diff --git a/DQN/LunarLander-v0/Deep_Q_Network_Solution.ipynb b/DQN/LunarLander-v0/Deep_Q_Network_Solution.ipynb new file mode 100644 index 0000000..15b0abf --- /dev/null +++ b/DQN/LunarLander-v0/Deep_Q_Network_Solution.ipynb @@ -0,0 +1,378 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Q-Network (DQN)\n", + "---\n", + "In this notebook, you will implement a DQN agent with OpenAI Gym's LunarLander-v2 environment.\n", + "\n", + "### 1. Import the Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import gym\n", + "import random\n", + "import torch\n", + "import numpy as np\n", + "from collections import deque\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Instantiate the Environment and Agent\n", + "\n", + "Initialize the environment in the code cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "State shape: (8,)\n" + ] + } + ], + "source": [ + "env = gym.make('LunarLander-v2')\n", + "env.seed(0)\n", + "print('State shape: ', env.observation_space.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of actions: 4\n" + ] + } + ], + "source": [ + "print('Number of actions: ', env.action_space.n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please refer to the instructions in `Deep_Q_Network.ipynb` if you would like to write your own DQN agent. Otherwise, run the code cell below to load the solution files." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-135.58804185135858\n", + "-86.54432171057229\n", + "-72.78626923061778\n", + "-198.7339423827267\n", + "-121.43908619100935\n", + "-210.73540314401555\n", + "-103.04037296111133\n", + "-116.00013591586416\n", + "-151.54106885337308\n", + "-170.35394902211215\n" + ] + } + ], + "source": [ + "from dqn_agent import Agent\n", + "\n", + "agent = Agent(state_size=8, action_size=4, seed=0)\n", + "\n", + "# watch an untrained agent\n", + "for i in range(10):\n", + " state = env.reset()\n", + " score = 0\n", + " while True:\n", + " action = env.action_space.sample()\n", + " env.render()\n", + " state, reward, done, _ = env.step(action)\n", + " score+=reward\n", + " if done:\n", + " print(score)\n", + " break \n", + " \n", + "env.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Train the Agent with DQN\n", + "\n", + "Run the code cell below to train the agent from scratch. You are welcome to amend the supplied values of the parameters in the function, to try to see if you can get better performance!\n", + "\n", + "Alternatively, you can skip to the next step below (**4. Watch a Smart Agent!**), to load the saved model weights from a pre-trained agent." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "invalid index to scalar variable.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdqn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;31m# plot the scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mdqn\u001b[0;34m(n_episodes, max_t, eps_start, eps_end, eps_decay)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m 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\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_episode_started_at\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Cannot call env.step() before calling reset()\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mobservation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 32\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_elapsed_steps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Downloads/gym-master/gym/envs/box2d/lunar_lander.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mm_power\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontinuous\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontinuous\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0;31m# Main engine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontinuous\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable." + ] + } + ], + "source": [ + "def dqn(n_episodes=500, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995):\n", + " \"\"\"Deep Q-Learning.\n", + " \n", + " Params\n", + " ======\n", + " n_episodes (int): maximum number of training episodes\n", + " max_t (int): maximum number of timesteps per episode\n", + " eps_start (float): starting value of epsilon, for epsilon-greedy action selection\n", + " eps_end (float): minimum value of epsilon\n", + " eps_decay (float): multiplicative factor (per episode) for decreasing epsilon\n", + " \"\"\"\n", + " scores = [] # list containing scores from each episode\n", + " scores_window = deque(maxlen=100) # last 100 scores\n", + " eps = eps_start # initialize epsilon\n", + " for i_episode in range(1, n_episodes+1):\n", + " state = env.reset()\n", + " score = 0\n", + " for t in range(max_t):\n", + " action = agent.act(state, eps)\n", + " next_state, reward, done, _ = env.step(action)\n", + " agent.step(state, action, reward, next_state, done)\n", + " state = next_state\n", + " score += reward\n", + " if done:\n", + " break \n", + " scores_window.append(score) # save most recent score\n", + " scores.append(score) # save most recent score\n", + " eps = max(eps_end, eps_decay*eps) # decrease epsilon\n", + " print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end=\"\")\n", + " if i_episode % 100 == 0:\n", + " print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))\n", + " if np.mean(scores_window)>=200.0:\n", + " print('\\nEnvironment solved in {:d} episodes!\\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))\n", + " torch.save(agent.qnetwork_local.state_dict(), 'checkpoint.pth')\n", + " break\n", + " return scores\n", + "\n", + "scores = dqn()\n", + "\n", + "# plot the scores\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "plt.plot(np.arange(len(scores)), scores)\n", + "plt.ylabel('Score')\n", + "plt.xlabel('Episode #')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Watch a Smart Agent!\n", + "\n", + "In the next code cell, you will load the trained weights from file to watch a smart agent!" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "255.99381607266002\n", + "226.61080651426573\n", + "282.58898171199974\n", + "259.8534161803889\n", + "228.3168566284317\n", + "260.21060754539747\n", + "281.3071917871947\n", + "167.36565398302895\n", + "241.4566634286163\n", + "282.9761419691806\n", + "159.33329602627555\n", + "221.2857193629863\n", + "230.8217865160099\n", + "198.49594306350127\n", + "272.9984684474264\n", + "234.81127191151668\n", + "239.54560287485316\n", + "264.96463251789845\n", + "252.63479188826284\n", + "227.92611075204852\n", + "230.5965663049383\n", + "250.5077579546411\n", + "220.29714237409928\n", + "266.4194140017921\n", + "241.38565668693457\n", + "265.7548009293922\n", + "239.9381635424852\n", + "241.51114857256488\n", + "225.17947467336657\n", + "260.179166563512\n", + "214.94142604958\n", + "130.85709823134223\n", + "161.44084130673755\n", + "218.90533824159613\n", + "241.79581434303256\n", + "244.14998695024352\n", + "231.08081340784756\n", + "234.14822824664915\n", + "249.0188294631208\n", + "123.33931851587344\n", + "256.0148555006591\n", + "257.44467906829846\n", + "138.28448123096942\n", + "214.05063656485186\n", + "235.91972510946974\n", + "209.21987408525231\n", + "265.6289142825\n", + "250.3293354934932\n", + "263.814213360243\n", + "139.89549978812315\n", + "230.4530596422017\n", + "244.2485105308852\n", + "249.71951775313465\n", + "231.1530735683525\n", + "207.89451258428647\n", + "276.14351764609006\n", + "255.26664365360176\n", + "271.77481233156084\n", + "248.22274889885168\n", + "265.33803102546943\n", + "250.91266030533404\n", + "236.2335795940311\n", + "245.22346469385752\n", + "229.14752019842769\n", + "264.2398393246269\n", + "279.6962483457862\n", + "241.65264504145773\n", + "225.8569943233072\n", + "237.9604902249258\n", + "239.79981467542592\n", + "271.7759273630092\n", + "226.50290526593994\n", + "275.3559627790132\n", + "252.13441817479907\n", + "182.4758906679622\n", + "231.2762569260825\n", + "241.72863602138568\n", + "203.6749805337406\n", + "255.47176316164132\n", + "256.5679710157069\n", + "232.98591659243093\n", + "266.0587522052881\n", + "278.02802699164647\n", + "277.13446857687353\n", + "238.33636884002226\n", + "263.1770155496583\n", + "253.72797715963102\n", + "282.89850429583714\n", + "238.3859935698075\n", + "274.69905534316376\n", + "244.07902458793626\n", + "247.0631530883419\n", + "262.25723985708714\n", + "237.54637313983818\n", + "273.22454613617725\n", + "259.3541235632671\n", + "265.6688027446351\n", + "247.07535407544808\n", + "212.20906522047773\n", + "252.2595333555147\n" + ] + } + ], + "source": [ + "# load the weights from file\n", + "agent.qnetwork_local.load_state_dict(torch.load('checkpoint.pth'))\n", + "\n", + "for i in range(100):\n", + " state = env.reset()\n", + " score = 0\n", + " while True:\n", + " action = agent.act(state)\n", + " env.render()\n", + " state, reward, done, _ = env.step(action)\n", + " score +=reward\n", + " if done:\n", + " print(score)\n", + " break \n", + " \n", + "env.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Explore\n", + "\n", + "In this exercise, you have implemented a DQN agent and demonstrated how to use it to solve an OpenAI Gym environment. To continue your learning, you are encouraged to complete any (or all!) of the following tasks:\n", + "- Amend the various hyperparameters and network architecture to see if you can get your agent to solve the environment faster. Once you build intuition for the hyperparameters that work well with this environment, try solving a different OpenAI Gym task with discrete actions!\n", + "- You may like to implement some improvements such as prioritized experience replay, Double DQN, or Dueling DQN! \n", + "- Write a blog post explaining the intuition behind the DQN algorithm and demonstrating how to use it to solve an RL environment of your choosing. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "drlnd", + "language": "python", + "name": "drlnd" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/DQN/LunarLander-v0/README.md b/DQN/LunarLander-v0/README.md new file mode 100644 index 0000000..0709522 --- /dev/null +++ b/DQN/LunarLander-v0/README.md @@ -0,0 +1,25 @@ +[//]: # (Image References) + +[image1]: https://user-images.githubusercontent.com/10624937/42135612-cbff24aa-7d12-11e8-9b6c-2b41e64b3bb0.gif "Trained Agent" + +# LunarLander-v0 + +### Problem Definition +Landing pad is always at coordinates (0,0). Coordinates are the first two numbers in state vector. Reward for moving from the top of the screen to landing pad and zero speed is about 100..140 points. If lander moves away from landing pad it loses reward back. Episode finishes if the lander crashes or comes to rest, receiving additional -100 or +100 points. Each leg ground contact is +10. Firing main engine is -0.3 points each frame. Solved is 200 points. Landing outside landing pad is possible. Fuel is infinite, so an agent can learn to fly and then land on its first attempt. Four discrete actions available: do nothing, fire left orientation engine, fire main engine, fire right orientation engine. + +### Instructions + +In this, we will implement Deep Q-Learning to solve OpenAI Gym's LunarLander environment. To begin, navigate to the and follow the instructions in `Deep_Q_Network_Solution.ipynb`. + +After we are able to get the code working, we will try to change the parameters in the notebook, to see if we can get the agent to train faster! We may also like to implement prioritized experience replay, or use it as a starting point to implement a Double DQN or Dueling DQN! + +### Results + +![Trained Agent][image1] + +### Resources + +- [Human-Level Control through Deep Reinforcement Learning](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) +- [Deep Reinforcement Learning with Double Q-Learning](https://arxiv.org/abs/1509.06461) +- [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581) +- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952) diff --git a/DQN/LunarLander-v0/__pycache__/dqn_agent.cpython-36.pyc b/DQN/LunarLander-v0/__pycache__/dqn_agent.cpython-36.pyc new file mode 100644 index 0000000..ac2fb53 Binary files /dev/null and b/DQN/LunarLander-v0/__pycache__/dqn_agent.cpython-36.pyc differ diff --git a/DQN/LunarLander-v0/__pycache__/model.cpython-36.pyc b/DQN/LunarLander-v0/__pycache__/model.cpython-36.pyc new file mode 100644 index 0000000..9431a2b Binary files /dev/null and b/DQN/LunarLander-v0/__pycache__/model.cpython-36.pyc differ diff --git a/DQN/LunarLander-v0/checkpoint.pth b/DQN/LunarLander-v0/checkpoint.pth new file mode 100644 index 0000000..c07f05a Binary files /dev/null and b/DQN/LunarLander-v0/checkpoint.pth differ diff --git a/DQN/LunarLander-v0/dqn_agent.py b/DQN/LunarLander-v0/dqn_agent.py new file mode 100644 index 0000000..bb2e031 --- /dev/null +++ b/DQN/LunarLander-v0/dqn_agent.py @@ -0,0 +1,158 @@ +import numpy as np +import random +from collections import namedtuple, deque + +from model import QNetwork + +import torch +import torch.nn.functional as F +import torch.optim as optim + +BUFFER_SIZE = int(1e5) # replay buffer size +BATCH_SIZE = 64 # minibatch size +GAMMA = 0.99 # discount factor +TAU = 1e-3 # for soft update of target parameters +LR = 5e-4 # learning rate +UPDATE_EVERY = 4 # how often to update the network + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + +class Agent(): + """Interacts with and learns from the environment.""" + + def __init__(self, state_size, action_size, seed): + """Initialize an Agent object. + + Params + ====== + state_size (int): dimension of each state + action_size (int): dimension of each action + seed (int): random seed + """ + self.state_size = state_size + self.action_size = action_size + self.seed = random.seed(seed) + + # Q-Network + self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device) + self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device) + self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR) + + # Replay memory + self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed) + # Initialize time step (for updating every UPDATE_EVERY steps) + self.t_step = 0 + + def step(self, state, action, reward, next_state, done): + # Save experience in replay memory + self.memory.add(state, action, reward, next_state, done) + + # Learn every UPDATE_EVERY time steps. + self.t_step = (self.t_step + 1) % UPDATE_EVERY + if self.t_step == 0: + # If enough samples are available in memory, get random subset and learn + if len(self.memory) > BATCH_SIZE: + experiences = self.memory.sample() + self.learn(experiences, GAMMA) + + def act(self, state, eps=0.): + """Returns actions for given state as per current policy. + + Params + ====== + state (array_like): current state + eps (float): epsilon, for epsilon-greedy action selection + """ + state = torch.from_numpy(state).float().unsqueeze(0).to(device) + self.qnetwork_local.eval() + with torch.no_grad(): + action_values = self.qnetwork_local(state) + self.qnetwork_local.train() + + # Epsilon-greedy action selection + if random.random() > eps: + return np.argmax(action_values.cpu().data.numpy()) + else: + return random.choice(np.arange(self.action_size)) + + def learn(self, experiences, gamma): + """Update value parameters using given batch of experience tuples. + + Params + ====== + experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples + gamma (float): discount factor + """ + states, actions, rewards, next_states, dones = experiences + + # Get max predicted Q values (for next states) from target model + Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1) + # Compute Q targets for current states + Q_targets = rewards + (gamma * Q_targets_next * (1 - dones)) + + # Get expected Q values from local model + Q_expected = self.qnetwork_local(states).gather(1, actions) + + # Compute loss + loss = F.mse_loss(Q_expected, Q_targets) + # Minimize the loss + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + + # ------------------- update target network ------------------- # + self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) + + def soft_update(self, local_model, target_model, tau): + """Soft update model parameters. + θ_target = τ*θ_local + (1 - τ)*θ_target + + Params + ====== + local_model (PyTorch model): weights will be copied from + target_model (PyTorch model): weights will be copied to + tau (float): interpolation parameter + """ + for target_param, local_param in zip(target_model.parameters(), local_model.parameters()): + target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data) + + +class ReplayBuffer: + """Fixed-size buffer to store experience tuples.""" + + def __init__(self, action_size, buffer_size, batch_size, seed): + """Initialize a ReplayBuffer object. + + Params + ====== + action_size (int): dimension of each action + buffer_size (int): maximum size of buffer + batch_size (int): size of each training batch + seed (int): random seed + """ + self.action_size = action_size + self.memory = deque(maxlen=buffer_size) + self.batch_size = batch_size + self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"]) + self.seed = random.seed(seed) + + def add(self, state, action, reward, next_state, done): + """Add a new experience to memory.""" + e = self.experience(state, action, reward, next_state, done) + self.memory.append(e) + + def sample(self): + """Randomly sample a batch of experiences from memory.""" + experiences = random.sample(self.memory, k=self.batch_size) + + states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device) + actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device) + rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device) + next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device) + dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device) + + return (states, actions, rewards, next_states, dones) + + def __len__(self): + """Return the current size of internal memory.""" + return len(self.memory) \ No newline at end of file diff --git a/DQN/LunarLander-v0/model.py b/DQN/LunarLander-v0/model.py new file mode 100644 index 0000000..e547835 --- /dev/null +++ b/DQN/LunarLander-v0/model.py @@ -0,0 +1,28 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class QNetwork(nn.Module): + """Actor (Policy) Model.""" + + def __init__(self, state_size, action_size, seed, fc1_units=64, fc2_units=64): + """Initialize parameters and build model. + Params + ====== + state_size (int): Dimension of each state + action_size (int): Dimension of each action + seed (int): Random seed + fc1_units (int): Number of nodes in first hidden layer + fc2_units (int): Number of nodes in second hidden layer + """ + super(QNetwork, self).__init__() + self.seed = torch.manual_seed(seed) + self.fc1 = nn.Linear(state_size, fc1_units) + self.fc2 = nn.Linear(fc1_units, fc2_units) + self.fc3 = nn.Linear(fc2_units, action_size) + + def forward(self, state): + """Build a network that maps state -> action values.""" + x = F.relu(self.fc1(state)) + x = F.relu(self.fc2(x)) + return self.fc3(x) diff --git a/DQN/MountainCar-v0/DQN Mountain Car.ipynb b/DQN/MountainCar-v0/DQN Mountain Car.ipynb new file mode 100644 index 0000000..efaa843 --- /dev/null +++ b/DQN/MountainCar-v0/DQN Mountain Car.ipynb @@ -0,0 +1,482 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "I0506 21:58:40.464183 140568153229120 registration.py:117] Making new env: MountainCar-v0\n", + "/home/sourcecode369/anaconda3/envs/drlnd/lib/python3.6/site-packages/gym/envs/registration.py:17: PkgResourcesDeprecationWarning: Parameters to load are deprecated. Call .resolve and .require separately.\n", + " result = entry_point.load(False)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense (Dense) (None, 32) 96 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 16) 528 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 2) 34 \n", + "=================================================================\n", + "Total params: 658\n", + "Trainable params: 658\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_3 (Dense) (None, 32) 96 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 16) 528 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 2) 34 \n", + "=================================================================\n", + "Total params: 658\n", + "Trainable params: 658\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "[[-0.45182138 0. ]]\n", + "episode: 0 score: -200.0 memory length: 200 epsilon: 0.9960200000000077\n", + "[[-0.40103357 0. ]]\n", + "episode: 1 score: -200.0 memory length: 400 epsilon: 0.9920400000000154\n", + "[[-0.46454369 0. ]]\n", + "episode: 2 score: -200.0 memory length: 600 epsilon: 0.988060000000023\n", + "[[-0.45202495 0. ]]\n", + "episode: 3 score: -200.0 memory length: 800 epsilon: 0.9840800000000307\n", + "[[-0.57478255 0. ]]\n", + "episode: 4 score: -200.0 memory length: 1000 epsilon: 0.9801000000000384\n", + "[[-0.53477434 0. ]]\n", + "episode: 5 score: -200.0 memory length: 1200 epsilon: 0.9761200000000461\n", + "[[-0.45286772 0. ]]\n", + "episode: 6 score: -200.0 memory length: 1400 epsilon: 0.9721400000000537\n", + "[[-0.56940514 0. ]]\n", + "episode: 7 score: -200.0 memory length: 1600 epsilon: 0.9681600000000614\n", + "[[-0.52606454 0. ]]\n", + "episode: 8 score: -200.0 memory length: 1800 epsilon: 0.9641800000000691\n", + "[[-0.59602296 0. ]]\n", + "episode: 9 score: -200.0 memory length: 2000 epsilon: 0.9602000000000768\n", + "[[-0.42261749 0. ]]\n", + "episode: 10 score: -200.0 memory length: 2200 epsilon: 0.9562200000000844\n", + "[[-0.48767038 0. ]]\n", + "episode: 11 score: -200.0 memory length: 2400 epsilon: 0.9522400000000921\n", + "[[-0.52627714 0. ]]\n", + "episode: 12 score: -200.0 memory length: 2600 epsilon: 0.9482600000000998\n", + "[[-0.42721539 0. ]]\n", + 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val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0;31m# Get outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 353\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m 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1049\u001b[0m \"dtype_hint\", dtype_hint, \"preferred_dtype\", preferred_dtype)\n\u001b[0;32m-> 1050\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_tensor_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreferred_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/drlnd/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor_v2\u001b[0;34m(value, dtype, dtype_hint, name)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1107\u001b[0m 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This will put each scalar of each type only once on\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;31m# each device. Scalars don't use much device memory but copying scalars can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# %load mountaincar_dqn.py\n", + "import gym\n", + "import random\n", + "import numpy as np\n", + "from collections import deque\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.models import Sequential\n", + "\n", + "EPISODES = 4000\n", + "\n", + "\n", + "class DQNAgent:\n", + " def __init__(self, state_size, action_size):\n", + " self.render = True\n", + " self.state_size = state_size\n", + " self.action_size = action_size\n", + " self.discount_factor = 0.99\n", + " self.learning_rate = 0.001\n", + " self.epsilon = 1.0\n", + " self.epsilon_min = 0.005\n", + " self.epsilon_decay = (self.epsilon - self.epsilon_min) / 50000\n", + " self.batch_size = 64\n", + " self.train_start = 1000\n", + " self.memory = deque(maxlen=10000)\n", + " self.model = self.build_model()\n", + " self.target_model = self.build_model()\n", + " self.update_target_model()\n", + "\n", + " def build_model(self):\n", + " model = Sequential()\n", + " model.add(Dense(32, input_dim=self.state_size, activation='relu', kernel_initializer='he_uniform'))\n", + " model.add(Dense(16, activation='relu', kernel_initializer='he_uniform'))\n", + " model.add(Dense(self.action_size, activation='linear', kernel_initializer='he_uniform'))\n", + " model.summary()\n", + " model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))\n", + " return model\n", + "\n", + " def update_target_model(self):\n", + " self.target_model.set_weights(self.model.get_weights())\n", + "\n", + " def get_action(self, state):\n", + " if np.random.rand() <= self.epsilon:\n", + " return random.randrange(self.action_size)\n", + " else:\n", + " q_value = self.model.predict(state)\n", + " return np.argmax(q_value[0])\n", + "\n", + " def replay_memory(self, state, action, reward, next_state, done):\n", + " if action == 2:\n", + " action = 1\n", + " self.memory.append((state, action, reward, next_state, done))\n", + " if self.epsilon > self.epsilon_min:\n", + " self.epsilon -= self.epsilon_decay\n", + " # print(len(self.memory))\n", + "\n", + " def train_replay(self):\n", + " if len(self.memory) < self.train_start:\n", + " return\n", + " batch_size = min(self.batch_size, len(self.memory))\n", + " mini_batch = random.sample(self.memory, batch_size)\n", + "\n", + " update_input = np.zeros((batch_size, self.state_size))\n", + " update_target = np.zeros((batch_size, self.action_size))\n", + "\n", + " for i in range(batch_size):\n", + " state, action, reward, next_state, done = mini_batch[i]\n", + " target = self.model.predict(state)[0]\n", + "\n", + " if done:\n", + " target[action] = reward\n", + " else:\n", + " target[action] = reward + self.discount_factor * \\\n", + " np.amax(self.target_model.predict(next_state)[0])\n", + " update_input[i] = state\n", + " update_target[i] = target\n", + "\n", + " self.model.fit(update_input, update_target, batch_size=batch_size, epochs=1, verbose=0)\n", + "\n", + " def load_model(self, name):\n", + " self.model.load_weights(name)\n", + "\n", + " def save_model(self, name):\n", + " self.model.save_weights(name)\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " env = gym.make('MountainCar-v0')\n", + " state_size = env.observation_space.shape[0]\n", + " action_size = 2\n", + " agent = DQNAgent(state_size, action_size)\n", + " scores, episodes = [], []\n", + "\n", + " for e in range(EPISODES):\n", + " done = False\n", + " score = 0\n", + " state = env.reset()\n", + " state = np.reshape(state, [1, state_size])\n", + " print(state)\n", + "\n", + " fake_action = 0\n", + "\n", + " action_count = 0\n", + "\n", + " while not done:\n", + " if agent.render:\n", + " env.render()\n", + "\n", + " action_count = action_count + 1\n", + "\n", + " if action_count == 4:\n", + " action = agent.get_action(state)\n", + " action_count = 0\n", + "\n", + " if action == 0:\n", + " fake_action = 0\n", + " elif action == 1:\n", + " fake_action = 2\n", + "\n", + " next_state, reward, done, info = env.step(fake_action)\n", + " next_state = np.reshape(next_state, [1, state_size])\n", + " agent.replay_memory(state, fake_action, reward, next_state, done)\n", + " agent.train_replay()\n", + " score += reward\n", + " state = next_state\n", + "\n", + " if done:\n", + " env.reset()\n", + " \n", + " agent.update_target_model()\n", + "\n", + " scores.append(score)\n", + " episodes.append(e)\n", + " print(\"episode:\", e, \" score:\", score, \" memory length:\", len(agent.memory),\n", + " \" epsilon:\", agent.epsilon)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %load mountaincar_dqn.py\n", + "import gym\n", + "import pylab\n", + "import random\n", + "import numpy as np\n", + "from collections import deque\n", + "from keras.layers import Dense\n", + "from keras.optimizers import Adam\n", + "from keras.models import Sequential\n", + "\n", + "EPISODES = 4000\n", + "\n", + "\n", + "class DQNAgent:\n", + " def __init__(self, state_size, action_size):\n", + " # Cartpole이 학습하는 것을 보려면 \"True\"로 바꿀 것\n", + " self.render = True\n", + "\n", + " # state와 action의 크기를 가져와서 모델을 생성하는데 사용함\n", + " self.state_size = state_size\n", + " self.action_size = action_size\n", + "\n", + " # Cartpole DQN 학습의 Hyper parameter 들\n", + " # deque를 통해서 replay memory 생성\n", + " self.discount_factor = 0.99\n", + " self.learning_rate = 0.001\n", + " self.epsilon = 1.0\n", + " self.epsilon_min = 0.005\n", + " self.epsilon_decay = (self.epsilon - self.epsilon_min) / 50000\n", + " self.batch_size = 64\n", + " self.train_start = 1000\n", + " self.memory = deque(maxlen=10000)\n", + "\n", + " # 학습할 모델과 타겟 모델을 생성\n", + " self.model = self.build_model()\n", + " self.target_model = self.build_model()\n", + " # 학습할 모델을 타겟 모델로 복사 --> 타겟 모델의 초기화(weight를 같게 해주고 시작해야 함)\n", + " self.update_target_model()\n", + "\n", + " # Deep Neural Network를 통해서 Q Function을 근사\n", + " # state가 입력, 각 행동에 대한 Q Value가 출력인 모델을 생성\n", + " def build_model(self):\n", + " model = Sequential()\n", + " model.add(Dense(32, input_dim=self.state_size, activation='relu', kernel_initializer='he_uniform'))\n", + " model.add(Dense(16, activation='relu', kernel_initializer='he_uniform'))\n", + " model.add(Dense(self.action_size, activation='linear', kernel_initializer='he_uniform'))\n", + " model.summary()\n", + " model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))\n", + " return model\n", + "\n", + " # 일정한 시간 간격마다 타겟 모델을 현재 학습하고 있는 모델로 업데이트\n", + " def update_target_model(self):\n", + " self.target_model.set_weights(self.model.get_weights())\n", + "\n", + " # 행동의 선택은 현재 네트워크에 대해서 epsilon-greedy 정책을 사용\n", + " def get_action(self, state):\n", + " if np.random.rand() <= self.epsilon:\n", + " return random.randrange(self.action_size)\n", + " else:\n", + " q_value = self.model.predict(state)\n", + " return np.argmax(q_value[0])\n", + "\n", + " # 을 replay_memory에 저장함\n", + " def replay_memory(self, state, action, reward, next_state, done):\n", + " if action == 2:\n", + " action = 1\n", + " self.memory.append((state, action, reward, next_state, done))\n", + " if self.epsilon > self.epsilon_min:\n", + " self.epsilon -= self.epsilon_decay\n", + " # print(len(self.memory))\n", + "\n", + " # replay memory에서 batch_size 만큼의 샘플들을 무작위로 뽑아서 학습\n", + " def train_replay(self):\n", + " if len(self.memory) < self.train_start:\n", + " return\n", + " batch_size = min(self.batch_size, len(self.memory))\n", + " mini_batch = random.sample(self.memory, batch_size)\n", + "\n", + " update_input = np.zeros((batch_size, self.state_size))\n", + " update_target = np.zeros((batch_size, self.action_size))\n", + "\n", + " for i in range(batch_size):\n", + " state, action, reward, next_state, done = mini_batch[i]\n", + " target = self.model.predict(state)[0]\n", + "\n", + " # 큐러닝에서와 같이 s'에서의 최대 Q Value를 가져옴. 단, 타겟 모델에서 가져옴\n", + " if done:\n", + " target[action] = reward\n", + " else:\n", + " target[action] = reward + self.discount_factor * \\\n", + " np.amax(self.target_model.predict(next_state)[0])\n", + " update_input[i] = state\n", + " update_target[i] = target\n", + "\n", + " # 학습할 정답인 타겟과 현재 자신의 값의 minibatch를 만들고 그것으로 한 번에 모델 업데이트\n", + " self.model.fit(update_input, update_target, batch_size=batch_size, epochs=1, verbose=0)\n", + "\n", + " # 저장한 모델을 불러옴\n", + " def load_model(self, name):\n", + " self.model.load_weights(name)\n", + "\n", + " # 학습된 모델을 저장함\n", + " def save_model(self, name):\n", + " self.model.save_weights(name)\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " # CartPole-v1의 경우 500 타임스텝까지 플레이가능\n", + " env = gym.make('MountainCar-v0')\n", + " # 환경으로부터 상태와 행동의 크기를 가져옴\n", + " state_size = env.observation_space.shape[0]\n", + " #action_size = env.action_space.n\n", + " action_size = 2\n", + " # DQN 에이전트의 생성\n", + " agent = DQNAgent(state_size, action_size)\n", + " agent.load_model(\"./save_model/MountainCar_DQN.h5\")\n", + " scores, episodes = [], []\n", + "\n", + " for e in range(EPISODES):\n", + " done = False\n", + " score = 0\n", + " state = env.reset()\n", + " state = np.reshape(state, [1, state_size])\n", + " print(state)\n", + "\n", + " # 액션 0(좌), 1(아무것도 안함), 3(아무것도 하지 않는 액션을 하지 않기 위한 fake_action 선언\n", + " fake_action = 0\n", + "\n", + " # 같은 액션을 4번하기 위한 카운터\n", + " action_count = 0\n", + "\n", + " while not done:\n", + " if agent.render:\n", + " env.render()\n", + "\n", + " # 현재 상태에서 행동을 선택하고 한 스텝을 진행\n", + " action_count = action_count + 1\n", + "\n", + " if action_count == 4:\n", + " action = agent.get_action(state)\n", + " action_count = 0\n", + "\n", + " if action == 0:\n", + " fake_action = 0\n", + " elif action == 1:\n", + " fake_action = 2\n", + "\n", + " # 선택한 액션으로 1 step을 시행한다\n", + " next_state, reward, done, info = env.step(fake_action)\n", + " next_state = np.reshape(next_state, [1, state_size])\n", + " # 에피소드를 끝나게 한 행동에 대해서 -100의 패널티를 줌\n", + " #reward = reward if not done else -100\n", + "\n", + " # 을 replay memory에 저장\n", + " agent.replay_memory(state, fake_action, reward, next_state, done)\n", + " # 매 타임스텝마다 학습을 진행\n", + " agent.train_replay()\n", + " score += reward\n", + " state = next_state\n", + "\n", + " if done:\n", + " env.reset()\n", + " # 매 에피소드마다 학습하는 모델을 타겟 모델로 복사\n", + " agent.update_target_model()\n", + "\n", + " # 각 에피소드마다 cartpole이 서있었던 타임스텝을 plot\n", + " scores.append(score)\n", + " episodes.append(e)\n", + " #pylab.plot(episodes, scores, 'b')\n", + " #pylab.savefig(\"./save_graph/MountainCar_DQN.png\")\n", + " print(\"episode:\", e, \" score:\", score, \" memory length:\", len(agent.memory),\n", + " \" epsilon:\", agent.epsilon)\n", + "\n", + " # 50 에피소드마다 학습 모델을 저장\n", + " if e % 50 == 0:\n", + " agent.save_model(\"./save_model/MountainCar_DQN.h5\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0506 22:30:18.921825 140568153229120 registration.py:117] Making new env: MountainCar-v0\n", + "/home/sourcecode369/anaconda3/envs/drlnd/lib/python3.6/site-packages/gym/envs/registration.py:17: PkgResourcesDeprecationWarning: Parameters to load are deprecated. Call .resolve and .require separately.\n", + " result = entry_point.load(False)\n" + ] + } + ], + "source": [ + "env = gym.make('MountainCar-v0')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "drlnd", + "language": "python", + "name": "drlnd" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/DQN/MountainCar-v0/mountain car dqn.ipynb b/DQN/MountainCar-v0/mountain car dqn.ipynb new file mode 100644 index 0000000..89a41b9 --- /dev/null +++ b/DQN/MountainCar-v0/mountain car dqn.ipynb @@ -0,0 +1,1633 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'%.2f'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from __future__ import print_function, absolute_import, division\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "pd.set_option(\"max_columns\", None)\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "#import seaborn as sns\n", + "%precision 2\n", + "\n", + "import os\n", + "import ast\n", + "import csv\n", + "import time\n", + "import datetime\n", + "import json\n", + "import random\n", + "import math\n", + "from time import ctime\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout\n", + "from tensorflow.keras.optimizers import Adam, RMSprop\n", + "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint\n", + "\n", + "import gym\n", + "\n", + "from collections import namedtuple, deque\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = \"all\"\n", + "\n", + "is_ipython = \"inline\" in matplotlib.get_backend()\n", + "if is_ipython:\n", + " from IPython import display" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "class DQN:\n", + " def __init__(self, state_size, action_size):\n", + " self.state_size = state_size\n", + " self.action_size = action_size\n", + " self.gamma = 0.99\n", + " self.learning_rate = 0.001\n", + " self.epsilon = 1.0\n", + " self.epsilon_min = 0.05\n", + " self.epsilon_decay = (self.epsilon - self.epsilon_min) / 50000\n", + " self.batch_size = 64\n", + " self.memory = deque(maxlen=2000)\n", + " self.episodes = 1000\n", + " self.train_start = 1000\n", + " self.model = self.build_model()\n", + " self.target_model = self.build_model()\n", + " self.update_target_model()\n", + " \n", + " def build_model(self):\n", + " model = Sequential()\n", + " model.add(Dense(32, input_dim=self.state_size, activation=\"relu\", kernel_initializer=\"he_uniform\"))\n", + " model.add(Dense(16, activation=\"relu\", kernel_initializer=\"he_uniform\"))\n", + " model.add(Dense(self.action_size, activation=\"linear\", kernel_initializer=\"he_uniform\"))\n", + " model.compile(loss=\"mse\",optimizer=Adam(lr=self.learning_rate))\n", + " model.summary()\n", + " return model\n", + " \n", + " def act(self, state):\n", + " if self.epsilon self.epsilon_min:\n", + " self.epsilon -= self.epsilon_decay\n", + " \n", + " def replay(self):\n", + " if len(self.memory) 타겟 모델의 초기화(weight를 같게 해주고 시작해야 함) + self.update_target_model() + + # Deep Neural Network를 통해서 Q Function을 근사 + # state가 입력, 각 행동에 대한 Q Value가 출력인 모델을 생성 + def build_model(self): + model = Sequential() + model.add(Dense(32, input_dim=self.state_size, activation='relu', kernel_initializer='he_uniform')) + model.add(Dense(16, activation='relu', kernel_initializer='he_uniform')) + model.add(Dense(self.action_size, activation='linear', kernel_initializer='he_uniform')) + model.summary() + model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate)) + return model + + # 일정한 시간 간격마다 타겟 모델을 현재 학습하고 있는 모델로 업데이트 + def update_target_model(self): + self.target_model.set_weights(self.model.get_weights()) + + # 행동의 선택은 현재 네트워크에 대해서 epsilon-greedy 정책을 사용 + def get_action(self, state): + if np.random.rand() <= self.epsilon: + return random.randrange(self.action_size) + else: + q_value = self.model.predict(state) + return np.argmax(q_value[0]) + + # 을 replay_memory에 저장함 + def replay_memory(self, state, action, reward, next_state, done): + if action == 2: + action = 1 + self.memory.append((state, action, reward, next_state, done)) + if self.epsilon > self.epsilon_min: + self.epsilon -= self.epsilon_decay + # print(len(self.memory)) + + # replay memory에서 batch_size 만큼의 샘플들을 무작위로 뽑아서 학습 + def train_replay(self): + if len(self.memory) < self.train_start: + return + batch_size = min(self.batch_size, len(self.memory)) + mini_batch = random.sample(self.memory, batch_size) + + update_input = np.zeros((batch_size, self.state_size)) + update_target = np.zeros((batch_size, self.action_size)) + + for i in range(batch_size): + state, action, reward, next_state, done = mini_batch[i] + target = self.model.predict(state)[0] + + # 큐러닝에서와 같이 s'에서의 최대 Q Value를 가져옴. 단, 타겟 모델에서 가져옴 + if done: + target[action] = reward + else: + target[action] = reward + self.discount_factor * \ + np.amax(self.target_model.predict(next_state)[0]) + update_input[i] = state + update_target[i] = target + + # 학습할 정답인 타겟과 현재 자신의 값의 minibatch를 만들고 그것으로 한 번에 모델 업데이트 + self.model.fit(update_input, update_target, batch_size=batch_size, epochs=1, verbose=0) + + # 저장한 모델을 불러옴 + def load_model(self, name): + self.model.load_weights(name) + + # 학습된 모델을 저장함 + def save_model(self, name): + self.model.save_weights(name) + + +if __name__ == "__main__": + # CartPole-v1의 경우 500 타임스텝까지 플레이가능 + env = gym.make('MountainCar-v0') + # 환경으로부터 상태와 행동의 크기를 가져옴 + state_size = env.observation_space.shape[0] + #action_size = env.action_space.n + action_size = 2 + # DQN 에이전트의 생성 + agent = DQNAgent(state_size, action_size) + agent.load_model("./save_model/MountainCar_DQN.h5") + scores, episodes = [], [] + + for e in range(EPISODES): + done = False + score = 0 + state = env.reset() + state = np.reshape(state, [1, state_size]) + print(state) + + # 액션 0(좌), 1(아무것도 안함), 3(아무것도 하지 않는 액션을 하지 않기 위한 fake_action 선언 + fake_action = 0 + + # 같은 액션을 4번하기 위한 카운터 + action_count = 0 + + while not done: + if agent.render: + env.render() + + # 현재 상태에서 행동을 선택하고 한 스텝을 진행 + action_count = action_count + 1 + + if action_count == 4: + action = agent.get_action(state) + action_count = 0 + + if action == 0: + fake_action = 0 + elif action == 1: + fake_action = 2 + + # 선택한 액션으로 1 step을 시행한다 + next_state, reward, done, info = env.step(fake_action) + next_state = np.reshape(next_state, [1, state_size]) + # 에피소드를 끝나게 한 행동에 대해서 -100의 패널티를 줌 + #reward = reward if not done else -100 + + # 을 replay memory에 저장 + agent.replay_memory(state, fake_action, reward, next_state, done) + # 매 타임스텝마다 학습을 진행 + agent.train_replay() + score += reward + state = next_state + + if done: + env.reset() + # 매 에피소드마다 학습하는 모델을 타겟 모델로 복사 + agent.update_target_model() + + # 각 에피소드마다 cartpole이 서있었던 타임스텝을 plot + scores.append(score) + episodes.append(e) + #pylab.plot(episodes, scores, 'b') + #pylab.savefig("./save_graph/MountainCar_DQN.png") + print("episode:", e, " score:", score, " memory length:", len(agent.memory), + " epsilon:", agent.epsilon) + + # 50 에피소드마다 학습 모델을 저장 + if e % 50 == 0: + agent.save_model("./save_model/MountainCar_DQN.h5") diff --git a/DQN/MountainCar-v0/save_model/MountainCar_DQN.h5 b/DQN/MountainCar-v0/save_model/MountainCar_DQN.h5 new file mode 100644 index 0000000..7f17c81 Binary files /dev/null and b/DQN/MountainCar-v0/save_model/MountainCar_DQN.h5 differ diff --git a/README.md b/README.md index 67ef750..e6baeb8 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,9 @@ [image3]: https://spinningup.openai.com/en/latest/_images/spinning-up-in-rl.png "Spinning Up in Deep RL" # Deep Reinforcement Learning +>For the unfamiliar: **Reinforcement learning (RL) is a machine learning approach for teaching agents how to +solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning.** + ![Trained Agents][image1] ## Table of Contents @@ -29,7 +32,7 @@ The notebooks helps through implementing various algorithms in reinforcement lea - `MountainCar-v0` in TensorFlow.Keras - - `LunarLander-v2` in PyTorch + - `LunarLander-v0` in PyTorch - `Atari Pong` in TensorFlow.Keras @@ -83,7 +86,7 @@ python -m ipykernel install --user --name drlnd --display-name "drlnd" ![Kernel][image2] -## Bibliography +## Credits * [Spinning up in Deep RL](https://spinningup.openai.com/en/latest/index.html) an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). * [Key Papers in Deep RL](https://spinningup.openai.com/en/latest/spinningup/keypapers.html) that I've comprehensively went throgh. @@ -96,9 +99,17 @@ python -m ipykernel install --user --name drlnd --display-name "drlnd" * __UC Berkley and OpenAI's__ [Deep RL Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures). - * Udacity's [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program helped me getting started and implementing algorithms and clearing the concepts within Deep Reinforcement learning. (__Highly Recommend__) + * Udacity's [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program was that one course that helped me to get started with understanding the concepts and implementing the algorithms in Deep Reinforcement learning. This is that one course which I __highly recommend__ and [AWS Deep Racer Scholarship Program](https://www.udacity.com/aws-deepracer-scholarship) program as well. + +

+

+ +

+

+ +

diff --git a/TD3/Twin_Delayed_Deep_Deterministic_Policy_Gradients.ipynb b/TD3/Twin_Delayed_Deep_Deterministic_Policy_Gradients.ipynb new file mode 100644 index 0000000..a72318e --- /dev/null +++ b/TD3/Twin_Delayed_Deep_Deterministic_Policy_Gradients.ipynb @@ -0,0 +1,2425 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Twin Delayed Deep Deterministic Policy Gradients.ipynb", + "provenance": [], + "toc_visible": true, + "include_colab_link": true + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z1KRkwmfn7Ox", + "colab_type": "text" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "p-Akb9bzn-Sg", + "colab_type": "code", + "outputId": "65d7bb5b-8d8e-459d-99d2-16f14cd88805", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 552 + } + }, + "source": [ + "!pip install git+https://github.com/benelot/pybullet-gym.git\n", + "!pip install tensorboardX\n", + "!pip install gym\n", + "#!pip install roboschool\n", + "#!pip install pybullet" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/benelot/pybullet-gym.git\n", + " Cloning https://github.com/benelot/pybullet-gym.git to /tmp/pip-req-build-a876zo_5\n", + " Running command git clone -q https://github.com/benelot/pybullet-gym.git /tmp/pip-req-build-a876zo_5\n", + "Collecting pybullet>=1.7.8\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/0a/1c/26640b59ab18deb59104ed03ee4c26d1d998076cdf4a89c5ef1486831172/pybullet-2.6.1.tar.gz (82.8MB)\n", + "\u001b[K |████████████████████████████████| 82.8MB 36kB/s \n", + "\u001b[?25hBuilding wheels for collected packages: pybulletgym, pybullet\n", + " Building wheel for pybulletgym (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for pybulletgym: filename=pybulletgym-0.1-cp36-none-any.whl size=1513918 sha256=bf1edf6f5f13421d15122b38b1ee202a7c650c51e8d861d1ec4ccf618e5ccca6\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-99tjs2wj/wheels/ea/34/2e/1a4b77e473ea01bc931d1863c73abf7e4d1cc703904d7c74ea\n", + " Building wheel for pybullet (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for pybullet: filename=pybullet-2.6.1-cp36-cp36m-linux_x86_64.whl size=94540402 sha256=afd5b2d31cb50bc2de770143574b04857972e336cfa30f4b523f13e78bf884c4\n", + " Stored in directory: /root/.cache/pip/wheels/6c/85/95/de15ebf350270f905e8ac5b060e9668642ff251d3a3e7f65ad\n", + "Successfully built pybulletgym pybullet\n", + "Installing collected packages: pybullet, pybulletgym\n", + "Successfully installed pybullet-2.6.1 pybulletgym-0.1\n", + "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.6/dist-packages (1.9)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.17.4)\n", + "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (3.10.0)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.12.0)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorboardX) (42.0.2)\n", + "Requirement already satisfied: gym in /usr/local/lib/python3.6/dist-packages (0.15.4)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gym) (1.12.0)\n", + "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from gym) (1.17.4)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from gym) (1.3.3)\n", + "Requirement already satisfied: pyglet<=1.3.2,>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym) (1.3.2)\n", + "Requirement already satisfied: cloudpickle~=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym) (1.2.2)\n", + "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from gym) (4.1.2.30)\n", + "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.3.2,>=1.2.0->gym) (0.16.0)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ma5F4MOknpVM", + "colab_type": "text" + }, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JHXuB5zQnpVO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.autograd import Variable\n", + "import torch.nn.functional as F\n", + "from tensorboardX import SummaryWriter\n", + "\n", + "import gym\n", + "import pybulletgym\n", + "#import roboschool\n", + "import sys" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "h0E0YxDFic_Q", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import os\n", + "if not os.path.exists('saves'):\n", + " os.mkdir('saves')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K0He4LCpnpVT", + "colab_type": "text" + }, + "source": [ + "# Networks" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VP-7T-TRnpVU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def hidden_init(layer):\n", + " fan_in = layer.weight.data.size()[0]\n", + " lim = 1. / np.sqrt(fan_in)\n", + " return (-lim, lim)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "TmpmK3D4npVZ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class Actor(nn.Module):\n", + " \"\"\"Initialize parameters and build model.\n", + " Args:\n", + " state_size (int): Dimension of each state\n", + " action_size (int): Dimension of each action\n", + " max_action (float): highest action to take\n", + " seed (int): Random seed\n", + " h1_units (int): Number of nodes in first hidden layer\n", + " h2_units (int): Number of nodes in second hidden layer\n", + " \n", + " Return:\n", + " action output of network with tanh activation\n", + " \"\"\"\n", + " \n", + " def __init__(self, state_dim, action_dim, max_action):\n", + " super(Actor, self).__init__()\n", + "\n", + " self.l1 = nn.Linear(state_dim, 400)\n", + " self.l2 = nn.Linear(400, 300)\n", + " self.l3 = nn.Linear(300, action_dim)\n", + "\n", + " self.max_action = max_action\n", + "\n", + "\n", + " def forward(self, x):\n", + " x = F.relu(self.l1(x))\n", + " x = F.relu(self.l2(x))\n", + " x = self.max_action * torch.tanh(self.l3(x)) \n", + " return x\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8yGoEuJlnpVf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class Critic(nn.Module):\n", + " \"\"\"Initialize parameters and build model.\n", + " Args:\n", + " state_size (int): Dimension of each state\n", + " action_size (int): Dimension of each action\n", + " max_action (float): highest action to take\n", + " seed (int): Random seed\n", + " h1_units (int): Number of nodes in first hidden layer\n", + " h2_units (int): Number of nodes in second hidden layer\n", + " \n", + " Return:\n", + " value output of network \n", + " \"\"\"\n", + " \n", + " def __init__(self, state_dim, action_dim):\n", + " super(Critic, self).__init__()\n", + "\n", + " # Q1 architecture\n", + " self.l1 = nn.Linear(state_dim + action_dim, 400)\n", + " self.l2 = nn.Linear(400, 300)\n", + " self.l3 = nn.Linear(300, 1)\n", + "\n", + " # Q2 architecture\n", + " self.l4 = nn.Linear(state_dim + action_dim, 400)\n", + " self.l5 = nn.Linear(400, 300)\n", + " self.l6 = nn.Linear(300, 1)\n", + "\n", + "\n", + " def forward(self, x, u):\n", + " xu = torch.cat([x, u], 1)\n", + "\n", + " x1 = F.relu(self.l1(xu))\n", + " x1 = F.relu(self.l2(x1))\n", + " x1 = self.l3(x1)\n", + "\n", + " x2 = F.relu(self.l4(xu))\n", + " x2 = F.relu(self.l5(x2))\n", + " x2 = self.l6(x2)\n", + " return x1, x2\n", + "\n", + "\n", + " def Q1(self, x, u):\n", + " xu = torch.cat([x, u], 1)\n", + "\n", + " x1 = F.relu(self.l1(xu))\n", + " x1 = F.relu(self.l2(x1))\n", + " x1 = self.l3(x1)\n", + " return x1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i2KS-IRHnpVn", + "colab_type": "text" + }, + "source": [ + "# Memory" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JQwIQ14VnpV6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Code based on: \n", + "# https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py\n", + "\n", + "# Expects tuples of (state, next_state, action, reward, done)\n", + "class ReplayBuffer(object):\n", + " \"\"\"Buffer to store tuples of experience replay\"\"\"\n", + " \n", + " def __init__(self, max_size=1000000):\n", + " \"\"\"\n", + " Args:\n", + " max_size (int): total amount of tuples to store\n", + " \"\"\"\n", + " \n", + " self.storage = []\n", + " self.max_size = max_size\n", + " self.ptr = 0\n", + "\n", + " def add(self, data):\n", + " \"\"\"Add experience tuples to buffer\n", + " \n", + " Args:\n", + " data (tuple): experience replay tuple\n", + " \"\"\"\n", + " \n", + " if len(self.storage) == self.max_size:\n", + " self.storage[int(self.ptr)] = data\n", + " self.ptr = (self.ptr + 1) % self.max_size\n", + " else:\n", + " self.storage.append(data)\n", + "\n", + " def sample(self, batch_size):\n", + " \"\"\"Samples a random amount of experiences from buffer of batch size\n", + " \n", + " Args:\n", + " batch_size (int): size of sample\n", + " \"\"\"\n", + " \n", + " ind = np.random.randint(0, len(self.storage), size=batch_size)\n", + " states, actions, next_states, rewards, dones = [], [], [], [], []\n", + "\n", + " for i in ind: \n", + " s, a, s_, r, d = self.storage[i]\n", + " states.append(np.array(s, copy=False))\n", + " actions.append(np.array(a, copy=False))\n", + " next_states.append(np.array(s_, copy=False))\n", + " rewards.append(np.array(r, copy=False))\n", + " dones.append(np.array(d, copy=False))\n", + "\n", + " return np.array(states), np.array(actions), np.array(next_states), np.array(rewards).reshape(-1, 1), np.array(dones).reshape(-1, 1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9UDI07-nnpV-", + "colab_type": "text" + }, + "source": [ + "# Agent" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "n0l6u_4knpWB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class TD3(object):\n", + " \"\"\"Agent class that handles the training of the networks and provides outputs as actions\n", + " \n", + " Args:\n", + " state_dim (int): state size\n", + " action_dim (int): action size\n", + " max_action (float): highest action to take\n", + " device (device): cuda or cpu to process tensors\n", + " env (env): gym environment to use\n", + " \n", + " \"\"\"\n", + " \n", + " def __init__(self, state_dim, action_dim, max_action, env):\n", + " self.actor = Actor(state_dim, action_dim, max_action).to(device)\n", + " self.actor_target = Actor(state_dim, action_dim, max_action).to(device)\n", + " self.actor_target.load_state_dict(self.actor.state_dict())\n", + " self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=1e-3)\n", + "\n", + " self.critic = Critic(state_dim, action_dim).to(device)\n", + " self.critic_target = Critic(state_dim, action_dim).to(device)\n", + " self.critic_target.load_state_dict(self.critic.state_dict())\n", + " self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=1e-3)\n", + "\n", + " self.max_action = max_action\n", + " self.env = env\n", + "\n", + "\n", + " \n", + " def select_action(self, state, noise=0.1):\n", + " \"\"\"Select an appropriate action from the agent policy\n", + " \n", + " Args:\n", + " state (array): current state of environment\n", + " noise (float): how much noise to add to acitons\n", + " \n", + " Returns:\n", + " action (float): action clipped within action range\n", + " \n", + " \"\"\"\n", + " \n", + " state = torch.FloatTensor(state.reshape(1, -1)).to(device)\n", + " \n", + " action = self.actor(state).cpu().data.numpy().flatten()\n", + " if noise != 0: \n", + " action = (action + np.random.normal(0, noise, size=self.env.action_space.shape[0]))\n", + " \n", + " return action.clip(self.env.action_space.low, self.env.action_space.high)\n", + "\n", + " \n", + " def train(self, replay_buffer, iterations, batch_size=100, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2):\n", + " \"\"\"Train and update actor and critic networks\n", + " \n", + " Args:\n", + " replay_buffer (ReplayBuffer): buffer for experience replay\n", + " iterations (int): how many times to run training\n", + " batch_size(int): batch size to sample from replay buffer\n", + " discount (float): discount factor\n", + " tau (float): soft update for main networks to target networks\n", + " \n", + " Return:\n", + " actor_loss (float): loss from actor network\n", + " critic_loss (float): loss from critic network\n", + " \n", + " \"\"\"\n", + " \n", + " for it in range(iterations):\n", + "\n", + " # Sample replay buffer \n", + " x, y, u, r, d = replay_buffer.sample(batch_size)\n", + " state = torch.FloatTensor(x).to(device)\n", + " action = torch.FloatTensor(u).to(device)\n", + " next_state = torch.FloatTensor(y).to(device)\n", + " done = torch.FloatTensor(1 - d).to(device)\n", + " reward = torch.FloatTensor(r).to(device)\n", + "\n", + " # Select action according to policy and add clipped noise \n", + " noise = torch.FloatTensor(u).data.normal_(0, policy_noise).to(device)\n", + " noise = noise.clamp(-noise_clip, noise_clip)\n", + " next_action = (self.actor_target(next_state) + noise).clamp(-self.max_action, self.max_action)\n", + "\n", + " # Compute the target Q value\n", + " target_Q1, target_Q2 = self.critic_target(next_state, next_action)\n", + " target_Q = torch.min(target_Q1, target_Q2)\n", + " target_Q = reward + (done * discount * target_Q).detach()\n", + "\n", + " # Get current Q estimates\n", + " current_Q1, current_Q2 = self.critic(state, action)\n", + "\n", + " # Compute critic loss\n", + " critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q) \n", + "\n", + " # Optimize the critic\n", + " self.critic_optimizer.zero_grad()\n", + " critic_loss.backward()\n", + " self.critic_optimizer.step()\n", + "\n", + " # Delayed policy updates\n", + " if it % policy_freq == 0:\n", + "\n", + " # Compute actor loss\n", + " actor_loss = -self.critic.Q1(state, self.actor(state)).mean()\n", + "\n", + " # Optimize the actor \n", + " self.actor_optimizer.zero_grad()\n", + " actor_loss.backward()\n", + " self.actor_optimizer.step()\n", + "\n", + " # Update the frozen target models\n", + " for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):\n", + " target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n", + "\n", + " for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):\n", + " target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n", + "\n", + "\n", + " def save(self, filename, directory):\n", + " torch.save(self.actor.state_dict(), '%s/%s_actor.pth' % (directory, filename))\n", + " torch.save(self.critic.state_dict(), '%s/%s_critic.pth' % (directory, filename))\n", + "\n", + "\n", + " def load(self, filename=\"best_avg\", directory=\"./saves\"):\n", + " self.actor.load_state_dict(torch.load('%s/%s_actor.pth' % (directory, filename)))\n", + " self.critic.load_state_dict(torch.load('%s/%s_critic.pth' % (directory, filename)))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OJ1Kf78_npWE", + "colab_type": "text" + }, + "source": [ + "# Runner" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OYzKUCOnnpWF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class Runner():\n", + " \"\"\"Carries out the environment steps and adds experiences to memory\"\"\"\n", + " \n", + " def __init__(self, env, agent, replay_buffer):\n", + " \n", + " self.env = env\n", + " self.agent = agent\n", + " self.replay_buffer = replay_buffer\n", + " self.obs = env.reset()\n", + " self.done = False\n", + " \n", + " def next_step(self, episode_timesteps, noise=0.1):\n", + " \n", + " action = self.agent.select_action(np.array(self.obs), noise=0.1)\n", + " \n", + " # Perform action\n", + " new_obs, reward, done, _ = self.env.step(action) \n", + " done_bool = 0 if episode_timesteps + 1 == 200 else float(done)\n", + " \n", + " # Store data in replay buffer\n", + " replay_buffer.add((self.obs, new_obs, action, reward, done_bool))\n", + " \n", + " self.obs = new_obs\n", + " \n", + " if done:\n", + " self.obs = self.env.reset()\n", + " done = False\n", + " \n", + " return reward, True\n", + " \n", + " return reward, done" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xhmggP7TnpWH", + "colab_type": "text" + }, + "source": [ + "# Evaluate" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1g6CYzr2npWI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def evaluate_policy(policy, env, eval_episodes=100,render=False):\n", + " \"\"\"run several episodes using the best agent policy\n", + " \n", + " Args:\n", + " policy (agent): agent to evaluate\n", + " env (env): gym environment\n", + " eval_episodes (int): how many test episodes to run\n", + " render (bool): show training\n", + " \n", + " Returns:\n", + " avg_reward (float): average reward over the number of evaluations\n", + " \n", + " \"\"\"\n", + " \n", + " avg_reward = 0.\n", + " for i in range(eval_episodes):\n", + " obs = env.reset()\n", + " done = False\n", + " while not done:\n", + " if render:\n", + " env.render()\n", + " action = policy.select_action(np.array(obs), noise=0)\n", + " obs, reward, done, _ = env.step(action)\n", + " avg_reward += reward\n", + "\n", + " avg_reward /= eval_episodes\n", + "\n", + " print(\"\\n---------------------------------------\")\n", + " print(\"Evaluation over {:d} episodes: {:f}\" .format(eval_episodes, avg_reward))\n", + " print(\"---------------------------------------\")\n", + " return avg_reward" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P5uXjG0inpWL", + "colab_type": "text" + }, + "source": [ + "# Observation" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4zw822TgnpWM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def observe(env,replay_buffer, observation_steps):\n", + " \"\"\"run episodes while taking random actions and filling replay_buffer\n", + " \n", + " Args:\n", + " env (env): gym environment\n", + " replay_buffer(ReplayBuffer): buffer to store experience replay\n", + " observation_steps (int): how many steps to observe for\n", + " \n", + " \"\"\"\n", + " \n", + " time_steps = 0\n", + " obs = env.reset()\n", + " done = False\n", + "\n", + " while time_steps < observation_steps:\n", + " action = env.action_space.sample()\n", + " new_obs, reward, done, _ = env.step(action)\n", + "\n", + " replay_buffer.add((obs, new_obs, action, reward, done))\n", + "\n", + " obs = new_obs\n", + " time_steps += 1\n", + "\n", + " if done:\n", + " obs = env.reset()\n", + " done = False\n", + "\n", + " print(\"\\rPopulating Buffer {}/{}.\".format(time_steps, observation_steps), end=\"\")\n", + " sys.stdout.flush()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jZWuqwO1npWP", + "colab_type": "text" + }, + "source": [ + "# Train" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6877aX9BnpWQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def train(agent, test_env):\n", + " \"\"\"Train the agent for exploration steps\n", + " \n", + " Args:\n", + " agent (Agent): agent to use\n", + " env (environment): gym environment\n", + " writer (SummaryWriter): tensorboard writer\n", + " exploration (int): how many training steps to run\n", + " \n", + " \"\"\"\n", + "\n", + " total_timesteps = 0\n", + " timesteps_since_eval = 0\n", + " episode_num = 0\n", + " episode_reward = 0\n", + " episode_timesteps = 0\n", + " done = False \n", + " obs = env.reset()\n", + " evaluations = []\n", + " rewards = []\n", + " best_avg = -2000\n", + " \n", + " writer = SummaryWriter(comment=\"-TD3_Baseline_HalfCheetah\")\n", + " \n", + " while total_timesteps < EXPLORATION:\n", + " \n", + " if done: \n", + "\n", + " if total_timesteps != 0: \n", + " rewards.append(episode_reward)\n", + " avg_reward = np.mean(rewards[-100:])\n", + " \n", + " writer.add_scalar(\"avg_reward\", avg_reward, total_timesteps)\n", + " writer.add_scalar(\"reward_step\", reward, total_timesteps)\n", + " writer.add_scalar(\"episode_reward\", episode_reward, total_timesteps)\n", + " \n", + " if best_avg < avg_reward:\n", + " best_avg = avg_reward\n", + " print(\"saving best model....\\n\")\n", + " agent.save(\"best_avg\",\"saves\")\n", + "\n", + " print(\"\\rTotal T: {:d} Episode Num: {:d} Reward: {:f} Avg Reward: {:f}\".format(\n", + " total_timesteps, episode_num, episode_reward, avg_reward), end=\"\")\n", + " sys.stdout.flush()\n", + "\n", + "\n", + " if avg_reward >= REWARD_THRESH:\n", + " break\n", + "\n", + " agent.train(replay_buffer, episode_timesteps, BATCH_SIZE, GAMMA, TAU, NOISE, NOISE_CLIP, POLICY_FREQUENCY)\n", + "\n", + " # Evaluate episode\n", + " if timesteps_since_eval >= EVAL_FREQUENCY:\n", + " timesteps_since_eval %= EVAL_FREQUENCY\n", + " eval_reward = evaluate_policy(agent, test_env)\n", + " evaluations.append(avg_reward)\n", + " writer.add_scalar(\"eval_reward\", eval_reward, total_timesteps)\n", + "\n", + " if best_avg < eval_reward:\n", + " best_avg = eval_reward\n", + " print(\"saving best model....\\n\")\n", + " agent.save(\"best_avg\",\"saves\")\n", + "\n", + " episode_reward = 0\n", + " episode_timesteps = 0\n", + " episode_num += 1 \n", + "\n", + " reward, done = runner.next_step(episode_timesteps)\n", + " episode_reward += reward\n", + "\n", + " episode_timesteps += 1\n", + " total_timesteps += 1\n", + " timesteps_since_eval += 1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CfwWrVMOnpWT", + "colab_type": "text" + }, + "source": [ + "# Config" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xyOO_23tnpWT", + "colab_type": "code", + "colab": {} + }, + "source": [ + "ENV = \"Pendulum-v0\"#\"Pendulum-v0\" #HalfCheetahMuJoCoEnv-v0\t\n", + "SEED = 0\n", + "OBSERVATION = 10000\n", + "EXPLORATION = 5000000\n", + "BATCH_SIZE = 100\n", + "GAMMA = 0.99\n", + "TAU = 0.005\n", + "NOISE = 0.2\n", + "NOISE_CLIP = 0.5\n", + "EXPLORE_NOISE = 0.1\n", + "POLICY_FREQUENCY = 2\n", + "EVAL_FREQUENCY = 5000\n", + "REWARD_THRESH = 8000" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FE5NjQdcnpWV", + "colab_type": "text" + }, + "source": [ + "# Main" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Dgao3UPhnpWW", + "colab_type": "code", + "colab": {} + }, + "source": [ + "env = gym.make(ENV)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Set seeds\n", + "env.seed(SEED)\n", + "torch.manual_seed(SEED)\n", + "np.random.seed(SEED)\n", + "\n", + "state_dim = env.observation_space.shape[0]\n", + "action_dim = env.action_space.shape[0] \n", + "max_action = float(env.action_space.high[0])\n", + "\n", + "policy = TD3(state_dim, action_dim, max_action, env)\n", + "\n", + "replay_buffer = ReplayBuffer()\n", + "\n", + "runner = Runner(env, policy, replay_buffer)\n", + "\n", + "total_timesteps = 0\n", + "timesteps_since_eval = 0\n", + "episode_num = 0\n", + "done = True" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "alYTdprknpWY", + "colab_type": "code", + "outputId": "4c1f9a20-235c-4dc3-e2ea-3e2e820f7fb1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Populate replay buffer\n", + "observe(env, replay_buffer, OBSERVATION)" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Populating Buffer 10000/10000." + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9DzONcNZnpWd", + "colab_type": "code", + "outputId": "612e7739-7810-4193-f88c-657c2208db5a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "# Train agent\n", + "train(policy, env)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "saving best model....\n", + "\n", + "Total T: 4800 Episode Num: 23 Reward: -603.096721 Avg Reward: -1259.871510saving best model....\n", + "\n", + "Total T: 5000 Episode Num: 24 Reward: -722.750267 Avg Reward: -1238.386660\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -387.920471\n", + "---------------------------------------\n", + "saving best model....\n", + "\n", + "Total T: 10001 Episode Num: 50 Reward: -131.721027 Avg Reward: -698.314601\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.798520\n", + "---------------------------------------\n", + "saving best model....\n", + "\n", + "Total T: 15002 Episode Num: 76 Reward: -122.555019 Avg Reward: -505.503081\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -168.877200\n", + "---------------------------------------\n", + "Total T: 20003 Episode Num: 102 Reward: -235.918097 Avg Reward: -398.207284\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.847979\n", + "---------------------------------------\n", + "Total T: 25004 Episode Num: 128 Reward: -344.852213 Avg Reward: -160.288249\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.193575\n", + "---------------------------------------\n", + "saving best model....\n", + "\n", + "Total T: 30005 Episode Num: 154 Reward: -236.353658 Avg Reward: -164.682376\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.706353\n", + "---------------------------------------\n", + "Total T: 35006 Episode Num: 180 Reward: -117.075385 Avg Reward: -171.640776\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.752955\n", + "---------------------------------------\n", + "Total T: 40007 Episode Num: 206 Reward: -5.992129 Avg Reward: -149.138574\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.486963\n", + "---------------------------------------\n", + "Total T: 45008 Episode Num: 232 Reward: -5.075935 Avg Reward: -146.157379\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.462769\n", + "---------------------------------------\n", + "Total T: 48609 Episode Num: 251 Reward: -127.745479 Avg Reward: -135.610976saving best model....\n", + "\n", + "Total T: 48809 Episode Num: 252 Reward: -238.106626 Avg Reward: -134.679996saving best model....\n", + "\n", + "Total T: 49009 Episode Num: 253 Reward: -3.881976 Avg Reward: -132.295350saving best model....\n", + "\n", + "Total T: 50009 Episode Num: 258 Reward: -127.329855 Avg Reward: -131.159465\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.530695\n", + "---------------------------------------\n", + "saving best model....\n", + "\n", + "Total T: 50210 Episode Num: 260 Reward: -236.120966 Avg Reward: -129.917130saving best model....\n", + "\n", + "Total T: 50410 Episode Num: 261 Reward: -6.076339 Avg Reward: -128.778663saving best model....\n", + "\n", + "Total T: 50610 Episode Num: 262 Reward: -130.855321 Avg Reward: -127.654247saving best model....\n", + "\n", + "Total T: 51210 Episode Num: 265 Reward: -125.434175 Avg Reward: -128.767813saving best model....\n", + "\n", + "Total T: 53410 Episode Num: 276 Reward: -2.276500 Avg Reward: -128.053726saving best model....\n", + "\n", + "Total T: 53610 Episode Num: 277 Reward: -3.920771 Avg Reward: -126.823367saving best model....\n", + "\n", + "Total T: 53810 Episode Num: 278 Reward: -336.720104 Avg Reward: -126.603715saving best model....\n", + "\n", + "Total T: 55010 Episode Num: 284 Reward: -125.105746 Avg Reward: -132.576109\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -150.781722\n", + "---------------------------------------\n", + "Total T: 60011 Episode Num: 310 Reward: -235.339863 Avg Reward: -143.626958\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.517579\n", + "---------------------------------------\n", + "Total T: 65012 Episode Num: 336 Reward: -5.744457 Avg Reward: -150.754032\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -138.469024\n", + "---------------------------------------\n", + "Total T: 70013 Episode Num: 362 Reward: -232.966440 Avg Reward: -158.514407\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.945538\n", + "---------------------------------------\n", + "Total T: 75014 Episode Num: 388 Reward: -229.315751 Avg Reward: -156.356715\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.692870\n", + "---------------------------------------\n", + "Total T: 80015 Episode Num: 414 Reward: -119.444470 Avg Reward: -143.482099\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.947268\n", + "---------------------------------------\n", + "Total T: 85016 Episode Num: 440 Reward: -337.880809 Avg Reward: -142.975612\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -157.088687\n", + "---------------------------------------\n", + "Total T: 90017 Episode Num: 466 Reward: -323.300030 Avg Reward: -142.459951\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.270341\n", + "---------------------------------------\n", + "Total T: 95018 Episode Num: 492 Reward: -124.044936 Avg Reward: -140.017620\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -169.432375\n", + "---------------------------------------\n", + "Total T: 100019 Episode Num: 518 Reward: -223.578334 Avg Reward: -150.163502\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.569063\n", + "---------------------------------------\n", + "Total T: 105020 Episode Num: 544 Reward: -119.577739 Avg Reward: -152.176151\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.997675\n", + "---------------------------------------\n", + "Total T: 110021 Episode Num: 570 Reward: -124.805823 Avg Reward: -145.535917\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.697875\n", + "---------------------------------------\n", + "Total T: 115022 Episode Num: 596 Reward: -1.192696 Avg Reward: -143.355020\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -132.841568\n", + "---------------------------------------\n", + "Total T: 120023 Episode Num: 622 Reward: -231.953441 Avg Reward: -136.896938\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.439755\n", + "---------------------------------------\n", + "Total T: 123224 Episode Num: 639 Reward: -114.887502 Avg Reward: -126.847898saving best model....\n", + "\n", + "Total T: 123624 Episode Num: 641 Reward: -119.735718 Avg Reward: -125.725122saving best model....\n", + "\n", + "Total T: 125024 Episode Num: 648 Reward: -119.007238 Avg Reward: -128.475631\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.478667\n", + "---------------------------------------\n", + "Total T: 130025 Episode Num: 674 Reward: -115.216604 Avg Reward: -133.645939\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.141633\n", + "---------------------------------------\n", + "Total T: 135026 Episode Num: 700 Reward: -123.758406 Avg Reward: -132.691372\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.113074\n", + "---------------------------------------\n", + "Total T: 140027 Episode Num: 726 Reward: -125.405445 Avg Reward: -143.259755\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.298880\n", + "---------------------------------------\n", + "Total T: 145028 Episode Num: 752 Reward: -250.146860 Avg Reward: -152.858862\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.835185\n", + "---------------------------------------\n", + "Total T: 150029 Episode Num: 778 Reward: -127.709559 Avg Reward: -156.237790\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -154.768546\n", + "---------------------------------------\n", + "Total T: 155030 Episode Num: 804 Reward: -233.020598 Avg Reward: -149.831184\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -156.886488\n", + "---------------------------------------\n", + "Total T: 160031 Episode Num: 830 Reward: -116.007556 Avg Reward: -148.258933\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -152.030262\n", + "---------------------------------------\n", + "Total T: 165032 Episode Num: 856 Reward: -244.837696 Avg Reward: -143.321241\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.576442\n", + "---------------------------------------\n", + "Total T: 170033 Episode Num: 882 Reward: -353.122497 Avg Reward: -142.617431\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -150.948215\n", + "---------------------------------------\n", + "Total T: 175034 Episode Num: 908 Reward: -117.251271 Avg Reward: -141.022446\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.964292\n", + "---------------------------------------\n", + "Total T: 180035 Episode Num: 934 Reward: -125.182530 Avg Reward: -139.254756\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.482613\n", + "---------------------------------------\n", + "Total T: 185036 Episode Num: 960 Reward: -119.802244 Avg Reward: -134.859163\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.271954\n", + "---------------------------------------\n", + "Total T: 190037 Episode Num: 986 Reward: -115.013121 Avg Reward: -130.109018\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.811724\n", + "---------------------------------------\n", + "Total T: 195038 Episode Num: 1012 Reward: -118.445893 Avg Reward: -129.426216\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.517200\n", + "---------------------------------------\n", + "Total T: 200039 Episode Num: 1038 Reward: -224.806845 Avg Reward: -138.433039\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -139.943453\n", + "---------------------------------------\n", + "Total T: 205040 Episode Num: 1064 Reward: -307.324174 Avg Reward: -145.643810\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.328625\n", + "---------------------------------------\n", + "Total T: 210041 Episode Num: 1090 Reward: -243.205575 Avg Reward: -148.523811\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -132.994678\n", + "---------------------------------------\n", + "Total T: 215042 Episode Num: 1116 Reward: -126.190385 Avg Reward: -146.693333\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.720822\n", + "---------------------------------------\n", + "Total T: 220043 Episode Num: 1142 Reward: -119.622887 Avg Reward: -139.441252\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.124271\n", + "---------------------------------------\n", + "Total T: 225044 Episode Num: 1168 Reward: -231.630679 Avg Reward: -139.593452\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.835852\n", + "---------------------------------------\n", + "Total T: 230045 Episode Num: 1194 Reward: -234.607893 Avg Reward: -147.373665\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.323119\n", + "---------------------------------------\n", + "Total T: 235046 Episode Num: 1220 Reward: -116.640833 Avg Reward: -154.344614\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -133.649232\n", + "---------------------------------------\n", + "Total T: 240047 Episode Num: 1246 Reward: -122.403765 Avg Reward: -159.753970\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -147.636497\n", + "---------------------------------------\n", + "Total T: 245048 Episode Num: 1272 Reward: -125.662294 Avg Reward: -155.641135\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.904314\n", + "---------------------------------------\n", + "Total T: 250049 Episode Num: 1298 Reward: -121.751124 Avg Reward: -149.299052\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.929036\n", + "---------------------------------------\n", + "Total T: 255050 Episode Num: 1324 Reward: -223.802304 Avg Reward: -144.222016\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -133.855144\n", + "---------------------------------------\n", + "Total T: 260051 Episode Num: 1350 Reward: -1.667735 Avg Reward: -135.312656\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.474727\n", + "---------------------------------------\n", + "Total T: 265052 Episode Num: 1376 Reward: -223.188891 Avg Reward: -130.876768\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.901605\n", + "---------------------------------------\n", + "Total T: 270053 Episode Num: 1402 Reward: -1.951272 Avg Reward: -130.299024\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.034361\n", + "---------------------------------------\n", + "Total T: 271254 Episode Num: 1409 Reward: -121.365418 Avg Reward: -125.896203saving best model....\n", + "\n", + "Total T: 271454 Episode Num: 1410 Reward: -1.825065 Avg Reward: -124.679447saving best model....\n", + "\n", + "Total T: 271654 Episode Num: 1411 Reward: -117.506809 Avg Reward: -124.594415saving best model....\n", + "\n", + "Total T: 273654 Episode Num: 1421 Reward: -229.632021 Avg Reward: -125.075759saving best model....\n", + "\n", + "Total T: 275054 Episode Num: 1428 Reward: -124.739911 Avg Reward: -128.723424\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -153.461923\n", + "---------------------------------------\n", + "Total T: 280055 Episode Num: 1454 Reward: -117.669786 Avg Reward: -134.018677\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -147.203200\n", + "---------------------------------------\n", + "Total T: 285056 Episode Num: 1480 Reward: -280.009114 Avg Reward: -137.919228\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.154589\n", + "---------------------------------------\n", + "Total T: 290057 Episode Num: 1506 Reward: -124.076258 Avg Reward: -141.397901\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.985448\n", + "---------------------------------------\n", + "Total T: 295058 Episode Num: 1532 Reward: -123.033407 Avg Reward: -140.650200\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.972937\n", + "---------------------------------------\n", + "Total T: 300059 Episode Num: 1558 Reward: -115.808927 Avg Reward: -137.864783\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.544961\n", + "---------------------------------------\n", + "Total T: 305060 Episode Num: 1584 Reward: -122.425315 Avg Reward: -136.843975\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.368952\n", + "---------------------------------------\n", + "Total T: 310061 Episode Num: 1610 Reward: -120.898105 Avg Reward: -140.787549\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.264861\n", + "---------------------------------------\n", + "Total T: 315062 Episode Num: 1636 Reward: -125.534493 Avg Reward: -141.076193\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.257349\n", + "---------------------------------------\n", + "Total T: 320063 Episode Num: 1662 Reward: -114.279252 Avg Reward: -147.363485\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -133.098053\n", + "---------------------------------------\n", + "Total T: 325064 Episode Num: 1688 Reward: -124.549299 Avg Reward: -148.528338\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -138.404043\n", + "---------------------------------------\n", + "Total T: 330065 Episode Num: 1714 Reward: -118.862285 Avg Reward: -142.743474\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.839574\n", + "---------------------------------------\n", + "Total T: 335066 Episode Num: 1740 Reward: -129.135239 Avg Reward: -151.484712\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -158.840708\n", + "---------------------------------------\n", + "Total T: 340067 Episode Num: 1766 Reward: -0.777679 Avg Reward: -150.943629\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -155.902200\n", + "---------------------------------------\n", + "Total T: 345068 Episode Num: 1792 Reward: -128.171168 Avg Reward: -150.601466\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -155.374336\n", + "---------------------------------------\n", + "Total T: 350069 Episode Num: 1818 Reward: -122.461219 Avg Reward: -145.129279\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.295293\n", + "---------------------------------------\n", + "Total T: 355070 Episode Num: 1844 Reward: -115.024468 Avg Reward: -140.829685\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.656809\n", + "---------------------------------------\n", + "Total T: 360071 Episode Num: 1870 Reward: -129.110473 Avg Reward: -143.957106\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.798216\n", + "---------------------------------------\n", + "Total T: 365072 Episode Num: 1896 Reward: -113.606577 Avg Reward: -156.316337\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.410879\n", + "---------------------------------------\n", + "Total T: 370073 Episode Num: 1922 Reward: -117.735238 Avg Reward: -159.707628\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.078952\n", + "---------------------------------------\n", + "Total T: 375074 Episode Num: 1948 Reward: -114.898523 Avg Reward: -156.124062\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.820629\n", + "---------------------------------------\n", + "Total T: 380075 Episode Num: 1974 Reward: -115.586481 Avg Reward: -146.068599\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -132.008958\n", + "---------------------------------------\n", + "Total T: 385076 Episode Num: 2000 Reward: -128.076134 Avg Reward: -135.080016\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.196615\n", + "---------------------------------------\n", + "Total T: 390077 Episode Num: 2026 Reward: -121.769080 Avg Reward: -138.169571\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.408196\n", + "---------------------------------------\n", + "Total T: 395078 Episode Num: 2052 Reward: -124.228905 Avg Reward: -137.410341\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.129769\n", + "---------------------------------------\n", + "Total T: 400079 Episode Num: 2078 Reward: -131.999167 Avg Reward: -130.246776\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.726878\n", + "---------------------------------------\n", + "Total T: 405080 Episode Num: 2104 Reward: -126.790201 Avg Reward: -137.121146\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.130132\n", + "---------------------------------------\n", + "Total T: 410081 Episode Num: 2130 Reward: -119.960410 Avg Reward: -130.475035\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -158.631896\n", + "---------------------------------------\n", + "Total T: 415082 Episode Num: 2156 Reward: -308.325868 Avg Reward: -137.676154\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.590535\n", + "---------------------------------------\n", + "Total T: 420083 Episode Num: 2182 Reward: -122.583207 Avg Reward: -141.538790\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.147236\n", + "---------------------------------------\n", + "Total T: 425084 Episode Num: 2208 Reward: -124.846911 Avg Reward: -142.344370\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -150.633543\n", + "---------------------------------------\n", + "Total T: 430085 Episode Num: 2234 Reward: -127.332197 Avg Reward: -149.853123\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.459225\n", + "---------------------------------------\n", + "Total T: 435086 Episode Num: 2260 Reward: -123.192758 Avg Reward: -149.640447\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.432304\n", + "---------------------------------------\n", + "Total T: 440087 Episode Num: 2286 Reward: -120.086227 Avg Reward: -149.589741\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.945997\n", + "---------------------------------------\n", + "Total T: 445088 Episode Num: 2312 Reward: -246.348837 Avg Reward: -142.757755\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -128.555240\n", + "---------------------------------------\n", + "Total T: 450089 Episode Num: 2338 Reward: -124.964633 Avg Reward: -141.918950\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.931503\n", + "---------------------------------------\n", + "Total T: 455090 Episode Num: 2364 Reward: -125.900877 Avg Reward: -143.714676\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.585821\n", + "---------------------------------------\n", + "Total T: 460091 Episode Num: 2390 Reward: -129.567639 Avg Reward: -138.815683\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.797765\n", + "---------------------------------------\n", + "Total T: 465092 Episode Num: 2416 Reward: -116.888191 Avg Reward: -136.882432\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -131.833796\n", + "---------------------------------------\n", + "Total T: 470093 Episode Num: 2442 Reward: -123.828670 Avg Reward: -133.487363\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.398100\n", + "---------------------------------------\n", + "Total T: 475094 Episode Num: 2468 Reward: -234.718012 Avg Reward: -135.420118\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.145417\n", + "---------------------------------------\n", + "Total T: 480095 Episode Num: 2494 Reward: -123.679977 Avg Reward: -143.433529\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -119.734766\n", + "---------------------------------------\n", + "saving best model....\n", + "\n", + "Total T: 485096 Episode Num: 2520 Reward: -117.693266 Avg Reward: -144.661487\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.963103\n", + "---------------------------------------\n", + "Total T: 490097 Episode Num: 2546 Reward: -245.678327 Avg Reward: -157.834128\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.661861\n", + "---------------------------------------\n", + "Total T: 495098 Episode Num: 2572 Reward: -1.360227 Avg Reward: -158.220605\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.455966\n", + "---------------------------------------\n", + "Total T: 500099 Episode Num: 2598 Reward: -243.155762 Avg Reward: -153.103326\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -129.453010\n", + "---------------------------------------\n", + "Total T: 505100 Episode Num: 2624 Reward: -297.759534 Avg Reward: -153.873464\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -153.302473\n", + "---------------------------------------\n", + "Total T: 510101 Episode Num: 2650 Reward: -131.521448 Avg Reward: -146.969147\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -152.916022\n", + "---------------------------------------\n", + "Total T: 515102 Episode Num: 2676 Reward: -117.243756 Avg Reward: -144.429592\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -150.157366\n", + "---------------------------------------\n", + "Total T: 520103 Episode Num: 2702 Reward: -231.853279 Avg Reward: -138.233310\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -154.567423\n", + "---------------------------------------\n", + "Total T: 525104 Episode Num: 2728 Reward: -117.689522 Avg Reward: -132.136766\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -119.719215\n", + "---------------------------------------\n", + "saving best model....\n", + "\n", + "Total T: 530105 Episode Num: 2754 Reward: -299.577574 Avg Reward: -133.526023\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.351869\n", + "---------------------------------------\n", + "Total T: 535106 Episode Num: 2780 Reward: -127.261259 Avg Reward: -136.878504\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.548983\n", + "---------------------------------------\n", + "Total T: 540107 Episode Num: 2806 Reward: -258.774717 Avg Reward: -140.149025\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.566875\n", + "---------------------------------------\n", + "Total T: 545108 Episode Num: 2832 Reward: -120.449511 Avg Reward: -144.413965\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.535084\n", + "---------------------------------------\n", + "Total T: 550109 Episode Num: 2858 Reward: -224.515491 Avg Reward: -143.228104\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.011470\n", + "---------------------------------------\n", + "Total T: 555110 Episode Num: 2884 Reward: -1.359512 Avg Reward: -139.475467\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.020152\n", + "---------------------------------------\n", + "Total T: 560111 Episode Num: 2910 Reward: -237.123643 Avg Reward: -147.396429\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.781125\n", + "---------------------------------------\n", + "Total T: 565112 Episode Num: 2936 Reward: -242.438599 Avg Reward: -149.971620\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -154.799377\n", + "---------------------------------------\n", + "Total T: 570113 Episode Num: 2962 Reward: -120.210778 Avg Reward: -150.757960\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -131.735625\n", + "---------------------------------------\n", + "Total T: 575114 Episode Num: 2988 Reward: -4.229691 Avg Reward: -149.140124\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -161.022911\n", + "---------------------------------------\n", + "Total T: 580115 Episode Num: 3014 Reward: -226.948745 Avg Reward: -144.798712\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.948054\n", + "---------------------------------------\n", + "Total T: 585116 Episode Num: 3040 Reward: -120.554021 Avg Reward: -136.893223\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.834005\n", + "---------------------------------------\n", + "Total T: 590117 Episode Num: 3066 Reward: -221.598827 Avg Reward: -139.391132\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -153.016350\n", + "---------------------------------------\n", + "Total T: 595118 Episode Num: 3092 Reward: -125.336559 Avg Reward: -147.265765\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.506073\n", + "---------------------------------------\n", + "Total T: 600119 Episode Num: 3118 Reward: -126.106961 Avg Reward: -146.350242\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.654914\n", + "---------------------------------------\n", + "Total T: 605120 Episode Num: 3144 Reward: -234.821282 Avg Reward: -142.703088\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -147.022656\n", + "---------------------------------------\n", + "Total T: 610121 Episode Num: 3170 Reward: -0.583983 Avg Reward: -137.892869\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -138.988726\n", + "---------------------------------------\n", + "Total T: 615122 Episode Num: 3196 Reward: -122.176366 Avg Reward: -128.601079\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.384041\n", + "---------------------------------------\n", + "Total T: 620123 Episode Num: 3222 Reward: -0.506989 Avg Reward: -122.196971\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.317100\n", + "---------------------------------------\n", + "Total T: 625124 Episode Num: 3248 Reward: -126.930462 Avg Reward: -130.107257\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -150.572164\n", + "---------------------------------------\n", + "Total T: 630125 Episode Num: 3274 Reward: -116.715176 Avg Reward: -134.590791\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -127.705617\n", + "---------------------------------------\n", + "Total T: 635126 Episode Num: 3300 Reward: -131.825433 Avg Reward: -140.985506\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.581015\n", + "---------------------------------------\n", + "Total T: 640127 Episode Num: 3326 Reward: -120.814531 Avg Reward: -147.071811\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.695519\n", + "---------------------------------------\n", + "Total T: 645128 Episode Num: 3352 Reward: -122.260686 Avg Reward: -147.924768\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.831929\n", + "---------------------------------------\n", + "Total T: 650129 Episode Num: 3378 Reward: -127.985498 Avg Reward: -138.300934\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.954243\n", + "---------------------------------------\n", + "Total T: 655130 Episode Num: 3404 Reward: -130.780097 Avg Reward: -135.752944\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.790360\n", + "---------------------------------------\n", + "Total T: 660131 Episode Num: 3430 Reward: -231.667566 Avg Reward: -136.812850\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.450699\n", + "---------------------------------------\n", + "Total T: 665132 Episode Num: 3456 Reward: -120.583827 Avg Reward: -137.588174\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.451327\n", + "---------------------------------------\n", + "Total T: 670133 Episode Num: 3482 Reward: -121.368330 Avg Reward: -138.208478\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -159.942274\n", + "---------------------------------------\n", + "Total T: 675134 Episode Num: 3508 Reward: -118.340263 Avg Reward: -137.451700\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.723426\n", + "---------------------------------------\n", + "Total T: 680135 Episode Num: 3534 Reward: -117.103114 Avg Reward: -136.182716\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -133.463890\n", + "---------------------------------------\n", + "Total T: 685136 Episode Num: 3560 Reward: -266.640497 Avg Reward: -136.073236\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.707150\n", + "---------------------------------------\n", + "Total T: 690137 Episode Num: 3586 Reward: -2.587535 Avg Reward: -135.642127\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.567079\n", + "---------------------------------------\n", + "Total T: 695138 Episode Num: 3612 Reward: -215.018220 Avg Reward: -133.409454\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.443018\n", + "---------------------------------------\n", + "Total T: 700139 Episode Num: 3638 Reward: -1.741515 Avg Reward: -140.959628\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.851112\n", + "---------------------------------------\n", + "Total T: 705140 Episode Num: 3664 Reward: -247.642042 Avg Reward: -138.211304\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -156.994770\n", + "---------------------------------------\n", + "Total T: 710141 Episode Num: 3690 Reward: -226.160814 Avg Reward: -143.065031\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -154.664738\n", + "---------------------------------------\n", + "Total T: 715142 Episode Num: 3716 Reward: -253.516944 Avg Reward: -147.158539\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.064500\n", + "---------------------------------------\n", + "Total T: 720143 Episode Num: 3742 Reward: -124.357076 Avg Reward: -137.402631\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.337433\n", + "---------------------------------------\n", + "Total T: 725144 Episode Num: 3768 Reward: -236.302109 Avg Reward: -138.008423\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.758195\n", + "---------------------------------------\n", + "Total T: 730145 Episode Num: 3794 Reward: -1.705278 Avg Reward: -145.416049\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.485219\n", + "---------------------------------------\n", + "Total T: 735146 Episode Num: 3820 Reward: -121.787108 Avg Reward: -144.613415\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.011812\n", + "---------------------------------------\n", + "Total T: 740147 Episode Num: 3846 Reward: -121.603911 Avg Reward: -149.487111\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -138.030131\n", + "---------------------------------------\n", + "Total T: 745148 Episode Num: 3872 Reward: -0.727388 Avg Reward: -141.756732\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -147.740404\n", + "---------------------------------------\n", + "Total T: 750149 Episode Num: 3898 Reward: -227.083701 Avg Reward: -135.214338\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.927792\n", + "---------------------------------------\n", + "Total T: 755150 Episode Num: 3924 Reward: -243.118062 Avg Reward: -137.987211\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.417469\n", + "---------------------------------------\n", + "Total T: 760151 Episode Num: 3950 Reward: -2.353241 Avg Reward: -136.013777\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.600086\n", + "---------------------------------------\n", + "Total T: 765152 Episode Num: 3976 Reward: -8.211028 Avg Reward: -136.553330\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -138.736969\n", + "---------------------------------------\n", + "Total T: 770153 Episode Num: 4002 Reward: -116.168975 Avg Reward: -131.905270\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.792883\n", + "---------------------------------------\n", + "Total T: 775154 Episode Num: 4028 Reward: -133.513419 Avg Reward: -141.909892\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.896942\n", + "---------------------------------------\n", + "Total T: 780155 Episode Num: 4054 Reward: -127.728696 Avg Reward: -140.090708\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.760014\n", + "---------------------------------------\n", + "Total T: 785156 Episode Num: 4080 Reward: -246.544326 Avg Reward: -143.040507\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.741457\n", + "---------------------------------------\n", + "Total T: 790157 Episode Num: 4106 Reward: -132.763749 Avg Reward: -155.846864\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.544973\n", + "---------------------------------------\n", + "Total T: 795158 Episode Num: 4132 Reward: -7.056553 Avg Reward: -137.413391\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.700181\n", + "---------------------------------------\n", + "Total T: 800159 Episode Num: 4158 Reward: -123.495263 Avg Reward: -145.702523\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.115191\n", + "---------------------------------------\n", + "Total T: 805160 Episode Num: 4184 Reward: -229.253924 Avg Reward: -141.408891\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.965773\n", + "---------------------------------------\n", + "Total T: 810161 Episode Num: 4210 Reward: -122.288583 Avg Reward: -124.856103\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.152432\n", + "---------------------------------------\n", + "Total T: 815162 Episode Num: 4236 Reward: -117.957806 Avg Reward: -129.115129\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.487482\n", + "---------------------------------------\n", + "Total T: 818963 Episode Num: 4256 Reward: -120.064003 Avg Reward: -119.836271saving best model....\n", + "\n", + "Total T: 819163 Episode Num: 4257 Reward: -2.738928 Avg Reward: -118.665625saving best model....\n", + "\n", + "Total T: 820163 Episode Num: 4262 Reward: -114.935020 Avg Reward: -120.986460\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.828211\n", + "---------------------------------------\n", + "Total T: 820164 Episode Num: 4263 Reward: -0.003555 Avg Reward: -118.639586saving best model....\n", + "\n", + "Total T: 820364 Episode Num: 4264 Reward: -118.453637 Avg Reward: -118.589568saving best model....\n", + "\n", + "Total T: 820564 Episode Num: 4265 Reward: -1.581891 Avg Reward: -116.310270saving best model....\n", + "\n", + "Total T: 820764 Episode Num: 4266 Reward: -117.613503 Avg Reward: -116.234372saving best model....\n", + "\n", + "Total T: 820964 Episode Num: 4267 Reward: -232.323342 Avg Reward: -116.201013saving best model....\n", + "\n", + "Total T: 821564 Episode Num: 4270 Reward: -119.598486 Avg Reward: -115.154101saving best model....\n", + "\n", + "Total T: 821764 Episode Num: 4271 Reward: -116.931549 Avg Reward: -115.048580saving best model....\n", + "\n", + "Total T: 824164 Episode Num: 4283 Reward: -115.364326 Avg Reward: -117.247176saving best model....\n", + "\n", + "Total T: 825164 Episode Num: 4288 Reward: -118.553090 Avg Reward: -119.601442\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.881616\n", + "---------------------------------------\n", + "Total T: 830165 Episode Num: 4314 Reward: -131.253273 Avg Reward: -129.919240\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.050401\n", + "---------------------------------------\n", + "Total T: 835166 Episode Num: 4340 Reward: -121.335040 Avg Reward: -138.324514\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.588224\n", + "---------------------------------------\n", + "Total T: 840167 Episode Num: 4366 Reward: -240.106536 Avg Reward: -148.561125\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.099207\n", + "---------------------------------------\n", + "Total T: 845168 Episode Num: 4392 Reward: -119.081705 Avg Reward: -141.029944\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.721143\n", + "---------------------------------------\n", + "Total T: 850169 Episode Num: 4418 Reward: -229.648564 Avg Reward: -141.534058\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.599737\n", + "---------------------------------------\n", + "Total T: 855170 Episode Num: 4444 Reward: -125.121074 Avg Reward: -135.300500\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.100205\n", + "---------------------------------------\n", + "Total T: 860171 Episode Num: 4470 Reward: -114.524677 Avg Reward: -137.768224\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.322067\n", + "---------------------------------------\n", + "Total T: 865172 Episode Num: 4496 Reward: -125.015186 Avg Reward: -149.231360\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.575234\n", + "---------------------------------------\n", + "Total T: 870173 Episode Num: 4522 Reward: -222.072342 Avg Reward: -137.924374\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.626692\n", + "---------------------------------------\n", + "Total T: 875174 Episode Num: 4548 Reward: -117.156982 Avg Reward: -141.463162\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.851596\n", + "---------------------------------------\n", + "Total T: 880175 Episode Num: 4574 Reward: -125.667816 Avg Reward: -140.108061\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.793851\n", + "---------------------------------------\n", + "Total T: 885176 Episode Num: 4600 Reward: -118.322867 Avg Reward: -138.530417\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.638834\n", + "---------------------------------------\n", + "Total T: 890177 Episode Num: 4626 Reward: -118.038994 Avg Reward: -149.568908\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -147.235432\n", + "---------------------------------------\n", + "Total T: 895178 Episode Num: 4652 Reward: -225.996633 Avg Reward: -147.625660\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -158.206385\n", + "---------------------------------------\n", + "Total T: 900179 Episode Num: 4678 Reward: -1.501896 Avg Reward: -144.671597\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.592015\n", + "---------------------------------------\n", + "Total T: 905180 Episode Num: 4704 Reward: -117.453095 Avg Reward: -148.051892\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.071365\n", + "---------------------------------------\n", + "Total T: 910181 Episode Num: 4730 Reward: -225.324986 Avg Reward: -139.755337\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.061423\n", + "---------------------------------------\n", + "Total T: 915182 Episode Num: 4756 Reward: -221.773164 Avg Reward: -132.934515\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.526534\n", + "---------------------------------------\n", + "Total T: 920183 Episode Num: 4782 Reward: -124.049409 Avg Reward: -139.583605\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -151.042696\n", + "---------------------------------------\n", + "Total T: 925184 Episode Num: 4808 Reward: -235.793666 Avg Reward: -141.186618\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.711903\n", + "---------------------------------------\n", + "Total T: 930185 Episode Num: 4834 Reward: -219.421111 Avg Reward: -140.544087\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.117899\n", + "---------------------------------------\n", + "Total T: 935186 Episode Num: 4860 Reward: -120.611432 Avg Reward: -139.206966\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.931024\n", + "---------------------------------------\n", + "Total T: 940187 Episode Num: 4886 Reward: -118.256954 Avg Reward: -137.397199\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.955534\n", + "---------------------------------------\n", + "Total T: 945188 Episode Num: 4912 Reward: -1.268452 Avg Reward: -130.310709\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -138.343811\n", + "---------------------------------------\n", + "Total T: 950189 Episode Num: 4938 Reward: -119.253726 Avg Reward: -132.417351\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.548809\n", + "---------------------------------------\n", + "Total T: 955190 Episode Num: 4964 Reward: -117.151148 Avg Reward: -139.712416\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.114430\n", + "---------------------------------------\n", + "Total T: 960191 Episode Num: 4990 Reward: -115.949855 Avg Reward: -142.807804\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.707542\n", + "---------------------------------------\n", + "Total T: 965192 Episode Num: 5016 Reward: -238.995112 Avg Reward: -141.477765\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.901166\n", + "---------------------------------------\n", + "Total T: 970193 Episode Num: 5042 Reward: -311.902881 Avg Reward: -140.617272\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -157.416156\n", + "---------------------------------------\n", + "Total T: 975194 Episode Num: 5068 Reward: -122.772405 Avg Reward: -136.348183\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -159.752974\n", + "---------------------------------------\n", + "Total T: 980195 Episode Num: 5094 Reward: -119.105227 Avg Reward: -137.439344\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -131.691998\n", + "---------------------------------------\n", + "Total T: 985196 Episode Num: 5120 Reward: -237.345289 Avg Reward: -141.529229\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -139.591452\n", + "---------------------------------------\n", + "Total T: 990197 Episode Num: 5146 Reward: -0.995767 Avg Reward: -135.957440\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.230520\n", + "---------------------------------------\n", + "Total T: 995198 Episode Num: 5172 Reward: -116.415607 Avg Reward: -143.247252\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -150.038637\n", + "---------------------------------------\n", + "Total T: 1000199 Episode Num: 5198 Reward: -294.530948 Avg Reward: -142.189497\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -147.002503\n", + "---------------------------------------\n", + "Total T: 1005000 Episode Num: 5223 Reward: -256.679327 Avg Reward: -139.415353\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -150.268536\n", + "---------------------------------------\n", + "Total T: 1010001 Episode Num: 5249 Reward: -118.518673 Avg Reward: -143.660212\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.735483\n", + "---------------------------------------\n", + "Total T: 1015002 Episode Num: 5275 Reward: -273.147232 Avg Reward: -140.836778\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.227233\n", + "---------------------------------------\n", + "Total T: 1020003 Episode Num: 5301 Reward: -119.074925 Avg Reward: -139.697525\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -148.150052\n", + "---------------------------------------\n", + "Total T: 1025004 Episode Num: 5327 Reward: -123.006164 Avg Reward: -137.088181\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.453179\n", + "---------------------------------------\n", + "Total T: 1030005 Episode Num: 5353 Reward: -119.798884 Avg Reward: -137.571677\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -149.357889\n", + "---------------------------------------\n", + "Total T: 1035006 Episode Num: 5379 Reward: -124.218253 Avg Reward: -141.375183\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -155.415427\n", + "---------------------------------------\n", + "Total T: 1040007 Episode Num: 5405 Reward: -242.538489 Avg Reward: -138.803440\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.302818\n", + "---------------------------------------\n", + "Total T: 1045008 Episode Num: 5431 Reward: -228.758196 Avg Reward: -140.390593\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -139.293389\n", + "---------------------------------------\n", + "Total T: 1050009 Episode Num: 5457 Reward: -115.318761 Avg Reward: -148.254895\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.069658\n", + "---------------------------------------\n", + "Total T: 1055010 Episode Num: 5483 Reward: -233.367867 Avg Reward: -138.233515\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.380335\n", + "---------------------------------------\n", + "Total T: 1060011 Episode Num: 5509 Reward: -128.735016 Avg Reward: -132.039084\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.742289\n", + "---------------------------------------\n", + "Total T: 1065012 Episode Num: 5535 Reward: -123.908873 Avg Reward: -138.353806\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.242403\n", + "---------------------------------------\n", + "Total T: 1070013 Episode Num: 5561 Reward: -120.634253 Avg Reward: -128.718699\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.269467\n", + "---------------------------------------\n", + "Total T: 1075014 Episode Num: 5587 Reward: -121.193475 Avg Reward: -127.902842\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.320058\n", + "---------------------------------------\n", + "Total T: 1080015 Episode Num: 5613 Reward: -119.488807 Avg Reward: -133.707293\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.045586\n", + "---------------------------------------\n", + "Total T: 1085016 Episode Num: 5639 Reward: -123.367319 Avg Reward: -131.637888\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.034237\n", + "---------------------------------------\n", + "Total T: 1090017 Episode Num: 5665 Reward: -124.704078 Avg Reward: -131.764867\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.211623\n", + "---------------------------------------\n", + "Total T: 1095018 Episode Num: 5691 Reward: -117.716406 Avg Reward: -132.709009\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -147.395454\n", + "---------------------------------------\n", + "Total T: 1100019 Episode Num: 5717 Reward: -118.080312 Avg Reward: -144.887663\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -133.079177\n", + "---------------------------------------\n", + "Total T: 1105020 Episode Num: 5743 Reward: -117.727859 Avg Reward: -158.029989\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.986276\n", + "---------------------------------------\n", + "Total T: 1110021 Episode Num: 5769 Reward: -0.255192 Avg Reward: -158.621798\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.659802\n", + "---------------------------------------\n", + "Total T: 1115022 Episode Num: 5795 Reward: -126.660524 Avg Reward: -161.489052\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.053270\n", + "---------------------------------------\n", + "Total T: 1120023 Episode Num: 5821 Reward: -122.786710 Avg Reward: -129.562591\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.579528\n", + "---------------------------------------\n", + "Total T: 1125024 Episode Num: 5847 Reward: -119.323992 Avg Reward: -136.755981\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.547305\n", + "---------------------------------------\n", + "Total T: 1130025 Episode Num: 5873 Reward: -314.623404 Avg Reward: -142.900173\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.822515\n", + "---------------------------------------\n", + "Total T: 1135026 Episode Num: 5899 Reward: -234.281437 Avg Reward: -139.381844\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.110992\n", + "---------------------------------------\n", + "Total T: 1140027 Episode Num: 5925 Reward: -118.655738 Avg Reward: -140.318123\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -127.351684\n", + "---------------------------------------\n", + "Total T: 1145028 Episode Num: 5951 Reward: -118.327396 Avg Reward: -148.000376\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -127.483968\n", + "---------------------------------------\n", + "Total T: 1150029 Episode Num: 5977 Reward: -333.131819 Avg Reward: -142.705937\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.373243\n", + "---------------------------------------\n", + "Total T: 1155030 Episode Num: 6003 Reward: -233.250450 Avg Reward: -150.978314\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.509913\n", + "---------------------------------------\n", + "Total T: 1160031 Episode Num: 6029 Reward: -124.022789 Avg Reward: -147.272346\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -144.891718\n", + "---------------------------------------\n", + "Total T: 1165032 Episode Num: 6055 Reward: -0.210000 Avg Reward: -139.516759\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -132.469518\n", + "---------------------------------------\n", + "Total T: 1170033 Episode Num: 6081 Reward: -119.489986 Avg Reward: -139.537724\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.090899\n", + "---------------------------------------\n", + "Total T: 1175034 Episode Num: 6107 Reward: -127.790835 Avg Reward: -135.008936\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -139.292568\n", + "---------------------------------------\n", + "Total T: 1180035 Episode Num: 6133 Reward: -125.048496 Avg Reward: -132.239173\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -139.380858\n", + "---------------------------------------\n", + "Total T: 1185036 Episode Num: 6159 Reward: -123.703483 Avg Reward: -134.332520\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -139.372279\n", + "---------------------------------------\n", + "Total T: 1190037 Episode Num: 6185 Reward: -121.080102 Avg Reward: -137.113279\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.528016\n", + "---------------------------------------\n", + "Total T: 1195038 Episode Num: 6211 Reward: -2.891134 Avg Reward: -133.591208\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.981073\n", + "---------------------------------------\n", + "Total T: 1200039 Episode Num: 6237 Reward: -246.264437 Avg Reward: -142.020023\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.302387\n", + "---------------------------------------\n", + "Total T: 1205040 Episode Num: 6263 Reward: -232.388703 Avg Reward: -137.107420\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.221515\n", + "---------------------------------------\n", + "Total T: 1210041 Episode Num: 6289 Reward: -1.268200 Avg Reward: -130.311769\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.067026\n", + "---------------------------------------\n", + "Total T: 1215042 Episode Num: 6315 Reward: -2.147177 Avg Reward: -133.236337\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -131.755883\n", + "---------------------------------------\n", + "Total T: 1220043 Episode Num: 6341 Reward: -227.169642 Avg Reward: -134.442929\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.642269\n", + "---------------------------------------\n", + "Total T: 1225044 Episode Num: 6367 Reward: -119.643504 Avg Reward: -145.220307\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -156.984704\n", + "---------------------------------------\n", + "Total T: 1230045 Episode Num: 6393 Reward: -117.489662 Avg Reward: -148.212140\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.701055\n", + "---------------------------------------\n", + "Total T: 1235046 Episode Num: 6419 Reward: -125.987053 Avg Reward: -147.134115\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.121798\n", + "---------------------------------------\n", + "Total T: 1240047 Episode Num: 6445 Reward: -325.159137 Avg Reward: -138.041477\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -141.817139\n", + "---------------------------------------\n", + "Total T: 1245048 Episode Num: 6471 Reward: -287.998349 Avg Reward: -134.274280\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -133.180574\n", + "---------------------------------------\n", + "Total T: 1250049 Episode Num: 6497 Reward: -122.526683 Avg Reward: -138.501126\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -162.761224\n", + "---------------------------------------\n", + "Total T: 1255050 Episode Num: 6523 Reward: -114.903113 Avg Reward: -143.000298\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -193.901257\n", + "---------------------------------------\n", + "Total T: 1260051 Episode Num: 6549 Reward: -223.260529 Avg Reward: -161.790052\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -152.443888\n", + "---------------------------------------\n", + "Total T: 1265052 Episode Num: 6575 Reward: -121.093105 Avg Reward: -162.146832\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -153.431744\n", + "---------------------------------------\n", + "Total T: 1270053 Episode Num: 6601 Reward: -1.898893 Avg Reward: -160.646359\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -145.818628\n", + "---------------------------------------\n", + "Total T: 1275054 Episode Num: 6627 Reward: -127.616881 Avg Reward: -145.079070\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.177592\n", + "---------------------------------------\n", + "Total T: 1280055 Episode Num: 6653 Reward: -119.759752 Avg Reward: -141.040202\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -137.720431\n", + "---------------------------------------\n", + "Total T: 1285056 Episode Num: 6679 Reward: -120.808707 Avg Reward: -137.350840\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.563501\n", + "---------------------------------------\n", + "Total T: 1290057 Episode Num: 6705 Reward: -123.133641 Avg Reward: -136.781144\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -138.522657\n", + "---------------------------------------\n", + "Total T: 1295058 Episode Num: 6731 Reward: -124.124038 Avg Reward: -137.900261\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -139.806148\n", + "---------------------------------------\n", + "Total T: 1300059 Episode Num: 6757 Reward: -119.282404 Avg Reward: -141.070187\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -130.553016\n", + "---------------------------------------\n", + "Total T: 1305060 Episode Num: 6783 Reward: -114.992490 Avg Reward: -140.069090\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -146.640130\n", + "---------------------------------------\n", + "Total T: 1310061 Episode Num: 6809 Reward: -225.120112 Avg Reward: -138.672216\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -155.273360\n", + "---------------------------------------\n", + "Total T: 1315062 Episode Num: 6835 Reward: -9.016827 Avg Reward: -140.382467\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -156.811438\n", + "---------------------------------------\n", + "Total T: 1320063 Episode Num: 6861 Reward: -118.359733 Avg Reward: -140.861827\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -143.309605\n", + "---------------------------------------\n", + "Total T: 1325064 Episode Num: 6887 Reward: -124.126903 Avg Reward: -136.280656\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -136.136256\n", + "---------------------------------------\n", + "Total T: 1330065 Episode Num: 6913 Reward: -5.758314 Avg Reward: -136.240597\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -134.146833\n", + "---------------------------------------\n", + "Total T: 1335066 Episode Num: 6939 Reward: -116.378241 Avg Reward: -130.176583\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -135.244723\n", + "---------------------------------------\n", + "Total T: 1340067 Episode Num: 6965 Reward: -118.381882 Avg Reward: -130.194604\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -140.871424\n", + "---------------------------------------\n", + "Total T: 1345068 Episode Num: 6991 Reward: -3.528429 Avg Reward: -136.263096\n", + "---------------------------------------\n", + "Evaluation over 100 episodes: -142.284551\n", + "---------------------------------------\n", + "Total T: 1350069 Episode Num: 7017 Reward: -3.320812 Avg Reward: -142.315446\n", + 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0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "oxQ4-QrqnpWk", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Temporal Difference/Temporal_Difference_SARSA_MAX_Q_Learning for Grid World.ipynb b/Temporal Difference/Temporal_Difference_SARSA_MAX_Q_Learning for Grid World.ipynb new file mode 100644 index 0000000..9d3f548 --- /dev/null +++ b/Temporal Difference/Temporal_Difference_SARSA_MAX_Q_Learning for Grid World.ipynb @@ -0,0 +1,1159 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "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": "3.5.6" + }, + "colab": { + "name": "Temporal Difference - SARSA MAX - Q Learning.ipynb", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F1sa_QJ55-fu", + "colab_type": "text" + }, + "source": [ + "# **Project 4 - Q Learning**\n", + "\n", + "For this project, you will be tasked with both implementing and explaining key components of the Q-learning algorithm.\n", + "\n", + "All the code deliverables has to be provided within this notebook.\n", + "\n", + "# 1 - Packages\n", + "Let's first import all the packages that you will need during this assignment.\n", + "\n", + "* \n", + "[numpy](https://numpy.org/) - is the main package for scientific computing with Python\n", + "*\n", + "[matplotlib](https://matplotlib.org/) - is a plotting library\n", + "*\n", + "[gym](https://gym.openai.com/docs/) - Gym is a toolkit for developing and comparing reinforcement learning algorithms.\n", + "*\n", + "[gym.spaces](http://gym.openai.com/docs/) - Every environment comes with an action_space and an observation_space. These attributes are of type Space, and they describe the format of valid actions and observations.\n", + "*\n", + "[time](https://docs.python.org/3/library/time.html?highlight=time#module-time) - will be used to track how much time each computation takes\n", + "*\n", + "[copy](https://docs.python.org/3/library/copy.html) - A copy is sometimes needed so one can change one copy without changing the other.\n", + "*\n", + "[Threading](https://docs.python.org/3/library/threading.html) - This module constructs higher-level threading interfaces on top of the lower level thread module.\n", + "*\n", + "[Collections](https://docs.python.org/2/library/collections.html) - This module implements specialized container datatypes providing alternatives to Python’s general purpose built-in containers, dict, list, set, and tuple.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "M_PLd07ie8k1", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#######################################################################\n", + "# Authors:\n", + "# Nathan Margaglio (nathanmargaglio@gmail.com) \n", + "# Mihir Hemant Chauhan (mihirhem@buffalo.edu) \n", + "# Qian Cheng (qcheng2@buffalo.edu) \n", + "#######################################################################\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import gym\n", + "import gym.spaces\n", + "import time\n", + "import copy\n", + "import threading\n", + "import time\n", + "import collections\n", + "import math\n", + "import random\n", + "import sys\n", + "from collections import deque, defaultdict" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6AsLthEre8kw", + "colab_type": "text" + }, + "source": [ + "## Basic Environment\n", + "Here we define our grid-world environment. No need to make any changes." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "1KZhxOunpbNp", + "colab": {} + }, + "source": [ + "class GridEnvironment(gym.Env):\n", + " metadata = { 'render.modes': ['human'] }\n", + " \n", + " def __init__(self, normalize=False, size=4):\n", + " self.observation_space = gym.spaces.Box(0, size, (size,))\n", + " self.action_space = gym.spaces.Discrete(4)\n", + " self.max_timesteps = size*2 + 1\n", + " self.normalize = normalize\n", + " self.size = size\n", + "\n", + " # Generate State Transition Table\n", + " self.transition_matrix = []\n", + " for x in range(size + 1):\n", + " state_x = []\n", + " for y in range(size + 1):\n", + " state_y = []\n", + " for a in range(4):\n", + " one_hot = np.zeros(4)\n", + " one_hot[a] = 1\n", + " state_y.append(one_hot)\n", + " state_x.append(state_y)\n", + " self.transition_matrix.append(state_x)\n", + " \n", + " def transition_func(self, x, y, action, return_probs=False):\n", + " probs = self.transition_matrix[x][y][action]\n", + " if return_probs:\n", + " return probs\n", + " else:\n", + " return np.random.choice(len(probs), p=probs)\n", + "\n", + " def _get_distance(self, x, y):\n", + " return abs(x[0] - y[0]) + abs(x[1] - y[1])\n", + " \n", + " def reset(self):\n", + " self.timestep = 0\n", + " self.agent_pos = [0, 0]\n", + " self.goal_pos = [self.size, self.size]\n", + " self.state = np.zeros((self.size + 1, self.size + 1))\n", + " self.state[tuple(self.agent_pos)] = 1\n", + " self.state[tuple(self.goal_pos)] = 0.5\n", + " self.prev_distance = self._get_distance(self.agent_pos, self.goal_pos)\n", + " return np.array(self.agent_pos)/1.\n", + " \n", + " def step(self, action):\n", + " action_taken = self.transition_func(self.agent_pos[0], self.agent_pos[1], action)\n", + " self.state = np.random.choice(self.observation_space.shape[0])\n", + " if action_taken == 0:\n", + " self.agent_pos[0] += 1\n", + " if action_taken == 1:\n", + " self.agent_pos[0] -= 1\n", + " if action_taken == 2:\n", + " self.agent_pos[1] += 1\n", + " if action_taken == 3:\n", + " self.agent_pos[1] -= 1\n", + " \n", + " self.agent_pos = np.clip(self.agent_pos, 0, self.size)\n", + " self.state = np.zeros((self.size + 1, self.size + 1))\n", + " self.state[tuple(self.agent_pos)] = 1\n", + " self.state[tuple(self.goal_pos)] = 0.5\n", + " \n", + " current_distance = self._get_distance(self.agent_pos, self.goal_pos)\n", + " if current_distance < self.prev_distance:\n", + " reward = 1\n", + " elif current_distance > self.prev_distance:\n", + " reward = -1\n", + " else:\n", + " reward = -1\n", + " self.prev_distance = current_distance\n", + " \n", + " self.timestep += 1\n", + " if self.timestep >= self.max_timesteps or current_distance == 0:\n", + " done = True\n", + " else:\n", + " done = False\n", + " info = {}\n", + " \n", + " obs = self.agent_pos\n", + " if self.normalize:\n", + " obs = obs/self.size\n", + " return obs, reward, done, info\n", + " \n", + " def render(self, mode='human'):\n", + " plt.imshow(self.state)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "hy2YHvlNe8lW", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 264 + }, + "outputId": "3b885ced-6cb0-4fec-b421-f74d6babaf27" + }, + "source": [ + "env = GridEnvironment()\n", + "obs = env.reset()\n", + "env.render()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bc3VOQuJF1el", + "colab_type": "text" + }, + "source": [ + "## Random Agent\n", + "This runs the environment with a random agent that just takes random actions. Neither does he learn, nor remember anything. Try to run it!" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "z9XqqwQtFr8k", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class RandomAgent:\n", + " def __init__(self, env):\n", + " self.env = env\n", + " self.observation_space = env.observation_space\n", + " self.action_space = env.action_space\n", + "\n", + " def policy(self, observation):\n", + " return np.random.choice(self.action_space.n)\n", + " \n", + " def step(self, observation, verbose=False):\n", + " return self.policy(observation)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0S5tBBqfF-s3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "9c020505-d97a-4557-eadf-8fb2bbc88d2c" + }, + "source": [ + "env = GridEnvironment(normalize=True)\n", + "agent = RandomAgent(env)\n", + "\n", + "obs = env.reset()\n", + "done = False\n", + "agent.epsilon = 0\n", + "env.render()\n", + "plt.show()\n", + "\n", + "while not done:\n", + " print(obs)\n", + " action = agent.step(obs, verbose=True)\n", + " obs, reward, done, info = env.step(action)\n", + " env.render()\n", + " plt.show()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JCKzZh7u0zFJ", + "colab_type": "text" + }, + "source": [ + "## Heuristic Agent\n", + "This runs the environment with a heuristic agent. No need to make any changes. Try to run it!" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aVGLhrTG0yQp", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class HeuristicAgent:\n", + " def __init__(self, env):\n", + " self.env = env\n", + " self.observation_space = env.observation_space\n", + " self.action_space = env.action_space\n", + "\n", + " def policy(self, observation):\n", + " # 0 - down\n", + " # 1 - up\n", + " # 2 - right\n", + " # 3 - left\n", + " if (observation[0] < 1.):\n", + " return 0\n", + " if (observation[1] < 1.):\n", + " return 2\n", + " return 0\n", + " \n", + " def step(self, observation, verbose=False):\n", + " if verbose:\n", + " print(observation)\n", + " return self.policy(observation)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "kZGSRSzo07v-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "106ec0fe-efc0-40ff-9747-64e30f1212be" + }, + "source": [ + "env = GridEnvironment(normalize=True)\n", + "agent = HeuristicAgent(env)\n", + "\n", + "obs = env.reset()\n", + "done = False\n", + "agent.epsilon = 0\n", + "env.render()\n", + "plt.show()\n", + "\n", + "while not done:\n", + " action = agent.step(obs, verbose=True)\n", + " obs, reward, done, info = env.step(action)\n", + " env.render()\n", + " plt.show()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oSjsMD6Ze8ld", + "colab_type": "text" + }, + "source": [ + "## Tabular Q-Learning\n", + "This is where you need to define policy and update Q tables.\n", + "For policy.\n", + "\n", + "* \n", + "[np.argmax](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html) - Returns the indices of the maximum values along an axis.\n", + "\n", + "### Epsilon\n", + "\n", + "Our agent will randomly select its action at first by a certain percentage, called ‘exploration rate’ or ‘epsilon’. This is because at first, it is better for the agent to try all kinds of things before it starts to see the patterns. When it is not deciding the action randomly, the agent will predict the reward value based on the current state and pick the action that will give the highest reward. We want our agent to decrease the number of random action, as it goes, so we indroduce an exponential-decay epsilon, that eventually will allow our agent to explore the evironment. \\\\\n", + "\n", + "\n", + "\n", + "### Task 1: Implement policy function.
\n", + "**Instructions:**\n", + "- Our agent will randomly select its action at first by a certain percentage, called ‘exploration rate’ or ‘epsilon’. This is because at first, it is better for the agent to try all kinds of things before it starts to see the patterns. Select a random uniform number. If it's less than epsilon, return the random choice action space.\n", + "- When it is not deciding the action randomly, the agent will predict the reward value based on the current state and pick the action that will give the highest reward. \n", + "\\begin{align} \\notag\n", + "\\pi\\left(s_{t}\\right)=\\underset{a \\in A}{\\operatorname{argmax}} Q_{\\theta}\\left(s_{t}, a\\right)\n", + "\\end{align} \n", + "- Return the policy\n", + "- Please note, that the name for all the variables should start with self, thus
\n", + "\n", + "epsilon $\\rightarrow$ self.epsilon
\n", + "action_space $\\rightarrow$ self.action_space\n", + "\n", + "### Task 2: Update Q-table
\n", + "**Instructions:**\n", + " \\begin{align} \\notag\n", + " Q^{n e w}\\left(s_{t}, a_{t}\\right) \\leftarrow(1-\\alpha) \\cdot \\underbrace{Q\\left(s_{t}, a_{t}\\right)}_{\\text {old value }}+\\underbrace{\\alpha}_{\\text {learning rate }} \\cdot \\overbrace{(\\underbrace{r_{t}}_{\\text {reward }} + \\underbrace{\\gamma}_{\\text {discount factor }} \\underbrace{\\max _{a} Q\\left(s_{t+1}, a\\right)}_{a})}^{\\text {learned value }}\n", + " \\end{align} " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bg3eSln8A-kI", + "colab_type": "text" + }, + "source": [ + "### Training\n", + "### Environment\n", + "First, we initialize our environment. The environment, loosely structured like [OpenAI's Gym Environments](https://gym.openai.com/), has three main methods: `reset`, `step` and `render`. You'll only need `reset` and `step` here.\n", + "\n", + "- When we call **reset**, we initialize the environment with a fresh episode. This allows us to effectively run through episodes (only needing to call reset at the beginning of an episode), but, more importantly, `reset()` returns the environment's initial state.\n", + "\n", + "- The **step** method accepts an action as a parameter (which, for this example, is an integer in [0, 3]), processes the action, and returns the new state, the reward for performing the action, and a boolean indicating if the run is over.\n", + "\n", + "### Agent\n", + "When we initialize the agent, we must pass both a `environment` into QLearningAgent function.\n", + "### Task 3: Implement the training algorithm
