code: def _panoptic_to_standard_annotations(annotations):
      """Translates panoptic annotations to standard annotations.
  
      Panoptic annotations have one extra level of nesting compared to
      detection annotations (see https://cocodataset.org/#format-data), which
      we remove here. Also see
      pycocotools/panopticapi/converters/panoptic2detection_coco_format.py
      for reference regarding the conversion. Here, we do not convert the
      segmentation masks, since they are not required for the detection
      metric.
  
      Args:
        annotations: Dict with panoptic annotations loaded from JSON.
  
      Returns:
        Updated annotations dict in standard COCO format.
      """
  
      object_annotations = []
      for image_annotation in annotations:
          for object_annotation in image_annotation["segments_info"]:
              object_annotations.append(
                  {
                      "image_id": image_annotation["image_id"],
                      "id": object_annotation["id"],
                      "category_id": object_annotation["category_id"],
                      "iscrowd": object_annotation["iscrowd"],
                      "bbox": object_annotation["bbox"],
                      "area": object_annotation["area"],
                  }
              )
      return object_annotations
  
imports: import collections
  import contextlib
  import functools
  import io
  import json
  import os
  import tempfile
  from typing import Any, Dict, List, Optional, Set
  import zipfile
  from absl import logging
  import jax
  import numpy
  import PIL
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from tensorflow.io import gfile
label_desc: Use image node_id 2 for calling a global function with example usage: _panoptic_to_standard_annotations(annotations) and returns: object_annotations