//  [python]
def select_best_trans(self, seed_trans, src_keypts, relax_match_points, relax_distance, src_keypts_corr, tgt_keypts_corr):

        """
        Select the best model from the rough models filtered by IC Metric
        Input:
            - seed_trans:  [bs, N_s^{'}, 4, 4]   the model selected by IC, N_s^{'} is the number of reserverd transformation
            - src_keypts   [bs, N, 3]   the source point cloud
            - relax_match_points  [1, N, K, 3]  for each source point, we find K target points as the potential correspondences 
            - relax_distance [bs, N, K]  feature distance for the relaxed matches
            - src_keypts_corr [bs, N_C, 3]  source points of N_C one-to-one correspondences
            - tgt_keypts_corr [bs, N_C, 3]  target points of N_C one-to-one correspondences
        Output:
            - the best transformation selected by FS-TCD
        """

        seed_num = seed_trans.shape[1]
        # self.inlier_threshold == 0.10: # for 3DMatch

        best_trans = None
        best_fitness = 0

        for i in range(seed_num):
            # 1. refine the transformation by the one-to-one correspondences
            initial_trans = seed_trans[:, i, :, :]
            initial_trans = self.post_refinement(initial_trans, src_keypts_corr, tgt_keypts_corr, 1)

            # 2. use the transformation to project the source point cloud to target point cloud, and find the nearest neighbor
            warped_src_keypts = transform(src_keypts, initial_trans)
            cross_dist = torch.norm((warped_src_keypts[:, :, None, :] - relax_match_points), dim=-1)
            warped_neighbors = (cross_dist <= self.inlier_threshold).float()
            renew_distance = relax_distance + 2 * (cross_dist > self.inlier_threshold * 1.5).float()
            _, mask_min_idx = renew_distance.min(dim=-1)

            # 3. find the correspondences whose alignment error is less than the threshold
            corr = torch.cat([torch.arange(mask_min_idx.shape[1])[:, None].cuda(), mask_min_idx[0][:, None]], dim=-1)
            verify_mask = warped_neighbors
            verify_mask_row = verify_mask.sum(-1) > 0

            # 4. use the spatial consistency to verify the correspondences
            if verify_mask_row.float().sum() > 0:
                verify_mask_row_idx = torch.where(verify_mask_row == True)
                corr_select = corr[verify_mask_row_idx[1]]
                select_relax_match_points = relax_match_points[:, verify_mask_row_idx[1]]
                src_keypts_corr = src_keypts[:, corr_select[:, 0]]
                tgt_keypts_corr = select_relax_match_points.gather(dim=2, index=corr_select[:, 1][None, :, None, None].expand(-1, -1, -1, 3)).squeeze(dim=2)
                src_dist = torch.norm((src_keypts_corr[:, :, None, :] - src_keypts_corr[:, None, :, :]), dim=-1)
                target_dist = torch.norm((tgt_keypts_corr[:, :, None, :] - tgt_keypts_corr[:, None, :, :]), dim=-1)
                corr_compatibility = src_dist - target_dist
                abs_corr_compatibility = torch.abs(corr_compatibility)

                SC_thre = self.FS_TCD_thre
                corr_compatibility_2 = (abs_corr_compatibility < SC_thre).float()
                compatibility_num = torch.sum(corr_compatibility_2, -1)
                renew_fitness = torch.max(compatibility_num)
            else:
                renew_fitness = 0

            if renew_fitness > best_fitness:
                best_trans = initial_trans
                best_fitness = renew_fitness

        return best_trans