\n", + "**Instructions:**\n", + "- After initialization, pass the initial state to obs. Then check if it's already done. If done = False, you'll keep going. While it's not done, you'll need to update `state`, `action`,`reward` and `next_state`. You can get action by `step` the current state on agent. Use `copy` to record the current state. `step` the current action on environment to return the new state, the reward for performing the action, a boolean indicating if the run is over and some other information. Add the new reward on the total rewards. Use `copy` to save the new state returned by `step`. Update the `state`, `action`, `reward`, `next_state` of agent." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l88irSuqe8lf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class QLearningAgent:\n", + " def __init__(self, env, epsilon=1.0, lr=0.1, gamma=0.9):\n", + " self.env = env\n", + " self.observation_space = env.observation_space\n", + " self.action_space = env.action_space\n", + " q_table_dim = env.observation_space.shape[0] + 1\n", + " self.q_table = np.zeros((q_table_dim, q_table_dim, env.action_space.n))\n", + " #self.q_table = defaultdict(lambda: np.zeros(self.action_space.n))\n", + " self.epsilon = epsilon\n", + " self.lr = lr\n", + " self.gamma = gamma\n", + "\n", + " def policy(self, observation):\n", + " # Code for policy (Task 1) (30 points)\n", + " observation = observation.astype(int)\n", + " if random.random()>self.epsilon:\n", + " return np.argmax(self.q_table[observation[0]][observation[1]])\n", + " else:\n", + " return np.random.choice(env.action_space.n) \n", + " \n", + " def step(self, observation):\n", + " return self.policy(observation)\n", + " \n", + " def update(self, state, action, reward, next_state):\n", + " state = state.astype(int)\n", + " next_state = next_state.astype(int)\n", + " # Code for updating Q Table (Task 2) (20 points)\n", + " current = self.q_table[state[0]][state[1]][action]\n", + " Qsa_next = np.max(self.q_table[next_state[0]][next_state[1]]) if next_state is not None else 0\n", + " target = (reward + (self.gamma*Qsa_next))\n", + " new_value = current + self.lr * (target - current)\n", + " return new_value\n", + " \n", + " def set_epsilon(self, epsilon):\n", + " self.epsilon = epsilon" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "2SDbl2Kue8lk", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ba1cabd7-0208-42ca-e96d-c2acc3b85662" + }, + "source": [ + "env = GridEnvironment() # note: we do not normalize\n", + "agent = QLearningAgent(env)\n", + "episodes = 1000 # number of games we want the agent to play\n", + "delta_epsilon = agent.epsilon/episodes\n", + "\n", + "# total_rewards = []\n", + "epsilons = [agent.epsilon]\n", + "\n", + "nA = env.action_space.n\n", + "plot_every = 100\n", + "tmp_score = deque(maxlen=plot_every)\n", + "avg_score = deque(maxlen=episodes)\n", + "best_avg_reward = -math.inf\n", + "best_rewards = []\n", + "\n", + "# Training Process (Task 3) (20 points)\n", + "for i_episode in range(1,episodes):\n", + " state = env.reset()\n", + " score = 0\n", + " while True:\n", + " state = state.astype(int)\n", + " action = agent.policy(state)\n", + " next_state, reward, done, info = env.step(action)\n", + " score += reward\n", + " next_state = next_state.astype(int)\n", + "# print(state, action, next_state, state.shape)\n", + " agent.q_table[state[0]][state[1]][action] = agent.update(state,action,reward,next_state)\n", + " state = next_state\n", + " if done:\n", + " tmp_score.append(score)\n", + " break\n", + "# if(i_episode%100)==0:\n", + "# avg_score.append(np.mean(tmp_score))\n", + " if (i_episode >= 100):\n", + " avg_reward = np.mean(tmp_score)\n", + " avg_score.append(avg_reward)\n", + " if avg_reward > best_avg_reward:\n", + " best_avg_reward = avg_reward\n", + " best_rewards.append(best_avg_reward)\n", + " agent.set_epsilon(agent.epsilon*delta_epsilon)\n", + " epsilons.append(agent.epsilon)\n", + " print(\"\\rEpisode {}/{} || Best average reward {}\".format(i_episode, episodes, best_avg_reward), end=\"\")\n", + " sys.stdout.flush()\n", + " \n", + " if i_episode == episodes: print('\\n')" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Episode 999/1000 || Best average reward 8.0" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AHDJV4ZAE2l8", + "colab_type": "text" + }, + "source": [ + "#### Visualize $\\epsilon$\n", + "Plot our value of $\\epsilon$ over each episode" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "q137fw4je8ln", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + }, + "outputId": "174b11d5-697c-497b-fd9d-ab5cab494833" + }, + "source": [ + "plt.xlabel('Episode')\n", + "plt.ylabel('$\\epsilon$')\n", + "plt.plot(epsilons)" + ], + "execution_count": 35, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "30Yr74K2e8lr", + "colab_type": "text" + }, + "source": [ + "#### Visualize Rewards\n", + "Plot total_rewards per episode. We apply a rolling mean of window $10$ to visualize easier." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Raqojkywe8ls", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + }, + "outputId": "f620eaf2-51a0-4b0c-8401-7a34c20c4ccc" + }, + "source": [ + "window = 10\n", + "plt.xlabel('Episode')\n", + "plt.ylabel('Total Reward (SMA 10)')\n", + "plt.plot([np.mean(list(avg_score)[tr:tr+window]) for tr in range(window, len(avg_score))])" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "M6wai0MeiGVG", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "0cfc56a6-b31f-4b9d-de26-f717375be202" + }, + "source": [ + "env = GridEnvironment()\n", + "\n", + "obs = env.reset()\n", + "done = False\n", + "agent.epsilon = 0\n", + "env.render()\n", + "plt.show()\n", + "\n", + "while not done:\n", + " action = agent.step(obs)\n", + " obs, reward, done, info = env.step(action)\n", + " env.render()\n", + " plt.show()" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file diff --git a/python/README.md b/python/README.md new file mode 100644 index 0000000..542d42f --- /dev/null +++ b/python/README.md @@ -0,0 +1,3 @@ +# Dependencies +This is an amended version of the python/ folder from the [ML-Agents repository](https://github.com/Unity-Technologies/ml-agents). +It has been edited to include a few additional pip packages and PyTorch tutorials needed for the Deep Reinforcement Learning Nanodegree program. diff --git a/python/pytorch/Convolutional Neural Networks/CNNs_in_PyTorch.ipynb b/python/pytorch/Convolutional Neural Networks/CNNs_in_PyTorch.ipynb new file mode 100644 index 0000000..1f86483 --- /dev/null +++ b/python/pytorch/Convolutional Neural Networks/CNNs_in_PyTorch.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "gQEhrWODGxYt" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import torch\n", + "from torchvision import datasets, transforms\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "s3gVLCsfHGms" + }, + "outputs": [], + "source": [ + "import sys\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "_QOKO6R5HfR8" + }, + "outputs": [], + "source": [ + "transforms = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "5BTbBSIdHSXk", + "outputId": "f7bae1b2-45fa-43e9-f7e6-1f6fc4f52978" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "train_data = datasets.CIFAR10(\"Cifar/\",train=True,transform=transforms,download=True)\n", + "test_data = datasets.CIFAR10(\"Cifar/\",train=False,transform=transforms,download=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "nV5VjG0oH1Rk" + }, + "outputs": [], + "source": [ + "train_loader = torch.utils.data.DataLoader(train_data,batch_size=64,num_workers=0,shuffle=True)\n", + "test_loader = torch.utils.data.DataLoader(test_data,batch_size=20,num_workers=0,shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "byZt7yo-IYXE", + "outputId": "5eb81938-6490-423f-f629-0e7df5a272a9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 42, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RlycwcmGI7U8" + }, + "outputs": [], + "source": [ + "images,labels = next(iter(train_loader))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "XVMISqBOJzIM", + "outputId": "0538f536-c3c9-46d1-99b8-b193bb7b3274" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([64, 3, 32, 32])" + ] + }, + "execution_count": 44, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "images.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "colab_type": "code", + "id": "5DxNqhCAIvuM", + "outputId": "ee22515d-5fd3-4234-dd1c-302f686836db" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (fc1): Linear(in_features=1024, out_features=500, bias=True)\n", + " (fc2): Linear(in_features=500, out_features=10, bias=True)\n", + " (dropout): Dropout(p=0.25)\n", + ")\n" + ] + } + ], + "source": [ + "class Net(nn.Module):\n", + " def __init__(self):\n", + " super(Net, self).__init__()\n", + " # convolutional layer (sees 32x32x3 image tensor)\n", + " self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n", + " # convolutional layer (sees 16x16x16 tensor)\n", + " self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n", + " # convolutional layer (sees 8x8x32 tensor)\n", + " self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n", + " # max pooling layer\n", + " self.pool = nn.MaxPool2d(2, 2)\n", + " # linear layer (64 * 4 * 4 -> 500)\n", + " self.fc1 = nn.Linear(64 * 4 * 4, 500)\n", + " # linear layer (500 -> 10)\n", + " self.fc2 = nn.Linear(500, 10)\n", + " # dropout layer (p=0.25)\n", + " self.dropout = nn.Dropout(0.25)\n", + "\n", + " def forward(self, x):\n", + " # add sequence of convolutional and max pooling layers\n", + " x = self.pool(F.relu(self.conv1(x)))\n", + " x = self.pool(F.relu(self.conv2(x)))\n", + " x = self.pool(F.relu(self.conv3(x)))\n", + " # flatten image input\n", + " x = x.view(-1, 64 * 4 * 4)\n", + " # add dropout layer\n", + " x = self.dropout(x)\n", + " # add 1st hidden layer, with relu activation function\n", + " x = F.relu(self.fc1(x))\n", + " # add dropout layer\n", + " x = self.dropout(x)\n", + " # add 2nd hidden layer, with relu activation function\n", + " x = self.fc2(x)\n", + " return x\n", + "\n", + "# create a complete CNN\n", + "model = Net()\n", + "print(model)\n", + "\n", + "# move tensors to GPU if CUDA is available\n", + "if train_on_gpu:\n", + " model.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ZDjotf0uTUuh" + }, + "outputs": [], + "source": [ + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "# specify optimizer\n", + "optimizer = optim.Adam(model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + }, + "colab_type": "code", + "id": "30Q8BZMbN4DM", + "outputId": "555d8a8c-5fea-4e96-cb30-81170b548a48" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0/30 \t Training Loss: 1.017419\n", + "Epoch 1/30 \t Training Loss: 0.993548\n", + "Epoch 2/30 \t Training Loss: 0.970909\n", + "Epoch 3/30 \t Training Loss: 0.944078\n", + "Epoch 4/30 \t Training Loss: 0.926361\n", + "Epoch 5/30 \t Training Loss: 0.902615\n", + "Epoch 6/30 \t Training Loss: 0.885001\n", + "Epoch 7/30 \t Training Loss: 0.866697\n", + "Epoch 8/30 \t Training Loss: 0.846309\n", + "Epoch 9/30 \t Training Loss: 0.827201\n", + "Epoch 10/30 \t Training Loss: 0.813305\n", + "Epoch 11/30 \t Training Loss: 0.796012\n", + "Epoch 12/30 \t Training Loss: 0.777327\n", + "Epoch 13/30 \t Training Loss: 0.762648\n", + "Epoch 14/30 \t Training Loss: 0.746774\n", + "Epoch 15/30 \t Training Loss: 0.729428\n", + "Epoch 16/30 \t Training Loss: 0.718930\n", + "Epoch 17/30 \t Training Loss: 0.702704\n", + "Epoch 18/30 \t Training Loss: 0.686690\n", + "Epoch 19/30 \t Training Loss: 0.678095\n", + "Epoch 20/30 \t Training Loss: 0.663241\n", + "Epoch 21/30 \t Training Loss: 0.648803\n", + "Epoch 22/30 \t Training Loss: 0.638151\n", + "Epoch 23/30 \t Training Loss: 0.625551\n", + "Epoch 24/30 \t Training Loss: 0.613599\n", + "Epoch 25/30 \t Training Loss: 0.601236\n", + "Epoch 26/30 \t Training Loss: 0.591148\n", + "Epoch 27/30 \t Training Loss: 0.573198\n", + "Epoch 28/30 \t Training Loss: 0.568902\n", + "Epoch 29/30 \t Training Loss: 0.553698\n" + ] + } + ], + "source": [ + "n_epochs = 30\n", + "train_on_gpu = torch.cuda.is_available()\n", + "if train_on_gpu:\n", + " model.cuda()\n", + "\n", + "for idx in range(n_epochs):\n", + " train_loss = 0.0\n", + " test_loss = 0.0\n", + " for data, labels in train_loader:\n", + " if train_on_gpu:\n", + " data, labels = data.cuda(), labels.cuda()\n", + " optimizer.zero_grad()\n", + " output = model(data)\n", + " loss= criterion(output,labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " train_loss+=loss.item()*data.size(0)\n", + " train_loss = train_loss/len(train_loader.dataset)\n", + " print(\"\\rEpoch {}/{} \\t Training Loss: {:.6f}\".format(idx,n_epochs,train_loss))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mPb_WtZwZUy_" + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rl_PBx3hZbs2" + }, + "outputs": [], + "source": [ + "batch_size=20\n", + "classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n", + " 'dog', 'frog', 'horse', 'ship', 'truck']" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "5U-T924YVIMX", + "outputId": "4761922d-dad6-4024-a490-fc9d571b7143" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Loss: 0.730472\n", + "\n", + "Test Accuracy of airplane: 84% (843/1000)\n", + "Test Accuracy of automobile: 88% (883/1000)\n", + "Test Accuracy of bird: 71% (718/1000)\n", + "Test Accuracy of cat: 55% (556/1000)\n", + "Test Accuracy of deer: 60% (600/1000)\n", + "Test Accuracy of dog: 67% (671/1000)\n", + "Test Accuracy of frog: 77% (770/1000)\n", + "Test Accuracy of horse: 84% (844/1000)\n", + "Test Accuracy of ship: 79% (790/1000)\n", + "Test Accuracy of truck: 77% (775/1000)\n", + "\n", + "Test Accuracy (Overall): 74% (7450/10000)\n" + ] + } + ], + "source": [ + "test_loss = 0.0\n", + "class_correct = list(0. for i in range(10))\n", + "class_total = list(0. for i in range(10))\n", + "\n", + "model.eval()\n", + "for data, target in test_loader:\n", + " if train_on_gpu:\n", + " data, target = data.cuda(), target.cuda()\n", + " output = model(data)\n", + " loss = criterion(output, target)\n", + " test_loss += loss.item()*data.size(0)\n", + " \n", + " _, pred = torch.max(output, 1) \n", + " \n", + " correct_tensor = pred.eq(target.data.view_as(pred))\n", + " correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n", + " \n", + " for i in range(batch_size):\n", + " label = target.data[i]\n", + " class_correct[label] += correct[i].item()\n", + " class_total[label] += 1\n", + "\n", + "test_loss = test_loss/len(test_loader.dataset)\n", + "print('Test Loss: {:.6f}\\n'.format(test_loss))\n", + "\n", + "for i in range(10):\n", + " if class_total[i] > 0:\n", + " print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n", + " classes[i], 100 * class_correct[i] / class_total[i],\n", + " np.sum(class_correct[i]), np.sum(class_total[i])))\n", + " else:\n", + " print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n", + "\n", + "print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n", + " 100. * np.sum(class_correct) / np.sum(class_total),\n", + " np.sum(class_correct), np.sum(class_total)))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "CNNs in PyTorch.ipynb", + "provenance": [], + "version": "0.3.2" + }, + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/python/pytorch/Convolutional Neural Networks/Edge Detection.ipynb b/python/pytorch/Convolutional Neural Networks/Edge Detection.ipynb new file mode 100644 index 0000000..f115434 --- /dev/null +++ b/python/pytorch/Convolutional Neural Networks/Edge Detection.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import os\n", + "import cv2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.ipynb_checkpoints',\n", + " 'Edge Detection.ipynb',\n", + " 'FashionMNIST',\n", + " 'Images (4).jpg',\n", + " 'MLP for MNIST.ipynb',\n", + " 'Mnist',\n", + " 'model.pt']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.listdir()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "img=plt.imread('Images (4).jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.xticks([])\n", + "plt.yticks([])\n", + "plt.imshow(img)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.xticks([])\n", + "plt.yticks([])\n", + "plt.imshow(gray,cmap='gray')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "sobel_y = np.array([[0,-1,0],\n", + " [-1,4,-1],\n", + " [0,-1,0]])\n", + "\n", + "filtererd_image = cv2.filter2D(gray,-1,sobel_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(filtererd_image,cmap='gray',interpolation='nearest')\n", + "plt.xticks([])\n", + "plt.yticks([])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "#Vizualizing 100 random filters" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.random.randint(low=-10,high=10,size=(200,4,4))\n", + "a = []\n", + "for i in range(x.shape[0]):\n", + " a.append(cv2.filter2D(gray,-1,x[i]))\n", + "a = np.squeeze(np.array(a))\n", + "plt.figure(figsize=(100,100))\n", + "for i in range(a.shape[0]):\n", + " plt.subplot(50,40,i+1)\n", + " plt.imshow(a[i],cmap='gray',interpolation='nearest')\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + "plt.show()" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Convolutional Neural 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+{ + "cells": [ + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "import numpy as np\n", + "import torch\n", + "from torchvision import datasets, transforms\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data.sampler import SubsetRandomSampler\n", + "import sys\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "transforms = transforms.ToTensor()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "train_data = datasets.MNIST(\"FashionMNIST/\",train=True,download=True,transform=transforms)\n", + "test_data = datasets.MNIST(\"FashionMNIST/\",train=False,download=True,transform=transforms)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "val_size = 0.2\n", + "num_workers = 0\n", + "batch_size = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "num_samples = len(train_data)\n", + "idx = list(range(num_samples))\n", + "np.random.shuffle(idx)\n", + "samples = int(np.floor(num_samples*val_size))\n", + "train_idx, val_idx = idx[samples:], idx[:samples]" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "train_sampler = SubsetRandomSampler(train_idx)\n", + "valid_sampler = SubsetRandomSampler(val_idx)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n", + " sampler=train_sampler, num_workers=num_workers)\n", + "valid_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, \n", + " sampler=valid_sampler, num_workers=num_workers)\n", + "test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, \n", + " num_workers=num_workers)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 249, + "width": 1393 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "dataiter = iter(train_loader)\n", + "images, labels = dataiter.next()\n", + "images = images.numpy()\n", + "fig = plt.figure(figsize=(25, 4))\n", + "for idx in np.arange(20):\n", + " ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n", + " ax.imshow(np.squeeze(images[idx]), cmap='gray')\n", + " ax.set_title(str(labels[idx].item()))" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 684, + "width": 688 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = np.squeeze(images[1])\n", + "fig = plt.figure(figsize=(12,12))\n", + "ax = fig.add_subplot(111)\n", + "ax.imshow(img,cmap='gray',interpolation='nearest')\n", + "width, height = img.shape\n", + "thresh = img.max()/2.5\n", + "for x in range(width):\n", + " for y in range(height):\n", + " val = round(img[x][y].item(),2) if img[x][y]!=0 else 0\n", + " ax.annotate(str(val),xy=(y,x),\n", + " horizontalalignment='center',\n", + " verticalalignment='center',\n", + " color='white' if img[x][y] 1709.697889). Saving model ...\n", + "Epoch: 3 \tTraining Loss: 0.010343 \tValidation Loss: 0.000001\n", + "Validation loss decreased (1709.697889 --> 1503.887222). Saving model ...\n", + "Epoch: 4 \tTraining Loss: 0.010847 \tValidation Loss: 0.000001\n", + "Epoch: 5 \tTraining Loss: 0.010147 \tValidation Loss: 0.000001\n", + "Epoch: 6 \tTraining Loss: 0.009160 \tValidation Loss: 0.000001\n" + ] + } + ], + "source": [ + "n_epochs = 5\n", + "valid_loss_min = np.Inf\n", + "\n", + "for idx in range(1,n_epochs+1):\n", + " train_loss = 0.0\n", + " valid_loss = 0.0\n", + " \n", + " for images, labels in train_loader:\n", + " \n", + " optimizer.zero_grad()\n", + " images = images.reshape(images.shape[0],-1)\n", + " output = model(images)\n", + " \n", + " loss = criterion(output,labels)\n", + " \n", + " loss.backward()\n", + " \n", + " optimizer.step()\n", + " \n", + " train_loss += loss.item()*images.size(0)\n", + " model.eval()\n", + " for images, labels in valid_loader:\n", + " \n", + " output = model(images.reshape(images.shape[0],-1))\n", + " \n", + " loss = criterion(output,labels)\n", + " \n", + " valid_loss += loss.item()*images.size(0)\n", + " \n", + " train_loss = train_loss/len(train_loader.dataset)\n", + " test_loss = train_loss/len(test_loader.dataset)\n", + " \n", + " print(\"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(idx+1,train_loss,test_loss))\n", + " \n", + " if valid_loss <= valid_loss_min:\n", + " print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(\n", + " valid_loss_min,\n", + " valid_loss))\n", + " torch.save(model.state_dict(), 'model.pt')\n", + " valid_loss_min = valid_loss\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Loss: 0.152946\n", + "\n", + "Test Accuracy of 0: 99% (971/980)\n", + "Test Accuracy of 1: 98% (1121/1135)\n", + "Test Accuracy of 2: 97% (1005/1032)\n", + "Test Accuracy of 3: 99% (1000/1010)\n", + "Test Accuracy of 4: 97% (953/982)\n", + "Test Accuracy of 5: 95% (850/892)\n", + "Test Accuracy of 6: 97% (934/958)\n", + "Test Accuracy of 7: 98% (1014/1028)\n", + "Test Accuracy of 8: 96% (936/974)\n", + "Test Accuracy of 9: 96% (976/1009)\n", + "\n", + "Test Accuracy (Overall): 97% (9760/10000)\n" + ] + } + ], + "source": [ + "test_loss = 0.0\n", + "class_correct = list(0. for i in range(10))\n", + "class_total = list(0. for i in range(10))\n", + "\n", + "model.eval() \n", + "\n", + "for data, target in test_loader:\n", + " data = data.reshape(data.shape[0],-1)\n", + " output = model(data)\n", + " \n", + " loss = criterion(output, target)\n", + " \n", + " test_loss += loss.item()*data.size(0)\n", + " \n", + " _, pred = torch.max(output, 1)\n", + " \n", + " correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n", + " \n", + " for i in range(len(target)):\n", + " label = target.data[i]\n", + " class_correct[label] += correct[i].item()\n", + " class_total[label] += 1\n", + "\n", + "\n", + "test_loss = test_loss/len(test_loader.dataset)\n", + "print('Test Loss: {:.6f}\\n'.format(test_loss))\n", + "\n", + "for i in range(10):\n", + " if class_total[i] > 0:\n", + " print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n", + " str(i), 100 * class_correct[i] / class_total[i],\n", + " np.sum(class_correct[i]), np.sum(class_total[i])))\n", + " else:\n", + " print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n", + "\n", + "print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n", + " 100. * np.sum(class_correct) / np.sum(class_total),\n", + " np.sum(class_correct), np.sum(class_total)))" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/processed/test.pt b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/processed/test.pt new file mode 100644 index 0000000..14c1f3f Binary files /dev/null and b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/processed/test.pt differ diff --git a/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/t10k-images-idx3-ubyte b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/t10k-images-idx3-ubyte new file mode 100644 index 0000000..1170b2c Binary files /dev/null and b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/t10k-images-idx3-ubyte differ diff --git a/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/t10k-labels-idx1-ubyte b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/t10k-labels-idx1-ubyte new file mode 100644 index 0000000..d1c3a97 Binary files /dev/null and b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/t10k-labels-idx1-ubyte differ diff --git a/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/train-labels-idx1-ubyte b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/train-labels-idx1-ubyte new file mode 100644 index 0000000..d6b4c5d Binary files /dev/null and b/python/pytorch/Convolutional Neural Networks/Mnist/MNIST/raw/train-labels-idx1-ubyte differ diff --git a/python/pytorch/Convolutional Neural Networks/model.pt b/python/pytorch/Convolutional Neural Networks/model.pt new file mode 100644 index 0000000..0d84403 Binary files /dev/null and b/python/pytorch/Convolutional Neural Networks/model.pt differ diff --git a/python/pytorch/Introduction to PyTorch/1. Neural Network in Pytorch.ipynb b/python/pytorch/Introduction to PyTorch/1. Neural Network in Pytorch.ipynb new file mode 100644 index 0000000..cac9cb7 --- /dev/null +++ b/python/pytorch/Introduction to PyTorch/1. Neural Network in Pytorch.ipynb @@ -0,0 +1,766 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['C:\\\\Users\\\\User\\\\Anaconda3\\\\envs\\\\pytorch\\\\python36.zip',\n", + " 'C:\\\\Users\\\\User\\\\Anaconda3\\\\envs\\\\pytorch\\\\DLLs',\n", + " 'C:\\\\Users\\\\User\\\\Anaconda3\\\\envs\\\\pytorch\\\\lib',\n", + " 'C:\\\\Users\\\\User\\\\Anaconda3\\\\envs\\\\pytorch',\n", + " '',\n", + " 'C:\\\\Users\\\\User\\\\Anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages',\n", + " 'C:\\\\Users\\\\User\\\\Anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages\\\\IPython\\\\extensions',\n", + " 'C:\\\\Users\\\\User\\\\.ipython']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.path" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision import datasets, transforms" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n", + "# Download and load the training data\n", + "trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)\n", + "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "dataiter = iter(trainloader)\n", + "images, labels = dataiter.next()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "torch.Size([64, 1, 28, 28])\n", + "torch.Size([64])\n" + ] + } + ], + "source": [ + "print(type(images))\n", + "print(images.shape)\n", + "print(labels.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(2.2925, grad_fn=)\n" + ] + } + ], + "source": [ + "model = nn.Sequential(nn.Linear(784,128),\n", + " nn.ReLU(),\n", + " nn.Linear(128,64),\n", + " nn.ReLU(),\n", + " nn.Linear(64,10))\n", + "\n", + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "images, labels = next(iter(trainloader))\n", + "\n", + "images = images.view(images.shape[0],-1)\n", + "\n", + "logits = model(images)\n", + "\n", + "loss = criterion(logits, labels)\n", + "\n", + "print(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss: 2.323791742324829\n" + ] + } + ], + "source": [ + "model = nn.Sequential(nn.Linear(784, 128),\n", + " nn.ReLU(),\n", + " nn.Linear(128, 64),\n", + " nn.ReLU(),\n", + " nn.Linear(64, 10),\n", + " nn.LogSoftmax(dim=1))\n", + "criterion = nn.NLLLoss()\n", + "\n", + "images, labels = next(iter(trainloader))\n", + "# Flatten images\n", + "images = images.view(images.shape[0], -1)\n", + "\n", + "logps = model(images)\n", + "\n", + "loss = criterion(logps, labels)\n", + "print(\"Loss: {}\".format(loss))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autograd" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "x = torch.rand(2,2,requires_grad=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.6872, 0.4508],\n", + " [0.1086, 0.6609]], requires_grad=True)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "y = x**2" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0.4723, 0.2032],\n", + " [0.0118, 0.4367]], grad_fn=)\n" + ] + } + ], + "source": [ + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(y.grad_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2810, grad_fn=)\n" + ] + } + ], + "source": [ + "z = y.mean()\n", + "print(z)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "print(x.grad)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "z.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0.3436, 0.2254],\n", + " [0.0543, 0.3304]])\n" + ] + } + ], + "source": [ + "print(x.grad)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Network with Backpropagation " + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "model = nn.Sequential(nn.Linear(784,128),\n", + " nn.ReLU(),\n", + " nn.Linear(128,64),\n", + " nn.ReLU(),\n", + " nn.Linear(64,10),\n", + " nn.LogSoftmax(dim=1))\n", + "criterion = nn.NLLLoss()\n", + "\n", + "images, labels = next(iter(trainloader))\n", + "\n", + "images = images.view(images.shape[0],-1)\n", + "\n", + "logprobs = model(images)\n", + "\n", + "loss = criterion(logprobs,labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(2.3081, grad_fn=)\n" + ] + } + ], + "source": [ + "print(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before backprop None\n", + "After backprop tensor([[-0.0029, -0.0029, -0.0029, ..., -0.0029, -0.0029, -0.0029],\n", + " [-0.0040, -0.0040, -0.0040, ..., -0.0040, -0.0040, -0.0040],\n", + " [ 0.0006, 0.0006, 0.0006, ..., 0.0006, 0.0006, 0.0006],\n", + " ...,\n", + " [ 0.0008, 0.0008, 0.0008, ..., 0.0008, 0.0008, 0.0008],\n", + " [-0.0009, -0.0009, -0.0009, ..., -0.0009, -0.0009, -0.0009],\n", + " [ 0.0005, 0.0005, 0.0005, ..., 0.0005, 0.0005, 0.0005]])\n" + ] + } + ], + "source": [ + "print(\"Before backprop {}\".format(model[0].weight.grad))\n", + "loss.backward()\n", + "print(\"After backprop {}\".format(model[0].weight.grad))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After backprop tensor([[ 4.1413e-04, -2.9859e-03, 1.0058e-04, ..., -1.9176e-03,\n", + " -5.5404e-04, -1.7326e-03],\n", + " [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00],\n", + " [-4.9239e-04, -8.0323e-04, -4.3292e-04, ..., -7.3557e-04,\n", + " -1.0454e-04, -9.6155e-05],\n", + " ...,\n", + " [ 5.3708e-03, 4.2295e-03, 3.4071e-03, ..., 3.9200e-03,\n", + " 6.4069e-04, 2.1216e-03],\n", + " [ 8.6112e-03, 5.7455e-03, 3.9462e-03, ..., 3.4358e-03,\n", + " 7.4778e-04, 9.9920e-03],\n", + " [ 7.6290e-03, 1.8294e-03, -7.6870e-04, ..., 1.3134e-03,\n", + " -3.3456e-04, 9.9401e-03]])\n" + ] + } + ], + "source": [ + "print(\"After backprop {}\".format(model[2].weight.grad))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "from torch import optim" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = optim.SGD(model.parameters(),lr=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial weights Parameter containing:\n", + "tensor([[-0.0165, 0.0103, 0.0299, ..., -0.0077, 0.0246, -0.0242],\n", + " [-0.0172, -0.0024, 0.0082, ..., 0.0002, 0.0076, 0.0048],\n", + " [-0.0204, 0.0171, 0.0333, ..., 0.0253, 0.0275, 0.0226],\n", + " ...,\n", + " [ 0.0073, 0.0248, -0.0286, ..., 0.0319, -0.0026, 0.0312],\n", + " [-0.0151, 0.0035, -0.0264, ..., 0.0253, 0.0225, -0.0229],\n", + " [-0.0249, -0.0111, -0.0319, ..., -0.0301, 0.0005, -0.0001]],\n", + " requires_grad=True)\n", + "Gradients tensor([[-0.0010, -0.0010, -0.0010, ..., -0.0010, -0.0010, -0.0010],\n", + " [ 0.0051, 0.0051, 0.0051, ..., 0.0051, 0.0051, 0.0051],\n", + " [ 0.0029, 0.0029, 0.0029, ..., 0.0029, 0.0029, 0.0029],\n", + " ...,\n", + " [ 0.0005, 0.0005, 0.0005, ..., 0.0005, 0.0005, 0.0005],\n", + " [-0.0014, -0.0014, -0.0014, ..., -0.0014, -0.0014, -0.0014],\n", + " [ 0.0003, 0.0003, 0.0003, ..., 0.0003, 0.0003, 0.0003]])\n" + ] + } + ], + "source": [ + "print(\"Initial weights {}\".format(model[0].weight))\n", + "\n", + "images, labels = next(iter(trainloader))\n", + "\n", + "images = images.view(images.shape[0],-1)\n", + "\n", + "optimizer.zero_grad()\n", + "\n", + "output = model.forward(images)\n", + "loss = criterion(output,labels)\n", + "loss.backward()\n", + "print(\"Gradients {}\".format(model[0].weight.grad))" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After one step: Parameter containing:\n", + "tensor([[-0.0164, 0.0104, 0.0300, ..., -0.0076, 0.0247, -0.0241],\n", + " [-0.0177, -0.0030, 0.0077, ..., -0.0003, 0.0070, 0.0043],\n", + " [-0.0207, 0.0168, 0.0330, ..., 0.0251, 0.0272, 0.0223],\n", + " ...,\n", + " [ 0.0072, 0.0247, -0.0286, ..., 0.0318, -0.0027, 0.0311],\n", + " [-0.0150, 0.0037, -0.0262, ..., 0.0254, 0.0227, -0.0228],\n", + " [-0.0249, -0.0112, -0.0319, ..., -0.0302, 0.0005, -0.0002]],\n", + " requires_grad=True)\n" + ] + } + ], + "source": [ + "optimizer.step()\n", + "print(\"After one step: {}\".format(model[0].weight))" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Full Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "model = nn.Sequential(nn.Linear(784,256),\n", + " nn.ReLU(),\n", + " nn.Linear(256,64),\n", + " nn.ReLU(),\n", + " nn.Linear(64,10),\n", + " nn.LogSoftmax(dim=1))\n", + "criterion = nn.NLLLoss()\n", + "optimizer = optim.Adam(model.parameters(),lr=0.003)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "At time step 100 Loss: 0.11813484132289886\n", + "===============================================\n", + "At time step 200 Loss: 0.19559788703918457\n", + "===============================================\n", + "At time step 300 Loss: 0.11553216725587845\n", + "===============================================\n", + "At time step 400 Loss: 0.31023550033569336\n", + "===============================================\n", + "At time step 500 Loss: 0.0899040624499321\n", + "===============================================\n", + "At time step 600 Loss: 0.08915595710277557\n", + "===============================================\n", + "At time step 700 Loss: 0.18568871915340424\n", + "===============================================\n", + "At time step 800 Loss: 0.1915172040462494\n", + "===============================================\n", + "At time step 900 Loss: 0.20672257244586945\n", + "===============================================\n", + "At time step 1000 Loss: 0.025915861129760742\n", + "===============================================\n" + ] + } + ], + "source": [ + "error = []\n", + "for i in range(1,1000+1):\n", + " #prepare data\n", + " images, labels = next(iter(trainloader))\n", + " images = images.view(images.shape[0],-1)\n", + " #initialize gradient to zero\n", + " optimizer.zero_grad() \n", + " #forward propagation\n", + " output = model(images)\n", + " #calculate error\n", + " loss = criterion(output, labels)\n", + " #backpropagation\n", + " loss.backward()\n", + " #update rule\n", + " optimizer.step()\n", + " \n", + " if i%100==0:\n", + " error.append(loss.item())\n", + " print(\"At time step {} Loss: {}\".format(i,loss.item()))\n", + " print(\"===============================================\")" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting matplotlib\n", + " Downloading https://files.pythonhosted.org/packages/21/4c/35fa1837a705f33621604a1967b1505bd3f695940fdf02fad77ef11de196/matplotlib-3.0.3-cp36-cp36m-win_amd64.whl (9.1MB)\n", + "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\user\\anaconda3\\envs\\pytorch\\lib\\site-packages (from matplotlib) (2.8.0)\n", + "Collecting kiwisolver>=1.0.1 (from matplotlib)\n", + " Downloading https://files.pythonhosted.org/packages/44/72/16630c3392eba03788ad87949390516bbc488e8e118047a3b824631d21a6/kiwisolver-1.0.1-cp36-none-win_amd64.whl (57kB)\n", + "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 (from matplotlib)\n", + " Downloading https://files.pythonhosted.org/packages/de/0a/001be530836743d8be6c2d85069f46fecf84ac6c18c7f5fb8125ee11d854/pyparsing-2.3.1-py2.py3-none-any.whl (61kB)\n", + "Collecting cycler>=0.10 (from matplotlib)\n", + " Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n", + "Requirement already satisfied: numpy>=1.10.0 in c:\\users\\user\\anaconda3\\envs\\pytorch\\lib\\site-packages (from matplotlib) (1.16.2)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\envs\\pytorch\\lib\\site-packages (from python-dateutil>=2.1->matplotlib) (1.12.0)\n", + "Requirement already satisfied: setuptools in c:\\users\\user\\anaconda3\\envs\\pytorch\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib) (40.8.0)\n", + "Installing collected packages: kiwisolver, pyparsing, cycler, matplotlib\n", + "Successfully installed cycler-0.10.0 kiwisolver-1.0.1 matplotlib-3.0.3 pyparsing-2.3.1\n" + ] + } + ], + "source": [ + "!pip install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "error = np.array(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "i_episodes = np.linspace(0,1000+1,len(error))" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Loss vs Number of Episodes')" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.plot(i_episodes,error,c='r',alpha=0.7)\n", + "plt.grid(True)\n", + "plt.scatter(i_episodes,error,s=50,c='b',alpha=0.7)\n", + "plt.xlabel(\"Number of Episodes\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.title(\"Loss vs Number of Episodes\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "images,labels = next(iter(trainloader))" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [], + "source": [ + "img = images.view(images.shape[0],-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [], + "source": [ + "output = model(img)" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Wyvuav8XAliIlyWo8Dx061MX23HNPIJk8eNJJJ+X97AYbbAAkE2iqPcnzjDPOyItZbdHafscbqlevXgAccsghZXuOarPlllu6dvfu3YFk4mEcx43ep1JoJlZEREREgpPpTKyV0fJL7dgn1Pp8Ui32Z/zyEaHZeuutXdvOhS+UrFGXZZddNrU+idSHn+RVjA033NC1/eQtY9eRH3/8sWEdq0I9evTIi1199dWAymnNjj/zf+eddwKw11575cX8JC6bsd17771dzE6a9E9Bs7Psq5WVyvNP1LMEOP/kvbfeeivV5918883zntf+Tfz7g6+//hqATz75JNXnD8liiy0GJMts2ljdcMMNmfSpPjQTKyIiIiLB0U2siIiIiAQn0+0EK6ywAgAHHXRQ2Z/Llg8gWW8yZA1ZBrQN3P6yS20no0nD+fU4P//88wx7EhZbhjz88MPzvmYnd0HypC5JJoHuvPPOeV9/5JFHAOjfv7+LWX1I/2ebGqs1evHFF7tYFEUAPPPMMy528MEHA8lkuLXXXhuA2267zcWef/55ILd9oymwms3+yYWWVHj00Ue7mI1hfey0005Aso5px44d857XthH47/uHHXYYkNuS1xSdeeaZAHTq1MnFbGvBU089lUmf6kMzsSIiIiISnExnYu2c7vnmm8/FBg0alOpzjB07Fiic2BC6P//8s6Tvt03bkJuJvf76613shx9+SKdjUtAXX3zh2nZ6jdTNZlcOOOCAvK9Z0gzAlClTGq1PIfBnU/2TDY2tvHzzzTcuZmX7mjKbpTvhhBNczEo/HnfccS42Y8YMIFlOyn5m4MCBLuaXQGwqbNb5oosuyvua/3tcagLRZZdd5tp2Cl2LFi1q/Rnrwy233OJiTXUGtm3btq697777ArkVGQhztUAzsSIiIiISHN3EioiIiEhwKuLELn9Ju9jtBGeddRYAY8aMcTF/M7exBJpx48Y1pIsVyTa2+0uAhTZkL7XUUgDceOONLtauXTsgebKJTuuRSjH//PO79umnnw4kf7/vvfdeACZMmNCo/aomto2gW7duLtZUk+PatGnj2pYo5G9Pufnmm4Hkcqx9X6FTFC2Zq6myrSp+QqFtC/K3D7744otA4ffuQvxEZPsZf1ud1X0977zzXOy+++4rqe/VbMCAAa69xBJLAPD444+7mJ8oGwrNxIqIiIhIcCpiJtbnn5Ii+f7v//7Ptbt27QrApZde6mIPPPAAAPPOO6+LWQkzP7nDyhW98sor5eusSD2tscYarr3VVlsBubPXoXDCiNTNX5GyWcOmOvvqO/vss127devWeV8vdMqjzbZusskmLvbOO++k37kA2Wl8dionwI477gjAyiuv7GL+e1cxRo4c6doPPfQQkExMvOeee0rvbBNgSXD+zLjNlg8ePDiTPqVFM7EiIiIiEhzdxIqIiIhIcCpuO4HU7oMPPnDtY489Fkgmw1nNwmnTprmYLbH439eQ076kfoYOHZp1F4Kx3Xbb5cXsNBmAt99+uzG7E6Qddtgh6y4EY9SoUa7ds2fPvK8PGzYMyCV4AYwYMQIoPimpqXv00UfzYldccUUGPWka7KRDyL1u7fQygHPOOQeA6dOnN2q/0qaZWBEREREJTrMSP0X+45e4EPcpvCGDojEtQOOavpDG1D8t6cQTTwRgrbXWcrGff/65UfpRjJDGNRQa0/LQuKavUse0b9++rt27d28guRpbycmxpYypZmJFREREJDi6iRURERGR4Gg7QQNV6lJC6DSu6dOYlofGNX0a0/LQuKZPY5o+bScQERERkaqmm1gRERERCY5uYkVEREQkOLqJFREREZHglJzYVa6OBK5Bm7pT60X10bimT2NaHhrX9GlMy0Pjmj6NafqKGtNSb2JFRERERDKn7QQiIiIiEhzdxIqIiIhIcHQTKyIiIiLBmSvrDhQriqK5gUHA1sCiwHjgtDiOH8+0Y1UgiqI21IxtZ+BP4D7g+DiO/8qyXyGLouho4EBgLWBoHMcHZtqhKhNF0crAu8B9cRz3yLo/IdNrtTx0XU1fFEWLAoOBbsD3QN84ju/Ktlfhi6JoNWAg0AmYDJwcx/H92faqOCHNxM4FfAl0ARYCzgTiWRcKaZhBwCRgaaADNWN8ZKY9Ct/XwHnAzVl3pEoNBF7PuhNVQq/V8tB1NX0DgelAK+A/wLVRFK2RbZfCFkXRXMCDwCPUTBAeDtwRRVH7TDtWpGBmYuM4ngqc44UeiaLoM2o+OUzIok9VZEXgmjiO/wC+jaJoBKALQwPEcTwcIIqi9YDlMu5OVYmiaB/gZ+AloF3G3QmeXqtlo+tqiqIoagl0B9aM43gKMDqKooeA/YFTM+1c2FYFlgEuj+P4H+DZKIpepGZcz8y0Z0UI5ib236IoagW0B97Pui9V4EpgnyiKngcWAbYngBevND1RFC0I9AO2Ag7JuDsitdF1NV3tgZlxHH/sxcZSM8Mt9VeoHmszYM3G7kh9hLSdwImiqDlwJ3BbHMfjsu5PFRhJzQzBr8BE4A3ggUx7JFJYf2BwHMdfZt0RkTroupqu+YFf/hX7BVggg75Uk3HUbHs5OYqi5lEUdaPmg8F82XarOMHdxEZRNAcwhJp9MUdn3J3gzRrPJ4DhQEtgcWpmDS7Osl8i/xZFUQdqEjsvz7ovIrXRdbUspgAL/iu2IPBbBn2pGnEczwB2A3YEvgVOAmJqPnhVvKBuYqMoakZNZmIroPuswZeGWRRYnpq9W3/GcfwDcAuwQ7bdEsmzBdAG+CKKom+BPkD3KIreyrJTIgXoupq+j4G5ZlUmMeugLYUNFsfxO3Ecd4njeLE4jrcFVgJey7pfxQhtT+y1wGrA1nEcT8u6M9UgjuPvZyXI9Y6iaAA1SzY9qdlrJPU0K+NzLmBOYM4oiuYB/lJ5nQa5Abjb+/8+1NzU9s6kN1VCr9X06bqavjiOp0ZRNBzoF0XRodRUfNgV2DjbnoUviqK1qfmQMAc1FTSWBm7Nsk/FCuYmNoqi1kAvaurtfRtFkX2pVxzHd2bWseqwB3AF8F9gJvAccEKmPQrfGcDZ3v/3AM4lWWFDShDH8e/A7/b/URRNAf6I43hydr2qCnqtloeuq+k7kppScJOAH4DecRxrJrbh9gcOBZoDLwDbxHH8Z7ZdKk6zf/75p5TvL+mbm5BC2X3F0pjOnsY1fRrT8tC4pk9jWh4a1/RpTNNX1JiWPBPbrFlD/q2qT4kfAgrSmObTuKZPY1oeGtf0aUzLQ+OaPo1p+koZ06ASu0REREREQDexIiIiIhKgYBK76muJJZYAoH///i62++67A3DVVVe52Pnnn9+4HRMRERGRetNMrIiIiIgEp+TqBCFsQLaZVoDLLrsMgBVWWMHF7O88ZswYF1t//fXr9VyzHqtBmYkhjGlj07imT2NaHhrX9GlMy0Pjmj6NafpKGVPNxIqIiIhIcHQTKyIiIiLBqarELttGMGzYMBezrQO//+4O+mHcuHEA7L///o3YOxFpCD/58pRTTgGgc+fOLvbGG280ep+kaVhooYUAOPDAA12sT58+AEyYMMHFrrjiCgBeeuklF/vmm2/K30Fp0tq1a+fa++67LwDnnnturT9jWxhOPPFEF5s8uebwwzvuuCPtLpaNZmJFREREJDjBJ3Ztvvnmrn3bbbcBhZO49tprLxe7//77U3v+prype80113TtJ598EoCOHTu62HfffVfvx27K41ouIYzpUUcdBUCnTp1cbJtttgFg6aWXdrE55qj5/D1+/HgXGz16NAD33nuvi7333nsAfPnll2XqcRjjWps555zTtW3GsUePHi5m47/TTju52C677ALAww8/XJY+VcKYdujQwbUvvvhiADbddNO872vRooVrzzVXzeKmrfYBHHfccUDuGpmlShjXurRu3RqA1157zcXsOddbbz0X++KLL8raj2JlOabLLrssAM8884yLrbzyyvXuyIwZMwC44IILXOyiiy4CYPr06fV+3FIpsUtEREREqppuYkVEREQkOMEmdq266qoAXHfddS5m2wjeeustF9t+++0B+P777xuxd03DWmut5dq21OtvMWjIdoJqss8++wDwxx9/uNgDDzyQ932LL744AG3atHGxNdZYA4A999zTxbp27QrkljgB+vXrl16HG4ktXQ8fPtzFbHuQv8RdGz+hwdp+8s3TTz8NQLdu3RrU12q20UYbufaoUaNm+31///23a991110ALLfcci72yy+/lKF32fGXZZdcckkg9zsKMG3aNADWXnttFzv55JOBXHINwEknnQTkXouQHEtJOvTQQwFYbLHFXMy2ChVi9wKQ3F5oLFkpzW2EleK///0v0LAtBL7mzZsDcPbZZ7vYn3/+CSTfbyqJZmJFREREJDhBJXYtscQSrm2bvv0krg8++ACALbfc0sXKPQMbwkb5tC288MIAPP/88y72448/AskZr7/++qvezxHquNqswA033OBiVgbKn120T7f+758lK/nfV9uMpI05JGeIZqfSxnTAgAFAssRL2iZOnAgkT/F78803U32OShvXYh100EEAnHXWWS7mX0+LMXbsWNe+8MILgWRiXX1Vwpja7yPAwQcfDCRn83744YfZ/qz/+3/YYYcBsMACC7jYlClTGtS3+qqEcS3En0G19xX/2mjPmVbM7hFqW3koVpZjetVVVwG5hNjZufvuuwGYe+65Xcy/JtbGEhJtVbsxKLFLRERERKqabmJFREREJDhBbCewbQQjR450sVVWWQXIPomrUpdnysmWIfyEHNuMP3jw4FSeI6RxXX311V17xIgRQDLhpRDrm78l4N133837PkuWsceF3GvfPwmomE33lTamL7zwAgCbbLJJyT/7008/Acll2eWXX36232/bNyD3Wr3zzjtLft5CKm1ca9O+fXvXfuqpp4C6X6vFsn8L/7pw/PHHA6UnfYU0poVYbVjIneJ15JFHuti1117b6H2CyhtXe29/7LHHXGzdddeteaJ6bBO48cYb857DtirYdRNgzJgxQHKJvL73DVmOadu2bYFkDe1CXnnlFSBXxxhyNXf9JONFFlkk72ftWusnF/tbCctB2wlEREREpKpVbIktP4nLPqX5n6QsiSuNT1JNkZU3slkCyJ14VuhTlv9JcddddwXg9ddfd7Hbb7+9HN2saFb+6vHHH3cxm9WaNGmSi9mMl5VD8fkzhLUlilSLbbfd1rX90k61+fnnn4HkKTJDhw4FCs/E2usTcufb2+sdcgllL7/8sov973//K+4vELhCr9X6uOeee4DcrBnkyvwccMABLvbcc88BTe/68MQTT2TdhYrll8SyWXv/vb3QrKStuPqz/JZIWJf+/fsDcNppp7mYf38Rsk8//TTxZ138ZGsrW2bXUkiuFhibnS0meTgLmokVERERkeDoJlZEREREglNx2wlsE7Z/EpctNfz+++8uttdeewHaQlAKv0acbdHYeOONXezjjz8GCm8n8Jd7evbsCcCpp57qYjNmzEi1r5XK30B/7LHHAsllWRuHvffe28X8hMSmzh+X2mrg+sv73bt3B5I1SQux5KH33nvPxWyLxtVXX+1irVq1AuCiiy5ysSiK6ux7yOx3dplllin5Z++77z4gtzUDcvV3rWY05H4f/LqzluD04IMPuli1nexVSGPW1AzF6aefDiTfN+abbz4gmZxl7+n+9qErr7yyqOewbQJ9+/Z1MXsN+s9x/fXXJ56rKbPtblB4O4Hx/w3smlAJNBMrIiIiIsGpuJnY3XbbDUjO/NknKP+0lHHjxjVuxwJmZTWspBHA+uuvX9Jj2Mw3wKuvvgrkEmSaEv9MaTuJp5Add9zRtb/88kug6SQPNcQjjzwCJM+enzp1ar0fz1Z09thjDxfbeuutAejUqZOL2e9IQ06ZqzT77LOPa1vpOz9pxk7i8ZO9CpXIszHxkxCNld+B3ClVPXr0cLEOHToAyaSQpjATWyhpzhJnmxI/gcoSrAqVyfJnRLt06QIU/x7vP4etBvglzuw5/McrNilMcuaZZ56su1CQZmJFREREJDi6iRURERGR4FTEdoKWLVu6ttWR9Je9bKnBrz8oxbPlQv+0Dtuucfjhh7vYs88+m/ez888/PwC9e/d2MdvwvaTiAAAgAElEQVTWMXPmzPQ7W6FsefvAAw+s9fuaN28OJJNgTjzxRCC5Mf6kk05KuYdhmD59umtPmzYNgJtvvtnFrJaun8SZBn87jJ2MtuKKK7qYbTeI4zjV582CJXBaIg3krqe//vqri1nyy9tvv53K89oJcv6pSbZs27VrVxcrtqZltWkqya8+P8HKthH42wnsvcSukQBffPFFUY9tSeCXXnqpixU67cu20en+oWEq9WRRzcSKiIiISHAqYibWL7lhM4STJ092sWLLley+++5AMonDTgfxy3X4CWLVyk/QWHTRRYHk2ceWZOTPGBZiG+TnmCP3eefyyy9PrZ+hsISuFi1auJglHPknbdlMo1+Ky1YaTjjhBBf75JNPgGQpuabgiCOOKNguNz+ZyBJs/JlKS3SshpnYSy65BIDVV18972vbbbeda6c1A/tv/kl+xk+iK3S+fbXZZJNNXPvHH38EkrODTYUl+wFsttlmAHz44YcuZu/Lxc6++qd92bXTTwK3FRz/PV4zsOmo1NevZmJFREREJDi6iRURERGR4FTEdgJ/2cs2D7/11lsu5reNbR04//zzXcyWFfwNyDYFbid0ALz55ptA8UsYIbGTefwakZZUUVedUjt9xz+Fx5Z8n3jiCRcbP358Op0NiC0Jvvjiiy521FFHAfDOO+/kff/yyy/v2sOHDweSS6qWwNjUthNUqkpNWijWAgss4Nr+Urb5+uuvgcKv1cYQ+vgWq127dgCss846LjZ06FCgumoQF8uvzVpqbXKf1YL1axqvsMIKQOFEMW0hSN8111yTdRcK0kysiIiIiAQn05lY26Rtp3RB7lOVP8NqhgwZ4tr2M3b2sv+z/ukftoncNpVD7vSYapyJtZImVr4IcjPdfpmbjTfeGIA2bdq4mM3AWpkoyJ0zv8suu7iYfRr2k2BGjBgB5MrsVBsbr2LZKV0AY8aMAZIzsZIdP+nOVGrSQl0safCmm25ysY4dO+Z93wMPPAAkrwuNqams3ljypn+6kV9CTupmr2lbbQW4/fbbgcKnfSmJq3FYacJKo5lYEREREQmObmJFREREJDiZbiewOo3+pn+rK2dLsADDhg0DkssLtqzgJ31ZPVl/O4GdSGWnezQViy22mGtb8pA/zlan1D+ly5bM7dQTyCXBvfzyyy5mWzP8E6gs8clPaPBrczZlllTjayqJLpXCT3yypDqff80IiW2N8GtAm88++8y1/d/VcrF61N26dcv7miU3VruVV14ZSI59ocRkmT2rG1/XaV+F2Pu9/3oL9Xe7sdipeqHSTKyIiIiIBKciErsKfbryZ1133XXXvO+zxK+rrrrKxewTl5XjADjssMNm+xzV6NBDDwWge/fuLvbII48AyZOlxo4dCyTH5e677waSZ3zbCT8///xz3nP5s732CdhPaNBMbI1CpWWayuuxUvhlp5ZddlkA/vjjDxd7+OGHG71P5eavnjRGYpWVPDrllFNc7NFHHwXg888/L/vzZ8V/v7GVKD8Jxk6RknyWCOef2mnjWSiJy2cx/17BTuu89tprXcze9/yTP22FsanxT960JLgVV1yx1p+x0nD+fUEl0UysiIiIiARHN7EiIiIiEpxMtxNccMEFQHITti1L9+rVy8VsWeGss85ysUJ1ZM2xxx7r2pak5NftrMb6sGb06NGJP+uy9dZbu/bOO+8MQJ8+fVys0DYC429PCH1zeNq22WYb1/bH2DTVZA8/WePggw8G4O2333axFi1aAMlT/Ap5/vnnAfj222+Lel57Lt/EiRNd+4MPPijqcSqNX/86C/5S+q233grAzJkzXaxfv35AdZ9W5dcqX3LJJYHkyVKSZAndkHt9+FsHCiVxWcK3XxPWtg/62wmsBrwfs9eobW0BuPrqq4Ha7yOqkW03hOSWi9rYSZ+WtAjQpUsXAEaOHJli7+pHM7EiIiIiEpxMZ2LtU5U/C7LKKqvkfZ99Ips8ebKL2Scu28gNuUSx4447Lu9nR40a5WIquZFzxBFHuLYldl1//fVZdadqbLjhhq7tn4BmBgwY0JjdydwxxxwDQP/+/V3Mkgzatm1b8uPV52f+bZFFFnHtM844o6ifOe+88xr8vGnq3LlzJs9r/3b/93//52J2/fUTu954443G7ZhUPP+EzkIJW7ZK5Sdi1faeXWhVq3fv3q5tJ336z2vXIT8R+cwzz6yz76Gye6L6XL/at28PwMUXX+xiP/30E5D8d7FTAxv7vU0zsSIiIiISHN3EioiIiEhwmpVYr/Kfcpw05J+mdemllwLQqVOn3JPO6qP/3LXF/Lp8tmXBaqKlbdZzNmRQyjKmdVlmmWUA+Oijj1zMEu0qIUkr1HHddNNNAbjjjjtczOpn+rFyvR5rk+WY2pJfhw4d8r7mn250yCGHADBlypRaH2+LLbYAoFWrVi520kkn1atvs/Pbb78BuaQSgJNPPjnv+7Ic1w022ABI1su2usR33XWXi+2///4N6F4NS1qC3AlgURS5mNWe9k/8q6+Qfv8XWmgh17btE36d2P/85z8ATJs2rd7PseCCC7q2PU7r1q1dzJJv/v7771ofpxLG9fXXX3dte634yd22FSDtbX9+Uqktq/v3P3vttReQTB4rRiWMqc+2+tjrDuDGG28ECm9tS4vV3W7ZsmWDH6uUMdVMrIiIiIgEJ9PELuMnXdkJHv6nCOPP2FoC2AsvvOBiH374IZA8J3zcuHHpdrZKtGvXDkiWh/n111+z6k7Q7Mx4yM1m2+wr5GYhjz766MbtWAXp2LEjkJz5uOaaawC45JJLXMwve/Vv/myUnQB14oknupjNZvjPYafM+NeYp59+Gkgmdtmpdr5JkyYB8PHHH8+2T1l77bXXAHjxxRddzGZil1pqKRezBBb/lLJiLb300kDyVDP79/THxsoWNTX+yYS2quCXeLLSY/vuu6+LFZoxtfJyNiMIuXG28ocADz74IJAs4+WvZlS6QqsCjfE+7a8w2nXa/3c47bTTgNJnYiuNlSe162u100ysiIiIiARHN7EiIiIiEpyKSOwKWaVt6i7WdtttB8DgwYNdbM011wRyNeCyVKnj6idxWB1Dv1bmsssuCyTHcMcddwTglVdeSb0/pchyTG0Jv2vXri5mY/TEE0+4mC1P+8kBa621FpBbJofkVoB/80/ks2SOoUOH1qvfxaiE16ptD4LcaX2FTtO67LLLXOz9998Hkts0unXrBsB+++3nYuussw4Abdq0cTFLovOTx2z7RRoqYUzrw2rlPvvssy5m2zH8rTKWULvxxhu7mPXXH0c7mW7YsGEuZtsIbKtMKUId17TZqXL+/c+YMWOA5HWmGJU2pnaKliUZ18V/r/rkk0/q/bzTp08Hcqd5NYQSu0RERESkqlVEYpdkZ8KECa5dCTOwlcQvR9KzZ08geRqRP/tl3n77bQCOPPJIF8t6BrYSWDKHf9a2jd8+++yTynNYeT4/wcifla1m48ePd22bHfVPLjzwwAMB2HXXXV3Mft8toQhgueWWy3tsm53xT/ez03lKXMmrepagtNFGG7nYYYcdBkD37t1dzFYk/JWwe++9F0iecvbjjz+Wr7NNjJ8YbmWo6ipJFqLbb78dKDwT+8MPP7i2rVL518innnqqzL1Ln2ZiRURERCQ4uokVERERkeAosauBKm1Td7EsWcNPqll77bWBdBM06qsSxnXbbbd1bb8mo7HECr9Gp50245+EVikqYUyrUaWNq22DGTRokIsdfPDBRf3sV199BUC/fv1czJLipk6dmlYX61RpY1otmvK4+kmNttXGv/8588wzgdJPrGzKY1ouSuwSERERkaqmmdgG0qew8qiEcS00E3vzzTe7mJ00VcknOvkqYUyrkcY1fRrT8tC4pk9jmj7NxIqIiIhIVdNNrIiIiIgER9sJGkhLCeWhcU2fxrQ8NK7p05iWh8Y1fRrT9Gk7gYiIiIhUNd3EioiIiEhwdBMrIiIiIsHRTayIiIiIBKfkxK5ydSRwDdrUnVovqo/GNX0a0/LQuKZPY1oeGtf0aUzTV9SYlnoTKyIiIiKSOW0nEBEREZHg6CZWRERERIKjm1gRERERCc5cWXegVFEU7QOcDawAfAscGMfxC9n2qjpEUbQy8C5wXxzHPbLuT8iiKHoe2Aj4a1boqziOV8muR+GLomhuYBCwNbAoMB44LY7jxzPtWOCiKFoUGAx0A74H+sZxfFe2vQqbXqvlEUVRG2rGtTPwJ3AfcHwcx3/V9nMye6G/VoOaiY2iaBvgYuAgYAFgc+B/mXaqugwEXs+6E1Xk6DiO55/1n25gG24u4EugC7AQcCYQz3pjk/obCEwHWgH/Aa6NomiNbLsUPL1Wy2MQMAlYGuhAzfgemWmPwhf0azW0mdhzgX5xHL8y6/+/yrIz1WTWDPfPwEtAu4y7I5InjuOpwDle6JEoij4DOgETsuhT6KIoagl0B9aM43gKMDqKooeA/YFTM+1cwPRaLZsVgWviOP4D+DaKohGAPnA1QOiv1WBuYqMomhNYD3goiqLxwDzAA8DJcRxPy7RzgYuiaEGgH7AVcEjG3akmF0ZRdBHwEXB6HMfPZ9yfqhJFUSugPfB+1n0JWHtgZhzHH3uxsdTMykhK9FpNzZXAPrO2ay0CbE/NzKGkJLTXakjbCVoBzYE9gc2oWUroCJyRZaeqRH9gcBzHX2bdkSryX2AlYFngBuDhKIraZtul6hFFUXPgTuC2OI7HZd2fgM0P/PKv2C/UbNeSFOi1mqqR1My8/gpMBN6gZjJLUhDiazWYmVjAZluvjuP4G4Aoii6j5ib29Mx6FbgoijpQs6G7Y9Z9qSZxHL/q/e9tURTtC+wAXJ1Rl6pGFEVzAEOo2cd5dMbdCd0UYMF/xRYEfsugL1VHr9X0zBrLJ4DrgY2p+QB2MzV5Mqdk2LWqEOprNZiZ2DiOf6Lmk5eOGEvXFkAb4Isoir4F+gDdoyh6K8tOVaF/aNjRhAJEUdSMmkz6VkD3OI5nZNyl0H0MzDWrMolZh0CWEiuZXqupWxRYnpo9sX/GcfwDcAs1kwPSACG/VkOaiYWaF+wxszZzzwCOBx7JtkvBuwG42/v/PtTc1PbOpDdVIIqihYENqVn6+gvYm5pKGsdn2a8qcS2wGrC19sI3XBzHU6MoGg70i6LoUGq2ae1KzUyXNIxeqymK4/j7WQlHvaMoGkDNTGxPavZwS8ME+1oN7Sa2P7A4NbMHfwAxcH6mPQpcHMe/A7/b/0dRNAX4I47jydn1KnjNgfOAVYGZwDhgtziOP8q0V4GLoqg10Iua+pDfRlFkX+oVx/GdmXUsfEdSsyw7CfgB6B3HsWZiG0Cv1bLZA7iCmpyDmcBzwAmZ9ihwob9Wm/3zT0mr81rKL6why8Qa09nTuKZPY1oeGtf0aUzLQ+OaPo1p+ooa05JnYps107Y+X4kfAgrSmObTuKZPY1oeGtf0aUzLQ+OaPo1p+koZ02ASu0REREREjG5iRURERCQ4uokVERERkeDoJlZEREREghNaia1Ubbvttq697rrrAnDhhRdm1R0RERERKZJmYkVEREQkOLqJFREREZHglHzYQaj1zA488EDXvvLKKwGYe+65Xax58+YAzDnnnCU97qzxa1Ch41DHtJw0rukLdUznmKPms/ZZZ53lYmeffTYAffv2dbGLLrqocTs2S6jjOnDgQACOPPJIF9tll10AePjhhxu9P75Qx7TSaVzTpzFNXyljqplYEREREQlOVSZ2Lb300q599913A7Deeuu52DzzzNPofZKm4bnnngNgiy22cLFzzz0XgHPOOSeDHoXvtNNOA+DMM890sb///huATTbZpNaftRWWQr/z06dPd+0///yzwf0M1Y8//ujazzzzTIY9Ecnexhtv7NpDhgwBoG3btll1J3OFVusr6T1NM7EiIiIiEhzdxIqIiIhIcILfTrD99tu7tiV5tG7d2sWWW265oh7n8ssvT7dj0iT52wiMJSE9//zzLua3JV/79u1d+6CDDprt99l2AYD55psPSG4dsmtCt27d8n52zJgxrn3JJZcAcO+997pYiUmvwVlppZUA+OSTT1zs999/z6o7IpmyWvH33HOPi1lSaVNT6H3MVwnbCEzT/BcSERERkaAFOxO7/PLLA3Dbbbe52GKLLVbSY8ycOdO133jjjXQ6FjCbyfLH8fjjj8/7vpVXXhmAHXfc0cUee+wxAPbee28XayqzOpbMVRf/061mYmvnl8tq06bNbL9vm222cW0b006dOhX1HB07dnTtoUOHAvD000+7mJ/wVC0WX3xx17bX49VXX51RbyqbP1bHHXdc3tctgdhfDbz55ptLeo53333XtYcPHw7AX3/9VdJjSP3515ZHH30UyL0PAgwePLixu9Rg9n40cuRIFyt15tRWD0OgmVgRERERCY5uYkVEREQkOMGe2HX00UcDudO3fB988IFrX3XVVQC0atXKxazGmf99a621Vr36EeppHRtttBGQrKnbp08fADbccEMXs77V9Tqx7/O3E9x333317l9I41rs71DWvzshjOmyyy4LwAsvvOBifqJmGn777Teg8HaBKIpcu9gtRiGMq/FPLrSl7wEDBrjYKaeckvczltziX0ONf8Lh/vvvn/f1t99+G4DHH3+8pH5mOaYLLrggAKNHj3ax+r4/lOKdd94BYNNNN3Uxe62mJaTXqs9ee/54NGS72gILLAAkt3VZYteDDz7oYrvttludj1VpY1ro/ajUx6/rPW3LLbcEyrctTid2iYiIiEhVCzax66effgJg3LhxLvb+++8D0KtXLxezT1yFNmhfd9115exiJmxTup90ZeWKdthhBxezmdhiZxFffvnlvFjnzp3r3c+mxGb+Cym04b6Sypc0hhYtWri2rbAUO/s6depU1/7666/zvj5o0CAgd70A+OijjwB47bXXSu9s4PyZZtO9e3fXXnPNNfO+PtdcNW8TW2+9dVHPYTOKAN9//32pXcycJfxOmjTJxRqS5DfvvPMCMG3aNBdbaKGFgORM9tprrw1Ay5YtXSztmdiQ2OsO4KuvvgKSpTBPPvnkkh7PH+u77roLyM2+Qu56UFtJv0rV1N4zfJqJFREREZHg6CZWRERERIIT7HaCO++8M/Hn7MRxDEDXrl1dzJYW/ZqQIdppp50AGDhwoIvZEkyhJIy6WK1Mq/kKuQSbH374wcXOPPNMILmdYMqUKUDy9J9ql8YSTl31+JrCMtH666/v2oUSi2rz5ptvuvYxxxwDwPjx413sjz/+aGDvqp/97kKuRrT/+37//fcn/vQ98sgjrm1bB/w6pzNmzEi3s43AtqgUu32iLnaCnJ8oaFvZ/K1vUqNdu3ZAbskfclsBhgwZUu/H9bcU2nunv03u8MMPB5Jbj0LR2HVdK6nOuWZiRURERCQ4wc7E1mbVVVd17VVWWQWAv//+28Xs05wleIRqk002AWC55ZZzMft72kZ4gI8//hhIzrCOGjUKSM5k1cZP+OjZsyeQLNvx0EMPATB27Nji/wJNhJK3amczIPWx+eabu7a99p599lkXe/HFFwG45ZZbXOzzzz+v9/OFypKFVlxxRRezGVO/LJ6fKCvpKFSqbbPNNsugJ5XLf8+20m/+Ck2/fv2A4ldWrCwc5H73DzjgABez8mlHHXWUi7333nuldrvq+KdKpvEYhWaILdE5rdlczcSKiIiISHB0EysiIiIiwanK7QQHH3ywa9sJQP4y2QknnNDofSoHS+jytwRYLcJHH3001eeyZC7IJY35NWb79++f6vOFoLbN9HUtlXTp0iXl3oTL3/qSBj+J09r+SVUdO3YEwkzgqC87hcq2VwG8/vrrgLYQNBZL8ILc+5Jv+PDhAPzyyy+N1qcszD///K5tp5PdeOONLrbMMssAcNFFF7mYLUH72wJr428TsG0E/rheffXVQLKmcYjSWP6vz+M999xz9X7+kSNHAtpOICIiIiJNWFXNxF5xxRUAHHvssXlfs7v/ajJx4kQA7rvvvrI9h50A5iePGUsY+3e7mhX7ybPQ681P5kr7E3TIrr32Wte2skadOnVyMTu1aMyYMbU+js0oDhs2zMW22morIFm665BDDgHg3nvvdbGmmOz1+OOPA9ChQwcX82eszfTp0wG44IILXOznn38ub+eqiJ0a2bdvXxezE7t8tkLon+xVTex0rAEDBrjYlltumfd9f/75J5Asn2njVdfqyW677QbAJZdckvc1v/Tbk08+WWy3K1pt7yO1nRTp89+Xil0hbMj7V9rluTQTKyIiIiLB0U2siIiIiASnmZ+cU4R//NqgDWVT//5yVm1JV/5zF+q3LX37NeKMv7zgL+s01Kx+NGRQUh3TtNlSwxlnnJH3Nb92bNrJIZU6rv7SS2OckmLLbWkswVTqmPqsnqklX0HuRKm333675MezJET/uvKf//wHSJ4I9N///rf0zs4SwriusMIKAEyYMMHF7GQqOw0J4NNPP018DWCDDTYA4LPPPnOxjTbaCIDJkyeXpb8hjGmxdtxxRyB5upnxf6/t+37//fey9aWxx9VO34JczeYll1yy5Ce1rXN1bWNp27YtAPPOO6+L2VY3/x7AP72robJ8rdZ2/+a/tgptb7OtA2ltbatt+4Lfl2Ley0oZU83EioiIiEhwGi2xy2ZdTz31VBdbZ511AGjRokVRj1HXTGxtTjzxRNfu0aMHkCwbdeutt5b0eNXMEhEgN9volzaxhBiV5ik/+5RcSWdVl5PNANqJOg313XffATB+/HgXsxI+dh2A3Ax7tSbV2Oyzz2a9r7nmGhe78MILgeQ4PPDAA0DydDQ7VWnnnXdOv7NVplBSrHn55Zddu5wzsFnZa6+9XNtmYGfOnOliTzzxBJBM9oqiCEieJGevVfsTcslehVZe/dJZffr0AeCpp56q59+ishQ7c+p/Xxqzrf57UNqnbjWEZmJFREREJDi6iRURERGR4JR1O8Hpp5/u2pYY5G8dsI3el19+uYt98MEHQPJUj9dee62o5/v666/zvn+RRRYBkvXPbDnRX5qQHH+bhW0jsFqdAPfcc0+j96mpqoTlmmpw6KGH5sWWWmop1/aTm6qR1df0Ty2y+rxnnXWWi/m1NI0t73777bcutu222wLJpNz6JN5VK3vfATjuuOPyvm4JcYMGDWq0PmVh0qRJrv3HH38AcPLJJ7uYv5XF2GlQvXv3zvuav3XAro2bbbZZ3nP422fee++9+nS9YmVVY9xPDquk9yXNxIqIiIhIcMoyE2ufpDp37uxiVhrD/3T1zDPPAPDrr7+62BprrAEUPjmmkKuuusq1Bw4cCCSTOGxGd/vtt3exu+++u6jHbmrsdKOjjz4672v+7Oz999/faH2qNP4n0LRLlNhj+6VKKukTb7HeffddIHfSE8B5550HNP5rZ5dddgGSM4ZmyJAhrl2NSTU+KynkJ7dYOa262AlKvrnmmivxpyT570urrbZa3td79uwJ5EpHVSu/lFUaZa2uvPJK17YZWP+0SHvvqrbZ17QUKrvll420dmOUj0yLZmJFREREJDi6iRURERGR4KS2FrTvvvu69iabbALkkgn8r9tWA8gt9ftLBHvuuSeQTLow33zzjWtbosazzz7rYoWWvey0H1vilCRLcoNcgp2ffGfjN2rUqMbtWIUq9eQRKL6msS3vhLiFwGdbgvy/t9VhtsRNgI8++qjsfbElx+bNm+d97YcffnBtvw5yNZoxYwZQ/BYCn2218BNm7RQvfyvNG2+80YAeVpfWrVvnxaxmMcD777/fmN0Jnr1/+6dE2nvTkUce6WK2RbGa1ef9wd8yUG00EysiIiIiwUltJtZKaEGuXI1/6ssxxxyT+BNyZUj8k2CMPzNiG7f90z/8GR0pnSVkXH/99S5mM2i+rl27AjB27NjG6ZgEz0ot2Yl8kFt18a8TluyV1oxsmzZtgNyJcgBrr7123vfZtaPY0n1Nnc3iXnbZZS5mybGFZhybsk6dOgHQsWPHvK+99NJLrv3FF180Wp9C5Se42gyszb5C7iSupjD76qvPamA100ysiIiIiARHN7EiIiIiEpzUthOsvvrqrm1bAZZffnkX89v/5p8SY1sHzj//fBdTXdf02bKuXz/XEnFuvPFGF3vzzTcbt2NVoj61Y6tlaci2oPjLfFandb/99nOxnXbaCYChQ4e6WKkJmAcffLBrr7TSSgAsvPDCtf6MLYtX+8lz/varV199FWjYMnahxNlPPvmk3o9XjSzhzT9x0lx33XWN3Z0gbb311kBuu4DPvy/wt8JJOvyTTUOhmVgRERERCU5qM7F+MkW3bt2K+pkvv/wSgAsuuMDF/FmZNPnlfn777TcgeaJQU9C+fXvXthO4/HGxcmWFzvqW0hR74smWW27p2tUyE2un8/llXWw8/ISXBRdcEIBevXqVvU/HHnusa992221lf75KcOedd7r2xRdfDMAVV1zhYn6JsdrMN998QPIUKuMnKwl07959tl+bNGlSI/YkXH379gVyrzvIrQj6p+xJ+tI6fbIxaSZWRERERIKjm1gRERERCU5q2wmiKErrocrCr0VZV+JHtbItBLPz9NNPA01vm4WUx8MPP+zadprTxIkTy/68fkKIJSnaqVNQ/adzma+//tq1Tz/9dCC51WvEiBEAPPjggy620EILAckkrpNOOglIJufauL7zzjtpd7vqXHrppYBqmxfLXm9HHHGEix111FEAzJw5M5M+SeXSTKyIiIiIBKdZsee6z/JPs2bNytWXIM0av4YMStnH1Ert+KXK5pij5vPLhRde6GKnnXZaWftRihDGtTb+Bvnnnntutt/XmIldWY6p/ZydFAe5EltWkguS56AX45ZbbnFtK+nlv85LvL7VS6W+Vi1xDnKnH/mnqBWbxPH+++8DcMkll7iYjbGd5pW2Sh3TQtZbbz3XfvHFF4FkEtdmm20GwH/KTWwAACAASURBVIQJExqlP7UJaVxDUU1jWtv1sjH7WMqYaiZWRERERIKjm1gRERERCY62EzRQCEsJt956KwA9evRwsalTpwJwyCGHuJgladipaVkKYVyLZdsJ/OVb2zrgbycot2oa00qicU1fSGM6bNgw195jjz2AXJIswDbbbNMo/ShGSOMaimoaU6vt7dc5r/T3Ks3EioiIiEhwNBPbQJX6KWzNNdd07dGjRwPJ87ztOf0Td6wMz6hRo1LvT6kqdVxDpjEtD41r+kIa08cee8y1t99+e0AzsU2JxjR9mokVERERkaqmm1gRERERCU5qJ3ZJZZkyZYpr//bbb0ByO8Hll18OwJAhQ1xs7NixjdQ7EZHq4G+/WmONNQDo1atXVt0RaVI0EysiIiIiwVFiVwNpU3d5aFzTpzEtD41r+jSm5aFxTZ/GNH1K7BIRERGRqqabWBEREREJTsnbCcrVkcA1aCkhtV5UH41r+jSm5aFxTZ/GtDw0runTmKavqDEt9SZWRERERCRz2k4gIiIiIsHRTayIiIiIBEc3sSIiIiISHN3EioiIiEhwgjl2NoqiuYFBwNbAosB44LQ4jh/PtGOBi6Joyr9C8wKD4jg+Jov+VJsoilYG3gXui+O4R9b9CV0URc8DGwF/zQp9FcfxKtn1KHwa0/Tpulpeuq6WR4jjGsxNLDV9/RLoAnwB7ADEURStFcfxhCw7FrI4jue3dhRFLYHvgHuz61HVGQi8nnUnqszRcRzflHUnqozGNEW6rpadrqvlEdy4BnMTG8fxVOAcL/RIFEWfAZ2ACVn0qQrtCUwCXsi6I9UgiqJ9gJ+Bl4B2GXdHRLKh62qKdF0tj1DHNdg9sVEUtQLaA+9n3Zcq0hO4PY5jFQ9uoCiKFgT6ASdl3ZcqdGEURd9HUfRiFEVbZN2ZKqExLR9dV1Oi62p5hDyuQd7ERlHUHLgTuC2O43FZ96caRFG0AjVbNW7Lui9Voj8wOI7jL7PuSJX5L7ASsCxwA/BwFEVts+1S8DSmZaLraup0XS2PYMc1mO0EJoqiOYAhwHTg6Iy7U00OAEbHcfxZ1h0JXRRFHahJQOyYdV+qTRzHr3r/e1sURftSsz/+6oy6FDyNaVnpupoSXVfLI/RxDWomNoqiZsBgoBXQPY7jGRl3qZocgGYL0rIF0Ab4Ioqib4E+QPcoit7KslNV6h8adm655NOYpkfX1fRsga6r5bAFAY9raDOx1wKrAVvHcTwt685UiyiKNqZmKVHZs+m4Abjb+/8+1FwkemfSmyoRRdHCwIbASGrKQe0NbA4cn2W/QqYxLR9dV1On62p5BD2uzf75p6S95tqYXlhDZi00prOncU2fxrQ8NK7p05iWh8Y1fRrT9BU1piXPxDZrplUmX4kfAgrSmObTuKZPY1oeGtf0aUzLQ+OaPo1p+koZ06D2xIqIiIiIgG5iRURERCRAuokVERERkeCEVp2gVnPOOScAI0aMcLH27dsD0Lp160z6JCIiIiLp00ysiIiIiASnqmZiTznlFAC6du3qYpdeemlW3RERERGRMtFMrIiIiIgERzexIiIiIhKckk/sqrSivO3atXPtp59+GoDll1/exdq0aQPAl19+WZbnnzV+DTqto1LGdIkllgCgU6dOLrbbbrvlfd/uu++e+H6A888/H4Azzzwzlb5U07hWCo1peWhc06cxLY8sx3X11VcH4LDDDnOxvffeG4BWrVoV9RhzzJGbd/v8888B2HPPPV1s/PjxAPz888/16mN96LWavlLGVDOxIiIiIhKc4Gdin3rqKde2hK5zzz3Xxfr37w+kczRcIaF+CrPZ1D322MPFNt10UwBWWGEFF7Nx8/tYW2zNNdd0sXHjxtW7f6GOayXTmJZHNY7rIoss4to//vgjAJ07d3axV155pazPH+qYNm/eHICtt97axTbeeGMA9tprLxez0o8+6+8tt9ziYjZrOXPmzFT6V85xnXfeeV3bZlj9lbztt98eyJXCrMv333/v2j/99BOQHLdC7+ljxowBoHfv3i72xhtvFPV89RXqa7UhzjrrLAD69OnjYjvssAMAo0ePbvDjayZWRERERKqabmJFREREJDjBbifYddddAbj//vtdbNq0aUAy2eubb74paz8qdSnBP6Hs0EMPBaB79+4utsoqqwDFbxMoNnbXXXe52P7771/v/jfWuHbo0AGAN998M+9rO+20k2s//vjjDehKDX+5zZZh/PH69NNPG/wctanU12raFltsMde2JMUdd9zRxSwR5KGHHnIxf/mxVNU4rhtssIFrP/jgg0AuMQdyy7vlEtKYtmzZ0rVtm8Uaa6yRymOfd955QG5bHMCMGTPq/XjlHFc/qffss8/O+/ptt90GwF9//eVicRwD8N133+V9/6+//uraU6dOBXJbEiD3Gj3yyCPzftbuBQAOP/xwAIYOHVqw3w1Vaa/VQltVPv7449QeH3JJdcstt5yLvfTSSwBsttlmDX58bScQERERkaoW1IldCyywgGtfd911QHI2sF+/fkD5Z18r2emnnw7Ascce62I2M1Vo5tRnMX9D/fDhwwH46KOPXOzUU08FkiW27GdXXXXVhv0FGlnPnj2BwuOR9kysJTtAbqbCnwFcZpllGvwc1cL/XbcZHn9mv7YVpLnnntu1F1544dl+X7lnE7NiryNL3gQYMmQIkJzdKsSuERdccIGL2axLtY5XQ9k1EnIzsP5soyVq+eP37bffArlkWkgm2ZozzjgDgJdfftnF0rgWlYO9J0Pu73/HHXe42MSJE4GGJVn7j2czq3///beLHXzwwQDMN998LnbrrbcCuRKcAJMnT653HyrRcccd59p2D+CzhOtJkyY1Wp8ai2ZiRURERCQ4uokVERERkeAEtZ3A3zi+5JJLAsmEnJtuuqnR+1QJtttuO9e2LRV1JWKZJ5980rXt1K266rz9/vvvQHL5qBrtt99+rn3ZZZcBDUu+KnT6mZ+EZMleAwYMqPdzVAvb5gFw0kknATB9+nQXs8SNP/74w8XmmWceILmFwBJC/OVFM2zYsBR7XDksgcZfqrbkrLq2E9iJSIsuuqiLvfjii2l3sSpYAs2WW26Z9zV/S1uvXr1m+xhW1xRy2z8KXaeXXXbZevezsfhL9BdeeGHZn89q5x5//PEuZtfnyy+/3MWsLq2fFHb77beXvX+Nya/t7v/umhYtWjRmdxqVZmJFREREJDhBzMRakkehMjj+J64ffvih0fpUSfwZvmITtixx48orryzqOfwkLjtFxn8ua/tJDiGwhIljjjkm72t+ctH6668PpF8Gyz+9JooiIPlv0pByOiGy12Whsjl+YpfNDvozXksvvTSQPDXOZtD98lCW4FGorFqo/Nln+7ved999LmZJNXWxGRsrPQeaiZ2dtm3bAjDXXPlvo8W+F40cOdK1bWbRfzybZay2mcMslOvUzizZe9SJJ57oYpboZiumkN6Jb8ZWC/xVA1stsOswNE6SvWZiRURERCQ4uokVERERkeAEsZ3glFNOAZLJGba8cuedd2bSp0riJ2LZUr8/zW+nmtlJRfXhJ3Gtu+66ec9hGmNDf5ps7H7++WcXK1RbdNtttwXg7rvvrvdzWW3I2bFxtT8BXn311Xo/XyiOOOII17Z6h/7pZpdccgmQXB4vxJauunXr5mKFTk5qKolzlqQlja/YE5L8BEbbWuQnMD7zzDN5sabCvwbYyXuFToPq0aOHay+//PKzfbwRI0ak2LvKYO/tfq1c2zbx6KOPuljay/r2HP4Wja+++qosz1UXXeVEREREJDhBzMR27do1L3bttddm0JPK5CdTffDBB3lfHzduXL0f2076qSt5zMpzhebrr78G4Oabb3Yxf5O8KXQedanuvfde1z700EMb/HihszPNL730UhezMll2ShSU/rs+aNAg17bXqj8L4yfTVAt/JcFm/G32CnLj6pckk4axGSc/acZmU3fccUcX69KlC5B83dksuZWPg9zKls2+Arz//vtpd7viWYLQK6+84mKWLFTXqZO18a8zN9xwA1B3OclK5M82r7322rP9voasGtb1vP4sedY0EysiIiIiwdFNrIiIiIgEp2K3EyyzzDKu3blzZwAmTJjgYoWWzZsqvx7cW2+9Ve/HsVqw/rLXKqusAiSXcazerJ+QYxvMQ/XEE0+4dqHtBGnwE7usjqR/YpfZaKONXPu1114DqrPG4Q477AAkl6ZsG8HRRx+dF6uLJXH5j2fjdvHFF7tYtSfJ2IldV1xxhYtZHeSrrrrKxf7888+SHtcf14suughIng7k1/FtCt5++20APvzwQxezGsUtW7Z0sYcffhhIvqZtLP2axnYKnf/v1hRZndzmzZsX9f3PP/+8a//22295X7dkMP8ExtatWwNw8sknu5hdayudXy+/0Olcdg/w2GOPpfq8/vtSoefNimZiRURERCQ4FTsTa0kfkJtN8ctpTZkypdH7VI3svG7InW7kn8Nc6LQvm6kMffbV58+mWLKXvxqw0korAdCnTx8Xq61ki39ij822+idxFZqBNX4CwqRJkwAYOnRo7X+BANmMwsCBA13MShMVO/vqzw7642Z++uknAD777LN69zM0Np4bb7yxi9lMtJ9wdNpppwHw0ksvudg666yT93hbbLEFkEwystJvBx10UEq9Dpdf0u31118HcslJAPPPPz8At956a62Pc9555wHw1FNPpdzDsNjvvn9qnCUmFmLXayi8ymLXcX/8LVn8xhtvdDGbsf3111/r0evya9OmDZAsy1bo5Cw75THtFSd7Hc/uebMq6aeZWBEREREJjm5iRURERCQ4zUpMGPmn0ClN5eDX1Nxjjz2AZN1D21TvL/naMri/7FVoo3eaZo1fQwal0cbUZ9sIhg0bluvIrNeC35/JkycDuVqH0LC6s8XKclzHjh0LFD7tqVgTJ050bauvV5/kLFta+9///udi1j7hhBNczE/um51QX6u16d69u2vbNcPvoyWF+jUn01ap4+ov751++ukA9O3b18VatGgBJJcdLeb/rF1P/a0DljzinxSUpkod07pYUpJt1QA455xzZvv9o0aNcu0tt9wSKG8SZ6jjmgZ/OfyXX37J+7qdlGjX/2I11phaYlWh2rb+z59xxhlAw07PtK0LkLuHeuihh1xsww03zHte25ZU6FS1UpUyppqJFREREZHgVFxil53MYZ9Kff6pNIMHDwZg3333dbG5554bSH66jaIIyM0oNmV2+hbkTuDyP/UXSuLafvvtgcaZfa0myy23XCqPY6Vg7E/I/W74Z1TXNttTjWwFxk/mstevJR0BvPvuu43bsQriz5L2798/8SfALrvsAiRLGdlMrJ9Ee+yxxwLwxhtvlK+zVeKvv/4C4JZbbnGx2n43C62ESXn4J6tZMpjdb0AuYapcJRYbyzbbbAPA+PHj877mr3BbcrF/IqqdJOmXfvMTP2vzySeflN7ZFGgmVkRERESCo5tYEREREQlOxW0n2GeffQBYZJFF8r7mT2u3atUKSNaTtOnzrbbaysXsNI+GJOmEyF9+tpM7VlttNRcrlMRldV/33HPPxuhixSpUA68hLEkm7SSYuupOVhvbLgQwYMAAIJc057OTwACmTp1a/o4Fyk/UMH7dXWNbDP6fvTuPt2r6/zj+QuGrQpllHlKZZzLUl8rXlCF2mWcZigzJFCIKmVVkrCS2RBkyJmToi9BXZkIhGaMU1c/vj9NnnXU6p3vPvXefu8/e9/18PDwsn3PuOctqd86+a63PZ0nlbGvGNddcU9Tzd9ttN9e+7bbbStInybAT0QDGjh0LwIknnhhXd6rMTnz0t5H52yHMnnvumfNvn7/9yq5VO6nT53/3+bWlKxL1CWHF0kysiIiIiCRO2c3EmkKzYP4pR3Zy0k033ZT3PL8UjCWA+eWICv1MWjRv3hyAl19+2cVs3Aolcfmnbh177LG10cWyZ4ksF1xwgYtZ6RzfMsssA2RP84LCM4M2A1uTxA1/RtESl+bNm1ft10siv8xbp06d8h4fPHgwkD2lS6JxwAEHAPDss8/G3JPy5Jcj69q1KwBHHXVUUT/rlzKy7zwleJWGv6Kwww47xNiT6vnqq6+A3O+YU045BYBevXq5mK1ir7DCCnmv4SccV3Sdffvtt649cOBAALp16+Zia665JhDdamVNaCZWRERERBJHN7EiIiIikjhlu52g0FT3iy++6NoVbQmYPXt23uv4p3iljX+SmW2u9jdrF0risrqvdroHFHfqU1X68tFHH0X2urXJkgHt30tiy4iWZAi5W16K4dfou/HGG/Met7qT/laP0aNHV+k9ks5qwg4aNMjF7Fp+9913Xez000+v3Y6JAA888IBrW2Ky//117733Arn1t3v27AnkbkXQdoLSOvXUU117q622yns8idtl7rrrrpx/Q/ZkL7/WviW7+tdboURj237pJ9E3adIEgD59+riYXaM///yzi5XyVMSKaCZWRERERBKn7GZi/VO5Fme/0S6Jnedr5XcAZs6cCcCnn34aQe/Kk1/aoqpJXDWZJbXz2CF7ApidPw3ZMjP+pvM0sd9k/ZInfrsY/klyhWZi//rrL6Duzb76p0j17t0bgA033NDFLPHArjuR2mbJW3YqJGQ/E/wSWwMGDAAKf39Zso7/sxKt9u3bA7mn1ZkJEya4dmUrb0lhM6L+zGjfvn2r/Xo2s1uIX7Zs+vTp1X6PmtBMrIiIiIgkjm5iRURERCRxym47wdChQ4HcTdg77bQTkF1WBDj88MOB3Dp72267LQCvvvqqi3Xp0gXIXbZJm8qSuIx/gkehpeti38OWcP33sPf1N46vuuqqVXoPyWenVPmb9F966aW4ulNr7O8tFD5Vx7ZhTJs2rdb6VFe1a9cu7i6UjW222ca177jjDiD3c/C+++4D4Prrr3cxq9nt/6x59NFHS9LPuNn38hFHHJH3mJ08Bdnv+4ULF1b5PaxOt3/K3K677gpkaxtD9rPEr/VtW/AuvvhiF7OtW5LLatEWcvfdd9diTwrTTKyIiIiIJE7ZzcTab2Rnnnmmi/Xo0QPIJm4BNGrUCIBJkya5mP1W5ZfiqgtGjRrl2oUSXWyW1C//dPLJJwOFZ1OLjRV6Dz85wcp4SfXZ7IF/KliaZ2LttBk7kW9Jpk6dWhvdqXNsNspPctlxxx2B3FWvNK9sFWInFI0bN87FGjRoAOSWHgrDEMj9/io0A2vfUSNHjoy+s2Vg4403BgonU/k6dOgA5Ca4VsT/HrI/k3333beonx02bJhr9+vXD4BPPvmkqJ+tyxo2bAgUXt31y5nGRTOxIiIiIpI4uokVERERkcQpu+0Ext8mUGhzuGQddthhrn3IIYcAcOedd7qYbSMotBwQVcxq0PongGk7QeXmzJnj2h9++CEALVu2jKs7sbMtQeutt17eY7/88otrX3311bXWp7rEtgO98cYbLtamTRsgu3wLdW87wXXXXQfAyiuvnPdYs2bNXHvy5MlLfA1/25dt56pOQlOaHHjggVV6fmXb2oxfO91O93zttddc7O+//67S+9Y1W2yxhWtbndhC4/3UU0/VWp+WRDOxIiIiIpI4ZTsTK9VjM6LvvPOOi1mpK7/E1mabbQbknljSvHnznOcDtGjRIu89Pvroo7yftfetyQlgdZG/Mf5///sfULdnYu3Et0K/9dtsGOSu1Ej0brnlFte+6KKLgNxSO3Gdkx6XimZY/RO7zLx581y7Z8+eAAwaNMjFFixYEGHvys/rr78OwCabbOJiK6ywApCdhfb5J/SdfvrpRb3HE088AeQmedoKwtixY12sHJKPkuaEE05w7aZNm+Y9PmLECKA8TkLVTKyIiIiIJI5uYkVEREQkcZaqaHN0Af8USvCpyxaNX00GRWNaQF0c1+HDhwPQqVMnF7PED/8Emueff75ar1+uY2qJA5A93cg/XeeRRx4BoHPnzpG/dxTKdVxrwmpDAnzxxRdAbk1Nf2tSKZTbmDZp0gTI3WZhp0b6NU4teejmm292sXLaelFu45oGaRzTddZZx7WvvfZaIHsiKkD79u0BmD59eknevypjqplYEREREUkczcTWUBp/CysHdXFcL7jgAgCuueYaFxswYAAAZ599do1fv1zH1C/nMnHiRACmTJniYnYiz88//xz5e0ehXMc1yTSmpaFxjZ7GNHqaiRURERGRVNNNrIiIiIgkjrYT1JCWEkpD4xo9jWlpaFyjpzEtDY1r9DSm0dN2AhERERFJNd3EioiIiEji6CZWRERERBJHN7EiIiIikjhVTuwqVUcSrkabuiPrRfpoXKOnMS0NjWv0NKaloXGNnsY0ekWNaVVvYkVEREREYqftBCIiIiKSOLqJFREREZHE0U2siIiIiCROvbg7UFVBEHQGLgfWA2YAx4dh+Gq8vUquIAiWAwYCbYEmwOfAxWEYjo21YykRBMGmwP+AkWEYHh13f5IsCILZi4X+BQwMw7BbHP1JG12r0QqCYAMyn627An8BI4HuYRguiLNfSRYEwQPA3kADMt//14VheHe8vUqPJH4GJGomNgiCdsC1wAlAI2BP4MtYO5V89YBpQGtgJaAXEC76AJaaGwC8FXcn0iAMw4b2D7AGMBd4JOZupYmu1WgNBGYCawHbkPmMPSPWHiVfX2CDMAxXBDoAfYIg2D7mPqVJ4j4DkjYT2xu4MgzDNxf997dxdiYNwjCcA1zhhZ4MgmAqsD3wVRx9SotFqwa/Aa8Dm8TcnbQ5jMwNglZhIqBrtSQ2BG4Pw3AeMCMIgmeAzWPuU6KFYTjF+89/Fv2zMfBOPD1Kj6R+BiTmJjYIgmWAHYAxQRB8DiwPPA70CMNwbqydS5EgCNYAmgFTKnuuLFkQBCsCV5JZ+jop5u6k0XHA0DAMVSOwhnStlswtQOcgCMYDjYF9yax0SQ0EQTAQOJ7MdqJ3gadj7VAKJPkzIEnbCdYA6pOZgdmDzPLMtsClcXYqTYIgqA8MB4aEYfhx3P1JuKuAe8IwnBZ3R9ImCIL1yCzNDom7Lymha7U0XiYz8/o7MB14m8zEi9RAGIZnkNlOuAcwisx+Y6mZxH4GJOkm1mZbbwvD8PswDH8CbgT2i7FPqREEwdLAMOBvoGvM3Um0IAi2IZMod1PcfUmpY4EJYRhOjbsjSadrtTQWfZ4+S+YmqwGwKpnZ2Gvj7FdahGG4MAzDCcA6wOlx9yfJkv4ZkJib2DAMfyXz26yWDyMWBMFSwD1kZrs7hmE4P+YuJV0bYAPgmyAIZgDnAx2DIJgUZ6dS5Fg0CxuVNuhaLYUmwLpk9sT+FYbhz8B9aNIlavXI7ImV6mtDgj8DErMndpH7gG6LNsjPB7oDT8bbpVQYBLQA2mp/cSQGAw95/30+mQ8JzRjUUBAErYCmqCpBVHStlkAYhj8tSpA9PQiC/kBDMvu434+3Z8kVBMHqwF5kvvPnkpk9PAI4Ms5+pUCiPwOSdhN7FZllmU+BeUAIXB1rjxIuCIL1gS5k9hXNCILAHuoShuHw2DqWYGEY/gn8af+9qL7pvDAMf4yvV6lxHDAqDMM/4u5IGuhaLalDgZuBnsBC4CXgnFh7lGz/kLmxuoPMKvLXZOrujo61VwmX9M+Apf75p0qr81rKL2ypGvysxnTJNK7R05iWhsY1ehrT0tC4Rk9jGr2ixrTKM7FLLVWTP6v0qeIvAQVpTPNpXKOnMS0NjWv0NKaloXGNnsY0elUZ08QkdomIiIiIGN3EioiIiEji6CZWRERERBJHN7EiIiIikji6iRURERGRxNFNrIiIiIgkjm5iRURERCRxSnJiV5MmTQD4+eefXez7778HYOjQoRX+7E033QTA/PnzXWzhwoUAzJo1K9J+ipTKcsst59rnnnsuAG3atHGxdu3aAbDOOuu42HfffVc7nRMREUkBzcSKiIiISOJU+djZYk6WsFmoBx54wMW23nprAP71r3+52Nprr13Um/72228A3HrrrS523XXXATB37tyiXqNUFo1fjY6ci+O0jlVWWQWAbt26udhqq60GwGmnnVbUaxxwwAGuPXbs2Ah7l9xxNfvss49rP/3000DuqSz29+7uu+92sS5dupS0T0kf03KlcY2exrQ00jiuSy+dnYurX78+ACeccIKLNW3aNO9nXn31VQDeffddF7N7lELPs9VggAULFuQ8p7bG1Prnf08ceuihAFx99dVVftM999wTgClTpriYv3purG9DhgxxsVKvildlTDUTKyIiIiKJo5tYEREREUmckmwnqMgaa6zh2q1atQJgu+22c7H27dsDsMMOO1T4Ovfccw8AZ599tovFsbUgScszW2yxhWuPHz8egJVXXtnFrB/FXhN//fWXa9uf22uvvVbTbvp9SMS4+m6++WYATjzxRBdr0KABUHg7QcuWLV3sk08+KWnfkjqm5U7jGr1yHdMNNtjAtQcOHAjAtGnTXOzJJ58EcpdoK/LHH3+49o8//hhBDytWruNaHeuvvz4Affv2dbHOnTuX5L3eeOMN195tt91yHqutMbWtf5b8XlPFft/b82xbHGSv/Q8//NDFvv7660j65fVJ2wlEREREJJ1qfSa2MrZJu3Xr1i627777AnDssce6mCUh9enTx8Uuv/zykvatkCT9ZnvnnXe69kknnQTk/vb02WefATBz5kwXGzZsGAA9e/Z0MSsV5ff7uOOOA3KT+WoiSePaqFEj1x49ejSQe/2au+66K+95DRs2dLFnnnkGyJ2diVKcY7rNNtsAcOSRR7rYHXfcAcCXX37pYptssgkAp556qot99NFHQO5MlY1voc8vuxYBVl11VSA3+eP+++8HchMY/VWFqkrStVrICius4NotWrQA4LDDDnOxzTbbLO9nrDTcjjvu6GI27pWVUSxGuY7pLrvs4tpVXXUqtBLzzTffuJiV3fv8889r0sUKleu4FqtZs2aufeWV99rjpwAAIABJREFUVwIQBEHJ39c+rwEOOeSQnMdqa0xt5tn/jrXPt2LZfRNA48aNM29e5Exsoed1797dtW+//fYq9aUimokVERERkVTTTayIiIiIJE7ZbSeoyO677+7ajz76KADLLLOMix188MEATJgwodb6VA7LMxtttJFr2/L/22+/7WK33HILAF27dnUxq/FqWzUAvvjiiyW+xzHHHOPa9913H6DtBMavl+svOxnbBH/ttde6mF2r9mcD0KFDBwCeeuqpkvQzzjG1rRJt27bNe+zBBx907V133RWADTfcsMLXq2oSYqGlXP/af/7554t6nUKSdK36dbrterPrE7JLjIXMmzfPtZdffvm8xz/++GMgN1mxusp1TP3P2gsvvBCAo446ysUKjYspdA36Lr30UiA3USlq5TqulbFtBH498so+Ixb3008/ubbVk/W3EVkt2IkTJ7qYfS4MHjw473kmSWNqtWEBttpqq6J+xr6jCl2zr7zyimvvvffeS3xeVWk7gYiIiIikWr24O1AV/gzrQw89BOTOLtqG69qciS0HfsmPnXfeGchNQPjPf/4DZBNaAEaMGAFkEzSg4pnYYk/xqkuaN28OZMu9+f7880/X7t+/PwDTp0+v8PWsLFepZmLjVFH5FX8mK4rf4ivz0ksvATBp0qSSv1e5sDJvjz32mIvZrPjs2bNdzGaerFQcQJMmTYDcEn1+oqexExXtxEbI/nn+/fffNfsfKBN+EqIlH/rJLTamm2++uYsdf/zxAGy66aYVvnbcp0+WGz+h0Mo7FZp9/eGHH1zbkkCHDx+e97xRo0a5tp1YOWPGDBfz/x6klT9z6rcrYrPEhUp7+TO79rj/96E2aCZWRERERBJHN7EiIiIikjiJ2k7ge/HFF4Hc7QR1lb8sOmfOHABWXHFFF7Pam35NXWNLhQAvv/xy3uNW39NeAwon1fhJH3XFxhtvDBSu1ff999+7dqGl9F9//RWABQsWuNhOO+0E5I51KWtG1qbbbrsNgI4dO7qYf1pcVVkCof8a+++//xKf/+qrr7q21ZW0P4O08msQjxw5EshNrLMT4nr06OFib731FpC7RGsKbZvxffDBB0Dusrl9LqTlOi7E3zo0ZsyYnH9DNlHr//7v//J+1ra2QLR1NpOmXr3srYidTOV/t9s2Av9z9e677way9aYXf7wiaf+7X1N+cqYlHFbGEjtrm2ZiRURERCRxym4m1mb51lhjDRezGRZ/s7xf6qSu88/ptnI5F1xwgYtVlCxz+umnu7b9Nuz/ZjtgwAAguxHef73x48e7mM30pJ1fQqdQcovxS2cVYokH/fr1c7G1114byJ2BsPeoyYlS5cBm6fxkDUs+9JMG7SS5ytipcv7zK5qJ9R+z1Yq0u+6661y7ffv2QG7SYKdOnYDcmcRCbLa70Pj6yTJW1s9fSZDCJ54ZP1HMX5WpK+y0Q78044EHHpj3PPv8O/roo13Mn8WW6ttggw1c2757/FPJbKWx0H3EoEGDXNu/b6hNmokVERERkcTRTayIiIiIJE5ZnNjlJxsce+yxQG7tyGJ9+OGHALzxxhsuZqeq/PLLLzXp4hKV62kdV111lWu3adMGyJ6GtCTFnoJkiUqW2FQK5Tqu/rX67LPP5r/porHzt2ncddddS3w9f9vBGWecAcDSS2d/t7T3i2LprFzHtDpsGfL11193sYpOivJP9otauY2r1XN99913Xcw+/+xUHchu8ajM0KFDgdylXONvMbDTlPzlSasP+9133xX1XqbcxtT419HWW28NwD777FPhz7Rq1QrIHSv7nHjnnXdc7KuvvgJyT6WysV/8lKjqKrdxtW1qP/74Y4XPs6Vq+4wsJ+U2psWy7x7/7/VKK62U97xC9wX25+Vf+5MnT46sbzqxS0RERERSrSwSuy666CLXtlnD6rATN+zkI8ieYGVlO6D4kyqSrFevXq5t56X7s4j33XcfUHyZIz8BwT9zvq6xBJklsRmnimZffWeffbZrt2vXDqg4EUQy7ASqFi1auFihFQQ76acusRP8/FlDS7qqbPbVVgF69+7tYoVWxex0v+eeey7vMZtRTCM/udhKEq6wwgrVfr3tt98+r+2XobPVxYkTJ1b7PdLAZgv9cbAyZiqXVZi/InLCCScAueWy7O96odJvPnvek08+6WKFku/ioplYEREREUkc3cSKiIiISOKUxXaC6rATZbp06eJilmjjJzDde++9ANx5550uduWVVwIwYsSIkvezHMydOxeAJ554wsUee+wxILvMUBk/gcaWEusif7tLoc34xZ5uYiwJB7L1+PzX/c9//gOoJuLizj333KKeV+iUujSyGsOQWx/WvPnmm3mx5ZZbDsi9Bm+99Vag8iRQe15UCUdJ4SevWH3jww8/3MUs2Wu99dbL+1n/73UVE6rrPDt9zrbBQbbmsSWDQ93dWuBvH7StF/42l7XWWgvIve5sG0Fl16I9r3Xr1i5mp/09+uijLnbxxRdXq+81pZlYEREREUmcspiJveaaa1x7+vTpQG7ZB0tGsHItAIceeigA06ZNy3s923AP2SQHP9Fm8ODBeT9TV2ZljZV28ZPgKtrobclhkE0YqWuzMJD7W2uh32CrOsPil4yxcjP+azzzzDNV7WKdcN555wGFx9tPQJg0aVKt9SlO8+fPd23/c9JYaa3ff//dxWwVq3nz5kW9h63oADzyyCPV6mea2Kl79m/IjqVffm+dddYBYNasWS7mzyga+4ytX7++i7333nsR9rj82PW45557uljjxo2B3GTsLbfcEih8kqeVIYPsrGxdm5H1T9SsaulLPxHT/xwxzZo1A3Jndu09/JNBt9lmGyD3FMXvv/++Sn2pDs3EioiIiEji6CZWRERERBKnLLYTvPjii649btw4IHea+s8//wRyl1mKPYFrxowZAJxzzjkuZrUN77nnHhezU77SXOPQTjmCbH3SYjd6+7ULbclsypQpJelnXWBj2KlTp5h7khz+KWgV+e2331x7wYIFpepOWfFPPLK62/369XMxW671l22NnzRoP+Mnwlq9yQEDBriYfa5KLqvFve666+Y95ieFpn2bQLFs+XrChAl5j/mJyJtssgkAd999t4vZteyfhGZ1fAu9Xl02aNAgIJuQ5Rs2bJhr+1teTNeuXYHc+uWFPovt9C5/e+i1114LwMcff1ydbhdFM7EiIiIikjhlMRPrs1lAK6EVlalTp7q2lYhaf/31Xcx+S77//vsjfd9yYDOwfjLBpptumvc8mx3o37+/i9n5yk2aNHEx2zzfs2fP6DubYv5vspawVejENH9WrbIzxeuS1Vdf3bUrSkL0EzvrIput8meyLFlmhx12cDE7xeubb75xMUsA80t2Gb/MnhRmJbYKrWZp9rX6Pv/8cyC3jJOVd/KTvU499VSg7s3Edu/evWA7CrfffntezE6Ss/GGbGKXX/Ls4YcfBjQTKyIiIiKSQzexIiIiIpI4ZbedoFT8E3722muvvMfthJo0bifYaKONADj44IPzHrNlAcieDuUnw1idN387gVRPjx49XLtQ4oex09QgWyNZck/nK5SEOHv2bABeffXV2u1YmfK3ZFm7smW9f//73wAsu+yyLmY1N1944YWi3tevOztv3jwg3QmzfsKx1dT0xXWSURr5W1psKXv06NEutuaaa9Z6n+oiSxTzt8httdVWQO5n8iWXXAKUtt65ZmJFREREJHFSPxPbsmVLIPdkpEL8JIg0aNCggWvbOeeFXHXVVa5tpcyefvppF7OSJf6M7Q033BBZP9PKTjWD7EyMfzpaocSPhx56CFDC3OIsycifHSzEyuRZEogUxz+N74ADDsh7/N577wWyM92VqVevXsF2WtnsNcDuu+8O5J6aVtdOg1wSm6mbPHlyJK+npNfCLFl41VVXdTFbUZ0zZ06t9sXvQ6loJlZEREREEkc3sSIiIiKSOLW+1rPKKqu4tk17+0uvn376abVf25JlbAsBZE+eqSiRBuCjjz6q9vuWI39Z0Ja4fJMmTQJg/PjxLhYEQd7zbVnMrx07c+bMSPuaJscffzyQez1dccUVACy11FIuZtsJwjB0sdNOOw2AP/74o8S9TBarXVqopq5v6NChtdGd1Nlvv/1c205G8o0cObJKr1fXTvIrtAXj2WefdW2/Dm9d9sorrwDZ+q6Q/f7xT40q1rbbbhtJv9LA/26xxOA99tjDxexUM/+6rInVVlsNKHwCYG3TTKyIiIiIJE6tz8T6M6IPPvggkD1NBrIna914441Fvd4WW2zh2jYLVtmsq20IP+WUU1zs22+/Ler9kmL77bd37UJJRDar5SdxbbfddnnPtyQjzXJl+LPQf/31FwDLLbeci9nJbxMnTqzwdWyDvX+6imZgC7MZ6sr89NNPJe5JOm244YZ5senTp7t2VVfHCn3epJmtYEnFVlppJQBOOOEEF7Pv7Ouuu67Kr1doZeamm26qXudSpNDsqMWqMxN71llnAbml8/xyh6bQKYq1Ue5QM7EiIiIikji6iRURERGRxKn17QTvvfeea9tSaq9evVysVatWADz88MORvJ8t2958880udscddwDw3XffRfIe5eiwww6r8PFCS4jmpZdecu3hw4dH1qc0OPDAA13bagv7iTEV+f333127Y8eOQO6pSlKY1Tz2kxds6cpf9v7ss89qt2MJZwm1hx56aN5jdiIPZE/skuJtuummrm1JMHW9rqltu/C/2+3v9BprrBHJe2hLFpx88skA3HLLLS5mJ5b69wW21O8nIdspaD5L9vS3CRXaMmTbCAYOHOhitVHzXDOxIiIiIpI4S1VxE/4//mxIVPzEmEaNGgHw1FNPudgOO+yQ9zN2Ks+oUaNc7O233wZg3LhxLma/HcyaNSvCHmctGr+aDEpJxvS+++5z7WOOOaaon5kwYQIAhxxyiIvFNQtTruPqs5Ok7LdcgHPOOSfvebaZvtgZ21JJwpgaP2HTyvD4iRzWj/fff9/FLDGxtiVpXH3299wveWTszHOAvn371lqfTJLGdMaMGa5ts66+gw46CIAnn3yyVvpTkTjH1X7OP/HRT2ytKkvG3nfffV3MZhUXLlxY7detqnK9Vp977jnX3nnnnYHckzztPSu7B6zoef7qgiXsWSk1yJ4CWlVVGVPNxIqIiIhI4ugmVkREREQSp9YTuwqxept+26a/pXrGjh3r2hVtJzjppJNc207msWQ4qZglBp5//vku5rel+myrBlR+UpdUT6FxtSXat956q7a7kyqvv/66a7/44osx9qR82HJ0jx49XKx3794AnHfeeVV+vWnTpgHwwQcfRNC79Gnfvr1rd+jQAcjdvtG6desqvd6HH37o2oMHDwZytw5Mnjy5Wv2sKc3EioiIiEjilMVMrEQvDMOCbZEk8Mvf/fbbb0DhmcMpU6bUWp/Sxk+IMffffz8AL7zwQrVft2nTpq79999/A+kuL3XmmWe6dteuXYHc0oRz586t9T6VMz/pyhKuL7vssri6UyeMGTMm599poplYEREREUkc3cSKiIiISOJoO4GIlB0/WeOxxx4DsnUIAZ5++mkATjnllNrtWIo8//zzADRv3tzF/ISk6mrcuLFrz5s3D0j3dgK/zm6hmrsiUjqaiRURERGRxCmLE7uSrFxP60g6jWv0NKaloXGNnsa0NDSu0dOYRk8ndomIiIhIqukmVkREREQSp8rbCUrVkYSr0VJCZL1IH41r9DSmpaFxjZ7GtDQ0rtHTmEavqDGt6k2siIiIiEjstJ1ARERERBJHN7EiIiIikji6iRURERGRxEnUiV1BEDwA7A00AGYA14VheHe8vUq+IAg2AAYCuwJ/ASOB7mEYLoizX0kWBEET4B6gPfATcFEYhg/G26tkC4Jg9mKhfwEDwzDsFkd/0iIIghbAAGB74EegRxiGj8Xbq+TT91X0giAYD+wC2HfTt2EYbhZfj9Ihyddq0mZi+wIbhGG4ItAB6BMEwfYx9ykNBgIzgbWAbYDWwBmx9ij5BgB/A2sARwGDgiDYPN4uJVsYhg3tHzLjOhd4JOZuJVoQBPWA0cCTQBPgVOCBIAiaxdqxdND3VWl09T4LdAMbjcReq4maiQ3DcIr3n/8s+mdj4J14epQaGwK3h2E4D5gRBMEzgG64qikIggZAR2CLMAxnAxOCIBgDHANcGGvn0uMwMr94vRp3RxKuObA2cFMYhv8A44IgeI3Mtdor1p4lnL6vJCmSfK0m6iYWIAiCgcDxZJYS3wWejrVD6XAL0HnRUk1jYF/0BVYTzYCFYRh+6sXeJzPDLdE4Dhi66MZLqq9QLcalgC1quyNppO+rkugbBEE/4BPgkjAMx8fcn1RI6rWatO0EhGF4BtAI2AMYRWYPp9TMy2RmXn8HpgNvA4/H2qNkawjMWiw2i8x1KzUUBMF6ZH4hGBJ3X1LgYzIz2j2CIKgfBEF7MmO7QrzdSgd9X0WuJ7AR0BQYDDwRBMHG8XYpHZJ6rSbuJhYgDMOFYRhOANYBTo+7P0kWBMHSwLNkLtoGwKpkZmOvjbNfCTcbWHGx2IrAHzH0JY2OBSaEYTg17o4kXRiG84GDgf3JJHScB4RkfpmVCOj7KjphGE4Mw/CPMAz/CsNwCPAasF/c/UqLJF6ridtOsJh6ZPZtSPU1AdYlsyf2L+CvIAjuA/oAF8Tas+T6FKgXBMGmYRh+tii2NTClgp+R4h0L9Iu7E2kRhuFkvK0uQRC8jma5S0HfV9H7h5od+SqFJeZaTcxNbBAEqwN7kcminQu0BY4AjoyzX0kXhuFPQRBMBU4PgqA/maXw48js4ZRqCMNwThAEo4ArgyA4mUzFh4OAVvH2LPmCIGhFZilRVQkiEgTBVmR+8VqaTFWStYD74+xT0un7KnpBEKwM7Exm+9sCoBOwJ9A9zn4lXdKv1SRtJ/iHzPT2dOBXoD+ZWqajY+1VOhwK/IdMjcjPyXxAnBNrj5LvDDIb5GcCI4DTF8sAleo5DhgVhqG2ZkTnGOB7Mtfq3kC7RasyUn36vopefTIrhD+Sqb3dDTg4DMNPYu1V8iX6Wl3qn3+qlNyrTODCarKcoTFdMo1r9DSmpaFxjZ7GtDQ0rtHTmEavqDGt8naCpZbS9hNfFX8JKEhjmk/jGj2NaWloXKOnMS0NjWv0NKbRq8qYJmk7gYiIiIgIoJtYEREREUkg3cSKiIiISOLoJlZEREREEkc3sSIiIiKSOLqJFREREZHE0U2siIiIiCROYo6dra5HHsmcTtmxY0cXGzlyJABnnHGGi/3000+127Ey0bBhQ9d+//3MSbP9+/d3sUGDBtV6n0QKWWGFFVz77bffBmDrrbd2sfnz59d6n8rRMsssA8BKK63kYhdddBEA559/ft7zJ0yY4NpXXnklAM8//3wpu5hYV111lWuvssoqAEyZkj2Ib8011wTg0ksvdTGreenXArXY119/7WK9e/cG4P7774+41+VvjTXWcO0ZM2YAsMcee7iYf42K+DQTKyIiIiKJU+VjZ5N2ssQhhxwCwNChQ13MZnTCMHSxY445BoAFCxZU6fUXjV+NjpyLc0y32WYb13733XcBeOKJJ1ysQ4cOtd4nSP641gabXQPo1q0bAEcddZSLvfTSSznPT/qYXn311a5t/+/LLbeci8U1E1sO49qgQQPXvueeewA4/PDD8543e/Zs1543bx4Aq666qov9+eefAIwYMcLFhg8fDsDLL79coz5WRTmMqW+XXXYBcv9OLbvsspG9PsAff/wB5K4u+DO1USi3ca1XL7MYfNNNN7nYmWeeCcDnn3/uYs2aNYvsPX2NGzd27WuvvRaAyy67zMVsVrgi5Tamu+22GwCHHnqoi9mqzJFHHuliyy+/PFB4haCQJ5980rVPOeUUAH744YcIepyvKmOqmVgRERERSRzdxIqIiIhI4pTFdgJ/WcaWwjbeeOMKf+a5554DoE+fPi5W0VaATp06ubZtnPff99577wWy0+TFKrelhKrSdoLyYYki3bt3d7Edd9wRgJYtW+Y9v2nTpq5t/6+33Xabi5199tk5z0/qmNrf0zlz5riYLUP6f4fr8naCJk2auPaPP/6Y9/h7770HwOmnn+5iP//8M5CbrOR/Ti7+PPtshmwC2Ny5c2vS7SUqhzH1tW7dGsh+70D2Gozal19+6dq77rorEF3icbmNa/PmzYHc5Dh7ff869hO/onTBBRe4dt++fQH4+OOPXWzzzTev9DXiHNN99tkHyE1cP/nkk/1+VarY7QS+rl27AqVL/NZ2AhERERFJtVovsWXlXwD23XdfAE488UQXO+igg4p6nbXXXhvITTZYPJHF9/DDD7v2X3/9BeT+FtGqVSsANttsMxf75JNPiuqLSDFsIz3AnnvuCeT+Bt22bVsANtxww7yf9X9DtuQb/3r/9NNPARg4cGCEPS4v/mfH1KlTAfi///u/uLpTVjbaaKO82KxZs1z76aefBuC///2vi7Vp0waA7bbbrsLXthUCf9bKksFsRgayn6tpZN8z3333nYutt956ec/79ddfARg3bpyL3XXXXUDutXrjjTcCsMUWW+S9hv9naX82/gxwmpx22mlLfOzcc88t2ftacvdxxx1XsvcolZ133tm1H3/8cSB3RWr06NFA5bOq9pkwc+bMCp937LHHAtkkecgmiJVDCU7NxIqIiIhI4ugmVkREREQSp9a3E5xzzjmubXXZinXNNde49quvvgpUvIVgSWwK/qSTTnKx/fbbD8g9LcVqx/r16kSK4dcfvP766wE44IADXMwSFfwlH6sJ6dfjs5OpLDEHYMyYMSXocbK8+OKLACxcuDDmnpSHww47LC928cUXu7YtMT777LMuZslKthUF4NRTTwWy1x1Aly5dcv4N2S1gDz74oItV57M4abbffnvXXmeddYDc+sVfffUVkK3b7LNaswDrrrtuUe933333AfDvf//bxfw/ryRaf/31XfuEE07Ie9xqj9rf8VKwBEZLLPN99tlnJXvfKPj3ULa1zGrDAkyaNCnS9xswYEBe7Lfffov0PWpCM7EiIiIikjglnYmtX7++a9tvD7169arwZ+wO3//N0/iJVlEkEfi/Le+9994A7LTTTi42duxYADbddNMav1e5KpREJFXjl9qxa+qSSy5xMSt/9Msvv7jYM888A+QmbEyePBko7QyE1B1WBgugd+/eQO7pXHaKYY8ePVysUCkn++xeeunsnIeVIrRVLf+14yp1Vhv8v8PW9kvZFSrzuPvuuwO5JcrsBKVC3n//fde21cCkz776/LKOjRo1ynvcVkOLOS2rKvwyXRdeeCGQW17KSkx27tw50veNmv/9YKfy+UnDUfBXqVdbbbW8x0eNGhXp+9WEZmJFREREJHF0EysiIiIiiVPS7QRWCxOyp2EU4iet2KZkW1otJduED4VrqhWqvZg2VitSqs9OSAG44YYb8h6//fbbAejfv7+LffPNN6XvWIpZXcTqnDaTRm+88UZezF+ytgQs21YA2Wuwslq7tnXL6kpCdjtBw4YNq9nj5GvQoAGQu8XArLzyyq49ffp0oPITvixJ54wzznAx/ySrpLPtKP5yvf2dnTdvnov169evJO/fs2dP17Ytgn5i6GOPPZbXl3JkdYchuwVi2rRp1X69FVdc0bUtYdPqGPseffRR17aEw3KgmVgRERERSZySzsQufnb7kgwbNsy1b7755lJ1p0L2m4dflkakGJUl/t16662AZl+jZOWNNBOb4ZfOOu+884DcUm0qExiNTTbZxLWff/55oPDJXdVhSXJvvvlmJK9XboIgyPm3zy+36Z80FwU7jdOS5HxhGLp2nz59In3f2uCXwiuGrR4AbL311gBcccUVLmYJ7v5n6fjx4wE4//zzq9nL0tJMrIiIiIgkjm5iRURERCRxSrqdwD+hqNBSn21k/+OPP0rZjaI89dRTgLYTSPHs1J2jjjoq7zF/4/uXX35Za32qK2xMK0tKqiv8ZJS4tmTVBf7JaFFtIzA77rgjkFuPN03X96GHHpoXs9OxKkr8rg6//uzIkSOBbL1uXznVO60NL7/8smtvu+22QOVbslZffXUA2rdv72JW39xOmYyTZmJFREREJHFKOhNbmXHjxgG5J5mIlDP/tJmHH34YgFVWWcXF7JQk/7SkNM2mlAs7RUaJXfEaPXq0a/vlitLqhRdecG1LdGncuHEkr20Jov5M+llnnRXJa8fFPyVrt912y3t8zJgxQHSnvFlps9dee61gH4yVi/LLxtUF/slvNhNbmZYtWwJwxx13uNicOXMAuOCCC1zMf7w2aSZWRERERBJHN7EiIiIikjgl3U6gpT5Ji8033xyAZ555xsXsBB5/SeWmm26q3Y7VUR06dAByT0GKaklSCjvyyCPzYlYrFerGthm/LudOO+0EVL6d4NRTTwVyE7Z22GEHALbaaqu85//73/92bTtN6ffff69mj+Pl19Vda6218h6POrHKTqBs0aJFhc8bMmQIAHPnzo30/cvdSSed5Np2kmRlrIa+JR5C9qQ+f+uLnexX26d5aSZWRERERBKnpDOxnTp1cu0RI0bkPd62bVsg9+z5u+++u5RdytG0aVPX9k/uqEv22muvvFhNzmFOqz333BPITRKwU5AmT57sYnbiiZ1yAnUj4UXSy675QokgaSlRtOaaa7q2lWJasGCBi/kJMabY0nldunTJi1lJqUIzsZZIA9nyRkmdifXZauyLL77oYm+99VaVXsNPot1ss80A2HLLLV2sd+/eOe/l80+m8k+zq0v8mefXX3+9qJ/ZZZddAGjWrJmLvfLKK0D2+oTs6phmYkVEREREKqGbWBERERFJnJJuJ/j5558rfHz55ZcHspuEa5tf18zfWmCshl2arb/++nkxvxaiZEycOBHInlQC2a0YfnKL+fbbb13bthtccsklLvbee++VpJ8iUTviiCOA3CSsV752AAAgAElEQVSdtPG/Cw488EAAfvvtNxezZX//73VNfPHFF0U976CDDgLghhtuiOR9a9tHH33k2rZ8bUmykD0FauzYsS5m/8/LLbeci9k1+J///MfFll12WaDyBHJ73wEDBlTz/0Igd0uNbcc44YQTXCyu5H3NxIqIiIhI4pR0JvZ///ufa1sCQKHzk2ub/abtn75k7CQPgK5du9Zan2rbxhtvDGR/mwWYPXs2kJuUJBmTJk0CYJ999nExK5NjMweQTTw45phjXGzfffcFckvxtGrVqnSdFakhvxxUu3bt8h6/9957AZg5c2at9akUtttuOyD7d9Rnpz9B9lTJAw44wMX8xK+qevPNN4t6Xrdu3YDkzsT+8ssvrn3eeecBuf8vduKbP+u96qqrApXP7NnPVFbirH///nl9kZpZb7314u6Co5lYEREREUkc3cSKiIiISOKUdDuBv9T08ssvA7nLsQ0aNADgqquucrGzzjoLyK1f+tVXX9W4L1tssYVrW83Ptdde28WsXt1xxx3nYmk+zcO2Uvib5+3EjV9//TWWPpWbdddd17UL1c6103v8U3xMr169XNsSu6zenv/aqskr5cROiBo0aJCLWTKNvz3MTqlL+kmMP/zwA1D5aWO2pWK33XZzMftOqw4/ybMifuJM0v33v/8Fcrdf3XLLLUDh0+B8lmzs1yW2erOFavj6PvnkEyC3zqm162q92OrwT6O0Gv/+3/933nmn1vsEmokVERERkQQq6Uysz87p9X/j2nnnnQFYYYUVXMxKPvm/cR1//PFA7slIhdgGb3u+78orr3Rt//3MQw89BKR79lWKY7Mf+++/v4sddthhVXoNv7zcuHHjADjqqKNczE6pu/zyy6vdz7pi/vz5ADz++OMudvDBB8fVndTxT6uys9U7d+7sYnPmzAHg2muvdbG0rNZYySx/5vnss89e4vMfeOAB17bVlH/9618uVuj7w5Jg/KThQqefmalTp7r2+++/v8TnJZWfYGUJsH4ibEX87+4hQ4YAuSW2CrESW/6fcbEz4ZJNYN5vv/3yHvMT8vzxrU2aiRURERGRxNFNrIiIiIgkTq1tJzDnn3++a9tpHX4SjNl6661de9iwYQB88803Fb62JYq1bt26qL707t3btW+77baifkbSz64zPxmwqurXr+/a9erl/zV74oknqv3adc0yyywDQPPmzWPuSfnyP8t22mknAIYOHepiI0aMyHkMsp+XRx99tIsV2oplS6/2GqVg28i+/vrrkr1HRS677DLXtqTiLbfcMu95q622mmvvvvvuQLY2NGRP9nrjjTdczE6KquzEM9s24yc6q7ZpLr8+qdWc95OLCiUa2ufv9OnTS9y78uffVwVBABTeWuEn4D/22GNA9oRVyG7h6NOnj4vFda1qJlZEREREEmepKpZI+aeyTdRVYTMsVlYLsjMKNksQFUtOgOxv3f7s68KFC6v1uovGryaDEumYFqtnz54A9OvXz8U+/vhjAFq0aFHr/VlcnOO69957A3D//fe7mJUk8xO2KnLppZe6tiUVWkkYKHwKUqkl9Vq1U+XmzZuX95hfIs5msmpbOYyrXyLKPtP9zzw7w75ly5YuVijBtZDPP/8cyE3i+OCDD4DcM+/Nu+++69p2+pJfrm7TTTcFcpPzLPHXVt3iHFMrs2inki3J8OHDgdwZams3atSoqPfyv3eeffZZIHuiZCmUw7VaE2eeeaZr33rrrUBuYleh+5kLL7wQgOuvv74kfSrXMV1rrbVc277L/CROGz9/pdCSGps2bepiDRs2zHttW1G3VQaAv//+O4JeZ1RlTDUTKyIiIiKJo5tYEREREUmcWk/s8tlSin8ShE1Jt2nTxsVsA3d1WG0+/3QVfwpcsmw5q66bNWsWkLs09dxzzwG5y1mFEg2vuOIKIDdBxk5Cq2x5UgpbsGABAK+88oqL2al7kuFfl7Y072/JslqP1VEoIcler1Ai2E8//eTalgzy+++/u9jNN98M5NYMj+JUxqhY/VG/lmu3bt3ynmd1n/36z8Wyzw6/TrSfiCeF2YlyS2L3FBdffLGL1bXve0s0nDhxoovZ9oAPP/zQxWwroZ+YWGg7xh9//AHkJuXffffdEfa4ZjQTKyIiIiKJE2tiV0VWX3111958882r/TpTpkwBYObMmTXuUyHluqm7MpZw4Ze0ueuuuwDo3r17rfdnceUwrh07dnTtwYMHA9lT4QB+/PHHvJ+xEjz+iUbnnXcekJsoFodyGNOa2G677VzbVm+sHBJUPzmzpsphXP0krddeew3IlnuC7ClQjzzyiIvZCTs201IVduJckyZNinq+n/xU2cmLUB5j6pfJs5JDlswFhRNebHyvueaaCl97xowZQOm+l5akHMa1JvzT0Sx59qKLLnIxKxfVt2/fWutTuY2pzTyfdtppRT3ff2+7X/JXBaz9ww8/RNXFSimxS0RERERSTTexIiIiIpI4ZbudICnKbSkhLcptXO1EIUvcAujcuTOQmxBn2wj8hI3KTpqrLeU2pmmhcY2exrQ0NK7RK7cxPeSQQwAYOXJk3mP+dphRo0YBuVtarMbz3LlzI+tPdWg7gYiIiIikmmZia6jcfgtLC41r9DSmpaFxjZ7GtDQ0rtHTmEZPM7EiIiIikmq6iRURERGRxNFNrIiIiIgkjm5iRURERCRxdBMrIiIiIomjm1gRERERSRzdxIqIiIhI4lS5TmypOpJwNaoRF1kv0kfjGj2NaWloXKOnMS0NjWv0NKbRK2pMq3oTKyIiIiISO20nEBEREZHE0U2siIiIiCSObmJFREREJHHqxd2BqgiCoAlwD9Ae+Am4KAzDB+PtVbIFQdAVOB7YEhgRhuHxsXYoJYIgGA/sAixYFPo2DMPN4utR8ulajV4QBMsBA4G2QBPgc+DiMAzHxtqxFAiC4AFgb6ABMAO4LgzDu+PtVToEQbAp8D9gZBiGR8fdn6RL8rWatJnYAcDfwBrAUcCgIAg2j7dLifcd0Ae4N+6OpFDXMAwbLvpHN7A1p2s1evWAaUBrYCWgFxAGQbBBnJ1Kib7ABmEYrgh0APoEQbB9zH1KiwHAW3F3IkUSe60mZiY2CIIGQEdgizAMZwMTgiAYAxwDXBhr5xIsDMNRAEEQ7ACsE3N3RJZI12r0wjCcA1zhhZ4MgmAqsD3wVRx9SoswDKd4//nPon82Bt6Jp0fpEARBZ+A34HVgk5i7kwpJvlYTcxMLNAMWhmH4qRd7n8wMgkg56hsEQT/gE+CSMAzHx9wfkQoFQbAGmc/aKZU9VyoXBMFAMltg/gW8Czwda4cSLgiCFYErySx9nxRzd1IlqddqkrYTNARmLRabBTSKoS8ilekJbAQ0BQYDTwRBsHG8XRJZsiAI6gPDgSFhGH4cd3/SIAzDM8h8R+0BjAL+irdHiXcVcE8YhtPi7kjaJPVaTdJM7GxgxcViKwJ/xNAXkQqFYTjR+88hQRAcAewH3BZTl0SWKAiCpYFhZHIOusbcnVQJw3Ahme1vRwOnA7fG3KVECoJgGzIJiNvG3Ze0SuK1mqSZ2E+BeouyEs3WaNlLkuEfanY0oUhJBEGwFJmqL2sAHcMwnB9zl9KqHpl9hlI9bYANgG+CIJgBnA90DIJgUpydSqnEXKuJmYkNw3BOEASjgCuDIDgZ2AY4CGgVb8+SLQiCemSug2WAZYIgWB5YEIbhgop/UpYkCIKVgZ2Bl8mU2OoE7Al0j7NfSadrtWQGAS2AtmEYzo27M2kQBMHqwF7Ak8BcMjOIRwBHxtmvhBsMPOT99/lkbmpPj6U3KZH0azUxN7GLnEGmvM5M4Gfg9MWy6qTqLgUu9/77aKA3uRnLUjX1yZSCag4sBD4GDg7D8JNYe5V8ulYjFgTB+kAXMvvfZgRBYA91CcNweGwdS75/yNxc3UFmxfNroHsYhqNj7VWChWH4J/Cn/XcQBLOBeWEY/hhfr1Ih0dfqUv/8809Vnl+lJ9chNVkm1pgumcY1ehrT0tC4Rk9jWhoa1+hpTKNX1JhWeSZ2qaW0rc9XxV8CCtKY5tO4Rk9jWhoa1+hpTEtD4xo9jWn0qjKmSUrsEhEREREBdBMrIiIiIgmkm1gRERERSRzdxIqIiIhI4ugmVkREREQSJ2l1YkXK2t577w1Anz59XGyXXXYBoEePHi7Wv3//2u2YiFRb166Zk3hvvTV7CqdllBfKpH7hhRdc+/jjjwfgu+++K2EPReomzcSKiIiISOLoJlZEREREEqfKJ3aVW1Fev///93//B8Buu+3mYm+++WZtvH+NTusotzFdbrnlXHvChAkAbLTRRi7WunVrAD744IOS9SFJ47rSSiu5to1Xy5Yt8573wAMPuPZxxx1X+o4tJkljmiQa1+iVw5huvvnmrm3fI88995yLvf766wCsvPLKLmZbB9Zee20X++STzGnTF198sYs9/vjjNepbdZXDuKaNxjR6VRlTzcSKiIiISOIkPrHLZl/99sMPP+xinTp1Ako/I5smjRs3du3tttsu7/HmzZsDpZ2JTZJDDz3UtQvNwJr33nuvNrojIhH49ddfXfv6668H4Morr6zwZyzxq1evXi520kknATBs2DAXO+aYY4D4ZmTrkn333ReAq6++2sWeeOIJAC6//PJY+lQOhgwZAsDRRx/tYpdccgkA/fr1i6VP1aGZWBERERFJHN3EioiIiEjiJDaxy7YMHH744S5m/y9+H4MgAGDkyJEl6UcaN3X7yy4XXnhh3uO2RaNUYwrJGFdLgPO3CTRr1izveV999RUAu+66q4vNnDmzpH0rJAljmkQa1+ilaUyvuOIKIHeLwccffwxA27ZtXez7778veV/SNK4V2WKLLVzbEvD+/vtvF7OkvR9++KHG75WkMd1ss81c27636tev72J//fUXAA0aNKiV/iyJErtEREREJNUSm9hls66FSmwtvfTSec+TmrHf0AB+++23GHtSPmyWutDs6/z581370ksvBeKZfZXqW2aZZQBYfvnlXWzOnDlxdaegdu3aAXDmmWe6WIcOHYDcxCS7Bj/77DMXs1OlrGQeZBMT7fkAL7/8MgCdO3cuqk/+rNLuu+8OwGuvvVbUz6aRzcT6s4OWDOon0MRRdi+tLPkYst9dgwcPdrEoZmCT6I8//nDtadOmAbnlM/3ymkmhmVgRERERSRzdxIqIiIhI4iQqsctP4nrooYeA3KUr+3/xE44sCalUkrSpu1hDhw517aOOOgqAzz//3MX8zeGlUq7jajUHAcaMGQPkbl8x1113nWtfdNFFkfejOsp1TGtixRVXdO39998fgMcee8zF9thjDwDWWmstF5s0aRKQ++dWr15mZ5WduAQwe/ZsAA477DAXK7R1JM5xtdqjRx55ZFHPnzVrlmvbcqJ/utQqq6yS6VBE27BOPPFEIFuTslhpvFbXXXdd17ZkoyZNmrjYOuusA+RuA4laGsfVZ9tX/OvNlst33nlnF/vvf/8b2XsmdUzvv/9+ILdOrNl6661de8qUKbXVJUeJXSIiIiKSaolN7CqUxGWxm266KZY+Jd3GG28M5P5mZjMyDzzwQCx9Kjf77befaxeagbWkN38mVqJ38MEHA3DOOee4mM26+qWKVl99dQDmzZvnYiussAKQu4pjCVv2GGSTIHr37h1p32vKZo2h6okYK620UsF2qdifT1VnYtPIZr4hu6roJ7zdc889OY8BLFy4sJZ6lw4HHXQQkJusdPvttwPwzjvvxNKncjVo0CCg8Eysv7Jjp3iVK83EioiIiEji6CZWRERERBInUdsJdtllF9e2pVx/SdBib775Zu12LCUuv/zyvNjPP/8MwIABA2q7O2Vln332AQovvfhsnEqZnFEXrL/++q5tY+5fn7ak/uyzz7qYJWI1bNjQxb799lsgtz7iL7/8AsAHH3yQ975PP/20az/11FPV/x8oIf9zsGPHjjH2pHL+n49k2Sl+Ptsis+qqq7pYXaxnaidI+bW2KzJ69GjXPvDAAwF46623XOzcc88FtDVjcVOnTgVy65fb9qsjjjjCxfr06QPA3Llza7F3xdNMrIiIiIgkTqJmYrt37+7aFSV2SfXYTIA/u22/DdvsVV3ij4OVavNLOhn/N/xCs3c2a3jSSSe5mG2Wf+mll1zsiSeeAOD55593Mb8kUl1wwgknAHDzzTe7mP29fuONN/Ke75fE8hOejM3EpsEGG2wAwK233lrln/34448B+OKLLyp8nn/y1+LOOOMM177ggguKel8rQyeFlXO5qri0atUKyJ4UtyRrrrkmAMsuu6yL2XiOHTvWxYqd0a1rbAbWL0lqf8f9lbBCn6vlRDOxIiIiIpI4uokVERERkcQp73nixRRK4ioUk+Jts802rm0b6n233XZbbXanrNgJOgDHHXfcEp/nL/9PnDgx7/Htt98egIEDB+Y95ieKWfvPP/90sVNPPRXITV7wH08DO2UHskvl48aNc7FevXoBMHny5NrtWJmx0+L803QKsWQq/+/up59+ClS+naCQqn6ujh8/3rU//PDDKr9fXeKfjKatBRmfffZZUc+z0878zw/bglCXv7eiZicW3nfffTH3pDDd9YmIiIhI4iRqJtb/rVWJXdHo2bOna9sG+b///tvF/FmVuubSSy+t8HE7BcpPLjJ+skGxSTDGPzXKTkrzT02x08DScr1fc801rj18+HAATjvttLi6k3hdunQBck+IqolGjRoBxV/H3333nWur1FxhLVq0yItZ4qKVNayr/Otncf73vX13+SX1bOx++umnEvWu7vFXa8uRZmJFREREJHF0EysiIiIiiZOo7QSVJXbppK7i7bDDDgAccMABLmZjeuWVV7pYXR7Tdu3aVfi4Lef/+OOPeY81aNDAta3+biH+lgWrI7vhhhvmPe/qq6927QkTJuT8O03at28fdxfKliW8+LWDV1ppJQCmTJniYlEn/t14441FPe+9994D4Kyzzor0/ePSuHFj1x40aBAAK6+8sovZ8v8777zjYk8++WRRr23JMj5L3lywYEHVO1tH7LHHHq597LHHAjBq1CgX80+akrpBM7EiIiIikjiJmIk955xzgMoTu/xTfqRiW265JZCbRGRjmpaEoSTwSx61bdsWgBdffNHF7JQmX4cOHYD0zMT6M3fDhg0Dcs+Wt8f9cZkzZ07tdK6MvPDCCwDstddeLmaz9/3793exKBKDrJwX5K7WVCQMQyA9yVwXX3yxawdBkPd4oVUDmxE/99xzXcxK8K233nou1rlzZyB3JdE/OUkKK/Tn4J/O5SclS3H8a9Da/n1VuZd+00ysiIiIiCSObmJFREREJHESsZ1gl112AQondn377bcu5rel+tKyHFhqI0aMiPT1bAm9Y8eOLuYnjRh7vKr1Z8uVJQQBHHPMMQDceeedLvbYY48BudsnDjzwQAB+//332uhiWfHHq1u3biV5j379+rn2qquuusTn/fLLL65tJ4WlhZ9gaddZmzZt8h73T92zJM7HH3/cxe69914ge5oUZBPE3n77bRf78ssvo+p66jRr1gyAli1bxtyT9Ondu7drn3766UDulkJ/G2c50kysiIiIiCROImZi7TeBQoldr7/+uovV5XJQUbrjjjvi7kJZmD17doWPW5mcU089tdrv4ZfiMnPnzq326yWdzTLuuuuuLmYnxvgz3xdeeCGQm3wj1dO6dWvXtpPSLPFzSb7++msgOyMO8MEHH5Sgd/HxZ0btdL6PPvrIxexafeaZZ1ysU6dOQG5ZsjPPPBOAM844I+89br/99gh7nF62GtCqVSsX+/zzz4HsSo1UT9KT4TQTKyIiIiKJo5tYEREREUmcst1OcPjhh+e1CyV2Wb09qRpb9vJNmzYthp6UL/+UrAcffDDv8YYNGwK52y9smXu11VZzsU8++QSAzTbbLO81rrvuOte2epybb755hf16+umnK+17ubF6tzNmzHAxW6ItxE8smDRpEgBXXHGFi11++eV5saQvi9U228py9tlnu5glJlWWzGFLuGnbQuCbOnWqa9vf5z333NPFrP6rv/3n/vvvB3KT3IYMGQLA3nvvnfce/mfMd999B2TrAdd1q6yyimvfcMMNeY9b/ego6iLXZf4JcZ9++ilQ+LuqXGkmVkREREQSp2xnYn2FTufSqVI1s9FGG+XF7LQkyXj00Udd+8MPPwRyS7wss8wyAJxyyiku1q5dOyA3YcuflV1ckyZNXNtmwSozZsyYop5XTuwkIz9Z5pVXXgFyEzLt1CL/VCqz7bbburaN77LLLutimomtnD9effv2BeCggw6q8GdsVvaRRx5xsUsvvbQEvSsvgwYNcu0uXbrkxWw1q1AZvO+//961bUa30MlHTZs2de3nnnsOyB1n+wzyT6urKzOPdn1CtsymfzqXzYRLzfz555+ubSsstqKYBJqJFREREZHE0U2siIiIiCTOUlU8jeGfQksipVDoxAj/vS1mS7pxWdSPmgxKrY1p+/btXXv06NFA7vLipptuCpTHyTHlNq6rr746kF3uhuwSV9TXoG1dgOxpP35i2cyZM4Gqn6QS55gOHToUyD3dqCKF/q77nwmXXHIJANdee221+hOlcrtWK3LEEUe49gMPPJD3uPXDv7ZmzZoF5G59KbVyG9NRo0YBuVsv5s+fD+Reg/fccw8APXr0cDGrE+ufKNm2bVsAtttuOxe76qqrgNyTwox/iuL48eOB3M9pSyjzPzsKKbdxLaR+/foAvPrqqy628847A7n1o8ulLnwSxrRYlmjobyew7S3HHnusi5V661ZVxlQzsSIiIiKSOGU7E7tw4ULXriixy35ri0uSfguz3/Sh8ElHK664IgBz5syplf5UJAnjarMp++23n4u1aNGiqJ+1xKV69fJzKxs1auTa/qb7mopzTG3G385AB9hnn30A2GOPPVyscePGQO5MrJ057589X05liJJwrTZv3hyAE0880cXOO++8vOdZP/zkIUtcfPzxx0vZxRzlNqb2PeOXeuratWvmjbzvUJsxtesYsiX2/HKQkydPznsP+3vvz8Tuv//+QPZ0QIC11loLyE0oszJ/Tz31VIX/H+U2roWcfPLJAFx//fUu9uOPPwK5M9eVnahYW5IwpsWyxOX//e9/LmbX9/bbb+9i77//fkn7oZlYEREREUk13cSKiIiISOKU7XYCJXZFz04+Ath6663zHtd2gtpz1FFHAXDZZZe52CabbALAbrvt5mJRJi+kfUzjUq7jasmIAK+99hpQuD60z/rhJ4A9/PDDkfetMuU6pj47La5jx44uZsuxftKVnfLl146NSxLG1bZE2HYtgG7dugFw1113ZTtSxcTWUknCmFaVv53TxtmScyF3W1IpaDuBiIiIiKRa2Z7Y5W+g7969O5Cb2FXoLGWRpBg+fHjOv0Wi5icSVTYDayZOnAjAuHHjStKnNLGZWPu3VF+rVq1ce++99wZyS5cNHjy41vskyaCZWBERERFJHN3EioiIiEjilG1iV1KkcVN3OdC4Rk9jWhrlNq5NmzYF4IknnnCxQomc5q233nLtNm3aADBv3rzI+lMd5TamaVGu49q7d2/XttPOrB4uZE+NK0flOqY1MXbsWNdu164dAMccc4yLjRgxoqTvr8QuEREREUk1zcTWUBp/CysHGtfoaUxLo9zG1Uo6jRkzxsX8U+CMnU3vJ4DNmDEjsn7URLmNaVpoXKOnMY2eZmJFREREJNV0EysiIiIiiaPtBDWkpYTS0LhGT2NaGuU6rkcffbRrDxkyBMhN4jr44IOB8tlC4CvXMU06jWv0NKbR03YCEREREUk1zcTWkH4LKw2Na/Q0pqWhcY2exrQ0NK7R05hGTzOxIiIiIpJquokVERERkcSp8naCUnUk4Wq0lBBZL9JH4xo9jWlpaFyjpzEtDY1r9DSm0StqTKt6EysiIiIiEjttJxARERGRxNFNrIiIiIgkjm5iRURERCRx6sXdgaoIgqArcDywJTAiDMPjY+1QCmhMSycIgs7A5cB6wAzg+DAMX423V8kWBMF4YBdgwaLQt2EYbhZfj5IvCILZi4X+BQwMw7BbHP1JgyAIlgMGAm2BJsDnwMVhGI6NtWMJp++r6CV9TBN1Ewt8B/QB9iHzQSs1pzEtgSAI2gHXAp2A/wJrxdujVOkahuHdcXciLcIwbGjtIAgaAD8Aj8TXo1SoB0wDWgPfAPsBYRAEW4Zh+FWcHUs4fV9FL9Fjmqib2DAMRwEEQbADsE7M3UkFjWnJ9AauDMPwzUX//W2cnREp0mHATEArBjUQhuEc4Aov9GQQBFOB7YGv4uhTGuj7KnpJH9NE3cSKJEEQBMsAOwBjgiD4HFgeeBzoEYbh3Fg7lw59gyDoB3wCXBKG4fiY+5MmxwFDwzBU7cUIBUGwBtAMmBJ3X0TSRIldItFbA6hPZlZrD2AbYFvg0jg7lRI9gY2ApsBg4IkgCDaOt0vpEATBemSWv4fE3Zc0CYKgPjAcGBKG4cdx90ckTTQTKxI9m229LQzD7wGCILiRzE3sJbH1KgXCMJzo/eeQIAiOILPf8LaYupQmxwITwjCcGndH0iIIgqWBYcDfQNeYuyOSOpqJFYlYGIa/AtPRcYK14R9qduSjZB2LZmEjEwTBUsA9ZFZmOoZhOD/mLomkTqJmYoMgqEemz8sAywRBsDywIAzDBRX/pCyJxrRk7gO6BUHwDDAf6A48GW+Xki0IgpWBnYGXyZTY6gTsSWZspQaCIGhFZouGqhJEZxDQAmirvfDR0PdV9JI+pkv980+VJos0s1RYTWaCNKZLpnH9//buPNru6f7/+DMIRWpISEy/hCASVFOqqBZdIgirUuojraQoviISJTGkMa4ECSKEr6FBDRFLPxFjYmhDkiI1j6Wo1pAvQVQ0IRJCfn/cvPd5n5yTm3vvGT5nn/t6rNXVj/edtu2cz/3c997v9y4/zU2a7EcAABi+SURBVGllaF7LT3NaGZrX8tOcll+T5rTZmdg2bbRy5zXzj4CiNKeFNK/lpzmtDM1r+WlOK0PzWn6a0/JrzpxqT6yIiIiIREcPsSIiIiISHT3EioiIiEh09BArIiIiItHRQ6yIiIiIREcPsSIiIiISHT3EioiIiEh09BArIiIiItGJ6thZb5tttgFg1KhRIfbDH/4QgFNOyZ1COW3atOoOTEQyZfcBgEceeQSAf/7znyH2s5/9DICFCxdWd2AiIjWiXbt2AHTt2jXEzj33XAAOO+ywECt28MCUKVMA6N+/f4gtWbKkIuNcFWViRURERCQ6bZp5ZNqyLI9H69mzZ7h+7LHHAJg3b16IXXjhhQAsWLAgxCZPnlzRMS2fv5LOTdaRc4Vin9d//OMf4fqBBx4AYNiwYVkNB4h/TotZd911w/WgQYMA+P3vfx9iG2ywQcHXXH311QCcfPLJIVbK0ZH1OK9Z05xWhua1/GKa0yOOOCJcjxw5EsitarfE4MGDw/W1117b8oGtoDlzqkysiIiIiERHD7EiIiIiEp0othPYkuHMmTNDbPvttwdgiy22CLH58+cDsNFGG4XYRRddBMAhhxxSEPPp76+++qpFY4tpKSEmsc7rJptsAsBzzz0XYl9++SUABx98cIi9/vrrTfp+v/rVrwCYPXt2iL377rstGlusc1qMFW/5ws2NN964Wd9jvfXWC9eff/55i8dST/Nq/DaN7t27A3D88ceHWI8ePQD46U9/GmL2u8TeA5C/3as56mlOV199dQB++9vfhpgV0IwdOzbErrrqKgC+/fbbgq/1W5Euvvjigp9hS8L/+te/Gh1LPc2rsTkCOOOMMwA455xzQsyKOgcMGBBiL7/8ctl+fgxz+qMf/QiAiRMnhlixbQSfffYZkF8Ia3zBrI33pZdeCrGdd965PINF2wlEREREpM5F0WJr2223BfKf9M8++2wgl331/GZjaxXh/2K4/PLLC77GsrItzcjWk9VWy/1t07Zt25V+3nbbbReuDz/88IKPW+GMz3hddtllQC47CbnMuI/FxGcChgwZAsCmm24aYtZ65Pvf/36INZaJ3XPPPcP1NddcA8CNN94YYqeddlqJI47LWmutBeRnAqzFS3Ozr1LIz+Gtt94KQOfOnUPM3uc+W2RZV7+SZ9f2PQAOPPDACoy49m255Zbh+pJLLgHy2xaZcePGhes77rgDgKVLl4aYZW9Hjx4dYrZqYN8XWp7xjtnee+8NwIQJE0KsW7duQH67px122AGAo446KsTOPPNMIH+u641/D9sqtt1LPcu+Ahx55JEAPPTQQwWf54uVbZ633nrrEEuSBIA0TUsYdfMpEysiIiIi0dFDrIiIiIhEJ4rCrvPPPx+AAw44IMRsKaHYKRF2Ig/A22+/DcA777wTYrfffjuQ3zPt1FNPBeDKK69s1thqbVP3d7/7XSB/6dqKgvyyt137ObBiuc022yzEbHmhGvw2hlqb18b4XqSffvppww9376tjjjkGyF9mbYz1lYXca/6VV14JMdtu0NxipJjm1Lv00kuB8vfZbY2FXV26dAnXxx13HABnnXVWbiDLX7d+PLZU/d5774WYLS2+8cYbITZ8+PCCn7frrrsCTS9kXGEcUcypZ1uoTjrppBBrrIDm7rvvDjE7fdIXJdm9Y9GiRSFm37up9xMv1nldc801gfx7gPWD9lsCrFe8L9ref//9gdwWJMgVet9///0lj61W59QKBSHXQ3vx4sUhdsMNNxR83ltvvbXS72dFxgC33XZbwcdnzJgBQK9evVo44hwVdomIiIhIXYuisMv4v0YbO6fX/iJYGftrrV+/fiG24YYblji67Fj2FeDRRx8F4IsvvgixN998E8gvNrDWZNY+pyX++9//hutvvvmm4OMvvPACAFOnTm30+/g2PTHyWRdjf+VC0zMmv/nNbwDYd999Cz7mCxhjLYBrjt122y1cl3s1wLJgvpVRvfvFL34BwHXXXRdiHTp0APJXDV577TUAxowZE2J2OqLPxBp//7BMrBVqQvMzsDGx4k2f9TvooIOA/FUl47N+Vpj897//PcSeeuopIL+A0TJn/h7TkgxsjPwc2il7tnoAubk577zzQqxY0bZlu317Kd+Gs179/Oc/D9dff/01kCuSB/jggw+a9f1WlbW2Z4qOHTuG2Mcff9ysn9ESysSKiIiISHT0ECsiIiIi0YliO4Ftwi7WE7YUfhmtsQ3Ntc5vn7BeugsWLAgxv4Sworlz5zb6vW0JsdgS1uTJk8P1J5980rTBFjF+/PgWf22WdtllFwBGjBhR8LFiJ56sihUV+t68tgx0/fXXh1ixrRv1omvXrkB+wYs/AWpF/oQiW84q1gvRs0Ixvz2pHvlClr59+wL5BVt33XUXAJMmTQoxP+9N4U/2WmeddYD8rTT1xvd/vvfee4HcfcArVkDjC7bs/myvd8j13vRmzZoFtJ4tBJ7f+mLbCPzytBVsvfjiiwVf6081s/upFTdB/na7euW3Ydm2Fd8Ttrn8nFn/4j/+8Y8hZs8ZvgCsGr/blYkVERERkejUbCbWTtmAXIsS29zdEmuvvXa4tlOQ/Pn299xzT4u/d9Z8JsCyy36ze6yZzlpn7a/8a8syg6vKnFhhgS+g8QV6Zo899gDg+eefL22wkbAWZf69acUy3rRp04D8bKO9zotlYv0JNHZqXL2yllmWfYXcfcGKByGXdW1JRtoKuvzr3H6GtT4CGDp0aLO/dy3q1KkTAPfdd1+IFTsr/tlnnwVyxcMrfo2xItsHH3wwxKzl2+OPPx5idgpSa2IZRN/+0TKwln2F4hlYa3H2u9/9LsRsjm1Vq7Xwr6Ny8CvXxQoXjT9xUplYEREREZEi9BArIiIiItGp2e0EdnoU5AoGbKmmOXr37g3A6NGjQ+wHP/gBkH+yV2vY6C2l89tcBg4cWPBxK95YVX88WzKzDfKe78dnJyO1FlZ44Je9jz32WAAGDx4cYu3btwfgiiuuCLFi2zGM9ToF+Oqrr8oz2Brie4uOHDkSyN8m0NKTsyDXY7Z///4hVqxQzJYbJ0yY0OyfUeus/2ixLQT+lEfbOrCqXuU9e/YEip/m5be+lHKSXEysZzHkXr/+tXXGGWcA+VsI1l9/fSB3oifk7sm+B+ppp50G1Of7PivF3gfGnzhZDcrEioiIiEh0ajYT2xKWMfAnVVjMZ2msBURLMru1yFrlQO7fVyrjggsuCNfW0sln+Xyhxop++ctfhutzzz234ON2ApovjGkNp3N5lun2WVcrbvGn6nXu3LlJ38/+e4wbN65cQ6xJvuiiWBFXUzOwdv849NBDQ8yyrrYituLPM3baV72c0uVPdSrWRsuKLf17eeHChSv9fmuuuWa49u9xY5nzsWPHNn+wkTvmmGPCdZcuXYD8DOv06dMBOPDAA0PM5smv2hpfCOvb8EnL2e87gKeffhrIb1tmv7+efPLJqo5LmVgRERERiY4eYkVEREQkOjW7ncBv6rZrH7MN8aNGjQox6yt32223hdjRRx8NxN0HdlVsGQ+0naBSbBnLesMCvPLKKwD06dMnxIoVYlhPyF//+tch5pfGzcSJEwF4//33Sx9wRHbaaadwbb2gfa9BWbW99torXNt9slhRoF8Wt20Chx12WIhtt912ed8DclsHfMzMmTMnXPtC2XrgC4FsqdSz97A/xaux7QR+edwX4hn779Ha+plC/j3AXm9+S4ttv/BbWuwUKt8P9Sc/+QkATz31VOUG28psvfXWADz88MMh5nujmzRNgepvJ1ImVkRERESiU3OZWCvisLOSIfeXmd9Ab9mtefPmhZhlAmbPnh1irfGvWim/YcOGAfmnQVkWymcMevToAeQXExx11FHAqrOLvn1Xa7DvvvsC8Kc//SnErHVWuVgG3We+P/zww7L+jFpQrLDrmWeeCTHLjvjWOMUyrMXaZNnqzsYbb1zweaeeemqIffLJJyX+W9SWBQsWhGtb8bvllltCbKuttgLyizmvv/56AB599NEQs9ONfPGS8a34XnjhhXIMO0rvvvtuuLbXkbXCBHjvvfcAGD58eIg98sgjQG4Fy2ttrQkracSIEUDu9b4yvsC8mpSJFREREZHo6CFWRERERKJT9e0E6667brgeMmQIkL8k1bFjRwC+/fbbgq+1ZRnI9UCcOXNmiM2fP7+sY5XWzS+fduvWreDjVuTlexc2lRWNnHjiiSFmS5XFXvv1YrPNNgvXVghQrMit3I4//vhw7YtB68WkSZPC9f7775/3/5DbRuD7Z9qS69133x1idu3v01Y0ZvdmyG038F9bz+wUPdsaBLntbX6LxoUXXgjkb2OzU+j8/cQMGDAgXH/66adlHHFc7KRDyJ2Otskmm4TYf/7zHwA++uijELMixX322SfE5s6dC8ATTzxRsbG2Bn5rm+9vvqKbb745XM+aNauSQ1opZWJFREREJDpVz8Sefvrp4dr/9WXefPNNIL+9iZ3Icd5554VYa8kAlJM/2WTzzTcv+LgVMqg9SQPfLssKMPypJVYg5FtiXXvttUD+a7XY6VKWgb3pppvKOOLaZ6svUJ4M7J///Odwbafy7bHHHgWf57OI9cgXuNrKQLHzzX0mtjG33npruLa2W/5nWAFTa2MZWYC//vWvAIwcOTLEBg4cCEDbtm1DrFgG1px00knh2u7JvlCpnldlVsYy0qvKTB900EFA/klodj9tbauy1nIU8k/qawo7fQtybSB99nX11Vcv+BpbNfQrXFm9VpWJFREREZHo6CFWRERERKLTxvcXbIJlxU5taQ6fprYN3P369QsxKzawPrAAjz32GACXXnppiPnTT7K0fP5KmZSS59QXqpx11llAft892wzv+7wVW+L64osvgPyTOcaOHQvAk08+WdIYm6sW5tWzQpc11sjtwFm6dCmQX8RhfWKnT58eYh06dABgxowZIbbffvsB1V2CyXJOe/XqBcCdd94ZYuutt16LB2IFnb6ozvqZ3n777QWf709csveBFdyUqtZeq6WwOZwyZUqI2e8If++u9HauWOfUTj+zosWWuOOOO8L1kUceWfKYvFjntZi//e1vAOy+++4hZr1lX3zxxaqNI8s5tb7l9nsaYNCgQSUMZeX8e76xYq9yaM6cKhMrIiIiItGpemGXP8nECjH8XyHFNnVbBvbYY48NsT/84Q9ArqVGa1bsrOIuXboUvW6MZRv9CVRW1OHPRa+3k3mawrLUxbRr1y5c2+k9ln2FXMbvhBNOCLHWVrBhGb5Ssq++gG7o0KEALFmypElfu/7664frU045Baid1Zysde/ePVxbQZdfoXvttdcAFdM2RbHsvr3Xx4wZE2LWEq1YRsuvOEo+u49ArsWWP/Hv1VdfrfqYsmQrr8Wyr/a+hdxqtmenoza1wNY/a2200UZAbTwLKBMrIiIiItHRQ6yIiIiIRKfqhV3eRRddBOSfWmJ9xx566KEQ69mzJwDPPfdciH3ve98D8lPmWaiFjfK+J+FLL70EQNeuXUPMlgGnTp0aYrbx3Rd4DR8+HIDevXsX/AxflGTLEHaKSiXUwrw21aabbhqu/ZK3efzxx4HcyUdZqfactm/fPlzPmTMHgLXXXrvZP/S+++4D8pf/ixVu2Pf2S4pWIOrHbf1//YlAjW0XWZWYXquevfevu+66EOvbty8AixYtCrFdd90VKL5tqVJinVPrfe5fq9Z3+8c//nHVx7OiWOfVWDE45LYF+aV069NdTVnO6WWXXQbk5sKznq+QK6j126rsWcGfothU1iPZbzMsJxV2iYiIiEhdq3phl2d/rfqzj+1ElLPPPjvE3n777WoOKzq+xZNl+3wBzb///e8mfR9ro3XJJZeE2ODBg4H8v7jsdJBKZmJjMmLEiIKYX+EYPXp0NYeTOTtBx7e6amoG1k5G88UtV155JZD/Oi/myy+/BODiiy8OMcvM+P8eVsBYK62CqsmvvDzwwANA/sleNk/+1J9qZmBjZEUuAIcccgiQ39LNZ8SkZez+cfDBB4eYrajY67g1svubb8Vm73F//7VVAH8fLpaBveGGG4D8lprWxtAXfPsTVbOmTKyIiIiIREcPsSIiIiISnUy3E1hK2m94t6XDM888M8T8ZmRpnPVta0n/tsWLFwMwfvz4ELO+fJtvvnkZRldf9t57byC//6ux07wg1zu2tbBTZIoVCBbjX6sHHHAAUNqJO88++2y4XrhwIQBvvfVWiP3lL38BVr09oZ7YEuOsWbNCzHpA+yIuK7ZVT9im86fQ2YlRTz/9dIi988471R5S3Rk4cCCQ28oGuVOq/OmUrY3d1/yWIJuXI444IsRsW2AxNrcAEydOBHLPAgA33XQTkF8sXktbCZWJFREREZHoZJqJLebkk08GYNy4cSE2bNgwINf+BWD+/PnVHVgrYBu4fdsyK+pQJrbQOuusA8AaaxS+jfym+tbGCi58JsBO1fEFL1dddRUAU6ZMCTFr+1IK34rPim78CWnffPNNyT8jNnYSl2VfIVfE5bOura0IsRSdO3cGYPvttw8xa+/mf1dJy+y4447h2q/MmunTp1dzODXtgw8+CNdHH300kN9604qyfCvBkSNHAnDzzTeHWGOrU00tEK82ZWJFREREJDp6iBURERGR6NTcdgLjN8MPGTIk7/+lfDp27Biu7fSPLbbYIsS+853vVH1MMZs7dy5QvHdsa2HL1JMnTw4xf11Nral4a0VWJAe5IrvVVsvlLazIy/eElabbc889AejQoUOIWd/Ojz76KJMx1RPfK75Tp04FH/fL5ZJjBfOHH354xiOpDmViRURERCQ6NZuJleqwE5IA+vTpA+RvmLe2GjNmzAgx38KoNbPWWT67JVIr/ElS8+bNA/JPKRs6dGjVx1RPrAj5+eefDzFbzZLSFbuvjhkzJlxPmzatmsORGqXfviIiIiISHT3EioiIiEh02lgRRhMt88tREopYSpmUmpvT/v37h+tu3boB+b3kqtEvrh7nNWua08rQvJaf5rQyNK/lpzktv+bMqTKxIiIiIhIdZWJLpL/CKkPzWn6a08rQvJaf5rQyNK/lpzktP2ViRURERKSu6SFWRERERKLT7O0ElRpI5EpaSijbKOqP5rX8NKeVoXktP81pZWhey09zWn5NmtPmPsSKiIiIiGRO2wlEREREJDp6iBURERGR6OghVkRERESis0bWA2iOJEm2BK4B9gCWAHcCp6RpujTLccUuSZKZwO6AzeP7aZpul92I6keSJNsCrwB3pmnaf1WfLyuXJMlaNLz/ewHtgbeAEWmaPpjpwCKXJEkP4GpgF2AecHqapndnO6r4aV7LT3NafrHfV2PLxF4DfAxsCvQE9gYGZTqi+jE4TdN2y/+nB9jyuRp4JutB1Ik1gDk0vO/XB84B0uV/3EoLJEmyBnAvMJWGX2D/A9yWJEm3TAcWOc1r+WlOKybq+2pUmVhgK+B/0zRdDHyYJMlDwA4Zj0mkqCRJ+gGfAbOBbTIeTvTSNP0CON+FpiZJ8jYNWZl3shhTHegObAZcnqbpMuDRJEmeAAbQ8MtMWkbzWn6a0wqI/b4a20PseKDf8uXvDYED0Yu3XEYnSTIGeAM4K03TmRmPJ2pJkqwHjAT2BY7NeDh1KUmSTkA34NWsxxKxYr0Y2wA7VnsgdUbzWn6a0yqI7b4a23aCWTRkXhcA/wc8C9yT6Yjqw5lAV2BzYAJwf5IkW2c7pOiNAm5M03RO1gOpR0mStAUmAbekafp61uOJ2Os0bNE6PUmStkmS9KZhWXGdbIcVPc1r+WlOKyzG+2o0D7FJkqwGPAzcBawLbERDNvbiLMdVD9I0fSpN04Vpmi5J0/QW4AmgT9bjilWSJD1p2CR/edZjqUfL7wUTga+AwRkPJ2ppmn4N9AUOAj4EhgEpDUkCaSHNa/lpTisr1vtqTNsJ2gP/j4Y9sUuAJUmS3ARcAJyR6cjqzzJKO0avtdsH2BJ4L0kSgHbA6kmSbJ+m6c4Zjit6SZK0AW4EOgF9lv9ikxKkafoyDRktAJIkmQ3ckt2I6oPmtfw0p5UR8301mofYNE0/Wb7Z+MQkScbS8GBwFPBStiOLW5IkGwC70bBVYylwBLAXcEqW44rcBOAO98+n0fBQe2Imo6kv1wI9gF5pmn6Z9WDqQZIkOwFv0rAyN4iG7i83ZzmmeqB5LT/NacVEe1+N5iF2uUOBK2jYw/kNMAM4NdMRxa8tDdns7jTM6etA3zRN38h0VBFL03QRsMj+OUmSz4HFaZrOy25U8UuSpAtwAg09oj9cnuUGOCFN00mZDSx+A4DjaLgXPAbst3y1S0qjeS0/zWmZxX5fbbNs2bKsxyAiIiIi0izRFHaJiIiIiBg9xIqIiIhIdPQQKyIiIiLR0UOsiIiIiERHD7EiIiIiEh09xIqIiIhIdPQQKyIiIiLR0UOsiIiIiERHD7EiIiIiEp3/D7rjY5wnyeyCAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,12))\n", + "for i in range(output.shape[0]):\n", + " plt.subplot(8,8,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.imshow(img[i].reshape(28,28),cmap='gray',interpolation='nearest')\n", + " plt.xlabel((torch.argmax(output[i])).numpy())\n", + "plt.show()" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Introduction to PyTorch/2. Fashion MNIST.ipynb b/python/pytorch/Introduction to PyTorch/2. Fashion MNIST.ipynb new file mode 100644 index 0000000..1c32cb2 --- /dev/null +++ b/python/pytorch/Introduction to PyTorch/2. Fashion MNIST.ipynb @@ -0,0 +1,362 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.ipynb_checkpoints',\n", + " 'embedding.gif',\n", + " 'Fashion MNIST.ipynb',\n", + " 'fashion-mnist-sprite.png',\n", + " 'FashionMNIST',\n", + " 'Neural Network in Pytorch.ipynb',\n", + " 'Thumbs.db']" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.listdir()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![alt text1](embedding.gif)\n", + "![alt_text2](fashion-mnist-sprite.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "from torchvision import datasets, transforms\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "transforms = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize((0.5,),(0.5,))])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to FashionMNIST/FashionMNIST\\raw\\train-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100.0%" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting FashionMNIST/FashionMNIST\\raw\\train-images-idx3-ubyte.gz\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to FashionMNIST/FashionMNIST\\raw\\train-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "111.0%" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting FashionMNIST/FashionMNIST\\raw\\train-labels-idx1-ubyte.gz\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to FashionMNIST/FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100.0%" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting FashionMNIST/FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to FashionMNIST/FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "159.1%" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting FashionMNIST/FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz\n", + "Processing...\n", + "Done!\n" + ] + } + ], + "source": [ + "data = datasets.FashionMNIST(\"FashionMNIST/\",train=True,transform=transforms,download=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "datatransform = torch.utils.data.DataLoader(data,batch_size=64,shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "images, labels = next(iter(datatransform))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "torch.Size([64, 1, 28, 28])\n", + "torch.Size([64])\n" + ] + } + ], + "source": [ + "print(type(images))\n", + "print(images.shape)\n", + "print(labels.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "model = nn.Sequential(nn.Linear(784,256),\n", + " nn.ReLU(),\n", + " nn.Linear(256,128),\n", + " nn.ReLU(),\n", + " nn.Linear(128,64),\n", + " nn.ReLU(),\n", + " nn.Linear(64,10),\n", + " nn.LogSoftmax(dim=1))\n", + "\n", + "criterion = nn.NLLLoss()\n", + "\n", + "optimizer = optim.Adam(model.parameters(),lr=0.003)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After 1000 episode, Loss: 0.2700101636143636\n", + "After 2000 episode, Loss: 0.5298017345345033\n", + "After 3000 episode, Loss: 0.7793612696571963\n", + "After 4000 episode, Loss: 1.0291785699869398\n", + "After 5000 episode, Loss: 1.270998132389301\n", + "After 6000 episode, Loss: 1.5109612895195672\n", + "After 7000 episode, Loss: 1.7408327375774952\n", + "After 8000 episode, Loss: 1.9726359912398845\n", + "After 9000 episode, Loss: 2.1956617516527044\n", + "After 10000 episode, Loss: 2.4098692574718994\n" + ] + } + ], + "source": [ + "epochs = 10000\n", + "error = []\n", + "running_loss = 0\n", + "for i in range(1,epochs+1):\n", + " \n", + " optimizer.zero_grad()\n", + " \n", + " images, labels = next(iter(datatransform))\n", + " images = images.view(images.shape[0],-1)\n", + " \n", + " output = model(images)\n", + " loss = criterion(output, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " running_loss += loss.item()\n", + " if i%100==0:\n", + " error.append(loss.item())\n", + " if i%1000==0:\n", + " print(\"After {} episode, Loss: {}\".format(i,running_loss/len(datatransform)))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "error = np.array(error)\n", + "num_episodes = np.linspace(0,epochs,len(error))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.title(\"Loss vs Num Episodes\")\n", + "plt.plot(num_episodes,error,c='b')\n", + "plt.scatter(num_episodes,error,c='k')\n", + "plt.xlabel(\"Number of Episodes\")\n", + "plt.ylabel(\"Error\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "images, labels = next(iter(datatransform))\n", + "images = images.view(images.shape[0],-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "predsprobs = model(images)\n", + "preds = torch.argmax(predsprobs,dim=1)\n", + "preds = preds.numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure = plt.figure(figsize=(15,15))\n", + "for i in range(images.shape[0]):\n", + " plt.subplot(8,8,i+1)\n", + " plt.imshow(images[i].reshape(28,28),cmap='gray',interpolation='nearest')\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.xlabel(preds[i])\n", + "plt.show()" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Introduction to PyTorch/3. Inference and Validation.ipynb b/python/pytorch/Introduction to PyTorch/3. Inference and Validation.ipynb new file mode 100644 index 0000000..7aa64e5 --- /dev/null +++ b/python/pytorch/Introduction to PyTorch/3. Inference and Validation.ipynb @@ -0,0 +1,1446 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import torch\n", + "from torchvision import datasets, transforms\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "transforms = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize((0.5,),(0.5,))])\n", + "traindata = datasets.FashionMNIST(\"FashionMNIST/\",train=True,download=True,transform=transforms)\n", + "trainloader = torch.utils.data.DataLoader(traindata,batch_size=64,shuffle=True)\n", + "testdata = datasets.FashionMNIST(\"FashionMNIST/\",train=False,download=True,transform=transforms)\n", + "testloader = torch.utils.data.DataLoader(testdata,batch_size=64,shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "model = nn.Sequential(nn.Linear(784,256),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.2),\n", + " nn.Linear(256,128),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.2),\n", + " nn.Linear(128,64),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.2),\n", + " nn.Linear(64,10),\n", + " nn.LogSoftmax(dim=1))\n", + "\n", + "criterion = nn.NLLLoss()\n", + "\n", + "optimizer = optim.Adam(model.parameters(),lr=0.003)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After 1000 episode, Loss: 0.47717228531837463\n", + "===============================================\n" + ] + } + ], + "source": [ + "epochs = 1000\n", + "error = []\n", + "running_loss = 0\n", + "for i in range(1,epochs+1):\n", + " \n", + " optimizer.zero_grad()\n", + " \n", + " images, labels = next(iter(trainloader))\n", + " images = images.view(images.shape[0],-1)\n", + " \n", + " output = model(images)\n", + " loss = criterion(output, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " running_loss += loss.item()\n", + " if i%100==0:\n", + " error.append(loss.item())\n", + " if i%1000==0:\n", + " print(\"After {} episode, Loss: {}\".format(i,loss.item()))\n", + " print(\"===============================================\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "error = np.array(error)\n", + "num_episodes = np.linspace(0,epochs,len(error))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QRNGXXUtP9yImRkZUlMTKUiIiUj0GbmRISiuQ9HQPBAFwOLwoLuZUKRERqRsDNzIkUbQgOlpCbKwMAHA6Pcy4ERGR6gUtxVBQUIB33nkHkiRh6tSpuPXWW9scs2XLFnz44YcQBAHp6el49NFHAQDr1q3Dxx9/DAC4/fbbMWnSpGANm3TK5fIVJigcDi/WrbNBlgGBXUGIiEilghK4SZKE5cuXY+HChYiPj8eCBQuQmZmJtLS05mNKS0uxevVqPP/884iMjERNTQ0AoK6uDh999BGWLl0KAHjiiSeQmZmJyMjIYAyddEoULRg6tKn5ttPpRUODCSdOmJCUJIVwZERERB0LylTpwYMHkZKSguTkZFgsFowdOxbbtm1rccyXX36JadOmNQdk0dHRAHyZuqFDhyIyMhKRkZEYOnQoCgoKgjFs0imPBzh6tHXGjZWlRESkfkHJuFVXVyM+Pr75dnx8PA4cONDimOPHjwMAFi1aBEmS8LOf/QxXXnllm++Ni4tDdXV1MIZNOnXsmBkej4A+fc4Fbuf3csvMbOroW4mIiEIqKIGbLMtt7hNaLSSSJAmlpaV45plnUF1djaeffhovvfRSu4/X+nsBID8/H/n5+QCApUuXIiEhIQAj95/FYgn6c1LHLnQ+Cgp8fz9Dh0YiIaEHACA83Pe1qqqo5vsocPj6UB+eE3Xh+VAXNZ+PoARu8fHxqKqqar5dVVWF2NjYFsfExcXh8ssvh8ViQVJSElJTU1FaWoq4uDjs27ev+bjq6moMHjy4zXNkZ2cjOzu7+XZlZWU3/CQdS0hICPpzUscudD4KCyMAxCAmpgqVldJ535OM7793o7KyJkijNA6+PtSH50RdeD7UJdjnIzU11e9jg7LGLSMjA6WlpaioqIDH48GWLVuQmZnZ4piRI0diz549AIDa2lqUlpYiOTkZV155JQoLC1FXV4e6ujoUFhbiyiuvDMawSadE0QK7XUZKSssiBPZyIyIitQvKVcpsNmP27NlYsmQJJEnC5MmT4XA4sGrVKmRkZCAzMxPDhg1DYWEhfvOb38BkMuHee+9FVFQUAGD69OlYsGABAGDGjBmsKKVLIopmOJ0emFp9bHE6Pdi1Kyw0gyIiIvKDILe3AE0HlGKHYGGaW10udD6ysxORlubF3/7WssjlP/8zCq+/HonDh0thYeItoPj6UB+eE3Xh+VAXw0+VEqmFLLdtvqtwOr3wegWUlrIlCBERqRMDNzKUigoT6utNLVqBKNjLjYiI1I6BGxmKKPrmQPv08bb52rlebgzciIhInRi4kaG4XL6grL2p0t69vTCZZFaWEhGRajFwI0MRRQtMJhlpaW0zblYr0KuXlxk3IiJSLQZuZCiiaEbv3l6EddD1w+lkLzciIlIvBm5kKC6XBenpbbNtCoeDGTciIlIvBm5kKC6Xud2KUoXT6UF5uRn19UEcFBERkZ8YuJFh1NYKOHmys8DNl407dozTpUREpD4M3MgwlFYgF5oqVQI39nIjIiI1YuBGhnGhViAKNuElIiI1Y+BGhuFydZ5xS0qSYLPJKCnhVCkREakPAzcyDFE0IzHRi8hIucNjTCYgLc3DjBsREakSAzcyjM5agSicTrYEISIidWLgRoYhiuYLrm9T+Hq5caqUiIjUh4EbGUJDA1BaeuFWIAqn04NTp0yorRWCMDIiIiL/MXAjQygpsUCWBb+mSh0OtgQhIiJ1YuBGhqC0AvEv46YEbpwuJSIidWHgRoagNN/t08efjBt7uRERkToxcCNDEEUzIiMlxMVJnR4bEyMjKkpigQIREakOAzcyBKUViOBHvYEg+Na5MeNGRERqw8CNDMEXuHW+vk3hdHrYy42IiFSHgRvpntcLlJSY0bev/4Gbr5ebGXLHmywQEREFHQM30r3SUjOamvxrBaJwOr1oaDDhxAm+RIiISD14VSLdU1qBXMxUKStLiYhIjRi4ke65XP63AlEovdxYWUpERGrCwI10TxTNsFpl9Orlf+DG3ROIiEiNGLiR7rlcFjidHpgvIgaLiJCRkOBlZSkREakKAzfSPVG0XFRhgsLXy41TpUREpB4M3EjXZNk3VerPHqWtsZcbERGpDQM30rWqKhPq6kxdzrgdO2aG5+JjPiIiom7BwI10rSutQBROpxder4DSUmbdiIhIHRi4ka6Jom+NWt++F59xczrZy42IiNSFgRvpmiiaIQhyc0Pdi3GulxsDNyIiUgcGbqRrLpcFvXp5YbNd/PempnphMsmsLCUiItVg4Ea65nJ1rRUIAFitvuCNGTciIlILBm6ka11tBaJgLzciIlITBm6kW3V1AiorzRe1R2lrTiczbkREpB4M3Ei3RLHrrUAUDocH5eVm1NcHalRERERdx8CNdEtpBXKpGTcAOHaM06VERBR6DNxIt1wuX7B1KRk3JXBjLzciIlIDBm6kWy6XGbGxXvTsKXf5MZT+bwzciIhIDRi4kW6JouWSpkkBIClJgs3GXm5ERKQODNxIty61FQgAmExAWpqHlaVERKQKDNxIlxobgWPHzF1uvns+p9PLqVIiIlIFBm6kSy4XIEnCJRUmKBwOL0pKOFVKREShx8CNdOnwYQEA0LdvIDJuHtTUmFBTI1zyYxEREV0KBm6kS0rgFqiMGwCucyMiopBj4Ea6dPiwgIgICYmJ0iU/1rlebpwuJSKi0GLgRrp0+LCA9HQvhADMbrKXGxERqQUDN9KlQ4cCU5gAADExMqKiJBYoEBFRyDFwI92RJODIkUvbo/R8guBb58aMGxERhRoDN9KdsjIT3O7AZdwAX2UpixOIiCjUGLiR7oiib0ozUBk3QOnlZobc9W1PiYiILhkDN9IdUfRlxgKbcfOiocGEEyf4kiEiotDhVYh058gRCywWGb17By7j5nSyspSIiEKPgRvpjihakJ4OWAJYBKr0cmNlKRERhVLQrkIFBQV45513IEkSpk6diltvvbXF19etW4cVK1YgLi4OAHDddddh6tSpAIA777wTTqcTAJCQkID58+cHa9ikQaJoRr9+gV2MpuyewIwbERGFUlACN0mSsHz5cixcuBDx8fFYsGABMjMzkZaW1uK4sWPH4oEHHmjz/WFhYVi2bFkwhko6IIoWjBkT2MAtPFxGYqKXlaVERBRSQZkqPXjwIFJSUpCcnAyLxYKxY8di27ZtwXhqMpiTJwXU1JgCnnEDlF5unColIqLQCcpVqLq6GvHx8c234+PjceDAgTbHffvtt/juu+/Qq1cvzJo1CwkJCQCApqYmPPHEEzCbzbjlllswcuTIYAybNMjl8v1Jd0fg5nR6sHNnWMAfl4iIyF9BCdzkdppfCa02kRwxYgTGjRsHq9WKtWvX4rXXXsMzzzwDAPjzn/+MuLg4lJeXY/HixXA6nUhJSWnx/fn5+cjPzwcALF26tDnoCxaLxRL056S2qqt9SeQBA8wBPx8DBpixZo0JMTEJAS18MAK+PtSH50RdeD7URc3nIyiXn/j4eFRVVTXfrqqqQmxsbItjoqKimv+dnZ2N999/v/m2UrCQnJyMwYMHw+VytQncsrOzkZ2d3Xy7srIyoD9DZxISEoL+nNTW7t2RAHrC6fQE/HwkJETA643B7t0nm4sVyD98fagPz4m68HyoS7DPR2pqqt/HBmWNW0ZGBkpLS1FRUQGPx4MtW7YgMzOzxTEnT55s/vf27dubCxfq6urQ1NQEAKitrcX+/fvbFDUQKUTRgpQUL8LDA//YDgd7uRERUWgFJeNmNpsxe/ZsLFmyBJIkYfLkyXA4HFi1ahUyMjKQmZmJzz77DNu3b4fZbEZkZCTmzJkDADh27BjeeustmEwmSJKEW2+9lYEbdUgUzT/umCB0euzFOtfLjYEbERGFhiC3twBNB44fPx7U52OaWx2GD0/GxIlurFhhCfj5aGoC+vXrhUceqcO8eacD+th6x9eH+vCcqAvPh7oYfqqUKBjq6wWUl5vRp0/g9ig9n9UKpKZ6OVVKREQhw8CNdEPZXL67AjeAvdyIiCi0GLiRboiiL6BKT+++ik+nk7snEBFR6DBwI91wuXwBla84oXs4HB5UVJhRX99tT0FERNQhBm6kGy6XBdHREmJju6/eRqksPXqU06VERBR8DNxIN0Sx+woTFErgxgIFIiIKBQZupBuiaOnW9W3AuSa8XOdGREShwMCNdMHjAY4eNXfr+jYASEqSYLPJrCwlIqKQYOBGunDsmBkej9DtU6UmE5CW5mHGjYiIQoKBG+mCy9X9rUAUTieb8BIRUWgwcCNdUFqBdHfGDfA14S0p4VQpEREFHwM30gVRtMBul5GcLHX7czmdHtTUmFBTE/iN7ImIiC6EgRvpgiia4XR6YArCX7TSEoTr3IiIKNgYuJEuBKMViOJcLzdOlxIRUXAxcCPNk2XfGrfubgWiUHq5sUCBiIiCjYEbaV5FhQn19Sb07RucwC0mRkbPnhILFIiIKOgYuJHmiWLwWoEoHA62BCEiouBj4Eaap7QCCdZUKeCrLGVxAhERBRsDN9I8UbTAZJKRlhbcjFtJiRmyHLSnJCIiYuBG2udymdG7txdhYcF7TqfTg4YGE06c4EuIiIiCh1cd0jxRtKBPn+Bl2wBfxg1gZSkREQUXAzfSvGC2AlGca8LLylIiIgoeBm6kabW1Ak6eNAdlj9LzMeNGREShwMCNNC0UrUAAIDxcRmKil5WlREQUVAzcSNOOHAl+KxCFr5cbp0qJiCh4GLiRpikZt2AXJwC+ylJOlRIRUTAxcCNNE0UzEhO96NEj+A3VHA4vjh83wxP8ZB8RERkUAzfSNJfLEvT1bQqn0wuvV8Dx48y6ERFRcDBwI00TxeC3AlE4HL7n5XQpEREFCwM30qyGBqC0NPitQBTs5UZERMHGwI00q6TEAlkWQlKYAACpqV6YTDIzbkREFDQM3EizXK7QtQIBAKvVF7yxlxsREQULAzfSrFC2AlGwlxsREQUTAzfSLFE0IzJSQlycFLIxOJ3MuBERUfAwcCPNUlqBCELoxuBweFBRYUZ9fejGQERExsHAjTTL5bKErKJUoVSWHj3K6VIiIup+DNxIk7xeoKQkdK1AFE4ne7kREVHwMHAjTSotNaOpSQjZrgmKc73cGLgREVH3Y+BGmhTqViCKpCQJdrvMylIiIgoKBm6kSS5X6FuBAIAgAGlpHmbciIgoKBi4kSaJohlhYTJ69Qpt4Ab4pku5xo2IiIKBgRtpkstlgcPhgVkF8ZLD4eV+pUREFBS82pAmiaIl5IUJCqfTg5oaE2pqBERHy6EeDhGFWHFxMXJyclBWVoaUlBTMmzcPTqcz1MMinWDgRpojy77ihFGj3KEeCgBfxg3wVZZGR4e2WIKIQqu4uBgzZ86EKIrN9+3cuRMrV65k8EYBwalS0pyqKhPOnDGpKOPmGwcrS4koJyfnx6BtBIAIAIAoisjJyQnpuEg/GLiR5iitQELdfFfhcLAJLxH5lJWVAegN4FsA85rvLy8vD9WQSGcYuJHmiKI6WoEoYmJk9OwpsUCBiJCSkgLgpwDMAKY135+cnByqIZHOMHAjzRFFMwRBbs50qYHDwZYgRATMmzcP4eF3/HjragDRSE9Px7x58y70bUR+Y+BGmnPkiAW9enlhs4V6JOc4nWzCS0RAQkI6vN5JiIk5CMCMUaOeYGECBRQDN9IcUbSoZppUofRyk9kNhMjQNm60obHRhD/9KRbh4RIGDXqEQRsFFAM30hxRNKumMEHhdHrQ0CCgooIvKSIjy8uzISpKwvjxbowZ04iNG8NCPSTSGV5lSFPq6gRUVppV0wpEofRy4zo3IuOSJCA/345Jk9wICwOystw4dMiKY8d4qaXA4V8TaYoo+gKj9HS1ZdyUJrysLCUyqsJCK06cMCM7uwGAL3ADgE2bVLQglzSPgRtpisulrlYgCmbciCgvzw6TScaUKb7AbeBADxISvNi4kYEbBQ4DN9IUpYeb2jJu4eEyEhO9rCwlMrD8fDsyMxsRF+erUhIEX9Zt40YbC5coYBi4kaa4XGbExXnRs6f63gV9vdw4VUpkRMeOmbB3rxXXXNNyD+WsLDcqK834/nu+N1BgMHAjTRFFi+oKExTs5UZkXPn5dgDANdc0tLh//HgEXzCHAAAgAElEQVRfIMfpUgqUoH0EKCgowDvvvANJkjB16lTceuutLb6+bt06rFixAnFxcQCA6667DlOnTm3+2scffwwAuP322zFp0qRgDZtURhTNyMxsDPUw2uVweLFmjRkeD2Dhh2siQ8nPt6NPHw/692+5jKN3bwkZGU3YuNGGhx46E6LRkZ4E5fIiSRKWL1+OhQsXIj4+HgsWLEBmZibS0tJaHDd27Fg88MADLe6rq6vDRx99hKVLlwIAnnjiCWRmZiIyMjIYQycVaWwEjh0zY/p0tWbcvPB6BRw/bm6uMiUi/Tt7VsDmzTb8/OdnIAhtv56V1Yh//CMcjY1AGNu60SUKylTpwYMHkZKSguTkZFgsFowdOxbbtm3z63sLCgowdOhQREZGIjIyEkOHDkVBQUE3j5jUqKTEDEkSVNd8V+F0+sbFylIiY9mwwQa3W2gzTarIynLj7FkTdu5k1EaXLigZt+rqasTHxzffjo+Px4EDB9oc9+233+K7775Dr169MGvWLCQkJLT53ri4OFRXVwdj2KQySkWp2lqBKFr2clPndC4RBV5eng09e0oYNar91/2YMW6YTDI2brRh9Gi+N9ClCUrgJrdTBy20yiePGDEC48aNg9Vqxdq1a/Haa6/hmWeeaffxWn8vAOTn5yM/Px8AsHTpUiQkJARg5P6zWCxBf06jqaz0JYivuqonOvtVh+J8xMQAZrOMysooJCREBPW51Y6vD/XhOQkMSQK+/tqKadMk9OrV/u8zIQHIzJSxdWsPJCS0X6TA86Euaj4fQQnc4uPjUVVV1Xy7qqoKsbGxLY6Jiopq/nd2djbef/99AL4M2759+5q/Vl1djcGDB7d5juzsbGRnZzffrqysDNj4/ZGQkBD05zSavXt7IiIiAmZzJTr7VYfqfKSmJmH//kZUVp4K+nOrGV8f6sNzEhg7d1pRXp6IrKxaVFbWd3jc6NFReO21SBw+XNVuOyOeD3UJ9vlITU31+9igrHHLyMhAaWkpKioq4PF4sGXLFmRmZrY45uTJk83/3r59e3PhwpVXXonCwkLU1dWhrq4OhYWFuPLKK4MxbFIZl8vXCqS9xb9qwV5uwVNcXIy5c+dixowZmDt3LoqLi0M9JDIgZbeEyZPbX9+mmDDBDa9XwNatXOdGlyYoVxiz2YzZs2djyZIlkCQJkydPhsPhwKpVq5CRkYHMzEx89tln2L59O8xmMyIjIzFnzhwAQGRkJKZPn44FCxYAAGbMmMGKUoMSRXObUnu1cTo9+Oore6iHoXvFxcWYOXMmRFFsvm/nzp1YuXIlnE5nCEdGRpOfb8fVV5/bLaEjw4c3IjxcwoYNNlx7rfuCxxJdSNBSA8OHD8fw4cNb3HfnnXc2//vuu+/G3Xff3e73TpkyBVOmTOnW8ZG6SRJQXGzB1KnqfsNzOLyoqDCjvh4IDw/1aPQrJycHolgMYC6ADwBUQxRF5OTk4NVXXw3x6Mgojh0zY98+KxYurOn0WJsNGD26kY146ZJx5wTShLIyE9xuQXV7lLamVJYePcrp0u5UVlYGYDSAPwH4Y/P95eXloRoSGVBeni8Ia73NVUfGj3fj4EErjh/npZe6jn89pAlqbwWicDjYyy0YUlJSACjrZO8BMBEAkJycHKohkQEpuyVkZPj3gTIryxfgbdrErBt1HQM30gSXyxe4aSXjxj1Lu9e8efPQo8dEAOUADgN4DU5nBubNmxfikZFRnDnj2y3hmmsa/C6YGjTIg/h4L6dL6ZIwcCNNcLnMsFhk9O6t7oxbUpIEu11mZWk3czqdSEy8HikpxRg48A0AV+Dmm79kYQIFzYYNNjQ2drxbQntMJl/WbdMmG9ppb0rkFwZupAmiaEFamlf1m7cLApCW5mHGrZudOSNAFMNx770D8OWXj+Gaaxrw9tsOrh2ioMnLs6NnTwkjR17cTghZWW5UVJixf7/K38xItfguR5ogimbV7lHamtPp5Rq3brZnjxWyLGDo0CYAwOLFNZAkAYsXR4d4ZGQEkgR8+aUNkyc3wGq9uO/NyvIFepwupa5i4EaqJ8vnmu9qgcPh/XG/UuouhYW+q6USuDmdXsydexpr1oRjwwY2OKXutWuXFZWVZr+rSc/Xu7cXfft6GLhRlzFwI9U7eVJAba1J9YUJCqfTg5oaE2pqVLzFg8YVFVnRq5cXiYlS832/+lUd+vTx4KmnYuBWd7s/0ri8PDvMZhmTJvm/vu18WVlufPNNGJqaAjwwMgQGbqR6SiuQvn21Ebg5HKws7W6FhWEYNqzl2iK7HXjhhRocPmzBW29xdxXqPspuCbGxXaswyMpy4+xZE3btYnaYLh4DN1I9JXDTylSp0hJEGTcF1unTAg4ftjRPk55v8mQ3rr++Hn/4QySOHWPgTIF39KgZ331nvahq0tbGjnXDZJI5XUpdwsCNVM/l8l2AnU6tZNx842TGrXvs3u1b3zZsWPvzTM89VwsAeOaZnkEbExlHfr4v2MrO7nrgFhMjY9iwJmzcyIwbXTwGbqR6omhBSopXM3t/xsTI6NlTYi+3blJU1LIwobXevb147LE6fPZZOL76ihkNCqy8PDv69vWgf/9LmwEYP96NnTvDcPo018LSxWHgRqrncpk1U5ig8FWWMuPWHQoLw5CW5kFcnNThMb/8ZR0yMpqwaFE0GrqeGCFqoa5OwJYttkuaJlVkZbnh9Qr45htm3ejiMHAj1RNFi+r3KG3N6fSwl1s3KSqydphtU4SF+QoVXC4LXn+dhQoUGF3ZLaEjI0Y0wm6XuG8pXTQGbqRq9fUCysu1mXE7etTCbW0C7NQpAS6XpcP1beebMKERN91Uj1dfjWIQTQGRl2dHdLSEq6++uN0S2mO3A6NGNbJAgS4aAzdSNVH0XXC1smuCwun0oKFBQEUFX2KBpBQmDB3q34Xz6adrYDLJePpp7qhAl8br7fpuCR3JynLjhx+sKCvj+wT5j38tpGpaawWiUFqCMNMTWEVFvvVAnU2VKlJTJfzud6eRl2fH2rXMbFDX7dplRVVV13ZL6EhWlu+xOF1KF4OBG6nakSO+wEdrU6VK4MatrwKrsNCK9HQPYmL8n4N+4IEzuPzyJjz9dDTq67txcKRrl7pbQnsGD/YgLs6LDRsYuJH/GLiRqomiBTExUpc7lIdKWpov0GTGLbD8KUxozWoFliypQUmJBa++GtVNIyO9y8+3Y+TIxov60NAZkwkYP74RmzbZuB6W/MbAjVRNFLVXmAAA4eFAUhJbggRSdbWAkhJLm62u/DF2bCNuv/0s/vznyOYsLpG/SkrM+P576yU13e1IVpYb5eVmfP99wB+adIqBG6maKFo0t75N4XB42YQ3gHbvvrj1ba0tXFgLm03GokXRzG7QRVF2SwhEG5DWlHVuX37JyzH5h38ppFpNTb59AbWYcQN8laXMuAVOYaGvlO8nP+la4JacLOH3vz+Nr7+24/PP7YEcGulcXp4d/fp5kJER+A+RDocXffp48NVXvByTf/iXQqp17JgZHo+guVYgCofDi+PHzfBoc/iqU1RkRb9+HvTs2fV02X33ncGgQU14+umeOHuWWw1R5+rqBHzzTWB2S+hIVpYbGzYIaOraZxIKoOLiYsydOxfXXnst5s6di+Li4lAPqQ0GbqRaSisQre2aoHA6vfB6BRw/zqxbIBQWWv3u39YRiwV48cUaHD9uwSuvcEcF6tz69YHbLaEjWVlunD4toKCA21+FUnFxMWbOnIl//etfWL9+Pf71r39h5syZqgveGLiRarlc2mwFonA4WFkaKJWVJhw/buny+rbzjRzZiJ/97CzefDMSBw9yDSJdWF6eHTExgdktoSNjx7ohCDI2bmTgFko5OTkQRRHAEwBWATBBFEXk5OSEeGQtMXAj1RJFC+x2GcnJHW8mrmbs5RY4RUW+9W3+bHXlj4ULaxEeLmPhQhYqUMfO3y3B0o0v49hYGcOHy9z+KsTKysp+/Nd9ABIA+K495eXlIRpR+xi4kWqJohlOpwcmjf6VpqZ6YTbLzLgFQGGhFYIgY8iQwARuCQkS5s+vxcaNNqxZw0IFat/OnVZUV5u7dZpUMWWKjJ07w1BXx7WXoZKSkgJgGIAB8GXcfJKTk0M1pHZp9JJIRuByabcVCOBbT5Wayl5ugVBUZEX//h5ERgYuPfbzn5/FkCGNeO65aF4sqV35+XZYLDImTQrcNlcdmTJFgscjYOtWTpeGyrx589Cz5y8BeAD8EwCQnp6OefPmhXRcrTFwI1WSZV/GTasVpQr2cguMoqKwLrcB6YjZ7CtUKCsz47//mzsqUFt5eb7dEqKju38+fexYGXY7p0tDyeFwokeP+5GUtBsTJw7BbbfdhpUrV8LpdIZ6aC0wcCNVqqgwob7epPnAjb3cLl15uQllZeaArW8734gRTbj77jP46197YP9+Bth0TnGxGfv3d89uCe2x24GRI90M3EKooMCK0lI7nniiD9auXYtXX31VdUEbwMCNVEppBaLlqVLAl3GrqDBzc/NLEOjChNYWLDiNyEgZTz3FQgU6Jz/ft/YxGOvbFFlZjdi/34rycl6aQyE3NxxWq4xp04J3zruCfx2kSlpvBaJQKkuPHmU2p6uKisJgMsm44oruCdzi4iQsWFCLb76xYfXq8G55DtKevDwbMjKa0K9f8D48KttfbdrErFuwSRKwZk04Jk50IyZG3Z/gGLiRKrlcFphMMtLStJ5xYy+3S1VYaMXll3sQEdF9b6Z33XUWV17ZiMWLe6K2loUKRnf6tLJbQvcXJZzviiuaEBMjcbo0BHbsCENpqRk336z+6REGbqRKomhGWpoXYRovsFIybgzcukaWfVOlgS5MaE0pVDhxwoSXXmKhgtGtX29DU1P37pbQHpMJGD/et86N0/bBlZtrh80m49pr1T1NCjBwI5USRW23AlEkJUmw22VWlnZRaakJJ06YMWxY93WtVwwb1oR77z2Ld97pgX37eL6MTNktITOz+//uWpswwY2yMjMOHeLfYLB4vcD//m84pk5tQFSU+iNmBm6kSi6XWfPr2wBAEIC0NFaWdlVRkS/lGoitrvwxf34toqMlPPlkNCRtbthBl8jrBb76yoYpU7p3t4SOKOvcuP1V8GzdGoaKCjNuukn906QAAzdSoZoaASdPar+Hm8LpZC+3rioqssJsljF4cHACt9hYGU89VYtt22z46CMWKhjRzp1hqK42B60NSGtOpxfp6R5s2MB1bsGSmxuO8HAJ2dnBXdPYVQzcSHX00gpE4XBw94SuKiqyYsAAD8KDGEPdcUc9RoxoxAsv9MSpUyxUMJq8PBssFhmTJ4fuIj5+vBtbttjg0cdnV1XzeIBPP7Xjmmvc3VoAFUgM3Eh1lFYg+sm4eVBba2IQcJFk2VdROnRocNcZmUzAiy+ewsmTJixb1jOoz02hl5dnx6hRjejZM3QX8awsN+rqTCgosIZsDEaxebMN1dXaqCZVMHAj1dFjxg0ASko4XXoxjh0zo7raHLT1becbMsSD++47g3ffjcDu3bx4GoUomvHDD8HbLaEj48Y1QhC4/VUw5ObaERkpYfJk9VeTKhi4keqIohmJiV706KGNtHVnlCILtgS5OIWF3btjQmd+//vTiI+XsGABCxWMIhS7JbQnLk7CkCFNbMTbzRobgc8+C8e0aQ2w20M9Gv8xcCPVcbn00QpEcS7jxsDtYhQVWWG1yhg0KDSBW3S0jIULa7FrVxhWrowIyRgouPLy7Ojfvwl9+4b+/WfCBDd27AjDmTNcYtFd1q+3oabGpKlpUoCBG6mQL3DTx/o2wBcAREdLrCy9SEVFVgwc2ARbCJMO06fXY9QoN158MQrV1byA6tnp0wK2bg0L+m4JHRk/3o2mJt+YqHvk5oYjJkbChAnqOOf+YuBGqtLQAJSVmdC3r34CN8C39RUzbv7z7ZgQFpL1becTBGDJkhrU1pqwdCkLFfRs3brQ7JbQkauvboTNxnVu3aWhAVi71o7rrqvX3A49DNxIVUpKLJBlQVdTpYDSy42Bm7+Ki804dcoU8sANAAYN8mD27DP44IMI7NrFQgW9UnZLGDEi+LsltCc83Be8cZ1b9/j6azvq6ky4+WZ1BOoXg4EbqYrSCkRPU6WAb53b0aMW7j/op1AXJrT2u9+dRlKSb0cFr74+UxBCv1tCR7Ky3PjuOysqKnipDrTc3HDExXkxbpy2pkkBBm6kMkorkD599HV1dDo9aGgQ+Absp6KiMISFyRgwQB2BW1SUjKefrkVRURjef5+FCnqzY0cYTp40q2aaVKFsf7V5M7NugXT2rIC8PBtuuEFdgbq/eBUhVXG5zIiKkhAXp6/+C0plKadL/VNUZMXgwU2qWntyyy31GDvWjf/3/3qiqopvnXqi7JYwaZK6si9DhjQhJkbiOrcAy8uzob7ehFtu0VY1qYLvPqQqouirKBV0VsDndLIJr78kCdi926qK9W3nUwoV6uoEvPhiVKiHQwGUl2fH6NGh3S2hPWYzMG6cGxs22LjMIoDWrAlHUpIXo0apYz3jxWLgRqqitx5uirQ0NuH1l8tlRm2tOgoTWrv8cg8eeqgOK1f2wLZtLFTQA5fLjAMHrKqbJlVkZblRWmrGoUN87wiE06cFfPWVHT/9aT3MGv2VMnAj1fB6fU1q9bJH6fnCw4GkJG4274+iIt/8aLD3KPXXY4/VoVcvL556KoabgOuAsltCqLe56oiyzo3VpYGxdq0dbregyWpSBQM3Uo3SUjOamvTXCkThcHjZhNcPhYVW2O0yLr9cnVFRjx4ynn22Bnv3WvHuuz1CPRy6RHl5dlx2WZNqC6LS071wODxc5xYgubnh6NXLq5q2L13BwI1U48gRXzZKjxk3wFdZyoxb53bv9hUmWFU8E3njjQ2YMKEBOTlROHGCb6NaVVur7Jag3uyLIPiyblu22JjhvUSnTglYv96Gm26qh0nDL1sND530Rq+tQBQOhxfHj5v55nsBkuSrKB02TN2fhgUBeOGFGjQ0CHj+ee6ooFXr1tng8Qiq2eaqI1lZbtTWmlBUpOJPMxrw+ed2NDUJmq0mVTBwI9UQRTPCwmSkpOgzcHM6vfB6BRw/zqxbRw4ftuDMGRN+8hP1FSa0lpHhxcMP1+Gf/4zgfpIalZdnR2ys+qfNxo/3jY/TpZcmNzcc6eke1TT27ioGbqQaLpcFDodHs5U+nXE4WFnaGbXtmNCZRx+tQ+/eHjz1VDSatDFk+pHHA3z1lR1TprhV/54TFydhyJBGBm6XoKrKhE2bfNOkWm83xcCNVMPXw02f2TaAvdz8UVhoRXi4hP79tTGfHB4uY/HiWnz/vRXvvMNCBS3ZsSMMp06ZVL2+7XxZWY3Yvj0MZ89qPOoIkU8/tcPrFXDTTdqeJgWCGLgVFBTg0UcfxSOPPILVq1d3eNzWrVtxxx134NChQwCAiooK3HPPPXj88cfx+OOP46233grWkCmIZNnXT6lvX21csLsiNdULs1lmxu0Cdu+2YsiQJk1tQzNtWgOmTGnASy9FoayMn4W1Ii/PDqtVfbsldCQry42mJgHffstp+a7IzQ1Hv34eXHGF9q8xQXmXkSQJy5cvx5NPPon//u//xubNm3H06NE2x9XX1+Ozzz7DZZdd1uL+lJQULFu2DMuWLcNDDz0UjCFTkFVVmXDmjEnXGTeLxRe8MXBrn9erzh0TOiMIwPPP16CpiYUKWpKXZ8Po0Y2IitLGlgQjRzYiLEzmdGkXVFSYsHVrGG6+WfvTpECQAreDBw8iJSUFycnJsFgsGDt2LLZt29bmuFWrVuHmm2+GVc19AKhbuFy+YCY9Xfufhi6Evdw6dvCgBfX16twxoTN9+njx61/XYfXqCGzaxIyI2h05YsbBg+rdLaE94eEyrr6a69y64pNP7JAk7VeTKoISuFVXVyM+Pr75dnx8PKqrq1scc+TIEVRWVmLEiBFtvr+iogLz5s3DM888g++++67bx0vBp/dWIAr2cuuY1goTWpsz5zScTg8WLoxGo7qLFA1P7bsldCQry419+6yorOSU/MX497/DMXBgk2qbel+soHz0l9vZHVc4L18pSRL+/ve/Y86cOW2Oi42NxZ///GdERUXh8OHDWLZsGV566SVERES0OC4/Px/5+fkAgKVLlyIhISHAP8WFWSyWoD+nnpw4YYIgyLjqqhjYAvCBUq3nY+BAE1auNCMiIgGt/oR1zZ/z8cMPZkRGyhg5Mkb1VX4deeUVGbfdZsUHHyTh97+XQj2cC1LrayQY1q2zYNAgCSNGxIZ6KM38OR833SRg6VKgsDABd96p7r8vtSgpAbZtC8Ozz3ou6u9dza+PoARu8fHxqKqqar5dVVWF2NhzL5iGhgaUlJTgueeeAwCcOnUKOTk5mDdvHjIyMpqnTvv164fk5GSUlpYiIyOjxXNkZ2cjOzu7+XZlZWV3/khtJCQkBP059WTv3hikpobh9OlKnD596Y+n1vMRHx8OIBYFBad08+nPH/6cj2+/TcCQIR6cPFl1wePUbORI4NprY7FkiQ3XXFOJ3r3Ve3FV62uku9XWCti0KQW//GUdKisD8GYTIP6cD4cDiI5OwaefujF1ak2QRqZtvm3pwjB1ahUqK/2f0Qn26yM1NdXvY4OSb83IyEBpaSkqKirg8XiwZcsWZGZmNn89IiICy5cvx2uvvYbXXnsNl112WXPQVltbC0nyvfmVl5ejtLQUycnJwRg2BZHeW4EonE72cmuPxwPs26e9woT2LF5cC0kS8Nxz0aEeCrXj66+1sVtCe8xmYNw4NzZssKGdiSxqx5o14RgypBH9+unn+hKUjJvZbMbs2bOxZMkSSJKEyZMnw+FwYNWqVcjIyGgRxLW2b98+/OMf/4DZbIbJZMIvfvELREZGBmPYFESiaMa112prvUlXnOvlxsDtfD/8YEFDg6DZ9W3nczi8eOSR01i2rCfWrz+LiRO1FyDoWX6+HXFxXgwfrs2FiOPHu/Hpp+E4csSsq2CkOxQXm7FrVxiefLI21EMJqKCVtw0fPhzDhw9vcd+dd97Z7rHPPvts879Hjx6N0aNHd+fQKMTq6gRUVpoNkXFLTJRgt0usLG1F2YPxJz/R5sW0tV/9qg4ffhiBp56KxpdfVgRk3SZdOmW3hOzsBs2uo5wwwfdBYONGG/r1Oxvi0ajbmjXhAKCLprvnY2kKhZwoGqMVCODr+eVweJlxa6WwMAxRURL69tVH8G6zAUuW1ODIEQvefJMzBGqxfbu2dktoT58+XqSlebBpEz8NdCY3146rrmpsnunQCwZuFHIuly/7pOddE87HXm5tFRVZ8ZOfNMGko3ekSZPcuOGGerzySiQDdZVQdkvQ8vS1IPjagmzebINXX/FIQB06ZMaePb6mu3qjo7dJ0iqlh5sRpkoB3zo3XsjPaWwEvvvOqov1ba09+2wNBAF49lnuqKAGeXk2jBnj1sxuCR3JynKjpsbUvMSA2srN9U2T/vSnDNyIAs7lMiMuzqv5N1N/ORwe1NaacOqUDvZeCYAffrDA7RYwdKg+1redr3dvCb/5TR0+/zwcX37Jqa1QOnzYjEOHrJqsJm1t3Djfa4W7KHRszZpwjBzpRmqqelvydBUDNwo5o7QCUZyrLOV0KeBb3wZAF61A2vOLX9Shf/8mLFoUjQbtLq3SPGW3BC2vb1MkJEgYPLiJgVsH9u+3YP9+qy6nSQEGbqQCLpcZffoYY30bwF5urRUWWhEdLek2eA8L8xUqiKIFf/4zCxVCJS/PjoEDm+Bw6OPvLCvLje3bw1Bfz8x9a7m54TCZZNx4o/aD9PYwcKOQamwEjh83636P0vMpFw6uc/MpKvI13hV0fP0ZP74Rt9xyFq++GtVcRU3BU1Mj4P/+L0xze5NeyIQJbjQ2+n4uOkeWfYHbmDGNSErS3zQpwMCNQqykxAxJEgzRCkQRHS0jOpq93ADA7Qa+/96KYcP0t76ttUWLamGxyFi0KJpd74Ns3Trfbgl6CtxGjmxEWJiMDRs4XXq+vXstOHzYottpUoCBG4WYUlFqpIwb4CtQYMbNF7Q1NQm6Xd92vl69JPzud6fx5Zd2rF1rD/VwDCUvT9ktQT9/ZxERMkaMaOQ6t1Zyc8NhNsu44Qb9BOmtMXCjkDJS893zOZ1ernGDb30boN/ChNZmzz6DAQOa8PTTPbk2KUg8HuDrr+2YOtWt2d0SOpKV5cbevVZUVfFSDpybJs3KciMuTp/TpICfgZskSXj22WfR1GSMN1cKniNHLIiIkJCYqN8XWXscDi+OHrUYfsqsqMiK2Fgv0tKMkXG1WoEXX6zB0aMW/PGPLFQIhm3btL9bQkeysnytTTZt4jo3ACgosKKkRN/TpICfgZvJZEJFRQVko19lKOCUViB6XpjeHqfTg4YGARUVxv6kXFgYhmHD9F2Y0Nro0Y24/fazeOONSBw6pLMUkArl5dkRFqbt3RI6MnRoE3r2lLj91Y9yc8NhtcqYNk1/Qfr5/L5qzJgxA3/5y19w4sQJSJLU4j+irhJFY7UCUSiVpUaeLq2v9/VbMso06fkWLaqFzcZChWDIy7NjzBg3IiP194u2WIBx49zYsMFm+L8jSfI13Z00yY2YGH3/Mvwua3vzzTcBABs2bGjztVWrVgVuRGQYkgQUF1swdar+Pgl35vwmvFdfbbzABfBtc+X1Crrc6qozSUkSHn/8NJ5+OhqffmrXbb+pUDt0yIzDhy2YPbsu1EPpNuPHu/HZZ+E/fgg2xpKD9uzYEYbSUjOefLI21EPpdn4Hbq+++mp3joMMqKzMBLfbWK1AFGlpbMKr7LP4k5/ovxVIe2bNOoOVKyPwzDPRmDTJjR499J0lCIW8PGW3BP1+OFTWuW3YYEOfPmdDPJrQ+fe/7bDbZVx7rf4/BPk9VZqYmIjExETEx8fDYrEgPj6++T6irnC5jNkKBADCw4GkJGNvNnrD5zEAACAASURBVF9YGIaEBK8u9xL0h8UCvPjiKZSWmvHKKyxU6A75+XYMGtSk6+KXfv28SE31GLotiNcLfPJJOKZMadDllHhrfmfczp49i7fffhubN2+GJEkwm80YO3YsZs+ejYiIiO4cI+nUuR5uxsu4Ab51bkZuwmuEHRM6c/XVTbjzzrN4881I/Oxn9bjsMmO+FrrDqVO+XQXmzNHvNCkACAKQldWIL76ww+uF7lqe+GPr1jBUVJhx0036riZV+J1xe+edd9DQ0ICXXnoJ7733Hv7rv/4LjY2NePvtt7tzfKRjLpcZFouM1FT9fhq+EKfTY9ip0rNnBfzwg8WQ69tae/LJWvToIeOpp1ioEEjr1tnh9eprt4SOZGW5ceqUCXv2WEM9lJDIzQ1HeLiE7Gz9Tomfz+/AraCgAI888ghSU1NhtVqRmpqKOXPmoLCwsDvHRzomihakpXlhMWjSyeHw4vhxM4zYHnHvXgskScDQocZc33a+hAQJ8+fXYvNmG3JzuaNCoOTl2RAf78VVV+n/BTZ+vC9gMeJ0qccDfPqpHddc40ZEhDE++fgduIWFhaG2tmW1Rm1tLSxGverSJTNqKxCF0+mFJAk4ftx4WbeiIl/DUCO2AmnPvfeexdChjXjuuWjU1Rl47jhAmpr0u1tCexITJQwa1GTIwG3zZhuqq8245RZjTJMCFxG4TZkyBS+88ALWrl2LXbt2Ye3atViyZAmys7O7c3ykU7LsK05ITzfmNCngmyoFjFlZWlhoRXKyFykpxixMaM1s9u2oUFFhwksvRYV6OJq3bVsYamr0uVtCR7Ky3Ni2LQz1xolfAAD//nc4oqIkTJpknHPtd7rs9ttvR2xsLDZv3ozq6mrExcXhlltuweTJk7tzfKRTJ08KqK01GT7jBvh6uQHGmjJUChPonKuuasLdd5/F8uU9cOedZzFwoHFfG5dKz7sldCQry4233orEtm02TJhgjJ+7sRH4/HM7rr22AXYDrTLwK3CTJAkffvghbr/9dkyZMqW7x0QGYPSKUgDo1csLs1k2XMatrk7AwYMWQ01t+OuJJ2rxySfheOqpaHz0UZWhK24vRV6eHWPHGqs33qhRjbBaZWzcGGaYwG39ehtqaky635u0Nb/3Kv3iiy9gNsJiAQoKJXAz8lSpxQL07m28Xm579lghywIzbu2Ii5Px5JO12LrVho8/Dg/1cDTp4EEzjhyxGGqaFAB69JAxYkSjoda55eaGIyZGMkygqvB7jdvEiRORl5fXnWMhA3G5fMGKss7LqIzYy03ZMYGBW/vuuussBg+uw+OPA7feeh/mzp2L4uLiUA9LM/LzfXNmRmkNcb6sLDf27LGiutrvS7tmNTQAa9facd119QgLC/VogsvvK8bBgwfx+eefIzc3F/Hx8RDOy+E/99xz3TI40i+Xy4KUFC/CDZ5UcDo9zRcaoygqsqJXLy8SE1mY0J6jR4tRXf0C3O5cbNt2I7Zteww7d+7EypUr4XQ6Qz081TPCbgkdycpyY9mynti8OQw33aTvjOPXX9tRV2fCLbfo++dsj9+B29SpUzF16tTuHAsZiNFbgSgcDi9OnDCjvl5AeLgx1uMUFoZh2DBjFWNcjJycHJSVfQLgdQBzAayGKK5DTk4O94zuhLJbwq9/re/dEjoybFgToqIkbNxo033glpsbjvh4L8aONV5m1e/ihPLyctx+++2wWo3ZmZkCSxQtmDTJeC+41s5Vlppx+eX6D2RrawUcPmzBjBnG3Qy7M2VlZT/+az6AKQBWAhiO8vLy0A1KI77+2ji7JbTHYgHGjnXrfp3b2bMC8vJsmDGj3pAN3FmcQEFXXy+gvNyM9HT9ByqdcTiM1ctt927fBz9uddWxlJSUH/91BsB0AD0ArEJiYmroBqUReXk2JCQYY7eEjmRluVFcbIEo6vc9JS/Phvp641WTKlicQEGnvKFwqrRlxs0IlMCNhQkdmzdvHtLT03+89R2AXwAYj4iIV0I4KvU7f7cEk/7X5ncoK8u3DEHPWbc1a8KRlOTFqFHGXHLB4gQKOpeLrUAUiYkS7HbJMJWlhYVhSEvzIC6OhQkdcTqdWLlyJXJyclBeXo7kZDfM5jL8z/+kYtKkavz0p8acBuzM//1fGGprjbVbQnsyMjzo1cuLjRttuPde/S1JOH1awFdf2XHPPWcMsZ1Zey65OEFgh0i6SEorEGbcAEHwFSgYJePGHRP843Q6WxQiNDZKOHSoEb/7XQwGDTqBjAx+6GlN2S3BaD29WhME33Tp2rV2SBJ0l31cu9YOt1vAzTcbN0Dv9JS+/fbbAIBJkyZh0qRJkCSp+d+TJk3Ctm3bun2QpC+iaEFMjISYGGNUUXbGKL3cTp0S4HJZuL6tC8LCgDffPAmrVcZDD8Xh7Fl+YD6fLPsCt3HjjLVbQkeystw4dcqEPXv0V0yYmxuO1FQPRoww5jQp4Efgtn79+ha3V6xY0eL27t27Azsi0j1RZGHC+ZxOY2TczjXeNe4b7qXo3duL1147hf37LXjiiWjIjE+aHTpkgctlMWw1aWvjx/uyjnpb53bqlID1632tTvSWSbwYnf7ocifvDp19nag1UbRwfdt5HA4PamtNOHVK31mUoiJfe3NOlXbdxIlu/Pa3p/HPf0bgvfciQj0c1cjL8wUo11xj7GlSRVKShIEDm3QXuH3+uR1NTYJhq0kVnQZuna1h4xo3uhhNTb4KSmbczjlXWarv6dKiIivS0z2cIr9Ejz1Wh8mTG/D009EoLNTfVFhX5OfbMfj/t3fn4VGVZxvA71kzE7LPQAJkJkBYZA1CqBITFEmtteJCreBnW5dWURYRlwiiFqu0NIi4gSCipWqFWgWDC5uIEEGLQhZAdjJhCQmTyUqS2c75/hgzGNmyzHLOzP27Lq4rM8zyTk7OzDPP877PO8CJ7t35hbBZZqYd//ufFo0hFOPk5emRkuIK++kWlwzc3G43du/e7f0nCMI5l4la68QJFdxuBXr2ZODWrHm/1lDv5caFCb6hVAKvvFKFzp3duP/+eNhs4f3luapKgR07tGG/mvTnsrLssNsV+O670NjIs7JSifz8CIwd24hwzxdd8it+bGwsXn/9de/lqKioFpdjYmL8MzIKSRYLW4H8nMkU+r3crFZPRvGuu84EeyghISFBxJIlVbj1ViOmTYvH8uW2sJ3z07xbAgO3lkaOdECtFpGfH+Ht7SZnn33mOc5jx4ZQCrGdLhm4LVy4MBDjoDDR3AqEpdKzYmNFxMaGdi+3nTs9X5GZcfOdyy93YvbsGsyaFYdXXonCww+H5/6cGzbo0LmzO+zLZz/XqZOI4cMd2Lo1AjNn1gV7OB2Wl6dHaqoTAwfysyNMv6NRsFgsauh0IhITWWL/KZPJFdIZt+bAbfBgfrj60l13NWDcuAa88EI0tmwJrYnoreF0Aps3R2DMmPBeZXghWVl2FBVpZF9OLy9XYvt2LW66qSnsy6QAAzcKsJISFcxmF99kf8Zsdof0HLedO5Xo1cuFmBguTPAlhQL4xz9q0LevC5Mnx+HEifA6sb79tnm3BK4mPZ/MTDtEUYFt2+Qd1H/6qR6iyNWkzcLrLKegs1jU3DHhPDy7J6gRqmt9du5UsH+bn0RGinjjDRvsdgUeeCABjjD6NW/YoENEBHdLuJChQ52IihJk3xYkL0+Hyy5zom9ffnYADNwogESxufkuFyb8nNnsgt2uQEVF6J2Sp08rceyYgvPb/Kh3bzfmz6/Gzp1aPP98eCwYE0VPG5CrrrIjMpKZ3PPRaDyLFPLz5Ru4nTihxI4dEVyU8BOh9ylBklVRoURjo5IZt/M4u7I09BYoNO+YwMnj/jV2bBP+9Kd6LFsWhY8/1gV7OH536BB3S2iNUaPsKClRy3Yqxief6AGAZdKfYOBGAcNWIBfW/DuR65vrxRQVaaBQiBg0iIGbvz31VC3S0x14/PE4HDwYel8CfmrDBk9wysDt4rKyPGVkuWbd1qzRY/BgB3r14udGMwZuFDBHj7IVyIUkJ4duE96iIg369QOioljO8jetFli82AadTsR998XjzJnQXYK3YUMEBg50onv3EJ0Y6iO9e7uQlOSW5arj0lIVdu3yrCalsxi4UcBYLGqoVCKSk/nN6ed0OiAx0R2ipVIthg3jh2ugdO0qYOHCKhw+rEZOTmhuRm+zeXYEYNPdS1MoPKtL8/O1slv8lJfnKZNyfltLDNwoYCwWFbp3d0MbGjuw+JzJFHotQU6dUuLUKRWGDQvB6EHCsrIceOyxOqxeHYnly0NvM/ovv9RBELhbQmtlZdlRVaXC3r3y+mKYl6fH5Zc7vHOAyYOBGwWMxaLm/LaLMJtDrwlv88KE4cMZuAXa1Kn1GDOmCbNnx2LnztDajH7DBh26dHFzpXIrZWZ65rnJqS3I4cMq7Nmj4aKE82DgRgFTUqLi/LaLMJncOHlSBWcIfRYVFWmhVIpIS2PgFmjNm9EnJbkxcWI8bLbQeLt3OLhbQlslJQno188pq8CtuUx6440M3H6Of/YUEDU1ClRVqdCzJwO3CzGbXRAEBU6eDJ2sW2GhBn37utCpU7BHEp7i4kS88UYVrFYVpkyJgzsEEt7ffqtFXR13S2irzEw7vv1WiyaZVJfXrNHjF7+wo1s3mU3MCwAGbhQQbAVyac3zOEJlnpsoAsXFGu5PGmRDhjjx3HM1+OorHV56KTrYw+mw5t0SmttcUOtkZdnR1KTEd99Jf5Lx/v1q7N+vwc03M9t2PgzcKCBKStgK5FLM5tBqwltWpsTp0yqkpYXRHkwSdeedDbjttgYsWBCFL7+UT7ns57hbQvuNHOmASiXKolyal6eHUiniN7+RSXowwBi4UUAw43ZpXbu6oVKJIZNxKyryfLPnBPLgUyiAuXNrcNllLkyZEo/jx+X5N3bwoBoWi5qrSdshKkrEsGHS3/5KFIGPP9Zj5EgHOndmmfR8GLhRQFgsKnTu7EanTvyWfCFqNdC9uztkVpYWFmqgUokYMICBmxTo9Z7N6N1uYOLEeNhlWGnkbgkdk5XlQGGhBtXV0m3MvGePGkePqrma9CIYuFFAlJSouUdpK3h6uYVGqdSzY4ILen2wR0LNevVy48UXq1FQoMXjj8vvC8KGDREYNMjBCevtNGqUHaKowLZt0s265eXpoVKJuOEGBucXwsCNAqKkhD3cWiNUermJoidwGzKE89uk5oYbmjBxYj2WLFFh1Sr5RNU2mxLff6/latIOGDrUgU6dBMnOcxNFT+CWlWVHQgKD8wsJWOBWUFCAadOmYerUqVi9evUFb/fNN9/g9ttvx+HDh73XrVq1ClOnTsW0adNQUFAQiOGSDzU1eTroM+N2aSaTG6dPq9DYKN1SRmucOKGCzabi/DaJmjmzFpmZAh5/PBb798sjw7tpUwR3S+ggjcazSEGqgVtBgQbHjrFMeikBCdwEQcCyZcvw5JNPYsGCBfj6669x/Pjxc27X2NiIzz//HH369PFed/z4cWzbtg0vvvgiZs2ahWXLlkGQ24ZrYe7YMTVEUcGMWyucXVkq76xbYaGnU39aGgM3KdJogHffdSEqyrMZfX299L8obNigQ2Kim+1lOigry46jR9WSXKCSl6eHVivi+usZnF9MQAK3Q4cOISkpCYmJiVCr1cjIyMCOHTvOud3KlStx0003QaM5uz3Ljh07kJGRAY1Ggy5duiApKQmHDh0KxLDJR9gKpPVMJs/vSO4rS4uKNNBoRPTvzw9ZqeraFVi0qApHj6rx6KNxkt6Mvnm3hOxs7pbQUc3976SWdRMET9Pdq6+2IzZWwn+MEhCQU8Bms8FgMHgvGwwG2Gy2Frc5evQorFYrhg8fftH7JiQknHNfkraSEk8ppmdPZtwuJXQyblpcdpkTEdL6bKCfychwYMaMOnzyiR7Llkl3e4tvvtGivl7J1aQ+0LevC4mJbmzdKq1GvN99p0VZmYpl0lYIyOQG8Txf5RSKs6l5QRCwfPlyTJo0qVX3PZ+NGzdi48aNAIC5c+fCaDS2c7Tto1arA/6cclFerkJMjIg+fRKgCFBFRq7Hw2DwtG04fToaRmNksIfTLqII7N6twbhxgvcYyPV4hLLmY/LMM0BxsYDnnovB1VdHYuRI6WU78vNV0OlE3HJLNCIj5b/7w/kE8hzJzgbWr9cjIUEtmQzm+vWeY3zHHZ0QHR38LxFSfs8KSOBmMBhQWVnpvVxZWYn4+Hjv5aamJhw7dgzPPvssAKC6uhq5ubnIyck55742mw0JCQnnPEd2djays7O9l61Wqz9eygUZjcaAP6dc7N+fALNZicrKwP1+5Hw8kpM748ABF6zWqmAPpV0sFhWqqhLRt28drNYGAPI+HqHqp8fkH/9QoLi4MyZMUGLdutMwGqUzj1gUgTVruuCqq+xoaLChoSHYI/KPQJ4jI0bo8d578di6tRoDBwZ/CovbDXz4YSKuvbYJdnuVJHoMBvo9q1u3bq2+bUBi7dTUVJSVlaGiogIulwvbtm1Denq69/8jIyOxbNkyLFy4EAsXLkSfPn2Qk5OD1NRUpKenY9u2bXA6naioqEBZWRl69+4diGGTj7AVSNvIvZcbFybIT2ysiCVLbKiuVmLy5HhJbUZ/4IAapaXcLcGXMjOlNc/tm2+0qKhgmbS1AhK4qVQq3HvvvZgzZw6mT5+OkSNHwmQyYeXKlfjuu+8uel+TyYSRI0fikUcewZw5c/CnP/0JSqnkdumS3G7PfC22Amk9s1neuycUFWmh1Yro14+Bm5wMGuTCnDnVyM+PwAsvSKccyd0SfK9rVwF9+jglE7jl5ekRGSkgO1sCqTYZCNjX+mHDhmHYsGEtrhs/fvx5bzt79uwWl8eNG4dx48b5a2jkRydPquB0KtCjh4S+wkucyeRCba0S1dUKxMVJb77RpRQWajBggBNaac19plaYMKERO3Zo8cor0Rg+3CGJD9ING3QYPNiBrl2lU74NBVlZdvz735Gw2xHURUROJ/DZZzr88pdN0Ovl934XDExdkV+xFUjbNa8slWO5VBCA4mING+/K2PPP12DgQCemTYsPeubXs1uChrsl+EFWlh1NTZ7dKILp668jYLOpcNNNzKi2FgM38iuLxRN8MOPWemazfHu5lZSoUFenZOAmY3o9sHSpDYIA3H9/PJqC+Hn6xRcREEXuluAPI0c6oFKJQS+X5uXpER0t4JpreIxbi4Eb+ZXFooJWKyIpiYFba8m5l1tRkefbO/colbeUFDdefrkKRUVa/OUvsUEbx4YNOiQlcbcEf4iOFnH55cGd5+ZwAGvX6nDddU3Q6YI2DNlh4EZ+VVKihsnkgkp+MUjQxMSIiIsTZFkqLSzUQKcT0bcvS+Nyd911dkyeXId33+2EDz4I/Gb0Dgfw1VcRGDOmKWD9H8NNVpYdhYUa1NQE5xf81VcRqKlRcjVpGzFwI78qKVGzTNoOJpNLphk3z8KEn+xaRzKWk1OHkSPtmDEjFj/8ENgvEt98E8HdEvwsK8sOQVBg27bgZN3y8vSIixMwahTnMLYFAzfyG1H0lErZCqTtPL3c5BW4NS9MSEtjmTRUqNXA669XITZWxH33JaC2NnCZmQ0bIqDTicjK4t+Tv1x+uQORkUJQyqVNTcD69Tr8+teNXIHeRgzcyG8qK5U4c0bJ5rvtYDa7cfy4GoKMOiAcOaLGmTNKzkcKMZ07C3j99SqUlqoCthm9KHrmt2Vm2tkiwo+0WuDKKx1BCdy+/FKH+nolV5O2AwM38hu2Amk/k8kFu12Bigr5nKLcMSF0XXGFA08+WYvPPtNjyRL/7yO5f78ax45xt4RAyMqy48gRNU6cCGyG/+OP9TAY3MjIYJm0reTzqUCyw1Yg7Xd2Zal8FigUFmqg1wvo3ZuBeiiaOPEMbrihEX/7Wwy+/da/tS3ulhA4zfPL8vMDV69saFBg48YI3HBDE9TyeYuTDAZu5DclJWooFCJMJn6Qt9XZJrzymedWVKTBoEFOvhGHKIUCmD+/GmazGw8+GO/XbPCGDToMGeJAUpKM5grIVL9+LnTu7A5ouXTDhgg0NnI1aXsxcCO/KSlRoVs3d1C3U5Gr5GR5NeF1uYDdu7ljQqiLiRGxdKkNNTUKTJoUD5cfvpNZrUrs3KlhmTRAFApPuXTr1oiAzalds0aPxEQ3rriCC0/ag4Eb+Y3FoubChHbS6YDERLdsSqWHDqnR2MgdE8JB//4uzJ1bg+3bIzBvnu83o9+0qXm3BM59CpTMTDusVhX27fP/+01dnQKbNulw442N7O/ZTgzcyG/YCqRj5NQSpKiICxPCye9+14g77zyD116Lxvr1vk2pN++WMGgQ/5YCJTPTEyQHoly6fr0OdrsCY8eyTNpeDNzIL+rrFbBaVcy4dYDZLJ8mvEVFGnTqJKBXLwbq4eKvf63BkCEOTJsW711B3lF2u6ebfnY2d0sIpO7dBaSmOpGf7//A7eOP9ejWzYXhwxmYtxcDN/KL5jdyZtzaz2Ry4+RJFZwyeH8rLNRi8GAnSx9hRKcD3nijCkolcP/9CWj0QQLlm28icOaMkvPbgmDUKDu2b9fC4cdpZ9XVCmzZEoGxY5ugZPTRbvzVkV+cbQXCwK29zGYXBEGBkyelHQ05ncDevVyYEI5MJs9m9Hv2aPD00x3fjN6zW4KAq67i/LZAy8pyoLFRiZ07/dcWZO1aHZxOBVeTdhADN/KL5sCNpdL2M5nk0RLkwAE1mpoUnN8WprKz7XjooTq8/34nrFjR/s3om3dLyMpyQB/4Pe3D3siRdiiVol/nueXl6ZGS4uJ7RQcxcCO/KClRISHBjehoblfTXnJpwltc7FmYMHgwl/aHq8ceq0Nmph2zZsVh9+72/b3u26fG8ePcLSFYYmJEDB3qxJYt/gncKiuVyM+PwNixjZy/2EEM3Mgv2Aqk47p2dUOlEiWfcSss1CI6WkDPnjze4UqlAhYurEJcnID7709ATU3bP5m5W0LwZWXZUVCgQW2t7yOrzz7Twe1mmdQXGLiRX5SUqNCzJ+e3dYRaDXTv7pb8ytKiIg0GD3ZysnGYMxoFLFliw4kTKkyf3vbN6Dds0CEtzYHERO6WECxZWXYIggLbt/s+65aXp0dqqhMDBvBzoaP4Vks+53AAJ0+yFYgveHq5SbdU6nB4FiZwzgoBQHq6E08/XYt16/R4/fWoVt/PalVi1y7ulhBsw4c7oNcL2LrVtwsUysuV2L5di5tuYpsXX2DgRj537JgKgqBASgq/WXWU1Hu57d+vgcOhwJAhnN9GHn/60xmMHduIv/89Gtu2tS4A+OKL5t0SGLgFk1YLjBzp8PkChU8/1UMUWSb1FQZu5HNnW4Ew49ZRJpMbp0+r0Ngoza+pzTsmsBUINVMogBdeqEbPni5MmhSP8vJLf8xs3KhD165uDBzIL3vBlplpx6FDGpw86bvwIC9Ph8suc6JvXx5fX2DgRj5nsXgyRMy4ddzZlaXSzLoVFmoQGyuwLE4tREWJWLq0CvX1Cjz4YPxFm0hztwRpycry7fZXJ04osWNHBLNtPsTAjXzu6FE1IiMFdO7MScYdZTZ7gl+priwtKvI03uUHLv1cv34uzJtXg2+/jcDcuTEXvN327dwtQUouu8wFo9Hts+2vPvnE05SPgZvvMHAjn2tuBcIP845rzrhJcYFCUxOwb58GaWmc30bnd+utjbjrrjNYvDgKn32mO+9tNmzQQa/nbglSoVR6yqVbt0a0eWXw+axZo8fgwQ62C/IhBm7kcxaLiltd+YjRKECvFySZcdu3TwOnU8H5bXRRf/lLDS6/3IFHHonDkSMt/449uyVEICvLDt354zoKglGj7Dh9WoX9+zv2hdFiUWHXLs9qUvIdBm7kU4LgyQ5xzpNvKBSeBQpSnONWWMiFCXRpERHAkiVVUKvFHzejP5uK/+EHNU6cUOOXv2S2TUoyMz1Z9I7Oc1uzxlMmHTuWZVJfYuBGPlVWpoTdzlYgviTVXm7FxRrEx7uRnMwgnS6ue3c3XnutGvv2qTFzZqy3BNe8W8KYMczISEn37m706uXqcOCWl6fH5Zc7vPsuk28wcCOfOtsKhIGbrzT3cvPFfBNfKizUIi2NCxOoda65xo7p0+vxwQeR+Pe/IwF4ArehQ7lbghRlZdmxfbsWjnZOYT18WIU9ezS4+WZm23yNgRv5FHu4+Z7J5EZdnRLV1dKJkBobgf371SyTUps8/HAdrr66CU89FYPbbnsLu3apYbd/iNLS0mAPjX4mK8uOhgYldu1q3y4KeXl6KBQibryRgZuvMXAjnyopUUGtFtGtGwM3Xznby0065dK9ezVwuxXc6oraRKUCZszYDbe7DNu3PwFAiR9++AcmTJjA4E1iMjLsUCrFdpdL16zR4xe/cKBrV2ZTfY2BG/mUxaJGcrIbaunEGLJnMkmvl1vzjgmDB7MVCLXNG2/8DW73rQBEAKUACmGxWJCbmxvkkdFPxcaKSEtztitw27dPjf37Nezd5icM3MinSkrYCsTXpLh7QlGRFkajG9268ds0tc2pU6cA7ABwK4D7vdeXl5cHa0h0AVlZduzapUFdXdumaeTl6aFUivjNb7joxB8YuJHPiKIn48b5bb4VEyMiLk6Q1MpS7phA7ZWUlPTjT58BWOe9PjExMSjjoQvLyrLD7VZg+/bWz3MTRU/gNnKkg7vn+AkDN/KZqioFamuVbAXiByaTSzIZt4YGBQ4cUHN+G7VLTk4OUlJSWlyXkpKCnJycII2ILmT4cAd0OqFN5dI9e9Q4elTNMqkfSecrPMkeW4H4j8nk7nAXc1/Zs0cDQVBgyBDOb6O2M5vNWLFiBXJzc1FeXo7ExETk5OTAbDYHe2j0MxERwJVXOtoUuOXl6aFWi7jhBpZJ/UUanwQUEpoDN+6a4HtmsxtffKGDIHj2Egwm7phAHWU2m/Haa68FexjUCllZdjz3XCzKLABsUgAAIABJREFUypSXXCHaXCbNyrIjIYFlUn9hqZR85uhRTynPbGbGzddMJhfsdgUqKoJ/yhYWapCY6EZSEt+YiUJdZqZnO7L8/Etn3QoKNDh2TM0trvws+J8CFDIsFjWSktzQ64M9ktAjpV5uxcUaZtuIwsSAAS4YDO5WlUvz8vTQakVcfz3LpP7EwI18xmJhKxB/aQ7cgt3Lrb5egUOH1EhL4/w2onCgVHqybvn5ERfddk8QPIHb1VfbERsrsf35QgwDN/IZi0XN+W1+kpwsjSa8u3drIIoKZtyIwkhWlgPl5SocPHjhjP9332lx6pSKq0kDgIEb+URDgwLl5Sq2AvETnQ5ITHQHvVTKhQlE4ScryzPPbcuWC5dL8/J00OlEXHcdy6T+xsCNfMJi8WSCWCr1H5PJHfSMW1GRBl27utlYkyiMJCe70aOH64Lz3Nxu4NNP9bj22iZERbFM6m8M3MgnzvZwY6nUX8zm4DfhLSrScn4bURjKyrJj+3YtnOdJtn/zjRYVFSyTBgoDN/KJkhJPQMFSqf+YTG6cPKk67xtnINTWKnDkiJplUqIwNGqUHWfOKFFQcO72V3l5ekRGCsjOtgdhZOGHgRv5hMWiRlycgLg4psn9xWx2QRAUOHkyOFm34mLP/DZudUUUfjIy7FAoRGzd2jJwczqBTz/V4Ze/bIJez/f/QGDgRj5hsXBhgr+ZTMFtCVJUxIUJROEqLk5EWprznHluX38dgaoqFW66iYsSAoWBG/lESQlbgfhb8+83WCtLCwu1SE52cSsbojCVmWnHzp1a1NcrvNfl5ekRHS3gmmsYuAUKAzfqMKcTOH6czXf9rWtXN9Rq0buCN9C4YwJReMvKssPlUmD7dk+51OEA1q7V4Ve/aoJOF+TBhREGbtRhJ06o4HYrGLj5mUoFdO/uDsrK0upqBUpK1JzfRhTG0tMd0OlEb7n0q68iUFOj5GrSAGPgRh3W3AqEpVL/8/RyC3yp9Oz8NrYCIQpXOh1wxRV274bzeXl6xMUJ3ga9FBgM3KjD2AokcILVy62oyFMaYamUKLxlZdmxf78GFosK69fr8OtfN0J7bocQ8iMGbtRhJSVq6HQCEhM5ad3fTCY3rFYVGhoUl76xDxUWapCS4mK7F6Iw15xde+65GNTXK7maNAgYuFGHeVqBuKHkX5Pfmc3NK0sDm3UrKuLCBCICoqKOQKutxeef66HV1iA5+WCwhxR2+FFLHWaxqFkmDRCTyfN7DmQvN5tNiePH1dzqiijMlZaW4v/+bwIcjs8BAA7He/j97yegtLQ0yCMLLwzcqENE8WzGjfzvbMYtcAsU2HiXiAAgNzcXFosFwLofr1kBi8WC3NzcYA4r7ATs3b+goABvv/02BEHAmDFjcMstt7T4//Xr12PdunVQKpXQ6XSYOHEikpOTUVFRgenTp6Nbt24AgD59+uD+++8P1LDpEioqlGhsVLIVSIAYjQL0eiGgGbfCQk/gNngwAzeicHbq1Kkff/oXgCMAtgIAysvLgzWksBSQwE0QBCxbtgxPPfUUDAYDZs6cifT0dCQnJ3tvk5mZieuuuw4A8N1332H58uWYNWsWACApKQnz5s0LxFCpjUpKPH9CPXow4xYICoVngUIg57gVFWnQq5cLMTFcmEAUzpKSkn78yQ3gK+/1iYmJQRlPuApIqfTQoUNISkpCYmIi1Go1MjIysGPHjha3iYyM9P7c1NQEhSKwq+aofdgKJPAC3cutsFDL/m1EhJycHKSkpLS4LiUlBTk5OUEaUXgKyLu/zWaDwWDwXjYYDDh48NyVKGvXrsWnn34Kl8uFZ555xnt9RUUFcnJyoNfrMWHCBPTv3z8Qw6ZWsFjUUKlEJCcz4xYoZrML//ufFqLoycD50+nTSpSVqTi/jYhgNpuxYsUK5Obmory8HImJicjJyYHZbA720MJKQAI3UTy3xHK+jNr111+P66+/Hvn5+fjwww8xZcoUxMfHY9GiRYiOjsaRI0cwb948zJ8/v0WGDgA2btyIjRs3AgDmzp0Lo9HonxdzAWq1OuDPKQWnTqlgMgFdu0rrtYfy8bjsMiXq6pRQqYxISPDvc+3Y4TlPR42KhNGob/fjhPLxkCseE2mRy/EwGo1YsWJFsIfhd1I+HgEJ3AwGAyorK72XKysrER8ff8HbZ2RkYOnSpQAAjUYDjcYzObpXr15ITExEWVkZUlNTW9wnOzsb2dnZ3stWq9WXL+GSjEZjwJ9TCg4cMMJkcsFqrbz0jQMolI+HwaADkICCghq/Z8Ly86OgUKhhMllhtbZ/jlsoHw+54jGRFh4PaQn08WhegNkaAZnjlpqairKyMlRUVMDlcmHbtm1IT09vcZuysjLvzzt37kTXrl0BALW1tRAET0f+8vJylJWVcSKkhBw9quaK0gALZC+3wkItevd2ISqKCxOIiKQgIBk3lUqFe++9F3PmzIEgCBg9ejRMJhNWrlyJ1NRUpKenY+3atSguLoZKpUJUVBQmT54MANi7dy/+85//QKVSQalU4r777kNUVFQghk2XUFOjQHU1W4EEWiB3Tygq0uCqq7iBNBGRVARsadqwYcMwbNiwFteNHz/e+/M999xz3vtdeeWVuPLKK/06Nmofi8Xz58Pmu4EVEyMiLk7w+8rSU6eUKC9XIS2NCxOIiKSCOydQu7EVSPCYTC6/Z9yad0xg4EZEJB0M3KjdmHELHk8vN38HbloolSIGDmTgRkQkFQzcqN1KSlTo0sWNTp04cT3QzGY3jh9X48d1O35RWKhB374uREby+BIRSQUDN2o3i0XNMmmQmEwu2O0KVFT45xQWRU+plPuTEhFJCwM3areSEjXLpEFydmWpfxYonDyphNWqQloat7oiIpISBm7ULk1NnlWHbAUSHGazf3u5FRdrAYBbXRERSQwDN2qXY8fUEEUFM25B0rw3rL8Ct8JCDVQqEQMGMHAjIpISBm7ULkePegIGZtyCQ6cDkpLcfiuVFhVp0K+fC/r2b09KRER+wMCN2qW5FUiPHsy4BYvJ5PJLxk0UPRm3IUM4v42ISGoYuFG7WCwqREcLiI/3Yz8Kuih/9XI7flyFqioV57cREUkQAzdql+ZWIApFsEcSvsxmN8rKVHD6OL7ijglERNLFwI3aha1Ags9sdkEQFDhxwrdZt6IiDTQaEf37M3AjIpIaBm7UZm43cOyYCj17cmFCMJlM/llZWlioxWWXORER4dOHJSIiH2DgRm128qQKTidbgQSbP5rwNu+YwPltRETSxMCN2qykxJPh4XZXwdW1qxtqtejTjJvFokJNjZKBGxGRRDFwozZjKxBpUKmA7t3dOHbMd4FbYSEXJhARSRkDN2ozi0UFrVZEUhIDt2DztATxXam0uFgLrVZEv34M3IiIpIiBG7VZSYkaZrMLKv/stkRtYDa7fJ5xGzDACa3WZw9JREQ+xMCN2oytQKTDZHLDalWhoaHjDfUEASgu5sIEIiIpY+BGbSKKnlIp9yiVhrMrSzuedTt6VIW6Oi5MICKSMgZu1CaVlUqcOaNkxk0iTCZPAO2LlaVFRZ76KPcoJSKSLgZu1CZsBSItvuzlVlSkgU4nom9fHlsiIqli4EZtUlLS3AqEH+5SYDQK0OsFH2XcPAsTNBofDIyIiPyCgRu1icWihkIherdbouBSKDwLFDo6x83t9ixMSEtjmZSISMoYuFGblJSo0K2bm/tYSogverkdOaLGmTNKDB7MhQlERFLGwI3axGJhKxCpae7lJortfwzumEBEJA8M3KhN2ApEekwmN+rqlKiubn8vt6IiDfR6Ab1789gSEUkZAzdqtfp6BaxWFfcolRhfrCwtKtJg0CAn1L7bPYuIiPyAgRu1GluBSJPZ3LFebi4XsHs3d0wgIpIDBm7UahYLW4FIUUd3Tzh0SI3GRu6YQEQkBwzcqNWaAzcuTpCW6GgRcXFCu1eWcmECEZF8MHCjVispUSEhwY3o6A4sXyS/aF5Z2h5FRVp06iSgVy9mUomIpI6BG7VaSYmaCxMkytPLrb2BmwaDBzuh6vjmC0RE5GcM3KjV2ApEusxmN44dU0MQ2nY/pxPYu5cLE4iI5IKBG7WKwwGcPKni/DaJMplccDgUKC9v2yl94IAaTU0Kzm8jIpIJBm7UKseOqSAICrYCkaj29nIrKtICAAYP5h6lRERywMCNWuVsKxBm3KTIZGpfL7fCQg2iowX07MnjSkQkBwzcqFVKStjDTcqSk9vXy6242LMwQcl3AiIiWeDbNbVKSYkKkZECjMY2zn6ngNDpgKQkd5t6uTkcnoUJnN9GRCQfDNyoVSwWNVJS3FC0fx9z8jOTqW293Pbv18DhUGDIEM5vIyKSCwZu1CpsBSJ9be3l1rxjAluBEBHJBwM3uiRBAEpL1WwFInFmsxtlZSo4WxmHFRVpEBsr8LgSEckIAze6pLIyJex2BTNuEmc2uyAICpw40bqsW2Ghp/Euy99ERPLBwI0u6ezm8gzcpMxk8mTOWlMubWryzHFLS+P8NiIiOWHgRpfEHm7y0JYmvPv2aeB0Kji/jYhIZhi40SWVlKigVovo1o2Bm5R17eqGWi22KuPGhQlERPLEwI0uyWJRIznZDXXbdlOiAFOpgO7d3a1qCVJUpEF8vNvbuJeIiOSBgRtdUkmJCj17cn6bHHhaglw6wi4s1CItjQsTiIjkhoEbXVBpaSkmT56CvXvtsFg2obS0NNhDokswmy/dhLexEThwQM0yKRGRDDFwo/MqLS3FhAkTsHr1V3C7o3HkyAZMmDCBwZvEmUxuWK0qNDRcOJW2d68GbreCW10REckQAzc6r9zcXFgsFgADfrzmMCwWC3Jzc4M5LLqEsytLL5x1KyryLEwYPJitQIiI5IaBG53XqVOn4Pnz+AcAG4BtAIDy8vIgjoouxWTyzEW82MrSwkItjEY3unUTAjUsIiLyEa4TpPNKSkoCMB1ABoA7AVQCABITE4M4KrqU5u2rPL3c7Oe9TVERd0wgIpIrZtzovG6//RkAcwCsAvBvAEBKSgpycnKCOSy6BINBgF4vXDDj1tCgwMGDas5vIyKSKWbc6BxuN5CbOwAxMQpcddUa1NRkIDExETk5OTCbzcEeHl2EQuGZ53ahOW579mggCAoMGcL5bUREcsTAjc6xZEkUdu3SYtEiG26++flgD4fa6GK93LhjAhGRvLFUSi0cOKDGCy9E44YbGnHTTU3BHg61Q3MvN1E89/8KCzVITHQjKYkLE4iI5ChgGbeCggK8/fbbEAQBY8aMwS233NLi/9evX49169ZBqVRCp9Nh4sSJSE5OBgCsWrUKmzZtglKpxD333IOhQ4cGathhxeUCpk+PQ6dOAv7+9xpOXpcpk8mNujolqqsViI9vGb01L0wgIiJ5CkjGTRAELFu2DE8++SQWLFiAr7/+GsePH29xm8zMTMyfPx/z5s3DzTffjOXLlwMAjh8/jm3btuHFF1/ErFmzsGzZMggCswX+sHhxFAoKtJgzpwZGI3/HctXcy+3n5dL6egUOH1YjLY3z24iI5CoggduhQ4eQlJSExMREqNVqZGRkYMeOHS1uExkZ6f25qakJih/TPTt27EBGRgY0Gg26dOmCpKQkHDp0KBDDDiv79qkxf340bryRJVK5u1Avt927NRBFBTNuREQyFpBSqc1mg8Fg8F42GAw4ePDgObdbu3YtPv30U7hcLjzzzDPe+/bp08d7m4SEBNhsNv8POow4nZ4SaXS0gL/9rSbYw6EOOrt7QsvTmwsTiIjkLyCBm3ieWdKK80yguv7663H99dcjPz8fH374IaZMmXLe+57Pxo0bsXHjRgDA3LlzYTQaOzboNlKr1QF/Tl+ZO1eJoiI1Vqxwol+/hGAPxyfkfDw6ymgEEhJEVFR0gtGo816/f78Kycki+vcP/DEO5+MhVTwm0sLjIS1SPh4BCdwMBgMqKyu9lysrKxEfH3/B22dkZGDp0qXnva/NZkNCwrkfPNnZ2cjOzvZetlqtvhh6qxmNxoA/py/88IMazz/fGTfd1IisrCrI8CWcl1yPh68kJxtx8KAAq/Vsdvp//+uCQYOaYLVWBXw84X48pIjHRFp4PKQl0MejW7durb5tQOa4paamoqysDBUVFXC5XNi2bRvS09Nb3KasrMz7886dO9G1a1cAQHp6OrZt2wan04mKigqUlZWhd+/egRh2yHM6gYcfjkNsrIA5c1giDSU/7+VWW6vA0aNqlkmJiGQuIBk3lUqFe++9F3PmzIEgCBg9ejRMJhNWrlyJ1NRUpKenY+3atSguLoZKpUJUVBQmT54MADCZTBg5ciQeeeQRKJVK/OlPf4JSyfZzvvDaa1HYvVuLN9+0ISGBq0hDidnsxoYNOggCoFQCxcWe+W3c6oqISN4C1sdt2LBhGDZsWIvrxo8f7/35nnvuueB9x40bh3HjxvltbOFozx41XnopGrfe2oBf/5qrSEONyeSCw6FAebkSXbsKKCriwgQiolDA1FUYcjiA6dPjER8v4K9/ZYk0FP18ZWlhoRbJyS5mVomIZI6BWxh69dVo7NmjwT/+UYOEhNat2iV5+XkvN+6YQEQUGhi4hZndu9V45ZUojBvXgF/9iiXSUJWc3JxxU6GqSgGLRc35bUREIYCBWxhxOICHH45HQgJLpKFOpwOSkjwrS4uLtQCAIUO41RURkdwFbHECBd/LL0fjhx80ePvtynM2H6fQYzK5cOyYigsTiIhCCDNuYaKoSINXX43Cbbc14Lrr7MEeDgWAp5ebCoWFGqSkuBAXx2CdiEjuGLiFAbvdsxdp584Cnn2WJdJwYTa7UVamws6dWmbbiIhCBEulYWDBgmjs26fBv/5VyaxLGDGbXRAEBU6dUiEtjfPbiIhCATNuIa6gQINFi6IwfnwDxoxhiTScRESc3UZu8+b5KC0tDeJoiIjIFxi4hbCmprMl0r/8hSXScFJaWoq//e3P3sv5+S9jwoQJDN6IiGSOgVsIW7AgGgcOaPDCC9WIjWWJNJzk5ubixIlvATgA7AdQC4vFgtzc3CCPjIiIOoJz3ELUrl2eEukdd5zB6NEskYabU6dOARAAFAP43nt9eXl5sIZEREQ+wMAtBDU1AQ8/HIekJDeeeaY22MOhIEhKSvrxp2vhybp5JCYmBmU8RETkGyyVhqD586Nx6JAGL7xQg5gYlkjDUU5ODlJSUgDUAvBsbZaSkoKcnJygjouIiDqGGbcQ8/33GixeHIU77zyDq69miTRcmc1mrFixArm5uSgvL0diYiJycnJgNpuDPTQiIuoABm4hpLHRs4q0a1c3nn6aJdJwZzab8dprrwV7GERE5EMM3ELIvHkxOHxYg/fftyI6miVSIiKiUMM5biFixw4N3nijE/7whzMYNYpd8omIiEIRA7cQ0NiowPTp8eje3Y2nnmKJlIiIKFSxVBoC/vGPaBw9qsbKlVZERbFESkREFKqYcZO5b7/V4s03O+Guu84gM5MlUiIiolDGwE3GGhoUeOSROJhMbsyaxRIpERFRqGOpVMbmzo1GSYkaH3xgRadOLJESERGFOmbcZGr7di2WLYvCvffWIyODJVIiIqJwwMBNhhoaFHj00Tj06OHCzJl1wR4OERERBQhLpTL0t79Fo7RUhQ8/rERkJEukRERE4YIZN5n5+mst3n47CvfeewZXXMESKRERUThh4CYjZ86wREpERBTOWCqVkeefj8Hx4yqsWlUJvZ4lUiIionDDjJtM5Odr8a9/dcJ9953BiBEskRIREYUjBm4yUF/vKZH26uVCTg4b7RIREYUrlkpl4LnnYnDihAqrVlmh1wd7NERERBQszLhJ3JYtEXj33U6YOPEMRoxwBns4REREFEQM3CSsrk6BRx+NRWqqE489xhIpERFRuGOpVMKeey4Gp06psHo1S6RERETEjJtkbd4cgffe64QHHqjH8OEskRIREREDN0mqrVXgscfi0KePE48+yka7RERE5MFSqQQ9+2wMysuVWLrUBp0u2KMhIiIiqWDGTWI2bYrAihWdMGlSPS6/nCVSIiIiOouBm4TU1Cjw+ONx6NfPiUceYYmUiIiIWmKpVEJmz47F6dNKvPWWDRERwR4NERERSQ0zbhKxcWME/vOfSEyeXI+0NJZIiYiI6FwM3CSgulqBnJw49O/vxMMPs0RKRERE58dSqQQ880wsrFYlli9niZSIiIgujBm3IFu/PgIffhiJhx6qx+DBLJESERHRhTFwC6KqKgWeeCIOAwY48dBDLJESERHRxbFUGkTPPBMLm02Jd96phFYb7NEQERGR1DHjFiRr1+rw0UeRmDatDoMGuYI9HCIiIpIBBm5BYLMp8cQTsRg40ImpU+uDPRwiIiKSCZZKg+Cpp2JQU6PEv/9dCY0m2KMhIiIiuWDGLcA++0yHjz/2lEgHDmSJlIiIiFqPgVsAVVYqMWNGLAYPdmDKFJZIiYiIqG1YKg2gWbNiUVurxMqVLJESERFR2zHjFiBr1uiwZo0ejzxSh/79WSIlIiKitmPgFgBWqxJPPhmLtDQHJk1iiZSIiIjah4Gbn4kiMHNmLOrrlViwoBpqFqeJiIionRi4+Vleng6ffabHo4/WoV8/lkiJiIio/QKW/ykoKMDbb78NQRAwZswY3HLLLS3+/5NPPsEXX3wBlUqFmJgYPPjgg+jcuTMAYPz48TCbzQAAo9GIJ554IlDD7pDTp5WYNSsWl1/uwAMPsERKREREHROQwE0QBCxbtgxPPfUUDAYDZs6cifT0dCQnJ3tv06NHD8ydOxcRERFYv3493n33XUyfPh0AoNVqMW/evEAM1WeaS6QNDUosWFDJEikRERF1WEBKpYcOHUJSUhISExOhVquRkZGBHTt2tLjNoEGDEBERAQDo06cPbDZbIIbmNx9/rMfnn+vx+OO16NOHJVIiIiLquIDkgWw2GwwGg/eywWDAwYMHL3j7TZs2YejQod7LTqcTM2bMgEqlws0334xf/OIXfh1vR1VUeEqkw4Y5cP/9Z4I9HCIiIgoRAQncRFE85zqFQnHe227ZsgVHjhzB7NmzvdctWrQICQkJKC8vx1//+leYzWYkJSW1uN/GjRuxceNGAMDcuXNhNBp99wJaQa1Ww2g0QhSBBx5Qo7FRgbffBhITAzsO8mg+HiQNPB7Sw2MiLTwe0iLl4xGQwM1gMKCystJ7ubKyEvHx8efcrqioCKtWrcLs2bOh+cnWAgkJCQCAxMREDBgwACUlJecEbtnZ2cjOzvZetlqtvn4ZF2U0GmG1WvHhh3qsWROPp5+ugdF4BgEeBv2o+XiQNPB4SA+PibTweEhLoI9Ht27dWn3bgMxxS01NRVlZGSoqKuByubBt2zakp6e3uM3Ro0exdOlS5OTkIDY21nt9fX09nE4nAKC2thb79+9vsahBSk6dUuKZZ2KRnu7AffexREpERES+FZCMm0qlwr333os5c+ZAEASMHj0aJpMJK1euRGpqKtLT0/Huu++iqakJL774IoCzbT9OnDiBN954A0qlEoIg4JZbbpFk4CaKwBNPxKGpSYEXX6yCShXsEREREVGoUYjnm4AWAk6ePBmQ5yktLUVubi727k3H/v1P4qGHSvDEE9qAPDddGMsO0sLjIT08JtLC4yEtYV8qDVWlpaWYMGECVq36Fvv3TwKwFatXX4vS0tJgD42IiIhCEAO3DsjNzYXFYgHwBgAtgHtQWnoUubm5QR4ZERERhSIGbh1w6tQpAAkATABmADgMACgvLw/iqIiIiChUcSOmDvC0JNkOYAQAp/f6xMTEYA2JiIiIQhgzbh2Qk5ODlJQUAA4AnjUeKSkpyMnJCeq4iIiIKDQx49YBZrMZK1asQG5uLmw2GxISEpCTkwOz2RzsoREREVEIYuDWQWazGa+99hqXchMREZHfsVRKREREJBMM3IiIiIhkgoEbERERkUwwcCMiIiKSCQZuRERERDLBwI2IiIhIJhi4EREREckEAzciIiIimWDgRkRERCQTDNyIiIiIZIKBGxEREZFMMHAjIiIikgkGbkREREQywcCNiIiISCYYuBERERHJBAM3IiIiIplg4EZEREQkEwzciIiIiGSCgRsRERGRTDBwIyIiIpIJBm5EREREMsHAjYiIiEgmGLgRERERyYRCFEUx2IMgIiIioktjxs1HZsyYEewh0E/weEgLj4f08JhIC4+HtEj5eDBwIyIiIpIJBm5EREREMqGaPXv27GAPIlT06tUr2EOgn+DxkBYeD+nhMZEWHg9pkerx4OIEIiIiIplgqZSIiIhIJtTBHoDcFRQU4O2334YgCBgzZgxuueWWYA8p5FmtVixcuBDV1dVQKBTIzs7GDTfcgPr6eixYsACnT59G586dMX36dERFRUEURbz99tvYtWsXIiIiMGnSJMmmwOVMEATMmDEDCQkJmDFjBioqKvDSSy+hvr4ePXv2xNSpU6FWq+F0OvHaa6/hyJEjiI6OxsMPP4wuXboEe/gh58yZM1i8eDGOHTsGhUKBBx98EN26deM5EiSffPIJNm3aBIVCAZPJhEmTJqG6uprnSAAtWrQIO3fuRGxsLObPnw8A7frc2Lx5Mz766CMAwLhx43DNNdcE9oWI1G5ut1ucMmWKeOrUKdHpdIqPPfaYeOzYsWAPK+TZbDbx8OHDoiiKYkNDg/jQQw+Jx44dE9955x1x1apVoiiK4qpVq8R33nlHFEVR/P7778U5c+aIgiCI+/fvF2fOnBm0sYeyNWvWiC+99JL497//XRRFUZw/f76Yn58viqIoLlmyRFy3bp0oiqK4du1accmSJaIoimJ+fr744osvBmfAIe7VV18VN27cKIqiKDqdTrG+vp7nSJBUVlaKkyZNEu12uyiKnnPjyy+/5DkSYHv27BEPHz4sPvLII97r2npO1NXViZMnTxbr6upa/BxILJV2wKFDh5CUlITExESo1WpkZGRJ08AxAAAMrElEQVRgx44dwR5WyIuPj/d+89Hr9ejevTtsNht27NiBq6++GgBw9dVXe4/Fd999h1GjRkGhUKBv3744c+YMqqqqgjb+UFRZWYmdO3dizJgxAABRFLFnzx5ceeWVAIBrrrmmxfFo/oZ65ZVXYvfu3RA51danGhoa8MMPP+Daa68FAKjVanTq1InnSBAJggCHwwG32w2Hw4G4uDieIwE2YMAAREVFtbiuredEQUEBhgwZgqioKERFRWHIkCEoKCgI6OtgqbQDbDYbDAaD97LBYMDBgweDOKLwU1FRgaNHj6J3796oqalBfHw8AE9wV1tbC8BznIxGo/c+BoMBNpvNe1vquH/+85/4/e9/j8bGRgBAXV0dIiMjoVKpAAAJCQmw2WwAWp43KpUKkZGRqKurQ0xMTHAGH4IqKioQExODRYsWwWKxoFevXrj77rt5jgRJQkICxo4diwcffBBarRZpaWno1asXzxEJaOs58fPP/Z8et0Bhxq0DzvcNSKFQBGEk4ampqQnz58/H3XffjcjIyAvejsfJv77//nvExsa2ek4Uj4f/ud1uHD16FNdddx1yc3MRERGB1atXX/D2PCb+VV9fjx07dmDhwoVYsmQJmpqaLpql4fEIvrYcg0AfG2bcOsBgMKCystJ7ubKykt9QA8TlcmH+/PnIysrCFVdcAQCIjY1FVVUV4uPjUVVV5f12ajAYYLVavfflcfKt/fv347vvvsOuXbvgcDjQ2NiIf/7zn2hoaIDb7YZKpYLNZkNCQgKAs+eNwWCA2+1GQ0PDOeUL6hiDwQCDwYA+ffoA8JTbVq9ezXMkSIqLi9GlSxfv7/uKK67A/v37eY5IQFvPiYSEBOzdu9d7vc1mw4ABAwI6ZmbcOiA1NRVlZWWoqKiAy+XCtm3bkJ6eHuxhhTxRFLF48WJ0794dN954o/f69PR0fPXVVwCAr776CiNGjPBev2XLFoiiiAMHDiAyMpIfSj70f//3f1i8eDEWLlyIhx9+GIMGDcJDDz2EgQMH4ptvvgHgWYXVfG4MHz4cmzdvBgB88803GDhwILMJPhYXFweDwYCTJ08C8AQOycnJPEeCxGg04uDBg7Db7RBF0Xs8eI4EX1vPiaFDh6KwsBD19fWor69HYWEhhg4dGtAxswFvB+3cuRPLly+HIAgYPXo0xo0bF+whhbx9+/bhmWeegdls9r6Z3XHHHejTpw8WLFgAq9UKo9GIRx55xLuse9myZSgsLIRWq8WkSZOQmpoa5FcRmvbs2YM1a9ZgxowZKC8vP6fVgUajgcPhwGuvvYajR48iKioKDz/8MBITE4M99JBTUlKCxYsXw+VyoUuXLpg0aRJEUeQ5EiT/+c9/sG3bNqhUKvTo0QMPPPAAbDYbz5EAeumll7B3717U1dUhNjYWt99+O0aMGNHmc2LTpk1YtWoVAE87kNGjRwf0dTBwIyIiIpIJlkqJiIiIZIKBGxEREZFMMHAjIiIikgkGbkREREQywcCNiIiISCYYuBGRrCxcuBArVqwIynOLoohFixbhnnvuwcyZM3362D/88AOmTZvm08cEgNtvvx2nTp3y+eMSUXBw5wQi6pDJkyfD4XDg1VdfhU6nAwB88cUX2Lp1K2bPnh3cwfnYvn37UFRUhNdff937Wn9q8+bNeP3116HValtc//LLL3u74l9I//798fLLL/t0vEQUehi4EVGHud1ufPbZZ7JrQC0IApTK1hceTp8+jc6dO583aGvWt29fPPfcc74YHhHRORi4EVGH3XTTTfj444/xq1/9Cp06dWrxfxUVFZgyZQref/99qFQqAMDs2bORlZWFMWPGYPPmzfjiiy+QmpqKzZs3IyoqClOnTkVZWRlWrlwJp9OJ3//+97jmmmu8j1lbW4vnnnsOBw8eRM+ePTFlyhR07twZAHDixAm89dZbOHLkCGJiYjB+/HhkZGQA8JRZtVotrFYr9u7di8cffxxDhgxpMV6bzYalS5di3759iIqKws0334zs7Gxs2rQJy5Ytg8vlwh/+8AeMHTsWt99+e5t+T5MnT0Z2dja2bNmC6upqjBgxAn/+85+h1WqxZ88evPrqq1i8eDEAYPXq1fj888/R2NiI+Ph4/PnPf8bgwYPhdDrx3nvvYfv27QCAkSNH4s4774RGowEA5OXl4ZNPPoFCocD48eNbPL/T6cT777+P7du3w+VyYcSIEbj77ruh1WpRW1uLRYsWYd++fVAoFDCZTJg9e3abAlsi8j+ekUTUYb169cLAgQOxZs2adt3/4MGDSElJwVtvvYXMzEy89NJLOHToEF555RVMnToVb731Fpqamry3z8/Px29/+1ssW7YMPXr0wCuvvAIAaGpqwvPPP4/MzEy8+eabmDZtGpYtW4Zjx461uO+tt96K5cuX47LLLjtnLC+//DIMBgOWLFmCRx99FO+//z6Ki4tx7bXX4r777kPfvn3xzjvvtDlo++nzz5o1C6+++irKysrw0UcfnXObkydPYt26dfj73/+Of/3rX5g1a5Y3MP3oo49w8OBB5ObmYt68eTh06BA+/PBDAEBBQQHWrFmDp556Ci+//DKKi4tbPO57772HsrIyzJs3D6+88gpsNhv++9//AgA++eQTJCQk4M0338TSpUtxxx13cH9MIgli4EZEPnH77bfj888/R21tbZvv26VLF4wePRpKpRIZGRmorKzEbbfdBo1Gg7S0NKjV6hYT7IcNG4YBAwZAo9HgjjvuwIEDB2C1WrFz50507twZo0ePhkqlQq9evXDFFVd4N/IGgBEjRuCyyy6DUqk8Zy6a1WrFvn37cOedd0Kr1aJHjx4YM2YMtmzZ0urXcvDgQdx9993ef1OnTm3x/7/61a9gNBoRFRWFW2+9FV9//fU5j6FUKuF0OnH8+HHvXqNJSUkAzgatsbGxiImJwW233YatW7cCALZt24ZrrrkGZrMZOp0Ov/vd77yPKYoivvjiC9x1112IioqCXq/HuHHjvM+vUqlQXV0Nq9UKtVqN/v37M3AjkiCWSonIJ8xmM4YPH47Vq1eje/fubbpvbGys9+fmYCouLq7FdT/NuBkMBu/POp0OUVFRqKqqwunTp72BUzO3241Ro0ad974/V1VV5Q1qmhmNRhw+fLjVr6VPnz4XneNmNBq9P3fu3Bk2m+2c2yQlJeHuu+/GBx98gOPHjyMtLQ1//OMfkZCQAJvN5s2+/fwxqqqq0KtXrxb/16y2thZ2ux0zZszwXieKIgRBAOApd3/wwQd4/vnnAQDZ2dm45ZZbWv26iSgwGLgRkc/cfvvteOKJJ3DjjTd6r2ueyG+32xEZGQkAqK6u7tDzVFZWen9uampCfX094uPjYTAYMGDAADz99NMXvO/Fskjx8fGor69HY2OjN3izWq2XXBHaFlartcXPF3rszMxMZGZmoqGhAW+88Qbee+89TJ06FQkJCTh9+jRMJtM5jxEfH9/id/PT54qOjoZWq8WLL7543ufU6/X44x//iD/+8Y84duwYnn32WaSmpmLw4ME+ed1E5BsslRKRzyQlJWHkyJH4/PPPvdfFxMQgISEBW7duhSAI2LRpE8rLyzv0PLt27cK+ffvgcrmwYsUK9OnTB0ajEcOHD0dZWRm2bNkCl8sFl8uFQ4cO4fjx4616XKPRiH79+uHf//43HA4HLBYLvvzyS2RlZXVovD+1bt06VFZWor6+HqtWrcLIkSPPuc3Jkyexe/duOJ1OaLVaaLVa7yKBq666Ch999BFqa2tRW1uL//73v97xjRw5Eps3b8bx48dht9vxwQcfeB9TqVRizJgx+Oc//4mamhoAnoUYBQUFAIDvv/8ep06dgiiK0Ov1UCqVXJhAJEHMuBGRT/10zlWziRMn4s0338T777+Pa6+9Fn379u3Qc1x11VX44IMPcODAAfTq1QsPPfQQAE/W6KmnnsLy5cuxfPlyiKKIlJQU3HXXXa1+7GnTpmHp0qWYOHEioqKi8Lvf/e6clacXc+DAAfzhD39ocd1f/vIX9O7dG4Ank/b888+jqqoK6enp+O1vf3vOYzSvHD1x4gRUKhX69euH+++/HwAwbtw4NDQ04LHHHgMAXHnlld42LJdffjl+85vf4Nlnn4VSqcT48eORn5/vfdw777wT//3vfzFr1izU1dUhISEBv/zlLzF06FCUlZXhrbfeQm1tLTp16oTrrrsOAwcObPXrJqLAUIiiKAZ7EERE4WDy5MmYOHFimwJBIqKfYh6ciIiISCYYuBERERHJBEulRERERDLBjBsRERGRTDBwIyIiIpIJBm5EREREMsHAjYiIiEgmGLgRERERyQQDNyIiIiKZ+H/SSfxIh+VUngAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.title(\"Loss vs Num Episodes\")\n", + "plt.plot(num_episodes,error,c='b')\n", + "plt.scatter(num_episodes,error,c='k')\n", + "plt.xlabel(\"Number of Episodes\")\n", + "plt.ylabel(\"Error\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "images, labels = next(iter(trainloader))\n", + "images = images.view(images.shape[0],-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "predsprobs = model(images)\n", + "preds = torch.argmax(predsprobs,dim=1)\n", + "preds = preds.numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure = plt.figure(figsize=(15,15))\n", + "for i in range(images.shape[0]):\n", + " plt.subplot(8,8,i+1)\n", + " plt.imshow(images[i].reshape(28,28),cmap='gray',interpolation='nearest')\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.xlabel(preds[i])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([64, 10])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "images, labels = next(iter(testloader))\n", + "images = images.view(images.shape[0],-1)\n", + "ps = torch.exp(model(images))\n", + "ps.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[1],\n", + " [2],\n", + " [4],\n", + " [8],\n", + " [4],\n", + " [3],\n", + " [9],\n", + " [5],\n", + " [7],\n", + " [7]])\n" + ] + } + ], + "source": [ + "top_p, top_class = ps.topk(1,dim=1)\n", + "print(top_class[:10,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "equals = top_class==labels.view(*top_class.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 85.9375%\n" + ] + } + ], + "source": [ + "accuracy = torch.mean(equals.type(torch.FloatTensor))\n", + "print(f\"Accuracy: {accuracy.item()*100}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000.. Training Loss: 0.286.. Test Loss: 0.481.. Test Accuracy: 87.500\n", + "Epoch 2/1000.. Training Loss: 0.328.. Test Loss: 0.478.. Test Accuracy: 81.250\n", + "Epoch 3/1000.. Training Loss: 0.443.. Test Loss: 0.587.. Test Accuracy: 76.562\n", + "Epoch 4/1000.. Training Loss: 0.619.. Test Loss: 0.399.. Test Accuracy: 85.938\n", + "Epoch 5/1000.. Training Loss: 0.697.. Test Loss: 0.377.. Test Accuracy: 84.375\n", + "Epoch 6/1000.. Training Loss: 0.851.. Test Loss: 0.552.. Test Accuracy: 81.250\n", + "Epoch 7/1000.. Training Loss: 0.431.. Test Loss: 0.205.. Test Accuracy: 93.750\n", + "Epoch 8/1000.. Training Loss: 0.400.. Test Loss: 0.352.. Test Accuracy: 84.375\n", + "Epoch 9/1000.. Training Loss: 0.343.. Test Loss: 0.254.. Test Accuracy: 92.188\n", + "Epoch 10/1000.. Training Loss: 0.575.. Test Loss: 0.459.. Test Accuracy: 84.375\n", + "Epoch 11/1000.. Training Loss: 0.464.. Test Loss: 0.783.. Test Accuracy: 73.438\n", + "Epoch 12/1000.. Training Loss: 0.388.. Test Loss: 0.404.. Test Accuracy: 84.375\n", + "Epoch 13/1000.. Training Loss: 0.379.. Test Loss: 0.459.. Test Accuracy: 82.812\n", + "Epoch 14/1000.. Training Loss: 0.677.. Test Loss: 0.319.. Test Accuracy: 85.938\n", + "Epoch 15/1000.. Training Loss: 0.408.. Test Loss: 0.710.. Test Accuracy: 70.312\n", + "Epoch 16/1000.. Training Loss: 0.433.. Test Loss: 0.568.. Test Accuracy: 79.688\n", + "Epoch 17/1000.. Training Loss: 0.493.. Test Loss: 0.270.. Test Accuracy: 92.188\n", + "Epoch 18/1000.. Training Loss: 0.512.. Test Loss: 0.454.. Test Accuracy: 82.812\n", + "Epoch 19/1000.. Training Loss: 0.532.. Test Loss: 0.355.. Test Accuracy: 87.500\n", + "Epoch 20/1000.. Training Loss: 0.463.. Test Loss: 0.295.. Test Accuracy: 90.625\n", + "Epoch 21/1000.. Training Loss: 0.323.. Test Loss: 0.518.. Test Accuracy: 82.812\n", + "Epoch 22/1000.. Training Loss: 0.367.. Test Loss: 0.431.. Test Accuracy: 85.938\n", + "Epoch 23/1000.. Training Loss: 0.337.. Test Loss: 0.432.. Test Accuracy: 85.938\n", + "Epoch 24/1000.. Training Loss: 0.452.. Test Loss: 0.266.. Test Accuracy: 90.625\n", + "Epoch 25/1000.. Training Loss: 0.295.. Test Loss: 0.373.. Test Accuracy: 87.500\n", + "Epoch 26/1000.. Training Loss: 0.271.. Test Loss: 0.420.. Test Accuracy: 81.250\n", + "Epoch 27/1000.. Training Loss: 0.361.. Test Loss: 0.465.. Test Accuracy: 81.250\n", + "Epoch 28/1000.. Training Loss: 0.555.. Test Loss: 0.409.. Test Accuracy: 82.812\n", + "Epoch 29/1000.. Training Loss: 0.633.. Test Loss: 0.484.. Test Accuracy: 84.375\n", + "Epoch 30/1000.. Training Loss: 0.373.. Test Loss: 0.559.. Test Accuracy: 79.688\n", + "Epoch 31/1000.. Training Loss: 0.307.. Test Loss: 0.268.. Test Accuracy: 93.750\n", + "Epoch 32/1000.. Training Loss: 0.341.. Test Loss: 0.426.. Test Accuracy: 87.500\n", + "Epoch 33/1000.. Training Loss: 0.709.. Test Loss: 0.307.. Test Accuracy: 85.938\n", + "Epoch 34/1000.. Training Loss: 0.477.. Test Loss: 0.439.. Test Accuracy: 75.000\n", + "Epoch 35/1000.. Training Loss: 0.453.. Test Loss: 0.548.. Test Accuracy: 82.812\n", + "Epoch 36/1000.. Training Loss: 0.301.. Test Loss: 0.311.. Test Accuracy: 89.062\n", + "Epoch 37/1000.. Training Loss: 0.548.. Test Loss: 0.440.. Test Accuracy: 85.938\n", + "Epoch 38/1000.. Training Loss: 0.344.. Test Loss: 0.387.. Test Accuracy: 82.812\n", + "Epoch 39/1000.. Training Loss: 0.493.. Test Loss: 0.523.. Test Accuracy: 78.125\n", + "Epoch 40/1000.. Training Loss: 0.435.. Test Loss: 0.612.. Test Accuracy: 81.250\n", + "Epoch 41/1000.. Training Loss: 0.489.. Test Loss: 0.517.. Test Accuracy: 82.812\n", + "Epoch 42/1000.. Training Loss: 0.372.. Test Loss: 0.562.. Test Accuracy: 84.375\n", + "Epoch 43/1000.. Training Loss: 0.864.. Test Loss: 0.367.. Test Accuracy: 89.062\n", + "Epoch 44/1000.. Training Loss: 0.281.. Test Loss: 0.288.. Test Accuracy: 90.625\n", + "Epoch 45/1000.. Training Loss: 0.547.. Test Loss: 0.454.. Test Accuracy: 84.375\n", + "Epoch 46/1000.. Training Loss: 0.701.. Test Loss: 0.404.. Test Accuracy: 89.062\n", + "Epoch 47/1000.. Training Loss: 0.539.. Test Loss: 0.293.. Test Accuracy: 89.062\n", + "Epoch 48/1000.. Training Loss: 0.354.. Test Loss: 0.495.. Test Accuracy: 82.812\n", + "Epoch 49/1000.. Training Loss: 0.511.. Test Loss: 0.814.. Test Accuracy: 70.312\n", + "Epoch 50/1000.. Training Loss: 0.654.. Test Loss: 0.418.. Test Accuracy: 89.062\n", + "Epoch 51/1000.. Training Loss: 0.578.. Test Loss: 0.322.. Test Accuracy: 93.750\n", + "Epoch 52/1000.. Training Loss: 0.308.. Test Loss: 0.428.. Test Accuracy: 87.500\n", + "Epoch 53/1000.. Training Loss: 0.665.. Test Loss: 0.299.. Test Accuracy: 90.625\n", + "Epoch 54/1000.. Training Loss: 0.567.. Test Loss: 0.317.. Test Accuracy: 89.062\n", + "Epoch 55/1000.. Training Loss: 0.301.. Test Loss: 0.335.. Test Accuracy: 85.938\n", + "Epoch 56/1000.. Training Loss: 0.619.. Test Loss: 0.507.. Test Accuracy: 87.500\n", + "Epoch 57/1000.. Training Loss: 0.666.. Test Loss: 0.491.. Test Accuracy: 82.812\n", + "Epoch 58/1000.. Training Loss: 0.707.. Test Loss: 0.586.. Test Accuracy: 79.688\n", + "Epoch 59/1000.. Training Loss: 0.407.. Test Loss: 0.327.. Test Accuracy: 89.062\n", + "Epoch 60/1000.. Training Loss: 0.493.. Test Loss: 0.496.. Test Accuracy: 81.250\n", + "Epoch 61/1000.. Training Loss: 0.397.. Test Loss: 0.375.. Test Accuracy: 89.062\n", + "Epoch 62/1000.. Training Loss: 0.590.. Test Loss: 0.468.. Test Accuracy: 82.812\n", + "Epoch 63/1000.. Training Loss: 0.389.. Test Loss: 0.460.. Test Accuracy: 87.500\n", + "Epoch 64/1000.. Training Loss: 0.220.. Test Loss: 0.412.. Test Accuracy: 87.500\n", + "Epoch 65/1000.. Training Loss: 0.288.. Test Loss: 0.549.. Test Accuracy: 89.062\n", + "Epoch 66/1000.. Training Loss: 0.313.. Test Loss: 0.540.. Test Accuracy: 78.125\n", + "Epoch 67/1000.. Training Loss: 0.424.. Test Loss: 0.276.. Test Accuracy: 89.062\n", + "Epoch 68/1000.. Training Loss: 0.648.. Test Loss: 0.386.. Test Accuracy: 84.375\n", + "Epoch 69/1000.. Training Loss: 0.435.. Test Loss: 0.490.. Test Accuracy: 82.812\n", + "Epoch 70/1000.. Training Loss: 0.440.. Test Loss: 0.474.. Test Accuracy: 82.812\n", + "Epoch 71/1000.. Training Loss: 0.381.. Test Loss: 0.685.. Test Accuracy: 79.688\n", + "Epoch 72/1000.. Training Loss: 0.408.. Test Loss: 0.251.. Test Accuracy: 92.188\n", + "Epoch 73/1000.. Training Loss: 0.597.. Test Loss: 0.500.. Test Accuracy: 81.250\n", + "Epoch 74/1000.. Training Loss: 0.477.. Test Loss: 0.260.. Test Accuracy: 89.062\n", + "Epoch 75/1000.. Training Loss: 0.457.. Test Loss: 0.485.. Test Accuracy: 82.812\n", + "Epoch 76/1000.. Training Loss: 0.382.. Test Loss: 0.254.. Test Accuracy: 93.750\n", + "Epoch 77/1000.. Training Loss: 0.501.. Test Loss: 0.378.. Test Accuracy: 84.375\n", + "Epoch 78/1000.. Training Loss: 0.271.. Test Loss: 0.629.. Test Accuracy: 81.250\n", + "Epoch 79/1000.. Training Loss: 0.368.. Test Loss: 0.338.. Test Accuracy: 87.500\n", + "Epoch 80/1000.. Training Loss: 0.419.. Test Loss: 0.398.. Test Accuracy: 89.062\n", + "Epoch 81/1000.. Training Loss: 0.312.. Test Loss: 0.498.. Test Accuracy: 81.250\n", + "Epoch 82/1000.. Training Loss: 0.394.. Test Loss: 0.466.. Test Accuracy: 84.375\n", + "Epoch 83/1000.. Training Loss: 0.382.. Test Loss: 0.416.. Test Accuracy: 90.625\n", + "Epoch 84/1000.. Training Loss: 0.481.. Test Loss: 0.201.. Test Accuracy: 92.188\n", + "Epoch 85/1000.. Training Loss: 0.492.. Test Loss: 0.474.. Test Accuracy: 82.812\n", + "Epoch 86/1000.. Training Loss: 0.522.. Test Loss: 0.496.. Test Accuracy: 79.688\n", + "Epoch 87/1000.. Training Loss: 0.483.. Test Loss: 0.518.. Test Accuracy: 78.125\n", + "Epoch 88/1000.. Training Loss: 0.471.. Test Loss: 0.317.. Test Accuracy: 87.500\n", + "Epoch 89/1000.. Training Loss: 0.441.. Test Loss: 0.483.. Test Accuracy: 76.562\n", + "Epoch 90/1000.. Training Loss: 0.489.. Test Loss: 0.589.. Test Accuracy: 84.375\n", + "Epoch 91/1000.. Training Loss: 0.634.. Test Loss: 0.572.. Test Accuracy: 79.688\n", + "Epoch 92/1000.. Training Loss: 0.331.. Test Loss: 0.726.. Test Accuracy: 75.000\n", + "Epoch 93/1000.. Training Loss: 0.577.. Test Loss: 0.267.. Test Accuracy: 85.938\n", + "Epoch 94/1000.. Training Loss: 0.570.. Test Loss: 0.482.. Test Accuracy: 78.125\n", + "Epoch 95/1000.. Training Loss: 0.464.. Test Loss: 0.251.. Test Accuracy: 89.062\n", + "Epoch 96/1000.. Training Loss: 0.628.. Test Loss: 0.619.. Test Accuracy: 84.375\n", + "Epoch 97/1000.. Training Loss: 0.507.. Test Loss: 0.463.. Test Accuracy: 85.938\n", + "Epoch 98/1000.. Training Loss: 0.521.. Test Loss: 0.608.. Test Accuracy: 82.812\n", + "Epoch 99/1000.. Training Loss: 0.444.. Test Loss: 0.537.. Test Accuracy: 84.375\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 100/1000.. 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Test Accuracy: 79.688\n", + "Epoch 123/1000.. Training Loss: 0.394.. Test Loss: 0.307.. Test Accuracy: 90.625\n", + "Epoch 124/1000.. Training Loss: 0.428.. Test Loss: 0.396.. Test Accuracy: 84.375\n", + "Epoch 125/1000.. Training Loss: 0.687.. Test Loss: 0.431.. Test Accuracy: 82.812\n", + "Epoch 126/1000.. Training Loss: 0.555.. Test Loss: 0.329.. Test Accuracy: 90.625\n", + "Epoch 127/1000.. Training Loss: 0.397.. Test Loss: 0.519.. Test Accuracy: 84.375\n", + "Epoch 128/1000.. Training Loss: 0.512.. Test Loss: 0.769.. Test Accuracy: 75.000\n", + "Epoch 129/1000.. Training Loss: 0.295.. Test Loss: 0.343.. Test Accuracy: 84.375\n", + "Epoch 130/1000.. Training Loss: 0.616.. Test Loss: 0.672.. Test Accuracy: 84.375\n", + "Epoch 131/1000.. Training Loss: 0.344.. Test Loss: 0.584.. Test Accuracy: 70.312\n", + "Epoch 132/1000.. Training Loss: 0.393.. Test Loss: 0.470.. Test Accuracy: 84.375\n", + "Epoch 133/1000.. Training Loss: 0.667.. Test Loss: 0.545.. 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Test Accuracy: 89.062\n", + "Epoch 145/1000.. Training Loss: 0.737.. Test Loss: 0.430.. Test Accuracy: 85.938\n", + "Epoch 146/1000.. Training Loss: 0.398.. Test Loss: 0.450.. Test Accuracy: 82.812\n", + "Epoch 147/1000.. Training Loss: 0.393.. Test Loss: 0.586.. Test Accuracy: 81.250\n", + "Epoch 148/1000.. Training Loss: 0.509.. Test Loss: 0.489.. Test Accuracy: 82.812\n", + "Epoch 149/1000.. Training Loss: 0.398.. Test Loss: 0.413.. Test Accuracy: 87.500\n", + "Epoch 150/1000.. Training Loss: 0.440.. Test Loss: 0.323.. Test Accuracy: 90.625\n", + "Epoch 151/1000.. Training Loss: 0.418.. Test Loss: 0.410.. Test Accuracy: 82.812\n", + "Epoch 152/1000.. Training Loss: 0.442.. Test Loss: 0.578.. Test Accuracy: 75.000\n", + "Epoch 153/1000.. Training Loss: 0.617.. Test Loss: 0.448.. Test Accuracy: 79.688\n", + "Epoch 154/1000.. Training Loss: 0.604.. Test Loss: 0.450.. Test Accuracy: 84.375\n", + "Epoch 155/1000.. Training Loss: 0.413.. Test Loss: 0.474.. 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Test Accuracy: 84.375\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 200/1000.. Training Loss: 0.846.. Test Loss: 0.585.. Test Accuracy: 76.562\n", + "Epoch 201/1000.. Training Loss: 0.405.. Test Loss: 0.334.. Test Accuracy: 85.938\n", + "Epoch 202/1000.. Training Loss: 0.369.. Test Loss: 0.521.. Test Accuracy: 84.375\n", + "Epoch 203/1000.. Training Loss: 0.420.. Test Loss: 0.426.. Test Accuracy: 84.375\n", + "Epoch 204/1000.. Training Loss: 0.208.. Test Loss: 0.367.. Test Accuracy: 85.938\n", + "Epoch 205/1000.. Training Loss: 0.482.. Test Loss: 0.391.. Test Accuracy: 90.625\n", + "Epoch 206/1000.. Training Loss: 0.711.. Test Loss: 0.403.. Test Accuracy: 81.250\n", + "Epoch 207/1000.. Training Loss: 0.429.. Test Loss: 0.384.. Test Accuracy: 87.500\n", + "Epoch 208/1000.. Training Loss: 0.414.. Test Loss: 0.570.. Test Accuracy: 82.812\n", + "Epoch 209/1000.. Training Loss: 0.494.. Test Loss: 0.549.. Test Accuracy: 76.562\n", + "Epoch 210/1000.. 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Test Accuracy: 81.250\n", + "Epoch 288/1000.. Training Loss: 0.450.. Test Loss: 0.425.. Test Accuracy: 87.500\n", + "Epoch 289/1000.. Training Loss: 0.350.. Test Loss: 0.458.. Test Accuracy: 82.812\n", + "Epoch 290/1000.. Training Loss: 0.467.. Test Loss: 0.539.. Test Accuracy: 81.250\n", + "Epoch 291/1000.. Training Loss: 0.364.. Test Loss: 0.231.. Test Accuracy: 89.062\n", + "Epoch 292/1000.. Training Loss: 0.325.. Test Loss: 0.574.. Test Accuracy: 84.375\n", + "Epoch 293/1000.. Training Loss: 0.261.. Test Loss: 0.298.. Test Accuracy: 89.062\n", + "Epoch 294/1000.. Training Loss: 0.520.. Test Loss: 0.341.. Test Accuracy: 87.500\n", + "Epoch 295/1000.. Training Loss: 0.347.. Test Loss: 0.405.. Test Accuracy: 82.812\n", + "Epoch 296/1000.. Training Loss: 0.463.. Test Loss: 0.352.. Test Accuracy: 85.938\n", + "Epoch 297/1000.. Training Loss: 0.361.. Test Loss: 0.432.. Test Accuracy: 87.500\n", + "Epoch 298/1000.. Training Loss: 0.365.. Test Loss: 0.527.. Test Accuracy: 84.375\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 299/1000.. Training Loss: 0.393.. Test Loss: 0.404.. Test Accuracy: 87.500\n", + "Epoch 300/1000.. Training Loss: 0.491.. Test Loss: 0.405.. Test Accuracy: 90.625\n", + "Epoch 301/1000.. Training Loss: 0.538.. Test Loss: 0.539.. Test Accuracy: 75.000\n", + "Epoch 302/1000.. Training Loss: 0.422.. Test Loss: 0.362.. Test Accuracy: 89.062\n", + "Epoch 303/1000.. Training Loss: 0.331.. Test Loss: 0.399.. Test Accuracy: 79.688\n", + "Epoch 304/1000.. Training Loss: 0.570.. Test Loss: 0.319.. Test Accuracy: 87.500\n", + "Epoch 305/1000.. Training Loss: 0.575.. Test Loss: 0.432.. Test Accuracy: 82.812\n", + "Epoch 306/1000.. Training Loss: 0.399.. Test Loss: 0.269.. Test Accuracy: 89.062\n", + "Epoch 307/1000.. Training Loss: 0.281.. Test Loss: 0.496.. Test Accuracy: 81.250\n", + "Epoch 308/1000.. Training Loss: 0.736.. Test Loss: 0.410.. Test Accuracy: 84.375\n", + "Epoch 309/1000.. 