DQN_Agent_Arch1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cab-Driver Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import numpy as np\n",
    "import random\n",
    "import math\n",
    "from collections import deque\n",
    "import collections\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "# for building DQN model\n",
    "from keras import layers\n",
    "from keras import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# for plotting graphs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import the environment\n",
    "from Env import CabDriver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining Time Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the time matrix provided\n",
    "Time_matrix = np.load(\"TM.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tracking the state-action pairs for checking convergence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "11.0\n",
      "0.0\n",
      "3.0542857142857143\n",
      "7.93705306122449\n"
     ]
    }
   ],
   "source": [
    "print(type(Time_matrix))\n",
    "print(Time_matrix.max())\n",
    "print(Time_matrix.min())\n",
    "print(Time_matrix.mean())\n",
    "print(Time_matrix.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a function to save the Q-dictionary as a pickle file\n",
    "def save_obj(obj, name ):\n",
    "    with open(name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"def __getstate__(self):\\n        # capture what is normally pickled\\n        state = self.__dict__.copy()\\n\\n        # remove unpicklable/problematic variables \\n        state['process_1'] = None\\n        return state \""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''def __getstate__(self):\n",
    "        # capture what is normally pickled\n",
    "        state = self.__dict__.copy()\n",
    "\n",
    "        # remove unpicklable/problematic variables \n",
    "        state['process_1'] = None\n",
    "        return state '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def __getstate__(self):\n",
    "        s = self.__dict__.copy()\n",
    "        s['_db'] = s['_db']()\n",
    "        return s\n",
    "\n",
    "def __setstate__(self, state):\n",
    "        self.__dict__ = state.copy()\n",
    "        self.__dict__['_db'] = weakref.ref(self.__dict__['_db'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agent Class\n",
    "\n",
    "If you are using this framework, you need to fill the following to complete the following code block:\n",
    "1. State and Action Size\n",
    "2. Hyperparameters\n",
    "3. Create a neural-network model in function 'build_model()'\n",
    "4. Define epsilon-greedy strategy in function 'get_action()'\n",
    "5. Complete the function 'append_sample()'. This function appends the recent experience tuple <state, action, reward, new-state> to the memory\n",
    "6. Complete the 'train_model()' function with following logic:\n",
    "   - If the memory size is greater than mini-batch size, you randomly sample experiences from memory as per the mini-batch size and do the following:\n",
    "      - Initialise your input and output batch for training the model\n",
    "      - Calculate the target Q value for each sample: reward + gamma*max(Q(s'a,))\n",
    "      - Get Q(s', a) values from the last trained model\n",
    "      - Update the input batch as your encoded state and output batch as your Q-values\n",
    "      - Then fit your DQN model using the updated input and output batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQNAgent:\n",
    "    def __init__(self, state_size, action_size):            \n",
    "        # Define size of state and action\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "\n",
    "        # Write here: Specify you hyper parameters for the DQN\n",
    "        self.discount_factor = 0.95\n",
    "        self.learning_rate = 0.01\n",
    "        self.epsilon = 1\n",
    "        self.epsilon_max = 1\n",
    "        self.epsilon_decay = -0.0005 #for 15k\n",
    "        #self.epsilon_decay = -0.00015 #for 20k\n",
    "        self.epsilon_min = 0.00001\n",
    "        \n",
    "        self.batch_size = 32\n",
    "\n",
    "        # create replay memory using deque\n",
    "        self.memory = deque(maxlen=2000)\n",
    "\n",
    "        # Initialize the value of the states tracked\n",
    "        self.states_tracked = []\n",
    "        \n",
    "        # We are going to track state [0,0,0] and action (0,2) at index 2 in the action space.\n",
    "        self.track_state = np.array(env.state_encod_arch1([0,0,0])).reshape(1, 36)\n",
    "\n",
    "        # create main model and target model\n",
    "        self.model = self.build_model()\n",
    "\n",
    "    # approximate Q function using Neural Network\n",
    "    def build_model(self):\n",
    "        input_shape = self.state_size\n",
    "        model = Sequential()\n",
    "        # Write your code here: Add layers to your neural nets       \n",
    "        model.add(Dense(32, input_dim=self.state_size, activation='relu', kernel_initializer='he_uniform'))\n",
    "        model.add(Dense(32, activation='relu', kernel_initializer='he_uniform'))\n",
    "        # the output layer: output is of size num_actions\n",
    "        model.add(Dense(self.action_size, activation='relu', kernel_initializer='he_uniform'))\n",
    "        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))\n",
    "        model.summary\n",
    "        return model\n",
    "        \n",
    "    def get_action(self, state, possible_actions_index, actions):\n",
    "    # Write your code here:\n",
    "    # get action from model using epsilon-greedy policy\n",
    "    # Decay in ε after we generate each sample from the environment    \n",
    "        if np.random.rand() <= self.epsilon:\n",
    "            # explore: choose a random action from the ride requests\n",
    "            return random.choice(possible_actions_index)\n",
    "        else:\n",
    "            # choose the action with the highest q(s, a)\n",
    "            # the first index corresponds to the batch size, so\n",
    "            # reshape state to (1, state_size) so that the first index corresponds to the batch size\n",
    "            state = np.array(env.state_encod_arch1(state)).reshape(1, 36)\n",
    "\n",
    "            # Use the model to predict the Q_values.\n",
    "            q_value = self.model.predict(state)\n",
    "\n",
    "            # truncate the array to only those actions that are part of the ride  requests.\n",
    "            q_vals_possible = [q_value[0][i] for i in possible_actions_index]\n",
    "\n",
    "            return possible_actions_index[np.argmax(q_vals_possible)]      \n",
    "        \n",
    "\n",
    "\n",
    "    def append_sample(self, state, action_index, reward, next_state, done):\n",
    "        \"\"\"appends the new agent run output to replay buffer\"\"\"\n",
    "        self.memory.append((state, action_index, reward, next_state, done))\n",
    "    \n",
    "    \n",
    "    # pick samples randomly from replay memory (with batch_size) and train the network\n",
    "    def train_model(self):\n",
    "        \n",
    "        if len(self.memory) > self.batch_size:\n",
    "            # Sample batch from the memory\n",
    "            mini_batch = random.sample(self.memory, self.batch_size)\n",
    "            # initialise two matrices - update_input and update_output\n",
    "            update_input = np.zeros((self.batch_size, self.state_size))\n",
    "            update_output = np.zeros((self.batch_size, self.state_size))\n",
    "            actions, rewards, done = [], [], []\n",
    "\n",
    "            # populate update_input and update_output and the lists rewards, actions, done\n",
    "            for i in range(self.batch_size):\n",
    "                state, action, reward, next_state, done_boolean = mini_batch[i]\n",
    "                update_input[i] = env.state_encod_arch1(state)     \n",
    "                actions.append(action)\n",
    "                rewards.append(reward)\n",
    "                update_output[i] = env.state_encod_arch1(next_state)\n",
    "                done.append(done_boolean)\n",
    "\n",
    "            # predict the target q-values from states s\n",
    "            target = self.model.predict(update_input)\n",
    "            # target for q-network\n",
    "            target_qval = self.model.predict(update_output)\n",
    "\n",
    "\n",
    "            # update the target values\n",
    "            for i in range(self.batch_size):\n",
    "                if done[i]:\n",
    "                    target[i][actions[i]] = rewards[i]\n",
    "                else: # non-terminal state\n",
    "                    target[i][actions[i]] = rewards[i] + self.discount_factor * np.max(target_qval[i])\n",
    "            # model fit\n",
    "            self.model.fit(update_input, target, batch_size=self.batch_size, epochs=1, verbose=0)\n",
    "            \n",
    "            \n",
    "    def save_tracking_states(self):\n",
    "        # Use the model to predict the q_value of the state we are tacking.\n",
    "        q_value = self.model.predict(self.track_state)\n",
    "        \n",
    "        # Grab the q_value of the action index that we are tracking.\n",
    "        self.states_tracked.append(q_value[0][2])\n",
    "        \n",
    "    def save_test_states(self):\n",
    "        # Use the model to predict the q_value of the state we are tacking.\n",
    "        q_value = self.model.predict(self.track_state)\n",
    "        \n",
    "        # Grab the q_value of the action index that we are tracking.\n",
    "        self.states_test.append(q_value[0][2])\n",
    "\n",
    "    def save(self, name):\n",
    "        wts =self.model.get_weights()\n",
    "        with open(name, 'wb') as file:  \n",
    "            #pickle.dump(self.model, file,pickle.HIGHEST_PROTOCOL)\n",
    "            pickle.dump(wts, file,pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "episode_time = 24*30 #30 days before which car has to be recharged\n",
    "n_episodes = 2000\n",
    "m = 5\n",
    "t = 24\n",
    "d = 7\n",
    "\n",
    "# Invoke Env class\n",
    "env = CabDriver()\n",
    "action_space, state_space, state = env.reset()\n",
    "\n",
    "# Set up state and action sizes.\n",
    "state_size = m+t+d\n",
    "action_size = len(action_space)\n",
    "\n",
    "# Invoke agent class\n",
    "agent = DQNAgent(action_size=action_size, state_size=state_size)\n",
    "\n",
    "# to store rewards in each episode\n",
    "rewards_per_episode, episodes = [], []\n",
    "# Rewards for state [0,0,0] being tracked.\n",
    "rewards_init_state = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DQN block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Model 0\n",
      "episode 9, reward -195.0, memory_length 1339, epsilon 0.9955001547284723 total_time 725.0\n",
      "episode 19, reward -184.0, memory_length 2000, epsilon 0.9905350769930761 total_time 724.0\n",
      "episode 29, reward -342.0, memory_length 2000, epsilon 0.9855947626861951 total_time 724.0\n",
      "episode 39, reward -405.0, memory_length 2000, epsilon 0.9806790882997144 total_time 722.0\n",
      "episode 49, reward 8.0, memory_length 2000, epsilon 0.9757879309415182 total_time 724.0\n",
      "episode 59, reward -246.0, memory_length 2000, epsilon 0.9709211683324178 total_time 725.0\n",
      "episode 69, reward 433.0, memory_length 2000, epsilon 0.9660786788030947 total_time 725.0\n",
      "episode 79, reward -108.0, memory_length 2000, epsilon 0.9612603412910584 total_time 726.0\n",
      "episode 89, reward -156.0, memory_length 2000, epsilon 0.9564660353376199 total_time 725.0\n",
      "episode 99, reward 45.0, memory_length 2000, epsilon 0.9516956410848808 total_time 723.0\n",
      "episode 109, reward -22.0, memory_length 2000, epsilon 0.9469490392727365 total_time 723.0\n",
      "episode 119, reward 380.0, memory_length 2000, epsilon 0.9422261112358942 total_time 724.0\n",
      "episode 129, reward -283.0, memory_length 2000, epsilon 0.9375267389009072 total_time 725.0\n",
      "episode 139, reward -245.0, memory_length 2000, epsilon 0.9328508047832221 total_time 723.0\n",
      "episode 149, reward -98.0, memory_length 2000, epsilon 0.9281981919842428 total_time 723.0\n",
      "episode 159, reward -27.0, memory_length 2000, epsilon 0.9235687841884068 total_time 724.0\n",
      "episode 169, reward 158.0, memory_length 2000, epsilon 0.918962465660278 total_time 728.0\n",
      "episode 179, reward -229.0, memory_length 2000, epsilon 0.9143791212416534 total_time 722.0\n",
      "episode 189, reward 23.0, memory_length 2000, epsilon 0.9098186363486838 total_time 723.0\n",
      "episode 199, reward 23.0, memory_length 2000, epsilon 0.9052808969690094 total_time 725.0\n",
      "episode 209, reward -178.0, memory_length 2000, epsilon 0.9007657896589091 total_time 726.0\n",
      "episode 219, reward 68.0, memory_length 2000, epsilon 0.8962732015404654 total_time 722.0\n",
      "episode 229, reward 16.0, memory_length 2000, epsilon 0.891803020298741 total_time 721.0\n",
      "episode 239, reward 189.0, memory_length 2000, epsilon 0.8873551341789723 total_time 723.0\n",
      "episode 249, reward -131.0, memory_length 2000, epsilon 0.8829294319837746 total_time 721.0\n",
      "episode 259, reward -247.0, memory_length 2000, epsilon 0.8785258030703623 total_time 721.0\n",
      "episode 269, reward -83.0, memory_length 2000, epsilon 0.8741441373477834 total_time 722.0\n",
      "episode 279, reward -72.0, memory_length 2000, epsilon 0.8697843252741666 total_time 722.0\n",
      "episode 289, reward 5.0, memory_length 2000, epsilon 0.8654462578539829 total_time 727.0\n",
      "episode 299, reward -312.0, memory_length 2000, epsilon 0.8611298266353209 total_time 722.0\n",
      "episode 309, reward -55.0, memory_length 2000, epsilon 0.8568349237071754 total_time 725.0\n",
      "episode 319, reward -8.0, memory_length 2000, epsilon 0.8525614416967494 total_time 721.0\n",
      "episode 329, reward -150.0, memory_length 2000, epsilon 0.84830927376677 total_time 724.0\n",
      "episode 339, reward -80.0, memory_length 2000, epsilon 0.8440783136128177 total_time 725.0\n",
      "episode 349, reward -55.0, memory_length 2000, epsilon 0.8398684554606681 total_time 723.0\n",
      "episode 359, reward -234.0, memory_length 2000, epsilon 0.8356795940636483 total_time 721.0\n",
      "episode 369, reward -340.0, memory_length 2000, epsilon 0.8315116247000052 total_time 732.0\n",
      "episode 379, reward -294.0, memory_length 2000, epsilon 0.8273644431702872 total_time 724.0\n",
      "episode 389, reward -265.0, memory_length 2000, epsilon 0.8232379457947406 total_time 725.0\n",
      "episode 399, reward 339.0, memory_length 2000, epsilon 0.819132029410716 total_time 723.0\n",
      "episode 409, reward -106.0, memory_length 2000, epsilon 0.8150465913700896 total_time 724.0\n",
      "episode 419, reward 68.0, memory_length 2000, epsilon 0.8109815295366979 total_time 729.0\n",
      "episode 429, reward -375.0, memory_length 2000, epsilon 0.8069367422837833 total_time 727.0\n",
      "episode 439, reward -333.0, memory_length 2000, epsilon 0.8029121284914538 total_time 730.0\n",
      "episode 449, reward -238.0, memory_length 2000, epsilon 0.7989075875441549 total_time 726.0\n",
      "episode 459, reward -418.0, memory_length 2000, epsilon 0.7949230193281545 total_time 734.0\n",
      "episode 469, reward 16.0, memory_length 2000, epsilon 0.7909583242290396 total_time 732.0\n",
      "episode 479, reward 142.0, memory_length 2000, epsilon 0.7870134031292261 total_time 727.0\n",
      "episode 489, reward -125.0, memory_length 2000, epsilon 0.7830881574054811 total_time 725.0\n",
      "episode 499, reward 299.0, memory_length 2000, epsilon 0.7791824889264571 total_time 722.0\n",
      "episode 509, reward -177.0, memory_length 2000, epsilon 0.7752963000502389 total_time 726.0\n",
      "episode 519, reward -349.0, memory_length 2000, epsilon 0.7714294936219019 total_time 723.0\n",
      "episode 529, reward 110.0, memory_length 2000, epsilon 0.7675819729710842 total_time 722.0\n",
      "episode 539, reward -26.0, memory_length 2000, epsilon 0.763753641909569 total_time 723.0\n",
      "episode 549, reward -252.0, memory_length 2000, epsilon 0.7599444047288803 total_time 725.0\n",
      "episode 559, reward -238.0, memory_length 2000, epsilon 0.7561541661978903 total_time 725.0\n",
      "episode 569, reward -163.0, memory_length 2000, epsilon 0.7523828315604384 total_time 728.0\n",
      "episode 579, reward -117.0, memory_length 2000, epsilon 0.7486303065329623 total_time 723.0\n",
      "episode 589, reward -193.0, memory_length 2000, epsilon 0.7448964973021404 total_time 721.0\n",
      "episode 599, reward -206.0, memory_length 2000, epsilon 0.7411813105225479 total_time 728.0\n",
      "episode 609, reward -134.0, memory_length 2000, epsilon 0.7374846533143217 total_time 724.0\n",
      "episode 619, reward -112.0, memory_length 2000, epsilon 0.733806433260839 total_time 729.0\n",
      "episode 629, reward -124.0, memory_length 2000, epsilon 0.7301465584064071 total_time 725.0\n",
      "episode 639, reward 32.0, memory_length 2000, epsilon 0.7265049372539636 total_time 725.0\n",
      "episode 649, reward -134.0, memory_length 2000, epsilon 0.7228814787627905 total_time 729.0\n",
      "episode 659, reward 59.0, memory_length 2000, epsilon 0.7192760923462365 total_time 727.0\n",
      "episode 669, reward -191.0, memory_length 2000, epsilon 0.7156886878694535 total_time 721.0\n",
      "episode 679, reward -36.0, memory_length 2000, epsilon 0.7121191756471427 total_time 726.0\n",
      "episode 689, reward 91.0, memory_length 2000, epsilon 0.7085674664413126 total_time 726.0\n",
      "episode 699, reward -279.0, memory_length 2000, epsilon 0.7050334714590482 total_time 730.0\n",
      "episode 709, reward 27.0, memory_length 2000, epsilon 0.7015171023502909 total_time 722.0\n",
      "episode 719, reward 72.0, memory_length 2000, epsilon 0.6980182712056295 total_time 724.0\n",
      "episode 729, reward -115.0, memory_length 2000, epsilon 0.6945368905541035 total_time 732.0\n",
      "episode 739, reward -341.0, memory_length 2000, epsilon 0.6910728733610152 total_time 723.0\n",
      "episode 749, reward 105.0, memory_length 2000, epsilon 0.6876261330257543 total_time 727.0\n",
      "episode 759, reward -94.0, memory_length 2000, epsilon 0.684196583379633 total_time 729.0\n",
      "episode 769, reward 63.0, memory_length 2000, epsilon 0.6807841386837313 total_time 721.0\n",
      "episode 779, reward -119.0, memory_length 2000, epsilon 0.6773887136267543 total_time 721.0\n",
      "episode 789, reward 37.0, memory_length 2000, epsilon 0.6740102233228988 total_time 725.0\n",
      "episode 799, reward -301.0, memory_length 2000, epsilon 0.670648583309731 total_time 727.0\n",
      "episode 809, reward 199.0, memory_length 2000, epsilon 0.6673037095460755 total_time 725.0\n",
      "episode 819, reward 82.0, memory_length 2000, epsilon 0.6639755184099142 total_time 729.0\n",
      "episode 829, reward 23.0, memory_length 2000, epsilon 0.6606639266962953 total_time 726.0\n",
      "episode 839, reward -58.0, memory_length 2000, epsilon 0.6573688516152534 total_time 727.0\n",
      "episode 849, reward -215.0, memory_length 2000, epsilon 0.6540902107897397 total_time 725.0\n",
      "episode 859, reward -210.0, memory_length 2000, epsilon 0.6508279222535631 total_time 732.0\n",
      "episode 869, reward 180.0, memory_length 2000, epsilon 0.64758190444934 total_time 733.0\n",
      "episode 879, reward -62.0, memory_length 2000, epsilon 0.6443520762264566 total_time 723.0\n",
      "episode 889, reward -5.0, memory_length 2000, epsilon 0.6411383568390387 total_time 721.0\n",
      "episode 899, reward -151.0, memory_length 2000, epsilon 0.6379406659439346 total_time 724.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode 909, reward -110.0, memory_length 2000, epsilon 0.6347589235987051 total_time 722.0\n",
