def METHOD_NAME():
    rng = np.random.RandomState(0)
    X = rng.randn(20, 5)
    y = rng.randn(20)
    X_new = rng.randn(5)
    mat = Matern()
    wk = WhiteKernel()
    gpr = GaussianProcessRegressor(kernel=mat + wk)
    gpr.fit(X, y)
    for acq_func in ["LCB", "PI", "EI"]:
        check_gradient_correctness(X_new, gpr, acq_func, np.max(y))