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Training Loss: 0.391.. Test Loss: 0.372.. Test Accuracy: 85.938\n", + "Epoch 904/1000.. Training Loss: 0.535.. Test Loss: 0.521.. Test Accuracy: 84.375\n", + "Epoch 905/1000.. Training Loss: 0.411.. Test Loss: 0.562.. Test Accuracy: 84.375\n", + "Epoch 906/1000.. Training Loss: 0.464.. Test Loss: 0.450.. Test Accuracy: 81.250\n", + "Epoch 907/1000.. Training Loss: 0.528.. Test Loss: 0.490.. Test Accuracy: 82.812\n", + "Epoch 908/1000.. Training Loss: 0.442.. Test Loss: 0.330.. Test Accuracy: 87.500\n", + "Epoch 909/1000.. Training Loss: 0.524.. Test Loss: 0.366.. Test Accuracy: 87.500\n", + "Epoch 910/1000.. Training Loss: 0.544.. Test Loss: 0.516.. Test Accuracy: 81.250\n", + "Epoch 911/1000.. Training Loss: 0.459.. Test Loss: 0.319.. Test Accuracy: 87.500\n", + "Epoch 912/1000.. Training Loss: 0.576.. Test Loss: 0.357.. Test Accuracy: 82.812\n", + "Epoch 913/1000.. Training Loss: 0.425.. Test Loss: 0.333.. Test Accuracy: 85.938\n", + "Epoch 914/1000.. Training Loss: 0.260.. Test Loss: 0.356.. Test Accuracy: 85.938\n", + "Epoch 915/1000.. Training Loss: 0.342.. Test Loss: 0.287.. Test Accuracy: 89.062\n", + "Epoch 916/1000.. Training Loss: 0.520.. Test Loss: 0.381.. Test Accuracy: 85.938\n", + "Epoch 917/1000.. Training Loss: 0.198.. Test Loss: 0.271.. Test Accuracy: 93.750\n", + "Epoch 918/1000.. Training Loss: 0.357.. Test Loss: 0.362.. Test Accuracy: 89.062\n", + "Epoch 919/1000.. Training Loss: 0.280.. Test Loss: 0.549.. Test Accuracy: 82.812\n", + "Epoch 920/1000.. Training Loss: 0.465.. Test Loss: 0.467.. Test Accuracy: 85.938\n", + "Epoch 921/1000.. Training Loss: 0.489.. Test Loss: 0.587.. Test Accuracy: 79.688\n", + "Epoch 922/1000.. Training Loss: 0.484.. Test Loss: 0.408.. Test Accuracy: 85.938\n", + "Epoch 923/1000.. Training Loss: 0.383.. Test Loss: 0.611.. Test Accuracy: 81.250\n", + "Epoch 924/1000.. Training Loss: 0.404.. Test Loss: 0.486.. Test Accuracy: 87.500\n", + "Epoch 925/1000.. Training Loss: 0.483.. Test Loss: 0.259.. Test Accuracy: 93.750\n", + "Epoch 926/1000.. Training Loss: 0.410.. Test Loss: 0.392.. Test Accuracy: 87.500\n", + "Epoch 927/1000.. Training Loss: 0.337.. Test Loss: 0.354.. Test Accuracy: 82.812\n", + "Epoch 928/1000.. Training Loss: 0.535.. Test Loss: 0.417.. Test Accuracy: 78.125\n", + "Epoch 929/1000.. Training Loss: 0.443.. Test Loss: 0.517.. Test Accuracy: 82.812\n", + "Epoch 930/1000.. Training Loss: 0.536.. Test Loss: 0.570.. Test Accuracy: 78.125\n", + "Epoch 931/1000.. Training Loss: 0.313.. Test Loss: 0.399.. Test Accuracy: 89.062\n", + "Epoch 932/1000.. Training Loss: 0.690.. Test Loss: 0.381.. Test Accuracy: 84.375\n", + "Epoch 933/1000.. Training Loss: 0.569.. Test Loss: 0.495.. Test Accuracy: 79.688\n", + "Epoch 934/1000.. Training Loss: 0.486.. Test Loss: 0.443.. Test Accuracy: 85.938\n", + "Epoch 935/1000.. Training Loss: 0.479.. Test Loss: 0.399.. Test Accuracy: 87.500\n", + "Epoch 936/1000.. Training Loss: 0.385.. Test Loss: 0.347.. Test Accuracy: 84.375\n", + "Epoch 937/1000.. Training Loss: 0.428.. Test Loss: 0.334.. Test Accuracy: 85.938\n", + "Epoch 938/1000.. Training Loss: 0.571.. Test Loss: 0.442.. Test Accuracy: 79.688\n", + "Epoch 939/1000.. Training Loss: 0.453.. Test Loss: 0.229.. Test Accuracy: 93.750\n", + "Epoch 940/1000.. Training Loss: 0.336.. Test Loss: 0.872.. Test Accuracy: 76.562\n", + "Epoch 941/1000.. Training Loss: 0.455.. Test Loss: 0.470.. Test Accuracy: 82.812\n", + "Epoch 942/1000.. Training Loss: 0.434.. Test Loss: 0.451.. Test Accuracy: 79.688\n", + "Epoch 943/1000.. Training Loss: 0.481.. Test Loss: 0.353.. Test Accuracy: 87.500\n", + "Epoch 944/1000.. Training Loss: 0.339.. Test Loss: 0.313.. Test Accuracy: 87.500\n", + "Epoch 945/1000.. Training Loss: 0.619.. Test Loss: 0.229.. Test Accuracy: 93.750\n", + "Epoch 946/1000.. Training Loss: 0.573.. Test Loss: 0.562.. Test Accuracy: 76.562\n", + "Epoch 947/1000.. Training Loss: 0.478.. Test Loss: 0.429.. Test Accuracy: 85.938\n", + "Epoch 948/1000.. Training Loss: 0.441.. Test Loss: 0.383.. Test Accuracy: 82.812\n", + "Epoch 949/1000.. Training Loss: 0.342.. Test Loss: 0.617.. Test Accuracy: 75.000\n", + "Epoch 950/1000.. Training Loss: 0.409.. Test Loss: 0.425.. Test Accuracy: 82.812\n", + "Epoch 951/1000.. Training Loss: 0.419.. Test Loss: 0.447.. Test Accuracy: 79.688\n", + "Epoch 952/1000.. Training Loss: 0.531.. Test Loss: 0.442.. Test Accuracy: 87.500\n", + "Epoch 953/1000.. Training Loss: 0.510.. Test Loss: 0.491.. Test Accuracy: 76.562\n", + "Epoch 954/1000.. Training Loss: 0.412.. Test Loss: 0.485.. Test Accuracy: 79.688\n", + "Epoch 955/1000.. Training Loss: 0.413.. Test Loss: 0.568.. Test Accuracy: 78.125\n", + "Epoch 956/1000.. Training Loss: 0.540.. Test Loss: 0.197.. Test Accuracy: 93.750\n", + "Epoch 957/1000.. Training Loss: 0.373.. Test Loss: 0.496.. Test Accuracy: 76.562\n", + "Epoch 958/1000.. Training Loss: 0.417.. Test Loss: 0.276.. Test Accuracy: 87.500\n", + "Epoch 959/1000.. Training Loss: 0.378.. Test Loss: 0.414.. Test Accuracy: 84.375\n", + "Epoch 960/1000.. Training Loss: 0.296.. Test Loss: 0.462.. Test Accuracy: 82.812\n", + "Epoch 961/1000.. Training Loss: 0.362.. Test Loss: 0.476.. Test Accuracy: 82.812\n", + "Epoch 962/1000.. Training Loss: 0.344.. Test Loss: 0.447.. Test Accuracy: 84.375\n", + "Epoch 963/1000.. Training Loss: 0.430.. Test Loss: 0.610.. Test Accuracy: 75.000\n", + "Epoch 964/1000.. Training Loss: 0.464.. Test Loss: 0.425.. Test Accuracy: 82.812\n", + "Epoch 965/1000.. Training Loss: 0.268.. Test Loss: 0.525.. Test Accuracy: 81.250\n", + "Epoch 966/1000.. Training Loss: 0.587.. Test Loss: 0.393.. Test Accuracy: 87.500\n", + "Epoch 967/1000.. Training Loss: 0.652.. Test Loss: 0.438.. Test Accuracy: 85.938\n", + "Epoch 968/1000.. Training Loss: 0.480.. Test Loss: 0.511.. Test Accuracy: 79.688\n", + "Epoch 969/1000.. Training Loss: 0.579.. Test Loss: 0.204.. Test Accuracy: 93.750\n", + "Epoch 970/1000.. Training Loss: 0.264.. Test Loss: 0.569.. Test Accuracy: 81.250\n", + "Epoch 971/1000.. Training Loss: 0.420.. Test Loss: 0.545.. Test Accuracy: 81.250\n", + "Epoch 972/1000.. Training Loss: 0.539.. Test Loss: 0.442.. Test Accuracy: 84.375\n", + "Epoch 973/1000.. Training Loss: 0.528.. Test Loss: 0.357.. Test Accuracy: 90.625\n", + "Epoch 974/1000.. Training Loss: 0.439.. Test Loss: 0.342.. Test Accuracy: 87.500\n", + "Epoch 975/1000.. Training Loss: 0.404.. Test Loss: 0.471.. Test Accuracy: 84.375\n", + "Epoch 976/1000.. Training Loss: 0.412.. Test Loss: 0.425.. Test Accuracy: 87.500\n", + "Epoch 977/1000.. Training Loss: 0.437.. Test Loss: 0.579.. Test Accuracy: 85.938\n", + "Epoch 978/1000.. Training Loss: 0.580.. Test Loss: 0.333.. Test Accuracy: 82.812\n", + "Epoch 979/1000.. Training Loss: 0.495.. Test Loss: 0.437.. Test Accuracy: 87.500\n", + "Epoch 980/1000.. Training Loss: 0.284.. Test Loss: 0.338.. Test Accuracy: 92.188\n", + "Epoch 981/1000.. Training Loss: 0.463.. Test Loss: 0.332.. Test Accuracy: 87.500\n", + "Epoch 982/1000.. Training Loss: 0.386.. Test Loss: 0.341.. Test Accuracy: 89.062\n", + "Epoch 983/1000.. Training Loss: 0.663.. Test Loss: 0.307.. Test Accuracy: 89.062\n", + "Epoch 984/1000.. Training Loss: 0.270.. Test Loss: 0.335.. Test Accuracy: 92.188\n", + "Epoch 985/1000.. Training Loss: 0.610.. Test Loss: 0.598.. Test Accuracy: 79.688\n", + "Epoch 986/1000.. Training Loss: 0.502.. Test Loss: 0.448.. Test Accuracy: 85.938\n", + "Epoch 987/1000.. Training Loss: 0.306.. Test Loss: 0.212.. Test Accuracy: 92.188\n", + "Epoch 988/1000.. Training Loss: 0.427.. Test Loss: 0.510.. Test Accuracy: 85.938\n", + "Epoch 989/1000.. Training Loss: 0.546.. Test Loss: 0.409.. Test Accuracy: 84.375\n", + "Epoch 990/1000.. Training Loss: 0.317.. Test Loss: 0.463.. Test Accuracy: 87.500\n", + "Epoch 991/1000.. Training Loss: 0.656.. Test Loss: 0.394.. Test Accuracy: 81.250\n", + "Epoch 992/1000.. Training Loss: 0.503.. Test Loss: 0.455.. Test Accuracy: 84.375\n", + "Epoch 993/1000.. Training Loss: 0.680.. Test Loss: 0.464.. Test Accuracy: 81.250\n", + "Epoch 994/1000.. Training Loss: 0.375.. Test Loss: 0.466.. Test Accuracy: 81.250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 995/1000.. Training Loss: 0.352.. Test Loss: 0.410.. Test Accuracy: 84.375\n", + "Epoch 996/1000.. Training Loss: 0.366.. Test Loss: 0.414.. Test Accuracy: 89.062\n", + "Epoch 997/1000.. Training Loss: 0.400.. Test Loss: 0.523.. Test Accuracy: 82.812\n", + "Epoch 998/1000.. Training Loss: 0.457.. Test Loss: 0.451.. Test Accuracy: 85.938\n", + "Epoch 999/1000.. Training Loss: 0.272.. Test Loss: 0.525.. Test Accuracy: 73.438\n", + "Epoch 1000/1000.. Training Loss: 0.394.. Test Loss: 0.483.. Test Accuracy: 82.812\n" + ] + } + ], + "source": [ + "epochs = 1000\n", + "running_loss = 0\n", + "train_losses, test_losses = [], []\n", + "for i in range(1,epochs+1):\n", + " \n", + " optimizer.zero_grad()\n", + " \n", + " images, labels = next(iter(trainloader))\n", + " images = images.view(images.shape[0],-1)\n", + " \n", + " output = model(images)\n", + " train_loss = criterion(output, labels)\n", + " train_loss.backward()\n", + " optimizer.step()\n", + " \n", + " running_loss += train_loss.item()\n", + "# if i%100==0:\n", + "# error.append(loss.item())\n", + "# if i%1000==0:\n", + "# print(\"After {} episode, Loss: {}\".format(i,loss.item()))\n", + "# print(\"===============================================\")\n", + " test_loss = 0\n", + " accuracy = 0\n", + "\n", + " with torch.no_grad():\n", + " model.eval()\n", + " images, labels = next(iter(testloader))\n", + " images = images.reshape(images.shape[0],-1)\n", + " log_ps = model(images)\n", + " test_loss += criterion(log_ps, labels)\n", + "\n", + " ps = torch.exp(log_ps)\n", + " top_p, top_class = ps.topk(1,dim=1)\n", + " equals = top_class==labels.view(*top_class.shape)\n", + " accuracy += torch.mean(equals.type(torch.FloatTensor))\n", + " #print(f\"Accuracy: {accuracy.item()*100}%\")\n", + " model.train()\n", + " train_losses.append(train_loss)\n", + " test_losses.append(test_loss)\n", + "\n", + " print(\"Epoch {}/{}.. \".format(i,epochs),\n", + " \"Training Loss: {:.3f}.. \".format(train_loss),\n", + " \"Test Loss: {:.3f}.. \".format(test_loss),\n", + " \"Test Accuracy: {:.3f}\".format(accuracy*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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G1fO2t73N2bHMf4Nve4Xe8nJ5o7qysrLj58Z56aWX5G//9m/lne98p3zuc5+Tt7/97TI3Nydvf/vb5XOf+5z8xE/8hDzwwANy8OBBtxeAxlJ8m7b0GlO4cDhBVelPiu2TVZQVAAREowsAAAzTtoIPQLrMB/i2o50vv/xy6b+/8sorIjL+G33bHn30Udnc3JT3ve99MlVYKjU1NSXvfe97RUTk2Wef7ZpkdJTCfECra0whY9DKcNWgmgAAEIeo+vS+L6bF+aLKbwAA0MnmZugUoBEGcoiY+QDfz/zMz4iIyP79+2VQeH1iZWVFDh06JHNzc/JTP/VTE4+zvr61Pd+ZM2dK/33777c/FIlwaJMvICNQE1UFqMZ9AsCa0Xar+pMEGKOkE6BfiF+Nr3gAAFBK2ziBHWqAdJkP8F111VXywQ9+UF5//XW54447dvzbjTfeKKurq/Lxj39cFhYWLv790aNH5ejRozt+dnuF3gMPPCDPP//8jn977rnn5IEHHpAsy+T973+/pytBXSl2Uilec5+yLO4Mpv74UzWIJu/1oqwAWGe63QrdCLOCDwAAdDD8vTsACCmK5Wif/exn5Qtf+IJ85StfkQMHDsg73vEOefrpp+XgwYNy9dVXy+/93u/t+Pk//dM/FZGtAOC2n/zJn5RrrrlGvve978lf/MVfyEc/+lG54oor5PXXX5eHHnpINjY25Nd+7dfkne98Z6/XhlF5nk38cwxqTSCEnhiBGWzR6cd9983JwYOz8ou/uCYf+MB66OQAUaCNAtrj/umAzAMAAA1o26JzdK40UEIA9C6KAN9VV10lf/VXfyU33nijPPLII7Jv3z65/PLL5dOf/rT8zu/8juzZs6fWcf7oj/5I3ve+98n3vvc92b9/v6ysrMji4qK85z3vkV/+5V+WX/zFX/R8JaiDTgpohnvGvaWlTB58cF5ERO68c2EowMeg2irKCoA1ptstAy+qGUgiAADoSXEcUPhKlFdsKQ1gkigCfCIiV1xxhfzxH/9xrZ8dXrk3LMsyueaaa+Saa65xmDK4xsP1Bcw6oLZLo0GqiRsrK4ywY8O9AcA62rEGGEcDAIAO2KITgBbmv8GH9KT4PJ7CNcKfPt8sSwX3JAAgtKj6oqguBgAAxKY4VNG0RSfDKCBtBPhgDh3XBYWM4N0hjMM9058UX0Cwim8UAEBA2hrdGunRlmQAABCOpgAfgLQR4IM5TKADzQzfI9wvAADEIaoXFfpOfMuAnuk8BgAArWmeiywfs7AMAEgFAT6Yo6kT9aXWhE0KGQEnqCru1c1T8t4OykoHygGoj/vFIVbwAQCABgaDjM+hAFCBAB/M4eF6DDIGY1A1+kNe26H5DUwAaMNUOxa6ETaVWfAlY3EDAKADLQE+dh0A0kaAD+aEng9QI9kLR1NUFffIUwBAaPRFDpVkJs8cAABgW9k4gO/wAdCAAB/MSWEf6VYTCMw6YAyqRjjkvV5M3AKwLqp2zHTiAQBAigYDHfOTDKOAtBHggzlRTWbUVHaNOoYRsMZqgNzKFkoptEfxMlLJAOAC031O6MSn+EABAABas7aCj6HNThkZgogR4IM5tdtkLZtht1DrGpmYQE1Wg3rDrFZvq+kGANhjuc+xMFKxnL8AAMC9vqYdq154ZowCpG0mdAKApup0XLP79sn8fffJ+nvfK6u/8iv+EwXTrKwOa4vBnnvkqX28IwHAOtqt9uoM/chfAACwjRV8ALRiBR/MqTMpu3D33ZKtr8vco49Kdvp0PwnzqLRjprdGTcNVhWrjF/kLAOiL5T5nJMBm+WIAAL3ITp6U6RdfpM+AGnyDD4AGBPhgTtOOKzt/3k9CPGrVOdOjY4wYqobVVZYx5H0qKCsA1tGOAQBilZ05I7v/4R9k1403yuyjj4ZODhJkbQUfgHQQ4IM5bKt2QbIXjqZYwece+WgffQkABBS6ES6cLys5/2gSjb5tBAARmL/3XskufPBs4bvfDZwaYIuWAF/ZMIrnWyAdBPhgToqdVK1rTjFjUAtVoz/kNUTE7pJTBbiHgPpCx8hcevnlKbn++kXZt282dFKQELprwI5sYyN0EpC4snHWhZgzLLA8UAYqEOCDOSm8PUtADy5RVcIh7/WKaWIcQJpiGhPvvXdejh6dkbvvXpClpR6ui04AFlAvAUA1Ld/gK0MXAqRjJnQCgKZ4Hh+DjMAYbNHpHvkYH8oUgHWm2rEJiT1zJpPdu/VdjKn8hW3r67J4882SLS/L+V//dRlceWXoFAEASmjeohNAOljBB3PouC4gI5yJfXseqop/23nMCwgAgL5E1ceEvpjS80c+QIRac/ffLzNHjsj0iROy+K1vhU4OAEDKhwp9Bfhin7MC0A0BPpjT+PnfYE84eo01riH0xAhMoJq4QT7aRzAWgHWtxosG9NIetzgJ/QT6MnP06MX/njpzJmBKAACTaNmis2yMwrgFSAcBPphDJ3UBGYGaYvpGjx4785TbEQCABgodZyYaV/BV/xj9PwAAqRidV9GyRSeAtBHggzk8SAPNcM+4Ny5Pmfizi7ICYE2s7ZaWFXz06QAAYJLBIHQKto0GHxm3AOkgwAdzUnjYrnWNKWQEnBiuGlQTN7j97KMMAVgXVTsWOvGs4EtSllGAsctzkeVldjMB0F1Zn68nwIdKDNoQsZnQCQCaok2+gIxATVQV9+rmKXkPAPDFdB9jOvEArLjppkV5/vkZ+YVfWJWf//m10MkBEJnNTR0vEPANPiBtrOCDOaNvzuroUIOj924ti7wKsYLPv+18JX/touwAWGe5Hdv5Db4eBmYtluLlue08BlLz2mtT8vzzW++0/+AH84FTA8C6sjEA3+ADoAEBPpiTQkCv1uQBMwyoiariHlt0xSj+vsUC7iWgPtP3i4HE10migcsAkrW6ytgOgF9aAnyMR4C0EeCDOSlOrNe5Rh5fME4K90jf2KITABBaTGPinSv4etByBR8jbgAA0lT+DT7N4wLNaQPgEt/ggzmWJy9coquO39SJEzJ/990yuOwyWf3kJ1vvJcoWne6Nmxckf+2IaWIcABAGfQd8yGP/fgDQBg0uFBoMQqdgC7cHkDYCfDAnhY4rhWtEtYVbbpHpEydERGTz6qtl4/3vD5yicLTNc7CCLz6UFfo0GIjcd9+8rKxk8ku/tCq7d1MB0Z2pdszAWxZlSVSYTABjaHt+ABCfvrbobNOeMWYpIEMQMbbohDmN5wMiGNmXXqOBiRF0sx3cExGZ+Y//aH0cqoZ7428/++0NAP/275+VH/5wTg4enJW7754PnRwYFWv/3st1MY6GYhn1ERgVwbwObNO8RSfdBpA2Anwwh44Lrpl4VuiQyDy3cIG2MC9oX/G+oAzRp0cfnb343089NTvhJ4HxYurfe/8GX0v0/3Ex8QwAYAsNLhTqawUfAExCgA/mpPFgXeNpM42MgANUDR/qzQiR94lixhCAZ2X9i6k+Z0Jita7gM5W/AADAqbJxgOYAH+MWIB0E+GBOip1UitcMd4brD3XJjXHzguSvHbwjAQCKKGyE6yRJYbIRgZwXhQDAhMEgdAq2MB4B0kaAD+ak0HHVukZmp1ETVcM98jQ+lCkAS8yv4FOmbjiFPLaN8gMAtKX5G3xl6POAdBDggzl8NwlJ6vQNvuH/1jsAtWS03SnPV9onAABKFDrI4W/wBdmiM+xhEAjlBwDoQssWnfRnNZBJiBgBPphDm3wBGdFaalmX2vX2gS067WMRNADLWMHnWEnmlfUT5LFto2XKi28AgHoYewHQigAfzKmclI1g1rbWJURwnWjA0Qo+uFE3T8n79gYDkWeemZaTJ/uZfKOsAFgSXZvV8wUR1klTdPcNACAJVdNBBB+BtM2ETgDQVGUnRS+GCqnFRndu0RkuHTFJrQ6F8MADc3L//fMyPZ3LH/7hOVlcDJ0iANDD/ETOUGJV9Kk1T6oirWiN8gMAtEWfAUArVvDBnMadaqy9ME+orZFV6GpcHaJuuXP//fMiIrK5mcmPfjTn/Pg0oQCgR+/f4GuB7Rzjo7WuAQBs0NKPmH/xC0AnBPhgTuMtOg1i4rlfsecvK/jcq3uPkt9uDAZMqgLAMOv9y6RWvZdrq9GR15kss14OqaG8AAAAEBsCfDCHB7MLmGFoLbWs4o1z/1KrUzGgzHQqtleUE1Aupnsjz8XEBRlIIirw+AQAaEvzKjkt6QAQBgE+mNN4BR89HWJQ9VXlCbgF+kPzYxeBcACWxNy/aFnBV+fXJq9FhDaM0wAAqaCPKyBDEDECfDAnhUnYWv0OnVNrqT3cs0Wne6wy6hsZnAruJaA9U2Pkws2u8Rt8qY0XU0CZAgDa0zzO0pw2AL4R4IM5lQ9iET65EfBDFzFUjQ4LGL2IIU9T57Wr0FZhDeNeA8rFdG+oGLq3XsEHSyg/AIBLmvsVzWkD4BYBPphDJ3UBGdGaiomkptiiU5VxdYi8tstp2VERWiPrgLpGxwW275/hxPfwkoSjzLKd56D8AAB10WcA0IoAH8xJYQVfqyQbvM5QUssqtuh0L4JmBgCAcIpbdBoYq2hNF5pgi3UAQHzoz4C0EeCDOSlOrJddIxvAoa4U7pG+1c1TU99DSgxbdOqUYh8PtBHTvRHkvq9x0jrpiKkcUkB5AQDa6rMPcXEu+jwgHQT4YE4KK/haSeU6HTCZVY626DR57QqxRScAIKSy/sZyH5QNbdGp9TrynBd3rNNatwAA6IL+rQYyCRGbCZ0AoKkU2uRa15hCRsAJJqPc4/brl48Fcam+C6Id5QDUY/5emXABQa6t5UnNl0MIeS4zzzwjIiIb73pXr6ve6fvTwoYKAFwqf7lKb0OTfB+XfAYgJQT4YM7og1k2+QciEOElBVVZhyJD/XGv7gQReW8HZaUT5QLUZ/V+yXPZkfggW3TW+BGr+avN9DPPyOItt4iIyPL/+B+y+ZM/2du5U3sGAACtspMnZfHWWyWfn5eV//7fRebnwyVmbU3mHnhAZGZG1j72MZHp6XBpuaDpGIQxSg1kEiLGFp0wp2mbHO1jWyEjor1OL9LKLbbodG80H7Mxfw8AgHsx9zeaV/DFnO992XUhuCcisvjtbwdMCQBTaICjsnjbbTL96qsy88ILMv9v/xY0LbOPPy7zDz0k8/ffLzNPPx00LW6lNe8FpIwAH8wpvmk5Ms6LYODHFp39Kmbl5qa+7M0dfYMPrtQrD/LeDspKB8oBqMf8N/hGXlTTt4Kvx8OgJ2buG/aWBBC56VdeufjfM889Fy4hIjJ1+vSl/z5zZuzP9dmHND2uyr4sNDIFCWGLTpjTuI1OpVFP5TodmJRVzz47LbfdtiiXXz6Qz3xmWcPuDJ2xgs89tu2KD2WoE+UCpEfrCr48p01yLes5Q8dNzqqLp1HRACAM2l8ABrGCD+ZUTaxrez5zo+SqGHh48c1v7pLV1UxeeWVaHnlkNnRynLBYVbSnmW/uAX5wDwH1mFmJNI6JN2WqnypUJhtjUV6AYeoi8XAmdOPcYZvuvpLe5ht8obMVQH8I8MGcFFbwtUqywesMpe6c0smTcTSRVA336tYh8l4vE3PLADBGTG3WaHvcwyRui29Zs4LPPtcToGtrIseOxfG8AKhHA4w+KKlnSpIRFzIVEWOLTphT2San0mincp3oLIaqov0atKcPdfBWsAYEXoF6Yrs3ev8GH5LkMsC3sSFy3XW7ZWlpSj7+8VX5yEfWuiUOABBGhxV8WrCCr0TyGYCU8LoZzKmc/ItgdrDVJRi8zlAiqCLR014mrNizr5cVIgDQI1N90ITE9nIdLQeDjCG7y5Vts9e2DF99dUqWlramU555xvFHu5XlEQAko2GnwDjAjr6/+wv0iQAfzKFNvoCMaC21rBu+XqvXri3dbNEZH8pKJ8oFKBfTvZHnsuOCtFyblnTAJXeBs+EXhQYDAnIA0FroDlfhCr6mLxSFzkIAYRHggzkprOADRnR4k9fiSiXtt+349NnLawvMvchuLsEA7BltZ7T3nXUFuQ5W8PUnYB/pcgszyh4AIhRR4x7RpQCoQIAP5qTRSdV48GWGobWUsi7mawuJFXz2pdQOWEI5AIko3OwWvsGnNV2oz9c3ipzXDSqbF2QrgFIdVvDRrijGAz8SQoAP5lSuRoqw0Y7wktCT0TGNjZVF2sdi2tOH5ihDnSgXoFxMk0xa+9Q6eawlrajHZXlR9vpRRoARmm5WJWlpmoyYxoUAmiPAB3MaP1jH2qsxw9BaSlkX87WFxIo9wA8rLyEAocXX3/S7gi9rMRikfbLP1xadzussW317EV+7CcAJAyv4aL8cIBMRMQJ8MCeFgF4ElxAFVeXQ8kHfajBTfzp3lgcBP3us3hsAIDKuzTIUFJjQ6GpujzWnDe2o3KITABCG0QbdaLIBOEKAD+akuIKv1iVEcJ19SSmr6l7r2lpa+dIVeRUfylQnygWIX54XV9QFCFTWWsHX6tdQFHB1msvAOGWvHy9zAahFYePAyycO0AkgITOhEwA0lUJAr46RrYVQW0r9fJ1re/zxGbnzzgW56qpN+V//a4VdgWoYV4dSqlt98lEnUymbwUDk4MFZ2dwU+cAH1mV6OnSKJkulXICuYr5Xerk2AnpJ8lV+1AsAiMTEBn30oVRP+88kDpAyAnwwp3EHqqfHrc1gkqFUnYDT7bcviojI0aMz8uSTM/Ke92z0kLLJtN8DBPL61Uf+xlqGTz65FcAX2brGD394PXCKALgQW/Apk8CJb515mUjotFujbAWfym/wwQnG64ARoW9OhQ26i/ZLyaUA6AFbdMKcFFfwlV4STyytmcw6R9/gq3L2rM43v/JcV7pM1BlARL773YWL/3333QsTflKH0XtL170PwId637V1qsVJ6Pvt8xXgAwDET/PLVVrSoYrJiT+gHQJ8MCeFFXyAK03HNFq259R+244LQjCG9KOPLTpjLSst9zQAtzRPMtUxstX80J+DXEfJSeukw1Kewx/qAQDEoeuj0yOPzModdyzIqVM8hAHoDwE+mFM5KRvBE1atS0hldtqLAG+Kd9V6lj6OgaW2MtKWHnRHmepAOQD1xHSvqBzSDgZy+X13yM8d/JrsXjkuIkrSFYMIt+iETirbFgBmVbUhL788JXfdtSCPPTYr//Ivi17PVefnafMKyBBEjAAfzGm8VR+NOHqW5yKvvTYlg0HolNit/trTXXfCQPt1IH7WV/BxDwHlYpvIGf4GX5AtOgt/nj14UHY/+ai8+fTz8uFD/zT0Y8Yb1eSNlh8BPgAILHCDumNXgQ5pOXx45uJ/v/badJckAUAjM9U/AujCN/jG/GWE1+2L76y6/fYFeeKJWfmxH9uQ3/mdFb8nq0A18WNcvpK/dnBv6EQ5AImYcLNraAem/+M/Lv73rpUTIhJfUDWYSFbwuT4G3GOsB6CWmo2D5jaEMUoJOgEkhBV8MGe0jY7vTVr6Hb/GfT/NlSeemBURkRdemJHl5bD1M5a6FMt1QDOH96qiZXOKkgLAoZgmcvJcdiRewwq+ur8GW9yW36UONsbnUUAdGmD0oWE9c7T4rwT9CoD6CPDBnKpOs9gNRtstMsA1wVkxtZylbzp/pSUYYK16s0WnPV7LhoJvLZUXLWO9LvRJSYftgdb7I6agalCRrOCj7O2hzACUiuAlH81pA+AfAT6YUzn5F2HPVueS4p3mca/PCeTQAbMIbwcVxr2pTX7bRdkBsMR8m1W4gN6/wdeS5rShHbbojBflEpnQD9ZIg9KGQ2mybCETETECfDCncZtssBGvlWSD14X+2a0mOx/gtF1HKquMtDD3PK8owVlmq3JyLwHtWb1/fG+dXvOkbX4EbShbwefiWNQVGygn4yjAeIUu2w7n3/nir7v+LXSWRIFMREII8MGcyjY61UY81etuwWdWaSsGAlF+1M1H8tuOWMtKUayxlVjLBegqunuj72/wFWQ1A36Mq2xji860UEYAGpvQcGhuU8r7N+MPggBqmwmdAKCpxg/WmnvhMVjB18D58zK/d6+IB2Ai6gAAIABJREFUiKxec43I3Fzlr/icnPFWLI6+wWeF9nSPq0Pa043xKDsAlpj/HlzoCTRTmQVXfN03VCcbKCcApRQ2DgqTBEAxAnwwJ8aAXhUCfuPN/+AHMnfgwNYfFhZk9eMfd3bsNlmq7c3u0Od3Rdt11C1nbekGtOOeAdKT53a/wac5rWqp26IzE5HmBckWnQAQoYYvIPXV/letxjP/4lcfyBBEjC06YU6xY0sx4Cci8V5XQ3P79l3879mHH671Oz4nZwYDf8dup9kkivXt/PoSvlzRFZO0NlAuQDnzEzmhV/BVndRUZqIuijUtlDeAWhw1FrQ5ylAgSAgBPpiTQkCPFXstKYhOeZsfSnyLTm3XwRad8Ym17BQ0iwA8iK/N6nkFX4uT8A0+N3JlHZOLb/BRD6zQVfcAKKRkBV/T49IPAWlji06Yk+KDNQE/t3xmlbYVfCneL32om4/kt158dNwG7iGgnPkVfENUjFWsZh4acXnfEODrLltZkYU77hARkfOf+pTki4tOj0+5AKjFSGOR581e3jRyWb3JyBBEjBV8MKfxCr5IG3GmpksoWOWmrfqFPn9b2tOtrZzRHWWoA+UAJKJwsw9PumhZwRdTEFWVoCv4eILSZH7vXpl55hmZeeYZmb/3Xu/n4/4FlNJ0c3bYQjzkZWjKQi3o8ZESAnwwJ4WOq9U1ppAxVWpOGPjMKm+rghQEL0PSfh3jtujUnm5cEmtZKdsJDYAjsbZZInpX8JWN8WIuhxj5WsGHdmYfe+zSfx88GDAlAJLWYUm2li06fR0jKmQIIkaAD+ZUTqBHOMNeegkRXJdzLQN8Ka3gqwpAagkGhM63KmzvaJ/2Opaq4r1FOQHlYltdRq+KPrBFpy7+v8fImAKAO5rbEM1pA+AfAT6YQ8c1BhmjgvZv8MGN0XxlatKnPgLPsd4rWoL2ALDDxC2w/DdcI2eouWWnthe50ExsgXEAgAMdOoI+X/agv+qIDETECPDBnBRX8JWK9bq6qD2T7W/iSFv105aetrSlu256tKUb0I57Bqin/F6xGdHPc9lxQbQDsEbVzg6WbiDPbyHF8hwEID3N2yu2ER+RfAYgJTOhEwA0RRuNsRR8p664gm9roBWu0lq9X6yl21p64bnMFC2bU5SUVri3gHGM39wT9HLft5j5Z/WXIwE7JrdbdGZD/90yQY5NvfSSLN52mwze9CZZ+Z//U2R6OnSSAKBSpqURlaq0xDv2AmAbK/hgTuUbklG+qldyzVFeZz98ZpWvYml7mFiqhbbrGFfOfD/MLlVv3ycsqntmY0OmX3xRZHMzdEoQIfPBpkJis6GRjqnrgCnm75sKu268UaZOn5aZ55+X2X37QidHnZjKGuji6NEp2bt3Xk6cYEq4K19bdLo4Fm1eARmCiLGCD+akENdiormdth9q9zkQC18/bQactKdTe/pQLZUytL6Cz7LFm26SmSNHZOMnfmJrJQWAUirGTqUnZbsrL5St4HNxLCfHPX9eZGGh0yGyoRdKpl95Rda7psk47ldg1Pq6yPXX7xYRkUOHZuQP/mApcIrCy2o26KHblEnnD502AGER4IM5lR2XilmCHqRynU3UnDDwu4JPV0Ct6fkJBtTD7RcfylCHaO6t9XWZOXJERERmnn02cGLKbW5u/W9uLnRK0IbZe2Ocvr/B1+IkMX33MFW+VvB1Pcbc3r0y/9BDsv7+98v5T32qe4IAYIxTpy6t2jt7lhV8iFg0D5ZANVpzmFPVJsf4mE0/5JbPft7ZsRwdqPhNQCt1SXs6x6WPMaQffQSeYy0rgvaBKK9QS0uZfOlLu+Xaa/fISy+FfRw4eTIr+X4tqpjfalDbG/I1T8ouGw4oW8HX/ht83dIybP6hh0REZPaxx0RWVtwdWDvPdYFxOWBE6Juzwwo+X0lvelzz40IAnRDggzkpdFK1rpEnFpW0BdRCnz9W3H72UWbwSnlk9a675mVpaUrW1jL55jd3BUvH9743L9ddt0f+6Z8Wg6UB4eX5zm/w9XZSB79CXxKD7lv8u6wHGW88AEC/HDXijAmUo4AQMQJ8MKfxFp2xSuU6m1CxRaejcyU+yLSWbmvpxSjKUIdog+fKLuT48UuPAOfPhwtG/uhHW/uDHjkyIydO8FjShPVg06Rap2UFn/U8hlHKXxCxTOv9u2/frHzpS7vl4YdnQycFgJIVfHXPP+nftLZ5ANzjSRrmRDv5h+5aPhBv1yEXdcnZCr7CL7Z91I/lftG2JVbXLTqPHp2Sm25alH37eJCHX1lm9Ka3TnljOxjoalNFRDY2yv9+MBB5/vlpOXdOX5pDUl7Fmuv7G3wtaE0X6isbT7rYopO60VLPW3T6MH30qOz66ldl/s47W5/w7rsX5MyZKbnnngXqEtIUuuKHPn8JhUmyh0xEQmZCJwBwrhgYGf7z2prMHD4sm1ddJfmb39xzwuqrFSSgsxoV0wo+R0KfP1bFCaKm+Xz99btFROS552bkP/2nDXnTmyiovmm7V7ElmnIoq2CKVmVYyuf775+TBx6Yl7m5XP7wD8/J3FzoFOkQ05vaWlbstfk1q3keVITf4KMe2OCjnHZdf72IiEwfOyYb73qXbL7rXZ2Op2y4oAs3GvrQ4RvBIasot0cNZBIixgo+mFN867zJyp75e++Vxdtvl91f/7rI2prrpO0wGIjce++83HbbAm+dq7dVPqOTNs3LrWvgZ+wvJjYRpT2dddNX5+eOHaMr1kB7nWvL/iSR+QvYoqyCWfrE0wMPzIuIyNpaJvv3s+o5GiM7FQyv4At/32fK7lm4QbGmpe/ynn711ca/Q50EFIj0Roz0sgCUYFYR5jT+Bt/Qn+cefVRERLLVVZl56inHKdvpwIFZ+eEP5+SJJ2blu99dcH+CSSsVU9VxBZ+LYBjf4PND23WMK2dt6cR4lJVO0ZSL8rcrlCWnNo1bi4YS0wq+ol6uo8VJ8lz9rW0DK/iwree6YKGcLKQRiJqSm7Bpf6Uk2bqQKUgIAT6Y46qN9h0QO3jw0lvmzzzTbDdc35MH585lcvToVHz9nYIA3+iqiNCTkc3Or2e1j5qElGKCLz6UIbxSVsGUJac2q+n2Ia68yER2rOALkILCSXM9AyJ45iLAB7TFMwWgQM0bryroxv0LIBS+wQdzxnWqF5/D646Srfe+LZ8Glpcz+fKXd8vGRiaf+MR5+dmfXfeQuEBaTsaMW33lYgVfa4lv0VmkLd0ut+iEDk7LiolhZ8zeQ8obX2XJQYo6fOMmlJhXTfYqkhV83qQ0hvB8rcq7YgDbNN2cmtLSgNFk94tMQsRYwQdzvAVQHOvyvOIzafffPycbG1uJu+ceD1uHGsAWnfppT7fL9wZSmsfRxGsdU1SBqV9haM92trqMk6Kmp5E837mCLsgWnSVb35sIBqERl+XnbNUGUaikUfyAAjUb9H7vz+5jdQ3fNAbQDwJ8MKfyYTvCUXLZJYx01TWvc2Ul3k6+7XZKLlfwFbfoDB2QjvB2UIEVfP3qI0hFWekQbTkouzBlyUEL8U3ahN2iE2nwFbTtdD9S4b2xmLUW0wykbLj9933/8g2+hsgUJIQAH8wJHTCxbnMzdAo8UvANPlbw+aHtOkbLObaJVjgVsAJbX8Gn7d6vTfnbFcqSIyL16qrGdIdifnVZ6DfkW96jym9tE0J+39DtfbPzOqgLLfReFzyfr8X1UG8ABRSu4KNtcK/4vWUgJgT4YE5lm6xkaU2WtT9+raS1TP/mpvHZXo/cBPgcPewHCvBpCQZYG3uNWwUKoJloguXKowDF1eZASEG26ESSfG3R6fRA3ABJo/iRJMMVX3PSNacNgFsE+GBO4zcvU+nVal7nxobndJgwLgjXPTjn6hm97Rasjn4NjpD/dngNLLGCr7VY7iFtxRBLvqYs5hV8QdRIT54Ti3FCWcfkqgxDv9SHURbuVwtpBLBF8/2pOW1qkEmI2EzoBABNVU7CKlnB51KeiywvZ7J377wsLOTyS7+0ygq+Mo636GzD2zf4Wgp9/ra0P+xqTx+qeS0zKgSUNxLKkoMWYivDrO9v8MWWgahp9FmhbVXwVoWCRxx7pCzYCwBauHiRy0I34FXyGYCUEOCDKa0CMz6jORN0eV4pS9o998zLoUOzIiLyhjcM5Bfq/FIJvsE3atz2im1W9Hj7Bp+jFXyMcdwYVzfa5DdzGzpwb+hAOfTDaj5bTTcmczH+cp6I0h/JGFcZV15e3Z4fJh+7xYHgjcaspk0BFBi68Zp+p4171hAKCxFji06Y4vTbdMYa9+3gnojIgQOzrdMf9RadjlfwtcliZ981MlY/XWsVKFtaksUbbpDFG26QbHnZT8LGpCfx4vKujyCo0zJUVEGsB5A13VsbGyInTtTMUEV1oIzGbx1ar6t9U1almptwj2hZwWc+jzHCZ5mGfqnPJBp+AACAzgjwwZRagZm6D0mGHp5Kk9ryYXAw4EGqaPwKvvbH6nIMl6omcUOnz6X5u+6SmSNHZObIEZm/6y6v59JWzmhOY4DDB2tzZ1rvpcFA5Ktf3S1f+coeeeihuepfMNRIZJnetBUpzsbexZwXQa4t5gzFRS62POv6ewhHY5mNpsnYwA1wIfDNuWPVXoe0uHy+bL4dp7stqAHYQ4APprjsoDQPnX12xKzg87uCT/8WnZprfjezTz998b9nhv7bB591CKP6yEe+yYdJnnxyRk6d2ho279073/wAiuuAtSAwtpQHKmwWZp4H+AZfS/TzDgRsdHwG+EKP+THKYptI8QN6hQ6i0T40RP+KhBDggym1tulr80sWtbwuZ1tIatQxwNf253b+TjENLR8sHdXbqsNoHfNoTdc27elDc1636ERtWu+t1dWGbbmyC8nOnpXZRx6R7MyZoOkYpqVs4caJE5msrDT4hQkVoI+6UXlHU0HREN/gay7vOdirMas1pgn+vfTSlOzdO1d/63f45WgFX0guX2ABYA8BPpgyLjjVquOKrbereT0bGxEPIls+JJrYotPRCr7i38V2G1zkecLA6WriiG9JV1znUbT1vgT1y43G+agswLd4yy2ycNddsnjzzSNpmeJpwCRNEzmHDs3IV76yR770pT2ytNT9xSbNW3QWX+RKqT9xJtIVfM5QqZyxmJUW04xmNjZEvvGN3fLQQ/Pyz/+8K3Ry0ECfcyu0BR6QqYgYj/Qwpdbqs7qTaoob91qX0DL9UW/RWZPP7RWLdTT027zN9253ctrOtKRjnHF1RdmcPhpgi04dtGZV5/nowBc2/eqrW/9//LjI5uaOf/MZ4Jt02W3aS631IwRNeXHrrYsiIrK+nsl997XYwrZA07UVaU4bqpnYolOEt3MSwrNDek6fvjTwOnuWKVkRCV/xI3gD2miy/SJTkJCZ0AkAmhi3jz4r+KT29WxuRvzAGNMKvkBbdGqlPZ3a02eN7/z0Xl6KKoT1OUJFWdmMoYT7qiP33TcnjzwyJ//lv6zKRz6y7uckSQv7HZhxVlZqVqhCYnv/Bl/LAZuGPHbhlVemJM9Frr46wN79kazgc3Wc0s9LxFLRqrBFJ9C7su7P+vNCX/psQ1z0V7R5BWQIIsbrIjCl8NJ5ubqNtufG3fsgyUH6p6bi6uDqfsfB56JOb9847GmLTi1jHi3pGGd8+ti6y4c+8pEVfDpozSrTK/gqzu1jvLK2JvLgg/OytpbJ3r0LtZKltezRjIuXo7SoN7lmb1b0hRem5Wtf2y1f//puef756dDJ6RXbp6XFRhnx7AAE12EFn5Z7Vks6AIRBgA+mtAme2HvsHlWrs27Ro0f33Z3Os5TdH7CsreBDO3yDx682+dl1+2HKEJNkWcdXZjVVsEJafLzsU+d7v5qyxCKfK5HCCLyCz++vqXLLLYsX//tb31qc8JOesIJv8i/GUMmUspC1FtIYTCSZE/oyJjU5eS5y4kTWfxq1ZUrLHwt9GUXa0gPAH7bohCnjAnw7Oi4lK/i8c5D+mURbgHGD2tG/bz4B4Szw4zHAxwq+7rSnLzXf+c68PPbYrPzCL6zJxz62VvnzvW/RGbDCNA5MBaYo6zop9h6ZDIcvehZgBV+dY7Ypa6v1wQeteVE7XRN+sM34qy8xtFFra5fyt04wPnZty7B8NWdES1gjYCFrLaQRcZlU5267bUEOHZqVn/7pdfmN3zjfX6IcO3cuk4cfnpMrr9yU97634VugSlbw0TY4EMOgDagp0el9WNXqgd/nfowTWNjHfHo6zQ6ubpUIuoLPmckVMXz6bBq3RZe+8repST4uL2fy6KNzIiLy/e/P1wrwpcRCX5QETVt0Fv5MHUFoeS6SDdXLILcLHXYiXDZ4HhtPFw0zdXqEhSyxkMZgGLB4MVznDh2aFRGRp56alcHgvNkdn77znQV59tmt6e63vGVJ3vrWiq3Aam7RGf7+HP8iSfi09SfPaQ6AIqPNNVLldAWfYm1WgbXp36bT+uxGJRfBmWId1biCr4fTOqctXQTy9Fhfd3McyhCTNH6I1NRIKA3wsYKvG61bdLpYwdcLR1t0hr6MroKs8maLzsm/aL1SNcEMbdLFjzACvf/eq+3gnojI44/P9nZebXmoLT0uHDgwK3/3d7vl3nvnm/9yjBkCXECAD6Zsbrb4pRRGMC3NzESWBx0fEjWt4BvZ3s3RRFTx77TeBlrT1VSt4DxzGyOalH+bN0tT2qKztpUVmTp+PHQq1Gad6QBfBT9tUPX1GsoilWLOrxDX1nacZV1qYxCXxeysDUu07vWBfiYyFKAXZKuonCCJ5UVt1+68c0GWl6fkhz+ckzNnEhvEABMQ4IMp41bw7VC3ZzPeA45MRLS4nuhW8CkI8NWqo3X0tIIv9QffqePHZfqZZxoX3Lh8Sy3/+jIpX11NTmr+5pNv2cqK7PnSl2T3P/yDzBw8GDo5cK2ioe9j+yXaRvfMr+Cb8Du9XIfDF6eis7oqM088IdmZM/2cr8dMLb9v2vX/vpKd+Tw4/GoxKE39WQz9q/v+O3VxVOg8CX1+bZaXJ7e56T7dI0UE+GDK+Dn4CU13oBV8XSadayWtRfqL+Wd1T/Wxamb66Bao7r6fNnqM+hXhoYdm5ZZbFuTYsZKCSW4iqryMnJ7h7FnZ9Y//KLu+9S2Z/dGPGv1ul3y1Wyb96bKCT+U2f8oLfe7f/k2yC3udLv7rvwZOjU6mV/BFtEUn4jFpp4IgdaFmhWyzjb5mZff/wne+I4u33Sa7vvENh2+uTRA4wBf82NYrUReeOyD6GaAa94V0ygQti/9SLMcUrxkYJ7bpfUSu1iR/zVbe1tscbq671RanCfER4KvrpZemZO/eBTl8eFZuumnR6wq+nX8X10RVE9OvvCLZhYmr6ZdeavS7deuKymBTZIr513YuMtZyqDN3lp0/7z8hLWkpl6gCfAVTUz7SVt23aClbq2LOU83XoTltrsw++aSIiEydO9d4fFSLpvaxw+m9bdGZ5+ntnZowZbcDEsAKvvb6zBMX54q9DGO/PqCJmeofAfQYN3HbqmFX3Bv4WsG3seEhmLOyIgvf/a6IiJz/5CdFFhcdHLSl2iv4yv/sJsDXLo9ffPFSc3zu3JSzEXbTLTq16JyuOnXB0Zt6w39uE9DTWgaaNMmjwaDd9sNJl4Oii1eUlB06z7UqurAQ08buXnZg0nuboiq1Q9uXo7Kh7zYG2aKz5klj68Mr27Y+LjD4Fp3ujoWGeg9kWuhDMpEa37EFXGnyHGtR4+symhFGk91JKmUL1EGAD6a0+gYfjfhFGxvujzl/330y+9RTIiKSz8/L6q/+qvuT1NUywFf1901oq37NJzJ0Pvh6ycehg45809L9KSb+HXZqkkdtVvD1XQYhp2pYBBCIps6gIi0+tuuud/lUTtdM9S8GEptCHx6kjwjYPprYojO2ShaQhay0kEbEptuciRdGboTycUE28d/dnsvd8ZNAhiEhbNEJU2ptMVm3Effc2Lt8YC5NaouHwWKAz0UWzB04UPrfQXTO9O4rHIvBhbrHGEm6o/ppdQVfLzys4PP9u6lquoLP9zn6PVhaRrfi1hEEKrbRpt4YrTi3lm/waSlrK7Q2My6CHKzg609qL4GoXMFnvRJ1kVoFrCHl6pAKbWUc+wq+Wnp48de3quBjjJpen9WyBeogwAdTxjXgE9tpn8u1PPGVtM1Nzx8yN/KQVndyps2AqO3ET2WAr2WlaBrg03Jb1EnXgw/OyXXX7ZbHH2+5GH34oA0vvFVbhNq65ONg0K4d8lp2AStGrWbZSNsdUuO2UlHjOlK6Pazgqzjl2L9DfTHl3+jtordNiinfRVjB5/L0BPz0UdQVj2UhjYgLdaw98k4XygO4hC06YUrblRlRUrKCT5WOsxQuHrDaruDzZzRPwqepu5WVTO67b15ERG6/fVHe976zzQ/iMGja5D0CHuTduO++OTl0aFY+9KG1HX+vcYtO1GOpXAaDisCY5osppM3HBH+bds7Vz6TDeP++I7FZ/9/ga0lz2swKnKmuVvA526ITAHo0bgVfSk3TjpVdjV/8dZyYi5oO0I2PC1to/MIlEDECfDBl3MRtbCv4fPG9gi/0KpC6KwjHDV5dDGrbH6PiFxNfwVe0tOSgrrUcyHd9+5tt6apV1ctTpzJ58MGtAO/evQs7/k3DFp2atv+wvjhPS1Z2bSuDFkNFYvuoI6zgQxO91I2WN7XWcVNbqa3gc8lVsqtWWcMdjVkbye0AQ5ge62Jyp+k7DymjnVrlR57bf0AGSrBFJ0ypM3Fbe2LVc+/Ypc+oNdBv8TSwvt74V5ISNsBXcSCHh+nwglovspWVkZu9mE4nq3k9BE1ZgdKPc+fGD19UruCj0GvRnE2N2yBNM3aFc+eD4go+92mrc/m+VvmlQmte1E7XhAqg9dpipGKOq8cCL9v+1dXpqbcteK6AFspE03ABadIQ8Av+cqTKFXzNaElHn1K85hCOHJmW229fkBdemA6dFEzACj6Y0mpSX8OIRYniFp2p8ruCr92Dqq/nW2sr+GYef1wW7rhD/p/B5ZIN/ljyqfJBRK22YGNj64JmZ6t/1tMKPias2+mSJ1v3YPMDxFoOKiZvO9BSLo3bSk2Na+Hcg81CwK+H751pKceYxJynrODrj48Af6VCprXrtZ2ceuzftTmWs5f6rFeqJnoepKSUtcA4qU2PubyuPvPIxbliLdNOWMHX2A037BIRkccfn5U//dOzvXy/Hc1RLDBlMChviHd0XEpW8PmUZdLqYdD7Fp2htdyic9zft6kibb/BN5J0Rw/71qr54u23SzYYyMypE/LOVx8e+3NVAb5sc1N2f+lLsufaa2XqtdfKfyZQnlqfx1ldFfn3f5+Txx6b6S3tTfJsc9PNOVpzOXuYGM3ZNFoHG/anii6uj2/Ftnm5QVEWmdVHsNZHGvJc9H2DL891pMMzFXNcva7g6+1U9alMVCw0VPDJ6AsR3tZ9knRddLTFUVJ5pkDjFy7RWdu5FvhHgA+mtF3Bl+ci998/J4cPz8jqqvNklfK+RWcLxRV80fV3HWcp3KzgK/5NyzQ5KpyqWIPmgfz82rmL/11M17hg/7Cp5WXJVldl8ZZbyn/AYYCvySpQTXncxv33z8v3vz8vd9yx6HGbhvb3ssotOgNSMXkbgdE2aPLPq/qmUuHcxS06++FvW7xUmc+/CRcQ5Npqr+BLrFHtozACB/jcnd5R3TB/c+tlIWstpBG2qa1jahM2mZZVfTHOO1RpdX2xZ4pnzC3oRYAPpoybUKtawXf48Iz84Afz8tprU3L48MzYnzOlRfq9r+AL3dq3XME3LjjTRtuAWWXSPa02s3obNAn2T505U/1DDTKi/Eeb1P2dP2utDH70o7mL//3AA3MTftKfSXnm5PuMrlkrZEW0Zp3WdJUqJHZ0i07vp2y1oq/tz6QivrxQtoKvJktpLRN66C4iwQN8bQNzxWBv68tIuPHLVVTA0MiD1Gh7wfbS+Xt8Rg190UU1V/BpCeZhC3nkH3lsBwE+mNJ24vaJJy59bvL06UirfY2Wd329+CvGHygc9zYuBtuutj5zVTLl98z4o1vpwJ0EcaxcbIKq7kUfAT6vW3SiFs1ZV+wvK+uZptmbkRV8wZMw9u9Qn9Y8dbP7QQAqEpEITe1jh9N7HTdQH51QVtVqsZBG2Ka2jqlN2GSOdvd0mo5UpHjNfbPYj6ZqpvpHAD3GBaQmruAb0wK1/f5WXd636GQFX2t1V/A1CYAePTotBw/OyNGjO7csbL2Cr6eeU2sHneeyI1Oabo9X/yRjTlDz14p/V+feZZDkV7371kZb5YKRZlm9xm2Qphu9cO7itxNCreDTlEVwp3Y5Fn5weFzeR13w/RxghYo+IvgKPmXHpm4mheJOT9n4J2Rb7HI3o8Ynrfq7vkS6gi/+9qXixtFWzwwiu+wgwAdT+KDnBDVa3ui+wdc6yjNuINB+W4rrr99V+vfOAnwtC6s62GFzu8iQK/i65hGT2tWq8mTSv7ftJ7yuaKaQa9H8DNb5vtVyISIj3+DrI2may9aqmPPU/A4ThoSYVB4JrgYO8Llawdf6Mur8YugIgFEW20SLaUYzoct43PlH/z4TEU+J7XEQU+uwnpbhuW66JyUtdL0KIcVr7htzV3ZEulchYlVrUr/4RrCM6VQTbJm8B/j6fvB03Nu0PZyLgVadrMtOnxY5f77eASec32LVH10946CutR7Ij567/oOSzfwPr34guk4/4bVclBVwlulKTywqAxCanoYK5y5+g6+HU9aev3bxM6mIKS+2rqX/wLML1oORQWJGkQT4nNHUX/St5wqoMWtTLn5sCV3moc9/kZqEjKeyD5lAc9pcaHV9sWeKY/RRdrCCD6aMm7httdpDcctUJ2lt3n71vkVn3xytwBq3LUXQKlI4+cwzz8jM4cMiMzOy9L//t+R79rQ5TOW/K74tdtC2gq/L1iZW8ryMxpfJndQN1xQVcumbpEoKUlE2jRh9yaCFQnh0AAAgAElEQVThL1RcnNfFGYVzFwMS4fJdSzrioSEPa6ch5D1Rdv6S9GjIT99UNP+BA3yujuX1Mmoc/MUXp+WNR6blrW/dlLk5j2kB0EnovqXui6m9t2laXobrkI6yMXbbfrZpMkLXqxBSvOa+kcd2sIIPprRZwddoaY1SrpIa3Radji9A1Qq+4krUwUCyPJdsfV3m77mn3kHHnN/TDhS90vYNvranRblWL21c/Ld2T1GuyuXw4Rk5dGhGTp7UMGtqn5b7peukR6DPrJaeu7iCz0daLh4zz+Xy08/L9NEjJYFG9+dNSWz5V3xxLbbr0yq1FXxl2p7e2WWUHajhwZaWMrnxxl3ywgvT8txzMx0TVN/qqshrr02FLsKxeq9qLW6o0TQxfhxLa0VrSNtldHlRtfNJjenyjAr3KvNbWyA5AmSfXgT4YMq4iVuNS7O9b4vWIv0bG54fGIxs0dnnCj4f1SxbWvJ2fi0ddlVZaF3B1+ZnteS5Zk3yqO0WnS5sbor8y7cX5MSJKXniiVk/J+lIc32zlLbGK/gUyXtc5XrFqWfkIwe/Jm+6+XqZPnJk4s/WyTLF2aqChvxxtZLd+7UUX6RyOCZAM6HDGcHL0EHj9+STlzZmOnasnyme9XWRL395j/zf/7tbfvhDnWMe7zwM6oPXR/Su7zJ38RKzF55O6Ps6Jr0z7PrcTV8sj709if36NCCP7SDAB1ParuBTsf2MZ3UukRV8fg7fxwq+HT/bILoV61tmxW/wtbmOts1C1/ImwFetS73d3PRzzjrW1z0evCU92zHa1vm+HVmd1GO5FA5e7EJ8ruD72SduvPjnxW99y/t5U2L922+jFUDZCr7gCehHkO+0BhwIld03wVfwFbS5s9fX+28PDhyYlZWVrfPu3bvQ7iCeH9K9VzUHJ0ikqcEQbWWuZgWftowp0WcSDWRHcOSRf8xd2UGAD6bUmrhV+0pSe6VJbZF+79/gM7qCr+PhWp2raGSCxUXUcMyP9vmWmS+WV/Chu0n52rZupFJWmq9T87N+5xV8IS+kGODbDJSWwiBOUxbBHVflaKU+WEnnOJVDd+sXWODycpy1YSW/2PRb62trLc/dgZegYmT1rQ2yID1JruDTNuifNEHS4VdDBwRjb08qry/FTHGM7LKDAB9MGTehNrHRMTjz3ippNX4pthV8rldguXqDdycPD78XboQ8FzlxYvJ3L6yX8TjFYL+ve6b5r1WXd6xl4lL1w9H4fG6zitKVPB9z8KAr+Cb/GfWM5mPFvV6R8b2WS/HcvWzRWcyfrDIP6qD+XqKsqWmukNi2W2S6Or+tzDMu6Aq+en/Xp5HepEWCvH+GocTUVI9vQjpiYQVf6PpoitnM0rUCP8gKvhK6cqUdV3mYnTol02dOuTlYxELX2TSUPNNBpZnqHwH0qLUdUSqtfKsVfJ0PoYuTZVyXhFzB1+gXL/zbzTcvynPPzcgHPrAmv/qrq7UPM/kFNS0d9uR0FLfo7MzhqkiHp0ILbesG5RKe7jLouKWmoovTO5mopf+xQU+57eQqcKv1+oqspHMcFZ8xMBrg89qWNjzY2lr/BTnjYyZpMBCZnnZ2OO9VS2+HCsXCV5vy9iLky2f+TzjZ8EtGk144qkqiizyceukl2XX99XL1yUwum/6snH7D22sdj+anJjKqEbLLDlbwwZTx8ZwWDzWxtVSF6ynLK+/fZwi9RWfLX9sOHJf9fdfVQHWT2OQbfJLnsrycyXPPbT1ZHzgwV+8k5YcySdsKvi4BPvPfUfKg6cPTsDpxf2XPlL2ydp1a0ltMR9ctOkNOovSxRWed69NStjGxnKdZ39/gq1FJLeenauFnt51wdhkOGswQW3Q6jMNd4r0u6BtzR3I7hEFmOREkGyMdGLq4hMVvf3sryJjn8uEnb+p+wIgxj+IffZQdrOCDKXUmbmtvc6K4ZSp2VKVJnZD+hx+ele9/f14+8IF1+cQnLq3sKm7RaZ7iMvQqz+t9j1KaD3q0ZGnVQMJ1gK/J1mCu35zTkueaNRlYOl7Y21hxklpEghYyg3I3XAf4elUMLg6KgZQwD8eassii2PMr9uvTghV8CuqagwR4f4mzhJctOoMXRkPW048gQlcbtdNjWsbKHVbwdf15EZGppaWL/z23dq75ASLW+N5R2enbErq9Qn2s4IMpoSdumwj5wHzPPQuyvp7Jww/PyblzlxKyuckKvrJf2/5z2SRnXyv4RtI0mHzQulldNaax2kEX24I+V/CVvf3b7FAaZtN061Iv6wa//ei4jWMPNKbpEjv3Rtd8DLqCz0X72S0JwY8TA61zFq1fdBn5RZtjSmuCPK8EnC0qP5WrTGh5nLL8aJhH6+vtTt3FVGEmqU0xjrxg57guaFgJ3MMh0kHmODF+LmTyz7lU2lp6OqHvauN1bqVBJ611XOhT7NenAXlsBwE+mDIuwLej0ak5MmmyYkebLJPa17m6Ohzgq/UrdhQuoGuZ+hgUtf39fEIwO+sY4Jv071rrRDH46uQbfC1H403yqE6d0prnmk0OqlfXjb7zXFPYylp905Lepiv4au8mEMCk/sXZOVq8HKMoi0yIPb96n5iv+vdIM5wVfB3G6nV2XOlJiBV8xet1EmT0nInOD+8hvZE2NX4YzaxEupfJtEWjFK3g6/N41llaABLaiROZPPHETOO+mvbKDrbohClOJvW3KW6ZajWiNdM/3OmFXdnigeO3rduOM12s4Gv0ew1GMrGu4NP2Db4mh7Sa532qagMn5WHbgb6LcilfhRLWaHIykbJtRBVQlnU7dH64KfxCyBV8o1t0ejz3hHNoLm/4V/pSVp5fjDj1Xj9YwRdO8BV8gTl4G2xtrf9gY/EcGxuZzM11nP22toLPwQlV1kmlLL+gPSz0ZYw/f+CXFkJnjAMuLyHPRfJGr4mW7TKkoZN3x0WXofdJ2J/VVZGvfW23rK1l8uEPr8l/+2+r1b90QQS3ZTJYwQdTXK7g8633B+Yx1zn81xsbxUGb20TmobforFnW434t5Aq+kbeAG27R2SRoOTkdzX4+lJABvvJDldd9An79qxPgK7/Xu7dfSrqf5lTM8Oo2WrYVeaap8Atp6eNt1zYvJtFeNhN7XmhbwVf6fdUIqGj+A1dmVy/jtb4MBwdaLczV9RPg21l5Wq3g63ngxAo+aBS6zCfNhXg/qRa134CeHETTtLI7BY2/iZ6oxx+fvfgi0L59c41+1+z8RoII8MEUp5NShlumJqtEhn8s9i06fRzO5Qq+6aNHZfGmm2R2//7qY04K8A0GtTva6vRrmNkZVXV9VrborPP75u9DD0Ks4HNhbLoCFrKl+qZ5lddoG9TsF4pvnYdcwTfY7H8FXxktZWuV1vxrk67t3xkOorFyoB8avsEXeiTqquhDtqXnz/c/qVzsB4svkrYRui405qAuWxqnqWM0s0Inu/4LyD0nInTGOBDyEiLIvkpO2ssUMqqgyyUnmF1msUUnTKk1cVt3eZbilsplRzV8+Zubnh+bep4laHu2UCv4dl1/vYiIzDz3nGz8+I9LftlllWmqPGjFz1ddk6HbYgcXwWpX9Wf471iBEl7b4K+Lcslz/as82qysgoO2UlEe97FFZ7384i3nLszPjVUkNvi15PnONGw18GU/1r/BQLLVVckXF/2fq5+lYP7P4eFUzo5V1mA26HTW10fHPiECfBpX8PX+nBO84YpctPkbetPArfMHz14t0bEJ6Zg051I2FdZ5Linr1rYHL1PPYtuCVCOr84UpIsAHU8ZN3Kb25kaTONr2w1fZ9/cMZ8GW4pNlxwtqO1nW5rRTx4/L5qQA36RgdnHSaUIazJfxGM636GxwgCbnqlOnLJdRqK29JuVZ+y0626en8hiKCllRUkbUCQKFUkxb1y1h+mwHijk4sX/pkea6iPY6TbYMBdF6n4i3UiE3N2XXP/6jTJ06Jed/5Vdk4/3v73S4LOv/uke+o9VrgK9se7W2x3JznK7XX1y95+CQtThZwWf1PrzAzQiFl11SE7qMU1vB1+dLeVpilLFy0mWkkFFO0UdZwRadMKXVCr6uP9eSz4nvNgGGsgCfea3LsH6gOFgHNuHEZR8Yb7ItoZUVfMMflW48uV7rBO0u1nU90ZTnekweSHYN8PVFQ9nWyjsVH2HSrXOAT9ETv+N3Y+qccituo33FVgSsvsm8XfZBt+isMG51dt/pnH3sMZk+cUKywUAW77ij8/FUNP+BV/C5Or3TFX2Fg00qptXV0X/tYyzkZAVfkbUVfF5OoOGmVErT2KoDbZexff5e01FysqA139HFjx4m7P0cum75Vnl9sWdATcxVpYEAH0wZ97AysdHJc8mykgdzTbPABaODvhrr/cdmwtbvlm3Pab6xdjwQa796ZPzP1J1sG13BN+HaHK7gs1oHRlfzdhw8d1zBN64OqQoaJ6Jt0+5sBZ/yQleUlBGa0+a7jenzLek+tuisQ3d566c1mFe7XEMHfI0OkLJz5zr9vorLCrqCr97ftT1WiAOFWsFXbIPW19Nbweci/dYuGfbVnTfovW5quRkmpKMq79xeQiZNnj+0ZJ9PXl76xkTB2wXURoAPpjhdwWdIl7ftt392Y8Ndesbq+zXglr1Nm/xrq+2C0ol1fTBoEOBrViZabp+q63O+Raf/Xxv7+1ryXJMu865tt+h0gbJ0T0uejrbRFW1rxY0e8i3pwab/k9e5PtrCbpS/S9DO0AWEvpatFadD9/mYBPlK59jVUB3H2ionwwIH+Fwdq/WxO74dVraCr48stfANvqrTaWQhjehG2/gnyPm1DWI8reBzf0k6X+4Kpc85oNTkucjZsxnZZQjf4IMpw43L1FRePcE27pfL/qxY2cN43e9XRP0NPkcBmkt/bveAPOlnhv9taSmTl1+elje9aSC7XB304l+Vf5y7auxstQ5o+wZfn0HjFDXJs0b9Qstz1D2GhrLWmKZxNKetqPEkeeXFlbfhPmjNZ63p0kprfrlawdfnPSEiqjL0yX95QU788/0y/Z9/Wj76/33Y6bFV9AkqEqHm9OUaJOr8+bJf9z8J7OQbfEXOC8NzPnhYwaeyPmpBZjlRdwVf74kwUJ59JzFv8FKP0SxtpOkKPsKh7d1224IcOjQrV1+9c+IttjoVE1bwwZThidupodq7o5Gp2eJUfQumqy4v2FaNXdsEE5LYorPjBfkYFA3//sGDs/Laa1Py1FMzsrI8+TwTt+hstIKvfvrq/LwWbYM4O7S+2Elbshb/XJ1OK3nepy550nYFn6sAX+l3mhQVsqKkmNK4raxcwdfjI2fx3D2s4Om4IGXCcXlU3xbTRM52uar6Bl8hAeO+wefD2j/cInvOvSaLP/i+nHz2zM5/dLyCL7V7yuV942oMPVICDQ8UaotOC9/g887qg1QkNja2VphYo63abJ9/NB2Bd2cydO5xedjlsKHrhQWs4Kun6SWvrYkcOjQrIiIvvzztIUXwgQAfTBletTM9rp0pPpBf+PPIg7mhhr00aHDhL9fXRQ4enJHv3zcnS0vjH/CS2KKz46+1feivu9huuAyOH58a+3OV581Hv8HX6vuUDn6+L8V0xbWCz96DaWiTJiRVbj8WkLZJhKa0pLfpG6NNM97rdY4E+IrBRo/nnsB63Qwt9rZOXX0YmyC/ffjmsdNOj9dl239nAt78Lk/l7DLqDCwnHLwswNdH++BkBV/jzrXb4dW1K2IjjSdPZnL06FT4tA0lYHNT5Ctf2S1f+tJueeIJaxuThX32C16OIrrfUpqQjqYvT7sWy9yOO8yj+FB3fhO6EOCDKcNj/unpjm/5GpoZKSZ1+M/PPTcjp09PybFj03L33fMjv3tpBd/occ03zo4uYPxba25X8DX6vUkr+EoO2mwFX1bx7+FVpUvbN/ja5j/KVU12TCq6tk27m3thZ9lePKaiG01RUkZYSluovsHFyfpYwXPxlBde/NE8j2OV1vyrna6KSuH7+oo7edTd+r5vszNu06HyMo0G+LxpmMhQ3+Ar9h0uVvCZGyGrvKHcOnkyk698Zbdcf72uQNrRo9OydC6TPM/kttsWQyenk9DVxsfqs9on7e2EfjWbC+g3DTFx8k6I0oza2NAzRd33ug24QYAPpgy3xWMbnXEjE0MD8KoVPnl+6YeOHbt0Gz/11OzIsS59g6+HVrrnnqDtZEyoFXzDilk18nMTjpmVrOBrO6hUfBtM5GTw4zDA1+SQhpoitbq/VeZnMizP+93GrZ2SdppRfKXR+7YizzSt4CsYbGqvo+PRXl4S09yYymspBgDHtO2u01kc3wy/0OiC67arZSL8n6Pmqbf+zk0f6HQFX4Of0bJFZ5sVfH0H1p0f3kH6tT8X3HXXwsV75Pbb9QTSnGwJ20Kedz+3tjKG7CiUSZ/y6X9uhWe0SSrz28jN9vLLU3LttXvky1/eLcvL2stce/rSRYAPpgx/d2t4i84+V+/U5XK+tHTgf+Evt89TNfFQtkWnkf5uPEcBmnEx4A6naKzrw12TLTotlvvo21pjVkq5OkHDH7WYp5p1eXjq5WWGMczWAyUJ13xvdX1jtFgrQ67gK36DL9T8vZaytUpr/nVJV9Bv8CnJ0LW14t8UWo+ODxgagwmhp4rc5UHLKxkJJjcTbgXfzj+3Cnp4rpDe88HDCTTck8PW1kLfoUNqvnjhy+rq1rag1167R158sf13qbSVcYj34UtrlbaMcSD0Cr7YstTJCj6Fbr55l5w/n8mZM1Ny772ju7JpEludigkBPpgy3IBPjau9rpY2KdLlexnbb9wVAyJR6OEhsK8VfCO/N2mLzpLjulrB52OrNh9cbNE56e28SZrdf9V/Z6gpUmNSnoUc6I9Nl6JCVpQUUzrftyFX8BUDfD22QfmEqeo6/Q/t5Xh18ku10KtaawyQdvzImJ93nc719aotHrpp/EwR2Qq+2bUluerY4zKzcb7zsUK+qDEs1GcYinVppO62YW0Fn4MTaG+3taevT/ffPy8nT07J6momN964y9lx+87jcc/7wdu0gJVtx7xAixV8l4Kk7uZSlGWRShoXerQxvBL/9dfDh2kUZhFqCF9zgAaGH2Cablkz8oaX4lar6rm3bAVf1cRD2Qo+5/re5q3lBEGTYFjXalI/wFdYkVYR4Bv7ra+GFN8GE2nbonPcm491ft9qGfhUlSeTyr9O3fD1wDTcNmulOXmW0lZVz6q2HQs5idLPFp02XhaxTOv90iZdF39n6JeDr+ALlMHFIMlIMjqOtSsDfCGuu69z5rm8f98N8p+f+pZ88KmbO5++OJHb+jI6DkrKf91/Gzy6RWeLg3i+7zQG9AIccofRVcKThexrpo4elYVbb5WZw4fDJ0ZEjh93M32qrf9W8z68toxpwXWTljfo81MICDZ9HrNI+5czYqtTMSHAB1OGG/Dhhm+4kandHvbcMnU5XZeOa1KAr02a1tdFjhyZ1tGZ9vIQWF2jnAUGOhyzyZt4k46ttcMupqu4DaPWdJexlFatrNThsknrYGkY82cRUf0koak8h1X2gQ0b9T6vs/gCSR/nrtMXtf2ZVMU+kaNtYr6vreCK2xy6zofGx/NREIFu7Oz8edl99jUREXnLqeecn95ZgK/hzV32T308pxWfPVqt4LPeyDtIb59ZcOutC/J//s8eefDBOX8ncWj39dfL7KFDsnjLLVv7YxZZqy9j9H0Z48/X4/NASSKCPY04uAmbvOyLHhkcLI/dqa6D5nOL7tMA/wjwwZThhma67rbnY3rbttvz9aFqjDG8SqTuN/hcbNGZ5yI33LBLbrhhl9x228LoD7CCr+T3y/OkaqLI3RadVWWid2J/EhdbdO74pY5vSnf52V4n9nOR739/Tu68c16WluyUfZO35Ou0db7edlfcrVykOY2609bxpYLCL/R5rcWa3c/Eb52fan7Paa4jfVPxolWJ2mVUOrmXT/pnvxyPKdsKvkVniJusr3PmudNTaW6P+kibhRV8fdM8v3DqVCaHDs3KYJDJfffV/8aTlkuaOnu2/5Nubsr0Cy+0/MDkeE3zdDDwuxtTkOBUj4EX39tmTvr37pfUbQVffIrPY3bmM+ryEeBzKY16ZpPyqgNckuc7G/Dhhm9HI1O3xfHcMrns3EePVb8j2374Kl/B16xDPH06k1df3YqsPvnkbKPf9cJRGV46TLvv2bhYTTRSxhUTd/UDfJP/TutzdVU6yr434vwkDX5t+17SFtArOnhwRv793+flwIE5uesuvR9wrs6j8W1Xl7rRPaDf3yqPupxtIYYdKvvPhhnttVxGGvr+zn1pa6G2gffqn0mVwZeSxypPd+BJm+KLgT217cXt89yXacM+wUelCnVjjzlP29M7u4ySAzUJFJW92BQiwKfxG3xdnqHrGDlah5U/HQ5Ry/A3nppQ068MBr0nZuH222XXP/2T7Lr++q370lH1aVLmy8uZ/P3f75Zrr90jL7/c7xahwV+0CXVeNZV+lOKkBdHqZTflmZhl4dOnPIswBgE+mDHceE9NNV9CE/obfN0CfCUP49vXVfgG37jxSXFLwzbUvS3uMEAz7u9dTPiXKT4gdD3PuLJp+paZBXk+OpnR6jqGfqnJREqTc2mbsD5w4NKWPE8/3T1IH2pnx0l5FrKdGpsuRTeaoqSUCDNJWUcxHY3rWeEAId+SLn6Dr4+0xBSM0sJ8/pVcQK8r+Bx1xq7TWfkNvo7UjeVFeq3MPtsiZwG+hj8Tqn11EuAzNLFeylB6XQWyg6noM3yYffJJERGZfu01yU6f9nquce65Z17Onp2S8+cz+eY3d3k5x3bWhhyb9p+Adqrmkaw3adqlkL+Kv5whInHmeSwI8MGMnQG+CQ2fkleSXHY+k481+cCXAnz1jt1J6C06Wx9m/OorXyuyKrOq4qB161fTtFnosJ296d/6YrsFIVIYmHZVlUddA3yu7peq36ds49G4bBv+Qp91JVRAr+pntL0QoR150bOGGT517JjM/vCHkp071+j3ijvBjUxgdxxrN92is5eRfV+Vecx51AU+8rzRwfUE+FocxHMjb+FFgcDV39vvOVeocL1PgjvMiCbV5vXXL03Zrqy4uWgV02WaAnwO2yGXl5DnInnHXljN/etJ42+iG6AjwDc+EQazNBkzoRMA1DUcoBrdl3hCK5iP2U5B5Sus5Uofxi+0rFUrwbZXOvnctz0U19858DHObDuAHjTcZrBJwGL476x00MPpLAtWt7oOh2/rW8nHWEzK7+BloayCWAqQWEpb4wfKwp9DviUdYgVfmTbn1VxH+qasqXFj6AKCT8znO7/XVvWt6x02N2Xxn/9ZppaWZOP552Xlt36rdrLW1jIZXl/vOh9U1pFQEY487zST5qx/7fg2Ydk/9fOt1Z15t7HRfVbS97xm7+1Kq0OomN3VR8HcTYiJ9z5fygrePwRPwAUOA3xOLinPJZNc8mzy+pwU244+54D64ucbfOnVjRQR4IMZxe/vjd2buKTBHg6ITfo5l1yers6xqiYexm3R2fHZduexjKzgGzf4aTtZ1i7YUDHB2nC1h6stOpWPd0TE0ff3RFpHOpvkkbYVKRbKV6R5vR1W9i0aV+f1/ft9sJDGYVrS2/m+bdimO1UMLlat4HF6ym7fJ7XYR/VF60RO7TKqCOZZLutseVmmlpZERGTq9dcb/e76ekWAz/kKvoo3Bf02EP0qCdrmkjlLjrPLanigsmeAPrLYwgo+75wE9BykQ9F5fClr+bR997quJtXeR19f98XgPsem/k84geL7eHZjRf7ffX8nU/mm/Oi9n5E8X2yYLp1jxbasdxl1+AnwNaP6ZWqMpaDqAPVM+gbfxEZGSQvUJSjQbBC488/b+dbLFp19c9TD++zA2r4R1zzA4TZoGVrTAE6fb29NytM6g2gL+a9N3YB21b+NO17V39fn99tJLmhMkwVN6mDpLxSDbH2WQ8hzT6AlHVZpzb8uk0m9foOvqHDCkV0iur7dU9PamrtjlancojOAqh05Tp7M3CzeGXPxbfOg18nvCScrH5f6n9QtZufmZnk5nT6dycGDM3L+fI2DOs5U75PBHk7gr161qxMa2ggR2apwIRPj8EVmNXl6UaRLE9ua2N42yysXl7nr/ElZWD0jH3rqZu/nsqbymkt+QHvIU8cWnbCIFXwwY/iBYWKjN6aVH3nDy3MP6HK8P+lh/GJejDnB9u+62KJTXWfjOKDXNhhWJ0A48jMVE675oNm1uZqc0DowHB5MO9uis+UBus/x6Q8ChdY8wH2JsxWeLeR5+dvEIZvOui8DaKD5XhhtKyvyUfHFNO1fWp3j/2fvzYIsucpz0S/3XLuqelA3IBAIjCxjC2HMZLj3YjtwBMeXCMIRhDHHjhOHsK8Dh8MOPzgOEX5w4Af7gfCDn849vuCJ4RgLTYCEAEkgWmoJjUg9aWipW1Kru0stqWsedtWuvXeu+7CnzJVrHnKozu+lameu/NefK9fwr/9f//8rrC1FWX/yir3eXt7fz1EFJsolEXo93zKCJn0PH0LHePrwww088kgTb3rTAP/9v3fs9iK0ERfEqf+PaVNZGZM5xdOYH1jGvH4faDTiZb797TY2Nyu4/voefvd3JVa+ok9sRvynsy8w3yvmRG7MQd/wpwsJAJ0w0JZQ1YVkfdAmNbIODfM+22y2s+iPeEGgvR8rIGhnlizg0wGihD+UHnwlCoPoJqJa1RCwCDsHX5CDOO6qYApbnJmV52UgCtFpykcCBQnRySPjY4PMpy3ezMkUsKreJDaGkqzBSyrtyoBjmsPRdT/J8zfIK2yFTh0jvw6K8C2LwGMUeeHXVukhWx3T9AShQ3TmGVfCRt4UWYXl84rIC2T+LgxjkOmzOqANfInxa0x5CG3v44zxyCNNAMDly1W88krVilZA4iE6bd/VmTLcwwaEEPe5yll10KDz8F26VMXm5lDldOZMPfmA6obGEN4NFjkbL3saYWhtDC/BRrcMh44AACAASURBVBrGKW6lsmtZQMCHXLdCy6nZpY5wRSPPMFoyct4ouXOqKFEYlAa+EoVBPERn/J7KZi1tDz4aeoY02vjDn+XHCwBf8TAskKVXizfQyhfFRo4Wq4R9NLbXWOTGVBUo6odltDW8qdJj9Z0insiJ8uUlRKcGAdF+xMa4JHtmYaGC9fUrReKTGMAFbWiTg88WXD4zHFh5HdMs5JlXbR1knpRQCQMfEd12WuU4N6+KHsfX/LlXkde2MF3jXNCxqtRwzDKLRS7qGlmM8phpQPs1XX8Ii4+9u2u5vnOMtqYHB7z1Uc02ciYXa4JVBx1iVsZH4Q02DvhPqwlM6ebmk1CCV+pK8CBAEPhpjLT35qo00zx85r9CARzW6/sViqjHcYmiLxkq8DG36bbTld7PiooyRGeJwiC6eRG6LedCYnG7+CQVi8FkQ0QvADwl5BWRg0/zscqgh48f/yoON9ZR+9gnQMiHXFWReF6mDE7Uo1nxXvPgE/HlJUSnBvSM9SrX5FLcyZN1/OQnLVQqBF/4whbm5uz6fvHBbzObw99O2ifnjZxz9hLIK7/a/Yw2sqW5QZXWHR9PKysB1tcrOHTIrwJNpQ3y+v3zgL3YNpnm4JMhJe247xCd0hx8WTS8jTFVB2GImPxASPRPdrBkIKu5k2VYHHrwKY7jDBh3Tt7DYl4a+DjIDSP2yDoaAa/bZt3ExT7GOpz78maAyrp+3yhaVAIV0M4sJUqoouw6JQoDUQ4+pc1D5qutW/d81Rw60xx8xRaZmLD8pu987edoddcBQjBzzz1KVbgqI/2taRDk8SDbO2c+LAzgLHqPqCEUHzN43KjNf/KTFoChMuWhh5rqlRUUNoZplf6hM150wH0+RwOLyUpOYoHkqJmkkPKa48lVJD9sbgb4+tdncdttbTz+eLZhhdKku1eQtdJwyINaOZlnW+bfmiQVdTrPmoL2gHKaJA4KBj4arj+EhRBlzQr1TbXCriqRNxx/rPWCuqabfj6NTBSsOmgPVO1vFnmg3wd2JCn7NMilhq2tQCtyTlo8Ft3AFzDHRU6Y04SOiOi7/QMSZvKRmXNaVp3NUg+QF+SZN1dwsr3KeUPlYVue5ZxUwhylga9EYSAK0RmD6oyT45mJZk20SZsuAOxTqD49+BLP5mE1UsCY78buJnXdfYibMU3ful4evQKlmhQi+n7OPPg8zAFp6PQTyr8rADrtmmWfz+OykmM7U6FArw+yfpZYTaiGd5mjQxesAyRj9l59tTrxynjlFRc88Wmo9M2y//KRZThi15h819gH9vx+midJeIpk1wayRA4+GpayduZjKksDH8e6WXTDh+3BM1Ow1kH6QKnSIVzGta2tAP/yL7P46lfncPGiXe5FZX4cEHx1oYJ//udZ/Ou/zhqH283bAZm89HMdAb/+5JNo3XknKktLHhkqLsbfdLaziI8f+//w5tu/jmB7O931IauJSwUGfIwfyXyNvcKwF9s3/x58e2f/sdeQ+65TosQYtIHP2paUshbYqSEN4hP4rGcHA3aDFXlRDBx9Q55AxrumU2ZKW9ZhqQ2x5PsmhUc337cI/SH7HHx29ZeCvxyaetcYwjDITIlBCK0Ezp8AnOf+tqd5y/DlaE8p1tI5LhK951JucfX6ee4jaSMvurE9861dVmgoXwAOPKAkkMqjWQgpKRr4ojQCzuFIUxjTYbW5xndgz+n+5Q9WvTs7diFmx08fOdJEp1NBvx/g1ltnzBhMA9QLHnuqjjAMsLlZwc9/3jAhkZqBr3DrKWtcMBCsraF1//2onzmD9je/aa7z8dhAOt/C53f6tRe+g5mdNdRXFtE8ciT7PpEVA1p6AGekjKDbVzL/po7hZB7LeaMI01GlhJw3UQkOSgNficIgKptVqYN8sQmIMxulHcKB3ljZTJIqculYgUfXMw3RaV5/pJbYr8Q7pe3BZ7jC84r5EIq4xkPqQuJVZAZcxVeXvVMewnnpImsPPld9ooQ6ePOaannV+66/7eR3jj56jlgpFJJ9UFNJLrmV5ilp0foSLer7HJSKF2PZX/nIa9so88UomKscfIoyJuuyjWS121WowAK5a1feNcVHreu2Iudor2fJF0vWIMSernK9EWxtafR+QV9YXp6qqYrqrby5mS++szr85gyi00kRBFtb0//DELXTp93Un5uGcIFh35ztLAIYvlp1YYFbzgtSFIa1DxpY8FF4Q3rBoH1oqYQSsjp0UMIOpYGvRGEQFe4rFaJsS+Iu0CnPTDrVqQgGYwWdrB3Gi56LEJ15E1jW1wIsLFSt8zOMYSpnqiyAum0nrVdiIFSmk1OI2ksnr4VWJR5IqvQpXTaK+k1dQtYGeyU0rQsUqb/kmVddI3OuFkwFAx9rrTJiuddDdWEBZDBsIBKww1SbIs99JG0o6jq9w0VXt9Xzra4G+N73ZnDkSNO4DRL7Beq38UFBTYb6dFg/+nnLw3TSHHxZzF0pGfjob2zrwZfn+SidHHzJvkgb+IRtJBj4vs6Muv5myWMq0wpU3yGtIaedf1OznHfQ41exfZuPPGI2IFjGQ0f9Uuebp9v+KRqlUz/tJoBGvTKdS47Efe61vQSjtS7njeIjRGfOX7mEI9SyZqBECVVEJ29auFKasGihkJDhNUtJrd8HnniigTAEfv3Xd1Gva/CkCBUvK94mdZqDL1+nCG1BCPCTHzfwrlequHy5gl/7NfVEBzrGMNvvOH5etqmSKljo24M4QR3hxlqBmzGyD9Gpds2Gni84V2xkpICxNfDxnw8AC29v7on5HA20HLGihLx4GVsr4CIPZN1FRHVZhegkBO2bbkL18mUcuupGAJ/V4oNdn7toCHsNeW0LZb4kA0Hn/e66awavvz4M7/GWtwxwww1OwlYogjFHGX4cQoDeLmWQH7j90El5NOPoG0hRpUxIvHpiZ+BjkHf3oMaiM76V8ID1PEmwZC0tAx8Ljnn23r89WOd8fTaX0YUyQRiqHbygylRWV1G9eBGDa6/Vq8+j5TXrtlfVhaTOZ0YNw5wlDPWEmbfhHsdebF/H57icID+HDkrooPTgK1EY0Dn4lOF5Bjp2rI6HH27i0UebePJJfqx91wYAdQ++4V9eiE4dvqQLaoqr0c5OgJ3t4f+dTsBhiA3XBj6VBVDXYCEN0UntqnXeSVhvTpTpIrA9+NLj25XRt4Q6dIV5Z16emsjjty3SRqhIvBXbg09aBID+qdhgZQXVy5cBALNnn9FlSwl57iNpI2tDMa/OLIwlY+MeALz4ouL5Vc0GNJYyNF5kMEgeYnKdYkD7e6XRqZTld0tZj5rUXHvwOTXwacDZwTftepPXOp34Jl3IRwaTmDX5nZ04kRwb9GiYevDlBaphE1mzRNClYx8rIMUGuiKV6QV9MVOdi6u6rsi+IsBeeOcy8lAJVygNfCUKA9rAx7Ml8YQ/5gbZwWz60EPNyf8/+9n0f5tNn8tnx6/oYuHInb7SEQMiI5wvA5/c8CZRYigb+PJvsFNB9P2cCUEWJ+yT19hh6CwPZV+xsDeisvt9pxN43hjl/3R0HnkqAlyO29R1mgkDX7KycZGootgNT+ohOsu+qQel9ur3UX/qKdRPnfKmQXDx3cY0XOTgcxbaiBDzdzM0APR6QWK/Is/JrMekrBt4lxotJkDrvkZ/U0uCvuYs5l5WUNnkFqHGTwbGMlchOl3BJbn6k09i7p/+CTO33cYl7MYg72cUmsoxma3NLKWGqTDh4kQRUU8Ro0s6bfD6Qqp71LycUuLV61jPlBWyrt81eLpO5Qd41zIEfTA5D+yVhuRiogzRWaIwiCpsq1XiRgmV4qk7m6qYeVZoDz5OBeNn+/14DsNp+6mHpJMKfSn7kyc2UZYKAvZ1N+/kOkQnTVAvFw9/7BRhwWZ5Z5nwbZpAuwBy4p6HzHDNGg9HjzbwxBNNXH99Dx/60C6Hri1fauGCskV+jf6ux9bODnDhQg3XXttHsykvL4L2XEkVMJ1vnICqW7QZtjpMUa1Gfsj7WWn0s4NK29SPH0frgQeG5RsN9N/zHs9caXwzQcHWzioOPvUYqnNv1w6rFuuGAnCNKDxZlsMv87Jhx93dTT6bMPCxJiMN+Vs7TF8agzAtA59jDz4aWe1Hs5JLVXLwCdeCDBi3Id+6/34AQO38eVQXFjB4+9udbKTSWufMc/DlRG5MO/GsR9pF3H+nggI0hIzFHIn7VwT2wjsXzbs67/xdySg9+EoUBnQOPu5eVvVIEu+aJnhCr8uJz8bwNOYvGqIzqvyw4TNrAcb1dsO0i6iccNEVBl2c2C6y8lS08cljDj6bsnn9BllCtvHVtH8DAJ54YmjhOXOmjs1NP+JPHr/llaxE+M532rjzzhl873szzmmz5qEYBA2f+SEBRmWstUqbJ9qyovnO5XypB5b8SbfP2LgHAM0jR7zwUTl/AdedP4pmd33Cl+l3Gr/R+8/cgQOnHkP71luB7W09fipuOokzo7wGnX4/6cEn9QjS5NP6gJktLCZAa9YcL4iuyCVGsmYbjW+lnYOPRX5rK7BbRwoyyQfjeckDv76awNzA554Xo4pVTx15ErIC5CM3lgvwmiPVPUOKHcvoIIvh2pq3XJdZ1+8aTvpozhqF3lPmgb088FBCH6WBr0RhEPXaocPvZHVikkYQyDdfKlCSZ0eFxnVON3XJBYKQ+MJRM/TdtW2uyqVLmPnud1E/ftyO0IiXgJVIyDHcdRG9hVtm4COD+H0VRR/rehGV/1nlVxtDtAdQU1DnT4grGkwMfFG4yEmqggm98iOnjn4fuHRpaHC6eNF9wIpC5Uug+p8oB1/0vWzekRCgQsSTtYlBLzeeBBkjLwqNYHsbc7fdgusuPoT3n7nDGd39GwuT/2sLC4KSDJ5suoigjXiGNuYjhm29y3AuV/Lg00AuT4qnZOCjjbau8xs6hcbLsh2b/M+VrHrDMMDOTpQPAQGBQO0vFKIjwhwGTfpUWnsx0/1HLuYIIBGiU/mwtyk8fhjXh0VdIevDZplGu6BhwEsa7IvqyFPzpQXpWleARklDt+WyGQrQpFcsSgNficKAzsHHBUMY4wqAjjV00Xp8C+uqJ6/CkDaOkpgh0kbATDwr2Y3N/ud/ovbSS2jddx+ClRX1ig0YGgyAI0eauPvuFjod882crUfBRGk6SDSWkIa0WqsQnUUA/5u5CtEZ2yRqELBt1yy/S1H6hK0BT+ZdJZs/19YCvPxyVXuJICTPATCHUOoDGXUUl4eufa+52qeBiXjdTfOUtGqITlvPi0rYB9EYEUWZn/IAVRnQN6rnzk3+P7B+YfK/0twpGQiTf8MQhAxluptvnsEbb4i3r6ohOuUnrSwa0/DZXi9IGuQTRim7ujIfZxYTYK8X4PTpGpaXzVZaErJz8LlaZ5wdOHXwTcPQhiE1RMf53Nz0R6czHaPaBr6iIfEO+X2nwh3qpOc+lkFItb1NXjbFBkr7W+S2L2TFiEc9QAaixB5HfP03UufmrGGTB6/yoE3IAw8ldFEa+EoUBtF5uFIRG9NYSCMvkq/Thkqng0e/6QUiDOPeKqbee8p8KaJ6+bL5wxgr0vkMHD9ex1NPNfDMM3UcORJPvjReNOnndbzg1PkM2HSkFgw9C4epviS3Aj6FKF9eQnRqwcxbknctr22eJ+i2mc3Zje1t4Otfn8V3vtPGo482tJ4twrcsAo8u4Hqc0euDdlL3PFn2GbyML1kZ+ChEPfj2+hqVNvLkQerCYC1UZoQhXnyxiqeeauDixRpuv10ccld4CFDOCPt/1m/xZY0CU+zuJmXTQDuJsxhSDz4FpbpzKNbx4INN/OAHM/jWt2aZ3o5ScHLwLSzU8PTTNe1T9M6ahmW4MPnOkoMkrhFtzvn5aYXRPHy6r5Ho75bwtYZMyAgIquoF0lrnkk2rmu7DOStqoCseDMwEB1f1O2wIHVJpKvtTlbk8nnZzQsbgoEUeZdQ88uQSe+H9aNkj7++Ud/6uZJQGvhKFQVSpL8yvoSGZuBaXRIK8zUTINmiMT53G79ELRBjGn69W9Y2j/LIUXykGpicEQuZPnqxP/j99uh67Z2OMUX2OvicLsZToth4NfIQAKysBfvrTJs6edR+6zjd8ePDZPsZSjpcwh6wdfRn4CAGOH2+g3x/OZY880pQ8weCLpWDLUcfQOTCSPtytIa5fwbqJJIrX+CEGoNvVpG/IS/LW9BtojyOKbiWMxsKVH4xIU29XdOjIMVnAlI9EDjEACMNYmN2odxALyjn4NJnUCr1n2ABMDz5ZiE7NgZp5iE4HFe7uBgnZXqnqAW3gm+Kee2Zw+rSdTOzT4KdbXLZPcoEo+dnZaduKDHxRMFf8vExiqvBgEE/LwKdaT24+Sco5+Fj904WqY8hKtukaeHuBVPnwaODTJqtRr4rex5B0CQXkZsvqEGUOvhKuUDzNbokrFnSITm0BK4UVN3pqmG0Mc2dQGJNiHW6jn4168FWr7oQ3KzoOJGRRMnkd8iLjjG1bjZ/XP4AtCTFIKSl0Q3TecccMlpaScayKsJg7i1MefVlHgr1K2SK0cd7h08Bn07/y+G2LvhEy5Zf13jbLjnY7GjZ8rwf87/89i/X1AJ/+9DZ+8RftJzyV12atg7Z9JW7gM0PR+mtayE27cAaVEn+qL6E5oVt58EUh4a8y6OHqpecws7IfwFXccjoK/34/QABaYJQ8pN0ZJIqkLBaNtDq05N3uvnsG733vhkUFhouMxekGQqYHM5gGck+I1RsQzM1NK4wa+Fj7n8m0IRCSfZ0Z9d0ueQ5qVjgDn8phBp/MeZgL77uvieefr6PdVl/Xslzvi3gAhEXGiKzBQYu9tFwWBdoRVXjXMkQaB69c0sxZ85WIoPTgK1EYxPPIxe/FJhmdFd3x7OTKg09NnqVODI42dckE2oEwRKcNX1bNZ9n2NidTeY+Zrv8qXUx6AjvxnPg+rTDS4Z0QMI17mUPwztFbzvZ3DvuP3TjKszogG8ja05eBD7BTDnP5ypEkzD4wkg8roNvNh9txlVyXNelHCIjmkKeeamBlpYLBIMAdd7Q1uZTXDTDWIwBjtWR07NiMI0KACgkngpHKvGm73l5JMGkHX6EWXehOJuUZ40TGN307rRCd7154GDeevQvXHfkPBJubYqYUG4Q55iS0dGc6VxEQg81NNI8eRe355/WeY1006JsmBqCkB5+DvYgLsL6xInHhnsbjhBntR0EAzM5O6xLl4NM+HGMJb00w7oAeKvDFc9HXT+U1zKNC38bwvLxcwfHjDWxvB4l9eNrfJlkfX1bzhVx58HoyLDokrVj/3tcpFH0eYyGPITrzwEMJfZQefCUKA2UPPs5sxNzEOY7zL/bgM4dIWKDvyUN0kkn4OR5tPiRCQ4ohOsOQ8U1dGA0VrjmhLVNYDWT36RCdbvLS5WUxF/HhJQefBgHbMwN5aeO9DG3jywiE2Ht/2CoMfSPPBj6XcP1K1kpKRQbW1z2soxq8GE6LzAfkHnzyd92DXdMJ8pSDjwWn3ykMheKlcVtYMvnuiz8DAAQkROPxx9H97d+2pk2IgmxrOShsQ++O0br3XtRefhkAsHXoEMLDhzUJT2Ey6wWBiZBL/6aMpSY0+eTcPsgpE7ssOUjiEvS+PGrgi3vwCTxGs9aGu4CDRSp5ONeGIff1ZPZJkierzJ5zVb8l3c1N871JVvBat8fx7+TTFW0u4mCPvAYXRnv+nDVK5qHTNZF3/q5klB58JQqDqFCaVMAaus45np1EeT9sqmI+y/EIYxn44iE63fFh1XyWxkCmEkTjWZ3rcvDfZWKIlYbo1GuPgNAGPj2+conEqXT2B3EWotMQOvuRvBn89opAJhsvNsrvVMM4poAineZ0udf3beCT9TFRq4ves66fVkobrLmVdWjI1ohUIdPJ2tVcmIcxlQ/ke1wrfSdGIea6PxgI5+U+ZUe26iMCCzdPJjF9V26xxOQloaX5wtLHFemNjXsAUHv2WXMGNOqMwmitDsUefLp7JG+GGUL0+gwDpgedVEEb+KIhB2UhOtk/BNcs4Fsuo1vZZG+a1rpWuBCdNBhCCbO9XfUrxy8unrPSXdN54yI339oxtA/lCYvyQ5O71sfofo+9+v1E2AvvXLQcfHngrwQbpYGvRGEQDwXiaMbxODu53FCIhIWk0jG5QAwG02u1WlzAdMkXSdGDDwiMT6rqGGNsleNcgVnCMDuEWgSh3MBnYnQqwoLNMvBZn8zTEuzt6redG/JssOl2gd1dezoyA7iszWxy8DnL30QTzgmYrOTRMmkJ1x5OpgfJuQQ4qFY9tD1Vt2gOi76XbTeQefDtwW6XGowOlaTawHrrlHBdlQy2aFQKheLiSqn7xk1m+KBRdArNupJKfsm3SqPfpGTgk4XLp9MYSOm5NOgZlontj1PMwSfy4NveVjTwsVCUhcDjntdXExTNSyTB4GBgzrQLAx8haQYr4rKxZ+r2aOB3QsaASDrfKr96gDSgu3coQmvlMURniWKiDNFZojBIhujUm/nSiPPtKgef0rOck8UsD754/kIC06UuT8o4QpJebDa0on9Z91SeF96jFayyTZbUghG/n3becS/g9OlkMTchOn3kI1IZI4X7Lop4/fUKbr65jUoF+G//bQsHD/p7UZkC1+6UsjnfhAS5+8DWxu8U4bLazNcrQYWiecGLB1+iMdSKarcZdegmCAcgCh7uiiTNeNqjyFOITtY3UeJPcYGUrdVJDz6Hbtiq92zo0sVkpwksBwWv2Sd7GN+DzJFgZHIYh0g9+OzePW2DMP1odG/M7EsOER1nQQA0GtO6dnf5qSBivzMQkp2Td7BIpbWumbKa2bqbNcMpCiBpt3EuZKkUx7/LgwWiA1ZZ7/vZ9RfBxGUOE3k4by2Sx8MXYh7y1oIlxig9+EoUBtGJjw6hItws+NqsM+DF8wPinGMqeoCo8sPGgy9PijZCxGFQXBlbXb2jTPiQfUcaKiE6XRxWzAqivuYsRKehJtu2jfI0jmwRHWd33DGDXi9Atxvg7rtnbCkL70rt3xbKb5t5nMdXnsRg5kYvx53QlDXX4yw5R+t5vageKPDtwcdT+rJkClsjUlUSotPkG+W4q6aKaDtEw8MLFTkpNp6LqiY0whAiq3Q0SsWkuAtYCIuJr6Bj4KOvSbzO9A18eVqRRjDoMGYhOsVtp3vAwps8x9MUS3ioVgjzug/ED94SNJvT39FIDrohOl33Tu/TXmLsT3+benv54jmPSmQdBGGYkKVYTeysD+WkgdI1EGUbKtDVt3PCd46N9Vcy9pIeZQzfIbWBvdFOJeQoDXwlCoPoxGekgDU4FawLEV/ODWkcgkkPvoAK0enmlCgTKcatoJWUNG+VQQ/ve+EOfPDZb6PZXVenqXBNp8xEaUopFYiMsOQ+GdD01EnkdoFXlNjYyruU+16JBDY2phPg0pJb8UJXmJdtUPkKziCT8DtpQqn/ZtTJXVbrW5FlF6KTr5JKxYNPWJTveaFbTxAOnOTb9Q5CUHvhBdSPHQN6vRQqtEe0XTKdsziV6363cfn4wa0RbWmIzvhvVvF+H1hYqMbvaQp9Wrm1jDtukKhn8jsMgZ0da+2WdsQHBfpaXdDRiTSjEJ0Dvx58xrDYbMQMfFXquizcvwXoOSjqwdftRtcRTYPBqICveS2PMnxaCmtT401mhwIkp19TX/s8dp60+yWvz6XKh6vTyS7g8aCv61dS0Tv55iFPMDrURTXIa69V8PDDDaytZTPXJUN0Zq+MMOlnJbJHGaKzRGEgysEnOw3IhePZSeQZZ1OVaDM+pUtGZVk5+Ka/h+FN3fCV5eQuy1PyjvOP4dDiMwCAG1/8AYBPs4pRj7sJ/ciimaAj6x+yeqlOsSdCdCrC2bsaNpC9MTV/iZTzBkv7tzSWveh72XrwMXPgZPiRbQ6M5AGmrPneUKsqKdUfGMLmEI4qWIYKlnLH1hOqQqaWF7V5M5sNbXVhATPf//6Qg24Xux/7WCZ86CDadpXKdM7LYih719GFoVCRS9tkWf32tttmsLBQw/XX9/C7v7vjkDm3hyaGzorUHickQL+P2W9+E8H6OsL9+41oi4ob76NcwaBOF1FT6LnQNgefcdOxCCkSi+5dqpVhjrDxoz5D+dKpM2q1oSff+GDpYDA0OCZ5CDDZ5KSg4PfWnceTUlrWOQdwcfBJN0WKU6huABnXjA51s+hYiCl58+jMHB7Hv/rBTj4iM5VGnW7l2D377S1ge/iQRr8P3HRTG2EY4OzZGj7/+Y45c4Yound1ifyg9OArURjQGwldME/eOp49o2GSXFalo5Bl5eBL5i804ytPexhCxAwcfv25yf+HVl9Wp6lwTafMZJM9EDeedttSkoC90SkHoBiLG0osvEpU6tMiyjIEqwv0WY6j3H57TcjewyZlkY8QnYXDnngRt4Z0eoxLFaeCCkXzNa0AchKSmJD4vwwGXCuECQGqYV9aRoWO7jO6aB49Ov3/Zz9zX4EH2MrEvuHC0DH5V9Ip6RCddN3b2wEWFoaWmzNn6vyC9DWBTCKFYcelD4mMLzaefBKVlRUEgwGqy8tGtMfw4cGnBWdKXYNnBmLhQPeARZryHE/KjNYZBCS+x/PowcfaWzYa02vjPHy6ITp9yx/OydvME2wScGEkICQeKtVXPT6R4G4wsN+Y6yDFAZ6+2K3mfZ86X1ntPzwZFh2SNq5/r8MovGWkoZaWKhMaly9XeU94hexgchZQ0W+WyB9yuCUsUYKN+AZG4xSUQJHlV1AzF5pVQmjQrzA+mUZvpMIw2XbmfIl/pwmWEkT1dB7vPXwKZQk6MsLa9914H6o8s7wc4JvfbOOWW2YSm8c0QCvzAFPdkNnHFQs88gFmb2iwe74IsJ1rTA18hIgPashQhG+jdGAkoxdxWa3r05DWTWS4PjmJGKnBrPG5B8YDQahnnVTT2+VbIZkWeHJdXuYgl3wEhAhlVzpEJ123qdE6IVPqaDtsYWJkCwAAIABJREFU5AsGrWCdH2pe1zNFKutm0YkM6jQK0UkZvJIhOvVpuoBpzka6aBDEDf4+Pfiic/FYboqH6WTzIH21LPpftztlWBU8Dz4HcCGv3HrrDP7X/5rDqVPTQw1uPPjseLOBVWoVR5viLN4/L+u6c3hUvDhZkjkPFdHwkVe+TOC624jW/eq5c2jddReqr7xiXoECss59qYu883clozTwlSgMREYqJVtICkdqXOXgU3p2tEmVKTJYz8bbz9zzKPGsjtTrJC8PoX67o8+lq1lmakymr0tyhFFKiN1d4OzZGl55pTosm2GIziNHWrh8uYoLF2p48MGmO8KE+p6cF/ASotNQmSJ73Lb/lGBDPufLcvCZ0ZWB7rdpHCaRISe2O2OY8uv6PbXbUfCAzhzS6zlYy2jDW0ohOqtkACKQMUzmx6L1X39IKtel8NR4xuI1S3nKkus0PfiMTnNP6o/DWZMZGvgByAeiJpN2+UMd8GApb41hJObTHm1UBbkJ0alBPLY/BomFUCQphegcf4uogW/swSd8DcF7uzKkyJqxcvky5r76Vcx99auoLC66qBEA0OyuY9+rZ5InEBR4tMXSUgUXLtQQhgFOnnRr4EsVLCWHgjzj6sVsDO9MeoI+nbbRSH0f5NGimYJuTpWsm7cMUnml3I/bDCBsE0mDcXPvEoL27bej/vzzaN92mzlzCiiaB1+J/KI08JUoDGgDH09Iok93CU97OT7a6CsHn8qJ9XEJVqgikXFUjw/ZBRti+o+7TgPgQyhjKU1ZF2T95aWXanjjjQoWFqq4fLniLUSnSl87d26qAXnuubqgpCYkjT2+ncccfGk+fyVA14BHw86DT8KckC/zZ9OCCo9ZHdB22X6ulRRJPZMmPWUFf5yuEw8+mg0GL6z51fZ7RD34LPTX/lGEgUvBlVznC7pNKlT6W+bg4/KiqVG1CtGpCHZ+aZhrhpnF3eebtoWJZ47JWk2oEJ1JDz7dthT/NoXOkKYNbWl59LLCBDebLAOf4MBVigp+Hlp33YWg10PQ66F11138goqbqoAQVMI+/o+TX8O7Hv0Omvffr82TbRNEbYrR/13010zXG1W9jat+xVoHssxB6BFcfUXacMSANhmNPpN5GwlgovspGljvYnNwiZYlVA+XuUQeDXwi5J2/Kxmlga9EYSBSZsiVwZzfjmcn8WbT3FOOadDg5FVI5uBL1hs73Wljo5OchPUJQgIE9NFURY2XnvBjF3JxIjDrthV1f3l52rneeEPNwOdLTR/1Fuh2/e30eMo05rumaHUT7QFUNs+5UWoXCCpzYhQyQZnX5mGov74oIcOPrPLuWqHoCgLfITqlNAVtqjOH9Pv2c6wpBd29LV1PRZKDz4TqHuiaTsDyngEyaB+urGXW65gefJJElEkPPqOqqUodQ9nAz/AilOVR0zbwSa75Xg8cKVKdHOyifut68DmDoUEiWF0F6U3n2aEH3/R+Iv+3Q7AMfNEcfCohOpmzRIpKVQCxnJYVOr9lFLy+w/hOb15+AY3eFgCgceKElAfXQy76fPx/+/U0VQOfRPjn8uJpHvf56mmv3ar1eeUrRQO/k/VCoXgqr1QKwglY6TOpZ7kip8d2T8O72urgXYnCICsxtkQJKwiFS46U7DOEwxg2nh8iKG1wR/+wcvBFkZscfA5CdApPUxvQ9yGUqRp+Er9DAohygVAKHx2vNluFSbtNsLnpYZsjqZyQ4WfNYw4+HVJ70JbiHLqHNmjYePCxaKnm5aHnpTx+2zzyNIbLOTgNnbSwbzga6K5z8PHWznGR6KEg2zasEPcefHnuv2ki2g7KsmeKijMVHT3Pa4s+vBVIPPjoCHhO7AMZLfSEGDyr+V3TDOmujJQYkHnw1Wp2xlLj11AhRF2rP/UUWkeOAPUDCMK/BKlUEzn4WAZjV2Ab+KY8jj348paDz+s3Gt7IfEDxqi98iE6WBcWnpWoPb9iYr0LInnlF7U+n8eLOD54IiDDpCYScvfL9dOEy9Hi/Pzrsw+pEnk440A4Zef+OeefvSkbpwVeiMIieOhOF6GQ/q3vDDCLPOFFVlVdfRfub30Tr7ruZBdlC2PieOPcIa8EzP+1NLT6yU8UeQStBEu0tzPvDXkRN9TmsMq2d1ZignDhFSz1E05B2b4YHX7cL/PCHLdx1Vwvb2/4W37k5/9+dqeQaQcejVQTTZO1FFmqKyru+vpMeQWqCM0t3oLNpKGr77kUlRhqvZGpI0FEOpJWDj1XUJgwpIUMPPmLgTa9YBYDhSds90F21wTPw5aUtdPmYlCdk0j8n1yQefLSBTzb2lXjTUvYx+rimsvD48TqeeKKBXi9ghOiUKJUcGPiE9NI4LcGpw7ki1fvCkJ57U+vIEQBAdX0V11w+Ofy/Evfg87lPi/b7cWSPqIFvvHYJjUqM9h9TzV0oRF7fYe3bAz0Vm+tuGX0+vqbb15NliM5AshZM4Gpcp7igejcaGfKRukyRFyEGMOLFVJek9YDB5iNPzWoL7TaWvHxyX5H+JKc6taWJvdRnriSUHnwlCoPoJEML/bLNAs8YZKrg58F0/92+5RYEgwGqly+j/wu/AEI+FLvPXMc5BFmh6WjjqCpfsiqzNvAxlSCWNFWuyfDZ1vex/vApvHHVL+HkDb/HpiMhLJXdGAa+n/2sOcmJ12gQfOAD+q4fKu87Nxci6l7o7ECTYmM7i97j0MDH2+e76lNXGuQGPFt67A5LSDJB+mAA1JVTTXIGQoYfPfPNugZc8uYiFJXsedLrA3WOKK1ZIW8OcRGik1WPav027Rb14GND/o1E9V+4UMX3vz+DfftC/MEfdLILr5cB4jJxdnzwYGVEY0zCYg8+uh9ZjH1BYWHUCEklAcB9+vnna7jvvtbk95xOvQp1qxTP67rgWukd3bcEQbJtVfJQewFr4lWcjAkJUOsP42EGARWiM/R3mpsVJlg3RGcandGZLKD4PQJCtA18sqr0EfXEn/5fOAMfzWAYCpme7Edd9SvnAnR+Jto87A2YejhnjGiOe0aBbhcgDaDZlBadXHdv4GMMuDAEb2bP61rOQ7CxgeaRIyCzs+h+4hPG4dCG85yFUjOCiS41xUFSNO/qvPN3JaP04CtRWCgLmCkeiTL1jIueSKteupS4r2JQGG9SU03SmrXSWlS/I68BlbLRMu96Vx83dJ4EMMzBUOvvMAmZCJoxJAx8AU6cmFohTp1qKOvOdEEbO3Z27OjxkFS8DP+68uCLNYRGo/gwFKT5/F6ArQFQZ3Omo/CTzku5gIK3Sa7ewUyb5Hvt+8DpW7Hvn/5f1E+e1GZIRyxxHaKTz4BhODUBKqE4RKcJonROnapjezvA669Xce6cYhzdPYJ4eDy1vmWDYHVVcMTYUdhsjHKIUWu/zGtDFqKT2/197g805tSjR8XaQxIiBQ8+Pn3n+nwNDajz/pyQFTVlc/ppV0unoxelc/D5PIjJNvBN6xuH6KRhvf/JCpzThbSRIgDRtoK5fmXe9sZFPZkeKGF8A69h8VPsi+LlKL1GT3X4MSrz9aa679XpBPja19r46lfncPlyyipzTR2QC5JZoXXvvaifOYPG8eOoK+QrBdyfC6GfnciUvvbGhKD6yisIVlYml5IhOt2PBH32c3h6sIQUpYGvRGEQnZSEXmiM2YuQ7HPw2RiVmM9yCCZDdAZU2xFjQ2SibIZKYeY3Vaxfx/Cl/UqcXDGJhTph8NN0rWR48Nl4Z9o8s7npZilRFSOYYeMyllxt+lRmJ8YLBN2pRlZe53vZeIwqKZJThhIrGfHrslrXpyGjzx/YuIg3LZ/BubMByJ0/VnpeFjGAPrQzhhMPPpYCUq2oXv+nHq6EfU5Bdl1q8+X0/6jx06qdcjQ+VSGSiZUe0kD9iScw92//htlvfEO5QxgbSiKnLCbXJB58dDgl0xyssZs0D9IH3SFh4CShUwOfjEQmEonBOxi9Nu3B59Gw4pyQyAga6R9xA58jfhhQz8EnCJnuWlPLQJKcYQ9PrKN8hA7yzLt6XmTgy/3SJxFIuPy76leFazB1qL5K6q/sqEJtMtQDL7xQQ78XoNcLcNddLVHR2HUnXU/yAJ2nuMionTs3+b9++rQxHbtuE5+vJzKlp/Fff+optG+7DbNf/zqCzU0A2XjwPf54AzffPIOLF/UPSO6hqXDPoTTwlSgMlJUZCpOxL6WryHCmXBXj5VgGgIQycPQPvUA4C2eIfMm5w/cSMFDR31yJ2ln83PT/athj3qNP0VofzqLoMb3auHW43XhubHhSB3GN2H7pyx9TGI/+2cg1XConXEDVaMGqVycufh6/pc2BkSLD9XoVHffN3hYA4PLlCl58kR0XMmHQi/wW8ULf293V41OJqKCIy3aLhuj0rcfdC134/PkqbrllBsePq8QEjua/ml6NtsOxY3WcPFnH0pLddq919OiwnpUV1J5/nmIjGdaY5oMLTqGEAdrSg4+nOHEdpp9ZCe+3xrNSLyzN92AdklIyerriQdd4xYWB7BkhyA7RqUvOkfzrqC9WAiLMB+8SUdrjOSgaym5q4OM/l4aBTwfCOYE3ppnXdfuFv1Dc0f+TXiL6tDP14ONZUCK3zW6qw+b98xhOm0aqwy/F8a9LttMJJg8tL6sbQJywLyGyh+x7cagrSxNXhLohzX5Gy5Qqz+igdf/9AIAgDNF4+GEAKUdgG+HBB5u4eLGGm29uM+/rLIcl8oPSwFeiMKANfNpCUgpChCsPPiWMCNJ02Tn44tdcefDRSgevyhIGL9H6klXHO0jzxz/GzPe+h2BtjWvgddFFqoO4NpaEnP2IrG7N01uuureJHseVB59UoB3d1slJqVqfTt/Nm/HqSoSsDWW6Vd7zw/QeYm8QWb3SfEkZQ9R2q6sBjh+v48GjDX4hr3AX6s+nkZhE+FxfV5z/FOc3Gl5y8GkoyEw9+AiRe/DJ6uZdG6Pe2cCNZ76Pd194aE9MrLfe2saFC8N8bLKDM7RMTGN7G/jpT1vY3Azw/PPukhMG48RaEph+jgBTgWlCgxMZYYxkDr4kVWXeeEp78Od25phyKVMQItz06M4Q0jGdxVgyMvAZVDOYvjw72oYGLZfbSsv+My4dVGgPPn/fUubBp5SDjwXP/c98buIQoggGyD4HH9/AR5fUly+yzMHHDNcs1AW4rd9n30x72uXX5zZ/tTYTjirUJuPsRR3sYzR1QCqPFkJEtmDS5v2S+x6/HnxRBCNrYhYGvhJ7E6WBr0RhIFJmxCZBncnYpXsbzA1nNEyMLGPFA+vkskwRZAqfG0c5AmGITvo9GydPovbii2jdfTeXoqmcGS1TDfuxuiffhVq4m6uX0brrrkn+pqSBT1IpwyPQRYhOFdB0nXnwMTbJLLBz8NnXpwpdg08SKW6eHEOVV9k8s7YW4KGHGlhYYJ+K5E3j1bNn0fzJT9DaWkk+JIAq36xydt+bs0nIEExWRheffbaOTifAieM1LCwUW0T0GaLT6AFFJRR9z0cOPtbcOtVVupufoh58CmwpIfrMu078EG+7fAq/eOEoZl45o08sx1hdFY8/lnI9iu1txsUU5yHdqqLlK4gPXt0cfKYHPKSFfAv2I2iHn9fcy2i/muN+wxQPDOowYoveJzh+N6fkFDtubI+H+HzgeJtL1RsNCzpkgh2ik36O90NwzQLOyGlMJETTcOZaj8wz6rmoJ7CMa9vpBDhzpqYm2yQ17+J2H91iHtq0FThGyNoLz7b9c48c7Zd0D2wS4oh9x2v+XoPtXp1LZATvOfiY7ORPN5QHHkroo9jamxJXFGgjlY6AxQ2h4nrTKhC6vG76IlAJv+EtB58GbJuDEMkJJs7mqnbxIne9drEfGHvwjds4AGEKfPtePIX688+j9eMfI1he1q+XkmR0QnTqKJdVymxtpZMIeHxNO7yUaiWKRFy3316E7B2///0ZPPZYE9/+dhuKDiEItrbQvuMONE6cwHuP36JVv+qnZo1VZs5HxXoLAwbjS0v6MfnzBJ8KMx8M8NYhHzn4REVchvmuhAOQ0WKoEgJbZQ2O0tm3eG7y/9zLzxnzmUfIPhktE9PX05yL2N9Nod/yPvjo+uS2NAdf/LdMDp789mhc4Hr7qEB38tLkWS+ke7Zwzlfs5YlViM609nVKj496XIXaH2eeg6/Xw6FXTuDQyotsIoIx6MuQYtzUqpsqYu/BZwseqy4OPtl8F0KAm25q4847Z3D33S35AzQkOfiEc7sJUlXweyOtVB9PdvDKl8c1WPs9BAV0jKpsMpqDJkKEqccTevCx68rrGu8KNu+X3HcN/1rJcaoYTahpePDt9T5QYojSwFeiMOApM+h7TEGbxE/huBIARYov/wJSwFRGsxQdIuOoUwNfiisHIZAKQEY0E1ARyqZlqoPkkUSaVxrVV1/VN54yNjnJTZef3THN2saGrxCd7DbwEaJTB66MqZZsCNHpBPj2t2fwrW+1pR6WgwHw0ktVJUOtqyH/+utT49GlS0lDEotudWFh8n+rY+fBx5u7WYeDbUJ0Olc2OIAKK+ODCWnD5V7fNf8u6Ylpxceh6xx8svXIaoxThXU9+HTny9j/NhEFcjQ+x9Cx6VQqycJZv5JN/fo5+MRhlY2U2oxCWqf5LRogYXQKmQKecV3Seda3bM9qW04doqqNxD46RKfFu3nUTQOECPO4Ji6P+ofNHk8XLANfPAcf0HjqKbzr5N340HM3Y37rdTWeHDPt8pvQvwkBnnqyjqefrk1k6AAk9j1UmPA5xHj/+6hXhtdfr0y80194QSHXrALDyjK3wcuqKPit9CgG8O1BmGqf8DqJ0mTtcrYqXHbGuozT6Dq2p6DYgNrdRvJAct/P/gI+h14eDXxi+asACUWvULhLypAxlpaWcPPNN+PEiRPY2NjAwYMH8ZGPfASf/exnMTc3p0Xr/PnzuPPOO/HMM89gbW0N7XYb11xzDX77t38bv/Vbv+XpDUrIIDLwmcKWjH2oPrOyTOXr6IIkPL0VknSym9zDUBLGSNBJdAQz3bar0Aa+0QYwDOkTFRJPS00DHwsmAqhJX3EWolMRzgx8LBoKk4ttVbYbbJXyR440sbAwXOLvvbeF3/u9bW7ZBx5o4tixBtrtEF/4whZqKUsGPoygsjbm0wsS94oeCUXp3S2VFnmE63An2s9rDvTxbbqY6xx8gCxEJ/u6LgjRz8HHo6OCvOe+1IWOCKCcSyxF7aJpVawcfIHEg48O0Skz6CkpgTM8IUD3ZWnfdm3gywJcBvRledV6goBhTE17nXCMSkAf/vPHkDwHX4DmQw+BkKFQef0rR/DUDX8gNibnGYyJ5cUXqzj9XBW/vF7Bs89W8JGPcE7khCFQVY+KYKs8jRv1prRUovz4hG19gSREp3M4r0v0XdPdS6u+mnYT7Oyg/uyzGLzlLQivuUabL1dtbi2zR6AXNcwvL4BZDr5CwOrATQA42gdkEaLTdVqJElcu9oSB77XXXsOXvvQlrK2t4cMf/jCuueYanD17Fj/84Q9x/Phx/P3f/z3m5+eVaN1///34yle+gmaziQ9+8IN405vehE6ngwsXLuDYsWOlgS8nEIbo5JywY26QLWdPHQ++tMDiiVYEOcsVSJ+YT/GlCZHUp3XYOeBc1zc+VAfDeINBgEm/G/Iqp6NTLy3cOQtbqQB647m15ceDj46IMb7tLLyUYQOx+wm/DzmqVgtnz06X93PnxEv9sWMNAECnU8ELL9Rwww0jybbXQ+2FFxAeOoTw6qsB+JF1TWjInjHlk1VO4jxiVE+WUOKRkEwMmy7bL8V9mTYDOrw4ycFHsyEw6LoN0dmH7foqeib2f8EN8bqIvjsrB19qyoIgeShCuT5OIdcefGyZT61B4s+yn1Eyptp8AMcefNKw074nzwwtjLJ9iw4bLllW8tbjVEgIJiE6E/tjj83KmoNYOfgm5QLGWsB4J543pysYk2d8o4sXp/L1dK0mybISA5/PIcb737Rem0PW2s/SDErWAtHhDSfmM5I8YE0k03PRYNsXm0ePonHqFABg88/+DGR21l9lGjDq69w13/HBE10iBhErirA/tYGVPpM3zaTYaDrpQMxhlxtW9V6JbLEnDHz/9m//hrW1NfzxH/8xPvWpT02uf+Mb38APfvAD3HTTTfjTP/1TKZ0XXngBX/nKV/COd7wDf/M3f4MDBw7E7vfpI6IlUkV8MXU0q1hqMJNKBM8LfoQYofYPYyGEtUC4WjQSQm2GCjWm0dbDaqNLMhGikxCwvIKkvyXCG32ftcHwYThhlUnbEODKg081/FGyKnVjqoqOxoeQZEoz2raNxx5D87HHQCoVbH3hCyBzc7nilUuMudOmr/FzFCS/j2YOPpYhJ0eSsIoyOqsQnSyY8mGj1HfJhyqB8W26mAsPPh2FqdX8RBXWDdFpxU9eOqwj6BxiiBr4xtdZ4X7S1EHqfo7YXMkUMvgE6e2ZrrzFupmpvlZ3EGo2thf7mi0BgfHKaZUR2ZnlwacDp+3oav5KQ8AcIbq3HOeoajSm93u9+HAe56WjDU6bmwFmZ4lyOEtdOCPHa1tafiLJ3I4YDIC6QkhKTlX6mH6bKK2sc/BZQ3LC07nMrUBHp6o8K8zp/UvsugbGxj0AqD/9NHY/+lFJpZYVKpKWknVQr44uwIqXrDtLxmC9vlAXpdnPuPsur+up/6pc0rzCu2CuUfgcfK+//jpOnDiBN73pTfid3/md2L3Pfe5zaDabePDBB7GzsyOl9a1vfQthGOIv//IvE8Y9AKilHbesRAwxY5bIg4/3rAcBUC8EmCLDnNPQcUqEWx/rcJsvD74sZ/dh1YxvOv6tEdbHVuCOKdpGBj66jaUGO/q2pGJaYasju9ga5LzpD6gxygsjxzSw6Z5ms2DatUEzzWEk3zNM27b52GMAhiFxGk88YURPjSeWwVRfgzCzvYz/6/g/42Mn/x2V7S2KXryszxx8eYNpqMos3sVlnV7HqYoAInwZ9fXJiQcfw4Arq3cMOw++qUCitpbqhUiO0bTpPHkcuFJElevJu4OBp1Bs3kJ0Rt5nIuOOvJIkXhvJdxW/u+kegI4qIISGoETfSoaNJGIzlOZ7SKMg+DYSaQis7g18VA6+vIbo1CBECHKTg69SAer16fgdDCJ2MIY39z13N3HyZB0vvBDRsXiej10aYVWNxLJDNq6HHG+dzDoMnK5xUCUHnuQJjWcVniFsD740UagcfIWUraYYN7XOAWonr8zSWUYhEMydGRmzgAWTLrcAXA8+j42YPJSXvVtwIfpMiQQKb+B7+umnAQDvf//7UaFi1MzMzOCXf/mX0e12cebMGSGdpaUlPPfcc7juuuvwjne8A08//TTuvPNOfP/738epU6cQFj0Jzx4AbeCL3xMLdNwJynLm8hK6TbFgstjwgszAZ4NEO7u2lGnxIt4wmQjALg6SVfpxbezYE0aXjrQ89VHDUF0Ada3ASEsAIITfl7V5cCxEThQYmgbhvEFuANQr76JO1WdufPGHmN1ewr7N1/DW4/cJy4vGBlfQV0L2ArkLBMh3P1VBWvOU8lqjqJhKGvgc9KmEsoDPi1WOHqqeChloHbaxgfYhj5xD1jZx5Xq03YftzfLg8wVj2YmlMAdJ5pfp9y1z8Gn0aZ3FXASX2iZZ33Zt4MsCqQmS03+DQO2wHJeUT0W4oRG0WlGg4wgsAx8QD9M5NPCN1jbKg6/XAy6cH4atXFqKEHCsc/FmdCWEPy8lxnC6eiSfBr4sPfjoHHyExA2sk1uuProCHVdzRtpzME93lqqBjyUDOGJAew/gWKekVbfuA1e4XprVPLpNEosLl9j3B+wbKa2nvqpyuufKWmYswUXhXdJeffVVAMBb3/pW5v2rr74aJ06cwKVLl/C+972PS+fFF1+clP+7v/s7PPPMM7H71157Lb74xS/i6lEeohLpgzbwBbyjtCnuzjI7CTeqiL3AsUJ0Tv9Pej9qhp+L/rZQqLnYHwhpGEhjOqEXeWVq4W5CaiAk2VZSmUEmE1OdT0fBllsDnwJhngBnE2ZJVJ9FMW7ZNORFU5rc5zzu6JX7buxikp+D6+cn/8++9rIzXvQ3DQxlQ4ZI8qDmfZbF6UEXhyx4z/nS14+9FZQfkPBC30srOjzPrmHTbpWwD1Sb2jyIrnH5ycNgcwgDvVgMaRr4WNCtLybj09ckkzAvB9/ycgUvv1zF3JyBEYdjfFQsaliIUw8hwjXYNq2V+wcM6CkYr3TucUH1JSeyowtYTrzcHHweQe8tx2g2CbZGQRSi3rVkUkiSs9rzZGVKntesiQDwhBGiUzKH+ZRXXI+hTHPwQWwAEhr4TF5W4cOkvbambWC1ej/Jw8xX8dSgIrL9PsAU5TXXJWesS+aLxCEoJR7s0hSkAosGdNltJvuuVA18nqJuWCAPPJTQR+ENfJ1OBwDQbreZ98fXt7a2mPfHWFtbAwA88sgjmJ+fxxe/+EXceOONWF9fx2233YajR4/iy1/+Mv7xH/9RGqrzr//6r5nX/+Ef/gEAcPjwYeHzJYYYt/O4vebmKmi3h5PfVVc1MT8foN0envjbv7+Fw4eHs1Blbg5BpD+Q2VnMzc1jt7WC+ij+/cxMDc0m0Nq/H8Tie1SrQLs9TZo9P09w+PAwmXC7XcH29nSyPnCgCV5V1Qi/rf370W63EyGHoqgMemh1Wmi3gXp9WK5Rb6DdbqPdJpN2GmNurjVpq337WtjaCiZlRHzROHBg2uYAsH//gdjYI7OzaPOIhWHsPZv790O5Ygb27QuwWa/Hvmmr3cbMiGazNYMwku+g3a5F/p/D9naAZquJer2OWm14vz3TRkBJzvPz077FwxtvTNtlrldHu95GoxGg3wfaMzM4dGgf5uZWEUT4abVqGDdH66qrMDc3H/tuzWYz1rbj7wwMc1zMzMygTab35+aGG+pqNd7nov1zer0V+45RtNsEhw+z59NpXZUYr7UaweHDLeEzSqjVJn0dsnOcAAAgAElEQVSk2QSag3gbHD7cBCFgvtO++T53HQCAJt3XBoNYf5yUUQjFPDtbSYyxZnPYBvPz03uVSgXVKknM+dG5a/h7Om/wEH3nuTl5+dnZSmwOOXy4OaJTwc5O/HqU9oED0/4em5f27QM5fBi7u3xeotfH7aHyPtE6p9fibTQ7S3Dw4EFURjzVakHie9cj42umVo21+2BAz9XsMbB/fwuNBqh5Tn2OnJ8P0Gqtxealdtt+rbHB7GwFlcr0m7PmtMrsLIJ2ezLPzMzMYN++/dK5zzUOHgwS3yW6vutg//44rauuUv+OLMzMTPtPq9OafONGgy3XVebn47JIuz1ZH1nz2Lif7dsX57vRkM/JMgTz85OxAwCtRiMxfg4eHNbfbsfnsEolwMGDig23uzuZN1otoFmrgDQaqA/qaDQI2u1GbC5OzoUhDh+ei5HsduNtFV1vLjWak7RG7ZkZY/m6MjeHILJPSKwXKSH6nuPvwcPrr0/b7sCBMNaOhw83cfnykF50Lqq3KmgZvFtsLTh4MD6XLS5ia2ZmMneM+5XKvFmZnZ20e70eIAiAmVYLaA3HV7M57DMAcGDfvpgcd+hQc6LobDYrMdltZobg4MEZfOMbFXQ6AYIgLhdfdVUT+/cDlXYbAWVBb151FXDoENBqodpuo9mcyl8zLVouq4/qJ9g3Nxdrl+DAgdiYm9BlYHa2EjtQ0erOxNazdnsG+/fvj9GL8awpT9P7E4CaH3u9xL6EtX7FyszPq69xFH0AIHNzmGU8P1yPkzIfABw8KF4b6H0kACzNtCPfDWhRsvZQHovPQTzQc9PwebP1Kti3Lz5HU78BoHngQOw7j9twextoBS20223Mz82i2WxM+myrZT4vyhBdq6Jy3FVXTeXMRn84N9TrQKM5g3a7Pdl39nrATGsnNkcFARDOz2Pu8OGYPA1M5Vhd0HT27Yt/I+ZegAVCEuNiX38fNpqNyTu02zXUG3W0WjOjOWwoAzYPHAAOHuTySMsr+/Yl10IdRNeH6Jo5MxNvC1UdQFLunzHiq9+P0zp8uDn00H7ySaBaBfnQh+IWLEp3QKPZHMqrvVGZQ4eamJtLzr+AoRy+uhrXXRw4gLm5fbFvdehQE03FrrmxwZ/PWHuh8RwWfaZed7Pnnpsb9pHpfFjD7Ows9u3bH3s/2TxLg7V35CFgrGvj8W8LWkc37LfJvvTqq8B//mcFh7Z28Plae5JHtF4P0J5pgbTbIzm8FaGV1AMAw3WUJd+zvq0QlUpMHxLVAQHA/vl57rzO0lEAw34qGEqZIdpfhHrECNbWkm0skplZ80FUbqLX8rm50feq1+Pj/6qrAEYaL11EaYb79mHu8OHE3DzUx5nNszzQe64oWOvr6ip/vlLdm7PksBJ+UfgQnTKQkemZVtrTGIfgDMMQf/Znf4Zf//VfR7vdxtVXX42/+Iu/wHXXXYdLly7h0Ucf9c5zCTZoDz6lgjpEDaDjwefSG4IV9jFQDNFpc/LLyoPP8TEQQsQnmMw8+NSuicpUw91Y9eNvRROS0pW1rUKITlGeMRuk5cGXzEPjOUQnfeSVk7tV9KhJ2+TphJTMgy8tb0PbeoIwPhHS9ET9KFnWwss5R992DBWeJvNWysirB5+TE/CRgjq8uM7BR4jY48eq3eg5PDYO7T3k6d+x/6+wEJ3R+1SGgtFayTgNnMbJ+NEPm6pYXlWiOZ0VovPyZaDTGa9bjmK2p4Sk9497+VnlmtpNR8jAg081f5pO/cZNZTHxCvfHHj8dbw6KGjuiOfhoOXK4FjHGuof9oui3Mnibfnrdy7EHX95CdBICBCdPonLvvaj86EcITp0yI0L/62NjIa8+FaSdg8+nB18evtNNNw0PvGxsBBgFdEtAtc1Ze0gjSIiQgYkH396Gy6ilWXjwpRF1g6Z5YPUcfuHcETR31pTKlygGCu/BNz51N/bko7G9vR0rx8Ps7NADoV6v4wMf+EDsXhAE+PCHP4wXX3wRZ8+excc//nEhrbGnHg+Li4vC+yWGGFv6x+21vj6DTmfYZVdXO9jYqKHTaYx+72BxcagFa29uohrpD4PNTayTDexsd9Abaco6nV0MBsDOygp6Ft9jaamCTmfqSbO+3sfi4rDPbW7OotOZ7niWl7exuMiOtTUf4Xd3fR1bWx2hUrk62MXOzg46nRC93rANdnd30el0sL4+QKcTP22xuro7aauNjV1sbVUmbbm83MHsrFqSqZWVOjqd6Qmm1ZWV2NjrrW/ip3euo98P8NGPduMn2gaD2Htur6ygb9H2Kyt19Ha78W+6BWyNaO7s7CCIaEY7nd3J/5vNLXQ6FXR3hs+H4fD+Zr+D3d14u6+v72JxsSvhpYZOZ3jKZmdrDZ3ZDnq9Bnq94dy0uNjH2tomZiP87OwM0OkMJm2xsbEx+SZj/mNtO/rOALC7GyLc2ECnN72/sTHA1lbcO2tlZXvCV5zfndh3jKLZDLG4KPZ4jo5FYJj/Z3FxU/iMCoKVFcyN3nlnp4pu2I21weLiBgaDAJ1O8lTr2uoqdx0AgA26r/V6sf44KVOvA2GI9k03ofLGG9j55CfRv/HGWDn6/Yfkhm2wsTG9N/TEJYk5f22tORmPALCxMZ03eOh05rXKb23NxeaQxcWN0fX4vLS4uBGjvbIynUvpeam7uIhul89L9Hq/L+4T0bJLS8m5cXU1PtdsbvaxsrKCmRFPu7uNxPfu9fsTabS7M4i1Oz1Xr63txr5B9P13dgJ0Os3Is/y5m8baWjO21uzs9NHphNhZXbVaa2ywuTmHbnfaF9bWknNae2MD1U5nMs9sb29jZWUNi4u7SBPRuXSMtbWuER/0erW0tIVq1WwHOFwjpn12Z2dn8o2DIDnGAaC1toZ6pI+G9fpkfVpejvfHIb/Dfra62oj1v27Xfn6N8rK9HaDb7SbGz9LSFlqtEBsb7YkM0W63sbs7UJZbK8vLmJ3UU0GvV0O/so1er4ednRCdTj82F6+vt9Dp1CO/e1hcjB+soNsqut7s7u5OchR2tjrG8jUtNybWi5QQn4vF887S0nSsbGz0Yu24uLiBxcXh/XE/3draRT8MsGnwbtG1gJ7Laisr6G1vo9cb1r/d2QIJKlL+gXi77+42hnL59ja2w0aszwDA6tISOp3pyenLlzdQrQ7H5ubmfIxuGBIsL3cSY2yMxcVN9HoEs5ubqFDjYHNpCYQQBBsbmOt00O1W0esNx8PO9jYllw3bttsNsbGxgZ1ouywvT9YrANhaWkLI0ZbQ6/JMZA0BgO2tLaytr6PJ2+suLWnJ0xsb8fqA4fgPguH8WFlamoxjAOiurWGXQZ8lI6iApg8A/c1NbDOeX19ny3yAeF8FJPeRANDZ3Jy0ba9HEt80uo+TYWcnPmaHzyfnMBXQ68Xu2hoaVBvR+6b5yFy70x/O6Z3OBnZ3u5M+u90xnxdlWFmZrlUbG9N1utudzuubmzujMRRgpzvcpy4vdzA3N0C3i5i8tLW1i0oF6K2tYWdxMSZPA1M5Vhc0HXpfx9wLMED3253VVayvr6Hbje9Fe7tTObDbHe71thYXueMfANbW4uv+2ppZPxpjeXm6PkTXTHrsr66qybfRfq6yT+SBlsXfeGMDB269dfKb3HwzNt/2tslv1lwRxc5OFTs72+jUh2UWFzfR7RLUV1bQop7rrq4y5zERqsvLaFO6i/X1jdh6e/nyBmYUHW2Wl6vodNg6SdZ8Np7DOp1pfx3vN20xlr+i+5WtrQBra2uxvsjsI4PB8PRZK6lLiI4nXpsHm5toPvgg6s8+m7g3Hv+2WFmJrx2bmwMsLib70uLisG83tztYWemh0xmvDw1sdzroBJ3EnjYqJ0exvLwFQoLEN5atVTSisnRUBhljfW2NO6+vr7N5W1zcRLudP4tNtL8MNjfRUfj2rHEk0mfWIvqDMbZXVyfrKT0vLS8P14hgdXWilwKAzcVFEJYnhSai79wbyY50n6pUzOdZHtbXp/qneq+DG3/+rwCA2defx+LiZxLlV1b481VUXyQCSw4rkcTbIuueLQpv4Bs3xqVLl5j3X3vtNQD8HH00nVarhQp9FBZTA+DubrrKrhJTiE4osk4PaxM1gIM53gwk6cGHiQdf0jDois9Ec1EXLl6s4JHFqVD4m7/Z5ZZ1wouIpjMPPjmdmAffYLzYEQDB5BSnyNkQQZCoR+qNQDHLOrnEax7XJ3K8nfBheD1yT2jpMiE4NVg7cwbV0doxc8892KAMfKa5GouAvHjwJcvIY9MPKrXI+NOvg1dO51Qg97BfjjoIkxUG43nx4DOFy9OcieYxySKr6MFH3xsMguEpd5entpkMDCuwOuHPKFwJ+QKIyuFY5bbK0RhLA7RMHATTvJmEJGVBV81DFDuiUn2cQkyvngHbg4/l4SobL0LREWKnp8qgh7csP4+N2bcISkkqkYE1MExfiAGevPj88zU88kgDH3prHf+nBX0jGHjwGSHmwZf0tEpxC+mU+GRNYsjNvhDtR1HVSfT/6F6VBJUYTzLeXK15zpwweIRUCGp68NmC58Hnoi2CwJxZuhnCECCVCoLRjcBScBu/jzNxSamB1HObufjO6XvwxSsMtrfR/sY3EHS72P7MZzC49lp1YiM077sP9bNntZ6xhQ1ZUZtHZS9nkO0XXG5w9gi0m0TQxhNaqXrw+c/BF6V51dork/8PbFxkiplX2LZqz6DwITrf+973AgBOnDgxCbM5xvb2Nk6fPo1Go4Hrr79eSOed73wn5ufnsbGxgdXV1cT9CxcuAADe/OY3O+K8hA10kogHI0VldBPnSukqUoQprwmOZ0+Zocfl5E0boc69PD0z8MQTlHeM40WS/qZAoBzWRcfwpctmZTD26hhzFd/gKkP2APWhdRZmkRCkZmyhf3vahTPAN1Zr8iCoK9gWn962MfgMr9FCXMoZ0wVwYSd1UacJXRJMT77JphvR2EgK+uYhOouKrEJ0smDKh8slx0lbSIiIdIXWYTqpjaxOWLowBN54o4Lbb5/Bww8nvV75GOXxI25PQnmRpfLS2SPQYYklDyfD/ajLSFrgDDTTqgIQJg1a8Tu+xzrUJlujjY2PhOD6Cw/gfWfuxEdPfd2eHgcJo5PmgS8ewhA4fbqGjQ329v+uu2awtFTFo4823YQG5oHBL69vOjd80G1pVYHDw14Wpx2Sxn4XDMnBM/BF65ca+BhKVi/zlAtwvhE9Xoe/qbKaJ21tmyCujwiY11Xrcfk5WIp0wvAC06pcZgzRoSV7hiRlY1ftk3a3N32P5tGjqGxtIej30Y54X7LA2z1xjXs6jEig/X6sdYkjK/P0ajx9j2sdkChFjY6OK3ew0OO5HPv9fvp6mTRstrFmCOwMioXoT1coCm/gu/rqq/H+978fly9fxj333BO7d8stt6Db7eI3f/M30YoIDwsLC1hYWIiVrVar+OQnPwkA+I//+I+YsfD8+fO4//77Ua1W8bGPfczj25QQQdlIpTPjWM6mejn4FG8o8M/MwTf6wc7B52ahkrLqKjeiAoZtz6epeso89oyhUBYtk/AgImo5+HS7AU/ZpULDqJ/GyqTjwcYSrLm503Rz1AgajNTEzu2uDFI+4cooMrluSdc1WHyEATsRNKs8z6jK2pzpLhPMDWGGDac0tzAu5uVbm8JFrhkV8OgmlJSqSijGQmq92VR4+XERVu6273xnBufO1fDII02cP88fZywEAgOf2rrHf3dj2a8AkL1OdA6rVEhCyWSbz2MwAF56qTrJYyfmZfr/JGqBxZLMVF5xPPjo/HuAhoFPtJhPjIzx93/nq48DYMh63EoEdSkiAHHiwXf6dA0/+AE7lhw9lmJzDiGA7wg2Bu1jdDgqIiuycvC5NKxYPahLaNQ/AiTnAl9Q9eCb/M9wMsybvCSE6jciBAknt9Rz8LGVt8zcrFJadryIaBEiMfAZ0GPeMIRrNb8LtrLOwResrJgT8/WMA4gOvum0uRP2ZUQE84krnV/RYGdEjbcZLwefz5ZNa8+qWr+Mh7wu0yX2QIhOAPiTP/kTfOlLX8LXvvY1nDp1Cm9/+9tx5swZPPPMM3jrW9+KP/zDP4yV/6u/+isAQwNgFJ/5zGdw6tQpHD16FBcuXMANN9yA9fV1PPbYY+j1evj85z+Pq6++OrX3KhGHtZeLB628iCdlYZ2hTVYVuJlKbsbCHteLmG/+EmVpoc+KmB4IideXEEAZJ1MCxuZSxpIum9VBUglCiP6JcqmBj5CYpBGG47Ap/JOaqrRl4LXTuH3rx46h8dhj6H3gA9j96EeV6coPHSdDmUbuSOlXL15E4/HH0fulX0L/uusEBcUKbNtwqK438jqwr8u99yFrzmK2kYT5sKJj4FMrN+RPWK0S3TxBiUfiIeSMWrUFpOX2AA2rrt1dQJJKWo045GOJNVa2tqYa2/Pnq7j2Wo7RjkG3GvZ5twx1PxzjvO4hj4JDNi+ZKHKj+OlPmzh5soHZ2RD/I4wr7VUI2xg6mCE6Q3UDX1QeYUM9pFr8qbS0HZTRyZEH39GjTe49EYnGyZNoPPkkBu94B7Z/7/fsNcwak4HzNTUWojNZQYrbGGeI8pEYpx6ZjM7F0bCNNA+TtQ1xD76kPEn/4wcujbCsPgQwvPpS7iysdTwIzJTIblln7CGa/HnJuHJ3Akfit689nCodX6FrIzW4YyDTSdJBQ434FzkWxO8FbrqehYEvL+uSCWzmSRufDbracWSIRA/y2LjJEJ3u9+BR9ulUE2XU172DPWHgu/rqq/HlL38Zt9xyC44fP45jx47h4MGD+NSnPoXf//3fx9wcOzk3jWazib/927/FHXfcgYcffhj33HMP6vU63vOe9+DTn/40PvCBD3h+kxIixOdUwpcvdIQxy4nayWkLVkB6GQQVsT344r9dGfh0wny5l4gZChpKiSnrIomT5iRuIFNlM7a5pk51T7wtNZWPUiEnDIGIPUNHoBSRNt3sRa+1fvpTAEDzoYew+8EPAvV68gEVMCrievAp8N2++WYAQO3ll7H1R3/EJ2LgwafDh+1Q8Cm8c2l7PDLq6n1CQYhO1Y0eK0SnTmSlPG6slPobdbEM0engWQEDuvSG3jTuPghr7eYZGJMiimU4ZIH1RXUdG5OI3ctJf3UFWR/hK5iGsPXgO3lyGI51azPA6moFV12luPsnlFxlAKYXIMeDjx2iU9xHhbyJLO020KCX8P6RWyyV0GgAW1vse7H+RM3/QXeYT7v2yiuoXrggzrlkCgMDn+28zPLgMyRlxxPrQU0j6FhZVwmoQ5yG7KiA78EXX+smbFO5nAmB8D19eyppg/ON2CE6R/+P3yF1Dz72b5N6ks+YfxiWfEEsDXzMFCwpwkaPUgQkePb9wp4aSUpWUECUd9JL3jIdAVDxVhH7Hg++jagTcdO57pKPrD342PWpO7KUyA/2hIEPAA4fPow///M/VypLe+5F0Ww28bnPfQ6f+9znXLFWwhFEyoy4kic9SUuk+FVeEwxn9NimCVMBl7WHiF5zuWGijVbpnW4e6TtEAg7DUJd8d5Iow6pHB1UqBx9IMpxqAkHyxJe0XsrAxwKPhu0pHa126vfVDXwKjc0bc+trwKuvVnDoUCg8DDqGKMQIkXjwud54+4D5BlxPMeriXXTHHW88EUcefDbKDFphNfk3R5KwKit5V5TIablLWG6kZNbcgIvsCl7zYVH1y0QS0/XDpTKcuZ7naIy5gI2BjxB7A9+EtszYTyhjkGWIzmgOvhgGdFjyodGbJxNYG4YsO6woRK+MbNL7RzzoVE+9Hzo0wMqKXXaOoNNh37Dde3Gfd2zlSYToVGRDEc6mIVeEPHo2a+fgQ9LAxzTOeNgvukB0nG1vB7j0ahVkP6sCxhymmYPPFrzpJ2sPPqaBzyJEJ5e+K4GD0ZC+dP6qdHx58IlkUGeVpAgXVevm4HPGi+QBOk2LVV15ggXzLg8ecKdrj43rSmYXIUaT6rilB9/eQeFz8JW4ckAvpsoCDhmGGvMR59/JZMjw4JMtUiJDGjt8YJyeyINPRyliFRLLsu2HBr4w9tu6asMuEi1ToUJ0TpRjFCFZuxNZxdSHDsNA4xSZ3YkcnX2TzT6ElRuFN+aefaaGc+dqOH1azZgo5Is28CnkO5zeyza/hS3yatRRKaOTg48/dyeN7VeE0KugxCgafCmlGg2Cz32O4wbjgCGXhrAxEuGsNYwNOkEG5GGW4zdcKsqka6b4YfNnM0JM7mDs5liebdYVQd2YpKRs4dBihugcxGNx8hTWY4j6qcxgmTkS8iJxkoNvfl593KcOAwaMeHY46XhtM82FYJx3nPbg85k1SDUH33guIMH4RtzQl0DmnZGDEV/dLnD8eB0P3N/AsWMNZtHEHCYVJN1+J5cGPpeeJSxapNHgF5JUlrwtycWhC8cGvrx27SikPHoO0ekrnK2Nbk30yrwDDXTdGxtm7cakJ9C/2UQ6Kgp87JOimOQg9mXNj2LUgbL24GOtQWIe8uZiX2KMPePBV2LvQ3RaJjYBKc2Iw5O/tkKEaDJWPollrDwBYpPr6DlZDj6ekEIIcNddLVy8WMUnP9nFL/4iI7EJPZnrNJ/jlYoQhjAWFYgUqg/o7zUkKn1OxFRlpIRKCnyWYc1oKBideCRsP4WWcszxzkfWjltbFgIHr/5+fxjbSsDDXtngcXkL2AoZF++iK6jz7oUVvkijyjdrY+Tk4HWGH92k6gAkN4ZN06Zzazya/h8EQE3lHIFgbRf1eZ5ywAo0LxohOl0ZvCd0JO3AfY5xLXZvj+Xg0xEB6ENvhDj04KO9x2QdZPTbeLxFNGSxccfJwccLx6kUYUNzodGKUuFyApKNOa19g/yeTdhKFTC/GIc5E1lACCoHH+sgmSpcKhlt9qOEYNKo0fCYVgyp1otxvez/YwpvVojOFDz4aBivpyO+Ll6sTraL/X7yADHzW2ounLZrPm/6MWlal5+DKU9Qiomg2wWZmTEiqrU/1aTtgqzyfLa7i+qFC8D8vDhHoQWSvKiHTreoxM8zCmRUyKpGhlDWSY5w771DL9VPfGIHH/ygQkgOCbMyr/4pb9nkUzeGxbfX3psI5o20Q3Sy9IS+1QZlDr69i9KDr0RhIFpMZQ9OjDc0LcvZMzkZizyjOPdoQ81APsMONxPsRL4KVTA9+M6dq+KFF+rodCq44w62YC1b54Snn2TEdLCzgzc/+RO0t5e5RQg1vUWrG598EuVNMGGzQgaocJRhMm/HRD3CTUByQ+llg6FRhjumdCQGhUbgkdNWSGkc7w/6cWO3ipcshxTzWp4MfjJeVHnNwuApysGnw7ddF46PzTx92zGYfZXBaB5510Hyu9n5E0/+U40gYNgJXSqNVelHr8v6v3A8WMtUFuWK3mEp6MzFwhx8nMMZqhAdpBJcsvocTOUVJwcf34PPMFKB+Kb6IxYNkJRn3CzMzo1lLmFg4DOqhpbFqQps67MybBuWGV5mexz4/K7RMRbNURU38E3LjD34omsNM7XVqIC7UIRuvZkn7z3mkzVe6X2E5EO43hew6LGVyPK2cckbU76gL25vG1fm3EjMePk09nAz3/se2t/7Hio33ZTUtQjywbmA0/fJ2qKcQb0iMkeOKIajlfGiGKIzd3lMnUHvoLXu/Ju2gY91IM1HVaL5nq3bEtFywFAJLyg9+EoUBvSCxV20dBSVlrOTTgggblVa2rM4sVibKIbt5LWbSl6OxDvROfg0Da+maDzxBA6+8BS26CR0gtM4sbYiIYAqsx1tUI2E5xy3xThEJ91W0v4hFe7i93WMTl5z8NlYRxTq5Z3K15ZhGS8x9OtloM8ODaZIVlomTSHJ2IDHGdwueFcRKqO/eXWGghx8MvrR605t1DmEijJamnfLE1yeNPWllJKd1l1eruDll6v48E6AOU5BFi8/+tEMHn98gFZLQ4ZRRXKBERQVnyIVGU50eTHVaTONkUUYfALo91e+0ZmQ5HcyNvDRxjZJHCGdHHwspUsAwg7RyYlawJufVTz4REqfgCprDR1CiUFHnIToFBcTxO6PFXMwR6egSOWC8uD71fd18TI/JbP7+hVh6tGX2B97ZJK3t4waIKIyFSsHXxRpTeGujLDiQ608jXE64Bn4ZOXY8CmXBcl23dmx9yE2f1ntZ/TIqrVldWFhWPrCBW99R5Vvq3GZoYHPxfwyHse2ITr165XcF1QSleFFKXlyCQsmtR8VPDAJ0WldiRp0jWsuQG+3desrRH+6QlF68JUoBsIQrc2lyWyinmtsetNHDj6dEEjKBj4nG3VxWWcLvo705HAlaD7+uJwVkXjEWEkJYRCBmsJ5XKYaMsIuEAKptyVDcpTmN/QUotP0Mzkx8MUUvwFTgNWwf5uD3mwqePA5qTdtEILmAw/g156/HTM7ahou1SGvpwN0ozxwkYOPbeCL89ftAk880cDp08kzUtw+kGHnMN3k5qU/W89JjnkQnZ4OQ+DWW2dw//0tHD+mlg80iqWlKhYWNPqVKug5jbnWDf/KRBLVKZ3b74SKCTXa7Gdz0mEdQfY6ceV6snBSPjUPS6dlMCDqBj7GY3waHA8+nu5TKUSn6CbL+GgjeCsq5AC16BIxKA5Kr0PEljjneefyKvXQDb/Sw2c/27GjacsT60ENQoRM9zw20T10EaWtEqKT9uAD2H2bNubbQjplqQqsipsqkxCdro2dLHqmSmSXvKnQCnZ2tCpjRs3w1fFJOh58sf1w4oCwX7eszEUpTwzIyJqGpvbiJacjAApusfIz7wUYTLFa9PauBx//XhZGxhJ+UHrwlSgEZm69FR9+9HXM7fswTr/7d9x58FluIpwkRDXw4NM5IQ0Ag0H8NE+130V7ew2dmUNWhsLEpkYk5DheJaQbNoHRoEKGK6nrEJ3VATuu+nCDKyak3TwkaeBTNXxLhdzNzWH+gyrbYCI0Jpp4pCqCt0HlMiUjxrtG3+v1mMVsqzGhZ1KeZyg7QtgAACAASURBVJC4euk5NH7+c7x5qYFmdwOP/eof8Wlr7mC8tJFEcTxWHk3KhOFkd6PKD6t/0dceeaSJJ58c5mScn+/gmmumknmUT5160wSTJ0aDZ5G3wWV7uUxYHjfw8cutrwfY3Bz2uZXVAHgbm4guL9bTqKayWFS38DC5ibbQoDhrqvYctco7bNYBUQ4+gsDKI7dCJ4BjzhVi/rhgzemE7cHH86TnRyowDNGZQxBHHnzCOtJsEw3jqXO+IsryIBhWYKoEdcqbimAoaqNR/6hUsvHgUzLwsTz4WOui585oa4SdtO/4d8LARJKVpJ7giJ4vAoSMg6MqbeGSdXq+ZtGOGfi06fPvGUm0Sp2FG//FjBzi82E4oA18ajR0eeFtgW3qM/JEdvSCLsioefARIDK3OWFfQkTswRflTZlkqqgsLqJ1770I9++P30iTSUFdE71pSvxYR0ZRRPx17KJ85Kk/lYhjj9r1S+wlBMvLqF28CEKAa197cnjNwIOPeclydhLn3JOyAYCxSMs8t3j0BO8SFaCrvR28//6v4OPHvopr3jihVBevCqmXmSeQRoNzI7LJEixck5BTCgpXHX1ldbA7MaYkQ3Rq0pXd9+TBd/VrJzH7z/+M2X//94RCbfo8q9+zhSGbE+9sgyt7zGmfvBPxRfOhGKJTVbDPi1D05uUXJv/v33wVgJw3Fxs/X3qPRB+IfDdVPlnfkDZojI17APDYY/G5iFtPhh/d5JtlFaLTLfwlLA8qkbxCEbpRxWZi7rNgwCXvhIgPs8jq8qnsU3+OdbHYGeKT86K4bWRG5+jBLhZ9Y8ZkiifNA2icShM0glAvB5+SB5+q9XjMg46cYTP+k8K2XnluMbU9i6kngxUMDHxGfczhyQ+va6SuMDaC1v7YEqoGvsn/jBx8UaRl4DMG1Xcm40QwV4xv5SUHn6ycCi0bsOR/um2iBj6ZhECH+HTehxgN6fpbTelwUiFkpGtxggwNfPpk1esVy18phLFWNPDl1YNv5rvfRfXSJdRPnzZ6nvX6QplZc/7lqL+89c08eM+x5p+8LsUlxMjpsC9RYgpauQ7ou8MPJyhDyVYAnX0i956jHHwiRBUdh597FPXeMIH1e8/+ADqCiEyoFSoFbAw/NKlWS8qL8PeA7cEnNAYrgBWik5eDTwZZ+WQ+mkB5Yy/qYr/y/A8QEILK+jrqx46xeRMNJV/WmxFUUhEofTOdnW7CwMceMzfd1MZrr1Xxzlcfw2/9/H/i2gs/y7XBjxXSkstLEIjvW0CXpqoRLRAY+LgbaQLoGIaKKPzqGDvTBrtOh0YgQ8iMKax7iWIxhvx5xCoREKx1SZGEPnFv+j2Sh0BUxqWyDabISjDoK3hpJQ7dL3VCyIuQyMEnYdTWwBeAML0xaZln/NPEwKcEjRdgG5zNtcCJ6BKECCce1REZZeE3fqOLt79d/xCME5haGhzXPfbgc+nlYBMKV/ibd426HCA9D754rqdpPVGlcmx8UnIkIX7SZ/giJ17TI+VY1zUnJB8GvjyG6AzD5EXdEJ1M+lnOMQKY7E/JQH6g1yWcGjAzNPA5eY/RQzp6Ryfsy3Qngvtx2TD7Ps9CZX2dfcNGN+hwXuKF6PQSjTVgp/HxEUUnWg/tCKGrrsvBdFqCg9LAV6IwiE8kRNvIp0BUG5kZ+Eb04ps6fuXRcrVuh1tOGzrt51BiJM2mvIyKBx913zRE57hMNdxl3pTuM1iLu6xiCw8+kYgSEwy3tqRl6GtJj1SzHHys37wNKpcpnbpE9xRDdL722tBg9p5z96G5u4HrXvpJBmF5kuDNlaSiYeDjwMXejd1E+t4nOh58/Bx8w3rrvQ4ObFwECNEyaNDzSGEFYELy0HWt4DNEJ29MCQ9ZWDAQfRdCgPPnq1hbM9M4cJWqyaLM38J+ITDeyW9YorCDjQ0dEYDV75wZ+BKhzNmlaCgpJwjbGMgyEtIefOM6eXpz8fxlGH5JQW44fryO22+fweXL/JywChUlfgo5VXyPaLF2O9RKdeAdCsYr4T1VnhkLg+l+0qkNQXePFN9EcBXRPr8kz4MvauyLrV0BrXYKku8BeO9/rr7RJHwfJfMNf+vthVy/8nRPFk5+m3pouOSN2c1FBj5L+uo32VA5jOyqfXh0kjn4XNUnOujoCAWSy5gHizghOkVt5+SVZbK0Yg6+vIbo9AHt9xM8QEjAPHzgqxG5Y9/TuqB6b6/3mb2KMgdfifyDMbtoKdFGBpbowj0RfC1Zsz4hDBhpIXXD50QV1LRxy8ZGp5LDzgdIs8nmW0W5CExP+jsW3GuRHHyyEJ3JeiiDhiZjOiczbRX3QiHAxsAnqUNETtuoIiiUyFxBefDpvJJOmNE0EH3tgY4Hn+J9XR4Ai1NjhAh3L0G/H2l9NYMhIUCl18VvHPsX1Po7OPPOT2DwKx/W5035RvqgWdncDHD5XAVvirRRViE6vSuSHNBi5Tsb90VaFol1UdX1iYlphU8+WccDD7RQrRL86Z9uod12o6FT3WC6zAeo8o2Ur1mylTV025nukzScGbgNDQ/m440wD2HRAveYPq8eOkQp61nhTYMXWFsLcN99LQDAj19t4P+J5ODUSVGtHd7XRHFNzWMuD0RIoUFcqpAiBK0f/hDVCxew81/+CwbvfreE4PTfIEhqY3XkM6+KcNnEx5lHA8QNtz5zk0pDdBJ2Ll/eEKP3EU4O8jLqcU6IpaewXDhteSYhwa89fzuuWjuHZ3/h/wYh7zReu30dlhr+Tn5kXQ8+1j7L2S6L3lcw+rSNHkWlUFohOr3M+RnufUz2AKqH91TvGUPCLFNGYpXLTt1gBsX+wipmszdh0ev3gVpKBj4eaDWHU1CESw++vYPSg69E/sFQFohO0AtIJGGpqRIJecprgoUHH3WFWzZ2msdCDZZ4J53mc7hIklZLXijgf5vKiPHoKfGJkpauS0N3ygrRqUMnBu0QnRLDtzIv8uNeOgY+mk8h6GfTNo7xGkYxBx/rpopyOishieXBxwVnwnVj8FM8VSzb8MSMvIFxDr7W8iXU+kMFw6HVl4RTcl6+pQ5oHn/0oxZePFvDqVN1YbmsYMqHT2OhytwYwJ0XZJTuAw8M17/BIMAjj3Dy0eoQnGA4DpMhOSH8LafLv2X+jRhKQde7+5RhY9NhGZ3HBi5iGV55qEiKPMxoZ7rf29THe5gl8wzZYa9NOvO2qBDr3XhYXJxuq50eeHNk4BMZha2HgLVFwuz56ksvoX76NCpbW2h/97vyBxJ51ADoHhATwLsxiXOf62niiB0WZAa+gITScZhGiE5n5FUnaYbxNe0cfDOvn8ebl55Hrd/Fr565A2Fovga7/BxMeYLuyxoefDIjsbCwI7gy8HG7kzcPPrXrrtdyL884gM5cxFtLJ/okW8iIKIfoVCdZdOjoZlTAPCSW8hziujrd+Wev95m9itKDr0QhIUpoyxOifWwiXIToTLyKykkthuJBhNjBZwUDhKRa/gWdI8o2qFbZAmh0U5X0w8JkqxuGQBXT35MSLIOSnJ2JgW+wO/Xci3htEJIU0KX1aG7udTz4vCow6Js2IToZt7les4wNtTHozaaFBx+LmWTfTTkf1wisHHyyc69uDHoUH6ppR6l5LwChxjlFWJiDT1BPZKwGivzF6eZbSqZZOX++hmsZ+Uidh+gcDFBdWMDgrW8F6nV5eUMcP17H00/Xsb4eP8Om8gkqCwtoPvww+u96F3of+QjzWeYBI0KYoZZdzcG88r2e/oEHrlKVU09yvNodsoisxMaKpHG5aHmS5hjr94Fq1evxaPn4m9ZdYRzXlIboDEPUn3oKwWCA3Q9+kDsmZbmKeTK3sRGNkFiIv8n1AfugDU8mYKTvTvKmq9GQvJSwO2j0T5X80Ca0RfOYq4OA2oyIrvEvjxCgurhoV7fVvGEW8pAJXb44Mm8iPKRH7x+eIplr4JuMd/ZBQuG4zAPouY8TopO+BsBRyB911DrrMQ4IMc/BpxoBQwVM+V9k4NOszHkfYoxLp1OIQp1hP90cfE5hwKxKWFRPVXOh48GXjoGPv17Gc6M64CWHYDWP6zN+gwFjhU958PmsrszBt3dRGvhK5B8sBYClB58rAVB8MlFRIDb04KNpigQievPnKiY3/WxFRylgU7FCG4kE8CAcjAx8cdgqNaphUqM0CdGpqZ+RNo9SqFV9r6vYPQ2Fy+SaRX9WqUiVXL8PnD1bw2AAXH8947uIXoK+J8jBV6nEc7SlFabWBcJKUgTg9g1LLxBRHcpKB9YgiYWiopQEBjn4EsoGIs7B50jv6hWmPLj2lm3ddRfqZ89i8La3ofOHf8ip066OnR1MQuSZ0J799rcBALXz5zF497sRHjqUeDYIGMQYckpCEWTxcsL+qgKFunkKQNMp3ZXcJVPwT5BSGKvq2bOY+dGPEB46hM5//a9DQ58DJN9TPP7i34HOIxZIDXy1Z59F64EHJjd3P/YxZj2m4SKtxjLDEMD34GOTsJm31Qvxi1ophiyMParFxvuoa944gXctPIq5698L4OO6JN3BwMBHCPRlzDDE+OskDuRJ6tPhzQso2YS6CUB/f2yDmOwbqTNq4JOJ2mnkLHZmkOEY+MSbohE0c/BZtwWDns4+0SkvAlrMuVM3B1+MiOB9TF5EUXZyQY53z1eITlW+Xazl3p9xQZZRQOUwnA8Dn2xU8kJ0xvlyp+8rBvQWvkAyb/T78DAxs8Ed+46rE88/egeW9n5/Ki7KEJ0l8g/WgquZg49Hx7WBz4hcgoh8s6qbHymu6DB/50TT6gidjncIUn2H4MThWDCKLu42i+u4DFPgImwPvthCytiNywxFzGTQikKmc8+caF2am1o2Ef5wVc3Bd/58FcvLFaytVfDSSzUrIS3Y2UFlcZGpuJQpUlS8Qn0LSTwewyDpCeujb9CwrXMy3hIeuBRMPfiom3ZtErB5yxBMwx1DceW6L9TPngUAVF99FUGno/yczvjodNyJtZXXX4/8iisyeWMqNjdQ67TKesOD7VyeqFtDma41XnUWTEZxXYWTUPbzhPYddyDY3UX10iXUT51yRtfGpsPqj9PwQuPDGfFCzQcfnP7/s59x65F6k1HymG2IzgCELf9wcvCZGL+58kr0moayzzWSbR6K1xALA9/1r9yP2e0lHHjywakM63ssadGXrJ6anlEJT1/iVgmamtKLM49WqM2BT89mWplM/0978NFzA5e10Y3ceZ+MXmbyrmM+WTngRmNp8o5pCNcRsNYTXQXuGD5ZLz341Or0Wh9FV3q4z0mOHMfP+CMD4P9n782CJDuOA0F/L7POxkEAZJMQKYAERRKUQFIUQQ45lESORmayWdmMqJWNmSSTSTa7tjZm2vnaD/3og98y/YyZpB1pJa5EilQTEBYjUCRAHI2LIADh7Ab7ABp9oi/0UX1WdXVVZb7Yj8z3MsLD3cMjXmRVdSv9pyrfi/DwF6cf4e7u/hZqM0u7IXnBGFhcLODll6fh9OmSLLvp9k8NJPAyNeTep/r9Yv0VNghimyuPH4fTT+6Db39zBn78Yz+Fg2iwW6dLkhMYP0w8+CZwTUK2Q6vlRi3JlT5qTsuOr8fnZyKk0DtxXRCwIMVc9WjR96y3olaBWgtp3s1RyhiTNtliY7L7xtNAA2jeDIy4urFVdz2nBCbmaBYDH4JQDj7Hew61e/bsyKvi3LkSPugj8htktA4zL70EMy+9BCtf/CKs/Nsvgxj+QjHP15k/ZIHKwcfSVrjK4mD5CNB48BkTDttiG9iNkT34ODDGnd8hQ5dPZ0Ea3zcSNHsYpcRfZ31U3WwrUIXl0wJznhSFHwC6LhDc/oxJYmBy7+XcxQON0JwaaYw6IyLtgXK9DdhYywsXsuGKPTopY439LhiiUwnexaUWChiukF/Wf15U7gfV/cN56mXdCzQQOvtjGvUmQyQDqShWz5duf6BML9bWoKj6YDqljHJcWsOEQTHG9+wMwaD84BtJb+zI9jXPUpAFw9ShfbQu711A2Yh9cahvLqEi16fLavvnQa4QfRgv9zsZkVDEu4AW6cHXFozB3LofQlzbbk7a/OhGhBI9wsDHjm2uxRkx5m3RsXzeBnvwrfsVxTHtWSFZSErls/lCdFbwgx/MwrFjXeh0puG//bdF6BJ3ma81aLP3i1Uj9xEAhocc29ykn7/zThfm5gz81E/1g6xXee4cbPnud+HU89Mw8+E+vHj2C/Dxj/fgfe+jzx4836t+nL7qWp9r1zNMPPgmsPmBUAAMNjmdxos6LJpHLTWYMcYfdiNMiX8Vuavaip42ymev2TZcbZuToaqCfV8JIVIbRZFS6RkCW6DAB/AAZ+FNVwevoT0S5bbzCh1amHniCfil5/87fOjU63RbaP7GMGua1WTPZSrvUA2kYl36rXg38+KLvqKfHG8ZVc6lEAt2W1VRRiuVc9Mw+C2P/G3nD8CHDzwDxeXLbv1QRyoNfPbNTE/ZEGnouhYYXi2N65wyJgugaLoOtBkbj//ARqrmt2tl8eY2a9CQod8vYP/+Dly40C5ngkcjIk1rbGeh5QLQVncVxPXDVk1vOojQ75NnYTDChNZQE2u4Spzjdn2Sb4j24EsL0VlIhQIfZXdpTB69YF8Zk0XTgvexohjxaAODQATDm2tDrelh8AU/O/KApuSYzRDGLDUUrocHh+sd4/dwJIZz8I1+iuHyNzmM1oviGyIP69ZdUPg5iFNz8OU18Lm/SQ++0H6HYKye1cS6HLsMhxCa/vrm4MuJP8lgk4mAcY6Tr5PM3EYIUVXBsWMDP51+v4BTpzpetbLEbN616NJHQ5ZjA8txCPr9FKR54aGH5uC7352HEyfCqQBmnn66+f/uw08AAMDZs0IuerzPTDz4rhuYePBNYNNDQf0XE6ITIgTXSJAUKFrGwhfq9CE6Y5VhAABQ9ZOZEZ/UDToMCKJPnuzAvgNduPzKFNx775oqRKf3Phy5Tg0Do0HRCAOxeIIhOglJTTvnJIOKtJaK8+dheudO6KxNw88eeASOvf+zXlEPc8YcfFhA7XRGNhzsjalSmGmeCUU0uU40+85G8Y9VgRhGyZjF5OBL8XDV6D3qMjMrl+Bze++D+XkDMy/U1ps67CWan/i3YKHCuWMcnaU9xiDn4OPo1r9Yf6BJ8ftyrCQzC6et0NbrZRRghYY9HqQanDjBPTixU59/fhoWF0vodhMHxSdEV4x4Ngr9mAgBoVpDU7uCPuT2GEmDOOOtE5lBCNFpWuZP1XjwGbRnYvpY4IwUBI9W9PuO1Bo28CmazWA0iyyqAp9vCPCDCQQ0Bj7bW3KIJ2n/b9sJCQY+AIi7iYIvoA037VSnxLHapgJGUI7kYFQJJRw50oGXX56Gu+9eg3vuEdxhiXZjcvCRpI55P07fC2mCyZQJeA/bYA++nAa+NrSqU5tU1UDIi2xMnENjm1cZZATh3Xp78GU1jG2ggS9HuxoZHofvzEJ+AAmXgy/EF17PcG2H6JQH6/vfn4X/+l+XZBQtaRt48OXZyyawsTDx4JvANQOxinW7IqeEbqvU8RnVhNPUi38VQ5OlpBa+xVFm5zR0IlQSbi6kWRJUvtC4sFDCylWAZ56ZhdVVGX1tHPNCdAYUVxyMjFs0c8jhthFQimC5SphWrn4qEyTnzaJDZeZ2f3INfLayI8Bgt7FkC6+uhRCd2r2yrMJKnBy0x0yRref28fWFvjYGHHcuaVjEcLqS0ZPB64f+3ThI13HEGTaTCBkDrK5KNKd/jzdfFMpoSUke2x2LiwN2HRsw1WOE1gYX2YCiKyXIgJaWiFdkObxWr2WI1SP4PLE7xtlCdIZynbKIE8ObWzn4nG+ssCFngJ+bj3Eh9PmXlPGSAzE0YhseRMFD6tC6rl1FMVr0+OxioWB4vRjIpHw3BuI2JEJ2kMZb1b7iWTIyZXl77HBkiVR6HnhgHo4c6cKjj87B8nKQBAdYDz5Uj51zmffybOgQIinqi7eHRXvwteO/qO3nWvHgG71ogT8X0URHRm/tqYnG6+pReqF0COoqkpGuQx0S4lJKxOjHOBkyG4SIRUZfiiem+MJxwP79XdixY0qMoKIGNS8TWTVQgXrd6xEvxtSJMfx+Lrye3qyf2UI6gQ2DiQffBDY/kJsrfbD1egBv7+tAebkLH/5wD6amBDzcswiIVfyqkCgYwVq/mEJ+AcbpvVY6B/RbZHLGKSEgWFjw4hK4Veo+jhDW2oJ4a5EQGEJtt/Pg4/GKBQVPAFaoSozzxwn+9Wd315aha0oAmOFpGQI5LxOVZXa3U2HRNGG51olfbKDfB3jssRlYWSlgack19tttl1UPqopmCzgSc9AuKh2ETSXGg4+jc+u5fbCldxHeue0z0O9MewJuASZPqMp1ND6cP1/AkSNd+NjHejA/r9vTKIPPWMO1Mv3R9pheWYlukgW7Tyhh3vH8JD34CONw5nmgHiNFuwOegs+XpGpTEJjTFW9hmtLwulXPni1hbQ1g69aIiZ9Rs9PmXKA9+NrR0+CO9ESxDUYAAJcvF/DII7MwPQ3wH/7DMsz4R3bQ6EK1WxfhjNwymWEjFYk1kW8Igcez4BN3DDn4xHoJ39LaCzbiTHDajb5EhqLBmM0RojN6A+DOp0jvTw0sLpYwNyf3sxOedqhUxgY+bPTC/FZ24wwDyeiZMaIvdyJY5xCdFKmp/FWWy8xMe2y3MHI6jU9W1OteKutE4ugcOQJzP/gB9G+9FZb/838GY3jVq1ZPlGt5aLf/Vu3l6PNMoEHLXdqNiWje1jhvt8sB58FHySrjhOPHS3jooTkAAFheLuBLX1pth7DF2Of34APPUnKtOUX6a5mP3015Cstz+VrrjX89MDHwTWDzw3DH1hxar78+BXC8A7MrJRjThY9/vOfVdaC1gU9/O0ht4EsUxrQ3jwowzp7sCFoBCCoH2lgLY4AxiNV9cPZsJ2Ac6zvl69oaYwxDTvOPrfwdPhwwfJGMhzoXisDBZdTdqEgBgIj4KxIS/nVVFXDLxSPwub33wdR8F568+/+AlekbZUWdKnYeqBYD3oeuhRAYxhTwk59MB8t1DG/gG+GSf+ugXR4xvk30QrhOaAzATZdPwM+/+QB0OgCdq1dg/x1fIW8TjzsHX3n8OMy8+CL07roL1j772XAFAaoK4MEH5+HChRL27u3Db/+273WrEnJNZmFpPZI7AsDaWlrerRB4/AezEILrgymXg66YOlwvKewqWc+P1P2E3KpbEHbhfAH79tX5TPpwczKmfBBSEtnvy9I1UlSVXz+1e4KXVhA/hpX4jz46C0ePDvr2hRdm4KtfXXHqemBoDz7ewEfTLYWSTVUCx9zwb5WDz3+QKGDwxeRcogqUufn/BAOfMRDtwRfVQADGaocK9RFnbMJ8aQZlM9fF3PePPPjoCDrBfqujrGTir7ONU4RQhRXwUYboDEAb+BLGAgByKnIpusjLAZvEg09zP1Rqav6BBwAAoHv8OEzt2AEw82/CjSKEWPGexYC0XjDWTTJz0yE9AlNUTBuUCoGzmAvb6vKFGegIwHPPjW5sPf/8THsDXyuIXBeBPu73C4BOxGJPaFeLVnUWJuowm7J52aMJbCBMQnROYPMD2l3qBOLUZmcrsZvEorXQQ93waslwxyi+1AY+jYt0GyVhTmEWj02MB18LOgoqzI4FZ8+Wnq7fYca4HHwExEwR8hZnQC/DNs4YMV3c8q2+FAOfRmEaoxxrJ9T6DVUVwOd3fwfKqgfT/WW4+9Djg3aEG5zRHnwChDz4QoIZBRvGQBnKg29dmo0uQ9bBcw0X6vWsor5R8WeOPjuoVwDcdezHDUp3HetzEfV6CUpSANjy3e9C9/BhmH3ySSjOn9c1xsDx4x24cGEwMU+ckC86hCDrXFBuTm3XghOi02g2XwEixpEyOBXUHp55sSd78DGCoEb/nZqDj1K8aRRV6i5r0bcHDowuNrzzTjiZ/TgglkWSxr7nRFuOz8En8gGRxqQjR0Z9+/bbynullMKcidyRPUSnREOgYmqIztA6DvJRiQa+EuUOigrhn5txSTXwxbiqMnx1rrzksfXFitpDBxWNMSxrQXPOkDn4sBd7Q0vR/JV494xiowwx7jl2tTosKvUNmNhgDr48FzKa+kR4ws2Qg8/HFUihEtmYaOBLIZz4+NT+KBcWoppqnm9wDr6xNLIBkELKKPRxZmJCEOI1VB58m8Q7PQYSeJnIqmp8YwvRGdJdEDCOyy5e7tjUvXYCmw4mHnwT2PxAWNG42zI40S1+r3+RRloKaj9pt05jN2CK4+nnQnQmHR4xzec8CQK4zpwp4TbhTG4YIyQg14xcWY7yT8k5nRqMwpuBMIBj6Gt4Bon5NmbwHQY6bHmu/jiMOKlCLY2EB5xAem7lQrAO5XFDMlQqgdIN74TXTShsJIU+Qo+TFTBtZdXn22LCs+aQl6U8YlhJYdeXQlEZA1A4Bj4fByW4GeNuwYUxKoPGY4/NwO7dU1BVBWwlP0TXUZ2zZ6F3yy2qshQcOxY2TmhIkfLHJUGLiRNDRx2ic275HHzuzfuhKjrwys/+DqxO3zDCs7wMRb8P5oYb1A1jxTj3Pe5j3sCXq29zG78055A2RKemTOq0WA/hfr0h9uj05qRT19+zUo9iT5EUGLSCKQag4zPtHHwOMB58nH1HZeCL5AM4pVpbwE2RRhppkias76LwDROUwUINEYsoOvypBHigq4p1XcB8X1EMnm2KSAyxQgErv4TRxEK6B184B1/2zXycoLjVy/KnmyBE5+bIwUdE8KAaUPbXQBbOQJjUQPsiAMB7tIYQ0qHzxmd0Cm5FMQ3nEBITIZa/zJGDL3RUZwNmfUh84fUOrWUTDx/RgTkGVzg3OFB5Y5J4+YsjeL5TuT4zsJ0T2ACYePBNYPMDUohJHny1sEbVH4cQMQ4PvkJxU4sMX6TWjOmKkVVx12Ja2wr4WuA8+IYPFxZKn0G2DzUiRKctaE5b0QyleKiXeQAAIABJREFUkG8YbHyFpeUKdgulSFIMlH+jHdNK05487TU5+BJySvpIhs0RY4g96Khbnpi+siQk28ROcNs37KUCCdqGV8krbI/+13jwcW23sN8kG5w9YypuVDDwDZ8O6uEk5O7GGrzIsbg4CIEqGSrXC44fdw182mOPOjezfo96grRrs96vP/3292B++RzccOUMfPLQY02TxblzcMNf/RVs+eu/hs6xY2qag0Izw2Z4az2zRJRi4LOVwbiIRs/W+oJIhj4g2boWeDeDoBq7h0pz0vXga0dLtEcQ4tlt4BROnoGLjBXk/hylaOJy8OUP1xtj4GsTopO41iI3lmrgw7f7BT4q2GZui4QCrTEABQ7FzWxQa2sAJ46XWfOi0ufrOsWVZN7XMrIajwK15ptIDz5TkXuJMQBLSwU7hQpmD9nwfRrLKNzBD6O9oum7gKdp7m+j+o5uIzy2OS+FYhqCBr7Ijhn7HCFk+lxtavVEudpcl/WU0EjrfK5ZoZYT+RLjCNEZjBbARHCS+MJN1a0crCcfH+B31suDT4Mz1VgbQ+64PIUnsP4w8eCbwOaHCA8+Dti9dIwGPvX+jW8lR9xaSyHfE/4sfJo2RdwxITrbQADXlSulaJgbMUY0nulpA1evDuoLabxIcti+DdDsC9CKAz+gbFpPZo5dU23zKCCwlXaF5cQaNIhq6FJ0GB5rrwo2SjI3L2MgRdmSgrhjejxuZnGTinbit/Qudii4ueblPLEUKlSblABljDuohZENfAC+l68qXNMYoKoGYTlTgFo/MZHPgtDSwN45dgxmf/hD6L/vfXD1P/0ndj7WY3Hz4onm2a2X3mn+7x482MyL7v790P/Qh1Tte3v8kA/B898pR80dwfgRC0XVh5nL5wBgLr4yOfd1a1F7S1azJ6TzHQUYj7AWnUoRu8HXn0N94112sci1DXymuZij/x53HlcwUALzm712bLV5REgvQHy2Dn+mhOgMtU+1BxDpwadc5ANePiDUhFBFtFXDYCzc9To6BxIWZpQmSV82yAtga3a/D9DteuXuu28eLrxj4CuWl7u9lyeQlvdYj90Mmf7PodTFa4q7MMDhri9Ncevluedm4LnnZmDr1j7MUHONQZzPkJJLWzr8TgXPF2u0aPut106ITgZhnYdRgZPqf6q/kwxHkXJhlrIKOXKcMA6bRisC1g0tX0Bv4PMvLlB0BHmhkODJzAkc5ehfE1Bddvx4CU8/PQufXp6Gf5uCb50MfK30q6lNe/tMoHzEuwlsLEw8+Caw6QEL+pyBalQ+YuNsuTtlUbRjIUBjEKEYV61rXotvDhkzRBpyHpKWB1+vO0MW4fK1ANB9bCs3pqZGnln9fpGU56ieo02IzsoXtuwfscpPSijS6tCleYsV1FoYrU8XogQqbz5hKBDzyuF2a5alj1vqXIlmrCBLCdGJodu7ClM7d0J56lSwbE7AYRgdD75IBUvM8o4yGAj1pb42BhzrPKlsrv8KXgyDHDLSetk8EtSZM/LFhho0S9LLndMWWuz/xgDM33cflBcvwtT+/dDds4ctu0rkd68VXcagvT/k5uTQiC4WMGXXTTFiDPzbN74BP//cX8P088/HVo16558r7ea85FzSSlHWirfZaI1W/L7oG2vsunxo43haEH8S6CsywkRDZxyfagzAW291YefOKbh8kW4We9XXkCsHn3NGAhcWi7rtIytTpMekd3oGTQueM6XgZRi9BFpbJNLqF2jTp/j78+cLOHWqQ/NjJm+IzuRuiN2DnLGyzqcMHnxaA58NnAefA4iW06c79JzLfJ7m2s4xz0htZxx/Gs7BJ/+OBz+iSypPl/M4VJ9zyQxoQTeUCoqBydc/jDd6ZOi8VKhx5sJ94UIBhw6UsLwcucFmIiBlTWEeRcOytHU6CBUiy2+SEJ3Z56Gi84pz59TGqJdfnoF33+3Am2924coVfqA42WcsXZgwMVW8s2JvcuYH1pP2xxOCfgLrDxMD3wQ2PzAefFyITg4owa5tGACsQLDRqfdvz4MvXlCPgQIpKGrBMIURaHWrrE3fW3Vfuuf3lXgtAXjIGXj5OKz/7QvAlNJY1eTwJWWMC0HBGP1swMom8SC3mIN0uUlhGMwez41H58QkFxi1Qa4ZAVFdjkNQP+50om/HUajws4/u3w6zTzwB8/ffD7C8HCyfW1lRQ1n1PAVLqNEcAqGmj1IQSR58nBDk6VGNxtC1/sYACqj8e3TfKly4FfsPCVVFata9FiOU3Rg6p0+z71ZWeMbAGHDXfcTe5O2liNDRWegqWr29iMCXAu+9sB+2XDkLxgDMvPBCuAJjhPGL0YYh23CSCuzeoq2HnmVVLIxrkx0jCZIih1LIe/g05zkAlHjuKAkjDVdeoAK/rJ2D7+rVAhYWSlhaKuDSRfqSlL3ddKwtcLOF6OSoUW1DY5iPfg6+lkrYGBqJsty8ktAaA06uXQAgO7SeC56Xf+E3kOrpKj1LATFPNPo/t1IXdyGXh5j7VtbAF4TMxhkGktFzFY2/jrxvH0fycwGo5cmd7ym48tFFX46g0i+k4Ne/1NUR7nepcKmMTLhahkgwbSClrZUVgD17puDtfV3Yvp2+iJ21wQwg8TbrfgkkUIjbW/FZkOqdvllh5vHH4Ya//Vu4/ekHvXfU9zXGZWPEiFzJsnIKRMrb06uL8P6TP4Fiaal1U3LhuPrXw3y6XmFi4JvA5geco47w4Ks3GUpYozbOVEVTgLSkjdDPfRQWAtrk4BsklPcFwxwK9WxlFbgaugtmG/OE9tH/dZ9zl1yLYhCms4ZeT8fZUTn4pNvsArk6CFSSjBip+DnFKrumYuJkKaRI38BWj2WAMc8hoU5NBRUpmrw7+NHt7/5kUHd1Fab27g2SkXkpNVBW/Q3y4AvHPiHxhYwWAQ+++qE9jlUFgL15Qjn4VA/XgRO+eNHfC9uQEpuDrzxxAm748z+HLd/4BhSXL6c1qiOMfUV5MDqhqqz9SGuowNDs7faeOlTAOKRRQlumfuj2VqPQsbmJnUd0aKGqUiZ4B+VtV0YxXdOgRWGMcP7HQot8wpsBcIhO99gV9lAN4HmsVE608vQkFb543fvfZV/MUnnwSRukigYGr4A+VId6aAyI+572siLmX0rsrVDzUwyfJ9GYBRT8kgdYaxfrumkMAPb+3wiInDy+f9aIF5aiEmgAn/25PPjIi7ZCeMscbHtWYHhOMkQklv0ic/C1/VYKXyo/OE72LZiDTwMUT5GLaMWeNMamBjAm47BWnkuBM2cGt20KMPDWW1NxlTN1aA40/GU4q4x0STSVrkgkFNui5ds3FQS+e/onA33J/LEDMLV2BVWVLzDEymPk2SksmvLUKd1hGakw+YU374dP7P0BzH3ve3F4Q01hXQfhKTyBaxOuxaU/gX9tMFQe4E0q/jZNfqVrnOKXIRgbMMfuwZf+zfjwxLfK1itEpx2CxwA4k0HFjFV9kaaiAJiy+NFWHnw1VcK4Ut56Gg8+LslyiK5ssoJGqMoomBhD5OCTyRqV09AlKPbq51LeIwqo+SiNKw43FSrfCkxEiM6AwoVS1GuFx9QcfH7fov1IzMFXsGNjK0sLMEEbtTQHoseuxWC30ZFQ4U5jl+6WbdugWFuD8vJl6O7fLxPCfKdKASX0ERmiM9WDj9rfAjQE53ZToG2Yy3abguactJ+lKApyKQ45mowZja0WF9vGJjDwUXuUBO57tzLlcRPzSQ6/FAizh0EO0alrPGQIsPHb/dDpjMrkCtEZU9HpN8VlHwB6GyLXp7RfqXN3u/yTZ5hotYgSJ1gQpElT+B58lAf5EAUOSUryhlHQbp21AuZ8Wk8PPg6iPPgi1llq33qya+oYsUIWsV9F7puhpmIhbw6+TP1H1A0atSL23OCLFMJVBLfz+g0VGqsHnwKRVyRmk2Hwr/YKOHiwA8eOdaINL6kQ1KkIekJ9Dr5wO8n8hw1siE77jE/PL6uFzZSmguqSNiqomBx8M08/DVu+/W2Y//a34yeAoO8rTAU3Lb4LRQHQOXEi+oNEfRPmqSP3mQ0QkyaghG64yAQmsMHgefBVogdflEK95e6UZXOjrrOVRqU1TmofM44tZPIN29utHHzcDX5JgUkJncZYtzGLQR6+GgZeIUpNJBGCqlZG4qIadBJIeQbx77JUy0s6hMP2R0IkE1on8RZmjR+DZ2ALGeVguJS8fURPh/s8YGBMWF+OciZkTVbi1ADu346xFGUZlBGG2cbaTBEJkefFIMTjsJWZ/u1L4zzI0t8bxAmHdBwSWTHjgvNHliEPvjb9IRBGhui0mnTmSMhyG6FArQUjrOAft+IiZS+XtjdOAag28EVueG2mxaBsJuXCJhBUY/sCz0l7XqpCdGpxBwxVXPhdqj0vZy1XVqGcrX/ay9j24NPsX8EwiF55ISwWPiuI32xdxcMknkUsZry16HokMXxvwfB6Ofa2qnLjrAbAGPBudRRV5V/9Mfgf92WqEjRy+sRB7OFtRmNHhsKNUMrjtSOFNqvBRl9HicEpIUKAx2kcU6wVEBGFMHBzjcoNSdbLBFTfpRv42tOzY8cUHDzY9c6mHB58n9r/Pbhy7FbY/dH/BYx5TztCFZBs8EyUJ8Zl4KO+o23qGgycLubQoSlYODP4f3bWwHvfa413JhpyfgrOgYYNaXaboXazhOU2dIK4kKyy6UEplBoD3iWG0P4WOx/YvYmA6ddeAwCAzsIClCdOQPXBD7JlOZ5ZBSsrAHNz+vIRTcWmXdrw83gCLEw8+Caw+QHtIDikjA2Swt1mWkRhLwLwDWtx4+TekbnAwnRhJkJ7o7/tzX8Hl8Ig00BOSc0eS2WILru50gw0QtztZezBFwrRKTFUjZI3MDnw69A4GVNEefBlucUlKKyaf7EAnJkD4HLgkWF+hlCWBMedYlUy2IPPV6RQnlAEGh5WVoLlc3apsy6q3mhPY5S4XNupigOurqTOrPFKa8QYcDS/VB/aBn2nnMXkDtav7I0Yu3bHBVplIynwEB0Us0Rq4aaGan5eJIQbXc2cwUa67t690Dl6FACYEJ1c7MLEHHzMA/+xMZ4CRTJ+xEEkHobeYDGoQ3Ri5YayXapOZGVuDhsD2Tz4NocmOayssEHiOyjvv1TDRWEqb15rcFPPNAqnAgxpSPPzRw/+2v3kevBJZ4iCEOIDQgaLlGmkoWVwLsXGipaBzM9D8XRc/egWLYhgJEQ6qso3nIihTD2XagAzfi8HFcROHmasBpcx9ONIQa4cfF7+ToVnrog4EbJt7wwiSr/gfVdVQbmwAPN/93cwd//9ntU097yjSM1l4Iul9cKFArZvn4VDh7pw9KjrWxA08Cka6/TX4MalU3Dvnn+Q+YxWjMvo97j2CIkfHxdQF3hyemxyvOaZhdFFjlOn1kcdHTxryTM//PE+X6PXGaUW4qJ9efLotWjk4wBdysRjI+1voQuX1NwgoysoBi90mSNmT8F6Cs0l8EBT7EsqROeG8UMTaAUTA98ENj14oQqNu9k5ZVuHW4mDNunFGkjguFspkBHuVKUPAMTd9sg5LrYH30Ad5LcjfGcj5DM0YQ8+7XlK5eADqD34Ihk+YyCoQgmMpS/0h0FU5GFhnbrNjuu0yaNAfB82sKXgJn/bzwTlU/Q0VipDa9CE6My2lNAc04To5EGvULaf33rxMGw98RM2bj2+oSdSgOeng1PhSgjUvBv8L+/1m0OCoqd0ZE6CIQxCdPKGTQfW1qD75psyMS0msSSUTe3cCXMPPwzz998PxekzsLpKjYX1zN5AEhWoVA4+YLYOifYcoEaHvoUSUgfnlK6NGN5nBP7lgdRpMSqXZ+3hvJtqQsaoPYmYnl64aNGDr9F4yKEPrZoyYYwSn6Kf9CxCUJaMIQArIwg2zr6YlRyiU+ADsMEC13P2idwhOgNtawDvY2UR9kjStpXlMhcxcUV+qTcwkvT7AFeuDCeXMPBeTriWyzeXDQGA6L8QcrynExeWUonCXZgrBx9Zz/o/I9swHuDmPpWDb/jtTZV+H+Yeegg6CwvQPXoUpl94YZyUkiE6yXLpS14NZ8/yAignImqis2Do9lb8c64tKPC00aNooOpFXnwYIy2xwJ8Lluw5Jm101v05MgynBJq6waNJiiYwBNyv4xjvdd2TWxj4QsDKzymHUGhCR+qFbCiIS+CRTY1wKXnUFNwT2FiYhOicwOYHtINQHnwhYZ1933J38tMK8bfJWUUq8aIwFRgQwtQYKuyj7lva5eALPWiDLBGN0oPPbrYWvLBQJofolHG6//jvsTHUtSHw9STA+UQkKBwdd5pWA9vPSa85THSLEJ0UcB58QNEyhLIk6Ei5DW8UOfiMMM7MM6dPI5m3NoBvtJVVnzfwWbVskGTwkEJhy5WzcO/uf4DZWQPTr38JVj//ebYuqddiFMsNWFopaVq6t/ddhr7en7npYkymcCtjAq2OUAxpHNguipUV78aiV6VFDj4PLFyzTz3V/N997EkAuMvHOaTGmMKpWwSsVJziUVKg4v1RmjdtgA1tyIFSScXlsMCG4n7fDYdItZNLOSvtI9k8+JTzc5wQ21/8WYgV8miuKBa1cy7gUFAhwoS5qcqba5gcfEzEApuf6XZH9YJHfMIYhw0W8Z4PZBnqYQYPPm8f84xEEfjGwP9T4TUlKNZWoaoG4f9WVgq4884+vEcy8DGG42smRKfiPQ7RmUIP3u85Ax93cZE18AWI0chSGwpmJCMChORpf0Mvz59vfnaPHoVV9zXVVDJQ+FI9u9umUpePG+alOq8o8zvT4qTC6SWPVeL8jw2dFwXEx+RdZ8xatiaFZw+xeIgDB7pQVQA/8zO9sRkCa0j1JvbTPIRbCursYhhA5vF15b0HhGecp28JXWhF/a5ZjwmLPVgC6y4knPjMkTwOCDxR/MzEg++6gYkH3wQ2PyCvGemWIpeDz65X/8oBWTY+4pAuq97YdtU2wp93ziGmUxJ2WsWcxriqqjnItTn4XIWVrwCwD3IcojPSI77BATDyQBU/l2CoyXACDL3OM+e3HSOeL0fiphGyvxtDgWRJiQRqDG0B1V7vQuRe2rM3UfjDCrKUEJ0irLMHn7Muqh6PO2BQiOvOQad9/MiTTbmZZ58N04vwBnNDCQa+gTLTP0s4xW+UblUwcowTtGPgCjwsNgBoYbwc26TlcVUrjBaS2/wiwqh46977Ppo0aW5sLHB00AKyv6234KGEPtB2z4imTBqMcc7XRBJiQ3SGLvE0/EDkOefxJ4SSRbv3qEJ0MpE4OJ7OtufY6dvkEJ0CAYZZzBA28OF+0zSq8eAzhikYwC0VC/FFIV5gLGuGMM5JaMt+D5aWiib36rlzJeudDMB48BkiZ91GgKb/pMPbkl80nrISaEN0okaa/2I8+Oh1xglNaQOVbapyiEhZyC0aUk77TbWblBSpqeJY2/6T1leOEJ1jhdjzMmNZqVIIz9JSEfS8teWgBidxnqfQzOoEGuANfHWdQ4c68NBDc/DP/zwH+/fH+6QkivnRuGO9+3JcCsX9Sh3LZBjuzJAdp4QQTei4EJ3xzZJ7k+aDYz34GB7ahibPY+ASuI9H+vJ2C32jt+YJ8DAx8E1g8wPasSUPvsFBRr3XXBtOIY2/rauOYU6cSL/yyn+Hf/OTbw4MfQTUxh/HgKPWjMl528SqWGDRV817EtiJdqFwboKp4qWTITptw5FxPPi0Ofio5PbNWEkefMRvTX/5N9r5OWeHs2wj3DrMo9V+87yFZKCZT9iDjgJvnhYKjyIJgfVcYurJKpE3LzUefOOylXSMEKKTrV97R8m4KfBCGFF1hU7Gb/B8LFZXoTxzRqClVoiNXnLTgjNosHoopy5LwNhBbyRw6Qt5LgYBfa/2goeqm5hC/atymFdjkNdeCwOf/dd+75LG53xY9+mAvoVTgmtD3KSE6KTOiNR+GNRD4bnbgHdZSWyY/90C2qCSPPg8b89IhWXsJZVCuBygCtFZ0B58uGz9027H9ioNhuhM6HDOYFGvKdxvThMRe976hehk3qVE+mBoWFws4Hvfm4XHH58ZjQlHb0KITlfuAtFIGDOvNJBVgRy7t3DnE5OrMgbw2tWE6LRhxFPhfdUnJphLeROBx3PWPKQmRGfQ+yQXlQ1xHn76fA+foW1pCxn4SP1FIvMpnnMpH6JQxucaO452So6U2tyxYwr+8i9vgG9+c0t47Y6RpwHg17frwUfT8M//PNc8sv9vA+LnES/F0McpbSje40LkGmXWR6yOQoLiypV2CHKDdbZTPFxof9NO7bLqQWEquos1SEIGvojLHt6l8Yw5+Lyw9xMPvusGJga+CWx+IIRmAPrgKiQ3HkoYarlzxVzIY99VhDBmDNy8eALuOPlKK/oo0BoCU7pGVArkZCKtOaEN0Wk3VypCdE5Pj8qHQnRS0MxPjUI3UdkdUsJw9dVdHxgzTYjOUBg8sWmCUC78hOg9yngE0I3yZQtjHAZIE6JTaob6rcnBlwtwn9kefJxBhqM9Rj4ftZFqPaI9Uag5MPPkk6RADsAbOezCGkOX9O7EiQ78y79Mw86d03yhTBAKT0I9Gymj6EmWctP0xIkSHnl4Gnbv7rJlWk1qhqh+wIPPGFQ3ggZPaGa/x7104zWRaTGP8pwN21tbg/LEiVabvjH8fMHP2W09Zp8Ff85qdHI3LZ6E2x78Nsw98Vh00zxJyvk5ZmWYDSEvSYenQWyQOz5o72ZoLs+cgc7Roz7Pjb2eIvgCDDyPbin7CnoicsY1e0l3OoZ8jkHDj1G9L3okMedMCLR67Bw57vA+xvHAuCyJRLkWtm+fgbffnoI33piG116bEst6Xk4SHQBQDnPwOSAZ+BQefDHdPFblV4SWGMsvbT34cBdyHnwc2no/KiM9+Bx8kXNB2UQrPMSB7j0f8beBQzNARNu55fODdE7lyCNbXcdpeR09+HLpd8INiI9iqoch4qJovw+wffssAABcuFDCiRN8uhdMy9paAWdOF3D1anyoaXUjDdARhuw648iRmgqSl15siE4VXYFC0mWjGkgdhRJmH3kEbvgf/wNmrDQI6wESn+OngnDLhi4oYtQFUe6mxZPwlVf/HH7ptf8byuWlRGVooNPJDVU3UNIlcKrvpFRSHlnjDAU8gXWFiYFvApsfFB58NdChs3LHEx9BjO2CpUGQ7rcsL5DPRx58+vZtQsKeB2xV93fgMBib8GuHbS0KoA9Gt3H7O0eMERLKLKbS9uALXZiRBIr5q+fhPa8/BzddPiEjQaAJ0RkTLsoN36WowPx2cK5zDj5jCAOfYgJTCnlKkaSjQxBKgLhtFeNRDDLzlh0MlYNPH8YNoSJAVgqVplY86wxT4Xf+i+6xY9A5dIiYlkVT3g/ROfoteaPU5X2hwd973t7XSfJ6agsxYf5s8AxISuj3AQ4f7sKliyX88IfWjducOfiYQpyBz/bgi8nB57ZDrXtr/6uMXwWI/jd0uVSo8cx/5zuwZds2mNm+XVVHyrmoebYRc7mGD5/4F5g+8y7M7HoDpnrL7svEjvXYSg5PTC7XSIhVoroGQJevoxTy3hmNDrAt3/oWzN9/P3T37nXbVhBGzxufBm2ITo2nT43fzcE3ei8ZSGP2GYfniTHwEb/VtBj8rSDONY3xr/vWW/DJHQ/ArRcPD+oQBr5Wmmqm7v79o3j3u3dPkWUaiHQVozz4KN5OjEwwNkElEmKFMKa8lJ5AT4q7dtYIO6rXrlUlNQcfLquOhLNeoNjvWVko8LutEY2lo/5djXIC37R4Ej569Ecwd/X8uvSptO+zPKiSsCj60xZD8JEarULGpcrRuha6Uw8edENZBvUI1n6xsFDCW/um4ORJ3ig4aFo/oCyvadHfwc1ZupjcIPVHjhx8GsiSt52JCuWmZkk8C1ZWYGrPHgAAmH7ttVZkZoXAJYnQ8a7RK/zCm/8IU2tXYHblEvzUzu1piz02RKeirCoHH4Fammser0CWHcMinMDYYWLgm8CmB6ybp5mF8E3npJAzAeD2/Zi9W2Jiq6iMwrpGtYo9lRysZVa1DWjBqut58GmYMeIUs+cIzsEXCtEpwUeOvwC3vPECfOTo8zyZWBE0eBhGHvhWLpxlWKlPICAqkiE6MypAqbmKc/BRgJvMmYOv6o2Ql2U4d4vmArMD65mDD+FycvAxjcbseRyduA2R6Rb2ds+YaiE6+4FPNv93Tp4k6a7LY+O3I0wPywTDvSkgi1AXWVUv8NADG0tz3U+h/Igx34mVfNyZaVbpQTLc7YZE62cx9DBy1j67PhDtcot6QO11FgaXgaZ37oyqjx9RXVJVlNDIfIkwrtTekrItT68t8c0krh+1B19kHqU4GuJQO3wxGg57vwp53GGYe+QRF7dx82BrFdeqriHKFmBIHq1+9p5LR+FLO78B733xMQBjnCGxPfiCzXLrW6gXdalKHaKTMITmDtHZ68Hc978Pt53dD/fu/odBG4V7YdKRk0ieNNCWYsAbwzNXNjIHX0F58AmeX7lz8CWykTpkAUSU9wFAHg8+bQ4+Dm1MDj7Wa3SMt0hyjVHDQzrrtRi+Q3NtjGcHCVj5XQ0MpmXVgy++8bfw0aM/gs+++YCKjFY5d0FhD6KISPbgy6yQVqzLBH19FMSEztu1K3CJIoCI5q/T+5TdAwpBlidksxTIucQ2mwffOEN0FpobHW0gtYOCOfgUF9sC7UyvLjb/z144paMNP0vw4At1SYNSugRO4ImKNBfpwZd9r51ANpgY+Caw+YHx4OP3zzCzkuvQtw+TrQtvwed2fAu6u3bRVHFtCkpGwy1R086Dj/4ZHyrLOwxY3FTlFoNgwiE6uQTEg3d9r4z9vizTPPjsHHw5bp6FuqgMhDiUGFAKYkNxkYraEHchgWJOuB58o/LB0FIRhkd76FbvvReMZR3Fc94LOYaFJdKDz/3dL0c3LjXCVS6mygvRqcjBF6PbCzKsQg4+CRfPsI9+r8zdPHqsC1lMAAAgAElEQVRcVeL2IynDNJ5s49bTaIGmgxJ4CvG9Dan2+eDN/Zb7PwVcDr76G41BSrbEHHxSUfec4YW2XHNGjUdZkF7Hfmgvzbzg1gynmNaCl1PObi9xwtr7rhjSeR09+GLszwOl/uiBq5Cn+0uNO2SsH5aScNh0hqAsZeb2C7v+Hm5cOgU37dsBnUOH2Bx8EqQq2NYtRKek8GbalqBW2NXFpteW6LGIOYwTPjZks4kO0dnv+e9JI+HQ6MIYTnOG6MzGn1HzT2i8fiemqlBCSg4+J2R+LQe1CNFZBPi2GMjGo3H8PfENwQuTcU1Fg5f3fejBt+XK2ebZDVfORPPfKSCH6Czi9zsBmv5PEUokhC3RjEAhSyRO/KWlAg4dCnjfBdHq2rpwoYAzZ0o2SkUIn31507vLPsQRdcddCWJXGv+yh8Y5QDL+RdOgLKQJ0Qmg0/14kJGvJSGxg2z+wBi/HHdBUdMsrTdS7iGRlze8HUAo3zYHn0iat8/49aP7bAKbAiYGvglsfsAMNcEbNQyddKk8F7Nngb1x3n34cXjP5eMw+9RTYHoRt1AFGkwpM2pYmagBreipY0DEn2PjEZxDHgrHQyPkoWiMnReCFtZwiM6UHHy47RBTqfVSsXGGBsl+rfHgCwoVJj5EZ7u8MUhANbxXot0uvsVF9pWWa0HXoW2hmRI+NAY9/LtXTssFxgTYw6BTCQY+hiZJ1xcybkgGPk0XSB58VeFOeFLZzIXotPEOK8ohOj0LYZDW9YJQToL6PV6nNb2qS/TYOALE9yrntKqYvf9bg1etyR58xrh1Yw4ovCVwglFw2WRa257yMFQeKSC5fUqrZ0txrqDOiBQ9VmmExlMNfNr5mkvjrIAYHQg+iyiFfOjyBofbM1QH+kCam6qLT8bQIToRQmMAOqdOIQOfdjwYpXKN2P7r1BLOQaKfxH4THkd78EV6I9fh/7mQ4qrzKmEtBPeN2BCda66yyxi6EcxzNPUlY/56Q2x/Mvuof19JmOsM+Dn4giQ4UBTDCBe4v2P4IGIu5BqqZDwG8YzGXy8j3KgRqhNXV9nObf2teDpVNB+saaftsbeeOfhESMGl+Hi1UT/1W5QXH86cKT1aQk1KchSH49KlAvbsmYK33+7CqVOyKpnFV9gGPrqP219W0F08ogCXi7m0HcO7aQqR5RU5iZMNpJH5QqMhtYMQXbE5+KSX3N6oioARK3cEdGsUNLJ1pAefmBIG69j6YzbsTmDdYGLgm8DmB3SLb7QhEcJ30dagEAf2nl6HjSoEhj2IBL8qaANfK2Wx0eXgS2H6RR2ZpwRKBKxpZbguv48sI2DFePBZwpsdojMUrYAS6IpCZhwYBPbD8IEflYPPpkfZZxEhOuv+9dZfogcf9+04Bx8dxlPGzRaStBV1kco4j4NMv0JY8pAgBg6X7/cB3nqrC8eO0ftDzBboKImrHv1CgZt6vnfvFOzZ02WnUQF6Dz6yLWEDMtbeSenwOKXCwAvIb1O6NJcqMOaGFOVN6H1yWNHhs+ZVi/BU/jBbD6zkHXwYKeu5dTaLeThRO9o9HPMq487BpwanQVrhawx9Ngyeu89SQnTmgtoDn+RZUhV4Vj1J6e8Zwjc0B59FBzqLqBCd/L4p0xLkC5jKVDOct7RzDpUAJH9P8TtFwebgk2D27AmYefJJXeEQDTUolDQUqKdQCw++um5dbODFY5Bhwt0XWJQxTAACLtSjVSAKbdHzPfhkBTWzrh2ZSC+dpBpMVMhCyJnydiSRVHp8D75wn3ieL0p5XArRmSKXUhBdTznHpQt9Jd4rUKd2Fhbghr/6K9jy138NxeXLUfNOA17fVQQ/QpTT4NJAefo0wPJysFz0PhMoFnPOJTUgtZmGzn+naEBrZwi2l9BPdi7VQ4fQoevpCXz8/T6I6RdsXcx6Aq1bo/kZrIqyn4flLsV+GkDCRamSVWS6DvXykwu0JC2zVMHTC9Gpq6Zt1itP6AnIHsxg4NOWzerBh5siPBYjSJvAJoKJgW8Cmx/Q7iQx1KwHH5OMtq0xsDkDjYGyGh2IUXGM0ffZJHEGvkE5HDZL16avoLC8Gxg67DbdmnIl59NynQSWokJkEBE4TA+Tg6+GsjTZPfj8Z+k4m/kbYehVCfwKpbxTV6PdaBGik1qftjLPvp1m94XKwKfkWgxqyM6FQHvw4d/x8168oQUAly6V8P3vz8F9983DmTM+EeqlhgqWVX/Uv8r+GgkV/nx+9tkZeOSROXjrLTrpu6QsTWEqXQ8+a++sKsAjYwztwYfzjdVltm3bArt2+dpjjTBXFxw3M6xVNtI6Qnq8Y/cpvDeleoSKYC9w6yYG5/xh7DPOOWDTDHyUAYgSrqnfuSdB7jnFzRf8vFV6JKZf+fbdOVhWvuK3gRSDGz53YkJ0ZhyAWKUhp2ACcA0pvpcWU4kupQq3q917NAq7QQ4++jmFPzpEpzFw5+Pfhumf/CRYzqMhYrwHHvLhDyZRMvtLHBL+/ZblBe+CVPRUTpj79R7N9aOnWAxA2Sdu3xE46jlSMhM1pyI525YQMab2PEsOveoUry/sVfDxI0/CXTu/D8XSUhTaslTsHQKSnJcnooH7MF548osoNvRidRXKpSWY3b5d35QW8B7CGPg0EFtv6rXXYMvf/z3c8I1vAKysJHnwpY4/2/+5YB14ea9JpU4ppcs4L26n/RjexJPjfaLW1sDNjT0miNhuvJcxYxzraaiVGUVg9JoSX6gGfKs9916cmv88wH9L4+3lRNY0qZ1AsXIB8T5URZWDj8AjdjVxRkzg+oCJgW8Cmx80RoUhDDZA5mCIDXmjI43H7ZWNv/HOhuhsRXe6YtVj8jCDEYMz9RsaxTNY+fcULokW1CG+sHLD/j1tRU0MG/hqYZhmqIJnPXGoSvmbapwhDz7bGJaSg883rqPyGqE2s4cD9lrwCdAZ+MT8DJhDFjz4Qu1Qj0JKf2zgk+bBk0/OaEggATO8pSJEZ7TeAwAefniOfF5aITpTtgNJMDWBCW8be7yi7oHT/Mclr8foOeXluJUCWlnEfsbuBdhAp2wY7wNNCFAPvx6k9WIsDz5WN2yF6MyRg09a95hX4WjPrbxzIHCNG591IyiCOSxqGJeBTwNcaN/BmZiAMEYLNMYcfD4J+lXiG/gEfIo+Cs5jiRZh75Dyndpl6BCd4ZBUnU6Y0JCiR+IhQx586AGqSzdK9hP1/Rk9+G5YPksa+KicYmxbUVpfhwwehPx5FJS9Vf88iwzRKeXrDsFYz/RIJeHowlI+D76fOv0GfPj4i/C+E7tg5oknxDp4bZMGvgA4tBLemWPr79SGah6S2iuU6x8AoDx7Nvu3Ujn4jPHDjiTJ/oGzafappwBgIMtMv/oqcHoHgHCIztgznZVFUyH2vGxRlqOdWkY0nriQlOS7zN9L7QHYI5jjk3Pk4CtMBbedPwCzVy/QbQXr0zy7JAtHbt1phZjDNCirKCDGgy8JUjvIoouSY6jIIuJeFaDDUINJ8oWR/ROx2Ufl4CPwSLZH79LcxIPvuoGJgW8Cmx7wxlkWtRATgWQMu9Bg3x/dcrSbidkkJaFd9uBDeJTfWJh8ITpDlZyDNZf04uRf0m9h9ncWgY8uCjeXi9Yj3j4sY0L+0F2hUKoFCnCx2LkpFzRUI0WfF+KUoqmFBx8lFHoGPiVD442Ali5k4LO/OVuITqyoiTDwKZtkC9plSyMY+JjHgi40SJ8t+LXefxAj7lyOQGGea+BvqlpjbFU8fZrej7VTaSOY4VDIntFY0MSl2i+8PFzKAY4ed8uDjxW47dWfMQefs8dXvhKANCSsxySgrTv4AVmEIo96zhr4qDMB40lV2A2hNIIHX0r/4v1X8uBrc75hEKI3aFBz3uwAaHywgipSgafx4BN/WyDmyR5CWXAaMsxfDhDa36rx4GtjdAiH6IxXnpPXHaiKbeYawsd58LXy6GPAzq8UNDJHfmPZ9922KZlKnPuDTckvq4I4hboImorU4Y0ek0sskqh6TX3w9BvN76n9+wFg4IV54EAHlpZwFBkXKAMfabh2njFxhoeQ2reheqo8S+DPLTGikJBj2kc8fm8m7mJD7LSLbvfq1QBu5tvbevBRtOTY1DQyXURdVTUiN5Z2LINzP+Hyu/ga81ME/pAHXzFkFtsuC2MAPnL8Bfjc3vvgF3f8P9Bdk0PGpp5/Pp0y4cmpDwQkI1nc5Quj0sXUgMOhCAQnTekwc0s/DxgeKbnERiXRSr6rCF5Uw5uFOgWvEZMpBx/RdIxuZeLBd/2AMlvBBCawgYA2vlCITjb/AiUMtWD2JKFfE+KgAdHARxuwPOVpBHBGHM0h4JXBh4GEI5e2wA7RWXtmEFwgF84JYDReHDNXluCE6Azln4g8y/1nxHtd7oqQB59VVuPBh28KhXLwUf2HBeCMQpoxLgPHhegkBUYNAzbikN3nVufZIToHjLMctpYSqkO69hADF4JUPWCn6vF7YxuBjykrGfhqEPd72xBnlysK16hDMM/GLd5AVQEKfTL6/9ZbwzcmOWiVN1UJKQI/1Zf2C81cIh1pcf2MoQ2diwUqjb4VolPKwUcIXdSrGC9tf62nn90ObVgxb9PU67lJZCMaxMWm1q5A1Z/1yvH5DuMgcpsBAHvfIIwpWUJ0DnYPkpRM83j6pZdg+sUXYfWzn4XVX/qluuUo1O57rOjwDSkxfK/Ec8s5zuRnqhCdBW1I89v1w8urPPikEK82QqJQiOdy+UwdD6zx4MMyjNiw0EhdbHblEhRrq+C5hgwLqM4rYb+0oSwj7lMRcZZFhX2vB/a64faAxpOc8OCLhqtXYWrfPujffjsA3B5FbxREbAD2/MAXz1Loqfurvihl89RPPz0DO3dOw/y83JdlaaAMrBcAXg4riItZufo2uC61B3qzXogikRaCbPPGI2T4s2pzR4A2ZKvWDxEiH+MRleaRHZNDv2ODl3dXalMBqm08ceInjS95zhlHhvLIUeTPa3ARZ/mAP7Fx0Ex1Drv3z7zzDAAMItR85MSLYMy9Ynkvl6hCZ4brpOiEogsZ4yxCim2hdBQqwOdw5s1JExOLahF7FvoefH4dSt6lHzCXdTRrMUMOPm0Xx3vwRXgwRhyFoXcT2FgYi4FvcXERDhw4APPz8/Cxj33MeXfu3Dn45je/CXv27IFerwef+cxn4Pd///fh1ltvHQcpE7gegLoxt7YGt+1+BT5yfBYO3/6F0TtuHxvDLuQKIn32XRCkmzFCiM76bI8HQnEAujMsyG/ElE8dE1uoJZyQNcxYHaLTfV84ObncEJ3xtCkeB9+H6oXCvnMefBxe78ZpRA4+VjCJ9ODr9QDOni3h8uUSijlZkejGvR/9nzMHHxQFmKIYsX6Iqc8hgNRGwAYXuvEau1TUjCLKEWSH6NRupYIuVEEfrdhxfguInbXu7AuF693LKopG650hkDXyMMV9utAbWmwRkEWAds8WBZ4hRIXoJHCPLqEMv1m9/hQeEUzSLe4m+EgZUcgefAKNnjDvKX3s7x1VymUIkwAr2oqqAtPrwdQbbwBMTcHaPff45Yn+N8btkp9+91X49LGnYOrEB+DNrb8L9thoPPhEgltAne+YRJOCG4+lMGRaT48QzPzoR4O/L70Eq1/8IsDUVLROD89J76ICV15Bs4MbX1IJEEquhYbO8EcOrmcQz6ncyVA47XT4oBciHhIo2gJ1cb+F8AFE7LFCwdCFMGpKdy+cg7IYMbrG8MYWqi0PJ1Oh2yXyo3LII722yv6a/14K0closGIMYrPPPANTu3aBmZoC+Pf/JwDQIchjQbW3KA5v2ksuyHmgZgYdUg07xp56O3cO5syVK3IEFdJQH7NfEiE6cwN3FooVLJBC2q7HpS4R8HRicvAl5wnVQmCDYyPKtB38FCWGEk8bNiCJBGVUqFyfHKQnCqdfWKVTMaZ1iE5MZ1nVF0K4vZOoa4z7m6qHzo9UnQ9XiBvXwlRgmkhf/oUnijYNeIY0Y/LuZok5+NyzvfDKifMfC0oKMNoOiw1pGrGBFFhPsbIifktIBSfxqPYFdg2M+3yeQDqMxcD3xBNPwLZt2+DXf/3XHQPf6uoqfP3rX4fTp083z1544QU4dOgQ/Mmf/AnMzvq3hCcwAWxUKAsD06+/Du9948fwsSNd6JddMOYzAABs7g4suDov0ska0YRvKcY0JYRpEm9JIdAKE/r8UD4TFFJkY92N82mZTgInHrp0WEsHZi10YgZq+Bt78K2uxnjwocNYJoUtoBnPKGWTZirFcAaofVZ5GGkhOHq0AydP0lq6AeM8+JBaoUn1k8rAF7rqVQPSnNoMECl8JEl+qF9bevCplxoqWNqeDZHfkbK8KeNZefo0zDz3HGw9+REA+GXZG9qqj/PvVcj4739OMVJU4hAmVuEPf6TnviNo0Qpr66Gs8p8p9y5W+azYOASpKhTyNQnseeMY+Jjy3GYcSiTH7F2bIkSnJPz3+zC1YwfMPjO4uWymp9Xt2sU+efBRgKkpmD9/Et7X2Qenbr3bbiKdbIEWar56fIehQ+4CQBYPPvYZgT/VQ90ByuPSb8oDhwct6XeqPLkBiFbSKxRiTJVBe2yIVJ8fdS8x6RSCJQjzBxOD6zI815UrBTzz1BQcRR7Fmj6gy/BzcnGxgCNHOnDjjQbuuKOva4h4372wAEVxe7CcGpi6A69KnSwTu57KnqspNgbEPH6kBx/eywNdMLVr16Du2hrMHTsIAL/g05ACsRXRGVCv9UFYtkQahlCt9QfG06HyWOXJ73nwKY3p3FmWMUSnj6eA8+cLeOihOZju9OF/W0NbMNeQf6ADAL3PxoQCNoQHX9tvpXgPrdiDgaunnWdSG6EcfGHZI87zPRoUA5Mqc/nAKO1bGPikvVfUjQl7YkxeMz4Hn8DDDh96F4I2KWyIBx8MZQxUXOIL1YAtsCLPnoA/VEkZolOXg09oVpKjANQhOnPk4NOiKKqKlRko1FFBR9rsbRPYVDAWA9/OnTsBAOAXf/EXnedPP/00nD59Gm644Qb4nd/5HZienoZt27bBu+++Cz/84Q/ha1/72jjImcC1DoQH3+D28+Dk+viRp2ABagMfvRON4yado5DAOfgi9EUpu2etOEzbeH0lCUWG5htiUrNob/sGwT6QGy+duCtKtQcfl3+kKNyzs9fTCTPa8KfeM7YAf9OMZcwtsJkde21wSvvgjfOGJr/8SKgNcBcSGOMY9zTMW1PW2N/nodVNcO6dNfC2oEV58KWE6PRCwETk4IsJF0iBIwyYHhhTiEp47lvilnMxbM+fG/P33w/FygrcfuAduGnrz+iVxU4BN/yLJr79CE3hjKGGcfeEXWXfrQeE22TW1LBifK6IoSIVh5FWrj9VH7EefEBu1LUXgjHoZmqEB58NMfdKJIEyF1QV8lzq9RrjHgDAzFNPhQXZwPPptSXUZotb/wpaJKjPb7Kvs4ToFAjLGGo2QEaUDkTrwefs7cJExvyJyK8w+yv1TBWik/Hgw+eFMX6UAo1CC0fcYIFU5PDza+VqAYcPW2K1ckDJEJ1U1WH9XbumoKoALl4EeM97KrjpJoUwgEJ0AgB0rix5a3HU78KZGSM0AONVySGPDtHpu4JIIWRzXvS8eLGAXfvmk9BN7dgBUzt3wuoXvgC9T36SLhRYVNxSig7viutfvgx3PrgNvvp2Ab3OdIMjtpvK0m83nIPPek7mTo71ReThkUfmYGGhA2Vl4PDhLnzsY9bcCxySGh6gQL9FIBBqPOskwEYhU21QDj4mB3YN7JE9jhCdmc7rNkZFTVlv5JV8TdLnUeccyB5bnr1RUJBQcp7Wgy9HDj7NM+olPjNi5XCZrsSLk7hd4pI1l5olBrAHX6v8vxQkevCF0ipEh+gMAXXwUUha5uDT6KOcKC2rq4MIAiG8BGlSeSrXpwQbodOYgA5aOj/TUHvofehDH3Kev/jiiwAA8Lu/+7vwq7/6q/DLv/zL8Id/+IcAAPDyyy+Pg5QJXA9gDNjsTlm4m52Xfy03p6UAW2EQLUxJYXdiBBllm4WJu61anj0LxdIS/TJwSOXmCWykxvA5CgEYIXf4d8QU8QrcshzlczGmoPQOHu66rv0Xvw/V1zxv2grcEI3Nwee9yBGiMyMHIDGuwdBSCroK6h3SnNoMUOpNaZ+5R0qQ1h58ytvySIk3CF9C72EF8Z/bZgx9Q0yEcGJ/+y2Xj4oefFwOPs+Dj1EwjDz4nKLIiCvPq8YgqoCNYIZVAo9AWHpqAePWb2EY8YpSG+7wMRV+2akmZVwX9j97TRVeci0gPfhIJbkZnSnjApKnwOuZUYKzhjtUPDVEJ55vGkUZflYIY5zUq0hbJRr4QmekBpi9NfboZJaB+w7PUyV/6uDGfEbgmyWFmBQOuYaBkY6ZCJjHtM+vkr7kN7tyEe7d/R34+TcfgE5/VfYA5Qiv6Y/wygmgEsuQhpBhv9vdf/lyLQhpJ4t7QcsLSdZG288814RNbSDSa6vou2EcjZFxtPbgG3b+1asAu3dPwfGzvoEvCKurMLt9O3TOnoW5hx9WNkwAxX8PoY1ifPaJJ6CzvATd3lWYXbnUPK+qOCV3WZp26yXS2KvGO/zdXCY0Bi5eVOoRuHNUIwtF0tgaPB6F5jvaLPkUOiggL9KlhgkA5Z6oBcXBPK6mRs91bcZ6aOILjVwlDwfhUcgWJs7tXq8gx/zSpQIOHerA0pIffjEHkFE1EkHu1/S62kKuLouulqwexXtvTmUeMR884NpDdMXk4AvpUzn5XsWYR/YP5fAQ6hJnLAUdkS+iCBfjFWHkN0JvMYH2MBYPvkuXLsGWLVtg2kpi1e/3Yd++fVCWJXzpS19qnt9zzz1QliWcOHFiHKRM4HoAtPFRSe6DDDU6UFIZcLJN8A9ZbTgFAJAPBuF7NGdkDFBnWHfvXph7+GEw3S4s/Zf/AsbMyZUknLHaKw6a/oqNpT36n2WKhoVqJdH0NMDy8uD92lrhhO0MNhJ+zBdolMCR9RDEGvi8HHyBMbND03J1okIuBdqz5T0p/x3J0MSEVsMcMtN5ZPsJ87z2MGuMw+uUgw/vrWXVBzCGD5mTo02rLBniNaYtT7NXA2JbGebZi23flJO9VFl6BMgpWHKgVd7Y5di9ICYHH3G21np2NkRn6l6JC6G2y/4aVCVmbWnXpqI+RAtGIUTxDDZWTztOCVcsJa3AnpfeGCnyQSR7mzNNaOHll6cBPjYNP/dRHrcIxjShhMnhSlBEUPwaSxjGn0PxwcydGCMwd0ylevO4PLcBZ+biM96vTRVr6NS0p/OcdlvvdGgPvp878AO49eIRAAD46LHn4NjWzwRxC62ybyjvfbcqt+aUbvgpsgKq6/Yzf76pvN6Vi7e+KKep63kOBJvRbU58qOjBRqI1iNUXkGrjUEwahQbH6ir9Aq+rUP8y59VgTNG4Rmy05enT5JysI4NpYRBCv12Izg1TLiqZvaL5a7ynscZNLQnJCIwhz21NO1q+gISgBx9jzFGH6IygK6VTiTr+I+U+oNlXiQZMZG4sAVUW8AyOEh8N/lmI95Gan9u3rwurqwVculTCXSZ/Dr4QpHp4jyVEpwIoGRqrL5LwRuSUi/mWzjvvwOzDD7PhJYNI+64zRYyBj0QbkPHUG19bDz4ByMteq6s0J0rgjRFRJjn4rh8YiwefMQZWkHX54MGDsLa2BnfeeSfMz49uvBVFAfPz87DKMbwT+FcPWBHvb3b2zXoax3gYnNH/pcLTbsvCMeju2QPQ68GhQx149dUpuHoVxN03OrSohhEhwhzVreHn9e3SoteD2e3bfUZausUFSHERxYWLSJs/dYhOW8jWKC7rPsA5wLDCv9sdvZe2qJAiK1Y40oz7gF69B5/NKIeU+s3vCA8+slHqdwugv8fnalU5+GLmnxOi06bBVwylhOjESqqYEJ0UtNnvyqpHG/hYJXT9X4okITPXhfPCxy958Jki7MHXKMRqw2o9VlZhzd7uG3HCe9B6QWjvqSpGgGjKpkmI2EBI3VhMBlZRDtCp/Ng/zTlhiD1NslQJwh/+nvq3c6mCUuYw66gNeLjwLVesLLLmeFm638gKyAiHKjcjAVUF8OorU1r9qfcseDYmnDf9Ht8/Qfw5FIYMzTE6AqwI48ZHq+yn+CWJMC2Po1U4lUCvS0yLvXy5HHy3XTjc/P/+hTfVhpOCWKtiXeJwDPJLoJuy3LgV1MajoW3wsImI0uAjDBUsLiVP1Y25Shx7c4DyKiP6oi5DGl2o/ZGBYnjjT8IXXF7aPSO0qJjzqSgIg2XMPkWdWzDoVikfOZWDr/QPQL+eEKKTIC0LhM4U9isVc76ZGzHye8YcfAsLJezd2x3mObPwVWaQboKQ9UPQSozThOik9rbEDsjOZ4f29YxtsnjUZ27CxWeq75kISKMHCmJsfGifxHMTYLD11/vL8vLA6Ns2RKcHzN7GFFWDq+8JR3XR4KZy1GMc1AUdzBfGRJNqAMdQzaTHmf/Hf4RyaQnKCxfkggyhoRCdoTURrUtR5uBrEeqm+R2izY5OIUd5cheN1GW+XO5j2wi9xQTaw1g8+G677TZ499134ciRI3DnnXcCwCgE59133+2UraoKlpeX4eabbx4HKRO4HkCx8YUYam0YgkiyGggpQOaXF+ATP/4OzJ3twZmTS/Dgjl8BAIALF0r4jwm3ckc5+BJujxoD5J3rwBnWOXkS4LYA8ghBORnssGmSUUFQAlMx4V0BefB3enr0ECeFpiBVsc8ZSoMHfqAA9U0APC8Sq4RXrankMCvU7SwUJo8pS+a0wQ8k5QnuOCYHXyp4xgL8IOKadJscfJTxsax6aiWwDSmOmhoPPilEJ6vsgtLdGxjkTS5Oy4OPvE1sDKuZNgT/zxYcM2jlDqbbSEj14Bv9M9w3lYYRjaHAEfJQ291qDTjRxxiQLyCghjgBW1pzLgrewNcaBOHfE4KF2GpY+Nf0P4BuW5fWdJOqx2gAACAASURBVL2kYrtjFA7dP49T9i0A38A3Qka0n0Ozx8xBjCq09tzzkDnjCZ4szMko5jEATO3cCZ1jxzyPJNt715ubCpYVh47E72wanctdhTIHXyhEpwAU70jRFgMULQUYf31k8ODz2mjh6aWlIeTBZ8pytGfFevBRymnJwIcvUxXu31B7noEvUz44suEILbGt7A0q52uoKnbBcLxEzF3ssmQMqlqI9ubkQZSVtYce+Pt/09cCD6SiOZMlY3kZ4Nvfnoder4CfvuS+M5WBfj9fiE71WAQK5s7BJ7abgktRJwZtirxA58byT/LYEJ0DLAn9JOxV5ExG5dfWmItn6EGbZVEuLACs3RoihX2J93j/k3nngtxyFQ0FSS+mK8XAF+PBNw4oDJMDEtFF6YhscT1ItiBH1c+4y5yYXg5vqN1geYrHEQ7ikByR0/FinafFBCJgLAa+e+65B9599134m7/5G/iDP/gDuHDhAjz66KMAAHDvvfc6ZY8dOwb9fh9uvdXfhCcwAQDwdid849TOwccxA2qBJwIchR/KwYfxf/zIU82pcOn/+zHARwcGvh07puE/3srTllV4HLUABcpbhNutf9uCdy3YeoX4n+7vHIoxcG9VN146Ci6QGi9fgTv4Xcu8dhQBrQcfrfyV6UtVAodz8NFrg0Uf4AywR4proAmHuQtByGCJec6ioOuoPPhEz1m3EWMpQdz8bMR4E98f6pJQ+EZR4URMLb2xzUfcMbEefEWQRq/VWnCi5g/+TSKWvfmA9OAj6uB8PKYCY0pXWC2G4wP8rUztt4+bGdbqM+xnrFJ3+Cz14qYX4jPnxzO4jBmE6PSegzVHIwx8bg6+0WMyR1tgfVC05oJBGxZCQjjnFJMaxYStPOaaiAJrT9QcF/YzO8ci2YcJE7aidBnKxR4VgprBwRn44nQENC8mKu9Evmn0zvPCMQbKM2dg9oknrIejRGuasMYSDC7v0BXxno29+kMGPgOFmKdzUIiZnET7EnjnOtMZKn5FqK+jxUdB7WOckYikQzlhgzn4Op2mE2JDdBamAq+LI3LwBRvA7WE5iKibnF81djNk6NbwpTPPPANTO3fCype+BGuf/7xXlkJdVQWsrRWDPhfyn9dAGfiIFJm8kjUmHmgkBOXsSGaP2vNU4UkbBHk8+N54Y3rkHUUgpLpU104LSwszn5z26QmX2hwAhOXJaITW73VXbCsbjD02uHfBfGV4aAL7EuXBZyugqblfgO7CDgVTL78Ms88+Cz+9dDMcsulQ6NSyew0SoBpORaGQB9+Al0r4ICIKyLpOee7bqdiuRFXSwJeybo3RncttPfgYtO57axw5Dz6FvkkCO0Tn7t1d2Lt3ClZW1mFBTCA7jMXA9xu/8Rvw3HPPwb59++CP//iPm+ef+MQn4J577nHKvvLKK827CUyABLRhcYop+x2qPnxH404Hy3gS8OCTFPhpTCidg69WRKuBMPTZv6tbboHOwoJQRj5JnDMvM7NtDJBCpkZBwQletfBZd4udc29tTdGvguI5ph53YwzD7Wd3w/sX3oQjt38eFm75qPc+OkQnnschrxtKMM+hAGXa43IKsrTYvzWhQ9mOoT34cPgLAOKGuIYtziHZJ1QviD1k4ME3pTZgxLbptBXhwUe9Z0N0Ej4gZH3kwTfYP0HwqKVvP3u4U/cBdaGYqvS+dfx4Ca++Og033ywrrFSGHGEfiM3BF8LvIvXfUSE6Jffloqr4FcrsKaTXRzNnbL7AgDeVMp2DoscyFoJRAVuhhi9+aLdFLgSklp+pKoXin8RPG8OaZwn9S+ad4PCMMUSnNM1DaPA5xNXV9hFWjNhQGAOdw4dVOJKWvRFy8GGewNqTOx2dck7twadQVKIKMoqIc4H04EvR1NbALGxXQeheFGBRUppEAch1btftdEYhwWJvDtSa4AYKxsA3KORddBheElPn4Gs8+Gp86xyik3nP7ekUmmJ5GaaHupfZZ5/1DHxc/sWqArj5H78DX3n1Kuz4xP8KF278kFsPtVuWRmUQd86ysgswDM9bEKEdU49PsR57mCjKjhge75F0Fl66VMD58yVs3VrB3JwhN66Ub8WR9VyETEj22PNAXw0A6HG0gc35vVly8ClwtGFnyXeoECWTtBmTWEReH2N6An2C59zaGsBU6OJ9VSUb27oHDgAAQGfpMmBVtzRfbF0dHgsp1KMdUYiUCRFkkwUJGcWnLbJdAJUhbazAtGfrkwZHv1/OdkwPqp8YGa95pr18lSFEJwdcDj4tYFlNWpf1z7U1gO3bZ3V6zwlsShhLDr6tW7fC17/+dfjZn/1ZmJ6ehptvvhm++tWvwh/90R855aqqgu3btwMAwKc+9alxkDKB6wG8HHw+NAw1txeN4XCyUZZAKNCYBx6NWk+iAA0pYHulUOeNuekmlx5PcRjBeOeS1OwDPoL7c8IW1Dn4GGUG5cF35gy/XTbzD+hxDvLPiTrCWy8egfdeOAif23sfWcY1iIUVN0HBz9DGdqdomxx8gTnBGSwxLaTyOVUCQ3PMDpWSKnwElXYB4SoKf2TZsurzAreAI6lNm7mmdjtDX1eg6juKpKJwFL+coqU2MDYGm6Hi11vHjYBHogHNjf2Ql+a4gDPYfPe7W+Dtt6fglVemRSN0rCCK90LOwBdzVHvKBCFnGZmDbz08+EgU7pi/+24J9313Dl4RctClgCfQR+zB/g1pRtGIGmnjwUcqUYD+jZ+FPPhSLky1MvBl8OBLFaExz0LyHNRZEqnQ9QxixL5MnWnGFGF7KLG31l7TFOCwzn5O3PC3qT34Au3779J4XDZkOr48JNKl0yhi/g3PmYKZN20Ah+j0usm2ABIuRiIpyvk9ygVLr3V1iM6rV50y6x6i037G8TWhHHxUNBZUlmu6c/IkTK8uwuf2fFfGAbXxFMvv8jcNDHxDyBiiU4I2Hnzy+NN8Y1UBvPVWF44f78CBA6O5n+PbnKgt2IBSGTL3WaQdIR6Y+RTCfeEcwOOPz8Dp03E3gbizT2xMhZB/1EbmUoEyH2VsiE4s76hB6gCiQcxD9Pv49oqPwlS685wC/mLx+PInxkCOEJ3c2Kn4wgB4nvQEwaurAFeujMkAxHWQInSoJNJF70Na5UbsxT8iOlaMJCDl4BPEWP89XoND/dbaWqEy7iVHK5jA2GEsHnwAAHfddRd8/etfF8sURQF/+qd/CgAAc3Nz4yJlAtc6IOYQh+h0bsxrwspAngPdEZRRiM4oL56IvBpFYcCYgvS8sRoPQuO+XvAGPgDwtH8zS+cB4HaWvtDBkgUsRUXtwUfn26JpM2ZgxHDLuuNW98+WLaMPeOaZWZiZAfjUp6QrkiLJY6wQh4Y9lJESKJTQmMqh5mFuMQn8sHDhcLxskzHKE3teoMZ8D77AWieE26DcGTH+bXPwYahz8HloCQVhSptc2SjP4yE49DtjRoXoJIgg5rMxhR+GFfjwJFr+H5E4FtDqMzglO/WM89TSEtOsxRaeT955agt5zh5eQKciYlANF4mpCM8gpTTonPfUmmMum9vz4+DBLizODs6Sf/fvrvpIEsHrSoUQTEVCMEavIGqTg8/eE2PXRM1rsfUSzhucg09ko8Zg4EsN0Sl5tDceRlSOHkWnO/Md735CTke7PDWfdOPNK/bwpaIK5ZsJhvQqCn2YTUZRKVRwyypDdHJtu2uzaJeDj/oWdLZh2aXeu1hjUaKmtN9HZbsjVQStmBXOIIK/ksJ8egYn4bIGBX6qAr9uEJ2WiYqgi1P2UkD1D0ZGNV1VIx6806c8CNxKKTn4qtI19vqh26PQsYD3NxVPRD0f/qby9XL5Onu9UUSY5WU+zUTKtzpDS9Da6/nPNUOUkttNrOy89o09AADPPTsFb9w1DRdPTMMffEAfrnGkAhgTw03gzT4vE/cCrQxgA7W7htLaqNdLja++PDlsDHuaknRXLXLwpQ4IsY5DRjTMqwzOzRDh6RGhuDL1v5gvTOrDwABdulTA3/3dFuj3W8iIEnDfjkOHMh58HJokXZwGyRg9+JIFsCFEkYbl9glcszAWDz4tFEUB8/PzMD8/7+QEm8AEbGCTWlsQ4sVZwacFV+Yc8F6Izoh2RA8+9O2B0CsacJQGlhKcPG8QbXNLCyCByBTm4oAdmtL2jcIMFFReeKIRdw0AAF/84irccsuovVdfnQIKRp82Ykijbk0xc7xtl9mMl0Y4ivXgA6QMIuuMyYMPAK0Hq67GwKcOHeplqWba59oJCQcqpUzcPFffqqKUY6ZaPw8+CyoupKYoTI/eOWu5QCE6ie/EXhy21wg28NWVtTofekwNpPvq6EArd8SMVepNU28PUwo05GPlnmKMnIOPVDZauLSXE7Q5+Ox8sY1cOnzWKocdotXrM5yIHiv0DEC9dsrS3cO1CiL+gnR4csV68NkgeV8Zo2vfq9fH/UMrHEeNCL9VDdLz2X8s7xeuksk9j/h1K3wbh5sKQa/YGDTFWO9qzoOPMlgOodNR8jljC9FJthYsQY1zQRk52xj4iDlWgBF5vlOnSnjppWnYv5+5C4x5HeVa8KJ/dVt4bVF7m4SDQFZevAhzjz0Kt1w8MizCj9koROewbg65RrvX4GfOe/eiqyd/2GVDue0IfglgMIVi2HnSwCfwCgCuB1+r8P4IYuaQWIHl7xWykIQukw7MHlrKg48K0amRQVpN80CITu7crs/lAuLSMWrGut8fROXRfBe1r6WyAbGeOjVQUQZipi0HXPngnJA6gOKFkRG91yuCRsSq38LAxwB1OV47lm1l3GhcViFurMPGx4R2gbgEgvbixx4bhG8ci3GPaK+hS3FjjAulGrrIoV5PJF+I99QAEJMuas4IZ2NIXJb0T7XuQztPcrA/ExgPbIiB75133oHHHnsMHn74YTh27NhGkDCBawkQc+htSKT3lvgoO4SEfjE8W5DztfDUxiOl8iPclp/Y2ymKXs4unUNlZAbNEZQTlQEeOCE6+S2MVmra7yvvcC8apefg2U03GfiN3xjd2KXCm6QCK6cn1OdAzFlHQGwOvoL6hhw5ikaVnV82zynlviNJ0HDxnNbZ8eCzHysEkBDjFqlsxdDGg48qWxomRCchtKW2ScHFy124ipyaSAUk1xZe7wEPPnuu2yE6Kfwhg/tmZoY1QgsZ4x+H2Ixur+4z2VtYBZgI1oOPCdFZzwVC8atVIkpCM1vOmjf1jf1Rv47nLAFQeGjYZRUKAFrZ247+1D2K8pSw3mbx4GOQe+1L5SQQvUgTUbM5+Cg+qN7LhYnsViM80ST+yoz2Dq0Hjv28BD53F44agMN26wx8cdZ1d/5FhOgEHNKcBnr4A2EWJSKV7wcXFZgwxGBgdbWAfh/g9OnSDcXV8iD0wrPZIToTcvB5zRId2oTopDz4AGBq1y74/O7vBPkcbOBLkjnHwAjI55P7IN2DrxCHx8/BpzWIW7w7CtGZYbtt4JZL78DHjjwFc8vnRJ2C2JDyYgOLl0OdyYNPPJcNE6IzkGojlRa7slSfDVFszZ2kuw3MC2MAtm2bh299aws89dQMj5jDM0Y9E7uvKNvk8oTrGtVXwsvaaZU6a9ClmrU1v5z3yevkwcfvBYVfgKqTQKOKPA0v6/DDhYfbu5+s7ZZADr6zZ8dsPuAIxZcXiT2WEQ/TmiR4XbJgJN9CRUhieQ5KPhd4sDZnZr2uJx581z6MJUTnjh074IEHHoC7774bfu/3fs9590//9E9w3333QTWcPUVRwG//9m/D1772tXGQMoHrAdDuVIfo1Ci2bWaJyheWbGRCbZVISPYZZr4dX8HIhxwty1F4G+rcCUFcriX/pvfs0nmAeasExxgWPqORC2rnCWMsxW3B95lNlv1XVNRYc6vblRSKGOeoQAxThQ2lISUDSwRaFDbPkeTBFzLwKW6tFvVgabjgwAdrPRJVOfi0EiOK++V5dyGIvQ1LGo9aLhxt9YLwKitNHyQPFm7JS21SIUwwHH5nGl765hb4vyo0tgLiguhHAFB58NlhlQc0Wm1h4U2gASuZNbSOCxLk9CCyWA8+vBeOFKs6QlghawgXLhRwabGAqcUCbrjBn1eUga953yc+Rojn4p7NbihAMMhLQrj5WFV+xJvWApTVkIcrIGwaQxuqtu75MUyvXIbp1X8Pq9M3uOWFyxYcXSIBg5M8WhitPfjYcgkdi2/HGyM0gHNnZAzRiffiEGp8HlI8R+iWvIZE0qgp7cuhyzYKYHPvYKWgdU+1LI2KzykDCmeurRB4njHKyU09Ji+oSfSEaKXDGqi9eRx9H2amtTRQuADcEJ3Redf8fqLWJLcemnaty5NSuHBs4IuJKMMWqPnj2PnG8OPkmrfLqjz4/D6wQ3RqoCwNFCg/fYg/ruyLWb1eK2WlQ8vaCnx+17cBAGDr+bdh3x3/u4xU3dCwz0keSGcMvHChgENnp+D4+zreu1hwLkHitioD/T5AJ6FT23VRSK6jnzf9Z8zwrIvkIZl2T58u4dSpQV+//vo0/Mqv8PmsODxt5mXSHE64iKZpj+LtAMLrNBjtBz8yvgdfCMZh4KMu24f0OqFyAD7vlXQcRwIeO+pYxnyhGgJRQFZWxhx1j+tAi65BEb+cK9LhC2boskuIDOGyM9soV0Z6H7spcPObeI71YaKuyfBybAQZE9gEMBYT/AsvvABvv/023HHHHc7zw4cPw7Zt26CqKrj11lth69atYIyBbdu2wZtvvjkOUiZwPQBWqCCBQbKkjHPzcRUg+lt9HkRcTXOVF/EHbGEqdKOnVrQx+zx66IXojGFicklqtgefJIgH0BSm8pmjIU0cQ5SBZBWkJL2m5mCsB593uBNjxs17UahKjEeHmRX3ewbMf1NGUngTuNSDgq7A4Rx8HjBKFxaI/gp5n2LyFCjZtnHZ5qZl9HrV70ekEFqUsLhYwoULmAH3GU5SueYgRTn4iO8s8TkCo/nsXtAw0K1W4cbFd8FUtHDofU+EIjcnJCthhEKpSbQ9D8AMOfhWVwH27JmCY++U8Ogj0ySeTtUjjAC6EJ3SnHdf8R3NCU+rq3U/on7JAJ7Q2u+L3lk2HfUe9oGze+B9u38Mtxx8A+4+/DjC79duE2K0AJMcuRnnO/bKJSwykn3j8OTwUFcK1SHFnARc/1J9VFUAFy8W7M1nMqSlUiksFhOUcNyFAMxzYGVWiCk1UHiXO/w2CE2ZBhSKYApYBTeOUkAUbMoEGrIvxjXPDHHxMebblRMW70/9fuGGGA548ImkVMT7iBx8NmjCceMwA/EhWwnktdyh6U+KEUKPg14bLUJ0xnjwFYUuhKlty686cojOVB7qxsV3m/+3XDnr9leMBx+aH/WY8WHZZdTGALz9dhdOvttN5rVskHLwGWOGa08vX8SU4WAwjgFvcVIO0nnwkficf1yICfcpNyA+aocajx3hZZnM7yeANzethrxLkMxY2o/X1ijjFDJEZDDwafqI49lrSLnE0fY9gO5SpHOONkc3uozI1hDwBjz4ckazItvn+D/P8OiXkUQ6CciyFBPLHY7DV0ePduCZZ2bg8mVlqJfhb5ZWoT0NpITonHjwXfswFgPf/v37AQDg05/+tPP8iSeeAACAL3zhC/AXf/EX8Gd/9mfwa7/2awAA8Nhjj42DlAlcD2BwmBtJGen/NmZYJzM3hBUgzjvEjIkHdUIOvvoWUrTSxejpMsanbWplyS2DvjNGQZXc99Z8qJWYkqGPa27gCeAymDhEJ0BAUEZg574I1XOeYeY9pGSg2iYUDVjoD+II3MqTFBCiUKXlFgIfrFUqs4yatiIu6xj43NCO4X6VbwymGHNDoLZdErQVXIjOIXBzOW45E2Fkhga5uj8boV9CbAuZ9lrGvUowz1SoLi4H3y++/lfwpTf+X7jjyPPsGaMBj4alpVZnUHfXLph96CEoT5wQ2qRu4oc3g6gQncI+wO4LEfOrJuL8+dHG/M5hn301BqDTX/XHtr5lT32MFM+lqthX3PdQ32t78NUCaV4DH3rQ76uv7dbFfvr0jgbXB87u9fBjbO1CdCo9qAiQcvABhHmt8sQJ7yDB9h5O4Th66eKMBuZcjeGf3LOdV4QFvXlgoGDevXsKdu+eIo9Aki/ACkhCSWYMpVygCcW8ijYMfR95MWpz8K0HeOdrxJ6H86hJ9YPvANh5WiI+VcOPtFXtSR588aGuiHlIGvgKsrwU6p2CMkOITm3/BRW8+Nwd/g4ZrNJDdMYNjzZEp5uDz8pzToToTIZISwFXmgrDO/hjj0XhlJUMdwPeoFDJsBoQj6PKkMYtXYjOhNCPVkF56yrIS4313Im9ECSuMJNgNFJ9qBJpoC9iaGip9x/hyKEbs8sT/EyJzkLKE9jjf6roZRsGRT9yagCpS2I9+FTdGyjEjZ37yM3lqx5WvFGst8WHI1QRopM18FFrL2WghGfnzpVw9GgH9r3Vgccfn9XjVQxMXWRhoYRnnpqC3bvpIIxtRJTRWaBbeNnO5wlkh7EY+C5evAjdbhfe8573OM937BgoEH7zN38TyqEU9lu/9VsAAPDWW2+Ng5QJXA+AlSFYoINixHxKSocxkuV5Mnk0C4B2X4nmskw4qG06BCOQKldKsFH3duBY+j+Ug4/jzhAUpmLz+djMWlL88liIUXYzQCk+7TG15w4HnmIcc6pGYWynuIlMBj7JI1GadyQjrNeeut4wGUJ0BulouY+1madlVYk5+FLaVEVnHe6SnU6goI3X6mvnJmNROgqTgmDsqRCdlAdfUQyMRgAAHzn0LDNcOoOZXXf6+efhhr/8S5h78MFgXRLf4iIsb3sM3nn0EBTf+C5bTjMXJIWgRqFHfb1nIEwQaKSylBeOMYMQnewapBRZSg9673xAiiLp2DHGz8HX9ra+eM4GDHwD5dmgUm0QkbyaqG/ShOjUbLG6aWCv5dFNWdU+b8HcP/0TbNm2Dea+9z3nOZmDjwM8X7KG6JSLce9Gl4pGDxsDLHWWoGcLC4NJsLhYwMoKgT/EFyCQQnRqgMu96hGGfqoNfFDJ6y9uclp40w5ekhZqf0lKREW/b7wu8HqN+GaPaiUf2+u5D20PvugQnUoFXP1Ia9wl26yqxoOvwZcrRKey350+Z+pEe/AplN6DZ4V4sYPKwRd77leFxQCSrlZp56ZIhjE+Xo5OvI7EuRYh12XKwWcbTnzvjCFroAivmIOWBgKHAHeh0F6rMYblBpWwPqJAsT5y6QY42jVGWI6OEK+pCdHpyU+27IU9+BjC7CKDC29yG1VfF3Kba48HXtdF90Xm5iH+4iQHoRCdmhzbJN5rxMBHr82CfS1/P7MHa8Zh2D9nzowm7KFDQhY05ZlLwVtvdeHcuQJ++MM5L/UDXmcAgaAjTGEtLbn2vQnkh7EY+JaWlmB6etp5dv78eThz5gzceOONcNdddzXPb775ZpidnYULFy6Mg5QJXA+ANizPQ6rgN3MbNPnCIslqABtXPLTEpssX5t/hcDwS00XBiGFuOkDWZYROR0HZAoAMMrk44moU6iHm1uOouUEdcsyQ0hNg1OfdtWX46MGnYOrll4nvppVp3nuSnvhxpHBmuZVO0S/lqHLaFAT/TFyAlHMo7EiHaKcYVorOQIjOkLBYEMoDJ4RGTHggCj+tG9QBsS+FQnRy+rTUkHvNs+GHYIFOrUBDh4Rj/KdCm1lztzlTFIpdf48r1OvXLjfzwgsAANA9fFhugIHVYwvw5ptTcOpUB/bu7bJV2y69pBvB1j/U3jjQZ0Yov4YP7blOX2gAKKs1Yi8cIiXngbARMx585JpH68PlN4pRiM5hgZw5+Lz5p/Lgc/vUNpyRTaEXrUJ0GpvvUFwssiDkwSfFpuweOgQAAN2DB50PiMnBl+UsUwrVUlMx4bexsr+gXjR4G8aaJ6SqVP0wWOfC+Ao4uD20ROvK4cM1Br6igDIQojMZiAHUfC/JdlAefMTcXl0t4NChDpw6GZgEFRFmFfiLUiIPyp2TyrXheW90ZKOOjfaLX1yBr33tivOS3P8QcBdNOC9Jsq9WVvy8dyl8N6VxS9lX0Bkgjp1V1lPeEmU5UWAcHnx2Y/3CDdGZS2yUICVEpwZHjME/12eJITqr4X7sPdft5ZpnXGXtWWZDk47FGNGwzNJFNJp0fntrXimT6NDpQBmuNgl3EiJrP8EGPmIPxSE6ez3Xa5Pm+91LS1EQIcMFlzrxPUF5IAcoBpNKk2JPlWQDqeISCAdZ9mhmUyiQTBblwRegjVwGlX82k3sI4uGjU7NE8sJ16OtLl8IDLH2zb8jPJJ9OYMNBMC+nw/z8PCwuLsLVq1dhdnbgorpr1y4AALj77ru98kVRwNTUlPd8AhMA8BXxKsET/fTryAYZDWDmRaJDuo0sfU8onEwslFC5RhGBwTLGP8g8SjnGkOreXJKZTfNQiW97WImJ5i0YjBk3Toh5BYBPvPMU/PS512H22TUwN98MvY9/PERo81/wsGTmS0yXhQRp3c12ot2qGilgPEHNnz8S8xOEACPlJJCvFdNa0VhQ3gfLOu9cGkJhtIL5LiiFErOPaUFbngrRWQZCdHLttRVwSW9crjDxzg/RiXPwuQNVCiE6Y5Tn6m8PKDkchAqwbwrWCc/pcJxpbaaG6Kw3fy4H39oaDMIBXpmFL3+ghNtuiwvhVUNj6EFnWKffoz2OuI9RJmzwBHpWSUZf5mhCdMb0qwCil1S/r5bsa8/ugVeT9SKwj1Ehlqh6FLAhxoGewy6vFcjBJyafs/CsrICZnwcAgIry4OO+A/OjKQPJeAH63zMwYIUucVBhwZt3Q0NRXd4YoiECr4OfmhviGcXPTY3evABBsYfWu73Uy1J345/MKUi1EXnwevy60lOGnEJGF6Lz+PEBb7Z2pgsfPFXC+9+vm/8D+vBFBACCy1fhkp7jx9iDzwnRGVBkq8KWJubgA5D5yWIYntPBl8uDTwucltmCJA8+W4ZieJXYHHxlaUZGmrpMwBBWucNLgAAAIABJREFUddwQnRhSxUjVetdUQPx6QzslS1O8/bCWBlK8/B0ZCfV1by1u3drQil9hLhc4uFGEGWPc/I3SvIudE3k8+BKVMQE5AM+d5nmLHHy5VC822LI515/umV7LYIOHnucR+HRWvfZWBk/vQ+7v9IXbKP0L2m9DdVXzR0MAse/g8zIlCpV3SSZiA+B41ijgCB2eXdw6AXBJdWT51MvUmvM6+SasnhRXDhq0d/lyAbfd5hbyVV0RgzG6U6GCcewtE8gDY/Hgu+OOOwAA4KmnngIAAGNMk3/v537u55yyi4uLsLy8DLfccss4SJnA9QCIOaRDdPLVpYOgHfhho8JEIAyRwmYTTotTkClPCE1oMUpZ5hn8AsobVRikSHCEzwQuoq5bkiE6/f6p///gqR1Qj/nUa6+ROGNy8OmA/j4KXw4DX1A6wEpSSqiNCNFpDMCPfjQNDz00C+fOhcdS5KFiuCOuPMchW51XOeEbZWWFhgwN0yl9GkVDm/B/pekDdcs3BKk6quZZbaxHQ0AZnSnlmnOTkfDg8/rdM/AZK0Sn/dxtl5pGG8HkYq8jDjS0yXlt4+YSq9wa/nPuXAlXrhRwZamA3bt1F7uoSwNNfhb0rlOt8R9NaYmEHHz4tmjzXHOsWRWqCtbVgy8cotPPeeMbReXfreZ8QMklQRnquIBitoZiGGoPwFeeGSPgGUeITlYRrPsckQ0yrtFL/DbrlatMiNv07NxTmmPXq1/w/Ax+XjlhyAXDoAVBL9C6LVUpGTTfy3qwYB5S6nMw8Oabwn5qiEgoRh8KVfJajgV8OSAUohODQwv6LmMAoM8bCT2DE8Hr2+WdsoSBL6kPcjER+Ayo5btAmDnswbd7Vwf+4R/mm0gAFIkAtYFPvyqkdcxBv7S8OSNz8J05U8KDD87Bj388HS5sAXXRTb3/C4pa7ttp/lcv60kgjU9/OOwpoYRpOpRzIfAR3N6WmoNPbE4YL3WdFBxtQdlAbK5EY8JGdxqHLQejAsxY2o97vZGxr65iDDhym6lahOgUIDQ/vOhIggwKEL6AFU0DUYjjDTUhOlN0UcuX+0249qiKcUXVSNbWAA4d6kC1VsHx4yW89NI0HD/eGY8HnzIqHNuopqInf0m8HVFt+M/iYrzezJkfXhjnTPLpBDYcxuLB95WvfAX27NkD3/rWt2DHjh1w6dIlOHjwIExPT8OXv/xlp+zevXsBAOCDH/zgOEiZwPUAeCNM8ODTKn1SySrxrXKElhQgIN4wKMXT1ti6PKWdcFuJZrrjtDXO61wcsCXQs14/IVpGT8n3VIhOBzRX1iLAjygXYq79Z0FjsWZ+AIEDcUuu4o9QgHOaAQJOnizhpZdmAABgbs7Ar98qr3XsWeWG6AwoLTFdWg4GN2Qpj0gPPqJxaVqk5u0JNOk9+ODpnTDVX4F33v8L1i1pOkRnVfGiOydAtl3e1FrmcjGRc80hoHAUv9QYkOulVkyzIYt8hXVVEVOJ6YxYoU4uphPkU8cl3YOPrl9Ta+tvqVu8JPoah73eqz7gO2rGFNCp1qBkvAbIMc+Ug6+ZO7VyD50t+FtzevB53V9VwY2prl+fdbVHK6eczBIC2gJuzwg9c0OJEuEflR58YBn4yBx8Wp4xYSDxyEjCvYaMkBJs4KVZe/kKSGH0OW6fE3yfYmMxJjL/h0UE1yd43uNwVEGPayj0HnzREOCPI84FUukq33BK+yZnj/MN/2Id9Vnl/hY9+AIhOmkPPvSQ6Kf6URmiWXrvGPiGnuop+2KugxqdOTUE+VLrMDIG4LFHZ6EqO3Dy5Bx88pOXWXLiPfj8SAmhb6/sEJ2RHnwPPjgHi4slHDrUhQ99qA933tnCkK/c/6mIMRQPkEhFNEghOns9et2mhujUAucR6uAmZN5mbRmTNQcfftzvu1tQVBtyU6q6mkJtPPiSCAogcj34ED9AyQOegc/nK4wZyIFULnS7jOpet7B+tWMX05exue5y5eCjZBR8Xsbegz98qITLLxdQFNPwmc+swfy8ccYqJLe11QUAgNdB//N/zsHRo12o3irhyoK1WInGlEFZ2AIGCnc/V8j30aF/iUkYNd8aDz6f+Y8SUZjFoKUly1hPYCwwFg++r3zlK/DlL38ZqqqC/5+9dwu25DrLBP+Ve59z6iKXZEnWzTa2ZLWNbAsDxjSBwQhjJoZmItz91jHTxNhBTES7iYDggRfe/IqfmGA6AAPuoTsGdyPMxQbLtIUsGVuC0r2kknUvlVRylVUXqe519s5c85A7M9f67yv3PpJs9v9QdXbmuuXKlWv91+9/+OGH4bnnnoPpdAq/+qu/Cvv27cvKfvOb3wQAgA9+8IM7MZQ1/SAQ2p0q5KEbQ0gONkbhw9CqNyULolOsZwgAUgRf6WHQt8eMU50jsvnr44sxv5a9umU44pSSRhvOwCcoH0h3rECxENayZUQZKKxRYxU0SRtm0AGuuwMQnT4DMJB+tRxV7ryWwgScPz/M49mz9nGUeqem34KLPB5WUltKDr7phTPw40/8d/iRp/4SJvW2OkdsF45xjOEdN2fn+t/XnHwKPvDs38F7D90J7/jew8PYmO8/NDX77VsM7LICLvstGw1nEXzpWsQRfMxzclEcqUKhv2YIbjH6nl1y8hhL3uXrESS1KNKxyCPdXOLcR2lOPu988BCd9F3FCDBpKERnX2aJHHxZHSWaRXoHrcdyavhcUqGXPTfyOJ7P3VJ9H3G++O4l5aQ7go9ROJA+F3BNY74H4kyF2/YqZhP3ZNbWL637Uk9dx1i0xPayvTJBkFBeNc7lNiY6m+xdxqaX8i/+/WMY5HR+Ee3nOAQx+ZnUm0ycUOQ7pL0wczMJ7bkhOo2+teHi+1wEH+bfi8k5XwQhMs3B1zQq/0MUllyfBRCdUg5Cdm9JBj44rhjvHNFsBvDIwxtw9Gil88wSGfx4VfHjSfnW9DlYOU8YUtOEIn7Am4MvkxmrRHnLRPBpU5XKEC+8MMnuqe2UyAJiQ9y5x7fBdleA1qKRBklXz/jry6Dyegel1Y9RAE5M1s6oHHzCzaWD8J3fi7eui5yDLFnK7T1+Xu0IPrsTnGs+ZcOlSNMoyNod7URkUXa+OGEcyVmqF/c0WVwIn9eD6ovRkxT0+5e3b/Vln3lmSip2KSEkWsU7wkf8iy+24zjzKj3zMKXPX3J+9POHXihZh2MFbY08A0v+7Jzxz5zhFl9+jYosCrrLOoLvB4Z2JIIvhAC//uu/Dr/4i78ITz/9NOzevRtuvfVWuO6667Jy8/kc3va2t8Ev/dIvwU/8xE/sxFDW9INAWDAtjODrDsHR+MuOrjhPJHKB2VM54UcTPDT4oTSHlFgmNoIHrHAQkovKWBmyGO1RlJ084yE6A0Ty3npvTKtZQYuUzn+VIwSK4wAY4f3DDcl49yU5+DIi7lA83oOmrAhNw44uLcrmfUO/tQg+az2VrJTsfaCOYnIvBIAbD9wB4dTzAABwYddb4dRb3qG2jYfJKpSWXA9XfPPv4bb9B+Gla38MDr7nl+BDT/1lf++Hn/+fcPj6j4h1rRx8pQKk1EYJfJFcRlJ2hVxhwq3JrP9WmdpFfu0UROcKPvOeOIhOvv3lDEmlnqbD/opuMUnI+bUk492k74Iz0MYIUNUaRCfzMApEJ7u/deMgfYS8XHJ/ezuQ6zsawVfXEKtKicIFMqcEuk6J9l+aouFYpJAJr+hsOIXorLlvSYhqIGf1KiA6FQOf53G0HHwQVwHRyRg1HZGvXDHPHjidXYQ6U/pPYLL4djFqQJPl5fVBerUw1EoBNAljIa+9Th1cmdYwS5U1Siv6bclZAe8j3b7gkbPwN+XkGeoaGZmrCmJoowdDt2gmuYGm74Ph+7j9j6nJDwa1LY0Zt4sdV/q/Q1DXy4EDG/DgfRsAz01hY2Pe5qAtYSLS8UpjjYZxGEXwWUri9F43BSziAhPBl36vVWWfIxiiczxhy608IWN0A4SPV3hM16tdEURn50jE3kty8GV5WRt7n1mK73eckdx564XolNg2eT/Kn9VcZkw7hqha2lxyr3ME81Si78zSNXD3LEhfjjLnSsQPc/tSGsE3+Bcw+04G0cmzSsLRkI9P0GtxZ/IbIb+tqi3LwWQMRGfVDA4g/VGRLKyLF8v3imISPvh0bIvejKq8kXmorshRXXtYPuAK4nRGpi6WtmnNW8omdPVfe82O4NN+y3mifXznKr+JNa2WdsTA19Ett9wCt9xyi9z5dAq/8iu/spNDWNMPAmEhUisyRtMMLfLKY49twlVX1XDTTT6hIpNPCUQnHrOkLDKirrAXfcive724+3EyBixBVucVQR7OSDowV3USRArRyeUvoB5oehnJ64lljhwRfCkTygtdiqeawJxqfdqesiM1TUiBISn++uuFOfiMIhnVSJmXkqmAxt+kF5oPc8gogu+KV56F1xbB8NcdP0gNfGjOXJ9BxGvXUScpvOeJRwBgA95x7CE4eNP/mr2n+XRX/zfH8IYYVWOrrHBWFCiMk5kL2ioqOYJwH+m3jCA6uegGbChIhVQT7hX95oRIvqK9SXuN/d41sTzEpuNgUfaNNGIP319GQRQ6zT46bybNTDROFUN0Gjn4UgV8t4dzz9Tn34N0XuVuPZR7t6Ob3sbjYBBhYRiF/pjbRSTmEBZoevoUXH3qDJy44j2Z0pT9FjkjLjPgNIKvYSA6Rdiy1zmCT95vh7+tvHNiLjemcQ6i0zMwGbpZQdUQeMXp/AJsCxF8eN/GEJ2+CL7XyT3ZqUnkz87VRvAR3m1x3lXBp5hinalGakoZFM6WYe5u1HXGQGvNslGTSuSHFcGndZbyjFl7McKPfefP4YozR+Cxm/83eOXKf9XyOMz7u+uuXXDFYo6fe24CV13VQPsqnPsIw0xyZ1P3d/+q0gg+bNFgvkNJh9lF3mSRdgKleY0BACYTezNrwpBHshSiUytHfxtab8/GC7qC3WvwXiVpEJ1dDr7u3O/LOnhTib8/cqSCb31rC268sYaPfGRbqmw/M5MHN107K4PojNRY2D6b8t079rmdfKcA/J5Wsmw18shYpN3kQutcnswgJ09kBj7ZmB1BR15x883KRKivM+mz/45HGNEsp6AxegD2dlYGORm2v4ohOqkRLW/UiuBbybfA8YYx0vNb4WHx335SeFb2Aqg8PEsFvBML/yxBdLJ7UxD5kRJZZE3fX7QjEJ1rWtMqKSDmk80TtiCRiUfaIFzuvvu24J57tuCv/mo3vPpqueeCrfwaEvdmAlihooF4YifERxXgYcjCMFu1sD2VGRupDCCUwH1wsCYSs8oe0nhNLH5jIZm0JRn44sBMpcJsqVedBY/B1Q2NLgGNVnwpgObLQnTipvFUexJ9uyNLPJyeJJBnH14yPuSI3ioibQWAp08vWQqqSZMD5p/bdWVWFndvRfBxhD4lF+H1KkZBa4xvKmSiF5N5mDMQQdxaH67lwms+Jsr8eyM8PM/kJa9wP7qrRcXREJ3YkIWUkQEi7Hrte0Rzo51DWQQfwwfECDBptosgOv0Z2XNnGsnDmlMKZMnqV2XgUyL4LIjOdIxdsWqhgOnWMsljWADRGWOWrkocg2e9hvPn4T13/An8+BN/Djceudc857w5+NIIPpYNkx4QtzNm8yswEnoepwyic6isQUpm7UO+fwYGRhH32bUxhu2bzjBE52DswU5FGDLbzMEXCnLwFb5XE6LT6I60V+R5bxRwMKGZwnDMwSHxT+gyieALIfeII9p8tPeaEXx0fQ57Hp4Hnl9jH6Wm8MABIlxz8il426lnYGN+AX7sO3/OVMypW8NatJWLmI9LkgOzS64IPtpdXQ/LqAk0jIaP4Bsaal8xbTiD6Az5OhgrNpYs35IIPvqNa3NdwEOvCKIzhT7Ez9Xl4GuhXMv6kM7qL35xL7z44hTuuWcLjh+XYfZHqCYyyPxRkObCoOl+VNgO00bJe9LKSjKC1wGgVAbI9nxvJcjXlmvPSeas236kHHwdcSglS0MHjjyjS8p5yrpSJzg6tPbuMRCdWGcAACiCT6+/AvE2a6T7k02rYUTwlXyn/fwRiE5aMEaAxx+fwsMPb7R+SaWOf449RaNubzx9mqY64Nrx5iXsnnUN0fn9TzsawQcAcOzYMbjvvvvg+eefh9OnTwMAwL59++DGG2+En/qpn4Jrr712p4ewpu93QspZDKMYFSnYu2EeO7aIBIsBTpyo4IorbHexbExGDr7QSlGETAMfbqdvgwqhlhe31R+dq2Cfjis5yccTBxMz3PQzqF3xYW0h5hU36XCJ8sBIDA0Ll0cc+OJ9h3w0zEnij5ec9ISP9w7QEcFHlD4MsTn4HGMpzjuktdXkayPDew8VcJ9h2hxu2pNvsegzQ4ziW85/L7tdTxIPaU7ZEhvQvn1pLKXT63Vu4OZngLHhJzYyEJ1k3hNhoVMaDpFe/PePx9GNpVhALMEbEqjWnSyLmtOM0KOTwS+uSQa+W577GlxztoE982vg/L//9+oGxa5TIYKvahr5eQwlNx7B5MQJ2PXXfw31298OMX5UHF/aDqegST1eJeigYmIE4J7q2ufRAdDnbCJwYoSH8Ut8jz8+hdOn9bPZ+/yb+/cvNHATuPnw3fDku3+hHx7bhnODyiL4BIhOdkVKa6jETbqIj+KjCzBcdfo/bls8K1llV9tfjABvPX0Ybn7xm7Dv7FGAy9CoHAdWjMb+IbQxmV2ECnYPxZQIvnRZTibtHFSVrhA2YV6N8UlEEDecmPbcHIUYQWNvufLqcCVZAu0jLocpaf0454uL4IvTaf9NhrrORpHLOdTAx1JdA0wHFUc3x5Wyj5kG1SyCr+NBAPZcPEWKSltCVcXs++j/GKNlZs4SjD7Sv6pkPyM5+JwGvtQgGStbwOEgOi1qKpSPEdGSIgffDtOouHtwsj3k++GgkLblnqEh+/yIEeCJJ6Zw7lyAD31oBpubtExiuyXfcjOP/YO1576AruAdM6KjRyu4+moObcEJ0YmMjktH8AlUnINvlXqOsW05v4VSZ2KJinPHa0IuQOZU071L9qwxcvBZ0ZbaGKRbnH4xvT6pt2FSbwMAH1VOziS9+74nkzQeXymjpTLxrIWqoY4saUULolPiWYuIQU9hnfuYByoy8PGbsV4mRnjhhQnccUfLo87nAB8d6wmrjoPe6+diMb91HeDChQB79ujzLUHbSjzqGHZkTW8u2jED3/b2NnzhC1+Au+66CzjPk3vvvRf+7M/+DD7+8Y/Dpz71KdjkOJU1rQnAhW2sCSVavY7SA8vrKZYd8IaBTzrsSiE6AXLjE2EyTNznpi8rj3W47MotqIw5m8uRygDSfhrB12tAbA6GrBHl2awIPgmiM332IgMfJschi+9xES0pufSPlsSA7lcMM8wKc2MhOlF/OURnXnQVkXKD/Vzm3mOiDMZG9YbTyI34hsw6CVnv9fIzR7LfEwS/gaPPxkTwAehGLk74IRCZkme38yPIIz6q3PGDieCrGIVnmvOjhPDa5V5J1qamrXDO+6oUXRKNhZLEe2Ffn4H+iBFg8vLLUJ08Cc1VV5H7fbmFoK/l4Ev7lXKrcvMetBx8ALDxzDOw8cwzsHXTewDgh7qC5jmTEgfRmTorjCE1B1/T+Db89PtBBj4KheOL4Dv9WtCNewAqRCe5ht6ZpqAHABGik0SHJ27IzbwhcCZFBr6mcWmvT56s4KmnpvD+ywH2pmPpvw3a47IRfO39CN0MxJhUZl5Aeusjj/03tj9uYNJ6cKPFJmfndHYJQtzqV3h6rmoRfB4nN9wGP5iRWguG7zehAEEw8EGZzdjD/+Tdtx+g1wgptTmm2HwOABO0gNPvp1Cbz4oWyMAn7mslfCSTgy/ERnc0RLRrlzDnY9YckhP6aF3luwaA3AoklOOGkxpmWV4XETbwyTLR0FldbUAnyoT5HGI17pxcWvFobGiu9guUpZ4IvhdfnMBXv9oqlS9cCPCxj22TslpUaD0HgI22YBbBJ8FRC+PQrkmVzbLM/S7atjQHn7qII22rmEdm9xz/OnWJNLgQMoBa7ZSMZUwOypQ8e07oz8LIzlXnKNlkOfho2VKITjwsTw4+/PvGI/fCO489CE//7P8BOecmd21NX8n+od22Ivg446NFHERnKlO9URCdk8iMi9UHJ/qamN3wdplfZ1Lo/MM/DKlO7r57F3z0x209tdpZ4aSl5+rp04mBT9hrRTUe1jU14+T+Nb35aEcMfE3TwO/8zu/AgQMHAADgyiuvhPe///1w1UKBc+LECTh48CCcPHkS7rzzTnjllVfgt3/7tyGU7kJr+pdBaMOiG6cewScyMEn53MDnHlZPJAefUb4jTtGgbb59pEmB4ID7kyD92Fwp5iHE1JF+r+TUh9yzx4CmEsfC3E8LSQa+vg3HXtVFRkiUKboRA+GB6CSKCUNp5YLo5CJDY8yw9aV3KgomQA3FTHWXUUnzTDNp7PpTXOA4iE7Li9+zDr152FiKuVB0+dmXs9utJ6IwGGi95Mbl4CsbpuQU4ZmfVKHFDiDQCD6zf6kccqjgntOrj+zrroB79nrqLitILhvBp+0LPW0LuVsWxMOp1tkZlfYjrS32u3K+i72vvAidgY+FbVb2cE6fukqoIWJgLoDoHHLw6QKqd08yXmU/AAPlMek4fw7rnBNv485SA18dWQMfSyNd5GME+B//YzecO1fBobgL/q/0sZTF4NlvtQg+DMfWNLwDgtUfIWXMKR/hRS6KIcB8ugXT+SUIEGE6vwSz/l4awZc3mEZfds9ZVdrwgg3zag12xcR2w8gyViOaop44PXRrRuhD5VuWZALqOqTBEC0pEJ15szinUHI+h2rYHwQDtN+4S6MQQmLcSc+bhvnwpKnYtYthIsauM5Zf4f/O+FYUwcc5jXJDms0SOdkL0Zk4HlYVdb4ByNdmHRK1VF2PNmpY5XSdgtIA/o463iJTtK9OAZ6WvffewRF+//4tYuAjkbGoo7cdOQCXbx+As3uuySM9HQ5tvgwHwnN7IviUdREgFtr89chEfLl1RFbmgGlnVZ+wm5wdSLlwl21bk8uIgY+R5dIIc4zqkVZLHTMjwyOuIgefWpTTaQDAdH4JfvixvwaA/93drkaj5SqlDLd3j1Grc4a0114FePjbm/De987NCL6VfAvMc3kj+DSfTfJ9cPOHJo17VxsbYxemMDBubAvScvABtHn4rruORjxKw9Pej5qyiStf4NiwpteXdsTA941vfAMOHDgAGxsb8KlPfQp+4Rd+gRjvYoxw5513whe+8AV49NFH4a677oKPf/zjOzGcNX2/ExFMZWbLy3RRXc+wPouVtcB4t2NlX4ysVqUyGGuiZAv59dKDXMpbhNvqfxvRk+lzcopcNQffWEoYRaqWK2sH/+yeD8PcEJJy8CXKh+mOAyDntBKITsvogb/FbD0oQtXICD4CMcNF12oaCakzqwyRZlIHgFyhmE0PVGw/ateMMEQrFDBRqO4VJIJvUApw3ow9lIqwmXLPwilyLXLDpDHEjSFjggGgMZK1c8JCr4xSXljB8iYVB+a8TJnAFmPha0Y3J1YsZeD7Z4S8fkD3s78ZOJaUuPOuipEVWgIoeTlLc/AllBqMO4VGrkDtmqP7YBatt7i+yhx8hOqa5IkV2+l4ihVF8HkjEzMludYuNvAtvlup/upy8PHNuKP6mCLnzrXv5NzZAPGy5NGMvZWj7FtQXnVA97O9XTmrzO/euYCp4xjveBFDgPmkNfABAEzrSzBbzHaj5OBLh9HZiKylj3MKMoMm4/MQZxTPeHSjO9xWsXKu9FyJdK/cCYhOfHk+B4BUQRYCxMTAZ0F0ZuNNFcfVtHdgCk3Dt0HkSbltQjXde9w5vxe0tQWwjRV24mZmNMzMvyuCb6SBLzsqHRCdIeTONlIEX4b6Uk0AOtaMQV4YM01mvbEbr3B9FM+1Agd3HL2H1+bbjj2xWMJPQPWW4boHfdszReJzN415nnBtpc4cJTn41IjEOCIqzPGg7nduFJT2KW+fpf5H0j3tHGjrKONjGk0hOjVn3kynw7zH5X0j/fDwuNyes8eTX7k8kH++A6LN1vYZ2HvhBJzc966skGsMjofVHGABaF5ijzzHRfB9/X9uwtOXb8Ejj2zA+97HYGwL/Y8lTkYek4OvNDVe12pWh9Hnbm6ihsi7MjoacbDlfMdAp0/bKBEStO2yEJ1revPSEtpxme6++24AAPj0pz8Nn/jEJ9jIvBACfOITn4BPf/rTWZ01rYmQISRrkRopsyRFysxmuVJqDAPhgdpkhXiTs2bGLDyuB6JT49LZ80aaT6095+3RUUpM8plM+drPAT/27n9s7EyftxSisy+bPJNTv7roe/lDdrU5+BJCCvBszSFlW/6H0IZA3HrDlBrf8fyaAknJZJIPK5k8koMvqTYCorMUDhITF5Gb9rHr0uns9rSe4eIZ9RCdhaR+68RpLpgRQ1rDnAdothZRBF9ghPqAvv8QBthO4p2qD4cqHyxF54gJrk6ehN1f+hJs3XMPQKQ5pmS9k/3hi/Co4HR4YTtvr0k5+LK+rHOEEfRxpGm6/0wrHv6Vgw8OYw18UjmmiQxamDFWjqKkAbL+DIjOjC9aFKtiDWk+ydEGvrn9YBI/xBfO4X8txwAJopMo9h05+FgqgKAWu8cPr8C+yfZKuh6lV054xoQZkvQM5vvRIDqTb9KtYIkAs+nufrzZkZsYE7BjRkQON+n/bDchEHhmi5ZR3qrvvb8seG6v0sDHGEsyjRFqg2Er7P6cE8U6ASgRfClpOfg6Q3CMtI3B0cSW1VCzZFyZCBL1vZa0jwxwna1tlHmHOVNT5JDsu0/5VpyDL2nA/f0zRIyvgcpEFr9bxwFePTQNI9f4HUjU++l8LBPB1/EWGmT2ElTSloK8CgD5us3WiUMG8YxDPAZdlRker3v3sSyCz1rEeJxm29a+Xkijol6cOfhGfbeFEJ2uEjF7AAAgAElEQVQxMjy70XHK80njjjFkMnQL0ZmXW0UEnzb0It7U6H4yvwQ/89AfwE88/v/BTS99yzs8d6EYcwXEML/Y+Fi2N1UN1RFcOt9WPH++ggsX9PW7EnhH5rmqFeTg8/SD4ZI5WWBryx6Ht18A6gxmtZ2ei2fP2srGUohO7x61yrNuTaulHTHwHT58GKbTKfzcz/2cWfa2226DyWQChw8f3omhrOkHgLBSrjusWJis0s0mRoInPSYHX4UMHRxmM0emUQZV64VbRrAridDKDFiaB6rFxDGHo1R8ZYHcibK+JPcF14700zLw4RB+7tmx55TavQHRqdbt6pgGPsfHYWkYPe+/KAdfaljnJivvj4Po9BrIXAZldmJDpjHkFIr9PQGiM28WG5rscRTta0ZhDNGJi1cLT0tJgJOFM/+Q2PZR2W7/GQXRCUgLyD1nIizgfTUtjJXj3LBXCtEpzMvuv/ormD7/PGzu3w/TZ58lfTr1UcWFSuv372bxB87Bx1azIvgiNYBgQ3R6b1oJ3pRLRPA12KAiCkaLMobBusQjnSNNoRgIVpfcRr+HEcUlKutx8wffc3WqdpeCCh2iZuSvVzGbQXQWtMMZifURkeZCbPJmFAOf53HUsz3iSLBEKaQoR+iejSMpPRCd9B1rzzOb7uLvZRCdORx+uiwHA5++aYWmXomCjbRLzn4fSfmHOWc+9Zw1IleysXVG4WUi+LzXEc3ngBdwZuALKkQnNvANfzZVApuBJrX7yeXezRpT+Bw2NUKMrBwiTcV8ns/x9nYoW2dpWTSevl2mvez7TSxBMfLv3FTwOcZMc/C5NrPx+RizJguMrpxS3/tO2LkuriJWKlkaJP+eIkeQqO4VkKagVvsQ7ocsgq+gX22zZPp6XSP4vCTwl4XVFmQ4ezGVNIhm9pNU9iUAIBF84jmTouXUS+Q6VPZxptPC63TL4u5ffvblXt6+6vQLYn1PJ1L51GGFW/bevMQpTbATFVoj58+XReOOIubZvRF8+RrJnf9dYyMvlO6rGKIzIB7emnZW/+EY3ND+8N49ugCv0bMzNK58P1vT6047YuDb3t6Gzc1NmDow6qbTKWxtbcG2K3HHmv5FEmLIOFjL/j7Dh/UCjHBYYjxpv/dJGiHi8wol1wvrKenARGgW3B8XTYPbYjtgxmMxhipE58gTJD0Ye8GaixJWhJv2PvVA5yA6cZkY5QIZWxG0pPI+MuSUvG/r3RcYgLOmFM6AVTJza8IL0Wk8AxO8yY7F7MwiTaOU3KMRfIyyxliHwwXhwzbIiuDDVDXzwdDPRjU3rZK++PuUFxgXxUGYdVk7ILeXvosMojP3BOWiF/i8bnRefBF8wtDTtlPhYoSBrzp1qv97cugQiZSSmPLR3pSLxsanFkD1NYHb6oSp0xv4mD1pIin4R0ZfLRoe/uTOWqdCIeB5GU2K8F/X7hBybn1nuay6ckbkS0cu22K/R9mHEn609Ixiz0Upgg+3k0TwsXC3OxjBhxUN2tntQRy1cvBxZwRpxL6Vk1PxrhZDDzGXDHyJqKrB4XeGPYvXcedhW1bLgc9ioT1Wub1qiE4hgg8rAENyz6KxLgp1DeTdR2cOPnEtA4705CNMvY4KLHERfBCJxz8aFmki/d5nM5A3M4s4XltwTpQi+HA73qWPeccqziGcOZNfQxCdVSWMOZUZYy44YT7RO010rSsFSpStkgykDMwz5mWRPABoBJ/WZmbg22mITkMm4RTvMeb8uBf6G0A5uxeEz1TTKUk5J5UiYltaWW0OPWXHpAgujeBrGoZnYb4nfPZZe0uMAE2o2Dpp38tQO27FQZvZC13tMo6g2VmDGtOe48IFgK99bRc8+eTUFBUDnfZx6yshDNEZY653tSL4lmWbAIB1+vTm4HP6pPuHghE+ogei0yBmINf+41fgvS/8g3jGZGxCyvc4UDKk4XE63Rj9jyPNZ/W978Gur3wFpo8/7mtoTSunHTHwXXnllXD+/Hk4evSoWfbll1+G8+fPw5VXXrkTQ1nTDwIZeeCWwq6PNIJvFTn4OGGCFeJHCJtp1FLpYaXlWGMPQnSRUxAYPcq3xnIBiaKCE6ylHjHzozO1iTDMNSjm4MvLW57kQ31ByVJAVmSDR99rMvp4nBxEJ0cOAx/33WEhIhfEcA4sY649643jkJeF6DTII1wVfSqGAAkAUNW5B3c2ni66QeCMJUF/JXsRW47vDwDPXf7OGpTriTwnNmCk0aCFyhq8vM28PCvAMOHyiHDerWOVS0VnDKe0w+uFWT/cfLD9MXVbj+CAiwBAhAryzaQbCysgLgHRqUXY2BF8Ylc9aecHJ9j39wyIzrQim38p0FyilTeCj4G7FLr2Xcc5+PpzTng+bw6+7e2+LAvRKT1GifQsVcP7kRLBJz2n5uxCWlB8R+hvxeFLGgAuX6h8AGh37xSiM7tX+XLwVbK/V0YtHK1eZhyNa1SO4CtYl2AYCfD67yP4Cjro+ykj3CSJNAJQITopf51vft39LoIvRrkN7txP/9bOvTiv4dlnJ3DgwMZQpxCis64DH8GHOtzebhW9dBAJ/0kuYaMw3dsBAEXwBbRunN9/UqBq5vDRh/8Q9n7+87Bx4MBwHa1fn8NjgJg4h4eaKps9ZMqq2TzaPHhftudFQlauBKJT43Gcl1lyBu4DAJIHHZ14IvOlZnzpODgnXV8En3sgQlvF7DjT/srPE7JeXcMoHseY74k9042G0ry3GFqyayPGXIbm+LJy0cmWica8u/xMEvaKVI+kRERi2r9/Ex57bAOOvFjBSy/pmya376Rt0xx8anNtHWTgw46Ur3cEX/cOx+TgKznHet7XguiMETY30SXkWDjG4fyyQ0/Au4/cBze8coCpMLTc/lu2N8pzgvhCiAtdgt0mbWugPbffDhtPPgm777gDwunTfKE17SjtiIHv1ltvBQCAP/zDP1Qj87a3t+Hzn/98VmdNayJkGfiAKhG569KGi4WoMZ4LudCIBSZ5s+eU3JKiBKBlJKSEuR64wu5Q4AwjHNODGXPCpHBDlZQ7q+KA0wNZNagYc0Ei+IZJIfndOKxUY2wAAFoQc/6e+ZtuJSjYBhOXDkJR1Hf3s3XPMJdstJ7DwOcRErQcfBp52hYHFkIGyZp5QqN14YHolBQP2vBUpR6JZLRp2nTnMm1YiozSxsLopzJqmkDmADPrkmLNCx+VGTwcEXyk/8TbXIvg48gHt5i0OfZlJ8Q54a9C4F+2fr8PCBF8HGlwf1JdKQdfiJHkV+2J6yfdVJQxEgOfoCWQeJG+7mJePI5E2toTI+UBFjhwNrwQFyXUKVnG5uDzKBm7PdGn98sHWJnGYKEdbg0tovicjsHyDceD4HOT4/NKmkZHlNox4V84TdCCZIhO58AAr03FWx7VSiE6M58aAaITAIpz8AHQ/K+0hDlYvh7DL2v8fEcemcM1FP0Q5vnGZSA6R2pKW5jKtNOQMcsYohNTvzaQXNfl4AMAssF6zqH0PlfsyGGAY8cmrVGuG0thBB+GJ53NaN0LFwI88MAmPPTQJrz6apmTZJvnbjE2zgEuRjq/mZKz/d9ak+m8X37mCOy5+CqEGGH6ne/01ych72cysY1pTQzZB8w65TgI85vqOi/ZeEudOZwkf3d+47EF0ZlS5jxUeH5J1zIlsuIEyzXE6x+Gw6hI6dwMa91DI1Fg8z5HipfWvRgBXn4Z4LtHfB2MYk843kg5B5qG03HR9tJmu9QPbX25/TRan1sXy+bgU/WH6H7Z8UbrYd0JHolE+/dv9WM5csTwijD2bpKz1kHYwAeQ8/9vVAQfOy69KtFBjpE5OAMf5qnr2fIRfB1df/wxs1ru6KY7M5MyxrhiHOeDHM6dg40HHoDq+HEIiWJ94gj2WtPqycbQHEGf/OQn4Z577oHHH38cfuu3fgt++Zd/GT7wgQ/AlVdeCbPZDI4fPw6PPfYYfPWrX4WTJ0/C5uYmfPKTn9yJoazpB4GibFQAyBVveBMzlUgxMhCdvo0wZ15q9rpFYyA6007cSp6+P/key8yQi6iSAYWhKjccE3XuXIBvf3sT9u2L8JM/ud0+IxPBlwnYQruWgioVArlk8Vk1p3XJbYRiFENi0VABAPVAX4WBj+3XCSeoKrZdEJ3cABETlQhiw/Ms3tkqITqd7bgi+Jh1llIpPErJ+CTq8gJwiZ37/FA4esho16M3695ZJrwPJWh5QdiyIDohBCIoYuKMbNY3JJ0nxVHfy2oUQiDerVLk1HjFcHfG2BtH4H5FVL+PUmLaM6SJshx8ESrJ6M3lT1OFcGE87FZFz2SNPOWqSlkq3N7bUV27pXqSRzQC76igOMSk5NLHlijsQiDvXSWJJ+E6u3gRYPduaOacU4qgIFoBRCcRwpUIvq7pixcBvvOdDbjuuhquuy431pRAdKZ8XYhRRK6z3o1lhOlIzQ+COplN7Bx8WLGRrY3EwFc1c7j++ONwaWMvHXvkjV3L0zgehLssQXTq8ox8CzcVQvv+Jdh9V8QB5lvF58t75+D2opJ3TZNz+jM5hN7AFyOIEaZeqGGOjh9jLqaMjTDmlGgEHxDG4plnpv3Pgwc34Kd/WnCULpAD+zJzGhFXEn2W1Oz/yhSvybzjM0OKSMfw6lk+xqYGg5XjR2fJe6mjXgkPLspq/jn0fEtjaDxEp923b3goHNZZP3WEiFXVn7Eh5kxCXetOs7gzKc8VljWLc/CNK1JGiwZPnqzguecCwOW0g1Xwmvj770g38DGNMp3gs15yHsrKGzn4VgB+Yg7dM6/WnovnFe+F2nPs2rXQSzLyudaHZ2yeZ5swEXya0xalACYvZBEj37C8/+JmVQ3fdVEEH9+lfj1GwtPMLkXY7e/KGJj80no+Jqbvw+5OhOhEw1gmgm/XHXfA9NAhiLsQH7+Kj3ZNxbQjBr5rr70WfvM3fxN+93d/F44ePQp//Md/LJbd2tqC3/iN34Brr712J4ayph8Eak/9/qdkhBnK0p9aDj4M0bl8BB/HMNuCjdlJV0ewZY3Owde088syOcJ8chc05dRYuvPOLXj66RYO56qrarj55jr37CmARNzeDvDKK1WPnc0pMFjPV4aiUAC32SaVHw9twinbOgMf6dswTrggOo0cfNggpEG+ZrSiHHx4ve8YRGdKqCMcMUDeDWpjc/ssbD36MIRb3wXxiitMxQN3rUh4NIQCAIDJAqKT2zJ6w4noDcArs6w+myZfg27PbEVwlLywIwRoGE/Qq089DTcduQ9eftsHDbiPRDAjvDZ9frK8uX07PcdG5OAjfdZ0jaxyD+5e/9jUAlKuuci9MidEZ0pVbNjguxAjTILQ6RITFDHgRUSGk8W+wK5NZiweRyJtz9aUslZEZF8eP0M3TY4IPolckYk4skktjMzHVg6+hounAf6bvHixnUWhHZZG53HMedisilK/G/Y992zBgQObUFUR/uN/POtS6ov3k+9BUm6Z70eB6AxyMbXdLoKPoGKHNCcX6jfZBzsDQlVFeOfRB+B9h+5k+zGNxJ2CWC9lk+Msbovxzk3jcvBpFh4m8n2MIs7UOuoDx1FsJGG1YeAb4EXzMz+OyMEn5XLmHnESasAxBCE2bASfRGwEH9rMsKEmI/YADYuxyHJgt5/h/HtYzuuGkjZRVQwMeVJAMppOQpNJKh6IzqaBDKKzauS1oBE5Ewsj+MQ3Shn4trwyMM+YTVh3sPf5sgg+VzG1jHR2AIDbwairOCzlALGqICZoGhIvKY6j0eXu0hx8JDJbOTdNMs4EfO/559uPxjLkdOR1UDdJGSQbgcfuSwO1+25igBFk3Eynw6453/OJ36MiT+JOi+Ru0maOFFAC0bl3bwMXL07KoukXveBLmJfyPBOXg68kndAy89YR95a5NAHdHE0mKfpEGvThH1u/BlHv1BeZfsPz7cINwVyHehUtPY4l5ur9FKCrIJoeOtSO7eJFufM1vW60IxCdAAAf/vCH4XOf+xzcdtttsGfPHnJ/z5498PM///Pwuc99Dj784Q/v1DDW9P1OMVKjAj70Eg/v4k0p0gi+leTgQ8QxhKQRrh7nWRV4IVTCAc/H4TTILHq3tDOW51dWfARH3Bn3AAAee2yzr8cyg3gcTPtPPz2Fo0cn7H0s0GZtGRF8GCq1I1/OCSiaG+mZVxPBx3UoM+4hMEyGFSUjNN16DVFBKqXU87tY+VXAZIT8w8red0Q5+LIuAmXKbzp0F7zlnq/Dni99SVAkyAol11gZ5a3V1gDRybQXawAIxd9rCXPKCQwkZ1t3ndljulcZhHIxVDmk4mJOfvyJP4crTr8I73/2q7AxOz/cXwhBPUQnWef8c+DxWKR9I2oHmEKAGkN0Fnjdkea0iGFPm4r2Z1CsKuvcOnQFhUGey6MPbyfQYB1hZSHpW1NqZHv8SI0YyIZPtmxQ+sqUssxm6MDKDBDZCD5uH1slRGe3R3mUhgSi04zgc2oBARKITkbYLjDweY6iDDYp5gpI7dvorh04sLloJ8DBgxssvyI5vUkRfFyHbohO5x6mQnSmaziEPgcfaU6M4JNz8EnGvQg2RCcen3dftSDtpYa4yyGWR/CpkTgcRCfzEfZjVvhj9aJDccVu906ITim9QAyhh3KNkXbS7ZFaLlHLSXKj4hWMnMOf1Exd5+tie5vuH+5znFmf6XnB8i3Iesh95wr7y1IlaA8xVHZVMR2iOhHyDrEjmHduFLGlvbaqCL7+tyD32k2Q+mOpJAdfJps6JrUYRrgEohPfD+nZODC2Xt2MtIe17Y2ICmP3tPGGtBKeun9PzoXv3JJxCUe7w/PWNdOo8cGlUfMai5ae9bFZHqLTMx9j5CYip6K9NuOz2qvuPvfu9e0pMfL6NswblupKJlF3ArEIQ2SyuWQLGullSAVLP91uNGMWeW/ct22FPcZI1mFn4Cs9u9lbBBOHvmMpRQPZ36Iub1I5r/3Wvfub63nXBr43hHYkgq+ja6+9Fj7zmc/AZz7zGTh27BicXiRa3LdvXx+xV9c1HDx4EAAA3v/+9+/kcNb0/Ugco6AxI0QoNULFIwfRWT60gCA6NUE7S+puKRqwMIEUflgQs0jL+ecR4DWFhclILclVdc+dHWAMNpXl9XTiRMWWS5kYDaITAGSJFz2TZuDT5saG6OQYFU1xweThMQe1IMQtZcwjV9VqQ+jO891xEJ0dg2YaKTzrTSqThRM0/YO3EXyJt5hicK5OnYJw+jTEcIU9DuewxhbuIDohUoi2qmNehfUifedWt5jp1hRtHXGR1yH1OxCEyhgC44WXD3Brdi7vK1HuBXxdeI7hWiEzrCz2S9sBHti/ATfcUMPb3y6XIxF8wpkzVhFRYojK+xPqMwI3t2dw64h1aGl4iE4Odq7/bUVflXxownlmzXd312/g0+eEGwoAkEgNrQ9MMVAjiJo3MqGSwFx+L9EP307IF+vXDVGYnzkT4Ot/sRt+/LEpvO99c9hY+A11nqYsvK30uAXnm1RNiuDzNo2f3crVWKLgGfozKjk3BjcacQwwn271P7Mjt0KQfWm1Jmbncfq/RFXnHCCOZYTGr62oNyW0y09leQRfiWzERcFx5dS2Rs7TfJ5vahFAhejElI59aIbm3eWGbEXw4fIpTTnHkRhpdPdiPHg9NM2CV0ga5yL43MTIDWMi+PBZgveWNjIWO1qkyB4N4ccBACoW0YSSCdE5gixUhdGQ/oSPMb75JamkLZzPUXvGTI+wIojOrIyVxyotCkimb3NxtD+Tddby2Vw7Ql9EHwRw8QJVzptnlEN3sYzBT2t6MlnkLGXlJZ9jgfXuOFFPl6cZpx1jTjIY1si3H2P+drk1s6ytgEOPyHV58vFWKh5kEXzMWpTosstiVkftl5GdJIdzs60F0Qi+IOc1V4Y0nwP81/+6B159tYJ/828uwvveV+CBwDxXFbkcfO3NFC1LEuks5BDpXsOsQ1x2finfRHYqZQzHx+A8g+m+0OUIzudE9ljuUrSs8gzzyo5rWi3tqIEvpWuvvZaF4Tx//jx89rOfhRACfPGLX3y9hrOm7xdiDjgtBx9XvWUm5A2XQnSWM2oV4QTyn9IGZx0CUgQft/umkX1ye40b2pBj2koZnlU6buD8gwCgR/CNgT8V4F+5qFG5qeGHJNCm9SQFbnufYeBD1dfLhmR5JjuIXW+CEQWAeuhyZQDAnYMvEkUrVZD04xrt9uwgpHySQrj4HHw6M0iGOUYiS2hMBN8A0cnk4OsMJ0K+FEPvIdKXv7wbJhOAX/zFi0T4ERthjDVSX3m5dlJIPo+0BAf30V1THoa7hRUEUmRzaoSS6P5/3oBvNi1U3X/6T2dh926+bDNH3waTrwLAaUhS7rmWIlso5v0rAzGNUUzdDkoWC8Ft9kUh8rzFFsr7Tr9nZQgxhSqmfiWEV7EgOj3z2jpm8KMSBbyOlDnN5gp95zEGF0SnNP46T5nEUvctuNaWANEpEtPowYMbcOxoBadPV3D8eAXXX7/YE1YUwVdq4CN7dOF+0xKNKJWceCivMGiw6JYb2H7pOaF8z4J3sUYRoI/ga58jcZxJVlT6bWMetbMLmJClhRF8XiL7unEWz+cA+/dvwqFDvCjOwudqQ2oaABC8ykoj+MYSq+jNf1sRfDRPXK6wHIxJg2E84hx8qJM+6t+Z95ybZx7W24+K0j1WWmd7O6h1VOLk48D/3Zdh8D/xMYbbYw3mSaFsXtLrKJXAZGKvryYGBNHJRJMUEmtQQEy5iy9XrqffPmsAMciDcmIRgehU5ro0go8jXE3d672adbTgQmz6d+d3FuH3oAMHNuB78y14sNrM7o11YrOuldSXCnX8mXdfHvMsngjWXFbXvyfu+E0j+HLkjbx6FsFX0zPLPc+vs1GBM6SlvEpJDj6sN9Igm60IPgAehl8jdqwF89kVffjhDTh5st1ovvKV3fC+951xt5HpYBZ/sggsseN90XhpM6OJYxmkCD5/owqfFAKEMOT54/VyNMJRare9zPP1pN2FbDYm0GW5QmtaNe0YRGcpYWi2Na0JAGT3ZeCFeI8nWtIEQBQi+C5cMLnJjCc1lF+y54glDCPhe0knMSnpdPufzysrF4yYPhhFO6nH/TaojzpK1kTnq1+SA6MfGffO+rWVj61CEZpim8Rj1TkWfEFRArcMMH3eEox0cRxGDj4sCOfRpIqiQvyWygTh1Phe7N1egjGDBc4sB196K5+PVjGuC7Lc3kDGqghXJjkKTxpZYugEMTy9Vo4Ra3pfeGEKzz03hTvvbKM0sLIMR171gifLCFPGN2N4gckpis4GzpO/N/ArwvToFFzOCi+9OGwaTz0l+GCFwEbwjVY8qOdCWf3uz8Hwlu8LSK/WUm3sr0zdPlckrhcjVJKhw4Lo1Agb+MhA/QJU25y9gRFnnIz0jjSYu6EJus+0ynKaS9Rr4HMFXMQImjk1a1uB6GTHwHxbly6Fft5TJegQwWcNIhnOyI8sO1IwP9iPmfPI5730JaU+V7gEorO/ZfHSSg6+9Adehvk85NTl4CNjUiL4miyqv23ciuBLlY18h/L8lJAVHXfw4AZ8+9tbwBGF6LT3C/WdCetWGqOaM0ibH8ec1XWgC3hi589ri3IHSDviHMlDGpYuU6mf0pzLfU2NfNL66iDmc2MQFK0zzuEuPftEmNHumjMHX9+fYOCTeK+0MgvRyRIa70ogOnVjVzb+EohORBxEp6OW40o5qbkbEWX5sF0QjXabahmvPmWx4AY+0nBk4vqN9Mbp0wHOnm35gPPn5RQbrg6Uc5OjEvkAt6NBdHq33zHHmLYm2ldpnzX4rO9+eyE6uYGXQnRy17XXaSFjsHUY417qfIjbtdru6ywK9U4g7BjoeBX1xSh5LsYy/VJX/fTpJUwMDEQn72DT3kx1bWK0GjDPz8iuGKKT8FXMGlpFDr6UJHh2rlomS2JHrtjJm0I/DP+36gi+NUTnG0NvGgPfmtbEEgNdRA5T5fRKBR9px8IGvr0vPQ2X/f7vw94/+iOAhYc3R9mYmlzp5IW9sVATOWWSFKln5Y9o26MbrSSrc4IneTbmcEyJho6Pox/9zu3wY1/7Xdi4//78ANOSlFhzocKX5L/FiBD8E13HBr7JxGeY0pjBRohaxHA4WXte6dGSDtD9imOGuTUrvAt8GRstNKNSG92S/N4hSASNQ6Y5+AoFRRjGLSlIhWpqH1b5DKITMcC94cT5zgbyLbJnntmQBYYY83aEzliIzvT9h4WBLwPmx2uLMfAxEXweiBMX/5q+FyfDqyoNavx7CQMfQyWGKK0/bV/olTh2EhTSRAclwgkkk4DebQ/Rabw8p5ZGynWWNWFMvFfmkbYUoiTG5Ijgk9pnI/icED0eZNAOotK1NkNurK8yJxRm8BKsE6N0gu12H+QMI69rBJ8C0anoqVzEQnQqPBK/tzLkNIzTHHzyfjKbDAa+1JDTRWYBUAMvt5ZVA18IGaT+Kskbbd7RPffwxj2pLZO0KhE7Z+X/F/Wr8djKPq8UWRqik8ByCzyUzlMaY5wzUf8QSZtVrNn63WMRJbIin6okyIfc312XXLS8DtEpQMWmPJKg/MXn8GQijDk9ywhEp8+xRBneog90P9vf/Rsv5VXKvnnxfjG/TQlH8Gn6hUq3ozDj4B1NxN9EuNMjvjMxE0XwDUpqHy/KQXTS6MaBSiE6va8kRoDbb98N+/dvwvHjVXbdVRmSqHSG//LyC1p/XDSdVQkbrzhdEW4ij+CTz8rUwNfUdN25oVA5fo8ZF/ntkJ891M7RuFCybp13cyQZ+AD484zLS1xCnP6phB9ZxfwBXl9AnT3Sm/l+FvDthDyOhbqBLzB6k9IIPmsVWyhrleT4gMriVBmkKaZ8jCuO4Fsb+N4QWhv41vTmJkUhnhfjvedFJVBSABv4brzvSxCaBqqzZ2HrvvtcQ8PMl5dfyrzTLrtMHmdXvrM1GIKdWJ+DpRMgaVol/AhJxVI8um4OtBYgAfkAACAASURBVOviq3DNyaegggY2H300Y8A0iE6LtLnATBGJGnCOHcOfTae80oHzEErvZ7cWCk8iwCqhE8tAdGoRfKkArypQHBCdbTG/MWF4JkXZpHWmlcECXRbBlyg2kGwUodIZYU54KJXIHGRVnzYyRKdo4FMaLddPhf6buvLKJoNuyvdVHaIz9yJHSiKAbOGHOldspXj+Vj5HCf6jo84znwxQGLfK8Homkongk+r5IDr1d2uSorTTcvD1ZEJ0cu3XEGOAV1+r4P77N+HAgY3+3gSEsKElcvAF9N0TQs9nQXR6AwdFA5/laezooIXopMNrQkUMQ36ITqcSTtkz8j0VjdkIEWSNdZHfX3rFIZuDzzG4jkbk4OPG4T8KcqOnCUmpGPikbR7Po+vcGkbX/61F8OHr8+ku9mHS85fwbUmDg4FP37SqnYrgo5Ok3t/a0nmFYohO7Z0Ii4ucdV5+asQYsiJ4AasQnXl9DNffyn+pIxZVWnefKM79K30b7HTVjMyQQAim1zjqHwvJSLEpYKCYcumZU1XDBQ9EJ34XeF/2RPBVsWZTKNAIPsfagABxwhv0Swgf6+I6B8EQ5pRte57RK/cKJPELJW3hCD6Nr0vfhSc4pwQApbiB9BxaKDmG+R3q+SE6y27a/mX4+5a/wZSefXYKL7wwhbpW0DgM6uAavXmsVrn2pHbZXIgZ08bx68OZqzqQogg+qiMwhzqCtAg5/jfeI/Fei/ccajRzGJoWfyhxBiZEJ0UB8fDnlPfj+K63f+8ReNfL/wRVzZwpMG4tkkaSPydqBN9QXhTpnPoXggjG1KEQnYJTqUSaPgWCyNNz+yI28I0UUfr66wi+Hwx63XLwrWlNo4iL4BvwF/orPQm7ElYUp8Xw4ZnBAL72mjI4n9IhHzMdV0f1ddfB7MorAb6+X6ynQXa5cvBFmoOvU+QQZkbyYG85l+5HdhnTKiL4NucXhh/zeR6638GiZdA8MftfJlmAMyP4+GpEKTpFO+zGRh80wNbvSFW4C34ZVg4+l5HPeokxQrruK8bAV8JdEOYdR/AZUYlFMJ0l60/hkNPvokIQncTih4izzS8Lj8MzrPrEpBF8ZIyxAdGrUxlL6fR267XNMdfAhbPMXLTaJ9IHt9ayup2CYCJDPU0YQ4EYGZ0Nic5tqbebCy7WoAZFE0if/2hGfVFx7FCx56AWpWRGbDDQwVXTRvA98tAUtpB9UNqvNQ9QkxhDAnfbao7Mi1ZWUK7ijrg+vZDEaeRqr9wNgYlKcUbwzez5lCHLucIoKgHnYMPEPDcxKPRtdXUYIb8ggq/0aMUQftq7km6p+WizsTVA9lBlsfaXjPWDIWCxIoUbp9wZ9OdsG8V3NrseM15beBYYvhXT4NnUYqo6cXxeijHnFxRF3uamxisIkVNa1xpEJ4JuSqPgXG1z73ek0pldE26IzuTvdF3DsE5a/kKQuVLFWEUXgQrPPRPOD2IAMCA6UR9Nk3tbe5cdzj8LIK/97rFxBB9WREPkPkuOp0zOwwKITpbHSv5u00gmBr7GERLOkKXIzaD2vRF87BlC5U3NMCA1vQqNKolSU9rEEJ1o2yLEN6VEV5FvQt/rhh8505Ma0IvhGQ2eraPiCD7BiQjTa6/xjKLnVXc1h0/B16cn0tK63vav7IMN8z41XmLxQ4wigm4bzc96T+4zkRRdoDb0UZHzbPch2xdLcvCl+iQAA6KTkQMU9YVv7THzg8f/tlPPwAee+VsAAJg0c3juHR8t6sMeA30ur1N++j279l58D0N0MmcJvlY75B5rIJl8K0Aoc7oPauCjL1z69jjHF4alWYpWoe9YUzmtI/jW9OamYcftL+H8aGkxbjNXN6pIITolRYU4NMiVXzEyDKCHwwoBtn/2Z+Hk//Jv1XoqRKdBJZEalid8e0HvzzuXWt3pvM2Tk0Yu9u97qQg+ReBAcylGhBiElbM4gq/vr8B7U3pmy8DnoU4gF98bFto4xqMAwsyl+BGoBFpKUvC6O8pCuATFht5IMhBeCF/mW+Ha06g38DHFQwcvJXDGsnBWNkzWKQIx9Rz8VSMpZtK6bAQfyvmHjD0SRCdqhn1Ob5Sc+o0k5bS+OkIBiSvPwSdFdlv1sQCCHzXfThYKzyzfAqcUoZe6SFNuKkl+x04JxwmImjSYEoLolBQcnACmllNIdehxrhOLqoqbl0gNDFjJJfTpiuCLuhCpPU+1+G7FukyUTdMI67lbo0wEn5gXfBUQnRCJUE7LcPWGa1iJk/6fUsVB7CkT3CsCCnPweYtZ73w+3czWfROq7MHwt40j6gF0WKoIgSBuiIMxxmyRFXWxa5d+X5N12PIGegB7rUQD5m3TUw0tYC9EZ7o2sm86BFe6hixXb5hQSEtG5hzGxSsYl4ngA4CyCL6sol8O7ItaOfgaCuXKRvAlhSrBwIeNiR5nlSYGZOzFjgQ+YUYRW9p2Bd6x+JrnnoM8MqAlx5Xm4Csx8numQ3XoZb4dtq3Fgsu/1/aHtC2QsbEKoY4H5PbUQk8KzxhAXu/aUnnmmQ34L/9lDzz1VPsNYHQRq51RW7KjUvqTPdOND84D0QmQ6zc4mWbpHHzMLe73hQuB6AalOtx3SaMU/TwFliFUiE4jgq9dg2V7E+eAgtt47wv/0P998+G7s3ur+J6kNSSVy7Jx+EBZ1AJcEERah0TwzZyMrud+kCP4+iKWo2NfruORAl+WXoAYw6hzViRP3oY1rZzWEXxrenMTc5rjXGPuHHwM1fMIzUQ28GkCelbOgI2SPNazg5ThBksj+CwamOV0zhb/Ra6sQYpiWLit1k2pOxM26jbEsmOYMmWwZuAz2i+C6AQdonP4mV9PYQMA2oi+NMir/x9jfCvMYAwV+2halKHbE5x7n4o3dRWYd80Nzmngo3pm+R1SwWcJhgqXIR8Dv8dMAOVaiTykZFbXY5gsUDCSoQrl59NdvbFchehsGkY4salUL5jumZ3TAgddxwsblNgIPgWic9Lk2pBUuWd5r9JrOMrIUNasAqIT7RkszCAsL2yVOt/1ihlkUOGiDXoyBAAWvrWDkmXmmjhkdGWWzcGHDCmcAQw/PyYscGkkR08YYzWoN6wujHgc/9JF+6e/Pd17YLSKvETR4UXgsh2UntnZel78YA18wvdUcr7lhKPQAgzrUo5u5ZrGc1cS6ZXxL5rS0Homp5NCXbcMz1vPvAjzahPivmv4cS0eoqk20J2Q8XkBO9OlJR0GvrYNZwTpiG8sQEyiEJCxBLW3Jafga8++Qs971SgbqeGGO19dCBglDAlzia2eJqzWokOFG+2cyxF8wxmd1CkMkWzm/D6JB9gqs+ki7I45PLf1PPqVMZoiPcb2vOj4GOZ8ojn4Qi6z1U12NslR5EMdKYIvbjM8lsGnY4hOnC+zaM9P+2bRIYR73ka7awX8unhfqFSyBZEcfAYCStpJjPo54jmb1CPDzME3nIepsT/V9yyTg08jN/Rn34G9xwGUncspnTgxgW89tQUfuippy+lMWsKva7yqtnba+cLnBm04O28SvRML8bkonxn4mHN6rDwiX0CXYoTt7QAPPYT5EJ3wWY1z8OHzyHOEdkac7W1eXmjPOWro0XhDqd9w6hTs+trXIO7eDZMapwsKUBVsREuIJgNlL3ohrzh1dulceR3M0nsYopMz8OF26sIcfPxAEkc2JqS/NbwtSmYQnfniWwZlqFrkti9+h5qubm3ge0NobeBb05ubOKXg2NMjFTwWf85rIFA93r5y5sU6RQRlX4YTshBOQZbqOwMfxzfm3qc89fkCGO8UIitqEJ39n3ROhaIuGKuUZrO2RmeUSKHe+oO4f5BCTQjohhUC0emO1MppgtaWFMFHrirK8I4BJgLsCiA6LWMTgW3jjIrMoCVDOWGS5v5vuxiic4zkw3WUzkcp5B+nWLQ+nLYn11DFPgBge7q7/5YGzHzuXQkGPmVNAgTvZ9e3gR01uMYDxovCRQRlVw/VVVX9zJEcfAz00/ANJe/YISC5FARpxRVAVpB0BFEWAi1yQ1SJg5Hr92PSBOtaN9oM7yWN5GnEaUxzg+YdGtFXmpASm/wzxOvSinhy9MH264rgG6lJWhgRsNGs++5yODzfmvVAdHb9uO6FfG/BCl9CTKRA/0y47dkMdn35y3DzM4eABD8IA5RymVmElV2cobGkaU6Jw60Vlc9hzop+LSmOPeb95EfTAFxz8in40Sf/AgAAHrvs/wSAfaRc92nW1UZ+3BKIThSdm57HyjykVO04RKfQBGpvDESnNiTVUCFslprhA5fsz0X3x6sQXsAMROfJkxU88sgGHD48yYqmkUexYxHQOpH4hkwhqkB0so8x5+aQGkmrWANn4OshOrGzxFh2gFOkWxF8izCv114LcOJE1aYReGvOm2jsb3/d4oMBoNn2WU6IU2gK0VlsfUna0S4IvGM/JmMzxvv5srSKNkgEn/Itcrl3NfKcTdmrwjedOfjiYsH132tT95CB/hx8ZXNZmoPPKvLAAxtw7NgELruMyi/eXHpMD65hSfKZ3jRXSZa7WvhJ77mxGIERwdft45mBr1b4E4ucujujqKsNKYKv1FEUt93N8fZ2IOle2IH0l3IHDY+j0O6/+zuYHD0KAADXHd9M7gRWH+BBAxu91FHlfj4UXtaTg89KDdDfCwGqathvyLuKNIIPQ3Sa+7m2PiGIjp14bWTjixTxqKvghejs9mJ/2hFeXshobeB7Q2glBr7Pfvazo+vWI5m3Nf0LITbfCd4Yg3gAdEyDtNnWMwDYBFKH/yGXsxJsy0ydrqXhoTjHn5qaEYicCx6FpSkEKcZK4/TvctVlEXy4fQVleMwB23svBzxW3YCbHropM4UNfBuCUxiei3Tsp08jb6IdhOhk3yfiltIi7gg+YZ93yYAxsg9AIDqXl4/zPtOOks6yHHwCFKDYXNMAlk+KoBA9JDQ029gDcPEUAABMGxmis4M+LB1QSfEYB6VQP7WRg7rShdq0fKYkQkYBAMbAF1EEYRgM1tgRIkNoZdZoMRuzLERnCCQHX8MIw0ZXLtK8psNrr8HkyBEI23Q9kbwpvRGD+ZaNQcYmElknNDWvaADqkNGVqThFpHeCmtiD2mvONMPz84T3DI1UJwaD59AofUchRHYfayP4ku/L2YnX2N02V26Y5HJn4rY54vbmzQMHAACgrjfJPZF9GAnRSfNk+OpLLJZHqQ8AMKmYSDDcADMUi//z5tWoa+iNewAAtxz8GwD4D3L5hYFvGHMewZd+wzHmxpEhgs9WNqq0osPY+mb02yvOwYd5zEWklxTBJ/G4WVmWh6afjkehykF03n77bjhzJud3s7WRyQLDjX6NJ9QtV83Al+7r7Bg5iE7mW9pRiM5c0573i94d58gZ5nNoGoDHHx+EkZAY+NoIvqRNIYIP55di3wkbwac/U4Tc2IvP8rHTxBqyE0Ox2oDV+Up4ZUN2cFAJH9pBdHZzY/Xj4S1V3YkGu4v1IBUfse1WOnudreyhLcbAfK8CHTkygW98Y1daGg3ONzYJAcNqageXbU/YeEX2W2YPTQ18mnypOWn0dZegNJKQ62asoR3zWDEiJwjHeUrvtX9cuhRY53BJt5m37eMjOuOeNB5/OhLl2UqVBOhPLYLPktE9/fSXAEFkWgwNANQzWb719ptS7lyZlqVOQto+TORwOpC834XzYfHeoFVYG/jeEFqJge/gwYOraGZNa6LEeDZzG3pHmiDJHYTcvqMyqQJhxaGXwcwO/Q6iSDOKdTIsJ1gHAx4QpEPaz/1hhsIybC7DjHVwI1kEX4wLD9Nc814KtQNQZhCjEQ4+ohCdkTAikwkzd4sLJ09WcPJkLl0PEJ35KPTn8TF5bBtKhAtrjCiQMPDlcTn4IhkL1894z0lA+A/JGIiy2fgGmwaIY7djvnzCwHCBK7+9saf/u8vBx0Vw9BESwmbqUTh7KFMQCVsel9+mKAdfCvXTYIjO/He+r8oPI0Hm6Re6yx1zXvJChWLojHndc/DVNez5sz+D6tw5qVEAoI4ZrH5yBERnb4hmqBIhOhkBsQSiE7XHCYGWgFciKKuQ3B5DsIOqatjHhu84QoAmg5P2RrHj3JAcdZBcLsUTKlQtoHWlPScy7zg1UuP3s2ApmHaEwa0gBx9WqGrwtfJ6yr200/8t0or1YyhUkubtD3Wxg0Ca/zXrd2HEq6spieDLnN5UZyubF4DQ5uAbuy9a1EJ0SsPL29O2PewsFmP+P0vGucLd9kfwOclRP8aAPojcqNN9T9i4h4k49Tgi+NJ9rGEi+NRxCxCdnGMSLw8FMm6AhYFhzLxzinRHBJ+Wq4038Mn8AYCcHuCtl23DSVxP4TUAFhx0NaCU0Bx88tjzcgFfoL8To2R3+5VXKjh6dALVjRX80I+gRqUo2NEhmGkbPhlJI/zMOkRnzohZ/WD4TwA6HWr+XcuJK+0fGfgsvYbHoNCfkaxeoyzCjeuuK/Lss7lalTfAlOsrNJkkpTE6F9aRXDkW2P2KZezzPrrLGux4noNvOR3BTpK158aYQx97Ie7Te2paA6YhLs9aaaSu1EWJ4VPkVUo6T1Px9PMhC98M+7C4Tfn9GHVeOQYUQcesdczXzlcC0TlQCADvOPogvP17j8Jrl11PqrEGPqVNF/+4KCDJRFq7qtPyOpDrDaGVGPg+9rGPQRgLNr2mNWlkCAMAC4Gg27z0QCtC83kguz0HnWQNzYrwSr2XsuvAnMRKBJ8Utu1X7FBDj6hsxgrkiP8AsJhOTblhvZwuKKQz8PXRGilDw0SzDXNmHKAcUysYj7X8du3Y+r+y6xSiM/+9f/8mXHllA/v+NXrPmrFqByP42H61CD4m0S+7npw5+LjgjDynTXJdi27xdMYQO3Ytgg+tCzOK0LGfSeXcTQrC+vY0NfBZEJ2B7mHb27Bx//1w2ZFrAOADZAxliggh+pHbIzIBBjHsqQBvRENjA4UG0akbgzlFh70QM6X+klJq0zBnnaCQH6+vjX1fHE0OH2aNe1gA4c8NRAZEZ6aMW1AVG9HzWoySs4wz2hiTcqyTDRbGhbZWFsG3CgNfpBCdAAs+geFhUFWuuaIIPte4USHuu81IgOjklOEAq4m+9TiPYH4xMjelOeWu5Qomuf8q6BF8ogKtjF3Lx5MZ+HBBvlK3vJqqZZKyCL7UmIlz8CX7XleH5uDMiY3kzcY4etMkZ5K+paxYblY680LLFkUwOOfJ1bWSgy+l9h0P2qpBMYwi+Lj+yIcjyw9sG1wEH8O3SPLekIMvJyl/LkuaIn2xn3fXpQg+3FvuKIX3FiEHX7rO0+8puX75nhm8+91zOHWqgne+07fRxhgyQcnc78V28r+pIZuv8/TTbd9P3LsJ7/i3fC4kTKMNUNaAcD/GdsFByUnURfAB2PsUgMMZmus/IdUIimWWasgzz8mYJhXu3yYPQL5v2v4glxQclA7iFPqlTUnlJL0FgL6m2f2KnZOkPQOis7ueOWs3dB91i04iT8XBfko/lHIGkRx8RQayfC1pvDLn6Jc5wSIZYgxrE2OZE4PYR6mSAP+p8Cuygc/oX9rTmbNTq9bM8boq3wf6SyHARn0R3v/cHQAAcPnZl/v75HmY0EXumTteE3fLoYfF7Rm89alH4PpX9sF3r/6AevC45Pt1BN8bQisx8P3ar/3aKppZ05ooKRF8HNE9JsAgsdB65r7jPOzt/DTCIZIqKwrcsKU5sGr2CuzswOc3fh9EJ+1dVO6oXBWlzmOwg+jsvcYSYUAydnlI8wbCAi2J4HMyKlwOPvx6T56sYPv4BCCF79TWnZCDr4oNVM0c3vPSP0KIDTz7jp+BesJAj2mEmOKWEZfHVXGKxaQNDrYnp3wyShQdRKlZooG0CGs4BPwHl5EqvY8F2bazouGMLZxG8KkQnU0NTU1NqpPvfhcm3/0u/NALE3jL1VfDmb3Xom59ysqqipnAIENRdKrd/Jok4GV7aef0kXoCGzn40gho6liR9F+ijEPUz7UTolOihhO4hKXnEYjLztQFGVqRbo/0RPCZcH8LeEysMBCNj0jB30OurtLAhyjdMwHaOa0qOv9lEXxRdJ6zouc1SsfYGviQEi1AD7smKZi4730+9+8D2pixgJr+rki0Ia7Mtycp7qU1JL6mAiWBWCTmUV5aDj7N2x0TmyuLex2Kxqi/vCKvW9KMMVd1leOYRwzRqRjIu2edghERLBhgVkFSVDn324SGYz8l+ftS9xa0kETerNsrsSNl8vPsaYCvf3UX3HJuArfifpwTm8kXIWSKXe08SNkxDF2VG4Lp+HN5q1I3cvYxmAg+iJHkE95RiE7Ut5eu/PqXYevcLQDzOX3s9CwhEXyRN/B19UKQ1918Djfc0MANNxiK7qTDJgYE0Tk2go9cEQt0Y8I55IgyVGjfPtNpFAkpocpIzm+KjFeRRdDDWOcx54yAz3otB19U+ObMwBhCBtcbuDAui7o9TDEIpFSag09RHdg3jLmWyu0ERKfVf9EthrGX+DgSwZ0WyyL4RryrBWk6mkJVlK8/ZEQjMKSoE825Z+C5dR4f62ewDNKNa1kDXzvn/knjxrEMSTJIejNk6WKEgI2kihR12ZFl4MPtVrFeqR1rq75glgmxgRgmw1pS9jtRb8K8172PPgC7Hv42NEcmsL2xB05ccZM94HUE35uOxmvH17Sm14M8DFoYIvg4oZQ/GNsNcT4PjALZdxpySqf+HmscC91w1fZz1Trz/AJfMBqCsWfGDEiTrg3k7akVxwxeCdEIvvafVPgf5qqcg9Gmy4zgY5QHAEBglbBgjA1+HV04n/atwzw2osK3gXe9/M9w40vfhncfuQ/e9d39SZvO9ZH0+/TTU3jggQ146XBSAAsdwDBVTQMvv1zB/fdvwksvTfp67LM0+m+tLolEXYXnpCR0CGU4RZQJ0blixeL2doD77tuEBx/c6L8RjmZOiM5unNpAbzxyb/Zb0RcT6owGOAfeQpVLhUAiGCVtpRAouJP0f6BMJovnL2v1h/4dBj5JibUq5XnNRFhKEJ0u4s7ZaAkGUlPU+zT9g1ecDpIRq/zizsGmXiht6T2icO0MtxYEsUY4Ugjt9dzzcfu9F+qy60dSrq4q9gdDdHZ/4Gh/j2GS46c46r5zz3otfU4eohP6ziismNCONDju+giIzjFIEWJ7yiRxqA3a5PeX0ZiK9pZUYa9FlaTlFm+6mWwgiM4q44mxMTp9T30EX7QNfJ7xLwXrrbTbkXYMvGEQnQ76p/s24ODBDfinf9qE8+d9coNJRRF8eV8xLmSBNIKP4dFzSE/BwKcRm4OP8kpVrNlp6CAMiZyKlcFeQmdO/60zSs+wfQk2H3qozzuaPUM6noZCdFow0VWsWQjEwGg8WSeE9L0giM7VGfjkPjteJYUuZaPaSs6EQhrzHS4zjBLoPklxrebgI4WdPK8Fyewh5nzTxrYK2Ef33I9n0p3N+/c0zpBklQWgMOya3NlRCoutpTnABj7crOv5lLGU5HVnarN/c3uaFcFnHNWkjlg+0jJe3tBD3adUIrd4xmoRr1uR9S2uCD5jGNK8kch9hpfCTmPWN6U5MUUIImpY3kdbRnOixQY+z7n4lv3/2Jf7V4e/oZZ37SHrCL43hNYGvjW9uWnJCL5M8GGqzee00hgBAsMGYZIU6TysXF4vK9/xvsx1iza3z8K+c0dziJtkwJYwgxW1fKX82mhlBADMZu1DTRfGiF5JF9MIPs2b2ThgnQl7AZgIB6lNgwmXDHzp+2Dh37KSlbDGmuwwvvnw3XmbDurm7LXXKnjllQq2twN865tJFCBWZARmXmKEQ4emMJsBHD48WTC6hjJxQV4ZEGD4FtzCSYEUR3JjphCdyT0STeBQmlLGkKlasCcdOjSFb31rC+66axc88cRUVORtT3f3f3cQndJ6jbXlNasLSRoNiqAG/aZCORfBJ85Tuu9AgBjzqAAL6in77noFWT5m0n8heZhhVqjDiuGGcUwRDI9jYeA0qDL9xqJ+7AQL2buwJ8vgyZxRVZxDw8wDgALRt0QOvoD3aGGMqbCFc7AClCusRPhFdM6WrMuBLwIForNRDXxcf1puJ1xZG/NS+lIBolPa68S8QQKCgRkFKhDet7LfSgRfiTKCW5c4yh47TpAzuPtel3BCSOeazK/4QIvyixx8/bOEPIIvX4eBGt4BYKJF+WHo4FVT0vAyEJ2sYdbsW7nHRPCFSM9XKYo9fY5z56q+zKuvDoOczwHu37/RwxyqQ81eY3Ab+LriXf/9uEIewUd5qDzSrIX0pO2K516MIkQnmUMDohPfxHzvMnKTtGZUsS09S9gcfPpYgtQ4dyAY448RSA62gupsuRgpz5VB+y7udTJnV8mSiTvyp3BQmnHKSBrRY0iuXALR6TXwpXs90V1oZySe6wSiM83B5+UXeBZL5kNNHtn1AnniImQ81fEze51NXDnC0XUtXQnbhwXRyXRYxZgZGUSIzmRTXiaCT6MlXqdIeN/FzzgmB59Hl8bJi5mchPxYxjyrFnEpludvlHRK/qz4jbO9l5xRXlAWqQB2rsKyQIiRrMPuzHc/IlMwvTQJlAfC7ZPoPIGnT8tYZxo1VDo/uGXk+zXtCK0NfGt6cxO3+SJlhsUQaGThypvQYYsKZg4+yRMnZfYYKYo7JlWhX9hkt7bPwMce/M9w1avPi1VIVRdEp86A4GctoQ6ic1q3EXx9XrDESMLlZnOTMmASeYcVxgXP8ra3tWtjz54Ie/YIShskUKtGbAGiE6/BphoUJqURfBcuCB6UqNOKW9dYedGAyJVT5aI8JnKdi0rQyPPOmI8h4s6SMZZCdLIGBk64IsopucmUvvWtLVGA3N5IDHzNrG+Y/QzmdSFD7y9eVZ3CxcH0GUxwHsGHPqL0fwAC0clR5yFYLNw6KFOiGMoO61qcMwZY6V0WKBO8wwEA85vGwgeXML4vyyRUtwYxqWfiNFbCfh04D4IUQPcR6QAAIABJREFUepNvjpTjxoS3jgCROHRMJsIYFLKiJ5aiGFsINgRBDcw+thMRfN4xSpe9ayXG4brnzAFQeMkSDZpQpH1+TutCX7YHorNbI2K0p2KQFi+vQpPGNCM6lSwu1xWK4IOceSHOdMlzdM9f1QMENSaXwkJkim3CEJ0aD6zqPRbfpjQ0qY52z/e9jOOTAABeeGEC9927AX/zN7vhxAldvUC6TTZLP0Rn0h4EyPSKDB/qgegU9y8RfcG/V/YRfJhXHxnBR5X+eQSfu50sgi+SvUVyMkkj+Pp1k1T2RvCla66JQTXweYnIR4IhO/07NfCVRPCNHaOHRurEAUA2ChF5MEbQuB/JCUY8SzUNMzceZKgn77/022CYA8yXOoeWVx5TxMmnW1X5HHz0vYySATwyR3qsEd4IGYB6Xou3LIk8VwRijfLya+Jg8UjZ+c9PEk+z+R5J56qVcdPB0vNI7cehA8CGN+5YXgVEJ32WkTLjyM2s/5Op3029J4KvxFEgN/DZocrEMGbxUcZceM4UKYKP60cyutJzMW/KcizwyAscH7Cmnae1gW9Nb25yKsS1DVs7LDmFlCaQ5+WCOB7vALNNvIOXUbxQ+6glvCkbBpwbX/q2HMEiKL4wE8cyyObmr3AWRt0OonNj3hn4aL3e2AW5YOYhd34CYA5bD4ewoPe8Zw4f+MAMbr11Jr8jRkklkZR3kBr4hlw2bk9wjnFRvg9PtFEI4DbwlRjocR6WlUB0Sh1VOWxIf4txvVbHwQkXBWvJIk1RNJ/u7ietaubJ2JnFoUTwtcy+fw/G1Bn4Um+8FKIzpfa3IhgJEle/H6SKYUMybCOZcgmpRDGetWWtxVLlOSrf1Mz3Xo838LGUCAY+xTC6jM8V5pzp/zYEAC7qZtLMxfXOGt4BgHXtTrXs2mQZOfg44RFH8E0mkYWZkUg91x0KAHcfCKIzRCGCjxO6E3JH8IGOAox5sKJnY76tGGXhXoToXLGBDzIeBSvvyl8ep4zwRfBBwhhT6h8PRStZQ5R4PTy/kd0Ahj2b5OBDkVnYMYSbBy0HX1EE31htmJNW7tis9c3xZQy/IgUMe+jYsUHL9vjjw3tkzyWcgy9lfHXL51AtfZchACiyE1ZSRtAhOsmY65qN9KnYOWzysSyoO+bY6HvvumG+H6mqpNjVeNBWUZl8b64IPkGjyp7rjIyB94HMwDPunCNKXtxOQyckh+jU52nUoJTiK3HYcVI3vd4IPulz9MJdmzfxJp5F8I3cgwvWjbkPa2sHFTFl7cLvnNW7rKB5q6zu5OhrJ71exRquvvfvYe8f/RHsPfIsWynGXJczOoJPebAS/eF4ahFk8gg+/3psGuaMEJ6bf558/14FTGfJ/jSI0UxYo5cYp08ddYuXTUyZmJFpMHoCB9GJUzkV53VWCscQYBJsnRnhZZk2SZSfFRQRc3QV93vXHn5t4HtDaG3gW9ObmwohOjWmjqvHMXbS5i+Vw0r+Vpgwh9aPqb/OGPjY6B5OuYiMHZgmyLgn5bHIxitBVKVGHeO+yowZp+EA0XmpbyvGmB/8grErpc1N4TmczAIAwCRTGOlecwTeswK4/PI0mkN/bgKhivtyG/gGmCQ3RCejBNUj+JjIG27dWMaArionaAl1NaWmswl/BSGCIMQGbrqpgHnhIDoVSChpOBK1jDhfvg5TmE8GuNVJMxMFexuik/brLV5VEQBCv29ye9HwZySJxl2eaN1emkK7KRF8GPpY9U5cwll7+EZ0b2arfD1noEuFHHye92Ix8WwbnoZjAmWiwBCmB7G3r0mjRfAJe8Zo4wxQA1/kc/Clv7kIPpyvVyNNOB8LXZa1ARGqioEaBirkdte0/jKIM5X8Ofg8PBhqmik3eDqTM0eC5ypQ5nqQHvCZmjWjQnTy/AbHc3CRNuy3nTTALNv22hIRfOo6MXiBLgdfpmBJoZZRBB8HneuB6NQIz/hYBSA5X1FD2hRLEJ3qWNR7vHORGNnkkKXYMS4K7tmjzzMZywiIzvSZFllDh7aZubYi+FIegIyvruUc0ahwKUQnXZLOfZTbi4JdW+JBAQBiTSP4xMjgDmY9ycGX3eci+IyxNQ30Hca4mnOu7VdY58nfGKKTH5zd9ihyykga4bPMRj+J/f9aP3IEX0C/8R9Jn8aDdLfPnK3giSc34dy5hQN1BtFZ8F1I+xczjjERfKGp4Uef/Av4qUe/AJedf0WcPxVCMUa44sxLsPfCCbtbpgPvErXWkMgjCMQi7cT0e6J0/SuPwb6nH4HqtdfgqifuYxyHqcwGjLxXypZwZx7H8/Rjd35weI/MQH561BI5gk/rhju/POXI+irsVyJ8drrGVXRDL9v9yY5hcTOP4EuMxAU5KVPSIDrxGir9fjz3vUgoAMPzDmwRfWbxu8G8C5bNnPu2qtNYQ3S+IbQ28K3pzU0ejWUIwsGWFBE2y/k8kEoWU4VvaYcOWw95cg8/GIsF3nyRItpLswSez5Ojgb+gVzJfVemYZ+3cTueXhupNzA9XxcrTMWo33FDDRz7CQDYpAyZGOmzIEp7FdSgbUUFVBaoXdQwVJ7+QdViPgejkjJ6ZQQsL/4zQwRn4loDolOj1gujEk5c+XxVruOaaBt7znjns2pWqmIQxC0q2VZGWS6KZbEBdDQa+aX2JV/oBQDMrh8XxFu8/WQz5sVA2YmELP1G6RqRoYjaCzwPR2Y9JEW5HQnRmSpRlI/jmDRWQBYeMsXp6y5jjUUj91IEvwK37/xuE8+dVBUVnIHnuuQkcPszkb2KUrlUzA+wl249dyI3JCe/uHHzpPqh85P0zgQDRWSAoqwa+JQT3VHhrI1e5fZ+bV33ss5lvIO2xIUSG4iZKFZ8riuATtwAPT2pVw/uuYuBT4awWpEF04hwsfWWLt12RUO7OwdeVr6aQTj6J4MMOdRQEAybRjuBz0YizuQQRRMz/uChbDK2l7l9UlgBgeNaRfFK3XLr2LrssPT9Ypnf4KwQZYwtRFlWcfNMxBKQcpu8hO9NCBVAxcoP04ddNNqytrdY5IjDIA9x+CpAq4vLyy0bwdZIkzh0krx/lmyQ8vn0GVZITIBPSzc9v3mFq0KflfQpbk89m5jG1R1oQnTmfWmbM5l61BV3sIa9s0TrZqaqGjKQADHqWKu/G4dQWI8Bjj23AM89twgsvtHzgGPhTLcKOo8mFczB98kmAS5ecHUR493f/Ga458STsO/td+NCTXxolxl13/CD85IE/hY8+9AfEyKcZpEvJ5LMKB+/NwSet+z3Hj4jjwRF8tI3lPHpfjwg+Dm1KOhMkojDOtmNr1136qVH+r9zgFeM4A99Suo5sv+DPzfRaSQ4+iSdLv4dujwQAIYIvGUMfSVcwt4o8DMBH8GG2vQSiU8rBx/N/epAJS2MOkDXtKK0NfGt6cxMXwadaP3zCdffnfF4mkHPEY6PLQxMhOCsbbtKE6BTGO5vuZq8DyIphfKixzKARrSXC6XG/Ec1mASbznOFuahTRw0SzcXPDKb40ZoFELhiYFGO9hDiqKh3mcRxE5/hDuigHH6M47K87uuMEB9sLVS/X3x9t6cDhBEM/oWmgqgCuvbaBq65qRMVpf8kJOYz78fLFdQ1UedzdC5M8gq+eiW1bEXzcnukdYwidsi01mEQAoI0E5lq2ZSbrmijvIP9WRIjiZFz99x/zfUCN1OLIWO+qlyjz3nF5DqLTsYxcfXLXOjjFF16YwOnTwd3wvrNHYd+rL8HWXXeJQj8AAMzn8L3vVfCXf7mHbYc7Y6umhmZWs3tbJcBgWjn4dAU5FfKyn70ANSwWDqKTi5YT+1QdMxS+pYAwRGcrzFOIToB8Drj+ury5Zp9GxABIPBIi9pZg4INeiZiPUTbwCecOd444zhbsmJA1r0bw2df63HMOPqc7j7U3peXNlUjim/H8BqFcCtGJFVLZPo7zReYdA4DPwKeuP1FDZVM+33p9Te/BRceZ56x2s2mAM+xIfFSpQvnSpfxcsKHq0M8UllExLmd7Yva8ww2J58wj+AJoyk5ivNgexlRVAB/+8AxuvnnO5gizIvhY+LUxGzhSNAL4eH1NyRdrlEsokZ/2f/A/wHy6izQkyghOJ4GUv1ldDr703TLvOeXjhQg+t4J6JRYCuw3rmyrhwbPfxr4i7VN47ek5+DR5YtjbLlyscuSN5PtyT7OyUXJwuu+994uw+ytfgd1/+7fODgCuevW5/u+9F04MvLrieNp9592lH3n6r/t7tzx3h/AYQyQjd99zbQxpejEsp2syPkfzjV1yecGZtu/bsx0Y8pV2eywUqhnBh2U4xRm3lY+d/D0jL+L9ezUQneSqWp6jscMY5GBfBJ+cdcG/RvMIPir7ZXPM8JTmOjK+F0mG5frF+y43jm5OrM/UQp2QxqPqNNYRfG8IrQ18a3pzEyekISOMlrPOEog5hVQmGCjcRNcuVthxh7NEqSDY1zBOY16A0/ubTbbk9gTPdg+DbEV5LMNszucAG/XF7FpTQwZzqKmpOOim7L4iOGJFGYYJFGnsA0c0Vk2pGQb4mpTwOqwRRKcrgo+NhsmZ1LRfLrKRG7oX9kJjekmbkge6RJ4xSIo9wcCXzjlneCckKNnUsRSQpihqJhswT/aBaX0pG2+TRHzOL8kGvmWH2+fg640t/H4CABk8Tz/OzDsxuccoi8dH8Mk02k4cI/Tv3gnR2RMWDuf0/aw8Bx9qY//+Tbj99j3wJ3+yFy5cCO71ESPA9Nkh50bkXlnTwDe/KZ9RIHyWMJux5+Ek1nD99cm32TXAzfsSOfiyvvHzxeUj+AA0eLTxL7YXyoCD6Ax9VIpm4OOoJAefaawYSZJSKAhrSDpzinLwFUJ0knNSmQgJcivlU6t6GyBG0cCnR7tzPDA1hpc4Makw7ca+0Rn4ev4eQSlSyOak3y6SsZYXYkkOzFGU8nLcZpiQxe8UK+bGKDaNMXLEGQG74BdOnmDPJfwaUASfpv/FvF+MQCI9ubkmBr4CiM56NtTNcphFPgcfN/4+wgnLqU35ZljXAPfduwn3378Jp06hCIeYy8jc8+SU3ERzX1VDNAPGqOjXQcOfoYE5EKzHjMAYeArqS+XUiKhF4TwHX8KvGZ1LkODesQHI53nJsrCeuSMi3xoON1JknpiDj9PbaNDkCW8ckUOHZ79mdReMLoijveePw64zxwEAYPr8874OYnlEVteM9J4n5Myy92WuKe5MsWQ2M6oW97FkBN/21lvY6jHm+wsXwbcEcjgA8Gt97J4iUW/gU96hflRT+UqS7ThDEMcTefqVx1Mmt4h9FHTO6dy8aXWKIviY65hnJk59UYrgK3hEo6DHwNf90Ax8Q1GBmWQ+hlS+cOvXNJ3GOoLvDaG1gW9Nb25y5uCzNlUORiXGIeKF6dJsWDt01MNbitBbAqLTVARwgqw1Vs9JZZRJb8+2AS5eFG4ytL0denjOjpo65ga+QKMexTlDpCnSCUSnAPkm/CwmbIwcF8GH1nFgoO6scSgMVNu5LCTHCLISSYToDKiYgxuLkb7TtnO2D7Edb1kEGYQhOlFFnSFi5sdjYC8SLGJkmbk6TGE+RRCdibATw6Bgi3PD4ypGuPzMEdh98VTR+AAGA1+fv6Xb9hBz2q0npuvhGRIDH7eXpvuClfssBFmIyfZLxRPZon7sy0J01nSPSJlytk+FtGjmrvvOAFfXAfbv3yzPVdH/QccY6ro1GpqNIJrNOZQ1qGIN73xnDdddV8Nb3pLsDbxGhG26aXLEpizHCOeEwQhXbA6+FUF04v1u1BkUO4hOGhXVRvCh/d4RwecSCCPK80BvCz+Uch0J8FzlEJ2ewdlj5IoEiCyfOUYZcdWpZ+GDd/w/sOdP/5SNXMPvsJ0LR59L7HMpEYWj8EB9BN9kSq5nPLMSSeuD6JT3IdLmsowdKIoVoGsvdUqAXnHFD43tS3tngqXYa0Pk+k3rXrwYsmvW1JH7TgOf3EgewcfZtLP9Cyp2CUj7V72NEQfa/znZUuIj1Ag+NFaRFjcfeWQDXj3Vtnns2ESpIDbB36vp3PcGoYCNL4t5kIwwbA4+ndfo+unGOdbAR5S8WGZLvhU2go/pS/pWSiE61b3HrCuT15mCqBqMTrwRfGozXicYtMYqxsnP6lPn0fN7W7Oztv6IWTvUAUao65WRGUbP0ntxNJYPJMMhOo5UCPK1IfMuipychcKNNPCpsrMxzlK5Buir66GYLX2J0va4CL5AyrZji7AxOy86nlgkyeISSXCQy21mus4uZR/Sbsbw1ABI5scQnbisElk4ar4hiAa+lEjkoJKAU3wnuE1okGzmfACt4bWB7w2htYFvTW9u4hgPQRjgymsb+Xw+3M/wlgv3M28Ovl5ngIxRg76BKqU5uEmJVMMQowTpbzXCQYR+U0Utf5AGiOTgOXcuwN9/bQseemgTTpzwbTuzWSARfBFFKEnGLjw4TlHKvbe9F0/AdccPQpjn3nQ7noMv7auS2wcAaIQGSA6+SR7B5yE+gi9/yenPCkfPSOteUuph5t0RyT86F4Hnw2a1WLniKLMjMVE9aj+s4soel1+pIUdWNZMNqLMIvjwvZZMY+ObbUTXgXHPyKfjXB/5f+NmHfj8x8vkWWR9lwUEVcgIRuiZF8OXKu8X7Sha+BxpRgujM+nfk4DPXqAZZgZWWAIyBj86LpGPKIn0qYVyGFIRva7nW8GV8brDV5vMB4o0h0Qg+m9E9CFohZToFuOmmGt71rkG4YI28zCFW1wAPPrgBDz64Ca+8sjhjnEq89BzEEJ1VFU1lYPYcgRpo+ntOXsfugyqruO+u7VM38Hkj+IiBSyNxnTk9UrtLwvoTc9wVRPB5jN1YiZSOPzCObGy9oUZ//cNP/HeYzC/B5PhxuPrpfyYlqyBE8Gk8RgN6xAVDkpKMbHWC9qX7s0ERfFThKztbeQx8VQJHaxL3DRjHnFsxB/nc/MqvnIN/9+8uDO1EHqJT7duIkiEKLEbRiqPAhMbIFbx/m0q2Bg0GQXRK3yWO4OuWMsmAzOyPBN5ffZn5vdTAl0bwhdjQfMJgQHRio1XtN7R2dOJERet0a4blX0JaLB9v+ruRITpJo4uClfCy3BF86XvBEVyvRwTfgrLhRn8OvtGHbzqeFbRhPXNHq4rgw3WkCFUAMFErsv2CvH+Fd2Tbo4Wlupuz87rsIlSkyE3OL1jiZ9DXjB0euTPbzy9IVBilsyB8phMZ35INIm/i7vbx/jebg88xQKUQZ+RawacH6ZkoRfBh/lkSA9uzyt73YuTfXaajqQAuP/QY3Hb//w0//cgfjYJLjDEUyS1KQ6PK9jKV4oCe5+ALyd8jhoF4ryjJFN0YPLDviLj9PntvYgRfIs/3+2IQZZxufrw5+PC+g8d51VU1/PAPDwdlf1vb35tm+dDbNRXT2sC3pjc3cRF8yi6KmSyNOUqTwu/eXc64S4dOjEgXqDEb6ebKYnHxCoZShkwrH5JDIiNJaEujzZj7P/PQH8DPPvifF7j0bYl77tmC+bw9hJ580hdZNpsBieCrCyA6+zEygZFtZTonV716CH7kqb+CXfd+O7tuGQeI4qSU0DLQ32+r4aBCOjIChHKITkupi+8Tw6fkeS1G8Dn6Z+Yi/VS670HfF+QxuAaWQYR1kWdUWW8J6dx9sc5YqYNTTEArNBOIzqRsk0C6NrPaFtIWP3740NchRv/0VlVkBRjOEx4rINttfHgXEyOCL3tvSg6+7vvo323fFjeRYjMqZUFf3jOhI2zgm1OlomR4xAnXxxA+G0KgY7KIKCzSv5umjwDhByD0NZuz+9oE7df9NyYZZ5D0c+TIBLa32z326acX30Vm8JH3qZ4vgAGis1NYTacAYUU5+EoMCZjSMeIIvo4q0HPwcTSbyQ4GeABtMYfiMPIRyUrT7LXSCD7RwFeovOSqVdgAgPncGOHmw3fDB5/+MkzOnTXb697T1rlTpBwXLfTsMxU8+shUNm5GIBF8JesrNbqrBj6GcA6+CPlHEFC0aap8GQx88jfmyoGpjNHcQ1XlYn6WpfN/9dU4YtZx/mLSLOxCRQ1GTKL+OZKyOAdfKfuS5uCDuhbrcxCd+AZROEMH05tCdFaAw7+DkoysSSA6w6TquwTGGaJq+PGnBpAs4mAERGfXDibpvNCaz3isJhKeIU2HwUJ0pvla047YzdV4zhCg0SJ2nWTKFogRiTFP2aFFi2AqHSPXjCTzrcb4kFPn6OWFcJUCMCgCi9KYw6ktxnaNNYy8VULc95QqrPdcOAHTWetMsTE7r8+xwFBwqVkAdD1Dty+x/TkEdA/Pxxs0ypUSmg7CdHI0ZEeOt+pVdDk0D+27II0HR5ZhZUykJI3g6/qSQ1yPHZvA7/3eZfDYY1Qf1jQFOj68viB/3yEAXH/f30GIDew9fxzeeuhRX7vGeEr0sNxYTWKei98L2psIAIDtkvOVlNLvZO8UR/DFmK1DDaJTfGRLV+REQulITM+yuCaLKOSgRHOW37/ttktw222XgJD1btdRfK87rQ18a3pzkxKmPQgd9J6HUmZ+z558k0+ZQYm6WxbklsagZve4CD7Uv6jss6KHlHYYWb397YFoEvrbdek0fOjJL/Wv7+xZBrqrqzufQzh9mrTBRfDhQ6z39EsjdQJW0pMi7P203NYD92fXiCFLpOWlsaqyhX3OsIlhcppqfARffsAn7aLvMVVIcbjxyU3XZZdzWeQjEsxv36l4JmWR4ijbf2oa+aJ61DEGUA9EZxFJAiQAzCcDROcEw98mCrZm1rj7nywiAb3DDYtlUvWGUkiiCRhhi1HU9X1XPCc/RPAN+SorY3FlsIsxP2NWDdFJMP0tQuUjJwA6vjExn5tisBKbdn7T6dqn9xcTWzdw6aIyr0KUedyesxF86X6dvjvxLEbzy0UTYohODrYJj7ETOrv8lqU5+AIXfdV3aCt7TFrspYOAmniCMvvsqiL4uvquMZcqEiQFTuTXkBSdsPHaSZi88ALlnUa5BKMieI9GbV534gm46aVvwQ2vHIDr/umrbFtclxxcLTYSnzpVwcMPbcDd32ijU3mdQCiO4JOIKu14amK7OTXVNBtzDAEaJaKH85HTITrLI/jyb9qvmAn4PaOm+zwnYZELM3Vc4vgcgyotz2zkI/jI+u74m0IHgm7P5PgZdo3hCL5UQ6cY+OigBrnJjOBLzzQhgk90BNhODMvTdqxVtXhPSqRGSilEZ7qORgRVZGMlvxH/gknlNZtceZmuyza6jongS3NRp+ek80Aga23FEXz43eM+2XfOfbsOp9fRNJbfT8gLh8zJT1r3XohOja3VdClpBF+bSzN//5xzmEZYboiJQ+yk3oafeegP4Oce/D3Y2j4DW/Pzun+ONIfOdemGZcQRfMDAmJfyGUa1/rqjUvY9OVJp8DxK0G62t9IIPsbPtBSik45A4A+XazYjydCN+57PA3zta7v5frTOUgM4s77IOZ/Q9ALvMCZR96rG5OBbaktrqKzBRvD1Z93QmZ2DT2esOsdHtkGgfF3F8JSiXkcbWFJ3Euiz4s+GyGPKPul9JySCTwgyGdpd/LA+zLWB73WntYFvTW9uYnajEjz2TmHFCav9fhMj7NoVeZhOVbm0GI8B0Un7zu/1PxkDH2ChpIs0WWEEn3gokLnEf+R/4/qXnX8lP1QEjeDeP/kTuOzzn4eNAwfwLZjOEURnBAiJ8B85AR0rJYUIvhKGBXvrSfBoHpgVTujO8kEY7xdD2HSEk3RnsFbOnd7MwQc5c8RBl7JTIDHzeI2xDlqUSfYaa9MSHnr++QncffdWlnuLeIYLBj5JYZYRF+FoSl+FpNSbI4jOdL2mEZ/N3G/g67r0Fsc5+EhD2Z987tS+rdR4Y0TwVUoEX1+NRPBR8kB0Shx2f1ljhpk9Fa/eeh4Bb9CSPj4dCs4J5yU83MwYqvTX/g7ZAuGOj/m83dclEp0x5jN2PxWdbiwDn6ZI4vbGLNIoawIgDjxFl9+yqqKZC1LrI7tO5mRclFtV0flqI6I5YVo/M1OnKY0sSLBcULbL5Nc5BU4QBe7MYSD5PnYdewn23H47bDz8sN2pS+uUK2U4xV137YZXBl5oz3cPkZaIoN9td4yFTzLeB4jw7LM8mkLTAHUqMLa9fF9OlS25gQ5Xws9SV3hMAdK5w98P4yMHVS0bFkq/P0wlRjfNCSPd7irqp8b2d/RoBefOyQMIihJFRgrAa9fBw/7/7L3rsy3HdR/265m9zzkXuHgRIEACpAhBfIjiSyYpypZkWpJTSlJylSQz/hA7dlKOnSrnP8gfkcoHpxRJVU5ZlpPYkWjLpkgplMyXSJEijDdIgBcAeQHhgsB9Afd9ztl7uvNhpmfWWr1Wd88+B+Rl1V5fztkzPd1rerpXr/dSjIBjBHYlI5C0IpvPeW+es1qKznhjkp1SZ7M0gs/BZT6mfI2ORvAtiPNcSHklq9YRVfxSnnxOBB9TwGnycYVRTz7GI/h8QhNpDT6Oi2d/k861Gnw57SP6dUEds6Tj4qZscTItTA7X2udTdLK2M2vwad3URPCVaE/uu7Lrgj0+rhSdo4Exu/htmOT6jENHAQcAqQFKyXjTdiu898UvYmd1vYhXOmaYlYkhghllM+BYghqni032R8nBT4I8QhNaVMEsmIY24UCQ49dy/ZuohEJGiA0JjOY0n4vgy4EpHw/gCX1M3jUEPPS9L+Gnv/NpnLhxERD6kvnJoLneo779nBv5tpYc3F/rb86pwVeDU87Ah8Cj3HOOj5vMhQs+k6JTH9eiLVOKTv27W3UiqRzL7s8VyCJsDXw/cKjLlbeFLfyQQE1pJ53UCPMmGTuuKOL3ojLKoTfwqekqstzE8HxyEOtC0HToG6ePllauUoBTU9ZQKBwmuHEDIZzkjxiRKmyMgQGziH7Jo2/51FNorlwBAOx9/vNYfehD4/3VyiV1wmKKzjjHQfwFMlEqLoB/m4qDPfZ5HB6alTCJVYsMAAAgAElEQVTXazvCsuMRWXRdjqmESqCtt0yKziS9gcKQW/1ql2sDm6i3VpVhLYNDhGvXHL7+9V28dP8O7v+rJT76Tjbg9O/Qj3OYH4llzU/xsRmLIjNGRyL4Fp2M4KMe9N7c/3rfqQBtwajMVARkPQ2ITdMXbVBvjLwpq92UZzCZYT3zMpsqmNizR43g60KSbsT6XlxZJ2mgRIxdtIbPPJNplmm/v+/QnOjgsSx0IuDwcIzYpiDX1rhniWYiNM20fysMfEm9zQwtBPq1FPXAMZq6T9E5J4KvXqDaVLHTNFCitYLqSFE6B/f3lUh9DRTDTq7tnFulFEyJ4p68etuGRJm594UvYPXX/lr/w9q3ufoTb7yBxenTcO79AE6MSDA8MuvOen313TXeECkNLU1+CNg8rEgFfRFfu+bw3ONLeA90d/RtuobTgOCarFJKOkcBeWeOqnopo0CRNio6SeT2JflBpzfSCf790v13+nReZG/Wh/ZNpbaapmidVQtsaLte8+i0/paTzQQ+dNCe0FGaHGRaLIq36DiEIbKskHOQ1+k1avAZ7+9XjPGY0FackCwDH5U5eYpOPm7tJ9AMyPRsqpYlyIDOB2F8DiO/LSP4aIpOtS+tBl8JFeHA2IjJuP2lZ7CzvIjDD38Y2N2Vj2tolGUSQzGqGQe0R97MCL4NdeJZSGTkEPlCvYNxb/sOP/W9/w87h1fxzEP/Jbzn8589MgpObRPuThj4AhKlcwmCOHGMjbCzugbf2Kmqc4PKupOjfifrfOpM+Sy5pPEEIeCOOzwuXWrSexl05/AQ1k22XUoRfNY7ZsSryJKw11XGqRGdcmRP29cspWVpLw8KL011FyHykNI5oZb3D6GEhx3Bt3j+ebzrpa+j64C9g0tomr8Hui01p/gafEqZymR7+je5scH4gDF/w02eopO+YzpX5f3Aea9EzxP0NJa1PH3+xiCXGQY+2S5Cn0Y17dqR+9qwmuNL6WzTjdmFb7s18P3AYWvg28LNDUSAizAvgs+4AU5v9vZ6gSsyqDVKyQhqLnZDeEiJK7kwar6N+8CQsiv1Ts0qAkU/st0H//Jf4+QNj/su/Ab+6q7JwFbDjJSYIXrQqwfFjRvms6uVUyL4HK/Bp3iGj97jyhyx3zN4DaYYlt9X/NzEQEejDvplkEHO6YKYnKvcNze71r4RNRRmDv8Q0vtju8oafBpDbyoxRJsSXSgxIOfONXA7fZsL5x0QDXzOjXuTruOmQXWKzhEyKYcTAXtDZtgSrgCRotMf8u/nJg65CR7rVcAOyhAFhmqllIvfQ6ToVHDXli3znKOpGOm7xCcJbm1F5IYVwcdTdBa7MWF8t5xBQFs/sgZfl0ZYWREPb3v1Sdz73Ufw0ts+hqt3fLgaV6s2Qf5i+QzWjsb9fYdmL/N9xvSewnlmvVYXCTXwUcOto0rjxQI47BXiznuuWNBeTRr4BGRTdA7nVNtW1gAjUJtWdRNyEY14NMVMpHGsrlJsn/FU9R548cUW76gcFxmnAHMNiWu6fl5XCllpvqgygCyJ4hooIjhcv+XTn0bzxhu4Z/kCgP8BQKpkkudjjZe1xnOozknK+qHnlIZ6H8G3uYFPps2lSiU63pe+uIt33ujvXb/cIxpr8HGjgp2CQDPw5VJ0HhVK2RCkA5wFdN3FdI1HTdGZi+AzcbEU0hkeV97TUhqX6JF6v2mmPZY18AWOKzCk9iO8iIJ/UoPPNIin+NEUndEiazmXWY4Q0+cJzNlm47TfyiQeNYIP3pspOmPrUQQdOlLPtBB0i48mH8izzIjguvPKy3jg65/B7pkO7sYNHHzyk2ln4/DSE1geKvb4sX2tgvrNrcHn8Bd/sYODA6fuMwrp8WTgq+z53H6N6+GBc0/igdf6iPbF8yt4/yl9fG1dZs4TuueCkwa+DRhuuSedU/kCIGBnkxp83q7BV0LL5JuVjSub/up/fQ1nzt+CRx+1JbPN6u3Ngz6DyLw+zt31bvwYvgOgNA80tDlts6lYfGx9GownvTTV4BNy5BxHgAxS1hyF0Bv44qXbr76aBkRssD5y38tsP+tGCpQnnoxIdlYRmoGN0sENM+ozJ/LE2UjqKYwafG13OGsuqKysvWtC1pg8luqFKVgGPgl1EXyKbqDU8dbA9wOHbYrOLdzcoCnEVcI3eUaaIAgQj+DjQkwN0codOkzpkKkZQj09xyELrNImBiT5SMLge48PnvoMR9OKhqxU7CbXNK3dwvYxWK3SqDTvhTJMmQxZI2Vuik49gm+zVAu1/QeinI91PSyw1of0WucRfLXKolS5kYvgS+pfWUxLpYGvKv0h+Bzm0gWWcDP7Z5t0skBRRtdM0Znrd47SoNyk+JwElqJzfcg6ZjUbg6/ylKT41eLYNP0caKnStJQmVjpcQERTKBpvVs80p2AYFMo10Qs1a9RKeVPHDCvMszTwrX2qFDOUhD/57Gdx27XX8IEXPqfWq7PwZeOJz+Jc2WCegLb2Bzg4cPmoGwu/lV6DrzUEwVEQc87O6ZL+HJ8f/8/s9VGADlOKTk9SdM6Zt7zjThnnIgRu4KN9l1LoSjhzpsW1a/UiRYlmLJ98Ertf+QrctWvVfQJQHXdCoIIrn1AewVfo3KrBZNHw/X00b7wBANi58CpDiHWVIbYWX6UOaRgs0jWUn/wQAJfJhlECl3wEjkDs68UX0wn37ZLjW/Be43Sv/78mDedspe4AVJFUetZKxQpwfYeWonN0SpjB77t1puZZUj95+LsB4ZApOpnuJqQ8pK5bT89rugHDOn9exz7iXshF8PVtXGrgs/pVcPYkRSeWPa/UNIBDehY7r9cQjJEdTeApOrtOGdACsb5ybTaJ4Asd5/0aGug4GFHjZ6IRfPSbAAC8n5Hhgq8FT/k28s0eevlr4/87Dz+c7THhWbQoZu1/0r6WVz+61QGQ53mEp55a4utf38Wjj+7glVfyB5REw9rbGr3JvULc3/ddeHa8dtelF9M9kjHwlQSK6RHFwLcBr8kgsxF2Vtc2OgtklogaRf6o61GNLMJ5TVkPjZDjNZVKAQX9urH+rT6KGT0Ir3/ptgfw+Ps+hVPv+mUiz1i8M5fZgtdSdFYQtcwHLRmzi2tNqSUul9dk4BNnbqWBL4S8YZunMM4vguRYzDhLWSDTW5dgWufGjTmdFK6NKTrXB9g9uAygpgaf/mPUI4d8DT4py2oGvnecfRy/9PD/hlv/3R9Uvwvtr4HO/1i6OZqiU3s/k/zK9bLhmt3W4Lv5YBvBt4WbGxSClT34lLMuBE3hoEXwBUQmq0YRG2+VDr5qwUJRSqsHmkv7TIQrC1n1lhGBU3UY51N0lhipYGjUvO8NsDIqLdaGGRkIl0oqUvlgGvgqvRuBcnq/aS1sJujRp3qlQb6fqnGoIqBS0JfpcACBi2Bsjr0GX3CpMVqudUVh6QoKyxwOYx/ONo7RJyePMaRKMwOPSbBJUzdRj6nTp1tcu+bw4IO8uvisZZVpzGvwCeM5Fap9h2612VouwaRY9Ow3oH9rqfhgkQ+NEcEXBSBimC3tYd6HrSDbwL8i7eOIKTq1fPtW/UqqTGhdB6CuEJ9UN2h9ayCVF/QstRQS+/sua4BVBRf0Cm1NXNUMxawu12LRp4NLntPH6S8qRkPFCk+fXSx6nXVoWuzsWMY0G3K2jZzhuwSc7QhphPrgpZmP4OM3T51azEAkpApY3+GDL3wWu4dXsPP0j2PvL/6U3NVFFV1utuiv/m1lOrqEBywNCFSl7mROX1LJZESo5oakezJ+Jy26TDOAO00xJNGZSaMYXyC9xl1KE6x17V2b7KtsBJ+iaDtyBN+wKDQ+a9M6pgAYspphWaMncyBXe1DlzYLiLhY4/5yFoS2tvVmbTk/du2074hPWZQc8mf7OiuAb+XMR8acvQR1xvyLOXDRFZ0h5FDtF54QQT9FZwb9KLINLx1WN+eOQ6v/xuREyNfgC+gMp0snR7heU/GBKes6+rUaPyHeBY9kkIO9VTlPyvlKRSd7ZiuCruoZ5ym+rG0um+/a3jbTlFf1avKrm8JGb17huV4s9MR7vyHs3GB0UqKg7ncj1KM+teU6Lg8Ga853VdQR6vMjDyVgHSS3YWnJp8c3KrCWvkexvg9dW+jl3rsHJkx4nTqT3q9f/eK8sD8Sf1/begrN3vw97+2+we+b5QqP9fVov76g1+DTaPIumzKglWTK+2c/nU91zfih9V8aLCdZpsxSdlan3B6g1JtV2Ms2n7nS4e3gFD/6738Lf+nbAoz/53+D1e9690ZAU2LxJg3QITA8x0ajp2m3XzgIAFi++CP/SS+je9a6kDwscAtoNUnRqMqjkx7Jnf9+C0Z3E4Gf5UW0NfDcdbA18W7i5oSKCz6xpB53QxeujgB0yNfgyENuY6UkUfKmyE86Nip71GqqBTzPkmQq/zLk9KzVARLGiBl8pjRs1Hqo4GNqSKBtqEXxUM6Km6DRr8Infc2ohyffclGuwgAUlFoR9V8dsbZzaJNdPRkiemF9lIVYqC7t1KB9KQQo5YVBUFeZkbkja1DucS9dxrxhXIs5yeCgGvnjh6lU3euieOQPcv+ka0xR5A6xZDT6eotOzFJ3djOlyiSBaAy2J2JJGv/F68InigymfmoCdw6s43DkJJujENUgWSpuJEItNR6Nj5mVqo0wToN8lU6xEDbKT59qMFJ0UFk36UW+5cQF3Xn45O+ZRa/AF9HOqyEkAgIODQo0HIrh07RJtVGSv9G/aKCk627CeUJaR40L6UZVBhRp8UoCKypgPfGCFl9cB71yusV7PPHccVOckDebtP8dwpCk6gf5bNQmdTXGPurAQgOeeqxcnYiQzxfld338Ybz/3NADgti8+B9jlldjYCRh1W2pSdJaMNyZdsK7TfU550eC54iqz7qzhtD1Zyxs6wmWafNsRavClihiOwB/90R7aFnAgNeOEJ9ZotAxdPqsFndf4TIHWl8DJzUzvFfRjOYcoClqKTvrcJqBF8D366BKnTi3wdy+ItlYqe4N/Yc2ofOOOMYKPMO9WDT5QJTcZKxqftP41ZVVv4NPkSx1nTw2Oy0jrAntm6kPHfarNpNTgqwUmW+bXZ73jJZkrH1h23qYh0Yauj64afw8dNYqBL5sutgA8QiWfseXwEHj++QXuuy89myhoikyz8dA+7UN/ZBP5+jhAnoG1ZENGIVulFSLEdSsNfNoZ5L2uWMw5tcn0+jSbSBvW1U4DYx9SRm8aBEVP03arPo2enMTCpAaf8t/XrjV45plFcmxKGbk017lx52baiPDss0s8++wSOzsB//SfXsXenjmEORZtK1N0JvNtdMz0Xwr/PNLx8WLax1zfyBqYtZeNCD66ZOLZrsmzNUDrqWnA6GNh2iXJ3DRFZ1bvqrTX8Jk1MntYn8/+TsB7X/wi2rccAmGBjz7z+/izX/hf9G40PtvQYWYj+ETb6Oiu6UFCwOwsJL0eqZz1Kongy2yOqshXaHLSHKKbga2B7wcOWwPfFm5u0A5TIzexbFeC1WoieLu7QRRpHcaqSJ2kKb0oZO+HgCef3MF6Ddz5niXe8TFjENpfxgBUStnSP89vXbni8NRTy/RssIpI6yOoMOV9Tr1NEYKZojN6BKcRfG4wkqTG0AjTdxwEzVTXP42vgKZQTYy4hnI350E79Z9e8wljUVjIFQudGWIzhmHrmfHa4PEWldoWMxyZfI3BsRn+Cu+8CiVGiSnP4aCNZaXoZG0chBK0vn8KcSxKj6Tz8yx9X6ZxEsFHmdtmIoAueLXOQt+94fdeKTj0TLDDcp3W39RSmuUEzbtfeQa/eO47eOGdf5Mr2UaFYTs+U1L6cqNNhnbMWEfm5Vplx3jG8P60FJ2agU/O57Ll4564cRG/8PjvFN8pOdMcqtPu9hetGz0