code: def preprocess_image(
      image_bytes,
      is_training=False,
      use_bfloat16=False,
      image_size=IMAGE_SIZE,
      interpolation="bicubic",
  ):
      """Preprocesses the given image.
  
      Args:
        image_bytes: `Tensor` representing an image binary of arbitrary size.
        is_training: `bool` for whether the preprocessing is for training.
        use_bfloat16: `bool` for whether to use bfloat16.
        image_size: image size.
        interpolation: image interpolation method
  
      Returns:
        A preprocessed image `Tensor` with value range of [0, 255].
      """
      if is_training:
          return preprocess_for_train(
              image_bytes, use_bfloat16, image_size, interpolation
          )
      else:
          return preprocess_for_eval(
              image_bytes, use_bfloat16, image_size, interpolation
          )
  
file_path: gen-efficientnet-pytorch/data/tf_preprocessing.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: preprocess_image(image_bytes, is_training, use_bfloat16, image_size, interpolation) and returns: preprocess_for_train, preprocess_for_eval