def METHOD_NAME():
    """Test of singletask MLP classification API."""
    np.random.seed(123)
    X = np.random.rand(100, 5)
    y = np.random.randint(2, size=(100,))
    dataset = dc.data.NumpyDataset(X, y)
    splitter = dc.splits.RandomSplitter()
    train_dataset, test_dataset = splitter.train_test_split(dataset)
    transformers = []
    classification_metrics = [
        dc.metrics.Metric(dc.metrics.roc_auc_score),
        dc.metrics.Metric(dc.metrics.prc_auc_score),
        dc.metrics.Metric(dc.metrics.matthews_corrcoef),
        dc.metrics.Metric(dc.metrics.recall_score),
        dc.metrics.Metric(dc.metrics.accuracy_score),
        dc.metrics.Metric(dc.metrics.balanced_accuracy_score),
        dc.metrics.Metric(dc.metrics.jaccard_score),
        dc.metrics.Metric(dc.metrics.f1_score),
        dc.metrics.Metric(dc.metrics.pixel_error),
        dc.metrics.Metric(dc.metrics.kappa_score),
        dc.metrics.Metric(dc.metrics.bedroc_score),
    ]
    model = dc.models.MultitaskClassifier(1, 5)
    # Fit trained model
    model.fit(train_dataset)
    # Eval model on train/test
    _ = model.evaluate(train_dataset, classification_metrics, transformers)
    _ = model.evaluate(test_dataset, classification_metrics, transformers)