 [512, 683]
        ```

        Panoptic segmentation example:

        ```python
        >>> from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation
        >>> from PIL import Image
        >>> import requests

        >>> # load MaskFormer fine-tuned on COCO panoptic segmentation
        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-coco")
        >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-coco")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
        >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
        >>> class_queries_logits = outputs.class_queries_logits
        >>> masks_queries_logits = outputs.masks_queries_logits

        >>> # you can pass them to image_processor for postprocessing
        >>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]

        >>> # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
        >>> predicted_panoptic_map = result["segmentation"]
        >>> list(predicted_panoptic_map.shape)
        [480, 640]
        ```
        N)