq7VmzRr79tKlS/XVV19V61daWqqffvpJCQkJrqmwjk6cOCGz2axmzZrV2O7l5aWYmBgdOnSoWtvBgwfVunVrHrwDAABoghx+JXR5ebmKiopUVFQk6cISaZXblf87c+aMfHx8NGzYMP32t791W9GS7HX8ryNHjigzM1PXXHONfRpFXl6esrOzq/Tr16+fDh06VCUcHzt2THv27KnzmsgAAABoHBy+Yzx8+HANHz5ckjRt2jTdc8896t27t9sKu5TXXntNvr6+6tq1q31Viq+++kp+fn5V1iF+4403tG/fPi1evNi+b8SIEfrqq680Z84cjR07Vt7e3vr000/VqlUrjR071oiPAwAAAIM5Ncd43rx5rq6jzvr06aP169fr008/1dmzZxUYGKi+ffvq5ptvvuQqBv7+/poxY4beffddffzxx7LZbIqPj9eUKVMUGBjooU8AAACA+uSyH747e/asSktLa5xEHRYWdrmnr9Xo0aM1evToS/abMWNGjftDQ0P12GOPubgqAAAANFROB+OVK1fq008/1YkTJ2rtk5aW5uzpAQAAAI9y+OG7/7Vy5Ur985//VGRkpG699VZJ0pgxY3TjjTcqKChIsbGxeuCBB1xaKAAAAOBOTgXjjIwMXXPNNXr66ac1dOhQSVLPnj1122236bXXXtPZs2d15swZlxYKAAAAuJNTwfjEiRPq1auXJMnb21uS7G9+CwgI0HXXXaeVK1e6qEQAAADA/ZwKxgEBATp//rz9z76+vsrLy7O3+/v71/hmOQAAAKC+cioYt2vXTj/99JN9u0uXLvryyy+Vn5+vvLw8rVq1SlFRUS4rEgAAAHA3p4JxcnKyfv75Z1ksFknSzTffrKysLD3wwAOaNm2ajh07Zn8oDwAAAGgInFqubciQIRoyZIh9u1u3bvrzn/+srVu3ysvLS927d1d0dLTLigQAAADc7bJf8FGpdevWDr1wAwAAAKiPLjsYW61WlZaW1tjWokWLyz09AAAA4BFOBeOKigotW7ZMX3/9tU6dOiWr1VpjP958BwAAgIbCqWD81ltvac2aNerSpYv69OmjgIAAV9cFAAAAeJRTwXjTpk0aOHCgpk2b5up6AAAAAEM4tVybn5+fOnfu7OpaAAAAAMM4FYwHDBigbdu2uboWAAAAwDBOTaWYPHmy5s+frzlz5mjIkCEKDQ2Vl1f1jN2hQ4fLLhAAAADwBKeCscVikc1m0/bt27V9+/Za+7EqBQAAABoKp4Lx3/72N23ZskUDBgxQp06dWJUCAAAADZ5TwXjnzp0aOXKkpkyZ4uJyAAAAAGM49fCdv7+/IiMjXV0LAAAAYBingvH111+vDRs21PrGOwAAAKChcWoqRdu2bZWZmaknn3xSgwYNqnVVin79+l12gQAAAIAnOBWMX3/9dfufFy1aVGs/VqUAAABAQ+FUMH7++eddXQcAAABgKKeCcXx8vKvrAAAAAAzlVDA22sGDB7VmzRrt3btXubm5atGihTp37qxbb71V0dHRFz129erVmj9/fo1tb731loKCgtxQMQAAAOo7h4LxzJkzZTKZNH36dHl7e2vmzJmXPMZkMum555677AJrsmzZMn3//ffq37+/2rdvr8LCQmVkZOjJJ5/U7NmzFRMTc8lzTJo0SREREVX28aISAACApsuhYGyz2aptm0ymOh3jSikpKfrd734nH5//Kz8xMVGPP/64PvnkEz388MOXPEdCQoI6duzothoBAADQsDgUjGfMmHHRbU/r2rVrtX1RUVFq27atsrOzHT7P2bNn5efnV+NScwAAAGhanJpjvG/fPrVt21aBgYE1thcVFSkrK8ujD+nZbDadPn1a7dq1c6j/zJkzVVZWJh8fH11zzTW66667FBUVddFjLBaLLBaLfdtkMsnf39/+Z7hW5XfKdwvUHT83cDXGFFypvo4np4LxzJkz9dBDDykpKanG9j179mju3LkeXcd43bp1ys/P16RJky7az9fXV4MHD9aVV14pf39/HT58WJ999pmeeeYZpaamKiwsrNZj09PTtWTJEvt2XFycUlNTFR4e7rLPgep4/bi7fWd0AXCDS/1D330KDLou3M24MYXGqL6OJ7esSmGxWDw6PSE7O1v//Oc/1aVLFw0ePPiifRMTE5WYmGjf7tu3r3r06KHnn39eS5cu1a9//etajx0/frxSUlLs25X/2snNzVVFRcXlfQhUYzKZFBkZqePHj7t1zjrQGOXk5BhdAhoZxhRcyZPjycfHx+GbmA4H47y8PJ08edK+nZ2drX379lXrV1paqlWrVnnsLmphYaHmzJmjgIAAPfbYY04F8m7duqlTp07avXv3RfuZzWaZzeYa2whu7mOz2fh+gTriZwauxpiCK9XX8eRwMP7666+rTCNYunSpli5dWmNfLy8v3X///Zdf3SWUlpbqj3/8o0pKSvTCCy8oJCTE6XOFhobq2LFjLqwOAAAADYnDwfjaa6+1P9j22muvadSoUerWrVuVPiaTSX5+foqNjXX7izLKy8uVmpqqnJwcPfvss2rbtu1lne/kyZO1PkwIAACAxs/hYNy2bVt7+HzggQcUHx9f7QUZnmK1WvX666/rhx9+0BNPPKEuXbrU2K+goEClpaVq3bq1fc3joqKiagF427ZtOnz4sEaNGuX22gEAAFA/OfXwXW0PuJ04cUIWi+Wy795eysKFC5WZmalevXqpuLhYa9eurdI+cOBASdIHH3ygNWvW6I033rCH+GeeeUaxsbHq2LGjAgIC9OOPP+rrr79WaGioxo8f79a6AQAAUH85FYy/+OILff/993rkkUfs++bPn681a9ZIurCM2VNPPaVWrVq5pMhfOnLkiCRp69at2rp1a7X2ymBck8TERG3btk27du3SuXPnFBwcrOuvv14TJ050+/QPAAAA1F9OBeOvvvpKV155pX17x44dWrNmjYYOHaqYmBh9+OGH+uijj3Tfffe5rND/5eib96ZNm6Zp06ZV2Xfrrbfq1ltvdUNVAAAAaMicCsa5ublq06aNffubb75RRESEfSWKwsLCatMbAAAAgPrMJW/h2LVrl3r06GHfDg8PV2FhoStODQAAAHiEU8E4KipK3377raQL0yjy8/OVkJBgb8/Pz1fz5s1dUyEAAADgAU5NpRg7dqz+8pe/6J577lFZWZnatm2ra665xt6+Z88excbGuqpGAAAAwO2cCsYDBgxQy5YttW3bNjVv3lwjRoyQt7e3JKm4uFgtWrS46MoQAAAAQH3jVDCWpO7du6t79+7V9rdo0UKPP/74ZRUFAAAAeJrDc4wPHjyo4uJih/qePHnSvqYxAAAA0BA4HIynT5+uHTt22LeLi4s1efJk7du3r1rf77//XvPnz3dJgQAAAIAnOD2VwmazyWKxyGq1urIeXML5+8cZXYJH/Wx0AR7k/fZyo0sAAKBJc8k6xgAAAEBDRzAGAAAARDAGAAAAJNVxjvHJkyd1+PBhSVJpaakkKScnRwEBAdX6AQAAAA1JnYJxWlqa0tLSquz7xz/+4dKCAAAAACM4HIwfeOABd9YBAAAAGMrhYDx48GA3lgEAAAAYi4fvAAAAABGMAQAAAEkEYwAAAEASwRgAAACQRDAGAAAAJBGMAQAAAEkEYwAAAEASwRgAAACQVMdXQtcnFotFaWlpWrdunYqLi9W+fXvdeuut6t69+yWPzc/P1zvvvKNdu3bJZrPpyiuv1N13363WrVt7oHIAAADURw32jvG8efP02WefKSkpSffcc4+8vLz00ksvaf/+/Rc9rqysTDNnztR3332n8ePH6+abb9aPP/6oGTNm6MyZMx6qHgAAAPVNgwzGBw8e1MaNG3Xbbbfpzjvv1NChQ/Xcc88pLCxM77333kWP/c9//qOcnBw9+eSTuuGGG5SSkqJnnnlGBQUFWrFihYc+AQAAAOqbBhmMN23aJC8vLw0dOtS+z9fXV9ddd51++OEH5eXlXfTYjh07qlOnTvZ9bdq00dVXX61vvvnGrXUDAACg/mqQwfjHH39UVFSUAgICquyvDLtHjhyp8Tir1aqjR4+qY8eO1do6duyoEydO6OzZsy6vFwAAAPVfg3z4rrCwUMHBwdX2V+4rKCio8bji4mJZLBYFBQVd9Fh/f/8aj7dYLLJYLPZtk8kkf39/+fh47mv06tjVY9eCZ3mbzYZct2tEC0OuC/cyGzSewiKaGXJduJ8RYyo6Otrj14RneHI81SWnNchgXF5eXuMXWrmvvLy81uP+t19djpWk9PR0LVmyxL49YMAA/e53v6sxpLvNX9733LXQJLx3d7jRJaARmXAH4wmu8/DDDxtdApqYBjmVwtfXt8qd20qV+3x9fWs97n/71eVYSRo/frzeeecd+//uv//+Gs8F1zh79qyefPJJprfAJRhPcCXGE1yNMVU/NMg7xkFBQcrPz6+2v3IKRW13cFu0aCGz2azCwsI6HytduKts1H+ebIpsNpt+/PFH2Ww2o0tBI8B4gisxnuBqjKn6oUHeMY6NjVVOTo5KS0ur7D9w4IC9vSZeXl6KiYnRoUOHqrUdPHhQrVu3rnV+MQAAABq3BhmM+/fvL6vVqlWrVtn3WSwWrV69Wp07d1ZYWJgkKS8vT9nZ2VWO7devnw4dOlQlHB87dkx79uxR//79PfMBAAAAUO80yKkUnTt3Vv/+/fXvf/9bRUVFioyM1Jo1a5Sbm6vf/va39n5vvPGG9u3bp8WLF9v3jRgxQl999ZXmzJmjsWPHytvbW59++qlatWqlsWPHGvFxUAuz2ayJEycyfQUuwXiCKzGe4GqMqfrBZGugk1nKy8uVlpamdevWqaSkRDExMbrlllvUo0cPe58ZM2ZUC8aSdOrUKb377rvauXOnbDab4uPjNWXKFEVGRnr4UwAAAKC+aLDBGAAAAHClBjnHGAAAAHA1gjEAAAAggjEakUmTJlWbTw64ysmTJzVp0iStXr3a6FLQiM2YMUMzZswwugw0YtOmTdO8efOMLqPeIhgDAAAAaqDLtQE1ee+99+Tt7W10GQDgtGeeecboEoAmjTvGaDR8fX0JxjBcWVmZ0SWgAfPx8ZGPD/esYKym/HuMnz64xNmzZ5WWlqZvv/1WBQUFCggIUPv27XXHHXeoQ4cOmjFjhs6cOaOHH35Y//rXv3To0CEFBwfrjjvuUP/+/bVv3z699957+umnnxQWFqZ7771X3bt3r1MNkyZN0sSJEzVp0iSHakL9VB/GUklJid555x1t2bJFJpNJvXv3VkpKSrV+8+bN06ZNm/TKK69owYIF+u6773TVVVfpD3/4g6u+DlxCfRgvGzZs0PLly5WTkyOTyaSwsDBdf/31Gj16tCRp9erVmj9/vl544QVt3rxZa9euVXl5ubp3767f/OY3CgwMtJ+rcn7x/84z/uKLL/Tll1/q5MmTMpvNat26tVJSUpSUlHTZ3x/+T2MbSzabTUuXLtWXX36p4uJide7cWb/61a+qXbPynDNmzNDGjRu1adMmnT9/XgsWLLi8L7SB4o4xXOLtt9/WypUr1a9fP913330aO3asfH19q7ySu7i4WHPmzFHnzp01efJkmc1mvf7669q4caNef/11JSQk6I477tC5c+f05z//WWfPnnV7Tah/jB5LNptNL7/8stauXavk5GTdeuutys/Pr/VhFavVqtmzZyswMFB33nknr5b3MKPHy65duzR37ly1aNFCd9xxh26//XZdeeWV2r9/f7W+CxYs0E8//aSbb75Zw4YN09atW/XPf/7zoudftWqVFixYoLZt22rKlCm6+eabFRsbqwMHDjj+JcEhjW0spaWlKS0tTe3bt9fkyZMVERGhWbNm1Xo3+B//+IeysrI0ceJE3XDDDQ7X3dhwxxgusW3bNl1//fW666677Pt++YNVUFCghx9+2H6Xo3v37nrkkUc0d+5czZo1S507d5YktWnTRrNnz9bmzZs1ePBgt9aE+sfosZSZmanvvvtOkydP1rhx4yRJw4cP18yZM2vsb7FYdO211+r222+v60eFCxg9XrZt2yZ/f39Nnz5dXl4Xv9fUokULPfPMMzKZTJIu/CPsiy++UGlpqQICAmo9f7t27fTYY485VA+c15jGUlFRkZYvX66ePXvqySeftPf797//rfT09FrP+dxzz13y2o1d0/70cJnmzZvr4MGDys/Pr7VPs2bNNGDAAPt2dHS0mjdvrrZt29p/mUiy//nEiRNurwn1j9Fjafv27fL29tbw4cPt+7y8vDRy5Mhaj/nfvvAso8dLQECAzp07p127dl2y79ChQ+0BRZKuuOIKWa1W5ebm1npM8+bNderUKR08eNDhmuCcxjSWdu3apYqKCo0cObJKvzFjxtR6zuuvv77Jh2KJO8ZwkTvuuEPz5s3TAw88oA4dOighIUGDBg1S69at7X1CQ0Or/IBKF34RhIaGVtsnXZjn6e6aUP8YPZZyc3MVFBSkZs2aVdkfHR1dY39vb2+FhIQ4fH64ltHjZcSIEfrmm2/0xz/+USEhIerevbsSExPVo0ePan3DwsKqbDdv3vyS17vhhhu0e/duPf3004qMjFT37t2VlJSkbt26OVwjHNOYxlJeXp4kKSoqqkq/wMBAe99fioiIcLjWxoxgDJdITEzUFVdcoS1btmjnzp1asWKFli1bpscff1wJCQmSVOu/RGvbb7PZ3F4T6p/6OJYuxsfHh