code: def _compute_rows_and_cols_corrected(
      num_rows: int, num_cols: int, confmat_sum: Tensor
  ) -> Tuple[Tensor, Tensor]:
      """Compute bias-corrected number of rows and columns."""
      rows_corrected = num_rows - (num_rows - 1) ** 2 / (
          confmat_sum - 1
      )
      cols_corrected = num_cols - (num_cols - 1) ** 2 / (
          confmat_sum - 1
      )
      return rows_corrected, cols_corrected
  
file_path: torchmetrics/src/torchmetrics/functional/nominal/utils.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.utilities.prints import rank_zero_warn
label_desc: Use image node_id 6 for calling a global function with example usage: _compute_rows_and_cols_corrected(num_rows, num_cols, confmat_sum) and returns: rows_corrected,  cols_corrected