RPVL-gambler-problem.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0fc8162d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "# utilities\n",
    "colors = ['#006BA4', '#FF800E', '#ABABAB', '#595959', '#5F9ED1', '#C85200', '#898989', \n",
    "          '#A2C8EC', '#FFBC79', '#CFCFCF']\n",
    "bands = ['lightgrey', 'mistyrose', 'peachpuff', 'moccasin', 'khaki',\n",
    "         'palegreen', 'lightcyan', 'powderblue', 'mediumspringgreen', 'lavender',\n",
    "        'thistle', 'lavenderblush', 'gainsboro']\n",
    "markers = [\"p\",\"v\",\"X\",\"o\",\"s\",\n",
    "           \"P\",\"^\",\"d\",\"<\",\n",
    "           \"3\",\"*\",\">\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1badf72b",
   "metadata": {},
   "source": [
    "## Implementation of algorithms\n",
    "### Non-robut dynamic programming\n",
    "The following is the vanilla dynamic programming that solves the Gambler's Problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4124c761",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_iteration_for_gamblers(p_h, max_states_plus_1=100, horizon_l=100):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        p_h: Probability of the coin coming up heads\n",
    "    \"\"\"\n",
    "    # The reward is zero on all transitions except those on which the gambler reaches his goal,\n",
    "    # when it is +1.\n",
    "    with_dummy_states = max_states_plus_1 + 1\n",
    "    rewards = np.zeros(with_dummy_states)\n",
    "    rewards[max_states_plus_1] = 1 \n",
    "    \n",
    "    # We introduce two dummy states corresponding to termination with capital of 0 and 100\n",
    "    V = np.zeros((with_dummy_states, horizon_l))\n",
    "    \n",
    "    def one_step_lookahead(s,h, V, rewards):\n",
    "        \"\"\"\n",
    "        Calculate the value for all action in a given state.\n",
    "        \"\"\"\n",
    "        A = np.zeros(with_dummy_states) # can bet 0 to 100\n",
    "        stakes = range(1, min(s, max_states_plus_1-s)+1) # Your minimum bet is 1, maximum bet is min(s, 100-s).\n",
    "        \n",
    "        if h<horizon_l-1: # when the time < horizon length\n",
    "            for a in stakes:\n",
    "                # rewards[s+a], rewards[s-a] are immediate rewards.\n",
    "                # V[s+a], V[s-a] are values of the next states.\n",
    "                # This is the core of the Bellman equation: The expected value of your action is \n",
    "                # the sum of immediate rewards and the value of the next state.\n",
    "                A[a] = p_h * (rewards[s+a] + V[s+a,h+1]) + (1-p_h) * (rewards[s-a] + V[s-a,h+1])\n",
    "        else: # in the final step of the horizon, V[H] = 0\n",
    "            for a in stakes:\n",
    "                A[a] = p_h * rewards[s+a] + (1-p_h) * rewards[s-a]\n",
    "        \n",
    "        return A\n",
    "    \n",
    "    # get the value functions V_h\n",
    "    for h in range(horizon_l-1,-1,-1):\n",
    "        # Update each state...\n",
    "        for s in range(1, max_states_plus_1): # s = 0 means 0\n",
    "            # Do a one-step lookahead to find the best action\n",
    "            A = one_step_lookahead(s, h, V, rewards)\n",
    "            best_action_value = np.max(A)\n",
    "            # Update the value function.\n",
    "            V[s,h] = best_action_value        \n",
    "    \n",
    "    # Create a deterministic policy using the optimal value function\n",
    "    policy = np.zeros((with_dummy_states, horizon_l))\n",
    "    for h in range(horizon_l-1,-1,-1):\n",
    "        for s in range(1, max_states_plus_1): # money is [1,100]\n",
    "            # One step lookahead to find the best action for this state\n",
    "            A = one_step_lookahead(s, h, V, rewards)\n",
    "            best_action = np.argmax(A)\n",
    "            # Always take the best action\n",
    "            policy[s,h] = best_action\n",
    "    \n",
    "    return policy, V"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d799684a",
   "metadata": {},
   "source": [
    "### Solving inner optimization problem\n",
    "This code block contains the numerical methods for the inner optimization defined in the RMDP formulation. We consider four uncertainty sets: TV, chi-square, KL, and Wasserstein. We solve the optimization problem w.r.t. to each uncertainty set through their respective dual formulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fa5960a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "def worstcase_L1(V, Po, rho):\n",
    "    \"\"\"\n",
    "    Computes the solution of:\n",
    "    min_P          P dot V\n",
    "    subject to     || P - Po ||_L1 <= rho\n",
    "    via its dual formulation.\n",
    "    \"\"\"\n",
    "    if rho == 0:\n",
    "        return np.dot(V, Po)\n",
    "    if rho < 0:\n",
    "        raise ValueError(\"Uncertainty parameter rho must be at least 0.\")\n",
    "    if Po.dtype != float:\n",
    "        raise ValueError(\"Value function V must be a float array.\")\n",
    "    if not 0.99 <= np.sum(Po) <= 1.0:\n",
    "        raise ValueError(\"Po should be a probability vector.\")\n",
    "        \n",
    "    def dual_L1(eta):\n",
    "        \"\"\"Dual formulation for L1 divergence. See (Panaganti et al., 2022, Lemma 5)\"\"\"\n",
    "        ones = np.ones_like(V)\n",
    "        res = np.dot(Po, np.maximum(eta * ones - V, 0)) + np.max(eta - np.min(V), 0) * rho - eta\n",
    "        return res\n",
    "    \n",
    "    bounds = [(0.0, None)]\n",
    "    res = minimize(dual_L1, x0=0, method='SLSQP', bounds=bounds, tol=1e-3)\n",
    "\n",
    "    return -res.fun\n",
    "\n",
    "def worstcase_Chi(V, Po, rho):\n",
    "    \"\"\"\n",
    "    Computes the solution of:\n",
    "    min_P          P dot V\n",
    "    subject to     D_Chi(P || Po) <= rho\n",
    "    via its dual formulation.\n",
    "    \"\"\"\n",
    "    if rho == 0:\n",
    "        return np.dot(V, Po)\n",
    "    if rho < 0:\n",
    "        raise ValueError(\"Uncertainty parameter rho must be at least 0.\")\n",
    "    if Po.dtype != float:\n",
    "        raise ValueError(\"Value function V must be a float array.\")\n",
    "    if not 0.99 <= np.sum(Po) <= 1.0:\n",
    "        raise ValueError(\"Po should be a probability vector.\")\n",
    "    \n",
    "    def dual_Chi(eta):\n",
    "        \"\"\"Dual formulation for Chi-square divergence. See Proposition 2.\"\"\"\n",
    "        ones = np.ones_like(V)\n",
    "        res = np.sqrt(rho + 1) * np.sqrt(np.dot(Po, (eta * ones - V) ** 2)) - eta\n",
    "        return res\n",
    "\n",
    "    bounds = [(0.0, None)]\n",
    "    res = minimize(dual_Chi, x0=0, method='SLSQP', bounds=bounds, tol=1e-3)\n",
    "    \n",
    "    return -res.fun\n",
    "\n",
    "def worstcase_KL(V, Po, rho):\n",
    "    \"\"\"\n",
    "    Computes the solution of:\n",
    "    min_P          P dot V\n",
    "    subject to     D_KL(P || Po) <= rho\n",
    "    via its dual formulation.\n",
    "    \"\"\"\n",
    "    if rho == 0:\n",
    "        return np.dot(V, Po)\n",
    "    if rho < 0:\n",
    "        raise ValueError(\"Uncertainty parameter rho must be at least 0.\")\n",
    "    if Po.dtype != float:\n",
    "        raise ValueError(\"Value function V must be a float array.\")\n",
    "    if not 0.99 <= np.sum(Po) <= 1.0:\n",
    "        raise ValueError(\"Po should be a probability vector.\")\n",
    "    \n",
    "    def dual_KL(lamda):\n",
    "        \"\"\"Dual formulation for KL divergence. See Proposition 4.\"\"\"\n",
    "        # for numerical stability\n",
    "        res = lamda * rho + lamda * np.log(max(np.dot(Po, np.exp(-V / max(lamda, 0.001))),0.000001)) \n",
    "        return res\n",
    "    \n",
    "    bounds = [(0.001, None)]\n",
    "    res = minimize(dual_KL, x0=0.1, method='SLSQP', bounds=bounds, tol=1e-2)\n",
    "    return -res.fun\n",
    "\n",
    "def worstcase_W(V, Po, rho, d, p):\n",
    "    \"\"\"\n",
    "    Computes the solution of:\n",
    "    min_P          P dot V\n",
    "    subject to     D_w(P, Po) <= rho\n",
    "    via its dual formulation.\n",
    "    \n",
    "    Params:\n",
    "        V (array of floats): Value function.\n",
    "        Po (array of floats): Nominal model (a probability vector).\n",
    "        rho (float): Robustness parameter.\n",
    "        d (callable): A function that is a metric.\n",
    "        p (integer): The p in \"p-th Wasserstein distance\".\n",
    "    \"\"\"\n",
    "    if rho == 0:\n",
    "        return np.dot(V, Po)\n",
    "    if rho < 0:\n",
    "        raise ValueError(\"Uncertainty parameter rho must be at least 0.\")\n",
    "    if Po.dtype != float:\n",
    "        raise ValueError(\"Value function V must be a float array.\")\n",
    "    if not 0.99 <= np.sum(Po) <= 1.0:\n",
    "        raise ValueError(\"Po should be a probability vector.\")\n",
    "        \n",
    "    V_dim = len(V)\n",
    "    Dp = np.zeros((V_dim, V_dim))\n",
    "    for s in range(V_dim):\n",
    "        for sp in range(V_dim):\n",
    "            Dp[s][sp] = d(s,sp) ** p\n",
    "    \n",
    "    def dual_W(lamda):\n",
    "        \"\"\"Dual formulation for Wasserstein metric. See Proposition 6.\"\"\"\n",
    "        res = 0\n",
    "        for s in range(V_dim):\n",
    "            res += Po[s] * np.max(-V - lamda * Dp[s])\n",
    "        res += lamda * (rho ** p)\n",
    "        return res\n",
    "    \n",
    "    bounds = [(0, None)]\n",
    "    res = minimize(dual_W, x0=0.1, method='SLSQP', bounds=bounds, tol=1e-3)\n",
    "    return -res.fun"
   ]
  },
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   "cell_type": "markdown",
   "id": "a2e15bc0",
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    "### Robust dynamic programming and RPVL (and policy evaluation)\n",
    "Robust dynamic programming uses the true model. As specified in our paper, RPVL estimates the nominal model using a generative model and then uses the estimate to perform robust bellman update. This section also includes a policy evaluator that uses robust bellman consistency equation to compute the robust value function of a given policy."
   ]
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   "cell_type": "code",
   "execution_count": 20,
   "id": "8e43636a",
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   "source": [
    "def empirical_est(p_h,n=20):\n",
    "    \"\"\"generating samples for estimating P_h.\"\"\"\n",
    "    ns = [0, 1] # == s+a, s-a states\n",
    "    data = np.random.choice(ns, n, p = [p_h, 1-p_h])\n",
    "    \n",
    "    est_ph = [len(np.where(data == 0)[0])*1.0/n,\n",
    "                 len(np.where(data == 1)[0])*1.0/n,]\n",
    "   \n",
    "    return est_ph\n",
    "\n",
    "def robust_dp_for_gamblers(p_h, max_states_plus_1 = 100, horizon_l = 100, rho=0.2, uncer='L1',\n",
    "                           d=None, p=1):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        p_h: Probability of the coin coming up heads\n",
    "    \"\"\"\n",
    "    # The reward is zero on all transitions except those on which the gambler reaches his goal,\n",
    "    # when it is +1.\n",
    "    with_dummy_states = max_states_plus_1 + 1\n",
    "    rewards = np.zeros(with_dummy_states)\n",
    "    rewards[max_states_plus_1] = 1 \n",
    "    \n",
    "    # We introduce two dummy states corresponding to termination with capital of 0 and 100\n",
    "    V = np.zeros((with_dummy_states, horizon_l))\n",
    "    \n",
    "    def one_step_lookahead(s, h, V, rewards, c_r):\n",
    "        \"\"\"\n",
    "        Helper function to calculate the value for all action in a given state.\n",
    "        \n",
    "        Args:\n",
    "            s: The gambler’s capital. Integer.\n",
    "            V: The vector that contains values at each state. \n",
    "            rewards: The reward vector.\n",
    "                        \n",
    "        Returns:\n",
    "            A vector containing the expected value of each action. \n",
    "            Its length equals to the number of actions.\n",
    "        \"\"\"\n",
    "        A = np.zeros(with_dummy_states)\n",
    "        stakes = range(1, min(s, max_states_plus_1-s)+1) # Your minimum bet is 1, maximum bet is min(s, 100-s).\n",
    "        \n",
    "        \n",
    "        if h<horizon_l-1: # when the time < horizon length\n",
    "            for a in stakes:\n",
    "                # rewards[s+a], rewards[s-a] are immediate rewards.\n",
    "                # V[s+a], V[s-a] are values of the next states.\n",
    "                # Robust Bellman equation:\n",
    "                z = [rewards[s+a] + V[s+a,h+1], rewards[s-a] + V[s-a,h+1]]\n",
    "                true_p = np.array([p_h, 1-p_h])\n",
    "                if uncer == 'L1':\n",
    "                    A[a] = worstcase_L1(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'Chi':\n",
    "                    A[a] = worstcase_Chi(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'KL':\n",
    "                    A[a] = worstcase_KL(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'W':\n",
    "                    A[a] = worstcase_W(np.array(z), true_p, rho, d, p)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                    \n",
    "        else: # in the final step of the horizon, V[H] = 0\n",
    "            for a in stakes:\n",
