docs: def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
          """
          Produce additional matches for predictions that have only low-quality matches.
          Specifically, for each ground-truth find the set of predictions that have
          maximum overlap with it (including ties); for each prediction in that set, if
          it is unmatched, then match it to the ground-truth with which it has the highest
          quality value.
          """
          # For each gt, find the prediction with which it has highest quality
          highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
          # Find highest quality match available, even if it is low, including ties
          gt_pred_pairs_of_highest_quality = torch.nonzero(
              match_quality_matrix == highest_quality_foreach_gt[:, None]
          )
          # Example gt_pred_pairs_of_highest_quality:
          #   tensor([[    0, 39796],
          #           [    1, 32055],
          #           [    1, 32070],
          #           [    2, 39190],
          #           [    2, 40255],
          #           [    3, 40390],
          #           [    3, 41455],
          #           [    4, 45470],
          #           [    5, 45325],
          #           [    5, 46390]])
          # Each row is a (gt index, prediction index)
          # Note how gt items 1, 2, 3, and 5 each have two ties
  
          pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1]
          matches[pred_inds_to_update] = all_matches[pred_inds_to_update]