text: import tensorflow as tf
  from tensorflow import keras
  import numpy as np
  from stacked_capsule_autoencoders.capsules.data import preprocess
  import time
  
  
  def prepare_data_for_resnet50(data_to_transform):
      data = data_to_transform.astype('float32')
      data /= 255
      t = tf.convert_to_tensor(data, tf.float32, name='t')
      t = tf.expand_dims(t, -1)
      data = preprocess.pad_and_shift(t, 40)
      data = tf.squeeze(data, -1)
      session = tf.Session()
      data = session.run(data)
      data = np.stack([data, data, data], axis=-1)
      print(data.shape)
      return data
  
  
  (xtrain, ytrain), (xtest, ytest) = tf.keras.datasets.cifar10.load_data()
  #x_test = prepare_data_for_resnet50(xtest)
  x_test = xtest
  y_test = keras.utils.to_categorical(ytest, 10)
  #x_train = prepare_data_for_resnet50(xtrain)
  x_train = xtrain
  y_train = keras.utils.to_categorical(ytrain, 10)
  print(x_test.shape, x_train.shape)
  
  input_shape = (32, 32, 3)
  classes = 10
  bs = 32
  inputs = keras.Input(shape=input_shape)
  outputs = keras.applications.ResNet50(input_shape=input_shape, weights=None, classes=classes)(inputs)
  model = keras.Model(inputs, outputs)
  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  print('timing start')
  st = time.time()
  score1 = model.evaluate(x_test, y_test, batch_size=bs, verbose=0)
  score2 = model.evaluate(x_train, y_train, batch_size=bs, verbose=0)
  print('timing end')
  ed = time.time()
  print(bs, score1, score2)
  print(ed - st)
  
  