Clothes Classification_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.13.1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist=keras.datasets.fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 1s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "#labels 0 to 9\n",
    "(train_images,train_labels),(test_images,test_labels)=fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exploring the data\n",
    "train_images.shape\n",
    "#Each Label is between 0-9\n",
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, it’s time to pre-process the data.\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "#If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have to scale the images from 0-1 to feed it into the Neural Network\n",
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\asojasingarayar\\AppData\\Local\\Continuum\\anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "#Setup the layers\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compile the Model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 19s 317us/sample - loss: 0.4992 - acc: 0.8264 - loss: 0.5011 - a\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 19s 319us/sample - loss: 0.3735 - acc: 0.8647\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 17s 285us/sample - loss: 0.3367 - acc: 0.8765\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 20s 328us/sample - loss: 0.3122 - acc: 0.8853 - loss: 0.3117 \n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 18s 295us/sample - loss: 0.2924 - acc: 0.8919\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 19s 316us/sample - loss: 0.2781 - acc: 0.8972\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 21s 345us/sample - loss: 0.2663 - acc: 0.9019\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.2557 - acc: 0.9050\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 18s 297us/sample - loss: 0.2458 - acc: 0.9081\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 12s 202us/sample - loss: 0.2387 - acc: 0.9102\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x27da8db5ef0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model Training\n",
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 129us/sample - loss: 0.3395 - acc: 0.8798\n",
      "Test accuracy: 0.8798\n"
     ]
    }
   ],
   "source": [
    "#Evaluating Accuracy\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.3418897e-06, 5.6621285e-08, 5.4118578e-07, 6.8990357e-07,\n",
       "       2.4289504e-09, 7.5485551e-04, 7.4890795e-06, 5.0751649e-02,\n",
       "       5.4536895e-07, 9.4848287e-01], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Making Predictions\n",
    "predictions = model.predict(test_images)\n",
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making Predictions\n",
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.3418897e-06, 5.6621285e-08, 5.4118578e-07, 6.8990357e-07,\n",
       "       2.4289504e-09, 7.5485551e-04, 7.4890795e-06, 5.0751649e-02,\n",
       "       5.4536895e-07, 9.4848287e-01], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#highest confidence value.\n",
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, it’s time to look at the full set of 10 channels\n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'green'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let’s look at the 0th and 10th image first\n",
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 10\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Xu7qtILp86utdlcr937OnDkh7tu3b7TP1uv03DW1lLQUp5xySrT9ox/9KMT2evvss8/u1PHtb0Ktle8auhKuyK7SN01EREREBDWMRURERESAJpBK0aFDhzAdzJNPPhntmzFjRognTZpU9Rh2GNOmSHTv3j0qZ7e7dOkS7bNpDDZdYuXKlVG52bNnh9gOl9pV6yAeqp0+fXqIjzrqqKjcwIEDQ/ynP/0pxOm0NbWGkexQ8IEHHhjizp07R+Xq0kK08p00tjQdodp3LJ3Wza7a2L59+2hfQ1fTs9KUpmpsqket4dv77rsvxLauAkydOjXEaf1cvXp1g85DpKl717veFW3bdD1bj2qtElmLvU7VWsVyZ34PRHaGeoxFRERERFDDWEREREQEaAKpFK1atQqrv11zzTVVy9mVpJ566qlon01vePzxx0M8f/78qNxzzz0XYjuTA8RDOHY4Nh0itekYI0eODPFZZ50VlRs/fnyI7dBTLeedd16IX3311Whfjx49QpymSNj0ETukna4UNGzYsB06H5GGSuvJm2++WbGcnYUC4pSh9Ptq0y7skG2t4dZqq9tB9TSLWkO09jckTYO66667qh47TRkRaa4GDBgQbdvrj62/aZ2fO3duiAcPHlz1+DYVsla9UQqg7CnqMRYRERERQQ1jERERERFADWMREREREaAJ5Bg3lF3F7cwzz4z22e3LL798j51TY7v//vv3yOtoBSFpbGl+cLW83XQaM5uXmB6joavn2e1qq9ul27Vyke1Ujk888USI63L0K0lfy66KKdKS2LxiO9WhnfIUGp5jbFe1tTn93bp1i8opx1j2FLWQRERERERQw1hEREREBGhGqRQi0nTZKZcgXsXOTrX4+c9/Pir30EMPhThNP2hoyk+19ImGrqKVvs7atWtDfNppp4X43HPPjcrleR7iNO0jXblSpDmpNn0pwPnnnx/iO+64I8Rp+pRdrTadztRKV7ysdA7wztQKkd1FPcYiIiIiIqhhLCIiIiICqGEsIiIiIgIox1hEGkG6xLrNua215GuvXr1C/PLLL0f77BRPtZZtbqhqeZNpfrSdUq53794h7tmzZ9Vjp3nKCxYs2OnzFNnbauUYv+997wvxT37ykxC3adMmKnf33XeH+Ktf/WrV17LTsNWabjGdzlFkd1GPsYiIiIgIahiLiIiIiABKpRCRRnDSSSdF23bFuLZt24Y4XT3upZde2r0n1ojsSl4AnTp1CnE6Pdtxxx23R85JZHeoNZ3huHHjQmynUEvrQEOnWzzyyCNDPGPGjBDb3w2AxYsXN+h4IrtKPcYiIiIiIqhhLCIiIiICKJVCRBpBmjpgV7Gzd6s3dHi1KUpn1LBDx1u2bIn2dejQYY+ck8jukK7kWM2AAQNC/OSTT0b73njjjRA//vjjIX7Xu94VlbOzUrz55pshTuvUihUrGnROIruq+V6lREREREQakXqMRRrRVVddVXXf9ddfvwfPRERERHaUeoxFRERERFCPsYg0gv79+0fbY8aMCbGddqlW7u3WrVujbZvnaFfi2t3sa9lzGDJkSFTuPe95T4jXrFkT7TvxxBN309mJ7H7pqnPVXHrppSEePnx4tO+CCy4IcZpXbF100UUhXrt2bYg7duwYlfubv/mbBp2TyK5Sj7GIiIiICGoYi4iIiIgA4PbkEGXFE3BuObBgr57EvmeA975XYx1Mn+Fep8+zZdDn2LLo82wZ9Dm2HA36LPd6w1hEREREpClQKoWIiIiICGoYi4iIiIgAahiLiIiIiAANnMfY5e584B7gcJ/5WQ0oPx84xmd+RfL4Bp/5jpWfVfE4O1S+xnEuAR70mV9UYd8o4PtAR2A+8BGf+XUudwOBF4HZZdEnfeb/0eXuAOA+4CDgJp/5m8rj3Ax8z2f+2Srn8H7gKJ/5/2cemw684DN/YQPfwzE+81ckj38V2OAz//XtHWNnytc4zkDgXT7zd5TbI4Ev+MxfsivHlcblcrcNmEFR118ELvaZf6NG+R8Dv/WZv8vl7i/AF33mJ++hc/174KvA4cBx9nVd7v4d+ASwDfiMz/wfy8fHAj8G2gG/B/7FZ9673H0auAx4FXi/z/wWl7uTgQ/4zH++yuu3A/4AnAEcTH39bwM8ClzuM/92jfP/C+W/V7XfwMbkcvcQ8Pc+86t312vI7uFy1wN4uNzsS/G9Xl5uH+czv2WvnFjC5e564B+Ajj7zXc3jbYGfAqMpznuCz/yr5b5PAHVLgOY+8z91uXPAz4ERwH0+818py+bA0z7zv6vy+scAlwLPAHXXvhEU9XIb8Duf+S834lveYS53HwSG+sxftzfPQxpPQ3uMLwQmARdsr2ATdQlwYJV9twBX+cyPBH4N/KvZ94rP/Ojyv38sH3s3MAU4CvgUhMb1ftUaxaV/A26q23C5O5zi3/8Ul7vqqx40XQOBD9dt+MzPAA5yuTtkr52RVLKp/P4eCWwB/nF7T9hTXO5aJQ/NBD5A0Qi15UZQ/PYcAZwD3GSe+z2Keji0/O+c8vFPUtTRZ4F3lxfmrwBfq3FKHwfu8ZnfVm6/4jM/ujzOCOD9O/oedweXO+dytx9wO3D53j4f2XE+8yvrri0UHTPfMteaLRB9znuEy12ljrL7gBMqPP4pYInP/BDgf4HrymP0BL4EHFs+72sud12AMcBmn/mjgDNd7jq43PUHRldrFJe+DHzHZ/4W8++1DPibcjtqFFd5D7tN+Xr3ABe43LXZk68tu892v0Qudx2Bk4DTgfspenRwuTutjFcAR1I0Fv/BZ/XTXJQ9ML8G7vaZ/0Fy3H8FPgQcAPzaZz6r8vrfKF97NXCBz/xyl7u6H5P2wCvAx33mV1d6HDgTOAb4mcvdJuBEn/lN5iUOo/5C/CfgjxQX0Greouidsv92X6NGg8PlbhjFj4LtPfowxYXtcOA8ir+m63qdnirfc1fgEz7zjyXHew9wNfDe5PFDKX6kegFvAJdW6eEf5XI3kaJX7Aaf+R+UDYcbgHGAB671mf9ltceB64HDXe6mAT/xmf8W8BuKBswN1f4tZK96DDiq7O3/bdlYxuXuixQ9Ql+t9kSXuwspLniOopfmSpe7fwIG+cz/W1nmEmCsz/ynXe7+AfgMRW/rUxS9rdtc7jYA36T4A/MLFH9wA+Az/2J5nPTl3wf8wmd+MzDP5W4OcFzZK9vZZ/6J8nm3UTReHyif15rit+At