    "                z = [rewards[s+a], rewards[s-a]]\n",
    "                true_p = np.array([p_h, 1-p_h])\n",
    "                if uncer == 'L1':\n",
    "                    A[a] = worstcase_L1(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'Chi':\n",
    "                    A[a] = worstcase_Chi(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'KL':\n",
    "                    A[a] = worstcase_KL(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'W':\n",
    "                    A[a] = worstcase_W(np.array(z), true_p, rho, d, p)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "        return A\n",
    "    \n",
    "\n",
    "    # get the value functions V_h\n",
    "    for h in range(horizon_l-1,-1,-1):\n",
    "        # Update each state...\n",
    "        for s in range(1, max_states_plus_1): # s = 0 means 0\n",
    "            # Do a one-step lookahead to find the best action\n",
    "            A = one_step_lookahead(s, h, V, rewards, rho)\n",
    "            best_action_value = np.max(A)\n",
    "            # Update the value function.\n",
    "            V[s,h] = best_action_value\n",
    "            if np.isnan(V[s,h]):\n",
    "                raise ValueError('nan in value.')\n",
    "                break\n",
    "\n",
    "    # Create a deterministic policy using the optimal value function\n",
    "    policy = np.zeros((with_dummy_states, horizon_l))\n",
    "    for h in range(horizon_l-1,-1,-1):\n",
    "        for s in range(1, max_states_plus_1): # money is [1,100]\n",
    "            # One step lookahead to find the best action for this state\n",
    "            A = one_step_lookahead(s, h, V, rewards, rho)\n",
    "            best_action = np.argmax(A)\n",
    "            # Always take the best action\n",
    "            policy[s,h] = best_action\n",
    "    \n",
    "    return policy, V\n",
    "\n",
    "def robust_emp_dp_for_gamblers(p_h, max_states_plus_1 = 100, horizon_l = 100, rho=0.2, uncer='L1',\n",
    "                           d=None, p=1, num_samp=200):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        p_h: Probability of the coin coming up heads\n",
    "    \"\"\"\n",
    "    # The reward is zero on all transitions except those on which the gambler reaches his goal,\n",
    "    # when it is +1.\n",
    "    with_dummy_states = max_states_plus_1 + 1\n",
    "    rewards = np.zeros(with_dummy_states)\n",
    "    rewards[max_states_plus_1] = 1 \n",
    "    \n",
    "    # We introduce two dummy states corresponding to termination with capital of 0 and 100\n",
    "    V = np.zeros((with_dummy_states, horizon_l))\n",
    "    \n",
    "    def one_step_lookahead(s, h, V, rewards, c_r):\n",
    "        \"\"\"\n",
    "        Helper function to calculate the value for all action in a given state.\n",
    "        \n",
    "        Args:\n",
    "            s: The gambler’s capital. Integer.\n",
    "            V: The vector that contains values at each state. \n",
    "            rewards: The reward vector.\n",
    "                        \n",
    "        Returns:\n",
    "            A vector containing the expected value of each action. \n",
    "            Its length equals to the number of actions.\n",
    "        \"\"\"\n",
    "        A = np.zeros(with_dummy_states)\n",
    "        stakes = range(1, min(s, max_states_plus_1-s)+1) # Your minimum bet is 1, maximum bet is min(s, 100-s).\n",
    "        \n",
    "        \n",
    "        if h<horizon_l-1: # when the time < horizon length\n",
    "            for a in stakes:\n",
    "                # rewards[s+a], rewards[s-a] are immediate rewards.\n",
    "                # V[s+a], V[s-a] are values of the next states.\n",
    "                # Robust Bellman equation:\n",
    "                z = [rewards[s+a] + V[s+a,h+1], rewards[s-a] + V[s-a,h+1]]\n",
    "                if uncer == 'L1':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_L1(np.array(z), np.array(est_p), rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'Chi':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_Chi(np.array(z), np.array(est_p), rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'KL':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_KL(np.array(z), np.array(est_p), rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'W':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_W(np.array(z), np.array(est_p), rho, d, p)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                    \n",
    "        else: # in the final step of the horizon, V[H] = 0\n",
    "            for a in stakes:\n",
    "                z = [rewards[s+a], rewards[s-a]]\n",
    "                if uncer == 'L1':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_L1(np.array(z), np.array(est_p), rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'Chi':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_Chi(np.array(z), np.array(est_p), rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'KL':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_KL(np.array(z), np.array(est_p), rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'W':\n",
    "                    est_p = empirical_est(p_h,n=num_samp)\n",
    "                    A[a] = worstcase_W(np.array(z), np.array(est_p), rho, d, p)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "        return A\n",
    "    \n",
    "\n",
    "    # get the value functions V_h\n",
    "    for h in range(horizon_l-1,-1,-1):\n",
    "        # Update each state...\n",
    "        for s in range(1, max_states_plus_1): # s = 0 means 0\n",
    "            # Do a one-step lookahead to find the best action\n",
    "            A = one_step_lookahead(s, h, V, rewards, rho)\n",
    "            best_action_value = np.max(A)\n",
    "            # Update the value function.\n",
    "            V[s,h] = best_action_value\n",
    "            if np.isnan(V[s,h]):\n",
    "                raise ValueError('nan in value.')\n",
    "                break\n",
    "\n",
    "    # Create a deterministic policy using the optimal value function\n",
    "    policy = np.zeros((with_dummy_states, horizon_l))\n",
    "    for h in range(horizon_l-1,-1,-1):\n",
    "        for s in range(1, max_states_plus_1): # money is [1,100]\n",
    "            # One step lookahead to find the best action for this state\n",
    "            A = one_step_lookahead(s, h, V, rewards, rho)\n",
    "            best_action = np.argmax(A)\n",
    "            # Always take the best action\n",
    "            policy[s,h] = best_action\n",
    "    \n",
    "    return policy, V\n",
    "\n",
    "def policy_evaluation(pi, V_bench, p_h=0.6, max_states_plus_1=50, horizon_l=50, rho=0.2, uncer='L1', d=None, p=1):\n",
    "    # The reward is zero on all transitions except those on which the gambler reaches his goal,\n",
    "    # when it is +1.\n",
    "    with_dummy_states = max_states_plus_1 + 1\n",
    "    rewards = np.zeros(with_dummy_states)\n",
    "    rewards[max_states_plus_1] = 1 \n",
    "    \n",
    "    # We introduce two dummy states corresponding to termination with capital of 0 and 100\n",
    "    V = np.zeros((with_dummy_states, horizon_l))\n",
    "    \n",
    "    def one_step_lookahead(s, h, V, rewards, c_r):\n",
    "        \"\"\"\n",
    "        Helper function to calculate the value for all action in a given state.\n",
    "        \n",
    "        Args:\n",
    "            s: The gambler’s capital. Integer.\n",
    "            V: The vector that contains values at each state. \n",
    "            rewards: The reward vector.\n",
    "                        \n",
    "        Returns:\n",
    "            A vector containing the expected value of each action. \n",
    "            Its length equals to the number of actions.\n",
    "        \"\"\"\n",
    "        A = np.zeros(with_dummy_states)\n",
    "        stakes = range(1, min(s, max_states_plus_1-s)+1) # Your minimum bet is 1, maximum bet is min(s, 100-s).\n",
    "        \n",
    "        \n",
    "        if h<horizon_l-1: # when the time < horizon length\n",
    "            for a in stakes:\n",
    "                # rewards[s+a], rewards[s-a] are immediate rewards.\n",
    "                # V[s+a], V[s-a] are values of the next states.\n",
    "                # Robust Bellman equation:\n",
    "                z = [rewards[s+a] + V[s+a,h+1], rewards[s-a] + V[s-a,h+1]]\n",
    "                true_p = np.array([p_h, 1-p_h])\n",
    "                if uncer == 'L1':\n",
    "                    A[a] = worstcase_L1(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'Chi':\n",
    "                    A[a] = worstcase_Chi(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'KL':\n",
    "                    A[a] = worstcase_KL(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'W':\n",
    "                    A[a] = worstcase_W(np.array(z), true_p, rho, d, p)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                    \n",
    "        else: # in the final step of the horizon, V[H] = 0\n",
    "            for a in stakes:\n",
    "                z = [rewards[s+a], rewards[s-a]]\n",
    "                true_p = np.array([p_h, 1-p_h])\n",
    "                if uncer == 'L1':\n",
    "                    A[a] = worstcase_L1(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'Chi':\n",
    "                    A[a] = worstcase_Chi(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'KL':\n",
    "                    A[a] = worstcase_KL(np.array(z), true_p, rho)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "                elif uncer == 'W':\n",
    "                    A[a] = worstcase_W(np.array(z), true_p, rho, d, p)\n",
    "                    A[a] = np.max([A[a],0])\n",
    "        return A\n",
    "    \n",
    "\n",
    "    # get the value functions V_h\n",
    "    for h in range(horizon_l-1,-1,-1):\n",
    "        # Update each state...\n",
    "        for s in range(1, max_states_plus_1): # s = 0 means 0\n",
    "            # Do a one-step lookahead to find the best action\n",
    "            A = one_step_lookahead(s, h, V, rewards, rho)\n",
    "            policy_action_value = A[int(pi[s][h])]\n",
    "            # Update the value function.\n",
    "            V[s,h] = policy_action_value\n",
    "            if np.isnan(V[s,h]):\n",
    "                raise ValueError('nan in value.')\n",
    "                break\n",
    "    \n",
    "    # show the differece between V_bench and V\n",
    "    V1 = V[:,0]\n",
    "    subopt_gap = max(abs(V1 - V_bench))\n",
    "    \n",
    "    return V, subopt_gap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e7a2c21",
   "metadata": {},
   "source": [
    "### Gambler's Problem environment utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6574761b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gamblers_step(s, a, p_h, max_states_plus_1):\n",
    "    if np.random.binomial(1, p_h, 1):\n",
    "        ns = s + a\n",
    "    else:\n",
    "        ns = s - a\n",
    "        \n",
    "    if ns >= max_states_plus_1:\n",
    "        return ns, 1\n",
    "    else:\n",
    "        return ns, 0\n",
    "\n",
    "def run_policy(p_h, policy, max_states_plus_1 = 100, max_steps = 400):\n",
    "    current_state = np.random.randint(1,max_states_plus_1-1)\n",
    "    t = 1 \n",
    "    while True:\n",
    "        nextstate, reward = gamblers_step(current_state, policy[int(current_state),t-1], p, max_states_plus_1)\n",
    "        \n",
    "        if nextstate == 0:\n",
    "            return 0, t\n",
    "        \n",
    "        if t >= max_steps or reward == 1:\n",
    "            return reward, t\n",
    "\n",
    "        current_state=nextstate\n",
    "        t += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7632e256",
   "metadata": {},
   "source": [
    "## Experiments\n",
    "### Some preparations\n",
    "We first train robust optimal policies, i.e., robust policy trained with the true model, which we will use later to evaluate sub-optimality gap. We fix $p^o_h=0.6$ and $\\rho=0.2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de4852cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "H = 50 # horizon\n",
    "cardS = 50 # number of states as in Gambler's Problem\n",
    "rho = 0.2\n",
    "p_o_h = 0.6\n",
    "\n",
    "pistar, Vstar = value_iteration_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H)\n",
    "piopt_L1, Vopt_L1 = robust_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, uncer='L1')\n",
    "piopt_Chi, Vopt_Chi = robust_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, uncer='Chi')\n",
    "piopt_KL, Vopt_KL = robust_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, uncer='KL')\n",
    "piopt_W, Vopt_W = robust_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, \n",
    "                                         uncer='W', d=lambda x,y: abs(x-y), p=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2342b2f",
   "metadata": {},
   "source": [
    "### Robustness fairness study\n",
    "Corresponds to Fig. 2 in the paper. This studies how robust policies trained with different nominal models behave differently. Train policies on some nominal model, e.g., $p^o_h=0.8$, and test the policies on testing model $p_h\\in\\{0.2,0.25,\\dots,0.8\\}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e66352a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "H = 50 # horizon\n",
