def make_feature(self, keypoints, keypoint_scores, skeleton):
        """Combine original scores, joint distance and relative distance to
        make feature.

        Args:
            keypoints (torch.Tensor): predicetd keypoints
            keypoint_scores (torch.Tensor): predicetd keypoint scores
            skeleton (list(list(int))): joint links

        Returns:
            torch.Tensor: feature for each instance
        """
        joint_1, joint_2 = zip(*skeleton)
        num_link = len(skeleton)

        joint_relate = (keypoints[:, joint_1] -
                        keypoints[:, joint_2])[:, :, :2]
        joint_length = joint_relate.norm(dim=2)

        # To use the torso distance to normalize
        normalize = (joint_length[:, self.norm_indexes[0]] +
                     joint_length[:, self.norm_indexes[1]]) / 2
        normalize = normalize.unsqueeze(1).expand(normalize.size(0), num_link)
        normalize = normalize.clamp(min=1).contiguous()

        joint_length = joint_length / normalize[:, :]
        joint_relate = joint_relate / normalize.unsqueeze(-1)
        joint_relate = joint_relate.flatten(1)

        feature = torch.cat((joint_relate, joint_length, keypoint_scores),
                            dim=1).float()
        return feature
Combine original scores, joint distance and relative distance to
        make feature.

        Args:
            keypoints (torch.Tensor): predicetd keypoints
            keypoint_scores (torch.Tensor): predicetd keypoint scores
            skeleton (list(list(int))): joint links

        Returns:
            torch.Tensor: feature for each instance
        
mmpose/mmpose/models/heads/hybrid_heads/dekr_head.py