code: def post_process_panoptic_sample(
      out_logits: np.ndarray,
      masks: np.ndarray,
      boxes: np.ndarray,
      processed_size: Tuple[int, int],
      target_size: Tuple[int, int],
      is_thing_map: Dict,
      threshold=0.85,
  ) -> Dict:
      """
      Converts the output of [`ConditionalDetrForSegmentation`] into panoptic segmentation predictions for a single sample.
  
      Args:
          out_logits (`torch.Tensor`):
              The logits for this sample.
          masks (`torch.Tensor`):
              The predicted segmentation masks for this sample.
          boxes (`torch.Tensor`):
              The prediced bounding boxes for this sample. The boxes are in the normalized format `(center_x, center_y,
              width, height)` and values between `[0, 1]`, relative to the size the image (disregarding padding).
          processed_size (`Tuple[int, int]`):
              The processed size of the image `(height, width)`, as returned by the preprocessing step i.e. the size
              after data augmentation but before batching.
          target_size (`Tuple[int, int]`):
              The target size of the image, `(height, width)` corresponding to the requested final size of the
              prediction.
          is_thing_map (`Dict`):
              A dictionary mapping class indices to a boolean value indicating whether the class is a thing or not.
          threshold (`float`, *optional*, defaults to 0.85):
              The threshold used to binarize the segmentation masks.
      """
      # we filter empty queries and detection below threshold
      scores, labels = score_labels_from_class_probabilities(out_logits)
      keep = (labels != out_logits.shape[-1] - 1) & (scores > threshold)
  
      cur_scores = scores[keep]
      cur_classes = labels[keep]
      cur_boxes = center_to_corners_format(boxes[keep])
  
      if len(cur_boxes) != len(cur_classes):
          raise ValueError("Not as many boxes as there are classes")
  
      cur_masks = masks[keep]
      cur_masks = resize(
          cur_masks[:, None],
          processed_size,
          resample=PILImageResampling.BILINEAR,
      )
      cur_masks = safe_squeeze(cur_masks, 1)
      b, h, w = cur_masks.shape
  
      # It may be that we have several predicted masks for the same stuff class.
      # In the following, we track the list of masks ids for each stuff class (they are merged later on)
      cur_masks = cur_masks.reshape(b, -1)
      stuff_equiv_classes = defaultdict(list)
      for k, label in enumerate(cur_classes):
          if not is_thing_map[label]:
              stuff_equiv_classes[label].append(k)
  
      seg_img = get_segmentation_image(
          cur_masks,
          processed_size,
          target_size,
          stuff_equiv_classes,
          deduplicate=True,
      )
      area = get_mask_area(
          cur_masks, processed_size, n_classes=len(cur_scores)
      )
  
      # We filter out any mask that is too small
      if cur_classes.size() > 0:
          # We know filter empty masks as long as we find some
          filtered_small = np.array([a <= 4 for a in area], dtype=bool)
          while filtered_small.any():
              cur_masks = cur_masks[~filtered_small]
              cur_scores = cur_scores[~filtered_small]
              cur_classes = cur_classes[~filtered_small]
              seg_img = get_segmentation_image(
                  cur_masks,
                  (h, w),
                  target_size,
                  stuff_equiv_classes,
                  deduplicate=True,
              )
              area = get_mask_area(
                  seg_img, target_size, n_classes=len(cur_scores)
              )
              filtered_small = np.array(
                  [a <= 4 for a in area], dtype=bool
              )
      else:
          cur_classes = np.ones((1, 1), dtype=np.int64)
  
      segments_info = [
          {
              "id": i,
              "isthing": is_thing_map[cat],
              "category_id": int(cat),
              "area": a,
          }
          for i, (cat, a) in enumerate(zip(cur_classes, area))
      ]
      del cur_classes
  
      with io.BytesIO() as out:
          PIL.Image.fromarray(seg_img).save(out, format="PNG")
          predictions = {
              "png_string": out.getvalue(),
              "segments_info": segments_info,
          }
  
      return predictions
  
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 16 for calling a global function with example usage: post_process_panoptic_sample(out_logits, masks, boxes, processed_size, target_size, is_thing_map, threshold) and returns: predictions