7ssBjJ6vLRq1UqvvPKKduzYoR07dmj79u1avXq1Bg4cqAcffPCyr9e2bVu9/vrr2rZtm3bs2KHNmzdr5cqVVR40hms09rF0Kb6+vk4f25jw2xwuExwcrBEjRugPf/iD3njjDbVs2VJLly6lJtSZkf+/hYeHq7CwsNoDKseOHfPI9VF3Rv+c+/j4qHfv3rrvvvv017/+VUOHDtXatWt1/Phxl5y/WbNmSkxM1NSpU/W3v/1NPXv21NKlS1VeXu6S8+P/NJaxVHlHOScnp8r+oqKiy/6vsY0dwRiXzWq1qrS0tMq+Vq1aKTg4WBUVFdQEh9WH/98SEhJ0/vx5rVy5skpdGRkZHrk+HFcfxsuZM2eqbHt5eal9+/aSLjyY6erz+/j4qG3btrLZbDp//vxlnx8XNLax1L17d3l7eysjI6PKXeTPPvvs8gtt5JhKgct29uxZ/fa3v1X//v3Vvn17NWvWTLt379ahQ4eqPN3b1GvCpdWH/9969eqlrl276v3339fJkyfVtm1bbdmypdpfmjBefRgvb775poqLi3XVVVcpNDRUubm5ysjIUGxsrNq0aXPZ5581a5aCgoLUtWtXBQUFKSsrS//5z3/Us2dP+fv7u+ATQGp8YykwMFBjx47VJ598ojlz5ighIUFHjhzR9u3b1bJlSzd9gsaBYIzL5ufnpxEjRmjnzp3asmWLrFarIiMjdd999xn2tH59rAmXVh/+f/Py8tKTTz6pd955R+vWrbO/4OOuu+7ixR31TH0YL8nJyfrqq6+0cuVKlZSUKCgoSNdee60mTZrkkrnnw4YN07p16/TZZ5+prKxMISEhGjVqlG666SYXVI9KjXEs3XrrrfL19dWXX36pvXv3qnPnznrmmWc0Z84cN1TfeJhs7nwqBQAAAGggmGMMAAAAiKkUqOesVquKioou2qdZs2bV1pwFfomxhLpgvMBVGEsNC8EY9VpeXl619Rt/ifU84QjGEuqC8QJXYSw1LMwxRr1WXl6u/fv3X7RP69ateZsdLomxhLpgvMBVGEsNC8EYAAAAEA/fAQAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgDQaMTGxspkMunIkSMOHzN48GCZTCatXr3abXUBQENBMAYAAABEMAYAAAAkEYwBAAAASQRjAGhQ9u3bp5tvvllhYWHy9/fXVVddpT/96U86f/58rcf8/PPP+tWvfqWoqCg1a9ZMnTt31vTp03X27NmLXis/P1+PPPKI2rdvLz8/P8XExOjBBx9Ufn6+pkyZIpPJpHfeeafKMf+7/8cff9Sdd96pyMhI+fn5qWPHjnrmmWd07tw5V3wVAOByPkYXAABwzPr16zVy5EiVlJSoQ4cOGjZsmPLy8vT0009r06ZNNR6zf/9+DRo0SCdPnlRUVJTGjRunkpISvfbaa/r6669rvdaJEyeUnJysAwcOKDg4WCkpKbJarXr//feVkZGhK6+88qK17tixQ7/73e8UHBysQYMGKT8/Xxs2bNDs2bO1d+9epaenX9Z3AQDuQDAGgAagrKxMt99+u0pKSvTII4/oT3/6k7y9vSVJu3bt0vXXX6+8vLxqx9111106efKkJk2apHfffVfNmjWTJB09elTXXXedDh06VOP1HnzwQR04cEDJyclasWKFWrVqJenCXeTRo0dr+fLlF6137ty5mj59umbOnGmvc8+ePerfv78++eQTffPNN7r22mud/j4AwB2YSgEADcDHH3+sn3/+We3atdPLL79sD5uS1L17d02fPr3aMRs2bNC3336r5s2ba/78+fZQLEkxMTH605/+VOO1fv75Zy1dulQmk0lvvvmmPRRLUkhIiN58881L1turVy+9+OKLVeq86qqrdOedd0qSVq1adekPDQAeRjAGgAagcp3hSZMmyWw2V2u/++67az1m5MiRCg0NrdZ+ww03VAm9ldauXSur1aqePXsqPj6+WnuPHj3UvXv3i9abkpIik8lUbf8VV1whScrOzr7o8QBgBIIxADQAWVlZkqS4uLga24ODg6uF3EsdYzKZFBsbW+drXapNunBHuiaBgYGSLkwNAYD6hmAMAHA5Ly/+egHQ8PCbCwAagDZt2khSra97Liws1OnTp+t0jCT99NNPdb7WpdoAoKEiGANAAzBo0CBJ0uLFi2WxWKq1L1y4sNZjMjIylJ+fX619+fLlKiwsrLZ/4MCBMplM2rZtm/bv31+tfefOndq1a1ddPwIA1HsEYwBoACZOnKg2bdro6NGjeuqpp2S1Wu1te/bs0axZs6odk5ycrJ49e6q4uFjTpk2r8mKNn3/+WY8//niN14qJidH48eNltVr1wAMPqKioyN5WUFCgqVOnymazufDTAUD9QDAGgAbA399f77//vgICAvTqq6+qS5cuuu222zR8+HD17NlTycnJat++fbXjFi1apPDwcH344Yfq0KGDbrnlFo0dO1bdunVTaGhorWsJz5s3Tx07dtTq1asVFxenCRMm6KabblKHDh104sQJjRs3zt0fGQA8jmAMAA3EoEGDtHnzZt10000qKChQenq6srKy9MILLygtLa3GY+Lj45WZmakpU6bo/Pnz+uSTT7Rv3z499NBD+uqrr+Tr61vjcZGRkdq8ebMeeughBQQE6NNPP9W3336rW2+9VZs2bVJwcLA7PyoAGMJk47+HAQDqaMqUKXr33Xe1YMECTZkyxehyAMAluGMMAAAAiGAMAAAASCIYAwAAAJKYYwwAAABI4o4xAAAAIIlgDAAAAEgiGAMAAACSCMYAAACAJMnH6AIag4KCAlVUVBhdRqMUHh6u3Nxco8tAI8F4gisxnuBqjCn38PHxcfhtnQRjF6ioqJDFYjG6jEbHZDJJuvD9sngKLhfjCa7EeIKrMabqB6ZSAAAAACIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgSfIxuoDGYM+JEp0uPWd0GY2OyWRScIlZBQVnZLPZjC4HDRzjCa7EeIKrMabcp1WAn4aEhzvUlzvGAAAAgAjGAAAAgCQDplKcPHlSmZmZ2r9/v7Kzs1VUVCSTyaSWLVuqTZs26tatm3r37q2IiAhPlwYAAIAmzGPBeOvWrVqxYoX2798vm82myMhIRUREqF27dpKkkpIS/fTTT9q8ebPeffdddevWTePGjVOvXr08VSIAAACaMI8E4+nTp+vIkSPq06ePHn30UV199dUKCAiosW9paal27dqlTZs26bXXXlP79u01e/ZsT5QJAACAJswjwfjKK6/UE088oaCgoEv2DQgIUP/+/dW/f38VFhbq888/d3+BAAAAaPI8Eoxvv/12p44LCgqq07FlZWVavny5Dhw4oIMHD6qkpERTp07V4MGDaz2moqJCTzzxhLKzszV58mSNGzfOqVoBAADQsDWqVSmKioq0ZMkSZWdnKzY21qFjMjIylJeX597CAAAAUO957OG7w4cP1/mYDh061Kl/cHCw3nrrLQUFBenQoUN66qmnLtr/9OnTWrJkiW644QYtXry4zvUBAACg8fBYML5USK1JWlpanfqbzWaH5jFXev/99xUdHa2BAwdeVjD2spTLq7zM6eNRM5PJJNO5MnmVl/EWIFw2xhNcifEEV2NMuY+X2eRwX48F4wceeMBTl3LIwYMHtWbNGr344osOH2OxWGSxWOzbJpNJ/v7+6rpgpiyHvndHmQAAALgM5o5dpYT3HerrsWB8sQfgPM1ms+lf//qXEhMT1aVLF508edKh49LT07VkyRL7dlxcnFJTU91VJgAAADzIo2++y8/PlySFhIRctI/JZFJwcLDb6li9erWOHj2qxx57rE7HjR8/XikpKfZtk+nCrfkffjVDp0vOurJESDLJpFZBwTpdWCCb+M9KuDyMJ7gS4wmuxphyn1bN/RXpYF+PPnz39NNPa/LkyVXC5S9t3LhR77//vlJTUxUTE+PyOkpLS/XBBx9o3LhxCgsLq9OxZrNZZrO52v7zPmadN1tdVSL+fyaTSTa/Zjrv68d8K1w2xhNcifEEV2NMuc95n+rZrTYeW64tIyNDUVFRGjNmzEX7jRkzRtHR0W57sceKFStUUVGhxMREnTx5UidPnrTfyS4pKdHJkydVUVHhlmsDAACg/vLYHeO9e/dq0KBB9ukHtTGZTOrfv7/WrFnjljry8vJUUlJS4zSK9PR0paen6+WXX3Z4HWQAAAA0Dh4LxoWFhYqIiHCob1hYmAoKCtxSx6hRo9SnT58q+4qKivTWW29p8ODB6t27t8N1AgAAoPHwWDD28/NTcXGxQ31LSkrk6+vr1HUyMjJUUlJiD9aZmZk6deqUpAuhuEOHDtVeHFK5KkXbtm3Vt29fp64LAACAhs1jwbh9+/baunXrRR+8q7R161a1b9/eqeusWLFCubm59u0tW7Zoy5YtkqTk5GQFBAQ4dV4AAAA0bh4LxgMHDtSbb76pL774QqNGjaq1X0ZGhvbt26ff/va3Tl1n3rx5dT4mIiKCV0IDAAA0cR4LxoMGDdI333yjd955R9u3b1dycrJiYmLk7++vs2fP6ujRo1q3bp127typ7t2716sXggAAAKDx81gw9vLy0uOPP65FixZp1apV2rlzZ419hg0bprvuuuuSq1cAAAAAruTRN9/5+vrq3nvv1fjx47V9+3ZlZ2ertLRUAQEBio6OVkJCgkJDQz1ZEgAAACBJMtl4vcply83NlcViMbqMRsdkMikqKko5OTm8BQiXjfEEV2I8wdUYU+5jNpsVHh7uUF+PvfkOAAAAqM8IxgAAAIAIxgAAAIAkgjEAAAAgiWAMAAAASPLwcm21sVqtOnDggPLz8xUUFKQuXbrI29vb6LIAAADQhBgejLOzs5WamqpTp06pRYsWKioqUkhIiJ544gnFxsYaXR4AAACaCMOnUvzjH/9Qjx49tGDBAv3973/X3//+d7Vu3Vp///vfjS4NAAAATYjHgvFbb72l4uLiavtzcnI0ePBg+fr6SpICAwPVt29f5eTkeKo0AAAAwHPBuKCgQA899JA+//xzWa1W+/74+HgtWrRI3333nY4fP66tW7fqs88+U3x8vKdKAwAAADz7SugdO3Zo4cKFstlsuvvuu9WjRw+VlpbqH//4hzZt2qTz58/Ly8tLffr00X333afAwEBPlXZZeCW0e/B6TLgS4wmuxHiCqzGm3Kcur4T2aDCWLqxA8cUXX2jJkiXq0qWLpkyZoqioKFmtVp05c0YtW7aUl5fhU5/rhGDsHvySgCsxnuBKjCe4GmPKfeoSjD2eQL28vDRmzBjNnTvXvvrEwoULde7cObVq1arBhWIAAAA0Dh5PoRUVFSotLVVgYKB+85vfaNasWTp06JAefvhhrVq1in8lAQAAwBAeW8e4oKBAf/vb37Rr1y7ZbDZFRkbqN7/5jeLj4zVz5kxt3LhR7733nlauXKkpU6bw8B0AAAA8yqPLteXm5uq5557Tyy+/rNjYWL366qs6d+6cJCkxMVGvv/66evfurZdeekl//vOfPVUaAAAA4Llg/N1332n06NGKj49X+/btdccdd6i4uFhZWVn2Pr6+vpo0aZJee+01mUwmT5UGAAAAeC4YBwcH68CBA/btyj8HBQVV6xsWFqZHH33UU6UBAAAAnptjfNttt2nu3Lnav3+/mjdvrsOHD2vUqFEKDQ31VAkAAABArTwWjPv27avXXntNO3fuVHl5ue6++25169bNU5cHAAAALspjwViSIiIiNGzYME9eEgAAAHCIR4NxbfLz81VcXKzAwMAa5xwDAAAA7uaxYLxx40Z16dJFYWFh9n3bt2/XwoULdezYMfu+2NhYTZkyRVdccYWnSgMAAAA8F4znzp2rhx56SElJSZKkXbt2KTU1VYGBgbrhhhsUFham48ePa/Xq1Zo1a5Zmz56t2NjYOl2jrKxMy5cv14EDB3Tw4EGVlJRo6tSpGjx4sL2P1WrV2rVrtXnzZh05ckTFxcWKiIhQYmKixo4dK19fXxd+agAAADQUhk2l+Pe//63Q0FClpqaqRYsW9v0pKSl64okntHTpUj322GN1OmdRUZGWLFmisLAwxcbGau/evdX6lJeXa/78+ercubOGDRumwMBA/fDDD1q8eLH27Nmj5557jjWUAQAAmiBDgnFFRYUOHz6sKVOmVAnFkhQSEqKhQ4fqv//9b53PGxwcrLfeektBQUE6dOiQnnrqqWp9fHx89OKLL6pr1672fUOHDlVERIQWL16s3bt3q3v37nX/UAAAAGjQPPaCj/91/vx5SVLr1q1rbI+IiFBJSUmdz2s2my/58J6Pj0+VUFypb9++kqTs7Ow6XxcAAAANn0fvGG/btk2FhYWSpGbNmik/P7/GfgUFBWrevLkHK5O9rpYtW9bax2KxyGKx2LdNJpP8/f3tf4ZrVX6nfLdwBcYTXInxBFdjTNUPHg3GGzZs0IYNG+zbW7Zs0dChQ6v12717t9q2bevJ0rRs2TL5+/srISGh1j7p6elasmSJfTsuLk6pqakKDw/3RIlNVmRkpNEloBFhPMGVGE9wNcaUsTwWjN94441q+2r6V1FRUZFat26tHj16eKCqC5YuXardu3frvvvuu+id6vHjxyslJcW+XVl/bm6uKioq3F5nU2MymRQZGanjx4/LZrMZXU6jNe6974wuwe3Ol5/V9