    "cardS = 50 # number of states in Gambler's Problem\n",
    "rho = 0.2 # robustness parameter\n",
    "p_o_h = 0.8 # change this to see other nominal model\n",
    "N = 1000 # number of sample\n",
    "\n",
    "pistar, Vstar = value_iteration_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H)\n",
    "pihat_L1, Vhat_L1 = robust_emp_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, uncer='L1',\n",
    "                                              num_samp=N)\n",
    "pihat_Chi, Vhat_Chi = robust_emp_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, uncer='Chi',\n",
    "                                                num_samp=N)\n",
    "pihat_KL, Vhat_KL = robust_emp_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, uncer='KL',\n",
    "                                              num_samp=N)\n",
    "pihat_W, Vhat_W = robust_emp_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, \n",
    "                                   uncer='W', d=lambda x,y: abs(x-y), p=1, num_samp=N)\n",
    "\n",
    "policies = [pistar, pihat_L1, pihat_Chi, pihat_KL, pihat_W]\n",
    "\n",
    "# act policies on testing environments\n",
    "head_p = [0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8]\n",
    "n_runs = 1000\n",
    "s_range = range(30)\n",
    "win_ratio_seeds = np.zeros((len(s_range),len(head_p),len(policies)))\n",
    "for s in s_range:\n",
    "#     print(\"seed - \", s)\n",
    "    win_percent_data = []\n",
    "    for jj, p in enumerate(head_p):\n",
    "        data_collect = []\n",
    "        time_data_collect = []\n",
    "        for n in range(n_runs):\n",
    "            np.random.seed(s+n+50)\n",
    "            temp = []\n",
    "            temp_2 = []\n",
    "            for j in range(len(policies)):\n",
    "                reward, steps_time = run_policy(p, policies[j], max_states_plus_1 = cardS, max_steps = H)\n",
    "                temp.append(reward)\n",
    "                temp_2.append(steps_time)\n",
    "            data_collect.append(temp)\n",
    "            time_data_collect.append(temp_2)\n",
    "\n",
    "        win_percent = np.mean(data_collect, axis=0)\n",
    "        win_ratio_seeds[s,jj,:] = win_percent\n",
    "\n",
    "mean_vals = np.mean(win_ratio_seeds, axis=0)\n",
    "std_vals = np.std(win_ratio_seeds, axis=0)\n",
    "\n",
    "# plot\n",
    "markercycler = cycle(markers)\n",
    "colorcycler = cycle(colors)\n",
    "bandcycler = cycle(bands)\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "labels = ['non-robust optimal', 'RPVL-TV'+r', $N=1000$, $\\rho=0.2$', 'RPVL-Chi'+r', $N=1000$, $\\rho=0.2$', \n",
    "          'RPVL-KL'+r', $N=1000$, $\\rho=0.2$', 'RPVL-W'+r', $N=1000$, $\\rho=0.2$']\n",
    "for i in range(len(labels)):\n",
    "    ax.plot(head_p, mean_vals[:,i], marker=next(markercycler), color=next(colorcycler), label=labels[i],\n",
    "           linewidth=2, markersize=7)\n",
    "    lower, upper = mean_vals[:,i] - std_vals[:,i], mean_vals[:,i] + std_vals[:,i]\n",
    "    ax.fill_between(head_p, lower, upper, color=next(bandcycler))\n",
    "\n",
    "ax.legend()\n",
    "ax.grid(visible=True)\n",
    "ax.set_ylabel(\"Winning ratio in 1000 games\", fontsize=15)\n",
    "ax.set_xlabel(\"Test heads-up probability\", fontsize=15)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a28609",
   "metadata": {},
   "source": [
    "### The effect of robustness parameter $\\rho$\n",
    "Corresponds to Fig.1 (middle plot). This section is a comparison among the robust policies trained with different robustness parameter $\\rho$. We include the Wasserstein case here with nominal model $p^o_h=0.6$ and number of sample $N=1000$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "84fb5da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wasserstein diff. rhos\n",
    "H = 50\n",
    "cardS = 50\n",
    "rhos = [0.05, 0.1, 0.2, 0.3]\n",
    "p_o_h = 0.6\n",
    "N = 1000\n",
    "pistar, Vstar = value_iteration_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H)\n",
    "pihat_W_ls, Vhat_W_ls = [], []\n",
    "for rho in rhos:\n",
    "    pihat_W, Vhat_W = robust_emp_dp_for_gamblers(p_h=p_o_h, max_states_plus_1=cardS, horizon_l=H, rho=rho, \n",
    "                                       uncer='W', d=lambda x,y: abs(x-y), p=1, num_samp=N)\n",
    "    pihat_W_ls.append(pihat_W)\n",
    "    Vhat_W_ls.append(Vhat_W)\n",
    "policies = [pistar, pihat_W_ls[0], pihat_W_ls[1], pihat_W_ls[2], pihat_W_ls[3]]\n",
    "\n",
    "# evaluate the policies\n",
    "head_p = [0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8]\n",
    "n_runs = 1000\n",
    "s_range = range(30)\n",
    "win_ratio_seeds = np.zeros((len(s_range),len(head_p),len(policies)))\n",
    "for s in s_range:\n",
    "#     print(\"seed - \", s)\n",
    "    win_percent_data = []\n",
    "    for jj, p in enumerate(head_p):\n",
    "        data_collect = []\n",
    "        time_data_collect = []\n",
    "        for n in range(n_runs):\n",
    "            np.random.seed(s+n+50)\n",
    "            temp = []\n",
    "            temp_2 = []\n",
    "            for j in range(len(policies)):\n",
    "                reward, steps_time = run_policy(p, policies[j], max_states_plus_1 = cardS, max_steps = H)\n",
    "                temp.append(reward)\n",
    "                temp_2.append(steps_time)\n",
    "            data_collect.append(temp)\n",
    "            time_data_collect.append(temp_2)\n",
    "\n",
    "        win_percent = np.mean(data_collect, axis=0)\n",
    "        win_ratio_seeds[s,jj,:] = win_percent\n",
    "\n",
    "mean_vals = np.mean(win_ratio_seeds, axis=0)\n",
    "std_vals = np.std(win_ratio_seeds, axis=0)\n",
    "\n",
    "# plot\n",
    "markercycler = cycle(markers)\n",
    "colorcycler = cycle(colors)\n",
    "bandcycler = cycle(bands)\n",
    "fig_W, ax_W = plt.subplots(1, 1)\n",
    "labels = ['non-robust optimal', 'RPVL-W'+r', $N=1000$, $\\rho=0.05$', 'RPVL-W'+r', $N=1000$, $\\rho=0.1$', \n",
    "          'RPVL-W'+r', $N=1000$, $\\rho=0.2$', 'RPVL-W'+r', $N=1000$, $\\rho=0.3$']\n",
    "for i in range(len(labels)):\n",
    "    ax_W.plot(head_p, mean_vals[:,i], marker=next(markercycler), color=next(colorcycler), label=labels[i],\n",
    "              linewidth=2, markersize=7)\n",
    "    lower, upper = mean_vals[:,i] - std_vals[:,i], mean_vals[:,i] + std_vals[:,i]\n",
    "    ax_W.fill_between(head_p, lower, upper, color=next(bandcycler))\n",
    "\n",
    "ax_W.legend()\n",
    "ax_W.grid(visible=True)\n",
    "ax_W.set_ylabel(\"Winning ratio in 1000 games\", fontsize=15)\n",
    "ax_W.set_xlabel(\"Test heads-up probability\", fontsize=15)\n",
    "ax_W.set_title(\"Wasserstein uncertainty set\")\n",
    "fig_W.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a11cfc5",
   "metadata": {},
   "source": [
    "### Sub-optimality gap vs. number of samples N\n",
    "Corresponds to Fig. 1 (left plot) in the paper. This section studies the convergence of robust policy (in terms of sub-optimality gap) with respect to number of samples N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "291b23a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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1TDmlaJ3v9761Rapz7Ngx9u/fj8Fw7dZBd3d3KioqANDpdDzyyCOsWbMGh6Pqe+Szzz7DZrNx//33N0tdm+O669ev5+677+aJJ57gyJEjzJs3j6effppz5865lFu6dCleXl7X/ElNTXU5pqKiggMHDjBp0iR1n0ajYdKkSezZs6fOdSwocD5C5e/vX+/P11gyeKKN8/YPBocdygvB3RebQ7K4EEI0mX9NgKLau9hU1jJw2Gp+z2GDvPPwj4F1u6ZXMPxqe93rCHzzzTd4eXlhtVopLy9Ho9Hw5ptv1lwdh4PY2Fg2bdrEwoUL1f2/+MUveO2119i+fTsxMTGAszv03nvvxcfHp171qY+mvG5ZWRnz5s3jjTfe4IEHHgDgpZde4p///Cc7duygR48eatnHHnuM++6775rnCwsLc3mdnZ2NzWYjJCTEZX9ISAhJSUl1qqPdbuepp55i/PjxDBo0qE7HNCUJdm2cKaALAI7SAhR3XxyKluJyG55GmaxYCCEarSgTzJcbf57S3Maf4xomTpzIypUrKS4uZtmyZTgcDu69916XMpXhz2KxYLfbeeCBB1yew+vfvz/jxo3j3XffJSYmhuTkZHbu3MlLL71Upzp89NFHzJs3T329YcMGbrrppuse19jrXm3r1q2UlpYyc+ZMdZ9Wq0VRFIxGo0tZf3//Vmkxmz9/PseOHSM+Pr7Frw0S7No8U3BXgCvP2XUH4HJ+KX1CvFqvUkII0VF4Bde9rLWs5gDn7g86t+a55hWenp5ERkYC8M477xAdHc0777zDo48+qpapDH8Gg4GwsDB0uupf8XPnzmXhwoUsX76c1atX07t3byZMmFCnOtx1112MGTNGfR0eXvepXRpz3att27aNoUOHotVWNW4kJydjNpsZNmyYS9mlS5eydOnSa54vMTGRbt26qa8DAwPRarVkZGS4lMvIyCA0NPS69VuwYAHffPMNO3bsoGvXrnX5SE1Ogl0b5+Xn/EW6egBFUrpZgp0QQjSF+nSJOhzw74mQdsTZ/apooUs0/HJbsw2aqIlGo2HRokU899xzPPTQQ7i7uwOu4a829913H08++SQff/wx77//Po8//jhKHetuMpkwmUwNqnNjrnu1hIQE9bnBSitWrGDEiBH07dvXZX9DumINBgMjRowgNjZWnfbFbrcTGxvLggULaj2Pw+Fg4cKFfP3118TFxdGzZ896fKqmJcGujdPp9XhorZRdNZddSrZMeSKEEC2ucoTsR1e6QB22Zh0Jey0zZsxgyZIlLF++nN/85jd1Ps7Ly4uZM2eyePFiCgsLmT17drUyBQUFHDp0yGVfQEAAERERDa5vXa5bFwkJCTgcDt5//33GjBnDZ599xsqVK9m9e3e1sg3til20aBGzZs1i5MiRjB49mtdff53i4mLmzJmjlnnrrbf46quviI2NBeA3v/kNX3zxBWvXrsVkMpGeng6Aj4+PGrxbijyJ3w6Y9A6XFru0gtLWq4wQQnRmlSNkoUVHwv6YTqdj/vz5vPrqq/WeK23u3Lnk5eUxZcqUai1WAHFxcQwbNszl58UXX2x0na933TVr1lyzFS81NZXc3Fw++OADXnvtNaKjo1m7di0bN26s1g3bGDNnzuRvf/sbzz//PEOHDuXQoUNs3LjRZUBFdnY2Z86cUV+/++67FBQUEBMTQ5cuXdSfTz/9tMnqVVeK4+rxx6LOCgsL8fHxoaCgAG9v73oda7FYWL9+PdOmTUOv11+3/AtPzOR0iQnDhF8D4K63sfKhpvsl7uzqez9E85N70rZ0hPtRVlbG2bNn6dmzJ25u9XgeriYp22DD7+D2v0KviU1TwXqw2+0UFhbi7e2NRtNx2mdeeOEFtm/fTlxcXI3vr1u3jjlz5pCTk9OyFbuOprof1/odrU/mkK7YdsDkrof8Ehy2ChStgXKZy04IIVpPr4kw/9pLTIn627BhA2+99Vat7yckJDB48OAWrFH7JMGuHTB5uAElzilPvIKwO7TYHQ40rfBchxBCCNEcrrcea0JCQptbfqwtkmDXDnh7eQElUFoAXkGgKBSUWPHzbJ9dIkIIIUR9ff31161dhXah43TOd2Be3s7h5VcPoLicLwMohBBCCOFKgl074O3rHK59dbA7mVnUSrURQgghRFslwa4dMPkFAa7B7pzMZSeEEEKIH5Fg1w6YAq4sY3LVJMXpheWtVBshhBBCtFUS7NoB70DnenxXt9gVlFhaqTZCCCGEaKsk2LUDpqArCwnbLCgW56CJCqvMZSeEEEIIVxLs2gF3Ty+0it35orwQALtDg8Vmb8VaCSGEEKKtkWDXDiiKgkl3JcRd6Y5VFIWcIumOFUIIIUQVCXbthMno/K+tOFfdl1ZQ1kq1EUIIIURbJMGunTC5ORcJsZfkq/tOZchcdkIIIYSoIsGunfD2MACuI2PP58rqE0IIIdq+mJgYnnrqqUaXEdcnwa6dMHl6AK7BLlPmshNCiBaTmJjIV199xcWLFwG4cOECX331FYmJic163dmzZ6MoCoqioNfr6d27N88//zxlZWU1ljEYDERGRvLSSy9htVoBmD59OlOnTq3x/Dt37kRRFI4cOcLs2bOZMWNGveuYnp7OwoUL6dWrF0ajkYiICKZPn05sbGydz/Hll1/ypz/9qd7XbinLly+nR48euLm5MWbMGPbv319r2ZdffplRo0ZhMpkIDg5mxowZnDx5skXqKcGunTCZnOvFUmYGh3Oqk4JSayvWSAghOo/ExEQSExOx2WwcOHCAgoICDh48iM1mU99rTlOnTiUtLY2UlBT+/ve/s2bNGpYsWVJjmdOnT/PMM8+wZMkSXnvtNQDmzp3Lli1b1FB6tdWrVzNy5Eiio6MbVLdz584xYsQItm7dymuvvcbRo0fZuHEjEydOZP78+XU+j7+/f9V3XRvz6aefsmjRIl544QUOHjzIkCFDmDJlCpmZmTWW3759O/Pnz2fv3r1s2bIFi8XC5MmTKS4ubva6SrBrJ0w+fs4Nhx293fmvNItN5rITQojm9uPgZrVa2bp1q9oaVlOZpmY0GgkNDSUiIoIZM2YQExPDd999V2OZ7t278/jjjzNp0iTWrVsHwJ133klQUBBr1qxxOaaoqIjPPvuMuXPnNrhuTzzxBIqisH//fu6991769u1LVFQUixYtYu/evWo5u93Ob3/7W/z9/QkNDa0WTBvTFRsfH8/o0aNxc3MjMDCQN954o8GfpybLli3j0UcfZc6cOQwcOJBVq1bh4eHBu+++W2P5jRs3Mnv2bKKiohgyZAhr1qwhNTWVAwcONGm9aiLBrp0w+fqr23prZeLXUFxua50KCSFEJ/HjLjSHw4HdbsfhcFyzXHM5duwY+/fvx2AwXLOcu7s7FRUVAOh0Oh555BHWrFnjUu/PPvsMm83G/fff36C65ObmsnHjRubPn4+np2e19319fdXt9957D09PT/bt28err77KSy+9xJYtWxp03autX7+eu+++myeeeIIjR44wb948nn76ac6dO+dSbunSpXh5eV3zJzU1tdr5KyoqOHDgAJMmTVL3aTQaJk2axJ49e+pUx4IC55Kg/v7+1ynZeLpmv4JoEt7+Ieq2xlIExkAAsosq8DS6t1a1hBCiXYuNjXV5Vq0mWq0Wm831H9E/DnWV5b799tvrXtPNzY1bb721XvX85ptv8PLywmq1Ul5ejkaj4c0336yxrMPhIDY2lk2bNrFw4UJ1/y9+8Qtee+01tm/fTkxMDODshr333nvx8fGpV30qJScn43A46N+//3XLRkdH88ILLwDQp08f3nrrLWJjY7ntttsadG2AsrIy5s2bxxtvvMEDDzwAwEsvvcQ///lPduzYQY8ePdSyjz32GPfdd981zxcWFlZtX3Z2NjabjZCQEJf9ISEhJCUlXbeOdrudp556ivHjxzNo0KA6fKrGkWDXTpgCu6jbSlkheDm30wrK6B4gwU4IIRqirKyM0tKmmWGgsnWsOUycOJGVK1dSXFzMsmXLcDgc3HvvvS5lKsOfxWLBbrfzwAMPuHR39u/fn3HjxvHuu+8SExNDcnIyO3fu5KWXXqpTHT766CPmzZunvt6wYcN1Ww2v9uNn+Lp06VLrM2p1tXXrVkpLS5k5c6a6T6vVoigKRqPRpay/v3+LtJj92Pz58zl27Bjx8fEtcj0Jdu2EKbCruu0oK1C3k7OKuaGXX2tUSQgh2j03N7frlrFarVgs11/px2AwoNVqm+SaP+bp6UlkZCQA77zzDtHR0bzzzjs8+uijapnK8GcwGAgLC0Onq/4VP3fuXBYuXMjy5ctZvXo1vXv3ZsKECXWqw1133cWYMWPU1+Hh4ZSWlqIoSp1arvR6vctrRVGw2xu3NOa2bdsYOnSoy//35ORkzGYzw4YNcym7dOlSli5des3zJSYm0q1bN5d9gYGBaLVaMjIyXPZnZGQQGhp6zfMtWLCAb775hh07dtC1a9drlm0qEuzaCZN/kLptL6pafeKCzGUnhBANVpcu0a+++qraPkVRqnXH2mw27rrrriarW200Gg2LFi3iueee46GHHsLd3dlrc3X4q819993Hk08+yccff8z777/P448/jqIodbquyWSqNmrV3d2dKVOmsHz5cn79619Xe84uPz/f5Tm7ppaQkFCtpXTFihWMGDGCvn37uuxvaFeswWBgxIgRxMbGqlPB2O12YmNjWbBgQY3ncTgcLFy4kK+++oq4uDh69uxZj0/VOBLs2gm9Xo+7xkapXYu1OEfdn2luvqZ/IYQQ0K9fP5cRr4qioNFoqg2g6NevX4vVacaMGSxZsoTly5fzm9/8ps7HeXl5MXPmTBYvXkxhYSGzZ8+uVqagoIBDhw657AsICCAiIqLGcy5fvpzx48czevRoXnrpJaKjo7FarWzZsoWVK1dy4sSJ+ny0eklISMDhcPD+++8zZswYPvvsM1auXMnu3burlW1MV+yiRYuYNWsWI0eOZPTo0bz++usUFxczZ84ctczy5cv5+uuviY2NZf78+Xz88cesXbsWk8lEeno6AD4+PmoQby4yKrYd8TI4/wKpKMpDi7P52lwmc9kJIURzGjhwIAMHDlRf63Q6brnlFpeuzh+XaW46nY758+fz6quv1ntutLlz55KXl8eUKVNqbKGKi4tj2LBhLj8vvvhirefr1asXBw8eZOLEiTzzzDMMGjSI2267jdjYWFauXFnvz1ZpzZo112xNTE1NJTc3lw8++IDXXnuN6Oho1q5dy8aNG6t1wzbWzJkz+dvf/sbzzz/P0KFDOXToEBs3bnQZUJGdnc2ZM2cAWLlyJQUFBcTExNClSxf159NPP23SetVEcdQ0tEdcV2FhIT4+PhQUFODt7V2vYy0WC+vXr2fatGnVnjm4luee+Dln8p1Z3H/K0xTZjThwsHp2NJo6NqWL6hp6P0TzkXvStnSE+1FWVsbZs2fp2bNng55xA+fzVydPnmTUqFF07dqVCxcu8MMPP9CvX78WDXV2u53CwkK8vb3RaDpu+8wLL7zA9u3biYuLq/H9devWMWfOHHJycmp8v6U01f241u9ofTKHdMW2IyZ3A+Q7W+jcHOUUYURBoaDEip9n+/zLVggh2osft8pFRETU2j0pGm/Dhg289dZbtb6fkJDA4MGDW7BG7YMEu3bE5OEGFAFgsJeC4kztWUUVEuyEEEJ0KNdaixWcwa6hy6B1ZBLs2hFvL08qg53eVqLevfSCMvqGVJ/xWwghhOiovv7669auQpvUcTvnOyCTd9XM4JqKInX7TFZJa1RHCCGEEG2MBLt2xORbNRGxprxQ3b6YL3PZCSGEEEKCXbti8quapNhRmq9uZ8tcdkIIIYRAgl27YgqoWrrEWlaCQXGOkC0qb9ySLEIIIYToGCTYtSPeQeHqtrW8BA/FuXah1e7AYpNwJ4QQQnR2EuzaEVNA1Qzh1vJSPDTOYKegkFN0/QWqhRBCCNGxSbBrRzw8PdFwZVmx8lLcNVXLiWUVyXN2QgghRGcnwa4d0Wg0eOmdXa5l5RVqVyxAekF5a1VLCCGEEG2EBLt2xmR0/rfMYle7YgHOZtdvEWghhBBCdDwS7NoZk5tzuYkKu4Kbo0zdfym/rLZDhBBCNCG73U5ZWRl2uwxaE21Phwp2y5cvp0ePHri5uTFmzJjrrjP3+uuv069fP9zd3