code: def _score_macro_average(self, n_classes):
          
          # Gather all FPRs
          all_fpr = np.unique(np.concatenate([self.fpr[i] for i in range(n_classes)]))
          avg_tpr = np.zeros_like(all_fpr)
  
          # Compute the averages per class
          for i in range(n_classes):
              avg_tpr += interp(all_fpr, self.fpr[i], self.tpr[i])
  
          # Finalize the average
          avg_tpr /= n_classes
  
          # Store the macro averages
          self.fpr[MACRO] = all_fpr
          self.tpr[MACRO] = avg_tpr
          self.roc_auc[MACRO] = auc(self.fpr[MACRO], self.tpr[MACRO])
docs: Compute the macro average scores for the ROCAUC curves.