def METHOD_NAME(name, pipeline, data_loader, max_evals=250, scoring="accuracy"):
    def test(i):
        if i > max_evals:
            assert False
        try:
            X, y = data_loader()
            clf = Hyperopt(estimator=pipeline, max_evals=i, scoring=scoring)
            trained_pipeline = clf.fit(X, y)
            trained_pipeline.predict(X)
            return
        except Exception:
            test(3 * i)
    test(1)