code: def _pd_pearsons_t_matrix(matrix):
      num_variables = matrix.shape[1]
      pearsons_t_matrix_value = torch.ones(num_variables, num_variables)
      for i, j in itertools.combinations(range(num_variables), 2):
          x, y = matrix[:, i], matrix[:, j]
          pearsons_t_matrix_value[i, j] = pearsons_t_matrix_value[
              j, i
          ] = _pd_pearsons_t(x, y)
      return pearsons_t_matrix_value
  
file_path: torchmetrics/tests/unittests/nominal/test_pearson.py
imports: import itertools
  import operator
  import pandas
  import pytest
  import torch
  from lightning_utilities.core.imports import compare_version
  from scipy.stats.contingency import association
  from torchmetrics.functional.nominal.pearson import pearsons_contingency_coefficient, pearsons_contingency_coefficient_matrix
  from torchmetrics.nominal.pearson import PearsonsContingencyCoefficient
  from unittests import BATCH_SIZE, NUM_BATCHES, _Input
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 3 for calling a global function with example usage: _pd_pearsons_t_matrix(matrix) and returns: pearsons_t_matrix_value