code: def preprocess_for_train(
      image_bytes,
      use_bfloat16,
      image_size=IMAGE_SIZE,
      augment_name=None,
      randaug_num_layers=None,
      randaug_magnitude=None,
  ):
      """Preprocesses the given image for evaluation.
  
      Args:
        image_bytes: `Tensor` representing an image binary of arbitrary size.
        use_bfloat16: `bool` for whether to use bfloat16.
        image_size: image size.
        augment_name: `string` that is the name of the augmentation method
          to apply to the image. `autoaugment` if AutoAugment is to be used or
          `randaugment` if RandAugment is to be used. If the value is `None` no
          augmentation method will be applied applied. See autoaugment.py for more
          details.
        randaug_num_layers: 'int', if RandAug is used, what should the number of
          layers be. See autoaugment.py for detailed description.
        randaug_magnitude: 'int', if RandAug is used, what should the magnitude
          be. See autoaugment.py for detailed description.
  
      Returns:
        A preprocessed image `Tensor`.
      """
      image = _decode_and_random_crop(image_bytes, image_size)
      image = _flip(image)
      image = tf.reshape(image, [image_size, image_size, 3])
  
      image = tf.image.convert_image_dtype(
          image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32
      )
  
      if augment_name:
          try:
              import autoaugment  # pylint: disable=g-import-not-at-top
          except ImportError as e:
              logging.exception(
                  "Autoaugment is not supported in TF 2.x."
              )
              raise e
  
          logging.info("Apply AutoAugment policy %s", augment_name)
          input_image_type = image.dtype
          image = tf.clip_by_value(image, 0.0, 255.0)
          image = tf.cast(image, dtype=tf.uint8)
  
          if augment_name == "autoaugment":
              logging.info("Apply AutoAugment policy %s", augment_name)
              image = autoaugment.distort_image_with_autoaugment(
                  image, "v0"
              )
          elif augment_name == "randaugment":
              image = autoaugment.distort_image_with_randaugment(
                  image, randaug_num_layers, randaug_magnitude
              )
          else:
              raise ValueError(
                  "Invalid value for augment_name: %s" % (augment_name)
              )
  
          image = tf.cast(image, dtype=input_image_type)
      return image
  
file_path: EfficientNet-PyTorch/tf_to_pytorch/convert_tf_to_pt/original_tf/preprocessing.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import logging
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling a global function with example usage: preprocess_for_train(image_bytes, use_bfloat16, image_size, augment_name, randaug_num_layers, randaug_magnitude) and returns: image