def METHOD_NAME() -> None:
    shape = [3, 2, 2]
    axes = np.array([2], dtype=np.int64)
    keepdims = 1
    node = onnx.helper.make_node(
        "ReduceL1",
        inputs=["data", "axes"],
        outputs=["reduced"],
        keepdims=keepdims,
    )
    data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
    # print(data)
    # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
    reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
    # print(reduced)
    # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
    expect(
        node,
        inputs=[data, axes],
        outputs=[reduced],
        name="test_reduce_l1_keep_dims_example",
    )
    np.random.seed(0)
    data = np.random.uniform(-10, 10, shape).astype(np.float32)
    reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
    expect(
        node,
        inputs=[data, axes],
        outputs=[reduced],
        name="test_reduce_l1_keep_dims_random",
    )