576 lines (575 with data), 142.9 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time in seconds since beginning of run: 1682448440.1718328\n",
      "Tue Apr 25 11:47:20 2023\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "seconds = time.time()\n",
    "print(\"Time in seconds since beginning of run:\", seconds)\n",
    "local_time = time.ctime(seconds)\n",
    "print(local_time)\n",
    "# Quanvolutional Neural Networks by Author: Andrea Mari \n",
    "# https://pennylane.ai/qml/demos/tutorial_quanvolution.html\n",
    "# This cell is added by sphinx-gallery\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import pennylane as qml\n",
    "from pennylane import numpy as np\n",
    "from pennylane.templates import RandomLayers\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "n_epochs = 30   # Number of optimization epochs\n",
    "n_layers = 1    # Number of random layers\n",
    "n_train = 80    # Size of the train dataset\n",
    "n_test = 30     # Size of the test dataset\n",
    "\n",
    "SAVE_PATH = \"imageqnn/\" # Data saving folder\n",
    "PREPROCESS = True           # If False, skip quantum processing and load data from SAVE_PATH\n",
    "np.random.seed(0)           # Seed for NumPy random number generator\n",
    "tf.random.set_seed(0)       # Seed for TensorFlow random number generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "mnist_dataset = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist_dataset.load_data()\n",
    "\n",
    "# Reduce dataset size\n",
    "train_images = train_images[:n_train]\n",
    "train_labels = train_labels[:n_train]\n",
    "test_images = test_images[:n_test]\n",
    "test_labels = test_labels[:n_test]\n",
    "\n",
    "# Normalize pixel values within 0 and 1\n",
    "train_images = train_images / 255\n",
    "test_images = test_images / 255\n",
    "\n",
    "# Add extra dimension for convolution channels\n",
    "train_images = np.array(train_images[..., tf.newaxis], requires_grad=False)\n",
    "test_images = np.array(test_images[..., tf.newaxis], requires_grad=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "dev = qml.device(\"default.qubit\", wires=8)\n",
    "# Random circuit parameters\n",
    "rand_params = np.random.uniform(high=2 * np.pi, size=(n_layers, 4))\n",
    "\n",
    "@qml.qnode(dev, interface=\"autograd\")\n",
    "def circuit(phi):\n",
    "    # Encoding of 4 classical input values\n",
    "    for j in range(4):\n",
    "        qml.RY(np.pi * phi[j], wires=j)\n",
    "\n",
    "    # Random quantum circuit\n",
    "    RandomLayers(rand_params, wires=list(range(8)))\n",
    "\n",
    "    # Measurement producing 4 classical output values\n",
    "    return [qml.expval(qml.PauliZ(j)) for j in range(8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def quanv(image):\n",
    "    \"\"\"Convolves the input image with many applications of the same quantum circuit.\"\"\"\n",
    "    out = np.zeros((14, 14, 4))\n",
    "\n",
    "    # Loop over the coordinates of the top-left pixel of 2X2 squares\n",
    "    for j in range(0, 28, 2):\n",
    "        for k in range(0, 28, 2):\n",
    "            # Process a squared 2x2 region of the image with a quantum circuit\n",
    "            q_results = circuit(\n",
    "                [\n",
    "                    image[j, k, 0],\n",
    "                    image[j, k + 1, 0],\n",
    "                    image[j + 1, k, 0],\n",
    "                    image[j + 1, k + 1, 0]\n",
    "                ]\n",
    "            )\n",
    "            # Assign expectation values to different channels of the output pixel (j/2, k/2)\n",
    "            for c in range(4):\n",
    "                out[j // 2, k // 2, c] = q_results[c]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum pre-processing of train images:\n",
      "80/80        \n",
      "Quantum pre-processing of test images:\n",
      "30/30        \r"
     ]
    }
   ],
   "source": [
    "if PREPROCESS == True:\n",
    "    q_train_images = []\n",
    "    print(\"Quantum pre-processing of train images:\")\n",
    "    for idx, img in enumerate(train_images):\n",
    "        print(\"{}/{}        \".format(idx + 1, n_train), end=\"\\r\")\n",
    "        q_train_images.append(quanv(img))\n",
    "    q_train_images = np.asarray(q_train_images)\n",
    "\n",
    "    q_test_images = []\n",
    "    print(\"\\nQuantum pre-processing of test images:\")\n",
    "    for idx, img in enumerate(test_images):\n",
    "        print(\"{}/{}        \".format(idx + 1, n_test), end=\"\\r\")\n",
    "        q_test_images.append(quanv(img))\n",
    "    q_test_images = np.asarray(q_test_images)\n",
    "\n",
    "    # Save pre-processed images\n",
    "    np.save(SAVE_PATH + \"q_train_images.npy\", q_train_images)\n",
    "    np.save(SAVE_PATH + \"q_test_images.npy\", q_test_images)\n",
    "\n",
    "\n",
    "# Load pre-processed images\n",
    "q_train_images = np.load(SAVE_PATH + \"q_train_images.npy\")\n",
    "q_test_images = np.load(SAVE_PATH + \"q_test_images.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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kvn376uyzz9bf//539enTR//zP//jdo0AAAAAAA8La4/p3XffrSlTpqioqKhRfPr06brnnnv0H//xH64UBwAAAADwvrD2mJaXl2v06NFB8Z/+9KcqLy9vdlEAAAAAgPgR1mA6dOhQvfXWW0Hxt99+WxdddFGziwIAAAAAxI+wDuW98sordc8992jTpk264IILJEnvvvuu/ud//kczZszQ6tWrG+Ui+q1cuTJkfOfOnSHjoc5Meskll4TMnTlzZsh4VlZWUOyBBx4Imbtnz56QcSCSLr/88qBYv379QuZalhUy/q//HsaTUGfgbervaMuWLRGuBrGgqTPOhnrfPPHEEyFzf/Ob3zS7jrPPPjso1tRZeY8dOxYy/vXXXwfFtm3bFjL3v/7rv0LGN27cGBRr6ozeX375Zcj47t27g2Jt27YNmfvJJ5+EjANekp2dHTL+wgsvNHvtf/zjH0GxpnoToYU1mI4fP16StGjRIi1atCjkY9K3/5DX1dU1ozwAAAAAgNeFNZhyLToAAAAAgFvC+o4pAAAAAABuCWuPqSStWbNGa9as0b59+4L2oDb1fQkAAAAAAL4rrMF0xowZKioq0oABA9S5c+cmTwqA2Pfxxx+HjP/nf/5nUOyKK64Imfv000+HjN9+++1BsZycnJC5w4cPb6pEIGJCnSQkMTExZO6+fftCxlesWOFqTSb5/f6gWGFhoe3n//Wvfw0ZnzJlSrglwUP+9RwV/+qLL74IiuXm5kasjl27dgXFVq1aFTJ3+/btIePvvvuumyWd0M9//vOQ8Y4dOwbFQp2gBYgX99xzT8i4G19TnD17drPXiHdhDaZPPPGElixZohtvvNHtegAAAAAAcSas75jW1tZG9NNKAAAAAED8CGswve222/Tss8+6XQsAAAAAIA6FdSjvkSNHtHjxYv3lL3/R2WefrTZt2jR6fN68ea4UBwAAAADwvrAG0w8//FD9+vWT1PTJcQAAAAAAsCOswfSNN95wuw7EmMrKyqDYM888EzL397//fch469bBb7+LL744ZO7QoUODYm+++WaT9QEtLRAIhIyXlZW1cCXNF+rsu5J07733BsUmT54cMnf37t1Bsblz54bMPXTokIPqEG8efPBB0yVEvUsuucR27gsvvBDBSoDocHwH2nfl5eU1e+2XXnopZHzHjh3NXjveORpMR40adcIcn8/HP3oAAAAAANscDaYpKSmRqgMAAAAAEKccDaZPP/10pOoAAAAAAMSpsC4XAwAAAACAWxhMAQAAAABGhXVWXsSPs88+O2T8Jz/5SVDsvPPOC5kb6uy7Tdm2bVvI+Lp162yvAZiwevVq0yU41tRZC5s60+4111wTFGvq7IRXX3112HUBiJyVK1eaLgGIuNdffz1k/LTTTrO9xrvvvhsyftNNN4VTEmxgjykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEZx8qM49IMf/CAoNnHixJC5o0aNChlPS0trdh11dXVBsbKyspC59fX1zf55gFM+n89WTJJGjhwZMn7nnXe6WVLYfvnLXwbFpk2bFjI3JSUlZPyPf/xjUGz06NHNKwwAAJedfvrpIeNO/j+5aNGikPFDhw6FVRNOjD2mAAAAAACjGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAYZXQwnTVrls477zwlJSWpU6dOGjlypHbs2NEox7IsFRYWKj09XW3bttXQoUO1detWQxUDAAAAANxm9Ky8a9eu1YQJE3Teeefp2LFjmjp1qvLy8rRt2zadfPLJkqQ5c+Zo3rx5WrJkiXr06KH7779fw4cP144dO5SUlGSy/KjR1Blyr7vuupDxUGfgzc7OdrOkRjZu3Bgy/sADDwTFVq9eHbE6AKcsy7IVk5ruwwULFgTF/uu//itk7oEDB0LGL7jggqDYjTfeGDK3b9++IeMZGRlBsV27doXMfe2110LGmzpDIYDoFOos4j169AiZ++6770a6HCAinn766aBYq1bN3/e2fv36Zq8BZ4wOpq+++mqj+08//bQ6deqkTZs26eKLL5ZlWZo/f76mTp3acNmSpUuXKjU1Vc8++6xuv/12E2UDAAAAAFwUVd8xraqqkiS1b99eklRSUqLy8nLl5eU15Pj9fg0ZMqTJTzECgYCqq6sb3QAAAAAA0StqBlPLsjRp0iRdeOGF6t27tySpvLxckpSamtooNzU1teGx75o1a5ZSUlIabpmZmZEtHAAAAADQLFEzmE6cOFEffvihnnvuuaDHvvsdCcuyQn5vQpKmTJmiqqqqhltpaWlE6gUAAAAAuMPod0yP+8UvfqHVq1dr3bp1jU7QcfxkIuXl5ercuXNDfN++fUF7UY/z+/3y+/2RLbgFNLV9//Zv/xYUW7hwYcjcnj17ulrTv3rvvfeCYg899FDI3JdeeilkvL6+3tWaAJMSEhJCxsePHx8Uu/rqq0PmNvXVg5ycnPAL+/+F+vrDG2+8ETL3t7/9bbN/HgDzQp2szY2TwgAm9OvXL2T80ksvDYo19X/M2trakPHHHnssKPbll1/aLw6uMPqvk2VZmjhxol588UX99a9/VdeuXRs93rVrV6Wlpam4uLghVltbq7Vr1yo3N7elywUAAAAARIDRPaYTJkzQs88+q5deeklJSUkN3xtNSUlR27Zt5fP5VFBQoJkzZyonJ0c5OTmaOXOm2rVrp+uvv95k6QAAAAAAlxgdTB9//HFJ0tChQxvFn376ad10002SpLvvvlvffPONxo8fr4MHD2rgwIF6/fXXuYYpAAAAAHiE0cG0qQvV/yufz6fCwkIVFhZGviAAAAAAQIvjG/AAAAAAAKOi4qy88aJ9+/ZBsSeffDJkblNnHuvWrZubJTUIdcZOSZo7d27I+GuvvRYU++abb1ytCTDtnXfeCYpt2LAhZO55551ne93jZxz/rqbOxh3KgQMHQsaXL18eMn7nnXfaXhuAdw0aNChkfMmSJS1bCODQqaeeGjLe1O/UUPbs2RMyftddd4VTElzGHlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVZeZtp4MCBQbHJkyeHzD3//PODYmeccYbrNR339ddfh4wvWLAgKDZz5syQuYcPH3a1JiCW7N69Oyg2atSokLm33357yPi9997b7Dp+97vfBcUef/zxkLmffvpps38eAG/w+XymSwAA29hjCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUZz8qJmuuuoqWzGntm3bFjL+v//7v0GxY8eOhcydO3duyHhlZWXYdQHxrqysLGS8sLDQURwA3PLKK6+EjP/Hf/xHC1cCRM4nn3wSMr5+/fqg2IUXXhjpchAB7DEFAAAAABjFYAoAAAAAMIrBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFE+y7Is00VEUnV1tVJSUkyXgThUVVWl5ORk02XEHHoWptCz4aFnYQo9Gx56FqacqGfZYwoAAAAAMIrBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEYxmAIAAAAAjPL8YGpZlukSEKd474WHvzeYwnsvPPy9wRTee+Hh7w2mnOi95/nBtKamxnQJiFO898LD3xtM4b0XHv7eYArvvfDw9wZTTvTe81ke/9ikvr5ee/fuVVJSkmpqapSZmanS0lIlJyebLs111dXVbF8UsCxLNTU1Sk9PV6tWnv/sx3X0rHfEyvbRs81Dz3pHrGwfPds89Kx3xMr22e3Z1i1YkxGtWrVSRkaGJMnn80mSkpOTo/rFay62z7yUlBTTJcQsetZ7YmH76Nnw0bPeEwvbR8+Gj571nljYPjs9y8dMAAAAAACjGEwBAAAAAEbF1WDq9/s1ffp0+f1+06VEBNsHr/H6a872wWu8/pqzffAar7/mbF9s8fzJjwAAAAAA0S2u9pgCAAAAAKIPgykAAAAAwCgGUwAAAACAUQymAAAAAACj4mYwXbRokbp27aqTTjpJ/fv311tvvWW6pLCtW7dOV1xxhdLT0+Xz+bRq1apGj1uWpcLCQqWnp6tt27YaOnSotm7daqZYh2bNmqXzzjtPSUlJ6tSpk0aOHKkdO3Y0yonl7YN99GxsvKfpWRznlZ71cr9K9Cz+iZ6NDfHUs3ExmK5YsUIFBQWaOnWqNm/erIsuukj5+fnatWuX6dLCcvjwYfXt21cLFy4M+ficOXM0b948LVy4UBs2bFBaWpqGDx+umpqaFq7UubVr12rChAl69913VVxcrGPHjikvL0+HDx9uyInl7YM99GzsvKfpWUje6lkv96tEz+Jb9GzsvJ/jqmetOHD++edbY8eObRTr2bOn9etf/9pQRe6RZK1cubLhfn19vZWWlmbNnj27IXbkyBErJSXFeuKJJwxU2Dz79u2zJFlr1661LMt724fQ6NnYfU/Ts/HJqz3r9X61LHo2XtGzsft+9nLPen6PaW1trTZt2qS8vLxG8by8PK1fv95QVZFTUlKi8vLyRtvr9/s1ZMiQmNzeqqoqSVL79u0leW/7EIyeje33ND0bf+KpZ734fqZn4w89G9vvZy/3rOcH04qKCtXV1Sk1NbVRPDU1VeXl5Yaqipzj2+SF7bUsS5MmTdKFF16o3r17S/LW9iE0elYN92Nte+nZ+BRPPeu19zM9G5/o2djdVq/3bGvTBbQUn8/X6L5lWUExL/HC9k6cOFEffvih3n777aDHvLB9+H7x9hp7YXvp2fgWT6+xV7aVno1v8fQae2Vbvd6znt9j2qFDByUkJAR9YrBv376gTxa8IC0tTZJifnt/8YtfaPXq1XrjjTeUkZHREPfK9qFp9Oy3Ym176dn4FU8966X3Mz0bv+jZ2NzWeOhZzw+miYmJ6t+/v4qLixvFi4uLlZuba6iqyOnatavS0tIabW9tba3Wrl0bE9trWZYmTpyoF198UX/961/VtWvXRo/H+vbhxOjZ2HpP07OIp571wvuZngU9G1vv57jq2ZY915IZy5cvt9q0aWP94Q9/sLZt22YVFBRYJ598svX555+bLi0sNTU11ubNm63Nmzdbkqx58+ZZmzdvtr744gvLsixr9uzZVkpKivXiiy9aH330kXXddddZnTt3tqqrqw1XfmLjxo2zUlJSrDfffNMqKytruH399dcNObG8fbCHno2d9zQ9C8vyVs96uV8ti57Ft+jZ2Hk/x1PPxsVgalmW9dhjj1lZWVlWYmKide655zacYjkWvfHGG5akoNuYMWMsy/r2tNHTp0+30tLSLL/fb1188cXWRx99ZLZom0JtlyTr6aefbsiJ5e2DffRsbLyn6Vkc55We9XK/WhY9i3+iZ2NDPPWsz7Isy/39sAAAAAAA2OP575gCAAAAAKIbgykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAYxWAKAAAAADCKwRQAAAAAYBSDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjFYAoAAAAAMIrBFAAAAABgVGvTBURafX299u7dq6SkJPl8PtPlIA5YlqWamhqlp6erVSs++3GKnkVLo2ebh55FS6Nnm4eeRUuz27OeH0z37t2rzMxM02UgDpWWliojI8N0GTGHnoUp9Gx46FmYQs+Gh56FKSfq2ZgYTBctWqSHHnpIZWVl6tWrl+bPn6+LLrrI1nOTkpIkffsXkZycHMkyAUlSdXW1MjMzG957cIaeRUujZ5uHnkVLo2ebh55FS7Pbs1E/mK5YsUIFBQVatGiRBg8erCeffFL5+fnatm2bunTpcsLnHz9EITk5meZDi+LwmPDQszCFng0PPQtT6Nnw0LMw5UQ9G/UH5s+bN0+33nqrbrvtNp111lmaP3++MjMz9fjjj5suDQAAAADggqgeTGtra7Vp0ybl5eU1iufl5Wn9+vUhnxMIBFRdXd3oBgAAAACIXlE9mFZUVKiurk6pqamN4qmpqSovLw/5nFmzZiklJaXhxpe7AQAAACC6RfVgetx3j0e2LKvJY5SnTJmiqqqqhltpaWlLlAgAAAAACFNUn/yoQ4cOSkhICNo7um/fvqC9qMf5/X75/f6WKA8AAAAA4IKo3mOamJio/v37q7i4uFG8uLhYubm5hqoCAAAAALgpqveYStKkSZN04403asCAARo0aJAWL16sXbt2aezYsaZLAwAAAAC4IOoH02uuuUYHDhxQUVGRysrK1Lt3b7388svKysoyXRoAAAAAwAVRP5hK0vjx4zV+/HjTZQAAAAAAIiCqv2MKAAAAAPA+BlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUa1NFwAAMGPkyJERW3vVqlURWxsAAHgPe0wBAAAAAEYxmAIAAAAAjGIwBQAAAAAYxWAKAAAAADCKwRQAAAAAYBSDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARrW2k7RgwQLHC998881KSkpy/DwAAAAAQHyxNZgWFBQoIyNDCQkJthYtLS3V5ZdfzmDaDEeOHLGd+5e//MXR2g8//LDt3LVr1zpa24mTTjrJdq6Tv4/s7GzbuSUlJbZzgVjgpK8CgYCjtVu3tvUrQ5K0a9cu27ldunRxVAfiy2effWY7t3v37hGsxNuc/N/g5z//ue3c5OTkcMoB4lKrVvYPZrUsKyK5Jtn+X8bGjRvVqVMnW7kMpAAAAAAAu2yN5dOnT9cpp5xie9Hf/OY3at++fdhFAQAAAADih609ptOnT3e06JQpU8IqBgAAAAAQf+x/Yehf1NXVqaKiQj6fT6effrrt754CAAAAAPBdji4Xs3LlSg0ePFjt2rVTenq6OnfurHbt2mnw4MFatWpVhEoEAAAAAHiZ7cH0ySef1LXXXquzzz5bK1as0Ntvv6233npLK1as0Nlnn61rr71WTz31VCRrBQAAAAB4kO1DeR966CEtWrRIt956a9BjI0eO1HnnnacHHnhAP/vZz1wtEAAAAADgbbb3mO7Zs0cXXnhhk4/n5uZq7969rhQFAAAAAIgftgfTXr16afHixU0+/tRTT6lXr16uFAUAAAAAiB+2D+WdO3eufvzjH+vVV19VXl6eUlNT5fP5VF5eruLiYn3xxRd6+eWXI1krAAAAAMCDbA+mQ4YM0ccff6zHH39c7777rsrLyyVJaWlpuvzyyzV27FhlZ2dHqs6407FjR9u5hw4dilgdXbp0sZ27a9cuR2t/8803TssB4pLP5zNdgiTp+eeft52blZVlO9eyrHDKQZzo27ev7dxI/j70usmTJ9vOveuuuyJYCeAd27Ztc5Tv5Pfh119/7bScqOfoOqbZ2dl68MEHI1ULAAAAACAOObqOKQAAAAAAbmMwBQAAAAAYxWAKAAAAADCKwRQAAAAAYBSDKQAAAADAKNcG05deeknLli1zazkAAAAAQJxwbTC95557dPPNN7u1HAAAAAAgTji6jun3+eSTT9xaCgAAAAAQR/iOKQAAAADAqLD2mFZWVur999/Xvn37VF9f3+ix0aNHu1JYvKupqYnY2j6fz3buF198EbE6gHiVkJAQsbW/+2/y93n44Ycdrf3v//7vTsux5csvv7Sdm5qaGpEaEL0OHz5sugQACEuvXr0c5ffs2dN2btu2bZ2WE/UcD6Z/+tOfdMMNN+jw4cNKSkpqNOT4fD4GUwAAAACAI44P5f3Vr36lW265RTU1NaqsrNTBgwcbbl999ZWrxRUWFsrn8zW6paWlufozAAAAAABmOd5jumfPHt1xxx1q165dJOoJ0qtXL/3lL39puB/JQ+AAAAAAAC3P8WB62WWXaePGjerWrVsk6gnSunVr9pICAAAAgIfZGkxXr17d8Ocf//jHmjx5srZt26Y+ffqoTZs2jXKvvPJKVwvcuXOn0tPT5ff7NXDgQM2cOfN7h+JAIKBAINBwv7q62tV6AAAAAADusjWYjhw5MihWVFQUFPP5fKqrq2t2UccNHDhQy5YtU48ePfTll1/q/vvvV25urrZu3arTTz895HNmzZqlGTNmuFYDAAAAACCybJ38qL6+3tbNzaFUkvLz83X11VerT58+uvTSS/XnP/9ZkrR06dImnzNlyhRVVVU13EpLS12tCQAAAADgrrCuY2rKySefrD59+mjnzp1N5vj9fvn9/hasCgAAAADQHI4vF3PHHXdowYIFQfGFCxeqoKDAjZqaFAgEtH37dnXu3DmiPwcAAAAA0HIcD6YvvPCCBg8eHBTPzc3V888/70pRx911111au3atSkpK9N577+knP/mJqqurNWbMGFd/DgAAAADAHMeH8h44cEApKSlB8eTkZFVUVLhS1HG7d+/Wddddp4qKCnXs2FEXXHCB3n33XWVlZbn6cwAAAAAA5jgeTM8880y9+uqrmjhxYqP4K6+84vq1TZcvX+7qevhWjx49bOd+/PHHtnN79+4dTjmAJ5x33nkRW/vYsWO2c30+n+3cyZMnh1OO6y644ALbuSUlJRGsBPCWH/3oR7Zzk5OTI1gJ4B1paWkRW3v79u0RWzsWOB5MJ02apIkTJ2r//v0aNmyYJGnNmjWaO3eu5s+f73Z9AAAAAACPczyY3nLLLQoEAnrggQd03333SZKys7P1+OOPa/To0a4XCAAAAADwtrAuFzNu3DiNGzdO+/fvV9u2bXXKKae4XRcAAAAAIE406zqmHTt2dKsOAAAAAECcsnW5mHPPPVcHDx60veiFF16oPXv2hF0UAAAAACB+2NpjumXLFn3wwQdq3769rUW3bNmiQCDQrMIAAAAAAPHB9qG8l1xyiSzLspXr5HIFAAAAAID4ZmswDee6cRkZGY6fAwAAAACIP7YG06ysrEjXAQAAAACIU7ZOfgQAAAAAQKQ063IxiE07duywnZuQkGA7t76+3lEdTk6QlZiY6GhtoKVt3LjRdq7d7+sDiA779u1zlD9t2jTbuU899ZTtXCe/kyWprq7Odq6Tqy8AXvPOO+/Yzv3yyy9t59bW1oZTTtxijykAAAAAwCgGUwAAAACAUY4H027duunAgQNB8crKSnXr1s2VogAAAAAA8cPxYPr555+H/M5CIBDQnj17XCkKAAAAABA/bJ/8aPXq1Q1/fu2115SSktJwv66uTmvWrFF2drarxQEAAAAAvM/2YDpy5EhJks/n05gxYxo91qZNG2VnZ2vu3LmuFgcAAAAA8D7bg+nxS4F07dpVGzZsUIcOHSJWFAAAAAAgfji+jmlJSUkk6gAAAAAAxCnHg2lRUdH3Pv7b3/427GIAAAAAAPHH8WC6cuXKRvePHj2qkpIStW7dWt27d2cwBQAAAAA44ngw3bx5c1CsurpaN910k6666ipXikL0CHVpoKb87//+r6O1/X6/7VzLshytDbhh7dq1tnMTExMjWIm3/eMf/zBdAqKYkxMrDhw4MCI13HPPPY7yn3zyyYjkOuXz+Wzn/uvVFoB4k5ubG5F127RpE5F1vcrxdUxDSU5OVlFRkaZNm+bGcgAAAACAOOLKYCpJlZWVqqqqcms5AAAAAECccHwo74IFCxrdtyxLZWVleuaZZzRixAjXCgMAAAAAxAfHg+kjjzzS6H6rVq3UsWNHjRkzRlOmTHGtMAAAAABAfOA6pgAAAAAAo5r1HdPS0lLt3r3brVoAAAAAAHHI8WB67NgxTZs2TSkpKcrOzlZWVpZSUlJ077336ujRo5GoEQAAAADgYY4P5Z04caJWrlypOXPmaNCgQZKkd955R4WFhaqoqNATTzzhepEAAAAAAO9yPJg+99xzWr58ufLz8xtiZ599trp06aJrr72WwRQAAAAA4IjjQ3lPOukkZWdnB8Wzs7OVmJjoRk0AAAAAgDjieDCdMGGC7rvvPgUCgYZYIBDQAw88oIkTJ7paHAAAAADA+xwfyrt582atWbNGGRkZ6tu3ryTpgw8+UG1trS655BKNGjWqIffFF190r1IYUVFRYTv33nvvjWAlQHQbPXq06RIc279/v6P8Tp062c61LMtpOUBIkyZNikguAO9q06ZNxNaur6+P2NrxzvFgeuqpp+rqq69uFMvMzHStIAAAAABAfHE8mD799NORqAMAAAAAEKccf8d02LBhqqysDIpXV1dr2LBhbtQEAAAAAIgjjgfTN998U7W1tUHxI0eO6K233nKlKAAAAABA/LB9KO+HH37Y8Odt27apvLy84X5dXZ1effVVnXHGGe5WBwAAAADwPNuDab9+/eTz+eTz+UIestu2bVs9+uijrhYHAAAAAPA+24NpSUmJLMtSt27d9P7776tjx44NjyUmJqpTp05KSEiISJEAAAAAAO+yPZhmZWVJ4to9AAAAAAB3Ob5czLJly7738Vi8yDwAAAAAwBzHg+mdd97Z6P7Ro0f19ddfKzExUe3atWMwBQAAAAA44ngwPXjwYFBs586dGjdunCZPnuxKUXDm6NGjjvJPO+0027mHDx92Wo5t7733XsTWBlra73//e9u5M2fOdLT2tm3bbOeOGDHCdq7P53NUx65duxzlAwBgwrFjxyK2ttPfnbDP8XVMQ8nJydHs2bOD9qYCAAAAAHAirgymkpSQkKC9e/c6es66det0xRVXKD09XT6fT6tWrWr0uGVZKiwsVHp6utq2bauhQ4dq69atbpUMAAAAAIgCjg/lXb16daP7lmWprKxMCxcu1ODBgx2tdfjwYfXt21c333yzrr766qDH58yZo3nz5mnJkiXq0aOH7r//fg0fPlw7duxQUlKS09IBAAAAAFHI8WA6cuTIRvd9Pp86duyoYcOGae7cuY7Wys/PV35+fsjHLMvS/PnzNXXqVI0aNUqStHTpUqWmpurZZ5/V7bffHvJ5gUBAgUCg4X51dbWjmgAAAAAALcvxobz19fWNbnV1dSovL9ezzz6rzp07u1ZYSUmJysvLlZeX1xDz+/0aMmSI1q9f3+TzZs2apZSUlIZbZmamazUBAAAAANwX9ndMKyoqdODAATdraaS8vFySlJqa2iiempra8FgoU6ZMUVVVVcOttLQ0YjUCAAAAAJrP0WBaWVmpCRMmqEOHDkpNTVWnTp3UoUMHTZw4UZWVlREp8LunZLYs63tP0+z3+5WcnNzoBgAAAACIXra/Y/rVV19p0KBB2rNnj2644QadddZZsixL27dv15IlS7RmzRqtX7/e0TUyv09aWpqkb/ec/ushwvv27QvaiwoAAAAAiF22B9OioiIlJibqs88+CxoMi4qKlJeXp6KiIj3yyCOuFNa1a1elpaWpuLhY55xzjiSptrZWa9eu1YMPPujKzwAAAAAAmGf7UN5Vq1bp4YcfDrm3Mi0tTXPmzNHKlSsd/fBDhw5py5Yt2rJli6RvT3i0ZcsW7dq1Sz6fTwUFBZo5c6ZWrlypjz/+WDfddJPatWun66+/3tHPAQAAAABEL9t7TMvKytSrV68mH+/du/f3npQolI0bN+qHP/xhw/1JkyZJksaMGaMlS5bo7rvv1jfffKPx48fr4MGDGjhwoF5//fWYvYZpXV2d7dxTTz3Vdu6hQ4fCqMae7zsD8ncNGjQoYnUAJlxwwQURWbdTp04RWVeSbrvtNtu5Tz31VMTqAADATTt37ozY2pZlRWxt2Gd7MO3QoYM+//xzZWRkhHy8pKREp59+uqMfPnTo0O99I/h8PhUWFqqwsNDRugAAAACA2GH7UN4RI0Zo6tSpqq2tDXosEAho2rRpGjFihKvFAQAAAAC8z/Ye0xkzZmjAgAHKycnRhAkT1LNnT0nStm3btGjRIgUCAT3zzDMRKxQAAAAA4E22B9OMjAy98847Gj9+vKZMmdJwCK7P59Pw4cO1cOFCZWZmRqxQAAAAAIA32R5MpW8v4fLKK6/o4MGDDV9APvPMM9W+ffuIFAcAAAAA8D5Hg+lxp512ms4//3y3awEAAAAAxCHbJz8CAAAAACASGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAYFdbJj/BPkyZNsp37yCOP2M497bTTbOf++te/tp0rSbNmzXKUD8Qrv99vO/f4JbQAAID7srOzTZeACGOPKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjFYAoAAAAAMIrBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFEMpgAAAAAAo1qbLiDWzZs3LyK5AAAAzWFZlukSANe0adPGdi7v/djEHlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUa1NFxBplmVJkqqrqw1Xgnhx/L12/L0HZ+hZtDR6tnnoWbQ0erZ56Fm0NLs96/nBtKamRpKUmZlpuBLEm5qaGqWkpJguI+bQszCFng0PPQtT6Nnw0LMw5UQ967M8/nFTfX299u7dq6SkJPl8voZ4dXW1MjMzVVpaquTkZIMVRgbbZ45lWaqpqVF6erpateJoeafoWbavpdGzzROqZ6P59XaL17cxmrePnm2eeOxZr2+fFN3baLdnPb/HtFWrVsrIyGjy8eTk5Kh78dzE9pnBJ7jho2fZPhPo2fB9X89G6+vtJq9vY7RuHz0bvnjuWa9vnxS922inZ/mYCQAAAABgFIMpAAAAAMCouB1M/X6/pk+fLr/fb7qUiGD74DVef83ZPnhJPLzeXt9Gr28fGvP66+317ZO8sY2eP/kRAAAAACC6xe0eUwAAAABAdGAwBQAAAAAYxWAKAAAAADCKwRQAAAAAYFRcDqaLFi1S165dddJJJ6l///566623TJfkisLCQvl8vka3tLQ002WFbd26dbriiiuUnp4un8+nVatWNXrcsiwVFhYqPT1dbdu21dChQ7V161YzxSKi6NnYQM/iOHo2NtCzOI6ejQ1e79m4G0xXrFihgoICTZ06VZs3b9ZFF12k/Px87dq1y3RprujVq5fKysoabh999JHpksJ2+PBh9e3bVwsXLgz5+Jw5czRv3jwtXLhQGzZsUFpamoYPH66ampoWrhSRRM/GDnoWEj0bS+hZSPRsLPF8z1px5vzzz7fGjh3bKNazZ0/r17/+taGK3DN9+nSrb9++psuICEnWypUrG+7X19dbaWlp1uzZsxtiR44csVJSUqwnnnjCQIWIFHo2NtGz8YuejU30bPyiZ2OTF3s2rvaY1tbWatOmTcrLy2sUz8vL0/r16w1V5a6dO3cqPT1dXbt21bXXXqt//OMfpkuKiJKSEpWXlzd6Lf1+v4YMGeKZ1xL0rJfQs/GBnvUOejY+0LPe4YWejavBtKKiQnV1dUpNTW0UT01NVXl5uaGq3DNw4EAtW7ZMr732mp566imVl5crNzdXBw4cMF2a646/Xl59LfEtetY76Nn4QM96Bz0bH+hZ7/BCz7Y2XYAJPp+v0X3LsoJisSg/P7/hz3369NGgQYPUvXt3LV26VJMmTTJYWeR49bVEY159nelZ77yWaMyrrzM9653XEo159XWmZ2PrtYyrPaYdOnRQQkJC0KcG+/btC/p0wQtOPvlk9enTRzt37jRdiuuOn1EtXl7LeEXPegc9Gx/oWe+gZ+MDPesdXujZuBpMExMT1b9/fxUXFzeKFxcXKzc311BVkRMIBLR9+3Z17tzZdCmu69q1q9LS0hq9lrW1tVq7dq0nX8t4Rc96Bz0bH+hZ76Bn4wM96x1e6Nm4O5R30qRJuvHGGzVgwAANGjRIixcv1q5duzR27FjTpTXbXXfdpSuuuEJdunTRvn37dP/996u6ulpjxowxXVpYDh06pE8//bThfklJibZs2aL27durS5cuKigo0MyZM5WTk6OcnBzNnDlT7dq10/XXX2+wariNno0d9CwkejaW0LOQ6NlY4vmeNXdCYHMee+wxKysry0pMTLTOPfdca+3ataZLcsU111xjde7c2WrTpo2Vnp5ujRo1ytq6davpssL2xhtvWJKCbmPGjLEs69vTYk+fPt1KS0uz/H6/dfHFF1sfffSR2aIREfRsbKBncRw9GxvoWRxHz8YGr/esz7Isq2VHYQAAAAAA/imuvmMKAAAAAIg+DKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjFYAoAAAAAMIrBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwKjWpguItPr6eu3du1dJSUny+Xymy0EcsCxLNTU1Sk9PV6tWfPbjFD2LlkbPNg89i5ZGzzYPPYuWZrdnPT+Y7t27V5mZmabLQBwqLS1VRkaG6TJiDj0LU+jZ8NCzMIWeDQ89C1NO1LOeH0yTkpIkffsXkZycbLgaxIPq6mplZmY2vPfgDD2LlkbPNg89i5ZGzzYPPYuWZrdnY2IwXbRokR566CGVlZWpV69emj9/vi666CJbzz1+iEJycjLNhxbF4THhoWdhCj0bHnoWptCz4aFnYcqJejbqD8xfsWKFCgoKNHXqVG3evFkXXXSR8vPztWvXLtOlAQAAAABcEPWD6bx583Trrbfqtttu01lnnaX58+crMzNTjz/+uOnSAAAAAAAuiOrBtLa2Vps2bVJeXl6jeF5entavXx/yOYFAQNXV1Y1uAAAAAIDoFdWDaUVFherq6pSamtoonpqaqvLy8pDPmTVrllJSUhpunHUMAAAAAKJbVA+mx333i7KWZTX55dkpU6aoqqqq4VZaWtoSJQIAAAAAwhTVZ+Xt0KGDEhISgvaO7tu3L2gv6nF+v19+v78lygMAAAAAuCCq95gmJiaqf//+Ki4ubhQvLi5Wbm6uoaoAAAAAAG6K6j2mkjRp0iTdeOONGjBggAYNGqTFixdr165dGjt2rOnSAAAAAAAuiPrB9JprrtGBAwdUVFSksrIy9e7dWy+//LKysrJMlwYAAAAAcEHUD6aSNH78eI0fP950GQAAAACACIjq75gCAAAAALyPwRQAAAAAYBSDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjFYAoAAAAAMKq16QIAAO4pLy+3ndu5c+eIrZ2amupobQAAEN/YYwoAAAAAMIrBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVgCgAAAAAwqrWdpAULFjhe+Oabb1ZSUpLj5wEAAAAA4outwbSgoEAZGRlKSEiwtWhpaakuv/xyBtNmOHTokO3cW2+91dHa/+///T+n5UTEhRdeaDv3b3/7m+3cHTt22M7NycmxnQuYsnv3btu5mZmZtnNbtXJ20ExaWprtXMuyHK0NNGX9+vW2c3NzcyNYibf179/fdu6mTZsiWAngHS+99JKj/JEjR9rO/fTTT23ndu/e3VEdptgaTCVp48aN6tSpk61cBlIAAAAAgF22Pi6fPn26TjnlFNuL/uY3v1H79u3DLgoAAAAAED9s7TGdPn26o0WnTJkSVjEAAAAAgPjDWXkBAAAAAEa5Nphu375d3bp1c2s5AAAAAECccG0wra2t1RdffOHWcgAAAACAOGH7rLyTJk363sf379/f7GIAAAAAAPHH9mD6u9/9Tv369VNycnLIx51cdxMAAAAAgONsD6Y5OTn65S9/qZ/+9KchH9+yZYujizMDAAAAACA5+I5p//79tWnTpiYf9/l8sizLlaIAAAAAAPHD9h7TuXPnKhAINPl43759VV9f70pRXlVXV2c7NykpKYKV2NevXz/buR988IGjtd966y2H1QDe8KMf/chR/iuvvBKROpz+m92zZ0/bucOHD7edW1xc7KgOxJf777/fdu7LL78cwUpiz/f9v+27OnfuHMFKgPh09dVXO8pv37697dzu3bs7LSfq2R5M09LSIlkHAAAAACBOuXa5GAAAAAAAwsFgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUa4Npi+99JKWLVvm1nIAAAAAgDjh2mB6zz336Oabb3ZrOQAAAABAnLB9HdMT+eSTT9xaCgAAAAAQR/iOKQAAAADAqLD2mFZWVur999/Xvn37VF9f3+ix0aNHu1KYFyUkJNjO/e7f6/e59tprHdVx66232s7Ny8tztDYQr/bv328795VXXnG09oABA2znPvnkk7Zzzz77bEd1tG5t/1dGq1Z87onQKioqTJcQN3r06GE79+23345gJYB3/Od//qft3Lq6OkdrHzhwwGk5nuJ4MP3Tn/6kG264QYcPH1ZSUpJ8Pl/DYz6fj8EUAAAAAOCI44+0f/WrX+mWW25RTU2NKisrdfDgwYbbV199FYkaAQAAAAAe5ngw3bNnj+644w61a9cuEvU0UlhYKJ/P1+iWlpYW8Z8LAAAAAGg5jg/lveyyy7Rx40Z169YtEvUE6dWrl/7yl7803HfyPU0AAAAAQPSzNZiuXr264c8//vGPNXnyZG3btk19+vRRmzZtGuVeeeWV7hbYujV7SQEAAADAw2wNpiNHjgyKFRUVBcV8Pp/js0+dyM6dO5Weni6/36+BAwdq5syZ37u3NhAIKBAINNyvrq52tR4AAAAAgLtsfce0vr7e1s3toXTgwIFatmyZXnvtNT311FMqLy9Xbm7u955KedasWUpJSWm4ZWZmuloTAAAAAMBdUX2hufz8fF199dXq06ePLr30Uv35z3+WJC1durTJ50yZMkVVVVUNt9LS0pYqFwAAAAAQBseD6R133KEFCxYExRcuXKiCggI3amrSySefrD59+mjnzp1N5vj9fiUnJze6AQAAAACil+PB9IUXXtDgwYOD4rm5uXr++eddKaopgUBA27dvV+fOnSP6cwAAAAAALcfxYHrgwAGlpKQExZOTk1VRUeFKUcfdddddWrt2rUpKSvTee+/pJz/5iaqrqzVmzBhXfw4AAAAAwBzH1zE988wz9eqrr2rixImN4q+88orr1zbdvXu3rrvuOlVUVKhjx4664IIL9O677yorK8vVnxONfD6f7dzly5c7WrtVK/ufR1iW5WhtIF516tTJdu6HH37oaO0ePXrYzvX7/Y7WjhT+7UBTbrvtNkf5w4YNi1Al3rdr1y7buWeccUYEKwGi2zfffGM793/+539s54a6igma5ngwnTRpkiZOnKj9+/c3/LJYs2aN5s6dq/nz57tanNOBCwAAAAAQexwPprfccosCgYAeeOAB3XfffZKk7OxsPf744xo9erTrBQIAAAAAvM3xYCpJ48aN07hx47R//361bdtWp5xyitt1AQAAAADiRFiD6XEdO3Z0qw4AAAAAQJyydRacc889VwcPHrS96IUXXqg9e/aEXRQAAAAAIH7Y2mO6ZcsWffDBB2rfvr2tRbds2aJAINCswgAAAAAA8cH2obyXXHKJ7dP/O7nUCQAAAAAgvtkaTEtKShwvnJGR4fg5AAAAAID4Y2swzcrKinQdAAAAAIA4ZevkRwAAAAAAREqzLheD6OD0O71/+9vfIrL23LlzHdUxadIkR/lAS3NytEiPHj1s5/bp0yeccoC4NGDAgIisW1FRYTv3ueeec7T2+++/bzv3j3/8o+3c/v37O6rDiVat2FeB+HXKKafYzu3evbvt3GnTpoVTTtziXyEAAAAAgFEMpgAAAAAAoxwPpt26ddOBAweC4pWVlerWrZsrRQEAAAAA4ofjwfTzzz9XXV1dUDwQCGjPnj2uFAUAAAAAiB+2T360evXqhj+/9tprSklJabhfV1enNWvWKDs729XiAAAAAADeZ3swHTlypKRvz9I6ZsyYRo+1adNG2dnZjs/KCgAAAACA7cG0vr5ektS1a1dt2LBBHTp0iFhRAAAAAID44fg6piUlJZGoAwAAAAAQpxwPpkVFRd/7+G9/+9uwiwEAAAAAxB/Hg+nKlSsb3T969KhKSkrUunVrde/encEUAAAAAOCI48F08+bNQbHq6mrddNNNuuqqq1wpCpGVm5trOzfUpYGakpCQ4KiOP/zhD7Zzt27d6mhtwA27du2ynRsIBCJYSXSYPHmy7dxevXpFsBLEslWrVjnKHzhwYGQKceD55593lP+LX/zCdu4zzzxjO3f58uWO6rjtttsc5QNe8dOf/tRR/vFz6djx97//3Wk5sMnxdUxDSU5OVlFRkaZNm+bGcgAAAACAOOLKYCpJlZWVqqqqcms5AAAAAECccHwo74IFCxrdtyxLZWVleuaZZzRixAjXCgMAAAAAxAfHg+kjjzzS6H6rVq3UsWNHjRkzRlOmTHGtMAAAAABAfOA6pgAAAAAAo5r1HdPS0lLt3r3brVoAAAAAAHHI8WB67NgxTZs2TSkpKcrOzlZWVpZSUlJ077336ujRo5GoEQAAAADgYY4P5Z04caJWrlypOXPmaNCgQZKkd955R4WFhaqoqNATTzzhepEAAAAAAO9yPJg+99xzWr58ufLz8xtiZ599trp06aJrr72WwRQAAAAA4IjjQ3lPOukkZWdnB8Wzs7OVmJjoRk0AAAAAgDjieDCdMGGC7rvvPgUCgYZYIBDQAw88oIkTJ7paHAAAAADA+xwfyrt582atWbNGGRkZ6tu3ryTpgw8+UG1trS655BKNGjWqIffFF190r1IYMX369IitvX///oitDbS0WDxi5LLLLnOUf/jwYdu5H3/8sdNygJDee+890yVEjcWLFzvK37ZtW4QqAaLbH//4R0f5oY4GbUpycrLDamCX48H01FNP1dVXX90olpmZ6VpBAAAAAID44ngwffrppyNRBwAAAAAgTjn+jumwYcNUWVkZFK+urtawYcPcqAkAAAAAEEccD6Zvvvmmamtrg+JHjhzRW2+95UpRAAAAAID4YftQ3g8//LDhz9u2bVN5eXnD/bq6Or366qs644wz3K0OAAAAAOB5tgfTfv36yefzyefzhTxkt23btnr00UddLQ4AAAAA4H22B9OSkhJZlqVu3brp/fffV8eOHRseS0xMVKdOnZSQkBCRIgEAAAAA3mV7MM3KypIk1dfXR6wYAAAAAED8cXy5mGXLln3v46NHjw67GAAAAABA/HE8mN55552N7h89elRff/21EhMT1a5dOwZTAAAAAIAjjgfTgwcPBsV27typcePGafLkya4UBWcWLVrkKH/ChAkRqcPpWZl3794dkToAE2677TbbuQsXLnS09kUXXWQ7d+PGjbZzL730Ukd1vP32247yAZj1r1dUOJEuXbpEsBKg+UaOHBmxtbdt2xaxtWGf4+uYhpKTk6PZs2cH7U0FAAAAAOBEXBlMJSkhIUF79+519Jx169bpiiuuUHp6unw+n1atWtXoccuyVFhYqPT0dLVt21ZDhw7V1q1b3SoZAAAAABAFHB/Ku3r16kb3LctSWVmZFi5cqMGDBzta6/Dhw+rbt69uvvlmXX311UGPz5kzR/PmzdOSJUvUo0cP3X///Ro+fLh27NihpKQkp6UDAAAAAKKQ48H0u8d3+3w+dezYUcOGDdPcuXMdrZWfn6/8/PyQj1mWpfnz52vq1KkaNWqUJGnp0qVKTU3Vs88+q9tvv91p6QAAAACAKOR4MG2p65iWlJSovLxceXl5DTG/368hQ4Zo/fr1TQ6mgUBAgUCg4X51dXXEawUAAAAAhC/s75hWVFTowIEDbtbSSHl5uSQpNTW1UTw1NbXhsVBmzZqllJSUhltmZmbEagQAAAAANJ+jwbSyslITJkxQhw4dlJqaqk6dOqlDhw6aOHGiKisrI1Kgz+drdN+yrKDYv5oyZYqqqqoabqWlpRGpCwAAAADgDtuH8n711VcaNGiQ9uzZoxtuuEFnnXWWLMvS9u3btWTJEq1Zs0br16/Xaaed5kphaWlpkr7dc9q5c+eG+L59+4L2ov4rv98vv9/vSg0AAAAAgMizPZgWFRUpMTFRn332WdBgWFRUpLy8PBUVFemRRx5xpbCuXbsqLS1NxcXFOueccyRJtbW1Wrt2rR588EFXfgYAAAAAwDzbh/KuWrVKDz/8cMi9lWlpaZozZ45Wrlzp6IcfOnRIW7Zs0ZYtWyR9e8KjLVu2aNeuXfL5fCooKNDMmTO1cuVKffzxx7rpppvUrl07XX/99Y5+DgAAAAAgetneY1pWVqZevXo1+Xjv3r2/96REoWzcuFE//OEPG+5PmjRJkjRmzBgtWbJEd999t7755huNHz9eBw8e1MCBA/X666/H7DVMhwwZYjt33bp1EasjOzvbdm5JSUnE6gCinZOTp/3hD3+ISK4ktW5t/wTqlmU5WhuAd/3xj3+0nXv55ZdHsBKg+V566SXbuSeffLKjtdu2beu0HESA7f/tdOjQQZ9//rkyMjJCPl5SUqLTTz/d0Q8fOnTo9/4nyufzqbCwUIWFhY7WBQAAAADEDtuH8o4YMUJTp05VbW1t0GOBQEDTpk3TiBEjXC0OAAAAAOB9tveYzpgxQwMGDFBOTo4mTJignj17SpK2bdumRYsWKRAI6JlnnolYoQAAAAAAb7I9mGZkZOidd97R+PHjNWXKlIZDcH0+n4YPH66FCxc6+j4WAAAAAACSg8FU+vYSLq+88ooOHjyonTt3SpLOPPNMtW/fPiLFAQAAAAC8z9Fgetxpp52m888/3+1aAAAAAABxyPbJjwAAAAAAiAQGUwAAAACAUQymAAAAAACjGEwBAAAAAEaFdfIj/JPP54vIunfddZft3AcffNDR2q1a8XkEYMeuXbtMlwAADe68805H+cuXL49QJUDL6969u+3c8vLyCFaCSGFCAQAAAAAYxWAKAAAAADCKwRQAAAAAYBSDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjFYAoAAAAAMIrBFAAAAABgVGvTBcQ6y7JMlwAAAOLAv//7v0c0H4hmn376qekSEGHsMQUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAY1dp0AZFmWZYkqbq62nAliBfH32vH33twhp5FS6Nnm4eeRUujZ5uHnkVLs9uznh9Ma2pqJEmZmZmGK0G8qampUUpKiukyYg49C1Po2fDQszCFng0PPQtTTtSzPsvjHzfV19dr7969SkpKks/na4hXV1crMzNTpaWlSk5ONlhhZLB95liWpZqaGqWnp6tVK46Wd4qeZftaGj3bPKF6Nppfb7d4fRujefvo2eaJx571+vZJ0b2NdnvW83tMW7VqpYyMjCYfT05OjroXz01snxl8ghs+epbtM4GeDd/39Wy0vt5u8vo2Ruv20bPhi+ee9fr2SdG7jXZ6lo+ZAAAAAABGMZgCAAAAAIyK28HU7/dr+vTp8vv9pkuJCLYPXuP115ztg5fEw+vt9W30+vahMa+/3l7fPskb2+j5kx8BAAAAAKJb3O4xBQAAAABEBwZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMisvBdNGiReratatOOukk9e/fX2+99ZbpklxRWFgon8/X6JaWlma6rLCtW7dOV1xxhdLT0+Xz+bRq1apGj1uWpcLCQqWnp6tt27YaOnSotm7daqZYRBQ9GxvoWRxHz8YGehbH0bOxwes9G3eD6YoVK1RQUKCpU6dq8+bNuuiii5Sfn69du3aZLs0VvXr1UllZWcPto48+Ml1S2A4fPqy+fftq4cKFIR+fM2eO5s2bp4ULF2rDhg1KS0vT8OHDVVNT08KVIpLo2dhBz0KiZ2MJPQuJno0lnu9ZK86cf/751tixYxvFevbsaf361782VJF7pk+fbvXt29d0GREhyVq5cmXD/fr6eistLc2aPXt2Q+zIkSNWSkqK9cQTTxioEJFCz8YmejZ+0bOxiZ6NX/RsbPJiz8bVHtPa2lpt2rRJeXl5jeJ5eXlav369oarctXPnTqWnp6tr16669tpr9Y9//MN0SRFRUlKi8vLyRq+l3+/XkCFDPPNagp71Eno2PtCz3kHPxgd61ju80LNxNZhWVFSorq5OqampjeKpqakqLy83VJV7Bg4cqGXLlum1117TU089pfLycuXm5urAgQOmS3Pd8dfLq68lvkXPegc9Gx/oWe+gZ+MDPesdXujZ1qYLMMHn8zW6b1lWUCwW5efnN/y5T58+GjRokLp3766lS5dq0qRJBiuLHK++lmjMq68zPeud1xKNefV1pme981qiMa++zvRsbL2WcbXHtEOHDkpISAj61GDfvn1Bny54wcknn6w+ffpo586dpktx3fEzqsXLaxmv6FnvoGfjAz3rHfRsfKBnvcMLPRtXg2liYqL69++v4uLiRvHi4mLl5uYaqipyAoGAtm/frs6dO5suxXVdu3ZVWlpao9eytrZWa9eu9eRrGa/oWe+gZ+MDPesd9Gx8oGe9wws9G3eH8k6aNEk33nijBgwYoEGDBmnx4sXatWuXxo4da7q0Zrvrrrt0xRVXqEuXLtq3b5/uv/9+VVdXa8yYMaZLC8uhQ4f06aefNtwvKSnRli1b1L59e3Xp0kUFBQWaOXOmcnJylJOTo5kzZ6pdu3a6/vrrDVYNt9GzsYOehUTPxhJ6FhI9G0s837PmTghszmOPPWZlZWVZiYmJ1rnnnmutXbvWdEmuuOaaa6zOnTtbbdq0sdLT061Ro0ZZW7duNV1W2N544w1LUtBtzJgxlmV9e1rs6dOnW2lpaZbf77cuvvhi66OPPjJbNCKCno0N9CyOo2djAz2L4+jZ2OD1nvVZlmW17CgMAAAAAMA/xdV3TAEAAAAA0YfBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAY1dp0AZFWX1+vvXv3KikpST6fz3Q5iAOWZammpkbp6elq1YrPfpyiZ9HS6NnmoWfR0ujZ5qFn0dLs9qznB9O9e/cqMzPTdBmIQ6WlpcrIyDBdRsyhZ2EKPRseeham0LPhoWdhyol6NiYG00WLFumhhx5SWVmZevXqpfnz5+uiiy6y9dykpCRJ3/5FJCcnR7JMQJJUXV2tzMzMhvcenKFn0dLo2eahZ9HS6NnmoWfR0uz2bNQPpitWrFBBQYEWLVqkwYMH68knn1R+fr62bdumLl26nPD5xw9RSE5OpvnQojg8Jjz0LEyhZ8NDz8IUejY89CxMOVHPRv2B+fPmzdOtt96q2267TWeddZbmz5+vzMxMPf7446ZLAwAAAAC4IKoH09raWm3atEl5eXmN4nl5eVq/fn3I5wQCAVVXVze6AQAAAACiV1QPphUVFaqrq1NqamqjeGpqqsrLy0M+Z9asWUpJSWm48eVuAAAAAIhuUT2YHvfd45Ety2ryGOUpU6aoqqqq4VZaWtoSJQIAAAAAwhTVJz/q0KGDEhISgvaO7tu3L2gv6nF+v19+v78lygMAAAAAuCCq95gmJiaqf//+Ki4ubhQvLi5Wbm6uoaoAAAAAAG6K6j2mkjRp0iTdeOONGjBggAYNGqTFixdr165dGjt2rOnSAAAAAAAuiPrB9JprrtGBAwdUVFSksrIy9e7dWy+//LKysrJMlwYAAAAAcEHUD6aSNH78eI0fP950GQAAAACACIjq75gCAAAAALyPwRQAAAAAYBSDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjFYAoAAAAAMIrBFAAAAABgVGvTBSC61dXV2c6dOXOmo7Vffvll27lJSUm2c4uLi23nWpZlOxeIBfv377ed26lTp4jVQW8BsWX79u22c8844wzbucnJyeGUA8QlJ/+Xnjp1qu3cWPmdzB5TAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjFYAoAAAAAMIrBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFGt7SQtWLDA8cI333yzkpKSHD8PAAAAABBfbA2mBQUFysjIUEJCgq1FS0tLdfnllzOYNoPdv2tJqq+vj2AlkfOnP/3Jdu6pp55qO/f5558PoxogepWUlNjO7datW8TqWLt2re1cn89nO9eyrHDKQZy47777bOdOmzYtgpV4W1FRke3cJ554IoKVAPFr6tSppkswytZgKkkbN25Up06dbOUykAIAAAAA7LL1HdPp06frlFNOsb3ob37zG7Vv3z7sogAAAAAA8cPWHtPp06c7WnTKlClhFQMAAAAAiD+clRcAAAAAYJSjwfSDDz7Q/fffr0WLFqmioqLRY9XV1brllltcLQ4AAAAA4H22B9PXX39d559/vpYvX64HH3xQZ511lt54442Gx7/55hstXbo0IkUCAAAAALzL9mBaWFiou+66Sx9//LE+//xz3X333bryyiv16quvRrI+AAAAAIDH2b5czNatW/XMM89I+vb6dJMnT1ZGRoZ+8pOf6LnnntP5558fsSIBAAAAAN5lezD1+/2qrKxsFLvuuuvUqlUrXXvttZo7d67btQEAAAAA4oDtwbRfv35644031L9//0bxa665RvX19RozZozrxQEAAAAAvM/2YDpu3DitW7cu5GPXXXedJGnx4sXuVAW9/fbbtnN37NjhaO2bb77Zdm59fb3t3O/uUT+R0047zVE+4BXl5eWO8rt162Y7t0ePHrZz/+///s9RHf/2b//mKB9ww+zZs23nTps2LYKVeNvy5ctt5z733HMRrATwjpNOOilia1uWFbG1TbE9mF511VW66qqrmnz8uuuuaxhQAQAAAACwy9F1TAEAAAAAcBuDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARrk2mL700ktatmyZW8sBAAAAAOKEa4PpPffc4+j6mAAAAAAASA6uY3oin3zyiVtLAQAAAADiCN8xBQAAAAAYFdYe08rKSr3//vvat2+f6uvrGz02evRoVwqLd4MGDYpIriRHh1z7fD7buaeddpqjOgAvqampsZ3buXNnR2t36NDBdu6OHTscrR0NLrvsMtu5r732WgQrQTT6+uuvTZcQs44ePWq6BMBz6urqbOcGAgFHa2/fvt1pOZ7ieDD905/+pBtuuEGHDx9WUlJSo8HF5/MxmAIAAAAAHHF8KO+vfvUr3XLLLaqpqVFlZaUOHjzYcPvqq69cLa6wsFA+n6/RLS0tzdWfAQAAAAAwy/Ee0z179uiOO+5Qu3btIlFPkF69eukvf/lLw/2EhIQW+bkAAAAAgJbheDC97LLLtHHjRnXr1i0S9QRp3bo1e0kBAAAAwMNsDaarV69u+POPf/xjTZ48Wdu2bVOfPn3Upk2bRrlXXnmlqwXu3LlT6enp8vv9GjhwoGbOnPm9Q3EgEGj0RePq6mpX6wEAAAAAuMvWYDpy5MigWFFRUVDM5/M5OlPViQwcOFDLli1Tjx499OWXX+r+++9Xbm6utm7dqtNPPz3kc2bNmqUZM2a4VgMAAAAAILJsnfyovr7e1s3NoVSS8vPzdfXVV6tPnz669NJL9ec//1mStHTp0iafM2XKFFVVVTXcSktLXa0JAAAAAOCusK5jasrJJ5+sPn36aOfOnU3m+P1++f3+FqwKAAAAANAcji8Xc8cdd2jBggVB8YULF6qgoMCNmpoUCAS0fft2xxenBwAAAABEL8eD6QsvvKDBgwcHxXNzc/X888+7UtRxd911l9auXauSkhK99957+slPfqLq6mqNGTPG1Z8DAAAAADDH8aG8Bw4cUEpKSlA8OTlZFRUVrhR13O7du3XdddepoqJCHTt21AUXXKB3331XWVlZrv4cAAAAAIA5jgfTM888U6+++qomTpzYKP7KK6+4fm3T5cuXu7oevlVTU2M7t23btrZzFy1a5KiOm2++2VE+EM2Sk5Nt537f9+RDOfPMM52WE1P+7//+z3QJQEx44403HOUPGzbMdu4rr7zitBwgLrVubX986t27t6O1e/bs6bQcT3E8mE6aNEkTJ07U/v37G/7BW7NmjebOnav58+e7XR8AAAAAwOMcD6a33HKLAoGAHnjgAd13332SpOzsbD3++OMaPXq06wUCAAAAALwtrMvFjBs3TuPGjdP+/fvVtm1bnXLKKW7XBQAAAACIE826jmnHjh3dqgMAAAAAEKdsXS7m3HPP1cGDB20veuGFF2rPnj1hFwUAAAAAiB+29phu2bJFH3zwgdq3b29r0S1btigQCDSrMAAAAABAfLB9KO8ll1wiy7Js5fp8vrALAgAAAADEF1uDaUlJieOFMzIyHD8HAAAAABB/bA2mWVlZka4DAAAAABCnbJ38CAAAAACASGnW5WIQm5xcd3b37t22czt06OCojr/+9a+2c5955hlHawNu2LhxY0TWPfPMMyOybqwaOnSo6RLgEZ06dYrIuvv373eU36qV/c/9f/azn9nO/d3vfueojtat7f83b8SIEY7WBrwkUufH+eijjyKyrlexxxQAAAAAYBSDKQAAAADAKMeDabdu3XTgwIGgeGVlpbp16+ZKUQAAAACA+OF4MP38889VV1cXFA8EAtqzZ48rRQEAAAAA4oftb8WvXr264c+vvfaaUlJSGu7X1dVpzZo1ys7OdrU4AAAAAID32R5MR44cKenbs1aNGTOm0WNt2rRRdna25s6d62pxAAAAAADvsz2Y1tfXS5K6du2qDRs2OL40CAAAAAAAoTi+jmlJSUkk6gAAAAAAxCnHg2lRUdH3Pv7b3/427GIAAAAAAPHH8WC6cuXKRvePHj2qkpIStW7dWt27d2cwBQAAAAA44ngw3bx5c1CsurpaN910k6666ipXikL0OP30023nWpblaG2fz2c718kHHjk5OY7qAJpy+PBh