code: def _pairwise_chisq(self):
          
          return [
              self._chi_squared(
                  mr_subvar_proportions,
                  self._margin[idx],
                  self._opposite_axis_margin[idx]
                  / np.sum(self._opposite_axis_margin[idx]),
              )
              for (idx, mr_subvar_proportions) in enumerate(self._proportions)
          ]
docs: Pairwise comparisons (Chi-Square) along axis, as numpy.ndarray.
  
          Returns a list of square and symmetric matrices of test statistics for the null
          hypothesis that each vector along *axis* is equal to each other.