code: def _kendall_corrcoef_update(
      preds: Tensor,
      target: Tensor,
      concat_preds: Optional[List[Tensor]] = None,
      concat_target: Optional[List[Tensor]] = None,
      num_outputs: int = 1,
  ) -> Tuple[List[Tensor], List[Tensor]]:
      """Update variables required to compute Kendall rank correlation coefficient.
  
      Args:
          preds: Sequence of data
          target: Sequence of data
          concat_preds: List of batches of preds sequence to be concatenated
          concat_target: List of batches of target sequence to be concatenated
          num_outputs: Number of outputs in multioutput setting
  
      Raises:
          RuntimeError: If ``preds`` and ``target`` do not have the same shape
  
      """
      concat_preds = concat_preds or []
      concat_target = concat_target or []
      # Data checking
      _check_same_shape(preds, target)
      _check_data_shape_to_num_outputs(preds, target, num_outputs)
  
      if num_outputs == 1:
          preds = preds.unsqueeze(1)
          target = target.unsqueeze(1)
  
      concat_preds.append(preds)
      concat_target.append(target)
  
      return concat_preds, concat_target
  
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 12 for calling a global function with example usage: _kendall_corrcoef_update(preds, target, concat_preds, concat_target, num_outputs) and returns: concat_preds,  concat_target