YmIiODpp5+mrKxtByRvD6O6rVQUwZVn7nKKZfCEEEI0p/Pnz7Nq1Spmz57NL37xC2bPns2qVas4f/58s1539uzZKIqCoijo9Xp69+7N888/7/J9dXUZg8FAZGQkL730Elar81ns6dOnM3Xq1BrPv3PnThRF4ciRI8yePZsZM2bUu34/Pubzzz/Hzc2Nv//977WWaavqmyVeeeUVbrnlFnx8fAgODmbGjBmcPHmyhWpbXYcJdp9++imLFi3ihRde4ODBgwwZMoQpU6aQmZlZY/mPP/6YZ599lhdeeIETJ07wzjvv8Omnn/L73/++hWtePyZPD3XbaqnA/cpcdiUyl50QQjSb3bt384c//IH4+Hg1LFmtVuLj4/nDH/7A7t27m/X6U6dOJS0tjZSUFP7+97+zZs0alixZUmOZ06dP88wzz7BkyRJee+01AObOncuWLVu4ePFitXOvXr2akSNHEh0d3SR1/fe//82DDz7IypUreeaZZ5rknC2lvlkCYPv27fzyl79k9+7dbNmyBYvFwuTJkykubp1HpDpMsFu2bBmPPvooc+bMYeDAgaxatQoPDw/efffdGsvv3r2b8ePH88ADD9CjRw8mT57M/ffff91k3tpMJpO6XVFepj5nZ3dAcbmttaolhBAd1vnz51mxYgV2u71a92vlvhUrVjRry53RaCQ0NJSIiAhmzJhBTEwM3333XY1lunfvzuOPP86kSZNYt24dAHfeeSdBQUGsWbPG5ZiioiI+++wz5s6d2yT1fPXVV1m4cCGffPIJc+bMaZJzXi0+Pp7Ro0fj5uZGYGAgb7zxRpOev75ZAmDDhg088MADREVFMWTIENasWUNqaioHDhxo0rrVla5VrtrEKioqOHDgAIsXL1b3aTQaJk2axJ49e2o8Zty4cXz44Yfs37+f0aNHk5KSwvr163n44YdrLF9eXk55edXI08JC51qtFosFi6V+3aCV5et7HICnyVvdtlucc9nlXMlz6fkldPN3q/c5O7vG3A/RPOSetC0d4X5YLBYcDkeN4ex61q9fX6dyGzZs4Fe/+lVDqndNDodDrTvA0aNH2b9/P927d1f3/bgMgJubGzk5OdjtdjQaDQ8//DBr1qxh8eLFKIoCOFuobDYbM2fOxG6313ieutbvt7/9LStXrmTdunXceuutLudoyHl/bP369cyZM4e//vWvjBs3jvfff5+nn36a6dOn06NHD5eyL7/8Mi+//PI1z3fs2DG6deumvq7MEr/73e9c6nnrrbeye/fuWuvucDjU/9rtdvLy8gDw9fWt1+et/P9vsVjQarUu79Xnz16HCHbZ2dnYbDZCQkJc9oeEhJCUlFTjMQ888ADZ2dnceOONOBwOrFYrjz32WK1dsS+//DIvvvhitf2bN2/Gw8OjhiOub8uWLfU+Jic7T92uKC7AI6jqZm/asZdwQ0mD6iIadj9E85J70ra05/uh0+kIDQ2lqKiIioqqeT9feeUV9R/qNXE4HBQUFFz3/Ha7nR07dnD48GE1NNXG29ubZ599ts51t1gsfPvtt3h7e2O1WikvL0ej0fDqq6+6NDJYrVYKCwtxOBxs376dzZs38+ijj6plfvazn/G3v/2NDRs2cOONNwLwzjvvMH36dBRFobCw0OU89anfxo0bWbduHWvXrmXUqFHVjm/Iea9WVlbGvHnzWLp0Kffccw8AzzzzDG+++SabN2/m5z//uUv5Bx54gNtvv/2a5/Ty8nKpT1paGjabrdp+X19fEhMTr1t3s9mM3W5n4cKFjBkzhm7dutXr81ZUVFBaWsqOHTvU7v5KJSV1/27vEMGuIeLi4li6dCkrVqxgzJgxJCcn8+STT/KnP/2J5557rlr5xYsXs2jRIvV1YWEhERERTJ48GW9v72rlr8VisbBlyxZuu+029Hp9vY49anLw3eH/AqCxlbtMeRIROYipgwLrdT7RuPshmofck7alI9yPsrIyLly4gJeXF25uVT0bZrOZ/Pz8JrtOXUKgoij1+t7Q6/XExMSwYsUKiouLef3113E4HDz44INqiNTr9WzatImuXbtisViw2+3cf//9LF26FE9PTwBGjhzJuHHj+PTTT5k2bRrJycns2bOHP//5z2p99Ho9Op2uxvp99NFHPP744+rrb7/9lptuugm9Xk90dDTZ2dm89tprxMTE4OXlVe0z1HbeuoiPj6e8vJzZs2e7tGYpioKPj0+183p7e9O9e/d6XaOoqAgAT09Pl/MZjUa0Wm2tdXc4HJjNZkwmE/Pnz+fkyZPs2LGj3p+1rKwMd3d3br75ZpffUaBeAbFDBLvAwEC0Wi0ZGRku+zMyMggNDa3xmOeee46HH36YX/7ylwAMHjyY4uJifvWrX/GHP/wBjcb18UOj0YjRaKx2Hr1e3+C/6BpyrF9IV3XbUlaC71XBLjW/vN3+pdsWNOZeiuYh96Rtac/3w2azoSgKGo3G5e93X1/fa7awORwOtWutLvz8/K7bYufj41PtO+ZaFEXBy8uLvn37As5WtujoaN59910effRRtczEiRNZuXIlBoOBsLAwdLrqX/Fz585l4cKFrFixgvfee4/evXszceJEtc6VI2trqt+MGTMYO3as+jo8PByNRoOiKISHh/P5558zceJEpk2bxoYNG1yeCb/Weeti+/btDB061OX3Lzk5GbPZzIgRI6qdd+nSpSxduvSa50xMTHTpig0ODkar1ZKVleVyvszMTEJDQ2ute2V3669//Wu+/fZbduzY4XLeuqr8f1nTn7P6/LnrEMHOYDAwYsQIYmNj1eHUdrud2NhYFixYUOMxJSUl1W5S5b8CKvvL2yJTYFWwqygrdll94nJB256qRQgh2pq//OUv1y2zatUq4uPjr/m8lEaj4aabbmLevHlNWb1ar7Vo0SKee+45HnroIdzd3QFnS1NkZOQ1j73vvvt48skn+fjjj3n//fd5/PHHrxtEK5lMJpew9mPdu3dn+/btTJw4kalTp7Jx48Zrlq+PhIQEly50gBUrVjBixAg18F7tscce47777rvmOcPCwlxeNyRLgDMz/N///R/r168nLi6Onj171vFTNY8OEewAFi1axKxZsxg5ciSjR4/m9ddfp7i4WB2V88gjjxAeHq4+TDl9+nSWLVvGsGHD1K7Y5557junTp1d7aLEtMfn6qdulpWUYFBta7NjQkCdz2QkhRJO7/fbbiY+Pv2652uaJaw4zZsxgyZIlLF++nN/85jd1Ps7Ly4uZM2eyePFiCgsLmT17drUyBQUFHDp0yGVfQEAAERER1z1/REQEcXFxTJw4kSlTprBx40a1S7Ix501ISMDhcPD+++8zZswYPvvsM1auXFnrNDP+/v74+/tf97w/dr0sAfDWW2/x1VdfERsbC8CCBQv473//y9dff43JZCI9PR1wtsxWhu6W1GGC3cyZM8nKyuL5558nPT2doUOHsnHjRnVARWpqqksL3R//+EcUReGPf/wjly5dIigoiOnTp9fpX2+tyWg0YtTYKbdrKC23oijgrrFQZDdSWuHA7nCgqeO/voQQQlxf9+7deeKJJ1ixYgWAS8td5ffKE088Ue9nuhpDp9Mxf/58Xn31VZfn3upi7ty5vPPOO0ybNq1aqxU4n0EfNmxYtWP+/e9/1+n8Xbt2dQl3mzZtuu5516xZw5w5c2rsMUtNTSU3N5dvvvmGZ599llOnThEdHc3GjRurna+xrpclwDlg88yZM+rrVatWAXDLLbe4nGv16tU1Bufmpjjacr9jG1ZYWIiPjw8FBQUNGjyxfv16pk2b1qDnVX79i5lkl2nx1lYw6Sc/Z39JGJk254Oq/5g5AD+P9vkMTGtp7P0QTU/uSdvSEe5HWVkZZ8+epWfPntUeTK+r8+fPs3HjRnbt2oXVakWn0zF+/HimTp3aoqHObrdTWFiIt7d3g59Za2teeOEFtm/fTlxcXLX31q1bx5w5c8jJyWn5itVBU92Pa/2O1idzdJgWu87EZNSSXQZFNh0aRXGOjL0yl12WuUKCnRBCNIPu3bszb948Hn30USoqKjAajXV+Pk1c24YNG3jrrbdqfC8hIYHBgwe3cI3aLwl27ZDJXQ8FFuxo0CkOlylPsswV9A3xbMXaCSFEx6bRaBrc6idqdq1VnxISEppsubPOQIJdO2TydAOuhDlrmcvI2MzC8poPEkIIIdqhr7/+urWr0K50jM75TsbbdNXEjxXFLi1253Nl5QkhhBCis5Jg1w6ZTD7qtq3M7BLs0gqkxU4IIYTorCTYtUMmn6q57KwlBegUBwbFua5cfom1tsOEEEII0cFJsGuHTAFB6nZFqXNdQvcrwa7c6sBiq312dCGEEEJ0XBLs2iGTf9X6txXFZgA8NVVLreQUyQoUQgghRGckwa4dMgWGq9ulJUUAuGuqumCziiqqHSOEEEKIjk+CXTvkHVC1tElxaQV6vd5lypMsswQ7IYQQojOSYNcOeXp6ouBcCc5c7sDDw8NlZKzMZSeEEEJ0ThLs2iGtVounzhnsCi0a3N3dXILdhbzS1qqaEEIIIVqRrDzRTpmMUGQFs02Ph0GLu2IBHIBCurTYCSFEk8kpqsBcdv2ppExuOgK8DC1QIyFqJy127ZTJzZnJS+16DFjQKFVTnhSUylx2QgjRFHKKKnj2y5Ms+V/ydX+e/fIkOc0weG327NkoioKiKOj1enr37s3zzz9PWVlZjWUMBgORkZG89NJLWK3O74Pp06czderUGs+/c+dOFEXhyJEjzJ49mxkzZtS5bqtWrcJkMqnXASgqKkKv1xMTE+NSNi4uDkVROHPmTN0/fAtZvnw5PXr0wM3NjTFjxlxz7VqAl19+mVGjRmEymQgNDeXBBx/k5MmTLVTba5Ng1055e1QtQO0oc85lV9kda7VBSYWtVeolhBAdibnMisXmqFNZi81Rp5a9hpg6dSppaWmkpKTw97//nTVr1rBkyZIay5w+fZpnnnmGJUuW8NprrwEwd+5ctmzZwsWLF6ude/Xq1YwcOZLo6Oh612vixIkUFRXxww8/qPt27txJaGgo+/btcwmf27Zto1u3bvTu3bve12lOn376KYsWLeKFF17g4MGDDBkyhClTppCZmVnrMdu3b2f+/Pns3buXTZs2YbFYmDp1KsXFxS1Y85pJsGunTF7u6ratOA/A5Tk7GRkrhBAdh9FoJDQ0lIiICGbMmEFMTAzfffddjWW6d+/O448/zqRJk1i3bh0Ad955J0FBQaxZs8blmKKiIj777DPmzp3boHr169ePLl26EBcXp+6Li4vjJz/5CT179mTv3r0u+ydOnFjva8THxzN69Gjc3NwIDAzkjTfeaFBda7Ns2TIeffRR5syZw8CBA1m1ahUeHh68++67tR6zceNGZs+eTVRUFEOGDGHFihWkpqZy4MCBJq1bQ0iwa6dMJm9121KUAyBTngghRCdw7Ngx9u/fj8Fw7ef53N3dqahwfhfodDoeeeQR1qxZg8NR1QL52WefYbPZuP/++xtcn4kTJ7Jt2zb19bZt24iJiWHChAnq/tLSUvbt21fvYLd+/XruvvtunnjiCY4cOcK8efN4+umnOXfuXLWyS5cuxcvL65o/qampLsdUVFRw4MABJk2apO7TaDRMmjSJPXv21LmehYWFAPj7+9fr8zUHGTzRTpl8fNXtCnM2eHdzbbGTSYqFEOK6lqw7fc3nkq32unXDVvr7lnPoNMo1y/i461hyV596nfebb77By8sLq9VKeXk5Go2GN998s8ayDoeD2NhYNm3axMKFC9X9v/jFL3jttdfYvn27+vzb6tWruffee/Hx8alXfa42ceJEnnrqKaxWK6WlpSQkJDBhwgQsFgurVq0CYM+ePZSXl9cr2JWVlTFv3jzeeOMNHnjgAQBeeukl/vnPf7Jjxw569OjhUv6xxx7jvvvuu+Y5w8LCXF5nZ2djs9kICQlx2R8SEkJSUlKd6mm321m8eDHjx49n0KBBdTqmOUmwa6dMvgHqdnFhIcYgIx62q4KdjIwVQojrKii1klfSdMswNtczdhMnTmTlypUUFxezbNkyHA4H9957r0uZyvBnsViw2+088MADLs/h9e/fn3HjxvHuu+8SExNDcnIyO3fu5KWXXqpTHT766CPmzZunvt6wYQM33XQTMTExFBcX8/3335OXl0ffvn0JCgpiwoQJzJkzh7KyMuLi4ujVqxfdunWr82feunUrpaWlzJw5U92n1WpRFAWj0VitvL+/f6u0mC1YsIATJ04QHx/f4teuiQS7dsrkX/Wvi8IiM34eHriXFqr7LuSX1XSYEEKIq/i4X/tr0Gqv34AIk5uuTi129eXp6UlkZCQA77zzDtHR0bzzzjs8+uijapnK8GcwGAgLC0Onq36duXPnsnDhQpYvX87q1avp3bs3EyZMqFMd7rrrLsaMGaO+Dg93Lm8ZGRlJ165d2bZtG3l5eer5wsLCiIiIYPfu3Wzbto1bbrmlXp9527ZtDB06FK1Wq+5LTk7GbDYzbNiwauWXLl3K0qVLr3nOxMREl3AZGBiIVqslIyPDpVxGRgahoaE/PryaBQsW8O233/LNN9/QtWvX65ZvCRLs2invoKrmZHNxKWHu7hiVPDTYsaOR1SeEEKIOrtclei67hCX/S67z+Z65rQc9Aj0aW61r0mg0LFq0iOeee46HHnoId3fnYLqrw19t7rvvPp588kk+/vhj3n//fR5//HEU5dpBtJLJZMJkMtX43sSJE4mLiyMvL4//+7//U/fffPPNbNiwgf379/P444/X8RM6JSQkqM8IVlqxYgUjRoygb9++1co3pCvWYDAwYsQIYmNj1Wle7HY7sbGxLFiwoNbzOBwOFi5cyFdffcXWrVurdeW2Jgl27ZQpoIu6bS6pwMPDA0Vxjowtshsxl9mwOxxo6vgHVgghRPsxY8YMlixZwvLly/nNb35T5+O8vLyYOXMmixcvprCwkNmzZ1crU1BQwKFDh1z2BQQEEBERUet5J06cyPz587FYLC4tgBMmTGDBggVUVFTUe+BEQkICDoeD999/nzFjxvDZZ5+xcuVKdu/eXWP5hnbFLlq0iFmzZjFy5EhGjx7N66+/TnFxMXPmzFHLvPXWW3z11VfExsYCMH/+fD7++GPWrl2LyWQiIyODkpIS/Pz81KDdWmRUbDtl8q560NVcblN/kSpHxtodMlGxEEJ0VDqdjvnz5/Pqq6/We+60uXPnkpeXx5QpU6q1YIFzWpJhw4a5/Lz44ovXPOfEiRMpLS0lMjLSpfVqwoQJmM1mdVqUSmvWrLlmS2Fqaiq5ubl88MEHvPbaa0RHR7N27Vo2btxYYzdsY8ycOZO//e1vPP/88wwdOpRDhw6xceNGl8+RnZ3tMrHyypUrKSgoICYmhvDwcPr37094eDiffvppk9atIaTFrp1yc3NDr9ixODSYy8HDw9n076GxwJW5ibPNFfh56FuxlkII0b6Z3HTotUqdJinWaxV1VaCm9OO55yr97ne/Y/HixdcsU5OxY8e6THny42vV51yVevToUeM5u3fvXuP+s2fPXvPZvkOHDuHv788dd9zBHXfcUe/61NeCBQuu2fW6ZMkSl4EoV38mu91OYWEh3t7eaDSt314mwa6dUhQFkwFyy53rxbrrnf/yuXrKk0xzBX1CPFurikII0e4FeBl45Z5+slZsE9uwYQNvvfVWre8nJCQwePDgFqxRxyHBrh0zuWnILYdCqwF3RwngOklxtsxlJ4QQjRbgZZDA1sSutxZrQkJCg5Y4ExLs2jWTuwEKKrChwVGSDfyoxU5GxgohhGiHvv7669auQrvV+p3BosFMnlUjb4qz03B3d3cJdpdkLjshhBCiU5Fg1455e3mp2+bcDNzd3dEpDgyK81kQWVZMCCGE6Fwk2LVjJm9vdducl62OjHW/EuyKy+1YbfZWqZsQQgghWp4Eu3bM5Fs1EWNhQV7V7OOaqpa67KKmWwNRCCGEEG2bBLt2zOQfrG6bzYVVLXaaqmH5MjJWCCGq1DZ/mxCtral+NyXYtWOmgKoFis1FpVWTFCuuc9kJIURnp9c7J2svKSlp5ZoIUbPK383K39WGkulO2jFTYNVSMOaSsqplxa4aGZstwU4IIdBqtfj6+pKZmQlwZX3t9rmWtt1up6KigrKysjax0kFn19j74XA4KCkpITMzE19fX7RabaPqI8GuHfP2DVC3zaUW12XFrpAWOyGEcAoNdfZyVIa79srhcFBaWoq7u3u7DacdSVPdD19fX/V3tDEk2LVjXldPd1LuwGg0otFocHdYAAegcLlA5rITQghwLsXYpUsXgoODsVja78Ayi8XCjh07uPnmmxvdbScarynuh16vb3RLXSUJdu2YTqfDQ2enxKqh0KJBsVtxd3enuLgYN8VKmUNPjgyeEEIIF1qttsm+RFuDVqvFarXi5uYmwa4NaGv3Qzrn2zmT0dnsa7YZoDT3qilPnP8aLbc6KKmwtVr9hBBCCNFyJNi1cyY3Z6Nrsd2ArTCzxufssuQ5OyGEEKJTkGDXzpk8jOp2Uc6lqpGxV015InPZCSGEEJ2DBLt2ztvLU90256RdNUmxjIwVQgghOhsJdu2cyWRStwtzstRg5ylz2QkhhBCdjgS7ds7k7adumwuqBk+4K/KMnRBCCNHZSLBr50z+geq2ubBAbbEzKjYUhx2ANJnLTgghhOgUJNi1cyb/YHW70FykTnKoKGC80mqXW2KRha+FEEKITkCCXTvn7bJebCmKoqitdl5XnrOz2aGg1Noq9RNCCCFEy5Fg186Z/Kpa7MwlzmfpKp+z89LKc3ZCCCFEZ9LoJcXef//9pqiHi6FDhxIdHd3k5+2ITN7e6ra5zLnCRI2TFBdV0CfEEyGEEEJ0XI0OdrNnz0ZRlKaoCw6HA0VReOGFFyTY1ZGHhwdaxYHNoWCuUMDhqHGSYmmxE0IIITq+Rge71atXN0U9XAwdOrTJz9lRKYqCyQD55WC26aG8QJYVE0IIITqpRge7WbNmNUU9RCOYjBryyx0U2gw4irLx8HBOWizBTgghhOhcZPBEB2DyMABgcWgpL0hXu2J1igPF4RwNm15Y3mr1E0IIIUTLkGDXAZg83NVtc/ZltSsWwICz1a6g1IrVZm/xugkhhBCi5Uiw6wBMJi9125yTgU6nQ6/XA65rxuYUW6odK4QQQoiOQ6Y76QC8vX2AiwCY87MB52jZgoICfHQWcq/kuSxzBSHexlaqpRBCCCGam0x30gGYfP3V7cL8PMA5SXFBQQFemqoVJ2QAhRBCCNGxyXQnHYDJL0jdNpvNQC2TFEuwE0IIITo0me6kAzAFdlG3zUUlQNWyYj9efUIIIYQQHZcMnugATAFVwa6wpAyoarFzVyw4HA5AWuyEEEKIjk6CXQfg7eOrbheVOZ+pq2yx0yiAw9lql2GWueyEEEKIjqzewe6LL75Qt7t168bJkyebtEKi/ry8rprupNzZOnf1XHb6K8GutMJOaYWtZSsnhBBCiBZT52B3/PhxbrnlFt577z1138WLF1mwYAHPPfccn3zyCUeOHKGioqq7b+7cuU1bW1Ejg8GAm9YZ6AqtOrCUqi12IM/ZCSGEEJ1FnYPd0KFDmTZtGuvWrXPZb7PZ+Oqrr3jkkUcYNmwYnp6e9OvXjxtuuIFPPvmkySssamYyOqecMdv0UJKDVqvFaHTOWeejk5GxQgghRGdQ51GxS5Ys4Y033iAwMJDZs2er+1955RVGjx6NxWIhKSmJY8eOkZiYyMWLF2XEbAsyuenIKrFSZDNgL8pE49MVDw8PysvL8ddZuHClB1aCnRBCCNFx1TnY/eEPf2D27Nn87ne/cwl2lfR6PYMHD2bw4MFNWT9RRyYPN8gtwoFCUfZlvMOH4+7uTl5eHp5amaRYCCGE6AzqNXgiPDycDz/80GXfhg0b2Ldvnzoxbmtavnw5PXr0wM3NjTFjxrB///5rls/Pz2f+/Pl06dIFo9FI3759Wb9+fQvVtml5e1UNljDnpgNg1XlQYDNitVetDHI+t5Rz2SUuPzny3J0QQgjRITR6guKXX36ZF198EUVR6Nq1KwMHDiQqKoqoqCgGDhzImDFjmqKe1/Xpp5+yaNEiVq1axZgxY3j99deZMmUKJ0+eJDg4uFr5iooKbrvtNoKDg/n8888JDw/n/Pnz+Pr6tkh9m5rJ2xvIBMCcm0VOUQXvJWqwObq7lEvOLGHJ/5Jd9um1Cq/c048AL0NLVVcIIYQQzaBRwW78+PHqc3fHjx8nMTGR48ePEx8fz7/+9S+Ki4ux2Vpmeo1ly5bx6KOPMmfOHABWrVrFt99+y7vvvsuzzz5brfy7775Lbm4uu3fvRq/XA9CjR48WqWtzMHn7qtvm/FzcyqzYHHU71mJzYC6zSrATQggh2rlGBbudO3eyd+9eunXrRrdu3bj99ttd3k9NTW1U5eqqoqKCAwcOsHjxYnWfRqNh0qRJ7Nmzp8Zj1q1bx9ixY5k/fz5r164lKCiIBx54gN/97ndotdoWqXdT8va/ar3YwgKCrlFWCCGEEB1To7tix48fz09/+lNefvllevXq5fJet27dGnv6OsnOzsZmsxESEuKyPyQkhKSkpBqPSUlJYevWrTz44IOsX7+e5ORknnjiCSwWCy+88EK18uXl5ZSXV63cUFhYCIDFYsFisVQrfy2V5et73LW4eweo2wXmIqxW6zVKV2e1Wpu0Pu1Jc9wP0ThyT9oWuR9ti9yPtqUl7kd9zq04KhcSbSAfHx/MZjMGg4HHHnuM5557joCAgOsf2IQuX75MeHg4u3fvZuzYser+3/72t2zfvp19+/ZVO6Zv376UlZVx9uxZtYVu2bJlvPbaa6SlpVUrv2TJEl588cVq+z/++GOXVR5aS05mGt9t2wHAxOA8DDf9mm2F4XU+fqL3JXx1MohCCCGEaGtKSkp44IEHKCgowNvb+5plG91id/bsWf785z+zcuVK3nzzTd577z0WL17Mk08+qU6Q29wCAwPRarVkZGS47M/IyCA0NLTGY7p06YJer3fpdh0wYADp6elUVFRgMLg+b7Z48WIWLVqkvi4sLCQiIoLJkydf93/yj1ksFrZs2cJtt92mPt/XWGlpVcHOYtcwcfx4tm04V+fjx48fT/cA9+sX7ICa436IxpF70rbI/Whb5H60LS1xPyp7Ceui0cHO39+fZcuW8dRTT/Hcc8/x0UcfsXjxYlasWMGf/vQnHn744cZe4roMBgMjRowgNjaWGTNmAGC324mNjWXBggU1HjN+/Hg+/vhj7HY7Go1z1pdTp07RpUuXaqEOwGg01hhU9Xp9g29kY479MT8/P3XbXG5Hp6vfrdXpdJ3+L4imvB+iacg9aVvkfrQtcj/alua8H/U5b73msbuWbt268d5775GQkMDtt99Oamoqs2fPZvjw4cTGxjbVZWq1aNEi3n77bd577z1OnDjB448/TnFxsTpK9pFHHnEZXPH444+Tm5vLk08+yalTp/j2229ZunQp8+fPb/a6NgdPT08UnL3qZosCDnsr10gIIYQQLa3Jgl2lwYMH88033xAXF8eYMWM4dOgQkydPZtq0aRw7dqypL6eaOXMmf/vb33j++ecZOnQohw4dYuPGjeqAitTUVJdn5yIiIti0aRPff/890dHR/PrXv+bJJ5+scWqU9kCj0WC60tBothmgvO7NtkIIIYToGBrdFVubm2++md27d/P111/z+9//no0bN7JlyxYeeeQR/vSnPxEWFtbk11ywYEGtXa9xcXHV9o0dO5a9e/c2eT1ai8lNS2GFnUKrAUdJXr2ObdQIGiGEEEK0CU3eYvdjM2bM4NixY7z99tuEhoayevVq+vbty3PPPUdRUVFzX75TMbk7m+zKHTrS0s5htdetO9Zqt5OaJ/dCCCGEaO+aPdiBs5tw7ty5JCcn89e//hWj0cjSpUuJjIxsict3GibPqlGtffQF7Dx7joPnk7nJ4zw3eZzHUHyW/yWeJq2wal3fg5fSiT93npt6y5TGQgghRHvXbF2xlaxWK6dOneLEiRPqT0BAAHl5eWRlZTX35TsVk5cX4OyCLc7L4CdDbuKjvafw0Tpb7oL0dnJLLRxKy6SLtwmA7r7e9AzxR6NRWqvaQgghhGgiTRbsioqKSEpKcglwJ06cICUlRV0v9uq5kL28vBg4cGBTXV4A3j4+wAXAuV7sfTd0483tJ6mwOzBoFALcnOUyi0rIKS4lwNOdAE8PJvT2b71KCyGEEKLJNDrYTZ48maSkJC5duqTu+3GAGzBgAFFRUURFRTFw4ECioqJabLmxzsTkW7XiR2FBHuN6BhHsZSS33EGoOwQaq1rlEjOzualnBABp+bYWr6sQQgghml6jg913330HOOdRGzhwoBrcKn8kwLUck1/Vc3JmcyEajcLMET3IKUgh1F3BQ6fgroVSG5zLK2Bk11Dc9XoOpBaQXVRBoFf1iZmFEEII0X40Oth98803REVF0b1796aoj2gEU2AXddtcVALAfcO78emGM+r+AKPCxRIHdoeDpKxchoWF4HBA7IkcZo7qUu2cQgghhGg/Gj0qdtq0aRLq2giTf4i6XVhSDsC4nkH8ZHgvdf9/Hh7F1l/fSpCXkVNZudiuTImy/VQu5RZZrUIIIYRoz1pkuhPRMkwmk7ptLrMCoNEo9AypGhwR5qllYt9QPplzI2VWK2dz8wEoqbCx60z9JjUWQgghRNsiwa4D8fb2VreLyh1wZRCLh4eHur+0tBSAW/qF8swtA0jMzFHf23Q8C7tD1qAQQggh2isJdh2I0WjEoHEGs0KrHiqcExG7u1dNXFxSUqJu/2X6ELr5u5Nmdq46kVFYwbFLZoQQQgjRPkmw62BMV6Y0Mdv0UJwNuLbYXR3sjHotH80eR3J2rrrvP/urpq0RQgghRPsiwa6DMbnrATDbDDiuBDu9Xo9O5xwAXdkVWymqiy+/ntgbc3kFAGkFFo5dLmjBGgshhBCiqUiw62BMHkYA7Ggozk0D4MSJE1itzsEUJSUlXLhwga+++orExEQAfh3TH4dSoZ7jL+tPuUwyLYQQQoj2odmD3Q8//MCOHTua+zLiCm8vT3XbnJNOYmKiGuAA7HY7Bw4cwGazqe8pisLf743GanNOd6JV9Py/+OQWr7sQQgghGqfZg93DDz/MLbfc0tyXEVeYrhoZe+RikUuoq1TZegeo4a5noBd9Q52DLHQaDW/vPM/pzMLmr7AQQgghmkyLdMVKt17LMXn7qdvJ+do6HXPy5EkA5t3cA3Deq14Bvjz03m4sNpm0WAghhGgv5Bm7DsbbL1DdDrOeQ6/XoyhKjWUVRUGv1zNq1CgAQryNDAp3TnLsaTCQUWjlzxuPNX+lhRBCCNEkJNh1MKaAqmXFtEVpxMTEoNFoqoU7RVHQaDTExMTQtWtXdf+0QcHq9sDgAP688Ri7U7Kav+JCCCGEaDQJdh2MKaCLun25zI24uDjsdnu17nCHw4HdbicuLo6LFy+q+wd08aSrnxsAwV6e+Lu