code: def _calculate_tau(
      preds: Tensor,
      target: Tensor,
      concordant_pairs: Tensor,
      discordant_pairs: Tensor,
      con_min_dis_pairs: Tensor,
      n_total: Tensor,
      preds_ties: Optional[Tensor],
      target_ties: Optional[Tensor],
      variant: _MetricVariant,
  ) -> Tensor:
      """Calculate Kendall's tau from metric metadata."""
      if variant == _MetricVariant.A:
          return con_min_dis_pairs / (
              concordant_pairs + discordant_pairs
          )
      if variant == _MetricVariant.B:
          total_combinations: Tensor = n_total * (n_total - 1) // 2
          denominator = (total_combinations - preds_ties) * (
              total_combinations - target_ties
          )
          return con_min_dis_pairs / torch.sqrt(denominator)
  
      preds_unique = torch.tensor(
          [len(p.unique()) for p in preds.T],
          dtype=preds.dtype,
          device=preds.device,
      )
      target_unique = torch.tensor(
          [len(t.unique()) for t in target.T],
          dtype=target.dtype,
          device=target.device,
      )
      min_classes = torch.minimum(preds_unique, target_unique)
      return (
          2
          * con_min_dis_pairs
          / ((min_classes - 1) / min_classes * n_total**2)
      )
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.data import _bincount, _cumsum, dim_zero_cat
  from torchmetrics.utilities.enums import EnumStr
label_desc: Use image node_id 9 for calling a global function with example usage: _calculate_tau(preds, target, concordant_pairs, discordant_pairs, con_min_dis_pairs, n_total, preds_ties, target_ties, variant) and returns: unknown, unknown, unknown