code: def prepare_coco_panoptic_annotation(
      image: np.ndarray,
      target: Dict,
      masks_path: Union[str, pathlib.Path],
      return_masks: bool = True,
      input_data_format: Union[ChannelDimension, str] = None,
  ) -> Dict:
      """
      Prepare a coco panoptic annotation for ConditionalDetr.
      """
      image_height, image_width = get_image_size(
          image, channel_dim=input_data_format
      )
      annotation_path = pathlib.Path(masks_path) / target["file_name"]
  
      new_target = {}
      new_target["image_id"] = np.asarray(
          [
              target["image_id"]
              if "image_id" in target
              else target["id"]
          ],
          dtype=np.int64,
      )
      new_target["size"] = np.asarray(
          [image_height, image_width], dtype=np.int64
      )
      new_target["orig_size"] = np.asarray(
          [image_height, image_width], dtype=np.int64
      )
  
      if "segments_info" in target:
          masks = np.asarray(
              PIL.Image.open(annotation_path), dtype=np.uint32
          )
          masks = rgb_to_id(masks)
  
          ids = np.array(
              [
                  segment_info["id"]
                  for segment_info in target["segments_info"]
              ]
          )
          masks = masks == ids[:, None, None]
          masks = masks.astype(np.uint8)
          if return_masks:
              new_target["masks"] = masks
          new_target["boxes"] = masks_to_boxes(masks)
          new_target["class_labels"] = np.array(
              [
                  segment_info["category_id"]
                  for segment_info in target["segments_info"]
              ],
              dtype=np.int64,
          )
          new_target["iscrowd"] = np.asarray(
              [
                  segment_info["iscrowd"]
                  for segment_info in target["segments_info"]
              ],
              dtype=np.int64,
          )
          new_target["area"] = np.asarray(
              [
                  segment_info["area"]
                  for segment_info in target["segments_info"]
              ],
              dtype=np.float32,
          )
  
      return new_target
  
imports: import io
  import pathlib
  from collections import defaultdict
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
  import numpy
  from ...feature_extraction_utils import BatchFeature
  from ...image_processing_utils import BaseImageProcessor, get_size_dict
  from ...image_transforms import PaddingMode, center_to_corners_format, corners_to_center_format, id_to_rgb, pad, rescale, resize, rgb_to_id, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, AnnotationFormat, AnnotationType, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_annotations
  from ...utils import TensorType, is_flax_available, is_jax_tensor, is_scipy_available, is_tf_available, is_tf_tensor, is_torch_available, is_torch_tensor, is_vision_available, logging
label_desc: Use image node_id 12 for calling a global function with example usage: prepare_coco_panoptic_annotation(image, target, masks_path, return_masks, input_data_format) and returns: new_target