def METHOD_NAME() -> None:
    shape = [3, 2, 2]
    axes = np.array([], dtype=np.int64)
    keepdims = 1
    node = onnx.helper.make_node(
        "ReduceLogSumExp",
        inputs=["data", "axes"],
        outputs=["reduced"],
        keepdims=keepdims,
    )
    data = np.array(
        [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
    )
    reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
    # print(reduced)
    # [[[60.00671387]]]
    expect(
        node,
        inputs=[data, axes],
        outputs=[reduced],
        name="test_reduce_log_sum_exp_default_axes_keepdims_example",
    )
    np.random.seed(0)
    data = np.random.uniform(-10, 10, shape).astype(np.double)
    reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
    expect(
        node,
        inputs=[data, axes],
        outputs=[reduced],
        name="test_reduce_log_sum_exp_default_axes_keepdims_random",
    )