{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"2022-01-14-mab.ipynb","provenance":[{"file_id":"https://github.com/recohut/nbs/blob/main/raw/P920826%20%7C%20Multi-Armed%20Bandits.ipynb","timestamp":1644613989516}],"collapsed_sections":[],"authorship_tag":"ABX9TyPgqv1n0wEAKJ0oCISigkms"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Multi-Armed Bandits"],"metadata":{"id":"KR6cZY4I9aOu"}},{"cell_type":"markdown","metadata":{"id":"JL13uxO2M40G"},"source":["## Agents"]},{"cell_type":"markdown","metadata":{"id":"MHlgXpT9NRee"},"source":["### Base"]},{"cell_type":"code","metadata":{"id":"c1E-ptqGNRbF"},"source":["\n","import numpy as np\n","from collections import OrderedDict"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-w_5Of8fOAKd"},"source":["\n","class Agent(object):\n"," def __init__(self, name):\n"," self.name = name\n"," self.history = OrderedDict()\n","\n"," def init(self):\n"," pass\n","\n"," def step(self, t):\n"," action = self._step(t)\n"," self.history[t] = (action, np.nan)\n"," return action\n","\n"," def _step(self, t):\n"," raise NotImplementedError\n","\n"," def get_reward(self, reward, t):\n"," assert t in self.history, \"time t when the action was taken doesn't exist in history\"\n"," action = self.history[t][0]\n"," self.history[t] = (action, reward)\n"," self._get_reward(action, reward, t)\n","\n"," def _get_reward(self, action, reward, t):\n"," raise NotImplementedError\n","\n"," def reset(self):\n"," self.history = OrderedDict()\n"," self.init()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lS1tFETvNRYz"},"source":["### Epsilon Greedy"]},{"cell_type":"code","metadata":{"id":"ufQof1W-NRUj"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JIfMj5SROFrJ"},"source":["\n","class EpsilonGreedy(Agent):\n"," def __init__(self, n_actions, epsilon=None):\n"," name = r\"$\\epsilon$-greedy with $\\epsilon$={}\".format(epsilon) if epsilon is not None else r\"Optimal $\\epsilon$-greedy algorithm\"\n"," super(EpsilonGreedy, self).__init__(name)\n"," self.epsilon = epsilon\n"," self.n_actions = n_actions\n"," self.count_actions = None\n"," self.exp_reward = None\n"," self.init()\n","\n"," def init(self):\n"," self.count_actions = np.zeros(self.n_actions)\n"," self.exp_reward = np.zeros(self.n_actions)\n","\n"," def _step(self, t):\n"," if self.epsilon is None:\n"," epsilon = (t + 1) ** (-1/3) * (self.n_actions * np.log(t + 1)) ** (1/3)\n"," else:\n"," epsilon = self.epsilon\n"," valid_actions = np.arange(self.n_actions)\n"," if np.random.random() > epsilon:\n"," r = self.exp_reward\n"," valid_actions = valid_actions[r == r.max()]\n"," action = np.random.choice(valid_actions)\n"," self.count_actions[action] += 1\n"," return action\n","\n"," def _get_reward(self, action, reward, t):\n"," self.exp_reward[action] += (reward - self.exp_reward[action]) / self.count_actions[action]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OAyZI8S_NRSY"},"source":["### Explore-Exploit"]},{"cell_type":"code","metadata":{"cellView":"form","id":"28tOfZiONRQh"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"2U2TMoDROKjB"},"source":["\n","class ExploreExploit(Agent):\n"," def __init__(self, n_actions, n_explore):\n"," super(ExploreExploit, self).__init__(\"Explore and exploit algorithm $N={}$\".format(n_explore))\n"," self.n_explore = n_explore\n"," self.n_actions = n_actions\n"," self.count_actions = None\n"," self.sum_reward = None\n"," self.chosen_action = None\n"," self.init()\n","\n"," def init(self):\n"," self.count_actions = np.zeros(self.n_actions, dtype=np.int)\n"," self.sum_reward = np.zeros(self.n_actions)\n","\n"," def _step(self, t):\n"," count = self.count_actions.sum()\n"," if self.count_actions.min() < self.n_explore:\n"," action = self.count_actions.argmin()\n"," elif count == self.n_explore * self.n_actions:\n"," action = np.random.choice(np.arange(self.n_actions)[self.sum_reward == self.sum_reward.max()])\n"," self.chosen_action = action\n"," else:\n"," action = self.chosen_action\n"," self.count_actions[action] += 1\n"," return action\n","\n"," def _get_reward(self, action, reward, t):\n"," self.sum_reward[action] += reward\n","\n","\n","class ExploreExploitOptimal(ExploreExploit):\n"," def __init__(self, n_actions, n_steps):\n"," n_explore = int((n_steps / n_actions * np.sqrt(2 * np.log(n_steps))) ** (2/3))\n"," super(ExploreExploitOptimal, self).__init__(n_actions, n_explore)\n"," self.name = r\"Optimal explore and exploit algorithm ($N = {}$)\".format(n_explore)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"M-QO1m-CNROU"},"source":["### Successive Elimination"]},{"cell_type":"code","metadata":{"cellView":"form","id":"CWmNixf1Nat4"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"6FjtYgRVOUDu"},"source":["\n","class SuccessiveElimination(Agent):\n"," def __init__(self, n_actions, n_steps):\n"," super(SuccessiveElimination, self).__init__(\"Successive elimination\")\n"," self.n_steps = n_steps\n"," self.n_actions = n_actions\n"," self.count_actions = None\n"," self.exp_reward = None\n"," self.active_actions = None\n"," self.round_actions = None\n"," self.init()\n","\n"," def init(self):\n"," self.count_actions = np.zeros(self.n_actions)\n"," self.exp_reward = np.zeros(self.n_actions)\n"," self.active_actions = np.ones(self.n_actions)\n"," self.round_actions = list(np.arange(self.n_actions))\n","\n"," def calc_conf_radius(self):\n"," return np.sqrt(2 * np.log(self.n_steps) / self.count_actions)\n","\n"," def ucb(self):\n"," return self.exp_reward + self.calc_conf_radius()\n","\n"," def lcb(self):\n"," return self.exp_reward - self.calc_conf_radius()\n","\n"," def _step(self, t):\n"," if self.round_actions:\n"," action = self.round_actions.pop()\n"," elif self.active_actions.sum() == 1:\n"," action = self.active_actions.argmax()\n"," else:\n"," lcb, ucb = self.lcb(), self.ucb()\n"," lcb_max = lcb[self.active_actions.astype(bool)].max()\n"," stay_active = ucb >= lcb_max\n"," self.active_actions *= stay_active\n"," self.round_actions = list(np.arange(self.n_actions)[self.active_actions.astype(bool)])\n"," action = self.round_actions.pop()\n"," self.count_actions[action] += 1\n"," return action\n","\n"," def _get_reward(self, action, reward, t):\n"," self.exp_reward[action] += (reward - self.exp_reward[action]) / self.count_actions[action]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PH35rbInNarl"},"source":["### UCB1"]},{"cell_type":"code","metadata":{"cellView":"form","id":"DhZiSH-kNhT0"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"xt2Bkm8EPdoU"},"source":["\n","class UCB1(Agent):\n"," def __init__(self, n_actions, n_steps=None):\n"," super(UCB1, self).__init__(\"UCB1\" if n_steps is None else \"UCB1 with fixed total #steps\")\n"," self.n_steps = n_steps\n"," self.n_actions = n_actions\n"," self.count_actions = None\n"," self.exp_reward = None\n"," self.round_actions = None\n"," self.init()\n","\n"," def init(self):\n"," self.count_actions = np.zeros(self.n_actions)\n"," self.exp_reward = np.zeros(self.n_actions)\n"," self.round_actions = list(np.arange(self.n_actions))\n","\n"," def calc_conf_radius(self):\n"," T = self.count_actions.sum() if self.n_steps is None else self.n_steps\n"," return np.sqrt(2 * np.log(T) / self.count_actions)\n","\n"," def ucb(self):\n"," return self.exp_reward + self.calc_conf_radius()\n","\n"," def _step(self, t):\n"," if self.round_actions:\n"," action = self.round_actions.pop()\n"," else:\n"," action = self.ucb().argmax()\n"," self.count_actions[action] += 1\n"," return action\n","\n"," def _get_reward(self, action, reward, t):\n"," self.exp_reward[action] += (reward - self.exp_reward[action]) / self.count_actions[action]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PSJLN6NkNhCe"},"source":["### UCB2"]},{"cell_type":"code","metadata":{"cellView":"form","id":"57YFo814Ng_o"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"myR0nk0iPkeC"},"source":["\n","class UCB2(Agent):\n"," def __init__(self, n_actions, alpha):\n"," super(UCB2, self).__init__(r\"UCB2 where $\\alpha$={}\".format(alpha))\n"," self.n_actions = n_actions\n"," self.alpha = alpha\n"," self.count_actions = None\n"," self.exp_reward = None\n"," self.count_r = None\n"," self.selected_action = None\n"," self.init()\n","\n"," def init(self):\n"," self.count_actions = np.zeros(self.n_actions)\n"," self.exp_reward = np.zeros(self.n_actions)\n"," self.count_r = np.zeros(self.n_actions)\n"," self.selected_action = None\n","\n"," def tau(self, r):\n"," return np.ceil((1.0 + self.alpha) ** r)\n","\n"," def ucb2(self):\n"," tau = self.tau(self.count_r)\n"," radius = np.sqrt((1 + self.alpha) * np.log(np.e * self.count_actions.sum() / tau) / (2 * tau))\n"," return self.exp_reward + radius\n","\n"," def _step(self, t):\n"," if self.count_actions.min() == 0:\n"," action = self.count_actions.argmin()\n"," else:\n"," while True:\n"," if self.selected_action is None:\n"," action = self.ucb2().argmax()\n"," r_a = self.count_r[action]\n"," n_times = self.tau(r_a + 1) - self.tau(r_a)\n"," self.selected_action = (action, n_times)\n"," action, n_remaining = self.selected_action\n"," if n_remaining == 0:\n"," self.selected_action = None\n"," self.count_r[action] += 1\n"," else:\n"," break\n"," self.selected_action = (action, n_remaining - 1)\n"," self.count_actions[action] += 1\n"," return action\n","\n"," def _get_reward(self, action, reward, t):\n"," self.exp_reward[action] += (reward - self.exp_reward[action]) / self.count_actions[action]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jgWtcf2dNEh3"},"source":["## Bandits"]},{"cell_type":"markdown","metadata":{"id":"HyMfc-H_Pu7b"},"source":["### Base"]},{"cell_type":"code","metadata":{"cellView":"form","id":"W3f7BbovPtR4"},"source":["\n","class Bandit(object):\n"," def __init__(self, n_actions):\n"," self.t = 0\n"," self.n_actions = n_actions\n","\n"," def init(self):\n"," pass\n","\n"," def step(self):\n"," self.t += 1\n"," self._step()\n","\n"," def _step(self):\n"," pass\n","\n"," def best_expectation(self):\n"," raise NotImplementedError\n","\n"," def get_reward(self, action):\n"," raise NotImplementedError\n","\n"," def reset(self):\n"," self.t = 0\n"," self.init()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"249K9FEcP2oX"},"source":["### Independent"]},{"cell_type":"code","metadata":{"cellView":"form","id":"ZJbIs80YPtOe"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"NAr0nheJPtMr"},"source":["\n","class IndependentBandit(Bandit):\n"," def __init__(self, arms):\n"," super(IndependentBandit, self).