def METHOD_NAME(image_bytes,
                                bbox,
                                min_object_covered=0.1,
                                aspect_ratio_range=(0.75, 1.33),
                                area_range=(0.05, 1.0),
                                max_attempts=100):
  """Generates cropped_image using one of the bboxes randomly distorted.
  See `tf.image.sample_distorted_bounding_box` for more documentation.
  Args:
    image_bytes: `Tensor` of binary image data.
    bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]`
        where each coordinate is [0, 1) and the coordinates are arranged
        as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole
        image.
    min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
        area of the image must contain at least this fraction of any bounding
        box supplied.
    aspect_ratio_range: An optional list of `float`s. The cropped area of the
        image must have an aspect ratio = width / height within this range.
    area_range: An optional list of `float`s. The cropped area of the image
        must contain a fraction of the supplied image within in this range.
    max_attempts: An optional `int`. Number of attempts at generating a cropped
        region of the image of the specified constraints. After `max_attempts`
        failures, return the entire image.
  Returns:
    cropped image `Tensor`
  """
  with tf.name_scope('distorted_bounding_box_crop'):
    shape = tf.image.extract_jpeg_shape(image_bytes)
    sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
        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)
    bbox_begin, bbox_size, _ = sample_distorted_bounding_box
    # 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