code: def _roc_multi(y_true, y_score, ax=None):
      
      # Compute micro-average ROC curve and ROC area
      fpr, tpr, _ = roc_curve(y_true.ravel(), y_score.ravel())
      roc_auc = auc(fpr, tpr)
  
      if ax is None:
          ax = plt.gca()
  
      ax.plot(fpr, tpr, label=('micro-average ROC curve (area = {0:0.2f})'
                               .format(roc_auc)))
      _set_ax_settings(ax)
      return ax
docs: Plot ROC curve for multi classification.
  
      Parameters
      ----------
      y_true : array-like, shape = [n_samples, n_classes]
          Correct target values (ground truth).
      y_score : array-like, shape = [n_samples, n_classes]
          Target scores (estimator predictions).
      ax: matplotlib Axes
          Axes object to draw the plot onto, otherwise uses current Axes
  
      Returns
      -------
      ax: matplotlib Axes
          Axes containing the plot