__init__(len(arms))\n"," self.arms = arms\n","\n"," def best_expectation(self):\n"," return np.max([arm.get_expectation(self.t) for arm in self.arms])\n","\n"," def get_reward(self, action):\n"," arm = self.arms[action]\n"," return arm.get_reward(self.t), self.best_expectation() - arm.get_expectation(self.t)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gk0VpiIbPtK8"},"source":["## Arms"]},{"cell_type":"markdown","metadata":{"id":"HZLkZ7E9S1OW"},"source":["Types of bandits are:\n","\n","- bernoulli (default): bandit arms have bernoulli distributed rewards\n","- normal: bandit arms have Gaussian distributed rewards\n","- bernoulli periodic: success probability of the bernoulli distribution oscillates as a sinusoid."]},{"cell_type":"markdown","metadata":{"id":"3iJWMowzPtIu"},"source":["### Base"]},{"cell_type":"code","metadata":{"cellView":"form","id":"-mY9IFa2QL4H"},"source":["\n","class Arm(object):\n"," def __init__(self):\n"," pass\n","\n"," def get_expectation(self, t):\n"," raise NotImplementedError\n","\n"," def get_reward(self, t):\n"," raise NotImplementedError"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NPzkMf27QGA5"},"source":["### Bernoulli"]},{"cell_type":"code","metadata":{"cellView":"form","id":"0ylXypdDQVlO"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"b3hSvZiZQKwy"},"source":["\n","class BernoulliArm(Arm):\n"," def __init__(self, p_success):\n"," super(BernoulliArm, self).__init__()\n"," self.p_success = p_success\n","\n"," def get_expectation(self, t):\n"," return self.p_success\n","\n"," def get_reward(self, t):\n"," return np.random.binomial(1, self.p_success)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4JF5e13rQHGK"},"source":["### Bernoulli Periodic"]},{"cell_type":"code","metadata":{"cellView":"form","id":"OsZRM8PiQKHq"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"pOcnSJOVQa0A"},"source":["\n","class BernoulliPeriodicArm(Arm):\n"," def __init__(self, p_min, p_max, period, offset=0):\n"," super(BernoulliPeriodicArm, self).__init__()\n"," assert 0 < p_min < p_max < 1, \"wrong initialisation of probability\"\n"," self.p_min = p_min\n"," self.p_max = p_max\n"," self.offset = offset\n"," self.period = period\n","\n"," def p_success(self, t):\n"," y = np.sin(2 * np.pi * (t + self.offset) / self.period)\n"," return (self.p_max - self.p_min) / 2 * y + (self.p_max + self.p_min) / 2\n","\n"," def get_expectation(self, t):\n"," return self.p_success(t)\n","\n"," def get_reward(self, t):\n"," return np.random.binomial(1, self.p_success(t))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GU4RYgE0QHid"},"source":["### Normal"]},{"cell_type":"code","metadata":{"cellView":"form","id":"WvEkvePSPtF4"},"source":["\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"d7fLmQuTQfNW"},"source":["\n","class NormalArm(Arm):\n"," def __init__(self, mean, var):\n"," super(NormalArm, self).__init__()\n"," self.mean = mean\n"," self.var = var\n","\n"," def get_expectation(self, t):\n"," return self.mean\n","\n"," def get_reward(self, t):\n"," return np.random.normal(self.mean, self.var)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AJXsZY1wNJKk"},"source":["## Environments"]},{"cell_type":"code","metadata":{"cellView":"form","id":"2Dl0XpsyQjqm"},"source":["\n","import numpy as np\n","from collections import OrderedDict"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"9MauCBMiQnqz"},"source":["\n","class Environment(object):\n"," def __init__(self, bandit, agent, delay=0):\n"," self.bandit = bandit\n"," self.agent = agent\n"," self.delay = delay\n"," self.t = 0\n"," self.actions = OrderedDict()\n"," self.rewards = OrderedDict()\n"," self.regrets = OrderedDict()\n","\n"," def init(self):\n"," pass\n","\n"," def claim_reward(self, action, delay=0):\n"," reward, regret = self.bandit.get_reward(action)\n"," self.rewards[self.t + delay] = reward\n"," self.regrets[self.t + delay] = regret\n","\n"," def step(self):\n"," action = self._step()\n"," reward = self.rewards[self.t] if self.t in self.rewards else None\n"," regret = self.regrets[self.t] if self.t in self.regrets else None\n"," if reward is not None:\n"," self.agent.get_reward(reward, self.t)\n"," self.t += 1\n"," self.bandit.step()\n"," return action, reward, regret\n","\n"," def _step(self):\n"," action = self.agent.step(self.t)\n"," self.claim_reward(action, delay=self.delay)\n"," return action\n","\n"," def run(self, n_steps):\n"," self.reset()\n"," actions, rewards, _rewards, cum_rewards, cum_rewards_mean, regrets, _regrets, cum_regrets = [], [], [], [], [], [], [], []\n"," for i in range(n_steps):\n"," action, reward, regret = self.step()\n"," actions.append(action)\n"," rewards.append(reward if reward is not None else 0)\n"," if reward is not None:\n"," _rewards.append(reward)\n"," cum_rewards.append(np.sum(_rewards) if _rewards else 0)\n"," cum_rewards_mean.append(np.mean(_rewards) if _rewards else 0)\n"," regrets.append(regret if regret is not None else 0)\n"," if regret is not None:\n"," _regrets.append(regret)\n"," cum_regrets.append(np.sum(_regrets) if _regrets else 0)\n","\n"," return actions, rewards, cum_rewards, cum_rewards_mean, cum_regrets\n","\n"," def reset(self):\n"," self.bandit.reset()\n"," self.agent.reset()\n"," self.t = 0\n"," self.actions = []\n"," self.init()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4gQrA45cNLXN"},"source":["## Simulations"]},{"cell_type":"markdown","metadata":{"id":"_nxse79DNNfC"},"source":["### Experiment"]},{"cell_type":"code","metadata":{"cellView":"form","id":"MFeE3HngQtuk"},"source":["\n","import pickle\n","from datetime import datetime\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"Ku4vOfUxQzZs"},"source":["\n","class Experiment(object):\n"," def __init__(self, envs, bandit, name, args, save=True):\n"," self.name = name\n"," self.envs = envs\n"," self.bandit = bandit\n"," self.type_bandit = args.bandit\n"," self.eval_regrets = args.regrets\n"," self.n_regret_eval = args.n_regret_eval\n"," self.n_steps = args.n_steps\n"," self.n_runs = args.n_runs\n"," self.labels = [env.agent.name for env in envs]\n"," self.results = None\n"," self.entries = [\"actions\", \"rewards\", \"cum_rewards\", \"cum_mean\", \"regrets\", \"final_regrets\"]\n"," self.run()\n"," if save:\n"," self.save()\n","\n"," def run(self):\n"," n_envs = len(self.envs)\n"," n_actions = self.bandit.n_actions\n"," n_steps = self.n_steps\n"," n_runs = self.n_runs\n","\n"," n_reg = self.n_regret_eval\n"," Ts = np.linspace(0, n_steps, n_reg + 1).astype(int)\n","\n"," rewards_runs = np.zeros((n_envs, n_steps))\n"," cum_rewards_runs = np.zeros((n_envs, n_steps))\n"," cum_rewards_mean_runs = np.zeros((n_envs, n_steps))\n"," regrets_runs = np.zeros((n_envs, n_steps))\n"," cum_actions_count_runs = np.zeros((n_envs, n_steps, n_actions))\n"," final_regrets = None\n","\n"," for i_env, env in enumerate(self.envs):\n"," for i_run in range(self.n_runs):\n"," actions, rewards, cum_rewards, cum_rewards_mean, regrets = env.run(n_steps=self.n_steps)\n","\n"," rewards_runs[i_env] += np.array(rewards)\n"," cum_rewards_runs[i_env] += np.array(cum_rewards)\n"," cum_rewards_mean_runs[i_env] += np.array(cum_rewards_mean)\n"," regrets_runs[i_env] += np.array(regrets)\n"," cum_actions_count_runs[i_env, np.arange(n_steps), np.array(actions)] += 1.0\n","\n"," rewards_runs /= n_runs\n"," cum_rewards_runs /= n_runs\n"," cum_rewards_mean_runs /= n_runs\n"," regrets_runs /= n_runs\n","\n"," if self.eval_regrets:\n"," final_regrets = np.zeros((n_envs, len(Ts)))\n"," final_regrets[:, -1] = regrets_runs[:, -1]\n"," for i, T in enumerate(Ts):\n"," if i == 0 or T == n_steps:\n"," continue\n"," final_regrets_sum = np.zeros(n_envs)\n"," for i_env, env in enumerate(self.envs):\n"," for i_run in range(self.n_runs):\n"," _, _, _, _, regrets = env.run(n_steps=T)\n"," final_regrets_sum[i_env] += regrets[-1]\n"," final_regrets[:, i] = final_regrets_sum / n_runs\n","\n"," self.results = {\n"," \"actions\": cum_actions_count_runs,\n"," \"rewards\": rewards_runs,\n"," \"cum_rewards\": cum_rewards_runs,\n"," \"cum_mean\": cum_rewards_mean_runs,\n"," \"regrets\": regrets_runs,\n"," \"final_regrets\": final_regrets\n"," }\n","\n"," def get_results(self):\n"," if self.results is None:\n"," return [None for _ in self.entries]\n"," return [self.results[e] for e in self.entries]\n","\n"," def save(self):\n"," now = datetime.now()\n"," current_time = now.strftime(\"%y%m%d_%H%M%S\")\n"," filename = \"{}_{}_{}_{}_steps_{}_runs\".format(current_time, self.type_bandit, self.name, self.n_steps, self.n_runs)\n"," filepath = \"data/\" + filename + \".p\"\n","\n"," with open(filepath, 'wb') as handle:\n"," pickle.dump(self, handle)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IvPY1A1aQtrx"},"source":["### IID"]},{"cell_type":"code","metadata":{"cellView":"form","id":"wm9t2t6PQ7sa"},"source":["\n","import numpy as np\n","from datetime import datetime\n","import pickle"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"zFo2PM8vQ7o7"},"source":["\n","# from env.base import Environment\n","# from agent.eps_greedy import EpsilonGreedy\n","# from agent.exp_exp import ExploreExploit, ExploreExploitOptimal\n","# from agent.succ_elim import