TF1_Keras_CNN_MNIST-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential, load_model, Model\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n",
    "X_train.shape\n",
    "Y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18d15f9ad60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_test[0], cmap='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(60000, 28, 28, 1)\n",
    "X_test = X_test.reshape(-1, 28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = to_categorical(Y_train)\n",
    "Y_test = to_categorical(Y_test)\n",
    "\n",
    "#데이터 정규화 과정\n",
    "X_train = X_train/255.0\n",
    "X_test = X_test/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 16)        416       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 36)        5220      \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 7056)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               903296    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 910,222\n",
      "Trainable params: 910,222\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(filters=16, kernel_size=(5, 5), padding='same', strides=1, activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(filters=36, kernel_size=(3, 3), padding='same', strides=1, activation='relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240/240 [==============================] - 13s 52ms/step - loss: 0.2714 - accuracy: 0.9197 - val_loss: 0.1085 - val_accuracy: 0.9680\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x18d15e5a580>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train, batch_size=200, epochs=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 5ms/step - loss: 0.1029 - accuracy: 0.9688\n",
      "[0.1029006689786911, 0.9688000082969666]\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5, 1, 16)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "_________________________________________________________________\n",
    "Model: \"sequential\"\n",
    "_________________________________________________________________\n",
    " Layer (type)                Output Shape              Param #   \n",
    "=================================================================\n",
    " conv2d (Conv2D)             (None, 28, 28, 16)        416       \n",
    "                                                                 \n",
    " max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         \n",
    " )                                                               \n",
    "                                                                 \n",
    " conv2d_1 (Conv2D)           (None, 14, 14, 36)        5220      \n",
    "                                                                 \n",
    " flatten (Flatten)           (None, 7056)              0         \n",
    "                                                                 \n",
    " dense (Dense)               (None, 128)               903296    \n",
    "                                                                 \n",
    " dense_1 (Dense)             (None, 10)                1290      \n",
    "                                                                 \n",
    "=================================================================\n",
    "'''\n",
    "l1 = model.get_layer('conv2d')\n",
    "#l1.get_weights()\n",
    "l1.get_weights()[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_weight(w):\n",
    "    w_min = np.min(w)\n",
    "    w_max = np.max(w)\n",
    "    num_grid = math.ceil(math.sqrt(w.shape[3]))\n",
    "    fix, aixs = plt.subplots(num_grid, num_grid)\n",
    "    for i, ax in enumerate(aixs.flat):\n",
    "        if i < w.shape[3]:\n",
    "            img = w[:,:,0,i]\n",
    "            ax.imshow(img, vmin=w_min, vmax=w_max)\n",
    "            \n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(3, 3, 16, 36)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = model.get_layer('conv2d')\n",
    "w1 = l1.get_weights()[0]\n",
    "plot_weight(w1)\n",
    "\n",
    "l2 = model.get_layer('conv2d_1')\n",
    "w2 = l2.get_weights()[0]\n",
    "plot_weight(w2)\n",
    "l2.get_weights()[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "_________________________________________________________________\n",
    "Model: \"sequential\"\n",
    "_________________________________________________________________\n",
    " Layer (type)                Output Shape              Param #   \n",
    "=================================================================\n",
    " conv2d (Conv2D)             (None, 28, 28, 16)        416       \n",
    "                                                                 \n",
    " max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         \n",
    " )                                                               \n",
