def METHOD_NAME():
    for target in ["cuda", "rocm"]:
        if not tvm.testing.device_enabled(target):
            print("Skip because %s is not enabled" % target)
            continue
        with tvm.target.Target(target + " -libs=thrust") as tgt:
            if not thrust_check_func[target](tgt, "tvm.contrib.thrust.sum_scan"):
                print("skip because thrust is not enabled...")
                return
            for ishape in [(10,), (10, 10), (10, 10, 10)]:
                values = te.placeholder(ishape, name="values", dtype="int32")
                scan, reduction = exclusive_scan(values, return_reduction=True)
                s = schedule_scan([scan, reduction])
                dev = tvm.device(target, 0)
                f = tvm.build(s, [values, scan, reduction], target)
                values_np = np.random.randint(0, 10, size=ishape).astype(np.int32)
                values_np_out = np.zeros(values_np.shape, np.int32)
                if len(ishape) == 1:
                    reduction_shape = ()
                else:
                    reduction_shape = ishape[:-1]
                reduction_np_out = np.zeros(reduction_shape, np.int32)
                values_in = tvm.nd.array(values_np, dev)
                values_out = tvm.nd.array(values_np_out, dev)
                reduction_out = tvm.nd.array(reduction_np_out, dev)
                f(values_in, values_out, reduction_out)
                ref_values_out = np.cumsum(values_np, axis=-1, dtype="int32") - values_np
                tvm.testing.assert_allclose(values_out.numpy(), ref_values_out, rtol=1e-5)
                ref_reduction_out = np.sum(values_np, axis=-1)
                tvm.testing.assert_allclose(reduction_out.numpy(), ref_reduction_out, rtol=1e-5)