code: def _normalize_correlation_data(self, corr_data, norm_unit):
          
          # normalize if necessary
          if norm_unit > 1:
              num_samples = len(corr_data)
              [_, d2, d3] = corr_data.shape
              second_dimension = d2 * d3
              # this is a shallow copy
              normalized_corr_data = corr_data.reshape(1,
                                                       num_samples,
                                                       second_dimension)
              fcma_extension.normalization(normalized_corr_data, norm_unit)
              normalized_corr_data = normalized_corr_data.reshape(num_samples,
                                                                  d2, d3)
              logger.debug(
                  'normalization done'
              )
          else:
              normalized_corr_data = corr_data
          return normalized_corr_data
docs: Normalize the correlation data if necessary.
  
          Fisher-transform and then z-score the data for every norm_unit samples
          if norm_unit > 1.
  
          Parameters
          ----------
          corr_data: the correlation data
                      in shape [num_samples, num_processed_voxels, num_voxels]
          norm_unit: int
                      the number of samples on which the normalization
                      is performed
  
          Returns
          -------
          normalized_corr_data: the normalized correlation data
                      in shape [num_samples, num_voxels, num_voxels]