code: def augment_random_crop_flip(
      image,
      height=None,
      width=None,
      num_channels=None,
      crop_padding=4,
      flip=True,
  ):
      """Augment small image with random crop and h-flip.
  
      Args:
        image: Input image to augment.
        height: int; Height of the target image.
        width: int; Width of the target image.
        num_channels: int; Number of channels of the target image.
        crop_padding: int; Random crop range.
        flip: bool; If True perform random horizontal flip.
  
      Returns:
        Augmented image.
      """
      h, w, c = image.get_shape().as_list()
      height = height or h
      width = width or w
      num_channels = num_channels or c
  
      assert crop_padding >= 0
      if crop_padding > 0:
          # Pad with reflection padding
          # (See https://arxiv.org/abs/1605.07146)
          # Section 3.
          image = tf.pad(
              image,
              [
                  [crop_padding, crop_padding],
                  [crop_padding, crop_padding],
                  [0, 0],
              ],
              "REFLECT",
          )
  
          # Randomly crop a [HEIGHT, WIDTH] section of the image.
          image = tf.image.random_crop(
              image, [height, width, num_channels]
          )
  
      if flip:
          # Randomly flip the image horizontally.
          image = tf.image.random_flip_left_right(image)
  
      return image
  
imports: import collections
  import dataclasses
  import functools
  import itertools
  from typing import Any, Callable, Dict, Iterator, Optional, Sequence, Union
  from absl import logging
  from flax.training import common_utils
  import jax
  import jax.numpy
  import numpy
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 7 for calling a global function with example usage: augment_random_crop_flip(image, height, width, num_channels, crop_padding, flip) and returns: image