27lt27aNYCXR4Te/+Y3t3JNOOsl27tNPPx1OOYgTTn+34J+OHTtmugTAiP3790ds7QsvvDBia8c7x9cxDSU5OVlFRUWaNm2aG8sBAAAAAOKIK4OpJFVWVqqqqsqt5QAAAAAAccLxobwLFixodN+yLJWVlemZZ57RiBEjXCsMAAAAABAfHA+mjzzySKP7rVq1UseOHTVmzBhNmTLFtcIAAAAAAPGB65gCAAAAAIxq1ndMS0tLtXv3brdqAQAAAADEIceD6bFjxzRt2jSlpKQoOztbWVlZSklJ0b333qujR49GokYAAAAAgIc5PpR34sSJWrlypebMmaNBgwZJkt555x0VFhaqoqJCTzzxhOtFAgAAAAC8y/Fg+txzz2n58uXKz89viJ199tnq0qWLrr32WgZTAAAAAIAjjg/lPemkk5SdnR0Uz87OVmJiohs1AQAAAADiiOPBdMKECbrvvvsUCAQaYoFAQA888IAmTpzoanEAAAAAAO9zfCjv5s2btWbNGmVkZKhv376SpA8++EC1tbW65JJLNGrUqIbcF1980b1K4Zrq6mrbuR07drSdW1tbG045tuTk5ERsbaAp9fX1tnMty4pgJZGRkJDgKP9fv8JxIt98843TcgAAcEWnTp0itvZbb70VsbXjnePB9NRTT9XVV1/dKJaZmelaQQAAAACA+OJ4MH366acjUQcAAAAAIE45/o7psGHDVFlZGRSvrq7WsGHD3KgJAAAAABBHHA+mb775ZsjvEh45coRjrgEAAAAAjtk+lPfDDz9s+PO2bdtUXl7ecL+urk6vvvqqzjjjDHerAwAAAAB4nu3BtF+/fvL5fPL5fCEP2W3btq0effRRV4sDAAAAAHif7cG0pKRElmWpW7duev/99xtdRiQxMVGdOnVyfOkBAAAAAABsD6ZZWVmSnF3XDwAAAACAE3F8uZhly5Z97+OjR48OuxgAAAAAQPxxPJjeeeedje4fPXpUX3/9tRITE9WuXTsGUwAAAACAI44H04MHDwbFdu7cqXHjxmny5MmuFIVvB367OnXq5GjtUNehdcPf/vY3R/m5ubkRqQNwS6tW9q+odeTIEdu5fr/fUR2hLtHlhnvvvddR/n333ReROgAAOJH9+/dHbO2JEydGbG3Y5/g6pqHk5ORo9uzZQXtTAQAAAAA4EVcGU0lKSEjQ3r17HT1n3bp1uuKKK5Seni6fz6dVq1Y1etyyLBUWFio9PV1t27bV0KFDtXXrVrdKBgAAAABEAceH8q5evbrRfcuyVFZWpoULF2rw4MGO1jp8+LD69u2rm2++WVdffXXQ43PmzNG8efO0ZMkS9ejRQ/fff7+GDx+uHTt2KCkpyWnpAAAAAIAo5HgwHTlyZKP7Pp9PHTt21LBhwzR37lxHa+Xn5ys/Pz/kY5Zlaf78+Zo6dapGjRolSVq6dKlSU1P17LPP6vbbbw/5vEAgoEAg0HC/urraUU0AAAAAgJbl+FDe+vr6Rre6ujqVl5fr2WefVefOnV0rrKSkROXl5crLy2uI+f1+DRkyROvXr2/yebNmzVJKSkrDLTMz07WaAAAAAADuC/s7phUVFTpw4ICbtTRSXl4uSUpNTW0UT01NbXgslClTpqiqqqrhVlpaGrEaAQAAAADN52gwrays1IQJE9ShQwelpqaqU6dO6tChgyZOnBixS5D4fL5G9y3LCor9K7/fr+Tk5EY3AAAAAED0sv0d06+++kqDBg3Snj17dMMNN+iss86SZVnavn27lixZojVr1mj9+vU67bTTXCksLS1N0rd7Tv/1EOF9+/YF7UUFAAAAAMQu24NpUVGREhMT9dlnnwUNhkVFRcrLy1NRUZEeeeQRVwrr2rWr0tLSVFxcrHPOOUfStxeZX7t2rR588EFXfgYAAAAAwDzbh/KuWrVKDz/8cMi9lWlpaZozZ45Wrlzp6IcfOnRIW7Zs0ZYtWyR9e8KjLVu2aNeuXfL5fCooKNDMmTO1cuVKffzxx7rpppvUrl07XX/99Y5+DgAAAAAgetneY1pWVqZevXo1+Xjv3r2/96REoWzcuFE//OEPG+5PmjRJkjRmzBgtWbJEd999t7755huNHz9eBw8e1MCBA/X6669H1TVMd+/ebTs3JyfHdu6RI0fCKceWe+65x3bu7NmzI1YHEO2GDBliO3f69Om2c2fMmOGojnHjxtnOXbRokaO1AQCIBZE6n40kPfrooxFbG/bZHkw7dOigzz//XBkZGSEfLykp0emnn+7ohw8dOlSWZTX5uM/nU2FhoQoLCx2tCwAAAACIHbYP5R0xYoSmTp2q2traoMcCgYCmTZumESNGuFocAAAAAMD7bO8xnTFjhgYMGKCcnBxNmDBBPXv2lCRt27ZNixYtUiAQ0DPPPBOxQgEAAAAA3mR7MM3IyNA777yj8ePHa8qUKQ2H4Pp8Pg0fPlwLFy5UZmZmxAoFAAAAAHiT7cFU+vYSLq+88ooOHjyonTt3SpLOPPNMtW/fPiLFAQAAAAC8z9Fgetxpp52m888/3+1aAAAAAABxyPbJjwAAAAAAiAQGUwAAAACAUQymAAAAAACjGEwBAAAAAEaFdfIj/FNGRobt3G+++SaClQAwqbCwMCK5ABCu45f2A7wgJyfHdi7v/djEHlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxiMAUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAYxWAKAAAAADCKwRQAAAAAYBSDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMYjAFAAAAABjV2nQBkWZZliSpurracCWIF8ffa8ffe3CGnkVLo2ebh55FS6Nnm4eeRUuz27OeH0xramokSZmZmYYrQbypqalRSkqK6TJiDj0LU+jZ8NCzMIWeDQ89C1NO1LM+y+MfN9XX12vv3r1KSkqSz+driFdXVyszM1OlpaVKTk42WGFksH3mWJalmpoapaenq1UrjpZ3ip5l+1oaPds8oXo2ml9vt3h9G6N5++jZ5onHnvX69knRvY12e9bze0xbtWqljIyMJh9PTk6OuhfPTWyfGXyCGz56lu0zgZ4N3/f1bLS+3m7y+jZG6/bRs+GL5571+vZJ0buNdnqWj5kAAAAAAEYxmAIAAAAAjIrbwdTv92v69Ony+/2mS4kItg9e4/XXnO2Dl8TD6+31bfT69qExr7/eXt8+yRvb6PmTHwEAAAAAolvc7jEFAAAAAEQHBlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGxeVgumjRInXt2lUnnXSS+vfvr7feest0Sa4oLCyUz+drdEtLSzNdVtjWrVunK664Qunp6fL5fFq1alWjxy3LUmFhodLT09W2bVsNHTpUW7duNVMsIoqejQ30LI6jZ2MDPYvj6NnY4PWejbvBdMWKFSooKNDUqVO1efNmXXTRRcrPz9euXbtMl+aKXr16qaysrOH20UcfmS4pbIcPH1bfvn21cOHCkI/PmTNH8+bN08KFC7VhwwalpaVp+PDhqqmpaeFKEUn0bOygZyHRs7GEnoVEz8YSz/esFWfOP/98a+zYsY1iPXv2tH79618bqsg906dPt/r27Wu6jIiQZK1cubLhfn19vZWWlmbNnj27IXbkyBErJSXFeuKJJwxUiEihZ2MTPRu/6NnYRM/GL3o2NnmxZ+Nqj2ltba02bdqkvLy8RvG8vDytX7/eUFXu2rlzp9LT09W1a1dde+21+sc//mG6pIgoKSlReXl5o9fS7/dryJAhnnktQc96CT0bH+hZ76Bn4wM96x1e6Nm4GkwrKipUV1en1NTURvHU1FSVl5cbqso9AwcO1LJly/Taa6/pqaeeUnl5uXJzc3XgwAHTpbnu+Ovl1dcS36JnvYOejQ/0rHfQs/GBnvUOL/Rsa9MFmODz+RrdtywrKBaL8vPzG/7cp08fDRo0SN27d9fSpUs1adIkg5VFjldfSzTm1deZnvXOa4nGvPo607PeeS3RmFdfZ3o2tl7LuNpj2qFDByUkJAR9arBv376gTxe84OSTT1afPn20c+dO06W47vgZ1eLltYxX9Kx30LPxgZ71Dno2PtCz3uGFno2rwTQxMVH9+/dXcXFxo3hxcbFyc3MNVRU5gUBA27dvV+fOnU2X4rquXbsqLS2t0WtZW1urtWvXevK1jFf0rHfQs/GBnvUOejY+0LPe4YWejbtDeSdNmqQbb7xRAwYM0KBBg7R48WLt2rVLY8eONV1as91111264oor1KVLF+3bt0/333+/qqurNWbMGNOlheXQoUP69NNPG+6XlJRoy5Ytat++vbp06aKCggLNnDlTOTk5ysnJ0cyZM9WuXTtdf/31BquG2+jZ2EHPQqJnYwk9C4mejSWe71lzJwQ257HHHrOysrKsxMRE69xzz7XWrl1ruiRXXHPNNVbnzp2tNm3aWOnp6daoUaOsrVu3mi4rbG+88YYlKeg2ZswYy7K+PS329OnTrbS0NMvv91sXX3yx9dFHH5ktGhFBz8YGehbH0bOxgZ7FcfRsbPB6z/osy7JadhQGAAAAAOCf4uo7pgAAAACA6MNgCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAYxWAKAAAAADCKwRQAAAAAYBSDKQAAAADAKAZTAAAAAIBRDKYAAAAAAKMYTAEAAAAARjGYAgAAAACMam26gEirr6/X3r17lZSUJJ/PZ7ocxAHLslRTU6P09HS1asVnP07Rs2hp9Gzz0LNoafRs89CzaGl2e9bzg+nevXuVmZlpugzEodLSUmVkZJguI+bQszCFng0PPQtT6Nnw0LMw5UQ96/mPmZKSkkyXgDjFey88/L3BFN574eHvDabw3gsPf28w5UTvvZgYTBctWqSuXbvqpJNOUv/+/fXWW2/Zfi6HKMCUeH7v0bOIRfH83qNnEYvi+b1HzyIWnei9F/WD6YoVK1RQUKCpU6dq8+bNuuiii5Sfn69du3aZLg1ACPQsEFvoWSC20LPwLCvKnX/++dbYsWMbxXr27Gn9+te/tvX8qqoqSxI3bi1+q6qqikRLRD16llus3ujZf6JnucXCjZ79J3qWWyzcTtSzUb3HtLa2Vps2bVJeXl6jeF5entavXx/yOYFAQNXV1Y1uAFoGPQvEFnoWiC30LLwsqgfTiooK1dXVKTU1tVE8NTVV5eXlIZ8za9YspaSkNNw46xjQcuhZILbQs0BsoWfhZVE9mB733S/KWpbV5Jdnp0yZoqqqqoZbaWlpS5QI4F/Qs0BsoWeB2ELPwoui+jqmHTp0UEJCQtAnQPv27Qv6pOg4v98vv9/fEuUB+A56Fogt9CwQW+hZeFlU7zFNTExU//79VVxc3CheXFys3NxcQ1UBaAo9C8QWehaILfQsvCyq95hK0qRJk3TjjTdqwIABGjRokBYvXqxdu3Zp7NixpksDEAI9C8QWehaILfQsvCrqB9NrrrlGBw4cUFFRkcrKytS7d2+9/PLLysrKMl0agBDoWSC20LNAbKFn4VU+y7Is00VEUnV1tVJSUkyXgThUVVWl5ORk02XEHHoWptCz4aFnYQo9Gx56FqacqGej+jumAAAAAADvYzAFAAAAABjFYAoAAAAAMIrBFAAAAABgFIMpAAAAAMAoBlMAAAAAgFEMpgAAAAAAoxhMAQAAAABGMZgCAAAAAIxqbboAtLyTTz45Ius+99xzjvJvuukm27lffvml7dzly5fbzr3xxhtt5wKxwLIs27nHjh1ztHbr1vZ/Zfh8PkdrAzDLyb8d9DcQGbW1tbZzhw8fbjt37dq14ZTT4thjCgAAAAAwisEUAAAAAGAUgykAAAAAwCgGUwAAAACAUQymAAAAAACjGEwBAAAAAEYxmAIAAAAAjGIwBQAAAAAYxWAKAAAAADCqtZ2kBQsWOF745ptvVlJSkuPnAQAAAADii63BtKCgQBkZGUpISLC1aGlpqS6//HIG0+/o3bu37dzNmzdHsJLI8Pl8jvJ/9KMf2c4dPHiw7dz333/fUR1AS2vTpo2j/NraWtu5R48etZ1bX1/vqA4nnnrqKdu5P/vZzyJWB2Lf1q1bbef26tUrgpXEHie/l+vq6iJYCRCfnnnmGUf5TnrWsiyn5UQ9W4OpJG3cuFGdOnWylctACgAAAACwy9Z3TKdPn65TTjnF9qK/+c1v1L59+7CLAgAAAADED1t7TKdPn+5o0SlTpoRVDAAAAAAg/nBWXgAAAACAUY4G09///vcaM2aMnn76aUnSihUrdNZZZ6lbt26O96oCAAAAACA5OPnR/Pnzde+99+qyyy7T1KlTtXfvXj3yyCP65S9/qfr6es2dO1dnnHGGfv7zn0eyXgAAAACAx9geTJ988kktXrxY119/vTZv3qzzzz9fTzzxhG699VZJUkZGhh577DEGUwAAAACAI7YP5f3iiy904YUXSpLOOeccJSQk6IILLmh4/KKLLtJnn33mfoUAAAAAAE+zPZi2a9dOhw8fbrjfsWPHoEvIHDt2zL3KAAAAAABxwfZg2rNnT3344YcN90tLS5WVldVw/5NPPlF2drarxQEAAAAAvM/2d0wffPBBnXzyyU0+vmvXLt1+++2uFOVVH3/8se3cjz76yHZunz59HNVhWZbt3MTERNu5GzZscFTHf//3fzvKB7yitrbWUf7Ro0dt5z722GO2cwsKChzVce2119rOvf/++x2tDcB9X375pe1cficD9gwePNh2rpPfm5J011132c5dt26do7Vjge3B9EQvwvjx45tdDAAAAAAg/ji6jikAAAAAAG5jMAUAAAAAGMVgCgAAAAAwisEUAAAAAGAUgykAAAAAwCjXBtOXXnpJy5Ytc2s5AAAAAECccG0wveeee3TzzTe7tRwAAAAAIE7Yvo7piXzyySduLQUAAAAAiCN8xxQAAAAAYJTPsizL6ZMqKyv1/vvva9++faqvr2/02OjRo10rzg3V1dVKSUkxXUZEvfzyy47yBw8ebDvX6393kVRVVaXk5GTTZcScWO1ZJ0eN/OAHP3C09k033WQ7d+nSpY7WjpSjR4/azm3Tpk0EK7GPng2P05594oknHK1/0UUX2c7t1auXo7W9zsl/8Tp16mQ7d//+/eGU4zp6Njyx+ns2WtTW1trOdfr7zefzOS0nppyoZx0fyvunP/1JN9xwgw4fPqykpKRGf4E+ny/qBlMAAAAAQHRzfCjvr371K91yyy2qqalRZWWlDh482HD76quvIlEjAAAAAMDDHA+me/bs0R133KF27dpFop5GCgsL5fP5Gt3S0tIi/nMBhIeeBWILPQvEFnoWXub4UN7LLrtMGzduVLdu3SJRT5BevXrpL3/5S8P9hISEFvm5AMJDzwKxhZ4FYgs9C6+yNZiuXr264c8//vGPNXnyZG3btk19+vQJ+lLvlVde6W6BrVvzSRAQQ+hZILbQs0BsoWfhVbYG05EjRwbFioqKgmI+n091dXXNLupf7dy5U+np6fL7/Ro4cKBmzpz5vXtrA4GAAoFAw/3q6mpX6wHw/ehZILbQs0BsoWfhVba+Y1pfX2/r5vZQOnDgQC1btkyvvfaannrqKZWXlys3N1cHDhxo8jmzZs1SSkpKwy0zM9PVmgA0jZ4FYgs9C8QWehZeFtZ1TE05fPiwunfvrrvvvluTJk0KmRPqUyGvNyDXMY1OXF8tvnqW65g2xnVMY1NL9CzXMW05XMfU++Lp92y04Dqm4XP9OqZ33HGHzjzzTN1xxx2N4gsXLtSnn36q+fPnOy7SrpNPPll9+vTRzp07m8zx+/3y+/0RqwGAffQsEFvoWSC20LPwEseXi3nhhRdC7nHLzc3V888/70pRTQkEAtq+fbs6d+4c0Z8DwB30LBBb6FkgttCz8BLHg+mBAwdCHt6ZnJysiooKV4o67q677tLatWtVUlKi9957Tz/5yU9UXV2tMWPGuPpzALiDngViCz0LxBZ6Fl7m+FDeM888U6+++qomTpzYKP7KK6+4fm3T3bt367rrrlNFRYU6duyoCy64QO+++66ysrJc/Tmx7kc/+pGjfCffOfnoo49s5/bp08dRHfAer/XswIEDbed2797ddq7Xv0OC2GGiZy+44AJH+QUFBZEpJEqcdNJJtnM/++wzR2s7+a53tHxvFN/Pa79no8X06dMjsm67du0isq5XOR5MJ02apIkTJ2r//v0aNmyYJGnNmjWaO3eu698vXb58uavrAYgsehaILfQsEFvoWXiZ48H0lltuUSAQ0AMPPKD77rtPkpSdna3HH39co0ePdr1AAAAAAIC3OR5MJWncuHEaN26c9u/fr7Zt2+qUU05xuy4AAAAAQJwIazA9rmPHjm7VAQAAAACIU7bOynvuuefq4MGDthe98MILtWfPnrCLAgAAAADED1t7TLds2aIPPvhA7du3t7Xoli1bFAgEmlUYAAAAACA+2D6U95JLLrF9mREuhQAAAAAAsMvWYFpSUuJ44YyMDMfPAQAAAADEH1uDKRftBQAAAABEiq2THwEAAAAAECnNulwMYpOT7wB/9dVXtnOPHj3qqI60tDTbuQcOHHC0NuCG9evX28595JFHIlgJ4B2HDx92lP/666/bznX6e8iuVq2cfY7v5ASQd911l+3czMxMR3XU1tY6yge8wsn/MSVp6tSptnO3bdtmO/ebb75xVEe8Y48pAAAAAMAoBlMAAAAAgFGOB9Nu3bqFPKyysrJS3bp1c6UoAAAAAED8cDyYfv7556qrqwuKBwIB7dmzx5WiAAAAAADxw/bJj1avXt3w59dee00pKSkN9+vq6rRmzRplZ2e7WhwAAAAAwPtsD6YjR46U9O0ZXceMGdPosTZt2ig7O1tz5851tTgAAAAAgPfZHkzr6+slSV27dtWGDRvUoUOHiBUFAAAAAIgfjq9jWlJSEok6AAAAAABxyvFgWlRU9L2P//a3vw27GAAAAABA/HE8mK5cubLR/aNHj6qkpEStW7dW9+7dGUwBAAAAAI44Hkw3b94cFKuurtZNN92kq666ypWiED3at29vOzfUZYS+z759+2znJiQkOFobcMPx79bbcdddd0Wwkshw2ldHjhyxnXv8hHnAdw0ePNh0CTHr008/dZT/8ssvR6gSILqVlZU5yj927Jjt3H79+jmsBnY5vo5pKMnJySoqKtK0adPcWA4AAAAAEEdcGUwlqbKyUlVVVW4tBwAAAACIE44P5V2wYEGj+5ZlqaysTM8884xGjBjhWmEAAAAAgPjgeDB95JFHGt1v1aqVOnbsqDFjxmjKlCmuFQYAAAAAiA9cxxQAAAAAYFSzvmNaWlqq3bt3u1ULAAAAACAOOR5Mjx07pmnTpiklJUXZ2dnKyspSSkqK7r33Xh09ejQSNQIAAAAAPMzxobwTJ07UypUrNWfOHA0aNEiS9M4776iwsFAVFRV64oknXC8SAAAAAOBdjgfT5557TsuXL1d+fn5D7Oyzz1aXLl107bXXMpgCAAAAABxxfCjvSSedpOzs7KB4dna2EhMT3agJAAAAABBHHA+mEyZM0H333adAINAQCwQCeuCBBzRx4kRXiwMAAAAAeJ/jQ3k3b96sNWvWKCMjQ3379pUkffDBB6qtrdUll1yiUaNGNeS++OKL7lUK17z11lu2cy+44ALbufX19Y7qOHjwoKN8oKVZlmW6BMe+e63p7zNhwgRHa7dp08ZpOQAMeuCBB0yXABhx7NgxR/nsXIsOjgfTU089VVdffXWjWGZmpmsFAQAAAADii+PB9Omnn45EHQAAAACAOOX4O6bDhg1TZWVlULy6ulrDhg1zoyYAAAAAQBxxPJi++eabqq2tDYofOXLE0XcXAQAAAACQHBzK++GHHzb8edu2bSovL2+4X1dXp1dffVVnnHGGu9UBAAAAADzP9mDar18/+Xw++Xy+kIfstm3bVo8++qirxQEAAAAAvM/2YFpSUiLLstStWze9//776tixY8NjiYmJ6tSpkxISEiJSJAAAAADAu2wPpllZWZKcX6sSAAAAAIDv4/hyMcuWLfvex0ePHh12MQAAAACA+ON4ML3zzjsb3T969Ki+/vprJSYmql27dgymAAAAAABHHA+mBw8eDIrt3LlT48aN0+TJk10pCtKll15qO/eVV16JWB3Hjh2znduuXTtHa1uW5bQcoEX5fD7buUePHrWd26qVsyt1OfkKxZEjR2znJiYmOqoDQGzJzc21nfvee+9FsBKg+Zz8v9HpVw//8Ic/OC0HEeD4Oqah5OTkaPbs2UF7UwEAAAAAOBFXBlNJSkhI0N69ex09Z926dbriiiuUnp4un8+nVatWNXrcsiwVFhYqPT1dbdu21dChQ7V161a3SgbgED0LxBZ6Fogt9CzimePBdPXq1Y1uL730kp544gndeOONGjx4sKO1Dh8+rL59+2rhwoUhH58zZ47mzZunhQsXasOGDUpLS9Pw4cNVU1PjtGwALqBngdhCzwKxhZ5FPHP8HdORI0c2uu/z+dSxY0cNGzZMc+fOdbRWfn6+8vPzQz5mWZbmz5+vqVOnatSoUZKkpUuXKjU1Vc8++6xuv/12p6UDaCZ6Fogt9CwQW+hZxDPHe0zr6+sb3erq6lReXq5nn31WnTt3dq2wkpISlZeXKy8vryHm9/s1ZMgQrV+/vsnnBQIBVVdXN7oBiDx6Fogt9CwQW+hZeF3Y3zGtqKjQgQMH3KylkfLycklSampqo3hqamrDY6HMmjVLKSkpDbfMzMyI1Qjgn+hZILbQs0BsoWfhdY4G08rKSk2YMEEdOnRQamqqOnXqpA4dOmjixImqrKyMSIHfvVyDZVnfewmHKVOmqKqqquFWWloakboAhEbPArGFngViCz0Lr7L9HdOvvvpKgwYN0p49e3TDDTforLPOkmVZ2r59u5YsWaI1a9Zo/fr1Ou2001wpLC0tTdK3nw796yHC+/btC/qk6F/5/X75/X5XagBgHz0LxBZ6Fogt9Cy8zvYe06KiIiUmJuqzzz7Tk08+qYKCAv3yl7/U4sWL9emnn6pNmzYqKipyrbCuXbsqLS1NxcXFDbHa2lqtXbvW0QWjAbQMehaILfQsEFvoWXid7T2mq1at0pNPPhnyE5m0tDTNmTNHY8eO1SOPPGL7hx86dEiffvppw/2SkhJt2bJF7du3V5cuXVRQUKCZM2cqJydHOTk5mjlzptq1a6frr7/e9s8A4B56Fogt9CwQW+hZxDOfZVmWnUS/36/PPvtMGRkZIR/fvXu3zjzzTB05csT2D3/zzTf1wx/+MCg+ZswYLVmyRJZlacaMGXryySd18OBBDRw4UI899ph69+5t+2dUV1crJSXFdr5Tn3/+ue3cM844IyI1tG7t7Ko/Tg7nqK2tdVoO/n9VVVVKTk42XYarvNCzTjj5asK+ffsiVkf37t1t5+7atStidXgdPRv7Pet1/zqw2OHkJJUDBw50Wo5x9Gx89ezRo0dt5zr9v/H3fUcX7jlRz9oeTM844wytWLFCF154YcjH33rrLV177bXas2dPeJVGCINpMAbTluHFX5gtIZp+YTKYxhd6NjzR1LNex2DaGD0bnljtWQbT2HeinrX9HdMRI0Zo6tSpIQeVQCCgadP+v/buJ8TGto8D+O/wMP6ESDlO/k1MKUkZEeXPhljY2JCSrdhIz4L0NpQmWVgNiRVWVuwkC6FYSJSwGFEUk0gZCsX9LLzj6eR534fjHNec6/586pRzH4vfda772/S9z5l7/hPr169vbEoAAABK64cvJxw8eDCWLFkSXV1dsWvXrpg/f35ERDx48CCOHz8eHz9+jLNnz7ZsUAAAAPL0w8V0xowZcfPmzdi5c2fs27cvhr4BXKlUYu3atdHX1+cP9gIAAPDTfuoL2J2dnXHx4sV48+ZN9Pf3R0TEvHnzYsqUKS0ZDgAAgPz93G8G/9fkyZNj6dKlzZ4FAACAEvrhmx8BAABAKyimAAAAJKWYAgAAkJRiCgAAQFIN3fyIv82ZMyf1CECLvHnz5of/76hRo1o4CcDXv4QAZeXnbP58YgoAAEBSiikAAABJKaYAAAAkpZgCAACQlGIKAABAUoopAAAASSmmAAAAJKWYAgAAkJRiCgAAQFKKKQAAAEkppgAAACSlmAIAAJCUYgoAAEBSiikAAABJKaYAAAAkpZgCAACQlGIKAABAUoopAAAASSmmAAAAJKWYAgAAkJRiCgAAQFLZF9OiKFKPQEk59xrjfSMV515jvG+k4txrjPeNVP7t3Mu+mA4ODqYegZJy7jXG+0Yqzr3GeN9IxbnXGO8bqfzbuVcpMr9s8uXLl3j+/HlMmDAhKpXKt+Nv376NmTNnxrNnz2LixIkJJ2wN60unKIoYHByMWq0WI0Zkf+2n6WTW+n43mf01/5TZ4bzfzZL7Gofz+mT215Qxs7mvL2J4r/FHM/vHb5wpiREjRsSMGTP+5+sTJ04cdpvXTNaXxqRJk1KP0LZk1vpSkNnG/b/MDtf9bqbc1zhc1yezjStzZnNfX8TwXeOPZNZlJgAAAJJSTAEAAEiqtMW0o6Mjenp6oqOjI/UoLWF95Cb3Pbc+clKG/c59jbmvj3q573fu64vIY43Z3/wIAACA4a20n5gCAAAwPCimAAAAJKWYAgAAkJRiCgAAQFKKKQAAAEmVspgeP348Ojs7Y8yYMdHd3R3Xr19PPVJTHDhwICqVSt2jWq2mHqth165di40bN0atVotKpRIXLlyoe70oijhw4EDUarUYO3ZsrFmzJu7fv59mWFpKZtuDzDJEZtuDzDJEZttD7pktXTE9d+5c7N69O/bv3x937tyJlStXxoYNG+Lp06epR2uKBQsWxIsXL7497t27l3qkhr1//z4WLVoUfX19//j6kSNH4ujRo9HX1xe3bt2KarUaa9eujcHBwd88Ka0ks+1DZomQ2XYis0TIbDvJPrNFySxdurTYsWNH3bH58+cXe/fuTTRR8/T09BSLFi1KPUZLRERx/vz5b8+/fPlSVKvV4vDhw9+OffjwoZg0aVJx4sSJBBPSKjLbnmS2vGS2Pclseclse8oxs6X6xPTTp09x+/btWLduXd3xdevWxY0bNxJN1Vz9/f1Rq9Wis7MztmzZEo8fP049Uks8efIkBgYG6vayo6MjVq9enc1eIrM5kdlykNl8yGw5yGw+cshsqYrpq1ev4vPnzzFt2rS649OmTYuBgYFEUzXPsmXL4syZM3Hp0qU4depUDAwMxIoVK+L169epR2u6of3KdS/5SmbzIbPlILP5kNlykNl85JDZP1IPkEKlUql7XhTFd8fa0YYNG779e+HChbF8+fKYO3dunD59Ovbs2ZNwstbJdS+pl+s+y2w+e0m9XPdZZvPZS+rlus8y2157WapPTKdOnRojR4787qrBy5cvv7u6kIPx48fHwoULo7+/P/UoTTd0R7Wy7GVZyWw+ZLYcZDYfMlsOMpuPHDJbqmI6evTo6O7ujsuXL9cdv3z5cqxYsSLRVK3z8ePHePjwYUyfPj31KE3X2dkZ1Wq1bi8/ffoUV69ezXIvy0pm8yGz5SCz+ZDZcpDZfOSQ2dJ9lXfPnj2xbdu2WLJkSSxfvjxOnjwZT58+jR07dqQe7Zf9+eefsXHjxpg1a1a8fPkyDh06FG/fvo3t27enHq0h7969i0ePHn17/uTJk7h7925MmTIlZs2aFbt3747e3t7o6uqKrq6u6O3tjXHjxsXWrVsTTk2zyWz7kFkiZLadyCwRMttOss9suhsCp3Ps2LFi9uzZxejRo4vFixcXV69eTT1SU2zevLmYPn16MWrUqKJWqxWbNm0q7t+/n3qshl25cqWIiO8e27dvL4ri622xe3p6imq1WnR0dBSrVq0q7t27l3ZoWkJm24PMMkRm24PMMkRm20Puma0URVH83ioMAAAAfyvV75gCAAAw/CimAAAAJKWYAgAAkJRiCgAAQFKKKQAAAEkppgAAACSlmAIAAJCUYgoAAEBSiikAAABJKaYAAAAkpZgCAACQ1F9PMWIP2fl3swAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples = 4\n",
    "n_channels = 4\n",
    "fig, axes = plt.subplots(1 + n_channels, n_samples, figsize=(10, 10))\n",
    "for k in range(n_samples):\n",
    "    axes[0, 0].set_ylabel(\"Input\")\n",
    "    if k != 0:\n",
    "        axes[0, k].yaxis.set_visible(False)\n",
    "    axes[0, k].imshow(train_images[k, :, :, 0], cmap=\"gray\")\n",
    "\n",
    "    # Plot all output channels\n",
    "    for c in range(n_channels):\n",
    "        axes[c + 1, 0].set_ylabel(\"Output [ch. {}]\".format(c))\n",
    "        if k != 0:\n",
    "            axes[c, k].yaxis.set_visible(False)\n",
    "        axes[c + 1, k].imshow(q_train_images[k, :, :, c], cmap=\"gray\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def MyModel():\n",
    "    \"\"\"Initializes and returns a custom Keras model\n",
    "    which is ready to be trained.\"\"\"\n",
    "    model = keras.models.Sequential([\n",
    "        keras.layers.Flatten(),\n",
    "        keras.layers.Dense(10, activation=\"softmax\")\n",
    "    ])\n",
    "\n",
    "    model.compile(\n",
    "        optimizer='adam',\n",
    "        loss=\"sparse_categorical_crossentropy\",\n",
    "        metrics=[\"accuracy\"],\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 - 1s - loss: 2.5032 - accuracy: 0.2000 - val_loss: 2.2208 - val_accuracy: 0.3000 - 1s/epoch - 58ms/step\n",
      "Epoch 2/30\n",
      "20/20 - 0s - loss: 1.6996 - accuracy: 0.4875 - val_loss: 1.4901 - val_accuracy: 0.5667 - 116ms/epoch - 6ms/step\n",
      "Epoch 3/30\n",
      "20/20 - 0s - loss: 1.1630 - accuracy: 0.6500 - val_loss: 1.5023 - val_accuracy: 0.5000 - 144ms/epoch - 7ms/step\n",
      "Epoch 4/30\n",
      "20/20 - 0s - loss: 0.8277 - accuracy: 0.8000 - val_loss: 1.1998 - val_accuracy: 0.6333 - 156ms/epoch - 8ms/step\n",
      "Epoch 5/30\n",
      "20/20 - 0s - loss: 0.6096 - accuracy: 0.9125 - val_loss: 1.1389 - val_accuracy: 0.6333 - 145ms/epoch - 7ms/step\n",
