merForUniversalSegmentation.from_pretrained(
        ...     "facebook/mask2former-swin-small-cityscapes-panoptic"
        ... )

        >>> url = "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> inputs = image_processor(image, return_tensors="pt")

        >>> with torch.no_grad():
        ...     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

        >>> # Perform post-processing to get panoptic segmentation map
        >>> pred_panoptic_map = image_processor.post_process_panoptic_segmentation(
        ...     outputs, target_sizes=[image.size[::-1]]
        ... )[0]["segmentation"]
        >>> print(pred_panoptic_map.shape)
        torch.Size([338, 676])
        ```
        NT)