code: def tschuprows_t_matrix(
      matrix: Tensor,
      bias_correction: bool = True,
      nan_strategy: Literal["replace", "drop"] = "replace",
      nan_replace_value: Optional[float] = 0.0,
  ) -> Tensor:
      r"""Compute `Tschuprow's T`_ statistic between a set of multiple variables.
  
      This can serve as a convenient tool to compute Tschuprow's T statistic for analyses of correlation between
      categorical variables in your dataset.
  
      Args:
          matrix: A tensor of categorical (nominal) data, where:
  
              - rows represent a number of data points
              - columns represent a number of categorical (nominal) features
  
          bias_correction: Indication of whether to use bias correction.
          nan_strategy: Indication of whether to replace or drop ``NaN`` values
          nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'``
  
      Returns:
          Tschuprow's T statistic for a dataset of categorical variables
  
      Example:
          >>> from torchmetrics.functional.nominal import tschuprows_t_matrix
          >>> _ = torch.manual_seed(42)
          >>> matrix = torch.randint(0, 4, (200, 5))
          >>> tschuprows_t_matrix(matrix)
          tensor([[1.0000, 0.0637, 0.0000, 0.0542, 0.1337],
                  [0.0637, 1.0000, 0.0000, 0.0000, 0.0000],
                  [0.0000, 0.0000, 1.0000, 0.0000, 0.0649],
                  [0.0542, 0.0000, 0.0000, 1.0000, 0.1100],
                  [0.1337, 0.0000, 0.0649, 0.1100, 1.0000]])
  
      """
      _nominal_input_validation(nan_strategy, nan_replace_value)
      num_variables = matrix.shape[1]
      tschuprows_t_matrix_value = torch.ones(
          num_variables, num_variables, device=matrix.device
      )
      for i, j in itertools.combinations(range(num_variables), 2):
          x, y = matrix[:, i], matrix[:, j]
          num_classes = len(torch.cat([x, y]).unique())
          confmat = _tschuprows_t_update(
              x, y, num_classes, nan_strategy, nan_replace_value
          )
          tschuprows_t_matrix_value[i, j] = tschuprows_t_matrix_value[
              j, i
          ] = _tschuprows_t_compute(confmat, bias_correction)
      return tschuprows_t_matrix_value
  
file_path: torchmetrics/src/torchmetrics/functional/nominal/tschuprows.py
imports: import itertools
  from typing import Optional
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.confusion_matrix import _multiclass_confusion_matrix_update
  from torchmetrics.functional.nominal.utils import _compute_bias_corrected_values, _compute_chi_squared, _drop_empty_rows_and_cols, _handle_nan_in_data, _nominal_input_validation, _unable_to_use_bias_correction_warning
label_desc: Use image node_id 4 for calling a global function with example usage: tschuprows_t_matrix(matrix, bias_correction, nan_strategy, nan_replace_value) and returns: tschuprows_t_matrix_value