      "episode 919, reward -87.0, memory_length 2000, epsilon 0.631593050259626 total_time 723.0\n",
      "episode 929, reward 10.0, memory_length 2000, epsilon 0.6284429667796988 total_time 724.0\n",
      "episode 939, reward 466.0, memory_length 2000, epsilon 0.6253085944066726 total_time 721.0\n",
      "episode 949, reward -25.0, memory_length 2000, epsilon 0.6221898547810748 total_time 724.0\n",
      "episode 959, reward -49.0, memory_length 2000, epsilon 0.6190866699342522 total_time 730.0\n",
      "episode 969, reward -94.0, memory_length 2000, epsilon 0.6159989622864221 total_time 726.0\n",
      "episode 979, reward -79.0, memory_length 2000, epsilon 0.6129266546447325 total_time 732.0\n",
      "episode 989, reward 149.0, memory_length 2000, epsilon 0.6098696702013323 total_time 724.0\n",
      "episode 999, reward 72.0, memory_length 2000, epsilon 0.6068279325314512 total_time 723.0\n",
      "Saving Model 1000\n",
      "episode 1009, reward 180.0, memory_length 2000, epsilon 0.6038013655914889 total_time 729.0\n",
      "episode 1019, reward 153.0, memory_length 2000, epsilon 0.6007898937171146 total_time 727.0\n",
      "episode 1029, reward 8.0, memory_length 2000, epsilon 0.5977934416213744 total_time 723.0\n",
      "episode 1039, reward 188.0, memory_length 2000, epsilon 0.5948119343928097 total_time 722.0\n",
      "episode 1049, reward 342.0, memory_length 2000, epsilon 0.5918452974935846 total_time 721.0\n",
      "episode 1059, reward 136.0, memory_length 2000, epsilon 0.5888934567576223 total_time 722.0\n",
      "episode 1069, reward 339.0, memory_length 2000, epsilon 0.5859563383887504 total_time 724.0\n",
      "episode 1079, reward 333.0, memory_length 2000, epsilon 0.5830338689588568 total_time 731.0\n",
      "episode 1089, reward 330.0, memory_length 2000, epsilon 0.5801259754060536 total_time 721.0\n",
      "episode 1099, reward 419.0, memory_length 2000, epsilon 0.5772325850328504 total_time 725.0\n",
      "episode 1109, reward -16.0, memory_length 2000, epsilon 0.5743536255043372 total_time 727.0\n",
      "episode 1119, reward 252.0, memory_length 2000, epsilon 0.5714890248463761 total_time 721.0\n",
      "episode 1129, reward 504.0, memory_length 2000, epsilon 0.5686387114438011 total_time 726.0\n",
      "episode 1139, reward 361.0, memory_length 2000, epsilon 0.5658026140386287 total_time 721.0\n",
      "episode 1149, reward 169.0, memory_length 2000, epsilon 0.5629806617282764 total_time 724.0\n",
      "episode 1159, reward 210.0, memory_length 2000, epsilon 0.5601727839637891 total_time 726.0\n",
      "episode 1169, reward 636.0, memory_length 2000, epsilon 0.5573789105480766 total_time 727.0\n",
      "episode 1179, reward 209.0, memory_length 2000, epsilon 0.5545989716341581 total_time 728.0\n",
      "episode 1189, reward 486.0, memory_length 2000, epsilon 0.5518328977234156 total_time 724.0\n",
      "episode 1199, reward 916.0, memory_length 2000, epsilon 0.5490806196638577 total_time 722.0\n",
      "episode 1209, reward 344.0, memory_length 2000, epsilon 0.5463420686483893 total_time 724.0\n",
      "episode 1219, reward 530.0, memory_length 2000, epsilon 0.5436171762130925 total_time 722.0\n",
      "episode 1229, reward 487.0, memory_length 2000, epsilon 0.5409058742355145 total_time 723.0\n",
      "episode 1239, reward 358.0, memory_length 2000, epsilon 0.5382080949329644 total_time 722.0\n",
      "episode 1249, reward 592.0, memory_length 2000, epsilon 0.5355237708608195 total_time 722.0\n",
      "episode 1259, reward 97.0, memory_length 2000, epsilon 0.5328528349108379 total_time 725.0\n",
      "episode 1269, reward 425.0, memory_length 2000, epsilon 0.5301952203094819 total_time 722.0\n",
      "episode 1279, reward 321.0, memory_length 2000, epsilon 0.5275508606162479 total_time 730.0\n",
      "episode 1289, reward 67.0, memory_length 2000, epsilon 0.5249196897220061 total_time 729.0\n",
      "episode 1299, reward 20.0, memory_length 2000, epsilon 0.5223016418473468 total_time 726.0\n",
      "episode 1309, reward 14.0, memory_length 2000, epsilon 0.519696651540937 total_time 724.0\n",
      "episode 1319, reward -21.0, memory_length 2000, epsilon 0.5171046536778833 total_time 724.0\n",
      "episode 1329, reward 165.0, memory_length 2000, epsilon 0.514525583458104 total_time 721.0\n",
      "episode 1339, reward 396.0, memory_length 2000, epsilon 0.5119593764047093 total_time 725.0\n",
      "episode 1349, reward 600.0, memory_length 2000, epsilon 0.5094059683623896 total_time 723.0\n",
      "episode 1359, reward 379.0, memory_length 2000, epsilon 0.5068652954958104 total_time 726.0\n",
      "episode 1369, reward 845.0, memory_length 2000, epsilon 0.5043372942880178 total_time 728.0\n",
      "episode 1379, reward 316.0, memory_length 2000, epsilon 0.50182190153885 total_time 731.0\n",
      "episode 1389, reward 27.0, memory_length 2000, epsilon 0.49931905436335716 total_time 730.0\n",
      "episode 1399, reward 59.0, memory_length 2000, epsilon 0.49682869019022974 total_time 721.0\n",
      "episode 1409, reward 433.0, memory_length 2000, epsilon 0.49435074676023355 total_time 730.0\n",
      "episode 1419, reward 523.0, memory_length 2000, epsilon 0.4918851621246539 total_time 722.0\n",
      "episode 1429, reward 908.0, memory_length 2000, epsilon 0.4894318746437464 total_time 726.0\n",
      "episode 1439, reward 855.0, memory_length 2000, epsilon 0.4869908229851962 total_time 726.0\n",
      "episode 1449, reward 628.0, memory_length 2000, epsilon 0.48456194612258474 total_time 726.0\n",
      "episode 1459, reward 367.0, memory_length 2000, epsilon 0.48214518333386397 total_time 724.0\n",
      "episode 1469, reward 606.0, memory_length 2000, epsilon 0.47974047419983834 total_time 725.0\n",
      "episode 1479, reward 212.0, memory_length 2000, epsilon 0.4773477586026542 total_time 721.0\n",
      "episode 1489, reward 391.0, memory_length 2000, epsilon 0.474966976724297 total_time 725.0\n",
      "episode 1499, reward -151.0, memory_length 2000, epsilon 0.47259806904509577 total_time 722.0\n",
      "episode 1509, reward 963.0, memory_length 2000, epsilon 0.4702409763422352 total_time 723.0\n",
      "episode 1519, reward 673.0, memory_length 2000, epsilon 0.4678956396882749 total_time 721.0\n",
      "episode 1529, reward -88.0, memory_length 2000, epsilon 0.4655620004496764 total_time 726.0\n",
      "episode 1539, reward 324.0, memory_length 2000, epsilon 0.46324000028533724 total_time 731.0\n",
      "episode 1549, reward 299.0, memory_length 2000, epsilon 0.4609295811451323 total_time 727.0\n",
      "episode 1559, reward 499.0, memory_length 2000, epsilon 0.4586306852684627 total_time 724.0\n",
      "episode 1569, reward 599.0, memory_length 2000, epsilon 0.4563432551828119 total_time 730.0\n",
      "episode 1579, reward 1209.0, memory_length 2000, epsilon 0.4540672337023085 total_time 726.0\n",
      "episode 1589, reward 591.0, memory_length 2000, epsilon 0.45180256392629703 total_time 725.0\n",
      "episode 1599, reward 234.0, memory_length 2000, epsilon 0.4495491892379152 total_time 723.0\n",
      "episode 1609, reward 473.0, memory_length 2000, epsilon 0.4473070533026783 total_time 729.0\n",
      "episode 1619, reward 757.0, memory_length 2000, epsilon 0.4450761000670712 total_time 726.0\n",
      "episode 1629, reward 558.0, memory_length 2000, epsilon 0.4428562737571469 total_time 728.0\n",
      "episode 1639, reward 462.0, memory_length 2000, epsilon 0.44064751887713194 total_time 723.0\n",
      "episode 1649, reward 387.0, memory_length 2000, epsilon 0.4384497802080393 total_time 723.0\n",
      "episode 1659, reward 523.0, memory_length 2000, epsilon 0.4362630028062879 total_time 722.0\n",
      "episode 1669, reward 703.0, memory_length 2000, epsilon 0.43408713200232857 total_time 729.0\n",
      "episode 1679, reward 881.0, memory_length 2000, epsilon 0.43192211339927816 total_time 721.0\n",
      "episode 1689, reward 757.0, memory_length 2000, epsilon 0.4297678928715586 total_time 722.0\n",
      "episode 1699, reward 878.0, memory_length 2000, epsilon 0.4276244165635446 total_time 725.0\n",
      "episode 1709, reward 493.0, memory_length 2000, epsilon 0.42549163088821684 total_time 722.0\n",
      "episode 1719, reward 786.0, memory_length 2000, epsilon 0.4233694825258223 total_time 724.0\n",
      "episode 1729, reward 336.0, memory_length 2000, epsilon 0.4212579184225415 total_time 723.0\n",
      "episode 1739, reward 526.0, memory_length 2000, epsilon 0.4191568857891617 total_time 721.0\n",
      "episode 1749, reward 779.0, memory_length 2000, epsilon 0.4170663320997578 total_time 728.0\n",
      "episode 1759, reward 845.0, memory_length 2000, epsilon 0.4149862050903786 total_time 724.0\n",
      "episode 1769, reward 808.0, memory_length 2000, epsilon 0.4129164527577405 total_time 722.0\n",
      "episode 1779, reward 971.0, memory_length 2000, epsilon 0.41085702335792745 total_time 721.0\n",
      "episode 1789, reward 595.0, memory_length 2000, epsilon 0.40880786540509717 total_time 728.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode 1799, reward 851.0, memory_length 2000, epsilon 0.4067689276701942 total_time 730.0\n",
      "episode 1809, reward 553.0, memory_length 2000, epsilon 0.40474015917966877 total_time 722.0\n",
      "episode 1819, reward 857.0, memory_length 2000, epsilon 0.4027215092142031 total_time 726.0\n",
      "episode 1829, reward 465.0, memory_length 2000, epsilon 0.4007129273074429 total_time 722.0\n",
      "episode 1839, reward 635.0, memory_length 2000, epsilon 0.39871436324473586 total_time 727.0\n",
      "episode 1849, reward 540.0, memory_length 2000, epsilon 0.3967257670618763 total_time 736.0\n",
      "episode 1859, reward 661.0, memory_length 2000, epsilon 0.3947470890438561 total_time 725.0\n",
      "episode 1869, reward 991.0, memory_length 2000, epsilon 0.3927782797236218 total_time 728.0\n",
      "episode 1879, reward 815.0, memory_length 2000, epsilon 0.3908192898808378 total_time 723.0\n",
      "episode 1889, reward 1055.0, memory_length 2000, epsilon 0.388870070540656 total_time 722.0\n",
      "episode 1899, reward 626.0, memory_length 2000, epsilon 0.38693057297249134 total_time 721.0\n",
      "episode 1909, reward 939.0, memory_length 2000, epsilon 0.3850007486888037 total_time 722.0\n",
      "episode 1919, reward 754.0, memory_length 2000, epsilon 0.3830805494438854 total_time 723.0\n",
      "episode 1929, reward 579.0, memory_length 2000, epsilon 0.38116992723265536 total_time 724.0\n",
      "episode 1939, reward 653.0, memory_length 2000, epsilon 0.3792688342894587 total_time 721.0\n",
      "episode 1949, reward 442.0, memory_length 2000, epsilon 0.3773772230868729 total_time 724.0\n",
      "episode 1959, reward 662.0, memory_length 2000, epsilon 0.37549504633451936 total_time 721.0\n",
      "episode 1969, reward 599.0, memory_length 2000, epsilon 0.37362225697788115 total_time 728.0\n",
      "episode 1979, reward 628.0, memory_length 2000, epsilon 0.37175880819712703 total_time 727.0\n",
      "episode 1989, reward 729.0, memory_length 2000, epsilon 0.3699046534059402 total_time 727.0\n",
      "episode 1999, reward 963.0, memory_length 2000, epsilon 0.3680597462503545 total_time 729.0\n",
      "33470.61829018593\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "score_tracked = []\n",
    "\n",
    "for episode in range(n_episodes):\n",
    "\n",
    "    done = False\n",
    "    score = 0\n",
    "    track_reward = False\n",
    "\n",
    "    # reset at the start of each episode\n",
    "    env = CabDriver()\n",
    "    action_space, state_space, state = env.reset()\n",
    "    # Save the initial state so that reward can be tracked if initial state is [0,0,0]\n",
    "    initial_state = env.state_init\n",
    "\n",
    "\n",
    "    total_time = 0  # Total time driver rode in this episode\n",
    "    while not done:\n",
    "        # 1. Get a list of the ride requests driver got.\n",
    "        possible_actions_indices, actions = env.requests(state)\n",
    "        # 2. Pick epsilon-greedy action from possible actions for the current state.\n",
    "        action = agent.get_action(state, possible_actions_indices, actions)\n",
    "\n",
    "        # 3. Evaluate your reward and next state\n",
    "        reward, next_state, step_time = env.step(state, env.action_space[action], Time_matrix)\n",
    "        # 4. Total time driver rode in this episode\n",
    "        total_time += step_time\n",
    "        if (total_time > episode_time):\n",
    "            # if ride does not complete in stipu;ated time skip\n",
    "            # it and move to next episode.\n",
    "            done = True\n",
    "        else:\n",
    "            # 5. Append the experience to the memory\n",
    "            agent.append_sample(state, action, reward, next_state, done)\n",
    "            # 6. Train the model by calling function agent.train_model\n",
    "            agent.train_model()\n",
    "            # 7. Keep a track of rewards, Q-values, loss\n",
    "            score += reward\n",
    "            state = next_state\n",
    "\n",
    "    # store total reward obtained in this episode\n",
    "    rewards_per_episode.append(score)\n",
    "    episodes.append(episode)\n",
    "    \n",
    "\n",
    "    # epsilon decay\n",
    "    agent.epsilon = (1 - 0.00001) * np.exp(agent.epsilon_decay * episode)\n",
    "\n",
    "    # every 10 episodes:\n",
    "    if ((episode + 1) % 10 == 0):\n",
    "        print(\"episode {0}, reward {1}, memory_length {2}, epsilon {3} total_time {4}\".format(episode,\n",
    "                                                                         score,\n",
    "                                                                         len(agent.memory),\n",
    "                                                                         agent.epsilon, total_time))\n",
    "    # Save the Q_value of the state, action pair we are tracking\n",
    "    if ((episode + 1) % 5 == 0):\n",
    "        agent.save_tracking_states()\n",
    "\n",
    "    # Total rewards per episode\n",
    "    score_tracked.append(score)\n",
    "\n",
    "    if(episode % 1000 == 0):\n",
    "        print(\"Saving Model {}\".format(episode))\n",
    "        agent.save(name=\"model_weights.pkl\")\n",
    "    \n",
    "elapsed_time = time.time() - start_time\n",
    "print(elapsed_time)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The 'total_time' above includes the 'last ride' time also in each episode. Although it exceeds 24*30 = 720, our code drops the last ride from the replay buffer. So the total ride time per episode is limited to < 720"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.save(name=\"model_weights.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracking Convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       " 576.4116,\n",
       " 621.2918,\n",
       " 681.0606,\n",
       " 931.98444,\n",
       " 618.9637,\n",
       " 1021.9284,\n",
       " 700.3761,\n",
       " 549.91956,\n",
       " 558.3985,\n",
       " 512.83746,\n",
       " 514.5377,\n",
       " 521.8628,\n",
       " 570.25616,\n",
       " 607.6101,\n",
       " 580.55817,\n",
       " 609.58344,\n",
       " 572.5481,\n",
       " 1653.8176,\n",
       " 670.0542,\n",
       " 602.19366,\n",
       " 597.01886,\n",
       " 571.41425,\n",
       " 586.64844,\n",
       " 524.1452,\n",
       " 552.1732,\n",
       " 600.15924,\n",
       " 548.6019,\n",
       " 564.75073,\n",
       " 612.32166,\n",
       " 614.8232,\n",
       " 665.6932,\n",
       " 778.6497,\n",
       " 4300.262,\n",
       " 4920.344,\n",
       " 3332.9814,\n",
       " 2911.256,\n",
       " 2413.244,\n",
       " 1935.7666,\n",
       " 1523.5497,\n",
       " 975.8974,\n",
       " 930.9389,\n",
       " 750.6487,\n",
       " 647.29004,\n",
       " 589.62476,\n",
       " 555.37317,\n",
       " 502.0787,\n",
       " 576.1513,\n",
       " 512.9888,\n",
       " 515.93677,\n",
       " 530.0266,\n",
       " 509.86487,\n",
       " 514.369,\n",
       " 532.49335,\n",
       " 564.62164,\n",
       " 546.2223,\n",
       " 758.12286,\n",
       " 534.8825,\n",
       " 498.9247,\n",
       " 512.407,\n",
       " 460.79483,\n",
       " 527.6132,\n",
       " 543.58704,\n",
       " 546.785,\n",
       " 589.54486,\n",
       " 696.0051,\n",
       " 616.8823,\n",
       " 528.1441,\n",
       " 582.41693,\n",
       " 557.4938,\n",
       " 576.2131,\n",
       " 611.4716,\n",
       " 617.39496,\n",
       " 554.3475,\n",
       " 1030.4512,\n",
       " 2073.7908,\n",
       " 1622.029,\n",
       " 976.29114,\n",
       " 741.47003,\n",
       " 620.509,\n",
       " 660.523,\n",
       " 677.72766,\n",
       " 693.17615,\n",
       " 627.2199,\n",
       " 596.60565,\n",
       " 558.9688,\n",
       " 516.38525,\n",
       " 618.46985,\n",
       " 567.0651,\n",
       " 511.3101,\n",
       " 559.03265,\n",
       " 545.6666,\n",
       " 530.76746,\n",
       " 559.5648,\n",
       " 631.2338,\n",
       " 540.01526,\n",
       " 558.6892,\n",
       " 674.5825,\n",
       " 580.5946,\n",
       " 576.81946,\n",
       " 504.36548,\n",
       " 558.0687,\n",
       " 482.51913,\n",
       " 520.8386,\n",
       " 530.8323,\n",
       " 564.29553,\n",
       " 476.0479,\n",
       " 564.79016,\n",
       " 626.0955,\n",
       " 528.18604,\n",
       " 554.8444,\n",
       " 584.0596,\n",
       " 519.9509,\n",
       " 630.09973,\n",
       " 668.0918,\n",
       " 612.1094,\n",
       " 597.913,\n",
       " 571.32996,\n",
       " 609.07886,\n",
       " 589.46313,\n",
       " 566.64355,\n",
       " 591.2258,\n",
       " 543.3047,\n",
       " 637.32,\n",
       " 583.93756,\n",
       " 658.48944,\n",
       " 604.8427,\n",
       " 623.81995,\n",
       " 585.71564,\n",
       " 616.1163,\n",
       " 647.6255,\n",
       " 565.38214,\n",
       " 645.05096,\n",
       " 560.91785,\n",
       " 596.2857,\n",
       " 524.85535,\n",
       " 554.8884,\n",
       " 560.1766,\n",
       " 552.26764,\n",
       " 598.4532,\n",
       " 588.34265,\n",
       " 619.55225,\n",
       " 557.8006,\n",
       " 574.326,\n",
       " 616.7554,\n",
       " 596.3493,\n",
       " 628.55585,\n",
       " 611.2554,\n",
       " 700.35944,\n",
       " 681.6855,\n",
       " 794.71844,\n",
       " 595.4604,\n",
       " 563.67413,\n",
       " 568.78986,\n",
       " 536.6511,\n",
       " 535.06586,\n",
       " 558.7497,\n",
       " 531.6928,\n",
       " 638.11444,\n",
       " 592.17773,\n",
       " 593.9351,\n",
       " 529.5729,\n",
       " 527.90485,\n",
       " 621.80817,\n",
       " 621.51074,\n",
       " 576.8975,\n",
       " 578.6932,\n",
       " 585.53644,\n",
       " 576.3477,\n",
       " 578.5596,\n",
       " 557.7066,\n",
       " 554.243,\n",
       " 603.5017,\n",
       " 597.36536,\n",
       " 526.0726,\n",
       " 595.8651,\n",
       " 598.0438]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.states_tracked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_tracked_sample = [agent.states_tracked[i] for i in range(len(agent.states_tracked)) if agent.states_tracked[i] < 1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Plot the Q-Value convergence for state action pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0, figsize=(16,7))\n",
    "plt.title('Q_value for state [0,0,0]  action (0,2)')\n",
    "xaxis = np.asarray(range(0, len(agent.states_tracked)))\n",
