from_pretrained("facebook/detr-resnet-50-panoptic")
        >>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")

        >>> # prepare image for the model
        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> # forward pass
        >>> outputs = model(**inputs)

        >>> # Use the `post_process_panoptic_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps
        >>> # Segmentation results are returned as a list of dictionaries
        >>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)])

        >>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found
        >>> panoptic_seg = result[0]["segmentation"]
        >>> # Get prediction score and segment_id to class_id mapping of each segment
        >>> panoptic_segments_info = result[0]["segments_info"]
        ```NrŽ