code: def preprocess_for_train(
      image_bytes,
      dtype=tf.float32,
      image_size=IMAGE_SIZE,
      data_augmentations=None,
  ):
      """Preprocesses the given image for training.
  
      Args:
        image_bytes: Tensor; Representing an image binary of arbitrary size.
        dtype: TF data type; Data type of the image.
        image_size: int; The target size of the images.
        data_augmentations: list(str); Types of data augmentation applied on
          training data.
  
      Returns:
        A preprocessed image `Tensor`.
      """
      if data_augmentations is not None:
          if "default" in data_augmentations:
              image = _decode_and_random_crop(image_bytes, image_size)
              image = tf.reshape(image, [image_size, image_size, 3])
              image = tf.image.random_flip_left_right(image)
      else:
          image = _decode_and_center_crop(image_bytes, image_size)
          image = tf.reshape(image, [image_size, image_size, 3])
  
      if dtype not in [tf.int32, tf.int64, tf.uint32, tf.uint64]:
          image = normalize_image(image)
          image = tf.image.convert_image_dtype(image, dtype=dtype)
      else:
          image = tf.cast(image, dtype=dtype)
      return image
  
file_path: scenic/scenic/dataset_lib/imagenet_dataset.py
imports: import functools
  from typing import Optional
  from absl import logging
  from flax import jax_utils
  import jax
  import jax.numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 7 for calling a global function with example usage: preprocess_for_train(image_bytes, dtype, image_size, data_augmentations) and returns: image