    "                                                                 \n",
    " conv2d_1 (Conv2D)           (None, 14, 14, 36)        5220      \n",
    "                                                                 \n",
    " flatten (Flatten)           (None, 7056)              0         \n",
    "                                                                 \n",
    " dense (Dense)               (None, 128)               903296    \n",
    "                                                                 \n",
    " dense_1 (Dense)             (None, 10)                1290           \n",
    "=================================================================\n",
    "'''\n",
    "# temp_model = Model(inputs=model.get_layer('conv2d_1').input, outputs=model.get_layer('conv2d_1').output)\n",
    "temp_model = Model(inputs=model.get_layer('conv2d').input, outputs=model.get_layer('conv2d').output)\n",
    "output = temp_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 28, 28, 16)\n"
     ]
    }
   ],
   "source": [
    "print(output.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_output(w):\n",
    "    num_grid = math.ceil(math.sqrt(w.shape[3]))\n",
    "    fix, aixs = plt.subplots(num_grid, num_grid)\n",
    "    \n",
    "    for i, ax in enumerate(aixs.flat):\n",
    "        if i < w.shape[3]:\n",
    "            img = w[1,:,:,i]\n",
    "            ax.imshow(img, cmap='binary')\n",
    "            \n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vt9vY3d3F9vY2Dg8PIYoiJEnC0tISVlZWsLKygmKxyOH0rFJBM5dJoGkOTdOYzXd/fx/j8RipVAorKytYXFxEuVx+4ffpgAN+aH1RUJ6WOPJIrCgejyOdTnNTPvWH+ev7cliWhcFggHv37qHRaCAcDqNcLmNhYQGFQsEnR7kEOI7DbWaUIx8MBrxnicUrl8shlUpBEISZXuozNY7EHDMYDPDw4UPcv38fn3/+Ofr9Pq5fv45/82/+Df7Tf/pPqNVqL/wusQH7bRGvBxrN7Pf7GAwGMAwDsViMOTRphtUPp18Nx3Fw9+5d3Lt3D//tv/03FItFrK6u4t//+3+P999/32ffuSSoqorPPvsM33zzDf74xz9ie3sb4/EYn376KZaWlnD79m1cu3YNCwsLyOVyM+8lnYlxpDYImt9VFAVHR0d4+vQpdF1HKBRCvV7nr9M2l686+HqgHC8ZR9M0Yds2c+RR0yzRP/nV6VdjMplgf38fBwcHcBwHgiBwlZRGYn38MIxGo6n2qF6vB9d1eYKGxmElSUIikXgj0c5MNWTIa2w0Gvjuu+/w5z//GaZpIpPJ4Gc/+xk++OADrK2tnfoaftvO64GIEDqdDg4PD6EoCsbjMQRBmJpNPY1EwceLcBwHlmXh22+/xePHj5HJZLCwsIAbN274hvGS4Louer0eGo0Gnj59ioODA3Q6HU7/rKysYGFhAYuLi1yEeRO2YWbqg5ZlQVEUNox7e3vodruoVCpYXV1lV/kk/J7G1wd5691uF51OB+12G4ZhwLZtZLNZSJLEim1+nvF8ICLb77//Ht1uF0tLS3jvvffwy1/+EplM5m0/3pWA67p48uQJNjc38eTJE7TbbQDgwiF5jpIkTcl7zBozNY6kZ/L48WM0m03ouo5yuYyVlRXcunXrVHIJCgv92dSLg9ZOURT+IlIJEjKjnkb/4nk1SHri22+/5SLi7du3sbKygps3b77tx7sSoAv94OAAT58+xf7+PisKyrKMYrEISZJ4bPZN5shnYhzJMG5sbODevXv48ssvmazyl7/8JT788MMz1dmInuhlC0DElsRBGAwGmTSA8pQkcjSZTM7snbxqIOlKqvDTBotGoygUCpy38dt2Xg2KfD7//HP80z/9E8LhMOr1Ov7mb/7mzFTQD8G8yhNvb29jb28P9+7dw+bmJjY3N5HP57G6uoobN25gcXERa2tryOfzSKVSF967ZBeoJZBqGaFQ6JXMSTMxjtT03Wq1eC6SjNTCwgJqtdrUqNp4PGZDdrLZ+7TNYlkWV7NpUxF9F81ezussdigUgiiKSKfTGI/HzP9IiWyfPPh8GI/HnC8/ODjA6uoqcrkcqtXqpTZ7U/GSLno6vPPi2ZOwHtkKwzAAgKMcbx8uXfokWeG1Dd7z7v37eDzm6TAi4ia5i1cZ2pmcFF3X0e128d1332FzcxOqqrIeBMkleD98Uh/UNA2hUAiJRGLK+/FKiALPBv+JvZoEeoBnG6tUKkEQBKYxIjbheUAymUQymeSGZK83Mm8eyQ/FcDjEo0ePeL7317/+NW7cuHHpwllE8T8ajdhBEEVxbrR8SCbhwYMH2N/fh2mazGqfTCaRSCS4kk2EK0Q8QSqPtG5eZ+s0uWfbttlrDIVCWFxcfOmzzUx9kEI8qpR6DdbJcJpG2+gwewsydGMYhsEu8mg0YjeZ2oUAsGhUIBBgtb2rVngYjUaYTCbsHbuu+4LY/Lx4HbMEkTFblgVBEHD79m3cvHnzpdNEjuNceO2Jp5Nyw/P22VEKTlVVGIaBdDrNdoI016ljgJwn0juiM+Bl/CLWL/o3Xddhmiaz+tC0HfGe2rZ95rPNxDjSHPVwOMR4POabMJVKsY6JF6PRiJuUA4EAN38TuzU1kxO9P4Xh9LOj0YhvFBKoTyaTV8ooEoi5Ox6PczhGfV8+Lg+GYWB/fx+j0QjpdJpbz87KX3unuS5i4E7yGL6Kfu6qgbzCfr8P0zQhyzIkSYIoisyvScaRnCbKFxI1IhlF6uklwzgajfir0+nANE2mPaMC5cu0k2ZWrXZdF6lUit8UER30ej0cHBxAVVV+o7QwVKUmA0oeI0k3kgwjeY0kMyoIAqLRKKLRKEql0pU1jABYzInWwjAMLC4uolgsotvtIhgMolKpXNr7d12Xb1/Hcdi7pz+DweBLb98fG7yhmOu6qNVqiMfjKBaLLFFxGiiXdd51P9myRsWCeUoDAeDzHo/HkUgkUKvVeK0dx4Gmacz+TSF3OBxGLpfj3wEwpTxIUSgVYmgoggwl/ayiKG/eOBLi8ThEUeTQOhaLMQFFv9/nyrRhGFPFlWg0OpWonkwmzMxM/w6AbxB67VgshmQyeaXHuUizmqZfVFWFKIqIRCI4OjriggzlaunAkjfiVWrz/klfZAApTJlMJhgOh9B1fWrdKY8ZDoevlHKh67rsYQBgpmmvoPxZv/eq1/Wy4pM3RFNKtm1f2MBeBZAXJwgCXNdFNpvl0JoiQ9M0edKOdMNJTdO7d+nzodQTGUavFzkejwE828NeiZDTMBPjGAqFeOxHFEWUy2X2DO/du4dUKoVoNMqhMzFS04PTwfbmDsltptxjIBBAtVrl28Obi7jKm4tyL7u7u1BVFf1+H5ubmwCAvb099nbo0FWrVa780Xp75VjpkBqGweQgmqZxSGkYBhvWWCzG9PYUCUQiEc75XgU4joPBYMD5LRJ7C4fD0HX91EIJeSMky3oybURSuF6RORrvpL1PBYdIJDKlLnnVUavV8NOf/hT7+/tQFAWlUgmFQgHlchm5XI67WujyCYfDnIv0OlF0oZMSAQC2F4ZhoNlswjRNjmLJprwMMzGOpDGby+U40ayqKj/ceDxGIpFAMBjkm4E8mvF4zEUYmgkmIR5ywSkcoQNPRK1nVWUvW+z7bcIrWEYkwuRdHx0dYTQaMQkr8KyPjEYHo9EoBEGYSlzT79LNSiF0s9nkm5cMrSiKrDdOuberluv0etmWZfFhU1UVANjDI6+GvGvyumlP0voSG5JpmrxPh8MhRqMRNE3jVJKu6wiHwxw6zotxFEURsiyjWq0ikUhwozfts/F4DFVVpwq8o9GIjeTJqMVboPE6AZTCIwPqjUDPwkyMYywWQzqdxvXr1zkf1W63oSgKV1YzmQy3MPT7fTaikUgEw+GQ+/JIIyKfz7NLTUlrb+Ka3uxJ40hTI1fFQCaTSZZjJZlKMmIknNXtdnlj0PrTJUIhSywWY0F1Cue8VW8SQyNaKArXvfrZ1Dp0lXJkFK2EQiEoisLC83t7e7x+VPzzGjfaj6QQScZvc3OTDza9LqUuHMfh7g3yVCklMi9z26lUCpVKBe+99x76/T4URUEgEICmaTzQQJVsYrEPBAKcTpJlmfck1SMMw+BLjfY05XLpMzpP0Wsmu1oURfZOqKpcKpW4ZYeMI7XhUEWa3pAoisjlckin01yy94bO3soe8NyDonCbcpH0/79KOTHiDSSVO68XSP1czWaTCzZ0EKmKT/ka0sGmvlLKD9PlQzdzJpOBIAj8fUEQUKlUIMsyhz1XadSTRLVEUUSlUkEwGMRgMGDhLG+7Do3JUvcEfQ7tdhuRSIQvfmpappDbtm3eozQWRz149FnMCyhvnUwmuchKtQjg2dmWJAmj0QiKorCjQ5fyyWIgpfTodWl/UqRKhjKZTHK65CzMxDh6H44WgDZUt9tlOdGTCWk6vKRlK8sy38jU1uPNP1ATKLnJtFDhcJgNh7fQcxVAc9E0BkgNr1TBVxQFkiTBNE32DAG8kGOkQx4Oh5HJZJBIJJhZmYxDMplkPWGarolEIkilUkgmk+xFXbXQmg5QsVjkvBVwumicNzVB4XM4HIYsy5wyIkNIXuJ4POZ1zGQyvI7zCDJYdOmTtxeLxfhSH41GTNRM3iFdyHTmqSBz0lbQmlPRki4fQRBeOTE2E+NID0BvgMLhYDCIbDbLdGY02nPyTdEDU+WQ/k7fpw1JRtfLU0gutjfMIa/pKoDWanl5GaZpolqtsoDWrVu3mJWHPEfK09A60gEPBAJ8+9KmIW0ZSoKTF0/eO60tpTXoQF+lgx0KhSBJEu8fCumo+JRIJDhvbts2F7MonM5kMpAkidNA169fn5rKoIuc1vEqXi4XARkt4PnwCEUudIkrijJViKX8NzVzU42D9iqJbnk5S6nIS3/3DpmchZkYR3oQco3pzdKBo1CPDmY0GuWcFz00eTr08N5WlNP+PO1NnvyZqwA6VLSpyMuhS4aKAlQhJeNIrSney4NSFcFgkEMNKtwAmMpDXpXL5TygNc3n84jH49B1nXNWgiBw0ZAuF9q/iUSC9ZTJK/Fe2D5eBF0YNCDi9frICSIP0JtXJOeLqtaxWIyNIxV1zrP2L/veTHWrz+o3PE/Cmap1p2lzzEsl72Ugj+O0tThrCP8kTm6Mq3iZvC5isRgKhcKpUsEE4iP1FgLP+tPH6SBDSGQeZ+3X06rLs96/V6fMOKc4bSP4B/Jy8Kp19Nf58nHWmr6NtZ6fWMmHDx8+LgDfOPrw4cPHKfCNow8fPnycgsBFegADgUALwM7sHmemWHJd9+zs+luGv7azw498bQF/fWeJM9f2QsbRhw8fPuYFfljtw4cPH6fAN44+fPjwcQp84+jDhw8fp8A3jj58+PBxCnzj6MOHDx+nwDeOPnz48HEKfOPow4cPH6fAN44+fPjwcQouxMqTz+fd5eXlGT3KNF5FT3RRbG9vo91uv7M0Km9ybS8b/trOFl9++WX7XZ6Q+SHrexFKvVngZXv3QsZxeXkZX3zxxeU81RkgGn8SjiK23ng8/oNYkz/55JNLftLLxZtY21nBX9vZIhAIvNOjea+zvqQOSHIekUhkStHRcRzIsoxYLDZTpvSX7d13is+R1AiHwyE0TWO5ykgkgmw2y9oPPny8qxgOh1NU/0Ti6uM5yCCqqsqeI0klkB7PZDJBu91muWZiVU+lUm/MBrxV40gLQyy/pEmraRoLq5OGLwlLEV26j9fDafK1Pi4Pmqah2+2yemMqlZoriYlXgZRCSWqV9KVImsMrNaxpGtsDUsL0ak0Bsw2934pxdBwHw+GQld10XcdkMkEymWRjSNoluq7Dsqyp73kXx8f5YVkWBoMBksmk74HPCMfHx9ja2oJlWRAEAZPJBJIkIZPJvO1He+sgj7HT6UwJlNm2jbt372IwGGB/fx+qqmIwGKDT6cA0TYzHY0iShOXlZdy4cQOLi4tYWlpCKpVCNpudkmu+TLwx40gi5v1+H7quo91uwzAMFu4OBAIoFAosmqPrOnRdh6qqCIfDrEt91aRWLxuk2EgayiT0NB6PMRwO0Ww2WcaVvHEf54PrurAsi8M+Eo3z6viMx2MYhoF+vw9BENDtdhGJRFh8fp5Bom+6rrPo22g0wmg0wtHREXq9Hra3t6EoCvr9Pvr9PkzThG3bSKfTCIfDrI5J6YpEIsEifZeNN2YcSXbxf/7P/4mdnR3cu3eP9WiXl5eRz+dx+/Zt9goHgwFUVWXlN1J6I3W8eZazfBlM08Tm5iaGwyEGgwE++ugj5HI5tFot7O3t4Z//+Z+xurqKer2OW7dunSpg5uN0WJaFvb09XlvS+15eXp5SyQwGgzg8PATwTGfcdV3k83lW15w3kGNENqDX60HTNPT7ffR6PSiKgmazicFggKdPn8IwDJimyb+n6zqCwSDa7TaOjo5YWE5RFACAJEnIZrOXvrZvxDg6joPt7W1sb2/jzp07ODg4wM7ODssnkq50LBbj6tRgMMB4PEav1+Pwm27reb+BAfDNq6oq0uk0GzkSlBdFEZIksTylV1610WhAURTEYjFkMhnkcjnO/fh4Ea7rYmNjA6qqot/vc448FoshmUxO7UfyGtvtNoLBIAzD4NQRpYbmERTRDIdDdLtdmKbJBjAcDrMHSH/GYjEOvfv9PqLRKGRZRjabRTqdZtlhVVURjUZh2zZXvC8LMz8NjuNgPB7jwYMH+Oyzz/D73/8erVYLmqahWCxiYWEB4XCYNxrpA7daLViWhU6ng3g8PmUc53WDEVzXxWg04hzN8vLylHEsFosIBoNs7BzHYfHzcDjMeZ1IJIJKpcKC6L5xPB2TyQRff/01Wq0WgsEg0uk0stkskskkFwkImqah3W6j2WwiEolA13U+xKS1PG+gy0TXdQwGA7RaLYzHY9a3j8VikGUZ4XAY5XIZpVIJ9Xodw+EQhmGg1WrBdV2EQiE2mGRYFUWZ0hK/zIhyZqfBdV1omoZOp4ONjQ386U9/whdffAFFURCPx/Hhhx9iYWEBq6urWFlZQS6Xw8rKCgzDwOHhIQ4ODvDkyROMRqOpCvVp+rXzAtd1cXx8jF6vhzt37iAQCCCfzyOVSrHHqOs67ty5g2g0imQyidXVVaRSKYRCIVSrVfz93/89pzPa7TZs28b29jZkWUahUPCN5AkMBgMMBgPs7e2h3W5z2qdQKHCbmRemaUJVVRwfHyMSicA0TUwmk1eKy19l0HunCn65XMZkMuHKcyQSQbFYhCAIEASBu1Mot7uyssJtfoPBAMPhEP1+H8FgEIIgzOzCmckpcF0Xk8kEmqah1+thd3eXcwqxWAypVArXrl3D4uIi1tfXUa1WkU6nuaJHG6zf7yMWiyEQCPAtM6+GkUChSavVQjweRz6f55u02+1CURQcHBwgmUzCcRxomsabJ5FIsEi94zj485//DEVR0O12EQqFkE6nEY/H3+bbe+cwGo2gKAo0TYNhGLwP4/H4qSkeKjgoioJoNMpFxHk1jMDzlr1QKMTFKWopI2O4urr6QoHQe9apoEgwTRMAuKtlFpiJcRwOhxgOh3j48CGOj49xcHCAfD6PX/3qV1hbW0M2m8XKygqq1SqWl5c5iR0MBqGqKrrdLkajES9eIpFAIpE4c0POCwKBADKZDJLJJK5fv86NxrZtwzAM/Jf/8l8wGAzwi1/8AoVCAR999BG+/fZbKIqCjz/+GMlkkl/LdV30+310u104joNIJIJyuTyXl89kMuGc1cmUDfXjJZNJxGIxLC8v49q1a3j//fdP9bA7nQ62trawt7eHRCKB4XDITczzum8ptRaJRJBOp1EulxGJRCAIAnuVp7XjUIcKTcw4jsOVak3TOO8biURmsr6Xahy9VSnTNGFZFoLBIDKZDGKxGCzLQqVSQTabRa1W46kX+l0yjPv7+2i321AUBZIkIRKJIJlM8gjhPG4yOsCGYcC2bciyzDdmKBTCZDLhf5Nlmb3AdDqNQCDwQoWfckCqqsKyLGSzWViWBcdx3tZbfGvwRiYE13Vh2zZ0XcdwOOSOiaWlJeTz+TNbR0zThKZpiMViEEWR87zzDFpfQRAQjUa5rnByDalVyms/HMfhOkMwGORzQJ4nFRpnkba41E9tMplwFYlc4HQ6jVKpBF3XMRqNkM1mIcsyrl27NnVL27aN3d1dPHz4EHfu3OEwvFKpIBaLoVQqQZKkud1oFN4dHR3Btm0Ui0U2duFwGMlkEn/9138NwzCQyWRQKBQQCoVw/fp1vnFPHv5ut4tGowFd15FKpaAoylw2K9PB84ImNNrtNhqNBmRZRi6Xw6effvrSMI5SQoVCAfl8HplMZu4b7qlQ4u0HPQ22baPX66HVauHo6Ih5FciJSqfTMAwDqqqyN05fsyjSXqqloU0jyzK7zNTgncvluNInCMLUm3EcB6Zp4sGDB3jw4AE2Nzc5N1Gr1VCtVjmsmVfQ5orFYi9sBKqIKoqCQCCASqWCSCSC4XB4Zsjhui729/fRaDRQKpWQTCYhy/JcVlNPQygUgiAIyGQyME0TmUwG6XT6zEPY6/Xw+PFjbGxs4Pj4GNVqFblcDpIkIZVKIZFI+L25LwF56pTfNU0T0Wj0hQudIhvqdyZv9KTnfxm4VONIFalUKsWtJTQnKQgC4vE4EonEqbe0YRjY2trC1tYWDg8PsbCwAEmS+AYWRXGuD24wGOT+r5MbgYo0pmkiHo9ziEzG8eR600Zst9tot9tYXFyEKIr+AfaA+m4lScJ4PEa1WuWe0dOgKAru3r2Lvb09DAYDrK2tIZ1OI51Oc77cX9uz4Y06DcOAZVkcJVLI7M2Hi6LIVe1ZNdfPJEalw5vJZLj1hlzk097