UU3SSM7RrdkYDn1uv9D2l8Ag0ukVGji9OncLyiSfgDg7Md0hSdCb3hWA0/HPyZMB73hbQvhpw+bJLUjuVoDaCbw5QHJtGM672PIYc2yr8fvmyG9Nz7u4E3HJLwPXrNj2I65K+wr0XT6X41b5H8SJMJSEl4ZQdUbupiNQzO+eEzVQy9MhOc3fpUoMTSqlidY0a9faqHha3N3IQMsaUfNpzz/VRKbdaz7uAyGk2fq0GDGhjjWQ/F8kWccpNQWdHZxSNwLlxyA8eOZrpr3Cs+mYx0s6m4zT06lWHL36x1+Y+/dQS73snX0hOOV/nMBtais6jRPDNTdEp+TQawafp55iBDw0aiLT2mbm2UnQ2IT2LezqZdsYi+BbTM76zZToL+ubpt9OcT6Bf0sHz6ASaojMMrgFTufiBT/LdVPAlDrSpQs8pKRpj18qc/vmf7+Lxx3ewXNp8osb/U37RopHJnJkpmjeIMktb1TSaN44p44q/lRF867bOwKd2VuB5R37AOXTU2dCvZ+8N7TzVuthZX0ubV47FvvkwkZ/73InUWaO2a4X4pOtvsxSdEQ4PHb75zR188pOH/MZc2qM4UZklXchiG/V0mRSdPIIvdYidG8EnQTOwznl9F0Jxp9JUtdEBDiB0ZlSo6FDiv1i2HeUMYDoaqfvZKIJvrrxR7VmSH1T8qzuBBuwdXmF6uzGS2G02ZLH+ceAZSGiKztmDaePDo1XkRCkzzUnRaQ5b4BWSOpKWH9U2gu+mg20Nvi3c3JBRiE/Mqd1+JIhKP1HAdugj+GQhcWt8OZR28Ml0TOY7wGM16Cj/4usD80yop5WK00zRaUDJAHLlitJBxWlFmSWtee6gAICQ5A/o2xwO/OeiS1N0OiVFJ+3ZrDOleLHXtOtxV6IwjxPIemma/CClGo0RSgppvfOUQWApLQRex56is4J5f7NSdNoDThxNrzgi9KdLI4U2jeArKSTmgPUoj+A7YLh2xGu2CR1qgyBi2qR6pbwDVqtRKZkaz8RcC0afjhM9kX/ir/5cVwSRzmvqMkWvuTclgm8YAcD8byudLXyAVAipQnOQKXX5HPzk6T+zlUAy/YfSdy2EoRPrEe8LqSvJHqFr2Ko5pTlkMIFpuWRrY++LX0R78SKaa1Hho52HKU6aoCOFawDMoDg/gs+4V3AsmjWG2BsuBF5XqTAmTdG3u+uzBov4YKKDy6Uk2kwvwK+RNUS/Fa83lSoVIuzvA0890aq80uL559GcP59cpw5iNHWo9NFy3ptn59WrDr/7u9wUlgj6Gi8c30mpv1s6L/tMCfPWl4aPfUF53uBrmrBGyImqdF6jga+C1otHOUyeaSk+M2vwWZCr/TgnIqgj9LAR9PDSJSJLGDU19bTYyCqGZJsY4UPvFddL6tWGtDBdCsy5K5AafFURfOSaa7KymcSfGvia5ZSiEyGN5rFr8E1jsJrvc1J0MjqSf8aK/k5oL73gfXZtJtFO6PmmhMaYCj1lzul3YfHFQqmr0JHHH+/XP3WQI6hNj1rrXGs83k/CX5J2EesSJPy9HG+DA1zWmpJ8i0VHkvPZktsGiPu7a3i60HCY8l/dUF1Afv5avifAwbfTODTzRvUWEfvJSku4e3g17TcRSi2GYgJqcKG0ULbNycjy/NPoUC+f2DxKzfwcHBADWqSLFU5AjERoMlBhcFl6xowUp+2UGnxVayDTSDNm899157vkd3gEXxzLJ/LtnVdext987Dfx0Wf+rbknSvI/LT2Rk48jbuzZgv5Imxuq98j1LfuwdB1VoKToVOl0CPAi6wNtqsmu2f0e+80Y0uUebrvJMbT2FXP03oWAZqbDeAi6NTOlI/kzYtMIviKHf4SMIFvYDLYGvi3c3KBG8KUEx6KV1sHdK3YmZetiEZjgni0WLW4Va/BZKToBHl4/KqX1/NoAYSQ0Qp6LZAppP0WwmKAR99RzVUJGT6JfHKSCyIQu10qKToZi+iKjsiviqUXhYJ6itTaCb1Mtq9R1HEfKF+m0VRXBp3gisWCdYEfwJQ+yfusUDWraMctLWV47ljkbPyS/0UwpMeK9XjGuR76YoKQwjWNyYZ0za7OWVUZY7xY0go97cdK6Jy742YasOfqpxaGIJBj3rDKfArhCnvQhoy6B6hp8cX9M49svs2mKGCpYZmvwaRMpa/CtQ1qDzfCKpdA63s/C52s2WShZZ5DWdrymrMv4u+tcwcA3/UsV2m6le3dTx4NIT2mEYGjbLEHUFXAFhwkhGLH1OCxU5wbjQ+VmMem2Qu82OXqiopnOfRTme0cKKfzp65Yq84rGPTJOjaBd/XymH6uGiFRk587Ir351F1/+4hJPP71MIqybq1dxy7/6V3CXL9sDEFAjJjJ0O0ZHkqb6GtUi+Arrx9qv2ZqYBZBOVmYNvYxyJeLd+q6SYaUGviN6C2cNfHM6siM1s6lhZ+yFNVGGy8hFPm2GDLCJBlW00Ww5pW7UdUcml0XMEaBrI43gI32zuR6asBSduoFPU+BKfNwQwTfy6lK+Uuik9yR1G7yaMUagkAVN6TrS7KKMnFHyiRp8zKDtHACXzD8zqMeBCJFMHDkLQA0y0nFrDp95pAYaPTae0WqXz4f5fdBh56CgycS55+P+luu6PUhDy7vO4f/5v/fw8MM7OHeuspZe4BH9zNnQr7OynckDBL5utXZx3eb4kCpVibPDumuy3EQctaetvjQ4zvSV+TnPO4Qzx2/F2VLS53gtBNFOTbde661swAz9oQoVkzw6cghdhUPAh577Dzixfwn3vP4Cfuz7D5so5r916lDTP8f1BtqSqnUQp7iEoO9fi6yb87khnczpWh0wOoBpRtY5aFDZrUQbY/8Pvfw1fPSZ3x/bqTXoTaFYB4fUGVd7jKbxNCOnDX7GHlvo+CQ9tPyoCvviKCm7t7AZbA18W7i5QaFKCZGv4HK1w9IqJM66yVDFeEsqJhOCrjAyKl5RGU0Pb+39FQPQUSP4VMjM5a03LuAXHvs/sHN4lTRLkTDfNd6Uh8dwCFy+3AAh9GkECSSCcIzgo5E6CVXrx+hlU50xYjgoc5l84wpl+iyYEcFXrezaIIIvl6JTA6rI65lAwthTpV5lOr/aeXVy3JneTsX+hXDI1k0g60m8V9GjWhHuND2byRTWQOY5mqKz9zojSjAWwWen6LSGtPi7ey+ews986/8a62uFALSHSs45KHStYMhge53cGFN1UU/wCqXv+G3pN4bYPxXfRVuPy9V1tNev9D8qJXGLedZqgGj6eMmsazX4akBVQBrzYMoYmfbe578PdTyga9itV7riRqu3Qo2ZIoJPxVdCoQZfYjChwn3T9DWfNMV0BhIDXwj48Kk/xC8+8s+xXF0v42yAxFHW4AMMYdqI4GPpBpWIsaQfpCk6j8NBgyFFQAq/8V+pyM4J9U88sTPiffZsqtBzIWDnscf4RToxdOo0JV8mwlUDeUTRvwwvJeI96UC7RSbn4h3vwpM//d/i8MMfrsItMfDNVCoxpZjvbAMh+LqJ50GbceZgeBlTkHPAKNkqsuuYDMhTdOq0BCjzbl0zOTz4gxWefHKJl17S16dmsLAyW6TvQaIGRFsWtaIolEwl/NhRtOYSJyMrHaJxFsvIL22/pwa++vPQr2gu3ymCT3N01HjmLI2cw+9tyhfmuqBz1UkDH2kG8HNsrFmsnBVUobecjNCqTCvkYA9aY4p+s3o6UuKn2R4zIjlz81R1vQK3cbwNvuusPUZA0rDaFJ2Sr2puXE/aPvXUEhfONwhB1OU1a2oq39+RGnyEb6ueooS5yBuGWfNEKE0flHKqzxj4kq4sPliJFtV4pByPUjM/6ppR96QNmvMnmxONtpPLQRnv+i1349RHfi2h4bLpkVN0FiL4ivtwRIDoBNJPN/bFnFCDx4n9S+Pve974rjqEpHvJfSOFMUOPAFszMyP4Yp/L1XW87dy3Rrmjdw6slwHzN/Jtx/nUzusQxvmg7xnPW2pwK8kYxrLNngPvfunL2T5NKFggLQc7pv8gP3iKTlsXWzzTRB/VkZulb7s18P3AYWvg28LNDVoEn0LkLeJFGaQk4mE8NPr6e6zoeWy6QQ0+iYgWaq2lwQmuL9L8pS/vJu3H56LCb66gQa5Xy0iZd/+p7/4JY1Ssodl30ZhIOcaQm/PSJTfWCNO8csZ+FcVPOzIdQqBzgecuV1KQWUAjQr773QX+5E/28Nhjy6SdqUxjeKTXghCoj0XZuck3R8oguEAE/kRIptGpjjHktBB0rYFPte8pgrn+Pvac9QzzhnNKtOy0H+eQpOiM+JmQyReRE9Znoa4p8uLwroUf6tI1vmNGFe+aqbaK71SDkYoLTReiwE8/+we469KL+NBz/7GvpxSA5mBfbZsqxkLSMfXglN6R41OKxruUto1G8GW9Vzf0kr1l/3W8749/C82ZM9U1+EbQDHzS8Gmk6GR9By6U5ZYVS98q0g7lFBUaxPaW8Njvq0zNJYoLiVjBamU4JSRSzLj2ABw5gm+8lndk5tA0E42ojI5yjivjTl4/h7ed/zZ2htRSVeNWjJHgE/Qi75bSIRuNpEEIihCaCvSbgKY8CkHQh+HfXCo6TcVVqrUhazsy/oaexwgpCZi5n9Q1ahj4tGs5Guc92GF8fe8teP2uB7PWrawCtSp9gFN/9ik6M88rfE5tik4TMhF8xfVN8cmcxVbtx7lAI5q7/Q5/+qd7+IM/OIGLFx0/H2UaSZNfKa9DagQMQa/Bl1cRG/fp5GZSOlHcx71gupZzWW+8pkTwUefJhDclBj4awafxKA6pgY/O0aLxzEltkxp8KpKRN6Y8qtJUPsaMXcEnNfgA4O67PQIcdnaB224j+yMIZ6PYOXnhUFrg9FthMvBFfKZ79SqrYrpKQY+VHrJ4sr6PUK80j0Mect8012fCHmfoFDDxf/I9bz37Eu5+/QU2+OXLxr6vCqnpT6au5RF8c/dG8t4V5884xAbCF61fbnec54NrcCzrLOY50kxPldc6W2sVskYyjRQ3Ib9cOnk/Hv7Z/wnn7/upJAVwQodnGvgSPlPps3Z55XQZXE81GS/zBIMAACAASURBVEconZdfZ2d1rTCYDsygrKwvDaexzUwDH+U1P/zcf8BHn/l/x3ulCL6iMQl96nvN9qM5+JjOIYPTSU5XaKJhNJBO5BIPLZJU48vNz2jIKX3/Ho1L3z83n95P64u2K2V6sZyTTHpo9betwXfTwdbAt4WbGxQiKAutck8843GF+aCXNongG8dUmHtGYEWKTnoIy/Qjn/vcCVy7nkn34LgQSq9ncaSC7e4uws5OpvWIUNJLfz3grksvlp/HxFyp06ichtHr88qVBot1bwQ4cYIeYqI/5cUTj2QizDCDYNANReo1cnjt7zt0a+ALX9hLG26omWTrxaibMbatZOJlBF+Vji2kyg1qdJEKK1Y7QwgvvtGZUAoJ817JAyTvEtKUhQnUSAZWH0rkpxrBl+tjuGcxWImwriioqkBpfO3E3cNgDh2JgKKKUO/asUC8C35WynRL4Sxhub6BEHgEn+mRH7BRBF/f30AnMpEf4g36Z2uiejfc40BPR5bPPz87f0iydZWaPV4RuqW3aoOK+mgKSN6863RhywRh/Jd9AUBTOSc8gm+t9kmNhb3hNqChEXyLxWwDXyI4S55CCJTMEUFY6moVgk5Eeiw73TA+FziOQY3ga0KXnqWGEieNTkkn8D3vWePeeycDuqQZufVk3VKvK/tA0tO41KQiu8hHKecjG+dWXivPTNGpRcXT2sIVZzznGfq/2t5W93uBYIfgmNE3xAiISk8hqw6yhBp62/p1dQTfOL5IPz0bJvfvtO9CDT4r8s0aArBTdC6XAQojzmDN6lWthscdvva1XfG5rIikum9lKu0glrmyR1SPcqUGHzWQ16ToZPwCieCz9ruUt6QsaeEP9GmxRxyWNIIv5VG0lI3USUZG8G1S1zeEzYxCZmfxX6+vzYceWuMXf+kQH//4anIEw2Tcm6L6Bl7KiuCrwJlHqPB1sgn7pc0V+63xXBqJZPwpd4KswSEzXD0TPbdPBZLU2yUaY0Tw3ffkl/CxZ/4tSzXovTPmMz9H4+snEXzza/ClzEV53Zg6n4pBfaWcYdXJAtIzX4s0SyLjJY2aaWeeo+di42gRfMZipCk6p/HEw8O9Hv9jMPBlQF/rTvnPgJr1oJw32rO7h72Bb/HIozjxb/4N2tOnh+f1lPJjN9ThWDiuaXzlUYHSujuuvjLyhpYsWTvk+fMNHntsB489toODA3FT0X+oMkLQI/hyuscaeULri7VTOplFwjMNHYKZ9tk6d44zRWeJXqqOQ1sD300HWwPfFm5u0IiG5wIFBc17O4KpKA59VAOLbKhgfOKtuSk6KS6lqBMrRWd6LR85Rsc5/PjHsbr3bXbjiI/X58shsCiK3EFAlQI1kSkxgu/y5WasEcYNfKLfkR2jwnNMGyOEGGHgc0FXdute8LZRmeKzKYQ5KTorYU6ap7GdVoMv+ImpkHMqvyktjGx4mVFIZCnDSCF/8/fJM8MlHDSwU3ROAppzgJMK3NJ7amnYhgtHieiQz1EmcL3YxRPv/Y3pd5tGngJ9is6ozGlC96bU4ItRVLIGXwRdCcPpMp0n7h2pC5Ul3Khw4IJnXLq2b6rmJTdgV1+DzWSeQ5qiM9Tg5Xntn2xkDcFRRvCZwoRxOQw3dOVw33c2RScxgrMafGvdu1sqtoG+ltd4biwW2XfX8OQepYokNwpShtdI25KopHrreS4Khw+/gfd2iCk6FW9ZNTJST9FZU4Pvjjs8br+dOztxdcpx0Zv0Ru/dml7PGVnUNVCK4NsV2RfoeSh4Qk0RU0vyTUWC5vCE1OufRvBp/fQRfNwYEpWvNZDwR/UpBHqQ3nY10Q2EB66N4GOfhI55hBSdYgQ+v8OP5rXX0Jw/T/qcGv3Gb1zHA/ev8NBDa2oXMcE3pAYfoaFdx5U0DeXjMDkpJHyyqYWlMDH2IQDrdeoAVQLK643/0cm1jOOUHpI1HCwXflAFoUzRWeY3x+7WxGlkl9TgCx5yrhw5ayQOcQypJKveISXZ0un/5x5jvFNHHPrIlC6XwAc+sMKtJ8V7WEZSWoOvsJBlikYQ50BplN0YMh/kqCk6j83QOhMomZqjxx8NtG7ax7kVSA0W2pr6ydN/xq5VyfoE5FnkW07TssYOjd+U/GmFgc/KUlMDuRSdWuYAFWfjjCvRDavtUSGn41Aj+Axb1qg6IOtLWyORp6E6mkJyLBtyesBMyZEq8CnfJA3mdC1ZWWaAPoJv9+Aydr/wRSzOnMEtn/40Fs8+iw89/fu4+43vmShMc+Qg5WNG1xQn5BKhMPlBBSy2zJxfceOll9rxqPje9xZmW+38HnHAlJmLRcV7/lc+lUO2lIlrXL/B3iPsmnnDHsCqwcfwFMEjkwxqD5feSw65vEOdNS+lTbU18P3A4QiJQbawhR8AKARL84qxFB65PMJVNfiy4HR8yD15Py8wDMpo2MLyRFxnChr0sERlOFdmElaLE2i7FbtWZAwqOIfo9XnpkhujFUwDnxGWltQXYLp+Pre9YqU8F43vKo7bOkFPT5vA72f7qVWysQi+cupQIBW0Yj+TzkdE8Mk0BuR+FHpMBke5nCvB164P8M6zj6NzbfouoUL0r9jUmuEdbvpFBRPnQpKis5T6TFPiTnPOBSCNwa0Bqtt89Z7341s/8avMKEIjoNhzrh33ThO6vgafIrfO5VUpNIPiqz3kkUjU+1sqRK2oIUDudaEkAqoi+PragwcY020V4Cj1N0MA0HWpYZhAlXLEp3V/VNQT5a08q+xd40iKkBUn9X23lWl3gX7OrMjkMYIvdDhxIuDGDc2qOo21Jgptt16lbaGRyMANiIUIPrWOxUwvfUhlzMYGPiKwZw181V0yaBqMHrnSIJDgUpGi04pwkpE3kmfj+5wrLixQ39maIoWecgNfXe1Ac1ztBh1AKOqSFHKS9hVBGGuQKpqSgem1zGD9/qYRfKNGOP8MrGZlTRA7wRctnOv0e2knyZiu20CZ0DST86DwJLPPHA0dul9DMvvt6dO45dOfxv3nGtyx9z/i0m0PMOPyQw91eOiBA9z2mu3ISGFNavAtSOTiei2U1kFJCyvwBcrccAjTXo37gepuHnjtCQTXIIT7rCFsIBMR1pkzktARcpWvk4KCMLjGTFsa+6MQVn680ogUnamRtJDVRfDjiUGiAjQluQt1fH5ODl0fTv8nUc3OcUN4COZZxngcauArMJB9is7JMYvN5VxHAYrPzAg+KesA8oyi1+dFyVif2iHMNGJO56TWpyWLaDQst/yiAb8ZnGIzAc62MaCQoWFUQAPonEjRaTgl5vpLLlXOa8J5qC8pZJJcik7ZlTnRAj/N8FBI0TmXBxzporZOMp1p6QlZe4OXoXoEueenS1QO1iL4jqa76r+uoO3B+qFARYrOaV59kmVmtTiB5XpycL31xgUEook/8dnP4p4LO7hz/XzmDWiNUn7OeUkuO5GJYwNBQb5yNG6VUnSWbuzvT4hdvCg60xx0jHWqpW5WU3Qq/JyFXlEfHAIr3RPbybbVsgIBB48WHhoXy/gIZtx1ap8lmUV38HLIBstoPqyldbU18P3AYRvBt4WbG7Q8zAbT2h926bXxMBcHIT00ZA2+0WNtwxp8PG2oYAYJk8dqmGnKaEl8ByVZZQYkHZ+mUoCw3n1gUmpg98YbaM6erT/kBk3y5csNFus+Zl8z8HnvzLQY08Gczh2f4/oUnfIgl3gnntFzQUTwHcFPdcIjeLzj1Ufx4JlvoOl0RXgCCgOUq8HXGB5EAOBpSrpKY4AahTQ0+omXv4r3nv5PeP/3Pq94P9coLI+QqkLh9lTvOI3DI2ClmCB/pv83YMSBPsJsVGA3O8y4B4Cl6GRoNJOBzwVfXWuOC2d5iBF81MCX83AsOWbwAubTdxnrP1ZE8PUGvqkP1Zs7k49/NkgprAaUGnySvmmRhUnqMCWaxwLaTNoju87NeodReaPMHTXwnTxpWmfG56mBulnrqfi0d2x9fUSBCuR91TkMMh0qJaLRujWs0eoUnWJ/HJOBj/I+fQRfbYpOPYKPGcoKKQzjuKny9HiUpPLG8uGH8Ylv/jYeOPdU0kSrNZXrvxTBl/BrVEnBlIJ6BN8cUPHT+BnlukXnaN9UQd8bbIwBFEh43rkcjQinzHoUKxOxSQSfKgAofZdq8OXm1YWAE3/4h2PXHzn17/U+Z2xmer7TNMRdx2sByW5jBJ88R0b+xlBUpQosxyL43n7+W/iZp/81FlfeyOJdStGZi+AbDdo0go84LiY4nj2Hn3/8d/CBFz43XXONPs/G3Ic1of87NMIs3UtaDT7GtwjZQ57def00/ZYar8J5oZo+Kf5rkho1pmznnXMnyaoIvhlFJgMcz7RCZeQYSVzbF12zyYO2jB6vpY/og78ZqVLnNtcfNfCV54H2rgSibtb5Ll0TANaLqVyFmd2hMoIvOMci+CjfVg1SIHEOpQwHZgRfBY3IpugUeifJJ+YgkasSvQNvv6mIOyeSGTBSdHprMU6O69N+1Pv2Hnxx+uOowcdvafzCrG3nlSxHYmlNNStDwr+vFry0y7I7qDcKxfuMPsp3FY5jmo4i13fNkThcsJz5avljqtsDwNN0Kt/Q0rUG1ySy0iY1+MZ/C/ss6pxk1pmj6gam9wxmnXapm4tAs26wNplIXAOL7Gm2cQSf9BLewpsO2wi+LdzcoBEspQafRVum69qhPhn++hSdvFeLOZZ9a57ulNnJGQCZ9008mIUAlTyrRJqM18oTAde4KiNfLkUnZVJyytufe/i3cGt7gLd0f0+JOFE8X1cr7O8Dh4cOi24fTQPs7k5tosLCe87kUAWSVDDSCD76TBPqvF37tvywpQKJlg4uB2raP2m0yByWtcqyk9fP4ae++ycAgBsPHAIf+3gZN0UR1ISOMT4W8yCFF5ai0zTw8XfJMe8PvvKX07hyCmo4q0KbSeEl2iZKDT8yX66k9JeQScNG330uw0+hIwY+Dal1q9ff7FN0BgB9/aVuHYAaO8ioVCt4dQJoQ88QtwfX9a4KNQvkOMzAR9diFCorPkqMNB1TFRYmuyZFZ9YJpde82g9rAkdi4EsFVRUvSV+lgSZDS2i9D6rAHdERfb/8covz5xvmlTlC54GFvkamGnxrRus53tP1JEWn1t7x//safKReX9tmN6yGp6xpqjyUFfppik5LeEvGTAx8x6RIHKHne5wShaHzLbqBT6botKaWZiBIdHCJoiIdJ8Feu04vHhxg7ytfwS3Xd7BcpU1kis6SQrxk4Eu+j5bC2Tk9Wm+W4k962EaFi4KT4g1Wk6JTRs6EgGoDX1KDb67X02IBYDLe5+hUvEOH6BUv8/xXQ9tOo2QirEs1+IT2KFUwDn17D+ysrtf1mYGuGaKRQ0Dju945xzVYr12itNYjk8UZUcPDxn9CHxWo9Xv3t74O/Bd/mw5fBrKArZTTkt+mvA7LfkIG3f3D38et18+DPelcVjZLFMurblxR7Y5IISkaN0oaOH42yAi+jOyWgfTbifsud1dHrhMRfFqHNIJSjeALwazBp0Ga0teuwTcHvLcN8qxfQwtsKbaTRyq+Xc3ndQXlaq7POSm6eVYbFM+eyWAhU3T2P66euEfFiSNbb33qiOzY+DWpfVv5jkGcGM4VZQFNub949lnsfulL5eEyKTq1MdTlJg0zqtFN51FCAL785V2cPl3GI8kUU0JWu6VlMUnaSysyN9wlWXIGEshTeabjHL2kXLrWcwaR5Gk/TfqDr/wllqvrOLzvZ+BI1KlVg88N8ckUbtl/HeH2eW9Q67mhOiEfIQPN2G+G34wtetTEGigQwYsXG7z97bZlTncCnJRwlNZfu9bgzjvTVNm1kJvinhfWMrFoDigG3clvvjSoYHgka+DL7VmT9kgdQRiM8YV1Jfrd1uC7+WAbwbeFmxuqifygO1ENeXrX0qgyCtnOVTERo8HL8Gof8c2kP0hqD0AwelKAGwMBlHnJ8K98nEJjY2wKtRF8sY+7Xj+tKJtDeiisVrh8uSdLy/U+9va44ZV75ejvYKWCc0542gU9xYCuvM1HoPFXm89R8Bp8hTSPG8A7vvMV85M/+OAad9/d4a67vFmDz/JuTJSyxIDlN6rBl7axjdy8zXHOWbIviWf47Vdfxd/+5v+Kt7/8aKIILOKhCNIWzZqpEp2eZVnhNAOflaKzISk6PSwbhKELqWKkYyTpYsUj+DRFQz8HPjVkGRF8I00l9O3UqR2cOdNk6blvWjY+i3ZWaYHdVxVkavBZZ5gEF9J5qdGflFL8UOAGPn6v68A+xOFhX0/h+nW985ywMNbgC97WA5I1QQ3UTXdYKcAFtDNSdJYMR/EMlqlvTKWfc0DTjO2bykX0ZkXwyTEaLUWnEu2pOTMBdSk602fmK0lrgPbihpq+lkKH05K6faf1l3QcgUbBCSVS0keSW4n3K6PwNb23mn1AS89dmOv+LCdn+xDBV1tLL0Hf4tUo/0AfWvA03DWplsf23lcb0Nk0vAk1+LSU0/TfqCBq237M5SOPYOcb3xjXbd0gDh2twzesE5qi0zTmBoVfqVkbcWgEU29TckTTIviYhq6zU6GN6z1M75VE8FF6feGCit8cekoNjm7JFemJIreQolNG8M0y8AXjW8axDfnQwgXg+7BbUVlE6bwmgg8wI/h0/oavBc9S0PEIvuyLCKDyYi47RHUEkzHe/OjzTZS++ebao5ZDUFLGojKCrwmdmrpuvdhl19T5zDhN9LxTVI7LCL4NFMOSN3aa4t14hDQ88dnPorl2LW0rjCSzavBFOWV2ynfFcWaAp59e4pFHdnDhQp2hsThQoUnRwEflOBLBN94W7cc2AZx5UM6Ami2SO3mOK4Jv74Vn8d7T/wk/fubreOdzf641gUMqI7SCN5GOJzX4jBlqQrq+PA0uGMqIMBw2qMGnNXKYn6JTfhfJO9A0nb6j9HtyMEj6DFPNuFtvnQZ+9dVpjqbx82cnbZdL0Rn3cU0EnzkXGSSa4KvoA/321MA3R65K8YgGPqO9tblKk7o18P3AYRvBt4WbG9QIvgBQOSMj8GjEXd6LKTojQa9NAzI+X6gEXFuDT2OEUkY5MgxCWHFWe2XgWg2vZJIJA0zT2hnN+T3La0jW4CMGvsX6ALu7+kErI/iY0CmEfx7BJ4RTZSpUJZlUGA19d52eJnA2WBpIrekGpp/cJ//VX72BvT3gc5/bAxQGyIXADHwsHalY24yXpYWgZ9TrqoHUwFfRWcVg5v4RAzZ+jXd/5/PAO+9Ix8gJEFoE33AhieBjDeu/OU3RqekRTANfsxj1ay74qkg1CqqCQcxnE9bwoU/RqYn7ak2ZTIoJqz5Z3CPPv7BEd2YBn1H4ThF8QfXGl3CUGnz9gD5fj4SOFc8YJUVnjWI2nU8xboYw0Fsygq9XvE19Hx4W1mcmXUrU+zjfYbEoK9BYSjoj7YdOwycBI2xagy8E8zm59akyJri+dtHcCD5Ani/HnKIz9Otei8LQrtWk6FQNSvF5cjbnlMw1YJDa3jAVv5PBG/ZnWOB4i8jDHD0z51rc4PvWIdZYinVt2XLynp2t99zjcf58//H/xpP/Arfsv4En3vvrOH/Xu238lMwM2qcopbT23rHvr2WXyIH0Ut8sgq/yecHnYb2ePRyA6iLcJQNf0cGnaeC8ZwqZtgUWzzyDvSFaxBGlculdAnoDXzvU32v9Ch120XWTo2Lctwldy5wZ2dRO5Ic8G8YmNMNGreKLTm7OICBwjePlnCNV/NQ2+nN+NeHTLpshAnCYK2ng81rkAHGCEM4TG6fWU+QGa72UjEER1qswKobUtU5lKEsRGYJdg68AfYwLjeAjkfPSEFVYI1lFZ2lClDPKmri5Bj4L5p6Buawfw1V9HGEoKtmEJycs3cBHH/be6Z1VpOjsMXZJDT6h6imDwhz0tCEjVw88QZWcmDAvGT5aoGXSjMQATfGK48oIyv7vN7+pZ2Upg1PHtq6N42pbXqToHKeIIDy+iyFDhcCjsKEYno9ag08zZs8xiMR1fNuTD+PV4dLbXnoEl/BLsglc8KDlGR3S1Iu3Xc+UsLFQIAY+pkMMSJf4zBSdNVCK4Bv1DwX6GelKhJj95do1h8cfX+IbO7fiH/yD6+Qx/bz2wxo7edLj3LkeqddeawGsoDlT1ExBie/SvuWVe94JvPRKufMSEiGgsTQkhm65T29r7+M4D6V3b4YMEDnQoriLTEwFP7eF44VtBN8Wbmpwr7+eXst5R5oMuGzPjXhNMzmP1nh70aFKzH0+RSdRSkeiyl1+yohUgIxIqqrDZxUTFl6bhXNqMPwYCmmpEFuvcelSj9uy28fOTmCe9fGM8B6wDF23XXkVd155OcXbAUEIjrXe2DJcPr5Lrti4BVqEoVyLxx3B149bvq4ZKKMyEkD6vaRkSBj7TSL4ajVBSU20TYSyHHArkjp5Mv2FcxXfLVNTlE8jF5LnoN6taeMUbzNFp2tJBF9XzY9la/CJi223RggOi8PKFJ2ij8SIoqTo7A35Q/uBpl67ljHmDM4Kue+XeNsXISNgWjnNlGfHd03SrKSGSM1mVDTwZSAXwee9bbTXwBT4QJRHfo2dHeDv//1rePDBNT70ocOkgxCEgc+oLcpomuv3futX0/gbRPDFc4z2SeE7zy7w2799UsWhR5YY+I6pBl9lph4GjLQNTi5TZBp1gEkj+Cy86foo1Sjr+05xOS5eh0Hx3CFK9wpeQJun7Hhdh+vXHV5+ucX+PpKNwL6FePaeezxOnuyv3XbtLNruEO84+8T4nDyi6F+Gs+bIFPIpOvsIXUKHVN406ZLgoylXjcYaLEQNvoyDi1Q2OemxXgtk4boMYzenBp96njRROUfXXsDu1742/t55/PEajIdBHLp2UojHmlXrNRljeA+qHDWdAkuGMUIHEXIRfAVgbMqgfKow8E1rPfB95KY0fKriUw6vGPgoXU+UwCSapFnyfL5pCn+vHtnT/cB52EJ6LQtK2S1qjH19PwRPwj8mUc3yQAphjOqWkSosgo8a+FQZw6YX1OkqcYwpMKlyztmYFRF81Qa+irOr6vPOXAO0+RwjcaOQ89zQYw0+Q2amDh0WHiX5azzTnGMOxKUafOrelhGxG0bwmaCl3q4AOsbcqN/+gm7ge+ONzVS52XfOvFNdik4BDPFUJxW/P2OtfMprzXWG0PawVX6mCoZn5fGl7qXA6bwLPjkrNANfca1mggDku0nn0JLsUWX8GvqweGbzG5HOc2LwuXMN1ofAlSsNnnuOGPutCL5Brxf5ZQB47bU0gq8E1JCY6JhkO3L2AcC1E3fj2l0PZPqsvRGNhxURfEwPazs00mvp2Z++XEBjoqepw2p0cFY5jS28ebCN4NvCTQntmTNo/viP4U6fBiAVIbqxxSJqPdPIGzAl8eBxPh1W8wx88sBOiJ0UXqiikHmgDn9zyoyBuMqUZ0WFRhSQHHQKnXnGuFl+PmmuPKOk6LxyOEXw7Z3gzp+Hh5ERdKaXyQOnvorl2b9Irrs+XIHgFOCauDhou7RPK+KiV5jQ95ovpMvHSjX4NtFe5R7RlINM+RQ6jN6NyrMxKiEERDeivo/BdS13+G+g00hwBaAbkDeBoOxLAK7RmUUt7cymKTozJGMWeJIBcl6KznZU6LjQvSkRfNHI0h4aKToLEXxUsa0prenIfW/93xxvyQx8wXPFj7ZvjvBxQnC9ciAT0crqssZ/1Rp8/FptDb5ecCnTEJYpR3hayhSdJbCEi7Ev9HRmsQh4+9s9PvWpG7h40eGpp3aSBzvX9kXVlTpHGu5Avw7nRPCZ/Q7ejRrNdAg4OLAFb2oZUOsWaeOJozpRJLuj0Yq435rRcEXH0oRpPYKPGQ6MyHiAvEugKbli37ZAPQfG84Z5jittlOsbJjcwb4TO46mnlpM+8MeHcTSeNYnuTueRGrSF/pL9Zc9YXr2ZSe46zvOM53t2gpzyn9Wy5xk0mg0AWCw4P/ImRfCxM1dJ0akppefV4JvOrNEZqm2B9Zo1WyzS5+aAJyk622GN0GMml1q2KuuHcblP0alPNq+lpN1XgNDIsDa1hP3Yju6jGPmhWyzU8V2TVaIlz6yn/dAsebivXCdN4Jku0v6OXoNP46v7faUhXxLnppvrQ4/oQpMYs4cDiRqHzQi+tThvs8CJWUA01k78uJZSNZv2EVGOGfiduVr0+JzxTE4nUQKTv5gpw5RkBsuopvHOuekYsyyEbqB/xJAuxvHeMJhmsxYImtlMPB7CFClTvUVCup+KXzvIfzLQzVBWK2smhHQ+LEcLSW8lGEksatDJwjvOPoEbe3fi+2/94JCiO0/P5SBSBmWZsVT65Ib0knmrc5XYUUJQOaMjlPZgnz2l1L0b+kp1lTKtY2nvqf2LUjPKv/14DkmKziNnoAHRXxopY015j8pxeunW8V4cY7UCdnag7umIS5TJbrllOlcvXuyd6tSzP/Af+/vA2bOt6gClvUfU4dJv6ZtFIidnwdgD8Z5l8KePtYQX9R6IPvXy/Yzh5qKXhaID/dbA9wOHrYFvCzcnrNejcS+BDOHLpeGkQJmEKMREwT3WG6kFlRB7/X7uwBsZm4zgGG+bnpsWQy8EqCow5CEH7rmSU97mriMEI0Vnj9+y28fubp8+dbnsD/oQ+r+9HYkwnFS5JNOPkGllxduVCAXteUD5DsNvXhOQj7cpVEWCHSNwBbKWEjcfwReV3tGDaFT60rlmWq4JEobU8KTN4Tx2lNm0R1EYZw3iCZeaH0hVKI0MmBCgNlR4M0OPgrcVwReadqKFmRSdOi5G3VLRuPUrBB9YDT6/swtcPxzHZUJwQs85Ay4NLPH6KFQOa3C1sjelNPCVJnuu4TPtoBTBR8FNz1AISurSCsEtSfGTEVXzEXwKThWgTS018O0YmYaYItc16NolFusDjaT07TVDB1WoFAx8Vl/srFde5sT+6/jQc5/BarGHC2/7Sd7JTRLBx56PLwUOxAAAIABJREFUimbNkFFh4OvXp0h1aUQ49bhymnYUutw/b3xDwZRYut20nmeB5pL+YqpFBuL3jauezY1UwHtPlOgVip5o4LXmrpr3UOgHBe/B6wfG871yAJkgQoskzqUBWr39fuBbfQKsg53bsnRq7HMDAx8Das04php82sILbQsHRQapVcbLIeCwbibCGSNeVis3KebB+VXWr2IkUsdRlEaArbcpfrNiDb4KJwjKI9FzX/BP6qOuUdk18/2Jga/d4Qa+xPFCcT6RSmT6XTflKY4vgo8oXmfU4APSNGXjANTyQLw0iwp0xZlgdKyRqX+PKYJPWwhqGmljsR9R5CNozFsHRQOfMdfSIUSNVhygZ1f7ho0RQSadc9T5yKXoDNOZFvewbxYs7fAsUM9uZ9wYbhsyrorvugNAIr4r5XWqwE+yAlXW3OJ72uHVVzesuwdbPwYAu4dX8MHn/whds8Rr97yfP6dG8BmDKEQokW+Rfn9rnOMw8B0pgi9+J0kbDL2RTLFfE6lZtE+aNfiikTTihFTHNjOCzzetXqImzK/BV8M3SPC+T0tZ44jUtoORbzgvzp5tTb5lvOSB3/u9W8fSQED/3YqyVeClDbxr1cyzNXMhwcGbNYiZgc+JyGnF+Uw6E+Z10GWYzo7JiSkEmBvz4Od+DuuHHsLuA2l04xbeXNga+LZwU0L3Yz+GcOedcG+8kdxLDyhdiSOvyUiQCNELYqzB5wxltdF3MUWndjAND/MIvuHQBhegWF9RvlIMLQVslU4KT+QsdnMFER/0ehGy4Wo1egQv1vuj0XVnJ4xK+oMDlzCCNeBcanSqTtGZpODp8ZKZnDatwUfXQQmnTWvwlT45XVfSwDd5gosafDJNEhGKg5a3omDgq11XWoROCSxjgNFa/ZUYcuU+7Cchi4RUSmvKtxoFlQU0e6O2VjrNwOccvJsMfM7Pj+DTII3gW6NZHURuFF27g0DlU03SIdcOSdbGdJ+E6a/QYFhRBsBEE6IBQovgm2tIMRwbJ0a4th6JRVf6Ak5K53ZffT9i3MyGaIjwoNXgo+9QFEgz96PXo6zBx/WIXDPdR6wcmP0mSs4QmJKoFMFnQZw/K7Lzw8/9R9xx5QwA4I7VBeAucpss2HkRfGROxPdrmvlpojVFsxZRptfg0wehetY2c37lFIob168VwGhnofarpOfFMzLDa6o3pJAu+D7WnO4nOBUXmj4wOYsANfW6VhORRvBp79J1PFpmdHqrjHotpX1UlVyk7xuf+BvYf+z72D+7j8ff96msMVB6k0vlTA4YWjRFp+a5VQk0fSUdZ8RJyQZQjArMDujgWxLB5+0IPq0GX8o76bisVg5nzrTY3SX3Qy6Cr1CDT3tmRopOQO4juoFdMv/JWDFCSPZt7ItA+CG3aBkDoqXby9n+GxnBV+D32PoR/HbSkOzt5Jbyv7zQrSZEZQRfAMR7Z+Rf6iBQiOBLab8bxumG+0NKuMDrlNbW4Ov/2jRJ49fUM+rgwMD/eCL4btl/HatuHzf27tIbZPqpcbCLkPDOwTbwrQ86vPfFL6DtDrDXXYNTyilaupUaXOTzcaHLuqLzID0gfUFuHpubmnAC3oMa+GrfbRpDWTPyjFTWn6zdGQLwyivzDHyWscKCDz3/Gbx2z/s5/VAsGWYNvghuiuCTNcRZamXquK2NcxxsouiE/SwNYBheNJC8SBK9N7UESrwlAc4PifUln5/phJwcKW6iwxHitztKDT4t2m0yGE3zEfUZFo2VUeQnT3o0fo2u3cFrr7XqfNJrFy44XD5MX2STCL4+erkScjQjGJHxAhYNMTD6qc8561nTnUn5ogos2fAd74C/7z7gzjtndriFo8LWwLeFmxOcQ/jIR+C+/GUAE+EHJmHAUrKpIK5TZnjR9j8mYcalBNLyQkKvmHROHGpMeEiZiVEpEbhSR4J8NiqhTM886zq97FzqYq1BhpGpyhdPrwfDLKVE8MVLi+5gnPbd3TDW0To87A2wVtomyXSYEXzBiuBTrknP0QFS+bJ8KtYY2qzxrPZHYXpthclwLXojawYEuRZJmrGABp5Gw3qffJwavLXp0iL4jiPqcdqXQuA0PhpNETQaiHIMZTaCz3po3vdlW1OjW4ay1LtmVDQ2oVNruqngnM0QiotNt2LRe6vFHoB9pvhnXQuD27e/PWkXpFKUngsygq+UojOO31RE8B11mbmuM40Zqr7OuJGcK1N2XLPDOYoounTk/PUGgPr1mdOZxC3Uhg7LZf78DgFDxMoSfdaW3rtTeSC5woTrxSKf9o8+Fx1/iJAZFcGSdkbjHtAr66ab7k2K4AuIH92MZqsZR4vcrqjBF9tTYb1x+rkqH5Q0I0e/K9m6sad4w6ynpaxHiXOOnoWAbDT22KdJRBW8khSd/PsCZcOwXh9J0/pURPBR3DVPhwyUHHDGfWTxq7s7ePFX/iG+emIHcA57B5fswaQ8oGq5bBhteVr02CaMVe6ZEEZBQ0bw1dRcUbuEQ0dqVjWDMrz3fO+vqXydG/aHXHNxPsWNs2cJ73zb1MZKEZczyvYIKpuPpegs1eDjdCvAgUkZhfMpwM3jGcl+cC0nzC6J4Ou0oPvpvjTwCeVc7bKbE8HH+9SdzAAZwaco/+h7Y4pi4A45gafPZJbCwnkdDQFNgxC4gW9uis7clmKOXBknXAY3buidVXywmm/615/6l4Bz+MYH/3tcvu3+8gPEMDBDtZzU4OvfX39+8fiTePDMN/r/FwAGFvzUu34ZP3v5z/of5OW8T43rOZ0FbRf3MdBHDo34DrxbSVkfwclN5Jz5fkk/OWb1iEDHsErNmM9Af4MzZ44ewVdoVfkg3U9kGiN/ivQ8kl2GwM+NjQ18JdkkZ1wpnQkzzmlJ51vDwFd8J6HkoXPUJMwtf0waGd3MFJ3BtQDEIT+MOTuCj0AuRWcva/U/SgY+qfu59dYhqrzta/mV2HSdRU8jZeXz9OwDAN808KGB3I30XUwkFNhZpIhJstbIOoGZPueQtcjdacD0qbRPa1/MSnmxheOE7cxv4aaF8JGPqNeT3OWWghyCIBqHfqQ/lA4xZVmBIpaMMboHl0JCVSWKJcCF5FpO97JJis5cutNs2lEB8TBUbyg1+GJ/y/Wk+F/sTR/n4EA5S6jQabxer/inKTpnRPAlXEBkPojnWWH8LEjFoBUCtOEATvHil91Jhcl4P6ZrVPZZ8kxOqKrgLGoVLklgoKrEPBqM6BqbyzkkXGoxCkVT4irKN0a3btyYJVR067T+AQXNsBHQF7YfaaHv4NczBQHFwJBG8K2wWN0Y8VstTrCoE92rVcfj8FAoqZRNyFJ2GeCHxPWj0aagNKtKhZkT1ktFM7RzKqnBlyoItIjLfFoqfX1EmJOis+YMsGDcQiGweqsU5LfthogVq1+N1sX6jwB6Zf4GxopNDaQ9Ai0x8NVaz/Pj57xMLZACIuUn2L2qFJ09sBSdlRF8NNJYDl6nIMh8v9iBQm/pbb6syml5GC+n5JOVGCWK/+j5rPGsBq68P2LQCMSQaThIqEhBUYAK6GvwkfQ/mJTuNbBJBB+7H49cQcdrx6plkfb3HR55ZIlHHtnB1Wt1KTprwYpqiYZEeu0oKTqBPtpl7IvUh4r86RTBVz6jq/goA1dKv4s1+LQumJHV8NgXhqTxD+HTGC9q4Bpco35nK0o3MVgRRCSt1CL4uKJeqcFXCZzHNJ6ba5wg7Wl5MTVFJ8irK4aKsT+tDoYBzFkIQwYdJULS+Y6/2pwUnckyD/r/sX0hgk/KSHMg+3lCwEee+/dV/SRZPwYYa2lbDhSKfGnhtPPoo0m/QJ/qf9QJkPdfr3XaV3pnCZ1SVxTQ5ZdifyR6zIIx2rncewJZuVXja0Ka2laTfRKchTwUAnDp0uaOXePYGfxl2Q5TJCvRMBqZR3iXEYOBJSmfG+VvWWqQOgXWz2Gk1TU6nyTjRoWBT0PdC2cZmqJTrq/k+a4DWzPFs4a/TJSPeYuBj51rQaDnjBrBR/+God2g48lG8E3n6XI5OcLFsj4ZNExWj37TtE2/fqle0LvFnOBOFSJeTfBYNh0eemiNvb2gtgGARUNTdLpKfqbGk7Fij1nIy8tbA98PDbYzv4WbF+64QxWGc8rTnFGKAhV0pxSdI/eVKF708fo+GkWhQA9Ry+td/qCeTtOzQjHiolFlpmBXw43IRzKMwBzP15HpUDkXIRiv1ySC73A6tG/bG9scHrqeybOiqqT3OLWbCg1eNoKP4JsUSx7uyaVxHCk6ASWdae7ZI/L3/HlduRE6PbIpGkXGZwI3LjEvbmUfVb2mNe7sjsrDTN9B0ACqOKKgKBhqhT15Td7yXcDi1Cmc/K3fwkN//C8y6T3S58a+1MWhG/18s5hq8Pl1tdIpEOEsvckvtn6F9nCK4DtcnkjaJ4ow49vu7qYRVNP7CBflDHjXK+u06CpNEVId2WhAyetcVQwndRQ2r8FXCzyCj89j13GcavXhuSPAIbAIPm7YmvZIcM2QotMQaOWzw/+99yyRAitxnPqa0hZSukeeFM+JM5dE8CVetwbI9deIMY4qO40pOjUaq0S42xF8BCfDOaUUITdXSWrW/qK8xswUnbXgPdDdfz9W73tfeoOAWruE/OVK4lDEhxoMNaWH5mwm0wH27SidSQfqlQUcmV75ZgOn2/noBP1bE4OJWNf5aLAQURwRqf2Wzz23wGrlsFoBjz65O90QKTrnsBdWdML4z5iik8ggrXWAlqGP4EtTdAJkjyj8quXQ4rTNkRmdAk2xXEolH7TFXmHgo4+ws1JG8EU4PNTZLtcgpdkwvzlTyLcNU1xJZX2SSlL0J1PtWQ502rMMJykfir1u7YPk3cg8+PU076UafDKKYboOnqKTpr+tWFchcMXgFPEr1sSMFJ05ZyfLATWZp/39tJ31/BFgZ3W9qp2lqB6nzphrTX6yPgs9Buh68M1C1a/IT1JDRqIxNYQJOU+iklmKzioDHx84oIYeiX8yiKe35n3/4EPCy1l9JPKQcELSnDaq8ZDvXEIANq/P6LnByFjjTY6YnL5YRsMj+99ka/Dlv+UsHU/gTuRWqtlSl5L/YZmohLyYTP0Ra/CpvNfAN1iO6Kw2m9G5xr9rW2806JqTlJbVmGpVCydgjTdT1pgs35PMSUjPvtC06l5U0T48zGdsCAEIHm97m8eHP2w7BNMafHSOtAAVk4dQz75Me4XchIBtBN9NCNuZ38JNDf6f/BP4u+5C15JsskoNvp4Y1SmzAckMh+Hv8IRkdgtKIqkIk8KEFu02pgIsKKUTocTpBLbE847juV6Zr9VqScA8T6XXpnGYD9BPnxIZo2lnYwRfCFjQCL6Tk7f8wUGskTjfwMdSGxipxDQDX2OsgU0yORW/lTMO3YjWRj6GZXycyxgWva6AoLj2657g6RpuhFX6TqO+6iZSV37mn531jeRLGkwKZdLGucgM5DIRfBJ8F3DiM5+B8x47V17Hj33/P1fhTnVNagSfRRNpBF9YM09u9rzxeqqhJYngW2NJlBerxQlO79T6qvqAd9whFZOT8WVM81OxV4JrmIGvWB9tQ0XsCJkIPk2QAKBG8CWCt1YzUSoApfI7Q4xyEXySoS8KqBmdyWg8DHYEH1fWTCnp5gghDfECR6YGX259u+Dx/u/+Ce74vf8T7Wuv8TFzk+BcViGcA24EOnoEH39+1LgkfeiKW10xwlJ0tuVocVV5OlNJ1nUVXqazIvj0+eQeqGSemgb7f+fv4OATn0gfGpG0vY775voHNNOPswg+ZT0oyhvV4BomuqrNTx/BR5QXFTX4GB5JM51W//WfNWpaST63YtyxiU/5uuWyV9jIbU+jwK9cm4iPi+Glm2yqHLELQU3R2TRG+xpwjslHVIm4v9+/nxWVRrqY/q/Yh6M8IZrSUmulSAxVr9TkHcIAiSvpXxqf4qCmgc/NSlfGeL0Fj+BLHQC1CD7SXkYLEzxy9CrtT1FOqs4nUmlq36S1BpPAu2SOvR3Bxw6TvKpJZpiRtGaqwSfoSNHAZx+QjjdUcUrOqDe5Bh8dfW4/9P9CwKSaotMW/YjMbBn4WASf4+tp5GEzLy34u9j/OO7MFJ0IAcH373Tq1AJf+coe3riUX4PjWsnR7w1Ac9iTWZAAZW1re3tGaRQLauS03HO01hfHjTcep1FxXJc1+GL/3qftNChs+3qBpLI5a1sTWR6vCUcOJoMY4+tnlTDwQadrqiEssbirKOjgdEf6+O0sOmPb4qYbc1J0Ar1TjIpioo+cauMlWULGcQq8CXi0suwn/k+drju3qDK2L06dwsnf/E04K90zovEwH1gCTMEpEcecMW8OWdOWUYKj4iSiwtbA90OD7cxv4eaGe+/FtX/8j/Hkx//heGlODT4qZ1gpc6ICpLVqJpcMfEMNPhONQIkwZyLZweRSRkhlpAwDUFYHUsvB0HZSqUyZ07reAAwMv8YUKgqUvgafQ+tXUzqqxQK7J6ePEw18jBGkUY8Z5SIz8JU4HXIiNoFruCddUtlDqArEPFhREBrU6Nz6NjV9ckF7vGpE8AHTGo5K/7GZcwiYavBpCvCaZVmz1o8tRacmmBpWddV7rUKwyClXKEiDTbYOUea5BAU1GiKm6Bxo4YwIPsDWhcpv13Qrliqxa3ejRipprymwI9xzj8eDD4o9qdDSms3RG6Lz9dG44r+GQ9bbhAC4XEFA8eykHBE4ea87TAioi5zRgXtl83lM66zk5zknXERBT0bwaR2EABaxUqOwjsBSdGYMfLnUavdd/A7e+eqjaF+/mIxTpP1DBF8fMVf3HUoRdNIgtVoBzz+/wOnTbcbTOh1DXfNvcorOHhfmEmI+Z62boiLBNPA59hfI4M0UzQo+OSurTOEM3oHkM4tCtY+eyQMtEdFrtdGTpbUaAsd9TkS0hoc8+6PxWKvxNQ1D+OaMqJrIAyE1MN9+e8DHPnaIj33sELu7Bm2GKzoksXEsfIyzbPx/kTonHCVFZ59eW09nd3AwGPjGFJ38SfPg3hB4BF8B6LhqBF+5Bp80DLCU3PHe4aE+vGuyZ2bKp9kpJ+MajGurlKJTRtXO+gSkccJTJ2u/rmMezUYj+LTDwvG9Zsjjdg0+BeR3DBhq8HH8xnrgESpTdFJaqY25SQTfejFF/B53BF9tfWDLMNC2KQ2iMCeCrxNsdXyWGuDoOlOdwSSCY38haRvfvWP9E6hUGIfQp688f77BwWGDktE0wXPGpsw6dyn8fAhInbwq5GMtgu8osMnz6l4CEnkx6TuJ5OP0fzyKWARfXveWRTLX1tBtAYVvSZ6t2aPS+b81nEetfTxeE/zPnAg+Jx2eCuGPDBfJE4lG1hSIBAhq57kU+3R9lFJ0yn6dmxzhesNcYd+r3fb9Zf2NghbBV8bvxGc+U3RMQQijQ1Fuv9MIPtPwbjhlkivK+DZq1nIw903p7N/CmwZbA98WfiTAU+9HaRTSlCUDWMxlIlyDEvNQdeBPwofhwYj0/nhYCUVPP6pxEInDK3kBct1k6KXhpkaAyDA6WlSiKSB09s0kTH2I4Fus90dUw+4u9qYMnTg8VBhB2qcUNIlCwJObRWVYBaOvHeiVspkJpQg+OcBRxpOexPS7UgErrCdtqmQgd9Y38OFTf4ifOvVHzFt6kxSdtekvpGB4HAI2H1pP0VnES4tUpX1ncqQnz5CmllClQZ+iM2PgUq4F59C5lqXorA0yigKA+unExTasmSdvgGPkKElTRxRIFN761o5FC/Rt0wi+fGo3TG1Y+sSCAHTU9DCFGny6x69EQqnBpwkyUrEuzqpsbStyS6Isa3SVtu10Px2POknQCD7LeBZcg9BMQoM2tm7gI4reDSL4AODt579t38zh4BwTdIpRolofKBv4XnhhgbNnG7zySos33iiv/ejkou0xmUZOGz8EADdu4L5nvor7zz4JAGir2AtFeVpSysgegn7+UuV+iXdLcNDwZpE6Ib2eyeUTDC1/7IfdFgY+jU2LvGRU5KTroSzgxw5KPkQdqcE6Kt0zH5bhXjirIt7UA5k7adVHGkdthMUbR1guk9JpAinHvyWzIPOH3vOeNZoGuO22DfiOMUXndCln4CuCc1g3U4YLPYKvB+8B3/Cas8m4Yo3qoLdhCrGCekE9e0gHYa3TyN3vfBs7X/sadtbX08gv5i8w3Ds4MJSmLnl3K/oN4Ol2XduMuH7gAyvcurfGvfd6vOUtAw8iDVGy28DTmwVfKccm14xvpChgc0pkOo+U/1FtKYIeqmeZJM4zlXwh8MFH3k4yX5UpOtMfYu0aitFknkgE32p5S4JfNS7HBNY3pboMDbQIPgsof8bIo2sxlSmhOg5nRq1ZoM21Vwx8pTOI9ucQcONGvXBs6YqybWfCuCUU/t0yQMtzNetMTsByntGh/p0tQ4K5GKlhj57xyuumEXx5XIrIWrdLxsiavvVXZOAEnbci+IpDJik6dcJOed8Rr5yniTYW/YyDk7aEuFYtW7s1BJ0mK0XnxNtSHjk9q8c+xV7qjZqTI5wpIwyQq8FnGfiiPoZG8HnXoqtN0WkA0ymbEXzT/21DagB6vU1pb6uO90kfynNyCVrG+KMqRLewMRguMlvYws0FBr9A7jtVCV5D6GQNvrkpOpvgh0N8UnRLJXUEeajJGhL9xUGzowkdLracyWFSgS9j4HPBIwxFdc0IHoU5zcF63c98TYpOt1rBL/r6exHfsLuL3SaMUxLTKjEFARU6U234cD0wZqV1nR6FRfEDsrkEklsVp7lqc6EetC4fwScVIzXnp/XJUwUuWY+uBdAzpH4d1O3jHPD+730ed116sTf2/efbyPNO4QLEu9QsY3MfkNuGIWgOhDApSBkNIO+RG6KkYARQFaVB8dkEPNFLzk3RORn4OnSdzh5Ysl0Nvk3Hq14HxxlATiMcagTPsbVm4KuINZ7Wafz2XFhI2vug2amqIARkvfdMpaAqpImzrhC52TcS6aQzxIMqCNIIPo5TqSZGTCemrRFaJ8qM4BPFo3j6t/QdmHFw+L/t1tO+yGj6LXk4BOD2q99P+k8aakCkRefqFILaOElap2biO9Zr4OLF6Xx74w2Ht7wlj2ZUGKlR0oqFX8N796tfxdu/823svd7gxt6daN5iCOKJYkqm2JxP8GRtyHGs2FshRWdSk0zgB0A14LEawDmP83VeQ1XDZ1KIqcJHeisU+VrKwUYayzApQBMcKDqrDlEtf9QUnZZTnqUgSliHnCOC7EtJ0VmFdgh9J5E+W/mdALz1rR5vecshmgb4+td3+E1KMJD8q6boPEoNPgDwrV6DbzTw0dSurgFA83iLb1OxDy1eq/ab9UiHycU48lfMgpDuhzuunMFtf/o57OwA7/neIS4t7xHjET4t4pipwTdHjqL00C2aEec77gj4xH37cPtrnD7df9umFMGnpejUaIIGVIGrfLsj+m4yGl+qwQcYsmAIPAI4F3WVWNGmCD6JkxPGxFIkBKPvcyP4tDORRvAt90b6OHfvlpvX0Vn6fpo9tWaflnDiZ+TUiKbopA+vVsBSoX1a5otRzTEJW4jvTuuKMqiJ4At9is7xPQXPaDwiES6PMz1t3rEcbKSzXU36TU2/dRSxdzQozOgjBGe+k/ZTS9XcG3CIgyDVnVEjoOX8ejRRfyM94QgVqaPHa0kEn569hY5vnVXWb/kuCXoyi8QsD1VdcRSla8uIbG4hciFHuvl8OAShc/RNOzrcSKdq56Z5Tlm49Iy1avABXL5KDGghMJ7AN5PDw1HBhZDW76ZjD0CzkIXgsjqwjciahpv1ilbH2xSdPzTYzvwWfiRAZ84G5QYj2gZDmzm0myHMmXq9Sc/qHGgpOtmBI7zbCLIMX3po55gczct0krsq3t85k0jnvBpHZhCcqywdHH2KToMTlQWA12t4z5X1cA5N61Llr2WkzEXmmcYEpVtNCyie3aQGXw3MYxMqFDK1RkDSF/OgNJRdzgF3XXpx/N1euEAY+wZ+bgRfGc1xXP5c2ehd+kZS8c0Gq/VCKg2i0hL9mVKqTXMI8pxKN/UwlbEGn3N9Db5Yw6IGLEFTfpPWr0VbxzFUFMHVUZ1auuPqCL6JJhYFoAp0jhRRWkEn4X0yL1RQGZ1V5NmhKL8tOHHjDfz8Y7+NO6+8rKddshTYGcgZW/oUnfpz9IyXaWMq7CIARIH75dLc0/Noua0kTAxGxMBXG8EnIbc2z53jURI1QRMjzTdoe8nAGAKw8+ST4zd41yvfrEvRGUJypGzioGH63pCzO7fmmLBs4a3cYH1mnFikkXRK3+iT5omy3lAY0aGkklB3DDLmtTTf9ByJSvdaA5/cC/J3rKllKIiaRkxrBR0fnXMKaGZ5X7JpkuwSAiwfAS1F5whhGoNO/1Ei+GhNUoBHCYwBR0Pf3k9ZFWgEH7fZzNAEZdZsqQaf2n0hRec7X310/P/+c0+nEXxQ9mLGwJfjWRJlHR2qEcRxwJXyEPJ5aWxKDHwZsD6F6pihOLfk+khukv9jqkfWITO8hcToNvaRLPDpGdalEgoTHSgmA8Swp46QolMCnztlHoPgf0MgGwpYLfZI26OmdeBQWwTDMgwk302A9E3RIugieCONNavBR+ZPdQbrO8riFJeMVoOPtpFE1/q2LANKjaNfEP/MoMe1vEukCQ6hmKJT3dvSQc+Qu4D50VW1YEXwMRpmDEINeQntDtr3tZAtfM+isD+vOQM/ySIlkHS+pgafel+uecYP2Q87h8Thac5ZE5zTea/RAFbuw7phOefFdUB1bCEI3pfiJIx/ziFJZZ+DnFGuFMFHU65610JLOsDGN9KFK0+hKoLPSNHJ5Yr4XL1WsWbeEt7C4pe3KTp/aLA18G3hRwOyEVo2MapRIEX6Y3q9lWrwJYTYiXGn+1FRSmsKTA2JQGxIZlHpNl+BLIQoyzhGD8qM0ELTjp7/u/8d1u94Byyma73O4CvndhDGx/mJ2hrnsCOUT397AAAgAElEQVQdpY3xLO9tGtkDAG0pRWfMgZ1RirMafEfinMU3zn1fgUyNzq3KiUac6tQ459deZx5ye3EU1uUNpW0GSkqM2o7On2/wyitNuUC30qX2CpZyL/vtpNIAEwMmwdtO9lnoU3TaSHrlWnBuEKh7gaTxazjp6GCC+M6sY36x9StGigJcYlwLoa+dsVrZ85lTfrN9XrE5guu1yVMUjBK5NENAOk4YmfIkHCA9p1RPRPE7LVRuz49zwK03LuATT/9esme6zjGcykJU/v7QijlxqErn5Iah4FFeixr4chF8UhA6qkJkROgYIvikYTBXI6JU6hGAadyz2yspOslfh6BGjKUQFCFy/kQXFQkF3i1Hz8c2TeotzRRRGS16MGrwqen42NmgT2D8/iE4JtBPilrdGUk7L2vTaALAmPkg82HpuySGRqm8VFJ0Znn8zLiMVxzGsnjAEgS5oTba/Daf40IYx6C8Y1YHUkLeORbt0pK0UWoEnxT7xXzVyRZBbcuNsjOUsLHtjBp8so9Aog1CmO45IyV2cA1ev+fHRd8ZekhpSeOyxLdUg88h8MDgDSL4guo0GVQHUKMLhg/FPYIdwTd9/2bkuyhjF+bV4NPwo/QgOgTMTNHJvoF86cIcJdN4eDjR7eUS3hGj5cyz61h4imFk7f/ZQROKXBLBStHJDHzZdJNOucZBiyY3I/hqiLn2PoXnjmLgq+uYdO21FJ0ar+ISumHxKRJqthzl22rb1hhv6PwzRw/6PoY+gKfx1AcrGh/ytxPbSY2ecIRaj8KBwaXfq6YGn3o/F8En15ek7clBNC+CT9OzbZqik96wSHc/bW5sG9kwFq0maG9iSB3m2XuZJSTFT5Ufox6EHfHpxuMRfIvi0nDXrmXvU3rKghwMXKmBr7lyGc3Zs0k7SVtT/ZOyD7PvYXxc66NvI/h+aLCd+S38SIBaXygCEYxUYkWEPQ1kik5AEPOSgU+pVcOMchnvNnY4WwYrafyRkodzk/ey8ZpOtDeZ3dLJN1xP+stA1zlboSQF465D6CbFCjXwxQL2IxqOM44ldMa+Bsilwew7zSgJCfPR/yyPn7tP57N/3eP1CJUe8Rou0qOOCrDoOnV1aswHZezn1uCrFaoSBV6FovqFFxY4fXqB739fl3wYnQjCSFbBpPQ45fFIvIIBU7hKDTZ1cxM8/wYKFuq1+L3btk/RqSmpAP31UmV97JVf7A18UinoWPtXX23xrW8t8x2Pz7I3SG7UpehsEGgNPtKPasB+kw18LG1z/FeL4BNzS5nycbkaivUqPIjCfLXiE+G7MDNFp4qOwC1gscgLDyFE4dbJWzru4O8xts1E8Fnvoh15phFS3gNGjYtz5TqPVh+5GnwSoodsLj1aziFhpGcEGlPh4uI/2dovkyF/Xg2+Obo2RsdnGfiCep5pFxk+zAAowKgjBij9+DTqx/r+pmJVWVdmXb6CQYEZk0fiuJnIaEXwWUrIWRF88gWOmqIzAk0xuAnJt9azEsHXNJlzrqSYhjTwaTX4iLJMieCTPW4KfK1RGp2+g8pH0L1UUeCanZVSGRkHsGrwuQbPv/+/QnfPPelNDT/PD1jNCZM6KmUNfLJ+rnezF1lN85xy0OqM8lGagU++t+YQBYA7AClOEtrYNMKnpgZfKUUnSH+5qGJVqR8C6Bqm6TnD3h4zlB97Db5KjwTaJ11vsT51yVgxOYbYBr4uY+AjmJiI0bpzD7z2OH762T/A7VdeEaOkTom+MeS0GlkMaYrOEozj1zCryng1MOmMFAOfwvSU5OMsP12h4qnqSID3xvsmfSg0nxj4ZIrO0bDDmWoTh6PAcWdY0VRqieMReATfmkQAl9RuOQNfnKOmW2G5fzXhadMUnfl3Z7i4RuW9egOU7chnbiFyQepCaRO6F8dSyJSmUN1USCP4YnpLK0sIe8eMPF+O4OM1+Hwwsn0MAzbXr5tjUXAhwCkpygGw2sZRd33ixkW8+7O/g50nnkgfEO+8ES+r4Sh5C0s2rPWu28Kxw9bAt4UfCWDM9AzGpYaYNaOBb+hfMLslL6JGi/CiAhNL0dkzMSPfKSPrph/mSyQeXBVKbBkRZxrBeF4LPo7BnEYGQFOAAjSSoE5r51joQRxUSdG5QQQfv64foJPQM0Bnp2ClvBNTNkhBeCbknkxmoWIYS+lqGfhS45ylENQEjkGolsxhxWasExyUdw71dSlfeqng2ijf0zCIp5cq0knWWsygK8VqgKX2VPA26YVzwGIxKulav66OdgQMfMW7NX7FGWqpsNvQ8NE/qyiKKlN0OmLgMxVWsX2Nga9wZhQeLrdXFLM8Raf9HGPMCxF8Eeg6+OBzn8HPf+Ofo71wgXRbUEJXChcySnvEhUWglM+8HE0Mg8Heos8za9JPY5airier66x1nlMM5w18/V9/993Y/+Qnx+t+l6QWc2GWtkRL0QlMXfSRKbrgL8moXMK19LsqGCQq3DUlGfT1WHOOahF8rHs5l1JIJ3j1P8mgFfVZXaCph1yiRNKclgw1Snm+KT0d0hnW8jQ5JT4wOVHk0qlX06mKCD7ZtwYNfD4kthKyfmMhpDX4QujTfZoKkvIZxmrwdVMaKBnBl/Q3bELGB8Y1mou4MQh6hW6Wjy2bks0dFON44sgnN7Bm0cqk6DzYvQPX/9E/wuFHPzp1oxnfxQWZojMaIycDX6ecI8RQotXgS4fJXlP54vgtg9gThUfpt6ZOHBatpYYhswafVhRO6wuczoz4GTX4GO5zIvgyNEnNEiH1ACQ9Z9jd5W1nak1Lzd/sFJ1zELJSdHauJcYa+/l4a+fGZXzghc/h3oun8Nef/l3Whireiyk6a0AwF4nxv/TsXMg8oznsBR8UJ6+yzJCk9k3FgBFyjlay7zkRfP2eStvLFJ1Mfo6XxZjUABvpoxWdpuFSRNa6LWS4WXxopYOnFoFFnW9u7N5OW6u4jNekgU9EOe6sruFvPfK/4+f+8jex+/Jpga/QT87gaxI9kEByk0jhCPkIPvo75WGo8V86d/cOlLqBTwO9akp8v2C2c8D8CL6rV/MNxr5tGZF+j7bp27z/e5/fnF/V9nJBlFWvWw9tU3T+0GBr4NvCjwTwCD4hVGJK92QRqxx71woDH2NOgKoUnVlPSXJhtF0pAjKrwSeiWkhnvRwrhKKi3iURhPVmbKwc08oUBmln1Bi3XuveqS4E3Ut3tUoMksG55JywlE3mAeQ4Y9SqKTG4oghA9uBkNR4SqXkzA438vhpc37vLHEbzfDTrxVgGPscZOxpVmeAqDc6Esecee/MYiRxYBtejgCweznCLytyKYXRcBoQVJa6F+1Fq8I3kRdvoOaPfoh33WeNXVUZGlnZlgA99qFcyvv1tnItvu/VAX934LMgaOsp3bKhA5Uj/BfDgxh49Cmb6f9M1WwOy73HvKFanxNhCBE9rPmsjx2gfwBTRfuflv8L9557CziFPNXIcc9I4z2g8VxZzZc2mtBWATQzToY4EPALJsRp8JSOy1geQ0tCcgD3WuHAOq49/HNd/7ddw/dd/HQc//u6pPwTk8rGk4+d5IYRQJfRTni3tJA9mlKeG0IwafJZ9QNOiUgNfjjhY31k1IlQI6A01dAf6PXIKRmMdzVjovYGvfs+VzmctRac03nB/t/LYkm9jvE0pW0PE8RhSdI68q8ZLhPRaE7pCis7SgA5rI4IveslL3m54jHYxC7R3lP2UHGHU2yxFZymCL0DKQSwCLIJR94al8Wbylg4sSqDVU2KMhnZZK07glCjqCyk6VTpprM2ab5k1XrIUnYpMI3gly8DnLAOfpJGWs2gz1UgcHQIk4jMMfNnaf8aks2+WRPBZMvoxQOWGtAx81Fm5ajgEk77zM7Jft9F505LtVKOWsobV1xwu1qfoVPahfO+K+ZwMT1avadsasGRKuW8kb65FVWp7zfpuR2GRc9DzHcYN5X8ugwnaoeBYs6/mGPjUtkcJATSe1ZyO5fWGps/euV02tYfMpehEwHte+jKW6xtJjdJe1silz01B6j3yKToN3YVMZ6l0nishQI3AkQ2zdY7za/Bl+W7y3XIRfAiBfU/vGhbtrI1VStFJH4rvkOj3aATfcC4uuwNGZzWDtck2aHo5yZwZuj9229pT2xSdPzTYzvwWfiRAPWSOySGrHXYBLXQ/R/GiKoTJIUrzJHedY4ZInqaA9zDd4Irb/r11gd1Mt0Wva1aZAZbrG7j9yis9XuK9JzS4YO0VheuCON+t1z26qoeHMrc0go96eKXnxDQmU9BL5RLVw5GbtSk6nRLBF++ZjMwRuOuSgS/A4fH3fQr7u7fj2i334Mrt9/P7LtUWWWesZeCD8NwKXV4BYeFZTtFpWQctvKzB5iksrS7keIEtnuqe7FuawtnAe9NUkIwcacY8zSMvGsTaxfhI2602qlfoXMCv/MoB/tk/u4pf+sUbrF2SotO0Ds8HVVFUGcEH4iRBU9Opn/0IOFYZiDXhVNS7688Q28CXS9FZqzjXlOInDt5gOEQ4jhSdi4zXuRY5VQuJILJcpjcIWBF82rK1jJAqbFCDT8KcCL5x7w7nfffud6P7iZ9IHGPyKTrz4xM72thXH9xS6DNoNfjqoOQQWmM0nL6pzTeM1yn/pa3DjIEvWMqVkX/gSOUMjrFNVOJIpQegG/B1Pickgn8OpojoOsN4+v1TOgTo3zIqkZOjwaLlUpGWUx4XIBCEVOezqk5SgnHhQjvtR8FHOd9loy1KKekCRASfVzRn9EzRUm4Za2QuMFQL/CLP5hmZDjr/+Qg+QPBqUg6JA1sGPmKgUPmkZAlTA58uQ9E1KB20JF3auAafieB4I8UHCq9NgKer1FN0qrxw0CKRBtxoittcmmjDwBeUCL7ECFJgUOlWzKboNCL42G8awbe3l6X7JTiiqDICJVEig2zVQHTfW021FJ2xbrUmM8nfORSoI1rCUyoRfOyhDCQpOiufA1DHrCrjzelajdAp8Crjc5UiU41efXx2xrtKR1gLEdWwAr5m4rswul6xr468fxJcZ+zlSr5Aj+CbzubV4oQ6pDpvQqcjo/B3Dy5n8eVyypsfwVfzfWiKzoa/DluXMUUn18sQPiHwGnzOYUyd2fdVMLoZ/IHEi+r6Ij70POprkuZpTCmCj+Fl1GukBr7I23sSTW11OmfPBOFcJQ2o9O/wa2vguwnBOEG3sIWbCzJZ3QCQQ0HzRggARBg3hdHbzWJ2zQi+wbCm1OCjhH6HRLNJeSQRUOP/zKNUMM9OGsvslJsEITKODb/w2G8DAL77jp9HeKuhpBOKpZg+laLUe9kPnnhdvKn0p8xtsz7kSprh5aQSJLgGTRMSI5upqEsMf5YSUmgtFVcjmaJTEyAt0BWntoCkwdVb78Wff/R/RoDDL3/vX7J7GkNWSrXT48AFbVmDTzW+KcLGKB85oRA09+Z80CIEjsWLlrwAqwcQDWAZOpTuS95t0kF8rjKCryIIAQD/VJpjRDaqbbFA204Mb30NvlRRfsstASuBtDTwBTigIc/OTNGprV/nCKWp8dx1DdA4RD+MYo2CChRrU95cvuxw8mQw96dl4AOmOgMaXj2tTAXxjWrw0TGIcBHC1Ka0j2t0Jm2udpugTdXpljRc4mQba0Nf3+V+i9+cfOTGEN6SPiWN2aAGX87j3Sl9zhlfGviAkP2O07jzhE6Ltyg+YEbw6fRcPa6sc6vCwJfU0Yh8jail118rRF6P7YiBj+AORKVZSOhx+h0NfsyA6Ryv2zMp3dHpEOPpiKMa/Utx0M7AJEWnTxVrrL11HYHf9H526j0LXnqpxbVrDu/5mYm/iHumDeuC0Tq/1gMcDha3jL/3FGWfZkSRBtGpbcU7GwSdzW1BEaruS6IMCp1PNBQJSyY2MI3gG98jU4NPAu0zNfBliMXUaPp3Vhq4NAuC9Wz+MK1br9l3I/8z3ZyyMTdJ0ZnjSeI38V6MY9Tgm5WiU750UatuR/Bhd7d3bFXewYJZCtZK/sZ6BXoe1HYU1h2Wjz/+/7P3ps+WHNed2C+r7n1Ld6PRjZUAQQAidoqLLEikNs5I47E8mk3WeLSEIhwz1jgUdng++k/xxHzwF1mekCYkWZYY1Egkhxwuoi1RgBaAAAFiB0gsjaWBRqP7vXdvVfpDVWadc/KczKz7GgQQcU8E0O9WZWVmZeVyzvmdBWhbrH74h+PH7xWAr29aEUrRfv+q95YyAYDOCtEpFMb6MvA87O2cLmwA8NUu3lh17xMeUPfO47/TEJ32PJkDvsyRnc1tfazCnT8PR/ku0mEW7cUbhjy0X6WIERbVCiS19W1QOOFLwL3rPYm0VOyu2A+kBx+tJ9kH5B5ZaYwZK9AMWTDwj6U5Zp0z3vNuLRYeR0fT2R2eszz4mG4q48HX9/xc1eZ5c3jAftMyWQ8+DHKcc2Ofm1b14KP8W20OPgCq8Yr3/NsH5xFPjC2suWTdrwHrne/hMceykj68uay+pePRFuDb0geCsgyvJZDF2/kNpnGTNfGc0EmmotXzCnYW031pxcvYGW5uprYZzltmkFNh8SRDXromPyYf/d43cXD9/ZkSpB3llOcefJm2NICvW8FR5+Lx/ZIQnXBYLIKBbvkQGRT/5LBVgFnewPiOmVgCWflyw4Nt+L6EaXMNZ3QD2DQe9MvVZXZEa2sllxcp/i0816wQnVlZhjIaEDn4VA8+0Z8KZmPotxSajq+IyzLZlVZIg8LDrmi4p6Cihf7MEUBKOfhUa/XQp0U7jm2qALcoMOH6vVTB3fTrRPibrZDQ2hJJ2wFdiScpWCOHPmgefGyL3dCzEuDj+dRTC5w71+DECY9PfWoVBQWnldc8nZWQPtkGlWdqc/DF8o4DfEYzZjdy5RaCG3XqQCD11JhLRQ++TesWY+3Ej408+PLfLxd6cL0uf5fGzYNKaxQuOW9xjp84kzfL9bvKg897XLrk8O0/X+LmZ+xcPnU6e366Jpek+TElg0Go4TNNIKrv4H2bKD1ChWEfkW1p7ddS3E8rZ4t2wrD7SojOeM9oYth7tPEUijSfKtZqtgvnvAgRqRs1FesJ/RHv/PrrDe4ivyf5YY02N6krOn+wdwZ906LpO+wevY3F+gDrBcm1KfhJ2omYt00pW6JciM4iv6ghEmQc9l5/CT917v/A393zL/DO/rXF9k0PvtXKAPiI8i9jUBmvU8+2RaNudGx/W/O1ycJFEi/VsF+F6qtD4VqynoPKg+amMvdm0z34aAN0rZnGKrU5+DIhOmUZGYIuG+eNdKEEalh7JHtOhOgE1qzslaTaXKclgE8jVSSAx+mnHsbe818e6lossL7vPgBAp+Tg865NwBqrY9l5p/H940Xdg6+O9xvqI8ASAf9zTxU7jOoi2ec0YDznBZm7duaNZ3Dbk1/HarGHN66+Hc9/6H707bJSZK3n/kLf+x76AHiPnW99C7vf+MZ4YVzL9DvQlkk0ETrfa0J0Fp3ojgHwFUNY9vq5ZhnDUR5dhnTU2pfrWII6AFJwnY6fXEqSB70CHnxTiE7jucKZT7skMyZIkDL8tvIIDx58vNc0B1+pf+7ocOCp1dQ2U0Fel4u6jKYZ3qdzC3RW8ITAk9aG6IR+hgPSg28MRUqjEch6hKxhga68EXsviqxav8Ldz35jmNMH9+v6vUYPZb6lHwxtfSe39IGgJLm6vE8Ypzn3gOmQapqpcC52PwBgvcaNzz6Aj7z8IJp+nfaJPLO77MljMkSnLnizI0sqaR2/5tHYyimtjmLhgZqVdF0flfECxAiCNW2C5+BDVCRI0sIgWSE6ExnROTUfT2IRFr6/84yRbCxBXiqKtBCdCPOEACFUsVQ5xrFNcYAyAKtQz2J9mTNICkN2/BCdvcoIJK+ZKE3y4POmglLSDw9IJeJcCuuy1rskYehrPrc2341+bxjFDwckKqZWczYv32KRMNtl4gAfnWvakFArxqHp2Q2qpILgFQKsHydxVDYUBv44AB+lc+eGgbp0yeGdd/Qx0BQocT/LhPgx17vvcfPNm3mPAXhXAb6QMFztC62nSllDnpXnwRXw4NtEZqE5+Db24BOAeUmJs16nlUjeIhei01Ja0B4N1Yy8gfcpqC9aD+1KQb6WJBCsN+Pxta/t4uknG5w/nw6SNh+bBlifOKXWNa25cDaQweGoJX/WzH+inDFaflZtqyYefAmY1ffKnNAn9BzvtLjua5SrTlFWSh7W2wqiaVhl//JtT8rifFet6w28bbJd8Xyk8E0sgws6hwC4vmOKpLRjhUU+GmNd2rsmXjpx+Q1ehFm/c+WYpZzPGiqVeGeUz0m1+oafL6cuvYq7n/0ya9ky/IBzTPkV3+PoyAD4qLxV0T/aVKPz9+z914rhZygnQGmHNKSn2ZfM/ErAWgssFyDi1RdfxL1PfwEfeekBnDh4M/88Bd4IUMFlCSHbVYboZLmmSA6+aU4Kr6eil2gGLCgyMJkcfLu7/N5MZn1OPtN8PXqduRCd6vzwHtf/5Rfj9b0vfSn+Te2XIygtc/BlzvzaY8b7QXacPPjSHHzeoxiyOBSU+09Jjk547RnnYw4U0sKV+j6dMzVzaFjfvL47nv0arr74Iq5782nc/dxXcMf3vwnAzo+m0gxAjOqvZJkJ3DP4aAIKh2gD4VckvsEbHZoPTOYuzuFDa0N3lzz4zBC0tI4AgArwqQcfIzX3bKjD4L0sYnuKM2a25DszdWgUVHwHu6eB3R1TZwfvk7RGQNmDL4TO7PtyFCLvHZbdgXrTYu1DfxrfTUYPTYv+mCE61Wfk2JALLQnRSfvGH5gvZOVCdAa694n/hNtf/Evc+vKD2H3kYb2dbXjO95S2o7+lDwTlNs6SQt77/KEWrIipB19SgaDFY4/h1kf+M+57+gs4cXA+K/QvF9PmKD34EqAskGFROlwWSrka61OqWKrUTu6/fY79poduw6xpUsuXxIPPUiRqgEfXJR6HcGkOPg8F9MuQfG2aG1EtF15YC9GpAMGb9oO1NRIHHzMKWgCL1SG/PyNEJ+uXsIZmAhHJEZIjHhZFWH+Fwep77H7xi9j//d/H7jtviAoUgVRTZCSK73kKyyx5sp8QJa6XgKXxbNYCUA0Zp5dPQnRWApg04oQaojPn1bZoZwN8NLQcwL+Nlv+pWU8W9dLqfm6ITt4PYhEaPfjKdQVlRQ7gm6O4HArNm4slS0M1L58gLbSIpmj/2Z89xDXXdDh5ss+eBeotw1K0NgdfjnI5+IjVwCxwT6XKHHznrr0Hj/3Qf4M3rr6Nd4EQU5ZJQVoYPmziwRcseAMlOYgKQ1EC+JzDrEPM6veUgy+1yKX9pArtTQG+LBiCCfh69NFl0eOQtv/mL/0qzv2TX0vK0L/DemIh5HIfYS2tpznfVwL4NArg8MDX6h58lGRYr3it0pBleCDsp5UiYw4dwdRvipOEeWlahRvjPIGFYxtxPuuKGoscfJqDr09DqdbUYxKZ9FOIzi7Ln5XGPIwb9XI7efB62q5VnwSFaviMSRhglzXwzRw7hWngyvvh2vXnn5weEXt/ksM81ONJ/VYOPuUccU7vsPcKYFUC+ISSTNvHqayR8zbTyOJDcu+g/Q0Apy++jFtffhD3PfNFLNYTA5mcYeKig2GsIvPvZZhK7pkRvp8TQKLhwVeZg09tt+TBJ/naJAcfaynbDwD43vcWePXVK6tys75pAE5qZCerXCA+7YNCuRnBmtFIITO2UzjszHpR5utxc/CBfb8ZPOMGAF/N9x+qHMcLGc9X0g/JI8nv5D2wWPEQg6cuvTqUrZF9BF9jUeO72HfLg8+bggmVGTJen+Ey5U+tMqXhLoFYmfDJxcoDT0Au5YzhmDxM0PLetfjbe/8lmFE0+DqJIXElDy+NdFj3BX+fhOis4J9jO3kPvhLAJ9d86GvXOXz/xh/B1+//t3jzQ3epz4Z2huF2/JwggKc0UnJu8pSskTH6HliuD5LrznG9mczBB+9H547xnZoW2j7D3icTojNJ32SMbUdCZQaAbzC2qNvj5HjMjfzjHNA+/zw+dO6ReK0596q+VrcA33tK29Hf0geDnEv+TDdAZzAe8g/+cGBaqWUUC9OlKMDa114zldrecwaCh+gUB54B1FllNEbCS6tWlciB7MohOgHgxIVXM9URBl6xHqJKvr7PyGGacpGERqIAX6LYcy4CiTkPTz5f7HIJhT5khEgzB5/2oarJM2CkpOC5cPYj8e/Xz9yuKsLsEJ1kbmVz8NkhOlm9Pe83s/Ic/1g+/DB2Hn4Yi+efx8cf/1z23SxK3uUKgHuyCgbwjY2WmgmCpUmal4YlxNCxnPF61INP++jZvHwbePDlQnRqAF/bEQ8+x/euuaGOOMBCBKqgKKpgcYYQnVN9RfDlGHOt9GiyjwRScvDlKrcsuJ3vsb/v8a//9SX85m++g/39jNecoqylgqYllBq1FcvJcH3at/U+fC+7nrNn89bktR58r199O56/6cexXuxVroPcTS4tSqAu9xj7PRvgc8l5IAXoeSH5dKU15bHqZLpBebWJgUydscr4b2Gcafv9DdczyyT6TpKHZGcDM1AQYzlTAR1/O5una3wXc4skihZl/LU9uErzQYuH0HnGLM/yBFAUwIrySz6b8rn5iSWVxeLoJn/r7+2k8VvfzxqjhEpKxvH2EKIzU7C0yF0K8J26TAA+77GznpiCMI4RCJKgcBi/in0hNWpgh0L2WfV2m1qir5YnlIJI+uil8UeooCYHHwOtUj7Xez5nXduonkScF7YBPs2DL48rKO+lFMyFTD02i6wtTG/k4KNGkYUDwcznTmX0sfPJuSkNKARR0DQ/n7V7BQ++CiCC0rlzDZ54YoELF8pyRH2ITp0fyxEDHCyHZcrnEaB6CtEpeTBNeKrvFzX4Ca/UKSHzNFLrHyuLS6UC4Ev0RFdAppT10KpTDz6rvfwitjwB54VPzL8rCy3ZWzJKuR1PrlPDI8u43aLjAnzy/pxPbemahZUAACAASURBVAKZRhv0dZgHn2tx7pq78bX7/y2e+0f/Sq0i6CYT3sdQcnnPt97FAkMuYeV8ryPDoNLnDapLMirbVxsnjhQ36V+9noNP5iCkxr3OIebgS9jc2D7fN5eEP9IMZQEFgIOH81N0L83RgVHXqTn4vAe+850FvvWtHbzxBv3OUz8YW+pJiE6kITotAG+6nyjP1D5RuvWlB/Dhc38Xb+x+5Su8/M6O/tHneGBs6YrTFuDb0geC8mdS3hpcY+7poUU9+FQQwwKhKmmHhKsM2FXsD2X+SJ9mheh0RGA3iB3GleCT0wJKYzpQp/b1+qgXn7c+UO3YOs2Dz7bmv/HGoY4zZ3rs7MQqEiAwp2CaNAwrpZw3u5pWVKYExKWKCwuxHOnJe/9bHO5ehYPd0/j2Hf/UAPjKXZQApR2iMw+SUiWlNo8XT04W2afffon3R7M4rfTguyLkZ3o2yD4ZDyXCI32u0oOvlo4OCwU00C9cSwA+pay85j0ziKDrXnu3tptCZgUPvsicXyGhOs67GmHRNYMrCeoAvisl99c08MILC/zWb53AQ3/bFtvle4iupKVKyorIb5EmAd8C+PJ11YzZos2MuzgnLYXNbbetcccd/NySU+Cwr/PgC230Sn6Dmq09ASg2ysEn6hT5fssAn1JIKrONj1PDJkSlXFTa9KrHmGw6eEfkeDaLSiE6PdEizfHgk4oGWlj2x/tpfmRzIBshOlmoz3CvcmOhXiyaB1+yrpXvaAG7tvdcHuBL+ij3HfF78l5I50r0PpZNGZMqGTdFsVZDDj7VeG+CQOe+I1E8h2KN10LAs45VEfPgIwDff/X4H+ATT0yGVFfCg68mRCeMXEW03eRBBeC7tHeWlWM4kyHX0D0AmRCdlFeVDbPzTXo9WHNRef+p2hREZOH2akN0Zi/Wzfsq0EXuG9Foiuzj8MmZBCDxdqTNJXsBk3UmgI/uq873irYWRTmcLV9jnxjqV/hyKXcLDz4O8KXh4S168cUrp/S0eLBpG8vLTtPfXuX1AcCvU4Cvd60w3rT3ydwWKmXt4Z+R7zJCdNZYEB0nQkDNQwk/WNnQJAemY2bl5GNbnDgv2T4X6xnWRGUk0yqiAJ/3btbASr0WnTNxj3FOLf9uefDlJmXp/HOTgJAvp3rwEaB09FA93LkKq+V+vK4di/kcfLwjR0dTg8ulVzz4SjyN+F7H8uAzboDL6ImsRJ6L0UGsUM6e87LOgeTgKx+GfT+kudHISq8d1l3bTyE6+2ahm4mE+W5471244HD+vJ1DT1JHgMQ2elS39piPvep7h9///X187nP7soDSZz5ud3zvz/HDT/4Jbnjju2gOL6N9/XX+zbrOzsG3pfeMarJZbGlL7zlRq4UskFVhjQAMwkOQrcIm2TQ6U2WBUPwg9mCSOLnZoIdrJoNgGuLAKcLNWKH6TlSwou+SfVnesyAZq+VyREEKs9+kibb1WK1GxbnXFUoq4NGt4dqJgehHLlAag3jnDGWfx0c/2uPmm3vs7fH6L564Pv5dAr2c94MwqwiRU4jOVKEQGdYaJYBFbFzyDxycuhZ/fv//gnUf3CZ1gK/YLmVgBGPnuynqe07ZTYWqNMxnTQyiSiWnBsweE3lRQbhEGk7XIutDySNGURZb47GJbhHgyijNU/eKh+iEFwbbZO/TPPiIFWMScusY318rUBNSbrBG1vOjTcpEtjjK/btCgPPhocPhYYsXv+dw+00O11yjzB+lTRNgMZQIGtH9senX6NulGSqU5SdwTdJOzdLPGfuxc9k5M/TqTTdpOciG8Qhtf+m/nMLh1Xv4F20e4JsUmrxCXUGG/MsRRJVamJYoBWb4uJZz8CnrXxq5ZHPmifaVd2S6XMM7SqPIC4UflbRYeP5Brcph59nV5mO74Gc2PQ8mAHN6QM4TfnG8JTdxATxKPrNmGBqSgy/xBPJ+3H+nPuljMB3UtM3FwjMF0VR6Uu6XSBooJY1gev/GEYXl+Ec0TpBKH2MvT5RNfapYqwLk4fkmNMOIT+1PZrTYvPOaARkpWxkWVQP4Tlx+Hde/8QSvb/SMsQ2S6tdizmOsNJnV28ohcHn3atIzm18wPfisEJ2V49r3YF5MtfkoJWAnFfWsCj/l4FPl1ZzmUFyj39Xi5TZilRmyStpDut7Y2hHf9J57VqD2/5pnRt+Dh+j0vb5nVnpI070y1llS5GOGBx89F1xTDL9YHv86VN/iwXIGcyxqSyxn94kCfKF837QCrLHH1lY82/NzaGPzEJ3wQw6+WKdLIxkoj/A/jilPBtLGAt6n4dZrdFd9l46Z4DGit+sM3XpJdm76dXlYxA069vEaWzNWZ/R1RWmObFxzxM351JYhhmZEJa8zoJR4qHbGXK8C+ODZuNLjbnfXJ/qr0r7HVU+uWm/Hb+eRP+8RZTjF3ojx2yFaBf1ocjykvBnGue/17cJ7wC8WcOt1GqKT6m6JLoMOYzhPHFmPfcGDz8q/p/HbOaLpqprx7PWugWVjEdb2wYHD88+n80xb+9bnve+ZL6J96x+ldVjRLuZa2W3pitIWXt3SB4JylsNV1uAJwEQ2yYYcxFolWp44JS+bbG/qXz8opUaiSjcenkRnhFhdTu9nGbyhkooNPtVS0m+Xhh3hnjxGPRp41gsF+9jXNGm0m8ZVvI9zwP7+pDw6/PSn8cZ/92vo2iUpU+Dqwgut1+ZwbQrCmG0h/Txy7idKDgcWE3VODj7GfLKwoALgmxGuSgpVxxacaspfAWEsMm0CTIjUVFpZZQo5nyZ/N6uRc6v6HadyO3va3cza3wDgg/dsT+PrXglv3K3YHGF2DRt6NlFyjiQdr1CU9CTckHM1fdh8rpVCWWqKKGCYN++8Q8JXWQ+PZOfgqwf46Hu2fp2UtxSGmiKlZunnQnTSB3OK2ZpjrXMLfOc7SxwagpX8RszTI0Opsls8RAG+SlBNvk8z89xXPfjo8/AmCKbVL2tiIB2AJh/91DQGKSsdpgfb1laIsMphr+VpPhI+cNEkyoZQODnDAHRkD0sqjoV0RW94X8Y/SHDQYNNcBPhc+n7eK99MuZbwwwOVPPisD5usjwLQw8Idyr5ZuqHCXk7Bklw95nV4ZnEsc7vUU2GzE9fbUi6mSl6dAnz7B+fh+k4NBcxaH99RbSL77vo7svmjAMhm/YF3JRXENWoYPrJ+hHvaWlyt1D5o9VJw2nvgb/92iX/3707hy1/amZ4LfSyE6PQifCTvA5+jgyJTK2dTlRflMcishyrgfZ+cSQCyOfiuv77Dr/7qJfzqr16KdcSyYR54MH57aCddJ3KMk/vZKUxkroqHqQcf9rkXBN+Hj/8Baj2lLR4sNweESDNcgx1m35NvGT5lmn6hIj6jQj/3cwex/fBYePfO8uCrmOAhMkHM/zfnm1wBgO/mcw/h3me+iN3DC5x3JXxEyYNP2z912UAaFQUPPr3/2pwp8V/0jLJCdNbJtQ5T3sbJg4/pvoR3lkbFT1MqYIGRw6/so5qhgcWnWfcA7oXWOSU0PERIXOPZQWikAN/0984OUv3VDKWVh4seoZSG7y/GSZnnyWfg6OH4jwzRyddJUD3Rc0aGCJZGuZMHn+7F5z3gxz3ce2C5Sj34iE2m+i4OHo1fc69mZZ+J+mfD0GiuDpHOBQbwKdN2U17AZF2dQ/vm+fT6Wvfg24bofG9pC/Bt6QNBfUUCUQmsxWeVfYcqIVsC8FnK1YQSDz6lL+EefGIYPLWjV8LCkyhWspoHn9X/pI4NAT7KDFrxsElpBmqyCigpeSrcGMeUddMpHnywPPhEub09HH32s+g+fEvivaCRVBRBAXPDeG6Sg6809On3rQhJkgEEc5/bYvy8c9wiaT3lRazFins03PtF0WAkTFOlUKUqMbW1ewxZO1Va1igYK/pvhG0DIEBVe5yy/SBld3e1AgoTGhQrbTkHn8bo0iVCw+aqITr7NERntsEMlZRQdR58EzguAT5dcLP7aIUMPi5ZwJpVJtnDRmoSYMnur/TgA4Qyzsj/oo15jfIyp2RwZO8Z9rfNF3Y/GnmsO70ONjdhC0+zyDmeg29TD74kB1++Y+t1ukFzK2rbcKN276SPtzlXAFq350ZZc4D9xcJn813wsJf14XYHR15jTiuKMO8V0KsA8MXnNfCj0oPP9V0cu+T9+tSDNYIIGjor+mDJ43MMJgC+R+aUkur+avTX9MCQgF4B4LPIOZ8kXUk8MI9LPiiep0tFb59KT7Ou3cHB7umhzr7D/uGbXBEo6qPjRceqhger4tM22TOVEJ2MXwQP0ckd6ycPvrgHeJ/NwRfLqWvY4ctf3sPRkcN3HlWsFZUJxqrp+NxhhhCKB19uq1KHW7ko95fckukLOc6SZ5WFaYbopPue2JAcgFtu6XDddUMZTZYMAF98hnjwMSrm4KMvIeRo+tsYS2bsQxS0frk0jXBr8ucVl0/l5mUBfPUhOqf5Yip11xTgG88MwjOrHSCkbaE33zyAvNddNzGG07Ib6q314NObFUbfrswzTmd8tuKErr66x713T3Pjqosv4+NPfh63vvQAPv7U5/W+KgCf9q00WSvZqiQQXQCWNiHqwTd8z7JgOPHR5BrEGCh9tDxjM03NL5AF+AqPVhroZo4JAHx+9+2OWiY8m3jwEb2KlA0PDynAl3rwlV6WY3AOjapAnVGHcmOS44Y2LBbawU9pjcQ5IUMk0+MpGDZJu3RmSLC3N5ZxzIOPjidnB4Vs4D0a6sFXMjo0nEIsFtOaN50ncuS4f/eKYVSooxxNqLzvUGrfelPpVKc/tA3R+Z7SdvS39MEgKlRYCgCLQfUOt778AE5dOkcrzNYnFS9Jd6TiJgEcyN++Z4oTCgaxkGcVypMJUKHPNRkAhwt8amcNynubTPVp4KtzIleON0IXdqmVa+O7tKwC8MFNHnxZ4G68YCm+TApCsmaFP95jAmTCjddz2NnwMcnkygN4khkMVpc5ZdrQJrdCZfX0OcWtwSwnHcsoNzNUFe7w2Br4UIW314pzxWVkhqKlZIRtA7hyoFaQSPpAPfgUgE/fZ8Zri5ZZflYNq5cA3/S3FrJv0aU5ceIeXKnoLwHWPRTlu0FDDr6g9MwnDwfy8/HOO9f49V9/Bz/79w+ws7PhnNSUTQxYI9eZ/ioVTJK+JuCyTbTuAEhZ/eDVKgCycgyl7Yk90Hg3OFd1VloUchisO539TUJ0ZgA+s49KwckQR1PuVNSPdH1o05uec5pMmYTonKXdSPd6plgsAI5xnY/KU2rNnWmGUdMAvmQZGs5u04PP0WIAhhCdWkhjpnwkD0S+J2fm2+nrrSbskbnHkRCdGrilGb+kdei5Zi0DhcALfPuRHVy4YK/v0Jei8pyF6Aw83PQ8e6fYB31AtFBYSRmn/8375NMcfFrfC1tPWE+1YZoXsKOBVDVI6J39a+LfJw7eUHfJsodEff9lGbYUCjn4uNOTspZiG0rsQa19jd/MgLRzzhC2j5hJIkXznb2nhX7PGa+kIqMg75bNy+WMJNJ6wBYm22+0PVYCfIocoPF8QXGdA/jYOBYMZb7+9V088sgCgFPOuem3tR+b30LwIFZ0oHeb+h5wb72F3a9+FfvPTaF4p7HN719yu1NJycEXQ6mCf6P4tzLvaFeuu67HLbekuUep4l/z0pfzwiI3no0biYUVD0kw9Zqz0zy86bVH4t/XvvmsbpynrJtcmFNaJuUJxR68SQ6+wjvzHHwGz2gCfESmtdaM08tY/Tq2uJ9j1UuVGwvF4tUtIzxqQN17Fz3DE17KpTodeYbzEJ0c4EPH19kco7deSbcADGdyLsSsdUQxnYZ2niCVKSSvG/rFc7QKD75xvsq6WF8iwAcsK3LwsTQUY52DB5+PfcpZ1Fh8SI0NmaXfa914JqIxx7y0WOaE6ASAxVupB5/rdYBvm4PvvaXt6G/pA0E1HnyAzhR5D1z1zjm0Hcn9RHbMyYPPOA20HVhoznJg4+DBJ0+6yFmRPhErDANtVIGaeECm/acKtfDbA7oiq5Y8jx8f3OXl0KWePHVMYVQms0PNJQyrhwL6ZahpeKVWXjoqvAKoCtGZKN2JQreOxDemAGqlBXesSbSb8yqyFNRaDr7wsjmQizFmoo46RXId564qMTdQxrGWPakr/K7RDPJadEaHjhnzELPzWW7sPEAa29VCdGbewy25UF3zyQYrO7KfFgA+KiwO3LilKDkGBXCmQtkiw32oAo1YJ02/xs3nHsI1bz2blLvpph5XXZUPDZjpuOnxMj3L97Dpqlevy3p4a5meMIBP8+CD/rfmNVLxWXOhIqlRQDZcd8USDZaWKyU/HWmKePClBhVq29JIQ5ajHnylsHyi5viXzDWhfOflcuqH5sHHqGCQUAKLvBeeKTTceaae8Gzccys9GmN9y5wHH2LF5RCd07Wm1QE+K6xyF4x76MvRwciGtSZnDClfCuELDHMnjF1ire99Oie0c5GcUzL8qdrbcU0/8NApPPLIEquJlcZbbzk88IBugR7bktcyXtI2fmIrT2j5oEiZxX6FftIBIN9vjkIxe4YpIE4If2xSSUlCKuvayaKn7dbq/L8SHnxa26xO5V7h0YE0gE/wpVYldCcLSmg3TlRVH00NN6LxheD/tM6G860QohNiz7B4QGBo08/MwWdtmc4ZD4j6aj1DecXgwBt8PMvYu9MQncamogJ8xIPPC55MncsFD77DQ4c/+7N9vPJKk+wezvq+4T68PQcam2ecPa4KzQnRuf/5z2PnwQfxoa/+EfYO3xr6Ex/Py0TUEMycMh1XngOTTGpGGqLrMjMVpX6C7vGWR0yNXO0weMRSXq40pgnYdWwUKd+G5AHV+S3ESY3/skJ9zsrBV5C7pAefrmcrj9dg1DX1MwJY9NuwObWZHFU8hhK90wxmIehBSvNQ8CVJNRTg6wEsl0nXagA++S2CatK5sUpp5Vd7wIy1a7JKANCtOmrApvDNXeMYCMpyiWLKwZeGQab7r9CfjX22AD7vwUN0Kjn4nONhbqU+xsGj6SfPQXO/ClWYHnzzGFU1RGfTqu85lweObRjTwTuH5vz5pG7f9TqAuWkHtnRFaAvwbekDQTkPLRq+S9uZdEV5qiQ1hTutgoIHHwPlfG+GkmSHFqvEVnoPB1hG6NVI4xpmPEJ/SzBFa9+51INPI+1QaPwUojNWhnDg8kZqQnROxQsKWHk9HPRansDxm9BbWa+7TD+09nMMdykEp+bBZ3WJKXYE0FTrwZeCfEExzvM0SDS0Wn6qAu6uTIhOFvJJVlep6C8RVRZLZUwuRGct+EXLqQCf5mHFPPim63XRObgHHw3Nqw0R9eDzcKJM5TuWZCtMc7BEPVlAzqXx/DW69aUH8PEnP48fe+R3cOrSq/H6cQ3WrDUx34NPl7Dm5EqhdU9KaF3gYeE61RCdrtRc9psmILhSOPs8xWBGQexopT+QONgiNV6ZTc5F8GA4v989D74dgrVoOfg4L1W/rw8Pa4odAhC5/HvRs5WCg2noWLtZ54AmA/DRB3IAHxP+gwERPUeZ0mfcH+j818L7Ucp4/jvlDAxhS6cbeggr6sGneejK3DtO66ahgDUNpsaHL5y6Cat2F+fOTWv80UeXqvJDbWCkJEQhJoWZ2l+U9/KEb9N1hlmAnuXg63tLeMhTRkGseWkVQ3TO8AjqheJPm/+TYnGYRINXCO3r5psdq0cxCGNkHmT8tmU4kfDJTijxvQfGvGk6wGcplbX5msppuoKXrKtMDr7U69QXwar47MQ8Kc3zb+nEcNC/+6IHn1CCKwvTmmNMZjLkzRzAB7gkB98mIToDPfzwMuWFSh584trq0OPllxtcvuySuca21hqjstISmyFctC+/HP++9s1nxsfr+Dq6ZzJFMynkhQefc3TuGBMM9HLm24d/6ePjRZqXzHyBHPk07Gftc6FPMx6KfyVzSeWZNN6k/JwM0amFy9zIg69A3INPHxlpGKKNPeOlvc7f8NCLVgdLAmDhxay+1lAwIErRK/armIOPhujsAW8oszSATzBz6lm0XI5pbiQPU+Bp2Dnhcjn4Bgphdms8+HglZPFb3fMkRCf4OUHfWaY8CL+9d0l98c/jevD5wdA3ejU3+Rx8Vj7uuSwmDcdJDai1MS/pEscn0iuZR9oLigcfCdHpPfDKKw2ef77Fqt/m4HsvaQvwbekDQSUPPstSI9xLrpGNWMvBx57RGK2uMxmdRPHifaI4mRWik1mnBKaIKzxTS7hQXrm+ATLBFB8SlBz/k8Mk+RWVWVNy8CUhOomASL3RBg8+P5VB8udQLj4rlZtpd9S+rVYmgGuc2ZUNkKJS+KQHd6GeVH7m5WtBB/ldmeegOMBp25Sk0lQLt5GzEN84B98cJbVBMVwctSalwokyx2Vbm4TotML7zEhLxYl0UgP4skjvopyDL3nU2x582li0NETnOHknZc885aL5KkEJV6NsQROHvQZ8cfC4+7mvxN93Pfdf0v6Yyg79Nv+tnDcGwEeV+SrAbQiduWfiPTJ0QcCXYVm0PmljXpeDz1B4kE2FAsObUhCWV2t9Y4xtREVuZQ6+ZA8XP2IYWHscJTkH3H77pFypAfioB1/XpYW4Z0R9uFCNKEg39Kdy/8bmOfgAoFnah1oUqDP1Js51zcBDUQ++rpvSLkVjC/KQDOU6tEcqFXmaAcr3KeBHpaTfEOvkGA4yA2BZ+8KcEJ39uN76ZoFXz97FlMHJOwqlQs6DTyosASQAZWzHWPNTuEP+jWpAvaQeWtDKK1JJqpcD0jOuLXnzzoi24UXIvJwH3/AjFZycdUgpZbJey4WxK6qdtL3B4MOH+iZ+LewBbgT41PoL+zrjc+n5m2Go2fsnHnwpiDjxPYBfTWHFcn2J13Ih3CvmrZaD72h5cuqjHGsNefepJxIALhglydPlu1PF7bjP9EhCdBbbydDOjrIeJeIpiBlcAPjudxd4+ukFHnlkga6XQIRlqLsZ1Xvw8X04nVfKeyn7ounBJ4wcZIhOHqGlPkRn0hfC34V3D7mSsy9g1AuvvE/hu1Dgya44VsaqrZVbwrmpAeMaT5hszwoolnrwBY/aGWdXof+tn3iZvtfLp/uRDnbEfZ2e/eZhbfAjm7OsagV8ry/J72U9CJCuxaQasr/1PVKFGRD1fb2ohP62+rsbnPlFBCorHLFOTs0XTufcnXeucd99K/zKL7+TVJfIuX26zmle3eEePyejEWBiCML3X3o80bDnlpFyAPi8R1UOPsnzBu/10K5pkDA+oKb6gT301rwJPJ5zxPBI2+8w7gGF+TwnROdyfYBG46m6Lg70m286PPXUAt/7Xosnn7Yje2zp3actwLelDwRpwIwErxJgLTyrbVZk92yNsFKx6HFDdHo7RGdNHPJUiOZ5oqTls94v3k5NiE42bo4fpoH6jJCchuhUSMvBF8LBSQUpjufBJ0N0mt9bCB3WwQzwQ/+4ICp9lIerkxx9WjdjkAwPPlWINRi/JERnNtwYJ6qE516Am3nw1Xjm2SE65ynoeOgymFbeNnmUVFd0/Qzfhq5l3YNvDh3Pg28hXrP8zg4wc/Cpe5LvY72DUk7e25xCe3M8+HgOPr0PWcVlxZ6StJnRNalKGUVhkuuXueaz4LJoUwH4anLwbRyi01irScjc1KJhFoW5YQF8055OAb6KcyOn7MakGK4BkWlvb721w0//9CHuvHOFs1fn+Q4ALPejDNH54INLfPk/T5uCczA/zmRMRK4V+KvWBY8g/W3o2UrBwTk5+ACg3TmeB1/SRugY4YsuXXJ48MEdvPFGE/dizYOPdZVUauXcAHmK9WG9xp2P/Rl+5PH/G/tHF8wnsx58Pg3RGdaV3CsEmzOUtdKIkIuvXHtvUa9eAnpofppkjik4ApDby/U9zjr7s+vXCNE5h4ohOhOA78rl4JPK0Y1CdBahN1ImZ9RQ4vO0fDwADj/9af5cRpOaRJ0QBmUu68FH41vz55JnWB/GfwshOs0cfGS8Y0hj+OgpVTvlNP4wdwZJPEtbT127wFO3/Eyeb6IGWcYcS3Lw6dUM/xohOmUo0MkogryU8OBruyN8+Nzf4fTFl9j15TJdd2UPPv7I+deHf4+OHF57XQct6TvkaINtpVhPALWBVDdCifF1hE9Uj6u+Vw14/AgOmyE6jT4qd9Ny1LBKhiaUMplBgxzHZdFqqgL4RHuZ6lXe3fvUM0qgeTnDmEB9n+aWDL8tOwQJClttUUpy8FWARNrYS1BcO/9rPPiKn6ZUwOhrFcVBy8+pkgcfz4HqogdfKl56ZrgDQPz20OT0aOgno42ph6EuX3oYIToJ6Nc0wNmzHru7aR1JUxrP7TjAJz3u4rKgfYTjHnyij/S35ojhPfDyWyfxwgstjo6c6sHn3HwPPu1jx74fw4PP0u9RQzktr/imYrLlvLJYGwZTxBjuxRenef3897YA33tJW4BvSx8Iqs/Bl5J6npGS7foAzSuvJMr9qXFdgMkyBZRRU0J0xrbEoTUVoIwzsFhdxnXnn4Tr1tPzrERemcEsvTdRkFoefHDqYZ40YQ2WwmiFhK1RQUPBWMITMQ++HGngIGwFUBjL2Op6DTm+YfzNHHyi32Ui88WlioscDWNtzCNMQKsOXOgChXcCnOt0ZZf8zow5dKOfgFTUZBZObRjKZL7744foVEGXDMCng1fl/odcK6EODtZncvBtoBXY2U2v5eaTW7Y2s22R9wLgk1oH9ZGxQTGms0MXGmNiaYm1vpCQjxJ8yVlmTj2g4A//tklb8+TOqS7Tg0/vlyXcJ6EQc2uRAXxd0g8t6TigK2BqdCbmfmydkTNIMxaxcvAlITqrc+rYL+cBASLXeR7svv069v70T/HZkw/iF3/xACdPCEFWzcE3/S1DdH71q3up8VAGkEtfJD2veQ4+/TGtfoqfzPbgywB8TNFZCNE5VRj66ZJyjz22IADfRDEHH31pWmkmvrF2Tuw89BBu+v7f4IbXH8eZC0hHowAAIABJREFUCy+YbFrjh1x93qdKJEdyghTJAGC0b0jXwGtnPlp2nKlUVPJ3lB0Xc814scSL0eBTSuScTzU6m2jiM5udFqJzgVVSjj0zI6cXy89S68GHunOuRAkvuKGB0tFnP4vLv/ALej+kMY08GwQzWgL4ND5E8vaA2EdckzyjdM/0EjGBkLW9B6pgozE3c0YbtArVEAcNnrr17+HLn/nf8NbZW/VnRUWaItvJHHyKkDCBUBUAn/c6by02ojte+AZ++Mk/wae//dvYOboYr1OvdtkP5a1im3SoafvtwjEluzSOrKHLe2fw1/f9Ms6fTse5Nn97AgYUvIaGe179m9UVKug69XwPITqlxzDpmdlHWr0EImVZzcsUNfuhH/Lmxu/nXPG7JHJqTVntQuZ52kazwdkiPd59r3gC9vkQnWpXC32ROfj0DbUgiwFMHzCtGW/qviye+koDfJSK554WfQHpmit78PEQnYxxF/Vmc/D5Xp3bAXCTBupqrsTMt1P1RkpIfS1cpNWO92SfdNKDjz8y5ODj/dA8+OhcKYU9X60cvvCNs3jhhWF/0XLwAbkcfC5GSAh9L4a8NgG+eTItNdCiEUGmbnMeqTSfq51iBF0+ee30g3jwMRHoCuSj3dLmtB39LX0wSAp2lLyPCg9tZ1KV1KSSG77+eZz8D/8BJ/7mW/pmqFTgMh58w+FF7kHx4IMiuApGKN7yPX7i2/8nfvQ7v4cb/uILqWDLDkjezsTk8bZmW3aQfAjMOiYctAVhd7DAVMZAGdtWJm0nY8MAPuciT1QTenOwhCJWP4a1eqIoEiEOQouAkC/FN7E6UqVsygiMaQ4+wfRbHnzaPBbfiNYhve8y+jPevyBUbeLBV8l8Wsz0cUgKHnP44NAna54zIt9heIbUT60dN1SQ0U7u7uUmuHKtIkSnplRYE7CERWTyHldfrYeeic833CK8jgoKrLkefPR8KYENEuSgwlblIlHB5OlXUr4qB58wEgCU8RQNW0nshzqIoBMseOkeZ+g0NOVUTYhO+aUm45RJcIgKQEWgrT3TSh58dP8ChCJ4RpvJPbIwagGtW77+h1g++ij2vvhFNK+/noTL0UBRHqJzsnQ1ldkZFL80phIoU73FlPpkSLDakKWx/E7B6CX+WwfwNaPnnhXZQNuLPRRFPx3LDAo2nTHzAWsmzCsbSWLIpCiarBCdzulhOul665sFXr71frX5UAfbd7TzOxuic6pHvkmO5N47l8d1SHPwWcY3pXrov5SCjMLGXAmBxSusfxGp+Kv14Es6WcnkZT3AM3zUcN08yOD399U5nvC+Qq6Je3aQBXMhOgtnCEsjp+WJKwF8SnSS8Edif+R9LF+7J+j8ofDGzClONYXk+EDfLNLQsIrR1BA6s+DBV5CDGmWNBRk68uRWDj4xxre/+JdDnX2H215+IF7XQnQyDz5tjyJn1BCqWchHiYI53Ct/P++B1WIfr529C12jhaGpmwOaVwlQH7Ul7rXQPfhc3zMFtAzRqQHjAJDNZU5u0Pkfth2+LtNxqPbgk21XM4ie/zvnGf1mWswbuSs9/6ByK5ZzuO+U7zvWUQPw1RL14Ot7Y9/PixrDNef4mnbhOq2Griu9s8cF+OTeOWtMujpe1fLgO326x733rnD3fdxYUvPgC2qkPMCndz7m4pYhOjOGZ7L9wYNPAfNU0I/WIWQOrXI38dwWCx3q7Lp0j2W6PAHotdCjIoT63nnHYb2YIpksuwN1EuQ8+FoSCrcfvfesHHwuvIRCc8PNUv5tMtjVv1+NsU+1zlzQpZPXTT/WNIUP0WFtAb73lLajv6UPBNUcwMNGqgnVyqarAEIn/79vkE2KFK7w4EsAB3LT+T5JQyA5t+RgIBVe9c4rOHH5DQDAqacfTRQoNCazpElJKgThCl7XYo6lsGsqeQwQJsdnez8oPJLvOD7E87PMC9HZtrzxkhIyKjGyITpD0fmKOtYmnS9SQSaVHyVBRdyPQlzJa4R/cK4AGD34EnBHfOfHHlvi/PlJ+FMZ0U0kjBIN0m1yeTaQTdZlljmFrlwHdNGcPSq+dQIMh78ZQ1n/ImHutC3QKusjH6KTe/BVfSrPPfjYmvQe99yzxj33rNV6/dDhmQ3myTnED1/VfRGiUw1JIpTj/PnynpK0WSEMs/YNAIT1S9syRMVSMKsN0RnCyHGgkSj9hXWjpJrPau7H4JbY2cI1ND4b8qtJkkCYPOPMfuZe0jk+x0pK/ZGWb08Jzdvnn08AK60vMspgIO0YkwYGJdLmC7PwL+R8kIZIMSdNAeBLQKhKDz7LSj7BMIiyQS2v5OCLHnzWhmnl4MudMWmX0ut9F6tIlEjeJzns9DVu96HkwQcAz9z1D3D5H/9jHF51jdHJfPs5JaS1xG0Lac5XankJa04CB887JAGaMEeOw+op3Sjl4KsJpz9Vz+eiul7lC/gUZK0nwbey86hkKJO5x4CDnEe9kKHEWnSXL4c/0+YLITqL4fdLITrF62vhiCnA4tf2PND4x6wxVAXIrXlHcWOlCoAPRohOGbNdGV9pwEPb13LwaXlLc7IZrbdtlXOJD2paAZF3Dg95KMR1J5W5Og+fI+rBktyrrcPzv5mMbRwuGsAHGDn4FA8+5zzx4CsLCzmxjxr8aB3UFcRCHlaPNy/k1fJ4UvDNrtigzHmnioneq4Yd6ZrO79UayB/kFwvg0yJvzAvR6YwN1fj+bI7wcdb0RBZoXNGUUTb99okM54G9w7dw/6O/wzx/9Qp1Q+lUB6gDfM4B11zjcc31Qn7aFOCDV/eQGKo/DQWkvJMxzq7R56kmvxhGqLyIwnOKvssQncAI8BXOekrFvMYY+KAI8nmPxejFZ0XCkZ52MS99w70xVfI+Ses01as/Yh0BaohOQ+xKdImVVLO+1kuS6oEYw7EzaQvwvae0Hf0tve/Je6D3mbBykTnVGQ9duEuVsnwzpIDWUP+lS+SajGud8mGsfxLgoxbUsj+AVMLbQnS4qAJ59N0o4+9cKrwVyDcKUEP6OVhm8TrnKliAYdwGBlUkYB5/mCE6K5TsSW6a0u4XTl7lYA5jkFUCzBpj+o09OE+crydVhPELccwKHnyNYKBYPjglRKf2epcuuQn0tJiwjHJTVQZWAHdVnnMFUh+n86rGrKmiDzkwN5eDb65322LhdeVgbl5WePBp1HXTQzJE52IBXHttj8XC08tjTx3YXpvxKKNUWlo0gXeJ0hCdYi0mD9hCOFNwZppOx9XF66o1uU/HDrBDdA7bT6rgTZRxlR84CjOGpaDG1NP3D/tkDqg2vz3bL+q/K6tb2U+PVnodbP8Csh58oqPZDmySg0/OIQnQamdY205np/fT+wSWhYfoRJUicKJUMUK/aeNqrZu5dXo6HvnvW8zBF9uxPfjYdj4OZNNaUnWqVI8soLFhliyllZ91QDj6MXqkI2tmUuakCqVU2e8I+LMJwHfYLbC+7z5cPn2D0kN+FmdDdBI+L8xLy4PPa+HagBTQU8CNmn25gecMJuH7+jmicpwrxi3PlemlHHzFEO2GHGF68FGDwHERavxUFqiv4MmCh97+N7+Bv/fg/44PvfqILED6lFYW9hVpnMiKiXvMgw9gITqPdk7x5l2D06f7pH3qNaC2Y7uY8vqNHHx0zrM5u+5YuRJpOtZc/lPeFydyQI3XOVIhKlcAKa/kEoPY+wqClszpPlabAHxq2LVKbxognbNzQpcdHjrQWdL13EhWpq1IK7NBqU3Djst6qP4jp9jV9kLnvRoqzq+5kYr04NOUzUNPpodUz0CH5KwK/ICVsy1eq1AaO4QQnRO/OHucZ8iTbH/IPBfecehN3oNPRjmYZBNyUZPpSX5bqw/KVbPPANCSEJ3m61m8DQVsjbC2sL45ZRIJ0dd+7rkW//E/7uOBB5b8uQxpHnwfe/rPcO2bzyZl77xzzfV3tZF1xj6YYeuXU6XUgy8pVwD4rHcNAJ8TKWZKegwJvqkGr5pXnxKiM5H3FN0h1V8Cuk6t73mbSb5dQS3qDChX1ItP5OFzruDB1w+h1ZsG6llKyw6Avj7um+bgCzI+ABbVI8MuGZ2bR6HObkHy6/W9EaKzTk7b0rtDW4BvSx8MogrYK8ynqQoN+kzf44/+aB///t+fwje/OW5q67XYSMXhRhUcljKPHnbJAa4zRUNb0A9KhWK/xK4/22LX8dAKtN+m5YhQKNUcJt5PymTWR8eVP+FalQcfebYulCfpDAYGySpLcsumiiXjoZqxZ4r0RKBJK+B18vvRol8DDmhRqSxh4TV9opjS2ybVuWYEG8cCVpy+YzAZx6lDrRe24hN9n21GzhuzHLPg9uY+oIVfqaEgbC4WUD+Opqwcwvz6EeBLGfVse5A5+GjFZB/U5olzDIScC9Jq1pH0j7khOi3wJQvWaSE6rbLG67GlZyjEdQZeVyxoyesBJNJErQdf3JMNK00tLIeWmiz3eRvn0bz4IhaPPQZ0XWKcQusu9TdH4WwtheikbVr9ZmMkvSNlf8iAlHJEWB2zDHjkNeaINArMErgExrHN5G1L1ldB2RI8+Mx1Scj7yRhErrkiz1YC+MJZbAJ8jq+jZlr/annFgy+Oo5WDz/AusdaQWlbpT8M8+NKzNA3ROfUY9G+fnnMWfy15zNUIjmsgeaJc1vgONQff9LxGnQHwhffKAXxV5D0HFZIwg3UVFj2rxHg0vstPhBkvIsMGFnPwhT5p/cw2FNZXplwPuPPnsffAt7B3eAGffOKP65ugQKVxfWifrCXNg48AfIfLkwCAa64Z9rt26fDzP3/A6qWPWyE6cwCfyU/Tn8aL+wzAx64VDlNzD6NntNfP0ipehq4Ry4Mvl4NP9pd9wwDwOZ4WAroHn+UVIQsO7y7Hi8ro+qCHy4eHPFRd7xtWnWb0ylvKgH46Y6xcU7uo/m3nlxc8SyFEJwX46F4dgVi2TvXO5AChksyk54msIRHesmIPVQ0KzLLT31IfI0nzFvPeCEtu7RnGBRVoKXjwWXMmR3UhOgt9x7AOqB5JGvcMN8pKPlr3H/zBCXz/+wt87Wt7eOstlxYoVTD+vO78U0mxpgFuuKHnOTwjwJfv4/RtdJ7dLSZ+xns3z4NPerEqfYkp/eQeqQ2NsV49oBtwFEN0pnXRC96TPicAX9q9ruMe1DKthqQ6+cphtdiPv5bEgy9UbaVklnrKGGbZ2su9n+3BZxED+IihXG6fteg4HrJ9OwHqjihC2fzZevC9p7Qd/S2976nIpJGNxcx7Ia9RxpTy2UrhC296PP30sIH/xV/sDhcLuzLDpryiQPNSMJIFBMMD/lMqPQMjkFgpKkrSWpSUDYWlnHTOPAy4kKGHC1KeQuM7OC+U9eMP6h3EPPhqanb8u+fKDQ2MdWeEyEyEmFmKmURxm/HuLDVl5eBrlPFnwp5Idl/y4KvrFFe4AEi8X9l8qlQ2JUpBeGic6xwln1TAeC8qyH5s0mbJOyfJwacrBzbFLEN9i4UB8hhMqHODMsalnyxP3mc9+IxHhn/BFWvH9cIEINzaKvY5FqLTUFgRSgR28s2KXsEAVqt0WIqKfsNzjhlR6JIb+9XIEI/VAF9w/9L7wawbhScOLZt7z52L53Hyd38X+3/yJ1j+9V9P/aA5+IhQOIfYnI4efPrH6kXoRbmfmmBYbt44F5HvQTCrm+eyreT7aQBQw9dg14ewZ3n+ybiZ7d8XvrCPJ56YrGyajGIRoEe5H/s0Xp+Zg68t5OAblIf2Wh6UbIQHDIpOIxxiMLZgHnwbhui01rKk3DybAL40DJR+Nooynhqy8AdqPPhWg/GyGeZWa5+1Qc78ZI43aX+HPuRB3ZyyODc+tJ+eIeMEpJjj/ZHZ7LQQVa1f5+WJmkMl1C9Cd2n7DFcQea5op89W8OtMzpFihfdoLmbCndHq00nAFMFascQYx3EvHec93MFB/H04evBddVWP++8/wv/0m5dw9qzNb5ZCdGp8OdsKhHdZ3OsUDz4HH73RalkgK0dzzVwHJpCG1WmFLaW/Hc/5roabrsjBF28rITq9hxmiM2nHAjqFF1kiY5Hf9lwf+n5w4Fg7MkRnLqUCkFd0VgOC2rPK+RK6sFzoe8ruLnlvi9cfb3RHaThZANGbWnoM6310Sl/5vmPJQOpYVoxN2Ne4srlyD69hVmV7RT6cV+9ghOhkY5h68Mm9Wo30Pr77nBx8JX609ZNBe9/rlXijDpa/19NIT7a3sbm2yFWN3nyz8qzse7jLl9E+80w+V7LC4tWkSxh6qPMxkVqiX/GAXy7VYhrAJw9b3aN+bF8Y8Kn9NwF2p+7vqi5JqS4LUAejGsFzs2fGH+u11ImlHnz0Hc2oCKJDOQ++UKfmxScBviogy9AjWlFtzLXhFF0F2esSgLjSJGIudQTgQ9epHnxbgO+9pe3ob+l9T1IZbJXxHupBpcvO5HBUmDNazepI1Nn3ibt1lnesEKBrmc8JhOS7ePFxqpgtdlghQ+DuiXdDImzMbCLU0fRrJgRTZsby4Msy/YGRMOTVbGcA5k0SnyVKgDguGYZ1DqWgT956SypW5P1ciE5eMReyqALA9V0iMJXeNYT5jM+IkKdx3CrzUeUbu0IhOr23hfXCmlfBdK2dTFghK0TnrFcbC8uck/F2bq1UhOjUlAo1HnwqxcmbKtBLj1nEvJ+rPfhI3UWAz+5MSbl2+bLDX/3VDr77Xe56zPZQTUFt9KmhhoxSgeWQ5v+YAaRwgK/kwUf+JgJIoBorxXY9oQXtSy9N/dCUq0Uv5gyNBVdrA8yRITqhKKe0anMefERSdG5DD75SGyOxOYHUg4/ebJxtuBGK8dCIvGzMt0ratvrF6o7n5/hcYTzkubPYqwiJIwEAUUZTaFrfNl4mD/VBMGdKTqI8tUJ0GiB5LYWxCopCSq7vUw8+5Vs4P3132QeZw0+r5Oho+G2B5MXwdwGAhZ/2CcGnyX5beU6SEJ19GfQ0+2x48Hnn6hXEtD5BHkgGvPVddoOcAy7KnMdVHnxQxqcwMWtCdKaLTNzuxaIWlXllzSVnnPDgYyE6vfTgOxWb2tkB9vb1CaKH6Dy+B1/kHIg8GKeb9+hrc/BlL9okh1ELn8UU69LYQVmYzut7LDPiM3LwxbJCcQuM3hICPFLnstG+bGf4025XG8vl+jJ2zp8DABxc5nO16yWYTGViLbdhqsTe1FiJ10N4XT/xps4Bv/wv38HJkx433NDHHNg7Ox4f+Qg1rpgYT2069SsD4It7iP5dq4xYhF4kbhfkogZC1xg8TCE6x7or9tBkv5kndFVUPP30vbFupPGgApDkjAgCNb6rtAtx5P828Rx8xv5fMVyM5aLHvlwfBeDY+jSaLKQCmn2PE7/92zjxh3+I3a98Zd6nrgzRqecCJrTgITrXd9wRnpzqGMVjzYNvbuqW6eG8UM9vO7Wi0tw1gzUR78caDz4rRCcK/JjqHavQut2Nfy+6I7XTVpjOVsnBpxprBF6EPOyJkqTGbpyND5kLAWjNhegs6abnUqivZyE6dQ++I+IhuaUfPG0Bvi297yluXOIgkAp1i/FQzzN2iJKDWNmkEkVNLiwIAgM3VdCo3kWJhonfzQhETb8WIUFskmOkMVMWsWaFNSXtp8pAiYNlVojOzOEsc/DRvF51VH53ySQ6LQef8EBQKzHGWVW45YTPmQKg5cFXCj3IwnA03IOPxSfI1CH7kcTTBxKOhlnzq5ZhFcqkd4E04aqGH5rjAUb3HIB/uwT3LDQePR/G+tdr6NNdA/3GvdU3jRC6awbas0/KwuZ68a7Jk8I44ZggrWyoRrDviRAzgOu6x8BU6dTHthVK1bDfFt7jwgUhsBVeuyasn5xX0lAAAJzM61g53sEiUuuH3NrDeNC92hL4WF/EPKD7cGhjTm5FTvS8DOBECYUa26oM0Vla936Gl6hWv9aGpsRxjo99N65hPQefz4ILUrlQmi8hRGeZwvk5rrvZHnzlEJ1zAL5yiM70WsgLzd6WVmqEdM4pP+VvLdRaEOap0oOulXRO6HVYoahrPPhmAXzKIKhRLcI9C+AzPPgSS/mCYs28LgA+yufPAfey4Gb04Jvqa4oefIW2pUaH9EOd//JdtO+jyiz5tpOw0IpFdy1xycjmISjv6KVs0/ciB99JXofi6c8MIyj/xcbY6Az4evUixHqoQnsfB59EtjApyKg5D77CGvAeak7LWg++eAk+fgNWnL7LjBx8OQ8+athAicotrF5F4czIAKQo3f6nv4X2mWdwODmCDsaPvTNldN2oTH6IiefMhu8skCajAMPQfehDPT71qRXuvHONa68dvFZ/9EdXjD8vhejsjhQwENNebHpaFQCWcIPv2ZXvLHUlWv3jAR/vDYqPqvprAL5Eic7K5je8oKdSc/DRvUGrRn4k49xwPjX0mR7RBLECX0eE0SFEp1KFktdu6AyVx8g+mOEBWAtK36zuUoOJHJ1+9ZnoXb7z0EOmnJuT//gc0I34zToAFqKz74Hujjtw8d/8G1z86MeSPujhlPNjZI6tlrfR+OXh8Lf3/Pdp+co0Ftb+xIB8oSrjAN9AQ4hOYkRRgC5qc/DJiAcaUYM3zYMPAI/8oDbkOT+5t8duzaG5ITqzUXqO4d3XtQTgIxG+vAfeuPo2vHn6I3jhph/buP4tHZ+2AN+W3vc0bVw2g5ZYX6nPEyInStHSSXrudKmgsYmVhAyJKDpI/hLtC81/UM5LsACg78Y7a4WissgbAF/spwL0MeWn8Fg02/GI3mLheWo9zbyDnJ6DL/kWGaCt6rspHnyBtBx8ofK5Ft+sX4pCmhWQ5Z19vzYHX7s6jH+vFvvFHHyl1xu+W6ocdmN8r1DXJt4sOjNdz6yooP8oeKlMKMrKbVJT/i5R/DSN+NZUGKpN5p00MDx3dKRPcEsB4RyqPPiSJ71nQiMND1i1AtgYzwM+dB0UGUMFbJLEQ3SWrf/o91ouvVifmzPMWv3xGgPWyHjRTyzAVN2QQL5bhvknz0cPPgWcSECKYBWszKNagI8a1WgW/nNJ2x8tcCJ5HwHwWfteNgefc0JhuWkOPtlGOqBNw3maED5Xhh5l9WYW6pwxn0J08n6xb6so0aWyp7TvLHYzIc9GLdIcgI+uf7tSQ2k8IwcfPV9k8VAi/pWZZzGHoIIQyjmhhVMCgLbn53Bo87ghOvvesY2/tJ9V4AhDvVaIzghmePE7rTNHDp4fFMywq15BHEtZsojk0UsA3ww+kvNbutdTj8HFlxqjzGdVK8650iLObaokYogFxKSK9dSDD4cTT3u4OMHbyKFfELlFNVlNWShsDzMU3tRr1dH9MpODT5t7Gn+Y+5ayXnU9UV5Mhk6LBi98o1b54k1z8LlwVmEcXxfLWPy3KT+IfTaXBiE3V3e/9jUO8Dk3yn26jK578M0z6NokRGeUX6DrAnZ20ilLAREN5DBDdMYcfGXjH433C/2jAHco4xX+ndVXsVkFhfcc5XlOh3QcUr3yvEejAS2ZXMRyzABbRnR9p/KGss7aV+UefHUbTPzu9HuS3Mc04lEC2gojgkJTU9nKcyzRoc355JVJ07Tw6fS3BPgAwJ85g/XeSVbWOT8YoYpKSuCUlaliDqjjXYNzZ+/C39z7yzjYPT31XQsvWyEbsj2ZefDRZ5V9qJPnRJPdB8wQnbQrkPqpCSyjZIXoPHXp1bH7fjKW0fQtgRehZyIB+Oqmky4DT33VZQqpS7ySRM915z3Ti//d3b+Eb338f8A7+9e+K21vqY62AN+W3vcUDwtnhaKcmENtM9MslrgH3/SvatUsd2BNcSOboEKp10I8Qj/slN+NUIY0fSc8+OxlTIV4dnGuRE8APG41W+fdUMtBeT8qPOTzI1FrmsGDL/SjqvoqwUmOmebBpykBzIqqOiYUFzM8+OTnlGFNgs6qZHXVHk1S7Lrdq87Bd/66j1o900N0Sg8+ZoGrtaEoMhKATxcwN/kEbA8QFeR0UtO8KYQU6aVyU1/LLK9ZxfJxDvjxHz+K/f/whzsVyLdyftQCfOk1fsEK0al+C2qcgCvEjFKQdHxXmiQ9UbaQhN3OceFFUzbT+bpc8u89I10S7wMViLR5bHjw5ea3puRJQktX7svBgtfy4KOkgao1nqDWecFy8Fnaf/2SSmFOdH2j7t9SMZE741j7pVDDJAefmreoRD41klFBXOe51amfwp5J+vBT/y+W3/mO2lxYl1RhmaxPCVbAPidYveL8zFtzp9Tu/qA9+NLJrubgo4NsAHxzPPg0ouF4EiVSJkRngqH0PFR2vK4BfOLM6HuHvtdB8vVa7CvGftY0YArLJB+rBPiMEJ2hDToG2vOxbes6eA4+FlKJKJSK83R835tvXuO228QcGPd3OiSSn0+qm+GtTC3bqZcmo5q9s7SGFcA7ESsKGvZsE44qgnVjD0DIItIVwE85+LwHjpbCg0+0J9ugeYU1L7PSOJo5+Axw22cAPnZNGXvZBy08nFySOniS8eBDessCkV0mB1+aC6/Gg88IN+ttgE/us7mzK+vlcHSEo0N2JdnamTyujavBcw/l1UPc7A+rQ26z9LtXHCaU79b4Ay1EJ+MJDCCmNkRnkB2YBwrl30tz1KJx76G83Luag6/keUdo8IAzvKvFnNQAEvYahgIiF6JTe62S3NVU5OCbo+MJf8QzI/k2eSE0vLZ8/eNgsxp/ofIiY6NrEuJfnVrKHszqXi5k0aF6ISs5p8iswuBoVgSMnNAj+hI+0KvX3IVXz94R30XmAgfkmndpXckDOqPHukfkhFQnlo5ToMbruVkTsN0YQ1YXA/imGze88d2pnjbDm4Y6jwXwTUQ9+ILxXjD6S9osbUbHWDC9aydPbo/J6g/zeNYtvXu0Bfi29L6naQ/KbxrWXqVed+nhPAArChkhOrOK1pKCQ3q0WWgB0sPbyZAkGWunkjCbI9ZtK0QnnMoP6xiiT8ppbTZ9x6y7aJ+Z0thxbyGTmPnSDEVz6OhKTuO3AAAgAElEQVRqpQBKkxJAAkOldsxbxpzRmbtcnfz+FKJTmYekaHs0JRleLfbQNyLGHhGYXj17J/7qR/9HXPyN38D3b/uM/jqKwoXmr5wA45kcjtrYPEalrvhcJmVk6IvKMc5IWsqBTTz4fvInD3HbrSvcdFOH667rdU9dYwI6B/i2LQJ8yXOikBWiU6Nkjhwb4PP8s4110z5d2jvLn5AAX2E+Bs8XYAAOWYhZsndsiO+rlFXmubRMuJ6G/q2XiDVQU/MkTITsTA6+3HtKITrZh2kb6tqsnDvkwM/YbkSjkeF9Ks4N6Wkv5yHxEqM50Gq6CpT370DSg289KqrVEJ0OWD7+eLYP7PzJKGcBoHUWuCLn4SS4D/XUrxfngEUG4AuKzmH+62u5h/DKtHX2Q519qlTvlBx8TEFnheg85nnn+i5iJ5rS2uJVJAWQOQX4xAWdmcPhIXB4ZDIz2fYdbEDXDC1WUOxO++BAV189zcXTp8tjng3RqSnoC9YcO0uPD3+4w4kTVPGVjkVTyME3K9qGBLgMJXKaxzldn1XGHyVmPv8w7QC/xeQfW5ZintAu9eCjAN9qua82RS/Se2aIzkZZ9wqZIevIGp2AMh8BwVrduerBN4N/0jzNejru2j5A/x3bUwE2mYMvQzmAj8pWZh5iI0QnnV8U/KJ918qyGjzQX3cdjg4oj+4Guc/gCVQASswV3pX69e18j7uf+wo+8cTnsHv0djIvch58Gln5pQLxEJ3T9eCx0iseMJLUM3DUtcgl5D3fa/UxruHFeA6+GuaCrs+6VirJK/uAsTcXPfgkWR58mRCd6nZamCttRQ4+9T0BHpVGAComv5XRadG6Jf9O+UmjW3p73jIgUh4eG3nn4nRpf18xvsvweb5p4AzgiL37yHole4rjgHVtyMyhgfy3Y/ODC9PZdC+NK4+5J3JgNOhquONGR42UxjFdr11yTuRApBo5PtQTn/G6kGp58NG2wn6Yy8HHzsTdKfefGjJXENt7CcCHcPZSYwbJz2dAvk0MqmNf2pbpCCe9nqszwtjSu05l2HlLW3qPiTIKKrtHNrdad37mIdekhdkzMm9RIVeCtKQwGRl6qGY8+JK8O/2aW8vA8mzkAiS7OEf7PD4Tx1gAEt5DrY8pJ4XCwHu7C41fj0JwaHDqL2daaYhOzhRVvlKeRi3acHDpB9Zc65sakor50hE8DI8OEgFEYaeFBCHPLVaTB99qsc89QzsRJMI5XLzqQ/BnLwLuNbVf3jU8Vnrfq/krmceUpsQ2BET2WwLmBTJ5XD+tPGteW32gfclSxoOPc3Lz6nVu8Cj74Y+tsDjZTRcFZXN+bBKiU1rVU8UQVXhbirWCoKK2mVu7VKAc18LgwedwsHsaXbtkxROAT7NOJO213RSXbrnkuXPmbqtq9zVL84zAOrXJx1pT8iQefJn+MoCvkINPe5B78Bllrb4oITq9J8LvMQY6KhHhRi8Nq1NDG/2VyME3fhCa49L5Ht4KO6hRn/IRNQBfmJ5R+VHhEU2v5UIB5TxqLKLzMnjJNEqUgxwtdsc1a2oShlx0jXG/h/DeVPmL6ZYG8KkhT2l7Gp/opQff/HncoB+3BJd66vg0B5+VC0bz4NNYQ0uRcnjosFo57Cr32BMqqDXN5dieAFmSfig5w6b2ZD4n4LrreuztrbBYeOzvk7KGwiMB+KjFtWviHji9RGPksxnqb5ztyU+HpK0J0Zmb67R+slYb6N5Vi/4oEw1FdNQuoHYzqcLbuWGyRz0N0UnbyhgYSGMhZjHftFi1e6ijoQ2WC4gaZYU5UgjRKXlZTUadQGkfQ3QCg7LZBK6CjGqO38QvmAp0b60nZ/xNL/P9Tg1FJ3PwaZMjrHchTw63UwU29eSn1URvkmRu0HIu78GXme/96dM4fJP3se+EDoHmh1LmRQ70y3n3Sbr53EO4/ft/AWDgQ/vP/rPpmaBIRn0kCbpnBjZzABRG3kfx4ANAQgYaZ5/CryZD7P0Ecng9pGZNiE710/kh5OhGQGoFs5oD3ko8UjjzNV5Jho1NWGvBA1pGoHM9+EpE96IaD77z5x1efjnsL0QeIwbhUofFKS+EhkvUy3r4bb6CeD6dQ9kIAUrjFy9OdZw44fG63H8MvgsA0Lbs+zz99AJ/8Af7+MxnjrBQPNNKeT3VueSMvwueo1Yl1NBIBRRRIRtqC0fwIgNfzSvQQnSWdDNNv0aX4RkHkDQf5hQI+oxxP7QAPi1XECUB8K0+/nG0zz4L1/c4anaYToHWqxHVqQUPPtXbl7b9LtAQ1UIJR13Qm23pB0dbmHVL73ua9qf04EvKZZgBdk1YyZj1AakkVZODryQ8eC8OLFtzJz1+pPt51sLeTQcAq6RiA2ZVWR581OMo10TlIeM91Nj0k9KYM4WLxbzDa06ITnivAlLxHtIcfExZNfOQowwhY34rtLocF+L3Y4jOglKmOaI5+HiIzuDBR9uI68Y6ReQYeM/CnYbqrkQOPlU6zJBe1LGbDFDIPidqKYFUpJKm8aqCA7CFN7NdoeAEYHvwyfcav+fgwZcK50b31QuzPfiIcrY+z6FOVDkx1T/16fLuGciQxjQHH8BBAW0JU2vWxYKHWzxuiM7Qa0olq15r7FTQJpmb9nhzUHPwWqzJsxC8AGjfwvKsBvgsZVFFOOoS0dwPOQ8+2ia7xjsa/5JhN1UgYTmBy5pQlyVFe2EBUgyn6INwOvy7bpasbI5s5cJ4XxofjSE6q76F98Ti2p7jGhVz8GGcf8ZeLD34mpaDS5RCKElJaojOAsDnwBdBMtcq2DTmwSffTwH4rDWu5eAD0jllff/DQ4fDo7IWUeU7Rg8Dh/QdJwMR/twmITqvuoqDezly8MwqwQkPvmSsc+iJVaz3SZG2zwN8Hra3UFJ2/FZNw0ERSosKZVJti7kQnXP4oJzlAovwIM5mtuc6u9fr5cTPqp+NyVvDv2aIziYAM9lqWK5l1nUyVxnPRjz45Fy3QAztWs209F73iGUKT8k7KoLyYLip7HM0vG2BKTI9+IQMbIbitHIn0fCtim6gxiAw9IPm4APGEJ3Gvq8qPRUDVC8vVNCHzz0U/77hje9mzsk6eSisWTU1CbgHH0uPETxWDA8+bghjNE7AKiv6jR6+tMxkJ+dsxW5G1+dcorx3EnFJ4W1qQnQmbTgk61vuMbRN0xNe2U6LITr7DjRiR86D7+AAeOyxZXofkpcejLidwgSw9TLDgy+yXCW5U+G7ukyITkajZ+ili1Ml+/vperNy8AFIAD4AeO65BX7v907wNeVSHty5lCeY48En1wYAkydlbRQBPk02lErV6X4sLT34fDpxJajcV3Ao+nkhzmRFzyqjh5W8nJ2bCmn7VTh7qMFRf/IkLv3ar+Hyz/4cHr/1v86+R1JfM6V6iAZ+JGx/jQzBO7cZ+aaNa0byFlsPvvcHbb/Clj4AFAQqOwdfVHhUevBBYbgB3erWSUW7BfpYpAhdSS6WHIPDDtPBo0wyxKYySCj9NWaq6hVIwnOeg09Rco3EkvzCI6dMjgnc/eShqIFljClyDprhTNIVpnWg310XhmsAvinEWGYsj2HFws/HfD0JMC0O1wkAyCtcqAff2gD44lpyJDGy8Z4hiXF8xvDgs0PsjNVLwEP7ZsbcqtTBxWsJk5pOJrufYq2ZJL2oqDBM2us70ZcCVQN80AVe53BFPPhY2NwCWOadKwoO2bZVZR0BSce/z5wZ6r28d6acg6/Qh6YyROccygJfxbA9496eKFvLYWNqvV41Dz4rD4Z1tgobAYVI/4nypyHne7g4N8Y/m9OkjtxRHkPICOvIvtWVF0UPPgB+Zyf2ZdEdounX+OR3/x/c/+jvYO/gzfxLGB58KUAiAb7hR5AvXz17Jw53rsq3RZttFFfM0L4RolOSdgSfufh9fPTh/4Rr3nxmjHKQ4Q/EreVuXsAf2Bw7rNsA8KVSvA7weTUHX3ze0KQNSm5ln1UUIXOo8fkQnTLE5hwPPkAD+PRxfvtth8uHxrvTtazd1zz4ZBlxXQspSNvbdO+ldbB3FQBfojAq8HfauGtAQ+P7QojOfL8phTXRtsME0eb/YFjg1DUp+272SdEmJcOxQYhxWlmNwt3KwZcYnSz3sFpkPPimyRMv0bOBn632xGXf2gjRmaQfCM9203rMzXWrfq2x3BQt5oqzQFfihQ54PcQ+HTxN+WkAIsE7oe/BZAcLrAbc9K7CwKYphhWkfciMZd+zHHzB89+S0ed6nc3hZVJvMP733BCdQPg8QZ7l9zojBx+NgmD1LUcJWOV5vrxAtfkMU/LoOx/PaO/qx3ma2hueI3IeinNjqFbfmxt2bk79Fw/bv2ObNj9VbSxA62NryRjHcfK88kqbnd/hHvXiT7pT4VkFaCE6HW+kkjSjCrtwjwsXpjycy+WYi13KWgbfBSBJhcHuFTz4dnd9wvhre3BW91E5j+i+FYzgAOhGHQqon1RrgO5MTvTpntJ1wsCqgFw5V2c4bhkoUNJy8NE14NzwPXPkAD5ZFwv0N92Ey5/8URwtThT7Weyztz34NgnDWdWPptH3Zz9fPt/Su0NbgG9L73uqPavVg8sqSxW6VKbRlC9SIa9ZZgvZhebaaLR8I1FjObVskRQ+mn5tHpRmHdJCbi74pFi6DG03KsNHFc/TcxnF1mKyBAkeigzgG4keth5TTPDaA+Xuuwfl/O6ux5kz1lyZlBfhWztnM4BhOtSOsTn0RCjn4K8AJBQGMHch8h5qiE5SLsnBRzkbn8y5qOewQCTXCGbEC458eI4BJJsKVTOBIQvgG3rlzTKsTUsxWTIykCE6jX0gbb+gdFM1qda3SZU3AeArWayllUmAz75XoivCjCqay1OnenzqUyv85D/Zw96+UJK4Jg67BfCxddJRgI8LO5ti+lRfmnjiZeY2M1IQylZNyZPWbY83VRC0o9JCK5+EuyGeEnPAYjZ2mpkxyN53DOMJ+rFliJ+hOd5GAFT0jtY2OQqGu7vx8WV3iNu//xf40GvfwbVvPotPPPl567Hh78p40M5xa/t19OAbfl/evwZfv/9/xdHOqSqQJatsER/Vr6fz0qbhmU8//Nu48cWH8GOP/i7aPs1zmyO3aE3gKfQrB9bPycHnHPQcfIoHH/tGWg4+wfdtctwFa91hr+BW4s4roL5RTxsBPq6sqPXg++M/PoH1KsPjeI8zF17AyUtpCG8N0A1ruzG+BQU9rO80VLTBOTn2gEWqECE62XfLKZeEwUVy1AplTNOv83yPc1Xt0X61LTIefIfJtYQ25cNoNxMZRzYh+GVRkeTHtH5JpZ/pwbfYw8HeGbx4/SeApsHhz/yM/Q4K2MFkl8oQnZJ5iutMAUUdPHE/cdyoAsYwKhdT8FDfaywQMevBJzuN0YNS69wxc/C9847DH33uBL773UUso4XoBKbvJRW6SVjBpN3yHA86BebB5wblvgVu6eOq8Bk5oNgyWEyU1kJeGN9pTiSJ4ZycZJ7VCnjl5QaXLjn4lZBVRorv6PQx4PPORhrkq3sv5WZl3Gr0HeR9rHqU7vA/ZuyB3KAl78HX94Dz6XWtTY1/YDx1ZxhUZTz4tPqLHnx+Mj4w9Wwjj5SLVkhDdFJ9R/JNC4JDAFok/x7X+Uwey3vdg0+lrsdrrzVxju3v9yqvGYyYVDsJxYMv9qXgwbez40dzeV0nR59jbU6/CueyKDsSPZK00PfaJ7MAPn708wFiHnxjwfWarystBx9/X58YjmuAtzqGUt+qRG2X77VchwMis8/QQ2g8E9fr+brYYMhEZXzLaNC5dy8Hn29a/azztsywpR8sbXPwbel9T3FfHHaz5L6Zg8/lDjI3Vcmuphtlwoit9dwlsk+sIk0hkQFxbMZ5zFcjhHomKNKyJMY9q3uukrJp9PEggjgfak3QzDAVyyXcajUqrRQloVO+lyvkERHPAsBnfmKF1ZMrnDplh7KJ14XHWdcsCdNABLqR2TyOylm2n49Prz8T+yNeLJcYmZU7pADfPnZIeBaMgnz81rQNg1NNrCYJYDr+HPrVFyytpFJHG46ZCim7eEkpVa67NM4snIv81ixEZ7ktVq+i4DSVNNKqEqNHpnNFhUxaEx+UOSE6QZhVoDx28amcbp/c9GSfP3nSo7njNLx7SZSfXJ4GQS0P2LXSgy/JqbgJZRS4QrFlDamZ20epa6K6dRP2vZocfHTMm4YLRbmcY5ZylAHuxwL2eP88dAft+F4BcOjX3BSusg9aMb83eZEs1ge48Y3H4++zF57PV1iZg0+CNF3HPfiAYc5n81OQujlvIvgLocw6Osjvn7LPoejO6lL1pw0KEou8d1ERVgvwIe6Tad+bBqoHX+dd+gSt1AjRmfPgq2HTGgLwWUp+9tvQI9sefELJmLEDtXkTj5teewSfeOJz6t3owacoea3+UtAjYe29bugwh5z3fF51wjuMkpZXjPQl9FG9JfpXzME31Fa4z/s5hD/1zCMkttetkhx8qhyUHUc/lWN1CLkHmWoy1WuhswAwrzXnuPdYLi/Pejnsu9++65/htp//+7j3E/qkC3MSECE6mVxpWwOwva3Cg4/N2UyITo0sDz7ruyZ7jbb/03GXr6csTDV6AcANHZjrV7qPNkJxCwDPP7/ADRdIKGkDrGagVgbgG4qIfZH9nd8zjg64PNYluGZGlk9a48+qe6ixaEqhrHmahzpqmmkd9T3w+ONLnD9o8dgf7eOz+3r4+aDYNUN0ko7lvHkon6q9suohIuRN7TkHjB589GLliMwE+KZ36OHRpKHaxe9BHeRVDyP2fb0yJpIHNM4N11/ZHHytAMvV9TL2Zch3TjvDN6QgA1iezABfExuF6CyQxnf1TRqZQ++fxxtvtLFfJ06kZ6HaCKUcwMc82zyccwLgQwR5YrkkPYDdNIDhW2lJ0mVfhMHH/r7H228P3nQ7O3I/TfnaZJzpmTUtHK6/IvwmNbahoOJgPJ6XJSlvcObCC7jvuS/hqrdfnuqAQ2KArhBLC6TtNQ64+uJL6Q36jBfpacaxX61sHjqr48AE8DW+Y0Z/so7s2bYhrwwM/IM0RIpVXgE5fUvHpy3MuqX3PVGFW3bTMxSNallhLRxIs3bwSdyKvAef7IvqEeJ7LNaTJW0SxkCUZc92nSxhKoOkonewlqojrgDTGfkYRkVpnIXoLB0kBBVo+o4zf+TlmMAKB+1tcnOkXTicPetpKqQsUQ++rqUCJ1H08TQgSZ9riVsBZgRGUW/alLwfF1DaJinarjI5+DoRopOsRXudCcvzvh84mpFCXZQRq7EoUpWeKCnIOKnCpMafz/iGE0hVeAeyn8j8TlYOvipgUVv/FVaCyfOLiWnTAJm0L0LpTxXEhY57OAFCbs5wRlI0XOFSf/p0MlCDsBSK22H9AlFhN+Q6ChSn9xUDnKc1oQlNbO1XAOFzQnQyDD8AfMywgf8biFugpuUtYmcxzc9CQ3gpoaG0/mbvuQDMWB58ol/92lQUVeuMwssJD74aL7RIhvZCmzuN2LqHcnLe28CdXS7f+N6eT/udofi4eKa0fHzToG3LY1edg69J+Yvp3nSiciWGouinxhXWhPfz9nVJVJgP5x47xxtZqaG4qMzBl/uYFl7vvMe1bz1rPue8AnIUmst58DF+sdLTVSXNXFs0WAKYo0dRyH8tZQrRv8Z3+T7P4EHC3pvz4BuUMikPOYcmHkcCfLQzyE9weZCJDk23bX6CKjapd7fc67olCc+pMP/aPsw9+OgPo8+5CshPjT908OxdsiE6CwAEBSlZxBMxjL2yr/dMoatVPPwb/jTD61Nl5gY5+OTfdohOst5EX1qRgy8ZdzkgBvnOY0XSVnrXDHyDAUKrXg2qjGRsgLD5MhmKT77CJiE6nZvWUd8DFy86OPR46aUWfp2G6ASmbyMNODWylOKaUYb3/LTKjluOvIcnIeFlzrJsPysAPvOdoPDY2jfz3twHrHbUb2r0sYHtwUf3RnWP1eojxkAyH1rs3/ieSQQA6mUKx/IuO2MvtQw8ZL9TDz7HC8ygnMEb58MHD77Qxv6+Pl/Y+wnyTZPyBSPJfdk5vqcEYI2DoKkHn0XUKIJfTP5kbTSLBnfeuUbbAi3WuOce4SFntWW1EwxBZQ6+Xgv76OKzN93U4Y47O6PFsS+Onwef/vb/hTOXXk7KadFJJPDM+mZ48F04c4vZl/iA4tW+XjuTh6Yk5VfvpjDZDXrQbfQYmN0s8k0b882yOUPGVBoLbukHS1uAb0vve+IefBNNP8Mm4vgBknMTjopfWykaq5JhENZ68laWIJwKvspB/8kn/hg/8fBv6Q3DZnCCxQYv20ztmUpezilWsv7TnxmA79vfXuLoqAQ6cQErOYSaZojpPJ52TFgjle3vT4fv6TOiQYOySlSFGFBDAClL6DYTOxvtmt3xk3CWeKJm+yuUo2LeB11UMfTg0RSHZtVygO/1Vx3+/Bs7ePvtSbDb3R37m7FEkyE5tPC2VyIH35UAVIIS4LhhIovPUyWw+NYWwAfoiqFS/dbc0UJ0RlpOYHuNrlT2ywrRonbFObbN1L6jpRwermX2gquvTr+Pm4T/YX/NrxOag0/uxwcHMzWloV9k+7ZCdJYAvjQHH5AI7onyYSbAV+HBR0FVGe61epmqyjeHnFKsluIac/kcfKGNpuchOqtzudBiIQQ1AfgW64P0Ifq8/HaJYY/dJnNEEjn4AlEhMasQoJ5TxtwEBg/ZG27o1T1e6yMwfdqcMoSWG38BTQNLHxMVaE4/8wCgA88T4zIAH5DuxQCwDsolS4vedel8915dQxpZfXF9B++drqhBaqSmeXEBugefXLNAgY82SCpYkvuaB19Yb4aDlG9sgG+oZ97ZkZDvGRjhSKSOZAzadqM9KHwzBi53K7ijI/OZQaFT19YE8I0KZGV+PH77PwzVZjt6nPBNAIrMQ/YsYPIPOddkMer9FT2z0xdbL/bj36XQdVPeH3KN7iOVITrl1hP3OsWDjxqR9Ei9E9SxsphYqz9Mse9UDz5mzGHwiLQFNf8ewL+9NuD0mzJ50ql/O9/HthLWIMhNhRx8kiyDylfP3snKrVZ8HXmnhOgs5uBTrmXm696uHXqRUrLEFGC3RE0z8bABMAnt9CvqwUfGoAkefAQAAPHmVLx5ktVLjTxoeQoIqeNWpuCBPLGOM/bqDTXkZqhYBeBzMAwJvT4n6X22pju9r4MHnwEgbRCulnolUiCZV1w3bkwfEPC4HE+t9C1cSnPw2c/kqvQe6F0quKrypfd4/fUm8eBL9t6Mh6JMhcH6ohhoUxBoZ8cnPIEWojNLiVWmMV60jWbQv91//xF+/h9exlVXpe8rq7MAPu9J3U4AfN4l5YHpHc+c6fHhj/RFnigN0ZmWsfLZUeKOji4p4vf28MJHf2r4W5nHUR6hDMUoDw7qxQ34SAqioefjyjxA8/qoY+m6mqYc5ntzMX1LV4C2AN+W3vcUD4uMB1+QZbV8FGp5otClVMW4GCE6mbsyvVexiaYHQ+bw7jsOlhUsWYY6DK1wrk/0ECMhOnlie+Dll1v85bd2i3xxdhycAxaLqY1+nVitAIMw8vGPr3DLLR1+4iePZh8ghi5av0YOZee4Bx+tSfUCqRxjq5e5EJ0l7xV6/9QpHwGXLMC3XqMZc4t516Brd4Rit8fTT7d44olFfHBnR2mc9lPk4JMhT7X5VCNc6Uz3XA++skDBmFB+y+xHUkghCdjD+NbylUr7SFBw8rVulma/GL/NPPiyTapEGeKS4OgpaFNRvkTOpevj5es+BueA9e23w586pT/X0hCdBQ8+koMPAAvpeVyAT+1bJcDHgeMAcAhlPzVrHh6y26UAXwibnLGmDdQbDH4EJSrac9SDj3gwhjNdq6Z2u6WKNQ2LovxGqJj12wx7m2lzXBR+Z0cAfJmzW4BeTkEjLZye5eAbzycN4MtRmFu5cuHbnD7d45OfXA1rvzZOEiaL68Z3847LtkVT8OCT3rXstmu4jqMW4CMTIdp90YdopccM0WlRADUGYwDFg8/pyh75bmFNyzYTYC2rgBjuJQ5t3rNQxslTJEeQZWQn+0Fz5OT4to3PkB6mB1/Cg1WE6GyU91ABPvTY/epX7X45fkYmt5OyU4hOOv9fP3M7vvkjv4mLJ64f91EvHyN1FniNYO3u7TqKk5neTiannttbVsnC4YWzQe7XGHLwmf2U3RgfZny9xvgpFdHzOHjaJlUoY0bfwwx7FZ4J/KnBptB3sOoADINFduAYhwuZO1Z4fad4K1hU5cGHCg8+cV8CfLUefN++85/iqY98Nl5eH6Xroe/BxoeFpNQ09xk5QhNFdnd6FaDJhej03m3swTcBfLyd7kjKKqOug6y1QMywhOonCsp++jMpo6yxWoOTpD6lrttuU0Bgo8OX//k/R3/iRFLHFCFHn4fpN5t4nqSuzPdV9y0ZDYbUUziekr9zRHPwlUJ0Zr8h2Zul1xnrI11bSlsWwFfNesq5N0N94PseFy408VsFDz5pWJU1WpuRg897xwwvFouUH0nmXMZxYWjE3gvZ/KCy+cjQLBbAzkLnbac6nL6eNb2lUJX1nu775KykvG7bQPJE8ujaOAdfhuTW8OzNn8H3fuV/xjtX31R8lvGT48dfr23PYnP9AqNx5tCJ1vVcRpb8fGadHTcHn2okRM+ELcL0ntJ2+Lf0vqe4qTod4Evy3U137EqJIEyJhjeKVclk6TFsBTkcHKK7ctKPgnU6kDKt2QS6wpI/WOJr1hpTuzXIRB1pAtn583lrVilcJfxF0zBBsKUKP8ff6qqrPG69tcNVp8nz4luYHZlD3jMOksZo10J0zvG604gzM/T7zqvLuwY33tjhvvtW+NjHVlNXFNO9KLQdEO+9xT7gHAsVMcT55sxZjMFu9U+CnNKKidSdf6GES0ybsoT/CiGHXkuUrzPGnip2ssTyyIg+MA++Sis7WVeF1JYoLOnemsuSriEmP7MAACAASURBVLWbWLzp7atD6Xh44eN6TwJIAJiH7vpFvPXr/wqXf+mXQiPZzqUALEDnXCsEB6qciwDfTCUzx2RTQVF0VumfvvdbVsDWM/L5QK3iwRfAmWQvhwXw5YeFzQ8699m+sxnLyuommqoY0oeQ7OMLN/wI+tG446lbfobNn+rtgXnwDQ0suqO8cY6R+zdHE8A3XasJpZqjLMBHhG56zli6Yfm3auRRQ22LxthnAogyzP8cwJcqL0xnk6iU4kqM4aIBBGgefMhypWlZpXATw6k5df2m76yPUzBUkH2U+vgc//FTP32ET35yhR/6odRauskAfDlANyi207ORevCJvY5ayxc8x7Jrlrw8yxcsH6oI0cmU7aEeD3UTbM+d26y/gnIhOt+86iN458R1dW1Un1+Ul/HpeSTAzOp6iSKYKS8TgxUNhE0HbL0sAHyKQpl58NE1ZbmYyirh1WNM8+xoPPHgc2kIVUlDWW3t87q1cyA83xc8zcz8zVSB59faZX5WzQnRaXhvNV73eKJGDvkcfOleaaZEgMOlvbPx9+qIMy6DB59D79M5Mzxve+uFPg8P2fPVwSceMkDKEySe5T7sOQVmi1DTIAK1AdSOOflWVojOsP9NfV/kzmOFHFKZRcpcMp88oMguWv3ew/ckZzx55vTpHnfcscZ9961w6lSGPyYVr2+/Heu77squeUv+k/MyhLhUDW7pHJV7JzCG/SZzjXx/+n0aPzcHX36uyLWUo1QecOzvuA8GHs2l35StCaXDYS+TxtVmNKW0l0mnVV7K0DcO29tQPo6zjLiTa71pzKkkx2K1QgL6wQkPvhk5+DRjB7M43euJQZ0ZZlrwZSmOqKxz0dnOAvjo300+5K4O8KWFNA++XIhOzXDI7SyZrlJS5PksD74KBi+nJ2rdxOep8sbx1L0m+aaZUmbQbYuF6Hx32t5SHW2Hf0vve6IMRfHgOqYHn5bLyychOlMPPoAknvacfaiJHJQLmaeF6Ew88gyamE6ibJDAi9Un2m/iwWd5D8ouMYsylLW7zEvQ8OCTz8w+vOYcpsLjrDMS3xupWsy2Sl2QinkpMGr8tWy3aYCzZz3DanIMrDvk+feGdkQODAFglgA+mcQY3qs5+ChzumkOvvkefPZvmfNsTj/o82b9bD17AeYqybMy9apEARxDSZNV2i8n5WVViE7RL4YPFgbRw3FArlIpkV1Dyl7aX399ltsM+69z5bnUZkJ0znBe0vuhKIAn5l0H1DQQkl8XRD5qrbeLloMvKiQzwJGlYNTIAvga4rEUCtWGydSIevBl+zS2tV7u4+lf+A389X2/gqdv+en6dujQBg++3V3sjbrmZVcI0aka9tQRHfd1N/yQYGbJcCSwCSz3mbFxmuBsgczzvvRcVQ4+Pc8NMCi3WY6tVucHY3WqB5+uoIh1auMgUO5qwJtep+eH4sGnWZKHMj3hYahVPulgqqTJfJgbbuhx6pTHtdf2MbXZDTcMSsi2mxeiMw1NxZ/pDf4rvIRUFs8m780zIuxpsY22NcdFAnsaeBa6eLhzVXHeD5EQ6hZHULg0zaBApvIMP/PzvHORX68Z45JFR06GsbRqlLdxeh0lD76SMjtQ1wFtd4QbX38Me0cXpxvjHDG/P1H00y2Aho+TZ/YcD74wfyy5ko5LbhhzYSOHThnfhFQqjZ0ikRf3GiBKv6PB/1o5+BL+x/AmpOCjmjeMDyqpcFIeew+sV/IsVkJ703dQvRoyvKcJ8CkAkHgHmaebnQczeGnpwRfWrgXwRV0Hea9FQ86ljJe6vOGcZzIXHw9FYV5hmOTAc/DRb9q2wI039jh71gAjFIBP7k/aO8Wcq8k8FClVRj2VJme0/YrnFZVd8Bx84B58fE3VAHyaTkejpu/ivmPl4Kuabo7k4AMZVjm+1IgxI08nUSmCcqTAYyW8WSF/ANu6+mFfZzo1pZ8Dj2PUmQnRyQAuB6xWLpnzSeSbGTn4hgrsd2Xzg7TBtjWDt82BYYChO2wcGyf6/vxZsre5dExYWcdzsAKG8Z6Sgy/0jtYVr4qtYdXuArDXWqSu43OuwoMvRzS9wqLpYocky+Uc/n/23u3pkuO4E/tV9znf980MZjDADDDADIgbcSUBQhJIgitKKy112XDshXLY0i7XEXbsWuGX9YMj/Jf4xS9+88OGvJIV4V1thEIRtldr7orWiiIpkRRJgyRAAgRAXIjbYOb7zukuP1RXdWZWZlX1mSExG3EyApjzdVdXVVfXJTN/ecH5d36IK699NUXo4hUt45XZWHS9kbaIzJk9wvSh0jJT/T3t6UOgtKmKU0serKGcDYxoD2dypVeYK3mYGaGyRiNXTTEWNylFqezBtwV/T1tYnw9OsesvRcbITs2sZkmybalAZE3UDpKuy0J06hVZDVDaQWsmuhKqESE6Oz1Ep5XYeannHRXKl3gDys9pMj4VD774ezPlK8msmwRzdnQUP5atFGN1jKOKgLBvbViGaX3mZXYH+Lxz3BJ2oWCn1F6+SwEW8S6M2SsqJHLSFJwtVtjThfkSQei0JrNr4gJzbpAKuUpXlgK11QqVS1pOKtfNCtxaDr5+4PmSHn/kGP/P9PuXfmkGypfwzkVFvwCcrekpw6WZSh62D7QJtXGNHqyI4i71R1cEBn25RzzbavpeqbjQ5jMNm1l8vnAvddflITppH+la3Jw5jzfuuFRvSNSVfsdFcXSUQvqstsfFurIQPzt68EUgKgNi0ZiDzxSCjbUkLV4kYK1scd2CHHzOBaWxFaIzKa2MtQwgC9EZ83ZqffB+VqTS92/JwaeR6gW4gCj/pe2XmpFapLFbpbWsefA5lx/nFh/91FObdL70PfDUUyd4//0Od9454idXe6Zgl0SVX3LMLQepsWAZzHjs1nNSkvcsB59sgRXtuqpaRuNJvOf9e+vc/bhn+OtKRVyZJ+n6wdmsnyFUrsj3SMGTCpjaCuCpZ066nQPsDp6BJyYRkM6y4s+6U/DgG/qD9Fv9xHT9Tw2Po8Mzz/8RLr35LbWsCewTuVTNd6UoD/txQ+RcGzyOFEEC5Y7aF7WOChAlWUevHC7O8LzWlJkWtYTohPf2vjYNnARSegMoSe3S7yB5DDJwGxGiM54dlmI2Pc+Ay7xskZdp9OCTQIZm8FGjrvMJYKYshvMjhhOag480S+T+SDT6kQawZHzraPSVXFBl2ZbIAz73ftNIHSd1stQZpdlzW+hrxhzgA7wateCT3/gXOD44iy89/d+EvbIia1Hjc8avL/TgazKsJQB7cR+WtwwZOvK36pCSi8YuCyBnsZpt4KTn6ziq76TqGsZR8I7TD0VeN6dL35vgXw7wGXs1BVLEQZPpNSXPXYgOxHUj82/Gb5dyG0ykYaZsL0hh8bn+0tKBZHlwS2vRNXjwQdFxIZ9vJYBvuzoKOUw7pe/kGRYNguRvPjkxOlYgD4eRAHzrfp67cswjwPfx11/C0fG7+O79f5u/W6vRuEa9FaKT8uk3UP+ebpj2AN+ebnmaz6Ky1WmNsWBlp3+zEJ0NHnx+k+cucU4IJ6wvdYBvSaiuLAcffdRQprV6/JnU5QdhaHu+rjI98W9xkGjfyvc9A/hSHRYXSBmDXd7JIArwzRKPZzn46Hj+3u/lcflvlEo5+CQLkilWLJZYA88ik3ntWrq2XR2GekQOPinkxhx8JRCJhuSA9yyPFFXu3ghduDDi5YWWSKx40OrMfUyAiiV96DSvtWUhOi0L5nErhPcKM7YE4NPyOqbnz5xi7SotFfvBQnTWvosY31agtvhZdKm9qcLwPWwAFsjv33v3Bn/v167h6Mjj3nune63aBWJ5ly5nlqBlgG9WKMrretgZ5sFX7+XUh2BBT3MvaP0BiPLH8/GrGMlygVcYyCTgLYKHN+DBF9+61YMPkPLwDm2TEJ2Hh4EfaPPgI+PQBPBNys6bkIMv1JcrWnklCkgzzOEXPZTQaIrALD34qsun781QwKxfFsAHAfDVou4pHdIUtEyxPY7ZY9IzapTe+bJ8Zf+JvCVT7gnFUUeANM0LTraZAXzKPPnlXz7G00+fAF+aGz51Cjh1au5blquU6LwpeF1SRlFinqQ6OzgVvAF+wjJWkmueKGnywiWQxcER5fPQHwAVpaRpr9X1eO/MPXj1wpNz0zREp8hbJr8j7ZfGrzcpfQ1Ld2A6t+RY+JGcD5TH5US98OR8tzvjsmcjDSjPHa5Qnh/OwD2gGqKT8oE8LGbcFH0297txDuerWcXL97G8aFKdZF+O6y7+F/9W2ylOirnTMzDZoFGPxi3GBm/Kk+Ayp5bvj/LF5RCdGv/s9d+Oe1VsT0a2+kPu3oLnhQsKWN6fuWx69QIvY3vwyRCd8rkb9eAjoIEfmAcfNcioefCxPlpGLD4f/4y90BTm5Pd2q+e9Dh58PrVNQVt7257Xp9qZRupkuEQ1FKfNDB+evIcnv/8n2VkdvynrP6mDRUwaBxNAYgDOglekIELpwVKdHso4Q5dHSxUmgGXLn2sO0anysY2DMY7w3jV68OWPO4dgoGZ58AlD+e3WZXwinB5ekrZRJAt5k8XoHkwBPoPHklt8tva1by4BYMOgkBkvdF1ZZ+qQGZ9Uz31D7i+N5WZ1aIPUlKjsRiygd8nBB/Bz+mA183njyHkfWsdHX/piBvDdCI1O9+BjwPPeg+9DpT3At6dbnhK/ZW3oRGCOPI1zZSWgN5g9JmykOqW0qCSYJYqwTEnToHDIlChM4OQHj8vCorjqIZN5hDUAF+w9WIhOBclThGoqXFZBD5GDLyj8jJOqdE27XDit9WoJ2ENz8LnydnmzcvAV+289S8aKuu+zMoVvkOXgA0DF2s4PnDlzaA/RGR8TIU9Z3bEfGpNdVJgBDz20xesnJ7g4jHjzzc5kcExS5q7SbHbNqrs21yUjKYHTRNIqr1Zv7E9DJzWL4nhpvOsuAC9lVZntim9me/B5QBHkXDcrjFpDRso+s78L71arSAJ8LdT5AU88UQdfJG37g+DFRUh7/UypY23l2jrRQH3v0xcrjbccs27c4nCdA3y5Z5hL93OBz2yOb9MsjOhsOdyaN65EVLDMwlYa/WNKEXo2l4QwKtzGEJ0HB+g64OjIY7W9XhZS5V54wx58uSBtgSz0WksOPl6xMucq68/5Qd0fNPLrAwwXL6JfNZyLrR58/ezB+8pdT+He178+l/UgHt5UQdGJK+D7ncr3ca8qqrhpAfcAbhDTKWOdhegkPdTCjEslTCaUi45cuDDg058+QYkcPAtlnN8fs/1J8uTy/anCUwWkjH1wEVVCdCZakIOP1ePj/6Y12fXVteGRK4G2q0P86S/89wEgJBXEcl3ng1csm2slrdXyMat6PPhxOhwMZW2pSV5RvQiovKeAAjKH0Y1QnCPGXJlBuy3W3/oWurvPYrxyZTZmADfciGUjhXlePFhMg6+Y16rw6MznVZRzZgAc0oD0klBpRw8+KQObfFlUtMvQiLFv3qM7uZ7zRwxkFIrnCL756dgVTNd2C7jCQIf3yCPdNJM3PPiEAZwM0RnPg65D8z7YdUjKcOZ870eM2yHNCObB1+Wgbb9ziE6AiNxsDpbCyG42wF/+5YHhteWZXojNpRovIv5lDxUeToY0lRx83pe97AHg7Aev4Z3bLudjJ/MbG/nc0/mqkAq4NswV54PB0jjOkRuifodWUQJpvaMhOu29qhQ1grYhWWIa/rP2LvLBVtnTi/7PfIeQ10uG/ZUQnWkOOuDhh7f44jQeDz+8nXWbdB9uAbMoFTz4rIpYfw3liGHDQS6Sn5Hf6xo9+CjY19V1mDJ8tMYvUJ4ozYms4/leluZfHz34yrowukl5EiFpY7PIdt+dA42+EgwrfGqG7od974vn1I3xys4wErIjbezpZ0t7gG9Ptzyls8jVPfgoSFJmqqMyQSinFXNay4Mv6yfd7EZ+MLUqmFMbDFyRwuBgggKmtXxipr3QCrcRU2r6EX0fDhIaNknz4JsZwPJB4rsOjnjw9eM2cWne6m9lPqjUBJbFTvnEQToHDD1RMDUqHRYREXg6p39fQDBASpslINyixTn4MOfgK+V5k8yalkdKs8wt9V2+78EB8Nwzx1j/zRZ//ucHLXpwPhQE4KOW60EPt/yDVgUFKZQpSk6AC+/TlbYO0LEyv43UkM5ryd99F6lKUZZl3WoD+PR+ZFeK5VtIC7PGhV8NRZvLaZZ8JpjrsHPivbFbAwjrjhszS0XUqF3O+lXypuANLw/RCUwKy5VPqglToDeEwjSXqFbAao8oMpiSuvBBWpcqDS+lhehMv8nepYVzrLZD6yI5+IAJ4NucFPOKZUqIG87BJ/rXClRST27JX2hKZurB55yZK4pS35iD72uP/ed46HdO47ZTp6BEhwlt+nkPLgN85P3JPvmNh/8z/PiOR/H4i/8njo7fZQAffRfKl7LGIzWE6BwMHrU0FuydGgC+uU5f9eBzLhfKR9FHdt/oaAD4cgULNZqzlvJcP38PPd9HLNoO8BVDlynAncYH+L63lYhiY8ymB/Hs5GHfCx1WxnmYIi2wpokHn7SoWObB10gFvgwe2bdg+0XJEInyZAyIKfQlgjJK75k3mPZyDYBiVtaa+5Ohwv2v/AXO/PEJ1ocOV//pP8U4TpERlLnf+e1sMKOBGvKMVcaW9V98Fzol03+V8FrmOcdkQWKcZ3kM1XLwGXkiW3LwUZlfRgLpxi3gPT75zX+BK9sf4NpW9s/rvx0H1IMoStbR5PlvBelNho3iGu0zu2bwMi0efCzXoZ/vLxFbnJu9zjKAbzMm39dFITqVLmbLnQJ81DustDGRtl94YWWyRM57FjHC8kdWp6TC2HqxYLWty/IkzfImehTDV4f+RmNxKdzLjSDvB8A9grO6VfGnzqRp689g5Xl77BW4Z7Zz014oz9cKUpTOz4E/J43YjMcBP2K7BT74wAUgfRybz7ykD5TGFEsMcvve5ENG34HuzBcujPhbP3+CB9/d4tKlYMQs96iaB182nOqBEn+SvYrqIXuXbBas/NK0HXVteqJbIfsf65/FE4sQnTVWYGmITmsia1MxAXyro6o61XvHo6+QD7/d5hFOWogaZx6s5ohuIQsO2ZMLLDPQtu719h1c3xk8hC7/7+lnT/vh39MtTyU5MJUZ+cEVAD57140HV6ZcAGeYAWTxuiNIIfulJUefC1YU3XB48MEtzp2L5kGFw3sQ3lQo5eCDeXDViD0mcvBdujTg4kVunavmm1CUtFZHzRCdtCLxTFIkltizhSBNkkcBBkiNVBljWDC1tNnyOcohOkVZwWA0MUjkWQANOfh8NucOD0UlgjJBdxyZyVICc5mwoyiMLdCa0miDIGrfjLlNb9YAPnPNFSwOAYjE6IVvnQnv5ZfTFJxm+FTNk25S0Ax3UYCv2ORU01woWIzRimt95pO31XuuvKR3AH9IDj4rr4xJhrBToy3ZT9jWIee7EKx51bpie339PVx65a9xsPmg2LcSg68BfDcjROeW5EMy2zM9+CagrF0FbTZErYk1sgC+1vOEjQsJ0QkAp04FD75iN+W53+jBJ8P/RA8+LURn7VUCH2ULwapCcSh7LqhH+TjwM0yZlmPX47WLT2K44wKAivDqPVN4ZrcdB3fpPjn2a7x28UlcPziXdUjdqxcAfE4oV4p5og1yIgdfiYcNfSZdagrRKQffifv0lj2BpIKFPtf5UYl0yHlyWTVVJhRz8NFnlhoCtIborOSAsdqTyrUmgE9HpNSiLESnH7nHD6mnKQdfVXPL1eZZHaPPw9SyuV8g7i4wt1F4Kt0pgALG7XQxnP+V9+6MCar1aeLbDr/4xVmxrYwZDes3ul453/gkosCWaDG7Ym1Pen4zwotJ3lFZmDIcoUoVLR8F5iyAr2NzWQc9KLBy9qzHlSsDLr79Xdz5zou67GN8Z+nBN2a5LF0WGjCvw47Io7aXXfQ4d075loUcfPT+Erk/ePCF56hyOAB8Rg4+RB7KCNHZ0rZiAEHrBmawm83h6f4HH5S+gQeMLay6bHfUl6T6Kx58AKpGrd51LJQzoJ+LZlhBP4WRVED3XV8vevB578xv11L/fL+k4yDzUNnTIj8uWeLmEJ2Dx9e+tsbXv77GCy/05nmnjbkfQ9mM95JrseShWAjRuR1zg6pnfn7E5cu8PWl0r/XbouZIOXSvr4Xo9F436KRFxvybZx58lRCdoTNdxttxuwCfh8p1yl7D2hqzNmW9cti2/QFjBU25lE7UHTz4WJ+FXm3V0RCd0oOvmU1dRtOHHomsH//lhqM3tpfu6cZoD/Dt6ZanZAEmgKxssyZCT9VyYHpYlqPMWNy01m+8hodf+vez4n9rhejULd2blNbO4XOfu45PfCLs+JZ1jnMKw0iFU1GtBBDSKbd01xc5+NZr4LHHtjh9G1J/tQN9Bvj4Rp/xF86FpK0R4PNc4WcBfBoVX63hvVMRElLSuboyJozz7gcaD9FJGCHZ58o7mIpbhSmjAF+kTV/PwQfncHjo02+VXGBEqGXkrh58VUujhXl32PxzPE+gRcef/nSxTg1oUctJoYxZihIFhzQsKNZqLJHGHHwULHcXzs/lmqYzBfjkrXIF8a61T2hUXcIVpa42l2bhxas5IUttOiO5XFXGVIEupe/Uo1TUG7ZyAT54j4f//e/j8b/5N3jkh/+O1+Wwc46qAPDlBjBWiE6An6+x3GjsoxbApyoAF55fmoIIyAE+uS8o3eFnc6EbTJkaB+IoeEefOuWxHo6LQGXGN7SG6HROzcFX+k768TrNu1K4JBWJY2hotc8A0MvzXpCm6LJCdEYQJeRJaQzRqfGLfNJkHRkjv8O1GvNvIwcfHcPRrUyg3CJ2fjQZxPD2JOUAn7hf8uAzKQ/RKRWQ8ezL2RtdeVgK0Xnx7e9h/d5b4d7IFUGSSqHfNe9vQOHByEH3xBMb9D1w5ky+D2t9jQANEMDklvOs1aCBTkkHz6J/yPCHxTOtxaOjAiZpSlMuF9nfgfJklseqBQywvM8TUQ++Zqtya0FG8KcSolNWwfhg+bqTQh5oy8FHleyl9oF8e0pzT/MULAGh8b3JvSbZdsccfNKDL85lbU8Fcjmi74FT199Wn6HPhfr5XGQg0yDncQjRWZN7KLH3yphenee77bZ8bCVvmskIOwF8cznpwecHHeBL7VNej+yrMkRn9s1I/+jvXIeggATTtRI75HzMwTc3qKVlUadk6UAubJrx/eU30kLs1/LOW+le5N5hrb/ODyabz/ckl180iIIIWjoRe9jmQnRvtnLUAWXwiraRe/CJAga987bD8XF49pVXeiVCkU2Rr3TggFYWGr3wflitzHsjWQMJOJJ8SjA/n9teGqIzP1DUW0xGoOu/wYNPj1Cf7+Gt+ReT1OZiXwqyk2sIHy30oFq4+1hMdieum00fPPhKPIX3YMaZXuTgaz3HWZ1Ex7fu54hu4+iyEJ1FWoj2pz5N76CH+a4YU+3pZ0b7EJ17+k+AbEZ4LiGFal8M0RkPEamQtmKjP/KDP8Xbt13GW+cfSpxlJkTSzY4dTPUQnaPjefSK1jmUs2VMqyFw08O7YSxVMnLwxdw1gB6iMwgPTu2brJ+2wTz4DECSCprN77ME4POeSRFZeCvvi0KeaSFaGIrHXvy/cPcffQ/XPngXgO6tollfV8FQo2HnRxz98R9j/Y1vINp7bNYxBx/32pTzKOXgK4FIpC/r559Xu8Ny8KmDwyVE9fUWMkmsuODOKKBC1+HJc8/hR98+D7z8b/VKG8mLEJ12Dr4GZTohFWAszE+LupWijCh0g36z1crus4nR071uIUCuYt8VgE+TCyIQKvfL2naR7o9jPR6GoIF58E3KMw/IvbKWg0/eONh+gKN3X8eJZb/FvOPs8Y77SizS+wFr5sHn2L/puuHBFxQ9Dtt+Dd2HL38X52br+bAeb4JNWlJUukxBYA+HoRUqEKsrevAdhDdv8+AT82Aog2Cpa12Hvp+/UxT4tBx8NXJOVwRHUvmlcYQM72Yp1dK1cUApAuNUiv9llB9HJBDF2k9G12M0PPgi0VySKURnzYOPrq2GEJ3jZACzZJumlsmqB1+Wg490TwupI/aTDDgphHa0Ot6NA+Dlc5EXLCu/LL33yMCHuS4A+Pjz/wYPeMB95r/lL9R1mSclMzpwXeI/vIepqckUbMQ0+s47PT75yRN0HfBnf3Yw712WJz/p39DZij7W4dY9B9GDz7OwhuEd7HWsgZBNZ3HprNT4TRYhpfBODODTgQPtGb0jIa+R2U9xsZRDEYDJ72r1U4BByz2l9cV3PeBKruXlvS3niea1wuwPKvlzXOfAMlaUOl2iBTn4RlK3zI9kGwJGYCW/H8ME62KFAUjBsbCC4+AzHn0YbNBdyj1zD3mzFJDW+jbZApl9Dn9yxfwM8CmomkG0qxnAt6HGiPl4ZR58BVBJA6ti+1ZahFg/5UPnHHxlfRAF+CxjpqIsWZCjtFsW0CwjggSAr8GDT/EkkqERzT1g8raj4xZJjbTUaNDh/cxfhT7xcziWpMTmN90HfcxhpzBAilwD5VKeg8/xAta7aKFutfNKmR/jGJ/3bE3I5y0exzkAfQ/Lu4l68KXnxR4a9ijtoInP5XMnewlWodd+sgdrHnxS9C4BfCB75M4efBWAr/cyRGfIR8fARdfhvvsGnJw4/Ijk4KvtEQlg7g/g3Afz57HORjpRiZ5gs2lce4LnpmO07ue5G3LwzYV/WiE6U055IU/IEMsL8cM93WTae/Dt6ZYn7sFnH1wsp4ozGGfxoLRw4Dn4uFB06uTd8MPw4Bs72+qqTm5mdkjbUwWsJLVM8Shb90oAoVU4y8As5sHnU58cCVdTcgypWayh6ybLlum7jJt6V28KqJd/m/Sq3osQnStVETBX1ZBrsUAX3nkBD778JRy+9jK/sQSUBBeMWRnlG5x790cTuDdTUw4+RwA+o3vedUxgt8iaG5cuqNJecAAAIABJREFUDbjrrhFPP3rVDEWSri0O0UmFSJ6DzxTsDg7wxiOfxE9ufyA+yfvQOh29FJr19mjuz5JCh9clL+plSzlCug64evpiarc+pnOBPHRahaRG+WZwhJZQVXpkEl6Wr9/I6ReSuRk09HqITklMAY18ekoFg7yeEQUhFjD4wYMvFwSzvkcAzfM56X0wGtDi9pPHwm8y8ZwIXwboZ3vrt6PAjCHXZ22ws20XrjmGZVmt4FcrHB0FBXzJyjTbrxviuUSAm45FzMGnheisO3NKQ6n8zEvtxj5QZUENbJ+olpOG1hWfPzgQiiQX3zNotWIoJb0ukYOvL31Up+RDJZbW1sTTAL5pXsdHmIc7bbHEurJ5kT/cS3CAeHWUcj5Gyjz4xFqjHh+77tbOj6EeOhZTO2ZumgoC3I1bHHzpS/ybKxoOOrbfevA30u/vPfq5gkZEfBAButFQSNWQ4n5WPO8aotMEF6Y1EMPucfCEGO4oRmLs70Zwr3TmBFBGhKuzFKIZSm14DRcOypIHX3EPI+07N7dhAkpaTrm8KgDA1752gG9/e8X7UzH8Gxq8bDVDIGBWmltE+Tk1fw7z2JCTorLfWW3WcvBREJocrlYOPla3R8rLqHmx9MOJ2S9mFMyAas/6MWz5fV/z4ENuFFNKF6LVE0Hgv/t3r+H8edsL3Q+cv9o1B1+k1hCdx+sz2cMsB5+YsDqoRKrwM8DH8hXKEJ1uBjaKvIv3yv3yoKQulwC+wsDOkTRkiM7cQK8lRKfsQrwg91jZxdhmmwdf+lXsD22LyqIl8MMiKmtbw0kNxEopL8wQnZX+ZEbzrQYtmN9f7rXZq5TOqkKIzkHJI5gL1k5rkd8ukX6g5MXIPkYjZtg5+OZ61CJKiE4pr5jnKtnbXF83etJkK/nISKNdNcjsmVHS1PclHnw0RGc51HNh/pB+r7o5fLX3P5sQnb4L7UdehW6Z3Fv9p9D4nppp78G3p1ue0uah7FTJQNd7Zg0RFV1mnQ0efCZQp8SGcA4sRCcXYOrATww/KpUdgBLyYSAAn1B65ZY84lqjkDZ0a3Tb7WwFRu7R8JmOWPUUm8ksD0WDzvEQncy83tBWS8ZAtK3SErDM+6RUDYfZAg+d0twzzu073n1RfVwLpyj/bGGQNI7rYJhzdMXHjtdnQ7sZwMf7VMvBF5lQU4GevrWeg+/SpRG33eZx7tt/gusv/YVeSaSbFaLTKkOuxcs7M07jmEAC56RCXIlpGNurgORpzbGFqJcthuh0wNUzF3HmgzdCWV9TOM/trSRHwRQndqdvJhNay/tVCtFZnMolMrx1io+QEJ3skymWoLIM7dfcNwtxE894P49Araybi/TjhoXoTIY3BWWqDNG57Vamcjob4zHmRFA8+BZOGG1/jIpgOr8t61Xund7WNlOkkIHwh4dYb8KetxqO7T5LoGAY0IQudh3ja8wQnQTgK52jWS5WWsbYk2Y+pg0N7Rpy8MnN7O67B/zo0OP42OHy5QEvvdrDeWJhXjgTJLipYs5xnniimKihcfS+FaJzsuz2fsq31bZs574SRXanevDZVv4tgJKsr+jB11iHvNaNW1NJaLGopRCd6frJCc9d23XZbkP3pJfvfgbOj+j9gPev/Bzg9PWYnSkt3toJ/JZKeA7wVcfTKee1WXaWaxxggifGY1nfb4gU6yAZuq/2OKtH4Xu1C5qcOO4QNsrysknAV0OIznEE3nyzg3thBT+lNrY8+OL76jn4OMV5VCwQ30OcwbM8XQ7RadbeHOO0rXxbiE7P+AD2vOI5FdfcajxRn7EvTt837f0OfgQcBa/cZBhU8ODLQ+rlSs9YRjVWmt7pqae2eOqpLf71vz7Cd77D927vumKIzlYOieXrJVO+g8e4neu/+olncWZ4Fa9cfBqbN06nPszlyfiL2ZN9M6KXkGuAjl3K01WShxVy8MxDn3vwVWSSEsBXaRPI81LmYJ5r8uCjhiCpCwWAj7WwGOCrU/AKjHzTAoBPfE8aFlTTdwFgxgUlgG/XEJ0ShPXEK1HreuYFHf7H31+R181ps1rZAJ/mba558O0I8Hmf95UZMND5QddNQw4+efYpheY20uCK9W3l/BZ6kpKRknM5wKfOVUQDAp9Ay5YcfLRbwdarspBoiE7yLTcbLFuEiOcLDdE5Jl4gePDNZW92iE5ScXhcCE6ZgcZNYCP3tDvtPfj2dMvTfFDkgA7dwE5OZm1k11UYQSIIs8uEGdueCQnmqMURAGDQBI1SSNAGz66CJUiWtHmgBxdlXEMfP/Lql/Hgy19CN2yyg6doSUiIepeERq0QneX6WoVp33XBuyGCPr4eolMyjrt0QlMuUYCPefD1a95OQXmxC2LRT3OvpmCT1FpeEwakke4rdz2F985cCvWIHHxS2ogefHaet3rfQ91D8gqwlBZHx+/SpnO6EYBP9ocyodqzO3xbVhf1oHIAVaVQRVQ5QVhbY+a3Ud6BXvrRpU8saHYukDGUlYe1cEY1atlLS5e0x22Arw6AAdAtGiu07XKAL/xbBvgsq7i4dkrWtQCEB197v50fcbgm++GYVRf6RwRhmXR9wMpcPxrA59xs4EIBvmaFt0IytCIViCzh1srBV2yHfkaCfPvDQzgHPProFucOr+GBBwxFsh/RrqabnnFI1pVxPMdJKbObB195D7fnWmQWtLWYrykWktvsB1+jrnN45pkNnn56E8awm+fdOGIRwKc1zvAFJURn9OCjFtfc89RQvmHO3WJ58JWIhSzU8oUaCgrn6l5wKqslLjAedcHeLRWTM6/N+2s5SNG+m+zgdptrXwr98F2PH977Sbxw+Tn4PkRo0HKrZYrmUthMH/kHZU55sP4NfYONbdc152GOa7UWotO7lhx8NSUpL6OChALkNr18lPdjykXk60+SmudsomoOPpe3ZSrhKx58Gv349T7Nh5qhVjUHn/csD5YoaQLm01/ldho9+GoAPqMKGG4ZuOU5+HQPviTzZ9/Lo5vygKp2Gdqcci7THQyDF0C5K3teOJeD6VRWTc25VJ/VN+XxuR4RxjGAvqGfXQf9pfXuJspCdG7n995+9BFc+0f/CD/56M/NbZK+r2rK5OwFIu8we09ZxmIcCGpYd96H3ImRyH5S2baNycJ5Ro3SfJL6mh1DdKpdiQi9z+cuAy4NXYLZ94a5ohkaNhtLxH3fGblVpfEG3XuUvsW91PTgqwF8SojOmgdfNDCKITpzw/1cH1TaE20PPuWGqhhcpteilEWkMMaLORrUAD75rML/MAf+WLcI0TkqZ3H4TbyTHarvL4H29Cxtn5xlVBaWYOH8TvxfgLOC2l7uPXi4fiILjqNr9hxldTIPPiqTuyxEZ53PayfK/2ryxDiW+bA9/Wxp78G3p1ueZovDXLBIshY8Tk6kwsne2WYPPqFcIJv89vwFnHzkIfg/+TrvyOTZRoludkEhRO41JCKPGzYDtZTnkxIjPccPqXve+AbOXv3x/NzjnxJMoJufK1Bm6d1xYSv97soKV4uJ0ay8PAERe6HwU8+JipJCpcoD7Lb3aazj9/XOJSbFwe9wNNtELY5YP0Sfa+HpojdoRgpTRhUuV+9/DH99/A/VdjJlERwOD20lLgDVyordj2DuOKDr8u5Zn0q1ljJCdOo5AkQ5Mu+o8MSszMht1+qBZBAVpg4PvfC4I4oAOR6ts60B4JNjIufM2xc/iufv/xXc/t7L8P6bVvWhJnIh0+XQvmhKoUo+mxqpgkwF4FPr6Tu1XPP+onjw1ZhbqtwtAs7ippwucx/LY52uLwjRKffDgzUBOCoefN7zkNrjWPYg0gA+YFZWeF8GKFq/lQQJm0Au+o70bC60yZ6hC2Nyfb7rrhEX730fbjvgxReVUIINfEP2zDT/nSPRDZzDZpML3BRcKs0XPuYhDE+rspr1q0A0KkChlqzS1Qo4e3ZS8JK9ZHPii33LPfiUM0JjQMhHHb3DH/7hKbz0Uo//7o0ed10c5jLOAcOQr+vpfEngKzGi0Y4BbUyYcjBTMuV5ERko2RCiU5IMs90C8LUAAKvxhPffcQVsppTqygAfgDDhWwE+5WxPz1AvQI0PKHglSeAgUxDROdTkwdd+Rs4AH7Kw6oty8DU1xnkXfhaB8VKpzAKOOezPRKPmUDwok2GJ6zKBoebBx4pH5bm1fywI0RlpO9AUBjUPvnr4MTmPZPvyu9Dn5nYacvDJimWFLaSMF/my5hzNIogQha3vuszjIot044DVUAprnWtrPVw4qqOc7AG/9XArvjnXQnSWQuBn/IY6IdsAPvrK4dhZHqKTbmUUuAwA3zzm/UHOI1OWgsqSGiunglWxPipzUQ+QNOVmOU7ZVjJy8LlRVqtcoGnxjSJZHT7nO7Qc2jW+KYK3max1/Tou/MH/il/+isdXnvgds54O9jxgRmwLFBjR83wcZx0IN+DT6/zbv3KMf/fSiDffDJ7JMhepyiuQrWmZB1/bxM/2iimnXtYPKr90PbpximqFORJDqjMzzBQ5+mj/u85co2oOPilzuOUefM6Rb1MyfjNknSrAJ3jRUg4+ylMFnSRrlD4wX12Yg0/z4Mt4SgKUpTlR2Hst7L/q1G7k4KPesFq91g3Kk6/7EZup79KQM4uodJPIT++QhaL22AN8txDtPfj2dMtTyYOPbqwh6fIsVMvNh1JU3mQhOonyJDKFSQkWN+Kt7vpNBaZ+cx33vP4NHB6/qyphsv4gWA4my24BrrA+FvL2RHAPAB79wb9lzLOst0TRg48qPajlVZL1DA++nYA3FqKzzYOvRZHELlQ6xg5qcVrWwltlHnyWVZRx6MW5pzEhxXbF8JjllXaZRbVSMQvBQhWLDliv53J6c67a91jvvA41JrtaxY1xEmSNZ1UqSsCaJajRiKg40OnTHjAUHJmSsRWMaQD4MgWEWEtdB3zvvs/iK0/+DoZz58vtkn6VQnSanV64WSxViGZ5W7U5VgEai4AZsFuITuLBx+qUAkaynJ0VHLT9tBcLCdsMG8I1H83k/IiDFdkPh7w/oUohjMbrPsbs1xUtJsDHvFAc+zerxOo77Yfw4BuFkkx7aBer5fQ5hLePT7GNwXMzyD7vCvBNhxjlJYbB5R58cEkhUhrOLFSaVOJb/UA7vyFDdEpqGf9uNfdz2KLBg4/MU3X9E4Wi4sH33vs9vv/9FTYbh+/8f4Q3mLVPSo0enRvT2vQkRGcrMQMrRTGV6eUJTzzsAPDJ9bo0Ql+qRfRrNW7U6xaO0OLB56QHn+I9pPHYwLy/ai+ohug0J6zch8Vt4tUWQnQ2nJFZCzbPlR6RyubsXCw0IcC7FsreI2iD2aVmDz4AI0QECehnt6xDy8FXDdGpKf+tPFnOnidW/cNAPVf0aiO1ePBFJXPWtiJrWkrXLL+Z+MOUoxbyayUwHJDnuwHwwRM+CHxdx3WmfK9+tAE+GfEgtJl78I0DR5Ti2WEZWqpyT2EN34gHH4QnTkf3nMb1a+XNkh583VrzbKYhOi1GyugKlVOIBx8L8YZcToFzDemIhQcfIT6W5LvK4sr+VDKM7pxuVKTm4Kt48Gm5650Duvfew/rN13Hq+jv4+W/9ftbe5cuh3qP1Fk88ofOW3ttpM0pEv1HRk5rcuv/+AY8+ZvFhNESnaKuCqsRLkn3WQnTW5l58sHbexRQ8yYNPeugpxizmdFmtJpknb3PUPP9vQN5RSQLHZrH5Do3GaOfgm/9szsEnPPgsnRFdR7UcfM4BvdcAPrEvuQ5pHi5YFNKGbI7IYZSlThkC4KvxA7HvrE506V1W3Ww9IQG+rqTfg/3dqzQB1NHoblY98HNxD/B9uLT34NvTLU9p81C2o7RXeo/NxmE1Fe46Dwyl7cvx52N9JH9JZvWVuAqdOaMWxqfeegWfeOX/wLWj2+HuKXQjdYfn4GOClmQatzxEp+mxhcl9XAJPDTQWPPhYDpibFKITU4jO9CdV+BUAvuUN1bpBhQ7AEUkiKMfKjGdLN0yAbwJupe5Iy5fG//ScITY6oYfopBKd0lfXzWFfyHdfrec5R2PGy36WQ5CFCrpxi9Uq5FJqIXUqLARYWJ86oUioCS83ONfodzhzRmM4p99Zfo3lAJ9FNS/QsA5mBXe5srlAxlAaluTp9rS2b9LyVRsqKbyAqc+76a7mOsdxsTcv9abh2JwF8PGygNgCoyK/Bgwt8OBjCh+MQZgQ97TcbrGf0sJ325ADLLUXz3IfPfgcUX5qQnAj8kAUwUC0+p2VF6k+ZnXv1OtNJFBvCvAVu7kTwOfT+6U1PCnEmkJ0OpetWT6uIgdMwVLcuqGVDR58ZaVM7Rzs+jnIznabgwusLvHumqFSKRy3bJ97+01lCyE6KXi6HODjuY6y/UxqDKjSpQLwmYpkVmYHtFspGvNP5vuzAYyVwIdI2y0P+ad68EVw1ahEAQWzuVeLfQRjDnskgAZoy4kY+Ki2caagjcMcdeHgwDOlca2+Fk87FWCie4MM646J31TOMY3o7bmtAm9WOMRbc/CFcePnTkY7evBRQ1ENK0tzwulr9No1h1de6XD+YpfmkUkG3+UJVjW6DgcHHs88s8F26/CVr6yLHnxe63QLaeNF+9eUg28ECyfe98Bmw8AG7Xv1QyEHX6ybega6fuIHydk/cJkgzURlHGjoZUpJl+Bzryx9PMW6UVgbmYPPe5fGaEmITgt/7fzAzpr+QImeMPXdOeXcoX2VRxJRqBexHFJ/KgM3GXPb5Lzn/I6rh+ikz2adyea+rsnX5qAGPHeVbxOBzVKxU9ffztb5Aw8MuP32EU/94gdwK/tdveesnilbkkKOeAmZxivK46ycIx589JwW4xkjqgD6OWPZUO0aojOsoxrAF/s0hVX0nq2dmudmuu5mkEew2wCA7cgjZqSCsj8LPfjYs4MU2gzem1TU4sEn872a7fsZDHPI13dq3ouHprKuU+aMUIG15OAbid7VMiLS9if+zeoOHGx9SYBvoeZAGpCs+xHXpzqGgXthV1NF74rA9T2c24fovNVp78G3p1ue/BynQVfOTnRyMl8Piil71zVDdIowhHBKzHAl9BLd7Oi9U9ffMfuQ98en9+GHHGfiaA4++o4mBlZkVHWKlt7pUUs4mw59ablSaybb+LuOhegMh3NFkZSY/8opQhUb5ZK8Ge95iM6O54/KDuaKRXKNIkMiQZIWhTJtbnR6+AeN4ey7cp9pCDEqqKyIXmo0AL5afrX0rf046wAqgJB5/UZy8DmS+Jvc9L7c/xpwZD5HxvH0aW8LOkrS7mK98VE6hpZnWq49FQAf6UZhmsuLSz34lq+SBloY9rPrkF5412+aoyd1zpYyx0UFlJL7gt0X370adlOxnLaIWSpOSrYETvgYekkqAmflhLQOD4Yj+aCW1rSVo6etkpzmUG7hb/7p9DrYUmycX8njUgJ8B7rnpqRdAD4AmQcfEIQ+DYhNvM30nWph20JZ2sdyV7QoCirAV4hKIB+kyiJ2mygghk15/EbHx0PdJyMQzJTp+guzMYrr0AjRSRUaI/kGrWxa7sHHKQvRSXREu4ToLH3/GwL4RIjOJZ7xFjvoloTozDz4pn9VDz5RthSiMzVvKLBJ/yh/ZZG65xmDFMew68K+GefHej0rjbUqsnOvUelU5Nc8Uli3SAwEqPDL40JjupJH1JIcfLEtLedbuBHb0UmTRwaKdXod4EvNd73iXQp861srvPpqj29+c4Vr1/TcPRrgbwF80SB1vQZWUwhKOuYl3rEgjuVUzcFnAHxMBh4ZKEK9IJIBokhx4BzQDzEHn8JzTPVRT4/R9cyDz3tgHMAV4AWQp+tC4yUPPilTL5Xv5r52ihFgcmFq1q6acpYfWR6r6MHH5hON8EJzWIm5ybzlJb/qpv4qHcq9TN0E8Nnvk56VITrz6hkvQddnRg2HknNQwTyZ59r7Om8Xwgx7Y4ske67w0HYOuOMOj6ODcg6+TH9l7GbU4z/mhKagREuITrpfUO9qNwFC6l5CwSTle8wAH3+4VQ0gv0nMqZeVE3qV2EYCg6zzbPrbnDYKjx5pq62VRRtufbr+VHLwifctheicehn+Lzz46B/OmOveKXmJwc8FKVNkc805UL1Hlgop/TbAT1Lv/B2VPsmhXrWl5iiRzME3g+8EQHdNdmiLKK3jGKJT8K/egzW4B/g+XNoDfHu65SkxDgUPPofgwUcZjyzRNaVpE5KyBz3442aVGJJ42BgefKWQoNUDV4Iy9JCTlkED9+CzBK5YjerBV+mQpghSFVAVZWfNRZwUBFYrAvpsy8yNuN7sVSGVggVhGN6zEGrS2vpjH2uQMhSyDr0oTOmMCHleVZDnjFNWZgeAj4VgIQzTaj2X3RgJ573rmGLFIjcOdUsjWl5rbiHAx0MVsY/OQpFkY+8XzDWz8XnM+x5Yk/werC9SeK8BN0JwBuz1mSmQCgBfC5MW+1bKwac+17nFHGhJMaldrCkvo1LGrK+lLwUP0u0qeGy9dfsD7Lo3lLtZiE6UPfjSXkw8+FxpWMnkv/feBmBlonU/JqGcVpUJMAQYkWUHRXkZ38PqJwU4Zf481m7hLNK9jhxtJrWhdWoc1cvF+ZLqkovi6Mh+iDa/2Cc09McL5YFHyBkk85N4p4N+tC7nxDUhxM+KjrwfS6ieg08JiSYe6Ffz39UQneJcUnPwkfbi0WlbnSuaDStEJwHJxx1CdHbjDGKpQyb5RTKPdgL4RCstITpb9FFWiM6ig1Rtn14A8GWgnVC8s3uSj+l704CwHF7PMcWtdx18X/Gq7IIiqoWYBx/Jm7xeewE6lM/ICYmsNJa/J6/H5wBE4a+seup1l862Qp8Kss1OITotJXwsuzBEJ809NVOuPByg8wXXrs0Vv/56b/JWzoHdsxSUaj7bhsNt8akU87RLpDE2Q86QogefEqIzVBMB2YUhOuOcIqDM2K0YPwhMHnxsLkz8jSGHWakJKGjIntfGOVtbOWDnnePAo4d5HpfI2s8DqJrn4GN9IEYZpXQPJaPAsFdFmUuEeBNGH95FgK/8gs57DFuJYtl856gY6LQS5b+lcQ0ABpLG6s3wv7EMyWNdbJue7T21uq0AiFKWMN6ZRnLSDA01Y4bsPgX4qD6NhhcvyGwlDz4ZojMBfjW5U94fR7WdF5/4HMazZ/H9J3+DFk3fT+qLeBujvQ4nkEdbe4MG8HUdtvffDwA4vuteDP2BGsY1e866L882sY+k3xTAp8eFJu9mAF95X7MMuqzIGTRaSPDwVOqf8sY4R9ZdNNgxvKBTnXHtKnyN7I73wPP3/8r8bEuczUikI+Po8N6pi2ox8xs6JwC+eR1RJxcLPGdV7SBjxsrDec2dWqQH354+XNqH6NzTLU+JV1GALLppn5zMDG8wzrB3t9mDj1/vWZ6xuENOB1G8vt2qYJelsG2hqGBvycFHwxHO5Wxhj+cYaOuPBLOsY6BbdQjnYkVhUCE/mZvEce1pDj6jwigwVpWLCzrDmADvhQcf9z555hPHePGVU3j33YlJYOPs9DjlpbZ99OADmOC/8MC0c/DlX7GTIWkLdVGL5gMSZU6LGQ8gaXwsXnsGc4fkScsstpbMoSQg5l0otR0LaX00ry2d3IKYcOdHrFcjtpPAKpUa3pPmakKLplRtzMEX9ta5fu7B1/C+3gMu94iuCss7AmstdRp/ZtQC8FX7V1jrX338v4B3Hd657TKe/Zv/DXe88yKuHd2ODw7n3Iam9St0BcHcL5/dqA4l6evFCwM2D4w4OQGuXnVpL9PoYDUA44i+90mQVg05DWvpYXBhD3W50s0C+JyjITqJUrIFQTBIenrYITrJb3a9rZ31Oh5mwspxQYjOpeAP5XvSGnbO9OArAXzpGvXiFh4ii8PLdLp3edHaOTVWUXYRkG678bnrMW1OeqU1evBZ4c5V5bWlgBdne1DilUAhvQ4PpyqRsm9ClS43wYOP8UgLNu6wV83l+ylEp6xrrjIfv9F1JhjsHHLFk2I5ND/LK0mfS9MEyQZLptFxrlieB56PQwD4jrOytG3NIEdtOgNFQh/WawGeQMpSEoirr2srnHnEHZzPAb4lHnyaDFQMn0729Ux/ywBauw4qL5lK+EqITo2GkQMIGhaZlGQun1tyn+57Pd+X1i3ZBgWXZT94iE6j4oXM2k3JwedH/j2YB19cb/x7OVcJ0RkVv8Rwcey6wN8IDz4eotN+/1KITu8cFJxOBwoVOUYCmNr6St6MC0J0mjwvAVUBpDHX5HKmIEfedsmbJ1sD7FxVvAaBqgefk8YFpAIVT3U9fPTk1Pbvhjnv4HUPPmWdSg+yvD9d/r21M4/0cSSGIlH30CT/FmjoVoiaoMiPUn6oVndsnxv6RX2azfNRgytt/CwbKi0Hn0bybPajsjABvPrQp3H1t5/Gj//lKTz4tS+nqjsFSNdyH5vjX/DgG7xueHHt859H/4Mf4LXjh4A/Di1YpAFO1B7Aj2LXMQA++gLUgM5Sjlj5POVzdJ1PaiK1Te7BF3/7EPFL01et13DXr8M5n/Rps9OHSGWD+Qx0TufXJL13x304/qX78L03DvDCnc9N9ep6W/HKMxEPvnEErp26E9968Ndx19vPw7seF3/y3ayODAQl58uqm/k8aviQdGo3U7cSaXphaSTkfYm72tPPmvZQ655ueSrxWUmh7D1OTohw5iqKuKmiLEQnBHhGlf/JxFJh2BwP0Sn73KK8ouWoTp0yg84B3bBhz5XacA6sM+mAq3RoVEJ0UmuyVI2Zg692uORSKA3RyRR+TQPYSDUlIb09jjwHn+NW2w4+AwRbumnxEVGRvzwHn6jfmvcKx0UVLpqSkwN8xJqTMHubrR2i0wIbhwsX5nrHreHBZyhItXm+EExl84+s8cAotSmeqsCy1bIQAhMQIBV5nlj6ez0kk9q+L3/Tls7SfbFmRQrM+24pRKfapIv5hdrZwqVAQE0B3vcMRV3UVtoHs3Cq87cbXY+fnLsfY7eePqX3AAAgAElEQVTCXz3yD/CtB38df/nkPzZj1WceIJUcfKkPxIPP6rtz4Dn4vMeVKwMeemiIxo8mrfpwsNE9TzrMAPOewcBpRA++dYMHMnmAvA+rW62jdQE69s0t50ueg48/X6OuAx56KAiYXgJ8je7KLQJn9oyb+zeH+0YhBx/fy6QHn7wW6qV9VEIVsWfFWBW8XjRlN/0723dF3V3mwVdQQgljLO2YYt9/yBef6cEXFYRaiM4JFKFju8Nnns8QzWgnCz8182KD5rUD4IXLz+GN8w/j/Y9+PLsnv2ELwNeyFGP4PGuKWIrYcM/gDbZb/o2U+TYr4S3tYtlTJXS+YQ0bCmx6Nng4+HUZdK0BJIwEf0rPgoOjkkIw7/vS/ScfTp/xnF0l3BV/WlH0lfiJ6UL4rvzmMg++SSFo5OBLe4WWq9Hp0VToOVkLz6d58J2ckLHwHtutwQ8qTIIG8HnXKSCv2NMsBnfJfKTljQ2eztFRtk+e6UgozXlNkKg9igwQvYS1uTbn7pufG90q6A7IgTCK/FzpnvKhrRCddK+hMrVVj+ZFooaxFop5xvs1rt+iBx+RVdyqHL67FKJTH3+kvrKoKaxQtsjhfd2DD96D2VFCiQBAqtdCbLOxFd9KfR+nr225j1CAyOw+4aGtPgOCN6Y8zVAO0RmXStqTDPmLGgTFMuPII2Wl9q3pxvZErk+LSzzLp8bkVnseb0X0oEIgFV6/ki+vllOWeuCqMpZcs6VvrIHlE21J2FHG6x4cYHjkEfjJuvqm5uCzypHfdHqpvIHQf9VCdFqGoNZ7sXDgRuQfr3jwUaMHDShLS5sY8bE1Rn4PboWT557DCw/8UlobS9WTlJ+LY/SDy5/Glz/2T/CTc/e31UH6HSLrhIo2G+AvPvYFfOuh38h1MWpFO8JxMQefODz2OfhuLdoDfHu65SkxI4pAQveXzcalHaXrcsUUJcuDjzFMAJjyPx53hgefGvJElCn1hx9AVKAre/DVDnrV+rDSoaHijVgH+AJZwkO28U8hOrU2yujlVF9hDLx1WiuUMWxkrFkIjKmsGYJ0B0Cym7wWLcWFRZly1MzBl/eVzn81dwn5gAyYIg0MWiiGqR9hDfL77//u7+La5z+f5kDw4IsP1TkCdThuMAcfq5/Kd+JeLS9fU3tkTjl4E1hxEB48rRa5DZansia5t7Yw3LyCUKM0mKg+ucNYlgQ+CnCYTWh6KyMHXzM1SpbHh+fwg8ufxtVTF+zGDEHR+vxzNfPELb6HGZOyTKtugBtH1RrX6FAWotPyICrtVyxEZ+FcavV0HuFwdOSJBx+pwxgOBnJTJbrS7wce2OLZZ0/mSJxS0mqcZK05+C5fnufelStDlt8jhOhUPPjA8zUA+Rg6J/gaObcq8yeG2aPAyK5rLAupKP7uqQfftjx+mUKpL3ttZTlL5H0KjMYfwwBtB+w7AfCpyrwcHKIUlYZNHnzUyt9Yf2+efwh/+bF/jPcf/ljWbhbOtDXsuiDZz9V4ol4v6r0Vjw723E8hB18ERNi1Ug6+BiMR9q0rITq1SixwchRyA1WcH53m9S1gi/UuwRcBt86H+M3sc+zowZfA0gYPPqZEnmiXEJ0WwJdCTmogr+GhPPpOeK7o4C8Q8trIt5SAxmZT4q3s+ZfmuOPaTm3/z6Z4aWHal+s5+GhaDLHOaOjEzvDgSzI/+V5RJsq8hCnF55gH34rl4AMAP/gkE9BoDyXASA+fNz3XGhFD1CvnY+dHdi6ZwEOFTGMJ73mbveZN5+b+FcLUqUudGkD4CB7xcR0l/zC1WQX4sEwkZKGyd9RKO3h1z5B8iPdoC9HprRx8vM30DD1HGkN02hcCyRx8PtpsKMYrWlVyy2A8JwlVnslsBODTplVcQ7kHX9xj9f7Qd+EPerUg7X/cm8aR8F/0DFVCo5t8SgF9sTz4mq61Ftcs6JRbDIRbmNteje6iXBNHkdkvNr6KB59zECE6hUeuaEMaoLd4xMswy1NXWLSUwisEEh58rGyDDBv7HZvq3YB45m82Dm+f+wiG/qAtlP5Cf7u0jlPYbc2Dj5ydrSHj9vRToT3At6dbnjKLN0LJOBB+EnrIZl44AONGKmWPjgF8YvuLHRn0vEU3FKJz2rC1TVlaezmlfetVs9j9jZz/6DQPPqVvve5RUZEFtY5OITrNFzGvWQqiXYgpr7xPHnzOxbClhOGRlj436JxOga4SwFLNYWUNQNWDL3+EKvdoMnraxmiAXhb47E+dEiF2Qtg/TahtpundzpyZ66C/sz4w07RlXhSWB1/xGQ+8+GKP7363x/aEIwrRgy96DKduSQHvJnrwZQpC8bcEZuS7ZG0jAnwN/eMP3nTSQIoS3ZQQnSWAr2GilBhhbqXbZdt55j1VAYYYALxg4q+nNRq/8fP3/wpOTp1lVZw+Ped6uv/+QcnBt1KU1UbYRhGiM5YNL6HtNzbJ/fHgYL7YkoOPDVnlex4dgYP2mRVR26RvBfjuu2/A/fcPePTRLc6d8xnAB2fl4Mv3PTWPsFS+MEv9ilAsv3VzstXSGOlfOvfg0yMtAMoeQfbJ3/zN6zg48FxQHcsAAzvnSjn4vPTg02usTZE5ZKGmRTEUtomHUR5Jhl95+8X9dIGyX16L4fOo8o/+vRTgAxCM3yoAnxVOqSRn5J0vzGPFAyRrgwx0Sw6+1rDApbxlR6eaqph612hMVPJ68D7zFAjKqHTbJO+BEZxHrHdmUr4p348CfCXFl3PIDEsyihVoStqCAcPxcRoYuwOwPPhIHxE8+Mx1XgT4ggdUzGWWKT1ZiM5dmSFB1RCds8yee74R+YN6461m+XSOXKDk4BtaPPiIEafrw9KOc8lPOfimsqsVORcMfUQonaGjLDcxQJTFedeyDncdclAoaFN5KzuE6LQ+z+HmfawiQOocusP87GDfxwLvUQvRKTQtiuUBPy9dU4jO5HUfKxDjz+slhqiF/bs09zuyd7DrGsB3Ax58rDtUNlAAPqu7pVekZOXgY3q2Uv9cmKJJRiF6pJjPWauDhejEPE9k/6UHX3OIzizKQd2Db54/TgX4NMNMc/ynRaeJjTIHnxeDJPkljapbtDRYq1uPVu2QpMpVk2mZp64T6y0+5/JvD4AbL7R48PkGDz7MkdOsMOD0mZiuZBTfaJFTO+EdM5zVaFeSFaIz1OGmZtrW6E4UPfgSwDevjb0H361De4BvT7c8xYOCngmRqJzNwpg43ySiyo2ZKROnBhNDMlmsumHMmERNOKFU3WSdCNGpWK+mtihz6Gbh39JLqMxzpUOZpbcA+FI1N7CDeA+8887ErHcdPMnBR9vwVn9bTy5FYLCqyBi2LbfupN+lcxVPGeN0s5T5/bhVreNrCmXJJFvJl9XwIU4ZcNa27sFHe7ndGgoo57K+e+eC9pt8624cqkqX0t8AEmf/2GNbrFZB/n/ssa35faQif17jBhOqPbeAXnutw8sv93jttR7vvkXWM/Hg885l656thxpww1wPp59G2VwBwV/V8uw0398bAF9N0Jqkgpal/OM7H8N3Hvw1/PVT/6Xa50RdeY13YlSCUkYv2yLAAshDxDZMlOK4kgsdnY+rlWmvIUOMmH1fHM420KoLOfjiHvXiPZ/E1SsfZf25fHnAzz+7xUc/usWdd47gORmcGqLTCmsmQ3QGpW/kBTStQNth5OFweDgr6oZB/w5WiM7mUKCxnp8ywLdaBZDvrrum8h03XgoefHqITtkl7ayR3h7MKGTphmh4uOwkhIqHutX8PiEHn3LeTUVktAWqUHr66Q3++T9/H7RIOvoUQTrri5/4xDHPoRjzls3F7TDWJZqVFzkPkn0T8reZgy8qUqe9kwHz4pldc/DJ5bkaTtT5Y3mUAGWAzzkEfo3Wqebg8+SBho5C2W8KOfjmcJJ5M7lCx8FXYim1gntZY+B7yNFpss8ZBnm0o1WQr8UgS+YII17mpYe9NxR9QhZizxQ8+Abf7sGXarE8+GLZBR58Hg7Xrk1rjKxZbW74rs/2YSrbAmDGrGr3jHFKbUx7uuxHMdoJKdy67L2mVaUdAVg+qxLARz34mAftVJfMVVzvY3yuxYMv9JGBf8q67DqfC2SirDQk0Nd3vrZkiE4HfsYF4OXmhei88Pb3Ux1v33ZFDSs46x68MPwRSntN5lVAqMwDxNjvl4forBSn+g0N/RILxeLBVQ8+LS9fJQdfKFNY3/E3DS+7ynPwlWpvacv04FMAvnnYyF5alO8MXgo8HOQMBOdtbYWt+6DwafJdw7tkAhfkLHnz/EPss48EdG0J0QmFN0s0rSUN4NsMYkFalRQ2uNrel0WksHRVZOxYDj61cD1EJ/M4hpgn819qvxjYZxk9EcvKfpQ5+JT+sMgb7TI7lwdnj3zVoF0ONeEbMqNLg9djc985jGRNBYCPe8F7uKYo8ssVWlP5vg86duHUsg/ReWvRHuDb0y1Ps1DiMuE/ASKeWzWGEJ32gWQ6igkPPgjGr/NaXpVAqvV7I82WJLlWoCXUVJEHKDCqFmmKIK2apFSzlB1GM94DP/hBj298Y42vfvUA27FjoA97VgGKaIHIRN4MYvmFxnFmlDuXKQXv+KM/wMf++g+TJTpjcHboTzdu9XQVFQ++XJ40GGpl4vaVcFtWDj4aCmoYDMWw5sG3XofvSbiPzoccfC2hrczr07udOuXx7LMnePbZE5w6VVAUCoBPuymVTNa1FqIKGmrR2bnZgw/Cgy8KVFl/DUqPWtIngMcfD6avXZ/PKSaoknmxRMEoQ3SWFHL2RZ22qyO8cPk5vHnx0WI5CcDIJn5y10fT73dvuzf0uRKis9rN1uQPBsnvrFkueg/41SoToHMFf0W43xHgO1iNE8A39dV12AoBpe+Bj318i0uXxqxvMURnLkjZAJ9zs6LVeySBQvU2bp1LzuHgwKfOmcNB6rNy8DWBVbuG6Gz0opHk01yeeYnNRgnRqXhOehHKzrl8H9cUTOY5rwEjCmnzmNVD9l0Tl6EpaAwPPjp3WfvCKCDopYmiUfHgozxkZpBVWGMdBqYQiGOu7/GGki+Ou7LWS4rB0YgyIb1S6P4vn7lJrBbJwaef+1o7I1H4qv0YBm7NrBRKYIblwacBfM7hh/f8fPh9dITN44+3r2OlGP3W1jdJZFira8T5NeHBd5ou3PIZ2bL3WCE6Ge8qPPhac/BZBi/FfhUAk51CdBprOIbz1byRfUMIYgrwa2U1Dz42Ht6zdBTlwrliPIAoeYgzgO//2bSsyY5Nh6FSRMmxq/WnH2c3xsgHBFBrkvktQBb6UMV9gsk1XTeDdAjbeDLsdcjAP0nW+TK1qPdF26OUb5hF8hEhOulzS/Zoq+zFt7+bfr91x4P6Pkb241KITq4Qjw+T8g0Aw/R0swffwAC+mYfRmpJhyLOCLfMYo3EeSw8+h64m0BEZtNQFHqJTy8FnjGv2ino5loOvsU/FfZ3ykb4xRKcC0Iwh+jMzzovXW0h+p857tua++5Ffxl89+nkVeBpHJNCW6Yuk/qLgwUc9kCWN0hjFqGSR4Y+spmCQaqYjqKk3GwA+Nq8JP1/Sd6X2iazhet1wJBpLOTcbRMwefD5rJ8pAQc6MuaDK56csUpNdMrrBEJ1AOBcTwOfEuTfpS2MzJYM5pmdY8A5xr4n7JhsXKjftAb4PlfYA355ueZo3wXwHyozjiWWR5qVCS2obWg+e344dcPBqoutYZN7scm+32uYZoxxqyoeStZd039f6Zoa/MOvs8nyCxnNq7hpSvORU8fLLoY3NBvjhy6vsY1a7avWp9FylUnabxsWJFtukwPqN13Dx9e/goy99sdJRTtahRwE+Vn4hcGyV1z34NMlrJqp84hayc9nAZCvM1pRfiMlxBwfhh/Dgy4ChAqmCDuGUulXdeon1iYQ9vO2D13HPm3+TleHP3ZiGk1l0eo84JNKDz0F68O3ALYnB+jt/5xj/7J+9j+c+c5KVY4r7GqMum5n2mMwZodJnucdWyyIvLvstlbrSK/blh/8WXr/jEbxz9gq+FoW4XfYSSgWAzxJYGEBQ0lvSHHyFnFLUg49ez8reQA4+eM+UWIMIkeOcZ32k3R0GPURgzYOPKkPKHjzKHqQAtx6OhWBsy8HHGtILkRbYXz9lD778wVA/bTaE6OTFvOvwxBMb+ogaZlyeJwfbD3DuvR+hxctHfi9LAZ4BjWq15XGjHnzDVgfZLO8tFWNRAD7LMpztOUHzpHdSjplzOxmGxTNEy/OySw4+2h/yj/pMiwdfyzfuxxOuFErgol1HLUqGG0f+3RVGoJQvhRcg7cLhOw/8Gr7++D/E1S98AenQ1qhglaMq9ysefItCdMLh9Om5fRPgq1bk7c2Q9q0GBA7cIDKCABwoV/Ztz7+16flHnyEgtSxWBfjoPQX4YRTnh8ZkGmcZMyi1wIyYVwo9anvddut29uBLY+tyDz6258k+VAA+NUUFHSNNUwquuJXre7Oe48pGgwAA8CvCR0S+R+Tgq9P0nQk/PjruwRfzjTn4lGupFCY86B2UfcqVQnRqnVUAvkz3wNeo9xQEbFu/xmsA4OP95vmHqg93sBkptSsEjDQNZxL/QO4rHrpK5XreXEGJL1HClasFCxOrc3oOPulZCqCag88pHmVqORa+vz1Ep2SNLNmS6oHiNxpHVwSSMwCQ7olUn+Zz3iXd6/K9QjrtaixW8ogS6yKrXwHLKb109zPYrE+D5kqlYVPnEJ1kH5NrFh4mwDq9jJbDfLT23ewlCnOxAYxrucUMhlaVShcAfACR5zvbI1wzdI39UqMQTHyZDvApcoYG8IGXY+/kg6EknXt9Xx7v42OHd94hexbz4JMv7LSfshAP0UkBvnmhLdKptZJcx1JP7D13dNkDfB8uNWT33tOePlyiDLHc9GgOvlgqUikprIfT893RhN+aB9+4VTatySqlYonrXGHDmzZFTflAmS8LzyvyAIzjqjyAGZgBfdTloCWQewK19kuOwzDmITqrlRBlVLM10wKAz9H4D5Prv8ZU3PPGN/GdBz7XDKRacyDkomvopsv7IBXYrQ0zDz5N0UIteonwcv6OuYzlwadNVgrw0Xr7LhdIm8EVIDcRrQFLhsJ+vfkA97zxzbwMfbbiNVsjxkjCY7VKiywrx5du+Z1Sfyomlnfc4XHdUE5EskJ0mjS1WQrRqU6RyTuhaSwV5bNerFzAr9b4ypO/k/4+2w3NdRtd2ilEZ5GCaXqoO80Xp3o5pz5EYK9kOUr7JqxWa7TqPbCdc/DBubBva0rr/Oecgy8jvbMyF5L3BLBTznZV+W/sS4eH8z0Ti6EAzgKAL7v9IQF8NNfYduuYtfNv/dYHuOf5Y9zxdf7xzBx8pM+f/vP/BcPVY3z/vl/E9YOztEnZhfxcWJSsglO274q6aY6QYauPX8y3mXnwKYZKdI7FfD5MEdkA8Mm1EaGaND6GsrKmT+zIWs+WT2Evag3RydatDGdKFW16bWYTQ79OimOqQFa6UgX48mNeAYEKBhGWB5/XAD7XYegP8NrdH4e/8327g1AUfYrXGlOirfS8iHPb2l6XX3vkkQ3O9ls8Hj0jPfcoOXWGzFdNOVZ4B71jOr/G6hKbK1s/hSbGkfdRy4+TDUEEUFTPtPmaugXRMyutLStE5/SvkYOvRjUPvioIj2AUaQ1gOQdf+Hfo81DZgFDoynHaBeBr2O9ngM9n+/LJ+gxOX3sre2Y8fTq06ef3tTwu7XZzINd3PAffMLjkwdf3QWFfAubmENDSE7Fw5tdQEoSxkeCREyC89+DKbLtFtc8WbVeHePfsZQAfZN2lZ3JXADtqyn7LcEby5Q2Bg0N5z0N0hjPfZU3Feqkhq9M26UZSw3HukINPa1tbfrUcfPXqK7KSyOkap5xmRGjrtpx65lI5JQvRuVL2Y9GWDM8JWCE6lS4pAJ/TJgYh6gGqApxjGTRk1Jis3jkoffGsP5UqdPoZhOgsgfreQ6xta+x99juMCdQobL7vk8Fo5H/o/JHjUuMxZHc8HN57zyVg9syZEatV2Wjs+NjhG99Y44EHtrhyZRQhOu3+8D540LVKo6+sQEJM05yoO4TobFCdscq1EJ2sn3uA70OlvQffnm55SpugogiWzCk9eGsgiWbhwA/lUEncpBxmJjtT3LhcEbKIFEY2kgzNQSnL26VUqyqligCfERIz7y5crws7MzPatsO7vhyi01LU3mxi82lDLUZzoTt06+aeYFkOvupEnotFCky5Mre1kGVLcvD5kCvv6Mjj40/NYxMAPt3qPbseAb6OWGB5Xw0Vyt5DG3M1Bow9dFJhXxJOsuducN5RQT3k4JuYXvGtsxCdNaElPloRVABkCu08RCe5Z8nhtD5EgK9NcKj1T6NmLwapvJfKS+0M+SmE6KwztzkjrD1Dzxyv5ODLvnttnixQhFHFx8qF8Hdxj/Jw2A6K4YfhwTeOLigvC8oL3vgUonOc3z+esarxjnpNV9bTHHzUitYC8pil7dL1v2uITj9mYYYrzj6ButxYaLPhW+TFiyPuuDPvkgRQnENmLbsergMAHnrpP2C9vd70LokaPfhUqixkqoCwPfh0JWHdg6/WH/J7FMoi+RhR8NOxXSIMJ0WitpYKnj0Wj0qtqYFyDr4Wnsf6xkN/mP5eTXPH4ulrAJ9JFQ++eewXbPaCPy9TfR+mRot+VTEM7Lqm8MNPPrnF5359g1OT05PDCBr9gwJ8WRvZrdbJWADcAPiB19N3fGwsfkper74903rzM8l30tq/gKiTe6bcNU1QFQg2Q3TmisTs2Qi+oa/yOpuT8jrnhlXz7+NzFzCsDvDC5edUeZqtLwvIM25Z46HWR/tXCNF5sj6d1blZneJghgLUyeZ0isDgrCAdkgdfIOrBl3itCBQZ7AYNu8xJyBKKrJ/dJO+SA3xjMjyh12L5dg++crm3zj1gooB0X+oKkYZKITpLSvfozcR1AR2sPPZzXZ6NTW09MQ8+jY8W6JoqYnmvrm0V4Kvk4AvhPhu+HwV4+/YcfP/qX53Cn/7pIXk8f6Hjo3OMp47vEddEKJoDs6ohuHFsm6qdJg++/MGdMyV4zhVqRry6Bx/pc+bBZ7ESvsjIZzxOw9rL2yhfk6Gz2eloyEC1SOJLAD5AgGv0OXZW6udx0F8ozzvHZIxu3DKjh6w7EQx09rks+/b223NFt9+ez02LXnxx+ubkTJRj1MLfSr1aCPmfepvKqACfBPQq/JtJ0xgPSojOfQ6+W4f2Hnx7uuWJCsOSokI5WWmQeyXG1btOz5HuhxSkc/cQnXqbResIqVgRin7Sc7ERz5u9dag7eaFCLR58STiqePC1kutd5sHnKuCTpXTJigrhv1SWFSUhOn3fAwMvkJgIErubMRqWVVTh0JPKrZo1cizLdY3WN8kbpsBazSumH7f41KdO0HXA8aFDhPjUEJ1Tp7IQnSQJclAchptrt4ETuUcWgS1UoOm6KqffAvBpyqebwbDkAB/9SyqiHAC+x1VJSFj33bfFSy+tcOedQwrflXkviDlEgbrR182BZ4DP7ou6H1sSnkJRpq8Wn4esqXzfe3MvIa3rTcXyO4ToZGUkrg+fWmQhOi0jCJAzsJIXLa2Vhsk8dqsUYuigC2df3yOF+BoG5BmDlD0yNjv0y0N0RmUIFR7Uc0cTjvoe2GwyIY2G6GS5WjwvJ7oytUN+VgRpIFd81pT1n/nMMb7//RU+cfEYp9/g3+iuu0a8/LLHtWsODz6omDADBOCb1/owuAz30POm6oCoRVd+/FUApXNU3DBz8LVvrOyMpdcJwLfdeFXhGHMzZ2eoBg4zgC9p/9Q+sfqC9knvvPc8pZpzLHxgK83Wxpq3rm1k4eEwdn0eNiwqLhXF1i4hOrVxcg7Y9Ic4QPCA64cN45lk21rVpRx86TlqmKVUYiljktJYrldv7OGVddzk5eBccD0tsStO4a+sM4XJDbOHtvc8RGfVgM/X+Q0H6QGev6/m2d6y1qUHH9G+6+WpUkmOTdeZZ6ZKUXlthNFLe4WmpHV5nnjWJ++x3lxT5zgF+Gq02QAH1vmtWOVHevXZ38AHT92LH/7Ls2ztafJGdsZWtPUMdNMaN4jJt6L4sQLwnRyc4Urgmsel2W4O8PmuB/XSG8dwNjk/ziE6iwa1Psk94obyXGGjU75hS4jOKu+nUE05/d6ZS6YITfdspiwW69QC5LRn2XOa4ahX9lFSV/SwysMf5n2Y530jwFcgagBeIu/1sJ2srkYjOLp2WC7XIQegKL36ao9XX+1x111D1t7/+9R/jYtvfw+/+T8+gNP/8Uv49u97nJy4ZHCa8lIa9WfD5vQQneFPr16ne8+cgy/KwzaLlebGQkHdYVS/N9sfHdkTtHUmZbiSYX5h0WXAlzn3SnOyfnbz4nW+tkXXx+U9Rb6gQBDdO2hRa1JRXYIVtrzrpnM56O66cZueCzn45jkU9+n0jYlhlrXfjaPDO+/M3+78eT4PmmR95sFnCk7lOki/u3FMKi9PJmzSjQsdQXO0s1L7ychJhujk32UP8H24tAf49nTLkyeaXbn/lZjT3hUOWOghOqlQp4foHEwG0wrR2bJnlwStWjgHutlL6jqojEtp3/XQc/CpllkxLrcBnNnKFHFBSZrLmEJVGUnKtUo0NcUGDTNCM3mv18CJDq4tPcVOn/Z48039nhyvmodm7AMTuoz5oFn1ydwGkqgiyI0DC8/HyknAiFgSseuHswV/WC8TaFDQcDV9WvoNloaBMxrQhFLJmO5CUnEUxzRYZhGGWoborOwD6VGxUP/+37+O559f4cEHt/M+kym0jbUXq2tUipUAvnKn26n0iHPGGq397fRv2ty9HUJ0FhlhxcI9Anyyf/K7z/k7jM4vAPjeP303zr/7QwDA4WpOLB8VL9vBIYvyS9afDE83Oj1EpzrOMQdfWi9u9uCzwOKGaxHgowJ7jXiZhXO2ye1upgceGPDZz55g/eVj4N/ye84BzwgGzvMAACAASURBVDyzwckJcHSkP+8TwDc/FDz4yN7SQXyn6VnNg08aOpHXP3X9neK7ZDn4OptXKQEhJshCaLWe+z4OyMMQpTZyfsIRr435Iml/jEpk47yc+KOgrAR8MUQnD1mVGVNh7p7J1/lZMZfxxNTS2c+Kq6h8pqC9fBfVg08oWdsAvpyc89gSD75uOFGrKBvmkVBq1l4tcydn/ZjmteRhYrOG169akUJSOavNa2ZIsFo3AHx1yvZeEnbMe+D0Gdqv8hnZROZ7EkVs5ikQAem5X8YoZuHhwr/W3DA0ckATwMdHZlII7pKDz/TgC/Rz3/lD3P3mt4F77DID+uoH2W6Aw5rRkfL3iM50xcjWhFwflU1JVVrXPPi88Hrq+EZ8sjqTVXmyPjPLsWT+0D3NEBkZad95mEJ0xrk3jg7oJ5+8KKLOgmlWJ81PTKl0dukefPzbdh0yAFOG6AzXiLdKo1xaE5eG/sAcS+5J0ubBF4mHOTQWqODLJ1/KIsAXSzLvRiJHV3U3JYCvMKmcK+1PvPqqLFeYMVJhn+plMcrbwO7XX8/3gvfOXMI7Z6/g9Efew9HXyatTOWSi2tyJz0awwTJOzq7TbUMB07x3aohO710G8LUsg46EcqT9ZG2m+ePSWrS+RWzYnC6F+ImacYD25/IQnfRctgE+xpuQitpCdM4PV8PykrVtygTaWdz3KbVH/oCbvOin56oefCHaVVi7eohOujeNkB58c3jpZtohRCcjJwxItlt0nccw8OtqimA/6rmNl9LE50g98TgavPKePhTaf4k93fKU+C1FEZg2bYUpK4EGcHqITp6DD0lxA4TNkObg265mZUUQjsrLqSRwJM8ETY9QsgSaQIGi0ltjngsPqB58UM/mFKLTbLtRcdBNB7YtvysVtQI5C7QXrEqqKFpbIToNxqDQ5uXLA86cCZ5bZ87wF85z8LUprSmZTILCcdFIjZoXAz3AGZNVQUJSvgO5JpgHHxEO/YCuAMgXmgrXFlpbMvCu0xOsB2BLUQLupA0jzQkPvllfIBh5AfApKmhe3ujWmTMezzyzSeEkQifywo4PCWl1vmEK1szKkna5oc+V/Yu2IvuplhLvVisfvXpC2WUMb9oHVWmm1tH8vjZcLAfdghx8ppBXAfh+dNfTuH54DidPP41Ln7kMIGBUjzwYQup1nU/jtR1y5SkDqcXyHxQr/3CuK32JITqVEF6qt7GmmFcPVIejo3le00/HhsQZ854NrAUiEVoYorMTYezy+za4J8uFHsYcfKKNRiBDhuhsWaumEoII31r51Kb26pUzh+rotluoAF+cu7JfukfofG3OwafflyG+SiGyOuJ15xXlVguVAI+4D5y9+hp++S//Z/RvvDH3E46BZPT69HD4hypqSh58Vv+M4dyuDtLf/fbGc/CpzzdoIMOzRgXGntFK1BK89qiHAyohOgM/LvgQyxslA/jmeXj+TocrV4JW9N57y+EMY96xOhFDFO27ZTn4pvsVfs17fo7UIrTQslmvBX9XysFH+2aH6JxfNDtfLAMG53B0/E4A96CPVRJhGxbYSS1Ep/H36DuR8oLXwfZ5qxsmwKfsK5VEQFKpLs91LUTn8Zp78M3fa2F8vgkgYx58rk95WoEwfSOQljz4omxsAHwyhBoAVYs977nKWhbrzzlkOQaD8QZ/nd1CdJbvD91KlNGV9Czdg2hbXe7G/sjkDSU6UQvAFzz4csDG6gv7XioTrsiBss1GD77Qv+U5+PR6yDvSXK4NZ49oUL9MzxP4zENO3b+kzFqQ76zrHQ0NmmRL3pYWohPYMUynAMu1aAI0h2M0JuCiQA62m+NfMvqj554rnAeFj1tjYXxjDj60nAekDnbWVAA+y4NP29uZUXTM/1bw4Iv1deM25atOBhq0O0SnbIHzfI47vP32fCF68JVy8GVEvn0G8DUuWJaGZxjI957mKAwPPrnvFIzRijR9AwnwBb6tY3/v6cOjvQffnm55KlnYS5COJVQtWJRZMYqpUDdKZb7nOfiG7gArHKciIwHFSiEpVEoMTUQTdSViJicQZZWp0F0KfriOh3uI7SjV1Nz2mw8MBeBjwIelMVLaKLZZ6RA7DKmJ2HQoywM4zLOk2SvWHanvgU98YgPvgR/8oMfVqwREW5CLjvWBKTsMpZZy2i7JwccUHqQszWdFCoTnpQffwazg8ww8zJnhpUJBqrfrQKeOWiZT2Lc3dqMefMzDYpozR0ceJ1Oo0/XaY7NxgPdMaKpZh0rBudhJRdlA5xDdG8cRdVOgqc2mxM6FfhSbMMbduRIAU98fqEJn1zlXlCpb9lzxaVm4I7ruBMAHChA37kOOTiqFXr/zUXz90X+AT/3me/j4f/gzHL+8CbnguiF2Ie0L6msbOfgAYOzWqmJS3d6jcEcBzvidVIC67YwIHnxjusjDpDhWLvWbbn0LzeIyAV3p56VLA157rceFCwPuvbfmrVIh4cHnXfDgG6SMrHjwjaKvSbGx4NwsUuMG8dJLPY6PHR59lChf5foXjfYsRKeuREvAjni2W3W5ORgFDcZ8zXgxV9ItMlmyT+g9OkfXrEt5hpbQ7MGXn5sXLwxYrTx+7tv/Oy4dvZ09O3QrZIFyRSWnTnm8++5c3iy6YP92jufg68cTphRqCcHMFb4eTAGlPWeGhFV4kwLAop49FeZCNxpS+IlVHrY4b7tlnD3r05XLW7z4WpiL9903wPUdfvu3r+GNNzo8+Cdb4Mfzk9mrNOw9MkTna5/9e7hwx4+Av/irudAwcHYkWcvPcpRTvlFYQrmir0UR6Sc5wsfhcDw8ceuUNXPy0LNntWKGgGYOPu8L+xGnbb46Mxq2MMdCA4cijd4VeaViDj6i7db6rUawYQJV/j2l8Y6s92StefCdhsdsHGCF6Gz9zkzm71ZYkzCccd4EDz6v7husLnLmSrKeUyOnSH7QaSE6EZQU5CyJHttLZJPakTx0azsSD9kXuymUqXddxuNxHU5uAGHxOtqZXwb4wpng4OEXhL8bJwMdBl7pDZh1hPeoG6l6P5/fR0ce9947YL0GXnwx8DyhbZ9NAp1HJuuHfEhXCdGZ12PwNRRsmgxOvXdFz59M/HSOrAvRbjr7BT/W53uFBPg0Dz6A5whsJSc8+HRdyLwnaF5h8tsz411aD1zRSoqGbC/yQgs9+FqjgjGjfvLMal1TvPAxV2XDxBvNPHgmT9E5h/ycQOfSvMrIOXgC8PV+MIFEYD5z2NoV+6c8tjQPviVBoyh/mXvw1b99NCBJx/EUojPeiw+35OCjlOlRjDIAkiFTPPMpe8bCpS/Vg+/pptLeg29PtzylTUcRLFLOMsE0AkBfUXSqLswMFHSTsDgzXcyDrz9gz2ZhLWXdhb0uKiezjRrleO1ys9faZFaSREgz63RWkvCcukmpZoXFawJYgClproFNVLTu3peZHaaos0vlTbEQnXUPvuxm5TDVLIqyHHwKV5K/q8/uW0oGScxrTmVqicu/36hlP//5a5k0lKxcRQ4+EICPegZ045blAyxRVWhZCqi4Qg4+7doS6Vkhao0Wa3ryyS0u3TPis790nLxzHHbz4GvyZpQAiRSwDA8+s23oAB/ti9qVBvCgXEFOvuLBJ//ue7vu5k8tpRkCzlrUGquehlnz/SrbI+V372rWu4YH3/33zxLzZz97HOroO9x+uw/LdpKog5V6mCDbQRlrKqCJPkhvoFhI7WsM0UmUwSlEp5ovVKnEMGVdmoPPuq5R1o0GD76HHx7w1FMb/JMvXJ1vt8QO1Ujk4ANCDj76Dl1nARm5gNnKC6gkz66iRxWn11/vcPWqrmzSKIULBzBsdYW6loPPOX0+0WuqPsTxvqXxDeblah9Dy8SDD5TXyteNyfqY4YRCaPovfOEDPHHpDQaQxrWpemkKb5L77x9wcBAUHYenJei7zGCMFqUhOqMHn8UDadTiwcfL64WoUUcqS+SMUrvtlO/DEqfycFUvJ3W/Ufoo9977rmzx8APHePTRLc6dC56rfQ9cuqRBykKZ3GisRunq/Y/h+Fd/lYMyY14v5Zm9h+5J7MW3Th58BhDAxiOfUFWWSFMqWnIX7a/8dtb+Bs8NuzQF7NTH0fXVM2a7bf9GtK3Bdxnvy36Sv811aC08zSul5sEnAD7Zpu7BdxuT/ZKyfYfzMsr0kQa3CucmHYcJNAsefH4GKQprsClEZ3yHBiG56wwPRXLOxOJhjeUAkUXOlR2KZIhOtp+R9zz7na/hV//j/4QLb38vq0P9NDHHshMrlp2ry/bd9Kj3VcCGXlY9+JRNo8SDBIOHVg++GbS6994RFy8q3plN9VCAj3zEhWvBHCvhEZ6qLYDIqiG48DCi90MZIX+aOfjmNiwPvnEs6140CuOYg5yUD4t8GgVo2RhIgM/4hmquUnqfzXlvzt3S2lDPF2KA0erBxwy8G3LwcRBW6wT9qcuJqme0DNHp9HLeORYmW4bolO20hKuUz9AcfHfcwXm81hCbqT7ZZKMeIg/RmV+3QnSyvyXf18jOx1QLcg6OI/D2uY/wsstZyT3dJNp78O3plqekj1SAi0yhTDasrhRuzRkhOms5+PwsDERlRdrcbyAHXyykHRTZpuz4H9WDninZK5ojhE376qkL4XfkdZ1jB3aqpsJ/tzCBqaAoLN+zufIFVFIu0TBbfm1b1c7hsm68b7lQ3VaPJXSxMgrD2WcJtDhZORaooPORjwx48DNb9FdP8OUvH7D7maBLPfiyEJ27MRsZNTzIFPauAPBpY7KDxwUlxqxODZ865fH4E1scPzLiu9EK1Xu8/77Diy/2+OADB1cBQOVany+qpfXn028uTFUpCaxtggPrhgXwyCaS0FUCzj2kJ1ct7GbMTRV+1/vBn53+3RWMmWsCAHbWpDtUiFeUlfP7zYoJExhwMAG+U6eAp57a4LbnjnHfsyfkgennoOfgywQ5xTMs0tDl+6hpkJBCdBKAM4WyVr5pI8Dn4XB4OH9zM0QnfcazxVFsMns2Y1L0tXfunMd7qxHAVH7XOTXVT5tVc/ApXbJyje0cojN5XE0Da3i4KMc/gNlKO3wXsUbFAz2xMN5uANfbHnwZUFThL1IOPlOp3gbwxTro+Ixy7TeQYx58+Z57990jzl7J39/DmSA7gARqrtfAL/zCBsMAvHcJeOGtuegNhegkRnH9cCL2qvoeLPMB1tq0PfjyqANp/AsefPUG5zlS8sBlYGItRCfqeZhTd9wUst97rFbAffeezGcTY24LvPbUwSqAFNxd0p9BAcgVkl4xfAl8Cj/TtKpbPQ6mEvOzNwDwhX+ioUwd4PN9z1szc4x6btiVxLB8jAfUvZy3G5gbRklpN3pxrAjDhlYPPo1U443CnAOQAXyyiO3BR3K/ev17NfFynodV9F2fwmzSMgmYns4PwJYDtBCdXjlj5udbhGQj/KPiLZ68uhccKH3vsd3q/QghOo26yLpxDlhvr+HZb/4eXrj8HCumrr9kiMbBSM2DjOpFMi/UvDtZo3QPlfyqc8I4WwH4Wkayc75ojE2rbwnR2aJ8p/qo4E08/Sbg6Q0R2c+CB9/0Pdh6SF3WyREPvlZ9Bt0CFYPFkgefcuTU2yP5amljmg4qAHxK7jXRkBWi09ITynYSKVE11HIVCu1Og1PIGW8ZgFSjdQm5Uxdf6iCa1i8O8HVlnl2E6PS9LTNqHnyldXf9uMPJSeSRfYhsA2C14s88+OAWL7xgwCudHemtFSAcqcw8DJMue16QVojOLE/qruhbDJMqzrltf4gf3/nYbnXu6abT3oNvT7c8UZBJkpWDzzlACbo01wmnKip4Dj7OwQRrvximABj6WVlJmcQm5tN4kdrhLS9TDz5TcaZYJpXIw+G92+7BC5efw/b2O/DBb/2W3e1oNd/Y30h57OlgXqNa/ZRertbQknKwlVdOCdEpGYOG6otdA3J9lJYvSFPOaIxoRgrHRUE1lRmlFkGFkLdHp5hzHpLQKoVaApRSz5DOeRVw1/pVHeMGDaTFxNYa10DupeREDj5WIRGGnPf4/vdXePfdaW+oJWaXgnKhg64XCggBsrAQneSdzb0tWVmKGxUPvkUGuprQpZAWRrf0d9ch33/1pu1rBYW+uec6e1ytEJ1SOc+2tnj2KUnpGRVy8J075/Hoo0NKlcnGMgF8sxJ3GBXlKRUKxfeV4f7iQ2pfhQcfQIRj5QHVM8nIM3d4OH8XCnxZ+wLbOpcu/CU5+GgHdgD4vHPZXPZwKfQTgBRqjAOxE4jqcmVCPJtL3bdeKZv7C2P40uLZfib+piE6hy2ghWhPyls5RysvNdZCdAoPABPcScriqI3VoyXUpliX6m8X0GOdWqQJ6cEHhHFZr5FZnLMltVTJ5LrENzt4uO22ps/Knl/UdMlj1KqgkMCGKRnNSW8riFRDokqIzlHMV0A/UzTZgRmeLAD4WrzDZIjO1CUKGm1lDr6w91CvbG28x5F3SovQwsaVjgdRxIb+dDvxbRbAx7x95X5mGDB0vp6fK/Z3QF/t5GZT+kb8u9DhHSsefNxjQ1Tr8j2CtVoL0ckKc14FQLsH3wHPwaeF6AzNts1h6sE3dqssokOY5yP6Hhz8U94tnbklL+nG63KuayE6NS/TXQC+JR58lEZjP85DdGrCZa6Alhc8uG7DO8fkEUmcH26Py5sizhRAKqses02rLa97gJXGb3oyK9cPc1SdkZzTbinAZ/A1bB1QDz5ipFSoKp2zVuoRyqNSasnBVwzRuZBCG/kYzJ/dpzlCQ3SWvpkZorOin2gyPkNh34DOwrD9WU50y4PPmAt64QaAb5yNNSlPxfkq0qTmTb/Kc/Cl57uOGVSWPPhiHZoez3xF0ub58zOA+8ADA267bUS/Ah57bIvz5wt7r4typ1J/o0KkHqJT6uPig+UzoXm/EHxOrPatK09mOoq9B9+HR3sPvj3d8jRvhHqITlNOL+Xgc52Rg4968EXQKV6YhIHpORpuCOCKE21Ti/HhNYr7eoulU3ZIWR5b8eCknSEJ4i2K9X3nwV/DW//VZ3DnnSNWX/+6HnphVVCgFJqRQN7gxVjTZyUHkBVYQJVnTEvFFKIzV4yo4YMa+5bPZ9l+Xk9RuQN++DOqAHx6yAMK8BEmS1G2yj6odbIcfDT8R7sHX3VoG8ZeKjksDz6ND56n7m6cSzcOoSLnVCV03JescApXXvsq7vrJ8/jelV/Eu2cv5x1MDxTWuAIaU6GAK4Ua5vLUbranVj34jLW9gPKpWFvj/G+q0KlgCDbtYjbaWFwCXFJZ6cR8rCpnCwBfqjQ1TibCMOfgi4odVcgWHnzOzU2N3Sprt6bgouHcYn4K1ZpUCbOoArcuhOiMF4cBeP99hx/9qOfhOkm/lgB82e0GDz61oZvlwedcsjoFyOcxwE/y1/R8mR8pdCGvv+DBp1EBa8kAFopDbTce3YEO8MFxoxkZkk3rlB/KZ7ynHnzjmNaKBtxLg6tdPMKjwkPN61WaN87w4Iu3NXZOeJi1AHzWcHrvMHQHs2Ly+HjRnktz8DXtzQagnHnpgANCVrstFL+vNzwP2L9w9ZBdrV4PRMml7h2LEsVwheei9ukLDzzMuAzRafWLemvP/SmcbWItMhKGDOq8YYrsHHzi9ZG2BDISQ1dl5DmQpMlH6YxsmGveOztHYDZG89+lEJ2hJBkHK4y7a8/BV1Nm5x58XEberE6F66TcyepM2qjCNJ0AAC1HXY0896wcJ5BRevDBkxx8DWFr8jKF80UZTPpesUinfW/Dg2/JUgeiB4q+z8ioC7y7upJekspq0vdrWNepP2ObF3e2Pqab0euGXvbKfFrKwzvLy1JQkwcfcuFTG99+JGkz6DldygmudchqiIXo9IuqpVWlasRLWPOGbSVxfVOZdLRDdA4DTAMrKouw69IoS5EHZw9Qwn/RLshhtDz4lp73FsBXWG/aIyw32mADfHQYPv7UFtvjAZ/61Emd4VrowSdBVNL7rF8dNXjtFnjw+UHNl5iqZzoomoNPNySiFPPvAcDZsx6/+7tX4V66itv/YMS1a8Y3I8CmOj6UD3Hq5dxDnIXoDAVvO0v0SIU9kRkSN7CZSfYig3n98ByAEOLjtft/AXiZP7MH+D482gN8e/pPgKaDVQH4gLCRSZDFuQDwFWwcqzGK42acGGd49H6b+pOF6NwpT8fcH1pXixIxCMFTSB6DcYn9pn/V6qfvkeo0wDdqNa/3U++U7cGXtwEYqoZW7fsCAMEsqpg4Jua0YK2/lCQT0hyaiT1j0MS4UICAgWoaM+q4csTsqKUoEN+Ue/ARZkapcmda6sEnQjixcur4L1fIZjX4Ed71OSDnXAr3kAmB3mO9+QAf+94fw/kRd7/1Hfzfn/ofMLoeH33pi7jkV8D4dFv7MjQTHJMPpbVktT7UAb4bxPEKAoHsTC5Eloh68Ozax11CdDIPIKlToOPGPPh6Ux6niskwlQwhNylLG4gK+P8/e+/6rE1S1Yn+suq57/u7d9+BbpsGURqhgZ4D6CCKBgITIeN4wXE+HLxEOEacGT0f0A8ahp8M/wDDIGIMwohzwtCZINQPzqgR0nNwJsYBgR4UBLptAekL7Lfffa3nWpXnQ1VmrcxcK7Nqvw00zF4R3e9+qrIys7IyV65ca/3WckJ0NntgGSL4YtPFKIv8/Yo9q9kQna4SUCrPEqc8g8nBV1NVAZ/61DA6zyUDX5d+dAnR2RbW/N9dqZEHgNZZRMM18NnuJAwZjsHAbYJr1q+JvyGGsOPJHYL4g/mQyi2aN341oRl9JD43Fo59geSesdc872Zn7UY0YRnJwQelrNGadwrj62jDgjMGi5hyC0Ku6Iiy2S9/1X06ywBoVSP41s12t17DzJUuIZhpX7oYBiWUXa3U9fZAw4ObF1yv68+Y54IckJLRIwY+p9wwFbIr66T0S54dJI0R80g3BJ+nIMoUAM9YzoQCyzINJA18XlhgonzjKIbgq5i8ouHL0P2/+W5CuL0ogk/gb12V/0CdBy4p93PeZ/590zb9HNpPteDyQYf/R/I0s2dd9kDNz7lmpnh7O3O+Vwrr4Qyj1YW9tBpteXU1Bjjve3VSWEIjJ9+lasJR+ghBizxteBggnQ108Lwpm8ohG1bVfsMsEwyYXA4+BsFX3nsv1q94BbLnn8foU58Kqoki+LKhOJYsisbrOyAosyl/FPZVbp6Vcf+V9m/Cm7a2NYZzje1tjXvuCdehs69wBj5hr3DaBi9zsP1M8IKso+xH57zjuHPVEJ3+e3rrzHxHI3dwe1sQhUnJITqlPYs6jlOdHiUZwRdRhgkk5+Aj16wRWPE5+Ji9jpVJEiE6fRlcdpKQPy7bbj5on+A8l5lb3/nqNV7/2qK+7hluUu1eGcHHObDT1ElNiE6W/zYIPmvgq0pn/jjjovzoZ7LczP3wUXp5DmQ2TYAgpyQMfF2dubTK2pJlGRj4Dg4q4CJ8jtMpXYnIGH/+Zd+LV+48huX3fAcuv3pn2NdrA983jK4NfNf0oifLCJU5Frjkop6IUKy0HKRT8WEDlIPgU67iBrUXpjlblfkAf/9tP4jvOP2fOP/nrwX+6ora4aY/9buQtuVipE9tiCdJ8dYXWdbHwKdy/rAT0RcBCDc3gxJiZepgZ/YLdN8YU+8vyVLGU9c/0EiHGq1Upx4x+k+3Ku7dGeWMIyBJhmZmp3VCdDIIGDEkVsLAJ43zk1+c4C/+aguveMUG305RFEq/YAg+KhCLB1PH5i1XyHn5XwVx4VOmS5TIw7WplD1sc+i+0bpwhLRXfvExXE5u4IGn/xqHywqjvy7c+sQOhPcoP6R/dwvRGT4XfYD0UfPdYct2opTy0ptnWaa/JiE6nTWcqJTy8/r/goFP5QGPtLzWPpMY84SHsnOVrvPmRF0rvOvrZcWMNWMQMk1xBj5WsQcEITq1bg/H7Bk5lQOIdCjPXcQXOxRXZEDB7T4hOsnGGMvhtfi+78PgiSegigL5zZtsGeo1vVwy1xk+ye0dKlOd9zOfKv9dxZxoHfh6wglK5a2jk9LMYRb0ld19nB1pitSpdKBPcRSRBMGnANHgrwCrLLbPXWFkMy0j+FI8t+KQNhHHL99A7fBP4aNJ/FJDOZEv1GrVVXSo+9LXiS4y34K6Cb9eLoFPfGKEqgJe/vJNP01pB42GI1cnQ3RGwpRwxBru4wustyLYVkxkhpyZQ946yJSL4NO6nl+f/vQAf/M3I7zudWu85jVrRxEGECXvbSL4+hCLmPLr6xiiU+nKGmicPZvK7WaP7JCDD5DHwpejHeQLfASf+6w7dvI85pZhlTLwcfUkcvABwGowdQ18wzZEp9YIDStdNhPbqBeiU+W1fOMh2Y1Y4yiWJd6XcQg+pmniuJwipSCG4+Su1aoSch4dj7F+4xuRP/mkYOCTv3WZD0HS27JnTX/IFbQDJK6q8HnneC/oKGgIPXuto8KYjs2991V40xuXGP/dmt1vKIKvi4GPbU9pmWcQqg1ErQFOZ1koL2gdrG+OZ1AEn4MMswa+Lo4akTIOgo/m4JPr5oYthuADwjXgROhgEHxaC2FfEU0v55w7nH4IOficeohhpluITn4u9MnBFzt3xvlG+JI6a/PF6piVnLZFD+ZJAV1765QTsmAXvulhOZnZNR44rzalqHOMavZY0enKMfAlQnTSkNQdcvDRNofD/parqoKdyHyITu93nlvHWrdcZgdQVRVJb1NXsLevuxn4/PvCJy4mB5gtbrUXyF7/zB2vwZfe8RD2Ht6g/OPw2WsD3zeOXijMxDVd09eMrLwlIPhqr7rmAEgPnLEQnVBsnGQqoBkDnyGlXSFbqxxfvOdRfOy7fx6L73ok6K9P0f2xTSYYKRRSjeBLLGPaoR4hOruQlHi3r4GPU0A4PzmBq2sne2gvxKIGeRb0sZtRqmt7QQ6+jso/SQhxylwhRGclCZue0sT3MuPCDM3nwIc/soXT0wwf+9gIy7XrpddVGsevRgAAIABJREFUD3NlZRShrgg+9rCgPEXzFYj1SG/WQBuik+FPnhfvfc99Eq/44mP29/iv/9qtT+xAeMrNbwPBZwr1RvD1UMZ0RvB1QBz59yVbXGdl822G6IyRI5QPBqKBzx6EI0K8UkiH6KTkx8WBG9Jws2GqiYy/8TJO+QfQ9hxlsFEocRZhrpJmQvpGoSxLo8/dEJ3OSb4feYsiNupO1RFUaHnXXZj/+I9j+fa3M5W071i3p5z+W2Qfq8SQUH10H+igNLLFww0uprToWKl43/JOwZte1Zop59uKOfioAa9KGxg4BJ/o6U/mY3WFY5giyoig65JxsVGKxBB8rJNPLuf97LcJ1nyjzEfto1c08HHbBvucIFTUl4WGsgz/+I8DO4xPPjmo8+Chp8LUIlSIjOX7v0DF4TNAfxkwFtdW+s21mtofvPs+jwUQwm0Iytz+VAr/+T9P8ZWv5PjzP5+gqhpeH2nLbyaG4OuUt9E554VKRYeo4Z8x8LEcXvPKf64/JQbJ7+PnjArqFOSusvLC0XvyfXcEH3cQ53JeEGdRxkrjhCtmcvABoUNClXkIR+2iI8tsgCphNDdU7xMuCsrfD1Tj0VsriFvZR0qVwBr4Ypue9K29b8jOx4pZF1xowERbV0XwtWehcB9KniEYhDPgjqs/jlqpK4XoVArII34SVaqz0fFr53LXHHxOG76Mioic4ZFr4CMoRMNEu5CAngTgIJKdHHzalWGE6uzZKna25/ZU+tnbHHx0H+XTagCN6CXclL4/FzrdL69JpAU2h6JvGNKafbdgz7gqRfYI7pYr93WbGyonHyKloPHeN4bapY7S66O7sH7Vq1BlA3z6wR8iPKUdUzakN6Ob0ko5ITpzvbFzVdI/OOfnhJxBf8TnNH+d5hbmDKDajzYwZPYxpVxnGAfBV/+xfyDM/8AI7Zdzfy/Gu/jLR38Jn7v/+91SitfFc0b3awPfN46uDXzX9KIniuCL6PAaaoVGzsB3//0b3HFHhX/2f6yCWOwAb+BrPbPrcB5mHwiSid4OssdTynVXnMQNfHST5NrjqA+CLxuGmy31lIludPR3k1iea0PcH7yDfCdKHZylsxaTazDQmfQ0zjY1O78CBJSnjAyfCJVdsRx8vlDhhljiescrR4IDfmJctQb+8R8HTj6HFTXw6QqZ6hrmMDHOnUJ0hgohlpjDywuF4AO8OdO0ZbyxuMNiHSLY6yLjTchfkO/5IYv75uDrEqKTfa4TdM8UDg9d3G//QoqVxnLw0f3Ep4rxmO1DfohOh/ddAcFHKxIPNKZMjARFnmoQfHWIznqCbErGmOc1/My9rwMAfOnu19d7ONyDYAzBp5SrNDGKdnYJSsgV+psc3BOpMJz3uJ0cfL1CdHbNwccd/EzdCVmCu27+5EIgdnH4EfdNf+8SeHMHHWfawK/cqAwSgi9w3GLmrN9QWxUjoMAzghIDX0BaY1C2Rq0KbfhFNkKC8K6ZLkWFRHR9K8XnyvIbpuTN36uG6KyVfQobguJVq5VbIFF/StYNSJxvWg6Zl2VOSFux8s4yekiO/DGIK/w4lKdkXAAk40uoKOeetb87CNW+vMsh+FRVBvuaUu7aDJzDqvo/Nj+OIPvR9wnGinrpi9HOSJ/Nnn+FEJ3SXqag+bxN9Nnm1Toj+CRfgwgCodKZu6048pfubODj8hyzyJTeOfi4MoyS3Pyr3bGt68lRDkbdlqfWyDkEn4dkNHntuNydPlH5iNQg8huuPt8IJIXoNE6bdCvgEHztA3wfoga+3B1L/6zJVq11uxcrxaOtqIFP4DdaGZGGGA4i5xGnn2QdZLk8/vW+1BpvWs+AjkYQK5JqkWdEiTP4SHu7R86cyDKXl1Z8DjifXCN7ZM0TAx8Ne2hkRrG7StnvJ0ZA8mXUnLYbnsFiw3MlA5/mDaKOHEb0TeY7u+yNM/CFbVE+eddd8fkS+34xPsTmm6MHHs/xhvJYyQEkOZm88YsZ+Gh9SgGLd78bH37T/41/uvv1jpxvHNAd54bGiUZC8NHzlkHwKeXNN5Dx84zY4JzmGGLttIkHq6qdJakcfEB7dgzOlFIOvuadDg5c5wb7d+Bh5jqRhO0obIbTMDWGYKTmjj3XBr5vHH3LhOi8efMm/uAP/gCPP/44zs/PcXBwgEcffRQ/+qM/iu3t7SvV+elPfxq/8Ru/Aa01fuRHfgTvfe97X+BeX1MXogg+jrKMCByUYTEGvtlM4777Nli+vAqSgdbPuzn4fK9DE6ITqHMlAAJj7KGsAdrDlD2kSygsvx2irBJ1EELogYcfXuOppwbY26vw9NMtw+5l4GMQfC1cPHaYDRUWvmOb3QolbXUf4UMo5z8mnUdVo1AVY8hz1EVJkmi/c/gW5V3o0B/OmBg80jVEZ/CcW9f5ucKtWxnKl7aKaRreL1Mugo++U+TMwVPPHHzam9uUGAfZ7v2IkFX2eApjDXrYZg4cfQ6QUYYT3qPzgcpvbKgNn4zXe88QnYZ/dRnLLgg+Tlmfmj9Zhk5zxidqrI7l4OtjEKbD9dAX/yt2L5/FTvFVe813KvHXvvUijjWZQvBJfLWR3vO8ReiuN8za8Qb4s698J/727u/DZjgVirQKAOcqg/Ywh2NWJxLZI1o0B5lDPRYwF2LK/1ukPiE66WDG5pSN48tMbs9Q4s+/eA6+sG9+iM6r8L3UwTBFBunDV9pQRhAgmkcgKYUg6oGI4PNDdMJThFADPcinMxYKhjJd4o5nPw00USovZ0eo8ARbNkZ1SCnBsSPBc43c6jxiDefMOHjz1wE79JgMtSK1RfABANZ8yDSJYjn4fDL5qaW++EzSDhu3Lno59bgKYl8x6XdEJxB8lVadFp2kLAWQNrT436CLs5p3ILB1OOvGVyTWyjYadrnyDFotgq+thzvfid3yeX7Wfc64e2mHEJ3+t4uF6OScs7x9UGdZnScvNd90GMLPv89Rqb2cuX5fqEI/YuDj+UQO+K8oPaPD71kJCD6W/HM5ReGpPLmmbDWeUabKclCUnuljHWFEu8Y7bq9UNZCxZB0RwjOvVI95r7ZeIYcjo1HNUEKp3G0toYQQQ3SqGvEthmP0DDZOP5ohqFQGrRmFM+GPHHqq/tsfR4WyUqLi0lVmkzYTjoSOwpwz8HnjJ7GhLnk2Ax17lgWrPYYEpO+YewY+Tl6/LfJCdHJycGDM9oZNg4h9Sp4vTrPUwGcRfO39GEBRCt1Zt6nBHY6c70ZkaIfNUF0ImBCd/l4nOaOQ/ehd75rjL/5igi9/Oe92znYaiL0n1y6d4/5Nx6rX/nVbITqZIuSef55XgxxYwxl7e4/waDXI5PNbljUIvmbONAY+BvTX1pe350//m9Xnnvq9fJmES/Fk6xRZevtSnXLwMfuY9kAdilmUe0x0OiDOV3hq+urvZ56cY+butYHvxUXfEgi+Z599Fr/yK7+Cxx57DC9/+cvx7ne/G3feeSf+9E//FL/6q7+K8/Pz3nXO53P89m//NsbjcbrwNX1NKY3go0Y9oqSOhujkyTkoeJ7VCl68/qw18AHtoeAqDM1spL0RfEqFeW78IpzXuVLY3dV47WvXeOABlyv7yBKemg2UNfCFzfnkbwQmz4hgixR25u5j1JXEEExDV6FqhA+narHz3anLATfmvQ2EArclPx9KhmSfr5yDz5tDRVH/Xo227HVq4FPQyCMCU7Qpn/rm4GN+2+tgDi9X/LY01JDhM/lFuzeZMLWGn3EhnTgvXkPhJ4j0kynrrlv+wC1XV5fvq7/vheAzz6Qe8QqkylMFf59PW+UERXWVEJ1eY/SRw5On8OA//Tcc3XoS41U7R2plSaQaovgT36WPhzJdS2XrtWr4Qln6PDusM8u0Y9zj+sb21ebgq/999tk8GqKTQxWY/rcGvghCwa9PitqU2m+92wFqLWrBIGs+9n2EkNH0mlL8fmLlpUQOvlZhT9BpAonf078hhugUvJ1jU5RpNBWikzN6+gpde50x8IGTpeA5iWjtICso3fn85zBanEOpOpfUV268skWlRl4teA9diQokyQhSK9t4BJ+dM8ya8EPkdZFRxGmp3BCdarkUFcMctQa7cL6w+58wkGYs4m3Qa54HM9tgc7kZ/+lENvA5ToupEJ1s2NzInOmSizQxwba3ShwdldjdjSiaA6RY/dsZVx8pgCbUoYPKDmWsUM4K158r8zofpq7X+LH0DNFpnZUkBB8JWxbw9kxAoUHb+qIyqXcGilJHlArtYun5HvjrnfIi/x5tjV0jHDI4wSze8qa5/fuhV1bC+Su85irbPQNflgeoM4lMVJ722UGA4DPoHj8HX8wJN+Dpkc6I9Xi/2RCvOtxnMs/BACDfS+iHJLeX2RBQSlRmU4cp+u1oygWtMt7fpaJGEuEcyvQ3bcAxnWv/jivjtTt3Oxj4WNI9QnTSapk14oeMlJrOSIhOlSn3Q8YiQGh+nIJZR/q2d/E09v/qz3Fw+gXHmJNE1SslyF2kC167DiuxzqPuJen1qsp/J68/3HU/cpdX3pcXMi7HodemEyqXyooESXfjhsZP/MQc73rXgu8YQnnOjnsvpyOXP9v3rSrkTz6J7CtfactJ0yHFUD0DH/d9uDyH5hlORDE6IcrfdRbPwaedHHyldc6obzPzwjNiO0Ui0VfYrS0xRmXZPsjn4PNkIUE29EN0mogTxhA3GPLrzOdPLo+ReWSwxnPX6cN81usQnS8u+pYw8P3u7/4uTk9P8b73vQ/vf//78VM/9VP49V//dbz73e/G008/jd///d/vXecHP/hBFEWB97znPV+DHl/TVahLDj5auhqGxtnUHuVu9HAPXI2noDmAmg2z3XATdcfaDhB83Sn0cvPa7CmoOuFuzKOKN77lw/Cw08XA53+uiobI8J+VFDW0n3wz0Wc4Es+jwkbrbJ4dof2x7pixo4iTlJd8oFAW1ok/6HWkg4RRUvGCQkphRAWCpx/5QWyGE/zjfW/CcrRjr5faFawyybja4aDTr4A3FD3QW74XVx/Sihr4Krz8Sx/B4Z/+p7ZAM8/tVGMkoy5J3J36pFu5J0hCOcPApF6TuuT2LzKUbHciSlifOo97QrcS/uYV/GxlhJwQnS+Atywd26OTf2DLUPSNUsD60Tc6DNcYNqJDmjLw0YfpB21CdDqGI6XCg4pwIKXkOpPGQnTC9bBv1hCb+7WLYjuiwAweJW30CdEZ0BURfFG0imQU8NZTbYwVmk8Y+OhNLe0DCQoOrIICPFoHNYbQ9hlNtovg43Je6SDfr6hko/JF0tuWz8Hn0+7FM7bNf7rrdU0+qVD2SdGVEXxKoWRDdDZjy62JgY8avtqJ3ShlNhbBp50QnV0Q2pvBxJbpJAcIhTjUpvWgz5icvB0n7StfuYGCxmSi8ZrXrIJHWdY7TBj4uuHpYOUk1vjSD8E3yDUefvUaDz+86awUZdeltw78EJ01gs/tGx+i0/wrjQQ9C9R//83fjPDcc27Yui4GPosYkdA4tJKOCD5ozYdY9OaGzjKUJW9AC56VDPymMqaNys9dFRGOYvsj172Y40BYuO7Ew9+5xAMPbPDggxvcfQ9/fvqH+77b/v2Fe/9Z/Th1vtV+iM4MlRTCmumHL18EOfhgEHywBosYcTn4uDC7ph4RuOMYqATnPsaRhEtNkiIJwefkS22IM8yErJZGZGFkRMB2OioONYpsatToHqLTQ/AJDSnV8hozn6IVC5Spbgi+gNgwfN04fl62Bj5kmXsWEEJ07p1/GW/9+G/j0b/9f2qEE0VPBuf4lq/fOP0Ctj7zv/Do3/2/yPTGfhc/p2+gUlCKFc0dVZTXbjZwdQPm9Wgb0lZwpRCdDv/gZV0uZLSv/3GIGLwoAorbi8XIUbFpFzvfM7eoo5ZxWBt+8pOY/dEfYfT447Tbbb/oOSu1BgIDHyOHUKcDH8GnwutGJ0TDtKq8C4Kv+VltoLSOq3iydo4HZ4YBBXHIek6fpKGiOfhSITqVAo/gUx6irqRh0BN7U4KvcLpEINzbJXmSP/b0PPBd0wtG3/QGvueeew6PP/447rjjDrzjHe9w7v34j/84xuMxPvKRj2CxkD0kfProRz+Kxx57DO973/tw48aNF7rL19STYh5bgLc5ko3gnx7+/siOLhz8HeHYRZX5CL5StYJZl/N/l81aCqslkQ/X5qplEXyxOnuE6OSa7hKiM8zBFw6iozDgKupqGJBOJwyJtxslTHcjQ7dyoY6yg2DPaxDav4T54IeQ8A3j7KHdCWVGJb+EgY+M080HX4/P/8v/K0jUS/N3US8rqS9dKaXMCsib25T8wyT1LO/bRxpeUVUlXv6lj7B1xBz6Ywi+gGIdZO5RgdVxBO0RojNweLsCki1VLqKTqsn7/r5C2i+f5+Ezhizv5w5MebcQndL7BbmCOgzVeu8Qm0HtuHJr96VYfs/3uPIzOfmKW99qhckf/zFmf/AH6QYFS685gGsolKV7KNGeci0cWk+Rp2QDH+AqatrwWEx56cAH12HCFk8i+Nr7klK007bUx8DXMQef8ewMpoxSzoDXPNVtzzqRcIYAbu8I1hPbI7ZMsF8Kp+KEHjheiNy3nrpRBJ+rbBVDdJJrXIhOp58UWUsMfDEb+j/dVeemvIrDiKpKufLmupRnTauIIp4bh1gOvojSlL2mPAQfG6JTZoRP3fsm8nzCkSAimCvFj7uE0Je8xX06Oqrw1rcu8XM/d4HxqNvepxPJQDW4tRq2bYvEtKlyN7xGIxpUWsb8SZW4lK+WQg4+q1BVgYIYjbE8QFHFukLHg7zMk08OnEOKiERirilBWa+I0tP3sJccGOoQnXEEX92PDF1CtSnI3ycmH/nhUP29LJqvMHGOCnLNCuUoDbIK995b4e67q1Zx69Fzh6/CEy/7XnzhnkfxZGPsa7+3ahB8bp7iqgeCj4ZOrbJBmGevWQv+2VyKpFLne0uvQfoOLHlnM3Y+siE6IyHYhUGRzhxllgh1GpGFzFpz9kZazsqpbjhtN0RnOJnLKIKPtE/HLyHrOXnkYg5wTQOSz3RfBB/Ar5t6PPj9TZzXSnXKwffI3/8nTJZnODj7Iu5/5qN+Je5PjwmYvo/WBZEn+DLNr6Zr4UEqtj45A5+PGBLP7N78SPkwA77jLL+utTO3GF7u9UehImMkHK5NfVfQxMfkRvY9GXTn5MMfjjfSR3fmFeFRuw1PcOSsxqEmc+dIlmlkykRxoQi+9uwZdE0px8CX642DJvbPnYDLG1QTGcN+N8/AR4mVJRJjRN+b2+cDnYDQPkXwqbLEffc15w2l8I53zGW5N+BP3XRuqRx8Zo1dh+h8cdE3vYHvb//2bwEAr33ta5F5XHI6neJVr3oVlsslPv/5z3eq7/T0FB/4wAfw6KOP4q1vfesL3t9r6k+tck7SP5CwZIRhrbb28f898m/x2Qfe7tbX1TDkHfaVrmyYFa09hT1RGPjCR5fmkt7pEqm4gc92yJY37UWKcwa+sFkArqLXkBTqj1Jg4Gs+ruyg203BcrskVjlqcvB5RgZXwOv50ZliQQ6+WiuWrIPWI84Hb3CzzJ2onMDoe1e7/XIqCztF69FhPTS/Xaa6+6knh7bD2PuKY1FXyikBr+iRRA/LrKGu+ZDUQO4Tl8eFPt6ZGGUDFVh720h9Id1WHBcg/dyjMXqhcvD5JCr4ERdOoyE6E89KlHpmnY/xP17zf+J/veKH8cnXvLdmtvSAUqsIxeeVAoaf/SyGTzyB7OxMLmT6Q/e/TTv3qJIzePXxGMs3vxnVbIbF939/Ut8cQ/AB7qHE8DauTjaUXQTBxwEOKIl5KJOT1R3/XiE66QSIGY1jXgCk/kBZiVQOvhDtUq/R29tr6cFQ3srjk78Wx+Q9VisSZlhrFslgxiP4nAn5whr4BGcpTRF8VRXPyakB5DkW473mWTl6gTRVKIIvINO2YKjy83ia62xflYobeHtsOkZGdmSKKgxjFatyM5jgI6//t/jiD/4Ubt35Cr+3YYOSokP45lojuS7cSkKaTjVGI76Y4SMOnx/EGZEkg4nd6SIrp5R1HdidL6+x6QWCvET1GjZOG1oDJRui0/123PlOknn9uUzHLzVdlYL9OJm0GUuhDrjfpl5ihJLGVmuKhkmvK1Fe9g6glGeWlXIU4P60dsfRb5D0iVsjnRD0HtH5kQk5+JTCP7zku/HZb/tBG+rbN8D5ITqr4civhSetnVCsNYJPBxMlQxUi+CTekumQX0Qmnuho4MnOOXdmYEN0Mga+RJ9FBF+TZ1qcs0Tx65TRxFlZCtHprC9hX1Wh0bergc9BQObyXmDaCeoSKxba9gzNYlvOq2p2jfTPlYU6KosTj5d3UBitL+3f+2dfih86fBmLKRrkPPU/pZJCdMq6B3q+MPO5K4LvKiE6HQMS3Xecc0oWlBf1P4CXX7Td4zn5S9KT0f2Iv8kTx0cdR5rIJ3e+X58cfF6R1JpPIfgGg/Y9KMLSniE4+S3LvBCdm3REmyyDlIMvQPDFt0BLUntl2d7sEqKTd4hUQYjOrS2N7/quNR55/QavfvXGlZ0cvuznRJbfgT4cOGEJL8++07WB7xtG3/QGvqeffhoAcM8997D37777bgDAM88806m+D3zgA9Ba4+d+7udemA5e021TyzR4rskh+IBaqFtM9nG6fa+91seAphtUWavca0N0Ai3k/QWxM2VGUNZt2wz5bZk+yqR5pVRPYVcyglBPK3uNXJKaCbysVO6NdYdv5SnYOlFC+S9u2rkfpiretatS2D6vpPP74ivMWWIQfI6ujvFyFD16UwY9z0jMbfybys170GXepO75BaSyzlyO5JhihZMOikiOqJA0XTHGleZDxkI/ZFWJhx7a4L77SmxvJxTifQbRa5ca6uiBWhTWBARfUrqLKGHZsuAOQ4HrZJdqLMUMfDG63RCdV0HwbaoMxfQQz97xaov8cHLPdUDw9SJaCXnHfMi5RLYH+tVb3oLLn/95rB95hPV07GIkMAdkXwEH8LwK7DUPwScgPTgSDYB9B7YHgs9BiAlGIp3n8gak3CMqN7d9T1n6Z4imqfeAVIhO0X7gK4iu4qps+yE10v62CgEBwdcqBogCKeN5pZOD0XijSA4xjjECaX5AbVxXMJ5mumyNHv7jpo/CRsJ9Y0t+XXSumbY7fEJxjhB+rxRYlEGq/tVwC4s77kvy+TquHV8ZZ/imNwOURZ81H0OA+EWh2nyasTL+XhGT369gaBHnUOwZD/XQrnmqZA5zvijlGs/8FANVhQDBZ/ozkJyfJAsEOvIcZ/0260rKwUf3GYa3s3uTptFfBCOl7m7MjWm4Y2yyrNxznM/XHWSzHwY7MsbNA8w1Qag3MgqdBz3CN1NnWgU/RGfuOl9FyI/KU2U5a6hWVRmczeUzercQnfZ3h0OKUvx8pA7HhnLFJb+KtyUi+JgQnU4XiZLemR5wEY9sFCZBTuX2Var855xFveL134Q/xVDhSnVA8HU58Ov+OfgA2H06QDr7BqO++52A4HPq9PRDKcfdcEhCYzY3bDZqRAd0HNCmfgEAMRS5sP5oiE5/rEVVkoDgcz67ivcpNJ4QBB8Nj8nwSenMH5O1Y3sF95zTh5jzIK2HotVThXU6Bx+3Z0lLK891G6KTOuvlJgefQAPqSF3WwAz+uFpfI+lKTL5V290gfK57bhBfRiDquMQaw/w5LciGzpprzhrb2xpHd+hgrjrdS/Ankc8nUi20CL6wgmsD3zeOEvj7Fz8VRQEAmM1m7H1z/fLykr1P6S//8i/xsY99DL/4i7+I/f39K/fpl3/5l9nrv/VbvwUAODo6unLd/zvRoGHUOzs7mM0yjCYTzGYT+J96NgNm0ykwm2E0GmJYDjGdDrC7v4PZ8zNM1jMMG0Y5mQww28ow3d9HxsyZyQQYNsLFbGsbBzduYDQaYTgEJo1b7mQywWQCjLNtzGYz7OxoHB3NMJ2dYTgcYjgExuOWN0+nA8xmwHissF4HTTZtzXB0dITlEpjNckwmE9tn910HGAzaPo7GY0xmMzvPh0N3A5oc7GM6OW7ff1a3g8UCOXl/+txwPLX1HRyMcXQE4PgYQ+IVubU1wHAIjO48xGw2w2Qytm3MZhqzWV12sXDrbtvTzvXJ7h4Oj/YxHA7txrG1Vb/rZH8f+sYNp78AMD48BI6OsLWVOeM1m3lhc3Z3sW3WXFE49dDvbZ7lWMngrjsxm80wGo0xXA0xGNRlzfOz2QzTyRjj8Qiz2RDV3h7UYgE1n4eVEZpO3fa3t+vwAqbezWgczIXZbIZs0nZyb2+C9RqYzRojcVViWoTrRE+nmG3ai7u7Grs7O3Yd7OzuBnx0PJ2StQP7XcdHRwDhY9nODtTpaTsvR2Nb187OBPXydIWEfNC+187OFvbVtv1txhcA5nN3jMx6kkjv7UE1Bep1GM6/uv7mvfb2cDYes+W2d8jcAbC1lWE4ruf6ZNL2QysFpXVQhzG+DIdDDCYzDMsLAMAB5hgOh8676J0dzG7cQLYzw2jEG9RnowHuPqz5z6c/DSyXbXvjcbvuAEBvbWEq7DXFwb4zp8aTCQ4P93F0VDd6eKjs95osWn6Q5/x4zqYTbGYz3HXX2BH8su1t+y38tQYAO7s72NvbA2Yztl5K48nE8tuZwLvGY2C0s4vZpTvPj462yG/lzMWDgwkOj4aWLzh8qfnGZRn2fTjbtv3Q0ylmdD3MZhgOF6gqYDKdoWIm7LScOd9gOh3Y+Toej8Hx/529G7bNyUTj6GiC060ZhsM639Nrn/2rev3vTlBVYZ/pnJVofONGu7ZPTuy4aKWgmj1wOqu/xTjgTQMMjo5qxmb6vJNhsSDfaDjEJB9huWzGcTTCdDoO+jXZ3sbWbIbRIAea0GLTrW1AKezu7wTjM53NMJu4lVR7e8hm9XoaDgEMR5jNZjg6GmNnd8mOcdvvLTaMynQzw2xQt7Neh2M8mw3o62N8111wXu7yMtjLbNmDAzv2dO2Dvp+HAAAgAElEQVRQ0tMpJub7LJdOXXo6dWSb6VRhMp0562Vvr1kPm4191qzN0aTl94Y3ZXmOTCl7nco2hvx5ZcZlOK7rG4/rvWNyeIhiGvLa8bhGPnHz1eyRo9EEW1szHB3VediynR1nfCb7+5hOR/X6GY2A0TD4vrPZAMPRGOPpFtnXNA4ODqA9XjnbasuMR5Nm/x9iuGnGZzaDbtofjScYj6e1nLe3BzWfN/MunB+jETCdDew3GaP+PnQujUb1/uTLB4amowG2ppNmbF2+P9ndhT46qseHpCaYTIDpbAtq55BZO1vIxzPcOLzhzBU9mWBvb9fhl4eHY8se1I0brBzN8Z7pdICxmiCrNmTvGwKDteV5Zm3u7ZnvxWu+Dg4mWM9mTjhgf93p6RSTgwN2rU0mwEiP4cs7h4djDA4OMBqNQNIDYjKp98CtLY2jo7qRbHeXX59bW9g6OoJqeM9m047FcFh/V8OPZrMZDu4YiedXoJZBFmOXz46G4TPmu2Q7O4HcqXd2HFmA67sjY00m2JrNoLTGaKRYe/WU8P4qH+Lw8ABHR8BkOrE50KbjAXLV1r01m2JnZxsYD60MlW/vYXbe9uXGjTG0huVFOzvA/S8dQu9M8d1vyTErQ9luSOTNkTdWWT5AlmWYzWa1EuyITBJDeY7cfKtRPbaTYc7uD/s3btg61MGBM/8n+/tYbc0wHLqKtPFohOlogOFwaNe2zjLoTWXfQSlgsrWN2WAWvINPs9kMk/GILXN0eAPZ1hZUc9hcLsmZdnsPe3ujVrabTjGb1RN9NFKYbm1jMGzOfjcOnDk22d+3PHK5vYXh0NWpbO3tYzb39l9y9sq2t6EafY2VMc7PW/lidxe7622cn6etfNs7+825ExirEbYm7VgMxlNMdvcwu5wmZcrZdIrxMMewbPaCrR0cHtb8bjAaW6PaZDzE1tYU6zUwVjUvmJbTkI9OB9jenmExhytfj8f1uWHTXptM6jk7Wc6CeoZD4PDGDRhG+NWvAs8PB0G5STNX6d6xPRvj6OgI6rnn7Ny038E7/xrSmt9n8smWo+MAgIMDcjZYb9n9fWtrZOsYj8eYDurfejTCYLOyCnEz/80+tbenMB62e/V0OrXy8nhj6h80MsAIA7J3+0RlkzEZr929Xezu7rF71Wik7Dl3Oh3U+pSjIyhyNjZz38gclPcY2WdnZwtr5hv5NBy2yv3RCNje24M6P8dopEyaa4yGQ1TZAMOVQVAqZFmtZ5C+1WBnB1urXbvux/v72NnZxsWFd26h83I0woyc8dWgnk9KaRwdjaEa/ZfRW00m9XdArjAejzCZaAzGUwzLS4zH9TgMh+3Z1ZwHbpzsYTbLHD1RLRPWc2c6mTq85uDwoJXPRkPMZjPs71d23k2nGru7OtApAMDu7gS7zXfyx2o0AquDm5Dvlg3avXVvb4KjI43dXYUF2e9MeaovqnZ3nHcYDQeYTms9zGA0xlDXB57pznagAz4/d2UdKoNveef4s7O6rH/2ouTLQgAwnrXrZmdrC3tHRywvGAza73fj8BC5aTvLxHMLUMudu+sd+022tzVmM08GxASzWS1TTGdbGIxmuHGjllt2djJUlbLvtbVV6xVmsxlmjSyfZcDO3h6mdxxhTM4qtSw1wOTwEKgqVFu1PmE6zDGdjDGZ1Ho5qoMwfGY23gbO6314Nplgupk0sugI+uAAaj5v5fJGBwG4crCl5kzFnQsBYDQa4PCOO4DZzOp6nfEhOoGtrQGqRr4Yj5Xla5PpDJt8jOmA0fHt7NR6iIsL+60or5pOJliSh6bF2BnDPHd9E41cNdbbjuwyvvNO7O6254L9/XqdTCYZ1mv3vW/cGGN3t9XnX9s/vn70TY/gS5G2XkJxQe8rX/kKfu/3fg9vetOb8Ja3vOXr0bVr6kitopv/hk5ISMfrrfG0Y8JOiR4O1CsBCloRRXvjhdki+Fr4dsxrogsFoeo61hXLwWf66fr0pz3RpBCdnMFB5RmyTIshOiXnWd+jr2RCFzljEXFlSnqI9PguYrJjIQdftOoOriu+F0/gzci8e7IPUqe8xvIcwoDT9oXwRj1y8AE8oIHmdqMenynq5MmYIB/B1zVEJ/Usj44HQzQ0x2RxK6yj+dZKyR61sRx8neeBcE9at3TaxBB8WabDahMhOg0C+TZYZ0gqvj5CT0GI3+92QnT2pS6ebqUO1yNFDNzzzMdx93OPiyyzN2XUu7HtoEHwdeGH6ZRQcojOzM+/Zz265f2Aa9w6ZVNEso9Q6NBvAOncff7tIDFl5PkuOfhi4Tm9+jmkUpsrK5xLUg6+FEJefCXGI1wx494BxJBeyB4KR8rB5++pWYbkJK1KBnFCcwV5eYYoP+BDMpG6IyE6JaKexkFZIUSnkVHn0wOmQkEuZEKqdsnBx/ZZMUiWSMimVF3+OmSfi8j5Ymi82wzRGSsW8HilHA9yjlhPfabt6NmhL3K2C4JPyBEVGxebg4+szdJD5FRV7RhIQ3X9yL+s8O//fYW9XQH95kApPJmN4XMxMmtczMHXE8GX52jSO2xsH6o3vAHVv/t3QX+i6FraDHgEn62PRrJxAJXK2Vb8fdQJoRfb57iB5GAoCQSfH6KzKzsxqGetazSog+DLclSDPiE6/Rx8YRQdigJJIe/YHHzc2SqBBPQfZ2X/qgrAnHZt9dhQpNtdEXxBGRKRJZ2Dr66Jq8jP/aSViuYEp0g9ymNSsp7zvVhFh/vNWXCfIHMEbXVA8NXj0Q2ha4lB8PXvDFMnUzzTpT1jmLHrH6KT1Ou9FJ+Dz+2H1PXPfU6B+DU57Ulj54ToFBB8FGlu+iRFDzNlzPOVg+AL5UHxrBGbtrF9ljtrk4gzuuyG4HPWTYo5d0HwUdZkz3JNW94cGQw08oYXOyFRzT4jnQFJDj4TopPbmqy8mbXnTx/B54fopMRHz4zLsFXV3uyE4BPOemIanhhUEQhRpkI0gbYdxbfnnQvMkG1k9dQ1fQPomx7BZ6zpBsnn07zxZIx5SALA7/zO72A0GuFnf/Znb7tPBqkn0fHx8W238b8DGUv/yckFimKIYl1huVygKFwmtVxmmBcFChRYrVfI12sslxtcFhcoiikGiwXWjdvOfL7GsFBYnp5iwsyZ5TLHel1z7vPLAicnJ1guV1ivFVbLBfJqhcVigSzTuKzWKIoCl5cb3Ly5QFFssF6voZTGYqGxXtdMcbHYoCgqrNfDwLvB0Hwxx/HxMTYboCh2MF8sbZ+dcvMV8hxYr2vBe7leoVgsUeT1u5jrQM2wL05PsJjP2/dfLHB8fIzs1i1skfenzy1XK7uenn++wGxWIj85wWq1tnqr+XyF9Rq4vHULi8UOlsu2v5tNiaIom3dXWK9DL6PForLjAwDF2SVuPl9itVrbja8o6nddnJ6iHA6d/gLAxfPPQwO4vNzGgrRfFCun3PrsDAuz5uZz7JB6FosM6/WA/HafNXR+foai2MVqvcJ6vYbWdTtmvhRFgcVijtVqWc+D83Nkl5fIBb5kaD532zd9N/1ardaYzxfOXJjPCyyqdkxPTxdYrxWKYlxf0BqLRbhOKrh8sihKnJ2eYtRcO7+4CPjofLmyba9WFYqi3sEvT05QEQlnenmJQVHYebRar21dp6crDIdAUbiH7sv5CrOm7ovzM1zOd2xbZnzrsXDnkFlPEm0uLjBo2l4uB848a0mjKBoP57MzLBZLu+4pnZ6dY0n49fn5DLNmTObzNYbDRhmQ51Bl6awjoPXEW6/XWKxLTJr3y0+fs3WYQ2l5cYHlrVuYFQWqiucVq3mB+bxAVQGrlftuy2W77gCgyjJcCnvN5vzcmVOL5QpnZ7dwfFx/37OzAYqidgEs5kv7LZdLBO8IAIt5gcW0wPHxhXN9enZGvkUejPFlcYHTZg5y9TptLGu+dHGxcebpZtOO1WpVYVlcOvcvLjY4Pm4RDefnExRFO5/OzhY4vnlp+QLtx3xez7X5PORjxbq07fhjPbu8xHpd87L5fI5ChXwg99Z1UaywWNRjRPkppZsn5yiKHQCAUhWOjy9RLuZYr11l5GKxwXKpgvFOrR0AKE5OUDbvkp+eYsbwsNWm/hZ+P+fzFRY3b9YQGfteMxRF24/VaoW1Xtq5u1yvsVwunbkLAKuTExQX7TytsoEd78vLc4y88dGLBYrS7ev68hLDorB8YKXquXN8fI5inrNjbGixuHD6bd9Ht99zswnXw3y+cg5v5ycnzmnQ33spXR4fo2oOdXTtUKrGYzvX/Lq01lidnWFcmP4NMF8sPL5fr4fs5MQ+a9bmYrW2Y2LmSjms9w66D/g81Z9X63U9LmZ+mL1jfusWlut14EW9WlWNTOAeSQyPNXXV367mMVPC5wFgdX6O1Wpp148m72JouVzXeyqRCVerErdOTrDxeOV8McfQjMV8jqIosFqtMDByxnyOOer2F6tVvc4LjfnNm8hv3cKomXfLMsdgsyZ90FiuqnYuFwsUKBweY8bLl0/suy4KLIoLrNdrLJftvgwAy5MTrI6PsbVYICPjs1zWcspaT4JxKeZzrDYZbp3ewhF5phoMcHZ22soWAE5OLjGb1d96cOsWpswc1TqURReLNRbLJbKqbPq9QXEBqKpyvu/NmxdYreo90fA6n05OFsgXc4tWAMJ1VymFxckJy7/W67yWnb17x8fnmJ6dWZnf1r2o+5ZlNc8FgOn5Obs+y/NzFMfHGN66hUlROPLLclnLHYvFAOtN/T1OTgqc7+8jb9JN+HR+cQmsVs43W63Cvt+6dYnhsMKsKAK5s5xOUZD57a+dekzovlfg8vISWbMvc/4ri3lh+1RWCicnz2M6rbBcrSxMoijW2GyUncPF5SUuhxfIm2eLYo3Ly8J5lz/5kzU+85khXtO882pV4dbzz2Pz/DG2mj4BsHtlPR6tvLlYumO1KYGqqtfbcNh+P0rq9BTb5lutZiiKApvlIlgnAHBydmp50OjiwvJaoJEll0us11RxprFeLbFq3jnLNE4GA6yXS2x7cs/F5QLFMtxXfSqKAiumf0oBN4/rcVJleAY7PS9w65ZGUdT782LVynbr9RCX8wXKpu9nZyfOHFmenWFl5IJiHrR9XhTBnFxdXFj5eXZxYefl5fPPoxoOkd+8adfn5vISxfocRZFWS52cnDXfK8dyucDm8rQ9/200JusNFos5e/akNC8uUS7nWDf8+WK+xOnpAkUxw2qzQV42e8RijuVyjuVSYdHwqWwejv9iscFiWWC+HAby9Wq1dmQWI8+OF2E9ea5x8/gYurl+cpJjswzHfLWox9zst/W1SxwfHzu8eX1+jgVz7qfEyd/zTdXoOEocH5vz3NDOn2HT99Wqwny+afuwXGCTN3txvkG2oryr5oGr01Msj49xfj7BiJ7f5wsrX+VN/ctlifU6x3q1wsX5JabC2qCyyXrV1nlxcY6z6tyedd33HqJYrht+tEJ5McTl8THG5Gy8ODnB+vjY8nx6ljGyTzG4ABZFdN0CDT8g55XzomjOzu05ZrNcYJOPbF3D4RBaa7u+NptRYOCaX17gYj5v19hXv4rLy51AhnX3kQXmxaW9tta1jK2UxvHxBQanp7W83vSr1nENUFbAerjEYlFhud5g1HyjoqjPwKaJ+XyF5c2bOD0tURRThzcPBs08WOVWvjJ0fnnWymeLeTMHV1aHMBxWODlZO3KJoY9/HBg+tcQ77i+gVDuv67/bsXfGhHw3MwYAcHa2xPHxCmdnE5RkD14v6rW4WLTn91pOat5Ba6xXKzs3lus9KzdeLpaBDvjkJENRtFFmqL5SX46cs+WtWzmKYhbsc5R8WQgAiuXK8p+L83Nsjo8dXZih1arlA8/fugU9bsav2SMlWp6c4OLizH6j09NNwMvnRYEim2O5HOCymGMzBE5OChwflyiKLRRFZt+rLEssFnMUwwLLoj1Pn11eYn7zGOcbdy4XxQrz01MgyzCe1/vqelE0+s9aL7datevWnMsLzO1eMS8uMVwsrB5vM59jUBRWx7BctnLX6Wndb0rZ889jqyjYc2Hdxw2Om/PxzZv1d3Seb/S+StXvsyoKjIrC0fXMFwusBhkWq/q8QWlzcYH58TFyIvfSZ5dzVzdCdYtK6Ua33K6PdSNnVgtzjqvX7MXJCc7PW53erVsLHB+vcXKyHYTpPD6u5Xmjz7+2f8Tp3nvvTRfqSN/0Bj4zGFKOvWeffRaAnKPP0FNPPYWiKEQD34c+9CF86EMfwhvf+Ea8//3vv40eX9NVaZOHmzngOQlSz8U89JBLekZTr4TG24Ii4TLiMWfzARnPoNuBTGSup1qfukRvDlMNRdEk0Ad+fRKCj3rU5bk7xg6iMubJQn836AzR6bArYoOjnl7m7PWROSi6Hjq388mBEMloyI4D43YmxbHnf5DLHIKP0BaT043OhdEoEqs/8hzAeytVuu0A9ZAF4uOadCRT6fXuhN5TfDjMoBx4zzMA9fr1tGCBdzbx4psuTsLGyLeWYvLHEHxsfeI996eG8tZtOyDUY1YaJwXN9znhIdqHb3ZGeHDIlQh1ycHH3S4H7X6kPNc1pbW7hjuQM1TCuDl5K031HXi6LduXX0k5rAZmb71KFT7SU0bwUY/DFPqOzyGqxN+RbbNuQ7rfd64k0M4O0e8ey8En1MWi04Lu6LasR1w+uHpc4zzVv2blA78+pRoEX8/8Ml0KEQSIgsZsfpOtQisXRSauf0cWNLlApBnvRnqg365SAwBLr6+k7o6oHedxku8nGHv7B/+h5uO9ZiA49Jz3DMPUr4rgs+3Q/fOKCTqUCtc72xVJFlJAsAma7qQhx9G6LTKFv8tWM/8X/wKDJ57A8PHHkd90522lFZSUv4vrTpccfKnv1sHB3+XNNAefW87NLVWjGmjusBLuHPvMZ4ZNnQyqRogIQHmZD/DphOBjbog5+AjiUnvrQyvV5IIlaPcmUkZGveeNZ36DHrAe8FXrge8tUdtNPjKLR8I4VVrZqEb1PYFx+3+n7gF8rkPpGQHB15XoPqWgkZctf93kY1SDPjn4XLR1nq/tXdu1qgqRfZE1xKKDxcNlnI8ATb50BlGaVRU+97kB7rmnvcci+FJtCVRmJkSk0EWCsHM+tXOeS/BJ/7azlmv0H5Unyiqh7yB9sH9HZORaJrCJFyNQNK6zLmUC6pdSEPZdQPD5OfhSpDIVrLFgCXsInky7OfiCs62Qaytzcl7KOfhMZSnWEESZcBB84TyJIfgAYLUETk8V9va6jaEzLoz+wJ0jFFFGEaPkb5sfWYdRmFIyFe1XROyIIX/Z+mi70tnCmwoqI7MjqXhxxzqZg8+ba36u9sEANgdfRudoMyfXgwkpbTZF5eSHy3QZ6JYCouUbZLx9VQ/BJ+k5pXfyqaraucCOj/9gBMHXVd6V5ijgyhCMqrFFmftIai7Khb7Owfdio296A9+rX/1qAMDjjz+OqqqQkYk3n8/x93//9xiNRnjFK14Rreetb30rVqsQvfPMM8/gM5/5DB544AE8+OCDeOCBB17Q/l9TmowRZDMYg0uWLDFTLll28iFC2j9CCSE6O1YXL9Pc5EIZJGpNKxxpEuWrhuhUgvEty5B7ie/p7+hGR6jUEQMft/N4lXceL68ev1pJEDAH+badVoCzdfWJsWVqETa/GzcqPPlkt/fihodt3pMossztwNY2sLVV4fKyHYT7XloBT9VyxkteUnoP+5W15IQv03wEQ0fZjjahsqlBouTQdlnbtPqI9Mce1LlvwtThX6L8Yro8BeB1lXzIeg2F7ZjQKBzFDsp9++tGeukwl7UQYlXHv2mvEJ0J4dkvJ/xk1ry+WohOqkS6SmwKryOpQyvghjOz0TgE5UVPXQ77oKSUMm2yITq9Z/yDG9c3dl/XGjmI8o0YQlhjXkSLwOXSSyprYga+PoeWTsy5oS4hOhM5Xmj9nPGqa4jOqMGgI3HOR2qQAwjXCzcsdE1ozxAcNd7qCrPF80F99au4GyY3Z/0OGQMfGGWY6Vs9x7Sz4WntOna0j7bPxkKOSaSqUmYWQohOoA6lXmUDLEY7mCzPnP5zz+grhugUh1MpZy1yMlMXhYD4zfxCwtytl7CgjOgaolMiX4miwltaU6MqoHd2sH7kEQy+8AXAM/DpwLzHkyMvizc7ktasYtUvQ4njK8xDtZgTCdFJStr2FR04lpTwN1iFbRcSlfW0kiuG6LR7ldehEi7/9V93MKgRKFKIzlR3N6W7/pC5Y+Pwf/+zKP45AE74bIcSe4cT8lgwBHCkaVhjrTHctDH5NoM+Bj7XaU5nObJsbfvT9rOK6xMIcSE62TOD4XcJA5gpmjPOfUpXuHkzcxDxGRgDn+lzTz5QNmHoORkOcPtOq1bknOKPhS1XtQ4qkrE6SJGg+HygfB+ogQ/iuyvV7tFmPrU/3Io5edgO7RVCdDq8gNbJrO3kp+sQojMvXR3nYMPEsow0ap0LmvCTSrVyeSxEpzWGMLyY28tVnln+x4XD7HJW4gwwoq6QOqtIYUS9kL3+fefb071TqdqBvaGqh4EPMT7fcy1rGia06rZ3uC+YaK9LiM7mXaj8E1Tf/EFDdBpnG7peNgMml26WOXuyCdEZY380ZYC/fv21SfnUVY5FVdU+2CVEp+9AZMqkQnRG3BC9yuj+m0Hy7NL+GYYx/EvA5WsD3zeOvukNfHfffTde+9rX4vHHH8ef/dmf4Z3vfKe994d/+IdYLpf4gR/4AUxIyKgvf/nLAID77rvPXvvpn/5ptv7HHnsMn/nMZ/D6178e733ve79Gb3FNMTIMYp1P2PtZ1jIueii1SsgO8fDZW40E4yD4tBuvnz6TzlPDK+2B9jAVPaQzlzUUn6ODlqccNuN3uu/8zjWeeGKArS2NxRFh+sTAJzXgb3RXQfDpbBAIVs5Y3I7SooeQIt5OKFVZwb7DzhbT4b761Wt8VW3wBb9M5JDRp7E8d4VHlSncd1+Jz32u/aDf9nKNl12sbAJeSymPcO83/57ke3s5+OLvkxjXvjn4lBI/lS8Hy/qlsMN5rp3yjkOAnS++y5wRbvlmqDIi+c1jfM43kCjlHOLpt+6kcIUWlQDxBz0FU6SKFCvi6uTKswrrvgpQuAg+lI3C/Qr1kJ6l2+xo4JNYZrdupPllKm+UVB13zUF/UOqJ4GPHL8jBR+ZbAvkooWgb64BIt7Uu6Q/Jy5YyB25yZ3SO6EAu4ZQttm4h11gKFS2uMeYGN3c6zdVUITKPJstTG15tPZxhuC6cKrog+Oj8MNNQysHn7CFaO2h5Nq8WqZvmkyLVOf8GfdOV9WoOygjGEFpuPjlwDHzmZrAmSD4Trp6uxI27VE/Kft1WkOhXhAlK31xrXuHaC5FDK+ObcZ73eaFPFTJkHT6CHeMXAsHXYdP3c3vTb2xqDxBo2jXwaQ2UEoKVjk/CwOc4lAVGAWo0iytL677VZTJBWe8gkLn8qoGBT0NVms8jBFfuKXXrSJjn4RYwGLSKNM4oEpz34M0veE6U3vOuga/7wq+ynL//NcrBp7383QNiuKgRfKNOdTnGPZVBq4xFnhkDn1LtGEnnfZvnlVKt6ec7ITII18DHIfjMOjo7I3v+C2ngYxB8dA35yA7bh0o28LUPa/ZvxyWQ44ddc/D1UMb7BmOm4ngFQK2JuUoOPskJJXAm6nDupXIawm4PyqX3e+EY/Uo/P1zQt3D/jiH4TJk+juu1w6W79vyuVJW4FYSdICTp4By0tiDrOryR8nJbgFQRIALJ/sTsd9JZIzbtYmPJPac7HOo59KWl5CLSLIv3CgV1S3OkRvA1357yvsbpbOUY+FRbO3FKy3RVG94VI4sRnmj/bMpLfaQ8OKbvkL5bKgdfuGAlBJ9wZk7w+agDAnsebPrq65iZCbvZ8G2enWUYDCpsbzuZO67p60Df9AY+APiZn/kZ/Nqv/Ro++MEP4lOf+hRe8pKX4POf/zz+7u/+Dvfccw9+8id/0in/S7/0SwBqA+A1vfjJMMLNYMRuaY7wSYW6hgfFvOei7froOF2H8zDeQ0GIzrYY21TUC9jrTy9EWkJR6R/EOdrf13jDG+rYz8+ehfd9jynb3SyreT3pfzcEn/v71vkA/+W/jHAfX5yn21Kmu/TVg4dwx60nWBlGU884r01fMBAVmwLFhNS9PY3toxJ//XRcsOf0WLce+R4cPv3fsH74YYwef5xtzEfwQRkDX6thU5mwKScURj4KlHtPX/GSZ90SPyeHNksrdHwDX6S18DlOMZdlwcqiihsAKFV8u6WHWdHAp2WkGKvg7FjYD9FJ/y47hOiETofoZA0DfliZCHXmiV1Ct9HiEQOfde7gbud5/d2rqlYKlmX74QSFhVM3N7cStCHhib5mCD5KErIxxg+9a2l9c38DH1s+U+FgB5OyfZA3EtI+pj1npTHevOQlyJ9+Gqs3vzn6fEB0Y5QmRMzA513riuAzA8fJKPW125tMThd6GPgcHWDKokMOvVskPOfl9BD7jYGPQ2EoBXaS0nVVlXGlXxBOkMAMAu9XALeL4Mt0BMEXMYaYMSzG+zjAF4LrPi/RjIHvdkJ0Ska3++8v8cVK45FHVphOOxiXFNzwURx5Ck//eXE/YRUd4q2QgnEPHeYcn7uk8rnb3hftW093c1bWD0uRvxS7triKs0w7CrsqgeBzOiTJE64Fwq0ntW8IN8QQnSQEZ+Bhr5Q1FhjKMgCldsOrCwKe68ATKqNtc7obWqjuUjtmlc5QOcpst6xjgI8dV7si+FILxjPwdZ2mDs/U2kEibQYTizxLEf0mxgEvCMUJN3JGKgqOyqR9tN8Zv51lDTiL0QhzKNOMQ2FcURgs83hebO4sZPoVjJdP1BmbizLEPnsbITojY5AKBxx2Xfj2VzHwMQcnH8FXh+SL160y5Tp3VFVw/h1sXAPfcLNwrrlhD5k5y+wLSUcXpdp+SMYz33CYxRF8YV90YPTvgxxSCQSfLy9kVWjgc2iQR7cAACAASURBVMIdUucr5TrI9QnRGe+0PO6sgY/KoQKCL/i+3b2ugyKpyD9+yg1fd5bn2u5PvoEPANYCgs8JyV2VHRB8zNr3y5h+EB4cC9EpUcrAF8gcHH9VEQQfJ8cIPBGQ+W6y3h4Ivv/4H+s8g697XYaf+IluMss1vTD0LWHgu/vuu/Gbv/mb+MM//EN88pOfxCc+8QkcHBzgne98J37sx34M29vb3+guXtNtkOG5m3wCTsZRCqxQpiRP9ejG6GiUamZqZBOL4KtFbxp6yVVsUtGcVJfJEOigbA+ld58cfBKCj16id+ywRtB1YYhOWie/Yfqb25NPjVFmQ2vgczYPQSkUO9T3LffpB38IL/nK43jd92vMHvtL9yY5jEthBaIhs26HqHCc6AOls9e9GRf/6vUA0N3ABy8Mpy3EUMrA5xVnY3M7B2jPc0quOk19H8gyWVfqKWu40BJSm74HduV7RzJ1mG74a8p2tYoLq6k+xe7RdTuZtO2vw8jVYXXgDXzRPBKqY2jO8DHMf/iHMfmTPwljyisEmil//fi/jRFEK9Ur74U24UBMaO/NRrbMdqmPsmkhPBhVALaH5is3yRP9KJEQd4DAixIGPoPiMOSHXrRUuR7RFT24cd3KMsAftqZidh9LOMb0yYvh0+KHfgh6NuPhSAkFuC3WBcGX6GCWhd/IGi6ZEJ2BTINGcSQoZlJd4IxyNL9KL0rxC4pcJAfly+kh9s++1FRRv3sFdx2xq54ThOglTxFp5URi4KOOYE7VDoKv/3go3Rq/AxnLhD7z5g9dm/PJPsKbzPfiDJ+0vR4M3CjJNPlt6L77SvzwG5fYfK+rgHzNa1b41KdGGI81lstQBo1ya0luNO/AGfI0WHnAlwOi5J1FfEVI+y+vvAmq68zgw3Vt71wFwZfYCzMJKUbq9qvJlEHwaXtfDNFJlVtmLl8BwVch5HMxsspZ4azm7BuMgc/PY1obMCoHwUcdZGifaA4+7pM5odtjBn7BEOojX4KjEeXzEQSfP580MtbRVAcNkD6aDhnqgeArq3ZfDxF8I+hhNwRfXrVaScOnWScQEpKw5ZU81fulPz7ymUEMFRwg+ML5yF1jDXxemxzddVeJ555z53PpRSkK+ilFJOoSolNCONN5xiAEy4jhwK2rHb+YM1e9Hpk8eN28HNoi0KJTQKxtLgef0u5C5eTsJBKQ4Q8Bgm+zxHAzt783fpQsb03S5Wr3YINoZeyi1jDCB4ZgfyjlPmD4sIMc9feVLEwDwvE66TPS85Yk67oIPib/MV2v9H7mOshxYRclPVlM7RXbnXlH9XQOPr8d7V+IkXeuY5uozL7ffgxpjgwGQG6Qd8F41o4cbKfJ+BpHuGjXc5K7W/MbpO2jg+CTq5TaowY+zklAyqvtUzIHnyT3+vuFt4iYHgFI52IFZAQfqf6avs70LWHgA4CjoyP8wi/8QqeyfZB7b3vb2/C2t73tir26pheCLIIvH4PbByXhIbN5gtzr7h+RdptNqOWBGjlJDt83RCciMY6jQjmtghGGUwY+FsHXUfnPKUYdarwupeSz4sHA29y0F+IlJvQnK4+R94x5v+VoB0++9J+jesWXAc/Apxklrb+PBgr5jn27994SzzyTo6pqL3a2oS6HC65InocKY90qtjgE39GROw+LJR87gg9Bw9/XOh2OoE+IzuRwdAzRaYfCWeMusegKT+iLdYoexKouEk5TT5cQncKjlqJOAoz2iCKWZrP27+VailVIGxNCdEYQfMaw09mkRsZ989BDuPzZn0X+xS9C/Y1nkI9PzYAchY7vORs6njoV68EAqjHwqc2m+7swldJDq/SdNxVvgOGrDnvTm2VKBr4eITr5/dnlO2y/fAQfUdRyChsFBEZaswbsPuaE+ezfb9PfGNlDqBRrMPZ8FwQfrZfhw/QpzsBn9mdOkcEaMvw8FD3yIPM5+Loj+CA4S7EPCfPocnpo/65fxTXuiAheRiFtjNPBp6F7iPZCdHqOHbURifym4cG8pmMKKRG/xinPPSrGroFPQvBxITpv96AuhrZj1vTb377EQw9tcHhY4T/8B89R0+sYKzMKA8itC6A18AXUR/70jFHMNJKrYeqsEKKjomeNLv3voKyjrQWbql9G8SgN6RnK1yvh/MIi+CSibQayaHcEn1JtW5mE4KPd6ojgU1q7IRaNgOfxLC4EN23X2gUdUznfN+5a6fkhxjCwgb3AqZM5v3WpxCdvDnUN8+4CUDQGZYvgW+eTzjn4eASfqZXsj8TAlzrnZxyCrz5osOW75uDjvlUMwcfmhI+szZe9rERZKhwft31PKXZbI7zbt7wq7bVU/tK6XLc9XivV2cBH37/e1+TnKo5Jdxw/ewtadAqgFDiPcE403niICn36TKbc9UacQQ35Bj4AmK7akE2bBrFpn/MN+cxn8h2Y/GGjW6rrKM5/8yyDY1jMGASf1u6ZfTDwDHyaR/DF5CmOnM9OZcamvBsEQwd/m7OAE9GI+d5Xkam6O/6Ydm8vRGeSM3cx8NHitv/NfushpwcDjUxp5JslMZxrO35aZdgMJm4eSeN0a35WG2QkspazzM0rkrOs5Z3C0NI9/GoIvlYmZRF8vgFOqE/iqVJkHVJC7FssDL3OcnziVT+G71x9EsWPfkfQRAzBZ+gq6tpruj36ljHwXdO3Pm0GY4DReyrVHnbcEJ0Nc+I0vhGFvCHdhOikntlZtbG/Tcg9v7rQ+dVobeVdPAsYbzduaPqYKtVW21MoiMi6QL0B+BvdVUJ01hllQoVjtCJPeXsVss/GDkCRkGitEOoq3LqigYZD4HWvW2GxUNjb4yXS1FhwynxbRLnIJIU2J1NwmFa11/DhYYmbN+uPesedAJ5gOp70CHd/JxMKSwYihpK6qY5zXGtgsVCIRVXxvQJFBJ+wtvO8BnYBQMWE6HS6SoyvEoIvj4TojFfu3fJDLCFE8JmwJ+u1QlWZVxT4JjRvMImsAR1bc1x5D/Ggd3fZ+LH+u6Wq9+fdbKZRFPVD+/uRyZG7hwn7oQHnvaWDWCxEp3TgXG3aeWabZpFoOlDCdSVHtSseMPhv5x+EAYbPhEVYUtr1iKYHVHa5WQ0caY/LkdSxD9EcfKk6ruLeCbh9vwqCzxNGskwz30PuI2uQSyjHYhT4RygFDHgPZnaPILJUgPTsaHm6mN4IinTJwecx5/oSiIHPUX64Bj4Hwccgt50QnbeN4PNuCgY+qpwuJgd8vf6+QPKZGHIUbX36bHgDNaYkKM+BBx8sA+WBUjqJwHVCq0t94YhbFxza7jaJy8HHNaAb1XGKqMwXUF8NIll4orLLC+/EKYX97d+sH7O/1Qi+Hjn4wtu2fe5voJ+Bj1JK6QtAQPBxiKXWOVQp8pzX7kbnQLNO/U+W5/5nlA38jnHDO5v6KBz2vRDyAoffec4rlcp4eSOxYKgjBJs7UiBqRFVaO3nENoM+CD5q4KvrbB1giOKXINLsuVNyVBjwTrfBvEw52/rfkFsDHRF8XWTt4RB45Ss3GA5zPPNMPQilb/CBvO6cflXpEJ3OHBXkZQ7BFzMc0HLUQB/xra73RdWi6NhzewdFu1L890gSJ2hqz4DfZTL7YUhZBF8YjoXm4g1QUbEzpPm+Sub3pqAYGh2uXAKEBj4uRGe9PbXPDAYewh8mZKegF/G7WPEIPkouPwhlL/rtnTWo3P17a7e7gU9a90HjHW51MfAFdcQ2CL88OqxTi+BrCwesqfljMADuffJ/4O23PurKImS9rBsDH9Xj0X0k026ITm7PVKp7Dj6Hp0j5fCEPVUWQi9z4XM6O2rIHB7I8mDL4C2dUP0eo865ZxgJoDH31xivw2bsfxBvvL9j7KQRfbLyu6WtD1wa+a3rRk4nlvM4n6bMr2bisR0jCg0wi33imAOvBoXW7YVoDS6Kd2AHGF8r7eOfEFES10NnPk48e7JIIPsUZ+MLmfPI3N9ED1FSSODx2jh/t1eMr6tgNNRKi0z7GxGTvSpOJGxLRrTit3Am81/x+UKW31jDV5Tk/N97znjn++38f4447ShzsCY36nn3+uHprgVXeUoEZ1QuH4Ov4Eb7whfog+0w1w+uEc5nvLRrMl0SbjkNlIkSnWrSeaJIOX3UUMFPkK979HHxZBkynraFrswFGo9jh+oo5+LylFz13MApRtrjXUKC48n47Bo+qwrd/+xpPP51jZ0djasP8hy1plTm8QZWlaxy7DXnWF8QNLVbtIA+HzaHnG5CDz3g99vUiBVolr6FoiE6yL1YO+i58gK3DKOOYfSzLVXTSdcnBJ96+KgNzYvoKi61HDr48D79RLESnr7DSJhdKz+/cdiF8TgqXlczZkZrQQoi3y0mL4OOMINL3ouvKslripewqIj0DX8XPW65uDiUeU0gADX8Q5q3d01kX4bpiKURnMA4JA19XJkPHTDSW9VRYdXHkkFiwPKeV+y1NPZxsL/RXdUDw1cpHZhwEhU6Xtu0ljmcn5LWw0Q6bl1emVeLKj5ilRpWhcohOgmzlPA2diiMyPt03ugRRgA5DdQlNBYKaUsF6rnMOVk7uOylEZ9kY+MxzdDCV8qJcdBQwXKWrO7ejWSwjglN1cANAm+dUzMEnCfUcj1KhUVOiiqCeFTSGBJm0yU0OPvn7cZESjANeqzeg/L9V6Ft+Lsn8Sgdn8r4hOu3L2aI8YtPIiXnengHt2uoDYRKKpBF8oQGu7hcJR3s7ITqDsYnn4KNEUZ0Yj4CF/O5sDj6OfHnSe06S2522/Oq5kI0Mgo/pRliGW2OEWATf4sT+3QfBZ9QykoGPqm1adFb8HUxZ7Rld/PJ++E3BRtqZlIPc8mRD1OvP6TsN2cs1SOa2zjK87pE1vvhXtfPoy18V2pq/Lgg+OkiVZgcohuBL8g6tAyMsU4j8xcuBLYKvuedX5Bj4ppiinb/IMue+2ccl8VYp7TjISgZ6O48TCL7UlKOyASeel/kIH3343+Dus8/j4fc8hOHnP8/2W9T5ps6mwfulHJFlGdttSiURfNchOr/+dG3gu6YXPVnEXD4yHN+5TzcBJ9Etk2+uVWQJh3NfPvIUNzlB8PkhOpMUKRh43XXon7nQK0Qno+CKkX1XHY4hANQhOl2UQLcQne7vSuXO5hkYL3oYVaLkH/61p4Dj6owgJhwh9HY0+pEGJAHIUJYl0DreQVWD5JqgnpNNuf19jXe9qzkcPdFRkGh+7+5WODtz0ZhaK1Z5S/UgXcObdKKOkoTxUj09y3Fywj+zWndUOgptOgY+JheTU99yaSuV0ENZBMHXR2HKaeF8g0Zt4Kvn23qtMBrJicuVFlCHcYtd0hhSIwmB+VwwdHfgCx3sAk7B6RR4+cs75NLwwoEkJVyfGF7EKZ7sfZVhuWon1HjcVHM1Wb9bv6RKBAUz9z2T+maGxwEIQ3Q6fLpbn0xDrUJGObc0lKjkFL1qO7CXqAI9do9REgRFYgY+T+sQHfsuCD7m2/TZikPUApANOQRf6KQC+EMQR/BxvhdVlmNBjFm1cUE7/VIK7FiwoW+pcdpTRNq+VpVr4OMQfE4OPldp2IWUruRwQoIxhJZbD6Y2vNF8smf3pq95iE5nfnaQ8aK3Et75gQaupTqMHiOTaPAv2GfSc7kP219MNYwDHqGX3V/huR6KeZb39N0Munj4e57u7Lrwq6mqJmRlZe+XEMLAN2O1WpEIFBJPZJ6zTXbJwaeIslHrKBLHOSMyCL6jO4EvqvrdDg8rWyc1erhnitaQt6kyoEEU+1XXgNR2bYtbYGTd9wnRGUPIlvsuAlg08KXm3RVz8DlGVK0dw8UmH0MPR+KzSrVj4OTgy7wcfIQyUBRIQonK5OCL8SLueoB8VQCnPqbriPaVreQKxOXgi+3Btg+6hNYRdDzpX1Qc4hB8XNoEpjtOiN3pBHoRljfPcM4EXRyjXYeoON8Q2/Zk1LptV59QvVAhOjeMgW95av8OEHw9QnRyOcXqjvGGe2dIfR1drZwAoCIIvvZ3eF7WQZnYEsyqtg0pkpSbgy8urPpGyTvv1rjr0RWyDFgMc8bA18GZxLvWG7iQu3MjFUMzOMulJmANfyVNMOUpgs+r1iLAyF7MNUmBEqvB1L2plJuDr3IRfOxZhu47NQMIZJlaRlPOd7+KHEwdxiX2fGv3Zbg4fCn0jQtxAogIPiZEp1MutidkCsqfEh1Rilp3QfBFb1/T14CubarX9KInuw8pBT0OBXeXcbRCYzREp0AO07IIvvpiVm0sg9Qqd4RPpdp2RB4a2RF8wbCrd46Ba7P3jLDK7aYRcg924Tv5An5MAdSVqWsVCdEp7PSdjy+RTtRzKy7E6EjOI/Ozi+cepfKee1BtbUGP5IOoacDvkj83rKeTV8oSRWqQfuY5kodAUVEthOh86KENRiMddIiXJcn3xguYg08SbgTSKnPDezgKEe9Ap/l5J6FznUMJg1J15OfFwl4YDASjA3NouwopH0XDHMJoHj5ju5LOBD4qy1IEwaeZuU3pznuA179+jd3dyqnAN/4HdQQ5wxD9zYWP8Yk9aCjXwKeohaInzw0eYQ6Q/jyNIfheMGE6geDr0lBKXx5F8GkaOocc3Loi+MJSTvmYIU4yskdDSZtrkb0+avKm60VabFJuP9M46RSnYLOPM3wyyMunQiN8l3H2D8jOPQHBxxn4KCXlOIafrAczPkRfYoz8Mk7ody9XiClLHcEUDdHJ5ahJIPhSpBrFBUtGQcnMHxoW7n+++t/gqfvejE++8l85ShWnPIcu8GWzrn1uZOSrIPi4ulIhOmtLEl+mvszIeo2BrxOCT6II2oze6hqi8+HvKvGSl1TY3a2ws8PMPf9RTobtqY1S4D38nTJOftRWfgjCvDpboRuiE5ARfOY9lkuwYyopX/1v1SVEJ31TBTc0tNCtmrz1oZXCYKTw8MNr3H//Bg8+uKnnKrR12lEKTohOWt+GGDs5OUXM+RQhWk+NfOH5WhS94f2uDlwDX6VyfnAFod7+5Rn4OiP4vHeghovNYIwqH4rfmraRlcTAZxF89W+6F+Ygcog5E0shOvOuITr5622Bho9fXraoUo/MNTqMuWIMfJKG22/SM7JyITqd8gKCj0a0EXknMfCJITrhIk41uufgc2gahvJvn9HuHs3wmi5hGxXQ28AHQBA03cXYZf/RnuzniW0ABATfkiL4aq9BKg/6inxbvy0jI/jMH1xo9EDXRq93CtHZ/l546EylZWdUjhznC2fQ+HmZVWXIqyQDXyOH5A2L7J2DT3JqSSUR9yiY4xvGidTbq3sp87QO9hqxclKfeWYVRo/lx4VcXHsGPq2UI7NmeuPoJtg5x8w1ibLK05/5lBijkphcUhFLYiTygwSf99+Py1fP1sf/dOg6B9+Lj64RfNf0oidHwBuP4bti1R6hodeVVQJekbNoZE64HoOe0RrQOT00aqu84Ppsm+8RorOzUlglEHxebjhTb8Ks0/5lZF3JCzbLgo3cMVp0eI0qywPFpFjh7TTElKOKFkDY5MhOLp2B/Rx8qf4U//pfA1WF6R/9EQZPPSWW0yodolPMFWVIMPB1QklGg8OHvycT4A1vWGN/VOITzYavdTpEp4JG3tGLLfmpeyqz/PGlefM2pXxQ7zLlnKgYKQTfZmMr3dnRGI00Viu3EQ7ZJVcY6aC/DhB6jFsDn6oRfEDk0KTTITqZTjh98btbjiYAGKnfqYIRQBNCaag40/yNBGWDzFWA90TwxUYmYxSMWmXOIcga+KJK7CsQfVBaS1kGVIJyyms4CYhRKuRFAKC1sy+mQq1pKPEQKSH4OCV/rA2gg2GBtNv7Hu2/lIMvsh8BcDqeZTr4RjduuAZzp25fljDe5VcIxco+pxQUm4OP9wB1FDmqfw6+yrvGKaWUCh1SAO87U6UfMxRSDr66DwKCrylfWaU+r/xq68ktX1Co5LDgfvwqWicpfLF1Jz6/dadXyHuICdGZQpzFyFdChh3sfisZojPSVh1yS2iPQ7Z2DKcJoF23DEKFs/2l6sxyhUceWWE42+CJJwY4P0/w+y4IxJSiv5Idc0gpt59dHGUaJaCD4EuE6FytGL7ukVFM171y11Gw1qNU35fy7wHe8DIhOqEUdna0NcaaSBnOXibl4CszYMDLY9TAp8CHbFQKwVj586+z4jsyZ3wDn+hkmphnDkqqB4LPcaZlEHwql88ANPRpTh2IGoV5O+5EVqBo6YQStUYHe0bmaNxa4Z7WGH/4wxh9/OO4cde34zmOpzPzNKqkTg3waASglWOTITqFs5BZP1mmUQXyuNn42jGVzs6BAVGpwOnSrbtZ89q9qCaj6Luz4YClBpjnACC7AoIPAB+iU/vr25V92P0wz9z1xsiPnIGP5uBbEz4KIIrgs2OdMvCh297glHV0FmYfdfVStK353JtjiEYoD6+T+doNwVcxkTzInkmABlAK1f4+8KUvAQh5Z92u0K/IcKX0CUHvqEGwqngHMP/79dSxxaafn7PXf8Y4r7pLl9nLvBx8QQEOwRc5xtJOK1Qscpfq+dpne1iQG6JGvU65RBndSDQHX3K/jeTg42TPaHXuerxG8L346BrBd00venIY4Wgc3JcYh9nP+oTLcDaoJiGqDedBvP10Ip8W36GIMhGuoN5doRZRmAChJ27icCKR2x+3vs1GNkJ1acYmuI68c0qRGaWYYE9yoUhl/YTyXNWcsq289954vyIe5rQBVnFFKM9D9JTzmyp84XkgpZBGnLKL6zcVklS79gyxBj4q5OnuOfiS1Dd2gtcYRc+tPXua1sJ648bJc4DjcjFJfcky4JFH1njoIbcDMcVTap6k7okGPrS2K2m6KPTPwWe9Q4V+ViNXeO+sZA0UCmz1lmIhC2Ok8sxFUzEekX2IHlq576xV5niqGvDvC56Djz2Ze0UYBJ9/KBGr0Al+Za55h9BUDr6YybSdhrS/OqqAlA5wqWOdUojPpeipnTBKSdEUQZT7sg2HTjs6Km1ZH0Xkykq63X+47+w1yxJzQw3CsVEqGTEoTcxeqb0jjlEm0pHtg+CjSHM3B18GO7d8Ax+jiAnkTPmV2nJE5sxSCL7bHsyGUjn4ElTnwqL0wuTgi+0bXeqTvrlB8AXX+xi4EwYWcifspjQP7TqVnVHspWRM5C6kkpag0PvbbF7OC3m1ugY+QCVDdAIIjKY+OaHlIkr4bnxKR+UsR7HFRbJg5A+FqjXO1/HM7U1njRN+5e8/dSh+2g3h+0TmX1UpN+1d9zgoTkXl/n54m5NDyDOaWwhV5dzvjuAj46RL5OXKtrfxjRRylxwEX+mF6HQQO9Q4m0Dw1SE64/tyl3qgNUYf/zgAYPIPn2Od+7g9gA3RqUxbcdKewdrwb6poT/Ir1BFttPWbkd+Pq8JHxvtU9tzaNvkY+TA+sVIIvuDbmemryP6o5Rx8Et9RiuEhdWXur65nR2+N+e0OyrjDZBCiM3hvV36mfRODRak4gs/fy42Bz97vgOC7//44Gk14nfa6Zgz4kT7WYR89Pkudp+k8yDKs3vxmrN7wBszf+U7oHga+GPU28OXt2taejNpeb/8OZKyUvOXNt2D8CaqSmwfGmTkpa0UQfMgy18CnS+dbsXIkmWuZb1j3zkfJEJ2JMSoTOfi6ksgPEiE6s3j8mGBO+zrrmIPUNYLvxUfXBr5retFTgODzSCm627eMfGfXXIkrp8R2mwesga9qEXwV2SytXGWEaOncFUXa8ciDdB/jCD4IG1aM6EGNOYcRZWD94rHQWl1ew/afFHaQS8zhyC/fWQHDCOo6JcRQxERwn3j8etLR8nu/F+XhIaqdnW59k7qcQIyY0A/OM1TQot5wBBl0VQNfp3LKnUNsOAJHgVp7QHahpJzZqRZa3q3QBWUx88WWd72POaJ1pUJ0cs9SIxvgIviSayumMPXnlFKBQsm0bXLwAUFEJbdvvT3aBORWQ+XQ4/WesB3+sNVyj4m/uZBMQZXclB+44UCkEJ0SbwpCNtHpJOXgY0N0duvvVUgck/hy9q5zSAM6PsKzkRx8bFjShILRr+NriuC7qoGvC4IvkhPW3yv9nCLDocb+fkRx5Y9HD1RFV8p6GPj88FzRtW/ztpBnmMWhFCMTcgYTx9bZypdUcUUrMUWU1q5hmuP7TIjOFLKGhqhVuoKK5eCT5k5CTgqUrdRw05AztVMKjawNQW7HnePjibo66MvTD3m3uLEw8qD/LVgZW6qfUxBz7XSNBNDFEYzW01M+Y/tXpfdy3zjEGr4ZJZ9SrSI8iuBr6nnwwY0wpu3fm3wUPGeK///sveuzJclxH/ar7nPuY+bOe3Z2dmd39jX7xr6wWLwJEEuAhCWCS5EmQ5IlIeggBTlC8r/hf0ARjnD4g/zFD0ZYcoTkCFGyHBRphCiLkggRAgFw8SIggCCAxT7mec85Xf7Qr8yszKzqvndX44iTX+493dVV1d3VVVmZ+fvllBx8QJun6n2v/xOjoKhDQ/ApqOEQhdPQoN1YdYwRMa2GU3QmCJ9e0lEtvxWqpriIRk9xEkGPe4fv6HNLznKd5OCz+0P1yw3xYTF6zrpFannzA+1STXJaj/Nr6kSnCL6cs6WqooLgU94M8x4o4gTHDW0pTA9Houjc2eFjJYPg67+tZKhMpOg0aQgVJGSO0lr2ZbXYaz834yZoHxmCr8BDJCnNjwvB186PNICiIAdfXfHBrebgMxIRdrKWlKwOgm9opsD5qO0NmY2COtOryKhBNQef7Mtjj63x0kuH7KTmcMx9R1JfZn0M6bzOftPvdSjTernjqVO487M/i/Uzz+jtT0Dw9cc0ZghX6F61iQUUnaIDWcOLpOjk5XMIvsHWIvYvrI7A7Vkqgq8iSNZu3bWmvxZtTXRcI+ig/11J+9lEaZqK/G+XG7pvnQxBB4xMWFOS38ogtNiBZBMlCL45TuytHE22j3wrd72wOUnJwceQP6TwmTPdJlJTrB1lj7VLDtCE3NJYX2QEc2a4BUvHyQAAIABJREFUnz7wdEEFunhGG5msmWgedoXCOdO2YZfbbMqepyVFkUiOppOlnHGUlNbQkoGOsc0sN8oMC7+k6AQQ9/dx8/Ofx43f/E3byTfDepo6pIz8Z70saOT/qNT11EGulEaDW16TTrIIPkRUSYZfXYoMYqVlkSrudU0MRxtlg+Np3XZXZo3z5LHGdfmQ8QrKjZtH0YlR+eYRnLSCORSdvCLZ3U2H4BuVXf255wzFOVquKfQxyYV0bphI0SmF6dqKoUA6+Ib0nRMQfEW3mFH66fHjoOg0nYhNY1J0mikIjEAZLXJ03NzpcqQcfIXrayJHRfCB35NEKl28qOfwtI71hpaie1ZE0700Bx8AXL26mfbclN/yUD9mFgvg4Yf7HFiRXdvfp9fegOAjRgwzVxCJjo5Rz8HHgqgK87ttKIIvblBZ46Np1AU3Oy6105rxcYqDrxYG4rnfhRBtHU7evaNvu8/CmbDYJZbOKwzEyZ7Cac4ch93xvb0+6MaRY0DwUV3WekwJ3VNfzsnB1wdx0Wu9HHyPP77GpUtkD2OM+VV9RASfOH/hzW9lywBIvw9lbzkE1ZM8T31gkMxxtY6L5Dr6mxnKlWfR2ip9YzrVISpBe2depJyiz3W5uqmvyRYth/Y+M8EkTJcmeuhyfWv4P4fek91gOfi6+VWb32kOvpwRNVT5tApUzG+5wMHn5eXjBwv7Ixy3moOvZA7sjepSd2BC5keTolMJppyag2+12M8GHzInVQGCbyjq5OArnndUvnk/UEnb84Qq6N8YEY2ik0qC4BNOdz3Qe+wbf2Sjo1zbY5ljOoDNBX0gE/ddhmSb8uqrd/DKK4fdNZHtVbNtRurA1/sox3FSF/1eaX0Fa2/7rUwLkC1iBKLdo9+VCEIbjwu9WHbSbWBCDr7MODAYrPlJAKvlPq+6f9aMpnOl19OvvYwOVtpLud5Hg3SsOj2huo42PieJsu/PU2LbFJ3aPC3nTa/6LYLv7pOtg28rd72wiXBPp+gcJ6pRaTx9pjPWz5xZmi7auJ/vK4rgIw4+1eCjrGXaQv+tKx/BHz7717He5w6gSYi0EEwlogoGgi+zeRv60V3aaIb97p8jMtMNz9K8Z2XTnHR0btuNqEczmDqIieFxykhK+pwdBS+3IGtGSylZBB/pf80cfEqIm+yf1uDEHC8GG4TYHOmGZ02y5SYas+Q7oK97JaKSzMdlIvjI5qgkKXbGaFk5YV/aps8sqyD4LAdfDGFQ3njANY1YNBx8juTmuISis8DIKhV2TZJnOjMHX6grG8EH4KmnxLeW6wjStYZKEyqGbB4RfIahae70WGDY6Ck6rc0wlfx0YcxxTcPmVeoomb65Ste9dwvB5+YZE31ILybfVEEOvtSYzX+3Ecmj3HOPRz2n9C34NLo5URF0BoLv4CDiqWft/uXmi6gYiGOocOXKGq+8coj77+8MRcLoWELRqRpbhd7AHHyUem4Ggk9T05IcMMyYKPr/LlJ0ThkK0uAeHYrOaeatgo4436GM1B/6EKF++JMoOhvD8SXb0dYzTUiB++5rcHAQsVgA587p7Wg6pUolKctII3cWUkoRJqMRl0Z9awi+qhoNTTHCpOjcOwHcc09H3a5QdNJbWC/0HHzAdARfTtg3qTn4EgRfi1hka7pB0bkmEf7aKxueMSwEH7IOvnfeGSve2XHecebDv7V31j1vHqOSIPjsopqDL0ZgSVBJ/XzjLcEcwTeuN5uwwJUrJICO6jbUYJ+h1qzqlFWHIpfZMaOeGKVmor8njUpWpejsJfM+4nLJimwkokuWFzny7r+/7c8ibHDpUtM5TDLIp8D7mlJf8zVX0m5LkfVPRvANjdsL8uADpMi6yHPwUYSVr5oa/RLzKxfhAISipwmHC1BA0Um+H61d6VQLAWi6IB4jtg4IeYrO5Lh0uiB9TNQu2Ben+6w5KlDbhtHHzN6cvZOSwFYh5j7JqGsKgi8y+DdaCPS7TNHp5+Czq22DjdPgqOE3y8G3rxTgZerN2nQYVlVka3AOgUtzNc7ZEm1m5+BL58fgbazNOaVt9P77N3j2We6Rk8FGXj3y1BbBd3fK9pFv5a4XFtSuUnSS/+kGsAo4e7ZJI6kc7Z9PWrwc3aDFajpFpxbi+M0rH8UbZx5WDFNGHUKiYrjkBfJOHLd+L5itm7GPakvSKDqZWO+rUPlIlFRhgMhx/lOPjxXFFdCoCvWRRbt38dtjbJMFXASf1vnCaHA150R/LhpjRGygKyPPg6tIazJ1jItlkNpsNKVF3ZAb2gs30KaGrLXM05hsIvi9HhdFZ/q+0rG7vz++tBHBR/pCbzlGnYbFi0IOuRx8cq7vlV1eR9qovxFLlXynLq+ZRSW8wURhjhHnzjV46qk1/tJfvo1HHlE2Uw5FZzBy8FEZHHyKYn6keagIwac3oBmXE0Ml+FjzIrytiGg1iluLaByrStqyjPzjeWOeyY2T3MP3rqfP3lpYnZywsv6qiuwe77nHzxGjIe5UA4zdpH8ihDF/oyLnLoQWsdNJFJYAPp/mB3lvgEvWk8DHgXYD1AlHA8i0ZhMEH3l3m5Au0MzBV7gNYzn4iIMvNYjFFMlK+ulJcm9VZRvu1Av4uR4BMhQNSn6qkrqOo7y8VLnedPAdE0UnVcdLcsrGiuvGVQU899wKr/7cnZa6Uru05Llo966hWLwqhB5W0v6Yg29EuG6i7uBjgRSZ/qycHHzvpoNPo+hUDcExkhx8MOHho4MvpUyvazk05zn43n57/LG7tJ9rcobq6wG4uZvm4UtkIkWnV5zR3ZN3utwQB59EIClCn0WP4HviiTVeeHmNX/ql28N5ipChCL6YGef7+ylFJ4KT7VCtJ7gb6wcf3ODkSX2eD04Ovuw4Xy6xOjgDALi9e3oITrH2Y3I9uXp1gyeeWOPF525hsXCCZ0B1ndRZpTYGlM1L4pLVYj8bfJid+4x7kLalWQ4+o1nu4POd38P5it+HvCaH4FtJBKz4IKlPqD9F9Rvr9egIPsNpXYE5+6tBz4mw0YSRtxOj8Rr98SNZPfhYr5KyrG7SIUs3y7Wd9tcurwWOmRJC6jSdStEJ287RX0yLe5T7rpM+BB/Bxyg6uYOvf3exrodggxM7K1y44Ox7BFqU7k9lUHEfYG0+hsyHPtfBp4ozYCwHaj8uf/mXb+HDH76Ttf9l1PtBYtwi+O5G2Tr4tnLXC1uoDYrOgUJJoNXOnWumRd/Sdvs4SaETaAi+osnLMRyk1xf2OaO4t7laJiL4lBx8Udskd/ezXk9K1Z4Io7CaYvyZu2KQ686da4bfFy5s9HdEN/GG8yBBVh2XxV0x3kvRKDrZ4ssQfOMqPDcHXwnlhBy+mrItKTpLEWDZRzk1VEhUuFiMnV1vhIIb0/KWxCiMEoqR8ObPfArNyZOIVYVbr72W65pL0ZnshV1HBD9XVek4Gyk6RwSf5fsx35/jLJJRp7L9Zlc31mQNzMY3aslcis5QB/ZtSQRfCMD58w0efTSDmuqEPSolt4ocP0OsizX3z51y6A/LcT2sEYUb9kzf1NfYNKiYYW2s6HCVXqDebr/hifz30F/nIc2OOMw9eOf8zhe/iL3f+R2E69fLcvBpD5Ick9+k5eDT6ptinMgFXbBlZlGr6BQACSUUbyH/IadzYNr/KnCd0BwH7FinX5JABg1p0P6Iw1zQ6opKBA41hBRGc1GKTongY46GY6TobNf6wl2+0l8VyWIg+CbPv0fQA0IwAnUMB9+kvikIPt9OXRCAJ75L/da6emZSdDKqtWb0RJpdYtHuhIaNtJXqfa2uSimo1xblHr1nu0IAwKa2EXxp3lVbSl4zK1MwVqqq1duG/lDKjWAj+FJwINfzLSe+7uAbj12/Praxu1u+c5P65J/d94Hh/+9fel59eKYO2o+tO6PDIS4WGXsmMe7TOZPcL80htnrsWraePgffzk7E1UeCyHk9tlGBfgv+ILn2+AanO99nCULSXNc2Nh3cwUGDp59e6Qi+ON/BF+sa3/3Ef4nXr34S//aZv2a2L08M++CqpQHf3x9Pm1SCZH7JWRBGA3zM6gGyr6vFbh7B17MIRfJdFTj66L0F8DV5M9HBlzTFEE8+CjIq71ebH2i+Sk0Silt5v2Su7qc+qt+o5oQQ8tNkEPVSpwt5pvQaTT0eHENIHXxlc3sE39OQvzndif1P5ovCl1++32jrLkr5MXRIoIqbxqDoZJdM3gRJp49VOf+GUwd/T0WuPbpsDj4AqCo89FCLUvvZj10n7AJKcREcp4kci3Py7wFAE8eAubnAiOHZFeScT04Lpjt5bXJ5Sb75TnIIvrn2iK3Ml62Dbyt3vbCJ0KDoVI+FwBw42YuQLlCWMhkzFJ2qaJHrhnG71Ck5GMktQyzEdqRE0VcWaY+iU6NfnCLUWVqS0+k45cyZiEce3eDJJ1d47bVbels5xASQ5uA7rj63ux8maQ4+voFPmjcRfAUacKmxK/FEcSPPZpNeIx18JXlfcudkgZLXoD3PXqQjgc4JWUOPOKwp5PHUKdz4rd/CjS98Aetr1xK1K3msiuPHFNdaws9ptIMDRScCVquQIDF59VF/BMxrlWrXXhebZUrvlrSr1KEET7u/NcNkiYRaIPgcrmJ13xDSsTWcMnLwUekRfNZznD0FiV2eaqTrnlWC7FLKag4ba3PPpGmYIZh+P6t1fl5SqepEcW+dtTZxOYrOo1iRqxs3sPzyl7H7L/6FHQ6dg2xLYwn5feGCTiVt6jAhRdlOGVdq/mPFETe0Lx46QzwFfSwlfaXXK5HvISCJvFfHba0Zy/Q1ngWCNZz+KEfR2eflyNljKQI8xA2njWORJHG+9UBKXfugCWcwNNUyjYYPTn6qiRNWVkd26gshmiwZMShBfVqVzrwFJGrbWJeozK3TakNb94L8h0gBRSejQC6hGBMGO7f9vhsDgi9P0cmQvorRnTbDkCeifZr3xuzahLHnvq8m3W+GANQk1xsc5MWqaef2iNQoXlWE8j1GaEa6EMDfS13j8LnnsN45gf/w+GuibBx1CE3c7wf48blr+NrDP4f/dOkFvP7gz+jrYua5Vm+/PfzfnD7tLp0aRaeUPhdjCMDNVz+D79z3Cn5y9mGznh7BFwKGBX9Yi+g6quQCtuafugb+xt+6hfe//xD33dcMZRMmGYd9J0awOVz71ul3xO4PztyfG+eLBVZnLuCbD3wMN/YvZC+zA5T766LtnGIBBPbemU1F0Q/KauvhY3qdycEXgoKit5iPEh2TOvgERSejlLcdB83p00MN7Jxgr/Buuz/Pxlgzpr1YrG/j0e99ETurG3YlIaSUrOKDlCkaQiApVoxH1q5VaVDU2FXutE1QdAUOvv44fc8TgJjsvMVWIfdfyVxF16ZjQvB5MoWiE5KiU+iovfD3p3xr3pqQIPjEeAbXF2SVv/RLt7quRjzyyJqdY2U9is7+HrtgkTNnInbCil9P+hACGLV/koMPwK3PfW44/8ePfw4AT70yRWIIg5nAR/CNe3tTvEAuY78m6W7ZO9HSKdit82cZ8wi+LUXney8TZoitbOU/j9D5NiqojmFTwzSM9u/58w2iGOZejhy+AFTqRiJGINYLYD1ekxiWDEUnqeuo0cyjZqOfhqV1eVUSw5UTzNZH0mw25Q5JTbSF5exZob1pxlthYJsrD17d4Bd/8XZfUdq/2nZAjorBTC9ngcZZsCcrLkBzTrS5UDJjozQaPGNAUukaxOaoVAHIPY9souEg5hQx/qjytt4IBEI0OmC0Se2uWiRr3Fm2RtQTJ9R6ksdqbHacLqgijTHas9/ZGZ9FDwoxEXyxFMGnKPnjjh8gxzZLng9Cnz8VUQxs3u+h3xmDlpRqUTHnf6AOPukkm2YvVUWOH2qc0+8x41gr7Yj8YACgLh9sue/anLubBnGjU3RqvlT13rqDo5OIGxJcY/RMGpbjgIksX3/dRkrTgJPMXFTXwP2XGnzjJ8Cjj66xJ9WnwPUPLReKNHpMEW1tjx0VmxoYNGUXKO/dYEhQvw0xDjbKtVQPGiKy47hOSSqxUfUcHWwxApuQOi21HHw5aTwEn6DmMhF8GUnK5Bx8jrQIPmV8iWOTOjilfPbb1nV7KKhFdRxnFRE+P/c/X399gdu3A7BD+2L0uZ+/lLbMdW0mgm9TL7Fct0Y2mvvUNN4bqJtQkThxJYo/BF7/urEiKcj/GYc1pWaUTXKKTmMwTxh77qPcpCwg0hHT1ByBTZteKwgZ+ruf+ivYNLzyod/5+Z/Hv/r2Cbz9Dn/OJ05EVI1jEC94Jt+5/0Pj9fhhWoApicIiCCAQB188cwbh+3ZbTJcecvDxPq4XI8NPPHkCX3vkM7j0k6/hwpvfVrtBc5L3+6TxPHkX6OlVR2eEF6hQLyu+1raLjnlvajWZMS/H1dhXJcCgdHwbKEpmJBZGck9cHYsi+Ex0z7Q2Y71IvvGBotN7Bp09oR1P0paUXjvqk2OOWumQK6ZQ3NnBzV/7NXzvP30P937n345NRsWDZUhEleg17VzbdvTad38PV3/wh/wiodev6r2sTiUYddv7ZGuj0B870fZYw3NL1rGoIvikGUhz8A3txJjsV4GY/QyqCkBD+ziOUcv2M1ZP1vsZwd6tc5I/n7ZdXUxkrF45D7pukHXwDZ3wfqc1JEeu/MWXcOrGD/G9Sy+SUuk4ePzxNX7jN27gzOt3sPf7TnNkIbAomanNju7Lc7czjjViA7p2Dd/60F/B17+xi5+cecTul3ui61doHXzLJV+7qiomDlFThsFeAfIVWh9Vf1p+SzmKzi2C7//XsnXwbeWuFzYH7aUUnSEoaKTuxNmzPAolP8lwRdNSJlWKzhmGv145kv2fguADuigU5fwcik6tokZT3Lp/PvaxQ/zZb7eHHnwwVRrOn2/wxhu2MkIj02MIuHSxwQMPCAvukbwZmWvFJjSGwDfPikFVPsaq2czbUOUkt9vCGElnFaHKTrURFJ1ENEXSQ++4/SS/TUCBMKBaOfgmS4aaa7mMODw0dqwoBmXxywoQfMmGr+2MU+kRJbehJaLtRUPoUHxd2dWKR+VROpPWQat5x6WDj1IucuNWsonf4XO9GQwhb0sxsHkyRJdOfPahrjhyZiKCT6eIc4wXYkM3PJ7wLiL4gPZ5Sto5i/NEES3SlV1m9B9Ng2ZFc5qMFV15oMHbsrw2B0nDDDH2VpW/zs7JwVdyvthfYhhwsxSd4oF/7pdu4wc3b+LyZS8IxWjLQdvljrW16v2zqJBQ13weYN0KybiRdctDjergi6mBIXdTvTGSGonoGkcQfCFGZpzNIfjU/G6KUINO1XAEX0LROdMrJx9DrNIcfO4FRJpqkaA3YnDm8QkTVuTDR5eqsg0disFuqFdh2pji4NYM87QbP/4xRwC5Y9r1JvEXM1w6IwALEIiTZpMdQ6a+axjD+2uqCggEZUQRdmYfvUhDcASffFdm/ZkmZ5VREEbJ67CcXiDOzpDm4GsdfO2xen3HpjVUjHaVEpBzcBCBt5x37Ez8+nSp1OWN3xhRvfXW8LM5fVqvY6hqPGcFRazr3uBL8yjZyJue0SQEDAE1w3XUwUcDOHN7aLYG55/vHIrOXrXRAkt7OtG5FJ2TdEmxH9ZOm+tbw9Ek+caQpfJcnzgFCM1wtdgrysHH3oPB75igMGmQauR08o1kCbDaBtBcvYqvPfYMLn7vj1FvDrv6bASf9sx6x9ggZB2656d/yoo31QJ3dg6wf/vN4Rh1jltriXTwoa4QCZuGxVSiBkWJMuxf5uAjjmByOd+L8u5qFJ2iGbMbJmuXGMcJaxJlIKLsCseM4OvvYQqCL1aVGKtNklICULbrU+xucl8H4PT1H+DZ1/9PAMDZd/4TcLkrajiCz59vsCSgPG09oN/ggKbui/X3RB8mg5ala2riTJYfWlXhnSuP48dvjN/HXIpOIHRsVtFhQyK/nfk1VGUOPnY70ZhzkdpjzILKqS2C7+6U7SPfyl0vzOi5o9Ml1hWn+OtnHzUHn2L4U9tFQKMoCQOCjzc1OtssHXQKhclU66xlyLAQfIUT96BEGYobALz//Yd45pk1rlzZDIltqTz22BrXrq1x/rwekcipCwOuXVsPHP5D07mNyWxrtlK3VOIdiNygWMYN+51tI3ecnM8ZlxaLTDXEiVRHStGJbASP6pQucfAlGwPFmEaVvHh8CL7c2JbKWb3k5el5jQpQfR9G5zmCjze8rnezEVLeoz7Kc5DnrH7QnCQ9Tafat2IEX9oNlUoxjAg+2WfXIKocS4soSr5R1+1PfrK9Yn8/ORfqwAzrYSpF58Qy0ljOKTpnwmxKOqI8l368lDhKZiv2IjIgosLDD6/xyit3cPWhaQilJ59cDYVee62ngvHXDXMTl1uLjgHBB8BGaHiU0Ypus1gGPPDARkd6F2zg5Xs9ylILAKjTHHzDN+k8O1WPo+IEULHmQ0oJpY9x8j813gh9r29neF1kHmiMKH5at0fRSUUi+KjRWVJ0amNn1nvLOPi8WWdTLUmjvfHH0b2PPLDK67PmKiDo9zysPYXzrESAaEVK1jPnWVlFiwK1lHo3NUGjZ6jAQwDOnBotOhLNakuLKqm6Z+NSdFKdJDOe1w5FZyOCOvTGpow9+72GplECjETwpoPgO9yQYDzZ192dYepfbm4nDoCuOlU0SrGDg8xYnmLctc5bXokYEW7eHHOV7u0Bu7tuE/R5bJp0zgT4OBi+B0cfpDnJEwQfKUhz8BU5+8XL84KFzZt2HHz9MY2yvwqK4T7XVi9tYnalrVzwpT6WWv3eCLizKDpdSR3oVDYnDpLTm+WuM9+P75RRMxcGyLD8no1A8BU4+ELAMFZilM+KBJtbz1Cel1b3TiSVYdWscWPvPDs2OsdpvXx+SnLwVaNj1AM9lpgMZL0Des5B8J1/81u4/KP/OKxXXA1Kv/vc8Dd1wYCE0jspRj02dGwXri3m2mSMxak5+Nh62sQCik6kfffuRXHw3fejLw//n77+A7sdWo2z1wgBycbs+/c8BwDYXLyIeO5ce9DYl0tdi35/AMwAgoKthl5QSJvupP2fDpdZDkN1v+O3T3NDhiDuV6PonKAWUQSfRv193Or9VvKyRfBt5a4XFhC7myL4gF4vTVcNikABujXNUd6kfmQi+MTiWjJ5JcYmZyErRvCJzUgI4jFAd3yWSl+XmoOvW2CWS+DFl1bYe1M3CiyXwKVLDW7eNJ4lC6lUuqhpZuT35GD1qRtXoiyYFJ0y4rn0OecUAsWQ4+WM06plCD6Zg28ORWduIy9+Wwi+hKKTbSKP4LCo/I2VvKUzZ3lb1BiyWgcWlN1uwtJK1WMyoDJx8ClzmWOMyEveMTOcEigFS8Hc2xuN4eu1EsHZ14d5Dj5P7Bx8mTGbeYamsq7UtXr5Zawfeww3wwng9/4HXk9dcee/5eALQZ+jRHtZB5+B4FMj79Lqy0VD8Mm6FZSLu2F3yrL8TbRM06BZcyPHr/5q65zDYUjWOjWov6v47NmIp55a4eYDK5x+rI/a9x+4NVyztCVzDKEThDlzJs7FuS7Npeg0m9D64qACJWKQGY2ym2fd8KVeJnQvdSRoxjLFiJGUpQ6+UOuUY4yi00ecD13ojIa9wasm6JNk8lUh8/k1Nbm3up6dzm9TLZM5ixq6VT1vihToThZ/uYveLXQUuyJQlF5XpdFJPTnhG55L0dkEkqvZoZx/8sk1Dg4afOW+NW79sdKPahxpyVLS0wd2BtkYgXVj9E2iUQzHNQBsFmUIvuJ5yhG36GaT1T9YAIg0oHfOTi0HX9zbQ1XdQlUBi/Ud9nBb1pH+R5lx7eRJPkan0LNl58BePDYNgd7LFFcpOqWsFyOtu7amtW2MN0r3Q0kOPkbRmcItTAQwadz7tj2qzxjh0tJqW+LhNvq+zoAwTUbwZc678+wMis4Qo6uLrPdOJp3Z7Og0fmkDfL3Ppq8A0JDvtxIIvoiCeUec5EGv5Tn4BqYCobP0Pzc130u9deoKbuxfwMU3vzkcW4syWsepT6iqItZVXbw2tXlv0zlE6pe9g28oZ6Sl2H3jz/GBr/wvAIDTr/8s8Oxz5NvWEXwl/TRz8Fm5JPvfFoKvcG0pANkz0Rx8P7j4LN48/QBe+oVD7P/O7/DKaP1FOfiUTnidUnRj6TAb1GjjGUtRzwld9z9e+8v48ysv4Nm/fjDOzSxK24aWVVVMEHxasJLsx/wcfJWK4JsSTDr0zwOMGA81QfCJF57Tya0pUSL4dnYiVitfD9rKuy9bBN9W7nphC7Xl4KsEFWU1Tsx99P5yGXHiRKe4WRMgW9BDsqj30jAE34ikcEWimgopHHzpJ3u9n0nS2AKDgWbXUh18xzRjU4pOq85kOZ3S9tTdiYy+9CjR+kvKidcmi2dQ7uk5XT2MIviaoyP4Spx+ss8yObe8pkKDKpRZErOvPkPRKZWzs+dSRBet4vBw/L+NCsxv3rWuSKq21WIvq1AdaZhP0JytYEDO/Bbs4RINik5aRBq6NIVyrB3NrrLhTIopxoBq2jO0cvDFjuItnjuHoKw71SIU87mWGsldtIwYPxTBJ8UzAE3uhDcHFGwANUdaybfTUnQSo0kmp4laC9mgnT8fcfm+cROaR/DpLyMbfHNMCD5TvBx8QYlBPcok0n+j/GMvri6h0EHqxGPNOqGsJQg+ecjKwVeCPE0QfAxxwK+J6NcGMOqjGHQDGHfwJafVV9aiDChiIDVOA2hRRDO9csnzOxJFZ82QRcNzNowmk78Lbb6WfTh3DusrV/RLNeOJFcCjffMZY1dpUW8ujM48a9apnSiYpxsDwZd+ZhG7uzB1R9cZ2hkBqdHWcvDlEHysWvax8g5vSg3theLW0TTJ+Ekcdc4c10hDN72uQ7gtFrFF8CkInxCgvhdtScoi+KRkHp7qqKLXkP8X3/gGln/yJ8Pv3sHnNcH8vcYarDkpPPTTokPwhYCUqYUUpMgHFS0EsrHXAAAgAElEQVQlRerX2pqZM8bOnMProDj4Cvb9ACbn4MtJ+7z8C6atA76DD1XAau8kO9Ts7rv19u80QfAxp6PVG90RVYrga/tMdCS6J6b15fK/9ceYYyx1OPXy+gMfx819geBbUPRrZH3rJaXorJkdyzMntL+Dfj7w43Rv2NM5h8C78/BX/vnw/8V//X+x7oYYVRB9buxWVUz6MvzNzOssoGdGUL3cP+cu0yg6D5cn8N3LL6O5eFFWXkjRmWk0o/MUzw3KMy5pTo5xoP3W3jz9IN8X0e/AycGX9MXQM4qdr7kHEMJgJlBtYlNECbLV5nk2dxt0t0lB+5ApHMGXnt9SdL73skXwbeWuF8ZVvKvTU6WTxzjZfPrTtxF/f43Tp5suoqyw3ajTasQYEJXFlRoyo4N40/o419gxoB+s2TMaCL7CVXVc72hfFeXvCDvnrCKsrUbpAytvUBpRcnW7FJ3dgtmIyN3S51FQzhsbva3A1bsMBF+CetVkpoOP0ZdFHiy2XAJ37sjv0KboLLBN8ZoymsTVqxt8+cttmQce2OD75xrIrXRdd0mPQ8BqFbC7G4d7mULR6SH4NrVCSSQNA93QnwMO9SR1gumV1vW44ZGpnY6O4NOcB91lISDKyFtjVys/u/T9+GN8uF7WT43m2itfVGYy7xKR/cx9ig3d/FcRubiD2eNEdETL79oj+EoiMeXxN08/iBB+BAC4ceKiieBD0zDvh2fEBdIxrUlkzxDYOOuGeT9OOyFApXMtqrhUcmv3lAmzO9cXSb4dg1a3tEuqU06l6OzEy8EX8jn4pGg5+CQVlqk/CQdDr0dpNE40Bx919FvGTTqGSnOExRDQVPWwho/0cjGh6NQcfLOGXV3PdvBtqmU6xzGqOjHPTOxgMrYWCx6N0wVo3Pr1X0f1xhtYvP46dr/4xaHbJnLGoejMHuslQfBFWDoqG+8T2jCH/0wE3yYIJ3G2XR11w3LkyBvqHXydxhUjsI4FFJ1qZV2/az1X73C+yewvyImSIehOp02TFEjKJxSd49igzqPkle3tArHVnxfrO+CjJSj/jWI6+LyPe8LYA4x10ahj54/+iP2OZ86Y/eyFBtz0Aaey+ysNwefk4Ks3d8Y+dFZJbS1ke/vMXlN9og7ybE4Ovv6Y3BsAAm3oVaBJgYNPE0/3Ow6KzsFsIYKztftf7R6gxo3h9xAo6NxE+w4sxYN2gI+79t46vSBuRuSycPC5QudLow8lFJ3JXoo4XGjAxr9+7vN469QVxLe+zepYKRSd3gfZIu34c/P2qXJ/aL0OC8HXjncyVyrB92MbcVaMU6sX6H3Mvk/Foera5pS2p5RTAx77k4pHivW/AMGndts1NJU7+KxnLA9o9Wn2nWQZMxB8sr4+yHOcX1qgiLQJyCbn5uCL6HPwcRux6UDU9JL+2Aw9TyL4tCAhfoFTl5iqqPljS9F5d8jWp7qVu15Y9PfJk2qZWtINks3G3l5LEbk35N8uQ/A1MahUIDHy3CqmgVhKEnliL2SlFJ2Dwmk5GIJ4LhM3sBqCbzg3cca2ilPHh+VoKK6spOzE37GAorOKm2kRTKWSqbDvmnsLpP8050QJgk99xwXc35LCjzbTL/6SjuTUwahw7jmsKkd9xqdPR1y7tsbVq23OyLM8kLG3QQ99pDZDswPG98dz8PEyJRSdU8RTlHOFq1ovS51I0m7Mos6i7uDjNDdiTCB1zpFfaJY77LhK7abcYw7BZ0YpOlq8aryqKx6qRjXcEo+snGcyDj66od0RQ6fA1+EeZ+1IB5X6jNNziYGhE/lYv/bQqzi8cA9u7Z3FHz3xK4ghYGdHufmmYYYJj9pMFWrlU8Slj8IRcvAdHOT7dVwyxxo3pWz3nEspdUrqjzs7ZrRyqsdMaExD8EGhttL0P22M07HNEHypsZbm4KOR0U2VUnTGyKfCbO5m1osUwRcCZPKZY0PwZR18jmxqSdFp691TpX2G4rnKsGFi7GouXkzmqpc/sMLFiwoVpYYEBEFIlUjB8x9z8E1fK7T5b/hdYPjR9Dqe43GTH5Ab/bynw4cukIvmDtsYDj66hwsKHKNvhqK2HnhgnTyrohx8sPczpYbptjEtB59oh1Qo24xsXykc4J1SvFx2CD5B0Tn+SMeThkY/CkVnsRQGgU5G8BlBERs1B5/n4BsRfFSpknSCtE6PWpM1TA8pc1+2HjKHWK9C3Yo4FJ25qVxD16ftlBtvtbVyODeBonO4BvocMPyuAg53uf612fEDrnq91UPwWUK/V0rR2XSIQKuf7DhB8FlOJMlEkMwp3Xk2ZpuGGPLTfJ1pDj5lP2qMh+4UIkHp9/dg9VHq23QMJ850QZuo1akhs6leOYOhNnmGbFpNqH7FtewXYVYqnDenUnRqTuShj9rDl5TXJRSdSZtOpyY5+JxvI/fdFHjXomS0MPrQf3/jd1KG4Ju9NwxhmNbnUnSanaLl2XUpsnS8X37tFBuStNdQSk5tP79F8L33sn3kW7nrhfnt9nZw+9OfxubSJVamqvjk7E6zMZah+IKdGJs7pfw+D+Wk08NDrhWulOaC3ksjqEtDv6lwhNQ1OviU08dkrGHP0ooCPY7NpiW5uhW8uVxHZc6SYptYicZpbAyBUaFNqyGKLvHSTM7BpxloixB85P8YmFOlX/w5ZUTEmTMRjz22xr33blpDjSHZV5+h6ARah/8DD2ywWADnL6TleABYwI0bAd/9rpOPyOiUR9G51hB85VWXOTgMydFI9VLX4wa9BSXoyndruFNGvRF0wSrQxlgIyXeX8Mcb1+YMbKXvMKHSks9sAoKPJqe3JGdToN8LjZALm03pp+u3X1VYXbuG5r77+Ikcuikj8vLV8gR++Nrfwn3/3edx7/vO4qUXD1VKD8TIotGrha+uFtHoVWLNdR6SOY1kbr0xgpDMPh1BVMTPjLVSW9NDIAa/gjrUyFqlofWTT+L2wQV5uBXHoJSl6FQ2qK3hSzN05p8R10UiK6qh/odvl1J0olaMIkEg+FInmtVnuobUBkUnmkalGiobdsKpsLsLd8B7RnsrB19GZZ0tDsWe9vvhRzb4/Odv4sIF8r4i+Lu06rKO0YpizBQN6bkJlnzz9qz1NCPUwVeVOBdY9HdICyBdz0JsOoM+QfCVUHRK+gAim45atKoiPvvZ23jkUa6LF+XgM06GkDe8Hr74IoBu/XzuueSCZFp0AlUGSmMYCD60w3yxvs1OReqALvz2JyP4MoZXde9WaNUbHXx2f7QcfLL7m2pE4fV9zKGfeqH7JM/Q732TUoaicVrARYy2YVqrn4rq4Cvt80wEnyUtQsYYA6M3rZzSUCAhExpbBGyWPDo0LI0o2KQ5+iEae2NRB0uz0lAnWihy8MmT1jydRfBpbCgGRWdf152dU6yOSnOkOTawqgJiXRUHKKXMB/o77xF8o21F2fdBOvnbugZGo6jn4MuN43Y8pe+tnQ8y8wh5/1UWCpcKnd9unLg4zIUbuR+jTQqbgkUfHKuK2x9jNCg6x//VZ5X5NnPPd0C+FtJmq0uQ4g1zEXy5+iRaVH0IvIFZlJpo34/u4PMDF1gd/fgsdvCR04azvO1bek0yvzoUrhzBZ3dtK++dbCk6t3LXi5xvVy+8gNULL2D/t38bi+9+F0Chs0KrUIjUj2wE3yK9xtnYdp2UrZmnpiL47F0LV56HTZjzfH76yPPA14fL2V926XFRdFIlxdpdZIw17vPKGTxzdTsRt8MlzQZh5qLvimK0pGIh+NRCyFB0qrvFvDNPO8YMmCIx91Al3QB1b/Deexvce692ExNkYqjQyVO87+t16BSudty99VaFf/SPdnHjxmh8AcQtFyH4ZA6+PEWncUiVkrqsc9YevK4xuFhSBB8pGA2K1cxGxzbohMRgtOjolFyDaHupaMPukltQUHRGBDaXVgsHwSdEazOds/wXTTeYLELOcPBNkdXTT+P2Zz5TrJlrFJ1WWdU/WAc8eHWDB6/ewt4/aYCvKc3GyBB8zMGnzItF458ZEvx8LuYmznnYIeC9RfBpdSdrY74vgzFUeZ/Z59wfU06oNIgHB/jyJ38TP/7KT/HRL/2PvE6JNKJqneyL2jHhoFImtmheC/NYIHqUVpRRdDIHn/GuQ0BVtTTQnvNF3i81KFYEfVJC0VkiIQB3Pvxh7P7BH2D94INoLl1C/GrmAkM2VY2aoLzGMaKsn5m6StpOEHw5z0xI+9FTdEopCR6IVTUYeTXEmSXumC7lLSeHSlkXpNC84lXcmPloeokseST5Vip7zunpr/qAuBiBDYzw9XbRxVDQkB55srsLnDkT8dGPr/CV34u4c6ftU8OMidN09PaxdbogOzbKnY9/HM2FC9hcuoR44kSqW8nXsXBy8HlBnwTBt1jdEewIchDz49rrP3ky8yxK5kgik/NU0ms7ik6vOEfw6QU1ZIhmmA9BGVKSFiGDNpqG4EvLDwhe62txKDq9bf9RcvDFxQJBATXnXqN13tILkv7Rwxrive++QEK2Ohp/TzIwry60ctI1PMj+Gc/PzsFXFzsx+nGQC+7z6+jaEiit/ppKcfDJCndX1/XKjYarKrYIfxPBlzpFSpggekfgcL5bS6Vqq+Uu7m8/IM3Bp5mRpNDvO+ljbl5nVGOjs6uU6YruN/7oiV/Bp773P6M52+DOz/2ceU0TFqgxsjIhBFy+vEk7FyRFZyxA8E23Z7nPhB5m33gS3WbXZx4UUsihKZ3JahAx0vXTDv7M9U1H8M2qrzCghlbRBlhF/m15bbl7k/Ha9XoMYq4qg83pXdz2bkWXrYNvK3e1tIt0XimoBYLP14Zi0WwToVN0AlyJZLqfunPoRFIKOcbFYgffULcRBQtFy1Fk/cgj2DzwADb33IPVd86N/eguVZ/DMc3YOTSkeuIobWfqSnJOMcORruRJBF9xfws0Ts8QtljoY41dQxx8Vy7dxp8A2N2NuHIl02egiO5Ju5Yqk9zBN3aHKdLlmMcCHSo/X/DyrcPk8LAtfOJEg6qnTUHAP/7HecpCTYmPUey3lBx8Xt+dQ0XibiwKx+diAay6c00TzKmkKAefRDAjtGNBMbRqv+vNYXo82XTFLEWnZfuWCCQtopvueapFxb4tGhEpjW8ltt4sRSdD8JETioNv1phRYXTQNw3dM6bLgmXM0TaKrEqnsxVDKfkBJaqT2nkwrvFJ9pHVYV4CACaNuNmno0jJnD2lPeV5SaOHKVWVGg2k8ar7XS+CHtnvbMyzzmQF/ddoiMCoOHaVZ1TV46pEDX5tUcWA0hdupDGNl10sIlvXI6oiRrAW/TY+szoaCD6jstJhcPixj2H1wgvtOC6cuzRpqiVDggHdPUwwjLsiy1sUnZnraTELwVeUTympyO9KEeV0oWGeSYnOptSXUHQawoztmfYTBF9PXTYYnANWm0qdU0MVeHSR8Uw3VeuYYah2Ul9pjkt5HQDcOnkBe6t3gNWhWQa7u1h1KD4ACoOACDoQqAs2bHqnQEgRfLFD8C2XwPL2bYGg5OgMKXJaraqIEyfKndCyo6otMGdwdMZwc+pUUjytauyrheDrnwM15mtpFeQ2PQSgkfqeYegvzq3GOqbZHALOnm1w80fpOzg4aLI5+Nrj3MEFwM/Bl5tH6hrBudyqwtveFuXgKx2HMeMkCgFxwdeBfkzZcX29V8+ev5P9bR/4HCjSjAT2hEp8L+l70uo292zJPoefzubg0xx8ANaLvQEN/PYJPbK23Relg6J1xFX5MWX0mQ7JKI+HwPZjPfKb7acVB6pUg1Lxx5lEGdI+NvDndRZIH0fn6pznc+PERbz+i1/Ak794x333dC25994Nbl5u8Im/fAuQjDF0QgSKKDqznVQuLnUKllN0irWzLhtvCfuO0Zb8ZvocfPKCYgdfrl+Ggy9HA67piJIRjhU0n1FkRZgOoe2VnEdN42Fu3BgLLhbpewO2FJ3/OWT7yLdyV4uMKLE2w3UloNUehVghgs/NwTeDojPNg+FEeBUqBS5cG62yrzo+pcK6v4/DD34Qm0ceEafaH43mZJ2I4LOKsKjVQopOjSJrcsPWedmWpH5SivbQ/qlKXUnfvCLVaA8whfb/pedu49VXb+Ov/tWbqT1fq2Smg0+yndBLe6dkkp/HivaaamAsoOiUB595Zo2LFxs8+ugae3t+AFhpLjjZFbnRVSk6lXospXmqPdS72LpfGnUpWbLY52/V4SH4AnfwscsUzXKxkckQoX8fmc1wMbhF0jnKZiqxkSDJvKWUIPhyRv53laLTKaxRL2qbC6uK7HThtE1pCHMIPk28dULmBEnadoAlnryXFJ3q2nWEtTExtCgUnf2/kh6oGPmEjpJG++7FJjOKDrnrnDqPKJvWRmk79wEJBF+SO6un1ovAj34Q8c47YeiTfAaPP97mCaNIdjm3WusU1Tspgs9NljpB+nbjwcHww11/nReyUSg66ficq/P2IlXzRE9zxrbXXkRIjHlFE6zMcUMp0ko/Qc8KSvs4Vd+diuBr8s4Fen8WRWd6TTcEyLUWRScmUnQOqHbR/lyKzpv75/FvP/ybuHnyolcskRz9eaSQIjGnsZxecgju9xSdEYv1Hf78LYXbMFCeOpXSqSXOMkwUbf4t+KabkycHdKJXnK7HG4PyXNtTaw65tJ3oJjZWv1Grs5oObzzN555b4f0fq/DQQxu8+OIKy2XEzk6bI9ybw70uBCcHX1beDYpOS8fq0c4OgkcekmVTUqSAw/3T7JCzfWfCUPiFFJ10bKU5+PLzTq9jDU0aJtk0B59wfIRyik5qb/nDp/8qVot93Nw/j+/c94rRScO2VAGoazRET/B0mLqG/U3JekUBDVnVILXBDUEAMappEUpMQceB4CsGGxCRY/nMufy1DQloeOyxDX7mkyucPaukIKoqbluLeYpOVWU4opdmpOgs01+SNUruOSwpcfCFlkWDIfiMeVody4rQq0+cGH8NaPkwpquhwBWLotNdP2ttv5N2mFZROVTRWu5kL58yZRG6eXMst1zqVK3Hue3dSplsEXxbuauF6rjeBDE5aL1k4xEVY1knmvLWK7NWIugpTqkpFJ1USUwrKlNUrQVhvFQ5f0wzNtuAvRc5+DzDjCZkh6BFgwL+wnkkydzn2DUnYpz0f6de46WXiCNiDkR/YoT4ZkMVGV2BP9bn5z4zRYGrKpw4EfHEE4K+1KprggGNRRQmOfiUpOaKlA71SQZTGWVuBER4duOUolPzYpFNjwxwQEgU27FwaszvDXlZA1taldUlv6DQ4iVlWb0IDDWibZiybU4owyk6yYn1ev4YyZ4wzo0fcbZsdrpw2mY5QxcFu/NMX9icU/kbqLk0LFmKTnQGmbnQqEy9c9bKQUcx3qdOfVYD2PhNSINY97el/lV0igkIPs1hk3zrCrVV8WOnBjUSgNLWJ/vSGge///0KX/5xjVM3lnjxxRW7x2vX1jh1qsH+PrAOAXXdxgQww2J6S7wNhuAjiAEWSaI7+NJhESFzamjv0fUVOmOrqRYJwpci+CatV1rT8oCIWCrVt6W+G2M3bijqoUQf719oX1Gu6e6gNxeq+Y5zUhSAlXaI5eBzEHzjBXRgiBdtSIvgi4yicx31b76qlRejyEjRORrRuCGYGOEnPM6IgOUOlIGWmUAyhmAvJcGIQFOMZPstk8RyCSw3EsFHvikVvcuPDbquNxkac7dyqqwO46I7n/rU8L+P4Bv/bwxUJnsO6OY4xUFRVZHtSwAwil/L0E+PlyCRh38V1HhEwM5OxAc/uEb12K9j8fWv46X7v4XqjTdQVcChkwfTO36UHHxtgI02JsqCFbT+WU6rYT2d5AyJ+vMl8hcPvohTf/yH2D18B1995DPjHiNXt7YQ5HrDcvBxBF8JHWVyssDepBUbApkY6i2P4Hv71P343Q/8twni0Gu7l9bBxxkKPHOCdPZq31RbLpIGNqT/fE7Ugu8lRaeU3BCoKhn8NXh8C3J5Ggi+wkVHFjt7Nm8ToWs265T28EPgeo0SkFoSZGYJHW/DMWOtLP02kuCYcIwOvqFO0pfY8Fla09GQrqeaPPnkCt//fo2zZyN+/OMKN24EMwffVL0ESO0ptCLTxEHGpdQVhjyetLyzdxr0LQDXr499aRF8Zte28h7K9pFv5a4Wd8EhB+oqqhuebKV2lQDsHHya0sPybWlrs2Lg1uSpp2wkSNKXfrPhUHQWKdCqUjM+KvU5HNOMTTe8swzQOUk20ZlVTP5eLOxz/eFmMytqq2jT4Saep0ZHvVpGNSiUusAWeL39hLpwogFJ5uDTKDrhIPimSnFOI+dg/1zVHFIqn5TepkfRqSH4jtXkP8Fp4zn4+k8/RZnwiMWpCL4YbIrO9hlH3P7MZxBCmxD+e5deVMopt1ZnNsNWougMRacWPWwi+EooOuW8lHn5dPykCL7UoDxpypyyywKG6MHinBqJlI10SkMYFtwwmkw5cgxrHSLH2n7Z9z0nBx9Q5uCb8nKakycRd1v0xu1PfCJfT6FjAyiYKzXaluHafFSVjeDj5akxxLKzJRtQ5T6Tbz2Tu8YV2ZGxiwqCr0Xhfec7i8F58cYbVWvc7MqeOBF7Gz0QxuCGGFKKTmudoqhJC8EXChF8perb3GV5Uy0G425/PxHOxDT1Rcl3kEHwWdfL19w06dhWjZyyfQfB5+kRuTGtHvdEQ9IWRED2ATQAiig6rYFB5+GkSOQUnTECG8PBRx/54tvfxvLLX1bLjRSdYwfp7VGEYLGhHe07l/NUtg6lQPJKHSR6j4bR9LGwP1KRJgg+i6Kzq6/PH93Lc88pbAhJg+XrSHteOZaZZG79pb+E9ZNPFjVBg8HM1BmKEV5b47Rbk/qeuj7lOmlIiI3bj+b++3HnZ38W8d57xkfmBIx5XamPQtFpwN1ya4U9paeOTTl/pHq/006OorMKwM4Sv//S38H/89IX8Gf3vVLc93kIPtIX6uBDGYKv7fNYzrr/FMGn3YRwThGHi5qDr/9d1e6cpTJ4dMt4rLwcfPwa6TyzvimqCw7HOscEvUZDZhcwlbsSgr2nMcdx/5vpXISusnC+kI/53Ll856VNwRynnV5NmSakLag9ntnPefcSY7FOqQb3KQfU77tASm2TVYVknAHpuJHVlaT4299vUZUXLoxOtWOh6LQ6ZRYkp6njGcKmYYErDNklGWY4RecWwXe3yBbBt5W7WkqjHKqKT1buXKLy4XfX0c1WtPOF6Ai+OORpKqPopHWPF7z66h18uTnEtZtrfOMbi9kUSUAXzTdVUVXsWqrS42qc5V1lSkrpyiD7X4p4VMRz+EUjHEU+xmzE89zVLXNdrxi4xeimTW4as1ZF5XjB+6BOFtpkCMR5Rq6pHSfmUSg6ix67UqhEP2SXWRS5TnT+plq6yq1zSD1eUpd1znJmsLwnDkUnUELRmXQwi+BbPf88vvbZx/EHXz6HRkPwFYxF2U/T9p3uSPkpieCrI0eNbDbm2lJC0WmVG84xBB8paBiCpkw5bkCM6uAr5688PgSfNFKbl9l1s3Uuuvdt3qKzyQwBiCdOFHSsXOLuLm58/vOo3noLzb16nhTaAQ3ZlhN1Te9+S2NnLwlNufMtynFd14ahrmTnbInijFQdfIULSqhoDj5O0amUHozovZFgsYiIq0rXTQKn6Cxx8DUhMDRE3RAHn4guk+jQ9dWr+MmDHwS+OB6razNlIpPjoujsx8h/thx8xm95eLOxndfu2sM2D3yRaecMwyGWW8+846VlCxSaSNAANaPojNAmWxZQaSL4AiKjKuUOPs2ZOtRJx85qhb3f/V21XI6iU8vVpEoQdJmhwnKZzvfZVyGetUvRKaRHKGjT4M7p0ZG52NxmaAaTorMTaoADiPF4AoIv0fElAjg37op0tbLg256iM6EVJZR18pjVrUEILUII+p63Pa73HehQDZoOr337IUMvP9PBVwXn3eYGbwFF5xTjbVUBMZMDsyT/ujVHpu+xPdfUS9zcvwAgw8ZChOmC8tn157rjWtCRRMlZNhVe7TheYrQLyidk6TnsHigC1HHwZcXYk4YAoBr1m/aZ2PdcYkYAyDslL7dFpQbQQEZNjxjedUxpKoc+O1JVesc0/dbTK6tZCD7e4zNn9I0qe8USwTdAGHU9nr17A8H3wwtPAQAOP/UCznuNKxeXqimuI9GpJEkLYMkRcvBpirgMoDW3KUbf6f6qn9bnIviGPhXm4GNdct7RQPPL6rO7QW0Qq9VYMEn9o/VjK++JbBF8W7mrpRzBV5LERL/WEisHH6Ar/xQRMN3BN8r+fsSHPrzGpUv5KOxcDj5EieATnVZE03NVqtRjmrFz+QzVE0cxeEz5LSIabYrODV88C/uXy1ERQ3Dt2CV2dooySqK2SoydcmypuZbk77EDlAu/rkEQfGM9+7sbsy+THXxHNRTC3xSqU4KFoHW6stEMPdbmYo54F8rNikvROc5p9F0WUXRSEW3EENQIednF1cHZwbmXdD1nJGlb4r+sbsrrPGdDGKmR+2hBDzlzPBSdY//KKDq1CmfAcbQxubNMztm5ItPjpQ4+moMPckNTOIap0GeYR/AZdeQ8i5OjAwrK7u+juXy5bB2csTYO9itJI9YbBhR9R2629Rx4en+t715N9E6utQx96m/oxizV2a4gF2TOkhFxkDo8IsaNe49+CoHnu+H9CsP00oQ+qtcxerRHeY4u8m1EGbpO5qHV44/j1q/9Gu48+AirTZvetHZnO/jqZXKeIvimGHZK2s4i+ErGSxn4URcaECLPOd+pO6atucR5Vup3KIMOlDI0Bx9Fo1hNRyN/QvQCILoo//4baRqYeXVy83nf5Kaj6BzWRHFvxQ4+IS2CL+Jw75TabrZjVvmaBzQyXb2bL3p60ocfXqOqgHvv3eD0PTsd60FEvVnx719xbFF5/vlR7//lX76ZuQGj45n7MvOnT2nDERbDkEHwUTtnugYoRs6qYg1Iwy89zuoj6w2SMw0AACAASURBVNm5cw3Onk2ZFNom9TXDVdELJiJ1WunzlWkFc/vNuj6ySiGvMx1K3cJSUadNpqEUwZc+76loG83ekXPwaUKDfFuKzjLzKr1nW1/w6xoCmcTNDK+d5uCbavb19se1jeCTYlF0AsbxDIJPm9f7Pehcik5L1wUmOkYpurrQe3N4yDs35G1zpLedJXYnY+6m9/CVLwH/7t8t8cYbZO3uHHxfevJXcPvSA0l7OWa03PMteifJHo2IoSsk9RpIZDnmJILP2h+XqmWWjPsrEhzAgqWNdcN7oNp6azl4++pgU3Rq+fa8+6QUnVQsBN+WovO9ly2Cbyt3tcxF8LkTYylFZwhqMu8YpZIUk/5RQ/gwoSYRF552X6ZR5xx8NHcMK++0pym86nW56Ey7CSaMovO9yMGXE0qZkTEaDe/WMYgcuTszKDqZ0JCa9ZqfK3GKF9A7ecZ2TtGZOs92diLqKqI5JorOyWND+XY8is7+u+XKoi3nzwM//GF6vAlKtKxhVNBkqqGJilTULWMao0VqWkP0t698GHeWJ3H5yR8Ar3+lvR4GPUdmfHkUnWOXyqPMNcOMbLYUwZdQNtE6QoUl2eQOla5W7e8c9a3WXtbBNz5gRtHZNAgKNdWkz8AbKxpFT+fg0xxCUnJMIm4ePGLErJcTv+vMuiENDlLmIPgm9a1UPMWnxBo3pS2pB4QUwTdsVkvaMdquaz6eB0OdeOj0m7CchYOUIvg6+ZNHfwGPfe+LuPOxp4XHvKuevGeJ4EvHPYk2b0aaH8+hNW7qA2LUaXzlnMhy8DHnt+3g68eP3v67p0e9c/IyTt38ET8YjjEHnywuQ4dzeou27kS076LkuXhWH4miNOYMaaC20LeTNCPlOcaBG9YWiiyr4top2TVjdIoaiiSgPXTOlaoAwVc6z67rliuKIvhom5TKzTUyyfUYAcsl8M3HX8X7vvsN1M0Kf/Tkr+KpXLeqdC1m9dY25f/g4OuquP/+BpcvH6KqgBshALu7WDS32zL0+1e9FOMc/f73H2K5jDg4iHjsMYLOpGWPqn5nrHrqOi+Oee+HTXH9XwPBR6vXvuXknewsk/MNW59o24H9f/7cBk8+uWaI7KQ9pFSVgIJYZB6MeZEGKoKvdG4tcPBNWObbZyJsKMOeuQ+YYbObNU9CKauMl5A6TYdnbBm9NX3G4ne0HH8Ac8jGEIR+ozatnNQL5ig6B/CfXIO6sTAVwccehUrR2e2P64oV9ik6gbV1D0Iv5/+gc/DxsanZ5sZLovr55D6DEPg4GMZdSNfgpC7qLBFUiCXyzjv+uqGJDI4x0cWCovOddwK+9yPg3O2Ar351iY9+tKVsHnRWGPufTKfS00bQdmGVyTgv9BLZ5RRHGkWKdoCI4VV2HUhj3E3lxzjcfS+hGmzDMvC9tL6hZQ/BZxyqxPzF01ql+VeTfQ45zViEiCzTuD6ra1t5l2Xr4NvKXS3lCL44TaEtmG1++uYC/+v/doBPK33SlLfWaNVP3kqFYkJ2FcAJs2Gi2FFpOIJv2DQ7xjltr9hokPq5M3YI7F2xqLU5UaBH6QvgG4KMSCBZtIozc/AdUSyKTvYOaWTqHIrOAgSfZagA0hx80nm2txdTo6TRxeRcVaWRrsZY1n5bB/tHVoRKAVzLxNNPR5w6tcY/k/UU0tHNHkoTNOc+YlxKXY/321N0vn3yMv784jPAwT8fC0YjBx9rJA1wsCg6VbqI8SypQlFoM5Ra1nhKNgWKoW68dqTYi8vlgIwNm42OmzsG37Xl4Ov7JkpPGzdTdllQAh/6YkrZLEWnI5yiU/uYJ865cm5wrjERiXeTg0+rt2jS04skc5uDqIsFyHvLKVdVusFTb2/UWbxbUedqxcHXv9XvXn4Z3733/Xj5w9fV+vh7FhSdzjPuI/lbY4mNwBrmjy4Hn1EdO9gwR+J6LCsTQBc4+MoRfOVzw+raNWweeQTNqVO4/g/vSaLhvRx8U6fIJA/iMeTgAwKaJprXulXS77SAc1VF8Fjj6ojzRQmCb1ONTo6qAMFnBg66H2lkQYdNYztTVQoqRW7tnAZgU3TyHH9+sBDTmzsE33rvAP/y5b+LxeYObu+eQVW943dItC8fx4bOCUqbAAcqsGG1u4vq9u2kSeo0157mcgm8//3lud3Vjk827hZ805lnxc+NtW0afWxowQOa0y/5RBYarxidu/S5v5+DcowE1MFCyyVzML3WoeiM+lBv+6Ll4HP6xuqdieDzzjfG9z0i4kvmfuK0pGttMgzSQMPcvuTw5NnuvzCudTHyAI0wHid/BEUnR/B5+1AmOSc40jGs7bcTvUQ4nIbDpTSHw8VaAOz4D6fotKWuI9YG7S2V0cFHymYQfEm/CvqjiWT18OYRKcF43qX6+9tvT99XbGQOvhxFZ3dvt24FVI1O0QltTiuRKRSdDluF6/x7Fyg6mUNXm6dRZgIrkfIcfPnBGzw7nKW/Msez78wDcgi+tp9yf7BF8N09snXwbeWuFm6nsCe9NBLOrlPmJ2HnchvuTnSKzkw0pKToLEDwZZWUvpyH4NNyZTibN0NHTM/PnLGbULMI4eJIt7mS26gmyjLZwGUoOocqYmPSQsg63b5o550ifffcW6L34FF0WmO9qngXCiz21Dgq0awDRWf33vf2un4YG1r3G6iq1DE4cRBp78bdFBZqL32/FwvgwoW2j28fXMbp638OAHjn5L0ADvN1l0pdA3RzP8FpYymUdOj0Nsv+G6C6doDi4JObZGX+owi+TBf144ZR36unODDace7HEMb5nt64RMj25RUjuRYd5zqzWQ4+fu7I86ZXgYPgK2m4IB7AlJpsQsPCz0Surt2eI2Ymgs+7plENhYoc0WBv1qNZL2cOjt5gFIKeL68kEtxa9+R3P9Tp3GsWwRdSh0kTFINlcfgw+ZfmV1HGDXs+1MEXKtXoTI27/aY/i+BNEHzjt5EEiyjzrrzV46DoTE4tFlg9//x4XlIHekbPCeM0xoCEUig3KRq/5dpXuj7Ie2djV1aiBAXoOU8yfc4dB2Y7+CiyjBq6zKYMik6q+yV+TkRGZbdGba//uUCKKuCdk/fihxefBsApOpmfJM7bXww5+AKwXuxhvdgrqiNHM9gIoywzWjs5+IDWwWf1dfwxL9AwCUYoHYvD9TOseqLOUgSfht4BdN1Pz8EXwSZ4BcFXQicoz1kSoAcNzHXweW0PDj6NRSLX2YIcfFLeOrgfwHfUc1Xl6AkDgq8ZEZmZ/kkEnzZ/muucOHHnlVcQYsR3rn8A+Kp4r2LiKvEVVZQGM9Tldg1m68nbmwDg8OAM8JO3x/N9W4bxRtKH5oQ7ttI+DX6EqjIpOvPThx4gMqCdBIJP1uFSdMbe8cC/4by5xcgNjYLvnuY8pAGWhXPwZjOWs+gPkz5VhRSdVYvO6t/VZhNQR8PBN1TlGf8UmeTgKwM3JIgyk6KzQLmVbXV7G9aeyMHX/1ccNJCzZQTLwVeOCGyDCqKu5ynvn1Uh31FOV3BeaAht4NChMGFtEXx3j2x9qlu5q6U0/0ddGUi1gmtFi+N/IV3U+z5pyhuN/ilx8HkzXk7RHXubV4jpGmk+F2NBGBF89H5TBaykv5ZhkG141QXNF+p0mCWeJippn4z7rGTkz3GtZsHPwTcotF4hcg+zEHxCSqh2aKdlpJKkTNnbax1BnuPdFMN7cOeVVxCrCm888XK+Dm3MEaRW0WW5jWl3+j88/hq+deUj+H/f9zfRLFJaOK0eu2qh/BYiAgEkz81C8FXVqAAPwJDuN7sm+jn4ora7Cj6CjxSTl7EfSR6OzDRrIlJEQf95cgTfcLR38InvqmRoz6XoNHs4ZQqaaDDGsjUGe5GY3uXsnTltcwTfDAOkU6aqMm1b4CtHt7izc8o85/bLEZeWpsi66JQZnBxGWSenQ5I7V21Hf/51beTGqmswHcxzxim/k29dmR9KQ7slgs+9jo6rhhrTDKtSIA6+DsGnLcVyrqPP3ETwibU0Gg6+bM7UTp55ZjQEPfxwhrpRzqFiMo44PorOpC0ZjJUZL9rd90QCsyg6peJMgj0sx3/2GWRyqpRUGnd38zAWdONkgh5vauaeUQiRIV2o821KPc3Jk/jur/83+FfP/9fDGOsRfFFcWuzgS8Zuq5+ULDNTCmyqPEWnlaqgd/A9+ijPu5vQCx6HZG48Oe3lBDIkoYp3Hh1z8HXv1KLopPOnnIM2y910nRDBAYnh19T1fZ1j+DemiPWoMVjQa+fm4DsKRWeBg0+e/w9PvIZ3nn0Zh889p19nveOo9DND0SlfuEbRKdc1i6Jzc/Uq7nzykzg8dW44PyAjuzI/+lGFL31pia9+bUdtn78vmYMvrxvLm7Ccb5KJYL0v8oL2yBzmFOvmrsRZqXeG7inuu4/ci/IND3ufqmb15Sg6tWdiTTNBuRdatlHWDgmil1KgCqvfrzaPJGJRdBYGwj/66KgvPPtsGdqarSWA+VD7d0gZxmoDwTcE8Grd9h5g5GACAAmN9xAkXPQilL17Iap/EjtSbtBA7FdR/Ep5O4CJ4Ctkk+e/Z0TO0nEZAnjAhErF61an0nRaCL6p6v1Wji5bB99W7moxgkQTqSq4G5tShxkvFmyFS1FUKEWn3kl+zkqo3JYt/DT79iwEX4csS8o7K4i2V5yZDkCVxOhTCUOhlCOuDMmomLBjL1YUmk27iZu6tzYUMXreu/0SBF/0EEbvEkUn/S1z8A0IPknRaUas5j78tC+Hn/gErv/dv4u/eP/PuQqjVcdi0RuL0gtUaq3C7/Xm/gX86UOfwpunH9QLKB0sHf6JgcX7aOWm23BaUJupRJlQB5+F4KPtyTZaA4dlJrTHvW1s6Q7NpOicwsPR0nd1P4oQfMrBifMadYhIxVr7/CYp2dnFVcgynXjs95XefI7SqheaZ6xaZihyZDuZByCjdZO2J0ZpAsD1g0teF4vqOHLZWeulNt9FlTJz2KzmPOmO0MABJgpFmBlVrFmQhKj6W+EaTS8NcQwg0xzDDfk9UnQ6udyoIbIzLOaWYongY+coDbek6Owqk3WWIviuXt3gU5+6jRdfPMQv/MLt/AWiz6xo8J/JUSQWBmPJ81Lfna3ryvxHZC2ItU4BmHRR6oMF+rrbDyBF71nXV1WCLvMkWEZjarAWqJSAyCgENSPtUDajUjX7J9l9DAZqcW9rQufoqmkhpcu0otFdyeiCDcZnLFGc/fdtBj51Dr7Llxu88sohnniiHWM9IiKETCCCIyUIaVfmWPXEee/RMTushXQqQPA1O3tpt0RwQAiOM4K9r0K7QiFFJ6uvyMGn6FZGWyUyh6Lz1t45vPnhV7F+/HG1f1kE34Tnmebgy4/1nB5Hje+sbzHiT/90gRs3An7v93bV7SkbIywHXyWCFC3HwdgBT19InqG4qd3D691YT403Sf494xm/9totHBw0OHeuwauv3qG9TMqO7CWV+X5V9czaL1D9qZL/QKXo1IJAJVP5lKmwr990zMo9s3wsRIkLbkFdPvnJ27h0aYOHHlrj4x+/k78AYDT5AFyKzrZ8e75pgHrjU3ROzsE3AcHXP6EQ/GuS2yitvpCis6rAxxl0e2l63bTgJrpn6gOM6dicgwh0A+2t6zDm5ZSi27WEPiou1ZCmJ05EtY0S2tGtHK9sKTq3cleLw/DGlYIQ1YnZlIKVyFS2op6A29qUDU2lu3m7O4UOg6GPHkVniRPHNNQG3g49P5OiUz5XFpVuIImOVXIbV/pbGI2sMUHz7qh1lspiwTHvwXfwWYoBu4ZsXsN6DZnHQL+IyBwHHykjaXZHis72r+bgo7n1XIqwuk7eyFB8uRwUdrkxdPsO4MyZiLNnGyyWEZ/97C3803+675Y3wt3sNidIwSer92GCg8/6lBeLcczHGNgmlAWi5xx8WodDR9G50ZXook9I23RlNmLmY5EFNaNPH3RMEHzJ96VIidG4fb7O+YGOJeLee3mFc57V3HOxquz8SEo92enCaXsKgk+dm51BlUPw2Tn49DY21RJ/eu3T+IRZo9OvuWVnfTRpeUtH6TeRieMPZQg+y2hMc3uyZqVzgryCLKpKWSu9HHxZoToR0aOkEagvMTYwttBgjHCXY54h+EqcSiGoDpgQkCafOUYHH+Dk78qMvxTBZ/dn7tgdJJeDr/BbiTFkDTXq9dTJGiOjQ1fR+loXJnzPpXTwjebgMzrTVDWbc70u7e02uK4UctlToqDojI5DMbO/kP2iOfjaububl+dSdGoIq5I6MgUkYwldZ/p9pbYPikDHZ9/KYsENh2PB98aQVvT90ndYtOew+06Nqn0OPgvB19fTGkKFg293L8mjKBF8sm+smxNpRwGUU3RScSg6+3GpPvKQUnQWi+Hgo+/FfM0KfZ5L0dmtUVWIyJORpv1Q+xJC8nosik5N9xkeWYxoNjyAYb2GyjLTXy9pMItZhWi/rDVJ6jFijt07fCfVwZoGYYHUwWfIpUsNfuu3biCI5U+nuO3+qUeHYW7/YiH4AH7cysHHzhn96t91MIKti1Rp5bvX5pHE5PHOO/iZf//f40tP/JVZCL7z5yP+5t+8WVS2l0QfHPR1JMfpPbwrCL78aUDYE9Xy1rwLIHj5RalMyMFHb7QPjpAsP8UUnYbQsTUi+MY2JtXX73O8VDmW/joZwee/UI0x/OTJiBs3lDVwonq/laPLFsG3lbtaShF8LbMTnazmbe7VTUuJ4Wq41umkNF55NzQBwWdO9kBHiESU82q8zqly7GN36aZRFt3JM3aHEnApOjMdso5NMYLmztMI5IT2ybgkbqY5mI1ysr1cPd5Gb+xcxZGB1sbRqmSWg2/8TRWZqho36n3U8e5ua5AMksuzRHJGhYLi6oY8AM88s8Zf+2u38PTT3OClo/qmd7O8L2ntKm2fvHYSgk8v5lF0Mh+PRtGZQfABTqQ6iSab8qlrBYofi/wWM9SIwzim3+wEis4k0CGzd+nnzStXNjh1KmPoUI65iHHPkKwY0nsDZAkNUfYTddqe4uAr+p7oZi6zxJZGVf7w/JP4/Zf+Dn735b+Hw50Dv9JOSiP/AcwOpJkjGuJq/CHKMoOeH9GqJWLXNvJRQQzSQuyc8m7lIc3BN4uikyD4WlSCmCsMKneEgPHW+Zzg5eDTHruH4HMdfEek6DyKyPw8lKIzkYn65JUHxns8OIip7jSV9wjjo5tD+c7WixhZsMdGcfBFxRht9vEI+m7c38+W6fuT5GFTpR03D11dY3+/jcB/3/toUBopKWN8EFGR78Cj6HQdhXIuADE2iROlDr7km+4QfJMlh+CTtGqiTa8KKwefummTxzNyZASfJhOdYV5xhs4xaNa1PFkJReeuguBbpu/EDPBlxwv3AjMoOqVCePXqBiG0+bz7T1rV+zTqy9J3Jyiyeyl6jQbFW/pOhO5KHVDK/EPjUaWDTafolH33dZNB9xAT12bNn1/TBGVCIzqMcKQV5eAT9qXitTFwXWCxvs31te4eQkj7ZUkIERpLs0fRiapO1nhaH79G37OYKrqK4NNR47SuiqTrkZ9AybJZmoNPk/3bb+KFr/9D1vBxa1lsipiQgw+gDr5gOPhGfVX9btwFVKGzVu6eogRVUfYFw+9C3d3du/dl+iBd+Q0r82YxuY9xX3RsNQ1w6xY3w5noNq8+zwZkPEP3+Sk6VQ51pyH4Tp7UUZnv4TZ2K51sEXxbuavFQ/DRDVmSg2/OZgRiPu2NUwoCyKLodEU6+JzNtLZAubdElFU2hwvLUZkCScore4VBJs7YwyIno7rp71zuxOOQZCPvbGSl0ciqshEIvkybprwLFJ1DwR4ZuFoRnsz8JjAZiyoVmtzE6XVVFXD7NldClktgLSk6F4shAv7EiUxYoBSh3MQQEi74bB39qTpPo2XVMSuQWqlbfZR1Dci412RnZndAvi8LOUuH/8Cf30f4Mb98nqIz+e7QIfhUK4WSO4tUpf/oRLyL4scyIUzPQvBZFJ2qiI6VOvieeipto8QW5+aQ8CYZuW4tl+OhgoZnpAoYyxHjRLVIdo9upZoTjb76NqLYFuv1J4a6EHBr71zXhcII0yn6ScbRfCTxjFzkhKY3NLl3b3zXgBPZLx46R/DpdfHfQk9THXxps6rQdUQg+LyyQzOR0+rK8kOAAKHtcTsZgnCqjk1HyU1V5OCT9cxYsKaOAc/KNnEsnzkbce3aGtevB9x//wZNhqLT0vOETbScotO716ZhCL5V2IHy5vK6hWWw0w+1IudrxcGnvelQ2eNLbQYNXnxx1SJbHmhwi9RjSmwpOvv2PQefy+ahdIpSdNLTU3LwsbEQKoaSK5bMBRtlJNA2ARUM1Z4XDr7EqA/MVDyVdU0WSHR8eV6r07ugoE4iOQcfpR8cP5vUsdbs7KdLqpg75HNlRlIyvospOo1FJ9ExaJtiInrggQ0uX94wdVNVfzUEX7HCpe832f0b5y2bRZaic1JgrB/Ypi0vOQRf3+0YBIKPBKb2CL6+wECzxyg6BYKvxMGXC0Ih9bFidYXbu6ewf/stfqkIMgG4Du1RMNtOC9vBF6typKLLmMG+te4lkO+sgkbRaQS1i2WYi48QTudTGwls1bV/+61ZCL45It+nSendBb81g4MP6jqRnTKc73PI+VggsxF8ZCyHEBW9uRPDbqdNAYzq1aDSSB/ntDV22F+FgK98ZYk/+IMdF8HnqZZD8POMjTUbl4AwsOfXZylaDr4TJxoEi2lkK++pbH2qW7mrhSP4vMVZHJhjfBIyUNNJQ0EMzHDTn5YUBFLCwp7Fk+5MpOi0IlZSBF/+vqXBg7bDzufoVwxpRF/pb9UxdExGzDm/k6hw4/0GGCH4U/umIPi8qkZFw1c4aH6aQBxpRRFROUOZUsYav1UFXL/eRZPJUO+Nnth7fz/ioYfWOHOmwfveJ6LOjuI9KCzfbiy4kzxrmDt6s365xSI7Z0gDgVepNS/U9Wgg2WzGIR5CTBAuyavIIfgCj2CWSmxJ1y1DJS8vjP6lTniFopP+GBA4BRSdJZ+ZzMMlpemeSZ93p5fDF15Q18bE7uds7t3uyRerjb2+PeWExyQyXpiXeukbA9Vv0inUOpnstq0l+D3fqEwxEGj3fMS2reqS8aQUtL5jj6LTslfnDEkynxXQGjTksStXxnXmpZcOYYmc32hfNbRP0p/YG1T0j2UwksHLwTcebFClTtW+jIjEYOu64eDLGRWKJHNRQww5vX58HAi+Pgr80qUGjz66wd6eoqvl6hPOgL7eprHnBbdKURFdCw5hUXQ6CB6vweDQocmxWUjRefb8aADUDDdJ9Z1BL0G5JfslcgrHRNGpfOtWn6kzaMoYb/PsTneW5Zw+G2qUDFYOPuXCEBQEX2TfVAiYtw8BkNhK5XvMVaC1dYwIPk7Rqaw1yhys6SSbnd10jdlJHXxlubjsvcDhBz4wnPv2/R9S9lG+g09jWimZ4qr8m3Jlzha8pejUHUHWfN+vUWyZtcoGfo3VLxZ410ku8Hqsg8/f6zVva7OBq6BLik7XvkP6S0Vf39NApVAFvHVwZexbvQNIGtimNeYn/TJkytw4zIv1mGvQ1mFaKaXo1OxLiWNCVEDPUTavXJYITUoRfF5ds9icZkiCBh8+lFSPaA+N70qT9nhbRpuLc8i43K0OY0TRvbRKktso4rJHMQNUCEic4tq7Oz4EX4U336yYc0/r7lCNV18hgo+ddig6NbuWtRfrZYvgu7tl+8i3cldL6V6lCg1ffks3ykJ0RV7TeMdPp5+46jqq7YwKi9wweTdU+GnmJnRktC7luDR4AIbSM1txkRsdkrtEu+2CHcccSiWzfvq7NAdfQb64krajstJ7jm0LwZdsx2m91AlRQiUxh6LTGBt1HQcDawzVqCDEyB1SYid75UqDZ59d4/Rp4azJJhKKgLVRJyVM8RRRKgWKb0JNk1FuPdHvW/ye4OCz9GEODOk2ByGNxgzIOPiU/sW+fuP7dvYqarn+XNYRYG1u5LfovNMmVD6CbwZFp5Nypb2vUOHxx1fY3+eV3fnYx/Cja6/g+ol7kmtYexqaySpMRSJCFgvmnMhJNuF24Zg/fZb/dpHX1jFnbshdap+YsfZMMry+ewaCZH6TeoDyEQ6bVRmhrq3JxvO3vns5r7HvJve+tfqQjvkHH1zj+ecP8dxzh/joR+8k1wzV0RwwJFBKX0P0d5QY3/vjgefgsyg6eYeC7aR3KDrH8RPNS45NEiP2OEbqGvjgh1bYPxHx8MNKIMRRx3kGwWe1J4sVI/ik0HlSIPjWIeV6VAOFjD5OejZyvtYcfEp9n/jkIeqdGnUNPPlkARLdGrCU4l5O/TEyI93GcfB5gHOt/0MaNYEkKKLCUiSGUErgke0bFYbgE/pLj8iw0IsaRWeyPszNwScvy8yvyWktSCwXBJqp06qK5oUejpUgpgDE5U46DhaKg8/YE2eDZLvCdz7yEfz4Q5/Gv3n2v8KtvXPKGhHgIkE8hVDpVy8VjoDgM4oWTaXKgmIi9Un/SikkNdEAcLKvpTn4EgTfmpfdbILrMaL71yaEsvGYRqarxZLnUlX46iOfwWqxj6aq8aUnfjnVwSZSdE4JaBttWhzBl3PwaXWY5UgOiP4e2O3JfX3XeF1Dpei07kWeE/EXw99cDj527j1G8CX6u9QBBEWnJZShxwoy8S4unWZKv/Pkky3MwWcCHpIpIHJHcheoP4ybYY44mu6s7a+o5GiE5TG5PvWiHWNrFxRHeaYtT6wcfHPWkK0cv2wpOrdyV0upjipz8KU2OGl8mtAHeW2UtC9dJFpuzZKLrrfIFSP4MuWbZnI0kdAR+2rStqVlvbBeLwffXGrVooZn/FYdKYrIHHyzYyi19pwN+5BUOnNLEmU01FjygR2jgy8E4JlnVvjmN2vgOw2eXhKHCDVKHiUH39QxNPGbkEYZAMeqxLeUos5cBugUFLJgzmNEfxpGC0qhOfqFW+M0o0nSyox7/wAAIABJREFUlPsMgm+2AY2KptAmCD5+XkbPmQU9BB9GpbwEwXfuXIObN3ObK9uZ/+yzK+w+scIDn7mdntzfx5+/8Cr+/Eev4/mv/x9DX5M5YKLxZBA5tpdL9DOcmbPNuRyymPP9PfHEGt/4xgIHBw0eeiK6lF9Fnz0plEPct8VTGpi5j5HJlPniPQx9TJ5HbxhQyv74zCO4540/BQCsL17CMpTT01JkMMANRtZ7lA6RpE8a+k9xai9q4DOfsR17Q5/YPDIGSrX15dc7aizRxDQ8GtLn4KuqNDe1zP/2nuXgK7L8jvLBD63wwRcPcfA/KQplpq6HH17j299u59rHHlsDr1uW3E5y1j3rnVm0TzmRxlWyFtyJOzjlle8vs6ydhfoVu6avsxDBd+pMwEd+Zo3qx4cuDaDaNNWZnTZCbFCHDfons3YoOvNxE7ylAcEn3ntpHvd0jzgPwZebr3nOcakTVV0VSrshKHBJAFogxQzJ5uCbI7k6xLPyEXzj/+uNgpjOGDfpwbizC4CsV1MQfFWFwYfmDdKdHbzz1Ev46TeVHJidpAQxpM2CSAN1+xPfXQefXlVUX14IetAz7R/bNzv7xqEdWb8omMv9Jmsa6+bz92bD0S0UwUcDnwcnEPEItplGC555Mufo10jdJ1QBh/VJ/MsP/D0s1rdxuHOAEG4I1JvmQLX7ZH57iv1hdMJ5OfiUa4L2TXFdcFjyBIIP8BxInc5T1+x9y0+o7DMo3B+6NZC+HrNNi1Y3IvgiP6ks2q1TKO/g66+dvO1QbAAa4q5ltghqN+XBZAyVBq8Y9iO+fwjJntWqvxjBZ0g/J3mMJlY/zd+zEHyRF1GYPqjk7lOyJuzsRCyXx2OW28rRZYvg28pdLYqdYhQ6MQcdWn0UGSdjoYRFnlul71eWorMWN+AsZNbMKiNThvYcik4UPBcrUP7dQPAlOfhIwoliBB+9niQGniXJJpv8LowKDzGTg6+wbbnjy1VXbICw8oS9Ww4+w2FUVa0y89prt/Erv3pnyK8XBEVnsQaV6Uur1E4zMjDpJiAtethq05JiG12B4yLWdX7O8AajpPM0HgEdX3QuWCwiaA5RNceIRLElHQ4M9awq38g8t1kOvrSrakEvB18gVEASuaHU+/M/fxvLZeTfq2iPUqBKOX064pmnD6GkUxq6kNu8Nd55Z/wmAS4EwVdSRZai05GLFxu88sohnn12ncwpSXOawdwpIw0OmuQ2oG0bMxafKWvnu+ngk9+e8bFpc+jt3TP4oyd/FX92+WW88198Tq3eMhoPCD7Z/pTQWMOIwdqvlHmycJFmCD5C3SONv4BjmBNUXbSvNAefheCTc10Tapw5o8wxNMhAcmW9hxSdWerSoFA1F8pnP3sbH/3oHfzar93EyZOK4Vj+ziFgtHUn+hSdrjgIPpuiM1PnjH4koDlt0TDGZL2sioNuLHp3lqMsAfFHYEMRfA6iJJuDj58fVHUXwWdXKaXPwTdZMo1sYAcOjTn4yhZTqd+mKtYxWtVyeqnWVgbBlwMNUtFy8NHxFZW0GeowR0yRkJkcfOYeeoIlU+5RI0K63DHr/TwHX6UFiU1xTszY3oSQ2iYAXS8d6lIcfNm+iQlFswlZ61wuB2tPld33rVnzd05z8OX6Fivu+LKeX/LMzLEj9+BtuaZa4HDnYCzHLipH8IVgO8+090MdfIOOGNvSY53yGiOoy6pboehk04nsVzfRV9XQmUnBOr06qvWxnWenIfgGeRf19yRnrhUQ1AW/TUHwqcGmUr+T9JbyEzMoNfu5Ore/IluE9jd55+6cVJiDT1ILh6gDIrSxXNSAPGycN7c96prZBTEUbsLZ8xPvSN7rVP1BUnSePEmDILlsKTrfe9k+8q3c1VKO4It+4dxv7bBj3NLyOmRtU4lzzpGgGcTTOoZJ2bofiHwsBYq+NHgAPG/EcH7ijN1fJw3NXkSreWxOw52YUdJa+cThpvelatbQoP1TRVP4Sza92eFNDYDU4lEwNpI+HYmik9TLGhEOvlLLSpais0C88oUOPpUu4RgAEoDRvQKKzkk5+IxHIHNl9YWrKkXwud0xFFWKEGTnnHGvzdF2gbSImV/I27zI/qEaXwF9F/0YFs/j/PmIL3zhOr7whes4f143PGfHi5f/QxhS+o2RZjxUxd2tinM0B1/Bd38UBB+7PmcMyWwW5e8SBJ8qTp3FU8+UOWp6uOi08kSkcUP7DgeDWAD+4sKT+Oqjv4B47py7zsqhOxjdJH2lQOHFGNia6t6aguCzctoUiTG8VH3LmP8bEskvx/z4DBRHiNLtHsF36hQvvFqFdIwowTKW4dNr86jSzzk5AweQD2Y6eTLiIx85xNWrG7WuZA3OhO/35XOXOVWYJ0OMTM9aQafo9IIWaR9L9SsAsyk6h+irjAyXWtA4idSnKibiSCEIoIHdXhIQKTpxeJgcGhqk3ZmN4JuZg28qgk9bo612dcez0HdmKp5ZBF9ugihZfzPnPQrVOTn4VIkRcW+PH9vRv0+tm8yBnaHZ5ec0PdqZ+GdSdBbnqZpSZ/DPS0M5PW6uwTECqxWj3cu9Q+nUKQkeyxvPBfqp69tmw9+zRtHJxkjDc93J8ePZaeh11nFrDLLbEUEmIYDlZLQdfHaffAefTdGpXsN0Zd320H8TgVF0tpE3p978XmtrEY2FgGGipxSdcu531xSlL2xIyHnWeGZNtejWIGVcHbM0ln6lzN2tPn9EBJ+slwRG1D/8IU5+/Y/H9zNUOMpqBbz99rgx1Zcz+zmXzm25XIG07ii+e/JnkOI5xWkHsJ+/XO+Lls0ZkbMyBx8VbS+csC6J5yJtKSdP2u/nXfwMtmLIlqJzK3e1lCL46koi+Oyy6m/lsIfgow6vEcGnRBXTOhOKuswiVDCB5xB8MCJSPFHWO936konOtCRB8FHD9Mzo7kmSGwvUMFkaCdRsVEV4cl+ULOreo7Vz8HFh90Eiy6fmZwQMI0NifNerYsVk9NcMik5VkUsUdF1h1zslpEm5/9V3UqBQFttLHAV3EG1cym9nSg6+hd4Zy88qc2iVIPi0+7BfszfunXpD0OwoTD796Tv4B/+gRowBv/iLt+yCHkVn0BF8nlNV46tvdxjt/Ww2BgNXL170cIhZZ8ZsB59cc4wcfFYVuWWseNbMOPgSeufMPDUXweetUcXL4JTdznuJ4DPOG9bbtJh5Pf89otcCqJ3Hd6qXr9vjNU7eyYxww8IYmRpC2pdGiZV0KTqDloMv8zxDi+Crqoi6Hu2/J09GxKpCrKph/gkFDr73gqJTDYaZWVdWJLppSgKeTkZ204K+eHoR8T7FxQLrptCwao3pI8wXpRSdCKGcGh0wdUeXZj9yB9/cNSmG0Dq2tcvE72JiiOR9zkTw5Rx8tIeizd75Z+Y/MhB8LK5wQkBnc+4cqp/+FADw9sHltD3nd27sxn6y9PpSout2kg+yK0PwIUJB8Cm6nmHoz/oRJ3yrR0fwKc4XNGiAyYG9XtFc7krL4Cz3X7T+sFrh4O//fZy5+Dze6c5lA66ibwjXAPM5tM1gfCcIvgBgs+bXUYpOrQMti0/7DGKo1LmtCTWqSBwglj1HSFJXXTGG2aErivGGOkUaI4evNzx0B1/s/5lG0am0qelT8oIQG5z/v/8JnvlX38De5mH8m/f9jaRfoWkQh77pjpoSQJ+5pxmUP/9bWNe7xbSoR5WRorOTzCYsu0dMiR/c+uNyiXBnpLs/+y//KR68Diw2hzhx6w2cuP1TVv4HP6jxgx/UiOcdBJ/TZHHwgkPRSV9hKYKvmKLTuCE6x2himm+9yqbQ6PSnEflcnn3h/liXCL6Bkcv/nLfyHsnWwbeVu1pKddRcJOws40F3jb6ZGBcQStHpiqPMFTkeQkiN+EN/rM0uRzaaxkljM9MLjZgcbX7zFJcojGE8B5/fN1s8DTVzvTNWYiFFJ9Aq+EdF8Glh9f641xfU5BqLotO9yDh+BASfFbWHpplH0akZdac+e/edxq47Ef0Yy21GSpsp7aZWLipolcSwNgXBZ8wLIaQGjdhRa549F3HiRMTNmwEPXVXGFKWsUeauUHH0cTs2wlDeMtIkmy7nvrRD5883+I3fuIE7dwIuX3YiSJJNAh8Dg5OigKKzRFoDv6NQ5xB8GUKG43LwYbmctMFRjVAVK2C37fWjZAw74yOEmDUolaxHc1CAU+ao45zPplwbQje2DcrCkpxN1rPpl6N2TG7GS8U3x4Z87ltX14IKyTc1h6KT6FFTdBRK0SnnLZqXgyEVDYloJ+MQgOeeW+Fb36pxcNDOwTccBJ+lkrwbFJ1JnxWKzmI9IyNZJoZcAh7lvfTsprOQvdRQROFlyyUajYpS0+2OY98iUSaFFJ3tGm0Z6ZXf1oAldWgUnVUcx6ZlcAaQpeiUCL6x0sb8OeVxxtDOU1NfQW6+XkdKY6E7DEopOgFl3Z+A4Lv1uc/hxG//NmJV4fqrnwV+0B5/8MH15LGYrL9Trbfw1WhG0dkETqkI/blrXQjQEHycQlfqBszITO9TbUC/TqN1dnPwFSD4NNEcMlN0iHxRha0jQDWqaxSd7LqmwamvfQnoEc7G88z4LVjZqQg+1Z4RI5oNL5vLbNEG13SOFFTqOGiqBUc4lX5jUte1xrq4hxCQIAs1cbevilPFpui0RTJmZN8po0hvsP+tryOEGufe/jPsrG6wa0KAzrYjp1fnRrVxIPegEYFRtWuyrnfwnuXgE+unh/gP0APR2PVE9zGDTGj5ZN4Envz2v3DbaPtpO/jkMGJz7xEdfLJi+c1QNDEvyns1H8Gnn8+S0yi/1WCszBpIEXyAWCu0ebYKMB4JgDRo2aPoPObPYCsFsnXwbeWuFo7gs6OnqsAdWZYjTLs2e1g5yB1ebbsqnR2AfsnycvCpUnHjVGvgMWZ+Q3EJkU/iJYYLriN2ypuS32Aqgm9c5KSD7z1G8AmxuPkBpAZH5/nVG8Nx5olUmCZa3SwEn/xN6w3vQg6+RLc3xiO7PWlZm0PRWYDgy25uvfOeUbe0jqmSbOaUMsrz6Y3xg0xw8FXBLpsYm7pNdKgCnntuhRs3AnZfvANmb7tzB/V3vsOukZtS14CXoT6i9crfJXvmc+fGKE+roEvzQQ0JBRSdltANYzZllHPOMqSEMHalCUo+MlrQq5xINCg6Q4jqt3ZUik6roqJv0hkMcxF83jXF08CU+WJi6GNz7hw2Fy6g/slPsLp2zS0raQqT9U3OKaSsZcgkJZVD0njQlxlDaq1Hk6NDA1L9oae2OnzpJez8+38PADh8+WW/Eq2uLgffMKTkXKEFgUUkVF1j5ST/UrCde/I76QPLTpyIbV7KXioBXVCoL+SzyxkVZknuuWjf5HF1QF6fQ8BIA2/7y526WVkPzUFYEuJiYSAZyx18SZe8OYhE1AOYFiw1xXJleM5cHT6iHN2Q6beF4JMUncVApmTszqTozIxjZpRV8hYBBuooBHMx1WjGivpyzz24/rf/NhACPn5riR/+760S8tnP3p63NlIpGXclui45F0Ic96TyfRUi+AIiGmmZ3PFz8A3rYyHiyrwBIlWVvnv21mdSdFZosAEmOXpzdZZsoSwH65RnpOmw3vUlfaXrrFaQslHQHHwUwRfROpUlKtxyWGkIvhAUh0wpgk+sFaGugBUvEwL4N9c07ZUFOfjcrY5L0Vkzp5GfJcfJwSf1ctEp6dipNqt0fHffS11jeE+JCvD/sfemz5Icx53gLzKr3tGv7/sCuhtoNhogTuLghUvgbRQ5FEkJ1IwkyiTNrHFs58v+K2O2+0VmY7Na2w9razYfZLNam8M0knYpyiRqBIGkKC7ASyQOgji6ge5+71VVxn7IyqwID/cIj6ys6iex3AzoVxlnRkZ4+O0J+RLdr8H5NH7ECQ4m5drScvCpPfhMreBLHWRrZ/SZJgdfNxd3N7RoegzX8FcbJUsbiYDmsjOWT4bN7WWhw8Rjvjw3B19RgI/MkPDCc/dlkvZEWowehuhsEWhQt08R2Qp0sFLwrWBPg5ZGdeNus6DELh6RJnjsNLlVGnA9+OIMq8yQ8PVr63Z3crYovNk03jSygs+/sDRpCtzLq2nqfwfr/D8fghCdTqJglReG9EwLGm6lgWjMPB8KO56F2dJoGcCsIafgi0xXLY9x3yPXg08Tn4ASZQJl0HeIztwcfNnbhrEKhMlPRsxV0SpbOGLSU7K09Sg3oj+hWstpoME5aIXUBw9a7BLB5+Z/+A8Y/PSn/vzInigkxjv+OLrUlinvrHSJhOisjJODL+XBFxnGZRhr75HYyyU8+AQF32ysbh58wZ02HML1PkpBiD663RxRQwzuN9cHmVcXD75OngrzQK6AwBjcfP55lK+8gsmdd2a3pb/rRzyj6TXjmNFGMEo+uRui0xs2x4OPm7ug4Nv5yEdqAVBZYvfxx+P9NN259kteJATG85N7d0vC8ZH6bohOa5GUy9cmCZE7uihaIZcmRGeuFTILKYERpfXA3FPKvpL1O3vw+ZuzqjrMhY7vhugcDiN3XNzIpO0zYz7VoUPpShJN3UOITvfvYF/DqnJCAWle5cgR4a4lyGbis1AyMAqjTvJLZpC1NYvdKZF06qxTUFXsnKSQ6WyIToqHcxU7U97gwAGLr371ptNP5j3X5X4OlF7xIRwUF+wttYLPVkyIzowcfJR+i/ACsSVL8roCLemFYua6gENQdoJ4xAVWqViAxR3GAAXZy7FtId1vMmvqz7WOCmKjdWinbd/eRKyHNyymHnzKsM/WFOzdXxVx/MriQ2PC51IOPk5IrwiJnEtiel52BN9786FtmLMhXnmOAWjjWdXULayfEsUYeDn4mj0c5OArCkRdksiE6BybiBMxGJfrwAI9+FwIPOAz6bGg3PHgU4XoZDz4NNA4DLD1A/mH83fq2zUQoWOMme1TY0AUfFO+JOBX1N2LYwL8+nOGHqm9Y4yg4Et8AOrB50dZYowr6fcm67IK0bm3YbXkK9jToM3BV6DyQr9QIUJSOMhAS2wydSspB18EqAdfKlxBYN3lVLp6ddw2/NznZGvLaSAT50GecKX1/uDyw3TE2PRbeCE6FUQF2+c8ITrJe0zOnav7LApMTifyUjhQxkJvKCHI+WdMVATdWBintrfnwedxLum9EezDOUJ0iqH5ZslvmIoRUHnwydRYkCeEAsNI13vNJYz4PlK8YK4VmAcaJatC2dR2FzFL5M4kzcHn9fXGG4FyD8watR583NpBH6LT+60IMSZBcEdEqXjeg49d8xTR3RD/NqEfjBTW+zwu4OocopPepaIHH99P8ihrP1DgckQrKCxOA9yg+zbph5mQ00eXe3ZzE5O7704bqEwZ9HYPch58UK4TS3Px7VrlFmH0udDD4hXFzClo2zCtGxvY+cQnsPPcc2qjnZgHX8x7xHvuhOqic5+tgVEq+IxnDOUB9eBThLsOhKMdtnXKs2yRITqDudD2SgWf18cUB3cK0eniFjd+5GDAG8PRu0s5RyB+Hu3hw9h5+mmMz53DzV/7teRcWygKlrbLZZ3iUTj0ITqj/RiDe+4Z49KlMba2Knz5yzedIXyBmS/k1Ss9rGvEkwPMGt577xibmxYHD1Z49AmB/o53MZ0TsyYUz3bMvdZUF5sEglelcjoGGV75xhCbQGp4yxo4MetrLcw+EraW8eCTlC05SxryDTNglbguDpHovSKk87xiVIxmXT9pcYul2kj3zEBPv7ChwCODa+aa8uCb0T7A9esFbtwwqEN0MoYf0TzY7pz9+7g5V109+Oi+43Aj/QZmajyg8+CLvBezD9vUIINS7DOHnWJlYc67FEQxUVbj4FuY1hjXNVAnOEuRPzsul4uVzaBw76CFevARRZkwVmOOqVHwNZ1pFHw5BvDeOBpPA2bInYPHxal4kFDwAY6MgXjwzZWDDzy+EvmrWSu5Q6YzYwC7taXqxZ/ObF8aA19JzrxTautSO5l9+yKGJz2wzSvIg5UH3wr2NGhp1GN/+n/hiReHcuVcLtWpw3nwTZycGg0STDKDuTGROOw6fXbkSIX3v3+E889s4/DZCfBTiSP0Cf38EJ31v0kFn2I920suCNGZ8ODrGxJ7Yffxx1EdP47qyBHY/fu9stj6FdU432gyRQUnGMqmenL5XcWhEz6KhhxRzVFze2uEinSjdQjRKQs8ZjAabGA4uskVpRdugR58WmDblWV+DqII5Hjw1cJpPxyju48G3/9+2IkxjAdfQpqjKVII+dXEMxVORxC6NTMFn8ekZ+dNMZ5VevSTJTz4YsJSYA4FH90csRx8iuYxQ52sjqjAkfOsi+Cuurv42KwenXqJKZl+aR5JWKCAYPtTn8LWv//3MAZ44cqveGXGuCcnhWet6p2a/lr0nsBhnoeGhnYRPPg6gYvfwswg/jhsiE4TDdE582IsaqUSczVSoaE1BbsKgZFJBw++hTDg3PmTBsqdQGLvJGkbTpicMrJQju+G6MRggAsXJ8APFVOQ8FXm2uw+/jig9FT1xsrQaHkRF9yzErtmrAUUOaEAhCkNvML63vziF2/BMqjHPwpK/Ezvf1NgOOzqBeXD1pbFww+PYAzw3rrjwyiF6ORoI2OSHnxNn71AJs8c3L8afpfe4TFDM/L6NJ8Udw9LU66o58naMMDw7L2e4Meig5LnbPhXzTlPKPgApK0LI9BNOGtF3FFmSBl5fNBTDj5hP8/kEgY3bxq88MIQZ1838DJuGIPx2LTr2kY2EuZmDeN1hzCkYuDNzt1JUzNtrxqDH3x6DS1u0Sj4YhAL0WmL0ptbDPXQiBkSH9fiAAf/N3kLm2plNRI9+IoCoidWEocZWsd6/3JGlEEfXrSH9Ji54HZXGWWIzmlRpVDwxXLwBYZtkSg30XEiOfjCO6H+dzxYx48f+QwuaQaI8Exu9zTqmmt8FOuuqwcf98JVFUkr0v4RGuPpQ3Q6xdSDzwHJoCUGUohOlqZZuZMtHVZLvoI9DVoPvgBBdtzZnPyBtyoLFXxJBEYqpC5blkFyhDWHDlmcODVF9gIiphYpGgWaJ1RqFHxcuo2OhEsQtilBNKnY1dhcUkQ0I/kaX76M6tixrOH68ODLV/ApPfgcQizXg09lvqRUXksefIYo+PoK0WkMMBpshnWU0DAKnlUgE55Ns0fV8hKBwPWgKAIkF5ztDGVTTg6+NkSn9zCt4KMv0q4pYbSbMaSTP68cWF0xCBfocaSzPzNDdFJwBT2x5ixB38zGWBaPugKz2H0T3b9U6eJ48GmUW8l9r/1AfXAIRDiWDtHZUQiXgow+kjlE54Dq+HHc+O3fxs1f/wpeP3Y1nFfEo0cVolNY31a5BXL2qSea21cH4Wo072SqKw+f2pnATCGUABCEcvfKgMCDLz0hIyvxmxCdTVX3LmXwK5AvpJDmFPtdceVaRJE7ducQnbNHrT2cRE9HpuidUycPnh0M8MijYxw7Vnlrzg4jrWffOECSwCg2xUzmJGxa59wEHhSwqpBxQIJXifB+48uXMRnWpuU/PfmQ1CyA5vudOFHP7/iJCgcO8Aq4KAjr0vbjevwLCj7xMzD42BqiAPDCbs23b7z2KbqUCn81Y2toXQd8/kHmI6NDwsJs+q4HdqjLwUf5sZSBYZQt7WrM6u4fSZBKPfgyIPFK8pjGsOtBPfjiVwYna8nAxQyNoA7R6Yz9//zZmnd9tCE6VZMAY9hTn3MaojMMYS3fjd5Q7LiW5UVcBZ/EA8RIa4n9BDA1NK1/hE6jNmzDnA3pGjeuB1819uqVk10EXEsiRGddmD5z0Rx8Gs9+a71QiIuk34OIDgnayiZE/u73U7FbxFBCCzkKvqIAfnT2CfzJo/8GuwdlmRztQyNDMgaeF2JRTQBrZ+sg0M7RaG3S2YS8f1JbhMpGjLGwGxthxIVUR3YW0a2u6h5YBjcn7ii6xE3IztQdsoLlwMqDbwV7GrSGMNleAQpsIyY8tkBlXUFhPcmy5C/zlpAJLFITjAAXolOat3Abm2nmFmZIfpKkK9dSLTpmlsCSvFeXHHx9Qk7/kbpRDz6lUIslSiKM2kxvlwi35VpauZyK590pgEbBRxl1RQ4+76yQHHxq6aNiLqPBhq4vDrhcF4umXhRCD1sU4VkOuBE9g3/gQNw7jI4ThOicjmVu3EDx2mthJyb04EsJ8DrJNhkGX90+YaYn5Xvx6mWG6KRKi6ry67637wT23/o5qsOHMbr//ujUNTn4xKnkXK6uB59SmOVCrlWtOI/Uh+buSzLflEyQn5psHa/eazmbesGmj9WxY75Sw5jZR2oFA10EmXIbalXfticKPn+vKNasRw8+LwcfYYyDMOMSrchxzdNO4jn4QnxswXsFAKjDKxonnDcbopMK3BI0gwYSjYJcKzEaNncCqfPfIURn3azrhnHOgxuiczjE5iZwzz1jvPlmgb//+5oW4xTWgRfRMqUixqhCdLYg0RexOVuLwhM4x0J0yt1ExxgO8b2nfxOv/9XreO3oVbiqnOhyTgsvXx7jzBmDwS+NMIpU7wqWEcIHU2EdmUzyLjAGnRU7YofK/oK1VZw32nP89ewUZ9X90IiJ2hx8gIXd8I3+OAWfFDawK30ZhOjktr5GLlHMvLhFVkRyCU/0G+0zAt79TQwLs0J0svcob9jDXyVW7cE3k+GHwvfJGIEHX1XFz4JHx5uS3Y8pjyvufue8ATnvZmNIf60HX9pjOnXupPqmjNznwfysqDxzn7dzcWi5Yur13bTxjKkbmO47Tn7VtBXz/zoQ8wTWevCp0uL0AJPpfvLOHzupmjaUvv/16wbXrhXY3p4Zi2iMG1nDCAVIRmfSw1G5gUm5BmA3Vs2HsmSNnOv3qhsXhS9zK7h9hXAtclkybv9w5cF4wks2Z91ubsLcuCF3xDxyDazc1+Jxr//eHPq7cmWE731viFOnJjh8eKXg20uwUvCtYE+D78EXE6wlQlJkYJeWp2mEW0zbiZBW7dRFAAAgAElEQVSDbxITQkVCe3EQMts8A+5NIujE+qGKFOvAyO09yx9PWNMBYiE6F3ILaATBSkiG6OzQpweLCtHpEGJGUPCJnXRQ8ElskOTBh6ryibF5FHzES8f14EviCQpWJli8PjLPVU5Fth3n6UIVaAlvsitXxvjhD0scO1bh6JFKTB3OefAVhWURRfmDH/Ch0Zg1aqLVzKkTT0LXdafKdilcoIuTW8+ZDCGbOwzlR/7h1CP42bF78MBvT6JnQlLwudBXiE47GMgMzoKoeNa4RXNOKMyB++dqM08fS4htUi+vrcNKwrT55maSHE44q1jLhoYix6HZyoFFuTEhnZQIh+U3N55MWuX1J/UV9eAjc2Ry4zUefIGX4vSHy8SrlEom4cFH71O3jI4PjtToQTGgobX6QuwUEh58wdsxQqa5cvAJITrtYOCMlVCqSuvXB95KtLfGdPfgc/tz/g4U17CAMmRcNIR3AkaHjuKnJ08HzzXLZgywf7/FTixEaAxSd7/nxkkMMJsqkmRG8OATocf7OIUdpPytUci8w/2zSnBwwsCpfYYK2CDJg5IefDNc7feZkmTK/BWbTlXzvRIhOo1B2rgh0m9KOBst5xR8CZRC78WgPNKew5+pe07T2aCsghx84zGCsy2H6Az5M2MYOkHpgUPHEY0jyWVmDFA4c17bqHk3Sm9EPfgYfKYJ0Rmwp8Kr0rM2U/A5HnzWV/AVk5GnnDAGXrSd5u/gGERwOifXit3PIhljK8+Db5H0u9qDb1ok3bff+laY3oidNuUFuyr4OJpYAHd/ZF1nwsF3v7Mx1jN+p6Ffm78WGaKTe6xex337AFfBp9hrvgefA4zxCuXFOPjsZ7fxyCMjnDo1EclV5dRW0DOslnwF/yQgQJBcPp7YbwZmhEsocOJy0qVDdKYZkugcY0JO4QKsQ3RG+kwM2wrKGMIt17qYFQyQAbUefEEs8AyCIYCebp6y6mDvSydMYppb5//smFweB6ZbT1mRqeALEoGnOEDIhLQ2B5+oyE7MDQhXK+rBp9wwnlUgbabsIyncywESkg1gvktCwXf8eIXHHhvh0qV4KE8uHIXkwVe+9RbfiTGBLKQoZNwVE+ZHUXkxhwdfQjLgoS23bmaIznvvnZ2/I0f8EF20uS1K7KwdSHIUNJcAR2h3VfAF+2o4nN0BtAvNvdrFg4/lGBR3fGIzpEKQsFMT7h6xvrpjAZbEGbH3ulJ4LH17SQkodTuPQo7reK4cfM7emHnw8aF2WMt7GzlzJuXB5//bjGkLIeQoUfB5RjzCmi8kRCeB4P25M9nUnXdsY1A5YYwn587F58Z00yr4umwatw3x4OOFZmG+qBR978JcjloSDcdsCnEpBNoxilOtPkRnVLjU8VBnodJF4V3i8c+9SikoN+m+rH/6Z2oe9BmFxJqneG4N3xBVNBhKh/uhjX3vmogngQUwJBpUsu+NEe514xhmoKMhwBTEPIspiITobNFrlxCd0375KcT7atsw+KMc6Neo+YaXL8946fffH/InPzj/ERgDjE6dCSai9eCj96LnRWYsJhP/O08miMou6HtUgbeXxYRqKyO0ZHQcBjcag2SIzg99ZISvfe09HDrkMxqxkIN8Dr7ps7JoN51Ew8zaMA+58Zorj+Tgc5sOqt0wB99UflCWszl3ycHHfYOmmW+QJq0ZMdqYi6CV+m/mUwolBBq6WsOfOd5tXD8eEAWflorLDdEZrS+NoVF2EQVfUY1ZvKmxcY+PM51T7n0h8FRtfzRtB1M/OIeuB19in2qiSBcFcP78xNsKiqt+BUuAlQffCvY0uJZGMWY4iXAzsAutyiHliROisxm7LIHdCAIPlB6MQNYDKmCOCSxjITqdC0tj+UUFHgAw4SzMO4bojGUs65o7MT5ggvFUdvP44zt468d12zvuCF35i2oyM5nQrgedS08efEEbNwffeErEjMdxilx6rvDgk/oSPfhoiE4tBZWYizGJHHyRxbWDAXYfeiicKie0nlYYDoHGeJ8mINZuCS/UmtSuLIMCYwx2H34Ya3/zNwCA3Q98QDcgEBUGiDn4OEQhKLg43BW10DdyiM7UOubWn00obqanCtHJ5T0kE3j00V28806B7W3gcjXGS9+ZhQyhEAth5kJR2KQFe7Sv2CJRHDXFJUVh5xJwqcZugAsdp+kmsRm6KPh6uaNyuJ2lmz46gq/p2Nx3pgLQwOhGI0wgQjYYP48PVaprls0YxzBpnhCdXsOZwJgTlEiKigqGPyPGUfAhLRxrHgY57TAVZFD8yoToDK/xHgxOUrQVp+DT9pUCRth660tfwsYf/VEd0vihh4Jy7jd9HLPRiHo5up41joLP9+CLT0lFa7ENe4CiUArFpn9ICyWdX9S0v6lme7OKjKfNwZdTHG3WmXDIBEr3cqwVuaptUcCurfn5s5syqojqO0Qn93f4U793I21SS+7nsNTh4ACsDT34uGkxPHFggMIKYPniPkN0zqrzONyoLhTSb0TBF9kGAFyPriK47cqh/hw1a3Tp0gQf+cgurAUO/5/ANrEb/P/ufBY/v+MBnPiVNeA//9ugF298wQi2eREu3PygtLATv+5kMiMspCAl7QyCHHxTuiThwWfZpQppCI534ugSY+Ap+ExZYGMjk6SMKTymeyamFPO66hyiczxtM/XSm4zCeXk5+PipG41XeEoul2o+DUPdtl0g/d7kdEyONa3A0Y9SXQ1+oh58WsgxNnVDu2Z9jwGv3ghwmfHz9RXuwRfotmgOvsiYfeTg8/rb2srrCEDhxmryUvQYBNLQjjRQ7I5YwfJgpeBbwZ4GLY2aZDa6MG+ChLkJvdSAG6IzOjdKzKWEo1xMA+E9RMbcyR2jGhP80rDfIRP55yx5dsN5JqLs/0Mf2sVPXhrjzMYYhw4xRG+XHHwUMhV8DQ2T6t5THG5vY+vf/TuYGzdYS/8AFOZLUviraFc0WD6bNygBCmVjrgff5OhR7D75JCbHjgGbm+EwHJM/fXDPPSN8+9s10XvlyhiATACL30wh9GCV/YXBzkc/Wq9jWWL3scfEsXMgIPSNCZlmztWXtKECu0KyrAVCL0mvq5i1abgXtUePtosl6fbqugKX1mpWnuNgAHzqU9sAgPV/G5+fVmhlTLpuXyE6Q6tNp4tOHLGijcqoIN1v4AHh7MlbG4dgrMXa6Cb+5p4v4gFpaoESIzJvAXI8hJaVh0vEM6SQY1bFbx+5a596agfmhQJnz05mvHhRoFasMedHI7g2xMPCFHxfCjCsBx//mpzy3Frwoc2nPxp6sfHgS0Gdg4/BSRydWrleUt2ECn0AZwwTszLP6zzsqTp5Eje/+lVd/xx+sJiG/psPj7khOl0PPirUVKcWYOY6z/djv4ExeW6dXTz4YFF4/MgeUvAR6Ix3I4e5VcYn6k7uvBP27wYw4zGq/ftrum5tDdjZ8eq1Akqk++wEKc2OWzXlUc8BudfTHnz+3nFflRNes1OwFjh8CG8cuYwTb7+EH579IB7gqgkefElQbr5FhOhsf3dQ8GlDdHIQWx+agy+Khqc/NjYsTpxwDQjCPb2zdQTF4FbQVx8efGVReeyohfEC38izmj6nOfOmXU8KEqEnWC5+oQNDJc7gzSDgRYyBp+CbfWN/5tFzh9CQgyrh6vedeGFzu8pyWu9b14PPjuGudlmNRQ++opjtfddBwBgbvVNEutYBTQ4+NOHc2zVaoIIv4RHqPq+pav1cNDn4sLamGp5Cjgef+yzjOhL5d7aPwQDN/ioZOV6WB1/kHtLm4IsWGNmDT0Oz1PiAoX2ZEJ0pA1hxjA53yAr6h5WCbwV7GqJOPYzQqf2dQkwRbEMv+lApZlBZ96K3s/nF+iUefEmBbPjCvNLP/Ze2sH6ybR3CdpioKcHGhSTtTLjEElUXBgE9OQ9TBYV4T3nzrK0BV6+OsP42b7lcVqPZq2lvs0BJQ9a0lhImm4fDkUYOsTN86SXdXKTnXD0ybylEp0dzUUGHe9i165fpwRd0y41Tlhi/731kmIiS3Ji2n4MHLT7wgdp6fz1uJNx5izRzDNa8MMDGBnY++Uldxy5EPfhI1Q4efPUEab/+A687M0furA5yJrZi1INvVtfD04oQnXTMmEy1MiX27Uv3WRQ+s8fhBbWFO9e5AzMPPmQsrgwqcaRCwddJoOAUj8sNfOOB30Y52cV4uAng3ew+1XnMctZtSaaPTfocGqLTGMEwKEJ/TZ9GDYqeeGIXm8/sYvAOMewoamERwFhg08QrkTkBc+x5+PiJRkLQeo9UKMR5uoLHyifT2CbWCDn4ZhZms/YKYxll+p84JJBtIMhx7spkXymYV5HBCI0oGZI1HbcjRxFjB4PQU3X6I0mPSASeMb3qcQDUGyInB5/00NlYdI7r772DA3/+X/GTqeFT7HzG8iWlBFlR4au20SLwLl1fwYMPJ4/jxu/8DszuLqqjR2dzY77PnvHgo/Pq4MGXoga8s0pxMCO8ZlEvLIoC+O9XfxXru+9iZ/0ggHfDdgnhajOHqMFZZI26evBJCr6qKDEwU7zfAYlVx4+LU0gF62mfMS+Vk0uzUdJubLhGOoJsQ7hKEmR8UNG7m5o2hUVFnGWrCsHZiubgYyAw0CH12FDfJqSjODlOTae5DaeKMlfBV0re/Ox0m46CJ4EHnwmjEHDz0xTOlIezSZXkYwyq3WCwNgefcx6DY6C5Nxi6lsoEY10Za7017/ua9s/8QC5k2mno4ZwQnVbwkkuO0dKF6dUpitkaZpGICVrGOiHS6/eojbIKN9WO4ddiaTn4hPrtvDuE6KwV48b5W4Ye2PuF9LUCHaycJlewp0FrhBboRTSCfAEC5EovNSuH6Ix6yEXCMWgEiZbkWvDqREJ0puIs0+es3J5zU4gwfzGIh+hcwi3QWQMQr9uEkZgHAqZYEgAQSNbRUCRSJxrzJeWaSjn4DJWsKYUrrBCBjJ2dg09BJMVgfZ1X7qn70AhSqCV41gAMZIToBOQcfLGwXYEHHxMax2ngNtUDU1ktp1NLBgAvfF9miE5t8eHDFU6dtfjSl27xFUgfqRCdvXnwTZm6WqBAvpPiY3WRP7LWmAqPgWCoiFbDmjrHWa3cE7tk7qiOe1ULS1LwzebuCL4iY/eRe5D1+pgHjREBbIU5QnS6c5t68LVCRUqbRXLwseGFjK/Yrwgrxs/ZoCoGYVnzjdwCRsE3HAKnTtXP77hj3M9ejXRyxx3j9t2PHVMY7+ROKBeRCPclvcZiOfi0Qkov3Ljj8ZydSmBRUhGJ7unqwed2k0HDByHrHMhRDARthXXOsryPeDR1BprrTcjBVxQW9sABVMeOBXdUAFTTkeu5FYMcvq4L70b6T3vwzX5XkVANAus+BVsLlo2ZKve4sSwq7n4z6RCdIi+tCLOowmhCDr7ay2uq1Ll5E+Vrr8XnianxwWAAu7GB7U98QpxFahu0gnrmAxaJHHx+3/WP9XV/D/NnxBm3qVqEOfhanjOB/93vMygtqok/h/HYRCNzuCCF6Ex5XMVy8HniFsb4IaDXprjFCznYQcFH39k9h2bQKPjqB0kjJW4AgS90w2maauKhuWIy8toYg1kOPtcYlxqIKe4UrQefBMZW9X9N22V68CVCdKoM3qZ1NQq+ZXjwubxtjsgxUH4x7dq/HZxakr1V16Mer3mabA7HJJqI4BoVVAoFXzCWlYw/TLj2nbduxChgBUuDlQffCvY0xGT+LrMT4rU4oxwn02oLB9v+CpHmhMkNmEJgIXGRQMYBpWqSF3gAHUJ1SLxiUJ6Jsdt2KQ++2ISEZ1HFaiCMm0OIkqg7y/2j7DMxF0aW2KUbnTWtBB0UfBKz7zOk/kbzvA6065f04LPYHezjiuRxIgRa8yNg8jPPlTQ0D8x5KctQwbEg5XgQjtNMw8uxHJxwtg0XojOH8U8/l8uVgmCqnIwM5DFK1IMvR/BsZA++ixcnuPjZESYndR58KQVeDo50Icit5nrw9QFdEBwQGIqEzHj6bPt7MnHu2TaJu0eCnDtnyQo+6sHn/ev+SZ8pcWm0vCg8PGYtcOvWVBCnOccEN9uihDHdXLKCO6yxiDc6Dz5rGSG0M09fwWdgGCMqujyTggmPO90f3ndjCGdjgC996RZ+9KMSFy+O8eMf++zfvLoA2smnP72Nb9wY4+TrFc6cUdztuRPoScFHoV66DoshnFPr5MulAv9YVBLA+abz0KwcCGc1h040UsQF459fJlXirDzmwXebQ3QuIkJJYKgi1BUdI2jUBgYX7RUPPtWezezTi+oP34s1ZeDUPrM2qcem95l759E5aCH0wpIGTvQjePBZU7a/t/7gD1T9Ts6exa3Pfa7mJ6aGCPNc45whFs3BF+urWSPXg08CzsaR61/rwefen4WxmEwsmqYWptYf0Rx8xqDhL7zr1xgYRlFUJXLwCRqPcJ8JHnxUeGMM/Bx8gtFCVGEhBwNyzpzuHLC5+ujv5oHrwWfHXhkN0QmgpXmiAVUiOF2kaxNlQT9ao/oeoPHg4+YXzMvkefBx5ybgBReQg4+z2+iSg88eOMA+d/toPeGGQwB12ozCjmdzYOg2t50WUt9HvCqF8VucRRV8iom5IdID2XBPe7XLHbKC/mGl4FvBngYf/2SEwghCH+ixS0pQGM3BxwkiGYKl7jeBjJkQnaJyal4PPqZLwHXMmSJ/a9vv4IWpS/bqz0ks68j0zAU9ClDn5q2l2PqZELSZw4Mv2P+K21tifMUQnVXleR2oz+ucHnyad6HDxEJ0xkD9HRVCD2tMwOSZefZxZHJUEceF6GysPEULV2aNaA4+KvzUCumoDCpi2B0FrXUuQISPrkV1h9BI0TAlym9KFXyUMa3zd0W8mWKLROcwZerqMQnD2wuyUsyBa6c5y/RsucokVsEXfptOCr3UvCIwl3FGBsymFHLAqnfW3AGJ72EBuN6x164VuHaNF2jKMAtBU+/5jgq+Zj87oa4kRt2awqsLoPXgk/alu7cqW6BwBXHCe9IcPoCzP9yz6CYMcvbP5qbF1atj+jg6ZhQijQ4etPj0Z3aw9RaJbKBF7H2DsPfo46rqGNpGOqfDoaNkjRslSHNcdB7ONqdvDzn4Ut/RI/tiKz3HK3faYpr7ZF5gNhs3TGBU1QCzx9wQfsYg26BTDcm+IppcZZ85Hnw0JCdP/zDraK2KrJJy8CVfS6EEAAQlbldaCLU3j9Q8Stlu+PxRioyKljOFZULK6NH902/oevBNLlyAees7AIDr+8+0z9to3gREe9Qk/p+Vl4WFJTn4JhP/fm+ec2BRAITuMCYdUlHsb0q7N8NzxpEB/c2F6BQUfLFtR+U1rjLQDBrjogI7OwY7O7q9Hxt85sHnyNhIdKRyshv2M+W9yqKij9phUl7GlH8M0LXGpWkaojPm1doXqEPQT/FWjgef5vtRBZ/2m+d68HW5x6r9+5N12m49D74xkNDDR8kkZq4zmam0/nlGwW1/HUJ0Fk4OpEAWG+GPAb18k/tcKw++5cNqyVewp6FLWi5AgYgUnc2IrbCuS4i5ITqjQ9IcfNM+BgOL++9nCBaaA6pDiM7ahNwG1cPJ8YRZ65FmBcVGz7CQSyBFzea8R9d31kocmBCdNM75o4/u4tixCT73uVnovuS0NLHSpU66ePAJ3lleU7rRuhx2NnSfPz83B18ASkrEqxYId5lzqRhKq2xh69UaNmWHPOw+8AAAoDp4EJM77hDrcV4GdThiBxQ5+EQPPlEA3nZOi6LQ+XhnKOc8xZC7QCREZ1Iwa+I5+PQCV5vO6RqZSnSedL+3ITpttzCNif5Z6OI1zJ3LWBsFcySOkwvLuHMyoRnGExY355CZgyY8a5vHOOLJ5oFklq8FIoCNKrWTQPb3VFBGhUANcMKfCnyITgv/aLMh0LkxGAWfI82ePVMYy6jzRcagC22lpYVSQy8kB5+p/+vRK9cOBrzQjFMWZFxem5tzvL80Ti8hOqON/F+RylEP/8Re6bTF6D23CGZE6cEnfgY+bpqqz04Q0ewkj6tiL+caY7nKhYq8N4d/WfSDtIKP3mftn8H90h2/dQ5BK3jwVUXZC/+c80pBOevBF3qdxjoyxnopDnaefRbjk6dxY/MY/vZ9n2+fFwWfvoJuf3FN2sZTYbl71zOCb9eDLwW2KD0FdDNUKqSilIMvpJMEWtXTgDMefFMZVI5xz9t33CdPucnBp4h+ISpqgnqhYqyoxh45X1aj9hs1z02r4HONrNLrRsF/F+qdmeZ3AuqhZ/o9JouIpcgAdIZ6FvLZCh529eDL8Mgzxj0XCllmUzPlwWccIztHPlYyOfhSYroUzHgrH9bWLD772VvyVSnS7tP+traEhpG5CMpEzqi669btcoesoH9YefCtYE9DjjHioUMVrl0rsG+fxdp643CtbMxBcylSb0AYVNYl3upJFkX8AqUKvvveP8bFp27h5MkKm4wOgvXWEygzUYgD6wlCNHkSfIFH/S+re+kaojN2SXe9GeYUCvZet2s9RsF38+SdwOYxbN16Ez86+wQ+9sFdPPtsXOlBf3dLeC/Mqas3DW1KNpph8gYlQVFvMphxi5rPwp0l6sEXENkd9p/YRCNIIaHs4h3ysPOxj2H8vvehOn06uo5FCbhqqzq0mOURRZYHnzzfGo92EFSZsJ16WXK879w+5wnRibg8qFKeA58JCvuurfw7Sn8EDz4VA9gT8Lk2w0c7Tz2F9T/7s/bvsA05WzHPXAF6yRObs0630fSRw4WU+RQVRRxeTP2eV8FHIOYJnILGQ7V5PQPe26YdyxRwMaWt3RH5yibMwcfRurR5LESnL+1N57PtxYOvC/Q1UG85+Px+qgoouswx5sE3pW2idASYGy8Skv/BB0f4q79aw7vvFvjYx7b5elpo+IgcXCMwZ65BWjTEP4CDN14LKzT15gjRKdEOWai04z6tTp8Wy7gQndwwWSE6qZfw7crBp+HlEo1SHnz+a5IQnYxChQVrcfjwDD8eOsTTfZzxki2owUj8Hb350hx8XFhEzZqJOfgiUS9yolMkWHCuvP1uzAcsBvFDR9doY4M829zE9ed/A//vO1teASsOKYx4LQSPqfDe6XsytkGL8Rjt2XJDdHJ3NRc5oKbn4x580gcMw7tGmIa2Uf0O3rcvTFANiJ+7V+99Cvj7WyiqMba238K+4u1ZYaPgUyrX+agO/FxcI+Gi8g0ny2o3DHfaKPjK4FE7nyJyp9R0gA32mDd/TgE7HMKMZkqhZebgCyBmYCvwiFJdTUF3Dz5+H3IPjclTCDZQUeVXZCj3XqbeokBotxD9pBGeyd3/x49P8Ju/eRNFAbz6ah6ObP/mBMcJcD34QGXDAX/cjX7oKsZdQb+wUvCtYE9DVE5BMMaVK2O8/XaBw4erNGJSYJvW+pxaCqJgFV5J41cyp8Gawf33h5fJrD4jgaEXrFS3aQKfUNUIMSW5fSPs4ghiHfYOw/hxBPrSoU+B4rzGswFFaWEKg68/+DvYuvUm3t06hY/hRrJZAInNGfXg0Sj4gg8pdeUyG0Qx0kHBx4YPJQS6OoyF24iAN++O+6XrNhMVfDlcGgdlicmlS5pqmJBngwF4RBGxIAymG/Hgi3ne5K7jIhR8nrGGxFEqwJqEB1+Ogi9qRRveHULFEChSm07YGNqOD1GdhA6CwPoRFY4Au488AjsYwK6vY3LXXShefz3eT+IO0zCgnbwYc9Zp2Qo+Zk24vRUw3X0oRUyYqzMLqMA8IvRMQd3O+bauMFnAWRQqlPy6GOPdKRMzxMDukLFDqIpBWDaLET9rr/CGV8oW45BqlMPp505gATn4aCCBufpv+hwM2u+RnIJUgel7MAB+53du4MYNg0OHelqLHA8+YY9N9h+s7++p50gszctbhy6I3c+D9iJ6URkEeirX07U6fhzbzzyDwY9+BHPrFkr3DmIM+CgURcTDzJgpH8bzdMYQgf6cvM3kzBkMvv99VIcOBeHAkl1rEAyjsIx15+X/IuWxEOXekMZiMAC+8pUbePnlAcuD13wDc68bE+yH5BkWoBx0k4JqcvAFbSSNMbP/+D7iZ4ATlDdA9TAxsKbwwnN6/TOCf26uWvzdjNLm4XLO0WgXwRnLCtFpCp4eIB581KCC728WXr8Z3rBKU8vSAF6IzpIPjRjDtXZ9Ay9c/TIA4Ilv/QFKO1PwNXwQLz/QKbApHd+eXTdE5zReantNTcYY0zDGjRGNG/qcXlEZcj9vLkYuu/W5z2H4t3+L4UsvOZ0sKExyCmIGtoooL0BjwCsU0neZMwefhr9yv1vOUubk4KMefH6OzfqfT35yG3/910M8+igTbS0BHI8khRh2qyUDn9Aobwyep2t28J2fYN//+g1MTp+GobFIp/uk/dmjgm8VonP5sFLwrWBPQ849ORwCJ0/WN/o4xVxEOgvc+kndyhhW8VgUNmrxTj34ki/EEX+S2bUoEaqIB58wFsfMgMrtySXbEWMT+Ztfxl0o8xJIiW+fxb4n5tLuHe2c6VxoO2thCqAqh3h3f2MZzM04wXSqssoLoFDwxZhtsSkds0uIzoQbQrZATYCUYEizh8K5xIjxGXAJ5y2j4MvVY2oh8LQzYQ4+jYKPzreQ8szAV/BloXKG6VejjywPvlmnnsCFhOjUCMBlAaLVhdZthmHvHgtMc7dGmbvYPOk7TYF6rGvXOeBBNQ01IToNgOEQow98QO47ODPOd1SEGZIfZkJOH0vijNq9wuTgY+erOGjJsN6cpCmyNppl63z+mX48Ia9rlc0pPKnwztZnjrU+Nr5i/6enHsRdP/l6cs5ciE4uB58HSg++pYE0z9wPNac1FUtFTRV8nXJOSvMfDoGphT/dA0l6JLFWgwHylXtcv5E8A9l61EGJW+uHsG/77Wi9dw6cx2vH7xPLc63kNcUpOi5njBiMHnsMo8cew/p/+S++go+sr2Fy8CXPpSPpZ+99l47JUdgycOvTn8bg5ZcxufPO/MFcA6wAACAASURBVPXogqwT4NkEWgMXD8e8iF47fh9O/7zO43bj6kM4AuDcuQrnzvHCWmMoDuCjzqRCsHtnh9TtquDzeXVHmSEo+KrDhzE5dy7db2QK7rNoObPfBkM9T2aNYfkdSWDMKVtFMl4YmBO+j0eAd0MYgzfeKPDNrQdw77t/DAD4+eG7xO4rFDBE4WwMYIm2kyq3xHuH8k5CiE5vv02NnQobGs5S79EcWsDLwTedRzTSQyZIOfjc/twwiu3z6YcvHM/J3Bx8fofhO7BK2+PHsfPcc62Cz7Q5+Jo2PfALCWiHEBRuTTAJbQ4+8dogh4tT6GsgT8FneRo6NUZCwedGULCego93unjggREeeGDElrEDcGM66+8uXdYWIXTjrc99Dmvf+AZG990X5FPl4NKLf4Ty3jHKN97A0WtH2ueaEJ2BbFyeourZChYLKwXfCvY05HjwucCGt8wdc9qGEl0WkoIP4AQ/ovwlNSeFB1/7u+AvzFpcl2dNpF4qd35ZN6/rwUcIdI7+UPTdKWfKbBLqqqEiSzmutl4q/5uy++BinkOax3rJJSYgEdLRHHxdrN7mFGBoKRFvqh1DdCYtf4WCgwctjh2rww/fddeU+OQE4QuioEqiiGti9Affz/2Xggk9c1omlTLakIUVtB5TmlnfqZcjyXQ7nTNEZ2wLaxkozTtGwxVGOjCCxKQo/HY1A9lhD2razBEW2IUoXaAUUsTucfXrZ6zTMgQEHpA1kYYnRq7BO3VRktS4VKqcPlfcXu26fHU759s6lvCsR6MpUBQz+tFVFHF7xj3aL194Bq9dehzDl1/G+1/6j+zcmxzMtTDCEUz3pOBbyDbL2ee5ffcWotPvsqqYukIXmkI7GLSC3HAKlj7wf6e+bV/Q8DodQ3RSb6ebm0exb/ttMUTn2wfvxF/e/xuaKXUojBVn7Jk+NOC0D0WIzqRNj4tkpnlCPXzk5t+c9x02NzG+/362KPv6VdLahw9XeOcdng9y+UVrSj9EZ8SD77uXPoHd4T5srx/CQ3feBSBuzGWMPzfpjmMvK6eOH0KU8EicYZfmnAshOqsipJnHFy7g1he+oDYUk6YQm5Yxzh7mFHzJoT0tqOzBR4DzgLGQQ3SmcLr7fapbO14dC4PJ2OCP3v4o/uHn72Lf0Zv49tlP4cQ7L7N9WlOwwvAJNQIKeGvOeNaEtLuUg8/4vIgxCHLwccsT81L2xrVW8MjUyZX4EJ2UL5wO5bxjEzaxqVpOdoNcYm0OPtfLtwOJEPPg0+CERuYm44z5gHb30p3P4KG3/gTjS5dQHT8ebaThJdoUHFw3lL6m76581aiCj45p+PqptlZQdjWGjF4fDpIqmBx8fYG7t1y5iniXKujQ8ZUrGF+5Io4Zu6fXRje8gnp/uHI4sdso5N4hK1gMrBR8K9jT0JunewY30sqqG+wWCK58xiJi/DplSqZ/E6IsZU0UCHeNkRm2WIhOm4ewObl9VRnQuNm5ApmWWIrk4FuIVXnq28+zsQj3Ql37UxCsIRvCR6kYisGCPfgCQk/Y2940XGLZFRblmRPG59GT0IHmzlH1k1+l7p95h3vuGcO6OrXBIGTyFhTelloaW8gefJKSjEsUnwrRKUFqHTsf75wQne703E0teLvFILrdlYKZdEiy/j34uqLNlIc8Cx0NH1KbwffgSzevHyrGTUCW0m7JrlacBx+bj0gRojPpwUeBwRNic64eIzDPDbHndk89+GJjV6ZAWc7QSB3WXPBINIbYBRiM1vZhoLBUtiWv4MumDQODk3nji7OD6J7FnkuwgBCddbcGPzt6BYev/wOA2tNMNX4kBx8NBwcIBhfSHPukWbn2ESZmd/Mgvn35GWxsX0Npx/gI/rTuQrgvjQFubBzFcfDCb8CqcstG92Pi/btssQAn94F3Ke9I+2TWkBpUBZDSzLv39e2UqmmltwS++MWb+N73hjhypMIf/uGmWL0idATnXNDU3x1u4buXPgkAeKS8qZoWG53A+Gc2xQvEUFRnDz4pBx+K4MzYjY0s5Z40hdi0vDlwOfgyQ3TqPfi4u17vZdI05uiXC3/3X7E7YeiZosSLlz6bnBsN0dnSTHQxtHhdwV/XVTieLLRGn+c64ey6NTn4jJffxe2Q8IWNwTwTorOBshrP3q9p3nrwuSE6/TMco7vX1sKJ0+oSTqC8MLfmi4Lvn/8otu9/EHf9SjqMrtaDT1ymcSSlUAbkhujs4sGnqdwqk4kHX8fIo+K47RYQ9pZ4HimOQkM3LoBWj81j7n74XMMrWCysoqKuYE+D72KfsHbtCaiShvfgmz1zPfgoAeE62sSIBRYocxipIxIuVeUp1HQhIJz6U2K5XpN+GOCJQ+RqvDJVV1nOXpjnFqPfZC5KgAFFjg4Okq+UYvK0HBygUvBJhKR3ho3h923Npcnz8caZkyJRcrPR0KJLolw84caRIzCB4nUx8+Cs+YZDgVmL5QAImFR5TOtYI+cez86fJydEp7v2Uu6rDl25YAzyQnRGgFOwqjsQQ3TakOFd1J3c1aggsRncPagOMxTcyws+/0sL0Vn/662DIkRn9JM39IkYhTYsGJ0+CwDYXdsflO3spDZ6f4wkjchgEIbT84Y2oeVzHaKTAVJ3Mpl6/LUDRMIXDwjNkfDykmjDpVxbOXPKnVCmgi+ozQlPph58Pzr9GF458QDePHwRL77vc7oBhHNqBwN+LMUaLEaME0I7Fy78ujH46cmH8PKdT2NUOlbxwvobU3vwNVW4HHw0nK3UT7dCuTgn7GcvntMp2pnBV+lc7n4fFr7xpeGsTxcAqYgUqvVj6hw5YvHBD+7i2LEqqEoV5L6HXKhQ4UATFIEKw9v+ijA3tHqbkIrsPBSdsTn4pgcraN7h+3NTUAfrYV4qVGTK+8YKHnwcJqT7oa2p9eAjONml4yZUuRcoHWzQxsftRUAXGgNMSIht94w899x222ZtjfLJ/r4L0r0045PLrMYHobJJY7PLvZeBZUMLasLbX7kyxtp6/fepUzOegrZs1tZ9x3IaorPps6xGrVyrHWfKp5QFH6KTm5Qru7t4cSy+SwPine08r9d7cR58HEzW9yX5O4rTJIjm4KMKvuBcJLsHUBvD1fXTXszz8JUpnq79uyxx9Wq9x8rCtnuhr2/HnZPOITo71NeARQ/ytLZZD04JK5gbVh58K9jT0NmDL4WoIp2lcgRZUxAjyboBJwj34pXn4k5OgC9Zb4oefPBeSBODnAo8ACE9GntTpvt9+fxTOHrtxzC2wg+v/JJfZxk5+Obpn9YdDHDhwjZ+9KMalWYTBh0EABrZWMBspxi92Hw13AB5pgrR2YxLD1xZ6oV3Sg++qihRVJPwNZWCRsqEelUIgT83JPqyRYHq6FGGEVkMFcXl4Dt1qgLeSSAKAvSLUg8+97UrTlgBBPW436n6IvSQgy87RKeJMFPA3CE6Z88Nqpg9V2SRYkrLLjn4csaO1unCXNI2Hp4KO2D1ih0dIbtVFCaxLJjOkw1JSQ2femIOrz/1cbz44r1458B5PPXX/4tXdu1aYi2Ys9d1Xxrjv2ORDNFZwpW71R588sGkjr+uEVVszgFOSO2P2xiiszp8GHZ9HWZnB5MzZ/zB6LfKncCcOfi4e6fNwVeU+FZKsafd746Cj0IyH2nzu6ezlRwnJ9KDwJwZU3vwxaBSKPjmQXtpj/Y5OplnInR9q1oY7MJgEN/Xtiha7MMKPydhzq3bAl0Yl0RRmINvBhovItpHDDQefKl3ioXo5DzbVEpRRsFXTT3Gguadvn/eHeqVcQq+0iYCovqd5eXgC+uprwWC/93tRG3aAlqnqSzwKVLOa2rY4NKfDz44wrHHd3FyfYzr1w1ef31WN1AWigo+nwAxBkTBx9MXXXPw+YPHH62tAb/2a7ew/vrYV95LdLyzNoZ68E12ww/thOic+I+kKeLYsQrnz08wGFgMh8DOjl+RXr80FDUw5f9cGRusRysuAwdr7zWtMaJ4f+bmmBfnoWeiynKG2117V83Qdt8+mBs3+ELjeMINhzh6tMIjj+xiMMh2ek7CbP84XqkRowlWxuoUSiFUpbG31w9iY+e62GVTkLo+upLci+AtVpCGlYJvBXsauubg64Mhbi7DogTW121rPU4VfH50G0LEx5B4StkWvrBeANC2mC8Hnyu3pyE6uxIu22sH8PWHfg+bO2+jvHgReGNWFigTFgE9CkvscIgzZyoAtfXPiROZHjxk7IDBU3okJOvMk6uuQ4hOiY5UTaMsYbe2VFMLOhT25HiwibXd98ICpdDBI6i4/ZN5rqShNVAdOQKUZRjyl2H6+gBqgXvgoMWBAxa4xryApAxi1iiG/2wkB1/Uu4X9FEqqNEG93tw8in233gIAXDtxaVbAS+prUHzkKLE+pwdfUhilKBvdey/W/7QOyza6erV93hffmivUaoE205zlyB7kmF+VMINh+pPwj0TBF7WAZRQkQfvcEJ0AsL6OV088wBZtbyfaVxVMV8QaAPFStnGrbGsK3HnnGN/97rCpzufPmbanuWK4nH2s8JB48LUGBpkhOheyrRhjnZtf+QoGP/iBhztYWLSCT6KRyeOYnUd0rys8+EhvXiQQdoCEd2ZnEHBYyhDMO89KDz6uvFLE7cvxttMWZy1jD2tOX58z2KHDJGlkyjPUD2dFbg6+eXNU66fRbbmiCr7QC8Dz4DNEgRbJweeCRjhaK0t4esK7EuYwquvM67IHw7Q5YF3okoMxTUb56+d9Ey4H39C4AaWj+yYvRCcvExLxtzDw7HE3+nhW5EQ+IqG5G/anoh58xKPn7vdV2Hi1wrvvEuUe2Xcc+gz2LBOi0wxKWOZ1tB58sJbUrceoNKEfARw5arH/ZDyPW0vODBxlCMnBZ2zVhu2cPZsa2gvhC6VPuLlJoi5EjBapl7A1Blhfn2oGZ7VStOI80BXP1vPVhegUw0QnFHy5/I+m/vHjFTasxdZWhfe9b5Ru4IDdvx8gCj4XRTUBuBq8FaTt67LYkTYuPxVz/kgPkUf7Xtt/tlXwJTqO/1ZCjyLWFcwBKwXfCvY09ObBlyi3xoT5o4zBYGDxS8/tYP2nI3zjG2utVbYYopMQiVE37NQcmVAsuR58gYWoJFQXhJRtPkLOulwdtyMc68a+47ix7zjOrfku/yz9oRDcRhmt1G2TwwTRtoMBigI4dy5OtKpBkWuqgxx7rhx8YSLw9ATUHnxFEUjUbFFgfPkyxufOoXz11XjYQ6UH37Wt0zix+1LQXBsqLLq+DfeWADXRk+irTaS9DBcMhMTkmbM2HM9HFFwnISM33ZJsQB7HGnleYlFbf3zPPVh78cX67/Nh3qX/fs+XcfWH/xk3No/h56fuBXCrLiAefK7FoN30c8hwkxPRD+exLULC6n+OHHx2/37c/NVfRfn66xjdf7/fhN4bi6Lku2ojEpvHw1NO2Z13jrnqfJ99zCsCvYSKywBvvEawpBFwcXdyh7nP9brWggrquvZnDBH22FmITvf5gw/u4ty5CS4PxthweOhYDj47PffGWFhb05PWS7Iqgx0M/OMek2YDy1XwMVAdP47d5s5qQHnvxiD7syoukkbR2isMh/xYJMQgC8Ka9B6607dSpJPg2wjuEcYA22sHxaFqQeN8ITrTODFPyMsWCvmqsiDFazAbICdEZytkdiHltrIsSJy31Dfkil2hqLWEP+7Rg88YJvz49I+A9o8Majk8PYWuITrdhm0OKWP4Hb8QBV+kjBmPzTUogIUJhewCcB58QL4HX4MruihrpTaVKdn9GCjClDxcMI6Ug8/Fw9aK3mRdWUcDK4h90jyz/FBQGDsDFZWvzAOActIoe6bt2xCdkQ2goa8YWVjLA3vtLbC2Xj9zcTLJwbcM+j05xGAAU/E40prCny9nkNZAXx58Gbzo5qbFr/7yNsaXb2TbqXOG4qdOTXD9eoFz58Y4f376Pn277BGYiT9m7xaTDfP7rfvY1/afxak3vxvv0pgAr+TkT6Vdef3cxkACv8iwWvYV7GlYmAcf/c3cHE0s6sHQePJpLs9e/S8XotMdMjNnSA8hOmErDCYzqxdbpnPGpeT2kkBP26/kpi52uWACKYsAo0yqlINPTTHnCwA6waJDdCpvdFUezbIEjMGt55/He//6X2Ny4oQ4NW1YuO/c/RmMBpuwgwHGd9wRrc9ZvXpnggtNl/jek7Nn9ds4UbFq1iOmrOgRKIN+dqrM9mQXXRR8hVMGv7iKevDJwOkitP1M7rwTO08/jdH992P7M58Jym/sO45v3vfr+O6lT/rW187dYaoKxbvvtr/tQVnQ2YB4NCOh3bR9uLg6xrilsMzkzjux+/jjnsKSM2jpBJp3VOAc1f6PhBJeWweeeGIHV6+O8KlPbQPghXB0nE5hSnM299K5I2du07H97+wLxDT0QIsepAJ39Hm2lDKktQZCgRkvOB8MgPvuG+PwUf9+axV8kTm4n3Y0MuGackCFESkvL+H5kuxDeOhBwTe655727/Hdd3eeA6V3re24EMw5tVNaRnq3VI6gpPK2I4h0E8sHMfUA0fqSnhuu2qJDdGrO20InIPVB7x8GX6VCdHKRXbw7KMo0Lw6CNU/t2UR5OHVfuVAFdDjnwRfnybXzc3n/2P0S8pLOOSAlJSdT7qiAgODBtwgFX/R+ZQTlojcQ094aZHjwhfWsBQ4ejEQRYX6312csMohA57r7wX+PItiPxoQefAHNytxJMKFxnkjrGj+vvYGvbGqivITesXo5g1bJwqXuZkcR+EJDPFwDPDnZ8Z9P8Z4RAsLSe0kGmZ6n38GurwcVja3qde8oJ+sLdh9/HEBtZDq69976b0axNqEyQSMbnZqecvBF6WLmYTEwwb7T7FlOdnTkiMXjj+/i+edvzQwkhE3dRTkby3nthfzveDVHFbBCi+21A9hZOxCvZQr0F/3Eh9t0BH7hYeXBt4I9DQvLwUehLIMEshYGg3LW1iWmua7LEgyxEsnBl0Lw1IOPswZvfksKFVthOL4160NhHsfx8E24KX8+3bC22w/lBxaipOhL4seBltrVjkkFAExTjWyMY7ZtWYYx1DXz6xSiUydUFBV8Tdn6etbc6J5sfu6sHcCfPPZvcO/lm7h06s8x+Id/kPtMDaN819E998Du7gKTCW594hPAH6qaJWFy7Ni0A1KwICqKfrOzZyfheE04mIwQnbEQRTkefJQZ5oZWgTEtU6So6v2wRdG+u7l2rS2q9u9PdiQS+hnmiql3THrwdQBWQdDDHnTX0n0WgIa5TGweb0kKg6ee2vXK2WEFoUwW5LRZkpC2FWK6iCWmYOCEUQ7ccccY35z2dccdwr0TmUfw3NoZ7slo33VLUgV27cEXESqQ82qn8YJZ4eB0UmU5E4SNxwjWlJt7VQ4AN1JRIkSnTKeFIfCWBhoiJgH24EHc/NKXUL76KkYPPpg/JrN/Ux580SlyhY0BmPC+yRx8kvK274817a9LSL8QZtbntbI03GeVYpzstVYU5/TZi+cF7SMSorO5n3M8+ADGwGRJOfiylydzD3PFXjhI658fLX2jIauoMNYV1ntehAkDp5h9ZudP4xqUtXvHdMvBpzawkV/Ew58KD774ZzdYX9fNiXtmYXDffWN861tj/OxnJT7zme3IUD7+33/z58m62iJrCrJiTSjLMqinGsf4Qn0xB1/zhxOe01fw8TSbOkQneJkWhyu7OkW1XbGTmo1dVj6t3uA92dhRGSEhcqYpTrBra7Mfbq09kINv58knMb5woTYIHg7r+bJepQMAsxCjjWMDCwkFnxaqCO+gUQJrYfeJJzD89rdhbtzwIrQFvKpkqN8j0PX376GIYTTC189djtFwE9e3TuHE7rti28lgPRyeGrMq7QAWTa6uQAcrBd8K9jT40UYU3j8CpDx97GAA48XRrqEoQqLAt8yaWepwl2LXKJZShxLTKVqNbG+3WHk8WJ/eKqGQTIokMsvB5xI2jPt4zss5dal1H6vgm5MoS/bXce7AEjz4lF2phnOliTkdKBR8wb0v9Bcw11xfOa4FoVmXWLUqBpisbaYXiyn3BAswKlxkt7ZQffWr9d9vvqnfdon5tR58VBm8oBx8dD6Hj/QVolOWYscs/HM/3yKIy5iiunAUfPZA3GqOCow8yOCQVQLN2EJ0WCQtw9zUFT1jOCEolbKz0r78Dx3DU5z1oioHXxfI6eN2xjeJjD3zNuPDyVy4OMFjF3dw4Z3xLDet4r2lKidOVHjo4xGBHTD14PPDdHb9XDVD7pxrN8cOQ2/YoghQohSis3nmR/cVlIEUhkPAXYaUB99tDtHJglZim4DJxYuYXLzYbUxBeNI5RCdHyzQ4XHg3OpaWjuobWporof3w5hfz4APwjfu/imde+T+Aw1vA62/Aa6oK0Wlx85/9M6x//eso33iDFkbbyns7I3xbHweE8gsxfDqtm1JAuX20gkp3HHdTLTAHH4Xgk3Sgs2PFxhB8SegJX3htxSF0ITrB7ukkcRl5pyB9Bxe6UnPe6fev/2I9OxaRgy+m8OH2G+upKPSdk4OPVQ7Yenmef/4WJhNCQksykunjAzd/lp5gWMjPg3jw1d3YtAcfExa4yePrVWOMI7k9Sj34JM8p/TaRQnT6cPJkhf37lbSzoJh3c/Bxzd3IVABaBc7cN2bsDBOvzNZg3l1zS3BPz3c4fxaYtS4KTC5c8NrxHnzEYAzytRFNlyLMjYMml6F6abqu4doabvze78HcvImt3/99cf695qqN4RnvTumYg8/oPPi2n30W63/yJxidOYs3376EMz//TrR+NRgG+Gge3skFTd7bFfQPt5PFW8EKkrBUDz6mj6KYtW264EKBNF0EhFiEGEoKczjvJOm9hPcrbt1s/x4NNlVrKMrt52SAm+aVg3YCXc4i+NE5Gc1o3Z5jdweKWmXIsaKoPSYA4MKFcVgBiDL7UWtljcJNqWxSefDlKPgSezC5dkrOMakoiWlYBEGiCImKbdjH4OWU/WfC8eNVa1V7/vxkRgRyiCLDgy9mvFrj2A55dJjyRchIA4LVzYvievClFHxIhOhUQmz7NX9ESewOi2QMY+XeZbFpGw5PcYYPiW6AtMDcY2ikvCaJh/9UQnS297MjhLJlyX9n5w9JkFGWwONPjHHuXCW/bkaIzk9+8tZMUSi9Q48hP4N2Tt9S7laKEitJkNEK83lDkZhA1RKJaTMX8Q5XKvgWgSdFWOpgwpj84e6u4OP6iyj42Bx8FLdI9P286yf1J/BBbBcJBd/1A2fxl0//j/jeM78VlGtDdE4uX8bN3/qtWdQCJSTvw3k6yQF6yFQefHkhOgM+slpO/qdk16k9m+ggZTAbRJRR5gHTCBtpxBr3jvNwvKIfaTJFxxCd/h6ahcuuPfjIjBag4IuWMfs7h0W2GR58Ka+zYFyKbMld+9L5J/UTbbpEJEQns38W5cHnfXdChLghK2chOv3utR58xlqh7qzS+98/wuXLggxCuAdZYJQNnrxtsut32XjwCVEWtPxJDI8E+JQL0TnNe9g+uq2WVDMwRvDgK3wj9dqDT8BsCg++KqXRh6xoFh+mZDIxKMu0oa2EpHq8PykfFc/BFx9fM63Ro4/ixte+hmtf/ApgDH529Eq07aRcYxxh0uNo5nc7yP0VrBR8K9jj0FsOvkS5ZRC8hWHDbkpxlGsCwu8jisRT4SiZXAshcyfVnT52FHy7g33ysjAMugtV5VrsM2PmYHDDX3J1l5ncBYAHH9xt65w+nQ7hFVxicxBgvXvwBdI8fU6hX/mVW/jyl2/iC1+4xZZHrTkj83XXyxZxj4T2p7C36RRYAUSGV16ulDIg8tXEZMLyW7MmWqIn8g6Ts2dn5X2EClRAOTB4+OERHnpoVIfa48bpoOCLefDZYpaDL4tY1Cpp5oSgT0HBp/Pg44tyrApZa1k4jGkqRGeHReqI8pOF7Huz0j7FxkhsHhdP8R58TJd9hJHOWLxFCmk5eOXkg4AxGJ8/D3v4cP1QI5jh1roTpyfsZUVLbqyuy1cLRJz94QptuNkQD76qqjuJhegM7kPFWwa0qhvSmoNlKvg6xvDR5LHtG5qZEnlo97THUg4+OkgDxoRezcIaLNz+eTouRyN6YRAV+9NbTxjvDLT3UaHx4HN+dKHzGIiS+j3yBWKf5G4bXb7c/v36sTpPUlIhwnnwOb/NkkJ0UgjWPJMHTxXXKGK2GatIiE6JdgR0S1J7X/OTyrnSYriEtYHUKCA4Xn2KP4PmS1DwecAp+IZxPogW6nPwhc+64Mmm71dOPogfn3mM6TP/XuI8+AAG7wmbycOhJqTdqQefiCurCoUNPXrnuftjMq1kXxkDpTz4iooom6a8ZxaOz6xj4eMYLgdfQEQsga7RDsHt5UkR5uAT+5MiQE1h98EH8ccf/J/wzsE74vPIVfAt+h7r2VCfAxrxJnWO3AIPz8fqE7Cbmy2/+rMj78NPTj0i1p0M1oLd0XXZO7F9K+gdViE6V7CnoTcPvhQIOX7K0rYEdcucOsRWQCgF0QwjbtiJKbJhRSXMKQlxtrcB1HHCR4N0/j06Ty7yHr1scsFdv2RCeWkc59lTT+1g+MYIJ1+r2Fw/SQHpPJJqLWEgjZESKmRIm4ZD4MKFCAHWNQyBRpGrvtEVoS0zFHyp0LtssftQyaHELIe1azIPkbP9iU/AvPMORg8/POtvWSE6UW+dra3It0uE6OQEuJv7mH6mUGUkkg4TxsvT7Aton7Yo2l2RlYMP/Xjw7dtn8cwz29h6weL8eR4HRpNzd1gk17u97aLLYmsEq2zoYvqh88fyFHzMsKxDC81L0MGDL0tpt2QL4B+e/SBeOXE/Hvg1QZk/BTUezATtFSNVbOk0apDUaR7Ot3VDXXGbjdksolJ9OinapBEqxwQfwf3YMUTnbWXCb8fg4pj+/eGGo0914QJ7pqc4XKLiAnsYDS7sA6S1SNGIisPJkgXGwI3kOzHp+g7YkAAAIABJREFUu8179cz9osYhscJFKPhInzsf+xh2X9vFmz/ZxN9feA5Atxx8Hj6KWsX2B8kjSyrkhp/l6Dr3dd67wUS4UQyhXRLL2b8T4Xcy9UdEOd45RCeXgw8GVZccfAyk1iy2pdkcfAmPVKqkbdKa5cxp1lcMQceN4MaDDXz30idx4u2XsLn9jjOnyH0g2LvS0NwN+zOhnstKI01Ku5tI/nKKgE014weoLGvWRJdjEbB8Dj7B6F0DsgdfXMEXPJsqn6RYJVr+JOoJTM+3o+DzwrnbyWxN94wHn2X3Mg0bG8vBt/vkk9j4T/8JALDz4Q+HFcoStiiT+VD78ODLhgiuFo1pe6RLjZnlHgTiOHUhIktj8J27P4NDp+7G5df/96BeNVjrjS6/rbzFClpYKfhWsKehqwdfkvCmDQRhKiVuAHKRBwo9QsRHrTQSWK+k4RxMOGDTqYKIGA11Hnzu6jQEsxuis63akXCJEYNcbPkUbGwAzz23g61XhbAQKeh0W9bAeX4Cc1hcM9+3t8syJjmIderOSfrmEcG51JU4LheaVgJJ2CmASkicEhQw30TzvVOKqFjB+NKlwBss4GUXRURJG5CV5MkrYY3B1atj/OQnJU6cmGB93WJXqut48KWm45Vl1u8KwTZzHhQ3brR/t+FUJZjuJWOYpRvkKeQfe2yEAw+RvBSeMKp/D75eQnRS4BQlCzIl9PAUM/dl8eaT06cxvvtuDL73vTDfVM4k5lh/t+nucAsw77a/VSE6NfMJEGdGvh3leWiFTm1H3W5jup99Dz4GCjkHn3TvhMLP9JraAbG4jij4Yp5xe0TutDxQEFLWzpGDj+tvzhCd7F0r9DcXxHLwacYyDH5A48xgAII/bpcHX9ay9XBA6Oel95jdvx9vff55fPO9rfZZUiGSWot/oh58gP86r7w2gBu4VevBp7FztFbAmwEu7847ds7vy+Tgq0NCFuHn7im3VJTmdssYmUXKXnM4rP8bjWqjP+0ZVRkHKyAgSQI6WffyHt4TImakPPhEXjdFxwq40tjKM0zicvyRJlGQQ3T6fdnhEGY0wvYzz0TnDcjUGfXgi5Ay7dwAoDD8Ba7G/VG5on/2qkbBR9oZ6yhVlcNqYZ6rn83BR2MFG1nBN7rvPpjtbWA8xu5jobcrBgMUBVAlFHxR3kEpk8mGWB9SJK4ex6kfu7JPRQ6+QG6t4LkSUA0YCwoAVTkMUF3UkCACiyZXV6CDlYJvBXsaevN0T2AcTYjOlpj2csjFhfYx3UinEJ3Ce2g8AnYHm8k6Tpd1v47cPmqxrxifCgaBUK/K0gWZAobsuvNsrHkJA2bD7Hz4w1j/8z+HLQrsPv44zA/6IRE7JxJ25yhRfkVRezFVlRy2FMxSazjw2PdJCn4STLSSmEwyQB2IUi1RJ1deDhVFcUu7ohKi4GBa9+jRCkeP1gzYDpHCeOGXTMcQnR3qd4GYgq8BWxSw+/YFzyk0jGuo4JufPBNzc3CTyATqwdeZlaX7S/CmDx5p7tOEJMPtg2vPWTYH9bpsMNKmOngQux/6EMy7786n4OscX1AGquhqh6LMJnPw5gmZBdTehBdf+Qvsrm1hfOVKVvuolXCHyRROLhtOsMfRYLNcPLz3c5cQnRgSi+umE+W5mRUpDU4WANYY/01vhwSAETJZm4xCJUPM81h4v6QHX0xTMQ9I4/QdotPx4HMhKQCkU8m81CWPlGgzgc4JIhfkAF3PSA6+BpLXPif0c/GRm4PvNnrwpXjS1Ny4T+43Ieug2FN1P/k5+FooGC85OknSjwSdPfiMmXrEWv/uYe+ffLxRd2U9b7gYy+T9Zni31H42Brj33hHeeqvAlad5kz9uTgcPhoqcLuQPfZ8wT17sDg2fNUY9FfEANUaxRwXFB92LBdk7Hs517tY7/u//DfadG07DYvqPv1DxoyjX5Wi/orC48bu/C3PtGqozZ/yuNHxs25Fu77ZdNnhPaCbRsS6I574ppzjHVfC5Yy05B5/2mPMKvjAHn2hkUpbYffxxuf8mZ3din89yU+sO7KLTFCzLgy+a3iljWE0eWakvTtY9KddqvpZUDqRp6mFz8MsKFgUrBd8K9jT4Hnw+0uhVlCVYroZCTN5SsP0dcSgKlRuJOTGeTKJnokAYur9Hg03x4qhOnWLbzFJr6QRaGvBDnBIhU4ccfHNDRv+BsqPn5LzWGOw+8QSqY8dQHTsGu7UV1Om8HIv04CsKjB59FMNvfhO7jz4qdheeAaZijiIy27I7HSJUo+DzqkjMdbpbFthzxa4TYSDLYvG5ety5cEqXjBx8sQWxXLihdLNpYWA3H2nQDQLBPOd1trWVpGybb12WobC3s0JeGKdvD77OFuipNsoQnQEzohmatvFCdKbPvVRv1n23HGQxAfuyuKPY+sXyyHFCnk6DMFW+d+E5vHHkMm4dPIEPlLmaF54mygHvvV2BqlL4y+V5cx8ER5yh44whSpYytLjmB0F078yTh2duyJVoLHBMSu/GPPhyhPsAZtoa4U7vHKJz3vWSxjGmNdTKGkuoM0tHNFtrY0IhOt+lE0kkEwd2px0cmI757LM7ePnlAcZjg89/ns9vre6TudPDKDCJO4R4cAX86JJCdFJIbpPMM89fhc6eiCj4YihR58HHN7aGhOjM8ODThOhUUQ/NISL52a1hPPg6fn9658Q+lTcHZnFTHqlAnUN6//4Jdg5BjOpB4fDhsN8u+fIozaZVFE8bB4+qxktvWlYUMyOBQFEHcgE4bWbzMTh+0qB426lGeT/Kj05h8N41eGJeZbhuCQxsMndYUdS8Dye74GBzywI367+PHZvReNw7Ruc5tcwpIzR4SmZVlmBzb85kXgT3bjhpb9woLtUkjy5eAkiKtyAHH+aTMUnjuJDNi/axhrE++szBJ4xjjF+mEscRGrWrwYYLVRl+70m5xtbNQYWxMffIEfiFg5WCbwV7GsZO1MUsHJzCMAwz11jFNdBaskTahkISQsB5zFqexTRF5iZG4ahCdPoefDe/8hWs//EfY3LHHZicP8/Oq+EVq4q5XKLayxA44Vhg8NfRJTwLyFznEv+XZSgQYUAdeqMogMEA43vuEat0ho4KPnfuMUHLztNPY+ejH63HEeQgWs8n9dwS4TzZplR6GiuXp5lskwJRGaDsK8xBtiQqipGelG++iY3/+B+BXYE953BXRArjevBJw+dOt08Ivh2zQapUeM6gPzLRzBCdfL/tX3HhR8dF6pJ/Ljk2h6c03kmas0zxg6vgY3JYqnSsfZiNR85CFlO3RE6KevCpDRO8ThIhOo3B24cuYDi0AN5TzYvOZ64lcRobW822oSA5po9pLp72+fRfyYOPNhkNNvGzI++r61BCOOYlFnn5PWVVu4x9K5y54Jt1JQg5Wqb0hb1eWURpHvxe8Pp4Z5ezNqHzoe2dv12cORqZsH8gP0RnJq3ThU2S+Jv9+y3+1b96D9vbBkeOZG6O4L3DCdBhk3wupXnp3b6kEJ2LPrLcJ/cVH5T21yn4NEsS8+TJ2ZceLonKBoT+uLkVRYC7GgOuoHlP3z/GMnnvwRBMc8ltSJG7ntkefIkcfG21jBCdHv3Yfou6/T33TnD1+hibmxWGQ+F6FiZz/PgEP/95gckEOH5+gqd+3QL/86waTWUS3ZMuzBmis64bx4HRLcwUlgXwz//5DXz/+wO8//2zFAOcLChK4kzvrG5xI2oYDoEPfGCEr79R4cMfDnnZQPknhOj0YI948BnDK9ZoDj4Yk+Uh5kLjwZcy4LktOfhi81mCBx8gOzcsUqYR4DcGIY+nCr6AP+74+tE7YgVLg5WCbwV7GsbjGaboVcFHoSGaidlaUcwUfJSAa5p5w0QismQjS4aRE82uNQo+4sE3OXcON3/jN4J6bh0u8l5b3gX7E0F/8DpSG02/2rJ5qFuub04g0vVmVMyla9dRK2gFU6AavN3wyhBJgqC009xSdafFnspdLVCfX4ivRklaAjcXx/UNZLzhd787V3sXYl7SFPbvt3j99frvAwcqjJawLBpBit2/X90Ry1/0aFXY5ATJ2nMJYI+e0E+s++BkaRV8GkicEe++VgsDZVygXsYcYcBtDtEJwFd09Y3HFFW6fX6fbusE7tVnK6yt8Zb4dV0GB7QhOunUzLRJ6FHuznfnox/Fd35+HG9u3dla3gYefNNx2fs9snDhd+xh72j337LvKjCnlrv7rR+tIgtitIzwHYKxtAq+edcv1l9Z1gmxGNB4yBw8WKEoLKrK4Pr1ArduVQAxBNF45M0XolMqydjjzgQ2N4HNzQ7nI3TPC6rMq+ALcPNt8uALIPXNOnzD2J7gwr7Oo+CT7i+PPwbJyUfaxPZM5xCdTr1ZdRPOBejx+ytpHoZ3y5pCBl5bVIjOUHEsz4l7tyaFS1GaNh3BrIz0JcgNNjaAhx+ucXB1aILNg4RuJcZoMUWyV68N0ek/V4sGSA6+ViYmhB6MdzZ7duZMhTNnfIUaZ3AXFfNM17LoEp7ZgYcfGeOBSze8Z7P3JB58a2thJfqzZ1pnHv0XDRsLhDn4LLor+HI9+OZR8GUva6xBnzn4IsPnhuhkzB/Z+qlxXQhyeMNR8PWRfqJ7sxX0DHvJhnMFKwjA9eALQj3Mo9TRKBYANkRnFRF2FQRBRpF4KsY4p+jIVPC5Y86Tg4+N2NOBeaAErkrhs2iY57Y0hufIST0rePNEvSL7hkWG6FRCyEwpOPAcBV9iPFUF5llySop+1NtMsZ+4SXEMUS8g4M4sFsCYpPDSwzuF7MFHRz55ssKVK2Pcd98IBw8mPIJ6gmDbcSE6DxxQ98fOsZccfM58orklOva9gMXlhL88niDnSyO4I7+9O5hlItP7qZPFsHQWbmOIzhioQnRy0GF/zKvg44ROXcH1kja2isoBLBuiU5jDtCJFG1yOl1fPPIwbm8dmD+kkYouTEaJzqdC3wmqOOVB6NxGMIWjftuXeIRKi0xpGIK3pcxHgjEOt2VXnyGk/GACnTs0W8ZVXynBf53rwJSI1UOglsmkfB4QO2EOITvd+bIWGLp5akoIvXMsM/lxRnroHwn0Z8uX0/i4Kq9oDogefMSxNIMHFixOcPTuBMRYXL/khpjtHYHdoafeus8zcuuZgjKHn2Pppxovi10h71xiiKCw2GXFGFFctwIOPx+tFM8lUVRgaolOLuOg94b6a00fwyoIHX86eTtn7dlHwsVVZBV+EHm88+GIkl+bwK+WKxsDPwSe12wP0OyC/fxCy0WQq5V0YDGCMxoMvk65Z9Br2mWpH2s9k/edx/phHZMkxMZNiLXgGMBFGOtrv7ZEj8AsHq2VfwZ6GZYXotMYEFHcdonNWd9bEJzT9fv2fmVEs5cZNBwkheUxUOxru60zfWDu7lNtyLdXvVHEvdpao1Ao4cgREmXshCkxbTa6snWefhd3chC0K3PzSl+SKCgvfzhA7QFolmnIyPB3PMNccg7RgD74ugodFhOiUgAuhwJ7qDD60VwhwjgKqSoG7nOqGsUYmzdzfx49XdU4Opt0iZKQB4cvggEqZgwIQ5thniE6ziBx8TBd9mFArPfhoiFr2FRKbYbI+kxJV6xugsDDGRJgXf847aLd6mFLqeSBM494pdRhTITrbZ/pwZq3QM2YlrARXEVHaSevB53a6tTUV0nEKvqkHn3TvcTn4UvOl90NLI3F3aYYH31JhGUhaOab7eK4cfJkhOgEm76o0YN/rFZPA5NDSwvOzZ2fKjO3tEA9wCj7KS0U9+BKQi8vYwj72JH1vllfw3zvJ56bm6YTo7Krg6QXm5Lu4Yu+8BLxif9FPRA++FL/B0CRf+cpNfO1rN3D13rFXxuq4tQw6M48+PfhyPpU3hIJ3i5KIyg906BDfSWdDNQc4T1CxLefB1zzU0KN0MXjiZw6XOwJzhui8vv9MW7dyDRidu7Svq7wYFC0uHAwUfU/xXqA0dafYUQDW0pSMAdZswqFC1/a5IBHQvpYmB988Hnx1iE67FA++bIj00We++9jwcnoiIX0ToVF7STswTSvkwmSq5NXI5zRwO8j7FYSwCtG5gj0NC/PgY8CWpX99G1PnfgmQrBw+LmaNmYs8g7wVsRCdivcbDUIBJgdU4DH7l1wuXTz4aJLihAXzQm6GPscwhhdI02+3bx/e+5f/EmY0gt23Tz+3HiHG7Ectqpz30xJCahqtCxOdUbfTp44Q+GIVxQurP63Wg496w6Y8gruC9PI5e5VT8EXAOjn45j2uizhSwVHiBnGZPwliBHsPTIfHmBoDUQTSYZFyvkuWYFXrxUbH5/Z/YpI3zt2NW8euYm10E9cvPBI0V6D2biDNa4968LnzDZnPUOnV01DRZ9qO5pmWq4gw1WSaCxB49LFd/N01i4MHLR55ZBrOUAjRGXshKkjhQr4Fzen9EAsDmYNzgwQzC4TbIQFQjtk5yi3XX1TBZ0JlYh93rQZia9HFg4/AmTO+t5LGg68ohEghTWEGSMuVFe24hzXXROigVZLXPufB5/Z1mzz4soWDmetrLfDmm453Et1Tihx8WpIq7sHn1FOcDWPqUJ00dHznEJ2O0sCl71ih+hIUfF6ZSsHXlVCcwYEDvBJnXyyMbkcPPi3+o0J4zbsYq0ztEciCfHBfLarUF0J0xqY6mcwK//7ix2APvww7GuHWF74gdS9DBnFnygIXLozx6qtlazASba7Jwac9XwIEOGZjwynri3BdHKhy8GGOa6Px4Et0cFtCdMZgKR58luwfRQ6+tkBQAKqAaTscAjs77TMpBx/N9amF20HeryCElYJvBXsaOnvwpYDDQNNnFy+O8cMfDlAUFk8+uQN8gxJXs98pw6qYG3ZSIB90brIxp1u8O9gHIEwcHGtjramZHYYodwkavUzEX7ukgq8P6JPRpIR2UbAeVywMh7DEPd4sKmcSBx1DdFZHjmBy4gTKN97A+MqVfucUE4op5hasniZkp9ufkpjsRPAmFHzSa7H7iSNwgxCFS6KiOggdDaPga78dI4ivCeEOuRQYgXhfMq777hvhO9+pz+9DDxE8yoXozLiwakE/mXgPHnwtmMV78MX6uXJl3K7d3Xf7luzBvlB78KmmGe3HDAd44Z4vAgDu2j8GcCs1bHiOoWDUkp3wZ2pZFsDclLx5MEIuSw1+NLhU9S7zhdl1hZ65bYOZOIqIwo7b6Dbn76jwP3zlBjY2nJw0Uw8+YxzDKOnMTSfFefBFf4PBK80EuBfNQH7LJEX2ggSgeV2f3p0jBx+31tNvxS2thelu4LZAhV/MiCsVcrYBquAL7nhFfiXv9xx8j7rZIggHhYYprJI4iIL3Vgv/WHLwZc7N2pqOeOGFWhgZGMAqcvDlKXj5y5/uy+g+dYsIfc4eM825LkKjEWuKpSn4YmXzhujUKtUPHZp1cvnyGD/4wQCHD1c4ebICnz1UhpSCL/ZNOAH4zINvGjZ1iuSNYfCnpQY+wgQTZ0mbg6+LB5+bjnV3uIUbv/u79Ud0DX8zaC13TVL1Tp2qvHDPsf7bHHzKKKeAQPPElEl03SMhOgNZQ0+gFFmwdVgPPhpO0zB0iRIa2sEmggOmomsEe2QRd7ELRVGH2FfHZ+82vOTBl0sOzyGyrOcxGMA4Cr5JWe/jQKbU0/bdCzaqv4iwUvCtYE9DVMEXve0ZQVkMjGmx0JkzFTY3Rzjzmzdx6JAN3KLdSzLgtSI5+EIkl8nITePsS7D72GPAX76Aa/vP4tB7r3hlVTGYxtpOk79U4NEKq6bW5VGBXqJfd/69Iv15brx5Q6DN4+ahICh6oxG7egQZg5v/4l/AXLsGe/SotkkAaoVAjyE62aYxblV4lgxZofhIaiKuYw6+hVFRfQgZJ5MsKrYysRx8cViU7PjZZ3dw8GCFo0crnDlDzi2n4NMk7qaCTxeWmIOvyyKxYRMFzvDZZ3dw/XrNOH7849vxjtUefOSsd/Dg8+/ncO5zOkWJEFhgN51mhhxeGmjwZJeDpwzR2eWK7iOcjSuwLKuxF6Jz3z4yd86DTxJ8TR8kSQcqQAZCvPBPQMG3lFxzwv6k9O6E6KZiXaQKUyE6kwq+2LedB2I0cAZfJT0/eNBi//4K771X96v14JMg2B/J9ehmHKSekBZon4ocfMlrnzQI8pr3LRgVgN7/Eo6TIPfMWwvccccEH/zgDv7iL9YB8t6V01/zZ7j8OiQX8+DLzjHW9En6K0rG10gpqbdF4fUmGQL1lYNPHRGSbl5uTj2E6DxwYNbJyZMVTpzYhTHAdgc8SeeT48Hn0pxUPtQqeBwFX0Cz0sG136sLjoZ75iLhkAm4Hnxt/4IsQRXa0VmT9jffGdtUfNYo+MTXt+rzJUExGbVVjOHnqO2rT9C+VsXsZc6Dr7ORU1nz7akcu7nGd72QpqnBBgNglxjudvl+Qht6/lUkV3ipZk+LJZ2IbGJcDAVZmf+zaw6+vcLG/qLBSsG3gj0NvoIvA82nMAqHgRzrpiNHLAbHeeVLTMFHqYuYlUYy9AMNlcMQ8e6K7DzzDP7sb59D9fZ7+MgLv+/V2x1GwkIGMOvVVfD1wQC7lj1J5YtYKfJcU7fP28eY+QTxS5SqRZm91BqUpVq5lzWEgimNCgK65OBL1e+LqKPMnLZbrYLvdlFRLSebsXcjCj7Wu8HM8lklBUlLgs1Ni49+lPeAZvfoGp+4mgP2nXp1WU+cow6LmrP9Njctnn/+Fl+oEIKyc9d4rCYmmebP8/Czehm1Qo0lmj5K75rC27lKj/Q8wmddlqEPBV9lZmewqGYefLF7y5VfWVPwIaOm7el7cSE6gzmVgoKPUzAuW8Gn7CRfYdMDCGPSx52NuLm1jin4jAkFadK69LxeooEB0EuITgA4fXqCl14qwv6ZMYDEK2XSeTleHCL0sScVtGzIVsXPkHumjQHc4CoBj7mXTOfn3MN2Kp9/8sldVBWw/T3KC2d6hUbHCnntpgP3cc7ZoIYDordWCgRJrEszt7AED75oDj4GovhV+YFoiE5VM/XdpF8zblxPwUfjDtP2dBdwdIXi5dQefNPvE6JTeW1GGS6RXVCmiKNyo3a0OfgidGzH89U8GkwiEbCMwYEDFu++W1c+fLgCGOXZ7YL6rmC8vxllXGcaaBqic64cfE1BQgncN8loBwMYquDrEYyRPfi4uqm+csb1wQZRQCblWq0M6smDL5emWcFiYA9RfytYQQh9efBpLG1EQoMq1ZzfQWL4DMSWRJ5B0l6TZHTHgw32ch0NNhOD8V3WIYuE4WJUv9Svt3YKC+YlQNbVw3A+7L7Rc5I5o88HS0gk3IBabxYTinUZKKFUC4h85UST8iWOMUsoaKQtwobI0igQl5yDz+7bB6tUYpmqkq2VGUFrzIMvF0UsBaUsxIOvvxx8QHdBrQS9OZsphL88s99h/wf4YbYrOzoOdsPhWkH+HhHQBh44NO+T98e0jmGEpFo6QfFMbt9aJDUz0TcmsDN2QnRWE5USwjtziRCdfA6+hEEDwSvRnD971YNvL4AgKO8s3OJoAEbBd+zYbID77iPS054VeWqIKPhc0IboBHz7liCcIiNUjL5q5rp0oh0WgXs7KDaTdj0h8SnX3cs5+OaAwSDsX5ODT7sckgefpQq+iGwgaKsRt2kVEMy4Fdf/UnLwOQbBCqIwes8o53voUH+X1f79FufPzwRNFROyUALuevfaO22pgB/QKfg0YGNafhc6hOgMPPg4aOmZdNUApPtG7cHXWFPZ6By0Cr7YzhpUEQVQUeDKlRHOnZvgvvtGNZm2pPtbLW4iG7YyJYvnYlEMov03OfjoGRLmoV6eHtYxqSjvK+G60IYqPl3aP6rodMolD9zs6REeYtLk4KP4qaNM6TaIcVfAwN6QHqxgBQK4F402vAYQor+dp55qkeP200/zFKyElQhRFPNCi4XozGWAAuaBHVAnDRsNNsJ6AlAFnxui04MuHnzCBdeWxyYTgzgHkvc7Z5yimM/TZoExvwOIzXMJNzIvT1MIJWP7LMfbjxuuo4KPAoeZqtOno92KU1V68AXC42UrA4ZD3Pr853V1M0N0xiwAU0K62yKo5tb+Nnjwje65R+yXtfCOTiAOvaGMQPir1bSlu07dJ/EQ2v3xfMlOpr8D/LVHFHyBoIr8pnV6GqoFjlaYnDvX/m03Qvqmjxx8rqBirXSUMVplmjGhV57TPpWDjwqUAcAKHnws2svYP8skRW6LIksYk9K7XcNTsbRHg8OdsrvuGuPo0Qpnz1X48Id3/PqSwqDv9Ymsv0ZIn+wveEQEVWyoysjFnYkHJUFw9FUo4dDHmisUfP2E6IzzrrcFUuunWN/Ll2uce+zYBIcPz77PcMgYnSg8r7S2g9YK8wvwMYPbBahoHnsl7xHMDWDTd6jvJAXEPLqiqFuRXqGrB9/Zs7UwqCgsTp3K10DEVvbLX76FL3zhJoBwH2lD28+E8MSDLzYnSxZDuycUSrxYWc7127sHH60kIbzknULKphtL8uDjgI0KFhmknOzIVYzB+jpw4YKDqxaAf7teS5yCsypKlm7JoQVHV6/WbY4cQXX6dKDI4mB2RoRvdTv4oJ4j5lCg6x+TDacix8yTVciY0Ph8XNQKv4z0ownYG1GXftFh7/gPr2AFDLjExVw5+A4fxs1f/3UU169jfPkyj/W0cV0EJF3/tph4v+UpJj0qOEZbYyHHdJUTojMUeMwKvOHcHyoMbgPL/73gwTfXmMbwnlraPhXaiL6WZK4QnZmg5TU54jJLwJTgVNjixL5V5t2Ogi0KTE6dIn0oQ8RoCU1JGNg3RNZ4cuECtp99Fhv/7b/F++AUfFL/qC38M6p7sGA6nYc94sG389xz9dhra1h78UVnMon8Ex32zsI8+IzplmRdYLpjv92fWr1iWGc2zxMnlJyxNC96ppcaolMuC2mVjoIoxSbRyrOqo0ex/fGPo/zxj7H7oQ+FdWNnSwmugm9j4FB2kQvOo59gQD1N6+dH8U2dAAAgAElEQVQ1ULTBKgMpDPUhOv/RePDdRgWfe+OnLNejd7gyROdwCFy9OsZdj46wQ/XSwhw7G75pIWXpMIUcGsPFiwH+KMs8I/RMPNJJd7KIA6DA56EsMzEPB2m09Gw79445vXoA+h7JMLyKvfSZz2zjxz8e49y5sVe9LBl6hlG2ZK/tFMQcfEXESIobkPSZHDddpR6D7qtpiM4AeoraoiYdFeNZquhUDvTpT9/Ct789xF13jWXbuY54sSyB06dr2i1Q8EX6NAbBRwty8Ll1A/qz+3k1xvJrGduD07IcG9ocby7V9OnelfaMmeaadLRN0c/bhujkafBm/e++e4yXXx6gKOqcnqn5uY8GTg4+Tbul5BaGVrZgmb1dgqPlc4yctj/9aYzuuw+T06dbGaHWg08Nfaxjog9O7tKFIpC+uTH+nRJL3yQX5PM07DVJc/AN1vm2hUFR2HY/7N+vW5Hw3lU1W0HPsFLwrWDPQlXNLhpj7NxIojpzBtWZM/UPzlozoeBrkFY0Bx/BbHPNmZOgKgSWVc8hOl0icrYGzFwUwFr+KyczunoVw+9+t/77oYfkSUf6oHMAML85zDxM1F4J0bkEQpQlQrlxFVagYplmPRMKPu5ZbojO6vjxpAeX+FrqHHzR7vuD1JpyuZ+ogqaqsgQ8VYZlNDedL3zhJr71rSEeeijD/HQO4AR3OQo+FgV10FTaffuw/cu/DABYe/FFH5fHmKoOi70wlNHcLQkFH/WW18yH7uSUXJvDWXScZ57ewY9etdjaYjxyJJAm25vWtD/gpqC6xzvOPUc4P3rooYAWoHTaPEvohhLcGMQ9+CxR8FlTNFy9eF8EAmcGR9Z70HlO8UqzZyJKxz0HHYT9yxiza2gqqT8uRGdWH4tal9g4UXpWmE9qnoGiqwQ8fXnCuy4TL3ZatgXQ4prcg/RW+ifjwZcCxUdaWwMuXx4HzwcDnQdfV1ZP8uCza2vePskJe37qdIW31ix2d00dpjfnonOhKEKliDFiSNFFg0djKs5pNBJTZL5Hjlg8+eRic2QB4T46fz5yKRTGw2O0vS2K6A4pqTJK+b3svn0BieyO2f4tELw552I0Ss+pDYreZbvF7puy9NzJokem8eCLXVHG4NSpCvv3j7C2ZmtPYKJEjL1EmcjBp3p2G4GG8ZVCdGZFcyhLTC5dan8ak+PBJ1RQ0EH/P3tv9mTHcd/5frNO7wuWxtZobATREAiAJAgCJEWJpLgTXERwKJkSx5JsejSSHPfp/iPzPG8T90b4aWJuxNgPnojRxHiupLkztuwJWbZsi6IoeajVFEWT6OVsVffh9DldlZX7UpVVJz8RCPQ5pyq3ysr85e+Xv186b9qAPPiU0jK8lpCybmKYzDEvJgnB5z+/g3//7xdByGjDjWmekeqJBr5IsOTP3+t05Ep15d94vyufwccP0VmKsS4qhmwXC2MhJ90VySgDYGvg20938puhQjijJjipB1/uc/epp5DNziJbWkL/6lXt/IUFM72WZ+BTTJO01MCnLOvaGvi0y0GdeaRYUOkOOeqe4caGSrJMlM/g09mG6RPeQWW5vk1EITr3/i8qCuzO4LtwYYgLF2y0tZqw2kDFwLcHs05OFhyKyiij8dxRGA5Wv0iSwipT6T1VuYZ6TvkdiUtL5VWtyhl8R4+m+Pqrd9gyCg9e2UN5p2kEBj0bmYClobK1U00UdU5CdO5nPN/JCaSCQo4NcuM+y3zv9n6TevBZyKmAngdopR58IWgAGPOO0nlDPFhtzQjRSecvhJojx1g/KtG4qBn2nJkelUzZgy+hDHzi5FXWPby8VfOowoNP5Vwp2bTPbItJ0em1qr/3SvsVNrW2MWB58BWV15lVlmnKecfm50Hye3g02jfpEFy92sdHHyVYW0uxa/psCEG2sgJ88MHkq7EHXylJ6dkCRj4qdHH2oSa0cf/b3BzgRz+aQacjNph5NUhK6jqWZ+kN0seOpXjmmV38l/9SDgOe5Ax8tIGQVugTkpXGwYReVwrqP3zrLfT/7M9GYfhFawtRG3JDdPLbZlC2rwuS19xgC8gNfIUYoYL099YLpTbN372X9/IytXlQauAbXT8+q4x1CVPWCsiywTK8pUnCLKPNRidCMqaTQR6pbO5wrlBFWe8ig3NPYZqG+Aw+ek1ls6Zhpl06g2/vM2PT7JkzQ3z1q1uYmQGWlqIHX5OIzR4Jlrxgoa3r1B2YCRGGCsgnmRfS6IFLZ+eLbPcfczecUqiycro9QwMfQMs+nNji6prNyV/MqgjSyZaW0H3+efQee8ztjGEjhPH6jbI1pzqtWt0hOlUNfKzdqa6gF138i8RflS6hDXxjT2G9bEawFm+sdqLHj5oWE0r9X8HAV0gzp6zQUiTVtaCiFRtJoubZO5lbGOOAQYhOTvKjMumGRZHgM0SniqGrdJvKgeDUTefPD3DffT1sbvbxwANlb08l5+wsw8yMZv1V34UAVkb33LOvmNo3iOYVV6P/Xc1kprYQmmzybpmXJR9qaL4j8QYuGUYEnnUcAx9I2XNZ2s/HApqlgkkYOk09EbXr6jDwcfIsGvgcpg8wQ3QKr9eYI52SS78kJ2b5PzUMGvlk6DVUUuz42sYhyQ2sn2sZShWU2axjHoSwPPh47eEoRKMRBnK2Kupn8BXbUseDj+kRNz8v7oqSOi0uAidOpHz7jEqbEIL04MFiuUjCNo5V0OkL2XIUNcePp3jggT4efLCHZOPYKJyfNLFqoQ10k+8T4IEH+rj//rL3Fqt50/xYx6jP/16/Pvl7+9qNwm/MN3+cxunT2L11a+ItpWwcybM3HtDrDVE3GQzkz0RL1qIu4urdGL+JpkySpkCWcc/g45ZNQ9/wg/MvTP7efu017nUTvLx/apviWNfQzgmZCw8+ST40k8gW0OjDCjoZa7x78BVDpCpNzQJjoXk5UApHum+4Lq7rxmuNAwcyZeMeq3w1DutTTfTgiwRL0YNP7zBcoxGFd09BCVv0oqMFpTQj7qzmJQ0qo4wc5ThNf9aBB1/+B8MRuxjeNNPy4BOmK/pRqjHTUILRnwmxEwxa6sGnnIXKolSjbLRHJPNRy/oxQyjXldNVPPh41aIXNbxdrbq72o2R9VFeG+a1pbohOkkHhPC0rXUeFsWmpBSdndXrt6ymcbDgmCx+984/5RbJYBwqp5UZpcM8X0rFk40qgNL4Qn3udIDnn+eH1fS2KZdXrgA9+M6eBT796RTJz1KcOTMSypghOj3OIWZRtO3LkzdEzCl68E3uVQiJW7alGIzpQ8qFII+Ggr/1HnwKyhIdb4USrDDNuiE6VbFNj1ay5j8ryh+y8hTWEawz+MS3FykZtWToK0G9RNOgy+3Ag48pH/OK7nH+kL7CDtddNJ1OBno/AiuyTtm+qv6MWeN3Njcn7tciVOqv+K5lBw6Uv2OVxdDAK9rsQReRZciffMy9U2MFcX9tDbuvvIL5b34Tc9/7njhxXSzuH9ejbOBjz9W8/LK8Bojxrr599in0ZlewO38Aa6fPQsuVWQUFvZjOq6jnwadwkc6ARyUobZ405Rr4eGSdTvHNEcxlH6+s4zvXv47luS4uX1hlXyT7zgOq2dAGppSww47onMHHzCcnNxNSlC21j+mBoxW/JDPWGXyunx/POUQ5G5lhlH9L8TN9Bt8kRKd6unp5hqezmQbq1x5EIhyq9ODLWErF8W+lz+xBGtBTkkgXByoefEwBs/ydeYhOktvNk1MQGxhhRutQyeReheLHJg/WvTaH8ypslXLWBIKXyHVoFGVZl/XO2XjwuVDSMPKTLlxmZ5HuLbrTQ4eQUTtsOcmyoZ8T70ZaAVHRYqKEivGFYeCb9DnWGCZQjouqWdWh5iVoRZ5qeE6Ooh8Q72xVpTCWiwwOBu+NM90hY0wtKUJVLG0qg45m/2AvTKjvTMYcTrno/qsTYtEWUdPcfw24eHGAhUmUqvw8nskT4OWpHKLTwHBssBimKXrw7SvimL2CPoNvb3nFNKxP3vtyiK7CtczCU/eMZQgFo6OIOg18lYzbrHO3KXQUW/1LlyZ/D86fZ8t8e3O5wigixnf75NM3iPTAqkshRGfZ0sK9VidfnculSVQRopMxp2sboZhn8KldWykyhapFnx6dwSffUGE6/XPP4KM9+HQyMDSUs65Jcwa+Q4dSZBjV//Dh4nqS1caDCxf2/3Zw1IWwPUTrW1bf9NhfZbLUZN7mrKuYYwpDSc8P0Tl6VwczC3jnzOP42fFr5XddY0cZt6sYGPhETXPhwr4i7q67eNY+C1lLFqIzn4tsXE9T/nKZ0BfvoanL2lo8gq0DDA9US/nLN4TQBr4MacL24NvYMA9lQOdDN4tSJBmFDnr58n5EjXwfNcazB1+SoFAvnXC2LlWWQNmYOeB48JnKnVWocSNyogdfJFiKBr4KPPgMzuBjheh0FhSFJXiYKDUB9GaWAKitYenbe739LybylumITU0glUSQ8bjQZCqjdWhpiE4WyiFZTXaQc2C9LlJPVAUDH0thu/Mv/gVm3n57pPhjpFEuC+fZaxqHuIWsCpWFOitEp4CRB99IgG+EcEj32bk5rduZdXS54MjKZ9aUftfEmxDPmucUPB+U09bA1yvF9FrM/09/XzcMRRXjp/I9xgvEzDhkJG1gs2nCjTMZ8N7o7yOHciG6BMqcyU8K8mn5DL7ydbLykz0lKmuejGfwyclnrROiMzt8GNu3b6Pzy1+if/0624Nv/J2tvOy6vUTjqyhkmsYW72IW1BpK1zNDUwlrZOCziUumWhAlA58kTYanMO+51HoGn+wCKwNfWZ5heYZot+04rYzjEUcZ+KzC5Bt1UgBJUthAuLk5AMmGuPpgD3N/W76WZvfZZzG/sIDU8Cx75XUMIH6nTOtvyPD8eaQHDiD56CP0Hnyw9DvXg48Uf+eTFe4nRH4TK0S3KtykFeZ8nUARjz/ew4cfJkhT4IUXds3KlId+bx2F6AQADIcgyEYyKms/j4K+QWXtzoQlf3noz6avDV3/TmdPRqZuXl8f4pOf5Ec1UcknvzFOZOATrh1EnwE89FAPH32UoNsFnnlGobyyRnJ1Bp8g+/x8IZqLeLK4i3PFATDO4Bt78LmZowMS76eaaOCLBEsxRGcFGUoMfBPFkdDApz6SDRZXtMqTgSgtdFOGY25/pnxAtIi8gq3bHf2fEYLOWAg21HymVNs9+GAP3/3uHAYDgk99qms+M9gsrGzvtXHtDyVEp2OUhVDvQrE4ZKdqQVWKlB49it7Ro9zflbuEqgdjKFIUR7FZKI3IwMfsA2YefLXBCtGpgS8DX0EZ5dmDz/i50At+RuLs82WK3zHP4LN8R7yt+Vh1BmoN0Smsl0o7ul4M57qkUYhOSm4z4frNIT7+30McOJBifjZXIAUDn8kZfMIwurysRSE6NSpfq4GvRnQMfHSxh5ubGG5ujj70ymc0TcbwipXZUgTvMy3bKxn1mDITv0ORTjGPJJH0P83Jhr3nSNLBKwjRqRL+XTbtl7y8G+rBZ8PIg4828MlDdOqcwceUSxln8Cn74jqaQzOgEKJzbg64eG6IwUYGKBj4spUV7N66pVBgB4gGVB9jouj+JMHWV76CzvvvC49QyBsnAAhDdCYdhv4lF56wZHSmyqdk4NNtE4Xrdd6LxcUMb7yxI0xv/C7qeCVNEOklNDeCkPyZMooor7dll3iL5+8GdrMXNzKcOzfEfb+7bZ0PrSPNDwN5z2sbA9/sLHDrltjgrIOzaCmcSrHaxTQtzfgP5XIwz+DLCtnZdF3TeTfiltjskWCRhugUjUAGoxPXsCCYbEoDl0S4+NsLLyFNOvjNobuwu3FOXB7GorYcLpQBox792SVhXqIkupPNMWRfgDNQnI4uKbbd4iLw+7+/hddf38Yjj/S8CERcbwneZx0IsQulV2WIzgoNfCxYCh/mO+cxRKe5gY82FHrUhiqG6CwrejwhaVPmM6S+I2kqfw8LafIXAQGtmSaUlKKWBj5CxDtbTdIVKgKdnMFnCKvyCopRQvcxVnksO4/Smt1hiM6mrIyYfcmkQyi2nVHSDna7Lq4kOHt2iEOHFMpJPauUFb+L+qwSolNa/iaG6KQJZFAfDi3KwWprVwY+35NgPj1HkR4KyqIsLXxXro5ExtAsh1FzVxCik3cG3zgk2sbGUP7KqkR2GeNR5pd2SZl8atGHZ2ZY6bPk9uJn1VDPWcZOjz6DT+e9VMpZpU0IQba8XPxuOGQrqD08fy0RhfVOjROoOEQnAGB+HsNTpzhrvNH/5TP4xn8xwokzirtvwJBEzQAjHK/h+FrIX6ENdcdfVYzkNMsQnQXSlHsdy8DKyoPVEkp93ofBWhGTbDqd0bnsxUAZ9v2AENqDr5imkgcfK1EXBRNRhYE2l57R0Ezs1zSszCfPK5fwyCBpllETdDbTQPTgiwRL0YNPc+IxGVEMPPjoyUtmr/nZiQfwy6NXMOzM4STEu6JMPfhYOxsHnXlxXgLGHnyAfYjO/IQxXmwdPJjh4EH2LvRKzmaxUbQkiZUHX5XzXtUhOukwa0oKeMCpgU+aJ/P9KSNcM6k+a0Whh3nYM+s6uveEFKKTEGQLCyC7ox122eysslEjI+WwIazb9H/0CN1nA/HgyyMcSw3yKhu9tZNg36ho4CtFx2J58CmcuyXC25qP9y7U6MEnhFr4lT5T1zA/MxicO4e5Dz8EAAyPH+feauLBty+nWSgteKtw1rxBh+gUXb73BcuDj76O1axZkkxCcw7X986DsVSYVmngK41FVYzbnArms9YJ0VmiyhCdtohesLJbiX56VJJjAx/vcmn1HITolOLhBSilyBlPXnttGz/96QzOnVM4S6glZ/DZlG2snM5Thwefisc1F1ODAEM+Sj76iH1tBc9fVGTWGbtjrM8ldczITp5xz+CTyYITuWPPg09ljCsNBy7O4FPAWfQNKgGV7pYRUnxzBWsPsxCddgY+lcZQ1mVUNP6qDht5Oh0AKUF+fndlR0sVz+Dj6kDoPuKiHT3ORyr5EFKcq1WM6uMrbNZD7IQ562JPcng08NVDINqDSKSMzINPODx6MPCNyYfApG/JUvmgPYl3rFuekQQqvY0WUPszi9rtkb98EqITZP85GM4wSu75vIKYXieTtG0MfITY7ZKsUqsmUuJXMAOrCsUsz1WXZRiePDn5zArVwspPWATXXiiKHnxW/dglHMXyzu3bk487r75avoYjVOZD3LAIUli09uBjeLc69OAjyErKqOGxYwCAwalTSI8c0U7bV4hOEMa5pqYrmlwbmoRgUdrZ7NKDj+5HgYboZJ7BpzKWU3QfewyDU6cwPHIEOy+/zC2LajPsPvPM5N6/u/sFXrHU0TDwsc7GmvxNK6PZt5QNepzCb3/hCxieOIHeffdheOEC/9qavfa51DB3ZQvsMPU8Ax/L20c4rLPq4CtEp0eDXzlEZ/5vRVmE+mrfwJcxL5dWx2L9opxEBR58vPdxcRG4554BFhcV0mRGduFUzuP8Yf3MLPrw7Gz5fmEI8j10DHxlAyIB5uYKMkHpmFiddajDa8jHH7M3GNSwrlOWWQIMaUgIw4NPoPtmbSrLRJ77FD48+PLtyhvSaLnWs21DfJHDEJ1IU8E4zvaodBWiUyWSjQtcJTk6g08wdxiSJDIDX/4LbgcVf/aB5/GINvApieacdZd1sbgDQz5rG3nIz/gS0SN68EWCJb/YnpnRjKutmxkhyh58+dG1NNBqZCxdT7LOIZJoVefmgB3qu/6syqqRn2yvty/hWofoFLWdYjrOsVS0MBXxqmkqhOh0hkii8CSI5vu48vNWNXCxkHjwEQJka2vY+vKXQT7+GMPz55XK5GMDGa9aqgsO+hyb2hbHnP4/PH0aW1/+MpCmSNfXkfziF6VrWPAW2EFDt8Gc4iaOPZjDunMPvmIm3aefRrq0hOzQIaP0vD4XBQsPrVwhSTmENQjB1u/+Lmb/4R/Qv3xZuxhVe/C5DGPmlJIyVTKPj1lYwODCBcy88w56993H/H3ni1+UZae82Ozfdx9+/ukD+PPVNdxZOiYvnwSuslJg4Bv/JFT07X2mjUgjI3xOgcy5NT25ge0vfUlaJh0Fb60hOqug08H25z6H2b/7O/Tuv3/yddHAV1TA0OLZ7KygkQgZ7TzPN6TIwKeDR4MeQPUTR5EeCh58SLm/jT8LvScdePAFoWRy8Bx1zkisJALKHoQAg/PnMfPuuwCA/j33lC8QfdbA1INPdb8DM0Tn3Fx5AwaNjoGPNV6rFG4sKywuguyMogCRLKss5KVUNGMNnCoJMRPTw7a/E0KF1gaEZ/Cx5JLx/Sr6jrIHP6dQzK8zgPXuGxiobLuJjeFBJ0Qni3yeRBaik4XC3KI0dNU46ai0Oz2/jrxVqTWUg+mC9oItG/gaFKLTYT705gFR185KO0eUslBHIZqFpVqU+tz2xUWYRANfJFgGg/1Roooz+FQ9+EQHpd599wC/fG/098mT+zOq0U780uQrN/C9/PIO/uj/Hu1UvueekQtkf8bOwMcM0WnqwSc5ZNZ4GrBZWJnE/9ojA0BsFPEKWjVXa/SqQ3QqZcHadUpvk3e8iwoA0uPHgVxIOFl+wtdOsXzKIQ0VDZyHDmdYXMyws0Nw5EgaVojOve/SfBvz3sPS+No8Dz7XZ/ABcOJ9I+ynhCBbWzNOm9ndXHmzsTa3SO4jhD1/pOvr6I7DGGpis3dDCC+RUEN05uDWn/N+79y+DfLhh1qGZGNdcKeDO+cu4cOf7HtrWT0vAw++iYFPFPpo74tyNESLwloqeNPUQcdWff8dKvt1GN51F4Z33cX/PacEm53N0O8XyyXdt0FIoQ2EITpV6syzVLg2GOY/U53y+PH9+qytcaxwEpmJDtWnXR1tAx/jvCxZHj4s3FV4BRKiPp94LAYA7D7/POb+8i8xPHasKPupJqAI8ww+hgdfeamnfgZfKf29l7/4dQ3C6Fi2Xl1FZyd3zEdNBoZStqp5mo6JHqGV8IDEwJfbZDaZ9zkGPhY287+yHKZwiatuYpSO5hl81BRbZDgEskyvbQw8+JjjSI39WSWbvD51TEbNHa7saFkuCk/JwKcSErSG3Wa+PTDp8UDpXREYC23KwYrYwxyvDDOqSbyPUEQDXyRYiiE6PQ/4hPB3f+19z1Lc0IP06dNDzJweYjgEzpwZIl1dBel2sfP660ZlKpAxQgxQn0+cSPGV39/B0a0+VlZGbdazNPDtHaNVCNFpGkZR1HbMdHzMDDZ5MCRjGw++/tWrmPtf/wsAMBiH2vJFxR58NErPGyjHwbIom9GjZlxUqYCiEaLz3nv7+PhjgkOHMuz6KqRE2GaNm0xjsuoYIQm1FKSw6MPA56Ci+STodnWx21n02SohpTP4KAWcYfYivOnHFI3dVRr4hM9P8OPkJ8E4lR0+bFUWiz04fgx8gmtpRd/oS+pajoGvdKYTYZ/Bx4L5Pgd6Bl9IGoC8MSjveMJqOqEH3yix4mfLEJ3ZvPnZ2VoIzuA7dWqI+4728PHHCZ483wX+K+N+Zl322yqRnMEn7aaa/YX1s2wNKTovzBQfaWqdwVdpiN4M2coKup/5DPtnh+/8OLxcIXcFDz6dLEvy0t67WJCpqPFaS6YyVSjvXTM8cwadX/96lO/cHPslMnz+om4ra9Os01EzewYYojNJ+AY+VRk9zW9QFMpNWbkJXLRJIQ01A4ErDxuldHhzJAPWOpI28BWSmxj4yt6NXIOrgoHPVG9QZYh9Gf1+8fOouO5DdALijfxGHnwukGXmajwS3KNk3BQwnl9s2i3LRnNH/957sfNXv8YPzr8w+S3pUPrZaOBrNNHAFwmWvIGPKaeKRg2TEcXAg68Uy5xkOHt2f5frx1/96kjoYCh8ddd+JEvLCwhGPY8eTbG6up94f3ZJLyMq2YkHHyHlmPEWCftSamunabsdxsLAl544gZ0XX0Tn/ffRu3HDvBwq1BCiU5oFSyi2MfA5UKiwdh77CDHArRb9Ay92FSGYnQXW1jhhc6vCdMcup7z0gqMRwqJliE5fddxPpxzSytZ4RO9iJcTQyGZq4KsAJU8Qj2fwBePBV5KBFJSTVW/M2MPlEa46ITrpNimcwcepkHQHv4aBj0k08GkhixoiNfDR5Dz4SuE7Oew++STmv/1t9K9eRba6OrnfJaJQwKWwXR2C55/vAgA676jnUfTgK4ZOK88dnHBznDKZGPikto6mePA15Qw+CTYbjEayBn+8zX1b+GQTopNl4NNqBA3jnco13U99CjM//jHInTvYeeWV0jqB9s7xRUlGUs3TckOKDwhh9CMyPje0/C4Xxri96vA8+FibGkpNoPG8lNePDFzKSPkETLySdEN0CssqCA3LM/CZbFavTHelmI1K1kwPPhBsL+xvvnNRBUKKRu7SBgAFA5+XjTEyqhh7FBt4Un3O9S7e190XXsC3/ma18PXCInDoUIoPP0xw/Lj58UGBivdTRzTwRYKl6MGnebNDAx891uYXJtI5IUn46eoa+FiHBHNm+7wyoT+zUL5Gllcu2ckZfMhVxWAyHE38+bQMdnvZXse4VusxsNrf8qyswZUrGMgvs6dmDz5WFkwFqkMDX/lW9tPe/p3fwfyf/RmGZ89ieOpU6XcXCyLjJuYZ+FTCGFaBamg4VQOfgfKubmxDdPpbW+TCxZGkqLIKsSGB0fxl4MHnoz6+NplzFdahvNNSyoorl0tyljLM9F6rJjQK0TkyVhQUGZz72WfwSfLhwQp3HQ18UvJZ56da1mY26bBON2I+cUIErgf79G/cQP/69cLz9H42p8CDTylfxveFqmdiZZG0Opr1ZV0uFdN9nIftI82SBx+fqs/gs7vALkMVDz7V4fDxx7v4z386etknG3ZZBj5JmbTRMQLOzWHrrbdGrjlzc+i88w77OsdIh25VK2qgHny8M/iYqpak/OXYuMEzKI3R3rSueGl+zp+fl6gPcMoAACAASURBVJ+1BVQcopPO3NLAV5hn0pQbopPbtAoRc5TElQAN1nloeWbcP3cXDuHtc0/hxG/+Hr94+hHYxpGijeT05p26PPikc2EVHnyS6ESqSdpsOJdV6fLlAQaDkay7Y/iAfI0vET2igS8SLHmXcqbnmGjwcWjgKxuEBAY+j1oSAsZB2qIFdzYO0enGgy8fotNcQVNWDJqlo4HMoGdhHAQhViE6q0S0U86HIqA8ycvfjSxJSkK9T8XxmOHZs9j+yle077PJW5Rm3kBPeEqiUJSkqoKxsoHPQhoMpA0ySw8+V0wUXZkPD75yXs48+BRWCD5Cy9Ao6apczPm2GkkH6Cgufbc9rRSwnKLN0THwleatvFKDXSi1EJ2KZ7daKpjqNPCFYsjOT7VGHnyuGtH3ey96SRwZ+FgefCrZM9HcYcXec1TRs/OcZmkzkWgs9urBZ1k3y7KVPOwY7WCyBgGAK1cG6G6nOPeLIdbXRwY+1hl8zudBhXGwUANC9qNF1OT9XyoyI9/B6dOYee89AED/0iUAHFmx5nmAkKx0BrjqGXxjiqG5+TIMM2SwhmVKpamOHElx4ECKO3cSbG7ubyN2LWaO3wOTxyfUSzBDdAq8vSVn8DH7nMEZfMxLajRYq4zF99wzwHe+M5qHz50botfbf27vnnoU7556FC+c3gEst5sTYu/BV8cZfL7HS5ZXuJSqx0MyWn9o7k9mJSP8HKmGaOCLBEulHnyE4TVApTVOUhRf2uvExDqDj0fuurwHn2rx8kJUt7uf5sTQaujBJ2y78UWizz6wmdgJsfbgU8nCCZWex6EIXTmWYUSnASQefKZt6cODT5hGkvA994wStEA2aBh68NGpjrMpnYFRqpagPHVtFaPfLc0xQVmJb0FGkmLTWWbiqqmZGy4UPNlKyhUPjeZtzc55d0v1DGXro8pY43A8MlXMjq41zrYMZ85klmbiwbd3TUGRwW6bUllJOSSnMiEY+NQFTPHnCslnnab7H1gKWNm+jXxoKWlYydqs1uX7C2V19ALlk5QZ+HTP4JP1MlbzSKdkD2u39OjR/eRdrRNYwiivP4SyQYR1gWUfJnQvYBhTTKfTJAFu3Bxg9Vv7MjgvRKfToUslMVUju6f1nuwxsgw2uy++iPn//t+RHj6M4fiseUY96t7oQesoRt/xjVd5uWQy7yf7Hnyi+jD7oov3lXoP7r13gDQdJf3x5HvqaJlGe/Dl6jIcig2irD6nsLhXGkcs5S/fdDrAW29tYeFXfSwtZfjJTzqlMrt4/eh+b2Tg84EkM6VjBzyXQYaLM/iEOJqjQ13GThux2SPBUjyDLxwPvvznKgcubQ++PZydwQdBiE7F9hZ6P7LSMTBoal9rc2+SsDUHIW5ZqdjAp9TMtOJGoS3T5WUAQO/6dZviaeF43SVH5Vk5P0jBDKZgbBWiU9zY9G07t25N/t594QXUgmGITt8CO62MKmDZqZ3p61jKW5Pd6A028GWc73Wf0fDIEVdFEv7I3InqcfzRm6LdGctNzuBjGfh497NCdNLXKb9nqhstONQaorNGeEVhTcFaZ/D5NPC5Jpe+MKyrqnEBsjP4+Ncy0ZR12Aa+6j34spUV7Lz0EvqXLmH7i190k6iOB1+Im/rGVDAGWE2n9JzH8OCTZqiLyv28StTkwcfapEKTHTiA3Vu30HvkEf4gwLlXC1ujMcvAJ/DgY717qmfwqYbo5BkJdeSAJAF6N29y77XuKjrrGPoi0cYHwzP4xtfsLByc/NQ9cxf7ZoOIQaxkKjEQWSY5OwssLY3PlFTZTKsPIUUPPrpvtd6Dz0Ej0mfwOVtrV0S5fDU8z0j04IuEi5UHny6ESA18LMWNbpiS++7r4fvfn8P8fIaLF/Vc4ZXP4AMKdenNLE7+PnxY/0yI/RCd+8/BeKedQODlf2mJQ0ULy9tE55ybOjFS3DhEZcciM7QhVbbt3/1dkJ0dpMeOSfN0JRj5eMSismQzMyC9nvD+YLx9GIsw1vigGhqXPgNDdtvgyhVsLy4iW1hAevy48F5flN4t6xCdbgTifLr0Ys52tzTzdpOFGeslNTmDzwNK87urOgNGSrrtL3wBi//xPyJdXkbvk5/UL4sJrHnc4SrUpS3EqpvoKMhFRghOoUrJ0wY9WyNQQ2STEDUWRiE68/jcgONaES46g0/Uj/dgeljnxs6qQ3QavQqelIqDy5cxuHzZXYKMtuAa+Sp8r7Szsi2b4HmNk1YyqsgSGTPx4Mt56dYQolNlzQ9I1nsOKRVHtZErMojoMDqDryjbiOyRSadc3jRv4BPA2nCgMwJx06d+6F2/juGZMxjcfbfgXjdjn9L8SBvOtUN08j+TNEWWJJPvthaO4B/OPYuDd36OE09eweov/qqciUKITlOZsrZ3UBF6/HJl4BNtbrM6gsMnzGMgaqb0QDQM6S7yM8yoaQbJthINfJFgkRr4RKOGyYiiegafRYjOz3ymi42NITY2htpxjolhiM6XPpfhOz8c4OLFAVZW1KasfDaDwf6HiSelQfvSE79ROAfb6xzfmwEVePA5EjNMzlaxQGmSV9nNx7jG1JBjXk17rxAtocdkYVCXFGV6Bh8H2a7CssxLMMwtXmuB3gWqObhX4YzJ8g6ywZsQT4ia8ZrRD1xTXvs7GosVDXwqRtjh6dO48/Wvjwrr0Kgm+tG3B5/N3gWnyiuDM/jGP+UVheX2Yyev8o6qKvYAPSN+lZumpeErK4SXNStqiNa+DZni0NZ465J8+o42gvEuzStg97MUnKsE/c0orMu9bxKtCkUPvixJvPYba4OeZdmSRL5Z1WYeoVHZfOjc8K5zTUVjqjQbnTiossQrhhAgowObkbFSvTwfsIqregafcphHTptwm4pKOD1yBIOLF4V527wXTz65i3/+YYbDh1OsrhoIEZYhOgsMh8V7CMGvj1zCr49cwidXt4BfMmQkg8UXcx1gGUHBBtO5jZ5XnRn4EoEHn0p+LfTg06mSyakoGqnzf3I2h/gJARzRoy0ib6SF5I+gYoboFOHBwDdOMr+g0s1mfn4UE92E0pkDArK5OZBuFxkhOHlxAZ+/f0cvL6bgSvZlJ47CSlquQtsphF11JXG4yoNxr2gHWlA0wMCnsogW9rWKzuAzQStvFYk9kBCdpmfwcQXhJkYOd+7B54Z8uqWdk6FI3ax+EYgHn9Ir5tKDz1Qj6Vt7Xdrk5LftHU/RxuiE6CydwScax/YuknrwcbJiwXwmGrJJltWoXA3QwBeUB5/r90/QyVxFeuAmQ0gpNC0hkmFU86VgrS2ka8g6lIoGsIzjVYdMZmFt8NMkEW622Q9DV7hH4yzXUop7m7YKMhUsPK5ZWBj4Sv2ionUpna3yelgwh9ZFkhSNE4SIw+jnz4He39iT2+gjqA+zmTTqz91sRs8VCkYym2a/caOPzosDLL2jptcq9VPLEJ2Fz8MhkGV8XZ2KjKRk4FP8sqL+bPKqj4pbLLMbPUeGNCf7lsaHwjqU04d9zMWS58qUe2recFDWffHHIhe4WtdVtNckIiEQDU8kUibvOabrwWcyUHHvKS2uzT347FD34Os9/DCyhQX0b9wAFhflN1DwBBjuGXyK5NtYeQebb2y0h006g09UphqU5cwvWZ5PAUgLPgx8Im8gpUWyy1WaBcpnD6ga+DRDdAaBpQdfJXUK4D1iomDgU5rPPdTH2yvFexY1naOjj/n7rZS6xdDmdN+Djgdfqc+yd/LnP7PO4JMpjLn1MVAwOfNI1SWgsYhv4LPz4JN5BtRqThK9YAYhOlX7KU/pLX382ga+8neyPRCkIQY+5TP4PM8d1gY9y3e+Y2CHtmoSlTP4qkC1cp6ev3Qdo9pAqusGHSzvJyQrnxGmaOAbs38/PZ4UPzPPBNUoP+9SFUOv66Vj+tRjk3x3P/MZ8cX0PCjKXNNyRdIUZDjcN7Ym+4N+kpTzZuahMJfVaeBjV8Fs7vIRohMoG8kLv6mcwVcHjgrjY/NjbeKyo4yCXca2nOjBFwmWfIhO0wlMGYZSsfAb9gXZ/KRY5cBFskxZKdB/4AH0r10zHqBZ9ZrJRwAzCmtQ3NXOuoWeHJUnSxvjlc0kRkjYB9mrUolQIj9zILM0ltJKGleCkYvm0UpDZWAJRWHo2oNPN0RnANCLVFMDX5rMIEkH+OHFF3DJQbl8tp2ztFQWxrWdwVdxwjUa7YXGnooNrDqbIeT3WhREw8BX2ulfMIyw72d58NGflctvZOBzvCdNNbEGDOqsR89UyvKQ1VGnzr7bJ5++ozGHmwwjRKe0eg7O4JOK6U018BFOKE7P6xLbV9hWEdpR8MZzOZ1mLAOf43FMqQeqHiViUVmdV6FUZcV+p7wxsEIIKZ4BLhsWWcUde/DRFyj1RR87SRnPw7UtODt4ENtf/jLIP/+z/nEJgp0XumfwIU2Bfn/ycZjMsq8T5aGoy1L6MuAQnXnjdeE7SwihjXjUJjYVA18NHny+15hGITo5nT1AcblAA8T7qSAa+CLBEsoZfGWjU96Djxq1PS4SiYYH3+gG81GVuV6ckVygmbDTM/iqgjVzJcnoXJF0/0wIl72gkiaoq53pfFlb5B26ZJjeWnk0zGkO0WmgvKsdFU9Uhdu/9eAfYqH7EYbr6wC2rYs1bqugvRM4Y2oBi/7kkjR1lAen7EpnD9aBYOMN06DlNjutpF0Oi1pKUuoMPqEiY++L8vkkRYMe+63lvMuMstooeb0SkAaAlzU9BROS6SnSGF7IxrV03T6C99mnBx8ApoFP2k2dGPjaEaJT1bs9uHff8TsvCtE5kXuoa2w2Co83beWbles9aYpCm3BrUOF6gJBsEtK5lI1qvxNskqkLQsANL8i0RzKc9MfzPsuAkkc5RCcnDW7S9NiuYHB10ezpsWPAsWP6N1qG6CwwHILkNkXlPfhYEbAy1lqDmYF44zCAWuUv09DD5fHLfg4cGckD9OCTZeZqQwwnH7ddwZOs4miODki8n2oCkwAjkX2KBj7PZ/CxJnpOknVNUCSTHyruLC/WGRY5WclUcBGGN/VFScKwmBx5M5dBHPeg8FBepUmeFrgZwn7paYnKGrAHn9IiYXxlg0J0Mhc2zNWw+GGMH10jDXz0mKK9WBjd351bxT+vnmKG/jHBb1v5W2SEZOjyEcaQO6aZhCatgrKFSnqNTdnpNtd7/OrjrBSLEJ2pUohOqq6ElOquXH6VMVfvZ38EpAHgZU0bA2ZnNYspUxzqJOa7vXLpiWR77kjINPBxzNNJom+L0Kw/62epcbahBj7mWAx4f6esu6Rl+WYMjHVWWc7Pl9II6Qy+0vcVRZYpbVJRnawDPPOKkGJ4wbxRiFlcVojOJLfRh7URag+msZkQ5fYTbaCgMlJKrzJoOVEzRKdo3CFpCgwGk7knzT1LmxCdNMrHywTuwSeLlmMC3e/pJuB5yNaO5/HoxInhRI/98MNdtZvod8XzGXyuEi6/ow2RrVpG9OCLBIuVB58BwxMn2D8IVqOV6h0zTQ8+C7QXyArlIoRx6LFBOqb5T3C5kB/nOzNTCA3hkpMn9w27q6vVGXldoxSShOXB51C5ZSpoOBN8VTEx8HkaG6TeX6qCsWL5stK+owYIh5oeezS+N18TRhtmOgdK+YTVj1UapKr+Tzzofnllr9GwqTWFuvZcoLBphrrP4Nv/SZAxJUMOh0qXcrPm/iCZR0Ix8AVjyM5By7uzs3qDQHAeVHlEL5io3BrPiSsWkHLYWVlT6fYPVnpSkSpthmxdiigDwh6PQ+t/judrQvjPized2tg60pUVZprMjE2xMfDVdH5vqTgWHnyVtJ+AJGFs4CZjpXp5/GdllxYsTvzycPtip1McizhpKMsBrE2zdS6pFMo3hh2iMwNXthoOC+vVvAcfbXiafKkgMKoMXcw5qqIz+EwMfACQUuXzESGW/iw7pmd0UfUhOn3LoLOzwFe+soXf/KaD8+cHwmvp6le01C2Xw/C+usobKRKYBBiJ7CM18InQHVEIQbq+ju6nPoXBqVPY/uIXS2mNBbK8AFfpGXwsAaVCZmZzeZtWvFB+u0OmzfJ0nNZeO5jEcVdlaSnD7dvbuHath89/fscqre5DD7F/8NDuSp4ItLKCZSixMt76WcmYyJ9aQo+CJqIkkNY1NrB2nVoYZOhdhbJkQiBbXkbv+nVks7PYffJJ7ft91YlOd/v5F5AePozdJ56Y7Eh3iqsXI9efsoRzxlCNm10Kv7scDGpS0klReHd9jkc6STkthuJ5R/nvlDYu5T47c/5XDZXsIi9bghrE2e8v/Vy090PIOmJtnVqSXg0hOvMh/5TSl9SfbetuZ4hOZYOPY6xfYcsEOgrVs90vs337NganTmH3+eeBhYVymq7HMQsDX8nw6/H5C4cEVSuqB4NItrRkdT8h/AhNzOJ2SOn3jOx78DFDme8hNPAplpUJHQUiFBlyDD3OWnrwFRgOgX5/ck3+DD5eNibto/zaBxiis/vYY+Ob8ONTny785mI4kxr4cu8Xd8qtYy6uwKP48OEMm5sD440m44263tSkjuRM23k34obowRcJlryBj7U4E+64MByYeo8+Cjz6KDOt8aCc34FSGrh8nsGXpcj2dhECQGopzArzYi2Q86OFoUErFQi8zC9Vn6OVEUgDehE1/sN0C5Uim5tDbG4Ktvkr0nviCfTvvRdz3/0u5r7//cn3VeygVzLwsdpRp08ErKTRqoaKRBSSFGVSlhYZ+ACg+/TT6D75pNFz8GUbodPpX70XW9fvdZO4K+gxgDLwKSuVPXUML8my6szKLJSVUWney8/j7sdcm2Zw2i32Ni8QyrtHNF+y2kNk/JQt9lXrw3wKTTHw1Tio87Iuh+jU7Oc+Q3S6Jl9WUYfUMPBxu55JiE7N0MWsnz2L6NVBtwUIe8NFxaH5ZM/Q9QaQjkCp7Wo6HW5uYmdzk/o2y/3l1/CeEQLMzIDko8PULA/JslVuZEcK9d1nnsH8f/2vGJ46heHZs9r300VK6ShDe2VSDtGZP4NPAG/DQdbpFHsVJyFu+vQPoXnw0Z7SoobSDNGJNAXJssl3Ug8+aYK8S9Q2p1cVmUBnbuvdvIl0bQ0//ckxdP/6QOG3qg18lTrNyyrnaq1Vly7Nxb2Oyh6QeD/VtEXkjbSQSj34FNKaCGQFI1V1khJBBnQ62HrzTcy+/Tb6V6/6y0u2QDZuX08GPgFen9Be+UqGqYBntGxtrZK4/EqLa/oixyE6GytoqAx4VVVOZTVIx5lTWLxzN+9JDHxBY7hI8PcoPW44cVRGqWGiZiOXNHsXHny8zAI18CkpSjxnx8N5k9Ehs5TZ7xel4ucqNNp9zZaLVEQk0Q/Bnmca0MTMy9o2RGfQ5zIL2t+VIYZriE6S0rpJ18Cnln/RK1Dq5RDw5rACqtZRz/3NOnlrA598TC6vQeyfsajY1puOqWvSw4dBut2igY/3LtR09hr9Lit7RDmSIfoPPID+PfeMIlJY9ilCsvL6Yy9NWXEnRiWSa/dcW9D3cx+P5Rl8pT4Y2Bl89GYpEewQnZK0h8PJNUNSPIOPefa1Kw8+o7jQbpB6pxcvxuDiRXQ/LEdL8mHgKx8Zm5M1QppyAzwTdExt4rIj2S+UZey0EQ18kWDJ64u1JjDAk4Fv9JEXwgGAWbguZfYODt7YQHdjw2M+HANfXh4wHLFFbVcVVtnyZtrAhGgpvg/9YqAiFFt78JmUwQDvITpDMvCpoNCfmIsrBmUDXxhCtk98CcCTJg5qJSWBkKKSQjVMoqf+7yVZTtlLYbYCWWDSMM988mrgU++/zrtFp6N1vu44P1VZUGYHUi5/ACE6s9VVtwnWCG0MsN5kaNExVedOUwrjjG8PPp1refco1J+Q4rQne35+124OYXjwhXAGn3aXtOzDDOepUtI+FI2FNF0PoLSB7/hxdH72M+E1Exp2Bp/TM8v2wqfaQgiQJZ3C53EF2RtUy18VPPgE/UPZwKfbx2gZMjTdhOjAYRpdD77hsOAZoHQGn+JcIvrM/bKiM/hMHrGvPXrlNIrzaj6KV6VTrqxyAVmhZO3irahNWkNHpITToyMRirxOpYoz+GS/jcuQX0xVegZfhbMhU4gQefAptnfmyYNP2DIejL0Txh0g5N3aLGox8DFCl9Ff2HrwZZo7wytEpyxKu2BLih4/KC0QVc59UHyOjfbgM6QqW63PodBZQvSuWp1z0DzgZY7nPfAatz4Km7PizQQh7QBljn+a9SeC0Vk3kiM3awMFkwuPlp3PfhZZkiA9cAC9mzeV7gnm/FhB1nTT6XrwSc+l1amza4OeKH1HBj5ukWdmmE3x/PP750s///xu8QIHXhbSNWRDDHzK707gZ/DZbl4RhegcQ2fhwtaRTzMDcWvwo+4fHj+uPm5UOGnmszI18HmzMFhAiPkZfGPS3Bl8dNp5RCE6Vcuq9AMjvaBCdIrQfWGHQ5DBYNIEw05+Vzqn0q48+Coy8LEwCT/tq7i0nodOMyuYHTiduIYOypyPTMYjL2NYRXotR3J5SHq3aWb6NGmRRpBlwGCwP0owJ7CqRpG9fMYCWV4ArPQMPr/BJot5MZo2/wyMF2e5+1wa+LRw+Yz2yhfcLjkZFbSzya63bLYcNkLPMiZ+tq6qWchGMVGtJlfpTxUt6AeXLiFdXgYA9B56iHmNVJnJ+k7RwNcQvZsVdFO4qnMjBG2JgY9r7KbHDtfl2kNmBEkPH9ZPlPcu1Biic319f2f1wgJVZ5W2djinBGQL0Q7dM7HV5ltJUCbt6BSyjIuJC2+5dWs39/eO4Eo+g098Alvf+Aa2/tW/AljztwoBGvjoptMW8XxuonJsSCh8FpVbI9+8ou9vN1+e3L77/PPMYe7ChSFefnkHL7ywgytXKI9ZgwFBVaE+pvvww5O/VQ3VtaB4Bl/7Pfj4hgKf+2WcGvQkpOvrkgLk0DynUoSO/FnKRnWgZJSvblF/dAYfZeDbK6fMQX5cncL6RfAMbD34uFEN6PsDO3yUaHjwqYToLHweKQ4nH1OyX/ck4dxs8J6EZuAzkSHZRfN/nnbrz+BzgGz8rczAF3ayEQlhjfyRyB7F45wy74sH0Xg6/o0VorPKOaE3v1JZXkwD3ywBxutuQ+WFNERnFTOBjfacJ1026Aw+oJ4d9MwsytvkDRNSu9TP5iq1/uTdwOfrGc7MYOv3fg+d99/H8PRp87IYjBHTShMMfM70BhIDX4hj6fbnPofFP/kTpAcPonfjhn4CvPelIqM9i09+sof33pvBxx8T3L4tNvY42+3KocZmKKPpwcdsGkHywrplWUmRx82akZBMyXvXXUPcvr2Dfh+4dGkgvFZEtriod0MN8ocuJqKJKAErmct3+6hsqBCVg/F9PpmfH7sP3dklrL+a4djx41w96z33sPugSduVDXzi6/vXr4Ps7oIMh+h+8pPS9GuDqkjGaYrgNh06fudnOhl09cM6oZ75aVgnoZz48Ngx5VtL70hFHnwlDx3VfA1CSvuGEMb6Y6+yzL7DMlLmQnwaGfgce/CxnkdpA1eVWIboFO4/GQ5H//a+k4bohJohXOnVMpC/XOHKg89FcWXDfP794q1zawmX7eoMvkqeeUXtE6BcHlEnGvgiQZLbhMOfvASDj9Phby+fiQefKESn44npry7/Dq7//X/AsDOHn37iSbD9Z9zD3Aw+g30Dn+HA7ytEp/A6h5MUL7QR8+y4kKlAwaaUBb0YsfXg0yxTqKgoZyo10i4uYnjmDP93urwWITpTuk/Uva23BnTWwCJ8dpEzZ4Y4fHikYjt50qLAMgNfgGfwDe+6C3f+8A9H/d7lIlAl1K0n5uaAf/kvt5FljOKp1NHjOF3nuG2sKC8MXPxBzFl0b4Md5IQAm5vmhj1jAnrA/Fex+My0d8m79OBz3V6i9EQd0nBzVUYSvH/4IgYntwCkjOw1t6wbGfgkeczMoPf449J0a6f0TnPO4Atd2LUs3+XLffzgu2z5kLdfxkeITu6PhnQffhhz3/0u+jdu6J0t56OyBtkql9nzJiETkqR8Bt94ncWcXhlNnII9XiqPR7YCgYIH34kTKS5cGODddzt4/PGuXvq2OD6Dr5R2LkRnmntAvj34mE/TwzvI1M0FdAafVOzJfVGlB5/U2BqQB58Ml6Kk8EdH47GLjTURfRqmlY5MC8Ph/sAyM2MwOLgUFPfSmpzBV/BC8ztwvX/4Iv7fG/8HBp15nFxIAJiFUdKFVa+CMGs6GQoE3srwEKKzjjPtrKjlDD75lyaG0t61a5j73vdGfz/4oDxPB3gP0anSDhXu2JWiElpQ0gDjNs2mKXI4pxO4Nmr6CO9MCPClL21h9qd9rK6OH56bfAqLMa6Bj1EgD3BfK4tNHdwzsAIwfChtvGFt1HFY9vI5Hur9ynmTGYboLHwnSJ5W9MmakZu1gYGvNgLo57KsaaWZrQdfSHUuobKhQgDr7RQloy1+Gsir9CVN24PHRbXfhPbuO+7/i4sZ7r+/j91dgn/6J15dxedBmVAoNiHOX+Pe44+j96lP7Q9Aps+7oudPF6937Rrm/vzPQXZ30f30p/k3uvKYccjIoMeOMqRaXFsPPtVNXQcPZnjvPYULOem99toOul1gfl4pO3forBFob+UkYeimcp/pM/iS/UmbK9cazCVqMjKCDtHpq7jjdN899SjO/+z/Q7q0DIBtRA5q825AHnx0u1QlOlblcRqphraIvJGWkffg4+5OEQ1GHgx8k3Lk0q7iDL7u3OreX9XttGZP/oKdvIrtXYpvT8FVfMqoqi/w0m5YiM4qBE8l+xN90dxc6RKZ0NF97LHRH7Oz6F+/rlWmKtF5ZQabm5j/zncAAMONDfsEPePyDL7g350K8BWi03XTzs0BqwcsC8sqpMEZfL7wkg3vfQm179PGeKHJynl2ms3iWAbTDNE5uSRXDpGB3dVeG+Y8GZqSf0xAcxcva7rpdJVoMg97LWWK6/YSpVeKuZexr5OUr7e+4AAAIABJREFURyQCaO9NMujH5eennURDUDyLKzQs+zDJMiwvj/69/36xrrx+5t6DT/CjDQ5ccnx6/xeHC6oV5uaw9Qd/APLBB0h5axc6kf3E3BTQEEIyKspQbg5njWeM4vLO4FPui4oefE880cU778yg1wNefTW38VtmHchRuXEPKIRflM2BpT6cJKDfunwSJE0pDz55iE6TPqcarrWq/hxiiM63zz6Jfzq8icXXl4G//rfMa1XWuc6MTrK+FuraK89eg1VW1Ca0SYRLNPBFgqTowWeQgO7ApGAgSpLyDq/Q11AuKQizDjz4mAvTKhQ/HoywwZ13IaMGBRszCzoknfY2eQALC+g++6xSnq6qadaF1G9Kjx7Fzq1b6Pz85+g9xAnMG5LXqIryUvFh0GdgBLXLryLy858TQm5EiYHPySYPC7zM8U03dvvSDmhkVxXMuV1QIPbubn76uqIDN2vWD6HKJTbGroqw9eDLDh8ufmFTR9fto9P+uThaOrOIeNzU9KwyEORGStj960y8HJrAxvoAH707qufaWuHw+ppKxMGxMJ4uLRlkGfgZfDYEtB7IFheRnTolvob1Zc2NyzUCQXGDKoCU7E8conFVOUQnh6WlDF/72h10uwQrK0UvtsYgG6Potuh0QIhgo3uaFuovNfARojiXiD/zvqxKrjHbD1Dufy4NfCAEHx44AywWo47lDehBLUsD8uALIItIC4gGvkiQLC6mePHFFP0+sLPTY19U1SiXy2dmJhMb+IKascyRbUYyPf+LF/5CNx0tHKbJTanpHnweyqvSRUr9yMTAZ1mGqijnLR4rBlevYnD1qlKCmeJCxRsOPfiigc/d2QShD0MA5Aa+mj34dMJDKhPSwKQCbRBQOfPJok7aHj5usmVjGqJTceASDp1Zpl6fGhVM2gRULr5tXf8Mvu3bt7H4J3+CbHkZ3UceUcvIBM8GvwL5yUhrIwJfgag9VMQQnVyeeHwXP1ro4+L8AIuL+99XeX6rEZbl6z38MGb/5m9AdnbwkwdfAbb2f+P1Mzch6HIeSB7O4DPGR2UVsjKucoAhOlnDDNkb/1jFZX1n68FHv7eiOXx2FpidpcbZKg82s0VmmaJDdDIMfIXmmYToHLXJcM/YSkjGN/AZefAxvlTtIJaw93G5WaM4NfDtIVo/ZZlChlWNCaHPlzm8NYmndWlA4v5U0VKRN9J0FheBmzdHE8P77/f1E9AdUUTX537rdIrL1gbNCVow9fOiEJ2KZKyzewQZKyuodMpjYzHg3RvqTnkOgij2zlDZPUvo3YZtfaF84GSV7QiH53/QBr5ppCkhOn2QAUoGvvWTKX47C/T7wNGjaTMqN6ZkMFO7rjYEHWn8p8s5pEJdpRwHHnziEJ16szE364oUTE4IaGDiZW3iwTfc3MSdb3xjFPvM5SYq3wY9UT8xNPDpnMEn66YuQiG21cC3vJThgQf6WPhnSrEf2rvv+p2fn8fWV78KsrOD337rOPD3+R/Zocxch+gMaRyrqyzG2ahsAqwYZhNOhPHyvJx0yuXNe/CJ6qMcolMT0qQdkZK6skJ0CrvIXojOycfOaNLOe5WVMNgsorw5vSK9UIghOseImqDSriqrHGO+NCpeTZvlRWxu9vGjH43ehUuXBB6wIc1nEWtaKvJGpgJFo5zLfDodWgFNTQFNEq4EsJqvMP85CNGp9Ih8SBxVhOgMfWKsIZwLM4u+gfHeKk83z15p55mkLNZNHpICR0Vjp1jh/Dmd00qTNuFaw3ox8t9x+vnMLMHVq3189BHBkSMpdj0Vr9YQnaFQMkgqaAcs6hRU8xgqaVTP4JOJDjYefEHNEXmCesBsjM/gy7tR5bGps+/2EhmsVQx8DEQigLZcRnu1GOTvxRM7BHjrmVDf/T2cPI3ZWWFYfz8efPt/ZyB247NPPOanIJ7Jod/pAPorc1ks8OArbHreg7dBsWz48LRZuEGLB+kzZ4boLH5V2Oc68eAbfR6SmdI1hfwJMYpyoGzgq6BPE5IZZcM28LkYlSXht3MZB9VVAxh/xsj0S7pd9rnnujh5MsXJk8NiON9IqwmnR0ciLvFm4JOE6PRIlesDtgAjuECxcLWE6HQJbzHdtO3BFSjYVLIoefB5plUbbJ2sst1QWqhZVC6G6ATS1E1HnShUvfjoOkKyMBYpAZaWMqyvpyPvGk8vt5dkG2DgEOK5vOXmUe+/rovG6n+i0kzyVxy4aB3W1pmLAEZnTA03Nvjpq/wQkNJCSK39n/2cbM/gKxHSO2/qwaeaHvsro+zVLpDn0TQRXZksa+a777D/816tcj/zfAaf4Mfh6dNIl5cBAP1Ll6zLEQpOPfhqRuTBp+rxxAvRSc8z3BCdU2TgMwvRKbh+OARyBr7xGXzjzyxjXnryJNLVVQBAf3OTmazSfMVIu4oQ6Q722gu/s01XHKLTPj9lJJVjPiuTBgnwmS8tZXj44R7OnJHo2zzJqAEO9VNBW0XeyDTg0oNPMa2ZmeLu9SrP4Dt4sLrZkLlezIejMJQq8jVwauBz2RcMoIXygNXpI2rw4GN2mYEgXIADfOnUTF5z54pnn4nrEkN0WuPjEYakU9Yi4SlJIP/eY1GcofpgQnlgJh587rKzcnayRjNEJ3P2F8wX9E7+Xz3yAs4snsbw7FnmjnUuDQrR6SLkoit4WdOKqdJZR5pYKft8e+yJ0s9vwtIYp0QefNrip8GAQMtogb4K9nCEUd8eUdpdsgZhRNuQrJumTh06HWx/8YvovPceBhxDglNsNtlJhrqC15QrA18Asg6zCAIPPloMykgySSTLeAmO4Nq2bNfmTTLwyV5IRh8Rdps0HXnx7YVJHRv4uNkQAiQJtt94Y/ReXrigVGy2YbeeTRamG1eqM/Dxr1XSo1Q1LgR8zE65CTxpGAMYgyPuaKvIG5l2HA5U+aGUDtHpe/5+/fVtJEmGpaUUTzzR9ZtZDqmsYiiEBuHBF8/gq8TAR3tdMAXK6MFnjooRpCo0DobnMX61ptWDz9cjvHBhgKWlFM8+u+u/m7h4WHuL7gm8SbYiBZGXZANUbgmhDXy5z5M/HdbJRjG7sOB4wDA8g68QolPQFKW6LS2h/8ADSNfWOPdyFPqs70K1agTU//kGPuDKlVEI8ZWVFOfOWcoqDuvsfEq0PYOPgc6eO20PPoVy0Hru0IdYY6IHH/fV8m3gK210kXmJHDqEwb33AgsL9gWREfoGKIebAl0hWrNKNz0DSBN+vG36/hiiU8FbkW60LBP2E9LrTf5Ok5nJ/VwZdZzs+L3khdeWFIv7ZQV9Wjl0OEVVBj76EefHzE98wu8G7wKyylkcK1I13jYDlzZyOkw7UjnRgy/SXKoaNXL5dDrFxZRvD77z54f4xje2MDubVRpihjn554RZ01rmJ3clIUn1GetoE3wY+OiHE/qMFsBuWgBqZ/A5VBS7wpUdw1kCdfc3pyE6A393POGr2idOpDh+poc71/yed+mSQh+oWfHj4+wmZQ+mJr0LHhW2Ol3g0KEM993Xww9+MIvHH7ffFMU0kikY+Ipf8tPXPb6X+3uTlPwBGfh4dDrAs8/uYnNzgPX1Yb17uFy3j44iJy/saIxTrLC6PMOLtHoG/WVaNgY15gw+j++8alIu3mFhNQIcx/xj9qKF5MXNKwIhmJxBquIgX9rALHgHfXnwkSYNfJpjFEnT0rmHhWXwnoGPkH3vPYNsyvmqbLJiPacKhIbQQ3SymuDy5T6OHx/KQ0a6KpRKOo4MfC70Fzoe1E2gaeVtC9HAF2knuiOKooGo0wFS5IU4/8LU4mL1AptocQ7AiVSh9IgCmxnSgweZ31vHza+YKowoKkrarIqdrDnq7E7O8w7ICFJSgjsM0dmk9aoNgQ11tTL2XgKA9MgR9kUVGQm8JNu0h10qryftwCQpufe3iOef7+KZZ7pu9CvGieQ8+ASDmEyfp1z3BoXoDMnAJ/Lgm50FLl50tMs8oDqXEBmsVTz4mAY+UYbF90HWTUvyqkLbDYcBta9PmurB57F84+agh13nCuyQ3mEaj2VzsrewhmMiZDCLNDmDj6EToYxNGdmXFegQnSqGD8De635w6hTm/uIvAIzO8Q0aXdkqy0qiZ5Z0AIzmKNLd39CVN/BNnp2jOTgkDz6XITqdhzAGe4PkrVu76nlV6MiRERK0gXwcFcRbk4Qso0a0iQa+SHMRDD5Oh+hcPjMz1YboDIlCXQ1duYs73BR3QYXE/Dy2b9/G7Ntvo3f9+v730YNPgfLz7t28ibnvfQ9kZwc7t255zzMkA591WXIvZO1ebyaV450dE8/gm3qytTXsvPoqkt/8Bv1r19jXVNRglRj4Qn/4pfleobwO62QiZznbc2MYolMVWgEi2zQWPfjcwjfwOVb0OKyzdWvpuMiqnMHHwOkZfA5CdLaV7MABkPffL/8Q2rtfgwefD11tPq/RPLifSe1yeA0YV1lnDKoIZhfdG0hUptfS5kRBXsohOjUbeHj33ejduIHkV79C96mntO6tGu2N0Vm2Z1TNtd1MB0AxOgkhwDAXLnXShMZjkMKGFEZaVYwH5iE6JZv4HeHLU9ALhKhFLKiYysTlgOTyiD3RwBdpJy4HpoIHnyREZ0tgCrP5ePOGFZcqBk0nGEUPTBcMNzcxpA9JtxTKK6eG3ZPMLObnceerXwXZ3kZ26FA9ZTAgLwOqeiG6buJscRHp8jKSrS2+l1NVRA8+a7x6gzewEQcXLwIXL6rf0GQPPg3PmFoQlJerPPGTXeXohOjMkmTy053FY7LLAchFB+W600ZYQsLpPzSGG8R8wGsi50EZAnoWWuHxVCxljPtFIkC5j7uPSVXx8c6VsvPKK1j45jcxuOsuDM+cQcIw8AV7/uYYp/MFeyOfHyNveApgFj7H1FF7j+pu3M1Y81XNlJfF2UR2VjHqpPTmRAMPPuuJhxB0n3zSLo2q0O08WQZQBj6WkZAQIE1mC5+Lf9jBTKamCAqm3cWX4U025iptDszjalxQSSdJgtgZVFuIziatoSNSooEv0liEY6DuiKJoIOp0JB58DVSksmA1R7q+Drz91wCA4fq6/AYGpRj19O8qBbHEhwt+00J0VmHgU1ZUzs0hm5tTT8iiDK5458ZtXLrzH5AlCXafeUaxLI69CTsd7Lz2GmbefRf9y5ctE7PExMDHU5JPqQdfJEy8rNEVB8faDfccmIt0rx4aNcpVGh58O2+8gfTf/jG2Fg/iR2ee2L9cIK3SyRuH7HS4ycI7IVlwOThvvpDrLKqs8Rl8/Mu0m8JAXm3JUozJ4NIl3PnEJ/gNCoT3/tdg0PHtwRc0FRXUOJsAQ3QyiyDw4KOXKqW1i6BOyiE6A2gXb5h48JUspWU19siDb6bwmYli2yrNVzWNwaEZ+GRhkQnJwu3SLt49ep5bXAS6Xey+/LJFwYRZ+CPYBxVRIRr4Iu3EowdfXrnV1vGPVa/+1SvYOTiHbGFhZOwzIL+oUxaSbKniIcUQnVJC0De4quYH6/dg61NfQbawgGx1tbaypOvr6Bm+i06hHq6S8oZaCayuZvjVr0a7YJ2HR2sAoQ8ZwVHRGFa1B9/25z+P+W9/G4O77w7HwKfS1h49+GqdOzQMfMNTp/DTz/8hvvOfV5TboxyiU7uE7BtDmHB5BDTYsbty5t/AVycaY2f+DD6dWVnUftq2aIOxPsvya40WyhOyNgj5/ffMuGmqCdHJ+bHF5KvpbL4KoO2YmzAFHnz0dynhW1vKHnyKITrbjIEHH33uITrlNAgBUrKvk5lk49ODrzYDn2mITta39gOmdMx16XzhOh0Pz+vOW2+NAjkvLirfU5cHny8pKYChfSqJBr5Ic/FkxBP9Rp/BV5JRW7JtlCmrzHYwuHTJNmVhHkoFsbkO8LLqa7oHn49e62z95tCDz+WQkR47Jr9oSjDZdZpRRvFjx1L89rcpFk6s4Atf2HZZvEYQheA9AmuIKjz48uPv8Nw5bJ875yFTC0rl9bvJKSjdn+bcTmZntAos9+BTNAAG1WgSAiqrZ1s1P9GQno/lGXws+VFxSSW9FoD1zvrW27oa4MFXRXQWGh/R1goGPkKCeo0LVFQw424WoKeaaFxie/BRcpEDD77GHfdhgXYY4SxDKXgEpyFTh2fwqeydYm1qrSJMsksPPhfFlXnw1YaKTiJJit3LgQcfkgTZ/Lx+OhpZeEs4mIcXMSEsCTAScYWngSlJimm3dfxj1UsoSCg2hCxEp48GLS0sfRhhGyaU17HYDqFJXJXh4EH9PtRq2UlxS3734YcBAP3NzcKZizsvvQQszOPu25t4/f88io2N+uPgV43X/tCSjSd14MX7o2mDgYEHn00ItpCc0XTO4OP9JArR6U3PGXKfCrz/e9mvFVKdBRsMSuStJLwyc74ve+JnzMu1Pfg0adr+OxeEcKaZEIfl471aeS9OP3kF9E7TWJRFR1w0zaaONagM1lxMBCE6k07xy7xRiXvTHtwQnS0frHoPPbT/9yOPaN1LsgyE9thLkpKMRkiGYe4Mvsk85GgODsuDz+w+fxubxIlo51HluFDFbkUHSTQtIkEAQ/tUEj34Is1FZ4uoI6STaUsUqd5298h2/psKYHXPIHSIztCp4Qy+OpS0Lqv1+uvb+OM/XsTiYoZHH+1al6XuLusUxf7Ue/xx9G/cKIWrGFy+jDv33MO8z4eSJkRa1R9axNR58yjAOoPPpeQTwtwxQSNEJ8Auq+jx6u4NUu4qTepTgXnw+ehvTg0utusMnRdM5Qw+7ewtw9Jq1j8wZzb3aO/IDACHD4XXf3wsxyvvS8GP44aNHNQkP4LZ1Nn+poQkyZCmfB1GRvtM5Oqk7NkUoGejS7qPPIJ0aQnZoUP6R72wQnSOHkzJXTdVOYNPkfL9jD5fm4HPXYhOF11N2s9r6s9K8peHM/hM0qgrRKfLdenp0wO8997M5O9I9TRMKx2JKKI7MImuz422ppNp0yjL3m4Oxl07kuJXe3+fOTMsX+BD8VmSwisI0Rm6UF7DIiKMXUfmZTh/foivf/0O5ubM5PbQu4QVGofqZEtL7B9a3UByprz6wVJFiM7gH37JO09+jcPs6m0ehqI8PXCAezm7rCIPPtrY4elMvoAIyXuDbRvxL6uEIA1NEHmkqsQ55HrwsR0ArXekRwOfnNAqXeGgPk56Zsb9W5YvdtBn8FVUFuNsAjRkMYskGP9oDz5RiE7lISt0w7wt8/Po37xpdi/LwIds1GaDfSMCIUUDX+EH0WdFogcfH6lxqvT8JFQ5LoQ2Z3JogoHvpZd28Td/M4uzZ4fQOH4w4pBm9OZIhIVDD75sdlZ+EYC5OeDixT4A4Pr1HiOhoJbtxmiP84rt/eRTPTz0UA+f+9w2lpYUd0E1gaZ58FWw2A7hUbqu5sKCuQyotAuwqWgY+HRpyZAqxev70sZGDF2BJSAkA4cS5W245Z+8Gvjq6790+KfhyZPIVle51zOHPsH758uDL+gQfUFZcMvPphKjfp1IvGe4Zwfx6sD5XvVy7faOBr4iNSmXrahgQ8hddw1x+PDIQPPww/pRN6R5EVL63AaOHNk3ah04UDZw5atp3M1Km4bqbztmP8qNNTKbpBcDXwDtEgxpilLwiISUNljTBr7xcysFjDZsW2afr2kMNlU9VenB95ONUSjWNJnBz05et8/EF4F48OlmYUp+I9v6+tBpWVdXMzz6aA+nTjEcOSKV0DCtdCSiiMJA1X3sMcx/+9tIl5bQv+8+/oXUjPXZz+5ia6uLlZUWKk33oBVqrjaVHTiQ4Ykb6gstZQFMx9gbz+CrZfHvaiFoQ+iPpanQ72l7R0Z/uO6bgwsXMPPOO6O/L150m/gU0Xplf4AEZf+h5vb+pUuSG1gbl5STd+fYEHIfC+oBl5k6r13VsmmWmdeXq26KtjvFsOAaaeuihv6fJMBXvrKF3/42wdGjbs51FnrwhYSl98Uf/dES0hS4fXvHTzYN9OCTbTwq6SsMDHzBvbchwfDgI4SUHhwhwJAVotORxx4zGcZzq+JZls+5VcOXPZLVz3905gl8vHwCHy8dx2BuCcAd9fQqHBdCffd8TZ1vvLGN//SfFnHkyBD3398H/ock40ijiAa+SHOxHHx6Dz+MwblzSA8dAkQefNSMRQj4xr02ekpAYYHswhCnk05gNO5g7Bp2GdXxaEMoAy/vhnZ1NtGDzxrXXkq7zz6Lhd1dgBB0n3rKadrThJf3tGmDQcmAr7D916JO9LtQ57qb9PuFz4NPfEJ4PfMMPsEgVg7RKSlP4F1FiYD6PytrL+KcS4W27zP4kgQYKoTQl3zPe2+txYXowScn9IGiAg8+YOThcuyYG+PeCEHfC6nNLcqytpbia1+7gywbRS0RJe1sA0oQbSceV2TjFu3Bl09N+Szxpm0WrhLeGXysMOoMD74Sjgx+3C8D9uDztQ2X5cGXdmbxi2P3AgAWDQ2S1qg8ax9jkkEac3PiNnI1JGxspPiDP9hyn3AkCKZR7I1MA4qDebq+zpZgDdl96aXJ3zu3bjlLt2rK6/+KJmUfEwyVZrq25j4PWsoKfKIU7TR0RZDrtxppdXsEuBt32slWVrDzxS9i5wtf4J97GCKB9R0vc1/TBgOV8nqsQ50K+vTw4cnfWZIIw3MC+k0j0+eF3jWMCKiSbL1cBe98yPAsnJoGPt55ktaPX9PA17T9d7owvRxCq3RLojwUqhHYO50uL0/+Hp45Y5XW/LyaaqRNBj7WMoYIPfiKn+MZfJ5hGfgSjgcfqdiDryYDn6sz+AjJnLyC0jP4dDeyuhoXVNJx8bwMy/vSSzt7Rcjw1FPiKGfehsoAx+SIOdGDL9JcBIOP08WD4vl8ADDc2MD266+D9HqNDosm2+BrivS5VDDB9G7exMzbbyP56CPsfPazbhJNEgzX19H55S8xXF8Pf2KswSBTh5I2JHkl9C5hhSzOnAXT48FXdwkcUOXDanKDhTQwGZBVfgafs6S1GZ46hf7Vq0h+8xvsPv+89Hrdsup6MymnH3KfClzZ3/oQnZKyZEnCDj6oWWZVe2D04PNA6JUOeXwSkC92hrDO4Nv53Ocw/61vYbixgeGpU97yyVczhKMXXMEcFgtn8GVAbmSUefANrlwB/tt/AwDMPXwZ+PHoe9a5hvtpEOHnaYcVopMOrTj2Ghsz2bDjqC1ZfZ45I1Vi4HMTotNVN2N58PnIxwv0WcQu0lSs8OXLAxw5soWlpUx6/FNlBr5Io4kGvkg7cThQpWtrGJw9i5l//Ef0bt6U5js8f95Z3nWhvQBXbW/ZdRbPbfczn8Hcd7+L/n33Yf5/5IJJ02nOzmL7y18ehSAyj29QYue119B5991mPP8mefB5VBxXSaMEXU1Ki9DQlUsB0qb+0CZar+xXQTEigvY9iknVOpwQgl2NaAzMEJ2C63U9+Npo4AvPg6+CjOp8Pp0O+leuYPYHP0CPdf64btm4Hnzsz+XLNVVp0YNPTmgyWFCDujnCV6PmMTc9dgw7r79eaZ4hTzO6sLyaxGfwFT/TocuzpSVsvfkmOr/8Jcjly3jlH3fw0592cONGMex3gakcrNQgaTry2Mt/R8BssyHpFK8p/MH5zMuX1S9kF2mkb4M7Dz77sgABb8ity4NPo2GPH2cb/msTHds0uE8h0cAXaS6iwcfxwLTz+c+DfPQRsoMHnaYbKnTzme4Skibs8Pr+zZvo37gBDAZFAx8vXYfGPQDIlpcxuPdep2l6g96pVMFEXoesEJJOjSaksljj0SM02AWDY1rVH1qEl+cS8sAUANYGgBrR9+CjwxjWJGtVSUD9n5W1Fx1rYM9j99YtdB9/HNnKSvlHnqJL1SVPmky1Z2w21JZkRfCeP6GXj0NIHnth0Jy5WQZznMgtQGQ26lKITgDpxgbSjQ0AwKVLA1y6NBAXIhr4hDBDdFJtRkjxDL5KoHUqSVKRgS8sD7668nEB7QkaXmFHz7oqD772jOzTyRSKvZGpwPUISMjUGPcAg0nZUXuXJhQTg2Bwk3KAVGLQo5+mmbgwOH8e2Z4xtv+JT1iWqk5aLC55NBivrfHD2bSJVgxbGuGsm0IV3jytGBna6sGnCdODT9AUMg8+Y0IeUALS+rA9+Ny/kU7PPXax64UQtnEPECqZdeZ2VQ8+7aaIHnxFKrNSOyTk8UlAvti0x1ZT66TL6upIJk+SDIuLrZBeAHDGpUKITvr3Yt1ZBj5tQn9vK4aeb8ohOsuGGdrAN/nZmQefwkUVCa2m3cWXLSvL6ITY5/AqU+WYWrMHX+00qawRKQ1aNkciFHEw8kbZg89Twrq/m+QR+0mZJp3BNzeH7TffxO5TT6H77LNat4bUFUIqi3N0D5LS4OzZIa5c6WN5OcXt29vO0g2NpvaHnb3whRkh2H36afsEVT2rK2qw6MHHIFdebvgjN8kDaJaBj2muFRgkPB5f2hwC6/9T+QxylHay59EIRabqCKjb3rq9ZRqfZxbaZhuPc16Vw0fBwDel+0mfeKKLzc0+nntuF4uLdZfGHWVveuiF6MwZ+Ez3YITvRVQxEgMfCCkN8IQAw2S28JmVlmnbMueThhn4fA3HqWQ/bm3dWSXjQN+9JMHkXL7TpyUewC4JpP4RM2KIzkg7iQOTFbUp2FxIHfHZS3G6m5yDyyzS48eRHj9uVyDLMjQ5b+94FIwJAV58cRdZ1u42bGrdBleuYPvQIaRLS8gOHzZKo/fgg5j7q7/C8MgRDE+dclxCOyqZ+5r68PN4VNg2qXnYyh/R9ZY7nHkE3GhVyB+qVOb8FFCdpTgqG69v24oLqaZVofVOMawGDM3ARxNy/xcgKnbwYVEdsbGR4vbt3bqL4R2ZHkQlRKc2rR+s7Cg1MSFAh/4yU/PgU80zaA++sEJ00oZt63xcFczEwBcQly/3cWzYx32v7njLoz2+2BEgevBFmoxowJ4SQdsXZQ8+ydCv2t66Hnw+PPoitbRJHY8hpEffJP2eLiWHIWQBAAAgAElEQVTFhgdBuU3txWJlZX/ro48Qcd4gBMNTp4yNewDQffJJbH3pS9j+0peCe9DOzkQrJuo+zZqxDq8tuDXgdXcJpoOT4PqpDNFJE5iBr4qwvCE/n+zAAf6PGh58qkmotPfuc88h63TQ39xEqrkJZHm5QfOpI3x78GnPiw3q/yKKxW5mHSJsWBsPSMqXy0UefMZEA1+Bbi4qyO6TTzImD8I5g69T+MzEkcFvVA7GGXwV4MqDz1VxZZ6rtRn4FAjZe3Z2FjhxPPXrMd2SOToyInrwRdpJHJgsafCu8vjs5TQpRKdDQuoaIZXFGo8hOqeF9fUUZ88O8MEHCV55pf27owsQgvTEibpLweTEiek4A1KH/NlDPgygTV5nMs/gE+yN9TV0Bu1NEvgD9rLBIrA6ith95hks/7t/B6Qpdm7fll7Pay2emGny+Pv334/+lSvKIZxffHEHf/qni5idzfDMMxl22zylshpwbq76cujQoPchT77YpTP4Io1m/Gx/evIhnPvFXwAYRZfgQY9vqYM+HbKRoQ76994LDAZAmqJ/7RrIX/594XcCtjEt78FnG6IzZA8+1RMNytA6Pjcyz+HDxfVSMKKeSsYuCufifOS6mPKxpm1EA1+kuUQPPm+UPfj0buhfvIjZt9/G4OxZzPzjP5pn7CJEZ+wLZRoWorMNtLr+3txQpockAX7nd3ZaH4q0aVy8OMCVK3388pcJnnuuW3dxwsXhgC8L8xMyumWVDZ3GdQ+50QISDmKIzjLZwYPY+vrXgW4X2aFDxR81ys27tNwUikoxDW3mlSsDrK9v4cyZeSwuot0GPgaZuebXD9RDb7AadJ8GvdMROWObzI/OPIGZYQ/rF7tYfPTR0u9jyo/bwfOPHnxFOh30b9yYfCSdYhuTBApn8GX7PziAZbsrjWcVGfhMNyP5GroeeqiHH/5wBh99lOCzny2Hk7T2/PZJNK4Xmfb6N5zAJMBIxBFxYLJC28BHsfvcc+g99hjSw4ex+m/+DT9hWcYuaPKOGl/UIMhM+yvZal0AHZ6kVZWrlth0YUHI6AzIiITYcQHw9DoiDz4/O6mDhlb2B2bgqyREpw0VyLTZ4iKY8aA0QnTyPfg8RQihWFvzHNIqZEI/g68lUR7i8rI9jMeh4cw8/nbzZZx/fAfHFgal30d/Z9WE6IxyVQGSMBbSjBCdw85c4TPgMow846VvmAefr241Owv83u9tYzAY/d3r6efbu3YNc9/73ujvnHHXCpWMWzInGdNqJdX0EQ18kcYS5Wp/lGUV/V036dqaPGHZ73GC8UMFBj7jXdpeqa8MYbaHG0oK2vjeRiLThcN3vslKU6b9QzDvTKU+L6hKlp+N9MxpE9oyR2oY+FQ9+KZdr2YNbTDvdMJr1Lb0/xylEJ0tqFNtBDDpy5bF+d9Zj9qJga9kNay/XUKiZOBLyGi8y19DgN251f1LrB+LwoYUegxu2Bl8LocuQuz2l3QfewxIEmRzc+jff7+7gsmYdg++aatvy4kGvkg7iQOVFdoLcFft3cJFYJDU0M7T/ihbXf94Bl+kalr9QjWQOHcD0N+44W3oDLn9Qy4bPBWvLSEKNRqn7J3KTiLw7tA8Qj9/D2jHQ6+iI7ehnRpDcbwS6ftZj+XEyQz/sPf3xYuD8gUq0Amn8QzoPLSBjxBSelDp/ALTg8/V+8q8rSbjkKsQnb6W7Eb5LCyg+/TTfgvCoCqjbLBEwaxVRANfpLnEwccb3ib/+MyCoAqPqygrTBHRwGdGfCkikRJN3rSu+0onyUhJk6bE6H5nBamSgIQDVtZeohuG/Dx0cODBF8UFx9DG4xDDcwb0zrui5MEXaTRlGw1t8Mu41wLAjYcGGKx0sbExxMqKGyGGRANfgYT2WGOE6ExXVoF+7p4kd20O1dDgSvow+qKKzlJ0FaKzquG4rmFf6Vm3cI6KTC/RwBdpJ03WEAWAtaxiOjEaCmARTWrYTT7tSpxWd+VpD20RiUwTGkp+E5osvrHmOVnTJMn+Rv1o4KsWVtaLi+47oLvzfxDeC6Js4MuY30+7bOiaIA18NCGPTxoUXsWW1GlakY1LMg++hSWCRx/tlX+wIRr4CpQ9+FD24FtdBT6grin8YVkGhRCdVU1qIYborCMfJ4QuiPiW+xr1sCIyAu/NkYghoS2AG0ZZVnHTnunysl7GLoh9oUwtZ/A5z6JRtLo94pb8SMScJoRUk+FwgGvylD03l1mF6ZyKaAmBT4Y+DHyh1dEYrRCdqkk0+IUPAbpBo4GvGtpQh8gE2bI4/1nJi8sFw6H7NBtM+Qy+cmjFdGW18Jn7WAw9+EIy8K2smBmAp82DTwkXerEmL16CfjgRXaIWLtJcBINRNj9fYUHah7Wskktg59YtpMvL6N24gezwYeFtDZ4am0UNBpgoOygc1N1QaE/b6Hkb8U7D+9ju008jm59H76GHkC0t1V0cY1zvjm46CwvA44/3MDOzP94vL+//zRob19ZGSprFxQzz846koCY9jynw4AvdqKkKc25X9OAbf44efH4J0YPPqQdrIJRCdLagTtOMbFwqbsRhzBE+BrLowVek9I6VQ3RmB2gDX8a511ERgNKz93me2xe+sI3NzT4++9kdmKo6y3OzH+1bo8SeKIgUCfphRWTEEJ2R1jA4fx7Z8jL6ly6ZB6aOAHC7AB9cvYrBlStqk0WjpIEGU0E7h/go6yxDCPX3Bh0npNWVjUTs6V+/jv4DD7TnXYkefBMeeqiHe+/t4cc/nsHSUoYD/4+4Qs8/v4vvf38WFy8Oqjq6pVZCUvZHA58mGgY+3rohRvR2TAM9+NqyCSyG6GwPskAkshCdPgwEpOnCkGNIh/FQWGfwUZcU/9DMU2HqrlKmOX16iNOn7Tw7p86DTyHj0pw0beP5tNe/ZUQrSKS5UIPP8ORJ9B59tKbCtI2iuGJ9Bp/pROFigokCcpkaDHxxc1SRVslOMURnJKJPQweBCxcG+OFg9PeNG47PnEE7puzFReDq1YHStceOpXj66a7bAoTctwJSJNRm4LOhIS8I/zEXyx/FBbeE6MHn850PWWkcaQ4yr6b8OMU08sQQnf6ho8WAlL3nDhwofJ787GgMYnq7NUzhQdfB11BWlaegE6Z851FbNt1ERkQDX6Q9xMHJGWVZRT4p9y9exOzbb2Nw113mO0gdPcPhiRPo/OpXSJeXka2sOEmzVdDtXInCKGDBrgIC0mm6Jxr4IpGp4erVPnbXdnHkSIq1Nbfhj1pP67dKKxD4ZBg9+ARohehkt2NbmiIYaKV3E850bcFDjyE624bY6FGHB18M0UlROg4iYZzBtyy6xTZLdnr0l4GHYqhqDg5mrjeJIDbtxPZoNNHAF2kuwcwc7cNkM9LuK6+g9+tfIz1+3F3Ghs9059VXMfPDH2J4993R2FAT8fUs0ur6T/nOt0gNxD5WG3NzwIMP9gvf0ap8m92gDXFQCpqgd+MGJBywsl5Y8NABHcqh6aFDztJygmKIzvFlcT+QZ0I8oiKgd94VJQNfpNHIQ3Rm3N/4X1oSDXwFSnJNhrLRb1UxRKejkJ2sL32eweeCFg7H9gT+zCIRHWJvjrSHODg7w2gzUpIgXV+3ew6OpIzswAH0b95EurbmJL3WU4FGNYTXM6Qz+NokUJcWXSE87Egk4gXmbNGmAa0NhPw8SmG26oNupoWFzM/0ZSkAbN++jfTAAfSuXUO6seGwYJpolJt3aZtloRAIMkQnTUseuu8z+LqPP77/96c+5Tz9yD6yZYwsRKeXoy6igU8MISC7u4WvkrniBgf3IToZXzZsk2tVS/Zg5nqVjKddbxGQXB6xJ8BtXpGIInEw8kYw+vrAhaTW4MHAV1WMdx3q3NgcQv29EYwUH4lEaiG+8xFVApov6Ky9hOdkZaRZ5+HmJrY2Nx0WyBCNEJ08nWejzuVpAKXWC9HAF9A774wKdBCDzU3sPvccSLeL3gMPeMghEAJw25d78OV/Y5TXh6IknsFXhBCsraX44IMECwsZVlYzYGuLvoT92ZFBj/mYg1GaqdHG4dia0BvB9xgZO0WriAa+SHuIg5EzTM7g85JxfKaVUEUr1/Uob9zo4S//cg6bm32srNS/iBwTu3YkEomUybI4OFoT8gQTsJzny8AXjuRhCeNZ8eoWPfjqIXrwVUMlIToJQf/++/3nE5GOSzIPPi9hGaMHXxFCsLk5wAcfJDh4MAVJCNKDBwuXiEKr0mkZFkH+ZTTwKeVbGQoZBx3Wvgqmvf4tIxr4Is0lrhK9UZusEp9ha6lL3n3yyS5u3uxhebleFVurh6tWVSYSiWgTx4CwCPl5BDQZskJ0VpJRyM9HhEa54xl8FUE/k7m5esohomEKcCUIwWh7ZJb7HGkqMsNQ/vFWFaIzGvgoCMHMDHD8+Khd+oSgd+0aZr//fZA7d7Dz2mvcqbY0sys+L6Wp2+hcm/qoy4s+6CHSReEC8ER2RtAPKyIjGvgi7SEORs6oTVZpixKkaVQglNT5KEPw3Gt1V2515SKRSAGVMH0WY0Kb1sglNNullUNrwHJeqCE6g0EjRCdPadiWpgiVrM5Y9KrEhx4JDJkNOv+ZaZ/2YLSOZ/BRsHaNzM9j6623RuFMZ2dBdjiGWUdjDtN7kw7XG/j4NnVzcDyDT07rO8F00QApMBLhMHUzVHUE48EXn2k1eDmDT/x52mhze2QLC3UXIRKJ1InDAa3VBr6qCHmCCXgyrMyDr01wDXy8z8U2nna9mjW0crkBITrbMMRnIOh0cjVp8zs+BdAbEkQefcxHHUN0+of3jiXJpP25+itDuUPJ261hHsp1DVUhh+ic+vE7YLk8ok/YI1AkokPgE2qTKAu6bViORbhUYuCb7j5UlpVa1B5zc9h54QUMTp/G9u3bdZcmMg3ExUdYxOdRO8NjxyZ/D+6+u8aSaBJQ34khOsXoeCbwlmQtaYpwaUKITocPver+88QTuyAkw8mNIQ4ciAa+tiDrovnP47Ety71rwxMn3BdqOHSfZpNRGEd4oaF9FYH5ZdRHNo/4zCItInrwRZpLXCV6I5gQnZHWEB9tuxncey8G995bdzEiU0I2P193ESIiYohONh4nwp1XXsHCN7+JbHUV/Qce8JaPLaZn4VRB9OCToBWiU+my1jRNbdAefDFEp1ceeqiPq1cHWN7ugvxfdZcm4grZ2aDFEJ2jeWL7jTcw9z//JwZ3343s0CH3hWq1MGSAQihM/x58Chc1zFhU1XAcPfgCJurUW0UDpMBIRJE4GDmj4bJKRJcYorMC+Ae2RyIRPYanT2N48iQ6v/gFup/+dN3FicQBzQs6U3O2toadN97wVxhXBCwcVHUGX6vUtpznF9cNNTFlHnx12ECWljKQHerLgMaxiD4yvUf+9/Hf6YkT2H31VX9lih58BVQ8yLnPzdE5eSoGvixOfo1j6p9ZwHJ5RJ9o4Is0l4YdatskyoJuq9QRkRqY9tdz2usfYRA7hTmEYPvNN0Hu3EG2ulp3aSIOF4dx03rLCXjciyE6JWiUm1fl+Xng6NEh3n+/g42NqMC2Jnrw1UMb6hCZIDpzj/69skcfz+ATo+DBN3mOjgx6TDuQzP0zAiBwD75pJ7ZRq2iAFBiJRKomevBNGRV48E17H2qLfi8SCQZConGvhbTawKc58Ldyngh4MowhOiVohehktyUhwOuv7+Ddd2dw4cLAZekiQDM8+FqwIAg51HBEH50z+Cp71NGDr4iC7OD72bQxRGdVBD1ExmdWJOiHFZERe3OksUTh2h+1ncEXqYVscdF7HtP+egas04xEIhF1VJT8rbbSRawIOFzl8rKf0pQijLRJADA4g291NcP99/e9tfc0k83O1l0EOQ77fzCvUjAFiZgg8tijf/dpCxicPj35e3junL+MmohCw3NfQ59zcDTwBY2SlDHlzyxKYu1iuntzpF1E4doZwcgqUUnojd1nnkGWJBicPu1lEdFmfZYJ017/SCQSUaHV036cCIITDq5d6wEANjf7WFlpc+dzgEWIzogn6AEzxBCdgb3zTmhDHSITZHqPqkJ07r74IoZraxgeOYLdZ5/1l1ETsRlHHD206MFnDs+rPwimfTxv4xw9xQQoBUYiisQJ1RvxDL7203/gAfTvuWd0IIqHiZzWOURZoUhsj0gkEikTx8aWE5gi4dlnu3j44R5WVz3KuYHV2RitEJ2eyxIBAJABFeY0Nnw1tOWdjgCQH6OWf7w+1U3ZgQPY/v3fL2cacWvgc3Qmn/lFkcpQeR7TrkOOfbZVTHlvjrSKODg5I9pOp4SFBW/vTau9MAyIuoBIJDI1WEwAjz3Wnez0feKJrqsSRUIhwMnwwIHMbzECrLMRGuWO64aK6PfrLoGcNu74a0MdIlxEehDvnkiExP7FIoB5ND6WluLiwbZJ8RU7eqOJHnyR5hLARN9eipNUPIMvErEjDk+RSCQi59ChDG++uY2PPkqwuTmQ39Ak4kQwnbJ7W+oYPfiCo+TBFyJtUnzyiB2+0cg2JOQfb3zUNRGAB58KTRvtWt+fFSpYOic5NHzPodMol7eYuL8u0h7iYOSM6MEXsWUa1vM6RNkpEom0ApXBy3ICOHkyxaVLg7i5qI1M42TY5jpHA1+9NMGDr420+Z2eQmZmgMuXR+/S/ff3Sr/n9SBRJ1ITFu9cSSI1fl8VZNs4FjAJulmCLlwkokf04Is0lyhceyOewReJuCUOT5FIJBKJUEzD5NiWOmrUoy1VDp4m7IJo446/2MGtSJeXkWxtjf4+frzm0ox46aVdPPFEFysr5f4aPfjqx2oUcfTQlIy7sYOERTyDLzJlxN4caQ0tXD7UBj0X1rZ+bOOicEqIj05MlP8jkUhkyogDv8Od9A2iJRsS6TBWorBWDa1i4+hfvYpscREA0P30p2suDYc2Lgjod6GmYjSVnddeQ7a4iPTAAXSffLLu4kxgGfeA4rl70RZQEy7nUY17r1wZeXZubvYxP+827Wki6GYJunCRiB7Rgy/SXGIcSW/Epo3Y0sb1vA0t0e9FIpFIRINsYQFkdxcAkK6u1lyaAJjGybAtddaqRxQCK2F2FnfeegvJhx8iXV+vuzSRiBLp+jrufO1rIwVDA5QMMURnAFANrzXDWMzBt27t4pFHejh8ODXLK1Iv0YNPTuyzrWLKe3OkVcTByRnBePBFIq0hKrsiRdITJyZ/ZzNxv1Uk0ka2X3sNWZIg63Sw+/LLdRenftpi7NJhGupIMYVVro/FRaQnT4bb6B53/NVW5Wkcx1wzM9MYxXoxRGdcz9WCwTs3GXos3k9CgLW1VD2JOBYwCWasZtGQcSgSUSFqlCLNJQrX3ig3bRRmI3pkWXwf88ThKkLT/eQn0fnpT0G2trDz6qt1FycSiXggPXUKW//6XwOdziSUniqtnCemMLRdKZRlUx9sPIMvYkIbQ3q05Z2OKDE/v9+H5+ZqLMg0Y/POVfm+xrGgcTiZodo4z0UaSTTwRdpDnFCdET34IhG3lIenKAhOPXNz2P7Sl0aLgrh7MNIUFGQtEhe6BbKVlbqLEA7TqBhvax0F9YpTWiQSaQsXLgywsTHE7i5w3339uosznTRFdgi1XByqKm704ItEqiEa+CLNpSkTfQMJ5gy+qCRsLPHRRSIKEBLnrkj7iBOAE1rZjNMou7elztGDL2JCGweytrzTESXm54E339xGlsVHXRs2DR89+GqnrmYZnj49+TtbWGBekx49un/9kSPeyxSJ+CQa+CLtIU6o3ogefBFd2rietyHqAiKRSFPJOh2Q4RAAMNzYqLk0kUYzjZNhW8KSajy7aXisEUVauCBoX40iKsRxrUYchugshc12iM+0I/oMT59G99FH0fn5z9F94gn2RbOz2HrzTcz8+MfoX71abQEjEcdEA1+kuQTjZtY+6LVYlFUiETviOxSJRJrK/9/efYdZWd55A//OUIYZBqRIkSAiNsCCHRQ1lliIPTGYNXtt3uxmE1M21Y3ZTUxMW99s6m5McmVjNuq+blYXK5agSSxxLQRUEhDFQhJRlKbUaTDn/YOdkYEBBoQ558x8PtfF5XjO85zze065n/I9932vu/jiVD34YJqHD8+G0aOLXQ7lTMBXvgR87IQu+VHoju0YFNOunIOPTlfMt6Dx+OO3u0zziBFp7K4/YPT96FIEfHQZfk2363TBH1vSyXyG2nItAChXzXvtlbr3vrfjK9gBsDXdcWfYVbbZEJ3shA2bDH/W3FXnI/WBh91qi6PKUv3OlWpdQLcg4KN8dZUT5hLU3FzsCih3ru+2pbkCYEd0yf1Ed9wZdpVt3KEefA4C+V9VVVn3nvek5wsvpGnChGJXs2t0le80lIvNR+rq0Hewov1lzcHX6bws0DkEfHQd9hy7TMmEMyVTCDvKWwfQTdkBwJu6aqjZVbaD3W7DqFHZMGpUscvYdbrqdxpKVbkM0aktaJeXBTqHScsoXw6udxs9+HirXN9tS3MFQHdXNsNs7Upd5QDAEJ2wUVf5TkO56OB37KCDmlr/Hju2aRtL7h6FMhuGWNMFXYsefHQd9lC7jHAGdi3XAgDo9rrhzrCrHFIXdmiIzt1cDADdRwf3P6ed1pChQ5szfPiG9OvXOXvf+jPOSNWvf50N++yTDSNHdspzUmJcPKVECPgoW1s0o5uPzc1OKxScmfPWOM5py8UuoNuwA2BrumHA12W3WcBHd9VVv9NQqjb7jm3tKLO6upBjj23c/fVsounQQ9M0blzS06X1rTEvL3QOiQhdh4PrXca1OQAAdqkOXqTrUrpKGGCITtjIBxw6167cj+6OC13CPcqV/VmXIuCjfHWVE+YSNHr0+vTuvfHg54ADOn/8csqfkBigm7IDYGu647F7V9nGzcNZPfgouiLta3bguwDsAt3x2AFgB/mpAV2HHf0u07t3cvHF67JoUY+MG7e+2OUAAFDuuuNFuq66zQK+bqm62g84gM61I3PAAnRXevBRvrrjMD+daOjQ5hx5ZFNxT+T0Aihb5nHcNh9toMvSwLE13fEiXVfZRkN0kuTYYxvTp8/GNv6MM+qLXE2R+IBD5/KdK2vePugcevDRddhzQMlwfRcAaEPAV77bvEMBn4PArqqqKvngB9dk7drKDBrUXOxyiqOrfKehXPjO7Rad9TJ6u94CF9XYAQI+ytfme4pKHVKhVDgWAQDa6I4X6brKNu/AdpTrJtIxVVVJVVU3DfeAztdV9qMAu5FEhK7Djh4AoLj8woOO6g7H7l1lGw3RCe2zz4PdS8BX1rxdJcyb06UI+ChfdvRQspzrAgBtdMdj9a5yvqIHHwDF0FX2owC7kYCPrsOOHkqGgG/bvD5Al6WBYxua+/ZNkhT69DG8fjkT8AHQGQR8Zc3bBZ3DHHyULzt6KFmu7wIAm6ubOjU9n3026w88sFscu3eZwyFDdFJi/D4Augk7lbLW5d8+F74oEQ6L6Dq6/J6jG7KzBADoMpoHDUrjccelefDgYpfSObrI+UnBEJ2UmKOOakqPHhvPFSdObChyNcDusvn+Z4v9EbBTCj31+epKvJuULzt6KFmyWQDYeQ5rKVfV1Q4C2f2qqwv5q79al+XLKzNmzPpilwPsLg6IdovOelm9faWrefjwbBg6ND2WLEnjkUcWuxzeIgEfXYc9B5QMAR8AQBexAz34hgxpzgEHNOWFF3rmxBP1rGL3GTSoOYMGNRe7DGB3MjUP7B4VFVl3ySWpXLEizXvuWexqeIsEfJQvO3qgTAlAga6qQgMHb+oq5yc7eN513nn1aWxMevfejTUB0PW57lfWvF0lrkePNA8ZUuwq2AXMwUfXYc8BAABQdMI9AN4yAR/Adgn4KF929F2fXgBly1u3bS56AQBlw9znABSD/c0uM3Him8NmH3+8IbShKzFEJ12HHT+UjELB93FzEyc25PHHqzJ69PoMHmy+EKCL8gsPeFNXOT/xw0oAimEX7n+6+zDyxx7bmL59C+nfvznDhnXO9QiHCzuvu39e2TECPsqXE00oWY5FtnTCCY05/PCm9O3rxQEAyojzLACKwXW/XaZ37+SII5o6+Vld+4DO0GUCvuXLl+fGG2/MnDlzsnr16gwcODDHHHNMLrrootTW1m53/fr6+vzud7/LE088kYULF2b58uWpqKjIiBEjMnny5EyZMiU9e3aZl6trsqOHkiHga19trRcG6OLsAOBNXeX8pKtsBwDlpXKzmaXsjwC20CXm4Hv11Vfz+c9/Pg888ED222+/nH322Rk6dGjuvvvufPGLX8zq1au3+xjPPPNMfvCDH2TOnDnZe++9c9ZZZ2Xy5MlZsWJF/uM//iNf+cpX0tjY2Albw45oOuSQjf89+OAtd/wAAABFUqitTaG6OkmyYc89i1zNW6AHBQBFsMXPxux/yoq3CzpHl+iS9rOf/SwrV67MBz7wgUyZMqX19uuuuy533XVXfvGLX+RDH/rQNh9jwIAB+bu/+7scd9xxbXrq1dXV5corr8yzzz6bGTNm5Nxzz91t28GOqz/zzDQcf3wK/foVuxRgEzpwAHRTdgDwpsrKrLv44vR88cU0HXhgsasBgPLiByZ0Iw0TJ6bq8cc3/n3ccUWuhnJS9l2eXnvttcyZMydDhgzJmWee2ea+qVOnpqqqKr/97W9TX1+/zccZPXp0TjzxxC2G4ayurm4N9ebNm7dri2eXEO4BAAClqHnw4DQec0wKe+xR7FJ2ngus0K4KP2qB3cv+pqx5+3ZM47HHpmHixDRMnpymww4rdjmUkbIP+ObOnZskmTBhQio3G6Kxuro6Y8eOTUNDQ5577rmdfo4ePXq0+S8A2+ZcFwB2Xu/edqSUDkOkAVAUm+1vHB2VF4cLO6h37zSecEIaJ01KZBDsgLIfovOVV15Jkuy1117t3j98+PDMmTMnixcvzqGHHrpTz3H//fcnSQ4//PAOLX/55Ze3e/s3v/nNJMme5Tz/Qidq6U3p9epeKvfZJ6M1HJkAACAASURBVBVLlyZJqo48Mv28/2WppqYyGza8eTS3555VRaymOLRhQDnbkTasR01N69+F2tr01e69ZWefnbzwQmWamipy1lnN3XI/SumoGDAglZt9z2tK+HvuGIzdadN9Xp8BA1LwOWMX04ZtoqmpzXeuatCgZAdel03Xbe7XL7Ve092upubNYGqPPfpkzz27bixb2a9fKjb9fPp8JdGGFUPZB3zr1q1LktRs8oXaVMvta9eu3anH/+Uvf5mnnnoqo0ePzimnnLJzRQId1vye96Ty17/eeKJ00EHFLoedtN9+hTz11MaAb+TIrntABwC7Q01N8vGPN2flymTEiGJXQ7dniE4AimFX7n8MM9Tpuvzhgs8UJaLsA77tKfzvl61iJ1qVxx9/PNdee20GDBiQz372s1vMz7c1LT31tmbZsmU7XEt31JL0e726oVNP3fjf5cuLWwc77fDDkz/9qTqNjRU58cS6LFvW/Q58tGFAOduRNqzf//7gLknWr1qVOu3eLlNV5XCI4uu9alWqNvmeb1i7NutK+HvuGIzdadN9Xv0bb6TJ54xdTBv2pso33kjfTb5z6954Ixt24HXZ9PvatHp16r2mu926df1a/165siHLljUWsZrdq3r16vTc5DO22ucriTaso0bswl9xln3A19JDb90mX6hN1dXVtVmuo2bOnJnvf//72WOPPfLlL385w4YNe2uFAnQj1dXJe99bV+wyAAAAgHKkB3lZ83ZB56gsdgFvVUvauXjx4nbvf/XVV5NsfY6+9jz66KP53ve+lwEDBuTKK6/cpYkqAAAAlA0XWAEoBvubsjNmzPrWv8eObSpiJdB9lH0PvoMPPjhJMmfOnDQ3N6ey8s3Msq6uLs8880x69+6dAw44oEOP9/DDD+fqq6/OoEGD9NwDAIAdYS4K6HoEfAAUwRZHlfY/Je+MM+rz5JO9Mnz4hgwc2LXPC5rGjUvPF15IkqwfNarI1dCdlX0PvuHDh2fChAlZunRpZsyY0ea+m266KQ0NDTnppJPSp0+f1ttffvnlvPzyy1s81gMPPJAf/OAH2XPPPfOVr3xFuAcAAED35oIqtM+PWmD38gOTstO3byEnnNCY/fffUOxSdrv1Bx6YhkmT0jR+fOrPOqvY5dCNlX0PviT5m7/5m1xxxRX5+c9/nj/84Q8ZOXJknnvuucybNy977bVX/uIv/qLN8p/+9KeTbAwAW8ydOzc//vGPUygUcvDBB+f+++/f4nn69u2bs88+e/duDAAAlCsXO6Hr2eyCasEFVgA6g4CPUlZRkcbJk4tdBXSNgG/48OG56qqrctNNN+Wpp57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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 607, + "width": 892 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "plt.plot(train_losses,label='Training Loss',c='b',alpha=.50)\n", + "plt.plot(test_losses,label='Test Loss',c='r',alpha=.50)\n", + "plt.title(\"Traing Loss vs Test Loss\")\n", + "plt.xlabel(\"Num of Episodes\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend()" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Introduction to PyTorch/4. Saving and Loading Models.ipynb b/python/pytorch/Introduction to PyTorch/4. Saving and Loading Models.ipynb new file mode 100644 index 0000000..921b1fa --- /dev/null +++ b/python/pytorch/Introduction to PyTorch/4. Saving and Loading Models.ipynb @@ -0,0 +1,1540 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "import torch\n", + "from torchvision import datasets, transforms\n", + "import torch.nn.functional as F\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "transforms = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize((0.5,),(0.5,))])\n", + "traindata = datasets.FashionMNIST(\"FashionMNIST/\",train=True,download=True,transform=transforms)\n", + "trainloader = torch.utils.data.DataLoader(traindata,batch_size=64,shuffle=True)\n", + "testdata = datasets.FashionMNIST(\"FashionMNIST/\",train=False,download=True,transform=transforms)\n", + "testloader = torch.utils.data.DataLoader(testdata,batch_size=64,shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = nn.Sequential(nn.Linear(784,256),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.2),\n", + " nn.Linear(256,128),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.2),\n", + " nn.Linear(128,64),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.2),\n", + " nn.Linear(64,10),\n", + " nn.LogSoftmax(dim=1))\n", + "\n", + "criterion = nn.NLLLoss()\n", + "\n", + "optimizer = optim.Adam(model.parameters(),lr=0.003)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After 1000 episode, Loss: 0.2973772883415222\n", + "===============================================\n" + ] + } + ], + "source": [ + "epochs = 1000\n", + "error = []\n", + "running_loss = 0\n", + "for i in range(1,epochs+1):\n", + " \n", + " optimizer.zero_grad()\n", + " \n", + " images, labels = next(iter(trainloader))\n", + " images = images.view(images.shape[0],-1)\n", + " \n", + " output = model(images)\n", + " loss = criterion(output, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " running_loss += loss.item()\n", + " if i%100==0:\n", + " error.append(loss.item())\n", + " if i%1000==0:\n", + " print(\"After {} episode, Loss: {}\".format(i,loss.item()))\n", + " print(\"===============================================\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "error = np.array(error)\n", + "num_episodes = np.linspace(0,epochs,len(error))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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59NFHa1Xnap/32pbXO0caIm9vbwCCgoJsiTUrd3d3wsPDgf8NH9U7RBqT5ORkFi5cSI8ePXjqqacICgrCZDLRpk0bXnrpJZo2bcqqVas4e/YsoPYhAnXTDhrrt5eSa1LnrOO7qxtjnZWVBVQ/J5vIb9WaNWuYM2cOLVu2ZNq0afj7+1cpY33urXMbVFZaWkp2djaurq4EBQXZttfUps6fP4/FYqFZs2aaC0QanKKiIjIzMzl9+jSjR49m5MiRtp+4uDgA/vWvfzFy5EjmzZsHXP3z7uHhQdOmTSkqKuL8+fNV6uidIw2Z9Xm3Jtl+ybq9uLgY0DtEGpe9e/cCcOutt1bZZzKZaNeuHeXl5aSnpwNqHyJQN+2gsX57Kbkmdc76AkxKSrINd7Aym80cOXIEd3d32rdvXx/hiVwXy5cvZ/78+bRq1Ypp06ZVWTLeqkuXLgD8+OOPVfYdPnwYi8VChw4dMBqNtaqzb98+wPGHp0h9MxqN3HvvvQ5/WrduDUBYWBj33nuvbcibM897bepYy4g0JJ06dcLV1ZXMzExKSkqq7M/IyAAgMDAQ0DtEGhdrm/jlSqFW1u3W1dnVPkTqrh00xm8vJdekzrVo0YLw8HBycnJYv3693b7Y2FgsFgv33HMPHh4e9RShyLUVFxfH119/TZs2bXjzzTdtq7o50rt3b3x9fdm+fTvHjh2zbS8uLmbRokUADB482K7OgAEDMBqNrFu3juzsbNv2goICli1b5rCOSEPg7u7O+PHjHf7ccccdAPTr14/x48fTp08fwLnnfdCgQQAsXbqUgoIC2/bs7GzWr1+P0Wikf//+1/NSRZzi5+dHREQEhYWFtt6cVvv37ycpKQkvLy/bwjh6h0hjEhYWBsCmTZvIy8uz27dv3z6OHj2K0WikY8eOgNqHCNRdO2iM316G8l8zY52Ik7KysnjjjTe4cOECPXr0IDQ0lNTUVJKTkwkODua9997D19e3vsMU+dW2bNnCrFmzcHFxYejQoQ7nFmjevLndy2X37t1Mnz4do9HIXXfdhY+PD3v27OHMmTP07t2bP/3pT1UmAF27di1z587F19eXiIgI3Nzc2LVrF+fOnSMqKoonn3zyel+qyDUVGxtLXFwczz77LAMHDrTb58zzvmDBAlavXk2zZs3o1asXJSUl7Nixg/z8fMaOHcvQoUPr6tJErsqFCxd44403yMrKolOnTrRt25bc3Fx2796NwWBg8uTJRERE2MrrHSKNRVlZGX/96185cOAAnp6e9OzZ07agwQ8//EB5eTljxozh/vvvt9VR+5Ab0e7du0lMTATg559/JikpiaCgIFsC2tfX1+45rat20Ni+vZRck3qTm5tLbGwsP/74I/n5+QQEBNCzZ08eeeSRKpO8i/xWWRMENencuTNvvfWW3bYjR46wbNkyUlJSKC4upkWLFgwYMID7778fFxfHnY737NnDqlWrSE9Pp7y8nNDQUIYMGXLD/VZIGoeakmvg3PO+ZcsW1q9fz6lTpzAYDLRu3ZoHH3zQ1ktOpKEqKChgyZIl7N69m7y8PDw9PQkLC2PYsGEOVwnVO0Qai5KSEtavX8/27ds5deoUFosFHx8f2rVrR2RkpG3Rj8rUPuRGc6V/bwQGBhITE2O3ra7aQWP69lJyTURERERERERExEmac01ERERERERERMRJSq6JiIiIiIiIiIg4Sck1ERERERERERERJym5JiIiIiIiIiIi4iQl10RERERERERERJyk5JqIiIiIiIiIiIiTlFwTERERERERERFxkpJrIiIiIiIiIiIiTlJyTURERERERERExElKromIiIiIiIiIiDhJyTUREREREREREREnudV3ACIiIiI3mtjYWOLi4ujXrx8TJ06s73DqXFpaGnFxcaSmplJQUEB5eTkjRoxg5MiR9R1atd566y0OHTrEhAkT6N+/f32HU63s7GwmTZoEVDxnIiIiUv+UXBMREZE6FxMTQ0JCAgBt2rTh/fffx2AwOCw7c+ZMtm3b1mgTVb81mZmZvP3221gsFgwGA76+vhgMBjw8PGpV/2oScHPnzsXb29vZUEVERESuCSXXREREpF4dP36c3bt306tXr/oORa6BTZs2YbFY6NSpEy+//LLTyS9PT0/c3d1rLFNdQtYZN910EyEhIXh5eV2zY4qIiEjjoOSaiIiI1LvY2Fh69uyJi4umg/2tO3XqFAARERG/qlfZ008/XafDM61DLUVERESulr5gRUREpN507twZk8lERkYG27Ztq+9w5BooLi4GqPUwUBEREZHfOvVcExERkXrj7+/PkCFDWLlyJYsXL+auu+7C1dW11vWt83N9+umnNG/evMr+miZ/rzyB/Z133snSpUvZtWsXeXl5BAQEcPfddzN8+HDb0MQDBw6wcuVKjh8/TnFxMW3atGHUqFF06tSpxhjLysqIj48nISGBrKwsjEYjHTt25OGHH6Zdu3Y11tu2bRsJCQmcOHGCwsJC/Pz8CAsLIyoqivbt21epU3khheeee44NGzaQkJBAZmYmhYWFfPjhh7Rq1arGeCuff8uWLSQkJHDy5EmKi4vx9/enW7duREdH06JFC7vyEydOJCcnx/b3WbNmMWvWLAACAwOJiYmp1Xmd9cv/1keOHGH58uWkpqZisVgIDg5m4MCBDB482GEPyZoWNMjOzmbFihUcPHiQ3NxcDAYDfn5+NG/enPDwcAYOHIifn1+VYx48eJB169Zx9OhRCgoK8PHxoUOHDkRGRtKlS5dqr6W4uJiVK1eybds2cnJy8Pb2pnPnzowYMeKKQ2Wt8a5evZqkpCTOnTuHi4sLwcHBREREMHToUIeJT7PZzJo1a0hMTCQzM5PLly/j5+dHQEAAt956K/369eP//u//rnhuERGRxkjJNREREalXw4YNY+PGjZw9e5bNmzdz33331en5CwoKeO211zh9+jQmk4mysjKys7NZsmQJJ06c4JVXXmH9+vXMmTMHqOiRZbFYOHz4MO+++y5vvvkmYWFhDo9dXl7O9OnT2b17N66urphMJgoKCti7dy/79u1j8uTJ9OnTp0o9s9nMxx9/zIEDBwBsCwKcP3+eHTt2sHPnTp5++mmGDh1a7Xk//vhj9uzZg4uLC56enld1TywWCx9//DFJSUkAtthzcnL47rvv+O9//8sf//hHevbsaavj5+dHcXExBQUFlJaW2s2Z5ijxdD3t3LmTGTNmUFpaire3N6Wlpfz000/MmTOHAwcOMGXKlFoncY8fP87bb7+N2WwGKu6Fh4cHubm55ObmcujQIVq3bs1tt91mV2/RokUsXboUqPjv5+XlxcWLF0lMTCQxMZFhw4bx+OOPVzlfUVER7777LqmpqQC4ublhsVjYvn07e/fu5dlnn60x3l27djFz5kwuX74MgLu7OyUlJaSnp5Oens62bdt4/fXX8ff3t9UpLCzk9ddftw3ptcb7888/c/78eY4fP46LiwujR4+u1T0TERFpbJRcExERkXrl4+PDAw88QFxcHEuWLKFfv34YjcY6O39cXBz+/v688847hIWFUVJSQkJCAl9++SV79+61xRUdHU10dDTe3t7k5OQwY8YMUlJSmD9/Pu+//77DY+/Zs4eioiKefPJJkcnXWwAADkRJREFUBg8ejLu7O1lZWXz55Zfs37+fWbNm0aZNmyq9wD799FMOHDjALbfcwmOPPUaXLl1wd3fn0qVLbNiwgcWLFzN37lxatWrlMLG3e/duSktLGTduHP369cNkMnHhwoVa39f58+eTlJSE0WhkzJgx9O/fH6PRyJkzZ/jiiy9ITk5m5syZfPDBB4SEhADY7oG1B1hdz5lW2eeff07Xrl0ZN24czZs3p6ioiA0bNrBw4UISExNZsWIFDz30UK2O9dVXX2E2m2nfvj3jxo2jdevWQEUC8tSpU2zdurXKIgjff/+9LbE2dOhQRowYgZ+fH/n5+SxevJh169axfPlyQkNDueeee+zqzps3j9TUVNzd3Rk3bhx9+/bFzc2Nn376ic8//5wvv/yy2ljT0tL45z//SXl5OdHR0QwZMoRmzZpRXl7OsWPHbMeOiYnhtddes9WLj4/n1KlT+Pn5MWHCBMLDw3F1daWkpIScnBx27dqFj49Pre6XiIhIY6Q510RERKTeRUVF4ePjw7lz59i4cWOdnttisfDqq6/aklRubm4MHDjQlvSIjY3l7rvv5vHHH7dN0B8YGMgLL7yAwWDg2LFj5ObmOjx2YWEhI0eOJCoqytaLq0WLFrz88suEhIRQXFzM8uXL7ers37+fxMREAgMDmTZtGrfffrutrre3N8OHD+fRRx+lvLy8Sl2roqIinn76aQYPHozJZAKgSZMmtVoJ09o7DWDMmDEMGjTIlpQLCQnh1VdfJSgoCIvFYksgXQ9z587lD3/4Q7U/1iGnjjRr1oypU6fahgp7eHjw4IMP8vDDDwOwYsUKioqKahWHtQfZmDFjbIk1AJPJRNu2bRkzZgwdOnSwbS8vL+fbb78FoE+fPowdO9bWc8/X15exY8dy1113AfDtt99SVlZmq5uTk8PmzZsBGDduHP3798fNreJ34bfccguvvfaa7e+OzJ8/n9LSUp544glGjx7NTTfdhMFgwMXFhfbt2/PnP/+ZgIAAkpKSOHbsWJVrjIqK4vbbb7f16nNzcyM4OJhhw4bVeY9SERGR3xIl10RERKTeeXl58eCDDwKwbNmyWic+roXevXtX6TkG0LVrV9ufhw8fXmV/YGCgrd7JkycdHttkMnH//fdX2e7u7k5UVBRQMYyvvLzcti8hIQGA/v37V9tbqG/fvgAkJyfbJWesfH19GTBggMO6V2KNx9/fn4EDB1bZbzKZiI6OtpV1dP5rwWw2c+HChWp/CgoKqq0bFRXlsJeedbvZbGb//v21isM6pPb8+fO1Kn/ixAmysrIAbMm8X3rkkUeAimRaWlqabbv13gcEBFTp0QYVvTwHDx7s8JhZWVkcPXoUd3f3asv4+PjQvXt3ALvrt17jzz//fKXLExEREQc0LFREREQahMjISNasWcOFCxdYu3atw4TW9VDdJO1NmjQBwGg0Oky+WctkZmZy6dIlh/vbtGlT7aqZnTt3BuDSpUtkZ2cTFBQEQEpKCgBr1qxhw4YNNcZusVjIz8+3xVr5vFezMERl6enpAISFhTmc+B+wTcZvsVg4c+YMoaGhTp2rJo4WFqitW2+91eF2Ly8vWrduTUpKCunp6dx5551XPFb37t3ZsmULMTExpKam0rNnT9q0aVNtDzLr/fPz86Nly5YOy4SEhNC0aVPy8vJIT0+39Xyz1u3UqVO199763PyS9bkpKSlh4sSJ1V6PNXF97tw5u2vcvn07a9euJT8/n759+xIWFnbVc/WJiIg0VkquiYiISINgMpkYPnw48+bNY+XKlQwZMqRWwxh/rYCAAIfbrckNf39/DAZDjWVKS0sd7m/atGm156287+LFi7bkmrWHVGFh4RUir2CxWKps+zULCFy8eLFKfL/0y9gbmtrEXtu4f//735OZmcnRo0dZsWIFK1aswGg00qFDByIiIujfv7/dCp61uX9QMXQ1Ly/PLg7rn6t7Jms6rvW5KSsr48KFC1e8rsrPTb9+/Th69CibNm1i69atbN26FYPBwC233MIdd9zB4MGDa4xJRESksVNyTURERBqMQYMGsWrVKs6dO8eqVat49NFH6zukOmcdIvryyy/To0cPp45RXa+nq1FSUlLtvuqSjb8FlYfg1oavry/vvPMOBw4cYO/evRw5coQTJ06QnJxMcnIyq1at4q233qJZs2Z29ayrdV5r1cVv3d66dWs++OCDqz7uM888Q2RkJDt27ODw4cOkpqZy4sQJTpw4werVq5k6dSrdunX7VbGLiIjcqJRcExERkQbDaDTy8MMPM3v2bOLj44mMjKyxvIuLC2VlZdUmMmrb++t6qWmersr7Kvc0a9KkCbm5uZw6dcrp5NqvYY0lJyen2jKVhxT+ml5y10teXp5tMYNfss4rdjVxGwwGunXrZksuFRQUsHPnTr7++mvOnj3L/PnzmTJlit1xq1vkwsp6DyvHYf1zbZ+byqxDgzMzMyktLXVqWHDLli1tQ1lLSkpISkrim2++4eTJk8TExBATE1PjggoiIiKNlRY0EBERkQZlwIABBAUFYTabq10N08q6emflZE9llVdErA/Hjh1zOGwT4NChQ0DFNVROBFnn39q1a9f1D9AB64qYaWlp1cZ+8OBBoGIob0hISJ3FVlvWe/tLZrOZ48ePA9it/Hm1fHx8uO+++3jssceqnM96XIvFYrdYQWVnzpwhLy+vShzWPx85cqTaHmrVXZv1uSkqKiIpKelqLschNzc37rjjDlvS8Pz587aFGkRERMSekmsiIiLSoLi6utpWU9ywYUONvXisixHs2bOnyr7Lly8THx9/fYKsJYvF4jCGy5cvs3r1agB69eplN8zSOon/sWPHbCuHVqemFTOdZY0nPz+fTZs2VdlvsVhYuXKlrey1GIJ6ra1evdrhsNY1a9Zw+fJlPD09CQ8Pv+JxysrKqp1PD7DNtVa552SrVq1sC2AsXbrUYb3FixcDFSvOtmvXzrbdeu/z8vLYunVrlXoFBQVs3LjR4TFvvvlm2rdvD8DChQtrXHG3uLjYLuaahgBXnk/ueg11FRER+a1reF9DIiIi0uj17duX0NBQiouLSU5OrrZcREQEAN999x2bN2+2/eM/IyOD999/39Y7qL54eXnx7bffEh8fT3FxMQBnz57lww8/5PTp0xiNRoYNG2ZX57bbbrOtYvnZZ58RGxtrl2AsKCggMTGRDz/8kAULFlzzmAMDA7nvvvsA+Prrr9m0aZPtvp45c4a///3vZGVlYTKZeOihh675+a+F3NxcPvroI7Kzs4GKhODq1auJi4sDIDo6GpPJdMXjmM1mJk+ezNKlSzl58iRlZWVARdLtwIEDLFq0CMAuUWcwGBg1ahRQkfSdM2cO+fn5AOTn5zNnzhy+//57AEaNGmWXnAwMDGTAgAEAfPHFFyQkJNgSXydPnuRvf/tbjQmusWPHYjQaycjIYNq0aezfv9+WHCwrKyMjI4MlS5bw/PPP2z1T7777LnPmzOHQoUO25xQq2lFMTAxQschCdSvrioiINHaaNEFEREQaHBcXF0aOHMn06dNrLHfvvfeSkJBAamoqn332GbNnz8bd3R2z2YyPjw8TJkzgo48+qqOoq+rRowdFRUXMmzePr776Cg8PDy5dugRUXOOECRNsvZwqmzRpEp988gmJiYnExcURFxeHl5cX5eXlmM1mWzlrL7dr7cknn+Ts2bPs37+f2bNn8+9//9sudqPRyOTJk6/rkNC5c+eycOHCGsu89NJLdOzYscr28ePHM2PGDCZNmoS3tzdFRUW2JFOPHj2Ijo6udRw5OTksWrSIRYsW4erqiqenJ4WFhbZEW1BQEE899ZRdnT59+nDy5EmWLl3KunXrWL9+PV5eXhQWFtqGew4bNoy77767yvnGjBlDRkYGqampxMTEMHv2bIxGI4WFhZhMJp599llmzpzpMNa2bdvy0ksvMWPGDNLT03nvvfdwc3OzxVy5F17l3pKFhYWsW7eOdevWYTAY8PLysuvdZjKZmDRpklPzuImIiDQGSq6JiIhIg9SrVy9at25Nenp6tWXc3Nx4/fXXWbJkCTt27OD8+fN4eHjQq1cvRowYUYfROmYwGJgyZQrx8fEkJCSQlZWFt7c3HTt2ZMSIEXZDAivz8PBg6tSp/PDDD/znP/8hLS2NixcvYjAYaNGiBe3atePOO+/k9ttvvy5xm0wm/vKXv7BlyxYSEhL46aefsFgsBAYG0rVrV6KjowkODr4u57Yym812iURHqhvO2Lt3b/z9/Vm+fDmpqam4uLgQGhrKwIEDGTx4cK2Hsnp6evLqq6+yf/9+UlJSOHfuHBcvXrTNNdezZ08iIyPx9PSsUnfUqFF06dKF+Ph4UlNTKSgowNfXlw4dOhAZGUnXrl0dntPDw4Np06axcuVKtm7dSm5uLkajkT59+jBixAi7YZqOdO/enRkzZrB27Vr27dtHVlYWly5dwtvbm5CQEMLDw4mIiCAwMNBWZ/z48ezbt4/k5GSys7Ntiz7cfPPNdO3alaioqGoXiBAREREwlF/teuQiIiIiIg1MdnY2kyZNAiA2NraeoxEREZHGRHOuiYiIiIiIiIiIOEnJNREREREREREREScpuSYiIiIiIiIiIuIkJddEREREREREREScpAUNREREREREREREnKSeayIiIiIiIiIiIk5Sck1ERERERERERMRJSq6JiIiIiIiIiIg4Sck1ERERERERERERJym5JiIiIiIiIiIi4iQl10RERERERERERJyk5JqIiIiIiIiIiIiTlFwTERERERERERFxkpJrIiIiIiIiIiIiTlJyTURERERERERExElKromIiIiIiIiIiDhJyTUREREREREREREnKbkmIiIiIiIiIiLipP8HJ1wYHBdyO7AAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 607, + "width": 619 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.title(\"Loss vs Num Episodes\")\n", + "plt.plot(num_episodes,error,c='b')\n", + "plt.scatter(num_episodes,error,c='k')\n", + "plt.xlabel(\"Number of Episodes\")\n", + "plt.ylabel(\"Error\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "images, labels = next(iter(trainloader))\n", + "images = images.view(images.shape[0],-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "predsprobs = model(images)\n", + "preds = torch.argmax(predsprobs,dim=1)\n", + "preds = preds.numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 847, + "width": 852 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figure = plt.figure(figsize=(15,15))\n", + "for i in range(images.shape[0]):\n", + " plt.subplot(8,8,i+1)\n", + " plt.imshow(images[i].reshape(28,28),cmap='gray',interpolation='nearest')\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.xlabel(preds[i])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([64, 10])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "images, labels = next(iter(testloader))\n", + "images = images.view(images.shape[0],-1)\n", + "ps = torch.exp(model(images))\n", + "ps.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0],\n", + " [8],\n", + " [3],\n", + " [0],\n", + " [4],\n", + " [2],\n", + " [8],\n", + " [6],\n", + " [3],\n", + " [7]])\n" + ] + } + ], + "source": [ + "top_p, top_class = ps.topk(1,dim=1)\n", + "print(top_class[:10,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "equals = top_class==labels.view(*top_class.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 92.1875%\n" + ] + } + ], + "source": [ + "accuracy = torch.mean(equals.type(torch.FloatTensor))\n", + "print(f\"Accuracy: {accuracy.item()*100}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000.. Training Loss: 0.762.. Test Loss: 0.444.. Test Accuracy: 81.250\n", + "Epoch 2/1000.. Training Loss: 0.451.. Test Loss: 0.639.. Test Accuracy: 81.250\n", + "Epoch 3/1000.. Training Loss: 0.380.. Test Loss: 0.438.. Test Accuracy: 84.375\n", + "Epoch 4/1000.. Training Loss: 0.565.. Test Loss: 0.811.. Test Accuracy: 81.250\n", + "Epoch 5/1000.. Training Loss: 0.473.. Test Loss: 0.519.. Test Accuracy: 79.688\n", + "Epoch 6/1000.. Training Loss: 0.330.. Test Loss: 0.403.. Test Accuracy: 85.938\n", + "Epoch 7/1000.. Training Loss: 0.430.. Test Loss: 0.362.. Test Accuracy: 85.938\n", + "Epoch 8/1000.. Training Loss: 0.493.. Test Loss: 0.339.. Test Accuracy: 89.062\n", + "Epoch 9/1000.. Training Loss: 0.626.. Test Loss: 0.316.. Test Accuracy: 89.062\n", + "Epoch 10/1000.. Training Loss: 0.354.. Test Loss: 0.654.. Test Accuracy: 73.438\n", + "Epoch 11/1000.. Training Loss: 0.479.. Test Loss: 0.455.. Test Accuracy: 84.375\n", + "Epoch 12/1000.. Training Loss: 0.383.. Test Loss: 0.386.. Test Accuracy: 89.062\n", + "Epoch 13/1000.. Training Loss: 0.409.. Test Loss: 0.464.. Test Accuracy: 82.812\n", + "Epoch 14/1000.. Training Loss: 0.731.. Test Loss: 0.427.. Test Accuracy: 79.688\n", + "Epoch 15/1000.. Training Loss: 0.705.. Test Loss: 0.432.. Test Accuracy: 87.500\n", + "Epoch 16/1000.. Training Loss: 0.529.. Test Loss: 0.500.. Test Accuracy: 82.812\n", + "Epoch 17/1000.. Training Loss: 0.413.. Test Loss: 0.849.. Test Accuracy: 73.438\n", + "Epoch 18/1000.. Training Loss: 0.291.. Test Loss: 0.499.. Test Accuracy: 81.250\n", + "Epoch 19/1000.. Training Loss: 0.408.. Test Loss: 0.400.. Test Accuracy: 84.375\n", + "Epoch 20/1000.. Training Loss: 0.702.. Test Loss: 0.399.. Test Accuracy: 85.938\n", + "Epoch 21/1000.. Training Loss: 0.430.. Test Loss: 0.407.. Test Accuracy: 84.375\n", + "Epoch 22/1000.. Training Loss: 0.481.. Test Loss: 0.630.. Test Accuracy: 78.125\n", + "Epoch 23/1000.. Training Loss: 0.212.. Test Loss: 0.417.. Test Accuracy: 84.375\n", + "Epoch 24/1000.. Training Loss: 0.401.. Test Loss: 0.604.. Test Accuracy: 84.375\n", + "Epoch 25/1000.. Training Loss: 0.378.. Test Loss: 0.603.. Test Accuracy: 84.375\n", + "Epoch 26/1000.. Training Loss: 0.326.. Test Loss: 0.359.. Test Accuracy: 85.938\n", + "Epoch 27/1000.. Training Loss: 0.450.. Test Loss: 0.555.. Test Accuracy: 79.688\n", + "Epoch 28/1000.. Training Loss: 0.837.. Test Loss: 0.528.. Test Accuracy: 81.250\n", + "Epoch 29/1000.. Training Loss: 0.392.. Test Loss: 0.351.. Test Accuracy: 89.062\n", + "Epoch 30/1000.. Training Loss: 0.333.. Test Loss: 0.888.. Test Accuracy: 82.812\n", + "Epoch 31/1000.. Training Loss: 0.428.. Test Loss: 0.431.. Test Accuracy: 82.812\n", + "Epoch 32/1000.. Training Loss: 0.596.. Test Loss: 0.378.. Test Accuracy: 85.938\n", + "Epoch 33/1000.. Training Loss: 0.593.. Test Loss: 0.631.. Test Accuracy: 76.562\n", + "Epoch 34/1000.. Training Loss: 0.578.. Test Loss: 0.544.. Test Accuracy: 79.688\n", + "Epoch 35/1000.. Training Loss: 0.292.. Test Loss: 0.816.. Test Accuracy: 70.312\n", + "Epoch 36/1000.. Training Loss: 0.452.. Test Loss: 0.451.. Test Accuracy: 89.062\n", + "Epoch 37/1000.. Training Loss: 0.634.. Test Loss: 0.600.. Test Accuracy: 78.125\n", + "Epoch 38/1000.. Training Loss: 0.744.. Test Loss: 0.341.. Test Accuracy: 87.500\n", + "Epoch 39/1000.. Training Loss: 0.508.. Test Loss: 0.419.. Test Accuracy: 87.500\n", + "Epoch 40/1000.. Training Loss: 0.461.. Test Loss: 0.345.. Test Accuracy: 90.625\n", + "Epoch 41/1000.. Training Loss: 0.527.. Test Loss: 0.540.. Test Accuracy: 76.562\n", + "Epoch 42/1000.. Training Loss: 0.455.. Test Loss: 0.461.. Test Accuracy: 78.125\n", + "Epoch 43/1000.. Training Loss: 0.289.. Test Loss: 0.390.. Test Accuracy: 82.812\n", + "Epoch 44/1000.. Training Loss: 0.434.. Test Loss: 0.390.. Test Accuracy: 85.938\n", + "Epoch 45/1000.. Training Loss: 0.689.. Test Loss: 0.403.. Test Accuracy: 82.812\n", + "Epoch 46/1000.. Training Loss: 0.667.. Test Loss: 0.356.. Test Accuracy: 89.062\n", + "Epoch 47/1000.. Training Loss: 0.613.. Test Loss: 0.615.. Test Accuracy: 71.875\n", + "Epoch 48/1000.. Training Loss: 0.599.. Test Loss: 0.541.. Test Accuracy: 78.125\n", + "Epoch 49/1000.. Training Loss: 0.465.. Test Loss: 0.366.. Test Accuracy: 87.500\n", + "Epoch 50/1000.. Training Loss: 0.557.. Test Loss: 0.422.. Test Accuracy: 84.375\n", + "Epoch 51/1000.. Training Loss: 0.286.. Test Loss: 0.652.. Test Accuracy: 85.938\n", + "Epoch 52/1000.. Training Loss: 0.466.. Test Loss: 0.587.. Test Accuracy: 78.125\n", + "Epoch 53/1000.. Training Loss: 0.542.. Test Loss: 0.407.. Test Accuracy: 85.938\n", + "Epoch 54/1000.. Training Loss: 0.376.. Test Loss: 0.299.. Test Accuracy: 87.500\n", + "Epoch 55/1000.. Training Loss: 0.459.. Test Loss: 0.350.. Test Accuracy: 90.625\n", + "Epoch 56/1000.. Training Loss: 0.525.. Test Loss: 0.628.. Test Accuracy: 81.250\n", + "Epoch 57/1000.. Training Loss: 0.541.. Test Loss: 0.542.. Test Accuracy: 82.812\n", + "Epoch 58/1000.. Training Loss: 0.374.. Test Loss: 0.565.. Test Accuracy: 82.812\n", + "Epoch 59/1000.. Training Loss: 0.459.. Test Loss: 0.358.. Test Accuracy: 85.938\n", + "Epoch 60/1000.. Training Loss: 0.392.. Test Loss: 0.299.. Test Accuracy: 92.188\n", + "Epoch 61/1000.. Training Loss: 0.326.. Test Loss: 0.366.. Test Accuracy: 85.938\n", + "Epoch 62/1000.. Training Loss: 0.556.. Test Loss: 0.291.. Test Accuracy: 87.500\n", + "Epoch 63/1000.. Training Loss: 0.254.. Test Loss: 0.306.. Test Accuracy: 90.625\n", + "Epoch 64/1000.. Training Loss: 0.764.. Test Loss: 0.348.. Test Accuracy: 85.938\n", + "Epoch 65/1000.. Training Loss: 0.433.. Test Loss: 0.432.. Test Accuracy: 81.250\n", + "Epoch 66/1000.. Training Loss: 0.431.. Test Loss: 0.374.. Test Accuracy: 90.625\n", + "Epoch 67/1000.. Training Loss: 0.420.. Test Loss: 0.524.. Test Accuracy: 78.125\n", + "Epoch 68/1000.. Training Loss: 0.314.. Test Loss: 0.397.. Test Accuracy: 84.375\n", + "Epoch 69/1000.. Training Loss: 0.785.. Test Loss: 0.673.. Test Accuracy: 81.250\n", + "Epoch 70/1000.. Training Loss: 0.493.. Test Loss: 0.508.. Test Accuracy: 76.562\n", + "Epoch 71/1000.. Training Loss: 0.470.. Test Loss: 0.510.. Test Accuracy: 79.688\n", + "Epoch 72/1000.. Training Loss: 0.517.. Test Loss: 0.395.. Test Accuracy: 84.375\n", + "Epoch 73/1000.. Training Loss: 0.394.. Test Loss: 0.392.. Test Accuracy: 87.500\n", + "Epoch 74/1000.. Training Loss: 0.652.. Test Loss: 0.621.. Test Accuracy: 79.688\n", + "Epoch 75/1000.. Training Loss: 0.525.. Test Loss: 0.467.. Test Accuracy: 82.812\n", + "Epoch 76/1000.. Training Loss: 0.472.. Test Loss: 0.418.. Test Accuracy: 93.750\n", + "Epoch 77/1000.. Training Loss: 0.373.. Test Loss: 0.516.. Test Accuracy: 84.375\n", + "Epoch 78/1000.. Training Loss: 0.279.. Test Loss: 0.427.. Test Accuracy: 84.375\n", + "Epoch 79/1000.. Training Loss: 0.561.. Test Loss: 0.381.. Test Accuracy: 82.812\n", + "Epoch 80/1000.. Training Loss: 0.221.. Test Loss: 0.500.. Test Accuracy: 82.812\n", + "Epoch 81/1000.. Training Loss: 0.535.. Test Loss: 0.388.. Test Accuracy: 84.375\n", + "Epoch 82/1000.. Training Loss: 0.575.. Test Loss: 0.423.. Test Accuracy: 87.500\n", + "Epoch 83/1000.. Training Loss: 0.597.. Test Loss: 0.583.. Test Accuracy: 70.312\n", + "Epoch 84/1000.. Training Loss: 0.365.. Test Loss: 0.609.. Test Accuracy: 78.125\n", + "Epoch 85/1000.. Training Loss: 0.463.. Test Loss: 0.281.. Test Accuracy: 92.188\n", + "Epoch 86/1000.. Training Loss: 0.809.. Test Loss: 0.341.. Test Accuracy: 85.938\n", + "Epoch 87/1000.. Training Loss: 0.358.. Test Loss: 0.521.. Test Accuracy: 82.812\n", + "Epoch 88/1000.. Training Loss: 0.448.. Test Loss: 0.610.. Test Accuracy: 81.250\n", + "Epoch 89/1000.. Training Loss: 0.329.. Test Loss: 0.892.. Test Accuracy: 64.062\n", + "Epoch 90/1000.. Training Loss: 0.498.. Test Loss: 0.355.. Test Accuracy: 90.625\n", + "Epoch 91/1000.. Training Loss: 0.497.. Test Loss: 0.392.. Test Accuracy: 85.938\n", + "Epoch 92/1000.. Training Loss: 0.240.. Test Loss: 0.604.. Test Accuracy: 82.812\n", + "Epoch 93/1000.. Training Loss: 0.436.. Test Loss: 0.533.. Test Accuracy: 81.250\n", + "Epoch 94/1000.. Training Loss: 0.369.. Test Loss: 0.482.. Test Accuracy: 81.250\n", + "Epoch 95/1000.. Training Loss: 0.532.. Test Loss: 0.578.. Test Accuracy: 84.375\n", + "Epoch 96/1000.. Training Loss: 0.652.. Test Loss: 0.523.. Test Accuracy: 82.812\n", + "Epoch 97/1000.. Training Loss: 0.596.. Test Loss: 0.504.. Test Accuracy: 84.375\n", + "Epoch 98/1000.. Training Loss: 0.484.. Test Loss: 0.521.. Test Accuracy: 82.812\n", + "Epoch 99/1000.. Training Loss: 0.520.. Test Loss: 0.452.. Test Accuracy: 85.938\n", + "Epoch 100/1000.. Training Loss: 0.308.. Test Loss: 0.404.. Test Accuracy: 85.938\n", + "Epoch 101/1000.. Training Loss: 0.370.. Test Loss: 0.559.. Test Accuracy: 87.500\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 102/1000.. Training Loss: 0.396.. Test Loss: 0.428.. Test Accuracy: 87.500\n", + "Epoch 103/1000.. Training Loss: 0.778.. Test Loss: 0.415.. Test Accuracy: 85.938\n", + "Epoch 104/1000.. Training Loss: 0.364.. Test Loss: 0.493.. Test Accuracy: 79.688\n", + "Epoch 105/1000.. Training Loss: 0.557.. Test Loss: 0.580.. Test Accuracy: 78.125\n", + "Epoch 106/1000.. Training Loss: 0.389.. Test Loss: 0.624.. Test Accuracy: 79.688\n", + "Epoch 107/1000.. Training Loss: 0.379.. Test Loss: 0.233.. Test Accuracy: 90.625\n", + "Epoch 108/1000.. Training Loss: 0.639.. Test Loss: 0.408.. Test Accuracy: 90.625\n", + "Epoch 109/1000.. Training Loss: 0.210.. Test Loss: 0.573.. Test Accuracy: 81.250\n", + "Epoch 110/1000.. Training Loss: 0.340.. Test Loss: 0.650.. Test Accuracy: 78.125\n", + "Epoch 111/1000.. Training Loss: 0.469.. Test Loss: 0.569.. Test Accuracy: 79.688\n", + "Epoch 112/1000.. Training Loss: 0.546.. Test Loss: 0.478.. Test Accuracy: 84.375\n", + "Epoch 113/1000.. Training Loss: 0.712.. Test Loss: 0.482.. Test Accuracy: 82.812\n", + "Epoch 114/1000.. Training Loss: 0.320.. Test Loss: 0.462.. Test Accuracy: 82.812\n", + "Epoch 115/1000.. Training Loss: 0.634.. Test Loss: 0.319.. Test Accuracy: 87.500\n", + "Epoch 116/1000.. Training Loss: 0.600.. Test Loss: 0.416.. Test Accuracy: 89.062\n", + "Epoch 117/1000.. Training Loss: 0.477.. Test Loss: 0.445.. Test Accuracy: 84.375\n", + "Epoch 118/1000.. Training Loss: 0.631.. Test Loss: 0.396.. Test Accuracy: 89.062\n", + "Epoch 119/1000.. Training Loss: 0.481.. Test Loss: 0.470.. Test Accuracy: 82.812\n", + "Epoch 120/1000.. Training Loss: 0.497.. Test Loss: 0.491.. Test Accuracy: 79.688\n", + "Epoch 121/1000.. Training Loss: 0.514.. Test Loss: 0.541.. Test Accuracy: 82.812\n", + "Epoch 122/1000.. Training Loss: 0.356.. Test Loss: 0.363.. Test Accuracy: 87.500\n", + "Epoch 123/1000.. Training Loss: 0.361.. Test Loss: 0.289.. Test Accuracy: 87.500\n", + "Epoch 124/1000.. Training Loss: 0.438.. Test Loss: 0.606.. Test Accuracy: 81.250\n", + "Epoch 125/1000.. Training Loss: 0.775.. Test Loss: 0.332.. Test Accuracy: 85.938\n", + "Epoch 126/1000.. Training Loss: 0.440.. Test Loss: 0.801.. Test Accuracy: 70.312\n", + "Epoch 127/1000.. Training Loss: 0.411.. Test Loss: 0.494.. Test Accuracy: 79.688\n", + "Epoch 128/1000.. Training Loss: 0.556.. Test Loss: 0.494.. Test Accuracy: 79.688\n", + "Epoch 129/1000.. Training Loss: 0.540.. Test Loss: 0.399.. Test Accuracy: 82.812\n", + "Epoch 130/1000.. Training Loss: 0.494.. Test Loss: 0.287.. Test Accuracy: 92.188\n", + "Epoch 131/1000.. Training Loss: 0.443.. Test Loss: 0.551.. Test Accuracy: 81.250\n", + "Epoch 132/1000.. Training Loss: 0.698.. Test Loss: 0.382.. Test Accuracy: 82.812\n", + "Epoch 133/1000.. Training Loss: 0.341.. Test Loss: 0.341.. Test Accuracy: 89.062\n", + "Epoch 134/1000.. Training Loss: 0.476.. Test Loss: 0.599.. Test Accuracy: 85.938\n", + "Epoch 135/1000.. Training Loss: 0.323.. Test Loss: 0.784.. Test Accuracy: 70.312\n", + "Epoch 136/1000.. Training Loss: 0.595.. Test Loss: 0.383.. Test Accuracy: 84.375\n", + "Epoch 137/1000.. Training Loss: 0.371.. Test Loss: 0.328.. Test Accuracy: 85.938\n", + "Epoch 138/1000.. Training Loss: 0.357.. Test Loss: 0.740.. Test Accuracy: 76.562\n", + "Epoch 139/1000.. Training Loss: 0.647.. Test Loss: 0.640.. Test Accuracy: 82.812\n", + "Epoch 140/1000.. Training Loss: 0.569.. Test Loss: 0.790.. Test Accuracy: 70.312\n", + "Epoch 141/1000.. Training Loss: 0.287.. Test Loss: 0.439.. Test Accuracy: 87.500\n", + "Epoch 142/1000.. Training Loss: 0.623.. Test Loss: 0.311.. Test Accuracy: 92.188\n", + "Epoch 143/1000.. Training Loss: 0.577.. Test Loss: 0.370.. Test Accuracy: 87.500\n", + "Epoch 144/1000.. Training Loss: 0.387.. Test Loss: 0.398.. Test Accuracy: 84.375\n", + "Epoch 145/1000.. Training Loss: 0.296.. Test Loss: 0.501.. Test Accuracy: 79.688\n", + "Epoch 146/1000.. Training Loss: 0.449.. Test Loss: 0.401.. Test Accuracy: 87.500\n", + "Epoch 147/1000.. Training Loss: 0.747.. Test Loss: 0.418.. Test Accuracy: 82.812\n", + "Epoch 148/1000.. Training Loss: 0.463.. Test Loss: 0.536.. Test Accuracy: 87.500\n", + "Epoch 149/1000.. Training Loss: 0.466.. Test Loss: 0.455.. Test Accuracy: 79.688\n", + "Epoch 150/1000.. Training Loss: 0.491.. Test Loss: 0.736.. Test Accuracy: 84.375\n", + "Epoch 151/1000.. Training Loss: 0.867.. Test Loss: 0.572.. Test Accuracy: 76.562\n", + "Epoch 152/1000.. Training Loss: 0.694.. Test Loss: 0.535.. Test Accuracy: 84.375\n", + "Epoch 153/1000.. Training Loss: 0.589.. Test Loss: 0.379.. Test Accuracy: 81.250\n", + "Epoch 154/1000.. Training Loss: 0.332.. Test Loss: 0.572.. Test Accuracy: 75.000\n", + "Epoch 155/1000.. Training Loss: 0.501.. Test Loss: 0.440.. Test Accuracy: 84.375\n", + "Epoch 156/1000.. Training Loss: 0.471.. Test Loss: 0.532.. Test Accuracy: 78.125\n", + "Epoch 157/1000.. Training Loss: 0.380.. Test Loss: 0.282.. Test Accuracy: 92.188\n", + "Epoch 158/1000.. Training Loss: 0.422.. Test Loss: 0.387.. Test Accuracy: 85.938\n", + "Epoch 159/1000.. Training Loss: 0.368.. Test Loss: 0.432.. Test Accuracy: 84.375\n", + "Epoch 160/1000.. Training Loss: 0.508.. Test Loss: 0.299.. Test Accuracy: 90.625\n", + "Epoch 161/1000.. Training Loss: 0.699.. Test Loss: 0.508.. Test Accuracy: 82.812\n", + "Epoch 162/1000.. Training Loss: 0.531.. Test Loss: 0.404.. Test Accuracy: 85.938\n", + "Epoch 163/1000.. Training Loss: 0.591.. Test Loss: 0.304.. Test Accuracy: 92.188\n", + "Epoch 164/1000.. Training Loss: 0.503.. Test Loss: 0.379.. Test Accuracy: 89.062\n", + "Epoch 165/1000.. Training Loss: 0.338.. Test Loss: 0.287.. Test Accuracy: 89.062\n", + "Epoch 166/1000.. Training Loss: 0.416.. Test Loss: 0.528.. Test Accuracy: 78.125\n", + "Epoch 167/1000.. Training Loss: 0.615.. Test Loss: 0.492.. Test Accuracy: 82.812\n", + "Epoch 168/1000.. Training Loss: 0.540.. Test Loss: 0.453.. Test Accuracy: 78.125\n", + "Epoch 169/1000.. Training Loss: 0.536.. Test Loss: 0.277.. Test Accuracy: 85.938\n", + "Epoch 170/1000.. Training Loss: 0.381.. Test Loss: 0.506.. Test Accuracy: 79.688\n", + "Epoch 171/1000.. Training Loss: 0.542.. Test Loss: 0.437.. Test Accuracy: 81.250\n", + "Epoch 172/1000.. Training Loss: 0.400.. Test Loss: 0.522.. Test Accuracy: 76.562\n", + "Epoch 173/1000.. Training Loss: 0.491.. Test Loss: 0.342.. Test Accuracy: 87.500\n", + "Epoch 174/1000.. Training Loss: 0.713.. Test Loss: 0.317.. Test Accuracy: 87.500\n", + "Epoch 175/1000.. Training Loss: 0.516.. Test Loss: 0.542.. Test Accuracy: 79.688\n", + "Epoch 176/1000.. Training Loss: 0.296.. Test Loss: 0.722.. Test Accuracy: 78.125\n", + "Epoch 177/1000.. Training Loss: 0.409.. Test Loss: 0.397.. Test Accuracy: 87.500\n", + "Epoch 178/1000.. Training Loss: 0.337.. Test Loss: 0.419.. Test Accuracy: 81.250\n", + "Epoch 179/1000.. Training Loss: 0.687.. Test Loss: 0.397.. Test Accuracy: 87.500\n", + "Epoch 180/1000.. Training Loss: 0.469.. Test Loss: 0.462.. Test Accuracy: 79.688\n", + "Epoch 181/1000.. Training Loss: 0.459.. Test Loss: 0.758.. Test Accuracy: 76.562\n", + "Epoch 182/1000.. Training Loss: 0.464.. Test Loss: 0.318.. Test Accuracy: 87.500\n", + "Epoch 183/1000.. Training Loss: 0.411.. Test Loss: 0.564.. Test Accuracy: 87.500\n", + "Epoch 184/1000.. Training Loss: 0.489.. Test Loss: 0.787.. Test Accuracy: 79.688\n", + "Epoch 185/1000.. Training Loss: 0.511.. Test Loss: 0.508.. Test Accuracy: 82.812\n", + "Epoch 186/1000.. Training Loss: 0.708.. Test Loss: 0.341.. Test Accuracy: 84.375\n", + "Epoch 187/1000.. Training Loss: 0.541.. Test Loss: 0.631.. Test Accuracy: 79.688\n", + "Epoch 188/1000.. Training Loss: 0.515.. Test Loss: 0.451.. Test Accuracy: 81.250\n", + "Epoch 189/1000.. Training Loss: 0.251.. Test Loss: 0.554.. Test Accuracy: 78.125\n", + "Epoch 190/1000.. Training Loss: 0.381.. Test Loss: 0.598.. Test Accuracy: 75.000\n", + "Epoch 191/1000.. Training Loss: 0.460.. Test Loss: 0.328.. Test Accuracy: 87.500\n", + "Epoch 192/1000.. Training Loss: 0.508.. Test Loss: 0.444.. Test Accuracy: 85.938\n", + "Epoch 193/1000.. Training Loss: 0.610.. Test Loss: 0.429.. Test Accuracy: 81.250\n", + "Epoch 194/1000.. Training Loss: 0.578.. Test Loss: 0.560.. Test Accuracy: 76.562\n", + "Epoch 195/1000.. Training Loss: 0.907.. Test Loss: 0.533.. Test Accuracy: 79.688\n", + "Epoch 196/1000.. Training Loss: 0.455.. Test Loss: 0.288.. Test Accuracy: 92.188\n", + "Epoch 197/1000.. Training Loss: 0.629.. Test Loss: 0.394.. Test Accuracy: 87.500\n", + "Epoch 198/1000.. Training Loss: 0.396.. Test Loss: 0.414.. Test Accuracy: 87.500\n", + "Epoch 199/1000.. Training Loss: 0.503.. Test Loss: 0.441.. Test Accuracy: 81.250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 200/1000.. Training Loss: 0.309.. Test Loss: 0.430.. Test Accuracy: 82.812\n", + "Epoch 201/1000.. Training Loss: 0.326.. Test Loss: 0.399.. Test Accuracy: 84.375\n", + "Epoch 202/1000.. Training Loss: 0.422.. Test Loss: 0.457.. Test Accuracy: 84.375\n", + "Epoch 203/1000.. Training Loss: 0.403.. Test Loss: 0.364.. Test Accuracy: 85.938\n", + "Epoch 204/1000.. Training Loss: 0.475.. Test Loss: 0.441.. Test Accuracy: 79.688\n", + "Epoch 205/1000.. Training Loss: 0.335.. Test Loss: 0.487.. Test Accuracy: 76.562\n", + "Epoch 206/1000.. Training Loss: 0.376.. Test Loss: 0.719.. Test Accuracy: 78.125\n", + "Epoch 207/1000.. Training Loss: 0.366.. Test Loss: 0.554.. Test Accuracy: 79.688\n", + "Epoch 208/1000.. Training Loss: 0.451.. Test Loss: 0.450.. Test Accuracy: 85.938\n", + "Epoch 209/1000.. Training Loss: 0.455.. Test Loss: 0.467.. Test Accuracy: 81.250\n", + "Epoch 210/1000.. Training Loss: 0.551.. Test Loss: 0.237.. Test Accuracy: 90.625\n", + "Epoch 211/1000.. Training Loss: 0.443.. Test Loss: 0.425.. Test Accuracy: 81.250\n", + "Epoch 212/1000.. Training Loss: 0.630.. Test Loss: 0.405.. Test Accuracy: 87.500\n", + "Epoch 213/1000.. Training Loss: 0.369.. Test Loss: 0.431.. Test Accuracy: 87.500\n", + "Epoch 214/1000.. Training Loss: 0.639.. Test Loss: 0.344.. Test Accuracy: 85.938\n", + "Epoch 215/1000.. Training Loss: 0.535.. Test Loss: 0.348.. Test Accuracy: 84.375\n", + "Epoch 216/1000.. Training Loss: 0.498.. Test Loss: 0.341.. Test Accuracy: 85.938\n", + "Epoch 217/1000.. Training Loss: 0.635.. Test Loss: 0.591.. Test Accuracy: 82.812\n", + "Epoch 218/1000.. Training Loss: 0.609.. Test Loss: 0.351.. Test Accuracy: 90.625\n", + "Epoch 219/1000.. Training Loss: 0.488.. Test Loss: 0.340.. Test Accuracy: 81.250\n", + "Epoch 220/1000.. Training Loss: 0.372.. Test Loss: 0.509.. Test Accuracy: 85.938\n", + "Epoch 221/1000.. Training Loss: 0.601.. 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Test Accuracy: 84.375\n", + "Epoch 255/1000.. Training Loss: 0.469.. Test Loss: 0.359.. Test Accuracy: 89.062\n", + "Epoch 256/1000.. Training Loss: 0.485.. Test Loss: 0.393.. Test Accuracy: 84.375\n", + "Epoch 257/1000.. Training Loss: 0.469.. Test Loss: 0.338.. Test Accuracy: 90.625\n", + "Epoch 258/1000.. Training Loss: 0.510.. Test Loss: 0.501.. Test Accuracy: 76.562\n", + "Epoch 259/1000.. Training Loss: 0.538.. Test Loss: 0.359.. Test Accuracy: 89.062\n", + "Epoch 260/1000.. Training Loss: 0.379.. Test Loss: 0.427.. Test Accuracy: 78.125\n", + "Epoch 261/1000.. Training Loss: 0.347.. Test Loss: 0.466.. Test Accuracy: 73.438\n", + "Epoch 262/1000.. Training Loss: 0.417.. Test Loss: 0.358.. Test Accuracy: 84.375\n", + "Epoch 263/1000.. Training Loss: 0.442.. Test Loss: 0.393.. Test Accuracy: 84.375\n", + "Epoch 264/1000.. Training Loss: 0.462.. Test Loss: 0.451.. Test Accuracy: 84.375\n", + "Epoch 265/1000.. Training Loss: 0.480.. Test Loss: 0.691.. 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Test Accuracy: 81.250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 299/1000.. Training Loss: 0.571.. Test Loss: 0.445.. Test Accuracy: 82.812\n", + "Epoch 300/1000.. Training Loss: 0.714.. Test Loss: 0.472.. Test Accuracy: 84.375\n", + "Epoch 301/1000.. Training Loss: 0.488.. Test Loss: 0.357.. Test Accuracy: 84.375\n", + "Epoch 302/1000.. Training Loss: 0.331.. Test Loss: 0.416.. Test Accuracy: 84.375\n", + "Epoch 303/1000.. Training Loss: 0.290.. Test Loss: 0.303.. Test Accuracy: 92.188\n", + "Epoch 304/1000.. Training Loss: 0.430.. Test Loss: 0.295.. Test Accuracy: 92.188\n", + "Epoch 305/1000.. Training Loss: 0.708.. Test Loss: 0.342.. Test Accuracy: 87.500\n", + "Epoch 306/1000.. Training Loss: 0.458.. Test Loss: 0.554.. Test Accuracy: 79.688\n", + "Epoch 307/1000.. Training Loss: 0.434.. Test Loss: 0.646.. Test Accuracy: 75.000\n", + "Epoch 308/1000.. Training Loss: 0.372.. Test Loss: 0.437.. Test Accuracy: 84.375\n", + "Epoch 309/1000.. 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Test Accuracy: 82.812\n", + "Epoch 343/1000.. Training Loss: 0.313.. Test Loss: 0.603.. Test Accuracy: 76.562\n", + "Epoch 344/1000.. Training Loss: 0.362.. Test Loss: 0.277.. Test Accuracy: 92.188\n", + "Epoch 345/1000.. Training Loss: 0.626.. Test Loss: 0.373.. Test Accuracy: 85.938\n", + "Epoch 346/1000.. Training Loss: 0.341.. Test Loss: 0.246.. Test Accuracy: 90.625\n", + "Epoch 347/1000.. Training Loss: 0.661.. Test Loss: 0.616.. Test Accuracy: 87.500\n", + "Epoch 348/1000.. Training Loss: 0.329.. Test Loss: 0.380.. Test Accuracy: 85.938\n", + "Epoch 349/1000.. Training Loss: 0.424.. Test Loss: 0.357.. Test Accuracy: 87.500\n", + "Epoch 350/1000.. Training Loss: 0.329.. Test Loss: 0.440.. Test Accuracy: 79.688\n", + "Epoch 351/1000.. Training Loss: 0.619.. Test Loss: 0.484.. Test Accuracy: 89.062\n", + "Epoch 352/1000.. Training Loss: 0.466.. Test Loss: 0.818.. Test Accuracy: 78.125\n", + "Epoch 353/1000.. Training Loss: 0.859.. Test Loss: 0.259.. 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Test Accuracy: 84.375\n", + "Epoch 860/1000.. Training Loss: 0.546.. Test Loss: 0.410.. Test Accuracy: 82.812\n", + "Epoch 861/1000.. Training Loss: 0.513.. Test Loss: 0.369.. Test Accuracy: 87.500\n", + "Epoch 862/1000.. Training Loss: 0.476.. Test Loss: 0.420.. Test Accuracy: 87.500\n", + "Epoch 863/1000.. Training Loss: 0.440.. Test Loss: 0.670.. Test Accuracy: 81.250\n", + "Epoch 864/1000.. Training Loss: 0.556.. Test Loss: 0.422.. Test Accuracy: 81.250\n", + "Epoch 865/1000.. Training Loss: 0.490.. Test Loss: 0.312.. Test Accuracy: 82.812\n", + "Epoch 866/1000.. Training Loss: 0.253.. Test Loss: 0.497.. Test Accuracy: 81.250\n", + "Epoch 867/1000.. Training Loss: 0.503.. Test Loss: 0.398.. Test Accuracy: 82.812\n", + "Epoch 868/1000.. Training Loss: 0.572.. Test Loss: 0.711.. Test Accuracy: 73.438\n", + "Epoch 869/1000.. Training Loss: 0.665.. Test Loss: 0.461.. Test Accuracy: 84.375\n", + "Epoch 870/1000.. Training Loss: 0.373.. Test Loss: 0.472.. 