<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def _roc_multi(y_true, y_score, ax=None):
 """ 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 """

    # 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