06-Deep-Learning-Code/Tensorflow/基本图像分类.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-23 19:51:59.044177: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-12-23 19:51:59.230695: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-12-23 19:51:59.230733: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-12-23 19:51:59.242783: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-12-23 19:51:59.290160: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-12-23 19:51:59.291061: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-12-23 19:52:02.394606: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.15.0\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so,divide the values by 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images/255.0\n",
    "test_images = test_images/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 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": "markdown",
   "metadata": {},
   "source": [
    "### set up the layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(128,activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-23 19:52:13.152923: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 188160000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4944 - accuracy: 0.8260\n",
      "Epoch 2/30\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3724 - accuracy: 0.8655\n",
      "Epoch 3/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3352 - accuracy: 0.8772\n",
      "Epoch 4/30\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3114 - accuracy: 0.8868\n",
      "Epoch 5/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2957 - accuracy: 0.8914\n",
      "Epoch 6/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2793 - accuracy: 0.8967\n",
      "Epoch 7/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2684 - accuracy: 0.9013\n",
      "Epoch 8/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2581 - accuracy: 0.9045\n",
      "Epoch 9/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2490 - accuracy: 0.9066\n",
      "Epoch 10/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2393 - accuracy: 0.9103\n",
      "Epoch 11/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2325 - accuracy: 0.9125\n",
      "Epoch 12/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2246 - accuracy: 0.9158\n",
      "Epoch 13/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2178 - accuracy: 0.9177\n",
      "Epoch 14/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2115 - accuracy: 0.9199\n",
      "Epoch 15/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2063 - accuracy: 0.9219\n",
      "Epoch 16/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1975 - accuracy: 0.9252\n",
      "Epoch 17/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1939 - accuracy: 0.9266\n",
      "Epoch 18/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1895 - accuracy: 0.9290\n",
      "Epoch 19/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1853 - accuracy: 0.9298\n",
      "Epoch 20/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1790 - accuracy: 0.9321\n",
      "Epoch 21/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1753 - accuracy: 0.9326\n",
      "Epoch 22/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1723 - accuracy: 0.9357\n",
      "Epoch 23/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1672 - accuracy: 0.9376\n",
      "Epoch 24/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1626 - accuracy: 0.9389\n",
      "Epoch 25/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1600 - accuracy: 0.9401\n",
      "Epoch 26/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1544 - accuracy: 0.9420\n",
      "Epoch 27/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1537 - accuracy: 0.9417\n",
      "Epoch 28/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1489 - accuracy: 0.9431\n",
      "Epoch 29/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1463 - accuracy: 0.9447\n",
      "Epoch 30/30\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1432 - accuracy: 0.9462\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f0029bbf5e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images,train_labels,epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.4177 - accuracy: 0.8869 - 283ms/epoch - 903us/step\n",
      "\n",
      "Test accuracy: 0.886900007724762\n"
     ]
    }
   ],
   "source": [
    "test_loss,test_acc = model.evaluate(test_images,test_labels,verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model,tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.5718293e-17, 4.5014626e-15, 3.7136633e-24, 9.4852168e-20,\n",
       "       5.6487240e-16, 7.1025575e-08, 1.7054005e-12, 1.9434284e-04,\n",
       "       2.0062427e-13, 9.9980569e-01], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, 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 = 'blue'\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, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\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('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJkAAAPdCAYAAAAppLnfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5xU1f3/8c/uso0tsEvdZWEpUhQQKaKIX7EQLKgYjaLhZ4k1CRoTEyOJBWPsiSaWWJNoYmxRsYuKoAgKIh3pvXcWtrL1/P44GWdn7+csd7gLbHk9H499wLznzJ07szPn7Jx753xijDFGAAAAAAAAgABij/QOAAAAAAAAoOFjkgkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACKyZn0ZVVVWyZcsWSUtLk5iYmEO9T4CIiBhjpKCgQLKzsyU29tDOh/IaR2PF+wiNHa9xIDjeR2jseI0Dwfl9H/maZNqyZYt07NixznYOiMbGjRslJyfnkN4Hr3E0dryP0NjxGgeC432Exo7XOBDcgd5HviaZ0tLSvt9Yenp63ewZcAD5+fnSsWPH719/hxKvcRxp8+eLDBt28LefOlXkuOO8Oe+juldQUODJ5syZo7Y99dRTD9l+zJ8/X81TU1M92VFHHXXI9uNI4zWuC9KnuPoTNF68jw7MGKPmrjNVvvjiC0/2zDPPqG2PPfZYNd++fbsn69q1q9q2qKhIzffu3avmcXFxnmz9+vVq21deeUXNG5LG+Bqnn8fh5vd95GuSKdR5pqenN6jBAI3D4TjNlNc4jjRlXiDq29f20uV9VHe05zIlJUVteyifB20yyZU35t9HCK/xSEH6lAP1J2i8eB+5RTvJpI0L8fHxatvExEQ1T0hI8GRJSUlq24qKCt/bEBFp1sz7MdC1fw3p93Qgjek1Tj+PI+VA7yMW/gYAAAAAAEBgTDIBAAAAAAAgMF9flwMAoDHYv3+/J/vrX/+qtn311VfVPC8vz5Pt3LlTbZucnOx7G9FyfWVCy7WvRYiInHLKKWp+3XXXebKzzjorir0DgMYl2q/LjR8/3pN99dVXatv33nvP9364vn5VXFys5q6v0WnjU0lJidr2gw8+UPNzzz1XzQE0bZzJBAAAAAAAgMCYZAIAAAAAAEBgTDIBAAAAAAAgMCaZAAAAAAAAEBgLfwMAGp3bbrtNzZ977jlPlp+fr7Zt3ry5mmuLpWZkZKhtXYuopqSkeLLKykq1bWJiou/9ENEXpy0tLVXbfvjhh2quLUI7ZMgQte2XX36p5gDQmMTGRndsfsGCBZ7MNVa0adNGzYuKijyZa6zIzMxU8/j4eDXXxopVq1apbZctW6bmLPwNQMOZTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIExyQQAAAAAAIDAqC4HAGiwtGpxIiIPP/ywmrdv396TaZXeRERiYmLUXKvIU15errZNSkrynbvuz1XRqKKiQs2j2Y/U1FQ1j4uL82RfffWV2va8885T8/fff9/n3gFA41NYWOjJWrdurbZ1VTmtqqryZK6Ko1pb137Uth3Nxo0bfbcFAM5kAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAARGdTkAQIN15513qnl6erqaaxXcKisr1bbbtm3zvR8tW7ZUc1dVt2bNvMOvqwLQ/v371bxVq1Zqrj0e7f5EREpLS9Vcq6DXrl07te2XX36p5rt27fJkrspKANBQbd++3XdbV1/sqi6qcVUWjY+PV3OtWqjrPl1j544dO3zuHQBwJhMAAAAAAADqAJNMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACY+FvAECDtW/fPjVPTExUc21Ba9cC3z/72c/U/IYbbvBkAwYMUNumpKSo+aZNmzxZWlqa2jY3N1fNXYvNao9duz8RkQ4dOvjeRkFBgdq2pKREzdesWePJWPgbQGPz3Xff+W6bkJCg5q5+VFu027V4eFVVlZpr456rvWvs1Ao5AIALZzIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAqO6HACgwSotLVXzpKQkNXdV2dE88MADat6iRQtP5qrqU1xcrOannnqqJ/v8889975uIyNFHH63my5Yt82T5+flq28cee0zN77zzTk/Wpk0btW1lZaWaT58+3ZMNHjxYbQsADdWCBQvUXKsk5xqbXGPF/v37PZmrqmqrVq3UPCYmRs218dA1proqpQKAhjOZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIFRXa6RclX7iY31ziu6qk64aJUnEhMT1bYrV65U8+7du0d1nwBQVlbmu62rX3NVztFcccUVav7uu+/63kZeXp6aa5Xk7rrrLrVtenq6mr/22mtqvmfPHk+2fv16te3o0aPVXKsu5xpXmjXT/5SYP3++mgNAY/Ltt9+qufY3t6uKnKsf1SrJDRgwQG3r6nMzMjLUXPvb3bV/HTt2VHMA0HAmEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGAs/F3HjDG+MhF9QUARkc2bN6v5jBkzPNnZZ5+ttk1JSXHtYmCuRb41EyZMUPPbbrutrnYHQBOxZcsW321d/WtJSYnvbWzatMl3W5c33njDd9vLL79czZOTk9XctRB3v379PNnWrVvVtqmpqT73Lnquwg8A0JgsXbpUzePj4z2Za2wqLCxU86ysLE82c+ZMta2r4EVVVZXvvKKiQm2bmZmp5gCg4UwmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBUlzsMXJUkXKZNm6bm33zzjSdzVVv6xS9+EdV9RmPHjh2e7JNPPlHbpqWlHbL9ANC07Ny5M/A2tMo5WgUgEXf/6qrUoxk2bJjvtmeeeaaar127Vs1d1X4mTpzoyU499VS1rVaJTkSvOud63HFxcWq+bds2NQeAxmTfvn1qrvWN0VaXu/DCCw9+x/7HVTGuefPmvrdRVlYWeD8ANB2cyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIjOpydayystKTNWumP83ffvutmi9dulTN27Vr58lWrlyptv3hD3+o5hkZGZ5s//79atvc3Fw13717tyfLz89X23bo0EHNASBamzdv9t3WGOO7ravCjqs6mlYdyHV/y5cvV/PbbrvNk61Zs8a1i6qjjz5azZctW+bJNmzYoLZ96qmn1HzmzJmeTBs/REQSExPVPJrfFwA0VNu3b1fzlJSUwNu+7LLLfLd19cV79uxR89atW/vednFxse+2AMCZTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIGx8PdBqqqqUnNtke+ioiK17ZtvvqnmroX7tAW6CwoK1LauRWi13NV28eLFap6Tk+PJXAvCaguhA8DB2Llzp++2cXFxal5RUeErExFJTU1V89///ve+t/Hpp5+q+YIFCzyZq891FVbQFvgW0RcVHz16tNp2/vz5aq5xjXsxMTFqXl5e7nvbANBQlZSUqHlaWponi/bv4tNOO8132yFDhqj5jBkz1Nw1bmlatWrluy0AcCYTAAAAAAAAAmOSCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAILAGV13OVQlNq24TbSUcLXdVgXBVLtI888wzat6uXTs1T0pKUvP169d7Mq3iXG3b1ipJuJ6PlJQUNdeq3+3bt09tW1paquZaxT3X/QGAiMjWrVt9t42N1Y+haGOIq8JOixYt1PyBBx7wvR+ubWh99JIlS3xvV0Skffv2ar5r1y5P5hpXouEaD7WqqtFuI5oxFQAaKlfVTVc/6qo4rencubOaT58+Xc1dn6k0rrEMADScyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIrF5Ul9OqG0RTAc7FVV3IRat6E23Fm1dffdWTbdu2TW3bv39/NXdVOtq7d68ny8zMVNu2atVKzbWqQ4WFhVHth8ZVoaK4uFjNV65c6cmOO+443/cHoOnZuXNn4G0kJCR4stNPP11tO23aNDXPycnxZK6xwlVhUxtvUlNT1bYurj5aq1zn2g/XfbZs2dKTzZ8/X23rGoc069atU/Nu3br53gYANATaZ5aysjK1bV30gdrYJOKu6hnNZyoAiAZnMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAARWLxb+jmbhuaqqKt+5ayFW1/1Fs8j3P//5TzVfsWKFJ+vYsaPadvfu3WruWkS7pKTEk3Xo0EFtW1BQoObaY2/evLnadv/+/b73L9rFAz/55BNPxsLfAGqjFT9wcfWBWp951VVXqW0nTpyo5q4+UxPNmBUtV7+rLQjuWvi7WTP9z4ALL7zQk7kW/o6GVnxChIW/ATQ+Wv9aVFSktu3du3fg+zvnnHPU/OGHH1bzuhiHAEDDmUwAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwA5JdbloqxVoFXJcFdZiY/V5MVcejS1btniyCRMmqG21Sm8iIt27d/dkhYWFaltXtR9X1bn4+HhP5qouVFxcrOYa13OXmJjou31KSora1rV/X331lc+9AwDL1TdqXH1027ZtPVlGRkZU+6H1xVpFNxF3H1gXY5Zr25WVlb7busahE044wfd+uB5LUlKSJ6OaEYCmQuuLXZ9vunbtGvj++vXrp+ZlZWVq7hq3NK6/8wFAw5lMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgTHJBAAAAAAAgMCiqi5XWVnpqZQQFxfnaXcoq+a47Ny505OtW7dObbt8+XI137p1qydLSEhQ26anp6v53r17PVl+fr7atry8XM1d1X6059r1GF0VI1q2bOnJXI9Rq4oholfGSE5Ojmobqampnuy7776LuOyqygegadL6V62CmYjI/v371VyrkLN06dKo9qNZM+/Q6erPXaId4zSuKkXatl33pz2ntbXXuCrGafu3a9cu39sFgIYgJydHzYuKijyZ6zNSdnZ24P3QxqbaUF0OwKHCmUwAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwKIqQxAXF6dWOKtp+/btar5+/Xo116ovaJmISElJiZqvXbvWkxUXF6ttXdUX0tLSPJmras6+fft875/r/lz756rUlpiY6MnKysrUtllZWWquVbpz7UdGRoaaa1Xf9uzZo7bVqsiJiGzbtu2A23C9BgA0Ta7+OBo9e/b0ZKtXr45qG1rlNde+uaq0uSrDBd0PEb1ikDZ+1LYfbdu29b0f0VSX0yrBAkBD5uov16xZ48lcFd1WrFgReD9c1aJdoqlG5/qsAAAazmQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAILKqFvzWfffaZJ9uyZYt+Z44F5rSFQCsrK9W2roXHtW1rC3mL6AtXi+iLUbsWRS0tLVVzbbFs16Korv1wPfaUlBRP5lpYu2XLlmpeF4uuao8xNlafr3Qt1K4tWF7zdxjNgoQAGr/y8nJPFm0/oS38PXXq1Ki24Vq4VeMaQ7RxwdWPRrttLfdTtKO6nJwcX5mIyK5du3xv1zXuAUBDNXjwYDVfunSpJ3MVYZg/f35d7pIvrs8yGtd+A4CGM5kAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgUVVlmfy5MmeCmf/+Mc/PO169eql3j4rK0vNtSpwropsCQkJaq5VZHNV3nFVndMqnrmq/RQUFKi5dp+uCmsxMTFq7qoup1W/2759u9p2yZIlaq49Rtf9uWgV7YqKitS2SUlJvrfRtm3biMuu5xhA05ScnOzJoq2apvXpy5YtU9vGx8erebR9ZlCu+3ONIVoebRW+VatWebL27durbbWxSUR//oqLi6PaDwCo70455RQ1f+GFFzyZ63PMvHnz6nSfqnN9lommUmq01U8BNG30GAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIExyQQAAAAAAIDAoio3M3DgQElPT4/IZs6c6Wm3aNEi9fbTp0/3fV+uqj6uynCZmZm+MhGRFi1aqLlWec1VoW737t1qvnz5ck/mqqaTn5+v5q6KQQsWLPBkxx57rNq2c+fOaj5p0iRPVlpaqraNppKEq3JRdna2mtd8HYl4q8kVFhb6vn8AjZ/Wz0Rb6a28vNyT7dmzR23bvHlzNXdVPw3K1fdHS6u4F00VIRGRd99915O5xpW5c+equTaG5OXlRbUfAFDfnXTSSWquVVh2VUStWWG5Lml/c4u4P+NoDtW4B6Bx4kwmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwKJa+Ltly5aexePuuusu37d3LeT8zTffeDJtAW0Rka+//lrN161b58kWLlyoti0qKlJzbQE810KsrkWxtcXG+/btq7YdPny4mp9zzjlqri0gGK3zzz/fk23YsEFt26pVKzXXFhB0LcjuWhA8MTHRk/Xo0SPismthdABNk9bv7t+/P6ptLFu2zJO5ih9o/ZSIvni4azHXaBZWdbV15dEsFB7toq3amOoqNPHmm2+qubZ/2nMHAA1Zbm6ummt/L7vGG9dYtmbNGk/WtWvXKPbOXUwpmv442iIbAJo2zmQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIjEkmAAAAAAAABBZVdbmgUlNT1fyMM87wlYmI/PznP6/TfWpq3nvvvSO9C75RyQJAdVq1t2irpuXl5XkyV1UfV3U5VyU5jasSqZa7qsVFm2vV6FwV6lq0aKHmM2bM8GQ1K4AeiLZ/JSUlUW0DABoqrZJcRUWF2rasrEzN66K6XFZWlpprVUQzMjLUtvxNDiAanMkEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACOywVpcDAOBgxcfHe7LmzZurbQsLC9X8lltu8WSfffaZ2tZVCc1VMS4aWuW1aKrF1UaruOfa53379qn5qaee6snOPfdcte0f/vAHNdeq8GnVlgCgIXD1xa6++4c//KEne+WVV9S2rkqp06dP92TDhw937aLKNU5qXI/RVXUOADScyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYC38DABqEoqIiT6YtLi2iLxIuIlJeXu7J2rRpo7ZduXKlmnft2tWTuRZtPZSiWYTW9Xzk5eWpedu2bT1Z69ato9g7fbHx9evXR7UNAKgvol34e9SoUZ7sX//6l9o2ISFBzd966y1Pdvfddzv2UFdZWanm0RSgSExMjOo+ATRtnMkEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIzqcgCABmHo0KGebMaMGWrbpKQkNe/Ro4cnW7FiRbAdayLWrFmj5mlpaWpeWlrqyQYPHlyn+wQAh4uriqhWSVNE5Oyzz/ZkGRkZalutv6xt29Ho06ePmi9atMiTucbOrVu3Bt4PAE0HZzIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAqO6HACgQdAqk5WUlKhtExIS1LwuKvU0VeXl5WruqopUVlbmyVJSUup0nwDgcImLiwu8jdzcXDWfOXOmmhcXF3uyr7/+Wm170kknqXllZaWa79+/35Np/baIyK5du9QcADT8tQ0AAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYC38DABqEDh06eLL+/furbZOSktQ8moWnKyoq1Fxb/NUY43u79Ylrv7XHeNRRR6ltR44cqeZ79+71ZEOGDPG/cwBQj8TExATexnXXXafmvXr1UvNLL73Uk7kW+Ha5/PLL1Xzfvn2eLDU1VW37f//3f1HdJ4CmjTOZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAvO18HdoYdD8/PxDujNAdaHX2+FYUJfXOI60wsLgt9devo3pfVSoPEmVlZVqW9ei3dq+RbuNprrwt+t5Ki8vV3Pt+SsqKlLbBnnNNKbXeF0K0qe4+hM0XryPDo+SkhI1LysrU3Otz4z2eXP1u1rf7erPD0Xffbg1xtc4/TwON7/voxjj4522adMm6dixY93sGRCljRs3Sk5OziG9D17jaOx4H6Gx4zUOBMf7CI0dr3EguAO9j3xNMlVVVcmWLVskLS2tTsp3An4YY6SgoECys7MlNvbQfrMzyGs8Pz9fOnbsKBs3bpT09PSDun+2wTYO1TYayvsIOFgN5TV+pPsCtsE2atNQ3kfAwWoor/Ej3RewDbZRG7/vI19fl4uNjT3kM76ApkWLFoflfuriNZ6enn7Qb3a2wTYO5TYa0vsIOBgN6TXe0PsTttF4t9GQ3kfAwWhIr/GG3p+wjca7DT/vIxb+BgAAAAAAQGBMMgEAAAAAACAwJpmAgBITE2X8+PGSmJjINthGvd0GgCOrvvQFbINtAKi/6ktfwDbYRhC+Fv4GAAAAAAAAauM5k+nuu0WOO859gxdfFGnZMtidXnWVyAUXBNtGUzd5ssjRR4tUVtbtdr/4QiQmRmTvXv36devs9fPnH/x91MVr6GCVlYl07iwye/aRuX8AkTp3FvnrX8OXY2JE3nnnCO1MI7R8uUj79iIFBXW3zZp/J9SnMf3SS0UeeeRI7wUANCyHYqyoLxgXgMOvUX9d7tRTRX75ywO3mzBBZMQIkVat3BMo+/eLjB1r26Smilx0kcj27ZFtNmwQGTlSpHlzkbZtRW69VaSiInz9vHki/fvb2593nsiePeHrKipEBg4UmTXL32P77W9F7rhDJC4uMi8pEcnMFGndWqS01N+2GittwjQhQeQ3vxG57bYjsUdA43LVVbbPjImx762jjhK5557Ifq8x+fJL23dnZ7snw4wRuesukawskeRkkeHDRVaujGyzZ4/ImDEi6el2wv2aa0QKC8PXr1sncsopIikp9t916yJvf+65Im+95W+ff/c7kZtuEklLs5dDBxJCP+3a2fFszRp/26vv7rhD5L77RPbtO9J7AsCleh+k/dx995Hew2Aa+lhRfWzXfjp39vtM1A+MC8Dh16gnmfwqKhI5+WSRhx5yt/nVr0Tef1/kjTdEpk4V2bJF5MILw9dXVtoJprIyka+/FvnXv+wZO3fdFW5z7bUip58uMneu7ejuvz983SOPiAwdKjJ48IH3d/p0kdWr7QeDmt56S6R3b5FevTgbwGXMGPscLl58pPcEaPjOOktk61b7x/Gvf20/HPzpT0d6r4IpK9PzoiKRfv1E/vY3920ffljk8cdFnnlG5Jtv7B//Z55pD1SEjBlj+59Jk0Q++MB+ILn++vD1v/61SIcO9oBHVpadGA95/XWR2Fi9/69pwwa7/auu8l63fLkdx954w+7LeefV/Zmxh1Pod9anj0i3biL/+c+R3R8Ablu3hn/++lc7iVI9q97nGVN/D1w01rHisccifx8iIi+8EL787bf+nocjjXEBOIKGDDFm2TLzvfHjjenXL3x51SpjunQxZuxYY6qqjHnhBWNatDAR3nnHmP79jUlMtG3vvtuY8nLjdOWVxowaZdu1bm1MWpoxN9xgTGlpuM3+/cbcdJMxbdrY7Q4dasysWZHb+eILY44/3piEBGPatzfmttvC93vllcbYoSn8s3ate5+MsdeLGDNvXmS+d68x8fHGvPFGOFu61LadMcNe/ugjY2Jjjdm2Ldzm6aeNSU8PP67kZHs7Y4x56iljzjnH/n/1amO6dzcmP7/2/QsZO9aYH/1Iv+7UU4155hl73z/4gfd6EWOef96YCy6w+3PUUca8+274+s8/t23y8uzloiJjzjrLmJNOspn2HC1aZNukpBjTtq0x/+//GbNzp3v/Q6+ht9+295+YaMyIEcZs2BDZ7qmnjOna1T73PXoY8+9/R16/fr0x559v7zctzZiLLw4//y+84P39v/BC+LannWbMHXe49xHAgYX68up+8ANjTjzR/n/YMGNuvjny+lGj7O1CcnON+ctfwpdFbN8QsnChfb8mJRmTmWnMddcZU1Bgr/vkE9t/hPqrkF/8wt4mZNo0Y04+2W4jJ8eOLYWFkftwzz3GXH657Uuq759Lzf00xo6R7dsb86c/hbO9e+0+vvqqvbxkib3tt9+G20ycaExMjDGbN9vLRx9tM2Ps2HLMMfb/eXm2z6zZV7r86U/GDBoUmdXs440x5uWXbbZsmT7Gv/22vT6k5t8JNV8HtY3flZXGdOhg+/fq5s61z8G6deHHes014b8RTjvNmPnzvfvw/PPGdO5sbxvyhz/Y3zeA+q9mnxPqoz76yJgBA+zfgJ9/fuDPBX76rvnz7d/Jqam2XxkwILIvZqzw9xhcz8Obb9p9SEiwbf7859q3Y4z9nYX+Pi8ttZ9x2re3z0WnTsbcf3+4LeMC0HDENmsmcvXV+gTUwoX2DJ8f/1jkySftKZI1TZsmcsUVIjffLLJkicizz9ozeO67r/bJrcmTRZYutafuv/qq/craH/4Qvv63v7Vn5fzrX/bMn6OOsjP8oa+Ybd4scs45IscfL7JggcjTT4v84x8i995rr3/sMZEhQ0Suuy48896xY7RTcNacOSLl5fZU1pBevUQ6dRKZMcNenjFDpG9f+9WDkDPPFMnPD58x06+fPRpRUWEf/7HH2vynP7VHNEJfZziQadNEBg3y5qtX2/245BL7M22ayPr13nZ/+IO9fuFC+xyOGRP51b2QvXtFfvADkaoqu9/aOkp799qzs/r3t+scffyx/RrhJZfU/hiKi+1r5N//FvnqK7udSy8NX//22/Y19etfi3z3ncgNN4j85Ccin39ur6+qEhk1yu731Kl2/9asERk92l4/erS9be/e4d9/6DoRe8bYtGm17yOA6CUn191RzaIi249mZNgjp2+8IfLZZyI33mivP+MM2y9V/zpAZaU9gjtmjL28erU92+qii2yf9/rr9kzG0DZC/vxn20fPmydy550Ht79r14ps2xY5VrRoIXLCCZFjRcuWkX348OH2iPM339jL/frZx1lVJfLpp+Gx4tZb7de2/Y5lrrGipuRk+29d/d5qG79jY0Uuu0zklVcib/Pyy/Zs3txce/nii0V27BCZONGOwQMG2N939bFq1Sp7PxMmRH7NffBg+9Xzpv6VcaAhGzdO5MEH7WeFY4898OcCP8aMEcnJsePJnDn2PuLj7XWMFdE9hprPw5w59m//Sy8VWbTIntV85532M6Ffjz8u8t57Iv/9rz3b9uWXI7+ax7gANCAffmhnlktK7KxTaBb4q6+MycjwzkLXPFJwxhmRs8zGGPPSS8ZkZblntq680h6RLioKZ08/bY8sVFbaowbx8fboakhZmTHZ2cY8/LC9/PvfG9Ozpz0aEPK3v4W3YYx+FL02rjOZXn7ZzsrXdPzxxvz2t/b/111nz8aprqgofDTGGGO++86YU06xM/OXXWbMvn327JxRo4zZtMnevls3Y26/vfb9bNHCe1aPMfY5ueCC8OVRo+zvszqRyDN4CgttFjoSEjqCtHSpMccea8xFF0WeYVbzOfrjH72Pe+NG22b5cn3/Q2cZzZwZzkJnhn3zjb180kn2Oa3u4ovDZ399+qkxcXGRR2kWL7bbCB3Zqnm0vbrHHrNHOerCk08+aXJzc01iYqIZPHiw+Sb0IHyYOnWqOffcc01WVpYREfN2zUM8Ptx///1m0KBBJjU11bRp08aMGjXKLKt+eqIPTz31lOnbt69JS0szaWlp5sQTTzQfhV64B+GBBx4wImJujuYNaIwZP368EZGIn549e0Z9/5s2bTJjxowxmZmZJikpyfTp08d8W/1w4AHk5uZ69kNEzM9//nNft6+oqDB33HGH6dy5s0lKSjJdu3Y199xzj6mq3mH5kJ+fb26++WbTqVMnk5SUZIYMGWJm1Tyl8wiqfgZLVZUxkybZo4+/+Y3Ngp7J9NxzdhyqfiT5ww8jzxq9+WZjTj89fH3Ns5uuucaY66+P3Idp0+w2QuNebm5k3+mHdkT2q69svmVLZH7xxcZccon9/3332TMza2rTJnx2z6ZNxowcaUzHjvbfTZuMmTrVHmnevdtur0sX7xnANfXrZ482V1fzTKYtW2x/26GD3VbQM5n8jN/z5tkjzOvX28uhs5ueftpenjbNngW8f3/kfnTrZsyzz4b3IT7emB07vI97wQK7v6GzouoLxgovxoqmMVbUxnUm0zvvhDM//YqfvistzZgXX9T3g7HC/2PQnocf/9j7DYpbbw2fXaVtx5jIM5luusmO59rLv7GOCxrGCi/GioY3VsRmZdnJph07whNPGzbYM1juusueDVKbBQvsQq+pqeGf0NlDxcXu2/XrZxfIDhkyxC5mt3GjPZpQXm6PaobEx9tZ6KVL7eWlS+1tqp9dNXSo3camTbXv85HSu7c962b9ensUt7xcZPx4e5bYTTeJnHSSfT4nTLDrP7mUlIgkJUVmlZX26M7/+3/h7P/9P3sEoaoqsm3oSIeI/Q54enrk71/E/v6POsoeyUlIcO/LggX27KLqv/9evex1q1e7b9esmT0LLaRXL3vEpvrvt/rvX8Rern59x46RR2mOOSZyG7VJTq799enX66+/LrfccouMHz9e5s6dK/369ZMzzzxTdtR8Qh2KioqkX79+8rfavrh/AFOnTpWxY8fKzJkzZdKkSVJeXi4jRoyQoqIi39vIycmRBx98UObMmSOzZ8+W008/XUaNGiWLD2Lhqm+//VaeffZZObb6Cy0KvXv3lq1bt37/M3369Khun5eXJ0OHDpX4+HiZOHGiLFmyRB555BHJyMjwvY1vv/02Yh8mTZokIiIXX3yxr9s/9NBD8vTTT8uTTz4pS5culYceekgefvhheeKJJ6J6LNdee61MmjRJXnrpJVm0aJGMGDFChg8fLps3b45qO4fSBx/Y931SksjZZ9szButq0dalS+1YkZISzoYOtX3a8uX28pgx9ozYLVvs5Zdftuvjhc68XLDA9oPV+6gzz7TbWLs2vN1oj+IeSh062Oc1tE5G69YiP/+5Xbvj3nvtWa/Ll9t1sJ591r0dbawIycmxz2t2tj1j7K23au/r/fIzfh93nK2OGjqbaepUOwaF3l4LFtixPFRoI/Szdm3kuJKbK9KmjXcfQmdm1UUfX1cYK7wYK5rWWBGt6n2yn37Fj1tuseujDh9uz5Kq3p8wVkR33zWfB9ff7StX+l/v76qr7NlHPXuK/OIX9uyskMY4LmgYK7wYKxrmWNEsNElTfSKiTRv7h+err9qv0qWnuzdQWGi/flV9EeyQaDus+qp9e/s1gr17I78ytn27vS7UpmZluFD1uVCbmm65xVa/y8mxH5Luvdf+0T9ypL183nn67Vq3FsnLi8w++cR+hbD6V8JEbMc+ebKdNAoJnRocEhPjnYgaOdJ+6FiyxH4N0KWw0O6ntmh6aAKzPtqzRx+EovXoo4/KddddJz/5yU9EROSZZ56RDz/8UP75z3/KuHHjDnj7s88+W84+++xA+/Dxxx9HXH7xxRelbdu2MmfOHDnllFN8beO8Gi+2++67T55++mmZOXOm9O7d2/e+FBYWypgxY+T555+Xe0PfXY1Ss2bNpL3rTePDQw89JB07dpQXXnjh+6xLly5RbaNNjRfHgw8+KN26dZNhw4b5uv3XX38to0aNkpEjR4qISOfOneXVV1+VWX7LR4pISUmJvPXWW/Luu+9+/3u8++675f3335enn376oJ/funbaafbrygkJdtxo1ix8XWysXRGtuvLyur3/44+3C3q+9prIz35mv2pb/fT8wkL7ddtf/MJ7206dwv+vPpF1sEIv2+3bI/u/7dvDlS7bt/dO6ldU2D7J9bK//35bAXXgQHsQ5957bT9+4YUiU6bYgxQabawImTbNju1t20Z+Vftw/M5E7OTgK6/Yr6u88or9mkqrVva6wkL7/H3xhfd21cdg1+8s9NWJuujj6wpjRSTGCqspjRXRirZP9tN33X23XQLkww/tV67Gj7djxw9/yFjhGitcDuZ5iImp/Xc0YICdNJo40X4N8JJL7ITgm282znFBw1gRibHCaohjhVpdLjnZzognJdlZ/IIC9wYGDLCz5Ecd5f2JraV23YIFduY8ZOZMOyPdsaP9wJCQYNfqCSkvt9+hPuYYe/noo+33lat3Vl99Zf9YzsmxlxMS6qZazsCBtpOePDmcLV9ujxwMGWIvDxliv4NcfUCYNMn+ER/a5+pCa1KFvutdWRnuaMvLa9/v/v3t5E91//iH/R70/PmRP5deaq+L1oMPilx5pf2uc837qm7AALvmVOfO3t9/bQNQRYVdwylk+XI7iXf00fby0UdH/v5F7OXqv/+NG+1PyJIldhuhNrX9/r/7zj6PQZSVlcmcOXNkeLUv1cfGxsrw4cNlRuhL9UfAvv/VaM3MzDyo21dWVsprr70mRUVFMiT0Avdp7NixMnLkyIjnJForV66U7Oxs6dq1q4wZM0Y2bNgQ1e3fe+89GTRokFx88cXStm1b6d+/vzz//PMHvT9lZWXyn//8R66++mqJ0RamU5x00kkyefJkWbFihYiILFiwQKZPnx7VwF9RUSGVlZWSVGO2Pjk5OeqjMIdSSop9v3fqFDnBJGL/mAtVphGx78fvvvO/7aOPtmNF9YNnX31lx5aePcPZmDH2DKb337fX/W8MFhHbRy1Zoo9RdXHmTnVdutg//quPFfn5dv2M6mPF3r12PYmQKVPsRP8JJ3i3uXSpnYT54x/t5aBjRfV97dbNuxZgmzZ2zK/+nFdf1+JA/IzfIvaD3nff2efhzTfDa2iJ2N/Ztm329VTzd9a69YH34bvv7N8BftoeDowVXowVVlMaK4Lw06/47bt69LAVoz/91E6+hD43MlYEekjOv9t79BCJi7OXa/5NsHKl98yi9HR7wPz55+23Kd56y04QNbZxQcNY4cVYYTXIsWLevMjKa9XXWigosCvxDx0aruZT8zvPH39sTLNmtlLcd9/Zagivvlr7ukJXXmnXTrrsMruOzocfGtOunTHjxoXb3Hyz/a71xIm2zZVX2rU59uyx12/aZEzz5rYKwdKl9rvbrVtHrkF03XV23aS1a221s9BaTTXt3m3XiAitT/Xaa/by1q3hNj/9qV1LacoUY2bPNmbIEPsTUlFhTJ8+dn2i+fPt89KmjTG/+533/kpKjOnVK3Ltp7PPtvs7f76taPHf/7qfv8cfN2bgwPDlHTvsd5BD6ypV99FHdn2S3bvt5QN9H7rmeh2//KX93YSq4tVck2nzZvs4f/QjuxbSqlX2sV91lX1ONC+8YPd38GC7LtPs2bYaVagilTF2H+Pj7ffOV6ww5pFH7BpMn39ur6+qMua444z5v/8zZs4cu5bTwIF2DZiQl1+2lefmzbO//+rf487N1de1isbmzZuNiJivv/46Ir/11lvN4MGDo96eHOR3p6urrKw0I0eONEOHDo36tgsXLjQpKSkmLi7OtGjRwnz44YdR3f7VV181ffr0MSX/W7xg2LBhUX93+qOPPjL//e9/zYIFC8zHH39shgwZYjp16mTy/ZZeNMYkJiaaxMRE87vf/c7MnTvXPPvssyYpKcm86FqI4QBef/11ExcXZzaHyrn4UFlZaW677TYTExNjmjVrZmJiYsz9NRev82HIkCFm2LBhZvPmzaaiosK89NJLJjY21vTQFmo4ArTqctU984ztpz/4wPYh111n11TwuyZTUZFd3++ii2wVyylTbMXJmhV9Vq60tzv2WLuuRnULFthKmmPH2r5gxQo7Xowd694Hl4ICu43QuPnoo/b/obWFjDHmwQeNadnSVu1cuNA+P126hNf0MMZW4+zf3/Zb06fb6qKXXea9v6oqOwa//344+9nP7NobS5bYbYTWI9G8956t+Fm9L9aqy1W3e7ftN3/xC9ufv/yyHYujqS53oPE7ZOhQu520NGOKi72Pu18/u8bW2rV2DZPf/z5caam2NfeuvNKYq6/WrzsSGCsiMVaENZWxojauNZlq9lEH6lcO1HcVF9t+//PP7bo806fb9XxCa6syVrj/btfWZKr5PMyZY9evuuceuybriy/a57N6ZedLL7UV8ebOtX356afbv/VDbR55xJhXXrF/Lyxfbsfz9u3t57fGNi5oGCsiMVaENcSxotZJJmNsR3nSSXbB6sJCfWG9jz+2bZKT7QeIwYPtgq0uoT9I77rLmFat7ITTdddFTgKUlNgF4Fq31kuVGmPMF1/YSaSEBNsJ3XabMeXl4euXL7cTF8nJkY+xJq3cvUjkhFVJiTE//7kd0Jo3N+aHP4ychDLGDlpnn23vr3VrY37968j9CRk3zl5X3cqV9rGkp9uBwTUhZowdSJOSbLlpY+zi7C1b2kUQayottdc99pi9HO0kkzH295CVZZ9PbXH0FSvs89GypX3svXrZySnXWmSh19Bbb9kPjImJxgwfHjn4GmMnmLp2tQNQjx7eSaH16405/3z7R0Vaml3gMLQYsDH29XTRRXa/RMKP8euvbVb9Q83BqI+DwU9/+lOTm5trNm7cGPVtS0tLzcqVK83s2bPNuHHjTOvWrc3ixYt93XbDhg2mbdu2ZsGCBd9nBzMY1JSXl2fS09PN3//+d9+3iY+PN0OqzwAbY2666SZzYvVZzCiMGDHCnHvuuVHd5tVXXzU5OTnm1VdfNQsXLjT//ve/TWZmZtQD0qpVq8wpp5xiRMTExcWZ448/3owZM8b06tUrqu0cKgeaZCors/1ZZqb9A/aBB6Jb+NsY+8f3aafZPi8z044VoYMe1Q0ebG87ZYr3ulmz7IKkqam2vzj2WLuoqmsfXEL9Y82f6o+nqsqYO++0k/OJibY4Rs0iCLt32w8Kqam2z//JT/TH9Mwztg+rbvt2u81Qn1e9gEZN5eX2Q9bHH3sfg2uSyRj7/B91lO3Pzz3XjufRTDL5Gb+NsX28iDFXXOG9Lj/fbiM7244BHTsaM2ZMuNiD68NESYkdX2bMcD++w42xIoyxIlJTGStq43eSyU+/UlvfVVpqJzk6drSfG7KzjbnxxshJHcYK/Xo/k0zGGPPmm3ah7/h4e2D+T3+KvH7zZnswPiXFTph99FHkZ5DnnrMHkFNS7OM94ww7IRXSmMYFDWNFGGNFpIY4VsiBm6A++s1vvFUw4M8ll0T+0XCwSktLTVxcnKcDv+KKK8z5558f9faCDgZjx441OTk5Zs2aNQe9jerOOOMMc73PF9nbb7/9fYcV+hERExMTY+Li4kyF6/CYD4MGDTLjqp/meACdOnUy19Q4neWpp54y2dnZUd/3unXrTGxsrHmnepkbH3JycsyTTz4Zkf3xj388qIoWxhhTWFhotvyvDM0ll1xizgmVWQQO4MknvRVAG7OnnvJWODrSGCvCGCsiMVagvmjMY0V9HBc0jBVhjBWRGuJYUcuqSajPbr/dVlCouWA3aldWZhcy/9Wvgm8rISFBBg4cKJOrfam+qqpKJk+eHPV3joMwxsiNN94ob7/9tkyZMiXqxehcqqqqpLS01FfbM844QxYtWiTz58///mfQoEEyZswYmT9/vsSFvpAfpcLCQlm9erVkRbGK/NChQ2V5qPTY/6xYsUJyc3Ojvv8XXnhB2rZt+/1Ce34VFxdLbI1F6eLi4qTqIN+wKSkpkpWVJXl5efLJJ5/IqFGjDmo7aHpuuEHklFNqX1uxMYmPF4my2Mohx1gRxlgRibEC9UVjHivq47igYawIY6yI1CDHijqftgKakNdee80kJiaaF1980SxZssRcf/31pmXLlmZb9e/t1aKgoMDMmzfPzJs3z4iIefTRR828efPM+prfHazFz372M9OiRQvzxRdfmK1bt37/UxzF9wHHjRtnpk6datauXWsWLlxoxo0bZ2JiYsynn37qexs1Hcxprb/+9a/NF198YdauXWu++uorM3z4cNO6dWuzY8cO39uYNWuWadasmbnvvvvMypUrzcsvv2yaN29u/vOf/0S1L5WVlaZTp07mtttui+p2xhhz5ZVXmg4dOpgPPvjArF271kyYMMG0bt3a/Da0+INPH3/8sZk4caJZs2aN+fTTT02/fv3MCSecYMq078YCqLcYK9wYKxgrAFiMFW6MFQ1rrGCSCQjoiSeeMJ06dTIJCQlm8ODBZubMmb5v+/nnnxsR8fxcWXNl41potxcR80L11RYP4Oqrrza5ubkmISHBtGnTxpxxxhmBBgJjDm4wGD16tMnKyjIJCQmmQ4cOZvTo0WbVqlVR3/f7779v+vTpYxITE02vXr3Mc7UtEufwySefGBExy2sukuBDfn6+ufnmm02nTp1MUlKS6dq1q7n99ttNaWlpVNt5/fXXTdeuXU1CQoJp3769GTt2rNm7d2/U+wPgyGOs0DFWMFYACGOs0DFWNKyxIsYYY+r+/CgAAAAAAAA0JazJBAAAAAAAgMCYZAIAAAAAAEBgTDIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJr5qdRVVWVbNmyRdLS0iQmJuZQ7xMgIiLGGCkoKJDs7GyJjT2086G8xtFY8T5CY8drHAiO9xEaO17jQHB+30e+Jpm2bNkiHTt2rLOdA6KxceNGycnJOaT3wWscjR3vIzR2vMaB4HgfobHjNQ4Ed6D3ka9JprS0tO83lp6eXjd7dgTt2rVLzf/5z396shYtWqhtk5OTfd+f6zlzzWxXVlaqeXl5uSdr3bq12vb//u//1DwhIUHN66P8/Hzp2LHj96+/Q+lwvcbnzxcZNuzgbjt1qshxx9Xl3qApaIjvo6qqKjXXjpgYY6La9qE6ojhr1iw1Ly4uVnOtP3f1/S6lpaVqro0LQ4cOjWrbDUlDfI3j8Nu4UWT37uhv16qVSFP4rMj7qO6dc845niwuLk5t6/r7XOvnO3Xq5LutiMiOHTvUPDU11ZO5xiFX/tZbb6l5fdQYX+N8rsDh5vd95GuSKfRHeXp6eqMYDFydcFJSkidzTSZFM8nUvHlzNY92kqmsrMyTpaSkqG1dv6eGNMkUcjhOMz1cr3FlPI/qto3g7YcjpCG9jxriJJOrL3bdn9afRzvJ5Pqwou1LYxi7D6QhvcZxeG3YIDJokMj+/dHfNilJZPlyEcfn+kaH91HdadbM+zHL1W/Hx8eruTYuuP6Wd42d2n647tP19RdX3hB/f43pNc7nChwpB3ofsfA3AAAAGq1duw5ugknE3s5xAjwAAFAwyQQAAAAAAIDAfH1drrF544031Pzee+/1ZBkZGWrbrKwsNV+7dq0n69Chg9q2R48ear506VI1177ON3z4cLXt9u3b1fzyyy9XcwCoL1yn4Ebz1bhoTocvKChQ8ylTpqj53LlzPdnEiRPVtj179lRzbf8KCwvVtrsdC8m0atVKzfcrp2zcd999atvzzjtPzc8//3xP5loHBACasvz8fDVfvHixJ2vTpk1U2y4pKfFkq1evVttqnxNE3F9105bz0L7KLRL9fgNo2jiTCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAILAmufD3zp071bxz586ezLVYnkv79u09WWVlpdrWtZjrvn371Dw9Pd2Tbd68WW3bq1cv1y4CQL0WzcLf0SzwLSLy3HPPebLly5erbauqqtRc619Hjx6ttp0/f76aJyYmerKKigq1rWvx8LS0NDVPSUnxZK5xb/369Wr+q1/9ytd2RUQefPBBNc/OzlZzAGhMtGILIvr45PpMkJCQ4Dt3FSVybdu1MLn2GUf7LCQikpycrOYAoOFMJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgTHJBAAAAAAAgMCYZAIAAAAAAEBgTbK6nKuqW5s2bTzZ6tWr1baZmZlqXlBQ4MlcFYD27t2r5loFJRG9OoSr+l3fvn3VHADqO1cfGE0luaeeekrN9+zZ48m6dOmito2Pj1dzrYJP27Zt1bbDhg1T8wkTJngyrTqpiLvqUDT9/8SJE9W23bt3V/MWLVp4MlclujvuuEPN//nPf6o5ADQmb731lpprnzdycnLUtq7KcFqVU606qautiEhJSYmaaxVNXRWut2zZouZz5szxZAMHDlTbAmg6OJMJAAAAAAAAgTHJBAAAAAAAgMCYZAIAAAAAAEBgTDIBAAAAAAAgMCaZAAAAAAAAEFiTrC6Xm5ur5gsWLPBkcXFxaltXnpKS4slclYFclSRcFYby8vI8mauSRK9evdQcAOq7aKrLbdy4UW3ryrt27erJCgsLo9g7vZ/fvn272rZbt26+85UrV6ptXdVMTzjhBDX/8ssvPVl2drbadv/+/WpeXFzsyZKTk9W227ZtU/OXXnrJk11++eVq27qoKAgAR8Lf//53Nc/KyvJkrkqkrjGkWTPvRzXX+Na8eXM1d31mSUpK8nV/IiI7duxQ81mzZnkyqssB4EwmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJrkwt+uhUT79u3rybQFXkXci5SuXr3ak2kLdou4F+3u0aOHmmtci8q6Fu4DgPouNtb/8Y9Vq1apuWuh04qKCk+Wmpqqti0tLVVzrWiDaxt79+5V87PPPtuTTZ8+XW3rWnBbeyyu3FVQoqioSM0LCgo8WVlZmdo2MTFRzefNm+fJXAt/s8A3gIZq+fLlaj5o0CBPVlJSorYtLy9Xc+2zgmu8cfXRrrGiRYsWvjIR97i8ZcsWNQfQtHEmEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACCwJlmCzFUhoWPHjp7smGOOUdu6KuG88cYbnmzPnj1q28WLF6v5KaecouYDBw70ZB06dFDbuipMNG/eXM0BoCFy9aNJSUlqrlWMc1ULdfWXWrUfVzW7/Px8Nc/KyvJkI0aMUNu6tu3KjzrqKE/meozbtm1Tc60a0f79+9W2LrNmzYqqPQDUZ1u3blVzreKoiEjbtm092Y4dO9S2rs8mCQkJnmzjxo1qW9e456pGp1W0c1Wic23bVV0UQNPGmUwAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJpkdbmjjz5azSdPnuy7rauaQu/evT3Z4MGD1bbXX3+9mnfq1EnNc3JyPFlGRobaNjk5Wc0BoDHZtGmTmqenp6u5Vl3OpV27dmpeXFzsyVwVeeLj49Vcq4rXt29ftW1eXp6aZ2dnq/mWLVs82d69e9W227dvV/P27dt7Mlclvy5duqh5q1atPJmr8qlWQQkA6hNXNc5oKje7Kn26/m7ftWuXJxs0aJDa9rvvvlPzwsJCNdeqzrkq5bmqmbqqzgFo2jiTCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAILAmufC3tmiriEhKSooncy3y51pwW+NaENa1AG1VVZWaa4vrNWum/wr379+v5q4FywGgvnMtUq1xLXSqLYB97LHHqm1di3a7FkbVuBZL1fpi1+LcrsWyXQvIlpeXe7KtW7f63g/Xfbr2z0UbyxYuXKi2dS1kCwD1xYoVK9TcNVZonytcYmJi1Fzru1evXq227d+/v5ovX75czXNzcz2ZqwiD6/MGnysAaDiTCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABBYk6wu56r2oFWdi43V5+G2bNmi5lolueOOO05t66okUVJSouZatR9XlSNXpQsAaKjWrFnjyVJTU9W2rgqbRUVFnszVF+/Zs0fNteptrvtz0SqvuSrRufZvx44dvtu79k97LCL6eOh6rl2VUrVqRGvXrlXbUl0OQH23bNkyNW/evLmaa+ONq593Ve9s06aNv50TkRNPPFHN58+fr+baWOHqz13jkKsaHYCmjTOZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIE1yepyycnJaq5VknNV03HR2vfv3z+qbWhVfUT0/U5MTFTbUl0OQGOzceNGT5aUlKS21aq3uaxfv17NO3furOZaNR1XpU+t4qiISFpamidz9duu/XM9Rq2qm+t5cu33tm3bPJmrMqtrv7V8+fLlalsAqO9WrVql5i1atFBzrSq0q790Va2+6qqr/O2ciFx99dVq/swzz6h5NOOkqyqeKwfQtHEmEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBNcuFv1yJ12mJ8MTExaltXHs1C4a4FyMvLy9VcW3SVhfgANBXawqhawQYRkfT0dDUvLS31ZAUFBWpb17a1xbxdfa5r4W9t29q+ibgXZ9UWDxcRycvL82Suhb9LSkrUXHv+du7cqbZ1LXqr7feCBQvUtgBQ3+Xn56u56+957bOC6298V/7LX/7S386JyPHHH+97P0T0Pto1lrkKDfF5A4CGM5kAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgTXJ6nKtW7dWc636gquqT1lZmZq7KvhoXJWBjDG+t92hQwe1rasqEgA0VIWFhZ4sISFBbZuRkaHm69ev92SjRo3yfX8i+lihVScVcVeM03JXdaFmzfSh2tV+//79nsxVXcg1ZvXq1cuTvfvuu2pb13ijPSeuanYAUN+5+mJXZWmt33X1ge3bt1fzrl27+tw7N9fnHu0zTmZmptp29+7dak6fDkDDTAQAAAAAAAACY5IJAAAAAAAAgTHJBAAAAAAAgMCYZAIAAAAAAEBgTDIBAAAAAAAgsCZZXS4rK0vNtYpxrkpvxcXFau6q9qOpqKhQ85SUFDVPT0/3ZK7qdwDQ2GhV05KTk9W2ripAmmOOOUbNp02bpuauSkIaV+W1vXv3ejJXRbxoqreJ6I/dNZa59OjRw5O5qgi5tp2YmOjJ9u3bF9V+AEB90apVKzV3/T2vcVUtPeussw5qn/xwVa6Li4vzZK5KdHv27FFzPocA0HAmEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBNcuHv5s2b+85dC7y6FrpzLYyncS3wXVpaqubaIqquRQgBoKFyLaKqFVaorKxU27oWo9YWy87Ozo5qGxpXMQjXAuRFRUWezNWfx8TERJUnJCSoucb1/B111FGezPUYXdvQfl+uRW9deTSLrAPAoeTqj/Ly8tRcG8tWrVqltn3kkUd874frM4irSESXLl3UfNOmTZ6sTZs2altXP69tAwA4kwkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQWJOsLhcXF6fmWrU3VwUHV/UjV1UGTffu3dW8pKREzbVKPfv37/d9fwDQEOzatUvNtWpvrgprrko4WnU5V3/uyrWKcWVlZWpbVzUirZqp1seLuMeEtm3bqrk2xrkq5bnGQ63inqtykUtycrInc/2+tm3bpuZalTsAOBK0Ks8i7r/FtaqZrr74mGOO8b0frvHN1Uf37t1bzdeuXevJ0tLS1LY7d+5U84yMDDUH0LRxJhMAAAAAAAACY5IJAAAAAAAAgTHJBAAAAAAAgMCYZAIAAAAAAEBgTDIBAAAAAAAgsCZZXc5FqyS0Z88e321Foquy4KoksXHjRjXPz8/3ZFr1HgBoyPbu3avmWr+blJQU1TY6derkyVzVdIqKitS8Xbt2vvZNxF2hVKvq5qpQ5Kou56oMp1W/c1WuKygoUHOtKpL2uGvbD60Ckquy0o4dO9Sc6nIA6ou+ffuq+TfffKPmWp/uqizdvn173/sRbaXPc845R80ff/xxT1ZcXKy2dVUAzczMjGpfADQNnMkEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIzqctXs3r3bk7mqQEycOFHNb7jhBt/3N2DAADWfNWuWmnfo0MGTuSoXAUBDFRMTo+apqameLDExUW27fPlyNe/Vq5ev7YroVdpctEpqIu6qbtpjdD0WVxVRVzU6bV9cz6mrgmpKSoonc1VWclWo06qtuvZDq2YHAPXJ6NGj1fyFF15Qc20M0SpFi4hMmTJFzUeMGOHJXFU6XbRxT0SkY8eOnsxVuc51n67+H0DTxplMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgbHwdzVTp071ZKtWrVLbuhb+fumll3zfX58+fdTctRDrk08+6cn69eunth04cKDv/QCA+kQrwiCiL6JaUlKitt23b5+aa33mzp071bauBVq1xatdC3yXlpaqefPmzT2Za6Fx10KsrseuFYSIj49X28bFxan5hg0bPFm3bt3Utl9//bXv/XMtQOt6rgGgvnD1l67+VSto4NqG6/ODtvB3NEUpRERat26t5tu2bfNk69evV9u6ijMkJSVFtS8AmgbOZAIAAAAAAEBgTDIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAE1iSryxlj1LyystKTuarLde/eXc2jqbLgqg7hqoo0a9YsT1ZRUeH7/gCgIZg7d66aa9XKXBXWtm/fruYZGRmebPbs2WpbrQKciF7tzVUBThtXREQSEhI8mas/d23DlScmJvrKRNzj0IIFCzxZenq62jY5OVnNtd9NcXGx2tb1O/jRj36k5gBQX7gqr2l9oOtzgvY3/qG2f/9+TzZnzhy1rauCqmsMBtC0cSYTAAAAAAAAAmOSCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAILAmWV0uJiZGzcvKyjyZq2qCq1JPNLT7E3FXcNCqzrnaAkBDlZKSouZaJZzNmzerbQsKCtS8X79+nkyrpCYi0rJlSzV3VUjTuKqZlpaWejJXtbi4uDg1T01NVXOtcp1rG67xcN26dZ7s/PPPV9tec801an7JJZd4MlfFvqysLDUHgPpu6NChav7KK694sszMTLWt1m8fap07d/ZkeXl5alttzBJxj1sAmjbOZAIAAAAAAEBgTDIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAisSS787aItupefn6+2dS1MG434+Hg1b9ZM/7Voi3y3b98+8H4AQH3yk5/8xHfbwsJCNV+zZo2ad+vWzZNNmDBBbZuRkeH7PquqqtS2rsXDd+3a5clcxSC0Bc9FRCoqKnznsbH6MaW2bduq+cyZMz3ZDTfcoLbduXOnmmsLkyclJaltAaChuvHGG9X8zTff9GSuvnjv3r1qro1lXbt29b9ztUhLS/NkrqIZrjHONU4CaNo4kwkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGNXlqklOTvZkrqo+dVEhR6tmJyJijFFzrbKDaxsA0BRoFcxERI499lg11yrn7N69W22bmZmp5tFU+iwuLlZz7T5dfb+rn3dVKSotLfW9DRdtv+fPn6+2Peecc6LaNgA0Jh06dFBzrbqoqyKqq7rorFmzPFldVZfTxgVX1VJtXBFx7zeApo0zmQAAAAAAABAYk0wAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBUV2umm3btnmyyspKta1W6S1arqpIropB2r5oFfEAoDHSqq+5+uK4uDg1nz59uidr1iy6obB58+a+92PVqlVqHk11IG1squ0+taqoKSkpalvXGKJVS/ryyy/Vtq7qctrvKyYmRm0LAPWdqwKoq1/7wQ9+4Mneeustta2rAui7777ryS699FLXLkZF+xyyZcsWtW00la8BgDOZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmPh72ratWvnyXbs2KG2dS0qG42MjAw1dy38XVpa6snatm0beD8AoCHQFleNti9evny5J2vZsqXaVutzRfSFwrXtioh06dJFzbWFuDdv3hzVfrgWXC0pKfFkrkVbXYvNarlrAXIX7fcV7cK5AFBfRFtoQiuK8Oabb6ptXUUYNm3a5HPvoteiRQtPVlZWprZ1fWbZs2dPne4TgMaBM5kAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgVFdrpqzzz7bk82ePVttWxfV5dLS0tRcq/YgIrJ//35PlpubG3g/AKChqqysVHNXH71+/XpP5qre1qNHD9/b7tWrl9o2MzNTzZcsWeLJXBXWysvL1Vyrcieijy2uccVVSUh7ToqLi323FRFJTEz0ZFSXA9BQuao/u5x88smerEOHDmrbvXv3qrlW1XPBggVq2379+vnfORFJT0/3ZK5+Pj4+Xs1d1VkBNG2cyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIjOpy1SQlJXkyraKbSN1Ul3MpKSlRc63iQ05OziHbDwCo76KtSnb//fd7sj/96U9q24kTJ6q5VgWoS5cualtXBTitn2/btq3aNi8vT83z8/N9t9cqFIm4KwO1bt3ak914441qW62KnEu01ZkAoL6oiyqYnTp1UvP58+eruVbVbdKkSWrbaKvLFRQUeDLXZxCX7du3R9UeQNPAX3sAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBsfB3NVdccYUnmz59utr27LPPPmT7cf755/tu27dv30O2HwBQ30W7kHRycrInu+uuu6LaxoYNGzzZkiVL1LauRVG1Rburqqqi2g9tQVhX7lpsdujQoWqempoa1b4AAA7s9ttvV/P27durudafDxs2rE72ZfTo0Z6sXbt2altXkYgzzjijTvYFQOPCmUwAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjTSYAAIA6NG7cuKhv8+CDDx6CPQEAADi8fE0yGWNERF+otDEpKCjwZOXl5WrbkpISNa+L56isrMx3W9f9xcTEBN6PIy302EKvv0PpcL3GCwuD3baRvwVxCDTG99GRpo0VRUVFalvXWKHl0f6OKioqfOeu/XP9rqJdhPxIqo+v8dLS0qi33djfN0dSkLE3dPvG/uupj++jxqjQ8WJ09RlaX+zaRrTPZ3Fxse/9cOXa2FJff6+N8TXO5wocbn7fRzHGxztt06ZN0rFjx7rZMyBKGzdulJycnEN6H7zG0djxPkJjx2scCI73ERo7XuNAcAd6H/maZKqqqpItW7ZIWlpaozhDBg2DMUYKCgokOzs76jLl0QryGs/Pz5eOHTvKxo0bJT09/aDun22wjUO1jYbyPgIOVkN5jR/pvoBtsI3aNJT3EXCwGspr/Ej3BWyDbdTG7/vI19flYmNjD/mML6Bp0aLFYbmfuniNp6enH/SbnW2wjUO5jYb0PgIORkN6jTf0/oRtNN5tNKT3EXAwGtJrvKH3J2yj8W7Dz/uI6nIAAAAAAAAIjEkmAAAAAAAABMYkExBQYmKijB8/XhITE9kG26i32wBwZNWXvoBtsA0A9Vd96QvYBtsIwtfC3wAAAAAAAEBtOJOpgVq+XKR9e5GCgiO9J3Xv0ktFHnnkSO8FgIYmJkbknXfc13/xhW2zd+9h2qEj4JRTRF55xX97P8/J3XeLHHfcwe3Pxx/b21ZVHdztAQCHHp8rANQlX5NMMTG1/9x99yHey0Psyy9FzjtPJDvb/SHFGJG77hLJyhJJThYZPlxk5crINnv2iIwZI5KeLtKypcg114gUFoavX7fOfgBISbH/rlsXeftzzxV56y1/+/y734ncdJNIWprIVVfV/vvp3NnvM1E/3HGHyH33iezbd6T3BEB9sXOnyM9+JtKpk0hiov1j+MwzRb76yv82TjpJZOtWkQMVxbjqKpELLjjw9goKRH75S5HcXDsunHSSyLffRrbZvt1uLztbpHlzkbPO8o4dt9wikpkp0rGjyMsvR173xht2fPLjvffs/V16aThbsEDk/PNF2rYVSUqy48Ho0SI7dvjbpojIb34jMnnygdt17izy179GZmedJRIf731cAJouPlfwueJw4nMFcPj5mmTaujX889e/2s6uevab34TbGiNSUXGI9jagsjI9LyoS6ddP5G9/c9/24YdFHn9c5JlnRL75xnboZ54psn9/uM2YMSKLF4tMmiTywQd2kLn++vD1v/61SIcOIvPn20Gl+vP2+usisbEiF1104MexYYPd/lVX2cuPPRb5+xAReeGF8OWaH3pcz8ORFtqvPn1EunUT+c9/juz+AKg/LrpIZN48kX/9S2TFCjuhcuqpIrt3+99GQoKdnIqJ0a+vrIzujJtrr7X9/UsviSxaJDJihP2gsHmzvd4YO1m1Zo3Iu+/a/c/NtW2Kimyb99+3Zx59+qkdZ669VmTXLnvdvn0it99e+9hU3eOPi/zkJ3YsEbETc2ecYSewPvlEZOlSOzZkZ4fv34/UVJFWrdzXH2hMueoqu28AIMLnChE+VxwOfK4AjiATpRdeMKZFi/Dlzz83RsSYjz4yZsAAY+LjbbZ/vzE33WRMmzbGJCYaM3SoMbNmubdjjDFvv223FTJ/vjGnnmpMaqoxaWl2+99+G75+2jRjTj7ZmKQkY3Jy7P0VFoavz8015p57jLn8cnv7K6888OMTsftRXVWVMe3bG/OnP4WzvXvt43r1VXt5yRJ72+r7N3GiMTExxmzebC8ffbTNjLHP1zHH2P/n5Rlz1FHGbNhw4P0zxu7HoEH+H4PreXjzTbsPCQm2zZ//XPt2jLG/sxdesP8vLTVm7Fj73CQmGtOpkzH33x9um5dnzDXXGNO6tb3f006zv9OQ8eON6dfPmOefN6ZzZ/tchfzhD/Z3CwB5ebY/+uKL2tuJ2P7kgguMSU62/eq774avD41XeXn2cmgcevdd2z/Hxdn+0X6sCf98/rn3voqLbfsPPojMBwww5vbb7f+XL7e3/+678PWVlXZcfP55e/mhh4wZPTp8fdu24bHy+uuNefTR2h9zyI4dtg+tfl9vv21Ms2bGlJe7bxd6Tj77zJiBA+3zNmSIMcuWhduE+uqQK680ZtQoY+6915isLNt/Dxvmfd5C1q+3l1et8vdYADQdfK6w+FzB5wqgMamzNZnGjRN58EF7pPTYY0V++1t7iua//iUyd67IUUfZGfo9e/xvc8wYkZwcO2M+Z469j/h4e93q1fY0/IsuElm40M7YT58ucuONkdv485/t0YR580TuvPPgHtvatSLbttmjzyEtWoiccILIjBn28owZ9lTWQYPCbYYPt0cRvvnGXu7XT+Szz+yR8k8/tc+TiMitt4qMHWu/KuHHtGmR9+NHzedhzhyRSy6xX6tYtMiemnznnSIvvuh/m48/bs8m+O9/7Xe5X3458hTaiy+2X8mYONHe34AB9qh69dfAqlX2dTJhgj0SEzJ4sMisWSKlpdE9TgCNT2qq/XnnnQP3CX/4g+3bFi4UOeccO47UNu4UF4s89JDI3/9ujxg//ri9/VlnhY/annSS93YVFfbMp6SkyDw52Y5FIuF9rd4mNtZ+3S/Upl8/kdmzRfLybD9ZUmLHy+nT7dj5i1/U/nhDpk+3X8c7+uhw1r693c+337bTPrW5/Xa7ZsXs2SLNmolcfXXt7SdPtv1+6Aj7hAl2vL7nnsij3yL2K47t2tmxCwD84HMFnyv4XAE0YNHOSrmOOLzzTjgrLLRHHl5+OZyVlRmTnW3Mww/r2zHGe8QhLc2YF1/U9+Oaa+xR3uqmTTMmNtaYkhJ7OTfXHtGOhjbL/tVXNt+yJTK/+GJjLrnE/v+++4zp0cO7vTZtjHnqKfv/TZuMGTnSmI4d7b+bNhkzdao9erB7t91ely7G3HCDnc136dfPHkHw+xi05+HHPzbmBz+IzG69NXwURNuOMZFHHG66yZjTT7dHZGqaNs2Y9HR75Km6bt2MefZZ+//x4+3rZMcO7+0XLLD3v26d9gjrlyeffNLk5uaaxMREM3jwYPPNN9/4vu3UqVPNueeea7KysoyImLdrPuE+3H///WbQoEEmNTXVtGnTxowaNcosq34agg9PPfWU6du3r0lLSzNpaWnmxBNPNB999FHU+xLywAMPGBExN998c1S3Gz9+vBGRiJ+ePXtGff+bNm0yY8aMMZmZmSYpKcn06dPHfFv9cOAB5ObmevZDRMzPf/5zX7evqKgwd9xxh+ncubNJSkoyXbt2Nffcc4+p0t4stcjPzzc333yz6dSpk0lKSjJDhgwxs6ofum1C3nzTmIwMe4T5pJOM+d3vbD9RnYgxd9wRvlxYaLPQkV7tTCaRyCOhxoTP1DmQIUPsGTybNxtTUWHMSy/ZMSg0FpSV2SOxF19szJ49tl9/8EF7nyNGhLczfrztG/v0MWbCBNuuTx9jZs825okn7PZOOinyLKWa/vIXY7p29ea//709mykz05izzrJj8LZt4eurn8kU8uGHNguNpdqZTO3aecep3Fy7H5r+/Y25+273/jcFjBVejBWMFXyuCONzBZ8rjGGs0DBWNLyxos7OZKo+A756tUh5ucjQoeEsPt7OIi9d6n+bt9xi16cYPtwezVi9OnzdggV2djx0hDs11R7RqKqyRwi0/TrSOnSwR3xD331u3Vrk5z+338e+91672N7y5Xbhv2efdW+npMR79PxAaj4PS5dG/n5E7OWVK+3ReT+uusoeJejZ0x5t//TT8HULFtjFCVu1ivwdrV0b+XvMzRVp08a77eRk+29xsb99OVJef/11ueWWW2T8+PEyd+5c6devn5x55pmyw+equkVFRdKvXz/5m99FVxRTp06VsWPHysyZM2XSpElSXl4uI0aMkKIoFl3JycmRBx98UObMmSOzZ8+W008/XUaNGiWLFy+Oen++/fZbefbZZ+XY0CG1KPXu3Vu2bt36/c/00CkfPuXl5cnQoUMlPj5eJk6cKEuWLJFHHnlEMjIyfG/j22+/jdiHSZMmiYjIxRdf7Ov2Dz30kDz99NPy5JNPytKlS+Whhx6Shx9+WJ544omoHsu1114rkyZNkpdeekkWLVokI0aMkOHDh8vm0KI/TchFF4ls2WKPcp51lq2KNmCA9yhp9ZddSopd66O2t2NCQuRtovHSS/YMoQ4d7NlJjz8uctll4TWR4uPt0dQVK+y6SM2bi3z+ucjZZ4fbiNgjvqtW2aO/P/yhyAMP2HEvPt6ODdOn27Hwiivc++IaF+67zx4xf+YZkd697b+9etn7qq76c5CVZf+t7Xnr29c+d34lJ9f//vxQYqzwYqxgrKgNnysOjM8VfK6oibHCi7Ei7LCOFdHOSrmOOISODBvjni2+4AJjfvIT+/9//cvOSFf33/9GHnEwxq5p8eijdnY8IcEe5TXGmF697Iz3ypXen9BsfW1HVV20WfbVq20+b15kfsopxvziF/b///iHMS1bRl5fXm7X7Ajtc0133WXMr35l/9+/vz16bIwxTz5pzIUXuvcxdBTf72PQngftqPI779gjABUV9nJMjHffmzcPH3Ewxph9+4x57TVjrr3Wvi4uusjmDz5oTIcO+u9n507bpubR8epmzrSPI9S2vho8eLAZO3bs95crKytNdna2eeCBB6LelhzkEYeaduzYYUTETJ06NdB2MjIyzN///veoblNQUGC6d+9uJk2aZIYNG3ZQRxz6uV4UPt12223m5Dr+4v3NN99sunXr5vuIwciRI83VV18dkV144YVmzJgxvu+zuLjYxMXFmQ9qLPozYMAAc3to0Z8m7ppr7JlCIQc6Supak6kmv2cyhRQWho9IX3KJMeec422zd2/46Orgwca4Dl4tXWrX0igoMOaxx+yR6NB9iBiTn6/f7rnn7PpIB1Jaao8sX3GFvayN4fPm2WztWnvZtSZTTbWNub16Ra4/0tQwVkRirLAYK/hcUR2fK/hcwVgRibHCaohjRZ2dyVRdt272CGf10tLl5fY70MccYy+3aWPLP1efFK3+3dmQHj1EfvUrO5t94YW2uoGIPYK9ZIn9TnbNn2iOrvrRpYtd26J6Cef8fPud6CFD7OUhQ0T27rXfEQ6ZMsUeATnhBO82ly61FYX++Ed7ubLSPkci9t/aZv3797ePPYijj/aW/v7qK/t8x8XZy23aRK6rsXKl9whAeroth/388/b762+9Zb8bPWCAPXrerJn399O69YH377vv7Pfm/bQ9UsrKymTOnDkyvNqX6mNjY2X48OEyI/Sl+iNg3/9qtGZmZh7U7SsrK+W1116ToqIiGRJ6gfs0duxYGTlyZMRzEq2VK1dKdna2dO3aVcaMGSMbNmyI6vbvvfeeDBo0SC6++GJp27at9O/fX55//vmD3p+ysjL5z3/+I1dffbXEuMqS1XDSSSfJ5MmTZcWKFSIismDBApk+fbqcffbZvu+3oqJCKisrJanG4cXk5OSoj8I0VsccE12VNL8SEvwfeRWxZ0xlZdl1lT75RGTUKG+bFi1sn7pypV33SGtjjMgNN4g8+qg9QltzXBBx71f//rbPzcs78GPr1u3wPm/799sjzf371/19NgSMFV6MFRZjhT98rrD4XMHniiOBsYKx4qBEOyvl54iDMcbcfLP9rvTEicYsXmyPfGZk2HUpjLHfFU5JsTP2q1bZ71lnZ4ePOBQX2woDn39uj1xMn26/d/vb39rrFyywVXDGjrVHAlassDPm1SZ/fR9xKCiw2wgdvX30Ufv/9evDbR580B5RePddYxYutEdxu3QJf0/bGLveRf/+xnzzjd3f7t2Nuewy7/1VVdkKB++/H85+9jP7feolS+w2Qt8x17z3nq1AFDoyUJOfIw5z5tjvmd9zjz2q8+KL9vmsfjTh0ktt5Yq5c211i9NPt0ckQm0eecSYV16xR96XL7dnFbRvb6snhR5jv37GfPKJPSL+1Vd2fZDQV1hrO+Jw5ZXG1JiwrXc2b95sRMR8/fXXEfmtt95qBg8eHPX2pA6OOFRWVpqRI0eaoUOHRn3bhQsXmpSUFBMXF2datGhhPgwdAvPp1VdfNX369DEl/3tTHMwRh48++sj897//NQsWLDAff/yxGTJkiOnUqZPJd52+oUhMTDSJiYnmd7/7nZk7d6559tlnTVJSknnRtRDDAbz++usmLi7ObA6Vc/GhsrLS3HbbbSYmJsY0a9bMxMTEmPurl0jxaciQIWbYsGFm8+bNpqKiwrz00ksmNjbW9NAWamjEdu2yVWReesn2/WvW2CPU7dpF9hN1dSbTfffZM6SWLbNHPcvK9P36+GM7xq1ZY8ynn9r+7IQTItv/97/2flevtmNUbq77iPJzz4WP2hpjx5L0dGNmzLBHqKuvbVFTRYVdq6P6uPL++8aMGWP/Xb7cPp4//ckeCf/3v/XnxJiDP5PpBz8w5vzz7bog1Y8Wf/65reZUVOTe/8aMsSISY0UYYwWfK/hcweeKEMaKSIwVYQ1xrDhkk0wlJfa009at9VKjxtgO66ijbCd07rn2D+zQYFBaajujjh3t6azZ2cbceGNk5ztrlv2jNjXVDizHHms/HIT4HQxCj6HmT/XSpFVVxtx5p/1Qk5hozBln2A6wut27beefmmo/GPzkJ3agqemZZyI/SBhjzPbtdptpafbrEbX9MV5ebp+Pjz/Wr/czGBgTLjUaH28/UNX8KsPmzXZx2pQUO7B99FHkB7bnnjPmuOPs9enpdv/nzg3fPj/fvgays+19dOxoP/CESqq6BoOSEns/M2a4n4P6oD4OBj/96U9Nbm6u2bhxY9S3LS0tNStXrjSzZ88248aNM61btzaLFy/2ddsNGzaYtm3bmgXVVmI+mMGgpry8PJOenh7V6bXx8fFmyJAhEdlNN91kTjzxxIPahxEjRphzzz03qtu8+uqrJicnx7z66qtm4cKF5t///rfJzMyMekBatWqVOeWUU4yImLi4OHP88cebMWPGmF69ekW1nYZu/35jxo2z5aZbtLCn1/fsaRf5Li4Ot6urSaYdO8Jji4i9neb11+1i2wkJ9g/hsWPt1+Kqe+wxWwo71M/ecYe+AOu2bbavrvk3xx/+YBft7tXLftCozW9/a8fNkNWrjbnuOrt4bHKy/UBz/PGRf/TX5STTjBl2HE5MjPyKyvXX24VnmyrGijDGikiMFXyu4HMFnytCGCvCGCsiNcSxIupJJtQPTz4ZWZ2oMXnqKW+FivqotLTUxMXFeTrwK664wpx//vlRby/oYDB27FiTk5Nj1qxZc9DbqO6MM84w19csteLw9ttvf99hhX5ExMTExJi4uDhT4To85sOgQYPMuHHjfLfv1KmTueaaayKyp556ymRnZ0d93+vWrTOxsbHmneplbnzIyckxTz75ZET2xz/+8aAqWhhjTGFhodnyv0V/LrnkEnOOtugPmrytW+2EVH2qnrNzp92nOuqWGiTGijDGikiMFagv+Fxx5DFWhDFWRGqIY8UhWZMJh94NN4iccor9/nljEx8vEuVi+UdEQkKCDBw4UCZX+1J9VVWVTJ48OervHAdhjJEbb7xR3n77bZkyZYp06dKlTrZbVVUlpaWlvtqeccYZsmjRIpk/f/73P4MGDZIxY8bI/PnzJS70hfwoFRYWyurVqyUrVO7Kh6FDh8ry5csjshUrVkhubm7U9//CCy9I27ZtZeTIkVHdrri4WGJjI7vXuLg4qaqqinofRERSUlIkKytL8vLy5JNPPpFR2oI+aPLatxf5xz9spaH6Yt06kaeesmuQNFWMFWGMFZEYK1Bf8LniyGOsCGOsiNQgx4o6n7YCmpDXXnvNJCYmmhdffNEsWbLEXH/99aZly5Zm27Ztvm5fUFBg5s2bZ+bNm2dExDz66KNm3rx5Zn31L+4fwM9+9jPTokUL88UXX5itW7d+/1Nc/XtEBzBu3DgzdepUs3btWrNw4UIzbtw4ExMTYz799FPf26jpYE5r/fWvf22++OILs3btWvPVV1+Z4cOHm9atW5sdobJcPsyaNcs0a9bM3HfffWblypXm5ZdfNs2bNzf/+c9/otqXyspK06lTJ3PbbbdFdTtjjLnyyitNhw4dzAcffGDWrl1rJkyYYFq3bm1+G1r8waePP/7YTJw40axZs8Z8+umnpl+/fuaEE04wZa5FggDUS4wVbowVjBUALMYKN8aKhjVWMMkEBPTEE0+YTp06mYSEBDN48GAzc+ZM37f9/PPPjYh4fq6s/sX9A9BuLyLmheoLrxzA1VdfbXJzc01CQoJp06aNOeOMMwINBMYc3GAwevRok5WVZRISEkyHDh3M6NGjzapVq6K+7/fff9/06dPHJCYmml69epnnnnsu6m188sknRkTM8pqLJPiQn59vbr75ZtOpUyeTlJRkunbtam6//XZTqi3GU4vXX3/ddO3a1SQkJJj27dubsWPHmr01F/0B0CAwVugYKxgrAIQxVugYKxrWWBFjjDF1f34UAAAAAAAAmhLWZAIAAAAAAEBgTDIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBNfPTqKqqSrZs2SJpaWkSExNzqPcJEBERY4wUFBRIdna2xMYe2vlQXuNorHgfobHjNQ4Ex/sIjR2vcSA4v+8jX5NMW7ZskY4dO9bZzgHR2Lhxo+Tk5BzS++A1jsaO9xEaO17jQHC8j9DY8RoHgjvQ+8jXJFNaWtr3G0tPT6+bPTsMjDFqHs2Mcl5enppnZGSo+Zo1azzZnj171Lau2b/ExEQ17927t5o3Vvn5+dKxY8fvX3+HUkN9jR8p8+eLDBt28LefOlXkuOPqam9QG95HB1ZVVaXmrjEkLi7O97ZfffVVNZ81a5Ynq6ioUNu2bNlSzXv06KHml19+ub+dk7oZJ6PZ9qE4ostrHH5s3Ciye3f0t2vVSqQpfFbkfYTGjtd448Znk8PD7/vI1yRT6I/C9PT0BvVGqYs/nisrK9Xc9TxoT3hpaana1vVBxTXJ1JCe+7p0OE4zbaiv8SMlNTX47XmaDy/eR26HcpIpOTlZzRMSEjxZtAceXNuO5rlvDJNMh2PbNe+job3Gm7oNG0QGDRLZvz/62yYliSxfLtKpU93vV33E+wiNHa/xxonPJofXgd5HLPwNAACARmvXroObYBKxt9u1q273BwCAxoxJJgAAAAAAAATm6+tyDYH2tTbXVxpcXw/QvpJQXl6utnV9TaGkpMSTudbTcG07Pj5eza+77jpP9vDDD6ttAQD+1EWVmYULF6r5lVdeqeZDhgzxvR+uMeEvf/mL7/t0jYeu053r4qtuVNQBAABoejiTCQAAAAAAAIExyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAILBGs/C3a1FTzeuvv67md911lydzLeb6xhtvqPmtt97qyebNm6e2/eyzz9R8+PDhav7zn//ck1VUVKhtmzXTf7V1sZgrADQFy5YtU/Pt27d7srZt26ptv/nmGzUfP368J9u3b5/a1lVo4u9//7uaf/nll55s+vTpatvbbrtNzRMSEtQcAAAAqA1nMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACazTV5aLhqryWnZ3tye644w617TnnnKPmH3/8sSdbu3ZtFHsn8tRTT6l5586do9qOhkpyAJqyOXPmeLJ33nlHbbtlyxY1Hzp0qCfbu3ev2jYzM1PNe/bs6cl27NihtnVVl+vXr5+al5WVebL09HS17cMPP6zmw4YN82RHH3202rZ169ZqDgAAgKaHM5kAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgdWL6nLGGE/mqoKmVc0REZk7d64nc1X72b9/v5qvWrXKk3333Xdq248++kjNW7Zs6cmysrLUtitWrFBzl+XLl3uy0tJSta1WKU9EpLy83JO1a9dObRsbyxwkgIbptttuU/MzzjjDk7mqo2kV4ERE+vTp48nWrVuntn3ppZfUfODAgZ6sR48ealtXP//ee++p+ZlnnunJXJXhZs6cqeafffaZ77YXXHCBmnfv3l3NAQAA0HgxiwAAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABBYvVj427XIt2bJkiVq/u2333oy16KtrsVVjzvuOE+2efNmtW1hYaGav/POO56sf//+attdu3apeUlJiZqnpKR4st27d6ttV65cqeYJCQmeLD4+Xm3rWgwXAOqLRYsWqblrUeyHHnrIk3Xu3Flt26yZPkR27drV9zby8vLU/Cc/+YknW7Nmjdq2uLhYzefPn6/mJ5xwgu9tuIpEdOjQwff9Pfroo2r+9NNPqzkAAAAaL85kAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAARWL6rLRcNVqeeoo47yZK4KcG3atFHz/Px8T9aqVSu1ravy2uzZsz3ZrFmz1LZ9+vRR8507d6p5QUGBJ8vIyFDbuvY7NtY7r+iqZgcA9d2cOXPU/OOPP1bzf/7zn57s3XffVdu6+tGjjz7aky1btkxt+/7776u5Nt6sW7dObbt9+3Y1d1URbdeunSdbvny52tZV0S4zM9OTHXPMMWrbkSNHqjkAAACaHs5kAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAARWb6vLuSrDaRXWRESysrI82Xvvvae27du3r5rv37/f596JpKamqnlZWZknc1Vvi4+PV/PKyko1j4mJ8WTNmzdX27ry4uJiXxkANARTpkxR8y5duqj5cccd58latGihtnX181pl0PXr16tttbFJROT000/3ZKtXr1bblpeXq/miRYvUXKt+6qpQp1Wiq+0+NZs2bVLzXbt2+do3AAAANB6cyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABBYvV34e+/evWpeWlqq5u3bt/dkroVOd+7cqeYpKSmeLC4uTm2blJSk5unp6Z7MtcC3MUbNW7Vqpeba4rRVVVVqW1euLUzuWuDV9VwnJiaqOQAcbvn5+Wq+ceNGNR80aJAncy3O7SoG0bJlS0+Wl5entnX1/927d/dk+/btU9u6CjksX75czbUCGdo+i7jHsmHDhnmyt956S227cuVKNd+9e7cnY+FvAACAxo0zmQAAAAAAABAYk0wAAAAAAAAIjEkmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBNbjqcgkJCWquVc7JyMhQ27qqpmntXRXgYmP1+TmtGlFycrLa1lXVx1XRqKSkxJO5qg65KutVVFR4MlfVIVfVpjZt2qg5ABxurv7LVe3to48+8mSuPk3rc0X0aqbr1q1T20aTL1u2TG2bmZmp5mvWrFHza6+91pNt2bJFbTt//nw1nzp1qif7+uuv1bauccg11gIAAKDx4kwmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGD1trqcq6qPq7pcXFyc723s2rVLzbUKQ64qcjExMWquadZMf5orKyvVvKqqSs0TExN9b9tVuc7VPpptAEB9MXDgQDW/8sor1VyrkOaq0rZnzx4137p1qydzVbMrLCxUc62CakFBgdrWNVbs3r1bzTdt2uTJVq5cqbYtLi5Wc22cHDRokNrWVeHPVRUPAAAAjRdnMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAARWbxf+3r9/v5prC3yL6ItUb9++XW3rWqBVW7zUtbBqWVmZmmsLa7v22bWIuWuxcW1B8PT0dLXt1KlT1bx///6ezLWIuTFGzQHgSFi0aJEne/XVV9W2l112mZpr/WhFRYXatkWLFmqemprqu61rrNDy8vJyta1Lq1at1FzbF1fRB9d4o41PZ511ltp227Ztav755597sssvv1xtCwD1ifb3v6tIhKuAwoYNGzxZnz591LbPPfecmmt9ZnZ2ttrWNQ5lZGSoucZVfMg1VkTD9bkimmJKABoGzmQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIjEkmAAAAAAAABFZvq8u5KvI0b95czbWKBfn5+Wrb9u3bq/nevXs9mavigavKglbBx1WpwbXt+Ph4NXdVQNK8+eabat6jRw9P5qpS4arwBwBHQlFRkSdzVTZ78cUX1fyjjz7yZOPHj1fbav2liEi7du08masy3ObNm9V8yJAhnsw1rrRt21bNMzMz1bx79+6+t+GqtvrDH/7Qky1dulRtu2DBAjUfMGCAJ6O6HIAQY4yvSsbRVh+rrKz0ZK5Kz1OmTFHzJ554wpOtXr1abauNTSJ6lc5u3bqpbV0VsYcNG+bJnnzySbXtZ599pubvvfeemp944omeLNoqctrnNVf1bKrIAU0HZzIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAqu31eVKS0vVPCMjQ8216hTLli1T2yYlJal5YmKiJyspKVHbapUrXFxtXRUcXNXoUlNTfd/n22+/rea//vWvPZmrCkRhYaHv+wOAQ+2YY47xZA888IDadsSIEWrepk0bT/bWW2+pbVu0aKHmOTk5nszVn7/yyitq3rVrV0/mqly0detWNZ82bZqaa+Pkxo0b1bYFBQVqrjnnnHPU/LTTTlNz7fcFALXR/gaOtuKZVklu7ty5atu//vWvat6zZ09PNnr0aLXtwIED1bxly5aeTKtwKiIyY8YMNX/++ec9WVpamtpWq3wqolcLFRHp0qWLJxs3bpza9vzzz1dz12cIAE0bZzIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAEVm8X/o6JiVHz9PR0NdcWCl+3bp3a1rVgnraN/fv3q23j4+PVXFuc0LVgobYwYW2aN2/uybQFz0VE2rdvr+abN2/2ZMcee6za1rUAOQAcCStXrvRkK1asUNu6+tcdO3Z4soqKCrWtq2iDVhDCdX+uBbcXL17syVzFKlyFMFz9f3l5uSfbsGGD2nbPnj1q3rt3b0/mWlRW+72IiCxcuNCTucYbAE1PTEyM5+/9aP829su1OPfu3bvVPDMz85Dsx5VXXhlVrlm7dq2a33vvvWo+f/58NdcK/LiKabjuMysry5O5xhXXmKWNta7PINGMe6effnrEZQoaAYcPZzIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAqsX1eW0Cm6uqj6JiYlqnp+f7/v+iouL1TwlJcWTNWumP0Wu6nJadQMXV0UjV2W9hIQET6ZVixMR2bJli5pv2rTJ595RXQ5A/aJVMUtKSlLbuvri//73v57swQcfVNtqFdZERFq2bOnJXP2lVhVUROTHP/6xJ5s3b57a1vUYXdV+zj77bE82ZMgQta2rCtCvfvUr3/vnGlO1cXLv3r1qW+05BdC4bd682fP3u1Yx1NWPJicnq7lW1fmXv/yl2tZVvfPrr7/2ZK7+y1WJWhuHXJ9vZs2apebbtm3zZK5K27169VLzH/zgB2revXt3T5aTk6O2feedd9R82rRpnsxVxdv12UkbP12fkVxjrfa7Of744yMuFxUVqbcFUPc4kwkAAAAAAACBMckEAAAAAACAwJhkAgAAAAAAQGBMMgEAAAAAACAwJpkAAAAAAAAQWL2oLueqtKBxVVTQqlG4uKpRaJXrXPtWWFio5nFxcZ7MVQlBq35RW3ut0kKHDh3Utu3atVNzrTqTi6vKnfacaI8bAOrSnDlzPFlmZqbadvfu3Wq+fPlyT+aqIjplyhQ179mzpydzjQlTp05V8/79+3sy1zjmqmjkeoynnHKKJ5sxY4baVqtaKiLSqVMnT+aqLucah3bt2uXJdu7cqbaluhzQ9KSkpEhqampEplVq27Bhg3p7V8Vk7e/rvn37qm3/8Y9/HGg3v+eqROfqR7Wq1W3btlXbXnLJJWrepUsXT5aVleXaxUPmhhtuUHOturdrTHVVjNMYY6LKNTXHlWgqkQMIhjOZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIHVi+pyGlfltebNm6v53LlzfW/bVV2upKTEk7mqpsXHx6t5NFXWXNUXXI89mooKNat1hGiVlVyiqXJHdTkAh9qQIUM82Yknnqi2/e6779T85JNP9mQZGRlq20WLFql5WVmZJ3P1l65+W6vS6RrfXBXZXNVPtT66vLxcbeuqiqSNIa7KPK6qTQUFBZ7MVVkJQNPTsmVLT9Xoc8455wjtDQ6Gq+o3gKaNM5kAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAATGJBMAAAAAAAACqxcLf2sLkroWUY2JiVHzPXv2+L6/tLQ0NS8qKvJk2gKvIu4FV7VFVLVFWGvjWkRbW5jctQB5q1at1Ny1+KumLhYgB4C6Mn/+fE921FFH+W4rItKhQwdPtnXrVrXt5s2b1TwrK8uTaYtci4hs2LBBzTdt2uTJ1q5d67utiEhxcbGab9++3fd+uMaKHj16eDLXeJiTk6Pm69ev92R5eXlq2xYtWqg5AAAAGhbOZAIAAAAAAEBgTDIBAAAAAAAgMCaZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAEVi+qy2kVa1zV5Zo103dZq7zm4qpis23bNk/mqrBWWFio5qWlpb634aqU56oApz0n6enpalvXY3RVGNK4nmvX7wYADqUPPvjAk7mqXT722GNqfuaZZ3qygQMHqm1dffeAAQM82caNG9W2gwcPVvPevXt7Mlff6urPXZVL+/Xr58lcFVgzMjLUfOfOnZ7slltuUdsuX75czbXqfL///e/Vtp07d1ZzAAAANCycyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIrF5Ul9OqA7kqr7ls3brVk3Xv3l1t69p2XFycJ3NVWIsmr6ysVNu6qiK5uCodaY4++mg1X7Zsme9tUF0OQH3y5z//2ZMNGTJEbeuqANqtWzdPtnfvXrWtq9JnUlKSJ2vZsqXatn379mreoUMHT+bqW7ds2aLm+fn5aq6NcR07dlTb7t+/X821qq/XXnut2vbkk09Wc+3xuNoCAACgceBMJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgTHJBAAAAAAAgMAazcLfGzZs8GQ5OTm+709EpLS01JO5Fu0uKSlRc22hU9djcS3y6mrvuk9NamqqmmuPx/UYtYXQRUQqKip87wcA1JU1a9Z4Mm0RbhF3v9azZ09PNnnyZLXthAkT1Hzu3LmezLU494svvqjmeXl5nmzjxo1q26VLl6q5a9FubV/mz5+vtt29e7eajxgxwpPt3LlTbbt9+3Y11xYPdy2y3qZNGzUHAABAw8KZTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIExyQQAAAAAAIDA6kV1OU15eXlU7bXKa0cddZTa1lU1LTEx0ZO5Kr25tqG11yrs1Ma17WikpKSoufY8FRcXq22bNdNfHtE+HgCoC0VFRZ7MVfHMlQ8aNMiTDRgwQG3bvXt3NT/55JM92YIFC9S2rup3l156qSdbvHix2ta1f64Kej/+8Y89mfa4RUT27Nmj5meddZbv/SssLFRz7fcVTZVUAAAANDycyQQAAAAAAIDAmGQCAAAAAABAYEwyAQAAAAAAIDAmmQAAAAAAABAYk0wAAAAAAAAIrF5Ul9u/f78ni7bC2rp16zzZSSedpLZdu3atmm/dutWTJScnq20zMjLUXKuK56oAVFFR4XsbteUa137v27fPk7n2z1VdDgCOhIKCAk+2ceNGte2qVavUvHnz5p7sk08+UdtG03dv27ZNbXvMMceoud99ExHp27evmq9Zs0bNW7Zs6cnatm2rtt2+fbuaa+Nhamqq2nbDhg1qrv2+tPEeAAAAjQdnMgEAAAAAACAwJpkAAAAAAAAQGJNMAAAAAAAACIxJJgAAAAAAAARWL1Z21hZRTUpKUtu6FmLVFhMdNGiQ2tYYo+YJCQm+72/Pnj1qri2MWlVVpbYtKipSc22xVBGR2FjvnKBrEdUBAwaoefv27T2Za+Hcnj17qnk0C5ADQF3RFsA+8cQT1bYrVqxQ8/j4eE+Wn5+vttXGBBG9gMKMGTPUtq1bt1bzzz77zJMVFhaqbbt27arm33zzjZr/4Ac/8GSufl4rmiEi0qNHD082bNgwte2SJUvUPD093ZN169ZNbQsAAIDGgTOZAAAAAAAAEBiTTAAAAAAAAAiMSSYAAAAAAAAExiQTAAAAAAAAAmOSCQAAAAAAAIHVi+pyMTExvjIRkS1btqh5WVmZJ/vRj34UbMeOkFatWgXehquynla9aMqUKWpbrZKTiLsqHgAcSp06dfJkkydPVttu2LBBzbUqnQsXLlTbZmdnq3lxcbEnc1Vpy8zMVHONVmlVRKSkpCSqXKtQqu2ziLvqnFaFNTExUW27fft2Ne/QoYMny8jIUNsCAACgceBMJgAAAAAAAATGJBMAAAAAAAACY5IJAAAAAAAAgTHJBAAAAAAAgMCYZAIAAAAAAEBg9aK63Pr16z3Zvn371LZ79+5V8zvvvLMud6nRuvnmmz1Zly5d1Lbbtm1T86qqKk9GxSAAh5pW8fKJJ55Q286aNcv3dq+44go1nzlzpprHxcV5Mq1yp4i7Wujq1as9WXx8vNrWVRnOlWsV9LQKrCLuvrtXr16ezFWFz5V37tzZk7kqxwIAAKBx4EwmAAAAAAAABMYkEwAAAAAAAAJjkgkAAAAAAACBMckEAAAAAACAwOrFwt+pqamerLy8XG2bnp6u5qeeemrg/TDGeLLGtkjpRRdd5MkSEhLUtpWVlYd6dwDAt2bNvEPWhRdeqLZt37697+326dMnqlxz9dVXq/nAgQPVXBvjsrOz1bbaAtoiIllZWWp+zDHH+N7Geeedp+Ya12PRFhoXEenYsaMna2xjKgAAACJxJhMAAAAAAAACY5IJAAAAAAAAgTHJBAAAAAAAgMDqxZpMAAAAaHzGjRt3ULd78MEH63hPAADA4eBrkim0IHZ+fv4h2YnCwkJPVlRUpLYtKCjwvY1o97cpLPytPX+uhb/j4uJ8b/dQvDZC29R+L3XtUL/GGxvl7Rb17XmqD4/G/j4qLi5W89LSUjU/VPvm2g/XmFVSUuJ7G9r4Vlt77TG62lZUVKi5xvVYXNvWxnHGiqbH9V48kLp8jhmzDoz3ERo7XuONG/384eH3fRRjfLzTNm3apFaJAQ6HjRs3Sk5OziG9D17jaOx4H6Gx4zUOBMf7CI0dr3EguAO9j3xNMlVVVcmWLVskLS2t0Z3Zg/rLGCMFBQWSnZ3tLJFdV4K8xvPz86Vjx46yceNGSU9PP6j7Zxts41Bto6G8j4CD1VBe40e6L2AbbKM2DeV9BByshvIaP9J9AdtgG7Xx+z7y9XW52NjYQz7jC2hatGhxWO6nLl7j6enpB/1mZxts41BuoyG9j4CD0ZBe4w29P2EbjXcbDel9BByMhvQab+j9CdtovNvw8z6iuhwAAAAAAAACY5IJAAAAAAAAgTHJBASUmJgo48ePl8TERLbBNurtNgAcWfWlL2AbbANA/VVf+gK2wTaC8LXwNwAAAAAAAFAbzmRqoP7xD5ERI470XoQtWSKSkyNSVHSk9wRAUxUTI/LOO+7rv/jCttm79zDt0BFwyikir7ziv72f5+Tuu0WOO+7g9ufjj+1tq6oO7vYAcChddZXIBRf4b79une0z588/NPtzpEyeLHL00SKVlUd6T0RefFGkZcvobtO5s8hf/2r/X1ZmL8+eXae7BSAKR2SSads2kZtuEunaVSQxUaRjR5HzzrMdXF2q3uHU5rnnRE49VSQ93f3H9p49ImPG2DYtW4pcc41IYWFkm4ULRf7v/0SSkuxjevjhyOsnTRLp0cNu4/LLbScYsm+fvW79+gPv7/79InfeKTJ+fGSeny9y++0ivXrZfWjfXmT4cJEJE0Tq8ny1U08V+eUvI7NjjhE58USRRx+tu/sBgJCdO0V+9jORTp3suNG+vciZZ4p89ZX/bZx0ksjWrSIHKorh90NHQYHtC3NzRZKT7fa//TayzfbtdnvZ2SLNm4ucdZbIypWRbW65RSQz044bL78ced0bb9jx0Y/33rP3d+ml4WzBApHzzxdp29aOC507i4weLbJjh79tioj85jf+xmdtzD3rLJH4eO/jAoCQuujf65MJE+yB4Fat3BNS+/eLjB1r26Smilx0ke2/q9uwQWTkSDt2tG0rcuutIhUV4evnzRPp39/e/rzz7GeVkIoKkYEDRWbN8rfPv/2tyB13iMTF2cuVlSIPPmg/UyQn2zHqhBNE/v73qJ6KIyIhwY5bt912pPcEaLoO+yTTunW205syReRPfxJZtMge6TztNNvZHgnFxfYP4d//3t1mzBiRxYvtRNEHH4h8+aXI9deHr8/PtwNKbq7InDn2sd19t53AErFHcX/8Y5Gf/lRkxgw7ux66TkRk3Dh7XW7ugff3zTftRNXQoeFs7177Aeff/xb53e9E5s61+zh6tB049u2L5hk5OD/5icjTT0cOgABQFy66yP5B/a9/iaxYYSdUTj1VZPdu/9tISLAfXmJi9OsrK6M74+baa+2Y8NJLdiwbMcJO7G/ebK83xk5WrVkj8u67dv9zc22b0Fmf779vzzz69FN7YOLaa0V27bLX7dtnDxz87W/+9ufxx20/HPu/kX3nTpEzzrAfDj75RGTpUpEXXrATXtGcdZqaaj8IuVQ/YKK56iq7bwCgqYv+vT4pKhI5+WSRhx5yt/nVr2z//8YbIlOnimzZInLhheHrKyvtBFNZmcjXX9vn5sUXRe66K9zm2mtFTj/d/s2/b5/I/feHr3vkEfs5YfDgA+/v9Okiq1fb30PIH/4g8pe/iPzxj/bbCp9/bj/3NJQzgceMsY9r8eIjvSdAE2UOs7PPNqZDB2MKC73X5eWF/79+vTHnn29MSooxaWnGXHyxMdu2ha9ftcpe37atbTNokDGTJoWvHzbMGPsnfvjnQD7/3Larvh/GGLNkic2//TacTZxoTEyMMZs328tPPWVMRoYxpaXhNrfdZkzPnvb/27fbbZSU2Mu//a0xP/+5/f9XXxkzcKAxFRUH3kdjjBk50pjf/CYy+9nP7PMQ2p/qCgqMKS+3/9+zx5jLLzemZUtjkpONOessY1asCLfdtcuYSy81JjvbXt+njzGvvBK+/sorvc/r2rX2utJSYxITjfnsM3+PAwD8yMuzfc0XX9TeTsSY55835oILbP911FHGvPtu+PqaffwLLxjTooVtc/TRxsTF6X3c559776u42Lb/4IPIfMAAY26/3f5/+XJ7++++C19fWWlMmzZ2P40x5qGHjBk9Onx927bGzJpl/3/99cY8+mjtjzlkxw47JlW/r7ffNqZZs3D/rwk9J599Zseh5GRjhgwxZtmycJvx443p1y98+corjRk1yph77zUmK8uYzp1rH3PXr7eXV63y91gANB1++/dHHrF/kzZvbkxOjv27t6AgfH2oP//4Y2N69bJ/E595pjFbtoTbVFQY86tf2XaZmcbceqsxV1xh+7OQiRONGTo03GbkyMi+a+1au7/z5h34sbna7t1rTHy8MW+8Ec6WLrVtZ8ywlz/6yJjY2MjPPk8/bUx6evizRnKyvZ0x9nPIOefY/69ebUz37sbk5x94H40xZuxYY370o8isXz9j7r679tv5fa7eesuYU0+1+3vsscZ8/XXkdl54wZiOHe31F1xgzJ//bLcZcqDPfMYYk5trzF/+Epmddpoxd9xxgAcP4JA4rGcy7dljz1oaO1YkJcV7fej7t1VVIqNG2fZTp9ojxWvW2LNyQgoLRc45x57CP2+ePRPpvPPsqaUi9lTVnByRe+6xX4/YuvXg93vGDLtvgwaFs+HD7dHib74JtznlFHukPOTMM0WWLxfJyxNp00YkK8serS4uFpk2TeTYY0XKy+0pws8+Gz5F9UCmT4/cl6oqkddes7P22dne9qmpIs2a2f9fdZU9i+q99+w+G2Ofx/Jye/3+/fZMsw8/FPnuO3vU4vLLw6fbPvaYyJAhItddF35eO3a01yUk2LU3pk3z9zgAwI/UVPvzzjsipaW1t/3DH0QuucR+ffmcc2y/WP0rBDUVF9ujzX//uz3i+fjj9vZnnRXu4046yXu7igp7pDkpKTJPTrZ9tEh4X6u3iY21XwcJtenXz/bJeXn2LNiSEpGjjrLXz50r8otf1P54Q6ZPt1+pOProcNa+vd3Pt98+8Femb7/dHvmePduOF1dfXXv7yZPt+BY6u7e2MbdTJ5F27RgbAHj57d9jY23/vHixPatnyhR7pn51xcUif/6zPbv0yy/tZ4Lf/CZ8/SOP2LOB/vlP22fu2WP7x+qKiuxXmGfPtv1cbKzID39Yt+vKzZlj/+4ePjyc9epl+8oZM+zlGTNE+va1fWfImWfab06Ezs7p18/2wRUVdl+PPdbmP/2pPTM2Lc3f/kybFvm5QsSOH1Om2DNiXfw+V7ffbn8P8+fbpUEuuyz8rYdvvrFLkNx4o73+tNNE7r038vYH+sznMngw4w5wxBzOGa1vvrEz2hMm1N7u00/tEeING8LZ4sX2tqEjvJrevY154onwZW1WuzauM5nuu8+YHj287du0sUcOjDHmBz+wR52rC+3zkiX28rRpdva9c2d7FlNZmTH33GPMzTfbo88nnWTvp/pjqCl0xOfLL8NZ6CypAx3xXrHCtvvqq3C2a5c9cvDf/7pvN3KkMb/+dfjysGF2nzU//KExV11V+340Nk8++aTJzc01iYmJZvDgweabb77xfdupU6eac88912RlZRkRMW+//XbU93///febQYMGmdTUVNOmTRszatQos6z6aQg+PPXUU6Zv374mLS3NpKWlmRNPPNF89NFHUe9LyAMPPGBExNzseqE4jB8/3ohIxE/P0OmAUdi0aZMZM2aMyczMNElJSaZPnz7m2+qnIh5Abm6uZz9ExPw8dPrhAVRUVJg77rjDdO7c2SQlJZmuXbuae+65x1RVVUX1OPLz883NN99sOnXqZJKSksyQIUPMrNo6wUbszTft2aJJSbav/N3vjFmwILKNSORRy8JCm02caC9rZzKJGDN/fuR2QmfqHMiQIbY/3LzZHiF/6SV75Dk0XpSVGdOpkz0Td88ee/T5wQftfY4YEd7O+PHGdOtmj9JPmGDb9eljzOzZdjzo0cM+5upnKdX0l78Y07WrN//97+3ZTJmZ9szVhx+OPDJe/UymkA8/jDzzVjuTqV27yDN3jal9zO3f/8BHxRs7xgovxgrGCmP89e81vfGGMa1ahS+H+vPqZ9L87W+2rwrJyrJ9YEh5uT0rqrb+fudOu91Fi+zlujiT6eWXjUlI8LY//nj7TQdjjLnuushxwhhjiors9kJvue++M+aUU+w4c9llxuzbZ8y//20fz6ZN9vbduoXPrnVp0cLerrrFi+0ZvrGxxvTta8wNN4Tv18X1XP3975HbFQmfgXXZZeEzsEJGj448k0nj5zPfY4/Zz1wNDWOFF2NFwxsrDuuZTH4Xn1661J4dEzpDRsQuLN2ypb1OxM5q/+Y39qhty5b2KMjSpQee1T6STj7ZLgq7dq1dY2PtWruG0r332rOFrr/ezrjfc489Cq8pKbH/Vj8yHs3z2qyZXbgvpFUrkZ49w89rZaX9/nXfvnYdj9RUu5aH3+c1OdkeSWoqXn/9dbnllltk/PjxMnfuXOnXr5+ceeaZssPnqrpFRUXSr18/+ZvfRVcUU6dOlbFjx8rMmTNl0qRJUl5eLiNGjJCiKBZdycnJkQcffFDmzJkjs2fPltNPP11GjRoliw/iy+zffvutPPvss3Js6JBalHr37i1bt279/md66JQPn/Ly8mTo0KESHx8vEydOlCVLlsgjjzwiGRkZvrfx7bffRuzDpEmTRETk4osv9nX7hx56SJ5++ml58sknZenSpfLQQw/Jww8/LE888URUj+Xaa6+VSZMmyUsvvSSLFi2SESNGyPDhw2VzaNGfJuSii+yaFe+9Z49ifvGFyIAB9qh0ddVfdikpdv262t6OCQmRt4nGSy/Z/rdDB3t20uOP2yO0oTWR4uPtGT4rVtj+tHlzu67F2WeH24jY9ftWrbLrOv3whyIPPGCPcMfH2/Fh+nS79sYVV7j3paTEe1aViMh999liG888I9K7t/23Vy97X9VVfw6ysuy/tT1vfftGnrl7IE1tbKiJscKLsYKxIsRP//7ZZ3aNuQ4d7Bk6l19u12yq3q80by7SrVv4clZWuB/bt8+eYVn9b+Bmzbxn8Kxcafvxrl3t+NG5s83r4+eL3r3tNz7Wr7dr+5WX26JATz5pCyyddJIt/jBhgl3/yUUbP445xn6jYeZMe2brjh327KFrrw238ftc1Ta+LF0a+TsRsd+YqO5gP/M1xHGHscKLsaKBjhWHZOrKYfduu2bE/ffX3s4189yypTH/+pf9/w032KO2EyYYs3ChMStX2iOt1Sc46+pMpn/8w953deXl9myr0FlZl1/uPRIyZYrd3p49+v2deqpdC2TfPtuuqMjmP/qRMY8/rt+mtNQ+h598Es4qK+3+1TyTqqZ337VHtGuu/XTcccb84Q/2/w88YI8MvfSSPbq/cqU9k6n6Y6vtTKazzrLf7W4qBg8ebMZWe8CVlZUmOzvbPPDAA1FvSw7yiENNO3bsMCJipk6dGmg7GRkZ5u/VDz/5UFBQYLp3724mTZpkhg0bdlBHHPpVP2XiINx2223m5JNPDrSNmm6++WbTrVs330cMRo4caa6++uqI7MILLzRjxozxfZ/FxcUmLi7OfFBj0Z8BAwaY2w90WLKJuOYaewQ3RMSuQ1Rdixb2CLcx7jWZavJ7JlNIYWF43Y9LLvEelTXGrsGxY4f9/+DB4TX5alq61K4lVVBgx8KLLw7fh4h7jY3nnrNH6Q+ktNSYY46x65AYo4978+ZFrrfnWpOpptrG3F69jPnTnw68f40VY0UkxgqLscKtev++dq1d8/OXv7RrFi1fbv82P1B//vbb4fXh9u61/6/5cr/ggsj+rGdPewbQZ5/ZbyJ8913k2FIXZzJNnqx/3ujUKfythDvvjOx3jTHm/7d35/FRVefjx58kZCMkQAzImigqi6KIIopYqYKoRUXbL/q1WG3VWi1aXIrQakurdS9aK+LWKhWtoD9FRQVFQKqIqOwCsu+g7IQEyPr8/ni+05nJPTeZyQ1k4fN+vfKCeXLunTM3M+fMPffc86xZY9vNm+d+vmuvtX5D1WaGLVli///tb1XvvNO/nm3aqD73XNWvZ9w4e/41a+xxdY5V6I6M0FqHkecgIX/7W/TfsrrnfA8/bDOe6hP6imj0FaY+9hWHdSZTdrbdT/z00+7MNqGMBV26iGzcaD8hS5fa70880R7PmmXrC11xhV1RbdXKMtdFSkmxmTlB9eplzz13bjg2fbrdcxwafe/Vy+7/Dq1tJGL3SXfqJOIa6PznP+14XHZZuI6hbUtK/OudkmLHYOnScCwx0VJWv/qqXQmqqKDA7n3u0sX+Da0jJWJXgZYvjz6uAweKXHON3evdoYNdha9YB7/6ffONpVM9EhQXF8vcuXOlX8RN9YmJidKvXz+ZHbqpvhbs/b9UgtnZ2dXavqysTMaPHy+FhYXSq+LlpCoMGTJEBgwYEHVM4rVy5Upp06aNdOjQQQYPHiwb4rx8+O6770qPHj1k0KBB0rJlS+nevbu88MIL1a5PcXGxvPLKK3L99ddLgl9asgrOPvtsmTZtmqz4vw/PwoUL5bPPPpOLL7445uctLS2VsrIySatweTE9PT3uqzAN1YknxpclLVbx9h0ZGXZ1dvdum/k5cKC3TNOmtjbfypW2foWrjKrIr34l8vjjdqW2rCy6XxDxr1f37jZjaffuql/bcccd3uN28KBlLjpS+oaK6Cu86CsMfYW/yPZ97lz7zj1qlMhZZ9m6Pq7vu5Vp2tTa6cjvwKWl0d/tQ9+J773XZk116VJ1m1odp59uM1WnTQvHli+3mTmhj1KvXjbjNHICy9SpNmMo9J090rRpNrvn1lvtccX+o7I+rXv36PMKP6HnLSysuWPVpUv030TEZk9FiuWcz6W+nZPQV3jRV5h62VfU+LBVFVavVm3Vyq6k/r//Z+sELV1qI++dO1uZ8nIb2f7BD1TnzrW1nE4/3WbQhFxxhZWZP99m3Fx6qWWhixzgvOACy0awaZPdJ+xn61bbzwsvhNc7mj/fZl6FXHSRrSkxZ47qZ59Z1oarrw7/fs8eu+/7Zz+zkfzx4y0DhuvKwPff20ytyExwXbrYehWff67apEnla0/deafqT34SHdu5045fu3Y222vJEju2//ynXRUPXS0ZONCO/aef2nG76CL7fXGx/f6OOyzDw6xZ9ne58UbLZBF5leeXv7T7xteuteNaVmbxtWttltW6df51b0g2b96sIqKfV0iTMWzYMO3Zs2fc+5MauOJQVlamAwYM0N69e8e97aJFizQjI0OTkpK0adOm+v7778e1/WuvvaZdu3bVA/+3kEt1rjh88MEH+vrrr+vChQt1ypQp2qtXL83NzdX8WFOkqGpqaqqmpqbq7373O503b54+99xzmpaWpmPHjo2rLiETJkzQpKQk3exK3eijrKxMhw8frgkJCdqoUSNNSEjQB6uawunQq1cv7dOnj27evFlLS0t13LhxmpiYqB1di8Q1YDt2WJaYceNsnY41a2wduaOPVo28sFNTM5keeMCuKH/7rbVxofaxoilTbL2nNWtsLcFu3VTPPDO6/Ouv2/OuXq369tt2tfXHP3bv7/nno9v2OXOs/Z09W/WPf7S2209pqa0TOGlSODZpkurgwfbv8uX2eh57zGbhhtbfqMmZTH597owZ1q+FZuseaegrotFXhNFXxNa+L1hgbdLf/mZt6csvW6bqeGYyqdrMluxsiy9bZt9nMzPD7VlZmc3mv+Yamy0zbZp93413JtPOnfb70Pp248fb461bw2Vuvtn6menTbf29Xr3sJ6S01Nbm69/fXv+UKdbG/+533uc7cMDOASLrdPHF9voWLLBzg8rWXv373+08K9JPfmKzqr74wr7Xz5ihetZZtkZgSUn1j1XFmUyzZ9u6T489ZuctTz1ld2dE/i1jOedzzWTKy/OuNVWX0VdEo68Iq499xWEfZFK12wqGDLEPf0qKdRSXXRadJnr9eotlZFhDMmhQ9GKla9dap5SeboMio0d7b+OaPdtSZaamRncyFY0c6U29LBI+MVG1DuPqq+2LclaW6i9+EZ06VdU6x3POsedr29Y6M5f//V/v4t5z5lgHkZ3tnTZa0ZIl9rr37ImO79mjOmKEDYClpFgH3a+fNfah2Xi7dtlAWNOmto8LL7RGPfJ1Dhxor7NlS1tEt2J61+XLraNJT48+EXnwQdvfkaIudgY333yz5uXl6caNG+PetqioSFeuXKlff/21jhgxQnNycnRJaK51FTZs2KAtW7bUhRErdVanM6ho9+7dmpWVFdf02uTkZO0V+U1NVW+77TY966yzqlWH/v376yWXXBLXNq+99pq2a9dOX3vtNV20aJG+/PLLmp2dHXeHtGrVKj333HNVRDQpKUnPOOMMHTx4sHYOjcgfIQ4etLbttNOs7Wrc2Kbp33uv6v794XI1Nci0bZsNmDRpEv1luKIJE2wKf0qKXTwZMsTbLj/5pH3BT062E4p77/UumK1q/VteXvTFB1XrD7KzrX+oau3Pu++2/iVk9Wo7yejY0drrZs3sJCCyb6vJQSa/Pvemm+x2hyMVfUUYfUU0+orY2/fHH7dbgkPfXV9+Of5BppISO0/IyrL28M47vd9xp061C7+pqdaeffJJ/INMoUXIK/6MHBkuc+CA3TbdvLm95iuuiB6EUrXBnYsvtteck2NJeEpKvM83YkR0gh5VG/g54wx7rbfcEr4g7LJzpy26Hrm28/PP23lWixbWx+XmWmKfyAvJ1TlWFQeZVO2CeLt29jovvVT1r3+N/lvGcs5XcZDp88/tbxz5Hqrr6CvC6Cui1ce+olYGmRDc//xP1WtbHU5FRdYBffZZbdfk8CkqKtKkpCRPA37ttdfqZZddFvf+gnYGQ4YM0Xbt2uma0M3yAfXt21dvqmqhr/8zceLE/zZYoR8R0YSEBE1KStLSiguBxaFHjx46YsSImMvn5ubqDTfcEBUbM2aMtmnTJu7nXrdunSYmJurbb78d13bt2rXT0aNHR8Xuv//+amW0UFUtKCjQLf+36M+VV16pP3It+oMj3tatNiBVl2aTbt9udaqhZqleoq8Io6+IRl+BuuK3v616bdf65MorbVZyfUJfEUZfEa0+9hWHdU0m1JzHHrM1O+qKDRtEfv97kd69a7smh09KSoqcfvrpMi3ipvry8nKZNm1a3PccB6Gqcuutt8rEiRNl+vTpcuyxx9bIfsvLy6WoqCimsn379pXFixfLggUL/vvTo0cPGTx4sCxYsECSkpKqVYeCggJZvXq1tA6lI4lB7969Zfny5VGxFStWSF5eXtzP/9JLL0nLli1lwIABcW23f/9+SUyMbl6TkpKkvLw87jqIiGRkZEjr1q1l9+7d8uGHH8pA14I+OOK1amXr/dWlLEjr1omMGSNSQ81SvURfEUZfEY2+AnXFPfeI5OXZ2lf1XXGxrd10xx21XZP40FeE0VdEq5d9RY0PWwFHkPHjx2tqaqqOHTtWly5dqjfddJM2a9ZMv4u8t7MS+/bt0/nz5+v8+fNVRPTxxx/X+fPn6/r162Ouwy233KJNmzbVTz75RLdu3frfn/1xzBEeMWKEzpw5U9euXauLFi3SESNGaEJCgn700Ucx76Oi6kxrveuuu/STTz7RtWvX6qxZs7Rfv36ak5Oj20JpuWLw5ZdfaqNGjfSBBx7QlStX6quvvqqNGzfWV155Ja66lJWVaW5urg4fPjyu7VRVr7vuOm3btq2+9957unbtWn3rrbc0JydH77777rj2M2XKFJ08ebKuWbNGP/roI+3WrZueeeaZWuy3SBCAOom+wh99BX0FAENf4Y++on71FQwyAQE99dRTmpubqykpKdqzZ0/94osvYt52xowZKiKen+uuuy7mfbi2FxF9KXLhlSpcf/31mpeXpykpKdqiRQvt27dvoI5AtXqdwVVXXaWtW7fWlJQUbdu2rV511VW6atWquJ970qRJ2rVrV01NTdXOnTvr888/H/c+PvzwQxURXb58edzb5ufn69ChQzU3N1fT0tK0Q4cOes8992iRazGeSkyYMEE7dOigKSkp2qpVKx0yZIjuqbjoD4B6gb7Cjb6CvgJAGH2FG31F/eorElRVa35+FAAAAAAAAI4krMkEAAAAAACAwBhkAgAAAAAAQGAMMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgMAaZAAAAAAAAEBiDTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAmsUS6Hy8nLZsmWLZGZmSkJCwqGuEyAiIqoq+/btkzZt2khi4qEdD+U9joaKzxEaOt7jQHB8jtDQ8R4Hgov1cxTTINOWLVukffv2NVY5IB4bN26Udu3aHdLn4D2Oho7PERo63uNAcHyO0NDxHgeCq+pzFNMgU2Zm5n93lpWVVTM1A6qQn58v7du3/+/771DiPX7kWrBApE+f6m8/c6bIqafWVG1qHp+jw+Puu+92xpcuXeqM/+///q8nVlBQ4CzbqJG7q540aZIzfsstt3hiF110kbNsPMrLy53xQ31FuCq8x4Hg+ByhoeM93rA19O/zdUWsn6OYBplC0/yysrL4oOCwOxzTTHmPH7maNAm+fX14y/A5OrRSU1Odcb8BovT0dE+srKwsrn34xRs3buyJ1cTfo64OMoXwHgeC43OEho73eMN0pHyfryuq+hzVjW+GAAAAAAAAqNcYZAIAAAAAAEBgMd0uBwDAkeaTTz5xxseMGeOJ+d0ut2vXLmf8N7/5jSeWlJTkLOu6/U1E5KyzznLGX3/9dU/s3XffdZZ9+OGHnfHs7GxPrK7cFgcAAIC6i0EmAAAAHBIbNojs2BH/djk5Irm5NV8fAABwaDHIBAAAgBq3YYNIp04iBw/Gv21amsjy5Qw0AQBQ3zD3HQAAADVux47qDTCJ2HbVmQEFAABqF4NMAAAAAAAACIzb5QAAR4zly5d7Yo888oiz7IoVK5zxU045xRNbtmyZs2x6eroznpOT44nt8Jm20bVrV2d89+7dznijRt6u3W9h8ttvv90ZP/744z2xm2++2Vm2ZcuWzjgAAACOPMxkAgAAAAAAQGAMMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAARGdjkAQL1QVlbmiSUlJTnLPvPMM874F1984YllZGQ4y/bs2dMZb9KkiSd28OBBZ9lvv/3WGXdlnfPL0uZ63SIiX331lTN+ww03eGLNmzd3ls3Pz3fGt27d6on96le/cpZ99tlnnfGjjz7aEysvL3eWTUzkmhcAAEBDwLc6AAAAAAAABMYgEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwBhkAgAAAAAAQGBklwMA1At+meRcFi9e7Iy3atUq5v02auTuInfv3u2JXXbZZc6yS5cudcZd2dtGjRrlLHvfffc54/3793fGXa/HL/td48aNnfGsrCxPzC8z3L///W9n/I477vDEyCIHAADQsPFtDwAAAAAAAIExyAQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAIDAW/gYA1FuuRbhF/Be6btGiRcz7KC0tdcYzMzM9se3btzvL/vCHP3TGv//+e0/s9ddfd5Y99thjnfHOnTs744WFhZ5YcXGxs2xJSYkznp6e7om5Fk0XEdm0aZMzXlZW5onFs3g7AAAA6h9mMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACI7scAKDeWrt2bVzlXVnnioqKnGX9MqE1adLEE9uwYYOzbH5+vjPeunVrT8wvi9x3333njK9bt84Zd2W/O/roo51lExISnHFXZrh9+/Y5y/pl8tu7d68nlp2d7SwLAACAhoGZTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAmOQCQAAAAAAAIExyAQAAAAAAIDAyC4HAKi3Nm/e7Iz7ZTxzZWpr1aqVs6xfZrhly5Z5Ynv27HGW3bp1qzOenp4e8z7mz5/vjOfk5DjjnTt39sQ2btzoLOvKIiciUlBQ4In5HSc/3377rSd29tlnx7UPAAAA1C/MZAIAAAAAAEBgDDIBAAAAAAAgMAaZAAAAAAAAEBiDTAAAAAAAAAiMhb8jqGpMMRGRxMTDPz73n//8xxM799xzD3s9akJhYaEnlpGRUQs1AVCf+S38nZqa6oy72p7S0lJn2aOOOsoZX79+vSe2e/duZ9m0tLSY69eyZUtn2S5dujjjycnJMT+n30LoHTt2dMY//vhjT6xJkybOsn4LkC9ZssQTY+FvADg0/M5Z/BJQtGnTxhNz9ZEiIo8//rgzfuutt3pift/nU1JSnHEXv6QUSUlJMe8DQO1hJhMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgMLLLRUhISIgpFq/f/OY3zviGDRuc8R/84AfO+LRp0zyxY4891lm2ffv2MdbOn1/GpUaNYn/bPPbYY874G2+84YlNnz496nFBQUHMzwPgyOSXNc2v/Vi1apUnduDAAWfZY445xhl3ZZ3zy/S2c+dOZ9yVjW7//v3Osvv27XPGO3To4Iy76uKXkWfv3r3O+OzZsz2xrl27Osv279/fGXcdawCAl19mONd5yJo1a5xlb7/9dmf85ptvdsbnzZvniQ0dOtRZdsKECc74+++/74n9+9//dpa95JJLnHFX9rvGjRs7y950003OuKtfrnhM/Y4xgJrHTCYAAAAAAAAExiATAAAAAAAAAmOQCQAAAAAAAIExyAQAAAAAAIDAGGQCAAAAAABAYA0mu1x5ebkn5pcZriYyxvlldjjjjDM8sZ/+9KfOsqeddpoz7pcFyJU54bbbbnOWffvtt53xeMSTRW7cuHHO+Pjx451xV+anb7/9tsoyABApPz/fGS8sLHTGXe2KX5ZOvzbouOOO88RSU1OdZb/88ktnfPv27Z7YiSeeGFc9SkpKnHFXtjy/TD1+r/2f//ynJ3bPPfc4y/plxfP7GwAAosVzbuKXWfTdd9+N6znfeustT+yCCy5wll2yZIkzXlRU5In5ZbieOXOmM56WluZXRY94zk0A1B5mMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAAR2WFdPU1Vn3LXYXTxlRUQSE2MfLysuLnbGv/vuO0+se/fuzrK33367Mz58+HBP7JRTTnGWXbdunTPut1hqly5dPLGPP/7YWbZ58+bO+O9//3tP7PLLL3eWTU5OdsY/++wzT2zMmDFx7aNbt26eWNu2baMe79u3z7ktAIT4taN+C3G7EisMHjzYWfbhhx92xl3tml8f5Lcw+c6dOz2xbdu2OcsuXLjQGffrW1JSUjyx0tJSZ1m/dvaYY47xxPwWD/dbmNyvHwcAVN/06dOd8dWrVzvjubm5zvjYsWM9Mde5hoh/QqGMjAxPzO9cbevWrc74Oeec44n5vZZJkyY549dcc40nVlZWVuljAIcOM5kAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACY5AJAAAAAAAAgR3W7HJ+2QaClhUR+fTTT2MuO3LkSGe8YnYzEZF//vOfzrLl5eXO+KZNmzyxL7/8Mua6iYgcOHDAGXdl6hkwYICzbNOmTZ3xZ555xhN78cUXnWUzMzOd8R07dnhifpkrevXq5YzPmTPHE6uYhckvYxEAhPhlq8nJyXHG9+zZ44n5tbknnHCCM+7K1Pbtt986y/plM3W10X6Z8rZs2eKM9+7dO+Z9r1+/3lnWr51fs2aNJ+aXiS4tLc0Zd7Xh+/fvd5b1y1wHoOFS1ZiyUPqdE/ht6/qO7sosGi+/LJ0lJSXOeDzP6coKKiLy0EMPeWKu9lnEvy1u1aqVM/7cc895YmeccYazrF8bff7553ti2dnZzrKu7NQiItu3b/fE/LLcvfnmm864K7tco0aNKn0M4NBhJhMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgsDq7zP6qVauccVdmIBGR1157zRPzy/bzhz/8wRkvLCz0xL777ruYy4q4M0/4ZXsoKytzxv0y1x08eNATKyoqcpYdNGiQM37ZZZd5YsuXL3eWXb16tTPevn17T6xfv37Osn5Z7iZMmOCJVcyskZyc7NwWwJHJlanNL3tbYqL7GoorQ45f1hy/DJeufigvLy/msiIi27Zti/n5unfv7oy7+gS//fjVr2JWz5AmTZp4Yn4Zg1wZR0XcGY38+tQOHTo44wAaroSEhLizSVfcPlaxZLGrat9+2clqImvZ2LFjnXFX1tGTTz7ZWdbv3OSoo45yxlu3bu2JubJki4j8+te/dsa///57T6xz587Osn7nCllZWZ7Y9ddf7yzr6jtFRF555RVPzJVxDsDhwUwmAAAAAAAABMYgEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwOJaqW716tWexUDHjx/vKdeyZUvn9n6LlO7bt88TKykpcZb1W3j0vPPO88TOOOMMZ9kvv/zSGXctFOtajE5EJCkpyRl3Lea9a9cuZ1m/BVBdx0NE5MCBA56Y38LfrrIi7sVcO3Xq5Cx7zjnnOOPNmzf3xPxe49tvv+2MuxYhXLJkSdTj/fv3O7cFcGRyJYRITU11lvXrQ/bu3euJuRY/FfFfzNWV4CE9PT3m5xMR2blzpyfm6sdERFasWOGM+y247eK3uLlfX+Z6jZmZmc6yfnHXa/Tr3wAceVQ17gW5Y+HXrgXlahdFRJ599llnfP78+Z5YTk6Os+zPf/5zZ/z888/3xP797387yy5dutQZ9+sPzz77bGfc5emnn3bG77jjDk/M9bpF/M+pevfu7Ynl5uY6y/rFv/76a2ccQO1gJhMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgsLiyy/3jH//wZPJZuHChp5xfth/fSjgy+DRt2tRZdvv27c64K4OPX5a7jIwMZ3zt2rWe2DfffOMsu2nTJmd8z549nphfpje/bHuuDHV+/I61X6ajHj16eGJfffWVs+zo0aOdcVcWvpNOOslZNiEhIeZ9HH/88VGPCwoKnNsCODK52td4s8udcsopnlirVq2cZf3aeVeWTr/2ylVnEXfb6FePlStXOuN+r9GVrSk/P99Z1i9bUosWLTwxV7stEl82U79sewCOPAkJCb7fE4NwfY/2yzjnlwXZ1f77ZU3za/+vu+46T2zmzJnOsl26dHHG16xZ44n5nQv5nVf4nQ/Fw+/vtG3bNk/M7zzGL2v02LFjPbH+/fs7y7r6FRF31rkNGzZEPSa7KXD4MJMJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgMAaZAAAAAAAAEFhc2eUuv/xyT2a27OxsT7mNGzc6t9+9e7cz7lrtf8uWLc6yflnn1q1bF3NZVxY5EZHCwkJPzJX5TsQ/y47rORs3buwse/LJJzvj5513njO+c+dOT+ytt95ylv3oo4+c8Xj4ZWHwyw7h4pfJLyUlxROrmJ0jLS0t5ucB0PC5stj4ZUfzy7DpyuDml6XNL1PP0Ucf7YkVFRU5y/r1Ia59TJ8+3Vl26dKlzniHDh2c8ebNm3tifq/F7zi5MjG52m0R/6xDruPql4kOAETc2TH9lJeXO+N+meRcFixY4Iy72szk5GRn2WHDhjnj3bt398T8vhcvW7bMGXdl+vTLcud37F555RVn/Oabb3bG4+Fq59evX+8s27FjR2fclfV14sSJzrI/+9nPnPFTTz3VE1u8eHHUY9d5HoBDg5lMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMDiyi7XuXNnycrKiorl5eV5yrVu3TquSpSVlXlifplw1qxZ44y7sg5NnjzZWfbnP/+5M+7KbnDUUUc5y/pl2TncLr30Umd8ypQpzni3bt08Mb9MeX7ZOZo0aeKJ+WW08MvksHXrVk+sYta6itnmABzZduzY4YllZmY6y/pljDv22GM9Mb+sPn5tkCuTnCtrnYh/tlVXljVXtlYR/wxwflmKXOX9svClpqY64y5+x9RvH65+wa9vB3DkUVVPO+E6J/Djl70zPz/fE1u9erWzrF8mNFcGaL9Mn8OHD3fGX3/99ZjqJiLSvn17Z9x1HjJjxgxn2TPOOMMZd50jibgzmp5//vnOsn5c5xXff/+9s+xVV13ljLvOZS6++GJn2Z/+9KfOuCsjdsX+xi8LLICax0wmAAAAAAAABMYgEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwOJa+Ltp06aehb9dC9hNmzbNub3fwqPJycmeWLNmzZxlu3bt6oxXXDRaROTWW291lu3QoYMzXlxc7Im5FpoVcS8w58e1wGtlcb9FtF0LHLZt29ZZ1m8h1k8//dQTcy3aJ+K/yKtrQXC/hRpdfxcR94K6fousA4CIu81MS0uLuayISE5Ojifmtyhq06ZNnXFX8oM9e/Y4y/otTOtKiuC3ePiuXbuccb9FtL/77jtPzK9Pjacv8+vD/eKu1+7qZwEcmRISEiQhISEq5tdmxsP1Pfqdd95xll2+fLkz7mpfFyxY4Cz7zTffOOM7d+70xLZv3+4s++677zrjt99+uyf2ySefOMv++c9/dsZdfYKIyP333++J+S38vXfvXme8ZcuWzng89XBxve7KzJ8/3xOrmNCJhELA4cNMJgAAAAAAAATGIBMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBggVM4tG/fPqZYZVatWuWJ+WUGWrlypTPuyuyTmOgeQ/PLJFFUVOSJVcymF+LKiCciniwZIu5MRCIi2dnZzrhfVjdXtgy/TA0tWrRwxl31Li8vj/n5RER2797tjLtkZmY6467Xftxxx0U9dmUuBIBIfu1lPJnQlixZ4izr14e44n7Z5Vx9gohI8+bNPTG/1+LX36SnpzvjruyirqygIv7Z3lx9i19mVj+uLFF+GUcBHHm+/PJLycjIiIo9++yznnJ+WcH8MtG52mi/sn7fU11ZR/2ydG7dutUZ/+KLLzyxyZMnO8u6zkH8+GUi9csA58eV/e7MM890lvU7L7vgggs8MVf/JiIyfvx4Z3zo0KGe2AknnOAse9pppznj69ev98SefPLJqMd+mbAB1DxmMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACC5xdriYcf/zxgfdx8skn10BNUJf4ZWUCcGRytQl+mdf8snouW7bMEzv77LOdZTt37uyMuzKv+WVv2759uzPuynTkl/nGL+6Xjc6VYcgvW2hKSooz7so66vd8fq89LS3NE/PL+gfgyHPSSSd5sjjfeOONnnJ+7ahftmNXJjS/7GgHDx6MeR9+bd29997rjLvaUb+s1UcddZQzPn/+fE/ML8vdXXfd5Yz7ZZx2Zanzy1D3wAMPOOObNm3yxFq3bu0s69cPucr7ZU+tmI0wxNVfV+xv6H+Aw4eZTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAmOQCQAAAAAAAIHViYW/AQCoimvRUNcC2iL+iQOys7M9sVtuucVZds2aNc74vHnzPDG/hVUXL17sjC9dujSmuon4L/ztWrRVxL0Y+pYtW5xlr732Wmf8rLPO8sT8FoT1e40uiYlc2wJgMjIyPAs5/+AHP6il2qAykydPru0qBJafn1/bVQCOGHzbAwAAAAAAQGAMMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAARGdjkAQL3glzHOxS8j2znnnBPzPjp06BBX3KVPnz4xly0vL3fGi4qKnPH09PSY910T/DLoxfN38XuNAAAAaBiYyQQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAIDAGmQAAAAAAABAYg0wAAAAAAAAIjOxyAIB6ITU11ROLJ7OZiEhycnLMZf0y1CUlJXliquosG0/9EhPd130OZRa5eOqdmZnpLOs6HiLuTHLFxcVx1A4AAAD1DTOZAAAAAAAAEBiDTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAmPhbwBAvbBjxw5PrKSkxFnWbzHqRo0OTbfnt8B3TSwIfii5FucWcR8/v4W/i4qKnHFX+XgWXgcAAED9w0wmAAAAAAAABMYgEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwBhkAgAAAAAAQGBklwMA1AtlZWWemF+2stLSUme8devWNVqnqtREFrl4M9S5yvuVjSe7XHp6urOsX4Y/19/AL0MdAAAAGgZmMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACI7scAKBeSEz0XhfZt2+fs+yePXuccVeGOj/xZF47lOLNUFcTGe1cGjVyf2XwO6auzH8ZGRk1WicAAADULcxkAgAAAAAAQGAMMgEAAAAAACAwBpkAAAAAAAAQGGsyAQAANEAjRoyIe5uHH374ENQEAAAcKRhkAgDUC7/4xS88sblz5zrL+i38ffrpp8f8fH4LXTckrsXU/bRu3TquuOv4NWvWLObnAwAAQP3D7XIAAAAAAAAIjEEmAAAAAAAABMYgEwAAAAAAAAJjkAkAAAAAAACBxbSqqaqKiEh+fv4hrQwQKfR+C73/DiXe40eugoLg29flt01D+hzt27fPE9u/f7+z7IEDB5xxV92SkpKcZf2OWUJCgl8V652ysjJn3HVMXMdfRKSoqMgZdx2nwsJCZ9kg75mG9B6vaX5/m8rU5Gtr6O1rQ8LnCA0d7/GGjf7m8Ij1c5SgMXzSNm3aJO3bt6+ZmgFx2rhxo7Rr1+6QPgfvcTR0fI7Q0PEeB4Ljc4SGjvc4EFxVn6OYBpnKy8tly5YtkpmZ2aCu4KJuU1XZt2+ftGnTJq4029UR5D2en58v7du3l40bN0pWVla1np99sI9DtY/68jkCqqu+vMdruy1gH+yjMvXlcwRUV315j9d2W8A+2EdlYv0cxXS7XGJi4iEf8QVcmjZteliepybe41lZWdX+sLMP9nEo91GfPkdAddSn93h9b0/YR8PdR336HAHVUZ/e4/W9PWEfDXcfsXyOWPgbAAAAAAAAgTHIBAAAAAAAgMAYZAICSk1NlZEjR0pqair7YB91dh8AalddaQvYB/sAUHfVlbaAfbCPIGJa+BsAAAAAAACoTJ2dybRunUhCgsiCBbVdk7pp2jSRLl1EyspquyZexxwj8re/1ew+i4ttv19/XbP7BYCGZPlykVatRPbtq+2aGNpuAHXZz38ucvnlsZdvqOcndem8YuxYkWbN4tsm8tyDfgeofZ5Bpu3bRW65RSQ3VyQ11b6sXnihyKxZtVG9w+Ott0T69xc56ij/juPgQZEhQ6xMkyYiP/mJyPffR5fZsEFkwACRxo1FWrYUGTZMpLQ0/Pv580W6d7ftL71UZNeu8O9KS0VOP13kyy9jq/Pdd4vce69IUpI9LisTefhhkc6dRdLTRbKzRc48U+Qf/4jrUNRZKSkiv/2tyPDhtV0TALXpu+9EbrtNpEMH66Pat7f2dNq0mn2eWAfLV68WueIKkRYtRLKyRK680ts3HHOM9S2RPw8/HP79unUi554rkpFh/65bF739JZeIvPlmbPX+3e/s+GRmhmOqIs8/b31Ckyb25b1HD3t9+/fHtt9Y/OlPIqeeGh2j7QZQlYZ27sF5Re2i3wFqn2eQ6Sc/sUbrX/8SWbFC5N13RX74Q5GdO2uhdjWspMQdLywUOecckUce8d/2jjtEJk0SeeMNkZkzRbZsEfnxj8O/LyuzjqC4WOTzz+34jR0r8sc/hsvceKPI+eeLzJsnsnevyIMPhn83apRI794iPXtW/To++8xObH7yk3Dsz38WeeIJkfvvF1m6VGTGDJGbbhLZs6fq/dV1xcX27+DB9tqXLKnd+gCoHevW2Zfm6dNFHntMZPFikSlTRM47z76sH26FhXYikZBgdZo1y9qrSy8VKS+PLnvffSJbt4Z/brst/Lu77hJp29ZORFq3ti/HIRMmiCQmRrf3fjZsEHnvPbsyH+lnPxO5/XaRgQOtb1iwQOQPfxB55x2Rjz6q3muPB203gMo0tHMPzitqH/0OUMs0wu7dqiKqn3yilRJRfeEF1csvV01PVz3+eNV33okus3ix6kUXqWZkqLZsqXrNNarbt4d/P3myau/eqk2bqmZnqw4YoLpqVfj3a9fa88yfb49LS1V/8QvVTp1U16+32Ntvq3bvrpqaqnrssap/+pNqSUl0PceMUb30UtXGjVVHjqz8dVV8zpA9e1STk1XfeCMcW7bMys6ebY8/+EA1MVH1u+/CZZ55RjUrS7WoyB6np9t2qlavH/3I/r96teoJJ6jm51dev5AhQ1T/53+iY9262euvTJ8+qrfdpjpsmGrz5qpHH+09Jrt3q95wg2pOjmpmpup556kuWBD+/apVqpddZn/TjAzVHj1Up06N3kdenuoTT4Qfv/CC/Z0//tgeV/Xe6NPHXuPQoapHHaX6wx+Gf3feear33lv56wTQMF18sWrbtqoFBd7f7d4d/v/69dZOZWRYOzZoUHTbXFU71qePte+RPy4ffmjt/t694diePaoJCdH7q9gmVtSli/WJqtaXnHhi+DUdf7zqhg3+20Z67DF7LZEmTLD6v/22t3x5udVXVbWsTPXPf7bjm5JifUqoTiF33219VXq69bn33qtaXGy/e+kl7zF76aXwtrTdAFxiPfcYNUq1a1f7Pt+uneott6ju2xf+/Usv2XfNKVNUO3e2tv3CC1W3bAmXKS1VveOO8LnHsGGq116rOnBguEy85yeVaejnFbEeqzfftO/y6emqp5yi+vnn0ft56SXV9u3t95dfrvrXv9o+Q6pz7qFKvwPUpqiZTE2a2M/bb4sUFVU+OPXnP9ttAYsWifzoRzZiHJqmuWePjax37273w06ZYlNAr7wyvH1hocidd9rvp02zK7VXXOG9+itidRk0yK6+fvqpTaf99FORa68VGTrURtife85G+B94IHrbP/3J9rt4scj118c5Avd/5s61WVD9+oVjnTtbPWbPtsezZ4ucfLLI0UeHy1x4oUh+fngUvVs3kalTbQrrtGkip5xi8ZtvFnn00ejbGyrz6ad2q0OkVq3sSvr27ZVv+69/2S0Zc+bYc953n9UpZNAgkW3bRCZPttd92mkiffuG/7YFBfb3njbNrjpddJFdtd+wwf18jz4qMmKEXS3v2ze290aonikpNjPg2WfD8Z497fUDOLLs2mXtxZAh1oZVFFq/obzcZuzs2mVXh6dOFVmzRuSqq8Jlq2rH3npLpF276NlHLkVFNospMjlHWpr1Z599Fl324Yfttoju3W0WVuQtD926iXz8sdX9o4/CfcOwYfZ627eP7Ri5+oZXXxXp1MmOSUUJCSJNm9r/n3zSrnz/9a/Wr194ochll4msXBkun5lp/ezSpVb+hRfsSreIHd+77hI56aTwMYs85rTdAFxiPfdITBT5+9/tO/W//mXfee++O7rM/v3Who0bJ/Kf/1ibHjkzdNQoa8NefNHa6F27RCZOjN5HPOcn1dVQzitiPVb33GN/hwULRDp2FLn66nAfOGeOyA03iNx6q/3+vPNE/vKX6O3jPfcIod8BalHFUaf/9/9slktamurZZ6v+7neqCxdGlxGJHhkuKLBY6Krn/fer9u8fvc3GjVZm+XL3aNf27fb7xYvtcWj0+9NPVfv2VT3nnPAVV1WLPfhg9D7GjVNt3Tq6nrffXskQWwV+VxxefdWu7FZ0xhl2ZVdV9Ze/9L7mwkLb3wcf2ONvvlE991zV3FzVq6+2q98vv2xXUDZtsu2PO071nnsqr2fTprZdpCVL7Gp4YqLqySer/upX4ecN6dPHjmPF1zB8uP3/00/tCsnBg9FljjtO9bnn/Otz0kmqTz0Vfhy6mnD33fb3+Oab8O9ieW/06WMz1FyefFL1mGP861IbRo8erXl5eZqamqo9e/bUOXPmxLztzJkz9ZJLLtHWrVuriOjEiRPjfv4HH3xQe/TooU2aNNEWLVrowIED9dtvv41rH2PGjNGTTz5ZMzMzNTMzU8866yz9oOIbKA4PPfSQiogOHTo0ru1GjhypIhL106lTp7iff9OmTTp48GDNzs7WtLQ07dq1q3711Vcxb5+Xl+eph4jor3/965i2Ly0t1XvvvVePOeYYTUtL0w4dOuh9992n5eXlcb2O/Px8HTp0qObm5mpaWpr26tVLv/zyy7j20VDMmWPtxFtvVV7uo49Uk5KiZ/8sWWLbVnbo/NqxymzbZm3m0KHW3hcUqN56qz3XTTeFy40apTpjhvWlzzyj2qyZXU0P2bTJrgC3b2//btqkOnOmXa3dudNmYh17rLXroSvYLt26qd53X3SsSxe7AlyVNm1UH3ggOnbGGaqVveUfe0z19NPDj0eOtDq41MW2+3Cjr/Cir6CvUI3t3KOiN96w2e4hodmUkTNpnn7aZu2HtG6t+uij4cclJTYrKnImU0V+5ydBZjI1lPOKivyO1T/+Eb1fkfAMrKuvDs/ACrnqquiZTC6x9Nn1td+hr/Cir6h/fYVzTaYtW+x+6IsuEvnkE5vNMnZsdLnQaLmIXVXOyrIZMCIiCxfavbuhqxNNmtgIvYjd8ytiV0evvtoWb83KsoVRRbyj0ldfbSPlH30UvuIaeo777ot+jl/+0q6eRi5kWnFkvjaddJJdWV+/XuTf/7arGCNHiowebetznH22va633rL7tP0cOGBXyyOdeKLIN9+IfPGFzdjats1G+W+8Mbpc5N9NxNb/iPy7FRSEFyEM/axdG/67FRTY1YguXWzmQJMmIsuWef9uo0bZVe7PPrPXHRLLe0PE1l1xSU+v2YVqg5owYYLceeedMnLkSJk3b55069ZNLrzwQtkWOqhVKCwslG7dusnTTz9d7TrMnDlThgwZIl988YVMnTpVSkpKpH///lJYWBjzPtq1aycPP/ywzJ07V77++ms5//zzZeDAgbKkGjezf/XVV/Lcc8/JKRXfbDE66aSTZOvWrf/9+azilJAq7N69W3r37i3JyckyefJkWbp0qYwaNUqaN28e8z6++uqrqDpM/b/pfoMGDYpp+0ceeUSeeeYZGT16tCxbtkweeeQRefTRR+Wpp56K67XceOONMnXqVBk3bpwsXrxY+vfvL/369ZPNmzfHtZ+GQDW2csuW2cyfyNk/J55o7dWyZfY41nasKi1a2HoakybZPpo2tdmap51mV3RD7rzT1hc55RS7wjxqlMhTT4Wv2rdta2sphdZUyskR+fWvbRbnX/5iV6OXL7d+87nn/Ovj6htiOW75+dbv9+4dHe/dO3zMRGx9qN697Qp3kya2SGysx6yutd2HG32FF30FfUVILOceH39sM+LbtrU28Wc/szWbItuVxo1Fjjsu/DjyO+7evXaOcOaZ4d83auQ9T4j1/KQuqO3ziliPVeRHvHVr+zf0d1m2LPpvIiLSq1f04+r22fWx36Gv8KKvqKd9RSwjUTfcYKPkISKqFQdGmzYNr79w0UWqP/6x6sqV3p/QWhqdOtkI+8cfqy5daqPxkfsNjX7fdJPdfz1tWvTzpaWpPvKI+znKyvzrWRm/Kw7Tplk8cs0PVTsmjz9u///DH7xXcNesse3mzXM/37XX2ii7ql3BWbLE/v/b36reead/Pdu0qXxmUci4cfb8a9bY4z597Ip7pIEDVa+7zv7/8MO2HofrmIbWTPrVr1Q7dLDZBIsW2e+6dYveb16eXZnIylJ96KHo54vlveGqZ8jDD9vVi7qiZ8+eOmTIkP8+Lisr0zZt2uhDFV94DKSaVxwq2rZtm4qIzpw5M9B+mjdvrv+IvPwUg3379ukJJ5ygU6dO1T59+lTrikM3v6kQMRo+fLieU3HKXkBDhw7V4447LuYrBgMGDNDrr78+KvbjH/9YBw8eHPNz7t+/X5OSkvS9996Lip922ml6T1WXJRugnTttraOKM1gr8rty2ayZ6r/+Zf+PtR2raiZTpO3bw33E0UdHXy2vKNTf+V0Y/OMfwzOdundXff99+//o0dZ++gnNAIh02WWqHTtWXve9e91rotx+u61poWpraCQlqf7lL6pffaW6YoXNmoq82lzZTKa61nYfbvQV0egrDH2Fv8hzj7Vrbf3V22+3NYuWL1f95z+jv5uH1mSKNHFieE29PXvs/xXf7pdfHj2TKdbzkyAzmRrKeUV1jlVoDa4ZM+zxqafaeoCR/va36L9ldfvs+tjv0FdEo68w9bGv8MxkcjnxRJtNFKvTTrP7hY85RuT446N/MjLsysPy5XYVtG9fG5nevdu9r1tusbUsLrvMRusjn2P5cu/+jz8++gpyTTj9dJHk5OgU2cuX2wh6aLS9Vy9b9ylyoHnqVBvZP/FE7z6nTbNR+FtvtcdlZeHsdyUl9thP9+62JkZVQs8b69/utNMsPXijRt5jmpNjZWbNssxFV1xh94q3auVNty1i90FPnmyZLv761+jnqOy9UZVvvrHXXxcUFxfL3LlzpV/ETfWJiYnSr18/mR26qb4W7N27V0REsrOzq7V9WVmZjB8/XgoLC6VXxctJVRgyZIgMGDAg6pjEa+XKldKmTRvp0KGDDB48WDbEefnw3XfflR49esigQYOkZcuW0r17d3nhhReqXZ/i4mJ55ZVX5Prrr5eEhISYtjn77LNl2rRpsmLFChERWbhwoXz22Wdy8cUXx/y8paWlUlZWJmkVLi+mp6fHfRWmIcjOtvUonn7a3aaFMt506SKycaP9hCxdar8PtYmxtGMpKZW3wxXl5NgV1unTrR+47DL/sgsWWD/VsqX3d8uW2RXp+++3x0H7hp/+1LI1vfOOt7yqXd3PyhJp08abLnzWrPAx+/xzkbw8W1ujRw+RE06wq+eRKjtmdantPtzoK7zoKwx9hb/Ic4+5c22dn1GjRM46y9b12bIlvv01bWqzaObMCcdKS23fIfGcnwTREM4raupYdekS/TcRsdlTkWI996iovvU79BVe9BWmXvYVkSNOO3bYVctx4+xe6DVrVF9/3a7KRg6eVTWTafNm1RYtLFPBl1/a/dFTpqj+/OeW2aGszO6jvuYaG42eNs3uQ65s9PuJJ1SbNLF1g1Rtf40aWeaDb76xEfTXXou+7zjWmUw7d9rzvP++bTN+vD3eujVc5uab7QrD9OmqX3+t2quX/YSUllrWi/79LRvblCl2DCpeVVZVPXDAMl9EjuxffLHdf71ggd0f/vrr/vX9+9+j18FQVf3JT+zqxxdfqK5bZ1cIzjrLrmCHMu5VNZOpvNzWbOrWzbImrV2rOmuW6u9/b1euVVWvuMKuOsyfb3W99FLL3uR3NeHTT+3vFnpc1XvDr56R+65433ht2bx5s4qIfl4hTcawYcO0Z8+ece9PauCKQ1lZmQ4YMEB79+4d97aLFi3SjIwMTUpK0qZNm+r7oekTMXrttde0a9eueuDAAVXVal1x+OCDD/T111/XhQsX6pQpU7RXr16am5ur+bGmSFHV1NRUTU1N1d/97nc6b948fe655zQtLU3Hjh0bV11CJkyYoElJSbp58+aYtykrK9Phw4drQkKCNmrUSBMSEvTBqqbgOPTq1Uv79Omjmzdv1tLSUh03bpwmJiZqx6qmpjRQq1ertmpl2df+3/+z2TRLl9qV286drUx5ubVRP/iB6ty5tpbT6adbuxISSzt2wQU2C2jTpujslxW9+KJdWV+1yvrO7OzoK8aff27t34IFVv9XXrE28NprvfsKtcGTJoVjt9xi6zQtXWqzmiqbIfXuu5Z9J9SWhvZ51VWWteeBB6wtX7fOnuP888N95BNP2OzT8eNthtXw4Zb9aMUK+/0771if+9pr9lqffNJea+TV5ldftcw/8+fbMYtc368utd2HG31FNPqKMPqK2M49Fiyw7+d/+5u1oy+/bDPv45nJpGozW7KzLb5smX3vzswMz2SqzvmJy5FwXlHdY1VxJtPs2bbu02OPWX/z1FM28zjybxnvuUdkrD71O/QV0egrwupjXxE1yHTwoOqIEaqnnWYf7saNbSrkvfeq7t8fsVEVg0yq1lBccYU1FOnp1vjdfrt94VW11JNdutj011NOsWn6VTVMo0ZZozJrlj2eMsVuD0hPty/HPXuqPv985fV0caVeFrGp/yEHDtgCqM2b23G54orozkLVGuGLL7b65OSo3nVXeIAn0ogR9rtIK1da45yVZScVoVv+XHbutNsFI2+1eP5566RbtLDFBHNzbeBm3bpwmaoGmVQt3eltt9nU2eRkW4h28ODwIrpr19rzpKfb70aP9u63YkM/c6adePz97/a4qveG3yDT55/bNpHvxdpUFzuDm2++WfPy8nTjxo1xb1tUVKQrV67Ur7/+WkeMGKE5OTm6JDTXugobNmzQli1b6sKIlTqr0xlUtHv3bs3Kyoprem1ycrL2ivympqq33XabnnXWWdWqQ//+/fWSSy6Ja5vXXntN27Vrp6+99pouWrRIX375Zc3Ozo67Q1q1apWee+65KiKalJSkZ5xxhg4ePFg7h0ZUjkBbtli65bw8a+vatrXBoNAXVlXV9estlpFhfcagQdFpoGNpx2bPtr4pNTX6JKWi4cPtZCg52VJGjxoVbstUbaDrzDOtj0xLs37vwQe9CRZUVZ991r7YR/r+e0t0EXodhYX+dSkpsbZ7ypToeFmZLTh+xhnWf2Vl2QnFk0+G29OyMrto07atvZZu3cLJPEKGDbOTiiZNbODqiSeiTwQOHrT6N2tmxyz0naCutd2HG31FGH1FNPqK2M89Hn/cFu5OT1e98EIbPIh3kKmkxNr5rCxrk+680wb8I2+Xq875SUVHynlFdY5VxUEmVbv1sV07e52XXqr6179G/y2rc+5RH/sd+oow+opo9bGviGlNJtQ9v/1tdPaiI8GVV3qzH9WmoqIiTUpK8jTg1157rV4WSzqnCoJ2BkOGDNF27drpmtDN8gH17dtXb4rxTTZx4sT/NlihHxHRhIQETUpK0tLIqRVx6tGjh44YMSLm8rm5uXrDDTdExcaMGaNt2rSJ+7nXrVuniYmJ+vbbb8e1Xbt27XT06NFRsfvvv79aGS1UVQsKCnTLli2qqnrllVfqjyqmYgH+z+jR3oxEta2utd2HG31FGH1FNPoK1BUN7byiPvY79BVh9BXR6mNfUcOrF+FwueceWx+jvLy2a3J4FBfbfdh33FHbNQlLSUmR008/XaZF3FRfXl4u06ZNi/ue4yBUVW699VaZOHGiTJ8+XY499tga2W95ebkUhdJfVaFv376yePFiWbBgwX9/evToIYMHD5YFCxZIUlJStepQUFAgq1evltahdCQx6N27tyxfvjwqtmLFCsnLy4v7+V966SVp2bKlDBgwIK7t9u/fL4kVFodLSkqS8mp+YDMyMqR169aye/du+fDDD2XgwIHV2g8avl/9SuTcc0X27avtmpi62HYfbvQVYfQV0egrUFc0pPOK+trv0FeE0VdEq5d9RY0PWwFHkPHjx2tqaqqOHTtWly5dqjfddJM2a9ZMv4u8N6cS+/bt0/nz5+v8+fNVRPTxxx/X+fPn6/r162Ouwy233KJNmzbVTz75RLdu3frfn/1xzBEeMWKEzpw5U9euXauLFi3SESNGaEJCgn700Ucx76Oi6kxrveuuu/STTz7RtWvX6qxZs7Rfv36ak5Oj27Zti3kfX375pTZq1EgfeOABXblypb766qvauHFjfeWVV+KqS1lZmebm5urw4cPj2k5V9brrrtO2bdvqe++9p2vXrtW33npLc3Jy9O67745rP1OmTNHJkyfrmjVr9KOPPtJu3brpmWeeqcXFxXHXCUDtoa/wR19BXwHA0Ff4o6+oX30Fg0xAQE899ZTm5uZqSkqK9uzZU7/44ouYt50xY4aKiOfnusiFsqrg2l5E9KXIRdKqcP3112teXp6mpKRoixYttG/fvoE6AtXqdQZXXXWVtm7dWlNSUrRt27Z61VVX6apVq+J+7kmTJmnXrl01NTVVO3furM9HLtYWow8//FBFRJcvXx73tvn5+Tp06FDNzc3VtLQ07dChg95zzz1aVFQU134mTJigHTp00JSUFG3VqpUOGTJE9+zZE3d9ANQ++go3+gr6CgBh9BVu9BX1q69IUFWt+flRAAAAAAAAOJKwJhMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgMAaZAAAAAAAAEBiDTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAmOQCQAAAAAAAIExyAQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAIDAGmQAAAAAAABAYg0wAAAAAAAAIrFEshcrLy2XLli2SmZkpCQkJh7pOgIiIqKrs27dP2rRpI4mJh3Y8lPc4Gio+R2joeI8DwfE5QkPHexwILtbPUUyDTFu2bJH27dvXWOWAeGzcuFHatWt3SJ+D9zgaOj5HaOh4jwPB8TlCQ8d7HAiuqs9RTINMmZmZ/91ZVlZWzdQMqEJ+fr60b9/+v++/Q4n3OBoqPkc1r6yszBNbv369s2yHDh0OyfOJiCQlJTnjS5Ys8cROPPFEZ9mGcIWV9/ihsWCBSJ8+1d9+5kyRU0+tqdrgUONzVLvGjh3rjO/du9cTKy0tdZbNyMhwxtu2beuMX3rppbFVroHgPQ4EF+vnKKZBptCX0KysLD4oOOwOx0kQ73E0dHyOao5r0Mevs62J4xDvIFOTJk1irkdDGGQK4T1esxxvo7i3b+CHqEHic1Q70tPTnfGioiJPzG+QyW8fjRs3dsaP1GPPexwIrqrPEQt/AwAAAAAAIDAGmQAAAAAAABBYTLfLAcAhsWGDyI4d8W+XkyOSm1vz9QFiUFJS4olt3LjRWfa4446Leb+q6oz73RbnZ8uWLZ7YySefHNc+AKC+crWl8d4i5dqH321qycnJzrjrVudGjdynXqmpqc54PPX2K3vgwAFn/KKLLvLEJk+eHPPzibiPid9rBHDkoBUAUDs2bBDp1Enk4MH4t01LE1m+nIEmAAAAAKhDuF0OQO3YsaN6A0witl11ZkABAAAAAA4ZBpkAAAAAAAAQGINMAAAAAAAACIw1mQAAiENaWpon9o9//MNZtlmzZs74qaee6onFuzDtO++844w/+eSTntiFF14Y174BoL6KZ+Hv8vJyZzwx0Xsd3m+Bbz+33nqrJ+a3wHfr1q2d8ZSUFE/soM9SA8XFxc54ZmamM75gwQJnPB6uRb5dC56LxJ/EAkD9xUwmAAAAAAAABMYgEwAAAAAAAALjdjkAAAAAAFArNmyoXuLonByR3Nyarw+CYZAJAAAAAAAcdhs2iHTqJOKz3Fil0tJEli9noKmu4XY5AAAAAABw2O3YUb0BJhHbrjozoHBoMZMJAIA4lJSUeGKffvqps+xXX33ljJ9yyime2C9+8Qtn2fvuu88Z98sw1LVrV2ccAI4ErsxwrnZbJL6McR988IEz/te//tUZX716tSeWnZ3tLOuX/a5t27ae2JYtW5xl/bK6+e3blYXPL8vdsGHDnPHbb7/dEyOLHABmMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACI7scAABxcGUjatWqlbNsaWmpM/7tt996YkOGDHGWTUtLc8abN2/ujLdo0cIZB4AjQXl5uScWTxY5EZGrr77aE3v99dedZZs0aeKMN27c2BPzy/RWUFDgjG/dutWvih4HDhxwxtPT051xVza6oqIiZ9l77rnHGX/sscc8saeeespZ9n/+53+ccVc/2agRp6hAfcZMJgAAAAAAAATGIBMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMBYVQ0AgID8FlbdvHmzM56ZmemJNWvWzFk2NTXVGT948KAznpGR4YwDAKLNmDHDGX/77bc9sby8PGfZkpISZ9wv8YNLcXGxM75u3TpP7MQTT3SW9Vu0e8+ePc64K6mEX6IJv37F9dqvv/56Z9lTTz3VGT/++OM9MVV1lvVbOB1A3cJMJgAAAAAAAATGIBMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgZJcDACAgv2w/q1evdsaTk5Nj3rdfWb/scm3bto1532TwAdDQJCbGfg39ueeec8aTkpI8Mb9scWVlZc64q30tLy93lvVr513xLVu2OMv6ZSKNp533K+v32l318zv+d9xxhzM+adKkmOoGoP5gJhMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgMLLLAQDgEE9GnoyMDGfZRo3c3axr337ZdI4++mhnfOfOnTHvGwCOZH7t4meffeaMN27c2BMrKSlxlvVru13P6bcPv8xwrix3fhnqCgsLnfH09PSY6xdv/+HKOpeVleUs+5///McZX7x4sSd28sknx1UPAHULM5kAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACY+FvAAAc/BZzdVm1apUznpgY+7WcoqIiZ3zfvn3O+FFHHeWMr1+/PubnjOc1AkB9NWHCBGd8165dzrhr8Wq/Bbf92tGmTZt6Yvv373eW9VsQvKyszBPzSzThVz+/viUtLc0Ti2cRcz9+Zf3io0aN8sTGjh0b8/MBqHuYyQQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAIDAGmQAAAAAAABAYg0wAAAAAAAAIjOxyVRgzZowz/s0338RVPh5+2RfIAgQAddOMGTOc8dzcXGc8OTnZE/PLDOTHr0/49ttv49oPADR0n3/+uTOelJTkjPtle3NJSUlxxg8cOBDzfl19gohIaWmpJ9asWbOY6ybif17hylznlxE1nnMTV51F/I/1p59+6owDqL+YyQQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAIDAGmQAAAAAAABAYg0wAAAAAAAAILHB2OVfmhPT09MD78MvUEA+/LAZ+3nvvPU9sy5YtzrItW7Z0xq+99lpP7IEHHnCWbd++vTMeTxY5V2aIysR7TAAA0VauXOmJtWjRwlk2NTU15v02bdrUGffrE/ziW7dujfk5AeBIMG/ePGc8nmxqfucmfm3xwYMHPbG0tDRnWb+MbK59+7XxfvWI55yquLg4rn246u2Xic6vP2zcuHGMtQNQXzCTCQAAAAAAAIExyAQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAILDAC3+7Frq+9dZbnWX79OnjjMe7UPihMmbMGE+sZ8+ezrJ+C+C1a9fOE5swYYKzrN/i4VdccYUznpmZ6Yn5LeTttyC432J8QcWzWDkA1GeuBWT9Fkv1axtLSko8seTkZGdZ1+KxIv4L1m7atMkZB4Aj1erVq51xv+/Rru/L5eXlzrJ+bXGjRt7TLL8Fvv2+n7v24fd8rn6lsud08dt3PPvwOwdxvRYRkYKCgpj3DaB+YCYTAAAAAAAAAmOQCQAAAAAAAIExyAQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAILC4sssVFhZ6sjC4sti8++67zu3379/vjHft2tUTy87OdpZt3LixM+7K+LBhwwZn2ZdeeskZb9WqlSeWk5PjLDtp0iRnfODAgZ7Ynj17nGU/+OADZ/zbb791xjt06OCJXXDBBc6yeXl5znhNcGWNiDfjhl82DwCo6+bMmeOJ+bV18WT69NuHX9Yhv8x1rVu39sRWrVrlLHv88cc74wDQkHz//ffOuN/3fFemtngyrIm423S/dtsv7npOv+/cfvvw61tc+/HLcuqXua4mskuvW7fOE8vPz3eWzcrKCvx8AA49ZjIBAAAAAAAgMAaZAAAAAAAAEBiDTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAosru9yyZcskIyOjynKFhYXO+KuvvuqMn3LKKZ5YSkqKs6xf3JU5Z/Hixc6yxcXFzvgPfvADT2zevHnOshdeeKEz7sp+51fniy66yBnftm2bM75ixQpPbPbs2c6yXbp0ccZPOukkT6xHjx7Osi1atHDGXZnhyBYH4EixZMkST8wve49f+19QUOCJxZNdqLLyrixAO3fudJYluxyAI4Fflk6/76+ucwW/Ntcvi6irfLzZ2FwZ4Pwy4vll8faLu167X+Y6P/FknI7H8uXLnfEzzjgj8L4BHHrMZAIAAAAAAEBgDDIBAAAAAAAgMAaZAAAAAAAAEBiDTAAAAAAAAAgsroW/9+zZ41lQdNeuXd6dNnLvdu/evc74xIkTPbHmzZs7y7oWNBURyczM9MR69erlLNuxY0dn3LVwa9euXZ1ld+zY4Yy7FtfLzs52lnUdOxH34uEiIrm5uTHFRETy8/Od8U8//dQT++qrr+KqR7NmzTyxvLw8Z9mWLVs64507d/bEUlNTnWUBoC5Zt26dJ+a3wLffot2uuF/f6Zeswo9r3ytXrnSWPfPMM+PaNwDUdZs3b465rN9C3H4LhR9urnr4Lazt19/4nTv5JayIh2vffv1hPMd07dq1zjgLfwP1AzOZAAAAAAAAEBiDTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAmOQCQAAAAAAAIHFlV0uIyNDMjIyomIrVqzwlPvFL37h3P6YY45xxl1Z1g4ePOgs68psJiKSlpYW8z4WLVrkjLs0adLEGffLvObKAvTdd985y/plX8jKyop5335Z5HJycpxxv0x3Ln7Hb9u2bZ7Yli1bnGX9jtNf/vIXT+yaa66JeuzK1AcAtW3Dhg2eWKdOnZxl/bL6uPhlOfLLOueXYciVMWjx4sUx1wMA6rPly5cH3oerfa2JbGzxSkpK8sR27twZc1kR9zmSiPs1xtOviLgz2vmd3/jt22Xr1q0xlwVQ9zCTCQAAAAAAAIExyAQAAAAAAIDAGGQCAAAAAABAYAwyAQAAAAAAIDAGmQAAAAAAABBYXNnlPvjgA0lNTY2KtW7d2lPOL9OYXya0Dh06eGK5ubnOsq4sBn7PWVRU5CxbVlbmjLvs2bPHGd+7d68znpyc7Im1bNnSWTbe7HIuftnbjj76aGfc9dpdWetE/LNRuOJ+f1u/94Iri9Ljjz8e9djvbw0Ah4NfX+HKvOmXecevfXXxy7zjl3XOr49zZRjyy3IKAA3NmjVrAu/DldVTVZ1l/dpoV78Q7z5cKp6Lhfj1WX59i6su8bwWv7jfPuLJLrd9+/aYywKoe5jJBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgMAaZAAAAAAAAEBiDTAAAAAAAAAgsruxya9as8WRPO+644zzlunbt6tz+m2++ccY3bdrkifllJfPLphZPxgK/sq4sQH6ZgfwyJ7gyPvhlSPDLDpGenu6MuzLX+dmxY4cz7nrt+/btc5b1y6znKt+kSRNnWVcWJhGRlStXVvl88fxNAaCmrV+/Puayfn1TYWGhM+5qz+PJAFRZ3JUBdMOGDc6yANDQbNu2LfA+XN/z/bK3uTJ61hRXOx9vX+F3zhLPa/Q7Z3FllyspKXGWjec8ZteuXTGXBVD3MJMJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgsLgW/m7cuLFn0bYvvvjCU85vsWzXYqR+5ffv3+8sm5WV5Yzn5OR4YgUFBc6yfovaufgt5teokfvQueKuRfFERFJSUmKuh4h7wTy/Bbf9Fj10Hdf8/HxnWb9Fu10L+vkdj9LS0pj3/ec//znq8YEDB+Tmm292bg8Ah9q3334bc1m/dt5vAVRX+++3D78+y2+RV1d7vHnzZmdZAGhoVq9eHXNZv3bX9f3/wIEDzrLxLGgdL9ci323atHGW3blzpzPu933etfC33/d5v3O75s2bx1wPv+Pkek6/pEkA6gdmMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACiyu73KOPPurJ7pabm+spl52d7dx+x44dzrgrY4Ff5jW/rHO7du3yxDIzM51l/TKeuTJM+GVZcGV7EHFnnnBlbxDxf41+GRziqZ9ftgxXeb+/V7NmzZxxV5ZAv3106tTJGb/ggguc8Uj5+flklwNQa2oiI5tff+Pi16/4ZZHzy1zn6nP27dsXcz0AoD5znW/4fV/2a0dd7bHfPvza6HjK+sVd3+e3bt3qLOvXh/iJ57xi7969zvh5553nib3//vvOsn79oSvrnF+GOgD1AzOZAAAAAAAAEBiDTAAAAAAAAAiMQSYAAAAAAAAExiATAAAAAAAAAmOQCQAAAAAAAIHFlV2uefPmnuxyDz74YI1WCACAusAvI5sr+048GYpE3Fl2/Mr6ZSj148oYFE+WOwCoz1xttyuDmYh/1uq8vDxPzC9r9Zw5c5zxtm3bemJFRUXOsvG08/H2CX5cfY5fduqCgoKY99u8eXNn3C9jnKtPLSsri/n5ANQ9zGQCAAAAAABAYAwyAQAAAAAAIDAGmQAAAAAAABAYg0wAAAAAAAAILK6FvwEAOFJs3rzZGXctIOu3aLff4qXxLNzqtxCrX9xVF7/FZv0WLPdbJBcA6jrXwt/p6enOsrt27XLGTz31VE/MtUC1iMgXX3zhjKuqJxbvot2ufcSbyMHvOV1xv7Kueoi4F/nu2LGjs+zHH3/sjOfk5Hhifn0qgPqBmUwAAAAAAAAIjEEmAAAAAAAABMYgEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwMguBwCAQ35+vjOemprqifll3vGTlJQU8z78sv3Em3XOxS+z0tFHHx3zPgCgLnFl9Yw3Y+Z5553niS1ZsiSufcTTFvtxtf+ZmZnOsvv373fG481GF4+jjjrKE3NlixPxzy7nOk7x9qkA6hZmMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACI7scAAAOBQUFzni8WYpcXJlz/LLpuDLRxVuPkpISZ3zPnj3OONnlANRXrgygroxzlRk4cKAntmDBgrj24Wp3y8vLnWX9MtG5yvv1FcXFxXHt27WfoqIiZ1k/KSkpnti5557rLPvQQw85464