78dB7uzGXWVrmAwghhBCiwSTYdTAmH191O9UegsViqfUZR4fDgcVi4fvvv1f3KYrCbQOrunMHBAdyNqeIX3/2Q7PVWQghhBBNQ4JdB2MymdRtXdFll/cURanxebt+/fq5vB7byxeT0Tnwooe/Dx56HWv2pfB5Qmoz1FgIIYQQTUWCXQfj5eVV9aIkh4H9q0KbTqdj0qRJLuXDwsIYOHCgyz6DTkNM/wAANIpCvyDn9q/+s49L+SUIIYQQom2SYNfBaLVavJyrimG2GRjYLYCBAwei1WoZMWIEPj4+9O/fXy2fl5fnMq9dpVv6B6DVOFv3BocGolUU8koqmPXBHux2mb5GCCGEaIsk2HVAJjdnN2qhzQAl2QwcOJC7775bHf06aNAgunRxDrIoLS3l1KlT1c7h56FndA8fABRFw/CuztGysSfTeX1bUkt8DCGEEELUkwS7DsjkbgCg1K7HWpBRY5nBgwerz9udPHmS0tLSamUmR1UNohjbvWq07eL/HeLIpbymrLIQQgghmoAEuw7I5OWubptz02ss4+3tTa9evQCw2WwcP368WpmegR70CfYAoKDUxsKbnc/iVVjtPLhmF2UWW1NXXQghhBCN0OzBrn///kRHRzf3ZcRVTFcNoDDn1NxiBzBw4ED0eucDeefOnSM/P79amaunPunu60N0uC8Ax9IKeHZtQtNUWAghhBBNotmD3VdffUVCggSAluR91Vx25vycWssZjUaXgRSHDx+uNufdiO4++Hs6w9/RS0Us/9lojDrnr80bcSfZfCKtCWsuhBBCiMaQrtgOyOTrr24XFlz7WbjIyEg8PT0ByMrKIi3NNahpNQqTBgSor89mV/DqjGHq69kf7CG7qKwpqi2EEEKIRpJg1wGZ/KrWezWbzdcsq9VqGTx4sPr66NGj2O12lzIT+vpj0DkHWsSfzuMXN0Qyub9zMEVaYSm/+s/+Wle3EEIIIUTLkWDXAZkCq0awmouvP6FweHg4AQHOVjmz2UxKSorL+55GHTdGOlsBy6124pPzWPPwWAI8jQB8dfgCq/e6HiOEEEKIlifBrgMy+VZ1nRYWl1+3vKIoLgNcEhMTqaiocClzdXfsdyeyCTG58e8Hxqj7fv3ZDyRnXbt1UAghhBDNS4JdB3T1erHmsrpNSRIQEEBERAQAFRUVJCW5TkIc5utGdLjzvNlFFhIuFDJjSAS/HNcbgOIKKw+9twurzbUbVwghhBAtR4JdB+Tt7a1umysc4Khb2Bo0aBAajfNXIjk5maKiIpf3b7tqwuLNx7MB+Me9I4gMcga+fedy+PPGY42quxBCCCEaToJdB+Tm5oZO4xzMYLYZoLRuq0R4enrSp08fAOx2O0ePHnV5f1CYF2E+zufqTmYUcy67BC+jng9njVPXlf3TxmPsSclqqo8ihBBCiHqQYNcBKYqCyei8tWabAUpqn8vux/r374/R6Axvly5dIjs72+W8V09YvCXRed4xPQJ54XbnyFq7w8FD7+/GXGZp9OcQQgghRP1IsOugvN2ckwqbbXocRXVvQdPr9URFRamvfzxp8bhIPzwNWgD2nc0nv8QZ4BZPjmJcL2foS8ku4snPf2j0ZxBCCCFE/TQo2K1cuZKNGzcCsGXLFlauXNmklRKN53VlKhKrQ0tpfs3rxdamR48e6nN6eXl5XLhwQX3PqNMwoZ9z6hOr3cG2k85WO51WwwePjMPkpgNg9d4UvkhIbfTnEEIIIUTdNSjYzZgxg5deeony8nJeeOEF7r777qaul2gk05XVJADM2fVb9kuj0bhMf3Ls2DFstqrRtbf2D+DKI3VsS8rFcmUkbK9AE2/+dJRa7lf/2cel/OvPoyeEEEKIplHvYJeamorFYuHWW29VfyoqKkhNldaZtsRlZGxe9jVK1iw0NJSQkBAASkpKOH36tPpegJeBkd19ACgss7IvJV9975ExPfnpsG4A5JZUMPuDPdjtsiqFEEII0RJ09T1g1qxZAOTn53P48GFKSkqIj49HURS2bt3a5BUUDWPy8VO3zddZL7Y20dHRbNmyBYCkpCR69OiBm5sbAJOjAtl/rgCAzYnZjI/0Q1EUFEXh//18NHvOZnEpv5TvTqbzRlwST98yoJGfSAghhBDXU+8Wu23btrFt2zYGDRrEP/7xD6Kioti2bZuEujbG2y9I3TYXFjToHD4+PvTq1QsAq9VKYmKi+l7vIA96BboDkJpbxsmMYvU9f08j7z08Tn397LpDHL3UsHAphBBCiLpr0DN2mzZtwuFw8OSTT6IoCps2bWrqeolGMgUEq9uFRcXXKHltAwcORKdzNuympKRQUOAMidWmPjnu2t17a79QFt3SH4AKq50H1uyizFK3VTCEEEII0TANCnZeXl688sorALzyyit4XvWgvmgbTP6h6ra5uLTB53Fzc6N///7q6yNHjqjbo3r44OvhDH0HUwvJNLuuS7t0+lCiw30BOJZWwOJ1hxpcDyGEEEJcX4OC3fjx4+natSsAYWFh3HjjjU1aKdF4pqsHT5Q2brLgPn364OHhAUBGRgbp6c7pU3RaDbf2d7baOYDYE64TIRv1Wj6aNR6jzvlr9vq2JLacqN8IXSGEEELUnUxQ3EGZTCZ121xuB0fDR6ZqtVoGDRqkvj5y5Ah2u3OKk5h+/ui1zrlPdpzKpfRH3a2Dwnz560+Gqa9nf7iHnCLXlj0hhBBCNI1GB7u9e/c2RT1EE3MJdhYtWBr+nB1AREQEfn7OkbaFhYWcO3fOeR03HWN7O/eXWuzEn64+SGLhhH5M7t8FgMsFpfzqP/tcVrMQQgghRNNodLAbP348M2fO5MyZM01RH9FEdDodHs5VxZzrxRbXfy67qymKwpAhQ9TXx48fx2JxdvFOvmoQxXeJ2dh/FNo0GoXVD91AwJXVML48fIE1e1MaVR8hhBBCVNfoYOfl5cVnn31GVFQUTz31FDk5dV9wXjQvk9G5pmuhzQAljb8vgYGBhIeHA1BeXk5SUhIAXf3ciArzAiDDXMGRi+Zqx4b5evD2A2PU17/+/AfOZFUvJ4QQQoiGa3SwO3v2LE899RSKovDmm28SGRnJq6++Snm5PEfV2kweBgCK7QZs5qwmOefgwYPRaJy/NqdPn6a42NnFe/XUJ5uP19w6ePeQCOaO7Q1AUbmVh97bjfXKcmRCCCGEaLxGBzt/f3+WLVvGyZMneeihhzCbzSxevJh+/frxwQcfNEUdRQOZPD3U7aKcphmN6uXlRWRkJAB2u51jx44BEN3VRIjJGSQT04q4kFvzFCuv/3QEvQOdrXt7z2Xzl03HmqReQgghhGjCUbHdunXjvffeIyEhgdtvv53U1FRmz57N8OHDiY2NbarLiHowmbzUbXNuRpOdt3///hgMzhB34cIFcnJy0Px4wuLEmlvtvIx6Ppo9Hq3GOZL2TxuPsfds457/E0IIIYRTk093MnjwYL755hvi4uIYM2YMhw4dYvLkyUybNk1t3REtw+Ttq26b85vu2UeDwcDAgQPV10eOHMHhcDC+jx/ueuev1O6UfArLrDUeP6ZHIM/fPhgAm93Bg+/twlzWuLn2hBBCCNGM89jdfPPN7N69my+//JJ+/fqxceNGhg0bxty5c7l8+XJzXVZcxds3QN0uLMhv0nP36tVLnVIlJyeHS5cu4a7XcnNffwCsNgdxJ2sPk7+fHMXYns4WvpTsIp764kCT1k8IIYTojJp9guIZM2Zw7Ngx3n77bUJDQ1m9ejV9+/blueeeo6ioqLkv36mZ/KvWizWbm3YEqkajYfDgwerro0ePYrPZmDQgEMXZy8rWEzm1Do7QaTV8OGscXkbnkmTv7jnDl4dSm7SOQgghRGfTIitPaDQa5s6dS3JyMn/9618xGo0sXbpUfQhfNA9TQIi63Zj1YmvTpUsXgoKCACguLiY5OZkgk4HhEc7lzPJLrXx/rqDW43sFmnjzZyPV149+vI9L+SVNXk8hhBCis2j2YGe1WklMTOSLL77gtdde49ChQwQEBOBwOMjKapopOETNTD5+6nZhSdNPP/PjSYuTkpIoLy9nctRVU58kZl9zlYlZY3rx02HdAMgtqWDOh3uw22VVCiGEEKIhdE11oqKiIpKSkjhx4oTLT0pKCjabc/3Qq7/gvby8XB7AF03PZVmxWgYyNJavry89evTg3LlzWCwWEhMTGTp0KN393TifW8bZ7FKSM0voE+JZ4/GKorBq5mh2p2RxuaCULUnp/HP7SZ6a2L9Z6iuEEEJ0ZI0OdpMnTyYpKYlLly6p+34c4AYMGEBUVBRRUVEMHDiQqKgounXr1thLi+vw9vZWt80VCtgqQGto8utERUVx4cIFbDYbKSkp9O7dm8lRQby98wLgnPqktmAHEOBl5L2Hx3LbW1sBeHZtArf2DWFwuF+txwghhBCiukYHu++++w4AT09PBg4cqAa3yh8JcK3Hw8MDjeLA7lCc68WW5IIptMmv4+7uTr9+/UhMTMThcHD06FFG3zCWT39Io7DUyg/nC8gpqiDAq/ZQOal/Fxbd0p9lW5Mot9p58L3d7P+/qbjptU1eXyGEEKKjanSw++abb4iKiqJ79+5NUR/RhBRFwWTUUFDmuBLsspsl2AH07duXs2fPUlpaSlpaGrnZWdzSL4CvD2Vgd0DsiRzuG9Xlmuf4y/ShbElK5+jlfI5ezuf36w6x7N4RzVJfIYQQoiNq9OCJadOmSahrw7zd9QDOYFfcfCs86HQ6oqKi1NdHjhwhpp8fuisrTGw/lUu55drrwrrptXw8ezxGnfPX8h/bkthyommWQhNCCCE6gxaZ7kS0HpOHGwAVDi1l+U23rFhNunfvjq+vLwAFBQXkZVxiTC/n6+IKG7vO5F33HIPCfPnrT4apr2d/uIecoqYf0SuEEEJ0RBLsOjgvr6pBC025XmxNFEUhOjpafX38+HFu7eervt6SmI39GlOfVFo4oR+39Xd2GV8uKGXeJ/uuOWWKEEIIIZwa/Yzd+++/3xT1cDF06FCXgFBXy5cv57XXXiM9PZ0hQ4bw5ptvMnr06Ose98knn3D//ffzk5/8hK+//roBNW67vL19AGd3pjk3i6Bmvl5wcDBhYWFcvnyZsrIySrNS6Rfqycn0YtIKyjl+uYjB4aZrnkOjUVjz0FiiX15PTnE5Xxy6wJq9KcwZ27uZay+EEEK0b40OdrNnz0apXEOqkRwOB4qi8MILL9Q72H366acsWrSIVatWMWbMGF5//XWmTJnCyZMnCQ4OrvW4c+fO8Zvf/IabbrqpsdVvk0w+vuq2uSC3Ra45ePBg0tLScDgcnDp1iglR4zmZXgzA5uPZ1w12AGG+Hvzr/tHc+++dAPz68x+4OTKY3kHXP1YIIYTorBod7FavXt0U9XAxdOjQeh+zbNkyHn30UebMmQPAqlWr+Pbbb3n33Xd59tlnazzGZrPx4IMP8uKLL7Jz507y8/MbUeu2yeRXtQqEubCwZa5pMtG7d2+Sk5Ox2Wxo888T6OVFdpGFo5fMXM4vI8zX7brnuWdoN34xtjfv7jlDUbmVh9/fzY6nbkOnlScIhBBCiJo0OtjNmjWrKerRKBUVFRw4cIDFixer+zQaDZMmTWLPnj21HvfSSy8RHBzM3Llz2blzZ0tUtcV5B1RNb1JYVNxi1x04cCDnz5/HYrFwIfU843uMZu2xfMD5rN2scV3rdJ43fjqC7aczOJNdxJ6z2fxl0zFemFb/bnohhBCiM2iyJcVaU3Z2NjabjZCQEJf9ISEhJCUl1XhMfHw877zzDocOHarTNcrLyykvrxqdWXil9ctisWCxWOpV38ry9T2uIdxNVas3mItLW+Sa4BxI0a9fP44dOwaAR2EKRl0g5VY7u5Lz+El0IJ7G608+bNTAmgfHEPPmVmx2B3/aeIxb+wQzpkdAk9W1Je+HqBu5J22L3I+2Re5H29IS96M+5+4Qwa6+zGYzDz/8MG+//TaBgYHXPwB4+eWXefHFF6vt37x5Mx4eHg2qx5YtWxp0XH3k5VVNMZJfXMb69eub/ZqVrh7Jas7LJkyj4Sy+VNgcvP3NXvq6F9T5XD/roeeTlApsdgc/+9dW/nGDB+66pnm2s1JL3A9RP3JP2ha5H22L3I+2pTnvR0lJSZ3LdohgFxgYiFarJSPDdTqPjIwMQkOrr7Rw5swZzp07x/Tp09V9drtz8lydTsfJkyfp3dt1BObixYtZtGiR+rqwsJCIiAgmT57ssiZrXVgsFrZs2cJtt92GXq+v17H1lZuby+bNmwEotihMu30qKC33jNrly5f5/vvvARjgXca5bHAAaUowC6eOQ6upWzibbLNz7q1t7D2XQ3qpg00lwfzr56OapI4teT9E3cg9aVvkfrQtcj/alpa4H4X1eEa+Q0x3YjAYGDFiBLGxscyYMQNwBrXY2FgWLFhQrXz//v05evSoy74//vGPmM1m3njjDSIiIqodYzQaMRqN1fbr9foG38jGHFtXfn5VXbFFNj16WzG4+zfrNa/WrVs3zp49S3Z2NpryQiL9wzidaye3xMqRyyWM7ulbp/Po9fDhrPEMfWU9ReVW1uw7y/TBXblnaNOtRdwS90PUj9yTtkXuR9si96Ntac77UZ/zdpjpThYtWsSsWbMYOXIko0eP5vXXX6e4uFgdJfvII48QHh7Oyy+/jJubG4MGDXI5vnLFhB/vb+8MBgNuOiizXllWrCS3RYNd5aTFW7duBSDYconTONeM3ZyYXedgB9A7yMQ/fzqSX3y0F4BHP97HDT0CCfNtWFe4EEII0dF0mOlOZs6cSVZWFs8//zzp6ekMHTqUjRs3qgMqUlNT0Wg65zQZJjctZUU2CivXiw2IbNHr+/v7061bN1JTU/F1mAlwCyWnTCE5s4SUrBJ6BdU9mM2+oRffHr/EF4cukFtSwewP97DxiVvQ1LFLVwghhOjIOsR0J5UWLFhQY9crQFxc3DWPXbNmTdNXqI0wuRvJKiqhyGbAXpTZKuvIDRo0iIsXL2K32+lKJjk4A/eWxGzmTah7d6qiKPy/n49hz9lsLheUsiUpnTe3n+TJif2bq+pCCCFEu9E5m7A6GZOXs0XMgUJxbnqr1MHDw4O+ffsCEKYrxKh1jpjdfzafvJL6DREP8DKy5qGx6uvfrU3g6KW8axwhhBBCdA4S7DoBk5eXul2Ym9Vq9ejfvz9ubm5oFQcRWufyZjYHbD2RU+9z3TagC09faaUrt9p58L3dlFlsTVpfIYQQor2RYNcJuKwXm1//ENVUdDodUVFRAPTQ56PgbLWLO5lDhdVe7/MtvWsog8N8ATh6OZ8//O9QU1VVCCGEaJck2HUC3r5VqzSYC/NbryJAjx498PHxwU1jo4vO7KxTuY09KfWvl5tey0ezxmHUOX+Nl21N4ruktKasrhBCCNGuSLDrBEz+weq22WxuxZpUTX8C0NOQr+7fcjzbZaWKuhoc7scrPxmmvp71wR5yisqvcYQQQgjRcUmw6wRcgl1R3ZclaS4hISGEhobipy3DT1MKwMX8Mk6kFTXofL+e0I/b+jtXGLlcUMq8T/Y1KCQKIYQQ7Z0Eu07A5O2jbheWVLRiTapER0ejKAo9DVWjWTcnZjfoXBqNwpqHxuLvYQDgi0MXeG9fSpPUUwghhGhPJNh1ZAUXIe0QprJL6i5zmRXSDrn+FF6q5QTNx9vbm169ehGqK8JNcU53cviCmfSChnWjhvl68PYDY9TXCz/7gTNZrdvtLIQQQrQ0CXYdlbUc3p4I/5qA6YuZ6m6zVQv/muD683aMs3wLGzhwIAa9jh76fAAcwHcnGtZqB3DP0G78YmxvAIrKrTz8/m6stvqPthVCCCHaKwl2HZXWAD7hgAYvrUWdWsRsM/yooAa8w53lW5jRaGTAgAF0MxSgxRnA4k/nUVLR8PnoXr93BL0DnfP27TmbzdLNx5ukrkIIIUR7IMGuo1IUmPhHwI5GAS+t89m66sHO7iyntM5aq5GRkfh6utFVXwhAmdXOztO5DT6fyU3Ph7PGo72yduxLG46y92zDWwGFEEKI9kSCXUfW+1YIGwaKFu8rwa7QYsBeOWBU0Trf731rq1VRq9UyePBgtTsWnOvH2u0NH9V6Q89Anps6CACb3cFD7+2iqLx+y5YJIYQQ7ZEEu47sSqvd+VIPzDY9AOXomH16KqvSojlf6tGqrXWVunbtSvcgL4K0xQBkF1lIuFDYqHP+YcogbugRCMCZ7CKe+vxAo+sphBBCtHUS7Dq43Rnu/OH8TRTajOo+q0NLfGE4fzh/E7sz3Fuxdk6KojBkyBCXqU82HWvcmrY6rYYPZ43Dy6gD4J09Z/jq8IVGnVMIIYRo6yTYdWDnz59nxcqV2FEA11Y5OxrsKKxYuZLz58+3TgWvEhAQwPAeAXhpnKNzT2WWcD6ntFHn7B1k4p8/Ham+/uVHe7mc3/oTNAshhBDNRYJdB7Zhw4Y6lHKwcePGZq9LXQwePIhehgL19YYj6Y0+5+wbenHPkAgAcksqmPPh3kY9vyeEEEK0ZRLsOii73c7u3bux2689j5vd7mBXfHybWILL09OTmAHB6HFOd7L/fCEFpY0b9KAoCv+6fwxhPs4u581Jaby5/WSj6yqEEEK0RRLsOqiKigqsVmudylptNipOxTZzjepmcNQAero714y1OxS+TbjY6HMGeBlZ89BY9fXv1iZw7HJ+o88rhBBCtDUS7Doog8GATqerY2kHK5a9wg/fvlfnMNhc9Ho9UweHqhMq7zhdQIW14RMWV7ptQBeemtgfgHKrnQff20W5pfHnFUIIIdoSCXYdlEajYdy4cWg0dbnFCt+bg1n20SaeeOxXrF69mlOnTrVa9+zQAb2JcC8DoMyuYeOBs01y3pfvGsrgMF8AjlzK5/f/O9Qk5xVCCCHaCgl2Hdjtt99ep3KeuqoAV1RSxpYtW1iyZAmLFi3i888/Jy0trbmqWCONRsO06C7q662n8pukJdFNr+WjWeMw6Jy/9su2JhF7Mh2Hw8GFInubeM5QCCGEaAwJdh1Y9+7deeKJJ9BoNNVa7ir3LViwgFVvv8NvR9sYZ7qEQanqnszIyODLL7/kmWee4fnnn2fz5s0UFjZu4uC6Gt2/K4FGZ5jLtxrYevBUk5x3cLgfr9w1VH394Jp4pqyIY+GeEu78fztIzjI3yXWEEEKI1lDXh7BEOzVu3DjCw8PZuHEju3btwmq1otPpGD9+PFOnTqV79+4ADF3wPkP/+zClJ7fwvTmUeHM3jpf4U9mIlZycTHJyMh988AFDhgzhxhtvZPjw4RgMP157tmloNBpujw7lg++d67xuO1XAzdFluLm5NfrcT8b0539HL7HtdAYZ5nIyzc7JkLedzmTAn/7Hs5OjWDw5Cg+D/PEQQgjRvsg3VyfQvXt35s2bx6OPPkpFRQVGoxHlx8uI6Yxw3we4//chbj69mZt9LpGLH7t7/YZdiZfUSYxtNhsHDx7k4MGDuLu7M2bMGG688Ub69+9fx+f56m7CgFC+TMii2KqQZvFgT0IiE8cOb9Q5HQ4H645e5ERG1Xx5lR2w1ivz2y3ddIzVe87w1n2j+El01+r/r4QQQog2SrpiOxGNRoObm1vtQeVKuCPyNgD8yePOC0t5+bHpvPLKK0yfPh1/f3+1eGlpKXFxcfz5z3/mySef5JNPPuHixcZPT6JWR6vh1oGBV14p7DxTSEFBwTWPuZ6DF3K5++0dZJrLai1jd8DlwlLufnsHCRfzai0nhBBCtDUS7IQrnRvM/BAiJzlfW4rho5/SjUvcf//9/POf/+QPf/gDN998M+7uVevM5uTksG7dOn7729/y+9//nvXr15OX1/hQNCkqBN2VHJpq8eHA4aONOt+wrv4Eexm53uITDgd4u+nxddM36npCCCFES5JgJ6rTucHMj6qFO1L3otFoiIqK4rHHHmPFihUsWLCAYcOGuXTDnjt3jg8//JAFCxbw8ssvEx8fT1lZ7S1k1+LtpmNsb18ArGg5cLGM9PSGLzWm0SjMHNEDneb63auFZRZ6v7iOni98zZwP9vD+vhQu5BU3+NpCCCFEc5Nn7ETNKsPdJw/AmVioKIKP7oUHv4BuNwBgNBoZN24c48aNo6CggL1797Jz505SUlIA5/NsR48e5ejRoxiNRkaNGsX48eMZNGgQWq22zlWZHBXEzuR8AM5V+HL48BFCQkIa/OzbfcO71WtZsXM5xazJSWHNPufn6h3oxcS+IUzsE8LEvqF08XG/zhmEEEKIliHBTtRO5wY//xg+uR/ObK0Kdw99CRFjXIr6+PgwZcoUpkyZwuXLl4mPj2fXrl1kZTlHnJaXlxMfH098fDy+vr6MHTuWm266ie7du183oEX4uzOwiyeJacUUOwwk59noc/YsvXr1atDHGtcziGAvI5lF5bWW8fMw8GRMP7YnZ7I7JYtya9Wau2eyiziTXcS/d58BoF+I95WQF0JMnxCCTY0fuSuEEEI0hAQ7cW06N5h5JdylbHOGuw8rw93oGg8JCwvjvvvu42c/+xknT55k165d7N27l+JiZzdmfn4+GzZsYMOGDYSHh3PTTTcxbtw4AgMDazwfwG0Dg0hMcx5/tsKX48ePExERgV5f/2fgKrtjV+48pY6EdfnIGoWHR/fkhWnRAJRZbOw9m8220xlsO5XO3nM5WGxVQe9kRiEnMwpZFX8agKguPmrQmxAZQoCXsd51FEIIIRpCgp24Pr07/Pw/V4U7M3x4zzXDHYCiKPTv35/+/fvzyCOPcOjQIeLj4zl48CA2m3Mi5EuXLvHJJ5/w6aefMmDAAMaPH8+YMWPw8PBwOdeQCBPBJgOZ5gqybZ5klWRx8uRJBg0a1KCPdK3uWKvdwX3Duqmv3fRaYvqGENM3hBfviKakwsrulCy2ncpg2+kMvj+f4xIQj6cVcDytgLd2nEJRIDrMT+26vTkyGF+P5pn7TwghhJBgJ+pGDXc/h5S4qnD38FfQddT1D9frGTVqFKNGjaKoqIh9+/YRHx/PyZPOcOVwOEhMTCQxMZE1a9YwfPhwbrzxRoYMGYJOp0OjKNw2MJCP9l0G4JzFF99Tp+jVq1e1EFgX43oGceB3ziXXrBYr8bviuXH8jej0zj8SQ8P9aj3Ww6BjUv8uTOrvXPasqNxC/JmqoHcgNRf7lZmdHQ44fCmPw5fyeH1bEhpFYVjXK0Gvbwg39Q7GJCNvhRBCNBEJdqLuKsPdf34OZ7df1XL3FXQdWefTeHl5ceutt3LrrbeSmZnJrl27iI+PV9ektVgs7Nu3j3379uHl5cXYsWO58cYbGR/Zky8PplNqsXPR4k1/azbHjh1j9OjaWw1ro9EoDI/wV6+X5q1lWIRfg7p2vYx6pg4MY+rAMAAKSivYmZx5pes2g0OX8tQVPOwOBwcu5HLgQi5/iz2BVqMwspu/OhBjfK8gPI3yx1IIIUTDyDeIqB+9B9z/CfxnJpzdAeWF8OHd9Q53lYKDg7n77ruZMWMGKSkpxMfHs2fPHnVN2qKiIrZs2cKWLVsICQmhy8gZpOCHHQ3nLb4YUlOJjIx0mTi5tfm4G7hzcFfuHNwVgNzicnZcFfSOXs5Xy9rsDvady2HfuRxe2ZKIXqthdPcAtet2bM9A3GVpMyGEEHUk3xii/vQecP+n1cPdw19D+IgGnVJRFHr37k3v3r158MEHOXr0KPHx8fzwww9YLBYAMjIyyIj9BP2N81AUDWdKvejqSOPw4cPExMS02aW//D2NzBgSwYwhEQBkmcvYnpzJtlPpbDudwYn0QrWsxWZnV0oWu1Ky+PPGYxh1Gm7oEah23Y7pHohRX/epYoQQQnQuEuxEw1SGu4/vg3M7neHug7udz9w1MNxV0ul0DBs2jGHDhlFSUsL3339PfHw8iYmJOMoKcGSeRgnph1XrxqaDZwlJOEhpaSmTJk3CYGj7AxOCTG78dFg3fnplgEZ6YSlxV57P23Yqg9NZZrVsudXO9uRMtidnsmT9Udz1Wsb1ClJH3Y7qHoBeK/OMCyGEcJJgJxru6pa7czuhvKDJwl0lDw8PJkyYwIQJE8jJyWH37t1sPXCKPPoBoOk2krT97/Phhx/y5ZdfMmbMGG688Ub69evnshpGWxbq7c7PR/bg5yN7AHApv0QdiBF3OoOU7CK1bKnFRuzJdGJPOlff8DTouLF3kNp1OzzCH50EPSGE6LQk2InGMXjWHO4e+RrChjfppQICApg+fTp33ulg8efHSS+yo/EJQ/EJw1FwmZKSErZt28a2bdsIDAxk/Pjx3HjjjYSHh1/zvHa7HavVit1uv2a5lhLu68FDo3vy0OieAJzPLSLuVCZbr3TdXsgrUcsWV1jZdCKNTSecA0+83fTc1DuIiX1Dmdg3hCHhvmjbScAVQgjReBLsRONVhruP74Pz8VfC3Qx4eC2EDWvyyymKwp1Dw/h3/EUAQobfjuHMFi5fvozVagUgOzubtWvXsnbtWnr27MmNN97IuHHj8PHxUc9z/vx5NmzYwO7du7Faraxdu5Zx48Zx++2307179yavd0N19/di1g1ezLqhFw6Hg7M5RWqL3rZTGVwuKFXLFpZZ+Pb4Zb497pwWxtfdwIQ+wWrX7aAuvmjqsE6uEEKI9kmCnWgaBk944L/w8c/g/C4oK4APftJs4W5ML1/++0M6hWVW8nSB3DL8BoYOLcNut3PhwgWOHj2qtsCdPXuWs2fP8tFHHzF48GDGjx+P1Wrl3//+N4Bazmq1qsuePfHEE4wbN67J691YiqLQK9BEr0ATc8dF4nA4OJ1pVkPettMZZJrL1PL5pRWsPXKRtUecITjA00iMGvRCGRDq3WYHnQghhKg/CXai6VSGu49+Bqm7r4S7GfDIWugytEkvpddqmNjfn7WHMnGgcN7qR39DFoqi8MQTT2C329m9ezfx8fGcPXsWcAa4w4cPc/jw4VrPWxnyVqxYQXh4eJtquauJoij0DfGmb4g3827sg8Ph4ER6oTriNu50JjnFVWvi5hSX88WhC3xx6AIAISY3Yq605k3sE0KfYJMEPSGEaMck2ImmZfCCBz+7Ktzlw/s/aZZwN7FfAN8eycJqd3DR6kcffTZaHBw5coTx48dz++23c/vtt3Pp0iXi4+PZtWsX2dnZdT7/xo0bmTdvXpPWubkpisLALj4M7OLD/An9sNsdHEvLV1vztp/OJL+0Qi2fYS7j04Pn+fTgeQDCfNzVkDexbwg9A7wk6AkhRDsiwU40PTXc/RRS91wV7tZBlyFNdhlfDz1jevqy60weZTbIIIAwsklLSyMzM5Pg4GAAwsPDmTlzJj/72c9ISkriL3/5Cw6H45rnttvt7Nq1i1/96lftOthoNArR4X5Eh/vx5MT+2Ox2Dl+6EvROpbPjTCbmMqta/nJBKR99f46Pvj8HQDc/D+dAjCtBr5u/Zyt9EiGEEHUhwU40D4MXPHAl3F3Y6wx3H1wJd6HRTXaZ2wYGsOtMHgCp9kC6aLJRFDhy5Ai33nqrSyjTaDT06tXruqGuktVq5a9//SujRo1i+PDh+PnVvn5se6HVaBge4c/wCH+euXUAVpudgxdy1Wf04s9kUVxRFfRS80p4b18K7+1LAaBXoJca8ib2CSHMt/7r9AohhGg+EuxE8zGa4MHPq8JdaR68f1eThrsegR70C/HkZEYx2SV2SoOC8SjLJD8/n/Pnz9OjRw+X8gaDAZ1Op46evZ4jR45w5MgR3nnnHXr16sXw4cMZMWIE3bp1a9cteZV0Wg2jewQyukcgv7stCovNzvfnc9Su210pWZRZbGr5lOwiUrKLeGfPGQD6BpvUoBfTJ4QQb/fW+ihCCCGQYCeamxru7oUL+64Kd/+D0MFNconJUYGczCgG4BLB9CETgGPHjtG1a1d0uqpfc41Gw7hx44iPj7/mvHWKoqDX66moqHoeLSUlhZSUFD7//HMCAwPVkDdgwACXa7Rneq2Gcb2CGNcriD9MHUS5xca+89lXum4z2HMumwpr1f+3U5lmTmWa+X+7kgEYEOqtjriN6RNMoJdba30UIYTolDrGt5Fo2yrD3Yf3wsX9P2q5a3y4GxbhTaCXnuwiCyezKojuEU5pziXKyso4deoUAwcOdCl/++23Ex8ff81zKorCkiVLsNvtHDx4kAMHDnD+/Hn1/ezsbDZv3szmzZtxd3dnyJAhDB8+nKFDh+Ll5dXoz9RWGPVabo4M4ebIEF6YBqUVVvaczVa7bvedy8Zqr+raPpFeyIn0QlbsPA3A4DBftdt2Qp9g/DyMrfVRhBCiU5BgJ1qG0Rse+gI+vAcufg+luc5wN+t/EDKoUafWaBQmDQjkk++dqy+ka0PxVS7jcDg4efIkPXv2xN29qouwe/fuPPHEE6xYsQLApeWuchmyJ554Qu3G7dWrFz/96U/Jzs5WQ15iYiI2m7OLsrS0lL1797J37140Gg39+/dnxIgRDB8+nJCQkEZ9trbG3aDjln6h3NIvFIDiciu7UrLU6VV+SM3FdlXQO3o5n6OX8/ln3EkUBYaG+6lB7+bIELzd9a31UYQQokOSYCdajtEbHrwS7i79UBXuHvkfhEQ16tQ39/Xn64QMyqx29p0v4hcDenPxXDI2m41jx44xatQol/Ljxo0jPDycjRs3smvXLqxWKzqdjvHjxzN16tQa568LDAxk8uTJTJ48mZKSEo4cOcLBgwdJSEiguNjZFWy320lMTCQxMZEPPviArl27ql22vXv3bjfr19aVp1HH5AFdmDygCwDmMgs7z2SqXbcJF/OwXxms4nBAwsU8Ei7msWxrEhpFYUQ3f/UZvRt7B+FllKAnhBCNIcFOtCw3H3joy6pwV5ID70+HWd9A8MDrH18LD4OW8X38iD2RQ4XVQaY2BL3+PBaLhfPnzxMZGVltVGv37t2ZN28es2fP5ptvvmH69OkYDIa6Xc/DgxtuuIEbbrgBm83GqVOnOHDgAAcOHCAjI0Mtd/HiRS5evMi6devw9vZWQ96gQYMwGjtet6TJTc+0qHCmRTnX580vqWBHcqbadXv4Up5a1u5w8P35HL4/n8Or3yWi0yiM6h7AxD4h3NQrkHJb3UYvCyGEqCLBTrQ8NdzdDZcOOMPde9Od3bKNCHe3DQgk9kQOAHGn8/nVkP4cO3oUcI5uvfnmm2scyarRaNDpdA0e5arVahkwYAADBgzgwQcf5PLlyxw4cICDBw9y+vRpdXqVwsJC4uLiiIuLQ6/XM3jwYEaMGMGwYcPw9fVt2Idu43w9DNwV3ZW7orsCkFNUzvbkquXPjqcVqGWtdgd7zmaz56xzEmmdAjec28ot/bowsU8IN/QMxE2vbZXPIYQQ7YUEO9E6KsPdB3fD5YNQkn0l3H0DwQMadMpQHyNDIkwcvmAmt9hCgb4rnp6eFBcXk5WVRVpaGmFhYU38QVwpikJ4eDjh4eHcddddFBQUkJCQwMGDBzl69Cjl5c7lvSwWCwcPHuTgwYMAREZGqq15Xbt27RBTqdQkwMvIPUO7cc/QbgBkmsvYfjpDbdFLyihUy1odEJ+STXxKNi9tOIqbXsvYnoFq1+3o7gEYdBL0hBDiahLsROtx84WHv3INd5XdskH9G3TKyQMDOXzBDMB3J3KZNWQwe/fuBZytdqGhoS36nJuPjw8xMTHExMRQUVHBsWPHOHDgAAkJCeTn56vlkpOTSU5O5r///S9BQUGMGDGCESNG0K9fvw4zlUpNgk1u/Gx4d3423PlMY1pBKXGnM4hNSmP94bOklVZ1x5ZZbOqze3x7pfu9V5A6vcrIbv7otB3rGUYhhKivjvuNIdoHNdzNgMsJUJwF793Z4HA3sIsX4b5GLuWXczqzhApjFwICAsjJyaGoqIiUlBQiIyOb/GPUhcFgYPjw4QwfPhy73U5KSoo6yvbChQtquaysLDZu3MjGjRvx8PBg6NChjBgxgiFDhuDh0bFXeuji4879I3vw0yHhrDdlMnhsDLvOVU2YfC6nWC1bUmFjS1I6W5LSgcN4GXXc1DtYHXU7LMIPbQcbrCKEENcjwU60PjdfeOhKuEs7dCXcVbbc9avXqRRFYXJUIKt3XQJgy4kcfjZkCFu3bgUgMTGRbt261XmQRHPRaDRERkYSGRnJfffdR2ZmphrykpKS1KlUSkpK2L17N7t371af5aucSiUoKKhVP0NLiPDz4OFgHx4e3QuAczlFasjbdiqDi/klatmicisbEi+zIfEyAD7uem6uDHp9Q4gO80Oj6Zhd3EIIUUmCnWgb3P3g4a+vCneZV7Xc1S/cje3lx2c/pFNUbmP/2QLuG9mFbt26kZqaSkVFBSdOnGDIkCHN8SkaLDg4mKlTpzJ16lSKi4s5fPgwBw8e5NChQ5SUOMNL5dQtx44d47333qNbt27qc3k9e/bscFOp1KRHgBdzxnoxZ2xvHA4HZ7KvBL0r8+ilF5apZQtKLfzv2CX+d8wZ8v09DEzoE6I+oxfVxafDPssohOi8JNiJtkMNdz+BtMNV4W72txDYt86nMeg0xPQL4JsjmdjsDrYm5XD7oEFcvHgRu91OcnIyvXv3brMrRHh6ejJu3DjGjRuH1Wrl5MmT6lQqWVlZarnU1FRSU1P5+uuv8fX1VUNeVFRUq7dItgRFUYgMMhEZZOLR8ZHOCakzCtXWvLjTGWQVlavlc0sq+OrwBb467Oz2DvIyEnMl5E3sE0K/EG8JekKIdk+CnWhbKsPd+z+B9COuLXf1CHe39g9gw9FMbA6IS8phenQwffv2JSkpCYfDwdGjRxk7dmzzfY4motPpiIqKIioqiocffpiLFy+qU6kkJyer5fLz89m6dStbt27FaDQyePBghg8fzrBhw/Dx8WnFT9ByFEWhf6gP/UN9ePymvjgcDo6nFahdt9tPZ5BbUrX2b1ZROZ8lpPJZQioAXbzdiekTzMS+oUzsG0LvQC8JekKIdkeCnWh73P3hkbVV4a4o40q4+xYC+9TpFH6eekb19GVvSj7mcht7U/IZ268fZ8+epby8nEuXLpGVldWunlNTFIWIiAgiIiKYMWMGeXl5JCQkcODAAY4dO4bFYgGgvLycH374gR9++AFFUejTp4/6XF5YWFinCSuKojAozJdBYb4sjOmH3e7gyOU8dWTtjjOZFJRa1PJphaX858B5/nPAuSZwV18PtTVvYt8QegS0zRZeIYS4mgQ70Tap4e4uSD96Vbj7ps7hbvLAQPam5AOwOTGbm/r4ERUVpc4dd+TIEW655Zbm+gTNzs/Pj1tuuYVbbrmFsrIyjh07ps6NV1jonA/O4XBw6tQpTp06xX/+8x9CQ0PVkNe3b1+02s4zD5xGozC0qz9Du/rz9C0DsNntJFzIU7tud57JpKjcqpa/mF/CB/vP8sH+swD0CPBUQ97EPqF09evYI5SFEO2TBDvRdrn7w8Pr4IPKcJde9cxdwPWnLOkV5EFkkAfJWSVczCsjKb2Y/j17kpycTGFhIXl5eVy4cIEuXbq0wIdpXm5ubowcOZKRI0eqzxFWjrK9dOmSWi49PZ1vv/2Wb7/9Fi8vL3UqlejoaNzd3VvxE7Q8rUbDyO4BjOwewP9NGojFZudAaq46ECP+TBalFpta/lxOMatzUli9NwWAyCCTGvRi+oTQxadz/f8TQrRNEuxE2+ZxJdy9fxdkXBXuZn0LAb2ve/jkqECS45zPUG0+ns2ALl5ER0cTHx8PwNGjRwkODm7Wj9DSNBoNffv2pW/fvvz85z8nPT1dbclLSkrCbrcDUFRURHx8PPHx8eh0OgYOHKi25gUEBLTyp2h5eq2GG3oGckPPQBZPGUSF1cb+81Vz6O1OyaLcalfLJ2eZSc4y8/Zu57OO/UO81a7bCX1CCDa5tdZHEUJ0YhLsRNvncVW3bMYxMKfBe3fUKdwN7+6Dv4ee3BILhy4UkllYTmhoKKGhoaSnp1NaWkpCQgIAly5dIiEhgX79+jFwYMPXrG1rQkNDmTZtGtOmTaOoqIhDhw5x8OBBDh8+TGlpKQBWq5UjR45w5MgRVq9eTY8ePdRRtj169Og0z+VdzaDTcmPvYG7sHcxztw+mzGJj79nsK1236ew9l4PFVhX0kjIKScooZOXO0wBEdfFhYp8QbukXyoTIYPw9ja31UYQQnYgEO9E+eATAI+t+FO7uhNnfgH/t4U6nUbh1QACfHUjHgXPC4gfHhOHp6amWuXTpElqtlkOHDmGz2UhMTAToUOGukpeXFzfeeCM33ngjVquVEydOqKNss7Oz1XLnzp3j3LlzfPnll/j7+6shb+DAgej1+lb8BK3HTa8lpm8IMX1DePGOaEoqrOxOyVJb9L4/n4PVXrUE2vG0Ao6nFfDWjlMoCkSH+aktejdHBuPr0fGnpBFCtDwJdqL9qAx3702HzONgvgxrrh/uJvTzZ+2hDCpsDnaezmWgex5nzpxxKWOz2VxapTpyuKuk0+kYPHgwgwcPZtasWaSmpqrz5Z09e1Ytl5uby3fffcd3332Hm5sb0dHRjBgxgqFDh2IymVrxE7QuD4OOSf27MKm/8xnNonIL8Weqgt6B1FzsDmfQczjg8KU8Dl/K4/VtSWgUhWFd/dRVMW7qHYzJrXMGZiFE05JgJ9qXq1vuKsNd5fJj/r1qPMTLqGNcpB9xJ3Mps9iJPZ5Njxq+Qx0Oh8vrkydPduhgdzVFUejevTvdu3fnnnvuIScnR51K5fjx41itztGiZWVl7N+/n/3796MoCv369VOfy+sIg1Aaw8uoZ+rAMKYODAOgoLSCncmZbDudQdzpTBIu5lL5K2Z3ODhwIZcDF3L5W+wJtBqFkd38rwzGCGV8ryA8jfLXsxCi/jrU3xzLly/ntddeIz09nSFDhvDmm28yevToGsu+/fbbvP/++xw7dgyAESNGsHTp0lrLizbEM/BKuJsOmYlQeKlqKpRawt1tAwOJO5kLwCUlmEidGZvNWi3MVVIUhbCwMLKzs/H39+8Uy3VdLSAggEmTJjFp0iTKyso4cuSIOgCjqKgIcAbhpKQkkpKS+OijjwgLC1NDXp8+fTrd/7Mf83E3cOfgrtw5uCsAeSXl7EjOZOuVefSOXs5Xy9rsDvady2HfuRxe2ZKIXqthdPcAtet2bM9A3A0d6q9rIUQz6TB/U3z66acsWrSIVatWMWbMGF5//XWmTJnCyZMnaxz1GBcXx/3338+4ceNwc3Pjr3/9K5MnT+b48eOEh4e3wicQ9VIZ7t6bDlknroS76c5uWb+e1YqH+7oxKNyLY5eKyCu1EzhkNFlJe7Hb7TWGO4fDwYULF7hw4QJarRZ/f38CAwMJDAwkICAAna7D/NG5Ljc3N0aPHs3o0aOx2+2cOnVKnUolLS1NLXf58mUuX77M//73P0wmE8OGDWPEiBEMHjwYNzcZIernYeQn0RH8JDoCgOyiMrafzlTn0UtML1DLWmx2dqVksSsliz9vPIZRp+GGHoFq1+2Y7oEY9Z1nDkIhRN0pjtqaLNqZMWPGMGrUKN566y0A7HY7ERERLFy4kGefffa6x9tsNvz8/Hjrrbd45JFHrlu+sLAQHx8fCgoK8Pb2rlddLRYL69evZ9q0aZ32QfQmU5xVFe4AvLvWGu6OXDSzbIvz2bFAXSljPS7W2mJ3LYqi4Ofnpwa9wMDATrE2a00uX76stuSdPHmyxv+fer2eqKgotTXPz8/vuuftjH9GMgpLibsS8radzuBUprnWsu56LeN6Banz6I3qHoBe23wtpJ3xfrRlcj/alpa4H/XJHB2i2aGiooIDBw6wePFidZ9Go2HSpEns2bOnTucoKSnBYrHg7+9f4/vl5eWUl1ctKF45s7/FYlGXcqqryvL1PU7UwOALD3yJ7qMZKNknofAijjV3Yn1oLfh2dynaL9hIqLeB9MIKsq3u5FsN+GjLaz4vzt+hsLAwcnJy1GlBwNmal5ubS25uLqdOnQLA29ubgIAA9aeztFAFBQUxZcoUpkyZgtls5vDhwyQkJHD06FH1z4vFYuHQoUMcOnSId955h549ezJs2DCGDRtGREREjVOpdMY/I/7uOu6JDueeaGePwaX8ErafyWL76Uy2J2eSklOsli212Ig9mU7syXQAPA06xvcKZEJkEDGRwQzr6oeuCYNeZ7wfbZncj7alJe5Hfc7dIVrsLl++THh4OLt373ZZ2P23v/0t27dvZ9++fdc9xxNPPMGmTZs4fvx4jV/KS5Ys4cUXX6y2/+OPP8bDQ5YWam1GSwHjzryCd5lzlYUSfQC7In9PidF1LdiUMhOHSwIBiNAVMMQ9o9ZzajQadckth8Oh/lRO8Hs9iqKg0WjU4NKZ5oKz2WxkZmZy6dIlLl++7BKMr+bh4UF4eDhhYWEEBQV1qiXO6iur1M7RPBtHc20cy7ORVVb7X90eOhjoq2WQn5bB/lp6mDRoO9HvnxAdTUlJCQ888EDnabFrrFdeeYVPPvmEuLi4WltaFi9ezKJFi9TXhYWFREREMHny5AZ1xW7ZsoXbbrtNmtGbUtGtOD6agZJzCg9LDpMuv471wbXg200tUm6189svT1NSYeeS1UR/ezaehqovvMrRn/3796dfv361Xqq8vJycnBz1p6CgoFoZh8OBzeZcksrNzU19Ps/f3x+TydRpgp7D4eDcuXMkJCSQkJBAamqq+l5JSQmnT5/m9OnTuLu7Ex0dzbBhwxgwYAC7d++WPyO1cDgcnM0pZntyJnHJmWxPzuJyQVV4LrHCD9k2fsh2/v75uuu5qbezNW9Cn2AGhfqg0dT996+iooLVX29mzozJnfaxg7ZEvkPalpa4H5W9hHXRIYJdYGAgWq2WjAzX1peMjAxCQ0Oveezf/vY3XnnlFb777juio6NrLWc0GjEaq88cr9frG3wjG3OsqIFfuPP5uvfuhOxTKAUX0H80w7nvSresXg8T+gaw4VgWdjRcsPnx86E9OHz4MEOGDKnzyhN6vR4vLy+6d3ee12KxkJ2drf7k5ua6PG9WVlbGxYsXuXjxIgAGg4HAwECCgoIIDAzEx8enQ48irVzibObMmWRnZ6uDLxITE9XwW1payr59+9i3bx8ajUb9cz1q1ChCQkJa+RO0Pf26GOjXxY9f3dQPh8PB6UyzOhBj2+kMMs1latn8Ugv/O3aZ/x27DECgl5EJkcHq9CoDQr1r/YdGcpaZJz7Zx5aTJazL3cOKn48hMqjzzl/Ylsh3SNvSnPejPuftEF2x4Bw8MXr0aN58803AOXiiW7duLFiwoNbBE6+++ip/+ctf2LRpEzfccEO9rieDJ9qwogw13AHOFrtZ36otdzlFFfzf50nYHeDjruPlGb3Zsmljk94Pm81GTk6OGvRycnLUAFMTnU5HQECAGvb8/Pw6RbdkSUmJOpVKQkICxcXFNZbr2rWruvpF7969O3QIbgoOh4MT6YVsO5WuzqOXU1z786QhJjdirgzEmNgnhD7BJkotNl7efJxXNh8HwGp3oLvSyvfs5CgWT47CQ6ZgaRXyHdK2tLXBEx0m2H366afMmjWL//f//h+jR4/m9ddf57///S9JSUmEhITwyCOPEB4ezssvvwzAX//6V55//nk+/vhjxo8fr57Hy8sLLy+v615Pgl0bZ053hrsc57qdV4e7nKIK3om/SGKacz62u6IDMZ87zPjx412mMTG56QjwappuJ7vdTl5enkur3rUehtVoNOoUK0FBQZ1iihXb/2/vvuPjqu78/7/u9BnNSKMZdVtykzvuBDAlGHBCC4ENWQghhZAGgQQWwgZSgA35xV5CIISaZBPI5pcFUoCEUB0bA6aYuFsYXGVbtiWrjkbTyz3fP8a61kgjWW5q/jwfj/uQfOfMnTtzkPTm3Hs+J51my5YtrF69mlWrVtHY2JizXUFBgVFK5aSTTso5ki6y6bqipj5gjOa9sbWRQDTRa/tCl41YMk0smSbXHwiTBuX5Th6+4mNcOnP0CXNbwVAhf0OGFgl2x9HDDz9sFCiePXs2v/zlLzn11FMBWLBgAWPHjuXJJ58EYOzYsezatavHMe666y7uvvvuQ76WBLthoEe4G0PLFS9w+5IgyfSh/7O3mjUWf2byMQt3XSmlaG9vzwp6sVis1/aapuH1erMu347ke50SiQTPPPMMHo+HdevWsXXr1l5LqcyYMYN58+YxZ84cvF7vwJ/sMJTWddbvPRD0tjTw5vZGOmKpwzqGBijgfz5/KmeML6bIbafQZcMso6nHnfwNGVok2I0QEuyGiY4G+P3F0LINgJ2+s7nb/ZN+P/3uS6oZW3T8Zz0rpQiFQllBr7fLkp3y8/ONkFdUVITT6Tzu5zlQuv+MtLe3G0ucbdy4kUQi92hTdXU18+bNY968eYwaNUpGkvopldZZU9fK61v3s2xzA6991HBEx9E08LnsFLntFOVlfy12Ow7u67I/32GVfjpM8jdkaBlqwW5kX9sRwlOWWWrs95/KhLuOejj0lfYBp2kaHo8Hj8fDuHGZ4sqRSCQr6HWfFRUMBgkGg2zfvh2AvLy8rKCXl5c3Yv5gFhQUsGDBAhYsWEAikaCmpobVq1ezdu1aAoGA0W7btm1s27aNZ555hpKSEqMo8uTJk0f8peyjYTGbOGVsEaeMLeJ7n5jOjX/6F4+/tZX0Yf5/v1LQEo7TEo6zub+vbdK6BD1HzlCYHQgduGzmEfPfthDHmvymEyOfpzwT7p68GHov5j/kuFwuqqqqqKrKTPqIx+NZQa+trS2rfTgcJhwOs3PnTiBTYqVr0MvP733m43Bis9mYO3cuc+fORdd1duzYYcyyraurM9o1Njby8ssv8/LLL+NyuZg9ezbz5s1j1qxZUnvyED43bwyPvLnlkO2+eUY1eXYLzaE4zeF41tdgrH8FVVO6oiEYoyEYA3qWDcrFYTX3Hv6MEHhwhNCfZ8chS7CJE4QEO3Fi6Ax3//utw3ra29uaCUS9VBTYKXLbDqv217Fmt9sZNWqUsZZxMpnMmnnb2tqaVTw5FosZ693CwRIrnZvX6x32s0tNJhPV1dVUV1dzxRVX0NjYaIS8jz76yJiJHIlEeOedd3jnnXcwm81MnTrVGM0rLi4+xKuceE4fV0yJ205jqO+ZtI9eeUqvPxOJVJqWcILmUKxH6DO+Gt9n2kQSvc8c7yqWTLMnEGFPINLv9+S2W3IEQEcvo4J2fHn247pMmxDHiwQ7ceLIr4BPPQiv929UAGDJhwGWfBgAwGLWKPPYKfPaKS/IbBUFdsoKBmc0wGq1UlZWZtRqTKfTtLa2Zo3qdS2xkkgk2LdvH/v2ZWqZWSwWfD6fMarn8/mGfYmVkpISLrjgAi644ALC4TDr169nzZo1rFu3jkgkEwLS6TQ1NTXU1NTw+9//nqqqKqOUyrhx44Z92D0WTCaNK+eN5bG3tpDSe16OtZg0rpw3ps//0bFZzJQXOCkv6P+9n5FEipYeoS87/HXd3xSKk0z3byWYUDxFKJ5iZ0vf96525XXa+rgk3DMgFroG93/+hAAJduJE4y6mv5d7ukulFXsCMfYEes5e9eVZjbCX2RyUe+14nZYBu/xpNpspLi42RqB0XScQCGQFva6TDlKpFI2NjUZZEZPJRGFhoRH0/H7/sL4xOy8vj9NPP53TTz+dVCrF5s2bWb16NatXr6apqclot3v3bnbv3s3zzz+P1+s1Qt706dNH9MzjQ7libhUPvZH7TrmUrrhiTlXOx46Gy2bBZbNQWZjXr/ZKKULxFE2hWC8jgT1DYUs4gd7PewcD0QSBaIJtTf27h8OkafjybL1cHpbJI2JgSLATog+fmWojaXJSH1LUt8dpCCZI5xjBaA0naQ0n+WBfKGu/w2o6OLrndRjfl3hsx3SR9lw6a+H5fD4mTZqEUopgMGiEvKampqwSK7quG0ukdSosLMy6fDtca8ZZLBamT5/O9OnT+eIXv8iePXuMkNc5+QQgEAiwbNkyli1bht1uZ8aMGcb9fIc7+324O31cMau/dyEAqWSKFW+v4MwzzsRizfzZmD2qcDBPDzgw6chhxeOwMr6of6th6LoiEE30PSLY7bG2SO81/7KOrZTxfHpfhjrLkUwesWpSzEL0ToKdEH2YueyLjE1uAbMd8opIu4ppclVTbxtPvXk09RRTn/ZSH3cSTve8jBlL6tQ2R6ltjmbtN2lQ4rFlRvYK7JR3ubybZz8+P5aaplFQUEBBQQETJkxAKUU4HM4a0QuFsoNpW1sbbW1tbN2aqQWYn5+fFfSG4yQETdOorKyksrKSyy67jLa2NqOUSk1NjVE4Oh6Ps2rVKlatWoWmaUycONG4L6+iomLEj7KYTBpzK31A5n7O+nwzcyoLh/UoLmTel+/APXST+vmcVFqnNdIZBmO9jAp2/Rrrd13AI508kmfSGb3ptUwZmUNMHinKs2OXySMnDAl2QvRHOg7BvZiDeyljHWXAnC4PK6DD5KXeWkW9ZQz1trHU26upt1bRrPlQZI/O6QoaggkaggnW1mU9RL7T0vOyboEdv9uK6RiGCU3TjJVWxo4dC2TWa+0a9Nrbs//QdJZY2bFjB5C53Nk16Lnd7mEXeAoLCzn33HM599xzicVi1NTUsGbNGtasWWOUmFFKsWXLFrZs2cJTTz1FWVmZEfImTZo07O9NFH2zmE2UeByUeBxAQb+eE0+mM/cL9jpxJPuewaZQnFiy/5NHYkDL3kC/38PhTh7x59mP+1UFcXxIsBMnFI/DgtWs9W/lCdJ4qudDrAzCzZkt0gKq5y9fDcjXA+THA0yOb4Au92cnNBv7LZUHQ5+1inrrGOotVSRMjh7HCkZTBKMpNjdk3+RtMylK3Vom6Pk8lHudVHjtlObbsVuOzS9gp9NpjGZBZsJF10u3gUAgawWIzhIrnau4OByOrKBXUFAwrIKew+Hg5JNP5uSTT0bXdbZt22bMst27d6/RrqGhgRdffJEXX3wRt9ttlFKZOXPmiCoULY6c3Wqmwuuiwtv/Ue1IItVn+DNCYEeMvS1BQmltUCaPFOcYESxy2/E6ZfLIUCDBTpxQ/G4biz8z+eBlkue+gWreiqZ0lGZCK5oI//ZroHOt2F9mH0DpEAtAuAUinWGvS+jrvi/cjE1PUJncTmVye9ahdDTazMXss47tEfrazf4e557QNeqCUBeMQd3Be+M0dPxakHJLiHJHnHI3lOfbKPflke/1o+UVg8sP5sO/hGaz2aioqKCiogLITLjoLLHS1NSUs8TKnj172LNnD5CZuds16BUWFg6bWacmk4lJkyYxadIkPve5z9HQ0GCM5H300UfG+w6FQqxYsYIVK1ZgsViYNm2aMZrn9/fsRyF647JZqPJZqPL1PXmkc6WDCy+8kFhaO3h5eAhMHvHn2fo1Ith5v6DHMXATzI4XpRR1IT3nsoeDQYKdOOH43baD67+ecw388fKDD57z/0FfS4hpJnD6MhsTD/1iSkE8eCDoZQc/U7gFf6QZf6SZGeFtEFkJrU2QihHW3DRYq7IC3z7rGBoto9C17B9bhYlm5aU56WVjkkwR5vrMYy49SHlyIxXJXZTTSLm5nXJHjGKnhjnPB3lF4Co68NWf+ZpXnNln6TlRwmKxUFpaSmlpKZApHdLW1mYEvZaWFlKpg/cWJZNJ6uvrqa/PnJDZbMbv9xtBz+/3D5vLmGVlZVx00UVcdNFFhEIh1q1bx5o1a1i/fj3RaOYeylQqxYYNG9iwYQNPPPEEY8eONWbZjh07dtj/ARNDi6Zp5Dut5DuP7eSRpm7hMBDt/+SRpgPP7y+r2dRHGZnck0pctqETXbY1dfCtp1eyZHOEv7e+yaOfO5Xq4v71xfEydD4dIQbDhPPQy+dgql+b+TrhvGN7fE0DR0Fm8004dHulIBkmL9zMhEgLE7JGBN8nFW6jMZSmPmajPummXvmoN1VQbx1D1NRzrbSIycN2+0lst5+Utd8cTVLasZfy5C7KkzspT71BeXI3ZcnduNSBSzU2D+T5uwS/LuHvwD6zy09RXhFFE6qYMmUKuq7T3t5uBL3uJVbS6XRWiRVN0/D5fFmjesPh5ny3282ZZ57JmWeeSSqV4sMPP2T16tWsWbOG5uZmo93OnTvZuXMnzz77LD6fzwh506ZNGxbvU4w8RzJ5JJnWaQ33b0Sw82so3r/JI8m0Tn0wSn0weujGBzit5kOGv65f/cdh8kgkkWLRax+w+LUPjH2vb21k6j0vcPsnp3PHJ6cPWgDV1FAZOxxmDmdB3u5kAeehJbXln0Se/TauzzyEZdLCwT6dw5eMosJNtLe1UN8SpD4Qoz6kUx+1UJ9w0aIf3v89elNNlKd2Hwh9uylPZb760o30Od5kdR0IfAfDoHL66bCX0YyfpqST5ihED7G6gNfrxefzsWvXLj7xiU/gdg/BxX17oZRi9+7dRimV2tranO0cDgczZ85k3rx5zJ49G49ncP8P/1Dkd9bQMhz6I9Y5eaRr+OtjJnFTKEY81b/7BY+Ex2GhKK9/5WSK3HZ8rtwlqZRS/G3DHm7807+oD0bJUf0Kkwbl+U4evuJjXDpz9DEZqT+czCHB7ghJsBs5Rnp/xJM6DcE49e0x6tvj1LdFqQ9EqO9Iczi/R+16hLJUnRH4KpK7KE/toiS5Fxv9u1SjgIjVT7NrEk3uqTTnTSZkLerzOR6XnaIiP0WloyguLh5WJVZaWlqMUioffPBB1mXqTpqmMXnyZOO+vPLy8kE4076N9J+R4WYk9odSikgi3b9yMl1GCHOtjHKsFLp6FptO6Tp/eH8nGpnfZ73RtMwFmNXfu9AoG3Q0DidzyKVYIUY4u9XEGL+TMf7s2Zq6rmgOJ6gPxDOBz9hidMR6jqrFTS522SazyzY5a7+GTjFtlKf3Uh7fQXlsS+bybnI3Hr29W1vIS7aQ1/4uY9rfBSBmyafZNYlm10SaXJNod4zO3Mt4QEckTsfufdTuziyF5kq3U6SaKbJEKHYq3HluNHe3S8Z5ReAszDrOYPD7/SxcuJCFCxcSi8XYsGGDMQGjs2agUoqPPvqIjz76iD/+8Y9UVFQYIW/ixInDZrKJEEdD0zTy7Bby7G7G+Po3Sq+UIhhL9rn+cPdg2BKO09/hrLZIgrZIgq05Jo8c6hBKZdZTHoxC3hLshDhBmUwaJR47JR47syqzHwvFUllhb197jIZAnMZQoscvRYWJRvw0mv2sd82ELgNqbqtOhSNOmaWDcq2Fcr2eiuROiqI7MEUy9w86Ii2MDq5idHAVAAmTi2ZXNc15k2h2TaLNOQbVZcJIxFzAbgrYDRAFe0c7ReGtFEVepDiyhYLYHjTUwYkuecUHJ4dkhb/u9w/6wHT8fiU6HA5OOeUUTjnlFHRdZ8uWLUYplc7JJYCxnu8LL7yAx+Nhzpw5zJs3jxkzZuBw9CyPI8SJStM0Cpw2Cpw2JvRzwkJa1wlEkr2HvxwjhP2dPNJVf9ZTPl4k2AkhenA7LEx0WJhYml12IZnWaQwm2Nd+8NJuQ3uc+kCcWI7ruqGkiS1JJ1twAiXAVCDzS6+szE6Z154pzeJKU2GLUKa14og3UdKxn/a173J2UQsquoPWGDSlPTSbSmixjUY3HVzDNW4pYG/ByewtOBkAazqCP7KNosgWisNbKGzegqnpw368aw2c3gNhr/hg8Os1DPrBfGRryZpMJqZMmcKUKVP4/Oc/z759+4yRvM2bNxtlEzo6OnjzzTd58803sVqtTJ8+3RjNKywc/CW9hBhuzCYTfrcdv9vO5NL+3UbVffLIW9sa+dGLG/p8zvFaT7k/JNgJIfrNajYxqtDBqMLsCvxKKQKRFPXtMfZ1u7TbFkn2OE5KV+wJxNgTiHV7xIUvr5qy/GnEPJM5rWoSo315lHvtTHNm6l3p6TRtjXtpathDc0sbLcEoyS6ZMml20eCZSYNnJgBmPYEvsj0T9CJb8EV2YFG5/g9cQbQts7Vs7d8HYi/oOfKXq4RM578tuUfcOmsFfupTnyIYDGaVUonHM6Ujkskk69atY926dfz2t79l/PjxxizbqqqqY1tKpX1PZjZ2p1SKgshOaFgPli5/NvKKIX/UsXtdIYYgq9lEab6T0vzM7SxnTSjhoTc209hHWZdSj4P544oH6hSzSLATQhw1TdMozLNSmGdlWkX2JZFoMp0Z1Tswstd5H19DMEE6x43PreEkreEkUMCOfx1cSd1hNWUts1bhm874cXZOdVsJhzqM8irNzc1GGAJIm2w0uafS5J7Kh2Tu8yu06xRZwhSrJvyJPdgiDdmFphOhHueVU7w9s7Xu6F97m7tn2Ov2Nd/l5+Mzx/Dx0+aRwMqHH37IqlWrWLNmDW1tbcahduzYwY4dO/jLX/5CUVGRMZI3depULJaj+NWeisNvzoFwo7HLCiwA2NKtrbsEbqrJWfNQiJGqLZLks7PH8ee1u3L+DjObND47ewxtkeTBmqkDSIKdEOK4clrNjCtyMa5b4ee0rmjqSBhBr75L+AvnKIkSS+rUNkepbc6ud2XSoMRjO7Cm7ljKJk/Ca9Mxx4OE2zOrZEQiEaO9AlrjJlrjHrbgAcZTUFxAcXGxUUvPYaHnqiLhZgg3ZfZ1/ruz8HS8f4u3kwhltsCufjW3WZzMyitilsvPtfOLqE0UsqbJxuq9CXY1H3xPzc3NvPrqq7z66qs4nU5mzZrFvHnzmDVr1uGXizHboGBU5v3R17RpU2a07ggvRwsxHLWEEtz+7GaSaSsXTanutV0oArc/u5nFn5k84OFOgp0QYlCYTRplBXbKCuzM4eC9Lkop2kIxnnv1DcZOm0tjKGWM9DWHEj1mo+kKGoIJGoIJ1tZlP5bvcFPu9VNcbMZtTmBLhVDhVvRIgK5XLtvb22lvb2fbtm1ApgCxEfTKJ+Jyufq+1JlO9Ax7xrJyOcJgtK33Y3WVikJ7HbTXoQHjD2yf9UNzvoM1oVJWh0rZFPGTJjN7NhqN8t577/Hee+9h0mBKqZ1544uZO3UspaOqsi8TO7zQ/X1pGpzzw+wVWXLSM+1kNQ1xAumIpfq11jhAMq3oiKUk2AkhTmyapmWKiVrjfHxiYVadrkRKZ3+wW3mWQGa0L5Hjl20wliLYkGKzsccClGA1l+J3msi3pLDrESyJIG5TArcpgVlThEIhQqGQUWDY6XRSVFRkhD2Px5Md9Mw28JRntv7QUxBp7ba2cNcl55qy/x1tzaxT3EWRNcYnC3fxycJdRNIWNkSKWBMqZW2ohLCe+UOiK9jUEGdTwx7+8M4eRts6mOvezzz3fiY4ApjMli6zhbtMFnH5oaASgntB6egKEsqMTUtj0gDNDOUz4Viv1CKEOGoS7IQQw4bNYqLS56TS160mn1K0hpNd7uM7eGm3PdqzKHAyrWgIpWnIVNY7sGW4LWmcKmYEPbcpgTucIBKpo64uMyRos9mygp7X6z28yQsmS+b+NHdJ/9rraYgFul0ePhgGXZFmTgs3cVq4hXR4A1uadVZ3FLE6VMr+5MH3tifhYU+rh7+3VlNgjjHH3cg8935Ocn2I3dTzsuuumIeX28bxTkcFKWXGoqU53bOPCwtrGVN6Enz0D/BPgMJxYHX2eL4QYuBJsBNCDHsmTaPIbaPIbWPGqOzJG+F4OrPyRiDWpS5fnMZgPOdyQKGUmRB5NKWzS71YSZPXGfTiCdyRdtx7mnBpSWxWC36/3wh6hYWFmM3HcG1Kk/nAyJofmNxnUzMwVSmmxgJcHW5m386trF5fw5oPd7J1X8C4lN2edrC8vYrl7VVYtTQzXM3Mc+9njrsRryXOO8EKHq2fBYB+4DJvSplZERzFiuAovpVYyulr/3DgaBoUjAbf+MyayP7qTODzTYDCMXIfnhh0SimSaUUspRNLpokl9QNb5vtoMvd+4/tUpk1/18AdTBLshBAjWp7dzIRiFxOKsydvpNI6jcbkjexLu9Fkz9GrJGYCupOAnj0ypaEyga8jgXt3PW7TLjzmFJVFbkaVZiZj+P3+o5uperg0DZyFaM5CRhVNZNTJF/FpMvcSdi5xtnHjRhKJTNmXpDKzJlzKmnApWiOMLvaypzGQs7p+JuQpHq2fxShbB2McHYAy7gWk9o1u52IGb9WBoNcZ+MZnwl9BZSa0CpFDWldZwap74Irm/L73cNbPW+OGPQl2QogTksVsosLroMKbXVtOKUV7NNXjHr769jgt4Z41+RQaId1OSO9W8iMM9t1x3KZaPKbNFLvNVBV5mFBeyITRJdjtA18ipKCggAULFrBgwQISiQQ1NTWsXr2atWvXEggEMu9HQV1j4BBH0sBk4hX7pXxzRgJat0PLNojlmB2s0tBWm9n4Z/ZjZhsUjj0wyjfh4Fd/deZ+xUFeEk4cHqUUcSOAZcJUNFfISvVvlCzXfbODRSOzPKPFrBHKseTiUCLBTgghutA0Da/LitdlZWp5dqmQeFLPXNbtvIevSzHmXIuRx5WFeNpCS9rFzjb4VxuwtQ0zLRRY0xS7zYwqdDG+zMvYknxKPDZsloEJMzabjblz5zJ37lx0XWfHjh2sWbOGVatWsWfPnkM+X9cVb24NEM6fh99/Jr5pPvxuGz5zBJ9qpTCxF2v7DmjZnqnzl6s2YDoBzVsyW3cW54GRvW6BzzchUxhZZuMetWN1ebJzfzypH3IN1YFkM2s4rGYcVtOBLft7Z+f3lp5tnN3a2ywmTJrGzuYId7+wbbDfWp8k2AkhRD/ZrSbG+J2M8XebvKErmsOJrKC3ty2SqcmX7PmnLo2J1qSJ1jbY3BZj2Y4GoAENhddposLrZLTPRYXXfqA+nx2P4/j9ujaZTFRXV1NdXc2nP/1prr322n49TynFqlWren08Pz8fn+8s/L5L8fmc+BxpfKYwPtWCL7kPX3Qn9sA2SOeo4J+KQuMHma07m+dA0Jtw8J4+3wTwj8+sDzyCpXVFOJ4mkjazNxAnpRI9g1eqj9GyIXx50mzSjCDlzBG2un/vzLXfcvB78yCs0zoUSLATQoijZDJplHjslHjszKrMfiwUy1zWrWsJs6MhwN62KM2RNB0pM5kLPAcpNNqiirZohA/qI1mPue3mgytveDNhr6LATpHbdkwXGrfZbFjyCkmbDj3hQSWjEAv2+ngwGCQYDLJz585eWozF7T4JX4EHX54Fnz1tjPj5k3vxxXbhM4dwmrpd+kp0QP26zNad09ct8HUZ9bP3b6H4Y0nPujyZHaoOGbxSPdscrKFWxav/6OeKJ8eJBocOXpZDjJZ12W81y6X3Y0GCnRBCHEduh4WJDgsTS/M4d9rB8iaRWJytdU1sr2+jriVMUzhFR9pGSLcZxYa7CsXTbG2MsLUxO/BZTBpl+XbKvHYj+FUcKPzssB7+xIS2SArz/K9j6sf9bZrSuWNhGSraTmtrq7G1tLQY37e1taFU78NCnTUDd2ftNQGVBzZw2q348qz47Gn85jA+1YovvR+fOYrPGsNniZJnSmWuzkZbYU8r7PlXzxdzl2YCXufkjc7A5xtvlGs50suT0V7CWTw1PC5Pdr/0mDN8Wbq2N2OzaMd2jeJhwOOwYDVr/SpSbDVrx3WkvTcS7IQQYhC4HHZmTRzNrImjAUin07S2ttLU1MTu/a3sagoRTFkI6TbCupWQbiOmrD2Ok9IVewIx9gRiPR7z5Vm7rK974LKu147Xaen1D3JHLIXq56QFpZmwufIZW1XWa5t0Ok17e3vO0Nd1S6d7vyE9Gk+yN55kL9BZZDqzHaBp2Gw2vE4L+TYdtyVJntWEw6Zht1qwWi2YLVaU2U6sw0Us5CK2x0xMayRmChHTthKzeIiZ8ojhyBmsB0uuy5M2i0aguZGxlRW47Ba5PDmA/G4biz8zmY7YwbInqVSKt99+mzPOOCNr9rvHYZG1YoUQ4kRlNpspLi6muLiYadNA13UCgQDNzc0Htr1E4klCui1rCx/4quj5B7s1nKQ1nOSDfdkTFxxWU1bYqzgw2lfiOfZ/hMxmMz6fD5/v4L1v3S9PRuJp2oIhmlrbaW0PEQiFCYZidETjhONJook08ZRCmSyZmbQWG9qBr5htaOZM4A0c2AaTppEVpI7H5clkMslLL9Vw0eknZ63MIgaG323LCmzJZJIPLAnG+J1Doj8k2AkhxBBkMpmMQDRp0iSUUgSDQSPoNTU1EYu1AJmlwyLKaoS8kG4jlLYRxk5C7xkMYkmd2uYotc3R7NfUoNB1eH8WavaG2NUazVlbrLd7x/q+PGkBCjKblcxG5n6u4zXWpNIJSCUys3RTCSx6DFs6il1FcKkwbhXGQwgvHRTSTqEWJE9FcBDH4SnEUVCCo7ACZ1EltqJxaP4JkF8h5VrEoJBgJ4QQw4CmaRQUFFBQUMCECRNQShEOh7OCXjgcppSw8Rx1YI3XzrCXsLiJmZwEkxYCsZ7hSlfQEj68yvp/WdNwDN7d4bF0uTzZfdTLqumQSqAn46TiERLRELFwB9GOAKFgGx2BVqKh9qwgR7dPIglEc75yhoaNArPCZ9XwW9rwWfbhs7x34H6/zD1/PoeGtWhcZqZu1szdCZBXIuVaxHEjwU4IIYYhTdNwu9243W7Gjh0LQDQazQp6wWAQu5bGboriJwocKCBshbRFQ7fno1w+EuY8OtJWmsJp9gVipHouvHGU58qRl7Cw9LxUaTnK2ZOxWIy2trY+7/kLBnuf7avQCKQdBNIO+pqXmm+O47O04bOswGf9ZybwWWL4nSZ8xSUUllXhKJ3YpU7fhBFfrkUcfxLshBBihHA6nVRWVlJZmZlNGo/HaWlpoampiebmZgKBgDFD1awpzIl2SLRjA9zAOIcDvaqYZ3c6e3+Rbj45rYgKr73P2ZU289CaPelwOCgvL6e8vLzXNolEwgh/3bfOMNje3t7njN9g2k4wbWdnvKDng7UACfJMa/FZ3jVG+/xO8HkL8BWX4Ssfg69yIq6KqYNWrkUMPxLshBBihLLb7VRUVFBRUQFkZu91DXqtra3o+sHhuVgsRnu4ERjT79eYP8HLuCLXoRsOMzabjdLSUkpLS3ttk0qlCAQCfcz4baGtLZD1GXcX1m2EEzbqEvmZHe1AA/BRFPgI+Ain6dnMJV67js/joNCbTyKl2KDvpXjsFHxjZ5DnLRpS4VkMHgl2QghxgrBYLFlhJZ1O09bWZgS9lpYWOMxlMJctXYrPlsJqtWKxWLK2I903XAKKxWKhqKiIoqKiXtvouk57e3vP0NfcRGtTPS0trbQFwzmXpOsU1a3sTVjZmwA6gH2ZlTpe2rICWAGAzaTjc4DPbcdXWICvqDQz4je6Gl9RCX6/H4/Hg8kkEzpGOgl2QghxgjKbzVnBRNd1anY18dby/Yd1nHQ63WcduiM5r5ESFE0mE4WFhRQWFvbaRtd1Ojo6Doa+pgZa99XSun8fra0ttAbDtITTJPTe30dCN9EQgYZIEhqbYXMzkL0cm9kEPrfjwKXeEnylo/H5i/D7/cYMbK/XK+FvmJNgJ4QQAsiEkFHFhVhM+/s1gcKEorjQgwM7yWSSVCpFKpXq876z/jjWQbF78DuakHg8gqLJZDJmPI8bNy5nm85Z0K0NdTTW1rBh5Ru4HRYCrW20BEO0RtK0JqxE9d7rqKV1aArGaArGYPd+YGOOc9HwFuTj8xfj8/myQl/nVlhYmFWIVwwt0jNCCCEMfreNc9y7iPWj6onDAp/+5CVZ+5RS6LpOKpXKCnud25HsO9qg2HmcY+VYBUWr1YrZbO5XUDRmQVdPpXxMNXsjNi666KLsgriRViL7NtG260Na9+2gZf/eAyN+EVrjZlpTDlqTDkJ674WodV3R2tZOa1t7n+dSUFDQI/B1DYKFhYXYbAO/6oKQYCeEEKKbOVMnsGnTJuPfmqahlDK+dpo2dVqP52qahtlsxmw2Y7fbj/pceguKyWSSdDrdY19/QuNQDoqdYa+vUKhpGrqu09zcjMPhOPi4LR/nhDNwVZ/JqK4voBSEG6FlO7RuJ75/C637dtC6vz4T/DpDX8pBS9JJa8pBMN173ymlCAQCBAIBduzoveCLx+PJGfq6bg6H45h9jiJDgp0QQogs06ZlAltnuLNYLEYY6gw006ZNM9odT8crKHYPfEczujhYQfHtt9/Oub/vUcNpWMpnYq2yYrVYGGU2MybZgSXaiDVcjyVYh6V9J6p1G+H9dQTiadpSTloOjPZ1BsDWlJNAyp5zKbtOHR0ddHR0sGvXrl7buFyunIGvaxB0Op3DZkLNUCDBTgghRA+doW3z5s3MmjWL9evXM2vWLNauXcvkyZMHJNQdD12D4rFwrINiMpk86nM68hHFPGAKWKdAKZkNsJjAoumU6nFGpyNYkh1YEgG0ZJRELEIkFicSSxCOJQnF03TEdYJxjWDSRCBhQe8j/EUiESKRCHV1db22sdvtfY76+Xw+PB7PgIe/TZs2GT8fqVSKuro61q9fP+g/HxLshBBC5NQ5KpdMJlm/fj2jRo0yVrkQGQMRFLuHv3g8zqZNmxg/frxxmbqv4Hi0UjqkMAFOMDnB7occg6d5B7aulf+UUsTjcaLRaGaLRIhHQsSiYaLRKJFYjEgsSbqPci/xeJz6+nrq6+t7bWO1WnMGvq5hMD8//5jN+N20aRPvvPMO27Zt469//Su6rvPcc88xevToTNkgGLRwJ8FOCCGEGCL6ExSTySSbN29m+vTp2ZMnclBKkU6nj9ll58MNipqm4XA4cDgcvZZ8UUqRSCQOhr9etr5mSieTSfbv38/+/b2X6jGZTOTn5+P1evF6vRQWFuLz+YySP8XFmZnAh5rxu2nTJl599VVWrVplnD9kytbU1dVRV1dHR0cHMDjhToKdEEIIMUJpmmbcX3csHHVQTMZJxaOkkklS6TRJZULTNOx2O3a7Ha/X2+vrJpPJXkNfLBYjGo32GTx1XTcmffTF6XTicrmMtZg9Hg/5+fkUFBTg9XrZvn07q1atynlvZee+VatW4fV6JdgJIYQQYug6rkEx1EyyZReptj2k2utJdTSRCreSjARJKUiZHJnN4SDpOvC9qZCUuZyUyUHS5CCaNhOL9z36l0gk+jynznadl1SPVFtTw1E9/0hJsBNCCCHEoMgKio7RUDS6ZyOlINwErduhZZtRtiXzdQekogebAmnNlgl9ZgdJq4OU3UHKZzeCYcScT5u5mDbyaU/aaE9oBKMpOiIxQuEI4XDm/r+joZRi3fr1qGQMzTqwJV0k2AkhhBBi6NI0cJdktqr52Y8pHTrqjcCntW7H0rIdS+t2aK0F/TDuCXQAXi/4J5DyjqfNMYYWUwnNupvmuIWWQIimpiY2bNjQr8OllIlEGux93wZ5zEmwE0IIIcTwpJkgf1RmG3d29mN6CtrrDo7stWyH1gMjfoHdoHJMxogFYO9qLHtXUwwUd30srxi9eAJfMhWj64dec89kMmE7BrUXD5cEOyGEEEKMPCYLFI7LbN2lE9C26+Al3ZZtme9bd2TCYC7hJkzhJk4qupiNTVqfhak1TeOkk04alMLKEuyEEEIIcWIx26BoYmbrLhmFttqega9lG4T2c5XzTTaqjx/yJa666qrjcOKHJsFOCCGEEKKT1Qkl0zJbd/EOxrTu4IY3lvHI3/8F3dZP1jQNlOKGb32LMWPGDOBJHyTBTgghhBCiP+weKJ/F6Z+bxajRz/DK//8wb3dUkFJmLFqaMzz7uOALNzLmjDMG7RQl2AkhhBBCHKYxZ1zBNz98jK/tW0JSB5sJTBUz4YwrBvW8js2iaUIIIYQQJxJNg3N+iJk0DlMaE2k454eZ/YNIgp0QQgghxJGYcB56+RyAzNcJ5w3yCUmwE0IIIYQ4MpqGvuCHBO0V6AsGf7QOJNgJIYQQQhwxNe5sXp+6GNW9QPIgkWAnhBBCCDFCSLATQgghhBghJNgJIYQQQowQEuyEEEIIIUYICXZCCCGEECPEiAp2jzzyCGPHjsXhcHDqqafy/vvv99n+z3/+M1OmTMHhcDBjxgxeeumlATpTIYQQQohjb8QEu2eeeYZbbrmFu+66izVr1jBr1izOP/98Ghsbc7Z/5513uOqqq/jqV7/K2rVrueyyy7jsssuoqakZ4DMXQgghhDg2Rkywu//++/n617/OV77yFaZNm8bjjz+Oy+Xid7/7Xc72Dz74IBdccAG33XYbU6dO5Z577mHu3Lk8/PDDA3zmQgghhBDHhmWwT+BYSCQSrF69mjvuuMPYZzKZWLhwIe+++27O57z77rvccsstWfvOP/98nn/++Zzt4/E48Xjc+HcwGAQgmUySTCYP63w72x/u88TxIf0x9EifDC3SH0OL9MfQMhD9cTjHHhHBrrm5mXQ6TWlpadb+0tJSPvroo5zPaWhoyNm+oaEhZ/tFixbxX//1Xz32v/baa7hcriM67yVLlhzR88TxIf0x9EifDC3SH0OL9MfQcjz7IxKJ9LvtiAh2A+GOO+7IGuFrb2+nqqqK+fPn4/F4DutYyWSS119/nXPOOQer1XqsT1UcJumPoUf6ZGiR/hhapD+GloHoj46ODgCUUodsOyKCXVFREWazmf3792ft379/P2VlZTmfU1ZWdljt7XY7drvd+Hfnpdhx48YdzakLIYQQQvRLR0cHBQUFfbYZEcHOZrMxb948li5dymWXXQaArussXbqUG2+8Medz5s+fz9KlS7n55puNfUuWLGH+/Pn9es2Kigrq6urweDxomnZY5xsMBqmsrKSuro78/PzDeq449qQ/hh7pk6FF+mNokf4YWgaiP5RSdHR0UFFRcci2IyLYAdxyyy18+ctf5uSTT+aUU07hF7/4BeFwmK985SsAfOlLX2LUqFEsWrQIgJtuuomzzz6bn//851x88cU8/fTTrFq1il//+tf9ej2TycTo0aOP6pzz8/Plh3IIkf4YeqRPhhbpj6FF+mNoOd79caiRuk4jJthdeeWVNDU1ceedd9LQ0MDs2bN55ZVXjAkSu3fvxmQ6WN3l9NNP5//+7//44Q9/yPe//30mTpzI888/z0knnTRYb0EIIYQQ4qhoqj934oljKhgMUlBQQHt7u/zf1hAg/TH0SJ8MLdIfQ4v0x9Ay1PpjxBQoHk7sdjt33XVX1mQMMXikP4Ye6ZOhRfpjaJH+GFqGWn/IiJ0QQgghxAghI3ZCCCGEECOEBDshhBBCiBFCgp0QQgghxAghwW6APfLII4wdOxaHw8Gpp57K+++/P9inNCK8+eabXHLJJVRUVKBpGs8//3zW40op7rzzTsrLy3E6nSxcuJCtW7dmtWltbeXqq68mPz8fr9fLV7/6VUKhUFabDRs2cNZZZ+FwOKisrOTee+893m9tWFq0aBEf+9jH8Hg8lJSUcNlll7F58+asNrFYjBtuuAG/34/b7ebyyy/vsRrM7t27ufjii3G5XJSUlHDbbbeRSqWy2ixfvpy5c+dit9uprq7mySefPN5vb9h57LHHmDlzplFna/78+bz88svG49IXg2vx4sVompZVMF/6ZGDdfffdaJqWtU2ZMsV4fFj1hxID5umnn1Y2m0397ne/Ux988IH6+te/rrxer9q/f/9gn9qw99JLL6kf/OAH6tlnn1WAeu6557IeX7x4sSooKFDPP/+8Wr9+vfr0pz+txo0bp6LRqNHmggsuULNmzVLvvfeeeuutt1R1dbW66qqrjMfb29tVaWmpuvrqq1VNTY166qmnlNPpVL/61a8G6m0OG+eff7564oknVE1NjVq3bp266KKLVFVVlQqFQkab6667TlVWVqqlS5eqVatWqdNOO02dfvrpxuOpVEqddNJJauHChWrt2rXqpZdeUkVFReqOO+4w2uzYsUO5XC51yy23qE2bNqmHHnpImc1m9corrwzo+x3q/v73v6sXX3xRbdmyRW3evFl9//vfV1arVdXU1CilpC8G0/vvv6/Gjh2rZs6cqW666SZjv/TJwLrrrrvU9OnTVX19vbE1NTUZjw+n/pBgN4BOOeUUdcMNNxj/TqfTqqKiQi1atGgQz2rk6R7sdF1XZWVl6mc/+5mxLxAIKLvdrp566imllFKbNm1SgPrXv/5ltHn55ZeVpmlq7969SimlHn30UVVYWKji8bjR5nvf+56aPHnycX5Hw19jY6MC1BtvvKGUynz+VqtV/fnPfzbafPjhhwpQ7777rlIqE9ZNJpNqaGgw2jz22GMqPz/f6IP//M//VNOnT896rSuvvFKdf/75x/stDXuFhYXqf/7nf6QvBlFHR4eaOHGiWrJkiTr77LONYCd9MvDuuusuNWvWrJyPDbf+kEuxAySRSLB69WoWLlxo7DOZTCxcuJB33313EM9s5KutraWhoSHrsy8oKODUU081Pvt3330Xr9fLySefbLRZuHAhJpOJlStXGm0+/vGPY7PZjDbnn38+mzdvpq2tbYDezfDU3t4OgM/nA2D16tUkk8msPpkyZQpVVVVZfTJjxgxj9RjIfN7BYJAPPvjAaNP1GJ1t5Geqd+l0mqeffppwOMz8+fOlLwbRDTfcwMUXX9zjc5M+GRxbt26loqKC8ePHc/XVV7N7925g+PWHBLsB0tzcTDqdzup0gNLSUhoaGgbprE4MnZ9vX599Q0MDJSUlWY9bLBZ8Pl9Wm1zH6Poaoidd17n55ps544wzjCX7GhoasNlseL3erLbd++RQn3dvbYLBINFo9Hi8nWFr48aNuN1u7HY71113Hc899xzTpk2TvhgkTz/9NGvWrDHWL+9K+mTgnXrqqTz55JO88sorPPbYY9TW1nLWWWfR0dEx7PpjxKwVK4QYmm644QZqampYsWLFYJ/KCW3y5MmsW7eO9vZ2/vKXv/DlL3+ZN954Y7BP64RUV1fHTTfdxJIlS3A4HIN9OgK48MILje9nzpzJqaeeypgxY/jTn/6E0+kcxDM7fDJiN0CKioowm809ZtHs37+fsrKyQTqrE0Pn59vXZ19WVkZjY2PW46lUitbW1qw2uY7R9TVEthtvvJF//OMfvP7664wePdrYX1ZWRiKRIBAIZLXv3ieH+rx7a5Ofnz/sfhkfbzabjerqaubNm8eiRYuYNWsWDz74oPTFIFi9ejWNjY3MnTsXi8WCxWLhjTfe4Je//CUWi4XS0lLpk0Hm9XqZNGkS27ZtG3Y/IxLsBojNZmPevHksXbrU2KfrOkuXLmX+/PmDeGYj37hx4ygrK8v67IPBICtXrjQ++/nz5xMIBFi9erXRZtmyZei6zqmnnmq0efPNN0kmk0abJUuWMHnyZAoLCwfo3QwPSiluvPFGnnvuOZYtW8a4ceOyHp83bx5WqzWrTzZv3szu3buz+mTjxo1ZgXvJkiXk5+czbdo0o03XY3S2kZ+pQ9N1nXg8Ln0xCM477zw2btzIunXrjO3kk0/m6quvNr6XPhlcoVCI7du3U15ePvx+Ro7pVAzRp6efflrZ7Xb15JNPqk2bNqlvfOMbyuv1Zs2iEUemo6NDrV27Vq1du1YB6v7771dr165Vu3btUkplyp14vV71t7/9TW3YsEFdeumlOcudzJkzR61cuVKtWLFCTZw4MavcSSAQUKWlpeqLX/yiqqmpUU8//bRyuVxS7iSH66+/XhUUFKjly5dnlQ+IRCJGm+uuu05VVVWpZcuWqVWrVqn58+er+fPnG493lg/45Cc/qdatW6deeeUVVVxcnLN8wG233aY+/PBD9cgjj0g5hxxuv/129cYbb6ja2lq1YcMGdfvttytN09Rrr72mlJK+GAq6zopVSvpkoN16661q+fLlqra2Vr399ttq4cKFqqioSDU2Niqlhld/SLAbYA899JCqqqpSNptNnXLKKeq9994b7FMaEV5//XUF9Ni+/OUvK6UyJU9+9KMfqdLSUmW329V5552nNm/enHWMlpYWddVVVym3263y8/PVV77yFdXR0ZHVZv369erMM89UdrtdjRo1Si1evHig3uKwkqsvAPXEE08YbaLRqPrWt76lCgsLlcvlUv/2b/+m6uvrs46zc+dOdeGFFyqn06mKiorUrbfeqpLJZFab119/Xc2ePVvZbDY1fvz4rNcQGddee60aM2aMstlsqri4WJ133nlGqFNK+mIo6B7spE8G1pVXXqnKy8uVzWZTo0aNUldeeaXatm2b8fhw6g9NKaWO7RigEEIIIYQYDHKPnRBCCCHECCHBTgghhBBihJBgJ4QQQggxQkiwE0IIIYQYISTYCSGEEEKMEBLshBBCCCFGCAl2QgghhBAjhAQ7IYQQQogRQoKdEGJI0zQNTdPwer09FuHutHjxYjRN4+677x7QcxvqFixYgKZp7Ny5c7BPRQgxQCTYCSGGhfb2du6///7BPg0hhBjSJNgJIYY8TdNwOBw8+OCDtLW1DfbpCCHEkCXBTggx5JlMJr7xjW8QDAa57777Bvt0hBBiyJJgJ4QYFm6//XacTicPPfQQLS0tR328Xbt2cf311zNp0iRcLhc+n4/p06fzzW9+k82bN2e1ffHFF7n22muZOnUq+fn55OXlMWvWLH76058Sj8d7HPvJJ5807vnbvn07V1xxBUVFReTn53PhhReyadMmAFKpFD/96U+ZNGkSDoeD6upqHnnkkR7H27lzJ5qmsWDBAoLBIDfddBOVlZU4HA6mTp3KAw88gK7rh/X+W1tbueOOO5g2bRpOp5OCggLOPfdc/vGPf+RsX1NTwxe+8AXGjx+Pw+GguLiY2bNnc/PNN1NfX39Yry2EOH4k2AkhhoXy8nKuu+46Ojo6+NnPfnZUx6qrq2Pu3Lk8/vjjAFx00UWcffbZ2O12fvOb3/Duu+9mtf/qV7/KX//6V3w+HxdeeCFnnXUWdXV1/OAHP+Ciiy4inU7nfJ3a2lpOOeUUampqWLhwIWPHjuWVV15hwYIFNDQ08NnPfpZ7772X6dOns2DBAurq6rjxxhv5zW9+k/N48Xicc889l//93//llFNO4ROf+AS7du3illtu4dprr+33+9+yZQuzZ89m8eLFRKNRzj//fE4++WRWrlzJJZdc0mNUdPXq1XzsYx/jj3/8Ix6Ph0svvZTTTjuNZDLJgw8+2CMICyEGkRJCiCEMUGazWSmlVENDg3K5XCovL081NjYabRYtWqQAddddd/XrmHfeeacC1I033tjjsV27dqlt27Zl7Xv++edVJBLJ2hcMBtWnPvUpBajf//73WY898cQTClCAuv3225Wu60oppXRdV9dcc40C1LRp09RJJ52U9T7++c9/KkCNGTMm63i1tbXG8WbOnKmampqMx7Zt26YqKioUoJ577rms55199tkKULW1tca+VCqlZsyYoQB17733qnQ6bTy2detWNW7cOGU2m9XGjRuN/V/60pcUoO67774en9eHH36o9u3b12O/EGJwyIidEGLYKC0t5frrryccDvPf//3fR3ycpqYmABYuXNjjsaqqKiZMmJC179JLL8XpdGbt83g8PPDAAwD87W9/y/k648eP58c//jGapgGZSSD/8R//AcCmTZv4xS9+QXFxsdH+vPPOY86cOezatavXEiX33XcfRUVFxr8nTJjAj370IwAefvjhXt9zpxdeeIGNGzdy+eWXc9ttt2EyHfwzUF1dzc9//nPS6XTWqGFfn9eUKVMoLy8/5OsKIQaGBDshxLDyve99j7y8PB577DH2799/RMeYN28eAN///vf5xz/+QSwWO+Rztm7dyoMPPsi3v/1trr32Wq655hruuece47FcFixYgNVqzdo3fvx4AKxWKwsWLOjxnM7Hc9235vP5+MQnPtFj/1VXXQXAO++8c8h77V577TUAPvOZz+R8/KyzzgLg/fffN/Z1fl433HADy5cvJ5VK9fkaQojBYxnsExBCiMNRXFzMDTfcwL333svixYuNUbPDcc011/Daa6/xpz/9iUsuuQSHw8HHPvYxLrjgAq699lrKysqMtkopvvvd7/LAAw+glMp5vI6Ojpz7R40a1WOf2+0GoKysDLPZ3OvjuSZljBkzJufrFBQUGAWc29ra8Pv9OdsBxkjg1VdfzdVXX91ru+bmZuP72267jRUrVrB8+XLOOecc3G438+fP5+KLL+aaa66hoKCg1+MIIQaWBDshxLBz22238eijj/L444/zn//5n4f9fLPZzDPPPMPtt9/O3/72N5YtW8bKlSt56623WLx4Ma+88gqnn346AM888wz3338/lZWVPPDAA8yfP5/i4mKsViuJRAK73d5r4Ot6mfNwHjueOkf0LrjgAkpLS3tt1/Vyb35+PsuWLePtt9/mhRdeYPny5SxbtowlS5awaNEi3nrrLSZOnHjcz10IcWgS7IQQw05RURHf/va3WbRoEYsWLaKiouKIjjNnzhzmzJnD3XffTTAY5O677+aBBx7g5ptvNi5FPvfccwA89thjXHzxxVnP37Fjx9G9kcO0e/funPuDwSCBQACn04nX6+3zGKNHjwbga1/7Gpdffnm/X1vTNM4880zOPPNMABobG7n55pt56qmn+MEPfsCf/vSnfh9LCHH8yD12Qohh6dZbb8Xj8fDrX/+avXv3HvXx8vPzWbRoEZqmUVNTY+zvXOmiMxB1NdBhpqWlhaVLl/bY//TTTwMwf/78nJd3u+q8R68zsB6pkpISY23erp+XEGJwSbATQgxLfr+f73znO8TjcX77298e1nP/8Ic/5AwjL7/8MkopKisrjX2TJk0C4Ne//nXWJde33nrrqOvpHYnvfve7WQWaa2tr+fGPfwxkJjccyuWXX860adP44x//yD333NPjXj6lFG+//TZvv/22se/xxx+ntra2x7FeeuklgKzPSwgxuORSrBBi2Lr11lt56KGHCAaDh/W8v/71r3zpS19iwoQJzJgxA6fTSW1tLStXrsRkMvGTn/zEaPud73yHJ598kkcffZTly5czc+ZM9u7dy4oVK7j11lsHdImz0047jUQiQXV1Neeeey7JZJKlS5cSiUT4whe+0OtM164sFgvPP/88559/PnfeeScPP/wwM2fOpKSkhObmZtatW0djYyMPPPAAZ5xxBpAJdtdffz3Tpk1j6tSpWCwWPvroI9avX4/D4eDOO+883m9dCNFPMmInhBi2CgsLufnmmw/7ebfccgs33HADHo+Ht956i+eee47GxkauvPJKVq5cyb//+78bbSdNmsSqVau45JJLaG5u5u9//zuhUIhf/epXAz5iZ7fbWbZsGZ///Od57733ePXVV6msrOS+++7jySef7PdxJk6cyNq1a/nJT37C6NGjee+993j22WfZsmULc+bM4ZFHHuELX/iC0f6ee+7h2muvRdM0li5dygsvvEA0GuVrX/sa69atMwKgEGLwaaq36VxCCCGGhJ07dzJu3DjOPvtsli9fPtinI4QYwmTETgghhBBihJBgJ4QQQggxQkiwE0IIIYQYIeQeOyGEEEKIEUJG7IQQQgghRggJdkIIIYQQI4QEOyGEEEKIEUKCnRBCCCHECCHBTgghhBBihJBgJ4QQQggxQkiwE0IIIYQYISTYCSGEEEKMEBLshBBCCCFGiP8HB8KOJVDqgkkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train policies with different number of samples N\n",
    "Ns = [100, 300, 1000, 3000, 5000]\n",
    "pihat_TV_ls, Vhat_TV_ls = [], []\n",
    "for N in Ns:\n",
    "    pihat_TV, Vhat_TV = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=0.2, \n",
    "                                                   uncer='L1', num_samp=N)\n",
    "    pihat_TV_ls.append(pihat_TV)\n",
    "    Vhat_TV_ls.append(Vhat_TV)\n",
    "    \n",
    "pihat_Chi_ls, Vhat_Chi_ls = [], []\n",
    "for N in Ns:\n",
    "    pihat_Chi, Vhat_Chi = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=0.2, \n",
    "                                                     uncer='Chi', num_samp=N)\n",
    "    pihat_Chi_ls.append(pihat_Chi)\n",
    "    Vhat_Chi_ls.append(Vhat_Chi)\n",
    "    \n",
    "pihat_KL_ls, Vhat_KL_ls = [], []\n",
    "for N in Ns:\n",
    "    pihat_KL, Vhat_KL = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=0.2, \n",
    "                                                   uncer='KL', num_samp=N)\n",
    "    pihat_KL_ls.append(pihat_KL)\n",
    "    Vhat_KL_ls.append(Vhat_KL)    \n",
    "\n",
    "pihat_W_ls, Vhat_W_ls = [], []\n",
    "for N in Ns:\n",
    "    pihat_W, Vhat_W = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=0.2, \n",
    "                                                 uncer='W', d=lambda x,y: abs(x-y), p=1, num_samp=N)\n",
    "    pihat_W_ls.append(pihat_W)\n",
    "    Vhat_W_ls.append(Vhat_W)\n",
    "    \n",
    "# policy evaluation using robust bellman consistency equation\n",
    "# TV\n",
    "V_1_opt_TV = Vopt_L1[:,0]\n",
    "s_range = range(1)\n",
    "mean_N_TV_ls, std_N_TV_ls = [], []\n",
    "for pi in pihat_TV_ls:\n",
    "    subopt_gap_ls = []\n",
    "    for s in s_range:\n",
    "        np.random.seed(s+10000)\n",
    "        _, subopt_gap = policy_evaluation(pi, V_1_opt_TV, uncer='L1')\n",
    "        subopt_gap_ls.append(subopt_gap)\n",
    "    mean_N_TV_ls.append(np.mean(subopt_gap_ls))\n",
    "    std_N_TV_ls.append(np.std(subopt_gap_ls))\n",
    "    \n",
    "# Chi\n",
    "V_1_opt_Chi = Vopt_Chi[:,0]\n",
    "s_range = range(1)\n",
    "mean_N_Chi_ls, std_N_Chi_ls = [], []\n",
    "for pi in pihat_Chi_ls:\n",
    "    subopt_gap_ls = []\n",
    "    for s in s_range:\n",
    "        np.random.seed(s+10000)\n",
    "        _, subopt_gap = policy_evaluation(pi, V_1_opt_Chi, uncer='Chi')\n",
    "        subopt_gap_ls.append(subopt_gap)\n",
    "    mean_N_Chi_ls.append(np.mean(subopt_gap_ls))\n",
    "    std_N_Chi_ls.append(np.std(subopt_gap_ls))\n",
    "    \n",
    "# KL\n",
    "V_1_opt_KL = Vopt_KL[:,0]\n",
    "s_range = range(1)\n",
    "mean_N_KL_ls, std_N_KL_ls = [], []\n",
    "for pi in pihat_KL_ls:\n",
    "    subopt_gap_ls = []\n",
    "    for s in s_range:\n",
    "        np.random.seed(s+10000)\n",
    "        _, subopt_gap = policy_evaluation(pi, V_1_opt_KL, uncer='KL')\n",
    "        subopt_gap_ls.append(subopt_gap)\n",
    "    mean_N_KL_ls.append(np.mean(subopt_gap_ls))\n",
    "    std_N_KL_ls.append(np.std(subopt_gap_ls))\n",
    "    \n",
    "# W\n",
    "V_1_opt_W = Vopt_W[:,0]\n",
    "s_range = range(10)\n",
    "mean_N_W_ls, std_N_W_ls = [], []\n",
    "for pi in pihat_W_ls:\n",
    "    subopt_gap_ls = []\n",
    "    for s in s_range:\n",
    "        np.random.seed(s+10000)\n",
    "        _, subopt_gap = policy_evaluation(pi, V_1_opt_W, uncer='W', d=lambda x,y: abs(x-y), p=1)\n",
    "        subopt_gap_ls.append(subopt_gap)\n",
    "    mean_N_W_ls.append(np.mean(subopt_gap_ls))\n",
    "    std_N_W_ls.append(np.std(subopt_gap_ls))\n",
    "\n",
    "# normalize data\n",
    "def fun(x):\n",
    "    return 1 / np.sqrt(x)\n",
    "N_vals = [fun(n) for n in Ns]\n",
    "\n",
    "def normalize_fun(data):\n",
    "    return data / np.max(data)\n",
    "mean_N_TV_norm = normalize_fun(mean_N_TV_ls)\n",
    "mean_N_Chi_norm = normalize_fun(mean_N_Chi_ls)\n",
    "mean_N_KL_norm = normalize_fun(mean_N_KL_ls)\n",
    "mean_N_W_norm = normalize_fun(mean_N_W_ls)\n",
    "N_vals_norm = normalize_fun(N_vals)\n",
    "\n",
    "# plot\n",
    "markercycler = cycle(markers)\n",
    "colorcycler = cycle(colors)\n",
    "bandcycler = cycle(bands)\n",
    "fig_N, ax_N = plt.subplots(1,1, figsize= [6.4, 4.8])\n",
    "ax_N.plot(Ns, N_vals_norm, marker=next(markercycler), color=next(colorcycler), label=r'$1/\\sqrt{N}$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_N.plot(Ns, mean_N_TV_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-TV'+r', $\\rho=0.2$',\n",
    "          linewidth=2, markersize=7)\n",
    "ax_N.plot(Ns, mean_N_Chi_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-Chi'+r', $\\rho=0.2$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_N.plot(Ns, mean_N_KL_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-KL'+r', $\\rho=0.2$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_N.plot(Ns, mean_N_W_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-W'+r', $\\rho=0.2$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_N.legend()\n",
    "ax_N.grid(visible=True)\n",
    "ax_N.set_xlabel(\"N samples\", fontsize=15)\n",
    "ax_N.set_ylabel(r\"$||V^* - V^{\\hat{\\pi}}||$\", fontsize=15)\n",
    "fig_N.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b43069",
   "metadata": {},
   "source": [
    "### Sub-optimality gap vs. robustness parameter $\\rho$\n",
    "In proving the main theorem of the paper, we find that the upper bound of suboptimality gap is some function of $\\rho$, i.e., of order $\\frac{1}{\\rho}$ for TV, KL, and Wasserstein and of order $\\frac{\\rho+1}{\\sqrt{r+1}-1}$ for chi-square. Here, we aim to verify this through experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d8282ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train policies\n",
    "rhos = [0.1,0.15,0.2,0.25,0.3,0.35,0.4]\n",
    "N = 100\n",
    "p_o_h = 0.6\n",
    "pihat_TV_rho_ls, Vhat_TV_rho_ls = [], []\n",
    "for rho in rhos:\n",
    "    pihat_TV, Vhat_TV = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=rho, \n",
    "                                                   uncer='L1', num_samp=N)\n",
    "    pihat_TV_rho_ls.append(pihat_TV)\n",
    "    Vhat_TV_rho_ls.append(Vhat_TV)\n",
    "    \n",
    "pihat_Chi_rho_ls, Vhat_Chi_rho_ls = [], []\n",
    "for rho in rhos:\n",
    "    pihat_Chi, Vhat_Chi = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=rho, \n",
    "                                                   uncer='Chi', num_samp=N)\n",
    "    pihat_Chi_rho_ls.append(pihat_Chi)\n",
    "    Vhat_Chi_rho_ls.append(Vhat_Chi)\n",
    "    \n",
    "pihat_KL_rho_ls, Vhat_KL_rho_ls = [], []\n",
    "for rho in rhos:\n",
    "    pihat_KL, Vhat_KL = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=rho, \n",
    "                                                   uncer='KL', num_samp=N)\n",
    "    pihat_KL_rho_ls.append(pihat_KL)\n",
    "    Vhat_TV_rho_ls.append(Vhat_KL)  \n",
    "\n",
    "pihat_W_rho_ls, Vhat_W_rho_ls = [], []\n",
    "for rho in rhos:\n",
    "    pihat_W, Vhat_W = robust_emp_dp_for_gamblers(p_h=0.6, max_states_plus_1=cardS, horizon_l=H, rho=rho, \n",
    "                                                   uncer='W', d=lambda x,y: abs(x-y), p=1, num_samp=N)\n",
    "    pihat_W_rho_ls.append(pihat_W)\n",
    "    Vhat_W_rho_ls.append(Vhat_W)\n",
    "    \n",
    "# policy evaluation\n",
    "# # TV\n",
    "V_1_opt_TV = Vopt_L1[:,0]\n",
    "suboptgap_rho_TV = []\n",
    "for pi in pihat_TV_rho_ls:\n",
    "    _, subopt_gap = policy_evaluation(pi, V_1_opt_TV, uncer='L1')\n",
    "    suboptgap_rho_TV.append(subopt_gap)\n",
    "\n",
    "# Chi\n",
    "V_1_opt_Chi = Vopt_Chi[:,0]\n",
    "suboptgap_rho_Chi = []\n",
    "for pi in pihat_Chi_rho_ls:\n",
    "    _, subopt_gap = policy_evaluation(pi, V_1_opt_Chi, uncer='Chi')\n",
    "    suboptgap_rho_Chi.append(subopt_gap)\n",
    "    \n",
    "# KL\n",
    "V_1_opt_KL = Vopt_KL[:,0]\n",
    "suboptgap_rho_KL = []\n",
    "for pi in pihat_KL_rho_ls:\n",
    "    _, subopt_gap = policy_evaluation(pi, V_1_opt_KL, uncer='KL')\n",
    "    suboptgap_rho_KL.append(subopt_gap)\n",
    "    \n",
    "# W\n",
    "V_1_opt_W = Vopt_W[:,0]\n",
    "suboptgap_rho_W = []\n",
    "for pi in pihat_W_rho_ls:\n",
    "    _, subopt_gap = policy_evaluation(pi, V_1_opt_W, uncer='W', d=lambda x,y: abs(x-y), p=1)\n",
    "    suboptgap_rho_W.append(subopt_gap)\n",
    "\n",
    "# refernce functions\n",
    "def fun1(rho):\n",
    "    return 1 / rho\n",
    "vals_rho = [fun1(rho) for rho in rhos]\n",
    "\n",
    "def fun2(rho):\n",
    "    return (rho+1) / (np.sqrt(rho+1)-1)\n",
    "vals_rho_chi = [fun2(rho) for rho in rhos]\n",
    "\n",
    "# normalize data\n",
    "suboptgap_rho_TV_norm = normalize_fun(suboptgap_rho_TV)\n",
    "suboptgap_rho_Chi_norm = normalize_fun(suboptgap_rho_Chi)\n",
    "suboptgap_rho_KL_norm = normalize_fun(suboptgap_rho_KL)\n",
    "suboptgap_rho_W_norm = normalize_fun(suboptgap_rho_W)\n",
    "fun1_norm = normalize_fun(vals_rho)\n",
    "fun2_norm = normalize_fun(vals_rho_chi)\n",
    "\n",
    "# plot\n",
    "fig_rho, ax_rho = plt.subplots(1,1)\n",
    "markercycler = cycle(markers)\n",
    "colorcycler = cycle(colors)\n",
    "bandcycler = cycle(bands)\n",
    "\n",
    "ax_rho.plot(rhos, fun1_norm, marker=next(markercycler), color=next(colorcycler), label=r'$1/\\rho$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_rho.plot(rhos, fun2_norm, marker=next(markercycler), color=next(colorcycler), label=r'$\\frac{\\rho+1}{\\sqrt{\\rho+1}-1}$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_rho.plot(rhos, suboptgap_rho_TV_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-TV'+r', $N=100$',\n",
    "          linewidth=2, markersize=7)\n",
    "ax_rho.plot(rhos, suboptgap_rho_Chi_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-Chi'+r', $N=100$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_rho.plot(rhos, suboptgap_rho_KL_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-KL'+r', $N=100$',\n",
    "         linewidth=2, markersize=7)\n",
    "ax_rho.plot(rhos, suboptgap_rho_W_norm, marker=next(markercycler), color=next(colorcycler), label='RPVL-W'+r', $N=100$',\n",
    "         linewidth=2, markersize=7)\n",
    "\n",
    "ax_rho.legend()\n",
    "ax_rho.grid(visible=True)\n",
    "ax_rho.set_xlabel(\"Robustness parameter \" + r'$\\rho$', fontsize=15)\n",
    "ax_rho.set_ylabel(r\"$||V^* - V^{\\hat{\\pi}}||$\", fontsize=15)\n",
    "fig_rho.tight_layout()"
   ]
  }
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