def METHOD_NAME():
    class Model(Module):
        def test(self, x: Tensor):
            y = 10
            z0 = x + y
            z1 = y + x
            z2 = x * y
            z3 = x / y
            z4 = x.maximum(y)
            z5 = x.minimum(y)
            return (z0, z1, z2, z3, z4, z5)
    # fmt: off
    @R.function
    def test(x: R.Tensor((1, 10), dtype="float32"), _io: R.Object) -> R.Tuple(R.Tuple(R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32")), R.Tuple(R.Object)):
        R.func_attr({"num_input": 2})
        with R.dataflow():
            add: R.Tensor((1, 10), dtype="float32") = R.add(x, R.const(10, "float32"))
            add1: R.Tensor((1, 10), dtype="float32") = R.add(x, R.const(10, "float32"))
            mul: R.Tensor((1, 10), dtype="float32") = R.multiply(x, R.const(10, "float32"))
            divide: R.Tensor((1, 10), dtype="float32") = R.divide(x, R.const(10, "float32"))
            maximum: R.Tensor((1, 10), dtype="float32") = R.maximum(x, R.const(10, "float32"))
            minimum: R.Tensor((1, 10), dtype="float32") = R.minimum(x, R.const(10, "float32"))
            gv1: R.Tuple(R.Tuple(R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32"), R.Tensor((1, 10), dtype="float32")), R.Tuple(R.Object)) = (add, add1, mul, divide, maximum, minimum), (_io,)
            R.output(gv1)
        return gv1
    # fmt: on
    m = Model()
    irmodule, _ = m.export_tvm(spec={"test": {"x": spec.Tensor([1, 10], "float32")}}, debug=True)
    tvm.ir.assert_structural_equal(irmodule["test"], test)