      "Epoch 6/30\n",
      "20/20 - 0s - loss: 0.4682 - accuracy: 0.9250 - val_loss: 1.0415 - val_accuracy: 0.7333 - 101ms/epoch - 5ms/step\n",
      "Epoch 7/30\n",
      "20/20 - 0s - loss: 0.4163 - accuracy: 0.9500 - val_loss: 1.0099 - val_accuracy: 0.6667 - 135ms/epoch - 7ms/step\n",
      "Epoch 8/30\n",
      "20/20 - 0s - loss: 0.3172 - accuracy: 0.9750 - val_loss: 0.9290 - val_accuracy: 0.7333 - 148ms/epoch - 7ms/step\n",
      "Epoch 9/30\n",
      "20/20 - 0s - loss: 0.2667 - accuracy: 0.9875 - val_loss: 0.9345 - val_accuracy: 0.6667 - 144ms/epoch - 7ms/step\n",
      "Epoch 10/30\n",
      "20/20 - 0s - loss: 0.2283 - accuracy: 0.9750 - val_loss: 0.8869 - val_accuracy: 0.7000 - 132ms/epoch - 7ms/step\n",
      "Epoch 11/30\n",
      "20/20 - 0s - loss: 0.1849 - accuracy: 1.0000 - val_loss: 0.9000 - val_accuracy: 0.6667 - 150ms/epoch - 8ms/step\n",
      "Epoch 12/30\n",
      "20/20 - 0s - loss: 0.1559 - accuracy: 1.0000 - val_loss: 0.8375 - val_accuracy: 0.7000 - 110ms/epoch - 5ms/step\n",
      "Epoch 13/30\n",
      "20/20 - 0s - loss: 0.1375 - accuracy: 1.0000 - val_loss: 0.8381 - val_accuracy: 0.7000 - 186ms/epoch - 9ms/step\n",
      "Epoch 14/30\n",
      "20/20 - 0s - loss: 0.1221 - accuracy: 1.0000 - val_loss: 0.8241 - val_accuracy: 0.7333 - 115ms/epoch - 6ms/step\n",
      "Epoch 15/30\n",
      "20/20 - 0s - loss: 0.1096 - accuracy: 1.0000 - val_loss: 0.8191 - val_accuracy: 0.7333 - 98ms/epoch - 5ms/step\n",
      "Epoch 16/30\n",
      "20/20 - 0s - loss: 0.0979 - accuracy: 1.0000 - val_loss: 0.8259 - val_accuracy: 0.7000 - 115ms/epoch - 6ms/step\n",
      "Epoch 17/30\n",
      "20/20 - 0s - loss: 0.0869 - accuracy: 1.0000 - val_loss: 0.8151 - val_accuracy: 0.7000 - 120ms/epoch - 6ms/step\n",
      "Epoch 18/30\n",
      "20/20 - 0s - loss: 0.0807 - accuracy: 1.0000 - val_loss: 0.8026 - val_accuracy: 0.7333 - 125ms/epoch - 6ms/step\n",
      "Epoch 19/30\n",
      "20/20 - 0s - loss: 0.0767 - accuracy: 1.0000 - val_loss: 0.8078 - val_accuracy: 0.7333 - 121ms/epoch - 6ms/step\n",
      "Epoch 20/30\n",
      "20/20 - 0s - loss: 0.0689 - accuracy: 1.0000 - val_loss: 0.7835 - val_accuracy: 0.7333 - 126ms/epoch - 6ms/step\n",
      "Epoch 21/30\n",
      "20/20 - 0s - loss: 0.0597 - accuracy: 1.0000 - val_loss: 0.8049 - val_accuracy: 0.7000 - 115ms/epoch - 6ms/step\n",
      "Epoch 22/30\n",
      "20/20 - 0s - loss: 0.0556 - accuracy: 1.0000 - val_loss: 0.7858 - val_accuracy: 0.7333 - 121ms/epoch - 6ms/step\n",
      "Epoch 23/30\n",
      "20/20 - 0s - loss: 0.0528 - accuracy: 1.0000 - val_loss: 0.7695 - val_accuracy: 0.7333 - 128ms/epoch - 6ms/step\n",
      "Epoch 24/30\n",
      "20/20 - 0s - loss: 0.0484 - accuracy: 1.0000 - val_loss: 0.7948 - val_accuracy: 0.7333 - 99ms/epoch - 5ms/step\n",
      "Epoch 25/30\n",
      "20/20 - 0s - loss: 0.0457 - accuracy: 1.0000 - val_loss: 0.7926 - val_accuracy: 0.7000 - 114ms/epoch - 6ms/step\n",
      "Epoch 26/30\n",
      "20/20 - 0s - loss: 0.0422 - accuracy: 1.0000 - val_loss: 0.7668 - val_accuracy: 0.7333 - 128ms/epoch - 6ms/step\n",
      "Epoch 27/30\n",
      "20/20 - 0s - loss: 0.0400 - accuracy: 1.0000 - val_loss: 0.7786 - val_accuracy: 0.7000 - 120ms/epoch - 6ms/step\n",
      "Epoch 28/30\n",
      "20/20 - 0s - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.7714 - val_accuracy: 0.7333 - 122ms/epoch - 6ms/step\n",
      "Epoch 29/30\n",
      "20/20 - 0s - loss: 0.0378 - accuracy: 1.0000 - val_loss: 0.7699 - val_accuracy: 0.7333 - 119ms/epoch - 6ms/step\n",
      "Epoch 30/30\n",
      "20/20 - 0s - loss: 0.0327 - accuracy: 1.0000 - val_loss: 0.7928 - val_accuracy: 0.7000 - 124ms/epoch - 6ms/step\n"
     ]
    }
   ],
   "source": [
    "q_model = MyModel()\n",
    "\n",
    "q_history = q_model.fit(\n",
    "    q_train_images,\n",
    "    train_labels,\n",
    "    validation_data=(q_test_images, test_labels),\n",
    "    batch_size=4,\n",
    "    epochs=n_epochs,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to compare the results achievable with and without the quantum\n",
    "convolution layer, we initialize also a \\\"classical\\\" instance of the\n",
    "model that will be directly trained and validated with the raw MNIST\n",
    "images (i.e., without quantum pre-processing).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 - 1s - loss: 2.1728 - accuracy: 0.2375 - val_loss: 1.9858 - val_accuracy: 0.3667 - 917ms/epoch - 46ms/step\n",
      "Epoch 2/30\n",
      "20/20 - 0s - loss: 1.6843 - accuracy: 0.6125 - val_loss: 1.7618 - val_accuracy: 0.5000 - 132ms/epoch - 7ms/step\n",
      "Epoch 3/30\n",
      "20/20 - 0s - loss: 1.3629 - accuracy: 0.7375 - val_loss: 1.5760 - val_accuracy: 0.6000 - 123ms/epoch - 6ms/step\n",
      "Epoch 4/30\n",
      "20/20 - 0s - loss: 1.1032 - accuracy: 0.8625 - val_loss: 1.4474 - val_accuracy: 0.6000 - 127ms/epoch - 6ms/step\n",
      "Epoch 5/30\n",
      "20/20 - 0s - loss: 0.9144 - accuracy: 0.9125 - val_loss: 1.3506 - val_accuracy: 0.6667 - 111ms/epoch - 6ms/step\n",
      "Epoch 6/30\n",
      "20/20 - 0s - loss: 0.7667 - accuracy: 0.9500 - val_loss: 1.2775 - val_accuracy: 0.7000 - 119ms/epoch - 6ms/step\n",
      "Epoch 7/30\n",
      "20/20 - 0s - loss: 0.6573 - accuracy: 0.9500 - val_loss: 1.2045 - val_accuracy: 0.7333 - 116ms/epoch - 6ms/step\n",
      "Epoch 8/30\n",
      "20/20 - 0s - loss: 0.5619 - accuracy: 0.9500 - val_loss: 1.1561 - val_accuracy: 0.7333 - 123ms/epoch - 6ms/step\n",
      "Epoch 9/30\n",
      "20/20 - 0s - loss: 0.4882 - accuracy: 0.9500 - val_loss: 1.1255 - val_accuracy: 0.7333 - 125ms/epoch - 6ms/step\n",
      "Epoch 10/30\n",
      "20/20 - 0s - loss: 0.4317 - accuracy: 0.9625 - val_loss: 1.1049 - val_accuracy: 0.7333 - 107ms/epoch - 5ms/step\n",
      "Epoch 11/30\n",
      "20/20 - 0s - loss: 0.3809 - accuracy: 0.9750 - val_loss: 1.0674 - val_accuracy: 0.7333 - 118ms/epoch - 6ms/step\n",
      "Epoch 12/30\n",
      "20/20 - 0s - loss: 0.3371 - accuracy: 0.9750 - val_loss: 1.0511 - val_accuracy: 0.7333 - 107ms/epoch - 5ms/step\n",
      "Epoch 13/30\n",
      "20/20 - 0s - loss: 0.3039 - accuracy: 0.9750 - val_loss: 1.0491 - val_accuracy: 0.7333 - 112ms/epoch - 6ms/step\n",
      "Epoch 14/30\n",
      "20/20 - 0s - loss: 0.2744 - accuracy: 1.0000 - val_loss: 1.0283 - val_accuracy: 0.7333 - 122ms/epoch - 6ms/step\n",
      "Epoch 15/30\n",
      "20/20 - 0s - loss: 0.2467 - accuracy: 1.0000 - val_loss: 1.0157 - val_accuracy: 0.7333 - 116ms/epoch - 6ms/step\n",
      "Epoch 16/30\n",
      "20/20 - 0s - loss: 0.2248 - accuracy: 1.0000 - val_loss: 1.0054 - val_accuracy: 0.7333 - 119ms/epoch - 6ms/step\n",
      "Epoch 17/30\n",
      "20/20 - 0s - loss: 0.2064 - accuracy: 1.0000 - val_loss: 1.0053 - val_accuracy: 0.7333 - 117ms/epoch - 6ms/step\n",
      "Epoch 18/30\n",
      "20/20 - 0s - loss: 0.1901 - accuracy: 1.0000 - val_loss: 0.9982 - val_accuracy: 0.7333 - 116ms/epoch - 6ms/step\n",
      "Epoch 19/30\n",
      "20/20 - 0s - loss: 0.1742 - accuracy: 1.0000 - val_loss: 0.9926 - val_accuracy: 0.7333 - 119ms/epoch - 6ms/step\n",
      "Epoch 20/30\n",
      "20/20 - 0s - loss: 0.1617 - accuracy: 1.0000 - val_loss: 0.9805 - val_accuracy: 0.7333 - 115ms/epoch - 6ms/step\n",
      "Epoch 21/30\n",
      "20/20 - 0s - loss: 0.1488 - accuracy: 1.0000 - val_loss: 0.9805 - val_accuracy: 0.7333 - 114ms/epoch - 6ms/step\n",
      "Epoch 22/30\n",
      "20/20 - 0s - loss: 0.1380 - accuracy: 1.0000 - val_loss: 0.9793 - val_accuracy: 0.7333 - 111ms/epoch - 6ms/step\n",
      "Epoch 23/30\n",
      "20/20 - 0s - loss: 0.1292 - accuracy: 1.0000 - val_loss: 0.9737 - val_accuracy: 0.7333 - 114ms/epoch - 6ms/step\n",
      "Epoch 24/30\n",
      "20/20 - 0s - loss: 0.1205 - accuracy: 1.0000 - val_loss: 0.9727 - val_accuracy: 0.7333 - 122ms/epoch - 6ms/step\n",
      "Epoch 25/30\n",
      "20/20 - 0s - loss: 0.1135 - accuracy: 1.0000 - val_loss: 0.9777 - val_accuracy: 0.7333 - 118ms/epoch - 6ms/step\n",
      "Epoch 26/30\n",
      "20/20 - 0s - loss: 0.1059 - accuracy: 1.0000 - val_loss: 0.9691 - val_accuracy: 0.7333 - 116ms/epoch - 6ms/step\n",
      "Epoch 27/30\n",
      "20/20 - 0s - loss: 0.0999 - accuracy: 1.0000 - val_loss: 0.9685 - val_accuracy: 0.7333 - 113ms/epoch - 6ms/step\n",
      "Epoch 28/30\n",
      "20/20 - 0s - loss: 0.0947 - accuracy: 1.0000 - val_loss: 0.9719 - val_accuracy: 0.7333 - 114ms/epoch - 6ms/step\n",
      "Epoch 29/30\n",
      "20/20 - 0s - loss: 0.0891 - accuracy: 1.0000 - val_loss: 0.9661 - val_accuracy: 0.7333 - 123ms/epoch - 6ms/step\n",
      "Epoch 30/30\n",
      "20/20 - 0s - loss: 0.0839 - accuracy: 1.0000 - val_loss: 0.9676 - val_accuracy: 0.7333 - 129ms/epoch - 6ms/step\n"
     ]
    }
   ],
   "source": [
    "c_model = MyModel()\n",
    "\n",
    "c_history = c_model.fit(\n",
    "    train_images,\n",
    "    train_labels,\n",
    "    validation_data=(test_images, test_labels),\n",
    "    batch_size=4,\n",
    "    epochs=n_epochs,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cXCz+/pUB2LZtC9269cgRhCwWC3/8sZ0RI14CoH37Nrz77ods2rSeRYsWYDabWbPmN777bjEAycnJvPzyKLZt20zVqtV46aVXaNWqDQCHDh3kgw/eYd++vVSsWJHg4BCef/5F3N3dmT37U7Zs2cSsWeH2tvv3780jj/yL5OQUZs60rZa9bl0Iv/66DhcXlxzXs3//Pt58czxHjx6hQYOG9O4dxpQpk1i/PpIdOyIZPvwZfvttA+7u7gCMGTOGixeTGD/+v4At+Pzww/fExcVSrVp1nnnmOe68syMAb745Hi8vL1xdXVm2bCkuLmYeeugRHnnkcf7znxFs3LieLVs2sXr1b4wZ8xr339+HuXMXULduPYAc17ZjRyRjxozk9dcn8MEH73LhwgXuv/8hOnToyOTJEzlx4jht2wYzYcJkXF2vHinS0tL44IN32LhxHSkpqdx88y2MHDmGwMD6TJ48gcTEi7z55jv2/X/+eSkzZ37MwoURJCUl8sEH77J9+1aSk1MICgpm9OhX8fX14+TJE9x/fx9GjhzDrFnT+fe/R9K9e6ih91VhKTSJiFznEtIu0PrrZlxIi3fK+U4kxdBz4T0F7ufnXontj0QZCk5NmjTD29ubyMitdOnSnYyMDHbt+oPhw1/k55+XcurUKWrUqMGePX+TmHjRPiWXbcSI0Rw8eIAmTZoxdOjz9u1Ll/7Ia69NYPz4N3n77UlMm/YuX3wxj/T0dF588Tm6dw/lnXc+4PTp04we/QKzZ89k2LB/X7WvAwc+SnT0QdLT0/J91Mo77/yXOnXqMn3655w+fYrXXvtPgTXItnPnDj799BNmzfqC+vVv4qefFvHGG2NZtOhnfHx8APjtt1949tkXWLLkF1asWMaUKZPo3r0XU6a8bw93YWH9OXnyRIHtpaamEBm5la+/ns+aNb/x5pvjOXToAB98MJ2LFy/wr38NYP36tXTs2Pmq5/nmmy/ZvTuKr776Hg8PT6ZMmcSUKZOYOfP/6N49lJEjn+fixYv2a/j999Xcc09XXFxcmDRpPC4uLnz11fe4uLjw7rtvMW7cK7z//if28//xx3YWLPgJLy9vw7UsLE3PiYhIqefq6krr1m3tU3FRUX/i71+ZevUCadq0BZGRtu2RkVu55ZZG+Pv7Gzpv+/Z30ahRE9zd3enQoRNHjx4BYPPmjaSmpjB48FO4u3tQp05d+vd/MNf9U9fi/Plz/P33XzzyyL/w8vIiMLA+PXr0Mnx88+a3sXjxzzRo0BCz2UyXLt1ITU3lyJFL06U1atSkR49euLq60rlzVywWC8eOHbmm/losFvr1ewAPDw/uuKMDVquVjh074+/vT5069ahTpy7Hjh0r8DyPPPI406fPxtfXDzc3N+6+uzN79/4DwG23teKGG6qwdq1tSjMlJYWtWzfTtWtP4uJi2bhxHc888xy+vn54e1fkmWeeY9u2LZw/f85+/u7dQ/H2rphrWtaZNNIkInKd83X3Y/sjUYam56LO7mL07yMK3O+du96naZXmwP+m53wcm54DCAoKITz8c8A2Ndemze0AtGnTlsjIrfTq1ZfIyK0EBbUzfM6AgJr2P7u7u5ORkQHAyZMx1KxZCzc3N/vrdevW4+zZMw5/SvDs2TOALdhcOneg4eMtFgtz5nzOmjW/ceFCvH17enq6/c+XX5eHhwdgmx67VtWqVQew16Nq1Wr219zdPUhPL/jccXGxvP/+O+za9QfJyclYrVYyMzMBMJlMdO3ag19+WU6vXmFs3ryBgICa3HLLrfz1VxQAgwYNzHE+FxcXzpw5TaVKtoBcvXqNa74+oxSaREQEX3c/WldvW+B+raq1YfrOD3PdBH65QL/6PNZ4sP1f/K6uZvz9vYmLSyIzM+97oYwIDg7h7bff5MiRw0RGbuWhhx4GoHXrtsyfP4+UlBT++msXTzzxjOFzXn1QIueLViv2UJUXi8XYtWWHrstHRKz53COWLSsry/7nOXM+Y82a35gy5T0aNLiZtLQ0unS5M8f+ZvO1j7bkdR1Xjt5cy2jO+PGv4ubmxpw531CtWnU2blzP6NEv2F/v3j2Ur76aw7lzZ/n99zV07doDwH5f16JFy/Dzq5TrvNlTjAXdU+UMmp4TERHDTCYT40ImYTbl/evDbDLzeruJRTJFUq1aderVq8+WLRs5cGAfrVrZQt4ttzQiPT2NZcuW4ObmTpMmzRxuq1atGzlxIiZHSDp69DABATUxmUy4ubnnGLlJTU3NMVV0NVWqVAXg1KmT9m2HDh20/9nNzRYSLj//5dNf//yzm/bt7+Lmm2/FbDazb9+eQl7dJdkjR2lpqfZtMTHHr/l8V/PPP7vp06effdTqyn7Xrl2HRo2asGLFMjZuXEeXLt0B26iZ2Wzm4MED9n0zMzM5d+5skfTzahSaRESkUELr92Z2t68I9KufY3ugX31md/uK0Pq9i6ztoKB2/PDD99StG2i/b8nFxYWWLVszf/63tGlze65PqmVzd/fg+PGjOaa08tOqVVtcXV2YM+czMjIyOHr0CPPnz7Pfe1S7dm2OHInmwIH9pKWl8dln0/H09LqsLXdOnTpFfHy8fQoqW9Wq1WjY8Ga+/fYrUlJSOHToACtWLLO/XrNmTVxcXFi9eiWZmZn8+usKjhy5dD9SjRoBHDiwj9TUVA4dOsD8+fNwd3c3HCLc3d05cuQIFy9epFIlfypWrMjq1b+RlZXFjh2R7Nq109B5CqtGjQD+/vsvMjIy2LhxPTt2RAKXpivBNtr05Zf/x003NbRPMVasWJHOnbsyY8aHnDlzmrS0VGbO/JgXXni22BdUVWgSEZFCC63fm80D/+DHsJ+Z1WUOP4UtZ/PAP4o0MIFtii4m5jitW+ecSmzdui3Hjx8jODj/+5l69uzNtm1bGTjwvhzTXXnx8vLi7bc/YOfOHfTqdQ8jRw6na9cePProIMB2A3nHjp0ZNuwJHnwwjMDAm6hdu7b9+C5dunP8+FEefLAv587lHoF67bUJHD16hF697mHy5An07/+Q/bXKlW/gmWee57PPZhAa2pk//9xJ37597a8/9thgsrKy6NXrHt56axJPPjmUHj16MWXKJDZtWn/1AgK9e4fx008Lef75p3FxceHFF8ewfHkE3bt35Mcff8jRF2d68cXRrF27ip49O7N8eQQTJrzFzTffyqOPPmAPsp07dyU9Pd0+NZdtxIiXqFWrNo8++gB9+/bg8OFDvPXW1CK96TsvJmtpXPe+BJw9e7HIzu2s+fzrlernGNXPcaqhY1S/gm3evJFRo4azfn1krteup/rFxBxn0KCHWbgwgooVKzrtvAXVsGpVH0Pn0UiTiIiIlLiLFy/yzjv/JSysn1MDkzMpNImIiEiJ+uWX5dx7bw/8/CoxePDTJd2dfGnJARERkRIWHByS59Tc9aJr1+507dq9pLtRII00iYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIGKDSJiIiIGKDQJCIiImKAQpOIiIiIAQpNIiIiIgYoNImIiIgYoNAkIiIiYoBCk4iIiIgBCk0iIiIiBig0iYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIGKDSJiIiIGKDQJCIiImKAQpOIiIiIAQpNIiIiIgYoNImIiIgYoNAkIiIiYoBCk4iIiIgBpTo0HTt2jCeffJKgoCDuvvtupkyZQlZWVq79LBYLH374IZ06daJly5b07t2bZcuWlUCPRUREpLwq1aFp+PDh1KhRg5UrVxIeHs6qVasIDw/Ptd+3337L999/z6effsq2bdv497//zUsvvcSePXuKv9MiIiJSLrmWdAfyExUVxd69ewkPD8fHxwcfHx8GDRpEeHg4Q4YMybHv7t27adOmDQ0bNgTgnnvuoVKlSuzdu5dbb73VUHtmswmz2eT06wBwcTHn+L8UjurnGNXPcaqhY1Q/x6h+jnNWDUttaNq9eze1atXCz8/Pvq1x48ZER0eTmJhIxYoV7ds7duzI+PHj+fvvv2nQoAG///47KSkp3H777Ybbq1zZG5OpaEJTNl9fzyI9f3mn+jlG9XOcaugY1c8xqp/jHK1hqQ1N8fHx+Pr65tiWHaDi4uJyhKauXbvyzz//cO+99wLg4eHBlClTCAgIMNxebGxSkY40+fp6kpCQQlaWpUjaKM9UP8eofo5TDR2j+jlG9XNcQTX09/c2dJ5SG5ryYrVaAXKNCC1evJjFixczf/58brnlFjZt2sTIkSOpWbMmzZs3N3Rui8WKxWJ1ep8vl5VlITNTb/hrpfo5RvVznGroGNXPMaqf4xytYamdIK1cuTJxcXE5tl24cAGTyYS/v3+O7V9//TUPPvggzZs3x93dnY4dOxIcHMxPP/1UnF0WERGRcqzUhqamTZty4sQJYmNj7duioqJo0KAB3t45h9EsFkuupQjyWppARERE5FqV2tDUuHFjmjdvztSpU0lMTOTgwYPMmTOHAQMGANC9e3ciIyMB6NSpEwsWLGDPnj1kZmayYcMGNmzYQOfOnUvyEkRERKQcKdX3NH344YeMHz+eTp064ePjQ1hYGAMHDgQgOjqa5ORkAJ5++mkyMzN59tlniY2NpVatWkycOJF27dqVZPdFRESkHDFZs++uvs6dPXuxyM7t6mrG39+buLgk3cR3DVQ/x6h+jlMNHaP6OUb1c1xBNaxa1cfQeUrt9JyIiIhIaaLQJCIiImKAQpOIiIiIAQpNIiIiIgYoNImIiIgYoNAkIiIiYoBCk4iIiIgBCk0iIiIiBig0iYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIGuJZ0B0SuxmqFjRvNXLwIPj5m2ra1YDIV5ngrm09u5FTSSWp4BxAcEIKpECewWKz832+bOXT2FPWr1mBw52DMZuPHO9q+M47fGLOeizFx+OBP22rtir39kqy/M85R0jV0tP8lfbyj9Svp/pf0zwCLxcqsFRuJSThPLd8bePzuoGJvvyTr54xrcCaT1Wq1lkjLpczZsxeL7Nx6QvW1iYhw5Y033Dl8+NKAaL16FsaNSyM0NLPg4w8t4Y2NYzmcEH3peN9AxoVMIrR+7wKPnzA/gpkHx5Lpe9C+zTXhJp65aRKv3x9a5O1f78c7Wn9nnKOs16Ckj9f1l+2fQSVdP2dcg73dAn4PV63qY+g8Ck3/o9BUukREuDJkiAcWS+5/TZjNVmbPTr1qcIo4tIQhKx7FYs1db7PJzOxuX131G27C/Ag+Pv0wmPP4+7KYea763Kt+0zva/vV+vKP1d8Y5ynoNSvp4XX/Z/hlU0vVzxjVcTqHJyRSaSg+rFYKCvHOMMF0pMNDC5s1JeU7VWa1WgubeluNfJrmO96vP5oF/5DnEa7FYufHtVjn+dXQl14QGHB+9Pc9hZkfbv96Pd7T+zjhHWa9BSR+v6y/bP4NKun7OuIZc7Sk0OZdCU+mxaZMLfft6FbjfTz8lExyclfv4Exvou7hHgcffVq0Vldwr5dp+/Fw8B1J2FHh8Q89W1KqS+/j41Dh2nv3jmtu/3o83Wn/P2DZ44J/na6nEkVI5ssBzlNa/Q0drYPT6HT2+qOqn6y/Zn0HFVf/8+g/Gr+GnsOUE1wwpcD+FJidTaCo9Fi1y5emnPQvcb9asFMLCck/RLdq/gKd/HVwUXRMRkVJkVpc5hDW8r8D9nBWa9Ok5KXVq1DCW4/Pbr4Z3gKHjW1dri79H7n8lHT0Xx77kbQUef4vX7dTO419JcamxbD9T8L+y8mv/ej/eaP29YoPwpHKer6UQS3LlLQWeo7T+HR45G8f+lGuvgdHrd/T4oqqfo++B6+X6S7r9oqofGL8Goz/vnUWhSUqd4OAs6tWzFHhPU1BQ7qm5TEsmy6OXFdhGoF99lt230qH5/LXP/OrQ/QT5tX+9H799u4keP98Gla9e/0Ov/OLQPU0uF0rn3+G5cyaefMqd/a0aXXMNjL6HHTneHF809cvMhAkT3dhXoUmpvn5TXANWPP4rXl7Of/8sXuzCU8ebX/X6TbEN+PGhX6lc2fntb9hg5t7VLYq0/sQ2YE7XlTRokPfLRq8hKKBd/m0UAS1uKaWOyQTjxqUBeY8kmUxWXn89LddN4GeTz/LAkjBm/PnRVc9vNpl5vd3EfG8eNJtNdOG/YMnn28Ni5pmbJub7C9tkMjEuZBJmU97HF9T+9Xz8119XoG9fb/jlnWuuP9j+Dp+5adJVz2H6dQqLFlVw+jU4cvwff5jp0sWLDesrOFQDI9fv6PGW5W8zcqQHqam5X77W6z971sQDD3gyc4Z7qb9+64q3CQvz5vjx3Oe41uvPzITx49156imvAq/f+svbdOtWkd27c+9zre1brfDppxXo37/g9h2tH//r/4oVLnnu4uj3YFFRaJJSqVmz3KNI2apUsXL33TnvZdpxOpIu8zuwPuZ3AEJqtueDu6cT6Fc/x36BfvUL/JhqQgLsmNsfvl8A56/4Z9D5Brgtns/THXpdtf+h9Xszu9tX19T+9Xh8WhqMHOnOiy96kJ5uwvNIGF3jv8U1IWf9XRMaGPqoMsDr94fyXPW5uc5hjm+A+Yf5ZP7Vj6FDPRk71p2MDMevwdHjv/qqAr17exETY/ux/PRdoQyrmrv/RmuQ3/U7erzLhQb4/fI97LmXuXPd6NPHi2PHcv/iKuz179hhC4zr19smQEIq92ZwxdJ3/a4JDai3zXb9O3e6cM89Xqxdm/sXf2GvPzswTp/uBkCN+L70t36TZ/tN/p4He+7lyBEzPXt6sWBB7kmjwraflATPPOPBa695kJVlwicmjNCka/8evFr92p+ch8v+MC5eNPHoo1689ZYbWXn8yHf0e7Ao6Ebw/9GN4KXLG2+488knbphMVmbPTsPLy4Pt29N45x13AJ57Lo3XX0/HarXy1d/hvLLuJdIt6QA80+I5Xgt+gwouFewryZ5OOkUN7wCCAgpejfill9z54gvbD64vv0ziuMsGos+dxiUpgJmvdALM9O6dwezZefwT+wrX0r6zj992ZhOJxOODP22qBRd7+wUdHxNjYvBgT/74w/aLp149C3PmpNCkicW+mnD0udPUr1qDQZ0KtxoykOc5du1yYfBgT44ftwWU4OBMPvsslerVc/84LOoapqbCyy+7M3eu7T3n5WXlvfdS6dcvM9/+X8uKzM48PjHRxHPPebB8uW2krnJlC59+mspdd+X+zVdQ/axWW2B85RV30tNt2595Jp3XXkujQoXSef1g4v333Xj7bTesVhNms5VXXknn+efTc42AG/ke2L7dzJAhnpw4YXs/hoRkMmtWKtWqWbFYrISv3sKJi7HU8r2Bf3W8HbPZxDffuPKf/3iQlmY71xNPpDN+fBpuboVv/9AhE4MGefLPP7bvwUaNspgzJ4X69a1FVv9Nm1x44gkPzp61XfPdd2cyc2YK/nl8INbRn0OgT885nUJT6ZGcDLfdVpH4eBM9e2bw9dfp+Pt7ExubxAMPuPPbb664uFhZujyWr86P4Js9XwHg5erNB3d/bOiTFPnZvNmFPn1syx307ZvBZ5/lDEZjxrjzf/+XHaiS6d49/xGx0qI0v//WrXPhqac8OH/e9oOza9dMPvkkBT+/om/7/HkTTz3lwbp1tn+lV69uYfbsFG6/PXeNiqqGx4/bAuPOnbZfVoGBtsDYuHHp+nvKi8UC06a58dZbBQeH/OqXmmr7nvrmm0uB8YMPUvP8VGxp9NtvLgwd6kl8vO2Ce/bM4KOPUvEx9vsXqxW+/LICr76ad2DMll/9du405wj/t9+eyezZeYf//KxY4cKwYZ5cvGhr/957M3jvvVS8vQ2f4pqdPGliyBBPIiNt7/86dWzv/2bNnP/+d1Zo0vSclDqLFlWw/xB64olL8yYmE7z9dipeXlayKh6l39Ju9sBU3+8mlvdf5VBgyp4iAvDzszJpUlqufV59NY2AANs33H/+48HFosva5ZrVCh995Mb993ty/rwZk8nK6NFpfPll8QQmgBtusPLddyk8/7zt7/n0aTNhYV7Mnl2B4vin5O+/u9Cli5c9MHXtmskvvySVicAEYDbDiBHpfPttCv7+ViwWE5MmuTNokLHvi2PHTPTu7WUPTPXrW/j55+QyE5gAOnfO4pdfkmja1PaPp2XLKtCtmxf79hX8qzU1FUaMcOell2xT0l5eVmbNSmHChJyB6Wpuu83Cr78m06GDrWZbt7rSubMXmzfnfZ/Q5bKy4K233Hj0US8uXjTh4mJlwoRUZs4snsAEEBBgZfHiZAYPts0SHD1qJjTUi+++K72fUdNI0/9opKl0sFqhUycvdu924dZbs1izJonIs5u4yKWHfY7+dB1fJP4LvM4D0D0wlI87zcTX3bHftm+/7ca779pC0/vvp/Lww3nc6AIsX+7CY4/ZRqMGD07nrbdyhytnsVpto1+nTpmoUcNKcHBWoR5YDI69/xxtP6/jk5Jg+HAPli61/Wbw87Myc2YKnTuX3Kjd0qWuPP+8B0lJtou7//4M3nknFS8v2zVs2+bKxYue+Pik0LZtpkM1CArK4uOP3fjvf92wWEz/C4zpjBiRjrmM/jP2yBHbiFlUlO2XdYMGWYSHp3LzzZY86/f77y48/bQHsbG2C+7ePYOPP07F17ckr+LaJSfD6NEefP+97T3t7W3lww9T6d07M8/vgewRxj//tNWrfn0L4eEp3Hpr3t+fBX0PZwegadPc/7e/lQkT0hgyxPYz7Mr24+Nh6FBPVq2yhZMqVSx8/nkqISEl9z343XeuvPSSB6mptm+uQYPSmTjRNt1YHD8HNT1XSApNpcPl02OP/fc7fvd4OcdHTv3d/YlPi8eKFawmKqybwPq3hhNYz7FPUOzbZ+buu73IyDBxxx2ZLFyYctVvyiFDPFiypAImk5WlS5Np29b5f6+OPrA427W+/xx+YHIex9eqZbtP6eRJ2y+LJk1s907Uq1fyP4b27zfz+OMe7N9/qW+PPZbOjBnOrYGXl5XkZNubq1IlKzNmlGxgdJaUFFtw+O67S8Hh8cfTiYiokOP6/f2txMeD1WoLjGPGpPPvf5fdwJjNaoU5cyowdqw7mZm2v98ePTL45x+XHNdfvbqFpCRITDQeGI1+D0dE2MJ/YqKt/eDgTE6eNHPkyKX2a9a0kJ4O587ZtrVuncX//V8KAQEl/z0YFWVm0CBPjh619a1NmywGDkznww+L/uegQlMhKTSVDk8+6cGPP1bAs9VC0vren+eDGgE8zRVJ+2o+lv3dufvuTObNu3rIuRqLBfr08WTrVlfc3a2sWZPETTdd/dvi9GkTd9zhTUKCiVtvzWLlyuRcN2A6wtEHFl/uWt5/Dj8w+SrHZ+vfP4N337WN5pQWiYk5R8Fsy14UTQ3q1MliwYLSERidxWqF8PAK//tE4tW/Ib29rcyenUKnTmU/MF5u61bbTd2nTxeUAm33gA0fXnBgLMz38IEDtpu69+4teIru8tGc0iIuLucomCPfg5dTaHIyhaaSd/KkiVatvMnKAt+xDUhwPZTvvrV96tD38F4+/sgDgOnTU+jf/9ruhQgPr8Do0bbzvPxyGiNGpBs67ssvKzBqlO24//wnjZEjjR1XEEcfWHylwr7/jLRfp46FpUuT83lgMoSGenHsWP7H33CDhd27k0rl6ILVCh9/XIGJE93J64d1NkdrUK+ehS1bjP0dljVbt5rp29eLrKz8L652bQuRkeXz+k+dMtG2rbf9k215qVHDwp9/Fs338MWL0KxZRfuIZl6qVrXw11+ls/5ZWbbbJd5/3/2q+znz56BuBJcy54svKth+yNZdd9XABHDs4lHuemQt9erZ3vyvvebO+fOF/+4/edL0v1+Oto/ZPvus8eDzyCMZBAfbgtr777tx4IBzfvps3uxy1cACEB1tZsuWgv8lWVTtHz1qpnnzijRrlvu/5s0rXjUsAJw/b2br1qLpv6NMJmjTxsLVAhM4XoPDh4vu77CkZWWZrhqYAI4dK7/XHx1tvmpgAjh1quiu/6+/XK4amADOni299XdxgY4dCx6BLMqfg/lRaJJSIS3NNnID0KTdMUPHxGWe5N13bUsCnD9vZty4q/+rJC8vv+zOxYu2eyveey+1UMPUZjNMnZqGm5uV9HQTI0d6YHHCQOKpU8bCl9H9iqr9stLOtVANHFPS7+GSVtLXX9LtO0NpvYbS+7k+ua4sWeJqvzGxeydXdp8o+Jga3gEEd8jioYcymDevAt9/X4H778/Ic4G9vEREuLJsmS2oDRmSQevWhU88DRtaGDEinSlT3Nm0yZW5cyvw6KN5f+rOqKpVHXtgsaOMrvEyenQaN9+cu2b79pl5++2CA2xR9d8ZjPatPNfAEY4+dLusK+nrL+n2naG0XoPuafof3dNUsnr08GL7dhcC2m6C+/tzMvnqqSnQrz6bB/6ByWQiNhbat/fm3DkzdetaWLs2qcCbixMS4I47vDl92kytWhbWrUuiYsVr63t6Otxzjxd79rjg62tlw4akQi0ud7mzZ008+aQHGzde/d8zZrNtjSEjAbEw77/ERBgxwnYz/tVc7V4CZ9+TVRIcvYbyUANH6PpL/32Jpb3+xV1D3dMkZcYff5jZvt0MrWdxJrSjPTCZ8rmn5MoHNVaujH0hyiNHzLz7bsFzbBMnuts/3TJlSuo1ByYANzeYOjUVk8lKQoKJV14p/DQh2B6l0KWL12WBKb/gZVtI8MEHPfnoIzenLcR46JCJnj29LgtMeZ/YbM77gcnZsh+4bDZf2/GlgaPXUB5q4Ahdf8lef0m37wyl9RoUmqTEzfo/C/R5Ano/TRbpeLl6M6vLHP6v+9eGH9R4772ZdOpkuyl7xgw3oqLyf2tv3uxif7Zc374ZdO3q+Eee27a1MGiQbVpuyZIKLF9u/OZEq9V2E3zfvl72Z08980w6s2enEhiY819EgYEWXnwxHT8/W3CaONGdIUM8SEx0rP8rVrjQpYs3e/bY+n3vvRnMnJl3+0Y+5hsamplv/wvzMeGS5Og1lIcaOELXX7LXX9LtO0NpvAZNz/2PpudKxp+Hj9F19mNYA7YDtsehhPf4hlsrNwIK97DUo0dNdOjgTXKyiRYtsvj552Rcr5jlSkuDzp292LfPBT8/K+vXX/tU2pUuXrRNE548aSYgwML69UkFPoMqJQXGjPHg229toztXPnsreyXc06cvrSZtMsHhw7a1WHbvtoWchg1tKzA3bFi456ZlZcE777jx3nu20TEXFyvjx6fx1FMZmEz5t2+Uo8eXBtkrWicm2la0btPm2lYEL8s1cISj9SvrnPH374xV/cvy+684aqh1mgpJoan4rTm2in/9NIQUk+1xKHdUCeWLvrkfh1KY+s2cWYHXX7etnfTGG6kMHZrzpux33nHjnXcKflTKtSrMI1aOHbMFn127jD1K4UrJyfDSSx7Mn5/70Q2Xy69+cXHwzDOerF5tS5ZVq9oepdCuXflabNAZ9D3sGNXPMaqf43RPk5RZVquVD3e8x0NL+9kCk9VE3YMT+OH+uQ4/P+7JJzO47TbbL/0pU9w5cuTSP0f27TMzbZptWu6OOzIZONC5gQmge/cseve2nXfOnAps25b3t9iaNbaHtWYHpu7dM/jllyTDgQnAyws+/jiVyZNTcXW1kpRke2L4xIluZBYwah0VZaZLF297YGrd2raquQKTiEj+FJqkSFmtVjad2MCi/QvYdGIDCWkXGLT8ESZtHm97REpyZfj6Z8Z3GoXZ5Pjb0cUF3nsvFRcX2/O9XnrJg40bXVi40JUnnnAnPd2Eu7v1fzduO+EC8/Df/6bh62vFajXx4ose/P67C4sWubJpkwsWC0yb5sZDD3kSG2vGZLLyyitphIdf28NKTSbbcgmLFqVQvbotcH30kTsPPujJuXMmrFbYuNHMvHm2/1uttgdjhoZ62Z/vNGhQOj/+mFwqnj0lIlKaaXrufzQ953wRh5bwxsaxOR64W8FcgQyLbSTGO+E2kv5vITdWrMvWrUm57j/Kdi31mzjRjY8+yvtTbPfdl8GMGamFu5hC+uqrCowc6ZFr++UPa/X3tz2s1VnP3jp92rZcwebNtkJWrmzB3R1OnrwURn18rFy8aGvfw8PKO++k8uCDpf+G0JJ2vX4PO4vq5xjVz3GanpNSLeLQEoaseDRHYALsgek2vw4kfbgR4gN5/PGMfAPTtWra1EJ+H5lftMiViIiiXdfV39+aZ/vZgalOnSx+/TXJqQ8rrV7dyg8/pPDUU7ZHwcTGmnMEJsAemKpUsRARkazAJCJSCApN4nRWq5U3No61Tb/l4+DZGMj0wN3d6vSbsa1WmDw5/4etWiwmJkxwd9r6Rnm1P2HC1R/2ajabqF3b+R2oUAEmTkyjatWr/2vU2zs7WIqIiFEKTeJ0m09uzDXCdKWLbgehznr69cvkhhucGx7KwgNvi/JhrZs3u3D27NXbP3Kk9D6sU0SktFJoEqc7lXTS2I4+JxgyJN357V/nD8ss6fZFRMorhSZxuhreAYb2u7VWdZo3d/4UUUk/6PF6b19EpLxSaBKnCw4Ioapn1avvdL4B/7739qJpPziLevWuHsYCAy0EBRXNmkTXe/siIuWVQpM4XWxqLCmZKfnvYDHjt/Utevcuml/aJf2gx+u9fRGR8kqhSZxu3MZXSMywPUE211Td+Qbw/QKevLMXbm5F14eSftDj9d6+iEh5pMUt/0eLWzrHmmOreGBJGAADbn2ED+7+hM0nN3I66RQ/fVOHpZ/cjasr/PGH8QflluWHVZaG9q/nh6U6y/X0PVwUVD/HqH6Oc9bilkW7wp9cV5Izknlp7QsAVPGsyviQSZhMJtrVvIPERHjxi4qAiT59MgwHJkeZTJTo89RKQ/shIRb8/SEuzlLgM+lERCR/Ck1yVdkjJadO2UZKgoPzHyl5Z9tkjiQcBuDN9lPw96hsP/7bb11JTLQdOHiw85cZEBERKWoKTZKviAhX3njDPcdCjfXqWRg3Li3XPTFRZ/9k5p8fA3BPna6ENbgvz+Pd3Kz/W3hRQ8wiIlK26EZwyVNEhCtDhnjkWtn68GEzQ4Z45Hh2W6YlkxFrnifLmoWXqzdT7nqPZcsq5Hl8erop1/EiIiJlgUKT5GK1whtvuGOxGHt226xdM9h1dicALweN5caKdQp1vIiISFmgf+5LLkaf3Xbbbd64Vo3meO83oQK4n2vL/z0zkunJLpw8aezZb8HBWmBRRETKBoUmycXoM8lOnjRB52ehQjJYXEib/xnRpys4vR0REZHSQKFJcjH6TLLmj3zJrgYrAGh28UXa9GwEpHPqlImffy44POnZZyIiUpYoNEku2c8uu9oUXZ1bzxDTdCSkQqBffZY+NQpP1zTAdk9UUNDVp/j07DMRESlrdCO45JL97DKTKf9nl9045EXOp54H4N27puHp6pnreD37TEREyhOFJslTaGgmNWvmXkspMNDCix9HsDFpLmB7VMqdN96V5/F69pmIiJQnmp6TPEVHm4iJcQHgqafSaNPGQo0aVpq1ukjH758HLj0qJT+hoZn07JlZos9eExERcRaFJsnTsmWX3hrPPptBQIBtqu2NjbkflXI1Jf3sNREREWfR9JzkKSLC9um31q2z7IEpr0eliIiIXC800lTOWa1WNp/cyKmkk9TwDiA4IARTAfNjp0+biIy0Tc316JnBphMbiEk8zrvbpuR4VEpB5xERESlPSnVoOnbsGBMmTGDXrl14eXnRvXt3Ro0ahYuLS659Dx48yPjx49m1axeVKlXi8ccfZ9CgQSXQ69Ij4tAS3tg4lsMJ0fZt9XwDGRcyidD6vfM9zj41d+siwn1GMWnxoRyv921wL7V96hRJn0VEREqrUj09N3z4cGrUqMHKlSsJDw9n1apVhIeH59ovNTWVJ554gvbt27N582Y+/PBDFixYwMGDB4u/06VExKElDFnxaI7ABHA4IZohKx4l4tCSfI9dtswVbl0ED/TnePKhXK9/t/ebqx4vIiJSHpXakaaoqCj27t1LeHg4Pj4++Pj4MGjQIMLDwxkyZEiOfX/++WcqVqzI008/DUCLFi2IiIgoiW6XClarlTc2jsVizb1kAIDFauGVdS9RxaNqrim2xERYd8QD+j0P5vyPn7DpNXoG9tIUnYiIXDdKbWjavXs3tWrVws/Pz76tcePGREdHk5iYSMWKFe3bt2/fzs0338zLL7/Mr7/+SpUqVXjuuefo1auX4fbMZhNmc9EEABcXc47/F7WNMetzjTBd6WTSCXov7pr3i48X3Eb0hUNEntlMu1p3FL6DhVTc9StvVD/HqYaOUf0co/o5zlk1LLWhKT4+Hl9f3xzbsgNUXFxcjtB06tQpIiMjmThxIuPGjWP58uW89NJL3HTTTTRq1MhQe5Urexf5qImvr2fBOznBxZi44mmHOPz9vYulLSi++pVXqp/jVEPHqH6OUf0c52gNS21oyovVavvo+5Xhxmq10qRJE3r3tt3cHBYWxrx581i+fLnh0BQbm1SkI02+vp4kJKSQlZX3lJcz+eBvaL/3O31E86ot7F+npMB993mQ5v8H9HnaUDtxcUnX3E+jirt+5Y3q5zjV0DGqn2NUP8cVVEOjAwClNjRVrlyZuLicIyYXLlzAZDLh758zFFStWpX4+Pgc22rWrMmZM2cMt2exWLFY8n5WmrNkZVnIzCz6N3zbau2o5xt41Sm6QL/6DLzlsRwBdOlSV9KiPSG6DTXun8KptNw3gV9+fJtqwcVyPdmKq37llernONXQMaqfY1Q/xzlaw1I7Qdq0aVNOnDhBbGysfVtUVBQNGjTA2ztnImzUqBH79u2zj0QBxMTEUKtWrWLrb2liMpl4tuW/833dbDLzeruJuUbsspcaqFLFypt3TcRsyvvtkd/xIiIi5VmpDU2NGzemefPmTJ06lcTERA4ePMicOXMYMGAAAN27dycyMhKwTcfFx8czc+ZMUlNTWbp0Kbt376ZPnz4leQkl6ljC0Ty3B/rVZ3a3r3Kt05SeDr/+agtNPXpk0rtBb2Z3+4pAv/qGjhcRESnvSu30HMCHH37I+PHj6dSpEz4+PoSFhTFw4EAAoqOjSU5OBmw3iM+aNYuJEycyffp0atasyfTp06lT5/pcgDElM4Wv/wkHoFu9ngy77XlOJ52ihncAQQHt8hwh2rDBhQsXbNtDQzNt/6/fm56Bvdh8cmOBx4uIiJR3pTo01ahRg5kzZ+b52t69e3N83aZNG3788cfi6Fap9+OBhcSm2qY1n2j2NO1qFrwsQPbUnI+PlfbtLz1g12QyGTpeRESkvCu103NybaxWK59HfQrAzf630OHGjgUeY7HAzz/bQlOXLpm4uRVlD0VERMqmUj3SJGC1wubNLpw6ZaJGDSvBwVlcbXZs26mt7Dq7E4DBzZ4yNJUWGWnmzBlbfs6emhMREZGcFJpKsYgIV954w53Dhy8NCNarZ2HcuLR8w83//WUbZapYwYcHbn7IYDsVAPDwsNKpk0KTiIhIXjQ9V0pFRLgyZIhHjsAEcPiwmSFDPIiIyJ13Tyed4qeDiwEYcOvDVHTzKbAdq/XS/UwdO2biXXwLfIuIiJQpCk2lkNUKb7zhjsWS99SaxWJiwgR3rFesxfnl33PItNhGigY3e9JQW7t3mzlyxPY26NlTo0wiIiL5cXpoWrduHRaLVix1xObNLrlGmK4UHW1myxYX+9fpWel8sfv/ALi7dmduqtTQUFvZo0wuLla6dVNoEhERyY/T72kaPnw43t7e9OjRgz59+tCsWTNnN1HunTplbB2ky/dbeuhHziSfBmzLDBiVHZpCQrLwN/bIOhERkeuS00PTli1b+P333/n111954okn8Pf3p3fv3vTp04fatWs7u7lyqUYNY8/Au3y/2VGzAKjrW49OdboYOj462sTff9tGqzQ1JyIicnVOD01ubm7cc8893HPPPWRlZbFlyxZ+/vln+vXrx0033cQDDzxAr169cNNiQPkKDs6iXj3LVafoAgMtBAXZFqHcdXYn205tAWBw06dwMbvke9zlskeZQKFJRESkIEV6I/iZM2fYvXs3u3fvJj09nerVq7No0SK6d+/O7t27i7LpMs1kgnHj0jCb8x5xMpmsvP56mn29puxRJi9XLwbc+rDhdrKXGmjdOouAAGOjWyIiItcrp480XbhwgeXLl7NkyRJ27NjBbbfdxoMPPkjPnj3x8bF9BP6bb75hzJgxLFmyxNnNlxuhoZkMGpTO7Nnueb5es6btZvvzKedZuH8+APfd/CCVPIzdmHT6tInISE3NiYiIGOX00HTHHXcQEBBAnz59mDx5cp73MQ0cOJC33nrL2U2XO9lrJnl7W3nvvVRcXa08/7wnyckmXnzRg19+SWbuP1+QlpUGwJBmTxk+9+VTc6GhGU7tt4iISHnk9NAUHh5OmzZtsFqt9kd4ZGRkUKFChRz77dq1y9lNlzv79tlmTxs1snDvvbbRoDNn0nj5ZQ9273bhkxlmvqw0G4CQmu1pfEMTw+fODk2NGmVRv76m5kRERAri9HuaAgICGDBgACtXrrRvCw8P58EHH+TEiRPObq5c27/fNn12881Z9m2PP55B69a2r9/5aQXHE48BMKQQywzEx8OGDbZz9+ihqTkREREjnB6axo0bx80330ybNm3s2+677z6aNm3KuHHjnN1cuZWeDocP20bqGjS4tFioiwu8914qFSpYybjtEwBqeteiR2Co4XP/8osrmZm2c+sBvSIiIsY4fXpu586dzJw5E1fXS6euXLky//nPfwgJCXF2c+VWdLSZrCxbsLn55pwrrDdqZGHAv3fyZcVVALSyPImr2fhfZfbUXJ06Fpo21ertIiIiRjh9pMnb25tDhw7l2r5nzx48PT2d3Vy5lX0/E0DDhrmDjaWNbZSJTDfWTRvK2bPGVhFPTobVq22hqUePTPuyBSIiInJ1Th9p+te//sWgQYPo1asXtWrVwmKxEB0dzbJly3jhhRec3Vy5tX+/LTS5u1upUyfnjdoX0uJZeOBb2xd/PcSFE9V47bUMZs5MLfC8q1e7kpKiqTkREZHCcnpoGjx4MHXq1GHhwoVs3LgRgDp16jBlyhQ6derk7ObKrezQVL++BZcrFviet2cuyZnJAPSs8gzLgIULK3D//Rl07pzF1URE2P7Kq1Sx0Lbt1fcVERGRS5wemgD7Y1Su9P333/PAAw8URZPlTnZouvJ+JovVwv/99RkArau3YdrDTYn8ycKZM2ZeesmD339PomLFvM+Zng6//nppau7KMCYiIiL5K5LQFB0dzT///EN6erp92+nTp5kxY4ZCkwEWCxw4YAtNV97PtProSqIv2O4ZG9Lsafz8YPLkNIYM8eT4cTNTprgzcWJanufdsMGFCxc0NSciInItnB6a5s+fz7hx4/Dx8SEhIYFKlSoRFxdHzZo1GTp0qLObK5diYkwkJ9vCzZWh6fOoTwGo6lmNPjfdC0CvXpl065bJihWufPZZBfr1y6Bly9w3j2d/as7Hx0r79pqaExERKQynf3pu1qxZzJo1iy1btlChQgU2bdrEqlWraNasGbfffruzmyuXsqfmIGdoOnThIL8d/RWAR5s8jpuLG2B7wO9bb6Xi7W3FYrE9YiXjiiejWCzw88+20NSlSyZubkV8ESIiIuWM00PTuXPnaN++ve3kZjNWq5WaNWvy0ksvaXFLg7JDk8lk5aabLoWmOVG2e5lcza483mRIjmNq1bIydqxtWm73bhdmzMiZiiIjzZw5YzuvpuZEREQKz+mhqWbNmmzatAmAG264gcjISAB8fX05duyYs5srl7LXaKpTx0r20laJGYl8u2cuAKGBfajhHZDruMsfsfLuu24cOnRpEaZly2zP/vPwsNKpk0KTiIhIYTk9ND399NM88cQTXLhwgX79+jF06FCeeeYZ+vfvT6tWrZzdXLmUPdJ0+dTcgr3fkZB+AYAhzfN+ztzlj1hJTTXx0kseWK1gtV5aaqBjx0y8vYv4AkRERMohp98I3qdPH1q1aoWfnx/PPvss1apVIyoqilatWjFgwABnN1cuXf7JOavVyqYTG/hg+zsANLmhGUE1gvM9tlEjC88/n85777mzbp0r8+a5YrXCkSO2c+oBvSIiItfG6aFpxowZOT4ld//993P//fc7u5lyKzYWzp2zBZzUwEUEzX2ZwwnR9tdPJ51kWfRSQuv3zvccL7yQzk8/uXLggAsjRnhgsVyapps61R1fX93XJCIiUlhOn5775ptviI2NdfZprxv79v1vxclbF/FF8sAcgQngXOo5hqx4lIhDS/I9h4cH9O9vC0WXByaAo0fNDBniYZ+uExEREWOc/ptzyJAh/Pvf/6Znz54EBATg6pqziexP1knebFNzVuj6EhZyr7UEtlXBJ2x6jZ6BvTDl8cRdqxXmzauQbxsWi4kJE9zp2VMP7BURETHK6aHprbfeAmDbtm25XjOZTPzzzz/ObrJc2bfPDHXXQeWDV90v+sIhtpzcRHDNkFyvbd7swuHDVx9EjI42s2WLC8HBWuRSRETECKeHpj179jj7lNeV/fvN4HPC0L6nkk7mvf2UseEjo/uJiIhIEYSmy583lxc3LUV9Vfv3m8FU09C+ea3VBFCjhtXY8Qb3ExERkSIITc2bN8/zPptsmp7LX3IyHDtmAuudVKY+sRzKd99Av/oEBbTL87Xg4Czq1bNcdYouMNBCUJCm5kRERIxyemj68ssvc3ydlZXF0aNH+fHHH3nyySed3Vy5cvCgGavVFjgH1XqTqTF5r2tlNpl5vd3EfMOpyQTjxqUxZIhHrk/PAZjNVl5/PU03gYuIiBSC00NTXg/lbdeuHSEhIYwaNYq7777b2U2WG5c/qLdToxZMjcm9T6BffV5vN/Gq6zSBbR2m2bNTmTDBnejoS+cNDLTw+utpWqdJRESkkIptsZ6qVauyb9++4mquTMp+5pyXl5U/Ui6twzSryxzAdg9TUEC7q05/Xi40NJOePTPZvNmF06dN1KhhJSgoSyNMIiIi18Dpoem7777LtS01NZXVq1dTt25dZzdXrlz++JRl0bbQ1KxKC8Ia3nfN5zSZoF073bskIiLiKKeHpk8//TTXNnd3d+rWrWtfw0nylj09V+fW00Sc3AhQ4DSciIiIFA+nh6ZVq1Y5+5TXhcxM243gAFkNlmCx2lYD76nQJCIiUio4/dlzGRkZvP/++0RGRtq3LVq0iKlTpxa4htP17OhRE+nptpuNjvssBuCmSg24xf/WEuyViIiIZHN6aJo4cSIbN26kUqVK9m0333wz27ZtY/Lkyc5urtywf3LO7SL/pNlG63oG9jZ807eIiIgULaeHpl9//ZXPPvuMBg0a2Lc1adKEGTNmsGLFCmc3V27s2+cCgPnWCDKsthE53c8kIiJSejg9NGVm5r3+T0ZGhqbnriJ7pMmr1SIAArxrclu1ViXZJREREbmM028E79atG88++yxDhgyhZs2aWK1WoqOj+fzzz+nVq5ezmys39u83g2sqKbWXAdAjMBSzyemZVkRERK6R00PT2LFjmTp1KmPGjCEhIQEAX19f+vXrx8iRI53dXLlgtf4vNAX+RpZLIgCh9fuUcK9ERETkck4PTR4eHrz66qu8+uqrxMXFYTKZctwULrmdOWMiIcEEHRcC4O/uT7uad5Rwr0RERORyTp//SU9Pty854O/vT6VKlbTkQAH27TODORNu+QmAboE9cTUX2xNuRERExACnh6ZJkyZpyYFC2r/fDHXWg/c5wLbUgIiIiJQuWnKgFNi/3wy32j415+XqzV217y7hHomIiMiVtORAKbB3nwka2UJT57pd8HT1LOEeiYiIyJWKdcmB0NBQZzdXLvwT/wf4HQO0oKWIiEhpVSxLDvj5+dGvXz+efPJJZzdX5l28COer/giACxW4p07XEu6RiIiI5MXp03PZSw5s3bqVTZs2sXnzZt577z1Onz7N3XfrXp0r7d9vhka2pQaa+3TE192vhHskIiIieSmyz7XHxMSwcOFCFi1aRGxsLJ07d2bGjBlF1VyZ9fs/+6DqHgD6NNTUnIiISGnl1NCUnp7OihUrWLBgAZGRkTRv3pxz586xaNEibrrpJmc2VW6sPrkEvACrif7NepZ0d0RERCQfTgtN48ePZ9myZfj7+9O7d28mTZpE7dq1adu2LZ6e+jRYfnZbbAtaVoy9g+re1Uq4NyIiIpIfp4WmefPm0bNnT/79739Tt25dZ522XDt+8RgJFbcD0DCrbwn3RkRERK7GaTeCf/7551gsFnr37s2DDz7IN998Q3x8PCaTyVlNlDtL9i+1//nOKrqfSUREpDRzWmhq3749H3zwAWvXrqVHjx58++23tG/fnqSkJDZv3pzvopfXs0V7ltj+cLIlt99cu2Q7IyIiIlfl9CUH/P39efzxx1myZAlff/01YWFhTJo0iQ4dOvDWW285u7ky61zKOf6M32j74p9+NGxoKdkOiYiIyFUV2ZIDALfddhu33XYbY8eOZenSpfzwww9F2VyZ8svhn7FiC0oVDoZRp461hHskIiIiV+P0kaa8eHp6cv/99zNv3rziaK5MiDhk+9Qc5xtyk++tuLiUbH9ERETk6oolNElOiekXWXtste2Lf/pxy80aZRIRESntFJpKwMojv5BuSbd98c+9up9JRESkDCjVoenYsWM8+eSTBAUFcffddzNlyhSysrKueszp06dp2bIlH330UTH1svCWRf/vU3MJteBEW4UmERGRMqBUh6bhw4dTo0YNVq5cSXh4OKtWrSI8PPyqx0yaNAmzufReVmpmKr8e+cX2xZ4wsJoVmkRERMqAUpsuoqKi2Lt3L6NGjcLHx4e6desyaNAg5s+fn+8xa9euZf/+/XTs2LH4OlpI646vISkj0fbFP/0wmazcdJNCk4iISGlXpEsOOGL37t3UqlULPz8/+7bGjRsTHR1NYmIiFStWzLF/amoqEyZMYNKkSfz444+Fbs9sNmE2F83q5S4uZvv/lx22rQLunlWZtCMdqFPHio9Pqc2upcLl9ZPCU/0cpxo6RvVzjOrnOGfVsNSGpvj4eHx9fXNsyw5QcXFxuULTJ598wm233Ua7du2uKTRVruxd5I988apYgRWHlwHgd7o3ZyyuNGkC/v7eRdpueeHrqwc/O0L1c5xq6BjVzzGqn+McrWGpDU15sVptH82/MtwcOHCABQsW8NNPP13zuWNjk4p0pMnX15Nf9qziXPI5AFJ23AtAYGAGcXHpRdJueZFdv4SEFLKyNJVZWKqf41RDx6h+jlH9HFdQDY0OXpTa0FS5cmXi4uJybLtw4QImkwl/f/8c28ePH89zzz1H1apVr7k9i8WKxVK06yX9uG8xAF6u3lzc2RWABg2yyMzUN4ERWVkW1coBqp/jVEPHqH6OUf0c52gNS21oatq0KSdOnCA2NpbKlSsDtpvDGzRogLf3pUQYExPDtm3b2L9/Px9++CEAycnJmM1mVq1axaJFi0qk/1eyWq1EHLQtNXBbxa5szLQNETZooG8AERGRsqDUhqbGjRvTvHlzpk6dyssvv8zp06eZM2cOgwcPBqB79+5MmjSJli1bsnbt2hzHTp48mRo1avDEE0+URNfztP3kdmISjwMQmNaH/z2ql5tvvvq6UyIiIlI6lNrQBPDhhx8yfvx4OnXqhI+PD2FhYQwcOBCA6OhokpOTcXFxoUaNGjmO8/T0pGLFig5N1znbon9sI14VzBVwOxwKQJUqFq6YaRQREZFSqlSHpho1ajBz5sw8X9u7d2++x7311ltF1aVrtnDPQgDuvPEujq61JaWbb9bUnIiISFmhRR+KmNVqZd4/37Dn3B4Aegb2Zv9+W9l1P5OIiEjZUapHmsq6iENLeGPjWA4nRNu3fbB9Kse9bwT6aaRJRESkDNFIUxGJOLSEISsezRGYAI4nHoX774dbF+mZcyIiImWIQlMRsFqtvLFxLBZrPqHIbIEuo2nQQJ+cExERKSsUmorA5pMbc40w5XLDAY6bN159HxERESk1FJqKwKmkk4b2O51sbD8REREpeQpNRaCGd4BT9xMREZGSp9BUBIIDQqjnG3jVffwtNxEU0K6YeiQiIiKOUmgqAiaTiXEhkzCb8imvxczAapMwmUzF2zERERG5ZgpNRSS0fm9md/uKQL/6OV843wC+X8CA23qXTMdERETkmmhxyyIUWr83PQN7se3MJhKJZ8UPVZnz0d24ukJgYGJJd09EREQKQaGpiJlMJkJqtcff35sf/5sBmKhXL4sKFUq6ZyIiIlIYmp4rRtnPnNNK4CIiImWPQlMxsVph3z5bufXMORERkbJHoamYnDoFCQm2T8s1aKDQJCIiUtYoNBWTPXsu/VkjTSIiImWPQlMx+eefS3/WPU0iIiJlj0JTMckOTTVrWqhYsWT7IiIiIoWn0FRMskOT7mcSEREpmxSaikn2PU26n0lERKRsUmgqBgkJEBNj+7PuZxIRESmbFJqKQfailqCRJhERkbJKoakYZC9qCbqnSUREpKxSaCoG+/fbFrX087NSrZq1hHsjIiIi10KhqRhc/vgUk6mEOyMiIiLXRKGpGGSHJt0ELiIiUnYpNBWxtDQ4dMg2vOTmZntwr4iIiJQ9Ck1FKCLCleBgbywWW2gKD69AUJA3ERGuJdwzERERKSyFpiISEeHKkCEexMTkLPHhw2aGDPFQcBIRESljFJqKgNUKb7zhbh9hupLFYmLCBHdN1YmIiJQhCk1FYPNmFw4fvnppo6PNbNniUkw9EhEREUcpNBWBU6eMrStgdD8REREpeQpNRaBGDWPzbkb3ExERkZKn0FQEgoOzqFfv6msyBQZaCArKKqYeiYiIiKMUmoqAyQTjxqVhNuc9kmQ2W3n99TStDi4iIlKGKDQVkdDQTGbPTiUwMOeIU2CghdmzUwkNzSyhnomIiMi10GJBRSg0NJOePTPZts2VxERPfHxSaNMmUyNMIiIiZZBCUxEzmSAkxIK/P8TFWcjUAJOIiEiZpOk5EREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMaBUh6Zjx47x5JNPEhQUxN13382UKVPIysrKc99vv/2Wbt260bJlS3r37s3KlSuLubciIiJSnpXq0DR8+HBq1KjBypUrCQ8PZ9WqVYSHh+fab+XKlUydOpX//ve/bNu2jcGDB/PCCy9w7Nix4u+0iIiIlEuuJd2B/ERFRbF3717Cw8Px8fHBx8eHQYMGER4ezpAhQ3Lsm5yczIsvvkjr1q0BuPfee3nrrbfYuXMntWvXNtSe2WzCbDY5/ToAXFzMOf4vhaP6OUb1c5xq6BjVzzGqn+OcVcNSG5p2795NrVq18PPzs29r3Lgx0dHRJCYmUrFiRfv2Pn365Dg2ISGBpKQkqlevbri9ypW9MZmKJjRl8/X1LNLzl3eqn2NUP8epho5R/Ryj+jnO0RqW2tAUHx+Pr69vjm3ZASouLi5HaLqc1Wpl7NixtGjRgttvv91we7GxSUU60uTr60lCQgpZWZYiaaM8U/0co/o5TjV0jOrnGNXPcQXV0N/f29B5Sm1oyovVagXId0QoIyODMWPGcODAAb788stCndtisWKxWB3u49VkZVnIzNQb/lqpfo5R/RynGjpG9XOM6uc4R2tYakNT5cqViYuLy7HtwoULmEwm/P39c+2fmprKsGHDSElJYe7cuXnuIyIiInKtSu1dZU2bNuXEiRPExsbat0VFRdGgQQO8vXMOo1mtVkaMGIGrqyvh4eEKTCIiIuJ0pTY0NW7cmObNmzN16lQSExM5ePAgc+bMYcCAAQB0796dyMhIAJYsWcKBAweYNm0a7u7uJdltERERKadK7fQcwIcffsj48ePp1KkTPj4+hIWFMXDgQACio6NJTk4G4IcffiAmJibXjd99+/Zl0qRJxd5vERERKX9KdWiqUaMGM2fOzPO1vXv32v/8xRdfFFeXRERE5DpVaqfnREREREoThSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExIBSHZqOHTvGk08+SVBQEHfffTdTpkwhKysrz33Dw8Pp1q0brVu3ZsCAAezatauYeysiIiLlWakOTcOHD6dGjRqsXLmS8PBwVq1aRXh4eK79Vq5cySeffMLbb7/Nhg0b6Ny5M0OHDiUpKan4Oy0iIiLlkmtJdyA/UVFR7N27l/DwcHx8fPDx8WHQoEGEh4czZMiQHPvOnz+ffv360aJFCwCGDBnCF198wZo1awgNDTXUntlswmw2Of06AFxczDn+L4Wj+jlG9XOcaugY1c8xqp/jnFXDUhuadu/eTa1atfDz87Nva9y4MdHR0SQmJlKxYsUc+/bs2dP+tclk4tZbbyUqKspwaLrhhooF7+QgX1/PIm+jPFP9HKP6OU41dIzq5xjVz3GO1rDUxtb4+Hh8fX1zbMsOUHFxcbn2vTxcZe975X4iIiIi16rUhqa8WK1WwDaSZGRfI/uJiIiIGFFqQ1PlypVzjRRduHABk8mEv79/ju3+/v557nvlfiIiIiLXqtSGpqZNm3LixAliY2Pt26KiomjQoAHe3t659t29e7f966ysLP7++2/7jeEiIiIijiq1oalx48Y0b96cqVOnkpiYyMGDB5kzZw4DBgwAoHv37kRGRgIwYMAAFi9ezM6dO0lJSWHGjBm4ubnRsWPHErwCERERKU9K7afnAD788EPGjx9Pp06d8PHxISwsjIEDBwIQHR1NcnIyAB06dODFF19kzJgxnD17liZNmjBr1iw8PDxKsvsiIiJSjpis2XdXi4iIiEi+Su30nIiIiEhpotAkIiIiYoBCk4iIiIgBCk0iIiIiBig0FaFjx47x5JNPEhQUxN13382UKVPIysoq6W6VKXfffTdNmzalWbNm9v8mTpxY0t0qtdatW0dISAgjRozI9dqGDRvo378/rVq1IjQ0lMWLFxd/B8uA/Gp47NgxbrnllhzvxWbNmvHzzz+XUE9Lp5iYGJ599lmCgoIIDg5m9OjRJCQkAHoPGpFf/fT+M2bPnj3861//onXr1oSEhPDCCy9w9uxZwEnvP6sUmbCwMOvYsWOtCQkJ1sOHD1u7du1q/fzzz0u6W2VKq1atrNu3by/pbpQJs2bNsnbt2tX60EMPWV944YUcr506dcraokUL6/z5862pqanWjRs3Wps3b279888/S6i3pdPVarh7925rkyZNSqhnZUefPn2sY8aMsSYmJlrPnDljve+++6yvvPKK3oMG5Vc/vf8KlpaWZm3Xrp31448/tqalpVnPnj1rHThwoHXYsGFOe/9ppKmIREVFsXfvXkaNGoWPjw9169Zl0KBBzJ8/v6S7VmZkZWWRlJSU62HMkjd3d3cWLFhA3bp1c722dOlSAgMD6d+/P+7u7rRr147OnTuzYMGCEuhp6XW1Gl64cEHvxQIkJCTQuHFjRo4cibe3N1WrViUsLIzIyEi9Bw24Wv30/itYSkoKI0aM4Omnn8bNzY0qVarQrVs39u/f77T3n0JTEdm9eze1atXK8SZv3Lgx0dHRJCYmlmDPyo6EhASsVisfffQRHTp04M4772TcuHEkJSWVdNdKpcceewwfH588X9u9ezeNGzfOsa1Ro0b89ddfxdG1MuNqNUxISMBisfDMM88QFBREt27dCA8Ptz9IXMDX15fJkydTpUoV+7YTJ05QrVo1vQcNuFr99P4rmJ+fH/fffz+urrZ1u48cOcLixYvp2bOn095/Ck1FJD4+Hl9f3xzbsgPUlQ8Xlrylp6fTpEkTWrVqxYoVK5gzZw47duxg/PjxJd21Miev92OlSpX0XiyEChUqULduXR5++GHWrVvH2LFj+eijjzRSchVRUVHMnTuXoUOH6j14DS6vn95/xsXExNC0aVO6detG8+bNGT58uNPefwpNxSj7XwQmk6mEe1I2VK9enYULF/LYY4/h6elJgwYNGDVqFBEREaSnp5d098o8q9Wq92IhdOrUiXnz5nHnnXfi5ubGnXfeyYMPPsiiRYtKumul0vbt2xkyZAgjR44kJCQkz330HszflfXT+8+4WrVqERUVxfLlyzl06BAvvfRSnvtdy/tPoamIVK5cOVeCvXDhAiaTCX9//xLqVdl34403kpWVxfnz50u6K2WKv78/8fHxObbFx8frveigG2+8kTNnzpR0N0qd1atX89RTT/HKK6/w2GOPAXoPFkZe9cuL3n/5M5lM1KtXjxEjRrB06VJcXV2d8v5TaCoiTZs25cSJE8TGxtq3RUVF0aBBA7y9vUuwZ2XHnj17eOedd3JsO3jwIG5ublSrVq2EelU2NWvWLNfc/V9//UWLFi1KqEdlz4oVK/juu+9ybDt06BC1a9cuoR6VTjt27GD06NFMmzaNsLAw+3a9B43Jr356/xVsw4YNdOvWDYvFYt9mNttiTkhIiFPefwpNRaRx48Y0b96cqVOnkpiYyMGDB5kzZw4DBgwo6a6VGf7+/nzzzTd8/vnnpKenEx0dzUcffcRDDz2Ei4tLSXevTOnduzcxMTHMnz+ftLQ01q5dy9q1a3nggQdKumtlhouLC5MnT2bjxo1kZmayadMmfvjhBx5++OGS7lqpkZmZydixYxk1ahTt27fP8ZregwW7Wv30/itY06ZNSUhI4N133yUlJYXY2Fg++ugj2rRp47T3n8mqW++LzKlTpxg/fjw7duzAx8eHsLAwnnvuOc3hF8K2bdt455132L9/P/7+/nTv3p0XXngBNze3ku5aqdOsWTPA9oMXsH+CJCoqCrDV8u233+bgwYPceOONPPfcc3Tt2rVkOltKFVTD7777jv/7v//jzJkz1KpVi8GDB9OvX7+S6WwpFBkZycMPP5zn9+fy5cs5ceKE3oNXUVD91q9fr/dfAfbs2cObb77Jrl278PLyIjg4mDFjxlC9enWn/AxUaBIRERExQNNzIiIiIgYoNImIiIgYoNAkIiIiYoBCk4iIiIgBCk0iIiIiBig0iYiIiBig0CQiIiJigEKTiEghbdmyhVtuuYW0tLSS7oqIFCPXku6AiMi16tSpE6dPn7Y/X+pykydPplevXiXQKxEprxSaRKRMGzt2rJ7pKCLFQtNzIlJuderUifDwcAYNGkTz5s255557iIyMtL9+6tQphg4dSlBQEB06dGDkyJHEx8fbX1+/fj29e/emRYsW9OnTh02bNuU4f2RkJN26daNly5Y888wzXLx4sbguTURKgEKTiJRrc+bM4d///jfbtm2jW7duPP/88/YH8g4bNgwfHx9+++03FixYwPHjxxk3bhwAp0+f5vnnn2fo0KFERkYyePBghg0bRlxcnP3cERERzJ8/n59++ok///yTBQsWlMg1ikjx0PSciJRpkyZN4r///W+ObV5eXmzZsgWwjTbddtttADz99NPMnj2bP//8Ey8vL3bv3s2nn35KxYoVqVixIk8//TTDhw/HYrHw888/U6dOHXr27AlAWFgY7u7uXP6M8yFDhuDr64uvry+33XYb0dHRxXPRIlIiFJpEpEwr6J6mwMBA+599fX3x8fHhzJkzuLq64ufnR9WqVe2v169fn4yMDM6ePcvRo0epVatWjnP16NEjx9eXv+7h4UF6erqjlyMipZim50SkXLNYLDm+tlqtmEwmAPv/L38NICMjA5PJlOvYK115vIiUbwpNIlKuHT161P7nCxcukJiYSI0aNahduzbx8fGcP3/e/np0dDTu7u5Ur16d2rVr55pu++KLLzhy5Eix9V1ESheFJhEp11atWsWuXbtITU1l5syZ3HDDDTRr1oxbbrmFm266iXfffZfk5GROnz7NzJkzCQ0NpUKFCvTq1YuTJ0/y/fffk56eTkREBNOmTaNixYolfUkiUkJ0T5OIlGl53QgO2Be27N+/P++//z7bt2+nRo0afPLJJ7i4uAAwffp0Jk6cSMeOHfH09OSee+5h1KhRAFSpUoXZs2czbtw43nzzTQIDA5k+fTo33HADBw4cKL4LFJFSw2S9/KMgIiLlSKdOnXjyySe1+KWIOIWm50REREQMUGgSERERMUDTcyIiIiIGaKRJRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDCjR0BQTE8Ozzz5LUFAQwcHBjB49moSEhFz7/fDDD9x66600a9Ysx3/nzp0DIC0tjddee40OHToQHBzM888/T2xsbHFfjoiIiJRjJRqahg0bhq+vL6tWrWLJkiUcOnSIKVOm5Nrv4sWLhISEEBUVleO/KlWqADB16lT+/vtvvvvuO1asWIHJZOLll18u7ssRERGRcqzEQlNCQgKNGzdm5MiReHt7U7VqVcLCwoiMjMy174ULF/Dz88vzPFlZWSxcuJBhw4YREBCAn58fL7zwAmvXruX06dNFfRkiIiJynSix0OTr68vkyZPto0UAJ06coFq1arn2TUhI4MiRI9x///20adOG++67jw0bNgBw5MgRLl68SJMmTez7169fHw8PD3bv3l30FyIiIiLXhVJzI3hUVBRz585l6NChuV6rVKkS1apVY/Lkyfz+++/06tWLp59+moMHDxIfHw/YQtjlfH19iYuLM9y+1Wp1qP8iIiJSvrmWdAcAtm/fztChQxk5ciQhISG5Xn/++edzfD1o0CAiIiJYsmQJHTp0yPOcVqsVk8lkuA+xsUmYzcb3LwwXFzO+vp4kJKSQlWUpkjbKM9XPMaqf41RDx6h+jlH9HFdQDf39vQ2dp8RD0+rVqxk1ahSvvfYaYWFhho+78cYbOXPmDJUrVwYgLi4OLy8v++sJCQn214ywWKxYLEU72pSVZSEzU2/4a6X6OUb1c5xq6BjVzzGqn+McrWGJTs/t2LGD0aNHM23atKsGplmzZrFly5Yc2w4ePEjt2rWpXbs2fn5+Oe5f2rdvH+np6TRt2rSoui4iIiLXmRILTZmZmYwdO5ZRo0bRvn37XK93797d/km6c+fO8cYbbxAdHU1aWhpz5szh2LFj9OvXDxcXFx544AFmzpzJyZMniYuL47333qNLly45bjIXERERcUSJTc/t3LmTgwcPMmnSJCZNmpTjteXLlxMdHU1ycjIAI0eOxGq18thjj5GcnMzNN9/MF198QfXq1QEYPnw4ycnJPPjgg2RmZhISEsK4ceOK/ZpERESk/DJZ9bExAM6evVhk53Z1NePv701cXJLmo6+B6ucY1c9xqqFjVD/HqH6OK6iGVav6GDpPqVlyQERERKQ0U2gSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRETKrU6dQti2bXOer/Xv35vFixcUc49Ktx07Imnfvg1paWkl3ZVSSaFJRESuidUKmza5sGiRK5s2uVCUq/4NHvwI06dPy7Ft//59tG/fhrVrV+fYvnDhfHr37orVamXVqo20bRsMwN69e9i2LecjucqKOXPmkJmZWdLduO4pNImISKFFRLgSFORN375ePP20J337ehEU5E1ERNE8aCIoqF2uwBMZuRVPT688t99+exAmk+mKPv9IZOTWIulfUYqLi2PKlClkZWWVdFeuewpNIiJSKBERrgwZ4sHhwzl/hRw+bGbIEI8iCU7BwSEcOLCfuLhY+7Zt27bQrVuPHEHIYrHwxx/bCQoKAaB9+zZs3ryR999/m0WLFjBv3tc8+GCYff/k5GRefnkU99zTngED+rFjR2S+fZg372t69bqH7t078v77bzNx4uu8+eZ4AN58czzjxr2cY//stgHi4+MZO/Y/9OrVhe7dOzJ69AucPXsmx75r165i6NAh3HNPex599AH27dtDbOx5evfuhtVqpUuXu1i2bAmzZ3/KU089nqOty6ca33xzPO+9N4V33vkvXbp04P77+xAV9SdffRVOr15d6NWrC8uWLTFU9z17/mbYsCfo3r0jffp04733ppCZmUlqaipdu97F+vW/59h/+PBnmD79QwC2b9/G008PokuXDoSF9eD777+x7zd79qeMHv0Cr7/+Mp063WGoL6WBQpOIiJCQANu3mwv8LzLSzCuvuGOxmPI8j8Vi4tVX3YmMzHnMli3k2LZ9u5mEBOP9a9KkGd7e3vaAlJGRwa5df9C//0OcPXuaU6dOAbZf8omJF+1TctlGjBhNixYteeihR/juu8X27UuX/shjjw0iIuI3GjduwrRp7+bZ/uHD0Xz88QeMGvUyP/30C4GB9fn99zWG+z99+jSSkhKZP/8nFi6MIC0tnU8+yTndOHful7z88mssXbqSqlWrM2vWdCpXvoFp06YD8Ouva+nZs7eh9n777RdCQu5k6dJfqVu3HuPGvUJWViaLFi2jX7/7+fDDqVgsBT+S5fXXX6Zly9YsW7aKTz+dw5o1q4iI+AkPDw86duzEr7/+bN/3woV4/vzzD7p27cHp06cYM2Ykffv2Y/ny1Uyd+hHffPMVv/yy3L7/7t1RtGzZml9+WWvomkqDEntgr4iIlA4JCdC6dUUuXMg7CBXWiRNmevb0zuMVzxxf+flZ2b49EV/fgs/p6upK69Zt2bZtC126dCcq6k/8/StTr14gTZu2IDJyC7169SUyciu33NIIf39/Q31t3/4uGjVqAkCHDp1YtWplnvutW7eWm25qSMeOnQEIC+vPggXfGWoDYOTIMWRlZeHp6fm/djuwcOH3Ofbp1q0nderUs7/+7bdfGT7/lWrXrssdd9wJwO23B7Njx3YefvhfVKhQgTvu6MD//d8s4uJiueGGKlc9z5w5c3Fzc8dsNhMQUJOmTZuzd+8/AHTvHspLL/2b5OQkvLy8WbduLfXqBdKgQUO++eZLAgPr20PeTTc1ICzsPlasWEbXrt0BMJnMhIXdl2satTRTaBIRkTIhKCiE8PDPAdvUXJs2twPQpk1bIiO32kNTUFA7w+cMCKhp/7O7uzsZGRl57nf27GkCAgJybKtbt57hdmJijvHhh++xZ88/pKenkZWVRdWq1XLsU7Pmpb54eHg49Am2y8/t5uZGpUqVqFChgv3cAOnp6QWeJzJyK+Hhn3P8+DGysrLIzMykW7eeALRs2ZpKlfxZu3Y1PXr04vffV9O1a4//Xe9x9uz5m06dQuznslqt1KlT1/51tWrVy1RgAoUmEZHrnq8vbN+eyP79Bd+xERVlZvRozwL3e+edFJo2tU3/uLiY8fX1JCEhhaysS1NCDRtaDI0yZQsODuHtt9/kyJHDREZu5aGHHgagdeu2zJ8/j5SUFP76axdPPPGM4XMa/Z1ttZLrF7zFkv/HBS+/adtisTB69AhatLiNefMWUalSJX744ftcI0km07XfMXPlVJvZbLri68Kf+8iRw7z++ss899wI+vQJw93dg7Fj/2N/3WQy0bVrD379dQV33XU327dvY9Qo231d7u7uBAffwdtvv5/v+V1dy14EKXs9FhERp/P1hdatC77HpVUrC9OnW3LdBH65wEALjz2WaQ8krq7g7w9xcRYyMwtuIz/VqlWnXr36bNmykQMH9tGqVVsAbrmlEenpaSxbtgQ3N3eaNGl2zW3kp0qVKvz11585tkVHH6JZs+aAbTTn4sVLN2nFxBy3/zk2NpZTp04yceJbVKpUCYB9+/Zcc1/c3NxzjEKlpqZy/vy5az5ffvbt24Obmxv9+z+IyWTCYrFw4MA+mjVrYd+nR49Qvv32KyIiltC4cVOqVasOQK1aN/L772uwWq32sHn+/Dl8fHxxc3Nzel+Li24EFxERw0wmGDcuDbM571EWs9nK66+nGR7BKaygoHb88MP31K0baL9vycXFhZYtWzN//re0aXM7Li4ueR7r7u7B8eNHuXAhvtDthoTcyYED+1m3bg0ZGRnMnz+P2Njz9tdvvLEOf/0VxenTp0hOTuLrr8Pt4aBSpUp4enry119RpKWl8fPPS4mOPsTFiwmkpaUW2La7uzsABw8eICUlhdq1a3PkSDQHDuwnLS2Nzz6bjqenV6GvqSABATVJS0vjwIF9JCQk8MknH+Dl5cW5c2ft+9SpU4+GDW/h889n0K1bD/v2e+7pRkJCAl98MZu0tFRiYo4zYsSzzJ//rdP7WZwUmkREpFBCQzOZPTuVwMCco0aBgRZmz04lNLToFmEMDg4hJuY4rVu3zbG9deu2HD9+jODg/O9n6tmzN9u2bWXgwPsKveZRw4Y38/zzL/L+++8QGnoPx48fJTj40v06vXr15ZZbGvHww/15/PGBdOzYGS8v283wrq6ujBr1Ml9/PYe+fbvx99+7efPNt/Hzq8RDD/UrsO1bbrmVli1b8vTTg1m0aAHt299Fx46dGTbsCR58MIzAwJuoXbt2oa7HiKZNm9Ov3wM899xT/OtfD1GnTj2effYF/v77L954Y6x9v+7dQ8nIyKBjx3vs2/z8KvHWW1P5/fc19OjRieeff5o77ujAQw894vR+FieT1VqUa7iWHWfPXiyyc7u6mvH39yYuLsmhoenrlernGNXPcaph3qxW2LzZhdOnTdSoYSUoKCvPEabyWr9x417Gzc2dV18dX6TtlPb6zZ79KSdOHOe11yaWdFfyVVANq1b1MXYeZ3dMRESuDyYTtGunVaqvZ3/9FcX8+fP4+ONZJd2VYqHQJCIiIoX24ovPc/DgPp577gUaNGhY0t0pFgpNIiIi1+CNNyaXdBdK1HvvfVTSXSh2uhFcRERExACFJhEREREDFJpEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREpNzq1CmEbds25/la//69Wbx4QTH36PpS3mqs0CQiItfEarWy6cQGFu1fwKYTGyjKp3INHvwI06dPy7Ft//59tG/fhrVrV+fYvnDhfHr37orVamXVqo20bRsMwN69e9i2bUuR9fFyMTHHWb16ZbG0VVjbt29jz56/S7obZZJCk4iIFFrEoSUEzb2Nvot78PSvg+m7uAdBc28j4tCSImkvKKhdrsATGbkVT0+vPLfffnsQpisehBcR8SORkVuLpH9XWrt2NWvW/FYsbRXWd9/NVWi6RgpNIiJSKBGHljBkxaMcTojOsf1wQjRDVjxaJMEpODiEAwf2ExcXa9+2bdsWunXrkSMIWSwW/vhjO0FBIQC0b9+GzZs38v77b7No0QLmzfuaBx8Ms++fnJzMyy+P4p572jNgQD927Ii0v3bo0EGGD3+G7t3vpn//3rz77mTS0tIA20Nqn3rq8Rx9zJ6K+uabr5g58yNWr/6NTp1CyMrK/Xy+/fv38fjjA+nU6Q6eeupxlixZTPv2bQDYsSOS9u3b2NsCGDNmDGPHjrF//d13c3nggb506XInDz/cn3Xr1thfe/PN8bz//tt89NF79OjRiV697uHrr8MB+M9/RrBx43ref/8d/v3voZw8eYL27dtw5Mhh+/GXX9uOHZF07XoX69evpX//3nTp0oFZs6azZ8/f/OtfA+jS5U5eeeUlMjMz8/27y5aWlsaUKZPo27cbXbvexXPPPUV09CEAJk+ewKuvvpRj/59/Xkrfvt3JysoiIeECEya8Rt++3ejSpQNjx44mIeECgP0aFi1aQI8enVi+PKLAvlwrhSYRESEh7QLbT28r8L/IU1t5Zd1LWKy5nxQPYLFaeHXdaCJPbb3smG1sOb6FyFM5z5WQdsFw/5o0aYa3t7c9IGVkZLBr1x/07/8QZ8+e5tSpUwDs2fM3iYkX7VNy2UaMGE2LFi156KFH+O67xfbtS5f+yGOPDSIi4jcaN27CtGnvApCens6LLz5H48ZN+fHHn3nvvY+JjNzK7NkzC+zrwIGP0q1bT+6+uzOrVm3ExcUl1z7vvPNf6tSpy9Klv/Lyy6/z3XdzDddi584dfPrpJ/z3v++yYsVa7r9/AG+8MZaLFy/a9/ntt19o0OBmliz5haFDhzNr1nTOnTvHlCnvU6NGACNGvMS0aTMMtZeamkJk5Fa+/no+I0f+h6++mkN4+Od88MF0Zs/+ik2b1rN+/doCz/PNN1+ye3cUX331PUuX/kr16jWYMmUSAN27h7Jp04Yc1/D776u5556uuLi4MGnSeFJSUvjqq+9ZvHgZFSq4MW7cKznO/8cf21mw4Ce6detp6LquhZ49JyJynUtIu0Drr5txIS3eKec7kRRDz4X3FLifn3sltj8Sha+7X4H7urq60rp1W7Zt20KXLt2JivoTf//K1KsXSNOmLYiM3EKvXn2JjNzKLbc0wt/f31Bf27e/i0aNmgDQoUMnVq2y3Ye0efNGUlNTGDz4Kdzc3KhTpy79+z/I/PnzGDbs34bOnZ/z58/x999/MWrUGLy8vAgMrE+PHr2YMcPYs9yaN7+NxYt/xtfXVrcuXbrx7ruTOXIkmqZNmwNQo0ZNevToBUDnzl2ZPHkCx44doUqVKoXur8VioV+/B/Dw8OCOOzpgtVrp2LEz/v7++Pv7U6dOXY4dO1bgeR555HHuv38AFStWBODuuzvz2mu20bPbbmvFDTdUYe3a3+jVK4yUlBS2bt3M9OmziYuLZePGdcydu8B+zc888xz33deL8+fP2c/fvXso3t4VC319haHQJCIiZUJQUAjh4Z8Dtqm5Nm1uB6BNm7ZERm61h6agoHaGzxkQUNP+Z3d3dzIyMgA4eTKGmjVr4ebmZn+9bt16nD17xuEb3s+ePQPYgs2lcwcaPt5isTBnzuesWfMbFy7E27enp6fb/3z5dXl4eADkmO4rrGrVqgPY61G1ajX7a+7uHqSnF3zuuLhY3n//HXbt+oPk5GSsVqt9Ws9kMtG1aw9++WU5vXqFsXnzBgICanLLLbfy119RAAwaNDDH+VxcXDhz5jSVKtkCcvXqNa75+oxSaBIRuc75uvux/ZEo9sfvK3DfqLO7GP37iAL3e+eu92laxTbq4eJixtfHk4SLKWRlXZrWa1jpZkOjTNmCg0N4++03OXLkMJGRW3nooYcBaN26LfPnzyMlJYW//trFE088Y/icV9wrfuWrOb6yWrGHqrxYLHlPWV4pO3RdfqO6NZ/pzmyX3xc1Z85nrFnzG1OmvEeDBjeTlpZGly535tjfbL7qhV1VXtdx5U31V35txPjxr+Lm5sacOd9QrVp1Nm5cz+jRL9hf7949lK++msO5c2f5/fc1dO3aA7CFWYBFi5bh51cp13lPnjwB2EYji5pCk4iI4OvuR+vqbQvcr1W1Nkzf+WGum8AvF+hXn8caD7b/YnV1NePv701cXBKZmcaCRV6qVatOvXr12bJlIwcO7KNVK1t/b7mlEenpaSxbtgQ3N3eaNGl2zW1kq1XrRk6ciCEjI4MKFSoAcPToYQICamIymXBzc88xcpOamppjquhqqlSpCsCpUyfx8fEBbDedZ3Nzs4WEtLQ0e2A4duwYN9xgO+6ff3bTvv1d3HzzrQDs27fnmq8ze+QoLS3Vvi0m5vg1n+9q/vlnN6+9NtE+anVlv2vXrkOjRk1YsWIZGzeu46mnhgG2UTOz2czBgwdo1cp2s3xmZibx8XH2WhYX3QguIiKGmUwmxoVMwmzK+9eH2WTm9XYTr2kkwoigoHb88MP31K0baL9vycXFhZYtWzN//re0aXN7njdeg20a6fjxozmmtPLTqlVbXF1dmDPnMzIyMjh69Ajz58+z3ydUu3ZtjhyJ5sCB/aSlpfHZZ9Px9PS6rC13Tp06RXx8fK5PllWtWo2GDW/m22+/IiUlhUOHDrBixTL76zVr1sTFxYXVq1eSmZnJr7+u4MiRI/bXa9QI4MCBfaSmpnLo0AHmz5+Hu7s7586dNVRDd3d3jhw5wsWLF6lUyZ+KFSuyevVvZGVlsWNHJLt27TR0nsKqUSOAv//+i4yMDDZuXG//pGL2dCXYRpu+/PL/uOmmhvYpxooVK9K5c1dmzPiQM2dOk5aWysyZH/PCC88W6dpgeVFoEhGRQgmt35vZ3b4i0K9+ju2BfvWZ3e0rQuv3LrK2g4NDiIk5TuvWOUfFWrduy/HjxwgOzv9+pp49e7Nt21YGDrwvz2UALufl5cXbb3/Azp076NXrHkaOHE7Xrj149NFBgO0G8o4dOzNs2BM8+GAYgYE3Ubt2bfvxXbp05/jxozz4YF/Oncs9AvXaaxM4evQIvXrdw+TJE+jf/yH7a5Ur38AzzzzPZ5/NIDS0M3/+uZO+ffvaX3/sscFkZWXRq9c9vPXWJJ58cig9evRiypRJbNq0/uoFBHr3DuOnnxby/PNP4+LiwosvjmH58gi6d+/Ijz/+kKMvzvTii6NZu3YVPXt2ZvnyCCZMeIubb76VRx99wB5kO3fuSnp6un1qLtuIES9Rq1ZtHn30Afr27cHhw4d4662pRRbO82OyFndMK6XOnr1Y8E7XyFlD09cr1c8xqp/jVMO8Wa1WNp/cyOmkU9TwDiAooF2ev8RUv4Jt3ryRUaOGs359ZK7Xrqf6xcQcZ9Cgh1m4MML+KTtnKKiGVav6GDuP03okIiLXFZPJRLuad5R0N6ScuHjxIu+881/Cwvo5NTA5k6bnREREpET98sty7r23B35+lRg8+OmS7k6+NNIkIiJSwoKDQ/KcmrtedO3ana5du5d0NwpUoiNNMTExPPvsswQFBREcHMzo0aNJSEjIc99vv/2Wbt260bJlS3r37s3KlZeeHv2f//yHxo0b06xZM/t/ffr0Ka7LEBERketAiYamYcOG4evry6pVq1iyZAmHDh1iypQpufZbuXIlU6dO5b///S/btm1j8ODBvPDCC/Zl2xMSEhg+fDhRUVH2/3766afivhwREREpx0psei4hIYHGjRszcuRIvL298fb2JiwsjK+++irXvsnJybz44ou0bt0agHvvvZe33nqLnTt3Urt2bRISEvDzM76qbF7MZpNDK6hejYuLOcf/pXBUP8eofo5TDR2j+jlG9XOcs2pYYqHJ19eXyZMn59h24sQJqlWrlmvfK6faEhISSEpKonr16vavf/vtN8LDw4mPj6dFixaMHTuWOnXqGO5P5creRb7eg6+vZ5Gev7xT/Ryj+jlONXSM6ucY1c9xjtaw1NwIHhUVxdy5c5kxY8ZV97NarYwdO5YWLVpw++22hzXWqlWLgIAApkyZgslkYtKkSTzxxBMsXbo0x8MWryY2NqlIR5p8fT1JSMj53CUxRvVzjOrnONXQMaqfY1Q/xxVUQ39/b0PnKRWhafv27QwdOpSRI0cSEhKS734ZGRmMGTOGAwcO8OWXX9q3z5w5M8d+EyZM4Pbbb2f79u20a2fsadcWixWLxfnrfFqtsHGjmYsXwccH2ra1FPCASMlPVpal3C/sVpRUP8epho5R/Ryj+jnO0RqWeGhavXo1o0aN4rXXXiMsLCzf/VJTUxk2bBgpKSnMnTvX/syhvFSsWJFKlSpx+vTpIuixcRERrrzxhjuHD2fPoXpSr56FcePSCA3NvOqxIiIiUrqU6F1lO3bsYPTo0UybNu2qgclqtTJixAhcXV0JDw/PEZgSExOZNGkSZ85ceuBfbGwssbGxOZ4DVNwiIlwZMsTjssBkc/iwmSFDPIiIKPG8KiIiIoVQYqEpMzOTsWPHMmrUKNq3b5/r9e7duxMZaVvoa8mSJRw4cIBp06bh7u6eY7+KFSuyfft2Jk2aRHx8PBcuXGDixIk0atSIli1bFsu1XMlqhTfecMdiyXsezmIxMWGCO3rqn4iISNlRYqFp586dHDx4kEmTJuVYlLJZs2bExMQQHR1NcnIyAD/88AMxMTHcfvvtOfYbO3YsAJ988glZWVl069aNHj16kJWVxaefforZXDKXt3mzS64RpitFR5vZssWlmHokIiIijiqxOaI2bdqwd+/efF+//LUvvvjiqueqWbMmn3zyidP65qhTp4zd6W10PxERESl5WimrCNSoYWzezeh+IiIiUvIUmopAcHAW9epd/SONgYEWgoKyiqlHIiIi4iiFpiJgMsG4cWmYzXmPJJnNVl5/PU3rNYmIiJQhCk1FJDQ0k9mzU6ldO+eIU2CghdmzU7VOk4iISBmj0FSEQkMzWb8+yf71M8+ks3lzkgKTiIhIGaTQVMQ8PaFyZds0nYcHmpITEREpoxSaikFAgG2KTksMiIiIlF0KTcUgIMA20nTypMotIiJSVum3eDHIXo/pxAmNNImIiJRVCk3F4NJIk0KTiIhIWaXQVAyyQ1NioonExBLujIiIiFwThaZiULPmpUUudV+TiIhI2aTf4MUge6QJ9Ak6ERGRskqhqRhkLzkAuq9JRESkrFJoKgY33AAVKtj+fOqUSi4iIlIW6Td4MTCZoGZN25810iQiIlI2KTQVk1q1bP9XaBIRESmbFJqKSXZo0vSciIhI2aTf4MXkUmjSSJOIiEhZpNBUTLJD0+nTJrKySrYvIiIiUngKTcUkOzRlZZk4d06jTSIiImWNQlMxyf70HOhmcBERkbJIoamYZI80gR6lIiIiUhbpt3cxyRmaNNIkIiJS1ig0FRNPT6hUyfYMutOnFZpERETKGoWmYpT94F5Nz4mIiJQ9+u1djLIf3KvpORERkbJHoakYZY80aYFLERGRskehqRjVqKHpORERkbJKv72LUc2attCUkGAiKamEOyMiIiKFotBUjLKn50BTdCIiImWNQlMxyhmaVHoREZGyRL+5i1H2p+dAn6ATEREpaxSailGVKuDqqpvBRUREyiL95i5GZjNUr65lB0RERMoihaZidmnZAYUmERGRskShqZhdWhVcpRcRESlL9Ju7mGV/gk4P7RURESlbFJqKWfb03KlTJiyWAnYWERGRUkOhqZhlT89lZpo4d06jTSIiImWFQlMxyx5pAn2CTkREpCwp0dAUExPDs88+S1BQEMHBwYwePZqEhIQ89w0PD6dbt260bt2aAQMGsGvXLvtraWlpvPbaa3To0IHg4GCef/55YmNji+syCkULXIqIiJRNJRqahg0bhq+vL6tWrWLJkiUcOnSIKVOm5Npv5cqVfPLJJ7z99tts2LCBzp07M3ToUJL+99TbqVOn8vfff/Pdd9+xYsUKTCYTL7/8cnFfjiGXjzTpE3QiIiJlR4n91k5ISKBx48aMHDkSb29vqlatSlhYGJGRkbn2nT9/Pv369aNFixZ4eHgwZMgQzGYza9asISsri4ULFzJs2DACAgLw8/PjhRdeYO3atZw+fboEriwnq9XKxpj1zPtrHhtj1uPlZcXXVwtcioiIlDWuJdWwr68vkydPzrHtxIkTVKtWLde+u3fvpmfPnvavTSYTt956K1FRUTRq1IiLFy/SpEkT++v169fHw8OD3bt3U716dUP9MZtNmM3ODTFLD/7EuPVjib5wyL4t0K8+Fdu+TcJv93H6tBlXV402FcTFxZzj/1I4qp/jVEPHqH6OUf0c56wallhoulJUVBRz585lxowZuV6Lj4/Hz88vxzY/Pz/i4uKIj48HbCHscr6+vsTFxRluv3Jlb0wm54WmRf8s4vFlj2Cx5lxXIPrCIWj/AMQs4Ny5e/H3r+C0Nss7X1/Pku5Cmab6OU41dIzq5xjVz3GO1rBUhKbt27czdOhQRo4cSUhIiKFjrFbrVUNOQa9fKTY2yWkjTVarlZErRuUKTHYmC3QZzZGVfYiLS3VKm+WZi4sZX19PEhJSyMrS4laFpfo5TjV0jOrnGNXPcQXV0N/f29B5Sjw0rV69mlGjRvHaa68RFhaW5z7+/v65Ro0uXLhAw4YNqVy5MgBxcXF4eXnZX09ISLC/ZoTFYsVisRa8owGbTmzIMSWXpxsOcNy0gczMlk5p83qQlWUhM1M/MK6V6uc41dAxqp9jVD/HOVrDEp0g3bFjB6NHj2batGn5BiaApk2bsnv3bvvXWVlZ/P3337Ro0YLatWvj5+eX4/V9+/aRnp5O06ZNi7L7+TqVdNLQfommk6SkFHFnRERExClKLDRlZmYyduxYRo0aRfv27XO93r17d/sn6QYMGMDixYvZuXMnKSkpzJgxAzc3Nzp27IiLiwsPPPAAM2fO5OTJk8TFxfHee+/RpUsXqlSpUtyXBUAN7wBjO16sqU/QiYiIlBElNj23c+dODh48yKRJk5g0aVKO15YvX050dDTJyckAdOjQgRdffJExY8Zw9uxZmjRpwqxZs/Dw8ABg+PDhJCcn8+CDD5KZmUlISAjjxo0r9mvKFhwQQj3fQA4nROe/0/kGcLQ9p06lEBiYVXydExERkWtislqtzrmRp4w7e/aiU88XcWgJQ1Y8mufN4GbMWOYtgD33MnNmCv36ZTq17fLG1dWMv783cXFJms+/Bqqf41RDx6h+jlH9HFdQDatW9TF0Hi36UERC6/dmdrevCPSrn2N7ZY8b+KzrV7jsDwP0KBUREZGyQqGpCIXW783mgX+w9L7lNKzcEIDaFWvTu0FvqlXLXhVcfwUiIiJlgX5jFzGTyURIrfY82/ZZAP48t5NDFw4SEGALTRppEhERKRsUmorJ/U3ux4QtIP10YBE1atjmVPXpORERkbJBoamY1PSpSUitOwBYtP8H+0iTpudERETKBv3GLkb9bu4PwD+xu3EN+BuwjTT9f3v3HdfUvf4B/HOSQNhLVBAXuOoeVRlWa20rKiraPW2rbe20teO2tVauiPXavbxXax0d1rZindRtr1UBf2pvlWq1iuBiqYywIcn5/RGDIpAcOCGDfN6vFy8gOSd58nAgD9/v9zyH5y8SERHZPxZNVjShSyyUghIAcM77JwBAVZWAK1c4RUdERGTvWDRZUaBHawxvfysA4IguEQAXgxMRETkKFk1WNrmrYYouq+oUEHQEABeDExEROQIWTVY2NjQGLgoXwzd9fgDAxeBERESOgO/WVubn5o9RHe8AAAh9fwQgcnqOiIjIAbBosoHYrncBAETfTCDk/zg9R0RE5ABYNNnAmM7j4KZ0M3zT5wdkZ/PHQEREZO/4bm0DXq7euLPzGMM3vX9CVjYbNREREdk7Fk02MunqFB18snBBsd+2wRAREZFZLJps5PaOo+EKLwBAccc1qKiwcUBERERkEosmG/Fw8cAgjxjDN73X4GK2zrYBERERkUksmmxoTIe7DV94XsKOU/tsGwwRERGZxKLJhmJ6jAIqfAEA27MSbRwNERERmcKiyYZCglyBvyYDAA6VbUCVrsrGEREREVFDWDTZkEoF+F28DwBQgULsOb/bxhERERFRQ1g02Vgn3W1AaSAAYN3ptTaOhoiIiBrCosnGgtsqgeP3AAC2ZvyCcm25jSMiIiKi+rBosrHgYBE4dj8AoKS6GLvO7rBxRERERFQfFk02FhwsAmeHA8XBAIANp3+2cURERERUHxZNNhYUpAdEJXDsXgDA9rNbUFJdYuOoiIiI6EYsmmwsOPjqxXr/fAAAUK4tx/bMLTaMiIiIiOrDosnGgoKuFk0XIhCo6gAAWM8pOiIiIrvDosnGgoP1V78S0FdhOItu99kdKKostFlMREREVBeLJhvz9gY8PAyjTR2KDI0uq/RV2JKRZMuwiIiI6AYsmmxMEK6ta9JnDUSobxgAYD0bXRIREdkVFk12wDhFl5ujxOSudwMAfrvwX1wpv2LLsIiIiOg6LJrsgHExeHa2gNirRZNWr0XSmY22DIuIiIiuw6LJDgQFGUaacnIE9GzVCzcF9ATAKToiIiJ7wqLJDhjXNF2+rEBVFRDb9S4AwP6Le5FbmmPL0IiIiOgqFk12oKZXE4DcXAGTrhZNIkRsSl9vo6iIiIjoeiya7MC1Xk2GdU1d/LqhX+sBANjokoiIyF6waLIDNZdSAZCTY/iRGKfo/i8nFReLL9gkLiIiIrqGRZMdaNNGhCBcO4MOAGK7TK65/9PfP8K6U4lIydoPURTrfQwiIiJqXiya7ICLCxAYaCyaDD+Sjj6dEObbFQCw8thXmL5jKmLXj0X4qgFIOrPJZrESERE5KxZNdsI4RZeTYxhpSjqzCRlF6XW2y9RkYNq2R1k4ERERWRmLJjtxfdEkiiLmJs+GiPqn4vSiHvEp73CqjoiIyIpsXjTt3bsXUVFRmDlzpsntpk6dir59+9b66NWrF9566y0AwBtvvIFevXrVun/ixInWeAkWYWxwmZ2tQGp2MjI1GSa3zyg6gwPZKdYIjYiIiACobPnkS5cuRWJiIjp16mR22+XLl9f6vqKiAjExMRg/fjwAQKPRYMaMGXjmmWeaJdbmdv1IU3ZJtqR9ckqlbUdERETy2bRoUqvVSExMxPz581FZWdmofRctWoQ+ffpg2LBhAAxFk6+vb5NjUSgEKBRCk/c3RalU1Ppcn5AQw+fycgF+ynaSHjfEpx1UKpsPFjY7KfmjhjF/8jGH8jB/8jB/8lkqhzYtmqZMmdKk/bKysvDdd99h8+bNNbdpNBrs2rULK1euRGFhIfr374/Zs2ejY8eOkh4zIMATgtA8RZORj497g/d1737t616eo9HFvwvSC+ouBDfqGtAVY3vf2ewx2xNT+SPzmD/5mEN5mD95mD/55ObQpkVTUy1duhRjx45FiHF4BkBISAiCg4OxcOFCCIKAhIQEPPnkk9i8eTNcXV3NPmZ+fmmzjjT5+LhDoymHTqevdxtPTwGABwDg778rERc1D4//8gj0Yt3tFYICcyLjUVhY1izx2hsp+aOGMX/yMYfyMH/yMH/ymcuhv7+npMdxuKJJo9EgMTERa9eurXX74sWLa30fHx+PoUOH4vDhw4iMjDT7uHq9CL2+ec9G0+n00GrrP+DbtLn29cWLIh4cMR7Lor9FfMo7yCg6U3OfSlBhyegVGNNpfIOP1VKZyh+Zx/zJxxzKw/zJw/zJJzeHDjdBumvXLgQHB6P79fNZ9fDy8oKfnx9yc3OtFJk8vr6Au3vtBpcxYROQ+tD/sGHSFkzv9xwAQCtq4ao0P3JGREREluVwRdPOnTsxYsSIWreVlJQgISEBeXl5Nbfl5+cjPz8fHTp0sHaITSIIQFBQ7UupGG4XENluGOZEzkNbjyAAwIo/l9okRiIiImdm10XTmDFjcOjQoVq3HT9+vE4h5OXlhcOHDyMhIQGFhYUoKirCvHnz0LNnTwwcONCaIcsSHGwYMjRetPd6LkoXPNLrMQDAr+d21ZqyIyIiouZn06LJ2IRyw4YN2Lp1a833RhkZGSgrq73Y+dKlS2hz/QKgqxYtWgSdTofo6GiMHTsWOp0OS5YsgUJh13VhLfWNNF3v0V6PQykoIULEN8dWWDM0IiIip2fTheBpaWkm7z958mSd2/788896t23Xrh0WLVpkkbhsxVzR1M4rBGNCY5B0ZiNWn/gWbwx9G24qN2uGSERE5LQcZxjGCRin5y5fFlBdXf82j/eeBgDIr8jHxvR11gqNiIjI6bFosiPGS6mIooC8vPpHm4a3vxVd/LoCAFb8+ZXVYiMiInJ2LJrsiPGivUDDU3QKQYHHek8FABzOPYi0S0esEhsREZGzY9FkR4wjTcC1Xk31eaDHw3BXGVrBrzy2rNnjIiIiIhZNdqVt22tFU05Ow5d08XPzx6SudwMA1v79EzSVRc0eGxERkbNj0WRHXF2BwEDDFF1D03NGT/R5EgBQpi3DTydXN3tsREREzo5Fk50xth2or8Hl9Qa0GYSBbQYBMEzRiWLzXjePiIjI2bFosjPGdU2mpueMHu9tGG36u+AkkrP2NWtcREREzo5Fk50xnkFnaiG4UWzXu+Cn9gPA9gNERETNjUWTnTGONGVnCzA34+bh4oH7b3oYAPBLxibkluY0d3hEREROi0WTnTEWTWVlAoqLzW//+NWeTVq9Ft/99XVzhkZEROTUWDTZGeOlVABpU3Rd/LphRPvbAADfHlsJrV7bbLERERE5MxZNdkZqr6brGdsPZJVexPbMrc0SFxERkbNj0WRnao80SSuaojuPRbBnOwDAij+XNktcREREzq5JRVNVVRW+//77mu+3b9+OZ599FgsXLkRpaanFgnNG/v6AWi2tV5ORSqHCo70eBwDsufArzhSebq7wiIiInFaTiqaEhARs3rwZAHDq1CnMnDkTfn5+SEtLw7vvvmvRAJ2NIFxrcCl1pAkAHun1GFQKFQBg5bHlzRIbERGRM2tS0bRjxw588cUXAID169cjKioKCxYswOeff449e/ZYNEBnZJyia0zRFOQZjLGh4wEAP5z4DuXa8maJjYiIyFk1qWiqrKxEQEAAAOC3337D6NGjAQB+fn6cnrOAa13BG/fjMS4IL6wsxIbTP1s8LiIiImfWpKKpa9eu+Pe//41ly5bh7NmzuP322wEYCqjg4GCLBuiMjGfQST17zmhYu+Ho5tcdALCSHcKJiIgsqklF06xZs7BhwwYsXboUb7/9NgICAlBQUIAXXngBL774oqVjdDrG6bm8PAHaRrRdEgQBj/eZBgD4Pe8wjuT9rznCIyIickpNKpoGDBiAbdu2ITU1Fffffz8AwN/fHzt37sTYsWMtGqAzMk7P6fUCLl1q3GjTfT0ehIfKAwCw8tgyi8dGRETkrJpUNGk0Grz//vs133/99deYOHEi5s+fj7y8PIsF56yMZ88BjVsMDgC+aj/c1e1eAMDPp9agsKLAorERERE5qyYVTbNnz8b58+cBAP/73//w/vvvY/To0VAqlUhISLBogM6osZdSuZFxiq5cW44fT35vZmsiIiKSQtWUnQ4cOIDdu3cDADZv3ozbb78dL7zwAkpLS2sWhVPTXT/S1NjF4ADQr/UA3Nx2MA7nHsLKY8vwdL/nIAiNfxwiIiK6pkkjTTqdDmq1GoDhjLk77rgDAODm5obKykrLReek1GqgVSvDaFNTiiYAeLy3of1AeuFpLDm6COtOJSIlaz9EUTSzJxEREdWnSSNNvXv3xty5c+Hq6oqCggLcdtttAICff/4ZoaGhFg3QWbVtK+LKlaZNzwFAbNe78OZvr6JUW4o5+2fV3N7ZJxRxUQmICZtgqVCJiIicQpPekePi4nDx4kX88ccfeO+99+Dl5YWCggK8//77eOONNywdo1MynkHX2IXgRrvO7UCZtqzO7ZmaDEzb9iiSzmySFR8REZGzadJIU1hYGJYvr319M39/f+zdu7dm2o7kMS4Gb8r0nCiKmJs8GyLqn4rTi3rEp7yDcaHjudaJiIhIoiYVTQCwevVq7NixA1lZWaiurkbHjh0xefJkTJw40ZLxOa1rF+1t/GBganYyMjUZJrfJKDqDA9kpiGgX1aT4iIiInE2TiqZPPvkEP/74IyZNmoSxY8dCFEVkZGRg/vz5KCsrwwMPPGDpOJ2OcXqupERASQng5SV935zSbItuR0RERE0smrZu3YqlS5eiT58+tW4fO3Ys3njjDRZNFnB9r6acHAFdu0o/6y3IU9r1/6RuR0RERE1cCH7p0iXcdNNNdW7v1asXO4JbiPGivUDjp+gigqPQ2cf0WYyhvmEID45sUmxERETOqElFU9euXZGYmFjn9nXr1qFTp06yg6Jr03NA48+gEwQBcVEJUAj1/3gFCJgTOY+LwImIiBqhSdNzr7/+Op588kl8++236NKlCwAgIyMD586dw+eff27RAJ1Vq1YiXF1FVFUJyMlpfG0bEzYBy6K/RXzKO8goOlPrPl+1H27tcJulQiUiInIKTSqaBg8ejF27dmHz5s24cOECAGDIkCG4/fbbOT1nIYJgOIPu3Dmhyb2aYsImYFzoeKRmJyO3NAfnis8iIfWfKKwswEeH3sOcyHgLR01ERNRyNbnlQKtWrfDYY4/VuX3s2LE4cuSIrKDIIChIj3PnFE0umgDDVF1ku2E13x/ITsGOs9uw5MgiPHjTI+jm390SoRIREbV4TbtGhwm8tpnlGNc1NWV6riEJtyyEq8IV1fpqzNr7On9eREREElm8aOLiYssxNrhs6kV76xPqG4YXBr4EANhz4VdeToWIiEgiixdNZDlBQYZeTbm5AnQ6yz3ujEGvor1XBwDAnP1voay67jXqiIiIqLZGrWn68ccfzW6js+S7u5MzTs/pdAIuXxZq9W6SShSB1FQlcnIEBAWJiIjQwcPFA/HDFmDqtkdwoeQ8Pvv9Q7wZ/o6lwyciImpRGlU0LVmyxOw2bdq0aXIwVNuNvZoaWzQlJakwd64amZnXBhQ7d9YjLq4SMeMmYGSHUfjv+d344n+f4r6bHkKYbxeLxU5ERNTSNKpo2r17t8UD2Lt3L9544w2Eh4fj448/bnC7tWvX4u2334aLi0ut23/99VcEBgaisrISCQkJ2LNnD6qqqjBkyBDMnTsXAQEBFo/ZWozTc4ChK/iAAXoTW9eWlKTCtGlu0Otrr4fKzFRg2jQ3LFsGvHvL+7j1xwhU6aswZ99b+C7mJ4vFTkRE1NLYdE3T0qVLkZCQIKmLeHFxMaKiopCWllbrIzAwEADw4Ycf4vjx4/jxxx+xbds2CIKAt956q7lfQrMyLgQHgK1blUhJUULKyW6iCMydq65TMBnp9QLi49Xo4tcN0/s/DwDYfnYrtmdusUjcRERELZFNiya1Wo3ExERJRVNRURF8fX3rvU+n0+Hnn3/Gc889h+DgYPj6+uLll1/Gnj17kJuba+mwrWb3bhUUCkOVtHq1K2JjPRAe7omkpPoHCCsrgRMnFPjwQ9daU3L1ychQ4MABJV4Z/I+aC/e+ve8NVGgrLPsiiIiIWogmN7e0hClTpkjeVqPR4OzZs7j33nuRkZGBTp064ZVXXsGwYcNw9uxZFBcXo3fv3jXbh4WFwc3NDceOHUPbtm3NPr5CIUChaJ52CUqlotZnKTZvVmLatLqjRcbptdmzq9C6NXDqlIC//1bg1CkFMjMF6HTSX0NengJ+7j6YN/xdPLX1CZzVZOI/Rz/D60PflPwY1tCU/NE1zJ98zKE8zJ88zJ98lsqhTYumxvDz80ObNm3w2muvoV27dvjxxx8xffp0bNiwAUVFRQAAHx+fWvv4+PigoKBA0uMHBHg2e48pHx93SdsZptcAfQNLmIzTa3J17+4Gf39g2tDHsOrE1/hv5n/x8aEP8HT4NHT26yz78S1Nav6ofsyffMyhPMyfPMyffHJz6DBF04svvljr+yeeeAJJSUnYtGkTRowYUe8+oihKLoTy80ubdaTJx8cdGk05dDrzi7mTkxVIT5f2g/XzE9G9ux7du+vRrZsePXqI6NpVj3vucTM5RRcWpkfv3uUw1pQJwxbi1rNRqNBW4MXNM/BNzGpJz28Njc0f1cb8ycccysP8ycP8yWcuh/7+npIex2GKpvq0b98eeXl5NWfIFRQUwMPDo+Z+jUYj+ew5vV6EXt+8lxTR6fTQas0f8BcvShs+/OCDCjz6aDXqqwvj4irrPXsOABQKEe+8U1nrwOnu2xNP9nsGS44swub0Tdh+ZjtGdbxDUhzWIjV/VD/mTz7mUB7mTx7mTz65OXSYCdIvv/wSBw4cqHVbeno6OnTogA4dOsDX1xfHjh2rue/vv/9GVVUV+vTpY+1QZbv+rDlTunfX11swAUBMjBbLllUgNLT2wdGpkx7LllUgJkZbZ5/XB7+J1u6GPltv7/sHqnRVjQuciIioBbPromnMmDE4dOgQAODy5cuYO3cuMjIyUFlZiRUrVuD8+fO46667oFQqcd9992Hx4sXIzs5GQUEBPvroI9x55501LQkcSUSEDp07m66EQ0P1CA833X09JkaL1NRSLFxYXnPb/Pn1F0wA4KP2xZzIeABAeuFpLD6yqJGRExERtVw2nZ7r27cvAECrNbyJ79y5EwCQlpYGAMjIyEBZmeG6aK+++ipEUcSUKVNQVlaG7t274+uvv645M27GjBkoKyvD/fffD61Wi6ioKMTFxVn7JVmEIJifXpszp7LBUaYbH+uRR7SIjxdRWipg/34VRo9uuNi6r8eD+Pb4SvxfTio+OvQe7ul+H9p5hch5OURERC2CIIpS2iW2fJcuFTfbY6tUCvj7e6KgoLRRc6lJSSrEx6uRkXFtQDA0VI85cyobHC1qyIMPumPXLhX69dNh507TF+hNu3wUd64ZAb2oR2yXyZja92nklGYjyDMYEcFRzX6W4Y2amj8yYP7kYw7lYf7kYf7kM5fD1q29pT2OpQMjy4mJ0WLcOC1SU5XIzTVccDc8XCdphOlGw4ZpsWuXCmlpChQWAn5+DW/bN7AfHu89Dcv/XIoN6euwIX1dzX2dfUIRF5WAmLAJjQ+CiIjIgdn1miYyTK9FRuowaZIWERFNK5gAYNgww5ScKApISTFfKw9qc3O9t2dqMjBt26NIOrOpaYEQERE5KBZNTqJvXz28vQ0zscnJSpPbiqKIDw4tbPB+vahHfMo74MwuERE5ExZNTkKlMoxYAcC+faaLptTsZGRqMkxuk1F0BgeyUywWHxERkb1j0eREhg0zLB4/dkyJ/PyGt8spzZb0eFK3IyIiaglYNDmRW2651mogObnhdU1BnsGSHk/qdkRERC0BiyYn0ru3Hn5+hnVI+/c3PEUXERyFzj6hJh8r1DcM4cGRFo2PiIjInrFociIKBRAZaZiiM1U0CYKAuKgEKISGD4+JXSZbvV8TERGRLbFocjLG1gMnTihx6VLDRU9M2AQsi/4Wob5htW5XCIZia/mfS5FeeKr5AiUiIrIzbG7pZIxFEwCkpCgxcWLDncVjwiZgXOh4pGYnI7c0B0GewdDpdbhn00QUV2nw+JaHseWe3fBy8bJG6ERERDbFkSYn07OnHgEBhhby5loPAIapush2wzCp292IaBeFYe2HY27UfADAyYITeGn3c+zXREREToFFk5NRKICoKMNok6l1TaY81e9Z3N3tPgDApvT1+OKPTy0WHxERkb1i0eSEjFN0p04ZrmnXWIIg4MORn6F3q74AgPmp/8R/z++2aIxERET2hkWTE7q+X1NTR5s8XDywYsx38FP7QS/qMX37EzinOWupEImIiOwOiyYn1L27HoGBhnVNTS2aAKCzbygW37kcAgQUVBbgia2PoFxbbqkwiYiI7AqLJickCNem6Pbvl3cC5aiOd2BW+BwAQNrlI3h9z8tcGE5ERC0SiyYnZSyazpxRIDtbXpPKGYNewbjQCQCAn06uxvI/v5QdHxERkb1h0eSkbrnlWn8mKa0HTBEEAZ/f/h908+sOAHhn/1tIzUqW9ZhERET2hkWTk+rSRUTbtvLXNRl5u/rg67Gr4eXiDa1ei2nbpiC7JEv24xIREdkLFk1O6vp1Tfv2WaYxfFf/blh0h2Fq7lJ5HqZuexQV2gqkZO3HulOJSMnaz/VORETksFg0OTFj64Fz5xQ4f94yF98dGxqDV25+HQBwOPcgeq/sgtj1YzF9x1TErh+L8FUDkHRmk0Wei4iIyJpYNDmxqKhr65osMUVn9PqQWegb2A8AUFxVXOu+TE0Gpm17lIUTERE5HBZNTiw0VES7dsZ1TZa7drNCUKCosqjB+/WiHvEp73CqjoiIHAqLJidWu1+TEpaqYVKzk3Gu2HR38IyiMziQnWKZJyQiIrICFk1Ozth64MIFBc6etcy6ppzSbItuR0REZA9YNDk540gTACQnW2ZdU5BnsEW3IyIisgcsmpxcx44iOnY0rGuyVOuBiOAodPYJNblNsGc7hAdHWuT5iIiIrIFFEyEqyrLrmgRBQFxUAhRCw4fXlfLL2HPhV/lPRkREZCUsmgjDhhnWNWVnK5CRYZl1TTFhE7As+luE+obVuj3IIwguChdU6avwSNJ92Jrxi0Wej4iIqLlZ7jxzcljGJpeAYYouLKzaIo8bEzYB40LHIzU7GbmlOQjyDEZ4cCSSs/bh4aT7UKYtxRNbH8a/71iKyd3uschzEhERNReONBFCQkR07my569BdTxAERLYbhknd7kZEuygIgoBhIcOROHEDfFx9oRN1eGbHNHz/17cWfV4iIiJLY9FEAK61HrBkvyZTBgcNxbrYzWjl1goiRLz86/NYlrak+Z+YiIioiVg0EYBrrQfy8hQ4fdo6h0Xf1v2xftIWtPUIAgC8tfd1fPb7x1Z5biIiosZi0UQAavdr2rfPslN0pvQIuAkbJ29FB++OAICE1Dj868A8XmKFiIjsDosmAgAEBYno0qV51jWZE+obhg2TtiDMtwsA4KPD72NO8iyIoghRFJF8cR9++PMHJF/cx2KKiIhshmfPUY1hw7RIT3dFcrJhXZNgme4DkrT37oANk7fivo2x+Cv/OJYcWYQTV/7CWU0GMjUZNdt19glFXFQCYsImWC84IiIicKSJrmNsPXD5sgInTlj/0Gjr0RbrJiWhf+uBAIA9F3bXKpgAIFOTgWnbHkXSmU1Wj4+IiJwbiyaqYewMDljuOnSNFeDWCokTNkCtVDe4jV7UIz7lHU7VERGRVbFoohpt2ojo0cNQOFlzMfiNjucfQ6Wu0uQ2GUVncCA7xUoRERERsWiiGxjPoktOVkGvt00MOaXZFt2OiIjIElg0US3GoqmgQMDx47Y5PII8gy26HRERkSWwaKJarl/XZO3WA0YRwVHo7BNqcpuO3p0QHhxppYiIiIjsoGjau3cvoqKiMHPmTLPbrl69GtHR0Rg4cCAmTJiAnTt31tz3xhtvoFevXujbt2/Nx8SJE5sz9BapVSsRPXsaCidbFU2CICAuKgEKoeHDs1pXxek5IiKyKpsWTUuXLkVCQgI6depkdtudO3fiww8/xLvvvouDBw9i6tSpePnll3H+/HkAgEajwYwZM5CWllbzsXHjxuZ+CS2SsfVASooKOp2ZjZtJTNgELIv+FqG+YbVu91X7AQCyy7IxYf0YnNVkWj84IiJySjZtbqlWq5GYmIj58+ejstL02VJlZWV45ZVXcPPNNwMAJk+ejH/961/4448/0KFDB2g0Gvj6+jY5FoVCgELRPN0clUpFrc/2bsQIPZYuBYqKBJw4oUL//rZZER7bPRYTu03EgZwUaPT58FW0wpC24fj48AeYnxKPc5pMTFw3Busmb0L3gB42idERONrxZ4+YQ3mYP3mYP/kslUNBtINmN2+++SYqKyvx8cfSL9aq0WgQFRWF5cuXY+jQoZgwYQLatm2L8+fPo7CwEP3798fs2bPRsWNHSY8niiIEa7bAtmMFBUCrVoAoAh98ALz6qq0jquuT1E8wc5thSre1R2vseHQH+gf1t3FURETUkjnkZVREUcTs2bPRv39/DB06FAAQEhKC4OBgLFy4EIIgICEhAU8++SQ2b94MV1dXs4+Zn1/arCNNPj7u0GjKodPZ6Dz+Rurb1w1HjyqxfbsWU6eaHgVsbvXl77EeTwHVKryyewYulV3CrStHYk3sOgwOGmLTWO2RIx5/9oY5lIf5k4f5k89cDv39PSU9jsMVTdXV1XjzzTdx+vRpfPPNNzW3L168uNZ28fHxGDp0KA4fPozISPNnWen1IvT65h100+n00God44CPjNTh6FElkpOVqKjQQ2UHR8qN+Xv4psfgpnDHC7umo6iyEJPXTcCqcT8hKuQWG0Zpvxzp+LNXzKE8zJ88zJ98cnPoUBOkFRUVmD59OrKysrBq1SoEBgY2uK2Xlxf8/PyQm5trxQhbjltu0QIASkoEHD1qv4fJ3d3vw7Lob+GqcEVpdQke2HwXdp/bYeuwiIioBbLfd8MbiKKImTNnQqVSYeXKlfD396+5r6SkBAkJCcjLy6u5LT8/H/n5+ejQoYMtwnV4kZE6KBSGkbf9++1gmMmEcWHj8e24H+GuckeFrgKP/vIANqfzzEkiIrIsuy6axowZg0OHDgEANm3ahNOnT+PTTz+FWl37Yq5eXl44fPgwEhISUFhYiKKiIsybNw89e/bEwIEDbRG6w/PxAfr1Mwxh2qpfU2Pc1vF2/Dh+HbxcvFGtr8ZT2x/DmpM/QBRFpGTtx7pTiUjJ2s+L/BIRUZPZdAihb9++AACt1jAVZGxWmZaWBgDIyMhAWVkZAGDt2rW4ePFizcJvo9jYWCQkJGDRokWYP38+oqOjoVQqMXjwYCxZsgQKhV3XhXZt2DAd/vhDif37lUhMVCEkREREhA72epJhRLsorJ24EfdvnozCykI8v+tpzNk/C1cqLtds09knFHFRCYgJm2DDSImIyBHZRcsBe3DpUnGzPbZKpYC/vycKCkodahHfv/7lio8+qj2q17mzHnFxlYiJ0Votjsbm79jlPzFx/RgUV2nqvV8hKLAs+lunKZwc9fizJ8yhPMyfPMyffOZy2Lq1t6TH4TAM1SspSYVPPqnbqiEzU4Fp09yQlGS/65x6teoNH1efBu/Xi3rEp7zDqToiImoUFk1UhygCc+eqodfXPw+n1wuIj1fDXmuO1OxkXCy5YHKbjKIzOJCdYqWIiIioJWDRRHWkpiqRmWn60MjIUODAAftcIC71Qr684C8RETUGiyaqIydH2kpvqdtZW5BnsKTtWDQREVFjsGiiOoKCpM27Sd3O2iKCo9DZJ9TsdnOSZ+GFXdNxpfyKFaIiIiJHx6KJ6oiI0KFzZ9NnaAiCiLw8+xxpEgQBcVEJUAj1H94CBHi7Gs6U+OnkagxbfTN+PPE9F4YTEZFJLJqoDkEA4uIqazqC1yVCFAU8+aQ7EhJcodNZNTxJYsImYFn0twj1Dat1e6hvGJaP+Q4HHzmKh256FACQX5GPF3c/g3s2xeJMUbotwiUiIgfAPk1XsU9TXUlJKsTHq5GRca22Dg3V46GHqrBsmStycgy3jxypxeLF5QgIaJ445ORPFEWkZicjtzQHQZ7BCA+OhHBdd859F3/Da/99qaZYclO64bUhb+LZ/i/CRelSs39OaTaCPIMRERxVa39H4KjHnz1hDuVh/uRh/uSzVJ8mFk1XsWiqnygazqbLzRUQFCQiPNzQETw3V8CTT7rhwAFDv6aOHfVYsaIcffta/vU1d/4qtBX45PcP8PnvH6NaXw0A6BnQG3d3vw/fHV+JTE1GzbaO2FHckY8/e8EcysP8ycP8yceiycJYNDVeVRUQF6fGsmWGJpju7iI++qgCd99t2W7h1srfify/8Op/Z+BgzgGT2zlaR/GWevxZE3MoD/MnD/MnHzuCk825ugILFlTis8/KoVaLKC8X8Oyz7njnHTWuXk4QogikpCixbp0KKSlKu22ICQA3BfTEpsnbsHD4RxDQ8BQcO4oTETknFk0k2wMPaLF5cxnatzdU70uWuOLee92xerUK4eGeiI31wPTp7oiN9UB4uKddX4JFIShwU6ueEGG6IGJHcSIi58OiiSyif389tm8vwy23GIaY9u9X4aWX3Op0FneEa9dJbXq5PXMrdHo7PHWQiIiaBYsmspjAQBE//VSOZ56punqLY167TmpH8S/++ASDv+uLDw7+C1klF+vdRhRFpGTtx7pTiUjJ2s8pPSIiB8aiiSxKpQLGjjW/ENyer10npaO4QjDEfrHkAt47+C4Gfdsbj/5yP7ZnbqkZfUo6swnhqwYgdv1YTN8xFbHrxyJ81QAkndnU7K+BiIgsj0UTWZyjX7vOXEdxhaDAkjuXY1n0t7i1/W0ADIvDt2VuwSO/3I+bv+2Dp7Y/jmnbHq3VrgAAMjUZmLbtURZOREQOiEUTWZyjX7sOMN1RfFn0t4jtOhkTusRizcQN+L+Hj+ClQa+itXsbAEBW6UVsOP0z9GL9pwbz7DsiIsfEPk1XsU+T5YgiEB7uWWcR+PVCQ/VITS2FlObatsyfuY7i16vWVWNb5hZ89vuH+OPS/8w+9sZJWxHRLsrSIdfhbMdfc2AO5WH+5GH+5LNUnyb7PYWJHJbx2nXTprlBr6+vwBAxa1alpILJ1gRBQGS7YZK2dVG6YHyXiajWV2H6jqlmt//09w9RXKVBZMgt8HLxqneblnAZFyKiloJFEzWLmBgtli2rqHPtOgMBqalKxMZatnO4vZB69t2uczuw69wOuChcMDhoKG5tfxtGtB+JAW0GQaVQIenMJsxNnu3wl3EhImopOD13Fafnmsf1165r3VrEJ5+4Ys8eQ63+xRfluO8+84WTo+VPFEWErxpQZxH49TxUHhCgQKm2pM59Pq6+6OrXDf/LO1xvk83GXsbF0fJnj5hDeZg/eZg/+XgZFXIIggBERuowaZIWw4bpsGRJOTp0MBywr73mhrS0lncISjn7btEdS/H3tLPYNHk7Xhv8JoYGRUB5tY2BpqoIv+cdarArOReSExHZRst7xyK7FhAArFhRDjc3ERUVAp54wh0FBbaOyvLMnX0XEzYBLkoXhAdH4B9DZ2HzXdvx97Sz+GbsD5JGkHgZFyIi6+OaJrK6fv30eO+9CsyY4Y5z5xR49ll3rFpVDqV99rpsspiwCRgXOl7y2Xferj4YEzoO5doySX2cDuYcsMrZd0REZMCRJrKJBx7Q4rHHDJdb2b1bhfffd7VxRM3DePbdpG53I6KdtDPfpC4kn5cahylbHsSRPPPtDYiISD4WTWQzCQmVuPlmwyVHPvpIjW3bWthQUxNJuYyLcPW6flszknBn4q14aPM9OJTzf3W2E0URyRf34Yc/f0DyxX1cB0VEJAOLJrIZtRpYvrwcgYGGheHPPeeOM2fYg0jKQvJPbluEFwa+DA+VJwBg57ntGPfzHbh3YyxSr651Ml77bvzaMXhw7YMYv3YMr31HRCQDWw5cxZYDtpOcrMTdd7tDpxPQs6cOv/xSBk/Pa/c7a/6SzmxCfMo7yCg6U3NbqG8Y5kTOq1ksfqX8Cr48ughfpX2J4ipNzXY3+ffE3wUnoUfdfDW2ZQE57zFoKcyfPMyffJZqOcCi6SoWTba1eLEL5sxxAwBMnlyNxYsrajqGO3P+pF7GpbCiAEvTFuPLo/9BUWWh2ccN9Q1D6kP/Y3dxiZz5GLQE5k8e5k8+9mmiFmX69GpMmlQNAFi3zgVLlrjYOCL7IHUhuZ+bP14f8hZ+f/RPPNLzMbOPK7VlgSiKSMnaj3WnEpGStb/Ra6Lk7k9EZE/YcoDsgiAAH31UgRMnFDhxQom5c9Xo10+PqCidrUNzKN6uPhje/lZ899fXZreds38WxneZiIFtbsaANgPh7epT6365l3HhZWCIqKXh9NxVnJ6zD2fOCLjzTk8UFwsIDNRj584yXLigRHGxO7y9yzFkiNYhLvRrSylZ+xG7fmyj9hEgoLt/DwxsezMGtrkZ5doyxKfMgV5s2pqopDObMG3bo03e397wd1ge5k8e5k8+rmmyMBZN9mPbNiUefdQDAKBWi6isvFYlde6sR1xcJWJiWubFfi1ByrXvvF190Mm7M/7KPwad2PjRvGDPYHx559dQq1zhqlRDrXSFi8IVaqUaLgoXRK+9DWc1mQ3u72hrqvg7LA/zJw/zJx+LJgtj0WRfpk1zw6ZN9a9rUihELFtWwcLJBKkjPeXacqRdOor/5R3