mcu/OwmzPpU3r7+Blfkfssns+SlO/H7Ca7GmHIfHx8fh29ieiwYO1pQYGCgpk6d6uZq/s/GjRuVlpam6667TsOHD79oX7PZLLPZXGMbg9h9bDYb3y8ui7evv3q//JXRZXgUPzOewe8nuBpjyliGPHxXX+zatUtvvPGGEhISdP/99xtdDgAAAAzk8eXatm3bpg0bNujw4cPKz89XeXm5mjVrpujoaF199dUaNmyYQkND3V7HgQMH9Morr6hjx4567LHH5O3t7fZrAgAAoP7yWDA+d+6cXn31Ve3cubPKfm9vb0VHRysvL0/p6en6/PPP9etf/9r+hjx3yMrK0pw5cxQREaEnn3ySt90BAADAc8E4LS1Nu3fv1j333KMBAwbIz89P+/bt09tvv634+HjdcccdysrK0nvvvac33nhDISEhio+Pr/N1MjIyVFJSooKCAklSZmamTp06JUkaNWqUTCaTZs+ereLiYo0dO1bbtm2rcnxkZKS6dOly+R8YAAAADYrHgvHGjRs1YsQIjRw50r6vR48euu+++/Tyyy8rJSVFbdu21ZNPPqlnnnlGS5cudSoYr1ixQrm5ufbtLVu2aMuWLZKk5ORkSbIH5Q8++KDa8YMGDSIYAwAANEEeC8ZnzpypcW3itm3bymq1KicnR61atZLJZFJSUpLS0tKcus68efMu2Wfx4sVOnRsAAACNl8dWpYiIiNCePXuq7d+7d68kVXmVs7e3N0uVAAAAwKM8dsd4xIgRWrBggXx9fZWYmChfX1/t379fy5YtU7du3aq86eXIkSO8+QUAAAAe5bFgPHLkSOXl5emzzz7TmjVr7Ps7deqkRx55pEpfX19fjRo1ylOlAQAAAJ5dx3jy5MlKSUnRDz/8IIvFoujoaMXFxVXrN2XKFE+WBQAAAHj+BR9BQUHq27evpy8LAAAAXFSTfiU0AAAAUIlgDAAAAIhgDAAAAEgiGAMAUCelpaWKjo6WyWRSaWmp0eUAcCGCMQAAACCCMQAAACDJwGBcVlam+fPnKzs7u1rbDz/8oPnz5xtQFQAAAJoqj69jXMlisWjNmjUaOHCg2rRpU6XtxIkTWrNmjaZOnWpQdQAAZ6xIKzS6BLc7d+7/5hV/9lGB/PzOGViN54y9JcjoEgC3YyoFAAAAIIIxAAAAIMmAqRR5eXmSpOLiYknS6dOn7fskKSwszNMlAQDgMD+/AL35571GlwHADTwejKdNm1Zl+y9/+UuV7bS0NE+WAwAAAEgyIBg///zzkqSSkhL96U9/0p133qkOHTp4ugwAAACgCo8H4/j4eEnSmTNnJEmxsbH2fQAAAIBRePgOAAAAEMEYAAAAkGRgMPb29lZ8fLyaN29era1Vq1ZMrwAAAIBHGfbmu4CAAPuDeL/UvXt3de/e3cMVAQAAoCljKgUAAAAgDwXjc+ecf4/85RwLAAAAOMojwfiBBx7QkiVLVFBQ4PAx+fn5SktL09SpU91YGQAAAHCBR+YY33ffffroo4+0ZMkSde3aVVdffbU6dOigiIgINW/eXDabTSUlJTp58qQOHTqk3bt368CBA4qKitK9997riRIBAADQxHkkGCcmJqp///7KzMzU6tWrlZ6eroqKipoL8vFR9+7d9dhjj6l3797y8nL8pnZZWZmWL1+uAwcO6ODBgyopKdHUqVM1ePDgan2zsrL07rvvav/+/fLx8VHPnj119913KzAw0NmPCQAAgAbMY6tSeHl5qW/fvurbt68sFosOHz6s7OxsFRcXS5JatGihNm3aqEOHDjKbzU5do6ioSEuWLFFYWJhiY2O1d+/eGvudOnVKzz//vAICAnTbbbeprKxMK1as0NGjR/XSSy/Jx8ewxToAAABgEEMSoNlsVteuXdW1a1eXnjc4OFhvvfWWgoKCdOjQIT311FM19ktPT9e5c+eUmpqqsLAwSVKnTp00a9YsrV69WkOHDnVpXQAAAKj/GtVybWazWUFBQZfst3nzZvXs2dMeiqULaydHRUXpm2++cWOFAAAAqK+a3JyB/Px8nT59Wh07dqzW1qlTJ23fvr3WYy0WiywWi33bZDLJ39/f/me4VuV3yncL1B0/N3A1xpR78Xde/dDkgnHlknHBwcHV2oKDg1VcXCyLxVLjPOf09HQtWbLEvh0XF6fU1FSFh4e7r2AoMjLS6BIaue+MLgBuEBUVZdCVHV+WEw2LcWOqaeHvPGM1uWBcXl4uSTU+YFcZhsvLy2sMxuPHj1dKSop9u/Jfdbm5ubWusgHnmUwmRUZG6vjx47LZbEaXAzQoOTk5RpeARoYx5V78nec+Pj4+Dt/EbHLB2NfXV5JqDLKV0yQq+/yS2WyudcUMBrH72Gw2vl+gjviZgasxpjyDv/OM1agevnNE5RSKmt7CV1BQoBYtWji9XBwAAAAaLsPuGOfl5Wnp0qXau3evioqK9MQTTyg+Pt6+FvGQIUMUFxfn8uuGhIQoMDBQhw4dqtZ28OBBxcbGuvyaAAAAqP8MuWOclZWlP/zhD/rmm28UERGh0tJSWa1WSVJgYKC+//57ZWRkuO36/fr107Zt25SXl2fft3v3buXk5Kh///5uuy4AAADqL0PuGL/33ntq3ry5Zs+eLUm6//77q7QnJCQ4vZ5wRkaGSkpK7FMlMjMzderUKUnSqFGjFBAQoPHjx2vTpk2aOXOmRo8ebX+VdExMjIYMGXIZnwwAAAANlSHB+LvvvtOECRMUGBioM2fOVGsPCwtTfn6+U+desWKFcnNz7dtbtmzRli1bJEnJyckKCAhQWFiYZsyYoYULF+qDDz6Qj4+PEhISdNdddzG/GAAAoIkyJBhbrVb5+fnV2l5UVFTjcmqOmDdvnkP92rVrp+nTpzt1DQAAADQ+hswx7tChg7Zt21Zj2/nz57Vx40Z16dLFw1UBAACgKTMkGN94443asWOH3n77bf3888+SpMLCQu3atUuzZs1Sdna2brjhBiNKAwAAQBNlyFSKhIQETZs2TQsWLNCqVaskSX/9618lSf7+/po2bZri4+ONKA0AAABNlGHrGA8cOFB9+/bVrl27dPz4cVmtVkVGRuqaa66Rv7+/UWUBAACgiTL0ldDNmjVT3759jSwBAAAAkGRQMP7fF2tcTFhYmJsrAQAAAC4wJBhPmzbNoX5paWlurgQAAAC4wJBg/MADD1TbZ7ValZubq7Vr1yowMFAjRowwoDIAAAA0VYYE48GDB9fadsMNN2j69OkqLS31XEEAAABo8gxZx/himjVrpsGDB+uzzz4zuhQAAAA0IfUuGEuSzWZTYWGh0WUAAACgCTF0ubZfKi0t1Xfffafly5crLi7O6HIAAADQhBgSjG+55ZaLtoeFhem+++7zUDUAAACAQcF4woQJMplMVfaZTCY1b95crVu31jXXXCNvb28jSgMAAEATZUgwnjRpkhGXBQAAAGpVLx++AwAAADzNI3eM58+fX+djTCZTjS8CAQAAANzBI8F47969dT7ml3OQAQAAAHfySDCeN2+eJy4DAAAAOI05xgAAAIAIxgAAAIAkA998t337dn366af68ccfVVpaKpvNVq1PWlqaAZUBAACgKTLkjvGmTZs0Z84cnT59WomJibLZbBowYIAGDBggX19ftW/fXhMnTjSiNAAAADRRhtwx/uSTT9SpUye9+OKLKi4u1pdffqnrrrtOV111lU6ePKnp06crIiLCiNIAAADQRBlyxzgrK0sDBgyQl5eX/dXPFRUVkqSIiAiNGDFCy5YtM6I0AAAANFGGBGM/Pz/5+Fy4Wd28eXP5+PiosLDQ3t6qVSudPHnSiNIAAADQRBkylSI6OlpZWVn27djYWK1du1bJyck6f/681q9fr7CwMLfWkJOTo7S0NO3fv1/FxcUKCwtTUlKSxo4dKz8/P7deGwAAAPWPIXeM+/Tpo2+//VYWi0WSdNNNN2nv3r2aMmWK7rvvPu3fv1833nij266fl5enp59+WgcOHNDIkSM1ZcoUdenSRYsXL9bcuXPddl0AAADUXx67Y7x161YlJCTIy8tL48aN07hx4+xtvXr10owZM7R582Z5eXmpZ8+euuqqq9xWy9q1a1VSUqIXXnhB7dq1kyQNHTpUVqtVa9euVXFxsVq0aOG26wMAAKD+8Vgwfvnll9WyZUtde+21Sk5OVpcuXaq0X3HFFbriiis8UsvZs2clXZjL/L+Cg4NlMpns858BAADQdHhsKsX999+vdu3a6csvv9Szzz6rBx98UB9++GGVucaecuWVV0qS3nzzTR05ckR5eXnauHGjVq5cqVGjRqlZs2YerwkAAADG8tit0aFDh2ro0KHKz8/Xhg0btGHDBqWnpys9PV2xsbFKTk5WYmKiQkJC3F5Ljx49dMsttyg9PV2ZmZn2/TfddJNuvfXWWo+zWCz2edGSZDKZ5O/vb/8zXKvyO+W7BeqOnxu4GmPKvfg7r34w2Wp6F7OHHDt2TOvXr9eGDRt0/PhxmUwmxcfHKzk5Wf369VNAQIDbrr127VqtW7dO/fr1U8uWLbVt2zatXr1a99xzj0aOHFnjMYsXL9aSJUvs23FxcUpNTXVbjYAn9Hnlv0aXADf49onrDLnu31/bZ8h14X6/eTTe6BIAtzM0GP+vgwcPasOGDdq4caMKCwvl6+urRYsWueVaGzZs0N/+9jfNnTtXoaGh9v3z58/XN998o/nz56tly5bVjqvtjnFubq79BSVwHZPJpMjISB0/flz1ZJg2SuPe+87oEuAGyyd75pmNatf9sMCQ68L9xt0abHQJjRp/57mPj4+PwsPDHevr5locFhERodatWys0NFSFhYUqLy9327VWrlypuLi4KqFYknr37q3Vq1frxx9/VPfu3asdZzabZTabazwng9h9bDYb3y9QR/zMwNUYU57B33nGMjQYl5WVafPmzVq/fr327Nkjq9Wq8PBwjR8/XklJSW67bmFhYY3LsVXe9bVarW67NgAAAOonjwfjiooKbdu2TevXr9f27dtVXl6uFi1a6Prrr1dSUpK6devm9hqioqK0a9cuHTt2TNHR0fb9GzZskMlkUkxMjNtrAAAAQP3isWC8Z88erV+/Xps3b1ZpaanMZrN69eql5ORkJSQkyNvb21OlaNy4cdqxY4eef/55jRgxwv7w3fbt23Xdddd5ZGUMAAAA1C8eC8YvvviiTCaTrrzySvuqE5VLnXlafHy8Zs2apY8++kgrV67UmTNnFBERoVtvvVU33HCDITUBAICmqbS0VJ07d5Z0YTECo/IRPBiM77zzTg0YMEDBwfXjqdZOnTrpqaeeMroMAAAA1BMeC8YpKSmeuhQAAGgE/vKXvxhdgkf870pc8+bNk6+vr4HVeMbDDz9sdAk18tgroQEAAID6jGAMAAAAqB694AMAAKAp8vX11ZNPPml0GRB3jAEAAABJBgXjjRs3uvWVzwAAAEBdGTKVYu7cufL391e/fv2UnJysq666yogyAAAAADtDgvELL7ygdevWadOmTVq9erVCQkKUlJSk5ORkXscMAAAAQxgSjLt27aquXbvqnnvu0Y4dO7Ru3TplZGRo+fLliomJ0cCBA5WUlFRvXgYCAACAxs/QVSm8vb3Vq1cv9erVS2VlZdqyZYtWr16t9957Tx988IHi4+M1aNAgJSYmyseHBTQAAADgPvVmVYqjR4/q4MGDOnr0qCQpOjpaxcXFmjdvnh566CHt37/f4AoBAADQmBl6G/bYsWNav3691q9frxMnTigwMFBJSUkaOHCgOnToIEk6dOiQ3nzzTb399tt69dVXjSwXAAAAjZghwfjzzz/XunXrdPjwYZnNZvXq1UtTpkxRjx495OVV9SZ2x44dNWbMGL355ptGlAoAAIAmwpBg/O6776pr1666//77lZiYqICAgIv279ixoyZMmOCh6gAAANAUGRKM//rXvyoiIsLh/u3atVO7du3cWBEAAACaOkMevgsNDVVpaWmt7aWlpTp//rwHKwIAAEBTZ0gwXrBggZ599tla25999lktXLjQgxUBAACgqTMkGO/YsUP9+vWrtb1///7avn27BysCAABAU2dIMC4oKFBISEit7cHBwcrPz/dgRQAAAGjqDAnGLVq00LFjx2ptz87Olr+/vwcrAgAAQFNnSDDu0aOHVq1apR9//LFa2+HDh7Vq1SolJCQYUBkAAACaKkOWa7vlllu0Y8cOPf300+rVq5d9Kbaff/5ZW7duVWBgoG655RYjSgMAAEATZUgwDgkJ0Zw5c/T+++8rMzNT3377rSTJ399fSUlJuu222y46BxkAAABwNUOCsXThAbsHH3xQNptNRUVFkqTAwECZTCajSgIAAEATZlgwrmQymdSqVSujywAAAEAT55FgvGTJEknSTTfdJC8vL/v2pUycONGdZQEAAAB2HgnGH330kSTpxhtvlJeXl337UgjGAAAA8BSPBOO0tLSLbhvl8OHD+uijj7R//35ZLBa1bt1a119/vUaPHm10aQAAAPAww+cYG2Xnzp1KTU1VXFycJkyYoGbNmunEiRO8cQ8AAKCJapLBuLS0VG+88YZ69uypxx57TF5ehrznBAAAAPWIR4LxtGnT6rwMm8lk0l//+le31LN+/XqdPn1at956q7y8vFRWViZfX18CMgAAQBPmkWAcHx9fr9Yn3r17t/z9/ZWfn69XXnlFOTk58vPz08CBA3X33XfL19fX6BIBAADgYR67Y1yfHD9+XFarVa+88oqGDBmi22+/XXv37lVGRoZKSkr0yCOP1HicxWKRxWKxb5tMJvn7+9v/DNeq/E75boG64+cGrsaYgivV1/HUJOcYl5WV6dy5cxo2bJh+9atfSZL69euniooKrVq1SrfccouioqKqHZeenl5lDea4uDilpqYqPDzcY7U3RZGRkUaX0Mh9Z3QBcIOafod5RoFB14W7GTem0BjV1/FkWDC2Wq1au3attm3bpry8PElSWFiYevXqpeTkZLfO962cKjFgwIAq+5OSkrRq1Sr98MMPNf4fNn78eKWkpNi3K/+1k5ubq4qKCrfV21SZTCZFRkbq+PHjstlsRpcDNCg5OTlGl4BGhjEFV/LkePLx8XH4JqYhwbi0tFSzZ8/WwYMH5e/vr9atW0u6MPd38+bNWrlypaZPn66AgAC3XD84OFg///yzgoKCquyvfDV1SUlJjceZzWaZzeYa2whu7mOz2fh+gTriZwauxpiCK9XX8WRIMP73v/+tw4cP61e/+pWuv/56+fhcKKOiokL//e9/tWDBAn344Yf2aQ6u1qFDB+3atUv5+fmKjo627y8ouPCfAAMDA91yXQAAANRfhqxPtmXLFg0fPlwjRoywh2Lpwq3u4cOHa9iwYdq8ebPbrn/ttddKkv773/9W2f/VV1/J29tb8fHxbrs2AAAA6idD7hgXFxdXuVP7S23atFFxcbHbrh8XF6chQ4bo66+/1vnz5xUfH6+9e/dq06ZNuvHGGxUSEuK2awMAAKB+MiQYR0ZGKjMzUyNGjKixPTMz0z7v2F3uv/9+hYWFafXq1dqyZYvCw8N19913a8yYMW69LgAAAOonQ4Lx8OHD9a9//UsvvfSSRo8ebV8B4tixY/riiy+0a9cu3XvvvW6twcfHRzfffLNuvvlmt14HAAAADYMhwXjEiBE6ffq0li1bph07dlQtyMdHEydO1PDhw40oDQAAAE2UYesYT5o0SSNHjtSuXbvs6xiHh4fr6quvZlUIAAAAeJxHg3FhYaFWr16tkydPKjAwUP369VNSUpInSwAAAABq5LFgfPLkST311FNVVpv45JNP9OCDDxKOAQAAYDiPBePFixerrKxM99xzj6666iodP35cCxYs0LvvvqvExES3vgIaAAAAuBSPBePvv/9eQ4cO1ciRIyVJbdu2lZeXl1JTU5Wdna127dp5qhQAAACgGo/dps3Ly1NcXFyVfR06dJAknTlzxlNlAAAAADXyWDC2Wq1VXv8sSd7e3vY2AAAAwEgeXZXi0KFDMpvN9u2zZ89Kkvbv36+SkpJq/fv16+ex2gAAANC0eTQYf/755/r888+r7f/oo49q7J+WlubukgAAAABJHgzGzz//vKcuBQAAANSZx4JxfHy8py4FAAAA1BmLBwMAAAAiGAMAAACSCMYAAACAJIIxAAAAIIlgDAAAAEgiGAMAAACSPLRc25o1a5w6btCgQS6uBAAAAKiZR4Lx/PnznTqOYAwAAABP8UgwfuONN6psl5SUaN68eQoICNCoUaMUHR0tScrOzlZGRobOnj2radOmeaI0AAAAQJKHgnF4eHiV7Y8++kiBgYF65plnZDKZ7PtjYmLUr18/zZ49W5999pmmTp3qifIAAAAAYx6++/bbb9W3b98qobiSl5eX+vbtq2+//daAygAAANBUGRKMbTabsrOza23PysryYDUAAACAQcG4T58++vLLL/Xpp5/q3Llz9v3nzp3TihUrtGrVKvXu3duI0gAAANBEeWSO8S/dc889OnnypBYtWqQPPvhAwcHBkqSCggKdP39eXbt21ZQpU4woDQAAAE2UIcE4ICBAM2fO1Lfffqvt27crLy9PknTNNdeoZ8+e6tWrV43zjwEAAAB3MSQYV+rTp4/69OljZAl2S5cu1Ycffqh27drp1VdfNbocAAAAeJihwTg/P1/79u1TUVGR+vXrp9DQUFmtVpWWliogIEBeXp6ZAn3q1Cmlp6fLz8/PI9cDAABA/WNIMLbZbFq4cKEyMjJktVolXVjDODQ0VGVlZZo2bZomTZqkMWPGeKSeRYsWqXPnzrJarTpz5oxHrgkAAID6xZBVKZYvX67PP/9cY8eO1TPPPFOlLSAgQH379tXmzZs9Usu+ffu0adMmHvYDAABo4gwJxl999ZUGDRqk22+/XbGxsdXa27dvr5ycHLfXYbVatWDBAl133XWKiYlx+/UAAABQfxkyleLUqVPq0qVLre1+fn4qLS11ex0rV65Ubm6unn32WYf6WywWWSwW+7bJZJK/v7/9z3Ctyu+U7xaoO35u4GqMKbhSfR1PhgTjwMBAnTp1qtb2w4cPKywszK01nDlzRosXL9aECRMUGBjo0DHp6elasmSJfTsuLk6pqakKDw93V5mQFBkZaXQJjdx3RhcAN4iKijLoygUGXRfuZtyYQmNUX8eTIcG4X79++vLLLzV48GAFBARUadu5c6dWr16tG264wa01fPjhh2rRooVGjRrl8DHjx49XSkqKfbvyXzu5ubmqqKhweY1NnclkUmRkpI4fPy6bzWZ0OUCD4onpaGhaGFNwJU+OJx8fH4dvYhoSjCdNmqS9e/fqD3/4g7p16yZJWrZsmdLS0vTDDz8oLi5O48ePd9v1c3JytGrVKk2ZMkX5+fn2/RaLRRUVFTp58qQCAgLUokWLKseZzWaZzeYaz0lwcx+bzcb3C9QRPzNwNcYUXKm+jifD3nw3e/ZsrVixQps2bZKvr6/27dunyMhI3XzzzRo3bpx8fX3ddv38/HzZbDYtWLBACxYsqNb+4IMPavTo0axUAQAA0IQY9oIPX19fTZgwQRMmTPD4tdu1a6fHH3+82v60tDSdPXtWU6ZMYV4rAABAE2NIMJ45c6ZuuukmXX311TW279mzRx9//LGef/55t1w/MDBQffv2rbb/888/l6Qa2wAAANC4GbKO8b59+3T69Ola24uKirRv3z4PVgQAAICmzrCpFBdz/Phx+/rAnjRjxgyPXxMAAAD1g8eC8erVq7VmzRr79tKlS/XVV19V61daWqqffvpJCQkJnioNAAAA8FwwLi8vV1FRkX377Nmz1d56YjKZ5Ofnp2HDhmnixImeKg0AAADwXDAePny4hg8fLkmaNm2a7rnnHvXu3dtTlwcAAAAuypA5xvPmzTPisgAAAECtDH/47uzZsyotLa3xDShhYWEGVAQAAICmyLBgvHLlSn366ac6ceJErX3S0tI8WBEAAACaMkPWMV65cqX++c9/KjIyUrfeeqskacyYMbrxxhsVFBSk2NhYPfDAA0aUBgAAgCbKkGCckZGha665Rk8//bSGDh0qSerZs6duu+02vfbaazp79qzOnDljRGkAAABoogwJxidOnFCvXr0kSd7e3pKkiooKSVJAQICuu+46rVy50ojSAAAA0EQZEowDAgJ0/vx5+599fX2Vl5dnb/f391dhYaERpQEAAKCJMiQYt2vXTj/99JN9u0uXLvryyy+Vn5+vvLw8rVq1SlFRUUaUBgAAgCbKkGCcnJysn3/+WRaLRZJ08803KysrSw888ICmTZumY8eO2R/KAwAAADzBkOXahgwZoiFDhti3u3Xrpj//+c/aunWrvLy81L17d0VHRxtRGgAAAJoow1/wUal169YaPXq00WUAAACgiTI8GFutVpWWltbY1qJFCw9XAwAAgKbKkGBcUVGhZcuW6euvv9apU6dktVpr7Meb7wAAAOAphgTjt956S2vWrFGXLl3Up08fBQQEGFEGAAAAYGdIMN60aZMGDhyoadOmGXF5AAAAoBpDlmvz8/NT586djbg0AAAAUCNDgvGAAQO0bds2Iy4NAAAA1MiQqRSTJ0/W/PnzNWfOHA0ZMkShoaHy8qqe0Tt06GBAdQAAAGiKDAnGFotFNptN27dv1/bt22vtx6oUAAAA8BRDgvHf/vY3bdmyRQMGDFCnTp1YlQIAAACGMyQY79y5UyNHjtSUKVOMuDwAAABQjSEP3/n7+ysyMtKISwMAAAA1MiQYX3/99dqwYUOtb7wDAAAAPM2QqRRt27ZVZmamnnzySQ0aNKjWVSn69etnQHUAAABoigwJxq+//rr9z4sWLaq1n7tWpTh48KDWrFmjvXv3Kjc3Vy1atFDnzp116623Kjo62i3XBAAAQP1mSDB+/vnnjbis3bJly/T999+rf//+at++vQoLC5WRkaEnn3xSs2fPVkxMjKH1AQAAwPMMCcbx8fFGXNYuJSVFv/vd7+Tj838fPzExUY8//rg++eQTPfzwwwZWBwAAACMYEoyN1rVr12r7oqKi1LZtW2VnZxtQEQAAAIzmkWA8c+ZMmUwmTZ8+Xd7e3po5c+YljzGZTHruuec8UN0FNptNp0+fVrt27Tx2TQAAANQfHgnGNput2rbJZKrTMe62bt065efna9KkSbX2sVgsslgs9m2TySR/f3/7n+Fald8p3y1Qd/zcwNUYU3Cl+jqePBKMZ8yYcdFto2VnZ+uf//ynunTposGDB9faLz09XUuWLLFvx8XFKTU1VeHh4R6osuniZTDu9p3RBcANoqKiDLpygUHXhbsZN6bQGNXX8WTIHON9+/apbdu2CgwMrLG9qKhIWVlZHnlIr7CwUHPmzFFAQIAee+yxGtdTrjR+/HilpKTYtyv/tZObm6uKigq319rUmEwmRUZG6vjx4x7/LwhAQ5eTk2N0CWhkGFNwJU+OJx8fH4dvYhoSjGfOnKmHHnpISUlJNbbv2bNHc+fOdds6xpVKS0v1xz/+USUlJXrhhRcUEhJy0f5ms1lms7nGNoKb+9hsNr5foI74mYGrMabgSvV1PBnySuhLsVgsF71z6wrl5eVKTU1VTk6O/t//+39q27atW68HAACA+s1jd4zz8vJ08uRJ+3Z2drb27dtXrV9paalWrVrl1nm7VqtVr7/+un744Qc98cQT6tKli9uuBQAAgIbBY8H466+/rvLg2tKlS7V06dIa+3p5een+++93Wy0LFy5UZmamevXqpeLiYq1du7ZK+8CBA912bQAAANRPHgvG1157rX2N4Ndee02jRo1St27dqvQxmUzy8/NTbGysgoKC3FbLkSNHJElbt27V1v+vvTuPqqre/z/+OkwyiYCIqCig4ZSRqDnikOJUallKllbatV+ZXW+3VbnKbmnpTevbLSutW91ssLqUS9OyvGjfnHMGMYcSp5JQQEBkEA6e8/vDr+dGgGKeszdyno+1WrH3/uzzeXH4AG+2n/3ZO3dWOU5hDAAA4H4MK4wjIyMd83inTJmijh07Kjw83KjuK6lry8UBAADAfKasSlHTWsEnT56U1WrlRjgAAAAYzpTC+JtvvtGPP/6oRx55xLFv4cKFWrdunaTzD8548skn1ahRIzPiAQAAwA2Zslzbt99+W6noTUtL07p165SYmKj77rtPJ0+e1Oeff25GNAAAALgpU64Y5+TkqEWLFo7t77//XuHh4Y6VKAoKCqqsFAEAAAC4Up14wEd6ero6d+7s2G7SpIkKCgpMywMAAAD3Y0ph3KxZM23fvl3S+WkUeXl5io+PdxzPy8tTQECAGdEAAADgpkyZSjFy5Ei99tprmjRpks6ePavIyEhdf/31juM//PCDoqOjzYhW5527f5TZEQxRUnFO7VPSJEkHhnSWv5enuYEM4PnOCrMjAADg1kwpjPv06aOGDRtq165dCggI0NChQ+Xpeb7wKSoqUmBgIA/ZcHP+Xp76+aauZscAAABuxJTCWJLi4uIUFxdXZX9gYKAee+wxExIBAADAnRk2xzgjI0NFRUW1apudne1Y0xgAAAAwgmGF8YwZM5SWlubYLioq0oQJE7Rv374qbX/88UctXLjQqGgAAACAecu12e12Wa1W2Ww2syIAAAAADnViHWMAAADAbBTGAAAAgCiMAQAAAEkGL9eWnZ2tw4cPS5JKSkokSVlZWfL396/SDgAAADCSoYVxcnKykpOTK+179913jYwAAAAAVMuwwnjKlClGdQUAAABcNsMK4wEDBhjVFQAAAHDZuPkOAAAAEIUxAAAAIInCGAAAAJBEYQwAAABIojAGAAAAJFEYAwAAAJIojAEAAABJFMYAAACAJIMfCV2XWK1WJScna8OGDSoqKlJUVJTGjRunuLg4s6MBAADABG57xXjBggVauXKlEhISNGnSJHl4eOiFF17QgQMHzI4GAAAAE7hlYZyRkaHNmzfrzjvv1N13363ExEQ988wzCgsL0+LFi82OBwAAABO4ZWG8ZcsWeXh4KDEx0bHPx8dHAwcO1E8//aTc3FwT0wEAAMAMblkYHzlyRM2aNZO/v3+l/ddcc40k6ejRoyakAgAAgJnc8ua7goIChYSEVNl/YV9+fn6151mtVlmtVse2xWKRn5+fvLyMexs92rQzrC8Yy9Pb25R+24UHmtIvXMvbpPEUFu5rSr9wPTPGVPPmzQ3vE8YwcjxdTp3mloVxeXl5tV+QC/vKy8urPW/ZsmVasmSJY7tPnz76y1/+Um2R7TKvfWxcX3ALi+9tYnYE1CO3j2c8wXmmTZtmdgS4GbecSuHj41Ppyu8FF/b5+PhUe97o0aP1/vvvO/67//77q30dOEdpaammT5+u0tJSs6OgHmA8wZkYT3A2xlTd4JZXjIODg5WXl1dl/4UpFDVdAfb29jbtnyfdkd1u15EjR2S3282OgnqA8QRnYjzB2RhTdYNbXjGOjo5WVlaWSkpKKu0/ePCg4zgAAADci1sWxj179pTNZtOaNWsc+6xWq9auXavY2FiFhYWZmA4AAABmcMupFLGxserZs6c+/fRTFRYWKiIiQuvWrVNOTo4efPBBs+Ph/3h7e2vMmDFMX4FTMJ7gTIwnOBtjqm6w2N10Mkt5ebmSk5O1YcMGFRcXq1WrVrrjjjvUuXNns6MBAADABG5bGAMAAAC/5ZZzjAEAAIDfozAGAAAARGGMeiQpKUmfffaZ2TFQT2VnZyspKUlr1641OwrqsZkzZ2rmzJlmx0A9NnXqVC1YsMDsGHUWhTEAAAAgN12uDfXT4sWL5enpaXYMAPjDnn76abMjAG6NK8aoN3x8fCiMYbqzZ8+aHQFXMS8vL3l5cc0K5nLnn2N898EpSktLlZycrO3btys/P1/+/v6KiorS+PHj1bp1a82cOVNnzpzRtGnT9N577+nQoUMKCQnR+PHj1bNnT+3bt0+LFy/WsWPHFBYWpj/96U+Ki4u7rAxJSUkaM2aMkpKSapUJdVNdGEvFxcV6//33tW3bNlksFnXr1k0jRoyo0m7BggXasmWLXnrpJS1atEj79+9Xp06d9MQTTzjr7cAl1IXxsmnTJq1YsUJZWVmyWCwKCwvToEGDdNNNN0mS1q5dq4ULF+q5557T1q1btX79epWXlysuLk4PPPCAgoKCHK91YX7xb+cZf/PNN1q9erWys7Pl7e2tpk2basSIEUpISLji9w//Vd/Gkt1u19KlS7V69WoVFRUpNjZW9913X5U+L7zmzJkztXnzZm3ZskXnzp3TokWLruwNvUpxxRhO8c477yglJUU9evTQ5MmTNXLkSPn4+CgzM9PRpqioSHPnzlVsbKwmTJggb29vvfrqq9q8ebNeffVVxcfHa/z48SorK9M//vEPlZaWujwT6h6zx5LdbteLL76o9evXq2/fvho3bpzy8vJqvFnFZrNpzpw5CgoK0t13362ePXte8XuA2jN7vKSnp2v+/PkKDAzU+PHjddddd+naa6/VgQMHqrRdtGiRjh07prFjx2rw4MHauXOn/vWvf1309desWaNFixYpMjJSEydO1NixYxUdHa2DBw/W/k1CrdS3sZScnKzk5GRFRUVpwoQJCg8P1+zZs2u8Gvzuu+/q+PHjGjNmjG655ZZa565vuGIMp9i1a5cGDRqke+65x7Hv999Y+fn5mjZtmuMqR1xcnB555BHNnz9fs2fPVmxsrCSpRYsWmjNnjrZu3aoBAwa4NBPqHrPH0o4dO7R//35NmDBBo0aNkiQNGTJEs2bNqra91WpVr169dNddd13upwonMHu87Nq1S35+fpoxY4Y8PC5+rSkwMFBPP/20LBaLpPN/hH3zzTcqKSmRv79/ja/fsmVLPfroo7XKgz+uPo2lwsJCrVixQl26dNH06dMd7T799FMtW7asxtd85plnLtl3fefenz2cJiAgQBkZGcrLy6uxja+vr/r06ePYbt68uQICAhQZGen4YSLJ8fHJkyddngl1j9ljKTU1VZ6enhoyZIhjn4eHh4YNG1bjOb9tC2OZPV78/f1VVlam9PT0S7ZNTEx0FCiS1KFDB9lsNuXk5NR4TkBAgE6dOqWMjIxaZ8IfU5/GUnp6uioqKjRs2LBK7W6++eYaX3PQoEFuXxRLXDGGk4wfP14LFizQlClT1Lp1a8XHx6t///5q2rSpo03jxo0rfYNK538QNG7cuMo+6fw8T1dnQt1j9ljKyclRcHCwfH19K+1v3rx5te09PT0VGhpa69eHc5k9XoYOHarvv/9ef//73xUaGqq4uDj17t1bnTt3rtI2LCys0nZAQMAl+7vlllu0Z88ePfXUU4qIiFBcXJwSEhLUvn37WmdE7dSnsZSbmytJatasWaV2QUFBjra/Fx4eXuus9RmFMZyid+/e6tChg7Zt26bdu3fryy+/1PLly/XYY48pPj5ekmr8S7Sm/Xa73eWZUPfUxbF0MV5eXlxlMZHZ46VRo0Z66aWXlJaWprS0NKWmpmrt2rXq16+fHn744SvuLzIyUq+++qp27dqltLQ0bd26VSkpKZVuNIZz1PexdCk+Pj5/+Nz6hJ/mcJqQkBANHTpUTzzxhN544w01bNhQS5cuJRMum5lftyZNmqigoKDKDSq//vqrIf3j8pn9fe7l5aVu3bpp8uTJev3115WYmKj169frxIkTTnl9X19f9e7dWw899JDefPNNdenSRUuXLlV5eblTXh//VV/G0oUryllZWZX2FxYWXvG/xtZ3FMa4YjabTSUlJZX2NWrUSCEhIaqoqCATaq0ufN3i4+N17tw5paSkVMq1atUqQ/pH7dWF8XLmzJlK2x4eHoqKipJ0/sZMZ7++l5eXIiMjZbfbde7cuSt+fZxX38ZSXFycPD09tWrVqkpXkVeuXHnlQes5plLgipWWlurBBx9Uz549FRUVJV9fX+3Zs0eHDh2qdHevu2fCpdWFr1vXrl3Vrl07ffzxx8rOzlZkZKS2bdtW5ZcmzFcXxstbb72loqIiderUSY0bN1ZOTo5WrVql6OhotWjR4opff/bs2QoODla7du0UHBys48eP6z//+Y+6dOkiPz8/J3wGkOrfWAoKCtLIkSP1xRdfaO7cuYqPj9fRo0eVmpqqhg0buugzqB8ojHHFGjRooKFDh2r37t3atm2bbDabIiIiNHnyZNPu1q+LmXBpdeHr5uHhoenTp+v999/Xhg0bHA/4uOeee3hwRx1TF8ZL37599e233yolJUXFxcUKDg5Wr169lJSU5JS554MHD9aGDRu0cuVKnT17VqGhoRo+fLhuu+02J6THBfVxLI0bN04+Pj5avXq19u7dq9jYWD399NOaO3euC9LXHxa7K+9KAQAAAK4SzDEGAAAAxFQK1HE2m02FhYUXbePr61tlzVng9xhLuByMFzgLY+nqQmGMOi03N7fK+o2/x3qeqA3GEi4H4wXOwli6ujDHGHVaeXm5Dhw4cNE2TZs25Wl2uCTGEi4H4wXOwli6ulAYAwAAAOLmOwAAAEAShTEAAAAgicIYAAAAkERhDAAAAEiiMAYAAAAkURgDAAAAkiiMAQAAAEkUxgAAAIAkCmMAAABAEoUxAOASoqOjZbFYdPToUbOjAIBLURgDAAAAojAGAAAAJFEYAwAAAJIojAHAUBaLRRaLRZK0aNEi9erVS40aNao0h/fYsWOaN2+eBg4cqFatWqlBgwYKDg5WQkKC/vnPf8pms1V53aNHj8pisSg6Olp2u11vv/22unbtqoCAADVq1EhDhgzR999/X2Ouffv2aezYsQoLC5Ofn586deqk//mf/9G5c+cu+vnk5eXpqaee0rXXXit/f381bNhQXbt21YsvvqjS0tIq7deuXSuLxaIBAwaorKxMs2bNUtu2beXr66tWrVpp+vTpOnv2rCTp9OnTeuyxx9S6dWv5+voqOjpaM2fOVEVFRW3fbgC4LBa73W43OwQAuIsLRfHDDz+shQsXqnfv3mrZsqUOHz6s5ORkRUVFafbs2frb3/6mmJgYtW7dWk2aNFFWVpa+//57lZeX67bbbtOSJUscryWdL4xjYmIUFRWlAQMG6JNPPlHfvn0VFhamtLQ0/fTTT2rQoIHWrVunHj16VMq0ceNGDRs2TMXFxWrdurW6d++u3NxcrVu3TqNGjdKOHTt07NgxHTlyRNHR0Y7zDh8+rIEDB+rYsWNq0qSJ+vXrJ6vVqu+++05nzpxRly5dtGbNGoWEhDjOWbt2rW688Ub16tVLPj4+Sk1N1YABA2Sz2bRhwwadPn1aI0aM0AcffKDevXsrLy9P/fr105kzZ7R+/XqdPXtWDz74oN58803XfqEAuCc7AMAwkuyS7EFBQfbvv/++2jbbtm2z79mzp8r+zMxM+/XXX2+XZP/ss88qHTty5IjjtaOiouw//vij41hFRYX9vvvus0uyDxkypNJ5paWl9pYtW9ol2R955BF7RUWF49ju3bvtYWFhjtc9cuRIpXN79Ohhl2QfNWqUvaioyLE/Ozvb3qVLF7sk+1133VXpnO+++87xet27d7fn5uY6jh09etQeEhJil2S/7rrr7CNHjrQXFxc7jm/fvt3u5eVl9/DwsB87dqza9w4ArgSFMQAY6EJR+Nxzz/2h8//zn//YJdnHjh1baf9vC+MVK1ZUOS8rK8suyd6gQQN7eXm5Y//ixYvtkuwtW7astP+CV155pdrCeMOGDXZJdn9/f/uJEyeqnLdjxw67JLuHh4f9l19+cey/UBhbLJZqi/9p06bZJdkDAwPtJ0+erHJ85MiRdkn2Dz74oPo3CACugJcx16UBAL81ZsyYix4vKytTSkqKtm/fruzsbJWVlclut+vMmTOSpB9//LHa87y8vDRs2LAq+yMiIhQSEqL8/HydOnVKERERks5PbZCkpKQkeXt7Vznv3nvv1V//+tcq+y+cN2zYMDVt2rTK8a5du+r666/X7t27tW7dOo0fP77S8VatWqlTp05VzouNjXWcHx4eXuPxX3/9tcoxALhSFMYAYILfztX9vS1btuiOO+7Qzz//XGObwsLCavc3a9as2gJXkoKCgpSfn++4uU2Sjh8/LkmKiYmp9pyQkBA1atRIp0+frrQ/MzPzoudJUps2bbR7925H299q1apVtecEBgZe9HjDhg0lqdLnAADOwqoUAGACPz+/aveXlJTo1ltv1c8//6xJkyZp27ZtysvLU0VFhex2u+NKsb2G+6Y9PK6OH+uXynm1fB4A6heuGANAHbJ+/XqdPHlSXbp00XvvvVfl+MGDB53aX4sWLSSpxsc9FxQUVLla/NvzDh8+XONrXzh2oS0A1HX8SQ4AdUheXp6kmqcSLF682Kn99e/fX5L02WefyWq1Vjn+4YcfVnvegAEDJEmrVq3SyZMnqxxPTU1VWlqaPDw81K9fP+cFBgAXojAGgDqkQ4cOkqRvv/1W+/btq3Ts7bffVnJyslP7GzNmjFq0aKGff/5ZTz75ZKWHh/zwww+aPXt2teclJCSoR48eKi0t1QMPPKCSkhLHsdzcXD3wwAOSpHHjxqlly5ZOzQwArkJhDAB1SHx8vG655RadOXNG8fHxGjp0qO6880516NBBDz74oJ566imn9ufn56ePP/5Y/v7+evnll9W2bVvdeeedGjJkiLp06aK+ffsqKiqq2nM/+eQTRUVFafny5YqJidHYsWN16623qk2bNtq+fbu6dOmiN954w6l5AcCVKIwBoI75/PPP9dJLL6ldu3bauHGjUlJS1KpVK/3nP//R5MmTnd5f//79tXXrVt12223Kz8/XsmXLdPz4cT333HMXvULdunVr7dq1S08++aQaN26sr776SqtXr1abNm00d+5cbdy4sdJT7wCgruOR0AAAAIC4YgwAAABIojAGAAAAJFEYAwAAAJIojAEAAABJFMYAAACAJApjAAAAQBKFMQAAACCJwhgAAACQJHmZHaA+yM/PV0VFhdkx6qUmTZooJyfH7BioJxhPcCbGE5yNMeUaXl5etX4KJ4WxE1RUVMhqtZodo96xWCySzr+/PKARV4rxBGdiPMHZGFN1A1MpAAAAAFEYAwAAAJIojAEAAABJ9WyO8dmzZ7VixQodPHhQGRkZKi4u1kMPPaQBAwY42thsNq1fv15bt27V0aNHVVRUpPDwcPXu3VsjR46Uj4+PeZ8AAAAATFOvrhgXFhZqyZIlyszMVHR0dLVtysvLtXDhQhUWFmrw4MG699571aZNG3322Wd64YUXmPAOAADgpurVFeOQkBC9/fbbCg4O1qFDh/Tkk09WaePl5aXnn39e7dq1c+xLTExUeHi4PvvsM+3Zs0dxcXFGxgYAAEAdUK+uGHt7eys4OPiibby8vCoVxRd0795dkpSZmemKaAAAAKjj6tUV4ytRUFAgSWrYsGGNbaxWa6X1ii0Wi/z8/Bwfw7kuvKe8t3AGxhOcifEEZ2NM1Q0Uxv9n+fLl8vPzU3x8fI1tli1bpiVLlji2Y2JiNG/ePDVp0sSIiG4rIiLC7AioRxhPcCbGE5yNMWUuCmNJS5cu1Z49ezR58mQFBATU2G706NEaMWKEY/vCX3U5OTk8EtoFLBaLIiIidOLECW6KxBVjPMGZGE9wNsaU63h5edX6IqbbF8abN29WcnKyBg4cqCFDhly0rbe3t7y9vas9xiB2HbvdzvsLp2E8wZkYT3A2xpS56tXNd5crPT1db7zxhuLj43X//febHQcAAAAmctvC+ODBg3rppZfUpk0bPfroo/L09DQ7EgDgKlBSUqLmzZvLYrGopKTE7DgAnMgtC+Pjx49r7ty5Cg8P1/Tp03naHQAAAOrfHONVq1apuLhY+fn5kqQdO3bo1KlTkqThw4fLYrFozpw5Kioq0siRI7Vr165K50dERKht27aG5wYAAIC5DC+Ms7OztWPHDh04cECZmZkqLCyUxWJRw4YN1aJFC7Vv317dunVTeHj4H3r9L7/8Ujk5OY7tbdu2adu2bZKkvn37SpKjUP7kk0+qnN+/f38KYwAAADdksRt06+POnTv15Zdf6sCBA7Lb7YqIiFB4eLhjebTi4mJlZ2frxIkTkqT27dtr1KhR6tq1qxHxrkhOTk6lB3/AOSwWi5o1a6asrCzu0MUVYzzBWUpKShQbGytJysjIcDzoCbgS/IxyHW9v77q1XNuMGTN