SuccessiveElimination\n","# from agent.ucb1 import UCB1\n","# from agent.ucb2 import UCB2\n","# from sim.experiment import Experiment\n","# from sim.utils import plot, plot_regrets"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"DMi39h6nQto7"},"source":["\n","def get_envs(bandit, agents, args):\n"," envs = [Environment(bandit, agent, delay=args.delay) for agent in agents]\n"," return envs\n","\n","\n","def run_epsilon_on_iid(bandit, epsilons, args):\n"," agents = [EpsilonGreedy(bandit.n_actions, epsilon) for epsilon in epsilons]\n"," envs = get_envs(bandit, agents, args)\n"," experiment = Experiment(envs, bandit, \"eps-greedy\", args)\n"," plot(experiment)\n","\n","\n","def run_exp_exp_on_iid(bandit, n_explores, args):\n"," agents = [ExploreExploit(bandit.n_actions, n_explore) for n_explore in n_explores]\n"," envs = get_envs(bandit, agents, args)\n"," experiment = Experiment(envs, bandit, \"exp-exp\", args)\n"," plot(experiment)\n","\n","\n","def run_exp_exp_opt_on_iid(bandit, args):\n"," return run_regret_experiment(bandit, ExploreExploitOptimal, args, \"exp-exp-opt\")\n","\n","\n","def run_regret_experiment(bandit, Agent, args, name):\n"," n_runs = args.n_runs\n"," n_steps = args.n_steps\n"," Ts = np.linspace(0, n_steps, args.n_regret_eval + 1).astype(int)\n"," final_regrets = np.zeros(len(Ts))\n"," agent = None\n"," env = None\n"," for i, T in enumerate(Ts):\n"," if i == 0:\n"," continue\n"," agent = Agent(bandit.n_actions, T)\n"," env = Environment(bandit, agent, delay=args.delay)\n"," regrets_sum = 0\n"," for _ in range(args.n_runs):\n"," _, _, _, _, regrets = env.run(n_steps=T)\n"," regrets_sum += regrets[-1]\n"," final_regrets[i] = regrets_sum / n_runs\n","\n"," now = datetime.now()\n"," current_time = now.strftime(\"%y%m%d_%H%M%S\")\n"," filename = \"{}_{}_{}_{}_steps_{}_runs\".format(current_time, args.bandit, name + \"-regrets\", n_steps, n_runs)\n"," filepath = \"data/\" + filename + \".p\"\n","\n"," with open(filepath, 'wb') as handle:\n"," pickle.dump((Ts, final_regrets, agent.name), handle)\n","\n"," experiment = Experiment([env], bandit, name, args)\n","\n"," plot_regrets([Ts], [final_regrets], [agent.name])\n"," plot(experiment)\n","\n"," return Ts, final_regrets, agent.name\n","\n","\n","def run_succ_elim_on_iid(bandit, args):\n"," return run_regret_experiment(bandit, SuccessiveElimination, args, \"succ-elim\")\n","\n","\n","def run_ucb1_fixed_steps_on_iid(bandit, args):\n"," agents = [UCB1(bandit.n_actions, n_steps=args.n_steps)]\n"," envs = get_envs(bandit, agents, args)\n"," experiment = Experiment(envs, bandit, \"ucb1-fixed\", args)\n"," plot(experiment)\n","\n","\n","def run_ucb1_on_iid(bandit, args):\n"," agents = [UCB1(bandit.n_actions)]\n"," envs = get_envs(bandit, agents, args)\n"," experiment = Experiment(envs, bandit, \"ucb1\", args)\n"," plot(experiment)\n","\n","\n","def run_ucb2_on_iid(bandit, alphas, args):\n"," agents = [UCB2(bandit.n_actions, alpha) for alpha in alphas]\n"," envs = get_envs(bandit, agents, args)\n"," experiment = Experiment(envs, bandit, \"ucb2\", args)\n"," plot(experiment)\n","\n","\n","def run_all_on_iid(bandit, args):\n"," agents = [\n"," ExploreExploitOptimal(bandit.n_actions, args.n_steps),\n"," EpsilonGreedy(bandit.n_actions),\n"," SuccessiveElimination(bandit.n_actions, args.n_steps),\n"," UCB1(bandit.n_actions),\n"," UCB2(bandit.n_actions, 0.01)\n"," ]\n"," envs = get_envs(bandit, agents, args)\n"," experiment = Experiment(envs, bandit, \"all\", args)\n"," plot(experiment)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MnGmBa_cQtlR"},"source":["### Utils"]},{"cell_type":"code","metadata":{"cellView":"form","id":"EpCORFwoRFTq"},"source":["\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import os, pickle"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"BJitSmZZRDL7"},"source":["\n","def load_data(filename):\n"," data_path = os.path.join(os.path.dirname(__file__)[:-3], filename[2:])\n","\n"," with open(data_path, 'rb') as handle:\n"," experiment = pickle.load(handle)\n","\n"," return experiment\n","\n","\n","def plot_from_data(filename):\n"," if \"regret\" in filename:\n"," Ts, regrets, labels = load_data(filename)\n"," plot_regrets([Ts], [regrets], [labels])\n"," plt.show()\n"," else:\n"," experiment = load_data(filename)\n"," plot(experiment)\n","\n","\n","def plot_regrets(Ts_arr, final_regrets, labels):\n"," plt.figure()\n"," for Ts, final_regrets_item, label in zip(Ts_arr, final_regrets, labels):\n"," plt.loglog(Ts, final_regrets_item, label=label)\n"," plt.legend()\n"," plt.xlabel(r\"Number of total time steps $T$\")\n"," plt.ylabel(r\"$Regret(T)$\")\n","\n","\n","def plot(experiment):\n"," n_steps = experiment.n_steps\n"," n_actions = experiment.bandit.n_actions\n"," labels = experiment.labels\n","\n"," actions, rewards, cum_rewards, cum_rewards_mean, regrets, final_regrets = experiment.get_results()\n","\n"," if final_regrets is not None:\n"," Ts = np.linspace(0, n_steps, final_regrets.shape[1]).astype(int)\n"," Ts_arr = [Ts for _ in range(len(labels))]\n"," plot_regrets(Ts_arr, final_regrets, labels)\n","\n"," plt.figure()\n"," for cum_rewards_mean_item, label in zip(cum_rewards_mean, labels):\n"," plt.plot(np.arange(n_steps), cum_rewards_mean_item, label=label)\n"," plt.legend()\n"," plt.xlabel(r\"Number of time steps $t$\")\n"," plt.ylabel(r\"$\\overline{Reward}(t)$\")\n","\n"," for actions_item, label in zip(actions, labels):\n"," plt.figure()\n"," bottom_sum = np.zeros(n_steps)\n"," for action in range(n_actions):\n"," plt.title(label)\n"," count = actions_item[:, action]\n"," plt.fill_between(np.arange(n_steps), bottom_sum, count + bottom_sum, label=str(action))\n"," bottom_sum += count\n"," plt.legend()\n"," plt.xlabel(\"Number of time steps\")\n"," plt.ylabel(\"Action chosen for each time step\")\n","\n"," plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nbt-dDjyRRrj"},"source":["## Main"]},{"cell_type":"code","metadata":{"cellView":"form","id":"RXx7c32CRSZ-"},"source":["\n","import argparse\n","\n","\n","def parse_command():\n"," parser = argparse.ArgumentParser()\n"," _add_experiment_parser(parser)\n"," args = parser.parse_args(args={})\n","\n"," return args\n","\n","\n","def _add_experiment_parser(parser):\n"," o_parser = parser.add_argument_group(title='Experiment types')\n"," o_parser.add_argument('--plot', default=\"\",\n"," help=\"experiment data to plot\")\n"," o_parser.add_argument('--exp', type=int, default=0,\n"," help=\"experiment to run\")\n"," o_parser.add_argument('--n_runs', type=int, default=200,\n"," help=\"number of runs\")\n"," o_parser.add_argument('--n_steps', type=int, default=1000,\n"," help=\"number of steps\")\n"," o_parser.add_argument('--regrets', type=bool, default=True,\n"," help=\"plot regret against the number of rounds\")\n"," o_parser.add_argument('--n_regret_eval', type=int, default=10,\n"," help=\"number of experiments to evaluate regrets\")\n"," o_parser.add_argument('--bandit', type=str, default=\"bernoulli\",\n"," help=\"type of bandit\")\n"," o_parser.add_argument('--delay', type=int, default=0,\n"," help=\"delay of reward\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"AQS-_y7aRYvf"},"source":["\n","# from sim.iid import *\n","# from cli import parse_command\n","# from sim.utils import plot_from_data\n","# from bandit.independent import IndependentBandit\n","# from bandit.arm.normal import NormalArm\n","# from bandit.arm.bernoulli import BernoulliArm\n","# from bandit.arm.bernoulli_periodic import BernoulliPeriodicArm"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"-77SYM2bR5hB","executionInfo":{"status":"ok","timestamp":1632229412022,"user_tz":-330,"elapsed":1267035,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"2691bfb6-e163-4b25-b731-bea7c0e0af2d"},"source":["\n","MEANS = [0.0, 0.1, 0.2, 0.3, 0.4]\n","VARS = [3.0, 2.4, 1.8, 1.2, 0.6]\n","P_SUCCESSES = [0.4, 0.45, 0.5, 0.55, 0.6]\n","\n","!mkdir data\n","\n","def get_normal_bandit(means, vars):\n"," arms = [NormalArm(mean, var) for mean, var in zip(means, vars)]\n"," return IndependentBandit(arms)\n","\n","\n","def get_bernoulli_bandit(ps):\n"," arms = [BernoulliArm(p) for p in ps]\n"," return IndependentBandit(arms)\n","\n","\n","def get_periodic_bandit(p_min, p_max, period, n):\n"," arms = [BernoulliPeriodicArm(p_min, p_max, period, period * i / n) for i in range(n)]\n"," return IndependentBandit(arms)\n","\n","\n","def main(args):\n","\n"," bandit = get_bernoulli_bandit(P_SUCCESSES)\n"," if args.bandit == \"normal\":\n"," bandit = get_normal_bandit(MEANS, VARS)\n"," elif args.bandit == \"periodic\":\n"," bandit = get_periodic_bandit(0.3, 0.7, 100, 5)\n","\n"," if args.plot != \"\":\n"," plot_from_data(args.plot)\n","\n"," elif args.exp == 0:\n"," print(\"Explore-exploit algorithm\")\n"," n_explores = [0, 5, 10, 50, 100, 150]\n"," run_exp_exp_on_iid(bandit, n_explores, args)\n","\n"," elif args.exp == 1:\n"," print(\"Optimal explore-exploit algorithm\")\n"," run_exp_exp_opt_on_iid(bandit, args)\n","\n"," elif args.exp == 2:\n"," print(\"Epsilon-greedy algorithm\")\n"," epsilons = [1e-1, 1e-2, 1e-3, 1e-4, 0.0, None]\n"," run_epsilon_on_iid(bandit, epsilons, args)\n","\n"," elif args.exp == 3:\n"," print(\"Successive elimination algorithm\")\n"," run_succ_elim_on_iid(bandit, args)\n","\n"," elif args.exp == 4:\n"," print(\"UCB1 algorithm\")\n"," run_ucb1_on_iid(bandit, args)\n","\n"," elif args.exp == 5:\n"," print(\"UCB2 algorithm\")\n"," alphas = [0.001, 0.003, 0.01, 0.03, 0.1, 0.3]\n"," run_ucb2_on_iid(bandit, alphas, args)\n","\n"," elif args.exp == 6:\n"," print(\"All algorithms\")\n"," run_all_on_iid(bandit, args)\n","\n","\n","if __name__ == '__main__':\n"," args = parse_command()\n"," main(args)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Explore-exploit