code: def get_decode_jpeg_and_center_crop(crop_size=None):
      """Decode jpeg string and make a center image crop.
  
      Args:
        crop_size: Crop image to [crop_size, crop_size].
  
      Returns:
        A function that applies center crop.
      """
  
      crop_size = utils.maybe_repeat(crop_size, 2)
  
      def _decode_and_center_crop(
          image_data,
      ):  # pylint: disable=missing-docstring
          shape = tf.image.extract_jpeg_shape(image_data)
          target_height, target_width = crop_size
  
          offset_y = (shape[0] - target_height) // 2
          offset_x = (shape[1] - target_width) // 2
  
          # Crop the image to the specified bounding box.
          crop_window = tf.stack(
              [offset_y, offset_x, target_height, target_width]
          )
          image = tf.image.decode_and_crop_jpeg(
              image_data, crop_window, channels=3
          )
          image.set_shape([target_height, target_width, 3])
          return image
  
      return _decode_and_center_crop
  
imports: from typing import Optional, Tuple
  import numpy
  from scenic.dataset_lib.big_transfer.preprocessing import autoaugment
  from scenic.dataset_lib.big_transfer.preprocessing import utils
  from scenic.dataset_lib.big_transfer.registry import Registry
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_addons import image
label_desc: Use image node_id 13 for calling a global function with example usage: get_decode_jpeg_and_center_crop(crop_size) and returns: _decode_and_center_crop, image