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    `Panoptic Quality`_ where the metric for a stuff class is computed as

    .. math::
        PQ^{\dagger}_c = \frac{IOU_c}{|S_c|}

    where :math:`IOU_c` is the sum of the intersection over union of all matching segments for a given class, and
    :math:`|S_c|` is the overall number of segments in the ground truth for that class.

    .. 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([[[0, 0], [0, 1], [6, 0], [7, 0], [0, 2], [1, 0]]])
        >>> target = tensor([[[0, 1], [0, 0], [6, 0], [7, 0], [6, 0], [255, 0]]])
        >>> modified_panoptic_quality(preds, target, things = {0, 1}, stuffs = {6, 7})
        tensor(0.7667, dtype=torch.float64)

    T)