4CLg99vpXf0I5o89c15bXe4eB4aUv3tf9Jk/t3zN7wKTfeZ/XOPf7vMUv0UAt/jM3+hy91/AAjPi9FVgvc/8Nyr9Npaf2QPAn4ETy/d5P8Vn+h813pM0Iy53Q4B7KerF8cC5LnenA1dS1L37fea/VDbIVtT14rrcXQCc5TP/yTK+mqJHdZXP/Oll+RuAk4G2lI1Nl7uzKHp3V1D84TnSno/P/BNVGpvvo75X+FfAjWU8DviDz/ya8rwmAmcDs4B2ZUO/DfA2cC01rrVlg3q4z/zz2/k3ux7oQvGH8UKXu8uB/6Pozd5CMYo0yeXuH4EhPvNfLJ/3UPnvNBm4zbz37/vM/2953f4u0ANYD3zSZ36Oy90vgIUU7YonfOb/vfx9OIeiTkoz15C/Rt9P8UV/CVjlcne02TcG+CxFb8pgigZ0nY4UDaU7KjSKz6b4Eh9H8eUd63J3SoXX7gBM9Zk/GngEqGs83wZcWf71OaPW4z7zd1F88T9S/oW5idhMioYpwN9TNBbrDHK5e9bl7hGXu7r1KP9EMfT1FHCDy915wJRKaRrGScDU5LEJwC8pGsRpKsX+PvPHUfzbRn8wlGktVwHjKwzT3gx82md+LPBFTA914ijgPRQX2Gtc7g6k6KkbDYwCzgL+2+WuX43HrwIeK/9Nv1UedzKgdTuboPLiNo6iXuzocw8E/osixWA0cGyZGnQXxfejzgTgl+VoyATgpLKHZxtFoxOKOj3TZ/54n/lJNEx/4DWzvbB8rH8Zp48DfB14kuKPxL8CF1O9PlD29gz2mZ9fYV97ij+wd+bfbizwMYpGzgnApS53Y4BfUPwb1fkQcOd2fhsPA27zmR/jM7+gbOQfUA7LS8sxAvihz/wYisbwtRQdJWOAk1zuzt3O8zPgTJ/5UcD55WOfApaV15VjgX82o3snAP9Wjpo2VKiTZQ/3Rpe7rlSpq+WI4lKK6+BPy/e4xWf+uRqvcRxQa781GnhPmcr3OYp0wZEUde92l7vWNZ57PNDJZ35k+ZyflY//APhUeT3NgO+Y5wwCzvCZ//dyW9e+FqQhww4XUv/X4C/K7bpG3tM+8wsByp7DgdT3AN1H0Rv5M97p7PK/utSDjhQXg0eTcm9TNB6hqEz3lH9FdvWZf6R8/CcUF5SKjzfg/X0c+I7L3TUUf+3V5XYtBg7xmV9ZXtzudbk7wmd+HWWvUlnZ/gic53L3TeAQigtX+ldjP+rzx3C5OxZY7jO/wOVuIXCry10305t1T/n/KRT/pnVOp/gr9ezyPIKyZ/9d5b9F3cMHVHnP95V/IGxyufszxQ/QycDPy2HkpS53j1D8gFZ7fF2F4y6jesqK7B3tyroJRe/iD9nxz+hY4C8+88sBXO5+BpziM3+vy91cl7sTgJcpGm5/Bf4ZGAs8U34X21F8N6BoJN+9g6//ji5kitGLao/jM387xYgMLnd1F7VxLncfpbhwf8HH+cI9gTXJsQ4t/+08RZ15oOwx3hEnU/T6bizP5R6KYeDvuNz1Lv/o6AWs9pl/1eXuM1T+bXyVoof5yeT4dXVu5Q6elzRdr/jMP1PGxwMT6zpBXO7uAE6hyIWv5q/AbS53d1J/LTmbYoSvLh2yrocVil7PV3fwHGvVyXRxhLo6+enw5Nz9DvhEed09kqLz7dbkedF1czvuLUeUoKhzXytf8zmXuxUUDdlqXgaOcLm7Efgd8FCZEnIsxXW/0nN+lfx+LKPoOJAWoGbDuOyJOAM40uXOA60A73L3b2WRzab4tuR4f6W4EN1h0yvqDg1c5zP/fzt4vo2+GkmZanA2hJSH95SPb6Z8fz7zU1zuXgGGUfxlWOdyigb4iRQN6gnAE7xzOGUTxQ9RnQuB4eVQMEBn4O8o8p2h/t81/TedS9Ezn54HFL3/a8oeuu2+7QrbFWt/jccraUvxXqXp2JR+J1zuthKPFrXdzjFqfQd+SdHbOYuiAejL9JufmN4U601fn8PbUAuJR3IOAhaVjx9U4fH6Ey8ansf6zOcud09T1NX/oOgB/pMpuol3/ju8UqE+Nea/3V3ABylGoH5hyr/jt7FMpdhY4Riqcy2P/ZyrfX/eTvbZ7+GllGkYwHSXu6PKspf7zD9sylGmUlT6Xm1PXZ1cUo62dPCZX1t29Nic5IMoRmXta/4d8DjQDRjmM/8hl7u/lm2FN03RSnWymob8m1Wsuz7zy1xxn9A4ivSu8yl6iJfWuJ6m/2aqhy3I9lIpPkjRAzrAZ36gz/zBwDyKv8i25xqKXoxKw5d/BD5e9nLictff5a53lfP7YBl/GJjkM78WWG1SGy4CHqn2eBmvBzpVOsm61y1zn66myFHG5a6XK2/wcbkbTPHX9VzzvG4UPzy3UeQxvk3RwKxUkV8EhpjX+XuKGSoG+swPpMjX2u7MFBTLSH6AojfgCLuj7EGe54o7++tu3BhV5Tjvc7lrW/7hcxrFHb+PAhNc7lq53PWi6JV4usbjlf5Nh5H8CEqTtBTo7XLXwxWzrGxvaPYp4FSXu55lnbiQ+rp1D0W61YXUj+48DHzQ1K3uLncDduF876e4ueUAl7tBFHXxaZ/5xcB6l7sTysb4RylGqqyvUZ/H2I6ijr5NUWeDcrSmlSvutq9lATCiPJcuFA3sWh4F3u9y194VN9meT9FzD0Vj+AKK37i7ysca+ttI+Z77UsymIy3Tk8DpZV3dn+L78kjZW7na5W5oeU053zxncDmy8BWKe3P6U3yvLi+PgcvdYa64B2hn3U+RngTFH8YPlvEfKDrEupbXlzPNvrqUpSso7jOou25C0emWdtSF6+YOepQydcvl7kiK0aC5FPVkTHltHEyRmhLaAD7zv6K4b+rocnRstStSJXG526/8A6MaXftakO01jC+kuHnOupsKN6hU8VmgrctddDOWz/xSgdUuAAAgAElEQVSDwB3AEy53MyguCpUarhsphjimUPRc1011djFFrutzFLlF23v8x8D3Xe6mVfgxuNDl7iWKHq9FwI/Kx08BnnPFlGp3Af/oM7/KPO8aihvRPMWPzjEUOYhRPnXpUcoKWR73dZ/515P9I1yRu1uTz/xsikp/pytutrM+QjE8NR14nqLBXcnTFENGTwJfK/Ojf02RzzUdmEiRc7akxuPPAVtd7qa73H2uPO7p5XGlCfOZf4uibjwF/Jbiu1+r/GLg3ylu/JpOkfd/X7lvNfACMMBn/unysRco/sh8sKyLf6IYFq3J5e78ssfpROB3Lnd/LI/3PMUNPi9QXHj/2fQ6/xPFSMscihtuHzDHG1M+vy4t4YcUdfRoKg9FP8h2/uj3mX+tPJfnKHIRa81Eg8/8VIrfn6cp/r1vqTuf8n11ovg9WFw+1tDfRijSVZ70md9a6xyk+SpTFa8B/gJMo/i8635jr6T4Hj9MnGv/rfK7MwN4yGd+JsXNaC8D01zuZlLM5tKQm++/SdGg7Oxyt9Dl7upy181AP1fcCHsFxY25lA3K6yhGNJ8Crik7rep8hqIObKJIyWxbnutEn/kNycs/D/RyOz5r041Al/K4twEXlXVkIsVNhjMo8ranl+UHAI+WaVPfo5gJA4oG/xXl9XQmML7Ga55GMV2ktADOvyPLQXYHl7tvA7/xmX9ob5/L7lD2PD4CnKwLtTRHZUP68z7zF+3tc2mI8jfl/nR4XKSlcMUMLct9jVlf9jaXu4OBm33mx+3tc5HGoZXv9pz/JBm+bWEOoZgPWo1iaZbKntw/u3fOr9xUzVSjWFq471JMt9iUHUS8/oE0c+oxFhERERFBPcYiIiIiIoAaxiIiIiIigBrGIiIiIiJAw1a+26169uzpBw4cuMdfd+vW+B6x5cvrF9hp1ar+3pv99qv+t4MtV4vN495///ifvFOn+pmYnNuRtTR23vz581mxYkWjvdjOfobTl05n67bK9+rt32p/RvWpNg2zWFOmTFnhve/VWMfbW3Wylo0b6+fTf/vtt6N96XY1tlzr1vUrxHbs2HEXz65x7Auf476kpX6es2fPDrG9ZqXXL3vda9OmTcXHAd56q/7eulrXW/u8oUOHVi3X2JrC56hrZeNo6Ge51xvGAwcOZPLkdBG33c82hAH+7//qF5rq2rVriNu1qz4Hepcu9YvZpT8K27bVL+61ZcuWEPfuHc/Vf9ppp4XY/njsTsccc0yjHm9nP8MqS20CsJWtTM72/PeiOXLOLWjM4zX087QNzfSCVu2m3p394++JJ54I8RtvvBHts/XL1rvU5s31C3X26lX/23jKKafs1Dk1tr31Ocru0VI/T3vNsp1DBxxwQFTuzTfrF7GzDUH7OMDSpUtDbDuK0rpst3//+z03ZXBT+Bx1rWwcDf0s93rDeG+58847o+1rr702xN26dQtxv37xugTz5s0Lcf/+/UM8bNiwqNyLL74Y4rZt6xfTOuuss6Jy9kfhoouaxfSpIkDcyK01u02txvD69etDPHHixGjf1KlTQ/zAA2HdDg477LCqx9+woX6NgJUrV0blevToEWJ7cf6P//iPqNx73/veEJ933nkhPuSQQ6q8C5GWa926ddH2888/H2L7B2Zq06b6FZJfeeWVENvrIcR/VLdvXz+jqf2Dd3uvJdKYlGMsIiIiIoIaxiIiIiIigBrGIiIiIiLAPpxjnN58Z28OqHVnbN++fUNsbwZI8xnXrl0b4s6dO4f49ddfj8oNHz68YScs0sTUyjGulld88803R9v2Dvd0dglbNyZMmBDiadOmReXsTT92tpk0F9ne2NOhQ4cQp78FCxbU35/xuc99ruJzAK6//voQH3jggYi0ROnNcrZu22tgevO43bb37aQ31dkcZnvtTWduqHUjvEhjUo+xiIiIiAhqGIuIiIiIAPtwKkWa+mCngrFTy3Tv3j0qZ6eXskOza9asicrZoeVqQ0UAI0eO3JHTFmky7He81pRsN910U4hXrVoV7Rs0aFCI7aIbEA+52vm/Tz311KjcPffcE2Kb6pQO7dq6Z+udnQoO4sUD7FzlNsUC4Oqrrw7xrbfeikhLdPfdd0fb9tp50EEHhThNkbCpUTbdKU2ZstO62VQom44IsGjRohBPmTIlxGPHjq39BkR2kHqMRURERERQw1hEREREBNiHUykGDBgQbU+fPj3EdplLG0N8Z7odqk2HkeyQ7urVq0Nc6857keakVirFa6+9VjEePHhwVM6uVJeydc2uEHnooYdG5ez2yy+/HOI0Der4448P8aOPPhridEYJexe+XX46vSt+yZIlIb799tujfXYVy4amnIg0Rbfccku0bVeDtSlOto4C7L9/ffPC/gbY1e0gvsbaVfHs8wGWLVsW4qeffjrESqWQxqYeYxERERER1DAWEREREQH24VQKERFp/vp+vS9LNy6tuK9Phz4s+eKSivtERCrZZxvGaa6fnb7J5jamK3rZqdxq5Q4PGzas4uum+ZFpHpVIc1Frhcg5c+aE2OYQ2umYADp27BjizZs3R/ts3r4tl06NOG7cuBBPmjQpxGlOsH1tG9v7AQA2btwYYjs945YtW6JydgqqZ599Ntpnc4yVV7x7VWsUb2+fNIxdnRLgmGOOCbGdau2tt96Kytlroq2/aT2yddFOj2hjiH9v7NRtIo1NqRQiIiIiIqhhLCIiIiIC7MOpFOkw8MEHHxziESNGhDgdBr3zzjtDbFfxev7556Nyp5xySojtdDL9+/ePytlhpXQaG5HmytYHOwVTmi5hU5XS778dirXpGHYlSYinjzr77LMrPifdHjJkSMVzgHgaNjvMa6dxS9npo0Sau8WLF4c4nYrUTtFmp1BLr6l2OlM7XZv9PYA4zcKmY6RpV/Z5No1JpLGpx1hEREREBDWMRURERESAfTiV4vDDD4+2H3744Yr70iGbI444IsTHHXdciD/1qU9F5Q455JAQH3TQQSHu1q1bVC69c16kJVi4cGGIO3fuHOI0lcLq06dPtG1XnbPDqq1bt47K2bQNO7uMnTUG4hXu7F3t6SwXdgUvO2NFmi41aNCgEPfo0SPaZ1Ok7JCySHNg04lqpfjZNKT0WrZixYoQ25ksZs6cGZWzq1/atIo0haPaCnkijU09xiIiIiIiqGEsIiIiIgKoYSwiIiIiAuzDOcY2fxHi1e5sflWaE2zZvMc0d9JONWXzodKV7uwUUJqCRporm5ebsjmEaT7vUUcdFeI0dzjNMayTTsNm6409frrCls2HtNNC2amp0uPZY6TnbqUrXz733HMhtvmVIs3BSy+9FOK0XtprpZVObWrrlV0xdsyYMVE5u7LegAEDQpzm5ttrp66Vsjupx1hEREREBDWMRURERESAfTiVIh0OsqkVdgUfO60TxOkTo0ePDnE6jLRp06YQ2+HYdHg4HaYSaY7mzp0bbdtpl2y60MaNG6Nytt7YlSQhTneotepctRXy0jppV+my+9Jj29e1vwv2PUGcPpWmSM2bNy/ESqWQ5mbWrFkhTqdrs3XY1rc01ahXr14Vj33CCSdE29OmTQuxrZdpeqLdpykQZXdSj7GIiIiICGoYi4iIiIgAahiLiIiIiAD7cI5xunylzStOcwktuy+ddsayuYn2tdJpZpRjLC3Ba6+9Fm3bKQrTqcysBQsWhHjgwIHRPptHaHPzbZ4/QKdOnUJs65M9dnoeNic4XV7WvpadujG9L8G+VlqP7RRUIs3NnDlzQtylS5don71nxn7v0/txLrnkkorH/vjHPx5tf//73w9xrd8Km8+cTtko0pjUYywiIiIighrGIiIiIiLAPpxKkQ7F2CEhOy1MOuVTtTSLNDXDTvlkh2DT19WQkLQE6TCqTU3q3LlziNMpmNavX1/xORCnTNh6kqZS2OfZ46fDsjblYvXq1SFOUynsVIv23JcvXx6Vs0PM6WtNnz4dkeZq3bp1IU6vbfaaaK9zNgb47Gc/W/HYxx57bNXjVZt6EeI0RF03ZXdSj7GIiIiICGoYi4iIiIgA+3AqRc+ePaPtasM59g5ceOewax07TAvgva/4nP79+0fl0uFjkeZow4YN0badUaJbt24hTmeKeN/73lf1GLZO2lSnNB3Dbtvh3HQ1umor6aXpUra+Dh8+PMT33XdfVM7W3XRWCpuOIdLc2LqTpg/a+mK/53379o3KDR48uEGvZa/F9trbvXv3qNzKlSsrvq5IY1OrTEREREQENYxFRERERAA1jEVEREREgH04x7hfv37Rts0ltvnBdgU7eOeUNHXSKaTsFG12yqdaK/uINFc2ZxfiKZ7SXF9rxIgRIX7ssceifdWmRkzz8tesWRNim8+clrN5wPacbH1PDRs2LMRpXqN9Xrqi5dq1a6seU6Sp69GjR4jTa5tl7ws455xzduq1bG6ynYYtvQ9o1apVIdZ1VHYn9RiLiIiIiKCGsYiIiIgIsA+nUrRv377qth3CTYds7HCOZVMnIJ5Cyg6z2iEqkebMDrGmKUbbtm0LsU05SKc1O/DAAyuWS9mUpjQ1Y+PGjSG29Sudhs1u2+nkUvbchwwZUvEc0nLp+7dDzDaulh4i0pTY76ldJRLiej9nzpwQf+Mb36h6PHsdTVOcBg0aFOKFCxeGuFevXlE5W99sOZHGph5jERERERHUMBYRERERAfbhVAp79yvEqRB22Ce9Izcd3qkzdOjQaNvewV5txS2R5mzFihUhTtMgbNqCHQJNUyls/Urrmk2ZsLPGpOkINg3K1rV0FonevXuH2Nb/9NztPpvqUWuVSjsLB8Tvf8mSJSG2qRkiTZVN/0uvWTY1yNYdO8NMyv4GpPXoiCOOCPG8efNCnK4mu3z58hDb2WdEGpt6jEVEREREUMNYRERERARQw1hEREREBNiHc4xTNr/RTsmW5j1Wy21K86tee+21EK9bty7EaS6iSHNlV5xL60nbtm0rljvkkEOicjaP0E67BtCnT5+Kx0+nULQ5wTYfMs0xtuVs/nI61dr69etDbPMp7fmkx7M5lBDnXi5btizEyjGW5mDkyJEhfuqpp6J9to7Ze2vsCnapWvn548ePD/F3vvOdEKfTI9pc/e7du1c9nsiuUo+xiIiIiAhqGIuIiIiIAEqlCFauXBliOzz0wAMPROUuu+yyis8/+uijo+2nn346xP379w9xOgws0lzZKcnSKdTsdE+zZ88O8fDhw6Ny9nnpinZWrVXm7HnY103TluwQsD1eukKeTaWy0zja4WWIUy7SFCt7TJuOIdIcTJgwIcQ/+tGPon22nto0wYkTJ0blzj777BDXWtXS/iYcfPDBIU7TL+wxbN0TaWzqMRYRERERQQ1jERERERFAqRTBI488EuI5c+aEOE2luP322ys+/8gjj4y27XDsd7/73RCPGjUqKjd27NgdP1mRJsCmH6VpEHZGiLVr14Y4/f7b1azssCzE6Qg2fWLz5s1RObvynT2PdCjWnpNNaUpX47OzTbz66qshPvTQQ6Nyjz/+eMVjQzw8nL4vkabO1oG0ftjUIFsuvTbaVIpaaVI9e/YMsZ15YsGCBVVf1856I9LY1GMsIiIiIoIaxiIiIiIigBrGIiIiIiLAPpxjnE4fY6dvsjnGduo2qJ7blOZQ2bxKO3VbukKYSHM1derUEKc5tnZ76dKlIU6nNZs8eXKIba4wxDnCNk5XmWvTpk2Ibf1Ky9ltO62bjSGuy9OnTw9x586do3J2Orj0/dtVu+x7/OAHP4hIc5JON2i/6/Z6aK9zO8tOqThlypRon73PIK1vIo1JPcYiIiIiIqhhLCIiIiIC7MOpFOlqV1u2bAmxHaZJh1mrsc+HeNjHplWkq3aJNFd2VTg7BArw+uuvh9iuUpVO12ZTFbp27Rrts+kIVpoGZadvs+kSdiopiFfZs+kXaTn72zB//vwQn3feeVG5T3ziEyH+0Ic+FO2zaSH9+vV755sQaSZOOumkaPuOO+4Icffu3UNs69TOGjhwYIhXr14d7atWz0Uam3qMRURERERQw1hEREREBNiHUylSdhjIrlRlh4trSVcHsne22/SJvn377uwpijQpH/vYx6rus3eyz507N8Tp6nH33HNPiNMZK+wx7Ep1acrFihUrQmxTmtL0DjtjhY3TFfJ69+4d4ieffDLEl112WVTOrtpn0zRAK3NJy3HFFVdE23fddVeIbd1Zs2ZNVM7W+8GDBzfotTp16hRim4IF8W9A+lsh0pjUYywiIiIighrGIiIiIiKAGsYiIiIiIoByjAO7ipXNTWxormA6VY2dUsrmRjXGlDYiTZ3NuT3qqKNCnOYNrly5MsR26ieonpufTuNmj2HrXVrXbD6knfqpVp20rzVt2rRo3/jx46s+T6Sl6N+/f7Rtc/ztfQDplKV2JbyG5hhXW8US4jqbvpZIY1KPsYiIiIgIahiLiIiIiABKpQiWLFkSYruqjk2DqCWdrskO29rj2ZQNkZYiXY3O1hu7stykSZOicnZaw5RdPc4eb86cOVG5asO0tk6nx7DpUumUjLaO2mHkRx99NCpnUynS95+urCnSnNjvc/pd/tu//dsQ33333SFOU5Luu+++EF9wwQUNel17HV20aFHVc2rodVlkZ6jHWEREREQENYxFRERERAA1jEVEREREAOUYB3369AnxsmXLQmzzI2tJl6isNjWUXW5WpKVI8xCr1ZvZs2dH23bqJ1tPIM4/ts8bNGhQVM7mCL/++utVj2fzEjdt2hTiND/Y5kraOM1ZttL3XytHc2+56qqrqu67/vrr9+CZSFNX7R4BiHPr7fLQ6f0zCxcu3OHX7dKlS4jTKdnsNXbVqlU7fGyRhlKPsYiIiIgIahiLiIiIiABKpQjGjRsX4smTJ4e4oakUnTp1irbtkJCdGmrAgAE7e4oizYadotDWoQULFkTlbLrDsGHDon32ecOHDw9xukLeCy+8EGKbtmBXzoM4NcPWV1tXIR7CteeXrrhn9x1wwAHRvqaYSiHSUDYVMHXyySeH2E5nuGbNmqicTT2aPn16iEeNGlX12J07dw5xWt9at24dYpuCJdLY1GMsIiIiIoIaxiIiIiIigFIpgrZt24bYpj40NJUiZe96t0NCBx100E4dT6Q5qZY+8J//+Z/R9n//93+H+IEHHoj22aFZOxNFulqerWt21pfVq1dH5datW1dxXzrbhB2m7dmzZ4ivuOKKqFyaPmHVGooWaeoamv5zyCGHhHjatGnRPpv68Kc//SnEtVIp1q9fH2Jbr1NLly5t0PmJ7Az9eouIiIiIoIaxiIiIiAighrGIiIiICKAc4+CjH/1oiCdNmhRiO43bjjjvvPMqPj5y5MidOp5Ic1ItxzZdHeuaa66peoxXX301xHZKtjS/0OYO2xW7Ujbn0cY2TxLgpJNOCnHHjh2rHk9kX/flL385xH379o322Tp26qmnNuh4EyZMCLFdjRbi3P8zzzxzh85TZEeoYSwiIg2mpaVFpCVTKoWIiIiICODsCk175QScWw4s2G5BaUwDvPe9Gutg+gz3On2eLYM+x5ZFn2fLoM+x5WjQZ7nXG8YiIiIiIk2BUilERERERFDDWEREREQEUMNYRERERARo5OnaXO56AA+Xm32BbcDycvs4n/ktjfl6O8vl7nrgH4COPvNdzeNtgZ8CoynOe4LP/Kvlvk8AdfMU5T7zP3W5c8DPgRHAfT7zXynL5sDTPvO/q/L6xwCXAs8AV5QPjwBmU/yb/c5n/suVnrunuNx9EBjqM3/d3jwPaVpc7r4MfJjie/o2cJnP/FMud/OBY3zmVyTlzwNG+My/Yx4vl7vTgC0+849X2NeFoi4eQvE79XWf+R+VdfRR4IDy8bt85rPyOf8FjAOm+cx/tHzsIqC7z/y3q7yffsAPfObPdblrD/wAOApwwBrgHKAn8Fuf+SMrPP//AY/6zD9UYd8lwIM+84vK7V8AX/GZf7nSuci+Q9dKXSul6WrUhrHP/EqKioLL3VeBDT7zX7dlygrifOarz8TfiFzu9veZ35o8fB/wXWBm8vingCU+80Nc7v4BuA74iMtdT+BLwFigFTDZ5e43wKHAZp/5o1zuHi9/RLoCo+su1lV8GbjaZ/554JbyPBcCf+Mzv6aB72G3cbnbH7gHeNbl7htN5Uda9i6XuxOBc4GjfeY3l/WiTa3n+MzfD9xf4Vj7A6cBG4B3NIyBfwZe8Jl/r8tdL2C2y93PgM3AGT7zG1zuWgOTXO4eAF4E3lXWxZ+53I0E5gCXUDRuq/k8RWMY4F+ApT7zI8tzPAx4azvvr+IKJS53rcrXngksKh/+HvBvFBd62YfpWqlrpTRde2SBD5e7IcC9wCTgeOBcl7vTgSspembu95n/UvklW1H3l6nL3QXAWT7znyzjqyn+SlzlM396Wf4G4GSgLfAdn/lbXO7OoviLdQVwBBAtN+cz/0T53NT7qP9L91fAjWU8DvhDXUV0uZsInA3MAtq53O1H0UB4G7gW+EqNf4suwPCyotf6N7se6AIMBRa63F0O/B/Fj+kW4F985ie53P0jMMRn/ovl8x4q/50mA7eZ9/59n/n/dbkbRvFD1wNYD3zSZ35O2Zu1EDgGeMJn/t9d7h6naFS8o2Ej+6R+FPVzM0DaOwx82uXuvUBr4O995meVvabH+Mxf4XL3Y2AVMKb8/0nAtvLC+mmf+cfMsTzQqWwcdCzLb/WZ9xSNacrXaV2WfRtoU5ZvR9Gg/VeK34Rajdu/o6gvde8vTKPkMz8bwOUOoJXL3Q+AdwGvA+/zmd9Uvqff+szfVfaa30rx2/B9irr0M5e7TcCJwGPAj/f0xVuaD10ro38LXStlr9iTOcYjgB/6zI+hqODXAqdTXCRPcrk7dzvPz4AzfeZHAeeXj30KWOYzfxxwLPDPLnd167ueAPxbXe9PA/UHXgMo//Lb6HLX1T5eWgj095mfASwFplIMK42gGBp+rsZrHAfU2m+NBt7jM38J8DmKXoWRwMeA28ses2qOBzr5zI8sn/Oz8vEfAJ/ymR9L8W/6HfOcQRS9cf9ebk8G/qaB5yot34PAwS53L7nc3eRyl67zusJn/miKntEvVjnGMIoL+N9RNB6/5TM/OmkUQ3FBOpyit3UGxcXtbSh6Y13upgHLgD/5zD/lM78euBt4FpgHrAWO9Zm/r9qbcbkbBKyua+hTNGqvdLl7wuXuWpe7oab4UOB/feaPoEix+Lsqh33TZ/5kn/mfUtSfj5Tvb1N5/nOAUdXOSQRdK+voWil7xZ5cEvoVn/lnyvh4YGJdj5PL3R3AKcAfajz/r8BtLnd3UgxdQPGX6OHlX8hQ/1cjFH/JvbqD5+gqPObLx9MJnz2Az/ynw5Nz9zvgEy531wBHUvzlfGvyvH7U55Jtz73mon0y8LXyNZ9zuVtBUTmreRk4wuXuRuB3wEPlMNexwL1lL1jqV8mw3TLgjAaeq7RwZfrCWIoLwOnAL13urvKZ/3FZpK5eTgE+UOUwd/rMb2vAy70bmEbx/TsU+JPL3WM+8+vK548uL8S/drk70md+ps/8DRS9Yrjc3QJc43L3SYrfied85q9NXiOqiz7z01zuBpflzwKeKdNHNgHzfOanmfc3sMp5/3I772sZcGB5DJFKdK0s6Fope8We7DHeaOKK3zSK4RW7r62JL6X4q20gMN3lrltZ9vKyR2a0z/wgn/m6Gxrs6zXUQuBgAJe7NkAHn/m19vHSQdTnDVKW/zuKXMluwDCf+Q9RVHz7HqC4yKaPVdOQf7OtxJ9jWwCf+WUUPVOPA18A/rc8xlLz7zXaZ350lderO9amBp6r7AN85rf5zP+lzAu8grjntO7CtI3qf3Q3tF5+DLjHZ977zM+h6AUenpzLGuAvJDnELndjyvAl4KNlXTwy6QGGCnXRZ36Dz/w9PvOXU/RsjU/eG+za+1Odku3RtbKga6XsFXtrurYngdNd7nqU+UsXAI+Uf4GtdrkbWuYinW+eM9hn/kmKnKTVFEM2fwQur8uBcrk7zOWu3S6c1/3AxWX8IYqhYyj+Oh/nctfVFXcTn2n21f0wXAF8E2hP8aMFxc0H6QX0RWDITpzbo8BHytc7kuJO+bnAfGCMy50re7vGlGV6A/jM/wr4KsUNU8sp/n3PK8vs53J3VI3XHMY7b7qQfVRZv2zjcjS7trTpeqBTlX2vUtQzXO76AIcBc13uepU9xZR1/SyK/EXra8A1FPnHrcrH3qaom9ZLmJ5fl7uTykZEXZ0eQeO/v2FAzZxJEUPXyh2na6Xskr3SMPaZX0hx4foLxXDpk75+upYrKSrXwxR/fdb5lsvdDIp8w4d85mdSJNi/DExzuZtJkdu43fQQl7tvUlSSzi53C13u6m6+uRno53I3h6Lyfqk83+UUd91OBp4Crin/Oq7zGeAWn/lNFDlUbctznegzv4HY80Avl7sO2zvPxI1Al/K4twEXlTfwTKS4cWIGRS7a9LL8AODRMhfzexR390