def describe(
        self, images: Tensor, keypoints: Optional[Tensor] = None, apply_imagenet_normalization: bool = True
    ) -> Tensor:
        """Describe keypoints in the input images. If keypoints are not provided, returns the dense descriptors.

        Args:
            images: A tensor of shape :math:`(B, 3, H, W)` containing the input images.
            keypoints: An optional tensor of shape :math:`(B, N, 2)` containing the detected keypoints.
            apply_imagenet_normalization: Whether to apply ImageNet normalization to the input images.

        Returns:
            descriptions: A tensor of shape :math:`(B, N, DIM)` containing the descriptions of the detected keypoints.
            If the dense descriptors are requested, the shape is :math:`(B, DIM, H, W)`.

        """
        KORNIA_CHECK_SHAPE(images, ["B", "3", "H", "W"])
        B, C, H, W = images.shape
        if keypoints is not None:
            KORNIA_CHECK_SHAPE(keypoints, ["B", "N", "2"])
        if apply_imagenet_normalization:
            images = self.normalizer(images)
        self.train(False)
        descriptions = self.descriptor.forward(images)
        if keypoints is not None:
            described_keypoints = F.grid_sample(
                descriptions.float(), keypoints[:, None], mode="bilinear", align_corners=False
            )[:, :, 0].mT
            return described_keypoints
        return descriptions
Describe keypoints in the input images. If keypoints are not provided, returns the dense descriptors.

        Args:
            images: A tensor of shape :math:`(B, 3, H, W)` containing the input images.
            keypoints: An optional tensor of shape :math:`(B, N, 2)` containing the detected keypoints.
            apply_imagenet_normalization: Whether to apply ImageNet normalization to the input images.

        Returns:
            descriptions: A tensor of shape :math:`(B, N, DIM)` containing the descriptions of the detected keypoints.
            If the dense descriptors are requested, the shape is :math:`(B, DIM, H, W)`.

        
kornia/feature/dedode/dedode.py