def __call__(self, dataset_dict):
        """
        Args:
            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.

        Returns:
            dict: a format that builtin models in detectron2 accept
        """
        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
        # Load image.
        image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
        utils.check_image_size(dataset_dict, image)
        # Panoptic label is encoded in RGB image.
        pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")

        # Reuses semantic transform for panoptic labels.
        aug_input = T.AugInput(image, sem_seg=pan_seg_gt)
        _ = self.augmentations(aug_input)
        image, pan_seg_gt = aug_input.image, aug_input.sem_seg

        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
        # Therefore it's important to use torch.Tensor.
        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))

        # Generates training targets for Panoptic-DeepLab.
        targets = self.panoptic_target_generator(rgb2id(pan_seg_gt), dataset_dict["segments_info"])
        dataset_dict.update(targets)

        return dataset_dict

        Args:
            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.

        Returns:
            dict: a format that builtin models in detectron2 accept
        
EVA-02/det/projects/Panoptic-DeepLab/panoptic_deeplab/dataset_mapper.py