def __call__(self, images: Union[Tensor, list[Tensor]]) -> Union[Tensor, list[Tensor]]:  # type: ignore[override]
        """Detect objects in a given list of images.

        Args:
            images: If list of RGB images. Each image is a Tensor with shape :math:`(3, H, W)`.
                If Tensor, a Tensor with shape :math:`(B, 3, H, W)`.

        Returns:
            list of detections found in each image. For item in a batch, shape is :math:`(D, 6)`, where :math:`D` is the
            number of detections in the given image, :math:`6` represents class id, score, and `xywh` bounding box.

        """
        if isinstance(
            images,
            (
                list,
                tuple,
            ),
        ):
            results = [super(DepthEstimation, self).__call__(image[None].cpu().numpy())[0] for image in images]
            results = [
                self.resize_back(tensor(result, device=image.device, dtype=image.dtype), image)
                for result, image in zip(results, images)
            ]
            return results

        result = super().__call__(images.cpu().numpy())[0]
        result = tensor(result, device=images.device, dtype=images.dtype)
        return self.resize_back(result, images)
Detect objects in a given list of images.

        Args:
            images: If list of RGB images. Each image is a Tensor with shape :math:`(3, H, W)`.
                If Tensor, a Tensor with shape :math:`(B, 3, H, W)`.

        Returns:
            list of detections found in each image. For item in a batch, shape is :math:`(D, 6)`, where :math:`D` is the
            number of detections in the given image, :math:`6` represents class id, score, and `xywh` bounding box.

        
kornia/models/depth_estimation/base.py