def METHOD_NAME() -> None:
    # Define operator attributes.
    norm_coefficient = 0.001
    alpha = 0.95
    beta = 0.85
    node = onnx.helper.make_node(
        "Momentum",
        inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
        outputs=["X1_new", "X2_new", "V1_new", "V2_new"],
        norm_coefficient=norm_coefficient,
        alpha=alpha,
        beta=beta,
        mode="standard",
        domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
    )
    # Define operator inputs.
    r = np.array(0.1, dtype=np.float32)  # scalar
    t = np.array(0, dtype=np.int64)  # scalar
    x1 = np.array([1.0], dtype=np.float32)
    g1 = np.array([-1.0], dtype=np.float32)
    v1 = np.array([2.0], dtype=np.float32)
    x2 = np.array([1.0, 2.0], dtype=np.float32)
    g2 = np.array([-1.0, -3.0], dtype=np.float32)
    v2 = np.array([4.0, 1.0], dtype=np.float32)
    # Compute expected outputs of Momentum.
    x1_new, v1_new = apply_momentum(r, t, x1, g1, v1, norm_coefficient, alpha, beta)
    x2_new, v2_new = apply_momentum(r, t, x2, g2, v2, norm_coefficient, alpha, beta)
    # Check results.
    expect(
        node,
        inputs=[r, t, x1, x2, g1, g2, v1, v2],
        outputs=[x1_new, x2_new, v1_new, v2_new],
        name="test_momentum_multiple",
        opset_imports=[
            onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
        ],
    )