LxI3aFy910ioo8nupOA36/g+cpLVdH4Ccudy+43D1H0XD86i4c7zfA+S5301zu0vy8rwHvKr/vDwNXlsPJ/YA/l6//DEWO8W/rnuRy937gGZ/5RWWP8hPlMbzP/HT7Aj7zG4FXXHHDExQpG4+U5Z+lqO9378L7+zHw/fL9tSsb+Jt85hfvwjFlH6Jrpa6Vsuc579N0INndXO7+FVhucjObHJe7g4GbfebH7e1zEdldXO7OB8b6zF+93cK7/lqfA9b5zP9wd7+WSEuga6XsDVr5bu/4LtuZH7UJOIhiuiuRFstn/tcUPWJ7whrgJ3votURaAl0rZY9Tj7GIiIiICOoxFhEREREB1DAWEREREQHUMBYRERERAfbsyncV9ezZ0w8cOHBvn0Zk27b6hblatWoV7du8uX6e/61bt4bYuXhOcbvdrt2uTBfZ+ObPn8+KFSuqTYK+w5riZ2hNXzqdrXhFOp8AACAASURBVNu2Vty3f6v9GdWnea/QO2XKlBXe+16Ndbym/nmuXLky2t64sX6ufXvPRFp327atXyugZ8+eu+nsdt6+9jm2dPo8WwZ9jrHmfD1t6Ge51xvGAwcOZPLkyXvktexFM23IWqtXrw5xt27don2vvPJKiFesWBHi9CJ8wAEHhHjkyB1Zgn73O+aYYxr1eHvyM9wZVZb0BGArW5mcNd1zbwjn3K4sQvEOe/LzfPvt+lVV0xuB0zpV57bbbou2n3jiiRDbP1bTujt8eP3CeR//+MernlNDfyeqPWdHnpc8p9l+jvJO+jxbBn2OseZ8PW3oZ6lUChERERERmkCP8e5kUyIg7oFKe3hsD+9bb9VPm5imQWzaVL8ceteuXSs+B6B169YhvvTSS0N8ww03NOjcRfYF++3XsL/Nn3vuuRBffPHF0b4TTzyx4vFsHQT41re+VfEYac+07e1taO/xzvQQS+Po+/W+LN24tOK+Ph36sOSLS/bwGYlIc6YeYxERabaqNYq3t09EpBI1jEVEREREUMNYRERERARo4TnG1e5qB/jlL38ZbV9zzTUhtvmMd955Z1TuX/+1fkn0Z599NsQPPfRQVO6ss84K8eWXXx5ie9c8wP77138EO3M3vEhLMWvWrGh76dL6YfDevXuH+KmnnorKZVkW4rVr14Y4vT/glltuCfGjjz4a4kmTJkXlrrzyyhC3adOmQecuIiItg3qMRURERERQw1hEREREBGjhqRS12BQGgAMPPDDEV199dYjHjx8flfvDH/4Q4nnz5lU9/k033RTihq5yo/QJaemmTJkSbd97770hXrRoUbTvpJNOCvGaNWtC3L1796jcYYcdFuJly5aFOE2lGDWqfkWmLVu2hLhz585ROTul4qmnnhriww8/PCrXFFfPExGRXaMeYxERERER1DAWEREREQGaaSpFumqdTUGwQ6RTp06Nytnh2DfffDPaN2fOnBDPnDkzxL///e+jcna1u379+oX4pZdeqnq+s2fPDvHmzZujfTaFw66e16dPn6hcQ1cIE2lq7CwPZ555ZrTPpiPYlAiAI488MsTz588P8e233x6VGzt2bIiHDRsW4rSu3X///SF+97vfHeI0ReLJJ58MsZ1txj4O8P73vz/EQ4cORUREmj+1tkREREREUMNYRERERARQw1hEREREBGimOca1pjV74YUXQvzMM89E+2wOo81FBBg9enSIX3/99RBv2LAhKmenlxozZkyIV6xYEZXbtGlTiDt06BDilStXRuVefvnlENtVtlq3bh2V09RQ0pzMmDEjxDa397/+67+icnYqw3QKxcGDB1cst3r16qjcxz72sRDPnTs3xG+88UZUbtq0aSE+/vjjq5azef/9+/ev+HyAb37zmyH+3ve+h4iINH/qMRYRERERQQ1jERERERGgmaZS1GKHWYcMGRLts2kRvXr1ivatW7cuxD169AhxmsIwefLkED/99NMhtlNLASxfvjzE69evD3G3bt2icva17JRsNhVDpLmxK9zZ1SJvvfXWqNx9990XYlsXIJ5GbdasWSH+zW9+E5WzdddO67Z06dKonE1bstMh2ukUIU7HsKvsjRgxIir3nve8BxERaVnUYywiIiIighrGIiIiIiJAC0mlsCkSNm3BrkwH8d3xI0eOjPalK+HV6dixY7RtV9az6Q7pLBLbtm0LsZ1Fo3379lE5u23vjk/vlBdpTiZOnBjiQYMGhdjO/gLQpUuXEKd1zaYnLViwIMRpvT7jjDNC/Morr4TYriQJ8UwZNkUqTbmwaRbpMayFCxeGOJ2VRrPIiIg0T+oxFhERERFBDWMREREREUANYxERERERoIXkGK9ZsybEmzdvDnHfvn2jcjaX0E6nBvHqdK1atQpx27Zto3KdO3cOsc0r9t5H5ezUUzaP8u23347K2W2bv5zmNtr3dcABByDSlNkp1F577bUQH3PMMVE5my+c5vl37do1xHYaxjSff+jQoSFeu3ZtiNN8fjstm70Xwb4OxHX+1FNPDfHdd98dlbPTv6UrWirHWESkeVKPsYiIiIgIahiLiIiIiAAtMJWiTZs2IU6HSO2qczY1Id1n0yLsanQQD/e2a9cuxGnKhS1np3VLh3dtesfWrVurnrsdmk5X7RNpaqqlQfz+97+Pytnvcrrao02Fsiva2Tjdtivk2VXrIF7R7pOf/GSIFy1aFJWbNm1aiB955JEQP/7441E5W5fT3xMREWme1GMsIiIiIoIaxiIiIiIigBrGIiIiIiJAC8kxtrmJNsfYTruWlkuXcLW5jjav2C7nnNp///p/PrsENMTTsNnp1exzIM5NTvdVKyfS1I0dOzbEF198cYjTPF2b97tq1apo3+LFi0Ns85TtEvAQ32Ngp2FL66SdUs0u52ynXYN4OXb7O5FONWfzqNN8ZhERaZ7UYywiIiIighrGIiIiIiJAC0mlsFOj1Vq1zk6NZodmIR4WtUOudjU6iNMd7GvZFA6I0zFsWoVdOQ/i6aDGjBkT4jSFI11ZT6QpmTFjRrT985//PMQXXnhhiNOVH+0UhXaFSICOHTtW3JfWyVorRlrVVqNMU5hs3bX1+pxzzonKLVmyJMR//vOfo30XXXRR1fMQaWrSlRttipNNLQJ49dVXQ3zkkUeG+Oabb47K2Tpw4IEHhjit53aqVCv9rUinTq3GXitrpUKKVKMeYxERERER1DAWEREREQFaSCqFHUq1q1Gl6Qd29Ti7qhbEd7bb4Zd0+MYOu9qhnnTIpnXr1iG2w8Wpu+66K8TDhg0LsR16gjhdRKSp2bhxY7Rt0wx+/OMfhzhd+S7LshDb7z9Anz59QmxTJF5//fWo3IknnhhiW1979+4dlbMzRwwdOrRqOZtmdf7554f4xRdfjMpNnz49xEcffXS0T6kUsjtUS6mrljKQzsxi0/8mTpwY4v/5n/+Jyr3yyishTuu2TS869NBDQ2xTFQFOPfXUEH/3u98N8UMPPRSVu//++0N8wgknhLhW6oS95qdpjEqfkF2lHmMREREREdQwFhEREREB1DAWEREREQFaSI7x5s2bQ2ynfknzsWbNmhXidCo3uzqdXSEvzdGy7L40H8rmH9tpp1K//vWvQ/yFL3whxGneVLral0hTMmLEiGj7uuuuC/HZZ58dYrvCJMDdd98d4nQap4MOOijEtn7dcccdUbnBgweH2OZG2pXzAB577LEQ29+J1157LSpnV8+zxo8fH22ffvrpIU7fv8ju1NCpzNLVX6dOnRriG2+8McSHHXZYVG7ChAkhtqtYQjy1qb1n4IknnojK/eAHPwhxp06dQmzvHYA4j3/QoEEhvuqqq6Jy5513XojT66NIY1KPsYiIiIgIahiLiIiIiAAtJJXCTs9iV5azKRYA8+fPD7Ed2knL2qnR7LRrEA9Z2TgdsrJqTSFnp42z01AdddRRUbl06EykKXn55Zej7ZdeeinEtm4sW7YsKmenMkzTlmxKkz1Gmvrw/PPPh9imS6X139Y9O/2bXckLYNWqVSE+4ogjQpwOAdv3/Nxzz0X70vor0hjqrnW1rje12LQIu9qdncpwR1x88cUV49S8efNCfO2110b7pk2bFmKbMmjTsdJj9OvXL8S2vkJcz9PfFHsdrfZ7AHDGGWdUeBeyr1CPsYiIiIgIahiLiIiIiADNNJUiXQXODpfY2SXsSnepN954I9ru0KFDiO3qdmkqRTrkUidd3c6md9g7aNNVuxYtWhTihQsXVj1fpVJIU5amUthZX2yd+dWvfhWVu/7660Ns0xYgvvvdfv9tahLAhz/84RA/++yzFc8B4qHYcePGhdiunAfx0OznPve5iseG+Dck/Z2wK2na9yGys7Zs2RKuETZVCeI60a5duxCns1V89rOfDbFNNXr88cejcvb7m15vbX22196nn346KmdXv7QpjsOHD4/K/e3f/m2I7YqUdlYagHvvvTfEdoaZNC3S1sX0ummv03affb8Axx57LLLvUo+xiIiIiAhqGIuIiIiIAGoYi4iIiIgAzTTHuNZqdDaXKc3DsmweFsS5yfb46Ypzdpocm6NUa+U7m9fUv3//qJydAirN07RsznL6/nd26h6RxjJlypRo207/ZKeFmj17dlTO5vNPnDgx2mdX47L18JFHHonKjRkzJsS2zqd5g/Y8TjnllBCnK3bZewIOOeSQEKc5xrYur1ixItq3fPnyECvHWBpDq1atwiqqad6vnXLQ3quSXpdGjhwZ4h/+8IdVX8vmH6erzNn7cXr37h3iD33oQ1E5u4qdnV5tZ1122WUhtvcP2d8QeOf9Ppadoi2dOtVSnd23qcdYRERERAQ1jEVEREREgGaaSpGyw0V22pqpU6dWfU6aSlFtla10GqZqaQvpcI49p1pDNnVDY/DOYWarWmpGrXMS2VPSKc9OOOGEEM+cOTPEJ598clSuW7duIZ4xY0a0b8uWLSGutmIVxKlFtv7bdIa0nK1D6RSMdujY1s90+kc7LL1+/fponx1iFmkMrVq1CkP848eP38tns3fZlEmRxqYeYxERERER1DAWEREREQGaaSpFOvRph1nt7A12BatUulrOxo0bQ2yHcNMZIOwwa627X216g03TSFMzevToEeJqq+pBw1MzRPaGadOmRdtDhgypuC+dlWXx4sUhTleFtHey21QFewc+xHfh29Xt0pUk7Up1S5curXo8WyeHDRsWYvu7APHKXAsWLIj2rV69OsRdunRBRESaB/UYi4iIiIighrGIiIiICKCGsYiIiIgI0ExzjNNcP5tjbKdNs7m9qTTvb8mSJSG2+bzpynd2RSBbzuY2Q5wvbM8vnWbGnkea62jZ92WPJ9IU/Pa3v422bR78t7/97RC/+93vjsqNHTs2xOkqXUcffXSIX3vttRAfd9xxUbkjjjgixLZupHXc3hMwatSoEKf3Itgp5OyUb5///OejcnZ6xTQ/+ktf+lKIBw4ciIiINA/qMRYRERERQQ1jERH5/+zdebxVVf3/8dcCZBBUQBAQBxRBRSYVzXkoNSuHTCuHnBuszOFXpt/UdltLKYdvZqmlmX2b1MqxbFJTqdScRRQRFBVBFBWVeXD9/ljrbD57cfbhAhe49/J+Ph48+Jyz19l7n3PPOnudtT5nLRERAVppKkU6XVmaxlBjp4ICGDRoUOVj7PRqNm0hXdHO3rZTuTWaQi0dIra23XbbIh4/fnxlOaVSSEt26aWXlm7blfBsOtLAgQNL5WbOnFnE6XSFnTt3LuLail8Affv2LZWzU8DZujF16tRSObtyna3/m266aancvHnzitimbX3+858vlbOr+KV1Ml3hT0REWgf1GIuIiIiIoIaxiIiIiAjQxlMp0lke7EpV6T7sbBM2RSKd2aJqlb10KNVuazQ7Rrdu3eoeN11xz6Z6NFpxT2RNePHFF0u3bRqEfS9vvfXWpXL33HNPEd9yyy2lbY8//ngR27SIG264oVTOrjJnZ6947rnnSuVsioTdX7pq31tvvVXEBxxwQBHbGSqgvHpeOlOOTRHp3bs3IiLSOqjHWEREREQENYxFRERERAA1jEVEREREgFaaY5xKp3mqSXN7t9pqqyK2ObsAnTp1KmKbH5yWs9vSvEIrfVyVrl271j3fOXPmlMrZ6doaHVdkTZg9e3bpts3HtfGoUaNK5ezqdnY6RShPefbUU08Vsc1fBjjyyCOLeNy4cXX3DeVc56OPPrrynOxKeAceeGDdfUN5Grr0+Tf6XYGIiLRc6jEWEREREUENYxERERERoJWmUthpl6A6bWHy5Mml27vttlsRv/TSS6VtdpW8Ll26FHGPHj1K5Wzahh2aTadQs+WqUj3SY7377rt19w1Lr8An0pK8//77pdt22rSJEycW8brrrlsq97e//a2I0/e8rVOvv/56EQ8ZMqTyPOz+hw0bVtpmp5SzK+lttNFGpXJ2Gjb7uWCnVoTydJDp808/o0REpHVQj7GIiIiICGoYi4iIiIgAahiLiIiIiACtNMc4zeetWn42zfOz0zKlS0J37Nix7j7s1E1QzjO0y0Cn0zXZnMN27ZZ8/0jPyU4p1bdv3yK2OZpQXkq3Uc6yyJqQ5vPusssuRTxhwoQiXmeddUrl3nvvvSK2dRDKOfcPPvhgEffq1atU7u677y5iO4XalltuWSr38MMPF/H+++9fxGlds79NGDx4cBHvvffepXLPPvtsEa+//vqlbQMHDkRERFof