code: def preprocess_for_train(
      image_bytes, dtype=tf.float32, image_size=IMAGE_SIZE
  ):
      """Preprocesses the given image for training.
  
      Args:
        image_bytes: `Tensor` representing an image binary of arbitrary size.
        dtype: data type of the image.
        image_size: image size.
  
      Returns:
        A preprocessed image `Tensor`.
      """
      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)
      image = normalize_image(image)
      image = tf.image.convert_image_dtype(image, dtype=dtype)
      return image
  
file_path: flax/examples/imagenet/input_pipeline.py
imports: import jax
  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) and returns: image