def select_seeds(
    dist1: Tensor, R1: Union[float, Tensor], scores1: Tensor, fnn12: Tensor, mnn: Optional[Tensor]
) -> Tuple[Tensor, Tensor]:
    """Select seed correspondences among the set of available matches.

    dist1: Precomputed distance matrix between keypoints in image I_1
    R1: Base radius of neighborhoods in image I_1
    scores1: Confidence scores on the putative_matches. Usually holds Lowe's ratio scores.
    fnn12: Matches between keypoints of I_1 and I_2.
           The i-th entry of fnn12 is j if and only if keypoint k_i in image I_1 is matched to keypoint k_j in image I_2
    mnn: A mask indicating which putative matches are also mutual nearest neighbors. See documentation on 'force_seed_mnn' in the DEFAULT_CONFIG.
         If None, it disables the mutual nearest neighbor filtering on seed point selection.
         Expected a bool tensor with shape (num_keypoints_in_source_image,)

    Returns:
        Indices of seed points.

        im1seeds: Keypoint index of chosen seeds in image I_1
        im2seeds: Keypoint index of chosen seeds in image I_2

    """  # noqa: E501
    im1neighmap = dist1 < R1**2  # (n1, n1)
    # find out who scores higher than whom
    im1scorescomp = scores1.unsqueeze(1) > scores1.unsqueeze(0)  # (n1, n1)
    # find out who scores higher than all of its neighbors: seed points
    if mnn is not None:
        im1bs = (~torch.any(im1neighmap & im1scorescomp & mnn.unsqueeze(0), dim=1)) & mnn & (scores1 < 0.8**2)  # (n1,)
    else:
        im1bs = (~torch.any(im1neighmap & im1scorescomp, dim=1)) & (scores1 < 0.8**2)

    # collect all seeds in both images and the 1NN of the seeds of the other image
    im1seeds = where(im1bs)[0]  # (n1bs) index format
    im2seeds = fnn12[im1bs]  # (n1bs) index format
    return im1seeds, im2seeds
Select seed correspondences among the set of available matches.

    dist1: Precomputed distance matrix between keypoints in image I_1
    R1: Base radius of neighborhoods in image I_1
    scores1: Confidence scores on the putative_matches. Usually holds Lowe's ratio scores.
    fnn12: Matches between keypoints of I_1 and I_2.
           The i-th entry of fnn12 is j if and only if keypoint k_i in image I_1 is matched to keypoint k_j in image I_2
    mnn: A mask indicating which putative matches are also mutual nearest neighbors. See documentation on 'force_seed_mnn' in the DEFAULT_CONFIG.
         If None, it disables the mutual nearest neighbor filtering on seed point selection.
         Expected a bool tensor with shape (num_keypoints_in_source_image,)

    Returns:
        Indices of seed points.

        im1seeds: Keypoint index of chosen seeds in image I_1
        im2seeds: Keypoint index of chosen seeds in image I_2

    
kornia/feature/adalam/core.py