def METHOD_NAME() -> None:
    shape = [3, 2, 2]
    axes = np.array([-2], dtype=np.int64)
    keepdims = 1
    node = onnx.helper.make_node(
        "ReduceMax",
        inputs=["data", "axes"],
        outputs=["reduced"],
        keepdims=keepdims,
    )
    data = np.array(
        [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
        dtype=np.float32,
    )
    reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
    # print(reduced)
    # [[[20., 2.]]
    # [[40., 2.]]
    # [[60., 2.]]]
    expect(
        node,
        inputs=[data, axes],
        outputs=[reduced],
        name="test_reduce_max_negative_axes_keepdims_example",
        opset_imports=[onnx.helper.make_opsetid("", 18)],
    )
    np.random.seed(0)
    data = np.random.uniform(-10, 10, shape).astype(np.float32)
    reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
    expect(
        node,
        inputs=[data, axes],
        outputs=[reduced],
        name="test_reduce_max_negative_axes_keepdims_random",
        opset_imports=[onnx.helper.make_opsetid("", 18)],
    )