code: def _image_augmentation(image_features):
      """Perform image augmentation on batches of images .
  
      Args:
        image_features: a batch of image features
  
      Returns:
        The augmented image features
      """
      batch_size = tf.shape(image_features)[0]
      image_features = tf.image.random_flip_left_right(image_features)
      image_features = tf.image.resize_with_crop_or_pad(
          image_features, 250, 250
      )
      image_features = tf.image.random_crop(
          image_features, (batch_size, 224, 224, 3)
      )
      return image_features
  
imports: import os
  from typing import List
  import absl
  import flatbuffers
  import tensorflow
  import tensorflow_transform
  from tfx.components.trainer.fn_args_utils import DataAccessor
  from tfx.components.trainer.fn_args_utils import FnArgs
  from tfx.components.trainer.rewriting import converters
  from tfx.components.trainer.rewriting import rewriter
  from tfx.components.trainer.rewriting import rewriter_factory
  from tfx.dsl.io import fileio
  from tfx_bsl.tfxio import dataset_options
  from tflite_support import metadata_schema_py_generated
  from tflite_support import metadata
label_desc: Use image node_id 3 for calling a global function with example usage: _image_augmentation(image_features) and returns: image_features