9RiLiIiIiKCGsYiIiIgI0EpTKezqc+ntqVOnFnG6QtwRRxyxak/M2HDDDZtUzqZ32GHge++9t1TODlWnaRsia9pmm21Wun3PPfcUsZ3WzKYVATz99NNFvPHGG5e22dUfbXpDz549K8/Dplmlq8/Z2zbVKV1l0qZW2JQruzomlKd169+/f2lbOs2jiIi0DuoxFhERERFBDWMREREREaCVplK8/PLLpdv21+szZ84s4vPPP3+1nVNzOP3004t4iy22KG2zK3/Z2TBAw7ay5qWzUlx55ZVF/N///rfycccdd1wRP/TQQ6VtdkVLm2aUpilNmjSpiO2sF2mKhL1tUzrSlCtbn7bZZpsitmkf6e0BAwaUtqXpXiIi0jqox1hEREREBDWMRUREREQANYxFRERERIBWmmNsV5+D8kpwdgWqffbZp8n7tNMyran8wMMPP7yI01XA7Gp8Ii1Nhw7lj5JPfepTRWxXdEwNHTq0bpw66aSTinjHHXcsbbP13075lub99uvXr4iHDBlSWe7ggw+uew7pcW2e8qabblraphzjZTvnnHMqt40ePXo1nomIyBLqMRYRERERQQ1jEREREREAnE0hWCMn4NybwMvLLCjNaXPvfe/m2pn+hmuc/p5tg/6ObYv+nm2D/o5tR5P+lmu8YSwiIiIi0hIolUJEREREBDWMRUREREQANYxFRERERIAWNo+xy11f4IfATsB8YDJwhs/8hOXcT3fgaJ/5qyq2Xw8cBLzhMz/U3N8TuAkYEI/9GZ/5d+K2/wFOBhYDp/nM/83lrhNwO7AJcFXteC53PwOu9pl/ouL4nwSG+8xfEG8fB3wTcPHf9T7zly7Pczb7neAz/2y8fSlwl8/8vcu7L5F6XO7OBY4m1IMPgC/5zD/scjcZGOUzPyMpfwgwxGd+qYlpXe72ARb4zP+nzrYewPXAQGAecJLP/DMud1sT6mjNlsC3feZ/6HL3feBjwJM+88fF/RwL9PSZv6Li+fQDrvWZP8jlbl3gWmA4oR7OBA4EegF/sp8V5vEXAA/4zN9dZ9sJwN995qfG2zcC5/vMv1DvXGTtVlW3mmG/9wHf8Jl/dHnLuNydCpxBqIe9a/Xb5c4BVwAfB+YAJ/jMPx63HRi3tQeuq9X9VVU/feZnLfeLsvRxZvnMd2tKGZe73sCvfOYPXNnjSsvUYnqMY0W7FbjPZ36gz/wQ4FtAnxXYXXfgKw2230C44KXOAe7xmR8E3BNv43I3BDgS2C4+7iqXu/bAR4HHCBX1i7HsCKBdVaM4+iZQa0R/jPDBc4DP/HbADsC7TXqWS/skMMTcvrL2HERWlsvdroQvlDv4zA8H9gNebfQYn/k7KhrFHYB9gN0qHvotwgV0OHAc4UKLz/zzPvMjfeZHAjsSLsq3utxtAOwWy7d3uRvmctcFOIFY1yr8P8LFFuB0YLrP/LDYCD4ZWFj5yHA+365oFLePx97Y3H01oe6LlKxI3VpN/k04l3QWhY8Bg+K/LxLe27X3/U/i9iHAUS53Q9ZU/VwVfObfBKa53O2+uo8tq0dL6jHeF1joM39N7Q6f+SehaDT/gFDZPPBdn/mbXO66EXpsewDrAOf5zN8OjAYGutw9CfzDZ/4seyCf+Qdc7gbUOYdDCRdrgF8C9wFnx/tv9JmfD7zkcjcR2JlQKbtQfh0vBE6pepIud4OB+aZn7X8I39SnxnObR/wgcLkbCVwDrAtMIvSaveNy9wXCh1FHYCJwLDASOATY2+XuPOBwn/lJLncbutz19Zl/veqcRJqoHzAj1gPS3mHgay53BxPq4qd95sfHXtNRPvOnutzdALwNbB//3x1Y7HL3OeBrPvNjzL6GABfH44x3uRvgctfHZ366KfMRYJLP/Msud+sBHeNnRRdC3TwL+JHPfKOL5+HAeeb5FQ0An/nnAVzuIFzMryU05F8DDvWZnxuf05985v8Qe82vBw4g1NtRwG9c7uYCuwJjgBtc7jr4zC9qcE6y9qmsWy533wYOJryv/0PoSfaxl/dhwrWzO3Cyz/yY2OD8BaEOPRcfV9vX1YQR2S7AH3zms0YnVevgiXXAOhT4P595Dzzkctc99u4OACb6zL8YH3djLPtjVlH9jMe5DdgU6Axc4TP/s3j/LMKX6oOAuYR6O93lbgvgt4Rr91/NfqraFKnbgGMIXxykjWkxPcbAUELvaz2fIjT8RhC+vV4SK+E84DCf+R0IHw6XxYp3DuGCOTJtFC9DH5/5aQDx/43i/f0pf3ufEu/7B9CX8OH0gzhs/FitkVthd+Bxc7vR8/4/4Oz4LXssUPsQu8Vnfief+RGED76T43D0HcBZ8XlPimUfj8cUWVl/BzZ1uZvgcneVy93eyfYZsS5eDXyjYh+Dgf185g8nNB7/N75fxyTlniLUe1zudgY2J6QsWUcCvwPwmX8f+CPwBPASYdRlp4qLGnG/WwDv1BojhEbt2S53D7rcfdflbpApPgj4SRzVmUm4YNczz2d+D5/5XwOPAsfE5zfXZ/4DwhfZEVXnJGutRnXrx/HzfiihUXmQ2dbBZ35nwqhj7frwZWBOvG58jzCyUnOuz/wowijn3i53w1fwfKuuiXXvXw318ySf+R0JX0ZPc7nbMN7fFXgoXisfAL4Q77+CkO64E2A7jaraFKlHgT2rzl1at5bUMG5kD+B3PvOLY4/R/YRvvQ64yOXuaeBuQqVckdSLZalXMbzP/CKf+aN95rcHfk/4cLrM5e5yl7s/xIZyqh/w5jIPGIaeuvvM3x/v+iWwV4yHutyNcbkbS/jWul2DXb1BeThXZIXEXL4dCaMVbwI3xR7hmlvi/48Reo7q+b3P/OImHG400COO+nyNcEEtelld7joSRkh+b87vB7ER+nXCyM23Xe4+73J3cxxFSZXqYhyh2hK4BOgJPOJyt23c/FJtBGsZz++mivtrVB9lKcuoW/u63D0cP+8/TPnzvl6d2wv4ddzv08DTpvxnXO4eJ9Sn7Sin3i2PutfEBvev6vp5msvdU8BDhJ7jWqN5AfCnGNvXaHfil2rgV8nzakqbQvW4DWtJDeNxlL/ZWvUqG4RGYW9gx5hzOJ0wlLKipsee6FrS/xvx/imEylazCZD2Cn+F0HjdlVAZP8uSISBrbnKOjZ53lRuAU33mhwE5jZ9z53hMkZUWv5zeF4dgT6Xcc1rr2VlMdZrW7CYe5z2f+RNjvT6OUM9fMkU+BjyepFYA4HK3fQwnAMf5zH+G8GVyUFI0rYv4zM/ymb/FZ/4rhMbFx5PnBiv3/FQfpa56dcvlrjMhB/eI+Hl/LeX3bFWdW2rlrtgD+w3gI7E3+c+s+PWy6pq4zGtlc9dPF37Eux+wa+wZfsI8bmFM94AmvEY0vU2hetyGtaSG8b1Ap5g/C4DL3U5xSOkB4LMud+3jL0L3Av4LbECYWWKhy92+hOFWgPeB9VbgHO4Ajo/x8YRco9r9R7rcdYofLoPi8Wvn2YMwvPV/hHzgDwiVrl6Feg7Yyty+mJCG0Tfuq5PL3Wk+8+8C77jc1YZrjiX0lBOf2zSXu3UIFbmm3vMeDDzThOcu0pDL3dbJxWskK7e0aWU9jTmLHePNzxNmfnjPFDmKJT0+qQuBbxNyBNvH+z4g1E1rAqbn1+Vu91iXaz3SQ2j+5zeY8GVYpNCgbtWuITNi/usRTdjdA8TrgsvdUELaBMD6hC9u77rc9SF8uVxRdwDHudw5l7tdgHdj+uEjwCCXuy1iHToylrWau35uQEi5mONytw2wSxPO/9/x3KB8Da1qU6R0XW3DWkzDOH6rOwzY3+VuksvdOOA7hG+btxKGg54iNKC/GX9M9htglMvdo4Q39/i4r7eAf7vcPeNyd0l6LJe73wEPAlu73E1xuTs5bhodj/8CsH+8jc/8OOBm4FlCov5Xk+HgbxN+EOiBvxHynMay5Ne01gPA9rW8JZ/5uwi/4r07PufHWPKt9nhCPvXThA/KC+L95xPymv9Re87RjcBZLndPuNwNjA3nrQj5UCIrqxvwS5e7Z+N7cgihjq6oO4HDXO6eNF8Aa7YFxrncjSdcwE+vbXBh2qb9WTKMjNn2SeARn/mpPvMzgQfjELT3mX/KlvWZnw1McrmrfVEdCNwfyz9BqDd/XInndwNwTXx+XWJjZG7tdwwiRt26Fd/D1xKuJ7cRGp7LcjXQLe7nm8ROnPj+f4Lwxex6mvDDMZe701zuphB6fp92ubsubroLeJGQM38tcRao+KPSUwnXweeAm+P1s7a/VVE//wp0iM/3QkI6xbKcDnzV5e4RQmO4pm6boo59CT3u0gY57+uNJsiq5HJ3BXCnrzPNUzMf5zDC9D/nr8rjiLRWsY7s6DNfL+2puY91JvCez/zPV/WxRNqC1Vk/l4fL3QOEGS7eWdPnIs2vxfQYr2UuYulho1WhA3DZajiOSKvkM38rYTGf1WEm4XcIItIEq7l+NklM57xcjeK2Sz3GIiIiIiKox1hEREREBFDDWEREREQEUMNYRERERASonqR+tenVq5cfMGDAmj6NtcrkyZOZMWNG1aIpy01/wyWemv4UixYvqrutQ/sOjOjT/KsBP/bYYzO8972ba38t8e/56qtLVpmdO7c8r37Pnj2L+IMPPihil6zk+s47S34r06fPksWsNthgA1qCteHvuDbR37Nt0N+xbE1c45pLU/+Wa7xhPGDAAB59VNPsrk6jRo1q1v3pb7iEy6u/byxiEY9moFNYIgAAIABJREFUzf86OedWZhGKpbTEv+fppxfTGDN27NjStmOPPbaIZ82aVcQdOpQ/3m65Zcm0x3Z/Bx10UJPOwTa6Adq1a94Bt7Xh77g20d+zbdDfsWxNXOOaS1P/lmu8YSwiAnDfffeVbl911VVF3KlTpyJ+++23S+VOO+20Im7fvn0Rr7tueUbEXXZZsiDWzTffXMR33FFemGv06NFFbHujm7shLCIiLY8+6UVEREREUMNYRERERARQw1hEREREBFCOsYisRs8//3zp9ve///0injBhQmnb8OHDi/i5554r4i5dupTK9erVq4hnzJhRxEOHDi2Vs7NS2B/m2fxlgDPOOKOIt9pqqyI+5ZRTSuU22mgjRESkbVGPsYiIiIgIahiLiIiIiABKpRCRZrB48eLSbTtt2tVXX13EDz30UKlc165di3jnnXcubevWrVsRz5s3r4jHjx9fKmdTK2x6Q3pOjzzySBGffPLJRdyjR49Suffee6+Ip02bVsRf+tKXSuWuueaaIrYLhkB5zmNN8yYi0nroE1tEREREBDWMRUREREQApVKISDOwqRMpu4Rz3759Kx+XLuFsZ5E45JBDivjZZ58tlbPpDpdddlkRX3DBBaVyBxxwQN3j2jQNKK+Yt/766xdxuiT0b3/72yI+88wzS9uUPiEi0jrp01tEREREBDWMRUREREQANYxFRERERADlGIvIKmDzg20Ob+/evSvLLVq0qLRtvfXWK+I333yziPfZZ59SuenTpxfxzTffXMRbbLFFqdw222xTxLNnzy7iBQsWlMotXLiwiO1UcGl+9JQpU4q40XR1IiLSeqhhLCIiq13fS/syffb0utv6dO3D6994fTWfkYiIUilERGQNqGoUL2ubiMiqpB5jEWl2L730Ut3706nR5s+fX8Rp+oFd+e6VV14pYrsyHUC/fv2K2KZPvP56ucdx8uTJRWzTNNJV65xzRWxTJN5///1SOftc3n333dK2nj17IiIirY96jEVEREREUMNYRERERARQKoWIrAKvvfZaEduUgzS9wc70kKZIPPfcc0U8c+bMIrYr3UF55ghb7oknniiV69WrVxHbGSpeffXVUjmbPjFr1qy655oaP3586fZuu+1WWVZERFou9RiLiIiIiKCGsYiIiIgIoIaxiIiIiAigHOOC975u3K7dyn93eOCBB4p4r732Wun9NZVd3Quga9euq+3YsnazOcadOnUq4vQ9aVe723DDDUvbXn755SK2K+R17ty5VM7uf6ONNiribbfdtlRunXXWqbuPdAq5wYMHF/Hdd99dxHb6OCjnLI8bN660TTnGIvXZ6yuUfzOw8cYbF3H6WXH55ZcX8amnnlrE6XWtY8eOlce2vx/Q6pRSRT3GIiIiIiKoYSwiIiIiAiiVomBXu7JxI6eddloR25W5APbcc88ivueee4rYrswFsOmmmzbpWHbIuUOH6j/bJZdcUsS///3vS9vuvfdeAD744IMmHVNkRdn0BDvl2cSJE0vl5s6dW8QDBgwobbOpFTYN4q233iqVs2kWc+bMKeJ0pbott9yy7v7SIVW7it2DDz5YxEOHDi2VO+CAA4o4fV4ia5s0RcJeR1988cUiPuOMM0rlTjnllCJ+/PHHi/j0008vlbvpppuK+M9//nMR//a3vy2VO+igg4o4ndpx3XXXLeIvfvGLRZymcaXPRdYu6jEWEREREUENYxERERERQA1jERERERGgjecYp7m0K5JHbHOjAHbaaaciPvroo4t4hx12KJWzeYs2f+lrX/taqdxtt93WpPNolFf8q1/9qohvvPHGIra5nbBk2dp0eiqR5maXd7bTLqXvSZtzn24bOHBgEdsp2f773/+Wyr355ptFPGTIkMr9LVy4sIhtbrPNO0zP6ec//3kRn3vuuaVyNp85nVpKZG3T6Jpq8/vvuOOOynK33HJLEe+///6lbXZKxPnz5xdx+jud