E3bt3sbm5if/7f/8vd8DXajW89957uHbtGur1+txvrnA4zO04juNMraMkSUgmkzwlQBRvVMk7eaCfPn2KnZ0djMdjJBIJyLLME0jzmM99Gcrl8rmmW1qtFn7729+i1WpBkiQsLCxgeXkZS0tLyGazb/CJf1ywLAuWZeHo6AjD4RCNRgOu60IURR5ukCSJz34ymYQoiuwsUf5yFphZAi8QCCASibC1P+3Q0W1xcHDAHqOqqkgkEshmsyiVStwRP++N33R7xmKxF5h1KKdDCW5KZZy2aRzHQafTwd7eHiaTCU8eiKI4t8WulyESibw0YqH1PD4+RqfTQSAQgCRJyOVy3FlwMt/r4zmokk2pIWoOj0ajiMfjiMfjHDZTIzidg1nmG4E3MCHzsk2hKAoajQb+8Ic/4Msvv8Tjx48hyzI++ugjvP/++/jggw+wurrKFVcfeOVGcBwHhmGcOow/mUxgmiYePXqEP/3pT7AsC4VCAbdu3UK5XOY8sY/zwzAM/OY3v8GTJ09gWRY35X/wwQdYXFz0L55XgJh5qDpNl3o0GkU6neZ8LYXV3gjUOwU2C7zV0m+73ca9e/ewv7+Pfr+PXC6HSqWCGzduoFarIZfLcW+jj/OBPPbTUhDD4RBbW1vodDqwLAuLi4uoVqvIZDIvzaf5OB1HR0doNBq4f/8+Go0GACCXy6FaraJaraJQKDBXgL+20yDG/8FggF6vh+FwyL3N1L8bjUbZQ6TIib5mUYA5ibdqdRqNBv785z9je3sb/X4f1WoVa2tr+PDDD7G6uopCoeAbxwuCehpPw2AwwLfffotGo4HJZIKbN29icXER+Xzen4x5DWxubuLx48f44osvMBqNkEgkUCwWcf36dSwtLSGXy82c5v/HislkAsMwOCUxGAyYRo9SRPF4nJu8yWP01jBmjbdidQzDwOPHj3Hnzh386U9/QqPRgOM4WFpawvr6OtbX11Gv15nf0cf5QG0Op3UDbGxsYG9vD/v7+4hEIlhcXOQpJUEQ/AP8Gnj48CG+/vprHnRYXFzErVu3cPv2bUiSNOX1+HiOyWQCy7JYCsUwDDZ4FE4Xi0VEo1HurPA2er+p9XzjxpFyYnRQj46OMB6PEY1GUSgUUCwWUSgUuB/Sx6tBt+lZEgiTyQSHh4doNBpQVRWpVIqbmmkCycf5YVkWE6Ts7+8jFAohmUwyo4w34pn3QuJpoEIs5Rlt2+ZwOhKJcMsfeYp06VOXxpta0zdqHF3XRbvdxu7uLn7729/i4cOHME0TKysrqFQq+PTTT7G6uoqlpSX/wF4A1BuWyWRO9QBHoxE+++wz9Pt9jEYj/PSnP8X777+PxcVF/wJ6DXz33Xf43//7f+OPf/wjGo0G3nvvPaysrOBXv/oVVlZWkM/nmYHdxzSIvLbX62Fvb49npKmqn8/nIUkS05DZts1G8k3kGb14K8bx8PAQOzs76PV6AIBsNotKpYJ8Ps9TGv6Ne37QjXvaxqGcjq7rCAQCqFaryOfzyGazPqP6BUG8AY1GAxsbG+j3+3Ach1t3CoUCkskkr6sfTk+DpDuIANgwDDiOw9NfpC4Yi8XgOA5LopAH/qZp396ocXQcB48fP8bdu3fxzTffYDweIxAIoF6v48aNGzzU7x/Yi4F48E7DvXv38PTpU4zHY+TzeXz66adYX1/nxmYf54dt22i1Wtja2sKdO3eYa7Rer2NpaQmLi4vsAfmX+4sgUolOp4N+vw9N05iEmdp2SqUSh9wUblOR5rKZvl+FN2YcSS/iu+++w8OHD6FpGmRZRrVa5VCaZnx9nA9EjkDzpV6oqopOp4O7d+9ia2sLtm0jHo/zGvuFgotDURT87ne/w8bGBkKhEOr1OnK5HJaWlriAcNYUmA9M8Y6SLgx5jKQCQGqDo9Foai3fBlnwG/sUVVVFo9HAkydPsL29DdM0IYoilpaWUK1WUS6Xud/Ox/lAxnE8Hr/wPU3T0Gg0sLOzg52dHebGzGazLxB/+Hg1HMeBqqr48ssvuQhTLpexuLjINHxnjWv6eAZi81YUBbquAwAXYOiCp3FC27YB4LXyjJfFSfrGPMfj42M8fvwYW1tbaDabKJVKWF9fx89+9jNcv34dtVrtVIKE19loxEROIlTeMcarhEgkwi0jJ0GU8qIoYnFxEf/wD/+AarU6RXXm43xwHAd3797Fw4cP8fnnnyMYDCKbzeLWrVtYW1vDrVu3mGzFN4xng7guFUVhcmGa5iJSFdI7okZv8i7Pusy953o0GrF3GgwGuWPgdbsG3phxJB424BmjRj6fR71eR71eZ4nLy/JmSIBqNBrBMAzE4/ErR+LqOA5c1z2zeEVKbvV6HaZpYmFhgb0bHxeHqqrQNI3JhCuVChYXF1Gv15lY+DL2L/X1kfG4ShcZjQratj3VluN9z1SgoX19ViHGyw5O36f/pvYfej2qeBOz1Xkxc+NID0o6tevr6wgGg/jggw9w8+ZNfPTRR5Bl+VTv53VvYUqcD4dDqKqKZDJ5auj5Y4XruhgOhwiHw2emISqVCsrlMn75y18CgB/u/QCQd5JMJvFXf/VXWFlZwU9+8hNcv379BfXBH/r/8Y7UkejUVYFhGNA0DcAzlim6VEKhENrtNnt4JI1CLOqn9fAahjGVmyTvkwS7vI3mxA1JrULnPQtvxDiOx2MuydPUC7EkU3GAfpZK+F7FsrNAt4JpmuxS08xmp9PhJtOrmMckppKTME0T29vbTPlEOsCXgW63yz1pRKE2DwgEAkw7FggEUC6XUa/XLzXaAZ6JSB0fH7N4va7rSCQSl/b6bxuUX5RlGcFgkNePtKPo7HrzkGTwvGG2V4mUPEXgGekzGcWTxpHkn6lBXxAEpFIpTCaTM5/3jehWE9tGNBpFtVpl1TxZlqc+fDJ23krVWR3xlFc0TZP1ZobDIcs5UsIXwJWj/ifKptMwHA5x584dFItFrKysoFarXYpxdBwHh4eHcF0XCwsLc8WvGQwGUSqVkMlksLS0xBNGlw3LsrC/v4+DgwPs7OxgMBhcKeNI2vTFYpHtATV69/t9GIaBXq/HhpDYvr2hNTHbp9NpTjsQq8/R0RFUVYWqqqw1Q8aR5rWLxSLS6TRyuRzq9ToXfk7DzKRZbdtGo9HgUat+v4/xeMzjaq1Wi70PeqMApiw/ieuczCF2Oh12q08yCAPPKe6JKZgW+ypgZ2cHqqry5llYWEC320W/38fnn3+O/f19/PGPf8StW7dgWRYSiQTP+BJUVYWu63xptNttqKqKwWCAR48e8QalWz6VSkEQBNa7vnv3Ludwi8XilGTmVYTjOHjy5AnG4zFWVlZ+8F6ybRuqquLg4IAZ3GVZZnZ20zQRi8U4RLwqoJFgr5wwSXV89dVXaLVa2N7eZlEzr30QRZH3IkWCZGfIIep2uzAMA8PhcIoQmmRaSN+d8pqSJL20FjET40ge4GAwgGmaXKWi0DcQCEDXda5QGYbBrrO3ykzEl2QcKWdxeHjIkpfAs5td13WWf4zFYsjlcnw7XaVcGyk0ElsJ8CyUHgwGePjwIba2tvD999+zl16pVOC67tQN3O/3uc8sEAhAURQMBgN0Oh3s7Ozg+PgYgUAAoiiiUCiwcUwkEhiPxyzKZZomdF2HZVlveVVmCxIjI/VM2mOUu/Kmg7ysMScLBd7X0zQNnU6H6broghkMBrAsi2m7rpJxJIMfCoUwHo85OrRtG51OB41GA7u7uxgOhxgMBkw4QQXbfD7PBpaMIikRAuC2Nm+kSnlL+vK2W72qSDsT42hZFs9P0iHa3d3F5uYmh8lESZRIJFhuFABvMPJugGeeoCiK/LukZOjtlaKEa61W41uGxpHIwF4FUJi1urrKjd90QPf397G5uYmtrS30+31sbGzgv//3/84bJBqNQhRFZlfPZrOQJAm3bt2CIAhYXFzkCQWS1Y3FYmwwk8kkJElCtVrFxsYGvv32W+i6fuU6AU7CdV0oioJWq4XDw0Pk83lUKhVUKhWIoshe997eHnRdh23bLEccj8ehKArrg4fDYVy7dm1KE8V1XWxubvIlR/tXEARIkvSW3/3lgaJASgtFIhGsrq5CkiQcHR1BkiSk02mOCMkWUNSoaRqOjo64JhEKhZjzlVrWEokE1tbWeMCEPM1UKsVFG7rMXnX5zMxzpEQp5QapouS1+GThyfugggqF1rZtIxwOszfpfX1yt2kuk944iRqRrgqNJV2VsJqaZCn3AgDxeBySJGFtbQ3As8OWSqWYiIIuI2JEoibc4XAISZKmZoNp3b2CXjTBQPodtL5XaV3PAuXAE4kETNPkdA7JD1N+m8bdKMIJh8O8v+nip73f6XT4s6PqLPWrTiYTlga4amE1MN2BEgwGIYoiHMfB9evXUSgUUK/XX7AdqqrCMAz2qh3HYa7HTCbDjhFFOLVaDclkEvl8fiq9Rs6Vt+j7sv07s5zjyT6mdDqNarWKbreLyWTC5JXUpxQMBnkQ3bZtFn4iiQSqPFuWxZunXq9DEASIojjVbpFMJrGwsIB0Os0V8auyyQaDAfr9PiaTCb+nTCYDSZLwH/7Df8BgMECr1eKKn2VZLFh0fHyMr776iqniJpMJRFHEeDzG6uoqIpEIDg4OYJomM8vEYjH2YgqFAq93pVJBr9ebqhZeRdAlUq1WIUkSWq0Wt1J1Oh3uobVtG6IosgGlvUw0/4IgQFVVmKaJg4MDhEIhJBIJvpzq9TrG4zGOjo7YuyKFvasMOq9/93d/d+r3J5MJ2u02dF2HoiicrqhUKswp8EPSZm/cOJKgULFY5MbZUCiEQqEA0zQxmUw4/0i9