mage in the batch respectively). The class labels themselves
            should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)`, the boxes a
            `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)` and the masks a
            `torch.FloatTensor` of shape `(number of bounding boxes in the image, height, width)`.

        Returns:

        Examples:

        ```python
        >>> import io
        >>> import requests
        >>> from PIL import Image
        >>> import torch
        >>> import numpy

        >>> from transformers import (
        ...     AutoImageProcessor,
        ...     ConditionalDetrConfig,
        ...     ConditionalDetrForSegmentation,
        ... )
        >>> from transformers.image_transforms import rgb_to_id

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

        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")

        >>> # randomly initialize all weights of the model
        >>> config = ConditionalDetrConfig()
        >>> model = ConditionalDetrForSegmentation(config)

        >>> # 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"]
        ```Nrµ