algorithm\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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rINApz2Xh/l5VDNrLRs2bOCYY47hxBNP5Oijj+738YoEhb+PiJckHUDWx+By4JJ+n7nJIjqOdkRE6sjskoKZtYaI4LTTTmOPPfbgU5/6VF2OWWSU1E3p518Al0bEzyT1d5gLM7MhpcgjpPV222238b3vfY+3vvWtzJyZDTL9z//8z8yZM6fPxywSFFZK+g5wCPBVSaMZAnM7C6V+CuliBPKw2WbWQg444ICaWo/+K/LlfhxwC3BoRLwAjAf+tq65aJboZDVceWRm5VWkR/OrwI9z66vIRjVtcWlynVzRIHJbzMzKqOWrgfqqQ4lLucZluaHZzMqrtEHBzMw2VygoSHqjpIPT8thKv4VWVhkQj3wvZtUmmJmVS5HpOD9KNpz1d1LSFOCnjczUgMjPsBb5AfLc0mxm5VXkkdQzgLcDtwNExMOSJjY0VwNAEiiIXMkgG0bbxQQz671nD+3fmEO1tr/l9m63r1u3jgMPPJD169ezceNGjj32WM4777x+n7dIUFgfEa8rPaWTptNs+b+lo5Ol9vWWvzwzG+JGjx7NL3/5S7baais2bNjAAQccwOGHH87+++/fr+MWaVP4taTPAWMlHQL8CPiPnt4k6QpJT0talks7V9JKSUvTa05u22clPSLpIUmH9uVizMzKQhJbbbUVkI1/tGHDBlSH3rdFgsI5wBrgXrLhtG8GvlDgfVcBh3WS/s2ImJleNwNImkE2Teee6T3/WpmzuVGUX4hABOHuzGbWQjZt2sTMmTOZOHEihxxySL+HzYYCQSEi2iLisoj4YEQcm5Z7rF+JiN8ARedZPgq4LiLWR8TjwCNk7RgNVb0IxwIza0HDhw9n6dKlrFixgsWLF7Ns2bKe39SDIk8fHSHpLknPSVor6SVJa/txzjMl3ZOql7ZLaZOBJ3P7rKCL2d0kzZO0RNKSNWvW9DkTStPsRGUAJNySYGatady4cbznPe9hwYIF/T5WkeqjC4GTge0jYpuI2Doi+jqLwyXArsBMsqEyLujtASLi0oiYFRGzJkyY0MdsVJqTa5ub3chsZq1hzZo1vPDCCwC89tprLFy4kDe/+c39Pm6Rp4+eBJYVqTLqSUSsrixLugyYn1ZXAlNzu05JaWZmLaGnR0jrbdWqVZx88sls2rSJtrY2jjvuOI444oh+H7dIUPgMcLOkXwPrK4kR8Y3enkzSpDSgHsBfApUKsJuAH0j6Btmc0LsBi3t7/F7lhfZezVJUOzO7gcHMWsHb3vY27rrrrroft0hQ+L/Ay8AYYFTRA0u6FpgN7CBpBfBFYLakmWR1NMvJnmYiIu6T9EPgfmAjcEZEbOrsuPUSEQSR69ScKpA8IJ6ZlViRoLBTRLyltweOiLmdJF/ezf7/lywADRC1FxdqYoCfTDWzsirS0HyzpPc1PCfN0GGa5uhig5lZeRQJCh8HFkh6rU6PpJqZ2SBVZOa1lh8muzP5huVK+3JlOk4zs7LqMihIenNEPChp7862R8SdjctW40VkjcvZCNpRTXNUMLMy666k8ClgHp13MAvgvQ3JkZlZC/rRl5fU9Xgf/OysQvtt2rSJWbNmMXnyZObPn9/zG3rQZVCIiHlp8fCIWJffJmlMv8/cZNkwF5GWk2G4m4KZtZSLLrqIPfbYg7Vr69PUW6Sh+XcF01pKtFcepb4J1Q1mZi1hxYoV/OxnP+MjH/lI3Y7ZXZvCG8gGpRsraS/a/4beBtiibjlokvZ25srQeABR7aSw26RXGLnx1SblzswGu5HDt2HMiI0NO353xx6WvrLOPvtszj//fF566aW6nbe7NoVDgVPIxiG6gPagsBb4XN1yMBhEbni8tLzjkusZ9fAdzc2XmQ1aG2ZfzMjn/6dhx+/u2Boxgvl3/o6JEyeyzz77sGjRorqdt7s2hauBqyUdExE31O2MLcP1SGY2eN12223cdNNN3Hzzzaxbt461a9fyoQ99iO9///v9Om6RSXaGZEAQAtU0NKt9Rf0fFNbMrGG+/OUvs2LFCpYvX851113He9/73n4HBCg29tHQFfkZFCI30kXUDHthZta9U8+c2Ows1EW5g0IqGVQGwItUTPBTqWbWSmbPns3s2bPrcqxCQUHSO4Hp+f0j4rt1yUGTVHo0Z4OkxmalhXCbgpmVUI9BQdL3yKbQXApU5jgIoKWDQs8cFMysfIqUFGYBM3o7HaekK4AjgKcr8zFI+hrwfuB14FHgwxHxgqTpwAPAQ+ntf4iI03tzvt6S1KGfggANo9r47JhgZt1qayMi0CCegKUvsygX6dG8DHhDr48MVwGH1aQtBN4SEW8D/gR8Nrft0YiYmV4NDQhQaWCO6nJtQ/Pgvc1mNhjoySd5YcOGPn3xDoSI4Nlnn2XMmN6NStRdj+b/IPu+3Bq4X9JiOs7RfGQPGfpNKgHk036RW/0DcGyvcltH6rAcqdFZ7RujrQm5MrNWMfzb3+HZ0z/GM1OnwrAif1/X0bBhjCwQjMaMGcOUKVN6dejuqo++3qsj9d6pwPW59Z0l3UXWY/oLEfFfnb1J0jyy0VuZNm1an09eHTq78lhq5KqNPE2zmfVAa9cy4vyvNeXcw3fYgTf9ttOvyH7rrkfzrwEk7QysqoyUKmkssGN/Tirp88BG4JqUtAqYFhHPStoH+KmkPSNis2H/IuJS4FKAWbNmNfCr21HBzMqnSJnnR0C+LmVTSusTSaeQNUCfWGm8joj1EfFsWr6DrBH6TX09R6F8kKsqSiK/PkjrCc3MGqlIUBgREa9XVtLyqL6cTNJhwGeAIyPi1Vz6BEnD0/IuwG7AY305R++0Ny5XY0PUDH1hZlYiRYLCGknVRmVJRwHP9PQmSdcCvwd2l7RC0mnAxWQN1wslLZX07bT7gcA9kpYC/w6cHhHP9fJa+kYiOjyFWune7JKCmZVPkX4KpwPXSLqY7BvzSeCknt4UEXM7Sb68i31vAAZ44L30xBEBCqISC+SWZjMrrx6DQkQ8Cuwvaau0/nLDczUIeJgLMyujomMf/QWwJzCm0nsvIv6xgfkaAOk6yM3XLLVPvuaYYGYl1GObQqr3Px74BNk36QeBNzY4Xw1X6aeQLbcPlV351w3NZlZGRRqa3xkRJwHPR8R5wDto8OOiA6HDHM35yXUqO7ih2cxKqEhQeC39fFXSTsAGYFLjsjSQ8qWD2uZlBwUzK58ibQrzJY0DvgbcSfZt+W8NzdVg4JKCmZVQkaeP/ikt3iBpPjAmIl5sbLYab/PhbnOPqIKDgpmVUpGG5i0k/b2kyyJiPTBR0hEDkLfGyo9/12kAcFAws/Ip0qZwJdmQ2e9I6yuBLzUsRwNEuQWpNjUcE8yslIoEhV0j4nyyBmbSmEUt/8RmdJiZGaCtQ42RHBXMrISKBIXX03DZ2SCi0q7kJtsZqtyj2czKqMjTR18EFgBTJV0DvAs4pZGZGhiVjglqryqSa43MrNyKPH20UNKdwP5k36RnRUSPo6QOdlmxJ9p7NlefPEphoc3hwczKp9DYR2kCnJ81OC+DjIOCmZXPAM82PXiow3IaHC89hiQCOSaYWQmVNihA1j+h/fmj3GOoWeeFZmXLzKxpug0KkoZLerCvB5d0haSnJS3LpY2XtFDSw+nndildkr4l6RFJ90jau6/nLZQ3VB0Vb7MxUZVrWzAzK5Fug0JEbAIekjStj8e/CjisJu0c4NaI2A24Na0DHE42N/NuwDzgkj6es5DoUBpory5qT3JQMLPyKdLQvB1wn6TFwCuVxIg4suu3VPf5jaTpNclHAbPT8tXAIuDvUvp3Ixtz4g+SxkmaFBGrCuSx/hwUzKyEigSFv6/zOXfMfdE/BeyYlieTzf9csSKldQgKkuaRlSSYNq2vBZiOA+JV2xU61CI5KJhZ+fTY0BwRvwYeBLZOrwdSWr+lUkGvvn0j4tKImBURsyZMmND3c9Pea1kEoVyjsxuazaykioySehywmGwazuOA2yUd249zrpY0KR17EvB0Sl8JTM3tNyWlNVaHhmYRtTOwmZmVSJFHUj8P7BsRJ6dpOd9O/6qUbgJOTssnAzfm0k9KTyHtD7zYyPaEDu3JaY7mqD6W6pGPzKycirQpDIuIp3Prz1Kwf4Oka8kalXeQtIJsHKWvAD+UdBrwBFnpA+BmYA7wCPAq8OEi52gchwUzK58iQWGBpFuAa9P68WRf4D2KiLldbDqok30DOKPIceuj0k+hvb9Cdfwj4TYFMyulLoOCpNERsT4i/lbS0cABadOlEfGTgcleYylVE1VmXusQBhwUzKyEuisp/B7YW9L3IuKvgB8PUJ4GSBrviPaxjyrrAHJQMLMS6i4ojJL0v4F3ppJCBxHR0kEiav6thoXePyVrZjZkdBcUTgdOBMYB76/ZFgy5kkMNlxTMrIS6DAoR8Vvgt5KWRMTlA5inAZQePa02MFeGzsZBwcxKqUiP5iEaEPJ9FXJ9FJTfYmZWLoVmXhuKotK8LLUHB7Wna4uxSNs0L4NmZl3Q1ls17NilDQqZNPZRRDZfM0C0AfCVk7bgrhGrm5c1M7MubD9mLIsadOxCQUHSZOCN+f0j4jcNypOZmTVJj0FB0lfJejHfD2xKyQG0dFDoMNuaKj0VUrdmuVXBzMqpSEnhA8DuEbG+0ZkZWFGZmTnr0RwdNhG0NSlfZmbNU2Rgu8eAkY3OyIBTJ2Nkq707m8dJNbMyKlJSeBVYKulWoFpaiIhPNixXAyACpPYQUDtJc8hBwczKp0hQuCm9SqUtXH1kZuXTY1CIiKsljQWmRcRDA5CnASLae6tVll19ZGblVmQ6zvcDS4EFaX2mpJYvOWR9EqJ9NIuo1Bw5GJhZeRVpaD6XbArOFwAiYimwS19PKGl3SUtzr7WSzpZ0rqSVufQ5fT1HwZxUCwlRmVinMvaR8LNHZlZKRdoUNkTEi1L+MZ2+f2emKqiZAJKGAyuBn5BNv/nNiPh6X4/dq3zk5mOurldW5UdSzaycipQU7kvzKgyXtJukfwF+V6fzHwQ8GhFP1Ol4deM2BTMroyJB4RPAnmSPo14LrAXOrtP5T6B97meAMyXdI+kKSdt19gZJ8yQtkbRkzZo1/Th1mnktVwIKdWxuNjMrmyJDZ78aEZ+PiH2B/YCvRsS6/p5Y0ijgSOBHKekSYFeyqqVVwAVd5OfSiJgVEbMmTJjQ5/NXv/yzLs3t028q3KPZzEqryNNHP5C0jaQtgXuB+yX9bR3OfThwZ0SsBoiI1RGxKSLagMvIGrcbSNVOzYFyndcqJQiXFsysfIpUH82IiLVkYyD9HNgZ+Ks6nHsuuaojSZNy2/4SWFaHc3Srvd0g3zshGxHJE6+ZWRkVefpopKSRZEHh4ojYIPXvz+hU6jgE+Fgu+XxJM8m+oZfXbBtwrj4yszIqEhS+Q/YlfTfwG0lvJGts7rOIeAXYviatHqWPXsgPiFdZVlZCcD8FMyupIsNcfAv4Vi7pCUnvaVyWBkZlyOzsZ1u14qj6LJLrj8yshIo0NG8r6RuVx0AlXQBsOQB5a6o2NzSbWQkVaWi+AngJOC691gJXNjJTA6Py9FE21kWlhNA+5EXTMmZm1jRF2hR2jYhjcuvnSVraqAwNlMowF9W5FCKq3RYA2lx9ZGYlVKSk8JqkAyorkt4FvNa4LA2UNFy2cs0HHSZjc1Aws/IpUlI4HfiupG3Jvi+fA05pZKYGQgAoqj0UKkEg69ncVunDZmZWKkWeProb+HNJ26T1fj2O2hIC2lxSMLMS6jEoSBoNHANMB0ZUhtCOiH9saM4aLjcQXmpoDpQNeeGZ18yspIpUH90IvAjcQTZS6pBQ6aeQrUS1iaEyzIUfSTWzMioSFKZExGENz8mAS4+hqv1h1A4NzW5UMLMSKvL00e8kvbXhORlg7Y+i1sy9ltqcXVIwszLqsqQg6V6yr8gRwIclPUZWfSQgIuJtA5PF5nCbgpmVUXfVR0cMWC6aojJvQvty+3qbnz4ys1LqsvooIp5IcydPAp7LrT8PvGGgMtgoadaE6oB47dVH2bKDgpmVUZE2hUuAl3PrL6e0Fqean+3LbmI2s7Iq8vSRItoHAoqINklF3tf9QaXlZAPtbQI2RsQsSeOB68n6RCwHjouI5/t7rs7EZv+mpUprsyODmZVQkZLCY5I+KWlkep0FPFan878nImZGxKy0fg5wa0TsBtya1pvCA+KZWRkVCQqnA+8EVgIrgP2AeQ3Kz1HA1Wn5arIpQBtEHYbIltLgePO8rN0AABABSURBVGTjH4UfSTWzEioy9tHTwAkNOHcAv0jzPX8nIi4FdoyIVWn7U8COtW+SNI8UlKZNm9av01e/9yNqqpAiKym4CsnMSqbfbQP9cEBErJQ0EVgo6cH8xoiIFDCoSb8UuBRg1qxZ/fhzPo1rQXvn5Wx81KwE4c5rZlZGRaqPGiIiVqafTwM/Ad4OrJY0CSD9fLph5yfNqxNpDKRKaSHyD6uamZVLU4KCpC0lbV1ZBt4HLANuAk5Ou51MNhjfwOcPT7FjZuXU66GzK+n9HDp7R+AnaRjuEcAPImKBpD8CP5R0GvAE2ZzQDVJpaM41HEiVQTzcec3MSqkpQ2dHxGPAn3eS/ixwUD3O0WMecv9mP1Kv5jZguJ8+MrNyKvXQ2ZAamTt0avbTR2ZWXiUeOjv3b76jWrXR2SUFMyufIiWFA4BTJD1OSYbOFvB/xr2N7Te83uysmJltZsyorRp27CJB4fCGnb3JVC0pVBLSHM2Cw1c+yPQ1jzYvc2ZmXdlyYsMO3WP1URouexzw/vQal9JaWnV65ko1kSJ1UciqjhRtTcubmVmz9BgU0gB41wAT0+v7kj7R6Iw1nNTxJ5UWhqyXwjAHBTMroSLVR6cB+0XEKwCSvgr8HviXRmas0bJCQbQ3OKcgEJGNiSSPkmpmJVTk6SORzXlQsYkh/rBmAMMcFMyshIqUFK4Ebpf0k7T+AeDyxmVpIFUampVVGqWhs2NYm6uPzKyUigyd/Q1Ji8geTQX4cETc1dBcDYAIqoMcdRg4W5EmXnNQMLPy6TIoSNomItamKTKXp1dl2/iIeK7x2WugakNzpfNaVFZAuKRgZqXUXUnhB8ARZGMe5SvYK4OI7tLAfDVcVjpIzxsprVceSY1AbQ4KZlY+XQaFiDgi/dx54LIzWIQbms2slIr0U7i1SFrrSQPiVfs1V5KygpA7r5lZGXXXpjAG2ALYQdJ2tD+Gug0weQDy1liR/qlMwVZNCuQ2BTMrqe7aFD4GnA3sRNauUAkKa4GLG5yvpsqGuXD1kZmVT5fVRxFxUWpP+HRE7BIRO6fXn0dEn4OCpKmSfiXpfkn3pWE0kHSupJWSlqbXnL6eo2BOqj8i2ifgrPRVcEnBzMqoSOe1NknjIuIFgFSVNDci/rWP59wI/E1E3Jnmab5D0sK07ZsR8fU+HrdXKhVGUflX+Rolj31kZuVUZJiLj1YCAkBEPA98tK8njIhVEXFnWn4JeIBmtFF0mJs55U2izQ3NZlZiRYLCcKn9G1TScGBUPU4uaTqwF3B7SjpT0j2Srkglks7eM0/SEklL1qxZ0+dzRyoWVAbGax9Luw0pGOZ+CmZWQkWCwgLgekkHSToIuDal9YukrYAbgLMjYi1wCbArMBNYBVzQ2fsi4tKImBURsyZMmNDfbHQqEMM8HaeZlVCRNoW/A+YBH0/rC4HL+nNSSSPJAsI1EfFjgIhYndt+GTC/P+cokIv2n5XGhCRoqw6lbWZWJkVmXmuLiG9HxLERcSxwP/2YSyFVRV0OPBAR38ilT8rt9pfAsr6eo4iodlvLv9rnWHBDs5mVUZGSApL2AuYCxwGPAz/uxznfBfwVcK+kpSntc8BcSTPJvp2Xk/WTaJzcgHhtlRFTKy8cFMysnLrr0fwmskAwF3gGuB5QRLynPyeMiN/S+SQ9N/fnuL3PR6omSplRrodz5RkkM7Oy6a6k8CDwX8AREfEIgKS/HpBcNZkIvrzDV7j75W2bnRUzs81sPXpM/xp2u9FdUDgaOAH4laQFwHUMqWk4KwPiZb2YsyJC9syRBHe/vC1/eMFBwcwGnx02jm7Ysbsb5uKnEXEC8GbgV2TjIE2UdImk9zUsRwOkQ4/mtrQclQl34PUo8rSumdnQUuTpo1ci4gcR8X5gCnAX2WOqra3a0Nw+7pGq83O2sb7NQcHMyqdX33wR8XzqPHZQozI0UCIim4+ZSMtZeltbNg7S+k0OCmZWPv7m64QiWN82hJpPzMwKKnFQUM1y6tEsIcJtCmZWSqX95gsqfRNIg+FFdZA8ySUFMyun8gaFXENzW67NuY2s3PC6G5rNrITK+82XnkWN6tBHlWG0s43rXFIwsxIqb1DoQZvbFMyshEr/zVedjpN8c7NLCWZWTqUNCgHtLc3RRr6PcyVImJmVTWmDQkUom2ktk2ZYkEsKZlZOpQ0Kkf83gDSMdrbsoGBm5VTaoNAdVx6ZWVkNuqAg6TBJD0l6RNI5DTxTzc/21XBJwcxKalAFBUnDgf8HHA7MIJuic0YjztXePyENjpfvrxAOCmZWToXmaB5AbwceiYjHACRdBxwF3F/vEw0fOZIRY8YwatRIho8dw6ixYxg1YizDthjL8LFjGb/lqHqf0sysLsZvObJhxx5sQWEy8GRufQWwX34HSfOAeQDTpk3r84n222df2GffLrff2ecjm5m1rkFVfVREms9hVkTMmjBhQrOzY2Y2pAy2oLASmJpbn5LSzMxsAAy2oPBHYDdJO0saBZwA3NTkPJmZlcagalOIiI2SzgRuAYYDV0TEfU3OlplZaQyqoAAQETcDNzc7H2ZmZTTYqo/MzKyJHBTMzKzKQcHMzKocFMzMrEoRrTsmqKQ1wBP9OMQOwDN1yk4rKNv1gq+5LHzNvfPGiOi0929LB4X+krQkImY1Ox8DpWzXC77msvA114+rj8zMrMpBwczMqsoeFC5tdgYGWNmuF3zNZeFrrpNStymYmVlHZS8pmJlZjoOCmZlVlTIoSDpM0kOSHpF0TrPzUy+Spkr6laT7Jd0n6ayUPl7SQkkPp5/bpXRJ+lb6HO6RtHdzr6BvJA2XdJek+Wl9Z0m3p+u6Pg3DjqTRaf2RtH16M/PdH5LGSfp3SQ9KekDSO0pwn/86/b9eJulaSWOG2r2WdIWkpyUty6X1+r5KOjnt/7Ckk3uTh9IFBUnDgf8HHA7MAOZKmtHcXNXNRuBvImIGsD9wRrq2c4BbI2I34Na0DtlnsFt6zQMuGfgs18VZwAO59a8C34yIPwOeB05L6acBz6f0b6b9WtVFwIKIeDPw52TXP2Tvs6TJwCeBWRHxFrKh9U9g6N3rq4DDatJ6dV8ljQe+SDaV8duBL1YCSSERUaoX8A7gltz6Z4HPNjtfDbrWG4FDgIeASSltEvBQWv4OMDe3f3W/VnmRzc53K/BeYD4gsl6eI2rvN9k8He9IyyPSfmr2NfThmrcFHq/N+xC/z5X528enezcfOHQo3mtgOrCsr/cVmAt8J5feYb+eXqUrKdD+n6tiRUobUlJxeS/gdmDHiFiVNj0F7JiWh8JncSHwGaAtrW8PvBARG9N6/pqq15u2v5j2bzU7A2uAK1O12b9J2pIhfJ8jYiXwdeC/gVVk9+4Ohv69ht7f137d7zIGhSFP0lbADcDZEbE2vy2yPx2GxHPIko4Ano6IO5qdlwE2AtgbuCQi9gJeob1KARha9xkgVX8cRRYQdwK2ZPNqliFvIO5rGYPCSmBqbn1KShsSJI0kCwjXRMSPU/JqSZPS9knA0ym91T+LdwFHSloOXEdWhXQRME5SZVbB/DVVrzdt3xZ4diAzXCcrgBURcXta/3eyIDFU7zPAwcDjEbEmIjYAPya7/0P9XkPv72u/7ncZg8Ifgd3SUwujyBqrbmpynupCkoDLgQci4hu5TTcBlScQTiZra6ikn5SeYtgfeDFXTB30IuKzETElIqaT3cdfRsSJwK+AY9Nutddb+RyOTfu33F/TEfEU8KSk3VPSQcD9DNH7nPw3sL+kLdL/88o1D+l7nfT2vt4CvE/SdqmE9b6UVkyzG1Wa1JAzB/gT8Cjw+Wbnp47XdQBZ0fIeYGl6zSGrS70VeBj4T2B82l9kT2I9CtxL9mRH06+jj9c+G5iflncBFgOPAD8CRqf0MWn9kbR9l2bnux/XOxNYku71T4Hthvp9Bs4DHgSWAd8DRg+1ew1cS9ZmsoGsRHhaX+4rcGq69keAD/cmDx7mwszMqspYfWRmZl1wUDAzsyoHBTMzq3JQMDOzKgcFMzOrclCwQUFSSLogt/5pSefW6dhXSTq25z37fZ4PphFLf1WTPl3S/86tz5L0rUbnJ51rpqQ5A3EuGxocFGywWA8cLWmHZmckL9dbtojTgI9GxHtq0qcD1aAQEUsi4pN1yF4RM8n6qpgV4qBgg8VGsjln/7p2Q+1f+pJeTj9nS/q1pBslPSbpK5JOlLRY0r2Sds0d5mBJSyT9KY2ZVJmH4WuS/pjGo/9Y7rj/Jekmsl6ztfmZm46/TNJXU9o/kHUevFzS12re8hXg3ZKWKpsTYLba5344V9LV6XxPSDpa0vnp+AvSsCVI2idd6x2SbqkMe1CTrw+mPN0t6Tepx/4/Asencx8vaUtlY/YvToPpHZXee0r6HBcpG4P/iyl9S0k/S8dcJun4IjfTWlize/D55VdEALwMbAMsJxun5tPAuWnbVcCx+X3Tz9nAC2TDBY8mG9/lvLTtLODC3PsXkP0RtBtZT9ExZGPQfyHtM5qsh/DO6bivADt3ks+dyIZcmEA2MN0vgQ+kbYvopLcwud7WtevAucBvgZFk8yK8Chyetv0E+EDa9jtgQko/Hriik/PcC0xOy+PSz1OAi3P7/DPwoco+ZD37t0z7rSLrPTuWrNfwLOAY4LLc+7dt9v8Vvxr7cknBBo3IRnT9LtlkKkX9MSJWRcR6su7+v0jp95JV21T8MCLaIuJh4DHgzWRjwpwkaSnZEOPbkwUNgMUR8Xgn59sXWBTZwGwbgWuAA3uR3878PLJB3u4lmzxmQc017A68BViY8voFskHOat0GXCXpo+k4nXkfcE46ziKy4DgtbVsYEc9GxGtkA84dkPJwiKSvSnp3RLzYryu1Qa839aVmA+FC4E7gylzaRlJVp6RhwKjctvW55bbcehsd/3/XjucSZGPHfCIiOgwWJmk2WUlhoKwHiIg2SRsiopLXyjUIuC8i3tHdQSLidEn7AX8B3CFpn052E3BMRDzUITF732afUUT8Sdk0j3OAL0m6NSL+sbcXaK3DJQUbVCLiOeCHtE+rCFmVUuUL7kiy6pTe+qCkYamdYReyWapuAT6eq7d/k7LJarqzGPhfknZQNrXrXODXPbznJWDrPuS54iFggqR3pHyOlLRn7U6Sdo2I2yPiH8gm4ZnayblvAT6RRhpF0l65bYcomw94LFm11W2SdgJejYjvA18jG6LbhjAHBRuMLgDyTyFdRvZFfDfZlIt9+Sv+v8m+0H8OnB4R64B/I2tIvlPZROnfoYfSc2RDE59DNmTz3cAdEXFjd+8hG8l0U2qs3awhvScR8TrZ8M9fTZ/BUuCdnez6tUoDOFkbxN0pnzMqDc3AP5EF1Xsk3ZfWKxaTzcVxD3BDRCwB3gosTtVNXwS+1Nv8W2vxKKlmhqRTyBrJz2x2Xqy5XFIwM7MqlxTMzKzKJQUzM6tyUDAzsyoHBTMzq3JQMDOzKgcFMzOr+v9hD1tGtMH9dwAAAABJRU5ErkJggg==\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
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ViTBHKdZVGs2veva5WVT+JSHoo6uuvv9jbmteDUwILklTr9/v2V56X+1sbKp5orjADfccAN+6Id+CA94wAOwfv16POc5z8HHPvaxRcsd5uwjojks/Rtm9udy6+MALg/XlwP4mKQ/P8xCejSAe4e5b7zB0u9BcrauLTUzcInQa3N5yrbHP+r1oE96216b9XfbLp/SL1d531pSrY9OS3aX6Saf/V9/be7a13O/Om+6xX51rvk9xOLu91Lc76zJ1JnUbegrlg87d+7E2WdnP3rbtm3YuXPxfvQw6aOfBPALAL5KMp7N+lsA/hjAB0leAeD7AJ4d7l2D5iDv76A5jPyFQ7StoqKioqIDQ+sUzOxf0H8Q/YSO/AbgpcOyp0SmNXpQX1BpJaUeHA1BtLxCa/ubgS7pkI1ew4TQADNXSYliAkC2n2Wyq/Rxo12e1aAvSJbDaHEv36PSRIGcYUjv4ttEp6swy2wPTcqQbDNhi3oir1fYGDKVHjhF0WwgCru7xkc9/16EOiq/vOl9qnrGy4Jumt2yiopF46yzzsLtt+e5OTt27MBZZ521aLmrdu+j1FbJv95MNfjGtqQ42v/gO2idVqZAnwQB3bRJ+NzFdnBwgijbqHSXEDbKk3SUPzaflv4rtRT6nfIOio05m6upzsosaB8nvk1i+fQOWaK/bay3zb+XbHG39fpdMHT8qagYKh71qEfh29/+Nr73ve/hyJEjeP/734/LLrts0XJX7TYXPhYbXNgxJM/XskuecjfefYcQiHcP8fCT2y+BYiYVucGM6RKJpTYtzvVvN2p0HrEKz2Vg8bcM0CZbpbOkMzLcVhuL0UNRXTKKaPLoKMiNaqIMahmjzGKk4zoxZhUaLU7vTuQU78KPqnJdUO45kMUzQWfsGURGHSasTtzwSz+1rPrWrl2LN73pTXjyk5+MmZkZvOhFL8JDHvKQxctdAttGEi3vt49719cD7XST8+emTe6iK/w1+6QD2ijPYmBLtfr7HU+2RiGx/Nqwqzffz/Zu9d3lmU2ET+jbnnaNKNLfoLnviKC4tvJZf91dd9YtshVcruOEiuXDJZdcgksuuWRJZa5a+qiioqKioo1V2ykwUA+d/DuQeIpETbS4kUAxBQ/VCv6GZe4+XqyuAUi2RJrGGxR0RppCbQr8dqJMkAPpxg4bCk848ep5hGLCh5nzhrO1pvodrZLz0Re8hVYRY/ax7iejTiaKr8VlhbrTt6s17C+7YhfR/FSFYz56kKRIOct1JZVBqhhVrNpOwc//z1D6xFMprjko8sSH+1AH0ljPH330p7BFiw8Kt4UO0Q5G+Pq+DE//ihk02dvLLLTd2c2O3FD3o2nmma7vop8dHen+e1F+DyplVHH8YNV2CowevmljgXYrx8j8xxGBNnQ5uGgp7KkwFdMNcTHV21RpnU1OmlIqgVP3ZPit01qdKxxkxCmx4Vfyjt1UWbGFfmSTPOpWAUPrT4mPdGQzy9NdddQUM2vsO+rMwWp5L6VuliMkqdE4xTa9//wGYwDZLI4xolXy3hm7iFzPSLb7+VoVFaOGVdspRMJAG6TySjLCBRpd462et+bp9uCdSAB+nURsv2YPhKqnaq084S8LmX2M6bv+oEjr29DpsIJdtrSvVWp7Z1+Tai4b2D6y3Tuy7vQ+Ninl1sZs6e330m1rRcVoYdV2CisFtemoqKhYSVi1U1K9h2qJDUopMbhsZRAxX/aoCZF60IzWDu6W2eMvBj1Br+pv1k80NIXTySwuppf0C8rPorfVIUXuP+aL5RfJlgLwJgVQKqtF5Dh7E5XDdr1qsN4F4JXakvLriANSfkfxqV1pSbWvnByTF6+/DHS77wJzoDtZIC+/k06rON5x+tvPnjvTPLD7hbfPmedFL3oRrr76apx++um45ZZblkTvqh0ptBrEvi572cBEZqZ/QNOQm0lr5bHWlZVZ5mGWb3yKoKd12aKPzx4kNdOcBWXSRasJlWTMT/n8mi5W9VsPkWI+/agpL7c7tf0uBiDGOmIVqqfrjlKJ/YRWVCwdXvCCF+Daa69dUpmrtlOIE1ItebyWvFO2GgppQoK32DiMElzUgK4EV9k5lUc7GhO/t523cd59ExY97LRlRctBNy8g/NV9mFLjaN3No8mjTXA5+PxmYVqsFZ2ZpfLEevHOcmFT6guKaI4Vo5iC8+/aP9W/R19ssHuBYOfgzxkc9mEq9ZfvMwasZdqytWRVVAwHj33sY3HyyScvqcxV2yn0Rz+PVT8PElwsvGZtfK0rf9sGT5KUnnLHs61Rg6bP5WVr1n7eebSrn+x56u8qQpj5k/XPhY5RRWFv3Hiw/Yi1GnntX/sHugv9btRURwoVo405OwWSDyD5CZJ7Se4m+TGSD1gO41YDattRUVGxkjDISOG9AD4I4L4A7gfgQwDeN0yjlhN6EpeSHjGgmK5D4NWvJZDQbspvPlNrzqWfhulpEl0PAKEmmMUzONAxMBvsNzcFNj6c1ya0Y86J+EoZktmBTitPoiN80VypNKt+0DLKCCjSbymgXHI4bAe3VU+uo3YAOscCZM1GrLfCloSiXO3vAmRTvbI0lvRHM9pTbSsqRgODdAqbzOzdZnY0/PwtgPFhGzZ09GFTEn2gdJHF2IDlf/8uj/n8EOqnH1WU9FvI5pj1wVijLtmOyvAqo2BNdmUpygD4IrZudA5zLP8OZWsdd9mlvyyYoZuKsaL9buVpvyMWtFLZSSVdRX1SbZD37OIRol+v6xCwYlQxyJTUT5F8JYD3o/mq/xyAa0ieDABmdlfXQyTfBuBSALvN7KEh7QMAHhyynAjgHjO7kOS5AL4B4Fvh3ufN7CULKlFFRUXFMcAgU0iXGs997nNx/fXXY+/evdi2bRte/epX44orrliUzEE6hXhc5n8p0p+DppPoF194B4A3AXhXTDCzn4vXJF8H4F7J/10zu3AAe5YIygn4wKqbzh6yJgphTB7voAjo8gc3mUDJ48RZLQxnOPigcoddlHSleVSz2tQKAFumWorHk+4WlSasTKSCVOeYlskLLs9NQKReUr2gE2n9g45hdXU2GSi/4M1LOZsaa7voVtoSiL+WLTRQ6L00o6gos+lzpTz9qagYMt73vqVn8ufsFMzsvIUINrPPhBFAC2y28Hw2gMcvRPYw0EkwEEDnlNLygQ7aoJPmaX3IaX3pBpHNjnRHHZnP36GzbVYfe9Jf1d9VqFJ/R7KT07di+qDLFvPiOmi0VtkGqI+U5vvv/hkqKo5DDDL7aBPJV5G8Knx+EMlLF6n3pwDsMrNvS9p5JG8i+c8k+x5hRPJKkjeSvHHPnj0LNoDOy8uJsuN0cm67N6fLHmYKwEpA1nmQqjM8m9caSNB1LMqL8+5FgXqv8owLdLrRBeRENpGjpVDvn1KGpLMpTN4oTisQIQBMKb9ECOJIKGROj7da2lyPNkZNlX2p8tAjbZcdvHi6MmhdqL5YL+361BGNySjMn7wndQWkbcn9Nt5luSoqRhODBJrfDuAIgJ8In3cCeM0i9T4XfgbTHQDOMbNHAHgFgPeSPKHrQTO7ysy2m9n20047bcEGWAgiusVQ0QM1PZ3YRw3dltReYk53HnOf/GbFUzFJZEie9EsCn7EMZf62Gimb0xPKaOHKcglh+RmTILu1yhnkWEO95DzpkVb5W1b6Cmjq38zJj/XY7MJhOXuyuSx/1CnvUYPJUqZcn2XderNy7fQZ9Wj9DTQKqqhYeRikU3igmb0WwDQAmNlhLIIxJbkWwDMAfCCmmdmUme0L118E8F0AP7xQHRUVFRUVC8MgncIRkhsRXB+SDwQwtQidTwTwTTPbERNInkZyTbh+AIAHAbh1ETrmBIU/iDSLUkDNBnQxj3p/EqCEz5/Lo4roWIucnAmplM5sF8cKvoaFXHTRPcEufcxRJVaaBMbyh/vl2oRYfq9IrpR+KcoAmOgvzkqQZ1MeDeySUkdRbpSa7coUDqFrTvQZd/5C3IKjT8Q9lsnvnadeP8O3oBgzBv2pzBUVI4pBOoXfA3AtgLNJvgfApwH8xlwPkXwfgM8BeDDJHSTjPKnnoL347bEAvkLyZgB/B+Al/aa6Lh0K8keoiHw7X1PTlQfpmCefGopib//mPJdIjXSYUapmmWAdGfVzm7ax4rqLylKWprRlVp1a/nSz2H7O/N22nEJ/UZ8tM4DC1o46Eoqo7z57QLuXsSKTk1fKljzhFjvKXFExahhk9tE/kvwigEej+Wf0MjPbO8Bzz+2T/oKOtA8D+PCc1i4hokfYfUgN8vnLKD1yS56kC0Ym71DGFDqiQOOJu/ZCvfsoG5ZlJ1vQvo6edSEvesaMVhR2+woQvTIaIszpT3ZaLjOdXd11mAPFKO5ZKqva4jx99pJX77cxz+XQ1dA9GYm4QYCMYPwqull6ZfSpczQyKKOa3K94eXWwsPrwhOt/cknlffriz86Z5/bbb8fzn/987Nq1CyRx5ZVX4mUve9mi9M7ZKZD8tJk9AcAnO9JGFuq1Uq67cqXLFn8Rr9vPtqieTiOkgZWcGqjt82Thqbbc8XaHkQxri2ErRexzyR12DdD6UWUxXjal7t64ukOPm3uarwdxyqmdQOe02lJ9V+eR81s8c2IuORUVQ8batWvxute9DhdddBEOHDiARz7ykXjSk56ECy64YOEy+90gOQ5gE4BTSZ6E/M/gBABnLVhjhceqbU9WbcErKpYMZ555Js4880wAwNatW3H++edj586dw+kU0KxgfjmaTfC+iNwp7EezUnmk4Teya/4qfZGCqJYTGGmPFFDsGjo0st2K4JRHA7DNZ0dNAV5mpKn0WaWcZJ1AOjfAUTbeo6Wkt8oPNBGmXqTMWPAwUicxecza9xHplVD+rnhucd8Fw0uBBSeU1l0EGSlLixqL5WcqbWlLerVRyJiXld4Fm1qmXCchBX1Y6qmoWC7cdtttuOmmm/DjP/7ji5LTt1MwszcAeAPJXzGzv1yUlhWILkY5p+imdEJwSJzRXfeTbNoVtJn18rksu2PTNqVLrJXSn2myzpydwdB+dZJEFJRUTipIoPSAST0VUgtWJhNJOcrDdK/jukuYyY9+ljKXVF1MT7LNW1sSVQ3zJeVq1WPfL0ZFxdBw8OBBPPOZz8TrX/96nHBC5xKvgTHI7KM7SW4FgLCy+SMkL1qU1hWATm9Op4G2vECimQJZuJuxtUJXB2BeRvJAo7zsjbLU1+Vit/htc/nLbb3doxAPF0jTUx3UvpY9hcA4ImF+OJWfHSOVuBK5VaY8onKrr8ektGyXvHU9BjfqSvsT9e3qSltMhi1IHWCyqyy/yiSKYHSZr6JieJiensYzn/lMPO95z8MznvGMRcsbpFP4HTM7QPIxaNYYvBXAmxet+RjDWqtRdcqmTmUsGpUyT+cq2pzHedjx2Q7ZeUGyevJtD789VFBvN9iF0ETHEYd6tpApsTIbJxclr3LWVcR55XT4lbzyZHgqW6scbJdJYf2e0zp03r6+o470dq05bZruxmRlmVvvWVc6e/393ktFxTBhZrjiiitw/vnn4xWveMWSyBxkl9SZ8Pc/AbjKzD5JcrHbXFQEzHJs/PGNVVrsiuMXg0whXWp89rOfxbvf/W487GEPw4UXNptM/+Ef/iEuueSSBcscpFPYSfKvATwJwJ+Q3IDj4GxnBvrGEArDxtNkQRP0D2IWeeJFooQiLcTWY21LMm/jZQmf4zbHo6c0YhYAgCXevNwCnNIFUWTr6t5cfkMKdDtbot5IpbUD3brxX7lmIKa3q8GaFzETPH5dRR1lxqRYrrFYDj9aS6vC47tIRaXkKeqIhb1pmXf8KcrPnIfooI8qKpYBj3nMYwrWY/EYpHF/NoB/APBkM7sHwMkAfm1JrThWsI6Pwox4IkgpmFkEuc30EjGT83UcQ2ZZg5fvNsTrc+3s9H81TylbdbbuRtrJUOQpZfjfnZRMh+YEunlVHXpEtlBj7Wthuwr9Dc1j6b0g5bMcfwjP6F8tlDJZEJ3ZxPJzRcXoYpAVzYcBfEQ+34FmV9MRR/AVdbtnfwfJ97YcpHVnGmuLpj2GE8Tkhfrmr9DCRnaXkxmbzmalMZI8o29O+0RDRX++diMSXw2hbOzweMvm3pLX7Czvtxy4tejLm5oXtXl58bzsPJWVXY8nu9ofpfVPI5lcV1Gi6ahNpJu8LovDi2iXiFaD6mChYlQx8jTQQuFGXG7LhcKz7+fldiV1NKLtxwp57Ejr1DLWRK4AACAASURBVGn9zUr6S49f/7ZHD/pcXnDc7er2beRa2f2Iozu57D3bY5hSJ1v5OnT2q/9iHFPeN63bvu+iT3qf8tcBQ8WoYtV2CisFq7fxWL0lr6hYyRioUyB5f5JPDNcb47qFUUZiEJQlYU5wAWd3zXRpYxC6QIKYIpeygVvnNH19VgLNrZO84mljDtmLZXgomdtBEzGSJI4+Uf0isqSTxIxkTlGeRPxIkNoHmtnnWm3MMd5cWqX4+owKmG3Q3EzGWlGkQnq02YDODQF95kK90mLWeqSiYpQwyHGcv4hmO+u/DknbAPz9MI1aFvjIpAz6NbIIaZmUxjHJa+ERPcXMq4lp7QCspHdt+EbRg15pvLPLAN+QdVIfuXwuuN137+x+NJZ2RjlfV/n0k/XLW9R/zEdJcx0i9R1le8xyeld4vA9x5N9xZ2fg6y+XueOddZS+omKUMMiU1JcC+DEAXwAAM/s2ydOHatUygMF7TOcPI/5TzoFObeLSFcWLd9MWZaSQHim92qzLjyaA0s019Ujd5kDilXYFNYNys2yPHw3Jn6gjiG+vCI7XFmxpB7otTgktXWPV7/7G61x4qs4UxA0rpINdSYouuGOSlNN1CFjY1ZoSWw4tyFRvrvxhdTUJf460fBf8NuJ0I6SK1YF9T17cnkMlTvmHL8x6f3JyEo997GMxNTWFo0eP4lnPehZe/epXL1rvIJ3ClJkdydsxcC0w+o5QV4OfP/ejANrpemawy+keNneZ/cgejAUVYbPY1tG+Nk2QJsQGryOtJbLrNc42QuhKLq0V/Z26Zt/jiSKT7rmMwdrbLmOL64753ZG26tbRkV86f32ujhUqho0NGzbgn/7pn7BlyxZMT0/jMY95DJ761Kfi0Y9+9KLkDhJT+GeSvwVgI8knAfgQgE/M9RDJt5HcTfIWSfs9kjtJ3hx+LpF7v0nyOyS/RfLJCynMSGKVth39ZjlVVFQMBpLYsmULgGb/o+npaRerWygG6RReCWAPgK+i2U77GgCvGuC5dwB4Skf6X5jZheHnGgAgeQGaYzofEp75H/HM5mHB0SkWTxvzAU2lBpIXKPyzWQzaZq+4fCc+6Nlw1qQ4qRJRbQ5vCattZajRE/kpnbN4tJSbacmuUildvHljYUn6NDpjXnN2uf2hHFNG7zsniiaONkP2xPLEOsmUj6m9Giswyh5NWgblicr6QBuONlLjLa+eTsVzlZRt12zGLmavomKomJmZwYUXXojTTz8dT3rSkxa9bTYwQKdgZj0ze4uZ/ayZPStcz+nmmdlnAAx6zvLTAbzfzKbM7HsAvoMmjjFU+EalK0M/KqWdZBB+Of42WSWb27UO/eYFdelsHV/ZNsOxS5H371MCc88YcrC2K1cXOtI7ZkdZkilPBQO1/ju38tbEAU5Mi3RTehduE79cVl0FHS/iO8oSon5Xq/qAGpoNqAOgimXEmjVrcPPNN2PHjh244YYbcMstt8z90BwYZPbRpSRvInkXyf0kD5Dcvwidv0zyK4FeOimknQXgdsmzA31OdyN5JckbSd64Z8+eBRsR165adLnRdg6jo23yr90ojUa6tmLGqI/YpvhpWiVMpCCy6lSvWrzXFPRmcwZOHATEDidr1daPrQGCRTuSBX7EYLBi+BnqiJSmsfDQtUgdQ5em7HkmUfe0XDFK34UEnV0eyGhDYK788fF+QV8fxC8HG7Gush7tcuQqlN8oK7wrKpYZJ554Ih73uMfh2muvXbSsQeij1wO4HMApZnaCmW01s4We4vBmAA8EcCGarTJeN18BZnaVmW03s+2nnXbaAs1oN6gmqZrS9pb9VElHOLWmkHb56b5RiZ2S811N8lrUmZ/TKZGU/Ea5TlNYi5IWLrlvx7trJenssMVNz03y+3nY+aNrYvUZbVQ7z0ouG95+oxmpZxfQzvfLHaq83lJ3v/Jop1zqrqgYHvbs2YN77rkHADAxMYHrrrsOP/IjP7JouYPMProdwC2DUEZzwcx2xWuSbwFwdfi4E8DZknVbSFsFWK0NyGotd8XxirmmkC417rjjDlx++eWYmZlBr9fDs5/9bFx66aWLljtIp/DrAK4h+c8ApmKimf35fJWRPDNsqAcAPwMgEmAfB/Bekn+O5kzoBwG4Yb7y52ULMu1B5lXILlRYcsRlcDH4zGMuUU5ndnI95aGnOCcPM+7hHTMLZWJ6nYRkLomJBtPToTV03DBS1uL9lc+RJ8vkpCtTWfo4W0uwUdSniVxfqYzeP5mOw/SB4PjH5J3oiMGPFvRUuSzGZDduy4+zfBfNDZMxdN4b0G9CaDGyHH7MvNkVFcPEwx/+cNx0001LLneQTuEPABwEMA5g/aCCSb4PwMUATiW5A8DvAriY5IVo/gXfhmY2E8zsayQ/CODrAI4CeKmZzXTJXSpY3OI6MTaBSGCmUGTBc7ooaZhWGxc+xsbXJG+bmOqFRk7vK4VTUhPKbkMaZ5NrtJ51dnVKb9NGLdlCqTj9Ukft40A9OWOxzIgkVLjuqIOWLcqmobiOSSyeNbVL6CRnY05tyStqqBNSfpb6KypGEIN0Cvczs4fOV7CZPbcj+a2z5P8DNB3QMkGCusW/Yl1t3CQon5296eic5y2dmzylk5umrWqwtDgBh3M0JvJ00eAGT1cGGG5vI7Fd80QPueXWJuPNKXVBcCl/fi7HWprH8mbjLpteJztyXfQ9iKer93XNu7yXoqaSra3yi/CWey9jDCmsiYyu3ZQYvyN1uFAxohgk0HwNyf84dEuOBZyraK0b5f49s+UtfU0vuiuPuLotD9tbkT9FakVHEC2furmmifdt7VFPn+u8f5C/wVZiuxy6BbXSVEXubK3JGCI+qsMP96Sgtb15W4tJntaMIbE36+/SpZ/LvafK0nWXsqJi1DBIp/BLAK4lObFEU1IrBLX5qKioWEkY5OS1kd8muwsaWFYqyLXScemxO+HMcmCxLbWPMrko6BdlRUqOO5nQGklkKiPtveSPgWtx3Z6WofxxHFYmfYoT6eJ01zjRFgB6YBOELWIZuUy5rqLVrs5KXknyRhIuPtM5Hoib5qmwWAYry1XSPUgvIdNLvsyu/CEI7cY/QrXlQD+87oqKEUPfToHkj5jZN0le1HXfzL40PLOGj4Yl0a2jI9si1y5dnjNInvBM5l9CI2KJlRiLOjpkxoVx+Za1ZLdXBAcNemSmxXKY9A+eCvKTitu0R6yTKMP0ObWlRS9Z+6RNyrMin6nWmR5n0Eupv/yUvAtleVwPI/VXBvVbrE42PjJcrkh++XO6Tus0RJZ+F/Ij5spcUTFqmG2k8AoAV6J7gZkBePxQLFplWLVzVRa/7KWiYkXhQ39045LK+9nf3D5QvpmZGWzfvh1nnXUWrr766rkfmAN9OwUzuzJcPtXMJvUeyfFFaz7GYKADmusAOUkNLFcIZyonT9m37JHHx8KsnswY6T0TLseiIY0nKtRJSo/bT7sN27rK0vxiojKybop9JRUVH25UN3PwKeWPeuMZCoy2tGYC5Y1APJWk44FMt3g6i3kbjDBrh4V+xHoz5ChYWe9RftSXdIkrr9dJLmQbDrGfvfZ3QaYr+3JblgUvr6Ji2HjDG96A888/H/v3L02od5BA878OmDZSyJscmF/Q5do6bXTK5K7N3grqojM9kifCRbAcMfTxortsEzGqt7QhXXdu+VBSH+4B0Vcock92SNSzFpIYaz2lG054nd2lcg1uh0m56e6qhTIfQp1Y6znrU+byDIW+cisqhowdO3bgk5/8JF784hcvmczZYgr3RbMp3UaSj0D+t3gCgE1LZsExQo4z5xn12WWP3nGzwtYKD5ShEWfwcpPAJFvCo+WJbCkvXe5gSo53tgj6KK/5yXFRk8IEPVTp8ZesKI6eOhOTD7IJIrc8/eQhlxWn6X6jO1ch8U98LgV3fWA41w0lT7RV85rfV1DtDHVE0eDOggbzOgNnqujS91IegASE0WR+577c+ftRRwoVy4GXv/zleO1rX4sDBw4smczZYgpPBvACNPsQvQ75e74fwG8tmQUrAZZ9RLdyN1wmnzBcq7fr70keSS/9VlGc9ZvzUbsdZnY40k6U2hW9XeStI3RU49YjyHbSLa6/Y2Th6qs0IlWSlN8X1+WNxdcyeNOaoksRqPoDxaYymurMspvOL45WeklOlOEC3fquVKfY4kpgullgLkwdKVQMG1dffTVOP/10PPKRj8T111+/ZHJniym8E8A7ST7TzD68ZBorHFZv47F6S15RsRT47Gc/i49//OO45pprMDk5if379+Pnf/7n8bd/+7eLkjvIITvHZYfAFDmVob5QI+lQdt3DSLIpHdQ/0JtFdlIKiVqh0x0Ss3taBlDFVhNlXdSIOx9BaSU9WCFHSAveKZui1E9ZwESQRXvHct1lKq0sYq5FLz/eNmig2cSu5JRLmtJpOZBdbETRyemU6xTC54ImSu+/2BBQq6JYGlFRMVT80R/9EXbs2IHbbrsN73//+/H4xz9+0R0CMNjeR8cvCqqnPF6SVnYImaqI6SXNkSiJnNx5PwuyFpUDRB1Cr7QEKq3iz0vIC9oi9ZF1lAHdPM++T3rUX1JZFgiXWAlSGeW6CjU9hZRdxViy20R/13qQ9pqNXH7NXwb3VWXejiOnlNSWs6VI1ypM6zPclwHlh4pVgEGnkK50rO5OIXinObjKlGxAmpLqVhTHZwoZKL3EzmmJcXSSRwaUazdXU4LAaURDNCuN1Z74O8htj0ikScvDBKRRByEtW3MvTV0t6iU16UGHab6Uv2gMy2mgZZ25H6b8fhO6Jr3h/n3vGDu9WI95c8Ky9rN+tmxWO5G8/pgvTbglXPn986qn9RIqKoaKiy++GBdffPGSyBqoUyD5EwDO1fxm9q4lseAYIXqTzT/37Ff60YJ4lC2X0CSPbwitlKMNo07TBBqZvThigNxX19PrTyuXs5RQJpMnZWWwa7CkrN6SUFQ5Ma7TlqwHMDeaaq+YjmVsa8zlSIrhPXZfz2ZlWtAvpWltPBinmhLyvEgv+q80wtBRScdoId/I35FUs+ZzV1SMGubsFEi+G80RmjcDiGccGICR7hRWClZv87F6S15RsZIxyEhhO4AL5nscJ8m3AbgUwO54HgPJPwXwNABHAHwXwAvN7B6S5wL4BoBvhcc/b2YvmY+++SLNQQ/rFAiAEly0sUzlMNI6hAvNE27tcrEGAPANn6cnHHvRuRFbDHpa1g2/ZqIfQ6H6M8MRt3SLouOnWPpAj0gAOtEmY1kGxjz9Fe0Vtqd97erL5FOwKpZNH4ib3TF9bLzwMakjCn3klklnrSUzZ1F3pMBK3s8ZbOlvSmYO6Ccqz7FFdTXzakGc+uwmc6wwLOQU5UFWNN8C4L7zlgy8A8BTirTrADzUzB4O4N8B/Kbc+66ZXRh+htohACWR0vxWyqe9CVzIJayKo2/K/C1yJlynOfNxFW3kNroCmtZicVyg08QGy3+dHGQqyZFAVnwujG0FutFOV2H5UtZ7KMPSCtojryTv+OKmYDk6gs5aR2qXlSWK9ps8mwXlMxeEompRRx06tf6FXpLXVLEKcPiuozh4eP+CGt7lgJlh3759GB+f365Es61o/gSa7/dWAF8neQP8Gc2XzWHQZ8IIQNP+UT5+HsCz5mXtEsJ7sJbdyXAzreQVD12cWPe38bKjD1rIjsHJUn/hoLYCn3EfpmRDqcuakU0cDRSjg1QO0acbTbcOqkn7MolRLggbVy17Lzw2qF6PFlRUJs+bWXdZWelaYiJxpNJVuSk5HoKT4wxl3YnWzjpqvxRNb9dzLnfW2YxIVmYjUbG0+Pf/dRDAndh08t4+052HB44Bew7M3diPj49j27Zt85I9G330Z/OSNH+8CMAH5PN5JG9Cs2L6VWb2v7seInklmt1bcc455yxYeQwqJoe2mGdaeoSMnn14Lh9s1nKj/ZTVok1PDVP0MGOn0zklVOQ1RvTxYLPdEBtjnjEJhpLiySPrjl4vtQwdejTg66izYjRgSU9mYcxEv68yN4IpRwGI5Qk2UWxuBXpzUtt2AGAvvRe4/KFhZymjGEGKUDdZoXxfFcc9jk4avv6ppdteYj7YeMJ6vOi1jxiK7NlWNP8zAJA8D8AdcadUkhsBnLEYpSR/G8BRAO8JSXcAOMfM9pF8JIC/J/kQM2tt+2dmVwG4CgC2b99+HPzzOw6KsCCs1nJXVKxsDBJT+BD8AbUzIW1BIPkCNAHo58XgtZlNmdm+cP1FNEHoH16ojoHs6IiEmvssQd2xIrgYOAcCeRUvGO55GqYcJSTt1GtPWzRUSaZ6jJYDwCEAnlgMoVQ0SJxiwUrfCDeUyx/1By9XeRUUlFOk2WKIlomokfUB8ZlYL7Es5ui5FlUXn2C2OZ0Cl8yJvjtzHcW8siW5r04WVJnXmQPdMUcowxhTcDoHqH2eJgCf37ml8i8zl1BRsYQYpFNYa2ZH4odwvX4hykg+BcCvA7jMzA5L+mkk14TrBwB4EIBbF6JjfsjUQO4LSr6h5DdinmJlrvlM7TUDOegbZcSFUNbmZyAPt9L1hLVIoWRt/rlShDmDPT9k/pcrt6OTWjSJ0F2mT2cqK93WfiLJyTWjW4q70HsX59TXvqTAUUnu0W7urajnoi468rsV4vJe6kioYlQxSKewh2QKKpN8OoC9cz1E8n0APgfgwSR3kLwCwJvQBK6vI3kzyb8K2R8L4CskbwbwdwBeYmZ3zbMsCwNZ8NrRO8/BRb91drtRjZ6kNob5L1OerLPLEHPpDcMdvXc/AphVjNrqm0N0bfPtPuuoYyw0eGHfoexNx1Y2lzEOHvIcnLa9ebZo2cnqttlJGnK8wsKoITS40WtnfkephF0V0qesaYRHNYXoaspN6s3YKnkxYrPuBdUVFSOCQdYpvATAe0i+Cc1X/XYAz5/rITN7bkfyW/vk/TCAZd54z9I/Yt1wzYRqcN43tckvvUz9mJuM3Nn0eTa0aamRS42tNt79PNV+stv5+/jELhjsp4dq9xiTpUMsKbdObUUXKeIt6tagc9d2GH2cbWdXqdMKRSHdT0tFqvcm+F0IDcFob0u+pok8HRp1jGAqKkYNc3YKZvZdAI8muSV8Pjh0q1YRVmvbUR3pioqViUH3PvpPAB4CYDwGRc3s94do1zIgBzHTec2BB4p0UAp0GmDm88ervGmdNvDx1K/mYZ3fH0kpiZFmSxjkOUojO71BXHpCg76qt3SlXVBV5gzEvD57LJ1GHzIllIOpviob26hJmZVSyisE0HWqrtI3KiCN3uS2I6hkJICgn5Hv62bcUkA8pdPLcAVwVFaoFWvqIN0JxuTN+vpQdBUVI4JB9j76KzTHbz4OwN+gWXB2w5DtGjr8HPNMO+TfBsiCKP1xzWWxKjb+6HQt0Zrl00RSueGa6oxphY7UUuZrE3u9lVqO/Fs3kGvTPl6PddgSN5tLgW5mPT1IiVoyQgMf5MToiZ/f723yt9r2GgCyl3JrYN/n7KX03Jl01b8vc2eAHtpxWQ5uo6JidDFIoPknzOz5AO42s1cD+A8Y8nTR5UD2FOk45ezgmfPas0OpnrJMmjSZJineuXNpo6CWF1keRSP9QrxfLpmMwwaXMQYp5poUqW50ESx15fSefylUi8Y4gmEuT/SdY0NK96DotxhIVvXRLk0PDbJO/ZX3oYHfufTndx73QcpDFxZlMB2a5EGiqw/Kl6g7XF1RMRoYpFOYCH8Pk7wfgGkAZw7PpOWE9+y8D9gEFLU97zsV0ie1bji5Jk2/mZ/Z1JbSrajD9u5nc3prTKLeNnOSdEmFR96txzfZg+SX0ZL0ZeK3A+i5QRHK/M5T1yeL99VhpxMXRgi0shzS33YV1KWX+muHUDHaGCSmcDXJEwH8KYAvofkX8DdDtariuAdr41lRsSIxyOyj/x4uP0zyagDjZnbvcM0aPtrb3VqmCNDQF+kIHmFbLI6tCnqI8qxuda1UTQ6set1dI5XmVLMmII2x7F33GJ1RS3ryCWFiU1pFnE1ytuh23ZYzRRZIT1Rz5Xf2048s0iNZr4OunG5lMF+G9Eys/yZ/T0ZWeevvtpxkTKR0SupLP1MTIzWViyXV02F3+I7A55mdvquoWLmYkz4iuYnk75B8i5lNATid5KXLYNtwITRD19a3GjiE0AJWXJvml2tH18T87Ekwsoe8E6h0ByaSwyloLihaXBchWECCpmkFs3ImaaWvpTzNjCQta5BtpR6xxSQ96pGqjdSMp5Qslzl0XM2d7rB8m5axwq5Qj5I31aW+L1cH0b74Lqy5Rlx9LSfPucBxV114/eU3oKJiFDFITOHtaLbM/g/h804ArxmaRcuEVtDRpcZFXTJSCF65ld6iuvb0UtI1JU/U2RW1Fc9f9ZhITbZELzlNf9FWLzwR9TgjglzxpjWgrlNsY8W4o3rGUgFiJngvv2wMdUQiaaa3xS13lZj1+/OltfayLU4+VXMst9qkQyjJGaabaqDb0nnZsaPosFem4+p+ShUVo4ZBOoUHmtlr0QSYEfYsGvmvvPfmAaDXESP0vl8ENbiZ99AuvHI4TxuQviENCkRmuNS9fzpn3qiAqL/PKKK0Q6e16lbgzZbcXTq1DspOR8rfyuuvs19d1qivlzK3Xy0sWXSDOyLXRx9bnNfOXnd6cgTCr65Rll67p6wjT0XFaGKQTuFI2C67GfWTD4QctlOxSFSXsqKiYgVhkNlHvwvgWgBnk3wPgJ8E8IJhGrU8cNHDlJTZIDkj2O2CxrzoyrKIFmmNnD9mcuxJv+yt55pfkbSA6bVSNjFIXIRCneeaZRradqnPm9cPIHH/TB60z53qqLTdja68/uzpF/VS2qqSSrHWHks5ZqePd993nFsGusMoyqn0N7M4iQ/Vbr5ilDHI7KPrSH4JwKPRfP9fZmZz7pK60hEb0BSMTQ1Vu4kuG0F/F+6udTxF/aSriCWQ7TaHUzmuXRMaqKAptNHMNIiUs6MIiaqySInFG71MnzhapatG8nUv2JbqwXWa4TqVn6LT0ruIDbFISR2Sr9/uei4JHUi9xIwW7Ez70OqeG+69ZD1xjlGpv7uzQAcVWVExGhho76NwAM4nh2zLqkRtOyoqKlYSBokpHJfwM4TizB6ZcQLvxaZrfU5cYJLJ82aX10n5kdkpQkA46oNjWYajJZy87DvrHJrmFLJwT2YZNbvwNTNpmKS6AqVLmUwV7oneZE/mW9wcnqDL1YObpeQ4Hqffbw8SZkWNmaguaSupm9KG9FeeLzOyGMm4MugMJXPXrfIW3FMNFVWMKlZtpwBANkLLNEr6KMnFUwW1kfiIgs7I3UrcHI4m6YZwRkH60Dyn1EW/wAO7kvMMIsBcurM9sTe6aZ/oD+UxwB9aoxySJjrarSR4mnup/AB6MnOpZ4G+E67JlNZpkzK5+Frn5vWUdVDO1o1dVCo/4daqJAutl2kzL9qXM66hUDqwjgErRhSzdgok15D85kKFk3wbyd0kb5G0k0leR/Lb4e9JIZ0k30jyOyS/QvKiheodyLY4kV2DrummLLACJM5siNtbZw8/eJGpsc0UtR8NhOf1WmMAaf67etGByY6rehlGM60GKuRNgdjOXsPrNaRtpE3OlI7lT55vUT8mG9A1n7NMCyOUcg1GHjDkU9rSpuMygjKXOeu1Yg1AmS8389GWckTXzmtJfy5nGejO+rvuxzxRrH/ZdaBQMaqYtVMwsxkA3yJ5zgLlvwPAU4q0VwL4tJk9CMCnw2cAeCqas5kfBOBKAG9eoM6BoN4pkGcSea+y9AbjR0tJel5y4bfnAQjLsGmWFc9W8PLQkuc32Ms642rflN/ys7ryN+lj1JN1sSeWuVYvd3a6vbgGuil1AbHdtI5i2aXtjuOB9FwU3ZMRT1f9S3pLT36drh7jmhQzuIlReTzi6zCu4UgZkeX5NFe7+buAiorRxSCB5pMAfI3kDQAOxUQzu6z/IynPZ0ieWyQ/HcDF4fqdAK4H8Bsh/V3W/Mv6PMkTSZ5pZncMYOPIYtU2IHV6TkXFisQgncLvLLHOM6ShvxPAGeH6LDTnP0fsCGmuUyB5JZqRBM45Z6EDGB8kztSQy4Ac2myu46dIXzRbIHh5LpjprvWAeh+gzVs4xFyRpsoUTxuRpgnnAbTon2yXBXkaxNX8JjyX5hE/GnH7B4xlKgsgehJ4zSU1qbmoP9dFsw2EBHGDjKgnphuVVYtUmpYfqfyOr5N3lVLkOWXaUmGFGkxJ8e+Y2+gja1aZUkd1O7yKUcacgWYz+2cA3wSwNfx8I6QtGub4mYGfucrMtpvZ9tNOO23hupEH+nl/m3AnUQu5WWzNfo8UQ2dwF0qke/7G0SF5njxlw7fSi/ZURg4Wl1t1+B9t0v3vlF6UOdnlqLKO56Q8ugW2Oz6z2OKjqRPZ2sLyxnOtk89iGx1pMCgV1qoNqaWeq9fMSnkqyNWLFTaabFSoOsWW/B1RhsvbWFExqhhkl9Rnozl+82cBPBvAF0g+axE6d5E8M8g+E8DukL4TwNmSb1tIGy5cIJXOa48jgnJ6Zvonr5Fkxjsl90wXpJSBQ84VG9NiSqhv1lWetWRow1eOgdony2lQVmVoEB1pX6U4JTQFk0V+2iO0c3TUHuTEkZR2IAya1VNvyi9NeqqjsqFXyUWwV5LiMCiFmJltSZvgoShfuOzJ0MLGOkYS0RadulpRMaIYZErqbwN4lJldHo7l/DEsjlL6OIDLw/XlAD4m6c8Ps5AeDeDeYcYT1DGO3rFOL01/48euQCNi42ZuUWwrYGzZ8+wlLzZ4wPTqVE06+U1syY685RbVpBzlGMGAngxO/JTYmC4jELmWI5c7grgyvTXZaSlvE9DWEUo0Oerx22ukDiy9i6K6e9lD1zUWXoPYRa8zljPls1w/6d3Hx1yAvt93oT0e0KB3RcWoYpCYwpiZ7ZbP+zDg+gaS70MTVD6V5A40+yj9MYAPkrwCwPfRAQ9H7wAAIABJREFUjD4A4BoAlwD4DoDDAF44iI6KUUVtOSsqViIG6RSuJfkPAN4XPv8cmgZ8TpjZc/vcekJHXgPw0kHkLg2YqIPEPEQqoMVEmH8upLFISd6uJjo2IuuiUBIuH639rAuSmtMdvVlqsDj+DXpMxbC4pgZq9YdJRj5JTgUFnTGPZPCMUcHlKKVT1j/ggtgpz1hLaHiUyZZM4fQ/96xrtbRSgImmSukGXzQTCjCvy26f6saWvRUVo4K+nQLJDWY2ZWa/RvIZAB4Tbl1lZh9dHvOGi7TJWjmf39EA0iEo3RLboeJ2+lisa0gNc4tbYPkkIsljZs2mcUJpdHAWiBRHawVybCeVXjG9aYn50QcyrWPS5mb6Rm3xK4GLgHH4E/VTeChaWobm68jRQ7mem3uWdZb1b/lSKaPYxxvQLGFu0YCeHrKeuVupbjvWqbi6SCILuqmiYsQw20jhcwAuIvluM/sFAB9ZJpuWCXkMoJ5lbmPyFE4L0yOzhxuamZieW99OLcbiZnItsw/fOtdYoqQ9NjYwpkcPPclG4YRrwNOSrFTOeLRbgbRdOExsLmcGsQg2lyME8+WjtUcaIPyia6m/0ptPtoRP7uS32G2Zls6PrPRM7fCj46xmdBD1p2FPOiM7Dw0kPdavq8ayR66oGE3M1imsJ/mfAfxEGCk4mNlIdxJW/M7tSPYefbA45NQtF8Q7LVf0NrnLVbelJ1165vlXvMuW/uhBdwRx3SrebFevY6UxnF3ilYu/3+xDBB84R7yWspjKzmW28llJl0GAQ9cq8naSta/LeizLlnT2kiD37tJTUjYrZVvrPfsyz1KwiooRwWydwksAPA/AiQCeVtwzHHcjh4rlRF3gVVGxMtG3UzCzfwHwLyRvNLO3LqNNy4hIHzTX3Xsody+XahiRuABLyQMLwVfzK42JVkDS64rETZGPgN/gzfwtVR7kEHHb6mBDoqb0Wb8hXcHuFH/NyVbd0VNmzBP0CcvVabtu7d0VsgcQVjSH+nf0T8jtrk3qydeR16nlLd65g3BWJcFIZ06ql7ggsCswXlExKhhkRfNx2iEoxSJrFBJXXeQt567H34FOiN1FGQ9uN+eeajAC6PkVxymQKk+XWzuLarG3pHiQOj0lQtLvKCiuAYjPFXQS2n+K6z71FUW4U+WCLqFh3JoBpYaskOc1CF2TKSm109N1Tp0Lu7jaK7mqtCbDkGmjXHeylKOzjioqRg2r9jyFNHFRzyh2Xitceh5NyD78ef5M8jipeTsCooWLmeRnrz17qNp2de1JFELCEjIXe/uBHTa4ldlSLyG5V4wmTJ4vuwNpa9PndJV0a13kOnJl0/ocy1J076NUR6ny2s1xjgP7qbdp9KFWEyGI3r5OuVygW2xZtf+SKo43rPKvcqBCzLI3G/bkUc+6uRRKQtzSuBVEO2+87iVZkQoxQ15mHPKnJ3q9THOIHJ2GmZIL7znlNjgZupI3PqHbQ7vSurrIYxLrZeXJazZpWGN6lKT0m7XzmNYFYvca7rG0VkYtqkfLb5bJPNPuOugBoIfmpHeB3GU36b1ku+vwlJrqWrJs2e46VKgYZQx0RjPJswDcX/Ob2WeGZdRqwuptPyrpXlGxEjFnp0DyT9CsYv46gJmQbABGulNwmz1HysRzKvk6eIA+sJspjmYVrYhrNXjm20D6efoaBI5z4/320vBRW5e/VRoltcJzDdWUKagYZjYfdJXwcVKUKJdI5xTcV8qWaSBPwGUZrSIXdWS6XXmUqXx/ku0qIl9F/UoxderXNRvI9Qy/BiNvwK11oXm6u/Ta3VWMMgYZKfw0gAeb2dSwjVleZMpGA4eANJIW6Yr0SDPvH22awCzz3ZFVSVROblaixnzNDjYiMTWW5EFOR2spTily6rIGzx0FlfgTRGpHt3xmpHTcc8U187Vfs1Cm52eVjjKgmS2U1Eh9mp6CJwSO6kwVhFTGhvWSMsVche05PdNVTaA/l5vxnfcsUWlankh1pd86McC8zoqKUcMgMYVbAawbtiHLDvXCU5r8U45BTCBv11wGF+Ufvtvzs2Nr6/I4+ta50GHkkXJI0NXcVEvL5ndOVc2jEpen7VynTK3twkU/k+3l8z6Ps8XVaU73AWrRqfXUde1WSCMH5ueo8zgtVyxu21WUXx9u61c52sEV0jsGUxUVo4JBRgqHAdxM8tMA0mjBzH51aFYtA8zUW5VGxcpGWB7IH+SqO48GSb3PKF579E5R7HEUvFOlgUwbV9HTU70yEGh3YKGBtzg6gJQZ4hHLs+JNp228Y4m6HGFJdpNQNd0FmnuZCVNGhs0G4zQDx4K9Eph3B/vI757qFGPiiIM6KUCqO9Z5HEeUM5xyxQZtva7Cl6WuqBhNDNIpfDz8VAwDfRdPHd+oK5orKlYm5uwUzOydJDcCOMfMvrUMNi0TIuvvg6vRQ/deszZgBstHpUmqy9JHXeZ13ChAgquqLeqxxLeovcXgJQRC/eFfmZvxgfXSVN9AR8pIaahEn1ksf/TOg5TyeDpnVw+OM+qkYwoz6Ag5CRQ368hbNFQsfx8ToHWYNgTMw6rm9eTvQrqdRpMhQakhGZnl+rLyxVRUjBQGOY7zaQBuBnBt+HwhyZEfOTTtQzuI7AgJ5rxuozQ2maOMREUgrwlIREKQmQOuJSPtWRz9XUjK6YkGyamas+nQLN1JV6FP0bgoYj044seKBk/KUdhYdJdyV+tAmu4uag65Pn3cxQoZfhVzqZNWWlfWRe54VVs7WNyWo596+izLLLK6uqJiBDFIoPn30BzBeQ8AmNnNAB6wUIUkH0zyZvnZT/LlJH+P5E5Jv2ShOga0RLzN6AH6KGXZGMdmsBezanC1axkxuwK0UV52a3tJjur0bm8P2RHNjQ+lk1LhdN5sSz/zSmjT7J32Fg1kUBVHJL4DCD/NpkWNQy76fQcSfocRVFyVLMv4cmepIwKZkWQ6tEotcfuttcofb6nXHzsLszajl4ZKlkyOo5ZWDxBHJW4SQEXF6GCQmMK0md1L/y+l1y/zXAgU1IUAQHINgJ0APorm+M2/MLM/W6jsedmRWjTvcQaXE9ljbHu72ga4BqulQ3SFhNymm5PpDnFR/RYbyNwCO319qXltPC1ljY2u9iPZnvhJ1zr3G6+Ilq5Ad6cljZddzECFsQk6a0eVOp9OKqZMs45Ub6++i9gfuBFEK78fMbnAeUtPlLPgfxYVFSsGg4wUvhbOVVhD8kEk/xLAvy6R/icA+K6ZfX+J5I0eVnG8dRUXvaJixWKQTuFXADwEzXTU9wHYD+DlS6T/OchnPwPAL5P8Csm3kTyp6wGSV5K8keSNe/bsWYTqGNyVYHHL6w8hV6GW/O/M0egqWrdaVvkbpSqUqbJABCllE6gXAu70sLxddLmKOTynchItFHOrTxwDpzno3TohTgKwTHXhyJNEmxGZMsr7GOVN6GQM4OmecrtypbuoN8KVBIOjfrbyi0jHCGb9nZO+lELU1dXhM1ONMMgwuLqgF1FRMYoYZOvsw2b222b2KAA/DuBPzGxysYpJrgdwGYAPhaQ3A3ggGmrpDgCv62PPVWa23cy2n3baaQvWrxx5s4o2NhjKgcS5NSbBRWvSE6Ug6VSqJRMwua3rQZanhfIg5UuWacOY7irN1csyNKCsQdSU3nN5cgfQE5lZclz13MjuiYG5LvqFhHUb6bweIW5CZ0m/WyORReRaSzpLnkn0m9cqWaS2fNlgUn7L7wJFmTOTlusoB7ot1anXb8W77KK9KipWPgaZffRekieQ3AzgqwC+TvLXlkD3UwF8ycx2AYCZ7TKzGTPrAXgLmuD2ECFnMGtgMAY9dX+gpr1PPqFLb5Ky1CJAS52eot5xahhlimXMoiOW0vU0dXOl4aHq9DYlQ01dWS0zUkGY9lgK5Yx2yf5QaaTCMFCg1IWzx3+mTMnVclr5cBhZpPcSRxqpKnXUEUYNpkf1aH11GBT3L4qDgVSlhBllhlRz7eYQ6NYb5s/uTuOKWfcur6hY2RiEPrrAzPaj2QPpUwDOA/ALS6D7uRDqiOSZcu9nANyyBDpmRZc/mbzgwtmz1nPR9zefrs4tEEYPPn+e+lpMBU0ec/bazdkifm/0xJm3g26m2DrfOLVRaUqsFLkklIDs4WddWXa386uV5CssmmzMYwjLhcxlKOtQH053cx2280f0XHp6UtronhQvxvHlDYQOyFpt++x+vw2Qp6JiNDDI7KN1JNeh6RTeZGbT5OLm24VRx5MA/BdJfi3JC9H827qtuHf8YlU7lau68BUVKxKDdAp/jaaR/jKAz5C8P5pg84JhZocAnFKkLcXoYx7QMb/QGsr0wDxlQnhvWIKMKSgM9Rv9yWzWy41gZ69Knz/mlFBw89sSa5Khbm3iUTy34VgQlzfnSXrYjBpcADwUNHv7BW8WKSxry9ZN9QxIp5o1tRQKE/ioHJwuEerUINSfFET0twK+kT4rpeoahJK200UWKeYk+SHvvVJGFccJBtnm4o0A3ihJ3yf5uOGZtDzI9AxgIaDYNDk+j9IWmU9u0xieCkLxTGxQ8qluTPxFkyfpZWw+LXz0ulrz5aMI+EAzk63t7bSTJFNJLkwsxfAEUyugLXb52oko9FPTXTcR/hdJkfKKZrTKnzmgGPI167IlPtOTchfB+8RYadnh7Er2pZCSl1GG4CsqRhGDBJrvQ/LP4zRQkq8DsHkZbFsVsFU9f3E1l72iYmVikEDz2wAcAPDs8LMfwNuHadTyIG+toPNVkq+nTEPB1eTZQpZ+VELKGzgMZV+iSkObAtGAMNB41mW7qVvp0dkQEcY7rVkxzLZEBkRnGRVIUkMZTOmTuTxh0S1qwGh9pOnYXPfipn3Fc6keks54KoXlusjMn/xlVhpK6M5sS3mzpLIKDMzblUt9EUz0GZUy66iCiopRxCAxhQea2TPl86tJ3jwsg5YLSgektQmJDo+NQdERBOjJW55k8mRFpIliA1tuLaGUdWtaUCtf1uBmOWmjZFmXPt9Lc6Ty7KSmeL3AiQepoRpcbCGkK6WlNEuPWm9IlBx1/UCkbMa8/rSFByzbHjrCQpsrT7zSuon128pp5jewi+UU/YCla4NhLBbIDAxP+3euGrpTKoFUMaoYZKQwQfIx8QPJnwQwMTyTlgsxuFk0zu6+wlL++IcSXHQxzXIuI+VH01PD1WUe3WUeNGhD1g6WpyOZ0bqVe6a+wVXxmGU3OxfojtVQjmBKnUEGpfy6fsI1pSyfdD2TpvgRWXpvhmIxQYctUhflttxa/phaBrpjfZlkNev47ljHOpGKitHBICOFlwB4F8n7oPmq3wXgBcM0ajlgAED17UMDGAPDwVNUrzxe+pCseOduZpIPQaK8lkVR+Zzj3Em0Rgd9OhTd1C0aGD3b9HTIH6mXJDuMMlreu9uaO5eisw6lerq9er92Igf0LWyEF0xpHTnqA+46kuuuZSS6x4Dm3OU1cGXLZz7nkYF71spSe4tKnbkOOkYhdaxQMaIYZPbRlwH8KMkTwudFTUetKLCKPUp/1FBFRcVKwJydAskNAJ4J4FwAa+MW2mb2+0O1bOjIrXEME6etFcLt6FEzpRsYtzaA33efnSePES4SKlRD6yQ0Qw7ARi/fKBYJ/yFURXycEpTW0HainEhA10nE9ygb/cXgumxkkckaOd0+FSm16HH0E651JOKoIU93xTUQOYgNl8e9C6G2sicuJZWRFWMdUWvATwXIenJprUVxaR0xpSS7JDKeYtIEWHJrFRUjhEHoo48BuBfAF9HslHpcoGl0hZoIDaIlmiVcJyYpBE5l/4tyi4RM15Rz4LUZa4K76MW1BHT3M20C+Dn+YXM6s7RVg5+plBmRrmBuk1OCvz3hYkp70wM9TzdFKqjcnC50BD3LdFCS14v1AaCX1yaY8PGkNfmi7ZTrKL/Xyy9OZUj990zT47Vu6icb4oX/mg61l2xs1lJke50cV6cmeszp9DRiRcVoYZBOYZuZPWXoliw74vRMH1ws86Tf1NPBygCzFQOC7Kkm2VDP148q/Jrl6J2qbVlnam4oP852YcVdlLlVrKxfgsp5QMA8ilAHH4ZemiJaljOUn7p9uJRVZefhkR81leVMMnRkF9N949vln5vUk6t/kaRb6bmgtztL222UDlfn9KOWwsqKipHCILOP/pXkw4ZuyTIjT0XNTYM4oc5rjN50ui1uefaks4ee8oS/af2BrOiNHnEaXSDryV62GCSNTA5Me6e0DIDmH/HgE8+RPXItS7O/niV7o1QNrrtVzj0pM6Q+JajrcggtZwyB2V6+9qOmEJC2jjK7oHHIzzx60CZZRzs95BFHXqdQ+gatmssjiaJE7p3T215RMYroO1Ig+VU03++1AF5I8lY09BEBmJk9fHlMPM5R6eeKiooVhNnoo0uXzYpjAqFpNOgcqQbdTA6QgGx7cma5mjbGlPU8AqVZTOSlmyXFAT8zJy9SYz4WoShO1+a1ng1RCkaDuJEB8TSVXwXMlgxPKymlVvJFiLwSZEwh+kOFBcoqrnruqbxQ/nQUBD1h42pTXoiegqeQuHmO3CgFV9CAvfT+xRa066KOEypGHX3pIzP7fjg7+UwAd8nnuwHcd7kMHBaUHGiCiJFi0ZPK5KQ0WUugdE8+YUxpn7yCOFFCMW9ig4SUMV0rILIjcSNckA90C5mhtigpZRLc1VPYerkk6OlTPWdXrjBLdvacfl+jqS6YnyuDvpFeit2P6/7SaWfmn03RdaFvgi2RxtF3kai5ItCsAeUIFnWaX1HiwkIeXRsdvg0SFFc1FRWjikFiCm8GcFA+HwxpI44Ob1ZCieWdmE7G08nMBTrL8GgeaJgkNJ8bf98HThsfOv/2g4y4D4/fgLoMbZalaHnqUVvytLMiFo+kE9VCopV14oKsWhfm8hB5zOOfjSO1HHRu7300O5hGR7EGO/QnmfmkvXKI5cYuMprzikxGZ7k8XXW9yONGKiqOKQaZfUQTt8rMeiQHeW52oeRtaDbamwFw1My2kzwZwAcAnIvmDIdnm9ndi9XVBWv9DleWm4noJbqAovnr1IjEEUGQ1NMprKnhMqRpoVZaELxSGRi44KaYpION7DVnDTpVFYB4s3B5oyeebIyDlzVaScWzyXYdzTT3ZXwk9YY8IjFnffbEddpsD8BYzoNQHg0fx+eyOPHc5V008mRs1zPXITjvPtoq76XprsKZzf6AO3SN/mJd1pFCxShjkMb9VpK/ijw6+K8Abl0i/Y8zs73y+ZUAPm1mf0zyleHzbyyRrhWKebjGxxmO55Kv7U3i/DOmj7UZFccp1m9YtF/eF4PuffRGAK9C4xN9GsCVQ7Ln6QAuDtfvBHA9htYp+Ggt0wHxkKBvzEYJ9DoJSL67RF2VyIh5DE0ANAZtW+f/Rp1iU5MuoecQ6E1WMI5qfJ5cqBZTkvV36dTyx/GSbB+tJSobdE9l5YCyUjY+0J1/92iex2R80NIShWijvgsXRI73I2WUaqmD4NH6Z3g7SWf8LtC9f/2umJZLS5TMIdYdPYwzP/CnZaaKiiXBmlNPBX7rZ4Yie5C9j3YDeM4QdBuAfwznPf+1mV0F4AwzuyPcvxPAGeVDJK9E6JTOOeecRanPNLif4Z8DtVbcyzliAdyGa/T3U3qiMgoZsb011WNoToITG63QKXZliibTRDFf1J0ojWLVbQqqlpSN0DoUO/Uy6ehcF4B08lu56lgpmNRs94AmMBw7uEjN+OfRM+n+vN7EIlmRxhiAbmIWvSCbPZMHxPpUIebebS8ZVLoG8rlFCVZUjB6GNwaZG48xs50kTwdwHclv6k0zM3ZE7ELncRUAbN++fRH/8rI3a+IB9vLQIfmiOWfwyb1b3iG6CUyq9+nOX86lCQ0VhcZvpmRakV9X/ZaH4qRpmqFcMUisodHGI9YwqVyl8scpoSG/TMNMnVT02pum39uhf9OvjvKzyKx1xGyjTonNK6D1XZjo9SdZlyOmZl+pDhnpPYd3UQ6t3CeDjonyqC6OFFOXnY5RragYNQwy+2goMLOd4e9uAB8F8GMAdpE8EwDC391D0w84b1SnSqIXvEnz3mLyFMXJTB5lclWRg8WGRhZVqzqj3kNt0guvtZz2mdLSuCLfkkiyTl1FDJZKfi+1DPr2pC5iZ6l6xBzKtU4hbY8pgg09CQTn0QTD6Mh6GqiVMrS8+eavrlBWG2N+d85DT2tA2nOz1IjTqczfhfRlibdNdeYRhLkMFRWjh2PSKZDcTHJrvAbwHwHcAuDjAC4P2S5HsxlfxXGKflNqjwsoV1hRMUKY99bZMX2RW2efAeCjIfC4FsB7zexakv8G4IMkrwDwfTRnQg8J9NQFwnUMVkKCsWgc+yar7Fwq91VGYr5dwBMN5x8D2vJcI6ehJjoD0EmK5bS0PgB6R0uXg6ixbI1bnD3aSKsgBlfLEUSjx7NlQgNJHMJvJx51+vJTBPUrc9LtRlca9O2q92YsI1aLfrXHEnWlAf0eZRauFlaC1y7Q3anHchmP476u4vjHMdk628xuBfCjHen7ADxhKXTMaYP8Fj6nFQy2yJHo2oTY1FpuGjQo2tAxeZvqRE3JBncaMmUM6CovkpT1oNtrK01UrpnQ9QBmWbblB53BMYjMeEpZWSVBplJDuU4sMG0SFNb1ELJOINFXjvaS+u0ZbEyKL+WPdaRrif26Yn0ky07bn5drPeJDPelIgu3JljUmeXutLqdc16z1n5XVkULFaGJVb50NeI+xSS49bvV6EaZVRi89N+KUUUDH+l+NvqLxN6PnLm1h4WXnPY76HOwjM3HKaZMxsKwHzvutoxv95d5PqbmOU0IL21xVBQv8TuDZDgPzyEFivpY0Rs8937RE6vtAO5P+HPx3euks6BjB+UFhR1QccaqqDxmrqPK7kW0pFdYz5SpGFat462z5XQSTLQVmTfL3kuecff38Wadbph2pRV56Im7FE4Oe5p91B8tIYNONPDooI3P5e4WVIQ9NZsWIbGrHISUsprDmEYgvedYfs+ra5uixi7eOnBDrSN9FHMF0BdGzvU6zt1s+lXs5laOsRr8f1elIJUOHGaLHvE4xGBUVo4hBRgqPAfACkt9D3Tq7omIgsNJHFSOKQTqFpw7dimOEvANqTJCzmFNEM9A31qZT9FzeftHFdEv3maZSKBnOk07nNXt6ol8Ms6R10ogicVDCR6ke0F3DuucE5TUDoY7ovWE39dPYeOjKFckK4ZKUabZbClGWVP5gO/IZ2Yn0CqMVr7O7VihH4lnMWFB5ulpdT75z6zH0XYR6zOdiqy1W/K2oGC3MSR+F7bJPBPC08HNiSBtp5Lht/Ncu89GpZEFP/nlnqgLIzUikQcrVu0oq5GnuIZ3+Hpj1MNjStIs9rwuBEjGxzwq7LFBIiYLphSIqrSJ0Dwyg2Jva6yY95S9oq5LKUULJUTmx3JZtaTqNgqaJJlHKS6fNsTiNtJ7edXpRpCXrDQ1lxNK+YIvyf6HMcXP1LK/9XWDHdUXFqGHOToHkywC8B8Dp4edvSf7KsA0bOvzJOAC8B0vo5FPxIE08dstP5TaThbcv8sNDpunllNh4WTQrnRS1yo5hZ2avNnnqAe3DZGIeivPMVJhcVj1TGcXDSOkSv042aQNuzmvvuoaTp3Wto5m06ly98+i9pxFGUU5NSAf4ZAcg3dbzp60sYX4vxbixjUofVYwoBqGPrgDw42Z2CABI/gmAzwH4y2EaNmx4z97SP2LLLj20ETSZlOjGA5I3t9E50KveefQ38/BArDGRbRKYjg6y0DV5ZqlJxyVhX/rurJc88OxmZ3uifvHaY7CVlnbUTtNQnem+LvJYJx/so3l015Kmnnup3M7HN6kvKb+lA3j04BxAR2BleWhhJGVwEwoYymqA25LCTHTG9ysjhka7vv9co1oXdZuLilHFILOPiObMg4gZ/P/tnXu07VdV3z/fe84+Z+8byiMkOAKEJqFRBrXDRNJCqrZRhGJKhWIQbq2CpQQdRYEho0LrkGgdHVDk5dBSDK9qEVFByUgxMY2JVduRF4Q8CURewogSlYegDbn5zf6xXnP99j4n59x7zz13nzO/Y+yzf7/1W78151prn/X4zrXm2pjaDmwTizZu7R/s4cxHpxBYUmxlpvAu4DpJv53vnw28Y+dUOp5oNEVhHfxYsneIl+kJ96515EJPWbTdu213s3U9QONZ+kmDM8B29JB/L4fYeGRcaJ/e+K0c3tLrMurH145KWWBcLymOOZVRfmr+KxNljrIpsZ1B12ffSrjqXglodJvysyS+0Va9k75CC6nTpbcFOZlZjnXUX3nfEAcqlVQpujq7W1AUplh9FFhabMV19hslXUtamgrww2b2kR3V6jjAXPsxXsvfNS6VlvEUh0vHfcvRK4VMMWdc7eLX4zw9tdRIiEaJuMYlN/jVNgFgHZlRow5uJ2993bKBOylLcVHt365/K93SOq7ucPrSJuZ4XcNew+lW9pSOqzbN6ko+v+c6odEOaKkEeU294KJrU65RUr42S4Ya8dM38k2XIqPWkXU56H87nSE/OoXAcmLDTkHSQ83sK/mIzE/nT3l2spn91c6rt4Pwu2hr4yPww/bCSzuzospyzPzu4rTbtz9Ypns+bmSzTmX+4X0IqerlnF+XSMW4Ot7RO77ut0L35WDm4rh8Mi4L3wWWDM7LKePufmZVXrNRoJdTytY17t3L1vSS28U9B0FZEjvSq+42r5MQdeJ9V1NnIkAzHKv/XXQzFPezCvoosKTYbKbwa8AzST6PugFnvj9rB/Xacfh/fpXGII9C047a5iBibiTuR8bdwTb9yLQ87t8fn2RMG/nWAev8ckc/m5jjq4ta+Q3VuNYM3fmBmpBmPDUnca7xt6rTApHdiNgb7psba6tm524/gDXDcy+/T31RKfRdRbsbm7f7UunfrTOOOvPoY7R+IsVp/o7cTKOkX4LUaxYILCM27BTM7Jk4y9fBAAAak0lEQVT5+8zjp04gsDcQNoXAsmIr+xSu3krY8qEQMaMTwapxtq2HF/OD8/Syp1fa+yonfEkMfiezE1THo26d/ng6Vnb0jkU1Lb0uTf78Du2EoY5oVWcEaeeup56ccbs7qcxxYnPatplIb1Af70Hw4X1y3eNybW0vsy1aplVnFW7mI/+4UUNuQtBRaaZGStUAcpm4WUt3ClyZBc4n10KDPgosKTazKUyBg8Apkh5B+x94KPCY46DbzqL8w4/pA28M1YC8cdEbMmnkhjcv5gjuRLK0pr7tLqaLn5LqiY/Swpk7y8DahgHw6Vi7rg2aozm8dpUXH+VHrmvsHMb1x5alfQI1H1bTKGrZKA9NgyJ3qGl3ew182XnaR0NLW23/QtuiYHgDfSt+7xDQE3bmdBhaP60BY5FDbkc7KadUxYxpKZd7a3ICgWXDZjaFlwAvBx5NsiuU9ukrwC/usF6B/YCNDPV7ATFTCCwpNqSPzOwt2Z7wSjM7y8zOzJ9vMbMj7hQknS7pGkl3SLo9u9FA0iWSPi/p5vy58EhlbFGT+tXRJJ7t6egetRUvKWYdmXrneW01So2VrM3FwV2hOOZcVGQqx1Evg0/Sr3oqb7kDkuUU9w4yVJZeikSHtWVC9XpwdJdf299okkziuJmLT6o/T6FfeVOdxkmJvqoUz5imyqN6p4vLeZo7afyq54pcJbi68PLNv+IS82XuE5g/PSKXhaCcGrdYvqFYkhpYUmxl89og6eFm9iWATCUdMrP/eoQyDwM/YWYfzuc03yTpqvzsTWb280eY7rbgCAXK8snGKFljlkb0UGnY21L4UQdQ3slUiw8nx/eyF0QYNf6N1ijNziD/WnMIJyt7Ixrx4fcQSMDQr2DqdidYCRkwDnSnjfXdkfXhVuRbJzeVV19u/hyJzgmhTzWHe8rN59g7BCz7KbqykHvmyrflj271k6+I8aooL3URFp2wZ3WVVyCwfNiKm4sXlw4BwMy+CLz4SAWa2T1m9uF8/dfAneyGjWLBun2Tc7ZG/9j6qKOzeFWnC4Z70Oyc+bul5J3TuXE1fvxZGjF1s4aRfptRMAIOjEezRaabHbi20e/i9ppvaOhWH6+z9krdPo3mlnqxob2bqVSD+cZNclcHc1ZfmysbjeKWnd5uQtiM7mqS5w3d1pKvM8M6rUpfQ3QKgeXEVjqFFbn/bEkrwNqxEC7pDOBc4Loc9FJJt0h6Z56RLHrnYkk3Srrx3nvvPWLZ/ajRWqtnQzO6utlBmz14Iy51VG/OuZs31iYj89zcIMcdneZmTpe5BrGdINadBV2TyyNsykjaOfCzpmO3B8Kl04bTYxd7ztW1K4PyvJv31B3CzXBe9kiUmUw3xC9yVOQYzUlePZ06l8vi/Htdq8sMG9xI3bkWtz5+b+huxnB/+l0965lWdt1ucXPpucUFpqCPAsuJrXQKVwDvk/RUSU8F3pvDjgqSHgK8H3i5mX0FeCvweOAc4B7gDYveM7NfNrPzzOy8U0899WjV2HVs6n55j2Mv5zxmCoFlxVZsCj8JXAz8aL6/Crj0aIRKmpA6hPeY2QcAzOzP3fNLgcuPRsYWtGjffsjnURmBah123z31X+2OKl/W8RudCOfOonvYMVpy18CYvtg0X0UJ4c9KmN9r4NMrhu5eg5qDuX0C5sLc9QKbbaNp5imYXqbXRxtks6Qx4I3+naHdymlqac+IatmO6rzaZtyMrNZFU0xAd45Glu/vZKPrWJIaWFJs5eS1wcz+m5ldZGYXAXdwFGcpZCrqHcCdZvZGF36ai/YvgduOVMZWYJS1+f5TqIm2Pr7j1lWIHEdDFKZHY3qiSGkrUSrF4qirpk1tmjv5nrKpb4xokK5Ro6jp4xajd2mcreWn0h/+e+Scz2loI/m9sz9Hg7m4Xu96Upt8P+Jzm+VYMfhmjaw0zq28ybRS1b104nLyocnESPstHN1kc5JrWn2pWn+tkntPz9VRROxoDiwttjJTQNK5wCHg+4FPAR84CpnfBvwgcKukm3PYfwAOSTqH9J/3adI+iZ2DMyh2SyXrQLs/uxf3OMH6hrbaDUaG2252kBpAv3qxDojdyLoM9P3Sx6aiH527rLj0xp2Gl986Bie/TGqcgbzmPC/DbQ24N1yzwXVrHD2GolsdhPsuKOtY8u/KxTueWzTXUS3/UePsr6Uuz1VpZ2j3y1Y1LotaFwuz5spWnS6BwLJhsx3N30jqCA4BfwG8D5CZfefRCDSzP2Ixnfyho0l3+3pA2Wnb/t+tDghbm9UvwOwG4f6p9TMCbwL2QktzkQogj3IH6nLSToeqS0ur7DSuy07dssm2Rqktu/Q6kvcsGCR2YyWFa2zcztdNft98D26ncXWdkXWpHV/Rq2OMrJXtYLCSjLcamhtvZcG1Q+12TGd0J8iZ09Hlu9ZdW97KYDkLrfyAzP9nB4JFz5IfXwfdDMnw9dzpaIaGmCkElhObzRQ+Bvwh8EwzuxtA0iuOi1aBfYI9bGqOfQqBJcVmNoXnkFYBXSPp0rzyaA/9FzcDcKWJHMUgGq3hmZ3K7dAXhqcbarinLBiTH96qnPgfGzmw6xihkS4d3ePSs8K9VJqo5XNYUHuGurX5Pj+dE7j5V/uzIvwmgCq/S7DLf5PZOwQcnGQ/1h6Pu+tsa0510c5cKNf5nUrLufISeIeA5nLb10VPXBljWsvX/1j7QGB5sJmbi98xs+cDTwCuIflBepSkt0p6+vFScKfgyIe8UKTQDM45nP/rWaBMj/gdt5W+sGK/9c7feqNyCrfRdXusUZyexrGmS6aSStyap8FTHy1ttaX+nTzLHFYqi0Y4FaP3AubEfTuKpuji/fsNRt3dbdTzHUrAHDXkaJ3STaR0PFXU6qSqUqivLtUSx+/0LgVp+L0cVaLbcd3t07Amp50VkQJqMVeDNsTqo8CyYiurj75mZr9mZv8CeCzwEdIy1eWGG6m2OUA/Ju42dWF94w3N8JiXmJYBe7eRKb9bjLtteebo3U5mQtm8lQy9RRctjFv1rLOeknafk3pesjs9bpH8PHepc4WSQj1b2Xc23iDv5VNmLsacobuzV+cnnS7uFDpw+c9vmC8LV95z5d/qxZfTIvlJx6HmqZWFz7+XvyD/pdxin0JgSbGVzWsVZvbFvHnsqTul0PGC1eWRedSfG4lhqEP99I9fGzdrSx07F9S5sR3cCH4w98zPAtzo0QyywRYn3xuDoS3hxM0g2u5bN1o25vRqjRjtvZw3DVZH0HVFECUztDauNI6WiRUblcVo1Nzku/Jw8tuu49ywltF22QHsyqLUUUm6Ns1mtU+3TmZOe6BqVk+aK3Hdeyo5yKP8GlUuO/Iy23RB7ndRBNfSGFwdBQJLhm11CoFAYIuImUJgSbGPOwWNrhuF4KmAcdxun0A1aI5iaO415ozLzjDZDN1lbD/vlm5eE8u+7nqqypwynnBBzbhbRuvy75AN3Y4KcZOF0V9fFn1KlS6qeWtpjQ233lDcJI2mKfgy76/nUegeR2UtKPLeQu11KsGLTOyb1YVPIddc2BQCS4p92ylkZiDfFBqpPGncNTb04Z4tcdxyPZmrhNeILe1yQH01aBffb4PRuIt2ulehqqyT2Cip/u/gZDaHeOasvja4lAptU+O7srCWps9/c6jXl0XC0HZpjPJZo7m08aejFYUyBaacdqGtLKe/yNpdDfq13MmUT6tDTzE1w7BzeT14vZoMczKrk8Gil8tfluKOO/WW9kBgubB/OwVnaB7coNG7qC7nK5eB5bxx0aU3d2205ZZ5FrDBMsw2ah4tA10of97Q26EbGTv5LVW8dSJdu/nPAlfXydDdT3/G8tuMpJdfnraZgo3y79yFy+e5L38bFXhvaO4LoJsd+fKvmo7OrnYzqMHlsyu18fLaTQ3dcCBmCoElxb7tFFIr1ZZK+qWFzQu2G0G6ZrQaF6Ez0NZlptmIWoyytTEajM6ldR0Jl3eAoS2J9WsuO9cNnh6S18sNeN0o3LuOti4/bkRrzdCrmpafNViL62S6+Ym7qtp0y2I7o7BBOf+6lhlFpTKDMfe4ye+M211effp9WDdT62Yi5YWy9NWq2+1anS5L811xn2fLxWOxozmwpNiS76NAILA9fPXkdV7yk+u7rUZgj+Lk2Rq/t0Np7/tOoXDRME+uGObJCMq6+TYy9pRKNvDKXZe4mRYZVGik3glcvaRQWUnC0Mn3PHnTpUsji55jVkq4RtdFtzoDaP5RVWmdXn6dRXTyqXk2VxZ1bJ/LxZzsXn5DWSbc10WTWvdJuLfK8thFJ7GN5RdVh4Xy57ctqpZF8Y0k/G+m6u3qHMTXV437DjxAILAT+Lp27re1b+mj1Fh46sRRE249evFoOnTr9Nsu12petc6hdmvCR+vxW5TGQ5s1mdUwXCgQtRRVUrbWIFdD7uCJquawztx6+c4A7pZMtn0aVtOp9FFt3HtaqXWOOf+OLvHnU7uuwVFRqcyrjjbg9wOUSHVHN8mI2444GFpZVOrIp++oQLyhuVB6ZXWQO8lNTWZHL4muLEo6vbWk0FMlTwOHV+aJpkBgGbBvO4UCq6Nb2NS4WNGag8726kb49W9Nu4Q4I65Lut9du5nM8ZLMNkNoS0mLMmVWIhc+L8c12Yx9P+EN3Qt06dIr8uvj1Iq7NT5z4+85XcbGZJp8f900cRXgzsj2xuXydsun78rczKZEVUvbl8Wis7s3M3R/PTqFwJJi33YKXTOVR5/tOjUig7seNZ8ukWxcdLtofbTet34b+Saj8wNpRFqMq3V0PLj02xLVMoMoBmKfhzI0Lo/bjMClPSenjajpRvf+LGSr133+3btevhU10wPV+5yeM2hXL0Ku0OYMytYOxSlGeV8W5gzENTwvMe3WCWSRvs+qaQxNF1//tfuo047sOrw7r7ro1pf5/StBHQWWE/u2UwgEdhIxUwgsK044Q7OkZwBvAVaAt5vZa3dI0ui7v/XO8TbeRevNzi3Ix66nrHmD5mjN++LgdmTOZtLbdW+GLgnawsiLhY4dAj4Yml3VlZCj0bqrEetlLHqwkZwFlNkcrD4uS0rHu5U3LH/N53kjymzRb2HsEFAY9608wEMmJ22gayBwdHjI5OCOpX1CdQqSVoBfAp4GfA64QdJlZnbHsZbV9icUWgNHEzhGw613bwRFNWVWysYbNH3cERNENbBaa6jkqJyabHnHxjLLc0/VtPQ8DeONrzj9XKxaGOXJMFingzcOtzX+bjey03vwYW7fR0dx9Qq0MixRXBmNuR9v4B93gRuQW9UVNrmcK9XjqShfjoMrVx+vFVUzYON0GmXtUZM1/u/H7yQQ2BGc9KgdS/qE6hSAfwTcbWafBJD068CzgGPeKaxMJqxOp6ytTViZTVmbTVlbnXHg4IzV2ZS11SnMpkxmU9YmMzSbsjo9yGR1ik0nrE4PsjaZopmxuj5jcmDGgdkBVtcPssqUyWFjdf1+JtMpMGWyBodn60wmM4bJwOHZlMl0xmSywoHZlNXpjPUqf8ba6rTFWZ1iVf6MB2Zr6XoyQzNjZT1dD7P7WV0/yOTAjEHG6lpKy4b7WV0bmMxmrK5O0eQwNp2yOp0ymRxAsxmT6ZTJ6pRJzn+SM81lMcMOJvlrqwc5nOWvTmYwg5X1GZO1dZiusLJ2kLUHZhhidW3GZH2K7H5WJw8keaszhslKlj9jsmbYbMpkus7aZJ212ZTV2Yz1yYzh4IzJbJryVuRPZmi2nvK/NsNmB1iZzlhfm8H0cKqLYQbDAVbXZ6wOU+yBw0zWDrM6nbGiv+XA5ABr0ymT9RmTieWynbK2tsbkYKnzac7zLOV/Nkv6r03RwSR/fW3GMFtlZT1da/oAK2sz1jTjFNbh/kce659tIJBwcOd+W9rQXcIuQNJFwDPM7N/m+x8EnmxmL3VxLgYuBnjc4x73pM985jO7omsgEAgsKyTdZGbnLXq2dIbmfJ7DeWZ23qmnnrrb6gQCgcCewonWKXweON3dPzaHBQKBQOA44ETrFG4AzpZ0pqQ14PnAZbusUyAQCOwbnFCGZjM7LOmlwJWkJanvNLPbd1mtQCAQ2Dc4oToFADP7EPCh3dYjEAgE9iNONPooEAgEAruI6BQCgUAgUBGdQiAQCAQqolMIBAKBQMUJtaN5u5B0L3A0W5pPAf7iGKmzDNhv+YXI835B5Hl7+LtmtnD371J3CkcLSTdutNV7L2K/5Rciz/sFkedjh6CPAoFAIFARnUIgEAgEKvZ7p/DLu63AccZ+yy9EnvcLIs/HCPvaphAIBAKBHvt9phAIBAIBh+gUAoFAIFCxLzsFSc+QdJekuyW9arf1OVaQdLqkayTdIel2SS/L4SdLukrSJ/L3I3K4JP1CLodbJH3r7ubgyCBpRdJHJF2e78+UdF3O1/uyG3Ykref7u/PzM3ZT76OBpIdL+i1JH5N0p6Tz90E9vyL/rm+T9F5J071W15LeKekLkm5zYduuV0kvyPE/IekF29Fh33UKklaAXwK+B3gicEjSE3dXq2OGw8BPmNkTgacA/y7n7VXA1WZ2NnB1vodUBmfnz8XAW4+/yscELwPudPevA95kZn8P+CLwohz+IuCLOfxNOd6y4i3AFWb2BOBbSPnfs/Us6THAjwPnmdk3k1zrP5+9V9fvBp4xCttWvUo6GXgN8GTSufevKR3JlmBm++oDnA9c6e5fDbx6t/Xaobx+EHgacBdwWg47DbgrX78NOOTi13jL8iGdznc18F3A5YBIuzxXx/VNOqfj/Hy9muNpt/NwBHl+GPCpse57vJ4fA/wpcHKuu8uBf7YX6xo4A7jtSOsVOAS8zYV38R7ss+9mCrQfV8HnctieQp4unwtcB3yDmd2TH/0Z8A35ei+UxZuBfw8M+f6RwJfM7HC+93mq+c3Pv5zjLxvOBO4F3pVps7dLOok9XM9m9nng54HPAveQ6u4m9n5dw/br9ajqez92Cnsekh4CvB94uZl9xT+zNHTYE+uQJT0T+IKZ3bTbuhxnrALfCrzVzM4FvkajFIC9Vc8Amf54FqlDfDRwEvM0y57H8ajX/dgpfB443d0/NoftCUiakDqE95jZB3Lwn0s6LT8/DfhCDl/2svg24HslfRr4dRKF9Bbg4ZLKqYI+TzW/+fnDgL88ngofI3wO+JyZXZfvf4vUSezVegb4buBTZnavmd0PfIBU/3u9rmH79XpU9b0fO4UbgLPzqoU1krHqsl3W6ZhAkoB3AHea2Rvdo8uAsgLhBSRbQwn/obyK4SnAl9009YSHmb3azB5rZmeQ6vH3zewHgGuAi3K0cX5LOVyU4y/daNrM/gz4U0nflIOeCtzBHq3njM8CT5F0MP/OS573dF1nbLderwSeLukReYb19By2Ney2UWWXDDkXAh8H/gT4j7utzzHM17eTppa3ADfnz4UkLvVq4BPA/wJOzvFFWon1J8CtpJUdu56PI8z7BcDl+fos4HrgbuA3gfUcPs33d+fnZ+223keR33OAG3Nd/w7wiL1ez8DPAB8DbgN+FVjfa3UNvJdkM7mfNCN80ZHUK/Bvct7vBn54OzqEm4tAIBAIVOxH+igQCAQCGyA6hUAgEAhURKcQCAQCgYroFAKBQCBQEZ1CIBAIBCqiUwicEJBkkt7g7l8p6ZJjlPa7JV304DGPWs5zs8fSa0bhZ0j6V+7+PEm/sNP6ZFnnSLrweMgK7A1EpxA4UXAf8BxJp+y2Ih5ut+xW8CLgxWb2naPwM4DaKZjZjWb248dAva3gHNJelUBgS4hOIXCi4DDpzNlXjB+MR/qSvpq/L5D0B5I+KOmTkl4r6QckXS/pVkmPd8l8t6QbJX08+0wq5zC8XtIN2R/9S1y6fyjpMtKu2bE+h3L6t0l6XQ77adLmwXdIev3oldcC3yHpZqUzAS5QO/vhEkn/Pcv7jKTnSPovOf0rstsSJD0p5/UmSVcWtwcjvZ6bdfqopP+dd+z/LPC8LPt5kk5S8tl/fXam96z87gtzOV6r5IP/NTn8JEn/M6d5m6TnbaUyA0uM3d7BF5/4mBnAV4GHAp8m+al5JXBJfvZu4CIfN39fAHyJ5C54neTf5Wfys5cBb3bvX0EaBJ1N2ik6Jfmg/6kcZ520Q/jMnO7XgDMX6PloksuFU0mO6X4feHZ+di0LdgvjdluP74FLgD8CJqRzEf4G+J787LeBZ+dn/wc4NYc/D3jnAjm3Ao/J1w/P3y8EftHF+c/Avy5xSDv7T8rx7iHtnp2Rdg2fB3wfcKl7/2G7/VuJz85+YqYQOGFgyaPrr5AOU9kqbjCze8zsPtJ2/9/L4beSaJuC3zCzwcw+AXwSeALJJ8wPSbqZ5GL8kaROA+B6M/vUAnn/ELjWkmO2w8B7gH+yDX0X4XctOXm7lXR4zBWjPHwT8M3AVVnXnyI5ORvjj4F3S3pxTmcRng68KqdzLalzfFx+dpWZ/aWZ/S3J4dy3Zx2eJul1kr7DzL58VDkNnPDYDl8aCBwPvBn4MPAuF3aYTHVKOgCsuWf3uevB3Q/0v++xPxcj+Y75MTPrnIVJuoA0UzheuA/AzAZJ95tZ0bXkQcDtZnb+ZomY2Y9IejLwz4GbJD1pQTQB32dmd3WB6b25MjKzjysd83gh8HOSrjazn91uBgPLg5gpBE4omNlfAb9BO1YREqVUGrjvJdEp28VzJR3IdoazSKdUXQn8qOPtv1HpsJrNcD3wTyWdonS06yHgDx7knb8G/s4R6FxwF3CqpPOznhNJf38cSdLjzew6M/tp0iE8py+QfSXwY9nTKJLOdc+epnQe8IxEW/2xpEcDf2Nm/wN4PclFd2APIzqFwImINwB+FdKlpIb4o6QjF49kFP9ZUoP+u8CPmNn/A95OMiR/WOmg9LfxILNnS66JX0Vy2fxR4CYz++Bm75A8mT6QjbVzhvQHg5l9neT++XW5DG4G/vGCqK8vBnCSDeKjWc8nFkMz8J9Ineotkm7P9wXXk87iuAV4v5ndCPwD4PpMN70G+Lnt6h9YLoSX1EAggKQXkozkL91tXQK7i5gpBAKBQKAiZgqBQCAQqIiZQiAQCAQqolMIBAKBQEV0CoFAIBCoiE4hEAgEAhXRKQQCgUCg4v8DWU1Rc76XmJUAAAAASUVORK5CYII=\n","text/plain":["
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\n","text/plain":["
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