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
        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
        utils.check_image_size(dataset_dict, image)

        image, transforms = T.apply_transform_gens(self.augmentation, image)
        image_shape = image.shape[:2]  # h, w
        dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))

        if not self.is_train:
            dataset_dict.pop("annotations", None)
            return dataset_dict

        for anno in dataset_dict["annotations"]:
            if not self.mask_on:
                anno.pop("segmentation", None)
            if not self.keypoint_on:
                anno.pop("keypoints", None)

        # USER: Implement additional transformations if you have other types of data
        # USER: Don't call transpose_densepose if you don't need
        annos = [
            self._transform_densepose(
                utils.transform_instance_annotations(
                    obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
                ),
                transforms,
            )
            for obj in dataset_dict.pop("annotations")
            if obj.get("iscrowd", 0) == 0
        ]

        if self.mask_on:
            self._add_densepose_masks_as_segmentation(annos, image_shape)

        instances = utils.annotations_to_instances(annos, image_shape, mask_format="bitmask")
        densepose_annotations = [obj.get("densepose") for obj in annos]
        if densepose_annotations and not all(v is None for v in densepose_annotations):
            instances.gt_densepose = DensePoseList(
                densepose_annotations, instances.gt_boxes, image_shape
            )

        dataset_dict["instances"] = instances[instances.gt_boxes.nonempty()]
        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/DensePose/densepose/data/dataset_mapper.py