C77mH8HveYZPFjiVtnLTVYTqa83dYHuZPHuZPPi4EpxZLFIG0tIYbXer1AuLj1WC53zAp174DAHeVO4YGh2N6/+exZPQKHHzkKD649TOrxPj+wQXYlL4eBRX5DW7DheREZE+4EJzsTmqqEpmZpuv5jAwFDhxQIiKCC8UbYrz23cG8FJSgEN7wx+A2EWanxLr5d5P0+B/c+il6BPREla7S8KGvRpWuEscup+GT3z80u//ei3uw9+IeKAQFBrQeiJEdRuHWDqNwc9shcFW6ciE5EdkdTs9dxek5+7FunQrTp7ub3W7evApMn15thYgcW2OPPylrokytSZKyv6eLJ9QKNfIr644yeag80c2/O45e+gMi6v55auxCcmOvq5zSbAR5BiMiWNo1AK/H32F5mD95mD/5OD1HLVZQkLQ6/p133HDffe7YskUFLZc3WYyUy7jMiZzXYOEhZf8vbv8Sx6eewa5792J2xFwMbz8SrgrDRZvLtKU4cul/9RZMAKAX9YhPeUfSVJ3xUjKx68di+o6piF0/lpeSIaIm40jTVRxpsh+iCISHe5qZohMBXHvTbtdOjylTqvHww9Vo2/baIS2Khum+nBwBQUEiIiJ0TteyoKnHn5TLuFhy/7LqMqRmJ+P7v77FxvR1Zh//poCeGBIUju7+PdDd/yb0CLgJwZ7taoo5S7U9EEURB/NSUIwCeMMfQ9rU35Xd1P5yR7ocHf8GysP8ycez5yyMRZN9SUpSYdo0N+j1dd9cFAoRH3xQgawsBb77zgU5OdeKK5VKREyMFk88UY2CAgFz56prFV/O2LJAzvEn9TIultx/3alETN8xtVFxGnm7+hiKKL8e2Hb2F+SbWGQupe0BG3xaBv8GysP8yceiycJYNNmfpCQV4uPVyMi4VvSEhuoxZ861oqe6Gti6VYWVK12wd++N5zXUHo0ycraWBY52/Eltzjk0KBy5Zbk4pznb4FSeOYvvXIbxYbFwVbrWuU/uSJUlR7ocfaTK0Y5Be8P8yddiiqa9e/fijTfeQHh4OD7++GOT265cuRKrV6/G5cuX0b17d7z11lvo168fAKCyshIJCQnYs2cPqqqqMGTIEMydOxcBAQGS4mDRZJ9EETh4UIWSEkNH8MGDG+4Ifvq0gK+/dsX337uguNj0m0poqB6pqaVOMVXnaMdfYxeil1WXIb3wFE4WnMDJ/BM4WXACv+ccRF55nqTnUwgKhHi1R2efUHTy6Vzz8c/kt5FVmiUpBrmvoSGWGKmyh6LL0Y5Be8P8ydciiqalS5ciMTERAQEBCAoKMlk07dy5E2+99Ra++uor9OjRA9999x1WrFiB7du3w9PTE++++y4OHz6ML774Ah4eHnjnnXdQWVmJJUuWSIqFRZP9amz+fv1Vifvv9zC73caNZU7RssARjz+5ozRNuZRMU/QM6AVvV5+aIkS4OrJZXKXBsSt/mt3//Vs/xm0d7kBrjzZwV9U+Y9QSI1X2Mj3oiMegPWH+5LNU0WTTPk1qtRqJiYmYP38+KisrTW67Zs0a3HXXXejfvz8AYNq0afj666/x3//+F2PGjMHPP/+MhQsXIjg4GADw8ssvY9y4ccjNzUXbtm2b/bWQ/SgslPZfdE6OEwwzOShjc86mLkSPCI5CZ59QkyM9IV7tkTBsIc4Vn0Wm5gzOajJxVpOJc5qzqNZLa2XxV/5xaS+oAa/vmVnztberD9p4tEFr9zYIdG+NPRd+rbdgAq6dQTgudHyDo0YNFV2ZmgxM2/aow13/j8ge2LRomjJliuRtjx07hnHjxtV8LwgCbrrpJqSlpaFnz54oLi5G7969a+4PCwuDm5sbjh07JqloUigEKBTN8yaqVCpqfabGaWz+QkKkPW5IiOG/j5bOUY+/2O6xmNhtIlKy9iOnNAfBnsGIaCd9ail++Hw8/ssjDY7ULLj1PYzvMrHOfTq9DpvSN2DqFvN/n24JGY4A91YAUNMCQYSIK+VXkJK1X1KcRsVVGhRXaZBeeFrS9hlFZzDgm54I9mqHALcABLgFwM/NHwFuAfBX++ODg++ZLLrmpczBxG4TzebT2JU9uzQbwZ7BiGw3rEknAxRdvAJfRSuEBzX+ZAI5z29rlojfUX+H7YmlcugwHcELCwvh6+tb6zZfX18UFBSgsLAQAODj41Prfh8fHxQUFEh6/IAAz2b/RfTxMd+wkRomNX/jxgFdugDp6Q1vExAAjB3r7hRrmowc9fiLCYhu0n6PDn4QXp5u+MfOf+B0/rVCpGtAV7x3x3uY3HNyg/s+HvAI5qfORXpBwwdR14Cu+G3angbXNHX7vJvJ/Tv5dsKqu1YhtzQXuSW5yC3NRU5JDnJLc/Fn3p+1Ym5IdmkWsk2suzLlTFE61mSswoN9HoSP2qfebdb9tQ6v73i91uvo4t8F79/5vsn82cv+gOFnsffcXmQVZ6GddzsM7zi80UVbU/e3RPzXs8XvsNz82Ru5OXSYoqk+oiia/OGZu/96+fmlzTrS5OPjDo2mHDod56Mbqyn5i4tT4vHH1fW2LACA/HxgwYJKPPtsyz+DzpmPv5FBo3Hg4TvrHa0qKCg1uW9c1DyTI1VzIuNRWFjW5P3n3bIAvbwHoFc9SymSL+7D+LVjzL6+mLDxcFWqkV+Rj8KKAuRX5KOgIh8l1SVm9wWAZzY/g2c2P4Ngz3boHtDd0K4hoAe6+XfHOc05vLz7hTrxpxek454192DluO/qHakz2py+sd7Xb639jY8Rt292nSneubckmN1X7v6WiB8wvI8dyElBkd76I3Vy82cplhqtM/V30N/fU9LjOEzR5O/vX2fUqKioCN26das5Q66goAAeHtcWAGs0Gslnz+n1IvT65l0Tr9PpuYhPhsbkb8wYPZYtE+u0LOjYUY/ycuDSJQXeflsNd3cRjzziHJdicebjb2jbqJqvdToRkNCiYEyn8SbXVY3pNN5kPuXsP6RNpNk1WaG+YVgevareN4/fzv8X92yS/qZmHLHac/6/krbXi3q8sONZ7Du/r+7ziyJEUcQPJ1eZnB58aefzOHn5JFQKF6gUyqufVVAJKigEBRJS40zuH7dvNqI7xjR6TVdG0Rk8/ssjTW4ZIWV/URQxZ+/bsuI3xmCrPmFy82ck9+xNS5/MIPfvoM1bDgDAm2++icrKSpNnzz377LMICQnB7NmzAQA6nQ7Dhw/HnDlzcOeddyIyMhIJCQkYPXo0AODvv/9GbGws9u7di8DAQLMx8Ow5+yWvOaOhI3hurqEjeHi4DufOCZg40QPZ2QoIgojFiysweXLLHXHi8SePsSN4Yy56fOP+TWkQKufsOSktDzp6d8LHt32BU4V/41TBSZwqOIVTBSebPN1nC35qfwR5BsFP7Q//q2u5/N0C4Kf2w+Iji3Cl4nKD+3by6Yyd9/wGpdJQqCkFJVQKFQRBkJS/dl4h+HjkFyiu0qCwshCFlYXQVBahsLIQ6YWnsT/rN7PxvzToFQxvPxLtPEPQzisEHi7X/um3ZZ8we2mZYaleZ0ALaTlg1FDRNGbMGCQkJGDw4MH47bff8Morr9S0HFi2bBkSExOxdetWuLm54YMPPkBycjIWLVoENzc3vPXWW3B1dcVnn30mKQYWTfarOfJ36pQCsbHuuHxZAZVKxIoV5YiObpntB3j8yWerHMq5lE1T33CKqzT46ugSLPi/eWbjC1C3gqdr3WmNsuoykwWLkYvCBSJEaPX280+LAAEKQQGdaP2/B/5qfwR7haCdZzscyElBcVXD70sdvDtiXWwSFIKi5kMQFFBAAQECxqy9DeeKzza4f7BnO3w08nOUa8tRWl2CMm0ZyqrLUFpdgtOFp7D+9Fqz8b406BUMDYqAv1sAAtxbIUAdAB+1LxSCQnbBY6nCzahFFE19+/YFAGivXm1VpTLMFqalpQEAevTogaVLl2LEiBEAgO+//x7ffPMNLl26hN69e2P27Nno3r07AKCqqgr/+te/sHPnTmi1WkRFRSEuLg7e3tISwaLJfjVX/tLSFJg82QMajQC1WsSqVeUYMaLlFU48/uSzZQ7lXMqmqUWX1D5XGydtRUS7qDq3N3Z/URShF/XQilpU66uRcnE/Hv7lXrP7T+56DzxcPAxruioLUFCRj4KKAlwuv2T1osdF4QJftR981b5QCkr8XXDSqs9vL5SCEn5qf2iqiky27vB28caY0BjoRC2q9Yafu1ZXbfis1+JKxRWckNDSo6Fj8EYtomiyJyya7Fdz5u/gQQXuvdcDZWUCPDxErFlThiFDWtbPiMeffI6cw6YUXXL/y7f1/skX92HShnH17FXbS4NeQWefMGhFLXSiDjq94XN6YTq+PrbM7P6fjVqMW9uPhI/aFx4qj5pYpMb/y107kVWaheySi7hYchFZVz+OXPqfTYoulUIFTxcvKAUl8iuuWP35m+LLO1dgUre7zW7XIppbEtnakCF6fPNNOR5+2B1lZQIefNAD69aVoW9fx3pjJGqIIAiIbDes0fvERSWYnF6ZEzmvweLL1vtHthsmaSH9rPC4Bou2Ped3m93//h4P1ru/1PhbuQeilXsg+gb2q3W/1JG6WeFx6ObfHXpRD1HUQy/qoYceJ/NP4OPD75vdf9GoLxEZMgyeLp7wdPGquQaj1KJv6127UVCZj/yKfORXXLn6OR+pWfuxNfMXs8/fwbsjAt0DoVK4wFXhCpVCBReFC1RKF2gqi5Cctc/sYwR5BpvdxpI40nQVR5rslzXyt22bEk884Q6tVkBgoB4bNpSjWzf5z2VciJ6TY1iIHhGhs3pvKB5/8jlrDuWsqbL1/vZwweWmxm/rkTpj7E19/XKndy31Gq7H6TkLY9Fkv6yVv3XrVHjmGTeIooDgYD02bixDx45ik4uepCQV5s5VIzPzWsuDzp31iIurREyM9Ra+8viTz5lzKGdNlXF/W5x9CNi+6JMTvzMXfZZ8DUYsmiyMRZP9smb+Vq1ywcyZbgCAwEA93N2B8+cbX/QkJakwbZpbvc01FQoRy5ZVWK1w4vEnH3Moj6MupLfE/nI4c9FnydcAsGiyOBZN9sva+VuyxAXvvOPW4P3mih5RBMLDPWuNMN0oNFSP1NRSq0zV8fiTjzmUh/lrOluO1MllqYLHEq+BRZOFsWiyX9bOnygCPXp4obCw4V/Kjh31WL26DAUFAgoLBRQUCDVf//WXAlu2uJh9no0byxAR0fynRfP4k485lIf5k8eR82fLou16PHuOqJmkpipNFkwAcO6cAsOGecl6npwcx73oJRGRFE05e9OeNTx/QOSkmlrMCIIIf38RwcHS/hNs04aDvEREjoQjTUQ3CAqSVszExVUgMlIHPz8RAQEifHwAhULamiYAmD/fFR9+WImbbnKs4XYiImfFkSaiG0RE6NC5s+lCJjRUj+eeq8agQXqEhYnw8zMUTAAgCEBcXCUUioaKL8PtBw+qMGqUBxISXFFWZrn4az2TCCQnK/DDD4bPXMFIRNR0LJqIbmCu6FEoRMyZU2nyzLeYGC2WLatAaGjt4is0VI+lSyvw9tuVcHMTodUK+OwzNUaM8MTu3UpLvgwkJakQHu6J8ePd8eCDwPjx7ggP90RSEgeYiYiagmfPXcWz5+yXza4wn6RCfLwaGRnX/rcIDdVjzhzpzSmNHcFzcw3NMcPDrzXHzMwU8Oabbti9+1oRExtbjYSESrRtK9bav7HNNe2pT1RLwN9heZg/eZg/+dhywMJYNNkv2zbGa7josdTjb9yowttvq5GXZyjOvL1FzJpViTZtRMyb1/iO4qIIDBniiXPn7KNPVEvA32F5mD95mD/5WDRZGIsm++UM+SsqAt59V42VK10gisZKRgRgeqSoogI4dUqBEyeMH0r88YcCly6Zn3m3Vp+olsAZjsHmxPzJw/zJxz5NRC2Iry+wcGEl7ruvGq++6objx5Wor2ACAL1ewPPPu2HePBGZmUK9U3BSsE8UEVHjcCE4kR25+WY9EhIqzW5XVibgzBlFrYLJ11dEeLgW0dHVkp6rdWsOMhMRNQZHmojszKVL0kaAoqK0GD1ai5tu0qNnTz2CgkQIgrFPlNJsn6iPP3ZF797l8POzQNBERE6AI01EdkZqc80336zCc89VY9QoHYKDxZpF3VL7RP32mwrR0Z74+2/+GSAikoJ/LYnsjNTmmuHhDS/iNtUn6quvKvD441UAgIwMBcaM8cD27ZbtEUVE1BJxeo7IzhhHikz1WTLXXBMwFE7jxmlx8KAKJSXu8PYux+DBWggCMHGiFr166TFrlholJQIefdQdb71VhZdeqmIbAiKiBnCkicgOmRopakxjSkEAoqL0uP9+IDJSX6sgevzxaqxdW45WrfQQRQHvvqvG9OluzXZJFyIiR8eRJiI7ZRwpas7mmpGROmzfXoYpU9xx7JgS69e7ID1dga+/Lkf79jy7jojoehxpIrJjgmAobCZN0kq+hEpjdeggYvPmMkycaGhVkJamxOjRHkhNVUIUgZQUJdatUyElRdnoC/7K3Z+IyJ5wpImI4OkJLF1agd699ViwQI3LlxWYPNkdfn4irlxp3GVcjJKSVJg7t/GXgSEislccaSIiAIZRrZkzq/DNN2VQq0XodEKtggkAMjMVmDbNDUlJpv/fMl4w+MZeUVL3N7KHkSpRBJKTFfjhB8NnjpYROS+ONBFRLdHROgQGirh4seHLuMyY4YadO6shiobv9XrUfOh0wI4dqgYv76LXC4iPV2PcOK3J6UZ7GKmqG4M7R8uInBiLJiKqJTVViYsXTQ9CFxcLWLXKtcnPkZGhwPDhHoiI0KF3bz1699ahVy89vLwM9xtHqm4svIwjVY05g1AUDa8pJ8ewmF7q2jBLxdDU5yci+8OiiYhqkXoh35AQPXx9RSgUqPVRUCAgI8P8zP/ffyvx99+1m2p27qxHz546pKTIH6kCmj5aJYrA3LnqFjFaRkSWw6KJiGqRehmX//ynAhERdbuSp6QoERvrYXb/IUO0yMpS1BrVysxUmL1mHmAYqZo92xUDBujh5yfCxwfw8xPh5yfC11eEm5v5kaL58yvQq5eIS5eEOh9nzpiPIyNDgU8+ccWYMVp06qSHxw0v2ZKjZXLYeqTLuCasuBjw9lZgyBA9R9rIYQmiyGWNAHDpUnGzPbZKpYC/vycKCkqh1Zq+PAbVxfzJ09j8GS7462myaAgN1SM1tbTeN7/G7l9QABw/rsSxYwocO6bE/v1KnDsn7xwVV1cROh2g01nv3Tk4WI/QUMNH584ili51QV5e03J4PTlFj61Humz9/C0F/wbKZy6HrVt7S3ocFk1XsWiyX8yfPE3JX0OjJIDhMi7mRknk7C91pMrSfH1FBAaKaN1aD4UCSE5u/oH4DRvKEBnZ8DUE5RQdcn+Gctn6+VsS/g2Uj0WThbFosl/MnzxNzV9Skgrx8epa65NCQ/WYM0d6n6am7C91pGrv3lIUFwsoKgIKCwUUFRk+CguFq20KXMzGOGtWBe69V4vAQBFqdeNi6NxZj9Wry5CZqUBGxo0fQoProa7n7i6id289evTQoXt3PXr00KNbNz1CQkRs2dK0okMUgeJiYORIT1y4IH+kqynkjlbaE7nTm5aYHnX2v4HWyCGLpkZi0WS/mD955OTP+MeqqZdxaer+ckcppI5WbdxYVu+6LLkx7NunxF13NX20zN3dML1YVdVwsry9Rdxxh7amYCwsFFBYCBQVCZKnJU29fjkskX8jW67Jkju9aKnpSWf+G2itHLJoaiQWTfaL+ZPHUfMnZ6TLUiMdzTla1rq1HvffX41Tp5T4+28FMjMFiKJ1h11uvVWL55+vwrBhOrg0MDDXmKKlosKw7ZdfumLnTvPTm48/XoUXXqhCx471vw3Zck2ULaeob+Sov8NyWTOHLJoaiUWT/WL+5HHk/MkZ6bLUH1xRBA4eVKGkxB3e3uUYPNh8q4OmPH95OZCersCpUwps2KDCL7+Yn17s1Mmw8Nx41qC/v+HzlSsCvvhCbXZ/I19fEXfeqUVMjBa33aatORPQXNEiikB6uoBff1Vh924VkpOVKC9vfOHXoYMeUVE6DBumRVSUDh07ijZdEyWl6PX31+OFF6rg5gao1YaTD9zcAFdXQK0WMXOmG3JyLDM9aYnRYkfrE2bpKV4WTRbGosl+MX/yOHP+5K7LMrL2ujC501tS3nA8PUUIAlBSUvsdx91dxMiRWoSE6LF8uWu9RYsgiLj1Vi3OnKn/TEd3d8PbiqkCSqkUG5xGDAnRo6hIqBPb9ZpzTdRPP6nwwgvuln/gG0idHpVz/Dnq2Ys7dijx8MOWmeIFWDRZHIsm+8X8yePs+ZO7Lguw/rowS/yXLWWk5o47tNi3T4lfflFhyxYVLl9uequHXr10uO02HUaN0mLoUB127jT//L1765CcrERysgr795vvRH8jS62JEkXgxAkFNm1SYdMmFU6eVNb/YDcwVfhJ8eCDVZg1qwpt2zb8Nmwc6SwuNox0DhnSPCOdpp7fGgvhRRE4fVqB7duV2L5dhdRUpaTp6i+/LMekSeZfB4smC2PRZL+YP3mYP/lskUNLvOk1ZqRLpwMOHjQUUD//rDLZY8po+HAt7r23GiNH6uptitqY5xdF4Nw5AcnJSqxe7YLUVPNrokaO1OLuu6tx8806hIUZutPf+PwNjbSMG6fF8ePXCqVTp6QVStfbuLEMQ4fqUFUFVFYCFRUCqqqA1FQFnn9e2okAgiAiKkqHiRMN06Nt2lzLo5yO9pZa09ecC+Grqw0F1fbtKmzfrpJ0JYEbcaTJRlg02S/mTx7mTz5b5dAS04tNGen6+WcVnnnG/PSUlP/ym7ImrCm9unx8RAwYoMPNN+swcKAO+fkCXnml/qJTEES0aSMiN7f2m7RSKWLYMB3Gj9fi889dcf588zV4dXUV65wdqVAYnn/iRC3UahEvv9y4NXFnziiQnq7A7t1KfP+9+WtD9u+vRbduhly0batHmzZizceRIwrMmNE8C+EFQcTgwTqcOKFEcXHd+wcN0uHOO7X49lsXZGVxTZNdYtFkv5g/eZg/+WyZQ0tMLzaWJVsGAM3Tld7DQ4S7u4grV+R1j1epRAwfrsOECVqMHatFq1aGt0RrnD130006bNzogg0bVDh+/MaRLhFAwz/oVq30mDxZi/R0Q6F0/rx1z7708hIRG1sNFxfAxQVQqQAXFxEqFaBUAl995YqCAmnxeHgY1shFR2tx++26mulKnj1nx1g02S/mTx7mTz5ny6G1z1yqj5Q3zHHjtLh4UcDvvyuvfihw9KgSZWXS3qxffLESzz9fhYCAhmOwVoPXU6cU2LhRhY0bVfjrr8ZPFV7PxUVEdbX5HAwapEVVlYC8PAGXL0tryGpJ48ZVY8qUakRF6eDmVv821jqZg0VTI7Fosl/MnzzMn3zOmEN76DPUlDdMrRb4z39cMG9eA+/C15E6vWjtBq+LFrlg7lzz8fv66tG3rx5duhg6yXftavg6JEREVFTjil6dDrhyxVBArV+vwmefmW9Z0a6dHu7uQHW1Ie+GzwLKyoDKSsst4rbGyRxSi6bmv7iSCefPn0d8fDyOHj0KDw8PjBkzBq+99hqUytpV9tSpU3Hw4MFat+l0OsTGxmLBggV44403sGnTplr7hYaGYuPGjVZ5HURELU1MjBbLllVY5L98OTGMG6dt1BumSgUMHiytMKtv8fqNBAEmrw/YHPsPGiQt/m+/rWhwejQurtJk0TtnTmWtPCqVqFnPVFysw2efmX/+xYvrf36p07tS8g/I/xlYkk2LphkzZqBPnz746KOPkJ+fj6effhqBgYGYNm1are2WL19e6/uKigrExMRg/PjxAACNRoMZM2bgmWeesVrsREQtXVOKFktryhtmRIQOnTvrzY60hIfbxxvxjSwRv5yiV+7zO3r+TbFZ0ZSWloaTJ09i5cqV8Pb2hre3N5544gmsXLmyTtF0o0WLFqFPnz4YNmwYAEPR5OvrKysehUKAQtE8fwmUSkWtz9Q4zJ88zJ98zp7D4cNFGBYmA0Djc2CL/MXHV+Hxx9UNjrTMnVsFFxf7/XlaIv7YWD0mTixHSooCOTkCgoNFRETorxa9pveV+/z2ln+LHYOijaxevVq84447at125MgRsXv37mJxcXGD+128eFEcMGCAeOHChZrbxo8fL06bNk0cPXq0OHToUPGpp54Sz54926h49Hp9414AERHZtZ9/FsWuXUXRsCrG8NG1q+F2R2Dr+OU+v63jbw42G2kqLCyEj49PrduMo0UFBQXw8vKqd7+lS5di7NixCAkJqbktJCQEwcHBWLhwIQRBQEJCAp588kls3rwZrq7me1UAQH5+abOONPn4uEOjKYdO5xyLSC2J+ZOH+ZOPOZTHVvkbORI4cAD1jrQUFFgtjCYzxn/ggAoajRq+vpUYOlRrtfjl5s+e8m/uGPT395T0ODZd03Qj8eqJfEIDE+YajQaJiYlYu3ZtrdsXL15c6/v4+HgMHToUhw8fRmRkpKTn1utF6PXNeyKhTqd3mjNvmgPzJw/zJx9zKI+t8jd06LXn1DneMhpERGjh769GQYHWIfNnT/mXewzabEI3ICAABTeUmkVFRRAEAf7+/vXus2vXLgQHB6N79+4mH9vLywt+fn7Izc21WLxERETk3GxWNPXp0wdZWVnIz8+vuS0tLQ1du3aFp2f9w2Q7d+7EiBEjat1WUlKChIQE5OXl1dyWn5+P/Px8dOjQoXmCJyIiIqdjs6KpV69e6NevHz788EOUlJQgPT0dK1aswIMPPggAGDNmDA4dOlRrn+PHj9cphLy8vHD48GEkJCSgsLAQRUVFmDdvHnr27ImBAwda7fUQERFRy2bT8y0/++wzXLlyBaNGjcLTTz+NSZMm4aGHHgIAZGRkoKysrNb2ly5dQps2beo8zqJFi6DT6RAdHY2xY8dCp9NhyZIlUNx4yWsiIiKiJuJlVK7iZVTsF/MnD/MnH3MoD/MnD/Mnn6Uuo8KhGCIiIiIJWDQRERERScCiiYiIiEgCFk1EREREEnAhOBEREZEEHGkiIiIikoBFExEREZEELJqIiIiIJGDRRERERCQBiyYiIiIiCVg0EREREUnAoomIiIhIAhZNRERERBKwaCIiIiKSgEUTERERkQQsmprR+fPn8dRTTyE8PBy33XYbFi5cCJ1OZ+uwHMptt92GPn36oG/fvjUf8+bNs3VYdmvv3r2IiorCzJkz69y3f/9+3HPPPRg0aBBiYmKwfv166wfoABrK4fnz59GjR49ax2Lfvn2xZcsWG0Vqny5evIjnn38e4eHhiIiIwD/+8Q9oNBoAPAalaCh/PP6kOXHiBB577DHcfPPNiIqKwssvv4xLly4BsNDxJ1KzmTRpkjh79mxRo9GImZmZ4ujRo8WvvvrK1mE5lEGDBomHDx+2dRgO4csvvxRHjx4tPvDAA+LLL79c676cnByxf//+4po1a8SKigoxOTlZ7Nevn3jkyBEbRWufTOXw2LFjYu/evW0UmeOYOHGi+Oabb4olJSViXl6eePfdd4uzZs3iMShRQ/nj8WdeZWWlGBkZKX7xxRdiZWWleOnSJfGhhx4Sn3vuOYsdfxxpaiZpaWk4efIkXnvtNXh7e6NTp0544oknsGbNGluH5jB0Oh1KS0vh6+tr61AcglqtRmJiIjp16lTnvs2bNyM0NBT33HMP1Go1IiMjcfvttyMxMdEGkdovUzksKirisWiGRqNBr1698Oqrr8LT0xOtW7fGpEmTcOjQIR6DEpjKH48/88rLyzFz5kxMnz4drq6uCAwMRHR0NE6dOmWx449FUzM5duwYQkJCah3kvXr1QkZGBkpKSmwYmePQaDQQRRGff/45RowYgeHDhyMuLg6lpaW2Ds0uTZkyBd7e3vXed+zYMfTq1avWbT179sSff/5pjdAchqkcajQa6PV6PPPMMwgPD0d0dDRWrlwJURStHKX98vHxwYIFCxAYGFhzW1ZWFtq0acNjUAJT+ePxZ56vry/uvfdeqFQqAMDZs2exfv16jBs3zmLHH4umZlJYWAgfH59atxkLqIKCAluE5HCqqqrQu3dvDBo0CNu2bcOKFSvw+++/45///KetQ3M49R2Pfn5+PBYbwcXFBZ06dcLDDz+MvXv3Yvbs2fj88885UmJCWloaVq1ahWeffZbHYBNcnz8ef9JdvHgRffr0QXR0NPr164cZM2ZY7Phj0WRFxv8IBEGwcSSOoW3btvj5558xZcoUuLu7o2vXrnjttdeQlJSEqqoqW4fn8ERR5LHYCKNGjcIPP/yA4cOHw9XVFcOHD8f999+PdevW2To0u3T48GFMmzYNr776KqKiourdhsdgw27MH48/6UJCQpCWloatW7fizJkzeP311+vdrinHH4umZhIQEFCngi0qKoIgCPD397dRVI6vffv20Ol0uHLliq1DcSj+/v4oLCysdVthYSGPRZnat2+PvLw8W4dhd3799Vc8/fTTmDVrFqZMmQKAx2Bj1Je/+vD4a5ggCOjcuTNmzpyJzZs3Q6VSWeT4Y9HUTPr06YOsrCzk5+fX3JaWloauXbvC09PThpE5jhMnTuD999+vdVt6ejpcXV3Rpk0bG0XlmPr27Vtn7v7PP/9E//79bRSR49m2bRt+/PHHWredOXMGHTp0sFFE9un333/HP/7xD3z66aeYNGlSze08BqVpKH88/szbv38/oqOjodfra25TKAxlTlRUlEWOPxZNzaRXr17o168fPvzwQ5SUlCA9PR0rVqzAgw8+aOvQHIa/vz++//57fPXVV6iqqkJGRgY+//xzPPDAA1AqlbYOz6FMmDABFy9exJo1a1BZWYk9e/Zgz549uO+++2wdmsNQKpVYsGABkpOTodVqkZKSgrVr1+Lhhx+2dWh2Q6vVYvbs2Xjttddwyy231LqPx6B5pvLH48+8Pn36QKPR4IMPPkB5eTny8/Px+eefY/DgwRY7/gSRS++bTU5ODv75z3/i999/h7e3NyZNmoQXXniBc/iNcPDgQbz//vs4deoU/P39MWbMGLz88stwdXW1dWh2p2/fvgAMf3gB1JxBkpaWBsCQy/feew/p6elo3749XnjhBYwePdo2wdopczn88ccfsXz5cuTl5SEkJARTp07FXXfdZZtg7dChQ4fw8MMP1/v7uXXrVmRlZfEYNMFc/vbt28fjz4wTJ05g/vz5OHr0KDw8PBAREYE333wTbdu2tcjfQBZNRERERBJweo6IiIhIAhZNRERERBKwaCIiIiKSgEUTERERkQQsmoiIiIgkYNFEREREJAGLJiIiIiIJWDQRERERScCiiYiokQ4cOIAePXqgsrLS1qEQkRWpbB0AEVFTjRo1Crm5uTUX5bzeggULMH78eBtERUQtFYsmInJos2fP5oWwicgqOD1HRC3WqFGjsHLlSjzxxBPo168f7rjjDhw6dKjm/pycHDz77LMIDw/HiBEj8Oqrr6KwsLDm/n379mHChAno378/Jk6ciJSUlFqPf+jQIURHR2PgwIF45plnUFxcbK2XRkQ2wKKJiFq0FStW4KWXXsLBgwcRHR2NF198EVqtFgDw3HPPwdvbG7t27UJiYiIuXLiAuLg4AEBubi5efPFFPPvsszh06BCmTp2K5557DgUFBTWPnZSUhDVr1mDjxo04cuQIEhMTbfIaicg6OD1HRA4tISEB7777bq3bPDw8cODAAQCG0aYBAwYAAKZPn45ly5bhyJEj8PDwwLFjx7BkyRJ4eXnBy8sL06dPx4wZM6DX67FlyxZ07NgR48aNAwBMmjQJarUaoijWPM+0adPg4+MDHx8fDBgwABkZGdZ50URkEyyaiMihmVvTFBoaWvO1j48PvL29kZeXB5VKBV9fX7Ru3brm/rCwMFRXV+PSpUs4d+4cQkJCaj3W2LFja31//f1ubm6oqqqS+3KIyI5xeo6IWjS9Xl/re1EUIQgCANR8vv4+AKiuroYgCHX2vdGN+xNRy8aiiYhatHPnztV8XVRUhJKSEgQFBaFDhw4oLCzElStXau7PyMiAWq1G27Zt0aFDhzrTbV9//TXOnj1rtdiJyL6waCKiFm337t04evQoKioqsHjxYrRq1Qp9+/ZFjx490KVLF3zwwQcoKytDbm4uFi9ejJiYGLi4uGD8+PHIzs7GTz/9hKqqKiQlJeHTTz+Fl5eXrV8SEdkI1zQRkUOrbyE4gJrGlvfccw8+/vhjHD58GEFBQVi0aBGUSiUA4N///jfmzZuHkSNHwt3dHXfccQdee+01AEBgYCCWLVuGuLg4zJ8/H6Ghofj3v/+NVq1a4fTp09Z7gURkNwTx+lNBiIhakFGjRuGpp55i80sisghOzxERERFJwKKJiIiISAJOzxERERFJwJEmIiIiIglYNBERERFJwKKJiIiISAIWTUREREQSsGgiIiIikoBFExEREZEELJqIiIiIJGDRRERERCTB/wPqPXs1iPKSEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(\"seaborn-v0_8\")\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 9))\n",
    "\n",
    "ax1.plot(q_history.history[\"val_accuracy\"], \"-ob\", label=\"With quantum layer\")\n",
    "ax1.plot(c_history.history[\"val_accuracy\"], \"-og\", label=\"Without quantum layer\")\n",
    "ax1.set_ylabel(\"Accuracy\")\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_xlabel(\"Epoch\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(q_history.history[\"val_loss\"], \"-ob\", label=\"With quantum layer\")\n",
    "ax2.plot(c_history.history[\"val_loss\"], \"-og\", label=\"Without quantum layer\")\n",
    "ax2.set_ylabel(\"Loss\")\n",
    "ax2.set_ylim(top=2.5)\n",
    "ax2.set_xlabel(\"Epoch\")\n",
    "ax2.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References\n",
    "==========\n",
    "\n",
    "1.  Maxwell Henderson, Samriddhi Shakya, Shashindra Pradhan, Tristan\n",
    "    Cook. \\\"Quanvolutional Neural Networks: Powering Image Recognition\n",
    "    with Quantum Circuits.\\\"\n",
    "    [arXiv:1904.04767](https://arxiv.org/abs/1904.04767), 2019.\n",
    "\n",
    "About the author\n",
    "================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time in seconds since beginning of run: 1682448676.3245697\n",
      "Tue Apr 25 11:51:16 2023\n"
     ]
    }
   ],
   "source": [
    "seconds = time.time()\n",
    "print(\"Time in seconds since beginning of run:\", seconds)\n",
    "local_time = time.ctime(seconds)\n",
    "print(local_time)"
   ]
  }
 ],
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