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Test Accuracy: 89.062\n", + "Epoch 882/1000.. Training Loss: 0.474.. Test Loss: 0.280.. Test Accuracy: 89.062\n", + "Epoch 883/1000.. Training Loss: 0.480.. Test Loss: 0.386.. Test Accuracy: 82.812\n", + "Epoch 884/1000.. Training Loss: 0.444.. Test Loss: 0.499.. Test Accuracy: 78.125\n", + "Epoch 885/1000.. Training Loss: 0.378.. Test Loss: 0.432.. Test Accuracy: 84.375\n", + "Epoch 886/1000.. Training Loss: 0.365.. Test Loss: 0.685.. Test Accuracy: 84.375\n", + "Epoch 887/1000.. Training Loss: 0.347.. Test Loss: 0.433.. Test Accuracy: 87.500\n", + "Epoch 888/1000.. Training Loss: 0.448.. Test Loss: 0.407.. Test Accuracy: 87.500\n", + "Epoch 889/1000.. Training Loss: 0.705.. Test Loss: 0.517.. Test Accuracy: 82.812\n", + "Epoch 890/1000.. Training Loss: 0.309.. Test Loss: 0.405.. Test Accuracy: 87.500\n", + "Epoch 891/1000.. Training Loss: 0.611.. Test Loss: 0.303.. Test Accuracy: 87.500\n", + "Epoch 892/1000.. Training Loss: 0.631.. Test Loss: 0.371.. Test Accuracy: 84.375\n", + "Epoch 893/1000.. Training Loss: 0.380.. Test Loss: 0.669.. Test Accuracy: 82.812\n", + "Epoch 894/1000.. Training Loss: 0.300.. Test Loss: 0.331.. Test Accuracy: 90.625\n", + "Epoch 895/1000.. Training Loss: 0.265.. Test Loss: 0.833.. Test Accuracy: 75.000\n", + "Epoch 896/1000.. Training Loss: 0.545.. Test Loss: 0.389.. Test Accuracy: 85.938\n", + "Epoch 897/1000.. Training Loss: 0.575.. Test Loss: 0.585.. Test Accuracy: 82.812\n", + "Epoch 898/1000.. Training Loss: 0.494.. Test Loss: 0.462.. Test Accuracy: 79.688\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 899/1000.. Training Loss: 0.489.. Test Loss: 0.254.. Test Accuracy: 90.625\n", + "Epoch 900/1000.. Training Loss: 0.387.. Test Loss: 0.499.. Test Accuracy: 84.375\n", + "Epoch 901/1000.. Training Loss: 0.336.. Test Loss: 0.304.. Test Accuracy: 84.375\n", + "Epoch 902/1000.. Training Loss: 0.303.. Test Loss: 0.446.. Test Accuracy: 85.938\n", + "Epoch 903/1000.. Training Loss: 0.324.. Test Loss: 0.801.. Test Accuracy: 73.438\n", + "Epoch 904/1000.. Training Loss: 0.451.. Test Loss: 0.497.. Test Accuracy: 79.688\n", + "Epoch 905/1000.. Training Loss: 0.316.. Test Loss: 0.366.. Test Accuracy: 85.938\n", + "Epoch 906/1000.. Training Loss: 0.499.. Test Loss: 0.298.. Test Accuracy: 89.062\n", + "Epoch 907/1000.. Training Loss: 0.298.. Test Loss: 0.398.. Test Accuracy: 82.812\n", + "Epoch 908/1000.. Training Loss: 0.454.. Test Loss: 0.344.. Test Accuracy: 87.500\n", + "Epoch 909/1000.. Training Loss: 0.391.. Test Loss: 0.243.. Test Accuracy: 92.188\n", + "Epoch 910/1000.. Training Loss: 0.717.. Test Loss: 0.630.. Test Accuracy: 76.562\n", + "Epoch 911/1000.. Training Loss: 0.295.. Test Loss: 0.602.. Test Accuracy: 78.125\n", + "Epoch 912/1000.. Training Loss: 0.341.. Test Loss: 0.565.. Test Accuracy: 79.688\n", + "Epoch 913/1000.. Training Loss: 0.692.. Test Loss: 0.449.. Test Accuracy: 82.812\n", + "Epoch 914/1000.. Training Loss: 0.524.. Test Loss: 0.477.. Test Accuracy: 82.812\n", + "Epoch 915/1000.. Training Loss: 0.551.. Test Loss: 0.493.. Test Accuracy: 79.688\n", + "Epoch 916/1000.. Training Loss: 0.399.. Test Loss: 0.439.. Test Accuracy: 82.812\n", + "Epoch 917/1000.. Training Loss: 0.316.. Test Loss: 0.504.. Test Accuracy: 84.375\n", + "Epoch 918/1000.. Training Loss: 0.357.. Test Loss: 0.459.. Test Accuracy: 84.375\n", + "Epoch 919/1000.. Training Loss: 0.410.. Test Loss: 0.613.. Test Accuracy: 78.125\n", + "Epoch 920/1000.. Training Loss: 0.457.. Test Loss: 0.465.. Test Accuracy: 84.375\n", + "Epoch 921/1000.. Training Loss: 0.286.. Test Loss: 0.433.. Test Accuracy: 81.250\n", + "Epoch 922/1000.. Training Loss: 0.509.. Test Loss: 0.301.. Test Accuracy: 89.062\n", + "Epoch 923/1000.. Training Loss: 0.520.. Test Loss: 0.192.. Test Accuracy: 95.312\n", + "Epoch 924/1000.. Training Loss: 0.316.. Test Loss: 0.238.. Test Accuracy: 89.062\n", + "Epoch 925/1000.. Training Loss: 0.426.. Test Loss: 0.476.. Test Accuracy: 82.812\n", + "Epoch 926/1000.. Training Loss: 0.336.. Test Loss: 0.516.. Test Accuracy: 81.250\n", + "Epoch 927/1000.. Training Loss: 0.426.. Test Loss: 0.351.. Test Accuracy: 89.062\n", + "Epoch 928/1000.. Training Loss: 0.367.. Test Loss: 0.387.. Test Accuracy: 89.062\n", + "Epoch 929/1000.. Training Loss: 0.429.. Test Loss: 0.196.. Test Accuracy: 90.625\n", + "Epoch 930/1000.. Training Loss: 0.543.. Test Loss: 0.477.. Test Accuracy: 81.250\n", + "Epoch 931/1000.. Training Loss: 0.368.. Test Loss: 0.417.. Test Accuracy: 82.812\n", + "Epoch 932/1000.. Training Loss: 0.407.. Test Loss: 0.583.. Test Accuracy: 81.250\n", + "Epoch 933/1000.. Training Loss: 0.458.. Test Loss: 0.544.. Test Accuracy: 81.250\n", + "Epoch 934/1000.. Training Loss: 0.405.. Test Loss: 0.477.. Test Accuracy: 81.250\n", + "Epoch 935/1000.. Training Loss: 0.568.. Test Loss: 0.478.. Test Accuracy: 82.812\n", + "Epoch 936/1000.. Training Loss: 0.366.. Test Loss: 0.358.. Test Accuracy: 87.500\n", + "Epoch 937/1000.. Training Loss: 0.363.. Test Loss: 0.414.. Test Accuracy: 81.250\n", + "Epoch 938/1000.. Training Loss: 0.476.. Test Loss: 0.440.. Test Accuracy: 81.250\n", + "Epoch 939/1000.. Training Loss: 0.416.. Test Loss: 0.532.. Test Accuracy: 81.250\n", + "Epoch 940/1000.. Training Loss: 0.532.. Test Loss: 0.323.. Test Accuracy: 87.500\n", + "Epoch 941/1000.. Training Loss: 0.537.. Test Loss: 0.395.. Test Accuracy: 84.375\n", + "Epoch 942/1000.. Training Loss: 0.458.. Test Loss: 0.540.. Test Accuracy: 84.375\n", + "Epoch 943/1000.. Training Loss: 0.378.. Test Loss: 0.484.. Test Accuracy: 84.375\n", + "Epoch 944/1000.. Training Loss: 0.345.. Test Loss: 0.612.. Test Accuracy: 78.125\n", + "Epoch 945/1000.. Training Loss: 0.373.. Test Loss: 0.264.. Test Accuracy: 92.188\n", + "Epoch 946/1000.. Training Loss: 0.367.. Test Loss: 0.427.. Test Accuracy: 84.375\n", + "Epoch 947/1000.. Training Loss: 0.625.. Test Loss: 0.589.. Test Accuracy: 78.125\n", + "Epoch 948/1000.. Training Loss: 0.328.. Test Loss: 0.763.. Test Accuracy: 78.125\n", + "Epoch 949/1000.. Training Loss: 0.369.. Test Loss: 0.328.. Test Accuracy: 84.375\n", + "Epoch 950/1000.. Training Loss: 0.362.. Test Loss: 0.405.. Test Accuracy: 84.375\n", + "Epoch 951/1000.. Training Loss: 0.600.. Test Loss: 0.643.. Test Accuracy: 85.938\n", + "Epoch 952/1000.. Training Loss: 0.681.. Test Loss: 0.790.. Test Accuracy: 85.938\n", + "Epoch 953/1000.. Training Loss: 0.356.. Test Loss: 0.503.. Test Accuracy: 81.250\n", + "Epoch 954/1000.. Training Loss: 0.656.. Test Loss: 0.383.. Test Accuracy: 84.375\n", + "Epoch 955/1000.. Training Loss: 0.292.. Test Loss: 0.401.. Test Accuracy: 82.812\n", + "Epoch 956/1000.. Training Loss: 0.598.. Test Loss: 0.400.. Test Accuracy: 87.500\n", + "Epoch 957/1000.. Training Loss: 0.549.. Test Loss: 0.436.. Test Accuracy: 82.812\n", + "Epoch 958/1000.. Training Loss: 0.440.. Test Loss: 0.438.. Test Accuracy: 82.812\n", + "Epoch 959/1000.. Training Loss: 0.470.. Test Loss: 0.286.. Test Accuracy: 90.625\n", + "Epoch 960/1000.. Training Loss: 0.384.. Test Loss: 0.390.. Test Accuracy: 89.062\n", + "Epoch 961/1000.. Training Loss: 0.733.. Test Loss: 0.417.. Test Accuracy: 84.375\n", + "Epoch 962/1000.. Training Loss: 0.308.. Test Loss: 0.484.. Test Accuracy: 81.250\n", + "Epoch 963/1000.. Training Loss: 0.383.. Test Loss: 0.373.. Test Accuracy: 84.375\n", + "Epoch 964/1000.. Training Loss: 0.434.. Test Loss: 0.449.. Test Accuracy: 82.812\n", + "Epoch 965/1000.. Training Loss: 0.375.. Test Loss: 0.510.. Test Accuracy: 81.250\n", + "Epoch 966/1000.. Training Loss: 0.390.. Test Loss: 0.563.. Test Accuracy: 78.125\n", + "Epoch 967/1000.. Training Loss: 0.390.. Test Loss: 0.458.. Test Accuracy: 84.375\n", + "Epoch 968/1000.. Training Loss: 0.635.. Test Loss: 0.395.. Test Accuracy: 89.062\n", + "Epoch 969/1000.. Training Loss: 0.539.. Test Loss: 0.344.. Test Accuracy: 84.375\n", + "Epoch 970/1000.. Training Loss: 0.532.. Test Loss: 0.359.. Test Accuracy: 89.062\n", + "Epoch 971/1000.. Training Loss: 0.354.. Test Loss: 0.254.. Test Accuracy: 93.750\n", + "Epoch 972/1000.. Training Loss: 0.639.. Test Loss: 0.495.. Test Accuracy: 84.375\n", + "Epoch 973/1000.. Training Loss: 0.751.. Test Loss: 0.297.. Test Accuracy: 89.062\n", + "Epoch 974/1000.. Training Loss: 0.359.. Test Loss: 0.452.. Test Accuracy: 84.375\n", + "Epoch 975/1000.. Training Loss: 0.455.. Test Loss: 0.415.. Test Accuracy: 82.812\n", + "Epoch 976/1000.. Training Loss: 0.394.. Test Loss: 0.400.. Test Accuracy: 84.375\n", + "Epoch 977/1000.. Training Loss: 0.536.. Test Loss: 0.346.. Test Accuracy: 82.812\n", + "Epoch 978/1000.. Training Loss: 0.507.. Test Loss: 0.254.. Test Accuracy: 92.188\n", + "Epoch 979/1000.. Training Loss: 0.416.. Test Loss: 0.411.. Test Accuracy: 81.250\n", + "Epoch 980/1000.. Training Loss: 0.303.. Test Loss: 0.424.. Test Accuracy: 85.938\n", + "Epoch 981/1000.. Training Loss: 0.355.. Test Loss: 0.561.. Test Accuracy: 81.250\n", + "Epoch 982/1000.. Training Loss: 0.548.. Test Loss: 0.398.. Test Accuracy: 79.688\n", + "Epoch 983/1000.. Training Loss: 0.600.. Test Loss: 0.435.. Test Accuracy: 81.250\n", + "Epoch 984/1000.. Training Loss: 0.400.. Test Loss: 0.360.. Test Accuracy: 81.250\n", + "Epoch 985/1000.. Training Loss: 0.534.. Test Loss: 0.357.. Test Accuracy: 84.375\n", + "Epoch 986/1000.. Training Loss: 0.457.. Test Loss: 0.414.. Test Accuracy: 89.062\n", + "Epoch 987/1000.. Training Loss: 0.591.. Test Loss: 0.325.. Test Accuracy: 89.062\n", + "Epoch 988/1000.. Training Loss: 0.286.. Test Loss: 0.290.. Test Accuracy: 85.938\n", + "Epoch 989/1000.. Training Loss: 0.393.. Test Loss: 0.467.. Test Accuracy: 81.250\n", + "Epoch 990/1000.. Training Loss: 0.555.. Test Loss: 0.387.. Test Accuracy: 89.062\n", + "Epoch 991/1000.. Training Loss: 0.294.. Test Loss: 0.610.. Test Accuracy: 84.375\n", + "Epoch 992/1000.. Training Loss: 0.477.. Test Loss: 0.382.. Test Accuracy: 84.375\n", + "Epoch 993/1000.. Training Loss: 0.489.. Test Loss: 0.479.. Test Accuracy: 82.812\n", + "Epoch 994/1000.. Training Loss: 0.331.. Test Loss: 0.750.. Test Accuracy: 79.688\n", + "Epoch 995/1000.. Training Loss: 0.357.. Test Loss: 0.463.. Test Accuracy: 81.250\n", + "Epoch 996/1000.. Training Loss: 0.472.. Test Loss: 0.458.. Test Accuracy: 87.500\n", + "Epoch 997/1000.. Training Loss: 0.491.. Test Loss: 0.341.. Test Accuracy: 81.250\n", + "Epoch 998/1000.. Training Loss: 0.350.. Test Loss: 0.626.. Test Accuracy: 79.688\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 999/1000.. Training Loss: 0.375.. Test Loss: 0.436.. Test Accuracy: 85.938\n", + "Epoch 1000/1000.. Training Loss: 0.370.. Test Loss: 0.599.. Test Accuracy: 87.500\n" + ] + } + ], + "source": [ + "epochs = 1000\n", + "running_loss = 0\n", + "train_losses, test_losses = [], []\n", + "for i in range(1,epochs+1):\n", + " \n", + " optimizer.zero_grad()\n", + " \n", + " images, labels = next(iter(trainloader))\n", + " images = images.view(images.shape[0],-1)\n", + " \n", + " output = model(images)\n", + " train_loss = criterion(output, labels)\n", + " train_loss.backward()\n", + " optimizer.step()\n", + " \n", + " running_loss += train_loss.item()\n", + "# if i%100==0:\n", + "# error.append(loss.item())\n", + "# if i%1000==0:\n", + "# print(\"After {} episode, Loss: {}\".format(i,loss.item()))\n", + "# print(\"===============================================\")\n", + " test_loss = 0\n", + " accuracy = 0\n", + "\n", + " with torch.no_grad():\n", + " model.eval()\n", + " images, labels = next(iter(testloader))\n", + " images = images.reshape(images.shape[0],-1)\n", + " log_ps = model(images)\n", + " test_loss += criterion(log_ps, labels)\n", + "\n", + " ps = torch.exp(log_ps)\n", + " top_p, top_class = ps.topk(1,dim=1)\n", + " equals = top_class==labels.view(*top_class.shape)\n", + " accuracy += torch.mean(equals.type(torch.FloatTensor))\n", + " #print(f\"Accuracy: {accuracy.item()*100}%\")\n", + " model.train()\n", + " train_losses.append(train_loss)\n", + " test_losses.append(test_loss)\n", + "\n", + " print(\"Epoch {}/{}.. \".format(i,epochs),\n", + " \"Training Loss: {:.3f}.. \".format(train_loss),\n", + " \"Test Loss: {:.3f}.. \".format(test_loss),\n", + " \"Test Accuracy: {:.3f}\".format(accuracy*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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nXwTfyZPnPabGn/PnM1lY2IoYuHBhVU6eXCz92WIbFoILF3bKysrWfTx16oIsLDCbgTTsvHhRLi0uycrKpIisvyQ2P78oJ0+G873TkITYhiFt28eF586tyMmTfBsd5WJov86c6R9zTk/35OTJ8S8to7nTpydkYWF28//n5xfkqqvWPKYIqYqhDYN7U2fPynSNecfO/LzsYJ4SFtCGVXPttdcaO5b6b/BtROgtbGuUtltcXOz7uWGOHDki//zP/yxvetOb5KMf/ai84Q1vkKmpKXnDG94gH/3oR+Wtb32rPPLII/JDPjwaD4Ov6GXCBLkTvFYJz3ibGgAAAK4x5gQAAEAZ9Qt8G6udR48eLf33V155RUSGf6Nvw5NPPilra2vyjne8Qzqd/mzpdDrysz/7syIi8vzzz7dNMkLEE1PwsizBexRQuawQBA2FAipigBMs0gMANKC/co88BgAAGqlf4HvnO98pIiJ79+6V3sb+iJctLi7KU089JVNTU3LdddeNPM7KyoqIiJw7d6703zf+fiKy7QBTlrUcwY986OKJzA7y1RsW+NZpL3La0w8AgIhInjMwQVoYw7lHngMAAA3UL/C97nWvk3e/+91y4sQJuffee/v+7ZZbbpGlpSX5tV/7NZmZmdn8+8OHD8vhw4f7fnYjQu+RRx6RgwcP9v3biy++KI888ohkWSY/93M/Z+lKoBlbdCI1PPCuY4IR0I22DAAQIt5rdI88BpAU3uIGohFFONpHPvIR+fjHPy6f+9znZN++ffLGN75RnnnmGfnhD38or3/96+VP/uRP+n7+b/7mb0RkfQFww0/91E/J9ddfL9/+9rfl7//+7+X973+/7Nq1S06cOCG7d++W1dVV+e3f/m1505ve5PTaYImBJyYeAID0UO8BxajAQDSozkgNZR4AAABloljge93rXif/+I//KLfccos88cQTsmfPHrn66qvlAx/4gPzhH/6h7Ny5s9Jx/vIv/1Le8Y53yLe//W3Zu3evLC4uyuzsrLz97W+X3/iN35Bf/dVftXwlqCz0J5zQ06fUwLaqsedzQNdXlvW88BXULaoktSoEAAAQA8Zs9jFOBgAAGkWxwCcismvXLvmrv/qrSj+7PXJvuyzL5Prrr5frr7/eYMqgQtvRO6N/JCbVIp/qdQOxoA4DcaAuI368SQcAAIDx1H+DD2jEwKwAEwsekOne8EYrgBjxHU0AgAaMve0jjwEAQ+U5HQWCFU0EH9CKkUY6E5Hc4PEwFvlcWffgQckWFmT1p39apNttfbxUs177Qqe29AIAAIAXUlzQPs4HANiRLS7K7Je/LLKyIov/439Ifs01vpME9CGCD2kigk8nMr2RzrFjMnfrrTJ7990yuW+fkWNyK+LAfUTqqAOATtRdxI7FJgAAwjD9wAPSPXZMuvPzMvu1r/lODjCABT5ApPUTUyZ5/yF4IrODfGxk5j/+Y+vP99/f6BgU6Q28PQ1olm7bBQDQjP7LPvIYAFCme/Dg1p+PH/eYEqAcC3xIExF80MBUIbNQWCn/68gHQJGSCksdBnSi7iJ2vFwHAACAKljgA0R4YtKCJ91mLCxoa8767ksvycSBAyKrq7V/V/N1AwAAQCfGoD6wcwcAQOiEEbwJ3wkAvDDdONPYIzmZiOgr952TJ2XulltEROTSwoKsvPe9nlPkF00XUkcdAHSi7iI1lHkAAACUIYIPEGn0xJT6Q9biYiZLS45PmnqmN8WWtJumv/WtzT/PPPBA7d+PKZIRAAAAYWLM6V6eE7EHQLGMNgxIFRF8SBNPSK0cOdKRW26Zk05H5E//9KJcfbWj/ORJNxhkvU7cN6SObgQAoBH9lX2MEQCoVrPRookD4kEEHyDSOoIvk7z//yN/Orj99jlZW8tkZSWTe++d8Z0ciMjkk0/K3Oc/L5P79g3+I9/gMya2fNCefqAWCjwQDaozAABAcwPxfgyuhiNvEDgW+JAmGudWLl3aGgqcOeOwGUntvlW93jyXmW98Q7qnTsnMffc1P04Nqd0KAHGiLQMAhCi2l8o0II8B944d68jDD0/J/DzT062xRSeQLFpQoCEeAPxj+HJZr+f8lGrLf8uEq73uy/i2CAAgBtr7Y6CuPM8o946R34BdvZ7IF784J48+Oi233DLrOznp4U2SygZ2aQMCwwIf0mS6I0u4Y3R1qVnm8GTaeMgXbsU68gHQjToMAAgR/ZN75Dng1sWL2eYLqBcvMj3tHY3gcOQNAkcLCgDascDnTGzXHdv1AONQ5gEAWtGHAQCMoVMBosECHyBNt3rs/62RfSMdpx3kazkLEaUJB6lGg3uG5JVUAuoFAEAL+iy7eN4BkDQaPUAtFvigk+ktNU2jY7TC1r7Xly6J7NkzKYcPK20Sx+SLjXxLtYjz4A8AgH/0v4hdWRmn3AMAjKFTqY68QuAmfCcACEKDxnr7r2RCY6/Zgw9Oy759UyIi8hd/cUF27lR2P8tWnbJmcanDT1EjYhUAlKAtAwBoQZ9lF/kLIGk0gsORNwic0nAVoB2zSx8laPztsJSvG4t763+etHIOq8bli5V8s16L7GiZF5qrNm+CAwBiUXzxCEgB4za7yF8ASaHRA6LBAh8g0jqCL2VeJ1i4Ces8LPCR9QDiwCIBACA8vJjlH/kN2GV40yG0RaM3HHmDwLHAhzSZbpxp7K3LMok+ny9cyOTMmaz9ZRrOJyYYtqR63UAUqMAAAMXoxuwifwEkhUYPiAYLfICIkY6t7xB0lKhpeVnkc5/bIQcOTMpLL3Vr/e7YF9+I4LNGUz5oSitgS9knS4FQvfxyV/btm5SVFd8pCQ91F7HjBTsAgFN0MsORNwjchO8EAF6YXtBLmNN8iHhm9vjxriwvry/VnTu3/t+s6vWNyxcL352LKOtr0Xbd3YMHZfKJJ2T1He+QtbdeN/Dv2q4HAFJx5kwmN988JyLr44Jf/dVlzykC4F8mIgzebIn4URMAAESMCD5ApPXoPRv3oMXTgRkO8tHXPvCtLs1D+aJI6zB3660y+eyzMnvHHSKrq76TAwSHtgyheuSR6dI/Yx11Fymi3AOICYvqnnEDqiNvEDgW+KBS5cimYWxH8NH4o4U8r7nKOK68EcFnUP+9qX2vPMrY4w2pS7fhgkJra75TAMAnxt8+6BnXIwzHjnXktttmZffuKd9JUYn1JQAwgy06ARFGElo4GAH6KgpGI/gcXARVRoHCTeoxaQEMoC1DqFjgG426ixRR7u1isQF13XTTnKyuZvLCCxPy5jevyk/8RM93koDqaPSqI28QOCL4kCbjjXNOew9/LEfwOTqkCqquuzf+AVPV9QAGUOahBWUVQBHtAhCW1dWtFyiPHOl6TIlOrC+FhdeBAb1Y4ANEGo0k2KLTg4jztfTSql6v5ZFxxNneWtB5Uwj/GPutUABAMNbWmGYB0jbYBgQ97owA+Qv4pbUOHjrUlSeemJTlZd8pqUlrhgMYwBadSBMdmU4O7lsWwHxa7cv0UJ5TrUKqrruwwKcq7YAj1AuEii06R6PuIkWUe7fIb8CuGOrY/HwmX/rSnIiInDvXkV/7tSXPKWohhhsCJIoIPqAh+r51XvMhoptg9FIMR/SV/3oAK6EYKStu0RlPdQnK2lpUTRGAQFTYZTlptLuIXVkZp9zbRf4CbsWwRefu3dPb/jzlMSUNaMxwX8grBI4IPkCkdWOdFX+dxh8uEcHnjKqHkGL4R2liM2Hlr7lXXunI7bfPyvS0yJ/8yUWZnfWdItQVdB1G0no9XqTZjroKUA8AxI02zjNuAKAWEXxIk4GOi77PrRC2zrSpVXka88uZhQg+yr8ChfAP7pl5t902JwsLHTl9uiMPPTQ9/hfgHfUAWrBF52jUZaSIcm8X+Qu4RZ0zrOakWeRTbEa1nVMDbGOBDxAxP7JIqPF3eqmqwqea27ysitc3MDBzkC+RZv1Ymq57cItORYlXYnFxq/YdO9b1mBI0RbVAqNiiczTqLmLHFvn+0c4AtvW3aRrrnMY0DxXVxQBpYYEPOrXteAx3XFmkW9zRv7tjM4LPxjf41JYNwwkPOh8qbdEJAAgRC3z96MIQu8XF8T9DPbCL/AWqsTVGoQ4CQDN8gw9oKPZgsvvum5bnn5+Q//pfl+RnfmbVd3JEhLD4ocYVRgv5luqtUHXdhQW+vDeYeFXXA7RVUuCpAwjV2hqROkAqHnxwWnbvnpJ3vnNF/vt/vzT05+iz7CJ/gfH275+Qb31rRq67bnVke1UFdc6z2Cc1G+ocOybS7Upv167Sf89j/34QVCKCD0FaWxM5fVpkfl7k7FkLjafljsvHlokmHT3akX37puTixY7cddes7+RsiXgAEnIEn6NDqhR0PvABJwBQiwi+0YLuf4GannxyUkREDhyYkJWV9b+LagcNANG4995ZWV7O5Ic/nJTjx81OKdPGwbfuiy/Kji98QXb8679K58iR8h9igQ8BIoIPQTp3LpObblr/ntHk5Jz87/990er5mkSG9f1KZCORc+dY+/epdnGyXP6YYNii6br5Bp9bjPN1iPg9EUSm7Tsa2ZkzMnHwoKxed53kc3NmEuXRYN2l0UU8Nup7nmcjF/fps+wif4F6LlzI5LWvbf77g3UuE4n08zdB4sFowMShQ1t/fvllWb72Wo+pAapjgQ9pouMaqc5EtdOsjPi+mYzgGxgWE8GXJr7B5xTZC8CkXq/FAlaey9ytt0rn7FlZfeYZWfzQh8wlLBC0uYhJ1ZfpKPcAYkKbhuBsL5QUUChCmA6CtH2ByUmb2ugkIyZe6Ahggqly1PI4TDBsUfWSW4Xwj6DTDzhAHUCo2mzRmZ0+LZ2zZ0VEZOLgQUMpKnfwYFceemhKzp0jog6wjT7LLiKFgXpM72CisY3TmOZNqiY3HCEPoBQRfEgSQ3V9skwiH4C0e1N/5P9bobMWNdmOVyu26AT6rdd/nW0X0qPhM6oLC5nceuv69p8vvTQhH/7wgqytiXS75s8V9RAQ2GZU2abcA+FKsX62vWb69sBwA6rlAd/mQIBY4EOQdETwbckkpy90gUwuNy5fWufb4AAm1Vuh6roLs8N5T1PiAVuK7RkPaAhTmwg+Vw4e3FrJO3q0K1//+oz8+McT8uu/viTvec+K1XOr6o+BMdiiMwzkL+AWdc4zbsAgtuiEUmzRCZ1Mvypk+hB0BKjJ5Df4XNBSxLuHD8vMXXdJ99lnrRw/6HzQEP4BACjV6ht8nhw4MCmrq5ncf/+M9XMF3f8CLRDB51N/u0t+A25R5zzjBhDBB7VY4EOQtEXwjRPztoBOLy2RPRxqX9a4fLGQT1qyfu6mm2Ty6adl7qtfFVlpH02g5bpFqm3Rqel6ABuoA9Cg06lZUCOceKCuAiJEnQOISSLTO+EiwwcRwQelWOBDmkxH8EU2MolwXih4pUWmYjmyfbti2SIou3jRdxLcKkbwKbxngGka2y6gwxPbAOoyYhfL+FsT8heop+28EXUuMNwQIvigFo+LCJLzCD7oQGEo52GBWeWtMDIQUzSYY4tOYCyVbRmSwwLfIOou4jV8rEm5d4v8BtyizjlGhg8iT6DUhO8EAF4YWBCJLGhPJUVLLWNZ/QZfy8LJG8RKDWzROfgj3EdzeJEPgC3dru8U+Ed/BZH1cnDPPTNy7FhH/tt/uyRveENv/C8pUHWsTT2wi/wF3IphTk1jmofJhE1/+gy7uTz4I0C8D4ogaWsvs2I3GFMvP4bXb/BFKpHL9MPwYmfI96r4Db6BdgpA0HUY2FD7G3wJoO6m6cc/npADBybl1Kmu3HTTDt/JsWJU2abcAwAqo9OoLSPPoBQLfIAIHR+8MxrBV/j/toMU3iBWqrBFJ/cM2Ebbm0RIWqgRfPQrcO2VVwKtDI5Q5+wifwG3NL08GyVuwCC+wQelWOBDkKx/g8/AQen7AhD7Tah6fR7yIfasH0bVdRe/wacq8YAdPMdCI77BV1Z3mVxJUaxtdll55gU7/8hvwLb+to861xIZ2N72PGSLTijC4yIg0r4jjGzGMNj+Snm+jmIzgq/vn4K9uTqFPMFY3KKzrFxEXKUAIBrdLo11Ef0XYsUWnf6Qv4Bf1EHDxmVoZPOYRgSaB3kucvBgV559dkKK0zyAiMiE7wQAZfoj+Eom0E0vyNk+RKCdhCYprUvVLi6Wy1f5G8QKb4iBQqSqKrNFJzAW9QIhKpbLlMZAVVF3EYthZZkIPvfIX8At6hyCE2gE3+HDXbn11jkREfmd31mUt7991W0C1tZk6pFHJFtdlaVf/mWR6Wm358dYLPABImzZ2YLL6475g7dGI/hGMTQYifhWxIMtOgFAJRb4BvGSOUTSuO9E8IWD/AbsGqxjmYjoqnhBtRN1E8PgalCgeXDXXTObf/7a12bl7W8/7/T8k3v2yPQjj2z+/9Kv/7rT82M8tuhEkDR8g8/r8bGOfF5neWAW1RvELROuaQxcZYtOmEP26qCpDiNdq4WXcimnANax2m8TbS3gF3XQMDK0vgp5luJnb6Yfe2zzz1OPP+4xJRiGBT4EaqtRddInNTjJ9l/JlL1lpBYDlHIG8+X8+Uy++c1p2b9/dIA3t0KBChF83EcACE+x+W4twsY+wktCBTHe9+HXNDiBGOP1A0gXbZpfA70MN6TaFp0eBJQUBIotOpEm162jutY40PSqy8fq2owjTA7M7rlnRg4dWu8adu26KK97XS+uCL6WVF238RliIB755ZZTVZ1GMnq9/p699bd581z9Pp9E3yIVbNHpD+0M4Bd1rqW2jRg3oFoeeBhT57nucTzsI4IPQbK+RacBI9MVaqJjE3s+27i+MYORjcU9EZGnn54c+nOxZ31VQecDW3Q6pXzuHEBAeD9jPLo0xKJOWabcA4gJi+qekeFqcKswDgt8SJPx1jEXvonggINezdckfatLqzMyNnSBKgcYBq5d03VnhRnivKco8YAjmuo00tF2gS/OLZdaRjUiCinc941rZAcN98hfwC3qXGC4IZKxRSeUYoEPQXIdwZc1OEnbCL75+Y7cffeM7N07PEoKW4iOGcFyJWGCYbig86HCDHHQ6QcsoMxDg7W1loMeCjoQJaq2W+Q36qC8tKcxD0NKc+0XvEJKfCgC3aKzuDkTUMQ3+KBT244ogI7s9ttn5cyZjvzoR5Ny7bVr8prX0GKPxR4O5erkQ0oRfBbKi4rr3sAWnQCgUvH9jNbNt6X2n24FrsVY5oZdEy/YAYgd0zuB4Qb0CSnGgFuDcYjgQ5A0fINvuyzPa6fzzJmt6vfii13DKYqUg8IQVHmrmhjLI+Pyw4U03BkiqJvpXnGLTkk7O4A+OWHhCFjrb/BFOGMW4SUBpUaVbcq9XXnO2ACoo+1wmjYNwQm0UAaaLASEBT4kyfq3SZS3vsx7urf9gbJ28SGCzxlVE4yFCL6g0wp4wmQeQtTrFb83RzktIk8QizrjM8ZyAGJGG9dS3ckKVZMbjgS6RSe3BuOwRSeCpC2Cb6wxF6F5Qc3p/XFwMpX3opAvmYjkeS7dI0dElpf7f7bBBardIqhswKoi4YYUQkCykhC+lLIDALQggq+aPFc6bkNjkRblPhvXmMK1hiaRphOW0B/VR51DcLYXwmEFksqOALHAB4g0GkmYfHOY/qGhiEaArS6l5Jc7R4/K3E03Df4sEXzp4Bt8wFhUC4RIyzf4XIrgEoDWiFwFEBP6dsuI4Ksv0DwINFkICFt0IkjWI/gMH7QYGVP30YsFPvSrWSBKyvPsPfcYSotIWXpUDDBS+2ZjQfEbfCGnFQCwZW2Nj9pUkchlInJs0RkO8hdtUH7aIw9bIgPbC3aLTiaNMRoLfIBIwwi+1ofYpHmBj47GDKNjsTwfjN7aoLmwtWUgk1WVd+MhIIB+W9UgK/w/EI7WzXcib2RHelkYIYV7PmqLzhSuPyTkN2Bb8ZvDnpLRgsY0byr51EvyqmzR6VmWhZku+MUCH4JkfQ0i0IY6FMGuAaU2YRXwqrGKrFeRSIsqbNGZehYBQIhaf4OvKILGnsUOin5z2gAAIABJREFUgDJvm/b8zS5ckOkHHpCJ/ft9JwVACLQ3aqEKdsIUKeMbfNDJdEfV9ngtF57oH6rJIh6gtFrLKyt/QwpV3qCwRTOpZiSCz/ghrWGLTmA86gVC1Ou1fKOdgg2oMay6RjP+VkR7/s584xsy8fzzIiJy8dWvlt7rX+85RcBog3WOibFW6jZimiY3XFERwec7BQgREXwIkoZv8JlMFw00WimLzOoMad4bFLaJY0flJw9/T6aWL4w6ZXhSnxlhi04AUMl0BF+sw0y6NcRoVLmmzLumq/XcWNwTEZk8cMBjSoBqWF8KDDegWlABE7gIEAt8gIj3joz+Ybwsk6hHgK0uxWY+LC3JNV/+olx38FvyrmfvdHLKkKkqgoUtOjMJObGAH0HXYSRrdbX//4ng450dxKtOOabM2+Ujf5eXRVZW3J8XQALGNWp0KoMUDDiZP0YZtuhEkPoj+Cy0XoYj+AYmzmuuAtBAY7vWE3kjtuisa+LQoc3S/eozLww9ZZBUrcYZlueSFRb4yi+fxgdp2agHTbYrBlwpbtHZWqT9X6SXhRGsPBcGJxORXMMcI1o6caIjN900J1km8uEPX5RrruEGIy0pP65bYfrTQykq26IzsILq5TGWshE8IvgAEfdbdhZ+OMtoLCuJuFMxHsFnqtfPMr0LQxbKi5oiWLa/m5rEA+5QLRCi1jssBzYRYUuklwUMRZm3y3XTeeeds7K8nMnSUiZ33z1r92RAgGIYrmhM8ybVibfE8Ns1vZ7ICy905exZc/Nnw77Gg7QRwYcgFdcmjPc7tjsy5SOVYAMblOerNXUi+AzdXLI+cCzwAYBapr/BFwO6MIjEWQ7qXVOoD2lo4vTprVnakyeZsQVg1no8eA0xdrJ1VVngq5FPjz46Jd/97rR0u7n8xV9ckFkD73IEO18LrxhFIFimo9ryfMSESYOOrO9XEusIiTg0z3gRGvZaT83RwLBkqSjyxUhZ84cMVnF7ThE9aQdcol4gRMUtOongKxfpZdVy333T8ulP75Dnn+/6TgoMGbYj2LC/gznkL+BWIsMVd8jA9qps0VnDd787LSIia2uZPP74VJuUbfKywMeqYvCI4IMKbfuplRWRm26ak3PnOvLBDy7KW1x3fGNGLrSVFSUyYGk9kSdivVAlciv0qhjBx30E7FpdFdm9e0ryXOT971+WCUbeqKBtBN/ACMBSY++/D6n9bnpU5uc7sm/f+mTR7t1T8ta3LnpOkX3+y5wN1cfscV5/OMhfwC/qoGF1M5QbYDyCbztT39hm/hhliOBDsLY3Wm37mT17puT48a5cupTJLbfMtTtYlTTVTDANNIx+g8/kFp1Dfl7D2K90grNlwtW8ZVgSwUczA2yvs1nh/+3Ys2d9W5bvfW9afvADM29tIn6rq75TEB6imQYtLW39eXmZXt6US5dE9u+fMPq9nDqI4AuHy/xmPgApok1DcKpE8HkuuF52VKOyBo8FPuhUs3HZvr99k9+votaCHxF8tWWZJNepZFWvtywyy2ChYoJBIb7BBwThwQenN//80EPTI34S2NL6Dd/AJiJgB7fVjnvvnZF7752Vm2+eK3tfyivuOYCYMFwxrG6gATegmsAGA8wfowwLfAiWyQi+iQm7C3pZZNsDhdBhpDbWaHN9pZFqbNHpfiE/IGXf4AMwSEudRlqK72hQTsuRL1tSyQsX1/nss5MiInL+fEeOHbM/XVLnmlK5zwB0MD3loLGNCzrNQScuUCV55mrre6ANFviQhMnJMQ2w6QZ63PGI4GsmkY40qG/wKd6ic4CVBb9AK29pBF/JX2m8jwAQubbf4IvxLSl2ExiU+vW74OMZbdR95Z7bVRzXk99AP9PDiwiHK7pwAwb0RTWO2jO7ybHtTtEhcSzwIVgmI/imip+9cd1xFc6nrd8sptdFh1KaR8WFUW0ZOVKLTK3zDT5DeaYi6xOO4GOLTmA0k9sYA6YVg7Bbv0wSafsf6WXVsFUuyAu9iOADACAQVd4o8/4NPq+nR6BY4EMSxkXwNVko6vuVmr+f98J+Ogvx4THENAXDUgRfnps5jjcJF5qyLToTzg5gE/UAGqyusqBXhfXLXF2V7OJFyydBHSkUbSL4/CF/gdFs1xHqYEt1F6ICW7gKgsUIPlM0T9HBngnfCQCGMRnBNzlZ+AvHI5PiAqK2ftTBJ91Kz5mSVtdbZ4Gv7WL25t8pHFXkeeuCNeyFruAGWRUj+FKrZwCggfFv8Hlo7E33jc4v4dIl2fH5z0u2uCiXfvd3ZfW66xwnYLyyOSjEo2yszX22S9szOuBb+y06i+1caA/ViaHRU7EnfHBzTwgCEXxIQrfb3yAPBLcYWvSo+sMlwTVoIrCO1rim15fnIh27zbuKrFeRSEtKFviyso/wAYlT+bICoje4RWfNAyQyS23zsiZeekk6Fy9K1uvJxNNP2zsRaomxKNe7JvosIFQxtk9Qrm4EH9iiE2qxwIdgmYzgK1pZNnu8uhPnxS06Q+tXA+u/ttIQQkIsCTaCb8hxIr4VYyh5m7pkgS/IdAIJyTIqIapZW9O/RaeLJFg9x/Z+NNA38wK4zbBg1I5g3HO7yF9gNNPzRCHOO6nWMgNZNxIVnS8LfCjDAh90qvvNu8KPr662O966/lZ15Df5xkTwBdZfBKF0zSqRjDLxpn6eUK/fOX5csvn5wX9I+Imh7Bt8KV0/MN56G0m1QIhMb9EZw4jA+XyLsv0vFSQRQ9S5d9xnt1Tnd0LPgtAr4cd1hKrCgNP3vCQvjaIM3+BDsDRF8NVVjOALXQgDrRDSYJPxCD5Tsizol5i6zz8vc7ffLiIiF//n/5TeT/yE1fOFct1j8Q2+qCwtiezfPynXXNOTn/zJknuLyijz0KB1wFgiM2aRXlZlKV5/Ctc86hpTuH6fyF9gNOqIMnW36OQGlwtsJwfe30AZIviQpJWVwl+Y7sjGdJTFb/6E1o+GkJ6BNDhaZQqls2zzjUcRMfINvlF5EUIZEZHNxT0RkZmvf73/Hx0NWEPJiz5s0RmVhx+elm9/e0Zuu21O5ucZuplEvYAGGsupxjT3UXYBfE80PiG/YAcAImzRiQhtK4QbkXoDI6zGBZUCDnuYJUKwbEbwrRYX+BoYuSXnyB+u8POBUZbcVlRea3HLABEj3+DL8+HHCTGfsqWl8T8UYsItKNuis+63QhGOJ56Y2vzz978/6TElMWASHOFr3VUpH3eWK6u7buqz762Yhgk0WVbFeM1s0RmOKJtOwCDH78XDNm7AgNIxn7F8MTNuDSUoAWFhgQ9JKL7VurqaFX/AYWrKvsGnq4V20aEMjDWUbWtaV5siWHo7LN8kFWM/C4k0dcjuyy/L7Je+JJM/+IGZAxZV3KIT5Xo9kYUFXe0ymqFaQAON5dRFmp19gy9Qyj4TiIrYojNNTNhCI9qkwHGD6isbXAW2EEp/gTJ8gw/B6m+0zLZgy0vtG+Q2WyiGvlgVWP81XLAJM6Tq9ZXdsCG9vqm30IPM+uI1VynII/KqqiZ5MXfzzSIiMnHokKy+9a2Sv+pVrdIwoCSCj62eqlldFfm3f9shZ89m8pu/eUne+c5V30nqwz0zI/cQycfDGKpq/eKXsy2q/RZqZ+0hDS88YNzmHvkLuKVm3kmJ2nM93IBBVTpfz/lk4Gs8iBDFAkkotr8DEXwtZZLXauMHI/iMJicKxTyJfXvBVmMGS9/gG3WuaMpszQuxcd2dkyeNH7Nsi854bppde/dOyunTHen1MrnnnlnfyYFlVAtoYHzLTgdsb+Nl4xzuDm5GihF8rq/Ty64lRPB5Q/4Co7FF5yDfLzu5kucihw515cc/nih7lzgeCt6u4aVRlGGBDypY/wZfgxO0iuCLYCDjmquox2g6SwPf4Cs9rKaFVgWDM2vYorOxCxf8DI2efXZCdu+elEuXvJweQEC0LOgl060kc6EICcXOP+4BRqF8iPBt68CNK6Q1JiaPHu3Il740J3feOSsHDkS8GaDVLTrNNBrRzFnCKBb4kIRi+7tSXOAzbFxofB78Gy/9PYaPwWulcxpIWCiLraXnbbFFZ26q188L0ambEXwBjirGXbOBBT8ba4ZWcrIkUTyDVuOjDTh+vCNf/eqsPPjgjHz3u9PuE5CQUNp8uDWxf7/M/du/ycT+/b6T0kjdcjrQr1jbotPKYcM7v+8LHSLFCL4Y1bl3QY6/AYhIGu0wEXyBs5iB9947s+3PEe9yo+AlcRb4UIYFPgRre6MVYgTfdnXbV+0DGR8diqrIMdccFqCN+6ChzJYVU1PfIAxeyXVm4Y9Vg+Bjy5HHH5/a/POePVMjfhJAbXkus/feK90TJ2T23nt9p6YS420zW3TWpm28oCy5GIEtOgEdqI/mkaeGGYzgS4aCb/BlZRM7SB4LfAiWyQU+GxF8rbboVPYNvhAi+GylIZS8N/oNvjwf/g2+uhc8JGGh5FufKhF8LQuWghe61vENvsaaZpOrFx8uXuzIN785LY8/PsktbePyDSMPE1C2ZXHsEtmi09X27d4vdIhAkwVD1Iw5I1L1MSHPRU6c6FjfFQjaxB9WY3qOhjYNwSnbHiGwjw4SwYcyEW+cCwy3slpoEU1v9Thui87ABzJBpi+RyapGyhJtcIvOvsMGEMF3/nwm998/I7Ozufx+b/hapoa3r6ylIYTrUsrH+L1OdX3++a2h29VX9+Rtb0tw8QKoI7CHci8iiOAruubMC/Laf79VZt9yjSx+6EMjBgMNKetHlSW3sRi3qBy8d8OvMZX7HLpHH52S73xnWq68sif/639dlG7Xd4oAreJr072q2Um42tJdO/IJGhDBh2DZjOBbXW53vIFjjklgcZuf3lohoi/w/sFP+ip8BzCQjDORjDwvOVDVA9dZ4DNUmXxm/T33zMhzz03I/v2TcvSo2yfqQIrceGXf4HMV6aCcmnssIvv3T/pOgnqa7jcaKkbwKbjprd9FifClqOK53nvgRuksLMjESy/J5JNPuj15IAJNFizinttVdQH5O99Z/17yuXMdefpp3plPVYjvjdpm+5pjfInDq7a7N/X9UyL3piyCL7DKTgQfyrDAhyStrjo+YXGLzrD6h9q8dCiWMqntvTh3LpN///c5+cIX5uTiRU89bdlFGIzg2374ECL4Dh3aepA+eXJbN1a85rJ8sVD5Qqy/5Xe/ZNEvwLT7lszDS6Io8+nJiOCLMoJvu87Jk+YPqqKx2OqvVCTXgBivc9Q2kFV/FmY0eUxYWWHcCDSlrk1bWpLswgXfqYBFpd9gDqygssCHMizwIVhWv8G3bLmBHpPgYgRfaOpsFeMqDVnJ4kQI7rlnRk6c6MqxY125//7pxsfJ8/5rLI3oqyPiCL7KLAzOVFy3SGlCSwerGNA0m8heIFDFBT4FlXVwkrnmOMzRm2Q+I/isn7/sDW7AgVHFjfcVgHBof2k7BJryMDt/XnZ+6lOy49Oflu4LL/hOTrm2EXsh3wAX6rxx4xELfCjDAh+StGrgG3y1fmXghytsP4nxAoi+eumlrWiyF14IZIsWkwWqcKwQIviGqjLSCXEQayMNJTNAIVyqBqF/g8/E7wFJKW7RifjYbgwD7UADTRYM4f66R54D49idxwq5Dk5/85uSraxI1uvJ3G23+U5OJTwq1jRsrshQwTQ1XI1pRzWYwwIfgmX1G3wrZhungeiyMQkufgsr9LYyiPQp+J5Mu4625JcrJqZWZFbLt7pCjaQsNZB244cc+nfeqUloeMimtLi83yzI+qFxi87WTbiF/q/CaSr/mwrqLyBOcd6W6hPmcV5/yOi4UR31sz5NedY5d6707zVdwwDVibegagSf43zjNqEKFvigU8sWrrhXfpOt62q18YV/DGELzFFC6EBCSENdrSdvTe4NaDAD+w6laYvOMoWEH365I/v2TcrKiqf02FKxTKi9jxZpWgsoa3OOHu3IAw9My4kTdod4DzwwLZ/5zA758Y8DiVyuKQ+s34VFCrfobC2RLTpTxy6igFlN6pHqiAy0kmK7a3t4EXSeaqikIe5WpEmgW3RyG1EFC3wIls0IvrVVx41k4WSaJpBFAulQytJgYYtOX9damo6qialzETUvsDiMDTmCLy8Ousfky+KiyK1fmpX77puR3bunqp3DxuXbOGgQlVYnzVnX64nccMMOefzxKbnxxjlr5zl1qiOPPz4lZ8925M47Z62dx4bBZkHBwzraUbhFZxxB2PbrltU80ZfhSbB9W4rHdzGfW2c+kT4LCBfdRn2hzMUkq7jjQ+o3oOIWna7zKYj5Yw0L3IljgQ/J6pvvaRvBV3Nkom2LziIvD7sKMslbn+fhFXq1Ewzb8urll7cij773vWkThwxHHLPDXjTNpjb131TbcenS1oGKkeomnTuntP4jTcUFvhTawggj+JxTEB7Xn0TaZRt83PpAi1sSyHvUkWJ5SSpiD+kZViDLVtgcFl4WwlEFC3wIls0IPslzt29BjIngC72BDj19bYRybXneIjqurMc3tb1A8W0lCXiLznERfGW/UjvPlUygscDXmKZJUl6ka49qET+N3+Ar06qsRlDQ6dZGIy/MCD0fTabv+PGOPPTQlPUtvTULvTwgLCmWFxb8AlN3JYiVo35D8sP3I3cQjzKplw0FGM0hEYNNctsIvjaKEXyhCaHtrhTBZyGhbQ6ZVgSfu1Mak+eWtlNoeeMtpKnsOpkcrSaIAXTgWFiEKilWameN+/DGQH3/su0CQt2yKtBkWeX6ml2cb/g5BuuXqfTkucgNN8zJY49NW93SW5sU6xSao7y0x/pSdb2eyIMPTsl9903LwoL7h7Ek7k29PbPtpmXEqZK4F6htYvyPAH6YjOAbOLbksraWibT4ntj2NGWS19yyMxv9z4Hx8T0KV0LJ+9J0VE1cnQi+uvJ8oKzXSZpXFQZidSP4VFy3iPdBqGaasqnYFrtqmzX3AZv39/JFuLzfmvNNNYVbdBpvwhVccyu2K1eg+adgF1F1QsjHUWkwlb5eTy4/C9vd0lubEO4/EDLbEXsa66CrND/55KTs3r3+WZGVlUx+53cutT9oDDfAJFO7YBmmaYch+EMEH3QoTsrXbGC991OFBGj/Bp8LA2MNR1GPXiP4mp7cZgEaMuhTUWarJFLFhTRQcl2Nt4BNjOZgH1fFmYUqqFJc4EuBo8bAxULEqONZvcxYxwcITqABAxDyG/WkWF5SvGZf9u6d3PzzU09Nlv8QC3atDDzibuRfYBF8QBki+BCs/glEw7OJ+ZiIu0qapyn0BppxQTNZZiYidPN4AWa8qgi+IgORjWomW0oTxapMFT7uZ9O2g4W29oKsvzBqoC9VetPHLaaNbA+UXnMwAs2/FCP4bF9nCM2Fi4XzsuuMeUwxP9+Rr31tRmZnc/ngBxdlaqr851KpRzAjxfJCBF9kuAH9Ao3gK76AnPptQjki+KBDy1n1qj/6zW9Oy+c+NycvvNCtfOwt1fcU7a0RwTfOQJ6URiRZOE8g8lyqJ67OFp11L3hgS8tmh/HCwhadVtjIzMptZsSzOQ2pKNtDsEUnUEJhBF+drXi+/vUZ+b//d6f86Ecj3tu01LC5jOBzjWYOKdFeX8e5444ZOX68KwcPTsgjjwxZ3QNair0elWl7zYO/T+87jJPylWIh3i7QkPrUbwuqYYEPwbL5Db4BeS6HD3dkz54pmZ/vym23jf/YeJsIQG0vygSRPkdp8LVFZ54Hstg0TshbdI67AZYi+IJUts+kmsT7RTaNF9MCH/c7AQoX+MqUldUjRzpy4MCkLC1lcvfds5t/72oHAJf1x+sWnYE2FAqSaFyMEXw+5hNTKS8bTp3aenn30KHqm1illk+oJ8XykeI1b9LwAKRtojE0RPBBMRb4oFeLCL5M8oG/m59vErXXLAGhf+MphA7DVRpCuNahKiauOJGXjfjd2pN+eS7b36Rrugh5/nwmJ0+67XIqDcENFIAgy1CQidKh11Pw8HZZ8TnT1TOdhufbcXLeEE6HwsmOqhP7589X7FcVXHMrMTRKwGW+tugE0A71qIn+/ps8dIzOoB8RfFCMb/AhWH0RfD3LDarlzcQHF2B0bdE5sEDqYx7FWafqZ5Ko7qXMz2cyNSWyc2f7qLSRhgz66pzy9OlMPv/5HdLrZfLBDy7KddetGkxgTdsS3qQcW8lqR1t0ht7OhIJ8qiKeTHJ5v1mD8COLJIKvzNDy62jCxv8WnZlYa48UhMcpSKI6qcx1pnKdZUb1xSnlA9CE7baDOmgYGVrPsHlcQ3ORTZ8FQw8QQRiI4EOwTG7ROfD7hqN26kY1FSNE6HcHuXvwNDfj2nrydttF5rkMvejnn+/K5z63Uz796R0yP5/Vz5yKP182ntko63W+D3T//TObZf6OO2bH/HQLVcKZxiy2x6wsepO2Z5CmAbSvCD5AleICn4KK4flF4SB5zZPUMz8gMd6KYX037YB7xeebKvnNyzspY06nrRjyLKhraDsvFNTFeGBqFyzj6GgwHgt80MHCE07fr+e5ZJnZ45X/w+W/KkQkeu8vCkLs52NfiKmTx7ffvv6NyF4vk298Y6bSQpbphNU5/OKivQFJ7ctsWbi1TLb4H4TqpTnr2KJzvBD7N1imadW+JiL4LFJ2AcqSG6xU8jHlvjDUMUyo6QJsSrkt0iaJe8MWnVCMBT4Ey2YEX9liUZ2IpLJj1hHxXJMxlaIuLfR0bQ5p/MGsQmIWF+1F8I363SAHGePCmVJS0siUbnWMAZraZ1+TQTFMQuUxXAQqyTRV6su0vEwSNQX7X25/dgk0iRGw31fUm080kx4m1cuRD6hjsLzEP7a0XUeog4aNy1A6g9FGhdQ7VHyU4TahDAt80MF2BF9bdV9hHvi7sAeDAwukHpJrqxMLpXPM8xZRijYHZkO2tKxzCm/z6BXypW6eW5l0tVEIKyY0lPIPM3hGq89lHrGm6InCLTqrqhrBZ6vouY3gG7yKiG4lAhFCmXJTr9LtkOiLgXCE0OYCm4jgg2Is8CFYmr7BV/cHQ38DI4z0ZIX/c5OoUCL48lzs3Yg221JqiuArE0LCXaTBwGJmqkIoIqa42qJTRZ6pSCREzO4yLSK6wnIvK7/+GoMMVvvbUxHB5zsF8QstjwN5NIhWk6aTvEtXil3t4DW3mwBRlYcRvh0wcEVB3wAH6izwOTQ4fxxfWUR7E74TAFRROjFtMoLPcYOtb6u8TMTz4kASD7RNE2PpIsrGsE0i+LwpeWIY+DZdm8VOUxxF8Km4ZwHo9dwPmJs+Lxa/Hcs9Hm8jj/hOZZiWl0VuumlOzpzpyLvetSK//MvLMjvb8l4VIvj8j2ji4bsabZ4/wkk3lItxe7hhQ1ObAQOqJtUNK46dAIQjpbbICTK0lqELnkTwQQEi+BCssRF8NVq5gS0mS6Z22gz2R0bGlE60Z+N+BEWeO9UqfHyDr/TnyhayKhy37GG/eBxVC3wVmIhqq50XniL4orlplsWUTa5ejFARIBXTjY3YoUNdOXGiKysrmfzgB1PyxS/Oyepqu2PG/A2+qlt02ir/LrfodF6FFbQZCoIMYRgLfHY1yQfeL8CGFOqR+r7dAlfXUOU8tV9gHJjrSVyNCD6XeaXwUQYesMCHZG1vo5u8yT/qwWjc8bQ9VIXwNqur87Q5bxIPeJq26LRQ0Yxcd3Eg7SiCj6i+ajQPoH31LZrLkea0x2h1tb8dP3u2I/PzLR9XNFdqUyjo7ZB/CXH/EqaPHcFSLtJ1ntdSzieMR/lgwS94ljP0+PGOfO1rM/KjH0WyOWCdkHqnCBDBeJHUQsSoL4KvbEvLVhF8DpVNqqvborPferSj49WsyHuxPM/6osnyXKpfs6FZ/dLDDI3gU7ia6fnNq00uVmEiry9VTT7xhEzt3i3L73mPrLzvfZV+x0fWmXo5QOOLEc4MacsQlrKy1Hp9rrBFp44CO6hVBJ8lLiP4nJ9DQTnpj+BTOC5rII3rHH6N9oolmxeLqKj21SXx5im0U/lMEZOWN+Dmm+dkeTmTp56alDe+8YJccYXyGzjk+ktfyHZYWHlXEVUQwQc0VLk9rxA1E/pAJogIPkedqsYIvtoDjooXWfrC0uX/CXKQMSaCz9btaV0MLZRj34PQUMzcf790zp2TmQcflKr7/GnKpnFtDhF847mcLGaubTwbTVfrLTpXVmTiwAHpnDrV7jg1GK9TmivpZT636OSbneGwfStCf0Zji067yAfUkWY9MhtJlEaeOeT4hiwvb5WHY8ciWF7wEVJfQZptDeqKoAYiVjYj+Nb/YtxH/qobiAQYt0Vn4XrGnf7SJZEXXui2/g5NVWm8HbsulM6xVToMXUTpYYZG8Bk5pVuhLHr5iuBTedMMKkbxDNHr6W3/XA3+VT5k5IrbroSUrcW1vmctI/imH35YZr/+dZn7whdEFhdbJqa5Wsl2Vkl9RBphg8q2GAPq7Ahma4EP5VzmEy8BAYG3TZ4raaM5OoMZOv5QETRiPjrkCoJ8uR7BYYEPyXLWHleI4Bv36zfcsENuu21O7rtvxmDC6qXB9zk1bKe2vnVpi9+vuVA89Ofy3FwE35BBjopBxpiZryzLa5crI4vfnhb4WPOrhjypT0V7MAT3O3yt71HLAto9elRERLLVVek6iuKjXAagf/9Lf+kYIdBkqZbKomkq11lm1By9z3xI6R7EKoV7aPoaB48XwSKRJil3BmWURPABZVjgQ7DGRfDV6/rN72PWpi/MC3NNo3735MmOnD69XlV/9KPJ6iexKJg3DA30dCbHNG3yJYTBbZVrDzqCb8wWnbYS3fqwNtJVMqGtYZHcqooVtOpaQAh1YFyRt9eOFI8bSqcwAg+wKpS/RNGyfFWM3q30+x5Xs8tf0hiSN1r6v5bn2vy7y43h4qLIiy92W99yrVJo1uK8Rv99aJz5CthFvSEPRMiDDVHkQ50FvgYX3HS+sPj4EUVew7gJ3wkAfMgkN9so5nn/s9nyuxblAAAgAElEQVSYt37rzLt0u+2S1kQI86AD50ywF6vyzZcsk/o3zEAEX+i3Y2EhkyuKf2lgYGbkuj1F8AV/0wLRNJt8vRxQOFLf/7m65ZqLlua0x8jG/Rj4Bl/dk2z/fSUrR/6XC0TWU+F2+6IvfGGHnDvXkXe9a1l+8zeXWh2vLw8DbShCGK/HJoQ8ZItOu+qMuVLOJ9SXQnmxfY0p5KFVxR2Lav58mxsQxb0blh+e51aiyFtYRwQfgtU/+C7pmmq0cmN/1EAEX50frPMNvm63/x9dfYcvNLY6tVA6y+JiWq10GRpwVHlhqUkEn6uIz/zyif7jP6bln/95pzz00FThB0K62SP+38Y5LJ0maA0vWFMEny8qJ5VVJBJWnp/bRt1t//3gIvha/LIBvqvV9vO/9FJXzp1bf7Tdt29qyG8YOBGiFnr/Zuo76aFfZ4qC2SEHlVFv2iu2aeTpcOSNA3Ui+ByinqAKFvigQunWcm1atdxwBF/J8Vv880jLy2Fu3Wj7nMbLQMXzelU1MXUTPeTnSx/2B0MpL/9bFlZebbN37/rE3rPPTPbPxRrYttLI5LO3CL5qP5Y6HxF8TXnalXaApnK0kVYf2w0zeTeelQW+llF32yMAq0TWm9D6NI4ag1GHNX3KccczHlypoGFLcZEmxmv0ETCQYtnZEOo3+KBfCuXHdNuRQp41lZ0+LVOPPiqd+Xl7JyGCr89A90AE35YgEoFRWOBDsPq+wWd74NDyBKMWCcqeIeq8AF5M2lK7HYfUKn63MERmv8HX4pfz3FoHnG07rMdAhkoGtuINdFDias4/uW/wNXxgabquHsICX5GryOfQ2wIRCbb+o5+TCL66Bwxki06K8GjG82f7Acn8ZITQt48SWno0Khs75bnI8eMd6fV4EwfVUR/NI0+3zN51l0w//LDMfPWruh5QNQs2gm/0/wMifIMPWhie8TH+Db66Bhro4Q8TxXSuR/DZTXwIHYivyBOf5WJgAcZzBF/ZX25PY3ADiyYrrDUvwkRTNPTNMIMGvjmVoob5amr7qzpMRXb5qpPBtQVQy8YCX+v2cPuinqO2tWo7NDRvtEQaKj+/byGNYV2J8RrrXJO562e7r+3uvHNGnnlm0ncyoFyK9cj0i/gp5uEwnZMnRUSkOz9v7wUzIvj6DbmI0h08HF4wUzuoggg+BEtTBN/AIca89dvmdD626AyBhugjkxF8RiP6Gh8mG5mw4AcaxcXJknzxUq5cPMmUtTu98OuQVQ3yOcuG/06ID6S+0hTCtY9V58UGeGPl+bk4KdLmpRilEXyuthbdzvQpxx0vzQi+NJ8JbArhVm+lwd79DXEM40rZ81qbxb1RY0UgNim1Fd6ZyGyDNyyJez+scwwsgs8LvjURPBb4EKy+9qNCZFEtRhb0KjZwFSbaRyWn+G9LS2l+g69KdJmJ87RhvM9rE8E34neHJbPK6YKO4BsnsPJi9aAVy4S6e1iHgYsL/TstoWzRGUJe1KYy0fFzssBXU7bt910slBmJ5kkygo+JB9/3I0ah5amp9IR2XYBGUYyHPYshD52kOa+4A1nbnZ003gCThl1/YBF8qd8mlGOLTqhgI4JvaMRd0xPUmFeo14/2H9jFN/iC7DAin6zK82YnzzJ7E46laWoQwefyZZ/tya30DT4jeVdz21xPEXzJsbzAZ+F0rXltv0JHBJ8KVprptgfY3tl5DF0PIbI/2FNmWZIRfIEmyyrb1xzsS40V/s3WOWNTb2zHiwPAdqbbDk1tUe44gsnH7gttKEtuuRELfGtrIs88MyFrayI/9VOrkVxwDaldr0JE8CFYfVt0lm0t16KBMbEt38jTj5kUyFvMD/nYojOEh11bWylGEcFX93crTnSX7UjQfx8Cf+i18MRgpLz4WuBLfVDWcAHd4OGccxfBF3hbEAh2NhmvrCy1LcdZ2y06t/++xy06a4nwpahx57K6wKeEwiQHz2eeuhzKUXaA+qg35pGnl5maL2gb0ZeaEQt8L73Ulfn5jpw925HnnnMbK9Xr8d1cjMcCH5JgPYKvpjZvaobwDT4fk5SleRRRz9ZqDGcxHwZudYMIPmfGvcFf8o9BfNvRxv0ruzkR1ZdKDFxvx9EoqWmbOq7dcHXLNRStjSzeWEDaqPsa0p4SKxPabRfltifAQcc3Ym6h8s+G8Eq8i1MqCLKzimvWd/xQpHKdZYpjrpTzAqjLdH2h/lVUNaM8LuhFcS9HDMJPntyaGDhzpuP0gqPIW1jHAh+C1RfBZ3pxx3Akz8hFgtIIvnzcjwzlY4vOIDqUIBJh10A5ajiQa76dQ8mbQcVjK/oGX5UtOusu8BmZfA6ogoV+D1sZKLv1hRDB1yAY17qAijAiU16WWr5V1KbA5nlfn6ptu6RNltLtP4LP4htnClcPlSRTFdcL1dv/3+a5B4/t/wVSX0IZZxHlD43a1hGeKYYI5SEvNT465AqCe7EeQWKBDyqUTsLXaGSdt8djJgVqzi31CSGCz8VAzN09M5efbR7Mml5vlg35ZWtPq4oj+EJJsK8KlPqAvYJiFtWJ4Ashe4ngG0FFImGj6RrYorOO4u862KJz+PXW2L7UUXkPpVqVvI+UBCZGzQs9D02lL+Wyk2UJXWxKN9aL4sux/udpEImSRrpRda75S21KcBTNzYgIvuI/uaztUeQtrGOBD8EaG8HX5tjFyB7H6kTwFf9tacnHwHH0OZ3kpaMFizYDc2/f4DP0lF56mMJgZnv0QijrZUNVyZeaeWWkyDmYVSmNMkltZNggn9u8fGFS1cCRcZdoK40pTwzCLitNV5tFuWJH57Hja/dujvtK6vqUxs8XQ+RmhGK8FfXGHnaeA2PM16qiufbChXQPH5Ydn/qUzN14o8jqqqdEITamnwFiqH/e5sLa/BxKDe1h89xI/9t0vjD4eTcEgQU+6NByxsd6gMyoN2pKI/iafyTVRQRfGOMCNwuZYVzrZdsSUytdhiL4qpxT0xadRbZKVOvoEjPJ6OdggS/PRc6fD/hN1QbXWxw8u5p0G/WwbC0Y1yAVDx0DmZWX/7VFSUUNNORkga/OAYOK4KvxsxGuwo8rGxFcYm2D1xxwn6xGMQ/d5+nGfbU5lEuxvjShOZ+6x45J5+JF6R45IlOPP+47OVGKsKv1jijIyxw19gMvMLU4bxTlv3ARG/nj++Vp2hpUMeE7AcAwTiP4TJ9gzPHqNNDFf1tebpimFsZlj4sOJnc0gdzmWrx9O8HSDdiI4Bt2rvUBeECji8IWna6+wVebixFa6YpLje3dKvjyl2fl4MEJef/7l+S//BcPDZMFdRaqTN62pgt8446zfs/Nly/NDxma0poiK8/PLQ5QfKD3GcmlrezGFMEXauYHmiwREXnuua4899yE/MIvrMhrXmNvEJ/naX27zNacb8hlybZorn3EhXSOH3eYEMTM9rRZ0PXRZWdTHH+6O/NQQd8bU4ZdZFlAh9MFvhBKAEJHBB90CD2Cr+YPFrforPPrfrbodK943XUXYpqex5c8zwavsWniGq7IVhlga47gs/UqdO1DuHiSsfyW2fx8Rw4eXH9H6LHHpo0d16SBlrLC9deL4GseiV1kq6q7Eko6Rhra7qXRp2phvOnq9dq9nexhi041L5KI//rjbA1ORSMXTjIXF0W+8pU52bdvSr70pTnfyanFx2Rzmxc/XZwzNsU5+miuPZoL0SPlerSFcbQTVQtX3UJIIe5XZ4HPocHbRL3DIBb4ECybEXzFY7Z9I3vk4lPJsdtsA+dji86yt1V2nX5Wrj570GtnF1q35m37NZs3YERhCG5bvkIE30DkUqgDVkcLfCYXyX1EEtfWIF9DGSybiuCzVeTLIwV14ZtaYTK+wNd2S00PW3QO0ypvPJR322/5I9w8OXmyu/nnxUWz/UOo12xDStcaqmjuQUphrrDKdp2Ips61VRYk0CRviDKrZ9jDtOcIvuDm3RAktuiEWq02HstzaTsh2ao9bxF96GKBb5zXnHhKfvpHXxURkcd+7k9F5FXGzzHYt7rpQNucps2zU9lYovEEtMkIvsJgRlsEX18aLS96VeYpgk/D/bLKcASfyds46lij0zD6W0Gu7rmKslWx3YvdwkIma2siV1wR6oWb3Uq49RNxLBF8lsRcf7KqDXFAQkmmzbWEsu+Ymzyfjzxs8yJPc37GCyEggg+2pHgL2l5zWZuOEq7e2kz9BoyI4PMp9duCaojgQ7D6Bt8+9kcx9estJ9qLP7u05L6BL57vnQe+svnnn3/mq6o7nKDS3iAxWVa+EGgtOqVBBJ+zl0aL3+CrOWCtkmVlb6bVzeomW0fWZjwMpp+KF4EbXG+buXsfC3y+FNPEW4U6nDzZkU99aod85jM75PDh7vhf8MB005WVFM46zdfA73uskHXyxkk/Y++wjc4fxZvjNfnOfx98PwP5PreR9PR6zOluk/K1o500yw5R2U4obKQVJHG8QCP4FBYHeMACH/SqFQXXPxDJxPK2ktsOXjoEKpy8zqREr5fJ6mrDdFVUJ286uZ2Z3WIaShesLNxEX51l6XmrJqZuomtG8G2nKYJvoJ6XrUKEcBGuFvgMRiuqXOCzHMFnUtOq72vwH0I1GutyIrfSWvx/a6cMxt13z8jkpQsyvXBGbrtt1ndyShlfLG67pWYhQZnHLTqDk+cyN39YppfPD/tnw0Z0PANbdBsQ+psWJUJJpssxgulrDiUPh2mbvulvf1t2fvKTsuOJx8wkSCEVY9gmQi+8CeAWtBdDHtraiaHRccf9EitH/SpG8LnuR3iZFlWwRSeC1R/BV/IDnp/o+n48z2u9zNTrVd+KoOzflpczmZz098ZI38WWvc3iJhEhH9YtlwOzBhF8NtW69AoRfOMGa1YG7BbuF98Xa8ZXBN+oY7VZZLRXDNhOR6Pe0ZPy69//rIiI7H7Hh0XkGr8JKmG8LLV9saO4oOdxi85aeTPwco55k3v2yM889JBcNT8tD/7n/yMrk3MWzjKaq5f1QqUgicbs2zcp8/MduXjR7ayaizweHBYOv8ZW6VlclKnvf19ERK567AGRq643c1zlfL3M5VS0q5p+RVM+WmibB6GtL3VffFEm9++XlZ//eVl785sr/Y7muTCTFCRxvDqDcCL4EBgi+JCEsnBqZ41iy86g7EeXllqkxwJnAUiBa/sNvoHvwbWJ4GvwtDosinD73/enMbCHxXEf1Si5QBMRibV/z/YIbdjxekTwjf+Vdi9fNDXqvHXO46vd1Nheb3AZwed7C8F3PfWV9UTlufzC01/2mpZhyruzFvlmOIKv9fGMq5g3Fgr6zLe+JSIi3bUVeevL33FxypFSX+wTUZPM2l5+uSv33Tcjjz8+JSsrdtvRkPLQdFqylRVn5wpZTN/gm3rkEZm54w7J5ud1X0gkUrgFUUdN57nM3HOPTD79tMzce2+bwxhJS52Ddl55RSaeekqytlt9BXVDArCRHw0j+Exlp+9nSOhABB+Ctb3RzMsmpkOK4CsYFz3T9pMuy8uZlIc1mlFnPDGwKGWLpbdmtL8Nk2Vi7SLyPBsZBRBCBF/RqKwoDouK11cl26wM2E2r8eZZ08GiygU+O79i5HcHbW/jq7/J76o9U9luekhkaPk0u3RWNpanJlYveU3LMKa7+rJv8NX6fQ830UYEny0bp5lYW3Z2rqb/3uqEvivvEKG1MVvMJuTJJyeHn8nTkErbOUI4ZyhUjGEr6B48KNPfWX+5ojM/Lwsf/rDnFIWvc+SITLz4oqy8852SX3WV7+SghNe2qdeTzsWLIiKSnS/ffryMjcWXWt+LPndO5m64odknbQxmeAz9yrB5XFefCxom3PEeQkIEH5JQbABNL0qNPF7ppPrYH+k7etH6Ap8/LjoYjZ1Y2wi+an9Z8edMRfCN+CHvC3xjElw3ItJXGTO+naaGyuJCg3zw9Q2+pu2d8wnvIby3BQ1s1DuXEXy+dXqWP+BrgPHnZ8NbdLZdMKxi6of75W0vPSQTK4tjf7bypXgpjPFtpRieSFYqxtD8TT/TaXD14k5aLG2JWjcVWb2TTTz33Oafu6dOpX4Tx1telh033ijT3/uezN5xh5FDapyvaKvNNc/Pd+TJJydlcdvwJqg8DPkG5sN3IJv+7nfNzSeMOE7I2WNMxbfsSl+yr3C4pnmo8Vkb7hHBh2D1f4PP/BsTphf9hianbIGv5VZ5S0vpTZq4ihRsFSnQ8raMK/JDGYvYK/n/gQi+ehFvro1MU+Ef87z+9Rhpimw/yQwbARo8jcq3nyvkc53Bs8nbOOpYdSb6XNXJwfMoKBAOFvSGnHLo//vU63R9J6GU8eGesi06u4cOycw37pW3vTQl08sX5MDbPrD5b7XyxnHhc7abQ4HVIDsFEXxFoSTT9BhB5ZjDgPI6bysz0snktuUplHqGeronTmz9+fhxjylJ0+qqyE03zcniYiaHDnXld383zJ0kNtRZMLMy5Kpz0DZvhYT8sOLDsPwoW+BzKPXbgmqI4EOwTDaaAw2igRayzkJCm2OV/Zv9b/DVyPwRbxO14W+i2g+jEXyOQlKqLobUfSO1qrERenkufWU5lAg+2/er4ptnNk49Vp7LxDPPyOTevSIjvgVj6lx1td0+2RRT88qptaNNuIzg859P29rvjp53/Iwv8LWI4LP9Cu3U97+/+ec3HtvT/ECuC5uD89WdoxIRkaUlmXroIZl69NEoX3/236a4MeqZ0PYQyk0eV//+b5V/ryKVslNFNHkRzYVYYiF/Uszypm3kwYNdWVxcb+uefnpr2+WgxsohJUZh4VKY5EGGI/hMCWWOAmHT83SPpLVa/KhzzMK/V19krLdC1+tJ3/J63QU+2x+Yr5IG59bDrQb/LiBBvWHc4E2usjFtcewSVARfaQJGTJKUNySj/7nCKVvng6sFPoOalvXO0aNb2+KsrsrKL/6iuUQVNXhIaxPB14aplzLDiegL0OCbPs5PGVI+9bIwHwHKolPa5FvbLYucf4NvROPa6tSW0t3mpXGjsqz0fNOPPCJTjz++np65OVl517uqHzOkCltRKEkOajxcUyh5KDJ6ntF0Hod03bYV865uOzbYLXgq8JorWqRSqkd1DSuuQeVZxcSsrYmcOdORlYuZ7Nhh52X3AbZOEvLDig8VI/iqPkeayt7UbwuqIYIPwdoe9VO6pWWNVq74o6VbCRV+yGoj2rKht7xDVC3reWn+AaPSPbNwHq8ROxpGANuO7e2B9rJxEXzj/j3PRbYHF6YYwWf7lEV92+OcPGkpNc35KtNNi0QIEYY+02GCywi+kLBFZ0WFVf/M8gAsrzlhOzRvHBe+ELboLLOxuCci61F8Jg8eAAVJNGLUThC28yDWPI6pH08WN7EeIvhQ15Ab/PzzE/L00xPyxS/Otf7Uc91z1+XyyTaK+hBoBF9xjiKKvIZxLPABlxmd2N3e4pZF8K01X5wUEen10ovgCyEN41jdVrbuD484wPBklgwc8v4F3PAj+Eb8+9gEj7+BVgbsCS3w9T0F2d4uzXEEn62IPg0RfCp2vrucGS7brZDn3npZwgt8dQ7oeIvOUUKM4Bt1fFddm63zuTu4OUqSGTQfefj/2XuzWMmO60p0nZOZN4d7a2RRoizTlkHpUZI1W5J/2G24/dCGB7Tf63br76FbMJ5hwPBPtwB/GP6xPwwDz19yy3IbaltSSyQtcxRFSqQkFosSRbpIFotDcSrOQxVZt8ZbN++Qw3kfeTPznDgx7BhP5K2zAEp188SwY9qxY+/YO3T3Ch80XklzR8eDj4crqa92FQIM3JUwN0xlW6oHX6UXiImNO3NmokZfW0tx6lTDz7iHOkRcCZNWB471KItwf7/G7kFt4KsRLfJCgGsPPmQcV3pNDz7yLQoCnToKXMC/B59zu4kLONf68YuwKdJ5pBQCMUniL4TY3MDHL7tqpT7PQ0/aFZw17sJgqZ3P8wKyeRTcNB+5nHxCzxPIZDlWFd9e1p86NJTT+jkkx2y40oVf2uO6bZnfo5y/wedy33FQ9DvvpDh/PrHnM2x+3wKYpQff7O9Ai3RabHAPPq7B3l/MQttQr74QKVl15EDPiHXcFxW653CTNF7ALDTpsqsXZY0FQaX8LWLmuggrOOLuo0MkPzO/J0nYMala71ZjMRDnAxw1alQAK8OOwlhYqotnsNRA6BCd8ub4iTu+KwQEDbDGptmPlIwmlRkWtUgefLr9WdkhPpSbg8cBIxcd0oPPAHYefOZivisDX1VYBBrZ0QmhsI/ZEOrSwNf+0Y/QfOEFbP3ar2H40Y9alcVfg3pr6+TJJu64owsA+MMbMvTYBBoDkYS2+gvektv5WPolpjlVKeqOABBPN7i2JcjK8y1ChejTajz44rqAEhKykK8UXEl9VUOOK3EumLZ5IW3MbGO5jVBcMHZVt69yWMOVRb27Yj0QG0EN0elrGCvpax4RC7mwdy9qD74a0aLAKyyvdJOya3rw2VRu55ERPkRnFFgAgaGS/c2jMYfnwZc3mlVtn9H14OMKrLnfgglKviW0AAY+0yYUxsB3hxsQyYZPDhUmxtTAp/Ik99XFURwyJNjaqpqC+OHKwJeePYulJ55A2u+je8891uW58OCbGvcA4KEHLdvJvsHne+NbMA++XAXlXzxubTyPQWV9ugTFzugQJUlcLAqdiwIX/bkA0zsYbNseTd9FQ0ikqPvHC1yvn6g8+AjEBNP/1PM3CETDycr/puNuOoxV691qLAZqD74aVyQooYTC3J5Ul8X7Fj5Ep+TVNo5RxQ8Npon0iohFdiIbTDhzWSlvWHjwVWIQE0F3kSqM+JT2uFA+ez8HBDDwGRedl05DG/hkGI3QvfVWvP+Fi9i//fu4sPda7eJD7QlVlLNI+P73OzhxoonPf34bN9ywPf9QQWfE3P+j1M0bfMnampNypnDBY/MYbo2Bjnn+mEJ0WvWN58lYVQjLvEEz5vXmC3x5vfqOcH3JO+wFOk+ebRsbQLerTleo+wq80FkBbG3/0SBawnYvyrxu96/ZmI5vrmEyekniiWZXB82ADG5XsCBRvxsK4bvqTO9auKvhHLUHX41oUXiDj8fQNLgciR9bcE1pKEBOuWNGP6QrDIYO0RkFotjV5LAN+WLURo/GnCxDSWkWU4hOigcf+11WRlXtca4YteSNXqvMKcu9e8IwkHHZ1vHjaL7+OtLLa/j8098sfKO2zaYvZZcqdC6AhJrDbD2xeJX3+wmeeaaFLEvwyCNtadrp2vfZZ7FeIAGALHFj4HMN1wa+JOPwGU3jfwGRefCxEHrwVQCvJHAK997kCPpUhVhIdM37Qobo9IHOHXdgz1e+gqWHHuJ+1+2vyi5WLijcymcLiloJu4sGMy6QLyULpmDURlFC41KORt3JVNMppJ7b7kDUrYVmqVEMccwH2xoAagNfjUWBc96hVgw4O1xZMj5e9tAu2lUcNEvqeG8biLt3s2xgasTmh4I3GzASDbm/q1bqqzxxEzYmvkJzHExG8S0ciRiEw3qMmxDyDT6N9jbOnNHNAp++mIo7IpJv1bypE4t8PxhIPlZAZMw3u12+wecbVgY+Sz5TuoDhex7xNEWSqkPJoipQImPYQnTZaPK3B6NSLIxNggUgEcDi0Am4pzU5fx6tkycBAO2f/cwJLS5orPV0c9g6uETTd9EQEikC9M+VMAS+76VW2ocGxHjz4GOhusjssB5zxHPOMYbOxkvoK9VZnQrVsxw1agC1ga9GxFB58OmwRgo/zsbhuGT5nSe9/MOh381Tzz5U4e5S72xOlWoUmdbE4y1kbHopTZl83ZlPJ80G+j7J+NQIaVZZQpUGPiMhnN6d/gz+eiH7dL6bIqrDuCV8Gt2sDDKeMUriNPDxxsOmz9KME/JAp0DGgy8JEKLT5tK2KK+vWT6rj1Nx6Lnuvb5YFq8EsZDo3Ni6wB58CeFRWG+XTGsAqPurhk/sAoNGIFD1AVEZ+AjwpudwtZEq8i3CZaagIB78aw8+zt81Kkdt4KuxGHCsJaNEUtQ6XEmYHYX363lohA/RWcVBs1Smpw3Epli3Cv2kZDwjCVwmxhxH2sPKH/tVCayE9LoGSy/TMJSBz2MVVCNJYU5XPoHigatbeVXJ2Qsh3+8QKTNIeKoySsTqwefcKGrLZ9j8nvlWpvkGn0jK3PUKm9l6nrefHRprOXIBFBkRksSFTwOfayxGnxp2iGROL0a7zeD6HOuLNVjP8908iC5whct9vmDb5qi3WgIxPA8+J21YwENdVGNnCg0D39ZmhrU1OeN2NTdqtUkNCmoDX40aO2A9+PSFfbpUznrwycvl5I+IwScqr6kFg844mho5XILrwWc4IJRsMbxZNwWv7azhXWqIl+XVgHY+zycZ4Sz06GFBLifkpNGoa6pU1zEuuBxGU3t8Vbf82XJj2pNMEKqffNalBDNIY0koyCrhus8S9tFjXbA3qiJ7BJnMhzxPvNAhOnkfq5ZJYkAsfRC1wlYT1rK9B2Wpi/7cTWOkguq8tpvbXsMvrsS5Y9pm8Rt85rQ4h4oxakZZcIpQhxWd83NMY+cKov5gft/eBm66sYt/+IdlvPyy/3fNY9A7lrArJ8BiI87TfY0aDFwaMYTl+QKBTt2mjEZhQ3RWwbutb14HgEuSskxQYFUafMxpEhnJKlfq67adIbjc5+p15USY8r3AfGqEBEWRi86Nge3bWEoYEBkLm8nToWPgq4p3x9JvUlRApHcDX5ah8eabSC5cUKcdDh1W7A+u+8w2RGfwN/g0PfiqMuiVqvF4gYSHqRxf5JWOlfYxCMMKREgSF7Z0yj2bDOSyzU20770X7fvum2jpcojhzOONBtYDocpnFgLCtbgZLWuIhpBIEaB/rsQhCHWMDYKIzoq8SERGeoiAHbor5j9Rj7K9nWA4TJBlCW67raddnC4q17sBu2SAdzfijM9TowbYN/joQbtbTz6JZHsb25/6FNBqCfOXfmMYlo5dpcPvYMsAACAASURBVHRAUmhnM1UYoQLKtEd2gTwMr/ekKXXpdVPFnpcmnN3e0ChIoT9qDz7GGElRXFTSHt8TpwIJ0MTAF4ekWoQOm3E5jLKydMqtak1WzQtskWV+QsD57pfWU0+hc999yNIU63/wB8j27hWm9f52nCO47rOEFbh0wfCpZDz2N2EAb2/wLfwiZcFpuPMWLmCfxUOyW2Or6+XWfughLD311M4fbWz9238rTOvcUOwALooslxGBNwAHL7/cwEMPtXH99UN87nPb6gwcqPmk7GJFnP1SIw7Ew3PDYVcZ9BRIgJJsQZG7nLSpqluaGvXGoAtzDs8Hb9M+irKvoyCiRh61B1+NxQBx52y++CI6P/wh2keOYOnoUb3yAkG3al760Hpx+Ybipy8XQVHtkkbhYdPQWOcCWcYJKl8wiJkdep0ZQjK9kFysJ4aJoORFiA91UvI4X6M08BkQGQvfodJRIXtgsAAKsJ3OYHmIX/AuF7krvXPffZNaxmO0H3hAnpjx4KO8RVwFXPNY7lt0OgXyDKM+eZcvDz7Pc10ZMtsBdPldWXyxJKhWZJARu/Fo6dix2b9bjz1W+FbFMIvmLvWCkYuKY53et93WwzvvNHDkSBuXLvk5d8TadhWyDHjppQaeeqqFy5d5Z7YcQj5kGSsWdaB3CXZFiE7+T2Ha4Moy5BFRjaUpmEZMzxHeIw8pEEPf7vr3vXcBagNfjWhR9OCj5Wk/+OD83z/7mTS/zW0+JU0S9wsXQkHoEJ0m6ft9xzHKPW0ozoxNDmAUKsfk9C/4RtEHJuKpTa7OVb+x/VV6D5LzN4c653Qp4bsijTnhihRyOfmEoQ18Msze4LN5g5NenU7ZOks5lE5/t71f46+fwtWVqEJwDocLod/jswULwm07nEdQxAa+ytaiYcVvvZXie9/r4KWX9N4vKYXozDL3w7IAjG1ReLEtXTLeFWubZ/BAoHGR0XeWHJcumamuXPPJWPjuW2818M47DaytJXj22VY1RDjChQsJHn20hYsXAwoqMa3NhQI7Rm7HrNI+NKjcG70cRuOlLg2GVtW5MySEs5nTOJruzo2cVrk3eWWW7Ro6qA18NRYXFgyF8qafT37F1q9ri+FdKG8+/TR6//iPaD3+uCV1NBqm4N3kOHKkjb/7uxV897sdL3VSEm1tATff3MU3vtHDuXPmSnudtC6NhXmllQyu36fkIV9cvj6qMs2lAkptsKNm1v5sVCWpENfMJn8j25NG37QJhdtvoYVCQn1Vya6y/lwE2TnCaKtlVNCRQeeTYq2zBsBFenupyjXAvbFbkYGPB2HfBNK6GNwlKuCmm5bx3HMt3H57D4OBXcXem7wIzDgSuB+LcH0fk8LS5A6f67piAo/Gt99O8eSTLWxtyXKaX9qNuV/OrM4vRgwGiJtYCbIMuP32Lh54oIPbbut6q8eH98mCdnkloHrwVW7IyIN4OdbLZZsFnFwLSHIZIiGg4saxR48o+joKImrkURv4akSLghBA1h7zBQLvt55lN2oIhkMXBr7uD36Axrlz6Nx/fykUl2+wNB49ugQAePHFFtbWHAlpmgP00ENtvPlmE2fONHDXXeLDgkujXEyQHmIE33hrolRO3nhEbL8vQ2gCuYee6jvbPvMbVWb5pnB+4Cx0koErtGWVUoQ08OmUP/PgMy/ei4G/QppiqMc3Sl5AjhFVvwSWC0zh2ijKM2RqSSUcY57XMD2SN/i0ZNnAi9SFwXhjQ18Jn2+v8yYvAKNbABIB+KUr1jZPQeE35TZYRJEhZsyyuN7VNsHlywluvHEZ993XwUMPtYXpVOtEv+1xeM4uxOUqAsZj4OzZibHy7NmGv3YFYJiLuI7CYQE6RzWAHBkt2Jj7UlQ4bNCumP9aN2vUDXbVJ5X3beUE1KCgNvDVWFw4tswkmsI6uXqegc+SQY7Hipv6Wtegy9DZ51VKHVNSbPeQ116b32o8c0Yv9JMSs5OHY880E82ma22oAmkyP3XRPfjkf5uCtwqkZXPXvZ5yw8uNPNfjlR+YdL7Nu/TYMW5CLqHvWPYmVwt0lpNfOZdGfSyydix0SBEJkVWRkfBuBkUI3s1t2wtYVojcgy8ehJ3YvP1styi7bRAJm3Mu5oRcFotiNHWNRWgnS+Pjj7dy/14i59stKO2Xu6Sh3u4jeVjcu6TLteC6G6PmuRxigqleSheX40PUY2cKQSMynh7JoL2mfVR78NWgoDbw1YgWBceTMW0nFYWjC2wDkUK3Xl56pZ4ueOMCiByabTJVBqiqad93H1a+/GW0Hn88ij0tdShZcIW0knA5/7sKA18hL3tj3/KKbmU38BxXXJj6qXybd3erjLjg8pMmtDZWelNB34PPJWRTQkaTOiRMmONgDLyQipDyQEyyR0ljFumgRdVnAF/g8mksdfUGXyCty7RYnie6C3lXhJKBj1K/LUERrpkISQKw2AY+5yA0XtRfri8Ylbox1glEBHVeWB4PCrh0KYnmMkEsdNiC7f/h0NOCD6AhX/AlFRTVXKBUwIA58JM4mMOxHErDZI0XO43i6qPp2V2RUR0qJ6AGBc2qCahRQwSfkeV4N39Zpq2qM/9d6hmj8Bqi1MVCqVtyLPHr9EUZCZzc7NYsQmHXmBero1C6dAlLTz4JAOjcfz8uXP8rxmWp6BAq6UoZiQWSwBFGmXKKBsXwGhfddSkzAGZZUviN0mVelM+uGVy+vNxCMBVMVVXw/hYipIHPUb+GOICaG/j0/naFBdB7l1EBkTH1C/sGX6xwzWOt36nlpfXIuyKaMsFhMs5FXulYJolpARMRK8k+6fIpQvko3wVKsu3WFtDRe/d8EdrJwpRGV+ezhx9ewk9/2sbevWNhmpDQusQQsZW8bOALVFENI7jiHVkW4bQ0vJARZGplkieBFPmsvmtljW1ADSDoD54eJeR75pXv2Vyd9pV9bokRtQdfjcUAVePj0IPPimlKMru40TwaKTZPS+WTFo2KxFUcxgA/HnzJ5iY5rQlMFJGqPOMxcP58UjwsEQnn3SDOG/iq9uCj3ODX+R5KUPJ+gzpvtCyE6FRjYyPBI48s4dVX9cLaUptQ8PLw3eE6E8+BB58vg98i6CMW9hb59FZmIEOoz7qUYDRmIQ+lOnDdZ9ayBO9GVUUhOvl9w6ZP+Ik9Tzw+1R4VPZz2+XbOiFFtFev+4Hr6RacE1oGHQZoVOR6j981vYuXv/g7NEye0aVn0N/iocHUZ6qc/nbzzd+lSHCq0hZW9FFgkD77Kle4LBZqeLqo+5BATjL6oOoKGBSS5DKGBz01xVelGa1wZiEM6qVGDA5cefJT8tt4twjq4HzImiZ5CZ7E8+AIRwcCHB5/LvM7KEmXc+f3kySaefbaFp59uaY9jlpV/zBv4qDR7E9y5BekxjqKSW32gdEK775NMwQqqd0h+8MEl/OQnbdxySw8XL8ryGr4/meNNvt/g0+pXiYFPscRyf7tTSBTLpu8PoQ7JC3ljs4KTEb/Kivqq9uAzK5DDp7zyrjSFzhwhN2UXaAZUPHYXNFEbUStGc1gkA18Mfci7YFfEpEOaJ06gceYMkvEY3XvuMapjkcDSbBqiU/e7GtXs66X+qIQKe7DtCCauLOIiiACuum3O5wzPlB5AuYTLozfIXhzyNqJh1l2xpESNIj4Z5QvjccUcPqpbqzVEqA18NRYXni0zWsXLEjPfXPDGkoGPLcCz4lyHXp+3VHhvvsy+VfAsYCXCnGJCra5O2Hy/n2BjY9IpNl0TmwdfoSzFLTtWKev6gGKcwfXEybcztxB4lxjYqp96ain37xa5ShMDX3Ch0DXfdwhffLJ15jTa99+P9NQpswoc0REKpksr5Jm5qr5KhsOF8IKJqc8A8De6ijz4eLu33uUyj3Bwy13rTsaO4XaWJyuHrnLeBbEwugVASKWf72GxLt8jgenamvBbcuHCxKtve1tMS+7v3Ty9beZjzP0SM206YNsxGPipp6Q3cNCBu2UMbCDqgwsXEnz/+x08+miLmy7KvouJKFcdpplPpl9zXFWcEDQiG5nJ/r6GMYq+joKIGnnUb/DViBYFHYcl8yApjBwyKNnGmGV6Gycv6XicTMpJ+ImS8dgq+JZLpYy7btUrKE1p6WM59GWZWagcnTBrurbCLCv/aEJjOZ15xO6yQU98648UwlNTubEQN/Ly5RVCdOrVs7lp7jkmRKxv8Dnw4Jv+ZmJEkd1etZkeP3fXN7F0aIylxx/H2n/7b85uPkR5yFCAt10myLwGqoyqX9gr8VERN0d0RpkKDHw2ckhlBuTQIV85DXU+LLF0rgR8+SpG2L3UcqV58E2hI38UMByid+ONSPt9DD76UWz+1m8Z1xUTWBpj8eCrqu9KPE9GSIBF1HzxRaSnTmHwmc8gW1kh52PJXqQQnVciqN12991dnDrVANDCNdeUN+hpOVFvtYTLS1m2IPoBB/WovC2jGjtTCBqVcRpX5Rt8wVE5ATUoqD34aiwuLJgMhRk7OwwwCXUUyLJvUkXGLgjKXzrERbipBBFqTCeiTCC1INTEg09Ih2VeigefLGSnqVFVRpOfDBblU2PVciAz8MmqJCcMbeAjEOkyzKYNqAa+chMlIWo9zrtY2LNpk/3Rr+Fx5Rnct+QiBF9GcrwuNQaBG47TZ19qv8FHS+xLhgrJM6VNyMoefLbGrhjlzkVBSKWf+7LDh4vTDVGn+t547TWk/T4AoJV/m4+51BbrW6yuoZqPi7rUtQx8npGeO4funXeiffQoOj/4gVVZ3kJ0euif3TKXfGBi3JvghRea5L6qtA8JRFZ5IThI31hUspvnP/cNPpJuwU39ld9P0FVi16gEtYGvRrQovMHHCS2nc2wnbcRMHSH5lUldBf0SW4Bj5VP+4JllKCmgTAyUTiAp3NSuoVIgFf60fLfRGwSN0FUYZFn5x7yBz5QcZ3NCIXSTPPjon8lptAtxvUjyCpzcQtCdr148+EIa+HQg8eAT7zbujDey/jTlr0k29sZ/2XJ1wvW+9VYDL7/ciONgUkGV3sZEdTuf0ZjFqth13WfW7eTIU14NP5peFtEoFw2VYKbpSyE6dfKPRkjfektfVo5QkRHN+DNwTdcihBcWIoBRgUVtnGZhPoFi7sqYaGs+99z836++qpWXbYc3D75YGeaCg9KNPB4+zRfVMOgabCaKMW8GPenf1G+29WokjWosDSHcPx3p/VzpCmrU4KEO0VnjCoX9Wx15o5fMkMDKM1mWOFGsSQ18lorzKDcQTaJchW0pgH3DzTAWtxYxCgKFc8mBso2XKcG8zaaP/drNL9q64/7EGmgZA2aU894EBSunpL8gb7MPD74k5Bt8BuXrZPF5lqKWLcuna+C2AbXoU6dS3HRTDwDw27+9gY98xO01bVf96Ar5ctvba3jPuReQXHofsLLHT4USJN6uxLsFzyPM+fjoFLioHnzGCc0QQ4hOqhdU94470HzlFQw/8AFs/Kf/pFVHDTNUZeAbDoHjx1vo9TJ86EM0HljFsIv2LhEtShqpGQsycPxWVJZ807Oea6V0VayiNGYLy7OK7fAmrjD7uV3gYD4WYR3ZwvVlxqiNRBzipGctQTYTLOJMimrsTCGYkJOL0sVRcREVzpSs4Ah5a7WGMWoPvhrRQvkGH+83myuePOW/eXatguQXcvhtkhlXuCGlLFDa5zTC11TF9y0iE4rB9qtDr09j4dZksxV8p+gDkpwHH1157i7cEStUS/tNs1OD3ahijaaOF0mhvPxC0KxHz4OPyHtzayjJMr8MQmf8Dd7go1Rpinw5pmUmbCwRh31tyq9+8IPO7N933911Rg8JFRxM8sV/6vlb8ZGXf4C93721mo1xYQx8tN+sCtTBgr7BF8qDZ1aNq0tFlLoMv0/RfOWVyf+/+mrJWCu13UaoyIhVMRqSLlnZR48m+OEPO7jzzi7efLMhTmhYfpgC/BQZy1wJAXVbF1GVHlcgDJsJxWZdJA++K2kdiUDpA5kHX1Rgz+jELEHaYliJUh60WBexyiBWEOrJ3DTOXN8UPnx4jcVDbeCrcUUiyTiGAc0ypExV8jHLyt9NGLRPDz4ZdJVvrjYx3Ypd3eoslBmwn2d1Kr+bC2Gq77y5mubq0wnLp0MHGYqCKW/0uXiDTxu+pWGRgU8TXt7gqzyAvBxVGPP4ZSWSb+J8BQM4G6LTY19Tizb1+qXD9PDjh658/fvW3gIANM6uAtvbXuqTgrVcRLb2pqjADisF98KUTxlL9s2mbyId7yLo65AN0cmV69km87Rwg8Hsn4891sKXv7yCe+9tCwqIH7GSbEuXaf4f/nAuAx0+3PZaV1QQNaLw++IpC1kavURrMchbVd+Nd40HXxE5tuwWu9IaER7muh32F9nzCBWBoCxhz1be6K/nazUQGfh4IToJY+Jq2Cq/0BHbAa0GF7WBr0a0KHrwcRJY3C5xn4eugXVlIAv7Bh/zdwWPYbBegyqkqYcNh+lXlyE6TUO38vIkkwJllWnXM0X+Db4qDHwFA4ZuqF3F4gt2iK/KwKdZz9aWhzf4Sl6wHqVVjVuYhvc1rNLK8uX/Nq0/pAffQqAComM6Cy1OiE7ab1Rw91WdAjk8ynWUhGLhdvLVrGmaTHp1NcWbb5q/jRnDm46k+yPsvpBbF4cPdzAaJXjqqSVcvLgY7gYRkrQDt8YjF+00PRpZ122gANS9kGdSJ1DRJbcKUO7fxTNu8rCIdDdPnkTnnnuQnj49+62sxvCjZyhd1HXQgbUNhgadN/ii6kMOMawugpjNed2uYLcuWF66mN7QBQj6o+p5WXX93JGtmqgaJdQGvhrRY7m/iubmOi0xUTHCMwyUH071t0G58LoqeEKwiayVT3ptlymjXSm8dcNN0XVk6oTvvpvi0Udb2Fhn+znApmZ9yjcvKsuS0o+pUYhOPTp0yrJJ7Guu+ihgOASefbaJt94ibNt5o6XSwFec/52OWWPIfeCcV2nU5TiLT5nWRdklA59HUD3zfN8Noaxp6fu5numZV1zBAXgwKFTrrO2OF4Jzo6gtfTyrQEgDn+YlFBOsrqb4+teXcfPNPTz3nOnz7PaKKC0ZhVW4ZIa36AWuIpubZflnERQZsZAYsuuoZZteSosRxjQuQuMkKJ0NA3jwxYyS7BV7Qzc20L3jDrROnEDvpptmP7NkB/Pgq2EEU/5OzVepkUhFJEfWCDatKlNURFmVPwiVr+WfQl5uq9yDr8ZCwPQUV6OGdyQJcPWZE7j++LfxvvMj4P1MAi3DhntBQVq9xOLlauOT3UT1+QafLv3uSNHT+rnSnw4GwLe+1cN4nOBCt4X/O18948Fna7gyeTPHyFisWU/hplpMBj7CzbYC7TxPDM3by054iUGHHDu2hCNH2kiSDF/84joOHJDkKQ6YFmntdlYIzZlltHcTqGNaGoOQ0qqMyIrf4JP1p6xM6XxneYPDU5fx+CfuaNCGhEjvB9IITryJY89+X3DdVdzybBd1UM/jTBrBQOiJorFI7723+DbmRz6yRqNVXqxX8GQfUpPZ/h0OuVJUkogKqGEC2650MRRCryBl4ZZyH29iJoJ1y/wtkjmV8jyhw7LsSvbgk/9N/aaTxgd0tqIqou+wSM+dm/1bJpP48uAL8UzAbl5HNtDx4KsURN7J/uGlDVVZEi3qiWosTSFoBDdEJ0EP56pPKr9z5vwGZg0fqD34akSNj534jp+CeYYBhmnr8Cve2x+igrJM8Bst+wxVvcE3gfhgWqbXTEgvlaO5f1g8PVbAm282ZjckTzHeUy5DdAqhmoiizTb3+3rvkFFRvPRpQRlAG1tfAonqjT2VgcPVXLUGoUOOHGnvJE3w4IOKd2Ty6z9/oiLU02gU02xuKrNQi56A4U0mRm0qTEYzhDGPUhbdwCdupc8QnWU6vBWthcoPPwyiOgv5CtHpWGnous9sb9dylZCejaUFQ31uHVv1jSShiychebzchcxBhkwOl0HHVaRqhsJBrKEGQ/JiatnUZeuc1iqMCAaGP1dkpqdOofeP/4jOHXd4OI8W57srD75Y1o0uymflOC9RTSEaLjbrYr3BV73htGqYdqPF/YRw4BJTjATiTdb3ZRmyqMfmssTCQGTgC0xGqf6qCaixEKgNfDWihVIvzftRIOnzN167Q3HVTLZwu40lxlL5ZKpsVuW1gmZBpoc+WTUlhbnDg6tPAenJD/0ezu7/gFHhrNIuryw1b775YUhm0NPLXKbE9Jau9VhpFqCl2NF8g4/lixsbZkZccsJYQnRW7MFnWqaMbyTZ2G69SBGnUlkKyeD6on9ark9vSirKhqpFGLQJfHeX1MDF8/wOOX6KuoQ8YCEWZRE68iVvZ1LJUQlz+QkAEpkmeQH7MF44elvSApWFt4pUA5pl8EJL75//GY1z59A6eRKtJ590Xr4JfA9BVUMayVSiQ7AI2XYMh56MZgHW4sKNiQHM2kgPbVlpHxKIpOgVfSBYYAGLSnbF/Bc1guPBRzkPlKfUgl4K8GbJruESdYjOGosLC4bCu9HN/mZ7k1hUkK5QIKJDasPzzGzZMB86CmhyHexmqJnflQdfHqyBrxyi027D5noaKDow5YW8Y5RY0/BeE/rE5VEE75je4GP7q+RJy/lbVmAoGSWoclixEFRjMzHwqQ835CYFCI8jhIGB07I447JclB3yDT4qvTG8wRcSfGNLRQc7b1fi3cL5mCn4/iOPLOGhh5Zw/fVD/PZvc1yWeQpJnx58rAHK1INPYzG46XOaEkz2XYuOmXF+vp6ob4EWsPAefPK/q4Jruly0ixr2zzXtlFpFdYrrVpRqoDl3NXeSnLd44/Rp2Ow8rsZClxctCkrbU+wNEdBXNvB5qt/DGST2Ll8ERNWHBGYh1TX4pMX3bcSwWRcHO3q1kG0dj9W6zOCGwitisBcftQdfjWjhUhFIU4roeSTIDA2qfDrGRIqBj+0q12/tqPl58c2uPFzdmOUaRTQ2GhEdqls1hXH26MEnhLNTqV6oxlkyJm3ewGfafFcGPm1hS2Fsp5TFT6PJrCwF9ukczTLgwoVy3YW1ou3BV/y79uDT05O5kn2L5dAvgBT3pSJcHgNCDqMzzAwCwk8ekOz8bwRaeF9GKccKCN6B1ech9ic/aWM8TvDssy1cvMiphze5A054lXxJ7n6pYt/eqz7oxRVO/Tx5gEsSa0Dd0SRT0tagI6ThkVr2bvLgUxZBrCP2N/hUXUfXFdDePKTQ4BO6uo8Yx0wK4iKsPfgWC9Q2U7s/qj7kEMNrR6yXbQDoE3OFG/xE8iyPfdF0wHo65vvvb+Nv/3YFR4+2tPJVgiiJurJRG/hqLC54DMXGKhjhrRgZCjeVHWtbXR50XN221H1Hh83vQrdZ9uArG4w2N4GNDf2yTfuJNOMlxgsKDSJjtmuBXhdTWkQKPopSPQblhq5idJr8O9/p4mtfW8Hhw21+AqDobWtg4NvcpIU9piqIE5Y3hTTwBRhgHwY+HX7MXkhgDeK+EIt8H9sBO8+bSr8FRsJciXdmkHHcIB5LsKlCxzN+aysppeP2k2dLgWjdahmnNTrNxRCavHWou155+zuVVwohMPBlmQGBFWABSARgT5eLdpm+wWddt4HsNf3bWAFOyLgocyePKi4WxlC+CFF58FHqFqRhf/b2ZLCHMhdh3eSx9NBD6P3TP6Fx8mTQenkqujmfiyfsf4lM9lKQStfgEqGYtEOD3qKtBy6EwrZ/j7mNjQSPP76EwSDBkSMdZfqg/c3Vo9WIDbWBr0a00NRLS1HKT7yN46RCpiCe0sDEoFY4qPqwZhFo4H33tdHrlsMeekRdolNumhULYRV/586l+J//cwV///crOH3agL3yDt4mVjne/EZCLzOfjBVsjQx87gT3Yl7NvlGsO4uzqR4sF0mWTTz33nhjEmX7sceWxOVZh+ikzWNyE0Ia+HQguRxirGDTgGwcTPYHgONxHMGpy3eITim4B5OykcBHldzLBuMxlh5+GO3Dh81uhejCW8wrBpadqWXEcoxSPQLBoXRRwSHYJZJCXhd5O5F0Yiw6I630GbN2s4wWfY2VaXZCdFK2plqRQYfrs4CLOWoUwtUFPByMnK3ZCOQCGVRd5+q99ci7QYiFo5v8Bl+g+j10YMxjkpw/j/bPfobG2bPo3XGHcTmmXv/Uiwwx92EJOwY/LzQT9JXqD2ERCRl24Bh1p+OsSksoTgrZm+AL+3ZfjaCoDXw1ooVSaHesRLL1ELPJZ23gY+A/VBJdGe5MD6bZcWVh0T7UoOoNvq2tBINBgtEowe23d0n1Uep1lVGdVHK9bgfFEJ3h3zPJ5y09P8gRvpR18YyqFjQZZTAw8EnXVf5jIUSnPmmiEJ3GTWAI98qrDIisSglp8g6rKq1PA58pf/WN2JQEvEsV0382T5xA+6c/xdJjj6H94IPeaSl58Lm6dey4k50b+Gz2SBGjDXkxQbFHuVD2eLm4YpDFO//lGfk1DHwbG8C778Z1dI6N54ngly7x/pOm9vPSGhEZEWQ7tZcxsrzV42p+q/LFYIA2QekcFisD2AFV5h8MwoTojENyDYf0woXK6pZ58EUFwm0ACt2hDH5O8lkw2kWRQbQgELZdtU2qaxQwpSj61fkBrYYPxHVKqVHDFgKuSDHmWW1QmhY6F4q1gvKBrcPSg0/X4GhioLShwSS/aZdIFeYSJd/6uiP2qugIYfixvFIQCckllrIGkpxVjTpGboW/JPevTFoe2zcqr4tgArsDA1+jUfxN6NFrGaLT+Rt8VYbodFyMS5nW14WEUHI3tZ7YPPgIn5xUWeJFAJYef3z299JTT/khII/hMEz/W3ZmkPMjcQ8UCg4RvcHHoqqzNkWuVkFHB6UK0S0sj/1RYOBjefBwCHz7Wz1885vLOHas+C5KlYhVueaeLjPmxcpKJrCmndAZoiTGhjyivFdct/GZO9RdR+ugWNeJS1QqX1FB3Ht9PRnsYyJcCXOLhTvdDv8JkUr7ZZGTFgAAIABJREFUUJNfJwjowXclTK4YIOh3rl6MsAfpDFvU77DW828hUBv4akQLpV7atRKJ+cHqssvOTt86epRzM7/MuU0MZKORvzf4VDRkWuHsHJ04NMfbNESnSmFeSDtS9/MrrzRw881dPPmkXCFUPmgTYXHLipopny/N0RjdWxgqBZ/A2H7VhZdxw7Gv4qqH7/NEmAIGBj4WhZAOhQGz2+ade/DFelhxphlxU06+W/T2h7IBnFSQJky3HN8KKMr+o+IJLpE3PpQQWhvn24rpqB7nBj6LPVKoJPemfSzTUbpYJEkr+yBT+Nv0r8u8JmVReaUoUTJ7g6+4HsfjYtq1tWT2RuPLLzf1Cb3C4ZPVysqmGPjYtRHqQoHXIkUJYpG5CLhwIXG2veie6V2X7wMLGUZY2FFFyr158AUI0Rk1PLWXcilR/gafY4J8IqvwDT4B+v0Er7zSxOXLhuvGQl4s/x09F1JDcBBxpY+W6hpj9uDjIVrCrlzUJ5QaVwRUxjzeT40L59BcfRPDD34Q6KgfOWXRfPFFdI4c4dejdZjkc3qpfsmz8omFTMHi6zCmW7iLLikJPASt9pEjbayuNnDqVAPXXz9Au61ZqaKNaSlOJSdrwr8hp6qKlz5NxtLvpuVSwVPOi8uTVzxdi79y4iYAwL4X3kXjjQ9idO21tPolv0lB7JDV1RSHD7dx6NC4lJydeoNBgm63fFLKCiE61XyP/Xtri0uashxRIpM1ZAyDiaczvi5lWhmpOvVIDQMehfBFl+99275Kb5eG7jDe2ot00Jwb+DgQqR9K9Qj4U2yhhYtIUNr7NKs0gpNCNBRD7P6fZSXFEokkoQdf+e/pOo7l6ViAdqmhCrhmN6b5XdyjsJ7ahM7QHUcfLMhLmYYD8KMftfHEE0t4//uLoaXZNe7qDT6dvLEg8FFfDUpHVf0Gnwc5aEFEqwkqJ85gjw4NtQWLmyTIPBAYme67r43e0y2cP9/CZz+7Xb7Y4vjA+v53jqM12sLr7/0M7/PiQ8fAZ1icCOyelmXyfS5of++Kwd39qA18NaKF0ZnA5iSXY1rpeIir7rgJ3eY6Bq+9hs3f+R15tRxlytJjj5lUTYYwJB/slU+ulM2AO0WI7g0p9l2Cyd+UQ7WEhpIHnzpzvz+hYzSavM/XbvPzOBUMOcrcDLmbsBYn28TAg8+XgY/voaQjBZUPGunZs1IDHw/f/W4Xv/iLQ/zu727Q7gIQO+TWW7tYW0vx2mvl5GyW7e3c/C50km6IzmKfFDyFCzA4pPESxRKi05E3lSvh34bnTlEK0elQMDdd06E9+Hy1nwqegW+GkB58HtteaoWHumyK1OtlJrVIgxpQs5qXO3g3o8m3pSWd6GLITG6x69bL2//lvFL96M/Ug48XPVokS9c6DjViMfC5CNFpDYvGG18wombM/W1C5nhsHSiCiyeeWAIAvPVWUVXlS8G6iGt6OERBjsgyIBtH3hBiRw+HYd7gu+LgqP2uunFaTlRG0pLeQ5FEpXuwAFW+PnmyhU9gIppeupTgwAEL4UpSDwBcdf5l/PJLdwMAGqNtZNmnqVkXB4L+4LXNtQcn93JZEnG/RkvYlYs6RGeNhYWOEYtNOo2XLcLBi68i3egDAFrPPUcqs/Sd+B6gCqL0BQMWm4gaj9IYYuW+KyGtVI7moYXaJcp68woljTf45vnnfaVMLrgxpJ3HMCn3u0TQpVZNUrqZQEFAwhrCWKNnph/GUPT5tdea+OlPdd0z5YWurcm26GIf5kN0FnijpebFdN1wwVkAXj1hWBjWJc7mz0Bjurak/Mqg/auraTH8K5GORYMv+oWXKqYntlDgNNDZodRx5zkfC5s9UrRpV/QGH0VEkM45r7Av34rELDOTsQQefGyITmCuYYmJ30WlGJWimv0yijf4HBWYphrGOF4CZjO3bdeLLzbxla+s4LbbusHmnY9LVBPYzc8q1h3vAp70rBzDI30Ve/AtSiQDX/A1AyjdyDNSiO8hRDBXRWAMehMZrRxO2F1deuDueQ6Ju+7Vw/N/v8E+Q7SLl1TmzoNPpxyVXBu0v030lDWCo/bgqxEtNB1PrFEUih1UKBGkWcWaSftkHnyl3WA4RPfWW5GurWHjd34H42uu0axNJ1RMHOEXfNg804wphGTgM6uLCq6SNitatjIASObCJ1Vs5tGbDwnqwgihi4IBg73BrxK+DI13VLz0UhO/8RuEmJaWFTHDC4B5r0Jo4FPX6/WuAG+9LLAHn0s5V7ZGZGVKD8GW8+zo0RaOHOlgeXmMP/iDdbRyz4iarukqPfh4VXt9O0NQz+RiASo38MVY14S36b1TbFwR4eekCgOf5FINIbmLKp3l1VXSadExM7Yl7E/S8liKEmGIzjLtPK/BGnyk25tIxkvI0om20bbPTPObGPhcK5cpF5lEc9fVXOvcdReaL75YilChukAgw513dgFM3qR86aUmPvhBX3EV5yjxaHsHZkLeOI0NPPk8et5E3HvH48SPd6gHA1/0fe4Brtrsms85AWGOUKZRKIMfK7s48VqXED9qtAp/70qbuWBAuW1z3GCRSjeKfo2CiBoq1B58NRYXHCazLXiUuZxUfQvDRNFQ+NtzyDedN/iWHnsMzTfeQHrhAnq33GJc5/RbpnEHzFQPVqJBU3oq38a2H49yiM4ABj4mE6lfhJ8M3vQoKRvzygBan/oy8KkKInnnVSGsWHZIlpVvCpIMfJybvSpSqOuGlC60gY+FrJ+l71T6V+6oeK5JOQnsQnQeOTKJN7u+nuKpp1rStIsk81fCBqo+AcdiSDeuwnwN6hhxS00RvcEX0oOvEKKz/F00tXQ8FVwosE28sXWXhdUFHxF2DHxsH7AefEVZLh6Ff9WshYfGK6/gF//lK/g3x/4OzeEmADkLWl9X92dIA59zOBokrYuvTILW888jGY/RzMV8dzl3zp8PsybMWa/vNw3F+oe3305x6ZL7/tH24PMMSgt1wh7vsGa3UD22utvhqb2UYmUefFHtYypiWFkjy7jJgtACYItwr1i7HgnG6e45D4ogkp9DtM33ZR/niJawKxe1ga9GtNC1jz3wQBv339/Biy+aOaba8qdSfkmITp2mFXX18z8Kgj1rhGH+brz99vzb5qZG7dzipd9jEdLYel2EGjQL0UkrezIv+MokcgWiJEiM3uDj32Cft9mZ8dYwr1LBJxDC83/a3F5mYWrTT7JMq/IsK/d9QcDPKyULRBHmCnHdmKxz7sHeI4PwHf7TZ3Oo5cjGIXXIjLe25J7ZVIW3L6e1CxcSPP10E5ubEjor2Iym/VLiM1lWvQefoj+Mu8uin0VZnQ8dVckYQYhOb+PkuAwX5ZrQoS1/svKy4A0+Wd6Qw7+I6N16K5LhCJ2tS/jg6w8AEI/tffe18dWvruCeeygPGPMhmzcmHkDOzzEOjN8Au2Uk/IS62sAFU86x5Lry4KN0g0lXPf10CzfeuIyvfW0Za2tu9/yAT8G6g0YnenmHz4OSIha9BwmOiPN91om6D1HWRZDo3dzE0tGjaJw8aVaRAOz5zLQcanrWg8+2qighPDOJIzvoFKfTRzGF6FTNtOTyZaTvvhuElhpi1Aa+GosLhqM9+ugSkCQ4cyZV3vpKWMU/QNF9y6rnVEIXTKnMuZmzXUpDdDJSf+bjBXTvSJi/9AbI1FAho6Ns4NPb1E02YXYWleetQAjh/K57wJ3rCeZUmITo1KVDoyRXBeWK9K9MtT2y8oZX6MGnaUTwYRifoWoPPh1k9vPcsDqtOtUXEvLvRPiDTR8NBpO3fCheHDyMx8DNN/fwgx90ce+9ekpi3yH3dC9VeING/aMRcPPNXfz93y8j5+xBL9uirSJ2YNV9kszsp1L9Igbo2SOywMI1PfiEHzT6QQcu8+qUxVu7yvy8BOQ3+DTqqRCx0ba8eQ6AmK4nn1wCAJw40ZK+u2XaLrMQnWZ1kQvkVqCOQMP14HPIf73MHccXWUxpdOG4ZZJnKpOMxwkefNDwnW4BeOtlPIqMAbAgvsEHeHqHL0Jr3MWLCW6+uYtbb+1iYyMe73DX0PHgqxSqOUKWC4pjuXT0KNpHjqB3xx1Iz52zozEH6cVGDxiluz9E5+pqgpMnm/NLGdkkdhnl0pgtyv0pizAUAXYIS9bWsPwP/4Dlb34TzRMnKibqysYiav1rXCGwOROEZoKs8SnJMqkHH0ugzPshn7TZnP8hVbxbKp9km/XkAotYmBBtTLbQ3VSVijtFvtbRo+h+5ztYejfn/Whp4JOp2E3DXZL7INH34BOUOvuXbp+K/taqvaD4lBdUulVXWnduPfjI0NLS8pNKDXz5gckZ9xNCFdSxMhrTqt/gIxA5TWJyI49YBQlFnmp2WaTErxzCdE3ztsUf/aiDO+/s4n//757RdDh9OsXly5N5vr5eFGtl6z8EhPMpyxjv2kCE5CCa48eOtfDmm02sr6f4xjcI2nGH/UrlN77A1iPcYwK6Tqh4EXkteu5EXl/pVqlOL3+fkeWVFLlR5MHHC9EZ4xt8MdHCB73PTNsiy+ciRKd1HzsapPxdTaWBjyjvVCIDW6DEowN48LmauyRPGw1wQ3TK6Agpc4igcWnCiwefhzOHrd7jmWcmMtcrrzRxyy1dh5Rx4GmRuy62Ul5EEKrYswWF3sbZs7N/p7l/69DCk7Nc8xVevXmoPPgWHcMhcOyxFt59t/w8hSsZRWe/icmDT1ZZ+/Dh2fMF3XvuCUVRDQ5qA1+NxYWmQjwP7i0M1xxS02uOotgqPKeVe/OqtLWzyqeAQr2pYU1Vjr4HH/ueCvV9RiA9cwadI0fQfP11XHP3jTka/IXo3ElBI1D1nbUAMe+LZRntcXaeMTo1UAaQlG5EFJrF6S9Z2aSQjYo0soMbeZl5MPCJQnTyNULin9i/eQoEEU0mibyG0dQpW9IR1Rw05/1urHhyaFm3UY6p8MwzkwPU5csp3nijqgeT/OyRQiUsEHRf1llnq6uWY+BhntnML67skPGNDmQPvpBMQcGLXBj0qlKmWdWbG8Msm8xxI3lTJ0TnDmJ1PAfiMdJM6UgFa02WR/ebDDF68PE4P2W75nnwCd/aDD4RwuxnppdGVUYYnwboKSh8Q0cs4J7jKnyDjwQN5nmlePCdOTM/p73zTgNvP30JyYULfipz1F6TYnhz24fM5x2MQU9XP6VblwpOQnRqdPgoXZJmjXrsCOj3E4j0cdx92ef4o8ItnQfJISRx8hhkDReoDXw1ooXWY+LTdDsHDBO9me4GpTxkSIgw3QyoITpLBz7LEJ2lvgH9YORsQ9IsqGyooOdtnDo1+3feC4Z90ypTHFRYQ4xOP4kUw6aG6ekbfKMRcOcdkzdPWGU6V3BhDXya69LSliXPyyqOuJ2oWKeVG3L0K+cZ+IQhOi0vGrh8g6/yEJ0afRzag0/Wn0Z8A5MLCbz14gJKw4gAicKN1PUa9NV+3fq5b/BVQQgBjYYmbR7nlYcqpOWW5DrBxE58evCxey57sSgPDh3y/ZBUpRFcKDl06KBc8OGWx8rL29s7P3MuhbHygYaxKhRcXqDyAkd9ZpqfFYEq6R+LSgt7OsUyOOVZxDoLrzT76JtIQnS6aJvtWdd1//IMYK6i5hiB0kDBTQpeVh8efOyZ1kdv6Y7znj3zDPsvvYHt/++fsPy//hfSt9+W5DJExRtEufoIww8SBAmKrGGqr5GCU4bqjXRX9UwxSpvCb5OsEXgKW4J7cUZg4KNAJ5/p+boyRLV4awC1ga/GIkNLIe7hAVpZ9kwcfktXgZX/Pa9403mDz/YdLqYwaXojxb82DWqwG6LWW2K5xIXDNRvybiTfdV28+cDCpIwsJ0C/9VYD5883sLmZ4J//uaddV96LUeQVqSrDJ3SFqKKSMFB7HFiGWJ6Wf3e08MVSu2UahpVcWMgQnYS0POW4jWBOz2d2QJOlS1l+5UjJaPJ9CtVWFDyClGeF/dzAFwCyztPgObYh7Wza6mMcMknblQfp3A8Fmc7ziVtkmM7zp+6tt2Llf/wPHDpNfOvCk4FPaFAklavH96RyMlX5wiYaDoGs7P3Hl3/iM/DFDpnh11U/6pRD8QqyPsdkGRqvvYZk+s6SQYHc+cd7A9uCWFfnNdu8LuuhnrF12j795vpcqktHuTwNl6hIIPI45ZGdP9c4g8tJ76iIfP5PP38LLl9OkGQZunfeaVewqjKHxdjqemK6qFJaVUSmo0Wz6SGPI+ewb/DxoEoh9ATnYNRYYn6JZ+zcgd8IvlxoUDphv5lCdWcnaH/vjsHd9agNfDWihVLRZ6iANcugKMIjw8sXnffgkxpX2FOEpdZUV8HdGG7hvWefQ2vQd6YH0w/RWfybYoyaQaABSMaMlVDRuMGgWK9qQy86t8wf9pVCsuMXDK87b/BtbCTCvuQWlYlDUVDmhQNbljAvq2wr38w0uG0VQnix7JTxuJx8e5vvwZehqJjWfc9pPE5I5FLI54YJ9NnfpflAh6kHnysU1q4m/52idCHBIcEedCRewNunQ9IqVcJW8AYfpcqm/HKusGwXCH2A1THwFSyfAa/U8t7SbLz0EpqvvIJkMMCHny4qAWUGNxFc9G9oDz7eGUBppONUkGQZMBop3+ArFhPPDfXYefF0/vL6zMTIogs2X4jnM1tPPIHev/wLlr/+dSSXLhkb9FhwI9sIJj15htqcpSuAKwOfa5ieh2wwHJYLXdQQnby+WdQQnbpF5tO3Bv3Zv5N+n5PaEgEXOVtVktC7P3ZexOoiSPR6atTmppdihRgnzC1AZlONfewoqNKDT5Q32n6NlrArF7rH+Bo1osSMt2h4zZUP/k5JEobGyzI+QRT+mNcvST34WAHaMkRnCaV+Lv79iRfvwNXnT2K9dwjZb/w/RlXY7hc2HnyJKCyX5ht8bHgRF3ugzbzNMr2pwKM3zVkhKTpO9zYdeYxQG4NeFQd004KlBr78wCg0+mUZtpx+PKZ49RDUSVWH6JRBcuo0NbA5ql5L6ckatFzN190uv/s2IOlejHAOzjoTGWSsQ3RaNVAv8gGpRElmpYEvD8V7ps7AuajCfkrX1nxWGazc8ne64YwX9tZYyTYYYDxuF37i2U2CePANh+h8//tI+n1s/vt/j2z/fo+V+QNraK5KviqvccH7OpI8unR1fvxjAJOzRPsnP8H46qvtCtwB18AnIjaWTdtziE7TsZql29pC4803ge1fAtDVqlsXrs9Du+INPkkH+AjR6SW0jiUiIEEblHVHTRNd+wlMhm//0dD5aDMqcT4nITo1GGnp7LAT5tyq/sggerub2zaDBsuzsOMpl1mC9ressuAheGqIUHvw1YgWyjf4DBWwwJRx6ym+tb5nmbOwmPnfqSE6SwYqhx58lH65+vxJAMByfxXNNTcPRXPDbCnoyEP8lhinbwSJ2ZB3qoMUI/NIlYd55VHpgwS8PAkrMSfJJGWWTP5J7EueMdqNotp8PhaaxbZd86TvWigiLzPLUz7vQFQIZcOOfZJ740BD2T0FV4mgoxiXJJIp361hIXSH9uBzZeDLw6cH36KEZGENnixPDvU4OneAK/Dgo8A2RKcNgs0jgdGB/VsY7jjkhNfcx5SKf19wUL6p3K37O4tkONTa03zeS1l69FG0nn8ezTfeQPfuu5XpQw+zLqYX40wVwBTo5PPiFaSqkEAgpS8oBj47mSoOBZ2sCaq1J+YF/Lb1brkFvdtvx3sP36Es03ZtuX7WYDQqR2SRnkt9yxwU4tkDxSxPmbYgHnwRVBGUhwfcIGx4fqX7GIlf5yPjuCtXmUdq4DM8LFoiYRZqbDKIEQSTl7svO9YXiGXRCPZnTkMioKoGg9qDr8auAOGyjTqPxa03LnOXCNK89BQhpxiiU0IQ6y7v9A0+zN50I6V3tNEnvC7W2DG1QnQKDHy6b/ANBiE8+OgGmywD0tTu5JGfu9V48JULEhlDZGEzeIYclQGMLd8Y1ga+cthMUYhOlg8lyLSXJMX7lUR+1W/w6VjKskz4Sfa7KwUl1cAnK8elgU+Vlcpfq7zg9/DDS2g/yr4dMYG/A2my8796MoJzaBn4qtNO+VH20PfI0jyW8FKfEK1jX/PURbkmxnKrqcPJnB+/dDzky/U8BcVgwHHsEMcT86nAar700uzfjVOn/FUUCImkz8q/iW+qm4ZFZeswk2cs177FRFeyIFHZRCW1K+/yUEpd067k5tvamq2x7tuvAu/RK0OXDtfnIZ4BLHYPPp03+Lx48MV+I2IHWQYa28kytO+/H+n589j89V9HdvCgb9Km1WqnEc3/2Oes8hKFwPCjU6Ze1uLEoLzBRyjUPO0u8+DLMsIF8oBQ6mQXvL9ruEdt4KsRLUze4CPzON/cMMu0woVSUfTgk7g4VvwGn2lel+WwijqxB1/5N2GITkZhznsbJw8dA5+wvcwHkoGPzYO5B5csRCclvITtW4ii30zKi0kA0wJPwQgN/oXyEpca+IjrX9R9vJBWRuuTlyiWEJ0MfHt2hUDKGrg9ro9Yl16erheeb+LjFdZf+C10h3Hr49Og/QYfqS67rDZvnsne/lSJTgU5M01n+raQnse676aKZAd5qFI3XvU631xisqbmf+9fexOffvY7uLpJDFQzGJDknym8bluanRarvnpKx/wNPnoe07oo3yZnpxj4rz6ulBCdNjxFvIcYFGaXvIRkewtLP/sZsl4Pg098wvpsPh6XLyFIj6UxzAmNN/gKkUlcwQPD1ArNSMivg+Zzz2Hp2DEAQPfOO9H/r//VuCwbUM76WVb+bf9DP8TKmadx7enfxBvX/Io/AnVAmCNU46UykS44ZbAhOkMjYRZqDGzGGoIB5a5VUoPpYyQ6l0TRr66VejW8oDbw1YgWOnKvScgO+cZb9pBR5S+ll1tSKD/NaJnC+A0+hzfPVX3pS9lga1jSUsgI4oKUDHqKQtlwcMoN3qSzJleNlGVlO3M6STT7krmZVvTgMws348rAx1PSFsvW0yjZKqCslplGp/AOScIQnSwfkhiMRSS48uDjKpgj9eCj3Gp3ObdlpOopu3JhYxQXEHSg6kpqu4M5QU2ZHSUd7HgSmR4WAT3CdAxSbIjOkGc3LeWsExTHQGbgI/3uAfmxm/0zyrcuXChK6d9ZHp3vp88+822k4yGaFwfAHqYATiXJcFga+/G43PdBLn4ENjr4huzdQost2hjOIhJQwZtzhgpj3rIXeURRSdOVgUNATofcmEJl2Wzbzemhp7n2pZ+gfe4hAMB4ZQWj666z6nPuUTV2byiNN/AKF5ldwZeSwjGyDEgJtDVffnn278bZs8r0PFlwMACOH2+h3QY+9rGBM/FCNdSdzQtYOXEMyb4MH3n5BwUDX6TDMoFEL6JTBgUU2X1ry119Rum3d5eBT6gny9y9ay+Dlf6yCkwJjvJccmWiNvDVWAhwGarFocT9IZ2uvLPRFzWbk7BDey+fwmj/NcLMPt/gA6AV8tOVwls3EfuJcqif5aOG6FQcpNjwIkbKC4lBZvIDyBeDVDpvkoKB48mlU6boNxNQPPhU640tI4h4YtkpPH0R1YNPxvtEJJiGYiUV5FNaNih7liXwCcXcwCfhMQEVGc7WtIMF+KHXD+PnTx/DS9feAOAjuS/hT51SPlNxiE6q0U/7HRyLySDiNXbzi87zpAa+ig6vrjz4yAolXjx0eQ7hFx2DHSW9KHGWFcWxdKw5aTkhOrlzMdSFgF0EmQdfCAMfm2/Kz7IkIfNA6/G2KKDw1lPC+V3ExGzkHwOEWhMqo4HNGVsNuz3g5147ChyY/Lv9r/+K/nXXWZU3GnHOZLJzaQyMSzCAPNJ8vMFnYxAXwbaIoMPCqezxx5fwk5+0AQBLSxmuv57S8foXfNmz69JwQ7JeKzQWsDo1dRIavS4GmlMG68FnVI1OJrZ/hqXb7LsA/P6g6MWo+ciUZKoyAkYliGEPqaEEMXZJjRrhodKl5D/PmF+SFP5mv8t+040BLuVxWSY1gukYGPP1NBoZPvfMt/D5p7+J/+Ppu8SZWAOVzJtQE6q+tO1XWcU6+0pZWUM3uCa5/ivo9zSv1bAefMEOmGxFOSPPZCqYC3JAVnjHz4n37A7OnUvxwgtN6aGuMB6cf0nHSwFKcidjaGngG4/LyQeDuddxod3OQnTK05oa+LyGumOhoT00vwTi5lBaJEdepqhZSTaWG/w00O+nOHKkjRMnmtyiqEX7tpE0Bxv4pTcfQmu4gQ+/ch+ZLl/TkGdsmf0z0jf4yrxFnt7lGvarnC0XqKWkyctRIS8mFP62mDNkA59Z8S7mga5BsPA7dVvlGbsFHnyiCrXedNaFB7mlCrC8z6d8pXc2MIs8YQXChknZU/Oy9+y7yOJFXe++FYORhug0kWFcGnJEOgKdOkYj8TuhlYBSt4aBzws0PAhNoVuk5ZHQurKpcQ8Ajhxpl74bFqteq+Dr7Hj5swx46qkWHnus5Sd0q6hiwd/s2Uq7/aaHE04+J2/wMdCR55JBUWkTq0xCRZaJQ/u7apvsrCE6l0TRr0GZVQ1T1B58NXYJNDc3AjPyrvAz+L29vYZ9a28BAA6dPgHg/+RnDvwGn0wZbepa7vIgBdA9+GSJ04z5XdE43Tf4uGEBTU+zojo0ve1YJFlWKGM8Lod0Y3KQ6tnYSPCNb/QwGiX41V/dwg038G+ESQ0WBKVJ6fdYDsfaCvhyv25vA+12saySEkFSj+iTs5BWvPXiMfaEGdcr34oPMSXKdZhRny+n9GaoUYkTPPNMa/bvgwfXpfX6QnLhArLlZaDVKvyer7s5EseqCWpM3sG0ysrfC92pTyNq6QzKW/QOeWg+a5pmM2W8TXfJ354r/h2LB59sHatoqWhqgWdh0xVfdGgvrqnE7jLZYKA09uZlNK99bFl4pZ4PHCSI04PPpoyQBfiUYWMRf1nozQPaOYv3u+5KcTo/BReSdcCby+ORh0XkEKI37nnwQm4EBj3X+auojFYMZW1SM9fPAAAgAElEQVTSvI9efLGJe+/tAJgYtj//+Xi8xhL2vXMRTPqekIcUytahLqkUkWRQHIvYZBAnyDKxgc+xTll5LrEo2yvqEJ3RoPbgqxEtuI+J56GhgPVx4UCZX+g1x7lxR6SnmTOkSA0dlgY+l5uzq41HFA+bWq/ISMFV6jgK0alj4KOiVIZkcuc/zd/gy4QKT+5hPyvHmleuTQV4eY4da82E1EceEd8gZGlhf+N9dwkXY8hdXZoF8wS+2Xxjy8opEnghVlUkLKqBz0R7KDTIcLPQjNcUyEg1PZel8HNT+ZlnWsaKQVP5v/XUU1j52tew/LWvyV3KShXIK/StsOfPpx2aXB+GZOXxvGcFSdm+8H5bWlC37T6jWx+gMPCF8uBjoHoPVEhKYO29jgH91KkUP/xhG2+/XbwdpEdisV/IeTkJk8Gg5Nk1lX94iOlNlFiNNFMOM58XjiIBaIJ6FijCMW921FAuixeVTa1D4yxNLKaIwB58orGzGQLdLlWVA8DJ/s8L0RkVc+JBw4PPCy+rPfiCFavs6iQhr/UHHpjrBR580MzLkAwVs+AYerTH0NVh0WHRpoUkA/obfMnGRvw8CmJ5lvezia6piv3HCYIyqxqmqD34auwKzPcK2o04HjO2uf3LjenuSHlX0C/lHBdkYWZ8vsHH5e25viMZoDTr3PnFKr9OSCXRDcOygc9diE6qko7cv/lD+866GI8NorWW5naxDFchOkMLpFOolKdaZVHfLrLsAJEyczbfWKW0ZYhOVwoxrsAcq6DvSOnloHotA1/BQMLz/HEA3nSiXgox3Yo6994LAEjX17F07Bi2P/957TK9hyLjYDYeVWvhNflLHsOhwkvbadvmg5mmc95jVaSE3+oY+LIQFkdO2ep1TDRqE2n2fQl3YwO48cYesizB8ePFb6aXGbKMaGDMMn7vDAYYM3OcG6JToox2hl2mLEk0+szUsKPTZSRvB9cwUMpyFYk8FsQwrSTLJjtdKEVwYBB076R87I9ZBquzu/b8Fj4pQq+fN5edPYvhC+x8xeR0H2wusvtroGplKM2LBViYJvx8cnY1jK0ScqAITKXwE8fgRyrXgBbjbjAVrihpt1kPPn625vPPo3P33cj27MH6f/kvpWgslYLh/SXdULLDqwIsTeW5pEYNBWoDX41oodzMNRSwZgzZ8tFS7fj6/Pry6dvtuew3Gk3+azQ4Gz6jkZe9B+gEkrGoylWfrZfuwQdhDB9W0ZY5DNFJS2CRZ2cOJElZcJlOD8rY6XrwaZKnBCtUS+uQVD7/FMlBSnPs+Qa+HR6Sn5cOQnTyPdUMQvFW7cG3g/T0aSQbGxh94APCyW/6aLYr4V/HwCesf6roMylIpx63RSuR9PvktFS6fNE/Lbd0WMzk7/R6I0T1G+fnwUAVhplWrm5Wh08Hk+oD4gnRyRAx/5cGzzENSavbTNleyiPh6NElJ3JhwvBImzKT4RDsHTCZ90FM+teq7w5MkZw/j/aRIxhfdVXxd4mXtA7tpu1i85lEJLDt09LM1ChQxIJmv4uIpRr0HV5ycwUZHebhyugeuvplG+R35MGXlqJyVDiIlLoFA8jP6mHP9cAwnc4L3TJ1Kw84P8pVqfVclN8rR1aObOSN1qo6QaNeqgdf9667JukvXsTS449j+1d/1Zg8l0jfeQfdO+5AtryM/n/+z8iydlknMBEy+W0j9JWN/XTKMmNYJy4iUNXwjzpEZ41dAX3eYq+ZVSaXCO9cD0JC9UkCtFrzhP3LgoOcdYjOUs3y9JIbg6a3CVkauM5Rkk5ju0DrDT6qB99IZeAr/m20wTOZSv0imMtJXhCZ3RRNSlNB276SZUykMsXc4JJXzkNV5rJCdfmjeVhUyoHfiRxjaRnakTNL4IboTBLhrbQy+GPpLESnT4sYpb4sQ3rmDJa/9S30br0VzRMnxFlyeUMI1TY6B7GC308oIt52QuUjTmwkCp4ozBJ6/oE/nyqBoUIZMHiDzwIiA59dFTptl0zQUB58k8rm/+LVZfIGH1Xhb7hGKQbFy5cTHDu2ZFYBW18pRCdxTHjptrdLPIznwRfkDb4FRfeuu9A6eRLtRx4pfshkfeZKgU9fD4viwccDiSdaTM5Y5rWN/GMjr4nkY4rNVLvvhB589CJGo/LZOJO9wRcBShFyJA32Mh89GPhsEZSECg187Nl1+nQIJX9UHnwcQ4/28cJ0HBwbk+wyTZAM6SE6Z3nW1ozrc43eLbcgXVtD4/RptB9+GFmm3gts4eKMXwlkE71+gy8a1Aa+GtHCxINvGnyHqvBzJaxP3LYZ5Y+j6+esQaOZ87slG/gcXoXPsrIiR3YD1KcSXCetVohO8ht8qhCd7t/gY0E1oE4FaVbRnW8qlz72tyxDknvbS9UmqtBr48EngsyYNZNHiGMyGgEvvNDUmkfKylW/CbPzD0XcEJ2MgU9WrYgE3nIwWue8d8ACS67tH/1o9u/u978/p4NJZxrO0Yfwr7PGCkPvycDHK8pV0bbnAzYAn43O3wVmfIbjwRcSOt3KkqZ8g8/hZCga+PJ95ufgqEV6qDf4WPkqo++3snKocHtGLxb23HNNDIey/Uj/wtDsd4ttNdnYKNXNl4fiN/BVRVvj3Xe5dEznL/VcZvJNB1V48LkqkHfHQGgwoSqCHXWssBjnb/BZ8AjJj7bzk4JCfgeMlstLJTSGlre50HiDzwd89IFLfiEIIu0Ovtd7DrYBWmKYrgBIhzBvtLqaXLr5JOnZ87HOG3wxItnYmP07PXVq8g8NPY1pxB9q2umep7W3OURy8SJaTzwRlVG2hhx1iM4auwK6QgRPwHMet14SolPHg4/V1U88+CZlb6wLNpupJWdKg8M3+Ljfx3Oa2MS6G0+ysYHW449j7+vXAPjU/HfLN/ichOiEnoGPPXzJDqaiecHmYOkVefCJ6mAxuc0s16rI7EWu3uAzsUGXbtOzQrbm5JMpO44da+GBBzpyeqjLjFOHTkDgLOP3+8ygzA5YoR5xLaLuohg1TQ18QUN0ZpnQeM+C7TVK8aLfKJApl03LTDC2Wg/Ccnk6JaoivaILfiL+6h+C+MdA2M7gGdeF/VGkS+nB5xAi1mUzdWUKPfZTKfiBhJf6RKFaxbwl63+InWi6j1HW18aGmXJelTjLNGR4TiVpv4/xPjZZUqoj/y0vYjuFidwSMXRCdMokIVd6zZD8TEiEYVbufBMwMZOpGftcAtT8ztTAN/vbI5svyHk7hx6bPh+NRJFMIoaGgc9LU5xb7+0RrO0ewX/KQf739DdKW6NyDsqyQnvJITpNBrWqiaBT7/ZiG/gKSJLSeXEm6zHjbgP5JZbi36oQnV6RZejedhsaZ89i9Oyz2LrhBm6aGnGhNvDViBZF5Y5co0i4XGMHg5O8bpU04aD4Ju3MwMe33GDm7mcdorMIWWjEkkCnaTht//jHaD33HK59rYGVQz+Hy8vvmdTJeqMowA23ZJC5cLhmFVqKjmLeHTabhyqDKeE0myGZCS1AcfxUHny8sFdpYrf2+HmonaOn9dWeywKojHvW0JgcoqTsm48AgDRFliTzx6E1lN1TOPPg4yUK/AZfJnpMjFUyBBRYVWtEZ40V+RW8KKuSxHD8XcFgrXiXERTlcvfMqt/gIyaVeVxxMzhSZvt0mEvY9b4D6Rt8oTz4GHA9+Chzx3BcjN/gm2rqcgXokmBjxLFRsCXr61ohOqdFRaV03EFsuhaZB58r1qGnLNO/sOTkDKkosJykTGfCk71FZQeeCG5lJ/EYqSI8Ug18KnmYl9dll4re4NWpgxduVhqiMwbmIJT51QYiL/V7qMSFEcDbUCkKNoqmQ0xD3qc16vCBkm6HwK+DzV9JoSK51jkJjF4vGW4LUi4gkgTCi0YZ35Dr2oOPhfm7sw4wHKJx9iwAID19Wk5HjALxFYo6RGeNxUWBo5kxlUIRxT/0GKbGaVB0qKAcStg3+KQGvvyO4NCDj0d/frNnN35dA1/ruedm//75d47N/s018GkYK0QbJG/oRF4+6Zj5XfkGHz1cHCk8lCYo01L9vlL5p7yBz8Q+wxWQDA4VvJvhvO82dHkB3eopzM47QE6jZORDNmVAoXNNvJhcvcFXCiUF+DXwsdDQyMrCDhd+zzLsXXsb3Y1z0rQ+IarTZ4hOKg0sEmpMXB/gEOnbq4+n9KxEaWFl4HNMC7HuMG/wFfmB1MAX6PBaqkXReOE+r8g3HgOPPdbCv/5r8U08O090u8mtYwCkeK5wy+PRLTLwSbL52rp0Q8j5Cl9ri3kz6EpHHUOdKWbyTMUe1FSIWNDsd4sQnQAr63gIQW8wcDbzgGrgY3/MMn1vaV1Yiv4ljEblApxHI9KBqdUnJDzUbz8vwvGisLuFQq+Ru3jMQ5H3BZwnmga+BBmJxef3dvI+72q+6ubTSJ/sMg8+QDA+AgOf+wbz100U/ep6E6vhBbUHX41ooXP2mvGWWabi7Yuy8CRnRiU5gqMYroKfpUlW8ODbFIToBFDUyrOdqaHopqBwKHIoPI8b88bqevCx4N10FEIUopP3Bp/kmgT7dpGsK9LxkLRxlrqXd5ibWIDmfyIpvsFXCLE5XytkZRjB+CH7zvuNGqLTxoDHLScWi4y2gU9ShJZSWuxtMQXv4GK0zCkFuYTORDPkW9ecfRafeOF2IEnw00/9IQB9T0/VdNBZY4Whz/yE6OQVFerMqYRgn1bXM8+3uQm02262yGm9sXjwFaoUdAr7s+4bfDrhhlnkWUTxDT7DAiE+qPPKlSqbKrqdqrPf6uDEiSYOHy7zK7IiTdSv0m7yF6LTRq+drK9jzHi+8Dz44GksXCI2ukJ48MnAlkm5sOCcDgMeyaOhyIIE4Z81tIFe5kpgAx+1unI6TigCK7o0PUMd7Cd8kVqj8ypA6QKtYC8W/WaNIB585umzJEEiOOM5r8wkv0YxvK6OYAo6AXu2Wvh2WTQgGRQ9+GK9dETCzOjMkfUsxlnrrMF8C3kPuoQQQloN56gNfDUWFzkmYyqvkZWfBgcl0fY20X+UyyMdSrIMS0vzHzbWxZmTLBMfIMdjQBSqjlNcuW3iBCVSDLUuWQYMG/Ob5SVltQJsGB7qG3w7mblpuW/wSQxTszfRZHUBaL7wAn796P1ojNRhDpRjQcmbKySv7OAesJi/kwRoFBSvclUF9dBmdNZlD4as8EUQxqJ5vyKUgU9Sj9jA50hY570DFouBj8lC9eD7xAu3z/748Cv3Isv+gxMydb6L0mp58I1G0j0hD/4bfA686Dl/kzIRQeGVx461cPhwGz/3cyN84QsbDm1wrpieBLLyNE6IJgpxaQGGWV29wUetD+DwutAEcaC63CRcQ4rF9ZOftPn1mYboxGSNycRqn+NIvuDPk5fHY6Rbm8hf0CgnC3TJz6HRIQbIwoZR50fj1VfxkZ88gmxwPV669t8oy5HBRJ6x7mNHxjYuC2KNh9O/iTzf+QUCz4veVF7i/q5Nq7toOMULyRokMGlLck2VHnwUVP0Gn4dKbIsIysMVlbmkRblWXV74cQkVX+AQpj1/jRSV1HwcvYxuZ0rSszwnqeRhW7/Q8eDzHwmG/wTS/LvXytUVxS6EXoGoQ3TWiBY6uhTd7zzGzT6YWypgc5Oe3gDUw12zOU+ofINPVLijcDGz38b5f2fMN/O+GeU9+ATxsHlQdYcMWSYO0cn14JOAGqKzc+d3xcY9lTAu8uDjZOUVpZLLeGslTebtNokNbmPgY5WJpqhUHrE8yWaZ6OYu50Z3kpBDdIpIoL3BRxjA0CE6eQuA6ipqgMbY7JDDnw5qz0rV99KFBAFaTz6JlS9/GZ3vfpeUHoIQ1roKSm7JjhWpU/4oLJfhlT/+cQfjcYI332zilVdoBk8KOXnjQ5bJCPIEnlFDwAvYpLoefHaYTxCPS3UGpWiUT5AjyPfFhOI+VyYnircueHNK0xhp850NLUgeEkHCtL+uTJZfx962Lsu5FYuuZc77ph2l//bdFL1bbsHyhdO47o0Hsbxx1oiOKUJ78Mk8iClgxTllETMmod8Im3bP8jrwkJJlUZ9raG/bsftw8HXjYIPjyRLSM3cMzKFSdxQE6QMXthRvZDryYKRk4605ym+8/EFFHkLjWFmE1I0Gfa0bst0UNjJt2YPPlpoKwfHgm0Fg4KNAJ5+RTTcAEsFijeA0UoPBrvHgO3v2LG6++WYcP34ca2trOHDgAD73uc/h93//97GysqJV1uuvv44777wTzzzzDC5evIher4f3v//9+Hf/7t/h137t1zy1oIY2OIK56jaQpIhSmMn8t+5tt6H5+uvY/uQnsfUbv0GTNDxw5ATFEJ0bshCdeSHO0sBXagrzQ1FRyO5MWlUVMGI8+LgYj9F87jkgSTC8/nogTbldr3Vr15GBb5ux2bmYEiVjtaiDGck4b3/J5yn2C+2RaL0bv7QyTUJ0luZhSTFKEMYKfCSQmOJA4SMtQqCUVpGiY+BjQWIp/IVJyOgOmUGITsrhEwBGacMZ29crh+9Fy64HUaGd++4DALReeAHbp05h/L736VReKN7G+8cLKBNcgLW1FADxZoiiei6friBEp0lS9TuteqfRd99NMRoB11wzljoYu3uDjwPGsDtFDG/wscjLHVr9wNkfC3/7aA6rbNZUVJi2L8v4im2d8hobRQMfT2b0FS7VBrEqg1hQxB4K7d3Nc1jvXkXOV9qnDd4Utu1TmvJUfSGQt2ZLbxtrGPh4BiJrOOg8nfGkfuefZdi/5YXb0FVKk/C9MmyHI/o3+Cr24BOuFwtYj1khv2c5w9MG4YS/mxwiKgDvbKXdrR43av8yACNLDgZALiBErDIICTsGPt6eXRr3KTzvcXOWRbu84hSLImDWKGBXGPhOnz6NP//zP8fFixfx2c9+Fu9///tx8uRJ3H333XjiiSfwl3/5l9izZw+prMOHD+OrX/0q2u02PvOZz+Dqq69Gv9/HG2+8gWPHjtUGvoAweoOP+F0nRCYANF9/HQCwdPw4sk4H2zfcIK9TpIGf1UOvv2B0yDK0WvOEm33JQU52ilUo1VX9yfZf4UDh0INvmKoNfM3nn0f3nnsAABtpiuH113ObRw3RmWXixKmmgY9VEBntiwqFmY4BlWfAoihvi3VmSHPX2E08+HhCSmiZXjgWVQkvmvVKbWX5Qcl58KmUOaJPPEWniczHVXKF9OADyJZkindoqQ8SM48v9cHYLIwNyzMpSyxZX1enERREmgOKiLGkZbCTKD13Do0330Sy/GEAy1KaTJSCbIhlE8zK5TU0INPTqYklVenBp4G33kpx002TsfqP/7GPX/ql4l4rNfANBkCzqd1vMuW6sYEv5D6hqIu8hogGPnL38hQgFgpyyndZPnJeQULWg499g6/czzavTUrgQBaIEbYK4BkvN9xnp9B6j9sVHCnJKCE685veYACsrSXYty8jReB2Mpc8G0zYcwy9a1XnMfGbfBo20wm2t9E6cQLjgweRJB/lnr1c7P9Zxos4ZF2sXwgGLJSBL0aGKT4nB9hfPPaH6g0+9hLuJMQ3e+m4gvFiZSVOH1G6sayvMWhLSQdkISTZfJcgGe4uAx8guMxlYshlyzDAdB1V0q9GE71G1dgVBr6vfe1ruHjxIr74xS/it37rt2a/f/3rX8f3vvc93HjjjfjDP/xDZTkvvPACvvrVr+Laa6/Fn/3Zn2H//v2F78NdGGN4ocE7eAtuxBGKKPwhU1K0H3kEww9+EMAHiYQa0CL4PUmKHnxb/bFQaV94g48VVqAnMuWzc+mUHcZ0DHzMGsuSfEgsbkxCdO++e/Zn5+67cfn667XtnSzywtwsFBZPg2TrCekirWizzbcBCYAE4/H05/k3WSRX0Y95A5+JzMj7LSE+oMMeBtjfivNNXWY0N/I1KhcpM7lFOAjRSZnmJPIDh+jkqk8cevCxGCdmHnyqsdTjG7mx1nmDb5qHXlUJiqdduTDWNQyH6N10E5KNDey7+i0AXwDAU3ZlmLZKN6Qv64Ftgml7Kvfg48f05SbVNfDphBD63ve6s3/femsP//2/rwmzprm3Xt975gRWvnIbRu99Lza+8AX78GYCpaJUIePVpVAM2R4lJUNBo7WBj1Cn7vrW+V7qF8sxKXvwcRR7BvuCNiwLjlXXQqGLkmasaeBjyzTx4LOGQYE8oxLJwLfD68cj4OnjLWxvJ3jPe8b44AfLOgzWQGTTbiE9Fm3X/Sb7Tj3fmJTNflt6+GG0jx4FAPS2/gTr7avKGTzFoM5GEpk6AuYgegIjGGkE443jKvTyauqxtKE4c5k8l8H7W/SbtKwJQ9LO7xyqxmUG0YIEdZw+neLVV5v46EcH2LuX0ImhPHQlDSpFgxiNkIxHyNKGKmv0yGYhOukweS5GZ4+zlZttoBUidgG8b68ULPwbfO+88w6OHz+Oq6++Gr/5m79Z+PaFL3wB7XYbDz74IDaZ99N4+Na3voXxeIw/+ZM/KRn3AKDZ3BX20IWBzmVpHS+iJOFrx1ljn6zOxttvSzJLfpvSYHojKZvcwpwqUbNRhq0tQV0WHnwEQkp0zf5p4cGX5DSJbJNUb7vkM/G6QxyiU+Eds2NkTDNOfzoStPSUdAnzlyyz2oNQdZuZl0fHg49aptGhQqXANNEoBZBM7d9k4QugXAWLlsDFT0t7g49QPGeyeH3LilcXMWSpiQffOHUnI+gY+ERpTQx8lAWdJEVDok7xLvIAQPPVV5FsbAAAei89Ky5f8QYf75LAFOwbqibgrUkXxghzQvST6lyOUUHVpyLW9fPvPIFkOETzrbeQvvOOZq3itpd14xL6Qh1eWeURRfYpZid9iNGDz+aKAXmKCxI2N/vSZBWICiTEQgcLdg5TxB6SyJaWDXw6yrIpP8vGwNtvpzh1KjWMRKEBQkMpdeQvPczSC0IOrl2ae6G/+y7/6QIf8P1mlOm65KZjeaI1/5pgatwDgF96+xF+WQ5CdGYZx+swVoYwhUaITC9NiT5Ep2cEnC9Knq/QtS0MqO1gEg0GwLe+tYyf/rSNu+7qCjIZkRMo0xyNsVh/56M+n8jGfD0xxmPu2ZfSFj2+XqpWuwxvsNRf1QiDhbdYPf300wCAT37yk0gZxV2328WHP/xhHD9+HC+++CI+/vGPC8s5e/Ysnn32WVx33XW49tpr8fTTT+Pll19GkiT4wAc+gF/+5V8ulV+jYhQYyo6gTFAOUJQXSuUuZzMvHmYFm8NOugRjNAcbGLa6hd9F6Vm0WhlGowRJNka/n/CDk1m8wad9eyRv6GLT6jB+xmUiPw7C2Nf59DsJdEJ0sii1vdHcKdve+8jFG2+lPiD2b4a5USiJ0IOPl4a3VgvrTOnBJ+5zHzKKnWJUu7bSL9PpePkScPG1Bg4dGiNhPPjYuimGpPwcSfp9pKdOIRkVPZhNDXxeBUOeEk3kYsYq1Q1utVflwacyXlkT4zGrka5FModl+64ugS48+Kao3INPyxhTpEtp6NQQGFRNzmfNi9zJaDTbE0QeACZQikZ5w2wEITpdVuvKg0/HGGnKywSpC/ns3+C7DOR0bLzyiu8X08vWgvUgx3eLOhmPiDIXh/ad9e5q7k8vtZ1+J8Wrr+5c4EuH6EnpsoSFUl15NhSUnaI4QTc3E3S78kFw0m4HC0NGh0pm0DlL++KteTQSf1EquGtKdvE0BkUsM4DTiELBSAtg4NItMqTO3JUBnpaNvWQs35smITr5dQR1DlLNEa4+ikeghOgswzvvzM+jp06Jz6aFP00vlmuOs05oewBojLYxbHZMqooLnImW32uraJsL3aFF5YU/Q17KrmGOhTfwvb3jSfW+972P+/2aa67B8ePHcerUKamB76WXXpql/4u/+As888wzhe+/8Au/gC996Uu45pprHFFeQwXVZp7/TNiLC+Vyb+0HZFq/cOpR/Pw7x/DUh34P71z14SllKFvHGIXXjmGl1QI2NyfKlI2NlC9CyCw3FgewLJOHLSqnp/drItGoipSRWZqWHs3mVSlqsspgNr0tzFNc6W50Rt1uOi/zgohkMemGK0oSIM0dWFWCh+nBhRTuT1EQ6c2xWB6scDCXsmzy3yMPt7DnYgOnTjXwcSchOpNZpb1vfhPp5cu4uvMpAP+XHvmKEJ3DIbCxkWDPHkdjwiPKNP4MF8WyTD34XB7uWWU7+4YrtQBZUps3+Mpbh+E7pdSExHS8ZG7e4MuFeM7VFfy4tlP/JBSyHs+mvNPqCqyBL0kyZFlS5FsuFBWCec6yqEJPVRR+hlXU51FQ8rMeIQo5zYUHH1tF6X1m59trPgSxgXJ+ukly0NxcLxj4pN6cOnV6Rix0yFDai3TAMiDNBov42SsvN5HsrK2XX27iY2bUmRFBSEIWX0QZmd8vXy4b+LzYOhwoBGVZTC8JcNtK7IDp3DWhSyhzO/Hg45RfJUPgNYb48HJlBj4ncCkbWNyw8ZHeoljKG3yFvKz+JZa9jUNI4TkEaohOk/aF6gQdXR1HztP24Is4nKPQiCow8IUK0Und21Q4fLiN119v4Nd/fQvXXqtQAlI2qGgWao0pFt7A1+9PQqr0ej3u9+nv6+vr3O9TXLx4EQDws5/9DHv27MGXvvQlfOxjH8OlS5fwL//yLzhy5Aj+6q/+Cn/zN3+jDNX5p3/6p9zf//qv/xoAcOjQIWn+GhOsrjaQJAlarRaWljL0ekuF7529e5Ht9OXly0Cv10C73Uar1UK320SvB7R3vi8vpzNlXauVobW0hNawhV6viaWdYnudZfTSyXzpdtrojDsQTCt09u1Dr9dDozEps73UwtJ4Cb3e/IG8zp49SDkFLC0BrdYk32df/i7uv/YzAICDB9vgTY29exP0epNbpvt36l1ZSbC5CfQ6bSwtHcD+pX1oMHW19+/HtMBk374CLe0DB4CDB/mNY+oEgD17Ojh0aKogBE61WmjkQsX09uzFoUOTsLb9sxu4lHsocLm7TJ/zGxuzdnHHYXgAACAASURBVLTbk3GYruF2q4ler4f88stWVoDl5cKN/vahQ1hfn8yHPNI0w6FDnVKV+/YV27rSHaCXu8vbHXbR6/XQGgCt1mTOTNFZXxLyHh727Zv3Yx5JrzebE1MsLe3M4QMHkJ8Y43GxbZ2NTomGab9gMECrlaDX66Hd6aDZbKHdbqKTzPs1P7bsuAPAcqeHdnv+9mOn08SelRX0+pP8+/fz5+0Ug0F5LPbuLffD/v3Fug8ebBfem+Sl67SX0Gq10Ok0Z2v1wP79s3W4tLSEpaWlEu+YotVKZjxjkj7Dvn37ZnwlD7YNPKysZDh0aD4W072Cnf8Jhzfk1yulzr17O6WxmvZrq9GatenAwavQWFlBsr09WVPdHvKMLT8WoxG/zuXlnTQnT6IxmYC45s0X0Pv5eTl79mQ4dGhFSK+o3dnyMpYPHcL2NvC3f5ui3wd+7/cyfPzj9gJjsndvob7Ovn3A5maRF0555J49hXXY7XSw3ZPP8X37Eoxzk7TV7eHAgYPS9cDD5ma53/P9uWdPeV3mcfBgG3v3Tv6dX8PtVgvt9nz+d/bv587t/N4xTcPymTz27ZvwUZamq65qo6uIMrN3b4peb87rDhxoF+o5cIDf1wUa9+5Ftn//7Letrbms10m2ZnMfAA7sP4BDh5awsTHZT1oMU+l1u0CvN6s3T0u7rZ7TKqysTMYjX3en08SB/ftL87NtIBvm+2W8dy9WBGVMZYClpWTGV5eWlrh7c36+pWmK0SiT7uFc+UKQfnk5nXgV7+DQoXbh+/nz8zHYuzfD8vLE0DLh4z0kCZ9XssiP4zRvHtN53u8X0y4vZzh0aB4TIT9G4/37Z//Oej10PcnyyZ492Op0ZryonaN/yq+TAweQ9noYjzHb46cKk+lcTnu94kU4huaVlRRbW2Uly2QfI4SL2tpCysguvW4Xjfa8r1mZR8XLeLJB8Xtu/+90Zmuq3W6i3e2h15jUnV9r+aHPkgSdgwdn6yZrNpHsWHxWzg0L86TXHRX+ZmXSAwfaMjHaGGmvV7i0puILy8tpwRip6kNfKJ1B2vOzzkq3XZA1p0iS4vrj7rXr62jkeFev057tzdJ8mMhgaVrUEfR6kzXearVmF/fStCinyc5AFOT7ItuzB1mzqeSRbJ17945x6NAKVlZSbGxM+nHfvvEszXSc014PSb7sffuAQ4dwsbdS2O+Gw+JaAIClpQTdTgdbM/4y1trz8mM3m3dLS8V9aWVFuC+JwM6LPCb8ac6j2X4TyRAs71leznDwwIHCmbPX6xUi6+TTHjrUxfa2mK583+Xb3+u0ZvMvf4bs7Jw1JvXOy2w2+WdVHnq9FO2lpcI4L/dWhOfS8Z492mOhA1bGbx86VFLkp91uYV9qHzwIHDpUOgMCwMqKe16WdrsFo3NHcObTAatHaLXoYwhMePhULm7t6Feme1ciOEdOke7dW1z/irYkKyvFMbrqKvR68zk/nesqlHlkcV0CwNpacW7v2TPG/v3zs0N33EV7q4Nud7JXLPe6GDXm5/Wrrmpjz55p3hT9vlh2dAlWpmXPThO5Z3s2tbudDvbs2VuavywvSldWkFy+PCvzwIEDhf7htYmlpbVnr3B9T/UZrNwDqOd5urw8m2sAkHW76AjS52UvAOh2m1huLs10CyJ9W+kcF4k+vLBf7duHA/sP4EKufb1eE63W5OzRbLZKL32023O5UDQvV1fZtVBeL1Pkz0GTtBM+KNLRXHUVXRZ97TXg2WcnZdx11x78+Z8rDHztdnHcmPkIzNcHO1e73/42kCRIP/Qh4Hd/t7Z/BMSujzk59R5KFDcFxtNHqcdj/NEf/RE+//nPo9fr4ZprrsEf//Ef47rrrsOpU6fw8MMPe6e5BhE5AW0Wn1hw86l0K5x3Yyz/h+kVQd00hOSF33ea12rtzOtsjPV1wdz25ME3ISMT/l26+aLTDzkPvtLNcFHoJ07oXP5NsoTU7GTMbHZZsf5C2dohOvV+V3+kFZpfF7reGTwPTJ03+KiXfdjfRJ6FxXS8W3WKithP7AKb7hkPPYT0ppuA06cBAI2Geh7bhOh0wS/GY86tZIYoMw8+9h+Gt44lHnyP3rOKjbURsizB7bdP1vT6OvDAAwmee45QNg88oiShtnk3S3WKHydqIzClHJvvhTaw7x5p7FuypKJ5TmGHqq3I9Y1WajgbXpFaITrPnUPy+ONAv8/9zJ1Prt0nZAxIg+fo7hE67dAJ0VlIm785q91P4rYrRSMRQQFvrMrmzpQMbkhhBY0+3uBT3VB3IVpPUWozNW++kuW5gqWxUbwIWg73Xcwby6XlWOiQgRqik9uW3Nvc4kR0zPiZhgeBNWs2KIB3az8vvqjWOTt/L19Wt9dJKDDPE9LBkWj+N/OjzRt8om+qs6tNd3HzeosdTABlQbOHu5AMLMu0IhCFQlASAlbG62rpmUQhPwQDYY4YOTYxhzWj8w6BCXGT6DJODWaXZUA6Hoo+0+qLBQnnXdMphDTPfz9zBjh50kw3JoLqDT6dsk6dstzjNSpLLl5EcuHCxAunRlAsvAff1GLeFyhVNjY2CulEWN455LVaLXz6058ufEuSBJ/97Gfx0ksv4eTJk7jhhhukZU099URYXV2Vfq8xwXh8NbIsw2AwwNbWGP1+Ucu0fekStnb68ty5Bvr9Hra3tzAYDNDvD9BoZFg7cwZIEqyvr2A4nDC10SjD9vY2moMB+v3tmay5PlxHf3syjzY3N7Ax3kS/z+fQmxcu4PLl/uxtmu3tbWyPt9Hvzw+i2xcvYokzLzc3GxgM5org6dw9e3YdCSdW/8WLHfT7k5skly6cR7/fx2g0KWOrfxnvvnsRF/afR5epq3/2LEY7bh1LFy+infu+vroqCfpUrHPy9zZWV7cAAOfOpRhsbxc28wvnL2B1dbTz700Mcgfyy2uXyXO+8c67aF3so9UCtrYa2NzYmPXPYGsT/X6/4ME3XFtDo99HkqtvbXUVly8n6PfLt1DffXcNrAPuhQtL6PfnN26WBmvoD+Z9tdFP0G/1Mdq6vDO35re2Njc6Qt7Dw/nzm1hdHZR+7/X7GAyKXmZbWyP0+yP0z5/HKNd/Z8+m6PfnSql8H00xXFtDY30d/f4Yg0EL/X4fm1vbGAwG2NgYYasxz3Pu3BZWV7e5fQEA68M+tra2MBhMDqObmwOsX16bzY9z5/rYu1d8A4ild1LPvM5537TQ789vfJ05c5n7Xkg+3fbm1k6bBuj3J2kvnD+Hfn9y5W97extbyXaJdwCTUJCDQQvbW/P5urk5xsWLFzF86iksf/e7AIDxyZNY/6M/wtbWijJk3+XLI6yuzsdieluJnf+dS5fQYtfruXMYTa8qTn/rF//O48KFrdJYXby4jTNntjDY4YMAcPbcefT6fTT6fWxtNbDR76OPfiFPfm2zYzWpa4DV1U00zk/KAoDNzbQw7y5dGmJ1dUNILwC0OTxxeOkSRnfeiQPf/ld88uJ78NCn/l9kSYrV1TXcdVcHzz8/mWdf/OI6Dh7UU1yw9W2eP490fb3w29rO2LT/f/a+NNiyqzrvO+fe917365aEWiAJkBFShJAlBmMwwUOAYLDjilOKC5vESUxBKP9wXGWXk4p/OBVT/pWK+ZFyJU5ssKPgAYGMiI0dSrKRJcTQkhGmpaab1tyDep77zffde3Z+3Hvu3cNae6+9zz73dYv7/eh+95w9nT2uYa+1Ll5EV1uH62urWJ2v18jqeH/TcfHiPLZre8/a5gDnzp3H4mJcjLC1NXe/unhx2OfDvxewukpboQLAmTPL2NxUo7STvXtzYx0b5WT+r507hz6xF1+l9cfa2bPonzmDfp+ffxcvbuDSpdI4I+p2rK35if9Ll8yz5fTpZePbz59fw5kz7nrV29i7dAmD85Mzb22twOrC8G+1tmKcP+fPn8P2M3NYWyuwsW6eTcO8a1jFKs6fH46x/s1nz5rrmcVggB2f+hTKlRVs3nYb1u++e/yqHo/1tbVx3WtrfZw/dw5zS0vGPrB0+rRXAU1B75fNS5ewzpy13VF/9Xrd8V7e2+yRZ7M+3xYXF7GxobxnuENfnDuHap6er2trO7C6OvnGM2eWjPfnznWxujq8Pb683MfqagdVVWCzt4HV1VWUJZwzkYI+jr1ezzkj1y9cwOaZM2PascalS+aY69+2ubw8Hq+qLLHSEi2/cOkS1tbWsbk5JFZ662vonnoebzj8CObL1+DMD/wg5s6fx7bVVQwGwObmPFZXVsbuxOu5vHN1FcXaZE+227yysojVVfdSwvbtwnm/vo6dFu2yvrqMtUqnHc2z3l7/NijaQIc+N+01NVhewepgNFbjM71v0PGqKLB67hx2jMZxcPXVKNfWUCiF/lIPa8tL435cWjLnzcZGB+uddYNmV4GYgynYsbKCkjijOKysmGsq1Idt4Sp7jWm8zvrK0mhtbRhpbJrj3LlV7Nxpnp/laLw2NoZ718bayni/1/Ndc4177r7yla9Ev19idXWyDi5eHM7v3uYmytGFvqIw6TT7zF1ampzHEuh90b90Caosjb1+5exZw/qfqvPSpWGd+jpdWtocr596nHcsLxvzpT7n15aWjPPu3DlgZaVn6DV7vS7W1yf8gE53SKDvs/VaLy5exE79XLp4kT2XOHC0KAAsLZm0pt1vZ8+uYNs2ipc205VlhXNnz473gvX1DlZXV7FJbE/z8xXOnFnxtqseL8A6lzc0PnbEQwJAb2kJG2fOYGnJpP/KUuHMGZlAdHl5h8HDAMDy0hLLl6aMRQwWlpZM+vr0aSfWws6lJcPrzsrZs6jKEufPT87+GnqfZkFVufvU+fPYJPrk6NEOnnpqDnfeuYmbb/bT9Dbv3OnIxxAAlpYma3xzcxPVYCLHWhjdSOBoMJufDJ0XNl+0dPo0VlcnljVzc8O5HoK7X7k8oEtfbWJhYTDm4Ttra1hbWx/JKuaxtrqCfnfS12fOLKPXG/IUNr1i0445MXfhArZZvKM+R3YuL6PXm1waX19bxcWLlxw+7fz5VezYMfmexeVldOr95sIFnO+eN/qH+qb5CxcM+nr14iV2fdcyUJvuAUyZKYWdKyuGLE0NBlhm0q9bvNTa2iZ6xSWsdq8dPzt9esm5Q2Pzcb72TBPGfr20hHNnzxrft7raw9zcUE7T6206Oq6N9Y3xmPy3/zYMK/C+963jbW/TeVFzf1te5mUm9rqpz3pKngYAZ88uk5fx6bLNvSq0joqlJeM8Xzt3zpE31zQHJdsCgO1Koer3Z/qPAF7zmtdkK+uKt+CrO+P48ePk+xMj6wsuRp9dzrZt21ASApZaAdiLutI9Q6ugbtIKbqSXpfA2vQdUkGI9+/o6cOC7XdGNyajq7duGdb2hW3LBa+oN4bnVLLWgqCrgS/+3xBNPzOPkSXcNOtYoNYj1yn2eJN5cMaDNFcgYfJks+GIy2WXEzmWfdQaVjbp53IYFnw2RtSURS9MwuEiJr6cUuocPj3+WI/fOwXiAkXWInkUWMekzbT2iEN9U55pArxsr/pxkKRCJiqrCwu7dKApgx9pZvPL88+N3tXIPAL7zHV4oHAOVMQafGlixP4vysrqU2CS+pO87msTgs5FkwReDUHmeCusLPCGUJ06M94m5554jixft0y3evKf2cc6qxHdGiDJ4+jTOgk/RRnORkyTGeuaysOAj+vOH9v0prrvwIm74zqMoNX6Hml9sPymFYm0N2++/H9vvvx9zmwIlXqCd3hvqdJZG73UUhZ64EOUtlOXnoyxR+6xVyowjUw0I2kurZCsNZa40lGpAWoeJto6GFnx28sHg8o39E4K5f05iIxuorTis+TsYDHlTM6nHWiG+dXR7EuBrUig2JhfDbMss+Ar2RbDMEEhWQshztwHRWcssesn+0BgRvNdnP7uI/fvn8PnPL7a+1xs8bNuxyaZ4cNFbkx0TPhNdtbGB7ne+g/Ls2fQyxu0Kw2xqgjWe1ILPLsK3vptY8MWkp+RCVYhRuIIQaSpn0t/D2fO3fyt30RuqJueSjS4rpjGN3YLMkAtXvILvrrvuAgA8+eSTYzebNdbW1nDgwAHMz8/jDW94g7ecm2++GVdddRWWlpZw4cIF5/2RI0cAANdff32mls8Qgk4UJwsFKKEWU64hx2kohXjuuS6ePtDFvn1zDm8aC0q+NP6/VnpFCO8ANDothsXyXLmtB5MyG9/9bhcXT/ehFPD887WZ3SQv6+aEODi4T6cYQzttadVTvybrb1HBJxVmiuZ+URjMpN4LYRedblF6/MWUqSThsbhyjXSCgkJ0GsnQExotiYtOMTIo+Kj+qcdKX/uqMI/5FBed4+7wEGmi5nON1oruDOhLNDmU41CKt5CymC1SYG4jqPmQgZ4OvFtdX37zHIvXnpHuqwN1RhTvCHFyuATz1qt/jych9Up8dkvWhUSS3abSKGKjtpsR7IeIdsco+Mz0zCSXwNPXwXOHU/BNEYWqjH2xc+yYK5gWWpItPPwwugcPonvwIP7Bsw81axf5zH/+ZlXwOS463cwhQZcqCsNVXqkGVLLGbY1CQwVWSrvW14E/+7PtuPfeRVy6lH+el8wFPVHbrXO28PofCUNy0S9Hn+qQuAXkknBbEHu2jDYxigdcWXHpn1ddeB4/8PT9uE67XNUIGTovRrbcqDqlqD+T2sXB5inHyHSeOHuub3m0SWNQ5RO/Yy/GZkUi3bWx4X+fe7/IVYao4EwVJe/vGkL0gw/bHnkE2x98EIv33hvpX59AoOFKucq51kj6lsYrWE9k1o6l4Gt7q2kNRUG7Z4dP8Rv+2CbDWPPJfB75WdJYwZeQpvVLCzM4uOIVfDfeeCPe+ta34vTp03jwwQeNd/fddx82Njbw7ne/G9u2TTTpR48exdGjR420nU4HH/jABwAAf/Inf2IoCw8fPoxHHnkEnU4H73rXu1r8mhmiQBDmjvCaEOKwFwx0ZZLgNo7v/aVLJaCGAVGPHjVNfziBpmQPdWLfcVZtQCMLvuiDmZMwRxR2/nyJrnZ72hH0MQeoEsbgA2SMvXMLaXTQk0zaNGLwhdJqD/r9oXL5yT1dVvfgToWAIoF4aI6F/+CWfpt0inJKmEaV28lJBV9UUcKKAs8SilDKEvYW1vhGKjmAtNhqFMhLB20y+1R9UheIgg8qB65lQQpTE5oOqWfRtGPwpXx7sgUfk5ATEKS0bWNDyJQImBephVVriCjbThqtx24oKKih3UuZeCvIjOC5YyuDtgAkXWuvU+NDCuLZEHPf/e747+tP7MvYSqNmFk3oavt9aV3WS50fanS422cnRfLpY9HW0ZUSr01HSvavfW0Bhw93cexYBw88kH7znGsHF4PPl2ecd7QB1e9KIlGMQkjCB2RHps2L3IKYc4S6XEnN2ZuP/R2uP/s03v7dz7H8qQSsEDTzxi3QITH5BN/GZJ48ju+fsg694TC1zS34yAK2UrIeqttzya81BUmofgHatvq9EpUhKfu5c06P6bqwXCxEftWeDYqNDZSEoUYjOB9i/iyk9IfF2OUe9yZeWxz4ZAXEM1t2diXOaQCjGHzMu6l9VIKHJCGalpVEn84UfFPHFR+DDwA+9rGP4T//5/+Me+65B3v37sVNN92EZ599Fvv27cOrX/1q/PzP/7yR/td+7dcADBWAOn7mZ34Ge/fuxaOPPoojR47gzjvvxKVLl/D4449jc3MTH/7wh3HjjTdO7btmkMO1CrD/mGC4z7gEZZQgVLDB1QftiRMd3HzzIPrmOvW8LkK34KsqJrO2i9tVx27Qdv/6bqP6FFA+bG4WjvWOXg95Q10p8uDgDjAPbzGpZzAgd8a6fqPKaFco8YecncPXvy+91MGpUyXObHZwzbYObnzVSDCCArVjKrsPUoyQ9BvUYWKBvudvQyqEoadTgTFFps8Zj6Leu0cQHyVSYDWhYaKJJr4PDWVCwEWnRJEUdokELC8X2Levi1tv7WP7diYRNVnq2+b25uZpZyPYCj5mD5FY8BWOZUE+4j9ZWK2fFynxoQQKPk6YnqIIttd5mwxvrPVqHbc3CI/SmP0eqhOrCsXKCub27UP/pptQZfTHTzckJMwcItpFpwdxLjqFAu3ga15yGJSNGhql0pMwI9xGmK+IuZPidr4xzy2QyLYpe7aHQynE+9ayvRsoOW2zFQKs48dLnD9f4g1v6NeeRRspZWo899yE6D1yJL9oYOii030umh/2BtSw4yX0TPZ5m1BgnUQfX9IDDeeiU8CftLI+MxQao7CVvqfWc+zKSWlXOdqbswrfPXXaruOnitDYb7WCL7GS0KWApu0082dS/DJw+NtWDzJGNjd+K6Bj6ncxt4fyDojofWvdaNNUqS54M8g1a9heh5UCOurlpODjBUfUK84Ls5VVDG4blciJQ5DQP6LGxDRgpuCbOlpR8C0vL+P555/H4uKi4xrz3Llz+PSnP439+/ej3+/jrW99Kz784Q9j165dyfXdeOON+C//5b/gvvvuw549e/Dtb38b1157LX7qp34KP/dzP4edO3eGCwGwsLCA3/zN38Rf/MVf4Bvf+AYefPBBzM3N4Y1vfCN++qd/Gm9729uS2zhDC6CUShLFG7fPxBxGinfnBowED6MEVTUMGH7ddWlEty2LMOqpmeZYaWuDk1cpyiWIR4gqvC7S6xXoDng/GDEuOiW3wTg4FnzKU3+kAL1tguf48YmZ2ckTEwWf0watIT5DT+6hfXPeBykt4NIQmtIuULhJ1ze8gawUyc1lva2fIBiVFVE442sna+Si04OzZzt44IHtuPnmPn72Z+nA0RIXnVlBEaaUhqnbJZILzpKBq+BLWeNNBRtc2jYVfDHPfWlE68pWZngqci5EVP7vsdfD3JwSx96bFBLnopNtvlLY9sAD6B48iAUAS7/8y8C2PNY0TQ6ffn907ku7xTc+yQo+RfalrD1y18Je5ldrfFMrqxhQFks+pts3v3LDEdil7Dme8nzQXYUPzz04G4CovLEFX4Gi4gmi4U857ZMMpuClpQKf/ewiqqrAhQsb+JEfyejOumU0UfA5F2ka0t1b0j8RCj0fosKATlMI7asks4IvpJ+Q0icU79yGEq6er84lUYbWiqP9CsJrwRZuAKGP8RB8WzI3IVPytm312+jbo+mhPB0tmbfSS38cbRfT1KyuXwMf59A9UKTswdt+RlkU2xbqXZYh9hRCvbI92lyOdIgIHgVfzvimMbKqyz4GX51mFoPvskErCr4vf/nLuPfee/FP/+k/NRR8vV4PH//4x3Hq1Knxs927d+PFF1/Ef/2v/9VwoxmLV77ylfh3/+7fidLalns6FhYW8KEPfQgf+tCHktsyQx7E3NaJEWiUpS6coZUITYn8ojDLOH9+ouBrIhy1heE+F50GsRMpVW3EcNi+q4WHSa8HzFkEgmF9wX5oTAy+cDsMAY/+nPiQWIIyieAJEbyR2gGbqAi5HrGZxaIwFXwpt9wlz5JddJraPn/juDIIbi4rsSpgMkP1eWPw+Sz4vAXTc4GKwccVc+iQh6wgGn3utEJ1uhyXpxoo55MyMfFqzBt5dJuKvuuiMwWh9RBmBCbnmL0+jN9RGji/UiRy2wlX5yuDSiQ5/0PlEK9sBV9VCby6Ciz4fG4WaxQAugcPjn93Dx9G//bbA5ULQaw9bj5QcRI1PTiVwSzX04xUBV+MQk10RjKvLpcYfEa7iP615WESCz5nj2vBgq/tGHzGcBDG2CLYCfUYfLoLzgHxfVreaYeS2r17fqyA3r17YazguxIEaYWqcODAHE6fLvGud/Vwxx20WTD5LQJLeYHMk2pVQp5EZFB6AQwpZ6/r0cRMsuDLIcDMYCGUNp7+9yHaQlJnSrtYBV8GkHVmFEJnh8SNjuxVEkg6QlDJkE9W2NgYenbYscOfJ/YsykVPx9RZo02qJkC6By34ovogkvePAXdxUK/vSjiHvYj4AOoM7ihTbnIl9wcXg4/7KInMOEqOyvAljeTHVlnJSBnYmYJv6mglBt+TTz4JAPixH/sx4/kjjzyCU6dOYefOnfjFX/xF/PIv/zJ27dqFEydO4IEHHmijKTN8j8ArRINFUHCybos48O5hUYJSYHPTky6iyLGwy1bwUZl9V86a7vAO1ab9SRA+EvR6BToVHxiZVLApRQpXJcoh7llZ0URKiTjGRFq/tF2xVRrCfdsNlTZgYQs+97nEfaHv/ZkzJQ4e7HjzisaQWN9SOQpFsA37hlbcxqzPJFgVhOqjlndV1fm0zGUpbhhXZ7RLBw5Wv66sFHhqTxfPPz+M/ZPStihE7JMSgp1y0ZmLqYnhVbm0zp4paVzlv4jif5fi+szvwkdvE/vbU4AKMEYh9PgjaQLJ+jIaUBDPMP14lMxj6lmKK2cKZekfCMVsXcZ6DAymaKwZwZpXwbdFLjrJdewI6eRncqtoPDbys6a0vznxw5Wm4NMveOVsa1yDmg3g5SJYM86iUb+ePdvB//t/28k0o5ROOZMYfMN307QSbZLHyCwsYMfqGbzh0MO4evk4qVSi+FcuZEKKsi6Lgq/lCRii8aW8k1KEpUaOs8XJ5Cr40gsj2hPxDa1bngfa4lOwxfDByRBWYj8aDIYW1J/85E588pM7cOhQx5tegvLUKez41Kew+Md/jHJz4r2Iu+SYDZlcdEaKwWRpfHM31C1tWvDZr1PFQVqiArI8zmd78nj51thxjuAZgOJlY8GnPDH48lrwxb/L0ach9nllJeAGw1fATJF32aAVBV9toXfTTTcZzx977DEAwL/6V/8K73//+/Hud797bHX3zW9+s42mzHAFI2h0oj1k9xsiY0jAJIKyBblumfpBq1sExJ+xmlso+7aTqgAw5uQNLPjcNvBFDdvlySs8EIcuOi1pqk4MRbjo5A/H8OHjuuhUbP3FRKMiQhL9maBBi3HDGHQ9QhTV1ILv0KEu7r9/EU8+OcemS7Xgk8bc8oJ00dky4RKpiyAlBQAAIABJREFU4Ksq4HXH/g7veuoeXH/uGSOPYWVgrU7fDUn9705n8qMt9zRHjnRQT7Bx3W0TiLbAQbsaxzGa3FjYLjpzMsoxCj4Ovlipocb4knLbXko7RTH4mpxfwv2TS9brCeajPWeNuTMSSjOXj4LPMiFmVdEKPk8JKfNMUNTwXoq7DqN3CE97gk0PSdenAFJ5J+nzkLCVCRwi/jSHDg6fuQnkDAvbVXhMu8coCkNxq9N5lF5V/77W9PGRc+tKEKSVDP0u2jqc2wVhgXx0HRHlJcFee0ySH9r/GdxydDfetff/0FbXVEZOYC9QXOQEtzelKJWatDNKOOqMS7P9i0JRmTyk3U/u/JSfcOS2v4Uh+IIdRDETApozG4QWhC6fXOChh7ah1ytQVQU+//nFxk3Z/ld/hXJpCZ3Tp/H6578iaVYeTPHAoOY2xWdNaDu7bRHUnk/mFYvIQ4O73JmlqzPS121BqZdRDD545JYx1gPxSVjklD/56Nb779+O3/u9ndi9e378zFUwpxyCM8XftNGKgu/SpUvYsWMH5ucnE2QwGOCZZ55BWZb44R/+4fHzN73pTSjLEseOHWujKTN8j4AlmMe/9SDlRD7rR4piwBXITh7ksuDTPgzAkGHgNusmLjplbdAeVc7H0+882NiAoeCzmRyWgMpswRdy0WmPc8xcyUHw+ASQ3BmqUIxvBVaVmUd30Un2D1FeKYkoLMBDD03cMrtTVEIQpAswKIZCqdE/LbvoTHUTo6NcXcEdB7+Mq5eP4wcOfB7ARPGil6+Uab0pFXbr7vjG66mhJSBlGSkV/iTRlBLGiNksRBZ8dgw+j8tkH5pOB7a/7fZkk2zS80AabyM2D2nBZxQUdrUm3avtc2djI4Ex0drLKmSoZ20y8kRZMUIJLw3TEidvxOBzCSxxc6jkBfEXlVZ/q7aKSaU+yCMYY7tnGhIXpw6BhW7ie5uWp2hN0SfrFnyadDzl8tLlgFxnUE6USnZLiFyrloKPUxZKy9yS/hEWMN9bHqfv9lad96QRMfeBCS4622FS8haRemyS6SLbGtMu+0VbFnyiy0OSdzkQGozIGHzZm5tYYL8PXLwod+krukx8/vz472vOH05qVxKmqDCSGAv6eBTuvCcRQSNGIyTXUgneW1LyuE0hX5JpYitj0nP7pn05/nKlj4IgYvDl3LMl4LfN5iFMONnayZPlOMTKN76xwBdO8ZKhBswUfFNHKwo+pRQ2NjaMZy+88AI2Nzdx8803Y3FxcvOlKAosLi6iJ/KDNMP3EnLF4HMENoVgMwodvILdVK9DYsEnez5iFEbFlT6BsoeQjr1RGRQS62PhED6yOnq9Ah3Lgs+Mwdfcgk8iTI6x4PO2i0ASbZBKUAjzhVyvUXOlLHQhWCiGn6gZYj7QIPgpYtb8EV+xUmTlrcfb8TA2FMqVZaaIwmT4DSm53Lqz2538iAybEbUn2cK61t3T2I2rlbkeIpb7HspFZ44m5Xqf0h5yTRH1pQpjkoRzgUTGnhCI6cFDU4JrEFnweTav+hV5kSBC+NUYTNnScQzFag0WMEI4Bp9+GUs7/5tcpPG0xx0CTwOnZcFn0Z+FxD14Rumo2IKEog2otkbVLU/bKQlFemwlmgWfUqYiirycJuE7miKj0mErYXQzd3FuZWWi1OJgn7NTcNHZep8SFUh4QEkMvomb7fiPaOKCjG1PSjs8WVJ5jhBtwQmuQ+0JgYrBFyo7eT+LzpwZobZQl/wiinPyLi9j/vHH0Tl6NK19zDP7Ub/fcrfq/VLQtGg2ZCo4ZR7bZG86ve4iq/vZQFmpazapjRHru0n/NZkXSgFlZd8CvEKVOoSCr0YTXjtmm25zS+fYTOll1qRRnSn4po5WFHzXXXcd+v0+Dh06NH5Wu+C84447jLRVVWFtbQ1XX311G02Z4UpGxI5GCdE4cDIaH9ERapt9aCvluvJJdXNHtcuOwUcSDb7gag0EiSIBrPSdhs1NV8Gng2TqlYpS8EmIMtsqZzy3iBvIw75vWcHnIOAfe/jQfVVMLPh4N1PhviyKODdV8m8260510RlLGDnCU6VQkC46/eVEQTARQ+32Kt2MTpIf83o2/aZ4lHAfnraTFnyyjk1aO4LJQFkVArKzxI45YAvmcyFUpnmOaQqSlBh8itg7hO1J+XaJi077fIthkmuBpU9454N1V00GUsEnQNPJ42OiIiQS1GPfRZCY3SHE59nLUSTQboAgacRtilMUovoskSnal22avY44i/8mnxY4x2Lcz4VguugskhQaKAqoTmdUhrln2sqOjGR0FhQrK+i89FKwnTmwvFzgscfmcfSoP1YuB8qCrzxxAtf/6e/j3X//u7hq+QQAZv8fx+AbPSA/sKFbwziWLx6ZBoXcDzkzmVSL1oZIcg0egXDxEZ4mItuWtMW0HYPPfjaQxyieNmJj8IWw7cEHsfC1r2Hxs58F1tbCGRq46IxFzPeUzAWINtD2+kwBtyYMVjbkOWiKLjrtMzfVRaeomZfZeNnyzRodaw5fBtMqDUXhcdGZkfmNgBZJpHH1nCxayjdE3/gGZgq+LUArCr43velNAIA/+IM/wHPPPYcnnngCDz74IADgHe94h5H2pZdewmAwwK5du9poygxXMGJi8HGv7CC2RaEcBcUkU1z7ghuqlaB2ccXni7cUGLtgowr1SR8CkglbCGMUH6JibFpEHIMPaTH4CBednBBJZsFnnn71XCmZgZu2BZ9ThqRQbTHZyfXYStJ1VvhfRzePgkh4RjGGxpwJV+64s1SKlGZHdrMfGRR8dBGFQ3yrgAWfqRya/K276BwThMIPZMdOouDT6ti5cgpv3/8Z3H7ob9Nulks4Lo5yJoToNppY8B082MHnPrcd3/72XHhLjfh047yTKPiY7w4p+FKYWbq6RAs5KacT+J7Cugxhp8tlwefMJ6oT22QSmbKl4xjVNE/isAWfmTaHi07fBwV5V7tBWwBD6TSaO6K4GG3PLzX9GHzccAz7JdHyULOYcPragkiR2hSCgrv9dey45x4sfu5zuOXI1zNU6e+7L31pG77+9QV89rOLIjm6DUrBt/2LX0RRVSirAX7gmS+M2kFkbmjBlzJOucdWJFR3aEDKkifiDM9FMzUtI6HMEP2RUp2E1mgjBl+diVfwpZ8rlLA96aJDS3C+TDtg+/1hHO7vfKfL0pQhdA8eJP9mkUjchDzdNO1y2sK5JXoj0/yQ0fuuLCkoT4qsYwxtbrVNqVE0pqitqYydr27zLZ8mUF9Txe/L2UVnjSYW7k5ZEWdczstk0WXFzAuOR5op+KaObjhJPO6++2587WtfwzPPPIP/9J/+0/j5G9/4xrHyr8YTTzwxfjfDDAYiToeJy47wRiTaZ0KHtURxYLVlc7PAtm18Pom8cmy5Z1nwkfW3FYOPgC8Gn3Qcq6pApxoq+JwxUiqLi05JU2xBRJ2Hqt8WCIUh95/d5KY7pdiqRYJj4fIIIcZluBbceIhSSJexfIrK3E2OSg3WRwpKicqzEqsi4Y6/COpGac08GUJKmAo+6Yd0OpN0sdsFm57ajz3r55aju3HdhYO47sJBXLj4BgCviGuIBNp1NmOvlcxxZ/HILfjuv3/oqvyll7r4F//CjbWjr7lUoXiSgm/UHzHMR+i5D8kx+ISIHVP7G1IUfIVS45om+4xIGhJOk4qIsuukr1h6CXc98w2cv/YWKHVnlrJTFXzcDWlJc2JIObGCb4pjRV9IMwU5ORVPjfIHzrGmCj4dTixgiu4JCbqKAtAs+EwXnYAqJ8Nu005bacH3uuNPoPi+oXnxrYe+imde/Y/G79qYmkeOTMQFhw51cccdIcLRBCXALpeWxufc9o2LAOi253KFXWNLBI+CSimrEBvmFkRvpsUkQXSzsggwMyj4fCL61D2EbJaS7Zv1O38aps2jjaL0Cd+J+iSyCrKsSvHX91ue/MFLlRq9ffRoB0ePdvDNb8yjeH0r4kgXQgWfSxYX0V0nHUOA9iTS2lBJAuMlgJr/sTH4fBdQg8h5IPv2ME75I5taFhIW+RZrzobVu+PUsVx0XrEKPoCnY5mPSuEtU9Lm6FPunGLLlsy/UMNmCr6poxULvuuvvx4f//jHceedd2J+fh7XXHMN3vve9+LXf/3XjXRVVeGhhx4CALz5zW9uoykzXMHIokSwFHVjgZElHLF/xLrohCVcsRkHYGLBJy6SgN2uEtWQpqEy+xR8kaeEQdsE4pJESdc0FKpCZ+TyrtsdEce1S1KuEKUYCz4uucAF5YAWXHDxEy5HCz7jK62D1e4DPZ4FeW4TTYoRJqYKZbk4G8HyIhVlDoOvlMNsDdf0dGPDRfDJRh6H+I4grPQ6TQs+Zt0wjeT4LIqJtdeProhe2JzE5uluuEqwIKh9z+Xc6bSCOW676Ax5kOFw4YL8kgIFbvv1XQBhK6rofU7SNgl/HTqKyHUW0ML4BATkWS9oV41cLjolZ3B0fI6Yc308ruYFCR+v9s69f4RXXDyEWw4+gs6Z01naQRzXbFbL+FhUfrBQ61mw6fqDUONbAjmnrYaK6BA7T9MjjZzD04vBV5YE3Z0CbVzj1uDWWVgsbC553l5+wpR6XnT6G7j94EOY/7pldejbpy0XndxlOw4p8yKrTJWanJK1Q6UhhrbQzuylpQLrq6O+zGhtIEHMWScui0BqWADy+RSE566LTv3GQI4q2/8GMUL9qQ1e7e63gMJjj82D2rea8l3zjz6KxT/9U3ReemlYl1A4bT+apgXf1OOQt1hMiAWzPWj4vjxIr/jkULEIzOPkS88pbYz4liD9EqEEjeVHXk4WfNzZGXum5uqDWh4WdbYxSA0XpTWGb8DMgu+yQWtXZm699VZ8/OMf96YpigKf+MQnAADbt29vqykzXKkI7Fimaz3zGctogN9nTKJj8i/fPI8bS7gH/tAVYh5GR7fgYxN5+i90cHtfB+py6CLBgaiUKSzXBTd6oWS7iAGVxG/jYBMpdaYcFnw5DvvQPOMq0hUnep4Q4zKcK+kWfFJhU0CGT6eL2CO4cshv6bdMqIoKDO0/7rO6z4y9EcXQTee41HCfAIwFn7AjhnujbF7aFrM6w2eMXw4pHQFujojcuw7ctueaKzl4VU7Z5XvmnKGBttk1xkISg4+8BpzCHAu+306Sy0WnfSmiADHHlMLaGnD8eAeveEWFrFQx01/Sbpy7cBbAdY3qis1qKvi0tRVJv0jOgcnvkZtjyjXoFGPwGdUScWIn8FCriW0UZ6Po6ym66DRj8EUIX6xJpkbjqlRh0nNKmXMBeS0lm2Cun3LzIA+kshqdRypH/XrbS1/Fzcf+DguP0fG2yT51LPjiMAUdTnwjJGn0+LEjOFuQ9vL48RIHD3ZxcGUBH/iHk/wxzch1sTaq0syQCkHrczhH2T7YfLrkHGsi+99SwXpo7D3C4cbfYm1MxblzWPjmNwEA8489hrWf/dnkzun3XZlPCDFVURbOrY1jS+tTMn7BNAF6PLoBbUBRfJ7r6UhYVHweoUzNxvyePZjbvx/rP/mT6N9+e3JDuDHsvIwUfOJDJPC83wfm5ugkMTz2NFx0tnqezxR8U8fWXEcdoSgKLC4uYnFxEcVs8GewkMVKiDiI9Rh8McoCs20CWOVtbqbdwKAEGmMFXzXAOG6YL2PD0yLYNZ66JMTIMP7eRGBhC9d8SjQVZcEXfuYo+Oo2MW2YtgVf06w+b3dSBqsNIVfKFKXWsT3fggINIr6RXbl0uTSJwWcLoUPt9rnohDY+lZpIyW2BkF2P/vfIa9moLroNnDAoJgYfRzwOBnCErdGw8hRUOQIXnVzVpW3tqxTiRUa0tapv+xalnUwGf0HMwothPqTtTM7ThDMS1lO/t9OJFHw2UoKQj9IcODCHEyc6OHBgDqsr4fTSOmIUQJyl+/zjj6Pz4ovicsh2pLrobCiAiEnLMsCplTSFZ91SwmIpo94K2xU4x5JpeQJlLo+pYwUf76KdQmsuOgUfM9fnA+G1tg83QN2vNx/7u+h2OC46Iy343LThiZ9dBi4o0P6usItOs5yDB4d3tvubwBNPzIsanfM7x3kzLIyYIz502db73KERQ/RDgutQi4/N2+eFezb6eO62F3qgfO4cp8jVpii1YKHF6uqkIqrywCOK3woUEQU6Bl9LyHRwCUla/7PA+hP3q1LNL4Qa7XDL9/yUP0tpo7Nh8ElDJ1vR62H7X/6lrF6q7roc4vHLyYLPbbxpdS1FY2s5mNXm4L0bK/hS+NuZjmfqmJLTaxOHDx/GgQMH0O/38Za3vAU33XTTVjRjhsscMW4yx5YruSoPbFY+pVWd1Y3B16hKskyvC7a2JBMBKsbpG8GH9XoFuhV9oxfwKNEihbkipZFjLeJvQw4Fn5RYJJ9TggJdOVGvCkbJM7Qs9SBUfiaBnYSGcNKFmMmUuFdKOZZZUyFUIyuh+qeqhsUYFnxFzD2eyVzQXXRyFnyFqjDXX8PtB/8W/e4Cnr75x6HKTpyCz2mBGidt6u5NBM5Fp2A8ir7lojMxNlDM+g/lV9Z+ZShMJJWPx8gvyJK4O5ZUJxLOWfOGcqHL1qedRynj09SCj62bkmhVFdbWJvWdOlXCSx031WQxj5UCOgPzPN757cexMDgFAFj+t/8W6tprk9pVBPzYGvO1sNOPLINjN2SJgkxDVWkXHOwGbQFICz6BYJqyEJUgRqhmp21qwSeoclKXpexIteDTFXz6mWNbZNuX6bZSgNXVLPiuBEGaLcBWyn9R3oDARacPTc/XLJDQE/Z3EZdtHAUfc9ZsbvJuxkLtVAo4eLCDzc0Ct93WZz0Ts9tLBg2Wn30tcOpUiV27KoNODeUlmxW5nlPmjeuiM74MX1ucU2krg4OGxp6ZrylijCD0uuq/yfrD9W5u5iE7RIl1Xn0aaLEiaQw+du/IWVETBBRzBUELxRbbLFFDRNTBJd0yBV9VZXefH+uik7/snOa5zS2ncRFjRI+Lw2skYKbgmzpaUfDt2bMHn//853HHHXfg3/ybf2O8+/M//3N87nOfQzWarUVR4F/+y3+Jf/7P/3kbTZnhewQ+JZdfYKQnNQWBMZsgfSvNfBay4OPgS1/4YvD5CJLAaRHLn+l9J1FA2djYKAyBYk3wjWPw+Rh64mCPu6FiHjycBZ99s7sur4w4vJMIHitTU4LQfqXrsiSMsR7DUoJUIk/UFtKCL658UllpKfiy88sCiVOo33w3w4z1YrutEbroLEuFohi6HVGqGAq97XWuFN744pfx6jP7AADrC9fg4Gv+Id9fNqFIxZMZtb2qChS6YDvD2mGl+QQkSmxbEUwJvCVINfjypSX3TImkU2TBlxgfk4DERScpwJJWNk5Ht9neQ+xiRTH47HmtJgIrSnAhFYQ2ZcQk77gpMb9pmg/OnTsNXDP8e37vXmy8+91JDQ3zeZpbP+tolwuBLKVxJEtqrEdzU4wqJxfMvYiJ35SgeGqH5/bP4ZxCn44gBp+oPs6CL7Cfb6UcfW6wzr6bmmAtAhT9TF16pM4W24Iv1weqiAXQuErBQnCObEqQ7LHgG6dRFUunB5uhhrHRvvCFRQDAP/kna7jrroAf/xGqCjhypIPrV4BFUQ4evv5eXy/wx3+8A9deW+EjH1kR7zE0mx4nvEw4Yscv6osaY97D3sOF9YTgVexOe3PwMZxWmtz9oNc1vhSWWMlgUOTXGZUlGZd8/L6toWrpUJZ2LcXDc7/FTct9GAcqtl+LZYY+uZwQkvWdbe4oRRKK1Dh1qk3nmfdBhkbO7dmDhUcfxeadd2Lj/e9vXB6A0c0j89GEzuYy0S98IXBieGyOP5WUZYOzRhaX4VvUsxh8lw1a4VZ3796NZ599Fq973euM5wcPHsS9996Lqqqwa9cuXH/99VBK4d5778WBAwfaaMoMVzDs+BoOPAIznyBIZ3xCBDkHkQWfVUaqBR9FDNV7ZVkLIGKJ1swWfPq3hlwbUNjYoAUANZwb7EblcksSyWdzt5AuVxedVPWGQLCgCYUaKS4EitDa9NQnTScjQsKVh+u3GAylnImSndESrNeUfq2FnI4FpyMVkpWpy7MHAzdBATVW7gHATSe/DYBfZz5m1m6EY8GXMgiCftYZf+OVoL5iMB0LvpjYDuP9KiXoOyB20Um9lw2v+S2io8jnV9gCJyCw14UUIddMJIwYfHWMNAFzG7kHRK0JyhrE445sbnPNeTbG+rqbIbVdRN3aL9pFZ6zwhToHPGWwCj7hPtoUdnsl1p+i+SVEE0a/jBwb9718vekXQGI+16ihKKBG5ppKhem5qcTgExTczRyDr41v0cu06ftnn+3im9+cx4svdtk8Y1jMU6yLTm6r9Vm4psRT8kJCT9guOonvdNgdih+rL60kWPApBTzwwLbx7wce4CPB2lXv3TuP++5bxP2f324KNltaKOfPlzh4sCM+fsh0Le7jNep55ljwNaybLWcK38QiVDdnwafyK9CMumpGV3ibzn4UilUfK4IBYMZBEGXIAxEfJoCsuWEPHT6+iyO/go1p2pe+8hi5QsocEDXT6RM+04Q+iTy/IvqLS1oSVvptY9tDD6HY3MT8k0+iuHAhS5lq/A/7dgiB0sq8QJ9+oShnX85i8H1voBUF33PPPQcAeMtb3mI8//KXvwwAeOc734nf/d3fxX//7/8dP/mTPwkA+Ou//us2mjLDlYyoTYTZPGxBiW+PiWGGBAShXVVqDD4z0ajssdArzUVnTDwbuyiKaTTk8PZ7ATHZ6/njCHpdm5Ax+NJvqBSOopFvg0QgpCMHXe3OM77fjLZiMv9siwDf5UZqXZhWi3liEzB6Bi/IeSyscKwIIYgX10XnFIiTSOF+f0BZrhaO0MJR8HmqtfdKnf8kLYVtIfN4LTD1RbjoHAyAsqmCTwJGw50agy+lmaFpHC6TuGFJ7ZmC9VIIFXwxz31pRJ51Apl89UqVenUZSbyMp33jfcazT1P5iJ/x7RC846bEfJ8PAFjYCr4IhIzgOHoteENaKVfxKADtapbZv7bIgo900WlDhfer2DMmCQ3riBGI6cMxPPfi6hqjGJ6bNj3nuuiErJ+ngO4VZsFnu+g8c2Y4eKurYQGwTY/FXtTgl0uLUrQQKB7Sjh9duXS8c7nOp7AgPyfskj+kzLCrsTHoA8vLk3pi+U1f2U5dKRdwmIrs9R1ILkbIRWfqtOP3+ZRMmRDkEZqvXSkMRdZlaMFn04m1BVSsx4FotKk9sODS91Y/5qq7bXP6EF0j5f0sgmKKQ5GGCGbPtuBzzpqGe0EI4zibjQvy+A7XzuhKC3/CfcvQRSeNmM+vy8nRZfxS4eXowQJCDZsp+KaOVrjVixcvotvt4hWveIXxfM+ePQCAn/mZn0E54sw++MEPAgCefvrpNpoywxWMmBh8MUquonDdckXX7Uk/LtMqnLLgi93z7PRFNRAxJW4Q7jgXnUHoY+HE4Atn39govAJ1bwy+CAs+Sdyo0vEZV7eBLjRHnLAYnqPpAU/1gZShT2mENKl0ihrCjkAdsnVMMPiJLjob0TCRgtFqQAiIFKAGpntOm1mUuujUQhMN66sKh/xzy1KjtFyjJR05KaPx2iK4J2eIGsXgMxdOTgu+1PdjhZLQRScXvyWkK0qUkxB54mPwRSmylPa4RUGjAULBJ5lf0dtHTOMi9+x5jwVfYfstzSilsPcg0Z6qFLZ/4QvY+T//J+aeeMKlH4R7Xg12myqaCa1TYdNGRf0HtPW+BRZ8lBfApjH4YprtWOsmxOBT1kFHxYrji2lHaCGZW/pt+aroeFJeHiiFZzn56QILvljknIdZKoQ77mRcTYmLzjqfgN5KFlR74Fw8bfHcpWSx0guevjrKUuHWW13GKIb0GKNiFHyewiTfz+39Bg8wbQQZF6ptvDwmaurYxAKh4IuxD9fR74faEhmTWtkXSQt0Bxt2knbQUsE5eAKfi04fLZgab5hF5EIvoJJ0jEnNbsMFb0OZjlKCGHwtx0l0QmY0AdM2k8YM7ya6iKAJu+a9hB9ZdmNduGehs27PZwq+qaMVBd/Kygrm5+eNZ+fPn8fp06dx1VVX4dZbbx0/v+aaa7Bt2zZcyGRaO8PLCBEcWL1hUebpYoGRJSUNEWchFJp7KWBowWcLRiUen0yFhjLyFajSBJeZXXR6GRWB0IWy4BsKsnTpLFM+EainySHougod9TnjFijORWcLh5yPwR/9Gv43/L+qXCKa03FwHRbjpko6NTmCxpuOKNyYb97Kh/3hCJ6UchR8UxH2EPM/toiqgtFx4xtmetB2oVBhaME3eUC56LR/10xWEwXfpIzCWFsp7qZEUPRclijrir59ayPVgi//vtBuDL6454LqojJlVa5YipJA1bJEAQs+VsA3ZaHE8GyjBVPzm6vOsxqOgi+iHSE+T58PBr2mnev2+JfHjqF78CAKpbDtK1+RkSiecTfa4JMwtSWBs7+PclEemjsUXRB5xiShYaEx2U03/hFeG+yHnYmbP8M7AUFLTsVFZwhWxZudbb7XKUVmgbF0Gij4asHdZP3ncdHpxM9ucT1LBNDOd1HuDC2ekZr1Y0VSootOKT3CdleGc02aRbdoDOUlv8umLUZz4n3v28BP/IRrJZsyRexY8uz5HwmuHC/tOu1NS0DwFaqed5nrpmLwteSiMxoEj9kd9IZ/RwrDo+nhlhUtvqKp89Sw6nPaIuyL3N/krKm04r0yFY+syqg7YT/NNqQRBZWVqQUPypNyz7tUBV9IjqQn0YdPv/DH7Lm5mpTTQJWzKuSGIyXsklvITME3bbSi4FtcXMTq6irWNXc53/nOdwAAd9xxh5O+KArMzc210ZQZrmDEWEOIGQ3U+4xfqJdeoUl0dzqTy8GE3kBaJItydFOSZvJMQsJAg9MiKDCxhUgCJtO24LPhEw5Q1onV5gC7Lh5EZ0Qwa68cOASno/Dxt6HtGHxBoRwruJXJ84GJmxsuvS3jbEfIZVsc5U38AAAgAElEQVTyhImQkCWurxyqQKWG/9gxCnJ7/5C4Fg3166CiXNPCVC4U5fCZkLiyx9mJwWfBFjwHFXwC1OspSww+G4QUoRBY8HFV2zcWUxVPoekQKpZKK3bRaWPKFnwivrxJDD7NzZmEpkjiQe05Rc0dquBAZcGjJabDmbTcOM5velx0hiz4PAhtRc5ZM77MxNdRrq2x75xCBa+4GHyxgrcmoM4546Uyz75U6+FGECgp3Cmex6U3YF5AGXZJotStLJ19k0raptAlBjZdq4qtcR0bA1+M7SBsC74MLjrJF54zv/ElHIlQ1vF+wvGv2mvfQSxQXFBtaDqvHXfKLSoQaAs+Om2Qd9V+c3c5QnQRidG4FuM9OzIuWWR9rV2GkyA0GB4hSC6akqyLvcUqqyS7i06iHzr9K8uCL2UeD3/r+Sza2ScHionBlxsWIUDJYVKakMRveJSeyXRgROMpXqrOrvPEQVlV7jFL1cJT5yzXj0ZaSQw+2tiEqZZF6PyJKatxDD6PIJWVgcwUfFNHK1zB6173OgDAww8/DGDIdNXx9+666y4j7fLyMtbW1nDttde20ZQZXs7wCNEmQg9eYOQrL9rNkOeAmJubvLPPH8mepxNREwu+ejOteMbTt+NHuuik2sBmSDjFNjbodHWtzk1bvWzi8Ln+0b/EO/Z9Bj+070+9TDsFV8Gn6OejV20r+GIwic3oCiX1GHx2QzieS0bk+JH6zSmWPcNnkz8lrtlCrs2UJvxrFXa9gTo5F526+w5VdGAToTEuOvUYfLYFHzWPdOUcBV1x6ljeWu2rKrlbLw4ihZsgBh9bPuGiMweTN3zGMwaS/GJGj9mvUwRZEiFoiFcQCeGqSq5MjewGkcvQEKQWfJ58xM9GKIhx9fXhfN9Wmmk0QCDWnW8WpCr4jHeBSZTCOLPF2fuyxO1CZlBnlM9dslKeGCJmMVHPJWm3ykWnUggrxKlKisI443UXnQVh4WqIJpm2Pv74PD7zmUUcOtTcdSalVJ7vT6xrKRp0StMyCm5saxoUb+XE4Iu04BPVk1JIkwopfsf6Luo7HbKJsoiqvcckfE4MDeMXCDYT6EnbQJGRUfuasvbNEXLKI9kYfA3nG8/DNCq2XcQI0+nkYhj0TWQMPvtRSHcgLHbSNtKCL+AZIRdsvq3FqmIN63wuOrNWFEIgP0X3RF8CUcIYfG0rxnyIrGsrFXz2xWwxiHzcJQnd/bFxsYr5llwWfDm7Kmtsd2namYJv6ui2Ueh73vMe7N+/H3/0R3+EPXv24NKlS3jhhRcwPz+PH/3RHzXSfve73wUAvPa1r22jKTNcwXAEFh6Ibx5gJDQSSBm8KSQbnlK8gEprS6hI4znBAFKCdiejI4hPPy1oAsTzXqTgS4zBR2EwwOKhZwF0cfXycSz0lrCxcDWARAu+EUg3WZFtSzonQ4qf0IAIkg9vGdG30ihMw0UnRxjFMlIxFZPllKWYIBK3g2Ry48DOZYsAHcpa+BuTHGwXnZTQ2GbGJhaVgksHZUnTfKM0g4FtwSdqNl9f/durHCiIv3gUA4vrT5yIoX0pZY2luuismSRfnVQ3hvJwaSR8eSiGrCGvZxT23DkZ2ssk38TFMTTyS+ZiFil1XFrum20LPiNdRgs+pXg6yHbRydYXUPCRQmt9XjivNOk41SBFvMsJS0BegJhP9u9pCn2cyo2H3iQ5m1kUk9pYOpiCnY6z4CMEcDqofXtlpcDXvrYAAPj85xfxH/7DkqxNEZiz4mMmC0UT86TIasoqUcHXJwSGkR/Ixd4uYPF5IVqwCVIOR2YLCpZb720CSy5qfV4OFnzSLE0s+EhaQ5sR1DwP0UW+F5yCL3Wusd+5hRZ8IXeCpBB+tM9mX3M6E9nQRWd2Cz5CY9i1LbPbGsYp0gohstdxz+qRd3jPndzm9L55bNFnXJYhMig2YoiohnuKDHRIHQDoVJvoYzvdhjaJQSCrNs32BkHNT2XQ6PS36E1qwq7VbjVzdFm0BZ8FSn4cnOUzBd/U0YoF33ve8x786I/+KKqqwp49e/DCCy+g2+3iYx/7GK6++moj7Ve/+lUAwJve9KY2mjLDlYwIqaYbg2+ShhUYeaoIWj94CMKxoAAKtrBj+J67JSjfACeWWgNesOETiEUzyPoPoj0eDV+TGHzj8il3c0x9Tnwe7eaw6LOZdhSMgCKHgi9uOMJxgEgFrsdfeCxd1IY7sIDM1qjd2w5rsob6PGTBh27XERC9/vV9vO51LoMmHsfQehWUVTFCK9tFJwAxceXbK2Ux+PwWfEb6Dm3doFvwZXfRSdVHKWOE9ZUD13VYLuFqU6HP5RqDz80jmJsZbudGkBPNEbDgYyuN3AMacYyex0oVjhJBh+SClPSV77ftopMttk0LPh1bxKSSQlNm761fSzqBX8fC75TSHoI65e8nbSut+FsC8tB9OPJFXT8q1QDb1i+QgjK7X6myV1cL7+9oBCz4gMgLcFOE3j9SF51OnxKC8IK5bCcqr34eqQRpfD4IeLD4GHy0pe64fzx8qp7aeF81t+DjvKDEQbZumlrwcfuVHduv3nfEfUM0LMaCL6rL2hacxyDIuNAWp5Ks0dDqKuozM7GS3BZ8doFKAZ2qxyTOi5SYbhQkxcQWnSxbaDuuYIg2V8J900pkx0OTlNHK8s7E1G2pi86MMfhE81DAD+guOpsg3FXyemJj8EUI5zxEwUzBN220YsFXFAV+5Vd+BR/4wAfw7LPPYvv27Xjzm9+MG2+80UjX7/fxqle9Cj/1Uz+Fd7zjHW00ZYYrGE1i8PkIaD0Gn5EnO4+sBHuaX1hgP7f7pFSeeAm+E6HJbSeKadSYZXfcwuPY69n5RjfoR3UZwquimBCoFMFeVaygXkJPcIJx2i1QkUXYkD+TnjXebV6NWqjqCF2jLPjilUvifONMTYgH61tsYbHlonPXrgE++ME17N49j8OHzSO0Eb3qZA5YLdvTTo2ClWsWfFXhuhcthP0cctE5bCEtZJbsSarTAUAQ5KM0VVVYQuuEzpVwnCOmwGZ6JRbkuSz4Qs1MEYpL4jySz8ZMUrzVfApz6s6V8LX5oqrEfe0/m/xt454FEwVuibMKmIYuOr3KlYjClALm+x4LvoyoKltIa16A8ln5GoX4QAlfCAUOWZx9+Pna0RJI7wGBvXirME0XnZ1ycoIMz26p9N1K1+mMH91ydDduObob5665Gftv/Sfu+RmgfewpcuxYB7fdlhgbhsGc5T735eyi03aDPSwrnC+2Hvth9j5MORxZ/lV7TSlMavZIsOdT81usxArwDcGEmSCVG5M8xhSVYzWvmKtKNl+KADYXQh/nnZNSqyghbIH/YNDAgq9BOwjYLjoBoJsagy/L5tcWXAWWLdPy7RU+8ouvxS0nGr78BD2ZdLmTKCd0LoXa5qO/jh4tsbRU4vu+L2IyM2uDq6fjOed9nk5ygFpTIji0NGQuOmEfxi7yWfDprWuGxi46UxjlmYJv6mhFwVfj+7//+/H93//9fOXdLn7hF36hzSbMcAUjzoquIJ5Z71ALjOx8BBMVOqy9wuDRs1o5ZVnpSQkWCpMYfHUdaS4642Pwma3g2gUQigeB0GVjwxwHThg3rKyI+raQoN5VgPhdcdrpy4hTOwdd7e2bEai14yNGYts1DRedEjcC5HfqAn1KsGuV4yh2CCJRb0sWOkVAIKXMlaqC0VjKRac/3oW+VypDwVdV5rqj9x01aQddweRvzoJPUxK24qLTRgN/FUMFn9m3yePmqT5mjdVjGHSXyhXuUXzUiL4B6IHo4m0gkVeYKPgeH+IuGgxR2OsEQgVM7B7QhGP0PFYKmN9cdV/oGAzGazjmRnhon7eP+gm95i6IuW99C91Dh1BddZW3jtgYP74YfCIXEJnhszIf/xncm11wcztGsBiin2KF2XFdKqdD2EqKYhjnzsq/6+IhLK6fg/JwyRJ68tixErfdFtE2AXTrWqXyWPC1PZVLsVTJ6lNSwZfhUl2kkKpx/xBCRAc27zLmOUy6zCjSR/8nuGq0BfBJEC56pXh6OoZvkO4xIeG5b6uXtmWMsnS8IZTZLfhMWm+SdzrnEglKWK4/oITwHj4tCtaAUa7dpUNqZ+33/S46o2XeAhedrSH2UG5QTFPDOnH6KbvolI53Fnonw3gtLxc4dKg7/vuNSQ3hMd7jNK82EllVVqQq+CJi8BnnRRF2guhrUsx+4gshGipL2iYxreSb6FwhMwXf1NGqgm+GGZogixKBeVETfqxgMFChjL90y0iRFVHPoxV8Nlq04HMPdT8jBwyJZ+pWbj0HjBvsGuNEVjgYmPIb7YfoswkhawFFWurFCleS5LGxQl8rkRIcrJOzWWihEPEhqTSdTO9CrONIOEwgwXjpSSiXQHTbPBCs11BZVPw7pWBQb7WCTwmF0vVe8orlo+j0rkJZ7hi/Iy34nJiggfHQXwRcdLox+FpiDur+spi24PkzGIzce2rfkYlRji2KOrq2IgafZH/lGJf8mYisPmEo/TptSMn2EUxSQOgbXbeAY+TORqMd/U10LPezDu2yvg61YwdIRAgu/Qo+pdE62vpUCuXZs9j2yCOx1RNwz0exBV9bcOZFmBa+XCz4grRzw2YaZ7Gl7JCuX2fel66lOwB0KpOWtGltyR54/Dh9zjXBfN92n5tAI04BxlhlsODznm3C8iYv/Imz9qGwMIlrS4eUSxG2+aAIPoBL6tmLQlV/5SsLeOqpOfzIj2zg7W/fdN5Lmx5zZpNpLUGufYnWzit2XxzhojN1rk3y2YcqX2DQvXZTBMqn4zfx/SDubwqUBR9Vv6BPQi46Y2HHIlQK6A6GFnw1v9baUAW0bjnJHPsbnHNamV6CfHRMTAy+7JRaiD9XMiW1PtcKohxJGf409Fq6eHGyH/V6Eb1DVEbXPyyzo+QuOrPvRYkKPsr6k41rzMjW+Bh803LR2bysRvKrEGYKvqmjdQXfyZMn8dhjj+HFF1/EpUuXAABXX301brnlFrzrXe/CDTfc0HYTZni5Qttk7Bh8+mZsy2ck+0wTYcmECaVddMZurpzQeVhHmgVf7MEaTG4QLS7DpFRIwRcg7JSl4POgsBR8oRh8jqyHER5wlnp5FHwNDj+v0For10OMxDLqdf4da2ex68B+FG+6FcqKrxoLux7JLSMfsyiFLTy1XUIVynSDOxY4R6xtUcIQAyHA0IJPJ0DjwuwqBdz60tdx25FHcd2FeTz2w7+MmkygxqO5i06qzCGqgWqu4Atyly6jLa6PjA2UehPZbwmSRE8T+xK5yzAaF1+dTRgEpjp/GXYij4tO6vyRtE0p+oa2qO89HzW2qJSMp0BI0Tl8GJ3jx7H55jfHMU1MpdTj7kbAeg+Wgi9igoYEGja9xqE8flxcJ6h16RWuMhcipEGcM8A85/hFQgoeOJrQU4fkuSRhcxed8jntKviEDbfHtNMBuQcHzk+Jt7cTJzqEG9oIEItgzorBx9GslxNSXXRy52wwX2w9mdMHCyAPm3Cl9nQgaWBVDdMJzjPnNzOno+RztrDbqmR1tcATT8wDAB55ZFtjBZ+btvm5mJLc965QlT+WfAJ8fNtlgxDBp6XJfpxaTEtRVQ1cdBYJMmpmEQKMBd+G9aQloXigo1OO0aZlcRnE+duOwaeBG5UcLKookScTJ9sL3onMdCBGWfBlHqNcLjoB+hwcPo9rcy4XnfXvpjS7UrwHHnFjfHsoeyjNFHzTRmsKvl6vh3vuuQcPP/wwyXTt3r0b9957L973vvfhIx/5CObn59tqygxXKJrE4BO8cF/n2H01cDcBjTQZXHSy8EmII110Gu+oA04XLtnvbWaPwGAAdD1Cq8l3FqakhOLubAVNNTlhJYYfIRedOpSKVfAlHHIhgpfq20TCSZqtwFD58s7v/DFuOL2E7XN7scq4W05lFmT53HVsCkbDLjrNBAXJeOnzJllQF0IsY0N0WCgG3zBmJF+PUsArL74IAOj21rBz6SSA10/SWXXas5lyKcVWxnXkKE2VI+aFZBIxvi+CsZbIK71pCr6mfA41/0lhEilUpM+GkCCLLip+f7PzkH2N4bI8darE4qLCNbvkayUmBh/dvugsloKPL5hyIWX8HFhzcnkZi3/2ZwCA8sQJbPzET8gby7yjHnc3VoLpivV1/qWnHW5S3j23qU+zJrlnI25CygEBC74tctFp1OShN31ppGj0WQ37JCa7Qz9L89qVMBZ8VeEq/WMtfvv9AqdPl7jhhkQlHMEkzFsuOscb8mUsRJHSyU5/t+Sik+Jj9J1IMg+L5WWUFy5g8NrXBvtedKmSddGpPRNY8I3naMJa7BFeAvt9YG6OSk1/M+fZocbqar55KlmD0rR6fzZ1HWrnKdTkUpKI5jlzFuX8ANX113vLBdz+9bax7TMrVH6k96BGzZVeDKME/IRYoet11xzZtpeBi85QsZJn9qVI6eVjB7njuQX6iKILRN9vbTiiocgwXsndI1gb+jMzBl/AI1TuvSiXi06OwQVMNkTAC/hddPrOQfNdtFKOrTPhXcy4ce8uY9r05YpWFHxVVeG3f/u3sXfvXgDArl27cOedd+K6664DAJw9exb79+/HuXPn8NBDD+H06dP4jd/4DRSzCTCDjojNf5LU3YhM5oi2qiML9KWTCLBU2IIv1UVnjVINnNhYIjQgskkhqZZASvjo6PcLzAkt+FSnYw4NQYkbeoRIC77aKtIYO88HZBE2NBRI+tOO3H14ChEf7BjJOFWFbRsXMTeK09Q5dapxe11Zdzg+ECk0iVwLIQs+dw8x/0+qWtDuUFm0pVIB/eqZKjrDciJcdNbzuShM17i010G6LNH2EnDRqSsqAVogFw2KcB8w+0N403IeSS2j3bnuTxNSnJECIqGLTqfkBhZ8KW6MpBZ8hw51cPLkcM687W09/nhmGhfa4zieTjSkHgZoLISTaB8CndE9cGD899xzz2HjAx8QNG5Uv08QbKHbs10AEuWthdNQCM19e6/lXHRy+8cotfnLM7DUK3b/2iIXnSUUdDnB2bMlvv3tOdx+sYvrrqvf8DSYsBpR+r/5mwWcPNnBj//4Om4qR2eOBjcGX94+M2i7wrSUFZNilkm+YhR8dn2Sd9TcaaTgI+C66BzRrUW6O9C25f6l8MaO0w7CRafD51H5QvUELioGsbaGHX/4hyj6fWy8+93o/dAPBSoUCMkEaWJcdKbQTJs95UiGeAUfjdBZJ6ETmuxh/Fp29yKurcN+dvfUVBq/qAaOwpajF69ZOopr/uT/YG4OWP3gBzF4/eslVUyQg1ZORWgOExLvWkYQTX8FBsPxzsG46JRACzkshq8q29pIKd2Cr2UXnZkKlhQjNawzaDsGXvIrRgmRApMhI+RczYulfkvyWG/Tym0gN9FRVnIXnY3HyN5jM1rwSQhChcklQ4mLziafG33+MGBCoKLwiZHtfk5hlGf6namjFQXfI488gr1792Jubg4f+chH8OM//uOO8k4phYceegj33HMPnnrqKTz88MN43/ve10ZzZrhCEbSgIIRoDlgFhXsI2jf9m26kEjcysQo+yoKPZfJ8FbcZkJg4xEPVUfG9lNK+V5kCGR9iXXQ6+VnbfE0QXJYoqspQiEiQNKdixxbxlio+4oF61qn6UEImRPKeIi4knhNDa0zUD3bFm5vOe10wUJZ8manMf1xmHlVVAH0zBl8M7L3EFqDae24BBVWUzhpo5KJzVNZg03ZXmNA/gj4tEi34UFWOwEhKwgpkHkbZMVNjLDyS9hez8GIF20AeBR9XYa3cA4CTJzu4UXiGGR4kAh2ZvCwdYR5hwScp3I7LEiuoSxg06nE5cF2mOUi04LNBXbKvwVrwAdlMqammeqfWlljwmfPp0UeHHk9OnSpRFDWNZGVq0DYu63PPdbF377Duz31uEf/h5wmatrGLTmkrXWcO4vIMIqFgLfhKZVpO2ucjPU/cU6DfbyDcqImjwQDAwrBdFW3VpkbxYHMrVakmUVBFwSotSiU7HJz5RBwqMS5JU+dFaEub/9a3xm1bePRRU8GXepCkWPARk3C8Bol3oe/qb4JQ8NGuBn18t3IWzgSSuETSfUDocZF9zsXgawxHKFqhJEKI1LXqeMuzfw68efj39i98Acv//t8Hqog4f9s+swITjN4fXHlMjrpI4iLRRadSBSoPPRbdduLiQsey4GtrqELWaTlB9aNvb0i14Msezy1QXsy+wyayZAvStqTsUakiP2rnd5ZZ2QVGV9A61RRddDqHV2KgTKpdhDzSSStQWvkt+OTvQuMn7Uqfgk+MlPNlpuCbOlpxNvaVr3wFAPDRj34U73//+0nLvKIo8P73vx8f/ehHjTwzzDBGxOY/Fmoql1C092OxBZ/vNcEUTLIU4zLa3tPGCj7ynecbIl10Gv1JELnGM4IYCQ1lv1+YVnpOem1cQ4K9fh86w6S76ExzITf8BoOo0pQT01bwyQRqmqBCpHaglXVc/CpKwMQhVaAnCo1GrHdpJ1OKEKXoW2CRNF0aAuNsg3LJZgt8VO2iUxAMuq5Tv0TgKLmIjUERHcKNnbEncddh6zQ5YipIBG1MY0NMVA7r0UATyGI/9KFVfOQjK/gH/0Bfg64yMNVFZ9FAwZciuEuJwUcKNkcZ3biQMuFRlCA4BELBZ66lkSA8IPyi3F0nN44Ze1reFhZ4TduCz3inFFSUi06Bq2amTYWvQS3CqNaZ05O/l5dLJ410WsTO7WPHJns25zki1Dsp1sgc2PMpdrFzFnzE+oh10Qm4d4ai0O9j8dOfxs7f+z3sPH2IreSyiLvlXOTVXkXSZWNQQrsE3rBJGRRoF92eugR7t+PakhhTR6ntK6epAHUEbv6yvGdA4SRZD/KmF86eEnWWK3o9N3bRaSUslWvBx2GuP7k8k6S4yH2BNyc8bYsmqUNriorBJ6yESharP/C1neIzt8xF5xQR7n6eDy6cW0wacsfg8wrB6JjOFG0Tz3c0u/zA7RdheUI6qrI7Ln8rLfiyKfi4Z4AxPU3ZGp0+Vww+5h5yNCQ8erAxKbKPmYJv6mhFwXf48GF0u1285z3vCaZ973vfi06ng8OHD7fRlBmuYAQZVoYw59KMy+UuYEftnHxaSqDHId6Cz8znteCzCBKj3swEnvGtTl28ErJGv8/01yhjqRdgC/bs+hwLPn88JEfWE7DgAzAVBV8TIR01vjoxkuznvs4/yKfg4/g9ibeFoJUsJ8DWy7DHj7Dgs61K9P9zwQknFBoTe97Xbm60uF2Ugs/OZ28Ten+UGkNVVUydhJWgSGlQlmQf1mM62GyBCaUaVk80e66EzhdGyCq5RODSy3LGsNtVuO66ymB26T1AeMWU4SRSjgiZ4M6OLeBvDveQtJLzZnXjTwqqkfUDx43pryQF2Zte7Bj46ogR+gna6ovBV2xsoPPCCyTDHRp/oxzLRef4OeD1lyXpar+3J3q/dIptS1BG0FA2QhZ7TY6nRp/lOV8kZce8L61hovMK/OR3OqI9VLJvU/O5iQVfoRQ6Z88C6xu47eufHT4jlIz+C3Jh2HmeeaaLPXvmmiknE+C0nXLpF0FzyysynwXnrY8ApDJLBsU5R/z8K90w/WJeuE47CfVVsfO3sMu1KkmVw1KgulbCS4+fMRZ83F0OGa3hJixU5Sr4lKKSijFZ+9aeezm76KQsTj18mrdvQnUJ6g5XoifLyPQRoQEmLjrN50HETqBMyjDZluYq302Zlhr/BRB0jRS5FXxB2N5bhF6/LIZbtHc5NFX8tyXPXUqWpACdGh6UE//Nna100Tk6WPbsmcNXvrIgj/VKxuAT1Cmy4MuzZ9TVJu2TRnt8ZdNtdZ76aCeuITMF39TRiovOXq+H+fl5dH0RaesGdLtYWFhAj4rsPMP3NGJupE42JlfI4TAwBV02TXRwFYpaRd5A584HmVDKZhoUKXgPFtgk0DV14Fe+137CR6n6EOQTGcILS8HnKLMs7lF30Sn5bE4wbghVRnEAlTLLDyEHE+My5Hxm6aHPns2U8KAAykFffGCH2jC5mWQzAnT5oakef5PdSk9QQPq88RmQNmLIAuMsQVWZ87cq+NhCfLMmAo6ggk9VoERCZPxEXXg2Eqp6GgE1qKxYm+H2i2B3CKNJDs6jZB8tMrmDxQeKy6MEvt6CmMaIBVkaUpgZEeNJdZB9OxuKtlYOdJ5upZxNJhC04GMqjHXRGb+4RY8lCmfDgs96ue2hhwAAm7ffjvV/9s+85QzrUtbvUR26gs+mIyIYxlgBojEMNsFmWEPn25Z8oOOt8mlal215Kpqui05iTQna4AjEi4Ksd3g5zRpl5e9n6lkTJVlVAfv2zWFjA5iw1ES/N1Tw2fjLv9wOAOj1CrzznUL+3LMmky34yLLad9HZCJKJEdx4Jmn0pNuOH8RrT27g+CvvAlB4iAc1UhA2F6xFW/DZ42MltOkEpdypE8u7SPLSYx5XT8oUqflETsGXCoq2CJY7tcOBqc/jUja6aYE15VjJDQZuXD624rh1EyuCobTcl4sFX6osPmJLc55xc7kRT90EgTXUgP1rnMd3lnJrKSj7atB/g8vIgu/QoQ4eemgbAGBpqcBP//Q6kTFQDnjeS39eaZebuTHxWfDFbNO5prdfwScsJMVCfKbgmzpaseDbtWsXVldXceLEiWDaY8eOYXV1Fbt27WqjKTO8TBBixtiNiVFQULCt0GKULkMuwXRzmN0nuIb6G0o1tFYjP6klIp86+My+s94pvxC3PnDs+F7D7xoJbLgYfJSQ1LLgM110uvU7gkvL4nDytyXoq/90i2SRY0rIBCBEW4VWXEY5gkDKTcG5Hohx0cm9lyg/nbhyBGVFdSddZgMiRjgm43YwLjoVFYNP6KITMGPo+BR8tUCVsuAjx87qRMq1p96+qm/1R8qtZAGVPP5eT1ru/HH2DuEtTslcp/cgejlTIF1TRgke+QpIBS7SXG8lX7yV3oM1H4AAACAASURBVMY2+iF+DonaYwuWpNKeyM4Q3aTkQMwHdi8QlGtY8DHovPRSsGi/i07GzXmgfaK9glofTJu0Bm0Jo6rH4BsjtLcJxpBLEvOcop+aNCtmSttKTaX/kFbiicFXwO99Qjp3mtziPnq0g6WlAr1eMb6RTu0vWazaCHz1qwvyxC2vDU4ILMnjPK+IvSVq8kV8K8WnUEXa32XtYztWz+CGB+7DXc9/Ca8/9jjL+xVQ1oKIANHOaAtUmzayyiScZEiawVUl3mNEcoTR78YuOq30pAUfi6aXJzMwmokIyj08xG70PAgN/Ei4sLkJHDnSwfPPluJKWhTfACCUj5jIKqTx7ZORQCtIipGmCfIkBhpYYWWEcynILl4JlX4WY5cyFCmf5rQtZoMlHun9MehMLPh8Cr7ssR9t/mgwwJNPTtry9NNzdg4SJF8lOkTCczPVW7LbVYWUjAi0J96NrGiScnKUGjMF39TRioLvzW8eRgf+5Cc/6bXM6/V6+NSnPmXkmWGGGpyV3Xj/IASwEuF8oZn1cULUIBpsspSw1lc/JZBzXHQGMjr90sSCj3rfIAZffZHNx7QbwotADL6i3zfqy2LBB0tgPrI+GhI6043Bl6dQughpUeWACKTMZvYf7LVSLGWK+ty6Mj+pFgRem4R76erMoiFTAAQqINbZ0IJP2xeLclhOofex/3u5GHxcWspqKqjgA9h1PL6BOGg+r0kwQgD71eVqwRdyGxUrBK0GyrhMLI3BRymzUwTZEr6cvHVtj1uUVI9ORi9LwTd5BpQdD1JIb+1dscxhaNBIEAwfFWfXZtY9LjrHaQSCM7FcRimM26qUP5aP036+X6j6WAs+SeYcsOlWQkjks+prsVmBB+azq1ZOYm71UjBL6vsCppKmPjNi3PsD8LjoNMdBKbNs6RHQxIJvZYXUcDv15bbgS4IvBp/wPPILBWsFp9vxsZerYi8MxfGH1tqkDpfgxuP22RsOPzLu4tuOPDr8ZvIciYtz5qsTSIvBZ7yzEvZ65lg1CRkXQ4aRzwk6Gki7y8G5dgaGoR/actHp0rRbcGZx5dv9yww2T3+l11VPjkOHujhypIOHvzyHs6c95TVAdLf2XUXI1OKoTjFGo2Tbi0tAI/uFel/DVfrlzphqxmU7iSIK5B5n7K+qnBsX16kI2RDzoPGY2flTfT8LDhFqr1WCi/7m5RiePpIgByuSZMEXvYgJzBR8U0crCr67774bc3Nz2LdvH/7jf/yP+Ou//mscPXoUa2truHTpEl544QV88YtfxK/+6q9i//79mJ+fx913391GU2a4kmFtIqurBf7+7+fIuBAxl3e4QL06cZVCaNmbZKGY2+eCtrDpHRednsirnk7oHj6M7fffj/Ls2aj6qTbYdVGHum88xkJhzzd4FXyBQ74Dj8sAAmS8G7guOn3pc0DMpGoPdHdm9avxbUBPDD4O47huRnLVSgy+kOCXKd1foadyfZ2GkuuMu+4WzFe1F4L1Gk1D1Rl05QIVg89TrVLmg7LQ3H1WrjApOQbfyGqCQr2eqn7zG5k2A0H2ghaDj6tPIhgalp/G5MUIqQQGucP3Aheda2vAZ/50O771rXlcvFgYjUlhJnJY8EkrNAVF4b1NKUJRwpQd8ZqGoeBz3YezgrkQIdOE0WL2HOnctlFImGminJhPHgpZJweQ0W+REzRG6Otz0akkt7Iyw1beGLRq4aYZJwwgpk/IdpHPhplvOHsAP/zkH+KuL/8+ivPnZQU2hDiWqS2k4Sz4AvGjpWR3kxh8FEgLvi202hkjhwTKnlXkt8pB0cu7LryIuZPHvYlDCkPv1+QSUlv7Hum+jpuEAb7LqsKL6Is7gULt9UB/gtyKrdGxKFT0BoWgFhzas4pz0SmrJ5OZYU5IGT4NyXtXgHisacRTp4a8RqEqPPM0ERoggl6IgVcORYYGqBf6lIXhiR/rrrvw+Wv/tnkmn4tOo1uUwtyePZj/xjeA9fX8FnwJfIGoyhQ+3z3EorMELfgiiEGbBq3KSTiurXTRKeJJBOVAKZGLTqWrUJhvYSKARDcJGI5h0y6LUfCx51TK5J8p+KaOVmLw3XDDDfi1X/s1/M7v/A5OnDiBP/zDP2TTLiws4Fd/9Vdxww03tNGUGa5g2Af90093sbEx3CRefLGLW29zBWac27pxmZr1nh+xLjpd2NZ2dbbYDZoimhwLvtCGS7zvHjyI4ktfwuov/II3q+idp67asoiDyIJP9ytuKNcU7IDDheWis2soKfh2TMpkXEzphY6UE0OhcUvxQOr2iEsXFuhJLmZeB32agiTiqoXK5OrmPfBpwnxb8Gu3U0AIS277S/tF3O0CAilIL3FxRLWOqwrXMuHNz/4ltq9fxAvf92Nkfr3c0rZWoAjhRBedrCVuPaaDDAybJA8z0SQWfG7xccKiyW8/Y0wKnwOyHYmC72tfW8DqSoHBYBjn6Ud+pDfmAEJnAPVeIggM8QpkvXZsOuU+G1qTUhUGm6TVm+C+hEpEWfBRH2o/s7gv+2yLboeGmNuy0fxbpHBAh08u4zXW9wlHLebcbzFNjTmjxNsiJrWEFeuUJsKsnw0lAVQNEWvhrU9/AcBwH9r25S9j7ed+Lnuzagu+usxBbbkSKWQexnUm6OxArEP6YoZbThMLPimmacHX5jLwCQV9Z5t0C7rh7AG89Zn/i2sWegDmZYX4X4UTU3u9ZJMlNkfnfgGjMAkpp7kmUA8bW/BZsB07NTEoouuhJyjnWpwqkJ/jgstyzJydKPiaWXI4ZUfG4Dt/vsC2bcD27S1sFCkEXuK6s0ci5KmoVINkS+Lc6UkLvkgeMAaDwZCeImVgLdZL0XfeKSBsy6vOPzeO9Vysr2Pw2tcyBWaCxZD5zilxM5QrG5PRWPwr3wXH+Ipk6Qaagq+zxTH4kookMrH7hD5gAkJI54nFbdvchFLzzmPfOSkt23cGSsvwxoBkiYKZgm/aaMWCDwDe/va34xOf+ATe+973YnFx0Xm/uLiIf/yP/zE+8YlP4O1vf3tbzZjhSoa1UaytTTaIpSUhgUxt3IIL2EHDOisjJ0AKW/DpyenEhnxpHItlxISoit+wBbt159SpYBpJuXb8Qju9rymUBV9NBFIx+FSkBV+30IWm4UOGE4wb36grsqIEpy0cclLixLz+FirC+6Ic9N1gzikELniihZvXQaFFJfw2cQKzjZPp1yIDkZB+KFQpAE0xRsXgA4buneZ7y3SRmoDDcftG1UlscuQ8twTlilAG12UCeSz4JHk4l0GUkMbIR5UV2Ou48nK66Jy0JWyJd/58ScT9CUvbuDJTLlC661z4gdzVSIaJ1M8TDpGyJx4SF52BfADCbmpjGhezP6dM4sQ0PmFn7SaNPGt8ZVPKXyY59dxnwSciIJsiUG6hKsKbg3+/SqgmnJ7Y70oiXmCxsSGuM+69SWPX4xbtolNowUedgaGigWYx+CiQiseGniSyTGWPcDZZsE7Rtw2+9a3P/N9xi3yVB/uDYe42N+lvkFywsL/L3vdsl7R8Q/n9MdQMapx0i7sjRzrYt6+LPnHHb1yG8l9I2NyUWPD526mnk8qNY886Ka0lSVSqOAu+KEQIzk+d6uB//++d+P3f3+HIUabSFg+/Gs0fh8q2aMSCWYfZ3TsyzTGQatqTgBdf7OB//a+duPfeRQz6sr0oF8JV+S34dOjr8daj3xj/Pf/tb+d3OxqYW/ZrsfeWQDmSPcp78Y955/BZDSweHf614DxaBazxG85DZ7fIaC7HWnbrNILAc4yvSdTnb/urv8LO//E/cN3Tf+e8u2ws+FLCk8wUfFNHKxZ8NW644Qb80i/9En7pl34JJ0+exKVLw1gMV1999dhibzAYYP/+/QCAO++8s83mzHCFwRdTxGa07Y1I/+3IZ7gbLpExGTiEBHqcsDZm4zYs+KqAsCu28FD2EDcWSfjUt0PFMfjsg8Iq3LXgm0idgzeu7fnD1aNZ8FECLQ5Jw+AQd+b3k8cmN8kCVUiJ1rIiuPtEgUITF50hCz5nMgbaVQsLbFcg0u4Uj69AohE7VyjFmM9FZ3ewgR52OnXqVnuldtOBsuDjBG322PV6wINf3Ia37uvittv6mJ8fCsrJvqzryLQfk2Xr0Fx0RgkkqZvzQsFjylwf1yF00emb+0ZZzDoO8ZDU+xSvjSI+k+qgSAaVFUbayqMEBG+SWxWM6ZekzvBAwPjbc5xsqiAGn/ylP6nvtx0zWU/kPdUk7fHMc3Y9BmiPtmBXS41blBVHAI0+q+E5FivkNRR89f5D8QZco0IuOj11S12CZrfgCwippyi/NZEgeAwmE9BKKU0KzYuUPnzuuQ6+9KXteOVVa/jFSrsMJhWIRZ4HNh9co1QKZZHmLpxCPX/PnStx333DC9srKxt44xvpie27fKCXV2O4jprtG5K8kjGftD2+PaxAFPWljJZi8DnP+QKfeqoLvHV46eBv/3YBd9+9zqZtBTk3pxAxIbXgI9rU9h5akLG/R/OhDquRqQ1f+MJwzR4/3sGeb3fxXrstieUKRQBecG6JqZ+mmKkwefVpuuhUDN2cpKCWWxXXkChog3R2FaChuYzE87GcwX4eOsszj1GRSmgJzEzJpoos+AJlaCjPnsXc008DAG548hHgdf8o3AZh2VR77LzsEhKMW+F5N0wwU/BNG60q+HTccMMNpBvO1dVV/NZv/RaKosBnP/vZaTVnhpcZ2M2N2ohYwah5G8O7mUqEz8q9bekIZQQKvqDQmbvWwVFGAvgIFZpJ8hbmNOWBB7bh3LkSH/jA+kQoTB4aw2feGHw2LDP9DihCmobP9Y8R1y4xBl+MID+KSRWUYdw2Ys5r0RwshhZ8jRqmIVbBF1oPCaGIgqAt+FyI6xIId4JzlbtAMCAUfARqv/n22tTns63gM/WeQ99oEhedX//6Al54foDXXyzxwgtd3PEDvIvOti34HOaIvWIXEPYxrrFSmpkzBl/9d0nFErUKLUtGGMcwsKG2pcSayhGDz7cnGN0QGJxkWbJn87LPDx9cF00hLj1iskUcPnRbE5RbAsFZOAYfk9fzPQ595mkr9cprwTcl+M7iQlVQRceTQdbOxmejh2ZLrTOmTXaca/Fas+dOx3VlXRdoPw656KSe5YzBR1lv1s8vZ+Sw4JuMr9xFZ2sg9oK/+IuhIP30qRInTpV4zWtG7ZTuvYF9y7bgU4opW3CGE8WzD+v5++ijC+NnX/3qAm6/nffdaRRjlZnXgq8QH4tkPQwvH3KBHotSDVCqCQ+ZA+x+J6xgZaUFJ14pNIunX7yfEijb9s5RKIGsRFKvEN4yiNtwyZZ1EfnOnRV4McpI5kjd0XJzuRFPnRMObe7Sw6KpFeDzE8nqMbhz1qGzbQVfRP85bdZkAPrZHPr2xmMm5uUjy+GeAYZXFaMHmfQx3huKtbVAk5ovTEp0N+QXPPRCjnGbKfimjtZcdMYiOtbIDC97xLjZqTc+SQw+tlxKSiqom3uVK+g9Jbw09socdtvBNgRM0T19N3YdOMK+fV3s3z+HEyc6uP/+7eMD0BtcmVPwjRhZAx4XnSFBOickKWyXN9oAtB2DTzIX7SSxc098sI9QVoSLTkaIEW4+fVtRouALFe5jmGKWjJ726nOHMf/oo5hfPi8vIAHB9jFEl+pr832k4KvdaBrLNBBvzN0rC3ptCxR8Bw50x2WdP1+OrSYojN0Y2e4JU/a4COGC245w2UEGhoXZ9yHLYqrYoPtnykWnPX6UddSoQSlHYFsWfOQ6tt0vBc51yfc0ETQaoJSPkrlod0ZOK1Zamtvom0+eLPH3fz+HzR6TQFCQRMFHCjB9ezvZb3KXz155/Ba46CyszaiAcvokrwWfvK9i98CQkCJ239H7phLG4DNaUBRQhaskAIbW7L6ipGsn6mJ5oO3lwPQBSfEbaUdlfgFMjiObKqRpPFHueRF4byb295ceUkI6URwXnc4ZTvBIVDmZXXTGGkY4F52sSm0FXxPvehT7G8VnRQovRVOPWp9GDL5wmZJ6GJaUdztvIbdXQwBJspN6vkQLtUMd6bjoHKDE9FxjekEqI4j1nRsp7vWEkNBSQ4W8fZne2HnZMo3yrTNbOufF8M0tRuYS3Y0UDylqS2Q9cKdb0P0/V/cIBr2BQtvnPHRI5F4b27YiJUYE1Q6lnIvi2svJX5EWfKFqQ/0xPOsS6TSlsO2LX8Sr7/skdl08yFQrU8Z7Dywu00zBN3VcNgq+GWZw0ICJ82UNWT4Aaco5t07lCGakbYmqd8AopVjKqAXoh7ojXDOJmCNHJobDq6ul14Kvfma4YgzE4LNddHYKxoJvYwNzTz2FbRcncQh9yjoqBp9SeRR8MaAISyKV9pdr8uPmiXSLVfXdHIkCBd6CLyxkpNzrydeuUIipJgxIp7+BN3z9Xix885t47df+PJyXg0BoFTtXxt+trT9K+eakt+rU26HfxiP5J6VIRaGdtt8vzPo8Cr567toWfNm2MGK/oF45yk0bDSz4Yue6wRATSc2tfnRhgoklarSXu/ASUPBx7ySWKqGzWiKEUwri25r5z3VBooAF3zi5J9/wfaCemEURlTacvb+pcN99i3j44W3Yv49xBkLUGfxka67rSmij3zzfQ9E+HDMdFCraUtRpKPhCGEr2iWfOn/mr9T6YsgWfVVe9j6bE4JOkq63WJ/W5WahnOWPwdarN8fft39/FE0/MA7hMLPi8gycbWNEca4vm9iRuVI4wc0hJT1mskjNLqWRvAhS4c10sALR+uy46I8oWpOPyhmgLyVYvoouIRCWl4Gs4QJPs8vlWBPav3BC5Lx8hujtC68xx0dmeBZ/UXXMNShkx7puAMLy4eBHzu3ejPH48qo1AvovnUjQhWb3lBjxKtB1nsMme5csj4X8k52yIx6waXaC1FEGGBZ+HDs09GRymJE3BR6021uDAkK8EDB8Q56IztCGn8OQ1uk8/jblnn0Vn6RLese8zZF7xee5rJycHnCn4po6Zgm+GyxZNYvDpLxyindmIowgfwcHEERh2e0JFUgJK47ZeBNEqhe8cJuXGHkuDQvkZTd6Cjz48VayLTkVb8C185SvY9jd/g9u/+ifo9DdGbfWZjemF1u6xXKsmH5KGhBJuB5CLuGVj8PXdGHzc8R1qCkcrpLrus+5hCwoJo27L1asnx0r7hQunZc2hIFivobJYVxx9nQDlXXRyzdL3mGAMPsaCz2Z2+30zryoKdh2Pq6zcuqIhYSi0GHzBtKH3gb0upln8e/cMoNJKFHxlqejKgwo+2uIlhb/ilJt2e4hGmH9OiAAjWTDGnpq8zyYTyGTBF3QFHjORBPSIqJwR1tYK9HrDsbt40RzD8+cLPP98ByvLVNH2LVHfrVHXzbmeUCnauiQmnnJoGIyz9DJhUofWIIH9UTCGTQQGHOx9py0BHwDHZaEjtOLqs8eUcdFZqIoU3PrKpp7ltODrDHqo97gLFzTBmiL2nMsIUprUJxQcn23M5aTGoHhKDp69wLZKEVuXUC4FjXJNKOXmGecT1Cnd+2PP9eG64cuUWPBJxzPm3JZ8L+klJ7ZdRKKiGhi0BmBdXNWzJ7hYLhLYnqlY8EkOAE+n+nUZgbLt9VQNRHRxsN4cIC4WSt1Tbv/Sl7DwjW9gx2c+E21eS4o4Ej9WYk1EDZG9N+jPxBddbav7trXVhhBMbsEXmr8p9FHKcNn3ISlaKaoyg5+X0SHSS+Fi2Hv3YJBWJjF3WB7CKL+Zi04frUOhibO28vTpYBXiGHwULkei83scMwXfDJct/ALddFNiiQVfrHCXNkuvGQW7rDghESl4MNxxMIca+3H5YYwVwTD5aK+aefTdovG66LTzWJRMh3HJMb9377C4/iZefXb/8G87CLperj7GozYoZVo5hdDKMNBc6+Sv2j2jZ97VRUjN/0kXnTECZMF7kYtO6pmUQRC2R1dQD4oWw9Y2bPdYeEDF4POYfTnbhPZA37vsd8M/FXkzy/E0WBWm4MTnopOz4EtxVyjhuJiODrk8owSWqXGGOGO0ybrU6nANcul1ywiPbJDxnALMMvc6Rww+qdDR3uc5GPMm0BnRDDqTiKIH6LhZVj5RZySC6kNCwADQa81ZNkaayd+DAfDd787h5MkO9j41Fywn1UWnqhT27p3DE0/M4/hx26pfKyPBdWtVDQPed1580VTW1o3iPqYluFY7yhVE23NO1LaGtGjCgkmlB7j3hoJvNFQpFnzkHk8I7VJcYUbti4FCO1WfPnsuAwWfX4mXdjbS9bTsFp9MLx9DZ/5Jhc8MbaLT2A65xdAhqRZ81NqJteALrb/Ly4JPVk9UW6jzX1VjXjHX+pyUrUxy2lNBU1e+IQQV+R66OZXn4X7b9FSpBLKSjPAVS9KvSona1zl2bPL3qVN2aj9aVITl6NrkGHyZaWdnHof4c4aWdmAlSrvI7FnfRCNIN8YNLPicsvSLxMTexz1ofBGdyF9WCbdMow4R7U+Ph6QaOS34EgznRGVQcgYvfAlZouDyuBz5vYQWpZUzzNAMPiYhJCSalDH5F6CERbSVmOsX3F8/WfeIGfPRCVlkRZXCxUslFlcK7NjBCDMbHqRGdqIsXdjnCAcDhM/4APQQUKyCj2pPv2+MnRmDL+D6YlTPvn1zuOqqCrfcMoBSxYhh1jC24MM4cHoTeOmLEGE5ehZy3ed7EUqnFDDozKEz2BzqZgabQEe2CEPdM3HRKYvLYcyLwE3uXO5g6ttuVWfOuinLdHwKpGMySTH+y9jXBnoMvpFlAtFGen81FXEd20WndVmhVJwFH9Xc4cPx8g24RXNcD+fgwSnhwqi/HGWHQFDhJkkTqsUos0PTrc5TCvwsliVTSRW2XKGypVnweZtIJwLYGHzOfRpbSMqCE2IK1reIICEIgVC+QKNj3F9x1gJ6XfOPP45ieRnzvV3eep1ytGavr09aJbHMkCj4KEH3S4cLLC8P63rxxS5e/eoeXQj/iH3eWb6ExU9/2u3Py8pFp4nc7melR5vTRQ036qguVabCY1y3VkivV+D06RLXXluhW3O8honm8LIJNVcLVRlfo5RZNr11up0WZWQREPJ0Kzrg5Za76GxpLZAX/4hvjVnf43I8yrLg5wQWh7k/UlZ29LlrFmLlkfhOHmUsmFcpw8TPX97MzUdL5YzB1/gb7TFXE/o31kWnrwGlGgxdROqvxbRJuBqHLxFA5D2hIQ6+WOK5Zxfwgz/YwzXX0ITjcK7SXiG8nxKil5wYfBXr0jaqXrgXt6PHUCOW+b1LMD6RRDd5UTJxAkqyUUNk8/BRfFedTFJREwSII+lcFbCR4fQO0Rz3bSQdLi1D0I+Gq0rfRSPRx0aA5OebK/gKjsG10ipGfqxDeA/VLZuZS21Ma+5s4+oiLwCHN8xw42bIipmCb4YrF8RmKLEqErneUCr2cjNRTphojHbRSSRaX1V4+KEF3HRyDnfdtTkkon0F6mXPmTfsNzaAc+f8bv0owiBkwecrr74d6mNefQo+p0+sE7VUTAw+AnU9KysFVlY6uPpq6rtM66Npx+CzIbEMCYE74PWxq8qhgg/w3D5kWuivuyCzywQ2IUGnCtbvL38IWx5Y/19WfVSdyRoSd3sGyYvOXxr7iGXBFwNbgFna1hElNUhhCz5gMi5libFQlUK9nmwLviRI+pmhwIM3naVCO0GzQnOdeu9YVzptkcXgI5EYg28wKIKKAYdXTbTgI91gBkDulYFxTtqzqRh8jCCRyzf8mZEhZgTB9ePuM89g4etfBwDccnjesXt35mxFv7OFN6FmhBV8yimnANDbcIoON0iIVz35CAp7r5syWOEtMLQE8Wwi9fxaXi7Q7Sps2zaFRk4eetPFCrx8qI3B3eeTQk6dKnHqVIlduyrccUefrESVJSnsthV8blvdPPSyLoZWoZLjONAB5WCzkfV4HsTXJT8bC/tBlvrdeqQP0woPCq2pMXQEje7m6PCMHB0i4f8ouiHCgo9DyF2wqzAsYI+pdChiZI0x53sjF51UeUYMviJYWGw9Q3pwxEv5hPcRe3EStEI3NoC/+ZttOH/NPI4d6+Bf/+vV6G/2tdG54GQncGQBlYguDtULpF1+8bVtXC6ItesjqgVuCV+x9BLe9NxfYXnxVRjc8j5v2iaQzKdgv3r2AZ8797bUB1UFHD9eYqns4IY+xpeEfLKSGLh53L3QyRNZBxkruKEFn8E3FeWYH9jSGHwYXT6PBXmIMFX5CHMCuovOsPwxMO7MJYgYpFjwhaxZZYXMFHzTxkzBN8PlCw8hGnNOmPuxArgDKGbnFLiwirXgk4Byzza8lTYs9Omn5/DOd/bciphvU/Pz47/7feCee3ZgZUVokaO/98bg8wuL/z9779psyVUdCK6d59xbL1kSeoDBshtohMEGhMG0Pd0GuzscMUx0h2OiP3R/mE8d4f4y/hX+CR3TdrvDYWymPRiMG1vgkRFvEAiEQBYSeqCqUpUKvapKpXqoqu6te8/JPR/yZObaa6/Xzsx76zI+60PVPZk793uvvd6rM0CjQhc8T9gqiIbopEwxzcEHfgUfLXDlCm+hE1cefDGWKvhK3OwArl4N8JUHDsGVV4/A7/3eFsxmDqKpeZg/UhICe7b+sppDq8oK9YIVPgyB3oOPtKfELm+BI4i8uKGku93+JgKWeb0DO4mCz3egfcIuP3LAnlg49IYnRGfearuuVLCeIzMpBx/vwBRXfY2dgi8PX4yMNJZ5W3sC0gYU5ufSpQCvvDKD9+wO81jlykiW1BzNbC1l+7dXwcfOq0PBJ71fLAA28siM6FuZSZeAFWgLmiFJuOhRLJQKmMRCiYKPxhHwfdf8dnTaCxINsHq88aMfqUXVpgqQrDVkN32kCQqp9y9XvNsXjGJnWfMJDPbJgy8T9CshOrvzTvb9yZNzuPZE43H+wQ/uwJEjeTteurmkkBXRwHMOB79XhAyvvy6Edmc8+Fq63TIIhOERSAAAIABJREFUKFEuLBYAiNyWwZiAeb0rGHUdPA++9GwNPCsMH1hiwFZ0RAt4TjsHH/o9MkRnV0+s89Dc3N244gkHoSfmI8lJSK5fH8eUOfgA/EJPFtfntRl1ON4x+6iKS6TgS1+WrNOlSwGuXw/w1rfW8nlwVrjXOfguX64gHGp+v/rqLHvPfTO0Lc9vLCuZGkqHECQPvpjf7VrlHtzykR//JYRYw9Gt12H3zF15gT2iY7iqMzo7DvTgo/h3jzz4zp2r4IUX5nDu6ga8+qMN+PCHdyEwzXnbw2c1NERGMfgidfQVc7pktwGtY1yJJ5tGhwzEU+76YEW7j62H7kn8CkcrA5u+GerBx4F+1H2hfTSjNGl5uKhsxbBW8O07TKLg+8M//MPB3y6Ldv8a/imBRYjxl5yNiCpGqFzc9u4OvPuFr0EdZnDynt9irVY8hKTlhZEBNx5k8ZUwYp4KkcTh2WfnrHLP04dk/MxloF1MrSKHUzrRHHwxQn5R0P4oCj6RkYxpO2w/MDNm5AGUoPRefO65ObxxV4Dnn5/DY49twEc+sjtYIO7plyQEBQCIoYK6mnX1z+JCLOt43IGkX/GFLWSIFkpAFwLPf/Z7FG+/2XIHYONYcRuehq2uJyGFsaIGKxdCBQC9UNrirWPs56ypk4ToRF4t7bnhcjvygpTm29aDj8vdhztEhfTupOAacBMgeIJxOH2xAPjUp47B1laAq7dvwNtIVSFGl/igdK9TeTT+X6zbITxq0Bg/Jy5BFgOLRYCNDf9aUeUmW7cjRKcE7X2E8XwJDOJBaybXDhH6Ba5yS0hhdUZ7P7E0L2F0CQ7J+oQ2az4kef2rCsQQnRJDTSsZIrAVp5Eq+G4C1HVovAvJnFAP0Qe/eAh+a/X3yZNzeN/7CkN5KfdgyYeUZptUlhiZnGTgoL1pJ2az5HjMZg2uZ0N0Kr8B9Pykm5uOwRsTJFmop6Gxpt2jeZfK6x/q3c7yG8wzUSA35X5LOlEwB14NVob/09+sYZ7Ej3kEs865oQo5CyoiIF0uAL73vU245ZYafvVXF1l9fD98bXrxlEtgS+isorQHCLiepx58dh0cXLoU4BOfOAYxBvj4x7fg9ttTHsADloHCaECDY0PAcx6nK2V00V3MvSS/aaSZUC8LFHxT4FDFG0vQmvvD8K5gNcatLYAbN0IfwQnXifDI0YuvANxaUL8Cns+8Bn0euZm63FNv5lXlp071Lnvf+MZh+PCHdwfLOvhvyg0eS9vhDEgH5bTH7RPZUAuJysvyxh95OXMndKocfFbu7qaQx4NPbiZrlsoiGN5pLD1TyiJevBjgkb8/DL/83Aa89727jfHuIDfAtYJvv2ESBd/TTz89RTVrWEMCgy96/JswGpoBdkylcSoinL/8Erz9pfMAALCYHRI7ybVXyohyQmfqwdf9nd6uUvf7IkjBt7MjI+AyuSIhso0wiZIHX17HCow4R5Soxzn4rHGweau4PmAPPvATlmz7SqdwPqPTp+fwkY/kwh3OUhdbALaKlEQRwwnhHFBX/ZXRCpqscDNW3RJd7qHXSwQ9YwB78GGYEWGbu20HJ2vWhYXY2FAAWebVQQ63K4fPQww6qlcKu1nqwRfauGoojyXXr/0K0Rli5LOQM/D883PY2mr2+4nnZrmCD3iFp9WtsTn42HJeDz72W9v6X3pvpQSxxu7GkYQZmiL3V7GASSjE5btzGfxkOGDgIjjLSutcikNxP1kBvaLg8eTg47wyVYXBSCmJePeQw2cHU5oe2r1uefBhkLzhR9+VXsGItj9GACuEL5WCrCpJFXwRFovAh+g0xiI17c7DZ/R9Xu+IZ/mmwkTts/iDwBTeitY9M0oWSfGq8+MsFHF2j+QKbXbdY1xFTvEZX2l9AOjvdPeckPGfeaGC71QNn3zkyPWs+NgcfB4hudZX8mBUX6TGKk7BV3hmvvGNw91Yv/jFI/Af/2MzlwHiIJvTPUEZqNLKG+aaoZeY6uyXBnFRQT3WXdTdFQuCYFiI7y/XPlksYHs7wJOPb0KMAG9/+wJ+Tu3n3t0TBpnOlrPC+cr4ao89+LLW0vqo0qyRw/i8qPBvx3VXdBlx+JvNqOL0QPTQuE2koFwOZN7lY9eIow0myMEHMcoKUFS2xopNYSx1baet6IDJK4/lCdo96Zbhqecxv0cfeOAIzC81+c5Pn57Dvfcu9Makd6749GuYEiZR8H3sYx+DsNbOrmFqKBButZetJ1TF1CEa3vXTb8Ji3sc/KpUtlAAborPIKo3AEKRrCRdyiYg6J5IHH25Ly8GXVU48+HAOPouRFAUHVGiElBNVUYhOd9G+T6t5aZV9eR3T7WdaN1bORghQVxudUG1GLaWEybXGXBghEbiwTxLDoIUpKpKN1/0+pCE6B9Xp4ISK8UjLFHI5+BhEw6GeGPuzTAXrMTKd4ijXyHvstnXNZqvKRdzTjoMwegOEQO7QRbxGMvuM7jOPMNID1h7l3suGKs3/FWd4QCoSQ3Qul4NoeABbwUfBJdzLzkbwa0W732GQ8sG1pIJgSRXy0jsFAOKCcOKle96xaH5Bp/46ERSNuIK09VcjYiofJsy5MW52zSUlPfU83ithGXNOk1fGRo0xxe9D2DPvWZgKB0qf1zXAgw8ehkuXKvjd393OCg/x4EvCmq022RJ6mq4j74gAzpAfi88AWjrXnhtrqaqlHaJz6m0poS7LoFAV5DrbGsvDFc3FRMxb1j+nUiHjP8h3DT1j0GRt+3HKEJ1lCISO/9VXKoB3NH9/5zu5QezgexfYa7SsPoprVz/l5S33YgZY8elCR71jvUHyzibnK+mW0kdGbrJXUFUOHiLvlh+shec8+ELM0PAQQ9HRIs+JPPhCXcPx4/OuyOnTc3i/0iwb0jNGOLx9Ce47fj8sZhtw+iP/u1KDq1timdJ15ngbAIZGG9uQ0DCXZmcwbpWbUYHFGwW6RJZOWRQgWOYRzcHXgkqH7NEaYagGKPh8OY0b+i1VkPuQwHLZ5G40h5vd93Uyt047ZBW0u5F79+qrM/jF1Vxcvuy7V1hY64j2HSZR8P3BH/zBFNWsYQ0JaMla7Xuityah70Q8g4RCJbGxq1pOclyG0yQra12gFKKA9SXKaAAkVXGWLUpbIaYCf9qV0hx8pqkiEU7jEJ0WSCE6AShB0yvbSqyJJc8mfvnS/SAr+Pg6s0eOHHzZWcHPQoBlhYRgy528rK8rrrY9lk974cHHfd+uGxuic4/ApJfEEJ0p4R0juBFR02b/febBR882o4EIICn42r5GVcHXKsypgm9POaouUT06IxyuFTynu2fOvDfcXvd2lTPyYNt0dKQRdvAbXvu8wU18gUYYKH/sEZKbHwHk4Zc8IVUGwKDPV4NqvT0BeHpmuQT4yTNz2N0NcO+9uzAne8rMfTDJmRjGeMUafRrzsckP9NdUaCl68GlCfrSpJg3R2XZqH0DqQ0dzEOHwECOv0dvHJRgpa5PSPT/+8QY8/XST1PMLXzgCd96ZxjxqvNf991tIiJoelrGCVqPeXk0h1lnVGl/SluBgOg8+PkTYnuWohby5kyfn8JWvHIJ77lnCv/t3274j4dxsGv7o9rpCpw9s1ixcWk9S3uuiRgV8uX9H3ieBJ5xyP7Q8mpUdAb9g1zEEVlk4xqnKi7+1vnIwJERn905ak9W+9WyHIjpS4Z012OsQnRR2dgCOFeIudSxe5rJtJ9aix2tp1R6cp9aB6Nc4Q6LYyOTg02C5LDOqEyr9wIkvwG1vvNT8OPUQAPzLgkrLmkuexTQ862DjjT0K0RkYGpf5Kc6rJb8b5nhYpmhxhegcQQxGFClIpY/2KE8ihklCdEbZgy8JhZ8GJBWrbxV8ebPEC3RJoo0V4PUxOF+SwUn3izuncPLRWsG337D2mVzDwYUC4q2EeLcZdL+gVvpaAyrAsvqSlGdCdGJPDQmHiuF7JhKacxbzXdtGMx3zyFklt+PFa2148AVC7c6SPGL8BNFcf3zV6Afy4CtR8A2ZburBl71HlfIMuH2xeo9aHXoqZVbvJu9kAteqW1ZcFof3oAU4QYOzX0nZ1oOPKNWoF6O7Tg4vIa+fs2cruHHDZ6KXebkgD746zEzcSKYr2U8VyRFKDfZZJVis1RCdVQUQHTn4aIjOQTkDRIo1BY5gNe+JIimSXkyml/Oz4c7Bx/ym6yV68BXm4JvP+welITrz38zAClxlvMYLfL8YRtizpMKCXryoGKPECC+8MIOLFyu4ejXAc89tACwL97xzf8vv/CE6VYHGSkCzw9k7GEwqXcb0jo5yDj51rGY3WPzXv5I9+PRK9x4aPMwIASk9PLJvQ9GbRQ9ZYaxoG6dO9bTWxYtV8j0nhNfyj3WPqQcftAq+Bmaz9r7n8KB+L0hz5PaCMiZ5Vu+/Bx+Fp57agOvXK3juuQ14/vmZv1FHGY/gdM9CdJaAIbBK6h+KZDuacBXlJMacZxTwuoTbrS5we6vNmee9bjQhPafoHkFKsV4NJfXRO1bqu5eOkL6r4lIJ0UmRWIlBXm546IW9VvDR/bmzw7lD9WWKc4dqC1/nyjy8Bmo9A0A8C5cvw/zEidSbsK57fi8EiFieUOjBRyMWDepojHD7lZ92P++8cGJQVZ5zZ/ZVoReTVzRcureh5RLmx49Dde6c0ZEUknVh5AqDDCqYely8ZuF+5UJ0unPaM21l3RE8+F5/vUr4QXq6R4cV52iDKUJ0Ms86/E8MqLt2lbG0UcrM4VKj1UweGQajKkFPnbx13wdDLuy1gm/fYRIPvjWsYU/AwgeUkLSLiVZ5WcFGilIEEgOgKfGmiPYkWWu6Ls8hc6go4gCAje2t1S2F6EzaxIyRFVZ0sYCIhDVV7C98616SBAcB+pw/MUKaP2wPiJSuXbQ/2hyJdzz1PfhXjz8HJ37xY3D2zvcAK83kazPb1IQrEQIsZxvd71bBV1wnASlEZ/uOpmrjBCciEzAApH60LzUPPnfbykZ89NFNeOghIa+nAh0Bh8L8aR58Ug4+bESQCg1z5pwTpja5X/Iz2tZbVavKhXPceyIOD1mqfiQtMMVTHPMwkQcfvVz2JAefAy9UlfBxsYKvV+yVhvMakmQeAHKuVfgwnQf97poMVn15/fV+jx8+VANcTYtdvNCP/dq1kHjfAuTCxzHgtbr0zj+RpcFTT83hypUKbr1Vt9LN11tef41eU63+FYOjrGwJr7pfCj6KU1GzzTJGAKLgy/J3Ofb1UGG1XoGyLmUkKQDI+QPbwiUhOutaxnnLOAOABom1NAcTzY007/NGApjOg6+hOTh6fy+k9Q1oXbp0qQIQomQMOR7SHkuf20LHQX1w8kMWUDrALcg0PByoIidG5ptVe2Pzq85msTt7pWG3GxqQPIMmn1CrLEz7NFz45x2jOO5kzVPjAW97nkINnz4uB5/WjFeWgHHjfij4cH7w3d2gh9YunQ5N8cFoNUI0PP8L+uKSV9+4Acc+9X9D2NmBnQ9/GG78zu80z/GBms0AcNSLmN/tqoygdBEdk8zlVR8KOUojijmKKxU5EIXknXMeNh97DA5961sQQ4Brv//7EG+9Va18FB5wfDQIn2j7gfH+ZA2liTFhOQZOZUPc83/8x004dWoO/+k/XePprj0Q1gzy4CtxIWeMwyzgFKyegnnexymmTDZizetOZbOSN6tRCaQfr2G/YO3Bt4YDCyXWHV0IPcoMcoS2w4PPAo55GQLFYR5WTDwWfOFwTWJOjEkFUQ53/6S0LvRWQ3S249JCdFImmFzWOETn4Bx8TWNJH9qmuTCFYg2FyzCfpx+FN96Au594CI5dfw3u+8nnXPWK+YSSflllVhY+Yd7tMW8OPgu0z9QqLYH+hHu+nR2ag29SBd9qsF7lXqKIw26yOHk7ycFn9S/W6XnKhAEOAp0Kd7ByFAAdX6q5JXVmOQL2SpgOwAsDjHuCx2vD+mgxANz59Ifo1Aty42zuURknUEZjjAefB1gPS7zPkzuS3zfm3s+vEtd3HLR9wx58x46S/nLKCSuETelv450kBHZ9jmVpC4ArV5qxtv9LH9J6tCHTEJ0JXacNtUAxyq65tPcPCJPKemspyonSUHOe92JbEwqtAQx8wp2hKFM87Fyt/l8C9uBr/g+xhmvXqgQ/I3l1kVzIrSQx5m9WL0TPea2Kvbw+vQ0MUjobY50StNOdoVqtIs9kG3dx+zvBh1yEEamtkcztfA5QVStarA4sjSI3IW/A2dXL8KGnPw3vf+7+Thg7Zr9y93ZRfeLHIw4MJ3jmPPisvhW0tx+pYUuB0q03boC47wdtY20iGeRcxWVmRCU1UjqHXPnDzz4FYRXWYPOHP+yeJ+Hl5/OE3ij24FsuoS5RkDsOhxhdZSLQuuDFwdkt7wz/eOhb32raiREOfec7/k4SeZrnnlIei/UMvDpUYHH3CG+trM84RCd5eelSBSdPzoUPRyIqDs9O4cEnMYP0M+vcrqCl/azhU55Wjyim1yWBJlOTr8IJ1u2A8E7/lGCt4FvDAQYZiVDGRiPeqcDIA2NCdFrtSYSBh8Blvefq/va2kt7bnXEUsxhT5jLQLpXW40O7IANRrlmAu4BDdFrDFXN7xAhSmNCqYKOwijSFMMSCcwCAcPVqVs4rUNOIkbaKjODA5yAEWK5yBYQAUC2nCdFZquCz+JOxtAhXvg1nEZDnBMAqH85UMJElJrbMw4S363v0LARPDr7cIpaG6Gw9FxIhvcODj4boHBoCJfstjduxcUwPPo7xc3ZLK8fdKZZlKZvHgzRE17Rbt0IPvo3esVf3uHEAPcvnzlWwu8PgKzNfUVpfwzDpi8NvDTssSlYvq+DTlcgAAJF6rU4px3biZy++dJ9GKjzip4p9n1uMhu63ZliDz+akOfiyeMgjBRQKSHRkXYfOQyfpGh3nHvQtpw9y0Czwh+BGik/S90JoOmuvM+8XdW9wctttK0M6iHD1aoDjx+fsZyV7x+3ZbNABogefYpBy4UIFn/jEMfjLvzwK29u+bmBwbaWJ+A3XHiuiuQueJzzluHCBg2Rg9EzTCyDyCm3aXrXK92b1gXuPIy3gvEFuD1Ro6aDcUwcA4L3HH4C7Lj0Pb33tKXj7y48AwMHMwQdAUb0zzBop1NFjse7zS4/wWBSaSTzljK+6v6SUFaMgG3v/2w7Ryb2S+6iFZWSNwqTclAMOq0uWJFl1JPn3Zhlv7g432bZR0n0PmnZ68A1F+ZQn0XgUNx8yhJB1lOn4LcOz0MWfMu0NuVvVtpgK9yIHXyIPQ4eB44slI/7RNCrHo08UopPOT0874sZ8+NONZ/fFg09+JtWd7TeFRhVps7WCb99hreBbw4GF5AJxIjVXWDKJEC6VRDhhz0N0goNoLeJyDbAoNoYYsehT7rsYkZeS5MEn9CdldvsL3/Tg81LKE+fgk57P50RwA2UxuMcwyVkZCEkOvnlNEi45BcjSey9z1zHM0lqtCgwR7EqwnyE6S4F6uWAL1TYHn1ZzyiSl72gOPv3j1TdxmZyzTrBJJNZWDr6MwB6i7PDOKcp/2AIvMEUCBH5CBnVLEmLwZ2Mgk8gUxDg2ee1Q8OE+Y0MESxBYInR89NFN+B//4xh8/asbeUHB7XGoomPCK59V8B09lverCkRJWZqEvoRhHjnArKmCMJhaPR4PPq4STcjPefCV3L1i2bZTVsGxoI0ttoYV7e/cQKpItjXw3pZAo4eG1Kl5vgWIGTnIGZ20QL3Jmw+a+VtCT9MdOxbhl36p97h5/fWqyy9pzbNEY07nwbdrevBRuP/+I3DpUgVnz87g298uD/891TYf43mXLFnMJV2TsDjGucOQoaXab1QgtpUhQ9omt+4CAhs5H1UV4Ri6s159NY+44BYIosJ3XjrdPXrLhWfEevzrlvNERTidXGNcnntvvzScGpQcfG75hhAhmg3dKtXha2oSiBESj2dJwccao3kbkH6zCr4lrwg1ZAgceOTVIr7Dl0FVpbwQJ/dSOhMMWj0D1+GYbpdoPDwHbgMhugB7Em8WgEWkcbgDAK3H8Si/6xwRKvAnbA6+hXO+HH1MctEx89UZgJbwKwP7NihEJ9svoW/Yy86IjtNCP/+asRqYOfhKvO9KykkyOLHOIXzmWsG377BW8K3h4AJlbgTLRfJn8pt6VHgNsIu9RYS+5Dgtj/FfoojA1bXfV+gyKg7ROeCC5T6xLYpC+hNBe6epHnw4/FChgK0qmV8tBx+uZzZDe2ykgk8j3gkzwgnPzb3Kuvw4hXqEAa2rXsFXOXPwWdBaN3nz2XBMT+m8asASQCuCGjPSIdxcBR9e9yRE5zLNwdcXkL/vytdpnVqIzk4YQvptefB1lYsefCshCGFAJpOliwKxMuaDM1wY6sFXUo734MP4lVGoChU2ZZlztFwW0fCpB5/8ndQtCdpwtTe2GeaAhugUKsZOYDyuNPCI8lwsUNewXAJcvtz3+9gR+56geTHGWtnSPlFI9nBp3Qld4t8szFSJ75s9HnkBoHeDSvtC+d4T1no/gZI8WMEhrV96P0yDPDn6QKIZXN87ylj4hL1GhIYwX0BhUaOoDFWEe+5ZJl63y2WuSCjxPOJyj6mdFMCTg49WgQ0NXnpJCI09FhyCQA+tnNGBIl4ft6dj1GVOxTgf/W7IsQE4hKGlUv6PwaVMR1u6bNB1EXte8p3v7IWlzz03l75g68jWXlmvMbJ5v1dDmcZu6hx8VYzd/qcpRXiw90//ueDJzNY6FSHNQIyqYbYUorMtW8wWZXceAubiaDxbfY0Uk0MK36hCCGCutdaZ5bIsV7MHBw/04OO6yd+RqcdisvbOiac0Wh7RwAHk0HB7sC/C8U5pj9h+GjTr4BzkAnDnm12DCXmLNERn3lhnAFrIYw+BISE6WZwgHROM35xI1ztMy8Cz2SsDeZNVXRrNKtEs2VOO5rAGuVbw7TusFXxrOLCQXlQ6chCFmjzVJVTi79tQ0BQRHuFiEiKuFfQmRKst2JIqH6OcSKaUYey0ulkPn+7nitnEDZDcXdzFgh+lOfh0rk0M0UkqjTQPoBPGMNwAANucoDthpodtYqlfeO1iCLCsGkl+o9wi+TMELt0SdPTERf5OE+5ZHnwWlKxFz5CTHHzUi3EElOcu6gUxAMgKNgnRuRJKOpMwUaI/81ZgBFBciE5cjJ7vrj+Sgi/m45D6a4LzGyvUXMe8YPzL3iN7o+Djylt2Dh6r6EZZwIzTIRzE71MPvnGEPDsWLhyqVypYgA88d7C3ssuXQ3ff3HJLDbMZnufGkp3OFB2TKbgpYZgNOsNTxZBy/L2Of8sCjiy/KCpUGqZILlpgYb6PITolaPEwPd9DPfg4j9dbr74CcWdY+GltXYbgRopPkv0BfZ4wTyMsn9B68MU8Bx/2sGXpQI69EOid/czBh2Hz4Yfht/7xT+AtrzXeUkPkLA604lpcn4LP2Th5fsfT34Mjf/u3UJ075/rc1/jAbx33p4s3zScjDxvJ4fUYIdZld3gL2IPt3nt7HHDixIY3zRW/zopQkD9HfN2eckU4XdlvksdcSac6PFsvxf0/dNtJsgGNltlvOSu+DyTacPCdodE/bN7mZVJmsQA4c2YGZ15QUhkI4CIDnEIdKX2Gq0/ui2UFDE2Zqaom3CTW2c5o4IIQ38nvkR58Fy5U8Gd/hsJYZ7yujt8H2VBFPX0NLqf+Tl9mT7g22rQjxW2vHiXm4kGXh3WhnovGMaxvk4TodDKD3hx87q2JjbNjKMrB5wXtPIrLE3uawOzIQbl41rBW8K3hZxgQIvG6LrvlMyaDJiMriejm2iuWFRmMfbEH30Sg5uADKwcfU8fqSfdXYYhODBXgS1MtqipZEoFahSyWlFxAFNjqhTa5x1s3yhiRGHsiRPNK8NaJPfhmxINPqt2qu90bXDmvBx8Hg8O+cCPpG03O2Hx5E3PwrSDDaYiBU3PwcUCZmiR/ZcgnU8BHWJEuevDNeE+C7stSZQcHGpNGy2U4nxmbhF+t+gcCR3hLFqVZtxxSs7pO12WKHHyWvGGM0FGDrgypIJnDgetTKqwNdZ14zbzpTbzleEYfTLHnS8AjiO6KUkWLj77IhEekqIb2qgoST2IqxBYBeyIb90CRcPkA5OBrhCu64qmob6Tce089CL/5xJ/DrZ/9dI6DM9KuZPKGTZfqwRfzEJ0AMr5gQ3SuYBFR2PUuOkY+z5YAFj/DSn1vDj6rFI0a0H3H9fXyZTj03e/C0a3X4b7n/tbV/l5C5aCVpT2WC1T7um69+grc/aOHYP7883D0s5/V63M2bh4nA5EN2etWDj4ajSTGvF2AFX4o4Cs4qCqAt72thmPHmvq3tgK8+KLP+5ONbqAqcQoUckw5aa1cV5Sw5h0OqBfwvuNfgF975q8hvPGGQRfJp7eKKR9q0SO+8TfthRjlcNa0HxN5c7Mg0F4t7OwE3jBLOOOl7eHfXDtVXCb78OzZGbz44gyOH9+A06fTve3tyzPPzOGP//gWeO45Joy8RL9lQqn0nZqDj6E1x/B4/LNhnknec1wqw2DBEq55GkF1fP7zh+Hy5QrOnZvBI48cGpSv2wTmI1e3B4wNF2Hzok/pwYfCJKvysKE0qgDcLp0kRCfzDImAUAd8vAB3H7GfOEJ0lvAxpeWkd7kR94BDvVbw7TusFXxrOLBAGWlPOBVP/h02NB0htkvDWKjhJYV+DwvRifrYefAti/tbCpZgDQv7YswvAw33d8QHc6G2vEh5Dr5+0qsae/DJ/cjaoW2kkpt+z+1hDr4YQ7K221v5Zhqy9vSC9jC/AAGWWMFHBU0DBQq6cl5mGKRxe/MvFQl8Ow++9DlVcrrp1bFcD+A1jE0IttVvzNjWYcW0OkN0JjiQ8J00RGdXB3Pe0xCdIW9P8eDr6pOSXO8FcPeEIuxr3hshD8uaU8txgiepPvV8cOeeG6ep4EvD1WEPPq8guwT2wpKxr7uvczDzxAh7qYIPqGIgMl5omdcH245KAAAgAElEQVSq0c4gSYkeEsiPw0rb5esflIMPjHu34BCyqFgScO0Xk2r0P6DNytMhdj8l+uUXX30MAABm58/B7MyZtN2aaNsc9DXGMUNwo4VPuBx8Ji2CN91qTXGIztb2RPMmaH7rAs3Nzf5vKzep2AiBWb3L4nZW2HX1al5uYg8+rf2szJAcfELjeA5uv/LT/vn29vBqx0ieS/lHlubI8b/Fe4m0i8F3SdW1iL01rrj33l5gSs+ij2+wwTWsAv4iRoAXX5zBf//vx+Cznz0Cy6XcpYxfJWv39pcfgbedfxLuvngCDn/5y4PPQoh1bjBQgBc5GCSs3csrzKAvpRCdmhGOOjca8cJYhjTnqy/ToooAMaHXzHahn8cHHjjCR9bxVNIWI55A2Xxo9SwWk0t/9tqDL6H5oj9EZ/4K9bM0fzWB11/vFbzYkKGX06V10+pFeYSxf4fQ31oZTvnCe/DZ7UiNZXwjzsEnlRfqGgUcLhniwcftHQcz6A9l6zTuIv2ge8ofjroMODkD93tQJS2sFXz7DmsF3xoOLhRwJyJiisNy8FnSK/pdVS94IXBBiA8X48DgyArlY9mPHHw8xYbbyj6w6FMAWBG2klwNC0cL46ZQy0kNNAFE0gfkwVeNVfAVELRb2wzKVgdVZolnbZO6mndnaFaTEJ0DKY9SD76sU1LTE1JCXYhOskcla/pRjTgBh1LCv9MQnXIOPg83jS3ueQUf0y8hB18izAhBZCK7cZnhqiYEVuiQQxKik8NrGmOgN6eWo3cY7Yv7XiEFqQdfx4g4rILx+xIPPguGnGXMcGaMdt3vuxJjmoLmc6hruHq1x9W33cZYZQNjTU8VmVOF6Ix8+xiKLZbxnhwhYsp564SISUJ0tn0I0R+ic4gHnwg3KUQnl0Mvs6o1fot1K+Wq117r/p4/8wzc98X/Ah96+tPq3avRUD7hVYp5qZw2kn1X4sHH0iohQF0D1DBrf/beO4anpEWzbGz0BdyGD8YkzZa7/H3F5eDbp/3phSlDdCZ1KcKjoVNgolZKCxF+a0i7Fs1D6U9876VFbXwv96H5vw19++Y3l59nkTZiygHopO9scQN+44m/gI/+43+DW66dy95zJFeMAJ/5zFG4dq2CM2fm8MQTG3Jfpf21mue3vfbj7tn81Cm5o6DjiCqmITpVw0aBb5NZX/9ah1DGZxQBmUtKc+3sMFFA+E8HtZf8Xk0wflSRlAJdpJEYzVyvFFx5bYVFTpaRhOhk7xdNlrNcQpRSj7B9svm+/QzR2YAc6lCk25DXGAAMi4AjjHNjQ0Aq6E/vPWWVce17jfh2ALe3p4wOYim6RNw7MGpRjACvvVaxYUb3yoOPe+4N0SnJ17ImEPMcY343ufn8Qujke468g4NhreDbd1gr+NZwYKFEaCQytBxT4VAilQK+VLxImOaXcQmgsOBqRWBKTDNlOM0yClgWKFqIzooI/Cn04QNy5pUK+2ME2fMn6W//9wyW5hyLSoXkPa4UWXhN4MHn/Wh7e0AdjEZAIqLpOtMcfFqITjkHn969tk1LWEZhbA4+uT/yQ7rWsyX14HNaapUO1qoPN0sUfN4+AUASIjCEVAEhzQuXgw8PpRVsZkp6MQdfnfyvd8AA5j7QwktIuJZT943Jp+IdClfOUvCVCH2wgBDAH6IzKQupBx8bDoa2Wfh+kOdHIYxinmiBuk48dhpBP0Ob0KkiDOtkTLjAlHJtDNqbBZPHTJX4u6WR2MgMSpuu7aLQBLWGM29yDj6A1KjLQ/uq9h3KGKqLF7u/jzzwAFSLXbjr0vNw98UT4rdj2XhGfqkWzgStyt6QxrxY9N7uiYLPuP/UEOJAFXz8ENQKIF87Ke8viyO58I175MHnEV7uVQ4+TcAo9103eirG+YW4gF0GuvYknzGLS7k1Xp2BMehpnDyuNEQn2wMAALj3zDfgtqsvw5HtS/BrP/lsVso6gwAA58/LoeqTkPaIHsI57tO6HRPD4cVY50r4Ce8Pr+3JfopZaR92dwUFn3IXq1OknUHJgw+tNzbcrImSbJKl8VZCeXNtXBRH7PIGH2P65PVM8lRtPetlDHwkJ/eOHYmPMWxsQIYHkqoH0OUsX7Ncwp0vPQVvuvwCfmpXprZv38MA4M9xzz4PhJ/XDd7bd4NCOzLw4IOH4ZOfPAYPfvFQ9q5yuyYqfYiRZdcAyNo7L8khOfgA8jtzlAefxi9Fvki3biURCcSLZ63g22+Y20XWsIabBAajqxRNXkSCj32hZKYhvBvhQ4BTv/C/wPXDb4J3wefzMiXWd2yIzt4qrX22vQ3w6rkZLM9VqgXmkNuCzZ+eePDlt4TWTOfBFxu9GXcZJuvhCtGJvg0rRjnM7BCdTPxwljhBITpL5pAtagijkhCdTCiQKcKzaucHt7SseledqUN0cuUwvbNYNMmauXlniWcjxG/J1u8sxWJMFCycsI1ra3DDGkjUGNrkHeGthOhMBBaEcaD52WjYykzxDblQSvLga/JYykK2KRR8bjzO5rFhhDTYg08L5zsZBOCEZa4vnTQ5l1PKk9cjFWT3f7tD0TnqbaEoB5/AmUkCNSunlvZchBgTRefGRszODsTI7HHLgwPU31p/uj9Xe0powlNF89sr5MiE0lSQlv7GipD5XBFaas3j82wMsEjmcEBCdEJkDCug2U93XDoFm5cOg3d3aN5uWMGH4fCNy2rfBrxSvunH0XoVYRidgy8EWCx6j3KcGpYLDVySgw+H6JzUg4+Btq+bu9cg1HOxrmFbeJhSI6tlhIKPPk+8K6dSW4wRlGPjqIF3thVmzhv+NsQaYh1Z+iqtUqYLqy74gzYQft4r456loCnpbr/6UvfsyLaCd5hvu7ZlxzHHmg/g77jxEw8+ae1KgPKmLgXfXl5h2V2fPtvZycsA6OdFnRuJB4J+TtJzkcZqxeuVG2AYRs2eeZQEDrgPNJqJdanQ98slxMjk/5OAFeDQNqf04LNwEG3ZdxhiCGRt5b2g1cEBNszh+sUZHWl01OnTM/jiFw/DW98U4P9A8oH5yZPwz3/8Atx9pYKH7/t9uHrszdOyjwpw3m9DIVEIMwPQch/bApMcnnqq2e8vvVjB7rGU/xxkKF3CBGS8VP/Lqt5a2yxEZ4FxiZ+Pk8+j2T/PMt2Ui2cNHKw9+NZwYMEUvDGEmhWOCYAngikhGsCPMGmXuO9Ov+034I1jb1HrHHqxh5ibdTz77Aa8cHoGn/rU0YYYWb3f3gZ44YUZXLxYhmxNxK+slUWwtcRSE24pF4ICkIuuIsQE0zkiv0nCdKplPURhhJVyou13iQdfM+/zp5+GzYcfBtjaKtpr2zc4lG0IMFdEiBQGpAHZiw5DXfVW7pkHn/CxV1HAlWstlu6//zD81/96Czz55EY3h7IHn96eBVI/2jYx/pgzITpteQFfoDRcBVbqYy8XbJlXh5ksBOIs/ZDQ3uPBxwnsKqgTIpLz4AOAVJLKjWuI5bQFEoLmnhvC0TEepEOVKrgPlgcfexdyyhZO4FHXYDH5uKrZDDO/1nfl62gJhj1CIA8eGiRkYgqEuk4ERrOZoDCmzxbpPaUp0ba3VwIzDyQCpconCSwAb7hMDx+NLelnM8EoK+5tiE6xZuImsV8sK+WN+bFHuOfsP8KHn/kM3PuVP4ej27xyLv9Mnsfq9df1DhUK8j0zpm3HxMBn1Ra9RnAOI7Fu0sjubi+kwgqNyuBB2Nw2QohOt+HDQE/+EGt4y4Vn4bd/8H/Br33rvzUJr0ZEBdgLcNPX6oO2LsaQyVOf9tzAVWoF5BIeQgdy0RDoN3mUkxy0PJRGF3qeawxy8wpMlbt5CoFlC1Xlr48ay1KPFFc9SaGwqieKCj6zCrOMnN6Cwp7eWQJf2cKNG5qmtZwUUZU6K8FCyt/X7BkPUB6i0wWeAYWQEBxVwDn4mHuW0poLPj2M2JwHL7hzi9llTA9bOh4lRGdalOzkEXcdXXvWgw81zirHlMn4n/+zCRd8+vQcXnuNn9v3nvqS3MFsn8tFOcU2X6fRhvI86w66fzleTaK71HYdwBkGZHmiPfWU9IsKFx3g0PM3sCTphDJ+fRJ2jQFe/qeuGwWrzFrBt++wVvCt4WcGdA8+4WWMgAkBNtzTHkMnCyEECQ3RKZHellCe8+C7fj2siNYAly/39f7kJxvw0kszeOaZjUbgUMLQKn3QCNCG2ZUXrxXoBahFuWPCGDk8+JL+hv7Sp2E4srKKV07igYEVfIU3bvXiS3DkH/4BDn33u3Do299Wy9JLfmuLF2QMFkwbkChuqQcfjXUuNGb1QfPgizHAyy/P4MSJDVguA3zpS4f9Y5qSEmrrIgIWyZp+VBuFQEPz0BCdAA0TOKwPWCDBMOcxD3kZ6iXJwccwqUoOvpbzoIrzQeEK/VKi7BHe+0fPnYHNRx6Bauta/36EB1/pUnM8hR2iMwe6VnkOvv6FfW6xh1r/fKwHHwcu1qBlyBmmqK9nKE4wekAnq64Tj535nGk7MkK5TKDLN3fuXAWPPbYJn/n0kdTyXFo07NUrdd0QDORHf/jZEroGAKmwpaoioZH6OlWlbwFDKt07LKh4a/+gGTvBuxHgvc8/2PyIEX75ha/172RHafVMVNeuOeaSVK6cv7FyAhwKuC1cshydcRBBqNgoASsMK+CS1/hxyOZmX9blwbe7C2Fry10/hgAR7vvJ5yDEGja3r8Lm97/P32sDtm+5UoN/pBnaFbVFCmYC6Rs3oHr55RXv4ayvFBic38JQDz5OYZFuVeZscXhshR/HjL1S0jcn7TPACl6LQ3T6gBN6ZvuuKpH/p7w0VcoN7WuoGQ8+5m+1Dh3VHkgPPlcOPmUC1LkRFj5cvgzzkyezIlW9TPgIvCf2NUQnnSf0t0mj0joLFXyu+R+4SbxLm0dxkJUJonIEjBx8BZNCIyMtFvr3Y4xNr1+nY29+by6ur347Kplgc2YhOkvbwvcvlrFqIUILz/6Qvg1K5zBQqFYjFYomB0TiIx1oiE4ylinstQbRJSV88025eNbAwVrBt4YDCxZSyUJeNX+lhQoYXC2PHAXtNSeM7byomO+whfnGo4/Ckc99DqpXX0X18R3mLAzTscXs2bVrAZ7+5/8bAAC88YZuTZfUhKfGStJMBf4QXRdTJ8wj3zb/Cx58DqiqXrBgDVezPkr2BwrRycUc12DjBz/o/t584olRhChAgYLRlYOP/7R9XlfzVTURqpUHn0q8KXW2oO2NxQLg6lVh/wvW9a3gWfMqjVJ4SKE/vddgqoSWQnSq4BDCu4A5Z7SeLowJp3VxELV4b9W10mbyTU0UfGnZGAHaEJ0ctG1mYZ6GUKZcHcK4AxUGrn4c2nkD3vHNv4JD3/42vOnbX07es9bjLkWkj9hl7wtGwef+mBO+0zVe/WFNN37vzcHnE/BzOG6AFX1x4SAWLd56xINvPk/3HXunAECg+fDoXlp9eOJEE4Jve7sxgDAhEYbLhkRnz1bw05866gM6JwpzS3McEdAUfK0Hn6MDYpuTSvjHxHkuAYJb8jlgDCvIGniUKdx3u/Mj6fsrV/LutfiL4kzQBSy+8y+/45y+s2tEkcJLoaKaOyqnl9nwxaZXX19BGqKT7VLf1uXLcMuf/Akc/dzn9ILS94nyIEB19WoR/zMULPovaXuA8E3ySHjfr9yAW25p6qM47dgnPwnH/uqv4NBDD4n1st1leUqlfFEBR3kjRFeIjEJbyn1dD1QydiE6I1SvvAJ3f/sBuOvi8bJKCgW5pWH8aDkzR3wAEGkug14roT24s9DziXKIzqHQ4aSY4y4JqrCHXr0GfSkp+FqFcPE+4F5ub8Oxv/gL2HzssbwdwYMPGA8+U1bgwaPS3qLzhPZmFfr7y4Nb3cabQttsETd/MswjP5GRMaHGMbg9K4doP1aLSJVueEo7fgv1i/PgM6MGZD9S2NjdEouUGZH7ymZjKGgjuxuTw5DXo+7jETiQlT8U7INw5QocfuCBJpoWfq7QkKyA1wB3l+j9T8an5eAbc5V08j3JyGEs4Q6wVvDdBFgr+NZwcIEKMAZYElIBRAgxIXBbAiUTUgzNoWCAdjccvnwODn/rWzA/dQqOfvrTZgXpncpcdITob4tsb/4cADReBZMNMiGaaZ2+uZwz+VVaSJj8iljNMJVnax5bDz69D5IAIgt5gxRExUILr+UUbnsF2zc8jGwKHo8DaX2ScxAC1FWftpV6r0lEqK0okPvHCa+LiI4BwCr4VoxaM8a+3dkyT3A+WBBUOB4aohNRaX2VbegMhUtKGZBUqFyhpaEKvp55J3iaKPg6zwWK/CreG7U9T17ByhQeY9wObOf3La890z078sJx9J5Xgnms3UqEV83/+hnG9UnKIw4kD75M2Sl82wL24CuVN7jAIzz2hEw1GUzbi95VoK6TI9d48KUQIA2B236XVCso+PBv3I4oCCBjTMM1N/9duFDByZNzOHuWV/BlVQ/wqLVk2jEC402FQ1ahgprQi72T/co59bxNHN50CHAefJpyggPpHqVzLYbplPo20lNHK8OF6GTz8glCbHbMIcBi0Z+dJAcfpAL5/JjrAmkcotPy4Dv8xS9CcMfczSHb80LiMT2nGg9TbfNBOfgYmM8B/vXvbMOv/MrKyIyIMqo33gAAgM1HHy3qe5EIisHF+O9hc6bj+yyqQczLdCDwGdZ12D4MAeDwV78Kt556Cj5w/AtQ0YgdStOlHnx6GD8/7SM9U0N0ErxJ+eZBxkVMITp/+I4Zer5yeYLjDthnDz4MOzsCjYL5+xHthRhh48c/TpReCdsel0mQBOzRfdNCdAIkNEVGE9J6aJ174MHnDdE5oGoAsJ3t8J2O6cGELiM5+FR8bABV8O3uMncn+s3KkQYe4vaz1oNPLeRoizOIGW6IKqOLhG80QnR2tZjxWm1IizNOAQXyuMMPPggbzzwDgTv8Dl7Kn/uXlzNndyQJ0cndPXvBcvRyBr1/gyppYa3g23dYK/jWcGAhFzzaF25u1VwCFufjrIUQ3SH0f9DLCZDi6PCFl/vvmPjxAAKTFKWcI4hRib0XXUu8VRXTgGNMPBUnf+sRFgMAzCqZeRVz8AmQrUFN5lO4yTTiQPLgKyZQKOMdI3gFy5mQyGg7IfJAFkxad3P7/RIp+NTGCkAL0XnqVN5eR0hKzGydDyarWyE2OA+kdlvQHHwATC7CoTBRvpyI6mlz8AVGC2bl4AOIeQ4+QRicPksVX8UefCCcRWaD/P3fN7kZf/ADIcm8l+NkKef+98svz+Cxxzbg0iVsXCAJyPiuDAGuW1aITvUhc+5TO5GeEbOOM36PPfg0BZ8HRXBlSpg22ZK2XFDoeSd90IWmhXZ+mLNiECgWE94oOHz9aaEmwpv21anTPs893Hr3lzJBlgcfflYn13wzNjFEp9NrcFAOPqnqm8Sk5kIMDu+Ss01C04t1Z2crrQcr+HIUWYYAS8+/Jvjrn6W/A7m32LpJxYtF6GYLt5HvsQAg0R3MM+zBZxmizF98US9gAKfgKw0fPwocbfkUfE4alxgylUKMzLlQaEbar8xgI2r7RugArYPgexpmk93WwvxMEaKzWnnvzhfbMF9se5t2t63hZm/fOa+G/K5XeFBp/jrPHT/tofGE1KN6iqNJeSHP9aTlOJsaGvqyb0AM0TmmAfpbIYioQTCmNyjfV0L/iuBJvhVSg6tZJRg0CQ2H5bJQEO8o46RzPO16c/Dhsc4R2+82GByhndjaSse7XPbfc1MxIM1bU5c2+XQeFJgEd7ByKOe3dKqDHqpSlZWVys+M+7AkB9/8zBlfQ8CPwRp3C27RjhmicwT/oewvS/43SVqrtYJv32Gt4FvDwQVKyBQxY/0fhJbSBf8jQRQiMQqcVvmXJVQW6qOWt1zoAGy0iy0RQ4ydgEG2OnESdexD1AdmEjwX3IwJ0dnXqSj4jEVsQnTWaT/IHujC0gjhBQL6JkZIc/AVMIAA4I450fMiiCEhYRUCMKEFMxjuwUetTlsPPla5MPAw9Qq+vJ9nz1YZ8T0JZMr2HrjlwfsmU/AtU6t7cxpKJM2OeigeSazAWgKU45K49rKDSphhipOZUHFVjAkh2is6CCLm4q2hfuXWa2k7r71WwU9+sgF1HeCb3zys1iX+Js9T5iEtS8MhikIsxzp6l5rII5I7wJuDj2Omk5+cwMOVg6//++gb5+DQTuM1oXmqDGVeLQYjxiBiObxv7HrKnksFQoyw2O2fzefMvdhMvtpO5g2ftVOn16Eo7MXPSR659ryxX8pAjQHkgjq9hXFFmn+v+V+6azRBZUm+Tu7eqSWDm/xjdztFYG84YT+VN5GFyKL4nPPgU5h1LQdm6fk3hXuRV3DPZ3xDXd0YeYWQ5KPG9VXm3tWflXjwjYXsvuQ8+Bj6xQNDtzn9bpAHH9N4Q++gbajQc3t1RC3aYlCeJgvfEwOzGKHbcNk4HXyGdteWGIAyNfsaW4HGH1peEh6ZsebBd+lS6sGT48NxHnw9P56uB6anvdOsGmo49eKKGGU0WHXv7gb2bh6s6GXOSzx0SCxSxaWwNuUhOl3grATTYz/LHnxDu5DwOBBFg8FkGshuKwnN2H/U1MF68NGi2LCHacoVonPAe76Q/JFXGeO2mXRcrKmiS05zU37A9eKsp7hzYCdOzOHUqRls53YrOeONX+HqCxXhJsluhOi8ckWmc8aA1T+XAYnVsbWCb99hreBbw4GFEquBHrfY33it3KZgahNmDILO9HlM8cULskfAdC7a352AISB3cS8BisdkXdKMsNMj5Jkx1tmcNw8nnFT7G5h8NEwfaTtSX5rO9h58xSE6mbBr3r22XJCuF2zSJLqFIBzFhNiHn/4r+PWnPtXTOiHAstoozolkCTo4D76jR1uhRYAXXkgVQSqxiJ6r+IMMAuuaWLmIUudUCr5Sa3uq2O/qxR4zq9AZbciakr4150bPGRJizQpE2Bx8uFgIfX5AYVxc/hkMUm7GQcAGt9cnSzz3jnX0LnXGvAT+bw5YD1dG+M6Wc1gFt+9//rWn4Z4v/AV87Id/BIe3L7FC+Z0dgC996RA8+KCgiGXqxVCEYwXcLgu8+vdDhcTcUtSL/qPZLK28Z5yMerMtmd8dpR58UsjmocrPoraZOlIr+v7vFidj44WkWiXUtSufsrYvpLF2IRmMuicAiY4EaPCylbvGzVAbNJQaNpKhXYbkWZOAU4pRAQ/nwcfRkgByDr7lMnT9ThR8TIhOPvc3379Uwcd2aULg8TiGoZbYw4SQOUyl4AMAtwffaOG3tyzqj4t/ZApwUQvofk9fKx5RDiMdrV8tTckZkpKiGXBGh6Whe0toJEtwqin4nvzRBjz++AZcvcpF2Mnv4KFnoaLhtwkuSUBRWGvtua6mDPcVtOPsB3mYtrfLxjeUPy9pL0YARcFHc/BJtMdk4MnB14R46n424aXJfGC+jNa1XBZNHIuDKV8wMI6r9xxrOA1i6sHnzultxf3kQMvBl50pft+gBu3mHGX4I0TvmrJ62DWwwv876waANESnck9wK1kq90jm3nOHMvDaaxXcf/8ReOWVGZw+LUSlkuQ0mM7CUTKUtfXqnq0QnefPa3ROL9d9+OFNePDBw6yMRJtusZ9j6KIW1gq+fYe1gm8NP7tgMNrtC43RaN81yJSv22jaB5rF86o/tenBlz7AjBcndEgEm9HjwecDiwDJLKucylJJKNPUIXvwqSEBAJIQnZ23ABUCS2EB01pRZ4cr+DTBZFaW7EuOCZnCfZ5O4V2XTsCdl05l5WotRKdAHXgVBbjcO97RD5SG6dQISauNDggDPUMW/1penRDrLETivDREp0NgVQIZauFy8DHSRc5qlhL9aQ4iJoSNQGBzOfiS9VJCdLZ15iGNKBPmwGOcAMDJhVqMh4jXHOvovT+0pNq0N65GyLO6TgWE3Z+uMTRtfuC5v2tCKcYa3nv6y+y6fO97m/Dkk5vwzDNCKFW7NdI22yH+yxrv82G4coigdLHTz+HGBi/IMJVqNdljzL2VePZLHU3WuCwUlda3rh9aJUb3pDBZrRU3pnuwwEv14FvmDTmmxix7YJhUQ6rtWVP5HqXCJGLkYcDYHHwYaFhL7nvuGpFoyW7vEwJxd7fvN65viNI7pSvUz3uYIDx3RTsTghnyaVJwLO4YBV+mfMH7cgBPU3KHaP1ifzN8pwuoBb/WRvdMeDWQHm8h8+ArGpATqa6ejUnN5MHfQjrK5t2qr8ePz9mPOSV/SV+6djhPwJJLiS3WD8ybW5OW2ksFX8O/ps9YHlbaw5DTK1p7EGN+m2FUL3nwQdyTEJ2uvNAkRGcVDO98SgPWdZKWwQT0/e4uwMmTM3j88ZQul4zAlKrE91ZkKvrM68GX0WIj7lAaJYjz4EuaYujLoUDp+6H0OF+hUswrh3LUl6V/YfCAWFfhgJOjw3nwOfbBo4/2sdNff50hIGPMZCGdnKpOz64HOBkXC4Yb8fnzM7OOEyfm8N3vHoIf/3gDvvQl26BW718BXWV17KDwTv+EYK3gW8OBBTHHFgO9AD4XBGX1Oj34hgOy6lgZfEcImVIhMwZnkKnr7ov9l0mITjKGlmhprV1VBkPrBzenuC3WMsie9FmQE90mBC+WwHiECsiDT9wXyg3czlOiREVrWRV78A2Pfc6F6LSEEj2x7vd8PHzjjfwd9Dn4SkJ0DmGU7rmnp+pFxith1PIOWR58Sd4DJIhjo1mivH6jQ3RKUErco8KJl4szB591jqnnjBSiM3smePD1grpViEAxB1/d1UP7i5tyeUSUSImibi2f9XMfQnRuvPRTOPaJP4P3H/98Ps/CcZbwv9SPbI2Bt8DXoF3K+eIGuy6PPbaZP1T6lNVv4NiSddPqGCxvYwpgD775HACoNyw49hhlYLN2hnjwVcDdBWY+QHplOpql33HzKFnR4xCdnHJUC8OZ4DFDyMb2SRMq7pMHnwWZhzNVeBI6VIQMf5MxTRYpJSIAACAASURBVJQblmnKLEONBfI8aDHPyxejFf05PQ+wojFWz5IcfMmZzQ1VLHmVl1QNq1xnY8CVg4+hX6y+ed57v/OEs89RnMDDCWMx63MWLsb5pefE4qOYMm2IzmTcUrsOAS43xj4cV3ovlHrwSfUKX8h1OxZZYuda0Dz42puMyla7cQ9QiuMe93zi3uXg69odcDWJeZ3qevSe5vqwXJQPXBwLh9+UIpRmx/fBXoTo9IYKpwo+vXD+vtLyjimH49SpOZw9O4MnnyCGdxMJ4130NOU1os+DL1tpCwlwoHnwpUUSnEYifGfvxW4YfZovb/AvhozNgDG2PpRvbMztZG82VUZUOBZrPj34mkaF4kC6r3D9NcNHceBGo1mIzvT3zk6Ay5d1hu/pp/vDQ43jJbCWoISn1rzS17C/4Fv9NazhZoAi0KTvRSsvKrQlwiJNMLgXshtVIOyQ1OFRSowXvUxjbP7piJaxiNa8pHOm1HPBVUHLwYfGgqU3DqKqqgCqRevBx383xoOvkwx757UwB1+6XwMbomDsXm2/t+pRPfg8BC5XZx2ychtDHX0c7QFAtlaYoWCtiVvFx8QhOiMWwg0MqdQJfNqPkWCnEeYv3fHBKENaoWniFHwss0kEGawHH4Cs4Iv8WWyVHe3SDQp5JmlxBjEevBKsJP+XBXf9/WegOhbhred/DGfv+GW48OZ3J+1b/ev+imSPrEC0NSjci1PqPFiho4Np8yhySsPBaH2yYLlbdxf2jMsJxgioOeF2XaOjQhasik4Fn+Lt0rZZ7K2EPfi0bw2hOX4mhehkv9dQEdOQS9hkwT6G6KTNZs8I88/mYNNrXf2bbTpSLzOX7beFeNMzXZiW5zz46DrzHnw8vsAGDBhkDz7d64YfPhLWOk1oq8uXfQUVoOt/Y6eCx79XwTtfSvPGhpALtybZxo5KMi/DMfU6pWYlTWplS+i60hQPdd3km451mv1TNCLCzQr0RkmkEA7GePA1/abKeBnG2BDUdR7Okv6uKrkNGv1GzTdkhM7UBNk0Bx/GJZn8QgqjzdALWaj+/hewNCLZL9y8hCtX4OinPw1Q13D9P/wHiHfcgRr0e9TR/gEALHcjALH3as9LKXpgPd2UzVTVy2T8Le/plVGkTXmsCxxh/BkPvnbd2P3E8VxLJYoMLY+Uga+9xl9QXo9o69zJNJdcf+PB1/8WPfjo70ym4wfWg+8wTxfFyC/rFOGvN3avQ4yM11U2cLsyC3VnXogjCeQIVd+x5uDnVQzis1NIlKvcPaspu6FJF3HtWr/vWRotRlkBSs4u7stYCFnEhbyMFKaz7dZcEtEpssKHHjoE3/wmwIULhelwSmCt4Nt3WHvwreEAg/Ni0YpxhLbLgnX4Zc32Z0XEscLLkhCdXNXIiyYL0xFjx0i0Hnw1OvZegWcJczs0xxzLT3GKN5dZNLZej12ITu7CwoxWlQl0egYgkMXwxt+mwMU+996flKBZ7SrzK7NPtAq02bp3ZMx2JerjDrgcfJpgjBNIp3PiaxgLQLEAnrMYxIpheg5piM6is4IHWtdFTCYriCAMbuuts/u+94nfS31jPfiYOrJcULFOhC5dCidMYK48+LT9pCbrBjuMSvaBVS5jEPVvK9GDb9putXDb1ZfFOyBl5soEMCkuXH07VOIWAtv+aP5gjJTWlJAixn0oj8vMK/XgK/dq4AVP1MAjO/9W/1b5L81vDNCEK0m55L7jcGtg/06Uokz9miI9kjnSoGj4+8ikWv2i+6lUwNDfo3y9XuaevtbulVK8l3vw5WXpkmg5+NgxhbBqZ0UDJjn4eBqjf5bT9Kl1P4NbGagm9uCLEeBHT2zC8WerxFpd2iNOFCm1rBbC+fGmC9EJCe4rVah5GjcF+No946HpUYH77z8Cn/rUMbh6JfdQFT9v51WYH0nBZ88TOgeYHizx4DNoy/I+yeCTGU+0Pyon/ufu+pIQnU7QPpffpWPg7E0Pf+UrUL3xBlTXrsGRBx4AAICNxx+HW/7oj+DQ179e0L98vpacYZ5xP4hj4c5gRouRM4UUAJivKw3R6QKvsR+6xJocfASMzoSFotSgXkEMbU+NMqcCFzlKlZkkRKdoDx3oug7gWVZ1UAXfYgHd2nH3ey2Io8zmDBrt0OL6NPvOAyMMUZs+Up5CvudVWrJ4wDrvYu2DM2dSJdbmZiHSwde8U43MGbGzTQgh1W+/vR/T66/r3oeigk+B8+dnmXIv6YeDt+j4EZEoWCv49hvWCr41HFjIBBiCB19aTGdCNA++hJmZ6JZNQnQK77rmjctCysEHMcpjakoCAPAefEPGaVzSnMDH04x2iSTCC0f/6Zq3Id5ED74ohAVMa00qTRWlBcSlRoyzzaaCgySkSoHwzRNCofufC5kDABCC6MQwO3sWNr/1LajOn1f7RIFT8LnySRhrr56J5lB2gJ1COYaiFSZXkCv4qtIcfMnH6RVcchxTb942SAZFdo0AcveDH4Sd++5j60n2SJaDj54TMqcMQ92eBSqkzfoGqfCPtsFZ53K5/YpBwV8c41lUj/a8rEhWrqpTyYiFBj13WF0L56TOLfA1mJJ2Z8dihHbzCMtitE0dvEIx68MY+3NQVY2XUTbPUfZYRzWlCgNyBtyCbXxuBQ++girKQKENAICc6f5v7MHH4SFRiEArNfrF9kna+/vlwWfRvsCErTXm2d0WxXtaDr5igU2ZZ8DODv8c18YJ2Oacxyz+nlS0WODziutP57TBITztxP12h+i8dEl+6Ya0gRdfnGV7BOcQTr7cQ6HiVAo+Csn9NkB5xJAurnpu3AC4eDEwdA/izQqU7XUN8Pzzq9D3dH6ocB6jnpaGkjz1hobobOmvFb9Z4LCVvOD4wKyt1riV1WmsxmfgDG4d6e/mHApecVyFwOPcJqf1MMMyGkYR1zNU7o3pnix0q7NvHL8ze+ml/u+zZwEA4PBXvwrhxg3YfOwxCFevuurm6DIt8kYxSc0tvDVxaB0wr0j7ZVVTSneJzxkPPtXIht1bSrJaiqeYwxbI3vTiL+vcScOnNCwtiyP4YD5PrX9E6pPr1ykfHjK5RLsmy2Xq/YblcByU7KON3S27EABE6U4cYWguog7nPOJSexmi0+J/VBpjawvOPXbOppUZPMKOwckLuIe4THFTO5a777Zlhm0bWDnu+mBcEf9HawXfvsNawbeGgwtO7KIm6OYYDRee0dvWulb8rqMgHJ4PmIlE1qv4WXL/QO7B1xKTHqGoG0ib9KVH3tbml+AqTgS8DqkJvYOtHHyeEJ2ZB1+h4KIDNkchDxwzmRHHk6yhU/DGKGRa2Hz8cTj06KNNaBf8jcVvTeDBV9QgNMIRKQcfq+BD+6Y7d6v/ZzXlWA1FPf6BGg6FHnzddyhDfaiXXR31KhRXjAAwn8ON3/1duHTnO/oPmXnSc/AxQi1G8RKIMr1jnDHT2in4GMMH4SxSYp5LK2iCyBjl77qxS+GSmDBUWhuFRbpybdkqLpOucLwFxf3uRlaADSBK0ErbF+lKKalLEzoOAi+DOvBO56Cu+/3bW1TaeJ+e/yYXYrrQXIhms6N4jQNvVFDKf7n3msFM42d2iE4s7PG1yQmQpPa1Z11nbkKITrYrVg4+Y0F7wZUhGGNdtxXPa83rqBAleTz4qop0J0beCwJ/T5LoLBZ9qOzUg69cSeKlYzBMEaKTKvMiBHOPdGULhJASSDtitIJPArSG6p4r8FTAZbl+bG0B/Omf3gKf+MQt8NMzZHElgaDYWFMgNeCg88Pfw81+Tz0ns31YakhIYFSIzkKPuTE0g2tv5qQrW4Eku+2gqnz9sgTI7fOR90eMw890X46zHjEQF7W8EBrN+hcjbF/PP2v2ywABMDdIcl9l8p8VQ0Jl+BOk0GT64iA6CE3BijeMcxhYt8i2D9SDL8cL1INvinCDTNMdaHMXYhq9QVIImzn4PBACLJcA29uMnIWEsMTGCKkH30hEh2DT68E3QRqIMakkGuUTwnEQEr5CvOfHModZceYsSN6oOztwy5//Ofzig38J73zpYbN5j3xJk4dhcOGWGDPle9uHN7/Zj5yGhOj09G10mbWCb99hreBbw4EFaj3r8eBjY7LjOhlrcK69qaDrM2NeRxV0rNeUxXC0hTgh7epdLyxZPQq8gszNEHCEAa6HWgYRYXHxhQqEYPCaRbffBshCdNKp1BR8MTbcYSIoDE6ChoOC3CGUQcpCtsU8RGJWTzvaVAqWtZUACdEZI2RKMQnCTllOOkloVwLs/jIaxvwkZij4HHyr/eEIPeQV7DQVpvOZxcV31tPhEhQ2kiM+sdCXHYuS5JnLwRcYAQNV8LWGBZwHX82RIFE4iwSPuHLweRkK9kzqayEKsSYU+ifC4lpW8LFNc7id9K2uA+vBF5bLwmHo98gg4Q0Cj4Kvw+GZIg31ixOOaH33MkXZvPb7d2Oj3c85bZI/IjQCrTrGRAnmvXfStgMkN+DQ/eoUEFh3Pz56XIjO1js5q5S0L+39IZbJ4qubxKSyOfgsxRz22Fe6TQWw2fcavcLs4byI7qmiAZeDj3tA6QXJg0+K4LBY9POABXY07CX3rRZC10uqTp2Dr6VRs9BrkffIGkKjeaChG1G+mwGh4DJvFgCXd1kHwv5l7ykDp333u4fgxo2m0R/+gCQSq+vE2MoLnDdL91vB7x0tp2qQ3I+zPvTnQKZ95bqcdNcKVCNdh6ECrTqJcgK6gi+bc/obX5WljIkCbE5rA+hUKHoiuWrygjVopOOklUlSZIMXChCzcIhsQe/rjB/JN0PWh+WSeZ6H6JwEnOsr5eBj62HqnFEFn0fggiCPBmB+4qrahZoIfg8QEw8+vC6qeG+gBx+n3MPVcbiF8+CT5TA+QysAgM3d62Z5d2XG48zzu/AeyYopPEWHj738uAI0ogmdL+nO3HjiCQhbWxAjwLvOfFNvXqNrMV+tRMUSPiHPUd8ZRNyO5Y47/PJFNt870wmPvBufSQAn+yNetGsF337DgGita1jDAQEPL0UQdYNjHMLCONCiTOhP4jWH28F3ogcBJkL9/nJLEXB66bSCzc4aOvB9cTYrXAxyZZ5E8VKH+nB9KOyfg8mia9578OWEBhb8qgJTUmmTVFigAjVgCFHv51TBZ3kneCEjnAUis31ecleXCABb0D342j3Pg0sQEQI0Gr7mBfbgY0M/xh7ZTBqOMASAquost2KBgo/ztktD2TaTWHTOMw8iJTwb8HPdntl2n/Z0a4aIWYFJFy6X8Z7AffCE6LQMPpLn5BxWxj0h4jXHhPvXpN+jVb0Q954bfzDCDzFEZ8E+32unJtOIYYI2sWwoxBo++OzfwC1b5+HJd/0exHhHUV1YwdfhFnrnMFJNToFBDTqw8JJVfHEgMKX4lT3HijeVtgAFwjvOgw8X6spyUl1cvMRrhykqhugE2PvNDk6F9kir+2QuJ4Xx9bVdsjz4emUE2TuVpNRhBE0rD772Gd53eU5mua/cb4+Cr3r55eKw5hxkIWshV/DtaYhOSbi4RyE6E8So4QIlVKW2dty769d13NsqklxncdWAZrDBCTDR2+a/DteRckM9+FpeiFRXQvZSo87me5lvHLP/OOWgGoXD+YKltYLuwRdj0/ascECjzt/IttgcZ3Tw3nAZDH1JDxU+Q11zQ/eBAyFn+3Bl7EsVBXQeLPmPq6+CcUFScxaik2lDQ0wASqI6cBliZV59AzdkCbslPotpDj7Rg08w+DcbxxCCWH/mFIn4Wrw3rBCdSXPGvbCxy7i3cnVLOCvh5QyM7V1jphz/qXw3qrTmCPzFhugU7vsg4DBpniQaPIkc4hQG9cpipTw5w00zLU3qlylg5TjXiWQtptS5Weu4VvDtO6w9+NZwYMGb64Ii/OQZg3RE+Qz6MZU3X2NVt+oT7SshDCJzHLPy3HhImEY6ppbIbu83yYNvFGAiMufQfbQWG6Jz9a5UaoK/DanlcMZ0AFIqOAinttIaE+WOUE5d+xoxTssyfc0FvL6LNbE2EokXtaLRl/R99+3ArbfWcNddaaLzsLUF9/z0+3D7lZ8CgC9Ep7TnvQJrPBbMUHA8WTvnjYCM7B3LG5LCRILprv1uGBEq6MNGcgq+VLjPMNWkb5jZZC2OY77/emV8akiQWOJ3Zo+Mgk/wpqVhfEwPvhLcJuFVBcYo+LyUNdZjTBKiM8Ml6bNu79dOowxa/QREPNeumm+NPhQIBbfgFQDedOUM3H3xBBzZvgy/9OoPi890Xfch/1rc4mk7C9HJeGzTkG6udcK4EsCVx7akTm+IPK4pfJ9JOfi4tt0e/8b4igy5iJvEXrKslhzB9uDzAY0QQKvDYYO6I0bCA6b9UvZCoUyJk8mw+CGkY0iUw472d3fRvYNxLDcfQM86XyftF9d2deECHP2bv+m8SsZAth9CYHMr7ZkHH1uoPJS9+zx6+TVBEcjfIb6m2QpqXnlqV5PTZC2I0WZCj8NFmtERKURb1zZEpybAlr5ncYCKF/KJG8Oa5soapa+CkQx7Tqqg9uv69Qr++I9vgf/37w+J5EgLQzz4mC3XwVAPPnabEAYsE45LlRt0dIAI29v6HDqr5V/EyBqxJf2peQ++IekRTHAONFXw1bqBT+me0SIdtG3uUYhOiUJq5Robu9czmV2AmDiI4rMsyQABgM0t6AHps9ZLj977NAffWKdePIbNXWeIzgnkdnk06NI6UwJVi2ilymPH4EC2Pj08qLaHkocSHsH8DCNPUZpWgdu/rWdtCV1BlYHue48Bbd3sj2iDawXffsNawbeGgwsGodYSLhruoToxGqLTIsKdXUue83zuIM5PFfJwjJeYgw96a+gEZ3sJ0KhzDmmYJ/Iu+rwhWW+glfIy8SSqMCHB95/O2wxShRIdgxjebVU09Yxq53FgiE5vaATSt7ZsFqJzCmKPni1mvzYi1cCEidElRrju97xnAf/5P1+Dj33sRvesrgE2H3oI3nXya/DrT/9VQ/QHeUylwnbpG/wszcHHjH0vQ3Si/VwvCvYREkSEAE24zLoPrdjm4EsBK3mZKhHxSnGldm6SZyREJ1u2DdFZcX1symVhY4Dm4JvSlbKtWLHQJyDYW7hwaqmgG6Dx4Evat4Q53Blk9qrowVeAVvq+lAtvKFDFcP6juEJXFfjuni/7MMOz5U4xzuFy8OXh17gQnXm9qoJvgAffUEYr6+uAD7nuSSE62xBxGA8l3yNctX3oVnO/FMgkZThATCrFj5mhCcZl2hWNJ8BaoJFQivfMHHyrB1ShNxPy4rAhOkOA5TLAyXv+VVaXJ0Qn7VO6h9ludDA/ebITnscIcPZsBS+/XA2a8vy+ZIxkBDp1AhJShNJQ9tIaYwgBUoUag0s7QBJiU6il4KoYjeMfY7/engldlUk9+LLNJH8OrQEXf79pnosaYK/YobyFyMsJYNHnGnDlKA0v8uUlDQGAh77Z2Qlw9tUKzp/XDz/mv73jz/dkGmJ9iN0ODWcKADni8ir4CEj0zPY2X1CSE7jXTl3oBlpjCurB58n3WvK+qVdXNgBAlvqiYhkz/Y7WcCbFA1y/uVDOHrCKiTn4aoBff/pT8K9/8F/g7S99L6s09eAzPJilzjgPlbTnWrzMoWSqWG96w08GpUu1O0TKwefGwwW4zH0/eOUpDg8+duiFdwzlf+jnYg4+oZ3yewGfXVsW2VTF8LS0GcXtvCS4Gy3bVpuFOi+Y95KIBHIlB4d3+qcCawXfGg4uGJdhi08s5RMF0UuMKFKmgBD6iy8yzEEivKqbMBFdCAtnnJEAkRfSrn7E2NDn3XwBP1+jhqwQoGKuKgKitWpyoQ67JCwPvu4idSUHTj2k1O84cBJWEq+SyKZKQtcpngfsWTLq8AIXzgLzj3UNMDt/HmJslBjHti64rOJMglrbdISpShV8ep2j6RTaLzTYEqEMx9inOfhkotLbN1OBEHMFM1XwsevBKMn77+WziKvYcw8+RckMoAhLHV6YQ/i1KsohOrnybo+x5A4I6G9fH7U+jIF0LPa5kIw06H2oNcTtVe/9hSFT8LmFDelvzoMvwU9+KV7/iZAYnhXKGgJt9EMuZnrw9X9zITrZyAXkcnz6nf8rbP3ar7NtDgnvWkc5f+kk3o8W5BK7ojIx2t72vdDFmCsulKyS/0vzoCjBezECkHS+eW4txusOYhQVfN2YiYRudxfg1bt+FZ77Z/8GYN4TAz5PBlkwbAabQJfYK7e9G06enMPp03M4d66cNc8MQGLtzsEniN4K3oOIz6YK0UnrGoL7LE/wkggKGm1UEnIwydloGDV1dF5AP4Q+ezxa2P619JfLm1DYF5G5M5XJ0HLwWXm/Y8z5akobxigrGyjvrOUbipUeohOVFHN74bZKgX4zyPGXVMLWMdCDj+UrmXPR5uGjypHiOeHoglo/42EVezF9nofoLG2aBY+lBqPg0yJRlbKfHg8+ztN7CpDmaPP6Jbjj8gss70E9+NqzPDtzBt7zo8/B3a8fb8rTmfDk4GOeSetOo5a239Z1yAxhxfYcgD/bdIfoLKuXfU/vjcL+Yzqv2bHDPPjK581wNhC1yu2ZcijyJeEb+cC6m9Q2KDAhOgPy4LPkEVJbnSG0JdtR6yrYcNJg1wq+fYe1gm8NBxcUQQEAuDz4KKMRQmohJeKiAUI9CSR5ECUM6mWEH/1oAx5/fANefHG2IlhkK+z0+54poULeGFNvlyR0n3OQRUJS5p2rmRjzSyym3ntcSD8LWA8+QgRIYQHpe4BeuFUP9eArzcFHGCQq8G1mXL48PURI3n7I/2600T4QB9Q8zwRfqPys3nUp9mUm07HZMgVfL/hqYuyTNpEHXyJkYdorp1fR2RwRojNAJAo+JgdfouRlBHI05E/iwZciF2w9/Au/sIT3vW8XDh2KKN8l6SdiWnthOXee+bNIPZ48OfgoBIZw390FuHwpZM891v4sszxZeJuUz5jVyzQU3RA9A8cQUS9NyOfagil1HhyfYIVd0tr0Co9klOWYC1IgzcGX77kW55njiMT7PeY5+Lx3a/cnBGCZ5ALl8aoiHyQ4I38tKfg6z0dub8UISS4MCKbwP8NHUd4X7JzeRAbV03R2RpyXtWnYZggpS867t+yNGwE++cmj8P3vH8q+9+TstRR8tCOLRbN/Tv/Cb8K13/xo95x68MWY43c6Pfg3Nhyyxn7lep88pRWAe75rASv4FotWwZd2rgKGxi5ogwOLHvNauYt9QQ+wx3+IUaTBEhBivFnnvhTnp3emfee0K5zT82mdYvOACFamLclYzBsCtT1Tg0J0cp4VkN+DGg72AocTOFm/3EYJMeHjPz2yWM2YSsLdLK5ZfUyFwN45ZZWrFKFSSwupcoVOu+OOZYdPt7boPOr7QN5nTEEnoefZM6PBO0+iBx8jnDc6Rg2ihnjweXmYoTS1anwQYxJmsK4bBf7Rz34W7jr/HPzas59Fdx1aQ48ylVnk0hCdjQcfWi+B1uDAMpLYEDz4MiiUfbB3nVfWwBPDeTGFCVRphMKDZnrwMfcMbofjuTzz05XBz1PLMrPP6vAZTTM2NvEa99I2OtRdwjQLRVwskHjo1wq+/Ya1gm8NBxeciH+Ky6MVuJV+p/an+dVffAaCO3163ln9nTkzy8KksZbsYCgYVkznYke6lAYANzW4UYZwwDHLJRA9+Gh4Tkf/6TT5c/DxXkNp33IF355IubiiMQ/R5g3fIHlu4p/d/yAwnyG1vGT7SB5mexioB1+6nlW9dOXgE71WwfeeAm4z8w5rmejI5FlRmFoW6IQkITqH7aMWN1SI4OUUfBwvQ/EFhkAEgpTobc/Lm95Uw623xlWo0N6DLxG+cAIiLgefcBapcpuz0tYf5LCzA/DDH27C5+8/DOfOptbZlcHk7ocHH4aqTj34zFxK3BkkDedK2/6Pkj52Dj3F9sU5tO3SXHPuD+l51EJaZhDYcqXrVdd9XtY26Xm6XrlCuf0uLaWH6HR3DK8x2TiawFc1tMBWzGo/dGUFfoZDq3EhOhPAnoGhSveemhPYAQPmdWoorVo7eV5PTPbO1ISmDnrUKM7CU09twIULefhmCcdjIVsA2YNPCtGJ75IKeQ9gz2HvFKRVGwNGhWvc8ACY1b2XzXLZ4BfqmSENYqig1gNTefA1daE9kSJCuT7swecw2vG+8oRH9sxbYrDB5NWk/CQApB58EowM0Um95ksMlzyGkhg0Dz6vJ3JaH/1GDq2Z3l2yQS0AADg9+DyGNxwfagEtvlymczpZiE4y+ECIbXFFGF6o7d9dd/WWk12UIv1z87lHkyt9S0N01nWQ0nWW9SlpRMARtI+YrkcKClW25QXHt5nn8MD2tLvQeoH5+hDSXGKU15svtgECOdNDPPgoPZ1UF9hPlktgPfi8yjWVXig+ACNgSo12CInxg+jBpyJ6H2jyihayyAWorLc5l4fbhB58nNK7yHGg/yr51RlCC04Nrho9RfeQF1rDMFgr+NbwMwPZxah48KVC5ZRoryb0NrCAOj1xwrQ+RCd5bhAoPQ2eKnkSgoHz4Ot65BfGligteM8ge6KlvA2lHnwZI8558OVShKasxJhS1yYAqNFaWsqABKiSxCieW4znG1ilCwsUoto6R3CYESl1A+oKFny169GWq+qFS8FnFtAKEg8+ylDklrK94L8kTKJZIARi+WZ8i4ALJWTn4DM6ioVhK6WhbqRmh+ik/cWdrtUQnTknhB+ZOfiEhcD1njkz77ytfvJsKmC1CGBPSOHCrqnlaG4BS8HH9kMRwCSvR1yMU92pnGBzSBv9+Pj9Yo3f5SVHCtR1UD34uG+aR5n1QFaMKj49DG8iJI8p7usFKzlM4ZlpGVrg+8wK0ZmsDx4TpGMq6Sy7NBq9chNCdA6ynPWG6Iw13HprDR/96A1eIYXmugujpd2xypx4p+v113kCICfbmgeUXqiC5L20+oNcTliAOJvxdF3fNj3rch/NEJ14DyPvrtlHFAAAIABJREFUtCHbCnvwNYL/erIcfK7+CELb6XLw9V661NhRNTBAii7OIzNtS+mH1VFkkBmU40G/13KqcnkV+662gjuBBxgZojPzTFGE8lzvMnREeTnox1eIRhLgIm7wden4sI1+Q+nqBN1TpYIDpPIN3anzb1ZdlF6gqQ88lUwZolNbyLvvrru5bT2U+/OSnwUXMDyCFaKTu7rafg0KearBACXKjLu78HdGHfTazz34GBnUHoXolJVn+TMqJ+DCdKrgOUQDPPhQ4a6KJG9qSN9bTWogRk6RNrFSzsSJ3n4JeD+TS2F5isSvjUH0bTuGwWaMPJ3htHFDz8iGZL0WfdEJNA++DswQncq35DsM1IOvhMfvecNx+CCWDGANk8FawbeGgwtUuEwQbCfIbsOKOQSaAD48MzREJ8c0YgE+vTMSBaDCMDbleaFP85i/2Js5CbBEnkFDQnQCAIRLl+DIZz4Db/7OA/nLCSQE4iWSCEHKUFbnMQb48qReK6FjyF1JsVcrNjQHX0KrG+PhmOScwHGuoRKekWuHfTeAyuCW3grR6VLwCW24QhAx40jz8FEuSZbYjCJ+COFTkoMvq4YIUNgQnQoRzj4jODhT2jGC6NAJbYgwBBOYbYjOKldCauFycX2mzMHBTLR1cHOxsdiCjz72x3DHlTN5PUL/xHbLi3Tl2rKzWuZy+T1vN5Ln4Ov/8IbySmACIp47apqgs2vaY0GrTLwc8tWxWKReKwdfjJDd71z3AhFAhJjmivHinsQAiBrJrN6x+MDpwadBunR5hZ4cfKy3DAmVkygtmRx8En5wyxyckRhuFmTKC6cBV4AIt95aw7/4Fztw5x1MmKAmJlX2jVafBF6893M/J59li56AGGE+k77Hhn8rCCE5+7PEgy8hfsQ+YUhyqlnkKvq4FozXvHNG74cqRjb0mun5XQj2cVA81BjQzmmCu2ommgIHQohOX+MycDiJCsQ81SX4nMzPbPcGHPvqg3DXxRO0oz1fKUiQ5RCddp8AAGYrpmgIuhONUym9WLfh3BnhaQGN5CkjlyvY/F7+UxLUkz6lf3iqTedpuey/pzJUiX6zQlMCQD7OwSE6+7Zuu61X8O3ucnIKd7XyC2ahJRqLi4QwOaqgGkOOuCXKA2ycwu4RY3xZiE6HjjG7JwZ5DuUgridDO/Zlmz/m875My49r+JUzCDU7FNOQ9xhoiM7uOcnBZ6EEus+sbrr2VeGFzU7FgHQgahuJbIECQ3d1H44Yi4Bns8gF6EN3F3CEECkKifNy1IbYvWNDdPbNcE1xIZlpWx1t273ojdXd4L1kJTigPNP/32Gt4FvDwQWTUHMwUeQCCCGSsDuSwHu6yy+0wmzLgpC2SYUqhHDD30kefO3vKMSN1r6jdRz5h3+A+YsvygXaPxnakLtvuTq48IcVEKXFgJAt1IOP1qApFbLnjPfRYIK4qsSJpwRvC4NCtDmhI6C58SAhaokTQ8bLAG9p2pZrQhHylR7dugDznSYRteVBZQnn6HlMFXxpWWxEQLcfs2V1oAVwiE5HKFtaT0f8RYeCzzo7NPRIXafK2DrFF6H1nUGy7xa/UstqzoOPHe2KSedCdOL6dnZ4gw8TIhkjapPWcWT7Erz5wk/Yari5LuuIDbiqPESnTtxbHuDtTy4HH6d80mBKp6ZsLA6hTdIXIXSa1S8sdErCXsUIknBfglTBl/df6lAubCMWxnXNenCbgO9nQvbrfJn8MkErLg6WbwuPD4+t9agWUVYypjQHXwnzynadlRpMrBkpAK9RWgtFsi5Ed/GhUBkXGXRIs7rVs+o7R9R4Q6ga2l2YefAJnC0rMIXUQ2C+gehj5qhZ+UA9/eA+Nj3uDajqdNKaHHy84LZUqOg63hIdiyaxYoOE+ztSC/eset7FEJ26UilGgLsunoDfePKT8EuvPMp1VmyHq096aYWiPvTUk/ChZ/4aZosbvFeZJKh1MV05tHNUUYNSwVuCB58HX0srqvtL7S1LHojlOMiM1JSWvQam7hCdRt+0bwDyLefx4KPryHqtUdnD1pbeEQEwfXnoUL9QmQGlWa1wb7DyEB9w9xamP6a42rmQu9euBfjOdzbhpZdm/TWKaDJ67jIouH8AwPRoBGBCGg4cPFUeh2vXBG8quf42t6rGjxeF7Cd10zLSOenbDMm3DTmU0wgSDWx58Ik8slRIgSK6lxqACG2wpqMxLd9IARwhOp18kAae+WLpDKUdDhck9LRAgKcyJK3+VEHNgRWik1fw5c9oNZIH35SRhsZ/tIa9gHFB/9ewhr0Eg5BpcR93eehCq7zOjBkxkJRTnpWHS+DeSZ2oa3A5UZBxJ9XExhMjYSaAXDYxuiRIs5dfFt8lAlHm4li6cosJBEZCLAyzBJkj4QclTnAbnhx87fxpoYdCkD1gkqZnmlDHFihS5S5XvFvvghBmLDMPK02Sayn5s8gJKOoaAAf5poqMFn7+tafhA8/9HVSH5vC19/+fskDJQ0OSUDt9iM6m4czwciVEGWSBrhUgAy3x4OPWKFOGr0pq/bAIZuodwdXRfR8azwEARi6M23N4w9AcMC0uazcg9biyBM3iM+DHbaHERDFWzWDWrt0IYRMthMtV9VLs01ABbcMMMGeVE9wr0PZryhx8XThAJyNiMcdymebZzk5Ixs/1SQI6as6DL6M7HBMcIrlHiEAC59xsy7NAcQ5DBEkMpESeeGUJ1CiAgpRXs78aKVHDVEY8+LgcfC78oL1jpet7xMwOqZfSgU6r2cRAQ8AFHN2h1TcWpPDLGZ2++h/fUQEizKoaOB0hK2wLIWmPcSpP2ubOutRHK0Qnrgvn4Iv59uUBHVDqwRdizebgu3Klgk9+8ijM5wD//t9vwdGjw6KVZCBWgg0KBxjC4X0thOhUB4AVfEb7kdTzoWf+GgAAbnvjJfj+Pe+GEI66+iniHLZ7PiHhkZ0raXPEgy9ry2EsxvZPug8KNolkXCSFjC0OzVjYLQaF9X2Qot+wKMiXg88ji5BoDQ3y1AF9/4VrXevaqg5moIQBciv4FLrp0KFeaJ4pFTueoZB+5PgRNEmN0ZbgoUWjkUCcPkTnsk6lrDHC1752CC48M4djL8zgyJEIt/6zlHZhw0IruI5lyVKBj1oeAAaH6NS27sYTT8CdX/gq/Mtn3gIP3/f7abQW5sN27gNE2NjgPfiS7wt5FAAoDNHJd3W5TA1xe9qJB+pEoPZZGhO35jGqTKrJE+6hB9/tty3hKiLAVFxXuIhpBCthP5NoFmyYf6MLmBZIo54gIwBscKuMw8VqcMgH3cWWgk/i0zra1pDdaODCB1p9aw++mwJrBd8aDi54CBnmOS2UCQVEC6kyoZ4bkGAo7wsi0BmL0KgYDGKhTMqk5AQF5qukXGpqOC5rLjA9yRAOHs8kKWRgwhQ6LCg5Jm1jsQ1vO/cEHN55A+L2e4CudXeReiyoOmG2LL1p2nWEuXN48GHCgfYvaH0ugMzCSGFQXYJD4XvOg6953xeaCTn4PvDc3zV1LBbw7jPfgDNv+ZDQx3YtFWAopjxEJxpDu6djzOW8YxAF6Ue9KJd09KF+Y8KocR4BaRiNvN+ZB18kioksf2QqOcEhOj05+EShS8zzB9HyZtQghxBizBqmHnwzgJVI2cM4eZqj3hazOh2w5VDE7n/mTsXXIQ5hN3RbDwrtydZD//DB0NYlpYLERGrQ5OBrPhI9+NjvaNtEAEEFEjH6Bozxsasnq/aD0vVkr40TCB0/PoeHH96ECxd6xM+F6Ez2BGXAByrePAKVnwXQPPg4wHPp9eDLzmThPHmLU9wu1rH6QfOFzQwPPmwtHWOqWN7Y9CkXe0hpBSykTr2sdYQ9xIOvDjOo4oLtYIh1ZiRTQQ1nz/btfP3rh+Df/tttc12GKmBiJDSHoWDjBKBBmKNA9q1ISyCayDwjyt19ZPsSAPQKPi1HsCtEZ6fU6B+pOfeyEK4dI4Ie9QibDVNWAO3e0XCDRr/Rdx956v+BSz93T1ZOqqeEjjCF2doVXCL4rIKrX0UefAKt5XlGFWIYD3rHy53tQBV816/bnWOeY/JkcxNgFpbdcy6XloRnvGMJaKHPn6/gxIl58xOdC82bBt8D7vXTgFEoPffcBty5+vjs2QpuJcZJrAcfbsyQi2WfZjn48jJTefC1n4YAcPjLX4brdYBjWxfgbeefhJfe8kFUJj9DnRIvRtjYiAk/vthl+M6hnSO/rRx8+ZlLo0BZ4hArDWMJ+jGh5GNvw8zz5lyn/HwNFbRL9oH33YBf2LwBjzxyyOyW5UzBfGH2Gd997X7MaFgELM2R0AK57BYAsmgoErjopywHX5/H3aPg679Lf7c4LfdUL5BTtDSF40pm13Ot4LspsFbwreHgAhEuU6AJqlmExSCbao8NsGN67yXILX2nN87nt4rJ765SLOwiApCMuaHC9dUN6J2LIQIxrOCTmUJ+vVNB+rCL4s2vPQu33WhC7T32Rwt46795O3wYt9EqhaQQnVhQ0oXoxFZ3jHu9IBxNaPWKy5OWlxNBIVq65445ywkcJg9ACKuqAhse0AO9cowIxGZ9PVW9UB0bYwTY2L0+WrFJjwRWKkqpE/gQnWUMVz7ZwxBSFlI40hCdOSNr1h7THyHGldBgVRdVQMQ66QdV8CX4rq0V7UnJ44sP59QLQpbLXLhRcl5wf7v2qDeUdWwwzk3CAxZ3I4GqakIFVXGZMMKz5a6In9r6KO6nwIWXY0N0Gn3M6iXcbsdUDQA6Fl7R6+BsyPPmv7QMPvNSiE5JAKe1beXgA+AZoewRFUDEPAerteb4eYzNXuVySlherFzfXCDRJiuo6wCf//yR7HkrrORpAkLzAPHgw3SSgfX4vcTs4f304CPgO0u0LzzdmdUtePAlHxfk4NNuGe90eT342rZSDz6AWaXjSQyLZS+0n80iVDOsoGO+Zc661AYXwSN5hgrXkuugAnU1g0rMzRqhMqSLp0/Pucd5TQPuVg5MD74YIVKrRoK7OnBqHSM2LmTuv+S34m1cGV4uibe1QZdj6IaRMWppP2pCWXT4DnnwYYWvLwcfY+i5GteMKM2LFHzQ04wYbn8jTfFQKR58HR1gID+P1xeDwkhfmeecZU/l9+Cb5EwZ32APPoAUt3iV8qzXGnlYUQWfUxMXY/+sqgA2N/r3SRMePtbzAp2h48cb3PbTn/8QHN55A+5+/TgpmiPnZi/5z64F9SJC3CCyGqb7Uhhj9s6xcNgAOs3CbRJYyu626c1dW0HcKSIgwuZmbOhm8q7r34rvLF4jSsdE2Qhbyjff5uBrv+qU6kJn0pzZNi3GAsc/QMz4Z+l7Vr5Ukg7EAZT+5mgnK23EpUsBNjcBjh6V+8YZBqTVBV5+pUw898oTohMPUjszLH9Oq+JCdAq4XQPaRpfKhOLYwbKmwg4ArBV8NwnWOfjW8DMDGXHbIU0FecRcaCuG5MBCoT0Q3IjhyyTsz+VBEi56qUjvwUfKOKxgjGblPjCFl5IMQqoD10UEedZlwQlY8Cd3PPEd+MbXD8ONG6j86iMpv0ri5dIqO/YpB582t9SDSgWFGKFbcLRXoCDU5Tz4KF0zi3yIzqS+EGRr7DpmDzMmiGmgzfnE9QlRRahv0hzp+zN5G0KS16OI6G73LDoSZg4+69wz3KIkkEzODVLchtha6RKjAetgknYza/bYKzc4Dw83Y4vxvMB4a13r3iNGHHsWjA190m6H1goed20e+7gnlp7B8lZo6g78OXEIqLL2JtJ7DMFF4j4zOoMNCXZ38X7VhSh6B5qfnhCdlHbxePDhcEVNGw7mCXss0fLK4Nxe/doEKXhYg1bA03rDZ9+TMYl5MCXhoSZU1JjU/VDwDTl/ZJ4twTje65yCrzt/da9aoMKBSMMwk37QyrxjknPwkdDa7f9kqFQ50QInl8bCw83NdN6Ghbzr/64q0A350Me1wI6rR0th4SsuBx/BazT/sQQuZYFUCZ5P6ilCixpC20QJWtfQ4WntIyRhZQV/CIoEcBTnM/e+xZvi7hUp0JDhaET9wvOTeC4WyNc6wbWVC0yCFdHiEWpCjKy334DmhpfheCpYzVlds0r60v7xz3vBq7dOugWXy74BaqQontlIcQCzOSb04OvqCBE2tLCL0PT52NYFeMeLD8PRrQt8G1o/YkwGfvIXPwrPvPPjCW3QfjI2RKfHk/PZpyp48smN7N5MPHyJB1+WmgB3mv6d/8wfMjn46DdTevCxQA4Rm8al7UKMcOhQyo8viZO6B515cpA39DT/fd5m80fjwZfTCBKfUuT9LuECbs1HrlF2X3kJHKEojQ7EGhsoa3LixBz+7M9ugT/902Nw5Yq8wpTv4KpkPeEVGpTjDZIQnZDjj+a572LV9kBXH7MRe3qFXxuOPqVjkUJ0+mR8BYSDSjytFXw3A9YKvjUcXDC5ToKwnYIkj1vzFGEPu7YEDz7aFzZEZ+TLJr9jTAhG7gJMLFMhQMQee1MQcwoBCuDMwefpx1QXRYxw/XpOVIs5+PDzVR+WUVfwKbqLHpxJ2/t+6kRZTr/65ksSfiaPIGT72V9hA6KCD5Wvlrumd+voXF8hZMKxPETnqq3Yz2PFjcvDcAkFqBB2iJ64VfA1bGKudEr6wyh5k6PLEP0JEWmE6AToiWuKi0pDdHIEafuI8/Dw7GEJmrwXZXgwCOs49u7oQhMygq85CtPJH0PjXDB3JCvULxA6JdWPPZcIWqbICtXagmW92uBxslerPoeppFQYYim8XGIFnzArbKW5NJHe51mITrNO8pziHCetpJ0v1XrVyMEnQRKik2ubjCnNwcchUn5vSnspe34TmdRSDz7vtgBo5latnwnRWeLBZ5CGLGD8/v73p9Yc9DwA5HliJQUf1/4uyuW6saHsY2HslgefVx8cQ4WMcpx0m+L114S5XmbPMPR9t5TBw/Z+jOmdwNJPzDfSAyksvrofcQ4+426WUh0AQOYpyfFr9J1nv3e5Zg3ir5L6jvErDvNKw62zbSvtUaTrFPSWgqTgk+r++Me34Ld/ezspZ/VDK6MaQlHliNN4a0j4s+Sd867JlFQJrvHdd3sforPH0ZsbfWPYsKI7L3WEX3/qU3DvmW/Ah5/9a7O5bIRkodn881KdMVX2+M6u/j7EGq5eDXDuXMV+0P/s+zcL472PkveOvO7ZPeH24NOfiX1jXrQ51VkPPhqiMyLjjpLOleTgo/LFFSyXkKey8HYjpkZ9Ko88AEq+dxuiOs56BIA6pPIwT/QD/Oz++5soHotFgEce2RS7Y3vwCYY8kV9P6Vly/UkRzpzEHeZBRSBuqhh3UjqSa17qRsfX0vGXXN5jy64VfDcF1gq+NRxcoMJlym8IFzBTTQchpIzL0MtVZdQpYS14q+SWPwwl4WiYCtnpmHjmpswK3SxiFPAQE1L4w4QBcwiQOVkmV28S1mn1EafIa8tTqBUPPvFCzmIcBnHuPBexx3Ow313ymlMChCOQIprMkvua636eg6//fxaXqt4zxkYYZhGeHg8mDKmCLy3XMakht+gdbQyQePD5NXx5u2lIrjxni0NQh4VUAQBiek7yY0wYr9DvHRqikzIEGmQWpaSKxSIfh4sGjXl/8XNFD5KBlBvIl4NPrhx78FGYLXktFDt2Y9PHZQ1vunIGZsud/HUcnoNP7M/QOgory0KnKbsN7+2dnYDwXyqlKL3/Gpwhe/ABAOuhb3rwgc3gWv2jXvDtNivly9zLMnAzcCE6kz1BxlQcolNh+Fm4iSE6PVCRvCM29IqF0hCd3dwxuE7LweGdLqzge/e7F0puqdW9TK66KvD3KId3dxf9xxsbMalsiAcf/k37xQmVuz+hYsN0anNWM3d8C2weW4Puk8CzbiKqSDz4PCE6+cZjhE6h2XpXuTqIaRrLg0+7u6W47dy3Htyyeok9VzQQPfKwB19Ch9Rsee9d1tEhhodK1k+i3LTaaxR8Gj2Xvvv5n6/hXe9aMOVk0MvwBzgEYHlwHw6z868PyWtJve3qOohCYC+u9YTopAo+L/tHcaEVohMWSzi08wYAABzdvsjWkwB3LzFEPFaYtq95D76QPBkLLR+zvd0qr7perPoSVsZJ6BuiWNfuDO59jOlDxZmpb5Pm65yIrCkhj/B6bG6mOfhoBCgtd2dRh2JKX3ui+NR1mnKmu+OFzgxKJeEAO/S80aaVHNBZNwdUlqfycswzjr9niwu8GZYfqG13ncwNK5MzJNBZ0vOsnId+4kJ0RnyHM7R20m1+LGM8+Dp+2FFWjXq3VvDdFFjn4FvDwQUDK7ZxsDUkxCEdF64ZKeBM2rKI7pahYKR9msBZ8v7jLsCE+QyBtRAcNd4Y4dy5Cp55ZgOWFwPcTV7XiwgWwSzmQkh+CJozA7JPiMC1Y5ISD6gK4opQSAQDqx7V5sIy/cgIaVkQwFfJX9IxtkzMMGLy/2Pv7Z4tO6o7wZX7nHvrW0ggviywAUOD+DC4MQwz9mD3dIRnpp+6HxzREf00T/00EfM+f8NETERPx/TgmenusHuMsd0GjN023e42GBsLkIwQIIGqhEDfKlWpVKqve885O+dh79x7rZW/lbny3CtV4TgrQqp79s6duXbuzJXre+l2xboUHsGHM0dM0EURfDGSsBx1m1U1sDGGUGc6avSDJF4iJYgWer3aCk8Tvam3NPDNigiWYq2SolPue8eii9LApy18IUoDfMnAp9PSDBjgtYQNfDNNRik6h3UZxS8IXPkf0r6PZQYVjmYIAEdcI6nm4VCDbxgpwRDBNxQur6XngGcCa3Dyy1+mT3xX1iWZZIy+J2ovCdWsyEeg3wXVN60pNcQ94Qgk2/C1vV4zA18hCskDQw3FAe/FImYdWPjmMp5SFEalhNJ8ioGo2PWWsIqMNaUjrqpByPvNzpnY03ue+hotNwd04R2/Quu9uRYfT9GJDU/sT1VX8OjKipz/4w4udyJo40WNnvG1zulgBpmnRt15xhrM+1k4fd/by9GZ8Rj/ZcoP5Cim0BDAlUn7+yQNUg4lFaRF4PkaQlmaWQdwg46GEPu8tpLS9nrZmmNTSlYMfDBy1lK0xbID6AQ8RSfudr5WoFWL6I/gq+IEHqtG8Flp9HgNPm6c3hgGQSd+WapA0BjqTBvTUITYU98jZzT7GS0/eOZ5myjUgYZy2dWnF+AZEKz2kp745HGgIhDgStGZOkqZaDwGPp3ewMFn6LZdR7S/nPsVqcaT/C0U2hJPCID4BkgvHPMb21J0wj4UuBwsdIrO0eHG6jtXZeB3m6466FItlbMF254Lxf5jpP19naJTy519ntXBgVzGEyn++eTJSNevjzLMRnYzp+gMkgepOECUambnKBqOAWDdhNjT+378X2h/fZMe/9lfpcO9M20fxNvU3Os277JNBB+Hc+eU846RBa0pgg/87kPH5GwbL1SzHIJBr9I3Lb5+IUVnSwSf3u4T6WZ8golMBWqZtcx+71CZ6e867Ax8O7hzoSp1Dv+UCnTrA3wglEiIkxzVtlE5WNiwDwfLSEdERH2OO1JOk1K26FzYA05S+S1wcRD6ui4v0u/8zmlarwN9GCjf+804MQWpAytm1IQ2HhSw+Wgc0mmdiLSBZEFEK3Gfd8rT4Whh2EIzYyYc0mlpLVoRh6J7YEyppiqKxh4hytzS4XlO82qT92OGat/LvbeIK7cRHuHgSj2hU7pROUVnAliXpKLwq7XnL9tvWfh6MKzJFJ3VGnwV3JKXvGUTnIVIZmBUBj7cd91InxnCaXi/1OfWKTqr2hY/8L0nIi8a6amGUg2+xeaAiM4N9ysGvtr7733ve0S0j2/HNgeXbb3HEWTC0FE6E/3i1b9czN8ReTu6UnSqBjxF595efh89g4CvefQbGT8hcOFO0b4SGscSwVd45h0v/C39/NNfIyKiLq7p0ff8j9M9To/Tg2JN8L5CEBH1IoWSiigp4RTCoGBJCtqafvE1A8C31iB3inB+IIPvlXMt5zCAZz3jetcMp+/LpW2wa6XniP3MUnQaB96USlG9X+4ILw2EXiP5sKTbDHxlg2CsKm7huQHAVUPG6EQrr4uAzh12QaYBmwm6N0WnV6ZEEPpNeR+CfVRVBjL0aimmu7gRfBZK/CJTdOIIvhrMzo4RXq+CohW1KR9kIlsVpfl0vac8kXAlMUvLOjwiLlO69pE8dkIPz7ANf6Ob8hp8nA8vdauvZzX4+r7u7ObsnM+nTtGJjGmIXkUy0uMhPNSHnlJ0OmrwaZw8n8WzttEDWfbbgmwe42gwNgaFrGU/94hrg8rfWW3UI/DcdV1RRadBdorOif/YppYFEYxa47L+/j7R9evD35sN3sO89CtRnba2BMq1lEZ488vn6d3PfJ2IiG7u30VPvPNXfA8mPLw1+NCzaM2pNQyjyww5SPsPnDkztNv/2tdo/9vfpoNPfYpWv/RLRKTm05gvZOCbaRqTfUJHRMDAFyMJ3s/4yFM2lMrcuaK1FUEcUGBnEYDkDExz0wyV44jgc8kShTnwppzewfHCLkXnDu5ccEqdRUYZXGv2Pj4qJPqqGNWB5rEIGIVMv3bkVqB0cDEhWBy0w/91DT6h6jyOSeiZJzTC8fCQTv/bf0tn/9W/orOXn8Z9GHi0fi/eZrMcFNj8fFktTgoGZGCgh7nuVATUpOQjPX9EPZP0PLVFiPI6Hq0vpAUDyyO7FTRzUDz8Gw9rpKjkRnZdg28RK4qUEcPy77Z1M0Twzb/NFJ1AoX6kFJ0qgg/kwLQfVcbSQW2fp2uyBC4kxCOmX6RHyyL4QGraKX2KpDMwgs/40AsjRWfqD0Xwudc+309CQajerbIGc2cAPyKlJtrAx2HZz5JQFb/KusTH5UwUt/F4P84afLA2USFC0DpHRYQpeJDX3UKGY0uI1G30z4R3iuBDUeS5si3vWgu0RQHXoXjLvpFSyHKoemwmAbTikW2h93PPfmP6+53P/624h1J0io5UVD3v2hWNC96b04NYrlyNAAAgAElEQVTvfnePHnxwj65eDeomN1i8HgykDwTvMhop5995e37Wz++dQwARfMU1cwxzwhU+e3t15wFpk7OpEFL089In+/tRfl8XmyxHk+sppwvWhUg0paCUZ7ZNU1FKzwRdjPn5nNEpm54aaLY3Evtl+wi+GGlS1GfORyUEeSRbLUWnwSsRUVaXLKP5wjnL3h8akoGlVmtWOz1NFI9H8HGnQ2Hga9iT46ALlaKz0BTi5SUDQwSfr+/xToZTfayCkch6Ahn4AP2AYMmyBj3x4oaMBVwO8Ebw8dMya6dqQUFw8BnDT05LI+0t5/tymFG+iirqtJUwaQJfyBSi6eqQDt3n+OQChovWUWlZSNQpRbxUYbHUjAcoK4x+t051cuSyE3o8g+dE0IUhPadIl6m2YYh93Yju0Qcqfnp/X+4LxFtvNpIsoBSdZ268RG+5/EMK/abIr+coGnQDvMvJg6vz32Na2xadhztbkNmR3Gc9SeeSlgi+V1+VpogQiOjggE488ACFgwM6+ZWv8Lvsr7y/GI0MQIBnxU7Q6R3YXAbj/Ag5DUGg9WvoXj1FJ+za7C8BlGkJ8/ombi3EsMqg7+D1gp2Bbwd3LiClmLxdV75FFMGH+xLE+QjcHVIYuR/glzflFJ3TdVJpKVQh+kEXJjsSB9YxKGRqUVPvvPBXtLh0icKtW/T+r/8ObAOVS4hhbzgsVizlV4L1cj97Z5QiRHpH5x+iVoMPwTYRfAhP67dmpEXXViiWbqcuTMwJjbX3gqMGX0XA1YIob77o6xF8kYLNzHrmE0TwcUOW5cnJI/i2SXuEGoj6EA4D34RnzHERdZiQd38oM8c63aZ6BBoANfOXfpdq8M3W9RxFolyZNeA2d+GqwecR8NT1NtrNBYC2FJ0lSIJtBxR+vF6ewKQgQIjrYF9zmGSM2rlagXi4osVTTxn5nyrPqncRkZJGSjqPoG+14Z7CBwfjmHwfUCSfODQD96xPNfgsvEqXdMQeRZlGaqsafKY3qruL8bd9noh2gmb45zE5XIRgvKfmaZLjTU9N9bAEfWfKmhs3Ah0eBvr+92WOSDF/x8A7ecDD8mQpOhvWhnR0ADAuumlPGsb0Yh+4udFufmEdwceVsNrJxTNQpgAppOiEz1UUrDp9lzfqq4+hWFMPPl5L0an50ooCyoJt6oWlfsXuP3IEH3ak8UbwHSWte1dL0cnxqdAcfnPrFJ0JGK3rO3aYAR7KQAGPl4UZtZ81nseQI1PpuW2yBZTELJS2b4JtF77opuIw0HCG6KayZlz7vAx91C44EDGuJx0N0XC27i15BF/uKKPrTFZrrRWZWObszGUsfnSpCfPYNmvDJ4D72dobtdTD/DnH/mqVh830vxWojb0Ne5TWCIrgS+CK4KusjdTGNPDptKDE5FqeIUKlNz9x+Cr9Nw//3/Sxx36f3vXcN/II2Va0LV0KN/5sEdEYMx1Yy7PgWmZ4A7KmsWAmJzrWLCRhTEEp5el0HTnywDPZcHDSjIuR8aRU+9vC2QSUopPxt4iPtHTZHCZnudI5VwGXLFFTQuzgdYedgW8HP7UQI4k6dbWaQwk6mKJTQs07qImGMU05qkfGx+QwRPCp9lyIBN6rMVKm2MfCzfF6VKCUWBxOX31hvm8yIwYjA6Lniriwbg73zwx9sMfWYwQfZIKFIrSbfsrIvrqBT485XesHA99TTy3o8ceXdHCLsFKDP1NKZ8EFIFOT5J8zGDWj24r8QMZeiPi+ZeDjjbp+XTfwha6+CYuTmgvGWV1A0Bf0hG5UaGQTwoVPh1edrknE6UCtBl8VABNYMvBlBmcmlFs1+DjSTTX42D7RKT0Q6h4lBD87MmNKBUzDk2PCPUsTp+j0R/BBelrBjZ+brXx6CCNtiJHO/MHv0enPfY5OfulLbZ0wHNC53oMIF/1grnhlOALHBh65a6UzadrTNNDRtD6Wy/wAjhEoFsE46JwSBo5KjRd0PVKnjFTp33xBwTQ7Cr8qbKnw4TX4ZuGcfR/xTkQUOrp6NdCDD+7TD3/gqD4AkJmMiijC4fX0Qt1CSOZptrRSsKSI4+nKu4AJYOZdnL5HXzbIWGO2QDGCD3Q4RCTaA63XRBcuLOiJJxa02UhnEZ2i06MwL+lNWowRPQXolFOUQwoGwRD7POrL4Otq38V1LlY6OXMmjyhsBfG+3sMaGPvVn/i+4nW6jdb+K5ruSIWHbia9Xo2Wdsr4kcaPE22UfAiPymkx/PCUiuh6DVqjf6oGPkB35fv45JuSrI9+hzDQPR0d7KFh1hxAGaOB19LtMtlJbI+adzTrgwGKJKkiYlznP4cUnfMFb4rO0nCQ+AKBE8kZfa9S/lJsTtFZgk6fx/wPoWeQRoKs9qVGxsGbcjLrieDLUnQeIYLvm9/cp2eeKfDpVKYRyzHKkxv4NOkNsd9KRkHrhS/3Eyf4vsBRW1YNvgTvefpr0zn3vh//l6YafC2RUsLAV3C4Mi97j2InTlym0Ck6s/XPIMRIV6+Csi+Go0GN/xv6LOy9Wp/zVfbX0Xh/1xSCFJ2T44MjrgGQFiKaZVrtdNSWotMBHqXGDl5X2NXg28GdCx5lZIxUM2CUBG57vO0ZnIy/LBA34Z2uGTdUj4u1sbyuZYrOuZbM1MXodaIPvqIioaoAKGvxekdkEmJugu6v8aBYjxF8wgM8LDIc57oTXGnPDCSifTLw2YrWUc+dQRd7unq1mxngswu6r+WFNN46RSfZxjdZC6X8PaCxPITqeq6hjAx8MUrlyCLWDXwUgs2gxJiRBCgEOZm2obtR6VGJenN3OD0sCZKnBt9yGengQL7/YFjTBr7cOw15m1kMc+pTfCuYt9+uwSdeVaWlSVcRZKlsx+eTsHR4mD93FIF8xtl2wMifkUJ6CyKlJpORAabonCP4kJJLzHdRa2bsCXZWtMwnx2V/dZ2Wzz1HtCTaO3+ebvm7EYAiG0oRKzWw3oenjULrimqpgAD0/UwrUgSfXk/IOJIp5aJKlRorKTpLCCWwIpTAGV1a1tW1BhpmCo0C7ZwjqnUCTsoUeTF0FENH3//+HvW97LclVS1KtzR35HCXPUZoZXtE9HZsOZfKmTKIyI7gM7u0vnm7oJ9H8OVtMme5wnf58Y+XdPFiN/ZNtHr3fMDpCD60/pGTAAdRI1MZSUoKvRhDs1OOFdE89N27IzPq4zm+m6F4TmfjRz+6ojcsD+jZr5ZGKafoTO+bjP6zDFR4h4YUncIZRPHWXS+9irI1BiIFUe1zDUeO4Nv0lLKjRZayNUt36IWkVBxlIeRIOjcFxjfmdKhlTwSWgc9sH0gY8D1jxFiwBxu0KgRqK6Cl2iE+asA78Wqj4rWOlnlNRPM7nAnQXGU8x1FSdIJm3GBqGfgSBtyRVjs0e/DQGX8meceI4Es1uAYcIvV9XquvYXiFiz+CL6tTqr+jEOLK5092zbGOOqcjSK0fIqK//usT9Nd/TfS/rqTuoPrgCMuR7+MpOjdrKaseWwQfSRq2z0qS5/x5nLqI8IwfdQRq3DYS4nOYilHR9xGhpnI2R4xODmqficxhfV92mFad6Qg+ItvRQBtMEW7yG4xnMNJ3WA4EMaoUnZguRBqyoaXjKFCEp72rhnEhgo8In+FNEXy9OmdbDt2jtt0Z+G4L7CL4dnDnQoWoJMaHpxXz9NF0CDZCUc/FjWoElBJZBJ9OL2p0HmWavD6CFJ0lRtS+5YZaNhizpJbAAyuassifymHB33W1f3p8jB2Ug6+0fmq4F/GhjupAbXjOcacyrevXdPPmjP/hAdUXYeS4R/PeUQ7RzECAmMqWlVJQ6hJh40SCxWjcaakbAscofZPgr8khFdl1priZpjTW4EMRfDzqa+oKKP8k24sEoHx9CSWlFqhBBF8SPDwpOpsi+FgXKK+8RzDKDAOTkiUS0Z0RwTdF04zpfHlbK0WnMQr72793YxyEKz7uJz5xSB//+CG9/e02IZ+M9/36SOeqVoxxzFFdyWl8y1GmoBwhmj2GiebajkKwig4jGhLqx2s8RaeOBqh0I4zaqeOagFvCL8Z8zxV5g5qBr/FDtzTnKTqz54EiL1IoKsutsfn5M9X9q5wddyLoiIHaWTUry3CKTjFfysAnN2kbbWmF5VI5ENx8hd545UcD32toL0vfLxn3iIiee26h6v1hBU0JSgq8qtJdNS4Z7ODY2oLIIMQ+i8x4TSP4bEymv1ANY9kyr/1q8eQCqdILGCk664px2aBbS8ezbNWDc8ZnJAg2Quxy6HFtav5NN7wGn8PAV5YLy7ye97kadLFvSmPXEhXL21jtijIVqLvoGc+aAwtvvyySn918rG1q8L2WKTr5ta4j2lvMfYv0ojEO61tEqUZhLHZBjOLFJ4cJI4JPp9ZvzShf5OMLUUQ6MjEz8DUMBKecy3HQqGXjOuBwBAZ+hJdf7vzfjYErRSdtV4NPr4KgvrlI0ZnV/RvubTYuf7m5H+WQV0LRJWtMbZ3n39A6u4KddQE4EdLOw9A5yqAPR4vgs51MRHtwJhdTaSveDLZpdna325dq8FlqTzTHGk8rK01LBF9Tik4Ed6jM9HcddhF8O7hzwXNoaa8E0IcWuKVnSR75QNR20IIhxXjIe0yMZUbw5VFhvA1XyoiUUirtRIw0RgPyMJvWCL7yOwgbHPKq4d4wXkErNRaKvDZYjwY+Dl2/EesixtnjR6bi7PABN0VGMcVFQw0+YZw4IiMN03kwEApdqV3O2nGwFDFpPVcF7Ii/N4/06ro53ZyO4EttTJ6T7Juxj3XXFcUxeRVxyOhY8+jPEbQJkidF52SQmHCa+9XrtxmAFl/MC2k6IJ0LeAQfKmI/PVdh+FANPr5nj5SiEzUZ/5CCGxYepvtcWfga1OBDgv5SRBHke6xl6JI8p8+/979/RW99a09f+tJJeu45qYSWnzK4jOAevGahkBlSCwptC2r1kPb2ItFo2Jsi+CqCeA36fsZ7uYxElWh881KUBr7NOt+fucBbAcObGvImhW2alIv7h69S6WT2nP0IhIEPPWjU4MsGKiz0jO8rkaXXM4JPC+Me+Tg7c33zHmIvzhDYRikfaqnTtq31piFF7yX89lY36JMP/SZtbq3p8Z/9NaL7iCgQnTq1/XdYb2aa4kvR6acNXefXbQypc9tSdBIFk1E6zhp8ru8GGj1374dob30TOiCV+oj8M/B1zSLU+LQWFVA8gq8azct4GYXrol/RRjaWzzpS4ZXQqzkl6Ii86Vzr5zkTEXxZSk8fTlO2ilRbqnIG1C5Wx6uk6NTGGc2rD45kdadPEw+L5gXK06YVR5F9om6hAa6BKGodsKhjF6R84u1W69HDMRn49Jwvrlymk3SLboBx77n6E/qH3/jf6KW73yNxqZ0zQF6RDUb50orgU4LicaboLBkZBM8VJFUKFMliZdCF0t4hInLZC2vzaEBZZ2TfK6foHP7lEXy5sc2DHYCWGnzaQZVd55l25rPNd7Y2ryuLPokUnX1z5yhjx9YQQpbRCp45yOsg4hp8HgMfMvLGaOsPwEXzlr80UP1wLTmwTI+g92U6Hn8En7zIa/A1y4oAD29bATsD322BnYFvB3cuVAhQjGQy0sU+rANTMB3Hr7hBhwRXXqEafNsI1lrRleYpHURWYdijMLTL9S3aX12nw70zhOZOF/SFEOupoqLjoOBDrffzFJ2DZzN2u0ZROfP8jU3HudtQNxFQr1J7MPCpcStrWKwLrfgTkkHI1ljrN80EEWIMQ1D/bgGmJzFPeQHSM6KOqpG4lZfvi0wb7oan6LSUotU5Fxyq5Nw8yyhF8GUpOsmqwccXf4UZVahRjCxV3igUiL0UxUPa69ZkKKsRfMCbjbiBD3vktkLqr7kGn3q5VgNfcb8zZWgW3bqxUnTm/cr0b/nAFg59T7SIuWOMHhNBpJB/uy0PFoizEcEXY8HgX1FCLJkiARmOqeYpDAbhaSKXS6KAZDeQojPTT6kIvkzR4SykYUbBKHxKgObgU9/513T2xsXy4I41h2AydAdL0SfPZFnHBvEg5fFCiCZNnxDh/3o6PTZoGyfGujEjAc+cYD4DIlnEvwgB/2UTprNudLZ417MPULdZ0YYCve8nf0F03wkiIrrrrkhve9uGXn65o//hv79B4aLz3YPMSOdK0Qn2uvzd7jhERNRvkaIzUqA+dDDivYsbNKDRU5mwt56t/ZkzdOuTn6JH179Ev/D4F+ZRKgyO6eSX+h0nM4RIpOujmcgwRahQuoOHuTOCooeZga+AZ7V2GM3rKKGH0r9zMFN0svfbdLNKRyiAW1j2Ea8u06L76YnqqtzeNPAVnKu2IsG4PxSpmAxleq8Pjk/1yeS8qpAf0NFb02HIpgLKNfgKz5fO5GM08BENc3HfS9+hs//639M7Xuroh6PUrIfp+jW95fIPxbXp2/T9mIpWRTgDBgrxOkjOiFE5eVIUBlOnysKElhSdHI+OImX2AP6cQ94U1yDxDlTez8fD15h7pTBxiR8XEXzb1OBDNysGvqHe77DHrRSdMVqZuDAygh9QsgTi90s8Ar/Eef9aPW84T69hBF9LDT6KOIIvczSIkbJIOgM3Pjd6bCQjoznnOktL75hSdNagxi4TEU7RKc7w/GGPz+FxRPC5wKPU2MHrCrsUnTu4cyGWDT4xJoZ7+A0FC03Ugo7gw33XUhR4CV8IRNRhprGqLN0AwYcLkVwpMx1UMhc2qsGXHm5VttTaf/rBf0l3vfosnjugzNSADhwdnVhTRMiHA633TuaXY57eYaoTYEXwgRSdPBUqqsEHUep1BJ/nRWwQKUzMQcvGlOl50G+pvyIYAiR/1BJGF2OUUnHvOyKrioqGEEh+UvtDcGVprvQ4IoRA0ZtXZwSeUpB1Mwq3yMA3tzMF3fmHvKmVaEoo4Ckk0r+eGny1WbSUWWl6sCFGvZuDwGkvam/qlYxOHKOBLy2H0KMUnfOL17YhpKcOY0uMlAnPHgNfupcZ6BvzHqVxUbpFTwo7O7UYrtu5BDX4mlN0Kuj7OZJxsUAHMMFr+fYvR/DlOlgDUZ62SgmlJcNeLWCyatwbBqyihwCl6BQdqQg+6bDBvl8pulCt8WnvoWeOwcHl9QKg97TbxFmhbX4gowYf5C3JnvPWfZT2Zpry5eaAOJ3n47znPRv6+MdX9K53IcMWhhCIDhk/piP4fLjbSuG8K/uMGox1IK126VVCMA32yFEKOc5Ux6jcR7JWf++9dPjRjw01sLkSvZrL31C4pdt8frZI0dnyolkNvo2qwafXOKhD7lmG0/FYaWzO3fR+QczP1ga+ETTtR6wvRFnxGFW5L/ZltlchXzWaGyhttca3TNFpgY48bAU99mbD5J0t5mXoI+gL1WfM5QT4y0CRPnL+S0Qko7J45DQcY5QlThy+Sm/6d/8PnfnMZ6i7qPgNlB9Z8TpTZwqv1zpFJ9qvYfqX0QflmJvKBZgDqUGhEY03B/xdfU8ej5xr6tdKEXwLlvlihA0y8I09HQmhKA32iwXjO0muB56ik88pd4BFkC/RslLTrVvk9B3U4KvCEb5xliqYFP8d/Qa+fhPp2jU9J8GsBcrHtiL4dD3q9Iel56pGgFvZkPTZZBp5i90Pz6pGg97p6Ck61+tZps8GcEJr2u28gztfZvq7CDsD3w5+aiERrVZGGSn+EQHc9vyTz0UKTDFUVIbo8dfucBLxTr1S2GeeSBNDKT07jsrTdf2a7r3yhI1ltX/cIAuVrx4WowEudIOCgZRhKW7ysUbkkIEE4qDudwFEHAE0u7iWQkT0VLazlZVaCaGj0kwG21jrWoEn7tGYmjOE+icAzMS7n/5rOvO1vyC6eZOIlKFdpOgci56X+OBQ80Csw2o9I6Dr/EgI0/ug1KVHStGpbzXV4OPKz3z9luqVEWGmDeXlF0ykNvBRzNZSYko9NfgsCmen2BieO1INPjTeSEO9tWBkek5pMPEJx/Y4s4CZO3gselyDDzL3RQueDegs9Nk2hmcWUdXgazyg52dzY1spwsU0KrAUZgiWrKblcaboTA/t7c1/y71UT+mmIwbyCD4nYlzxZtgY4JwWFIbuOQFKP3bTfEwa+MA3jHJd8JNUGDJEu/L5WPTG9rjLHiMActkEXiWdqOFkpHgLvVSozU40BhjfvHW69vY0v+NLM+RXSMRjTdHJ/57qOZa+nTaGNKbVHoz1+JkFyoRgzFftu7hIuFqwnG+cLjsXAPrG2fww3sSdohOlzWMQbt6gcO0abJAZTAsE0RPBN79XyHBDoFN0zv3M3fUdTspkZs4onKM6Rac3Kro1RXctRWfW/tgNfICnTf07zmhrQPd5arRF10r1PvP+fYfGa5miU+PFA/DWfd3ARzHS/T/6MnVXr1J34wad+sIXZCO0B8F5jx0b83ImrSk6S214RK5VZxOViIHpDQuHaHXtbLOGt6HT4N58BvghRfDxtZI7tvX1M965gYQRL0RR4x7Jg32fR+kPOGF8WmrwwYsmj8NTdLbzoZlesJE5y3RIzJzQkV7DeP0TEd28YdAqbeAbny3N54wbPuczemmd0zEqZ2bbiUtwwRX+Ct8en4cRfGUe0iOSxBgGh+iIgxpKwM8od2PQ1hPluIPjh52Bbwc/tZAYn0RPbK9hLLBXet8epwZITCwRMBSoCD6Nu8VYaKa1ild0tHHCol9t7cUSIojY1JJZQzg8UaDN/snssRBj5o06eUCZEXw5DhsWwbdQHVpodtpLx5kHffJ8VB9JzPURDtHsbC5JpxUlad53oDddeYLe95O/oJN/+y3af/hhIrINfCH2rvRLfMoRo1qK4IsUBD+lM79I/NmYLjfmMuLirgopbqnBF9SnDxRl5BuYQLNOFbg2RWQZ30kKnjMjylNPmQxl5eOmOowcuLcecu5z0y+khADCQ0sEn4fJN1DIIM13BxQ/y0qKzpbxLAWQTvvEx6pG8IWQK0K3cVVnKHgi+NyCKri3V0nRCdPm6DZqTcWYhNw4fE8kqHlqo1Qi+CzlYLljS9ubX6plUGiFlme4gQSlShP7mHDa8QwKZxdXHBcNEduESbzOEGNdgOe0b3ol631UBN/r9d4pTdf8XRxp66PO+mBDCHLP6xSdstsCvQR/z2PYfAi/0FOYaoy6pzcwRz0FXZ8TM8v44uHhtgZuMK2kFIbyCp+jQgSf+Q49VoSi9qcffYTO/OZvUvfMM1mDpgi+whgaphp8lTXbKcX/RAN7fj5ilU5LbbbZ4XS7VNt6jmuPdXFTN1Lw/iXL7HLKKuFRpPW6Bp8jEw2R5Bl4e5HufgtaqptuNkHsfm+KTuEQrNsdxcCHmrDzpdXAFyjSXdeem651r7xSx0PwBSkkP1fQ98DTia8l32ex116xDphiPGrpxYVcpm7B71xINZy68+iESvDqqy26GH//KEVnmsp5/fbitxsyRYDkr7tO6lrkVmByLddXVJxMrOzmXhShwSQO8uGEQ6LVRudQnQMcSL0At53gv40zBzyYyTRjM5iiU3VhRvDFPIIP0nnQZ440CV2gaOKMIHWJwJUUnQjQHCM81+uQ3WgxCrvaemWmHbxusDPw7eDOBc/p05cPKUTUTS9llZbrOEDL31qhJNqqZ/t1niYN4RVUx71O0dnzLC6jxxiPJjxOZU3IDxIicqU5sw6RbcPDY+igga+LmzwSbvqNlQkogo8LCAsQwYcgSz04ctq1Ncy9yuQ9ZZEAzA5KU1KLOrM8oEKIvsNaKbxPHcyCWbh6lYi0sKuM8KtVWblMON2exteCTR+m79d1eToijg9Xlh4XXZj6VtoKXrzbAr0WuHK2Fu1U7R1wy2JuAJMo6GaYo2d0rQJE/CwGGaejmvdJllYIoN6ihEg1HdyRCqJvbVxw9FGAuf5Lnw1lpeiEegMLkap0HzKa5IvgG76nNvBtu2eQMsrMVTKOXurIku+4IgGl6Gw9H3nKtSkyGAnpQFmolZX63Dw8IHXfaTxlc6AN0lOEI3islrq4ZeymZyg37mR98jOKbCVZyUtXr/HJqIiGvI1CauvQpZqUWd/kiISyUnQaDkrHlaIzRfDNTg8bP41zAo/k90TwWdkP9N+ugE+xN2R6ffMZfb8hgm/b7+KaTq20RudHjV4hWq0UbW1IEVlhOdZ5EPqeTn/+8xmuC2Xg0x3E1hSd402UilrCeCb1eQSfMCQFO5qzggKEmT9vPAMK+wNBawQf0Xb6Qrs/IDSlcTKG0EtTjetIF+CQjS3QDmme8iN5H4rnOIKBr/beXOZbVQx8RH56IX4DLz25L2b5PSqZ2MgK6B6eA5ShY+IvGH2oGPhiVAM1yDVE9JpE8P3lX+7TZz5zlq5f306F7EnRKdK5KtLbRZ0exjkuWC/awMczbqAUnX0fsmdEl2oXoCVq3SOyeSoNQkZG8kWV7ip519rT5l5X5z0PLFD7tqhrtHD3RPBxfUdnj5+eN/kKgBbS+eWNchqyLeAUnfMZjM48b1KRw0Ogg22lI8Z4GU7HrCPbwfawM/Dt4M6FWK/BN3JI7IL6M1PilPuc2lUJtd1JRt+YMhsRTDNFJ4rm4cIHUOzHSFJJoN4fHlRTg/LEICXyS/f8PPUdrvvAIZBDeQ4OhkBRfleaDZRVPENH6+WJvE9YoHlUJInInDDrwoWSYfiXG1IXQQpFlmI0IAOfA6aorWxdqjWzpQ5Sf1uYjscx91mHNDBkok5EL+u2EWmmNlLYbKjriE7fvES/+Ojn6O89+eey/wIO1n7isFrPfaCadhwGdIc2C5Bv/8gpOvm7OCxMSfDRhrVsmPS9xHA5M2oJg8mhAKWDmn/L/Tk8M/SRFUZXQm0JajX4XBF8iJ5EmVJUr3dPBN+QllRF8DVG9ZSaTKkJgZHTTNOF+rUGqSgfE72TURtp/+bSqsaj6zfmmvJAehYpPmPoqDe8KRNktIuvT0DXlsthDd/16rPUH6zHdrqLmmUTrKnYzx7rSbEjlolHKJfn5q1b8m7pXQWgujRZm3CxTCYAACAASURBVPK63VZu8yosNPAUnZAfUzX4XMr/SgSf9XyMoxesbvhaCbNH7FcqB/D65TxyzYBveVOrP+F9x2UTUARfFWJ08O9zvzzds47g83xqK4Kvy4NHIK7TszFQ7Oo1RsXjVDDqwPNv2wg+DzJqPyY2jPMclRp8pQi+GGenO82bFL+3iODznUXh1q3spRf9WkbQOc4Zz7zpOsAWIJ6oWAuYt2uRC0Y8sqwkbuNWhTbo9rEvRuHp86ro42P1EYPJhkCn2dR/n0dNNu8F1C85eAoAep42GzkW589KEXz8mcyed8wpOgcHkuGHiMqKZVrHHaPMOa8YDCYZiDs0Jf4yBtKc3rGm6EQyDOArNH6tKTqL35ls/quJh1bwjW/kehXUvz2G3T+K4JujvEZnBwcfUIrY4r/5paEGH99D+R7VNfhq2Q9EOk8QcebBEYGowZfks6aorC1SbxdQ0kZqN5tsfCZXBB9fY2zBwFrzsTFFpSEcbMOapzVQXJLgY0yZxRxnnpabOWwbwTc/ckQ5ZxfBd1tgZ+DbwZ0LFUoaI1Uj+KCQqwSQmRE5fmVNLaWZSN+lx+9xFIX+PTwnFQVTGxpq8GlGRUTwpWtbvP6rp99C59/5q6ITK8VEvX/DS0VHcFhPK1eqSAHW4NP522OkOd0NZwwmYTkohnL0AFQ5xzO8AaqZV/U0ZslgJZldDqLWEBgwxpkzKKUwQ4pldG+ISHXU4NN9805GDkSkk9GRa+tBmfLRH/4hvfnl8/SuZx9Q2OUMyzxeQZk5dT/XbCql59TP8uhfi7FvYuCzfEP1TajHjZEpwUsEQ1+r0MbUppTeSbdfLGgyYg+pz7B1cDY+4oXUAQGOK1dcEXwO0Awsqq2AQHv3udIDwnFzmBTDIIWKpaBE/ZmKtpHmFAV8LQwVjJ36t47g86SdFe11XZJMYZMrZgYaXusXX1suI33gR1+mTz3yb+hTj/wbKAjpZzcbovPnl3TxYpfjGOfnJo91qGyvC87ay/bWTX3fmWZHfUy4XpGAqb57Qc/kG1sra01FaJQGEi3cx6goS8GDGCzk9ZpodQjm2zAYPPzwHv3l107Q+fOqWOs2RGcLaFdokxs3K4JPPK5TdKY2jgi+rdbMCCmCjxv4MlqGwDlOCESHzMCnI/hEl2xfo+v679YIPgrUHIFFIZhpGSGYPFP5sS2zLA/ADaa1gYAST9y2jPiFfhe3rtM9ly9Q6DfCaFXT/2olsk7RmS0yGClT2LhR8hxIhuA4CYVuiDm/R/b6aSFZ3BlPPOtU9OZvXCZeltHMwnPGy09MYj5V/K7sn//WKTqL/cg+0LeHEXZOouiZo22OpmNN0Qn4IH5NiOiOQ61JCZ9+IxkejNVr9WfUepI6fiW6WIrgS2tuEP1l5HbrO8NPIeRtvG+L6+OY2Jo0hjeVIRGvwTcjkdWeHnU4rfzEKy8TXbzYiSUuZb4hW1L6BiKCjzmB9qDsj/XdSmsE419gukUrdh5Agyeec85LeSL4TFByWU/S0d+KVs66sSJMjQg+yTKxH6MiJ0acohPSCgsti47oZloW3ZK/IiJMd9mDR4ngQ2U3fEglg3qZL6v2uTPw3RbYGfh2cOdChZokJcbk6e80LLk8ACsK0CpeesDx3xJDrqFfgxp8DqR6wCBMjJb3RGgAEaJupE6wBB7RxpJgPJpmoskKNr9rR3GZ57YMcZMrcWMP6mpJ/Kd74/zKGnySSTEV4ppTdc79HGWGD+kYh+9QSw9Rg9S+Aw9an68mYMSolAZRKhB0HyEQhfWaQoh07vqLeMzQUReAAEVsrIKUstrMqRVQKjghH/Wz8UBHam4FBSLgiXaxlO4Dq8nXaT5cC4OWvpVVg48oFywWizlCbrVSabo4HjWlTyVFpyeCD46gGfeUpnVSthXRmvp4PSL4OmC8Cfr7ZP1hg2o2uMMxBpHdjK6B9aLrJ7Ya+HTXKBVMCffcSG3+ICKivWWkN185T0REZ29cpJMHr2SbRo/30EP79IUvnKLf/u3TWS0SrpDVylFhLAdHZb6G5bmpDXyZctJUBvAnML4oonDAGV72Q0mQNkD66tT5gmFNSMcmiEYIdONGoG99a5+++pV9euEFppQIebqlBDduBNrEjr7whVO+F7jNwM/cUhui8dyoKKm08qF6xh6Rp0ygz2YdFWHx/C0ayikyk1CKTrB2CwpW/jdaS6VpidRNxjrvmT0Ytv0btDPmpfa5ijiwdTSBiuDzRmCWIviISEY4GrX1NJy+8F362CO/TR984k+b1mXmvNSvivS6OUXnCKUUnWI9GRF8qU2/RQRfCb9OPeOO4FOGjdocZIpmjVvOdODLBSjhgZSXplHTqxfgMrchvvLITdynfEE8R0HIVrUafHPP0Wx3lBSd1lk/0W/GKpecGWZ60XaO6AwdyLl1+i69vB5I11yrQ5EPNdIEwn5aIvgacBg7h8/U+P/jAHvP2c8sihF86XlfJD+Hw0OiP/7SCXr88SX9+MeLqQ3/5lMNPiQPsmuROSRbPGMCkRUmM0pup4+LUTrBBvWv7saiba4oYqQLQmtKDc7X8MlXXqRTf/AHtHzmmfxBy0nMjODDsnacPLWDaVxHeiLVDR9Q/AXbOA+htAbgvEXViF1PRlxLZ+09A5OBz3r/4nNeKBLDnYHvdsDOwLeDn1pIjE+RaHr7IcZMHlFByyFFPFn3uo5FvGnFQUUxyg9qLqT0IEVnjm8QBx8YvjSyQCILzTc4ytphYR04MBc2ilZTYW4xdBRGKZU374D3F0y7RwMzkTHYY2cigs/pTWrV4CtCjENkVIiZwOOuwUQkFVcNXoIZeo2HdfY8iODL2qzX1WEsT3rPOl6z6DLuKYhAKFnAd9Z4rFbYAGX2zw18jhp8ety+xwoJlKKzVsw9W06KUY9qXaT1yAX4xBhnBj4UwWcY+nQUGB+TqOrsZlywn0n7yMPQZjTOiohyjAv7TzX4xlSX1hweiWeuKQy0MCC+L7uuFkygSN1GKkLjuk1rku1jsabJVGjXjA3WKy+Xkq7qCG/kKfzVrw4pivo+0Ne/vg87D7Gf50vtEyKCxvy85I88Nw+yCL72lGFNKf0KzsRufqF0ltQfmaOTSa0J1UjH9E231F59/PEl9f0QBfzFL57it4rRKgR4j9dKbA1K+bLdXi/PO6d95femKa024hnbeO22F5kj+OKEa+t6L0EIRCtm4Nvfh/rQYpeyXm/+zby+H0NNqMYUnYOA0fAARqA2Xa1RCCaNdvKrYmuzH8l5MQTK0ifW+rrvxYeFI151iSj63CkDXxblDlKmeZTovGZrCZBzYJaisxK14YJ0VumIQqeit4kgUF5X09vNVlHNqJ9px4ep4bR3Wy0+FagZAxKg/WPOB+ddBK3x9ZFlw3hNUnTO+CU9fC3yGJ77uo2+oQ6kkpzRR8rOci63eZZxqU0pikgYlbUOpWYY1Hoi9J057dqiBl9LqscS1PdcDsjAlzL8aHnNQ18TXLrU0fpwuPbqq93Yj1GDbwSUKUbX4JucgJxna5WHLj3AQJw3sWBB0s8xZ64jRfBlnSo9IJu69/7N79HyySfxsxbtd0bwTb+PMYIvxEj8+BM6Gt1NRaeCnkGQ13uV+pWagW+Wm9GapZwOOJCag2ecYG74nYHvdsDOwLeDOxay+k8KkoLZJODjtUxRyftNHl6QLrUTJaTwmDzWKGTEl79fZrDY+CL4dIpOne4hilSfgOGtKEBVM3mNCqmxFI7180SmA0zPQU0zAnbAEw2HcghEB5/+dGZNyvCsCMsifU4y8LFvuex8KTpR6sEaJGYJpZIU76FSfRCN69GxjrVSvZjyIegUnXn/mScZV3CPf/OC65lhq2YhG3GAy0G/DOVrd7XpJqatlqKz72cvWVSDT3f+h394mj7zmbN0+bIx75n2mjGPW6To5J7AKJ2tQA8op/l05WmCe6mU0BF82st9MTOlq5UaG+xj622xtzqP4HMoLyytEVoXiK5a20bRin6LCL4STLXHkDK70relGM2+Q6zQY3RuUj4nWpAjirRQ365ft81HGhdF5ieFdtMUs/MNeYQvl4aiyP7pGY6IYmbgq3WKmvWbSM8809HDD+/Rjevynh3BpDthChGD5tjpMuFlKEjidmXUEGSpcgvvRESZW5GZ/jQEunlzNBJQpIMDOU41km1CiHd654G11iXkCu3OSmFoRPB5UnRqvFqgXoMP8cN+BWXfzz0sFoMjlYzgYyMl3shwLNJ/T0bJwjYRxqsYoPG9PGf4GXu8bWvwNVjS1M8sw0cJP/AXp98igo+N544uq3rtM1ANFpt1VitaNOd1mbxjUDmCj0PHDPGcTnHZ7jgi+NIcdY71W3o+3a/Ngdtor/pvjeAz5fnS4FkUiW+8krwbZrI7yUae96/OY/BH8PHOsnYe70TXB8N0NMl9rbXcXfc1fw8Y14m/jLljHuc7jvpNLENdh2RIpQfK5OsCE1VjLbepwVe66d2rnr2PYA+k6Nzoeu7Dxmnqd8jEkzsqaQPfYjFzk5mtO0ZQ93L8N/FEalmLtK+OvZ5lyTHamWnoKzClvjcip3OEMC+d8UFKD8jX8N6ta3b3RgpZy8BnGSXjQqYIRZ1K+bji7MP7Nkwlw7FSN/CVIvgmAI4VVsae0jWTHkS9ruprRutHyvSusOF3Br7bAjsD3w7uXPAoKByKyuIQTFk42fyOyXuJaKBrlvfHVGdmkoTVgamMHrmiCz/niuAL+XtvrasSxFsONgscxxzBhyCL4BuMQIef+AQ9/I/+F+qZcoB7jSZmpZSiUx72Aw48RWcWyWKgmUUmOQW8ZODTcyR+1xh96wblChwY3WVET8Y4Ksqiugj642uYd9OLfUFjis4MBYGLqUQsvEOC9Wb2OEvpTy29LWeOvJGaN28G+tM/NdK5RfWyTCrfJp1hjGH2dgXr1Bq6hNuk7I4qRWeMdOlSRxcuLOjgQNemTML70Icngs8SX6CBj+aolvU6f5lt6NdEAxsN75JWHG8EHxfAdFtRdwF671WHrkreSOlkGfiQoNv1a7l/mmvwTT1mYyQlJtLrtE3C3HS5kLQ/pxuVeoXqXE5/drHsoITSpCJF+itXAn3uc6fpP/2nk1mErXvdFuhCadpKEXxuAHPTAu4IPqFgAHxS6qwwTskg25IK8c4A37wLhaLVzkiXVOoTYtT4/efU5GO/ms8yOmwx8CU6s7+fHi4b+HAfeRtXBJ/al31Y4HYGaAe7GtjzUl7bJXy0Egj1bRtocWcWLyneta9HnmrgDlo1xTicKqZ0zKKnLUWlBepda3vGStE5tdsiRWcJNK/rlosbN7ll4KvpClsNfDUl7tQ/p4cg6sHLY7nPU6NDfdmSn/l3KWZFMUCTdh5Jsn7HO2j1gQ/Q6r3v9XWGznrFByU9vKU0F7jEvnzuoqgU5fhDBOR5Iupjl32Q40zR2SEDwjgXpQwgmkZma8Qh90i6tEUEX4FOt23vdqP6YjGMXUzR2RAhPf8EOoMoMyV03VCDLz2bpa8d9Vgygq+AA5VZp611bkRTrfvh737CD4HkS6bF2LzeS6Aj2dy0CPHakbLoaXTUWxF8cv3OcqSpj9ByVwRZetA+cwaBuPgnUO91+q4BrzOvDiCra19DaoRi+at8EPveT53s9HcDlvUmO9jBbYISh5+Idd9jT3/GUGVCN2M4IyLcSMg8AgQmkeg+ObNXi+DL+6XpOZGiU9Wi4RF8iVluKXpcguEY5MxpLnAMnlMOI1bfQ7OSbGTjHTtZvyRSN8/R/t7I5A+HqDa0hdiLKL1UVysJhigyihtSdQ0+C2CKzhqM63W5zKVUGcFnPO84XE9feY5O/f5/pPc89V46f99/ayplhwAa2d96TfTgg3sUAtFHPrKiEydk30MEHOujn5mWuZGahy1TdIZgKf0C8bW0Xs3rdlk5BfueKNWP7oCBxWJ+XnzRoXhrcrvF46b3znCpRPAhQREJQPyRwwOiH12YJyyvwTf/vVopxIUyc4AX3/5h+rmfXCANsAYfoyGnX3mB/v53/j0d7N9F37r/n1K/2KNsA1iSL1jb6Bta6y+LKqaQ1R+oQamJrMFnG97lp8yRNSN4POdbD6LHKZ8TpHDXtPU4a/Dp86alI+t9l0spzAU1Zu3syg18s0CZvLUDb5vaeVJ0EtGPnlhQTKmmoYLCxo23m/7UZ3bq04rGatfRqIbqmRjpzS+fpy5uXDV24Ph6H1vv1AAhzBkE8POJd1J4vBZwDP3WjCmcR05vZTqwbGS64Gl+LDQN/qEVUrqs2UDkVTT4Bu37ed2kdKBVxqOgYK0p/ixZI/3ZlJpvHKSW6k403zKCrzVFJ78u5YPyQFBxy370gXvpc5pt1xOSSj1/ilA0V2E9MzU6SmvbGnwpDVw1upHxRElu5MYxr4GvitPYIF+KQDoDfW1Xg6/kzadkHopEpLOIlMGDB2880Rto4KsPXIoU7bpZl9v3o2TiJWsOGNUjs+wKvj3HL8uGwRTNm3e8gw5/+ZeHZ7/8Zdp/5JHh+dqmN8YiYga+Sg2+zBBWGW/6zXkioO9I93QEX1AGj6N+ExQhFIikU/eIY1bmpOBIVzp/pmuMFlkRUsX3K9xsMQrp75Cg9F2XXaRDAhF8TE4PsW+uz8hp+vRo32c1+AanogFf9K5DPTOGy/RqGB9dg8+FaMVhNMYw1Y23+rWmh/NSrhSdcHx5PXM06ivOjbyvY4rg48oHflaay6TGo/L77N0ykuNw7p3O6MY9xx3jdXazdN3RzVSDD/VdgmbevbbodvC6ws7At4M7FyI+JPrQzRFYNQpk3J/7ZUa3JNgckfC11BkR6e8047aRB71WJErGggnBughtlGlcAMJoeDeUUnQOCs7RIKZkpf3Vdbrvxe/QlXP30cvn3jkJJmCE+c/SQYFq8E2K6SjxzAwI0TzQiRSzPhn4ZoZCG/hM40Cfe+l4jDpdF6nTVe+JhGE3jqppC0oK0Hdc+Cta3vgx/fxPnqNn3/hBrAiZQnRywWy1Gu5duLCkD35wba4pK4IvYz42mzpPUFM8Fxb0aj27RNVSdMY4z9cCpGK1GHsTfzgpxa5wc83wKsUmjOArrY+IaR9nLK9dVYIa8zAjGpbGnKKzHMEXiOilN7+fVu/8NJ06eIXuefUpOn3z8tCPoexK7/oLD3+WFgc36dStV+jdz3ydLvzsp4vKUwsmwbslgi9G6kjTihYNWkUxUKg3ZUYmVfpFc1Nsq/ejZeBTOARCBr52QZyIk0VF/w1vSstRpvY9uMeu9UypC00O+ZrSe7Vm4LPea9KTgJqHsJNS6F1GmELexmha0DOZIJRNkehNV56gX3zs93wPq8HgmOOa6I09KEqkFDTd0ksWfBsCm2BbpmkLsPkjo72zMVQo6jZ5ccj5r8Je1NA6XTq63usg5X13/lrIwFdzotAooG1WnFulJOtba/A1RvDVFFCNj9mNAuZFvRF81q2+W87dbxF+0FXWrcWvTNdKRbqAwtWj0JtTdJYn2cpqMP+wnV/kGsyNHXA8laLTu6cWUZ3/lcdqEXzZd+h7Iua86QHLoFEyxKWxtJzi3QseXWcLPTT1Coy3SP9xHqom32SOeHyNWw87GE2L5RgMN066VdNNoD3II/jSGGCx9DFIEhUjrLlWRs9ub6UAhDqtY47gEy8GI/gCxdLiK9zzkl1rz9X6Xy6SgY+NuY5Ey0Z5TY3BjRxcR5Cn6JzpQrYeYsycEWr0sVT6DNK9oTgk7EuMe9QafJMD6THxsJxvUjrJIlg8nBH6qM/o9DuO+r9Bj4GjZ019BJCPNW+GkdSZQywHqrLehQi8r+qvFsGXAKkTcZBF/ftwZ1VHY/vWzsB3W2Bn4NvBnQsmwQjz7RirRChjrjjhbiQ8MYQmz6EQJBnVjw7KQQPvtQpRD5hjCjEKnMSBM3o7ZgdMyA2bWwn5UiuWHexcYaafv/+JP6O3XnqMiIi+8kv/s2o/Ph8jcceVooFCh2KFQLwOSmTKk4Uq7D6MI+tq8fStEvnRu4ul6PSmbkRCem3iEyOyXGKFegm8S3XvYC7udGJ13azflSL4rG1z9WonHuLzNz0DIviyMVarogAfSBpZ5L0c9Dys1rMyJHkKWvgIAeA4+BT9skdO0UnMo5vdmCL4mEKH31ZKqBhzgx3FWqoNeYELR6UafMnAFynQE+/8FSIi+qXv/bvZwKdT2dLMzMdItDy8OY1897VnDNzqYCqRqE0x25qiswQ8RacWklsiMEzaEKPLa9yjrM4EkJgMfLPguG2KzlmYZ/eoLWJF94vO7qHmRrmeRVERqjyy+Zoq1uCDKToNxOcfQ99B7tcqIKVXcZwZjiybqbn52A//sOlx5Mkf2NxNabhU5gKISuVlisqa11lIrSkFa1BTXqOzOagTYqLvG80vjY9aNWMdvLgH8gg+n2LPG8EZ49y5N0Wn7pvzB1pZaPWBLvAafO55MgxpZvMtDa+uCD5rwfL57DdEZFscahF8m25vvr5ez85HzgnryG8URIr2UgTfNqnwiGaFeUuKznTW5hF8loGvLEvLK0NbX4rO/PluM8+R5/27uHHXixP4Ndq14RhInuYOD1vmsLO+5RwlLnUYnj1szWV2Njayon0faLNhtZ/5RHEri6djJDdG6UDiieBL0NUibg0GKuMNrAi+TvIM3gi+c9eep5977pu0f/4+Ivr7sA1PocidMLtOnc1h0M8kTFqzD+CznfW/RQRfCYditK0DtxLwueHqnCxVZuxz+lsZ3FuDr5aic7Np87TSZ1k1AIA9sHz8cdr/m7+BzeT55MeH83r9uqfl+fPUnz3r2tP8knCWUTKwdhpr+U7TJSOCTzZnP9iB0HF5Z5IjGwx8+qLpsS9lCmvfbFuDj8t7cHhQM9vk9dVi9qgsS/qR4gMadga+2wI7A98O7lywlASqDkOzPjVWPJbhQTJC1Y0aRxcQkTQY0WwbWxhpobwpOvXhvqGOidBDis7pgAk5w9s6gby5Vrjq1AmzsjoxNvO9ZNwjInrbS983x9ORPyZkKTpZ7Y9AlQi+svJceOMlAx9nDJUgMofUS3w7FMEXHelLg0xZgXDOaoGN/ePaeVowYoyvoSCb6xnUvZH0b9FfzJkW7bVWjeAbJRS+HTPDQIEpWbMIPleKzoiVHkQ2o+TiaYLE0lSYgn7Tk32fXyOy9oq972dlA2sdo3jnrAA4itgdv83hYSGCDxkf2YRZhvCkqCkqrM0LlEm2M/MP0nGZPD2o19moVSk1mVN0lj320ZCWYlT24cBRCaLWWaMFuUAxc56I6+NL0VncVJaEU3jXGIn2ltJBJpBK2Rlj8dwpRfBpA1+JBFuoalyIiE6ejHTz5nCeovoRWR98zav1Ou1pa70YS9vLNugl2VzvEk19L8+94V/sSWutH6QY9dLsefBW5tMJx9Gvsw+xh61nsnooBR6Z8rltRGkCHcGXrZ1jnP9ais55KHBugr9RN6Uzqjkab4tn0N6zzlMxzhbTDPnwyPKde8djP3gdbW6IKPF6tlIPf+fLlwM988yS6F15n9x4lfNP+ULwKDZLPAiHmsxSStEJhq028KQzRX0thHOWLQ9PLcwIPsNYOSk9c/mqhGftbIUAokjc555DLkA8d2t/ut+umyPRrOgh/d6rFbPlMcV6bDXwGfjyRxNP5EnROXTi4Pn475in6NTEOMaU6Ui+k2XgC0HyWZ/83m/RYrOiN/zZQ7S3/wFa7Z3OUINZSOKctl1cJm7gK68R7XyEDde8wRZnceGm3+Zt731r3/EyJFI/oL5HIw9JlNLhKrqs9vNioVKDGik6y3K6Xmv52kN/Z9f6nk598YtwBM1Hp7VWNVoRCV7v5CPfplMP/znFEOjwk5+E7W/cCPS7v32aQiD6x//4Jp05Y6wnqz5uBfBchoKBj/Hw/CV5BJ+x96zfcK443wHqvhMRhS5IfZgx6S4SDzZy5nCt73t4TJI6rOlZh9FOZoeyz2Rz4B3cVtjOFXoHO7iNILxc42y8stJVaUYzxLnWSh+WE/NfK1Q7ddAAIQyHQK0NGjducP3A7PkoFZJ95IouLNygQ2l7+mwfcFwxXPLSLJiMIGKrdUePP76kCxcWdHiYDTb2OT/XdSQMkVx5HuPAIGVKe/Y6yHByLDX4KGfYM4hxNPBRkUnxCrsIhIJbzwUTloKWPnJUxR/8dzGCT9euqdTgCxTtqEkwn/rSat1lEXwW8OLcyAPc9tY1O5S/G2vwsY5kd3qjT0Y0Z28Rv4skFfK+FmK5PmC91gJeea1GwvtT46j5fn6vfMF+pjVFZ+aB2+wwgTnzdz3zN/Qz3/pTOnFwFUfwVQx8coTC/ig4xgxnYVSCVy5064FlBN8MfaOBb1IMK1oyYDEotItkUAEX6q313XH6DQ7MYv9GZD2BCD5p4GvYmKxPotkY4c4mUIjgQ/Oc4DgcLzWOWxn49Hki6GTFgcXSNIJxLEP2cAUrC39agfM2sx7USCum6UVazyUaA3638pi1CD6L52+JgkjfdX8/TQhybLBxl0oP5qTSgT2v6b6gbR1M0VllD1sMfGBePAa+44rgqwJaJzMDaqfodC6srvI9YyR67LE9evXVgKOHVsBBL2HHzsv5zKm/+5QGrvIOOoJPu/OVjL1HiQAuGf8Ryou+LYIvqwWlnrWylrS+U02Ji2SV7HyNPgNfSdFedcwynjP34Iy42X8NUokFIpL76hgMfMP5ImVxIh/dqjmSwGcAr4MeHyKmpcOPNcd6rS2Yof/MzUsGHjyCT+1xte5knVJgHClMAFw7oB6os7sBh2IEX/lZNE7mIGcgcPLkzEvPTtKABowWv+J7ZDx8MBxc+HkfaW9vHg+n6JSXTFoBINPxlWiSJeimvqKSWQqAjuYQezr3tf88/h3pxAMPwGe/8cAevfDCgp5/fkF//ucnzOG2juAzIt+bU3R2tn4vjSONg5jXnS/wi8whMjvTcZ8cPPyTToOfzp4BGAAAIABJREFU5PCp7+p5V9LLhXy/NNBwN1gP/B2RmX7aYBfBt4M7F2KEdIEbX/K8wunR4UF46DJC2nfLXJAoa/M8aONnQh7BRzTkHEcP1lKbCcMgO/x6ddDGHtXgy72rmon5iIRMCSoq4agIvkI/xrceMZ2bjb2/8GJHFy8O1154YUEf/vCKTiuDBmeUdQSfTiNHEdTV4sw/UPgeWw0+D2MYBu82yOgWuoiRcNSmBqbAC9pdbhtQzFgtgk9D4OmXjP6rEReFd1iBCD6rL5mi84jzovFSL+GN4Dt3bkbq3LlBgNaRR3PqOvVw+hMJG+DbiHfWJEm1n9L8EIjgiw24IW/1UQC36mS4lmyMomEpvbO1/l6rCL43XXmC/t6P/zPdE9b0oVdu0bVT92btaimLLAZ+oAHsnImRSgKB7stwhIYK97wG33YGPui4w4ypMSo9clJwZeu69J4jDeiimbytlgpIO96kPztQg0/g5djrafzp7xhpsSC6555IV68aX7DG82Sq4cJzBqhtVG7by/22jYFvnoMRb56ic5xk6diElFrl9R6C5Fdgg7zTYp93CpTOfsEnWe9zxAg+Xit4uhbq6aqqEXwWbPFZkjGxFsGHeMEEnFeYaWaZj5n+ZAYaOUSJUTp6BN9m41nGdRqqQfZZ5/mGnsC6Yj82gaks1m213ogcKf9qHa4KKTpbFenjzbkGXzn9NooIGp4ZznaUwWNqZ8w7JHPJma3D12vPo/TqJaimHT8WQbX8rtn1tGcV3XOKa0Xjg2QVg/t9vK9d8he05pTX4eOK9dYIPlzaoazHKUFzzc6RMZl4g8QrqrH6nkaHQonYes0V+fP1rsNK+iJfiAwIcTjzpEO2lAFLcll1UNAE8d/DNBWIcWGMlqy1Xv4gwalTim8I8/tznnPbGnwzHzLLffx9hhp8UTyj8X7T84/RW194uD6+D60tGoy4oCwVjmcnHrci1yR49tkF0c8Mf1+4wE0GSi7Tmby8IrF1bhoRfOKb8GcZrbL2HoeigU9dNM/VLk9NisD1SbONNctKlq7r9YrgcznMlYTCnYHvtsAugm8Hdy4YxEJH8JmCAKn7RJPnZ6I3IuULKm6soUMCuP2Mh65NNcCyCD5nik7NyDBFl8XwFpE2wHxn4cEiifzkxVxTklqClj40xpc+PJDv8fjjy3yy4yxYdJ1k8jtdgw8p7edulJJ5uJYiAoeoUGCQAFMNI5MqLnHJGwrVnOAKi0g5F2CetwWhOZsLvXaCXYNPA1R4RaDs0fg4IvhMbzjHel6tZ/a0VoSeC1gLkF7FFHZNg7VqJCRyl/aA/sk/uUlnT/d011093Xcf9mSf02DyJ3Oue/q+ep2PIKIWK3M7MaJxKEYuvCPFHsoXM8cNR/BFIgqvTQRfi+KPlHBAHZxXz7gcfubid4e+A9G9L1/Aqav4PgVDmjRaz3dBATUBShmiDXx6vcRIi36lFAzbKeR4n9OfgMa1PK9fOu3tRSeFZe2QUJqrcorOwoeJjnoc7Nlz53r60AcP6WMfO2QRfM4U5f3sCILWAhER0plowXIr3SpU+vkBji/oHPLSl+cZ7Ewbodi7QmU2iuDbUtlcBcS3HgGKkWGMTzLfR9Xg4wQHPeKpFep5Jx3Bl50Lxr5qieBLnaMUnb4IvnIb73IZakK1RfA1vins7KgRfLBv5szIU3R6+zA+6xTBR0RbpejsQM0aqy1awyJFpx6TT1LhPTTMSrTyJHd9zhPnNfjqEXx1nIYGWeIZY+1o0BF8tfGGGnz5AjFlly0i+MxzFgwi1lJWt8gfwWe1y2paO/v0zofs3zdJxxbBZ5z1MqB33Bslp5PaeZTaaaW1oe/QY/U9wcwbm3WEe7cmIyKAUUTgWw/GRq5DqUTwaX1PZe9gZ6ci6sWN663Btw1rdOqUHJfzY5o2N558Qr6defRyDT5tc7n35Qv0gUc+T2evX4Q4epBwzT1R9dAVEd3grLJkwtnhyDd/XscOTR9az/ysu8YIPitF53RfZaGoRvDx49xyRsgMm/hdavI5EUHLOaftNQPfPEbeMBn4IA9fgLmJg58p3dwZ+G4L7CL4dnDngkUsefRH7cBEN0EE39BsJKalg89R2Tsb0oigmhhyo1C8NvCFQNAAkBkm1TjI4yryaMIjKKm0wlUrRzmj3lJInQOMSuIRKTQKKDqCj3kOhiCVXLoWHkVp1OrDoLSHCkUKzAMwxzGNhwB6tzqYviGCj2itGx6TFlCm6LTVRiE4x9HMGMdzStHJ5rRXa4a7lFr914Q/vm5U09W6ozhGXaIUnZY8BVP8lebKA1uk6Hzzm3t656/dpL3z63ms7Lu1rYdET6f+pl7wd0KQ7JWBIkWiOX3uNMD4ZyWCL9ufNCtXcgOfwXxaGjyghHCnOiRgAMqsIO6usn5Td0RcgMNG0pq+xVwLSgmiAe1ZpDwiypURgWIefdmUdpbjEaY+p2tMoIpxxouvXdCR6A+BNuiJeQaRGov1AW2WQ7oay8AnhFywr4Z0t/U9mnC5556e7r7ekwgWt9Z44VpLik4LmliGI/AXYwfZLA3K/XSeJ56AO/AYCo8CURbbGG4oYOB7HWHyZt8CkIFv6pfxSZYBJGxyhyjdRvY5g9yb2HhmQYrgmxzFsu9qTUj7RO3v5096cAR2HSLCokLp+/WxoEgyIAai9eKEu30tRZk5TgFvSD+kRh9ehvjVIviYgY87ano3BlS6W20Brx4Y86GNPRGk6PQcfXMEX1k5GFSKTiLFy7AI0AzvLUiWzlbhVfQudAS/RSOCVMby87w0bqfI8NkbF+k9T/8VvXzXO+mpt30cjjWwPHnnVd5d15LyEmDAY4p+5+7dXLqXjeJj+GvwHZ+Bb7MheuaZxRD5Q0TB+K6lWu7DcA73hYp+Z9oPaiw0l2duXaL7H/g8nT61oZu/8RtENNNVS+1T1GmjNZBoBrvXx07pEoCjuSWQGjiIrAnb1OArrMrjiOCzzufTpyVNs4xnaY5a+KEhgk89p2h4iuCb6bechw898ScUrSN6C+asyKoX+uN0c7xCRHUjoziWHXqnoU/jeuns6Vsi+MClSA0RfCP/382lGzzG9ZJ+SN/nL5PRc6X/RFCSPa3vrb8xn8+7rz5F73jh2xTO3E9E78X96WtR76H6xy9mwsMP4Os7A99tgV0E3w7uXLAEHsXBWkwr6iaEQTmUuth0e0yA6ovjTh20QuWZpRHB54vmyQ1gIlUVRRL61gmX/PT1evtppbwOzeeQeSsaYHllBiXEzhpdgJN6WKbojJUIPlyDj/fFcej7ebxkXPGA9kCPkaqS26JfZYwnwitGpAolmr51SbmZpXs4ooJzWlNzn2VltxqKK28QvjTU4CtHd9jfZBBoh4eXFTcXXpzbW2uxCELZLo1D3hSd6JreK60pOokCpKG8KLwHv8VixuPwEI+HeNp6BN/QnqfR0fdaYab9+cPm+ot5tK8nTQcaV4ynCmonAcwSSqDSRPTL15kd1W3iqI3uYEz+Mykdu17S1uOswTdSEl8HAOC3iXVFEsfl1O/+Lv2Db/3vdN+LQ5qecgSfvCjmr/F8v/vuPuvHm2ZHG/hgYXhjDzib+sbeYo/y8xUKr8hL31KIcfqnBd5AZNV8MWGbF7oNwCOfMogOb2vDm9qCzJAEvp+HjZjSZo6ADLcZxNjksJHV4m3k8QWtAxkLvA9HCqJONOo/h0CHe2ccA6XWeWeeFJ2uGnzG9Sq9ZoAMfPxb9srA1wpdLKeP5DLQss+dzMJaXhPvXlDClyApkWtp57DTU3sEHwfMh4z/OiLl0PM8yrE2DSHM722mk4TC3nzu/uJjv0dve+n7dP8Tf0Znb1wkBJqPQgii8XRdJC2OWtAawZdgubpJy/UtiKepOy04l3qXYWuKTou+Xn0l0Le/vUdPP70waeEkwjek6DTfA93gkTqGc3WK4KMwRxd/5PE/ohM3rtDi0iU68ZWvKMNPSXmBN4hwGE5/gu84OAvz52TNwgyAIaDYxKhxVl0bRgMX2QXnPZ9Da/2gFJ2pI9GX6wUk8DSFnJ/nRryui4LngM7xMYihc//Ogp6FcpkuH2Bs28BjTW0dc6Lly2p7aGTK37FUg68IAIkYyeQ5LXmYK3JQdJ6m5cX5VR9pqPsOHERUik5rQqvOGTHmZw3x95Dz+cnv/hb9zMVH6P5vfjbjG9BY89pvOxy2lfMy2Bn4bgvsDHw7uHPBUDzkKTrBYQOVgyMwA9/g2Tz8SMqDYroXRwQfhzDbEXID1PjTYh6jDikP8lCaD2qZprGPkoijlHYiOm1uuhVkBxzrSAYnFQ6XgpJJp8es4YqUSCFIgYJ758ZIWSrQ5FE3MQfKm2cQEIY2XQeYB+NdoIKqMvFnbrxE7374S7QMWMCfugCD+o22M7MXYj8w0KCP5LHkP6/BB0MCh1Z2V1J0EkXEY7ogxqGOW0odNdWNKypBRqVCyPef9f1KBiLxkxv4jpjOsOZl3Vc8yVD61IURYSzGZe1TBB+R8gxWysxsfOEogKXIvrdrj2foVfYV3/JIuVakR+pdMgNaBaBiTSnUUPpFT+q7uS0XUGwabfaFzho9J8MhLMbUStTWNZ3oDcr/b9epKpzbQDAUt9TZkzmVMNrYXbxIy2eeoa7f0Dte+PbQXp3L/MzQBj4xtiM1Hn/27jf0xdTKpWv8fDIj+LaMtKwDo29bkLfqWTM22FjpgJ2dBe4wgr4X6mNbpqkGx9xvyXAkMh1Y82bU4DOVJOJ6wGvScX7XavBZ47em7yJivGol4kyPWYvgKzoyKbrRHMFHgQ73Trvbo/nybPsWOjUMZCi9a9981sxnfcY4G/hCkIYI7/deUK74g8MTUVcx8GVzCRw2ijqv8WZ6jdo7dCqCL8QoU/QrRyMxVgOvXDtHK5dkik4WVWHhZBlxZt5M3tB80qlbV6Z7b3zlSRN1uTyd9AFo+P2kuf4tZh0G0blrz9OvPfgv6Fcf/D/o1K1XwHyUrbRIn2Diqm4cVwTf3z60pANVPsN0vC7QunmoWHYQQEpxXp/XcG4d5nKQAfkrpucWTz8t2m8TwVeKIuJz0kdlJFDvlNaINSjEodA+XarpUJ76SUfPPLPI2rWk6Mx0B+lvY03s70t8NV+i5bXiXgTzhCL4UA2+UgS2Sc8aeI5pfaIzqC/c5OPxefLIc6ntEZwD1Q3ZqdhjkfauvEQfefyLkyNkS/cxkoiWH/AF7ZlsRot5rVp6NnEGCCMgUjRLuRNCJcBBd2XqqitW82x62fXF5lCNgce3ztESzOvflqGzgRDsDHy3BXYGvh381MGs4Asqgg81juJOCKRSdC7EYZ89ug1+2XNl4rYw6vTEtSLIQbaxaCZX4g8RfOCgOiYl1aBw5Z59Mje6FcGXKWqcQqVM0alAXetYBF/XqRSdmQFBCstm6MKIQ4zzwc69UGug09dlzLsB9z79XXrDlaeytp7IExThkB32vJ6JoXm2hCVzYFLfPAnVU4pO2XZuNzB3xQg+wLDosbkMz5uu1zR9366TwiRqb9aRK1wb3sOxrwrrrPQIkWLwQ8IjZg1rQj5nDOd3YX0b82LBYjE3PDhg6ChDje6P72md5omIJo/DzcZaGFqxACDid2lVCMsIPp8Xn4FGdnGiWSrKWD/YFFmlN1M8pgg+QPeyFF3r9kgLqYCQaxrR/uK3rCg7st5izIwe02NM6EwK4BAGZA8OiC5e7KYIUx7Bl8YQqFc062fORPE+Z88CZZa3dmQv12v21sZiuF0RfL/+63MUw0zb9KeU5xragydvXaH91fX5UsE5hb8rdJR4nYVUeXYera9NKEfwTXvMWge9djaYvwd6xOOI4PGV0zX4XHxWKz87dj4pehlikM1Ua0PyClm3ZdTYhZ7xlFwRVHyd0GjgA+u67wNWcIk29j2IX5gdCJ0+ZkQ0KKuW61tYJgtBppoVWR4cKaeJaEHls4i/53JzmN0XKTpJypbibPYqxNiYtbUN77NzvN+iBl+LgtyfonM28PWLvbyBwqsewYcHhTTReChGox444ucj67sxcpm3Q2flcMao83Bs8Is/+H3q+vWksLX6qwGfl3zf4s0oKiLwd2ZRMZL8Y2Ru3cJETxh3ku2+6MyQy3C4GWgAnJmQU1MqLwINoqroXqNfNxFpA8L8h0a51xFhRpYJ+Hf+M2++MdKVF2C9Jvr93ztFn/3saXr6aTkXHoeQzCBH0VXHUDvLcQc5gXOsZ93IHXFAhppYrsEHerXf3+SdNnTi4CpsA3mnGWFjoFFWj0pnQz5Znc+pLUtzfHx0v2e0pYs93fvlP6S3X/wufej8H5f5C8sxxkjRGZV+c7rdYUPbhGtjDT7Rt3F+DoEDZf3QOHQZjChbbqj0ygDw22zQPb+eoiUjBoSdge+2wM7At4M7FyxmXUXwNdd2Y4LhptubmqFDMoOK0lxfD4FAtXIJycCXp+jMIzgQ8EMgi8zTh3hB67AtDS+lp+PMsxCwDCUsPgfYQVswls4HbZIgZsEiRsnkB5VGLkRdg28xPxeVIjUE2myI+rHzEJRxkGzjDkoRZB2eL7zpA3xI2l/fAM/Kb7/9QSoFCMgoJuXoFgY+lLJBKh3UzfW6KFQlY9Y2r7vZjPuEwhQhgCAxjEPu/qHdopSqxQv6e7MXRcKYBtO2HWVqMrhXaswo2IcdS1vnoRHcA5Kn6KxrrBluIB1V6sIdwWcBoHd8H62XJ+hrv/jP7bWVMf/tRloEeYrOnohs4b+uQFY4skaebZONtVrR3T/6Dt396tOiDTf+BRqiowUezlSUGqDigmbjVH6/fCibn0XX2Rkbw0/KlQ/jGu26gUY88sgePf74ki5cGKNMiJ0DW6yJkycj3XVXP+IS6e1vY8plZuQvGQ0m6GfP5ajZ/vGQa0Gx6XU4Tao8d+ZMT7/xGzfowx9mkTKh/CBS4gWK9KYrT9Cn//b/pPt/9GXWln/ovM+yIg9YuY8q+DbAUWTk2OXaNXQ2Z8rnBDqCD2mv+X2Dv6seAQpqEXw2+L9LooYogs/XT4B/W04RcnA5IaVaifBxCnSwd9bdHs2fJ0WnNxOEMapvLojo44/+Lv3qg/+CFi88nwaecaBgpuj0BmTVavDxa4sNiuCz68tF7iRXiADRD89KtLJRvOs3ah6jeia4DHxVUHvbcsRLY2rgDj79cs9cW4ltSt/EbGfg547Co1mO42MPfWN6Z76z85wMZOslLIeZk8kIYIA9P/I7lUp6W33wCD6RopN35jn3rPMA8aqVCD5u/DXn3OB1uMxKlMtCqcRGpJC9VoxEcbEQNE/wBZqfMZCTtGbUFaSx2TNDik4pl2VTLQiNHA8PX/5Wej9AGBt8/vOnxOWtUnRGZeArDc7uWYZ4Px8wg67Bl+aVo5Ii+ArIYR1fAX7l2/8X/epD/5LuGzN+pPGrUGmUl1XxQUqDGmJPV6/6dTlVUIt2cfWK3ZZ3b8iHwZGiU3yP5dI4S0e8shSdzmh+5BA5QrdQ8pQxVxV22d5U7AxG553X+bIHWXQ8RrvWpC6mymRn4LstsDPw7eDOBYMA9UKRI/ObZ4+qA1x7CPUdOxSSkcYi0lqR60K7rkydGAo9bp/jDpkfnaJTMYuIuKcotKENHj57Bsp3IEQd4Kif90bwBVL1VCaLXR3PoQbf8GOzkYdMbmjTdbVkVI40nHQjU96Wlo8or6OBmMwET7/lo/TcvR+afi9CD+aprDET/RaMO4FH66h6hBkUoiA0oDR7KVoQRsikNpUafMNzRIQE9MQUGYtkMniHUPYqBMZ8bcjNGuSPl9uHoMJcC/hUxg2kvBoB/jW5Cs1ZF2a66FnmXTcPJBQHtQg+4QEI0tFSOYLPZegwbmuB4GD/nPlc0LSCVGosxxwh1NL8TFFfKEUnW3/V/aFolhi88C0nQUFF8O0/8AC97YH/QJ/47m/TyYNX8IvEmNG43mG0RjjMdFyu6Woau8J3h9E3WvADNHbmEebu0xrtOqIrV8JkzJbnj8RJC2m1/fTzP7+mD96/ol//9Vt09xtKNRoqUEvRaWvQjm7PQnyZAffe29PP/qyk/dY6n9bp2EBkLog9ffz7n80HLGwayd5hnkn38ZqJrVtNtA1lwxEz8Klx+57o8uWODm4o7+dahJKhBEWsXAm2jeDbxuN4ckbj3xfhCOYI3Ur9FfcPl0WiHYFlQmiL4EMfrHekCob3Y6T3/+g/0ru/8v9R98ILOZ1O+7Nxlyw2K7r7P3w+Gzgy2h/CyEc2fueOWgx8KIJvJRqLPrZ0ZJn4mcq7QJ5I1OArnY1+3KbzD/CXGpACkKc2jXv75bGYPO6OjIn5vqoB5yeIqJg6W8gqW9R5TP1an7NovCn051W2Fh0nUxv1Lc0IvsYUnUhZrw1W6W+UNrqE52pF9OKLnTRKQG02T9E5/qsWS4w0OcRC/zyVWlbUj9Oys6W7MCL49DM6RacVqWsBTCPJvwNcOKG67BIe2qHSsyWSrOadQyKi06dzusMd5CT/0Y/XCkRAvWCMYG6jrsE38By2wSxk78XuwCdO3bpCFCN96MKfZKjBMyh9u8qGF7KgEZyA+r/77rntlSv+Opi6X+1MrPWAXrnBjBA0IvhEoIA4sBfsup0eN0FXSNctShqYDqUzDUF9AtTNe7KEQk4L3E5i1ljMwXPq28ETaP1dkWbESJcvBXryyQW98srOoHcnwM7At4M7F6IVocMIbt+XGQ5EkZjxQBj4qhzPlkTLeC5dXkxpvBShVqnNTE/2TLDm40Var+RBnOEElC9eiKqvwddl7ujIKTrVvTkdl8KjwnD1vfSO7hTz1MVeRA3pOk/acJI8AMef+fsYSwUJ6cVUDEyhMRj4dH9SkaaVKTEWkFE4zIprqTjRihqPN05mNOYfSAvomnkPRLRaVZmaLlj0oQzrNU0GXB7Bl/XFI/iS8QV5UTVZ5SjXcrKBmyL4IsA9gr0OHzaEgYLCwxvBx2vwHQodmR83K4f+cdfgQ7gBdPJ7QvHYkVuaKTWZNwsR4RSddZuxpP3zgwo/R3S4TgF74oEHJlr37me+PraRBsdAUdQ3JSKKa7ymu82K3nLpB7S3yiOTiQo1+JBxWAmcEoHK+ab4jGQol0aPWQk78wvMGxi9YqzU4HPspVOniP7hf3eTPvzhNdzvcE9WeIQYuiyiM/Z4b4dAUGncwitwZVPtORRBl9ZV9fmKwjDbS4jWGYaun3ZABr75vJ/XqT4TfvSjBT322JK++cBCsiklDRX5FB1W7Wl+X9fH1fyTTaMbvt/YCY7gq8PetSt08k/+hPa++c2qEr60V/sYROYI8xn+OAU63DvjxjXRrLPXX6QzNy8RkS+CD73XG649Sz/33DfpzMWn6PRnP5vjZtATD3TXr+EbIVDf5an2SzyY0AFWIvhEis4+N/B1LKovj1LNLb0eBXQas6us2aBq8GkcYliY6VAt9qSmsNPPGk0E8MjHfrlPFteSG+2B7IJAp/h3QiZjUME4k/oGIXCeaNZSZKCUiUMm/0AotVHfaRuHHLMGH0vRua2Bz9LjZJkEYIfzuf/oo3t0/vySfud3Thfp7KAsT7LqRNRBQya/61crpOjMDHfGXIiMQMwJXTff9JQ5ZReNwI3yRaO9UOCB2rpq8CmeO1AsZ+QJRO97H8huZOxTKB9WgMvxHDeZojMOS/4o/F/JicxpKKk1ilE7i/vOvxCIzp1L3yLSrVuBbt2ynpQ410AYqR0O2tNzKIVsH830yNJIxaaL1eBDxvWgiEYtRef0J3AOmPoISpdh0cXaJ+W4LWYesJaxxwsoyKOJjjjahhjpi188Sc8+u6DvfW9Pfr6jIL+DraFQmGEHO7jNYBFLlaJzjkQrE9fxl0jJxVO+JKWOJ0WnF+0Q6s8suon7k/1sykrYMKXOkweV9GSPkCHLUnk2gH4uP+DwoZTOsEEZ68wdEiNWjhYYdn7Ip/E3G6LAFIBZGrkY5TyOSlDEMFAYvZ4rNfjQZ9d1PUoCgo5WmdeJ7mBEarAA476owogoAUKvqQQ1pdzUHjD4WmguyoubTRbtwiGLVssQkJ3y/geP6WG+rAi+SchbrcT7cJyOjWcp5dRpgeiI4ANrgE+XTvdDxIyaHkUESdmYp+hE6UMt3FCKzgG3kBn4gnqPIsQI22dpjktRB2Cej6MGX9pzXPGlyZ82us39lRejMNKMnVZJrjEW0TxfKBXfol/Jvo3Iho+c/yN666XH6MapN9LXPvbPxSDD84H/mBCpRbmU0wMCXFAeqwiULGPb9HdKRRYjVmzxGnxIWVpbJpnRHrpq+w5x6SEK6tGUok86/l3KZ0wN3O+sr5WUHYnOEaY1XtDKVxO5bTSoFVj+4Ae0//Wv0+pDH6LVJz5xLH1y2JT2DFvrevpfeGEg5utbPb38skyByh7PwOLvWqYL6pY9HTAa3wLIwAfXo1r/7/z652nvjc/S3qOP0v4v/wwRfUA8W1wu6kx8rSP4AkV608sX6OOP/i4REf3NL/xPFOMbqtOK7p+78eLc73oN6nzOOLKe3LjqgWcHswURjUakpEVyfm/oYCeGY05GfSVFpz5DRRYM/3qfFGEV5aCOjNdlBQau5BhTdOo+nE15dpR+uSymTw1hfof8GDbkenCW1kDzUSXHGyUsqOXrXb95BJOFlxe8bV0pOlVnr32KzpyXrPJxSa4OgS5dWtC1a8OD1651dPNmGGoUo/FEBF/OGyQ0ORef0qyPj1OXRfAxvLQzr2Fs0lFEic+KMShsOkF3ahF8etkj3r8WwedZm/OZLftvieCbf5OZovO9713TqVNxqjkN5Q5Fa0Ps6w6n6iZP0ZluhxjF+3QdFct2IJgNxNvwgkg3N96pfCBUg89DJLqO6K67err06vDMK690dPKkrXcwI+y0roE76N66CdobAG6G2OfvP/6GjmZEYoHpszK11WvIREE4wNr6gNCBDGYAYrT1rTFS7lQx8hk8gq/o2M0uQ5K4yflrkExcAAAgAElEQVQRj+HWOoMPDohefrmje+7p6cSJ+fpVFrl361aY9/TOwHdbYBfBt4M7FyyBhxv4jAi+4oHCHtiExcTs6EMy60O7eh0RJq9kaxfqorCGoksLeRtm0AsUacNl0oLWYRtdFQrN55B5KxrtkjGuqlCZpAOAS/bwrLgaUnQyAx8Q9DuoBM2VzCm9qTTw+SZvuQbuUkV3xNnLcBFyrlwaHgGzGHPhBg6jmEX+PpMOhQLt7w/jlLYB349wWiopOkMYFCnFrRZjFk3nXb9JRxOpnKJzrsE3I7KA2gpLqLU6li/L121R0a77VYxeoJit09LQ8B5osBA1+AxGl122avBJNR/ATexPHKYX4+uRopOKazxL0blFBB/sd3yumKJT4GkJXrK/oWWwG1r4TMbeXKBIBlgUiZ1F8BlRqW+99BgREZ2+eZnO3riYoZdQzCK4geFat2sCVIOP+Cdl9CnOqbICqx1kpaYqKhLty7gROvu975wp35XCy6qDEYhQZFzTEheNy2dR10UKV67Q/le/SounnppwsJUMc5/aCx62L3lXh3mto/McZg84JgPfqS99iRaXLtHJr36Vws2b+Tl/RNazD7kvJ99fMMJC0UdB+xPPaJ1XjvVerndItLeHFRkuaPgu6btOqfIrBj4Np668MP198snz098THa/wMQn6GKivTQqA1fJUvdE84GTcIyL6yONf3DqCT9f+667Imjtcyb51tBU/w8ZO+m45y07jWeOlg7UafCKCD9XgUxF8loEPKiM1KLlLn5vZ2MiJkCktB6fEoxv4rBSdaMLQHC423MC3b66tRNOa66xtYeDT0Fl170nSQx31EZ10RZbeVeYcZYCz/FfEtVio6aejm5gTppcMihSd3IjdmKKziUepnMX82RdflOs6XL5MJ/7iL2hx+fJ0bbOhIRppwzJfIH3HiGYcZS90tkZl4LOMU0JvpKBTc2HJV5uNxA9G8GUdzVDz+7J4odrasL6bywdV65RiFLXteN/33NPTuXM57STKdQQTz+JM1S1/BvDt5L4aDHz+80T4sfiYeYEa1o+UbvLxuM6mTb64++5Zx1NN0+kkIsIZ/eCGP4IP8ZBrW6mr1/b0uxLBl+lTt4zgE6CIRy2TgHkWcvmMe7YBh+vsWTUmjLBF4zq+q9XkBz/YoyeeWNKjj841dvV3FMEAOwPfbYGdgW8HP3WgI/hqKToz40GcGShZgy9n+qVuykek9HjcAx7BcpGUSupGLQd3Yn60wUBFk1jdaCG6Ru/N+5o5ZQ2PmqLTY7Tgrfk4PIKv73FqH95eXNMpOnWdQx3BZ6Sk4tD1ayzEFya+FwY+JOCzNHHRwYykP4FXrHzX+T6vWbe/H6v7gBcR52tMG6aKXvlWHkaGf7CiCdV+RkMMhungitiIkdEGWAfFWLtOA59o6JCeSsYImbIpDRfyhpSvAf6eom+m8PHw+rIGnxog/VmIwCUqe+AdOUUnonclGoqeV+/SGsGHIPUwKS+B4sty8kACo1wLsm5t+VsGoYxA6w2lRgshN+4RUZZq2gMxGrV1A5ZVqp3RLNDrW/r15ujgtOcjvfRSR489tqTNmp1tfVlZPETwyQ+zlR0YfNz57HdG9bE50ErgQEYKlwSMSBYVExZg3Y0Jp/7oj+jEN79Jpz/3OdL5g5CQPJ3VIoLP0oba9E8oa9Aqe52E1HDzZr1RI8Su5Mkyn80oBTXRsNZrSkMO5SjasU1lOmUEX+P32IIGF3zHin3znz0TqQu+dLhbFoEVnXsmOTyslydrWBNRntpsb3PgMvBZ9I3f655/vjr+0Qy1o4GPpZsVirHycU9E2rGv7ACFavB1QthUZ+gWa24oIzjgwSMiLZzmNTWcT9rRqNXAV0RZnVllJ4sZFj1P0blXGoCI5m9iRZtldDr9C3ihUhYf4WAKZA9ImzKkYPcm6PnR7D7ns+od+T5iSZyw5lRE8Akrd2OKTuNVxNma6KwrWhl3ePZLX6D9Bx+cfq/XRA89tE8PPbRPLz4/DzbxBqjuMM2OTvx86XsCKToZ75XpLowIPiXDxEizEpxNVF6Dz+bfsr/zn/mjwIBSMjZMeJgGvjoR1wY5IuXQUzzUcj4363/UUbSQ3MGYnqdX1Tgul47JOSJ46G5LNh+rBp8eK83nYFAdbty8Wf6eVg0+8VvJwIubN4rtRf8A55KezDzn2QKDjjzKwNeFAu8g5n4g2kj26TpFoUq6vNLnBGmRB3mVGXHBOYxIMkIByXceIzaSwTcbmqKpb9yY52W9LvS5M/DdFtgZ+HZw50I0PItVujFEhBJAZqnvJ4ZOpuisHKgON2pM3/Ezk1LF8CaMG8nUW8pd7YnC07ToCL6pXR1pH4QglRrKOJQxzpQfXDUcpKJ6NLq4otJUBB/DEzEQ3HgzFABnKTpVFEnfz8qcZDQWfQH0UPRejMH2vmIKHyKiRdgAYbesMXN/WsZ9BIpCEcQNKtzAZ22FzWYemM9fbuCzhOvoi+AzIg9qMMjKg3BnpdyMkUQNvgSWAhSBCzco8Tsh47K1YBKyZqV9w/clR6kzvpMFi8W8Lg8ODIU6iMKqKegDDbReR/CV7MRZH5ZCBwjpRVt5IYKvJfWF1Wf6Dd/JayyS0oy8HrFw3HcLSPMmnCbj45wajeOxAGnNrBSdNcB4hMz5InvAMC6YoCP4xvnh13784yX98R+fooceXM41k2oRfLzQvPebAZgekdrk6V9XP5UUnVYnIXgVcTZEY89Z4y1enBXdi+efrzrQwDRc1kAFj44QyqnbSvWLiYjC9eu0OH++6pyCIEaiq1dDPfp9S0A1+BKgs1lDVg+UaRNKxp9Clw4DX3kCzLPMkBtq/Uw6XU7LkR9KyUub4YSWmk2zRkUvVEQXYERw5TTwZcrh5KxWGQfd11kwFi/MkYxcIbYNkzaT8nxgHuU4KdGdm2XBMgPU9OgLUIMvrFkEH+K/0j2PJShK58s3XHt2+vvGqTfWn6dZkU5Eg0OXMddNBj5LU4hoInheGPgW20fwmZ+UnaV6DVq1uXSKtJLRUjRU3sOeDBu8X0gbgYHPs3xr+v5Zn+B/JoFwxDtSik50DdPjvkIXhu+bv0DXr2lx+ZK49uyzi+kdLjw+R98VU3QyR77s1Vg0kL7fKSORJRJqfp7zWXzd9bGTsk8m1wYlKCkajpYwH3pL/tv6xp4UnUj+dwens3HnZ+z0ip5+iFKKTtWkZyV+Qpwi+Fph+F71eZaRvY30t9Qvc8L1HLeLBXYgLfatr+t1yCP4br2WEXwGHVrm5ZZEE0UMl6VIZydNpuBL0ZnGwH0GKZ+x9+iYPsbi0ZGjdgY9+NaOF9QyeIwyKxOH9cp8wZ2B7zbBrgbfDu5cMIhFYgwTw1T0KIp5BB/nUOYIvlDPY82orFs5F0ZDRqH9Qnn5T6hnKTolAUVe/Kh+1IZ5b0wGMi6AK2asGaR2VNwyI/gMQ1V+Dsh3hmmy9L10KKkafEt1EM/djgoeZvSL1Mnwf2WcGOp4z8KBpwbf3uYgx7km4AXGfNZSdFqAjKIFJiFErPGZUnQaKRIT9P1cYyDOYWSs/3zsbLiKgW+OsmH4pW1USXHBhb8i38GKHaeHUDBuc2pAjZeQyB1CAmD0BiW4nBNU5w7hweUJHME3ewd6aETXzeuUewabEnO6XeUBjzeCj5lO8nSWhYWB5lnwzY5vWBLsaoqvsUhI2XuvIHzNkgb6BjI6PuGTYDpzjBSdizGFmdBHGCk6SyCEoajnejtHhqENaDg5/CjD8WxZm6498p09+q9SRrwYWZ1EYLCleorOEmTGQdQG9BsIvCWfw9BlZ0G5Bt/RIvgso3plqAFGGowiuOZtPLyLqD3s8STV5xDb9sUIPtTHZkOnf+u3qLt+nQ4/8hFaffSjtHzySVrdfz/Fu+7CuDA4f35JFy92dPZspPdsq5ArwIal6IyR6PLljl56aTFdCCwNM4IQcQRf7WyZn8/b11N0sufB4i8bntvnENXg8wDf+0LZA5ZLta9SpCVqT7OBz5OoM1M6h0B9H6r70uWQwq0EowNVwnGbuRgHFrgSDelmJ2NGc4rOsuG5nqKzYLznDztlqvTIiYOrdPLgKhERbRb7dP3kG+n0zcuFJ0d8hOzni+Ar2AqGtkkWq44unz9z4yU6d+NF2mOOjP2irGLifI63XtzEi3e5o6aV5jRG5ahXoT3TfG0bwce+v0a/VFu81J+pltCytPjWuWFL/jGA4NM5g21E8Flrw0pVDiP4CvEFnPfR/N7pm5cpqkd1phDNG2jCM8zLcOAjH8tSik5J94Ipg2vjc4w4kqbv1VwUDIPzhQFWq6HWlQbxec0afBUDq7HYW2rw8fUm5tCIQpqQU/eGLSPltWK2IgAxKnoZ5XrlBvJF1y6zTHxug+EENZ2uFazzUNZFOhuTDZ7bV9F19iuM1OsV0X4dD+smPGcBvmKN1lJ0Kpl36fzGpXUWOvXehQPCHcHHN8r4XTWNmsYLlM0L5GuOGsHH2h4oNWYab7XWCDJJdGfguy2wM/Dt4M4F2+ox3eY5tKveeAkYpeURfDQJNkdTsGRDhgAFg0TzFlY9gIpiFNHMTQxSUR6j5hfT09mVLXSQhGrwWSk6+QGHIvgmxj90MjRdRXCMA0NcNKQ++z4I5ckCRfDxlGuhE4y2yKeflCKiBp9SdoBvA+vvDcjh6wmP9HzXkw66RgadZohRzbe9B/b3IxEQKDhsNkRLxXQIIU/V4EPCazWCj1QNPtDYStE5CFS49oIYgUXwzQZoZ0o8jNJwnXtrLRZSgb6FMUSOVZ6TMjMaZmMCF8iZd2B9fBnBd8ic4LHxkeNYX7/f/vY+3XNPT5+otjTwZdckPZJtS8bfQEqACLkH7jaoZYqaSopOdzQYU7ZOjQ2FUQyzYgNFcyZAKTqJcASfa01nChhJO3g7K4JvTqOrEahqr7N+pMMDv68chvoNEQXqaxF8M/pyJM+ZCyS3VnlJ0BzgzY4y4k7jQG2oH4GaQjkbjz8LiqTGqNZ+oiVCue1XdvCxt5VDF08+Sd3160REtP/II7T3ve9R6Htanj9PN/7ZP6s+f/HigPu1a4FeeSXQ6WPEjUhG8F2+3NEPfsC8nflaN2hXnjK5fCZkEZezxnVuU1F08wg+TvemLgOm0QN9bmBmx06QgQ/PucKDr2/wfvI99QJn51HMaVuVdDEDnwcyp6jkrFbZz4hFRXWs8aDtCzfGxIeKjgZcOpCiszBRwqgcN9PKrLAHRopOLjfoFJ3l/tDNpNe7m0XvXT37dpN3TTDIHLODCdFA/3rDaJLR1ZGGerZJyekh8W97qxv0X3/n/80MbKUafFP/lUgSK91vCBFE8OE1qfXfVjREGm/au1kNPoxjFecSFIx3LW1K/ic1mGx6Mcp3PoYafPrRqakjMwCax7M3LtL/z967NtuSVAdiK6v2Pue++t0NNAxqEAM90CAkIxC2QowmFKEYB1bIkkZjfZFDdoQ0H/xzHDFjyQ4xX0Z2aDzYkgNrJGEh9FYjaETTDZe+qLuhH/Tr9uO+zjl7V6Y/VGXWemfWPhfuVcReH+49uyorc1VW5sr1XnBBaaxA3g9qBB+6Rh2Rg5KiE+HEjAeWgc+L4KN0n50btbTr6O+33urUKU9KPVCrOxOMRktSdGIgNbkaFyZ2PCL7N0UIqcI/s5tYjs+38Tzhb7zqF+xfjrOz9+d7yPjBYYGhkPfdmqJzab/V6zySbQG/qi13zcCn+lwguohlBXEOpDTVq52R6gNO42nzZWVmtWkQL+rzIeYZxyL4sFGRBz+4Wbw46vnatM4pv7KbnsKK4Bs2AGFF99eM5N7Adytgb+Dbw+0LKbmMYUoAEKPqUWQplUIYiWmJ7Ap9IbxaBB9RJoXQ6DVUUxCw1jxuP0NUUrRViPLIfNHDgXi3oXulq0ZGQuU7QmDMKVOOGjKBFsVV2oVQlLTBkMysGl6JKSPLdx5oShDskZ7/J3m7ux7CENU5kjX4ksnkY9BTdIJz6lOmqYPoGwGMxVYz/HFvu5F51riuqQbfiS+YqTX4RAPKsIh2tQi+NEbHzN6gMiLVgjxXXFEsxstrDfXbGyk9VRyt+7yI/UKJ3FJOBKD1L6sRfAxGpZps3BlClgW4Bh9hBhXcyPgVgT+/2+uv6+1qHqkcDy4wzj8q/TAap7oAt6FAgOfbDygFpPawNmTZw4oil4xl4IBrQGlR1bNHbVRxzgo+cv5qBr6GOVJr8IElOXqW+vl7azX49NCBROjTw898Hu668gK8dtd7IB0ixVAaIKUwpdOW4+bjSDM4NMvvjsDfaWlctY6rKTqd8Z111gJLUnSKFMiT0N55grPCE7TU4JO3/IhLL0Wn4Gem+e4b6pKJccxXTVAzxFgwIAMfNu7lfjXlAaePHv/GwbLdLVE28Qg+rrTxcViuoCsKyKqBzxlVieAj9xla9Lzu3FSqKkyDbPtGAx9XOkN36hp8Fl7qM0uXL56fvM/xHDXli5sBG/g0wMprNUUnUjyGxPihGIv/XauBJ/Msd115vlx748I74c5rrXSDno2nTadM+kQvpxnYcZN3vfK4Gj3n1eDLbFN2BhjnouGsQPxPawQf769r/U58fS1hpgHgiSfWcP26vehd+Y+B6QfKnm9J0TlmF5izH2lK28T4WjKKgXM1VTkgma3G7wedxtxx4xVI5+la0QzY+Iaqv590F9zQnRKM0UBoKbk1+BoNfDGCaszIDqfzc0myGIZC6803DZmf0yXlfm3ZnSaCLxteSvpLoCk63X3H6Q7qr+l5pZ/xJ422TAlMA9/aIFtcbqXrLrH/66ip3yBfXFCDDz/X4rhRE1dT1xUetqUGn+h0bOW3V9oV0NL0aHI7ftaL4FOexRF8fKo1PQpDY/6NdXTOi3L9RIgDnDl+E1I6ZN7GgfJPiUXwVc5l9Xtq6rymdTrtXzR//KzI4203ybYo7Q18twT2Br493L5gMZELavBZyq5Mb+YUnXb+/gKNbiliyMozlrfQmB/c7sbqlqff0viEpno1DbBeJ6oQAco5aik6R0UrRwoxJ6EDAMMTMxstrHshEIViRu1d7xrgNVKDT/aPr8UQiC8sNwBQA5/C9CtLJafoFAyYxxSgMfouibaCYXXWmhlllKjikNdRxHB4CADXgv4eE2AlCWZS+EUL1RAAwiDrDZI2LIJPfW9zXmdFrbs1SwTf+KI81QzGZQkElo6B4N7A1LsGPqUhaeZw9pgRxs3MCGMDcATfdquvuZxOjyyLqvLaH7/J0GEYG8S7VdaGF8HXWqdFQS4PPSm0ZeqqEJKMgAX/3RMEILYBR7onCuoypmw3n5VMuaQp2Iy0TRa+HEXhKaoYrsnaXarcT8a+RvP10AuPAgDA3Veeg3Tf3GY0OqxEXcgRjygMRkvswGKft/I3GqAaDLJmpJ7CpeCgRPAtYRmWRFN1lvpd2bfl/2nNkhSd1piKZyy+ZXnJjuMoBj4Fv5sFtMvT9Z8cw1Gn1Iqs4RO0iwhaFCw1VppH8IXIDXz2Wlmy5jI9aU3RqToJTEDWoLNctIeHuNxAM0fwtSToHJ8gENpq8GlsSf4eNceanTNLjJ3jEUdcUNYVnKKz5ZP3MAAKVnKH65Uogo6k7XTOMGW9a4NlVvCuqy+Wy2/e8S5Sj0+D0TCWiINHZHRdtFf4dffbKfyl1SQa6zb2a3cORiOdHsE3/7bnmdMEL0UnpT2ZV9Abl2k084b6kOeKG/d21nOmeorOmd7oPC7/vV7P6dZKis5WhYOFjHJ9FwOfdx5duP6KuFzXhUilNHYWE0ZRlqKT9M94ETPqXUnRmWUDKgt1hEVW5Uxjvt98E9U967HxzZdHHBGgOmaL3Umk8E6JB0U2AebHyPdI0XRStGCswcf0JUguwmug7xZ0DJk/mb5theaj//QmZY1Uu6E4TKVVWrbpMn9UyoeVZ5iMues5r/Fqmn4uj0fXnx7BF7CzfgIi/2RYdayNPpxLtLuevXeFfy4ybYrwk3//v8K5G5fh3GP/JcD7kFDJnL69TDoAcq+p76Ia+Oqg9cVTdGbYbimDQdihvYHvlsDNcPfawx5+oDBH8I0EZXENPmbgw22zIl+FXYx77nNj497yJtwhXV9MXHmXiPLO80JfIuxlWB/IFJ0YsOcb/k6inSLI43coaNQ+AZvrLOx87GMncP/b0XiKIEgi+GCcR03JnCP4IsyGrhalco7gW6/ZIahFaqKxCn6a8nOJxswA7p1vKcjGGnz13PfDAGhNTWOAFJqpd57ss09OvROxT23FLcYj/11jSFMCZOBDY2qPWEpOq3tcxJ4xc7sbh0B4c2nv59WpsoTVlhSdGDrFEJ0x4rjRbb98/Vo1PvUL9DoXGDlu1vIYIyWR9x+z5u+cohMrrgLGiSGSqEJH65dHRAt6b9CcnKITC3Cq8avU4Gv4bIZXt/d7HHqir/j6DlEKRQCKMqXXOHYS7b33IgbXOEaCqzUO0pzqy1qLTVtKkdwsxYfZaSWCz6M7uP0ShUp5BvVdO65EildnDaKexqZEnDGUWjXjzWy5MsfR8NtpYjzQFKWnkJEHLzIMnWtL60Ml4+y2DPhL2BXsRd91SgSfS2cXvMeESNEPYVreMOdUkbEsgo+e1yAi+KrLahpkuzqsIwrSwzyFrrEGnzJ0awRfzZHKG0/hZ2LoZgNNUQT6irWClpWLeAKSnVCN4JufVyP48j2u4PbGSwnuvIYMfBceZGcSXo/srNqhBh8Glw/xrEPs0tAfiHtDv3Z57EJzKjX4hCyCzoSep+g0vi83aLQ4NKhItTxTASF6N/AGwUBFg5YIPgAqh5YsP967NVgFLDqh0sIdU3Sev/5q9bmyhLOuSMtaEOYIvto3wcYpnk3ArsHHeVrqfJxhiDwLUiUt7HTh+Bjgxo3ZQHnHHVF9yMKvtozz3K82N+DsZz8LZ//jf4Rw7dqCCD6qP+mRIzteDsKOjDYr1RHgNg2bgb3guBcYXUW8KcZvvdqtVMbY627yn4CFEXxLHBur53GHdXptffI91mpEVGWP1hp8+AeK4BO1dpW57NmLxfPndfwg2BMWmP5TOA3OvzEKb3/1G6XG7oW//jNRg4+n6BxpVBJj6LXJJa6nzaKD59mM4Nsm9foebh3sDXx7uH3BoBCxGPiApOhUDzitjwFHavXFUJP7MJy1CNF1D6xGJUa+V4xgXPhmSliuwFf75hdTImdl6LIyTKY0dHl7g3lYrxO9wwwvlgJepluKs5cpZxS0X9ohViL4NDwBPvXTW3jHO2yvHWLg4zgwA0CMUOY6BIDDa2/AR576/WLE075NjuBbiQxZhrCElNkhjDnDZf5tjNd4RYDqSo6ZbSmwqB7wIRCh0IKoyhnSOOYqqcFPt5Ofn71BAxRFLxIqNMjCHVc8UfxCWbzZ8MXTjNTAVJbyIvatEnkBvd8uKKkjYSGjpTTOhuVdIvgweGkvrGvW8wW3XQx8at96SloVuLaInxoN39BTmOZ1nb0QRfoQBT3fzqB4GXLlZL6lGCq1PVJq8LEUnRwfAIC01YxqtTRHRsrdgNNkG44gQiGIxjCMX9xD21vr/BxLyYhwSTLl4zLPWdZQM/q0KDrYs2rEqbdmTxnBh+kVFua17dVZBj7ch/i8k5EjYqWEgaDzAYgQrSmzHeeomwnfD2dXL/Ujcb5x3ofcqry3VmOZP1Y7S3kEn1ZXxUJ0UQ0+jk/VwGf3jWUJ7f18A99M21rRXxzBx2UMCDun6HQznmBZCWrz2TZwnhu8ljMOrd+745799nDQD7KebEDXON+qKe9dSKnUhSpjhQDHB3fICFiOR5YF8TnOlI1sMD603fcCh66i3OukgS92aypnKoAdmex2nODPZyk32B70dgQfBi9FJ1GgMt5g8Zl9U9tam4fyFk0RPABweDjf/H5H8Gn4Nhn4FBpz7vgNMYyi9iA3zBp8RYZn/AXjj3epwcdreI5tOYJAdAmQseWfmhIaAAC4cmVG6o47oikL6XSu3XPl/d/9U1g9/TSsvvMdOPz855tr8PGxOY/dApaOwOTlUfdbRr5jlIafOOjyS2u0IZGNGuim5lQrwBfm7L5ThC748oBOIxydEeg0UqAWAlHJLTnntbM7NKboBLwu+AaYiYD6bI/m6vLd74Frv/ZrBoKUJ+NrmsjMSiYvDaX19gZth/mcrgMgpXkqfC97N3W/KzX4djFEp2TX4Ntuw6mCY/Zw82Fv4NvD7QvJqMGH6gRlIUl5tPwhmAzU75iuiuVuRg9curSCa9fQqbgLoao8Y6XAC4N9WHjAUzFi55DWNESyU10hvD4A5sFC5w8Pk79TSoqBL+rGNVmDTy+anQdLF+bq20O/pgdsRaOEDUpcCUqEzBAgJWp8AwB48JWvw4OvPlGu8Sm2Ivg85QQ18Ml2Xs7vVuARfFqtvwwHBwkJRXp/OIJPT+3kpxjM1/skFSylDaDoGIASeSQ6Uy4VRgh8z/LUz/U5R6Wh7om6OEUnL2JPJmJBR1xBDXSvVKPkuLIvQTEYq4KLA1w40qdVfmQTt9rzE+BowaatwAVrpW83FR+AiODjKQ9bPqKrWINR4RCs90IKrvmSpeEwMDImK6KzUBsnQzHwtbyr4j0oBCGln+I9KO4tNFzn97CiN7R9BPZyJLRyiuBTa8+Ab+BrBkVym/tRtTcSFyeCD6CSolNBetGxg+cGR8Aoc9Fzp45pIFGbTyDJU3Q21g1h3bjzysZb1PkthgEaI/g8Q6qmHLD0ScZ8ePpjDrwGXy36aleYU3RmIWCZown+GZOM4HP14vg8SmHnCL5X73pvpeHUnEeHhLYUnep5VTHMnGZL6M9O+xxlXQmap78DWgR3fVz0PBam+LlBfrfxJDn9+/zUxFs3Om6M7aZ5gUAMzC2w5Bt5il4N36FbufgURybwI/isQbtOZvk4u25N0ekPUu4rAkwTP2y0MXmK2uc25G8NqJ7bnn9MX9UIvp0MfPJSgLwIK5wAACAASURBVN1TdAYNj4a5KCxTHoP1kdJ8LQQlrSH77paBT/udYTXQXHYxznyWTNFp8y5Z/qQXKIqHh+zzYJ7rlDX43vHqN8u19aVLi2rw4d+LUnRyflnQWvvsiRHgq19dw5//+SFcujSfFXwex+88/yY1+BZG8BWHC1hmOFG/Qb7oON15zy11SFDbkAi+tuivxMwJrXiospO2yIo8jPcKFsY6SB12/kF9KHOJU3R655SX7WkMmLBf1PLdltnamJIWTV6n6GQIDg1GY1W+a1gcmgxuGfiGTZL7qyC5N/DdCtgb+PZw+4KlJEARfCHZNfgs+oUPj9ixhAvsodde6+DJJ5eVqhQ8srHLiiGjnEkMYSWNGk/jJsZm4eQBALBeJEfwYTwrss7URr+5PuTpJewDjipGqSBGI9ECu6cxExISANz49KfLOI89/K/oHHWdG/ViGRkB6HvlCD5u4AMAePCVJxTMRlgNo4HvgDu7mguVeuR2QeHKkREgBZ0RmesW6t9JMMsp6QpSCGMNvsphjZUWswKA20CoUlGbgs5T3qQEHfF01HCylYyjt3xFiT8pvzL0nS6oVpUEHJB3Wur7xRF8JqMX2F5RjGhaHUa8D1RvugUe3QB2is5qBF/Fo1fDDQuNglZO/5+cALz8cgcnJ7QhodPKeveWOU/ZSyP47Odc4LRTEWJDAJTiVjLTM01H97i3wSTda59TSzeCxyl7NtPKynkEAG0GPkWpgelRuQ56lIJQgiigCo0AMoJv6rDJwJe8FJ2yBh/vxz1zeXpPY081bUtsSODe7KAL2QUQfXIVEy2AaJ9qOOZ8gTFv5P/pfYYGz3JvU+NtYkWwm8jcZODftUU+9tpkw5H6WqAb+Fyo7TVOBBWauyyCz0ivqx77u32T9gg+B1D7Ttctm7BL+uEMVy68Ay6+52fg1XveB9fP3jujw3X0XBnUmKJTi9pwjWUhiP25Eyg8Q0RKvKwYa4+49g/nGvsV4kCcQIkBBafoLGPWeGV69mXnIrF/OB6FTqF2XX9TUnRqRF7wlqyphu/QrQGn6bOgZij2UnRqNfhIqkL0CP5WXgQfkRs0nULbgWveETJoSnVDddIdmQHwWpP9m0bTlGC1mp0kYwzEMVN0pCKuoKnwEV1KKg9Z5feR0USM47wzaafIvvn5hK5r34S8Ayo1IiL4jG+9UiL4tPeJUcrmGj9a7qPnyrWgrNMywHLeE4/DkWky8LExxhSduO/K4NODeJ9yec3q44UXerhxI0CKCX7v9+bI9lG+pd8OfxNSg2+HeoEcd++eO/cpwfExwF/8+Rqefrpvnm/LMZuONdNOF103NY7xzK5GHE2uaU3RCXYEOz5XNGdZHMHHSxvhtl5a/xC4zGw7BpDXxHxNAiVFp3xQ49E1Gumd6XzuasB16zEiZxA23sY7wvYGvlsCewPfHm5fMDl+ejppwufRUYAvfekAvn2pp0RtUpAWJpPVLdAYh5K+gitKG6H2RA6rF4JMJUWnBsLIw9qHiVHV2tSYDg3WqyQi7qwUnYQhVWonjFObpJJZEzoMXOPb3w5P/9yvw1/+6G/A5bvfKwx8HnStUTkBhIEvN83KFW2Z3Hv2BhwcJJImdES6XoMvhDFUX0unSRDbAdQ+DYRwBJ8FlCHNCgu5trGBTPvEXdzaCgtIJKqjXuiYGrZHWb9SGwYVWg+QYNXXGaImwOknTmHgE8Z0ZgBeqlgbhVU6RukX2pW/ZopOkLjhV9hFEdiSovNb31rBpUsrePLJNeA0WlXFn6MVI8ZKRit2rcGAI8ew8iolptRkDL/aH7lAlWyrS5cgHB2pOKTQwZUrAZ54Yu0W98YpOqt2D80LfoGBjxut1chU3Bef/zxflhJNGPgSMXpwILQq5jSqsrE2NzvJOa5VTfuI47WjI4AvfOEQ/vzPDyBu7bMtpGQa+EYHpdOJCXjP4UhCrDTLsOJR22Wt23uK1GSuTbCzT0PAr6ooNTJ9usnCqr93b9IYoYMUgq4swgpFZ2xVOZB0A3PgdFZT4lSKu+AIE4C6ccYafx7PajzeIGeJa+Rj2QPQUBFFSpZ0r410OqKsEO6Wp5iUv59950/AVz7438GbF95ZrglFpeiwLUWnnoK4LUXnLgZTbIjgDyclRWfrnjltBF9Kdur4xkAHAsPA+KIsU1RSdGbokILTrRUE+jr0+BBx0zBOAOi172K3qvJ0IczP8mgz9fyHeW+Pz9Jv0aUt3Hefvi7xGvYcb3Lf3v0aLEoRnBIEnk9Qb+aPmeWmBeKEiOLTBLEMFaW/dd3kERv4/eYU5BYaQT+zxww8c0pkEfUYI2gGYQ0nO0UnjeBLCWY+C83TrjX4+KcyHT9bDTTiuclAotUvrD070SUiY2F+D/OEGt5CxmE6ArCdAo6O9ImIkaYQHPGbr9EafG37F+OXO12S0UeVwVKAL37xEL51sYcXX+zh8uVG/rvhw7T6qOFsV+r7aOcyd8xvPusVmqGdscjIZOGBI/iw/Jwfsg187Tw9GbJrD3BwI/joRqGO/TyCj+t0GyL4VBLVsF74mVx04QoMGxD7a0Zyb+C7FbAsNGkPe/hBgkGAiBCcklmDb7sFeP65nuQqBwBCaVP2eszCRCW3txYV40EIYBK3fNlK0ZmMtFn8ee9GAG5wmxleVYi2ujSYlgMW0RUmv7gZFZ3gd9FmkrnHDElnUTzyJOSD9uTCPXDt3JlpfNSg4pbFU3SSe6wG3zAARJARfDzvPoaf/uRbsHqGCnKW8mvGY+68V/KrLxKADIMlT9E5Grl1g0dLis4YQQohiSrFRqW6v+76tIUUgl7kOTEFPPYYL3vZoB9IIeLi0HUsgi+p0VmnSdGZVquKBtABwehR54R6nTuFMVTG73gkUQW6zmhLjI/K7ZoySHmqpsyIEeCtt8ZG168HiANOa+vP+xjtrFxnNBUbnay+OOhKcRQBEKgQG7vVrERzDHy60Y7id/joo9Nfkh6m0MFLL/VmZ0WJlI1/LWtfS8GyIEUnXTd2lIuJS54vS9vF91GNJmPFjxPBB9gQsKOSBWBepVr0Pp7Hl17q4PLlDvoXenjwYYC//dtD+MpXxnDxR94I8NDdY2+qwsbDhUXwvfpqBy+80O7iTOiwVuwdgZmi0x1gnIwYx/rMlhI/JbAPLfCVZHicZi1JI3DjqhtN6UAIjt4VRuvlZqNEwZEIvhmiWCeS4FiYWnvR0x9z4DX4mlN0cs3iBF3nRx8Q9tCbTHYbN6tFpXgGvgQyRWcVlEkkDnIhAf6qnK8bI/jqy1e732osq6VCbx14juBbzet1ctpodj5KfqRUS22qPm5KmlAyLziCD5K8LwcjvHJ+EkA3mGmA6X9kzqqkXYOzindz5EfG34fHb8Hh5hq8deHBcl9bC0OlBt9Ib+dsIc0kFBv4GB/RpwEeeGCAZ55ZiUco7dFlbgCgEVSJ7p+UJuNFBcVFKTpTqtYbh2RH6s9j5XkxlKzod6b563UqKdc2mwBn1mSSGpBn/Ru8nmbv9yJjStuGc0S/T/kc3UgVABTalOUgpnOfcWqowRfiIGp4EgMfvy6MI/qHwzKae5ZiumnwntX9lg0qbO6GoYGYMxxJunpooNeJrmWR0cB5ASu1v4iWTmg+Ge/Xd7sZloVDunYf/LkPkODxxw/gganNCy/08MADFaMvZL1Nmy1l0XmsIuvzHXwM932Vm1r9W3XdE30HwFjCZ5yrDhuBFdmv7/CzGvOfHYoMwSCE0WjtOA2ORu0iwbGeUVeIMU0s09jYp85DjecyBd3A58+dBXj/1tq4Qeh7A98tgX0E3x5uX+BK/AkiNrLF6HqpBUhw/ToirAFoBF9g+c89otfC4Grne+U50+5UieAzhTZH2ZwdXHaL4JMDrlgEHyQa3WLVA+ACYYkICYzxFxNqG/g04yvpKgSX6cA4CUGZK5mRshD3mdNwat+mOzlWlYiWQo8rs/sgvcOEQUdV+KjdowayT9XrD6b0opX1HOO8UkwGAa9j7p03PbxKfgQfSeuqpRY0vImKcMfWg1gbJIKP5mxnL6PjaE0Ti+Aj671BuWt6agaGi7IfPCNaVl5waIngI8KRQc9qEXxV+qoqbSVtwO2FQjfpa2Ip8Bp8JOJjocFX6zMEIIqvGFbl+pyic36WK5spDgZd0AQBNUUn6ikrVuMcwcfvlTFznzcxgg/TOLmntR9oDEMCsRwBLOM/iQTIBj4jgk/W4KN9Nq1BzZJQxkiQUoArVwJ8+9sreP31Dv7iL0aj3t/93ZwL+rlnsXZFGqS9CD7szXt0FOBb31rB1asLBDbcNVJca9tdKODzvEHkl5ABYYRxr1fwMpxc8q3ZcKqdf9o3PgURyf2yuR/JuPKtl7yaBn0P262qYQZQzkzobGNToQ3W3uFKzxalJANeg69mUNLww6BFjALM39Xa70LPnex6QniNLEnROdK75Sk6NYyJ9zfrTtQPhLYIPkuhaN2rOTU2g9I5Vji3GH2pYthvX4t4SmlyBFRlJutA8voLsp4vgFBEc8AOHoUOhptk4HPOm8OTK/BTj/07+OTXPgPvevnvi0ynfYehX7kGvoLbRNuPjwH+9E8P4QtfOISTEyQvGlaqEKRhrItb+PEf34yOiMZrjc/6OLlwSpqvyqBbOX+cpzHXJpfxGyP4RgPf/HuzCe4kER53wRyYEXyVGnxc30GerQw/ZthBPCg7u7NRba6/Sp/l7+ell9QNFNLRFxvzcR9NEXwpwaOPBvi3//YCPP41mW6b6xUISsYiqO/Licaxj9eSMlJE8Dlnpofc7HjE1nilBp92kxv4cpPc92lSdEqDjA8OiZ3rNE64nj3bttcs46J3flj3SQ0+xatZdST2vePMO6rssVXW7IRojOP+ettrF2G9uTGfE4FG8H38if8A52+8Nhtxgb7rKqBSTYqSbG6r6CoA6TCUtKDlN1btRLofCDgpOlucYccuQxlH3NMcKxtouLWXNNhu6VdepNvZw/cF9ga+Pfyjg1zMNRNuy2MnN+K0KTADXy1F53zT8ORwwGteoiEsL3+mePMY3vIIw5Ef+th7zpCbdFwNgfNA1OCzc1Dn75RSUGrt2KkmVWZCVSrQ9EYAbP5FqjE6Do/g0+qV5U5jDDB0a9iuzhDF0Hp7jJtROJ7vxR55mHrFlEkaUM3VemZcqkoa8p3QmpiUBEWhniLoE9yaohMJVta3cFJ0Zhi/hzXWmKKzrGfQx1SfRMKd9yopBMLg9IaC0AKzb8bM4YYt0RvUuxhd5xF5hTE1lG2F7CAGlglWAMsj+DRc+POEhjN8XXAEb7k1klBo43c0A7pmwmziwD3niUGrYZ60b5KF1rE2yhzNlNJpU3Tqc7uLgS/ThSz0NRkzFWGtxcCn/kDGqdo78+utKTpzhGaLUjRErwZfclOBNW8nz8A3zf9zz83r4+WXlBqFw6wQ0RRregRiHmRu35wuiAyOzprBN/CJuquKMVt0H2bBthoV4NwPoVabiY43/lhOGznwuW+N4BOGJ+fVYwpTBJ+8N9ZIkn3waDLNmGG+dsN81D4Vj+BrNvAZzFBNaSci+OSfBaxsdcRZUJlTj2YloAoqtT1/XKHrEQ0oDXwygm88i/yPoaborBjXCu47KHcuXRrrD/H5AWDrcpij2lu2YDPODvRxq9OHhRF8ISWZonN6xyff91/XEQFgfEgoWUXUtjWlbm5n0b6U4APPfqFEFz5y6XOln055JnbZeuTT3CyLPProIXz5ywfwla8cwN/8zeycYqbcVmhCiAOcO5fgV37lOvzETxyjR6i8a/HQeTzrPjYIuNB8HgQIANBpESsLu2yhN1jBPBr45gabDftS7qFrKJkXRfDVeYmQYpNcwNEZt6Jv4Mv6HNU4lrjTKZWXcVttPaxYes7pyTI/JCtRCkQesIynf/iH3eRgtZ7OcfuMIt9hxxp8xSjCaEqbgY90AQGYga9FSAe6T8n3cPR0llyHHY/zEFgexGflqjc6YRMttsjYof5sue+//tUrU99TP5qzgtU3d/K1xqkeydjA1yi7ePvZ/dwaDVZkNbymPnrxs/CjF/8TrLc3UINA+jp//VX4oRenbDW1FJ2GEyyAQTvwkF5AhcEnirXr6IQ6luodP9uiEwbQ5btOyQjGAe/fWhs3y/TewHdLYG/g28PtCxYTiQ05MbopA4S3VQCAYSj0JgbFW8I7FXcgVMFSFE9gpuic3s/sV+02iAOJpOjslYcamA4eZVWu8xzU2nzTYUY8RATfbFRqqcHnpx80GN++1z0Js5CKcOJpKYhiZDL8pK6Hr7/v03B05wNFAMgMRwiJ1II4PEwQkIFvu0bFn6OtnMDv2WueVBNely6t4MuPHRrCkL/+ZIqRpFwbv/VqBdU9YKXopEMkc21k6L0IvsRq8CGDdVmDFv1AHl/uq/Q9EbzWK30vLo3iIOkYdqjBNz/M9hqPvFRezturo7JBU3gsMOZMw9a8/lTcFkYvAIxT5ynTNIV2Ga9RYSKuKzTJy8PfCiEOsFohr+qyh+bUbZih1/dPmHHM95q/HPsGyjgZcLpQj6YCgBrB1zEDmi5EGu9SoT9mtLClmdC0YM43pOfY6BwRDcXWbDCa/m9UspK2nhEpJdhuAV5/vZIacMDCbHuKzhAAQt+R36cC9A200n5dNCq4Y0W/wBUbfHc38E29W4N834RUK0VnhZQvLI0YIFkRfMlQaDO+lSrY5nlSp8qM4LONTxx4BF9zik7QzypzvOnlyX3PEAzJNvCBv1f4XIWEvnUIVLZpARVPW/Er5iW0RfBp4NVERHpT4kjVuoU2G4DPfvYcURplup+6mY8vkeT4jDh7Fo4/9Sm13/6UEXwATgSfoUivjcdlCwCAy3c+BH//8C/Ci/c/QtrzeRTPGhPc4qziwcjTJVhtj9Tn1Qi+bg0x1aO0zhy/BW977SK88Pzc9ktfOrSRxAY+JYIPAOCBByI88siGPEJIm+fIwXgaOndtC7g120UZz81vBhOdNb6tK3P7xlUvgk/wWS3Mi6asN+hEk4HPjFbxf4/OPjRrAW+P69uLV2PZk5bW4FsZEXyaATSyCD5gBlFshAIY52S7ZTW9tHfAAzNo4ZXK3LO507JVZLj3zWfgk1/7bfjAs38y7Tl8FtF3aAFrLXulSSyjREogjLPYQEsNfDvKcFwGr+CoNX355UnWm3BtVQlko+dSNlUVd2o1+LR+GN/RjEejgQ+fufe/8Q/y8RAgHhySRw431yElgGtXEjz7LK1niA1cbmpuJt+XZyb9kZe9h6t2tKj0cV1SYkNr8NlyOB7TFRN3TNFZXXuT3i1GqvcTeOwNfLcE9ga+Pdy+UDHw5Ta1MGJBpHANrClSCzMFbgRfAwjms8JIWV7+nCGR3hrG/DgeJfPULdA0gncYJGEg5Sk6AzrTLIEwv6fwiOGjNXwCUzFW0SgFnqKTPEwPrryEXr7vYfiHT/1KubdCEXw/8zNHcPZsgvU6wS/+4nUIR7NwvFmdmbu2PPaRwieE0ePISlUBAHD9eoWcG+8j+9SZ1PV66iLMgpEGwwDlI+DoWq7Qxx5J2hLsBi9FJ17LdSAGh6xMg3qKTox/x1J4zfjrYM3P5sYw48MNfC170WL0OgCyTpVIJ693TG+ox+pyYYfPFR99pxSd4DPQAs3J+EEuNQlLE26WsgxSye8/tmO0omG61PWeBjh3LgrhaAAeZS4ZfqE4FsZURUDh0SbcUKkIFvnvklK5ZUKV9CDCq1sRxtXIfIQj39M1gcWsa6RE8E1DqUANfAMABNC6JoaAHY2+5Fms5EG43bhBET1/TkEGnTFyvSYngi+RtjvJafjdlVRkGHrDwKd9W2xAABgjx30vYrqxOF0LOSLc4js1Su9J1byNAZw/TVHTuMhLIjrLqSebACB13ajEZYDPZhJZ0BDBZ0GLYtZbSyEO8PYv/t9w7nd+B7rLl0dl/oIIPq3vmxbBl9Kc7pPz7KixlqJTO6PKn7BLik5N8eSl6GQRfABNBj4rBTGA8axtIVkEx8SeNO3zMGe/KAYehMSNf/kv4eTjHy+/l5DelrZ93NSVYy0dJSWCL8yL5qX7/hk8//aPul3hFJ3RSdFpo2DTNMH3OTTJrsHXNhU/evE/wX1vPl1viPFTaIJlwF1k4FPGIj8bv60G1lao1lxM9QjVwpstStE5d7rZgE+gmwx8yjhG2rqWFJ2tBj7+niMvMONrRfBp6Zm1b0xSdDbU4NMi+MZ+5QvIFJ3KOzNm14xINdpr3VXrvWaDiqjBZz/y40/8Dtx59XvwwOWn6J5LdgSfuieYjCN1BMncE5YBMiVmwE70W+MU3jh7gA+yXZtBLLD/ZyikoOhQTudUoK0T1yAMTPbV1g+RJ7K8vGMEn1bCwTXwWQqQANcf/rB4JiWAP/vTA3j++R7eemvGcYXqLEYmG5AubA2P0Fd6Gcxc4xqrwad9HM0Jj8sr45p25BSCfX2dzo/pbXMgRNb5mWuwOtIevh+wN/Dt4fYFQ1CfvVzHA9v3MuBKHJgUetPhqUXwWYBOxRYevzCp9nkEAHNBX03JmQabmKtzA0yJxaJNuskrezRqUm8S952sm+IFlYME3c7fSqRWSVFn/NmhQdIJchQ1owFIRgR1TaBjhl+KH+WS8JpLh7Oxbj0clXV7xx0JfuM3rsK/+TdX4Z1vOymRW6nrYOjnFDReSi6aorPC+C8Eu4C1nv5iNXl7YpWUBpghxQw+a6QyLCNe4/992trMVUokCmhMNUX78xjevE9c3RMy8AEArBd69Wl9//VfH8AXPr+GixcnJdVqxTbI7tqojnsP1miVprzQIh9KDT4bXAEzt2HKTIFOi3c0f2dcKJsPmWSKzgqKBFxsuGJ2ocOE1iSkAc6fZ8w8KOlDOMNf6deszcmWijg7kNMFgC70iXQhGh6V6DY8Fn6+O7oOd159Uc710kjP6XHTS17hM5ak6AQA1cA3nl2RPMTnqIl850YLaX2P6JVW54kM4TiZ4Egu7ODRCiTNeKxF8LGJdNZ6aTK9T4x1GlKN/vSUigE1YvidBsTcG31ywZ3zA9XP0vV6RDNe/3ivOekiS+SZlclKRDlIHtPzt3roe38Hd3z3W9C/+CKc+dznJmX+7tHt43i+8oHMZ8WoTRWPmN+Rz7nfBc1LaxplFRH8DDHw+WsrhY7WhjFAk7FqBlf1jFto6yNZGvL/aF0GNXeoPUht/JbtTFL58z3BzsVaf0LpzA0RlZp6hK6Db+DTyJb6jUC/qTm/FiccZW8O3cqlx9vDcwSnO6+9pOPN6TE6E0SpB6PGK39PS/bI42VZWzO2tChGm6Ne0jSOm98MtW244Tm95d8ZP2rgC/6CbTLw6bzeEr6UP2vccZ/jKTr5OFwGFDoKN4KP7YsFBj49go/ue857pkTHzMYuzr9TfkBRuvBxK3x0XiO8XWtEGcaRR703PQz2Pm2J4OOyLI+WzvjpEXxtaKpyr7OHWqKtioMh2BF8mhEnKAaWndlTRQ6sgcgc1somq7KwRjAqZ2oIcPWjn4DH3/9zqJ9xH3/nGbn4esJbOLzXRCC0cbsO3BfF/NeuNfiyM7HmhKf91mD+hDOuuhM2ey7vX0smCWN5Eu5IjZ8dO1jI+O3hpsDewLeH2xdcojIzPtmgoKaXS3oNvrmvQIu2Oyk6eX27ZpQrz7RG8DVB8JXN5VUXElw3B3OlhqGm1OSKvBw9F4JUwump/WwDgWLjGKHiws1r8FH8mJIZMVj9uoNhNRnsUiLM/WoFcHgIJD1nOnOGpm11uB88Fz3ItUkVt4YiP88L8xLkDMN8mOt7oHi1VdYOjuAzv0XiQozssxtOKsoan0GzoDDXUKnB11GjYd8Ze9H4flrff/VXh9DFLVy+3MGNG2GnFJ2m0MMUMHoEHxUk8X0uSGbYpQZfjXlUv9cOTKBbgw9ARvAhAcyswZe9ehtr8GkRUTUQTVKCbqofA8C6g7keKHbycBV2/Cyp7I9Md8lTfJyuazLkctAKfI8OHYn8JnDjBjzy//0mfPJrn4Ef+t7fUUSNdVKuyskd71te8qoWzDbw4XXTpakGn2XoYMpS+c0alNGKNsA3Goz/rVCZV75eE29vfccAEJixo6YYkl2gvis1+HgEX4lWJU4+fIAwd+1MTG2phjCRYluDoF4dBoBnn+nh6tWGBaMixn4axtZaN+5ZBqEpgo+mvVZSuaJn8kVdSaU8l+y6RhweuPxUORb7l18et31rik5DyWYpGDNttFN02meilYp07EIVBsz2eMzWo7Ylgm+7mtNW8dTZCcZ60nVDlLxWdYZ0cGwFHFmMa/CV7geZotODGovREi3RxY3K32JlMQbPMDMMCg+Pm9TObWQ88ZxfWpxVvJsWz5l/asbe2K8BZ8fh8PgnfhW2h+fmMcz9rZ3PoBr9uzQzfJ4zTWeUxdCeVYauwxLNetJTdJIuUs2ReQZMd+oRfPPv00bwJYOPCBCJ/ho7jFnr2zPAapf5vuUpOtWzDI0vjKJctlpcg0+m6MT9kkj5pGfOkA+XH1Kn5RwrVopO7sDDociHOxr4OJAoyAq9RiJHaU+WZkMNPi7LjjX4FPo1tWuqwWfhi3QpyyKjnD4rcqpo78gsantvO1dSdFIeNb/8jue8RUuMiyLjRW47GeFwdqyybjS6lFCpJuhNWiSjfye9SsgpOmU6zTKGMcdiTolQ2UFCuoc5W5CxaBAvYq0rXIO99Ntk4CuEWL8/OdYXR2pTjtwb+G4F7A18e7h9wWLusOJ6EpKcTpjxIJHooeyBkCGAw3w0EKklfH2GzjAcpBRo4fZAKXgLzeQMRz63ieDVgjRSzpDmjEkPSeKoeRTqNfimNjvW4APljCFzxBTU/H06lqLTCr0flSJ4jATDemYqeJ0KAGrgGy1+SAGiKMDzOLnAdQjjQe977yw/REOYjRWzgU/fUOsDysi5CnCmAPEYGsG8Z2Wjo9ATomHFPZkrYrKhAD8m3gd5cvvwLAAAIABJREFUbWkpOjPT06pcKt1O8zsMMHprKX26zxvz3gV9r7TC+OisPCH9wjJliOVsUcZSjY91WJ6ikzHn0WkvBrMMSUyI4zRw4XoAmGnPbOBDCgCgHn1l76DcaiJlD3eK0Az/ROE1PRXkexRcFA25SCGtgZaChaXo5B/j/Jf+GvrNqCTpB1RLB+38dmXbNGZjBF/t+5F5KwY+/ZlSyF1FtnF/qgdvvTlOM4SN+XoEn95XYGtn/HMhXcFnJYmwkO9jpejUhFGs2M6/T2NMqPlvlb7ZWfOVrxzAF//sEB5/fA05C/fLL3fw5JMrePPNUP1uag0+oWDcXbFW+u26agQfXvuDl6Kz8k6LHdP488zjf1TmS37A/F4LDHwZmlN0orY8OiXyjBtA17mpgDHW7i5nFDfwDd2cLYIrPW9GBJ/27HYI8MoreZKW1+Ar4+LUwdPDESmmZ6c8xENyI9kClqiFxPZxsHmh5NF7CVzpLAx8CyL4InQm/VtmsFK1q64iVq3BF/wIvs3heXjlPT8242jQDCtVaNfJCL4+2gY+bARyDUi183YXIX9+WO0v+EoMIn9zCIxn9+1ws5A8GvjmBiKCbxcDn9IUO2GruBgQgs2LGUuiwDBQeqql6MQRhMLZlM23laJT+w2gR/CRGnyo8yGFMUUg7pNPNdapJBA0m/NShKdQl1wQtZiVRrl3cnkY2og4UQWlRKKZWlPj4n1K+e6ss5C4WHySjOAbZSYtgq+WztuC8zdeq9AHqhfxDFm71uBrhVYDX7PcgfV2oUou/JvONbOvoq+RA2t7lJTZYDo9MqzJNMgUnRw56oBs61xJ0EnHddJyL5R7nmEfX1cTHdS/a903cfzm2ZHadMTcG/huCewNfHv4RwekEH1E3mEKdZN1VoB4kcfQQ8TCfOWQrKV4EmMBmHxsvt8bCvSUQK1f5EEtRacalVI7OBXc5htBGORkBN/820zRSQRExuwqh0arXMYNfB5gnHiUAleYY3ms64AU910rBj5A9fcSM/B5Ey9C9XlaLlFTRbO2LNNqWIa1nKKz1h9lCgpXIhT6FvM+47F1hewuzH1gg7VbMHnGBrwIvpRApOhcWnjb6jsrIUKAMcymo/SsGTR6h/eK1sxZdykpyhTc5wLFhsY8cq9VDjgdrQneXuG3kqzBhxt5goKLSEpMsUZpbmqYJ94k079s4MMw1uCj363/7nfhjt/8X+C/+tr/Bv3kLcwF6tLc8KLnCoJxDEU4ypf6XkxJkwLGMPARBT6LLOnefMOwiTkpOjO+hse/Rde0NG84wlkOM9/o4nacd6OGHY9+bRXI1Eat+29qV4RLvDAUK1ZK4KfobFQ61/ABgGoEn5Wi072UFf+xIX1rsGl+ftWqkoTtxT/7s8OSKeK7313ByQnApUsreOONDp54Yl39bu0pOnUUvGv4Zur6agQfpRu2534xGEcDKeVsEUq6yqei6SXTshp8yjc0lXYT3nYEn4LbZLjnBr7aN9JwLf/toASppboOAWDo5zAdcb5PvGyNtGj3LYNMSgB//Mdn4POfPzPhsztk0vXMMz08dWk0VOLaUZl2a6nJd4EW9suK4MMX3IhjdIMrnbnsw89u/mokgi3X7jmlMq3MJZednegUbS3UIvjGSAVkrG3c30WsVoz+gTjEztcJb4Tu2U5rSf1w2CDg46h/NEvx3Q3fnxSdfgQfrYuGM68UZOkDdEw2rhYhBTCuUeoQO3XBHMpUHFNU2/C54L9jZBOtOkLMMiB/NZ75ifKqibXVDHxWBJ+4DCnKyxRdanjlmVdye/P4NSL44o4RfDVbNB8n90XOV8dIge8HpBsj72RNJtj7hH+rcQkHZOCbH1yvbKdrDz75tc/AA69fctt4OALM857/11N02p220D2tHUVCGpjIZcwnThd3dq6rCafsWi2Cj+ojx8YdyEnsADmZBmlkn1HQI43HCL5kzhVAWw2+lECm6MTnYmOKzpQc47Yin96sFJ0JghHB59PfPXz/YW/g28PtC4aSjddo84VT5T7qdyRQHSJkp8iDBMbBW3lmZrIVpYjDoavdBnYYWcx/kIKXaxhZolzkgqHCcHCPzzAdtiFwhtLhUPjQSmQHGbtm4MMpOqEDGvhJO2UR9SSCbz0cSdkIp+hUIvisuZ/nYpwHbp+t1TWjjXWvoHnNT4ouK4IvM72V9TwMs0BiMggx+gwmjNEcpgDOI+q0dzOULHQsZ133PeA6lTcjRScAW2d9T3Fv2Gcakz1ep3ullqJT7VuJ4MtM8hLvwF5jHlXcgri2BGpriCu0s/A+Pd3ctwAi4DDP+YY6ihzXvOf6Oehh7g6oRx+kBOd+93chHB3BHddfgYdeeFTtc8bPToVEwY9ETIqG3DOEFVA0AjyCj6faCjdu6DIeQJkce0/zh5KJBwCQmry5vZfuhih+JgW/ldqtCJbK2K1HamlYU4qw5jmCj/A0ee7w+ROTa+Bj7NYiJycOnVEjKYMVwec6XU1rdhh83LIRxTqTQqik6Gx475QAbtxYZsWVEXz1xzQ9vrsmIACETq2VAWitE7oTHAWgpvx2aEd+ptUOE1KkURNB8owuKPhZNfjyPXKWMKW8wC/PF68vhX6U9GLeGaWciY+//+fg6PBO6zWqIFJ0onrPgqZDgGvXAly96vPGGh7W97h8uYMrV3cMgeDjxjEa9oUX+lmR2MkUnRSxqd3Zs8vHa5jvPm5VJTtVIOu8Jx8sxkBOZpFK0HRYmGl7OR6mnrQUypbdr+nchPyssuenS1p9zFyDzxyjCxBWyw18WPnPI/g6iEVm9pxprLIYAGiPKApzct9HEv1VOTeSnqKTt2lN0Uzpja601qKWcOYV0RH7vX7qKTj/m78Jh3/yJ6xvRX5IEc6e1QhIAIsHD8wxioPUXdDfw0D3gSS7dGxBo9lk2/Wig6oz6rcogq/wq6Gc9SRSPjK+u7ZPp7WgGU2NB2SHAMKBxxp01xSdJGKWG/hqUPvAMPKS2vIwI7GS1AmMdkK5F3qDV9glOwt5niHsGuqmddUaMRlSbDLaNAFO0dlag48twmYxwVcVVC7yAWVGsZT0PYoNfC0RfIoYNt2358qqwVdL0QkKL23yoYmuaQ00foWnE9VgRsuWiRLMAQ/7CL7bC/YGvj38owMcwZeSX4MPQDJCYUC5lwPLvawYqMjDqJmKm8IfW7QtX5/reSiHP/dIrCj5tAg+4oHTo3b5WpLEX+BqEfguCOmRR2OoHotcMMMh6l4EXzYMOJFqpoxipOgsbY00oRoO/DzeYgNfJUWniOAzgKcQCSlBzyi25SnaAmX9QVuKzoOczqUyDhYUSz58pjDXagAKvKLjWl6U8NM4miLEEw6R96YJKIIvQIKVYeBbyvRnZUgIMFp0UARfi4HP7Jd7WCv7AQNn+rCwQ/rdoQafmqKT4KY9FPB/zf1yYzK+vksEn5qKj+FAaFwIxBmgZT1YBj7UZYEIHY1uZJLI+RuvlT5NAUJ5F67wSoFHQDHBgtfgS+xssWhrQ4pOkfbo6IZ4ZsRIphaS78zoSVZmRTtFp9a+xcCXldxWis4OphpRirKQKIcNpeyID/2/BnkqcRrBeRy9F8vAN8rM8zMxNiiGvL4Hxs/w8biic1o7VHHBlJbT3DV5ljvEZWRj7CgVK0XniBEV5OmDPj3g3rVaik4OmrKsdvybEXx4L6Jx+ZnKFXa5fTk/iKOK7nauKtlVZKlCsOsWGABAoX9gR/CpfEDFwJcVgBwnxZZO7ytnFIcXH/gI/NVP/E+wefhhHWEDz9IlMfAlkqJTo4HPP7+Cb397Ja5TNJV1o3jFAwCcsOCVcX5342vikODll/OHG3EYwoxrfh9t6V//hV+AeMcdFOcd9UxkTgff8Uw8i+l8pyjLFSPv3KFuIMMGZo5jzSiIcVL71hAHmJKH0GuZJlh703MaDV0A7LG4NEUnAOhG5i3KkKE/Vl0HXNbW+nCfx9+Uy5KKMcYrRwCQv4nNj2JYUtKbRpgEf5LY7+7aNTh47DHoXpv5z7zNQwB45JENHBwkuO+eLdx/vzz3a9H23lnMgX8XEeXD5m7kYwIM/VoYv7McRM8qtOZYFJi2KHCKzm1/WNpqfFZSUnTS+/I5/n7qO+R7VgSf58ADSD50+AAPhN5NkZHMfciMbiKCD2x6UfSBSdIqGcGH5AP0mrg2pQc1HZ8FTXSkQS9HcOHO8LX2TupwWqrBRI1ecyQUDy1tT3hyPl9/5Syb/qYRfPYeJc6pIUBMFj2y3queohOvi4sXV/DWW53eDm+Wvic1+ALWF2nPIj2ONc/a8bJET2HppSLSxWNc5WB7A9+tgL2Bbw+3L1gKemxo2/oc7Jj/nV1kNfgSTvnipejckUjVPPj6UvCbPZeA1GdrHd6N4EMH2iJbQrIjJR96aEuUQbwuE8U7C4RMoWvV4GOnlld/qRbBV0usjoUsUZuR1eAjado6gHhAa/AJ2QgXvzk4oB/bScOK08dClB5aIkWnpvDRjKJY4cwYyc6QCte58HTFEIM/mbnGRq5r7MdoZyrkAZQIG8dyq+E3GU+J0MEVAmyPWF59Jo6GUktE8BEDX91bzleWOcoiABgSkejpk0jYIePt4LWoehKyfSyECvDXFe9DtpUPihp86PGqJ6qpJ0vQMQVO37evP63J+TMb+MhH9DRNvJYNfqMQxlRYok/+bStn0DiPlFhjZQ39I9+PTe+qRTTyFJ3inDqSNUwKDhXDNYcifFhe8iyauNY5UfzkGnyGfq7sA2Ox5b7cdEkpQf/887B+7LFyqYUXWE16cHJ2zSH8FAdrM8gDfDEfROhHLUUnT1XmKFlLsXt07i+J4BN4GgK0aFAZQyi1G84jMoz2F+uSR5xV0AIIAYbQqZ85kLN4RoYrACmeyndxPJm1PmqOHLIG34L01Qp4BsWlNXdmpzzGfynv13Ke5bVJYLSuu3joqaNYDT6UopPzVK1nu1qDz6AZw8Aixk+h3ElxrrFe9nmHU3Tmm3Li44MPwrVf/3V47Z9+4tSoDCgKsk+09m3BFfFvpqMWy9k1DLbso/3mgCP4ZscX+ZItzirioqEU1ZpqhrY+bn2+IwSy8SzHQgu/EKZvwWFaMNzgoTkXeIpkk243MB74rb1vWGQuJUVnzWhlju2wouX3pNwWSn6HQFtvHa5eLfjh+bzrrgQf+9gGHvngsSpntaXoNHRAlbmJkfEC/F0menu8vjDdZvPQHMEHqqMDTtG5WR3OOGk1+Fg6UY3lIkZtGKOSMO3xjNnaFMZYN/AVGscut6boJPuNy4ONzDt2cuOPZN6bA3bKFdc1+sWMiSO+u6XorIHqVGu0mSP4FDy051IUa2dsp68Tj074N/k1yehoa9hkq5tD+Cq3Mm/PHM0svcoYwZfXuIzgmw2HRopOCKNjLOHYmRyL1tTjj9uZFPBHljX47BSdIdDf5tzEKO4vSdFp3g8B0Baycdkb+G4J7A18e7h9IUklBgBV6BC5QhNIIAlPInwhhW6soYTauwqeWdtpoCwPU7XuHbrvpQuJzIBZi/BJ7GTlEXzdyjb2eF3bioAAP/uzR4Cz4QiPE4xfFmiYYJZ/jzoNpkjWopJsVG0ZhUegMOgSMrwIoYx2StdUguEAp+hUlNNkzTGPcY+ZARq5wxlPbnhcAnmpiLQCtRSdLbk2+Jrie9lJ0Zmveyk6gUVRcWMcgL1mMeNmvUpKQCL4IMkUnTWFsMXgEkUGStFpGdhM4EaRkKq1aLZOqQ9cDwY/mlM5LMGtZij0InAF2oyeWaB9BhnB57eneDj7iRkr+5V+rwV++Zevw//w31+BO+6gjHuGmAKdG9b/MAnqWIlF5towavClwgUZIRSxPdylockgnRQHhgDR9IoGAAhHRzrOhrCV8Rv74g9OAryFq9K+PUWnb+AruBhKx/K3owQMb70FZ3/3d2F98aLZhuA3KZH6nioKxh96pLNWowEARo8gxL+MirPdI/hCLUUnP3tSA+3JCv0Y6tGFaC9okeNd55wb2rrTeDbuXV/j2fg2a0jxuyjdVe436FFaeK0TAx/YBr4564Oi6AFjDlN7is4usAjfAKoRQesDGyxJn5aDTlD4AJyiShmjGAh4DT707ZpSdKLreH211BEGAN2hS6TonOtDCwNfa/otQ6Go3dtugbz0aZSicUDyW97nWg2+6Xtfuxbg937/LPzhH56ZDfqOobwVcJpTr15aUHglMj/MksyNIvx7WnM371fJ/1v02ZPHWsCLTtSMHN2wUR25CD5oPiyDsZXOzKQJajpsikehBd4EWHx9Cx+c9G+qrr+UFEZVtrFRTaRvrALghk0MXAk/rkXU60LHHmwsIc4NMqQd3awY+CBV25SxEXBeQBjOJ3p7fHChnPvzs7JDy8CXEhBnvww0gu/MjKPBL/B9jsfbbhlPkBJJ91fwwucM4bnkvtpumfOwAoW+71iDj2Q5Cg2p/DGwdaSt/xYnIo4PoZeJytwkgm+1TIa7qcB43lYavTSCzwXM/zh0n1xzspB4oEe1ynbVTGMTb891m76Bb2oWHAcKpJ/h44cArmObyadLgkUewpH+vAafB+ba37EGXy07Xr6D9Xx6s72B71bA3sC3h9sXDGKBCWp0op9yH6IbbGzpeiKEtAq8i6BC2zxCmxBhFspdnq9xGoz0xjh8U8AAnzcgKUfYAXfnnQl+7Me38L73jUKKiM5jCo6UpGCG00RRxpPi76XonK/pApUQntn70gg+ZmRkuZdEDb6DWYmy3t6Q88wPcKzMsQJhwpwyL3976WnOH7QNJ5rFCXsAqQYoBK01+ABmps1SphJjgSG8jt/DELJTstO2VZSuKRtMau/BvvOqP4XAD3MR+x4r2VYrkuO9i1s4OLna1F8NDx7RGiPAED3GPQBXFpB2jYZMvW8A/C1jkoofK4KPe5p+6EMbWK0A3v/+LelTCoBJRPDdDAEoQBJe993CCD7SXwCRognPwZA6Kjwxeo4j+HRGW1/rqrJdMaZiIzeGDtUi4jgTUAS4jhkLtXO3RhObpzkLzDiCjx1KFJc0XdMHIM4dUx0QyyjDI/hMo6HCiue2B1/6kqqksSDPS47g64CuVfUZM0VnIIbulIx0yD5G85+7RvAZ/MfYe5i7bqDp3i3NgO72oeC3NEWnOBobU3Qu0b+60RLsLC6XO6kUVTqe/3QsWZpy06uJx8/aEBam6FSQ9aL0BC4VpXx29hE1+PQuCoi2hsWzGKeqNE4OEhn/i6PP6gjpoEbwGd+jVQHcNO6QRA0iHO2MaXpKAE8+uYaXXl7B17++hm98YySASx0SNBi6OQqyT3oNPv0Cu40XYdLqAzEZha0Bvqa6JCP4lihaVUWqwu9lusi7yU20FJPXz94DR8eduz8C4ptM2VsQ/PH3mLaXGawDqCk6TRwMRzTrHLCMNF6//GwXeKV6ik7PwKfVkuWGuxgBXn21gytXZoVtCADr46vwsSf/d/joxc8CHB+b9Ej9LTExcVcvO2eSdh55XXKHiBiB0lNDLj4+yBF8rG8236QerJN9KAMx8K2wgW96Bq2PIUknhDNn5vtHR4Ea7EAa+LghAd9Lyhxut4FkrtLgtCk6+VLC/HRVZmYGPj1Fp2f0HvvgNdC4HgvvZ9y2N2R9DpZqxcMJ/efS34zrkhp8S5w4XPoYKnQZz2PWX1QUnea30m6ovCaTR/nj2cCnGNzUdOJxLtWUoDPT5PKyR3Pnk6O+IjNnsAx8Is3vwJy+ibMcR0iey/NlnwaTPdmgt+o2x3DfG/9g0+GS6UwaAqkKYm/guxXgJ97fwx5uQ8AEnATDGDWyiF4PEsm9nCCI9JIt3uImbip/XCFumZnRPGUsz3oHFe+gzYwqSbe/UGDRkAghmEy5xkiImlPkNzOuOR6uGHgxXD62peHRcOK1GQljCFLojuszRYxbbY+AlSGRBziO6vSUt0xSE1FktaIvpCtdEOSe2JaweTB5tbWwvvmV8Boj6KEIPmtteSk6ARLxClSVOOgjHR1R5VmOBCKKfW4LgjkNSoAE/cI6MnxblQi5zGyFTmhq++EEfvrL/zM8/k//G3jxgY+o/ZZ5E/tMj+DL77DZAHtJKWxoilHNK70GquKE4CYVP/M3TIBpQOx6WMEWzp5N8Eu/eA3+yfsP4MITJxACwAsvINp9cgz900/D8O53A6xWMGyTUDammCAHprgMPYAZeT0quihNWh3IefVAfKbBNvCNtAgBoxdZ+WiRcTPFCBfAIQnvRwCAkxNd2d3FbZtQ2VKDr3FtacJWHqvWB1YGD90K+smYxI1ntX6IYiUNrgDeB04IaT9l73kRfMcyItw15kx98hp8KcGcDph/Z+eVSUr0yme6664IDz4Y4Zln+pnmGmvBNPDhdeHxRkiwz11XlQz47NboZ6jzOrVzdqmBTxhXFWGcQ9dJQ4qLVggktSG5BSgdJjZUAlfsoWcUDRVWZNve9W2CPk+HvdTAp02eGa0PSgRflZea/mdKRqKIzWk8HUUa9krfJdLNi4QHmCL4OrugUDPd1RSRxjfebgOkQ4exWgJJpuiMoZ/nH6foDBOPM8Fzz/XwoQ9tqcJvR1QGHsGn7NGRf5L03lN0Zaev0pZHGtUinJpr8NEPOG/fhoMkj+XQYL4337jz3fC9+x+B7RPfhvWLBt0J1HnL2t+ac2B+XpNVQoyZsyWP6AYRff27Br6l0LDogpdaY4IFPj6C5nzvez08+2wv2jz0V/8XvPLGy+OF5x4C+PC9bZ0q18ctmL8NooOcvyoyW8N7NEZp8d/DAKSOlcYzjhF8dwiDaK5FSLMxofdpqsGHUnRqNfjQM8PQMSNwgsPZX1gY+LLxUWSmQoB/WjX4NrGm/kVyKoL2CD50DvGUl7WNVIxu6Dd7yZpTYADqEM3lWy43YB5gtTBl91JooSPCwbL2fNIyj9ibzDXwsVTSLeNbjiXVd219wXzmWhkusoGPGMfGvaJF2XZ43weZohP3q0HWH1nOtwC2E5twEkAR3KnrdCOloS9rSdGZYhJLwUpDizv7kS//B1i/9rLdhOtxDL5nl2NzD6eHfQTfHm5fcL0GRhi2WKDWu+Ee97lt7Mb0eEQxwSLGBCww8JVHKik6vYeHDVOIYe8Npd9ais75GXm6uwdx0r2lymFaSX/JgRtvZP27CUsxqKI0ZPfoI1Sqc3F0BG2eXpArxwdSg+9YMtyJ44GUpk5KLh7B59Xga6qzxdeGZuBrjOBzlcyKXtsy8Ml2E8M9bMz0ZqRAMtBvos0nN/BlhGpKUZzPHxtXaXSDQacUr1I8t6UPVIMv//GRp/4fBzEDXZDrFMNm066YpsYXmgarCRd1KBs3ACiGBGlo7eA979nCRz+6gXf/E5nqcGyU4Ie/+Dtw7rOfhTOf+xwAUMPU3Izh4C0A456I4IPlEXwilbMjNUfo6dzgMyBQA59l7GqrwZdYcfWxjzKPXHhJA4vgM95beTdegw/v8y5uhUIOIzpHptrfh46VCr4ZIk5XqNTg81N0ojlKA2w29t4otbqUFJ2kT8/Ap6bvsteYjMge1+owgOkgYwZPAI/gCz6uAeDeeyNJN0vWK3oXDZXeiODzlBEkotNzAEr1feClvvSeJR75bLmb6U+N+20pOrUafJVxLI99dJaS9/BSdJZ2gZyn8j7twOQHGAgDHyQ1HZ8Ko9ZV9mkp7YJv4FMN0VYNPn6GRsdh0Dkn8lquHiUKcjRFZ3Ij+E6TojPTU35PS9G5q2EtRcSXZENCZ6foxO3w+PjXLkBq8LHIdQxVZyimcRYpOhmNqaXbxBGkZnYTh8/F3+4DH9iWiz//89f1yAH2sQt/jNbRVz74y/Doh391qm+vj5vfIazqBj4L6dHAp5yNLILvvjf+AX746S/AwfU3ydgj3uoA4z0L75Y1ZMiS1nfwnRnBlL8xPsT5ikUuceMewDjfZy+/WH6f/94zjsCm/GbXcWrLlv0+6l18A4Q11zUDH08lLvbVRF+P1noEH5c7Ca/KFOrLIvjk+6QknQ1bIvgwSCMl/lufw+NtJYKP0ZUMu9Tg4/z0zYjg0+gFb8MNt0IuQt8Ef2MrWOC0Gb5m3lzTVRWsyFimLKT0XeMByTfwDIGVFJ18ZN6htv/Nd2j1mpja1SL46ODJ3HfYyTQ7c6spzzXaMQ9JDYpO9jJynUcUYhmv790UnZy7wfpby0ipyhSVaT979DpcuPqS22bUocxGvqpz5B5+oLA38O3h9oWkK9loBN9MUKwUeoQ50jwekZLi5qborB1cmYOyhUKRohODqo1i0RoGo6rVRnDtmp6no4qc0gaNYXprBk2olQKwl6LTlFEWFK5JodMRnzpl9joY1jRFpwBWg48oQIw0s2MKE8RoxSheQRS9dxgUW17jCnH921w4N/AHTchKSxwBR8bA3RjC6yj02ooUyp9KnPCYR0fIADgpejXFE//kGC+idMTMrOWIIITOQAT5mAu3BbluPcDzRq5z5wTW72ZD14JYO4pgC4DSDLYiiJ/B+DHjmuxOrlOxD/kTkxLg7NHrcGbyRF5fugQAACfHCr7oLIhJ3xtJ56QZ0HnGNfhqNbcA+FQm08sZIKfw4UVC0M+i8MQKdtaZRhfIK4zRn2ScqcHJCW43/93FoW1NVISr/DvDweaa9RjUUg0aN8YxkGYiduMHO3dOkZ6njd8yTBejTAOLoNBSw4u60KSbKAAlvl2RYFwER2K0A3PuktLWNXRpt7CBj9TgU2iNZeBzeKk5RWeopg+t4e4pFWcJ26b9o5BNx6gZ+Ljw3ZKiU5tn1+EGgojIK88h5ZugvbiPpPzgDhO5z0paJwCfHVsBcwBLw8IUnfKalxJUGP8q+9GswYf+Pnv5RTj/W78FH/ziZ6DfHov7GLizSbPOS3WUoQYF18DXNoyKjyUriYjmU9A2vDdwBF/pepiNUrxd+X0TIvhwFGQXt0SphqEawccW2jDoMilG2JXLknSM5GeJRxNx3/eyukFgAAAgAElEQVTcE+FDH9rAf/Fjx/C+99GzXXMAwIDLMgwdXm+VswIrkptr5M7Pi0wwIRED3+HJFfjYk/8HvOe7fwMfeOqPZnzz/vXk2mJkYN+yYXPifvn8C5kjJdXAx+ntAvbbdYjJ+N3xyjOk3dGF+xzhWfmt4Nti/MQ6g1qNSUhJl2kruothYHwi3xNTtP+wOoS4Xsv5ipSu0xSdXO6S67avRPAJh2E2D+v1nGI9RoCjI/K2Y41BJkOQX/gzGlr/qoEvG6M4H9DggAQga/CpY1hLytp7+FmFXhDjY6KGUFxjPveHjX4kgs8IbhR6jO+D3aK8MnrhluhdnB0og2cc9J3mqJFMgNKpxl82zY8q6NnXPP48JXbeT/yZtlaIDBpkBB8xHGp0KoSRp3Qc22yHDibv44Vr5Nu1eP6WCD61TEZDRqramTM68dhZUQiie/iBw97At4fbFyylEyIWA1KemB66wwD3vPksrLZHJIIv5yCPLLVQS4pOU1DX+ONW4qZ0Kow/3LLE2zNFAVf6k0gT1qdLzCuEOyleoqrSaOqG1+nD7bjHLS21VjeGWDLKqNykh6H5HT2hGObUjXmMYT1H8K0HGcEnivbVJLD8GLAafNyzfVGKTja3kxKbpy/Qit2fP5/gnrvbDXxoCDQWGoel6NSiccYUgPbcBOAeWHxQZNBOAJcvd/DGG8jLS1mzBHdATB+f+wZjsTTwsQi+rKhaYHgGoMIvv+6lUdpsZtqgDYkFdCIbV7zSVcZTbesoD2Bem56S2dorvaL8PZGZDYXCMDnzEczFkYSxsl9VBCIHQgA3RecQekrTkcUNK6IIPasaUxUcQBdWyt5kRq+colMDMp7iwGCl6Dx79Dq87bWLLqLckUO8m5BWM77YwNfD4WEaoxaijkuTgS8Noz7RmGBZg09vZ0ZXGeCSYGTQA5jPiLFGncIzxGQaoVIIZHNwz/gmwHOztAafEqHDv3tenmPTihIS/a0J43g/yWeD6KM8ixXcCyP41IOiAiSDUsv3CEHU1EM9qGcKX5OE59G+h5OqKF8jvBkkOHvjstr2gCU5D9uTep0qNI52/ljywZiikynGMB1UlhSuwcfHzvDuL/wudNeuwdm3XoEffv6v+G1Go9UDqK7U185RZtDyUnS2Wg00/sxyBNtuOQ+/O5C9M71r7FbzaytrgvMRuNbUrnomGsG3QWcjH9zmvwBAqWvJokoYgrUIPsy759qLQiE/pTTVRA4uK919d4IHHtDTbnu8ZTAysHgQAkBYYQfGthSdJW1zsFN05vsPvH6pXL/v8rfL31pKYty/dw40QRvDVdp2QyWCD/Q9iMcivJntD1bg3he+QQ0oQ5QLwsJZud5s/MRyfk1m1WpnTXcw6Efo3EZ3hBj/35y5IOeLnVU02wTFSdNlaBF8MQZzfiLP6AE0iu/qlfnvkGQNPj6NJBWoMebxpsJzKgaV0UjpP8YeL/iFAPDgK1+HRy59Ds7feNV9VnwtxcAd0lBlnXwDX5hk3owj0iUujOBrPleYrk37NEX+QGO1pedNgs63gsAD8z+tdLDBAUAFJSRUjTrNcg0vu5GbZgMfUNwtAx+pTZ51PYp+0UK7OBw5jm0tNfgAgOYW73uahaHgaSyalLQ/CcwO9wi3moGvYSGl0AFWMYv0xQv62sPNh30Nvj3cvmAqWlAE33YmKBYxff9TfwwPPPc1uHHmbuj++b8GgGzg65QHbYaoPLgUjGe4UkUToCJ6Py50dH2ACxciXL3qavvIr66fLrV4XWBcTxXBl4Aw28kW5gCkgEi93Twk5eFM0FoaweeMQ8dIEA+Rga8SwccNfElhXvM4hNVVUskRnIG1J/3o8ho3EADo0ZUPPjjMh7fRH8GFOTKL9cOMBdo3c9PWpLFuUHlMQSbAKCRdvz7eu3iRHnfjNvDXPsarD5aXlUGnFOGDRPB1/UjJeIrOHYEb82WKzvnvvk9CSWWl2Gnx9OJQi+CLKUgm3lBWkkhLxagJMNIT3h31epXPjwqTbOBLKPIgd2rQbSYwpNCdMoIP/Bp8aa4HGgKwmmzyO0qEdQ9E7qQwRvDZwgpPi5tTdFaPRKMGH1eanDl+E37yq78FXRwg/YjeFY9qJn06ohgAkDn+sY9HuGe7gRBAVSF570VrkQ2w3VIlbQpdmbsinBkRfPiZmw2Fnk+4xWh7zFtpnACCTAXewAORJqhv7E2uGvh4LSKFHmk4AmTjo92QC/+ilzCeB00p2Bh++B1F3U8jQj+DlaLTU+BhFmLbH8J6e6P6WVKni3t4X+PzcGAGPrKEM8+AHSaIcwBLXanoJn7oK5+DBx77Jnzv/g/C1z7wC6T9OlEDXxf1CL4l7LjH/u2aorNjSkbC5m2Oy/N3Xvue7IRaO81bHmjrOTIDX7wJNfg0yI5gHFcewXcaYx+ezzwMfr9uzHEJWgq30gd0RWK8GQY+HMGHIaWZ1ppe9UoEn8ezaeUA+KAl3Zth4Gt1HhE8lqZIZNdKhg6sjEbjWd8+G2lTf5oUnUl/BkXwrbdH5RHixJPpnbL+SzPjGzdFqTF+gI8bQ0doR6jlPbRkRGt8IlspslEc4O7vPQUB1XqDXQ18M4pV56hafzfO3AVnjq8ATM6b7dlRlDY405OIRp/H3p65AGF4rfzebAK89GKAo3fNbSwDn2o8SKlegw9HeCoRfACj7Jp1O9evAcBEgjKt4wZIdc6dRXM89HDBvIvWMDMghDRA6pfxq11IsH7rMnzkqd9ve2AaE0fa8lfRDKs4ewLfp+Maoe+CjdKYNK/MI/OUQjoaG/+vAYkCbjDwdY4xV+3f0L+MnVUiq5UzS0bOFwWTPkbuX6N9akrJCg3MBj5Mb8t5rMiggGTQYNfgy3ytSkcDmBOZa3prICLP0RyMNfjkOtbOLD60OT/Kjdo5xgMZzDZg7zkV0T38wGAfwbeH2xcs5g4RcGwn4tFNGd7x4tcAAODs0Rtw7oWncy8kRWeGLi1U8DSgXKVts6VN3Iob2+M9dR188IMbeP/7kSGEKXND0g8ELY2ne3iiw1X1zAhtdTZmI5JuvBkNrxh//dRSHXxYZAdGbxy085ka3NdkWNAgJil04xSdWg0+ovXrWUROQw2+ceBorvEWEHMLY1Qdr8EX0iDwv/9+JPw1fOjMLM3zJCUyrFDI7YaepUKyDMtAIwKj4Z6MvSAxWCk66SC8Bp++sFo93EYDH2LkbnaKTqac5vthu51pwxjMypU1OjO/S4pOXXFiK7IA5rVOPqWXKhf3reCtRvDhv9O8H9QIPsv7DtgZEVgEX8M0cfohU3QiRTv4EXxBod9i/jXjqVjOVAtWi8Lkik5zLxkGPvyOIUV48NUn6f5QhAuc3tgUGAWtma4jL/m06pFxmDlOjFK/8TJ03D5uieF8xBF7X6YyBoC9lK0C9buAcK6YLsQIszMBF8qMMyglikiMfso3A6N5rLgwgi8rcy2FOczv0hLBFxW+B+PDnai0cTTeCePHDRy1wDMxXIOiBtOr7WrU/NWcf6Jh4SIGPuyE0VCDDyveKN+mjMP6uOc7TwAAwDte/QZ1+EoJVomugz5uTl2Dz5yfoNTgqwBN0anTTOKkoRlXk7Omp31XPXI1ug7UoDB0a9sp4hQlCbRnU5I1+FplAw22yM6b1xcxWBW+2mL6oUYS2vBARlKvBl9Vc8tq8MkUnVxJqtcpK05wuK4sSNqe+9Dm3yQzyjuEoPMDszyHFZQ4nyEd+Fvv+Rl48f5H4NEP/+p4myiSTe2kOmgIfg0+AID15rrAdXzW31juem3igz1eNwGPdhbn3ogFaWMpoDWjWi2C72BzHfotdaIYi/R6+8iYlLwvG5PJYB0En5uvPvyv4Gsf+G9dAyyAQS95GyeCb2w/XtuevUDWw5UrAb75jRW88Qamo5RX9Qbvh5NybegPSI3gkqKTPMJ0NtOfh4dzIxzBBwCLIvgsJf1mWB7Bx7PQtEIXEpx/8enFz1mybr7GL1NbEU1lyuXbTP+KMx6uwWcYZ1prrFlQdcZEN2kEn7Z+9eeq7VoeNWrwuTwm5zHWa+LAYUFQaZ82QCJOBOgy7o3pZsYUnZoxuGM1+GwjKu0T87shKLIUHqM1gg/X4Os6kWYUwCG/hmyEs1JoToMtEXy19TPqSXG/Bk+7N/DdEtgb+PZw2wKPGMhADXxI4WgYP4hCDnnVZkVVBFQktCbwLiBUpanxTJNBjCveGFe3XgM88MDsidKcohMT7wYuwGS08+HWSSES2f4E+l66JZ6uSDUMqIqNioGvgdkofVUi+LgwEw/Plt+r4UgqD+nJSyhvctyziNEjJfdQTkEKTAxt8Xs0EFADXxcHOHs2wb33RlitAD7wge3oIWh1pOFSi0JgKTrLZVzrZBhsoT8l6EiUjc6MnD1rWQGCqngiaxV7MKUEqx4JBw1FqDk+MQZTETL2t5AJYgN02UhTEKP7YbOZ+7e2gpqiUxH0aqCm1kl0H1uKemHg88ZRFF0ZNAMfRms08I3905R3LYSZvgvWabXQU7Evtna06gCY4U8Ax/TFsNCI1yvGr1bHJBsKNeMF/k2USK0pOk0DH/1NPK4t5aOiFKqdY3l+iDIQh1xy42rFC53MWxymGnyYhqGoBJai04KlEXy1JYrnDxv4tHFSstNI6rXBltIpxI9UUnT2C2rwzYJ2VqDZ0Z1zex93S5GNEa4pV5am6FRr8FWg6+a9uO3PYPT0MUIQfFWG8X0VZR5rT99bU1RRxYpEIkm6R3AYoYtb4cjUDZuFNfjkHK7X9rz6EXzyubyv+XtaGdOHycBHlK2YRt9MBQjrazTwNRCzhaB9j+3k84F7PU0EH43CnwwJyFiaaTpPmY0hMoPnLiAi+PJYbPp02oHoJ1toYwCivQ5qc4drQM41+NgY03nE5TBx/gs7FH8X6fyqGvjwOc7wf/G+D8LjH/h5eOvCg+O+aojgE3u5YuDLZ0wINIIPg5ei0zPSagrmGoiMNAEgr4k0aUdDU4rO9hs1n7guDSAiTLZbuSisTk00FsyNsujGmk6V6CFzbKV/7W/INAob+NijTqo9ktovybYkPWd/SPkGY01FxQiHZdfr1xJ5YDTw0Wf4Nw/XrsHhX/6l+R6pEtmLI/jwltjFwAcA0G1O6o0KcpQH9yK0MaiRnIUFD3rd+Swf4ChNsAx8u5+XGk4aBI0Xa9gGIUVVP2TyXN52rhEQfElR9IUAkNZr6uxhQKfJvgZddqPsQk7RyfsJOo3ETqaBRqvh5jWdGqVXidyz5ljsIR4AgINOAt0LwMbIv/mZ3ld0El2oL6omAx8Kdx2jr5Vn9wa+WwL7FJ17uD3BoSzEwIdTdBrGD3LAYS9+ZOCjjb3Tt4XJ5c+4j8zMjMb8OSk6LVw8JW02gi4VuttSdFbawHwIdeCkW3IMlJhZtt7AnCLFCGnaj3jkEEFUjpF6rHSQEXCYMxk9f1ADK3oCKbOLgW+HCD5qOJHvJLyOJubj4YfnVDfkHYI0xHDgOb+FsZ7ts9xuQPVVumh7dvEoKjWqIkUzgi8/U+M7aKFwjECDkZMNzVN04jWjtbfAXpZRVXTNBr754dFgSwccvQQzM4kUri++CIfDtUXKQEGLuWClGWSVdaWm6NQMTyySAsAw8KFnx23nrGWemhCPx5Ryq3VFIHIgBPBr8EFP5Slk4OMMfrkulIaNhg1OezHws6RWrzZDnL4Z7tuowVcDnKKzVeFVaBCmwasVacBpk+VcBMBoQq7BR3DEAt9AHrKUrDczRSdP4YRr8KW8ptnLeUYtnKIzpR1wxQJpJYJPDF8UPW08WY2v8VLO9L0e2Sz6VpQfHZo/kaKzZrDjtxu2AkZh2x/aDRGYqfrwWsf0MfSmB2g5Z7Dyz/FkRkOJcfjvVTwRBjcrRac5iKH0/vCHN/Dqqx1cvdqV9GcJAvR9ko0doBF86mugrpKuTMVnNVuX4z5r4DGU9c5TAsYu13RReO6FxgryLHMMA3D9VHaCoxuzL1yJ1A1z9LWq8ePz1t1cA99Yg2/CiX374m1vebKziLVhCDSqhBvVaw5OyrOyBp/O55pykmLgyg5A9tmKIgnx+NywwhS+JJ2sub91WhGCYWxABr6DrRXBN/3vyaw7CfNTK7y3lRSdfG9Xo5PTshSdo5xI+X9yP81zNOMQjXeeUHBwm3og9RFroDobocw59jfQjg+fDqpOYROSw7kLEC7TZz26KKK4gBv4ZkPWtj8gclipcsFlV2YcAWA1+K6ivQhjVJ6nCkoJ4OAv/xIOHn8cUpp53dUqwcnJTEddyN9y4idDyDTOWKvO2ulCUg185jppNPDxa5j3ymsxxpHsYvqcnx3VEBMPx3mOTh4vp4l41xBW+VGF/tYy+AIourJae09kbXBg5iD288EBxC6pZyXBIzsWVIyKhRzoTFZ5jKbojOK7l3s4QhI6UoOP963qK2CaJkdmtuQWkeYXM05GDT7rzKK8xoxL389lWZJSg6/6XVvWUghw/MCDkF4Y69ve+9azZrs9/OBhH8G3h9scJJHBzDHmi1eGuZp4NcTZaJEZHF4k1dQlYUWS0UYVIiyFvDj5ZKdxi681KCGY2wg3hIh6OgDVA9i9mR9WjGcamrPS0fDWDJJRqKX28+5RY4HC5BsQK+No35kzFrRDWoOP4Bpt4Y2kvaqm6KwppSRjMAruTFEzKfyEB1L5eC2H9SRERPwL3WVKxdnAhyP4nFRIkCCgKAYNpy4NbopOMBQfcxuMf4JVx76hpaw0LvP0JkQRgvsDgM3qLFigKWPn37bUt9nMBuO+n4UBLE9oTgYHjz8OP/WVfwfrQbOYGcshybWGH1CFNcXYFgPNR8+Vt9kwJTy5UyIRizpe8z6kwl3GQ18cARJTrAVag69BIOLvL1N0zn8PqS9zEAKQFJ0AdP/qS1F/D0HD0i4RfHOqXE8+Ex7RzEM2pAj9lq4vXb/GiRK6ZSnr1Ag+pNzQIgzVnnLz+W6X4liDz1Do8Qg+02ioKFs82lQjweNamNZMieALoOadTXYaSS2Cr8XARwTbBQY+cQ0pmZTuZhy9To22Mp1Omg3dGmQlgrZHEC1dnKLTyNLgKfBois5DtQ2FYCr0DjbXYPXGa1MrpFRa6ARWjbxITgQfat8PJ+LcXpSicxxMXAkB4M47E/zwDw9w4UIl1ZaicMWQvZ+zAqmMir4lOcdKBB9GEdMMnTet6VhU/SAzKIw8lUEzT5WiU36PfOaS9HgVPsuElOAIl7PO+w8ZiYKWopMBj0TdBQYeNajsUXrBoFdaDT6HZ7N4pSJbNkTwWZE6VQNfA8z8cVsNPhFNieTR5rWIDXxROkaRCL6NUg8d/Ai+Er1jzEPbUrb39niXpejktWfFA8tSdBLao9nAozTwwcAovrIWLdzyOLXsB/ielqIzwUjTagZYVfThbVDf99xLnT1Tmu+3RPBZ+yOlqfYZAiuCj9eA83AHoCk6T3Akc8oRfBQ/juPB44+LPolfW815IH9LTGOSE2nr0OAuJMHfu8CNbo0Gbp7Kcrw2/8/5yJSQfID1I2mZM/XS863JdsKMQLXns9NkxU42t69kg9Lw8PqlezmNTpRO7vMczT7qXFkmBA21ad176wzv67HvZBr4OkBOpsGpwac5B8DM13gys6kT5XtoiwNPgtR5GH2N13ReAzutaU6GNT1FS9RsDAFuvOOHSrv73nhGxWVv4Ls1sDfw7eH2BIeyEOFxQIqBri4ghImRxoakhFN0gh+VsEsqnZZouY9//KQYPQ4OEGHm6Z3wvCieqYJpZvOIi8oCAFy/HmC7ycKqjWftMNDmRTdwTgYGN0WnrdAvXrya53LxlDOUMwtTdFrfjRv4coFrlyliBj6qNPXWG0rRGfUUDBgv7yA1mY3ybSvrdHqHJs9ynqKTGQfwb6JU7NflehcHkw6ElIghntQqyQxRim6KzpYIPvKdA/uGuatGIXQYqJEhEqsQXVOb1RkfMWWA0EE9gk+iz7rT32U1nMBDLzxaxyn370Twaal0AWhqw9JW+0BSqwZ9GujlGEHLCIPbxARlU6jCnKvQoNL1+mBuGxtS7AnlmhPBl7qeRoWxCD6aolMZAOpnUEnxzJxDONLEwJc8Azx5TCpMlN+reEKesYRIXgOuKixneoDTYDkRfDUlFUnPHIdKDb7JEKBIjyQlzE2M4MvKkMLTVFJ05mc06DpgEXx+Guhq35UUnZaBz6JL/CHXASj5jjvsWHb7ESgg/PinrqXo5Pdb9NwY1xzB5zurBPfd7/2dz0D/zDMUr+AleJloTuSELN9m75QAhu2sMLC8kN/97i189EPX4e1vp/SwG7aLIvhqqbQIqhAka1gz8E3biCt3rWEzzbIMfK1mAwmKQpPh7tXgc/dVBbS6NkVPRSZtt3cLkOAYKbYL74+MbcFJ0fnqqz38+39/Dr7z3ZsdwWefe1UjlVqDD13iysQlEXxKTfl8XfrF+BFAGT/2hHjv3KSLEg+tYxHNhlN0GjKhm6JTownb2Yl3vb1RHtHe1+VflM08sn71PWM5/JSxs7F66k+tJVhHh/aJoFaDz4x8dJwOaga+5rnBugplfdAMCG1GX1U5P/X9L/7FEfza/3gEd93FDCbT/eHMOeXVbEW9MLywd+5RBN/Qr6m8g2rj4c6jorM4ODB4JNANfAT7pP+9WiE+pcpzSt5rdFLVv4n3rawIPntoyoM3y9gJvxPVQcSB6ZIS/X5EJk6puS5vOmzLoABAZTX8v9aG6EWaWB9Jn/kYlS1sNNQEMXneClp/cECdPXg3q9npqWfZmtTvbRh5RWpNTRdpRPBhWoQj+NqMU4Hokfk4Idj2Tc43Evmw7wE0GTKjwZ819jpfzwKHWg0+Y75JE+jg6MEfKr/zeSva7Q18twT2Br493J7geC1YNfhaaAhJkacIRKfNsa0KERW8AgB88pPH8MF/dgKPPLKhnmZbRyGmnR7M+4OH7WcdeoIAJycBvvrVNfy/nzsU9bVFtxbzhl5yiXLM88b2IvhaXAPpN6AnXqt3k5s7PFjCsa0c5wc4eQ3LIBBYTaEFTCfvp3SB/u7C5PGlpFpSYcHemBV/YfqXPRujsbd74tnlAWZQtBQKXRpgtaIGcwLKmuWKP2ww6YLFPbXBmKIT1+DzDHwNEXz8+uj/KhrOBr5QPP77Xn6TGGf6p81LH9sFNO4dyFNGakspR2/Sb8DmmWlqxvVrRPCd+MIONlaQ2hRKJCEHrsA5QDKecMpQYImBL4a+XAgBZA0+Zf+K+dYcIhQ6Sc/Csd/779fPIM8Az8epGfggJVl/TYNROybwB9AFYzom2ntM0SoQZkY/fjvDmKKTK6kwjc3aBT1Fp/bMzQDNwDcMUBY6cZKKSfXwPHcuwd33Jnp+pLrSWUNmGAAef3wNr740X9Y9iA1lrrw0/8+ihXywFRetKTrJeIqmRqTorNEDQ4HtAaZX2YBTffXeNtiFAHDm85+nhkonhRehf0nOC9+D168H+Mxvn4Pnn1/lh1Sl6U/+5DH81E9chfWaPA592qqKRfOdFxj4AJTjvDKZuAYf2ce7RvBpRrAWRyrV0Q0bURLEbmUavneVd0LS0/lxWmjh2DrGkaIvShBK5Nfo6BVVGeGFF3p47bX+pjhPbDtWgw8bNTJeCZ1BTJFc/mYLTaTo5M4rCl8LgMQutP6yAV/U4Kul4gO21Czts7NWzNrS3NiIoy8DsJSlFr+v49F1oBrG+ueeg3B9TM15YCgc52F1RXIISX3f1u1C6Z+yJ5hcFxQNPqcVpgOughTlM+RznRLtA0PFalTxeMLrvwmU/nBpjPE/4yxuGCY7OuQ0epz3Kg4DB4eC/nuyZ9VZjfDe8/ukFFTEo+E0pTmdZeS5wVo4Jxnzg8/VWn20IgsmXIMv2Lob56OEkKDbLI/gKy/SYGwAANgmRF+mB4rqRXWym+dWS9FZgxAAjn76p+Fbj3y63piNi//XblJDcv0MtQyvFri6MJZK2kDRh75311hCi5Eb+FSn90wDNXwgwJ/8ySF87nNnqYPldD52yoYgWWRCJYJPo1XFwOfo/SxdjZeNoggiGU87Radn2CciLne4V3AVOLbQ8hDg5M774OTgvLh1SlX6Hm4C7A18e7g9waEO1MA3X/fTF46AI/iy4IOjfyzBtRXURw0qjy8fHAC8850D3HknU7pv7UPbVmYF9mvus+ul1fHG9QAvvVQhBdacNBj4BGOdnDQPAZS0Q/hU6sSluZWu+C2wwCjjKQVGoRsd6gHEQS+QYDX4qPbH+cbY2BArNa8MRT7GU72mKCt0ZLLGuM5s5lcy5UVk4OMpWGfDlyMwcIObIt1kBvm971WEZ6hH8GHlSgA63qiomR6uTdxzz8GZP/gDOPjOM0wRgtbYZtPoNWZDBzRyga+FMdIoC71SgTH+TOrw3FOtBRel89ybvATYwEcFZJLii0u240Xo4pZejhFOKvbIlKC8qEoaLLqtzPN61v/B0iMkBFAjvDJEFk28PIKvxcDH/xgbvOc9W/j0p4+UPnOKTuqxqPVtGfioAiMSz2cLtLRO9YcmAR4r0VY4CkQ6GwhjOQJegw+AKXSJ9+V0vaLAW6qEdo3PQBUb8++gjpMSkEwI73jHAD/6oxv4kR/ZQGBnFd4zS+Cb31zBH/3RGUL/zPMIQzaMOn2TWjsV3Lzg2lEg1r2gCXLk7KZnTUpaik6fGAhbd0ME8Go1p8ZNYYwwcl892Ck6S5O33qIGvuStSamh8lJ0Xry4ghs3qCLEUmAGHhILAN1w0p6i0zDQk/ECvSEiuLHSRpnXLGuI1FKKgQ/LGibK3BBiKQAblO4yRefKpC9337Uk7Ska1lAmaik6d4ZEI/jIhGIFJFsXXhTcrn4UQ0+VkbUIPs1eNj6sRPB5DowVhElqzGxoFyk69drj9PxnB7ZAXD8TuRMJgL/W+bfACuDWCN1ieIAkngkBYP2tb8H53/5tCCfHakQBflfXwJ3HUXmDCopWWvQAACAASURBVBh0sVxja7SrODchMa0JXOU9gJmi0x2k4vHk2Qa1blSelPO6SY/G5vyljjY2FCrPTzeO77wPQs+civnaZ7wqbSud1cqfiF+NUfJkuXk0DDhE7injywg+watifQO6hSP4WqOD8dpzdTeevA66ga/moIN1BC1GrkHhWXK9Qu0zpRR0A1+SztTemr52x9uruJVB7Z+8cflLOI1pz6VxpdX2foZWA5/6bIvT/XpNzjs8xva97wVYrWZ9YItzp2HkTQngpZdX8NhjoxDOM4ClBOpE8Ai+hHMDk4ZUX8H3HU+3jB+zptF1XuclfCqEf5b/qTNQNUVnZTu1nDmjA0MHV+5/yOihcbA9fF9gb+Dbw+0JjSdTxCk6+wo1gjHVT+4iMziRpYSoGVF89BTFQI22OcwkZtQCp7iKEmIUwezDYbaPMSVU1D1Fy3NQ8aBSCLimD5sFQskkzh57tC817aBDukxBQzlxrXeKYOfrkhFI+QdVYNEOWXpHkvbMq8FXktCPTKejJ/JSigLk15FKNjV9gdYVM/C5xjElRSfvSxNeAKBE17QoscszmgfkJMTed1+ED3+YMZCT4rrGd2DUOizYOV5b/Nn+M5+B9ZNPwn1/8H/CajtrrEiqKWV+LLDmDRt7OI4A1LteC2AaDTGG4oqtHRUffI17shGv1mwgZzRomg9CL3Dq1YwkZ6JTNCL4JF749SISvtVgaDOrWRLz3K9C6SNAwin1qxAC1FN0YsWYUYPPoj8JgoqPRifxPuq7BL/0Szfg/vtnaz3Gq0uDc1Zi+sb2DsE595VIis43P/HP9S0QZiOVtPMaWvGsLLBSdI6NEOa6Yo8PAwCq0YHWYp0aVyL4ltbgqwHeJloEn/oAGvfcuTR7vnf0W8aFiKUI8J//8xiRjCOYm/xtkDLXbELoiI+b7wCTa/D5z5LvJoyo0l8HC9fd5ctw8Ld/C+HyZdHH/ICusXz3u8e5OzhI8La34YcCbPsDcGuqQIDB4ZlCAEjnzpF3d9tjnixvO+dM3GyCPBOw/J/Q/lAMfGFJis6FkCC4EXyeITpMqaHzHOAzFhsN89kWI8DlyyP/SIxALdbuIJ0bapF/IQDEfq2+xB13JPjYjy2IqsBD4EwESPGqfLomPksdI0U1RScAQOplTTxLAXxzDHxWBB+ljWWNK/sDAESZgDFrAjaOyQg+DFymItE1pQYf46saavDVGmTZk/OHc51XXFvaMfBx+Rin6DRT/xm8sZfC//gY1hcvUocFwl/a58qcCUDB31AWe8DfedQ9oLMknS5FJ3eWAKin6NTqdQkDX6uxOc1nn2UU1RFXUnRCIAbp8dvLF+cGPhUtzNuzl00JfZfVCt74hX8Nzz748Rk1pQbfpz51BH2fSHq90UBkR/ABsG+tpOj0nNawgQu/GWcR8JoGAOhOdLqO2d6qww9ylG0y8HlRj7Bbis6lNfi0FJ3ZGKrVecP7mRj+wSjboAHjkT3IDn+FhGl7MxscrTPEQkOhA257W01II8gaa/DxvpMSwXf8qU/B8Sc+AUc/+7Pw5KWz8OUvr+Hy5Q56Tv+0pTStAY1mv/wKGofxofyMRR0iHW17DT48Tgh8PEk31EdRO3G8dR1x/i5OpIbex+I1CE+rRPBxmVxAw6JLYSxvde2ed/qP7w18twS8Igt72MNtCcRje0gAk3Plkgg+ACiHj0gt1GhcVHFbxvuThwJnagAgbhPAgTG0gguPDuNel53GiKT0/7P3rr+WHded2K/2Pvfebjab74f4EEmRIiXqbcuyx4/JyAkMG8bEmsTxWDOB4XwxAuRb8k/kaxAgD8DBBE4yY8zYiGED1sTjsQWP5Zeshy1BHFmUSOtNUWSzye5m33vP2VX5sHdVrbVqrarat1tSfzgLIPuevWtX1a5dj7XWbz2axntUI8G812J9gx0iSmMkrHwLwPI9nCPj4vk96H3V7sk+yRA5Zh9qHnzCUmauMtTzVwmAz9GOTfpBG4WdVKf3aqiBXrJ0SL0hOpOg3XFYR6+F9IhkPo0QnXAOfnOg3BAUZo+6BEw4kkcztal7F6UqNFDQGfdDGaKzNV5S4eQ9cHR6Nd8XzG//3mEoNcTeJdfDdsuF3kIQDY4rWmndLqzyMipGlnN76rtOQ8mOqG0qDO7ohQekkYNv7lmem3HNsm0hAtg1oU2M87gBMGQlx26HIsxc7RUkAkfHX4bolB58WdlozCHnsJscUI86XDasjDMD+PxkevCJx7py8LHcJecv2OvhjMKCGaJTVDnrSmxh2XuixI/CFwOwqcHQ0qbyMkxJdTNz8IHPBZqDz7K6qYY6F4oxXfVpPsLGZrUHnwLwFTgu7V+lb7NSzw491ArRGduR4U1pZ3a7cm9LYxsCzv+bf4Ph2jUcfO5zuPZrv8brEHVKPubRRyfcfbfHuXPcujzAMQDCotYcC+fPw13NjU6VDcMFj3/4mf8VR4++gryxOHa/bEBqZYNaXvfg21UViGY7tM9O/xsoweaWJ+jMz4RC+bXbBmCz7GfUiGYZ+49/fM6t+973nuJnf+pNViN/hZKXjTmrGI6rygDCg89tzHxLDgHjGAqv0xZZ3yIBnJ1rstXG8XX9o0kFWM2wqQCVGjSNB0W46OAG+GFMcosMKUb7XCMmf4Rl3AVvxCuse6xTOSoaqloefIUsVJGTekmT51QDF6Uh58AMHa2xK+qIcnIL8C/2EU1orOwV1r0OGcziB/I18t5J+K7zm2aQB6WfFJzQXqNQGrN+xAvrAD5L+S6JymzFvkdCWgJ99ZmK+ZS/fuEhRC5hylP7Rx7Bl554Go9/+6/Ndj/0oS1++Ie3+I//4ykCDTVeDLAcQ7KXG3ygloPPrBOlQbZc3+NJ9lyl5dYAfJTRooCUZpxdNCRoGHDDOfhUnY+4tsMIgK/75O0oCkceuTdEp5NbNeRc7qC0Vlzqm1KIlQX6cvDN0cfMJucyvft8Ff3j18zz6eAAYeB51XdPP41w1134xjdGfP2l87jz1OH0VDlPjTa18aJyPFA6bFgA3xCm/P3g5jGmYHys3ziDo2GYleJpXpP6mqjpPzEKg96kkyllIefybyn/z2F7c1hTKbutCqdsUIxy5RXdDaM9wPcDob0H355uTRIHPLtFNim/I8qjjtk8sBCdyyF7hqTOq8jY3ExlFj1bd8KzwzTRIM9w22D+vOLBF61catQ8DNR31J9pAhmirq5wAMjvVGVoaH6ISsgHGSpE9o+OV26jH+Djn8gekCykzOUGV/lQmhQvbksQUjIIVUruCj2HtWBglco168QAl6x6W80wKyjhMQQAAwERCwUe5rXQaoOB2bDWXt88D4ErJSRTxOZtj6AsGrCUKfEdtts8P0ZFtrPyB6X7tktbcUUmcC49+MrxsTz4WChV8WCcv5JhdgBOtwpTznTM2YK45v0iaQ7RyfPmzB58y/4dfFNhWoT4FRuwBPjYXiQ9+IilKr1Gv/F2p4dmlO3V8mhJmq3O21ajIaDwdCuUc4Hn4PMHOmBBjR4k0JMt7w2FYNWDj1PNGpaH6CxjEVPFScrVNSkhsWgXbybAF7iFd9wnpslQNoaKatEJ6+SGwlntjg9kbZwR4KvpPuneXx3Het/HkQvMZTtKe8vnj9awqqdsnC/ThOHatbn8G2/kdyvK20rn228PGA8JT7D8sRtuLERnUmh38JeRzh9fnvsb16HIK1WjYn3RPV0D+PxKD75W+47/KM5DCUQYdUjjqPj9B79LdWpgwec/f8iVZJo+S52rNniVrjk+P/yoW5rEPGONrVAl+i2uX3f44hc3+MY3xoSpsH6d1SDDMFgCUIZ2ZOU48T2hAzAYyvHyGNj1qJAsdfvGGZQ6y9eUDNFZ+56UEu8cSkGkDFFaelDKpa6tRbFECmPRVDSU/JCsSyPnwBjRbuBg+T2GutebjHQwEOM1Tfag9VsAX1NGUgpKYwHnwAxNurT3oc+DibYRSZPt8/ollUqPyDMAfD3ntcZrpqoUfr8nRGetIXpO0mdoZBupqNfkDGAxAhLeN6WxGqmG8Bze57NdrvmmBx/tSyhDdBYGKyd67kkWorOVg4/w05TXlufw4elVPPGtv8Kd175drWvYHpv3zeeo3GfUTGnnSwOuEEpP6fl6nIjKnA0rQnQ29C68t1zXQo0FZb9rOfjU4Vj2rRY2R7tt1l8B2adJ53OL7mg5+JZBvXRpYAa9RQ4+tUI73CZMfmMGuNQoVSHzoMHZHnwFzyXWXS00vck7tkJ0rgldvXTohRc2+P3fP8/argHk8rseHAT84i++ife+91S9rza9GNa3UoDs6QdDew++Pd2aVAM9NAtqlEpljYbtSTI4jky2B/X+0UN0pu40DnKN6Wq670tNJSUaorNiDUku8l8hQDgelc+GDg8+U+mVERRLHuhlOGLZyPBq4W6q9SjWNyXA18eINa3dNeV4YblKmCQq8MmM2B1z3TnYXm+ibLUOQc7pApRWdvfEE+xmtS+GF0L6m76LaJ+GX7LIxVmiCG80tIjWNrCMSRNBJPsCSg++GBLYCmtZMqOuCvBxBrOhGEkNkOuKsof2ZUsAr7nvYtwruZ/iuuwnXSmz1KZO+ZyDjzxWWPQCRQLuUObgm7YeWlj/UjEw1y09YoqO8FoKAX3cBERMrydEJ3+F0BGiM6+5AgwkSuZUb8ieGQEO26mRU0HZw7TzlPZrCFN97it9ZL8d/01DdPqDQ13p7eL/VlAE+KiXQcWDLw5kQ5+11Fl+aJ6Db3nvRojOmhJXox5QlfI0cxcIOCqAXBPcFwDfmbxxFmWJ9w3LVYVoviUytUWhPjChFER5RcNgK3ZFg6xO+gcN0RiJevAVHXKuzNFnKDUTjWOhtfXjQZM3qHnkAYA7OWH72hQWxLNTUueWzIpChY63cXY5BzXO4zhtV3nwtXiaQiHaG44rPh+NoyTAt3R99FuWMkCTKSZinGh5DPI9ogxpp310yffEPLYqeY/NJuDkZN26pm3sdrPCjkSdvSkkZTH6DkWITkIlwLUuROc0bFBAfM4xfi2GFJP8hFMMPthUVHLwbSxwLF5Texn3RRLe0/Dgs0N0kvWqjYuyX8l1VYQYHUZWWTVsnwNXZGpng8oELOd5I7WAu3qVb1/Lodidit3a9zr2Q2veavIKvK+9ZrNZzRipJV46P5W4xBk9+PKcyDJ73WAuMiZ6yGFqoOAEr53KidC4eitCTh0GxNh/EuAbx9hgnjAzz1HKukWoacnbQowh1UsYugzLgy//Tc+SOJX5M/S5jQGmUWOOplGZopea9zi+Tt/zld/Hfa99pVrV4ALG7SlkGBETABH3ez34pkDSXsQxu3IVw8uvYMDb1OdVD741AB/QHaJTzpWaYT3jxTo9+NwZfXaKsR10I43TU4df//UL2F0KeLRV6cGBiJjl0vnnfZb3AWCQHnyaLmIBtsvIQ2AfhwNuSp7bdE/m4NNfI+qKNLnDOd629K6zzhoLtAvLQ/IdYn2yDfn78mXO61Bdkey/lO+HAXjiiQmvvDKkZ1oUAT4t9yXv5Bnkxj3dMO0Bvj3dmlQDPcjmR5UjETep7UvDbgtEvcUiXHim7Kq03WGps8ZqoVDUi4187lwlBJoiOEjgQjLIw+hyOdZv+4ADABf0d0segcq4zILlljH7XRZFoi41RGfFYqQO8N2EEJ2CGUhyQaHgMqw0RQ4+lZlZKmb98L6piKqG8xkCAD436L8qvnzhAvz998PffTd273xn6leLJMCneTS6TSjvOQc/lp5cZQNlmDL5/Xk4OIXBGxXlGBWM2XcO3HJzGHB0BDz44ISvvQ7ccYfHG2+I0EgB7Lt7D5ZMWlq3vf76jXnxWB6NkYoQnVJZUwnTJxnZ4p6gqgcfXBJSWfujDvDJdxH4AxxCYc19eqIz9aw9Yjmr5+CrKTT4nB1GqruLIc/K9l99dcAXvnBQMOKlBJefLTz4aF/IQDJQB7NnxunpXEgDHlQFnyGsxAYYwOf7wuaFgCK0MBWu5rZEiM7NkXFWOARn7FdZk6d2gnkRai6suQXznmzXNUJ0DgjzqlQk+RbAd1aKxiU5PxIJ0anM6RAA6zPKkN/ydy+N4wwE0D1ZXXOyaqJs0/o994mEbG8AkPXzsc4eaMB/3DM1L9r0XAzBLW9O0yzMy/NfUSQwGkdgu2U83zRs2p+lwfu442OA5o0LwtDKoKzfrewdrGBRnCtG1Bx8kw4aWu+87FU1Vj4VhSv5Kk0pL25TgC9SBPiGacu2GK3vPE2S0QhkmVIhXjzmS29hM2d1CLWt0KSmVTndM85iFIBlTgjDoEQjzxFUnaMr96tJ8eCLayw1P2nh5sp8XIB4hSIHn1hfSl/n/c0IX0nyY9VCdGpkyUlOMtLxurEH07kg26p9F+cC25PWhuAd/K66M0Vvadkf50RECLWMfi/e7+gk+UsL0RmjELi6lj890I6yQ4nn4Cvn1BAmSBtHN9XdwkwjgUDPvn4FSFDb4Pz+HHawnx+TdQHUqFk8s7Q969SXd3BDDl8afDNFAAP1tftExvCeHEiaAkF/C8g9P86Fmp5jOLmeUrvwcvN+P01lvk9JWT9A9hhfAustcC/WNY9T52ET13h/UAAAwE4Y/427E9z9r/5PHPoTPPX1HyuaoKBrafTDf886BCGLLnLx+hCd7CevM4478+BrV90V9YfmGawdi3RfFmfPtWsDzhkGDLTusNkwQ8oQcr3TNIfCjjROIkWFwT+2wHz5dy1E5yyDRoHZzeuTgvH0ZYw2pd6vxuPKtlVaxocCoyPsHHxV0NnN1UWAk+oHtD4UOp+O+RTcMO9FFX3snn5wtAf49nRrUhcSRJQn6PXgOwWOlgNIs2bvFDK6DlwDsFpTaaDW3UEcqqzehfmQVnHWgSOUMS0PvuZm75QwMG5OFrtWHzhb8eVcIxwc4P/y55Z/K4xvGDjDob5z0t4YB7uw5tG8IjVAiz7AcvB1gNlR4KwaipnchDNva0oqSv7ee3H9F3+RX+wASePrUoCMT1f9nQNmxrBFhWWnMvb0vYouO6fno5T9od9Z2V+eemrC47cdY/OhU/zRH50rnyXATQhgyaQ9mYveA6+8kn/fedFWoFnKiaHCdIfAvUtUpV5lHrbmSVG+YmWmWsNh2S+EMkuGGysXbQAQCiv+k+t6XxmoQhh6LQdfjZhHrhuwOfBI6YdC0JxQAAC//dvncfWqUII5NEJ01mPJOmKVTGlMnupODx2o7GH07JBJuN1s7prvh11hsUjrnvfwoCpBivCyIWBDlKbh6KjscOofF8KaZ3EIs0FBBL5cieTIvakWopOS5nWg5SByZ/Dgq1GrbwzsJQCfqezt9OArlE1Gv+R4zt5Mjq3TLr4gKV+kop8r0XorvZEQnUn4L7tXnYTJcEJqaMtDcv4pc/KB81ZhHPlbOIdpqHvwwfEQnRL8cg5wux3chpxPYV4r3TxxJVQRwMdVri9mpXxagifDtFU9djRygPo9WHuK9bJVWOebwmIkw72n4z47+h1Gkjtbm1Mnx/maV7ySrTOyRVp+Le0lIk9Jw7b1UjPXnFiTZ7AJKEOqUSXekMO4z4AmUf4poAH0WypNSkjT4AYeenkZY/mNhopBwtxvrrGeJqHIVT34jG8H8Ww8FyXAZ8hgNTlJFpjvl95UIYAZzgThLdiaJ24Ue0YIdTmK9GsIE2ows7t2rVRaBg9H98GaXKvJ451LxVUGVyqFq97aHQ27sgmTz4g0LAZW7LmbkIOvh2I1WmjKGLEjN2WsI10tIuoSci/VG4X8DaIifCmkVrz57ksYr28xPfxwcQ41jQsIaBAflQaPLQ8+FqITOsBHnxtPjhPAJ2mzmQ0Qe3PwzTz8zHOF4NaFyl7I1s1ZH4/wA0s59TwM87S9csXhwoWAyXMP6ce+8xm4u0+BA+CtL31WayJ9i9KDrw+sWSrpImlMrXrmZaYyXerz4JtTuGhApFq+tke4lfuTRpsNOw9CyOef9zwUNo3eondo4fqDbnBmywChCvDR581cnpWxnPXIuv6Y7yucTF52EdqpnlIz2KJtwAccnl7F6cEFwbdmI/goh7M+FDywWHMd3z0awffyr3v6/tIe4NvTLUm144l78OXrPZaowy6bzUYGJ7oZAzBDdOaONRRIFmBUu1yx+qDCkxX6odaetIIdNK8ldFgHWrs10egV3UknHGFmG5u+U6QVNfRg5zgXlzpAnZZlWwh8hsRPIcOe8Uo5wMc1+HqIFkAI6yHArQwlVdRHFfgxP1TQAYLU16KOmapLQQh9NN45AB5utOA8BKijN8AE2xQLnIIWJIlymYPPAvgyc0w9+FxAEaIz/ekqYAD57jPAR0AMwsRdvuwSKHR4GHDx7vUAH2diheAcsjfX3P2SbW3l4JN51GqkeX+lP+FQGOcvNI0HcI4omeU+p6DyLsw5+Ojl0xM0Nxsq5OsefPpz2Ro1v8+44XVYOfgkuEce4G3QV3ZDCXSScnFasqEhoArQk4MvpHdJ1+g3FMq+EIBBahkkLeaDWg4E7Tf34DO0E1xrzW8Z57bDrHmPXfWKt1M53/sAvqSUFIBvrDO9Z05GmsvR4VUn29n2emmtGQFgmoOPhbvzlRCd4oKVcL5KIQuaAxOA6yAMABUEqykjamf3LIiSx6SBRDyWjTmdFOj0u0WFXUWRnfLuyH3LewSgDNEpmLHCQlrkqJ29i/Q8a7TvNERntOyVNJ4ek2eseUnqJfMsIO8NzRkix4KCf1oOvjUhOpX6JUnwq+bBpz4fYojOzOdQBe4G20JhKGlLdFqqd4xzYo9Q1p7yHM01lp+1v+PZcvD1f4uzevBFQ4tETIPNwTZmcCPa8ysBPi0Hn5S3TJ5ZsTphZYSQOk2irGYJP4yAtOJfaCAefNrePhcqeQfJRnGdaP+5EwIw0jChBWhg1xVlPT+MyVBmCBO8a0zICPD5XQHwMdn56tWyzQXgqysxuQdfMWdWai41gHlNvtJImiee9Tz34Csf0ZTGzk+F4Qhqv0X7IeT9sGedqXuSEyE65R7Am1z+1huLnpMawEd/O0eM4RQjlTuufAv3/Nb/jfPngesf+UjhWVUN0Ym8pr2H+q2oHGIT58W9L6MIURpPr9PirNxmA5yctM/2/B48B5+ZK7NCm+mk23kvNbS0DWQZR9I0AZ/5zAG2W4ejo4DdffydhmmriYypiRDKtuLNNTn4eg305Lmh9SsBjmfx4BMFa89VAT7OvJQP94A/m02Zgy+F6OQe8YPIp6Fvy5UwrUxG4d792hoFZgNWujd4D/0jO76i83nritydhWGzpaK1vm304FPSPCRVq2jjHV/9YxydXsF373kan33nL7G2E8AngOWevvZ68IWQowfs6dai/VfZ0y1N2gbJLDlXe/Btl3pD2kR9kMx2haHs4VxF0eYjtdO+onjTYvsXVnFSkaJ4aswMY72LzOJMG2bLvGSzqTMSClEvDbC/iJJNY4hp2BOrW4RxaYFqJtPtnNoGE6wtwT/2gRY1LYdy5VHYGStzXIZnVbqtXwvK4U9CF3RVJCgrLZMkLTobVIYlOAfXUf+sYDMUI1HIrOTgA6ACOPwTCiGKejTVPIBoN6QHH2FkaU4X6r13//0NIwODejz44jiNI4rvXlPyO9cRlouWl8wjBUwWxaW63QlPlICBe7CIB+O6kB58p8cdigGyXrS5ZOZVUBSP4yYriV1o5+Cj5ByKrOVsDAY7ROfcXqlsdCCh15zDVg3RqbRHz5TGHIwhOs3lumiOVSWI+D36rMD3wwg/bHRhDm1hWs2/RUPlulFfsLHsyrU3hIk94zXlnerB1wdMnYVCyIq9qCigOfjUBwxa48Fn1R1BBGaw1HO0xL28spblTlPtSuX+nINP3zdo5xj/6e3+5TLLHys9+PJlHeCjNI3tEJ10jlnW6SPJ3VPz+NSopbSW3WPKZQJKOWXzHPxUKBbN91X4maIvBQYSzALWPGUAPjhgen7DFVZnCdHZw2dr8/nVOx5P5U4efsws5xzgvD8TwNc09lkhJ5lVBA+6utm6q3mLFmPU5tUoTUM5IGroQFmGnHOmvCQ8+GSIzt70shpAlUJ0ivdteupAjIkhjzqDaeMefGKsG2vQuca3NDW6bZBZ8+Cjcgeg79vJGNEAY9byB6pXZjxLAqoGnrThNftZKy2GaiwhQnT25PukDcxjUy/K7imFpP7CCtHJAb56Q1qEndhWvJwiWCj87/ue/9107fzv/q4C8JWyLWuDfOssY9EyZRoC8QrrPfhO9Rx8AFJuWC/BF9l2/DdwgG9tHmUAONgdr1MlCKA4Gq1pFHPLn5w4XLnG9+5hCS9aA/hUDz7lt97/pZ+dbJLcO0zQPnZwIWkwaq3pNcdurey16wOee26DL35xc0MefDBCdMocfKPMZW58MA7IkluUNxC6oGkqDUwBss8vz6z34HMAgtjfuW7C8uCzQPKgePAVERmE/uPo9AoA4P5Lz+Pc8WV2zxHrXzlslr68bvwi+qvIRHu6dWgP8O3p1qRObpbnjWpvSPRQox586X7wZz/Q0BbKKVNlKrPo+UQAzEKwsKxNhACTwoQ5ndmdGUY9bF4qE4xE4HFjt8LAjCN/n0quP4vx18LQaKTLpaKxDjdPGWamICet58oXKnKQUU+uYeBK0wa6qlnz6OWM60SQkd/duXqdZwX4onjAX42sVSFos+cIqGlTaCpGeA4+2YwMPWe0EmKvAs8j1hGmVAX4SDLpicyz69dzX+6+u6G4MBgw4VdaPEcdI7Tu071Ga1OCaDUqmEch9Fp7wDSI/aKRAwOY33v0W3b55Lgsp1UTld7qtmBMQGkpOYfodMxabi3AJ0N0UqoBUs7l784E2ZBDrwX0ePAt18i8Yd9QCBUAMLZCdEZhhSg+U1khjG6IZ/00HM65ybRF7bICqPi8te+9o6FxSwWuVHDa+5PSJaHwYN6W8bueIQdfl5JMoxCKuZC6YCSkqYXoDd2UYAAAIABJREFUpBfOlIMv5HBH1T3ZeLZ1yRTulee48ptXFEN0NgVbys8lx0z7mTQ9ZJlJD/MnF2ZhrCE8+OBcYRghKThuZTuOwPVzdxblxhPqwddnmZ6B1myIoSqQhZJSBRUAM0Tn98qDb/bmtwuonqaY5zQN1bjb5XaPhi15XgcFTk/FupKvIAzdIjdfluE0bY7wyff8Cl582z/Eax/++aV+Y38JISl819CabxHOsGUAs8GSGepwLBVgJq1sXM/BJ+REr6/dZuhSMdGKEJ2VfG1mnWT9qeUNXrUlw/AzX99ZQ+Dj76XhW2U8MsBXhiNLvyveI4M34qCntpV1u+Rd6lJi3gDAV/Mona81XOxk8xWAb+4Pv9ny4NPC+A5+1613YeRzCPAepbDGa7KbjpwjQs6LRJtpK+aXwlLeI3LmrBsJ7LvEubuZTgUo3IhGIQwt6XtOO318qJG5PsycF6+Be0Dbg2/uW2+ITuIlHDr2W4U2WxtwrLWd5nED4I60m/hgxL5bz87XS73bzLPysuYx4spz2SQChgPG3I3v7ohndM9xW3tRhWpGAH/9qSNcvjzg0qV2iHYrN2fYbMyzaJokwCf2cgPgU3NcB/CPI/rjJ30PcWFi+tBgMSrUGGO+wG+RZ4ai3waI1srBpxi9NPcFAAdTlqPpviCjusRrrE9ye+w452Zdji4nsKE4CwO4pxumPcC3p1uTFLArklcsqAFgHFYo5VzeRKniowp2aB2qNKh5QX3y3b+CV+5+CtfO35sLVg5lUyFWfVGhaaWPGBYXzRjfLVTO2SE6y67WxiwqlvR3KA/bsi+WTgAAwsiVuyof0fDgk9Y+WVA1+gzwAZYhOltABAs5UP9QtZpM3kUwnaw+NXZhFIxqSk1e56oQnR2LeI43T+skTFdk2Cs5+ALqAN+XH/tHs9hMlOQU8JC5VMwuVz34soD10oPvSX+PY525sgE+mstFKALDYum4XB/HkultbS83y4Mv5rDU5lvhwTeMy7sQCVPuaUH34NP6S9ucfFagajn4qgALU8oBLE1eCGaITrUuh2qITu/qHnz5pfL7UA++4BymXV1AzhbMRLAQSgXZN+enuvBHAD75LZwIO3IwZQXANB6oMf0BMItopWtL3SjvsxCdI+o75dJS5yec340qdQjAJyR5a9tfm4Ovh/LeS0N0Gi9lnZluXQ4+tY5Ac0HcPA++VISOXeWjtfQf8Vg282cpwHKPB5/fGSDv8rtYQl6uVcFbCWuEGKKzPl95zp1hAP7mmV/E8499GCeHFzNov82KQR9sz4LUthDiqwAf6404H6gHnxKi0+22/Z4DKxVdgGLg0cODOP6e1BvraGjnlKE5+Mwwc2yPUBRQRj8vX3wUX3viJ+Fvv5ifNRo4W4jOFh+6fs8oKxFGXLQesidJo8wCBF2bg08D+NzAxnAw1nvmwYxv2wjRqXXQ8r7U+kj/TdcHYRCAxhKh2nxxvQg/KMJvS9Dg9YsPp79PDi+q76DlNiz6Qp9Tyl6++Ciun7urKCuJhWWDtW/bHny95BBwsEwjOf+kUrhPew8hczaUsw3xMqYvYM/dhBx81Ji4Sc7Ip1XwTw352AJvlnqyEl+sCwEADgPvuBnyvZC7ynXC7tJPvfyQ+9UaDz6zDN0erSTgoABfX4hOFp3CY1WqhtTm9vo6Hb/Q/9U8+Ch5GQc0lMCGaCJ5Gd9QDr4Og9+5rRxtJQQjfO4y3puBj7ssZdVd6Lzqx0tRBgC++e18BqrgWGNNOgfg4MDch7wH/FgB+IxOWgYWhb6DzO0wGU4blRx8jO8S6zMdjwu/a0VMW9SfKjUBPpoCRnzX2tjLHOc1gNwC+HraiZRCdPaGHtjT95X2AN+ebk2qAHz0YiAefJ1nbKoiHgqeepUpQgzpTmo76e0k06jsifQd3rj4MD7z7C/jqw99yFZm0fOCeijKfhnCYMEsSmbJhUICmEM+2Ju0yewmaWtAwYg7VyT46GHSZP9p26+8OuLyZZ0h1hRwBbOzIkSnSU6Pf8+s/+RYiBx8UgGq8h/EIjeWrwPQxvdzGcRg4U8cV1J1y7MdnLoMM6YJQRojwcMmVlsQlpJKDj5wZXIhryoA33Pv+Mf41Lv+GV585MdZ/4t3EJVpYGcIqHrwBeJJ9MLbfxrfeuB9ua8dzJWkVg6+3S5/f22v5LmgSubPYkpVRX3le9doVlQTwcYNgGSgFQF7CDt2+aQD4JtD0NnjUZOG5Jy9EQ8+APUcfMNYycEXqut31ic6DMGzJugwSkAn/dkA+Iawq4PCZO+Xc0eG12IefOMhTC9Pl60FWwILu0c9+DpDdK4D+DIVCqoQVAHzRgC+KvgslCEp9Kk3zs0AZihVa6ymjKrRZjP3Y7gJITqLoaT9a+Tgq/V9HGP0bGMsIu/HDMyWW5XDM42t0NIkr13xrPwWVg4+2q82wMfPG+eAN8/fgxcf/Qm8dsej6dkNCe3lDcvcot6k8KjwP/KaOA8YX6Pl4FvCAXdTg5mRe17dg095fgEB6N5L99dzYztEJ8vBV+Fna9eqoZvdXGL+v+HB5/33xIMviD3jLGSF5wPAQCsnQiQXBiAr99ZpLPO/Sm9K7cwNIe8DdD949dUBX/7yBteuuWLtei9Dq5/dgy8Zqrb2C/Fc8YgB8MnIBZHoWS7DgZ4cXsTfvuO/xDcf/AA+9a5/xutb5mc1RGel4/Qs2W2O8IkP/Ld4/cGnlDbKZ7PsodPMgxtNdwhK9927wzPPzHuAuo4JD9MVMUjIiIeH/J4kbn+ogAFKDj4ZojPe9B74kz85wh/98ZG2Nde6oVKWl536EL1izQe57moNpW9d8eADFvlY85wxjFFi21UPPrFveM2DTwinDIBOf9t6oOa5Lz5pjOzRDN1L9gG6JazJvxppTfQXSundOidXEUo5jZXB9xLA6CwhOtONDmGByvUWuJduAtiMxDO6I3Kx5u1aG7aaEUABWp3B2MHfd5+5wbZy8FmIrJnHsjDqIWtuCuo+wkJ0uoHpA3hd+jtgMbyzzi/mKS7ewzJWWxuiU5J0CKE6CfmYjLJU8MAd37wG8J1hyuzpJtMe4NvTrUkVgI9ZZxBlCHVp76HI4HgpuK3amUpFelnEOiG4IKUy+pZZicVUSGaRHPrOzf8rlMQoY7qbfdXaA5I1i7wXBJjVxSs4XZgGgJe+s8G/+BcX6n2xZf0CcNSomQ9JAfji2Gp9LjolPfiaITrz2NYsp1uKFFPYTX3t0bga1/SqOXBOH/NGvHjnylAqWrGlvggG8XeX76V02zloKQhOz9+BS3e9LQkKUTh24B58lBuyBPQQHPu2ITiMPmv1KBN3srk9gYqtUMMWozcUgmWmaXIp3n9i/EIUNpZ108rBt8JyswhRC8pMz+tb3e5E+MSAoWS6JdgUAkbRt+2JztTzuvM+Q601k0hozMMiJJJzGDc5t4ULfr0Hn1T+0+2h4cFnheh0LvdpCFMB8GntFflPlAdi2dFP5bomxcOQQXcrRGcCFogH3244NN+3C2AqEaAiRKc2v+nz85ru4wNmT8Zc1ieAb6nHU6FPr6MnT9Iamptd1jvx4EsWokIJwtc+n9tsHfSinqwzc7PjjiUd66oqKcyNOTZX3w8mTJ7Ob6koq3tuaAZEcdiqHoYWCBhDm036ddomq09I4gGumU8nwGESQvgvf/RNvPe9p/jxDyu8GyLA1/+9aYhOde3UGD/ynTUPvmFbhu2s1rUC4JtDdArFWOO9494lAb7swScAPmU8eA6+kkojB+V7VPo5DHM+32q58L3x4DsLjSPw8MNEQSUVlgwRphbu+fzTqMgL15jSWg4+algC8O/Oiik8xyuvDHj55TmnUaA5iXyYeS9SSQvUTWCFsV4BwHcCnFU5aQXVcvABwHfufSe+8NTP49pt9/HnljXk2bfkXmS1EJ3cc3BAGEacHt0uC/M2gy88dYr3MQDOap8qZbQcTZbXh0kC4Ds46N/frBCdUoXgvO7B95d/eYhPfeoQX3juCN/5TnnOuLT+nA4cVjqpGlPDJR7EQTeOYuWtdS+NGeW6EJfH0fgugsdt5ZPmNzm/Gs/6AohBls3aJPkWpYQyJqcHs94kbkEtPQf14OMA3/q9f/X+UgDxQQWqi8ccPyNrITqprFT0MfSF6KQAUQ/Rc6PluHtADG+qufpI3Teyj/PKDP1iJDagudynn/1lXLnwIK7/2E/A33uvCSK2c/Dpbdrf0T7zgtfX6CB0Evb3sMei0PuJjqsBsGogbNKnaqChclZJdtAbAJ/Cr9wMD76oC/VaaPE9wPcDpzOw9nva0/eBaoodqtBhOfjajATdxLSQJi74urJGgEi1gz31pRckSe8sTsJcwKyDKmg1sEM2yWoKoclAOONwbVKRg6/ShuPfhTxF/nImo9fjwdeXg68iRUN4fIKEtyPjXsvBJwG+EAIkg8L6Q3PnNEDsmmLKmoYUICjKVkJ0VvvhK3UuNyxloHMOCB1ruWK1DZSAlFTqtdalVLCxxMgiRKdJRYhOPRdYARj3MFdSQU29IMS7pbRGzqUlUA8tw8m52UuumwqTQB5O1WpqEuBL4bmmSGsOc4hOksYCpyfie4luzdXkvVL34NP7CHBlfXADxsOBzWfN0tl6Z+cAV4mR7N1QXQw15j0qLZyfFtCxsS6dvYdlmvcr5ydUs8uTvdZSisTm6FqdxoMkOBREFCaaEYFFdHy1HHxFeYSebQ6AEqITZM56z117ZNL5+PfKHHxVCjwsUQT+vQdOd+X5J5XMRSfEnri6Y2EOd7SZ2gBfYdwQeaLyUr5KKpI5oCTVQsmMdcfOLHwzD766MpiV8R5Xrzq88sowAy8pB19jH5adkjn4sHg+N7wwgwABH3o44KHHTnD0Hw6Ab5WPrPXgg6uH6KTnjVxfrPyNAnwdJIe0Nm3UeQruwQfMRjRxMA6HLWIAgfn58tu0PPgk9Xr50X4fHgIf/eg1hGsTTp4PePNNUT6EJlig1t304qAfVweQJQ1DwBNPTDh3LuCFFzaFsSV713EEFnZEAhNrvBw1oqHDUh3SM0R5f27IonzvrWMTLQH/DT62V3kclWzSWzOMpfe/ZKMkb87+jfe9Ht2GhaldYaiSlav5mX/4mf8Nb56/B5969qM4PneXvq/GcNcC4ANQLORCASoMi1p90+roIW7QpvSJ6hE6Q3TSoTg6Crh6NbclqZWDTzWciVYKrKPAX/zFUfr97W8PePRRPYzqDP70ywjzfFUAPsbz63sT450sgG/pf5Zl9X0h8aBDYPLzw698AceHF1HLuacZr0l9BR3kJP7J/aoGIIDLG6VhEv9X0nM/8lG89vVjuODxw+53cm7uhlEQ3cvYXnGGEJ1nBfjW5uDTjEFrAF8IGWSWvOcaD75aug9WH/3uFsu99Gcz0PDfZd/V50K/3FLTy0U9Yhqb4Ns5GwG8evdT+Iu7n8L7f+wqLlSUOK0cfOrQhMC+F7tVtENkgp3viD60AHxCvzv3RZc3Z8MwwV+IM1PlHWvrJ4XotMNWMz5a8rHCg48aSki9adMTuGPB7UN03tq09+Db061JFYCPKXQIwDcOHRwAqSJuorpLf1f3ynwHK2TlroPYc4tW9rBVAQX+CHcTQ3PO+Al/55YHnxkiK3PHZXecAzabKiOhURCWyuxwjgewJggruraC3zMUrLz9G/fgSwVOTuDefJNb5Q8D23lNPXp61zy2LWtBjbIgwxmumMOuGqKzAYKZJL6FFrI0vxPvaw+vIK27KRiXwySJsICSYe9gyil4YHnwAfqQhIBqiE7qdeF9ZmpbAJ+57CtKru2WfHdjn/TCWELSjXrwxTeM30rV3SwAH1eO2JqpOH/VHHzLmqMeCgzgWwRruZUWigFJIRRjPR4MzFouevAdH8//0bYlOYcbCNGZ5wofmvmPcZwL1Tz4OPBNrtNfUtkHYONPq8IGNptiTaaylcThu/EIcHr+r3l/6LM8Zl3fblNfvBuLtc8UekngrzaTSIYsYv3znkvnFsC3Ugnd6hudC3QffPNYCeMWgu1lKJUXnYMiFYchAJvp1C5jUQL47P2Qe/DV5wbz4BN1NkN0pgdJe9Eiv3Iup3y0k8fnPneAb31rxOc/f5DHXAJzNaAOKMOew2Ea2yE6J5KfhimZj47U8j3esnIOJ4VCi9ETiii65jQPPun92aSGQlDO8bUhOs9dfnmeK0aIzqOBn0eD0pnTE/u8nhsuL63x4Iu3HnnE4+l3eJw7pyh0QmjavLng8e6vfAwffO43cf76JQDtMG0HJIRg794mFdXS2JLyhVWjTDEm6z34FIBPKOGjIq3A4uO5Zk0+MtG8sneoITqba3C+nwA+WYd5Xhpz3Oi7tS9SpWILNGD1LUMq+3vb9Ut414t/YLepnAlpjBofV3q5WKGEndMZ1KoxjNlo2SfmZWkAfKyZwBW0Bwf8Xrkt1IGE/M14OScAvuMcWAEBDrffrjHteQ+MOcZrnyHdc05fJ0J/0QpvbuGjVeM90g5RYbD99emvfhzvff73WPj4uU82oBf7TIkuCSsUenuPDMbf/FunEuTSG3c9im/f/560r+Xc3A09R5Kjb44H3yqQj+qsAOZFWH0MXF50sD34UjMG2F3m4OvjQS3islrFYyxE2c0G+KznFHzTlPW4CKDIWnQdyjmn7b30PllTRlfrOfgMRNYCaiWzxEKEW4YpVM9QcRyo6Vnl+SWNAVT7+I689TxClJCVKwuB6miGIbBnCl7F2EcS/9WhC/dYovRofMvKY3JPN5/2Hnx7uqVJVZwzBVreRdbk4AOyQOQxcmWYwVBq23zLghaACiRwa1DCPck6qOKtA+ALjitlzN6JZ2V+7bLittKrYC5QhuisU/wI/IF63jneB4B3tZgTXR58ZRgz3ssS4JPlHQLc66/jwm/8BjBNpQcfVUpWGFDWAHpCdLbnY3ktAwRF2YoHX+27FumFpEIvZMBH1u066peAm5aXS86bmYHJioBhdCimFmUMKWgoZkPo8OBrAXxU4ToDfOS9KxPC5DcrHnwni4wakD34FLM9sw3n7LmnzqtCIOCCvTXnpXItWs5TRlXbTQe/Ywz66Unu72YTsNvxp9L8XKzw1DE1BloLHTZsOMC32wHf/e6Af/kvb4NzwD//52/innsqVoQVgK8WolMC9HJMxzHM79gA+LQ9jIG0GsC3O8FuKPMVpUdm98H5mUaITkrTeJgBWAnWEUVrS2Bh95gHnxJmSir9Vkgnc34wsheR/FAFGDwMiK49qqHMCnKGrvG+11/E+PIOITw5lyNjf+26zvLPQrDRFcK/dIVILWvHNLlODz5xIXpraFa7kW2i4XgaSqupEqlgXr/2t9ciBJgGLISiIn/acoVN5AkKpR9VqCmLJJ49dD+9fPsjcK+bXZhBQIxgMz+uo3Pn5KWlG30efKl8K5cW3UNge/A5JYHp7MHX15ejP/uzZhk5zwq2sLEeH/nz38Mjd57ApzPXsRCdh054HKoAH7mtnWpyKrqSv6utRaagtN4ntEN0Pvzy5/DId/4GAPDer/w+PvmeX+FeW8NYGP/cdtv6PE2yq+U+TN6B7ElSqVZ48NF52bF1aSE6gxu6csXRsHYqaQYeFcOs2HYXSYQ0kmHIZil+07WCP1R49sBzHa/LdzjvAZrX372XX6w8NveB8/8NjfJCyfO2Q1m6FujU2gE4mJf/pAyet5XLpDQVIQ8P633g+7hyXwnRGQJYKHM4h7//exJlBE5vN2T5scgXVelbTb7O/azIVrlUtSFLlpUA4Myu6vPHOqvmdxa8rZANNgf54Wmn7A/q/OC/TQ8+R4xqNF3ZZgO/yFRxbdZy8AU3FLkHZfSmmxmi05Zli571AXxi/xlCzYPPLXq9ZT8Rn75bn+hcV+FhyLJACHbYTerBF1djlwffyvRC1bNQ8hpnRGuY8R3RoU6TyMHnZc7iyrpXX162S2SCSffgm/e/rHOsGQrU5O+e/YpSdf0s84iuzZ491apb5uBjW0cB8PF/uyjqclbLhHv6ftDeg29PtyYF/dAF+GZCvU6iR1IPUcuLKVDBTbf2SA8J5WLpwVd2wFIeF0KGZklUy9/TEhyWOplHggLEIazz4GPXo/CofCjnUGhOmmAW4velAry8Zz2n9E8OR0fCkWZSe2HtkwQW+j0QcO4P/xBuu+XgHqCE6LTbAeYQfUl+7QU7KQnQivZxDr1YAfi6tLAl5RCdLrXFC+S1WtzrXcNUSejK+cIto7mVfoAeVsPy6nvolS/gtj/7D6QgZ35V4UoB+OggWyE6ZfUmiYeooGl58EVAC8jjnqqpmAm2QMeifOV7S4CcUgnwlQpu+bALoYjhP+fgiwBf+ehsdTYrsEuAb5mzloen2MSCG4AhA3zDAvD93u+dxzQ57HYO//7fn6vvrycS/CDzxOXYgeq80Nbv8ncKx5pCdCptO/2H5cEX6WB3HQ563ocQ5sbjY0WYkXTWlfXuFstO3atFAx06FstOD42r0nJm9vIS8t08/VYiRKfb9HvwnQHzAwC87Rt/jnt+6//B+SvfnethAJ9uaWlZls/8Ajk/ztKpMA+DBPiW6qu/CYJWXNL61Ap16oN9tkeLV4vXSeHZqdIiRbGqLO54FhaeerrnvCxXzA3JU8Hh+vl78N1/8LO4fu4uvQ9i3UReEIDpweeDwzTaAH5qP+6pxBDD8owRT5aVAIWxA4AzhQWrU1bsAIpX+yDGiv0xP//0l/+Q8U50fz10W/a8Nh7NEJ0S2IWy91XWo9zXtbXmQsA41HnKB1/9Yvr7rje+Pj/rqcFEuZ/eRtNkazJHBxUefHTdkTUwyNxhRUXrGrc8+BjUmIxqhAKzlfeXheiMCvT1HnwagGN58GWDE7GvNAA+adTjUHpTOQQ+F1YBfFwOp5TmlK6ZB8BloaTQFS9SzHnBq9c8BLW2TQWzIM4/lH1iBomdfDUtRoE2jVdphegclFx5IaDw4HvhhQ37rQKRS8e8zx58XeR0D74csSaq1uvjYyrmSS5kACXQLXjqYQhdewUz2IrfT7P6Wd5gJENoHmNGu/ky/96pPqcbUKcpfI7IHcs+HOUhC+CT7Yidj5/FnXNXE+OqJHRhPcAJgMUQo9wcK9uIrndDX4jOGeAxbiplqcxN5632+IbwJBIM1DGuoMpTPVTwoILXqK5BpfMtsKgI0Tlx+V0HMGc9pWrsJ89I0rCfrHOZ6yTo9ygNq8pjIUWsEHwhJS3zRo1HiPyBFqJT463L803PwUeNBlO9MsqSJXtVKIfobEc929P3n/YA355uTTIOXYAzIWGlBx87+60QnSuExVqIznx4dJLSrrNOnRrHVChpyVg6K0RnYwMOAaoilZzkJQCJJQefYIgrRBVPkVjeucpBEeNAVwVXKmAbn7ppheqkB19kFOiYegyXLunPD0NXeMjcn1y2CvA1DlHTGW+lcqRnPoeCsRZCKAnRKQWiOI4tnsAJQKtgYKggoniStj4zy7XYQOC6AT5av6MefHk9t76JueypMkwUoiE6x1GvW4mE22zT7KO0DhNgrLX2/CBydsqPJB6MioDZgy8XOzkOCfCMFquxJwARrpwj61e+REV5KsFU5/J8CgHTDrh8Off9tdeGyicNcKd2jinpPSC7qIXojOPdF6KTCDqWUFeM+ey5xhU6QoFIkFUrB59G03DIPFpZnRVrytS+do7u8vzwbmzPbwO012hWeNA9jHhYSUmeesOdTRbvohCAd37l/ysaevN4s/RRKEGsM9NxQ4gZFF+5GSyC+Y2F6Kwpzmn/6gAf8+ATHyCG6LQ/zMJfQFHSV07FWMbvxDvEBSkfbVl7yBx8yx9Xnnk/PvuO/8rsh5fiXgPgm7xTwY6yq0s9HR5O4sFcHiQMkeLBZynXzkotZZ7O9yiKLJ+9cacJ6b0owAfo47E9tc/r5SlV8dZLch1b95s2b9qzFFxRFMXnz+e/11p3M77QYObjnjSDlD5ZqGvtrQ7RaeXgo+0bYe1r0VgAMHA+HgutcGesXaPvNHJLIZMavIMpJ1UOpiI0awhsLpwlB5/2TLzWG6Iz7WWNjztHDuChy0y6gQOa8+IKaMv2yo52hIL2sGF3wTGOckwGxYMPAI6vTjkFqnP42teIQZJlmEdkvZiDr/YZqFJd/b5ynalAq8vKcGP4WEQUAE50St6fQ3S2lUiq8Qr1QBfzknrwJYP0QuZFddDYHAl8btXAFArwxXbifq8D6zSeaO4rnUOD9a4VWn1WRxBfeCD1NcTnj+UdG4+XuH9RHsCFsMqjr0efQ+X6QiygbS//1nLwqfUHD6k+qxyhRTnZWTNNDtB/opM6aBve13PwWYisBdTKHHDcCC9UPPjyD5qDz6qrqAB8HbG14VRWVvHgKz8KB6llOgtbN04NnSnA18qJTevTgESLEsCn8S37vHw/cNoDfHu6NWmzQXjsMZw8+BjePHc3u8U27ylvXNIioUbO5Y2ZKj4sxQS1gpp/9wkU9BlWH/o8+Jw4MGp1xnoL5Z14pATRzu7Bl0jhfoJzCONYZyQUmiFJwlyoArA97vxgEQ2O7S0vHdiVMdasrxjDLDk4Vj+XrFr6vIDMxG9c3ZK9pkyZ5zxh1F3MF1IqK/I72Zq12tSP09b83pYCnSmU65PFVIxE4YAqheU8dEZibPpd6L4ghWERotME+GjuRQnwFTn48rheuNDm6K28Zhol/MhRT0YByPhyr7B+N/smy2sCrUKR6adrinuGoBhIF0JSLETanmTLcpqvpLTC09ZbfX5Lq+JUnuwt0ylfp4eHwRTSXPBF3ik2V4exuhJUZePyI4Xo9DbAR4nvu5WQogsd7q7rfQ4AhgxqWgCfNsYpRKe196QzuP4urC/Sg6+coLmJfpMcAIuyTChdUlc1SV7wELnwzRWIhsUqlu6Dmgff1asDXnxB5GdbqPSg5X08OupTTnoPbHbH8kYX0Arwva5cw2TPVpRWdKtuheh0zv7+2r4hIsxHAAAgAElEQVSVlPQdOfhkbr3IuxaPio2iACyMMOOlNTGvYzKsbGs5+DSwoyhHeGOphNALovA6Sax7CGXEg+8BUSUzUAf44p90LNJ5RD4eDdF5gLbC6oQthw7FkrJH1FYfe6dKqPyxwVOqdRPjKS2kJfVaWbuvaWMLiHVeWLhbaBXfE3q64jVQ2znOD1rhykV4u4JoiE4F4OsGluVZSy4UQL6yX1gGVkUh2RMlHOFAedwVHnxxOmt5+5pe9hAhOuMYdYbopL/NMiqw1MkfEJlAm3/sm/bsd4HzjwcHHPCR06blwafN39NTh4/93gaf/vQh3nxzVvCfnHAepQrw+bDag08dY8p/B92DLwSkeW3b44i9XS6jJMe2ZRNLJ5OuVXgG7sFXgtOBzAmLH2JeO6wt3Ts68UbneOQQ57KBp+4trHjwBc94LgZQfK+s1KQuzAB3JEkPYi00oWiCRBPhN+W2aZ4dCtCjEc3ZC8jU3CWASz3rJb6rj0Xb21XpdlF/rIktmNXf2QahgNmg+Sw5+LzX7rkS4KNOIJM3x4Xu0eY2bMhmMRqSpW8FoEbNqRq/aR58wtu6puYeBMBH948C7DUAvkQd3zzOEzn+e7o1aA/w7emWpHDxIvyv/iq++5//N/jbZ/4LfpMq4Vfn4MsHj08AHxHmZcL2Vm0dHnz6cxWujJBpGSbqoJYoTDmBwBkYp3jwhb4cfOp9AgQVrzRzkwUjYbWjCj/IQJR2j3UxWrBWvkEY24JjywpVWjImQZWNaUVRNY5Fx/SxdeTfucCF2yY90TnssakBFrMSWrdGrj7UIqGElaFxqAffmUN0Bs/mfQGUUkFkHMX+sOTgq1A1hEYlB9/h9ho22xn4cFWAL89Faj3mHHDvvR7PPrvFHXd43H67nEf6IpIhSSlpIToLOqOwpk2HIvwDqMIjfqvywWk4EABDET+TK4qX+TsKD77tKc/BJ4kaaFg5+CyrzDiSrBMAhk0uP23FnDi0BdThtB660C8gp7yefy8CuzItuAcfOTeV/Us2QIEh2iztw8H2Tf2lAOYaUioGPWCcs9O4ePAZ4co0a8G5jVi3Uq8A+KpbWFgXolMqyyjAVwAWWg4mRNG8crYpJvtV/oJ+XzL210826sM1nocC11Twf/vbd/ihH+pQ6IU5QoD04FO6YQuZtW9B+UHVgy+/WxFKhgxUSkrfYFA0gb42flGpFyaxJ3bk4AMU5YLw4DNBKtF3ytcwpaaRg88Hh92aEJ3Um0A7nyoKhQSMEOAoDENXSPWzkDGkaoF8bo1FkWgEEcIZQnQSr/peBUkB9jYMuvQf/NKBcj62iCkqFX5Znp1rQodRvtACv2reojfswWfk4GNnY8hnLqu7ITuyEJ2JN26E6OwMTSONUHNn13nwaV6IM5+leEIEnoNPy29b6fIih5cfxMc8oxoAp+y5llFmcb4kw6LyHYvnDICvh09m/C6UPrGN1nex3rRMKwdfK0V4BGWLod9N8B54/vlN8azlwZe+h+fh4SyiSnUVvKPniAWcdQB8jA9TOiW/i8VzFv1nOfhccY0ZMzpgJHJBLdK0phNq7ZvN/pIw+XEsa0dqYaS8PMf2CvqdW576sZzCxsTrredEd6oUUIborOmcKGBfhuTsWOdERdP0HiZ8gLT702Vorjtovf8wv0w3VQE+x+URLeVG+rPWKD2zSRWzBx/Nwae4ukkydJAhlHw9/RZ+anvwzUb7el8LfkKcmzI1D72ne/B1AHwKf9Mjjw5ehujM51yLV8lzuS3TRIq6HDXywNnUSXu6ibQH+PZ0S5Oag88I0WmGWjMoEIAvbUatkzQqeeK+Wdn80yOWojheDvpGC8AG+IwXPZsHX3szlomlu8mwNjfbiX0zFM21D9wD8FkKVlZPzI9gMS4CTFIFCMnBURIhOlvjyQQe7/Ge92zx/vdv8da3CmnB4qKV7gGZF6RMZ/Mh65qg+Ormu9VCdBKgq0YsBwfJaZEYdjpn1RCdikBFrlFvjyrAR+7fcfXb+E8+/b/gw5/+n3Hh6svVEJ1BAnykLhcCfv7nj/Frv3YNTzwh8pdZ01JYdlLiITqXEkJJRfOZNhXvDdIEglxHLUTnhrXXCtHJrsc6PLA7nUN0Ose3oPSuabzdsmyUBW29tFB0JUUC9eDb8rV/dGQDfE4B+Ch5N5JxKCsZUsVc0KDv7qohOnO9/OyA/kAnBapcUDz4rO1qNxws38fYe8QZ3EOOAnyNMI5L77vn/OBFriiqWPKe59EdB/Wcmp+zlZOBxrzrJFX5pCmRhCK9AAYYQ5L/PjwswxipSyfEEJ0yz2T7HTRlLql2/pfyg60QnRUFWwzR2VTSk7/Tp61MxpSPduJrIHvwBVFe8A1yoKSGbrlfWBMLmsZDvHrXEwCASw8+nW8QgI+SD3O43BpJRUg3wCd5XgXgw2aT3K/Xnj9F28Z5FtdcocxTGqTGDHFZbHbznJ4BPqT3OnBcs6OH6Mx/m+GIpYJ9xcHMlPwVD76DTV1Jq/WNgjrSA+upp3Ys/Pn6EJ3Lmg/clIPVQ15uCL4a5rBsv76+Ne8xKVdpRnEhkO9sWvPkflfD9bG263taCFi85ZdzxcjBV9TLeABlbRbvECDHzgUveKH1ITq1Z7zryMGnpW5oefAFL8LhVXjJePacYe9h36c4k0LiQUJAtwcfB/hIWxoISo1alOqj12uBBS+RMI6PXSHnFr9J3wAgnFLvkQ7lMJz6fYt1Zqyl0AL4hOFLIe8JOXOa+sKPq2BBxRBb8+ATA5v+NZuXG038k5652rOKriIZNCoP6CH6A1sSdO9fE6Jz1TqKa3xliM7CmAO+KjJywJ6P8RoPvhnoaew9Lrc1t13K5HOfI8+r7POw5/ssl8o17cDkws5vMAOlRrqGDtLmJO13mYOvN0SnHta3ZiAVvLFm2R7NjaxlQdW4wWnOErydHg8+Vq+ag48DfLVvQT0h6Zrr8eArj86OPXwJ0alFB9kDfD942gN8e7qlSQX4hKdUpHEoBRCLnMuWpzwsQYPZlgBfT0gS9ezpO7llTGetL5Kk5U1iGBaOoQR6wszcVobOPFSIYFV2RwvR2eYwCotHqjyNytEK8FQF+DZtAbTF5DOeEHmO0n4P0S1SI8tlaCF5iyqknZ8F1AsXVoDZFcBsBpPsEJ124r4Gs7hUlkOYdYRjA+zQmQq1cpcwSymZCxJOzcFn5YuUfZVWgvHnsy/8AQa/w+AnvPfvflcAfLySieZyCvkdqCAg+6R2biEGeIrx4CE6FYHGqDP3wbyllxf7KAdA7L1GWs978BCdkkvVBIgYKs35qXCWpQBftFLUlmPLWISFDIyKhA1R3ol8W3MUPL3ClgdfGEqPX0ZMaOTXaIhObk1oCH2OXteVCibArCodl3tCqJFCC6U5RKfu0TYruYlyjPyb5pg2uRohOhmGRc/LDpI5FaqhmhUPjp7Gwm23Fdc6psRcjnrbWMr22tqnxihE0R3bf+KJnHfn8ccVk9VlCCTAR+uwficlK90/5LZF3mlVDj5BjWNZBWbVMHvlgwCUHHyWB59kQUWnokdPXJ81D33a9xCAz7zzl/FX7/1VfOn9OSqGHaJzWBWik0WcbVj2m6Hy6CY1jt8HDz6uBI4ko2NooI9zwNH22lxesHs0RKdz+vzYntaVlroBgJgLFfAsn/M6xfnDclxr5RqgDj2z3/e+LR580GOgPNxK5oGBxBYTRoxp5r7YzFpPiM7Tw9sBAJcvPoqdBmo7B82q/uTE4fJlV5xzXSE6lbNRkz1YCgkLOCFnbbEHDuX+UBvaooOIcoISojN4HJAw3d6NasQEjaICUs3BN7Rz8A2aXChlR3m+0KgfVv3xOQ18Co29nvYohRTQBtfQfJvV8TxoNESnFm2A/tZDdOoGVuNEcujJdaQpuWn/dxMra5FUPGsFchk7ROf2gx8EYOOjxbkoNnl5f06TsNaDr7wmwTueg8+uV/fgi7Xw752fGdSUC6kLVNYigLW1RqVcC3BZBxAAX7cH30ptvwTXe9ECcUYOim6DNkHXM5senfoKUaJ6dxjyXK6IBXl/W/JkisvmUMi9rVYW0OVidpPyQEpoZrWiohH98s3OwTdVQ3SGYg6n7hE+sLWPlNe1fZKfmZoHX23NRN6e6fwCD9FZk8WpJ+QwQN0b0rOSryvOSrObub/LmbAP0Xlr0vdGetrTnm4SxRBjlCzLkhXh/2fLi+TBR0IXhXVxrKN3lXb49iiLrL7l/uiWYWVuHPKwccpngC/w9oOdHyq3bVyPgrwCHMK51SE6Yx8ZWIaQ37wyqGs9+GTZdK2Rg48xzaLf6RqCLXk0QKxYTQYzCXha+VCtMG9zXbyPVPmkgq/WHGtQ8lqQyvdYRSVEZ4/CEhAMrSbICICPMdFOB/jo+FGmpRBQjBg4t19/Jf194c1X6h58BKgIwVjPCnUVE4V2O7KejH3yZubgK1/2Bjz4ejeQhaYJS16+iec20LrHlAnLvTgHjJd2wpI9Gts5EvJ1e8Lb3WzsPbYdonM01/WseMtCrFxv4ziv+SFMZggS9d3BFWg1oUIbpgDHwwOtzMHXsqZcQyEAbqJhxBpsr2oJa5MLPL51zYMP4xxCSG4nEcYyu7TSg48aDAzwGMclDLfmnS54Hvbe8wEB7Wb886GHPM6d2+LoyHQGWwA+HqLTYmGKF0FDdUIqaefgAwbCsDlkXigbA1QYFPCxi3tmVWiPnnrSgy8qROV+5rnCv5jvhmm5ppSlZbwHwjDi9YuP4I6xBNLcblc8Pmn5yHjF7O+q4pbuIaKraT+Ycr/COCZvxTOyId1UCzTh3LwnaWGkIsAHzNbaKQxaEDlVtfmxgIKbjSEPuFKhvsaDj92qePBtjDMy3RfrdtydMOOp5G0Fojg2zpQapbOCzCFTFhvyHuuEB99ZQnR+5p2/hHOnV3Dpjsdw/uT1sm/QFZ7TBDz33AEee2xi1809pJGDTxsrLZSbKmvFOs7kwWfckM2IefxTf/vrODy9CgB4/PEJdz+zwwM/eoKPf9w4CESXqRxOic4pq+NcHl7+GIbSQIK2qYBhVpmapNkiOh+9yOUNiHc2xlthoRO1ct/yHHzlm1g5JFkua7n/KPsR7ZjbbdPIhM0BgEbYPeegjSUH0gOCoWneve1tCOOIVz91DV87HvHYS59W60mym2HEQQE+k+c3dDKafCvXM/Pg88rcTTOtMjHZwHNeswqekW9G5/585ijNKLo1moMPAPey7wTe5rW+AuTLwnzqSxcOTtIZALTv5dhqwCelGNHBezusadapKWezUpbKahzgC6mPjrwzkwP9Is9Z7CmC+lHtcbMHtBYJbBVxYSL95b1j4ZwHv1sENT4GvFNhAWS1W+LMFzy6NMKMXaO6Nr7+yfMNXa3cryg9+eQOn/0sNxhyNZRfCdFpGcNq083y4BtC24NPUl+IziEZ8xb3zjhl9nTzaO/Bt6dbmtSwQ4bCd+wI0ZnvB1XZJcPC0GZoBaa1pNZmb4hOZUfklmHiYUXZVoI8VHHHgb50HbPyubohG9YziZQN3jmsDtEJlN9bTaZee74C8NEcfNb7tL6pD44xFBKQS5VrFqCKm4AsVoISpHwtxlhV2aOvDcl0FlVVPPhqpIXolEqEYv7Ppfo9+AqljlBaixCdRTg5LUQnHeozhOjcboRio5aDbzl+i/CcPa+v7RUVpVr6HsiAlmRGawBf0XxPOBL6mwgrxbciNI1lDr7iTStgE7AoWhcvA8uDLwp9UUDWmGdTYAuBe4cuc4F68F27EuQjtmB2WuYmo0RDdKprmFiFsgYRBdN5PKwQnbTemxGWBVj2uaEUUlLZCiCyizn4NKGcNFSMZ+2A2uUcjd6N6vdOf69871Eo/pliyXu+Z1dCM1dDdCoefDXigpxPCkFz3VpryrnCg08OnnPAPfcEXLig1xFCzMGnAdlB/BYFYq4gxTo9/UsVB8qcYXu6EjIHIPn3ANNyVQP+pZJIIysHH5b91svrktfSNE8KzcutorSpWdkelEBegMN05hx8Skik6rqa7w0ib244A//YQ3m/m/8o2Bwx5rvxoOC3nQsJ3ACWqboMBvXgmxvSFVZxa1D3OnW4bEVWUdJZPzhtxrp13yjW7cF0zJRUVFGXFEo3oFlgc8gC7kYawkoJVU+oR0bbjYd4+Z5nsNucK8svDBO3qudj9rWvjYvyMSpndaL59Gh433RZ+U4aQMTqDHO9eT8UZPDTTYBPTECHUJzjdP5fvOjx1NMB73nPtglA0TY1D74EShrrJvYnXYpjZPDl6bf0XNMRK/NeCPb5IAtmXXF5XlJjVNeTgy9wAzGWkrchC6ghOv2kyhqMl3Fczm158IXTvOf1el5rnjW1kHesyWHA9od/GN95/3+WPHB55Y7+g3JV8vtziM4eD76WnoYvLOrBl3mAcvEF50x+iAGIzJhMl1Fr3wlYgCLtzFGBZztE59CKeKX0red6AboF7sFKibIuMp9nBPisbYTu2ZoH32OP7XB0FPDYY+0ccS3vz9loIMtqpgcf6bsawcySIxUPvlZ/zDodD1d7Vlnw9be8PV07ufuB9HcE02iECApOWfOX8li0rFSLyTROo5L/m/aTAuFqIYXnD84V+mm6Np2bjV5+5m3/Ee+4/EkMSxjS6ppRQ3TGlyvPveJxloOP8BWK/rbqvb4806IUotM39s01E3NPN432AN+ebmkyBUXl8GmFpJGPR8FiCiKUWG1jKwA+12asLGRFPNRiHItqOrgm6ZEYD+31HnwWV5GVXgWYBi1EZ72dVCd5aMDE7y09Kp/jodtY8UgdCqMmky+YgQQQ0D47IyxAtDyuNFGGi+rz4LMo9mIWHvi6sQC+XEgZZ2I9DQAnhxfx+ad/AZfveGtuU4YdK9AtnRmlCuQWT8BAI7IuHQJjdoPob2xnGMsGmLJYsZxOZCgSTg+yIj4EVD34ouKGKqo15koFIxpCvXwovYvcN0PAY1//Sxz9u3+Hw+MrRZ1WHyzBMpKE5Zg1ogXQAPADz7nUnYOP1BdDdM4efHr/KODZUu5KiuMWKc4j6sF3TQxlzYBiOD3O73BYKtRrITrpXJBtOJdDdI5+x8KFWHtkEPuD9kAfr+5YKNZ1OfgWgE/ZJGmITqVrZb8jMa8o25vlLCQtMjlIKoS+cYB2drUMV7ziwdf7DkPwODwUAJ9UhFSERgbwNda9+vzCX1CgwPoGxbWkzLWJ6n1aITot4JiWafE6dL4FxSJfUgL45JmYzoaCcbI7ByTQK+2R9EHjo0jlbLGfG4Y8u6YHH2+D/qoX5kYVCTigYd42m8Sv3Wz9gKyvkB044oFpOMTnnv4IdhsjnGmwQ3TC8ERzCAlwtjz4WBtO8easDAy7NZSeHvF+y4PvYHfMft955Vt44LUvpd8aQFMYBawgygNZocDKcJltmY3+KYFrxveJPST/Jvy94QG1KkRn2juyIlc/80qAT5MFKdButSkeKurlDSs8fEWAc25u6/AQ+JVfuYZf+IXrOHeuXt65oHoYTl0hOntcG0WbNyFEZw9x2UT5pvTaGXLw0RCdgLaf1au3IihEL2XnlHVrKcGjEQ6VdTb1cyO3q1ToiBEidDk6hPykbQw2X7Ny8MlIMdPUt1dpxkbPvvAHuP/S82ofqAfftKtNICV9iiIPsvoVvYusQDv37RCdmgcfn3ssbUx3iM6VZ3hc4/HbwTYwj7wtsOyVVA/jd61tK/OYrrz28MMeH/zgFm95SwU8i9+g8YLDkOsNwTHPWi1E56xXyh1vbxOh4Ks6VJlquWIdaGeBVZbQlfsex/OP/zReuu9dePnDv5Cux63CW2E6DYDPfJ9K3uoweYy+BPji+RPfwfs8SRmPb76fg+S55XmyefEF/OSL/xr/ePy3ePrrfzKXWenBV+zVlY8qo0xEvnZeP3WZr+APe0DdZX/pN1Db0/eT9gDfnm5pGseA46M7iuuaNeXaON85RGen14JygAfYysKWzKExFPI6EyQLTaauFLA9+PK/0lvAe1ccAIyM3TpVY3l6rQT4NMGi13oowOHKlboii+busPqihjETndTAH9/zokNZ98yA5t9JHo/MhhHOpVDSKd4VsqxU4FNBVhs39S3k3BkGfPv+9+A79zxDO7PU6UhbpDaflTIFo9FxKjnHhYvC65OuGwXggzM8BUkhH0rFiqxzbjz3fzdyBSCzapU8ctx/CPiW1mhLkdIgy4NPPv/Aa8/j7S9+HIef/zweev4vzDbk3tr04JMzJ+Tvb1oCIyt24twIznEhNvA308ZiDtHplxx8ot9kXsb10sq9VZDofAT4qAff7pSXqeU4dScE+FhiHLKthISdUbd8KB64wir19QtvYd5Lcq1L8JXWKx/oOWdjiM6oRJQK0Ti/1e8XPfi6zjdyqwZSzSajc99k2FfZj7AuROcYuAFKobxrmermh807ZwnRmZpsePBJwJp930Izsz5EKsLsqXSwy/N8Jz2dK88uzRaX0m+ay7SZg08xGIDQgTeYlLUhOtNZKDz1UshOxfglk3KmyzDjlb2BlqkarKi8bduDj3W9dX7ROYZQrDlAObe/xx58aX+qGHk4N+9JL933Lvzxh/4HfP0tPySLLABfDtEpc8roITpDMrqwFCTFMErQDw6PPbbDhz98jF/6pTftosr3jZdaOfik5+37nv9d3HH1pfSbhjxOddLP1lJCG1T34CO8mZ+YEronRGdtXpd7iCvqtdZ7vD5YZyQZmIj3u5AVcdo82HmNDxUgQwXg07z/q0rvzCSxyxaoyQvNbd15Z8DTT++Y8l1SHQDL40S9AZ9+epf3Ci2ySyv6x3Ku15Sl6ey3ZN6Vxgts3ih8Vq8mlBajdmCq8UDneisBPs2YlvRBO/tjx7Z5z/Ob+rlROyfK0IA6wBcrmab8t+Sb2TUDPWMAX8fAaWv/gUtfwg998bdweHq1MEoYNyXfrYUUrnpjUz0Oe5Z4zStTajayyzJ4HEst9Q0gjTUoOJDrPXuIzq6ixXO0LxoxgE+E6Kzn4JvHRvXgOwP1RLWxQ3SScrE/BlhnypFExq71Qftb4zM0kKmX6Jx88ZEfx+ee+SfYXrw73Y/vTvP3Rg+3pUe6AbAv97sQANk9ZrAzAZudBfDlHy0A1TozrXHaTCe47Xd+J7V13+UXijKx3vS3moOvf+ytEJ0qONrS6XasbR89+MINLp49fU9on4NvT7c0OTcrgz739Efw0KvP4e8f+tF4JxeKSpoVe4xzMzABlIKbxjCnvU5YeEhLdPUAaFns15T5lgefwTFJkIcmqrcAx9mS2OxCKqN1NzOnZb0BWAXw0cOWC0WGkC/oTz9xhG99hofqKIaoI3SItOAt7gcNRAygc9JZbvjJg08oIUh9MyhBhAIW+soeQGtsgqgr9TF60SgAgewvr09SKaAUzKhiAaYy7y7PtJpA4BwK1CqnP+HhOVMIRfEqWmoS2mZNIVoKgpEpF55Kl1/LJaQeFwMAX4TPrPXJrEzeliCsV4TIEPDEN/8y/bxw6Zs4TcryVv0tYYY/L4Vei1n2wwHb2uZ25ODTB8t9aZocEGYPvhm8KxvSQpby/sOcgAxYdkMCEYcDElZDjN805TYHv8O7vvJvcbC7juee/DkWojOcPw+88QarP/6n9sXlPYGP6fzHZjMXeu7Jn8MDX/kNAK+lsuxd01NEOKbv0MHsF8oVkoOvBPjmwZB7HTALfbPgXRL14LMNUkoBENPEFMDVvQXRc6RPeSGtJtlTs+Y//x5HG7isdEoL0Vl7B7Z3IaQwRuY8st7VuRv24IvCJQUKtptz9roTz5b9sxX4eg6+PMcsPocCEi0FrublXRuKCAL6nTgb/DTPsoa3e3GuGzn4Bt05VK22GGfDQGt9iM55rNeG6EzlqZvxZqN6398MkmdCgSOKeb6LoaScw3ZzWzF35TLfkFxWmT8UTaARorNEYdXwkUdHAR/8oAgJCvFJK9rLA8ObY+5EKHLwSdJymt4MDz4HLosxmWAUfCzsya3tCZPsM3lGeiSm86ahdAsh89Ime0S+QwT8WbQJLR9dcIkrM9fBMCT+V/XgUxXc+e/W+qLK6RpJT+A6Pj/LAJq8leZUCLjttoBnn91huwXuuCNg5xUQNcrljbnm4NnarxtmGHqAFaCGdsjN70z0CGfw4GPAqWKMFJvtkbfDuEm5TzMvE/Q9SfMJiGc0yZ+Kg17VotLBJP/n9aE2ubwA9eDbbAjOKEE/uS8IwxjvbR6MXa544Nx15ZvFqNFIMTX5XVuEKqso9roWMKXJ4F0efHGuB+nBR8IArgR+ekkaAEovQkosHLAYsJoHnxwX9ujKNe6cvl5YXwbA+SyrsdTcI1/Pyx/dujNAOQcbz9X2/CAM+QojbHouVyqy+h/1ERPz4KuHQbXmQAjlPJRGlpoHH+1fcMP6vO9uyb1o6HIf/+qfA/fmduK7Vg1lYvs0vcWy32S+SJmvqSznO6kH7Ny9yjeVZ0iH/BswA3x7D75bk/aw655uaYob1Ev3vxuffecv4bU7HwcgGJGoXO+QIynIFZlVj/4QnfJOLVxYasvwFGKWEuRButHSg2uQlrYawFfZpDMzMt+hnW4BfKoFCO2DccjLEJ09VHhjaaFYFDo5aTPIMhyMDsjqYcxopdp3tmJx8/bLuiUDKjHINedkU5niygNeDQ1rCEXaNfVwj0pN69MpQmmsy8rBV3h9KFaQkWQun8iEs/La+iH19Obgo94+Raz3SwbA5xwJ0akNhL4fpN+1b6aQ6iWIgHOnBEyqKHyKddRgHYrMeWSMogeftkQmERJuTYjOSLOVn8cQfLmWAvnXZe9ACXLVAD66H1HvwoFY6g7TFndd+Ubav2n4zCe+9Vd4+Lufx/2vfRnv/srHMJzaHnwxv1HVupdZqS5Ki2W8Y4jO43N34RM/8t/h+s/9XPk8zUGo7A+kcllE/Z36SwA+GZYkKjWffLI8ePywWeXBZxoRUK2qAs8AACAASURBVKLhozoVKb1E340CkABKDz7DCml+J7sTugdfhU+hChk2XxXv9Ar/USoo8+/amPH1hCUHX94bt2PpqVp7kZoiiZ7pLcODyQ9qbhEWonGNZNox9+JZmDz24nUjdGeQdRU8jA56VcfSndGDrxGiUzP2ir/qhfn5n63WyVwdxy6DrB6yxqo3B99EPPOLPLuguR0DDg4CV3bD4AdDI0QnXDG+xd5XMVZgQQacHf6tFqJzM520z1vFg68nCoNFVKlrMSXUO0caqNU8+GIVu0qIzkJGUGQcez9a5rGxN7McfPFsxPocfIXilObgk4NvIAAmv2d8b2m8Jsk5FIheLW1G4nGUEK8R9Iv7wm23Bdx5p9gzAj3Tl3cUsoMczugVk+eY4Ylp8Ng9R0NKfeD0NRtL0UqrAMTyg4b0q3lGAsqxXSFPYkgOoRKiE8Y+FTu6XR+i0wS8BsJXW/MueruQ0HoUKAm8WLkOFD6mJdcAnC8ujDaHoZBLB8V4iI2iy2eA+c0sfYKyt8nigewzac/fGN9XGgBnc9t0nQF8ndqJNfORdjrLqvoQ3HGHx+23U7CJR+dwYTINAePxkgGTOg/Y6n/L+5PuKRLg07boQYQTju9hAnZxtzHmQY3kGAXxwdaKRwpOxfoSxTErB5/FL4Xg9G9TAHzkPN/uuGcy6SPVSdDvofO1yhiBryO6Hu6+9AIrH/nGKsifPPhIXuMVITotD76Yi5LS5vgafuqz/zvuufwiu97TTqSUg68W/Q3A7q1vrd7f0/eG9gDfnm5psiwAmZKGhOhcw0TkEJ38IOuyXEh8T5kY+UxkbKa15M3sAGZMKj/hpcWHZN4duPCg9sMYk9Su6unllg/IGeKWEVsMyxeJ1pz0aYZFTUnyVLvxHHwyxKAmnLfy+NQ9+HI7sT9d87pSiAqctI+O5AqsAri1a4rWIXheZ2EtRBR8BcOjCF7Xzt+LP/7Qf5+SqTsHnsdPjH0rBx+cnoOPlvE1pkWMQQrNVQB8l9KfzIKNeKZxb7JQFu4k3iXeP0u5e+7Uzrtn191eI5rFH/1W1uv5YcP3siXkCmM6DTAiXt7tlvkVPMaxFeakEqLTWE9cmHBpvdIQnT/y3G/iRz//f+H9X5pDdNAQnQ+98oVU7r7LL/AQnQLIkTnTtKVXO69oKJ7j7SZlpNfG/7knf07sDxXlV4PCMMz/Le0UHnxL3efPB3zgA1u8611bPPDAAoYuAJ8mVlJg3hR0G9qy2SOy2vtV628k3jpFnqwQWH/mPI19TAqbt+c6Q1rmp9NfA+oeInNpG8Bl+zqdH728lo97Iw3ReVTUoSmBrPw77N+GBx/b0+u6wrl8g//jnupRYVcB+GJoTtn4ouUoPPi8J++mDIoAvZgngvFRJM9SkHFGUgVMi+jQN8EAoYhSPfjGkeTgW69wq7Yvnm2F6NwNGRCKeXZpHbHbLgCHB56/B2ylTspF06tCU17aGoci1K5Bo7Ot+2R4To0mJRQCG0+3LkQnVUpZHnxUMBwW6xlrfktZwTlgGmzPVF+oRUpFnja/Q5gBJKAvRGc6XkMG+DS5hvKhPTnpizq0HNQ2dpqV64LP6vLWER2seRclmUTbs8kmb50JVIRK79xwZ5I5+MaaN4Ulk7f0A4Y8Q38yIKUzrzot1grRqTRr3qfhNEcSJk/jkTSZKLH1xKiBgobVPjTk5LkjQb/ksvwUa6HLS4botHLwMR1Bx0ZV+17BjfydHA/R2VR+GJcsYzvf0g0QWYsaplh2M4XhvKIboAZtN5qDrzXcaTmLcbt4cZYb3v3uXSEvsuf9ZOqcsm6iry8mRXmkCfAFZozSCtFpgXW23HO2MJpanYVhQlGA/q7rnbRHWjn4aFhYWgEFZFk7xbvnhsedzsOwORnXiTVJFZkzstt2iM6sC5ofX84t5cx55ZUBX/7yBq9fnRdmoKFLA/fgk12jRIHMYaDzKqjr4Lbrl/Ajz/1mtc4qzUKHyrdcvu9JnPzUT+HaRz8KrJZf93QzaA/w7emWJj1yUOCWVi2hSiHnkCw1Wa4t45CkgB797YXwqglNmkdSYa1lKK1rihKdoeCcFGXSogJIUwTt6t7xFa7C8X/pIwvAV2MkjErZrwHcQ8LsonKv6BZNcm9ZdmmxG0U7GtbKlJ+WEBDbpwK5OHhl+AwbTOmc7wQc0JNoKzm84lqycivmf3SlRJFeSAjqBJwrwitVmCwZViXV7gZYQogdolOZLzS0FA1nIIsaioSNyL2DK1dJfXp/Ne861iep4HBQFxIXdPhDGUQk5cTEMxU+yu+z5eDLfbCErhg6iwESNc2UQtM0h4hzKURn+QIqqJo63wjhyEJ05hA5jsyng911AMB9r3156ROgCULOAe7kONcnPPhiLpqqZ24SwvO+pOWV2O1cUtDQIfzas/8pXv2Zf4JvPvgBcXaUbdC+ae+SiifjjuWeEaITmK3z77orpO/g3bh48Ol7z+owkwDcpFj7G313oRSua1SE6JQefKRtZ1ksOV1ISqRoZGr9YwYo1IMvGY2IuirnO+NfWv3U+uLn/TgqD4Mbilyl9sM6gMaMJQgvqHmDMCBmmgVSSV2p3hRlV5zGVQ/D5MFXAnn0fr7eONcT6LX8Fuex2Y/KeSZD60XaVYCQuU7yt5ErOLVpKCmBPH7M842E6LzZlN9//qPwNBIAHwU6t5vzBZtC85sebaYSHNGUUgjY7Zb1qBozSD1aWUvN64PtxxcumGd61YPPUI6xPmhrjk6F7v2Cz+PibZlgREJYSYWZ9qKZ6QGAErim/HjhwdcXopNe7zES9WTvYPyOIG4dX4Idc58bSJpCNX6vKIDyDFfrXBGiM8lNirxVHT/tTHD6OireKwgPPs2zI/KqqnGJa/Kf1KDWUvqnczigL0QnJCAQzHu5TFe18GQt8LWk70lWx9yOgIMNz2smg1QL6JdCyB9xmvJ4Utk5y4qhrID8rgFEWttVYMRpHnxCHyMeTHOhspcz/psaPTg9Bx/tbHz0lbuewu7BtwAAXvvRD+s6G3GG59B+uRvcg6+PVoNnAtSVgI9zc+he5/g895A5+OohOgM5ZNl66TTw42Nff0nnuGc3NapXAb7Cg6/RNeNFrX2+qpcTclavp6as22oj8kt2Dj6FKkY8hXxA+j5u7RDjVIdlOTmwvZq1oS28XGhg3nTZ0FPyDqenwJe+tMHLLw/40z9bDHwHwWsQ/Uk1RGcwQnRWxs6iLkcXZ4foPD26Hac/9mPwjzyyruE93TTa5+Db0y1NJr5AdreowJJWo60dLXvwlZupSWJX7QnpoJ77hYSmt8ly8HVYmgW5LSuKWQlGAfUQnedOXsfBdIzr1ysMjBUGZnUOvjKW+fxGNS4W5r3iUocHX8qPUCmjMk2kscKTK1LKwWfXJ4XiKuMvylkFswKBrxuqfFK/TQ/iRNvPPwAA5974Lu64EkoFsvdqNRRwKQAD1oXAQFTOhIVl3RCQUoA2AXYo0EjVEJ1uCe8olA3yuzMFKFHUqAAfbacCqFjKB1GK/aKAVqSj7VVWZh3AVxkbADLrNQVMAhE6JcmQcF4kTZcd1faWaZrbG/yUcBHvxiTsATlvR3CD6sGngYLpnsjBF5Ut1IMv0uAnIASWg6+oT+bgIxQBC1vxEFSAngrIDG9ShJU373kYJ297AOGTQ9r7ikJUITFwr2yNAhzCZpMeG8TL1wCR2YPv1FB6O1gCFy1VEM3B54aVEFWdnFSKxfMjrndhqqsp/Wp7N4DVoQolwFdTIDvBNbBuOGcq63sjFwQf2L64G4+SYspSOCTypcK8ELQbITppvYUHQlLu2OBTWWFuY3PtDTxw6UsFyEvZz5QbS4TozAnY5AtVgA3MPBX7nUCqsiyt40whOhs5+CQA1V+Yz5/EQtEzfRhKMPMmkayv5cFHx2G7OQ8IXVTy4EPAuVHh/QwNY5oj5o5EeAYoOaJcH8A3PfoogE+q5cbB3ot7PPj0EJ2thW1T4v+qHnxDvrrsv6YFPvi+IL+nrLvIwacYRUgeIAF7JJyWuotQINFn3jHmCdU9+AiYqbAB8b1N5achoqoGffSGAcJXSexNYwU8jkOhGmXUgFLFoDfJ8Cs9+Ox8SFlZLlmiHg++/AwvHc+FwgioQS4EO6SfuJfbInJzhfxGz4O1OkTnjhhRHRzCSkMfa6qSsq9ZZxZVzLPpJwA8y4OvJme2qFhzgfNScDxEZxWcbukv1HWp7/9vvDHgb/7mAA8/QAxfncMb//S/BsZruP71O4EXynOK8v8xEo5skhqdr/HgW0WpwRaqJfYux8/I0ZOzwWhGA0x0lZ21wUZ9Uf0lqa4F4Dl7dXCRGxnmNW7IpVGfcxN4JclrnDXXog3wLf9aOfgMfsl7/V4RopOM0WZ3LIunvnGAL6/BPr52ycFnGP6MIh9e4nHFmXPlSn7++kk26KV8xawvixuJvRaoB/b8fnm/aYgVyvW2fOcXvmMKSpjtmypl7+kstAf49nRLk2YBOOcML0+OwWXmPSp0JVGQKwoWNAdfwaAZxJSFrCuaAsw4kGNfhOUNYzRqAJ+hZC/CMFIGxpUeK7PwoPfxgVf/Dh/40v8LhIBvDSMeeMCwlrWUlmfx4BN1UYAvvVsnF1MUEzmQVD6iAdrOHnx87CVoOu50gC+Fi2SeYrwfSShOjH+nKWYDeJtv84/hHFQmPlVV8eArfosPPbz0Et73iX+NJ95Qwn9RIbiD80iMFxXaRIhOSwiJClGek8ZQfJC2qwrReJEKbCGwMBPFO1AdCkqArwhFSeie1/8ed7/xdXzjgfcD0EHqmqDqfTlfx5bFXIXO4sFH7xaXFoohOjPjLeaOZY5JaJrmOT2EKYXo9MPIlPBp/To9B19NWShDdMZ5pQF8sbz3jp0x7P5JLQdfPUTn3IOQqo1Vz9eWuUn2mghas7U+UCWBdn7xE2w6PAfgOilX9ilgYHmBJAAi80tE8sO4rGVDUlUkWGtcecVlHjpZbfob7TlGaRReAEX4LaaZc+hRukla68nEvq/wOG09wNaCKz34es5eVocPOJiykL3bHPYr0qSiRyuyIkSnZcjUE6IzXqcgYQgOH/jibxdlN5uA7XZZa9GDT+pIUhhrMRae7y/FWJ3Bgy8qMFKtUlmlhfHDuhCdGDpDiaMcY8uDr8+1sqM9a9ovY1saVXFiITo354s66bySAB/lsdh1qnRR+VCV6SjKWO9G5/T08MMI44Y1FJ+r5+DTlWOUJleqEYx0V11knvUMmSGgl29H+JDXa56pRV719FtXePphxBjBe02Bb7WzrHsXfA7RqexfLMKMJf44V+4jZRH6q2rQtXSQ3dfyGBX1Fzn46uWlglS2rb5RlPe13OxSdpSiilCCS75kLlM5ayqf1QWPcTploSGLkN2xT6uFYgGyiCE7iwdf4jEJwBfHo+hjbCfY3wpb4cFnO87Y8lTqSP0cp/y59/lvagcVv8NQLt/lPq/3Ix+5jq/+3XrejJIEjAPmEJ0pSEtjX7BEa/qs5cEn6c03HT7xiXMIP0TqGBzChQs4d640pJ77K7zwQ5Yt4r/M67WbT+6PiEHrTcu6Mm6cX+XzxjVCdDqX27qRYAHmPiaI7lkc4Cs7OASfDD8omYYcyoumdzT6bNbphJwiZVY2B8sGKmpBADREJwm1LXI7auB59d1pUWqEZxnb0/45Z9pZzJK0IjcnHQXlC3KBYeIAX5pnEowkdUZDlWgEkviMaYJzbV7c8uCT61ijYu/pU9YiBD108xkx4T3dRNqH6NzTLU1q8tlBKNFQHtBNBbQDtkuYKOapg5UefAXApzyieQrRSzXrJHroKUKd2j+OpBTFJfPuYHvw/YOv/jYu3LbkRvLA174mhPkkKRlAUAHwtYXQwnrIiQIG9YToDCQ3gGnVFUPyWEpI1jfqmUTCAlihjVTOhwvb0lnDYuJ1xbreZ5UZSUx3RaCtVKbVGcl74PzHPmb3txKiM66XpkBABk0yWUyhZihAnRKikwF8Kli//D4pv+/ot9W1zJI4Ew8+CTYtF1PZg5Or+JEv/Cs89fU/xbte/APGLHKyNTY0DKj8qkU/ysfLddSxv7Lf1INPzHdK0oOvSJpOO6y0AywhOhfPuWEAMLgkuLJE5YvyRV1HVYCPz7uk0xqcvg0GP4cRsoQTJQdf3lPaITopQK8JIPT8yd7q+dosY4WiHSaIkIr9wWETSIl7f65LKAWMwYjeINHDUlKfB594JoB5Bnk31L13FyOfXoUEA46lQi9wT2NneZBbisBICtBRBXTI/NU9+EQfawoUaowCe4+wyAcuZO/Go7TXMQWWVp+i6JXTxzOAr84DTF7wFst78+HtB3etOUjPcJ9CdEoPviKOtf5bvpMJeoXqvK6uF4N/k/txtc7GhJAhOp38qBDKw3HU8+dq/VipnaOKHaDuwQeIEJ1LDj5KMdQmQsDR0AsatDz4ykO4KOf4+fXkk7ntd7+bGPBsNrh+z0NKG8BY8cTdGIZqlDQPPuq1stYrJp1bFQ8+ev4Moe6lQcvHumshOguQKJ6NSpv0eggOUZU/OMtdn7wD8+DT2wZ4iE5zHRBjGjnXtFD0c3/Vbpl0lhx8PSE6ax58at/SmVBG8Gh78PF9x/Lgq46HMtEGv8NP/M2v46f/+n/CW155jtQh5fGlikY446K850a40iBFk6u7z+exBPgAm/8qLy7fgyi0w2aFYYhWt9J5CoJQ/oaG6KQeo4WHXrnJs/tPPbXDj//kFrffXn4Pa3uQn25QIkDxdIQEKRP9bOVw06xBZt0AB8N4aw7XrpUGAkdH+pyj0RBkZIdIDIi5wRx8Jq0A3Wp6vyHYAF9sRg152ClcMF1ai/9xwuvY8DzVgNy5PLB99tkqyGXKsrS/yt8FCyp1cCt4YtYn5X1DIH8rqZZMCrMHnxXuXDSc/jyoGClRnQQ1FGDNmnpA8f1JP6KBMW2H3rOIzt8IfjoHri+rjBM3NiVhPYPvndK6Xq7S3xCASTP8WLXg9/S9oD3At6dbmmyAj2yqIQpV5DBRhAZK4cIFXD9391xWhujUyseqXTykFKX8CrKEFoArK84WopOOTeZunMvmPFIJaQF8zgFve1tm3C9fNkJxqteGMpxUl1GIreisKgsayj0AXRbhLfBi8iXDPD9HBDAr7nfyyLHrL6zJOz34ekJESGDcOQoQKIqEDmQxKHMq+AB3fFwFdC2BpKbsYkJQxStnCFOufxyXdcq0kRgUZahUBlt9ddev898IaljWGogcu6968BG6+O0vp7/vv/T80l5JtXXS9EZs0FqArxzawOaUJXTNykJRVloSGoMaL08TMC7M9WbD99NYhgKe8oyphRAFIDyihvz8MOjnVZgYwFeEQ6QhOlMy6rlMDtGpfzRTSKOWhFQxQiwE8zWnrneTwR8G7Db1pNnRezu206MUmJV9BOCzJqo1FkmA0iZWFnACSoMJJvguNpu9JPPWFOG3hAefqTirLcwbDNGZrnfEayvGxsjB172P+MBC/U3jkVpHHeCrGKGs8OCzIhUwhWmDSWHnnFGUnuEx914B8Fk5+BoAnwSzur6Ha4ToVDaumx2i84DkR5IAevbgI8DJOPZ78K081GTxdg6+PA678ajwVozddgg4GjXPeEUpRY4ydU/QziE5FwTP8DM/c4z3v/8UH/7wMZ54gjP11+/juVCoIkfmfY5UU46lPmg51Jz1o58GUNlF1DNSGSkCfEoI09hHGaJTGhL1GNAwuYruqdSQJSbW0+tihgk+X6SKRkle8OVByJwhgAF8PVTdYrLFV3+F8dGVAB/ADTTSPQUIkf3Twxz2hejMik9F8A1RLirbjoDAva99BT/+t/8H3v61PwEAPPzdz+PC9Vfhgse7v/Kx/D0NWTYbKTkzXzs7w7/xTdz32vNwfgI1Ko0FNfm91+Zh2uS9jc3rxlzMF2MOvn6AT5X9GsyAnPNxeKhiXgvRmfnzcu+k9ToH3HNvwIULZ3c90YwSqCFpnbeohyXV+NuaB3dRAfl5221BHWO+fvL+S7cEd0aAbxXFORWfqxnBMx2M0AP4mJah0oEQyjW1kmbZ5UY8+Hh/ADDP7k8/+8v4zs/+U+yeecYG+KL2pPM9auWCG+rgG2f8uuqOj+x2ekFqsIGw1oNPhujMZBrbk+apwTXtKy1k8WKafkIL2a8asQnKxkKcr3CKQTw9xyLxvH9U3RhMA2C7M30An2WMuw/R+YOnfYjOPd3SpAN8gTODirWPJnRSmt76KPB8uUG7hjW7JF/x4EsbsgIkVJXW44gYwJ56UYyDqFwoIbTrQCkIFcIybMUXAFy8ODMNIcyH88T1maID/LXOEqJzHhvl3ciPXuvCgmHelMr+op7IqFnKAuXaXDSXN5mKoax7/vz5d1S2JCGwxcTzTqjXNQV+ZDajwKDKmT0hOuN703fywY6HBs6wyA+hefDl9c4nExWYczXCg28YAO/5/gDd24o2OvkcDLN45eNjPpZOB/goMQaSKO/UcSeFRz9B4TdrXVfW91Lt/8/euz7bclx1gr+s2nufc+5T9+rqea33A1mWhIVk4zF+jdEYN5geBtwMjGc+8Bh6oqP72wTfCILgH5gvRHTMNE10TEwwpj9MdEPzYQKGGHpoYwy2ZUDIsi1jG8mSJUuypHvPPefsqpwPVZm11sq1MrP2ubJudOwVId2zq7IyV2VlrlzvlVlLOeZP/u6FwgyAWpMwthEGJTke7UxV/GoKqASv4UfXTZ7IbeuBvk0Vox7RmJyra6OBjA6NyuEm1OPjzzW+Q9/bKUDclUmJmtTga8oRfI0SwWc5gwRPO6Y8ah3bR/ExEXFDuztqd7A8umzi1DsHT6MK+lQpoE1veN9hrvI0fpZCs0sjLigcR7iXwlzOwOfaSq0bRgMHelz58Ic3S9FJFAUqbgRMnscNKaYCsBotlXM21OCbzsN1u1Kj0tX+Ij1JLk2/56To7DlvEYB6/ptzEZRdVQY+QgdiBJ9oPK7JxMDXi8guOTFGis5cDb5c1DSQKuZDv0VHOdpnYY2ulrYyMCotqPJjsYjvo73WcYxIkt6VavCxlI7Ood/ZhXMT3aaszk7DDXyDsUBP0RlAj4yRP60IvqnvU6c8nnhC5z0vX7iIBT6njOPx8z9/GU89tcTREfClL03vmktvFaCjiqgRPRo1NtdMRPlCPcIWIkUnT2mrgaQLJcM1fzgcYlRO1GlqdDbN0FPZr/N9jHjU6Betb+McxslgWtL8+jf5PZ2WRwNCxulDHSbiNoGWeo62d86g2TNr8MUITSFryzp0SQ2+XP07w8AHAI/93acBAKcvfQcvXHgQS6XOk8ZTSXwBMOcjC5ZPPYUf+ruv4tKJC3jq3n8E4LoSqsVvFvFrUzWgc/Uytqq5X26iWqQfpklq+CXvM9KAvp/oIj3Hg4wyGfB0hoPR/hlppjUImUPoGK0SQasap1X9hcaD03XvyPtV4De2OXvW450PruH+iqPL5DpCf5m8EN7RuWpdmWaMyOEc5cjwbplxuFw/zclgjEzLjwChZEMX+07wUDaUOb+absJoRmmWlaJzMuD4uGZfue4uXL75CvzyWbv/2TkRyTrSjFeKrmguaLo/+t5Wekvre3vv9G8jDXxkHS+zdYSns73vnVr9hEYzqo6x8SCbdB9aqZY5EXyDgU/IkcmcpPPQ9HaKztoU3jV7bsJgkCs6TX88l/HbwlWHbQTfFq5p0DwApTGFCgOBOFneNOF+d9tt8ZqM4HvgB45w3XUpEfbk0KPRAKyNJo8onAETRKSBryFp6by3D4SMMifXPOFzkY/gc46ndDg4UPo3PMDnGvjigangXQRtnuWlikiIkheo9KiNz5F5b6z6ZlI5F/rskybV+PC29mQlQpvnyifVwJPhyqlhRwUhuLJmhGHh+dyR5ZOZ16XS+cDUe55CKUbwidfIpelDWreO4SEMMQCwUJg6yyuMp+icBqHKrQDpWvLFjZRG8E2bfxMDXwp5aS0x8FEhytXXi3G+T6QE68lJgHCRwW5bMNokjapDqbdEuqk28DEDoRHBl6TolAbtEMnTNMBqUDxOe2skBoaim+4DdowQ4zdVtvY0RWnoozEcAFTlwtDmaLFHfya4bRLBB0zp3hIBRw5GgBr2AV0wcZ1QyKYSPeuwrv7AAKUUnQlxN2iqXNnP//Qv4fLP/iyOHntssxSdoR2JSNTST8uag9JwwumkQ2HrBgymv3qe6i+k6EzfwVbmyk3PlVLT3Myuwac4h91xe742qenpS4DVAjJSdHojRac0+CVDCB6Gnok5R4BcCmbtexSjSiFQz5wtALBccgUONyooEXxNmgHChE1TdFqPUzrYpAahfodHMNMUnSstRacqGORTdEqjLJU/aJtaePPGO7C/e3bCKTza97jxxh4f+cgBbrmFb5CFYriQQFN0xr4TQ0sZP2lHcyTdXdN4tg9o6tZAf4czw5CJxPpODHw5vlnQzojbCD1RymnGJ4YHpVs9EIyYQcGr0dYkk4Tc/x7MmSbBv4Lf26yBArMi+EbHUS0ClOBgKd81A3ldBB/hjYwafIl8wRFgv07sv4IjM6NBumcTOp0zMoYRxyFPXn4Zjz39aWC9jmtGrVeFvHGV9b00jN1aPcgMvXHMwHf8CL4cjQ4OeoBdgw/gez7hIeR9CzFxOafPaHyXrEtqP9WM57U0XONrZTRvcj/T9xNPHOC97+UOHFSf5YijrHzPeF6/xSk6p2eEzGR9D9cAwmlTqteGdi1rUGPg04DKPsUUq+DfsOs4jyHHpyk3B17Awa9WGbKUBifkXiO3jhNnokxHOcd63cCnf7yiQWnkl1T5Longm/rN8TB0fk0yXEET5FzlIviyNfhoik7qoNCRjFSZb9H03eRk1Ey6jbcygs+S1efwplt4a2Br4NvCNQ1mik6hoJLMbMnzuL+dG/howfNTp3o8+OAae3tMezH+IxSkGQbLYk7UtuJHr3mFA8MU0QAAIABJREFUMo9FnWOSShlHFOLBeJYaejwPm1fANPBpJznBZU6KTsbM0O9rHaQVkBhmEmFSwbuYojNlzMLcBmiPdK+h6ClP6xqJgzexQTb6GkvezfJexlTvSOZVpwY+FdTvmjRKxvc98oKrxlSPfek1+NJ1RqMAk+LaSg0+Rkuc0+RXXm/KUIj2J0/i8PHHE+a0FMFHp4PSp1J0naaEKHoYio4qdAhZSNZawfiSMOGJsDK0KkHju1TQsIwRI/AIPgCLdlIAEfl62AtWDb6cwo8aixxuummMxLEMfH1n1uBjtG13N9lbU4pO22GFetlO9hAyR9TAp6TodG2j7iMzgq9xWC92VHxII1GDj0SxZVN0hgi+TCrLFKUi+Ew63wSP0SBWq5BIBDah8HWVEXxSQbA+dXZwRHKuPlVh6IsYqFui0OwNlj9JG0t/XLwY6/S8cvZOvV0OF5Gi82ixQ86k/F6Ontw5BUMxgm96dt3p70/37Xvfe4h77lnbinH6t2ngowZO3cAXDftJik6xP2RqMVGXjhovzG/i5qfonA0FhxmaojNxcgjGeVLHCYuF6RCVwCztYfpczsDXtj6p2dbv7pmG41Vje2/LazkDnwbJ+p7z3osF/vKdP4+n7v44ruycIZ3ae2uR9X4foG/SCD665zbl2WkNqETmI3R0Tg2+0P96RorOsOOtaAPKy03R6SkyB6vT6vlFU7FJh1GA17dpGsALp8nwUqYDkZYRo6Ts07TiFTAnRWd8JhPBlwOnvEDJF1I6FmRr8CVGBkQHQokrdXgKz4zspS7LUj1CjSKVNFl2h3CXLxfntvp8NpxeNazUWn8+pd39zBp8g65l6nvd6chrugBeg4/fp8bcxKHTpftCi+DLzyO/KQ18cI452GWNGIouif1WeCGabl7FM93YmXtiLxKDkbRLhn1TG8E3k32NAmvUr+SMGhTlEX9Wi1EhZYxmwx+L/Ylkt0KdXpuiM+pmgl7JuaF9xsAX5FLt7IltFNXdOCSDRI+oOOzWgG7gI9fMWqSp04Lz3s6CI88LMnAuCwGlxwwvyqpiIOTJvJP9Kp1/2n6dzHX8PhtG8FG+SLwig6D74PJAhQ5a0JHaGnxD7wotmc8+bOEqw9bAt4VrGuwafFQRyRlyoKzE8+fPxYMw581eAplqSlfiKkyxONCltKV7iwrmzLD6WGnWKAGnfLoDL+CtwQ7R56qCocUhbZKiM8fkZrjtmhSdWBBvfwOX3KElr1vGx8Yw8E1ChVDaEVwCcxqNErO4z7xgJGtXSgOBbF+XojO97vtBsW0qEYwUnZyxTJVEVd8Enqf6UpSEHlzwmnAkihvFwHd077249Mu/DOztJQzw3Bp8QVgu1cdjSk+jjbyeGIqOnaKT96jSV1o7VBEIYh9OYZbZWORv36deqsbD4XLfI6ZfaVuvRxcHgdWlRrnJG1yfqGDQaVvg5Cngfe87jIirBlrfDXnqgxOJxTjv7ERco0Gm4bRIN4KkKTppW/pMKYLPOju4gQ84XJzQ3yE0H507ohKT7MdcupcQDWIVPM/Rf807Ot7rOvLNC4qUmcAN8EIw9l6k6CwbLlWcNB4igzNzTqDRzFr66UHLaPbrTuzhM+/5F/jcuz6Fr9/6PlhnjIXbYOCbaGPX7lR5O0+48bWYemMTr9eCSCNrO2opAHd3PG66qcfurqHUIM9bCgemaCxE8KUpOumgg3LB7JxAPkVnQaE/55yn/TJGMt9+mUvRGXjcTpzbWc0geaECj6Tt9wcfPMKFCx1+5mcuqwYTigZL0Qmg3zvBzsTgle7gsXLpeV2q7a3VtpIRfJBe9cif5xKcA/b3zuMfbv4hHC5PqvyXhEWmfk0ALT0h461mEtd4bpEIvrbl/VBHPVkDVYPjpOiM54rgnWnfk1IsTT/91/f9Y/zDTY/irx78OeJYBBKNQBTMylzxteGTPRGcOeYq1IoGPtD7lZ2LfViTolN1xlXofu5eVLJLRz+570UNPtXAl+EjtAgSB287FFv0OEm/lofkW3UdcQTV57i07SKPuUj3gnM6T6JmRgrfg9Xgy2fKkbhdvNixc1lBKXlGj+DjOqAcDzW9H32mnp4C6RLRIolYBlR17eZl29g3MSVN+M5LKVriexMHP1OADcbGOs/Rtq13mAOQ1uDLGBucSNXLsqoglYsAfn4cO0VnuFd4Qec4PaRDsBrQ8MRRhK9zn4mMnVteiIJmvLKNb/Wg6f5oMAF3mqFOoENWMUYax2v6O9r6iQWpI9wLB/9J7p3O0dxLsGmg14VMoUXwBT5CnjnMmBhq2nlhhJ7hoU0NfDGewCsG09J+rHE8wRBZ2iu5TdV6rVv4vsK2Bt8WrmnQ5Pum8YmwJZWZkpBTWN9+e1TGBk8J6k2ppkIIl4hwBqQMlio01Sgq2WnvEkMMoBxstI2oNcD6pZ6IkYE1kDZgtTKU6kpkFbu3SYpOqSSlz5fGU66y/heKcCyfKBmHFQOfZPDateE1ZNTgo5Cu+RkanOLtqU3jeqZ8Ur9NZu1qRoEAuqGbN5iUInJdZ/AQY9P7bO9RLemY8jYRbgq1MZPUSMCQmlMRXp3TU3SyvgnDw1N0Tu8WhRXvsXjmGbTf/jZ23nwTdDU5h+JGkgr0qDd1UzRnDvYffhQnnn+K1Yfj/XOPeP6H8k2lERf5V4ifOxqGdKWL9h7rtUjRiTauqckI6BAUpnRp1cgxYb88/vghcO4Il4Myom2NFJ1D/nstDTKjbcTAF69BMcgkz8fDiXnxh09Eu6TRALEPEdk64W0opeGwv3tdcj2h04NWdsDBk9oAuQg+YuC7mhF8kDX4cuvf+81TdMKxOnWyBp9liKg2eFUCYyfQq3uUgvm+zqFtgcOd07h89g55qxoXGgnUtUvcf9c+nMi+q/YX504I8OTnrBSdvRXBlxqzrffzadMEmIHPBwOfaNwZXvCi02RtJA4r05mY+yZzI/hqIq8YqoUIvtUSCAdZoiQPWSqII0B1ek4AvmkSbJ+76d3Yu/Iqzn/vG+oz113ncebiGpfuzBPmtvXowCOW+z3u4BDJi/dqBJ/OaBZSdCaG7NwZUAZGnw0Dg+yrJoJPS0+o8uyVQA18NRF8gUfwHvDGGow12sbb3QYRfNR5gCrp+mY4970fFGkAX9/fOX8/vn3DQyOOb0zPjfylTMVm4Q6M87BYqPvXJOHWnOQMfBuAc4BMBVP2TdSNY47wewnfHpT/o9zPoh1Khn7RH3MElHgbBr60T11nMNxMiTJV2vtRs2yQB6PLwXmS1Xm13qECeqUGH2DRJGW88D3WhO4VUnS+fPM72e+mAR56aI0/XQzG7vMXgMMX0ucoj957hxYTLx/6YY3lILIz2WymgS/BT6vBR1N05gwwmowqZGZA8GqOyy8S5DeU75q0NyP4RD+GPGZBOMqljGXO9STIDO0y4/AafOk6GHDnz/fiPbUUmdUQdIkVpVQs58aE1fFTBF/oNxj4bNQ2T9FZ6knKbDJK1epb0/3RYAIZaEDh859fYbkcHLG+9a0Wl19cYP8GHekkiwpZ91z22EHTT7Xj6bnrjSCHHG8QnxfrSdbgo3K6nEvKlwcDHzDpsJ3DkKIz0BHqbKug1vZHOBrv8Rp86Ztp7xLxrDAWe9ewCGp2bzOb8BauImwNfFu4psFyLNYOaMYLKULDlZ0zuHz/zWh+/APsOjfwTUebegCKi734rTEvRfteorR2DP8pHUIeFwAiAiqosFOGcK7QTVN0MhRCahlVQTRcl+UGS5AypalxQEfGZjQiyPoViiAS15bBAVEloaU0aQoGPpdJ0Sm9eUtRCbEf64bTI3QGJlL3KIvXiot3+s1TdAbBwELW8OgjAnGNwBLeWgrMDU3f2bZwSIU/Ta6VuvnkOnO143M5J0UnY+RIdF3s2nvs/f7vAwDOPNfiDekhVdxINl2SoDks96dP481f/EU0r76KE7/3e5gbwSfTzFEvSS+MsQnmBPWj5QkAdQQk7KMhgm89MbiuBcAVOfFbOJeeMTnCDTDvSkbcmmY0FPDnAv2edF+Gs8RqlUTwBU/TnPCo1tCkEVkkmiIYrRm9aedH8F3ePTf9Vrz7/ZiiMzxGFfdBqaZNb9eEFJ1GA+3MC2d3RjBZH+gRF1q3s1N0Jil0xXqVUUlGv5LWHkfRxNaCiDbRIJeiM6kJSwxKVbj0U/+nT3v8Nz9zgNXhGvhCzcNlYbOcojO5krSRKZJyuHAjsz4JLJJgbSjDxu+SZE8Qke8JLZcRPOR75CLcsxmzDWUnMBowDEU4h0INvgU9AMX+Ci+c7JV5ay12t7ODv73nx/HA1/9v08CXBWbgA9ae84ndzp48/iMsUZ+iMz6vfTflnEz4sRkKaWl0m+qz6G0AYGkY+F49czvOvvkcXj91K7573d3J8+1ic+IVj9+RDgOpUyfdsDURfPS6c0hSruYmsRzBx+s5AUBjfFvvEbXcgVd1RMFcqmXjHGZH8Ck+hYo3P7/QPv88r6lWCdIhLGdkkgpsdi8XGUQY1mjgMxw/k+0Sz/XxOykRfA5+mOLCOUAupH246Q/5/QaDJOf1S5DoFbou2tBywZVduzLlksjzmTX40j2hRmUotNsvddXi/o/9GNx6ja9+7lHgdX7v3HmPX/nFN+EccOr/AF4poBNw6TqgUc6J4cybLsiMLSofU0FMc/uoUYy9Om+RKopKWRw0/rYYwTeTp2SlYUgkWeKMGo2NtRF8Vc0IIjyCL3EIcvrfob52wrOK7yRTdG7Kazvns9+Pt7X5WOlgNtVR85FW9L3LpuiURi4TX4LPNGTKV2iZLuaCtlf4sZKX7Y+OgCefHAjd64cNvvSlFa6r8YYgAy9JFoJ1u8LyKDXwwbmszKl+W1GbPP7l+6h/YN14/cyRjt+hnEcpRacFWgSfVoOvCDXnUtBjWbroLbytsDXwbeGaBitFZ0JQRTvNK/Brt30Qt37kXpw8OeX3DsRJKglLMNG+ICinitPp8FA6kCefeFBL0SlpqIqpPIwUbw9N6CmBZeDLgYcbDWrTs0Motx3BEf6w0mDO5cSS5jJCZmcHXduj7SalzJwIPpZygVy3Ivg8dWUL18SaITYuAJm6SZqsVfA20mpsNZqBwMk/7IHVlIYKc8CY8qoUnYUxSJ/JdaUGnzQUq5+Z9KMack3Joi+m6GR/E6eCYorOlPdWQQq3FIKgW5vSy+/uAnt76Pf2xihcruyRjg0SgUZqHZhC12WZzRd/+OPAH/wx3jh5I168/gHc8MpXWD/GkAAmHUPTr0PgJigBlsbM40TwOTel0BwGNWrwje07o65IxE2J4Is1+DJGlSA4DMbNsR1pS50JQioN9p5upoHPVUTwOQdPogqoUJNTCvSjm2Ii4IR+lW9ZA5/9zBKLUKupmNZo3lnHBDZxdmsRfPrYDimjkMcj9w7MgMuM62n66TQVEz8INqlPwknj1L9zHsuVA47S9ay+j2Lgk7RjjoEvOR/HjphCqLCwqJLTaqqn6BSN18GzI6WVFN8EZ/ODZBRVJUXf5OpLehugb5amgY+hXojgO7HncfNehxdeaHHbO9ZMgxtpqqzBl4EcXxj4rKKSoeIwbVug82mKTk1n6eCxalLDiErzvDcNxOPtpPcCqlkw5yuz3lvDwPf0nU/g0t716JsFFmOKTraXhTPVJgpU5/tovGhbQfupgY9E8FlwvBSdI38sZE7a9+QgqshqBq8fjgWZvjxFQMhCmrY8o4y10muq/P4IJz79afWZPHh0Fy+yKyUDHyB4KNIXEOZG3uIGPtZZMYKvZ/3pdG38HsqC8j6d38b36KzFJ4W5aYQsntq4tEt0XSzlYMnvzgGX9s7jzJtKKBzt28hIUsqgIJFraASfUYOvv/569Lfcgu7JtGYhnMPeHr2Q4qSd5X0PNIQ/DnoduedTcqrw1CZ/pkNq4OMRfEOKTntv0vcopgf1o56J9N+7fA2+XASfV55J9E5EtmD9zIjyaRruZFAnY/n4rIoAbcvkjpCik+vkEgNfM635QX9o82DaOOq9ioXTOF37laToVHj2rgPQtgx3ifOmhrikK9eA8RsFHpUCPW84OR7aWTX4mJOh+r3r1xw1VNMIvnXLMzFQuddygLMzDtlyc6NE8MU9U4zgG/UjVIfdddV0qdVq8M0QlmsNiaFx+J7eNZwv2hr43nbYQHTfwha+f6Cn6ETiXeIc+KGjpOjshWKPHqBMQDMYe/ZQuC76VHPxG8yqPgAAJzypgjcPFdQygoOlvBuY9vEAZs/WGPiMGxnu0gNJis61Kytt8kypY//y8crznETO9R4Hq9O8n4KBT6vNNhjPpjatVbskCBVU6a4YeLTC0SXIee9rCoRYjyLn+ZVROOSUEtKQojWwGIkwNzkFXtIduJGeKd3HSIAkQ0shRSf/LsFSYRtbyyk6yd+agW+OIqwkgIiOSkbEBL9dknZLEXq1guJU6SaHYAJLwcD3xn2P4HMf/uf4zCO/mKTWShAVl6OBz6+nQuttm+yN6EXtyN6I75aRmAHVeAwgMTKPQycRfFR4YY+sVonSQRr4ElzI/rXmlNmblBp8NILPVBSyBxyL4NPAw7EIPjpnjXHeDAbTkKLTEC9qrVryMa0OXabbeSk6hUcmFQi9531NVmf1masFjJ0gc99b6wjGmoTCg2mKsQIuydds0n2dM/BRtPseODwMSrGmuAdr8Iy0go5ZAVZTqjO1avAFopzW4BPt5Dkl5j+ko87V4Bu6TfmWaQzbmG4qlCDevzDRjevxyU9exk/91GU88QSP9InKD/Luvm3jmVL8hkb6tUrUEmB2yyZVgg81+PQnl0oNPg2CV/XwVJkmeNUJoP7dpCJUU/7Ivqhy7O/u/jG8cfJGfP0d78cbp25G3y4B52Yb1R988CiLdxRtCF5ybYcUnc5xBwuTBxZGsjkpOmOaOrUu+nBGh7N3MvCVaUigi9RBtRTNFhTJbH6Fpv6F8w/E9F7fvuEhe04yBr4Eh4o19uYHP4r++uvZtboIPiVFZ3jnXLQGkSHU2rLamDU1+AAzgs97l0TsN/06a2TTrkVHm8qjJnEC7fuS/wOcAy7vni/2rdXgA/Q90XslUjToF4hzhjMi+LRzP3ZDz3HD0Eb51CCD0HTu9NsmQUniHK2N4Jtzdri+42vBORHNHJT82ofPj63XxKPyS3kxld6F05/JYCTleZd9Dw6Ut5or39YYGzRdSakGHyvhk6Mx0HlKBuN96vRqrV0tRWeqyvORttFghpjxxzCeS9oWXiPne0DbcQycaXybB2Qexz+po6vlNKNmPJjj0WkYJ00Dn4hE47oagi/b2nSNcN6g1RxHwpqW6URJn71rmcEs4MhSS4u9IWGK4POY6E2frAWpU5nLIwccg5Pa1S4zsYXjwzaCbwvXNFgRfEzB7H3igaMVg5aCROi7R7kGX+xjGnLsU3iGugVCoZEsvZMKayZkGxF8gnHMSsgJxoRZcvJgLR+cVg2+OJQqybmEUV83KyyQT2UolXbVhp4K5jz53XU4WJ3Gif3JnVyLcqBgF4+drjNvRgpGik6JX9v6oqEhHT6zJjAc+GqETvTSU+bcypFLIPQpMzQeis/MHmMpB+rfAZpQplxgTJQmVDqXpGyRbTotJ7uhkXbQU3SGb3t0JJlGI4KvEAURhy0Y+NIIPqURwXF/X3S5Nxn4VGGb7U+NNiRaAC6cZ0iOc0C3c4ITLIqsjgaASXhou/VkmCB1N+WcD96eel/WOjTpZdNEw0OAtvUxJeV6rfVHhISmSZhkmaJTFRyJEO7lAQWAlCvCOqboJN+vmR/Bd7A8lX8XNMwyxFJ0GhHpwGTQHDywlbWqRPBZCicKdPweTWI0Ydt6picsrS8oBWNmdQbUVNb0WQunucBsikSpoClCnXhgMAT52M5CuRa/YOiMzxg0Xu0vCsUTfi++2OLFF/XUtUXjrVMcoGQbY3/HfaasQQmawdBLw924LqSBL117eSamIfvJ+ibUAUbpInvOd5UGPloXTQPnBkexe+7p0H5DvHPoiOZvatt6DbjE3yTqClLaZVGXbrnboiM2SdvAB6yckqLT8EiP55HGU1ZusDns4TQygUwNPor3S+fuxbdufqwKAcZbifvXXefx6KNHcM7j85/XjQuDUmuSxRg/PFwgOPIIag0akRY6oRO5SfQ+acMj+Cg9SWU1i17Qszoa+DQlv2Lgy6G5Xu7hL971P+C6N5/D8xcewrvcS9n24tU2gmfu+Cje8chD2JXOehlDZ6BXulFzpMvaOUzOhIRvEXQgXc88gs9pKTq9R65Mr3Toafu1zZfK88YhMciSVzIh+VZdN6WBts4rB1zesR2xIgqGMc5KG2whR1O6+sViiEiTuDlDfjMv0tseLijgyXlG5zMYRYa6UHV7nF2uiuBLjRbxcXAHcQ/wGnzTpk8QGCL4MgvB+0RWLmVkUTIvGj9Ce6p3msYzjboVfHIuPaeJu0jRmXXeo+faqDsqGfi8SNGZDfyNmzbfBNo+T9rpcruch8CzU/oYHLQGg7ySJQA+FbuLOA+Q2o0F3SoJ7MZPbQxWi17KIVnI0zvW0nCKX4vI/TqdhMFYO9sxVkbwhetAeuaYpVvoO5AIvjkpOsMzy/V+XucieQzUfI/BYTTqUlwDWgplG8H39sPWwLeFaxp0A59nBLVBj0EnpXj0EZDRdjpxr1PuUQMfMwq4NrKVkVAqSvvkGvMY4vg3lkKUKdkp9vxgZkosRWleQ4bbdqibfSTPLU0JH9/DAaIg/Noti0RHetfmDBdsvCrvZsGc9z2uHCOCj6WeqJnJJhgPyXhGRGAtPjWgMSJhvwSlhCrwa4yNFcVG1vSrrzb4y69I5Q3XbqiyIpwawVdM0UmjwvwQwRfvjRF8sj/NwMciRbySotOowecAs9bFs88Czz4rUnw5KqCSQTVaoQlpJRIlHqIGLW3ev/AFjp/fIyl0tOiOQg2+JCqTCb35CD4p8LB9VWDug45hiOCb8ArrhhuFXCIMRgTov/K2YjwOfx8c8GeaZmpfLGlDctmEoWNEeE45MU4mnVMaMcmihZUIvuFTjkaICq9N1wzzdrg8idXRJbWNx4YpOmME34BBOrhNY3Pndk0dOtlPrdKTKfzE4nVKik5LWDy2lpUA+75EqWnW4CPzs1zyg8CqwVcLfT/1PymyY/dZyCp6ITyxUTbwWbhrKTpN3GhKUGPJ8Qi+8Q8jgk++GqON2rqYtF4Jvua3cU6PSA+Q0XDlIvjkGLnv2Yo6M+zRsOdIFEionTt2XRybQeSzSgvMuC8Y3dXK48qVqW23u6dvY++x0JRvCs1zvhDBJ8/IGgV1BjhPSb5VTnkqzm0NtEgYKd9IHPf2ygpTGrGSRKcqKTpzOEqlWtW6EPNiOvyxCD6FdpAf1BjR9xiV9iUD34SHxZoMdY2ni6+fvhWvn751aKvx02JtHffosdILVqXozEXwQaFVhImL5TFCZ6UUnSNfFJq3Sf1c0o3yLb2HGsFngsg2FPuZKdMlMmLXFeuaNY3H/s7ZXK8Dj6ZkOxruKniPvDt7pTBPVHO/aNU9FL5PIeA6Q+uG7+3JeTZke3DxfrImwrOVEXzH2QtOyebA5cwc3cvLftKA48d3zIsreTosgfP/Nq9upRvUgK7T6rmdWYMvLLWg/2pFgJ5Mhd2LuqkJXsqZaLHt4QbT2TVpIgbngAapQ4Hcx85POFGeborgWwG4DAkDn19v4ZN0jYIXDZL5r7QkamNYEXy69wvpq9bZS/ZLQIvgC3PsvV5rr8T3JG28R6vU4Iu8jHgPaeAbavA5vkaNFJ3atbafMiSE+ztHl5IpZXvI94CkhzXz7Rwx2Io9pjnIb+H7CtsUnVu4psE5XucsXJNe8pLQaUKDZKqp0isqRhVPhnEIVdmSGPiI+UpTsJrAhLjNI/h800rumzziI0NYq6SjXWlRfJbnZBy6SQ2gpbH6ZpFVVCWIacgal5ImfY+DFY9GiZ4zBjeqpeiUP0xGVong0xRvdDqtGnwSinMG7hXUIKToHJX72nlew5GPbWQe+WxXRg0+2k6fW4PZkntbSaNYk6KTjqmltbRYHud4is5Q48V74Fvf0gT8KX0hG8foP+0gP79pBF/F2qSwl0/RWUpFLJlxVkulgEAipFQK3d5Pc9n2PEWnhOgNiXwNCxU/6nAhlI7UiBdI9JSis0Cf2jZZpGG/luh1UkSbnIn0U2k1+FzbqN+Gp0yhMDws6/BxBXLDrByu2sBHIvgKkUsWlIRP75ryPpshTEoFXxLBJ2vwqUdlKlyWXjd3n6LfGKlkKJw+1eHcuR733rvGkvjXeKdH8Bnsh4rboPgmHslAURHLHoZNF6WXvqosrlg2JWVpGI2glPzN+yNzHiL0ZCrOYPCTtNIqBhIbcEGcGk9zCp5ct170CUz7TzpcsecqDFABkrqs9NFAE6iS2ErRqfK9IpIz+aD1+xkAi0YBhNEbQL97wnx2Cbv+CruOUorOSlxrzy3WTlesJfxyjWOEggCLoNjAk3vgS6cIviFrC+mnTeWjHLAI8qREQYqj+j2MidZkNSsNNZ33oHSek6JTFbNKC8WY/lz9x6SLQtNcCvFcn84ZNDsYEDIRfDxSyo3/5senSnCrHAcwZgRS7nkPtJ7ThrY/Mg0QQ8QZl8fnyN90XAZdF2lSNuNQyZDYNHALRVfiDRxdGukSjdoka41W1y88T/6hIya8dF52J5Ejnt80jeWGVZHzrRvoEOgQPp+iM86VunYLY0uHA1cRwZfjKRW5KuFdhW6AyhfDe9QY+OzzxQTq2DFcyDaX9DOpqSeA1v1UI/gYA13mVZPsSJYqRtWLKLKy7xFpBa3BB6AjKXU1I5LkvS1HjtK6meNYa4Fu4NMbsDIBBr9k3csOTGBtpCMO7ZWjZaB5cOn14crwfyE3N10mgk86AdCa3s5F3cSUtcdn9WUSaLaCphnKBbXdIbwfePmuTfl56hAv8c0BjTjewY3/AAAgAElEQVRMsg9twPdt4erC1sC3hWseJOGRwl4UkAjhk57dwBA1w3nIQJzrU3QmB4FgsDowV/BiP7EzcQprNfgSJYdyiPVuqjclDwRTkV15eO/sZIwKmtIFabRCj7wmzbk0JVTOcFGCIkPplQi+AllUI7sqcYuKKGp0s2rwFSwPyWWneyDx9gzhqEgBDEawIkVnAL7lataUwrAZ7xDlCs3rUlxw8JyJGpV9iXCjfWbSqFO+C73PGBrPU3QeLm0F4PDslLucKV5rUnQac5tTgtIIvqpvUzDwzY3go4JHKYIPSIUedk8oJCluwYjW9EdRae+UFJ1RgaEJucjvO8YMC6XE3XdPyp97712P+8uPuKX4847TCL7oXW0oRiiN74m+itdUo2taUSIQMl2VonNco8E7XJsmD8dq8FFP+ZoIPu/TNZYoYWU0YuU5JlNAA0LwrVBamH3DcYeMvk/qitVrOTYHRsuVCD4pvN9wocc737nGjTem727V4KvHxZnOSUWFQ5g7Y8hSis7z53lKNu/4QFO0Ztlq9+rp24bbEOnBFGARfOEgkRa2cO5Kw1/O1VYB0+GAdolUMVscY7xWXYOvcHbJOdaUl+zcXix054yMkxgZjP9baidBRPDJGtQswp12h7QG3/CAbqjIGfjkYzZvV6t8I0pl6piYS9Ep963Wq5I+mm3DDehdPDcTRS8fU31QAVlrLZkxqZxS5Rnj/YlTilYvPUnRSWTIYX79tDc0A4OIZtOi5fK8VHltHfdIko6uAepq8GWMmrkXozxl6KNEg0idKqloTfA2DHxJBF93ZOJprpnKGnz7+w7f/GaLN98kZ5YDXN/j4YePIlLXX5+e2/2OTqNoP2gaxRmC8MfyunaOxAg+Mi9LwynE4GOtdvLS9L0nPOwafMLp8y2I4EuNnR3gPU6eHIxGH/jAAUvRWdIJ5cZ2kNmlOP41eGsyIuePuAwna/AlUMFzL2rfn4KM4CuMw9aFm5eiE/D5uSsZ+JQPYJWt0Bw/kqQe3keH2IE+cj2BVTOzOY7cItFyhRp8ioFaA/peAX9q4LPq2tYhmWlq8AediOALOOaM7KZxj5bZETJFq9Z21Y3iegQfWGT1IENyfZm1ZmmKzqYBdo7ejPcOVyehCVNqYEvFfA8GvjB3QhbbzCa8hasI2xSdW7jmIeRUp78Thb44Y7XC3ZbhRqa0PM5BQpUPEadCis7Fc8/h1L/+1+Sm4znCgyAiD0DGUITxObegpeiUiqDacHvpxUwHzgnEFgNpQd8s+IFZyXDXpOgUNhm4rseBkaLTTMljpNSsSrtSWYNPKxy9KVAjNH2nxnnAQTXwTc/akHyXkiKSKoD6Xm1OU3TWDCqNRgCwd+U1PPD3fwQEG9WYotEJpYuaopOAGqlppuiUBr6T2Lvymtk3dSooKVqSayaNspluWoOPCsEadO0SbrmI/fm2tY1gBqI1isK8Ukofq0SfwznR9h1L0SlhULSN6yDa3MeL1ktEHPQUnd453HBDj26s/3fhQo9vf7slEXwm2sPzSvrGSIsiDTCMu2MRbdVZgKzzDqliyaqXZBn4gmAjI/gY3qPxNH47P3ileteYNficA3oXUnSmi7S+iHfhPCsZ0YVDzGyg61v5KGq3yqaU46/vvhuLZ58171ug1uCTbRpbuNYiw+bMzfD6QlGvKJfUPhWPd9a3kaLzgQeOsF47nD/f48UXy+cnSyNF8Hnp3L04Wp7Ay2fvwpVxvf/BH+yh/8IS9967NkkRIznBC16k6HQxgo8/66XyxGBiIu9B+DvzuziXP2c0Ghkj+Cpr8BX2VZNL0RloAq1X2baqcaJv2jQtnlWDb0OQEXxJ9gqrVpM3UnRC319RSa1EU2kRzDU8rgXWmVobwWdlktBwOn4EX4ghGHBrW89lglaJ7MyM5foOxLZWxKlpHOQns2hnkPm8R4wKY/SSIEmXfTgWmhkRfJJelgzEw7ukz0pcNj7rCh3kt+FIF9UUkVyZyW9Nlh35PYtrjRgFKQ0ZaMoY+eD9cB4YxD0x8Pm17XTX6JH5NbyM9w5PP73A/r7CF3Qd7rqvw0c+cgWnjta4y6c05/D0dcBBYZC2VZ0oAn+cXHeGgc/7kWaODxkRfFOkuK1LmP5O21DjnRbBF5TaSX+Z3+zyYNlVca+Bxnv0zuORR47QdcD6jh5XtAg+TR6qieCjvMF44GeXUtHoxEGmroy8i5BVp2iqtyZFp1qrMAMDbzEZpUoGPuq05LySJYx3buNJj1FqrDLEY8vAl7SlGS+igW/kx0gEFj0DgmGwFpg+JDGA8nrwc5weLZ4+jLFxik54856E2hSdAUc7Qg9ikjlG0/vx98hF8ElnI8vAx2rwGfoyDXiKTo+dwzdHtBwOd07Cu4N4P+Lm0/6rIvjgmG5J3tvC2wtbA98WrnloW+5Q2zRDNF4E75MD2krRqR0+NJqGRvBph5NUTqc1+NIUnbPBcWahUQq3W9oc38gIvlR4pwfa8FAdU1DLSMZuZxr4QrtcDb6JGZ6LIGkiDmyZonNeDT5dSjZRUVJO6QY+ysjXKatUZSCBQYHOOV/nrIPcM3xrgDHypTWVS9Gp1QzJCEFJY3EpRE1Wpegk1ygjGru3jD6Op+g8WJ4cxjamoScGPs0okwM7tdAEUoEwZ4yjxR6fK0WIVGuc0ro460Nc/9qzePX0bejbJVOsDMbY/DqVF9haybx7rMHHUnQSIxyNmmyDgCzoPSWUGn5K+tdh0AZtC1y8yBVywUGDriftcerKSc8mwBZzuZJfJypNg6isjN52QjE/fRty3YrgGxvL+i70XYKjCT2HG79G51ZZYTGcn12n7XUhQHh5t+4cK6VhmiMkSzh7ro/f7IYbxvp7SQ0+Ay9FEUjhysc+huXnPw+0LXY+85ksHrQuDXUM0qJTHTwWVkqjsZ1U2M/hbbyf6k7E81c5VyyDSRhV7VsKlePc7+4CJ05ohmQ99ZBWgw8ALp24gGfu+GiC6OGhw1NPLUuBCkN3Y4ReEqnX6441ru/5tcJc8/SnduO5Br7QqDaCr2g0sRwGMCkKWRRI27LUoYFt05z3LAOfGfEfBzZuKDX45GNDGrp0XbZeSdFpKKzyBppC1CXpvwYkHz2dHTmotwKxo1A4T20iC1E+Z/icpBMjp65lOGl8B6aHKiij2gXQSZuJ0TeNxkrODjkOMUZEBxBfqMEHYeBT8Mhm9bXwnmHgK923IviymRicIpOEe1mj86TolTyba3lf6bNTBB9VtPbNgqVxzUXwybp9bbJQAGvfRBpWEe1wcIDEuBe7G2syPfbYEXZfPMTy79Lnj06fKxr4fCZFp5HiRDfwBa00HPqmHb6DviDoP/yyqGdtPJrgQZXhVC2S8FMyCjje599KxS3zm93rO6Ad+lgsgLXjjqTUSDGthYkny40VHB5ofTfKi2l4ZdNGajobwReazr8FxysKNEWnBJM+FJzsrHka3lerwcfbM0d8jbcUWTcMshsHl7yXpTbR3jd5F/hJN0OcTmMEXKun6Ay6S6lbqjl7EwPfOI9Wg5rIfnlrMvDJcdI+s/XUawxPBkrrNo1+pGeqVqOeOcgbr831tX5I2yy+deB/pfzLxmIpL/UafKXPSSP4nBvq74VxjnZPAXg1eeY4EXzheyYZdrYGvrcdjufmuIUtfB9AGu+Ct06A6H1ACFKvnLBSKU09vWTKTxOicDb+K5RdWgSfdgDmoofMGnwV9LJ3UwqwqFgQyjUpJG5SMDdeCwYRK0pGPKcdsHKMvl0CBmOaVWAlfaWpFwaBkqfsS1J0Fgxq2iEvc7CbMCNFZ/QqOmYEX+hXfvcGfWQmAYN5maGZUQUZgUMEIpzXGK7jehD3ws/Ju1x5VjHwWRF89BsWI/gYdmkEXw48aAQfGedqpegU8zClUSj3f7TYA9tNildraU2eeOEbeOyp/xOPfvnfRpwjkxwE24xNIbvspLBB2gYjGk3RibYxDZ40gq8WeF0i0q+qlPBRQRd0xia91dLRhYi7wncLEXzRUYXMN3MEVCL4uIHPFupoewB46bp7chgN4xHv/BbBQ14/X51D9ObvOiSLIDHmRPQETSgJJgXl0XFSdP7AD3S4484ON97Y45Zbgism6c9I0VmzL/3Jkzj84Aexvv/+clu6fSsi+NqCga+uRl3y2ISLNPAZir4ENI93etvpEXwWLhZoEXeA/l3oNSsql9Kw05e+g8V/+nMsXxeC9SgZF1N0GhD3d5xb+7nB0zaj7NMcj8b3zNXg0/owb2e+Q9yzNMrRSNGpRhTKF4r5mTdTMsgIPpme3jmg39mFhEXr0XR2eiZ+qaAkk/TOoBG1r2gppLjzhnimsBfiQ+JBylttFsE34GXW4Jt5aCd1byROBP/Vyqt8mJnRI0bwOVXJyvpQbNysBpQmKxoRfDW4Wc8kTgXH1MVd/RSdxHqTDJYa+OL7F9aFw+RYSA18MmWfVYMPSCP4hogJnSfVIviASeueI/VvvGHIGs6X67QCuHTTHdn7wajmW30c9exzioGv79lB2DeLRNaMUFoQZBztNv3eNEX7a6ffETNKBH2RLJOQ7GmNB7Eisyuh8R3YMewc469ztLYm+hTeE4OZm/Q5VwnkuSD5LkqzgDpeeZMIvsAzR3VJgSeiEdBDik5ybhUMfNTBIkInUoVXQCnrlHOcF6e4S91IA5/Qx4BSv+ARfPFvi0c22Izst5A60VmRgfrfAQ/GWmkNLKjkiwEkDhTAEPmoywc+r8+wJsrZcnOSYQLTHMoIPgq9awfnTC90yYpDvKUnCQ4oTTOs61WM4AMOV6dUpzdam1bimwNukGxmfc4tvPWwNfBt4ZoHzUiT5AqXzNKMCD5AKPNnKPhknyz038k/COROV6fX4Gtk2heFoZBpAixDy1XzrpgsXOa9g8cfBzAw4d87dWuxPxl9aaW7qUVNgqx/cLA8pTc03klLp1SNW0zRSfBRDEncGFU5nNOESc54Jgr80ilshSgo48+yQ2ZSdBq2vOm+di8396NSnTVxDlpmIG4sUq5nuFetBp8lh9MUnaxNzRrqe93fX1HOalAa4mixy9toKTq1dCRNeu361/4+rrNpH5cjE8x3qY7gIyk6W27Qj90YArIW5cRwI0oi1kZRXjQNome4FsHHoFWcQwJOSp2jgC0A7By+CXd4QAKefLzHolLVCD7j/DAj+IZ/D3bO4IsPfBKv3PWD6G65RWDlkv4Wjhv4tOntXDDwabSM/7a8ikug1eATLUz8SrC76/GPfuIQ9967jlEATkTwqf06lyi1zPE1xZQAZuBTavBJMA18oY9CRG8OvKffnHv9cHKaV+aqfRs1+GhfNXTRTB9ZeNGaJXf60ovY+bM/w+5r3+E3WI4x5TqGs7s01TU1+OBcNopbjXwar33z5sfMsVkkdmGu5BxLBQMAOKIY8U2jp4/TaKGgvb7WAGTg7EXRvZXwS3MO6FeKgW/hk+g/QJcpWASflqIz9VhTca3di3IfxN+MARFKHl92Dkj6Q0ov5oJzYewBn7YV/TQpvaztt0T/VyuvOyxaKTqbSZ5wkdcZn9HOrPFm3yNGyYThNM4ul6Iz9ptR5QS0JX29qhF8Jl3N9+mcnm0n0gPNWKoZ+KaXzOLZ+ElxTo2+0mmARvDJLjUDn2mAsOhLTakKQ3ZwDiIMZhr76IEH0J84gaN778Xrdz1UHIPWfWb4eaueuyaUDek5ozzTLO3vPs6Hel8s7qzahJxnfe+wXuziP/3gL2P/o0/g9Rvvxldv+xAu713PH66swXccSJT342KTDkRsXRMdisXfA4gRfMHe5Ed5P5t6U9ER2RcUvdo4fnI+FRyvKMx1DmP9h6kpjCPpQOn4Z/XNNBpDU4VnDHyB5xp0aopMLNtrfIBmWPGpY0uM4FvsGM9qKTrr9ABaBB+nUZtZa/g8hPeYLtp1/hS9JSa6P3NgAEDXrFSaRvWhpVTpbGhnv4eM8gbImZbRL7MafJTPJfqy0l6g51PTALtHk4HvYHlSnRtKt+Ltygi+6Dwu6Nc2gu/th22Kzi1c8yAZBOnN6bwfhBjC8FspOmU/AYbIhikHf4D93etiLa2DD34Q7XcGJU2UMQRR6xUlt86o5pkydmCE2gAVkU69oyk6PeCn5ygzwiBDyC2BMtswdDvicfSRD+P/+7P/Avu753DXc/nUYn7RAj0X3lVluvZsxYESBXz6nLD0rNaXs2OYYfo1URhKBJ/W3yY1+EpGnVQB6FWPMoqH38sXah8aESWMuKb1Ge6bjERmLZkC8+jRqS5lJToB0KNoqTFEjeDLKNlakqIzGPisrUU9n0oGvgRv6Ck6qwTVCuW8TNHpFaNETiGcXCZpj4FpD9VG8Hn6W3nQElSiQnnQDrL7WgR28o46elyZY2o1ybuMDH30XDS8eLNzWlDu/sgX/1fc9MoCn33P/wjgHLtH1/m6TyP4mtap69uswUdQ+c75+/Hco3fikfXvw7kX4vWQQns4h4d9EQ18mbodwUFmvdaMN3wOLPtICbRaUtzYUNchrdsToeGL13kPLww29gasFIgqlFDU0Ksp6ROPU5fTJirpjWfKbpIHCR0U68IwRZLG74g1Ygjw1oWpxpcyptP7s17+7Nker7/e4M47pxQ5zKFAvuoYrVaM4Cvs/ZzBXOJgQqYG32tnbsPf3PsJnNz/Ls5c+vbgtKGgyiIVtCGo0VXiGdYHde9eLOA7rniQns0RD2OBbqpkOPjgB7F45hm4vsf+Jz6B1XfTybMi+CAi+IYzQDde03MouV3hCDMHzLMmO0idFWigK+RbiWwJNfRCHqsOfZy3pvHw0kHGOY4fjDNUjtO26dlC5n9nx9ubSeuPRMXLvZjDZ3pf4vyk8X/CwKd9rU3WCX/meAvNTtFpPxPnSPG0yyoxA32mMoQmOGnjEwNfS1N00mgJP7MGX2/X4IOQueI7H0P56RwSGhNgfddduPLjPw44h/Yz+TGcw+BwZKTorKZJ3rOwnK5pFVmTDpreyp3RGlA8wr9du8LBIz+Irz7/Pjz7bKraTOQ9BRepz6nCld4Tsk4co3FAx41iNfKGlJkR1ubYE22j4ZVN0UmuT/osYdTxwcAnnpkVwafLO1noBU9TIHATvW0AcIPqoBfgz8tSJQn7QPfX6ORcxF3TWYnbTUWKTgAslXD4JlMNPjtFZw2e3jlOOzUQckojvzOZz5wfksZmUBbPypymy2D2GvjmLY/zlorObN2u9POV0CqVTXOTvoBxroRWyHqFWgRf+Paa8Y/iHSM16TsY9F6bk9ZP8scQwUdTdJ7WZSRNn1QTwQdHIvgcTx98FXnXLWwGWwPfFq55UMtrCCWNbKMR+N614vAhhxTtLxjUHPD3t7wXvmlxy8daXLj1VrQvvTS0JwwR70cRVrTcRJnT1TXCwAf94FaPwIan6ByelwY+nk7yOCk6LcEKmObIOWD/xHl4n0bnJV0tl0ndAE0oqjEwWE1yOFDE7RQlhPERiozc2PQBZkhSU3ROSobjp+gkY1GmDR4oMOhVBr4wSjNjTREGUxp/tBp8k8JOjCkFfG2oGSk6KfSeGuvHNWFwrw4eCxLBF6JCcwa+6PE6swYfpEJY699QINSm6NTmnvWjrUnFuQEYvcM0ZUwGeBNJ+Oz319LQybosQ7uxKzc/RScTdgiiWtSIc8BNr3wZN776FbgTDwF4l703lAg+1SBj4XV0gNue+n/wzLmfYWNwZWtq4LNSdDJDHDVKVqSRDd+MKu8WrgP8VI9N7mbnJgNf32vvzH9LD/fqNDKlNF7CAGstN1m3J2DIaIRWg0+BKsNUcl83enF8JoWmFNoptI3PaphYTZOS17gGwiO7VAcxXhMRMUm38ix3Lpupwepo0xSdFB58cI2+n+ZKCrvJOrIk4ZmSMU1jqPFAQVmbPWcMJ5gAz9/4CADg4a/8exuRYopOw2EARDkl09kq0Guh9xIyNdpY2knjhf2ZM7j0K78Cd+UK+uuvx+ovFVq12kmea5seLong0zzrwSL4zKwQrBfYCqoKkGeqpjyVfTW1EXxCMV53RtjgHHDzy1NhsbYFIM/2poFLHBMqxi2kblitgN094M03eBOTp2pCik7EqBeTXvlpLD/mKndEwaynq5zesWkmMypTnubeW+PfBD2qVrwbcKwIPs3hxme0hArdnDIGFM5DEs1OI/hkndEc3q2I0lKjNQivq8uRKR9WC84JOplYxofxhnK/+QFyNfgs5xYNZxnBZyrjM+eMJVvRS3ReNQfJISBRH1vSpNBK8gfp0PUfyfWd+lGd5C8ITxO5YWVsNiUjbRkMZuMvm6VLOkjaFeQ652kEn3iUvksBjpOiU9MRaCBrmM6twZfgVRnBF5B0js+dLCMUryt8c0JryLwP/Q79TDX4jBSdXuczEjo/MoXs/PByb+i1qucDodHBEE/Jp9Ch5CCmohftvnHre/G1d3xAjJrOPTWM0jl3jpwfwmkgYCbfQcEOtBF19p7GGQ18RE8kYdALORbBB4BnvCp8i6Y7Gscb/tsZI/iAMYIv4kNwC+ftlStw/e74RhV6JmKQZDwl6nQWW3hr4bia4y1s4S2HtAafIH7hsCKEryaCj+nhqDKfCFR9s8A/3PQoDu64V8VN8wRNUgjNlZ4cN/A1ysFmRQLE9ybMourZw35cBVcLlVlMmcuiciYKG+mzJeFNHiiWgU8Tor96+4cBDMaNFy48mBkloyir+c5qis60i00i+HIK3OkWmVfflyP4dlMvddkmAP20ag5yylAYKTrhHDT7a+nbZhXkmlAZlCwZiWpWik7oKTptA9/0LFtPNYoxS4isXIulZTo3RWcEqz5h3zEhqhTBl8WxEMGn7U23aHQGflS+yKBar31rOp6fl6Lz+te+jguvfg13f+7/Gp61auoo3ucy1SVjoJs05/1q//WhHZlv2m2I4GM4WhF8dJ4z2sAgNLLvELxOieKsVIOPtl+vU1qmpjvbALQUbVKREq499NARzpzR8e0dF/yj8Cw/CiUkbavXd3Ll8y22rWkTnGvIWmUeoWKsUg2+2XUqJVmlTkqZc2ouaHQoa+ADVMUF9TJnUIlneCVLmaVH8HXTTdqXLxtuaf85z22p+NJwYw0J1Bg3GeqFuZLKLX58Bu0PSdHZtobTg8IgqF6AyjvMWHf+5En01w9p3kTGzsEwoUXwLaB7W2sLwJMUndr4cq6PuWfYehTKI62NxDmvsOH3jpuiU0KStcVho9xvzg3nZu5dViuP++5fx3KpIUu9xYdTY43zvVCe2vslTDsz8Kne9dKTRZH7MgZia/qvqoHPrMFnn1ZDe69G8IUdUZ+ic/jDFSL4Qq0h53hasl6k7JtTg6/pj2xmxDjrKuz5JjgHM0UnHavqzG4a1QEuH8EnD1QvIvgyNfjCdxpv/f3F98Vbh+97H2uniWZShgjD0zaWPSY18HFcANgp1Cuh8V2qpxFjT/ylZ3hoZ752RkayN0xyNb417XhGhCn7QzLXQSdVEcFXWcJOhbiGixF841y6ZpTpuGFJGoEZzfE+pVOVNfjYmVpytnOAZjQZzjLq0O/VCL6AUi6CL+F1K2WkhDUBp1sSb22NayB5YICXqrCNiDnEp3svn7sHX77zCawXghdTcOraFZoWOH3a413vOuJNx/Zaimw5FxGoY6woqaRG8KHOwBdTdI5r1DmuL7MigAMEh5Nh/Xjs0Bp8O6fUGoDO9zjz0tdx6l/+S9z86f8Ni/WVqsVDHdWl4+c2gu/th20E3xaueUgMCY2IQPN9PKCDV3tU4jFv3UwNPmrrNiI06G9O1HiTvmnRdr0YyzPGOK/I96LA6hRRyPCgB7BQ4jG8PJkbF/KFz+di1QMlzIcmxCgpDqWnpHgCWKQ13yxhuQTWAUhrZoTv+OzF9+Pls3fh8u45xkAlGDrdwEcZsSwoiifen6fNkrY5KKXolG0c7BSdAIY6NBVSIosgGCFJXZd5TnICjRbBF3AyPiplNBKwvPlHI4/1oJqi05iPpltHYadv2shwWnU0etcSoYm8k0oXJHOtp+jkTxhrQRGcu3aJtpsY3vVil7+mlqKzYICn7YfC81QZE75VpWAghYDMu6u6DvH9aRdM8JboGGuNCbVs0edTP3o/0HJLKPbaOqUERsJyiZD+MkA8x2hUhlqDj7x3a0Xw6fPsGoeEbKSTN+BDzrEFRsGjysBXVs73ST3BoHQoSBaWIdoRJe0Ip097PPTQGs891+Ib3+DWG9VZxTmR+1vs1cGt3gB+w5SbKzQ1ke7Esz/tfxrVDwY+jWSPY7GlqdCQEjiKB+FdLH6MQd+bRi/LwMfGrlDCaBF8MtNBDlFdlznhrJKtoDyT22GmZPzm3oWIg+QrKQ5adHMEbU9kFLQaqiXnFJeL4AsGYKFYk6m1AIOHlHhZfAsNq1Ses2C1SvHQnJ8WredpRimKSDNnUDki6d8L3uCYIPeBlA9mdSBA8mU0O4L6bk3Do5AKQ2nRqbVG6XTsvGFwtfLY2XN47LFDeI+Y7s/qO5xxfY+Yok/jiQDCc45/JxF/Gm0htNOil5s4S11tA99cmGRWuwZf9sX6fpL3ayP4qOMTS9GZRvBZPTU9T8HcKik6c2ybdhbPVoaaKdvoOCWjCIaD3ci+UxvB5/per8Gn6QTEuf+1iz+Czi1w8X3A+QcemBpmeLTQjxXBZ9n9rRSdJWcEQwWkj+GN8gkN36xqpFXJQDTSiliDD47RcVVeVuQ0+wKY84z1LhQaQ0agkEvRac2lPBuqI/hGvR9P0Zm2LUXwOSWCL8ePUyMRxSdp6nvIuBqmQ/IAMDGLlFZMNfgMAx95Toya4Cuflb6c3jUs29McAlU6o5hvAtUZkG+spozMOPZKUFN0Nks8/p41Hv46l5mpPtT3SqdDA/YOAeM4nuDr2n6drqmxk4US3ReghyMpOgkh67rqM3q53o9oOwfsHE0pOg+WJ3Xne9/j/s/+HtwDHRaX38A933mSKpkAACAASURBVP2PRT4pDBL9FIV+fRvB9/bDNoJvC9c8qDX4ZDFgh+QQSuqzCMUU/Zsa+JqYHmASyGPXUmCTnovORSZpUpoqkOUSXeIRAigCh8aghXQxzo7gk8xIbc0hVVhpnHmT9hqLyJfSY44RfFZ6TDJyEUFLuJqKIfPF8PrpW7FeTikpdWHxmDX4AnNFGH5t+qnH8hzhOed175zoyxA0YtsZ6TkBLkCVIvgATGk4qDcYXCGlrSURiH3KEEsjdjxSQRUAuGO7IhwZBKQ52I9/r9tddKMS0tpaIRXDMA7p0vLwo78NxZikQ+q4iteqXF+9pGmaAkBbk06ZL6RKL01JkOlqEqCAcc3abVXdLTlAqDdnWGvpVGUkZoAbqGgbNYJvetG+d2MEn6GcyUXwad9tsUgEKE2gp/uyV1J0jodn8j5WDT5VSE/21xjBRw18jkfwaf3EyL/eqXuWQkY/nIUiPa3xXEQm3bTQLtD9qqVhGiBN5ZVTKBTxC5+Tedra723W4Av3jykpJN9ciao2X8unCpgAkscDUp6sRjC2UnSaUMFrMJw0Y02owSeGe/11h6efnhTOmqL28ic/ifXeKbx0/r6YccBWJg9/5OxaqoNWhpfQwF7bA+RTdPpkr6BpmPd86Fv75maRSKnM2iDqC0gNfM4BV264mLRrW8+LzNCxE6PmBHWG5OMpTNg6ss42cW4HKPOg/NsXI/gKG0beTpw6nWHEraGNBcXVzs7Qz2IxRe/l+g7nAHUOsdCgkTCRRvsum6Lz+Rsfjn0e3XuvIWvZ7x3O/4RX0BzYDChOq7H3a1J0+qbFN259L7+X4Te0CL64zxMHBzEmiYyhMkovInpkDT6WUUepwWedGa5xKk/2lqboDHhVRvDpToVQyY0qN40RfFGB3yzs9TLeCHxxt9jB127/EC498niRGeAyixJJOLaxo/HTd0mGOiajY9fgo2j4YXRFplR564iwH9cm4dULZ26ub+26pR+QEXyWc64GG00pSdE5nFt5447cV/KY52VVvHASyqfo5IeADoPskp9r5zIpOsUkNZhfgy+nz0kHTM+DBFkRRMGAC5ClodgjzMBnjqHoQaID54Side5p1/tmYaMa9EexwdSQ9sVJrbGH4XX9V2UEX/AJ3TRF5+rockSp8R2W428Ph8PFiejYwvg1zw2IJ668mh2DQjyK3DZF57UGWwPfFq550GR3eTCksvBkaAtQ7dktFHIaRIbHqPXH+leYsBxT5pxegy9x9VY6mZSOxMBHQ7pVhtCyQsyQPkp1jYJyJhvBB5WZmvDNHxhzU3RuIlwNgsX0e4oclQManY+cJzfwpULZJik6S6kSJI4N+mw0zVwDH2cY7ELCsT18qrx0LgpD2v6UQllk7DPjWIog6k0txwGmaCeGiyGEuitX4t/rxQ66ZljHeQPf8PfcFJ2uwrOyJpqTXGG/ZK1SmQoSKBv46BcJNfjos4VAPFWpPXWQaBjUW5NSdzLwynGbVmlfACtFZ6muWPDwp9GtReVCwLsyJWpM5UnnnzTr+pT2aQbAoQ/aMVG4VazRPhr4pv4Wrj6CD1DeWe5V8S2tGg0JKAZ/2r3+PL/mnUsUxZG+SslZpOhUYQNZqGa90rVK16eMii2l6Exr8M3ElSq+yRwVdHoRP4vC16Xo5ERB8xpmn2WD0BaL16BdJmSr9+oN53vs7+fpSnfHHfjGz/xP+MID/4TPpWg7nY9SMSuQyaSxNl8KKR3J1q7KpevznkXU+JAfUdkvCX9N8J8GMxzFNlTgrkRSB+eAS7ffj2/f8BC7no3gU+he5AHMCD7y+yoqTGjtcMuYUpuGK/THoLRvKuUF2tyL/XSsCL4MfquVV+/XpOjEGEVhyywD7YvpOeGZ0lfD//kbHsbfX3wfXrz5IRz86I9OPVUq06xXvZoRfL1haKgx8AHAl+98An/8w//zdC86tSrRNYqBr9YZktXgYyk6RQSfmzafc16UzJgi+ACw7BfaS2pBuIqbWhZv0WVVBF/TAC+d00uLRDyaRq1F7r2R9tUZBr6ORkO26lk0IV8Bhs5kOssnPKgjZi6CL0lFqvAgun6kHpyQdeIYIoIvGVe9kNLjoQaf3mY23gUaRyPC1G+OPC8fgGa4rMZRnEklJ/TJQYKXXQhdSb8hFsHnfUqnqA6wkGOUYMpw1sDcEpQn9dMZQoMMphp8doYpSS/ltJn7kkBwFK3J5qLiYPD0k4Gv0CCHGIHc2aIb+FpdxCZ7s9fs1k6vo8plfx6QodbgG/eTdi/207To+4G2MT6XRPCxSEcFrdUYsdc0wOLKpXj9cHVywFONjlR0y5UK0hhJ7Zokec0W3l7YGvi2cM2DWl5DKJObBglBSurjCcUPI0bMYJgyz5NBjzO4Wu2ByRswo9DIKUmdrMFneIUq1D16kzKGgSvXnOMHoMU8WWlH1Isas0jGqI7gWy6S/qqZQkO5JbEq1gGcelCvWqmuqhQwsQYfYSoUz3qWQ94QWkuGGg2kYtd6wrmygS9RwpBp1YTeBN/RKylZZ4W9oYEVkTcgps/f7q7dHwCsO+We0Vd7OBn4jtrdyITnDHxaipm6Gny6dYytxcyHla+crAIpCG9k4COPj3XnpDKmFkopOi0ZIV4X7xPbOD1aNI5nvA+PbDAOFAW3vkeWuZeS+0CnBU50XK02YmhHlF/MEXBcGNxJgeCpGD+GvylifEx6rkg8epnyB3mlADPwyXfWhLayrTtBtphKUDlvNVorz7LYhqwD1/epdsFS5FSdX7kbE0SnkagoGHC2QCnDw8ayAqRqQToZzerAe5NHmVtr2U7RSZm6vMJdu645JEglk/oKfQ/fC3rGnCGc/dUq5rBa4J5Rg89Kf+UB3Hhjh+XS4+TJ4T8TXRGVmdQtCbTQueigE88PhX9LDFDGOZ04+1SuwzRFp0fTOvz1ff8Yf/Xgz8Wri9YDRxONp+MlSjJPUnQqB/bcCMo5YDk0UpgTwSeN/l7cS9Z+0cCnrR2iUPPYuAafTewGMA18Fi0IWVO8G3gd4rimpugE3zchWmNorzssPHPHR/H0O38S/tQphpHsVwWLZ55h4CtvE73B6dM5osORpme/o5yP7Foz8LlUrgKGdcTq67EIvgm39CznBJt+x1aUH2i8naJzub6CZG6ameefgMTAZ3zItgUOlyfxuXd9Cl+97UP49oV3pf0EA5+mS/DamaAbexylZULRy0BzJFF+Ww4FVIbQeL+RLdaHFobMaT+T73zcGnyyNEUw8JGxhxprinG65DglIvhkik71EbL2Er8TVX8k9QN6loHIz1UYfnIpOk1I6sLmxwn9du1q5OU4bZTyTi8MMrHMyCuvwL3xRpIqvIS7c8LIo7S12N6m4edNqMEn+51q8E1O8Mk+yjgRyYeytjWJ7Cbe8MYYdB1aRkQ1RadYc3LeKKjnKIzau4RYlfgwrvvVS1vADxF8ybfBaNzLzGWowSffoTbLGsAj+Np9auA7ZZ75ct14uGqj7rRVy/L6Fr6/sDXwbeGaB1kAN0nRqXjgeDjVwEeBKV9Ba95RYpcqRFkfyg7SIvjSBzPEr+HKw1CDr5EpdjQhNAozlPHjHmNSmWimafSyjmDmNYoGvmGMnHHNOagFjal3lta3dc3Cl66D+XxLxpBE59T6vJUGvp2diXmZc1Cqhh03FL9Nv3te2X6sFJ1aIV+JW8yVyBWq7cJeaKaSZfymh1r2g3by2L7zzoHxuu/+Nc6f71OlupE6VV3n5O/mgBj4FruzIvikt5gEjVHUgLcz5kkRCL1r8PJ1d8ffL58XXr+K0Mvok5vaabi4vmOehb5AU+XzzHhUCP3TjO+ubdiYzN6ieC/rSJDLs1J0UtzyBr5smjwplI2GIi5ATZ6GLIKPvGOX1K0b7muCCnN0KWkDpSIz7FeitCul6AQ8Okcj+MRdTSHB0tsquMr2VdHQ5UPBOz1Fp3c8QkyrwacuK2d4iWqgCOgJHuOQVFHKBFUx1s5SjwgIT8+N4LOUspKOWv14sXHMdmoGhRkpOn1QFpD29L6pQKjjNaZhDL6h65T1upkyRUOEznd2C1emO/ROT7sEDGfnmTMejz12hEceOUpSpTHdu3TSAE9rxLzm44AjD6k5iUlcwzPW9Zmg1eDTvnnb9Fx+oGmUlXeOPIAmiotvZp5H88jGMB4VH4wIPjF4fefKvfkGvnxz792sFJ3UeLR/+kJ2bGng03i/d7yjG/hHTM6hPlg/MhF83gva4XlatWyqzSAHK7RTjbYSaEtl69WM4BvocLpHLl7scf/9R1gsPC5c4GdMokM2HLmss4TNc/hDzWqg19tqZAQfGahtpIGPONyOPFxo3na20nb3ymvst3PDikicl2aQfOdge2uwdTt0+urZO/DsbR/AwepU2n7kRzS+SFtTquFNMtWBL80ciolMI5uqchBfsGlk1nD/ppt0XsaqwVfEpYzaNIbvVPnMyc2n9VMYOKnBN24gbX/Hocg6s4I++R7kDilu2mrs3zkRfDk/DPOVZ0bw9bt7+NbNP4SD1emk7IJm4KNOQje+8gze9dn/HYunn8bJ3/kdnPxX/wrNyy9Pz2ci+Og61xza1fbaNVmeYaRTNJhBi+BL1m5N2FQhRWeM4JPRnAK/2D6jd9PGYNmFGd2ZHGNUHYfi2Guelxrv2rRQ1Y9U51Poy9bnELqLoQZf0g18Nj0nMGS+Gfap4zTZcuhQYLneH3XGHsvDy/H60eqE+czxIvjG9e/yGTy28P2HQr68LWzh7Yc0gk/UYwiGKOEVLBUBUghh9ZE87y8+oxmS6GEo73sfmQcqfMxhIp1QEtJ0JawDRQiN6WLImA0zoNjvleCRMG8Kwc8IVlqzUgRfYKY4fmHceVKolebPa3Wo1HbKt4dLAjKGserUcl5RPGl4nDvncfHiGs+/sMT7P3AEPF3Rd8X8SKatgc4QOof5Bj6yBDQGJ0VG8c5V9gqQGjoonuGPl19u8PLLipGJIHbrrT1uvvkQ+//V4fDmGUUt9WCd9hf5buRvauBbL3aJV3f6LsOz1MBH9nEFg2Sl6DSVJenosb33wx9P3f1x3P7CX+GVs7fjcOc0gDen5kqk2Hqxk3Zr0IDGdyJVYFPce9l3yRr4aB/jey6sGnxAs0hpaFZittoCFRF8Lr8vlAi+KDzKhdE0qoIzptgke4ul4FS2eymCz73xBhMw1Do3Yq7CN+uIwkAa+CQ4J1NlVdCzmbYQLeI+waOwt8YrdopO+h37nnkDeyOCT986lldR5bw0jik05XMnTnicOOFx84/so30zP5Ebli6LEDzBh3RnOv7sMuN/PKy50CNebJpuzZ2WojOrQBBweJhvp61T70eFjLjZVCjNaiHSXHAGJZmGasncjXzzSJcUA5TVFavzKJU1vud1QGgniwWwnjyi1TTvkv5Ya+AqRfAN7Pdwja6RpSNGSuJYBCh0j0XwpWNu6DRvAn9VXbvHlHNUiVeYp8ToT/kkxSnON012Z2nyEp3nvlcawd6vTz78KbzzC/8G/sQC37rjR7H+up3mbGcH6iKm0S2nTvW4/XaPL31pCTg3ZmQYvi+NyLOAKZwraiNCR2nqLzebFUu8chvY42cY15/8ySvoOuDJJ5f4kz/hjmHJmiGKxWBgSHCLxoVUyWyVzIhur5EP5Q4FUi6VGYGo4SM8R/GSfF2Wf61QGOfAOc8jjAyoIultSxymeZ+0RAEZPcV5PMNiTfGRx1Kjz40IvuR8N+RAep5Z2Rt+4AfW+MpXjvDCCy0+9rFJLguGn8mpwqW4FHj4Egw0Pt3PlGdm2QzIH5rjFPsdI/jIpYKzFS+pp9DL5NwUBpcRVyuCr2YBM+ep2rlMzqT0/I1Nd3fxtz/+z/F3fzPIpNRhMXTFDXw+2e+nX3see//hm0PfMutGZQ0+TR9n4UwhGPHi2vSeRRqHcycYUo7aFVbx2YkXA9Kzxyv0M+zL5Lon5FfWDr0KEXwB1munNmhGmdUMphBrbnaKTqen5ubzlfK1g7FTwYfQKKmv1Rx5G1c28NHMTkyPTOXvCk3j6ugSnFvE4BAA6NzCjuAj6ybwWnPSsgLDGVmvg9rC9wO2Br4tXPOg1uCThgpZ38O5hHkZlJ96/SNWe6hPi5BGwuiEJ7SS7kutESIhy5UJ75lZKTrDcyLqhTS3BKYEDaGQmPs+jEHIKWcoZJip4oGRU+yRa1qKp2qQ33/ugRYYfqMp/UY/+99ewdH6AHvf7KoMfOOTyZVJCJCGcd3bMD63u1s76NAfEaAaLQe5FF5Ghk7ioNaEIELQ0Bd/Jjf3wdMx9k/pSVaiUi4b93kE32T8shzqzBp8NUuoogZfboEljD0cruxeh2fuHOq7LAUt9cozhwvFG8wQcBKvVufsiJYS+kCqHNboNL0uFK2s5mUugq8G2IvOT9HJ3lOxotD0PQy0tl7fB7xWt0vSTNNi4TJlyvLJJ7Hzx39s1mmiFzVaSOl9i5oafMQYK73ctfOO1WUvCyTxXM0IejVeyd41djSgMPClKTrTR9wx03Yl+ElPa5fO5+nTPe65p8PBrR3cl/PGREkz56LqBA+inYPJXghCbcYrWY+ibO1+DTANmObD5U5zSqbYpu/hE+sHs5oVx2H9CX60EXtfww3Q91ZNik42dok9yyoMuMKdZnLwbcswUZ3E3uIafDuKP4vGEyw9UeAsl/xMnOEFP1yTv49HH9h6pI4ONSk6C8l+kvVTXAz5+2p35GLfQ09RbfT72nV34v997F/g3f/0Ctb/4ST6dokv3/mjuP2Fv8KzF9/P2q5WHv2JE2hefVVipeIZox2CnNZPhmo1UkusCZauL2MoizRK26sb8FKzo8YyUDI0tLpuNYHeNTGyLqTOSxw1MxF8qjxOU38SRzNW87NpmVJTRvAx+UGmYQRMxa1ct/HihLj6XBGowtfCUy4lRT4eeHuvrrvOSFenrpsk61GenyiuBYOXpvskjeCb2v3kT15JnrdkPXZ5nI9Nwfle/R6s1rfc84bzqrwUIviCrivItWE47fXmOmYlaQEnxddwX/CVloOw+Q6OG6TMZ0SKzqyByTkslpS3SR2H6eNtm88kJWGK4MuvaZ6i09Cpaby/EwYihHn3GCI/R1kqRPA1tnMKapzDhJxOnYzYmszwLpoRW/tJ5yHwnywYTTgaxWfV7z05fsRxDEJiZfbQ5H0raxN5CX0Mto75ezRGBN8iV6IDGFN09mhb8Q4zDayro8tw7kx0rATyUf6N4AGtWn1ZiLRzXK++UQ3MW/j+wTagcgvXPEgmRRZcnyLcOCQpOptWywAJgBv4YKVgU37LdAAOqXeQphDLMZHOidpF4mALjVSFJ0udFzwd0xp8+cMb7DmKl4YrYCmIiNdjk+KngSe1V5IxCydFVVSim9bFJk5JMoIvKoWdzlQkML6fVQdKKiZXK5jvnVyuOEmlAv84EXwJM0bGz3m0TsiMDJ30xM1No/WOJe2CHNp6zlhvcb8ammMewTfNW00EH+OtKmrw5SKgYv8zFAfFdHNNmn7paJEaf83InL4bv7WPuFkKbxUHJ5wVqiP4xn8X4hwIAo1zzPhVI3CTXoZ/aP5+pb1M0akx/mpjCJoi+vZtq6Z7pCk6nfIaXQemFAjDaspqB2D3j/4oEa5T2pVy8eE8pfR+MTrX5BT9rAZfQk+VM0YxFGedFlARwac8r82zWYOPnM2u77lE2zRwCgLaeWDiWaEkiyQZhvKYPLzz53+uKLN5u2NH8FUY+BjIKEijnVWzymwPlypTIPgxJeNBOkaZvtbW4CvpyI4jIKvnltanFllhMHy5FJ05YORNRO8PEXzEoES/v8Ct13BVC3Wn77BpDT752HqtGzSW4BF81DlGr8EXlCGGEonZeo+nKTH5g4oIviLvrcg49Nlk7RcMrQlNdxznvi/3IWG92GVGwW/c+sP4jz/0z/DcTe9m7VYrj4OPfSzu8a+996cHnBSHRTeGmFF+jpU2kOuPByEMTUANqRklnHC8ol33OX5Pk808f5fjKuFqZB9NlE7HFevSp8p6yjhHmgB9vzs30QsAsZyHc2DRDdJZJ63Bl3feoo5bL1x4J/rF4Kj64h2PJc/R6JjNIvhg51ukw9Rsj8hDamtET4Gb4Dx+p4mvblhqwdiMykslMlyQeQcntULaaYmmaFMbwTcHmj51ZgR4phDpcE3xyYsccuLHs8PgH5oGaEshHMk3EvvA52vwXfUw8wDkpWo+CWMXkgi+VN7xFu+uQaYGX9ClyfPJapvrgyAcI6qoDBhr8DV2DT4t+8OcPQJMxj2LR9gUVH2H4MVl5DQFKYdm14V25jWpQ5Bsq6YlHuIdh7/FXGqylUNv1uBbdAfx91rVoTQxMpnpco0IPut7DhF8nKqbzhnQdUqzI/icwpdv4W2FbQTfFq55kMaw4TDnh4+U5zUFnHcNlsupL6aoIO2avotGBo0RLx2YSYpOBbLKECcOjF5RjBoKm5gaUBhyYpvIjFCENzfw5W5qOclzEXzOoT6CrzCe0WQQ+KrqMNkdmN+fMivWXMWFWj9krdBRMurojGRGGT4zRWcYI6SZKIKmuHUOjbo3xn1ofOPsu+dqtaQIxL9U3ZqVhpJG8LUT42ZNA1XIqHXEtLHjA97UPMf5z8BAe6jyorBvFE/5I2LE1JRZUnl71VJ0KppysaST6zxFp0ix2+o46xeUW7SN6nUszq4uU4NvfP7wkUew+tKX0C1WeOHCg8M9qYxQpEXveYrOOCZRLsQC3pagIkQCDbSzSypPwg/mqBI883kT9gg7s+X7KbuD750yzalSRGrCjrI20khwYkxpmjjZjlaVN2rwOeerzq/8DYLJSIOHfTemvtlE8goKKsYrza/Bl3jdFqJdWQq/DLHQovFTZW0eV+D4KTo1oN6spoGv6+D7/Lq1cNBSbqnpc5U+kjnRaldVRPAxHArnCDNQSBouvZ7pB2klHz+jBp+Eq1Qk5PBw6orO08IT+r5cAkfT74SfBq3BV+Znh4eOsx75j/jbMvBRg2uRbirEnMBViaSkirheGIEtPPLdqLCzA/Tnz+PSL/0S3OEhvvcXtwGv5eeAGS+U+tOxnWIcoU52uTHilOlayuJzJT7pWOA2OBNSFqYuc00hgs87x5TBPanrO5xDw71W1OAbnht+ywg+TWak9J0a+A6Wp/D8x/87nD33PL71t++G/5sXkmePu49ZrS3DISWpW6mdASxFJwdL2Z0Ye8ZQOmrgKynStbWQvyAvOTU5QhYaPYWcSRdtVOwhfKduLkqqQiReXD9jG40/Y/QYk8H73Lkel/cddnaHVOsank1Tl6GEPseMCkQ/YBn4arJdbATiTMrqKZxjvIXkgSTuTZM6mue+sa9I0Sk7mZPlgKbojO2IKBGzocTSEjRSUPTvyyk6KXJUXzAFazr+Lwrzn9AVnZ8I/XcdOcdFlFp+D3NEc/y55vBipugsRPCZMgDpi/GARMb3bTvoucaOaaT3ul1hseaRxkMEH9IIPoXe53Qoq/XlQcyiEZRwiWNP/Fvs4+F9Zhr4pFzoBhp9XOfQLWwO2wi+LVzzoDnnpik6kVC8JILPNSzVDmNsQCIN9i/F++uxoC09/LJetd7HQ4sKH0mzjEA2CCkVgo5yWAXGhTLZ8nmpTKyN4MtCZQ2+UnpMb6VWop1UgtW8KtLOACng8Bp8FfgZnuXTbUXynqEMseYtCNNyXTlDGeHcfAOfa+w5V6+PBr6qFJ1BUWoMkPWwzeQHKhq35HWjgbtCI/gqUnQ2bUxZwYwUtRF8cyxkxXv8d7LcFKOEWoPPEOBDik46h7Mi+CTkFP4aA9s0zFGD7d+Fvl/KSCiIVtTv0HLzy+cPPvQhXPnYx/D5d//3k5efJr1nzhUWwacIMFYEX66wOrnBf7oUv4BHR/proVgXBbAIvozS+MtjOll1f+XWRzgX8wssc2/CTVUShH6pVEOU/BYtmpWiMygzc0oJL/8Q92carqSQNlc/mfAwyjtMPILYR5kIvl65IRWMUuikC3h1NBShl9FlZXA4d27C64Yb8rySRu8ODx3+9q9bHGWCemuMqVksa5+t5TGEMm1OBF/OcJ7U4BMpOmlbNS2rwD/8LhqWNpzcoyP9u1ADn6zBlywAnzfwQfCaVztF5zSK3oYbDvJjz72v1Y8tbTvah/c6vaxxYig1CU6g/uxZ9DfcoPJ+lEUYE+dN94iiUk3RSf6d4yWfW7qZLMZ62sq5aeELbfqayHjlOE/4F6FUjrI9a5Qa+CznT+d4Oj5a24pH8Ck1+GYAc9xyDkfX34j1Qw+laXr58ZO8Ug04h8oIvopOm8Y08HXamtJewPukHqK6Fgz5wMJLe3ziUwNtnNGn5Jc1PspwwJJ4mPekPD02ZjKt+bHT9KCSHgd6cc89a9x3f4cPffgwY0zylSUIqLOn4P+FMUE6Fc2N8tFogAo0RSdSeTeRq0TAvzx2WVmGxs9K0Wmm/WLIeF7mxxAr9eueG93gGZ0K34TqC/7hpkcBAK9fuBOHy5Ps2SIY+1DyIux9pHNS5WfX+ueki+DidX3QdF8YfXNIKBPduxaq6o85LzYMV9mXvK7xBkzGXywYnVmspwg+mQXJjwq0+J3pmVRB7ynsHF0eq0FQuiwdq8l8EiejsOZmZyrW9BFz+9jCVYWtgW8L1zykyiVZS0yPBJJMa9NyvtESeJv15GVx6cSFpN/cbytFp4Sscs4ZBr5MBF+AODYhtpNyjbjz1AjC0css94hnNzUGGpiEja6QolOL4FOFOBVhl/sZr0Uj6AyFQrzmpBfMPFfYmLqtwpATYYbyrTi+MOweJ4IvLiWyBmbpy4KALjxQq4xcYhnn1oaq9DH2ATOGaMKjQUB4Db55KTrZejpGik6KUtaBQDJhGQ88IFXGrRc7dXWQwmVhkOwVL+Asji6fotNSdMXrLcdrauPgWiqUzedG2dwormqJc4pI0cleJTy/s4Ojhx/GpVM3Jv0xIMrVigAAIABJREFUgUIqtb2+ZukxSL0nLRzj81Zkt7ZGxaIKwiGNtqmrwUeEaYOYtK3HN295Dz7/zp/Fd3/kx6YbFdJEqDmTA+29Nf5C3QOhIVkL1Nte1gOlz8lRN7Q9DOP4lL7XOjNojdqWn3Oz9wrxuqWaDoqDeZZmtNdabTB5LfeeP/zX/wYPP/Pv1Ai+0sM7Ox4PPniE22/vcOedSs2NRMnE7z/zzAJ/+ifL+YK0guY0JnfZtqJ+tPWc/FbePY3gy3XKIcfGOPAIvhxdLdZxzg12DEv1LbdMSpbbb+/Uud1tSS2uUbmT8EmYfmcNfNXa0Dpg65Eq4a0afBnaoXVu8f7DeBlkFNDW9pmz08VbbukM1/B63tGC1arsFMHYRyGvMUO1nAePlDDQTjPI5Tzh5/B72rDHhU1TdCb9SKVy7kz3RD6dGMRk77MIPqMG33CWE7rphNGq8H5UqUv3lnMaf32VU3QaH7NGbPTBwKdFvCgpOtU0b2NOuSSCT37gxua1E5nEkCWna1oEX1mooONqEXxzPsfX3/F+HK5O8SHGupGyP+pISCPxKB76HvbsuTCtqxVw220do4kSBhFh3gKTdb+sCL7Iz82M4KumNUZUudVpPkUnxz+JjiqM4UspOsO/pIGlK9P45mSvkHmn/FyM4PPAU3d/HH/26D/FV9/3T+xIr6k7cyztKIrr0DqnCsB5+rQLFowm9FGNrH3KwJN24xXjw6m6u8Zw9CbzZ0XwqfSavht1ZCVRep4Yh50DS9HZtdxJmqZi9R4sExyrSVlBpUKKzkahdxo0UofuXNU4FHo3BRKE8bYGvrcX/rNJ0fnd734Xn/70p/Hkk0/ijTfewLlz5/Ce97wHn/zkJ3Hq1KlyBwo89dRT+I3f+A147/HTP/3T+Lmf+7mrjPUWaqAcwYds7uYAOyLlsUw7JaFfrHBldWa4T7pm0Q+K0KWl6EyEm4wi3wnl4SSI2MJgJKrj2H02gi/BWMfjKqbolPhZEA9ERQFID/OalEYWQ+YLB/qmMCeCz5IX1Tk2Jj4RimAr5eJyoWsGfkj3aPS9WYpOD03Jor6CEpnh4QoGhA20EZpmZAZjyIfXN3VzsB//PiKMW42BL6lPX0K1tyuuWIol1kamMCkJO8Krdd3u6P0bGgXXd2pdGnNuvNj3jJPO7zKrBp8nY/IIvgYQQXWTwD0PSil2AM78JyDWaZHeKkqQyetywp5+lilFJ6XNItWVc0wBoQ2d/Ba4hLmgyrUW3FFF64fRZhn9MP5eLICDrsHL5+7FlVM99sSHyqfzsbVe03lZYSiElqKT3KcpJikYHuIDT1NJ2yraeY+42CdWYQPaOY513MyGNSk6IzQNVwbmavBp30DWRbZoyQi3vPy3aA/fD2BkECtSAIVvdfasx9mzdhR8bG8sKakQvNoglYjyeoDoeGS0p5CLGMgBVTYkdT19z50fqNd8RQRfqQbfxH9uvpA/8Yl9fPGLS7zjHR1OnkwVtE0DnNiZ1oJfLkHrbWrK0EmplkItb7oR2TAXJ/1GwjEhAwldFe2TdLIbfIcf+/gV/MULh7jjjjVOnkwNObVQmq8dJTkBoJ8dgUYkmTGMfZRE8JG2pdIBUVmvyEZWDUerPS8LexXoj8KLpE1So07yjJRPkLZxRNMd55lmzZFrj50R03lIDXy0TcSrYOCjw1h8nTq3ztXJigZIhW+1gU/Mi3OI/Ii2v7teF0ZUYw9zYjK2Zk4mqZgOeY5LXr7Yh1yjyr7Q+DOr34PFCfzpD/0ztN0h/svP/S/D432nEu5W1OCTGU2sgdglws8BZZrsnBjXaMT59jSSLAzNHgt70zDwNc0QCX3ffZn0BDkgAzYVHlDUAc01XHZJcG+bWQa+ugg+MXeVshOQpuiUEXw9MfwA41ZzDpf2rodrj3jUM3UuKeBr4RNlZWF8Y11UBBvIW1MEn8GcQ5E5R1kUIDwjk00t/jwlQL1r9WotRB+inqPG+w28nQ+Yx+shmts5sAg+AFiIFJ0azlMNPnrA6/TemvrV0ZCikzsb2OWFaDR7fJ/ZytEUma2B7+2F/ywMfC+88AJ+7dd+Dd/73vfw+OOP4+LFi/jqV7+KP/zDP8QXv/hF/OZv/iZOnz49q8/9/X381m/9FnZ2dnDlypXyA1t4y0AyybKAs/N9bDMJVikDJIPDTMZmvL5/6npDm5n5TSL4KAOXE2QScEgExlte+huc/oN/B1BZRelDew9q4AuecTwCss7Ap7YJ3VSm6OxcvgafLzFTVcioP+O1l6+7Gzd992kAwPrsuer+gHRNMd68wDgBmJT4JtOQXtuodpIBiWEcPfSv7OF30yLAOXDNvAi+Ycp4lEnTwKjxlTKd4Y6uKSAgc3YwJDJrRvme1rdoDg8ADAwbLZ5sMTi9YeCrEnSR83Ibx80pEAq0KMFBCL1Hi91ixAFtL1N0asJ5DpKxMhF8zHgXdLysBt+kbPQigi+BuVqHqhSddgSfXFs5kuqbJk1L56e5ooE8NE1PEA7lPEnlSc7oUBV9HIQV10STY02KTgqW0nix8Dg4GP6mXuZVNfhQoYis6cc5dI1ikA2dW+EWbavLpCXlDh27iN2wxqdaLwFnI31BDsZ2x67BN/ISU+q60E5XgLIOchF8iiCfKm/4GBrtateHkAa+iIsK5TmQkW5azbykZk8yTOFsU5rT/VutaJqVonP6OSuCj+lyEi3xEOkdgOyfhM5phhA5dkzRWWg3Y27PnPH40IcmBY1cVydOiHSCbZsuAjo0wc6KlqEvcNwUnbQzllLRUhhZ7upqz/n9UEyVKtsrW+LsdcCPvmvygNeU8cefI7A67QCX4+S1QPPTGnz6npYRfA3KysrYNjNluTKe2nfpifGmrkZOgd/coOyBRtoSQ6mm9AUgGUj23UWNqd4wvlKHglCDLzxD+5fyMu07AOPrpLwv1s1x5TnnUJmis6KzpoFz/lg1+MJcTc4KRgRfRj6uPY8oLyMj+IrTKr6LFsGn6mpyODSLce00E49D03QG/knU4NPwLdHQJONOgTdwDmhnqlPY2vR8PLblpj/Uft75ziOcOZPu3Wq2c2bEEqtxqHxD7tSZGtgt5yTvXBWBlDRCNSQZn0vdK6MRa+jHsXeQshs/e8o6O85PTQ7ZU//0cBv/3NAJTZPNWfCx0EM6x8divCx1PnLa2vJ473sP8dnP7mD3RIpL7xpVdi2VsKA40uvU0Yx+g0XPI/iCk5dzPILvqFVSdCKczZ7Xmq6g94erU1gdvglgiuCjqTd7b0fUJbpe52ZH54bIbcovbQ18by8c0y/32oDf/u3fxve+9z38wi/8An71V38Vn/rUp/Drv/7r+Imf+Ak8//zz+N3f/d3Zff7O7/wOLl++jJ/6qZ96CzDewhxQI/ignBwFapIKbnkPvf3TNyRDSKKVMApeN/BJyDJyQgjYPXwDD3/l35eZOzd5kuZq+KkMhYZHRpGuIp25FhV7pQi+0QpLpnu6V8C5JoIPAJ678Qfx3E3vxqtnbsN3n/ivs32mwDstRYF6yRzGCD4dORXnq5CiMywXprIpRQ9UGm1rx08gRGYIhaqKEVu8Yl2UlDq5CD6BWDFFJ21vvC/NrW7W4INh4FPWxffufxQvnb9valORonPOtyjuGzF/63ZX7d+Kjmh8l6Q7KjF+ZgqfGZbB+EzL0y+xtD6t0r6GNmptlfffKEVnBg05rvUth100dM4NfFyYoziGOZ/orOH4UZOicxQSOxbB1xHcyiAVMDSCL8DM0gTTuZjbH5laYbyvzPeylAJDYYT0cutwtFCk0gxkFeoeoDvbUlKyZ5ZL7H/iE+q9Y0fwxTm1HTLiJaWIivWuqmJZIFtzVrB9aSmOLXwrwBvC9XEi+OzocEXRJM85iXtiRNNfTqbozHWasKeGKxEw8JosRWebEuasstCowZdAnTWjCiQtPnmSv5+X9bfkdx6V4tQxIwdXxTCg/TCYlAZ5+YhCgpv4nazVTQiK7NPSos7rBgBw/nwH5zweffTQDNgwo3nBDXwsgi9lkofrfmob+ylG8GVeIKPK0ZrzCL7ssPaQBGTd05o+1DNVyvVeqcE33nP0XKA0QvIiJKVv460afEr0zZwIPqrUJYYa7dylOFaqLtLnK1J0lhxbnAPQtpkafAqPryltRQQfnNN1DORDFtecS400qfLYjkhRQToEOCXV+Ay6RHUZtKZb06cGviQFOI3Eg2NtKfBL/DnrGXqrZp7ZCMwBmKfIZXMdZIvMwi2ohJLf/z97bxZjS3aWC34rYs8558lTg6vKrsFVrsHlctkYIzBlG9MYM1zwbTNd3RYXDFKLpiV4gSeeEe9XTfsJ8dD9YLUstfrq0kjdyA0SpjHGLttFGQ8YsMtll/OczDyZO/YQe8fqh5j+9a9/DbEzy1Wm8384J3fEijWvf/3zb+DzgIKP18NDdFKwPD1Tj8EvB2Kk4xyPIEtwKQxddXClW00rKdWuSe35Zin4PLnyxOUx+mmXbTz4BBmiVHEsjSzJO0TPdxferc8NhLODki98z3uW+LVfu8CHPmQ75egkDfKuIiVkIp72OTFop2tgeHMbOfi0oeBb9WQPviZEJ/VKF84DX9sFycVY5hY3FfS+1Ch0fZs7q6N2zubXO1dxDVcM3/cKvu985zt4/vnncfPmTXzwgx803v3iL/4ihsMh/uqv/qqTF96nP/1pfPKTn8Sv/dqv4fDw8Kq7fA0dwVbwaSuEZQxdNhx6FFbCpTffudE80xrI8/ZvV98AXDpEJ5QZ8mX34mVnOQqGRQlhMmqLspoY4UxHrAefV6AoCGEki6Zg/pRYAUwUI+8gDJXCC4/8FP72qf8O+cFN+0MPuJQhlBAzgLmNhnLwybx7HJqmscKlSq11h0biELptIktyyG3dIAiWktRfiTT/WsnjNoQsLs4issNckOD7llpm8aO1u1uFPHF58Dn2hUkIu5VcEuFrQccQnTwfBvfgCzFyliW26ubBZ3EJ0R9WTGi/HZ8hi1CqDNEpNCM/EICeTdGDz+yrkYDbV1dM82Vc6uan1mZi9KYegW+WFXzNF/72E/sMcoFMvT/onZTqld04r0d4wIWlvV77/WrN9kbVugtiQnRK/ZPOhC8HnyR8rvGQEidW4x8f/EBzP37+0Z9zz3/EvtS6xBNSaNyyNbuO/O1vR3HAPNolAZUgfAuBFaKzqmCP5JDZ3a2l3sop7OEgrQGf+5hznawdoaQcA41RyMSgraAHn7sLUcBz8jnBjufm7AyNqGEIzgKevaYMyxxzogso6vngi+QQIzlsPPjk5966IoF/ur3N1jFNjbvTMpgjghppC4gGY5fwUOP4WcJ3xpgihXjVl37BMxckX4GC7ypDdL73vQv89m9f4Md+bOEsL0ZHaehP8q4IhEmjvKZXGWiCL31kp4gN4LnLvM1GwYYmCtaXlqJUKFMWNJUdNGg93VtKmXeEmYOPeDeQHHyNkDmg4KNQh2WrK2jaSCCehUufY8+dWEN4XTW0Up1y8MElJC4KoiBIRN7bWFs/ehAPaUk2VDiz6keXfcz5GNFjTqBtXMfSCCdPc3Ayb07ADJXZxYPP5KM28eAL7DP2vXlWmIKA6vsCmumNrlWHt3swRCejSZJEO6dGKZQhOiONwZGmYdxW4aFQiE5Xnzj/pYjxApXZ1Hudel/b0cwEnOpRgnMalT4UnSg6gnQmXSE6aW69plnHpEmPa5Lx8FAbfH0NhZIVfAZqd3gtN3/TaaCdJZ1548t/15ZnRl5miE7Zg69W8Bln0RWSmYCp4Ks9+Cit7jaISKx7WGzCDwLuvFbwvbbwfa/g++IXvwgAeOaZZ5Aw7DAej/H4449jsVjgK1/5SlR9Z2dn+NjHPoZ3vetdeO655668v9fQHSSe3LTKWzc5+BpmSSBYfSE6abzj+s/Z9lHz7M/+bIz//J+38YUvmlYXElJrEXMrsLLAR5glpvBwmE+tT/jFV84JdRlPDILB6m8EBRaj4JOEh9IHEpMsQu3Bp1pmq62mG7EaM+2+CygUhpD/FAUyXFBlS9O9/fOV7QLiOnGG4bJteEJ0ihZtQkgGkSEGmdtN5qKD1b5BAEoMaYiLVKoJ0ck91fb2Cjz22Ep8JzRpPCy4lsa5cVsG2NlF/gVr1Boiz8HXMUSnqvJS+HCzDySrbrFZ1ziEUJZNGYER6LTXDCrZb+UN2Aq+rh58xkvfOSHzTS1a12u7YgsnBnC0G0dRa8GKWSExpTf14GuntfxtevB1CwMSE0osrn92Dj7jXpVwjs8SOFFYDHbwl+/8H/DXb/9NfPvmU+71j9iXtQef79zZ6y7sKcXXILoLUoesM7m1pfHMMzne+tYcN25Uxg/cg69jiE7lU/A5Oi4p3qlwRXoZmgMuPNnEg28zQbCMh3nNBrC96fPgc16ngQkxBHV8MrQ2wtxZkQ9YH+zKLStAsWuXycHnarLuz9YWGxPLvyJ58NX/ibSjtWcuRwc6z4FDwRebZwcIK6esrMFXQNNKGzFO8S6ft8FAfOytW+JrDKMKXkcjF1dNWVKbt/1aiC3NtU/fIwtZ7XovA5t68Il3UP3eoQgpC2ozhzfhFy2lBTUqrTwClYKVg69uu5kPQ8HnXnvAbbglXqeJTLvFg4aK8OCzhOXS/kpTSzlRg+zBZ/MtqsoJbir44Bi89af4O4bP4mxQV3Jd/CDifqg/M0P0mQbnvLAZohOyF1JgAFZqBgcP0PQjsb3ZQmDuA216GNOm67PZMYyfd4gOo66YHHw0m0iv7+F9FaACRsQUNA+zLRaqeW6zHVf79jPTCEuhnVd6NtdChoMk0dbdE4TQXS7sRy9P5OUnTQUT4A7RKcmDeJna4EPyZqd5GKUxFhEefC6PR5nmJB58Lr4yTY27yfDgS81kv7WMpzHCpXVGhuisYZBnSFgY01CITnPK/Gk6JOAhOksjjCug865hY/i+z8H3rW99CwBw7733iu/vuecePP/883j55Zfx9NNPB+v72Mc+Bq01fvM3f3PjPv3+7/+++PyP/uiPAABHR0fi+2swoVdJ8g4OdjGZtMjuxo0RBpMt9Ctl0LifYG9vF7t6F4PBEP0+MBqNqr/75LsdHB21Vg77+6qptz8i9Y17mEyA3r0PYvJSGzar1wO++MIe3r01Qb8iIiZbEwyHw+b3cDBAPp6g3+9ja6uH0QgYHh5ia2uChFwuOzsLTO7IIbmWOzsYVnUAQB8aSPqYTHqtUGF3F8XBAdJJWcdwCPQGI0yq3/3hEKPRAP2+wrDfa/qT7O9jb72L8Xja1K/7veY7CpPVEMPhEJNJyflOp2jGWcPh4SGOjgAsl0gnEwwGqrmLhsNhs9d3dxOcnpbf9rmmtW5v0kN68yYmkwmG1dqVa9lDv6+wGgybfvK1BYDxZMsQum5vaxwd2ePa2kqai2d/f4jJRBYmjbOR1Uaf9AEolTZHR9v47neB0Xhs7SG9v29Y3wyPjoCjI8wOU9wR5qH+rikLAEXRrDOFLDPXYzQeY7iw+zwajzHY2cb+vsZ3yb7qjYfY3tnGafX7zu79GM1PMFhOUXzgg148lU4mGI3K9geDASaTCXZ2dzAYDERaZDAAJhOzX8P9fWxvb2E4HLTzNhnh4HCvGW9zrkbl3h4My/ENh8BkMsB8Xu6nydaWNe66zeHNm0h2dqDIHA6PjoCdHSTb21CrVrC4s7PT4IRhkjd1TiblugwPD4FqXpKdHajzcwCld2+/Xyr3JlsljukNhgBKa27a535fYTSZoBhv4ehojK2tBJNJhU/WE0zSmTGOre1tDIbzpi8He3vQW1vinhgMzPmSYKu/jSRfYjAomeQB29PluRk3v9WNGwDBecnWrjHfw6HGZDLAaH8fSVVPUbRrNxn2Mcr7mEzGJT4aT5BMyv1I8XoNBwcj3LmD5t1o1PZvtLcHzOdNO/WYl0ugSPtIiCX1ZNLDcAgM7rqJ/mCI/rLub4lPBsMRdvZ2MDmvcahCv48K502wf3DgxFX13hrt7UHX50Qpa00WC/OMTvqpUWe/356L4V13lYi8WYekyTVXli1xuFIl/sfWFvLxuKl/MACGw3LdR8PyTI5GwMHBXjN/47HGZLmFXm/VGLwcHZU4fHs7wWql0B8MkK60gYso7OxsY3LWvtjfH2Ev3cdi0u6R0XiCo6MjjLd3mvFuTYaYFBOMBv3yzE7M+sdjGPtwOCrP+mBQMg017j040Dg/r4Tq23sYjUblvhr0SxzR6yFVMqPbH46wvb2FfXJ3tvNbC2NSpKrsH+0bXcfVYIj+aGKs5WAAbG9vY3J0hGRvD+riwqhfj0YYHR3hX8bjJhR1DdvbW5gsJwAmKABMABweDiGi4OqurfGvC4b9HibjEYbDYdn/ITlHoyGGydCY/9H+PvSNG8a8jPb3kfZ61X6vzslohOGipQsk4HfTsN9Df9XHeNzDzuEhMBw27fA9pre2gMkEalbiweH+Pi7IPqcwGE8sPDdguK/Xg7Ev+4MB+qs+Dg6AkxPg4AC498ZWg9fV9jaSyQTDocJoPBbxaL/fr/CScwqM9alxDkcnk+Ggoh9deGaA/tgcT00X7O0pA38eHQ2xs5Og3+8jqYSzo1EPvX5qjeHgYISjI8LAz2ZIJ5MGbyfpQBz3cDTCGENxLfb2tjE5If0cmuM92N9t53hvD0mFL7QGVD/FZNDSoaODgwavJru7UNV65LmJF2rcSe8eABgdHpb4r6LJajxbsHJ6ZweTDfmx42NgMkkxWpV44OioZ8ooDw+hplPo8Qz9vsJkNERB+1jRPeNxD4Olvc929/awqPiZcj6HWCd9Y69MJhPs7bG1dMDZGRpadzAaYzIZYzIBRjs7zVwvl22ZSdHuy97QTU8AQH8wwjAZtHfZ0ZFRfjAYNTQOUN5fitwXQCkLq3EuPyuTSQ/DGzdAEaLa24NmdQyGQ6ufteHvZDLB0dEQu7vmuQE8uBZoyo+Kluap767RCBiNxugPh+ivqnejISa9El+tGW21vz/E1vY21HjV7ImSfu6jN7D7TmF/v+Q1alp2MFBNVJmt7R1MpvK3+wf7DS1Qz1WJE8v35b3r127y+896P9nCzZs7Xln44aGNr7Ru9xtQ3vH9dTWP4zHG+RCTwdjCs8MbN5BMJhiNynkcT7agRhPs74/K+a0kmaMR0FftPT3s93BwsI/DQ40xocUGk20M+n2kqsBgABzduAG1s1PdA0B/afNVg0Eb1UelQK+uazjCwcE+jo5KHH06Pjf2jZ5MMJ6VeClJymf9vopxyivnetzD1miEMeVD6rv54KA5I0Vhzu1oNGS4o4fhwQEOt/fxynCEfs74s+1d+24djioas32mx2OMd3bQ7w9KfmI4wtHRDWxtbUHlLT2ut7ebPvO768YN8/ypvT1kE/POH41K/NrvK/QHQ+zt7ePgQDdj3Nkx+RYO6vAQg8GgWbPxZAu9PnB0RLRDWWa1y3nXWg4yJLRHOhyhj7LiUS9paaWKXzzd3UK/Xwr3x9X9VctnJpMJVv0xBsMhRqlJU9R8pVIKCUqnhYkuz4Pe2YGu7tEaDJprCOzsbmEyb98fHZlKhWR7G4PBshH8j7Zaer2XpBgR2mQ8bmmY8ajlMyTaxcU7DId2H2vco8fjhubT43GzV3a2c6uNmucEyn31znfu44UXEpydAY+/YYTJaUkvcFlErweMt7YwUibtPhrJ/dU7OxhXNLGEA0ejHm4cHmJ/fw+3iWylPtN8TigtWsNk0sP27i76/XLsg8EA49EQW1sTKNXy8kmicXQ0wvZ2e3b29kboDYbN2R0PBxj3RoQnbM9M3d/t3V2oLKvmscU79RwUeoKJmjR0VlnOpAfH03ZfcPnB1lZ7Dm/fbnFQKVfYwmiUtP2ZtTKz4WCAnZ0trLNl8yxJB2WKjyrs7WQ8xng0wmAwwHAIDPtt23t7bbvzw0NBDjbB/n7fwmnjSUt7jZItTIqJsU9H4wlGlSyolkUCwGhrGzdu3MBkkjZyKaO9EbCztwdcXEClKYZDhQla+Wdve8/4ZlXRWFtbGru7GsPJvGlzezLB5OiopDerM1fjpbo/6WQH6WjSyEBu7Gxjb2+3nd/RGJPJlrhHx/0UYwwwmZQ4fDgcordyy2qVQnV3ExpvOMJQDcheG+Hw8Aa2K71jLc+/1n987+D7XsGXVYjKRRjXz6fTabCuv/iLv8Df/d3f4Xd+53ewv79/dZ28hkuBZJxrePAVq7LMyo7JT2EwcHh+AHjl5pO4cevLxvPV1p7YH8OChltIaSEHnwBdQnRK/ZUecA++phjNwackDz5HmIUOITplb5IWovM8VJeAaNHc0UTUZ0UViDAR3Yf6p2ixCNhuo5XV8auSgy8CuDUUXeNVOsTfvOt/xHh+gvve2eES1q2lbSfDVJ5PANW8eA9O+e6hhzqYbhIrqtA3LgvP5k9HWIYGZ5D8e8vBNrQ+E+suOoToVAnDBx4PPh/+a8uY70IhOi0PvnQUzH1leAIUazFEZyzEevAVSc9Q8DVns5caVvvGfKdCnyM2sbgfYnLwrT0efF1CdCplrUsZnaVFbM34yZ6KCdEZ8oKIycFX10E9+JpQ0bEefNUe49aa1INv5YisGKrTBzH908r24Cs/ruZB8kBK3XSBeO47Gj/Sew1AE8q3xctEwB7hnU6f+cLDxQHzxvBVIHjwuYp3zcFXPQEAPPGExnwObG8DBRFExiCnmHxo/Gy6PPiuPI4N9+IIeJ+0BX0FSvDm4OOhn7mnh3JRmmVhI7QZpZuIBbSzb/yZLwfmFYEjvXILlQdf0zVrncMefGYDm/a0BKf3SkSITgTuhM4IQQphHDoGvA2eUwuberxGdt/DUBgh+nQbopPjCc53dAnR6TuiMXmQaHkq+DY8HzaEqHs1ggS3+FaBRygLyt7pSsGaKMoXK2cOvtSsg23GED+eOEJ0ljjP5hnrZ5ugfqUQlYMvyoMvSTrl4BNpdyGcqcgPS/xfycGHAAAgAElEQVSUCxx3Fr0DOnvwqYjwmwJOcYFOZA8+I9R0BSlR6igIoTYRPv/1d8Y96OXzTM/BGKBytZpvq8Fcd78Hn0/24gTXnRRBj/f7wEc/WqAogN7HFXDqJhNUmkAX9pkUodcL0wwVfRqTtsJJujA6geKpOrpYUdipLfjZpTjNCYF91uAmnhYkEgzeX6CBaYhOM+ej4MGnSi8wRcpQL2w65wbJKPBThRLkQKysaGRBvjH59TTIz+gbN6AqJySlgDSnOfj8ITqNVE+GprqlG83vFZb9CUbrUuaULGZmiF1t7/k3v1njq19VGCY5HnywfR6KLMJ5zbr/HEdfh+h8beH7XsEXAt0In/1I7ZVXXsGf/umf4od+6Ifwwz/8w5dqs/bUc8Hx8fGl6v//C9Sa/vPzO8iy1vLo7GyGbLlCXgllVrMM5+dnuJPfwWKxQJ4nmM/nWC6XTRkAmM/v4Pi4RbDn5yNkWXkr/NP2m9G/+Sze9K2/xXyeo3jjfTi/OG/e1zCbLzDVM+SVxUw2m1Vtlsg4Xy4wz8u+zWZLrNfA9OQEs9kRsqy9zC4uzhvlNIdpNkW2yI2+A0CWLRvkmZ+fIz89xaSqY7lMsVitmzrzfFmNP0G+nDf9WZ6elvM5a+tfYSn2ZTibYrFYIMtW1fypZtw1nJzcxnBYAKsVdrIMed56cM2Xy2avT6djZFmJbvLVSuRssmyJ5Xk5L8vlouqfxny+Qp73sVzmTT8XbG3L7zODYJhO1zg+tseVZdtNHPPbtzNkmWwcMJ/PrTZoHwDg4mKJ4+MFTk97mM0XTfn5fIUsK7Cez5GS8tOTExRJgtPTc6tuAFgs1siycgLPq7lTt29jW1gfvh6z+RzjhT0vs9kc+fQCd+6sMF+0fVxlGfSdO83vxXKJ8+Ua58kuTs9OcXzsJhZ3sgyLRYo8T7FclvvnYjrFIF8izyXmTCPL2HrduoUsu4ElmbfFYo6TU93s6zwvrfTmiyXe+MYzPJ5e4OZWhiTRyLJyDpZ5gmk2s8ad5wWybIXz27cxPj9Hj67brVvQ8zm2sgwJeV7imrLN9bKtczYrrRyz01OsKw+9ycVFs7arVdnX6TBt9sffPfhTePD0f4HWCnmeI88LzGYr5PkA2XyJbJrh+PgCZ2ft2ZipGTJtrvX5xRSLZdH05eT2bejlspkjc8x95LnCfCGfaaU0stUUarlEnpfWWgu2p8dj89z07tzBqDrbAHCxgjHfy2U5z8vzcwyqemazdm8uZxkW8znm8xJvZvMlFtMpTk6WyDKTyAWAk5MZ7txJm3WgY1mcnkItFk07ZfvlmBdqhKGB75dYrYCL01Ms85ys5Rp5nmKxXGI6y5q6l8se8jypcF6G07Mz8YwCpVAsy3Is7tzBsj6nd+5Y55Sf0dXsgtXZnovzkxODmZxOJ8iylkPP87y5A9bTKTTQzCkALBYF5lk5nuVijtksw3oNnF+cNWPs9QpkswzLJZpzenJyC0WhkWXbyHOFZb6CXuVYLEocxmGaTY39cufOAme9O9Czdo9k8wWOj49xMVugV5/t7ByZLvdCeR/lRj69+Twx6p1XuKzX0+X6Vvt0Ps+be/nk9AJHsznyvIflsly3PF+iWMvrtshXyLIpTk5OrPOzWpX7qFgXSIo1sqzNl7BYJMjzllxeLnPMCA1Sro/GxcUFsuNjjLPMwDcAUKQppsfHxj1RQzbLkM3M8rdvT5EkAseZ59jJsma/1sCZrny5wHyWYbFYYDYrqrFX52ixwGK5aO4aAFjcuYPVyQm26BqcnmJvtYLWCllW5rGeL5bVXLu1q9a+T8o1XyxyrE5PocdjEX/V86S1bnBrdusW1rMZOO0BADMBzy1yE5/VuBko78jlcol+RQ/1esBsBsxeeQWrm2U+3tGdO+hnGRaL8k6X8Gi+zCs84b4jl8u0oQtns1V1X5p34yKbYrmw6YymjjxHsTD7UNMFp6d9A38eH59jOp1gmedNTqjlco1lDmsMZ2dzHB+3bSanp9jKsmbdVjoVxz1fLLFM58ZZqOHiwsQLi4W5P89OTxtc2T85ae4UrUthcp5dtPtzOm3Kji8u0MsyrFa9hrbP8xzLZYs7F+fnGJK2Z2clzqvPWp6XZfOLC/Tp3phOMduQH9vbA7a3J1gcz/CmN80sXLmocMCs2rvzWYYsJfdWNY4sW4r77Oz8HEnFzwDAE09MMVv28MBehi9+seI/sgx37ixwfLxECE5P04bWnS+WmM9nSBKN5dkZFtUcnJwkyLIqAkE2bekygjskWCxzLFWLEy5u325oGgCYzc1zuppO0SM0BVBefTXOre/0GrJsWdLORIo3FOqoaVEKtZFvlmU4Pj7H+fmwoS1qODnJnPRuzSeqWXtO5/McaaqR5ylm8wUW+aqhPeZZhnlRrvkyNXHR7dsZ7qF7IptWfc6xzMfeOZ5OcxwfzzE6P0c/y4w5Oj+/cH57duesujdbfDSf6+bbmk72wXyeiGe+6dtsjlu3Zl7Z8elpD1nWeljdvn1R0tRZG1psuczRr+m0LMNgPsccM2SZyS+ef/e72Lq4qPjEFNl8jkXRx+npHFNC0y8WKWar9p7O1RKnpycYDArk86x5XvPbelXSG8fHxxidnVX3QGrQjzXkuSZ7NEe9e+aLso3j4zUuLkaYET5yNluiUKp5plTF9y4H0cLQxWKF7HyNixo/3rnT0BrZ6SnWzVlWxtwuFibdMZstMTs/x9n6BItljhEb38WZvacWiwVmydzAdXq1wvLkpJI3lLzHycktDBhvVfT7mFZ942fw5GQKRaIuDM7PsWJ3/mKxRp6jod1v3z7Fzs66wVdZJvP7NfROTpAT/jSbzdBfaRwft5EWkpMTqLnZbn131FCfhSnlS1ZrpNX8rdWsed6sx6Lll/b/9dMo5lPk+Qp5nmKazbDuFVgsllimJk1R38f9fh+6WGOWZZipGdJUYz2dYnXnjnHvUVyY5wXm88xYw+Pjc2NOtrMMeV40czKdzbFcrRvF3ZLcAdPpssE581k5xoWDduG0fQ2cZl0sWtxTKNXsF71u93c2VYIcppWTFL1es6+All7I84FlAKiUxmw5x2xl9pvWR2E9HmNe0cQSDlwsVrh1fIyzs54hW1ksVlbZ+TzHaqWEOnJcTKfN3izp9nJ+k8S801555Rynp63c8uJiiQG5exazDH209NliUaYvqvH+YrHGxXRK5BbtHVLucY0MKTJkBp+ynM+w/c3P4YFvfwbfvPtZzLU25EZ0j6VpgePj0qGG0hxnZyu88soMWbbTrhXBjcvFDFl2UdJptYxKJ1C6aOjZbDrFYj5Dni+hlMaiaNuez9vzvz47tXmsRY4751MccB59ud3UMcUcGcy1zmYz6EXNs7brms3nuHXrFrJsGzNBHvmtu57E0aOPYvK3f4uk4teKeSv3O88Lcw9WNNbp6Qrj8RpZNf/z+RrT83NMj4+RZTtYVM/TVGM2y5szv1gssVgVDR46u30MkHmYL5aYTtvzPJstsb8PPP64wlt+8BbyFzPkeSm7Wc7nWBf2mGpIklIZyvn1ZVLt4VxhPp/j+PgWFosSD9Ty/Gv9hx/e8IY3XFld3/cKPkq8SzCrXL59oS8A4I//+I8xGAzwG7/xG1fbwWu4NNiWixprZXrwcas7OQefSXAY9SqFf3zTBzAb7uGJR74K/WPvBv7a7otWpteA01IEpiWDxfh4GCGlZK8aywKXW4cq04Ov9lrhHnyAjkqgy+Mye3XkASsnwyBfJWacegp12FBqTenpABVqRidOJhAbFqUGt5WdFi24rJwvtcGB04NPWItIq3OXFS813PHG2A8kiQ6339Fyr7J8o5ZCrnnRqjSme/ChNQbnHc2CJGv+jgNsLYX9ddC46mc79+GrP/jf48Uvarzt0/8zAGqt7fDgU7wRWBbiqiictlXcoll8HzBjdVm11mct74/l+XN48CV6DYUCzcCUbYnoGofYIacHn8Nzh60/ne9ESEIfkxNDtNyL8OCzc7W4EazT2rOqWMRFwqRaOfhYMavbobMhnFHbN6Us4/Pgi7Xi5nNt5uAL1MH7FZMrSNyYtjuSTgW8XFcu4Zyq45Kzg3etI19YVpXMgy/o3SKZGFe/O6QxDTYRsjzv4sFXqBR7ewXOztpJdXnZ8wcGbSN48JV0mGOu4fAsYcMgQ3B78G2YB9eNP206xCoh4PgoUO4cfPzuFj093C9N3Ch48NXtRnmfOnId+3L7dYUkAf7jf8xQfHOK/Y8LhCTPwce9HYI5+EwL6NFY4/6H1hgsN9svxpGCz7OwKk+9NyJwh2Gtz17z70Pr4MqFY4C0ZyPouk64Fi1NbuSHM8aqoIl7JfXg43A5Dz53BBpunS+Vo+WpF0VMDr64u9pfj3S1SF4AzfvKi0Bsu4MHn3Z48CW6ZeSLxPTgU3UbdT9jIuoI70R+bgPeyqo7gvCJSGMHXdH20R585Zfmz6IAisLOwSc12PwpezPTsnYdumm77K+JH2P2aBCFdMjBR8+84cFLPdHru4vQrA+99CmxXl+Oz7KAts9DgEdJBf7GC0qhSFKk6/KuomORPPiSjh583nL0TuiYg69LH5I0gV5FdjBJvLmzAQCjkXWG3OO3x2LhLOLB1/Shgtq7i3bPkDvpwuAvRD7b0blGHiHyvxpv/9L/BgC4cfp1fP6xnxfr4J+xYVloy+x7tbetyAGkjId3pAplJfBmhUpk+RLrIz+GtI8GCWvgOfOj2WgP33n7T+PpnXnbJwX01q0h1io1jZvbHHzlmOvfHN/Xc2DdnVBGXlCsi0ruUtWv7Zx4SpWhjfPUNBBTuvB6bbrwu1IatTOVxrUH32sN3/cKvlrb+fLLL4vvv/3tbwNw5+ir4etf/zqyLHMq+D7xiU/gE5/4BH7gB34Av/d7v3eJHl9DV5BC4YghOmEzXhSGZvhxm8hUCv9677tw5yefwOGhGzOtCHFgIToSotMH/hCdMsFtlzPrMJM+EwbOUOKoOGEfbIGEl2gThGb00jMuepUAkBkUH+MfJaB0/3Q8d9cpt+dpI0ZyUBOuHiFt3MNuIAl5E10gJUl/1+nAKB+GitgiRFenroohOl2NS0QnEcIGBMaW8CRi/sWQGwll6mzCj4ddyLbvwsVOYjG9VMFnMKmJAjjPpBQMXtsTolMcCH/MhZ4RIToprNKRLJB0/Er02iAWL0P4ycejpMoL1WPP6w4k7jAcibDGzgcCBEJ08irSwjQpbfYAV2oI35r7R9lCVU0rbM8WZ7K4MM4VotPJICdCP3lfamaF7K0UgnbRA3WpJu9s1S9DwUcORr3HfIxJHe7GKxhxMeFGPYm13sb4hXtMN4ICu21ftM/YF9Z+gUYCbj1Q9QWS0ZEw8qqQsYd8fXNAQo2MEBAFc3zuscLRKsHOjqngmy8deAAOnA60yZQAQcAgdNFKSm+DqeCTC3PceFmQBJgx4UQtobjLWMgXojPUjE9goAtTmEjPD9+D0ngsSwqXFvLytBSvrj9wzFUVGrxBOWzXU0FNoWMEy5fru02r2hZGtIwURs4FIdrcer/JWKzLVMCxG1QTC04BPMNXBm7gtBZDnjGGAjX49r9XwScZoVIjpyuIWlsggXLkvW36IWwB/kyzuanDsVmgdYU3W6Ox+j9qVKkUCzuoi2bbJGQSKO8srUfRYdPwO8YyPE3U5UN0uu5EyvfHhOhMU2eITnFPae0J0VmNSdVKQ7b3HQaAIggF6H7RULVesYG4NCCmBF+igUSlrAEV3iZEG90/hvFyfXf56LsImhSAGNrT11OlIkJ0crodqjIUL+khanRDDU6ULg0DXCE6fX1ygsWoRHwjFNANLrC1NUkCIA3zWg1I6T0qmI32cOttT2Cnch4xz73DaMZBulhGaZQ/IwaaJetvttMtlKZ2nsNWQa8AZnSUsHoNWs2zQNzIzbJLMPB9ZdvHDYt4J7Vu5pf20SAHhD7pJLVTKsGkqwqtAMVZACcjbPaTQN4bizRAj8jbqDE4QHjmSrxj4GQhRKcwECMcdZGvrbnUGviHh38ST/7T/2l+S/gfpQRcYzdlgXWHqGsF32sN3/cKvqeeegoA8Pzzz6MoiiahNlB6733pS1/CYDDAo48+6q3nueeew3Jphzl5+eWX8eKLL+LBBx/Eww8/jAcffPBK+38NYeCXZangIwSV4MEnYSDuwefC2356iyXulZgUGjvZUZGXaFFyDj6rAp+g3pWDT2jbhchdCr6X7n47bpz+E752/3vwfjJGi6A3FHzkonGMTSm0ltv04q+JekP6QBg6h8QxtL5AdwaLj5EKx0Um0CWMfTVy8Ekcc/miea3Z4NWqvdipgi9GVGLtI5tudpYFUHlmmCZmjcCkEygUSQ/fPXgzbp58FS8fPYVEr7Gb/wNWDz1kagSiOuYp5ihfL1HeM5O8t4JdZTKoKmmYU0P46+iOtszM/P30evBZdZvgsmqtz9qqN5Qrcu11XSDB2hh7yIPP7B/bs44PqSCHdod70VKPX4ng75THh4xZYnSsu4t58DVDEYSVoTtCxAuFqXQHTCa/ttynTKJlHBM6EyKKMR/WwiHD075SNjnvG36PVg2Vd0d7eGjOIG7RGgKNiBwrURUqqJ65ZgZ+ldbmCvCQ7xO7itbLQRI0ihU6Ku2ag89633hD6HL1PRVwZXdj2SuVVQkUgJs31/jud9PmWUxfDXLCoeBz4tEIwRY3SHd58PkY6U1yipmeRoJJMoSzFt5MzXO3go/Rfx6lg+WFrbVp/MBzF8NW9Isv2W+rbGSuwU7gqqPfN+8eB14pFXzCc6vvl+srF+RK/TLOhCO6gwS14aBYEW8PCNK0MR580n3bNTem75mvEOWlSqG47MFneS4yo64uc+zNwee5sqSxXbWCL2YCJRRj46E4Dz4FNHcb4MeThvIOBdK0/I4qYnTSCvJrWYJJZ7poO3kzGbx2YMN15UGVAlR9oXjmPWpdmxx8dj2S0YGCrOBTVPHHcQHtuP2n+DtoLKfsKCAxZ/jS7RKge9VQ8IkefOE7ONg/5rUaGrBSwGDQXcLuGosx19D2wwjwKU811wbVfdjQg09uH2UOvki6Svd64tm4tf8QPvPkr+D9T8/xIHLrnLvWVnrO8yQraJPmNYxTlYW7DXovRuHq6Ge7lBWPKMgQm3ZdEbhYnbYHH7/DmeeiuCzm3NT18rKGfFe68xyOF1QGIG3nym/Wfp+0eI7f3Tk19Cb3Sko9+HpMwVfJeGr5stHfiHPWKucrWK+Ns1NU+PKb97wDt/Yfwlvf81n0/9+/LPvGYtmGcoNLd59kGNw1Qto1XC183yv47rnnHjzzzDN4/vnn8ed//uf40Ic+1Lz7+Mc/jsVigR//8R/HaNQeuJdeegkAcN999zXPfv3Xf12s/5Of/CRefPFFvOMd78Av//Ivv0qjuAYfSLw7Feameh1FzMYq+BzRfQCUSMwIS8y9GaBN12pXRR7iTCXK8HxwFudCVWVaPdcCFlFRZ1KOcj8cCP6rD/woXnjkp6pf0/YFT1DtGKP2eDhyzwezCjeRHAxv4QBfpBNZPsXWO9A/JAnyRx9F/ytfQbG/j6LK89NJwedSnHDm2EO0toS1SSylq5bgWFNL101kSYkSL35nfbXwmew/lchUsCtsIv352cd/AVuzW5iObwAAZnvvxpt+fhsSuEgX1/5tie1EfJ+mwP33r/FSauZ2aUIiejz4rBCdVqcYAenx4Asxf5yZKPvi29Mgnkfl79zhwecK0Vkmazcb6MQXci4kQsFnMFNpypgk0uWex6rYh6OleY4J0enIDddFsNC8lAQpzdy0zJLlvaj4FNbKl6oMv78C/ZKEWFKIzrTIgSSSCUW757lgn8r+zTBSEUxQjGe8ALYgUvlD/0reJbWCT+iCPNduS035MbNO17bFt7cOz4ajwxG9/wJAvby1Un7po1JOYQ+HQqVIANx1V9Eo+KRQvare80rJtIIj14RzrqM8+Lgi2v4g0WENddijwPdt5HOG430efLHW6aKnh+Ol0gUSghs1VYZXFTfzSQXxTb9YU66BX2GIzhDoUIhO4sEnh+g098wmJJnUXllZOERnjKK7eS9cCtvbBS4uyn21t8fa2ESrxNtI083o1IiqJeDh7el3tjDUI8hW1BMmTkkFkK3ro48lkElnu14PBOcnSvls8992vYzWc7WttRG6WVOahXvwKerBpxs7G0WE1KIHH93/HegGej8qZa5Nc/9dYt82/VuvS6Mhh5bLCr0q0PmFR8HnCtEpogs2V0r5lY8iDcke+EiU2oNPMlTzgd2uPUexOMWImMTCwDbP676lqqVBGDTKgxBPVasamj0v88pNeQW8+dE1Rrc15nOFd797YRfiHVKmF5ARxpaynZVnrYuW3wgvcz6v7kOI/omUj9TPVJqYSlh3FbImCQ5lBjXycOxFuU/aGrvfg8/s3oobRXjY5ah7TrgwVGEKyvhvF9DzVRQqEKKzsDz4LFmBZmFqmQym+UwM0Sl78BmygQjDoqYrhkOFWSbvTYKymFViyorqcvUat/hFQ9EQnR7hlYGTVqYHX0E8cGejA2BAGGmD/9FBw0NZTiV4A1+dbv4aNoDvewUfAHz0ox/FH/zBH+BP/uRP8IUvfAH3338/vvKVr+CFF17Avffei1/5lV8xyv/u7/4ugFIBeA2vf9gkRKdELNkhOuX2Qpegmb/AlmpYOfiEOv0hOrt78CllEpkGYSgo+Hjsa7EJhwefi5lc33svgFfEd3aITgewHHwUokJNEYhZ3+WyW51WuBXlflcXmP/kT2L1xBNY33dfK5x6NTz4XH1o6mXhHAAk65bQ7R6ik7XR9QNBcOtS8BkcMwNKkE4nR83zbPduIJk5OlsxVQLTa9VLn3sm5o1vXOOuH9bIj3N8+cvlPq6HqJnSxKngE/ObJdEKvvYbGaTua9gCcQOY8JdbnjXlnTn4SoEXxR/dPPjoD/vDul7uwddAmhp707A09uRRi8I3tEyEFx7PwddY9Avfep1NBOZeawUIDDe1Hl6vAfQVNGWgPQYtEsTgrjZEJxUW1H3zCO6ECvnZo7kWVmIuDZ9wtVK0X1oyrADBg68BSWpaTbQ0fV56wCpcz0uEwVIHAbIoca1+X9bLg0cR8M4/74cnB1+9nnfdtcYLL9S0Q7iz1lBp9A4iWPZ78PkRWGwOPn+gLTjn6sknc/zVXw2R5wrPPLMkRRldKHzvwvGhNv3rZv60ZIfuogAYbhQVfO6mXR58Fg75XnrwpSYfYIXoJBbzMR5rruO7yRC45bxUl+ldFkTWVj/+/b+f4b/+1xF2dzXuG62BOS3evdPWFIkbIlzvprjMjEhCm1RG9A5D+Gnd0WadXTz4bHzfGnTE5OBzQUwOvhDEKMAcV4uzngQ10pS0Itp9t7GKCyYAr/Xu1GO4SHpEaK8tOnPzHHyelxtC83mt4HPUHevBB2jxzhQVfNoRopPm4INjiJ4QncH7COzOVsq6U2MMYYLtqrCCj/JwNcTIa5LENCjmtJCUu9C0KdDmeZDoNdbP/kDhP/2nKW7dSvDAA7JShldBafaU4DLuwWcpYzYAo21DZlWNVanubfA7n71SaYJesZQ+sSFNLf5X+kCiu8S2Hc+t+hz3wnpte18bd4+V30MAJfeTRrah/wO2x16qWRgzufqOITp1eRcxwyKDJiG8I6fPDZJRkqMkqUgeKCHUpmGLRqLHmB587Xm1Q3TKHnwU1swYvDGKXVfyERoJTsjBx+vUUIZxoyq0cXVaCjfCo3b34LOfFUoJe1whxCddw6sH/yYUfPfccw/+8A//EB//+Mfxuc99Dp/97GdxcHCAD33oQ/iFX/gFbG/LXhzX8P0BNk+usU5a6wMxRGdZ0PjJPfhcBLBX4KkUDFwoFLpsiE6V2Ao+iRD1MTJQppUofR4r4HR6WjgIhNVjj2E0+g7yvHw4GMrMBvdONMDrwdcCt6CKCY8n1bkgRm29nmYCYxc11oKxV6TOJgkwGGDFQwR3EdbEMoSOcpT4sCymiAcftSiKadJmRtzMkUjY1gw6UzZ4yQEnhRz9eCMQz7LUwKBvPPZ58NUQUvBBmfggJvmxSzBdW+sa29Xa07K1fV0sT0eEwg6bByZ6beUB9cFw6A7JN/j7v3d+t6bKJIpimGexERLVl4MvBmhDEV54rhx8Xe8IKOUP0UmEXzwXGNeRtjisFBiG1ofvUQn31fvPEBZUOfgoWl68610YfOYzUEWB777vZ4EXzDroNVd/Rhm51dpxzznAJ4iJEdgZfeu5XR94WFgAbceltRbOfdf9aOHjxhK43AuhddViJeVvnt+iu1cZUeoGBFO2xMmTgy8pvXieeCLHpz41xHyucO99dt9qVKWr/W2FHKVEXQRO2yQHn6jgC1hC+9ZsOAT+w3/I8O1vJ3jssbL/MgkcsbeazurqXxct4V46voe7Wu+mROhGPfjqOegUorNaYCtE4lUSBSEgwsGycaagCXjwWaAuJyzhwqD6N8WbkjANiMEdNt64ebPAr/5qBgBI/qcI3Kzlv8XOAeL9FzOPNMSzq2rxncD3tEJGwuswYyajf+wAlXiECOy9/Xa/84XDDRmPbOL51L2ABBo8r6QVjh2OHHyVdoevjVKwPfisHHzVXVRQDz5B+dTBg9UAFQjRqRTYMOON3QgfoYqixMeOj/m8icapjQef4PEi7CkxPzFX8AmKKt6h0HbRAq4zaUFV6xWj64xvN+4bSts6ZRpkI3AFn9W2lDCPefBFdw4tH7e1pbG1FedxxfuRGDn4aKHNPPhCPI1Wqt3PlYIvEXJ7GvV04P+VAlSqcLpzP5b9LQzyqbdf2pGDT/Lg0xIOiehTkpT/NHwMTIRAt4Wcd5LcJzExEY1z2BqJtOdXAdo8C1zBF+vBxxV8PBe1K1Q46aAdGlbL8gHjXhcKFCqFeEzJ/Sjeo67FpIpBK0TnGH3pbqr7kqTyeUe7vkZENsSxEvgAACAASURBVLquDnzPQ3SO+jmSwn2HKerNyCKYbOTBx428lMNw7Rq+Z/BvQsEHAEdHR/it3/qtqLJdPPfe97734X3ve9+GvbqGqwA5B5/pwSeFWuDA45GHLmHpvYYylECWsBNajPVs1RXgKF3I3weGezbLCUHrbpnSprTcDSbECikPVo8+ije/+S/w+c8PoDXwI8/etr4t+xZBkIllJOK7JlDiCXP6nHrwDYdcwWdDrAdfM1cO7rmLB99VCaU4s6mgkRg5+Ppm2a71d80RI4XoTOXGXSE6jXfezglMtvA8tJ5BaYgyiZxGwYdSKN7sC+LBZxCwDqP0rh58bqZHW+98e9r6FqqxPNNg1pW+EJ3KFHq5hBp33bXGww+v8fWvEwFvYH3renmITgBtPi9KXNdelVBI+5d1TRIQJAHLsaRwhAIUvf88a1yNy9ivGmIOPlWFzm2YFxXy4LMZEqNfDiW0eXZKoCE663vIEBzv72P6G78BNZ9jcXaPoeCr62zPTPkH9eDjOR0At8CrrCMiB18MKCUYo5B2pRCdzKOHQievkkiBBmeG6T0izhCdbFZpV68XqxruweerkIdFcjD0QGu9PB4DH/lIhm98I8Wzt1bAF/39sYT8ggdfCEL7iG4BV67IBA5EGAlHRwWOjlrmvygc91YI2Hq4aA5/KEB2r/BhBTQ4PWJsJOXg84UotJ659lfXZJIx4CM0qbCfC0N1q+CThOnc4vqyXXXS16bk1vE80LiKD21nd8YGUZnF95e0xhGd8EVW9oEkwFUK0Iw+o8JP+xyVv1ujFBrOz9+JZriS8PJSHnz+9zGwqQcffyblfRL7zz34aPtsQGYOvjpEpzb44kK1gvzSqa3Fy4rT347xNF3jSl1B+b3pYS55tMoqtWEuqBC3hVgPPilsPwCsC7kC8bpa0z0vR0nQJIRS0JE6QOOU/FNH/CjgKKnd2KWhMhpXfi+KKJpc0uxVYwwXit0uhSf0dFZaA3sQ9mKaHnxmDj6qhEqSTmrvtk++l0q1jRRFaZxJu6uScIh/5eZflAJUmqBIevibp38VD7/0Kdz/nc/CJQODQ8HH21AqTHd55VHlP9UTbYyRRpnhCjKltHUv+eY4Zk/U3rzUq5cr9IzcjJfw4DMjSjm8Qj1ngF57BskoIL/CwfsZ8ytsA8NAnrwvVGqeRQJ5b4SmOxJu5eE5SbmaVzBpClvmYx1dVaY26PeBGzfW2N9eQS3o3ZCYtpJUXiN58HlAmkcepUrs4zV8T+HfjILvGv7tghSi0/DW0KsoYnYghzy2IEQTrYwcylz4pBvET5F/kLBk7zixJxLA5KFSzAJMKZGJlOpyhuiE/J3Tunt7G+Mx8M53LqE1kBTHyKp3ZojOeOWlIeD1KPG6KMFo0cWi/SHIlPwfV71qHneg6q4iBx8HGkrA9b1BhML04Ns8RCchIrpc6HXoNcOCPDAG9m5joVeAgeTP2j0YKMyFC02ITrNsAdUwqEaeHUcHjL1fFE7GKnRGReIrMIlWfqqqfGlt6aicQKLXBo6pCUG6V97+9iV+8AeX2NoShPmRiywp+GqvKRfRSa3Y7rtvje9+t0zA/uyzyzDzjPB+4I94Dr6mLxHhPY3fqrJ2tdBRqzWm9w+1HpbyOBhVhBR8Ur+s/V3lQ1O2gs8QnCUJ9M4O9M4O+plRhRWepWaAqQdfGUaq6m+Ed0tMfhgJeHENBXjCu4pSZIdXEQBRse/rYpSludZMSBrAnR7pAFWI2esSBqXZPRkigBKTudWOtdVomfa77y5w990FBn/pmZvqD0vPI+Tg40KbrkA9oV2G1WV+0qvjgqX7V6abmLAgdkF9ODFk4BNS8FEPvtSkZYF2PmM8+Jrx8P5ehTaDgw9RUhqde6QEPPgkAY5TENmxm4awK4L+DylwrPcWwvT3ezbax2x8gNnPPIrxf/kvePjhFZ5/viTIH310JX+04VryqIYhkGgqc3vxCAsej2Muu+wwxz7dtA9PuY5CDVdyJCLwRxQPzM+Ly7ijkoLytSkF3vQDbeQUg9ZIkzIRsRFVguZQE85FF88uyu87+cINoVC9trr1GoNPfQrpyy+LZa11dfArLg8+OQefEKIT5f1OQ/xJe0qPx86uWF0LRMOQPPg28USV2o1dLsqFOQ2yCa8U6p9ofJa0OJ97dgX5NtVNLlKDQbMzBR+v/0pz8PEPqwZpDr4uCj75lW4UOvPRPr518624/zufdX/iCtFpgclTuNbaJV+g68RzZ/Mc6lTJV0Y88k+2TEfY/aHnl7/kHnz8twt4AINQDj4uD7KUh9W8JIk9LsqjuEJ0ijQq4eNqWYxBqqKVvxhtksFxeW3eG1uySTrX1JietgOQaB+qzIepVHUG17aBLP/+vjcB7xwuoRQwW68bZWgTvcRlAUL4H6XCvLS0jzlfaLV3Dd9zeBW4nWu4hqsFKb0GFeamxdpiYihSroGH6HSHcCTCKPul6TUgCFktYk9iDnwnT20aopMmfXYwnEoK0SnPQ+JSDDoIBACYv//96PVKZVn+tre1dSUmkRYCkWghjVkKk8S+MLm1kwTUg4/vD1HoEunBJxagjzsQgM46pD3hgHq7cE+whCgcuobotNrwCPjE+gTm3ZWDz+fBFz0/XcHBEIfeG4I0koOvXgOlWhxhhZkRPfiUtW7BLkfMCQ2pw4ZgwpLmdzJqM55rB15QuijzqZCXEuG3s9OGY+qydnVZU8FXCU9rrT2pkCpdqfXeZKLx+OM5nnlmiR/5kUVcJwJce+w4XJ6+3opZ5Vq3uJ4nKqfCQR7WqAseAdx71OhLtTdWWvDgczDoFv5VieHMVddpevAJXfEwJzF3TxQoeBV8vhCdsZ7O3mUQ6A7rd00FKQDQpiWqS0niqDRKOOvrLlU0ipvO04+IEJ3W99Yj0xuJ7iEAhtUWD2nugtAc8DMnC0ZD3tiBuWLAQwCWVcTjsVDRGEWC0RfXA2HM6drhwVdV7PNgsp6F4u+7vrtC0E17mj4hBfwKPuueVMq2UuwA5lAJ/iDny7y3SVdDQkTJS8ko4LHKB/DK4WP4+6d+Gau3vAVAGVbumWdyPPVU3nioWkp4yUs6QiEv55yLMA4hdVM6RTMFX6LXZHj2HU3/T0iUlGBobE8oTx8KCYfovJwULtYrXkJL/JmRT6oKEyYe9yxzKzssWsTMYd1Lyjmnyou16pl7h4WYiMnravTPw67wgXcRgq7TVsGXfuMbGP71X8sNN834K6+NxKQQk2KITi0r+KI8+EYj+svbL4m+KR+3Z8DOweevUioTQ/u6ch2bHnwBhl55FHw1zSh5iSbmnrTOwyXok6ZOjqqNEJ1mDr5mvuvvnJvXJVvz9EUp2+ULJj0v4cioiFC0CZKTvJaZufq1eYhOe/wusjdJSn7B2MuURyJ7QMrB5+NpxJCTjkg7XMFn3HeMZ6O4kw+Kjp3+XRTKlskZ39rhmLViKSMaz+3GOrl5ZeTgE3izQqVIUns+Qh58TllKSu49VibvjUnBeq8QvjV103GzmcJXv1oOhso0JI9tq5+UH2KbRYPN/yU8+JztA8acxESMvYZXD649+K7hdQ8hBV9SrKp44H6isWuITgnKEJ1mYeNC0xprZQudughRpRx8IsHE6jBCkRDhgISsTcsZBzHmUPD5CIr82WfLCzjPkT/1lFjGm4OP94He7R4PvnViozLfPVgDzcHH90cMmJ6Jwrg6hugU17mD8D8kgKP0noJGeokQndIed+UCEeurPPgoAe8KF1ODLJq5Cs7Ofix6MoUmRjlCdDpy8NkKPoegr2OITtecJAmAisizLPba5gwo7r23eb4iyaMtxsoxN6XAi4fojA+vE+tFs5assIWwiAaD1DPrPjzU2HpijeUQcRAUytXnIlBPRP4+Crpihq0yhjds9YhV7wrnxfG7s327UfsuqgRia0XuaSFEJ/2Oy641u1vrsmYOPmYiGgCfMNI339L9zQ15jDKS8NmjRJEYz64g7wUW0qlTBe0znoOvc9+opzv8wmzNJSGFO99dlzsXaO8PyxvGEaLzch587d+u0N+JvmoPvrj+huhR5/o4BK+AfXd1td6l3s1aOD8+3ZzVX+Yt2wplXzsPvoRNCPXgk8KzSTqx5bvfjf7nPgcA+McHP7BxN125b2gZI3xkiGZX6hInRYatLWbwIYRy6cJX1SB58MXgfmeOJWUak1LhJw/dxxXwNA1CSInk8+BbewRpoSnx5faLgVijGbkflnsJeVULc+2vkum0yjNsC6ON+VVo6Od6P/eSAusihSJeKIZSox6OEHUiPJ5yRPU7pewRaqUMXNkFT1IPvt4//3OwPI3cINL5tQefsPckDz7l8ODT67UREaSed6MMUfAFWQcHPUx5CDkfmR9i2o0lb+iecEYlqoXe67Wzf41CRSU2Dk0TAETRxSJzeOVIGyJkKp9JjRx85FxBY3tbd1YEdFr3OuoD7HPoHRtXNPBXntxpFiSJTAuxNspzrvhr4bMYGaTpwUd5Ayn6StAZgNfO8WP93OPBx9e5NmIZj/3IKxiik8khuTyIA+Uj+DiNe12YAxqG2YBU1UdMxIOuEJ1mygOz3rw38q7DyhOiEwAuLir5EJXpCtasFk1HlJV1WM9G/sA6ZChBrRx8GtpjHCKNTeKtrz34Xlu4VvBdw+sepEtRUvBZhRhwAWLIjd6FoKmwJkllDz7OAHaCxHbhFgWM7L1JcDouZ6VEZC+CAzuH8rDk73yn9TioCONd8hBV0iMpBr5bwadRUwCmB1+4PeuSpHtFIpy6xGpwPY7cP87QqYbrvEnRXU2IzrbqjUJ0GuEoHI07DmW57zvMZdRLT9GABx8Pm2jm4KPlHAo+l9DACAGlnRPd7O0Oc8Lnj5cpDg8xf//7cetfv4PPHz5HS5oFXTn49BpKmcLCTvsksFatAM5W8EkefC2jqvzW7THMc8Q+UjHnIkLBZ+FHLkDRMBPUEyjDl5Tl1479RoUnPpDQtxUutPpBhQWJXhl94zjT8qoSQn7wcquVMLeeyS46nHsvJI48uXX9ktS05kA73m0uCAlLFHQnLxxxT1W/u6Yus953DdFJ33vMQAvBmMp3lkpvG+UP0UlDyG6GHqxuSJ6mQC1AC3kz+F9TkHLw+QyjGrAJaLH+EO1nlO3qwVfIHnz5s8+i/+UvI02Bb9182u196vvten4F+MC5ekqwPjc+1OQ/gZZgIY5UoqDHY0w/+lF8+mSFk50HOvXTSV87Q3TGu8d09eDjNIdrX+VPPYXeiy9i9eij0IeH5jfCGsco5DdWaBGhLhfsaiYUb/BNIij4yP+cN/OBT4FRaPfLlsaR13kTxQgFMVR4RB0uIWHzvlZsSNflxUX7jiyIUhDC9ZpC+jQpoLTpnVSo1AylZu3XzY0sxbOxIdopEhKiM0JTTRV8IiTukOVUwUdzOEtGJGpN6Xt5P1w2RCf/gOPNuD0YULB0uA+ozCGUg09Np84cfDXoEpmYnzOFl4WTQzxKaDza7BMQG6JT48d+bI5//r/KdR+PNWazbmshdpjKUHQZmD0x80DI3zmqE5sgRp01fnb2t9czcUv9nXRfS/If3raECtg6KW0qTs0QnUrw4AvczWzLuM+hAtCGODZlDiYSGQ9WePe7l7i4UPjad9xthxR8xrhR5nX00grMi5Wug8E/VjjIVMglsiEluaPrabciJ7QMvzG4VvZg4ivDg08w7CzEEJ02FCpt5zAQohNKQfXStiKW+Nsy8OiR9WULo3QB1TGvby3jMnitawXfawrXCr5reN2DjUy0RYQohL1aYoVU3jBxSvLgs/vGmWnrYveGTVGYDfdxfPAIjk6+hmV/CwNciH0xWo7MwWdZBrvCKVgefHWF3Qk5g07dMDKwj3mXYlo7896QanwhOuU+uOvqFKIzsPfCDztUSl6bDDSQrC4forNRKij3he6qTzwXQmFviM7LAKtPEoI5BWMOKl6STVPrJqVai32uqxNjxyMgKBQgxoOvLZvYZRjk73gHXnx+gvPbhBiOkdgASIrCDAOllD1uT1WxXjSlICcxrOKlHHymB58wWNVhr0WczSTx6ihKiMjBZ71ke6102KqVE6alr1HOENzY7Uk5AyhwJkkqV3sJrkENcTp68EGZ0RqrP8wcfBSfxZwL3+XuBnt/+j34RE8hwZu0hs6ORUIlEp1UM8Mc90dLIIT+cWOFLl1uhBm+CpRiIca1KNcBTLwqdpa0TYHm6wBgWbCSL33d9ALFeUZYd1qmWHkjT3TNoSNWFbNXuFB8Awk0l4F3VfAZ4cDIIV/ffz9mH/wgLl44xZe//hxunP6T0Djrb1JbQMvPvyfAcLQLP3GlbAj0zg5Odnfok+juNF/Qc+MI0dnFk9XyPuHzvqGkZ/He92L+gQ/ICbKJ4KxL9dbZR9x1IEV0aEKBU0MWQ3HEzlVlXGmFukN4jhsSQbif/fRFvJfFJrCpB590DZgeHXCH6GwUfNqeN2ZoVtOFNfSSAsgLp4dq014gxIRzzxj0lqw8o/dK7N7VKjFkC2o+F9um4FtbpdB68AVy8DWKQpfR74oIiB0CFOrBF7Kz0EqBs0JceMw9+ELhSCWQ2g3hAkmoX4RC9WdZMERnoVJBOEPOAzTAIzJ4OtvQWj4QFHxmiE7ijUznWmvs7mo895459MtLvPJKipdeCltOBLuTkHukapDn4ItthBpy01cJzbkW8AjUSSIeIm20UT0LKpuFlCT1bxKik+daVB4PvhL/eSJWVOjnzva92L14GavJDvJnnsHgC18Qy5b/K2s89E4rf7dOFb4IQNwhk4folHLX2iFZ2RmoPf2YYYJh7yCtWZJCuguVkYPPeu304DMQFFtYw4NPuq8j6frhROHgoDp4gRCdWiXlGOulYnlqXTQyB6VQhcd2n2ep+9LZvFbwvbZwreC7htc9iHhJKRRJryFAeNJX7fFicdUbel7XywXyhlOPFhR8Uhu+fGWVkuPvn/glqGINnaT4gW/+7wA+2/ZDIO4oM+kkhGrhuiNEj1HUEX5hE+FPjAef1IYxRPOH8ahQ8SE6KdDIXDHpTVzeTkrJ8+IKCeVaf5kXjC/rUjJKQgSlC6icevDREJ3hyZOI1S4XuiqKUhHBQ0QFhL/R9Tv3jrseWe4db8neWMRWQEN00ipMDz6yJpcM0ekk5prHgsDBsaeddbdP2joBpwdfHYaO78HYEJ2xUFth0yFqQcFntCvlUevSqYgyUcLDTTz4uFBet8xRwpRoVH+4Dij4Woaqy2Fmd1EdopMwCW0eRt3+T76zDCxU2IPPa50uwNXl4FOWUtbAXVKIzlrBJ+mUhXPv3TdKga8PF2jUnr4hnGDU6Shz2TBuVvO+vvCz4MvBJ4WF8Qljqz9iPPiAzWidGowz5/TgW8kvCHTpQVHAFArGfiwIIQJF7edWiM5LIHXmnbJ661sx3Uux/OYE4oxYXjvNgpvlurqixoDvwqRrQejpJGm9MVwRNSzBWWTuzphuuva1cR90CNF5mXPCG5598IMY/s3fIH/6acPbwAKJto5YTwmXxZCckjC4xvncg6+G0Lx1mWMfTb72Wdsn/rMdk4PPP62be/DZuNsU+Bp0Y6/X5ApKiAef5TnDKi1PWrt2qdJQVAmb9Mxzqqp7k9wDkkDWP2btL7MB3qkFuA3POZsJhcy1NI6I1GblhRKt4LObKJ+xXE9i/z0efBY45rtRKqgEq1V8mP+6gL0H/TSnD6iRuVPJUt+rqxUSlkZE2vtW/yh/otnMBjoaSnfhAsN4nsvViBJKKaCfrpEKKHqja5UjBE+IzmA9cF8Pqkc8LyU5EwVHDj5OV2jtpp2NtlkRV15hlwcfc8qylPOuLnzjnnfg9s4DeM+H+jjo3xbL0xC7AIzoFXwftLycH0wPPiWE6KQ/7Bx8ZZm2IzfO/hmpkeO2BcNwRyjQLQefecnLHptJMwI7ROcYSq2dfZFoWM63PPzwCj90sEZyUn0RsBLWqPb2mpT3GRB5jBJCDjOS8tzy4FPXHnyvNVwr+K7hdQ8uay+q4Eu1FKurW738eSzTYhFqzFJE4t9jGMq2LkALOeYsoSoNE+GxFhMFCrWpD4GEWRxFCwrlZtumGJH2yuFj2J1+G9OPvN9bh884vRDmJybB62LRdowLmKUIbPxSCzJQjrnqKqzRSRJxwXuY/HqfUkYWVxuiUyUdL3Qh5rovRKdUN2fwoyHiG1HJHAjRyZ91zcEnLqHqruCLEeA1fbEs8ALKQ8d3zr2u14aw5NUL0VlbahMGQAiL2HhVQlk5+GLaM4pElbWJYQs65uCrXds4gyYaZShlMD+ukLCUMPd2VQpzwvtSjddQ8NUefJxDraCMhkNCQXHBYfXD9OAzvSFCG+uqcvCpRPkFzL4QnVL9Gwjvw1uvDfWjFLNEdTCY1uxJwpIOQjARlGPu6HvagCcHnxgaK3iW7BCdyPOWBiJesD4IzYGQTsYuU+SXVY0YELJArsHqOxcwOZT7Tz25BL7uaFwgKZ0PQheAcFZqxX5UpASHRjrW6/xKgO9zbsjUKPgcAiQ+RZfsKseljaIgIkRnTHhfxX4b0OHCX731rVi99a3hgpIRhaefTz+9rD7bUOrkEBxXftLN76RYtXyJMwdfVRf3yPGAz4NP+0J0BpbuKnLwxfLK/LcPDylo03tnawvq7Kx8R0N0Wh5I3JhOGcrZZHqBvf/j/27KFDWPTeeV0BLl3WnOr2+8lG7h9279cJMcfFopFDQSiKTgY3iTh4Vk3Sg95RJZgUmjI6RpeUW6cvChowdfEA076IPmDlDAfG6HKwxBTLuxV4SREkUWFhgfi/0j70XlFaXP+Z2sVIBWj9hYWlvjoziL5sUt51o5v4uB4Nwyug8AEpLeoRCMLGLbqp9RjzjnutWQpmKIRX6miyJO0WaPX9svWChWlSbNUpZeq2xPRS7EbHwI9OfOXdF48DXjILiYKfgMwzRP+5SGDubgq+9CMz6mUf+TX/szAEDygP29mYOP4R6VWHi3KbqhBx/dO+l6ST+p3pkKPkn+w3pi/NraKqDPSLlAiE4NM/IJ1msvTeYzblY8HHAMqFrBR++cV5G+voYgXCv4ruF1Dy6iwMzDVyK/9pJS+O7+I9i/8w0AwPnWXcF6Q8+ll9W90f52hui0iTN3G/Yjb74f1MQ6DdHpFqIbljrESsoS9FshOqvvg6EIpK5yt/sWPvf4RwCt8c43XTRslBQmzkfMriUFqLMv7d8+Bd/Ojj2BrnAEnJELMe2dPPgiIeS1ajGbWl9JiM4GPAo+8bmgsBSVByD97tAxSTngL1S2xN+ZhH2gTsbFUWWSsVcqAi8mUTzP8RKjufbmJZSIX15GgFCeU1cOgqRYg4r/Wi/oODzSJURnXWdTX6Pga8sZ852qyuvPoT0OthkuE7Vlo0J0mnuzbJvgVQ0Dn7vq4rlV7DL2vvd1TCpXr5llDazN/BL8Duv12miJWiVGiM66ThqtjebDjYFmr18G0aK897nXpYEnJKlp/UxoW1ImhekQ9xoDaDwgmj5tKoXFFeTg4898FfCwSF4PPoG2CuTgA2whv9K6TOjY75uX1SX2SYzAMV3nAQmvrPxxgRWdAfK6h4dl9unDH85wdpbgqXwJ7VDwKX4fbKjg072e2EEpSmMDjhx8lpHOqxGi00dbKDlEZ5qiuXNKr0v7cytayKVlJQ6PJEvxWP1dhbrW2iWQMvvqL+DnfzbxAJT2tfTsjW/UeO65AltbCwCX8ODjEQvqd8rjwRfKwWeE8/TPgW/r+jzZQ8Yj36sQnaH7CjCj0Bih1gEU29tIagXfdNrUERKsa8KkJwkw+MxnUHz7JQAlvzMf7Mr92vTwKdkwyfWbponwgRWikyn4Vo88guLoyNmUiHGrEJ0SAiqIC0+Tw1k7FHxrweOIn3GvB1/Yk04pTYT4CvN5d/wYLNOBnjdCdAZy8AG2pyxvKhhuXGvQ0PtBQ7wNPfiMEJ3azMFnhJEEgkbHHLx8nlJmzsGGeQ4oGhzny+nB1yVEp+HB55YNxITZ9tLEPEQnaStJFbCq2zH3vBRe13G1iwUk+6P63Jthp8115h59LuDLaW0XY1JKDz5uWOQyUOPzaeTgqz2T6zHVuRalu9Dw4OtwGRJjgN564S4nypHC53I00qbWsskNVRuG2TwV9U5tQ3SWuNuUT2qvjEhp24i8SNLWSFeYf5sOUFFOFtfw6sGrwO1cwzVcLbg8+NbUgkLw4PuXN/wgXrnxFpzt3IfPP/Zhq96QzF8SuonWeOxREWHRGAzRydtNmQJLwLAGkakcyhbyPEiQOBR8mxCNxkUvEcOeOrngGbAFN7IHn/sCq4EKiHmIzu0d2GD1M8BhXEWITk89MUCVSzwETkKEEYURojNcr0VXw02LuxR8in3kYkpcCr7y5waSLw9x430WkfxdIrBdHnycCJJDdDLlscdTSTorFEJWpMJP57fxHnws1FJXD74ANDhApRbD1oboJLhDt3tJ3G9K3mvexi9XRFwYr0BAwP9liE47bwWU6bHkCp3X5BMK3QtWKD7euXZ/r3UrXFMNu9ZaxvPvBgOz35JhCFXOUMNxWrcLYr0NOFjfKOX34JNyQHhy8G0SojMgNzRCdEp4UhyToxPG/tnQKtOgH3yD4+99yiBJuBzhDSs5U9bh32JDdAZpPMcWoZAWeXjPdsCVnOYJ0bgu4AYPDz20xrPP5hj03J0R8YLrgW9QDk/XRoAjCUz4gELhOVzfbQKhvVz/SejpNG0FWbEhOmmZp57Kyd/hMK92Nwn+cOTgM/KfRWx2636i8GrEaop0PRuPgYceapfe40jthwovGXNU0WZ0H1HvBrcHX/W9wWP555h78FEofCE6A+TMJp5PFCxP+0iQrwET76eEBtJbW83frhCd5UvuwZc0isMk0VDn50bxL7z5Z422kwSmNgPyPeMMRckM18RypI+f/zyxXPBf+maITpKDb/ZzP4fZz/+89X1w6AjBfQAAIABJREFUbauUAjEhOmsQvVwkDz5exuPBJ/VLgvrs6krB973w4JOgSMzQjS4FH52qUPhBaQ3onUrzj7UP3Z1NU/97FxgefIWp4DN/tGckOK+xIGy0ruldpKraZ9rIu+5UzNZQKfg4jrGMqCKMYAB7XsQQnVojMbR4ZJ8JBsEhepxGCPCtE5cLGl7pVohOki/Zw1dwBR/PRW3KoxxeocJDiS7xe/C5FXySB5/pRNjOn3Yc6H6Egs/sT/g+mUy0QeNERfCiXnksB59lgO2hn7hx6POPfRh/+c7f9u7xkg5gytlXgey7hni49uC7htc9uC4lmnctsXKZlDn6PveW/5Y8Mwl7SYEXk4+Ad8aoR2uDCG/LWJ9567RAMqFn5cwQnR5ELA2xlA6bTThy8PFuxICs3PDVJRMkrm8kBV/Xy4V78G1v2+P3ETOxwkZAJjR4faEXUQJa/poKItZLJJXQrEh6USEmvPV39eATQ3QGxJ0xgjB30U4gEsXkh8uCnD6mltWGoEW1BKNhhXpFOfi8cxJgVqKFwgHBSvNYr6E4wczH7cOPkQupVWJb2QtuH5ROTtOq3xLx7GlXDK3iLBss4szV6S3PhBFat56IxilSLAefsCeNukNhcPj+qYSW9HFBml8nPaTrvLTOLMwkEnwXUyatjelflyoboEu6XndjIq5KwaeTxB/exxeiU2Iyu4bojLgPGoWqhBOkSZAlrgDcRlYbgSAwoWDlF77KEJ0Vsy7ytstlqQ2IdAuImYM0tVHLOu03oa+SImwJ7TJUkqAUNG2Ok4LPfaG5Lnvh1k04FXzVe+lu4zjJFWf/KpJJxoJSBo42PfjaC1Dyumyes3uzhve+d4677lrj7rvXpSAosjtN3VQp4yHaQvTExnAVEmFxLcN4UeLxYpvXYDnXK3rK9HYgQnEx7F57ZznxswCcXzXoTB+OCOCwS9PJl6jAulcpD4LCCtHZvKPJ03kkCK7EJwrYJAGwXjfz/4VHfxbn2/cE+ynRyKXBjswjG7S+9NJB7xUqcXrH1B58NtUE6OFQ/MZnmFPPlVLyPqUhOht0CpcHn6Dg4xefoeDze7O56Btq5DGfx/EtvjLWN2mK+U/8BO58/kt45fAtePO//j9iPXy+nILvqoHVww8j+Zd/Eeqh/KRAm9KINo03jnsADzywxje+kWIw0Dg6KuBRO7iqMLxoTVo9sUmjK/Tg48hIaY3a96jpgwOfSr8bPK+UMQ6VljI7rVWY1zGibrjvDZ77WAIJ1za430KEZn9r4Dn4uCdWFCo2ypO5bYyRbeTFQ3T2dJxREdfXekN06tqDz+yr7MGnodg7avjJ57PmE0S2h8yvZCjjDNFJeN7eag4OfG0N+itiH08m2gq5CcAZUh1K2QpBn7GiLwcf8+CbD3ex7G+ZxrkMpLN0reB7beHag+8aXvfgIgapUketSw++LghlE76EI8kkZQwftC10kojVzh58Ya83Q+DokdRYFzmYQLh+5vLg2wA2tcKi4Msls078gnzpGw7cg29nVyjEPqZypKjcMPRxhJDWaigArj5IQoQkX7bzlw74Jx2guvQ75uCTLLZ8OficHbuMxJ59a/wUCLNgW8qt4GuhnaSSBiMfOKrvGqLTK6jneMOxp+1vzcXt4sEHJsjqpJSJDH1SNDn4CHGd2pZ7Rg6+BIJAqAUpTK/Y+OWKRIbo9BfQ2sbZdTm6dpSJoVU0f3c+Tzbn2oagVa31pKryE3m8nqmRhWZyMKpwp8x7u5e4RNwG393jHzanQWwhnYHTpfxQdS4PoXafM2AsWOVrK+v6Z+je9UggjOGozT025B/+fjgZWshCMTkkpVmHKOTPc+vZZRUbUjvrtBXGxnjw+cLvceBXQym87T4GOufGGDxrEfTgi33piMXJPfgMEEJ09npt2PnJpJyYK/HY4+CjtR3SDhqKyikctM5i++d4DLzjHTnuuy9ewOoUMAVCdFrlBeDW25tAZ2GQ5CUdoeATvXcD6KipX8B9PIxYWuSEJuEefIze7DDHPt302rMNfKzDVei7pTnZuC5L4Evur34femDzKZZhokiLlM+SxMTzNK2FJmVCHnxKmRFP+EuDvZDuVAf/H/KQsNJ/1O8cODNosFyF6JTOzYp42zTCc5eMhVp01WNfMPWSRxEh3eFSGdODj+cjC9PqoXYBIH/6afzN234dL918umxLqFbzcx3I5TZ///tx/qa3eNuWjJW8HnxC5++/f423vS3Hs8/mcSE6hcG5xlIQcbGcL430OxIf8DvDUGhUxIyUC9ZXP1eo0PQtSsEwuvEpfgAid+N7h7URE6JTaoeupWH0Qfi4hIaQlDz4OoToVEobBeRI+OV7ExfLITpDZAv9uyiU5cFn4ARUCmymlIoNMc89+Iy2PSE6FZvfqMaAzh58Oztt5aJxeMCDLyYHnxyi0+xH86dPwYdCPHcuWrF+wM/ItYLvtYVrBd81vO7BdSlSBV/KLEo4spS8sWQXfne7rs5IhFpYDxAvKAFge3gIwjgj6bMjDB4TU5IXYQXfZSBGwWcQJtwThxdQ5vtCIEp9hi4cej3bOq6rB5/EwLgFSnbOw/K7Dp2W+uYpx5lhmhh4xZjVzQW4DsZVGpa2mYTNQnT6+nPFEFK0Mq+qmmBsiFfWL5NYL63SLFBX7MHHYFMPPssyjQg0admkWCPRNnFrDsEnxI9bSJprpfmkEny4mB7O8PAePfpojrvvXuPeex1S9gjFe1T3I8IKnu++ofl79eij1t1TOmHXe4wrNNq/gx58ISaa3zuCBx8NaUKZ6DoPn9gxcCMLV9g3bTBzXYyIG4XQJZGDSpQZDqWpvxq3tC88Hnyd+xPJ9CaVAJmP2WmM4iC27OEwwUyvh9Wb3hTotG7/9Y23Qw4+kdbySK3rO9LpwUcgtE9ilkzaBlTglBb+HHydjSGsspsJ4Jy0XwcFH2+PKg19zYvGbGj1flGGVEmCj3wkw3CoMRppvPGN6+b59ww4rcJCdHb14LtSesZBhBgkNloFiycCZFznbEmj+XoDRbqIY0W8aLZ9GaUWNyho6XzZg6/gxkPVXdkIcx35aCUQw7nV7Xg8+Ori4v0QGbHGK1C/hBjJpilpyLbCEmZSL776e59nQs3zGIZfa5r30GNYFQrRydNm1M8h00K0ARcP7FXwqcSODlS/49apbVOOH3WdSlQSAKYhWChEJ53TWmBOQ4h6+yV1zYGnk6QNPam17cUXgmC7BEJrQaFwla3pwf19vPKj/04qYNRp9Y/n4OPGcQI/vL2tveF8Q+AKW1kweZFS2MCDr4PBZB2iU/nPoWuM9dRRZWz9rJ6fbh58pGvsTFNvQBdI5LWM0811No1ThRx8EXenwUMr17kun8s5+FiIThoxLUAj0zXnNnRa2lN2LdaTpGb1yQeGjYMgG1VKywq+nm3k0cyx//JsHn/j7mebx98+elL8ZH9f4w1vWGNnR+Oee8PnxqXgIz1nP5Vd3hGFQSm4o4s1tLqb+JTlBvYev1bwvbZwreC7htc92ERZrdRhIToFbHL//SscHBT48Idn1juHLL1z38y7WQ7RedmGrBCUrGGlGJHpDflVX+Dke4cHn4MuINCdQbxKD74auoTolMYxGGjLunZ7W6jAcdGVjKYAnks01jIJcAgzhI9DylPD0ng5b56vk8t48LV1u+h92eqzEj5zYtbXuPVOVpTGdcCuT2LMnWc5QPSVzSpvcUvZIjHhYOtqxe4SwMn0CMmVI4g33/NQoVqpw/fgVQkvm3qTtMGBzTOfNE/JHnz0UI9GwCOPrHH33R3dgbsVEfvJv3vxyX+H/C1vweJHfgTrhx6yPQ+JB1/CiHNafeGQKTZ/B6X/nLkVnlVrXBTEelLpMlwrDfPBvqOJ0q28leQ81uUozlFcACLApnePNSVKyR58NUj7rvYmFerv7MGn4qzRqQA5iCclCUT1m4f64sWmH/0oZh/5SLB6ZzusoIGjPCE6X8scfDG0j7SuVIBmebQK4PPOseoWy0pj8Cv+jDuZ4ghP292u7e4efPxIGVUIHnz33VfgF35xhmefzdsqpVD3lwVHHbrqhzSnSdLiqlI4KNVrGumlvSvoa9O3sNeViTsuJyrweeFuDJ6cpkbb7H3XEJ3mO1cOPmI4Z4ToFHLwkf8v48FnoEjtscb38ILx+m6fAdZmYa8Bx71a/wkzBx+UQiEo+DgfLNEi9dzaHny2RW/Jk8Cr4FNKNiq1xiDR9ALtUMObHnQje63cHnxwhOj0rW8t5HUp+GjY10bPAdmDT9NLqsZ3HRR8MQXqR71emzMwy9pyob1c7O0JuMBd3sdLcyWYM5cbaTDtgY2LKa8d/GRTleTB1xX58dfCYroVfO19ZORnvUpgdB/AFXzxjbYKPubhC1vB58RvdUEHj9jgboeRDgcbFWjrhdKmB58RQpJF/OFnV5oea4k5fiJ1A4Cu7wujT9yDz42nfHzJasVwMwvJzOVBFi/gaAMwQ3RahtYV3pRldG1BLgO0Fbn0u7bv37nxOL52/3vwjXuexYsP/YTYWaWABx9c4+mnc+wfCP1ggxqPtSEXaHLwObzoNBQ0MTrlITr5uvvOUrkHbT7odPeB5tnp3gPGNzUdQOU81wq+1xauc/Bdw+seXPLXtZAMmFte/NIvzUrBpyT8CjwTL0uG8JOUMXzQNhEuCeN8Cjj59hL6YoJBmCkHcjWeh4i/qwvRaVjx9GxmJBRb3vWs2QtCiM5uCj67/M4u8DKvk69/4npnE252H+IIF/8LEyRLwjp8EvdWokT6JiE67T1tRmox+uBR8Jl1OJgWpXDjRiETaM457k5dBLddCEE4JdHtXNMiNGSFk9lUgoLPXbQs4jvbXADC9ozLqpv3L6QobB4TpU5drpt+Mm7vi4xO7cHnmNs0LZXnYgs+AliFy0RU4y3EH80nB5j/zM8YBQwCX6PZGwqmFs9wiHIIA9u9E+g33z+Cgq9A2ryjVrKqWHu9FgYDk6loLDVZWdPBK/5iKiIE2zFQhxLWKpG9naT7WggXa9TXAWKZ3lKALN9FPuEqh645+IJz7JOs8XceK3FRuBzIwQcwYUANlQefcjDFzjo9ULbDzgYRDKfrHHBEemvKd9jfgL03YsIW2pXIQgQv4g5dOZHcvtcog3TI6zxQ7YFej/X/1fDgc00mF8CS8dsGF3IdXJh3ZeDoF19rn9cYhRiFobN9xAlHLRB5ovBe7xqi06hfJQZ9pBQs+iwpVk19orDcITiNzcEn4f71Gh484j53V7Kn1OXDs9ZQsLlJWASXGA8+C/+ppOGLkkQbOfgkfqkZi8fQQymgcHjw1e/p/xyaaBOkjUceWWGw38dLjjpLBZ+sTHV58AXX15ODjyr123oc67xqLRXFuoTQqr7fvju812vXYzZT1nsXFHffjdPH3o75t/8F//jgjwe/qc8uF/oDwh6LUPb2+xWeJAZuoRx8Bq2oWZQhfr9sCLwKZ4hOlQJYN30B0CgdIllBf3eVEiM3bJqDT8LB9bMSh6uWhnedU5KDxbgymfFvTA4+L1j7nRgDsRx8PESnj64POM47yiZVDzz8ADViCdxbSdL2mXrwKWUaZzd3oYV33Xc6/Z6H6KRQODyfAZghOrmCr/pIMkoucWcd+j3B1974nNhHseGI8OKWB19lgNgahvGBKGMsWK+BNBXnCoA3lEGdVoXWDQBfeOSn8a5/+F+xnBT4hwd+2tt/oLOD7zVcMVwr+K7hdQ+uSynGg0/6vvkmYPwtfid0xhZ88wdCrjGfMCRpkwA3VXBmQqjAIMw8CkTx0o8I0XlV9MvX7v9R3PfKF5AUK3zpof/G+x2lYX3MexcPPglo7qcaxmP7GSfsDUYu8iKvy3fx4IuBOkdDqIzUHg/R6RMKcDAsfboo+IQQnVwJ+eSTOb785T4ObhR4xzuWwGeFfb+JcKhtMPiu2X8xDFXSzZrZyk0vSumVKfjwUE1eohLydoydP/tbua/WI12YgiyVlF4LkR58sYIvMQdfRfWLQpFKgRSDnJ39i1jssn+Bchvk4NNKWbXWij2uUKVCybVDv2bscx9w5qlAE56DltG68uAzQnQWZp9ZXTzMiuM4tMyccnsNS9DczR0RrVW8ZvyIgs9glj0hOsUxiddEN6GpWG/R5sowzpHrrnKcBcODz7Uosf0L4VH+3hm6B1W0BDN0Tehe1ZBDdDaeHVdodiriW+rBp1fwrrNS3RTYrOxGgjaY+MMo61mLmqaRvAIsGnMDD77mU+IdHKyPd7arpvoywPaxK0SnK38Pj4igkq4YwQ2GMYw5ke1zYgB2KRrLA489tsKXv1xqYX/mZ9wePxLEh+g0f3cN0clxh3lnlt5EdH6SwuEhBoH3Mjz4/HPs9+Aj+4zxjrWSQKr+KnLwOcMTRoDdJz/etxV8ZkQKpSCG6KzrtTz4CM9sCO3ZQtmKBQ04FCF0j/x/7L1ZtyXHdR74Rea5U1XdujUXakBNqAKKmEhAACgQBMURMEiREEVSlCjLopfplqxe/TP6D3Q/+q3X8ovXspf0Ylvt1mpqaJEyJdESTVLggFEgKKIwEai60zkZfsgpYsfeETvy5K1irT77oeqezMiIyMgY9vjt4NxtLhpTj1uby6rva0RuBw/RaYuCt1oj5oDaFxAhgl20l9KV8Zh6AkGG1LW66v1OstwRy+Rk0suIORF8AHDtsU/hz673fYkZpmMRtUFuzQREJwDs31/h3cAhrL/PGkaDCPC8CL4h54UYwefIbN3ZkA3RmVGgc1Z0DT76vaaP4CP7A/y5IkbiAg6sPtnHyXtUVXy+uG1rbhZeBJ9jgCI5+NrzJ008789B73YQnTEDn5UTQ8dYLjeCr4Xa7Z9j9ntj2H5wU99z2iOyozWlqF8pJn0HaYR9VG4vSwCK+c+0qeGnyhKcJ5hIFiYK0Qm6DmI5+Ijut3OoWDuCP3/4D3DXXbvYfnUZcFIP0gi+Rika7fOC9pYWBr4F/cLTsWMV1tcrvPtugQsXHO9Iz8BHlDyqcHlBEZFBJtCJ2VAxyWzMqQg+Y4g+JCHM1gKDI6xEGM7uEHOfZ1hBg7SBTztebrmd5QP484d+H2vb7+Dt9bNsPak8K/Q+Z+CTFGPcd19ZYd6f/UZUgSZ7eQKIGvi4w08cTy1EJ8esuuNGopzaW7kQne7c2Vw9VD9TGGxsVHj7bR0TPnn+edz/7X+Lzbff6tstfc7t0CGLRx7Zwc5HdrBLGEJNP3OJn+MZoaCCUcIt7t5XQXQa4ws2VZWEvMrJwacdU2mNcgykS0WVhugcg6wpwz41AlrtORuSMVZeW4rJpYGMUc1RhYEvqIeZa+288JWS/iu+/PIErYrDV1a25fMUdm4EX3tueTn4CtfA1+fg4+a7B9EJPzLA/bauMKdS9Le3B8KJBeNcNEq6ouy8Wb33iUB0cvqx7LOV+/bMPlW4SvrUns412vxOIhvmDGpCMWVJDr6YQwMbORTx/m/nEGvgIxCdrHKWqTNGKYjOcrabzHWc7wWb3pfTfXc8yN13SEWPU8bVvac08FlBWd3db/n/AQY+Npf1vBQ7MCnEWkNl6UJ08nVQJO6i7OIn5iaa+4Yj457ZCSWijS9pUrYvfPRohbvvnuL4+7dx4vI08SQhhRc8kFAECmVEYvbdqpiI+YoCQ4Bt+LlOUa6HQY2JADRfWirPbn9Px4TFx2d4VHyo0HXWiyKCr34mzii5OfiKAl0kBKDLwWdMOP/rs1/IwUeVy0KEYV0nWdGxdWYEiM7lZZn3TuVYLAoYK6xv488poN4TWIhOB7qFk5stgRAdwtu25EbwuQa+Iedx7Jm8HHzpCL6LF2d4ft3ixjvA+fPT6NxnO2itrzLQvPCAhSkZ7N3rHaub6VQfJXpecga+jAi+du4HEXzGd8qPygQdzx5MUK9p6qSTrRdzzwRy8BceRKfvGFuWNXRqjAJHWuP2M5SjOtktMv9bA1/umnMj+CYTYNeL4Kv5jUAXGeNfnXueX1gRrk9JvwLHwEcRqIJIXZeFTciS3b1UJEnwgNOGux5mfvRsUNYYL5+gqXzEK20OPqDmSyQkE2sK3pGdjtUCovOW0x7glSxoQeNSUQBf+tINfOITW3jmmT5vmG/g2x1FYxzArxAKvfF4IclTnHJQhAnlXUCcho2U8xkZw3vaGXfTbRkhsGNX7KEn+/bKwRrPOcEhxAQl9/5szhx8S0u66RNT+LEQgcL7xXRC2htc2VgEn8jgIB+ic/NLXwLKEtPJKr538enmIeDSpSlWV21gMOXmYvmzn2Ht3Te8awUD0SnB9HV1a7jMMeayYhFT4T5R3DNCF4UVBp6MiSKCzxpeuNdE8El8H60vEAAiinsvkqh5LhbB5/1W7hGuIqe7FomaApptNZKDryfLFtFIOKrpqVjfQZEog+6T5KnvnVPknaR+01nFKahtkz+qVmj2Bj5ThXCtLnlITkaG6GxtADQHX4piQlmWUqIx8AVK2baSCEQnK9tFc+bqiOu/G13o9lV0oAmEtJr8PivydwnVRwtIhSM5+NgbzAB3W2bzjmx9DUSnNgffEIUiUCswXN61nO2GhRyaZUJ0em2Z+DuIzw2A6IwrOzLO34SBr93d2rPTGqO3gu4FRKdEZD25w1N3wzHwMWTJvWKEPaKv22Ws+W/t5VlKTvZhfTOmduA8d77K/zQDw8/4x3Tzk54fxvjIAe51AIHDHf3WhQu7lxjjmJOFC/UelIvMmzEi+IbmtWXrcpWQsIGBr9q3zytfj4GsuGxlHi8Hn7NX0LQW3X8kB1FK+eqTH8HHMXIuH+xRZBHUUKNMBJ8AzxlUJ8gr+/ZZHD0ezn8/gq+pAv4cfustg9deK1Dt5kXwpdZ6jB+eTHpoPxeiU7N/5OwxnJzS9oNGfbnz6MIF3lHBGODBD0zxyCM7OHOmhfbr+88ab42/HoI5OYQRC4r4+48I0elC+7fOH2NG8BnjrykmB18Ok67JwQfUfLEo6yghOus8ujo5lW2H3JRy8FGITrq/1FPCjbaNd4QZ7n7dRzpcI0+0fReLNW30nfAj+Pzow47f8DotRfBZv6/g9jrfMCxG8DkRkuF4+WPR3TdGL7OxMgqzF3M8FI3IA2R0DfgGPsxmnpMWnSfxHHwViZxN6yc4Z8uFge/W0iKCb0G3BR0+bHH4cK0IaR3wZg4j0npNuhtwigbJ+vQgLghkS+f1F9/Zooc9c9+WE9/Zj1PGeRCdEUGhjeBzmBQ2go+B6MxSgjqUPdaCMCIV4yP49M25uZ9cunx5ih/9yKmb9MHLTaDsc1tNFkSnYgBjEJ0dIxpcr69QiM7Ud57deSde+Y3fx5/+8XpnHDQGWF0FHnqoXqff+EYveGoP+hhMpUxzKL7oes5tmi3MRcPyTJnjTBxVYnvKAJo82SvaX59Mep211wbdNwTlFKWDB9OeddLDLmZ/W0a7PjUY/0AjZDd1d0JFl4OP72tRxBQ1CprjnDEGeN/7GuX+wAi+oK3WoEOMaBLEojtf+j0iT2nAenQ33nvWmm5vNsaP4KN9AUgEX6NM5c4rt5xrJOfOMa9fA5WR4bdolHSF61Tj9J376J2BjxMyM84CsU9MmWqmV9LHBL7UNM/ZJFOMBIUKTWwUmnWh2WtYiM6hDE9DbLSQrTArlro9sazkyKUhXrBBFDND6deSDXzSs8k6R4rga6mLUnI3CVoXvXCzI/gcxSCF6GxlBQmi0z3mjRHOsAyi3ufcDV+WceEj0/umeijnESRcKnvHkVgn6CXl9HLIV3R5Y2TCCD6XuAg+GDdao+rY1zTEm7xmXCeAZLS1Q2PYu+c18LmQon4En/UD2uqDnzzLrB3ywpWjJG75Qk75HzzurAsCLF63K+Tg8/sjRzhR5yBjwH6QdnuzMDxEp9bAFynw0Y/vYLa1i81Ng+efD9/Lh+is/79+3eD736/562PvJSL4iIGPjovmDG8vTSb9DzeCT2Okp06KcbFSvmkZiM5z52Y4eLDC+rrMPxhjIH2upMHXdQZo6x5jH22rbx1nJIhOU4asUW4EXxGuI7lDTAQfx9sIjHCvGwp5dHcecGuqo86yHd9jOJjb147dh/UbP8PKzntYmm6y1bPGF/jfeWm5v7e97bdRFHH5nZOdvLa8ssYrG8/Bp0gTwvx29R1LS8Cm2xcOklmY411ev9A/o+k7ESHaiDNuX3ENqJVv1Jf0d20uVBWPzekFEzJ2t98yBj6JaohOp94ZkbWVEXzGIIgi5fRkNPUIB9G5MPDdWlpE8C3otqPu4Da6HHypeqRrGt7JGH6fvHbqXgDA7NgxVIcPq5Rx7s3gPosp5W/YHrSCtLm61wkjFBRNQEfFnqWUggrR1SN/nJnRR/BxtLwcQo9YACdOVPjAB3Y7RbyUw467B2A8iE7lZOS871zmTup/LkQnAFSr+7zIPzfXR8CoKaMpjWDg6x5nGA2Jhsg+WQIfVziSg69T9Dn3/8N/6D2Sa2adfzhQyCUnt2HzSnJRgrE57dKjj+7gwQd38JGPbLHP2cjDhROlwkF0DhW0AT/XAoWXsQkX9WgOPs0EGqghu//+XTz88A4OHbJiPVShFyj4uHOCEYrrchmbYeJc4ITb4JopOqOKKwwV1bTrGydwucoPqky1zmfpPqtxIvgUCp5aCOGdMXKog+jkoliBaAQfpwAbwwGG5WdcJX1qnSsVRkOhV9RDTvsRi+Dj6mUj+NJKCxrBx52X0XYZ4r+rxcxxqCmrHa5QXzrlIp0i5Vxxr/sRfDrDnOic015WGvi0Ofg8PlbrLTJGyBKlyGB657nzzjVEZ3uZX3dTJyprDD2uh5JH1hfbDgOrLdHcOfoGvCDnRKFLzZCp6HfrNz68W30GlEEF3bhxZ6Xzf2x/puSeefV/jgHKg+j03611HhHYVRXFxkf/7SN7A/OjBqYn65qsX2OogtsGL8UhO6ysAMePV0BZ4MoVX7br9qoEgxrPd54+Q1jFPGMA6edUqq6cAAAgAElEQVRSwbZJ4S9d0uTgA4DJksHGhhUj/ro/nTF55ZW+L9ded8oyzkpDcvBJc24y6SN/3MhVZRaLeLuEXrnjIfZ6kFvTFFhZsTh40IrzmXaS6gBYdAmyHui5mWTDBuyrUk46Fwqy47VduH0NJSBouZCyAPEj1VhzPxbB5zUT65OQg4/WV1UI+vXmxnn85Qf+Nb5/6WmvbLQioD53Hd3bqhO0fP26Iag/CZ1UYhtjc/BpIvi8fVknnwO+ga8srdf3Ti4EneM8/8oZVdlGURvkJRm4XO7Lhmykbu+OEavT4XgXp9zqat1XV3+Rguj0BWN0HmIcL2KMjfLCYQRf2FSg42P0BgsD362lhYFvQbcddREbBTHwDaxHusbty8FhWvBwTy+8/zO48aUv4cZv/VaWgkW6HyiqmR3WO3QUEXx9VZZVjnIGvjEUDRryogsVyj0ugi8HojPiBIl9+2zvOCoI8pyyGkCUMdSEvbekjTJKRvCJOfh8pdogYSniYa496E2RUNMzAzQXRKfi2a6IRjo0EnydHPnQkoz440fwxXJSucoitdGAdEx67sABi099ahuPPNIoRIT3TzXVQ9/ICsycfabdGuu5T5jMNoJP2MtjSmlWwUF/58wfhyYTonBVRPAFxBkFbWft8iryq+ffS6toZLQQjtKxL9Mb+PwcfN65QupyjdLt9+mL9GXF3OOJjWY0OLFIBAAAXnhq4WIZZxfBF0cmJU/RCuLB+TRg7brEOcOkSL1mSA6+bCmRU8Y6c0TMTzpleMg5GR4WotNWHr8ymcUNfNnkrMVcPqMnOYIv2qxQcXA5FsEXUTxMJoxx3hgZnjfgkec4aHKp+QAdj+iMqQuLLymqvMjkyNhqaW3N4tFHd7CyYvH4h7b7G1IUhhNJoDWcqUijpNUQ50Sh3NZyotw8hZVhIvhMGUR4tcRG8EkXEmMSzcFXyeVi1SZztCloaF5bqa6W2Bx85OXouqj5y3DdtNfcx69cmeJffm0TTzzh779dGQ9pIGxXiuALZABuvzI87xCT82oDHzPW0Qi+xPdt20tMmh6i0wZGaqnOnQce6C7tPuQbygbxts0zS0tDz7T8AO7vX3oG/+3J/xWbn/6090ywrqV6Ms4bVQSfBzuo2EdzeLqmLI1ObGnmQHQaNI4JubxZKgo9AdFpwXx74R07A59rmGyK+ogmwyE6Wz6pqpjvlyMbuoZfWBTOd14jBr7+2Xo0NBF8LrmX3LJBDr6BTjv0tf0cfP1NNxIXaPkN2ml+jhuTmHrkmVgOvnKp7+B0RgyDZCzcSHNj4jqd2L2Uz16X4iYC0RnKYMYrb5oIPk5+NgZRhsLYypPTNU7gFOrWwuCnPy2xvR2WXdDNoQVE54JuW/INfD5E51CBNHkmkwIiROekxOzcuUi9kdOJMRJQxp+LlvHzCQCnT8/wgx/UY3THHbOubjZsnzktNRF8WkoZGxQ69ihTP38OPqYwo+DXRvCllJkcs5Gr9wjmCHhvNAmewa0jNwcfVybK0Ci/RRKik+1/ZIwzaU69d5xpypBtXLIwfiLtCESn2xgHRcWNrxaiM7zPTADFYHERfJryErVKDNdTuxPunKgprrmiCD2+pbnGkqKMxtDKKXZS64vL99i+JfV81+Xgs95F7bxnI/jQe5q6EDg1hKis1PT2YUGoNqaHMHFz8Gkm1NAcfOG3aCP4Sr8Mp0FoKQLRmY0bx3x7tlhGhIj6gys2yWhVig3GE/FzIToZ6pxfY2PAaC7njeBjgRes9fmV2Lw1Bi+dehTAtwD4SlOJxoDojOXgi66fGL+j3fAjEXyTiaDgu4U5+MS3IudhANHpKAe5cZsRT/1BDA2hj3xkG08+uY3yjRnwd3yX3f52e0fKMWLevg15XvktuarLEtiNp76Uagvqi0F0hogaPu/TKcoBVIkx6M7GxFzJMV6OEtCqPIs0RHNDBvm3uAi+hMKf4wtbWl4rYdqzweV7SOgLa+AT5p+LPCD1z5jQ2YCTZdwtpIXoDNqLGPi8JmLnduFHONI+ewaRNtJH2HLbZ3c+/GHY/ftRnTiB6vhxuV9c15i+tpcmEwC74f0hOfg083Z7eZ2JCKL8rjQXyG9jupGlc2NIBN9ekAjR6eAhtn2RHGvEcS0KgEblSg81dXsGvgyZrHNOKUp6y7ebxhwU2omfmDgczDY1DuXwxC6vsO9Af+/dd0MHbX9MeP5MQk1h/ehaB9xIh9uxZfc3QrKBz/rG13qHI5FjMj9ZVRF9A/leVaEz8AUomEL90npnH2U/CPd8X65z/s2A6AzOx87AZ9GOLt9B/nJM/8uqeppzzb3+N3+zjOefn+B3fud6vO8L2hNaRPAt6LajehOxc0fwcftbKoIvqKPgI2VSDGzqsA94Hc5bkDKZJAff0aMVLlyY4syZGc6dYw4HR6CRIvjmeg+HxoAgc69Rpmn+CL4QolPzsBYCha9L0Z6mHoekBNkdMyg0OsvMwSc2Midxuai8upk25oGH4oVsgTSTP6LsiLwCAB4+s36AKBhSURSox8TNU+bdD4RT2qZOiGSjIcQBNH45+K+RNe6EXCiWQJjqIviYqCnTPKtYW3GBNf/ZoD8KAx9XIPgEQg6+WB5A+ncUsobpGA/RGUbwAahzwkU+vKvbrxVljsDqnFecwgkAyiqute3WUeZeJQkzktcuCx/XGfiYBiaTYE7kbqfsPLN9BJ/bV3bPzGhwT/VMTA6+WNc46FpKnjJSqqxzYeaVIWG7cp9a4iIo6gi+OAylS+/tP4HNZ5/F9pNPYvvJJ9XP8Qqg9DNtH+k1ANFokRj0rTHwJ01sAkWM3VIEn5rZy7F+zEvGCBq0phuJCD5Xn5MFsazoltuv8o03MPnud4N1ZuBA9o05TlE+IYOKgpnbYb1cUzQ3prY77ZnkPucakGh9Ug6+lgpPiRbvBLV7u332DXz6dxsDojNlmMyp23OStFUIGclE8AUQfAQG0eULg/etQ2nb0s0zNmngA3iZk75QTBbQQnS6fWAhOiN7csyAa91z1uGt6raonFuPxbVDl9QRfHbfPuw88QSmV67InZAoBdF5kyL4AJ6/Db6DJPdlKFBY51yvvA34+uQLKF6QFqmETWHmIq4MNTZyEbjC/c5Z3nf3Yh0bY00F+4MxHl8WhdptFlBo0CXrxYbtWLK2OOpuUT7BGdt9+/tb774b7i3UsdzbMxIh7R7McyuvkVx8uURf1x1r16mmc1ryOhwalbhZZoycu7gt4O+dpegkVUz6+VDZul4azUh1FhTyOUpcIWaNnTrdM3yPP16HvblyZO/oyK+7OoIv4gjO7D2xiPGYnM5tPfVYuWdm/f9bbxX43vf08s6CxqNFBN+CbksyBpi5isPZNLoh8XVwCvA40xLcJRtdF0Exj8Mrw8AEhhuywxrjMyqtF+Hp01Xw3KlTFb7zHTAQaKSokwC+pbcPneMLJ2gEPjTKcPAGPj2DsrzMeCQynZIi+CRlYBRakzk0tWU5sjBZEJ3u9WkZwShVdmkIRCdbB6upTjPLqbqHUldPSlgDROGj3pvie4uoUDY+RGcsgs/tqxjBJz4UKcO3FlxxPVSl/rXvog26SFEH2WzKMDdGMwjS3sEa+DIEM51PgKKUAqIz1GVy/WOEYmNw44ZxSqQUUCZyL7zhKkBcodM18LXHVWFnAFVSOORBdJpCdKBwFc9tpODy7nV89K//D6HTbV/G2RS6HHyMdzCAaAQf1eNURdnVl2f0TityPSedMQ5hDDcO0n7c+NKXsPTtb6M6cQIrf/mXTv2kvsyNglttHdxhrJ97YLVknb9gWcSBGE0vX9YX7tair7hmiuRUBwDYeegh4E//BsAM08kKJtNtuTAlpYHPRg18QgelORJqm+T+DSXhndt53B0nzsz0FIxCBJ8LFyXyBgOJjv7af/kv2Hn1VUw/9imsv/dTnPunv8XB916DaTzJUxF8NF9qlAzjTjj03QJkE91cHw7R6Z9JWCp5bZdTnr3eBuMpoyQnExudutbLwRfcFZ8btBzIQUWhuYZU19XlKslBIvgAIQdfjFGyqODm4AvrM8Q3uHs8kovVGODEKeCfEu/ErttWWRx8b5uUF7eXD4TXh+bgc/rVtttf8su2w/7i6Q/i+O5PMDv1Jnafuw5gK+xmCoYR6eXOOaW0345C+7WkSysx0ImKMRq7pIZ+jzSYjOCjMmRk39G0xxRu+iFAdLrxINZG+TKx2cT89r47Y9Co5zCRP4T11cuFBb2lh+jssGn5NtrLfDSZcfqs1+/Q3KMra7UBqqr8/Hvt509BdObm4KNGrXnJrWY67X+0vJyF6eA5C5ouiM4Jp9+DIDo5KktMJm0KboPnnpvgrbd4R9AuermI1Ee7ENNnOXTlyhQ/v7CN9XWLixdnXd86aiE6hRe3poBZ6hlkCtGZgzAGtLJ6+Gz7GH20XUfcGfLSS3uQ+3pBSVoY+BZ021IQwWcSmz6h1L7L3Zdy8HUyTycUxBnJ1GEfbJ6KCD7PuBMJ2bjvvl288EKJfd+3uPfsLn784wk7cO5Bu/3EE0BR4PvFIyLCQoxyBUmtErG9RHPIxYgb+6Uli62tNEMT2gzjXp7RuiJwnwEpk+KxBj6XGTV8m7Ni2bmWzhfHUzjn89MnyVEAQMjMqxVf6nDOSIc17TBrlz4u3Y/BkwYRfKlcYzCBJ3fXNmVaMyE63Tba7nQPKh7mIDqjeuGEJ2HLA1vTeyt0y6XLe8Y9WTs4iEoV5fceUiS4tscRfNeu8e/InXdJgZQTepgDroNF9CA6SQQfeW8vSoG+n7OPiTn4EqSBVuEoeL2yzsEQ5PfgNAgtNXORfmoxEiDRoZiw3hVLCGnxCzINsYVRoXJ27hxm586huHbNM/AFOfhG8ATQVNHNyxEhOqUcfFp+ZZ7o9BixvK0x4M4/r+zyMq595Wv4xuYuTrz1Q9z1yp9H69TcC/oRhei0mDGK6urQIV3dWYy4kmIHvqeg5SE6xQi+PYDo9PpGaPk738H04GF88H/8dYeIYlbre6PB+45NZcmeEYhfYtENVET2XdNFeIRKZCAWwdecZZE8Ny4tL4fMkvTeOf5K2hx87rOzYgmlmzdU+e0PHNBswn1dZ3729zi5/R7QBIBZhLxaXZwoLpkBkCA63foCiE63HMNAnj0PmDtm2NoyWFoCXn+9iVwjxm4eopPPnWW4CEUnauTd/SeDfsT2y6jYyMAVdbCG5Jt2ea6LCb5z3xfw4a9cx/W/+o8o8CpT7x7uqWgj+LIe6WhoBB9tjzpcxyKJpN/G+M/V84G05HXYekr3+hAZf18WITptf70waRmU7c4koej3Fg7vrJhEJCAyILc3e+xlwvjTPhhb0w8/vINXnquvnThR+WUiOoGgY92T7jsD+/ZZvPeeX4kUXc8MoX+PzMGWupQKxDCZ7G7Qvt+oD9HZ/92lWTAG3SM0cpox6LZNSo5RbQFvHFrHHP4QxNKSxc5Ofa8z7kEeg9aRW7W8hDaBOiL62NvP13U+eC8+dD/Jx+16tCUgOq0BC9HJ9aP7syzFel3ZkZtjkvzIOTT/5CcLA9+toD1wZ1zQgvaejPFhBdscfC2Nk4MvzcBIwtP8EXzEWEIZIxN6wXpY45H2iwL43Oe28OijO1hfr405EkRnS7M77sDOY49hZ3Vd/x5eOUX0UuK6903JYVIJ0JTatjwBOv40Wxdl1rs2bmIEHxAKHvRxScnrriW9TEDmaARepu9f3JhkSoFrinQqhtM+BlHFdNeuwLil5rJ0P5aDz2PONJ6TRh/BR2e+etwGGASM6QWuuIEvhHqQqMvBV/QRfF19HUQnLyRQgYfvQeRVFWOgUiIMyMHHGgU5odsYnDkjM+vB5e4P3Z7oClrdPmOKzqgycwx8ppqFMEMOeRF88KFFXcNj981hRD0DzSvaPlzv1ewtPbXvKZ07kT2M7pOVmbACU/55EL5V0XhwhsqkvD02LMd39sbnPw9bFLDLS3j9sDLqjHvxmJYi8Tj3Hp53svSazYTVIkFohouHSLMBJPaYFMBHKedKcl9oaXUV7x64g+clnMJHjvTn1IkTifwhLkVw5djoDWNQnT6NnQcfRHXwIG48+6zcefp7rDByjuqNqv/pjLkOopPhJ8cigdlY/dZfeekOJN6Hoyy+IWuzi5AiiVxEv6ZqnsogXtmmfQnWOjAEUIWrMsI6YsPp+kW61LcRebchEXzUISWaw8qhjQ2Lxx/fxuHDFT772U22f64yfmXnXZRUych872DtBPnSGMevlqiB2O1PIgcfJiUuXZrh3nunWFvzzwyP12caMEaCZGSMfp3sBmwvHUC1b5//TAo2WSJ3MBIRfL6/TX2vktSHY0RJM3JU+3sy4Q2uGqSMcN0r5X6a00sbwZexz7F1uHKftb6eS2Pgy6CWT5B42pklZ9mQszNm/CXnZVt/QfZf9jmuKQfZhV5znV/nycHXPnfu3Awf+8QOzpyZ4fz5qVdGYygLHJddqMqiwP79DG/PGDDdOgGBdXbnFFO2M/BlOqxL5A6dy9P0+gmnPwxfykflJ1LqMLoa0Xm8KLxoQvqce70fI82Z5yg9hP59965P4x9PPoQfXPgEZhcuhDW4/GMbwSe2Z0LP11QEX0Tv4O41GmSEyshG8OvXC7z1ltjxBe0RLSL4FnRbUlHUirGWTDUFCmUeNacOSslNmxYo/IPeS2brPZYHDUHvW05q8wpZj+lUMZyu0MNF8BEmQ9NviXKfs2E3o5XkREFw1SwvM0PAMEL0oAtyRFDKMvBFYKsUBgAJotNV+El47awyPEEp2YX9XEURFQyKIRCdIwo5+Up1QqKBLx0VGfPSr9x5F4HoPHiwwquvlrAwOHduihdf9NcFx+RG53SEWMifjG+h36vjdbqCjhtMCDD7pltrKzkOceuljQskQraWBbxQ6JEgOvuca/7gPvLIDl58scRsZjAte0inGWP3y43WYD2cYTrYsBrypy5RVFP/XCF1ebqqyABw0DKUrCkwK5dQzna9a0O2C2nsufwefF/4vgO1c4rk1zBXH+ErkFPy+sCYFq/x2aVLuP61r+Hau6vY+t//iu+f5uBwz95cRRIzEKdPzzov0kuXhHzNJAcfMOeYgI+QMdZ6vOteUT+MaeE8dl36XKxiySl86dIURTHB0pLFHXdUmDnjKsEMAYhaMyYTiy1BCbP9qU9hm3soRmPAssaUtu5tp62i6L30Xdgtl6bUwDcinyPVZba3ASwHxarEmZAVaTqmUrooAOQpooBomsd4e/T8mJRABTEyPDAENBPCheh065bIdXyhymVKdM+JsShDbDGzYgJ3heYogj/0oR186EN+lIKkFKT3ALA5vLzcsoQXae+33yF4X49fd8bVWm+PYue3wFu6RmD2GzG8Q3c5eD/r8FG1obI6fhzFSy/1hZQQnaHuIozg696B/A4ghdHn6qI0BkQny9s67Ho50Z9pLg2Z7xShop5TA3PwRWSNijMaRCL4YrKN2H60SMPDuXo10x9bMxQou5K+gU97jCbRYhgDX+gQmIiI6/bHNkLMH0PaLhs5SfsjyMzuOn/gwSnWz9ffZzZzDWWRvpI+AzVv5MlvxrAGvpa/ozKI927dY857C7JIt67BvzPXXapT4h6T1lx7prlOuYaB6JT2AskxiqP23Oa+sjVGHdFPc/CpiOl/+/z2ykF8765nAABF+R6CHjIQna5jmFcnDIyzL5rZrJlLYT86Hr4oxBEsEhF8oWwTX5c//rHBI4+MwG8vSE2LCL4F3ZZkDIHonPlKG82hym1GEZ63rjdQhhv/uWbXnTeCLyCOmSPlPOOOhtt1GCE+gs+5NqeBb+h4xIQ/9/4YEJ1DHo4xOmJj7S1FxJvuZl9G8iTmmEGX3PEb+o1V78MIsMHtWAcyOjeKIj+3HhVTzM81FuqweVCbg+/QIYv779/Fr31+E+fPh1ETnKJwKETnoMFyGHaaT2AodZ6acOFbmvFplMXc/tNd2yOIzurAAVz/2tfww2f+dXAv8LoeGMHHyj/WoiBC0vq6xVe/egP/6l9dx/+46zPdrb+/+vmw4wkFYjB/mHlrTdHJIy4ETmErT0Chs9gVtCyIIY+JUogJeBYGs9JXfo3lldrl4KP1aRQq1MDXnNvJ7+0QZzhml2OVjtzsSKn9ikVNAoBdX2+Ujkrhm+tHBkRn8FrMe549O8PJkzM8+P5dXLpLiCYjEJ3imS43ExDLtlmLGQe5PrQRQkkIK6la4TkRxo/hhdy2l5eBu++e4uLFGbtHSRTLwbe0xPQzh1+gv/cg76LXlqe48yE625kvnfuu88XNgOiMFhs6p5SFB38FhXMOL07pHS59ZbAfVe5CuHEU5OrytxhPAReH6JT71/aLdqklV0ahNGRKBRF8KYV7BrEQ/M4PFk7dKWQp09D0rzXAxg18pLpkBF96D4+dIdQobAyiEU5tH2YnTvjXI5MjdqR7Y5mI4OMg0UUDXznCXpHgRZaYV9awL8MgOg0TwbdXEJ3y88beHIhOK+SVnln/+pAlH5sblgozrS7NuDKBAqKzIS6Cr5V/vfkcQd3pBReFYyD5tpxBRXzEK2CZCL6QB3ZRTHxyxisRwcc5SraPjAUPL/GPfWqNXmZ35cO2D5LeT3KMcsu49XC6j7YjnU9ZIMf6v998s2iup+dDlHdiz2JmnDgDX6zBCERndu70yLNcGp92jKX3fv75EfnXBaloYeBb0G1JxviwjEU1E04zmVIRfCr9bhfBF/eazNFJgFHeBTn4qPLAEAZRo7ROKHJ971LDVqvlH/M958KKOXmibX9QHiOHJlwaQublot5K3GCMBdFJ+yEIlyJkXKJNd/y0/dAoVgNKeB2aVAQfc3mv8hRRUuXvEQSBaMRCQzdu8O+xbz9CA59AxgAHD1ocP2E1PgHsRe33H5KDj3pb57QjUScoFCUK6mAR0W6JDDjzjPjpyY3NT3+6+3vr05+GPXgQ0wMbYaeJ1zWfBzA+QNLdQCRy9nljgPf2n8D/94H/Bf/tgX+Bl/dfpcWS4x2Dd3THpQuIojn4mp5zcHseRCcVojzBVDd5piQyebQIvsbApzYYRuZSVfAQnXkdkuTIPoLP72umsDegP2GRViGReJYORqaBrzp8OCgzmQB33TXDxz++LR9BTARf/L3yIcGAmq+qMhyShpJ2PQcPDKxXVKC0pN3wI4pzLq9stM3UO+1lBJ8xUYhO4zk6hHXMKtczf2RDZISf8n9LSkRdfXOXTVGZVrQLOr2BXSI5+Ca9wo/WdfXqLlDyEXxd2dEi+NyzkfQ48m7DIvj8vWvomdoSVcT698h7JyA6reXl4la+ceuzReHJ2l5UDs0FxRj4pNzNpAvs+SbyDhFevO1fNdDAxyJukILS3s4ZAiQD3yjrO2Z4BTBZ0q3zsEwekpLbn57C/IlaiM6Yccrlk/tr7vkBDz7Z7hFEp+u05sErWn8MhkF0JjYc930ZA1/DZESrCFBcmPdxtxFVXm76HUkEn1c5KZPqL63IMA6a2gg+2qd+G2sv0vkfRua6uQMV3U3OQek2heg0xueRYhUYk8jBR6jLwcdRUUQi+PixyDrzlHpBTi7x1j8X0eqWpRCdjYGvXwdMl7QMgOJlU7LwG2+Mw24vSE8LiM4F3ZZkDDFKzKaABxyiUTjprkULEGGr3XxTCshoO4bxiCzTwkAQwcd0QXToESA66UEwFF4/Na4xHYnzq/uLMlicge/RR3nAJk5ZMsZ7cRjXWm8zWldAtIOTCVxoorZ9F+7ANbx0zCCjlDPGh2DSdjkmgNW/Leg6jEECAEBRCh3oFph+0areIyJQB9dSL9xc083lkN57j2oGajp9xuL8xQrFtxt4OctZo8PGuD2oKMI5GVWqJNoIfqeYfdFOGRG8E3V2noym6CKWVJ+q6YukqFFNIPLs9J57sDmZwE4mmJ09C4CHE7IrK8CWXA/XfNAdod/evg1+z7++71j9R58KJxCItfuA9RQ99Zq3MKiqumX3XPqVD1/HW1+f4eyBKRuV4uqqao/Afg9x52lgwOXI+HCkgA5+RkvGhN7Bmv2+IF7MkoEv98xk5cgIHGowMzI2fk3fhni1AwCKwu+Zs1FsrRzE6vbPAQBvHTzHPo6VFdz44hcx+dGPUB09itU/+RO5LZc6zWVa6f7O+hkY84JcV0PcEn351COeom58chQmBqKCRHdRHrIbq4dV5Tpyzyw3SpJCDSUi+KKK6hQFGtQ9NvC5a4VAdM46iE6+jgA+eURlrpbh5ZSkHGVFcQ3Z7KR2mRxqXHOUNOh2bHum8PmjZq5y43PkiMVXfm0b3/rpNr75zfocCiP4HAeMCGesRhhBXnSi1lHGrYOL4JuHPF4lhiZhTBLJho2GNQYVJ7+WfOR8UXjg6c25znR8IjlFMhCdzkbXRt9rjIbuUhkWwRf5vk5bLd8iyTpuPUkDX1HAH8EBlCFHeM0maFgEX1gwgN4dGsHnzt2UwddaH5FBYeDTrG5ahc/T9vy3a+ArQAzg2u1JkdKg70ho0FBFITW/3dQN7i07mfjzmTGsAsDuffc5ZfiJE5VTTS+3JOcZXfcEPUsy8LX1c9cB97vwTmtc2dbgKebYzCTpk7fnlKc3QwjRyX3z7tsqETY6gxwrJEVy8EnR1wrHzn7fZuR/5nl2nCIQnVyDxtETmwDpifnuitQggKw35B7k5uXv/u513HPPyqgs7ILStIjgW9BtScYQrPDZNFtY5/d6G70feLaVzEGvajsCUcQ/QAqFpbzoLQ2362zILEQnQuXg0A0614DGHayegtf6inyfibN45JEdfPCDfq6H/j7XP2X+xkDwcS6zVoSYdC0zZql67GTClm+Z8+VlXsjnPI0BiLAcOUQhOk+f7ufPyZMNc8IIsC6J8ySQmPeGcj8hpZqZ4ydS6hVmM45rAmCAz/3aDh57bAcnTlQ1rFOqI8awgRDsPDUZ89ChQapRp/IhDqAcucX03IYAACAASURBVFAsdF/o2410ha6teRTGRYHplSuYXbzYC5ql8aLNAQCrRCkzRw6+4Lq1/t5NaG0tPj9VOSP85pgxLDrDnwvRecfxXTz4wA6OHWv6Rxa8Nwy0TgaiM0U5EXxZUTKt8D4A8jPMwccbNPJ5Cuaarfr3cpVJAyL4fvmXa4eZycT2+3m0ceW+EnxnufD1taP4u7s/j5dOP4bvXPms2NfZ+fPY/sQnUB0/HrYl1G2I9p1Twrb091c+h62r92Hrox8V+wD4/OSrJ96PV+54GC+eekwNKW4hr9d5iB0CI+QLDa7V/fnZkbvxxqGLmJXL+Lu7P5+er4yBj6WIgW8ysXkGvlSnbhJEZ81j+xCd6Qg+h98dW1JXbi7SGTkPZZ2vKbrZEJ3wITrNpD6IKkFpurq/8KNFaASfoHillIToVETwzcvbtjSjBj5q9MykmJI86B9z8LtjT3nK9lab89QbGwpL2f3RGC88iDIqt1hlDr6InM/NVQXii6UR6pFnvMi7mLAVUc7SolVVQ3RLBgDDOSPTMqltOVGAOklp6gSGG/gCp0gmgk+1liKFWEMQ2Ss8h63RITqb+SVCdJJo9AECXLtfit3zNtuGD4OPJKV1JunkwqLAD89/rP6xNMHOL/0SWRf+t9v+8Idx/Xd+B1tPPy13jeOfpQi+SGf58zU0cnEGPsmAyQyh355TwI/MNfn9VtyTtqfOqGbIvKI5sIUz3c2l/fTTm0EZ+oCoo4tF8LV7Lrk8bwQfl6eUHSeXF24MfLEIvqIkMNbTKbund8gMGWkZXOLEqGBdNj/OnZvi2LEqbyta0Ci0iOBb0G1Jxlg/B1/jER0cUok6wmu5PWkj9qTfA+plFC3B87OZz/wZ4nlVGBrgFVbkMPXrN17H1Rf+K949cAd+eO6j9XWCA17/NwziIiUAykpX4UyOeMA/8YRs3JOIldUYoT2VUNiagh03ljL0VEE9S4yC0JiOOV9aAra3aZ+tyCwFOPUKCuYoYVpOnZphd7eequfO8Qa+oM4GotMaw8NaMgoAEVzBnep7ocgTNCbsZfRQCdl7TDMe3dBpBCtj2DnNDf/QHHwsZGnqYacDVSy4yG1HC9FpCpgcA187XwdE8PX7QZqKooX06Tdku+xHlnEfK3mGiIaKKqo4fOqpTfzRH+0DAHz841tMMcM9JtbnKi3dfbJzOHQ8ZAOvwoTCw7K4IjoPToswgm+sHHztOlfvm+5ZQrowa8Ynl0dQXPLPojmVQY8/voPTp2c49fNNTP6f1LOMIUYqy2kEnGvlG284tRr807H34Z+OvU/V7axdv/UYV0Twba4exo2nnkG5brH69a+LVbrL+ruX+9yXgcFfoLU1i89//oaqbEtuNEZ9PiqVodJ+In0uY/A39/4WTDVr1vhmxuERUXxHDHyHD1u8lGSOI0T5h5sJ0ek0VZa9okbKwTed+vznmByMtq5uP0/tm8Zk1Ir0HqAlERYtXvVgiE7K33UKa6ECEmEYjeCLjLHnsEfOWvoj592GIHQFEXxjnalMXfQdecNPf01azm2fPYV2KoIvwafYjDBQC+PxZMbw78q/fx1F1UWNGYPde+/F0ve+h2r//gCyk76L2KdYDj7yvkVR87Ft3itrfQhhptPzUQLRgjMiaozMQ9Z9YJQ3CFNhxM4AbYPdeUGTr/YdCSA6UzTgW7g8rQTRSQ0xakoZsH1Lcv0I/a6pMQ34nwIvnP5lvH3gNE7+5gpOrK35zRiyR08m4ZoaEMHXii4xg2SHPkOgSWl6nH37Qpm/e78oRKeBtZa9R39TiE5tZHaqXCqCz5PrCA/RGnQpGQOsr1t89NFtvHnPJu66K46I0TnmcPt4Ucg5+IR3o3OGI2kvBbhvFua0a/vWlVHk4DMG9SbXyjKzGduPWN+kuulP+mhr9KTXabDBgm4eLQx8C7otyRhfSWJm+Tn4WIUYr08UL/Y5+JrfNAdURtux+8HvqmIYipBhj5GrCHr/c/8RAHDs7efx5sFzeOPQJd9bbE4X4uzHEx+nZXRT31BL6v4xikz3cm2YUlbMHJrasraWuEhPDIngcw/0EA7BrXoUAx8jKF+4MAsvxp5pPTON8T25jOEfcO+NQPx8ChlRsTCTO2KEDoSCjwKik8tXxDKREW/dRCNsuxx5efoams0YhpP7nRjQlqmsTFnnYmXrC+vo3lNh4BO7oBisGhaVlFtOR/Ak5xFVyrTPUeGbFLjrrhn+2T/bxPa2wQMP7DrF5D0i1jEOYs6aAlXzLWauMETP6YTCwwv68aIUdGc9Z+AzJtGugjrhWqvocSiI4DMTlXKKtqPZjjy41tR5k+h7UQAXL85Q/ijdV26MRaGS+y30pSrDdZNzbsbq5nLwxWCKNFNoMmkUxkQ/o4ngu3p1Fwffv43NU8NCnWNCvIa3bSm1xfVrgHcg6gsq5zjnvNTQQw/t4MVv1HP6ypVGqZPYQ6J0EyA6u5+O8iqIiElE8I2xZ3mUC9E5MOXBfAUVVJZoDSBS3VxzORCdnsKUeu4zOfg84vhBd07sCUQnvSI/O0SsCyLOmbMohyQY7qZq/0cyBx+8Nd0+zxn4KP/kpn0wXWXNPU6JLk0iY0DH3JMLjZyDjxqu/HHtf2x94hOYXr6M2alT0cnsf9/IuSuMhVS8qoBKmlaKSZWcLymev132OXUi7Jp6/lNZm83Bl3ZIpTIFH1VJsZmbP23VIZQAAMpyZKePui1JF+AZ+MywCD6KWhTeDw18fsQSf1Z65OqJioY/MQZvbZwHDl4HUPlR1XQdcpOCyjea85CReQHg/PkpXnqpRmC6887QaFM7A2si+Hi9hHdecRPE9A7UzHBnRfBp1pwk23CwmAFEJ8I1cscdvdHqjpMzHL2ShrvvoswFnY0UwXfP1SlevnOKN35A68s48DieRIveFYHoDKDt4Rj4dhu5Xojg60jt4UN+cmKUMd71tr0U+sCC9o4WBr4F3ZZkjA8VYmhEl2IDlnTpGVWIEJ25v+lNlu9zaTYLITa8zst1079pW0feeQlvHLokRPDJ1cUoVc5lAlxvpYMHKwcb3GHc5jDwyQJ/+iOljSFZH1pdlAul5zz4WmaVUwhISiIqbA4V1jnYgaB/qQi+lKKb10wm282rb46yczDF99+/yzOOxgw08HGXQyaXtqkdEg4qLcn4ChF8We0QaofHmkKE6GSHNWXg05BisOq+kY9BldgKxWSwNoS2aQ4+TgC8776IUCScC1J9Yt6bVmAsiJCijeAzBUkh0Jft5nZi/EOIzvmUkS5l7Zuu0oHASwVGQm3j6UtyDj7lwcm+kvJZdW6mJLNT03Sygpfv+CVdnZF+iZSRg09btTE1D/P22349M8dQubpqcc89U7zwQomf/5zMp0GTNbF+pXvC2tDyrpKHdEdKiE4bMfBNJsBv/MYmVjd3evSinBelv8fCiRbads9Dl58uCusraph3mAmBHGP1LYdS53p2HrYsgSjS7kBjQuj8pFOV009lmoNIhr6nBj5yFisjrD0lGXM+c/BbLXlpBAgNmVfUOaEa+O1a8t4jhSYRkXkAeTm3RgtDw2jrGvg2IwY+qS91bf0+ur7eafm9Bijv0LbLwna2ClO3/PIypleusO1zz7IUg+hkeMai6Pek2sAn8J8KOTBJiYlZMAhFmrlM14Zm6vI5+Pxvz+0e7J6Z6iRjgezkRvgRfJhMhik7EkVkiE4nsm+vIvi8zaBxlne+WS60fI+eUlO7ZF0HwSAai+sj3ccTMhLdm93fp09X2L9/Fysrtt/XSUWeIbcosJ+BaW+Hi8JDM0PI9ivoZ/NJW6c2dWR2Qn+QhujsxzKMDDVo987z52c4eXLmI7hHvn1ttDNNGxHHayeCz51fd945w+mzVS2/k3lnTZG0VXftcbIRGVtxWTAGPgmisz5XbJ2XmDxD2+y6lIiU7q4xBsmAlRacIRcRfLeOxhYbFrSgm0LG+J6ERaM4zOE5FI46qn74z40QwccoWoLyDUTn9a98BTsPP4xXnvkX3m3xcFYqBgEEMAHJfkcoR5D89V/fxOEjFidOVDh+nBeASxsLV49PAl6JpZw4CWYmmucgs65UP2wZetLC1Pm+YgqBdm64c9dl6rXRMUF/Nf1PRPB1jISGc44UC+4pN4aoglOjlJIi+KyNzrEHHtjBE09s6zqmeRcjGfiYrgX5RdLVuwU7GEWO66PEeAxybZ4+3a9vLhIxqLYR5No9K+gGw41360HyWoi8S4x5p1SWjDJnOe1blZxukQg+UQhQtJcbwcfm4ENv4HMFaDOb+bB4zPidOTPt6tjYcM6gSJ4hqZ+zyV5BdLbCtdIgQ4Rwl9pcwkN5D+k3AN/YOy+sQAZl7aNcQTqfigJ/8dDv482NC6q2OsrxSOombL+HxOaLlqc5dCjUOleOknxtzbIe2mN8DmP0xhfJWKLtR1Z/hTOsOnAg6fJrCj+/7FwR/HsYwWfpPaetyaRX1FQVz7FOaYT7iOtTW1d/JowQlZNdUEEUAnOPITo9qHQAWAoNfGFDjoK6OStdiM72fmyvSUbwubJRxrtp5R63jjD6eLzvGXU2a84FFhq+fV54nRbtJxbB58lJNAcfZ4zzBtrnado6z52b4cKFOoqig5Fr+WbOmBl5/yG8S3RtMBCdsXoCH0PB2MLlx4v1S1vAvTReDr70/Lc2fDAw8HHfJsoEtX+GfI5fr39+uIYfS6DMWcrZa5l56SGEuu9orcfHq4/R1NwgUJUAgejseMNIg2SM/YjEUD8XRGMqBGercaLy+uhHFR86ZLG25twmUKx0P1laCg0lfduRPUMaJmevo+UrJpJTeBzR79CQtLV08KSRDlsnYtMYG6ZnjnyALs87nL1K0NlwEXxlab293OuXKZK+YTF5h55hYhC2c8MkGuwMye4aciL4pHfniBZV6crpWDU/Fga+W0cLA9+CbltqFWMAYGZ+RMJQSBnGqU1VR+gZFvcUi9ZtTPr5ZrOvTp3C9sc+hu0jJ/37GV50gUGnPfwZ5eBQT+Ic3eLJkxU+9rFtXL5cH065EXwpkgT+/fuFDO0OSRAylNnqFCNx6Zr8jByE3ABOqBdhy6CX0UM1FKJtlJnTkupbpDwzy2BAvd+sp+SIEXypdZksLDCE/3DxU93fr9z7ce/e5cu7eOqpbRw4IHtmeUybMoKvfozx5k68h3aNDxp3p60Yv3rx4gwPP1zn/PrVX92SCzbUvlcP0Un2T64rahe8yLxQTPqisJ4hCMAoEJ2i7JbIwZdqT6NIWl3t6z99us8H69bR5mvx9hZFBN8zz2zh0Ud38LlnN1lHBcDJu5iYgyJEJ0O5Z4kxA5VuZKutikn+OcY+wBiJPIhOtyR3tmlqFNpWXFOvIQZ+ujp+HDtL+1WP5/arIyaCL165TnDd2AjLzRRRm0N4GwCeQkSqKIMtSfKi3vVYxQotYCyflNiBSJtBi7TsXuTmddtyFWlOb2q7Dw+11NLMQ2oT4KXm6ZtD1cZGvFii7VsVwUc1Y1oDXyTNY/R5i8JbD20En8g/0/5Z/383N3NsJqYi+CJNRpWxwyA6Q+PGWFMz6mzWricmNUGK2Bx8koMUp9Dl5L4INKYrF37hC5v44OM7OHmy6i7Whgc+Ukjqy1xyTkP79tVz4MwZHwGod+BllO4Ih91agwrh+1fGXx+DqWm/dfQ7caLy1ix3fGrm8pD5zubgo0ahQWgFzDjR3IJuu7BdjvG67N7k4LOm6N7Pj3RzZFDYQdHvJuHwzBl73HPTNfi4z0lE0yO0zfsQnbxhwiXRCVRDgk4gVl/hMgVN2ysrfGR2ylHAWnTG0pTO0/NrcPiXGEVhlcGvuaJwc845PJKdhc6pAw8XNzjAmgL/+I98xKstChYNIoZIYU0RoHKIxPHfRPcgLQvLRPBJnIIH0ek8w6Xe6HgOJVa5JoKvbZ9eX0B03jpaQHQu6LYka0kugOkUtyKCr62jg0ERcvBlybMMQxCUJwlXg/uS8totmHhZwzAZQ+XyeRSYXP6usXPwlSVw9eoUf/u3M7z+eoFPfYo3KqSYmT2L4GMG3k4mAMKor8qUnsdvePD6dVnjewIO/aa14GP8eUOJMBTq4XIlZnpZ6PAogqZbfUrJ1QwyV+yNjYt476O/CrO0hdd//BDwQn/PGxJpQhvjj63SfczJtyxWH87pYdGsQcQC94gH0SnPOWOAj32sntvlCzvJrnTQK5bfF6KCzYAIvrCSeJHAEERg6LgRT76DsCfD2kEGPlpefMwYfPGLN/Cf//MqDh2y+MAHdoG3wzI7zWezhZyDj3OA2Niw+MhHtoEtPxozNwefhWEgOtuDej5lWfu0r9iNKOHds4t4MVdFOmF7rL7IJV8hlGqEq1PRFQlWTFR6JSY2V9/sxAngnxSdyWzLu0W174gbcLXfrFWquuRG8EnbTexs05JiOiYLZ/EDscIKiM7ZsWPKhiK/c2gMiM7YILt8lYOIYYwNFJdB17ycR/N306OyRLWxgeKdd2DX1mD37QPeeSco1u29KUcGjRKTVjoCaRSv/NmvM1pzlXki1FItg86Y9dw05P221t/fPJSUOXLwxc7G2LsNM/D5aqN5o+K9/kVkom7cXKxIUsjlKd1bbA6+svTKeFE5jqa7lms4B0vHwdjtJ/Mdy9IAhA9njZkxA9+cEXwA8OCDu9jcNNi3z2LGRPBJMqIx7byqr4sRfGojZNpB0RYFLlyY4cyZWYhoH0HkiNFgO00QwUcj7XRGbsrbRKMq6W9rUVbOvNdAdGaQ25eqKFHOqvj8G+AcQ3NM+jf989JFUnDLJCP2XV6byF6tnOjK3AFEp2D04JvVySEqMqbfcxjo5tVVi3ff9S9Le1PVpKsQIToFfU+9rh0Z3ZSdTM3WoyDujClLXq6reSISDS2h8iQ6UpZ1FN+1a7V+68KFqagI4CP40J/3gYxS1PVGKKY3CiE6hXnEQXRK687U6AKu0c5Mp959Td84KkoTpAaleiKah7TPwbeI4LtVtDDwLei2pc4wYW0d1aL2vJaLuBstW4Xlhae+rOwpk2o7djO4pAjXTlJTqSTk8Tn4hm3WYxhbuAg+NmphwNi3zNJv//YNbG8Dq6sArjHPkIfD/BYZyqcEXGWsLADRe9kWZSAQuYJvKql0lkKP/naYVI6SOfgkiE5JCwrlXI8wRZGf4jX2hjHs5fbi7j1XsbtugZf8YzeJKe++e/sesxhEbb8qJpM+3zLAe6UNVdDk5kOg9xOv0D+iKNg5WlR8Dj62q5KBT9OnDAaZNfApIvg0wmNfzheS5orgU0B0njxZ4atfvSHWAQB//Mc1Bo0H0UmjT2N9iyjyu0+WeDc+gm/+s6gVcIK8Bpr5QIpQhelQYh2WnAi+ZGL3oRt/brnId+1+k/06HtkV+Z45G3ozN11v59ha0G4bVG8FcDB36WFRk3cOWdHfN9mBePX8dW2nJYjOkSP4xhvUOLGOTbVW3C3V/VWzbvo9OuZNPoiMwebnPofJc89hevUqVv7kT6RiADKUlBntR39rSbEIuappBJ92ClFFvpnU7c+8PdxRcjEGPvd/Y51IhkgnciA6c4wYQ7ZyunfRHFPzUBiNwkU5lR5T6xpbRIhOw0B0CqGOxsA7B/h+RWQZw4yHN2mMWGdsPgsp7xLk7zFFgR4OmumTtN6NsV7XplOwk2fMaM6WuJSsXASfhq8bauALDHOUn9fyGYkGA8Olq5SHBao9hOh0qM4ZvhvsfS0de/NH2PfvvtX93tl/CDvLFku7NzArlwEITpkpXQPFgQXYHHzecwk5wXXC6/U1/tr2quBDzuAbL4T+c8TtB0wZ7yeTHsdFTXG7SSOz6n3EdA4FKTusa7i31q9vDKcCbl26Bj7vexkLt7iFmQut69KlKTY3l2BKgwffv8sXKgp2j2kNfBxVpsDx41oFRlhHUhbzOtGUSep8QohOOBCdXASfVojhtjvujPOuN38sDHy3jhYGvgXdltTCJkzLFUymdbSV2Y7ksOJrid7VHCjt4UXLjh/BR/pKDHxRRkkqSJj6ljomijHw7aEcLhIbwSdE6kjXYvdduABjGuOeUFE6gi9uPPPKkntR2CBmAkneyxz0y40b/Zyi+RRtEY+q05LquaSBL1HZHinoVKRRTDOXWwqNwTUlc8y1DxRF79WogOisHyHGH04JS36r12qrqFBAenQKLYexVftjTEMPQkqt40Gbo0Kz34pMrrsZpPqnmI/UwGcMYJaXML3zTkxeeQV2eRnVyZPBc8l34DxcAR+WUdlHr1hiHrM3IvPRg22azfQfPoDi6kmDLGIb/sDry8io9GpoY3cuFfVe3AXjNhCd9RjpNa6aOV54nsCJwiNu/Makz8qWgpnDFJxFDD8pXkpdmDiJ0cj2rHYJ3ffAFP+1sJ2jDWfgm6d+jlaaqS9FWGob1CpFjeHbyqHq+PH8h+Zp8yZBdAL+mV0U/m8eMjdxbs9J1YkT2GnXlVg3YwxgS92aCL6hEJ1KZKqALEgOvrKEMRZVKUTw0RyB1AaciOJsyYW5SimXJYhO9nz4BYvg05wZtixpqb5vxO/VPWODtpqXZ6NcNPuCMImSa8HUKTjCcQsRAFyeQJMHk2lKplgEH/OgK5/OZgxUIhDCHc5DjrwT3JqEY5Gjr8l5hoXopDC1WmdHKlNwiqOK/HY64kVTaXCGFS/IffOWr22PsDryuO/L6vbPvZxgs+VV/NnD/xKT6RauvPynAHrjn0epDYfiRcLfI3MnV52b3Xi/AQLRSb+lAtorx6lYnQMZpnvXgkHeCA18tuG5wv7W79TkMrdkznl/+K9GI3OlvT1HvuR90x1oatPvPVzu+KiBL9H2ZFJHLV/+lU1Ul6dg5Ssngi+IhG/2M/oNl1YMnngijizUvR9zL4zg4+vw5mICorOTCQOjYJMneA4DX5jzNOyD+x1dWkB03joaV9uxoAXdZHIhuMxOv+FqDtUxIDqp8CRBdOa2k7yfgOjMycEnKmwQehHl6MtyygX3nQtcDr5SitQZ0JZO2OcVHfT7Bx5mGRCd6+uRqExu4Cd8xwPICWNw/bp3wX8gAZuZ06UkJTz4xMgcofK5FV+sQM0XSSovE0ZwSdGijuBjmT2BmmdCZQ/DmCkV8annVLRnBr62Uy6sklMp04AI0dlWlWxVp9Cuc/ARIXIywdYzz2D7Ix/BjS9/mRXWU0tAbJlCdCqJ7mHJgkryBGhFDj7pni98hdc42iuIzrYKdQSfcG4AdS7h7O4oH3CNvaN913nHLjWxiyLgb2KGn9EUitb6m1KCh8ppd/0g8Ad/8B4ee6x2QnMjfqRzYcjZ9uyzN4Ci3k7On0/vm2GD6st8uVhhTQ6+Q4eUDUV+Z5RNOspoSTq3XUWaY2wPjhyNMngvKQFLVo3ZATIuXkO5JCeci1ZNnarUU8gQA8ak3rtFg30ygs/ZnyOGshyITsWQqO5JNKMR53RMMskbHy6qjf4IHH/6QocO+TJUB3gRSdq2vAwcPTqDBbC+Tj5Q0y7LI0cMLDF5tlMas2ERzCXj/JiDonCQRHbhYUb7MZnN+P7UOSrTfVHJ3BHZWZG+UFWlpq/WmnDOMRCdASmUE0mITvcMgO0QSgCQMKj5iUJ0Bvcpr+tQZQpUxQQ7yweibZjAUEALOPc7B0bfCSKQuSK/jZEi+Jxm6JwVI/jCNjTsvhZNyzNEInSupwa+6LnhpcKId5DaVCsC0ZnodvKaNJzsXlOF+eMlp10tGQOUS06UN12DQgSfhJpw9GiFTz61i2PHlPDuikES964MiM7WsYTm1ePOj27suRdnSJeDj5/3iwi+W0eLCL4F3dbkeuib7e0sh1xu33U3Ws2BQmWOzgMnyUjGYaWSh2RKwT9HBF93+xckgq8lDqJTw2Ck7ot9YwUYeo1z/1R2hjS8sZEHbWmJgOlCdFLyIvhoDj6Ci6//xgzDmXo4MRFKJtmx95v94ELvXEybMRR5gXAWvx/e5qN9PWVTbEIz8CWRxgAgwJbnvhFleLXfnxW0lJGFQJgvRSTF5sHut85FVv5umFZRYIwoWDuhUzFYPETnMuz6OnYefVR8LrmVCGNdjATRmbXXR5K4txFqAEaE6NStZw6i0wpYVznv2wo43F6redgThAo/F5CGtFFZPtSPI+QKnqxjkTERxwGFgqZ46y3/mlIQZTuiuQbwEXwRyh2ulZVeSaOBZR3Ca12+PMPdT25j9fUdlGXLJurmitSglqVJ8l3OuhcNa4pBzTp3U53a4wg+L9+a03NjkIzgc2l0iE62gZD6JtNMtbp7YyqlI8aeWHNDI/gqAudmiwJFwRv4rDGBgY92yIu2j4yL5wXflePnrjZKSYPwx9VB964xo+Lp0cz2j3zzRz+4g+d3Kpw9O8OFCzPAOTq6CD7D7LdODr4vfGET22/s4sxOA+NWEedWThaUJpHyzAn47sJEnUrGgMvzyBnHlNHEGN+eOZ0KkUNKiM6TJyscOlTh7bcLXL0qQOdFKgojSnRzeZiBLywYOOxxUZsJBRPbdlm2gVdB+TqCz4foNEo5MJfcaJyiaPzyovMv1CuxFMvBB/jj0zrLu1HOhrSVICp7cQa+gIfn1jXl2XNkJM3ZWAvm/U8hBx99hOOzAcDFsGXFLUG2tdagUkXwZfIuhLwcfK5cZ6zX4RqiM6LrHHKAUT2FE8HnlvNy8JG+l8sFUu5z0XOdDIqI4uRG41UVPvTf/y0O3HidLWpNUcvFghM9F8E3vXgRSz/4Adt3b4jIfssdie0eSK8vDHy3jhYRfAu6rWk6We1/OBF8mo0/R/fT0vbyOq6vHQUAvLlxPmJ3GA7/ycFcBOUDiM6EoYm7buJMiid8NofGUB1gdkRj4kP0OfjmpyREokukX/S9AuNZ5D3eveELnWtrmf0QPEg5r6vr112ITirlDDXwhV1KEVXIBPNcysGX00jb1h4657M3OiO4YHgp/P9bSkbwNcTDNcT7RtviFIV0zurXw44IGAAAIABJREFUOLNJpQH/uz+1EXzTu+9GtW8fe2/38mXYosBrD3486IrHHDMNpCL4VKSYj2UZer9S4/ygqiWjmrXAHAa+VPncZeXtRzQaM0M5z0YpJPrKRfCNqdMfJ4KvHLTnagwvsRx8yQrnaZi5nrOP7l650v2cXrqU1R2Xsj51EMEX3xeyvhnhtfYyB9/KmvEhqDINfMP7kTHazCKclUosn3kGipZNKUjnIUfbYa2vtNPw6yyk0l5RwsCXMi5kRfNzWqChL6g4u7mqWccnFRHZrCxrJXJRhlEvjFNkAAfpOmBExpCL4PPqdf7WsjN5Q+5EbzEQnfPMT19xno7go4a1U6eB3/u96/jVX90SlzfriOPUs75ucee5qjekeh9KiODz+Ev3HYbl4KNK/qDeeQ18lJ9ivJk5ZXB73Y/g4yE6YYxq/hkDfOUrN/Dsszfw9NNbfKFIReUkws9HaIiBjz5oTPgthkbwhbwP5XmdojSCbzJJv0CGHszVVbg8O3cGBMp97dyMyf/Wh9NkITozc/AZ46/9dslG11VGBF+MhqCosNS0vbbGdyEVwWdt3xdOxnNfpfa/dObBANlYUIt45EJ0ut+UG9VoBJ92AcfeoyhYVVpZOvPRG68QSjlKXNmEHsy94e7TrnGvPaKOHq0PuSNHKxw+bGVELqaR6T33xPvePEcdajkWLtgTuwi+dBML2htaRPAt6LakdnPzIDozNXfpc5ypzxj89b2/hWPvvIDXD1/GU51HkA/tkeIHonpNN/G69HxKMZHB7Up9KZgIvrBa3ZjPw4eur1usrPiH6nwRfEOF+0hUQjMOAZRp5Du4B+LaWvjNPeImFBVamwpaiM6iaDHVSbRU11DT5yAH37BvGlVwt5SYl62nkDXGH2mG0RI70pBKd6dVQmso8bD0CilbT/c1XCZTGSmngmsaOAbt/PW6kuiXa2RI2Sg7mkxw/atfRfnGG1j+5jcxeeml7tbWZz4DWIs3/98DwHeAzdVDWNt6G8b4ebv4927+lzZrzUAoowsDgVkRkZSUKWM5+Jzf2lORCowxoSOgaA4+x4BFITozIIxd0sqdswkXwTcbRWNuDJPvVKiXwsW6uTHdCMesxh3aWjkIY94IihWRCJGoErKh6uDBzI71VYkK6yRzY1CdOoWtj3wExTvvYOdDH0q2lUOS042x1t9XlRCd0/PnvT2JJTJhxZxdfk/nnqfS4+wSlsZFybsmz//YHlGU+NZ9v40PyE/nd0hDNxGi022rKHwFYMpAlq1UyiQ27xD6Y2oQHPfNoDKEo6ek0K8lhpZEFLjPTvq9uyqWQgg9MFPAGEeJasX92aUURKerGtWAt9Tlhs39ijonmPEgOqNnVPuD+XjUUND/Wd+YMRF8MedL7xwQlrYUwZfKwdc68IaGBROFQhuyBqPfxb1JITqZ71ASPy0OCIFGuMZobc3i8mVZCLBFIb6x1p42pEzQD0UEn7piz1DA3KcRbpEIPg1E59DTzXVWaPNAxox42rlpUhF87vu08g18J4gcqPla9goNk14zCgNfAOuYEcE3BKKT2/fCHHxc4fZmfy3D5zbMwTdCDBCnT6rb9HVmAJqvSyA6Yzn4IjS9eBGTF16o/445CRZ8Dr4+go8Z9xzHIq5scBZHKirLqDLrypUpTp40uPrrmzBmLZobtu9b805LS9i9fBlLP/qRWD93nlHWtr1YX/OjIRcRfLeOFga+Bd3WRCG4WtIcqilbgXSgbK8cxKsn3t+U2WTLhjJIhlGpqtgN1aOEdlz01ncqsgWvyLUwNVfAxPYPFeS0RqO+D37TDz64i8OXt3H63Db+/M9XvHsUMia3jxLSCtvjXGNIpIDLMB88OABqIwHReebMDK+8Uv/90EM7sqA4EKJTYywKKJHvT4Q+jBhdpLU+xDk/uickFqW0nlri4EHq60qITveeMoKPeqqzEXwDIziDcdc8KAgeyUfX1jA7eza0hhoDTCadIvLv7v48Hv3uvwNWC2w9/XS0/qiARK6L/VO8cw0TQ8ZcYeBLnQHV0aP8c1RIUlK79ubJ1cYaDopeCWuqSu+IExmAds3E+mqNCfkDY0SBN+fcMI1ilvN0Vj3vKhiGGPhIHVsrG+AMfBzUT/A3ubb52c9i5etfx/TyZVSOkVx8VrF+3J8piMUWEm03Al+rppyBJfCxktED8PfsraefxvI3vgEznWLp+9+P9qPtTmUa5VxkLQxWmiu+T87ZzXnvis1GNduW/fvvrzyL1w9fbozx78rPS9UOHihEx38UciM/nJlfFEgfgERROXxCKMjp5+XLU7zySokTJ6resJRq2+iNPNYwp9PAd9NAdHKkCKDn2zOFJ8u0EJ1AHZU7gRNFFsEBdXPwddcifecgOpXbrkhDgQu4CL55yO2vKgcfGdfY+j9woB7fqpiEisaY9xtdm1wbke8byNTGd1g0hnlX5oPExiaXcnLwUQ10w2Z3tLNjgjJu2b0mDqJTGzmYS9aGc4xGNo2Vgy+YA+T8cJGLbFGAQyYZg2wigi8s70SMxfbg1Eei1iYQPytmLUYN9cY3UvWitAv7Sb4la5Ax7F4UHX7bn526zxTf0FdWOAOf5SMrnb2JRcrxZNve2dBaYuATITr7v2Wnd7efPrnBp973sYQHR8LAFxnYraeewtJ3voPZ2bOw+/dHOyjl4OO4Q62BL9pHop9NGvh2BRjj5tlDhyzeW6n5KhElS9BnbH/0o5i8+CIMRddpyBrDHpXhGVew1xcGvltHCwPfgm5Laj3zghw7GXuJcI5nUVs+wpdFn2NJEcGXDPFOwXE5dXDGN8+7uCgchmbYZj3XeKD2JD5z1uKVSdg+hcZR6CI80uTjkJST1PYSMJuRFz92wgJv1n+fPJkZkWmM6EHaeqSdOTPD7i5w9z0zPP74Dq5dK7w+ahQROaRaO8oIPrFyBbPUkmfgG0Wrr1NsS1X2Bj6/L0mITm6TUeZe0HiqJ/NKJtrwH9VH8LlRpdp9JRC+mj60isifHziFr//S/4bzX72Bw4cmQTm2L/NE8CnKFEUohA2B6AwY6gMHsPXUU3jz5Vew7x9/3JezYaLyLEoJr5nf3fPqdgQVKbeNhrRbVlVM8Nqx+3Dq2nfxkxMPprqaRVRJRw0cm08/jbU//mMA8IzNVODX5GNjG3doc2WDP8czIDrbp6d3343p3Xfn94l0Tx1xoNxXtY8PJgrRqVSo2vV1bD/1FIrXX1cb+GAMZsUE5WxX3G7Gei81ROec7Q418E0nK0GkbbqhyO8cGguiUzq3XaWKY8yhEXxDqh+VnDPwxIkKJ07UfX333UbxN6dxwaMxX0YBUcCyjEqjdXCPKmsnky4aOzB8sRCd/pwohBypYX/l/gH+GtfqHofqKOl5lYpYy6GUshhA+ps7L7ayAnzmM5v46Xct7jVEgRljIioaNcQ0WRS14S7AXY3w8A6FBr44PzR6BF8MotOE38FdMzs7/HylBvC5KOZgM+Hu5ber5gUDR4LQsSAYa/abm/hvyqO5+421MNaH6Ex2f+DC5BzXuEi43HaSEXwkYhEIz8nUOqDO4ak82UE0pkIxmLMWNWUteEOlKyNxOfjqMkx/MxRudMjd/gaw03I1IiUhOl0eiX5rYxyIzswggQMHsPP448yNcL+mEXz1fgfMmr4F56vq8Iw4R5FrsepsWepmW1unIoLPu76xgRtf/CImr76K8sUXMXnlFdI9HnY50EeATw2wgOi8dbQw8C3otiaaY6cjFcPBKMScQ0Sn37W6nHk53auq4DAzBtj89Kex9p/+EwBg65OfjNavgsdiIGS6YoLn/1DdSra3qHAocu3TBOq5jIAIVcMxXIl8ZQGkQeTFr95n8ca1ChsbFQ4c0EEuesRFM6FnWIsCuHRphhOf3IJdW4sYIRVCtapLaRipX/QcfFFeLNV2IoJPuk6j7MQH3bFTR/DRLtrw+4fSWrzuhrgIPik6q7vstKWG6HRJUO64HnizyQqKlV24+zu3xtvhnCsCRPFsHcFH9gFVBF96Au8+8AC+94EP4u63/y8cfO+1+qK1/rPK92udZoYokqpjx5o8ibt4c+O8f89VFrgfPXfcCcRduo763neufA4/PPcr2FrZoNXMRcbEBeDp+96HTQCYTDC7eDF4tqWhEXwu1QY+po+WH28pf46KBjIByejsljKZhfgUyBjYqlLn4GOrVVgJPGW/KQHInrljKM1z6th+8kns+8M/BAD8+M4nxTpGOJoDL+0sGtPAt0fE5m5x7tMIvnYMisJ2ji80B99c51SKBKVQxzMmlaqZRp6EoltNCk8PrmoVdDlDAUSnAxE6a2B3u7oYB8r2k3ef3nHmzHWI8PrsKWz5A26sZRMY+Oacl7FoGo6PkZy8Wtq9916s/MVfwOzsYOfhh3H16hTvO7WLfS8S5bE2gk9yjGkhEglDoUFAMAaoqKxownfxz4t8I7vawBecdWE/XFlie5uHSrxZjgBDI/goaXhBDqKTGo14Y2ecx2JvJyP4QgjgKCnWJlekfT/HvhT9tm5SAA66tWurlOd3nSwuXIOFCc/JnC2H63d0XXHjKkwuVT90isTk9dDAV58d7BT2Ivi8Qa7/NX1EsSeLUIhORQRfuGdxkYZh9+i5ZwxY9Jl2zmfz3BIxi56K411MA6NzpIg8p07N8NprkbXI6RHJuEbhsrUbW9uOIgcf7VJ15gx2zpzB6uuvg5I1oYHP3Re6bjbf1J9vv5Ds+f9vaGHgW9BtSX0OvuERfAn+S18HewgkIlli7QgRfNOrV3Fj/37YlZUAOisoL3hKub1qBRzWO4PJvyeV1VBqXLWMUtv+T4+9D3dc+z6MAV5tIjOG9iXrnSICmHtBYxQ6eAg4ejcfFh9QAAliohCd8bkdF1aGHsjtWkgXkklS3Fvyv18lX6eNSRptmRwBKKUhidTVMuNcMY8fi3w4V7Ghz8Enl7t0aYoXX5zg4l0zvBXydWlq++Ua75T9AgYGTwjKHWokpTLahLGndTz2wAg+bQRanQfCz2mhgeikJBn8aiHPUfDBz8E37FDLvF+W2Pzyl/H8Gz/FP5T3ebe8/UiAAcmlrKBjY7C1eqj7WVVg32H/fou33uoeSVUJgPPk9js5vf9+9mG3/pkpAYRnfpQIjyAZ+NwcfEnl21wbP3NJW99YmmdN3TEKIvgiCkZuKGNtMfeo5zg7DGOMhfR9CM0uXcLm00/jhz8zePHow9GyYjOxwmNE0XPlM8dd1adckvrERCTUt31FVkpxOZTv1lIq/+KoivsxKYisCYl7tXkgOr3fRR+dHuSmYw4pzljQ0U3WhA2H6PTf0xp93jWO4spi5kcKlmJlBdf/+T9H+bOf9bmXuA8eGwCPOWWMWWSv87YRBfNgDLOmmAg+Y2qIs60tHa+ZatclV55wUV0oP9lepwY+VnE9YjRnNIKP4f+GtKuRQaz1+8J+O60yKVQY+D8jRmcKz8lq2TV9EMg9A9xovd4RUv4e6vMhsel4Mm4H0amAy3XJuW+MDXPs+UWYdciHK3F7UawrrSOEfk24/Q47KuXgYyE6Uzn4DNMWap2J63hgaXTjAEpBdHp9QUU3UxR2HJmxq5EZkE5/QPUmjC7AGOut089+dhP/8A8THDlS4Q//cJ9Tjv7hdsK/loTo1BCjJ3Kbct9arJIXbFjEA6qPWFopgKmzp95kfmZBIf2Ccu0LWpCOaATfhQv1YWBh8OST29Fnub2MQa2IklbmyNJhsSdy7d00O3cO1cmTio6li0gRRwWqzsBnjA2Y2yE0dgTfcxc+idePXMH1S+/Dj89+eK6qxcNOEGC4Iv0hSoSo2IBlDEowI0wMojPunhzm4Btm4Bukl00wK2IEH8NopRpWQXTqqor3qaVIBJ97jX52V3Bme8m9eyL8ra0nBh18xx0VHntsB5/45I7/rFLnyXoqpyA6HceDUQx8DXEeeC5x+h3RwNcVGEd4LktGCBuQgy9GnpBqh+Xg6x5PFZDW25EjeO3cI9hZPuBdn7kR1nNE8Pk5rNIRirmRQWfOzLB/v8XSksV998mRVW6zrnNEjkHGLTYEotPcuAH3S0lRgAV0BivV/cxyItxa6vkxI/hyHrDWF/5HjOALYLHRR3+OLgcT5Y0aotMYTO+/Hy+f/SBmDl8dluV3iKKQKm4f4yE6s2kQ4yFUtZcQneQ6hej0xrEp59kDOYXfXlEiQkEblaQijZJWSZocfOy+OBCi0xoyVJNJ93taEsMXg5ASRPC593PGj9Y7CKJzGEQsB9E5FtGoNva7KMIv7eHDmN5zT8dnsbJSTGj3PhDTRm5aA/d5wxv4JEfFu+6adeV/7dduDG42elOB5e86C+7syBF8YzkjRHPg3uIIvkDG1nYgk/dx9QdF5fCk7fwb9WDo6+LejzUktX2b7kBDSYhOr8EQotNyxvagEaqDCdv0sl2Qd2XnneKcAYCdJm90tbGBa4fvYvvDkhHey2lXhOjk1qFrvLfoxzLeRA1i4ThGD8nBF5YNF5kL0UllV5fqHHxyBN+80ePt8yHSUfMHw9dQiM71dYtHH93F0aM8L8lqc4MIvkgfnbNmfb2vLUD9ajuqgOjMM/DpWO6lRmQYhMy0oD2hRQTfgm5Las8Bmrvj8GGLy5enKD+0jSsPxZmOlCI+x8CXq3OICpRMBF8GpDaAiEcVw9QHfbeVOoJvLJ2gph6Xz95eXse3r34J5z62ieqPfME6t635IviEQ1ZReUyACUhyg2LaZoWPusX63wQkRS4igPc7NfgKA5/lKpcajZQdorvjq1cqQhjFS0sxxwEvyi72fiNAdNJvxCll1QY+2lXNIvY8C/MVmBJDTyP46DcrJ5xCgBnXnM4oiZ0/A6DFokvCVSTDChpEHc0T6cUKYU4En1FCIKZoHiWSNL/LEnjwwV1Yq9fVDIpsMT7kiRRxHaPi5z/3fotewm7UkNsmp2MfOu+Z56JnwYgGmuTjGXWbAKIzc55HK2eU8oGCN93GWKQ1aGh+e9fVG/kcEJ1swwNprAg+Sk2ffF906902zBjIviZp+PO5KKXYSbS9R6OYpjK9d3L3h6edJvtsWXZD10bw0TEL7EaBDBHpqLpbaV5qrP2Fj4jZm0hYb1q2a4p+PM1hzXl4xSYB4Z8Cx822Tc64mIK/bt+DiwJjvtHamsXDD+/grl/extpdedrTqCJeEEqM4Z1z3OESI/gYhfhgilTE8fND2lUb+ALDGx/llexQEAlIhUG5DjeCT23gUwyKxLO397r9LcLrTjavA+vJpqI8VX2fWJuAOqrL6Ww6xYR/i9OHxPgwTcIxzmELqGHOd69cQXX0KOz/mSEbKDZt6hNaVREZxHE8TImCFGTAy8E3QuR+KoLPBQCq0WcoROe0uz86ET1JwIcxjVIDH4SisXOdGrq1EXyXL+/iu9+tJ8KVK8QJtW1HAdEpOfdw81oL0TlZNsCNXoQa0/FnQcNoYeBb0G1NFKLTmDpJ/P77d1ElgiQybAUi9Xx+HHc6S7gSIDpjFJRPMVKAmIOPGvhchmoeHaAxVgWbKDZkDCNIhgdV/jfUC6e5EXxZmrQYcWUnxPNMgugUJlN3WQjpzyWNgi9IKi4NvbQAMjo3lnN+0Afhdyv0p/aVgDHSnsIC3BdLTSMUopONstB+k7CR8FIqgk+zL8VIGcEXQHRyEXzC/A8L5PWFUlmiSxSeQ9qpToU8Y4mQlLmgcz1kU7dECK85JDaazDsnYtGy1q2+S5pu1WV8+J8cA0d9jtVlZyY/B5+ZTvvnpTzEtDsjKIP05WzwRdTvmDkY0eI5dREDX8x4O0aUQqt4ikLvDlkjKuZRv17W1jKi32P9lSL4sid/JnMco5sE0el60feX4xF8vkKGaWNMEo0dglNYUGrOCL6h76aI5uKqHmrgo2eZLctOfqDQlbEc59y0C3KyJYhX1urn89A9LKUwz6Wosp0rOMQhi4NLjdRjUvtTzMCiuFYbHsg1AaITAFZWgIMHbSRjK08rK5H54L4/RekJ3sF6ssT2NgDmW80L1+pRjM9MB1mNS0QHojLwJephb0cgVzyIzhENfF2dQgRfr9uQB3hp+7rbqNydiWB8ABB41lUOihTpo5bnk76Vt+dQvFdmIlljiG1S6IcxqE6d8p+NdFXqFGeUoW1tbzf6Hu79HANSGqLTQQIZEMGXIm5d+hF87sfo/mH6qW9TTVQXxtw3xl8bkoFPqJqtnMrY0XPbObvW1oCHH97t+8E0GAYO2KDNfIhOei3UUS+vNPLoLF9Xt6C9ob08Ehe0oD2jPgefoNxSbC5jQXRWJ050Za+vHWXrDuuKbOgDDHwBKZTWkkEiiODzFA3DFSKxd1B5gBohXD6jHe6+eFYnGC6ursADJsYI5GgZuAklWIakCL6eUSedJt5EQ/W8qudyQ1E1lQv3hujuorJ56oUjzOIoEXzufExZLzmFoVB94J2aCdE5NAef4nJIwvyhEXwhRGfYr7aMqEQfSXguCsBUvYFv7PVVCyAOUYjOeRXocz5qTTFso46QSpkz4r4gVT80gs/tWgt5NnQ4tlYOsmdjUKf/I1E4g27i/Molu7ISXpTapzn4ohCdzCSKvRfDa1WJ3LdjDBNVTmifcenQoSp6v6fEwtprY9oQ2uMIPu/Mhg/RyUfwycbevaRkDj7VHNKNpT14UNkrRV0DITpzWG93SwgMmU4EocbAF4vgUzs/RRSGOfNkaNkAUnrEySnCOsOZXUMMfMaEik9lBB+bazk3go/0xRgmBytjxPfOmQEWrMuXp93+/dDDxDwYMfClcvBJEJ24WTn4JukIGg2pHY7pcxoLY5IhAwI+LFJHOWMgOveIaM5wgDgR0m7vbOkqzpD/l37wA6z82Z+h3N4i93VyeEsphwRqaGfHlpTJ4alUzi8mDdFJaWenqZfRU1kBCpXTZdDzye1HSr6h7dP6gHQEH5yzizpqWhg8f/YJtl75YgYJ603U+TSPcPyS2BWubBkfM5doxHohiNNdJYoIvhwDn4UJHMW5I7GF6BzdsX5Bg2lh4FvQbU00gi+HUvyXUm7A5jPPwK6uYjpZxd/d/XkAw+0YAICqytZhBPc1wkDJe5DHIDqT7UZoHijM9lrEwU3dJw0/J1Eqgi/JsGvvUeIEyVKC6CwQU7YEUXQBQ5DhsU9+JyMOUsYkRjEWvS5dA1BVVLPCUII5dSn5bpQ5dChm4PPstLH3EyA67TLj5NA8w2LLJzYXtc6TteYMi+BT7yPKCL5Al8NEc3eySyC8KTul7HRR+BF82nfVngH1uiMRfIYUyKB5IvhE4gTOOYQzVxGeG+U0hk6/bTLwdFb2xVeY5kN0urS5rFSYjzSfg+EThN3BOfiyByPyQZeXsfn005iePYvNZ5+NV0Mj+HL5nli/mbPBilDa6eq0D8b2DG27Gxs6iV11/jOUDeWTyxzHaK9y8DFntmvQk6DdvflBc/CNpjlnKAHROciRwaEbv/7rsEUBO5lg6+MfH+8bKpTqPEuVGfEtVeYZ+HxHDc6BksvBx3nYZ1PTSFHoz7exIvjmheLy90SFs2FJ21eSmGiJaczbFxjlOzHwJcecKBaMsTxEp7YOJRUF8Lu/ex1f/ep1fOADJNd2BKKTk4vc4dvelnPwjbZNRSriITr3xlmD+7ZJGFbuN/wxZ3kkyidLEXztxxhzzjhlXYjOVr6Y9wwAQsNGQGRNLn/rWzjw93/d/b6+eiSLx6CyUUsHD7pIDWQ/SQnwbcV1aV0nNMTNl8izO81y5iK/g0jQCNGgSSmScyhxTkuTCZ+Dz4DkwQbwwulfxkunH5u7Hywlvg0HOWyMLodDTN4JI/giFWm/ZduOYODzoxDj+jCqP+C2tkCPtVxfaPMEWhjs37+w9t1KWkB0Lui2pD6CTzDwKQ7VepPyYSNzmcSiAOyRI3j5i7+PP/2/Vx24rNDjQd29ESL4RG9Qd/MXIviKjBx8WX3KUCppy2XYe8T71Dslp3I6Hj1DZtOdmcfAZwyvNAdCyAlyaKcw54cKaKy9xxifYVNaU3OiqqSiowcMpCZfN44cbKyff8elJERnq0QQDHzVxgbK119nnwkZuczIkwixcLSpQd8jiE7Ow8ylpSEQnSlSjltR2C5R+DzVx5aEpwyGDTwhNeRFYuZ0THEr2JM07QSV9O8kOdl6KICC4nGvIvi0Snia0qUqJqzAFKPp2bPAm68CAF47fr/oJRwoT2PvPrYBgVESaij386Tqnd5/P6b3369omCgX9sDA51IP0Sk/MgiiU1FHTrWHDsX3Vu+6+iPPAyFMfu+l4UtL0mbtXZchOlMRWFljO4QEviympHLp5wdOwUSOuNnFi7j+ta/BLi0Bq6vjvcuAXLbKxzpy0x1T3tm6OfioFxEHC8lE8EU7Gimn0UPHaKj4wZ7lcxBVJrrEQdYGjhfKF7GTCcyOY+SihkK3bZqLlfSrGwOtAOpea98jkMPCdryfA/nUyQQ4erQC3pHkFdqQDZgkY3xZYmcH2M8aJEY08MXO35H8xXQ5+HTfOCc3nEhUpnfqKKvdPgY8EkGa2wdO50Wh59/cOBeN4JtNeifTmME/MGASivEqu0v78NOj78O5n/4NX4D5TXn0lk6erPDAAzv40Y+W8CsP7QDfdm5mRPBpPrE2gi/rOnqITv4gIPtIw2tw57m3t1vjGQw1EJ2xqGupe7XOI3RsCXLwGYPZZAXPXfgknjr6GvDGj9OV51DCsYm7IEF0il1RHNRRfkSrMxPKc988N4KPs2/T12ohOk+dmuGddwyOHJnhY1/YVPV9QXtDiwi+Bd3WJEJ0Kil2GOnOjubgXF7ycuHMFcE3Qg4+UZHu7uyRHHwFeEXXPOfpGBF8Y/DQ89SRYmaSjbl15TTMA5mzRVMQnaDMaYLplogdR3oxIYyPEcEnCRQq53yFElrNuCkNH1SgSkF0chFl5bVr3d/VxobYriqCbyBEZyj0KB6ZM4JPWjOpVDwTNoIEifSoAAAgAElEQVSvKSR4ZybfR7mhDY3g0yJRUSHWzAnRmfLIH2QjYvaYWIRUivxHDXNNpqrC/IJhI+D4e61y4ZC2h+Tg2/7kJ7F5+gJeOvUoXjt2n65ZZ+1RpQMLQyZWRNa9oJQR59HAsT9yJN9IntV+FkRnRr3OPS+fzAge8bG2+j9lpRlH9NykEXyD7RPCwTJvBNBca/kmQnQWts8pRCE6uzKMQSO4vheUaCA1Sm8fvBPvPvAoZidO4MaXv8zXsb5eG/c4GvoNSz/6+WZAdNKK2qGbFkQWVUbwtffpGNd5iiy++MUbyT5a5wxUglWwin0N7WUOviomX7U/FHkXWaJ8bmzOe4MYUaDH+HWXBGOYe9sYE/DH7PuPRVrldnNNFcEHg3wXHYEi78tF8A3ZI6W14uYuXF4OjZ2qb5wQJjXysuU2DyCeAzLVBxX5Dqkv3/EoJDRTWxR48ZHPdb9j0VZRA5+1bH/bSy/f8UudE0WWzkZwSHjqqW38m3/zHi7fQ25wE6kohq9FTVnjryfOKENpe9sEsl9HGQ47NILPzcGX1iWlKQnR6fDGhjgXeGPC6Q7mluOoDiNcw+wSnTOCjz4frW5XmXm11VsIlWly8AUBCk29GqS05SbWpiiAe++d4jd/cxPHjy8i+G4lLSL4FnRb0hgRfGM8JskcKeVsdEMfAaJTUqS7m782B5/7zJD0B9Kz2cQcNEMgOillQXQKBqFB0y2jYRYWLWLgizGjISxIPBeQRKo5WpY+nORQy3dkoCU96U3HAhfWk3OLvT8IotMhNqdN8wyrC0kY5nJz8OU8rM4zI5EwBqlpVZaNYM4Fk0oPJxaC1kBflvD2U/1ekQGV6yoO6HNDD7UB9yWlYQAnPCep9n2hn3uag0851jSCD1AKce0zR4/iZ0//Bp7bWuva5fcdV1CLVDi28nCeOoXnnn12C9/85jIOHarwjW+spIrnN0sNfKImb0CbzAPVUMeKAW3lFOk8w1HPn2SEuVtnbG9wISrnedlc5jhW1UiDbo3xlxd3ZjttFQVYJwyv+G0G0fnOBz+KwxeURviRvmHAT0YUxC6trkbgUgm5EXxBZWXZ7bE0gs8KDpT0QveTvMvv/d51TKc95BV9gOPxi8LuOUTnmOsvWTfnqDTUwEc3spj8Rc4BzrArta3NwcfyDpF3mcchqqvfpcia5xAg3DzW29uG3ROsMUP9RcO6Iu87htwPyGfus89u4t//+33d38Fzo+Xgo3XovpEaojOD3Hm7uv1uV70xwLXDd+Ho289397u8fGWJ61/7Gt79y6NAAyKzuXoYN770JRTXrqH8yU+w9Nxz/XPB5CAfgH1fi1m5hFfueDjoZ9dJ4bcxodMALRrIcRmTSzP8Wgemuy5P8Y/fq/++cGEq96Wh6bQx6HCRlRl6FvcdcnPwBRWEPwXfdAeiE6QDUjPz6g4YSuUe7vZqD94ycy9WzKcYD2K2t3XtCOcjZ2gUUcsyIvjotaUlUudeOc8tSE0LA9+CbmualctzMTlFITtta6pty6cMfJSidVsbbPjZryh1QBvB5+C9u8/MB9FpEdcyeoXZayEvx8EhavrRk/hOAhMUaytLYTQPk2AMMOEFVMljTVTWDDTwMS2HlxLCuDhc2gGPlPUMfOrvEplPKcMYp3hpKLavpCL4Uga+an09hEJ1FD5SP9yyGxsV3nmnvnn27HAlnTT/u8sZXsMsCc+7ygepgaLglXUitM4chi6XigL+fqokrR7NGOsrg6uZs9bzF3O1B5FFrCfoPIp5U8/tqjLdO9ZzPV3nKDn4mj64e636dcg5ZosSxuzuvY3N/TFPY4qJSYVir9jAto8cqfDpT29hawvzGfhiD0yddVoUgLAVSvuoRNzZYBMQnWOQMZgLzmxjQ89fZRmh5oDoHNXAsEc5+Nrv7ccBEYhOxlNdyo2yV/mluvZTBj7FvrqX81ikIB8bvxdxj335yzfw/e9PcP/9cecKF6KP5juyZYk2BTLNwcdH8DXzgvmctO9ra/nffK8gOlM01nGiyk00FKIzQ0j3esEZ3ppne8N+fABcPsx214hivrb6+/2Qzu8xSB3BWDftDt90yowJwOasGkyRirh9csh8lrb//8nem33LcZx3gr/IqrorcAFi3wiAAAgQ3MANhkzJ0ELKNEnRlCyrSVvSsWVb0vTD/Cvz0g89j9NzfKbtOd3H04vtcfc5bY/dPl4k2ZZsSyIlmVpsSaREkSKBu1RVZsxDbZkRX0R8sWRW3ov6PZC4lZERkZGREV98v2+5++4cn/70bUg5CucIAINLl9D75jeRHz2CrR8fJDrloTACLSNRc5scBu7D+ryMUtk3Dl/B0be+CQD4lwc+CJlntMfm8jLkvn3a7/nZs8jPnkVWijTD6jdxvdg4gL+7/Ivo99a1frqeY2VFukMKq0bvhnld+dsjRCcLQuDU6QLLO0P0esDGhpz+Xsb6eoHbtyf7iqTnEODlwVeJKsEk+LhHZMDtwafl4DNFn/GUuVlwyZJE/dwQnbYzuDquts8iFcHH8eAjZRYhtPV2tCVWf1tSg+ktCL65Y0HwLbArUc4XNPLiUxZB5sJvW885h+qZ7t1OyPnoJCgluasvWn2mBhg5+DSCr2T5mFIvyL1WLeQmPn37aLRmoTsx/Vf5nc/aVGKd2+BzIjFpKQg4EyOrwmowwUe8C7XuLKuKn1mmk1FUjaGkSySs1TM/atecVK9zPSQqymelArm+DnHrltaIWjephM0y/NIvbeGLX+zh7rtzUqlLYSIwVl6l04OPVbWlAvoFHTggcexYjjfe6ODee2mFXacjkefl71f9R5q+UMXKITpDq7cp1qshOmOV1vwDNPfSUKYl+ADdOEetzqqUpg5dWQbhofAXgiBDOc9UFJW9IzRvDef8VFl3bAqoJrXzHgqakNtjIGwTytUHS/nBY49pv6n7dG0efBEDpobn9GlWQyVBJh2GKaihmAnRYIjOihe3wcvapNDPMqCoc+In8ODz6l6qd+gTKkvBmTM5y5ipsiWolXU6OHo0x2uvdZFnphx8igJVqWM6VTw9FChizKooTPjZlBEbYreqTMyM1yZ/cLw2SSjCsLUe17pgC5HIOACoMhtAK4JN60EK2LxX1PVJCP24SYfoTJiDz1IRI00aC7bXfOxYde/bfu45DL77XQxOngb+rfu9k+Pr+Ag1DzfTQzE9+EJ3t+8feRDd4Tb6Fwv8+NzjwNcNbU2+SVP8Tu2sqSmq7OUBvPPxl/Dm75ya/u304Cvh6NECR++S+P67Es88s00XKk8myyEr+FtU7zUgy2ZkcuXHEl54YRu/8zsjz9KPfGT0PNV1ZPzGPfYSMkTn+HY1ygQH+hTXZ2HFg0+VkcpiYiW3fA0wroEzgpV6nlhvanWvT0nwxYTo5BpgU8OmhjJeEHzzx4LgW2DXIyYPX+yhZ1LeN0SnrZ389GmIn8b1i3S1zjLdWokomsl8SvAJUSWAYjwLvfZEgzDJM970I0M5HnyzmtOJGVE5+IQwhr0rVIWxojzSrM8yAfD0mlaYyCOyoCtRiOGDoe4y5+BjPAjjYadFmIppqhhNBo9QWTssh6ju975nbFdubAAEwUeuS8TYHjpU4Od/nilImvpqe68TGOL3sOecRQB96aVN/OAHHaPSTnMmNRF8pjlINcpEJeQxU+712TMqh6TAj3maHyiC4DNhUMRrZFSlU6cz4bzdys0yvL2GCWzsz/E2CCt8znpSQ+xgNafeBJUx8dH+2sBdMw2K7FjEhAl3omREYdufSYMrpXyxvo7hpUuQKysYPPSQVqTI6vfgM9XPbZMi+Cw6MMciZTLqCRS6XR1yXbP0KRkyXfkmBKZh+EeET0YXL4G7tgQjKuQIu0hy8EJ0xr3jste/Oldlp4MTJ0bfyITgm8nVtAElQE+7Rx4d4r+/Ovr39esWWcxyLqnLgy+hKORVEalUDw3Rqd4XE6LTokDneruSsoPtWRoM0Un9phnCUvMw0FiJhOV5qRx8Id+51/Lf6yG/eLFyVq4gE4rLdsDGy/ROnehknN33WLcrnltZB985dQMbl/rICqFdD0aABx9WDXlbTSifhQTwr36lj53OLSwbsvlU2jStCWo4dQ8PPta4Gb59VQY9fTrHyy9vYjAAzp3LjfnETbkOKVJNjSJezsEnSbe5apOu53N58KE0lqMzHl1fLTn4DO9VrV8N0Zl7ePC5D2b2rYhN8E3qVOqm+mGKFGGag5SYpRbVQnQuMHcsCL4FdiXKghmZh4+58AdG+9DK6wugH8k0vHgRYnMT+bFjo39/zV5ehyp8Wzo7gS1Ep0zvwRerX6TCf3iESw/ql61yUn6vK0QntfGahH9XSAr1EN3Jggg+lp6NOoyXXG/YynYb6WLocIge3frsrge2KHQqArEt7YDl5sEDD6D3j/9Idq3Yvx/UW3d5Fpt/dCM4Bx/xXrhdsCndl5ZGhx66YWE2wrBqVS3wIvj8hV+f77B8EMuknsM1acNW4oP+fVCDB99I4WRfjykYrY2VfKEq7rtvgG9+s4e1tQLnzuX4nmB4S1MYDlkHphQgFaTws4L2a2SG4BCdjuvRBJ/thvL7t4VwY6yjcmUFO08/bSwiOR58Ie9F3d8JxcjcCb6UxJrnGG09+yxW//APAYy8MmoBIa9UchD2+1OCr3x+qSjbAta2YDQdotOHpLWhU803ndIAb4KyHEmFtz9+fLRm5B3aQ8wm+5WvP/TwEG8f2EFRCNy40Xf2i9Yb8r8rn7IhfQm5l5XzKZDg00J02upRQghrxK7tjMXY34SoeuROlwtlrTbt30lg++aJEJ1qNBBy9hBn9GBY9t8U534gbBsytiMyN+PmCNEpukzhZlJP7RsDvU5Mm52EquWe4x06Dy0UZqcDsaR4RvvuHZkwk3tQ9CjMs6DpCMG511iGqds4fVo/o0iRVSO32Bgjpd5yE0VR7YdTl2SvGoCZ4Ju2UfHSq34R5XEWnXoPSJcvD9D/l9G/p967Bp0jRfBx2pj9phZJkTdiXKlJL1jaW/08+HTdCTVdFyE624cFwbfArgdJ8DGhr71+i9JsTfUL0am2kx8/jv7P/qzH/Y7r1EJtOOBoXnmyQKeYCRHlg1GMci1KAZCgfdM9xs2OItUMSpcgOdslhJVBPLhJuaz9Pu7cpI9aqKdOBxhoxZ0g5yghoGuFygKk+rnZiDzD76bQRiyCj/Gw5gMd/yMtX7JwCNab+488guyHP0R2+zbE9nbluty/n7xVD9EpvQ9dJpAkgYvgiz2QRlgxqznapvPG9B6dB0d+X/7h0vN4/Ku/AwD42tUXcIlxj94tU36mamFRJvgCxrseDz5C1IwNczJRPo//oVZnIoNDPfgOHZJ44ok+sgzoZ+PpHuDBN/He8WiaROU5DIo1p/FASAe4aya1f3KVHR6XU+q4RAzB51moaCAHH0Dvj9w2ueGap3VyK55jDr7hffdhCwA6HQwvXvRr27dPlXW59Mzb29NLw+6SdpsK0vM+IZw5+FKH6EwFNQcfJR9G9qsSCYKQnffvH73XPBu9x+1tUekbxRtRRiadnsDNm25ib1ah/g2NRDDeA1Ov3BQ+3zqGkQNcyQFlMgop/aGRANz2VWHYJn+UDw8Zoc30JViIw6LmHSN0IrFSfUMefADIEJ0awUesCUk9+CwVpcrBFwJjt1TjRVJ/YP5rVIfiUWRqjBmi0+dlmNqadslCQLFlanW9Np3/J9dXV5EpxI7rfKI9h4fXoKavKJWpVlvVqVj7A8EjcCzj62xjTPBNxQ6PSDnq/lTOM2ve9/nyG62KLIfoVFwIK0M1qzur2YPvQx/awSvfyXF6mOPo0RnBR93CkTOsZ3BlUGxTdOu557D6B39gLqA2aPDg44ToJPMFgvLgk9pjLUJ0tg8Lgm+BXQ8yRCdz4VeVvuWFjF6XZeXANynjkb+brlv5IVaJRSl1VOFFWjz4RIngS+fBZ17wuQozjuzmK/NyLFmpzZpsu6YcfNrGKywhOg1WV7MqlLnWDc3BR9TvekFC8Fw5VUE/oHOprDPZZInFg68a595T0T7+rThxApu//utAv4/9/+bfzK4PhygMBB/Fr1JzKQjq4W6kXaq2pToWdzKAIDjZXQg9xQtB5yOMrJOLNw/cg7+78nF0igE2j94HjFTMSaofvdMS0VT45/sLatjj1iERotMrRDGB0Ehd2hlyAsY8UOWDEA8+MRzWpgxXv7mKYqEuJa3hXquy0gK5Yg/JFD12tgoSevBRhco/qflNUp2H9RxffhWfPTvEd7/bnf5bhXn4COORyuWW5ODLMgzvv9/vHhdMfSobx5XysE48+ADVg49WnAnha3roiQQefD49TCaDMIzk4gk+y8Wx4nd5WU5DdE6j/BJ9k9LSoYiOpsjBJzsd3P7MZwApsf7v/h2EKeczo+5QcHLwpfLg4+bgo45T0zMTQXCQ34pC8GkyG3V2Un9KLDS4SH2ltG5ARebg05XBobDJh3SIzoA2vFyxXGCsaY6PkJuDz0hEudoLKDJpijRu9lR0OWV+VSZaXdXlF1UPxH0AA+T6+qidrS0Uhw/ThTR9hGA1XS5rL2OAY3xnJJmqd6HbdOmxRjn4Zj+Yzje25+bo6CohOsVsLEdeiLM2JWWQy+0IA+VxW1+XuHFjBys7pTMAoY8SAvHRt5R9zlbd8MoVbGUZVv/rf2U1Z8rxW+6HFm7Z0RHqDKE+lhaic0HwzR0Lgm+BXY9hN9yDT4Uih2vodoF+v1xmtIi5c/A5QuWZ4iZb+uJTn6FTdFtSolMO0Wnx4POB115sEI71cfG0NiWu+zxTSg8+r0S9lJBgdMXvWPuj5+BT5x5vY2bNUUc29DpDdFbqduX8s1dl79OkCSbBZ/PgM4W9qUCJhZD9+MfIL1wg61MFOToCQ5iQHBKiMzqCVpQHX/Xv6cHVZ66V4EVQCYE3Dl8BAGwIXuxY7h4wUv6WDmwyn5X18RIew+mtYXlu06UdyoPPc96Zt7iJclPN30DXb1LYyixjT0+5tARAhnnw5TlRTLeIdPbBpVRUulM5IPsqSUyVGu41Vme4sP3BD2L5z/4Mg6tXIQ8c8Go+abRHJsFHer9w6i/rkDnkcAqlrqcH34c/vI1/+Ice7rknh4NrrTbjWppTvShfpeEcUYl+Ucxk6irBNxvkGrgfHpyh09rpwae/+zSK/zIq+4VmsTSq/NixHG+9pYSSM4XoFIJ2YPXdDw1yJjf0pvbKhZitvR59iQ2LWhmfUl3Hj+d0fupQyx7VwouZg6/TERoRND17cc8jDoJvYhxhikYCJFjnmALl6Ge3B59ZcV3POl8GR71RJ4SQOjnYyQAXJ64SBSq4OWNqCdFJr50cDz42XEZN6vWVlfhHdE2MbhebH/sYut/+NgYGwx9OrtdoRIyvHjpaZeNtxvWyUqySg4+x77u+dkqfVPHg00J0msgnZ1f84dLpCIF8SBQh5pRRN0mdj5R13jpFswzDK1eQf+lL6PzgB+Zyk3YYHnzG9igdluDl4FuE6GwfFgTfArsS5bUjj8jB50ukdbsS/b4upIV48OUnT04XbDVUUCzBRx4UDAccneArKsqIspLCt1/cstx6OHKWr+zlE6LTKaD7bGoRgyeFQG+DJrZdITq1ujq0QODZJVrBTYQcsSrSx/cbw+v1evqPhrJWT7kx+o89ht7XRgkv+w89ZOuS5wG5CtVKzgjmhJYrK9MwncWxYyj27SOr4+hCgsUwajw4OfhIMHsR4W2nkp3TvjDzLnhfN4CbG9LnO6zE1y9o4wwuYkJ0mi4traQ/nWnvUwArK3IWIs0A4xRlzi25soL+Y48BX4aWo4I1i4siSahpDsrtWN9rDR3Q13Dz6AweewyDRx4J+r5jz5GVsHQlrxVpUbaSw+VJfAblbwyAGhbZZcBz8KDE+95nDhVoJaG482iOITprgalPJZl7IlMLAWBnZ/weRCUCCfW9TpUpdT6nR86eiCLpb1a9smom+Cp5jko4dy7Hj78Rl4OPrcwd/58ivzodiStXhvjTPy2wuZnh8cfN37F1qdXWDHufUk1NKTIcPlzg4sWhTihNyoQSfB45+Mo1droAhoZzDSWbQ4AjCZDGQbZxT81gWTz4REEr5csgPfhShui0etD7G/ZSSKuDZuxL6nlYrYHpwddUiM4RwTfqJWXcPLknVQ4+TTexukp4jjra8L0OoDh5Ev2TJ9nV+HjwsQoJQes8mB586tzTcjlOoZNOqm6i/J5NBJ/tkTgcNeXBB2BE7hlkQzK9R+xiw7g/z4m1zudczXivnKWd7RBgOneVfvdNS8SJlLYI0dk+LAi+BXY9yBCdTNg2I2pdNp0JnB56CoQAtp5/Hktf/CLyU6e00ACxOgwhxh4JpdOpKcm4JoPJvKqMqIToDF+0/TzlTBW4PZJ85b15efBF5eADsHRgFQeffwxbf/b3GLz3PcBPR7+rob9UaGFwlH6EykyUEooUSkpl2Dn4Jn8T2bJN/eUQKcWJE9j6yEeQvfUW+teuQbxBlRofcFwDY/HgK383XIKndLP20+ZLL2Hlv/wXyLU1zeqwTIKyDA8CFQchlts+uQHINiMEen3tHr+TULbFlrndAq7cy90DRjkBZn93igEm3AE7lE+5f67H93wHWSbx0CMF8HpcPXq9o/+Xw5Pdd98QX/96F50O8I7JSMAUJs3xHfQffhiDa9dQbGwAS0sascomZIkQnbHnVFlaNo3cSen51G/X1xvV+vf0Jz8lcfA65CuSUALAxHOv5MEnagjRWcaEHJ4qy2o6DzdFJo++B0vlqUJ0Ug23DZM+Zdl0TIQsxgSRgCiFAamG6NSrsnpQJILr++e8o7m8BgbZE0/wlZShkg6/8NhjfXz9i6O+3HPPsNI3jeATgv7WvffVUr0lhXO3C/zar23ijTcynD1bWs+05cmy4HgSA8aQX74Yt0uSe6VvqgKuYQ4jX+MUJSE96wrdM2six7I3/ephUZUdpr8rRiXVENuJ10nruAV68IHOBRwES/8cgWHY8D6LldrSvuGO9oHRN5agzUH1fGSK0sP14EvwMjgefKEEX0VWlZIM0akRfI5nEqEv1QavcLZVsL4JIVjzxdiGqOoeRKdjNzGoEGelemR1r3cZodHHKLeOrtvV5T8hoHnvmfRtyeASjoUg14io8xLxG8vrntsmw4PPK0Sn4HvwDe+5B93XXkOxtobixAlefxeoDQuCb4FdiXJ4hGGjHnzVvycLn1F5bGlHbhzAzlNPMfvl8IzhPIcqPBkIiZEH3+xgaPPg84HtPMGqt6OHngyRiXSZkxe+Eagqv6n76srBZ6rw7t94L/CZJ/GjH3eA/3PSR2WcJgomYqMHoFl7hZxZjaAUMCGTqKww63SqodQM9VUtr83fz/DKFWtVxu4aJ6PdujTPLc/PHJviyBFsfuYzld82X3wRva9+FYNHH53+Rnk5JWMX1MOdafDKefksXjEsRHnwKVV1ZnPKdI8NpDdpQnCNRoSQkKX4JVk+ACbvPYDgU4kZokH2pU98YhMHDxY48D9FbQRfubq1NYlHHx0AAN74R0+lo2tuZRmKY8cq7WnGEpx28pzcz32NZziEUEWvaPv2ajg9kzn46mgnVrfc7U6JvfK+YgtPFHogL//E8uCL2Ssnf9YQ2ogKURb8an1v9CEfAo0wvGHaU4WohrgvhhCiB7GzMy0y7NIefKrcViuaDtGpKro9bq3cx5AHYsevImoaPPh6PeCTv97H6tv9WVhbgwElYDDC8BS8qa19MhxraxLnz9tz8fp48LkQJGYQTdk8wKZDFmqx4ArRqTGxs2Iawe0M0UmHcp38W5UdWHtjYx589PlFHS5qTUjqwWeTMw9Wc44L4R/iHAD2709JBvkpJ0gdDzG3NzYKvPvu6M/19fG7SejBZytSfu/O3JIcOPcZZV8gQ3QKZc1QCtRB8MWcmSM+CK7Xlm60nZF7KrXXVAxFpEBeGj4TkVUZf2VemHSkapnJvlpuwxaiM1MJdITLDbMG3d/PUPXeBgzz2HBep1lQd3VEX1hgVGbar6fGaMr77WT6s6nd6fUktn/hF9B95RXk587FCQULJMGC4Ftg1yPGg89G9lDrKakwR1iITvt1P49Asv4sqwo7hk7SBF/JZLEcZijCGtwlSLoKyywLNd60Vu23DyXUtsQQfMSBcQKz4pCOCa56VcXwPXqyZ2LClH7ThsDMYkz/KZeXITY3nffUIeeb+gTA+j5jQnRyhdj80iXkly5VfiMNDxIRfNqBTyHzAKAQWdXqPcIS0nY/71bDmmpa1FwEnxZ0nofUHnzqxU4x8xCTpjhXln7FeNSofTx0qMC+fdKujeTWqQzctMpxAVXnVhhcEU3j7zxMq+sm9X5YbsNFqk+wXAP5K0UY0LdHbOQmIqsRZiLeg092OrPRY+bgIx+NOy5jTPbp2ofJMW9DqyTTkdkqn0PIHnngAAZXr6L7yivoP/lkfQ2pc6r8d68HYDSvsmIIiV4lB19e8eDTx4ibUy0K8w7RGYqmQ3RapLHuaq+Ss5JUlpm8xwFv2aYyL5Q9kHe/+ZoaRt8+hrrxlA9symISgSE6TRFsSJRz8HUJbeb4XkoB7iRChIAQdP5e0iiG/CMeNi8U1SByRPS4Q3SqZ9EoKBN05/p1iO1tDO+9F9lKVfb2afPFFzfxn//zKpaWYA1F7QtWeE39wFstTig4LlwY4tVXl1EUwL33Vr2D04JeO9VIGdO+YtZfrgef0/NJLb+66j+favLgo9YpTt/YHnwBmJ7zNAMEuj6qmYpMWlTJwpAw8hxdaLc7e00VDz6pPEmZ/KOeKXaxYXjw5Tnxu8dGq34akmDHWNVx26Q8+JQ2jcsH0YYUtAdfrtgPdbuA7K5VDMwXmC8WBN8Cux6UBx/3SGw7PFN7h6ovnZRxuaXHKvQ85SJS2WI64Gh9lQU6kkfw+SBaUZHp1oEugYVCxF5tCZ5x+g4AACAASURBVBkw0Y7zn9HLzZ8dix1aXigVhWLtpZbndosTZogMEVgqc+ZMjh/+sAMpgfPnhzxyZWkJqBB8Hv1zwDqfmBPLNSdVwSioU0ywdCERXnHaT8qgS9HBRLkJINqbRK6uBt9r8uALDvsZHKKT1x63W0Io4TeKwexiCgsIBbbPSteDjcPbhsRSdkDfc3kfvJSEwi6gP2RxhnKhjhCdpnooJfDk38mUh4Z7d40HX3lulnLwmcIJG+H5bFr+xlQ8DoeIrgHO7ZEI0amuXXU0vP3cc8DTT4/khqZQ6tNIBpoRfDkwzsE3uj7orFC3NQuXZ0XqEJ2pJqV6JnCQ6iE4dy7Hd787OgPtX1djNVb7UmnX1L4hhJbXho+qPFMOU+24zfh3KLaWD0Sd7dj70PiaKkuw5TePHHzlxTjr6MSbak1UWdqY65nqdSMFFaKTaDMUzIOvEDqRLYSu+6DGvUhI8Kn1F8ePT6OtqE1w8q1PcOlSjs9+9jaWl2XwlkA9I2nwaLmRcxaTGG1b16+P3sfWliJP+yo7AjA1Nk+hbFH1A+qyQYToHJGMcvqOnd9XjR58QqhGGglDK1Ll2B582sGL3VbVg686vqwcfErbuhOEPkadjsQ0p175m1By8M3IP2kkLaPAmL9kxCXivRinOnEhSAfJnEdU1hv1m/Eh+EDk4BMCGAyIogu0CjUEb1lggWZBhuhkwvfQU44dXS7v69kWKRfx6nO5uxlWedWDL1WITsbZzV6YabXm20dTPGryV4NgNL1cU4hOrS8WJZ5qdaVHUBSVv0U3TQ4+Eg4Pvl4PePTRPh54YICTJ4tqOUOnuKERr11LLIG4PkqDR2zpEgDHYTQB8VCGOrezjKgvsH7XnIQQeugQD8tCCvnZsxiePg0pBHZu3uTdNG7AqM8JHI/QEJ2pPfiEqB7EOsVgVtbDg2/av4gQnSqmzQcmTclPnZr++41D91auqZbFanWmYTaOf6TbvRDgKRfy3JTqwAtUDiZrnV4bsAWMssYitTAYkXWW5ZtyzmJLveRjsD7Y2eRTDW34DfkhOiwytx0Be3/L5KkxUWRNqJvcsz1D2UBu7F1d9uArRyCh1oXUkfkoWEkSIeK++TrBOBPE9uuxx/q4dGmAEydyPHljm3/jdC0pKSzHxiXUHuRLkdFO8fxaYkJ0fuXyRzHoreH7Rx/C2xt3J3Uocu4dgR58qiykzfnKIarswUd0pev5wOW6J6QgFaLTcq5L7sdri6ZBevBpJbV7JfQoO8FIdE6hsH9/OLln6grHIEjz5lEMhk0RcLSpUUOITpP3M2edMEbF8OmPIQefqU1jl+qIFKARr/y5yCprO9xx2iBCdFb+VvVSZVKtRMAVBVDI2b2c0NwquB581LwzecjXdZ7QDE+JNYc0yI5xlSf6zNq3Az34AH1dMbbn4cFHhi5doFVYePAtsOuRdwlFK3Pht1lScDimyebo2tRiCTvffYxUtqgHHEV4nMhFmcwhymH1yjlEagrRyUKiHHyq2MnpFymM6Gc2P/icih0DXxHSHGEVNIEzMEQnS44mDuOqULWyAqysKFaCNoJP8ZySip3K1asjguPJJ3c8O+uYT8pFLV+AoIkGoPr6rB58yQk+RlWhBN9Y+LfJnepc9PaKUSEEtl56CWJzE3J93etWzbJw0rVAjURoiE6ugakfwTe7mOUzYpv0oHXAeaizzBfVWnPafCDBt/X883jzJ1/GV1bPY3P1MF23oTop6ecwjn9kiM5xo/Y6xh1Iobu6cGGItbUCm5sZ7rtvALxunzNloodlbc6EMU9HhKzg1b6nPkdbt8tyEdODjzZ0te8PahE1RGddHnxq7pK63oOL4BODwejjI7wU9hzK8kppoerIHEOMCL4Jht1yiE5rVfXBsvZxlZle/UylvNcX/bB6LOh2gRdfHBF7y//d4sGnYpLXs0zSSH1dMMmXLkyU70Uxu9dehT3KjKutMn545H788PDV6YVUIToB4K2TVwD8o7lgEyE6y8WoEJ2e+WzL73ziAFSoZ0nh8H5r0IOP+m003xyeVK5n8AHjzMuNhtEETOfBCoT+zit/c3OQ1BGi0yCvcEJ0xrRhuy7HMY/L2WacIS9r2ANMRCV3rjvLTT4wFVwPPjVaRpd/llHTh5RTGxSGaFAmNQ1gMCqu3CuRZSW5t2zooObgK49zI4KQjuFQH1+XbF/5myHrsB6N+/xkhIDqvcblg5zXCw++3YoFwbfArkezHnx0eRPxZ4b9um+/9OtSy6NgO+CUCT4hJTrF7HBaVoC5n8sMe/gYS1LyMagwb/S4+I2ta7Mz/RbSdgUxJ2zLZZkpRKi6aasHpZQKQNfgcieyrZxCrKjC13PPeVhZG5p3/Wa7ma5jNi+aDNHJSXgdUv8zz2zhq//H6KB1/vyQrOfCxRz/+N3R+79wYVQm1oNvUtiX3IPQPfimXQm1WggO0ckrx90DhKh6A5Vz8Hl5CeuuvryOlaB6p06VA8QiywmtJTc28PqjT+GH2/pYTw+S43q4j5rKg498P5wQnXmeRL/d7QKf/vQmfvjDDJfQB/6TXqbySJ5KydiyuzJEZ2lxtuYoSnAgd4XSToVUXIq9zvFLILyaZbcLMSFO+/2RRU9KJdycFD9lWL2BFA8+ABClEJ3VHHx63Y08nkVhzLXib8FrgJD6+pu0Xz7zltgLJuHdqtVYQuBRKJXrdMZ5k0p/c2E9y6mK9vHE/LsrHyfLJAvRCeDbT/wiHnjiUXS/+10s/+mf6jeEymuqMGw5m5Q9ubOOUPWj1hCJvBx8xHcl9HYq1ad25fUI0TlBtztawgF6/5Iii9IRaB2x/F3WWTQN8oynyleehI0QYJ+Xp/J0hKwOAPnRo8B3fgIA+PHBC2SZaZepuT7+7cABw4twvENOiE7iZ3sbNeXgK8MnB18UC8iVtR0efGp1FYNQJbpQJUQnI8ifK/wjFTiswmeW9SZFASpE5+HDRaRyxgDGmY+MuBSju8syqHpCzr7NDkXN8OAztWfKKauu6SOCrwUC3wJWLEJ0LrDrkXcJRStzMSTXXksVenxpU1mGMtCjX777GKlMs1iHVZ5bCdGZylos+nxC5OCj4Du2Po9nzsHnj6gcfBZlsyv0lyYMaiE6052ayNxbnIlg+QDU0IiNnfGYE8slh37gAzPPwps3d8wFue1aoCpehECSU/EDDwzx4Z/fwSOP9GFKi3f0WIHLV3JcuDDEsWOjQ5dpajahHNQ+ocmBPNSDr00hOpUcfNOydYTotMBIXgd68NngHB+TckTS18i1ylIf+akyXq7s9ZLN9337JC5dykdeBkSfKn/bGo3pkG1ipmzHgJQEnyhPYJcyjvWjuQjLgy9kvNR5WoMnpWmeUV7NZY/7qecaocTZ1bDJK0SIThhCdHrqhNPB5sHHnjAeH2IdrDMAgXoJPuGhPJ6ULbcvJcw5+AJetCv3u/1e5YdSR7efemr27w9+ELc/+1n8+SOfwxuHr5B1JQ3R2clQHD9e9awu9U/bp7kPrXTSut+XF+NM6HYLthxogUp5CD3XH3v/5rSntW+pjwjRCVRTlAwzwpgj5VrueK9tMCioIEA5oekTEnrwcdbtrY98BJuHTuKNQ5fx2umfJbtLefBVCgC4cWMH+/cXyDKJF1/c1K4b/3b0eULweX0HNebgm6CWEJ0hZ5Ly/SjvJfz+2Tz4QozQXEbFanRZW4jO69d3cP36TpTRthWu+SnEKKgHQ2lorIrxDaTMwcfx4PMJ0QmhG0cDshzsZIGWYuHBt8Cux5Ai+Jiw8SbUesryiIGZCLTVbbvumx6IJPgsB5zKc6sEX8WDz94PG+wefK4fMOq/ti/pG5U/Gcq0PlN+K2+SU6LXR9sYYwVkJfjsYRU067PA0zlLIUl9YCbFu+3jKyvMQkIjst+LZT65PmJmDr5jxwr80i9t4tatDFevKnEOEp9aOfHwQyAEcOy4xEqJ3NOSO2cCp+4GsrdKB65GtJU6JMwG3NqhkltnYIjOUILPVk7NwTdtK2S8fTenEowEX4RryoULQ/zFX4z2+MOHZw1M3994PYv24HPdp/ytLweSpVzYfuYZiFf/LwDA31z9BFlXOCRgMEKp6BNFNcyRj7EJe/ga0sD5vk9tjy5PnPKp1aIkCfWEro55M2uh6llQ52shCb6VFeD27VHb/b5fXp7divIzlUPcEx58g85K6TYP+SMlrATf7vHgy+r24PNRHpsIPkHn4PPa8Mfw2QOnhHJ3GUXWg7yrAG6/TZbNL17E1nPPQfT7GDzwANDt4vbafmPdKUN0GiudFKwpRGdlD1Tec09Z1qYeMhPS0WcPEroH3+S8bs2XOkcPvsnQVJzdCb3LyIOvnv5Rzl7e0VASgXpGVthzy+CM5gRTUcT54BgvQh46hNee+jT+7q/NOrTpnlSqT92TlpeB3/zN2+j3YTT4pPrk/G7G8kR5KjjHuUYPPiGqBoKsuR5A/PpCNeo25QilZAmV4MtLHmumvd9GuOpGxaq3mjkfIGRRWXsOHQZu3uyT7Rh/8wHLgy/gPqUO9V5f/a5Xm4RuUF1XjHa/lPcfEaITWHjw7QYsCL4Fdj3IEJ3Mhd+XeLMRdzdvbuOv/moZ1671nQmc/XWoASE9XVYnlRCdM8WgQJXgk5Vy1m44+hhnai+zDFAE+pD+MIxxzH1IaaEYk4NPQUVG8g3RqXnw8bpElnMNrhDmZ2ESfK4QnTGwPnsE8aE+8j335NAms6mOiI+OsrxKFtfGtXgKPd/ifD341DDA0wtB9TVP8JkTkJe/gaxuDz4rwWcg7yMIvhMnCjz11DZ+8IMObtyYebxSuR0qbRqegxp/aTE8MPWX3M8YyoXixAm8/sufwV/+5x7e2XeSqtofBoVDZYxsjTTwAZIySQJ4L2fKDVLVcEx+txAb5LtnCJMVHfLUECexDzpxwE8N06OSa2L5N8KDL3ln2oByn8oefHLkwSf6/anMXfbgo5fJ+mMUBBmCKPB6Db4HL2ZVdYfoLA4fdhcaY+LhrxF8WVaZ/jEErho+jzNX3l07ji/e/yt4+DduY/nf/G90ISEwvHqV3Q/1XOwD41rC9V7ijhvXOheA+oJUDz6V4KvcGhCic7o31rl2M7XKo2Kavx+A6hAOM8KYIyHB5zIEmeeyT7bNCdGpkmTq3yqpaXjIkNzaJrjEQo4HHzD6LDVyz3edX11FfvIkOj/4AQaXLk3Lj85uYlqHtZoGPPgcP4cVpMpwc/C5QnRKhVRTmi3nOMzlbG65okFR8PXgKzAjT4Wqn7AQicbffOCYnxKGMy3jvZj2dTrVEGP/ZM8Fap2s/mZcPigjdWHKwddCuXuBChYE3wK7HnlnKVhHoq/v5Yr0Sk05+ADg+vUBnnhiwNqHfLkC332MFBYdOfgmyIocHVkiH8oKigg9QJQCYNy4YMhuvmPrZQynCuSGOlnPqr6fXg9yaQnZ7dvYfv/77RVaDjsmD77ZdfWgF3Zw2tiQOHw4x5tvdqY51lRowoyN4CvfZ+mEqkQsak60buyK4cCcXA5NpPwCkDYpPedDUwk+Q26AJmD0ZgzN6RJI8PFh9wIv/17x4MvjwivHEXyGC5EhOh95ZIBHHql6u06UipO1wmtvotbTyMOiEGArF4rDR/DOvrWUzRv6RIdCjDKKYG52TYVejCX4Knt6yYMvZK2SQsw8BB0vtFD2C3JtTjApmvDgm9ap5CWVQlT26z0bolNFaZDLcvdkbZ6OA6qeMKERzaNhC0fL/MDawLPWTfD1H3sM3VdfRfbOO9h64QXt+vYHPoCVP/kTyF4P/cce09o3hYf26mhFqV712GZ7sWcddHvpBiZpiM7JnmUg8theTgq0s4it0+V9XIhKaEoA/iE6icMimYPPVlXqD8xK8NG/lYncsmHCBCZvjyA45PI5BQMxIyAHX50hOtNAkjn4pv8MCi1lx+YnPoHOD3+I/NQpXhdVXQjvLj9oxI9nK66QmaYDAJ9BrP7V9Xsvk1yu6jWXB58Q+li4ogbN1lLCM9R25q1BwOCkZRgO9Wek9FNsPRGBOkN0Anr/vUJ0QicgRwQfrzsLzA8Lgm+BXQ+RCcjOUuXQzF0MyRxVlipc1ikBZzTWdXe9xHO4mCxmiM5UHnzRAnmnAyg8UohMZOBlWBWlzMFHEXy3f+M3kL37rmYp7AqhZiX4xhdnZezCoM8cfumlLXzvex2cOzekb1bD4MAvHFz5vum/ayRWrF1zfJRSG+cZ2IqX5OwgsG9fgVu3MnS7Eisr6Tz4nIoWiuAzHHRqVw4KPY68keBjomkPPtMYZZmsKv3KITpDCD6X1WYIwRfhwWeCUxdiIp5M4dF8Nw525TqS62cMfSuPiTX8Vw0fIEks1XFI913OVBK23CdmDj6rzGAh+CrKfochTjAcC0cKjzBTE9qayCD4WsEMxcI25ooHH6QEdnYgxutsOQJJDcskD9Y9sAaCL5CkcVZbd4jOXg+bn/rUaA0hFvHBY48hP3kS8uDBqTuL1r4pB19AR31CdFrsGqIRF6LTML+YITrZ5wmfw7qyPmnLmq8Hn9KuECYPPksXY18Y++ALjdSf3FrWf+QUwVdjiE4V89w2yLYZ3k7luaovgZI/RzmROZgD5Co28+ALuFkjxqqoTLPJH70e8rvvJvswqkOYejNCzSE6y//gz0FGwQjFgcuDb3v5QLUJjSyfGYpUDAE5IToVqN7cXjn4pKyQfJUzRA26EWd9IjxE5+z59Hs9lmLPQno56txl3K8N+xm1LD3xRB9/8iej8PKPPNLX7ltg/mibDcwCC7DwwQ/Okq4+9dROMqW/aw115dYzIQlh5wEh3BaLnBx8QiBZDj7bvdo1qnCnw2rft49GaxYC0mAtO2nTJwcfeTBdWqLDADmVdqV6HYmR1Rx8oSE6AWB1VeLy5aFquD+Dj8aqMgktzxsSojOC1GIrYCxCf5RSNVKI/cQntnDjxg5efnmzdqvX4sCB2b+PHNHWHCvJUDPUYZzmGggc33CCj9ce912NUpNWCb7pI4Vo3iLmW1EY7o304KMQalwT7CllWXMnfwumckHfc+oJw1fpIydBk3el5nupnIUhxh3JYcnBJ0oEnz1EJ6MdB8Hn8rRPhdDvxAaTgoJcE8vCQV9XBOwJDz5VkVn6W5bk504xREcOIYoCQgBF1kGR2SNkNPLJWPaJhQcfUaFpvIRAceoU5FrVO3sCKcdGbqYw0Z5QvWvsBJ+fMt4HMSE6jTAtXKEhOj3JiDJ0Dz5P2ZGQHaoEnzSECZfEv9LANN9sevTy0JtSoyT73hxyhs+5vQloZxtPmVIIsJOms0IqJ3gRQtDjHGyUENinCsHnkj9rIPh0ad2X4HOAoxOxQJOhMoG/v/cXkXd6ePPgPfjRXZes95tC+YfIqO4QnXQOvpF9Q9UA2UnwxYKVg0/5Rg33GbvHOC9xjuls+YDaa5ghOk0G5tRWfO3aANev9/HII3287307+n0LzB0LD74FdiUefngAOTZ4uv/+AeSXloFbt2YFmIuhTflh4JiSwPesEVReVTpYCD/1/rIXiK2cD2LJBZllLILVd6x8PPg0gTwGPpo3H4JPtaJU46+r9yoEX9R70iaSRw4+Wz0lqErEunPwTX5zHiymz2U5EHkihUL80KEC73vfTLHqQ0JbQYzH1gsvYO13fxfIMmz9wi9g5b/9t2qRDl+pkBTjBpaXJXZ2RkqItTWJIqZxD4JPCOkdHpW7B3S7yjyRcla24Rx8QzpSby2eqS4938mTJndCAlnmvXGQxdkEn7Vqf5QOycZ6S4dlCUURF9OBSOVELLz1OTZXlhLBZ1qr1FtCMTHEme4vJs9SX6jztAEzzmmThKVPxYNvZ6wIuJNy8JUWqqwYopvvTIsMOyvKbfq4NBGKziZnqGGzjOV8XoPvAYeJrAmCzxNa+1kGUJFAAhR4Lm+JMupwbOG0G4rkOfg8JoJQQnT2VDFqvJdOvpvKcuaSdYTuwWe6N9keTcESjs10RijPN5l1UGQdZEXVKCZZN+uwTEkEqiuckKumqEmzOpjrYkty8HFCHFagRi0KimbimAcNevCx4ShujGwU6sHX7eAHR6/g9cP3VQyITP2vekia69XqIepyLc9qdOOKoYPq2FC3B5/re5v8rZ6vInLwUR58LANw7rNqfatG+BFCmrtPhvekl+JuF7h5c0HstRkLgm+BXYluF3j88RIJFehN4XvODD3IJCHsfMu7yBYbwZfTYd7qIvi0eg3CzsGDEmfODPHP/9zFtWv9JASfj6zsCtE5EVBY4+TTsHPiWQQEpTOucA5RMpPaFiUZcBTClg+gaYKPXdigZAc81o2aFF+1gOhrcfw4bn3+86MH7vXaE6JzjKtXh/jRjzIcOlSg0xU0wcc8dfp48Anhr9Pmn/Wl8RtgWfpOyk7657uAlmA8WxNrnU/fKEwsi005+K7cN8SlAwN0u6N+vfpqD8DIIIgc3EiCTwiwlQs1ODSSqHqZOzQ5XAQqVEmZJAk867R48IHpwccyCiKetfxTUx58deTgM61N7BCdqTs0b9ieoRyis8jRGfaBbELwVcerjSE6ayH4EqLyybWc4Jvk4EtF5ushOs3vamWl5BmRIEwv1Y8QGOUcUw6+JvJiKCE6ez1+Dj5SFiP2Be0s6dgvkiuzPeSvya2qvVjeWUZWbE7/TkrwOQTgeebgG30/yr7K8eBTz8euHHymwZxDiM6gF5voPGv11lTq7D/+OJa+/OXRvx99NKg9VxveHnycgtRawmxALTfR6ZTJvUoTVoJvds0so5rfh8ubWyP4KiE6i2qITtfjp14Tievnzw/xnddGnT50qDDeZ6yK8Q2kzMGnzQVhjjxm6khlKxS0B98C7ceC4FtgTyA0XJpN5qUV9WkORqkJPlYlqlBYuq6RXkVJEVO5LybUYeTYjQ98n/jEFt56a6SkN3mM2PtBVut9M6nL89Hi+5xQNJLOrrTbft/PYfULf4nBI49MyW9KoAIAdOx1RcHHg882duVOhXzrgR4D/3LsGh4VXwSA0VjaEJKzSUUTklNdHnwTlL042hKic9zXtTWJc+dGSvxtU1Hu+PR6CTpmBncPGHnwGUj6Gjz4bKOT54Z7a9DG6IeOas+WloEXXxy95a2tEfdWFALvf/8OoDr3pfruAkN0RjdvMC4w5uCLUPaZ2nb/3JKQWsr3XQlVXhIobGsV6zDPJPgmv50+neOVV0bf66lTHt6njs4JgdqHfvLtkXK4IwffngjRaYFUPPh6xc6M4OtWPR4ZOuF60DTBl0jxq1XbQoKvDCl1+dekdDWiVM4nRGe3C9x33xDfwRC/+qub5oIBaILgm85CZ/JdZkM2qB58CsEXJccKQRN6Utr7mHoiGz349G9+0rSqvB92euiV7Kxl6b+x8Mk73wpwtOCWTpsIPmocmg3ROW6zQkaO37Gv3Gjrk+XcVSEcXOeTu+7C5i//MrI338TggQf8+meC4d0mI/hM1wNJnaznWJA1gq+Ug690lnTlY1fJI8C9F2i2EaWxFDDn4KtFhFU9SonN6MMf3sZ/+k+r2Lr6EO7Z9yX0H36Y9V5M+zr17daZgw+oPpf1/ZBtCO1s3bq1dwESC4JvgT0BynKYA98QnU1Zjfnq3/R9SU/YTFk+yiyDKIrKBg8A3Zwm+GrQA9LXLIOfZcDhw4WxmD95ahAdCDKvagkTJ3J4hV90DLxa1eDGzyC/8QQdEkQRTtUQnVGiFIc9NT23YjVrukf91gvP0Ic2qM2+cu5D+ODjy8D5QyiOHbMXtgj9dwTBx3hw1Vu01Kk0ffIBZx6G3B9X1PueTseiGKojB5/lem7iJWpwWZtV6VZurq7OyD4AwLvV65ITolMBOf2Zc8fHsTkGlb1Na8B2zVopq1hTxE3KaI+VCWwjPQIJvjLUXLmHDhW4554hBgMRR/CpXcpEhdBOMc9MS79UQ3SKqgcfdmoI6dMGbYNtLyzn4JNDdIfbQHdUJFfyWFFTbipn1vmcthx8NRB8KT/Ztw+fR+fd7wAAfnjkfu36/KdHicyuyYNvApeceehQgY0zQ2ydSBvGLuYsZBTDEofoLA4e5HdE8+BTihrlWEN/bHtv6bLV2zp2IjOVCpQcMyP4qmXVPHxNhuic53dNtq3ytY55IES1DEnwmRrjyPXMAXLpZawefP7KFlafrNUId3j5/Nw55OfOBbVl68BMDxR2f2i7Lkio52zTfabQu3SbrBCdWl2+Hnzl7qmGFI5GU6+JxPWNDYlPf3oTwAews/kzlfy69qoMcps6f5HWg89oQDQGh+ArvwbqHDd/mWoBDhYE3wJ7A0TuDw7YZM8Y6+thB5m6wrN5VUKt7J0O7XVQtrBuQQ4+kwI2xZ6fIpz99IDmI/rF5ODjXDYMuLphq9ZeMe9YPdSYSGUnLJ0IJfM5UKsa9laxdf09WD5IvFeDlEbPyUAFSJslKUbf1Hc9Nw8+HyRlDEZIQfCZ6rB58MmAxa2IIGZMBF8dOfhCDfnJtgMYNvKRIkJ0RsFgXMBVEEbl+jTdSynkaljPvGUr9R0ZQnTaCT53o9SYln8qiG/25MnCfAMTmnd/hhoIPj1EGQDSu54K0Slsxjy7EZYPT1ZCdI49+MZFBgyCr5HhESNPEcp7van2Q/HNB5/H8uBv8NP1k7i9ejhl1Umg8UZZRtuyBRyQpmGqGUYudSLlfjYdDxORF/iQcmMDOzdvovvKK9h58kl74dIeIYVAt8sM0cnYC037giA8+JISfApMe74Q+jl2UlQL0Znp6RKSzUHH2M4zRCcJToe0Mg6Czyb0N4Spd74r9Cx9s/Vvbl5yUwjJxqAay6cO0Wk6f4R6J7vO2ZZvqRqi019X49oLJvNpRvCJ6e8C0izQ13F28PScMJF7VjC+6aQefMQeUvXgM59dyCsEITlvmWoBHhYE3wJ7AqFKf5vrMVVFryfxwgtb+OpXe3j0USKfSCIYrUGY5UlhQ0anjgAAIABJREFU0UTwDQbY2JB45x1D5V0iSW8AfPgr7fDhJc27lG/V60aBxKGUjuDjHA3rcCXf5mzAU4FqKrTRYTaSbt4+ripMDz5ViVig3pMedzxsKcxWVwMbq0OSmqcHX9esVKgVRANGUqOGJO0hygg+wUfl4Bu/44Y9+IrCcK0GDz5VualVV/OkIt9PIMFXV1en4ZWEUMLdRKwzAUpN3yZ84P25WnLwVQiOEGME50PO6ldDdNaFJrYUE0khhkM6B99eD9FZHmSF4OsMZ16MqgcMRRw3psgWIlI28Lg34SQcrOzHd859qImmgqASfEa5g9vRirKu+lvq3HpcNJmDT4PHBtC/fh3969d5HSnB6MGnKoBNuZA5e6GU5kdcX4dcXzf2jwXmmdp2dtRDdCoEX6MefC0J+T0B58EdZUwKdq1cwhCdLj0G5cHHNUrQnidwclibaWKBj20jlOBjtqsZeGpRmZTqbGR5xRjB34PPxT1rHnylNgSqxgVZR2C6ulPfgb2peHi8d+M+xlh3OR7wbCNMUp9HyAyMe4HRnjZvGWqBMCwIvgX2BFLl4OMsZJcvD3H5sl/yt7o9+KjyTm8qjDw8BIAzZ3Ksr0ssL0t85Su96j1UmMcARCkqDLtSiEwUwx06k9XG5OCLYAxjlHhZNyHBp95MHdINDVit+stCZ405+Lzmk2Ei0QQfUwmxmwk+CmoOvt0gKbbEg88UxklFp5PGg2/y2E6FO/NhKoeWGlxTfMOTWZFl3l5sk+LfPfkEzv5glKez/zM/w7y3npwGtkNm459ehMLChevXd/CFLywjyySuX/c0tFK/b1OfUrDylstqiM66YA37FlqnZZuS3W4ll2ElukZff1fRBF8b9hSbvFIh+PJp+HshgKHiATM3Dz7AGM0jgHfyR8TNLkX/vKeHSvBBCNqWLaCjSffACDQSotNYML08K5w5+Og+sWQIYi8WAqBy8L399PNYWnkH+cWL6V+urT5DiE7Ng69Jgk/BPL9rsm3G2U0ql6XLg8/UWMIQnS5QOfiC21C/F2n6w3LbHF68y8A6Cag6md+8ev5TdTpqE1rOvtL6XX7PITKqay/QnVhL34AszIbedYy5Y49JsrMQcydIBxkhiJXnhzWE6nhdkepCpdfI68sCc8WC4FtgTyDcg6/6d3mhrVE/5USsPCEEcZPFi2KS104NsaYesmLOGLZ7Xc9nUlSnIfg8QgKUhZFY2cOLhUuRj2AcGkHZnLNOSF1MUARfrPWhShrV4HFVhqm7JutEqusrK3dAiE7GAcV0Xqj9MX0aYCiMfJXSIc/HvWcUotPC/qVumNmxijKI6kekwkq1LNa65WMwwVTMUfjm3TfR763joWcy4MwZdz3wsntgwTRjq3sU00PahcB7SZkkEO95Tx+HDhU4dKjA/v2ea6vFg4/1uw0eQgHHgy8qdOoYte7vRJ1yeblC8FU8+CY5+GowopgrbN9E6WPPiiG6+SREp0Te5eTgS9ZLOyIb8ppXDco18xah2PsSs6PlNaEtITpTRgycPh5XdqlD/tcIPuW6KUQnRy4kFLwAACm1fbzYtx+DR6+46+SAqVUWQj8jTiQMjVBe1fUuyb43h5wx7+9ahZaXMYSwSUnwMeESk205+LxDHHr2bQKVgGqc8NOeI47YZF/n7gmOtCsuhObgU0lqIMCDr3y/LLSyNg++6HfvOX99MHs+d5upQ3TKLJvqxdR3ZLXrGL88NQdfE/z2AunRtijWCywQhsAcfDbeJOV+Mg8PPu1Haud1CIoyq94Ts7BHhdSo8eTqwze5DFu8cvCpFdgOqo4J4fMMQhGi6vImAQwKSkYDVos59dmH6ULlRn3zVg++wA7UIUklUq5ywq9oB0CPsEBzA0NhVHRVjY8dR47MLCf27+cppLhTIcuknmR9UtZD8/bggwMAI4H+0CGPtciA8tZSRw4+1RIxan8JYNgmxYfdFfzTmfdi8MCDQWNTJ2zKmbqVJHXm21xaAh58cIhTpwKUu8r6Z1JSaQq78jXTozkEyPJPE4IvOeawmFaaVLThrhCdsf1tJVVYfqbSGtyRw6oHHysHX0NPGLk312GgkALzli0q5Pc4B596PZTIn+wj8yb45hqis46IFMr6pHrwwRCik1W36bKU2p5Z6/uMyPk4QXetKl8WCT34tAhEWl/StBMCqm1W2HOXUM+4R2ZZ2BnaADbBx7zHWjBwcsx7DfeKtkTezyP+VXDfoRbBxdOQlsrB1+vJIBnVtRfYPPggZUWHVnmsRGfInfe8Z9RUlrkjrnjUzzoTjNtl2loE98V2r/X9UPoC19lxgdZi4cG3wJ5AHR58nPL1IY50EQJ6YmBDiE5rLzopCT6Pa8wXE7Lnq9d9DqdaSA1DnUHjZDuoMsKVPPfcFr785R4eeWTg1U6s7Gq9OdSDz1VvuYnc8bxAoyE6KayuMpUQu4jgY/VN9bbszEky9BHUGWWKjh/B98wz2/jt315HUQC/+ItbvH5oHaHfW7dLFh7d4fGtnT+f4wMf2MatHwxwQfiFoKZQUQbVkoNv9P8kys2gm8P3aJ/UpCwYbp6SA8JhWe/TuHpgNd2r/C6EZzt1genxYbJeZsNxMG4sRKe2Bcev/7ZtSpPFS8Z3ezUHn80gSSoefBjLK0IAuWJAN08PPpll5JvgzhevTzvhOuAr7zcNlTeSELoIFknwzf6eD91dR4hOLsEn6vAG1gg+oCwRTYm4cZ+m4c05e6FpLyaeI6mRDDM09ehnWrbp95U9fUU1rL4zPPhI0ovjwaeRlowzZKjCItEATdb/2kN0cm9zEL+1wPXe3BW4648gsNT+2IzTqHorBN9Y7l1ZkdjcVnSIIoOQRXVPK/0hhGQ4qSoGdmU5W8qkxl8U+jduoDh8GMWhQ5D79lUv1qF/YdSR1INvUtbgwcch+BYefHsDC4JvgT2BOnLwJTIYCYLvgkqW5wiGZELWGXQCMFycqiMHHwVv4ypTvxwTgD7URhx0bdIuY0JcvTrE1atmxfzklokH3/TvlJs3h+DjNODRqU6ezoOP0xXjhfHfVPlggq8O1EXwUX3XQvzalAoNw9Qoy4PPb785eFDi85+/haLgO5tzifdulzjgidJFJoQAHn98AHF7gKVXHAUZqHz6xPodG37Q6RzqUb8MYNhi1k21bKooY0abA0WpyLI2D23U1EZsO3UhoQefFGL21I5nnYxP8iHRlDf1K0ZtBF/Fg297Gyt/8AcQg5JRThvnRCQqa1vZg68YQhRlgq9qKEKRaXsyRGfSm+dWdRDKCs3JthO6F07PIBbZk42Im1N6pE+6wTZOqkOeJXLwlc2yhNGDzzCGZa/mXo+WHYgcfEkJPoeQUdx1F7K33oIQwK21Y3r/AGxvV/ujRrIY5eBL9D4cAvA8PfjIT4XzqlzfWEqCjw177jsqB9+0S74W8TaCz/IdUx5mjSLWCjrUCiUwB5/oOlLaaN+SbnC1siJxW6m3yDro5OZ1hNNdtUw1RKeHDsz0mwvdLob33ccrWwfBRwwSa8306UtWlTGosN6mNiShM1gQfLsTixCdC+wJSEVryhUx9bW2noTtsSE6g+Qo9aaAPEgpPfi8cvCpgqDlZt8QkzHKVJNwmWTDs3QsRS6eCbQcfCk9+BSo700CvI/BoxMpQ3RS3z/3wDp5R/q3KLG8vAdz8DGgvv+5efB5oNi/313GM0QnMNLx+ESS5grVnY40exuFKAMi3Niffnp7+u8Pf3j27zq0MdNHC1FuUoPbIMGnIlpHaWg8WBGToKw6JVuzlDFz8AXtuY57KpfrGhBVSVwzoTf6uzSmqrFdp1MxFOt97WvpOzRvWAZZ9eDLiuH0loIRAr+x72aRg6/29icefNZCHliE6ATk2lp44yYoXiTdLr1naGEkDX0cPPgg5Ooq8mPHMDx3zkjwqYRe0rnrEDK2XngBcnkZ+coq/v7i82Q/VIJPzSEqE4bo5EStmRdIJTnnIO0oI7Vkj0SZpuK7o0rwBW1OxEQ/dWqWruDMmRwcVD3MGpKjylDfgS/JGBqCkflsk/PfpHjWDffgm1xbWdFDdEpHyE7O1NQIvlJfhBKis9LPObznWgg+oXvEsT5pXw++aqP8tnq9qgcfMQbzlqkW4GHhwbfA3kBgDj5VeV9eF+d52I7dZ4TQbyJJspaE6HTCSvD5efVLyeyIYwLom7TET/edwpGfvgYAyI8c4bUzQUQOPg4mt6g5+BoN0RlYj+1omjJEJ6Mr5gvTv6ttrazwZTMttX0dC06TITpVgm9eHnyOb3nrIx/B8h//MYYXLqA4edJZXd4J8xj3AXdMqBCdkz9DlAHO2WHp2IMPDpBlwPKyxLlzs8M7ufdEvnRVyRLjwZdiAsZUURS1nSWn/7AqReYl2MwBmqe8qU+WrrIeoyUH41qVxkSdlJJSLi9DbG6S90Zb5LdgTmko96lkET0K0TmLsqB68M0zROc8c/DF3OyrY24aVYJPxOejLZVlpjiuHXEhOg33Wg4m2x/+MJb/x/9Afv488lOngts21U+F6KwUVby79+2zP39+5gxuff7zo2eyvWtVjkv5Pl0efEeP4tbnP49/+tIStv68Grpu0q0TJ3K89tpoPVtfL1BsKh58DYbojJlzsSCfkSNTWQZHCADdLraeeQZLX/kK+o8/DoA4A3I/cuaLcBWzevD5Lr5ZhjNnciwtSaysSKyv1xD+uQbYQnDzwBgnDiFsu798m4HgM703ykNyeVn3KFaJxHFl03+FEHzl+2VhCdE5D4VsHYcy4p1yXrOPLqgcbl2I6r2uoD4jnUElIPXcjosLxGFB8C2wJxCag09F9bZ0AmSsB1/IIVbbEIiV3aUALpTrMQdIuwefQ+mWMERniAffVGFuyMEHAJ/85Ca++cWn8MDXfhtyrYftF17wayQyRKdHQ0pVGq0UXrXrvalsLLcey/NyQnTKAweAN9/0btbatEFwU39eXXU2a65zt0tSag4+18FjThheuYLh5cvsjoR48PmCOxU6HYnCfDG+YQ90OsBDDxGEew2aa1XxEK1gjpyEvrefPz/Et7/dxV13FThwIFLWMDRuCtGp3VIHGWognecOVQAwzUOHUZHzQgoFW8ig1akkppuo6sapcPlLS4CB4GvPxEgIC8EnpF8OPrYyNRI++VopeHUv4bNsbNQTdSUVVN4oSFFuQJQXe0KkdSoav091PpbW7cHDD2Nw//1eIch9IDSCTzkzKV07fLjA0aMFfroj8fLLt+lKS4NErp9EiM6UhC1ravR6ED1ziLbHH+/jW9/qYnNT4KMf3cKXf1cJx1ynB9+8P+QSyPfC6a9KmBBvZfjggxg++GD1nvJ8bDQHn6zIkBp8J6gQ6HaBU6f8lDBVMncO86CBuRdl+CHt65OrusqUGhdaWSGacYw9Jwesqm+q5uAz30deSv1e6tC/MOpkfUaBZ7TRP8t/O95RrwdgZ/rnIgff7sWC4FtgTyA0B5+vBWRTC5vejq8CUD8sBOXgyzrW6z6Iic2fMixFVL6j0gCoc+Xo0QJHn10FnvlN3JbS/9Rr61iCk96k65PXMPm7Vitg6tCTOERnv+tm0Lafegrr3/sekOfY+qVf8mqW3RWDkqWOCEK7Btr6MifJkPNiPfrmm4OvTox0XAYyJUQBFmJN4gK1FiaeC1p1tvqpd1/Hc1vw3HNb+Kd/6uLs2TxZ1SZjUUkQfGVEeQqb7iV+r8Uj2RdcD77YjZCodx6P37jOgpDFQ+XzXQPLgFRCdMohUA7R2eF48DXkqbJLPfgOHLAL9PNfckr5jUwhOn3WGk15N6sz50W8m6L/6KNY+tu/Hf177DEUgjpy8GlQz0eJyb3KV1ZqSwrdg09OrPZKMv+99w4hV4a4ddp9wNSXC/obbzIH36wv5t+Wl4FPfWpz+psqB2vhEyPg8ppqXw4+90abQv5JHaLTLfaO5mZQaGEf4cOiF6gYSaTMH82FlmrEs83As0V4bla//phy8JnuM72PoBCdZQ++vJhviE4VNQjLMsvYR5AKAmUEwO6YoEJ2u9X+zV+AWiAQC4Jvgb2BQA8+m/xBVxF22K7bg0/3yoIulIR4UcwrRKdJU8ko6tzAPN6FFKJizcnaKENPHjV78M0IvoL8PaJq483anAvVFil/b37sY1j7vd+D7HTwyrkPOauTGxu49fnPQwyHkOvrvD6MYXydzDnqRfA1YCqlhagLrsjdN/X9Z4YDwG6TIbf3Haq9De6YdLsw5uAL8sqo42XUEKJzMo2TkEau0Fk1YHUVeOCBWSiUFOuu8dNWCL4YJYnWhEk5QZHObfjQlUEyzp9O5LfDJPiSD4mqvOkQ76HGJskQnRaCLzpEZxtRHpASEdEp8grBlwtVvqZyANfTRQ2RBJ8XElZ68GC7Cb7KtyGre/X0WqJO+hJ8O08+OSJ+ul30n3jCWrbTkchzup8xZAv70VPJrQFt9XoSr55/Cvd+54/x+uErOH34CAAirnbEexSEB1/SucscPx8j51wl+JDQg89R0Ty/a9qDTzn7cjZ7zkMIUX13DYboFMKeg68pg61KMyJhGNigDjRH8LHfoSKVm6aIab+p5jgc/bG87L/ecjz4NOJwenYR4+eYY4hO9T3XQPBBiLCtzKcvFr2bc/lQDGcWHny7FwuCb4G9gSyDXF2F2NoaKTWZVk6+/MO8FrYgQyn1R2pMnCE60xF89hCdETd71hXqwaeOaXLZwrbrJwxX4hIGkz4XFaIzBMp9+YULuPVbvwUsLWH7fz8IcN7p8jKkI1enlwxpkHrUn1dXPaS53SRJcfqmvv95md0mOBzcXjuC1e23sLO0Dz+992fBm3T1o9ORNJkC1OPBF4A6cvBNlsskhHWbvzMPGC1DXUqRulmfmpoIgjpIJmIjNkSn/+UENxBV1JCDz7ZNUWSe8MkvHNuZecAyIBUPvmII5LMQnTwPvnTdtCKxZ4gXIt6hi+CbN9RHo4wEvRSJFlnGRMAZsbKCnaefZhX9lV/ZxBe/uISLF4f4/d+vRs2I+QRta8ng8mX0Xn0V+dGjozD7daLccHm9EiMPvm+fuoHvHX8UeWcJj+OWfk9gU1NIqa/VLfPgUyF7eojOZOuV42A6Tw8+yqua9a5CiCKOHodzXwAqBB9VtW8bgX2yvusm9v9YJQmXyHW1a4LkEXymtqjy+/bp6wWZDoFBHv3cz+3gz/5sGaurEg8/XE3hUP4GlnpKm22Q7QJh3NuFCNNB+ix4Fo9TVzWy2wXK6xLxDnbxa7mjsCD4Ftgz2Hnve7H0F3+BwbVrbMWmulCVF78mDEZMiNXzCwF9JScEQ1Xxqh1Gs2Zy8GnwEGr1sbErfL02V8V6zsfVnQPVQ9Daj0hMqlA9+Gol+KjKEuXgq+PAH/PNTwQ5tXyUB18daNISWl1fMoEskyiKlir/LXj90BV85+R1DLsreKCbA9ied5cA2D34gpS2dWhO6iT4TOu9r9LUsz9NfkZOTK1gzddrI/hM97b1o2aH6DT3n0PwcZX2yYfJQazWzuUSBF/21lvGe/eEB59tkNUcfMVEwSX9cvDVDJO3dy3tJ5yUBw/ushx81F4duxeOv6Hh0FEwAsePF3j++ZHM8/u/P/s9dQjZ8lBsP/ssBg89hPzUqWZfpJKDb/Jp5J2l8WUBKg0Gn6glIu7UnIOPW5nPGlT09MhJyV6T42B6330DvPbaaP28++4aJz4B8lgbSuJ43qOG6MyPHEHnxz8ObstVTM11HdJGLCoeZnPNwScxOnGkJfikqQz3Hfp68CmgcvCtrUk8++wW1v+hwKFDBb797a5zjpuOm9ev93H27BAHDxaaeFjeD1eWJS5fGiD/icTZs4o7ehMK2TqEZULJHETwxRgBlf52VtPrVbY/yoMvNJLdAs1iQfAtsGcwuHZtRO55wLZwpdxPYkN0+uYGFEL/kYzd7lAAS82DL3xht42da1xtoeZ89+TR4SwMVUuYRJ4jDRF8U4zbm1SZlOBzeUfU8Lx1K9o5ytzy3+rPq+4UgQGNR6DBEJ2UB+e8FW5TBHRk0JuwtZ6xsBLA9NqyDFpS9an1YAjBV8cLooxLUrUTMJ/VO+QcQnSqiNlbGZX7/e1Tl89tbfj4mR58QdpVx/PN4/FNa0NUnZbpQ3nwySyrTy3XhjmlwkLwZeUcfJk7QkZjniptHEcGyFxBJcz7sTSCL7GRYBkHDjSveKshVekM3S7y8+fjGgjoiHDEOp0SqYH7KHmblPrvCb/9/MwZFAcOIPvpT9F/+GFL3/hhgvUcfDWG6FT+vnp1iB//eAfvvJPh5s2dRI3yQOqGVIMgxmLOUkU4lEFbL76Ipa98BcX6Olb+5E8YFfIhBB2ic/pPpztQmvWoMgRNWCxZOxAAl3dnYiM551lCMyTQyy8vS1y4kGP/g0Ps7ADf/ra7W6bjphDAiRM0q1X5BooCDzwwQC8fQAhgu9zQXITneoTlEB2kz3lZ1Zf6e/CVIMbGLOpPC7QeC4JvgTsa6kboS6Rx4U/wxS2oQhCkWAKC7+BBicOHc7z5ZgeXLg0Md9HwIsTUB/ZQVLvGKjREJ6tyX2RZWIcCBOdJ14UWWjDh5u3yjmBWzs3zNGoy3TvRz1IeeRHH35v6c9ty8DXpeqQJpdkohI+qP6lbYGwqV8S8YMyz1RaCrwbry0OHxoYK0rB+7vF3TsG4lbgOaTFjZbhXD1nVEqvPBAQfa7jIQtQYeOwxHCj3JA3zZkA5XwuVg2/n5k2s/uEf0jfvhe/UYb1WZN0psdcdbk9vyTN7iM7t5Q1k2ZsAgOLYsXT9pdBkiM6Eco4Qo/OSSQ5s0/TSPfjG340PS6Y80H33DfA/14CTp4e4eLFZTyYgftrMQ1/vgtguRWcgOrR/vzReCwbhwZe0/izD7U99Cp033kB+5oyxmN4F8/6kEXzQ5YxgOL4JIYCbN/tp2vIE2TXOu7O8T+M4O/Q48uBB7Ny8iezNN9ltcYuN1lbL+trQx1o+h8/Xgy+4grA2EofonBp+agSfXnZlhWiGMGKON3pXLGDK9bsIvtTzr471l9CBzNeDz/GOul3Ngy+mKwvMDwuCb4E7Grb1vIn9hAtXu6x9iRGiU0WhhOgUAnjppU38y790cO6cnxeLS5C0IlbZVkIMwZfc+jbU/DWCpFFDgtbqwUdNzEQhOlsHA8G3Z3PwcUB48NFTvmbl/zwTdjSArKs/n6yY33qgJoJPZlk1F1c0wVfgySd3sPGNApcv+BmbkP3z7E+rQnSOwQ3R6WNAQdXFuZfMC9mC9UwLiW3qU+y304JnBSzkfwRUJUHZi4oiooZXr2JzZQVrv/d7akXxnWkjlOcqE3yZzKdFXATfl66+jEcf/nPIB89DRoUCYKDJEJ2JceCAxNtvt5Pgq6y7Jg++iE4eOiTxv/zr240Q+RRSi1bzel/FwYP0hXGHfvmXN/FXf7WEK1eGWFuLI/jILVRKjQdILraurCA/e9ZaxOc8KDrVwlkxTPb+omSUmkF2hTNw2m/uZ1LlUs3DhnmfCZxiQlTrm/4z5QS1CNTWPs5jXni26Sw+YVID2+GE6LQRO1T5stGW3g26rhBjD6kQfMaUNfNQyNZA8MksCzs7RuXg41ejri+SmJotWooXsGBB8C1wR8OX4GsKvgsqqzy1snsSfMAo5OClS/4h6prKwecaq1QefEnmR4OTbNKUOwdfhOZakVyCa5rTx+c1lwyF1Z+9PPhcbaRAkyE61cmVJQzh4wNq/YjoyIEDMYtIGGyvTV2mhcA4dmfYM7Jzg/pA9VZOMBF+9mf7WP+HHWTv6H21KjgIq0onap64KaqfSw4+nzrbcDJU9yhDn1TlZeWa4TEqdRGFGhkSpUJ9m4r/rtU6Kx58GxvYfvppdL/xDfRv3JjekF+4gGL/fmTvvhvdfuugKnEIgk9Dlmn52NR3c3vtCLY/9BS6++q3JtitOfiA0X789tvtJCjV9gtE5uBjritNITZVwbzfzwTD++/HztYWel/6ErLbt2cXxh08dy7HuXNb1ZsC57GZ4FPW7jmQthHOpOgkJPhsSup5I5UHH8sbjSur1qCFn1QxFd2F0PJTWlFTiM55rBmDq1eBL3wTP7rr0jQXJxtcFlVBaE5P5zes1Osi+Kp9MhPZQd7cmgVMSZZsWiFbB5NF1BGkg4wh+Hz0lkoOPhA5+NqyZy9gx4LgW+COBqF/tqKphS12QWWXd4XopBQTgfDRt+r94G9uSWVOi0CexHitQQ++yaP86K5LOPaTVyHEKEF3UivROXvwxSovffT+WkuGwjEhOmsJLdmk65EaTgYThZBdCZ0aQbnoFLz3vTt47fsSGxsSjz8+n9BAJnR66tyTcbGzuN+pDzqdUvIapHvpKfq5208sjjGQLmI9RsFsutdBNM0NuykHXwolXQ05+FSoedCM+bAtuUGC0YaJ5ehDThjKodthKdoai5zZhnEMxMaGWWM278fS9JfUnI/t5BwfMnUOvrk9Spahf/068mPHsPYf/sOsP2Wyz4WYzhN7+DzGwud9qOetTjGojeCb+4dcAkVqawZBUYSNBYk3BI5eZvIqJETFW6ypk2R5KmjrZ0PzYvvZZ/FXX93Gj3HU+15nFhGmDG28nRGi01f/VnbkmlwXUo6MPw11dToB+dDLAmpRmM8y1Pfk3Zon6iD45hGiMyIH3yJE5+7FguBb4I6GTZidpwG63o5fInkhUA2Hxm+o2mpCYbItHnxROdt8LGEYkFkWpuKKUGp//9jDOHDrB7h0+g1kL3yg3nMUVfkuCtHp1ey4sHrP6iqwtUWU5zTYZkmK0TftQDsn68sUi/mpUzn+9Yu3sLTUvtfSISQ5biifpiA7nepa1zaCz599iW83FcZjYNvbKopmpe91GBKQVbbhw1HlIqNyxVZJwB4WV4wNtWdNeIGoBJ8RLVbaJoXyXIXQ12LZ1XMV0kE2GlKjGmTsWmyMEjdy9905/v7v6WvznmJVgk9UFJrIyLrSAAAgAElEQVTTaxGdnHd+4dQE9LzflxrCUi6ZvXUo+ZYD421CYHVVYmtLYGVFIuuowffqh68H3+bqIaxt/QQA8Nb+M+kMRuc9ESwIlm1C5ouHLqQuTAk+ISDKjqZOtiDN7K00M695IQQ2148Am/7tC1efTeePwI9pVJ05L63bEE0ai6gefOWzRHSIzjmjlrDAxDdfd4hO1SHCK7WQkoOPdc8CrcTeTkyzwAIO+OobQhc6X1IpiZ4/KhbluApKcxwI2/7kfL6EHnxew6IQeslz8IUiQnCWIsNXLz6LN5/7BOShQ/U+h1J53YfV1IbQ1vrUZzMUrjt1jjfm6MGHLAuJFJy+H4FYXp7fd28N0ank4BMCcYNaxwshLBmTIMV8jghn2iYY9zaCWA9+XGZFGtHUluFtyoOPeOB5jEET6Ue5BJ9N8bCr4fgmyBCdPf036l01Zqexi/PU3nffEJcuDbC6KnH5cjUfa2vWnTEKqZ8hfCKUtO2B9kqIzimEwOZLL0F2OpBZhuGVK3U0oWMcovPq1QHOnRvi/vsTesN5ofo+XZ4/f3PfJ/DPxx/FVy5/FDvLG+n63GJjELIrQTn4/BszrRWh5ITr/QLlb3w+Bqg+5/BWIoTI5d4HTL0q7Xnz+PUao8DKUfQd0+0xITqlhBais9wQqV+p+93XQfBlWZCTgdcua5FHfT34kp4dF2gU7TLxXmCBhlHVx0jlb3v5OuG7oOrXZRKCL2WITp8tSi1pOwD7jlWUPthjo2ShwRCdjVTNCdHJwZwkihiCr/z3Cy9s4W//tocPfKCI81ytA03m4FMlfiHI8bAYSadB4hx8bUOnW30WIRCnGa5jbIi5kAQh85ki5+uy7mkCUw8+w3XXwhbxbMYcdpTnWBvGkEnwheTgcxXyfvwECkH1PdShs1hZCbwxBdowp1RYCL7ppZ7uwZfQiN8fxhx8NciaieU7IYAXX9yGlMDXv97Fq6/2KtfmCdWbQsthlKKBOWLPhOgsIT9zBrc/8xmgKCDvustcMLDz1G1iTPCtrACnT4/O7rfnMBg+vJoQwObqYXz14rOs8rV1pGHQOfjUv92LeVAOvjl48JH6JaCxzam8B+1KoyDO2SJw8//Yxzbx1r/NcW7fEMvL1VuNKc8d/bFNMdv+FWLsUTGalxLSJ8djzUiiKiQW+yCZKiYHX8UxwdG2IpcuQnTuXiwIvgXuaNgIPXoRC1vyfXWQhNGHV3khmI06VuqmQnQ6NwxriE49p5cNTz65g69/vQspBd73vh17YVURXGknkefIHoIaC75pSSB1c14Ca+ldXr48xOXLQxw5su7VXi0hIuYJ4pBOeynU61WYJIxVk56PJMzPkHX1a1Frdx3zriaFjbbmJK5/V4BB8CWzgmaWVQ+GQni20xCMa0NDHny1GyE3QvCFheiMiZY+raMFoYhdofpIDz6i31pOq445VFZqeHmRtRQtXF4AjPo1WZspD7461t+mED9t+B5jTUIeOOAulIidNJ3X5yFx+hJ8nN9C0OazEJnjjJGDz/YMxktcgi+YbDbPskkV5RCdlaoTvpPhhQvGa2oOPh+yqg0IJiUZz3bhQo7V9+6g+62qQb91XfYk+CbFBSSkyIzjHxSis0wY2s7YTQjPTVibCIErV4b40z8dhWJ+4ol+WN98ypb+dlUjO53qayDOjrvgk1sAC4JvgTscZeGGs2g1tbDFLqhCIE2IzoQefFGeTAlDdB48KPHyy5t4++0Mly8PPTrl1w4Lc/Tgq4WvcHlHCGFWyCvl5gMPZUMdUs8ek6Q0pWGWaQdKIWT9YciIk8e86bqUyDqEEr9lBJ9KOCYb/zmF6Ey9ftbKQbsIvhqU+03kfgsC1wjFI2qA+0KDEOpaUL8CvRwaygptTMM6s/Pe92L5z/8ccmUFg2vXguqoFcog54LYf7odQBE/1eFp1Fmj1Ljs9SAGo1CX37v8/vRtaYZzdw4KKjNKG9aNQMzBoag9SOTBB2AaonPe8HmkWnXuLfbgI7vCWdNC5otaL1dWSzBeE9lh1qRn/x1Ccn7iBOTSEuT6OgaPPWbph0ebLYSzy0KQhmZsw1RinGMCphgNm6W0kpXROfiKwhiicz7WcfXU3+sBv/Zrt/HGGxnOnct5NyXy4HNWo0aWmMOwL5AGC4JvgTsatv0j5SIW68Hn6gtVnkWkOCCzdKc334NCBR6SA+e9nTpV4NQpTwJUCC9LGA6CPYvm7k1kAKdfNfa9UcEjgoQObiMFEo0/qxaK4FUeqddr4L3tAe8EGzo9YgBbRvDVtsGGePCRG+YuPrUQY1B5nKxqdasd0uvwIKGaaMEYC9XwyZhwxFIHh+AjCqk/adbQ7Ib4EBT5H1unUkeoB19oZ/o3bmB4992Qhw41EN85AMpz0Tn4eq0l+Ib33IPhxYsQW1t4/RtPAP/SYD/2OJJ78M15TU2fg6+lZxsKgWM/uW3QXUVvuAUhgOLQoVa821gPvmTvrwWyggnknOe8O40EDLAq524KCcdP9eCbINbruzhwANsf+Qi7fRItnidTMAg+r98ZGM3RsHptHnyF6BjPEkF7QaUycw6+Rt5zE+vvuM71dYl77mGSe0AUwVd+Dt8cfJrH7PjXBdqPva31WmABB8oLF2f9bE6WiLO6FgJJQnQOe9zkKm7EePDNLXyQRcBI0qUWCqchCYBLN1f/bvj5YpuLku/q0Jy2cH5Mwemb6rUl9BCdvV4DwuIez8GnhugUooUhOtuUg0/FLp8Lzt4TxLp6Pbxx/r1JQuXGgrFHySwL80As1UXNSorgSw5NTklP8KngEnzJ5DghUJw+Dbm6mqa+WDj2bdGbrX2rq+OxItzWdYKvQUVKeX3OMgzvvx+Dxx+H7Oq5AqOxm+ScBCg/Xi6JbyDmu5g7wRd3/66eCuo51bPzf3P1XyHv9JAvL2P76adb8fCxBF+yo/pu8+BjPHiQ/MMl+CLJZhtmxI3wa8olmzNldzVE564Db5B5v1FwGfipf3sSfOV2bHM42oPPJpszCPPkaNGaE3POivHgE9kiROduxYLgW+CORnmx41gpNLWw+S6oZPkEITqHy2vRdUxQVw6+eSHJXAgVymv0gouqmnFznWRtaoLPx3IwifK6AUmqHAql/9BDyeuvgJGDT40IUQf2Qn4hG8gQpxFrZi3jRYRrTQLTPufrFTHnU0vq5itKkZHFj7mxGjxI6sj9lgSuMNKAs7OsZ2EUaoLwrONTzhXDY3aI5Tr2zDZCea4b783R6Yz2unvuGbvt9TgEX10d1FF5F6WO7NVX1CSqHg/E2O7iQd7jopUdgfvoJPThT/efxv/3+P+Kf3rxX0NubLSE7eQfAGvVue8ygk8zCOIMToCM0GyIzur/oyI/jNG/fn327xs3vPox+aO6nu4CcBR4VCQC7vgSehfr5+Oo1+aJp83H0p9JcvDNMzpVK9ZfGl6yspbrenavqxrNMJi4oUXDsoAFixCdC9zRsBmIpBRe6w7RSXr8pSD4Vtaj65jBYxC8mJYaYZkgSTa5Oebgq6VqhsXezs2b6H7rW7WSlKng5fVSh9RTQ53De+/F9vvfj2xzEzulw5Y3OIdTFsHXwDzYA1oo2+dCefDVn9jQD5yDQ1jFkWFhgKAcfBsb8ftrMrgseEWNHnymLkGfk60gdDgEX+YInWmCw+JY/cnpwZfA4r8OonVnJ7DORDn4WgfNYrqKQ8cyHHm8jyybDYFY0i1b1HyJ3W6DMlI5B19prb5xo49vfzvxXtJiRVodKD9eEZnURpsRcx67hQdfHIbdFaAHAIPoulJAF9nNaxB1Ldn7a/HEIOd8CMHHgXoP14OPTTa7y0yfd1x4ek/Ax7/znveg2LcPxYEDKI4dY/ZxNs/2qgefpCxdmeNLRTLweTXDM2eQd97GK+c+BGAUPrKMKdELCQmzbBwUcWBcmZTwD9FZ95pQy7koEBFGmFWjIkcPtBx8Cw++3Yp2aYAWWKBhlDdBTvzo3bKwjQz2Gd5UtmsiQ760ilQHD7sHn72vUeHmEoFSWEbjTiP4AMi77sLmJz+J7N13sfSXf4nO669HNFhF7DuJEmRSkEhNLDBCYPDEE/H1cL5JwmtLXWeXlhYhOqOxtoYi6yArZq41UV54TZDVdRN8vgciR3m1lePHC1y71se3vtXFBz+4w2+rDjBC9Niue32B3PfY1u/LQcYAcK7lxkdzPLNO8HVqHyaVQ6yD4GNjDxhakHB8E3JlRbe3IDz45mrXZghncuZMjmef3cK772Z4/fUM3/hGApf7tq4NNaFC8BE5+Fph+BCI2DCyd6Ly0Pidt2AwYh3nkol1LZ4I5LrMeXeqZ00IWcU13EswfpMqpjn4fD34KNl8aakSRYaD3Z+Dzy2MkwQf89l2bt5E99VXIfIcW889B0DXZ9rk/e1nnsHyg4fwz//vOoSQeOop03lGWo3Sdn2Izhasv0ZE5OA7fHRmjHrp0lAtXYGag89pHLpAa7Eg+Ba4o1EmltrkwZfE4y+SBOr31pNucDE5+NqgGFJT0+3VHHy1Yjwni+PHURw/jqUvfIEu15Jx8fJ6qYPga8k4UCiOHkV++DA6b76JwQMP0IUIry31kRpxNGuBgUAsbMt5pydwe/Uw9t9+A8DYWypmYOuYd3V58CVAqDLp6ad38NRTO/N/FJLgq1rCVg/49a8ztRjEJMDWCy9g7T/+RwDA5osvJvXgky6FhFa+/hx8+pYSb1AxGJRJCo/6LKGD9hRUgm+NCHXfra6HQsj57I0TKDn4yrj//pFS6I/+KF1O7gr26jwYo/x4ZA6+COv8eY9dC45m80OidzEleiO7kwIxU9H3fitaHKKT9uBj9Ddkvqh7SeIPztYFleDT7mno46/0sUXzgAtWl5eX9d+4Hnz79uH25z4HsbWF4vBhd5vEPLzv/gIHD93GyorEXXcZPPikJM7wsz+CjtmlyqxRnebx3pswdK3jPmXePProELeP9nHqVI6DB/09+GK6ssD8sCD4FljAA7tlYRMCEJEhOvu9dPn3gEhZ0CI5SJV5Swmrwi7+OBYsrO9iD75K8cQfVGoPPqtXbx2KFvUw1+YFRwhs/uqvovP668hPnyaLUCE61UdqIkQnOY4cBXyWTdfR/OTJ1N1Khm4XuL16ZErwAYgjNXcTwZdiLRQieDVP9Rh33VXgu98Nuzc/etRewEVY1aBgJn9uwXqWnzuHzY9/HCgK5Pfcg853vqOVkQ6rVeM1J8FXnWXfP/ognhB/NurXqVPofP/7zIb4yDr1jjmllzJBX4fnPx+SwPFNkASfokgRQq8m1jvKB2UPAtKbAGnI4XFFaerZJSg/LuXBl6zyOeBODtEZGi7V+MwtGAwfXq1Wgk9dQxNVmwYqASLrsxDXNwVWOe7ZkVNseg6e0/ys5JOGIpvthgWDsUhSe67PnJdraxU5wysH31jePXnSpTOUKKwefP5faSVkdVFUST6XPJ363Tcxv0Pr9NhoVb3L2jpw8yYzyoxiVTYK8W/2Bl2gvbiTba8WWEAJ0Vm9lnI/qTvdmN4vZrJaywPt9NaT9ttr7HSmJV1HglGDR0JbJ1RN0J625QSfF+qYo22XpJaWkN99t/nZyRCdWhX1I5Ds2nz5ZeQnTqB/7RryixcTdyodul3g1uqR6d9CRIY1rmHe6cnZ07RhtPr00UxR2vWG8b737WDfvgKdjsTHP77pd/PqKjY/9jF878Rj+MrljwJQPj2VsJrDQbY1S5kQyM+fR37hgvm912CsQf30rTPvw9a992F48SK2P/Sh+DaJRtQcfKmxvLzw4HMSfESOHLHUrhCdw3vvRbGxgWJ9HcOrV5trGGjR4lAPKgQfiH3Z50W3bKxigyO0gNMKR2Dn2bfNYTB8HqlWnftu9+CjoJGWjGfiEnyu+wIwqWLyf9/ID7VMhRbNAy7ynvuQK6mDcIQAUF9YU3M0kKDuViqrypLS8O+mENNm8mlagxEmBSpEZ0xXFpgfFh58C9zRsBmI7KZFjOwrw4MvP3MG+Ou/Jq/1e+vQ1RLhsOfgs9+bOiwFG6WOqcqoJPPjDvDgK3tBFfv28eoJHNxkFuacbtSxYOymRYcDwmtLnfJNePCF5uArTp7E5ic/WUOH/GEN0dmRuLU28+ISAneOB18KtKAvKyvAb/3WbQwGo3/7Ir9wAV+7cA37Nn8EQLfY9VrLEqCgPOtbMM4aAkJ0sh6DcTDOO0t45+mPYOVYAXH7NqNSf9StJ40h+PYsVEUuw4OPGpomQ3TKfftw+zd/c7TR1B3Suo3rQEOoevAZPGN8MOex5OSut2FXT4Xgc0r1b6OH1BygnqH87KT0MMMRHUlUUXqQeyrnPBjyTOpeYtpDE81FCtMcfOPCU8KvoXfU4qnAglxaAfqOQhTBF/Hg3a6H15WLqJ0QvbKBHHw+BpupJ0ablcARRkBe36nqwUfYQLZpWBYw4w45bS2wAI3yQhVggNVaCAHs/NzPTf/eefJJslx+/jz6jz+O4enT6CuJj/tL64n75HEQDLVaqxNKn+aag6+lBB/lTbP5K7+C/kMPjcKiqUJsyxR+PoKM+qRJDjt7TZIiPDZ0gq/5fuw1dDrA7bUjld9iPPhqMaho2iLbp/4sa8W31umEkXtlTA7MLoJPvZ4cTRzIU4Dqk2P+p/S4mN6Samw0Dz7r5WjEEHxLpfCejRh61AXHoNIEn5qDz6L4bwpZNh9Zu43rQkKUzz4FpXbZ1QRf2vp21VRILa+3QP6PcSZN2t0We/CRXakrBNQcdSGTpo1zwmmZnWb/Kq+foWFx54l82S3Up/bg0x2xDKEvqb9LKA4enP779uphSGE2fgsi+MoCalFU58y8321T5yIGvPRLMZuyqucsBm3YlhYIwMKDb4E7GuV1kLeIhQksTYfoFALIz57F1rPPQmxtYfDww8Ybdz7wAQBA9+tfr1zq91ITfGHXAMxNQS+FKNkXtciDr0ak9uArTpzAzokTdPnEkkLq87XX62mRMNgaqAMopUb0N5KDj3qRu31sS+h2JTZX7qr8JvI8vMK97sFHbZh7ZT4wFEu+oZZCwAo/1QKQB2dXzkITyjcxPPhcF1OsjGqIzjYRfFfvH+KPhISUwMc/vpW2Y/OE+r0RITo5ltJNevBxcCd459SB8uOSOfh8xqNlY5c6B9+uQqDm03hbCwbD55FqPb62YCxMoJ47hHhiZX7mEnyJ5iJ1TfXgm6Ih/YU1B98uQNFzJyom895GPKhVdvCod+uFF7D27/89zlwQ+L3DL+AXBv/FaBwYZJCk9mWv5+ALhY2E3dio/pDQOKIjI/QIC8wVLTs+LLBAs6juH+7NqU3rvQ1CjP4zvP9+/k2K4LjTW4dKasXAK0Snh9VaY+noVMv4eRJ8LfXg87458QeV2hvMSyGb4rDTZgEzBUSLQnTuMtg+rSyrWkIKAWQ//WkDveJDUw7UPbdTK013wbe4f3+BfMyTbGzMQnRL0XyITpLga+MYEuu2DA3RGUDwTX5LtgqqFrg1O0L4eJyqa8C+fcBnf/U2igLY2NhDHnyE/CpXViC2t2dFlnrWW8a33RlIOCnbnp66KEZ7tZClFAoRzz/vPJaLHHyWv5m3BXtI1QCf/aLRd9eiD5t8X5oRFQOlZ2LJFEDyHHw80XfST1G9p6H5aSKUmuxDDIolhpC0TJCAUR58lhCdHmNYHDuGW5/7HO4SAp9ZXsHa/y2Bfza16d/PyrnVllaoJgM4K9pktK3MhWL//pHBdJ5j68UXrWVjniMjCL5d8MktgEWIzgXucNj0MSkNRnxl01ieJKSf6kGx3yPCCkUghv+YWw6+ch8cirMUdfJvjBdtylU89NAoQHyWSTz44DBNpRwkkBRefHFm+f/cc3FeAF7fUQ0HjfYcYdNhcPkyAGB49iywsqIN09xCdO4hKTW5IriOsWmTB58CSbnP7EJ89KNb2LcfWF+XOHu2dDBTCb4GLLEllYOvjQgQ9O6+m7FH+irYapp/rfLgIxrft0/ubnIPYMkChRKmUyghOqkowZ3OLh8XE/bAWuuD8uNKKfRcVq2Ji+iPRvNetw2JOts0YWKDn27YI89XLFrlzkw8d02WNKqOoEldyKTpqQefb+SHRKSs/si7a1/Ml5r34LOeq333kNXV2dldI7JnfyfPwecwmKsdbSL41HHfvx+3P/tZ3Prc51AcO1a9lqpNAFkRoQ9cYK5o0465wAKNo3w4aRPB54skZz5Fitrp7QvvEAEvi/hQq7XUqGpFlUsJXmoLDnQAcPPmDo4eLXD8eI7V1YjnSkXweYzLxYtDvPzyJno9iWPHLBZgAfB6PQsPPhLbzz+P/vXrUyF0Lh58WTbyYmqRFXBKTMb0K5c/iodf/X8AADs3boRXWMe8a3MOPgbBV9x1l/V6G3DsWIFPfXoT+wdK3gQhrPtV1FdhGLfd7MFHhei8//4B+n3g9OkcJ04Y9pnSTdSYWmXKVAKnaojUST/mjz7ax9/+7dL032y0OK9SUlDW5mtrwE9+MiuiefDpM6ZtHny1va69Og/GKD/exIMP2Buhr+Ln6C6WyRJ58Bn3gDl8F3oXzO+n7u5uPf88lr7wBQyuXWsVwcfx4OMMBitEp4o5huic/DC9Z04hOito8BsJbYrlwdftamfUGO/s2oyDLH1aX/fXv0w8+KSEPQdfSoWsCS1Yf41QvjUpxOg3w/mlgph5JIf/P3t3HmRHdd/9/3PvbJrRjPYd7RIgJKEFLUgIsNgsxGK8gRf8JCapxzhJ2S4vKeJKYbzGcVViJzZJKpVybEiIYwrb5MFgsJOAsWOwWBVL7PnhGARCIBZLmpk7c+/t3x/DjO5+ezm9nO73q4piNHOX06e7T58+3/6ek6TkaXiQnCsmEIPKds9NXyVJ7X0rJgJ8ScrgS8RadWHse5/bZTpQMWmStHHjaPAPiiGDL5cbG3Q1wcvNbSSdQVsanFbyeZUr1mCsPeQbrS0eVjkUZF26mLWeonPsjwdnnqLC2n6tv7JQVeeeRZHBF2eb3ujcbbDNhR071PXIIyqdcIJKy5dHVLhg8h0NpuNssG0tM/oMsD3AV2vWrJK2bGlzjWwzINF4851Wfwys9mNNdB3OOKOgqVPLmjOnrKlT/a/Bl8jjwYe6LItGr6lZhy/X3X4NvqQF+EwJc7woiYdUbYAvF2Squ4RtoOk1+ExnBEbJ7YB87ctqAyhxqq1/Lw/mmu7SFVetUnHVKrMfakDDy5ifjXdzMfZ7UTB4LPmeQtbQOEXYfVW3/G5OucdFgC+XG7sZLhSqf+dTbQZfyzr08j01B8PZbynox0+XtXp1UdOmea+gugy+ZtKSwedXgH0ULIOvlKRnK+ABuw2ZVtkOJvnhjXbCKPtoV58cx1x6tpcnAWt/kcQpOo0cHwlcgy9Kca8f0k7L4tXuAxPbYnMj5FLtQtyRZPCNfXF1gM+Cuu3ocFQqjZVzwYLmwcmJe/5cTq9NWazyvCPBvjiMKRtdDILHpsn2Fles0Mjpp1txrExo8qRr5a+jeAramktUo21vswafp89q82dfg/wttJuex8R+mTRJ2rTJxwNBCejHRaJZBl/lS7rrb7/rx3JtOYkCsql99aE2wJfPGQx0x1x3aQ1Cu+K7v+4yiJaADD4vr035aTwh0gw+t2Mhho7FRn9rNkVnu9LXPtTiV+XYUZwZfH45jdbXa/S6ri7lKgN8BtfgqxLkXqzmvSedVNLy84753g2Va/BlIoPP52d6WsPecAZfZ+fYQ32PPNKtrVsL7d+ERMjI3RbQWLvrR6vXexHJOlMV/JQzNzxc9W+n9iY0oEDXykTcRVZvgJGxKt+9ooQO/CS1XGEIY1szcMdcu0mRtY2JaEO8ec97BrVgQUmbNo1o6dLmAT7j4+ZRZPCFfGx7enigSQZfw4Wxks5FgM/Ve0yUw4L2rNFxMvY7H+sLee1QVr7M1ABGm8GbOC/RngYpbOYmwFezBl+jqrDwkuVOWve7C+Xy8UFq08H9OATNuLN40w0WvnEWdxxNtZex4aw057UabndY2em1nxNDOs3EOZ6rabfa3HiMrl2r8pQpkqThc8/1/f1JSfz38r2rVx9/AGrlWnf7rC4QGFIGX6B2pcE+D7Q/qgrWYg2+du8NQ5IaNC83+SYz+JyxcYft20f0B39wVJs3G5jpC5Eggw+ZVtnuuWkT/baTZ51V0JNPdqpczun884fbv8EjIx3tkJ+ubvXxteWt64C0GOmIbMCKDD7zktSB8iqCfZD0DEc/YlmDT5LT0VEdoregbufPL+t97xts+zrjA8Fh1E1S7tIbfXeTAJ8zycXUOknjouMSVwZfItuzRtfgBuX0XHSvnxFR3cTadUhSG2CSiw54fQZf9Qicy5li08ngcZDErnH1GGau/uFJLzs6YVGVoH2PhG2OJ34PtdptTNIUncEOxQSefCGonYVEap8531ACpuh0E8BtlsHX9js6OnTsqquUO3pUzrRprsrTtowxNhheri07dw5rzpyS5s0rqXeGuwy+uqhcgA6Ap+x/D3Vo+iGtqmthq1mRItjPdQ/DeXz/ggUlvfDC2Pk5f36TB3L9bofXB1ZNfKekfMUsbgm4PMEDAnzItO5up+HPY+qbd78N3MCAo9/7vWM6ciRvbL2wSiYa3uKKFW8+bTWspxfvDP6BNaxcg6+iYsOYojPOAc9QBkK8fmjCR7Ai3z0Z6EHVZ/BFNCiQ4rptNOAQRCjtUsQZfJ40aYfcTq2TKE0iBXEElxzl3E1DFacmmZt+qsTxMSBhPIunzc19kgJ8iQz4muAiwKeu+jX4aplu1xMjrfu9icrNLZeljtpMmCD14fW9hhc9Nt2Ft+rQ8DmQ2uxtvoJEhnnZJJuDs0GEkcHX9OW112/DU3R6CvB5zOCTJHV2Bgru1X5Nw7WdE6i3t3Iac3dBWae2bQ4pgy/QiWv4JK/cnzmva/AlLIPvkkuGtH9/l5YsKTa/zPots4f2xeRyGDPAcFoAACAASURBVB1l8+PViAYBPmTa3LllrVw5qhdf7NDGjSNtXx/kejJ1qqOpU902lkEvXD6a9Deftvqvl8o61jsz4PfX85LB57pTGyky+Npp2UFr+IbGlZjMrasWStcyA3fI9Rl8EX1xkgNMAdmQwVfXhiep/hsNyHd2xjIdUlDNppxseU8fUgafk8sp8Q/1uwyIhpXB13KQ38R+qQvwxXjeJaIfFwIfGXz5nuoLX6NdbcsUnZMnlwO9P+lNRFCVmU3lslQOsgafjzZh6MIL1XvnnXJyOQ2fc47n97cS9JROUjfAM0MRLuMPeQQQZIrOrGi43T6OBT9r8MVxUTh+TIyXpfGUsuF/f4PvjPAgDPuragN8QR6A8rIGnyem7+NyuarPyJXL1X97UyR9hIDH1sCAo23b2o8j++KlbAH30SvTlmvW6/+fJOnleas8vRfJYd/oBWBQLidddtmwHCdZT6NNmRLshtm3zk4d6x0I5aMD1WdcIx0tMvhMjFWVFi2S9u4d+3wvC1InNMDnuVwJv0NsWTzW4PMlzik602rWrLKmTi3rjTfyWr682P4Ncajd8WEP9nsYmWp0BFqZvSdJkybJyeerb5Rl8Alel8aaRwvaM5dzI7oquptBvZxTFWQzXiVtPjBJGXyJPB5MaBTgq5nuN9dRXRdNEkkTbdu2gg4e7NDZZxe8vTHE/W44Qc2I2gy+ut+HnMFXXL1ax2bMkNPXJ2fqVP/f1UDQLFOru7xBA3pvanqeJyCDz8trrdp3ATS8jPlprA1O0Vk3/1SIGXyu3mxQVo6ruotXoABfi48Ksh9D6JRUZWU2CfDZkMEXqgjX4Nu/4iKd8uufaLSzVy8tPE2Sx/4dEoEAHyB3145czomsvZ8zp6wNG0b0zDOd2rkzvsbV9GBQPu+oXHbxZLuHp9aiG7CqLWTwLy6edJJG1q1TxyuveFuEmgBfJFoVL5L1uRJeP/5UHyORZfAZnLYiafJ56YorBvWb33RoxQoDAb4wRpaTnEGZz9e1XVauvydJuZyc3l7ljh2r+l3L6g5pX1gxBWOjYIzPKTrbDki8+evKQ61ykH8s49Fwy1SXwWf2472w4njww00GX80DXLl8rqo/3ChQkvRnUnbsMPS0usHjYvnyoqZPL+u11/Latq0gKVkPapTLqluDL9B54ea9uZzK8+f7/44WZs8O9jCq1UEin4Vv+rYEbryXWfOS/kCCKQ23M6wMvnbfE6LxrzqegRzPDq/MgHZU3Ze1pU/hdHcrN9L6eukYXIOvNoPPWIAvhAa76npYbnI9iWI/R3Ex8vuZEe6jQs8UPXryuyRJM/NM0WkrAnyAS1H3I847r6Bzzy0ksc/vm+ttabXYbpRaDNgZKVIup8IFF3h+WzngnPahMRXgs+CgdyZPVmHrVnXt36+RHTvMfKjVox3u1Ab4IxsUSPpoaUBTpjhau9ZQ9l4YGV1RZ+94vSGqDfB5yahOGKe3V/IQ4AtjkMSWDL6G297gd56L7mewt2n0L8D3JijAl9oRYDcBvsmTNbp2rTr379fI1q3NXlYlaWvwGTt2QmwH8nnpd37nmF5/Pa+ZM2OaCaVG1YC0k5vIWPAV2ElAG/rudw/q3nt7tGxZUXPnJqOOYxF2gC8B+7r1Od8iiJBiDXdT7XXWVGXUBjzcfq6BDL5xzbruUV2dqtbgs/Qgc3p62gb4TGbwhfbgbBgZfLnc8Xam4nivOr6ynsHnhcF7bVurAAT4gKaS0LBF8bBHlJqXq+ZGISkBvhZliLpIg+96l3pvvVVOT49GzjrL12d0dTkaHR0reNAnbxvyOAJUmj9fXfv3j701gYN/7fbxyFlnaeTMM80dDAm8wTetFNcDYVmZHs6EMOomyfWfy9WXx9YpOtUgOFmTkeaYXEE0SfvRD5dr8LniIoOv1VuikKgAn+3HThPNBiGHd+2Sdu6caFvy+ePXw0ZvSfkzKccZPg46O8emrU6Kys1rlMEXaPtjOIeWLCnp//yfQSOfZXWX11DhJzKULKsMy4prTC7XILDp6/7VxcW4WUZTLd/HXvu/TUzRGVsGX9W/IvlO05yeHunIkdavqQ3wJTCDL5SHAXP540t1NJuisxHTZYmgQSsPGFoCqUW7YHIq3QQOy8Eldh2QQrXzb/tlfopOly9026mNUs1FMuoLX2npUh29+mod+7//13d2yXvfO6jVq0d1ySVD6usLYaTP4wEzeuqpGl25UuXp0zX4nveYL09ArvpFYd7VpvCOOa4AX5rX4DOu5rgrzZwpp6tLQ5dc4v8zaxrMuHNTqm6C8vn6J7BtnaJTktPXV/2L9qlC4ZTDhhHARmVqEODzei1oNhDS8nNM1E+bgsfatUprgM/LdlQ8OFBZHZkK8KVlv7tUG+Ab/4WJDD5bM1rG2XCJMM2mbfYyRWeSt8OkRt2lumpyURk5N8n6fm+YDOyM2gBfoHYrgDQEGdzcT9RN0RmgfluOAQY5cWuvP+7fWefCC4ckje3fxYvHjvPatcObfW8kDH3n0IUXysnnVVqwQMWTTzbymS0F7GeffPLoxM/r1o22eCWSjAw+oAnbOq/nnz+sX/6yWxs3jhoL8JnWrKNWV7dJCfBVDtjVPDkWy/EQcNq4OXPK2r172FBhDMjnNXzZZXGXIjlsa3R8KBZj2qY03CVGpea4G7riirGnS4NcWGpHq8PeHwFuWqU3n7i1VOMAX/VoUssnek2UwZIpOptl8NUy9bBHyyY+jPa/dkCGDL7wudyudgG+2kyR1ErrcdBAuSyV38zga5q5BTv4bK+bvs2y/n99cbPRXtVfxpzQrm2u1+Q1dCw20jSDL4YAX+0afEk/Rya4uZ8wOEVnfQZfwKnfxxm8b1uzpqi5c4/phM6iusZXl2iWwde4g2SsLKF83puKa9bo6Iknjs2bGsXxGrAtOvfcgrq7HU2aJJ16KgE+WyU0DADEz7K+ttavH9X69clujF3XYVICfJVqCp/04yEWsY4emhf1PvbzFKhtioaWifMsK4PLJjS6+AV8aiTR2Vy5XOoz+ELPHKstQ8VaU4nWYMDC8TtFZ2UQ2/VAiOFrZpLOqxq2Zxs15XO7xgbdxt7b6HAhgy8dKjc36IMPaesjNpzu0BaGgiq1GVJJ0uqWLqtd6obb6WPjcyan6Kz7cHMZfLUPIkx8dBxTdFp6kLl5YNBkBl/LjzKYwRd0f8yaVVZnd07yE+ALm8nvrA3eBhVianVfn6O3vrXgo1BIEh5pB1yytF+RKPl844tSXd16mJYi1JhSi51OQlADpnZGQk62hBQjVUqlmCo1taOl5tUOxBs5q2vrP+yTq93nV2Znpy3AV5vp3TDAZ+iJ3laSHNQd16BM5Zkz3byszuhJJ8nJ5+X09qq0bJmrrwt98IoMvvAZGOhv9JbMXLLSchw0Ubl55XKufg0+biYm2HQo1PWTfAf6a39o8u+Esay4xjS8jPm5trm5GIf8sLMNGXyVDwHEVYagXAX4DE691dHhoaPnpQ7DuFbVPwHT+jWtfmeqHGF8vkEtM3trl8NI8HYgPGTwAU1Y1NZbw20dup6WIkpk8CFsGWh04srgc9I6uByGpo+YB5Dk+m9QljRN0emo5h46gkGShpfwJO3zcbmcnFyuqs9RnjOnQVPcvk9SWrZMx66+uuV0tp4CfH7qq81nOE6M+yDJbYBJLreLNfiyoTrAd7z9bRrYQarZ1M1vdb2waTtMatgd9lEZRjP4QtgZ4x/RNMM0ogcT0vD8g+Mmg6s27S6AVh8VKOATRh+u4jNylQ/3t3gCKoqgVQJHIY9rMUaa6NlyEJkUNJtANLLYRpqOszWrw7rfJ3CKzvrpxhJ9+Y9FcdWqiZ9Ho1hMOGSRrykRQyc2an7XjA8sDXeJUQnjBiFpGXy1r01TBl/tFJ3tppwMKcBnxRSdqn+gqDR7tu8qcfr6PE1nG/X0U2TwhcBne1kd4KvfMc1mvEidtBwHTdQF+Goy+Dzt5ZQN3rXMLE8633VfvY1NAygJ2LchzgRnsQaVEta1ze8F20cWebO/Hc/gq/59VGdqbR/JxuPMGRho/yKjGXweXuyhQkPZ55XnTlqn6DSNuZPRBiNeQBO0iea5HWMvz5oVbkHcqupgNP8TxhR27lRx6VIVlyxR4dxz4y6OfTJwUC1ffjzCN3duhNG+qANMNgth5CbRTxXm8/UBPosz+MrtpuiMal8keZ83UZ48WQoxuOspg8/4F8T87FRWHrIwlMGX2uqysF0IojJ4Wy5rYnuNZPBZXndWHwqGCp/kTM7WAT6L108MwFUGnxsugnc5vxl8Lrl52/iDJnH14Svr29YpOkfXrFH5zSBfafbshq8xOUVnbbUUi4bqKewpOkM+3j19piXHVp20bAcCYYpOoAnaSPOa9Q1q67Y8c6YKZ56pjl//WiM7doRfMDdqCpnawZcAnN5eDb3rXXEXIz1S2OicdtqIXnwxr6GhnHbtGo7ui1M731kIosjgC7sB9fLkc4MMPtUGySzSaA2+lq8PowwWZfBVGl9/L6qYZ8s/mgis1/47xgSZRAf5A6irUtcBvuPvbPSWvj6LspnQVOW+dZz6DD7P2eZIBp/tWausxdKcOeo4dGgsCGDZvrasuL65SpBxE7xT6/Zfkv8ncozsDKf1R8UxRaetB1lnp4598IPKv/668q+/rt7bbqt7Sd3MGwYZWxojjD6ciwy+WHpCST7WWrULZPBBBPgA12gjg+vrK+u119x1CkdOP106/fS2rwtzwKpqQKrmAOB4SL/I93FKB0ArdXZKl10WYWDvTWkdXA5FBAG+0G/YvAb4at9ucQZfXQZaqRR+5liNsYFs+865cpOnq8NSNfifywUOibab1jlJU3SmZgpqAwP94z+ff/6wfv7zHq1dO6qBgZQG+CxsF0wpl3N1U90FGii3vO6sPhQMZU1V/nvo7W9X5//8j4rLlyeiMpiis16kD/eGvgZf8x08/hHHt9dg5rEHnh6KSrLubpXnzFH+9dcb/tkZGNDo6tXqfPxxjWzbZvSrS6XkZvBV9QMrG5wW42+hsKlBY4pOtEGAD2iivq1P6c12hHbsGNF//Ze0cGFJv/zl8QFUG64/tYNRNpTZWlmtXJs6mLYhg8+9sJ/SNPWZrXiJZDS4abV5Db66h1GGhqoDSSanOWry3kbVb0NApxTy9OBRB1qZojMGrjP46t+yfv2o1q0bTfWlP2t3UrUzkI1n8PkaKE9ZH9HqzTGUwVc1BeHAgEY3bAhYMHMI8NVztd1u+p9uXuPzgm2yr3V8Db54dnjl2JuNs0J4Mbx7t3TeeVJ3t9HPNZbBF8Z9XMVn5OIM8NWytUEjwAexBh+ACC1aVNJ73zukrVtH4i6KO9Wjb83/BKOSkj3DPk4R1uBzL4QbBCdpNx2107+Mjlb/PUUB4dzwcHqegg5ZecGCUD/f07pFhgdPYpe0NsCUmu1wG8BqNn6VlmpxLUMbXC7bu45UGFK16YYy+pLES4CvcsrhNDP1nErOzZUi5JT7VsdeXQZfq8h0iKoC4LXlTfLJ45fh4J5kcA2+CB5Cc/WaKMqR5GOrRbtg48wpMI8AH4CmoprOyYrrT00hs/IwehyKq1apPGWKJKlw9tmxlYMpOlOEE9a1UDKtQhrcLy5e3PgPHqfozBUKRsqTRLUZfFG0Mw2n6EyooV27VJ48WYUtW1SeMSPU74o8g69GkqboTA2f51NldaS1ahqypF0wpVkGX8MXBPnwFLBqcwxl8CV5m70E+LLC1UM6hi60Ob9TdJr47tz4/8e2xUnEFJ25zB53XoURcI/k3nBcq6ee4piyM0lIrUYbTNEJIHImrz+RDVgxRWd0Ojp07KqrlP/tb0MfcE0UOmahqcsggytOLmfmOAwpg3J41y71/PSncvr71f3ww8f/4HGKzrQH+FrmFYXWztjRnhXXrlVx7dqWr4mk6GFk7NVml9U9Ah+dzDxZ7DrAd/ycTGtVuJLyja/cvKGh49dTpui0fHMMZezZ2jW1et8FYG47j7f/TY8Bv3Nq+ww2NzJetrqMw4h2eMvzIysHnQ+dndKI6cmyQp6is5Lj4jVGpaVBS+ta1/DE0m4FED3ayIyr2//ZmI4kNp2dsQf3yOBLEabodC+M+eJqbwwNjWo5U6Zo+NJLVTjnHP8fksIMvtL8+cd/Xras9W4MYYTRcdK1XkpYDxKFnllZF+AL/pG+2TqS3Y6BTJ5MrfGdsWtv5eYePtxRl8GX5QE4qzfdUHttax1k9XbFVYzDxYV2w7rjfc4LLhhu/CK/GXwGAnx1U3R6+2hjqqboTEOfMqJOWGdnCN8TZQZf1OWI4zv8anUMpbWfDU/I4APQVCSDWg3+nUS1HUsbyoxgYt/HsRcgRej0uhfGlI0RjxLk2l28aoOYsUY+zBu6+GL13nabnK4uFbZulV47/jendpqjsPaFjRf6OIX4NPQ4vwkBRsQ9UhgVH1N0ZlUWglu1mxhoqruUt6k2bU5djyHjU3QmeTtMctVuu+hPLjyhqLefOqjeXkezZze5MMd6wR4T93LUoT8EFTGntzeS7wllv0WYwRfKg6YtVJ6xSe+XtLy/zUo/Gy0R4AOAZlp0MBicgXF0xEITdYDJalFk8IVd/x6n6Cycfrq69u+XJA3v3BlOmSLkTJ2qwQ98YOLfuddbvJhzIRnC2A9JzuBLy3Hnc9CRNfiyoS7A92YGn68AX42kD0S2Y/V4vaGsqSRn77aa0tnqfReAqe3O5aQVK0qtX+T3gm1gZ9Rl8L1Zljj3s6Oc9c/ilRYtUnHhQnUcOKDC2WeH9j2dYYzwhzHbRxLX4Et6Y8aTF2iDAB+AyJm8/rAGH8LCPk6RuB9DtUkYAb6op0j1cmHI5eRMn65jH/iA8keOqLhsWXjlikmrp6DDuIQ2nKLT4gY1kqKb2C9tOlcE+EJgIMCXlqrwLAMb3iyDr+kLMsXi0fratjVjGXy1+y4rDylEuZ0jmzdLDz449vNppzV/YQgH1fhHjAeg69bgS4IknzzN5HIauuIK5YaHQ83mazpFZ5A6i6vxiuJ7bTqWWjTMmVnrGi0R4AMANwjwIWocZOakdXA5DGGMPNcu/G3mU/1rEMQsz52r8ty5MRUoXLEc7im60YwiMBZFNk6cAT7bs41ccx3gO74zslI1kjK2sfUZWoEy+FLUpjZi1eYYKqxV21wh5YdiU7Xnc6PtbjtFvEujGzeq3NUlFYsqnHqqkc/0KgmB23zeUbmck3K5dDyrmcuFPlVnGBl8ofTh/GTwhS3pjRlTdKINAnwAIld/vYl9uLexioKyBl/2sI9TJBV3hREJ48aq9gEJhdzqexhgyULgoeUmhrD9jpOrz1RB68MyjPVMcjlt21bQ/ff3SJJ27BgJ/h1+pXXgwcBId1qqAi7U7uwkjKDHxOogkc/C2rT7vY0jJ/Q+3jBT+8tVELCzU87554/9/MorLT4svAy+JFy2r7xyUPv2denUgWHl7z3++2wccf50dIRQO1FmzlWNv0VcDqsuRDWScMIidgT4ADRl+1znRtVcJJN8UwYz6BelR20Qh6athQhudEKvf49TdKZdy/Efk1P2VEjTVDGJyID0+Rlbtowol5O6uqQ1a0aDf6ZfDDxUyewUnfm8nHxeuXI5pIWCkm08g29CkAw+y1m9Ob6vb+0zwJLCy1JPWVG/2xtUUpOKK0+frvxrr0mSiosXmy5aZaECv6w+wOfU/D86c+aUde65BXU8z52bW5GswWeiEXAzmEYGXzUy+NAGQ9SAS7SR5lgz7kcGH5AOZPC5VnXrYLChG129WpJUXLpUCnl6mrYBvrQ8relSPJuYq/7uDNRzIGFk8Enq7pbOOGNEW7aMxPtgUlqfivLZoa2sjrRWTUMdHRpdt05OLqeR9evjLk3oag+H8XuJ4eEmLwjy4ZaxZjYXN3wGVZK8C70E+JK8HSYFaauHLrtMpQULNLpqlUY3bDBXqBCNb6+paUeNyspB50PTNfiCCKOj4iKDz9PfTEj6cUXDjDay9+gcAPhRd5FMYGcXRmVlyplMqA3w0eltLqTg1/CFF2pkyxaVZ8ww9plNeRmMyNToegOcC8kQ5fRHcUjrk8U+s8OrM/iS39cwOb5bOO88Fc46ayz6nHJ1Ab43fzE6mqv6t68PS8s5ZCPW4Gv577RydRlrlsE3c6YG3/c+84UKwfh2uVlzMDJZOcgMCOWZ1hBOeicpa/DZ9NBni85YmmZOgX8E+ABEzprrT4sLftbHhBG+5A/5WYQT1r3KzGWTjXMup/KsWeY+z5TEXoDMiXoNPokbTc9CyOBL0jWkbprktB4PPjL4MikDwT1JKhZrz8mxf8+fX4qjOIli9SXCZ+Frx2aTvM0kitRztZ1JzHbzKUnXqUjWYkuJSKbojErUfUebj6uafZSelgheEOADABeYojN72Mfp0fQpQdRLw+JQrMFXJc4pOmMuRKI4jodph/zUV5LrOKVtsN9Bx8rMiCTvNgRz5Ej1zt28tagZT41qypQ393+Q88LyA8fqIJHVhXfHS4Avpc17W+VyAve7kWNxbOdPTNFp8qP9SuE5FpYwpugMZS17Pxl8Yc92kfTjjDX40AYBPsClLLaRKXoILbBcngAfYC2m6HQvDXXjZQ2+TIxMtZhmKawMvtoAH6KXpHM5E+eZWIMPVY4erd65i5eU1f1KRXvMFJ128r0v7NlnZPC116iOyjNnRl8Qw8b3Z6KvTVk96FxomsGXtDVf3azBF/F+TvrsEi3Xw6RhhqQkN9sAEK/KqerI4Msc9nGKJPouNWFsepLRgCw8xxLHFJ3jnzv+8Vmo50BCmKIzUedvWp8s9lnnaUiURntDQ21SnTK885PcXLXD9SxBa7PFaHysffDd71ZpxgyNrF+v0qJF8RbKJTf7bKK5SsIT31k9yHw47bSRiZ83bRpp8UoPwriPZg0+78jgQxtk8AGAG6zBB1irbopOOr3N2XSj00TLJxzrXmznNnoRS4AP3tg82u1GWrfP53aloJmFR/395UDrSKVtDSqrmwRDa/DZyup9Z9D4/iwtWaLBq66KtzDjDOyM8Y+onEra0Ef7x0Hn2pQpjt7znkG9/HJeq1ePmvnQMOrfzWdEsJ+d7m45+bxy5bKcSZNC/77QcE5AZPABsFyoN0tVIzDN/4R0Yh+nSO0UnWguDSPPTNFZJY7dWDdFp63HUlQM1E+iAwC1ZSmX4ylH2MjgQ4V580oTP594YtHsE/YcOPHJQLDB20xwKYlcepSWgG2t2ik6c0nIWU3hORamhQtL2rhxVD09hj4wwgy+lkdbGMdBT48KZ52l0ty5Kpx5pvnPN8lLw4xMIoMPcIk2M9uYojN72McpQgafe1kI8FWydRtNiWiKzszXcxuhrPtBnYfPdwafU/GzyQIhSS64YFj/7//1avJkR2eeWZAeCBAYStmBYnWMLAMZfI7TfJus2lchSutzKuO4dcKEEA6Gpv3eGO5DRzdv1ujmzZF8VyA2XUQQCwJ8AOBCdV+DiysiwJ2UOWTwuVbVumXhGMzANoa1ia2CUnUZfGjNxGi3Rceyp2l0beIrgy+ldQHNmVPW7//+seOHRc0gaaDAvkXneyOpCvBlTO3mZ2AiBGuYuJrUZvAlIqhgdYNhv1COAD9TdGZ9v3s5F5Nw3iJyXI4BoJkmTxBlvW+RFezn9GANPg/SMHecl5uaDIxMxbIGHzfl8Utynadl4MHncZ6GZhbuVO1fg+1iKFm/8MVtazZzZllTp46lfa1YUQyvQCHj8j4mLZexWsfX4Hvz/0zRiTDuo91k8NVK60nnFgE+tEEGHwC4UJmNQB8zG9jPKZKBII4xFQe+tQOIXtbgs3UbPWh5rxzVd2egngMJY7AkyXWelrnNaurY7fk0ZUq54mcGYTIjyCBpks9nX6qPe6s2z/fUvNJ73zuo3/ymQ8uXJy/Al8s5LafmPP661v/OCjd1ZbNE79dEFy6FIlyDj33bAgE+tEGAD3CJa032OE0GgTkWAMvUTtHJSdxcGoJfbW5qiosXq+vJJ1WaNUtOb29EhYpPy8G4kPYxt5X1Wh6WWRsxTevAg8v9dvLJRR0+PKJyWTrllNGQC4XEyNp53oLVVRGg8P39jlavTl5wTxrbDDdNs9X7zqBEXsY8BJsDfkS0ElmoDGGd6GRIZKODJCHAB6ApG55Mi6yMBPgyh/2cIqzB514aDvw2N0DDu3dr9NRTVZo/Px3b20Ycm1iX/ZmBeg4kaxl8aRmk8FnnXV3SW95SCKFASLJA7aJN57cLVhff6sIHl7JD0be0JKK3lYTrNQddcEHqLIwpOv1k8CXhWLQEZ0g2MWcVADTTJJOF2f4Au1g71WQcMpDBp44OlZYskbq7oylPzGJZg+/NW0tbD6HIZe3p6LQO0iS5zhG/2uODG4oJVp06GQ825HIWT69qUBYuYyefPKqcHE2ZUlZXV3xlyuxBlhRR9lHZ180xRSfaIIMPANwggy9z2M8pwhSd7qUhwAf3QpuiM9sDoJ6ZGDC2qY7TMvCQ8YF+eGQyC8LyY62++Ba1CSk9791uRko337O0XMZqVe7Piy4aVvHXBU0fSti0slk96GISxqwcPHwbsrQ2UGiJx8YAl7gGZVvlYCXHQlbE3DHiQDOHKTpdcyoHIDkGU6K6LSv39x//S8XPRk0cO07Nv9EQGXx2YqQbXjBF5wSbi1/Xetm8MRV6e49vWUdH8za6/lBMSXvuUSIvY4aPxXxemjbNiT3ZOIlVnSlhHABk8HmWI4MPbRDgA9BUVNcFG67juXzlFJ1cMAGr2NDIJJGt9cZNTZXa3XjsXZersHWrjl15ZejBb1sPociFsAZfks8CT4MUQFqEsY5RSlhVFVYV1r1LLhma+Pkd7xhq+rqUxZp9S+tlrG5/JmFDOeiCCxKkC6O+/azBB6AlpugE0FR3dwI6dAlRN90YUo/+ZYrU7sxyOZ5y2CAFU3QSPKhWv6e5owAAIABJREFUd/jPmKGRs84K9TuZorNeq8MyjOmPEi0lbXDm9huCCRKET9mxZvXmWF345k44oawPfvCYymVp9uzmbXRKNz9TurpatT41f0tCn5qDLLDiihUq9/UpPziokXXrvL05jIdT2KeujK5era7HHhv7edUq1+8rDwyEVSQkGBl8gEtZuQadf/6wJKmz09H27SMxlyZmTQa6s3IsZB37OcVSMrgcihQE+BIxGJEgsexGRgCjZ1Odp/UcTXKdI3Z1AeGkZVREyOri29TWejRzZrllcE9K9eZ7ksjLmMudMW2ao2XLxtbVW7VqNMwShSLKqu/vP/5tVs/i1NmpwQ98QENve5sK55zj7b1hTNFJBp8rhZ07VVy2TMUlS9rut6FLLlG5r0+ja9eqvGBBRCVEkpDBB6DKunWjmju3pClTnKq5+DMvBWPeAN4U92ISSUaAL3XiGIwj690j1uBLhyTXOeJncg2+lLF686wuvHcE+MY4jt0b/o53DOno0bFteOKJronfJ3J/xlioiy4a1g039MlxpHe9q/nUtTZwBgZU9JPZFcZJ72cNvrT2HVtwens19M53unpt8eSTVTzppISexIgCAT4AVXI5ad48O7NbQl3ku+JCSWwgG3p6steJTLORTZvU/dBDKi5fLodpK5qraOvqsg1skcEbwFaSkMHHHmnDxOCJTedrWs5RRrrhhclpziw/1qw+dawqrHlW77uAcjkn2YE9Dzsjl5MGBhwdOZKr+32VJFyvYzzoZswo60MfOqZiUZo6NQF1EYMw7gcbfaa1951JQh1mGgE+AGiGKTozZ+fOYd1zzyT19ZW1YUPMU5ZwoBlV2LlTI6edRnCvncoBSFuPwSQMRiRYlLvV1kMoclnL4EvpNMm0PGjJ5CB1ks9vF6wufpYjXKrf3Cw9+JrLZbCLmYQNjvkcmzw5AXUQpygz+AD4RoAPcCnU7DAkXuV0Y/RH0mvTplEtWVLSwEBZXV3tXw+7OFOmxF2E5KOBS504dmndFJ0cV63HyMIYLKHOw0edw4sgGXwpO7asPnUynqFu9b5DHSv3pxWFTJGo1uBjvwKBZOh5GwDwqGkGX9Zu5bJl1qyyenriLgUQE9bgS52od+PppxcmptlZtKgUTyEMiiQ7oXbA2OL6ciUt56iVI6OIjcnUp5Qda1ZtTubP+9r2OyXtuQtZylZMlMydYwkTxoHvY5/m0tJ3BELCJQoA3GCKTgAZUHXrZGtjxw1glajHIrduHdHJJxe1dGlRs2fbORXjhg0jkqS+vrJOPLEY/hdmLIMvl9IpOpNc54hfXeA+QAaf/Q8BWHydtr7ug7HoUmNc4rfVT9DEhv1pRSFTLIz6J1oOGMcUnQBSw3hfr+IDmaITQCaQwYeAurulk04uqcupCOJYdizt3FnQkiVFzZtXVqetd0tJrvO0nKMMOsKLjE/tWMnqU8fqwgeX5c3PwrbWbWMSrtdZqPgkiyqDj/0MBGLrLSsQOa43GZeCMW8AaKty8UmLIhtOLjcxdYszbVrMpUmW2mtWJGM1lo8AdnRIK1eWovtCA/WTgCE495IwYBgGy45zRIw1+JqyafPqWi+bCm+A5Zf3QNK4rVZuk5WFtlgI9e2wBh9gHHmxAOAGU3QiahxoiEHphBNUmjdPTne3Rk49Ne7iuDZ0+eVy8nk53d0aPvfcuIuTaMyKk0BhtPdJvoakJcCX5ZFueMfxMsHqTc/4fjS5lKRtEr+rDRQwkduYyEJlWFx9uLT0HYGQ2PNoNgBErclUdVm6kQGQMR0dGnz/+6VSyaoMvtKiRTr24Q/L6egYmyMSVXbtGtKDD3brtNNGzV3DWg24ZHwA1Csj62nZVOdpWYPPpjpH/Gob3yCNseXHmtWnjlWFNc/qfRdQPu+oalqfFKrbvwkIqmQ9azZxTPThyOADjLNn5AYA4kQGH4CsyOWsCu6Nc3p74y5CYq1dW9TatcXovpALZR1PY2TUn53Yb2glQGQk/QPc8QcRXMtyhEvBZpq1XRq3te02JSDAl8qKt5mJY4I1+ADjyEMBgGYqOhmOCPABAOBKxgdAPWOKTjsluY6RPFmOjNRI1aanamPa4/KeYCFM0Vk4/fSJn0c2bQr8+bCfM2lS8A/xk8Gelr4jEBL7Hs8GgDhUZfDRuQAAoJm6KScZAWwtY/WThCm/jKjdjoztR3gTKAsvZW2q1ZtjdeHNy9J9cTp3dev9V1y9WoWhIWl4WCNbt0ZUphqcc7EbvOIKdT38sIqrV0tdXcE/kAw+wDgCfADgBlN0AgCAMGRt8Cota/ABXpjM4EtZG2HV5lhVWPOyvPlZ2Pa6bcznNbJlSyxlmZCFik+40qJFKi1aZO4DWYMPMI4pOgFYbfr044NEM2caHjCq7GQQ4AMAwB8unK1lrX7SksEHeFFzntdlOmeI1ZuetQcyMMHPrIKR8nUsWnD8cs6lTsNeIPsVCCTplyggMbjeJNOllw6pq8tRd7ejiy8eDu17msT6gPBwoAGwFQPZ3mStfgjwIYuCZPClbIDb6s3h+pZZ7GrAENbgA4xjik4AVps9u6yrrz6qXE7q7jb72ZU3bE7F022Jf3oPAIA4MQrmTdbqKy1TdDLYBC9MRrUsbzOsLr7VhUcQ7PqYWP1EABpqsA95WAIIhmFqANbr6TEf3KvDFJ0AgIwrzZkz8XNx5crmL+RC6U0GBq/K/f3Hf542LcaSADExmcGXMinfPKRELsdDHbGggUgf1uADjCPABwBuEOBD1Do64i4BAFQZuvRSja5apcKOHSotW9b8hRkIWBmVgfoZuuwyOfm8nHxewxddFHdxgOiRwTfB6kuEVYWFSYnf9YkvoE9WNxhoqNE+bDf2wawJQEtM0Qm4RD8i4wjwIQKFrVvVs2ePnK4ujaxbF3dxAKCKM22ahi++OO5iWKnVuEQWhizK8+bp2NVXS5Kcvr6YS2MIg03woG76MS9z/qdugJtzB/ax/rQDkqLR9Y+Hm4FACPABgAuVa/AxPQfCMnLGGSotWKDyrFnSpElxFwcA/EndYHTITNSXBcGm1AT2MsZxOH+NoF1siqqADThOY1JT8cnv7aCtRmvwEeADAmGKTgCxS2xnuTJrL08GHyLQ0aHSihVypk6NuyQA4B8XSm+oLztZEFRFggRZg6+G7UcesU7YKOnHqZ92obfX0bx5JUnS8uVFswUyJekVD8/qMtolMviAgMjgA1yiX5Ft1Rl8MRYEAICkY/TWG+oHSL8g7WLK2lTLi4+MSutxe/nlgzpwoEOLFpXiLgqyolEGX2eb8AQPVQEtEeADgGYqn7Qlgw8AAHdSNhgdOuoLQIbR5MEGiT9OfRawu1tatozgHiLUaA0+L+vSAqjDGQQATRSXLpU09jTRkZmLJ35P3wMAABiT+FFDALFK2UMAKdscZARjAMlAc5ECjRr9dhl8AFriDAKAJopr1+rY3LlyJk9W8ceTJ37PTSgAAM3VTaLDhbP1zEIG6ocajgHTRcELk+0gbSoQuVyONh8wokG03GENPiAQAnwA0EJ59mxJ1R167qkBAGiB9AxvqC8kWEcHg9qxo00AYsdpCBjS6GQiwAcEQpI5AHhE5x4AgBa4UAKpsX37iPL5sSDf2WcXYi6NvZxJk8x9GG0sgFq0C7CE4yfAx6wJQEtk8AEu0V/COKbnAACgBTLSvKF+7JSRwabJkx397u8e0xtv5LVkSSnu4tirs1NDb3ubuvbv18jGjcE+y/I2g0SN9LD8UPQkS9uaaBm59qZag5OJKTqBYAjwAYBHdO4BAGiBC6U3BESRcDNmOJoxg+BeUMUTT1TxxBO9vzFlbUJHh3T++cN65JEubd48EndxAskRbMiMBsuGAfCj0clEBh8QCAE+wKWU3VchAI4FAAA84MLZclyCIQtLMdiEuKSgTV2/flTr14/GXQzAtTPOKOh//3dsCHXbtgROV5yCdgEZ0ehY7SQ8AQTBGQQALlT2QXh6DwCAFmpu3AmDtGFiUI5gE5BeZPkCsVuwoKzLLhvSkSM5rVlDcDo29Hfs12BAzWGQDQiEAB8AeMQ9NQAALXCh9IbBewAAEm/lymLcRQDsRwYfYBwhcgDwiHE3AABaIGDljYH6Kff3GygIAACwEn0t2MLPGnwAWiLAB7hEfynbKvc/xwIAAC0Q4PPGQP0406apcPrpKk+dqqFLLjFQKACJRZuKhJg9uxx3EZA1TNFpvUZ70CHABwRCDiwAeJTL0akEAACGGFqzcOTMMzVy5pnBywMAcCeDwYYrrhjUvff2aOnSoubMIcAHwCMy+ADjCPABgAtk8AEA4BIZfN5QP1bKZXBgHwAWLSrpyisH4y4GMsTp6FCuVJIkladOjbk0CKxBv9dptwYffS6gJQJ8AOAR43AAADTHLbhHdCys1HYwCkD60X4jKVJ8LA6+//3qevRRFU86Serujrs4CKpRBl+j3wFwjTMIcCnF/aVY9PUdn85j4cJSjCXxjmMBAIAWyODzhvqyUvGkk1Tu75ckFbZujbk0AGJBVgkQuvKcOSq89a0qLV0ad1FgQqN+Lg9NAYFwBgGIxeWXD+m++7q1cGHJisW5K/sgPFwEAEALBKw8cagfO3V06NgHP6j84cMqz58fd2mQJbQZAGrRLsAWDQbUHAbZgEAI8AGIxaxZZV166XDcxfCFvjMAAC1woURW9PSovGBB3KUAAACwg48MPtY9BlojRA4AHjFuCQCAB1w4WyPjEQAABFCePj3uIgCu1WXsdXTEUxAgJQjwAYALlWNtjLsBANACAStvqB8AAODR4NvfrnJ/v0bXrlVp8eK4iwP45hDgAwJhik7ApVyOlHCM4VgAAKAFAlbeUF8AAMCj0ooVOrZ8Of0I2Cefl8rl4/8mwAcEQgYfALhABh8AAC6RwVen5dIh1BcAD3jUMEFYFwpxo88AG9Uet23W4APQGgE+APCIPjQAAC0QsPKG+gEAO9WuIwUAaK+m7axbkw+AJ5xBAOAR43AAAMCLadOOZ3lMmVKu/iMdCwCwxsjGjZIkp6dHo6tWxVwaALAQGXyAUZxBgEuMvWRb5f7n4SIAAFqo6TQxgZl0ySVDuummPknS2942VP1HMh4BeEEbEavC2WertHChSnPnSl1dcRcHAKzj5POqupI1WIPPyeWUYxpkwBUCfEALJ5xQ1IEDY6fJypXFmEuDpOCeGgCA5hwulHXmzCnr6quPSZL6+moGK6gvALBHZ6eKJ50UdykAwF61DwM2CPABcI8AH9DC7t3D+o//mKT+/rI2bBiNuzhICMbhAADwgAunpAaBPQAAACBrau8NCPABgRDgA1qYOtXRO9851P6FSL2+vnLDnwEAQA2mnPSEsB8AAAAyiwAfEAgBPgBwYcOGUb32Wl59fY6WLy/FXRwAAJKLgJ4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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 607, + "width": 892 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "plt.plot(train_losses,label='Training Loss',c='b',alpha=.50)\n", + "plt.plot(test_losses,label='Test Loss',c='r',alpha=.50)\n", + "plt.title(\"Traing Loss vs Test Loss\")\n", + "plt.xlabel(\"Num of Episodes\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving and Loading Models" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model Sequential(\n", + " (0): Linear(in_features=784, out_features=256, bias=True)\n", + " (1): ReLU()\n", + " (2): Dropout(p=0.2)\n", + " (3): Linear(in_features=256, out_features=128, bias=True)\n", + " (4): ReLU()\n", + " (5): Dropout(p=0.2)\n", + " (6): Linear(in_features=128, out_features=64, bias=True)\n", + " (7): ReLU()\n", + " (8): Dropout(p=0.2)\n", + " (9): Linear(in_features=64, out_features=10, bias=True)\n", + " (10): LogSoftmax()\n", + ")\n", + "Model Keys odict_keys(['0.weight', '0.bias', '3.weight', '3.bias', '6.weight', '6.bias', '9.weight', '9.bias'])\n" + ] + } + ], + "source": [ + "print(\"Model {}\".format(model))\n", + "print(\"Model Keys {}\".format(model.state_dict().keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model.state_dict(),'checkpoint.pth')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "state_dict = torch.load('checkpoint.pth')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "odict_keys(['0.weight', '0.bias', '3.weight', '3.bias', '6.weight', '6.bias', '9.weight', '9.bias'])\n" + ] + } + ], + "source": [ + "print(state_dict.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "model.load_state_dict(state_dict)" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Introduction to PyTorch/5. Loading Image Data.ipynb b/python/pytorch/Introduction to PyTorch/5. Loading Image Data.ipynb new file mode 100644 index 0000000..d70fd34 --- /dev/null +++ b/python/pytorch/Introduction to PyTorch/5. Loading Image Data.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "%config InlineBackend.figure_format = 'retina'\n", + "import numpy as np\n", + "from torchvision import datasets, transforms" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "transform = transforms.Compose([transforms.RandomRotation(30),\n", + " transforms.RandomResizedCrop(100),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.CenterCrop(224),\n", + " transforms.Resize(255),\n", + " transforms.ToTensor()])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "data = datasets.ImageFolder('imgs/',transform=transform)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "dataloader = torch.utils.data.DataLoader(data,batch_size=1,shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "image, label = next(iter(dataloader))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 3, 255, 255])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "image.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\User\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\text.py:1191: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if s != self._text:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 289, + "width": 572 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "for i in range(len(dataloader)):\n", + " image, label = next(iter(dataloader))\n", + " plt.subplot(4,4,i+1)\n", + " plt.imshow(image[0].reshape(image.shape[2],image.shape[3],image.shape[1]),interpolation='nearest')\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.xlabel(label.numpy())" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Introduction to PyTorch/6. Transfer Learning.ipynb b/python/pytorch/Introduction to PyTorch/6. Transfer Learning.ipynb new file mode 100644 index 0000000..4adfb69 --- /dev/null +++ b/python/pytorch/Introduction to PyTorch/6. Transfer Learning.ipynb @@ -0,0 +1,822 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "from torchvision import datasets, transforms, models\n", + "from torch.autograd import Variable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = 'Cat_Dog_data'\n", + "\n", + "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n", + " transforms.RandomResizedCrop(224),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406],\n", + " [0.229, 0.224, 0.225])])\n", + "test_transforms = transforms.Compose([transforms.Resize(255),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406],\n", + " [0.229, 0.224, 0.225])])\n", + "\n", + "train_data = datasets.ImageFolder(data_dir + '/train', transform=train_transforms)\n", + "test_data = datasets.ImageFolder(data_dir + '/test', transform=test_transforms)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset ImageFolder\n", + " Number of datapoints: 12500\n", + " Root Location: Cat_Dog_data/test\n", + " Transforms (if any): Compose(\n", + " Resize(size=255, interpolation=PIL.Image.BILINEAR)\n", + " CenterCrop(size=(224, 224))\n", + " ToTensor()\n", + " Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", + " )\n", + " Target Transforms (if any): None" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset ImageFolder\n", + " Number of datapoints: 25000\n", + " Root Location: Cat_Dog_data/train\n", + " Transforms (if any): Compose(\n", + " RandomRotation(degrees=(-30, 30), resample=False, expand=False)\n", + " RandomResizedCrop(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=PIL.Image.BILINEAR)\n", + " RandomHorizontalFlip(p=0.5)\n", + " ToTensor()\n", + " Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", + " )\n", + " Target Transforms (if any): None" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = torch.utils.data.DataLoader(train_data,batch_size=128,shuffle=True)\n", + "test_loader = torch.utils.data.DataLoader(test_data,batch_size=128)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DenseNet(\n", + " (features): Sequential(\n", + " (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", + " (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu0): ReLU(inplace)\n", + " (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (denseblock1): _DenseBlock(\n", + " (denselayer1): _DenseLayer(\n", + " (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + 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BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer10): _DenseLayer(\n", + " (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer11): _DenseLayer(\n", + " (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer12): _DenseLayer(\n", + " (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer13): _DenseLayer(\n", + " (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer14): _DenseLayer(\n", + " (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer15): _DenseLayer(\n", + " (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer16): _DenseLayer(\n", + " (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer17): _DenseLayer(\n", + " (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer18): _DenseLayer(\n", + " (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer19): _DenseLayer(\n", + " (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer20): _DenseLayer(\n", + " (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer21): _DenseLayer(\n", + " (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer22): _DenseLayer(\n", + " (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer23): _DenseLayer(\n", + " (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer24): _DenseLayer(\n", + " (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " )\n", + " (transition3): _Transition(\n", + " (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace)\n", + " (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n", + " )\n", + " (denseblock4): _DenseBlock(\n", + " (denselayer1): _DenseLayer(\n", + " (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer2): _DenseLayer(\n", + " (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer3): _DenseLayer(\n", + " (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer4): _DenseLayer(\n", + " (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer5): _DenseLayer(\n", + " (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer6): _DenseLayer(\n", + " (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer7): _DenseLayer(\n", + " (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer8): _DenseLayer(\n", + " (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer9): _DenseLayer(\n", + " (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer10): _DenseLayer(\n", + " (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer11): _DenseLayer(\n", + " (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer12): _DenseLayer(\n", + " (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer13): _DenseLayer(\n", + " (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer14): _DenseLayer(\n", + " (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer15): _DenseLayer(\n", + " (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " (denselayer16): _DenseLayer(\n", + " (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu1): ReLU(inplace)\n", + " (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " )\n", + " )\n", + " (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (classifier): Linear(in_features=1024, out_features=1000, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "model = models.densenet121(pretrained=True)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "for param in model.parameters():\n", + " param.requires_grad = False\n", + " \n", + "from collections import OrderedDict\n", + "classifier = nn.Sequential(OrderedDict([('fc1',nn.Linear(1024,500)),\n", + " ('relu',nn.ReLU()),\n", + " ('fc2',nn.Linear(500,2)),\n", + " ('output',nn.LogSoftmax(dim=1))]))\n", + "\n", + "model.classifier = classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#for cuda in [True, False]:\n", + " criterion = nn.NLLLoss()\n", + " optimizer = optim.Adam(model.classifier.parameters(),lr=0.003)\n", + "# if cuda:\n", + "# model.cuda()\n", + "# else:\n", + "# model.cpu()\n", + " \n", + " for ii, (inputs, labels) in enumerate(train_loader):\n", + " inputs, labels = Variable(inputs), Variable(labels)\n", + " \n", + "# if cuda:\n", + "# inputs, labels = inputs.cuda(), labels.cuda()\n", + "# else:\n", + "# inputs, labels = inputs.cpu(), labels.cpu()\n", + " \n", + " start = time.time()\n", + " \n", + " outputs = model.forward(inputs)\n", + " loss = criterion(outputs,labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " if ii==1:\n", + " break\n", + " print(f\"Cuda = {cuda}; Time per batch: {(time.time()-start)/3:.3f} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Full Model " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "model = models.densenet121(pretrained=True)\n", + "\n", + "# Freeze parameters so we don't backprop through them\n", + "for param in model.parameters():\n", + " param.requires_grad = False\n", + " \n", + "model.classifier = nn.Sequential(nn.Linear(1024, 256),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.2),\n", + " nn.Linear(256, 2),\n", + " nn.LogSoftmax(dim=1))\n", + "\n", + "criterion = nn.NLLLoss()\n", + "\n", + "# Only train the classifier parameters, feature parameters are frozen\n", + "optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n", + "\n", + "model.to(device);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "epochs = 1\n", + "steps = 0\n", + "running_loss = 0\n", + "print_every = 5\n", + "for epoch in range(epochs):\n", + " for inputs, labels in trainloader:\n", + " steps += 1\n", + " # Move input and label tensors to the default device\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + " \n", + " optimizer.zero_grad()\n", + " \n", + " logps = model.forward(inputs)\n", + " loss = criterion(logps, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item()\n", + " \n", + " if steps % print_every == 0:\n", + " test_loss = 0\n", + " accuracy = 0\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for inputs, labels in testloader:\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + " logps = model.forward(inputs)\n", + " batch_loss = criterion(logps, labels)\n", + " \n", + " test_loss += batch_loss.item()\n", + " \n", + " # Calculate accuracy\n", + " ps = torch.exp(logps)\n", + " top_p, top_class = ps.topk(1, dim=1)\n", + " equals = top_class == labels.view(*top_class.shape)\n", + " accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n", + " \n", + " print(f\"Epoch {epoch+1}/{epochs}.. \"\n", + " f\"Train loss: {running_loss/print_every:.3f}.. \"\n", + " f\"Test loss: {test_loss/len(testloader):.3f}.. \"\n", + " f\"Test accuracy: {accuracy/len(testloader):.3f}\")\n", + " running_loss = 0\n", + " model.train()" + ] + } + ], + "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": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/pytorch/Introduction to PyTorch/Cat_Dog_data/test/1/1.jpg b/python/pytorch/Introduction to PyTorch/Cat_Dog_data/test/1/1.jpg new file mode 100644 index 0000000..ada2e37 Binary files /dev/null and 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