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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def imagenet_preprocess_example(example, mode, resize_size=None, normalize=True):
 """Preprocessing used for Imagenet and similar problems."""

  resize_size = resize_size or [299, 299]
  assert resize_size[0] == resize_size[1]

  image = example["inputs"]
  if mode == tf.estimator.ModeKeys.TRAIN:
    image = preprocess_for_train(image, image_size=resize_size[0],
                                 normalize=normalize)
  else:
    image = preprocess_for_eval(image, image_size=resize_size[0],
                                normalize=normalize)

  example["inputs"] = image
  return example