MellZWXHVA0DdwkwmAAAAAAAABMYgEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwFj4GwAAh4MHDzrjGRkZnpjforJ+cddirqWlpc6yrkVsRfwXBHct/nrsscc6y/q9RgCor1yLUftp0qSJM56Tk+OJFRYWOsu6Fq4Wcbfzfgt/x2Pfvn3OuN8C3379kKvefq/Fj2uBbr8+y6+Pcx2TeBdqB1C3MJMJAAAAAAAAgTHIBAAAAAAAgMAYZAIAAAAAAEBgDDIBAAAAAAAgMAaZAAAAAAAAEBjZ5QAAcJg1a5YznpmZGfM+0tPTY477ZURKTk52xv2yAKmqJ+aXRW758uXOeLdu3ZxxAKjrXBlACwoKnGXjybDp1xb7ZVNzZXvzywDnly3U1c77Zajz6xP84q66NGrkPjVMS0tzxvPz82OKVcZ1/I466qi49gGgbmEmEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwBhkAgAAAAAAQGAMMgEAAAAAACAwsssBAOBw8803O+MPPfSQJ1ZcXOwsu2/fPmd869atnlh2drazbElJiTPul43Olf1u//79zrLNmzd3xgGgvvrggw88sR07djjLHjhwIOb9rlq1qtp1CikrK4sr7soW6pcBzi+LnF/mutLS0pierzKLFi3yxP7whz84y8a7bxweI0aMqNZ2Dz/8cA3XBA0JM5kAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACY+FvAAAc7rvvPmf85JNP9sSWLl3qLOu3qGzHjh09sVNPPdVZ1m/R7saNGzvjy5cv98SuvvpqZ1kAOBLk5OQE3odfsoW0tDRnPDk5OaaYiH+CB9di2X7PF8/i4X789uFKKCEi0rlz55j3DeDIwSATANQAsnMAAAAAONJxuxwAAAAAAAACY5AJAAAAAAAAgXG7HAAAAAAAOKKx/EXNiGmQKbRgXH5+/iGtDBAp9H6LZ8HC6uI9XgsKCoJvX0N/r+++s594tWplPyIiRUVF1XruQ/2e43NU81wLcR88eNBZ1u994VoQvMDnM+G3eHh5eXnM9WvIfxPe44dGHWqicRjwOapaaWmpM+53zPza6HjKuvbtVzaeffiVT0hIiGvfrj6urv5deY+71YXvr3Wpv6kLx6Mui/VzlKAxfNI2bdok7du3r5maAXHauHGjtGvX7pA+B+9xNHR8jtDQ8R4HguNzhIaO9zgQXFWfo5gGmcrLy2XLli2SmZnpO8IN1DRVlX379kmbNm0kMfHQLh8W5D2en58v7du3l40bN0pWVla1np99sI9DtY/68jkCqqu+vMdruy1gH+yjMvXlcwRUV315j9d2W8A+2EdlYv0cxXS7XGJi4iEf8QVcmjZteliepybe41lZWdX+sLMP9nEo91GfPkdAddSn93h9b0/YR8PdR336HAHVUZ/e4/W9PWEfDXcfsXyOyC4HAAAAAACAwBhkAgAAAAAAQGAMMgEBpaamysiRIyU1NZV9sI86uw8AtauutAXsg30AqLvqSlvAPthHEDEt/A0AAAAAAABUpv7NZPr5z0Uuvzz28uvWiSQkiCxYcGjqcygUF4scf7zI55/Xdk28/vQnkVNPrfn9jhghctttNb9fADjCnXuuyL//Xdu1qFpxscgxx4h8/XVt1wQAGq6dO0VatrRTpLrirLNE3nyztmsBoKZUb5Bp+3aRW24Ryc0VSU0VadVK5MILRWbNquHqHSbff2+DV23aiDRuLHLRRSIrV3rLzZ4tcv75IhkZIllZ9s39wAH7XVGRyM9+ZvGOHUU+/jh628cei30Q5dlnRY49VuTss8OxmTPtubOzrY4nnCBy3XX2rbwh+O1vRf71L5E1a2q7JgAOo5//3K4DJCSIJCeLHH20yAUXiLz4okh5eW3XLtrBg1bfk08WadTI/3rHJ5+InHaadY/HHy8ydqy3zNNP24BKWprImWeKfPll9O/vvNOa+/btRV59Nfp3b7whcumlsdX53Xeti/vf/w3HjjkmfMzT0+3xlVeKTJ8e2z4PlZQU6wqGD6/degCo3777zr5yd+hg7XD79tZmTptWs89zzDEif/tb1eVWrxa54gqRFi3sNOHKK61djvTAA/a1v3FjkWbNvPvYtcteQ5MmIt27i8yfH/37IUNERo2Krd4PPCAycKDVP9Kbb4r88IciTZva85xyish999lz15SxY92v79577XpzXev3AVRP9QaZfvITa93+9S+RFSvsW+wPf2hD4/WNqp0prFkj8s479rry8kT69RMpLAyXmz3bBp/697ezga++Ern1VpHE/zuEzz8vMneulbvpJpGf/tT2LSKydq3ICy9Yqx5LfUaPFrnhhnBs6VJ77h49RP7zH5HFi0Weesq+kZeV1dihqBVlZdaj5OTYQOUzz9R2jQAcZhddJLJ1q11VnTxZ5LzzRIYOFbnkEpHSUv/tSkoOWxVFxJqr9HSR3/zGugiXtWtFBgyw17Bggcjtt4vceKPIhx+Gy0yYYINII0eKzJsn0q2bNX/bttnvJ02ymUcffSTy6KO2/Y4d9ru9e0XuuccGqWLx97+L/OIX4a4q5L777JgvXy7y8sv2pb9fv8q7KdXK/x41YfBgkc8+E1my5NA+D4CGad06kdNPt0Hzxx6zr8xTplibPGTI4a9PYaGdOiQkWJ1mzbLrw5deGj2gUlwsMmiQXcN3eeABkX37rM/44Q9FfvnL8O+++EJkzhzrb6qyf7/IP/8ZfZohYv3KVVeJnHGG9cPffGODVgsXiowbF++rjt/FF9vrmzz50D8XgMNA47V7t6qI6iefVF5u1CjVrl1VGzdWbddO9ZZbVPftC//+pZdUmzZVnTJFtXNn1YwM1QsvVN2yJVymtFT1jjusXHa26rBhqtdeqzpwYLjM5MmqvXuHywwYoLpqVfj3a9dafefPd9dz+XL7/TffhGNlZaotWqi+8EI4duaZqvfe6/96b7lFdfhw+//+/bbPbdvs8YUXqr71lv+2kb76SjUxUTU/Pxx74gnVY46pfLtYjqeqvabOnVVTU1U7dVJ9+uno3999t+oJJ6imp6see6y95uLi8O9HjlTt1i38eNUqKzdkiGp5uerBg6p33aXapo397Xv2VJ0xw1vPd95R7dJFNSnJ/kaqqv/6l71XABwxrrsuukkPmTbNmtHIZlhEdcwY1UsvteZl5EiLv/22avfu1qwde6zqn/6kWlJivysvt3Lt26umpKi2bq16223hfT79tOrxx9u2LVuq/uQnwep9992qJ50UHbvqKmuOQ3r2tCYzpKzMmsyHHrLHjzxi24S0bKn65Zf2/5tuUn388djquG2bakJCdPemqpqXZ91KRX/8o3U/335rj2fMsGP+wQeqp52mmpxssbIy1QcftG4pLU31lFNU33gjvJ9du1R/+lPVnBz7/fHHq774ov2uqMhee6tWdsxzc21fkc47r/LuFgD8XHyxatu2qgUF3t/t3h3+//r1qpddZl+XMzNVBw1S/e678O9XrbLft2xpZXr0UJ06Nfz7Pn2sfYz8cfnwQ2tX9+4Nx/bssbY5cn8hoa/Jrtf1zDP2/6VLrQ9Uta/o3brZ6UMs3njDTnEizZlj9f/b39zbRB63MWNUO3Sw/qBjR9WXX44uW9npX6hPifwJ9eOqqr/4heo118T2OgDUbfHPZGrSxH7efttuEfOTmGiXUJcssRlP06eL3H13dJn9+0X++lcbIv/Pf0Q2bLC58iGjRtm8yhdftEubu3aJTJwYvY/CQrsk/PXXNg82MdHmpMY63zL0GtLSouuemmrPKWKXl+fMsRuYzz7b7ufo0yf8exG7FP3ZZ3b73IcfirRubbNzXn3V9n3FFbHV59NP7Xa7zMxwrFUru+T8n/9Uvm1Vx/PVV0X++Ee7HLJsmciDD4r84Q/29wnJzLRjvnSpyJNP2gysJ55wP9+iRSLnnGOztkaPtss0t95qs7nGj7ffDxrkvf1w/36RRx4R+cc/7P3RsqXFe/YU2bSpbt0kDqBWnH++NatvvRUd/9OfrDldvFjk+uutybz2Wpv5tHSpyHPPWRMWmpHz5pvWhD33nDVDb79tt7uJWLfxm9/YrJ7ly+1q97nnBqv37NneWU4XXmhxEbtaPXdudJnERHscKtOtm9Vt924re+CA3Xb32Wd2Ffs3v4mtLp99ZrdedOkSW/mhQ+1r/zvvRMdHjBB5+GHrNk45ReShh2z207PPWhN+xx0i11xjd3WLWLeydKldkV62zCao5uTY7/7+d5v8/PrrdsxffdV7y0bPnvZ3BYB47Npl7fiQIbayRUWh27TKy+12sV27rN2aOtVuaLjqqnDZggKRH/3ITi3mz7evspdeal+tRaxvatcuPCt061Z3nYqK7OtxZBKntDRr9yNPI6rSrZudSpWW2mnGKadY/NFHbWZTjx6x7efTT22mV6RXX7VTu1//2r1N6LhNnGj9xF132UynX/3KZsrOmBEuW9np39ln2+2FWVnhYxZ5mkLbDzQg1Rqa+n//T7V5c7tEefbZqr/7nerChZVv88YbqkcdFX780ks2hB056+jpp1WPPjr8uHVr1UcfDT8uKbFhcdfl45Dt222/ixfb46pmMhUX26XUQYPs8mtRkerDD9s2/ftbmdmz7XF2tl2OnTdP9fbb7bL4ihXh/fz613Zpt0cP1U8/Vd2504b7N2xQvece1eOOs31u2uRf/6FDVc8/PzpWWqr6859bHVq1Ur38ctWnnoq+LBLL8TzuONV//zt63/ffr9qrl399HntM9fTTw49DM5lmzbL3wF//Gv7d+vU2M2nz5uh99O1r75HIei5Y4H2uvXtjmyVXx4wePVrz8vI0NTVVe/bsqXPmzIl525kzZ+oll1yirVu3VhHRiRMnxv38Dz74oPbo0UObNGmiLVq00IEDB+q3oakIMRozZoyefPLJmpmZqZmZmXrWWWfpBx98EHddQh566CEVER06dGhc240cOVJFJOqnU6dOcT//pk2bdPDgwZqdna1paWnatWtX/SrWy3yqmpeX56mHiOivf/3rmLYvLS3Ve++9V4855hhNS0vTDh066H333afl5eVxvY78/HwdOnSo5ubmalpamvbq1Uu/DE1paSD8ZgSp2myeLl3Cj0Ws6Y3Ut693Jsy4cdZ9qNpV1Y4doydkhrz5pmpWVvTE0aD1PuEEb33ef9/qvn+/NY8iqp9/Hl1m2DCb4RQycqQ12V272kTYoiL7/9dfW/PfsaN1vxVnKUV64gnrgirym8mkal3GLbfY/0NXnd9+O/z7gwftCnXF+t9wg+rVV9v/L73Urki73HabdXGVfRSefLLqybv1EX2FF30FfUVNCs3IqermgY8+sq+rGzaEY0uW2LaVHbaTTrL2N6SytjRk2zbrZ4YOVS0stBlWt95qz3XTTd7yfjOZ9uyxNjY3V/Xcc62+K1ZYn7Njh+qvfmUzeQcNsrJ+Bg5Uvf766NjFF9uM1KqcfbbqL38ZHRs0SPVHP/LfxnX653p9qnaTQ2KizZY9ktFXeNFX1L++ovprMm3ZYpcjL7oovMpp5OqmH38s0revSNu2NjvmZz+zNZv27w+XadxY5Ljjwo9btw4vSrF3rw1xn3lm+PeNGnmH6leuFLn6alvdLysrfEk0dKmhKsnJdjlixYrwotozZtjNwaFFLEKzokJD9t2726XxTp1sllVoP08/bQtyfPWVzfC56y675Dx/vl0+X7jQ0idUdhn6wIHoWVUiIklJIi+9ZLN8Hn3UjumDD4qcdFL0pZPKjmdhoa08eMMN4dloTZqI/OUvFg+ZMEGkd2+bPdWkia3EV/FYbthgK/P+8Y/2GkMWL7ZFSzp2jH6OmTOjnyMlJXwJJlJ6uv0b+R6p4yZMmCB33nmnjBw5UubNmyfdunWTCy+8ULaFjnsVCgsLpVu3bvJ0rAusOMycOVOGDBkiX3zxhUydOlVKSkqkf//+Uhi5plgV2rVrJw8//LDMnTtXvv76azn//PNl4MCBsqQaC6N89dVX8txzz8kprr9xDE466STZunXrf38+i+dSn4js3r1bevfuLcnJyTJ58mRZunSpjBo1Spo3bx7zPr766quoOkydOlVERAYNGhTT9o888og888wzMnr0aFm2bJk88sgj8uijj8pTTz0V12u58cYbZerUqTJu3DhZvHix9O/fX/r16yebN2+Oaz/1lapdAY5UsQtYuNCuJEc2Ob/8pTWN+/fbZMoDB6yL+OUv7UpsaF2hCy6wJfg6dLAu6tVX607z86c/iaxaZc3qFVfY7KF+/ayr+ctf7Ar4jTfaLC4/ru6kKlUd81Wr7BhdcEH0MX/55XAzf8stNpn11FPtCnZkotSf/9zWqurUybrCjz7y1iE9ve78HWoKfYUXfQV9RU0LLYValWXLbDHw9u3DsRNPtBk7y5bZ44ICm2XTpYvFmzSx38V6ehHSooUla5g0yfbRtKnInj122lRxrbzKNG1qa/WtX29fq0880U5LHnvM+q41a2x2aOPG1if6cfUL8Ry33r2jY717h4+ZSGynf37S0+2Uq7IbZRo6+gov+op62lfU2HDVDTfY8LqqzR5KTbVLzrNn27pH//ynDduHbux1DWVPnBi+qXnPHvv/zJnRZS6/PPrycadONjvo44/tJuVvvrHtQiO3Vc1kirRnT3gdpZ49bWaSquqaNbaPceOiy195pS084TJ9uuoZZ4TXlRo2zOLffGMzovz8/veVzywK2bXLFrz44x/tcVXH87vv7P+vvKK6cmX0z5o1Vubzz+3Szl/+Yjd3r1ihet990fsdOdIuof/wh3aMImdTjR9v23/7rfc5tm71r2dIqI5xjAzXtp49e+qQiMVVysrKtE2bNvpQaHGVOEg1rzhUtG3bNhURnVnxsxOn5s2b6z/+8Y+4ttm3b5+ecMIJOnXqVO3Tp0+1rjh0i1zzqxqGDx+u55xzTqB9VDR06FA97rjjYr5iMGDAAL2+wqXCH//4xzp48OCYn3P//v2alJSk7733XlT8tNNO03vuuSfm/dR1lc1kOvlkW2YvJLJpD0lLszWMKjY5K1eGr4bu36/67rs2i6ZVK2tiQzObSkpsXYxhw2zWz/HHR68/EW+9f/ADu2Id6cUX7Uq2qs1ISkryvo5res6aqgAADjlJREFUr7X1P1yWLbN67dtns3wGDbJ4QYEdE7+ZWM8/H57RFcnv6vuOHbZOyGOP2ePQTKbI4/HFF+EJpxWPd+SsgG3bVMeOVR082P5Gd90V/t3evdZd3HijdQcV18F6+GHvulb1HX1FNPoKQ19Rs3butDas4mzSivxmSzZrZsuDqtrMoA4dbFbUokXWxnXrFt2+xzKTKdL27eH29Oijo2/WCKnsa3KkF19UveIK+/8VV4SXWH3vPVtDz89PfxqedRrym9+oNmninvEbqXlza9cj/e1vNoNKtfqnfyHjx9v6V0cy+opo9BWmPvYV1ZvJ5HLiieFsbHPn2lD0qFE2c6djR5v5FI+mTW0mzpw54Vhpqe07ZOdOG7a/914bNu/SxRaxqK6mTe2Sw8qVtiDGwIEWP+YYkTZt7LkirVhhl8ErOnjQbgh/7jmbhVRWFk6DVFJSeUa47t1Fvv226ssKzZvb8Yl1VPnoo+01rFlji3tE/hx7rJX5/HN7PffcY5euTzjBLplUlJ4u8t57dinkwgstHUSo7mVlNnuq4nO0alV1Hb/5xi7Tn3RSbK+plhUXF8vcuXOlX8TiKomJidKvXz+ZHVpcpRbs3btXRESys7OrtX1ZWZmMHz9eCgsLpVevXnFtO2TIEBkwYEDUMYnXypUrpU2bNtKhQwcZPHiwbIjzsuG7774rPXr0kEGDBknLli2le/fu8sILL1S7PsXFxfLKK6/I9ddfLwkVp3j4OPvss2XatGmyYsUKERFZuHChfPbZZ3LxxRfH/LylpaVSVlYmaRUuOaanp8d9FaY+mj7dZvH85CeVlzvtNGuaKzY5xx8fvkqcnm5rafz97zbxdvZs27eITZDt188miS5aZEvCTZ9e/Xr36uVNkz11qsVFbCLn6adHlykvt8euj5uqXa1+/HG7Cl6xOxHx71K6d7dU3rF2i08+acfs8sv9y5x4oq0tsmGD93hHzgpo0ULkuutEXnnF1uB4/vnw77KybO2TF16wybNvvhmdIvubb6zuDQV9hRd9haGvqFnZ2fa19Omn3V+P9+yxf7t0Edm40X5Cli613594oj2eNctmXl5xha3j16qVd8nQeJM85+TYrKjp0+2r8mWXxb5tpO3bbbZSaBJDvKcZS5dGx376U5u5NWaMe5vI4zZrVvTvZs0KH7NYTv8qO2YNre2PF32FF32FqZd9RdzDUjt2WOqXceNsHaY1a1Rff92G5EMjbAsWhNMUrF5tqQfato1vJpOqXc7Mzrb4smV2I3BmZvjycVmZ3eh7zTV2iWHaNJs9FO9Mptdft0u2q1fb4hN5eao//nF0mSeesEvRb7xhz3XvvXZ5NnINpJDf/z76su2ECTbLa+FCm/FV2c3LO3ZYyobQmlKqqs8+q3rzzZaiYtUqmw11991243Jo/aJYjucLL1jWuCeftMsLixbZpZBRo+z377yj2qiR6muv2fM8+aQd/4ozmUIjwvv2qZ5zjmX3C6WOGDzYLg+9+aa9N+bMsUtKoVHTyi5hjBzpXY+qDtu8ebOKiH5eYXGSYcOGac/IxVViJDVwxaGsrEwHDBigvXv3jnvbRYsWaUZGhiYlJWnTpk31/fffj2v71157Tbt27aoHDhxQVa3WFYcPPvhAX3/9dV24cKFOmTJFe/Xqpbm5uZofx6I5qampmpqaqr/73e903rx5+txzz2laWpqOrXj5LUYTJkzQpKQk3VxxrbFKlJWV6fDhwzUhIUEbNWqkCQkJ+mBVl1YdevXqpX369NHNmzdraWmpjhs3ThMTE7Vjx45x76uuuu461YsussmOmzapzp2r+sADdlX1kktsMmiIaybTlCnWbP3pT9Y0Ll1qTVjoosxLL6n+4x/WpK5ebU13ero1tZMmWTM3f77qunWWNScxsfJ1jpYssfKXXmoTOufPj+5e1qyxNYuGDbNu6+mnbebSlCnhMuPH29XesWOtvjfdZFfQIzMbhTz/fPRMnzlzrCuaPdsmsp54on9dS0sti9CkSdHxvDybpLp1q80+mjnTuteEBOt2Q1wzmVTt2B51lNV/1Sr7m/397+Er3H/4g3WlK1fasbzkkvB6U6NG2dKAy5ZZN3TDDTa7LHINjrw8b8ai+oy+Ihp9RRh9Rc1bvdralBNPtCVkV6ywdvbJJy25sqqtCXfqqTbzdO5ca1dPP90yxoVccYWVmT/fTmsuvdROQSLfqhdcYDNQN22yWUp+XnzR2uxVq+z0KTtb9c47o8usX2/P9ec/W/8X6lsiE3OH/PSn0WtDPfKI1X/pUltfqbJlXhYtsj5z167o+N13W181bJjd2LBund0k8j//E846N3GinaKMGWPHddQo2yaURDqW079Zs+zxxx/bMSssDNehTx/rm45U9BXR6CvC6mNfEf8g08GDqiNG2FzMpk3t23SnTvbNff/+cLnHH7d5+unplrv55ZfjH2QqKbHWPCvLvoHfeafdUxB5j8LUqbYybGqqrVr3ySfxDzI9+aQtKJ6cbINB995r9zRU9NBDVq5xY7vf4tNPvWUWL7b7GiJzp5aV2UqqWVk2CLZypX9dVO02vBEjwo/nzbOBtGOPtdd51FG26t+774bLxHI8VVVffdV6zZQUm/d67rnRKyQOG2b7b9LEVt194gn/QSZV6/3OPtv2U1Bgc23/+EcbaEpOtvfAFVdYr+ZXz5BOnezssJ6oi53BzTffrHl5ebpx48a4ty0qKtKVK1fq119/rSNGjNCcnBxdsmRJTNtu2LBBW7ZsqQsjEgBUpzOoaPfu3ZqVlRXX9Nrk5GTtVeGW09tuu03POuusatWhf//+eskll8S1zWuvvabt2rXT1157TRctWqQvv/yyZmdnx90hrVq1Ss8991wVEU1KStIzzjhDBw8erJ1D35QbgOuuC6cybtTIBkX69bMv5RUX/3QNMqnaAM7ZZ1t3k5VlAxrPP2+/mzhR9cwzLZ6RoXrWWfblVtWa8D59rClMT7cuZMKEyuubl+dNwVyxmZ0xI9zMduhgzV5FTz1l3U1KitX3iy+8Zb77zp6v4veQP//ZTlI6d7aTo8rcfbfq//6v/2tISbF6XHml3eVd8XW4BpnKy+0kolMna+ZbtLBuPjST/v77rVtOT7d6DhwYviv7+eft2GRk2N+kb1/r4kI+/9y6+8ivE/UdfUUYfUU0+opDY8sW1SFDrK1LSbGBjssuCw+GqNqgzmWXWVuUmWm3IUcO9K9da9fU09NV27dXHT3a+ovIt+rs2dZvpKZ6+4FIw4fbtfjkZFuoe9Qob/KDyL4w8ieyzqrW3/XsGd0/FhZa/TMzrU39/vvKj0/Pnnb9uqIJE+zrfGamHZdTTrFBn8g+YMwY69eSk231jIoXBKo6/VO16+ZHHWXxkSMttmmT7bMaTVKDQV8RRl8RrT72FTW3JhNqzsKFqi1bui9fNFQffGBnJSUltV2TmBUVFWlSUpKnAb/22mv1Mr/FVSoRtDMYMmSItmvXTteEzuYC6tu3r97kSn3iMHHixP82WKEfEdGEhARNSkrS0sjpKHHq0aOHjogcdK1Cbm6u3nDDDVGxMWPGaJs2beJ+7nXr1mliYqK+HZleKwbt2rXT0aNHR8Xuv//+amW0UFUtKCjQLVu2qKrqlVdeqT+qbDYkEGHrVhvoWbeutmsSmyuvtJlsDQl9RRh9RTT6CtSG996zr9x1KYvb3Xd7M9cdaegrwugrotXHvqLm1mRCzTnlFJFHHrFMdUeKwkLLoNeoUW3XJGYpKSly+umny7SIxVXKy8tl2rRpcd9zHISqyq233ioTJ06U6dOny7GhNbYCKi8vl6IYU3z07dtXFi9eLAsWLPjvT48ePWTw4MGyYMECSUpKqlYdCgoKZPXq1dK6deuYt+ndu7csr7B+2ooVKyTPtX5aFV566SVp2bKlDBgwIK7t9u/fL4kV0sYkJSVJeShTZZwyMjKkdevWsnv3bvnwww9lYGi9OKAKrVqJ/POf8WdEqg3Fxbb2yR131HZNahZ9RRh9RTT6CtSGAQNEbrpJpC4lH2zZUuT++2u7FrWLviKMviJavewranzYCjiCjB8/XlNTU3Xs2LG6dOlSvemmm7RZs2b6nWtxFYd9+/bp/Pnzdf78+Soi+vjjj+v8+fN1/fr1Mdfhlltu0aZNm+onn3yiW7du/e/P/jjuNxkxYoTOnDlT165dq4sWLdIRI0ZoQkKCfvTRRzHvo6LqTGu966679JNPPtG1a9fqrFmztF+/fpqTk6PbQlkfY/Dll19qo0aN9IEHHtCVK1fqq6++qo0bN9ZXXnklrrqUlZVpbm6uDh8+PK7tVFWvu+46bdu2rb733nu6du1afeuttzQnJ0fvvvvuuPYzZcoUnTx5sq5Zs0Y/+ugj7datm5555plaXFUKGAB1Cn2FP/oK+goAhr7CH31F/eorGGQCAnrqqac0NzdXU1JStGfPnvqFa3EVHzNmzFAR8fxcd911Me/Dtb2I6EuuhWB8XH/99ZqXl6cpKSnaokUL7du3b6COQLV6ncFVV12lrVu31pSUFG3btq1eddVVusq1uH4VJk2apF27dtXU1FTt3LmzPh9aoCcOH374oYqILl++PO5t8/PzdejQoZqbm6tpaWnaoUMHveeee7TItdZbJSZMmKAdOnTQlJQUbdWqlQ4ZMkT37NkTd30A1D76Cjf6CvoKAGH0FW70FfWrr0hQVa35+VEAAAAAAAA4krAmEwAAAAAAAAJjkAkAAAAAAACBMcgEAAAAAACAwBhkAgAAAAAAQGAMMgEAAAAAACAwBpkAAAAAAAAQGINMAAAAAAAACIxBJgAAAAAAAATGIBMAAAAAAAACY5AJAAAAAAAAgTHIBAAAAAAAgMD+P0lWcmdpLfjqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in 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[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}