code: def panoptic_quality(
      preds: Tensor,
      target: Tensor,
      things: Collection[int],
      stuffs: Collection[int],
      allow_unknown_preds_category: bool = False,
  ) -> Tensor:
      r"""Compute `Panoptic Quality`_ for panoptic segmentations.
  
      .. math::
          PQ = \frac{IOU}{TP + 0.5 FP + 0.5 FN}
  
      where IOU, TP, FP and FN are respectively the sum of the intersection over union for true positives, the number of
      true positives, false positives and false negatives. This metric is inspired by the PQ implementation of
      panopticapi, a standard implementation for the PQ metric for object detection.
  
      .. note:
          Points in the target tensor that do not map to a known category ID are automatically ignored in the metric
          computation.
  
      Args:
          preds:
              torch tensor with panoptic detection of shape [height, width, 2] containing the pair
              (category_id, instance_id) for each pixel of the image. If the category_id refer to a stuff, the
              instance_id is ignored.
          target:
              torch tensor with ground truth of shape [height, width, 2] containing the pair (category_id, instance_id)
              for each pixel of the image. If the category_id refer to a stuff, the instance_id is ignored.
          things:
              Set of ``category_id`` for countable things.
          stuffs:
              Set of ``category_id`` for uncountable stuffs.
          allow_unknown_preds_category:
              Boolean flag to specify if unknown categories in the predictions are to be ignored in the metric
              computation or raise an exception when found.
  
      Raises:
          ValueError:
              If ``things``, ``stuffs`` have at least one common ``category_id``.
          TypeError:
              If ``things``, ``stuffs`` contain non-integer ``category_id``.
          TypeError:
              If ``preds`` or ``target`` is not an ``torch.Tensor``.
          ValueError:
               If ``preds`` or ``target`` has different shape.
          ValueError:
              If ``preds`` has less than 3 dimensions.
          ValueError:
              If the final dimension of ``preds`` has size != 2.
  
      Example:
          >>> from torch import tensor
          >>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
          ...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
          ...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
          ...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
          ...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
          >>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
          ...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
          ...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
          ...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
          ...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
          >>> panoptic_quality(preds, target, things = {0, 1}, stuffs = {6, 7})
          tensor(0.5463, dtype=torch.float64)
  
      """
      things, stuffs = _parse_categories(things, stuffs)
      _validate_inputs(preds, target)
      void_color = _get_void_color(things, stuffs)
      cat_id_to_continuous_id = _get_category_id_to_continuous_id(
          things, stuffs
      )
      flatten_preds = _prepocess_inputs(
          things,
          stuffs,
          preds,
          void_color,
          allow_unknown_preds_category,
      )
      flatten_target = _prepocess_inputs(
          things, stuffs, target, void_color, True
      )
      (
          iou_sum,
          true_positives,
          false_positives,
          false_negatives,
      ) = _panoptic_quality_update(
          flatten_preds,
          flatten_target,
          cat_id_to_continuous_id,
          void_color,
      )
      return _panoptic_quality_compute(
          iou_sum, true_positives, false_positives, false_negatives
      )
  
imports: from typing import Collection
  from torch import Tensor
  from torchmetrics.functional.detection._panoptic_quality_common import _get_category_id_to_continuous_id, _get_void_color, _panoptic_quality_compute, _panoptic_quality_update, _parse_categories, _prepocess_inputs, _validate_inputs
label_desc: Use image node_id 1 for calling a global function with example usage: panoptic_quality(preds, target, things, stuffs, allow_unknown_preds_category) and returns: _panoptic_quality_compute