09OhR3XDDDfrrX/+q6667Tv7+/tW2LSkpUXp6urZs2aJXXnlFUVFRmjNnjhExAQAA4MYMKYyvvfZaPf744woODr5kW39/f/Xs2VM9e/ZUQUGBvv76a9cHBAA4xZfJBWZHcLmysv+uRLHy83w1aFBmYhrjjLwj2OwIgMsZUhjfddddf+i84ODgP3yukX44WazTJe7xg9FIFotFIcXeys8/wz8r4YoxnoxxzHbW7AguV27778/7n21l8rG5xwJP246fMTtCvcbPKNdp5N9AN9ZyKoV7fDcDAAAAl2DYqhSHDx++7HNat27tgiQAAABAVYYVxk8++eRln5OcnOyCJAAAAEBVhhXGU6ZMMaorAAAA4LIZVhgPGDDAqK4AoJKzpSW6c0AXSdK/16WqgS/rzuKP82ngpyf/Z6fZMQC4gKFPvsvLy5MkhYaGXrSNxWJRSEiIUbEAtzZnXabZEVzuXHmp4+N5GzLl6VP/C+MZ/VuYHQEArjqGrUpx+PBhPfTQQ9q8efNF223evFkPPfSQfv75Z4OSAajvPH381O3Fb9XtxW/doigGAPwxhhXGq1atUrNmzXTzzTdftN3NN9+s5s2b82APAAAAGMqwwnjv3r3q1auXLBbLRdtZLBb17NlTP/zwg0HJAAAAAAML44KCAoWHh9eqbVhYmPLz812cCAAAAPgvwwrjBg0aqKioqFZti4uL5ePj4+JEAAAAwH8ZVhhHRUVp587aLW+zc+dORUVFuTgRAAAA8F+GFcb9+vXTvn379M0331y03apVq7Rv3z7179/foGQAAACAgesY9+/fX99//73ef/99paamqm/fvmrVqpX8/PxUWlqqn3/+WRs2bNDu3bsVFxfHA0EAAABgKMMKYw8PDz322GP66KOPtGbNGu3evbvaNoMHD9Y999xzydUrAAAAAGcy9Ml3Pj4++tOf/qTRo0crNTVVmZmZKikpkb+/v5o3b674+Hg1btzYyEgAAACAJIML4wtCQ0M1aNAgM7oGAAAAqmXYzXcAAABAXWbKFeP6xsNaLo/ys2bHqHcsFossZWflUX5Wdrvd7Dj1VoNz5WZHgAuY9TPJ61yZKf3C9TzKPc2OUK/xO891PLxrf9+axc67f8VOTBsv66EfzY4BAACA3/Fu004Rr31cq7ZMpQAAAADEVAqn+HHSszpdXGp2jHrHYrEoODhUBQV5/LOSC724McvsCHCBJxKamdLvjs0lpvQL1+vW29/sCPUav/Ncp1GAnyJq2bZOFMY2m00HDx5UXl6egoOD1bZtW3l6Xj1zmWzePrL5MIidzWKxyN7AVzYfX35IuFCZp4/ZEeACNh9fU/qt8DxnSr9wPbPGlLvgd57r2Lxr/3vO9MI4MzNT8+bN06lTpxQYGKjCwkKFhobq8ccfV3R0tNnxAAAA4CZMn2P87rvvqnPnzlq0aJH++c9/6p///KeaNm2qf/7zn2ZHAwAAgBsxrDB+++23VVRUVGV/VlaWBgwYIB+f85e5g4KC1L17d2VlMe8RAAAAxjGsMM7Pz9ef//xnff3117LZbI79HTt21EcffaT9+/frxIkT2rlzp1auXKmOHTsaFQ0AAAAwbo7x9OnTlZaWpg8//FCrV6/Wvffeq86dO2vy5Ml699139fzzz+vcuXPy8PDQDTfcoMmTJxsVDQAAADD25rvOnTsrLi5O33zzjebPn6+2bdtq4sSJmjZtmh5++GGdOXNGDRs2lIeH6VOfAQAA4GYMr0A9PDx08803a/78+Y7VJz788EOVlZWpUaNGFMUAAAAwheFVaEVFhUpKShQUFKQHHnhAs2fP1qFDhzRt2jStWbOGtfsAAABgCsOmUuTn5+vNN99Uenq67Ha7IiIi9MADD6hjx46aNWuWNm/erMWLFyslJUUTJ07k5jsAAAAYytDl2nJycvTMM8/oxRdfVHR0tF5++WWVlZVJknr37q1XX31V3bp10wsvvKB//OMfRkUDAAAAjLtivH//fo0fP95xJXj8+PH685//rOPHj6tNmzaSJB8fHyUlJWngwIH66KOPLruPs2fPasWKFTp48KAyMjJUXFyshx56SAMGDKjS9vjx4/rggw904MABeXl5qUuXLrr33nsVFBR0RZ8nAAAArk6GXTEOCQnRwYMHHdsXPg4ODq7SNiwsTH/9618vu4/CwkItWbJEmZmZF32c9KlTp/Tss8/qxIkTuvPOOzVy5Ejt2rVLzz//vCoqKi67XwAAAFz9DLtifOedd2r+/Pk6cOCAAgICdPjwYQ0fPlyNGzd2Wh8hISF6++23FRwcrEOHDunJJ5+stt2yZctUVlamefPmKSwsTJJ0zTXXaPbs2Vq7dq0SExOdlgkAAABXB8MK4+7du+uVV17R7t27VV5ernvvvVft27d3ah/e3t7VXoH+va1bt6pLly6OoliS4uLi1KxZM33//fcUxgAAAG7I0Ad8hIeHa/DgwUZ2WUVeXp5Onz7tmNf8W9dcc41SU1NNSAUAAACzGVoY1yQvL09FRUUKCgqq1RXfK5Gfny/p/LSL3wsJCVFRUZGsVqu8vb2rHLdarbJarY5ti8UiPz8/WSwWWSwW14V2UxZZ/vt/3l7gsvAzCc7GmHItfue5zuWMXcMK482bN6tt27aVpi+kpqbqww8/1K+//urYFx0drYkTJ6pDhw4uyVFeXi5J8vKq+qlfKIbLy8urLYyXLVumJUuWOLZjYmI0b948BQUFyaPBOZfkhRRczR8xcKbjZgeAC4SEhJrUc5FJ/cLVzBtT7oXfec4X2MCz1m0NK4znz5+vP//5z0pISJAkpaenO4rKW265RWFhYTpx4oTWrl2r2bNna86cORddWeKP8vHxkaRqV5+4cDX4QpvfGz16tEaMGOHYvvAXSEvfClV4sZqFs1ksFkVEROjEiRM8ERG4TLEB1ks3coH9Hr6m9AvXM2tMuQt+57mOl1ft30/TplJ8+umnaty4sebNm6fAwEDH/hEjRujxxx/X0qVL9eijjzq93wtTKC5Mqfit/Px8BQYGVnu1WDp/RbmmYwxi17Hb7by/wGXiewbOxpgyBr/zzGXYOsa/VVFRocOHD2vEiBGVimJJCg0NVWJiovbv3++SvkNDQxUUFKRDhw5VOZaRkeGSq9QAAACo+0wpjM+dOz8ft2nTptUeDw8PV3Fxscv679Gjh3bt2qXc3FzHvj179igrK0s9e/Z0Wb8AAACouwydSrFr1y4VFBRIknx9fZWXl1dtu/z8fAUEBPyhPlatWqXi4mLHVIkdO3bo1KlTkqThw4fL399fo0eP1pYtWzRr1izddNNNjkdJt2rVSjfeeOMf6hcAAABXN0ML402bNmnTpk2O7W3btlX7MI09e/YoMjLyD/Xx5ZdfKicnp1If27ZtkyT17dtX/v7+CgsL08yZM/Xhhx/qk08+kZeXl+Lj43XPPffUOIcYAAAA9ZthhfEbb7xRZV9168oVFhaqadOm6ty58x/qZ8GCBbVq17JlS82YMeMP9QEAAID6x7DCuEmTJrVqFxQUpIceesjFaQAAAIDKTLn5DgAAAKhrDF/HeNeuXdq0aZMOHz6svLw8lZeXy9fXV82bN9d1112nwYMHq3HjxkbHAgAAgJszrDAuKyvTyy+/rN27d1fa7+npqebNmys3N1fLli3T119/rf/3//6f4wl5AAAAgBEMK4yTk5O1Z88eTZo0SX369FGDBg20b98+vfPOO+rYsaPGjx+v48ePa/HixXrjjTcUGhqqjh07GhUPAAAAbs6wOcabN2/W0KFDNWzYMDVs2FA+Pj7q3LmzJk+erK+++kqnT59WZGSkpk+frjZt2mjp0qVGRQMAAACMK4zPnDlT7drEkZGRstlsysrKknR+CbeEhARlZGQYFQ0AAAAwrjAODw/XDz/8UGX/3r17JUnBwcGOfZ6enrLb7UZFAwAAAIybYzx06FAtWrRIPj4+6t27t3x8fHTgwAEtX75c7du3V0REhKPt0aNHK20DAAAArmZYYTxs2DDl5uZq5cqVWrdunWP/Nddco0ceeaRSWx8fHw0fPtyoaAAAAICx6xhPmDBBI0aM0E8//SSr1armzZsrJiamSruJEycaGQsAAAAw/gEfwcHB6t69u9HdAgAAABfFI6EBAAAAURgDAAAAkiiMAQAAAEkUxgAAAIAkCmMAAABAEoUxAAAAIMnEwvjs2bNauHChMjMzqxz76aeftHDhQhNSAQAAwF2ZVhhbrVatW7dO+fn5VY6dPHmy0tPxAAAAAFdjKgUAAAAgCmMAAABAkgmPhM7NzZUkFRUVSZJOnz7t2CdJYWFhRkcCAAAAjC+Mp06dWmn7tddeq7SdnJxsZBwAAABAkgmF8bPPPitJKi4u1v/8z//o7rvvVuvWrY2OAQAAAFRieGHcsWNHSdKZM2ckSdHR0Y59AAAAgFm4+Q4AAAAQhTEAAAAgycTC2NPTUx07dlRAQECVY40aNWJ6BQAAAAxl+BzjC/z9/R034v1eXFyc4uLiDE4EAAAAd8ZUCgAAAEAGFcZlZWWmnAsAAADUliGF8ZQpU7RkyRLl5+fX+py8vDwlJyfroYcecmEyAAAA4DxD5hhPnjxZn3/+uZYsWaJ27drpuuuuU+vWrRUeHq6AgADZ7XYVFxcrOztbhw4d0p49e3Tw4EE1a9ZMf/rTn4yICAAAADdnSGHcu3dv9ezZUzt27NDatWu1bNkyVVRUVB/Iy0txcXF69NFH1a1bN3l4MA0aAAAArmfYqhQeHh7q3r27unfvLqvVqsOHDyszM1NFRUWSpMDAQLVo0UKtW7eWt7e3UbEAAAAASSYt1+bt7a127dqpXbt2ZnQPAAAAVGHaOsZmy8rKUnJysg4cOKCioiKFhYUpISFBI0eOVIMGDcyOBwAAAIO5ZWGcm5urp556Sv7+/ho2bJgCAwP1008/6bPPPtPhw4f1xBNPmB0RAAAABnPLwnj9+vUqLi7Wc889p5YtW0qSEhMTZbPZtH79ehUVFSkwMNDklAAAADCSWy75UFpaKklq1KhRpf0hISGyWCzy8nLLvxcAAADcmltWgNdee62WL1+ut956S0lJSY6pFCkpKRo+fLh8fX2rPc9qtcpqtTq2LRaL/Pz8HB/DuS68p7y3wOXj+wbOxphyLX7n1Q1uWRh37txZd9xxh5YtW6YdO3Y49t92220aN25cjectW7ZMS5YscWzHxMRo3rx5atKkiUvzuruIiAizI9Rz+80OABdo1qyZST3X/gmnuLqYN6bcC7/zzGVaYZybm6ulS5dq7969Kiws1OOPP66OHTuqsLBQS5Ys0Y033qiYmBiX9d+kSRN16NBBPXr0UMOGDbVr1y4tW7ZMwcHBGjZsWLXnjB49WiNGjHBsX/irLicnp8YHluCPs1gsioiI0IkTJ2S3282OA1xVsrKyzI6AeoYx5Vr8znMdLy+vWl/ENKUwPn78uJ555hnZ7XZdc801OnHihGw2myQpKChIP/74o8rKyjRlyhSX9L9p0ya9/fbbmj9/vho3bixJ6tGjh+x2uz7++GP16dNHDRs2rHKet7d3jQ8fYRC7jt1u5/0FLhPfM3A2xpQx+J1nLlNuvlu8eLECAgI0f/58/fnPf65yPD4+XgcOHHBZ/ykpKYqJiXEUxRd069ZNZWVlOnLkiMv6BgAAQN1kSmG8f/9+DR48WEFBQdVOMg8LC1NeXp7L+i8oKHBcof6tC9MhqjsGAACA+s2Uwthms1306XKFhYUuXTKtWbNmOnLkiH799ddK+zdt2iSLxaJWrVq5rG8AAADUTabMMW7durV27dqloUOHVjl27tw5bd68WW3btnVZ/6NGjVJaWpqeffZZDR061HHzXWpqqgYOHKjQ0FCX9Q0AAIC6yZQrxrfeeqvS0tL0zjvv6JdffpF0fnpDenq6Zs+erczMTN1yyy0u679jx46aPXu2WrdurZSUFL3//vs6ceKExo0bp/vvv99l/QIAAKDuMuWKcXx8vKZOnapFixZpzZo1kqTXX39dkuTn56epU6eqY8eOLs1wzTXX6Mknn3RpHwAAALh6mLaOcb9+/dS9e3elp6c7lmuLiIjQ9ddf73iaHAAAAGAUU5985+vrq+7du5sZAQAAAJBkUmGcm5tbq3ZhYWEuTgIAAACcZ0phPHXq1Fq1S05OdnESAAAA4DxTCuPqHvVss9mUk5Oj9evXKygoqNql3AAAAABXMaUwHjBgQI3HbrnlFs2YMUMlJSXGBQIAAIDbM2Ud44vx9fXVgAEDtHLlSrOjAAAAwI3UucJYkux2uwoKCsyOAQAAADdi6nJtv1dSUqL9+/drxYoViomJMTsOAAAA3IgphfEdd9xx0eNhYWGaPHmyQWkAAAAAkwrj22+/XRaLpdI+i8WigIAANW3aVNdff708PT3NiAYAAAA3ZUphnJSUZEa3AAAAQI3q5M13AAAAgNEMuWK8cOHCyz7HYrFU+yAQAAAAwBUMKYz37t172ef8fg4yAAAA4EqGFMYLFiwwohsAAADgD2OOMQAAACAKYwAAAECSiU++S01N1VdffaUjR46opKREdru9Spvk5GQTkgEAAMAdmXLFeMuWLZo7d65Onz6t3r17y263q0+fPurTp498fHwUFRWlMWPGmBENAAAAbsqUK8ZffPGFrrnmGj3//PMqKirS6tWrNXDgQHXq1EnZ2dmaMWOGwsPDzYgGAAAAN2XKFePjx4+rT58+8vDwcDz6uaKiQpIUHh6uoUOHavny5WZEAwAAgJsypTBu0KCBvLzOX6wOCAiQl5eXCgoKHMcbNWqk7OxsM6IBAADATZlSGDdv3lzHjx93bEdHR2v9+vU6d+6cysvLtXHjRoWFhZkRDQAAAG7KlML4hhtu0Pbt22W1WiVJt912m/bu3auJEydq8uTJOnDggG699VYzogEAAMBNGXbz3c6dOxUfHy8PDw+NGjVKo0aNchzr2rWrZs6cqa1bt8rDw0NdunRRp06djIoGAAAAGFcYv/jii2rYsKF69eqlvn37qm3btpWOd+jQQR06dDAqDgAAAFCJYYXx/fffr02bNmn16tVKSUlRkyZNlJCQoISEBEVGRhoVAwAAAKiWYYVxYmKiEhMTlZeXp02bNmnTpk1atmyZli1bpujoaPXt21e9e/dWaGioUZEAAAAAB8Mf8BEaGqqRI0dq5MiR+vXXX7Vx40Zt2rRJH330kRYvXqyOHTuqb9++6tGjh/z9/Y2OBwAAADdlypPvLmjevLmSkpKUlJSkjIwMbdq0SZs3b9Zbb72l9957Tx999JGZ8QAAAOBGTFmurTrh4eFq2rSpGjduLEkqLy83OREAAADcialXjM+ePautW7dq48aN+uGHH2Sz2dSkSRONHj1aCQkJZkYDAACAmzG8MK6oqNCuXbu0ceNGpaamqry8XIGBgRo0aJASEhLUvn17oyMBAAAAxhXGP/zwgzZu3KitW7eqpKRE3t7e6tq1q/r27av4+Hh5enoaFQUAAKDOKCkpUWxsrCQpIyNDfn5+JidyX4YVxs8//7wsFouuvfZax6oTfOEBAABQVxhWGN99993q06ePQkJCjOrykg4fPqzPP/9cBw4ckNVqVdOmTTVo0CDddNNNZkcDAACAwQwrjEeMGGFUV7Wye/duzZs3TzExMbr99tvl6+urkydPKi8vz+xoAABA0muvvWZ2BEP8diWuBQsWyMfHx8Q0xpg2bZrZEapl6qoUZikpKdEbb7yhLl266NFHH5WHR51ZtQ4AAAAmccuKcOPGjTp9+rTGjRsnDw8PnT17VjabzexYAAAAMJFbXjHes2eP/Pz8lJeXp5deeklZWVlq0KCB+vXrp3vvvdct/gkDAADUDT4+Ppo+fbrZMSA3LYxPnDghm82ml156STfeeKPuuusu7d27V6tWrVJxcbEeeeSRas+zWq2yWq2ObYvF4lhZw2KxGBHdrVx4T3lvgcvH9w2cjTEFZ6qr48mUwnjz5s3q1q2baVdmz549q7KyMg0ePFj33XefJKlHjx6qqKjQmjVrdMcdd6hZs2ZVzlu2bJmWLFni2I6JidG8efPUpEkTw7K7o4iICLMj1HP7zQ4AF6juZ5gx8k3qF65m3phCfVRXx5MphfH8+fPl5+enHj16qG/fvurUqZOh/V8oyPv06VNpf0JCgtasWaOffvqp2i/Y6NGjK62uceGvnZycHFVUVLgwsXuyWCyKiIjQiRMnZLfbzY4DXFWysrLMjoB6hjEFZzJyPHl5edX6IqYphfFzzz2nDRs2aMuWLVq7dq1CQ0OVkJCgvn37qlWrVi7vPyQkRL/88ouCg4Mr7W/UqJEkqbi4uNrzvL295e3tXe0xCjfXsdvtvL/AZeJ7Bs7GmIIz1dXxZEph3K5dO7Vr106TJk1SWlqaNmzYoFWrVmnFihVq1aqV+vXrp4SEBJc9DKR169ZKT09XXl6emjdv7tifn3/+nwCDgoJc0i8AAADqLlNvvvP09FTXrl3VtWtXnT17Vtu2bdPatWu1ePFiffLJJ+rYsaP69++v3r17y8vLeVF79eqlL774Qv/7v/9baRrHt99+K09PT3Xs2NFpfQEAAODqUGdWpfj555+VkZGhn3/+WZLUvHlzFRUVacGCBfr000/1l7/8Re3bt3dKXzExMbrxxhv13Xff6dy5c+rYsaP27t2rLVu26NZbb1VoaKhT+gEAAMDVw9TC+Ndff9XGjRu1ceNGnTx5UkFBQUpISFC/fv3UunVrSdKhQ4f01ltv6Z133tHLL7/stL7vv/9+hYWFae3atdq2bZuaNGmie++9VzfffLPT+gAAAMDVw5TC+Ouvv9aGDRt0+PBheXt7q2vXrpo4caI6d+5c5fHMbdq00c0336y33nrLqRm8vLw0duxYjR071qmvCwAAgKuTKYXxBx98oHbt2un+++9X79695e/vf9H2bdq00e23325QOgAAALgjUwrj119/XeHh4bVu37JlS7Vs2dKFiQAAAODuPC7dxPkaN26skpKSGo+XlJTo3LlzBiYCAACAuzOlMF60aJH+9re/1Xj8b3/7mz788EMDEwEAAMDdmVIYp6WlqUePHjUe79mzp1JTUw1MBAAAAHdnSmGcn59/0bWCQ0JClJeXZ2AiAAAAuDtTCuPAwED9+uuvNR7PzMyUn5+fgYkAAADg7kwpjDt37qw1a9boyJEjVY4dPnxYa9asUXx8vAnJAAAA4K5MWa7tjjvuUFpamp566il17drVsRTbL7/8op07dyooKEh33HGHGdEAAADgpkwpjENDQzV37lx9/PHH2rFjh7Zv3y5J8vPzU0JCgu68886LzkEGAAAAnM2Uwlg6f4Pdww8/LLvdrsLCQklSUFCQLBaLWZEAAADgxkwrjC+wWCxq1KiR2TEAAADg5gwpjJcsWSJJuu222+Th4eHYvpQxY8a4MhYAAADgYEhh/Pnnn0uSbr31Vnl4eDi2L4XCGAAAAEYxpDBOTk6+6DYAAABgNlPWMQYAAADqGgpjAAAAQAZNpZg6deplL8NmsVj0+uuvuygRAAAAUJkhhXHHjh1ZnxgAAAB1mmFXjAEAAIC6jDnGAAAAgEx88p3NZtP69eu1a9cu5ebmSpLCwsLUtWtX9e3bVx4e1OwAAAAwjimFcUlJiebMmaOMjAz5+fmpadOmkqQ9e/Zo69atSklJ0YwZM+Tv729GPAAAALghUwrjTz/9VIcPH9Z9992nQYMGycvrfIyKigr97//+rxYtWqR///vfuu+++8yIBwAAADdkynyFbdu2aciQIRo6dKijKJYkLy8vDRkyRIMHD9bWrVvNiAYAAAA3ZUphXFRUpObNm9d4vEWLFioqKjIwEQAAANydKYVxRESEduzYUePxHTt2OOYdAwAAAEYwpTAeMmSI0tPT9cILL2j37t3Kzs5Wdna20tLS9MILLyg9PV3Dhg0zIxoAAADclCk33w0dOlSnT5/W8uXLlZaWVjmQl5fGjBmjIUOGmBENAAAAbsq0dYyTkpI0bNgwpaenO9YxbtKkia677joFBQWZFQsAAABuytDCuKCgQGvXrlV2draCgoLUo0cPJSQkGBkBAAAAqJZhhXF2draefPLJSqtNfPHFF3r44YcpjgEAAGA6wwrjzz77TGfPntWkSZPUqVMnnThxQosWLdIHH3yg3r178whoAAAAmMqwwvjHH39UYmKiY7WJyMhIeXh4aN68ecrMzFTLli2NigIAAABUYdhl2tzcXMXExFTa17p1a0nSmTNnjIoBAAAAVMuwwthms1V6/LMkeXp6Oo4BAAAAZjJ0VYpDhw7J29vbsV1aWipJOnDggIqLi6u079Gjh2HZli5dqn//+99q2bKlXn75ZcP6BQAAQN1gaGH89ddf6+uvv66y//PPP6+2fXJysqsjSZJOnTqlZcuWqUGDBob0BwAAgLrHsML42WefNaqry/bRRx8pNjZWNpuN+c4AAABuyrDCuGPHjkZ1dVn27dunLVu26MUXX9R7771ndhwAAACYxK0XD7bZbFq0aJEGDhyoVq1amR0HAAAAJjJ0jnFdk5KSopycHP3tb3+rVXur1Sqr1erYtlgs8vPzc3wM57rwnvLeApeP7xs4G2MKzlRXx5PbFsZnzpzRZ599pttvv11BQUG1OmfZsmVasmSJYzsmJkbz5s1TkyZNXBUTkiIiIsyOUM/tNzsAXKBZs2Ym9ZxvUr9wNfPGFOqjujqe3LYw/ve//63AwEANHz681ueMHj1aI0aMcGxf+GsnJydHFRUVTs/o7iwWiyIiInTixAnZ7Xaz4wBXlaysLLMjoJ5hTMGZjBxPXl5etb6I6ZaFcVZWltasWaOJEycqLy/Psd9qtaqiokLZ2dny9/dXYGBgpfO8vb0rrcP8WxRurmO323l/gcvE9wycjTEFZ6qr48ktC+O8vDzZ7XYtWrRIixYtqnL84Ycf1k033aSJEycaHw4AAACmMKQwXrdu3R86r3///k5Ocl7Lli312GOPVdmfnJys0tJSTZw4kXmtAAAAbsaQwnjhwoV/6DxXFcZBQUHq3r17lf0XnspX3TEAAADUb4YUxm+88Ual7eLiYi1YsED+/v4aPny4mjdvLknKzMzUqlWrVFpaqqlTpxoRDQAAAJBkUGH8+zsBP//8cwUFBenpp5+utI5dq1at1KNHD82ZM0crV67UQw89ZEQ8h5kzZxraHwAAAOoOU558t337dnXv3r3axZ09PDzUvXt3bd++3YRkAAAAcFemFMZ2u12ZmZk1Hj9+/LiBaQAAAACTCuMbbrhBq1ev1ldffaWysjLH/rKyMn355Zdas2aNunXrZkY0AAAAuClT1jGeNGmSsrOz9dFHH+mTTz5RSEiIJCk/P1/nzp1Tu3btWEMYAAAAhjKlMPb399esWbO0fft2paamKjc3V5J0/fXXq0uXLuratWu1848BAAAAVzH1yXc33HCDbrjhBjMjAAAAAJJMLozz8vK0b98+FRYWqkePHmrcuLFsNptKSkrk7+8vDw9TpkADAADADZlSGNvtdn344YdatWqVbDabpPNrGDdu3Fhnz57V1KlTlZSUpJtvvtmMeAAAAHBDplySXbFihb7++muNHDlSTz/9dKVj/v7+6t69u7Zu3WpGNAAAALgpUwrjb7/9Vv3799ddd92l6OjoKsejoqKUlZVlfDAAAAC4LVMK41OnTqlt27Y1Hm/QoIFKSkoMTAQAAAB3Z0phHBQUpFOnTtV4/PDhwwoLCzMwEQAAANydKYVxjx49tHr1ap08ebLKsd27d2vt2rXq1auXCckAAADgrkxZlSIpKUl79+7VE088ofbt20uSli9fruTkZP3000+KiYnR6NGjzYgGAAAAN2XKFWN/f3/NmTNHo0aNUl5ennx8fLRv3z6VlJRo7Nixeu6559SgQQMzogEAAMBNmfaADx8fH91+++26/fbbzYoAAAAAOJhyxXjWrFnas2dPjcd/+OEHzZo1y8BEAAAAcHemFMb79u3T6dOnazxeWFioffv2GZgIAAAA7s6UwvhSTpw4IT8/P7NjAAAAwI0YNsd47dq1WrdunWN76dKl+vbbb6u0Kykp0bFjxxQfH29UNAAAAMC4wri8vFyFhYWO7dLSUlkslkptLBaLGjRooMGDB2vMmDFGRQMAAACMK4yHDBmiIUOGSJKmTp2qSZMmqVu3bkZ1DwAAAFyUKcu1LViwwIxuAQAAgBqZto7xBaWlpSopKZHdbq9yLCwszIREAAAAcEemFcYpKSn66quvdPLkyRrbJCcnG5gIAAAA7syU5dpSUlL0r3/9SxERERo3bpwk6eabb9att96q4OBgRUdHa8qUKWZEAwAAgJsypTBetWqVrr/+ej311FNKTEyUJHXp0kV33nmnXnnlFZWWlurMmTNmRAMAAICbMqUwPnnypLp27SpJ8vT0lCRVVFRIkvz9/TVw4EClpKSYEQ0AAABuypTC2N/fX+fOnXN87OPjo9zcXMdxPz8/FRQUmBENAAAAbsqUwrhly5Y6duyYY7tt27ZavXq18vLylJubqzVr1qhZs2ZmRAMAAICbMqUw7tu3r3755RdZrVZJ0tixY3X8+HFNmTJFU6dO1a+//uq4KQ8AAAAwginLtd1444268cYbHdvt27fXP/7xD+3cuVMeHh6Ki4tT8+bNzYgGAAAAN2X6Az4uaNq0qW666SazYwAAAMBNmV4Y22w2lZSUVHssMDDQ4DQAAABwV6YUxhUVFVq+fLm+++47nTp1Sjabrdp2PPkOAAAARjGlMH777be1bt06tW3bVjfccIP8/f3NiAEAAAA4mFIYb9myRf369dPUqVPN6B4AAACowpTCuEGDBoqNjTWja0lSRkaG1q1bp7179yonJ0eBgYGKjY3VuHHjWA0DAADATZmyjnGfPn20a9cuM7qWJC1fvlxbt25Vp06dNHHiRCUmJmr//v2aPn26fv75Z9NyAQAAwDymXDGeMGGCFi5cqLlz5+rGG29U48aN5eFRtUZv3bq1S/ofMWKE/vKXv8jL67+ffu/evfXYY4/piy++0LRp01zSLwAAAOouUwpjq9Uqu92u1NRUpaam1tjOVatStGvXrsq+Zs2aKTIyUpmZmS7pEwAAAHWbKYXxm2++qW3btqlPnz665ppr6sSqFHa7XadPn1bLli3NjgIAAAATmFIY7969W8OGDdPEiRPN6L5aGzZsUF5enpKSkmpsY7VaZbVaHdsWi0V+fn6Oj+FcF95T3lvg8vF9A2djTMGZ6up4MqUw9vPzU0REhBldVyszM1P/+te/1LZtWw0YMKDGdsuWLdOSJUsc2zExMZo3b56aNGliQEr3VZfGSv203+wAcIFmzZqZ1HO+Sf3C1cwbU6iP6up4MqUwHjRokDZt2qQhQ4ZUe9OdkQoKCjR37lz5+/vr0UcfvWie0aNHa8SIEY7tC3/t5OTkqKKiwuVZ3Y3FYlFERIROnDghu91udhzgqpKVlWV2BNQzjCk4k5HjycvLq9YXMU0pjCMjI7Vjxw5Nnz5d/fv3r3FVih49erg0R0lJif7+97+ruLhYzz33nEJDQy/a3tvbW97e3tUeo3BzHbvdzvsLXCa+Z+BsjCk4U10dT6YUxq+++qrj448++qjGdq5alUKSysvLNW/ePGVlZelvf/ubIiMjXdYXAAAA6j5TCuNnn33WjG4dbDabXn31Vf300096/PHH1bZtW1PzAAAAwHymFMYdO3Y0o1uHDz/8UDt27FDXrl1VVFSk9evXVzrer18/k5IBAADALKYUxmY7evSoJGnnzp3auXNnleMUxgAAAO7HkMJ41qxZslgsmjFjhjw9PTVr1qxLnmOxWPTMM8+4JM/MmTNd8rpwnpKSEsXGxkqSMjIyHOtFAwAAuIohhfHv7zy02+2XXNi5rt6taLZz948yO4IhzlWcc3xc8dAYnfPyNDGNMTzfWWF2BAAA3JohhfHvr9ByxRaX4u/lqZ9v6mp2DAAA4EZMebrGvn37VFhYWOPxwsJC7du3z8BEAAAAcHemFMazZs1Senp6jcd/+OGHWs1DBgAAAJzF3Ocx18BqtZr+qGgAAAC4F8OWa8vNzVV2drZjOzMzs9rpEiUlJVqzZk2tn2kNAAAAOINhhfF3332nJUuWOLaXLl2qpUuXVtvWw8ND999/v1HRAAAAAOMK4169eqlly5aSpFdeeUXDhw9X+/btK7WxWCxq0KCBoqOjFRwcbFQ0AAAAwLjCODIyUpGRkZKkKVOmqGPHjgoPDzeqewAAAOCiTHkk9IABA6rdf/LkSVmtVkcBDQAAABjFlML4m2++0Y8//qhHHnnEsW/hwoVat26dJCkmJkZPPvmkGjVqZEY8AAAAuCFT1kT79ttvKxW9aWlpWrdunRITE3Xffffp5MmT+vzzz82IBgAAADdlyhXjnJwctWjRwrH9/fffKzw83LESRUFBgdavX29GNAAAALipOvEUjfT0dHXu3Nmx3aRJExUUFJiWBwAAAO7HlMK4WbNm2r59u6Tz0yjy8vIUHx/vOJ6Xl6eAgAAzogEAAMBNmTKVYuTIkXrttdc0adIknT17VpGRkbr++usdx3/44QdFR0ebEQ0AAABuypTCuE+fPmrYsKF27dqlgIAADR06VJ6enpKkoqIiBQYGql+/fmZEAwAAgJsypTCWpLi4OMXFxVXZHxgYqMcee8yERAAAAHBnhs0xzsjIUFFRUa3aZmdnO9Y0BgAAAIxgWGE8Y8YMpaWlObaLioo0YcIE7du3r0rbH3/8UQsXLjQqGgAAAGDecm12u11Wq1U2m82sCAAAAIBDnVjHGAAAADAbhTEAAAAgCmMAAABAksHLtWVnZ+vw4cOSpJKSEklSVlaW/P39q7QDAAAAjGRoYZycnKzk5ORK+959910jIwAAAADVMqwwnjJlilFdAQAAAJfNsMJ4wIABRnUFAAAAXDZuvgMAAABEYQwAAABIojAGAAAAJFEYAwAAAJIojAEAAABJFMYAAACAJApjAAAAQBKFMQAAACDJ4EdC1yVWq1XJycnasGGDioqKFBUVpXHjxikuLs7saAAAADCB214xXrBggVauXKmEhARNmjRJHh4eeuGFF3TgwAGzowEAAMAEblkYZ2RkaPPmzbrzzjt19913KzExUc8884zCwsK0ePFis+MBAADABG5ZGG/ZskUeHh5KTEx07PPx8dHAgQP1008/KTc318R0AAAAMINbFsZHjhxRs2bN5O/vX2n/NddcI0k6evSoCakAAABgJre8+a6goEAhISFV9l/Yl5+fX+15VqtVVqvVsW2xWOTn5ycvL+PeRo827QzrC8by9PY2pd924YGm9AvX8jZpPIWF+5rSL1zPjDHVvHlzw/uEMYwcT5dTp7llYVxeXl7tF+TCvvLy8mrPW7ZsmZYsWeLY7tOnj/7yl79UW2S7zGsfG9cX3MLie5uYHQH1yO3jGU9wnmnTppkdAW7GLadS+Pj4VLrye8GFfT4+PtWeN3r0aL3//vuO/+6///5qXwfOUVpaqunTp6u0tNTsKKgHGE9wJsYTnI0xVTe45RXj4OBg5eXlVdl/YQpFTVeAvb29TfvnSXdkt9t15MgR2e12s6OgHmA8wZkYT3A2xlTd4JZXjKOjo5WVlaWSkpJK+w8ePOg4DgAAAPfiloVxz549ZbPZtGbNGsc+q9WqtWvXKjY2VmFhYSamAwAAgBnccipFbGysevbsqU8//VSFhYWKiIjQunXrlJOTowcffNDsePg/3t7eGjNmDNNX4BSMJzgT4wnOxpiqGyx2N53MUl5eruTkZG3YsEHFxcVq1aqV7rjjDnXu3NnsaAAAADCB2xbGAAAAwG+55RxjAAAA4PcojAEAAABRGKMeSUpK0meffWZ2DNRT2dnZSkpK0tq1a82Ognps5syZmjlzptkxUI9NnTpVCxYsMDtGnUVhDAAAAMhNl2tD/bR48WJ5enqaHQMA/rCnn37a7AiAW+OKMeoNHx8fCmOY7uzZs2ZHwFXMy8tLXl5cs4K53PnnGN99cIrS0lIlJydr+/btys/Pl7+/v6KiojR+/Hi1bt1aM2fO1JkzZzRt2jS99957OnTokEJCQjR+/Hj17NlT+/bt0+LFi3Xs2DGFhYXpT3/6k+Li4i4rQ1JSksaMGaOkpKRaZULdVBfGUnFxsd5//31t27ZNFotF3bp104gRI6q0W7BggbZs2aKXXnpJixYt0v79+9WpUyc98cQTzno7cAl1Ybxs2rRJK1asUFZWliwWi8LCwjRo0CDddNNNkqS1a9dq4cKFeu6557R161atX79e5eXliouL0wMPPKCgoCDHa12YX/zbecbffPONVq9erezsbHl7e6tp06YaMWKEEhISrvj9w3/Vt7Fkt9u1dOlSrV69WkVFRYqNjdV9991Xpc8Lrzlz5kxt3rxZW7Zs0blz57Ro0aIre0OvUlwxhlO88847SklJUY8ePTR58mSNHDlSPj4+yszMdLQpKirS3LlzFRsbqwkTJsjb21uvvvqqNm/erFdffVXx8fEaP368ysrK9I9//EOlpaUuz4S6x+yxZLfb9eKLL2r9+vXq27evxo0bp7y8vBpvVrHZbJozZ46CgoJ09913q2fPnlf8HqD2zB4v6enpmj9/vgIDAzV+/Hjddddduvbaa3XgwIEqbRctWqRjx45p7NixGjx4sHbu3Kl//etfF339NWvWaNGiRYqMjNTEiRM1duxYRUdH6+DBg7V/k1Ar9W0sJScnKzk5WVFRUZowYYLCw8M1e/bsGq8Gv/vuuzp+/LjGjBmjW265pda56xuuGMMpdu3apUGDBumee+5x7Pv9N1Z+fr6mTZvmuMoRFxenRx55RPPnz9fs2bMVGxsrSWrRooXmzJmjrVu3asCAAS7NhLrH7LG0Y8cO7d+/XxMmTNCoUaMkSUOGDNGsWbOqbW+1WtWrVy/dddddl/upwgnMHi+7du2Sn5+fZsyYIQ+Pi19rCgwM1NNPPy2LxSLp/B9h33zzjUpKSuTv71/j67ds2VKPPvporfLgj6tPY6mwsFArVqxQly5dNH36dEe7Tz/9VMuWLavxNZ955plL9l3fufdnD6cJCAhQRkaG8vLyamzj6+urPn36OLabN2+ugIAARUZGOn6YSHJ8fPLkSZdnQt1j9lhKTU2Vp6enhgwZ4tjn4eGhYcOG1XjOb9vCWGaPF39/f5WVlSk9Pf2SbRMTEx0FiiR16NBBNptNOTk5NZ4TEBCgU6dOKSMjo9aZ8MfUp7GUnp6uiooKDRs2rFK7m2++ucbXHDRokNsXxRJXjOEk48eP14IFCzRlyhS1bt1a8fHx6t+/v5o2bepo07hx40rfoNL5HwSNGzeusk86P8/T1ZlQ95g9lnJychQcHCxfX99K+5s3b15te09PT4WGhtb69eFcZo+XoUOH6vvvv9ff//53hYaGKi4uTr1791bnzp2rtA0LC6u0HRAQcMn+brnlFu3Zs0dPPfWUIiIiFBcXp4SEBLVv377WGVE79Wks5ebmSpKaNWtWqV1QUJCj7e+Fh4fXOmt9RmEMp+jdu7c6dOigbdu2affu3fryyy+1fPlyPfbYY4qPj5ekGv8SrWm/3W53eSbUPXVxLF2Ml5cXV1lMZPZ4adSokV566SWlpaUpLS1NqampWrt2rfr166eHH374ivuLjIzUq6++ql27diktLU1bt25VSkpKpRuN4Rz1fSxdio+Pzx8+tz7hpzmcJiQkREOHDtUTTzyhN954Qw0bNtTSpUvJhMtm5tetSZMmKigoqHKDyq+//mpI/7h8Zn+fe3l5qVu3bpo8ebJef/11JSYmav369Tpx4oRTXt/X11e9e/fWQw89pDfffFNdunTR0qVLVV5e7pTXx3/Vl7F04YpyVlZWpf2FhYVX/K+x9R2FMa6YzWZTSUlJpX2NGjVSSEiIKioqyIRaqwtft/j4eJ07d04pKSmVcq1atcqQ/lF7dWG8nDlzptK2h4eHoqKiJJ2/MdPZr+/l5aXIyEjZ7XadO3fuil8f59W3sRQXFydPT0+tWrWq0lXklStXXnnQeo6pFLhipaWlevDBB9WzZ09FRUXJ19dXe/bs0aFDhyrd3evumXBpdeHr1rVrV7Vr104ff/yxsrOzFRkZqW3btlX5pQnz1YXx8tZbb6moqEidOnVS48aNlZOTo1WrVik6OlotWrS44tefPXu2goOD1a5dOwUHB+v48eP6z3/+oy5dusjPz88JnwGk+jeWgoKCNHLkSH3xxReaO3eu4uPjdfToUaWmpqphw4Yu+gzqBwpjXLEGDRpo6NCh2r17t7Zt2yabzaaIiAhNnjzZtLv162ImXFpd+Lp5eHho+vTpev/997VhwwbHAz7uueceHtxRx9SF8dK3b199++23SklJUXFxsYKDg9WrVy8lJSU5Ze754MGDtWHDBq1cuVJnz55VaGiohg8frttuu80J6XFBfRxL48aNk4+Pj1avXq29e/cqNjZWTz/9tObOneuC9PWHxe7Ku1IAAACAqwRzjAEAAAAxlQJ1nM1mU2Fh4UXb+Pr6VllzFvg9xhIuB+MFzsJYurpQGKNOy83NrbJ+4++xnidqg7GEy8F4gbMwlq4uzDFGnVZeXq4DBw5ctE3Tpk15mh0uibGEy8F4gbMwlq4uFMYAAACAuPkOAAAAkERhDAAAAEiiMAYAAAAkURgDAAAAkiiMAQAAAEkUxgAAAIAkCmMAAABAEoUxAAAAIEn6/xTmvHBqu5B2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\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": 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c3FzbvoKCApWVlVVq99lnn8nLy0vXXnutS2sGAABA3eOUqRRlZWU6dOiQcnJyFBAQoE6dOsnT09Ph66WmpqqwsNAWZtPS0nTmzBlJ0qhRo2Q2m21t169fr8DAQMXExFR5rZycHD3xxBMaOHCgkpKSbNf79NNP1a9fP4WFhamgoEDr16/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==",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\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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",
      "text/plain": [
       "<Figure size 1600x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\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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",
      "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": [
    "OPEのより発展的なトピック(累積分布関数の推定など) やオフ方策選択 (OPS) は，以下を参照してください．[examples/quickstart_ja/rtb/rtb_synthetic_discrete_advanced_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_discrete_advanced_ja.ipynb). \n",
    "\n",
    "連続的な行動空間に対する例は，以下を参照してください． [examples/quickstart_ja/rtb/rtb_synthetic_continuous_zoo_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_continuous_zoo_ja.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考文献\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": []
  }
 ],
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