def heatmap_and_dense_descriptors(self, images: Tensor) -> tuple[Tensor, Tensor]:
        """Return the heatmap and the dense descriptors.

        .. image:: _static/img/DISK.png

        Args:
            images: The image to detect features in. Shape :math:`(B, 3, H, W)`.

        Returns:
            A tuple of dense detection scores and descriptors.
            Shapes are :math:`(B, 1, H, W)` and :math:`(B, D, H, W)`, where
            :math:`D` is the descriptor dimension.

        """
        unet_output = self.unet(images)

        if unet_output.shape[1] != self.desc_dim + 1:
            raise ValueError(
                f"U-Net output has {unet_output.shape[1]} channels, but expected self.desc_dim={self.desc_dim} + 1."
            )

        descriptors = unet_output[:, : self.desc_dim]
        heatmaps = unet_output[:, self.desc_dim :]

        return heatmaps, descriptors
Return the heatmap and the dense descriptors.

        .. image:: _static/img/DISK.png

        Args:
            images: The image to detect features in. Shape :math:`(B, 3, H, W)`.

        Returns:
            A tuple of dense detection scores and descriptors.
            Shapes are :math:`(B, 1, H, W)` and :math:`(B, D, H, W)`, where
            :math:`D` is the descriptor dimension.

        
kornia/feature/disk/disk.py