def METHOD_NAME(
        seed, batch_size, num_boxes, num_classes, center_point_box,
        max_output_boxes, iou_threshold, score_threshold, ctx, func_name):
    pytest.skip('This test needs onnx, test locally.')
    import onnx
    import onnxruntime as ort
    rng = np.random.RandomState(seed)
    boxes = rng.rand(batch_size, num_boxes, 4).astype(np.float32)
    scores = rng.rand(batch_size, num_classes, num_boxes).astype(np.float32)
    vboxes = nn.Variable.from_numpy_array(boxes)
    vscores = nn.Variable.from_numpy_array(scores)
    with nn.context_scope(ctx), nn.auto_forward():
        voutput = F.onnx_non_max_suppression(vboxes, vscores, center_point_box,
                                             max_output_boxes, iou_threshold,
                                             score_threshold)
    ref = ref_non_max_suppression(boxes, scores, center_point_box,
                                  max_output_boxes, iou_threshold,
                                  score_threshold)
    assert_allclose(voutput.d, ref)
    assert func_name == voutput.parent.name