code: def _distorted_bounding_box_crop(
      image_bytes: tf.Tensor,
      *,
      jpeg_shape: tf.Tensor,
      bbox: tf.Tensor,
      min_object_covered: float,
      aspect_ratio_range: tuple[float, float],
      area_range: tuple[float, float],
      max_attempts: int,
  ) -> tf.Tensor:
      """Generates cropped_image using one of the bboxes randomly distorted."""
      bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
          jpeg_shape,
          bounding_boxes=bbox,
          min_object_covered=min_object_covered,
          aspect_ratio_range=aspect_ratio_range,
          area_range=area_range,
          max_attempts=max_attempts,
          use_image_if_no_bounding_boxes=True,
      )
  
      # Crop the image to the specified bounding box.
      offset_y, offset_x, _ = tf.unstack(bbox_begin)
      target_height, target_width, _ = tf.unstack(bbox_size)
      crop_window = tf.stack(
          [offset_y, offset_x, target_height, target_width]
      )
      image = tf.image.decode_and_crop_jpeg(
          image_bytes, crop_window, channels=3
      )
      return image
  
imports: from collections.abc import Iterable, Iterator, Mapping, Sequence
  import enum
  import itertools
  import types
  from typing import Optional
  import jax
  import jax.numpy
  import numpy
  from packaging import version
  import tensorflow.compat.v2
  import tensorflow_datasets
label_desc: Use image node_id 10 for calling a global function with example usage: _distorted_bounding_box_crop(image_bytes) and returns: image