+++/v4jTqR2tRtdUWbupx1hEREREBDWMRURERESAVpRKYadPSYdsqrY1WrVuwYIFpduvv/56EW+//fZFnE4tc/bZZxfx8OHDi3jy5MmlcnZo1a7AZVfSAujRo0cRf+tb3yriT37yk6Vydnqpf/3rX6VtV111Vd1yI0aMKJXr37//UmVEVgVbH2waRDo12jHHHFPEo0ePLm2z71Nbl22aBpSnb3vjjTeK+KmnniqVs/XVro5lp0KE8jRvdgq5NOXCpmpoeieRarWpQgEmTZpU2rbZZpsV8Q033FDE6cqVNu3QrnaXtgfsFG177LFHaZs99p133lnEn/vc50rl7Ap5svZRj7GIiIiICGoYi4iIiIgArSiVotEvXqu2jRkzpvIxWZaVbtfSDKD8S/R0ZospU6YUcfrreMv+6t0Os37iE58oldtggw2K+Oqrry7i66+/vlRuvfXWK+IZM2aUttmhqF133bWIH3744VK52hC0holkVbPDmb169SrimTNnlsrZejJo0KDSNpviUJtRBZZOg7J1yKZwTJ06tVRu9913r/uYl19+uVTO1jU7K026kp79xXs6A4adsSJNwRBZUVUpO/YaaMuk1680lamKrXt2NpdG+7DpSQAXX3xxEdt6lM4U0bdv3yL+6U9/WsR2Bigo16MPf/jDRdyzZ89SOZtqaGesgXJ6xh//+MciTlMpNGPF2k09xiIiIiIiqGEsIiIiIgKoYSwiIiIiArSiHONGJk6cWMQ2h/F3v/tdqZzNUzz//PNL2+z0anbqtnRFK5t7ZXOl0rxdm9tlV5qzK/YAfPrTny7iQw45pIiff/75Ujk7zUy60s9+++1XxDZ38qabbiqVq+WANXXVP5GmSvN+7W071Vqab2tvp3m6ti5vvvnmde+H8hRtdh922kUo10Nbzu4bytPBdevWrYjTXEab62/zJKH8GWJX/RJZGU357G5Upik5ylDOsW1qvq2dag3K+f7Dhg0r4vSaaleG7devXxHb3/MAfOUrXyni6dOnF/E222xTKmevh+uvv35p20knnVTE9nPj17/+dalcmnMsaxf1GIuIiIiIoIaxiIiIiAjQAlIp5s+fzwsvvADAjTfeWNq20UYbFbEdBk2nTbLTydjhzn333bdUzk7/kk61Zod77fBLOjWNTZl4++23i9gOnabnaKekSlMp7DY7bLv11luXytkVfOxqeel52NWB7BAVwLhx44DyaynSHGw6E5RXu7P189133y2Vs0On6ZCtTVvq0qVL5T7syne2zk+YMKFULp3msCZN77B13p6DncYtvW3PAZb+jBJpDsu7wmJTp2dL2ff9NddcU9r2xBNPFLGdivGEE04olbNTqv32t78t4meffbZUzn4+7LbbbpXn9JOf/KSIzzzzzLrnA+Xrt52iEcpTm9r40UcfrTyurH3UYywiIiIighrGIiIiIiJAC0ileOONN4oV35566qnSNjsca6VDrnYmBrvSTTrkalMzunbtWtr20ksvFfEzzzxTxOkvY+0v4m0aRJqeULW6XPqc7BDxqFGjiviRRx4plfvxj39cxDbtA2C77bYrYvvr4rTcVlttVfccRFZWOlNEVSrF8OHDS+XsbA5pXbOpRXYWifRY9j1v91dL0ap3HnZI2s5CAeVh5N69exdxWp+q0qBg6c8ekeawvDMKpdchm1ph0+7SumfTE9LZYo4//vgivv/++4vYrioH5dXu7HU5vVba63Ij9rnbGSXS52hXnUxnyjjggAOK2NZZm1YB8MorrzTpnKRtUo+xiIiIiAhqGIuIiIiIAGoYi4iIiIgALSDHuEePHhxxxBHA0itLvfrqq0X8zjvvFHE6FdLUqVOL2OYb25V30m02pxjKq/HYHOY0r9Duw07zZFf2gfK0UXYqp1tuuaVU7u9//ztNYZ+zzaFK2dzp2kp3NbVcsaauZCTSVDbnD6qnWktXiLN5v2nuYZ8+fYrYTnOYvn9tuXvvvbeI02mh7Ap0dsrD9Lj2fG1OZlqfbM6jfR5Qzj8WaW6Npm2zq642mq7tySefLOK0DqyzzjpFfNZZZ5W22RUl7fXmueeeK5Wz+fk2Zzk9d7vq3CmnnFJ5vpatby+//HJp2+DBg4s4/U3DrbfeWsTHHntsEY8cObJUbuzYsU06D2mb1GMsIiIiIoIaxiIiIiIiQAtIpejSpUsx3djmm29e2mZXxbLS6VnsMJCdIiYd3v3LX/5SxOkqPXbIxa4Ylw6frqyDDz64dPuvf/1rEY8YMaKI0xQOOySWTg1lh6ZsSsi0adNK5WopGOmwr8jKSleVs6vC2ffbFltsUSpnh1/TaaFs+oRNwbApVlBOW7DpWDYlAsrDvnabTfuA6ukM03pjy6XDw1pdUlaF2vusajpQKKcapVMRTpo0qYhtCkKaCmjTkM4+++zStptvvrnu/jfddNNSOXsd/ec//1nEdgVaKF+nbSqUXTkvZa+V06dPL2377Gc/W8Tp9fZjH/tYER999NFFnKZnqv6u3dRjLCIiIiKCGsYiIiIiIkALSKVo3759MdNDOuxzzz33FLEd7rS/mAXo3r17EQ8dOrSI09kbTj311CK2v1AHWLBgQRHbYeF0iMWyQ7jpr9Dt0Kod2urfv3+pnB2OHTNmTBHboSIoD+OmvzS2w2r2OadD03ZoS6Q5pe//zp07193Wq1evUjk7jGpnfIFyypBd7S6dlcKmD9mUi7fffrtUzg6Pvv7660VsPz+gus6nKRf2dnpO9vNEpLnUZkJp6sxCaYrP7bffXsTPP/98EaepA3bGCrsSLJRnWbIr2t1xxx2lcmeccUYR33fffUWc53mpnK2LF154YRGnqRR2NclGq+XZ/aXsOVl21gxYejYLWbuox1hEREREBDWMRUREREQANYxFRERERIAWkGNspdO9pLdrJk6cWLptcxhfeOGFIrZ5iVCeAs3mV0F5aqj111+/iNN8Zrvalc2BTFftsznBNs8rzX+yqwPZY9nVi9J92FUAU3aarPScBg4cCCw9jZVIc7Pvf5uLm+bpjhs3rojTKQrtbVuXbR2E8ip29rhp3bXve5vbn+bs2/xgW1/T3yVYac5no9UpRVbE7NmzefDBBwG45pprStvs70kardxqt9lrRTq1qc27T6f9fOihh4rYToFqr6Epm/tvc4VTNn/5Qx/6UGmbvc7vv//+RWzrP8CNN95YxKeffnpp26BBg4p4hx12KOJ09bwrrrii8hyl7VOPsYiIiIgIahiLiIiIiAAtLJWiqbbaaqsmlUtX8xGRVSNNb7BpDDblyK50B7DbbrsV8TbbbFPaZtMYbLqDnSIKysPDdurCdHUwm2Zhh3PTKa3sapc2pSld+c6ek52eDpZOGRFZWV26dCmmEfv85z9f2mbrhE21S6dRtLftFG1pOfvePu+880rbbJ2waYfpdKB2CjSbmvH1r3+9VM6mEzZKufje975XxFOmTCnidIVcW5/TbTadyq6EmX72qP6u3dRjLCIiIiKCGsYiIiIiIkArTaUQkZYlTUew6Q02zSKdKeXLX/5yEb/44oulbY8//ngR2+HWsWPHlso9++yzdfefplLYYVqb6jF16tRSueOOO66Id9lllyJOh3bT87DS2QBEVla7du2K4f8999xzDZ/N6mdnwBBZlfTpLSIiIiKCGsYiIiIiIoAaxiIiIiIigHKMRaQZpNO1WTbXd4899qgsl64sV7XS3N577125DzuVVLoS18qu+GjznKHxc05XrhQRkdZBPcYiIiIiIqhhLCIiIiICKJVCRJpBp06dSrer0gzsNGmpdHo1u/qWnQ6uUQqDnSZtRVMnqo613nrrVZ5fmjqxYMGCFTq2iIisWeoxFhERERFBDWMREREREUANYxERERERQDnGItIMZsyYUbq9cOHCIra5uHap6OVhc33T5acb5RyvCJsvbM89zTG208Gl2xrlUrd255xzTuW20aNHr8YzERFpfuoxFhERERFBDWMREREREUCpFCLSDNKp1mwqwaJFi4q4X79+K32spqZONEq5aDT9W1UqRTr9m00Xsc8Rlk6tEBGR1kE9xiIiIiIiqGEsIiIiIgIolUJEmoFdcQ7g/fffL+KZM2cWcZpyYaWrx9k0hhXRKOViRWaySGfUsM8lnYWia9euy71/ERFZ89RjLCIiIiKCGsYiIiIiIoBSKUREpBXTgiMi0pzUMBaRlXbiiSeWbj/22GNFbHOMd9xxx8p9rOiqeM0tzZeuSaeas7fTc+/evXvzn5iIiKxySqUQEREREUENYxERERERAFy6OtRqPwHn3gReXqMnsfbZ3Hvfu7l2pr/hGqe/Z9ugv2Pbor9n26C/Y9vRpL/lGm8Yi4iIiIi0BEqlEBERERFBDWMREREREUANYxERERERoJnmMXa5Oxc4GlgMfAB8yWf+4ebYd9z/PsA3fOYPaqb9XQ8cBLzhMz/U3N8TuAkYAEwGPuMz/07c9j/AyYTneJrP/N9c7joBtwObAFf5zF8Vy/4MuNpn/omK438SGO4zf4HL3dbAT4HuQCdgjM/8F5vjeZrj3Ud4/R5dwcdfCtzlM39vc56XtD4ud32BHwI7AfMJ9eQMn/kJy7mf7sDRtTpTZ/uZwOcBD4wFTvSZn+dydwlwMLAAmBTvn+lytztwdTyno3zmJ8Zj3AQc6LP6P6ZwufsD8E2f+Rdd7roBlwH7AfOAt4CzVuSzzOXuWz7zF8W4I3A38GGf+UXLuy9ZO6yq62hTPv+ryrjcnQqcAQwEevvMz4j3O+AK4OPAHOAEn/nH47YD47b2wHU+86Pj/d8HPgY86TN/XLzvWKCnz/wVFefVD7jWZ/4gl7t1gWuB4YADZhLq9qzlflGWPs4sn/luTSnjctcb+JXP/IEre1xpmVa6x9jlbldCI3MHn/nhhIvKqyu73+biclev8X8DUO9NfQ5wj8/8IOCeeBuXuyHAkcB28XFXudy1Bz4KPEaoqF+MZUcA7aoaxdE3gVqD4EfA//rMj/SZ3xa4crme4CoWn+eVxNdC1l7xYngrcJ/P/ECf+SHAt4A+K7C77sBXKo7THzgNGBW/uLYn1D+AfwBD42fNBOB/4v1fBw6P5/PleN/5wEUNGsXbAe195l+Md10HvA0M8pnfDjgB6LUCz414HgD4zC8gfJ58dgX3JW1cC76O/ptwLuksCh8DBsV/XyR8Ka1dL34Stw8BjnK5G+JytwGwW3xu7V3uhrncdSHUsbpfjqP/R2gMA5wOTPeZHxY/F04GFq78U1w+PvNvAtPil3Fpg5qjx7gfMMNnfj5A7RslgMvdZOCXhB6edYBP+8yPd7nrSmhsDYvn8B2f+dtd7gYAvwK6xl2c6jP/H3swl7udgJ8RLoLTK/ZzAvAJoHPc14ftPnzmH4jHSh0K7BPjXwL3AWfH+2+Mz/Ell7uJwM6EStmF8ut4IXBK1YvlcjcYmG9ep37AFHNuY2O5E4BDgHUJ39Zv9Zn/Ztx2AJATephrvWazXO6+TXituwD/IfQ4FI0Cl7t2wC+AV33mz2uwn8nA9cABwI995m90udvQ5a6vz/zrVc9N2rx9gYU+89fU7vCZfxKKRvMPCBdED3zXZ/6m2At7O9CD8Blwns/87cBoYKDL3ZPAP3zmz0qO1QHo4nK3kFAHpsbj/d2UeQg4Isa1urgusNDlbiDQ32f+/gbP55h4bsTyHwKO8Zn/IB7rReDFuP3/ASfFx13nM//DeP9twKaEz5orfOZ/5nI3Op77k8A4n/ljgNuAi4HfNDgfWXs1uo7W/VyPvbwPE+pld+Bkn/kxscH5C0LD9Ln4uNq+riaM9nQB/uAznzU6qVoHj8tduulQ4P/i9eUhl7vusXd3ADCx9mXT5e7GWPbHQMf4OdGFUF/PAn7kM9+ocXs4cJ55jYoGus/88+Z5LVUP4/2zCL3XBwFzgUN95qe73G0B/JbwOfNXs5+qz6vUbYTPj383OHdppZojx/jvwKYudxNc7q5yuds72T7DZ34HwjfKb8T7zgXu9ZnfiVCpL4mN5TeA/WP5zxJ6Uwsud7sB1xDe3C822A/ArsDxPvOlRvEy9PGZnwYQ/98o3t+f8rf3KfG+fwB9CR9OP3C5OwR4zGd+aoNj7A48bm7/L3Cvy91fXO7OjMO/NSMJr8Mw4LMud5u63PUifFDsF1+nRwnfqiE0YneK36a7ED4MajoQLsoTYqO40X4A5vnM7+Ezf2O8/Xg8d1l7DSWMkNTzKcL7dQShh+mSeKGcBxwW32P7ApfFi+M5wKQ4UlJqFPvMvwZcCrwCTAPeTRrENScBf4nxxYQvzGcQLsLfI/QYN7K7eT7bEYZ4F6eFXO52BE4kNJx3Ab7gcrd97Rx85ncERgGnudxt6DN/DjA3PrdjYrlnCA0SkXoaXUcbfq77zO9MeN/XGrlfBubE3tnvAXYd9nN95kcRRjn3drkbvoLnW3VNrHu/z/z7wB+BJ4CXgHeBnSoanQDExus7tS8LhM6as13uHnS5+67L3SBTfKl6GO/vCjzkMz8CeAD4Qrz/CkK6406A7eyp+rxKPQrsWXXu0rqtdMM45vfsSBhOeRO4KfZ21twS/3+M8G0SQk/kObFH5T7Ct7zNCN/QrnW5Gwv8nvCNt2ZbwoXvYJ/5V5axHwi9UG+v7POL6lUM7zO/yGf+aJ/57eP5nkGoSJe73P0hNpRT/QivU9hJ5n9BeG6/J/RWPxRzlyGkdbzrMz8PeBbYnHBhHgL8Oz7v4+P9APu63D0cX78PEy72NT8FnvGZ/1683Wg/EHIzrTeAjeu9OCLAHsDvfOYX+8xPB+4nNAQdcJHL3dOEPNv+LCP1wuWuB6GXaQvCe66ry93nkjLnAouIPbA+80/6zO/iM78vsCWhh9m53N3kcvdrl7t6xyzVxWU8t1t95mfHz7tbWHJRPM3l7ilC7/WmhKHlpcQG9wKXu/WacDxZyyzjOtroc73e9XUv4Ndxv08DT5vyn3G5e5zQQN2O8jV2edS9Jja4H5/5H8Qvi18njKx+2+Xu8y53N7vcnVfncem18klC3b4E6Ak84nK3bdxcVQ8XAH+KsX2Ndgd+F+NfJc+rKZ9Xuh62Yc3y47v4oX8fcF+svMcT8ngh/BgGwg8KasdzwOF2KATA5e47hPSIEYRG+zyzeRqh4bs9cVi1wX4+BMxegacy3eWun8/8tNjb9Ua8fwqhstVsYs6h5iuE9ItdCZXxs8CDwB1JubnABvaO2MN8PXC9y90zhJ45WPLawZLXzxEa/UfZfbjcdSbkao3ymX81vpadTZH/ED5gL4sN7br7MdLXr3M8d1l7jWNJ6kKq3gURwnBjb2BHn/mFMU2nc0XZmv2Al2IuHy53twC7ES/2LnfHE3rNPpLmD8fenfMI9e/HhF60AYSc5XOT48w15zIOGOFy166WSrGs5+bCj4L3A3b1mZ8Th7YbPbdOlD/TRAr1rqMxFaHR53q96yvExqgVe2C/Qeipfcfl7gaWXRerVF0TO1bcb8+jNtoygZD2sJfL3Y0ud4N85l8wRW39BIovELcAt7jcfQB8PH7praqHC81nxDJfI5r+eaXrYRvWHD++2zoZ0hjJspc7/BvwtdoQhakoGwDT4oXpWMKPbmpmEvKGL4oXpEb7WVF3EBr1xP9vN/cf6XLXKX64DAL+W3tQ7OE6CPg/Qo7jB4RKV69CPQdsZR57oMvdOjHuC2wIvNbgHB8Cdne52yo+Zl0X8pZrx5oR86TSBszPgbuA37vwg8Sq/VQZTBgOlrXXvUAnl7vacCQudzvFYd8HCOk+7V341fZehDqyAWH2l4Uud/uyZFTifaCq9/QVYJf4nnTARwj1pvaL97OBQ3zm59R57PHAn32YTaZWFz+Icaqoiz7zkwjDo7n5PBnkcndofG6fjOfTFTgMGBOf2zvxYrwNYRSmZmGtXsd9bQi8uYx8SllLNbiOLutzvZ4HCA08XO6GEtImANYndHi8GxuTH1uJU74DOM7lzrnc7UJId5oGPAIMcrnbwoXZWI5k6c6hC4FvE0aIa9f4enV0Akt6eHG52z1ea2szvQwhvEaN6mGVf7PkB73HmPurPq9Suh62Yc2RY9wN+KXL3bNx+GEI8J1lPOZCQqV4OvaQXhjvv4rwLfkhwhuv1GsZh2gPBn4Se4Wr9tOQy93vCL25W7vcTXG5OzluGg3s73L3ArB/vI3P/DjgZkI6w1+Brya5iN8m/NjIExrrowhTTF3L0h4Atjd5SwcAz8RhoL8Rpoeq/IFb7EU7AfhdfL0fArbxmZ8ZjzeW8MOAR+o89nJCrvCvCFNRLbWfitdrHUIDYoWme5O2Ib6/DyPUkUkud+MIdX0qYbaKp4GnCA3ob8b38W+AUS53jxIuQOPjvt4ipPE848IUbPY4DwN/ILxXxxI+p34WN/+Y0KD+h8vdky53xQ8BXZjO6XiW/Mr9ckJe48XEX80n/sySH9tCmB6uLzAx9thdC0z1YRqqGwgN/YcJP757gvBZ0CHWnwsJdajmZ4TPpdqP7fYlfDEVqafudbQpn+t1XA10i/v5JrETx2f+KUIKxTjCCOUyfzjmcneay90UQs/v0y5318VNdxF+mDoxnt9X4jEWAacSrmXPATfH62dtf58EHvGZnxqf24Oxrvl4fgWf+dnApFrnDeFH6PfH8k8Qrkd/pHE9rHI68FWXu0coj+DW/byqY1/C54e0Qc7Xn8lIViGXuyuAO33m717T59IULneHEaYRWtaPmURaDRd+vf9PYPd6P7pr5mPdAvxPmvYlItXitWdHn/l6OchrjMvdA4RJAN5Z0+cizU8r360ZF1F/aLel6kBY+ECkzfCZn0vIQe6/Ko8Th31vU6NYZPn4zN9KWESoxYipYperUdx2qcdYRERERAT1GIuIiIiIAGoYi4iIiIgAahiLiIiIiADNtMDHyujVq5cfMGDAmj6NtcrkyZOZMWNG1YIMy01/wzXrsccem+G9791c+2uJf8/585esddOpU6dpEpSOAAAdVklEQVQGJZtm7twlc/N36dJlpffXHNrS3/Gp6U+xaPGiuts6tO/AiD4jVvMZrX5t6e/ZFDNmzCjdXrSo/t+/Xbtyf1zHjh2LuHv37s1/Yitpbfs7tmVN/Vuu8YbxgAEDePRRTY+7Oo0aNapZ96e/4ZrlnFvWgjrLpaX8PRcvXjKD2uTJk4t44MCBy/14gPbtl6wXNHbs2CIeOnRoqZxzzfadcbm0pb+jy6tfw0Us4tFszb+/VrW29PdsimuvLU/bP3PmzCK2jeRu3bqVym2yySZFfNhhh62is1txa9vfsS1r6t9SqRQiIiIiIrSAHmMRkXoWLlyyevKrr75axI16jO30k7aHODV16tQiHjZs2IqeokiLlk7HWjUakpazPbzrrLNOaZsdienQYUkTIk1xqjpWer9NazrwwAOL+C9/+Uvdx6fnZ89BpDmox1hEREREBDWMRUREREQANYxFRERERADlGItIC9W5c+civu6664o4ndJp5MiRRdxoRonbb7+9iK+44ooi/uhHP7pS5ynSUjXKMf7ggw+KOJ1CLc0rtk499dQitnnF/fr1K5Wz07DNmzeviBcsWFAqt9566xXxk08+WXlcy+YVN5p9RmRFqGEsIiIispbre2lfps+eXrm9T9c+vP6N11fjGa0ZSqUQERERWcs1ahQ3ZXtboR5jEWmR7HRtY8aMKeJHHnmkVG748OFFfOKJJxbxBRdcUCpnh3PTRT1E2qI0RcLWqUbpEnfddVcRX3rppaVtkyZNKuKePXsWcZrG1L9//yK20yOmqQ/2cTb1I03NOOuss4r4jDPOKGKlTkhzU4+xiIiIiAhqGIuIiIiIAEqlEJEWyg719u3bt4jtqlcA48ePL+KvfvWrRWxntQDo0aNHEffu3bvZzlOkpbIzT0B1+sRRRx1Vun3zzTcXcbdu3Urb1l133SK2aRCzZs0qlZs2bVrdY9mV7gC6dOlSxDbNYv78+aVy5557bhFfcsklRXzllVeWyh1xxBFFnH5WaJU8aQr1GIuIiIiIoIaxiIiIiAighrGIiIiICKAcYxFpBWwe4muvvVbaZlfOsqvi2VW5oDxdW9euXZv7FEValX/+859FfNttt5W2bb755kVsp3iDpfN2a9IV7SZPnlzEQ4YMKeI0d3jmzJlFbH8XkP5GwNZZe04nnXRSqZxdCXOrrbYqbbPTwTVaJVPWbuoxFhERERFBDWMREREREUCpFCLSCtihWLvyFlRPQZXeb1Mp7KpcKQ23SluRrnxn/fSnPy3idPU4my6RrlRn64edDi6tb/a2XfkuTXGqqm/2/vSc7L7T53jmmWcW8Z133lnapvosTaEeYxERERER1DAWEREREQGUSiEiLUQ6dGqHPe0v0tPVq6qGYvv06VMq99Zbb1UeS2RtYN/3//rXv4rYrmYH5Vkf0vQDuw9bLk2RsOkZNuVi9uzZpXJ2xhm770Z11KZVrL/++qVtDzzwQBGPHTu2tG3YsGGV+xSpUY+xiIiIiAhqGIuIiIiIAGoYi4iIiIgAyjEWkRai0VRKEydOLOJGU1DZVbXef//90rYNN9ywiF9++eUVOg+R1uymm24q4rfffruI0zxdmxOc1ocNNtigiOfMmVPE6Qp5dpo3+xsBu28o11m72l2j3OZG99vbl112WWnbDTfcUHcfIpZ6jEVEREREUMNYRERERARQKkVdV111VRE/88wzldsa0epZIs3nn//8ZxFvttlmpW12Fax0mNay9XD8+PHNeHYircN//vOfIrbTqaVpEFbHjh1Lt+fOnVv3cenKd3ZKte7du1fu314rbfpFmjJVdU21x4Hy8xozZkzlcUWqqMdYRERERAQ1jEVEREREADWMRURERESAFpZjbHOXoLxUZKNyaQ5UFZt7lPrTn/5UxFOnTi3ijTbaqFTuuOOOK+Lvfe97RbzpppuWylXlFdscquU5P5G1zQsvvFDEvXv3LuJ06VnLTiWV1kF7e9q0ac1xiiKtyuOPP17ENoc3nfLMXlPTejRv3rwittOrpbm+VfUt3V/V9XvBggWV5eyx0nO3nw/pUtciTaEeYxERERER1DAWEREREQFaWCqFTVMAOPXUU4t47733LuKqFIuVYadh23nnnYs4HebZZJNNitiuIpSmXBx22GFFvN566xVxmi5hUyuqVvZZFk0H13L0vbQv02dPr7utT9c+vP6N11fzGbVedtjXDqum7/eqKaPskC+Uh46nTJnSbOcp0lpMmjSpiO21KL322GkP02nTOnRY0mxolNJgy9l9pFPDpSkYVcetKpemJ9rjzpo1q+5jRBpRj7FIM6pqFC9rm4iIiKx5ahiLiIiIiNACUik++OADZs+eDSw9vHnHHXcU8Zw5c4p46NChpXI9e/YsYvsr1HQVrFdeeaWIf/GLX5S29e3bt4h79epVxHfeeWep3KGHHlrEM2fOLOK77rqrVM6urLXlllsW8f77718qt/nmm7O80qGjqmEvzXIhrdnDDz9cxPZ9nb7/7RBuo1/a2xSMfv36FfHEiRNL5bbaaqsVPGORlm369CWjVvY619T0BijXMVun0hQnuw97jUrL2f3ZculKevYcm5o+OHny5NLt9957r4jXX3/9Ju1D1j7qMRYRERERQQ1jERERERFADWMREREREaAF5BjPnTuXZ555pu62Wu4xwG9+85siHj58eKmcnVLNxmnu4NixY4s4XVVnzz33LGI7TdRHP/rRUjmbw2yPdeCBB5bKvfHGG0U8YcKEIn7wwQdL5bbddtsi3m677Yp41KhRpXJ25a80d1i5xNIWjRs3rohtHmI6haKdksnmHjZaicvmK7711lulcsoxlrbK5t3b60Z6PbR1Jc3pb5RXbNl8YZvPbH8vlN6255T+Rsiy59SoXOr5558v4p122qnJj5O1i3qMRURERERQw1hEREREBGgBqRSLFy8upj17++23S9vsCjbvvvtuEd96662lcj169ChiO0RqV5wD2HXXXYt48ODBpW12qNZOBzdjxoxSOTvsY6eJS8/dplxsttlmdWMoTx8zZsyYIn7kkUcq99e9e/fSNjvlm12Bb5tttimV69SpEyKthZ1qyaZPpCkS9rb9zEiHh6se88ILL5S2fehDH1rucxVpiV577bXKbTYNYkVXXW3E7tOmO6T1116z09XuqtjHpKlVjZ7LSy+9VMRKpZAq6jEWEREREUENYxERERERoAWkUrRr146uXbsC5dkbAE488cQiHjBgQBGnaQvz5s0rYptm0Llz58pyTz/9dOU5devWrYhtCgOUh2dff/31Ik6Hc+yqOvYxNnUCyr/WtakZKXvudsYLgKlTp9Y93+9+97ulcp/73OeA8op9Ii2VXaly6623LuJ0lS7LDg/btAqoXiHSzlYj0pbYWRgaSWd2aGpKQyN2hgk780s6i5K9TtvzaHRONh0jvfY2mqVi2rRpyzptEfUYi4iIiIiAGsYiIiIiIoAaxiIiIiIiQAvIMZ45cyZ33HEHAP369Stts7mzNjd3yy23LJWzU6DZ3CP7eID58+cXcbqaT3pONXaaOIB11lmniO3UaI1yjK00Z7lPnz51zymdasrmYaW50/a1sc85XZXo8ssvB2D69Ol1z01kTUrrpM2rt/mFjaZhs/mF6fvf1n+b52h/KyDSlrz44otNKpfm49spz9J6ZOtio3KWnSo0ree2zjb1uDZOyzXKMX7zzTcrt4nUqMdYRERERAQ1jEVEREREgBaQSjF//nwmTpwIwMCBA0vb7Ap0zzzzTBFPmTKlVK5qurJGQyrpNjs8a+N0mMYOCdlhmXRVuS5duhSxTb9I2ZX17Dm9//77pXI2vSPdZqeXs8PP6YpetX00GopeW51zzjmV20aPHr0az2Tt9fLLL1dus/V69uzZpW22flUNy6a3bTqSnRZOpC1Jp/askl7nbLpDOr1aU1WtfJfWS3tsG6cpF/Yaa1Mp0ukbG11v06leRepRj7GIiIiICGoYi4iIiIgALSCVol27dsUw6UMPPVTaZof87dBnmgowZ86cIrazQdhV5QBmzZpVxI1mpbBDR+mvde1tO5yTzkph2aEdm/YA5aEu+zzSFfJsikQ6dGTPyc7KYR8DkOc5AFmWVZ6ryJoyfvz4ym2Nhk5t3bPl0jpuh3BtnXnttdeW/2RFWoFJkyZVbrN1JU2XmDt3bhE3Sk1oxKZPbLzxxkVsV8GD8nWq0cqV9rrfo0ePyv3Z8033oVkppCnUYywiIiIighrGIiIiIiKAGsYiIiIiIkALyDHebLPNuPLKK4vY6tmzZxHbac3SHGObY2jzdNOpWdZbb70itrm4UM63snlJ6bRuNvfK5kOlOcb2HKv23Wibfe4A3bt3L+J05Ttbduutty7i/fffn3p+9KMf1b1fZE1qaq5vWnetRtNC2dxkW3fT6Q9F2gp73YTyNcbWh/Q6Z8ul9ciy29Jy9to2bdq0ymNVPSa9VtpVaPfdd98i/vOf/1wqZz8f0vzoNB9ZpB71GIuIiIiIoIaxiIiIiAjQAlIp2rdvX0y9ctFFF63hsxGRNSVNaWjqsK8dOrXb0tW8LDtk2yg1Q6Q1S+uUTS2waYebb755qZxNO3z44YdL2/r371/E8+fPL+JG9a3RNsvWX1tHoTzdqmWnboNyukSajtFomlaRGvUYi4iIiIighrGIiIiICNACUilERGDpWSnssK8dYk2HQ6uGadOhWHvb7s8OB0M5bWNFV/0SaQnSVIouXboUsZ21aeTIkaVyNgUhXZHWzj7RKEXClmuUrmT3URWn+7PpE4MHDy6Vu/vuu4s4Xf220YwYIjXqMRYRERERQQ1jERERERFADWMREREREUA5xiLSQrz33nul2506dSriRqtvtW/fvm65NEfR5hin+ceWzb3s06dPgzMWadnSfPyqnHm7khzAuHHjKvfZqO5Ytv7Z6d/sNHGwYtMlbrjhhkWc5hHbHOP0XBt9jojUqMdYRERERAQ1jEVEREREAKVSiEgLka5s1dSp0uzwqI1tikWj/dnp2QBmzpxZxEqlkNbMpiNB9cpvhx56aOn2k08+WbnPqlUo07QFu83WywULFpTK2cfZcuk0ilbHjh2LeK+99iptu/jii4s4Tadaf/31K/cpUqMeYxERERER1DAWEREREQHUMBYRERERAZRjLCItxLx580q3u3btWsQ2NzLNk7S5jHbqpzS/0uYc2zzHLbbYouF5iLRWNhc31a1btyJOpzybPXt2Ead5ura+NXWJZbs0dZqLbOtzoyWhLZsrnNZz+xmQnl9VjrWIpR5jERERERHUMBYRERERAZRKISItxL///e/SbbtaltWlS5fK23boOJ2ezQ7N2mmh0tSJ559/vohHjBixrNMWabFsOhKUp0RslDJk606aqlC1gmQ6PaKtbzalIU2RsLft/jp0KDdPOnfuXMR2lcx0xUwrPXe7Yp5IFfUYi4iIiIighrGIiIiICKBUChFpIU455ZTSbbuClZ1Fwv7CHWDatGlF3LNnzyJOV7SzaRY2TWPOnDmlcj169Fie0xZpse66667S7RkzZhTx3LlzKx83ceLEJu2/0WwxNl3JpkWkqRQ2BcPOKGEfn3r66aeL+Pzzz6887trknHPOqdw2evTo1XgmrZ96jEVEREREUMNYRERERARQw1hEREREBFCOsYi0EBdccEHp9rBhw4r42WefLeI0N3Lw4MFFPHLkyCJOc4fXXXfdIrZTsh111FEreMYirUu6wl0Vm49vp0mD8lRuNk5z+m2ur91Ho1xkKy1nfxewzTbbVJ67yMpSj7GIiIiICOoxFlnt9OthERFpq1r7Nc6t6alNnHNvAi+v0ZNY+2zuve/dXDvT33CN09+zbdDfsW3R37Nt0N+x7WjS33KNN4xFRERERFoC5RiLiIiIiKCGsYiIiIgIoIaxiIiIiAiwimalcLk7FzgaWAx8AHzJZ/7hZtz/PsA3fOYPaqb9dQeuA4YCHjjJZ/5Bl7uewE3AAGAy8Bmf+Xdc7nYHrgbmA0f5zE+M+7gJONBn9RO3Xe7+AHzTZ/5Fl7uTgDPj8doB5/rM394czyceawDwJ5/5oSv4+I7A3cCHfeYXLau8tH4ud4uBscA6wCLgl8APfeY/WA3H3gb4BbADoS5carYdCFwBtAeu85kfHe9flfVzMvB+3NQeuAW40Gd+frM+8SXHPgjYyWc+WxX7l7bN5a4v8ENgJ8L7fjJwhs/8hOXcT3fgaJ/5qyq2nw58AXDAtT7zP4z3fxr4DrAtsLPP/KPx/uaoi92Ay4D9gHnAW8BZK9KmcLn7ls/8RTHWNU7qavYeY5e7XYGDgB185ocT3syvNvdxVpTLXb0vA1cAf/WZ3wYYATwX7z8HuMdnfhBwT7wN8HXgcOBbwJfjfecDFzWo6NsB7WNF3wQ4F9gjvka7AE+v9JNrJi537X3mFxCe82fX9PnIajPXZ36kz/x2wP7Ax4GlGmoVdWhlvQ2cBlxq73S5aw/8BPgYMAQ4yuVuSNy8SuqnuXtfn/lhwM7AlsDP6jyu/XI+zyp/Bg5xuVt3mSVFDJc7B9wK3OczP9Bnfgjhvd9nBXbXHfhKxXGGEhrFOxOukwe53A2Km58BPgU8kDysOeridYTPh0Hxs+kEoGkrlSztW7VA1zipsioucP2AGbWeFZ/5GbUNsRfml8DBhF6pT/vMj3e56wpcCQyL5/Qdn/nbY6/nr4CucRen+sz/xx7M5W4nwgXrcGB6xX5OAD4BdI77+rB5/PrAXoTKVqssC+LmQ4F9YvxL4D7gbGAh0AVYF1jocjcQ6O8zf3+D1+UYoNYjvBGhN2pWPOasWuxydx/wMLAv4UPqZJ/5MfECPDqeTyfgJz7zP43fpm8HesTX9Ly059nlbkvgj8AXgccr9rMPoRE0DRhJaITcBlwM/KbB85I2yGf+DZe7LwKPuNx9BziepA653J0FfIbwPrrVZz6LdflmYBNCT+uFPvM3udyNBg4h9ET/3Wf+G+nxgDdc7j6RnMrOwMTaRdLl7kZCvXyWVVc/09dilsvdKcCrsZd6OEldcbn7HKFh35FQf2uNi58DowgjQ9f7zP+vy91pwCnxtXjWZ/5In3kf6/5B8fUTaap9gYU+89fU7vCZfxKKRvMPCF8sPfDdWB+rrhujgYEud08C//CZP8scZ1vgIZ/5OXHf9wOHAT/wmX8u3pee20rVxVj+Q8AxtZGr+FlQ+zz4f8BJ8XHXmR7s24BNCZ9XV/jM/yx+BnWJz22cz/wx6BondayKHOO/A5u63E1wubvK5W7vZPsMn/kdCMMrtYvjucC9PvM7ESr5JfEC+wawfyz/WeBHdkcud7sB1wCHxspStR+AXYHjfeY/TNmWwJvAL1zunnC5u848po/P/DSA+P9G8f6LCY3xM4AfA98jfAtuZHfgsRg/RWjEv+Ry9wuXu4OTsh185neO+6/12J0MvBuf207AF1zutiAMLR0WX6N9gcvih2HtNdqa0Cg+0Wf+kQb7gdAIOTf2OEDoBdhpGc9L2qhYp9qx5H1f1CGXuwOAQYT3zEhgR5e7vYADgak+8yNiGs9fY2PyMGC7OELy3eU4jf6UR5ymxPtg1dXPpfjMvwe8RHjOYOqKy922hM+n3X3mRxJSyI4hvC79feaHxp7nX8THngNsH1+LU8xhHgX2XMZ5iqSGUv3e/RThfTiCMHp7ictdP6qvG+cAk+LI0VnJvp4B9nK52zCObHyc0PhsZGXr4nbAkz7zi9NCLnc7AicSGs67EK5l28fNJ/nM70j4Unqay92GPvPnsGRU7BjznHSNk5JmbxjH3s8dCb2TbwI3xR7bmlvi/48RcgMBDgDOid/k7iN8y9uM8E32Wpe7scDvCb2YNdsSKtzBPvOvLGM/EL79vl3nlDsQ8hqv9pnfHpjNkiHZquf4pM/8Lj7z+xIa1lMB53J3k8vdr13u6g1h9YuvB7GSHwgcAUwA/jf2ytVUvUbHxef2MLAh4SLtgItc7p4m5Ev1Z8kQWm/CN+/P1XoQGuwH4L8+8y+Z57kYWOByt2SRelnb2C4gW4cOiP+eIIxCbEN4H40F9nO5+77L3Z4+8+8C7xEuxNe53H0KmLOCx69pOPn6ytbPJp6LrSsfIXzmPRLr1UficV8EtnS5uzLmSb8Xyz8N/Cb2MtvcxjeAjZdxDiLLYw/gdz7zi33mpwP3ExqCja4bdcVe4e8D/wD+SujgaZibuwrrYu253eozPzu2O25hyRfL01zungIeIjTeB9Xbga5xUs8q+fFdfLPdB9wXG7XHAzfEzbUfryw2x3fA4T7zz9v9xMbidMK33XaEi2vNNELDd3tCZWu0nw8RGrz1TAGmmET+P7CkYTzd5a6fz/y0+C37jWS/DjiP0Fv0Y0Lv7gDCkOq5yXHmxvMFIOZX/Rf4r8vdPwi9Sd+Jm6teo6/5zP8tOYcTCA3gHX3mF8Z0ldpx3iX0tu0OjFvGfvah/mvUifLrLmuJmIKzmCXve/v+cMDFPvM/rfO4HQm9SRe73P3dZ/4Cl7udCQ3GI4FTMelMyzCFcq/UJiyp76usftZ5TuvFx04gfB6lr8Uvfeb/p87jRgAfBb5KSDs5iZCSshchteR8l7vtfPjxT+d4HiLLYxyhk6Weel8sIYxoVF03KvnM/5yQHoTL3UWE+rlMK1EXxwEjXO7a+aV/BFz3ucVr2X7Arj7zc2KKUqPnpmuclKyKH99tbRLyIQzjLGv5w78BX6ulAJjhkA2AabFCHEvIWayZSbjAXBQrQqP9VPKZf52QO7h1vOsjhPxFgDsIjXri/2kO4vHAn33m3yHkUH0Q/9X7Ac1z/7+9+wuRsgrjOP79rVQW0kKlF0qKkBJdxUJXgbB00WUKRhlB2B9QKqgbSRBeDt4ISgWGF9FFkBoEG0aopNWCEV0km0VIgYF0VUkk5p9wxePF87w0zM44a7Xbsv4+lzPve+Z9hzlznjnnec4A9+V1LVXRSMdz032PNqvolmxjdaZ8DAO/5ZfbKLCi45zLwFpihvipAe1MoaK7gTO1qZMDrs3mGRUtJtKU3upTJPMJ8GzmKqKiZSpaoqKlwMXa1L1EId1IHjNcm3qIWFJ98AYu5WtglYpWKqrInyT6JcxQ/+yW178HOJBtdfsMWK+iJXn8XSpaoaJ7gKHa1DFi+XhERUPAvbWp48AWoo5gUbazmljaNbsRnwO3qeiF9gEVPZRpjMeAJ1S0IPv0GmJCpt+48SfQd/a04zO+nEjTeH+a1/iP+mJt6k9EilHpGNdXqeixvLe1Krojx7B1wBd5b39kUHw/kWbRmmzHvmzLY5xNMRMzxouA3YotWa4Ap4i0iuvZTmw1811++E8TRSh7gDHFVjDjdM1o1qb+mvm5hxXbn/VrZ5CXiaXNW4nlz435+A7gAxU9B/wMPN6ekDlWzxDLyQCvE7m8l4ENPV7jIFEo9CmRIrIrg4i/iGWjTT3O6fQO8Qt7Iu/tDBH07gM+VtFx4ATwQ+dJtakXFFtBHVXRheu008socGjAddn80RamtNu1vUd8rqeoTT2SubVfKQpuzgNPEwPaThVdJQpvNhMD7UcqWkjM8rza3Z5iu6njwJ3AVRW9AjxQm3pORS8RgfgCooCtXf2Yqf7ZGs8+MkRU/W/v816cVNE24EgGvpPEDPElonahnYDYmvewV0XD+V68UZt6Np8fzWPMpi0LN9cBb6roNWJMOU38CD1G1AZ8S6QgbalN/UVFPceN2tTfVfSlir4HDvfIMx7LYHISeLH9oZivv5uYhT6oohO1qY/mc/+2Lz5PbNd2SkUX+Xu7tgkVvUsE+hDFd9+o6CSwKdNEfiTSKVpvE/HBROYZe4yzKVR775hi/zEV3U4E9w/3KiSYi1T0IbC1OzXFbL75v/tn5lrur019ZLZf22wumc2+6DHOevE/382S2tRLRF7VskHHzgU5e37AXxh2M5gD/XM5seer2U1ttvqixzjrxzPGZmZmZmZ4xtjMzMzMDHBgbGZmZmYGODA2MzMzMwMcGJuZmZmZAQ6MzczMzMwAuAalsX+Sb2HcugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, let’s plot several images and their predictions. Correct ones are green, while the incorrect ones are red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n",
      "(1, 28, 28)\n",
      "[[1.3418897e-06 5.6621285e-08 5.4118578e-07 6.8990357e-07 2.4289504e-09\n",
      "  7.5485551e-04 7.4890795e-06 5.0751660e-02 5.4536946e-07 9.4848287e-01]]\n"
     ]
    }
   ],
   "source": [
    "#use the trained model to make a prediction about a single image.\n",
    "\n",
    "# Grab an image from the test dataset\n",
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)\n",
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)\n",
    "predictions_single = model.predict(img) \n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#only image in batch\n",
    "plot_value_array(0, predictions_single, test_labels)\n",
    "plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction for our only image in batch.\n",
    "np.argmax(predictions_single[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}