code: def _score_micro_average(self, y, y_pred, classes, n_classes):
          
          # Convert y to binarized array for micro and macro scores
          y = label_binarize(y, classes=classes)
          if n_classes == 2:
              y = np.hstack((1-y, y))
  
          # Compute micro-average
          self.fpr[MICRO], self.tpr[MICRO], _ = roc_curve(y.ravel(), y_pred.ravel())
          self.roc_auc[MICRO] = auc(self.fpr[MICRO], self.tpr[MICRO])
docs: Compute the micro average scores for the ROCAUC curves.