def forward(self, img: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor, Tensor]:
        """Run forward.

        Args:
            img: image to extract features with shape :math:`(B,C,H,W)`.
            mask: a mask with weights where to apply the response function.
                The shape must be the same as the input image.

        Returns:
            - Detected local affine frames with shape :math:`(B,N,2,3)`.
            - Response function values for corresponding lafs with shape :math:`(B,N,1)`.
            - Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size.

        """
        lafs, responses = self.detector(img, mask)
        lafs = scale_laf(lafs, self.scaling_coef)
        descs = self.descriptor(img, lafs)
        return (lafs, responses, descs)
Run forward.

        Args:
            img: image to extract features with shape :math:`(B,C,H,W)`.
            mask: a mask with weights where to apply the response function.
                The shape must be the same as the input image.

        Returns:
            - Detected local affine frames with shape :math:`(B,N,2,3)`.
            - Response function values for corresponding lafs with shape :math:`(B,N,1)`.
            - Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size.

        
kornia/feature/integrated.py