    "plt.semilogy(xaxis,np.asarray(agent.states_tracked))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_tracked_sample = [score_tracked[i] for i in range(len(score_tracked)) if (i % 4 == 0)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6NaQHR1qopWmGNG1eHgwAn3hizZSO7xS6MRDNIFr/4v5z+KL//Fe4sDkyPyMXM/DExPLgDV0WvJepyXRDZJlFa9D0gVLKb6/41ZdXPP51AF5X8vO7ALyg6esyDMMwDMMwVw+kS6628mASO7O4f8uC7bSantaJTqv6/fnNEZ5aH+KGo90dr4Pc21lKsZ9aHyJOJc5tDHHqUBuA7hf1PAS+N/G83Q+nlY574F295cEMwzAMwzAMMzdIzPCc1oOH6Wm1Rt1MEnm2G/qJJ9bmsg5yWme5QdCP1Puz1o/Nz5JUOa2hJyb2qlJP616mJhvRusRO6/KunGEYhmEYhjlw5HNa93khe4xxWg9wT2tsO636DZ4k8mw39JNPzqevdSc9rQP93PWBJVqzDIHnwfe8ic7xxkCVB++l00rlwa2rOIiJYRiGYRiGYeYG9S1erT2tBzk9mIRaZonWSU4rCctjvXCOTqsWrTO42oNIic61QWR+RunBob/36cFn1wb42OOXax9Dx52dVoZhGIZhGIaZA3TNf9WVB2cHf04ruZC20zopuIhE60tvPYb7ntqYyzpom9EMbmdZeXCcqjmtjUbeaId20n435b/91UP4wf/1idrHcE8rwzAMwzAMw8yR7Gp1Wkm0HminNXdXzcibhj2tx1dac+v3zZ3WGcqDtWjdGBR6Wj1hgpjqUo5zp3U+ovXcxhB97f5WQa+1zOnBy7tyhmEYhmEY5sBBDtxV57SmB7+nldxFx2md2NOqnrPSDpDJ+ZwXw4SCmGYoD45LnNYsQyvwjJNZJ8Tzntb5nN+XtkYTt0XrYdHKMAzDMAzDMHOATKqrTLPm6cFXhdOaNS4PJqd1paUmdcZzKKsd7SCIyZQHWz2tudOqRGtdn26eHjyfmxMXt6KJri0dw4CDmBiGYRiGYRhm55jy4AOuWv/n+x7F/VaPZt7TepBF67jTOjmISR2PXttXz00lHjy3ief+63fi9JX+TOsYGad19vJgOz2YelpDzzPfV2HmtM7h/JZS4tL2CMmEkuTcaWXRyjAMwzAMwzA7xgQxHfCe1v/4jvvxtk+eNd+bkTdJVitAlhlykzOrp3VSaWuUZAh9gZYubU1SiScu9zGIU5y+MphpHXRjIJrFaY11erA9pzVT6cG+V++0ZpnE5kiXB8/w2mNriVIT3FV3HMnVDbzllX7Lu3KGYRiGYRjmwGF6Wg+ocAOUQ5Zm0ikRJdEh5WyptstAXNLT2mTkTcv3TD9mnGXGyZxFdAL2nNZZRt6UzGlN1ZxWcjKrBORWlJjy90kBVE24uDXK11BTNk3r4fJghmEYhmEYhpkD8iooD06Ny5hZP8u/PqhjbxIrPbjsGJQRJTrkSAuuJJVGhM0qWu3y4Gld7UHpyBuJwBfwtZNZde5uFEqKd8rFLWtWbFK9H/RaLQ5iYhiGYRiGYZidk5cHY+Yy2Td+6HH8xFvvmeOq5ospjbXEje3OzeIALgN2Tys5g016WtuB7/SL0nNndaTJaZXSdribnWt9/dxBnJr3KckyhF4urKsEKSUH1z1mGi5ZTmvdsaDjFbBoZRiGYRiGYZidY5cFz1oh/NFHL+O9D16Y04qaMYhSvPPTTzV6rHEZLafQHv1yUMfexCVO66QQoTGnNZveaT2/OcT2KBeMtpM9SlK89vc+gR/5g0812lY/SrHSUqFQVCJMTuukkTeUHOyJyaN+mnBp20owblIe7HF5MMMwDMMwDMPsGFu0plOo1k+dXsNT6wOzjXROczCb8u77nsYP/M7H8eTlyYm2iSXYCLc8eP+d1o1hjJ/788/ObTQLkAcC2T2tQL3bOkoytAPPuIRJOn1P6z/4tQ/jv/7lg+Z7+/iOkgwPXdjCZ57eKHuqQ5pJREmG6492AQDrfRKtGUI/X2NaISCpPPj4Smsu6cG202qXBz96cRsPPL1pvieBzHNaGYZhGIZhGGYO2Nf70/S1/rP/9XH8yt88DEA5tPMIupkG6nXcjpIJjyzvabVFzCyjWObN+x+8iF/8ywfxGUv87BR6T7KCaK17r8hpDb085IhE2KihoL6wNcIlq//TPr5RkqE/Sp0e1SoGWuxef6QDIHdak1SlBwdefRDTxlCdGydW2nNJD3Z6Wq0Pzuv+7D786B/lzjGdZzzyhmEYhmEYhmHmgO20TpMgPIhS46BlUu55+jAJgyalvVTKaYtW29FcBKc1LnGDd0pknNbM2W7da4w5rQ3Tg+1jGCVZpZM9SjJsRwmu9CNMoq9vSJBoJaGbZGpOazBh5A05rSdWW7XlvE2x04Ptc2ljmBSCoki0Lq/0W96VMwzDMAzDMAcOV7Q2f14qpTPjda+dVnLXmrikpp8zLRdui5AeTCJ6HoFBxW2mmURmlwfXlHIXe1rjNH9vq0Tr5e0In/tv340PPHwRgHpPbCd7aD1vlKToj1L0o3RiABa56dcdUeXBa4MYUqoe29AT+VieqiAm3dN6rNeaT09rRXrwKMmwOcwdfzpePPKGYRiGYRiGYeaAbZBOUx6cZrm7mmb1Qmg3IOesSfIvCRZbSCULlh6clJQw73ibae7e2iI9rnEdi+nBSZqZ0to60TpKMpy+PECSZmMzcUdxalzRYZyZku71CSXCfS1ab7DKg1MjCD34E53WBIfaAdqBV7vPTbm0PUInzOfXEqM4xdbInSMLAIG3vNJveVfOMAzDMAzDHDjsC/5sCtGaZdIIXinlVCFO84Cc1iYuaWl68ILNaTXCeo7in0R6mk0XxDSWHkxOa1ou7imNeJSkpiTZfo1RkuFINwSgSnbpV1cmiFbqab32cAdCAOv9yHExbTe4jI1hjMPdEIEvaueqNuXSVoTrtesbO+5xhmGcWW65ei2e08owDMMwDMMwc2DW9OBU5kJof8qDp3BaTb9o+cibRehppR7QXUkPTgtOa81rRLqnNbRmoCYTelpTI1oz02McF47vYS1a7V7WSX2tVB7ca/k40g2xNojzflHPM05mXU/roU6A0Pd23NOapBku9yNcd1i5vvbxHOnzZ3uk/qU1cnkwwzAMwzAMw8wB+3p/mjClLMsfn8npSovngUm0ncZpLfS0tgJ1ab4I6cGxcVrnt5a4qqe1idNqyoMn97SSHhwlmTmW9mvYovWyNet0bYJopfLgbsvH0W6ItX5s3nfHaa0QpJvDxIjWnTrYV/oxpMxDoaLUdVoBYHNEQVHc08owDMMwDMMwc0PaQUxT6KVU5uXBmXZd5R6WCMemp3XG9OBMYrUdAFgUp3UXyoP1tlJZdFonpQf7VnlwZsRqVCGo6ebFKE7NY+OCqDvcUcf6yrbttE7qaVW9r72WjyO9luO02unBVSFLgzhFtxUg8MSObwZc2lbJwddp0WqXB9P5szVS67Xd4GVleVfOMAzDMAzDHDhs022q8mAriCkXr/NcWT3GaW1QHlyaHpxmWGn7AIDhAgQxxSXCeqeQWE8y6YygqXNaoyTV5cGUzCut0KsJojXJzPtBIllK6TqtU5QHkxjstgKstn30R4npr235wioPLl/XME7RDT2Egbfj9OAtnQ58fKUFoFAerI/LthatSSrhewKex04rwzAMwzAMw+yY1HFam13YZ1Yvq/3vPGZhNoXEXZMQpbJk3jiVWGkp969JiTHx1PpgV0qhKX15HqNZCNpWmmXOmuuEsZnT6uVOK22nsqc1s0VrLpTVa6nRSBTEdGU7d1fXGqYH90If7cDHKMmcZN5JQUyDOEU39BF6YsfpweQy9/Q5E5t+4XwGLo29idPMHL9lhUUrwzAMwzAMszA45cENndbU6mW1n7eXfa3xDE5r7LiNqnezFXiNndbNYYwv+Zm/wZ/d89QMK64nT+jdmbjqRwk++PAlZ1vFIKaq90lKiShVx4Wc1iSV5lhX9rTqzY2SNBethaCsIyVBTNP0tLYDD6MkNWsJfGGEYdX+DOMUndBH4HuQO+y7pn0nd75sTnBeHizN8VtWlnv1DMMwDMMwzIHCSQ9ueFGfWqWfahvTPX8e5KKogdNa4mImmSrh7AReY6d1e6RE2cXN0Qwrrmde6cFv/cQZfMcbPoR1K7SoOPKmyhGPU9Wn3C6OvKH04Ak9rcPYKg8ujCQ63HGDmFZa/uSRN1EKIdR6OqF2WvXaQ98uYS5f1yBSonXS45pAIrXotNrnnykPzrKlDmECWLQyDMMwDMMwC4StX5o6rUVnlcqF99Zp1aJhqvRguzw4Q+h5aId+I7cWsHtE518GXTen9Y0fehx/9qlm7u76QKXcbkWJEcBp5jqtVSXIJErd9OBcKFanB+eud14eTOXb5U7rjce6E53WQZyiF/oQQqAdeBjGaZ4e7An4E53WDN2W74zvmRV67kpLOa1FJxmwy4PZaWUYhmEYhmGYueH0tDbUnPl8Vnoe9bTuZXpw8/LgPD3YdZUDX6ATeo36Yuk5xe1My/mNYalYS0pKmIk3fvAx/OHHTzfa/lCX1A7j1ByjVDYbeUOitB3YQm9yebCZ0xrnScNJ4f1Z7QQQIu9pvfFot0F6sEr/VWvyMEqyPJnXt3paS/YnzVSpcyfwJ6YMN4H2q6cTp6OCkwy46cEh97QyDMMwDMMwzHyQM5QHk67KFqA8uIngzNOD3SAmVR7sNx55E88hLOmf/q+P49//6f0AgLufXMMbP/iYs7Y4Gd/20BKDkxgmucMZV/S0lok8IBeYrcBDQD2tWR5+VFUeTKeQE8RUEHWdwEM78BClGYRQo2MmOq1Rgp52Ntuhj1GcOTNQTXpwybry5OF8X3bitEYFpzUucVopYThJM/Oay8pyr55hGIZhGIY5UDgjbxqKTnIuSazIfQhimmbkTVLikCZZhtD30A69Rn2xgCV+d1AefGU7wmU98/MPP34a//ldDzhrLNv20Jp/OomBcVqzyp7WqhExudOapwc3clrt8uCYRt64oq6tE4ABYKUV4FivhbV+XDvbtx+luWilIKZkPD24zOEfkGgNfbRItO7g/CSRSk5r2U2TbT1XNtYu/jLDopVhGIZhGIZZGLIdpQfvn9MalQThVFGWHpykEsGUTmtZmfG0RGnmiEDbDaXfFxnGKUYNXUISayPbac2kUwZetX46luPpwRN6Wq05rVFa77QCQK/l41ivhSST2NQltVX70glz0ZrJfP9agXCE9dhztXhX6cFUHrwDp5XKg/V6qDx4FI/3tCZpZoTysrLcq2cYhmEYhmGWklGS4tNn1sd+buvMpppzvDx473tac6e1+ZxWe+xJYnpapxCtpjx4Z4E+JICiJHdDjdNaIsBGyRTlwXpfhklqtlmc09qkp9X3BITQ5cFZ/bHOrJ5WCsbK5+jm4rEdKim00g5wtKeCmdZr+loHltNK4pX6RgMvD4sqc47t151HeTA9Vwl6UZpebY+8YaeVYRiGYRiGYabk7Z88i2/47+8f6yNsEtBTZHxO63TPnwem/LSB4LRFTe5sZgg8zwT8NHvNnYvzKMmMazpKlSCUUlourrsWKaUWrc2ENYm1QZQ5At0Ww1Xize5pBYDQ8xCncmJPKx1eOz04LQjddugZ95GcVsCd21qkWB4MWKLVr3dayeFV5cHVj2sKPTf0lVguivLDncCMvIn1ubXMLPfqGYZhGIZhmKVkfRAjzaQpYSRmKQ/OjHPp/ru3I290+ekUc1rV8/KS2WmdVtq/KvHWhDiVpi+TnE0lDMuTiUn0NX1NKp/dHOYOZpZJR7inmcSD5zbxwYcvlb4WCcRAO4rRpJ5WuzyY9imj8mDteAaFntYVGoFT47TGdnqwei4Jw9D34HkCnig/70xPa8u3xvfMfn7SsQl9gdAX5n2in59YbVvlwdKkLy8rLFoZhmEYhmGYPScpiAhiliCmNHNFal4ePP/5pVXkc1qbOK3j/ZzU09qeYuSNmdO6wxRaEqC5gM4qnVZ6v0gMnt8Y4q7HLldun/aFHElPjPe0JqnEL/zlg/iHv/5h3Hs2Lxm3e1oBNQtVubT1Pa3SEq3k1haDiuzy4F7bx1HttNYlCPejBF39HHru1ig1awOAwPdKxwQNTHmwPRpnZ+XBLd+DEAKhnzutRrSutMwxTzJ2WhmGYRiGYRhmakhEDAoiT+4oiEl/n+2905pMcP+cx2a2YMuFYuB7aAd+8/LgHY68kVKFGhWDjeJEVvbLjgqu7Bve9yi+49c+bARSEQogItevE/pj6cFJJrE9ShCnEv/X733SPCcqOK0kzpIJDjP9eBTn5cGZVA6vSQ+2gphWWgFWdQpv1X7QvvSsOa2A67QCSrympeXBeU+rSQ9u+D6XESVZXjbtj5cHn1xtm32JUokwWG7Zt9yrZxiGYRgGg6g80IZhFhlyGEmgELaYaWpEjZcHj29rt4kz1+mqw3FarT7P0BPohF4jt9bezqyjU9JMQko4QUxqe7kwLJYHkyiin2+NEkRpNlbaW3w8idZ24Jme1pY1e3UUZzjSDfHQ+S285eOnAVSVB+fpwUXxS2Ql5cHqdaTrtOoS317LN72qxfPRRpUHU0+rWx5M7im5wVXHoWsFMTXpRX7Th5/Ad//6R/CTb/s0zm8Mzc/jNDMlv2EgxoLATqy2sD1KVH9ymiH0uDyYYRiGYZh95C0fP41v+uUPNO6DY5hFgMpPi06rUx48o9Oa7UNP6yxzWoHcbUtSCd9TTuuwYchRnvA7m2MXFR1Wq0y4ujzYfQ6l8773sxdKX4P+Lm2NVK8ope7GaWZKbJNUYpikeN71hwEAa9uR8xotXz0n8FTprb2mMmfbTo+mWaXq+8ysx3Fa24FxULdH5cd+SzvBR7qq95XWvmmlBwOqPLisLJ3EcLeVj7xpkh782x98DB959DJ++4OP4933nTM/V6I1D6jKRy6p1zmx2kacqtCshNODGYZhGIbZb7a108GilVkminMzCSeIacqe1uKc1r0ceRMXeibrsEOIbHEY+sppjdNyB7FqO3UptGfXBpXuYZy4ZbYjS0DnQUwVPa1pppOE1ffvqRCtAyNalbjratE6SjLjVioxmWGl7UMIqz+YSnlDKoMVztqAetEKwAn6SjIljluBCk1qWXNafU+gHXjoR+Xlwaev9AEANx3rqjXptW/p7bes8uCycu2BFQAVejTypv49TjOJRy9u4xvuvAGA2/89qiwPzntaAS22den5MrPcq2cYhmEYJi8R3EESJcPsNSZtdw49rfmcVvd5TUXvPIhndVrT3BUOfGFEXZObUCbEqaaO+hv++/vxq+99uPR3Rac1cpzW8nE6dvlzlGbm+ycu9/HYxe2x1xgUy4P1/kVJZpzORPeatkMfoe+NpQMbQahdTDXCRTmHo3T8ONk6e2OQpwEnqcQozl/XpAfrftaVdoB+hcA/fXkAALjpWE8/1+1pnVwerEfetHyEgdDrqb/BcXZtgFGS4Xk3KAfaPvaxVV5NZdPqMUqUH+qQc5w4pdjLynKvnmEYhmEYc5G/k0H1DLPX0PlaWx485ZzWYk/rXjqtieV6Tlq3HdQTa8cyyVR5MPVWVoknZzumPLj89aSUuLA5wrmNUenv7XJg919p9bSWO62AnvGaZDi5qly9otsqZd5DmgcxKfkRWeXBqRaTnUCFFJnSY2umKqAEIa2NhOYkp3Vj6JYHj5LUlCjTdldaeW/rdkOntWPSgwui1fdKxejAKkumUuJJvcgPXdgCAHzOdSWiNbHKg32rPDjO0Ak8c3w2h4kj8pcVFq0MwzAMs+TQ9dFOZv4xzF5jelrrgpia9rRWjLzZ0yAm24GcEMZUdFppnaEnzBzQukCg4naqbliRyNmuSMSl52VSuX52b2tiCVibMdEap3jWNas4sdLCZ57eKH19IBd3He1uKsdT97dmEsM4RSf00AryUtei0xpqQRinmRGapaLVOr5Fp3XoOK008kYd817LR7+ip/XJKwN0Q9+U3ZogJi1yqeQ38ESpGKX9UyNqmjmtD59XovVZ16yiHXiOix+leXlwy/ccp7Ud+jhkpSHHqeTyYIZhGIZh9he6QK8a/7BXjJIU/+vDj+9pSSazvJj04Jry4KandN7Luo+iNZNGBE0qEXbGvViluL4vcqc1rh69km+HxuWU7yeFJFX1aTqBRgXRmru4Bae1pDy4Hfg4sdrC5W13xqktcLcKTusoSRF4Ar4nkGb5dkJfOD2tvieM4Ap8VXobp9IIzbK/e/bh2By6otUuSzblwS0SrUGt03rTsS6EEPq5ntkvTwCemdNaPvJmEKWm9Jsc0knVMQ9f2MaxXojjKy0lWq1+aTs9OLCPmRblq3Z5cJY/dlnZsWgVQjxHCPFJ678NIcRrhRA/JYQ4Y/3871rP+TEhxENCiAeEEK/e6RoYhmEY5mpGmqTM/RWtH3j4En7irZ/Gp8/y+B1mMom5yK4uD57WaaWH70cQU5Jmpo9wUhhT0WklwRF6nhmp0qQ82PS0VoifYghSEbfcVFo9svmaooIAG5WUB7cDD8dXWriyHTuPtW9IkHjsWEFMvidMcBE5kXapa5RkTi9m6OXCrc5ptVOnN0fF8uAMrYDG1pDTSr2tfqXDffrKwJQGA3lv7vYodVzMwCtPDx7GuWjN04Prz8+HL2zhmadWzeuNCm6+XR6cC/0MndB35s4mqTSPXVZ2vHop5QNSyjullHcCeCmAPoC36l//PP1OSvkOABBCPA/AawA8H8BXAfhlIYS/03UwDMMwzNUKXaBREuh+UexDY5g6qIRyvKd1liAm12GVe+y0pplEJmGEwmSn1RKLWe5qBr5AL5w8L9R+XaBa/JDTWSWA7eeN0jR3WpOGTqsuD26HPo6vtHBp2+2dtfdhW3/dsYKYSLSOEuU2d0IfrcDtaaW+U0AdH3Kgiz2tZ9cG+Ll3PwAppePW26cQifGWFo1UXus6rRXlwZf7JoQJyAVvVJiBSm5wkUGcoqOFNpUSTyoPfsQWrTXlwcqdzoPN2oFnzsWNoUqX55E3Ll8O4GEp5eM1j/kGAL8npRxJKR8F8BCAl895HQzDMAxz1UDXR/E+O63ZhP46hrGhC/aiaE2d8uBmojMxotV93l5VH9A5v2JEa3OnNUlzhzPwhJkX2sRpTSbsJ817rXJai/NOTXpwlq+p+Hl2nNa04LT2Xae1zHHOy4MzU/pL62sHHlq+29NqO62B7xkhTMeJROtbP3EGv/hXD+HcxmjsvCGBGesS6Faxp9UKYiorpV4fxNgYJq7TGrjrMl9XjLwZxqnp5w3NeqrP77V+hItbEZ55zYp5PTc9OHN6fW2ntR14OKznyW4MYiRpZoTysjLv1b8GwO9a3/+QEOJTQohfF0Ic0z+7EcCT1mNO65+NIYT4PiHEXUKIuy5cKJ/9xDAMwzBXO5lxRPbXaU0muD7MYnHP6fWxHsS9hM7XQeQKG9sZa2qUpmM9rXC+323o3DeidUJ5cOqUB2dGdAa+XR48uaeVhH/xs/+hRy4hy6QReJVBTJYIskVyYvW0Fj/PxRLVUaKcveO9Fq70I2ffzGxSyy2lPtJRohJtA0+Y9XX0yJt8fJDrtIaeMEJ4RZf0jvQxoNCiVMqx84belzSTjkNJJb4rbctpLQliOnNFjbu5+XjutAohnDmpRFV58CBOzXtLSb51NxofvqDGB+VOq+/2tCbSKQ+mc5DSkTuhj3bgYa0fIZNgp5UQQrQAfD2AP9A/+hUAzwRwJ4CnAPwsPbTk6aV/UaSUr5dSvkxK+bJTp07Na6kMwzAMc6CgC/P9djhpHZNK3pj9Z5Sk+NZf/SB+8m2f3rc1UHlwcR6pHeTVNNSLHicLYnWvbuTQOd+0PDgpilbHaW3e02puFFni5/6nNvCa138IH3j4khF4VYm4dojRpjUaxhbSE0fe6BTg4ystSKlcyeJjj/da5md5eTCFLIk8WTj0EPoinxs75rQKI4SLTuvDejxMlsmx84YEbqyDmGibd950FC+79RiuP9JRj2v5GJTcLCiOuyHaVnmuvcZypzUbC2KqOz/pNW89oefChtXlwYF1zOx05KO9EBe3Iuc1l5V5rv6rAXxcSnkOAKSU56SUqZQyA/BryEuATwO42XreTQDOznEdDMMwDHNVQdeU+y1aJ/XXMYvDp06vYxCnePe95/bNbY3NRXbNnNYpg5jynlb357sNnfMkWicFMblzWvOZqIE/nWgtm9N6pa/ez8v9yJQHb0eJ0+dJ2CFGthtrr6nYnzksKw8OPRzTo2Ds84kee9QRrYXyYM+zRKvuabXSg8mZBZQTTQ70ajvvjZVSGmdS9RcXRKsWuDQuh8TeC286grf80y8wQrrX8tGP0zHR+6R2Wu2eViB3jW0X0/cqelqj1Oy77wkIUf83u18ogy6WB9tBTC0/d3ftY3akG+LiluozvurTgy2+HVZpsBDieut3fw8A3cp7O4DXCCHaQojbAdwB4CNzXAfDMAzDXFXkTuuilAez07rofOTRywCU6Pijj5+eeTtRkuHzXvcX+N/3PDX1c82c1pogpqaiszjixny/R+XBdM7TmJEmTitl9yTWTNTASg8uc/zKtqNeP99PEoqDKMFQC59Mlgtp+3muaM3d3zgpOq2Z83WUKmfvxEobgCtaB0a0huZndnpwoJ3Wbauntdif2QrKy4Ntp/X85sgI31TKsfedbgSkmXTEXpFeO4CUeS8wcfpKHystH8es/VD7op1Wq1809KvTg2nf6XF1f7OHprSako798Z7WwB55k5dU07qUaFXvR8A9rYAQogfgKwH8kfXj/yyEuEcI8SkAXwrg/wYAKeW9AH4fwH0A3gngB6WUk28lMQzDMAxTilyQslzTW7vPgVDMZO567DKeeWoFL77lKN780SdLXbgmbI0SXNgc4fHL/amfWzWnNXOSX5s6rfRcdxt75bQmBad1UhBTmuWlonGWu5qhL9DyPQSeaFYeTD2t1meOeoS3R6kjvsrCmOwbTPbvk1Tm5cGFz/PI2aYqBW4HPo6tKEHniFa9D8dsp9USob6e00p9pO3QR8svpAdXBB5RyW+UZqafFVB/h4qnDfWsxpkcE8LO41r5GBubS1sRTh1qmxmtRNsqz7X3qazsd2CNvAGUAK/7m03nEAlQNae1UB5sBzFZlQu509pip9VGStmXUp6QUq5bP/suKeULpZQvklJ+vZTyKet3r5NSPlNK+Rwp5f+exxoYhmEY5mqFXIVov8uDF2T0DlNPmknc9fgVvPz24/jml9yEB89v4ZGL2zNti8TFLDdMjNMajYtWciGbis7UlAW7QUx71dNKwo7KUJs4reSgxUnuavqeByEEui1/qvJg2w2lmwCDOHVCrsrCmOy/GfbvI9tpLRxD22mlPlhKDwYK5cF6XbbTSk4yADPyxpQHB6o82EkPtp1WS3jZTiv1swLaac3KndYkzZ3hMmibxXOyHyVG+NqY8mDPXWNZefDQCmIClACvmyNMTiu9Rjv0nXLuuFAeHGe50LedVno/uKeVYRiGYZh9Za8v0KswF9BL7LRmmZwoOJadB57exOYwwefddhzXHFIlnU1mgpZhROsMjmZilTPaZDIXAU1P6f2e02qc1k7z9GASrUmWmc8MOXa9lt/oPcmDmMYTe7dHidN/ul1SblzttGZm22kh2GgYpzikBZwRraFn3FTqqQVgypPLyoMBJdIDz7PSg73x9GC7p9UrcVqTzPSzmvUWe1r1evM5rVWiVTuthWO1NUrMDQkbSjZ2gpg8r/QmzqCkPLjuRuMwzhD6yokG1I0B+/2MU2lG5zjlwXFmUpGP9kJrBvByy77lXj3DMAzDMAszH7UsFGbZePNdT+JV//mv93sZu8pHH1P9rJ9323FzQTyruItSdRE9y3tO52tRnEkpjXhrmh6c97LC+XfPe1q1kCqGSxVRTms+q5PWT72RvVaA/oRtAPZnzuoz1cezH7nlwWWjXGyHdsv6/bBQmmrfiBomGQ5pcb4xzMuDO6GPlZaPS1vjPa12ebArQlVPKwnkdqBG3uTlwakzLscuwyURGaWu05pl6rwRIh8tkwcxTe5pBcbHDfWjFL22P/b4vDy4MKe1cN5KKTGMs4JonVQenM91pdeiGzxSyrHyYLq5MNQjiADltNqvt8ywaGUYhmGYJccEMe2Rq1RFuiDieSc8cbmPcxujxmJpGXno/BYOdwLcdKwLj0TrjOJutBOnNSvvaU0zacRG0zmrxTmtaeb+u9vkolWJhMk9rVZ5sE60BWBuInTD8tErVa+byVzg50FMqRGwQLnTWlUeTDcSqAfTvikxilMc1mJoyyoPBoDjqy3XaY1TBJ5wSmttEUrlwfbvWoHI04Nj12m1xWavlfcPP3x+ywg0mtPqCWHWRYIzybKxkmOblYrk5u0Kp7VjRtjUj7yh88Huaa0ajUMMLccUcIOYyFUtzontxymkzNdli1YOYmIYhmEYZl+h6/JiyudecxBG3pDD01QsLSODOMWhTgghBHwxnaNZhC6i0xlKwpPKIKb8IrypmLbntNrhTXs2p1W/PpWsThKtSSYR+h48oUOP0jyICdCjV6boaQVyN9SUB0eJcUyB8p5W+7NqlweT8O1awtr8znJaN4uitdfCpUJ6cDf0HaFqi1BfCEdMdXQQk50e3A5dF5MIfYHQF9gYxDi7PsSzr101xySVEr4QRvStWv2vSSYrRWu3IoipH6WmdNimXRCNgCp5Lt7EyW8CuCnDxfLgYZziij5+o9h1me05rXR86Hyhf4s3Eeyy7ICdVoZhGIZh9pNFSe01QUxL7LTSReEBNlp1b10+LxKYzSkFcpE/y42KyArbscVXNkt5sOW0zjLndafEppzWR+CJiX3RaabGvYQ6QKfYd9g0iMl+34o3AQZR6pQp90vKgyOnPNhyWmncSotEa8Fp7SgxtEnlwVocHl9pGdEFKLew0/KdMlfHafWFI6Y6oe8k4Y6sUlfALcMNAw8t38OD5zcBAM88pUSrOgdUeXDutAbOflU7reXlwdujSUFMrpgu/i02x9NJD/bGbqr83J9/Ft/2+g8CgFPmq17LM6Xk9L5ReTAJ/zzNWX1/2HJaq/p4l4XlXj3DMAzDMAszp9WI510WrWkm8aFHLu1KCS8F6Bxkp3UY5SmmJFpnPZaRcVpnCWLKzxNb5Ek7iKnhqZRZ5cDunNfm52KSZjOHcFFpfugLPZpkgtOaSvgkWhNpbvSQ+GkcxGQdIBJAJFT7UYpBlI9ZmTTypq482HFa4xQr7QBCjDutx1ZabnqwvkFCYo32mQi8PGiIthMGuQNZDGIKbXHoeWgFHj57TvWzPuPUCoA8OMr38vLgYtlvZRBTm4KY7PNRTnRaA8dpFUgLf4tJtLrpwePi9vFL23hqfaj2vdADS8chSvJycgpion/zYCwdxNRlp5VhGIZhmAWBro/22+EsSzLdDT762GW85vUfwq+//9G5bztKD75oHcR5wIu/w57WPD14tvJgen1boKWZ5bQ2ntOalwfbz5nGQf4v7/4svv31H2r8eJvEiE4P7dB3ApDKoH0kVy53Wkm0BujHk3tak7LyYBPEpMqDaRRNeXlwvdNa2tOqR6q0fG9MtJ4oiFYSzVTiS8FLhCdcEZuXB+eOYpXTGvgCrcDDhU01h/T2k9ppzeyeVrX+PGCp3mnNR94Uxv9kstxpLUkPJvfcZljitAa+h6ggbtf6sXn/hklaEK3qtUZJav5O5SNv1OubebclQUzc08owDMMwzL5CF+n7ndprUox3ubeW+rb+y7sfwOOXZpsvWkXutM51swvFwJoX6YmdpgfTnNYZ0oOzvDfS7mvNdD+iENMEMeXPtZ9SdLzqOLs2wBOX+40fbxObnlSvmdOaSTXuRfdv0o0eEhbdhk5rWlMe3NflwYe7IVq+57iH9rrJhSztadW/iwpOaydU81Q3rfRgQDmtaj6sK7zywCLPKaW1nVYa70KCkoKj2k4fqFuGS489vtIy/Zs0p9UTeSkypTr39T5WOa0k0u2eViqrXil1WsfntPqeGPs8lYnWVkl68Fo/RqLFukobdntaAXXTgG4WGafXI6fVfT84PZhhGIZhmIVhYUbekHjeZcVnlw6+9s2fNGM35rntA+20RqlTrgnMvr87Kw+WRrTavZdSAp6nQqKabpfKgKmf0fx8iv1KsqxRH2kZdjBOJ/QbpQcHnkDoqfmaSbE8OJy+p9WMENKCmURrJ/TQa/ulTusoydDV7uJ2WU+rNUuWGMbK/WwHnjOnFVBOKwBc1gnCdK6Rsx/6wtwoAVRPK4mptvUYwA4Vsue6uo4mic9bT/ScGzBSSnie5bRSr+qEnlbfE+iGvtPTSuK5V+K0kqi03WN6T+1AsEFUkh5c0tO6NtDHLU51P+94efAozpybJIBVHmzNuwWKonW5Zd9yr55hGIZhmIXpaU32SDzT9n/k7zwH95xex7f+jw/i4tZoLtumnka5vFlSExklmbl49s2F/mzbMkFMU4pWKSWSTOKQHhFDF/WAOp89oYRrU9FJ68+k65JPI6aTVGIQp47YaPxcLeoCclonlAcnuucyDDwkuvxUPd/qaW2wFqenlUbe2OXBuhR8pRWUjryJUyVAW75n3MWW7433tCZUfi2Ne9ryPWxFxbRaJVopjGlYSA8OfM91JYWAr7/vmFJbtz/TndPqft3SQu62EyvODZg8PZh6WqnsN3Veo4yVtu+40nTzoGzkjRHa3vga7XOvWG6tHiecMmIpJa70Y7POMafVLg9O3PJg6vWlGw/GAfY9rGqxzT2tDMMwDMPsK2bkzT47rXvl+JKL9fWfewN+/R99Hj7z9Cb+4K7T89m2dqn2KnV2P7DDeehae5aRNQAwSmcbeUMCyzitiVse7OlxPE3fBjd8abae1iRTpcWTXNIyYmtkjRKtk5xWlR4cGKfVdc567QBSKldz0prN18ZptcuDM3RbPlbbQWVPK5XZUnnwSts3bq0RrVmeEk1zQFuBZ94fEkl5+q4uD9bCy5QHewK+75YHk+CibZALmifhVgUx5eXBtxzvOTdgMgkIa04rjSKiY1DltALjpdn0HAppssmDmCz3uCSROy+3zl/XHu2jHpOX/do3HIqvNUoyUxFSnNO6NRwvqSa3lZ1WhmEYhmH2lYUZeaNffrd7a+lCrx14+KI7TkIINzhlJ1wV5cHxeHrwjp3WKd9zeg8PdchptYOYlODwRHOnNC0Rb8B0qci0pllKhBOnp9V3yp1LH59Z6cFpZj679H70TNpt/XntzGkt9LSOkgzbo8QqDy7rac0Q+p7T79hrBWM9rdSnTjcX2oHnCD8SSd3CuvM5rbnzZ/e0+p5n9rnKaa0OYvLQ1t/fdrJn3YCh9OBc8HZCH0JMDmIClPC2BX690zo+p5WOZVLitNoCPPCF87eSSoPpNYdxZlKAgTwReJSk43NajdDX7rT1OiRa7eO+jLBoZRiGYZglhwRWlOyv0CK3Ldplp9UujRM6fbSYwjkrV8PIm0Gc97TShexO04On7WklgVUWxCSlEhxeSaBNFVXu6jROK21jklAswx5Z0wo8Z/5p1WvRnNYks5xWCmIK3REtVbj76qYHA6q3tBNqp7Vkv6JEolUQoPa4nbyn1R2n09ZOK2HmoWrRSs+nGyQdK2XX7ksNfGGEqHFaa0RrVRDTrYXyYHLr6bntwEPoeea9bde4jr3CjFwSgqUjb0zAlCvEATcEjJz3dqHU2XZar2znvfl5T2tJebDlyLYK5cEXt5Twpc8VwE4rwzAMwzALAl0b7bvTukcpxnGhNK5YZrcTTE/rAdWsNEaERASF18w6p5WO17ThW+SGHi4JYjLlwZ5o3F9aNeZm2p7W4lqaYkSr7+n5m5N6USk9WDhOqz3yBmggWq3znm4EjJIUpAvX+jE6oY9eqzyIKdJOK32WSHQXezBN+Jm+qdMJPEcEkXjqFeahDmMVJpQHMblOqyeE+b7otJJYbBfCiwh73bedWLHKgyXSTI+80dtsBcrRpXWFNU5rrxU4Ny7o69KRNyVzWknA2v2qo9L0YM+pUCg6reNzWvPy4OLfQNqfxy9tQwjg2sMd8zxKVWbRyjAMwzDMviJNENOClAfvsngeCyHRF/7z3PasI2AWnWFBjOTlwTuc0zrl8c97WkNnXUDej+iJaYKYLKGazua0ksiYqTxYv07L93TJb/3r5unBeuSNfvws5cEkGE1Pa5TimA5EAlSp6Eo7KC8PTjKzZkCJoND3xnowE0sQA/k8VUCJKaEFoykPjnPR2m358DyBlhb0XmHkDQl1Emimp7W0PNh9bsv3cKgd4FgvNNtVY48kPKs8uB34CPxctFaNvAHGndbtmpE3dq8uUfaZGhVG1ND67c/NWj93WrdHCaK0GMRUVh6c32wAgMcubePaQx3HBTflwRzExDAMwzDMfrIo6cF5efBuj7zJQ2/Uv/N0Wg92efCg0KtoxoTssDy4iThcH+QX5XlPq5vqCljpwUI07rVNHad1tp7WvDx4Bqc1yZ3SJjdRkkyqcS+B6muk1yYR0i2U2dZth9zERI96GcQpTqzmorXb8irLg+M0QxiIvMxU97fS54BubpC4omCojlUe3HZKi+n9TJBoMU7b6IQqOTgoCLzcaXVLbfOZo+Xlwb4n8MXPOYVv/bybIXRwF6Cd1kJ6cOirUuxGPa2FY9WvGXlT6rRqN9g+B0ZxCiFcsRz4npO6bYvWK3pkkOO0WnNaR2M37tS/F7ci3HA0d1kBqzzYW27ZN370GYZhGIZZKuiCd1q3a/7rwJ6sI9LuELk7oe/NrZ+XRNgB1azGQSvOaZ3ZaU2bOa0fe/wKvuV/fADv+ZdfipuP94xzZ4KYrJTcTAsO32suOrOKkuAq1/8TT1zBO+99Gj/21c81PzNBRrOIVhpZ4wk9f7NperCHrVQJPCHKnFa1lkwLsWKJZ5opUbg5TBCnKlU2k8DxFddpTVtAvyKIabUT5KX2hbLfTqE82PS06jmtgFu+SwJ1EGXWrFfPbKvY0+p7+cibtlXuDwAbZuTNeHkwff6//eW3ONuiY5JJddPja194Aw53QiVqPdEoPbjX8p1jRU6rPa6GyEfL1DutwyRzHGlACWn7PCGhCuQCtqqnlVzltvW+ETce6zlrvO6Icl7tftplZLlXzzAMwzCMNfJmf5VWtkdlynGaORdprYCd1qbsVnnwpOd/+sw6Mgmc31TzdElM0igSO4gp0/2IvhCN3wf77W/S0/oX95/Dr77nEUc0pDspD9ajYygYbNJn0U0Ploh1uTDRK5TZ/tJfP4Rv+uUPjG0nTvO+xziVGOp5tydW2+YxKojJR5RmYwFRUarKi0koqhLe/LNFjm9eHlzvtPq6J7YfJ3lZrXYolWgtzGn1hHFPx8qDR9XlwWWlrnZ5cJZJeJ7AC286gh/80mcBUCW8dJ5NUx7cjxJ0Q98R24Rxcr3xNdrnwEj39toUzxO7EoHm3Lo9rZPLgwHgxqNd53W+/eW34O0/9IXOtpYRFq0MwzAMs+Ts1XzUSSRmHbtcHpxkYymi89j3LJPGUTqoPa2DyC37tBNXZ6FpefCTl/vqcak7Iqfle+iE3lgQkxCqr7Vp2XLVnNaq95FOF1sskzAbzBDElGTSiLEm52OeHqwem1rPB4CuVWYLAGeuDHD6Sr90O+RkJmnubp6wndaWb8p2i2FMqjzYc5zWlvXZMnNaC05rJ/ScnlYbSh8m0blqRKsSxLb2CzxROfImnzlqzWktEWmEO6dVoviQwM/nytY7rQEGcWrOne0oNTdXipTNaaU12i4/zat11+OeJ1e2I5zUNxsum/Jg2/XOy4Pzvv682oS48ZgrWjuhj8+57nDl/i4LLFoZhmEYZsnZK4dz4jr2aF5s0WmdV0+rParngGpWq2RTi1axM6d1ZMqDJ4hWLbjSwg2WwPfQDd25plKXdvqeaFwePO3IG3JVbYEam5Ex04+8iZLMcQEnpgdnlB6sRt7EaeYIn15h5E2cZaXbTLK8ZzTOpCVaLac18IxwLPa1RsUgpoITam8bqOppdQVdL1RO5XZBtL7gxiN47nWHIKzEYE+7zfZ2zJzW0XiJLD2vLAnXzGmVqkfYE65qtYVundNKfdZ0rPqjpDQ5uGzNQH4jKHFG3pQ4rZ7nvKdrgxgnV1to+V7utAZlTmtJerD1+jcVnNaDAve0MgzDMMySQ9c9044dmTdGkOzyvFjltLqidR7hTyOrdLLpqJVlY1BIhfXmVB486UbFE5cHAHLxE1thWp3Qd/pIUykRageu+LZe3Brhjz9xBt/zytud/kAniMkp+S3fL/qsOK+bjgcxFXuAq0iy/JwMfc8EM1VRdFqT1C0P7hZ6WtUYlxLRmkrjRCZpZvbnuBPElK+9WPoc67Jm22m1xXNHP5f2x+5pNaK14CB2C04rCb6f+9Y7zWM8TwD6GJDIy8fTqO/L0oOLwUM2dAMms3pabez9qnNaSWRvDRMc7oTYjlLjVBex58/ma9Si1e5pLXFaQ99T/be6lHmtH+FoL0S35eMK9bSGnrNdIfJQJ/s42K9/wwEVrey0MgzDMMySQ05rsV9trzHlwbs98mbMaRUTRUITaJwHcICd1qg8iGmn5cF1oldKidOXyWl1g5tC7bQOCuXBnhBQusbd7ls/fgb/4c/uN72x5jnW69vl6dXlweOlwCSobWH3z3//k/jnv//Jyn0jklS6adZ6P//8vnP45l/5gLM+qZ1A3xp5k2TS6SVtB6qMlt6vJJOlbrbttCapLC8PDvLxNMW/ETHNabXEj+1Cmm3r/XF6Wn0aJ1MsD1ZzTovlwTYk0J30YO0k0nY3TU+rFcRU09PqBjHJsR5U20GuFa3aaaX196OkdNwNANx0rIfv/aLb8apnn7LW4el1WOnBJU4r3Tii92ytH+Not4Vey8/Tg63nCCHQDjynPNi8b04Q08EUrey0MgzDMMySQxf2++207lWZMpU0Eio9eA7lwclkh27ZGQtiEuOu0DSQ0K/rY17rx0aA0OPo9QJPOa3FOa2entNaLA+mvs7i+13VxzrJabUFalLo21SvN6jcL5s4Lfa0qu3fc2YdH3v8CjaHCY70QmdNNKM0SaUKcrJElhACK60gd1pTWepmp1nu4MVZZtbuzGkNfSOio8Jnk6oWqpzWrhXyZB+bTlBdHtxtlZcH25CgtEfgmJE3AY28SSCE6yLmx7isPNgKYirtaXVnvFZBzvCmdnq3RykO67ExZfvxE1/zPOdn9D46QUzJuNO62g7N66y0A6wNYhxbCdENfVzYUjdl2gWHvx34GOlS9MDLZ95SENSRblh6vA8C7LQyDMMwzJJD16H73dOaj97ZXcFX2tM6B5Fplwcf+PRgmtOqD+M080xtmjitT1oBQmU9re3QM72SgHIiPT3+pbjdM2tKRBZFdtWc1sqeVhJhtmgtEbK2+KqDymwBJaxSPTOV9nNtoJyzRy5smdfxfUoPzszcVptuy8cgTszaMjn+Po05rREl9vpGJHVbuZNarEiIU6lKfamnNfBLR97Qfpie6FYe2FQaxBSPlwfb5E5rPuO0XejP3BzGpWNi7OfbOHNadclt2Wu2CtsscqjthlbVOa1l0P7aacDDkvTgQ8bRjSGlxFo/wpFuC92WbwRz8dgqpzVFnMpCi4Tan2Jy8EGCRSvDMAzDLDnUf7nbYnESuSDZ5Z7WdLyndR7lwZHT07rjzS0kg4LTGphSxtm2Z+a01pSEP3k5dyvjQnATlaMWXW7jtMqi06pFa7obTuv4zwZRaka31OH2tOZOG52Xa/0Y955dx5f97HvwscevAIDV0yqRZNIZmwK4o1fo+BZFeJJKR1gOk/z9XdF9mG3LFS1+NumzRO5myxfOZ6sVePC9POV2EKVqrI1v97QWgpgq0oNtqITWL3FaabuqD9TdduBXO62mPFhKE+ZV9tx2TQgTMF4evD2q7mkt47aTKwCARy9um5+VOq36dTaGCfqREqLHeqEZdwSM91K3Qw+jOBtLUKd9P6j9rACLVoZhGIZZeoru1UFfR5xIpzy4FYyPGPnAQxenLhm+GpzWYnowmVFNR8sUaTLy5onL404ribDAU+JnlNrHXpV6et54b/EZLVqL4qsqPbi6p3U8PTgxP8ud1X6UjCXulhGnckxQxWlmRP3aIDZjf8gtztODMyRpNtaDaadi034U90eNvKG+U+n0LJObTvNRaU32c9NM6p7WXDAWxVDgCWccUDf0IYQondNKr0cOte+JMbEG5K6nKpH29PPyBGOiuG0qvQ1Lelo9K4gpzaRxXouvWdfPCrhBTIBKEa4aeVPGkW6Ik6ttPHJhy/yszGk93Mlfh3pYVRBTLpCLx47Kg1Vfv9vv2g193HycRSvDMAzDMAvKooy8SY3ju7vrGOnZkkRx5M3pK318xxs+jHff9/R027UEzKwibtEZRip5lMSAoMCjnZYH17jrdnkwiZ/IclrbgVdwuXV5sHDLg9cHeW9s0dktm9NaVl5s1lGSHlz2s36Uoh+lE9OkE7s8mOZ0plZ5cD/C5W1VLkqln4FH5cHSEb2Eb4lF+rcYcpZkmUmYTdLMKf8mx67b8s3a7J5WWlsYCMtp9Zx1GCfcKg82jmjdnNY4xdZQldWWleKSQPfsICbqaXVEa7nTWjxW9jZpTmvxZetcWhsjWqk8eEqnFQCecWoFj1xwndbicbJ7Wtd0WvDRXsuMOwJKnFYqD04yZ54uAPzqd70U3/+qZ061zmWCRSvDMAzDLDl0PX3VlAeXBDHZr0kXgCQOGm83dYXTQWQQp+gErpBQo2V2JlptMSWlxJs+/IQRf09e7uO6wx0AuTCkGxuBLjONnORmXR7sueXBpy3x28RpDTxRWbZcTA9WPajqd1SSm2USoyRDqv+tI7ZG1rQsgRjp8U/rgxiXt1W4zobudVTpweqxoyQdcw9DP5/jaZxWa79ptAslzMZWenA39I3Q6lg9q3GJaG35nimZDX3PCYTyrbAoQAl6EsPk9I3NaTXpwWllKJDvOK1ub6zvCVMBUBR6QU1Pq101UJYeHDZ0Wlcs0RppV3OanlYAeOapFTzsOK3ZWBk19bRuDuNctHZDZ0RReU+rWlNY+N2rnn0K1x3pTLXOZYJFK8MwDMMsOSQ4ismge76OPRp5o4KY7HAWz9l3cpts57QJo9gtUT2IDOLUuSgGVFnlzHNaC+WrAHDv2Q38+FvvwV/cfw6AEq236z4/05vpOK2+8/5RenDRaT1jJfmO97Ri7Hct36t8H4s9rbaYI+Fnlw4X55sWodExgOW0ZpnltMbGad0Yqn8DTxjhsdaPxxxA3xO5yM/Gbw7Q76jvNMkyDKJ8JE1vQnkwCf9W4KYHO/3inudUMgyi1PRDV85pDX0M4wybw9j0bRYpHXljibqqbVPfb5nwtKsGyua0+g1Fa+h76IQetkaJufHSmzKR9xknV3GlH+PKtir7VSNvyntat0aJCeo6ttJyPp/jTquPUazOq9YEx/igcXXtLcMwDMMcQBZt5I2Uuzsypui0tny3p5UExrQi3ulpPaCqdRBlRnQQdWW0k6BjFqfSuNPUn0fpqRc2RyYgJi6UudKM0Khw7D1vPIiJekGB8XPdfpwRxIE3ldNK0PljC9VJCcJJlqe5kghzy4Njc1yoAsAWbPecWcfn3Xbc2WboCyPAi46r/bXddzqIUyNijdMa+kYcx0n+fFMe7OdCVY28odJxVb5rVzL04xSdVkG0lpQHA8DFrVFpcjCtmY4VhYHZ/ZuhKT0ulgdXO6203VSWpwebfWwg9lbbATaHeT/z1E7rNeomzSMXlduqgpjcbay28iCmK5bTSuXBvifGbmS0Q1UeHCVyYpnzQePq2luGYRiGOYDQdXmayX0VW3Z58m7219quFjCeHmxE65RBTFF6FfS0JulYuMtORKsjNvUm6AJ8YxgjSTNsRylOrKq5oWnBaQ08ocuD3RAsT6hxPLbmtGemFs+vqvLgql5bejy58va5S+5asbe1jjjNjKAiMRelltM6iHBpm0SrdloLSb1f/YLrnG06Tmuht1XtJ4VZ5cJyGOdOaK/lo+V7WvzoMmTruNExd+a0+nkQU+jMndUzbKPUiKpWhbAk0XphazSxPNjzBF5w4xF88bNP4VnXHDK/r+qXNQFOFYKNZvtSX3TZaxbLastYbQfYHiXoR9Vje+p4xslVAMDDF7aRZRJRSU+r5wmstgNsDROs6xsaR6z04E7JOuvKgw86V9feMgzDMMwBxHaZdrs0t/E6dlG0RklhTmvg9rRS+itdlP/AGz+G//m+Rydud+TMCi1/zBOX+vi/3/xJjJLpSo8XhWE0Xh7se+OjZZogpUSUZrmzqM+9dctRJFfx+IoSrcZpHetpLSkPLvTauuXB1U5rarm4lXNaTXnweLATua99K0V4UoKwPTeTnMMklSZwat0qFc2d1tzhvPFoFy+66YizzdD3jNOapy6PO63UF6rKg3PRutL2TXlt2ZzWyDitorQ82LcEYmKlLdP5065wWin99vxGtWi1HdPrjnTwW//k5TjSDc3vaT1Fd1IIYUYFlUE3YFJZkh5MvbNNnNZOgK1RYsYdTZMeDAA3Hesi9AUevrBljnNxXwDV10o9rb2Wj3bgm+NX7IEF8vTgOMka7cdB4uraW4ZhGIY5gLhicf8cQtvt2s11lM1pjdLMlKdSXx+5Snc9fhl3P7nWaLtElYj70COX8NZPnDHjS5aNgeXEEcXe0aaokmAYEUNC0jitg9j0b5JoLYovEkzFYy8ETHnwpa0RHrmwhTNrA1xzqK2fP9lpbQVe5ftoRFjklt+utgMjZKcqD7bSg/M5rZkJmFobxLisRavd00pC6qtfcN1Yym5gOeC03tTab/qM+b6HwPNMEBO9H99454344S+7Q6+pPojJLp01gtLsj2cCpfpRYrZf1XdKTuEoyWrKg11hXCSscFppXVWlsb5QNzqyDGPHs64ftgg5oJQgPG16cOB7uPWEShAmN79sX1bbShxf6cc41lOfka4+npVOa5xqp7X82B1UpnsHGIZhGIZZOMpCaPZnHXZf4e46rfYFYGicPonQF0Z0kHM6irOJ5Z3244HqICZyssmBWTYG8XiiazGltykkNHstH5vDxAg/O715Y6DeC7ogNz2a5LR6qqc1TqXpZc0ynR6sSz1/+p2fwR987DR8IfCSW47h/OaoNj2Yvm41cFrJlScBd7gT4Oz6EFkmMXREa/37nWTSOKy2QKR1qpE34z2th/R78dUvvH5sm76XVxCUJXPbTiv1v9ojaT7/GSfw+c84oY5FUCJaE7px4JnPU2g5reSgh36ewjyMs/EgpkJ5sO3kV5YHC9rHKtHqJgo7v/M8c6yL0Pmj0oMLr1ko365jtR3gzNoQFzZV4vMpfbNkGp55agUPnd8yfd/VTmuCOM2M02z3IhdRPa2q7PxQRcjVQYWdVoZhGIZZcuzxLPuZIJw6Zcq757SO9bQWLsgHhSCmUZIZcVKHXfJb1RtMbuKkctFFZRClYxfDszqtVNK7oi+ySYiu9XNHkVzFI91Qj6ChpGvXaVU/U8/PpFoTlQdf2orQ8j2kUuIFN6oS2ro5raan1a/uaS3OZKX9P6yFwyBOnRsd/Qnvd5RkYw5lkuVBTE+vD03ZMY28CTyBL3vuNfi973sFXnrrsbFthr4YE6uuo6y2bcbSZNTTWuJOejSGZ/xvhV0SbPe02nNNzWfLcuqr3FDbya8sD/ZcYTy+7+WCWK1rQnmwHnlTTA8OPdcJr4N6Ws9vDgEA1x6efpTMNYc6uLQd1TutnRCb2mk92lPnnim/rikPLobRXQ1cXXvLMAzDMAeQVEpzQbSfs1rTTJV1Am7v3DxJ0gyZdN2S3NnKE04BJSSo77KJ01oMAyqDLt77S+q0DsvKgy0xOQ10vHq6348ElZ2SSwLtcDcwwgpQ72PgCQghzLmbi1YJz6NRPEooveimI/joT3wFvvsLbgUwYU6rlYpbFaiV97TSyBv1PblXgzg15xEAbE84f5IsFxHmfNTiovj8DctpbQc+XqHd0CK+J4yzX9bTao8NCj1VYl12UwJQpbItS3yqfXYTnAFXwJrQI0/kn60oMeW/Nx7t4uRqC888teq8Vs9yWielB/sVjmm7ovQYAP7+S2/ClzznmtLn0TlTlh4cmH2c3J9KPa3nNkZYafmV4ruOw13log51BUfZvuQ9rZGpRugVeoZt2oGHfpTgqfXhWG/6Qefq8pUZhmEY5gCSZdKkSu63aG0HHoZxVjlqZKfYsyWJltVDCFhOa5KZ0rxBk/LgkiTc9X6MI708ICZedqe1QrTOkjptRGuonVYqDx6M97Qe7oQIPS9Pwc3keNpukjutQgj4nqoi6EcpDnUCnFxtl6boAoD9relprSkPpueb9GB9vh7qaKc1SjGw3uP+xJ5WafWA6vPRcloJO6m5qsSVUEFMrli1y+7pZ76n+lCTNMMwSZ1AI3d7wg1iSshpFYhTq6eVXFDr/dkaJapkOs5Ht5w61MZd/89Xjr1OzykPLhdWtO1iWJK970C5cPuJr3le6XMAwPeg04PH57SSCG828ibE1jDBuY3hTC4roM75NJPmJk6nRCwf0r2zaSbN35muma9bJlp9/fkA/n+vfMZM61pW2GllGIZhmCUnk3kp2b6WB2fSXNBGye6IZ3tMB1EMmSkVrfG0olXigac3cee/fzfe9+BF83MSDcva0zqMs9L04FnuddCIIHJaE9O/mfe0rhunNYRv9UbGaZYH4+j3j44/jSvxdNnyME6NEMpLbwvlwbqfGbB6Pf3qsuei05oUnNZ+5JYHT3Ja4zQb72lNsjFH+DpLAFX1c9q/p3WaUUFV6cH6hsDWMEGvwhVUKdvlTqs9p7UVuCNvaAbsUJfPT3L4ulZo0WpF36VxWitKdevKg+ugICaVHuz+rniTpI7Vto8ozfDklQGuOTx9PyuQ3wA5r/tiq5zWjWGMtUGMYyRaQxKt4/v+opuP4HNvOoK3/MAX4IWFtOmDDotWhmEYhllyMrs8eEqH8+n1oQmImcc6yE3YLafV7sMjcpHglgePktT0qTYKYrJFa6ZSa6UEfvW9D5ufx0bsLK/TWrwY9kR1D28ddLxIUNJ7vtbP55FuDBJ4Alhp+SbhFnCdyaLTSuNKPN2f6PRRem4pOJFmcqxMPPQ9pHpmZxF7hIv6Xve0aqHRjxJzzgSemOi0xqk0+2FG3mQqMIeSkwE1CoUIJvRW2vNRi/Na7X2gIKY4k7i4FeHkamt8Y6CUbTvhu2ROa5A7rSQsO6GPQZyam0G9CaK1Z51fKxWpu3bpcelazcib6aSKHcQ07rTSTZJmPa0A8MiFLVxzaEantau2QWFOZSJ0tR1iGGdIM4mjXSoP1kFMJYL9S59zDd72Q6/Es65ZHfvdQYdFK8MwDMMsObZojad0OH/wTR/Hv//T++ayjiST5iJzt0beGNFqXXiGhZ5IKuuM0swkAjcrD7aCmGQeLPW3D17Eg+c2ASy305pmElGSjQkB29GbBlMe3MrLg7NMYn0QwxPKnbzcj3C4G5r5mrljmFk9hoXy4Eyq8mCdHty3Zssap7VQUZBm0ggge04rUJ4EbdKDjdOq04O7eU/rME4hBHC012rU0xoUgn7iVB3vU6u5U3fTsZ75ehqnNS8TtsqD09xRDnwPm8MYW6OkMum25bvzcCOr1P6aQ214QgUOBYUgpiPdEOuD2Aj8MvFl0yQ9mARl1TFozeq0evnIm7GeVm8Kp1XfvNgcJrh2RqeVboCQaC0rdbYTgCmIyfS0TinYDzp8NBiGYRhmycmy/OIuntLhPL85NCWcO8UuDy728s2LOBl3Wos9rf2S8uB+lJQ6bjbFICZbyP3GBx5zXmMZe1qpf7PY0+rpksppiYpOayqxOUyQSeCGo8pRPLs2MBfvqu8yT8MNCz2GkSkPhh55owTnMErR1X2zdjKvTSpzp9WeAau+Hz8XTXpwwWm1e1r7UYpe6GO17dfOaU0z6Titdrl6lGZGRHoCuO5ILoCqXMb893k5b14mPJ6S7Hsq8ffpdZV0a4tkG9u5BazPku/h5uM9fPQnvgIvvfWYeT9ofYe7ITYGcWOntR14oF2rKg+e1NNKJcrTCjdKws50ibn7mtPNaSVm7WklQVrrtDqiVc9ppZ7WKQX7QYdFK8MwDMMsOZnML5inTe3dHqUzJceWriOTprd2twKhotR10eyv7bEcAIlW9XUm3fLfMkaWC5npsRmAKul8zwMX9GvoIKYJ5aKLCB2X0p7WWZxWfbxpe3bozK0nlKN4+srAuJf2yJskLXFaU3qvdE+rXtcgTtFtuaW3xZsimRXslBrR6jnf26SWaJVSmvM1Lw9OtcMboNcKasvBt3Qa8CFLnKt9VEFMJFqP9VpYbechSZOc1sB2Wk0vcNWcVg/nNrRorXJaCz2txc/SCS126X2h/TjSDTFKMhOwVbzpUUQIYdz36vRgXYI8sad1hvJgfcNpbOQNlaP7DdKDrXVfM2sQkw7EurBV7bQetkTrsYLTOm1p9EGHjwbDMAzDLDnOyJspxcf2KDHllDslyawy5d3qabXcIaIqiGlkOa1A7jRWoUSrumBUF77q5ydWWkb8knho0iO7aNBxKTo+wYyilUqvqW8xznJhc8txJVrPXLGdVs8RXySKqEpgZPe0eqo8eJRkSDJphJLvCQhRlh4srV5SN2G6bN/oMVLfzMjTg3V5sE4P7rY8rLT92nLwPCFZPdc4x6kKYqIe0+MrLaxYabqT0oMD30OsS65pF8rmtKogJmEeUyVa7XmrgN3TWi7ubKcVgHFyJ4lWID/Hque01ve0zlwebJzW8fJgukkQBg16Wi0xeW3F8ZxEsTy4qqeVoPJgclgnlWFfbcxFtAohHhNC3COE+KQQ4i79s+NCiD8XQjyo/z1mPf7HhBAPCSEeEEK8eh5rYBiGYZirESnVeIdZ0oOTVIm6WQRLGZm0yoN3aU6rcYdKgpgosdgpD47zdUwSmlGSB/5kWT6rtR34RiwnS+y0VpYHa3dqWui96JU4rTdr0TqI01y0enZ5cObMBgXKRt4IbI20u2cF+oSeN3ZTJM3yaoOk4CCWnd928FQ/SsfSgwcxlQcHWGnXO60kWg9Z4pz2J80kVtshuqGPYyst59g3dVrt0u26nlaiujzYDWJa1ynPYcEBzOe05j2tgCVaG8wHpXOiSrTSvhfdULOGYHanNc1QXx7caOTN/MuDJ/e0qpsbnifww19+B77qBdfN9LoHlXk6rV8qpbxTSvky/f2PAvhLKeUdAP5Sfw8hxPMAvAbA8wF8FYBfFkLwrQSGYRiGmQG6njVO6xRluRQuMy/RmmYSnRkd36aQGG5bF57U/1bsaR1Z5cH2z6soOq0kbNphPu8zSpfXab3r8SsA4KTZArk7NS3FIKY4zYwQIqcVyMONAt8qD85sp7XY06oEhxACQ33TwRZ7dm8skVlBTOM9reVOKwmrQZwaMUiuYj9KdVmyj5VWUBvEtKnLg+39pO0Cytk71gtxYqXllMtO7GnVI3ucxOCS8mDqaQXU/M7i+0u0fM98frZHCd74ocfxkluO4lBBWIYl5cEA8PTGdKJViOr+18ZO67Q9rV5e2l/sl6Ue6iZC2BaTs4686YQ+WoGHS9skWut7Wu35uv/8K5+NF99ybOzxVzO7WR78DQB+S3/9WwC+0fr570kpR1LKRwE8BODlu7gOhmEYhjmwpMYNLO/1q4Pco3kITCmlMy92t4KY6pxWek1yFKPULQ+elCA8inPRKqV9bP189IgWDVtL5rSe2xjiP77jfrz89uP4P55xwvmdN2N5MIlMKnm1nVZHtBqnNRf/9lzTViH9OdNBTLYhRj2tajtiPD1YSuOkNe1pzUuBE7Ou1XaA0BcqLTdS82F7Lb925M0GzaLV+0mCi9z4lu/hX3/t8/B9r3qGI/iaOK2AW9ZeFsREc1oBVcoeVDiJYZAHMf3qex/B+c0RfuJrngcxNhrGFZRjorVB2SqJ/eK2CSrdrToGYaF0vCl0AybVCdTO76ZID6abC4c6gbkpMwuHO+HYjUUbOgcPtQOnT58ZZ15HRwJ4txDiY0KI79M/u1ZK+RQA6H+v0T+/EcCT1nNP65+NIYT4PiHEXUKIuy5cuDCnpTIMwzDMwYHKOlsziFa6qJ6H00rb6Jh17JLTakbe2CIm33cppRHjUaGnddJs1SjN8lJXKz24E6r5olJKq6d1uUTr6/7sfkRJhp/+5heN9/qJ2cqDR6nrgiaZxJp2Wu3RLuRe2mIzSaUVjFMsD5bwdE8rQenBgO7NLKYHZ3mKdNJAtCZZZkpAB1FmbkaEvodrDnVwbmOoyoNbPlbazZxWEiAk9siNbwUevvqF1+PFtxxz5pZOmtNK4nNoVQvETnlwZrZD2zpZURpM+xanGYZxil977yP4mhdej5feOu7m0XHzd1geXFUaDKhjpPqTq9KDdzCnVbdMFAVxWChHr6MXKqd41tJgwu5zLn7uAOCQ7mk90gvHfse4zEu0fqGU8iUAvhrADwohXlXz2LKzs/QvpZTy9VLKl0kpX3bq1Kl5rJNhGIZhDhR0DUuOxFTlwSOaUVn9nHMbQzx2cXvitowrGbp9hR98+BK+6hfeOzEEqSkkbMLS8mCJUZKZQBrV05q/7mBiEFNamh6cj/GRVnpwiu1Rgq/8uffgY49fnsOe7S73nFnHVzz3Wtx+cmXsd76V6jsNudOq57SmEmv9CIc7gVPqSBfubnlwidOaZKZHm9KDCVsoqfLgQnqw5bQmhYChaqeVSoETczMk8ARuONrBU+sDDOIUnVA7rQ16Wslp9Y1oTfQ68nO1N5PTmu9rldMa6mNZFcJE64hS5YYP4hSvvONkxePcebNjorWB07raDky5dBl+4aZE2VqBnTmtxcNrgpgaOJqeJ7DaCmae0Uoc0seuqsy5E3oIPIFjvfKSbiZnLqJVSnlW/3sewFuhyn3PCSGuBwD973n98NMAbraefhOAs/NYB8MwDMNcbWQ7KA9u4rT+x3fcj9e++ZOT16FflpIvaR33P7WBzzy9aUoodwo5p62K8mAqAV5p+Rgl6VTlwVGS5UFM0tqnMN9+3jeb4PFLfTx4fgsPPL01hz3bXfpRUhuKk+1AtOYjb1R68LGVFnxPmNfLnVbPCEo7PdguD6ZlqDmtttPqpu6OpQdbPa3F8uCqnlY7dMmMj/EFrjvSxdPrQ/SjxDit6oaIe/7Q/pPTSv2JQgi0fM+4s1WidWJ6sN4f+3XLRt74nmeOZZ1obRmndbxP2Hld47Tq9GC9X+c3m4vW137Fs/EfvvGFlb8/1AmdvtEiOxl5Y+a0jjmtrrM/iZOH2rjZqhiYBTp2VeJbCIHVTmCSg5lqdixahRArQohD9DWAvwPg0wDeDuC79cO+G8Db9NdvB/AaIURbCHE7gDsAfGSn62AYhmGYq5Giw1ksm6yDLqqTmvE0G4O4UVIubcN2Je2fT7OuOmi7VSNvyE092mshk24Z7zRBTFLmqa22EE8sp/Winr84r5FBu4maOVp+4ex7wkmobUoexJS/51f6sUlBJVFy2Jpfave0Fss1oyQzN2E84TqRttgLfTF2PmWZHBOpVeXBWabcXBLVwzg12/M9geuPdPDUOpUHB1jRr923xt589twmnv9v3okH9A2ZXst3xGngC3OTxB4pY/dHTnJafSoPdpxWqzy4MKcVmOS0CkRJVjn6iKDPVh7I5GFVC/eW71X2zNo89/rDePntxyt//z2vvB1v/v5XVP6+bdKDp3daqUqimExcdPYn8T+/+2X4l69+zlSvX4TO/boy5xMrrdr3jVHM3lmccy2At+qa9ADAm6SU7xRCfBTA7wshvgfAEwC+BQCklPcKIX4fwH0AEgA/KKVcvgg+hmEYhlkAZKE8eJpRMyTo6pzWKM0aCZoyVxLIL6zTOfW4RjVOa5RKI0yP9kKcWRsYFwwA+pPKg+PcaU0zNz1YbT93WqM0M+WSu9W/O08oVKgMTwhMYdAbojRF4AkjctJMYr0f4YgWrYc7IZ5aHxZ6WvXNjJKe1pElWkXBae046cFeRRCTMNsGqsuD7dAlgEbe6JJiz8N1hztmxm+35aOnH7cdJTimk3nvOb2OOJX47LlNbA6TMdcw9D0niInoOXNa60VraMqD8/PWdo2dnlb92KpxN4D6zNg3dqpuYtBxtEX1kW6IrVEydY9pFUe6oVNCXsQ4rVOnBwuMEhUKVyw/9gvO/iSecWp1qtcug0qk6xzjX/nOlxpxy1SzY9EqpXwEwOeW/PwSgC+veM7rALxup6/NMAzDMFc7xfLgOte0CCXgFssnf+8jT+CFNx3B8284gjiRjUpH00L/p+ldpLmcc3Ij40K/IpCLgthykajczhatgwZBTHQhn1npwbnTKp1j9cTlPoD5jQyaNxe3RjjWa6mxKdaIlyK+h5nKg0dxhlbgOQ5nP0px4zH1OnTBbkbBeJ7lvGdjczNHSWZufviecHoSnZ5WT4zdKFDlwe5noFXxmaD3i3pai+XB1x/Jw3d6oW/Ck2ynnt77i1sjbI5isy0i9IURh7ZI6k0zp9U4rZZotcffWO5w0MhpzYOYgJryYNP7ma/vcFfdBNpJku40vPr512E7SnDNlA6k5wmkEqU9rbedWMHRXrjjkt9pyJ3Wasf42dce2qvlLDWcrcwwDMMwS8z4yJvm4oPKHW3RtTGM8WNvvQe//1EV9N/UaS0KhXxETDb2Gjuh1Gm15rSSe0wuzsYwHktzLV1/mqk5s1Z5MAk5U/KcZI7D97gWLvMS5PNkvR/ji376r/GnnzprhHy3QnDMXB6cKtFK4ivRLh4dLxJyJqDIDmJKpXESPU+Y0tXK8uDQLg/2xoRoJuWYs0pi+tzGED/9zs/gDX/7CB65sGWeu2pG3qTmcxN4Hq4/2jXbVU6rem27TP5J/d5f2BxhY5CY3kUi8HKn1S0b9sy5O3FOq+lpHS8JtvczsOa01jmtoe855cFVolUI9X64TqvavybJwfPguiMd/LMveVZlunAVvshLqIs9rbefXMEnf/Lv4ObjeydaD3UmO61MM/bmdgnDMAzDMLvCToKYypzWjz9+BVLmMzPjNHe/atehHxP6SsTQOqhXcF5zW82c1gk9rUe6qoxzc5ig2/IxSrLa9GASBh0TxCRNKJBd8hxZNwWeuKRSledV+jxPHjy/iUGc4vSVAba1kK8vD56tp1X1OOajZoZxasQQCTkqDw6d8uDM6Y1saUGVi9ZCEFMhPbi43jSz0oMLovVP7n4Kb/3EGQDAex+8iF98zZ0A1GxMwA3oKjqtNG8UqHFah3kfLxEGwjy+mFa70vIRJVkDp7WkPDgdF7CqPHiy06rKg6VVHlwtpALPc4Ki6CZQnWO4CPjWOVbsad0P6NyftjeXGYdlP8MwDMMsOB9+5BJ+7b2PlP4uL6d0xWITyJW0S0M/+pga3xIludhsImjIqfOFKPQu7pLT6sxpVRenUSqNADlmyoNjtAM1tqQuPdgk4Zqe1nzNdMEZpcpppRsE5LTOMi5mt3lEjynaHiVGPFWJ1sBzReCHHrmE17z+g2N9o0WiRDmtJG7STB3/ruW0egImyCjwPfM6cSbdEu/AQ5Sm5kaBELnTJ4TrVIWe55znUt9gGHda1ffnNoYQAnj57ccxiBLzfnVC5VD249QJNTq52jav3Wv55rjZTusTttNa1tPqedacVlc89VpB7YxSosxpdUbeWGN6yL2dFMRk39ipE6Df/8XPwFe94DrzPYnWqnNoUfCEMDe2Jt0U2AuaBDExzWCnlWEYhmEWnLd87DTecc9T+N5XPWPsd+RM+Z4bdNOELZrTaovWR68AsJ1W2ah0lNxGTwfz2M+3/90pcZoh8IRT+kcjRlR58HhPazvw0JKitjx4ZETr+JxWk8yse1qPdEOc3xxhra/G+EzTRzwPLm2N0Aq8sT5Km0cu5KJ1YERr+WWfVxCtf/PABXzokcvYHObBQ2WMCuXBJIjIFf2qF1yHduAZcRZYN1WSNHOcvFbgmTmtgCrzpLe4G/qOwAt0KTFhRCr1tKau03puY4jjvRa6oY+1QWxu0vieh06obmZ4bRgh6Qvg2kNtnF0fohsGJrCJHOtBlOL8pkqOvrgVYXMYG0eNCH2vdE4roIRfE0FFx2dUFcRk9bR+w5034GgvHHN8i2tKMom+Ft91ovW1X/Fs53sSrU3G3ewnttO6AEarVR682MdtGWDZzzAMwzALzsYwxnaUlrqodtqqEm5T9LQW0oNHSYpPnl4DkKcQR0k2VRBT4Ak12qQw8maeTmtZ+mfoC8RJZhKC6SKbRGtngtNKszBJcEkpzZrzMT4qPbiYerrbTmuUZPiBN34M955dBwB8z2/dhX//p/fVPufRi2p27NYoNe9zZRCTHhNCPHmlWa/u5a0IhzqhcTQHkXJK6Xh94bNO4v/52ueZx9tlvYk1pxVQF/VRkrv6nnVjoiiUAt9zRt6Yc8+UKbsl5Oc3Rzix2lJBREnmuKrkwCfWnFdA9VTSMaObFiM9eua0Pj7d0Dc9rUWnNfDzsKgy0TqpnxXI027tkTe2+22L9RuOdvHtL7+ldnu0Dgonm0aALkt5sGfdGCmmB+8Hh7vstM4LPoIMwzAMs+BsDNRF5vogHvsd6QpPCH2h3Nz12zZOq3rOPafXjYNl97Q2clotsRFYQTnFMuGdYs/3tAlpnIcJYlKO08YwRivw0Gv5zszWIuTCrmhXzUkPtoKY4jQzLi6x2z2tp6/08c57n8aHHlGl2+c2hjizNqh9ju209ieMOPELTutpKnuu2S8pJT59dh3Pv+GwcQ03R/ViKLDKeqPC+6jKgzO3PFiLjqJQUr2x+flk91MDbq8noETaiZU2WoFwyt19T6Ab+hjEqR7Bk6+Hwpi6Ld+UotNngkqDP/fmIzi/OUSUZmMjS4KSnmuCyoMnQc5x5cibTEKI8cChKmg/Nobq78g0AnRZyoN9kd8sWISeVnZa5weLVoZhGIZZcOgik8pRbezy4LJU1TqoR48u4j/6mCoNvu1ELw9SquhpzTKJ33j/o2Pb8LXjm/fEkuM6J6c1LXdaA8/TPa1q3U55cOijFwa1QUzjolWCtDr1U6qeVmkEMbHbTuvFrUitUR/r7VFibmSUkWYSj19Swmo7ssuDK4KYvKLTqgRxnWh9/FIfm8MEL7zxiCljNQ5eg97ZpNjT6rvlwXZ6cHHdtpMP5DcXaHv0O7vv2TitaeaI2m4rMHNabSF5/WHltHZD35xvdEOHkoNfeusxI7KL6cEtx0We0WmlOa1JxcgbXSrfFDo+G4PEKetuwuElKg+mv11Nxfxuwj2t84OPIMMwDMMsOOSwrg+isd+lVtpqaInFJpDzSBfx5zaGONINce3hTu64VpQHP3BuE//2T+7DH378tFqHMzNSWGXB2nGdU99nlEhHjBAtX7lv/VhdkOeBShLtwEN3QnkwuVm90IcQSpTnQUx5r2R5efDu9rRe3FL9k9t6/YM4LXXdiTNXBsYV3Bol5sZCL6wYeSPyUTTbowSXt9V5VlcefM8ZVar8whuP6F7QPI26Stj4vkCcSVN6XexpHSW50+qLPKioKIJVefB4mSy5m8WRNwBwcrWtRas056RyWj0MdRCTLaLt8uBwzGkdoNfy8ZzrDpvHF/uL7X0bc1rbAXxv8iU4rWdklwcXRt5MIzxb2u3bGMZTi0/T07rgTqsnchd+ATRrnh684GJ/GWDRyjAMwzALzoYRreNCRVqi1RaLTSCRIaUSaar0VphSTaA6iImcyY89rtzZ3PEVxtEC8pE30wRE1VHltOblwSl6Ld95TNuUB092WnutAJ4Qqjy4pKc1ySQOd13xN699q4JEaz9KECUZ4lTWitaHdT/rydUWtoaJNeJkcnnw6St52XHdfn36zDpavodnX3sIgHJRNyeUnYaehyTNrF7PQnpwkjk3YejX5eXB+dropgrNfc17WvPtn9ROa2Q5rb4Q6LUC9KNE9dhaQvLLn3st/t6Lb8RNx3p5eXCSlwffcrznzEQtnhOhnXbsu+rppmNdXFOT8kuUOa1p5grYsIH4La7jIItW38urOxYhPXil5ePESgvXHe5MfjBTC6cHMwzDMMwCk2XS9AqWlwerf8lpnW7kjdsrRxfulMQrpVQiseQeNwUX3aVLivMkU0qJdXtZ51VCGyfZmAgAYFy0fpSiFxZFq49uOEm0UmmrB08oES6kSiBtWeXBcZqhE/rohJ4JyNn18mCdVLtthSptDFUKblkJ5KO6n/WFNx7BA09vThx543vCCD8qfQXqHeRPnV7H51x/yBybwPOwNaE82PfUzQA6R+2+z3bgYWuUmHWI2vJgzw0kkpOd1hOrbVzYHKkbD5ao6YQ+Lm1HiDO3PPj2kyv4+W+703wfWonFT17u4+bjPWe8TNFpDa1thYWbLK/9ijvwz77kmaXHyNmGTz2t+b7aAVRJmpmwpibQe7UxSKYWn8uUHkwu/KSRQnuBEAJ/9S++BL32Yh+3ZYCdVoZhGIZZYLaixPRWlonW1BKLKy3fhCs12rY1dzLNpLlwJ9eLxFiZeKGSxTNrAzy9PrTW4aneWjPaZG96WslFG8QpOlZ4DqBG1nRbfm1P69C4ka7TSj26AIzLGXoCK3p8zKF2MLdk5CouUE9rlJgSYSnz4KMij1zcwuFOgFuO97BlzWmtLNv1hBF+lBwMVDutFML0whuPmJ8pp7W+PJhuNlC5csd6H/OeVvW9Z5cHF51WXWZMZAWRSueafZ6cWMnTg/NyYkoPTpAWyoPH157fEFofxDi+ErpO61gQk9uva9MO/NpxRYRxWvW5KYQb+lVMPJ4EHZ+NYTx1CvBxPfqIxv8sKp4Q5hxahPRgADjSC0vD45jp4CPIMAzDLDQXNkd49OL2fi+jMR97/DI+9MiluW1vwyoDXStLD7ZG3hzuhia0aRJSKlfS9GtmmU5QFca1pIv0TOZlyISdaHrX45fz8mAhlKig+aymPHh304NbOjmZyoPbpeXBk9ODe6GvRatEJlWYi3G8ktwhXGmrBNiTh9pTuduzYPe0Dqx92KgoEX78Uh+3nVzBSjswz+mGfmUwjSeESeB98rJVHlzhtNohTITvi8k9rRTYVDInlG6U0HnkebloGxt545U7rSQ607TcaVUl5NKqCvCc9OC6clJaH6CqDNqBj8PdwAjS4sgb+7VnFSymp1W/bifwnfdk2p5WI1oHsZlH3JSjvRZ+9bteim96yY1TPW+vsY/HAlQHM3OERSvDMAyz0Pzsux/AP/2dj+33MhrzC3/xIH7mXQ/MbXt2SmyZSLFH3hzuhJVCpshIO04UFJJl6iI48D1zgR5boU5FM9Hus7vrsSvGlfM8HZRjnNb5ltCOkqw0iImcsH6UoBcGzoiJduCj2wowjKtnzho3suWr8uBMIpMSnsjFAwlGcuiOr7QQ+mLXnVbT0zpKHCe9qq/17NoANxzpYrWjXODL23HtqBLfQ6nTSjceisfss+c2AQDPvT4PIgo8z/S0dlvll5djTmtRtKbFnlY98mZSerA1dxWwRt5YqsXpaaXyZE+gq3udq26GmPX5eZ83zQoWQuDkqnIgq0Sr74mZeyv9wsibduiNjbwJZuppnb48GABe/fzrcLTXmvzAfcQec7MI6cHM/GDRyjAMwyw0m8ME2zUO2aIxijPjyMwDW5is9cfTg+2RN4e7AdZrRqHYkHCgUR1Jpvo1A+0sRmlmLtIBjAkz6rO7/eQKPvb4FbMOuycWsOe0zqmntaY8OE4k1voxDnfD0iAmwBXb7v6kEEI9tlgeTD2JJGxb2mk9udqG73lG3O0WttO6PcFplVLiqfUhrj/aMaWcF7ZGtSLFF0p4Synx5OW+EWBJKnFuY4jP+cl34u4n18zjyfGnklFACcCtEjFqQyKSel/tMSAt38MozpxgMRIdvbHyYDc9mL4M/LxqwB6ZAyintVVwLn0tWodxqm/Y1JcHj6wZxnR+nTrUhidgysXzxwvn31mg40Wl+J3Adz6HSZrVrrkI3exJM7nwvamz4jqtLFoPEixaGYZhmIUmzSR2eaLIXKGwnnlB5b6BJ0rLg9MZy4NJgJHTmmZSj/3w0Can1dqPrFAePNLuz0tuOYYHz286vbUqxZjKg93RNzslqnJatVP39MYQ1x1pu6I1zEVrVRgTBTgJLZYyPZbF8/KeVnpu4Al845034FtfdpOePbrL5cGbeU9rv+C0fuzxK/if73vU/GxjoHpYbzjSNULqwuZogtOq9i+TKj34GadWAaj37un1IaIkc0r0SSzbfZyBL4wbXz3yRr0Oidt2idNqB4uR/hgbeePVz2mNU6nStPUG2oGHFWt0DfU2B55AL/QRpxKDOK0dQ9PWpcUqZVua8vNTh9pYbQdjrh4J6J30MpIgpRstndANWkumLQ+2PhPT9rQuC/bx4DbSg8Vid1MzDMMwVz2JNS9zGYjnLVq1QLjpWLc0iElavaSHOyGiJMMwTidelJJwoFTQRI+8USNrxNh+jDmt2nU6vhKaUmPATjF2HdZ5uZF1Pa2bwxhr/RjXHe6Yskw1p9U3x6NqVms/So04MunByEf4qMdQebCH73zFrQCAt999dlfTg7dH+ciafpSiH7ui9d33ncNbP3EGd958BC+99TjOrque1OuPdkzp6IXNEW481q18DTqc64MYW6MEtx7v4e4n15Ck0vRQ2qFdG9opXbVKYu1S3CpXlxJ1jWi1g5iKPa2WU1o8lwNflclKKSGEsMqD8+3ZTu3J1TaEsHqT9TEkpxVQFR3tEgffXV9qqg/opsjn3nS09EYI7WvZDZam0P4YpzV0ndZ0ypE39loOqtPqlAez03qg4HsQDMMwzEKjAnGWTbTOb70kEG4+3ivvabWcKXJNm7itJMDILUv1zQEzpzVxy5yLs1rpwv9wJ4SU+feB5+nxIOp7EnTzuvFA/YRFQt/DmTUl2K7VMxHpIt0uD65yWoexLVq10yqVY+d7yvWj59oln0XXb95QafDRXoj+KEHfEo/rg9js88+86wFdGqy+v+Fo15QHX9oejZXY2pC4MyXjeuZommWIdF+zHWK1OYxxSAdREfbXnaAqiGlCT2uS5T3anjCio+gS5/NYdc+tGXmTr0GIXEif0H2n5DTa5yq951ujZGJ5cJRkRkBSz/T/+eV34E3f+4rSx9v/zgKtn5xWcnuJqZ1W/2pwWvOvWbQeLFi0MgzDMAtNki2baJW757SWlQdnuTNF/alNwpi2RlQeTD2t+ZzWUDtZo8TuGxzvaW35+UU/CTrPU2LRjMvRx8LuQfzY45fx83/+2YlrLCNOZakQCP18biqJ1nY4Llqrxt5QgBOgSq1VT2t+4Rv6nnFpbUeP3NzdgkTrrcd76Mep43iuD2I8tT5Ar+XjQ49cxvsfuoSza0MAUOXBejaklNUzWoF8NAjN3u3psuLYclrtAKiNQWJukBChdYOgKgAnNOXButzVDsvSfdS2Y1+ZHky9q6l7Q8Q+L+wApBO697ZlArUspzUkpzWuDTVqacE4SlPzfR2mPDjYQU8rlQdTENNOe1qttcwSxLQM+Oy0HlhYtDIMwzALTbZk5cE0y3NWLmyO8M5PP2W+39Cu1vGVFtb60Zh4zEeECFPq2ySMqV8oD06zDHGmLoLp4n+7MMfVZhinaIee6UskJ47KacmlNU6rdUzecc/T+OW/eWjiGotIKbE+iMeSWgFXsFx3pOC0hpPLgwdxZlJqfU+9VpZJ49y0fM/MSLV7A4uhQPPmgu5nveXECqQELm+r7w+1A6wNYjy9PsRrPu8WHOuFeOsnzuCp9QECT5heS6JXM1+TxN0gUvtBQo7CuQDXad0Yjr8HVaW8ZY/JnVa771g9j4SzJ1DZ02p6V02/NPVTu4LFiFY9T5VE6SCm0UXCiPmNYb3TSnNk6bxuT3BQW35+s2NW8nJm/ZqFntbL/XhsPmyT7QEH12n1HPd/HxfCzB1+OxmGYZiFJsmypRKtO+1p/aOPn8YP/M7H8eRlNXpkfaDScI92W8gksFVIUnZG3kxRHkwCjC56c6dVmN4+u5S2WB48SjJ0wnweKm3PF0K7UtphNU5r/nwS9sXZr5NY66uey5uP98Z+Z5fsmvLgIHf/SMBWzR4dRIkpofV0n2QqpXFuwsAzI2/CQlnsXjmtgLqp0fI9nFht4ZELW4hTidtP9vCSW47hk09ewdm1Ia7VPb0rtmitKw/W+0guNAm5OJWmPHjLcVrjMac1qHBFbeg9KksZbhUEmgpiqprTqsuDC06rbwlVYfXEFsuDB1ZPK60hSrJapzUMPIzSzFQftCfMOSWndSc9rbR+clqLPa2nL/dx8/HqXuUitmg9qD2tttMq2Gk9ULBoZRiGYWYmTjN82X/5G7zr3qd37TWybHxG6CITW3MgZ4EuqP/2wYsA8lLMIz3tohbCmJyRN1qANikPzvsXtWjVZc2B7+VOqyWQi1pvFKdoB96YwCWnlS7uc2GRb4DctGnF3hNayN9SKlrVOjqhZ8qkbdFKv6+6odCPUiPWaORNJqVxbkJf5OnB1sV/4Hl70tNK+3xha4Re28eRboj7n1LzUq8/0sWLbzmKhy9s4zNPb+J67TTborWuHJQcRiNa2/nIGxL5rtOajDl8dExqR+toUbg5LO9pBXKB5lnlvWPpwaY8WDut9Bnw88RhT6jU52968Y34sudco16jUG4beMKUQtP3VbR8D7HltE4So3RMJ5UR126DRt6QUA4885nZHiW4tB3hpmPjn4UqXNF6MCWA47SyaD1QHMwzlmEYhtkTBnGKRy5u48Fzm7v2GkmWLWFP6+zrJVH1vocuAFCu6eFOYJX+lotWIfLy4CailS7cqYS0GMQEFMqDi0FMSaqdVl0ePMrLg22nlUSPLezoInza49REtF53uGMcljyIyTcigpzDIoM4NeXBQqcHZ5ks72ktBjHtYnnwxa0RjvVCc3PhwqYKVTrcDc25cMPRLl58yzEAwP1PbeD6o8p9W7HEXl1Pq3Fa9f7R8+zy4O0xp3X68mA6bmXlwUa0WuXB9LOVdvkMVHLvM8tppffeE+oz8XPfdic+/xkn9PM8Zz89IRzHsa48uK1H8hjROkGM0rm3k/JgYTnHntDCWZ9rFMBVVnUwaU3AAe5ptUvEWeUcKPjtZBiGYWaG+hTtlNm5v4acX/LsPKna5yjNEKXZ1KWvBAm59z90CWkmTSnmUS1aimNvMmvkDfUZUuJwHSQce0agqDmtFMQEuEKlLIipE3pGeNAoFnK44sKom6RQHgy44UzvuvdpZ95oGSRabyoZ30KBN1QaDOR9ku0wLw+ucloHek4roC58pVTnnu/lopWcVvvi37dm0r7hbx/BX9x3rnYfpuXiZoSTq20TqnRxa4ReO7+JAQA3HO3gRTcdARlLN2inNfA9I8rq57QWg5jy8mB6/4o9rUWnlYRknYNH41lMeXAwXh5sC8pXPOMEfubvvwh33nTU2Q6V8Rqn1epptZ3Wsdcvzmn1hSPe6pxWlYhtlQdXJCQXtxVOEZRUt53A81Qpun4/qH3g5ppRRkXsUKiD2tPKQUwHFxatDMMwzMyQ+zaaQbTGaYaHzm9Nfo0FdFofu7iN5/3kO/HZEoe5eCE9LSTo1gcxPn1mHZu6FPNoT/XlrQ0i5/F2Tyv1mDZxWqMkgyfyi+9MUnlwhdNaEsTUCcadVgpyIueWjoddMm2cVuu8+cOPncavTxCtT17u4+Rqa8x5A3LRQyFMQB6WM0t5cJqR06p+r8qD9T5a4ia0Rt78xvsfw9vuPlu7D9OyHSVYaQemjPXiVoSVlm9Ea09/fagT4o5rVgHAlAcDuUvZbdUEMRWcVnqtxOrPpp7lLJPYGo2nB1Ppb315cN7T2vLdlOHcaVWvJ4QSmd/yspvH0ojJESVBnZrZrnkfbFk/Y1G02nNa1XYnpQc3d1qDOTitQH6u+Z5A4HvGXX7S3MCZtTz4YIpW+1xh0XqwYNHKMAzDzAwJmVlE65996il81S+8d6xHc/w1Fs9pfWp9iCSTOKtL9Ig0k6b/dtYS4STL55D+7YMXTCnm0V55eXBqyoPV93bZaB1Rql6HhESS5kFM1Pu33SSIKSwPYgJ0KFU27rSOCjNcASUaNycESD1xuV9ZDkkX5LbTmve0+iaEp6r/tKw8OLXmYIa+Zzl09miVvM9wp/3MZVDJNgnqNJPotXKn9fojeTn0i29WJcJUHgwAq+3JTitd6A8LQUxJJs2NBbqBsRUlkDIfr0RMG8RUDDKi92oU5+dR9XbcUC26ceNbs13LTFMa+TK0RhfZAVV1rijNkR1ZM1ProJsoOwliAvJzLfAFAiv068krA3RDHyd1yFSjbXnC/J04sOXB1ls4zQxbZvFh0cowDMPMTLID0XppO0KSSSfspwzltM60vF2DSn+LYtp28WYdgxInEsd7LXzuTUfw1k+cweZIOa1HKsqDpcxLIwE1wqZJerAKUsp7PVMqD/a9Uqd1vDzYDWIyZZ2eMBf/oyRPfrbFIrlVdol1P0qwOUrGXsfmicv90n5WYJJo9cyaohJRmWYSUZKZOa2eUOXBmXR7WulGhC1uQj/vaVXJ0fM9WRPdV7tiOaW9lm+czhssgfqSW48CcMunyWmtLw9W/9IoGBI0KohJlwfrc4Fc/LEgpilH3hQfVxbEVMVYerAVRkairEl5cNFprRM5NMapudO685E3gF0eLBD4wtwUefJyHzcd606VkCtEPs7qoDqt9nvIRuvBgkUrwzAMMzPUX0XuwzTQxd8kF5V+Xydm9hq6SC4KFFsQxTP2+VKJ7j/+wtvx8IVtAMo97YQ+WiWlv6lVHgwoB2yjwZzWMac1y5BkGUKvYk5rMYgpVkFMJD62rdJZErKRLVodpzUb+1k/SiElKm9ixGmGs2uDGtGq9uM6W7Ra5cF1Pa1U9kvCzhdCBTFZotV2zELHac3Lg2MrbXdeZJlU80TbVqiS1dN6w5FcoH7ji2/EL/+Dl+B51x82P8tFa92cVlfMdUIfQqhzIiqUB9O5VQxiCkxPa53Tmve0dgpOa7sgKOtMsmKpN/1tsMuDywToWE+rPldJ3NSNvGnpkTd0PCaJVnqtnaQHA/lx9T1Ph37lTus0IUwEnccHtaeV04MPLixaGYZhmJmhC/RZnNamY0/o90XRtJ+YNRWdVus4zOq4RWmGlu/ha150PW7ULhqVYvZavrngJuyRN4ASuM2c1gztwDNOTkpzWv1c4G3V9rRmaIfWyJuR7bS67ivgzkcdxe4MVyAXq5sVIVJn1wbIZHVaKomDaw+3zc+oBLUd+LU9rUasWeXB1JNryoMD110lbCERpdncx9+UOa12T6vttLYDH3/3hdc77ttqE6dVuOXBLd9D6ClnOSkEMdG5VXRap+lp3R4lY0FGLePYU09rjdPq042WwpxWz53TWoTOAZoF63sqbZiEdl15cNtXPa107k4qDw7n5rTq8mBPwPc8s8+nr/SnCmEqruvglgfb6cEsWg8SLFoZhmGYmaGLxVnSg8vcttLXqCjF3U9IKJIQ+4W/+CzufnLNEapVgT+TiNMMoZ6V+j2vvB0AjEDphr4jBO21COO0NutpHY05rTqIyROm/7Mf2enBhecXR97EeuSN1dNqu6ZOenA6Llppv6pEa924GwA41AkghBtMY5zW0BsL77ExAUShO6c1lbDmtLqzWc3XOnRK6iCrWd/3KtJM9Rl3wtwRdHpaj3Zqnm0HMTUoD6Z05MAzpaixea8kRkmalwcXgpjCKXpaM4kxp3V85E1debB7A8INYkLl8+l8GFlOK2A57DVOa+h7kDIX7xPLgz1yWneYHmycVlV2n6QZ1vsxNofJTE7rQS8P5iCmg0t1rQjDMAzDTGAnPa2Ny4O1yFikBGHSJeRC/de/fBCDOMV3fv6t5jFxmuH9D13Eb7z/Mbz+u17a+K5/nErj6n3H59+CJMvwyjtOAtCitcJppQu0I92wcXpwy/fMxXXe0yrKndax8mDl1JKbSYLHt5xWW/Q66cF6H2wBSeN1qsKYJonWv/vC63HbiRUnPdjpafXykuUi/cgNIPI81SucZdIEuziiteC0xmmGJJOQcvJNmGkht1dot3VrlKDX8nHHNau45XgPL9HzWatoFMRUcFpDPy9Ftd+j/ig145TGndYmPa35MexUOK3DRkFMbk9rZjmt9pzWsefpz5Xd02qveVIQE5DfVJk08oZu/OzUac2DwJSLnEl79BOL1iLuyJt9XAgzd1i0MgzDMDOTO63T97TmCbL1gpfE0gIZrVafZqZdNiWGnJ7WVOIjj17GX9x/DoM4LR3TUgY5rYC6mP6+Vz3T/K4d+qa0kTDJqeS0dgNsDBNIKWtLLEdJhnbom4viOM20q5cHMdmzOYs3F8hpJfFBotMVreXPNy67NR6IhESV0/rU2hC+J5ygJZt24OPFBQFnpwd7njACs0ixPHhST2urIGBT7VIDszvsVdglyr2Wr0Rr28c1hzt477/60onPp7JiCpkqg7ZPx6GlRwTFaeb0hm5HieW0FntaJ5cH26OCiuKW1knvf51JRq9FfztSK4ys6ZxWW+CSoJ/U0wrkN3ImlgeXOPSzQDdb7M/V45dVr3vZvOJJ0H4c1J5W+3zl9OCDBZcHM7vGle0Idz+5tt/LYBhmF9mJ00q9YY17WhdIteblwblYGSVuaWhshbYU3dE6oiSrvNDthp5xooixkTedEGkmnXE15a+Tou3n5cH0Hoa+HcRklQdbTmuqHbhO4CP01RiNgeWQtUvKi+Oy9OCS41PVjzuIU3SscuYm0AV6y3K9ypzQYnmwEAKpVPvpWU4XURx5k+j04eJ+zoNUSiOm6MbHSk2oUpEm5cHFkTehL8bEOEBjiZRoW21PP/LGdqiL5cHk3NIIrPryYLfUm/42uHNax59n91rb5xGtOahxWsNC9cGkUTbzdloDLz/3L26OAADHV5qPuzHr0vs4SXQvK1wefHA5mGcssxD8xgcewz94w4f3exkMw+wiZk5rPEN5cNqwp3UR04OtMS6JdQzixO1pJSFT7EOtI9ZBTGV0S4KYiiNvqNdwUolwlKieVhIAps/PHnlT4ZQOTcqsB2GJVCGqg5jKnVY35AeAKT8tMkrSqZNYX3H7CXz1C65zSizry4Np5I0uD5bSONh15cFAHu6zG3NaPc91BOtKfYs865pVnFhpjTmjNrSPAyuIKdBBTG4Jd4KNYYzVduAIdyA//2pFq+VktguPW9VhY9SPXWN65nNaC60D7pzWmp7WJHNc365xWpuVB4e+mFjuT9vaaXpwaPW00jbX9DFa7UxfMNkKPHRC78CGFLnlwQdzH69WWLQyu8bmMMbWhJl7zPIipcTvfeSJqS7G581fP3Aev/H+R/ft9RmrPHiGC3USupP+RuwkPZgCcuaN7bRSz22UZs5s1iSTpgR6Gqc1yWRlb10nKAtiUv/aPa1AtWNJjBLVk1p0WgNrzmrfdlpLRau62Kf+PrpgLBO99niSYhCT/TpVPa1Rkk3sIyzypZ9zDX7lO19qvm8FXm15cLel1u3p8mA3PdgaeeONC1jaBt3E+N2PPIHX/t4nplpvGRTEBFilvlM4rV/7ouvx0Z/4itpjZwvvlu/peZ5q/qyT8DxSQUyHS8QSnTOdGkFtn9fFnlbfE1htB1gbROr7RunB7s0PX4gJc1rLx6GQ0K5z8dtWefAkl1W9lnb5a9zbJhin1bdEq3ajp3Hc7XUd1H5WIA8VA+pvfDDLB7+dzK4xKpR/MQeLB89v4Uf/6B789QPn920Nb/vEGbzhb1m07if5yJsZ5rTustOaZRJf+J/+Cn9w1+mp1zaJ3GnNheooTt2RN8lsTmtdeXCn5Zt01eJa6AKNyiwnzWodc1ot0dr21UWt/ffbvmkwTNyxH1TqWSylLXNai32/gCtuq3paab07gfo0iwz063fJafUEssxND3bntI47reQW0/bveuwK/vqBCztaL6CdVuq9pFCldnPRIcRkV5B+P4hSx9lLUuk4x9uRclqLycHq8ZMDfmxRWCwPBtRoJxJkdf3YoUkPdp1Wz0NtebDqY9VfW+8h3QSoK+U15cHDZMwlrnv8jkfe+NbIG/31lX6ElZY/U8/mQRet9s0KntN6sGDRyuwadLE2S68bs/jQheUso07mRZzJhUqUvRrZ2cibhnNaZwxiGiUZnt4Y4rFL21OvbRJ5EFM+x1L1tLpjXYxoncJptYOYinRDH8OovDyYLtYOdSjQZjqndWiVB4clYzqc8t4Kp7VYEun0tJb0P5PAs8VtpdOa7ly0Br4o7Tntj428UeedlNIE+zgunWeLVjf1ls6HOM2csudZSbJsbDTLLA5bHeYcsEqwSeDHqXSE+cYgGUsOVo+fXB5sn9dlQUCHOqE5P+r0mHFaTZCX/rnV91km6JSDnIvA4lrqelrppsVmQ6eVtrXjICY/72kNLad1ltJgQO1HnRu+7Njve92ND2b5YNHK7BomaINF64GELjLnnZQ5DWkqFyqc52pkHkFMTZ3WacuDSSjuxo0zulliB9VEhSCmJJXmtacTrTXlwaE3ti07hAbIL5InfTZzp9UVXaE18sbGvkFE/ZvklpHjasqDS0bepCWuPDn1245orehpjat7fZsS+l5p9U9eHpzPaZVUHlw4rip4yg5lIkHnpmGT4Nvp/wPTLHcFe6Y8eL6iw4y8idJc1Plq5E2UZqbkfHuUYmMYmxsjNqantVX9Hk10Wq2+29ogJpq5W/jb4HmoLQ8G8nPTXktvip7WrVHc6OYJvU64wxstvpUe7BvRGo0FYTWl2/JxaMbnLgN2VQGnBx8sDu5Zy+w7dGHC5cEHEyrn20/RmGQsWvcb6ufcSRBTOmnkzYzlwSREimm784CWkugxMYD6m+eWvuZOa9EdrWOi01oceaPXQuIq7/erP17kXJIgou362q0SArDvE9h/yqlEmcok24Xy4OLInJbvGQcycpxWHcSkE1lDX1QGSEVpZl5nVlq+55RwE4MohRC5+FY9rW4IUl6mWR5A1Dc38nKnFVDhRa1g+pRXIs0y896uzBDE1AR75A2lDasgpgxSChzphbi0HSmndRjj2dceGtsGCb66USqOu1nSY2s7uHWCg8qDyWk1c1qFHcRU8VzLuSS6DUbe2OXBJ1fblY8jjq20cKgT4LYT089SdV7X7mn18yCmY73Zzql/9ernjP0NOUjwnNaDCzutzK7BTuvBZhC5oSP7QZplM4XzMPMjKelTbIpxWmtGhKgEV/X1tDco6ByddIH21PoAf+fn34Oza4PG23bKg42DWBh5YwUOTV0eXOHOdEOVHmyHS5EDKvRTSBjUHVdAlfi2A98K4cmdViHG3dY0kxjGKe5/aiMPYtLCo1MoDw4LTmsn9EpdeSPs9OOuOdSpdlqT+TitZX+z+lGKXugbB9Xz8jmtuXvs9uuabRac6nxe63i/7iw4c1pp5M2cnTI7PbhlOcqJTg8mMbk1SrG2HRvn1cbMaa0RrULkbmHZDQjbwa2f0+qe4/R5tOe0VpWGhiVOa5ORN/ac1iZO62o7wD0/9Wp80R2nJj62DrvcmUT1le2o1O1uwh3XHsILbzqyozUtMvb7yunBBwsWrcyuEaUsWg8yphRuH510O7mV2R/sGarTngtNelrt303bv0wiohhcVOSh81v47LktPHR+q/G23Tmtdk+rG8REwnzaOa1V4oycTVv4mXEfwnWQGjutY0FMlHrqzjjNpMTbP3kWX/ff3odzG0O9Hs/51yuI1oERrb45P2xXnkQHBSFde7i9y0FMojI92J5j6gnl3jnpwRXBOkWnNSk4rf0ZEtbtmxL2GqgkdLec1mGcH+PA80z5eztQ4T2PXNjC5ijBM06tjG3DzGmdsLY6R9YOeKoTHKYEPsvwlo+dNrNTPa+J0zre09rEaSUXPk7ljs/DaQgth5/WvDFMZi4PPujY581BHetztcJn/ILz0PktDOMUL7hx+e6K0YUJi9aDCZX97a/TKtlp3WcSq7R3lGRjsxvroL8Nde9hMgfROpogGGf5W+XMabXKXotzWkclQUOTqOtpJUdoGKfmor848qYYUlNGpsV2y/eM2M2DmKwS35F6zShRZdAbwxhJJvHIBRVuRQ7rpJE33ZZf6soXndbrj3TxiSeulK55lGQ4OYf04LL3eRAVRasqD5Yyd/zsXk8bM/KGyoOtnlYARlA1RUqJL/vZ9+B7Xnk7vvMVtyKxRt5844tvxLFeC4dKgpB2gu1O2fs5TFIIKGd3pR3g44+r9+Y5JeXBpw610Wv5pSFNNoEnMEJ5ebDtHtb2tOr1fvrMOt5xzwM4dUiV66qRN6L2+XY6MmF6WmucVvtmRXsPRasZeeMJ5+8ri9ZyXKd1HxfCzB12Whecn3nXZ/Cjf/Sp/V7GTBinNd2/OZ7M7kEXaPvd07qfoplxheS0N6jI2as7hzLHcZpubU2DmOj30wQ25eXBmTP2JyqWB88w8ibJanpa9cW17dzSWuga3ZQH1xxXWmc79ODp/lUaYxMWUk8pMCeTuav82KW+8zv6ly4Yi0FMncDPU5attds9rUIo4VPttKZTz2ktYo+8+cijl7Gux6sMohS90BZMan9TOT6ntejG0ff0nkjdCxuZfZvu/4EbwwSPXtzGE5fVMc6sNdx4tIvv+PxbptpeE+zTLXda8/LgwBNYafs4u64c9s+57vDYNr72RTfgb//Vl04sXSbhVVYebAveOsFBx+Ph8+rmyYXNkfk5Pa9atI6XB9MNoKqbRQAcd7W1w/NwGkhI+9acVgAzpwcfdOxzmUfeHCxYtC44/SjFle36sQWLSt3Im7ufXJs4joFZbPrxIvS0yqnDeZj5YvdNTpvSS38j6nov7fNr9p7WetFAN9amucFmlwfbvZrF8uBIlyY3LQ+WWhhWzmkN3bJbeg5gOTK+G1JTBr1XLatUko4TpZW2zAxWdYGeZtKEZj2uxwgVR94Y0VoIYprU09qPUnRDH4e7IbaipPRzPY+RN6EeeZOkGf7BGz6EX3+/mvPcj1NnDIgdxFTsaS2uwYQY2eN90swEPk3b00ql1/S5SKzy4N3CFngmqMiMvFE3USi5+IYjHRzplc1pFTjRIKCItl818sasqWaf1egagUcL46w8K4ipSq+UOebktPo15cH2+76XTmtgOa32eXCQE4B3gn0u88ibg8WOP3VCiJuFEH8thLhfCHGvEOL/0j//KSHEGSHEJ/V/f9d6zo8JIR4SQjwghHj1TtdwkEkzubTiji5Miu5LnGb4lv/xQfzuR57Yj2Uxc2JQ6N/aD9hp3X9sIbkrTusOyoPz9OAJTutM5cHq3yTNherYyJssm3rkDTmPVeIsLw/OX4deki7W/AZO66iQ/ut7wqyV0kqNuAhy0Urre/SiEgt08W5G3lihMZ7IndZ26BtH2j7OiVUe3GsFONwJICWwVSL06np9m0JOK83UPaPDtzaHMVbbBdGqb4oV+3SLY1HoONnvcWKNQtqesjzYiNYsQ5ZJSLn7ozvs7ZOLGOqRNyRaKbn4OdeNlwbP8lrlPa3NyoMB5XAXP7O+J8z7Vem0mnPVSg8mp7Uusdi3j9Eeila7p9VaAzut5djnMo+8OVjM41OXAPgXUsrnAngFgB8UQjxP/+7npZR36v/eAQD6d68B8HwAXwXgl4UQB3fK8Q5JUonNUfld50WnymmNkgxRmuHSdrQfy2LmRN+MvNnHOa2ZO+6AyZFS4j++4348cqFZuNBf3HcOb/7o9DeSbGE0mhB4ZJNZybp14monPa0kRic5rVU32OpwnFYniMnuac3Lg5uO3SGhUzUvki70bYFEa6Gn0MV1bXmwXlfbuiAemZ5WN3DILg+mGwxUwps7rTQqJn+NVuCZm1vd0M/HI1nH2ZTQRglW2r7paSwbezOaRxBToEQrvR8kEM+tD3Hd4a55nOdBpwfbx5UcuvogJsC9mbE9ZRDT0+skWvOe/d0uc7QFXss6J5I0Q5KpHmtKLv6c68dLg6eByqk7Je/loYblwUDulD7rmlVzjnoif16VYCHH3P6M3XSsB08A1xzuVL5e2/etr/fHabVL01fb8+1rPig4QUysWQ8UO/7USSmfklJ+XH+9CeB+ADfWPOUbAPyelHIkpXwUwEMAXr7TdRxU4iyDlDuPzN8Pqi4E6QJvq6JviVkOzEzC/exptcrnGJdL2xFe/95H8FefOd/o8W++60m84W8fnfp1Uke0Nhd9du9n3Y0P+4bE1OXBDXta61oZqrCDmOwEZVuc0g06oHlPKwmd6vLgPIiJkFJCiLwUji5s644X7XPLckhNerC+qDcuqikPHv+s0XroX/uiOvQ9Z+RNbPX+EkmhPJhES1lfa5RkOy7LbPke4lSa/t3zGyOkmcS5zRGuP5ILFiHEeE+rNQrGxvS0Wv+fjtIMxRm0TSEhnVrnll/TazkPAsdFzAO94lQiTpTTSk705+zQaQ1qyoMPOyNv6veZ3o9nnVrFK55xAp7+DDQtD7ZF7XOuO4RP/dSr8axrVitfzykP3uG84Gmg88u35rQC7LRW4QQxsWo9UMz1UyeEuA3AiwF8WP/oh4QQnxJC/LoQ4pj+2Y0AnrSedhoVIlcI8X1CiLuEEHdduHBhnktdGop3tZcJujAZKw/OZiuZYhaLRQhiotee1oG7GshnRjY7Nmkmp+5JBYpOa/Pn24/dLac1Lw+e5LRST+v0opVGghCbw0Q7IkKVB0858obWUDenFXD3KbVmiQK5M1M22oWg49+2QnfMnFav6LT65nXsPtnQt2ZuktNqXSS2A89UZHRD3xwz+/8JeU9rgpV2YIJ4qkTrvEbe0L4+vTHExS0lXK+zRKsvBKRUN01IBJGYK95QICHhOq15JcH05cEqVCjJLNG6y06rX+K0hnrkTZRKBFZPa1kI0zTUjbyxndZJpZ20nVtO9PCPv/B2fMtLbwaA5kFMhd9PSuN1yoP30mm1nGHbHeae1nJ4TuvBZW6fOiHEKoA/BPBaKeUGgF8B8EwAdwJ4CsDP0kNLnl56JSKlfL2U8mVSypedOrWz4czLCl1wbixhXytdmBQvBGcdA8AsFsWZhPtBnuDKorUI9Tw2nZ2aFFzCpqTOyJvmz7cf23RO69TpwQ2DmEx68ITeVxsS0LEunyS2RwlC39O9k7lwGTTctulprRp5U5IerEpYXXfBE9M7rXkQkxs4RGWcWaGH3B5ZkvfG5q9BxwBQAiVOJaTMb454Iq/U2B6l6LXy8uBilkOqX3vnotUtD14fxKY/13ZaPaFEepnTWizdpu+dntZUzl4ebPW00vHe9SCmipE3lI7d8gUOdQK0fK90Rus0mPLgsvTgrpvgXAet8+ZjXXzxs0/hp//+iwDAGnlT/7y68Tal6/Y9s812ieDeLej88otBTOy0lmL/LeT04IPFXM54IUQIJVj/l5TyjwBASnnO+v2vAfhT/e1pADdbT78JwNl5rOMgQhecy+a0SplfrFWWB19FovU9n72AEyutpZy3W0WeHrx/Pa2J5XYxLiQKm5Zvp1Zo0DTYImaanlD7sc1F65RzWvUxGDYsD56H07o1SszF8DBOzeMGDVs8KHG2sjxYC0W73DjL5FgpZOB5tS77qCBaA09ga1Q18sZOD863aZdItkvCbWyBac+UpXNzpR2Y/e1HCa473LFEqzpev/3Bx9ANfXzti24Y2+Ys0JxW+1y/+8k1AHCcVo/Kgy2nNS8PLjqt6nv75kicWenBMwcx5enoVT3O88JxWq1zgpKWA9/DP/7C2/Clz7mm8txsSn15sN3TOsFp1du5+XjP+fkkp5Uc81luBLQCD8N454Fg0xBYN03sY8/lweXY7ytr1oPFPNKDBYD/CeB+KeXPWT+/3nrY3wPwaf312wG8RgjRFkLcDuAOAB/Z6ToOKnRBuGwJwuqOuvq6LD0YUHfWrxb+3Z/ci1/5m4f3exlzhS7C93vkjf0vk0NOa12JqE2Szua02j2nu1EenFolwXLK8uBhlLcoSCnNf1VrmS6ISf0bF4Tc1ihBy/fQ8j3nxlzT8mC6CVQpWltaIFlrted4EoEvanuFTRATjarxc6eVRFg+8iYPYrJvUrXLnFZrGfY+kECJ08y89qF2YN77fkROK5UHx5BS4hf/8iH84cdPj613VmjkjX2uf1KL1huO5EFMQghkmZq5WhStRYeOREWxPJhuGkzrtNo9rfvrtKogpjhTI5huOtbDq56986o3Ux5c8l62A88IwkmCg7YzLlqpp7W+PHiWGwFh4bOxF9Dnsei0Tipnvlqxb8BwevDBYh5n/BcC+C4A9wghPql/9uMAvl0IcSdU6e9jAL4fAKSU9wohfh/AfVDJwz8opbx61MuU0AXCsjmttmNRdC/of8JXk9M6SrKZBMEiQxdo6T6WB9Png0XrOJQE27Q8mHpaVahP8//Rz9zT6oxsaei0ztjTSmv70T/8FDwh8HPfdqe7lor++zqoPDjNMqdEfkuXB0tI9K0bc02DmKJEbatKtJqeVmt7aTbuKvnaJat8HT2TNnfVPCPE6WKexEPXclrtfe2UOq3j4qe4jVGSwRNAp+Wb/z/0oxS9to8j3RC+J3B2fYjTVwa4uDXCjce6GBXWOytm5E3sOq3twMPRnt1PqW6SpJk0Jc92qq6NX1IeHKd5CNc0QUxJmuHCZt7TSudZ3fzQeWC/b6bP2ReIzcib+V38kwgrCzMSQuBwN8DFrWhiaWfoexACuPFo1/m517A8eBZB0w48bGJv57Sa+cueMP3mAHCI04NLsT8q3NN6sNixaJVSvg/lfarvqHnO6wC8bqevfTVAFwhl8f+LjH3xV7yQvRp7Wu0LmIPCwKQH7+fIG3Zaq6DS2KZBTCQ+ozSbys2adU6r/XmodVp3lB6cv8YwTvHQha1Sd2cWp5XWEqfS+QxsDROEgYCUwkl9nzQrlqC/j1TCWKRq5E3x+jv0PbPGr/+l9+Hvv/Qm/MP/4zbzexJtZaNqyEnMndY8iEmVyyqn2S7vLBOtbnmw+jpJpRld09JOHqBKaFdaAVqBh8+96Qg+8PAlPFePVhlEydiInlkJfQ9JJp3jd3Z9iNtO9JybNZ4QpqfVzGkN3ECmfJu6HNy6kWDfpJzm/3UXtyJz88DtaW28iZkISm42hNYc1J2WBJe9VpXwO9QJcXEralQefO2hzliZ8cQ5rRU3H5rQ2genlc4v3xNOivRKe+/6apcJN4hpHxfCzJ29+9QxM5EHMS2XwLNDVorhLKZk6ioSrXShdpDoL0B6MF3Q7Wdf7ax84okr+JE/uHvXZsyOpiwPpvexqbgiZp3TOrLTb2veP/v8mjo92BERGbZHaWmPrwlimmL99g2TMqe15XumQqYb+o3LgyeNvAl9z0n6BZQjWBzt4Ov0YgB44OlNPHTendcbGXE8fgEfmPRgLS6oHzWTiDOJ6/QsS1t0mDJjW7RaF9j02CTL9Oga34xUIfeVQqZeeccp3HN6De95QE0N6EfpWA/urNDziyPX7H5WgEbeqH32i+XBY06r+r5vvSd2qXB/ivJgKg0GdA9xujdOq1fW0+qPC9l5EPgC7cCrrOigsTeTTLJu6OOWE72xn5ue1ooltywROC2U6r3TMvVpoPfeTg/uhv7YvGBG4Zvy8Mljk5jlgs/4BSdd1vJge3j8WBBTXg62rA7Z5jDG//m7n8Dl7ajR46N0tpCbRSWznIp97WnVF3RLqFnx/ocu4i0fO71rM5jJaW2a7kzv4zTCDSjMaZ1C8LpzWpttf9piBXtfhnGKrVFiwnGcx8XTBzGRgE7SzA1iGqqe1sAXZtzLkW7YvDx4gmgFxkVwKuWYqxR6wrz3dh8pYURgSalkOOa00txX9f+klXaAo73QcbjoMWXix/eEudhPMolRkqIdeKZUl47Tih6p8kV3nEQmgbd98gwAdfMh72nd+cgbANjUN01pjdcfKZaY6vJgOz3YGxdzQHlPq/21/Rk/uzbAk5f7leuj5OBu6KsZwHKPgpjKnFbrHJxrebDnlYYwEYc6YSPB8W++7vn4d9/w/LGf5+XB8+9p3U+nNdA3rAAOYaqDbuBxcvDBg0XrgmPKg5csiKlOtNrlivO4YD+zNsCffeqpHW9nGu47u4E/ufss7j691ujxSSqnKj1cdIaWGGjaM7kbLLPTGqWzOZtNMUFMDY8N3SCbRngC6tymC95pRJ/b01r9vB3NaY3sio8M26Ok1HmuSjqvwx63ZAvr7SgfeUNhc0e6IQZx2ihIiv4+1omEdug7Tmtx5A2ggpVobZkcf2/MnNZwXIgFBdFCrpKa06re71uP93Cs18rXpB9TVmZqO0RJJjGKVXlw6HlIUmkEXk+XO95581GsWiFNgzgdG9EzK+SSUrjhrTrE5/qC0+p7AmmmAgVJPFF5cFiw8OjY2efPIM7/32ZXFf3k2+7Fj/zB3ZXrI6f1xmNdndasxwPtdhCTtXk7PZiYd3lw2bgb4nA3aCQ4XnDjkdKZsRPntJaUsjeFjs2eBjE5Tqv6mme0VuNPuGnBLC8sWheceEmd1lGd02pdoM6jRPjNH3kCP/imj++psKeLqTLXpow4zaZ2sBaZvhMCs//pwdOKmUWAxP5unRfDeK+c1gw97ZpMI3ibpgfb7+20pdSDODUXsP0oQT9KS0UrlSpPUw2RO62yIKyV4Ays9OAjOuCnyfYnjbwBgG7LGxt5M9bT6qnezbhCkOc9ouNlvSaIybpA90Q+pzX0PfzSd7wEP/l1zzPPIfHrlgfn4pcutpM0wyjNlNMaCMRpZv4/QE5r6Ht4xTNOAADuuGYVg3h+5cEkWOj/qbeeKBetnhB5P2lx5E2hp7VM/Bgh3vKdQK6NYYxLNRU65zaGCDyBaw+3EWeZqS7YbadVCGHOoZbl7BHTzjStI/BFvdPaDnckOHZrTqv93L0MYrLntNKa2WmtxvQ0s8I5cPBbuuDQBeeyjbxxRGvhIrFYSrdTaJzAg+e2JjxyftA+NAm5kVJd6B0kp9W+CNvP8mC6AbKfa5gVOod2y2mlz+A0I29mWU+SSbRDVbZGibRNsB9bd+PDFt2zpAcf6SrBSKX8ZZ/ZnQQxJVk2doxVT6swYozW0KREeFJPK6BKR+33qWzkje8JJGke5DNeHlxMDy4RrVQK6atRG6n+W+Z7Ajcf7+Haw7nQywOdLHfO6o2ki21yWtuBr2bJZrnTSj2tAPAVz70GLd/DK+84CSnzMMKdzsckQUb/T6VxKdcVyoOFyN8Lesmqntai8wrk7/XRbojtKDEue2KJ9DIubI5wcrWNlg7Sor9xe+Ea+YWbFbvltD7/hiO48+ajlb8/vtoqTRZuysQ5rTvoad0Xp5VuIlgjbw6xaK2EjhE7rQcPPusXGBI7wHKnB9eVB88jQZgcpYfOb+Kltx7b8faaYPeKTYL290CJVqv0ramTt1MevrCFDz9yGd/x+bcAUK4PaZ1l7I2m82K3ndam6cHpzE6rEjGtwMMozvDQ+U2EvodbT6zUPs92ZZs6rdO+z8MoxbFeC1f6MS5tkWgtKQ82QUzTiNZ8TcXPQCsgwaF+TqK1H6eY9BeKgqLqREKn0NNaVh4c6JRccm7p5uETl/oYJuPltvYFvOnhtJxSTwjltKZZqetHzpntXrUtkZc7raqntUU9rUlmRCs5rQDwrS+7GV/8nFN4973nAABr+v+B7RqHrgm0TzRPl2azljqt+n31CiK+WLptJ7pSWTHt05FeC2fXhxjGKmgqyWTtzdq1foyjvRC+Lp2mc363nVZae5zKiiCm+b3+D3/5HbW//55X3o6veO41M2+/+ZzW6YUn3ZzZaYr1NJiRN3ZPK5cHV0KVEdzTevBgp3WBsS/Qlq88OL+gGg9imrdoVdv/7B46rXT3u0kPHz32IAUx2eXBe9VP+taPn8FP/PE9xrFIdyBmFoFol51W2m7T9yeZsac1zSQCz0M78DBKMvyL378br/uz+yc+zw4Cqpv1awvaKqP1T+4+i2/+lQ+M/XyYZKY09+K2mn1ZWh68gzmtcaE8GNC9Z9ZF7dFpnNZCQFIZJFrPrA3w2MVtVR5ceHignVZqMaF9+3//9/3457//SURJ5jg3dmIwXeyTeAl9z4ixJJOlZZWlTqsl8uh18vRgD6GvEo4p26BnjfDwPIHrj3TNfNe1fjTxuDSB1rQ5TNAOPXzBs07gC555As88teo8TqUvS2efwpKyWcB1WqlUnv5GHtPnH+1jnEpsRUllqTuJ1kAfb/rbNosrOC1jZdDWfs3TaZ3EydU2Xnrr8Zmfv5tzWk158A6c4Flf07c+r6s8o7USOm1Zsx48+FbNApMssWi17+IXhV2TntbHLm7jaC/EUSvoowoKBXrw/F6WB0/htCYHz2mli2+6sNoLEh2KMkoydEJ/R/M7FwHT09pwFMq05E7rlCNvZnBaPU8F8URJhjNrQxzqTL6gos8DuU9VZA3e53vPbuBjj1/R4i2/UhlopxUALm81KA+eIkjKHXkzXh7sWwr7qBYuwwbvtSkPrpjTCqjy4LV+hB//o3uwNUpw87FuidMqdE+r+/dnc5jg6fURRlo4EkXxSvth9ofmlmYSrRK3s2zkDe1D4Asj+Gj81+FuqNODpfl7YjutZl91yfA6lQfvOD04F62d0MfzbziCN33vK8YeZx9O49x4Atcf6eCmY24psb3PvbaPzVGCgRap9N73Rymwqj73UirXvcwtWxtEeMbJVfi+0D2teydai47ybo282W0mzmndSRCTKZnfy5E3eXmwECrUjMuDq7E/r8zBYnn+Cl2F0IWcEMvX00oXf4fawZhzY4u3rVH5Rdw/+c2P4mfe9UCj16KL/gfPbc6y1Jkg4d0kiCmewpVdFshFONQJGpef7hRK0RyWjNqZRbT+49/4CN5979PzWdwM5OXBu93TOmUQ0ww9rYHnoRV46McpLm+PGrm75G72Wn5tkJbzPlc8jkSj/RmTUo1lItFwcWs09hiC/iZN1dNKPYqZ6hu1BWAYeI74Mz2t04jWWqfVwyBO8ZmnN7A+iJGWlQfrkTfFYxMlGS5vjzCMU0cA2hfGRN6/J+B5eXlw2cVgeRCTurAPPc9yWpVopbFAdhBTrzUuBOhna31dHrzjOa15T2tdgq19PEkECSHw3n/1pfj2z7vFeax9zEh4099IuvFKVUV0PleVCJPTGhac1r0qDwbsXuTdGXmz2+zmnFb6TOyt0+qu9wufdRIv2aNWqGWEe1oPLixaFxi62FBBDum+jhaZFrrwPdQJMBpzWq3y4AoxvjFM8ETNLDsbKoN8an24Z+I+d1qbjLBQ6ytzZJYVmqt4uBvumctJh47eb7ukdNo1pJnEXz9wAW+/++zc1jcteXnw7jitI5Me3NBpTWd3Wn1PoB14eGptgEw2ez+iJIMQqtS1zmm1t1VVUpkHo+X7SqL9aFeJBkpsLTseswQx0VooodcWXC3fMxf+gOprBJqVB9MopElBTBc2Rzi3ocRnJsfTgwPPc0Ki8r7dFJkEzm+OykWrb4tOct58J4ipTMB0yGl1gphspzVPD46SFO1QjbxRc1rzGxhl+wpYPa1zG3mTmDWXYR9Pe3dD3xsbP+N5efIuOcN9K4hJfU/lwep92BrFeOsnTuMV//EvzTkupcTaIMbRXmusp3W3R94A+XvXNuXBS+q0TuhptUfITEtonNa97Gl1e3B/65+8HF//uTfs2esvG0IICLE3nxlmb1mev0JXISSIjq24d2qXAeO0dsKSnlarPLjiIi7NMjy9Pmz0WsM4NaVce1UiHJc4O1XYPbwHxW0dWE7rXiX3khuXO635sZxWtNL796nT63Na3fTQ52BakdgU2u7uO62qN7Idejh9ZdD4NY3b5onaOa1pA6eVwovs16XzJHdalWgtE9Xk+k4TQmWPvEkzqYOF8t5HW9jN4rTWXRR3Wz6uaOdxGGeQUk4uDzYjlugm38CU9AL5BbztruU9rQKeEEiz/CZFEdUL67pXbdOLlzutqXZaaeSNPad1paRcdjfLg+vcMlt8N3HkSFD0jGh1y4Pp/3X5RIAEn3lqE09vDM25OoxVv6/T0yr3zmn1apzWWUKL9gt666qCeJZtTmtYckOJqce3RjgxB4fl+St0FUIX5cf1Xfpl6msloXqoEyAqXAg2SQ9OM2mGrE9imKS4/aRKKt2rEuFp0oNtoTqtIFhUTHlwO9wz95g+D3Th30TMVG9LPf6Jy31cqZmZuJuY8uDdGnkTTzfyJk8Pnj6IyfcEWr6Hc5tDZ1u169PCxfdEbQK1/d5WbTYucUrpPKEgnMs6iAlwj4mUcrbyYP1Q5WZSGJUuh/U9J8SGRGujnla9hroLVFtsjuK0VEhSefC406pF69qw1Gktc9fCwIPvKXc5TrOxICJAuRv0fo493xLxsRGtauRNlGboRwmEKHdRe7rc1gQxzak8eBCntU6r7dJVOXY29H51K8qDt015MDmtiSl5pvP/it7Ho90Qvg6pSvbQaS2OOnJLxZdHAUwKYjJzaGc4piY9eA9Fa1npPlOP5wlODz6AsGhdYJKC07q+RGNvyLFYbQeVQUytwKvs60kziY1h0qicbhhnuOOaVbQDb88ShMvKEas4kE6rvvg+3A32oTx4vKd1WrfXFtr3nNkftzWf07q7TmvTY5Nks61H9bQKtAPfpPs2ec1RkqEV+Aj8+jAv24Wdpjw4rwYI4QmYkTeA+zlMrNFJ03w+bac1yTKEvjAXsqHvOUFKU6UH6zXUXaDa80yHuty3KKzMyJtCEBP9e2k7ctzcMqeVQqyOdEMniKlqbcd7LSeEy573SSWOqZUe3ApUCez2KMVKKygVh8We1nmlBwP5mJ4yvCmdVnoMpQfT30h670m00vuxNUyMSKXzjvaRelqTTJqy/T1xWgvpwUsbxKSXPWnkjT+De0w3X/bSaaUE9MNdTgxuii9Eo5tNzHKxPH+FrkLowo+cgmV0Wlc7QeWc1mO9sDI9mNyVpxu4rcM4Ra8V4NSh9p65ZvTeTNPTChycBOF+lMD3BLoT+hHnCQkW09PaoNexCvt9+9TptZ0vbgZmmQ06DcMpnFZ75u2sTqtdatnEfR8lqXbmvAk9re5rlUHlwbboJNHQCf2xvln7RhLtb6/lI05l43OJ1pLoOa3U1wuoC1q7nPKw6WtsIFoziZbv1V5wdS2xFWs3dbynVY+8KbQy2CXQ9nvml/T5fcEzT+BPfuiVeOap1TyIqaI8GADe9L2vwD/9kmea73Pxk8+XjPWc1nagfhZrp7WsnxXIheVaP4JfGCU0C65orQtiyr9u4tjQ/vWKPa1a+PdNebBOcR4lpk+X3qO1gfr/15Gu6mlN07w8eE9G3niuILPP4WUqD86d1nrROsuNABPEVOPSz5vPue4w/uSHXomXcfhSY+zxQMzBYXn+Cl2F0P/cyGldpgRhCllZaZWJ1jwgZbNCtJK50qREeBhn6IRqUP1eCaikUHJXhxsOszuu2l7Tj1L0Ql+5OXtWHuwGBRWd1qfXh/jpd36mkeiw+2Hv3qe+1qQgwueNcaQb3FixS3Cndlp1aaztgDUuDw49+MJ9/PmNIf72wQvWtiyntaqnNRkX6LQf3ZY/VspXdiPpsHYIm7qt9lpGSYrQ95weUPtrEjKNelqTbGJSa1Fs9aN0vDzY93T4m+u02uXo5U6rWxb7wpuOAEAexJRKp/TZ5raTK6YUGnB7Ymm7NPJGzWlVf7O3o7S0nxXIReDmKJlL+I0tWtt1Tqt1PJuU5pKYboc+hMhddbrpTK0wdINle5RgvVAeTN8fWwlNT/KezmktlAfb5+EylQeLCeXBO5nT+oIbjuDFtxzd8zTlF950hJ3DKfBE9fvPLC8sWhcYuqg9oUXrxhI5rRSy0gq8kiAmdad+tRNUOq0kKpqI1lGcoh3odMs9Eq1TzWktcXWWnUGUotvylZsz4Zjfd3YD3/fbdzXurayCRMLI9LS6ZaN//cB5/MrfPIwza4OJ20qskrv9clpNefAu3cgYlQi5KuzPzdROq9RzWi0B0OTmUWSCmDzn9d/4ocfxPb95l7n50MRppdez/9bQzYCudlqLr03QjSSae9h0/+21DOMMgS+cnlYSge1AiWZPNJ/TGk4oPSSn9YhVdjxWHuypOZ9jPa3WAbXfM9/0+ZW/ti/U39ckk+axk8h7I3PneRinkBJOcNX6IHbc47J9pefsFFv41qcHW6K1we7m77cS4xTEtNIO4ImS9OCS8mAK1zrabaleb2tO696UB6t/jdO6pEFMtOyqmw2tYPwGTVO++oXX463/7AtZQC44vic4PfgAsjx/ha5C6MLazHlbIqeV7qS3A2/sIjDWaaOr7XLRKmVeqtgkQXiYpOiEvik12wvMnNZGPa0HsTxYOSOT+hEB4K7HL+Pd951zegpngV5nUNHTSse5iWCi9+15NxzGuY0Rzm82C/2aJ2XO1zwp6/2tIslc13Aa0mzcaW1WHpyhHY7fbOpHKaI0M+0QrtNavq26ntZO6I2JVqf6wRrPBTT/jLqiNVVBTKHV00qjMQJV6tsNfWwOE/zVZ86V/t148nIfj1zYQpTKif2DtD8vuPEwAGA7SlC8/g48gdQKYiLXzt6/Mqe1ykHyPIFMSpMW3QTHadXP2dbirR34Zj/XBzFW2uUC0rPKrucRfmMLlbr0YKc8eIqe1tAXCD1hyoFbunc31inTZNDXlQc7Pa0UxLQHImm8PNjqad3DHs6dko+8Kf/9TpxWZjnwPcFzWg8gy/NXaIH5H+95GM/88XfMXZDEWe4UANOHzewnUapCVlqBKv+ySzbjRPVsrbaD0vJgezcn9bSmOmikE3p76rTm6cENHKWS2ZHLTj9K0Q1V+uck4U7v/U5vKKRm5I2+CLeOfSbzwJkmgok+S8/QqdNNxyvNk2K587wxPa0Nzjl75u205crU4+j0tDZyWnVfo05JJWLjOqkLeKd3uao82LiJ+e/p5kY3HC8PttdHn8/VHZQHD+JUh1GNlweTMOy2fLzpI0/gn/zmXfjQI5fGtvfv/vQ+/Mgf3I04zSaWwZ5YVTcyX3yz6nEbRGnpyJvYCmICxtPa7eNCF/BVF/LktKa6HLwJoe20arG4PVLvSzv0jJO3MYhNSnAZVCI8D6c1nMlpnXzxmyclq/2iv/Whp25gREnm/A28uDky1wx2eXA7UDdZfM+DlPn5uBdOJ/U156Ob8tcMl0jg7WZPK7MceILTgw8iLFrngKd7suadDEv/I6MLi70SZPNgFOfpkEAxrVOV0lU5rfYF7PmN0djvbYZW2AqlZe4FxUTOOpz04AMiWgexCk5pcqOAdn+nnw8SVsOSkTeU4ApMF4513ZEuAODiVv15thsU04M/fWYdcsrRPXVQGXVcMwOVsD9z0wcxZY5gUz+bbuSN/fjcddL9fvZoo8r04PogpmLvolMePC+n1eljtcqDtZjvhL7Z9uWSwLitYYJzGyNVHjyhbPFLnn0N/uyHX2mc1n6UjpXCUdm1/d7WilYxnh5s43lqTmuSycZllbQfgS+M6KIy2ZbvmdEj64O4MogJyMfezL08uM5ptY7ndE6r57x/YaA+G3GaOefyk1f65ms7PZgSm+kY0znTtCR7J9ChycuDlzM9mLTK5J7W5dknZjp8T1Q67czywp/YOUD/E5y3IKGLjc6SOq3tIC8ZtC+E41Qi8D2stANz193Gvsae5LQa0aqTKJMGF+jzgF6niRCLD6rT2vJNWEgd5LQ2CQSqY8xpLcxppe+bnAO0lusOtwEAF3dYujwL5ICOkgz3P7WBr/1v78PffPbChGc1Z1RwceooCrBpoB51W1A0cdXpxlaxL5rcUnJabUezsqeVyoOdntY8iKmj10ZVK2XhaIenFa3WUoZxhtAvzGml8mD977e89Gb8M52sW5YEH6cZrvQjLVrr/9fseQLP///a++9o27KrvhP/rp1OuunlVK9yqVRVkqqEIpJQRpIRIGzAgNsEgwdJ2GCJaKAbuxu3sQEz2jb0j/4RhABjMBbQBAUQQgRJoABUlaSSKueX37vphB1W/7HWXHutHU6+eX7GqFHv3XfvOfvsvc+5a67vd37n6WVTjCul1f0eX7dL2K+HwvyCgg0UyIuiuoLZ9zC1PTjwxFCl9doIpZVG/MwliMkKFBo+8sb+8/jpwcXkaLoX4jRzXCBPXsl772178IoObqIimN7H26Ea0XOEFUnSe9EeXHfOTi43cdvxBdx+YnE7D4vZRjzB6cH7kb3zKbSLoQ/zefdT0mKTfrHuJaV1kKSIdE+r+rultKYZQk9goeFjvZ+U0l7tJNNRts1ekhf2vidmLozGZZI5rbGzIN8fRasqOMYLYqLrOXMQk0nbLSutdkpqXFC2q/pVqbDdSaV1kObBUhfW1PN/6vGrc3lscn4IoYr7UQqu29M6qdIqnRCiI51orM8qtbFVVuvNAl4Xrfax1b2OQVq+x3qWPZg+QynJtXiPADDzRcft6c2cc+bagyNLbaPC7bvfeBu+6/W3AqgpWjOJzUGKtV4ytqpF9tZBmpUKq9DPg5MImot9crnpHBtgpQePEcQ0dtFaMfKG+jZbYWBeZ5rJ2p5W9b3q34al/Y7L+CNvJgxisop+W6EMPKGLVteqbW/I0mfXlc3YhGsFpmhV9+N2LMA9TyD08wAbN4hp7xQAo+a0LjQCfOAdrzHJ2Mz+g3ta9ydctM6BrVZaaWGxl5TWfpKZAArAVSTjNEPge1jQysZmQdkhG2gUeDi/1hs6wsS2B9MibTuYKD3Yui8G6f4YeZNkGaJAqFmCI4oiuiaz2oOTQtFqK6qpHcRkPc+vfvQxvOk/fbh0fHT9lpoB2pGPi2vbr7TS8ffizNg2P/30fMbv0EJ3QY8RGWWZnkVpVXNa8w2qUyvNsT6r+npjyy9sfMSWVRJwi8O05j4zI6icIKZ8Q4uObVlbL6vcD4uN6e3B3UHqnIMwEKY4si24LR0YVzW+jF7D+dX+2OM0ms6cVfdnfM9DYgUxAXmxfFpv1rg9reUxJzaedrJIOb6t0h15o/78tE73PrHUcJ5rHKW1MQel1S6+hs3anDyIqSKES8/bDX2BQZo5n1n2rUzX6NpmbJRWU7RqZ8m2jLwRAnV9rHvJHjyqp5XZ/3B68P5k73wK7WLoF/O8rZ/2WI7AE06K5k7x0+9/AB964PzI7xskhZ5W2x6cSYS+MHP5in2ttDA9s9JCnEpjE6wiL1o9tUjb5jmtcTL6+Zx+wS1Kit1uYp1wSouaYZsF87IHZ9ItWotKKyna9j1wbrWHq5txqWgz7y3fw9GFxs70tBp7cGoUsPufXp3LY5OFmgqxUZbpWZRWUt6oADq51BqvaI2pp9Vzg9oK4z/osdSmVPVjFce6AKqnNfJVUVxWWsv24El7Wm3bck/PVrV7Wu30YEIIgcVmgNWKopWO6dxab3yl1VIei6pSqAOu7HufnvfUSr3SOiyIic7N+D2t9sgb9TNPmaK16bzO4T2t8wtioiISGL+ndZzFL30W2v3MdqBRnGS1n4ED2x7cUhsrvu9u+G6X0urcE/oYPLG3knZHzWll9j88p3V/wkXrHKiywM6DfLFWViN2il/6q0fxvvvPjfy+gVZaaSe7ZA/2vVqFmoqR03phNayvlRbnjdDf1p7WeAL10LEibtNInq1mkKhrSH1ww+5N+qeZ04ON0lrR02oprVUW0eJzUzhR4AscXYhwaWMngpjy10Mp2s9c61WG9EyKUVp1ITZqcyV1NlYmV1o9IXDdoTYWGwFuONIeqb4DlDBe0dOqzwvZg7NMQuhFc316cNka3otTU5TQ/w8ZpbVcpFN6cH/M+7TYaxsUeloDYw92i7HFZlhpD07M646nKlqLdaQ6X67d2SitK6S0+s73A8ODmOhcTdzTao28oZaPY4sN53GGFa1kD55H0QrkxfTwnlYriGkMxc4eF2OShIN84yJO8/Rg2iAhbHfBSmfnlFayMpu/V6QI7wVGzWll9j++x+nB+5G99Um0S6EP9Hn3tNLj0S/8dAv7NT9/bg2feOzyyO/rx9lYr7OfZDodklTofOGUpNJJ2iwqO1ScnFhSReuw+Z59E8S0vT2tVQVSHVUzIfc6lHBKC6thRSsp53OzB+t7yX4/0OgjwFV0zdiXwnPT94SeVlq32R4spTSFs620AsCn56C20us29uAxldbIL89VHkWqldY333UCf/Mjb8SK7skbZdXPldZCT2tSVloD3Z9Unx5cvWFBtlIqTlYqlVb154mDmDLpBAOFhZ5W+rfiuJ3FZlBZtNrvj3FDb2ylsNzTqv7NHmFENnQqWquU1rrRJrbSOm4BZY+BoZ+JU4mjC5FSo63nJ+dNFcYePOeidZ72YNsSXBwZQz2t9D6jzRMiSSV6cYp+kuVKa6GndTt6Sn1PFO7p/Pj3EqPmtDL7H09wT+t+ZG99Eu1Sqvo25wEt0AJPTKy0SinxXz74eTx+aXP0NwP4qfd/Dj/8nvtGHs8gHa9oVfZgv9YeHHjVIU1AXuQcXVDJrkPtwUluD96KntY/+cw5vPe+Z0tfTyboaU32odJKCacU2jJsQ2Vu9uBST2sxPZgUWKtw0fdH8bwn1obQkR2wByslUh9jnGG9H5tF6v1z6Gulc0Tq4ahzT//ebvgTK61JJuH7AkIoG+446juQBzEVHRL5+A89p1VKM3OvTmml57I/SzYHqSlW6bPm0LCeVhPENK7SWkjftWbV0qxOoKwOqqJVFeT/6r//LT74WeVcsa9RNG5Pa1BvD6b7ye5Rps2R6w+30Y58HF9slL6/Tmn1vbxoHbeIofMeeOr+oMLr+KLakAyt3tjtsgcDttJa/3j2+Rxn7WuPvAmsAlZ9TTi/Ow913KJ1oJOjgXxjhQrf/oQbBbPgiaI9eHii9G5FcE/rgUf1tO70UTDzhi/pHNiyICar726ceZg2VzZj/OT7P4f33v/MWN9/rRtjczB8sVpntayCrH+VRatWYWnhUCwoqDg5on+xUyBL9THZ6cHe3IvWX/iLR/B//9lDpa+TPXicmaCDigXybuVPP3seP/yee0d+H/W00qJmmJI3r/RgepyuvubFnlb6u10s5fdsIYjJ6pM8thDh8ubACXDaaoo21vV+gsOdCGdWWnPpa6XXvWiCmIa/Njp3nSgwidzjkhZGoFAhMqxoTdJMKZXD5rTq932aaqXVE7WhbKTO2u+1zUFqwn3KSmtVevDkSmuj0P8X+WQPzvsmi+rgkrYH9+IU7/nUU/jow5ed161+fgp7cOFH6Jp0B7Y9WJ3Tw+0If/Z9r8Pb7jlT+v46Rc+2B0+qtFIRRz93Qo+aGjeIycxpnZPiF5me1vpC2Z/QHmz3sVb2tKZ5Tyv1Vh/u5JsodL+TU4GCnbZ15E1BaQ2sQnwvQbcnu4MPLpwevD/ZW59Eu5SqwmwSrm4O8O3v/gSeudZ1vk6FQOiJiUOGunrROu4xbQySkd+bP+Y4ISupU7TafWJJljn24JJ1U79O+oU+XhCTUmxG2SAnJclk5XkxaaVjnF9jRfXF2OM0doo/+9wF/NrHHi+FYxUZaKWVFqFUdDx8YR2v+okP4pzVh5xlcypaR6QHD7MHF68TXT/f83B0sQEpczvqdlDcyFjrJVhsBLjj1NJclFbTpzlm0Urvuc60Squ1OjT3xJANHXr9lT2txh5sKa3abVKXHkzv+9gpWhN0CvbgaqV1uiAmKrqJ0M+V1sDalCvbg0OsdmPTu0zPN03Raj92cYFmilbrelLvdCP0cGyx4V43U2DW2YMxcU+rKdoKxQ+1ftiqbmeI0tqcd09rUH1tbGyVZpwi3aiSdk+rpbjGVnowFabHtJsoSaWxblMfOp3jQZJCiO3pz3zt7cfwprtOmL8LPety7xWtrLQedOwgUGb/wFd0DtQphuPy3vuexXvvfxYf/twF5+u0+PanSA/uDtQvwHGVvfV+MnJhO6nS2giqw5biVGpVYngQUxR4WGwGYyqt3tz6fv/Dez+LTz5+xRxL1XWdxB4cp5lKMA38XT+nlc79o5c2hn5fkmaIfFFS1R54dg1PXuni4Qv5z2dGaZ3t2tCx9SvSg5MJg5jsZG6yoW+nRZiOtRX6RmldaAa449QiHrm4MdXmxvvufxaP6euW24MpPXhUEJO2B0+ltLpzO8dR3ykYKvQ9eKOUVqentebx0mp7cLEX8lCnoqe10P87GHdOq5SFnlCvMKfVtYgS1NNKvfoDc9/m52DcIsGzlLFiUUMFoVO0antwlWJJSl7tnFYv33SrsxAXyYOYXKX1uC5a7eNoD+lpbZvrOPucVmDyIKZxCkY6b5HlQHF6WpN8Y21Fb54c0/bsOM3MvUuvMbd3Z9sWKPMNX3gj3vmm252vqXCmvVX85Urr3jpuZn78yFvvxPcW7mVm78NF6xyYNT34g59VI2QevugWCrTQJHvwRErrYHwlEFBjZ0Z97yRFK81ppZ4hJz04yxBa0frF56Uix/cEDrWjsZTWVugj8GdPWJZS4mc/9BD++NO6z6xGaa1SduqItX2yEXq7vmglJeCRi/VFa5pJZNINV6EibENbETcHifX96v9bmR6c1Yy8oQKseN6Ni8H3jA19O4tWWrwuNAOjtC40Atx6fAGZBB69OF4vus07f/Pv8CsfeQxARRDTSKU1/347iXkcEj2nlTB9zkPei3T+IwqZc4pW9WfaSEu1kusJVNqDM8saXlRaTbFj7MHqWtv3wyBV7Qr0PeNuPhaDmAJf5OnBgWUPLhRGS80A64MEF9aVG4GszbZzIArGX2yTulsOYtJFT4U9uFHRy1m0tBbx7JE3Y6p+C40A3/zKm/Da2485j0324MCxB29fTysd/7Ci1e5pHaf4sQtVeg+QohsGRXuwW7QO0sxsCJhZv6anNd3RcTN2j+5egUfeMHefXcHzzizv9GEwc2ZvfRLtUmaxB/eTFH/54EUAcNQpIC8E1GD2+n6uKmh3fVyldaOfjhz1UJfEWgUVrdTj5fa0qvTgOoXaKMxC4FA7HGrbzIOY/LmMBaIFMy2C0yyrLlor1JHax0ykSRPd7T2t9HoeuVBftNJrD4NcUaDzTgq/3R+dzamn1cxpTYYrrYmjoo1QWn2Bo4vbr7Sa0Re6qLy8McBCI8BtxxcBAJ8/vzbxY/bi1Hrfu5bX4n36R/c+g+/7rb8zf8+VVl///PjXqqS0jpEonSejl+cr29fq6mZsRurUjbyxFV37dW4OUnR0L+SdpxZx89EOrj/cLn0fpRhTsTBuwncmpVP82bNqw6FKawgpgcd0SF6cZipN2jqmOrWzCiq8yiNv1GPY70WyoFYplmMFMVkBZuMghMD/+mV34o5TS85znKAgpjHntM7bHkyPMyyIadKeVnfkTdEWXQxi0vbgxdweTL9n6Njo+g2SbOxNgq3A/l29VzD2YK5aGWZfsbc+iXYps6QHf/zRK9gYpFhsBiV1yyit3hRK6wRFq5TS9LQOm61oelpHFGpSynJ6sHVu4kz1Q9Yp1LbSutKOTIpoFWZOa6B2t2cN06FFBZ3rJJWVVk0qesa55tTD2wgnswev9mJHsdwOqHgZprSahasnSqpatdJatm5OQ14Yl9ODMykty/YYQUxUeOuRN8Dw0Urzpjiv8eJaHwvNADcf68ATwOfPrU/0eFmmxmlQwUX/p8cvvi8+/PmL+P2/z0Pa6FySMtsbs69VSmmUUKKovldh24PLSmtmxs9c3Ry49uCKz6e6dG7bHvyiGw7jg9/7WtNL6GxsJCkaur1AiFmUVg9vuOM4vu/Nt+OmI528aK1IDwaAR/V7zB6FQkxSJDRHKK3dODWftWQPrurlpEJ0WBATnf5piyj6vKCeVlvV7QwNYtqakTfNMUfejLOHYCvVdT2t9L4nxf9wJ4In1D3fT/LfZfbj9ZNsR4uvwPPGTrPeLdDbp5iozTDM3oaL1jlAC5dplKQPfvY8It/DP3rhGTx2acNZTNl9d8WF3ShoYT9Ob9zmIDWLkWHKoSkARhQf9Bh2I7wd7pLo5Nk6hZpep7IHh0PtwX29IKNxCrOmB9M1zJXWGnuw1T85rNCn7wmN0jp+r+I//+WP43///U+P/f3zgF7XI0N6WmNLEchnL6qvbWolZ6Ofv04qNGZVwYsjb9LCeyW3B9s9rdXugNx6L7DUDBD5Hi5MqLT++B98Gj/yO6OTlquw7cGACshZbARohj7OHm7jwfOTFa1UaNH9RWr0QqOclgsoxc0uzqgXvN1Qi/j33v8sXv+THxp5v9IltYvWovpeRW7PFiY9mN5HgyQzPY9XSGnVQUxVbhP72ro9rUlp9qevC9Piz0S++gyJ/PEt/Co9OC96Qr3J9vbX3QrPE+Y8lNKDdeH8iFZaB5Zt1BQ+E9iDqfAaNvKGzoPpaa0o/ozSWtfTaiuPU86SCAr2YEdpbYy2B8+vaJ3MHjxeEFNFT6ulvNubE2dWWjjciXDHqSUEvqftwUWlNS9ad1JpVU6vvbVU9NgezDD7kr31SbRLCWewB3/koUt4yU2HcNeZZcSpxFNX8wThJMvg6dTAydODk7GPyU6KHaYyjGsPpoVuZBemToGR6XCHGnuwfp0eKa0bw4KY8lmM/hx6WulYqPCxg5iyTJZmhEo5vHcPyMfDRMFkPa1PX+vi6au90d84R2jxPExppQKIVDJguNJq0oNnVFpTYw921XBPKKU1NX3Gdk9rdYq2PadVCIEjCxEurk2mtN771DV88rGrk78Q2Pbg0HyNCtjbji9MXbTS6ywGMRXfsxv9xOldNenBWu16//3n8PDFDVwZ8t5TP6d+3ilax+lppfTginsoTqVJVb26OUAqSWkFqvbU7Ottbzr14gytQlEihPrcsd0i/SQzfadRML6FvzSntaBGRSOU1kcurptjputHc1MnGe1iPv9KI2/UF1TRqr5nvZfAE9VKqRl5U5ceXLExMSl0HY8suD2txVErRVqhHnkzZ6V1aHqw9RInG3njmYC6yOpzjZNcaV1uhfjkj34xXvOcY4h8Tzt63CCm3dLT6u/BICae08ow+xMuWucA/bKdpl/xwnofZw+1cfPRDgC3r5VSdgFMobSS8jL6mNbtonXI9+f24OGPaVIQw+qE4AGlB9eMvCF7cKCDmNaGJBv34szY44qjM6ah2NOa6FEqWSbxPz75JF71Ex/Ui/1ycEz9Y2ZqHEbgTWQht3sUtws6f1c3Y1zRIzmubAyccUzGWuuXe1o3TdE6/55WUgMHSeaE70SBhyTLakbeVN+zpvDWi8ujC42Je1rTTDrvnUmg46GiEshV0VuPL+Lhi+sTWd3p/UXv9zwRVy2Ak0KS77pW3AZWkQfAKHL3PnUNAEba0+nn7CKoqL5XQfbgwPdMsZdk+X1yfImK1hiJVlq9up5Wu/VA/5neN50K9S70RGnkDRUvk7xHM+nOaQ0L6mOnEUAIVaDYLDbV35+60jXHTNf6+FK513MUdfZge+RNWxd9KtXdr7RNkno6LIip+NiTEnjuqB36/dCOqo+JIJv3/Oa0qt9Nw2y3dqE4js3UDl8qpQcHwlHU7XMc+sJJD67qad3pIKa919Pq/p9hmP3B3vok2qXUjW4ZhZQSVzcHWGlHuPnYAgA3QZgUSQBT97SOp7TmBcaw7x/XHjywVBT65VxMD478+vRgO4hpRQ9hv9atVnx6Sa60Bp7n2AynwSR5ForXQZrhycubuLg+QDdOXXvhqGRWq0gfN+QFUIVfd7DdRWt+fHQv/vgffgbf+WufNF/Pi9a8p5XO12ZFEJNJD55xQ8HuZ+wnmXk/NAIfaQZLNVT/l1IOsQfnLgYAOLoQ4dLGZEVrksmR82xrf5bswZZ91VZa41TiscvjJwjT67PtwYEnzHujuLFC8zr7sXvOyIZJM0Q3R9x/dA1cpdVVTqsYWPcQqViZlKY3lxTHK5sDZLqn1RfV9mCnp1W/f+k+bFX0SYaB2/s+0KFxgLqXxn2PFue0FtXHw50I//M7XoG3vuCU83VSWumlDJJ8w4Ve92RFq7pm5ZE3umgdpGiGHqjuqlMrjdJaO/LG/vOURasvTD+r+rt60GH9rOrfKQV6TunB1kzdOryJ7cGje1rpfWbbbUP9b8X0YLundZJgrnlju6L2CnTtuKeVYfYXe+uTaJfiafvMpErSxiBFnEocaoc41A6x3Arx8IXcFphYqZyTzmntTVC0rvXzgnCcorUqiOmZa1385z/5PKTMw2Ai3WvaCDwnmdgUcTUKNakpyh6sita6MKZenJqernEWy6OwbcH23wdpZmyp/TgtpZ3+9SOXa5W6gd58iCZQcaSU6MbptgcxJfp+BPKgmCsbA1yzEpxpgR3ZI2/0eaINELuYM0rrrPZg68e7ceoorWmWWUqd+r99X1WlB9sLx6MLjYntwUk6vdJaDGIC8iThW4+rDaxJwpiKSqtyIPhmsVllD7a/n87lQqEHdFTRSup31ZzWYZtsibWxld9D0vS6rrQjBJ7A1a5WWkXe+1p67c4Gkt480fdhpyKRttIeTPNVJ1Za88ev6vt74fWHSkm99jWnY6brUxVQNAp6/GJdlduD85569f316cDqdYy2B09bxNx5agkvufGwdYzqMYclBwPALccW8P1vuR2vv/3EVM9bJPS9of2sAGDXOxOlB1uWd7t4zWT+fgsL53KQWL83CzNt+3E2VhDUVvH8M8u449Tizh3AFORKKxetDLOfGL69yYxNOEGAB0H2y0PtCEII3Hys4/QSJjplF9BK6xjjVYhJgpgcpTWt//5hc1r/+NPn8FMf+By+9O7TWNWqKNngir2ccarSdOsCrGzLIc2zqxt7Y9uDbZvhtDPoB4kbGkSHNkgyEybV071JrdBHN04xSDJ8/S98DP/8i27C9735uaXHTFKl5DQmUFr7SQYpsSNK601HO1h78pq5F+NMlop0gGxj7kZBtdI6J3twpgJzBmmGnrVxEPke0iwvVqkgss812VHz1yCdhePRxQYubfQhpRx7dz7JVB8aBW1NgrEH20qr/vMtumh96ML4RWtceM09HVBmxs9UBDEBebFL57JdKlqHF+VGabVTdMfqac3twWazKZWIRX5NV9ohrm7GSmn1BbIMlfZg2x0QG6VV3X9VxRCpXkQ/yXtfVRDTeO+5ktI6pvq41HTtwnavY1VA0SjM51/RHkwuFwqC0/26dQrjqCCmSZXHKv7jV9/t/J1e57AQJkBtYH7na2+d6jmrONKJTGp47XPac1rHuBz0eRIF+VxTmrdLr5N+17pKq0CSZWZesGcK3ryndSeV1p/+mnt27LmnhT7D95hAzDDMCPgtPScm2aEnrupCjNTEm48u4L6nrhmFS6lB+e73JAoiLdrGOSZbFRvWA0t9slXFBykXz1zr4plrKjzo1LJSDRqFcJMkkwg99cs58ESp2KfX6dlF60a90toIXaV1lr7WcnpwHnDTt5XWVObhJv0E/STD5ZrQmjiVWmn1x75HqFjd1IXyg+fX8Ef3PjPsR+ZCkko0Qx/LrRBXuwP9tcy1U1ohRn6hKKoceUNK66z24Cw/5704RZopBdvXLoRiqFAvsTdjyvZge+F4pBMhTqVjQ/9PH/gcfu1jjw05HlKXx1dbP/LQJfz55y+Y87XYLAcxLTQCLDaCyhE8z17r4b//zeOlr5t7M8lHXQ1TWqmntV+YeVtUJkdtmkzd02rbg/UxJpk0RWfoC7SjAN1BgiST8IWA51UXwvaGhEmx1vdfu8oeXHDF2PbgccPSpJTIpKtajlu0NkPf6c1UtlH1Gm440sF3vvYWvP65x8d6LHo8oGyFLKrfDcsCXcVESuuciihfBzNVXaet5HvffDve9c9eMvR7qsY4DUM5i9zNvFxpJat24vydvidOM/TjzA320uc4k9ybOSkeBzExzL6Ei9Y5McmoBIJGuRzqqMLsm191I3xP4Ct/7q/wuXNr2h6chzJMNad1DGXPtjgOHXmT1CutVDA8e61nQnuoaF1qhqYYoAAde4Zh7cgbYduD63paM6enFcgti9OQz2l1C6BBkhmluRer3iQKB6Fjq7OKkhLXmCA9mIpVKhp+6S8fxQ/89t9P85ImItYzN1XaZa6QOkorBYb4Xt7Tamao6pE3dhDTDOnBn3jsCv7uiavqcWS+uFXXQB1r4Amk0g3yUd9T36utEp3zBc0x3Ut40SoUf+vjT+CH33Mf/t+/e7ry2KjwHNciPEgy/Mvf+BT+4/seMMfoBjHlf25oFb/Ib338CfzAb9/r2LXt1zewXnsj9Kyi1bbDptaIHPc+L46IGd3Tqn7etY7m6jvNcS3ihHlZtn7z9cAzToZMquvsC4Gqj0CyFHsif/3DlNZAp7USdhDTuJuPdBx2kTGJOkoWYRq/Y4fwfP9bnouzh9tjP1ZtEJN1PBQ8RH+uIigofEXmobRWEfpepY17K1lshib0qg77dI5T/Hz1i8/iZ//JFzjBRcVZvXRf2vdKoO3BgzSt3QTZSaV1L8JzWhlmf8KfhHNiGqXVFK26MLvr9DJ++ztegbV+gt/46ydUEBONBBCT9WrWpaZWsTFuevCAitZy2BEtep+51sMz13qIAg+HO/kA9cu6GIhNEEW+E108RlLmfE+Ygr5uVms/TtGk4ApjD57ehlpMU6X/9y2ltZeoXmRK5KR+27VendJq2/PGsx7m9u4MaSax1ktGFhDzINWW9MAX5lrZ8wXp74CbkpkWelqdkTczpAf/+B98Gj/5/gcAqOtKhV0vSZFqBdsjpZU2GvTx9Wx7cJrh/GoP//Bn/xLnV3s65MztaQXg9CXTvfDO3/w7PFERikTnxLbXD+MP7n0aF9b6WLfSsBetItHudWxFnjPbmDi3plwMxWAy08dr7MEqJbbqPWEfr+lp1cdDgThnVloAxk8P9isKmiSTeNdfPYo3/NSHSj9nj02y+6IHVjHbjHx0Y5W46g9LD04oRCooBTFVK63uZ47d0zquhd/0UzvFx/gLZLrWxxYazvtrmnRc6ukvj7xxlVYTNjXCHlw3g3UeI2+qCH1v25XWcXDswWMUPyeWmvgHz1ehW6X0YP1/2oy0r02k7cFlpdW2J3PxNQn5yJsdPhCGYeYKF61zYhqllRS65VZkvnbzsQWstEJs9BPEVhDTxErrYHylddyi1S64ioosLcKfvqrswaeWm+YXx+FOZNJIi5H/UeCVihmzEPYEOpGP0BdDelqtOa1zsQe7Pa1VSiuFvJSU1l71Aj/JlKoX+ePPgLRVQgpkSiwlaqtIyMrse9a5cO3BVSoZfa/pabUKo1nSg9f7ibmHsyzvfesNUldptcYQUbFtn8M4zfDZZ9fwqcev4gFyMVgL76qitZ9keP6ZZQzSrHJuLd2n4yitUkr80l8+CkCdG3qfLdYorc2gWmm9sKaOb7VXrbTaNuF25Bsbp/1+te9Tek/nSqs6v3efXVbHOq492C+rQolOQH700mYp9ddNoLaV1rx4a4WesoFbSmvVxp3px418yx48rKfVtQerdN18Tuuwjb5BojY/qHiexh4M5LbwU8tNPQrF3cybBJMeXNPTCsBsmhWP2fl+GtlS8zqqEqLngbKCb6/SOg6zKMvFOa20GUG/l4uhVjSr17mf/K053wcBtgczzP6Ei9Y5MW4vlA2ph2SBJdqRj804ddSgwMvHPaz3E2dBXsW4M1XV440bxFQuXAgqGJ691sMzV7vGGgwARxYiXCoUrYH5pV4u5Ow+OSEEVtrRkPTgPIgpLFhVp4FUm6LSOkhTc5zrOm2ZFlpXjNJaXbwMEtU/2QgnsAdbxcLmIDGF0VbPbSVVOPCFWUjHiau0OuNKConNZAveqFJap7AHb/TTPNFZSldpzVQCsKeLmeKMXccenEpnhmkxPOnIgto4urjmFq1UVFZa4jO6H0YXrZ98/Cr+/slrWG6F2NAbEEDBHuworX7le/y8Pr6y0ur2tHYHKVqhjzCgnuP8+KvSwumcLbdCNEMPL9UJr+MWrVUqXJplpWK6eLy20urYg3W6a0+nRKvex+HpwZ1GYP68MaRopSKB2OgnxhY9avPx1z72GN7403/m2JjN657AwrnUUs93crlpChY6tkkx9uDiyBuvumiNanpabzrWwWuecwwvOLtS+e/unNb5LR2+4oVnJurh3S6cET8TFj/lOa1kD04Q+sKxrQa+QKzTg6Oa+2kn57TuRXhOK8PsT3asaBVCvEUI8YAQ4kEhxA/u1HHMiyrFcBRXN2MsNoLSQoUCSFJLDfK1hQgAvvVXPo4f+737hz52rrSOkx48mT0YKC/iSd16WtuDTy+3zL8d7kRm3qJd8ACo7PO0R94AwEorHNLTWlZa59LTmrr9eLY9mIp8WhBf7Q7vaU0yicj30NAF+jhzZO3itDtIjaVzq9OEqUCwF/Zxljk9gImlhtG9m2QSidWbN6/0YFKY6Vp0qnpadUhZyR5cSKymIrCfZEZRJg61I3gCZnNFSolBkpliprqPm+zBo4vWD3z6HAJP4CvuOY3NQa60LljFkh2QM1JpLRStfas4lFJiU6uHgVFa8+N3lVa3p3WhGeAD/+o1+KcvvwHN0Bu5SZJYG0yEHYhGz1t8HKOoBm7RSucl9IXqaR1Q0erB82rSg1NLaU3c3upiGjKQFwmA6rfeGKR50Tpi8/GJy12s9hKzgeeLXCmeyB7cIKW1hUGSlRwokzBq5A097qiRNwuNAO/65pcaa3gRp4iboz34f/uyu4ytdjdhF5ZiwpVSUChWSXHdHJSTgEPfQ5yR0pp/BkwaBMXk8JxWhtmf7EjRKoTwAfxXAP8AwJ0Avk4IcedOHMu8oFEck3Blc4CVTlj6ejvysalnuNIvP7JAAsC51R6evNId+tgTKa2DxIRODLOvDk1j1Yuup65s4txqDyctpfVwp2H6MqmwsHt96lRb2t0+1I5qe1pte/C8e1ptVce2B69ra2Yrcntai5ZNwoz4CcpWzTq6llLZjVNTGM1atN731DX81UMXa/891onVgWUPVkFMZZU9sPsR08z0azVDD5uD1BTneU/r5JsJGwOV1EyXol2RHuwJoYvm+iCmOLGL1rSUHux7Aoc7DWMPpmOlorJqNjEVbOMorX/x4AW88PoVHF9qIs2kUaKpeFkozO5sRr7jbABUIX1hhNIqpTr2Xpyipa319usBXBW8qLQGnoezh9sIfA+dKBhZkOdKa1kVSlJZ6jEtHm/gCafIHVgKJgUxpZmEL9TjVgYxWf24RXtwq2IWJxUJQN5juKDvq0bgD/0MpPNO95KnN03occdluaXmcpOlOT8fMyithQW6q37nG0x1ResobLWxzkK8n3DswRMWP3R+ivNaNwdpaXODRjD1k7R2hBIXrZNBl4vtwQyzv9gppfWlAB6UUj4spRwA+A0Ab9uhY5kL08xpvboZm5EuNi1dtCZZZn75+Z5w+ixHLZSpaI1TWeonK7LRT7DSUovn8ZXWQhCTXnSt9pQydsrarT+iw5QubfRze/Cw9GAriAmAmddYREqp7MGBO4x9Pj2tbmLuwFFatYoTltODq1TUJJU6PVh9/zhhTLYytTlI52YP/k8f+Bz+ze99uvbf1T3nOX1/VDTSfWSr5XbBQX2sRxfUJgWdr2mDmEi5TbLMGslSnR6cyVzVMyNvYneTpWepkcX0YHXcES6sDfT3qJ+lHs+k4thNT2uNLZy4vDHA/U+v4lW3HjMpqVT8RIGybS4UFMFm4JXswau9xJzTup5Weq3dOEUr9CC0EmhvOqwNUVrttXEr8kduklQprbb6PqjYQAAse3DgmYI3tUbeNHQQU24P9mrtwfRY7UY+UkopzV7lYt+e00pFOSmtrWi4ukzXje4PX+SF5iSFxT//opvwU199t960c23Rk9Ko6Wm1Hyty7MHT/dr3DlgRZb/ESV+v/fsNcIvW4jWmpPZB4va0OkorF18T4XEQE8PsS3aqaD0D4Anr70/qrzkIIb5VCPFxIcTHL1y4sG0HNw11trIv/uk/w7s/+ljlz1zdHGClomht68UipWYCrtIaJ9nIVE97sTlKbd3oJybpd9j3OnbLwmstKlGnrHEClAB8eWPgBLAA1cEnWeYWrQuNoLJIpwV3ozDyJplC0SPsOa2O0qp3woHcHlwMYpLSHfVi/2xoKa3jbG7QTFz151xpnTVB+NLGoFYRBrQ92C/YgwvhVPT1yPfM4sxWD2l8DB3rtPZgUsGSTJrCl4oLUuBCX837TVJZep5+oQebrPL9WCuthRXNscVcaaVrVLQHn1vtmfugWPjU8ZcPXoSUwBc956ixq9LImsAXaAaeE8gEVPe0XrD6bVe77nM6abhxqovW3IHgBDFVtAOQam3b6cjxMQxKja6yMqqeVrLrFntac4u5ayfOk6nJHqxG3pDSWlW05vZgW9mtS6S1xznRuaBNg/YIdXnVKK35686V1vFXyLedWMQb7zxRMQpla4KYAm/0nNZR+FvU07pbmTQ92KY8p5WCmJLS506gNwj7SVY7Qmmeac0HAVO0ctXKMPuKnfrNU/VJUlqNSCl/Xkr5Yinli48dO7YNhzU9qvhyX4KUEg9eWMejFcmjAHBlMzbjbmzaUYDNONGps+U5rXEmR47ZsNWCUYm16/00L1qH2YOH9LQWlahTK1YQk1FaB2aBaZIqfVF6zqRQtAa+qLT8UlGSz2l1Q4GmwS7UikorLVTrgpiAatUtsea0AuNZtu1NiY1+YorhUQFco7i6ORiqDMapROiV7cFAbrumDQs7RCdOM6O0HlugolU9D126Se3B9HiJdS1IqezFFenB5EQwPa25XXmQSKfv07beE0c6ES5t6KKV5qhGrj34LT/zYfzKX6lNKKO0jthA+ovPX8RiM8ALziwbpZg2OgJPoBn6FUpr3tP6b//fT+Ptv/5JnNfjboB6ezC9vu4gRVOfq9ArBw/l35tvDBTVpFYUOFbiKvJgtXJPa5zmSmu5pzW3B9tFrq04kj2Y5lXXKa302dO27cH9tDaRNgwqlNaIilbfjJmqIlda1c97lr15mkIutHod1d+nGXlDvyPcrzvqdzA6PXgUdgFwEIqomdKDS3Na8+tcvMYR9bQWlNZZlN6DDp1itgczzP5ip4rWJwGctf5+HYCnd+hY5oJSWsthI1JWWwsBVezU2YOV0prPabWV1iTNRi4mKT0UGG1H3egn5jgGSYYsk5UzR3tJ/pilntZMOr9kTxWCmIA6pdUvFfuZLBatXkk93egn+NgjlwDkPV0UDhLP0tNq9fgVe1pJqaMNA1Lh7AKi6rzFqVp006JxnDFEdnFK4UDA7D2tVzZjrA+qbcwA9D1XtAe7FlJnTqu1UbBZp7ROaQ+me9y+FqRu9+N8TqvvCed+TAojbxaboRPE1IszvZFQtAc3cJHswbGrtCapKmSubMa4uNGHlHmRPEyZk1LiLx68iFfccgSB75me3KvdASJf2XcbYZ3Sqo7h089cw4c+ex7nV1VB7Ynh9uBurJKubaXVfv9UBTHRubTpjGEPrk4PztV3+kys6mkVuk/VKK2pG9TWinxkUt3z3pD04LynVX2/uheHFK1ePoN4vWAPpp+pswibojUme7DIE96nKORy22gy9WPQpl0xdMbelAk9K4ipZk7rKA5aMJC9BzHpy817WV3FtVvR00rKv1Ja83tWWCFfbA+eDJ7TyjD7k50qWv8GwG1CiJuEEBGArwXwezt0LHOhKoiJlJ6q+ZRJmmGtl5TG3QCqV3Kjr4OY7J5Wq8dw1EK5G6fmsUfZUddte3CS4Y/uexZf+H9+sPQc3UFqRjUUHzNOM1OoNgLPUZDtopUW+mYHuqIX2CyE9S+e0BOlgudf/LdP4Vvf/QkAMON1Qqs3zma1F+PXP/b4WKm9bk+rq17RAp96AluFnlYAWKu4LnGaIQwse/BYSmu+aLbHsEzT0/rg+XVc68ZIM4nVXgwp623GStVSC/F87qmrYDo9rSb8SprHpJmndP9MbQ/u5zZcsowrZdJDL8l7Wn1PFGayUk+rer7FZmDsdwAFMcmSMnZ0sWFCr4zSatmDiz2+xDDl+v6nV/HU1S5ed7sa6UGK3rVuYs7dG557Aq+69ajzcw0ruXdzkGJjkOJvHr0MADh7uF1SWu17iv6N7s9i2NlaPzHFGb33qpTWcezBRVeE/efESgMu97QqF4kQwih4qdWXHOmRN4D6fFKbE8qCX4Q2vch6PUjUpt449mDagFowPa3q/5s1n69Xu2pTgz7bPUspnkYltXsdgXye5yQU09OJupE3jSmeAygEMU35GHuJPIF28hRa+/eb/ffNOK21BxeVViC/pgdhk2Ce8JxWhtmf7MhvHillAuC7ALwPwGcA/KaUcvgMl11OVfFFak2V0kpjUqqU1nakbHFx6s5pzWeGKntjXTFKi/NlHa402h6cYLkVQgi1OH/88ibW+0kpsbcXp1hsqscsBzFJdBo+DncinF5pOb/km6GPTuTj0vrAnAtasDcqFGp6nbSYDXwPxXClxy5t4BW3HMGHv+91eP1zTwBwU0tt3n//Ofzr99w7MnFZva5qpbWflOe0UkiPXUhWzWqNUxVuRL1kP/ehh/D2X/vk0OOwH/PCulW06lTeB55dG/laiK/5/30E//VPH8S1bmwW/cPG8wS+cCyURXuwGc/heU4fMSmjRwtK67TpwbbSahdH+fxO1YfpC+Hc47bSGnhqdEpx5E11EJM67ovr/Yqe1lwFTKwCFnDnHBd5733PwhPAF9+p7lEqFq9tDsxC9se+/C580ytvcn6uFfrG9UDF/4ceuIAo8HD2ULs08sb+LKB+WVKlKezHHG9Pvd99TxgXBs28dY4hCkZukhRD0wBr5I3+nAKq7cHFhNXinFYqujf6CTyhisO0MuhM24MtF0h3mNIa5JtgeRCT+l6yn1cV6/0kNRsh9sgbuo+mKSwiS4EDULoG45CnB7tfdyzbdtFakag8DrY9+CDUUFTwTKNylua0Wu6DyiCmivRg++e4aJ0MOl1cszLM/mLHtkullH8opXyOlPIWKeWP79RxzAs1p9VdUNEiuSoYiMakVCmttNu/0c/VGDWn1Q3EKSqhVzcH+PE/+LRRWsZRWmmHt6PnxfbTzIxbKS7cekmGpWb13EoVbOPh7KEWrjtUnvN3eCHC5Y1+ZU9r8byllqqmvqdsD768McDNxzq4/kjbfI3OVVFppYX5OCqlnUBbZ6mkgq9VoeTY3/e++5/Fai9GJl2l4z2fegp//vnhwWK9ODXn+kJBaf2zz13Am3/mw3jsUnWvdPFxLm0M8MTlTbf3tqJopVmogecZC2WaSVPo2iNlfE9oy6b6tzSr6ml1i1Z78+bdH30Mr/vJDw09drJMxmnmzO5thZSuXa20JpbS2gx9dV8XR95YG0LEkQW1gXRxvV9KD6b3CaCUZ/ses9+Hv/axx/DHnz5nzucf3vcMXn7zERzR54SK4KvdeKhaRepZL0nNeXzqahfHFhpYboVYLWyOOEWrfv/bo6Bs18DGIMFCI3BmJFcqraE/euRN4f1Mz0ePSY9fDmLKzAxLW5klBTQMPLSiPIWYRhtVJaHn6cH5Z9PGIB2qtNIGBG2MFO3BVUWrrW6b9GBbaZ2mp1X3OtJxzDOIyb6eak4rjfWZVmlV/y8Gdu1X6PRNE+Zjfr8FruIKlC3gtHbos9I6N1hpZZj9SfVvdWZiqtKD+9Yit8iVzeFKK6BGXNDClpTWzFowbwwSk8wLAH/10CX8P3/+CO46vQwgV1qHFa0bVnpmQ6vFtOi3F26pXoAu0WOmxUWoUq5+Uo9xKHK409BBTBXpwSNG3oS+cPpUkzTD1W6Mw52G83MmFKjQ00qvZ5xeUntOq51UahcJZClsW4rF0QWVPEs9rRfW+vi2d38CP/LWOwCohYq9cBllEd4cpDjUibDWT0pF67lVFchzZTPGDUeGv57Luh/2wlrfbJQANYFR1mYBWSjtzQnb5kvXT2ilKalMDyallF5zfj4fPLeGRy5uIMtk7aKQzrOtegeecNK1A8/TiqGbFAyogq8ZetYcxDxVmBRlm2NGaR2YHtNG4CPQ9nS7x9dW/u3+8p/904ew2o3xJ9/7GlzdjPHwhQ38s1fcaP7dVvLo/VkFqYy9OHM2GI4tNrDUCoYGMdF1rrUH9xJ0dNFqelor0pTbjfFH3rhKq9XTakbQFHtapfXZpr+/2NNqvb9MT+uI9GBAfd51B0mt0hpZm2DlkTdB5fECcNRtUlqFyI9/mn5UKiRpw2e6IKbqojUfdySdTbNpR97YwXgHgVmU1jtOLeINzz2Ou04vAcg3J4DyNabPl1SK0rUJuGidCp7TyjD7Ey5a50TVnFZSdtKKYKArG/VKKy221vtJvtMq1OLDLsiKCcK0+/+wTisexx5sj3ygApLW43ZfFz32EtmDS4m/KsDnthOLlc9zuB3iwnrfKD55qmK5F5jUG/qFE3gepA5Y8T2BK5vK5nqk4xb8obX4tbGLmFGQ0pNkbnqwHXxDFmB7UXxyWRWtdD6peH3mmiowI98zvb0nl5pOEmwVFKTVDn3HHrw5yHuixkkSpqL1/FofVzby11CltNozdKnPyi526M8DnYZM0Axh2uQ4ZnpatdJa0dNKltp+khkba9U5AFxl0xMCnYZKtaX7wfeEsyFhz2ltBD6iQPWHuvbgrLR4pOtzZWNgFI8o8PJZmnRvpK7Sam8AbAwSrPUT/Njv3Y8sU4unN9910vx720oJHq60attonDqbR8cXG1hqhkPtwVcLPa1qUeyOvFlsqve7aWGo62mNlR29TlkzmwlWIUMPk1jqdNWc1sivUForelrpNdSPvMmckS5xmmFzkBqVvAjd2+pcuBtQw+zB15yiNVdaTVjeVEFMrtJa3DgYhzp7MB0fbdBEVkvGNJh2jQMw7gaYbdbnSjvCL3zTS8zf7fd6UZEPqf0lk6VxRKy0TgeNEhy2McgwzN7jYPz22Qao+LLDfozSWmkPHqa02gtb+qVVnvFZTBCmf3tEF60r7dFjbOwkXCpaSWWwZ45SAbHYzHv8bOzQqCoOdxq4vG6NvLF6fuqU1qCws08LTSrEyM5J2ItfG/r7OEprXU/rmqO0qj83Qs8saKhQI0WWFr2kiga+wO0nF/G+73k1vu6l1yMbkioNqGKlFfloRb4TxNSLU/Mco3qVgTx5+Pxaz7EH26/nXX/1KH73b58yGyKhNafVtkjbc1odu5sObdocqE2WZb0RY5TWCnswncNhlu2q9GBfK62bfT1r1df2YL0hIYSrrDdDT1vQ85FFyh5cvl+pyKP0XUAt8AM9lok2V+JMOq/F3gDY6CdYbAT4w3ufxQc+cw7f+dpbcNyaWWyrh8OsoFSwXdtUAVrHtXqtlNbQsTsDrop9taKn1TneHtmDfcdZUFJao8BRS6tIKua02ur7sJE3xWKvrqeVHl/Zg6uOQSmJVBhQ0doK6+3BiXatbPQTdCLfFGStsYvWvKd1ppE31pxWSlOelGOLDXzna28xvf3O41s9lfnIm9nmtB6UAoou5zxmfQ6buWqrq8UNBdtpxYzPPWdX8MfveA1uP1m9ic4wzN6EldY5ke/ySzOTrW96WssrLVoALQ9RWoGy9axnFV6bJaVV/dvDF9bVYxultb4wWLeCSKjwpkLWtsj1EkpipSCmogU3q+0hA1SB6dqD65VWUuZosUCLeyqaaJbm4YLSWtfTSqrwqNE/9utKCgWbPcqGFuCB55l+ycVmiIVGYFQ3KihoTAm93ttPLuKDnz2vn0uibv1IQTKtyMfFdXfkDR1jfyylta+PJ8MTVhCV3av47o8+husOtfDq29QsZN8Tps/Kvs6mpzWRpUVYmmXY6Eu0I9+oVWWl1VX71HHVvwYzMqcQxNSJAjy72jPqm+8J03dLoUv02NTTOkjcICZyBthQwdKLc4WwESh7cZLlX7PtwYuNwJzLfqISv7/9NTdipR3hDc89jhuPdpzn8HVPbjcuz2u0oaKV7vWX3HQYf/D3z+D4YtNY9Fd7sfm+YT2tVEASG/3EOCuGzmkNc+tqXaFT7D+3Xye1FAA1Pa0FpVWlB+fp4rYC7+v04KqRN4NEFcD0eH298VYbxETFbZapotVSv9tD7MFVSqvnCXMfTWcPzovW0POm6hUVQuD73/Lcyn+zWyxmtQcXP4/3O94ci3S3p7VYmOaPzz2t8+PW4ws7fQgMw8wZVlrnBP1SsgswKiKLyp/6N7XoqbJqFRdr9v/tRX7R4llWWifraaUE5G5cDmIipdWMvKmY0zps0Xa4E6GfZFjtujY46qO1FeriAjpPqFXPeUkXcUcKPa0mtbQgx1BP8TjK5KBGaaXjdp7P6lPtNAIsNAJT3BqlVduA7YWJGX0z5Hg2jT04X1CvtENsDtK8MB5HabUK3s9ZicP2vbPeS5QVOKNkZ0/3wmVOPzadVxrhY86DJxDrOa2dKEDge2gEHjb1fUSWTtuJsD6O0modI50r3xNoNwITxETpwUQr9HN7cKKL1qDc01qVHkzvxW6cmucz9mCrvzfWM1sBtem0MUiRZdJsIh3uRPiWV91UKlgJsq0OK1qpYKTr97KbDuP2E4v4ghtWzGaUfU/GaWZsosWe1qDgZljr5z2tZi5xWh4BRMe5OeQaVfW0Auo9ayec02dKfrz5xgddv9TaGCj2tJLSWpkenCnln67fei9BJmFm4hah92ycSqzrAt685iFKqz3aiu4lX8wYxGQXrVtQDNpzQs2c1qmDmA5WATVLT2sR+9qGFe8VgntaGYZh6uGidU7QLyV7cUgFZtV8SjOvtGKhYysE9LhVfYxFNYAWUrTgGqen1Q4iIXswKWR20UDPW6e0xhWLXhtSRckuayut9PNEKt2iNTT2YPU9dfZge/yKDRW74ymtdk+rvdBXC1Z77RD6nrH3dSIfi83AFGN0DYpKq/2a+2n98ZBKSBsYQqhz2ItTY+3txyoF99ve/XH83RNXKx+H7MEA8Lnza1jUC3S7aF3rxU4AV0hBTKl0epfpnq3qaU1T1dNKhQJZeAE4hUYx+XocpRWw0lqFQCdSqbbU02pvljQdpTUzQUyDdHR6sBCqUOo59mC/1N9rK9D0HtsYJLlrYYjjQJ0b9e/j2IMv6n7mowsNvO9fvRpfdNsxkyptK3+DJMNCI3S+ntuDc6VVSmWJXWy6QUyVSqs+zm6F6kikdUWrVt/pPBUDnewwL3tUFX1dCOH0tFJadWV6cCIdpZV6eu2gtOKxqecrK610zqoCqOp6Ws1n9Aw9rZuDZKpxN6Mw4Umehyig9OAp7cEHrqdV/X8eScm+J0w4UPE+CYfYg+1MC4ZhmIPOwfjtsw3QgsAuWs2CsKKnNUklPFHdL+PYgwsWOtseXBxHUSxOV1qje1rX9GNQMAvNOASqiwYz8qYYxGQtQqs4UlO02n1oRJZJ55c0nQMqIi9tDCBEuR/Yr7EHT9TTatJUq5XWJSvYQfXRqedsNwIsNANTUNKi1liJrQVpg1T5IdelGyt7MN0LnShAR8/NpAK6n2S4vDnA++4/h7959HLl41y2lNZHL27g+FIDke+Z40wziY2BUhbtAoRel62E0n2cpNLZbAk8D3Gmw28iGh0SmJ5UW/imx8jtwaM3VOi1Aur90tGW3ESP57ETIpuhZ15HL07RDHzV05rIXGlNlIJcdb+2dDIxzQ6OtD04tvozkyw/V+Rm2OinpfEpdbSjCZRWvelgP6ZtDyYGaWb6zYtBTHZPazdOkUlY9mArPbhwPqjoq1IdiXqlVWCQ5rbuyjmtBVst9bTS152iVShFvTKIKcuc9yLNqW3XXAd6fGqFsAObcnvwqJE3+f1IeQPT9B26Suv8fx1TgRkFuT24Ec4YxHRQ7MFG5Zz9sVSfN93v7gNGFS4cIv/9z0s1hmEY/iScE7liaBetefppkbiip46w53+GZndbF62WWrgxSHF1c4AHtO2zqCTSgnqYwkjFy2IjRKT7MzdMEFNeNFBPmkkPLqqZmRyqFJxeUbNbH9WzRWnhU2WVJdsnEVhKDABcWu9jpRVWLpSB8sgb0wM6hp3WntMaV/S00uun56OFyELDx2IzNJsAxUVv1cJkHHswFR6dhq/nkya50ppkphCvCvsCVNFzelkFAWVSFfqdRj5/c72fP5bdb2wvpolae7Cv+hc3+olRqjoNS2m17n8q/CbpaaXjA9Q57+hUW5oXa98rrci35rQqtToie7Dd05qWizQApt+UjjO3B1tzWq1ijJTW9X5ihZoNV7KoAB0nPZg2HTrWRlZuD86LqDjNjM2VirY8PdgzQU1kLe/oIKZhSiup5sWUchtSPovqm+8JR6Etvh9se7abHpzbhu02CU/3Llf1tNLPkGJ1tavO2bCRN/RzRXuwr1OI63pa6V6je8kXAqE+tmkUOfos6G6RPdiEXXnWyJspqzC2B88Gnfdh9uCiCl4MI2QYhjnIcNE6J4zl07EH58EtRZRaVf2LqO3Y4tydVtu2ttFP8F8++CD+6S98DEC5CBpr5I1ZxPrGHkzPYT9Xbg+u7mmN06z29QDA2cNtAMDDF1TRGhoFoNwLXJzdWVRjL28McGTB7WcF8l/wpSCmCezB9nE46q9+yOWC0koLkXYUYNHpaXUXvVV9S8NSWVV6cGAVgQGakY9unDlqLilYdUnElzf6uPFoxzznSjvCgmVjpuMdWOFCgZ+Hy9j3ABWDw0beUHHVjgLTC2nbg2Pd1zqePdhSWq3gm3YjgJTq/g+0bZRohb7ZtOjFGRqhKjoHaWZ6gLsDpTZW2RwboRqPQ5sBjcBDGJA9OE9BNj2t2s2w0U9KMz/raFu23TqKQUx2yBltnKwWlL9WBk1dtgAAVmVJREFU5MMTudLajMjNIMz98cA5tcGVj7zJNxaq0oOBcj+qzbCe1o2Kzw/CUVoLc12N0mqpTgGlB1fszZDLg96L1HtaZ9OmDZdYb9AVr1c78iuV1tVujKP6c4c2Dz0PpY2TSaDXujFItkhpzXtarzvUQugLnLDSrCeBDm+a3t29CF3SeaQHA1arTzGIybrPixsK9L7ieaMMwzBctM4NUtJcezAVFFX24GFKq20Pru9p3eineOLKplFWisUpJRMPK47W+zFaoY9AF2BxaimtlsLSNUVrXXrw8CCmhUaAI50I5/X4FnvkDVBWWp0gpkJP66WNQSk5GBjW0zr5yBsgP5/2QsIuWu0+uk5D97T2qouxqoVJndJKqautMLcHLzQCtEIPvUHqKK2mb7pqNY+8wKeRKYfaIRYaoXkMKl7tntbAy2c62oUjFWrFGaehp+ynG4PEWDJVTyvZg6U1u1MaiyowauSNPdIlD76hwni1m5QKhmbom5m+fR3ERPc1nSu6vyvtwaGPvlZahVDnQtmfZUGFd3ta1/uJOVejelo70ThKK/W0kj04/0ygMLRVa2xRnGZoBB4agY9Un2+6zwI94uVHf+c+fP0v/DWiwMMtxxZUEFM6RGkdEkpE0AzqYtEW+MJ5DwyzBztKa5KZey+wNoVUEFN1enCs+5NpY+YK2YNHpAcnFenB6ueCWnsw9dH3rZE3tjNhUuiYpdwaNY0+E0Nf4AuuP4R7f+zNOLk8XdE6zzTdvYCY8+vNW2Lcx7NdOEXrdj5O6WCcc4ZhmGFw0TonqtQzUlqLdlX1teqeOkCpO/aoAsDqabXntPYTnF/rY6CVH7sI8gSwoBfHw4q19X6CBa2eRoGHXpya43ZG3uhFZzvyVVpsKT24vggnSG1Vr8tNsnSUVlmdHkzPeWm9b3pkbep6WqnY7U0QxATkmw6tCmsmoIo1Um06UYBFq6e1uOi1VehR9mBa4Lciz6hdnSjQ6mVi1NF+kholtCrsC1AF/pFOlBetnQiLjQDrui+Wjnegx8AA+Sgf+1js54hT6RTyZNvsFpRWKjozKU0RFqeZEwI1rKfVVVrz4ojOyUBbfItKKz1PL1aFHIVK0SYEbSxU3a/GHpyoRFohlIIXJ24QU7Gndb2fYF1v8iyMUlrHSQ+O6pXWRuCjGXqlIKbI98yitxX6ZtEd6jmzv/Opp/CG5x7HR3/oDXjemWVlD47zHu6i8myPvKmjTmn1PVGZPk6osUnugjzTSqvd10c2aRptVJUeHKcSYZDfsw/pkV/UklCEXucgUfbgTqG4VUprtT24qLTScU1b2LipslugtBY2B5s14VTjYIKYDohVdd4qZ9FZQDgunMJnAp3ream9DMMwexkuWudE5OeLZYKKnip1oCq9lBBCGIuwCfkwc1rtntbEpNP2E5V4elQrAa3Qh+cJk5xax1ovMZbfKHAXwlX2Ppp7WeyhpEXzMK53ilbd01qhOqaFICaae0sL5Mu1Smt1TysVYxMrrfr7bcVmqVZpDbDQCNHVib5FZclRWkcVrQMqWgv24NDHajdxRrfQJkaVPXiQKCvx4U6E44tKXVlph7qnVT0HFXC2/dW2B7s9rbbS6irhcSpxZXNgivpm6FpPzTzRNDPPCQxXWu1iyQ1iclNli0orHas9p9UO1jI26IqFIAUx9ZPMbKiQPbhvnetiT6ttD64btUJ0xkkPDtye1mIhvNwKSz2tFBpFr4MIPQ/nVntY6yd4ze3HzHsnGqG0kgJZVcARaVp9LgNPmPOh+lsLRWtWo7QWVHx6Hb5QmxOysmjNdOK1epzPn1tD5Hu1RSt9nnT1Bt249mCnaI3z+1F9DkxXVNifmXaf+LyYZYZsERPEdEAKKGMPntPLpc/9oenBhU2FYIaQL4ZhmP0GF61zoqoQyXtaq9ODhy0kWoW+t7o5ref1HNBenGGQpDjSaaAT5aNSVN/acKWVxqBEvufMIuwOyopYM/RM76vzeip64opQ0WqHltQFMdXNaU0ziavdeHhPa509eJw5rRXpz3VKa7FopeJ/vZ+UFun2uclH3owoWq0gpgUdxOQmmNpKa/keu6LndR7uRDhm7MERFpqhUTspgXagw4kANxW5qmgdJG5hEXgClzcG6MWZ6ZcLvHzMSiZzRT1JpWM77w+1B+cqWN9StmzVMfC80pxWQL1PkkyiGfiOcmfP/qxSOpuh6hvuJ5lJBFczRzPHHkz3FCVYX+vGxnY8D6U10NdgY5BCiFxxJBaboZserK8JKa22ohZYI2+ec2LRfL3h9LRmFT2ts81ppQ2J5VY4lj1YzWmVbtFqNu9UenD1BqD6GdsefP2Rdq36SY9PRX/xetHGRZGrm8oeLET+OewJgZVWhJV2eRNtHNz30db1tE4bvmRD77ODM/Jm3vZgV/Uufh2o72k9KJZshmGYYRyM3z7bQNWcVpMeXFGcKHtw/emnBWNxp5UWS6Ev8NSVrilWSGlthB5OLjfNolX1rdUvOtd7rj04sRaFG/0UUkq8+yOP4jPPrALIldaiequK8PGUVntxXDvypqan9crmAFKi2h5sKTY2dKzjzWktXz9XabWTnXNlqxP55jyu9RJsDlKjetuvExjd09q1rNhtS2kt9uj146x0jz15ZRMXdN/wJa3SOfbgdoiFRlDuaU2LI28oiCnfuEisoq242H7yyiYA4NRySz9GPnrGVlrL9uDhSutSIUzMr1Ba7XEQtMFAKl8z9JxFYXHToUhTz2kd2EprYWZtnOZW6qO6iLmyGWOjn5j02WGQ0jrq/dIM8nFHxWTaRuBhkFipzIlSWil9tK44LxWt1liu4sK4EXgQonpmKUH9s8XjC/zcHlxZtFr2YCqGjNLq2IPzotXzVBBTUW0d6J+xF/w3He3UHjOdD9rUKSqtHWtcE0Gze5dbIULPy+e0CoHv+eLb8K5vfmnt8w2j6nNhnuTps3MoWg9YAUW39LzswZH5PCn2tNpKa3VP60E55wzDMMPgonVODE0PrrEHD/tF1DILW7evhh5zuRXhscubznPRQvv0SsssjMdRWklpsBUpGvtw/9Or+NHfvR//81NPmREvkS9Kc1qV3W/4L1bqabV/SVcpral0f0nbwSlUiFXZg4UQOsk2w8MX1vGRhy6pn5to5I3d00r2YH0tPGHOqyeUXc6e02pSXXsxunGKowuNfMyB757b4mu2ITtmK8wV84VG4Ci+dHxUUJDy9y/+26fwI79zLwBlowbUuTq+pIrWlXaEBWvkzZqxB+dzSENLQR7HHux7woTfnFxumHNFhV0qpdO7bBetdfZgKSU2BkmegG0F39hFRuAJZ44iLfroddHIG6LYk1ykFXq6QEnNMUel9ODcahwFHlZaIa5sDLDRV7N1R40+oc2HaMT7pWn6g8t241CHSxEDrTbSMTv2YP08RxcazvuGgpikVK+nWMRTm8KwkTc0K7eIbQleaoXoxZkZj6N+LldaPStkKU4z57wYe7D+HgClBOEkI3vwpEWrThkeQ2klVXapFSL0hWVXV2nOZ2qsyKMo2uznTd7TOgd7MCmtB6WndYuCmEo9rUM2LkwfMRetDMMwGO5jY8aGFotV6bOVSms63E6bK63uQoF2+FfaIS6u9833kxLQDD18zxtvw2o3Mcc1qqd1oaEW8vbi/thiAxuD1DzHW19wCie19TMM3AVzmkmVfjnCNnb9kbbzWuzn7DuPlzl9RPacVgqmObJQbccjW+rPfughfOyRS/jz73+9KbbG7WkVQqV5Fntam6FvbJphoRhdiII81bWr7MHtyMehTohzq33XAjZmEFMzdJXWYohKP0nz9GB9/q51Y3PN7HO13FYL61uOLeBjD182vbdOf6leqAeeZ66RXVRSYRwX7cHWazuplVaa3Qoo5Zx6tZQ9eHQQUz/JkMl8vAu5BXxr44D+7iit+nnWLaXVLnJspXzonNYkDwQie3De/5k5tthDnQiXNwdoh/5IazAw3pxWOnb7+22iYtGqi2z6HHLtweprt59ccB8j8CBlHizlV7x/241g6MibYmgaEXr5rFPaKOglaR6iVbKYe9U9rQV7sHlO5M8ZJ649GABuPDKsaFU/e22T+oWrgpjcopXup8VGgDDwzH07a0EjhNr4sufTzpM8PXh+SutBKaCoSJ93EFNxA8H+e1FpNRs7PPKGYRiGi9Z5QUFMbk/rkJE3WTZ0IWGKVtP35enHVI+/YilG9Fz9JMVyK8SLbjicH9cIpXWtF5tezIZ1PEcXGnjw/LpRNr//zbfjBr0QLAYx0eJ51A78yaUmQivkB6gPYrILYNtCTLa9I51yTyugFlSpLoy6A3fTYFx7cCtUi9aiPbhh2S/NAsRStmyldXOQoBX5ONSOdNFafj11mwl2UnMr1OnBBXtwQy+cuyahOldBn7zSRS9OLXtwA4c6Ef7yB18PAMbGvNFPTRIxkAdv+V4+89JWnGi8yUCntRK0iBUCxobs9rRKZ1NnTRcAQtQrrZuWSgfkGwieEM55KCqtVORQ7287ChyFneaqAtWWyaZW2ewUW7rfB2YTKu9pDX0Ph9sRrmwMINvhyBmt6pjc93Yd9Fo6FcFOKvzKnims7LbD7MG3HV90HoO+l2b0VhUjdaFERFKz+eZ7+UzVJX2/qY2cwDle+/vVnFaJdlRdtFIQUJpJ2Ps3caZSpO3juPFoHvpWpKS0FkYUtSvswZsmHE21R/QGuT14VtT9lc5FDS0yz5EpdLvOw2q8FzD24DkV6fYIqqqvA0DDd9/rBy2xmWEYZhgH47fPNkDJj4MKpTWpGHlTZcezocVaWFh00KiF5VLRmlUm+DYCv7Y4klI69uCwULRuDBKj2tnBR8WeVipORi26fE/gzEqrcvyLq9y6CwU6T0kmjYJsK2bF50gyqdUyd9Ng3CAmOvcmiEkXjo3AM+oXHVOkZ0lGgWeuybVujG6coRUGxo7p9K5VvGYbe4Gc24N9pxA5utBwlFayQMeJUr0furCOyxsD+J4o3SsUvLXWj00BCeT9q6HvmfvNTpCmjQpl4cxfD22oHOk0rHAdD2kqIaVEJuGkB5PSeqgd1fa00vfQdaZrEfiuPdj3vMqeVppdvNAIantaw4rFaCv0je26bA+204Nzpe1QJ8LljQHW+2lpfEoVxro/yh4c5uODiqj3oNXTqotse+RN8XXefrJQtOrv7cepVlqrz0exaP29v3saDzy7BkBtZPgVr8P+bKOxQI5qn5XDvFI9p9X+OlmkfZGPlckKPa10P9J4ImA8e/DVbrU9uF1hD7b7zCPfM5/D8yhoiq6NeWLswSP6rMfB2IMPiNIqhIAQwLzqxeKIJ8IuYut6WllpZRiG4aJ1btBiyS5EcuumLIWHqDENo5XWYvhFL64pWq0gJue4KpJ+/8cnnsQ3/OJfoxunyCScICbi2GIEKYGnrnbRCDxnMR4VVB4qmMZJlTx7uD2G0po56pk9p5UK0brQktD3kGSZUcvo54DhoT9EnOahQSWlNfSNOpXb7vJgIFIFV7sxuoME7cg3RWuVJXrkyJsh9uBjiw30k6xkD6b/P3RhAw+eX8fplWZpYU2LdKW05kUrFaiBL8wi1w5iSmt6WunPp5ab5muUWEtqG41wiRNVtHpChULVXROjtDYpiClPa20EnrGPB75wFpV0jq5285AdOxjJtQdX9bSqn1/tJSV7sDnH1vicwBNKad0cYLOfjKe0jpEebL+WqkI49D3TV07jfCLfrxx5Q6/zOScK9mBL8U8q0oMBCnzK71MpJX7gf/w9fuUjjwKoTw23v2bswfpaSylLdliPitY0MyNpgPx6BH7e01pMELaT2ENfoBl6OLHYRB10Pzx+aQNAOT24HflICnOv6X5sR74Z8QTMR2nNR6FsoT14Dom/By2ICXA3S2albnNinPTgg7JRwDAMMwy2B8+JqkKkX7S8Wr+cklQ6RWKRVuSqn8X04GWtXuT9l2mN0uqVbLF/+eBFfPhzF4x9tCqI6ZhWVh+/vImjCw0nXKYYAhOn4ymtAPD1L78BT1zpmr/XBzHZdtq8p5UK0bpzRzbDXpJZyuD4SmucZmbBX+xpbVhKFh3Tl999BjcdVcXAYiOAEKpo3RykaIW+9f3lQr3ueEjVaUU+bj7awe0nFnHX6WU8bgVvHV2I8MTlrvleeo10fh48t4aPPXIJb7jjROnxF8xontjpad00Pa25YmWrbDT/Nk4zZ4FNCysad0NfS7I8kdj0tGYSa70EHT2Dtq6ndaPQD0n3B41L6kQB1nRar1/RA3nVhOz4WOvl/z5OejCgruFJHV4V+p669yrmtJLSemUjxqF2grOdelsqYea0jlC/jNJa1dMaiNJGRRgIc57tDY5Ty00sNgMnORiwldasVmkN9CYQsTFI0Y1Tc1/U/Zz9/qVzTj9T9XlBwV11Pa2eEEZtKhpX7J+JAg8nlsobNTZnVlp4wXXL+LsnrwGoUlrz+bRRoDaduiYcLXCObx4FjQlr24LCZCuCmLZCEd6teEKMDFYbl9BsTriPl4f1idJ9m7cHcdHKMAzDReucqEoP7jt2OInA6cOSaA/55d+JcoUBsJVW6mlVi6lTS008fa2Hnp4tWaW02sE3APDsNTXb9aEL6wBgelrtQvCo7k184vJmKfRIqTxuEqg61tGLmTfddbL0WEDFyBs7iMlKD6bCoRFU2zADTyBJJXqD1ChQdHyjitYsk0gyWbYHVymt+gBfddtRvOq2owCUWrTYCLDaS9CNU7Qi3yzYo4qidRyltdMI8L5/9WoAMFZt+nrPtgdnbgHzB/c+gyubMV5+85HS49MmxVovwVq/PJc38OuDmKpUMtpQcZRWj5RWXbRaluiNvhqz1Az8eqW1X+hpJXuwfq52w8daP1E9rRVzWsn6udgIcckfmH8fnR6c98Sa/uVAYJBmxo6byfzahb6Hw50QgzTD+bU+nluw4FZByvwo9aRFQUw1SqsJGNPHEtnpwVbR+uV3n8YX33miVJxRH34/qe9pDbxcVQSAi3qcEn2m1KUH249Fank+Uzg/dwRtNhXvLWPH94bZg6VTtA4LYQLU+/QnvvIF+LL//BdIMlmptAKqyF7RexC20lpUiGelbn7nPJinOnoQlVZlD55vT2vxc4d+v1W5h3jkDcMwTA4XrXOCfhHVKa3FsTdJmg3dWS/PaVX/t9ODAeC6w208fa2HfkJKq7vAjXyvVKydW6Wi1bXH2b80ydb6xJUuXnmLW/iEgYduNy92qGd0GgtTVbGfZO44IDpPdhhOnXLg69RaKrYGSWb1tA63B5OS2KqzB1s9rXV2u6VWqHpaB6po/aoXXYfDncixa9KonLpe402raLWhvy80leW1bwcxJa6qTNf25TcfLj0+Xe/1foK1XmISc8kebI8PsXv7El3UA24/Ji26TjpFq0qmpe+n8zZIVJhWR4/wWe9XJ9MWldZ8xIh6XqVW9uF7bgAPKYzXLKXVLgaogFLHXaG0Rnn6MN2blNRbZf33PYFDbfVeubwxGDOIqbxJVIWxB1c8Zuh7VjBU7j7IR964hVXVY9ijl9K0Oj049PMUYAC4oDdO6P1Vr9DW97TaIVbm+/Vm06BOabWK1rSq1UI/37/+kjtGFq0AcMepJbzzTbfjtz7+hLk3zXNaRSthF632vT+vICZga8J2aJb0PNTCg2hV9eZqD64OVaKvN8Ly5tRB3ChgGIap4+D4fLYYKkSckTeOSuUWKHYfVhXFOa30S6tb6Gm9Xs8+VUprWlJaG6Ff6kl7pqS0uiNv2lE+umOQZE4IE1Duaa1STsbFnt9JZJn7S9oorWmGvg6cqVuEhZ6H2C5arVElo0beUMFn7MFGaVXnolmhtBZZbqlRREkm0Q59nD3cxje+4sbS90W66Fjtxfi5Dz3kzLDsxSoEqKjiUBGz2AzQCHx35I224qaZNKmXZw+3cN2hsl01Tw9OsN5LzAbFpi4gfU+YopwW655Q57/qWtMi9uSS29MKoKSMG3twQ72GOnswFUqUPEsbCFQkUBEWWKmyQuT3sN3TavdIOkprzcgbwu5pzaTbE20rv/bs03FG3pxabuIdX/wcvLHCul11LMV0Wzp2uhYDR2ktpwfXYezBSaqdINXFp73hdkErrXRfJHVFq620Fnpa7XnABG02Fee0NqNclc7twVU9req1vO2eM7j77MrI1w4A3/HaW/An73xN6bOEzre9YdMtpAcTc2gV3dIgJt8XcyuGD9qcVsBNrZ6VfHOi2NPKSivDMMw4cNE6RyLfDS3pJZkpIGyLHUBjGkYHMZXntKrHp57WG3TRujlIEaey9IuvqLSSdRUAHjyvilZaaDesotVOLK20B1ekB0+zmDHjX5yeVlkoWl2ltTHkvCmbYWZGUrhK64iiNSkordTTGpaV1rrQqaVmiPOr2sY7JEmWArL+9LPn8RPv/Swe1BsIgNqYaFf8LH1tsRmiGXqlICa6JjdppekLK6zB6hjVtb28EWOtl5jra3pafc+kYW8OUjUCJ/B0umtZJaNrZSut9DW6rnTeyB682KCeVlf9HiQZfvsTT+L/+P3PoBP5OKXnvvYLczHzoLI86Ti05ste3YwReCq0yXYf0PsGqL6GdrFH7wf7XBBUxFBPK1GV9FtECIF/+YbbcHqlNfT78p7WmiCmYk+rn/dcF2f6VmHb1GsVU88db0UWdbIHpwVXBDG8p7Vq40PPaU3qlVYqmmylVcqyOjsJVZtfdG/ZY29ypTVwPufmkepKvY5bMfJmpRWWxqNNS660Hpxlw3zTg6t7l+nrxQ1nIC9wD5K6zTAMUwfbg+dIFLijYPpxik4UYL2flMbeJKkcag+mgicfISLMYwLAYW1JvP6IKlpp3mZZaXWLVupnBYCHzlf3tLYjdybo0cJM1CiomdM6xWLG17a/YhFsL3pNMrPuaR1mq/SppzXJF8h5T+sIe3Dq9rD2kxRC5AWAO6e1Xmn99DOrzuNUQUXrRj8vrgkKcSpCX1tqkkqZmuIpSaU5/udft4yHL27glbcerXzuxWaIU8tN/P2TVzFIs1xpJXuwL8y17A5U3ygVL0Yls64B3aMnCz2t9utq0kzQJMN6P8GxxQaagecUrX947zP4kd+5D5c3Brj77Ar+/T96vtlQoXuY3gdVSmvg5wrx1c0YnUYAIYQpOoFxgpis+86yBwNwesNp44fmtBJVM1WnpTlUac3fg0ZpDbzK9OA6Glbvdl16cOgLxyVCSiu9/s1B9QaL/f4wPa1D7MG+J5BSv3RQLlrtebx2ejD9eZ4hRnTubKV1M1Z2cd+yzgNzSg/ewp7Wb3/tLfial5ydy2PRaz1IBZQdADYrdSnRvqeSsYcrrQdno4BhGKYOLlrnSFihtB5diFTRmpZ7WocprdQnV1Rce7FSvp53Zhk/+qV34k13nkToC1zTPabVSmu++Hp2NS9aL20U0oN9mgvpOz1wVUrrIMnwrr96FI9d2sTb7jmtvz7dL/eiQl0KYtKvnRJchxWtoe85ycED68/9JIOUstZaTAVZ01rMB1plpK83K9KAbZZagbkWVYt585r1BgfZYO2ivRunxhZp0zJKq+ppzSRMT6hSWtXr/ILrD+GfvPR6vOTGcj8r8bwzy/jIw5cAqPmqQG7J9e30YL3xEvhKwTb9k7a1s8IebJTWVN17tJmSZBIb/dSM8KFCJssk/t0ffgZHFyL81FffjVc/5xh8T+CZaypp2qQHi6LSmve0Bl5uhbzWjU2BWtfTWnUNm5X24LLS6vS0TmgPHhe616qLwnyDrG8VrVVzWuuga9zTo6/q0oPtezNXWlP9/6SyqLYfi+zBVATScdubBr5QxXGpp9Wa00rFg93SapKI5zCHlKDPvqKyToFY9mfsPOe0bsXIm6Vm6Nzzs0B1U9Vc3v2KJ+ZzjYH6nlb1b16l0pr3tM7lEBiGYfY0/FE4R2ylNUmV5Y4WsfbCD1DpwcN2rF93+zH86re8DDcfU+NUTHpwokJHfE/gW151E1qRj2bgY1WPLimGOTRCtyB8VhcBdoFBizT6parswfnjFHtayZr4R/c9gz/+zLmJ0oOrKM6SLVoVfVO0qkXtKKV1vZeHRA3SzChFUqIy/ChJM/zB3z9jrNd2erBvFa3KHjy8p9VeILbC+gKGCnW7N5Do1qhXjcCDEKowMoFDukCOLaU19D287OYjQxdbLzizbMbCFO3BoZfbg6Uk5VUgzmSlqn77iUW88PoVZ6OD7m0qqEihjrXSWrQHf+yRy3jyShdvf92teN1zj5cCSMycVv20Havn2x7FQffwej8pBYyFvnDnl1acH/vf8/Rg6u/NlVa6VwJPYKkZ5LblORatJnirauSNXx55M21Pqz3qqEjou+nBRmnV52Ktl5geaRt6rCjwzL1cnClsF3++J6wUZKunNcw3J0wQk/VeofC0eaqUdO5se/BGPzXW73mPvMl7Gnd3MXgQg5h8T8wtPTi3B5fv1cj3WGllGIYZAX8SzpFm6Bs1oacXYLTgrEoPHtYDGvieGaUC2CNv0tIvvUbom+Kl2O/Z0IqM1PLEs9fUovMF1y2rfw+83Aap/99puPbgIx1XaaUF89NXe+jGaa52TLmYUaqRa/mzF4NC6JCrTFbOorUJPOEk0pLSSsVMVV/rb33iSbz91z+JD3/uAoA8ebWfpAi8fDHRCHwdCFO/SLbtp8PtwT76OkkXKCitNfZgIQSOLzZwarllCg4qPJM0Hwc0juL9PH39gfz6bg5SoyzYRWngewg8D6ldGFsbB//4JWfxnu98pfP4tMiyratCqNe53ldBTMoerO7N3/7kk1hoBHjTnYWRSB5dC7dYpj5PR2n1hbO4I6suXatm4Js+VfvrNpU9rcYebCmtSWpmxgqRJwgvbIE9uKoQDn2Vzpxm0jnHdMxVSn2RRqEYr0wP9tw5rRf0bOfNOIWUEhuDpLKopg2shu8h9FXf8eag3h4c+MJ8dtqbUsf16K1DnagyPTie4J4fl3aFPbgbJ3nLhnV8c7EH19hGdxu5PXh3H+c8EXNND67vXQ58UTnGzcxpnVPhzDAMs5c5OL99toFTy2pmKpD3npIKEacZfur9D+BHf+c+ADrxcoJf/nbRWix2m6GH1W5dT6sPKXMb3bOrPRxdiEyq7KKlktDiqRW6QUxHK5TWXpzhmWtd9AZpPvJmykVXo6i0ynIoTOB5pjAbpbTaxcUgzRBnmVEBiwnCUkr80l8+AgB46qpSoe0gJltpbYYqtbgZ+rVqw5JVtA61B+uRN/nsSktpjVOTWFzkd9/+Knz7a24xBUfXpAfnBeWoUSoA8PwzedGa97Qm5p60F1aRr3r54iy3II9ShYrpwb5Q/aZr/QRpJlXRqs/P5Y0B/vDeZ/ClLzhVKvT9wmZDSWm1FLjACmUCcgeBUcpDzylaqzaNnPTgwiLTVt56OqCKONwJneOaB3l6cIU92Jp7G1tFYNWc1joiU7TWK62BL5zWBprTKqVSm9d7SWUfrwnHso6nW0wPtq7Fcis0Y5rsYvaF1x/Cn37va/GcE4vG1m+nB9Nm4DyV1ip7sN27a2/OzaN+o/tstyuY3gFUWudpDzbXueJeDX2v8nOb04MZhmFyuGidI9cdauHJy5sAcqWVFrFJKvHJx6/gE49dAaBsbZOoA1RMZLL8S68ReFjt1fe0Arm98tlrXZxYauLEkipEbZWkYSmtvidMT93hgtIaBp5RWNX/yz1qk1AMsMoyWQq/CLRNcZQ9OPTzcwGoIl9KWIE+6jw8fbWLd3/0Mbzv/mfxuXMqkIr6fYs9rXReaCe8EXi1BbqjtA4pHJQlOjUFdlJSWqsf/+RyE63IL+3KF4uXURxdaOC0Dk6y7cF0DYVwFczQGkkyznOYIKY0n68a+sIow4vNwIQz/fnnL2JzkOLL7j5dehyjtFIPKfW0GqXVy4tW3w3JWSjY3huBDyGE1as6vKe1Uehfdnpak9RZvJPSOs6c1nGhAr7qMe3zS33Dqqd1AntwQDbYvD+3iJ1SLKXEhbW+eezNQYKNfoqFRrlnkh7LDoba7BfSg63n+6oXXWf6ZYv31k1HVRo2XXtbaR0kuU17XjR04NJ6P/8cscPR5h3ERPfnOJtNOwm91oPV06qcNfOArnPV7317w8nG/mxjGIY56Ozu35J7jOsOtXFpY4DNQVJSWpNMoh/nM0PTTE6kTNoLymJh2gx9rHapp9X9N1JSqef12dU+Ti03TdKr3Y9mBzGp/wdYagalxZS9aEusmahVvTrjEPoCAyssKqno9w19ZVPsj7AH+wV7MC2Uiym0v/KRx/Cjv3Mfvv1XP4kjnQiR75lkZSoWBrqntWEpdYA633UbDkut/HyOSg+OU1kbxDRqdEqzcJ0ppAoYX3V6nlZbD5sgJrcQCy0FKPA9J6F41HMUR974ntrsoMKkE+V9uTQv+Bbdv131OMX04IVGldLqzqTMe7VzpRywbb/la2gvHHOlVf0/zaQpXHpxVlBa51+0vv65x/FvvvwuPPfkYunf6D0ZJxkGCanfHu44uYhbjy+Y+c3DMPZg/X6pnNPq5XNaV3sJBmmGG3Ri+dVujEGaVVqi7Z5WQF2vohXeVlq/5PmnzEZa3fubvmwHsdMmVLGXfxaEULN3L2krNOD2mdtp1PPsad3ttlu6P4Z9/u437F7qWQmHbJYtNgMzL92Gzvm8EowZhmH2Mgfnt882cN0hNXfxqStdE9RielpTVXANdIJtPGLkTRG7mCjbg/3cHlxQ4I7rheB5rSKS0np8sekcH2CPvMmL16I1GChbQ2nczixKa1zoaS1asgJP2RT7I+zBgSecdFFaKBftwY9f3sCJpQa+7O7T+KEvuQOHO1FetFJhotU0Yw/W/7fPXxE7iGm4PdgNYrJf/+YgHTlns3idB2m+ITKugn/32RUIkfcNAq6KHxhlQNlu4zQvkEYrrW5PqydUn+zfPnEVAHDbiQXTO/zwxQ34nsCxxfK9ZpKjMwlP5HM1qai3F5Wh7zkbJwuFojVXyilMq/waPMthEFUUt3aokH0ODpmidX7FU6cR4BtfcWNl2jU9tz2GKAoEbjuxiD9+x2ucROM67IRooD49mOzBFMJEBTHNIx7W02rC3Rq+GRlU1dMa+h6+/uU3qD8H1fcvLdwz6w1+TW/WLVWEQc3C0YUGLlpF6+YgKQUxCVE953VStnJO6zxZbIb4P77ieXjrC07t9KFsGz/y1jvxza+8aS6PNayn9b/8kxfi+958e+nr9Dl6kCzZDMMwdfDImzlCfaJPXNks2QWTTKIXpxgkmUm/nFZpLf4Ca4aetXB1H5OKq3OrffTiFFc2Y5xazu3B9u6uPacVUGqYrRwSxYJlTau40/aV0cxRIs1kyXZHcykHSVZpoyKKhTMVhUV78OOXN3HHqSX85697IQDgl/7yETzw7BqAvGiVUlnhllshQl/gmD6X7/rml9aqDbY9uD0sPTigolUv5C35qBdXBzHZ2OegFfroJ2llKuswvukVN+L5Z5adYtG+t2ylUUI69uCoprAg8pE3edEa+QIX1xMsNQPcdXoZT11RPcSPXNjAicVGZdHk6eCr4kgW6vN0lFa/qLS6ybNNo5QPXwi2Qh+9OLNm8ubns93wcWlDqeGO0krv9zn2tA4jL1rzAC5ySoyL56nRRkZprUsP1vcmqeSktF4g1byiaDX24CCfNWtmEqfVlt7/5WU34OOPXcHzz6xUHy/Zg62eVmoFsHvJ58HRhci8XkBb9gsjb+YVjlNU9Hcz/1RvLBwU3vK8k6O/aUyOLajPuOWKe/XW42U3BcA9rQzDMDZctM6Rs1ppffJK1xR+9sibfqLUsMQUrdMprcXFTdNS3YoFHaloF9Z6OKfVVtXTqgqwRWvB2Y5UuioVMW++60TlYrD4/KumaJ3uF+tiM3CseKmUpXMT+AJJlmGQpCOUVvffSN0p2oMfv7SJL7j+kPm+w53IXJemMxbFw0o7wgff+VqcXlHXt2rRQdjnqxnVH2cU+BikmVnIx1rBlFJqe/CIotUqahebAbprqSn8x51Z2WkEePVzjunZtTTepqy0Br5AJutH3lRhei4tWy8d1xfeckQVkfo1PnppA3ecWqp/LK1K2ws32myJAs8KiPGc+8butQx9YdTrYipwkVbo4wpiS2m1itYwV+zt9+RXvPA0WoX5xlsJvdcGaWbZbSd//zUCD+t9UlqrgmDylGKjtB5RPabk3lgcMfIGUO+/Z7SToS4w7FAnwi//s5fWHmtVejA5TOY1i5Q4utDAo5c2zN83rfeksWzOLVU2dwow+5fXPOcYPvS9r8XxpWqXThXF0V8MwzAHGS5a58ixxQYagYcnr3SNhS63B0v0k1SPYKEgkumU1lLRaieeFhaCRxYa8ARwfq1v0nHPrLTQaQQ4utDAUUtla4Y+3vs9r8bpFfVL9R1vKtuVgHJRlNuDp1t0LbdCPHIxXyBWBjFpe/AgHd3TakPpvKS69ZMM1zZjrPYSp+/PtkHbKic93tkxegTptdDPDTtOsgcTpGYN9HzfYf2wgLs5sdgMcH6tb17rpItfIdSx9guFYWgpQFkmkWbZ+EFMxdRfkYckvfJWNcqJNls2B6npsa58LE9gAFfZevnNh/EfvuoFeOH1h3DfU9f0MYmCPdhNAi4GatWtA6mYripaW5Y92L5Gtx5frFVLtgK6t+xe5ml6DU8sN/GEDo+rSw8GVKFplFb9XqAitjIoyhp5Q99DVv0qe/A4UJEo7aJVb5hVOUJm4UgnwsU12x6cX+9wzkqr6Wnd5fZgZjY8T4z9e4Sge4KLVoZhGC5a54oQAmcOtfDE5U30b1AqXm4Pznta8xEx4/8ionlxKsDJ/Tk7fKnY6+h7AkcWGji/2seTl1XRSr84//u3vRxHO24fISV1DqNhihmV6GvswVP+Yl1qhmbOLKCs1MVf0pRiOmrkTfHcbJiiVV2HXpzicb1ItxcQdkKyXbRO2kvUCDxTIA3rd4sCVSRSEUj3RG+QlY6hCjuIaUGrTJumaJ38OlDRap8/e+RLAtU/aUbejFD1ij2t9jzVV9yiila76Ds9omgFXGUr8D384xefNY9Nz2nbiTuFfm2jtIYeQl/UXp9mUFRkyz2t3TiduyV1Emx7cFWw0bjceKRj5hNXpwfnPcUX1voIPGE2tc6vDelpNSNv1P87jdH24FGY9GAriGnLlNbFBrpxis1BgkbgY5BkRmWncz+/VFndQ81KK1PgZTcdwT9+8XXGks8wDHOQ4d+Sc+a6Q208eaVrrJq5PVj3tFqLzEmVSRM4U1BohymtgLIIn1/r4Ykrm/A9gVO6QLjl2AKW25Mv9mgheqO2Ca73KH10eqV1tRsbBSWrKVqTTI4uWgs/Rz2jtj2YilZbaaWxL4BbTE26wy2EwFIrdCzGVTT0yJs8iEndE5txUjqG6p/P/51CaOi1TrP4NaqidW/ZQUyh70008qbY0+p76jlOLDVwyzF139iF98nlVu1j0X1Vdy2KYyECS90zr8/3HHvwMHtza4jSSrb/XqGndbsJLHtwfwal9aaj7aFFJJ2nWIeGtS0L9Pk1ZfetKlqLI28WrCCmSeYJ29AlK/a02hsS8+KI3sS6uDYw76t2oad1bvM7KVWWlVamwMnlJv7DV91d2oxmGIY5iLDSOmfOHmrh3ievmoXkQkFpBWCNiJlskUI2yWLvmm0VrQopUkVrH09c3sSp5ebUxSVBi/hbjy/g8+fXsdafLT14qRUgk8B6P8FiM0Qqy0FMak6rVlqHBM4U+/I2iiNv4tSkgtpK65EapXWawmSpFTgL6ypoNi0pkaRgksV3ZBCTVfAtmqJ1OnswHQ/gXkN7FIeUGZJ0cnswvT4hBN76/FMI/VyBtnuxx1Fa65S54r+H+n1iF1M/+CV3mJ7zRuAPvVfp3FcF5FDhksmdTfSkY4sttX6aovVGy1lRqbTSaJ0s0zOSfaM4kj14oaKntTh7tNMI0I1TtfEx4Wgmoio9eLWbzF1lBWDaJi5u9M3mSm4Pnq9lM7LeZwzDMAzDVMNF65y57lAbVzZjEyxEvZSbg9SMYtno14+YGIZtg7QZrbQ2cd/Tq2iGvhnLMwu02KS5mrk9eHqlFQCudWMsNsNqe7Cn7cHplEpr01Vaj3Qip6g50nF7e4mpitZm6KQhVxH5nhmLBFhKKxWtEyiti42CPXgKm6gpWgsWXIAW6Wpe52BMazudN3pdvhD4ttfc4nyP/RpH9bQC9bMKTRCT6Q30AKSO0vrld582f24E3tCCia4/FSu2PdgeabOTSqspJnVPa+CJqZS/m47kRWvVNaWNNeqdjXxhrtv5IT2ttHlkpwcDagQVBZ5NamM3QUwFpXXe/awATNvExbW+SYbOg5jm3dNKBT4rrQzDMAxTB2/tzhkqCh+6sA4gV8HIGgfkhdSkSoNRkopzWoekBwNqVuul9T4eu7SBs4dm741Z0UXmnadV4uuasQdPt+iionVVz1yssgcHun82TuVYPa2klm2URt5keOLyZikQ47BlD7Ztq9OoabcdXzDW6TqKryHRxR0Vu5OMvFks2IOn7WkFXIt3ZNmDA0/N6+zr4xtlx6xKDy7iKK0r09uDi+8L+n+VbRVQycvDrms+2qQ88qZljTGa1bEwC8WRN5NabQlXaa1ID7YCn2K9YRQFqif46qZyWFSN+SleEypsN/qJNU94OqX1yuYAv/axxyClxGo33iKlVX0eXNoYmM2grbIHh2bDiH8dMwzDMEwdrLTOGVIfP/rwJQD5Yo3GSgB5ITVpkVevtNqFRrU9OJPAxfXBxOmFVbz0psP4ve96pVHHTHrwDEFMAEwYUyqrilYPV/QieeicVv1zrchHmkl0dSFHC+t+ooKY7jm74vycHUgVBZ4JvZpGTfv3X/mCkd9TLDJirR6RdXzkyBunaHWV1ll6Wh2llQbb+wKpVPM6ySXQHlG00nmz04OL0EigwBNOenORUbMK6bHN8Xr5mJUqrjvUwhNDiuRmQCph2R5sK607aQ8ujryZdlzKyaUmGjoUrG5OK6DtwUn+PK3QR5wmaEd+5XWhz7aGsQfrTaR+YsY7TXrM9Dzv+dRT+NADF/DKW45itZcMHUE1LYdNT2sfXdNnroOYtCI67/RgHnnDMAzDMPXwb8k5c8epRdx93TKevNKFEPlcR0dp1X+edGfdBDEFxaI1D46pSkQ9tphbL88ent0eLITAC65bMWrgai9B4NWnsY6CUlhXezGyTELKcoESesIoicOKVlKLWqGP0BemyKLNg81+iqeudp0QJsBVWkPPq90gGAffEyOL3eIClfr8qPAcpWQKIcx5IKV12pE3QE1PqxXOFOoiXqWpeiNVRjqG/hClNfI9CKHmBg87X6PGPtjzZO3vswtMm3d+8XPwG9/68trnI6W1Kj14lpCueWKPvOnF2Uhlvg7PE8YVUFl8emWlFcjfT3Vzac2cVt/dQNjop0iyDJ6Yoj1Cf748q+e9nlvtYa0bmyCyedIIfCw1A1zaGOQbNcWRN3O6/g2zScL2YIZhGIapg4vWOSOs3r2mFfhiF60bU44moQVkMcCJlNZGTSFxfClXseZhD86fVy3iBoVRKZNi97SmuvG3KoiJFo/DrJB0TpuhsjGaRN1AIAo8fP78GtJM4vrCCIFO5OtUWdUbOErdm5WSPVgrrcYePEJpBVAqWjemtJ3bP2MX6cYeHAj42h5MCbKjMOnBRmktf48QAs3AN2nW9Y81vIeQvk491caSWmFbBZRqP2xToGVtAqnHs4KYrJ+bxoY9L2x7cDdOHbfFpNx4VL0XRs1p7Vs2ZLo/F2uK1ryn1S1yyR48jbWaLv+5VV20rvV1T+vWjB46utDAhfV+3mceukXrvNy8r739OL7/Lbfj5qML83lAhmEYhtmHzPRrVwjxH4UQnxVC/L0Q4j1CiBX99RuFEF0hxN/q//5v62deJIS4VwjxoBDi/xLTynO7mDffdRI3HGmjEXpm0bduFa1kWZ125E2xQLTnT1ZxYslWWudXtKpRKG7BMA1Gae3GJmSl2C8W+N5YI13oHLUiH1Hg5VZsT81O/ejDlwGgZA8WQuBIJyqpKFtlAS1uMAwKQUzjFIYNfd3L6cGTH3OVqmjbbUNfIMkybAwSM/ZlGKanVb+uuv6/VuQPDWGyj2mSkTftyJ+653ChEcD3xNCRN+p5d27Pzx55o4rW6UdiUF9r1eeRPafVtiHThkCt0mr1QwO50rqu7cHTWNjpOlObwLlrvS1LDwZU0Wrbg+m1mvtxTr+6llshvvO1t86tR5ZhGIZh9iOz+qo+AOCHpJSJEOInAPwQgB/Q//aQlPKeip/5OQDfCuCjAP4QwFsA/NGMx7Gr8D2Bf/cPn4/PPLNqFv6VSusUI2+AspJm7GU1C8Fjul8wCjzz53nR1L1tsyiti40AQrhFa7FYVPbg0Uor/Vwz8BH6Hq5sqBTnwBdoBD4urvex1Axw67GyqnFkoWE2F7ZdadXF3bgjb4BcYV+w0oNDfzqbdlRheQyt3s44zZCkEt0JlVayPdct8L/7DbfhuScXx3qskUWr9f+6Ymocvval1+N5Z5atXsP8edu7pKc1spTWXpyOpczXQQnCVdcotwernlY6r/R8dX3Dxh5cVFoHCZIsm2pjpXj9H7+8iUGabUl6MKBmN3/+/HppIylXWrnIZBiGYZjtYiapQEr5fiklVWMfBXDdsO8XQpwCsCSl/IiUUgL4FQBfMcsx7FZeeetR/PMvuhm+JyCEq7SantYpldZS0WqU1uqFaxR4ONQOcd1Ka+4LLSquZklS9TyBxUaA1V6S24MrlFZiWNFqelq10roZ532eVNy/6IZDlefhcCcyj+1bIURbQblodYOYxrMH+2iGudqtitbprkPe05r/fGgVgaHvIckkNgYp2mMUhFTskNJaV3B+4ytuxMtuPjL0sUjFrxt5kyutuTJaZ1sdh2OLDbzuucfN34XI7eKzzvCdF8YenKii1U5inpQ33HECX/fSs7jtRHkjJ7cHq+Ruel4q4Oo2B/xi0aq/f72fIp7SHly8/p8/vwYAW6u0rvfzjaRiT+v+MwkxDMMwzK5lnv62b4armN4khPiUEOLPhBBfpL92BsCT1vc8qb9WiRDiW4UQHxdCfPzChQtzPNTtJfS8OacHF+zBgTuKoYozh1q46ejwMSzTYBZyMy7gl1ohrnVVEBNQXqDaysyw15n3tPqIfM/Mxg19z9inX3TDocqfPbXcNIvwXGndGguo/Ro6kW+Ku+4ghSfGSwBuBKo3kxTR7iCZuWi1r6Ppc/VVr2+SZugOElOADKPc0zr9/TG20moFMs2itFZB5yLSfc/qaztYtFpzWrszKq3HFhv4P//RCyotxvS6E50eTPcl2YMXa0KQzPmi7zdBaAkGU9qDi5f/wfNqrNhW9bQeWYhwdTPGqh7p1S70tO7kpgXDMAzDHDRGruyEEH8M4GTFP/2wlPJ39ff8MIAEwK/pf3sGwPVSyktCiBcB+B0hxF0Aqn7Ly4qvqX+Q8ucB/DwAvPjFL679vt2OChGqmNM6YUFk9+zZmCCmIWEsP/M1Lxyaujst81BaAdXXtdqNTSBRsaB3AoKGKq1W0Wp9X+AJU9y/6IbDlT/7ji9+Di5ZdmL6ua3APrblVugora3QH8vi2wg8lZJMFvRZlFazELfOmU8JsAJ9XyiltZ/i9MroIoEKun6S6sed/jyOSg82DgR97McWGpi3CBb6At1Yz6zV52Ine1rtkTezpAePgu7/JJUYpJkplltGaa1+3qLS2o58CKFH3qTzsQdfXFfv1a1IDwZgxjA9fmkDkZ8nZtN7ZR/GMTAMwzDMrmXkb3sp5RuH/bsQ4hsBfCmAN2jLL6SUfQB9/edPCCEeAvAcKGXVthBfB+Dp6Q597xB4btFKKbiTK620aKoOYhqmXtx6fGuSKZumaJ1tAbc8Qmm1H3+cntZW6Dnng5TWwBOlECbi+FITx3Vo1Xb2tC61QsRWEFNrjKAjQJ37ZpgnVHcH6dQL+LAiiMlWWkNP2YM3xwxiGic9eFxGXYtAjyiiQuqn/vHdcy8obKU19Dz0UD3XdLugAj1OM3QH6dANq5mex89t3q7SSj2t1RsYxZE3Qgh0ogDr/RTXurGZLTwJ9mfCscUGLqz1AWyd0nr3dSsAgA9+9ryjZOebKFvytAzDMAzDVDBrevBboIKXvlxKuWl9/ZgQwtd/vhnAbQAellI+A2BNCPFynRr8DQB+d5Zj2AuEvuf2tJrRJNMFMZWV1uHpwVuJGQMxo+q01AzdkTeFgsAuQIcpxnRuWkWl1Rc43I5wz9mVsayUW50eHFkhR83QR2yNvGlF453LduSjHfmmsNgcJEML+qHHYwpUu2jNz4Gv57RujBnElPe0VqdBTwJd09qRN57AL37TS/A1LzkLAFhshrUBQdNi211HKb/bgaeviZrTmm6Z0mrswVpppVmitLGyUKO0BoWRN4BSZTf6CZ662sWZlcnnRdvn+3mnl8yft6qn9XlnlnDzsQ5We4lzz3NPK8MwDMNsP7Ou7P4LgAaAD2hl46NSym8H8GoA/1YIkQBIAXy7lPKy/pnvAPDLAFpQPbD7Kjm4isAX6GvFCciV1knthfUjb4anB28lVADOQ2ld7cXGJlsOYrJ7WusX6MYebBVzgCqq//1XvgCy3o3uPh/NBt2qotVSrEJfOOnB7XC8t+U7vvh2dOPEFJeZnG5GK5BvBNg2bEdd1M+x2o0nVFr1vT7DAn/UyBsAeM1zjk39+GMdQ5CHoOWBTztbtIS+yNODt8oebEbeZIjTXGltj0gPpl7XZUsF7TQCrA8SPHWli1ffNvn1sq//XaeX8acPqJyDrUoPFkLgK+45g5/+wOecjS46B5wezDAMwzDbx0y/7aWUt9Z8/bcB/HbNv30cwPNmed69RlAoTklpnVTFK1ruCOrVbMyQIDot8+ppXWoFyh5MSmvRHjxmT6s98sb+vjAQWG6PP+5ny+e0ml6/AIHn5fbgOEVzzFCdO7Xa9NTVrvnazOnBVUFMnmc2WPpJNqbSmvdcArMV//4WbyCMAzkJQl+YsKqdDuIJfQ/9ZPY5rUOfw9iQJQZJPqd1VHrw2cNt/M7bX4nnn1k2X1toBHjyShfdOMWZQ5MrrfZHwvPObL3SCgBvu+c0fvoDn3OVVr2BwUorwzAMw2wf3JWzDRQVGTOndcqRN8VCimzB01pDZyHvp51dae3FmRn5UiwIwjF7WunnaOQNUdw4GEVuAd2i9GAroCYMPMRaYe4NUpNSOi5OH+rM9mBbac1Tcutmldbh6VFPpqd1hgKPisSdVLac9GCrmN9JIt/D5iBBJscbkTQNRmlNtdJqbbYA9UorANxzdsV5H7cjH58/p8bUTGUPFvn9eNsJNduX7PVbxQ1HOnjZTYdxUve6q+dnpZVhGIZhtput8VUxDkUVMp/TOqeeVqO07oQ9eD4LeApTubIRA5h+TistKFt65E3+9UlDr7Y2bMUoVo0AoScspTXB8cXmsB8tP5atQk+5eTBMaQ19z7kenTGDogJPmKJ1FlVqq1XvcSB1ze5p3cnjAdR1WdPjWLaqcMvntGaIU5nPXdUbFwsTBH8tNAJs6g2766ZQWuk+OLrQwAldRG6lykr8wje9xPk7vd9YaWUYhmGY7YOL1m3AtvUO0ixXWqfsaS0WYJ4nEFlzSLeT1hzTgwHgyqYaY1EqWq2/D+vdzUfeeNYYFzFxmuyWz2mlxX+k0n/NyJvB5P2Jtro6sz3Yuo5GUfSFs2kwrqoXeJ7p5Z4pPZiUrR0sEgIvL+BN0bLTPa2BwGpPbfI0tyo92KOQL9cdQj2rkxSNtpX49BRKq2cVrQuNAAuNYMv6WW2KarKxB7PSyjAMwzDbBtuDtwEqBFqRD98TeU/rlOpfVWFyw5E2zh5qz3ikk2PSg2fuaVWL38sb1UVrOKbSanpaQ98Kz5l8cbld6cGU/htneRDTpFbPKnV0UmxV1Ryjn99v4ZRKKynIM6UHb7HqPQ60ARIGu1Np3eogJipayc3xqluP4T9+1QvwguuWa3+2CBWtrdDHofb0I2+OLare9ONLjW1RWouwPZhhGIZhth9WWrcBUmkagYdB4pm+zcntweW+Q+KPvvuLdkSJotCgWRfwtPi8QkXrkDmtY4+88acfx7Nd6cHtKIBvFXfdKZJgnZTkGZVW+/XaKbnF3sRx8H2BuF8drDUJeZG4c1Wraw+mc7Wze36ht/VFqz1Oyf57FHj46hefneixSLE8c6g11RxduoeOLkQAgNfffnxH3CVmE4VrVoZhGIbZNrho3QZI6WuEHvpW0Tq1PbiikJo1vXda5qW0kj34Up3S6vRtjqe0Vllex2XLlVYaedPwkWYytwfH481BtfE9AU+okTc0R3NSGiPSg+3rO+7x2Y81D6V1J5WtoCI9uOp9uJ2EgcDFdbIHb5HSql8jtTTMEvZGCv00IUxA/p4kpfVHvvTOqY9lFoRQ7RhsD2YYhmGY7YPtwdtArrTmhZQnJl+E1wUx7STz6mldaQ+3B9uWzGHn7Y5TS3j9c4/jeWeWjb11mvNl1JQtklOoD7kVBgh8NfImyyR6cTZVARJU2HsnIar4+dCyBztBTEMSY23sn5llgW+UzR2sEUJfzaoVQuTJ0jvd0+p7Vk/r1hStdN0oPG6WzSkKb5pm3A2gCuZ/+7a78NUvmkzh3QoCX+xojzXDMAzDHDR2T/Wzj6FFbiPwKkeLjEtdENNO0jL24PkorfVF63hjfQ53IvziN70EhzuR+d5pFLHtSKz94bfega980RmdHizRS5SaNc34EqP+zTGIKbTswfY9N0kQEzFTENMuUFqjQJT6fndDTyuNStqqkTekKs5FaW3MprQCwDd84Y248Whn6p+fF6HvcdHKMAzDMNsI24O3AVrkNkMfjYCswZMveAJ/tsJkK2gae/BsC7jQ97DQCIw9uLggpPM1yaLZFK1TLLS3ek4rAHzjK24EAIT+U0jSzITdTGoPBvRrHKSzF63WfUmJtI3Ad87D2EFM1j0xywLf9HLv8JxWcz/tkp5W2ya/VenBgLqOXSpaZ3ifz6No3S0U3QcMwzAMw2wtu6f62cfQYrsReJaiNb3SutMKj8287MGAUlsvrfcrH4/O1ySzaGdRxPxtLJQCrZhRYTCVPVgf79RzWq3+VeKVtx7Fv/uHz8ddp5ec6zGuqjc/e/DOjxix+3qDXeJ4cNTvLbIHA+r1buggplmU1qMdFaC0G5TSWYn84W0KDMMwDMPMF1Zat4HQKriqFK1xMWEwMywc58287MGA6mv93Lk1ABVKqz+D0jpLT+s2LEwjXyDOMvTi6ZXWaEYVPqywBzcCH//kZderf7eu7zRBTDOlB5M9eAftmF/y/JO48YgaKZUrrTtdtFqzc7ewaA19z2yozOLyePnNR/Cb3/aFuOfsypyObOc4sdzEcR0IxTAMwzDM1sNF6zaQ97T6Vk/r9H2W04xw2Spac7IHA6popR69ujmtw5KDi8zjXG+X0iolsNaffnyJCWKackMjTw+u/nnfsmePW7jY9tlZVKnd4DB4wx0n8IY7Tqjj2EVzWonGViqtvqW0zlC0ep7AS286PK/D2lF+9VteNhd3CcMwDMMw48FF6zZAi+5G6Jm5gtMok3l68O5ZLDWNPXgOSmsrMn8uFgSBKZrGX5xHIwqxYWyn0krXc7WrkmCnK1pnU1pvP7GIf/H6W/Gq245W/jttSkyiAs/rHNJr2i12zLzHdofntG6bPdhSWneRy2MnGTdBm2EYhmGY+cC/ebcBUkabltI6jTK5m9OD5zGzclmPvQHKVtBx04NtoinUWcLfxg0COr7VnlZap7IHz9bTGvge3vmm22v/nc7HuCFM9s/MemvQ48xiMZ4n9P7b6c0j26a8lZ8JoS9weYOCmLhoZRiGYRhm++EVyDZg7MHhbEFMeQDM7rlsrbkqrXnRWrYH63M4wfOEM9iDc5Vw6881PZdRWqcoWrc6WZoed5Jjm1cvKl37nZ6LSuyGYCggVz2bgQexhQV94Hvo6n7rSYLQGIZhGIZh5gWvQLYBN4iJgosmX2R6Rv3bPZetHfkIfYGFOdjlVtr1RatJx50iiGm6pObtTQ8GgDWttLbDyc+lCenaonsjV1onKFrnVNzRtdgtSutuGMED5KrnVs1oJezXuZs2zBiGYRiGOTiwPXgbyEfe2PbgGZTWXdLbB6ie1v/+bV+I55xYnPmx7J7WOqV1qvTgGWbibk96MNmDldLajCa/N8x9tUVKWN7TOv5HRjCnYjPcJcomYezBu6SndZoRSZM9T/46Zxl5wzAMwzAMMy1ctG4DgaO0zpJou7Vq2rR8wfWH5vI4dk9rsdAJpuhPDWfYINjOPsp5BjFN29M6Crr3JgliMj2tMyutOz/yxobuxZ22K9N9vdVFq/1Ztds+exiGYRiGORjwCmQbsBWRhlH/Jj/1K+0QjcDb8kXqTnGobSmthYIgTw8e/7w1ZtggMD2t21CYhEZpnX7kzSwF+jjQ+WhPYAMP5hTEtFvsuAQp9zt9PFQ8b2VyMMBKK8MwDMMwOw8rrdsALbrdIKbJF7z/6AvO4GU3Hd7yHradYmWI0mrmtE5jD54lPXgbCpPQUloj35uqBzfcpiCm9gQFklGrZzyHwZwU23lhlNYd72lVz7/1RWv+Ojk9mGEYhmGYnYBXINuASQ8OPLPom6YwaQQ+bj62MNdj200MSw8OpuhpNenBUxQX4TYWJrSpsdaLp96Q2K4gpnZj8iCmWW29uyWtl9jqpOZxMQFv4dYeh927y0orwzAMwzA7Aa9AtgEzriXwZwoH2u8sDRt5Q+nBExQKs6UHb1/YDoUnrfaSqVUzeowo2Jr7Kg9immTkzXwK/91nD94dSmu4bfbg3Oa906+ZYRiGYZiDCRet24CxB1tBTLz4K9MMfbMAL9mDg1ytHpfIhDfNMqd1G+zB1pzWSYrCqsfYsp5WsgdPlB48X6V19wQx7Y6eVtqo2PqRN5Nb8xmGYRiGYeYJr0K2gcAKYppl5M1BgPpay0FMU/S0zmDF3taeVqO0xlOHbG21ZbUT+fi+N9+Otz7/1Ng/M++e1t2y0bPbelqbwfakB/NnFsMwDMMwOwUHMW0DVUrrNEFMB4HlVohnrvUqgpgE3vDc43jRDeOP15nlXG9noUTP1YuzqVWzrU4PFkLg7a+7daKfCSxb6SzsliKR2GpVe1zoc2WrlVbTO8tKK8MwDMMwOwQXrduACWKy04O3oVdyL0JKa/H0CCHwC9/0kokeyyTqTnGuaS7pdmwu2MXP1PbgXVhYzGtOKyut1ZBCv+VzWmncFCutDMMwDMPsELwK2QbsICYzp5WV1kpWWmpW6zyK+sD38E2vuBGve+6xyX92W0fe5K912gJkq0feTIMJYppXevAu6Wl92U2H8RX3nMZ1h1o7ehzGHrzV6cGk4u+iDRGGYRiGYQ4WrLRuA0cXGvAEcLgT4YnLbA8ehlFa53R6fuzL75rq5/J+zK1fqNv3wrRKqyksdtF9tV/ntJ493MbPfO0Ld/owtj09mJVWhmEYhmF2Cl6FbAOvuvUo/vwHXo/TKy22B4/gcCdCFHgQO6yqvfb2Y/iXr78VNxxub/lz2fblqUfe7JI+S5t5pf4eW2hipR3ixiNbfy32EqZo3ab04N10bzEMwzAMc7BgpXUbEELgzIqyEkZsDx7KN77iRrzkpsM7fRg4stDAO950+7Y8V2jNVp3eHrz7xpLkCulsj7PcDvG3/+ub5nBE+wu65ludHkz35266txiGYRiGOVhw0brNzDKG5SBwYqmJE0vNnT6MbcVW3We3B++e+8qfU08rU02ki8nmVqcH07ipXXRvMQzDMAxzsOBVyDZjlNZd0p/H7Dx2MTCtPfjUchOLjWDqoncr2G29qPuNhYbq/15uhVv6PGTzZqWVYRiGYZidgpXWbSafHcoLQEZhBzFN25/4ZXefxutuP77l408mwQQxsdK6JTznxALe/S0vxStvObqlzxPuwpAvhmEYhmEOFly0bjMNU7TyApBRzKNo9T2B5fbWKm6Twkrr1iKEwBfdNvk4p0kxc1pZaWUYhmEYZofgVcg2E/mqKAk5PZjRzCM9eDdCbgJWWvc2u7FfmmEYhmGYgwWvQrYZUitmnV3J7B88T5j7YTf1pM7KvNKDmZ0l5J5WhmEYhmF2GF6FbDOHOiGed2YJd55e2ulDYXYRVBjspp7UWaFCfNY5rczOEnB6MMMwDMMwOwz3tG4zjcDH7/+LL9rpw2B2GaHnoYcM7Wj/vCVJaWVXwd6G04MZhmEYhtlpeBXCMLsAKgz2U08rpwfvD4w9mJVWhmEYhmF2CF6FMMwugEJuWtH+eUtSIc7pwXsbM/KGlVaGYRiGYXYIXoUwzC4gL1r3jz3Y172QXLPubbinlWEYhmGYnYZXIQyzC9iP9uCQe1r3BZwezDAMwzDMTsOrEIbZBZDSup9G3nB68P6A5rSy0sowDMMwzE4x0ypECPFjQoinhBB/q//7EuvffkgI8aAQ4gEhxJutr79ICHGv/rf/Swhe0TIMJe029pGaReoxK617G1LMSXFlGIZhGIbZbuaxQv5PUsp79H9/CABCiDsBfC2AuwC8BcDPCiFIQvo5AN8K4Db931vmcAwMs6eJAg+t0Md+2sOhnlZOD97bGKU12D8uAIZhGIZh9hZbJeu8DcBvSCn7UspHADwI4KVCiFMAlqSUH5FSSgC/AuArtugYGGbPEHhiX1mDgVw95vTgvQ0p5qy0MgzDMAyzU8yjaP0uIcTfCyF+UQhxSH/tDIAnrO95Un/tjP5z8euVCCG+VQjxcSHExy9cuDCHQ2WY3Unge2juoxAmwO5p3eEDYWYipPTgfWRdZxiGYRhmbzFyFSKE+GMhxH0V/70Nyup7C4B7ADwD4KfoxyoeSg75eiVSyp+XUr5YSvniY8eOjTpUhtmzRL6375TWkHta9wVHFiL4nsDJpeZOHwrDMAzDMAeUkUMhpZRvHOeBhBD/D4Df1399EsBZ65+vA/C0/vp1FV9nmAPNscUG9lvrZz6ndZ+9sAPG6ZUW/uaH34jDnWinD4VhGIZhmAPKyKJ1GEKIU1LKZ/Rf/yGA+/Sffw/ArwshfhrAaajApb+WUqZCiDUhxMsBfAzANwD4z7McA8PsB/7t2+5CVus52JsEPKd138AFK8MwDMMwO8lMRSuA/yCEuAfK4vsogG8DACnl/UKI3wTwaQAJgLdLKVP9M98B4JcBtAD8kf6PYQ40i81wpw9h7vCcVoZhGIZhGGYezFS0Sim/fsi//TiAH6/4+scBPG+W52UYZvcTcNHKMAzDMAzDzAGOg2QYZkvwjT14hw+EYRiGYRiG2dPwcpJhmC0hoCAm7mllGIZhGIZhZoCLVoZhtoSARt6wPZhhGIZhGIaZAS5aGYbZEjg9mGEYhmEYhpkHXLQyDLMlcHowwzAMwzAMMw+4aGUYZkswPa1ctDIMwzAMwzAzwEUrwzBbgu9zejDDMAzDMAwzO7ycZBhmS4h8DwuNAIc60U4fCsMwDMMwDLOHCXb6ABiG2Z9EgYcPvOPVONJp7PShMAzDMAzDMHsYLloZhtkyTi23dvoQGIZhGIZhmD0O24MZhmEYhmEYhmGYXQsXrQzDMAzDMAzDMMyuhYtWhmEYhmEYhmEYZtfCRSvDMAzDMAzDMAyza+GilWEYhmEYhmEYhtm1cNHKMAzDMAzDMAzD7Fq4aGUYhmEYhmEYhmF2LVy0MgzDMAzDMAzDMLsWLloZhmEYhmEYhmGYXQsXrQzDMAzDMAzDMMyuhYtWhmEYhmEYhmEYZtfCRSvDMAzDMAzDMAyza+GilWEYhmEYhmEYhtm1cNHKMAzDMAzDMAzD7Fq4aGUYhmEYhmEYhmF2LUJKudPHMBZCiAsAHtvp4xjCUQAXd/ogGAZ8LzK7B74Xmd0A34fMboHvRWa3sFvvxYsAIKV8S/Ef9kzRutsRQnxcSvninT4OhuF7kdkt8L3I7Ab4PmR2C3wvMruFvXgvsj2YYRiGYRiGYRiG2bVw0cowDMMwDMMwDMPsWrhonR8/v9MHwDAavheZ3QLfi8xugO9DZrfA9yKzW9hz9yL3tDIMwzAMwzAMwzC7FlZaGYZhGIZhGIZhmF0LF60MwzAMwzAMwzDMroWL1jkghHiLEOIBIcSDQogf3OnjYfYvQohfFEKcF0LcZ33tsBDiA0KIz+v/H7L+7Yf0ffmAEOLNO3PUzH5ECHFWCPGnQojPCCHuF0J8t/4634/MtiGEaAoh/loI8Xf6Pvw3+ut8HzI7ghDCF0J8Sgjx+/rvfC8y244Q4lEhxL1CiL8VQnxcf21P34tctM6IEMIH8F8B/AMAdwL4OiHEnTt7VMw+5pcBFAcu/yCAP5FS3gbgT/Tfoe/DrwVwl/6Zn9X3K8PMgwTAO6WUdwB4OYC363uO70dmO+kDeL2U8m4A9wB4ixDi5eD7kNk5vhvAZ6y/873I7BSvk1LeY81j3dP3Ihets/NSAA9KKR+WUg4A/AaAt+3wMTH7FCnlhwFcLnz5bQDepf/8LgBfYX39N6SUfSnlIwAehLpfGWZmpJTPSCk/qf+8BrVIOwO+H5ltRCrW9V9D/Z8E34fMDiCEuA7AWwH8/60v873I7Bb29L3IRevsnAHwhPX3J/XXGGa7OCGlfAZQhQSA4/rrfG8y24IQ4kYALwTwMfD9yGwz2o75twDOA/iAlJLvQ2an+BkA3w8gs77G9yKzE0gA7xdCfEII8a36a3v6Xgx2+gD2AaLiazxHiNkN8L3JbDlCiAUAvw3ge6SUq0JU3XbqWyu+xvcjMzNSyhTAPUKIFQDvEUI8b8i3833IbAlCiC8FcF5K+QkhxGvH+ZGKr/G9yMyLV0opnxZCHAfwASHEZ4d87564F1lpnZ0nAZy1/n4dgKd36FiYg8k5IcQpAND/P6+/zvcms6UIIUKogvXXpJT/U3+Z70dmR5BSXgXwIaieLL4Pme3mlQC+XAjxKFSr2OuFEL8KvheZHUBK+bT+/3kA74Gy++7pe5GL1tn5GwC3CSFuEkJEUI3Mv7fDx8QcLH4PwDfqP38jgN+1vv61QoiGEOImALcB+OsdOD5mHyKUpPoLAD4jpfxp65/4fmS2DSHEMa2wQgjRAvBGAJ8F34fMNiOl/CEp5XVSyhuh1oIflFL+U/C9yGwzQoiOEGKR/gzgTQDuwx6/F9kePCNSykQI8V0A3gfAB/CLUsr7d/iwmH2KEOK/AXgtgKNCiCcB/G8A/j2A3xRCfAuAxwF8NQBIKe8XQvwmgE9DJb2+XdvoGGYevBLA1wO4V/cTAsC/Bt+PzPZyCsC7dNKlB+A3pZS/L4T4CPg+ZHYH/JnIbDcnoFolAFXr/bqU8r1CiL/BHr4XhZS7zrLMMAzDMAzDMAzDMADYHswwDMMwDMMwDMPsYrhoZRiGYRiGYRiGYXYtXLQyDMMwDMMwDMMwuxYuWhmGYRiGYRiGYZhdCxetDMMwDMMwDMMwzK6Fi1aGYRiGYRiGYRhm18JFK8MwDMMwDMMwDLNr+f8AY2PbXIlbDfwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0, figsize=(16,7))\n",
    "plt.title('Rewards per episode')\n",
    "xaxis = np.asarray(range(0, len(score_tracked_sample)))\n",
    "plt.plot(xaxis,np.asarray(score_tracked_sample))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### We can see from the above plot that the rewards converge at around 1500 near to the END. Since the initial state is picked to be random for each episode, some initial states may be less rewarding than others inherently regardless of the model quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "time = np.arange(0,n_episodes)\n",
    "epsilon = []\n",
    "for i in range(0,n_episodes):\n",
    "    epsilon.append((1 - 0.00001) * np.exp(-0.0005 * i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TosnMuLNDAp+M7ED5UlH8avwinpqxjtMZWV5HEwlr+Sn0OGBHjvup/mW/YGalgZ7A1LM8PtTMks0sOS1N32MZ7hrVKM+nozoy4IravDprM/3G6rIBIoUpP4VueSw729Gu64F5Zxtucc6Nc84lOeeSYmJi8ptRirBS0T6e6tuUsbe3ZNv+41z74hymLUv1OpZIWMpPoacCtXLcrwmc7SySAWi4RfLQs0kNZjzYicviKvDwlJU8MGk5h4+f8TqWSFjJT6EvAeqbWYKZRZNd2tNzr2RmFYAuwCeBjSjh4pKKpZh0b1se6d6AL1btpucLs5mfogOmIoFy3kJ3zmUAo4CZwDpginNujZkNN7PhOVa9CfiPc06DpHJWvgjj/m71mTqiPaWifQx6fRF/+WytLvIlEgDm1ckfSUlJLjk52ZPXltBw4nQmT81Yx9sLttEgtizP3Xo5l11SwetYIiHNzJY655LyekxniopnSkX7+N8+TZgw5AoOHj/DjaPnMebbTWRm6QxTkQuhQhfPdb20GjMf6ky3hrH8/d/rGThuITsOHPc6lkiRo0KXkFC5TDRjbm/Js7c0Z+3uI/R6YQ4fLk3V9WBECkCFLiHDzLi5VU1mPNiJxjXK8+gHK7nvvWUcOHba62giRYIKXUJOrcqlmTS0LY/3ash/1+3hmudmMXPND17HEgl5KnQJSb4IY3iXukwf1ZFq5Uoy7J2l/HryCg4d1966yNmo0CWkNapRnk9GdeDBbvX5dOUurnluNl+v3+N1LJGQpEKXkBfli+DX3Rvw8cgOVC4TzV0TkvnNBys5clKXDhDJSYUuRUaTuAp8MqoDo66sx7TlO+nx3GxmbdRVO0V+pEKXIqVEpI9He1zKtBHtKVMikjvGL+aJad9x9FSG19FEPKdClyKpea2KfHZ/R4Z1SWTykh30eG4283ShLynmVOhSZJWM8vFEr0Z8MLw90ZER3Pb6In7/0SrSNbYuxZQKXYq8VvGV+OKBTtzTMYFJi7drJowUWyp0CQulon384brGTB3RnnIlI7lrQjIPvr+c/UdPeR1NJGhU6BJWWtSuxGf3d+Khq+vzxarddH9uNtNX7tI1YaRYUKFL2ImOjOChqxvw2f2dqFW5NA9MWs69byez+/AJr6OJFCoVuoStS6uXY9qI9vzh2kbMTdnHNf+azcRF28nS9dYlTKnQJaz5Iox7OiUy86HONK1Zgd99tIpBry9k6z59U6KEHxW6FAvxVcrw3j1teLpvU9bsPEKP52czdtYmzmRmeR1NJGBU6FJsmBkDWtfmy4e70LlBDE/PWM/1L81l2faDXkcTCQgVuhQ71SuU5LXBSbz6q1YcOn6Gm8fM548fr9bFvqTIU6FLsdXjsur895Eu3Nm+Du8t2sbVz87ii1W7NcVRiqx8FbqZ9TSzDWaWYmaPn2Wdrma2wszWmNmswMYUKRxlS0Ty5+sv4+ORHahWvgT3vbeMu99K1pdUS5Fk59sbMTMfsBHoDqQCS4CBzrm1OdapCMwHejrntptZNefc3nM9b1JSkktOTr7I+CKBk5GZxVsLtvHsfzbgHPy6e32GdEggyqcPshI6zGypcy4pr8fy805tDaQ45zY7504D7wN9cq0zCJjmnNsOcL4yFwlFkb4I7u6YwJcPd6FDvSr87Yv13PDyPJbroKkUEfkp9DhgR477qf5lOTUAKpnZt2a21MwG5/VEZjbUzJLNLDktTV9MIKEprmIpXhucxNjbW3Hw2Gn6+g+aHj6ug6YS2vJT6JbHstzjNJFAK+BaoAfwRzNr8Itfcm6ccy7JOZcUExNT4LAiwWJm9GxSnS8f7swd7bIPml717Ld8uDRVZ5pKyMpPoacCtXLcrwnsymOdfzvnjjnn9gGzgeaBiSjinXIlo3jyhsv49P6OxFcpzaMfrKT/qwtYu+uI19FEfiE/hb4EqG9mCWYWDQwApuda5xOgk5lFmllpoA2wLrBRRbxz2SUV+HB4e/7Rrxmb9x3jupfm8OT0NZq7LiHlvIXunMsARgEzyS7pKc65NWY23MyG+9dZB/wb+A5YDLzunFtdeLFFgi8iwuifVIuvH+nCoDa1eWvBVq765yw+Wp6quesSEs47bbGwaNqiFHXfpR7ij5+sYeWOQ7SuU5n/vfEyGlYv73UsCXMXO21RRPLQrGZFPhrRnqf6NmXj3nSufXEuf/lsrb7TVDyjQhe5CBERxsDWtfnmka70T6rJ+Hlb6PbsLKYt02wYCT4VukgAVCoTzVN9mzFtRHuqVyjJw1NWcvPY+azcccjraFKMqNBFAqhF7Up8fF8H/tGvGTsOnKDP6Hk8+sFK9qaf9DqaFAMqdJEA+3E2zDePdmFY50Q+WbGTq/45i1dnbeJURqbX8SSMqdBFCkm5klE80bsRMx/qTJuEyjw1Yz09npvNV+v2aJqjFAoVukghS4wpyxt3XsGEIVcQEWHc/VYyd7y5hJS9R72OJmFGhS4SJF0vrcbMhzrzh2sbsXzbQXo+P5u/fLaWwyc0zVECQ4UuEkRRvgju6ZTIN7/pyi3+aY5X/vNb3l6wVV9YLRdNhS7igaplS/BU32Z8OqojDWLL8qdP1tDj+dn8d63G1+XCqdBFPNQkrgKT7m3La4OTwME9bycz6LVFrN552OtoUgSp0EU8ZmZ0bxzLzF935n9uuIz1Pxzh+pfn8siUlfxwWPPXJf90cS6REHP4xBle+SaFN+dtJSIChnZKZFiXupQpEel1NAkBujiXSBFSoVT2/PWvHunC1Y1iefHrFLr+81veX7ydTF0fRs5BhS4SompVLs3Lg1oy7b721KpUisenraL3C3P4dsNeHTiVPKnQRUJcy9qVmDqiPaMHteT4mQzufHMJg15bxApd+EtyUaGLFAFmxrXNavDVw1158vrGbNyTzo2j53Hfe0vZnKYzTiWbDoqKFEFHT2Xw2uzNvDZnM6cysrj1ilo81K0+1cqX9DqaFLJzHRRVoYsUYWnpp3j56+95b9F2onwR3NWxDsO61KV8ySivo0khUaGLhLlt+4/x7H82Mn3lLiqWjmLUlfW4vW08JaN8XkeTANO0RZEwF1+lDC8ObMFn93ekaVwF/u/zdXR7dhYfLk3VVMdiRIUuEkaaxFXgnbvb8O7dbahcJppHP1hJz+dnM2PVbn3HaTGQr0I3s55mtsHMUszs8Twe72pmh81shf/nT4GPKiL51bF+VT4Z2YHRg1qS5Rwj3lvG9S/P5ev1uvhXODvvucRm5gNGA92BVGCJmU13zq3Nteoc59x1hZBRRC5ARET2VMeeTarz8fKdPP/VRu6akEzL2hV59JpLaV+vqtcRJcDys4feGkhxzm12zp0G3gf6FG4sEQkUX4Rxc6uafP1IV/52U1N2Hz7JoNcXMXDcQpZuO+B1PAmg/BR6HLAjx/1U/7Lc2pnZSjObYWaXBSSdiARMlC+CQW1q882jXfnTdY35fm86N49ZwJA3F+tyvWEiP4VueSzLPQi3DIh3zjUHXgI+zvOJzIaaWbKZJaelpRUoqIgERskoH3d1TGD2b6/ksZ4NWbb9ENe9NJcR7y5l4550r+PJRchPoacCtXLcrwnsyrmCc+6Ic+6o//YXQJSZ/WKAzjk3zjmX5JxLiomJuYjYInKxSkdHMqJrXeY8diUPXV2fOd/vo8fzs3lg0nJS9qrYi6L8FPoSoL6ZJZhZNDAAmJ5zBTOrbmbmv93a/7z7Ax1WRAKvfMkoHrq6AXN+eyXDOtflv+v20P252YyauEx77EXMeWe5OOcyzGwUMBPwAeOdc2vMbLj/8bFAP2CEmWUAJ4ABTnOjRIqUSmWiebxXQ4Z2TuT1OZt5a/5WPl+1m95NavBAt/pcWr2c1xHlPHTqv4jk6eCx07wxdwsT5m/l6KkMejWpzgPd6tOoRnmvoxVrupaLiFywQ8dPM37uFt6ct5X0Uxn0uCyWB7rV57JLKngdrVhSoYvIRTt8/Azj521h/LwtpJ/MoHvjWB7sVp8mcSr2YFKhi0jAHD5xhjfnbWH83C0cOZnB1Y2qcd+V9WhZu5LX0YoFFbqIBNyRk2d4a95W3pi3hUPHz9C+bhVGXlmP9nWr4J/0JoVAhS4ihebYqQwmLd7OuNmb2Zt+iua1KnJf17p0bxRLRISKPdBU6CJS6E5lZDJ16U7GztrE9gPHqV+tLPddWZfrm11CpE9X6g4UFbqIBE1GZhafr9rNK99sYsOedGpVLsWwznXp16qmvkEpAFToIhJ0WVmOr9bv5eVvUli54xAx5Upwb6cEBrWJp2yJ857TKGehQhcRzzjnWLBpP6O/TWFeyn4qlIpicLt4BrerQ0y5El7HK3JU6CISElbsOMQr36Tw5bo9RPkiuLllTe7tlEBiTFmvoxUZKnQRCSmb0o7y+pwtTF2WypnMLLo3imVYl0RaxVf2OlrIU6GLSEhKSz/F2wu28vaCbRw+cYZW8ZUY2jlRUx7PQYUuIiHt+OkMpizZwetzt5B68ASJVctwT6dE+raM08yYXFToIlIkZGRmMWP1D4ybvZlVOw9TtWw0d7Srw+1t46lUJtrreCFBhS4iRYpzjgWb9zNu9ma+3ZBGqSgfN7eKY0iHBOoW8wOo5yp0TQYVkZBjZrSvW5X2dauy4Yd0XpuzmSlLUnl34XauvDSGuzom0LFeVV0zJhftoYtIkZCWfor3Fm3j3YXb2Hf0NA1iyzKkQwI3tShe4+wachGRsHEqI5NPV+5m/NwtrN19hEqlo7itTTy/ahdPbPmSXscrdCp0EQk7zjkWbTnA+Llb+HLdHnxmXNesBnd1TKBZzYpexys0GkMXkbBjZrRNrELbxCps33+cCfO3MiV5Bx+v2EVSfCXu6pjANY1ji9WVHrWHLiJhI/3kGaYkpzJh/hZ2HDhBjQolGdS6NgNa1w6b68ZoyEVEipXMLMfX6/fy9oKtzPl+H1E+o3fTGgxuF0/L2pWK9OwYDbmISLHiizC6N46le+NYNqUd5d2F2/gwOZVPVuziskvKM7hdPDc0j6NUdHjNjsnXHrqZ9QReAHzA6865p8+y3hXAQuBW59yH53pO7aGLSDAdO5XBxyt28s6Cbaz/IZ0KpaLon1ST29vGE1+ljNfx8u2ihlzMzAdsBLoDqcASYKBzbm0e630JnATGq9BFJBQ551i85QBvL9zGzNU/kOkcXRvEMLhdHbo0iAn5i4Jd7JBLayDFObfZ/2TvA32AtbnWux+YClxxEVlFRAqVmdEmsQptEquw58hJJi7azsTF2xkyYQnxVUozsHVt+rWqSdWyRe8gan7m88QBO3LcT/Uv+4mZxQE3AWPP9URmNtTMks0sOS0traBZRUQCKrZ8SX7dvQHzHruKlwa2ILZcSZ6esZ52T33FqInLmL9pH15NHLkQ+dlDz+vzR+5/w+eBx5xzmec6euycGweMg+whl3xmFBEpVNGREVzf/BKub34J3+9JZ+Li7UxbtpPPvttNYtUyDGxdm5tb1aRyiF/xMT9j6O2AJ51zPfz3nwBwzj2VY50t/P/irwocB4Y65z4+2/NqDF1EQtnJM5l8sWo3ExdtJ3nbQaJ9EfRsUp1BbWrTJqGyZ1MfL/agaCTZB0W7ATvJPig6yDm35izrTwA+00FREQkXG35IZ9Li7Uxdlkr6yQzqxpT5aay9Yung7rVf9IlFZtab7GEVH9kzWP5qZsMBnHNjc607ARW6iIShE6cz+ey7XUxavJ1l2w8RHRnBtU1rMLB1ba6oE5wTlnSmqIhIgK3bfYRJi7fz0bKdpJ/KIKFqGW5Jqkm/ljWpVohXfVShi4gUkuOnM/hi1Q9MSd7B4i0H8EUYXRvE0P+KWlzVsBpRAb44mApdRCQItuw7xpTkHUxdmsre9FNULVuCm1vGcUtSLepVC8xX56nQRUSCKCMzi1kb05i8ZAdfr99LRpajVXwl+ifV5Npml1C2xIVfRkuFLiLikbT0U0xblsrk5B1sTjtG6WgfD3dvwD2dEi/o+XS1RRERj8SUK8GwLnUZ2jmRZdsPMnnJDmpUKFUor6VCFxEJAjOjVXxlWsVXLrTXKD7fzSQiEuZU6CIiYUKFLiISJlToIiJhQoUuIhImVOgiImFChS4iEiZU6CIiYcKzU//NLA3YdoG/XhXYF8A4gRKquSB0sylXwShXwYRjrnjnXExeD3hW6BfDzJLPdi0DL4VqLgjdbMpVMMpVMMUtl4ZcRETChApdRCRMFNVCH+d1gLMI1VwQutmUq2CUq2CKVa4iOYYuIiK/VFT30EVEJBcVuohImChyhW5mPc1sg5mlmNnjQX7tWmb2jZmtM7M1Zvagf/mTZrbTzFb4f3rn+J0n/Fk3mFmPQsy21cxW+V8/2b+sspl9aWbf+/9ZKZi5zOzSHNtkhZkdMbOHvNheZjbezPaa2eocywq8fcyslX87p5jZi2ZmhZDrGTNbb2bfmdlHZlbRv7yOmZ3Isd3GBjlXgf9uQco1OUemrWa2wr88mNvrbN0Q3PeYc67I/AA+YBOQCEQDK4HGQXz9GkBL/+1ywEagMfAk8Gge6zf2ZywBJPiz+wop21agaq5l/wAe999+HPh7sHPl+tv9AMR7sb2AzkBLYPXFbB9gMdAOMGAG0KsQcl0DRPpv/z1Hrjo518v1PMHIVeC/WzBy5Xr8WeBPHmyvs3VDUN9jRW0PvTWQ4pzb7Jw7DbwP9AnWizvndjvnlvlvpwPrgLhz/Eof4H3n3Cnn3BYghex/h2DpA7zlv/0WcKOHuboBm5xz5zo7uNByOedmAwfyeL18bx8zqwGUd84tcNn/5b2d43cClss59x/nXIb/7kKg5rmeI1i5zsHT7fUj/55sf2DSuZ6jkHKdrRuC+h4raoUeB+zIcT+VcxdqoTGzOkALYJF/0Sj/R+TxOT5WBTOvA/5jZkvNbKh/Waxzbjdkv+GAah7k+tEAfv4fmtfbCwq+feL8t4OVD+AusvfSfpRgZsvNbJaZdfIvC2augvzdgr29OgF7nHPf51gW9O2VqxuC+h4raoWe11hS0OddmllZYCrwkHPuCDAGqAtcDuwm+2MfBDdvB+dcS6AXMNLMOp9j3aBuRzOLBm4APvAvCoXtdS5nyxHs7fZ7IAN4z79oN1DbOdcCeBiYaGblg5iroH+3YP89B/LznYagb688uuGsq54lw0VlK2qFngrUynG/JrArmAHMLIrsP9h7zrlpAM65Pc65TOdcFvAa/3+YIGh5nXO7/P/cC3zkz7DH/xHux4+Ze4Ody68XsMw5t8ef0fPt5VfQ7ZPKz4c/Ci2fmd0BXAfc5v/ojf/j+X7/7aVkj7s2CFauC/i7BXN7RQJ9gck58gZ1e+XVDQT5PVbUCn0JUN/MEvx7fQOA6cF6cf8Y3RvAOufcv3Isr5FjtZuAH4/ATwcGmFkJM0sA6pN9wCPQucqYWbkfb5N9UG21//Xv8K92B/BJMHPl8LM9J6+3Vw4F2j7+j8zpZtbW/14YnON3AsbMegKPATc4547nWB5jZj7/7UR/rs1BzFWgv1uwcvldDax3zv00XBHM7XW2biDY77GLObLrxQ/Qm+wjyJuA3wf5tTuS/fHnO2CF/6c38A6wyr98OlAjx+/83p91Axd5JP0cuRLJPmK+Eljz43YBqgBfAd/7/1k5mLn8r1Ma2A9UyLEs6NuL7P+h7AbOkL0XdPeFbB8giewi2wS8jP9s6wDnSiF7fPXH99hY/7o3+/++K4FlwPVBzlXgv1swcvmXTwCG51o3mNvrbN0Q1PeYTv0XEQkTRW3IRUREzkKFLiISJlToIiJhQoUuIhImVOgiImFChS4iEiZU6CIiYeL/AUHfcY3nBeUXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(time, epsilon)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Epsilon-decay sample function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "Try building a similar epsilon-decay function for your model.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = np.arange(0,n_episodes)\n",
    "epsilon = []\n",
    "for i in range(0,n_episodes):\n",
    "    epsilon.append(0 + (1 - 0) * np.exp(-0.0009*i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time, epsilon)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}