SvRv5CVSEYFyFCS2Q+V90rWmkjzlEeh7kiTxjXKVqqq5XA7RaBR7e3tIpVIol8sAwGEGeYpUyScDR9KruVwOrVaLc12hUAilUgmJRII9xmAwyKJHoVCID7XrukgkEiiXyxzGALjSB5g8Dl3X2ZsjkPenqirnyKPRKEctgiBw7hsACoUCf07kGVEEEIlE4DjO1EVPYvbzDNK6p9CY8riiKM6cH2AmxpGSnplMhkPpRCIx5SpTlU7TtKnqtLejnfIykUiE1dy8MpnkLmcymbkhFpVlGeFwGIeHhxiPx2wcgecqhKFQiFMT2WwWiUSCm2rX19fR7/ehqira7TYcx+FCCxUCyKMnA2sYBhfQqEIoCALK5fJLZRmuAii0peiDcr6BQIAjGZr8Ap5dFIIgQJZlTnfQAaaLKpFIsEwA7Xm6wFOp1NRrXaWL/XVAnADA9Nq8Ccysz5GqTKlUCtlslhOsFFLTHCVx5HkrT6Ql4S04UFn/pNgOhedXqSL9MlBvKFWfFUVhb5ryL5T7IsEiuniGwyGazSZT0x8eHjLHYzqdRqFQgCiKsCyLE9lez4cuoJMSsFfZcwSe7b98Pv9C4Yn26vLyMu9luvS9+5UuGgBTlxDlbL1715uimIfL/l3GTJvAvRuEDigZR8oV0ASNtzoFgEXpfUyDwmbyxL0HKx6Pw3VdSJLE3HnEdkItTV59awpTKO9DN/N4POawMJ1Ov9Irn4eL6bys3uFwGKZpTl3gFA1523tIVvRNs1v7OD/eiG419Rid9f2rHJbNAoFAAOl0+oV/p/A5l8vxJXPy4Hk9E+/P0M9VKpUX/l8+zg+6aE7idRmmfLw9+CIXVxAvIwb1D+jbgb/uPz74SQ0fPnz4OAW+cfThw4ePU+AbRx8+fPg4BYGLTDcEAoEWgJ3ZPc5MseS6buFtP8RZ8Nd2dviRry3gr+8scebaXsg4+vDhw8e8wA+rffjw4eMU+MbRhw8fPk6Bbxx9+PDh4xT4xtGHDx8+ToFvHH348OHjFPjG0YcPHz5OgW8cffjw4eMUXIh4Ip/Pu8vLyzN6lNlie3sb7Xb7nZ3+99d2dvgxry0AfPnll+13uQn8x7y+L9u7FzKOy8vL+OKLLy7lobz8jvRFzMgkyBUMBhGPxxEOhxGLxZjC/3XwySefXMpzzwrLy8u4c+fOhX/vMtleXncg4Oc///mlPcMs8Lr7lghsSSfHcRyWQxiPx1PqgV7ZAxJ9I+5MImR+XQQCgXd6+uQy7cKbxsvswluhLCP2b/oaDocwDAP7+/sYjUYwTZM1Y6rVKmvWkiyCj2fwabBmB8dxWN+IZDwsy0K73WapVTKIZCgnkwkkSUIikWBWddKBIap/Hz8evBHjSBtoPB7j8PAQg8EAOzs7UFUViqJM/QzdsvTnaDRiBcJ0Os2M1vNuGGbx/uk1532kdDwewzAMdDodaJoG0zTRbrcxGAzw/fffQ1EUqKrK8hx0kefzeV67UCgE0zSZyJk8yHnftydBUsC0Pu+SNMTMjSPdqCTStL29jUajga+//hr9fh+dToc1UFKpFOLxOFKpFIcygUAAhmFAkiSEw2H+c57hH7DZwXVdWJaF4XCIVqsFRVEwHA7x9OlTHB8f4/PPP4eu69B1nVUDZVlGNptllUzS1LFtm1UwSSfZ/+yegxyi0Wg0JRtx2s8R3uT6zczKuK4LwzBgmiYGgwEePXqEnZ0dfPPNN2i329je3ka320W32wXw7GZdXV1FPp/HysoKb7xer4fxeMySlaR1Mq/6Mv7hmh1Id2drawvHx8d49OgR2u02ut0u/uVf/gWtVgudToe1eOgwd7tdaJqGbDaLSCSCyWSCfD6PQCDAqo3RaBTJZPKd8ozeJkajEXq93pRmFKXbSGMHeJbeIBswGo2Qz+chSdIbecaZGEcqsIzHY5imCUVRcHh4iM3NTezs7KDf7/NtIQgCG8JMJgNZlpFMJhGNRlmgi25zSoafVIGbF/iGcXZwXRej0QiapuH4+BitVgvdbpf/fnx8jH6/D8dxEI1GIYriVBhI6pmmacI0Td6vtm3Dtm2OhHyADZ1hGGwIvQUvEiSzbRuWZaHRaHAtgtZUluWZy9bOxDgqigLTNOE4DgaDAfb29nDv3j18+eWX2N3dRTgcxs2bN1EoFFCtVlkEfW1tjTWX6Y0fHR3Btm0EAgHYtg3DMN64fu27AN8wzg50MFutFlqtFr766iuoqgpVVbG3t4f9/X24rgtZllEul5FKpSDLMoBnRlFRFIiiCAC8R3VdRygUgmEYSCaTcyUf/DJMJhMcHx9jNBpB13WuLVD3SrfbRafTQbPZxOHhIfr9PjY3N1k//ebNm1hdXcXf//3fI5vNzvRZL9U4UtuD97Y0TRO9Xg+BQADZbBaiKCKVSuHTTz9FoVBAuVzmAkwgEICqqjg4OGBJUdu2EQqFIAgCt/X4ocnZmEwm0DQNvV4Px8fHnOtKp9Nc9Sev3MczUGW61+uh1WqxHjdpS1cqFSwsLEAQBKysrCCXy6FYLKLdbkNVVezs7LDXQ7Ks8XicDaK3Fc3HdCdAJBJBIBBAv9+HZVmcbms2m2i1WlBVFYeHh+yRV6tV9tAty5qpcumlG0dyeym01nUdrVYLsVgM9XoduVwOpVIJf/3Xf41cLodsNgtN06BpGu7du4d2u42vv/4amUwGqVQKhUIBqVQKiUQCgiD4m+wVsG0bnU4H33//Pb7++mtYloVAIICVlRVUKhXEYjHWovbxDNSm02q10Gg0ADzTn47H4ygWi5BlGUtLS8jlcvjJT36CcrmMer2Oe/fu4fDwEOFwGN1ul3+XNKpDoRDnG+PxuO85/n+QcfSG1a1WC4PBAHfv3kWv1+OLR9M07O7uctvfrVu3AACGYcAwjB+PcaS+L6rWHR4ewnEcrK2t4dq1awiFQqhUKnwb0xujZKyu6xgMBjg+Pkan00EsFsMnn3wCURT5Bo7H475xPIHBYABFUfDw4UMcHx/j+++/R6vVwtHREQKBAARBQKFQwHA4RLPZRDgc9vvuPKDDSprflPAPhULI5/OQZRmiKCIajXJHRSgUYuNXLpcRi8XYIfDmF8kzGo/HftSDZ+mhWCwGXdd5P+q6jo2NDSiKwikK+gzC4TBKpRI0TWPvcjAYoNfrIRqNcpvfLHCpxpFaFcLhME+6BINBZLNZJJNJiKKIarWKWCyGcDj8QgOtoigYDAbo9/sAnm3O8XjMr0238TzfwHToKIUxHo/R7XbRbrfx+PFjNBoNPHz4kG/WWCyGaDTKExuU7jjtdedZeN51XYiiyIn+YDCIWCyGxcVFFIvFqXWhve26Lk9xCYIAURRhmib363p7dv1izHPQJUGpjHa7jSdPnsAwDF5PqitEo1GMRqOpFBsVc0zT5PrELPbtpRpHqi4Dz0KV5eVlGIaBwWDAN7IgCHBdF71eD4qioNfrwXEcqKqK//W//heePn2K7777DqlUim8FmoyZ1/Yd4LnxosOnaRparRbu3r2LdruNXq+Hzc1NjMdjZLNZFAoFLCwsYGVlBfl8HtevX0c0GmWPB5jOEVuWBcMwIMvyTEOVdxHhcBiJRAK3b98GAM5/U97x5MGj6EZRFK5KU243EomwQ5DL5SCK4lTv47yD1igcDiMQCKDRaODJkycIBoPIZDJYXFzktEUkEoHrujg6OoKiKGg2m7yOqqpye1QikUAqlbp0A3npiSd6QCqiUEgRj8cRjUbhui5s28ZoNILjOAiHwzAMgzcY3bAU4pDHSR3083oDe5vpTdPkit7R0REGgwFUVUUkEkEymcS1a9dQKpVQq9VQqVS4G+BkSEceEOWIyQDPG6gBmQzhWYdsMplgOByi3W6j0WhgMBhA13W0222MRiMA4MsnmUzy3p3nS/0kqDOAco507qnGsL6+jnw+j2KxiGAwyC2BkUgE4/GYaxpUoBmNRjPL584sKx8IBJBKpbiAkkgkEI1G+XAPh0NEIhHk83lOZKdSKUiShHQ6jZWVFayvr6NarfINHIlE5rZfjDbJYDDAYDDA5uYmGo0Gtra2MBqN4Lou6vU6KpUK/vZv/xaZTAaZTOaVr0kXleu6fJvPG847tjYajbC5uYnDw0Nsb29zH++jR48QjUZRKpXYONIBz2Qyc+eJvwqWZUHXdXQ6HViWhUgkgvX1ddRqNfzFX/wFd6ZQEcZ1XQiCANu2eWqJPMfRaATbtmfynDMtWVLPV6/Xg6Zp7CXatg1d1zm53W63sbe3x280EAhAlmXU63WUy2Xuio9Go+xBzhu8YR4VpnK5HG7dusWVfPISiaTjLBDRB4XUqqpCEAQkk8m5NI7nwXA4xGAw4CoqefGWZbEBpH7GcDiMXC4HWZaZpcfHcwSDQUSjUW4rW1pawocffohcLod0Os0N+f1+H7quQ9M0DIfDqeERSgnNsi1t5saR5lQpZKHmcMdxkEgkEAqF0Ov10Gg0YFkWJ7klSUKpVEI2m2Wmk3A4PLfeDQA2jpFIhCeLqN0pk8mgVCqdi7lI13X0+30Eg0HYtg1FUbgX0seLcF0XmqZBVVW+6CeTCTP2pNNpxGIx5giIRqMcAc3zfj0LNBmXzWaRzWYRDAZx8+ZNpFIphMNhDpmplYcawBVFYftARUXKo88CMzWOtEm8XIwUyg2HQ55f/eqrr7C3twfTNJFKpfAXf/EX+NnPfob33nsP6XSa8w0UTs/jhqOqZz6fRyaTQaFQ4BQDJfxf1SpC3k6j0cDR0RFfTnTpzKtX/jJQs/Lh4SH3QVInQKPRgG3b+OijjxCNRuE4DlZWVlCtVlGtVjnn7uM5gsEgjwjT/DkZS9d1maWL8ufBYBCmacK2bRwdHTEDEnXCyLI8s2LXTI0jJbpFUWRvkXIylExtNpsYDAbcECqKImq1GgqFAiRJ4j6xec01ehEIBLioRUl+yhW+6vakJHa/34eiKNB1farP1FuQ8PEcVMXv9/sYDAa8bym8ozlryofLsox8Ps9r6uNFeHtEvbPp5I0D4DNPU3a07kRZKAgCe+qzutBn/umRx9jr9TAcDpFIJDhM6Xa7ODw8ZMOYSqVQq9Xwr/7Vv8LS0hJkWZ4aSqc2gHk+wJR7pA1xnrUgZpNut4uNjQ2Mx2NMJhNEIhEkEokpso95XtuTcF0Xg8EArVYLW1tb3IRsGAaGwyFqtRrS6TT3OHrz5NSD5+NseI2aqqqwLIvDaF3X2TBubGyg0+kAAARBgCzLPPE1S3vwxshuiTKemE82Nzexu7uL4+Nj6LrO5fx8Po9sNotEIsF9Tuf1juYN590UrutiOBxCVVVutI1EIvyVSCQQi8V8w+gBjcJ2Oh0cHBxwIZEKWZTvTafT3LhcLpchCAJHSP56vhrkHZLnCDzPrdMUjaIoTHqdzWZRLBZ5rHOWa/xGyG4dx+EwsNVqod1u4+7du9jf3+eNR/m0YrGIfD7PBRiaDaacmr/hLo7JZMJhoa7r7CVGIhGeRvB78aZB+dlms4nt7W2MRqOpeWAiZ5ZlGePxGIIgYGFhgft6/cvmfKAWNeq19XZkUDqDBkVkWUaxWOT+3Vmzc83cOI7HYyiKwr1Lh4eHLJVgmiZc18Xy8jKy2Sx++tOfIpfLcf7GNE0Oy2eZW/gx4rwHj/rCBoMBxuMxJEniMNrbP+qv7XNQs/fe3h637lDeazAYIJ/Po1arMdUW7d9YLObnxi8IunCoLYryuYZhYGdnB61WC7ZtQxAE5PN5LC8vY3V19Y1MHM3UONJUh2VZ3F5CuS9N07g7ntp2SqUSkskkz13T3KR35M3HxbgdqT/MsiwA4PwYTW8QiYKPZ6B9R1NIuq5PCW1Riocqqa7rQpIkpFKpqbFMH68GrTXZCKJ803Udqqry5NfJ1p9sNvtGil0zlUmgpDQN5A8GA2xsbGB/fx/NZpPzYKIoIpfLcZmeiENpssav+j3HRUM1GuwXRZHDFRrV8sPpaVAKqN/v4/DwEPfu3QMASJLEod3a2hpEUcRkMuFKdTKZRCwWg2VZXIX18XJ487eRSISNoq7raDQa2NnZgW3bSCQSWFpaQrFYxE9+8hMUCgUkEok3krKYqXG0bZuLMcTw2+/3MRwOYZomTxIQr2MsFuPqk1eB0M/dXBw0Fkj9od5cDvEL+hRa0/CS3g4GA24r8SpjUmRDQwzUZ0dr6e/V88HrNdIXTc6pqop+v8+puEQigXQ6DVmW3ygv5kyMo5dOiwTO9/f3cf/+fTQaDfT7fWiahmq1itXVVdy8eRO1Wg2SJHFoQpMG/oZ7PWiahna7DcuyOG9Lfac0KuiH09MYj8fQdR07Oztot9tTBAeapkEURWQyGc6TEbkH5W39COf8oAvHy2pk2zb6/T5arRaazSaq1SqSySTS6TQymQyKxeIbfcaZfJrU0U5KbBTaNZtNdLtdGIaBUCiEdDrNvWKUkyQvhw6zbxgvDmJgJ2VHSlXEYjEkEgmfMPgMkLzE0dERNE1DNBqFpmkYjUYoFosQBAGWZUEQBORyOeTzeaTTaf+SuQBIlZQMIpFIUBGm2WzCsizO46ZSKSwuLiKfz7/xZ52JcaTbIJVK8exuv99nj3E0GrH0QaFQ4NYSwslG5/OAKoSnVQrnycBSOoOYj0jZkWZ/U6mU3+x9BnRdh6Io6Ha7TLNH65nJZBCPxzEejyHLMgqFAtLpNE9/XaTn9GX/7cVV/IzIOJLO1HA4RLfb5ak5yu0mEgn+KhaLb2Xuf2bGkeakh8MhHj58CMuykMvlOD+zsrKCer2OTCaDdDqNZDLJ85LU/E15npcZSkqia5rGkwsU9lByXJKkmdEavWsYjUbY2dlhvWRvSB2JRLhp+SoevB+KTqeDRqPBa0PeYy6XQyaTYZIUOry6rvNkFwBmrSd4uSGJacZbiPBqu1M/r3ceO5lMXsniTjAYhGVZOD4+5miSOgQcx0E6nUYul8PCwsJUPeJNYybGkRL/JNKtKApTnNNtTIwlpGtN9P3emUuaJabpGq+2LTWNEu0WGUdd13kDUvHB231/1UEXCcki0AElcgo/BDwb1GcXDofZ+6Y8rWma/DPklU8mE8RiMWQymReImL3RD/07FSKJ8owazYmzMBQKQZKkKQfhqjGI07oQVZ6maUxLRqS2iUQC2WyWSVbeFiHKTIyjLMuYTCYs/EQVasMwUCgUEIlEUCwWoes6vvrqKzx48IBDP5KxpGo15SNzuRxXYHd3d5lbj5K6NGZITDM04kUCSLS5rzpisRjW19cBPA/ZyBPxvcWXgy5bCuG63S5EUUQ4HMZ3330H27ZRKpVYqVDTNJ7cOMkk7vXW6VI/PDyEoig4Pj5mQ0xOwcLCAtLpNOr1OpLJJFKpFOc3rxrIc9zf32cilJ2dHQQCAayvr6NSqeDatWtYWlqaqYDWqzAT40h8a6qqcuhBuhuaprG4Dg3xUxtENptFJBLhnjxvOw8xAdu2jV6vB13X0Ww2eVabNiVpY1erVSYgFQRhrgoQvjF8PQiCgEQiAdu2EYvFEIvFoGkaOp0OkzF3u12OSKh4SDyE3oiHLmuvjjvxCHirs3SBETGut8mcGvWvErw5cUVRmAqOLiLy1L2Rz0XhnVKii4ouoclkwp/VqzAT40hviAow1Of49OlTDiHo78S+EQqFuC3CO9JGOUS61SkvSfxuNH9JfXy1Wg3FYhGFQgGxWAzZbJbzbD58vAyJRAKSJPE0jCAIaDabaDQa2NzcZNJmAOxhkrIjtUjRRU9e4XA4xGg0Yro427Y5dAaeH95yucyGMB6PQ5ZlJmC5aiCZhG63i+3tbezt7bHAFgBOo73OmfUqmtJFA4A/D/pc35pxBJ5tHgqriYdNlmXuf6S8C9GcB4NBdDodtNttfmNeGVL6O7UAxeNx1Go1rmaRiBTlbT766CMeNSKChauAwWAAy7JYj6fVarHOjo8fhkwmw/21qqoiHA6j2WzCtm3uqiDd71gsxnPAxBEwHo/ZMXAch9l6YrEYUqkUSqUSk7gS9RYxt1OfL4WSsiwjlUpd2bCa1qnRaODBgwcolUoAnnEBZDIZdLtdKIrCqQkymNSKRqkz70x2KBRiZdPRaMSOFaXjyMsnrapMJvPSWsRMu1bJqNFspCRJnIyljWOaJoe91ELhpTEi40neZSwWY+IEIk9YW1tDLpdDKpVihpS1tTXO3QC4MoUI6gejHjFi9CYPgxhjAPDaXyQ0oc/Mqyd+WruJtwp+VYgWaPqCRtpo7l8QBBQKBS4cUGHRMAyuUNMB9OYdA4HAlHQoUcPRRV4oFNh4ZjIZiKLIRLmkN35Vq9WULhuPxxgOh8hmsyypQnUKSj+QDSAHi8Y1qUuFGvXJOLbbbY4oqeBlWRbC4TB/jlT8edkc/MyMYzgcxo0bN9Dv95FOp1GpVFhk/qRkAg3x93o9br2hn6GePOJ7LBQKXMGmkMQ7ukUtEZS/uGogiidd13F8fIzvv/8evV6PQxJRFHHt2jU2cNls9kLkEjTs//jxYwwGAxwdHb3At0c5sUwmg2w2C03TZvZ+3yQSiQST1jqOgxs3bkCSJNy4cYPp3prNJoBnlxAxqquqyh0B1JZGJLhUbY3H4+wxlkolHoujfCTlKelyonzmVcsbU/ormUyiVqthbW0NiqJAlmUIgsCktqRpT+15dNHTGtZqtRdUHanWQXld8uyp7zocDkNRFOTzeW40f9nFPjPjSG+EPMVsNgvbttmQkZWnmwEAk0zQzUA3J93myWQSsiy/NFFLh5hueOKAvCreDfC8kZYG9ZvNJvr9PoeCJHVLlVRvGw/lcqhiSIwoVCAjI/D06VOoqjqV5iDtcVmWAYAJGK4KvMaILhMK92h4oVar8c8Ts9RgMOD2KUEQOLohA0gFAEEQEI1Gp8L3k5rhVDigsPwqgrzAVCqFbDaLcrk8dYGbpslhNXmT9HtkC0jamfaftwBmWRan7+iy8RZ4A4HAucT6ZqpbLcsyJElCsVjkBzsN5Pqm02l2c4k55iIEtyS/QEplNBFCvHtXAWToe70eV+0bjQZUVcWDBw+mClyUayVvnDjxqIBANzNxPpKOMIXspOlBlxSNcd2+fZuF0676KOLCwgIWFhbO/D7tOfKIKPEvSdK5Zq1PC5vpkF9FUO8yTcetrq5yNER9nyTFCjw3epSGI+eKLhFqoCeNmZMkK9R1QOeB8o3k4b9s774RJnBvSEZsv9QXRl5gJBJBLpdjD49uEjIG1D3vZfFQVRW2bTPBBY0g0W3hrW5fFXi9E2qWpR7Rer3O/I0E13VhWRYODw8BAFtbW7wuwHOxIwBcIAgGgxBFkW930sWmC4sKQMVi8cpOcZwXFE7T3ym0+yE57ouOzv7YQJV90twBwHK33kk2SjnQYAfldAFwmEwpC9KboroEOQbE7ENOArVrSZI01TVw6nPOdhme34Jeph66PbziOFS08f6e18BREYKM4Xg85mZa0zT59bx9kfQ6Vyl3Qx96LBZjlhgK2yqVCobDIacyKGQbj8dMwUWe9Xg85mooVWLT6TT/W6lUgizLqFQqU0lwGkEkGilf2wdTHuJlrcVV2a+ngQwYkVwHg0EMBgPOvVLah/YpDZBQPtK7Nt6zQGkLIlmhYhjle8mbJO2kV0nnztw4UmXPqxNBXp9XK4JuhtFoBFVV2ZhSJZtykxTKxWIxLC0tcdHG64UC09VUcrOvAmgDrK6uwjAMNoiGYXDOSlEUDjeI8YSKJpSmCIfDkGWZb96TnHlUIaS/e/M13uZ8AFfay/ExG8RiMWY6qtVq6Pf7cByHOwYCgQB0XefxYxq7pDCZeDXpT3KIvEMkXhtAe9f79ap9+0YI6E7yCVJSlR6OvgdMJ8W9b4QSqBRCUkKb8gre17jKoA/ZyyEoiiLPBLuuO1Wto6IBzQnTmlG/KK0rhRte1h4ygD5RhY/LBu1FYuSiAi0Jk9EeH4/HEEWRG8fpsqb9T84TFcToQr8MvDF2TnJlgbNpmrz/fnIumPCq/54HUJ6LwoWTOG0dz3qdk3+fx/X08Xbg3cfUj+zdf+elIZzVnn0r1MVnvRn/YF4cp62Zv44+fmx42T5+W/v56sehPnz48PEa8I2jDx8+fJwC3zj68OHDxykIXGSsLhAItADszO5xZool13ULb/shzoK/trPDj3xtAX99Z4kz1/ZCxtGHDx8+5gV+WO3Dhw8fp8A3jj58+PBxCnzj6MOHDx+nwDeOPnz48HEKfOPow4cPH6fAN44+fPjwcQp84+jDhw8fp+BCxBP5fN5dXl6e0aPMFtvb22i32+8sI8ObWFuv3C0wTQ/3Q/BjWNvFxcUL/c5JsSsv+TL9eZIX8DTOQOCHr/PXX3/dfpebwGe9d09KNQMvUhu+Ll62dy9kHJeXl/HFF1+89oO8TXzyySdv+xFeilmuLemckFgR6XWQjrLrusyP+Tp419d2cXERv//97y/0O8PhEIeHhxgMBhgOh6zTs7+/j1arhf39fciyjEQigUqlgnQ6jXK5zLIStVqN6bhEUTyVWu68SKVS7/T0ySz3ruM46HQ6rAvl3btEfEsE2K+Dl+3dt0JZ5mP2IIJbko4gnV+SnCB511AoBE3TIEkSstns237st47JZILDw0P0ej3s7u5iMBhA0zTouo7hcIi9vT2oqspCUK7r4sGDB5BlGUtLS6jX6yiVSnBdl9UygWlNJB/nB2l/E1s9ee6iKAIA61XPQq3RN45XFKS9QYzJRB3vOM6UJEUgEGC98Hk3jqT13Ww20W632RCSEdR1nRUaHcfBYDBg9cZUKsVSFYFAAIIgMDt7NBpFOp32GdVfA7SWp4HEt0h65bK1onzjeAVA+jzxeJw1rVVVhaqqfOvSJqvVatB1HaZpwrZtmKaJXq+HUCgEURSRSqXmVk1wZ2cH3W4Xm5ubsCyLpUDT6TQODg4QiUTwySefIBqNIhqNshyF67osNUGi85TCkGUZuq6j3+8jk8kgFou97bd5ZRAOh5HNZmHbNgzDgCiK55LDPffrX9orXQAkBDUajVhEh8IQ0pXw8WrYts3CWXRrOo4DXddhGAZ0XWfNXwKF2KFQCIqi8MYyTRO6rrOnOU8g+WBd13lNgsEg66aTVrLjOMhkMixKNhqNWPqXCjOkz0PyrMFgkPe7T/Jy+SDdpFkojL4V42jbNtrtNp48eYK7d+9ie3sblmXh008/xcrKCn71q1+9jcf60aHb7eKrr75CtVpFPp9nb9DrOdZqNd404/EYvV4PqVQKqVQKg8EAlmVB0zREo1EIgoB0On1pAkU/FpB4E+UXM5kMRFFEqVRCvV5HsViE4zgApqn7X6VtomkaHj9+zMbSN46zgVef6jLxxowj5bioAri9vY2nT5/i+++/h6IoCIfD6Ha7KBaLfBOcBLVQXBWZ1dfFZDJBs9nk3FilUkEikUAgEMBwOMTR0RF0XYeu69je3kan08He3h4mkwkAIJfLIZVKQVVVKIqCVquFUCiEXC43lweY2kTi8TiLvYuiiGw2i1QqxbK/F0U8HkepVOL/vsyQ7yqD8uKapmE0GqHdbgMAotEoisUiF7lmjTf2adHNfOfOHRwcHOC7777D4eEhdnZ2kEqlIEkSer0ehsPhmcaRFu08mrNXGbZtY2trC61Wi4XOJUlCKBSCYRjY3d3FaDTitgcA+D//5/8gGAyiXC6jXC4jm80iHo/DMAwcHh4iFothYWGBPaR5Al26lC9cWlri/OEPQTQaxcLCAsbjMUaj0SU97Y8TZ53p0zAej2GaJprNJvr9Pu7fvw8ASCQS+NnPfvbjNY6u66LX63Grg2ma6Pf72NrawuHhIRqNBkzTBABcv34dP//5z/H++++jVCqhUqkgk8mcafgopzPPhnF/fx/tdhsPHjxAKpXCv/t3/w7VahWu60JRFBwfH+PevXvc1xiPxzGZTHD37l3UajV89NFHEEURruvi6OgIvV4P9+/fRzgcxurqKhvTeQL1IWYyGW4TuczohPasZVlzub6U0x0MBqxDTZrTXgyHQ3zzzTfY29vD5uYmms0mdF1HNBpFLpfDzZs3sbe3B9M0sba2BkEQZlr9v1TjSDewpmkYDAbodDpQVRXHx8dsHC3LQjAYhCzLqFQqWFlZwaeffopyuczJ77Mw7x6j67ro9/totVoYDoeQJAmLi4u8yVRVRa/XQ7vdhmma7Dnato3hcAjHcZDL5fjga5qGXq+H4+Njzj/Oo+dIDfBntYycBB12wqvC5UAgMNc9jpS2IA/a25fonXxRVRUHBwecbmu327AsC/V6HZIkIRaLYTweQ1XVN3LJXKpxJHf44OAAR0dH+Prrr6GqKvr9PiRJQrlchiAIyOVy+Nf/+l8jk8lw9Y8O7Ks20LxuMMdxMB6PcXx8jFarhQ8//BDFYhHpdBqBQAC2bePzzz/H1tYWTNNEKpVCNpvFxsYGRqMRPvzwQ3zwwQf4y7/8S7iui9FohI2NDe7nW1tbm1vjeFEoioJ2u43RaIRgMIj19XU/n/gSBINBBINBFItF2LaNXq+HVquFZrMJRVFgGAbC4TAmkwk0TUOhUMCvf/1rDIdDAM8izFKphJs3byISiSAcDiMSiczcFszkEw2FQlO3cSAQQLFYRDabZYNYKpWQSCS40/1lcF2XPU76mjcjOZlMMB6PuaeuUCggk8kgEAhgPB7DMAy0220MBgMUCgVIkoRcLoder4doNIr33nsP165dgyRJcBwHo9GIq7T0ecVisbn2zM+LYDDIh3OePcLzgjxty7JgWRZUVcVwOISu6zg8PESr1ZoKtSVJQiqVwmQyQSgUQq1W41HNN7nel2ocKexNp9OYTCZYWlrCZDJBNBpFtVpFsVjE6urqhVtFKF9BB5j69OYJ4/EYmqYhFAohlUpxzgV4Hh7TRMeHH34IWZYhyzICgQBGoxH+43/8jygUCkin0wCedQ8oigJVVZFOpyFJEmRZnrsex9eBIAh8oVAvo4+zQcMG7XYbw+EQ3W6XDeXjx4/x3XffoVAoIJ/P4+c//znq9Tpf5K87738ZuFTjGAqFEIvFkM/nkUgkEI1G4bouQqEQMpnMuaYvHMdhLzEQCHDvHt3S0Wh0LjdjOByGKIqoVCqwLGsqrBgMBmg0GlhZWQEAfPjhh9A0Df1+H4VCAZFIBIVCAalUCgBwcHCAg4MDNqblchnFYpFTHPMGy7IwGo1gmiaPok0mE4xGI8RiMR6tDAaDMAyDD3Y2m/XD6XMgFApxm1QsFuNoZzQa4datW+wRyrKMGzduIJ/PI5lMnrm2ruvi+PgYgUAAhUJhqsXvnR0fpJBXlmVmJ6EJDVEUz3XwJpMJTNPkCp+maXAch/OS87oZ6X3ncjnYto1wOMwbgdqkarUaRFHE6uoqdnZ2cHBwgGw2C1mWIUkSe+zNZhOPHj1Cu92GYRhYXl5GNptFOp2ey/Udj8cc7lmWBdu22VMXBIGZdUKhEFRV5e9LkuSH1OcA2YVkMolYLMZpMsuysLq6ClmWoWkaUqkUFhYWuKXqLFBHDADuzaWppnfWOBIo1CBP5TxVZtd1uWJqGAbnxGhiQ5blufQYCZTnoh4v71qsrKygVqtxH2i/34eiKOj1elhZWUGlUpkyet9//z1+97vfQVEURKNRLC0toVQqXXoLy48FNPIXDoenOBxt28bR0REODw+xubmJUCiEQqGAYrGIhYUFPwVxQVAxxbvHarUabNtmzy+RSLz0nNNww3A4xGQywfb2NpLJJKeLLhMzcxMoDKa/vwpU6qcbhZqYKbczjx7NSQQCgVPXgXgDgWebZ2dnh/vJRFHkophlWVAUBc1mE0dHR0wHlc/nkU6n30gF8F0EzeUStyUALnwBz8cLKccYjUYvTCDhjw8+X2eKIC/i7NDFT5wBZFCpd3QWjtNMLc5FDxpVtQzD4JuERrl8nA+apuHLL79EoVDAr371K2SzWR4tbDQa+N3vfoc7d+7g6dOnWFtbw/LyMn76059iaWlp7tfZe8Amkwkbs0AggFKphHQ6jVu3bl3YMFIY7rdJPYN3nenSeFXEMhqNuL/XMAzYtj11UUWj0atBPHESNENJJKyBQACpVArxeHwmb/oqwnVdnp4Jh8NIpVIcKofDYZ5r//3vf492u41EIoGFhQUsLS2hXC7PJCz5MWM0GuH4+JhzjGQsvT25F4HvOZ6O855ty7LQ7XaZbo9o46jwOwsb8U4YR6Kipw0YCAQgy/LUKOFFZjPnEY7jYGNjA91uF/F4HNlsFtVqlb/X7Xaxvb2Nf/qnf0KhUEAul8P6+jrW19extLQ0d0w8r4Ku69jd3eUCDRnI1xlfPal/4uM5zks1ZlkW2u02c2iWy2XEYjGk0+mZdVi8E8aRkrD9fp9bVqgh2Rva+Dgdw+EQqqqi1WphPB7j/fffR7FY5O8bhoHf/OY3+Prrr+E4DqrVKm7duoX3338f9XqdSV19PLtIFEWBoigwTROFQgGyLCOXy0EQhAsZRppE8sPp1wcVag3DYCKQYDDIPbuzHFx4qyeCblSikqfZav+wXgzD4RDtdhvj8RiRSATFYpE7BYj4g3gzaYi/Xq+jXC4jl8tNtQXNO7xkwY7jIBaLQZIk5PP5C3sopNnje42vB+oYIBKVWCzGLTs0XTfL7oq3aoFM08RwOOSDSQeVxIgA32M8D+7fv49vvvkGv/jFL7ihm27TP/zhD7h37x5+85vfIB6P4xe/+AV+/etf45NPPsHa2hoXa3yA2ekPDg5gWRaSySR7kl5P/Dwgj9E3jK8PIh9++vQpQqEQkskkqw6+zmV1UbxV40jtPsSwQZ6jP696PlDjsqZpmEwmTNRKF4vjONjd3cWjR48wmUwQj8exsLCASqWCQqHgz1KfgGma0DQNhmFwUZB66C4aTtPUho/XB3ne3nWMxWJIJBJvhLrwrRpHUsWjiqBXr8PHq6GqKh48eABd15FKpbjQAjzfWF9++SV+//vfQxRFLCws4MMPP8S1a9ewsLDgr/MJ9Ho9JlwmHWoi8TgvvAfa9xp/GOiSCQaDfOHTGOKbSLu9FePoOA4Mw+AZVppp9U4n+J7jq0HGsVqtYmVlBYIgME/ekydP8NVXX2F/fx+u62J1dRXr6+tYW1tjQgof06DpC0mSkEgkWPP7IiD+TN8wvj7IgzdNE67rQpZlVnf0ptxmjbdmHE3ThOM4zFPo37QXg+M4GA6H2NnZwfr6Oq5fv45oNMrruLu7i9/97nc8oF+v17G0tIRarYZkMvmDjONVvbxoOovosV4niiHP0Qvv1A39eXKve4W7vH/OI0ajEfr9Pv83qUBeRt/zRbpf3rhxpCog3QDhcJjp6Wl8bZ43xnkwHo/x6NEjHB8fY2Fhgcf/gOf0bnfv3sX/+B//AwsLC1heXsbHH3+Ma9euoVqtvnZIMhwOMR6PMR6PEY/HkUgkWFT9KiCbzUIQBBSLRcTjcRbXOg9IYJ4uJ6/3SAaz2WxCVVV0u10mhgbAY5zxeBy5XI7HQefVWRgMBtja2uJx2Vqtxik4L4v4RUCDJoqiIBKJcF+vbdtn/s5MjKO3fYEYObygQsw8s+z8EJBcgmVZyOVyTPlEbCfEFt5ut7G+vg5Zlpl157zMJWTw6LIimimae6cZWYoArgKIcJX0008jlqAZX2Lt8fIBkIY4ANa0pt+ZTCY4Pj7GcDhEp9OBZVlMx0ehYzKZhK7rSCaTTO83j0QgZB+I3JmIa2jfnpV6I5szmUymuB3OwqvarGZimagviQ4QtYsEAoFz63T4OBuTyQStVguxWAwffPABF2ECgQBUVcXvfvc7PHr0CIFAAJVKBevr6yiVShAEAYqiQBRFxOPxM42k67rQNI1nXmOxGMLhMHs7NPhPlPfj8fhNvv2ZoVwuv5S8lnogu90ums0mvvjiCxweHmJvb4+lhykU73Q6PApL6SPqwqBc2mQyQb/fx3A4RKlUgizLuH79OnK5HIrFIpaXl+dyrDOfzyMWi2FnZweGYfCIoGVZ7FRRPti7h2mKSVVVRKPRqUJaIpFAIpFAJpMB8PysvMxRmIlxHI1GHGJEIhHYts1TLz5+GDqdDhRF4bA2mUwym8zx8TH29vbw6NEjDIdDFItFVKtVVKtV5tik1IWX4APA1KZzHAf9fp8b9ImHz7IsAGDG8Gg0CkVR3uZyXCpeRa1HzD2WZaHT6eDo6IiV8kzThGmaUweWfh54nqelf6Pm8uXlZW5qTiaTTACSSCSuXKsVFV7JsJ0VNUYiESQSCZTLZViWNZX/pfU9zaiRp0hM7afB+3uvIs6eiXE0TZOJQ4n0IBgM+sbxEnB0dIROp4NkMglJkqaMI/U0fvvttzAMA7VaDUtLS1haWmJ2dsq1kNdCBi8ajTKpK/DMCNNGdhwHqVSKGdoLhQJ7QZ1O58rkiM8j7kYKeMfHx9jf38fu7i6ePHkCwzD4siEWH0EQpqImuoiSySQKhQJu3LiBarWKfD7Pa53P5zkMv2ptbZT3o06As4wjtfQlEokLvT4Zz/PybL7q8pmJcSTBp6dPnyIQCCCdTrMCoSzLiMViPN72OqC81/7+PjqdDra3tzEcDmGaJmRZZoVDuo1DoRAnv3/sOD4+xuHhIer1Ont/xBTz4MEDbG5uIpFIsGbPp59+imvXriGTyXCDfb/fh6qqfIHpuo54PI5kMonBYMD5GlEUeT1p7JB6zua5qrqwsIBUKoXl5WV0Oh08ffqUGXvIAFJlNZVKsS42eY/kdVMRhuREgsEg/53++yqBcuJHR0dIJBJYXFzEaDSCbdu8P98lzORpqFrX6XQ4f0XepGEYEAQBpmlOHbCzXGZKmlIfJOV9dF3HkydP0Gw28f333/NwOvHuLS8vI5PJIJfLIRKJXJmKKrEgBwIB3mzEbddut9FutxGJRJiyjNpSiMSDDKqqqsxwous6gGfhDBVbvAeZPk/6XOiiIWKFeauqJpNJJJNJiKKI4XCISqXCMgBUraa1o5+9SCP5VQXtH1VVudd5MBhA13XEYjHuYLksuQOyF97+aeB5QedVHubMTPVkMmGB+UajweQGFDJQwh94lsMSRZFpiCi0c10XR0dHLPbd7/fR7XYxHA65dYKaQ8lNr1arKJVKWFhYQDweR6FQQDabvTKUXETwSXkYYjIieQTaeMPhEIeHh/jTn/6EJ0+eYGlpiXMsJI1JRlaSJAiCgFQqxcU0yiU2Gg2YpsnFBgDcvE+VWsMw3uaSvDWQNk+5XAbwnMTVW0k9j0TIvIBSQBSxbGxs4IsvvsDm5ib+5m/+BqVSCaVSCalU6gdfJrSH2+02+v0+t5x5i4off/zxm2/liUajHEqQMDc9BPXJKYrC0wTD4RCxWAyKorBgN6HT6cAwDPT7fa7+kaGjQ53L5VgSgPSx6/U6isUiM4lflQ1KIRndsuT5UbKbLiBi49na2mIhLUqEk7GjGWLDMNhYqqoK0zTR6/W4Wk2Ke97EOBlGr7TAvIH21Dy227wOvCxF9KXrOjqdDr744gvIsozFxUWkUinWSgKeXzBkG7w2gmRfKedLrVG0/zudDjsB1HlB7ViNRuOlnRYzMY6UX1leXkYkEpnqdidtmMlkwi0hpGO7ubnJlp3ca2qBSKfTKBaLWFxc5D404tqr1+vI5XJ841A+hwwJLfBVgCiKrDNNYTJ501T0og3T6XSwubmJQCDAs9TelhKqRsuyzAaX0hOtVgvAM2NMn1etVoMgCEin0yxlWiqVrkyfo4/ZwjAM7g11XRfxeJzbwf7rf/2vcF0X169f57YbunwoF0tGM5lM8gisoihoNBrY29vDwcEB8zTQ/id7U6/XkclkWO+dbAillE7DTIwjdbIvLi5yEYY8RsoBUBOspmlcxRoOh9xOQlMC1AJEHqC3Qktyr97ENnlHr0tn/66jXC5PSd56VRmLxSK34VAvHcmt0hc1btP60y1MVVbyFqkiTeNa1DPp7RVzXReZTObK9K4OBgPmcKR9SLnueDx+adEHaWJT8YsmZLw0fTQdclUiHgA8rDAYDLgGUKvV8P7770PTNI5cVFXl3yEvEHjeLRCNRrlIKEkSJpMJLMtCPB5HLBaDIAi83ynFQc4TpdsEQWBmqrMwE+NIQujlchmyLKNQKLDcKr1JCu+owOAd7Umn00in00gkEjzSlU6nuRePwrvTQF7MVdpUXlAVntpqBEHgA5zNZjEej3F0dMTvn3oUI5EIIpEI/zyptlGxhV6DPHbafPF4nEOZYrHInwldRNSgexVA+VZvexR5zXTYTuKiM9Lk7VPq4mTISL/jZaK5KqBLRxAE3n+lUgnAs9x2q9VCq9XiEJn2pqIoHFHSepIdWF9fn6KWo32aTCaRy+X4kkulUhAEgesalPp72d6dWUEmGAwin8/zqBXlGLxtIJSDsCyLK0q0KWh0ystSTRXtl22aq2oUCVQl1XWdN5ooisjn89yKs7q6yjlBuq29cpatVgv9fp8nmCqVCueIad3T6TREUUQ2m2WD6W3eJe+GvPSrgHQ6zdV8Gg8kfkea+BJFkfcsjQgqisK5cPp8KJrJZDK8z717mij+6TM5ycZOnQBXDeFwGPV6nUPfQqEA27axtrYGTdMwGAx4+oo6MciJarfbU7aAWO8lSUI2m2UHIJPJsI4SOVTeHkhvd8zLKtYzbSyivjoyiKfNO3qH9E+GFSdbevzKH/iDpsqzd7KFqvJeb5DGBDVN44vKa/Qod0skouQlJpNJCILAYfvLcrZXJZ9La+v1qmnPedtAvO0hXjYpL8mtl9eR/u7N9dLFFAwGz2wzu6q5XBpHPXmeyQjSunnFzQzDQK/X45+ltANFmERrRnuXLqpXEWe/8fHBk//zl1nnV32fcNVCjB+C02bUg8Eg0uk0UqnUKyn9vT1fLwsDr4rRuwgo/+dtMwOmqa5OhtKn0WB5SRJO9ouSMZzXKj8AHjIgkFLmWWvyKpq3k3v1Mvbuu9WS7uMH46y5Ux8Xw8sO2w9d33k2imfhXZy4mu8Y1YcPHz7OgG8cffjw4eMU+MbRhw8fPk5B4CL5j0Ag0AKwM7vHmSmWXNctvO2HOAv+2s4OP/K1Bfz1nSXOXNsLGUcfPnz4mBf4YbUPHz58nALfOPrw4cPHKfCNow8fPnycAt84+vDhw8cp8I2jDx8+fJwC3zj68OHDxynwjaMPHz58nALfOPrw4cPHKfCNow8fPnycgv8HV8JNxuaJJqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_output(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}