def sklearnAUC(test_labels, test_prediction):
    n_classes = 2
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(test_labels[:, (i)], test_prediction[:, (i)])
roc_auc[i] = auc(fpr[i], tpr[i])
return round(roc_auc[0], 3), round(roc_auc[1], 3)
how to implement custom metric in keras?