def METHOD_NAME():
    class Model(Module):
        def test(self, x: Tensor, y: Tensor):
            z0 = x + y
            z1 = x * y
            z2 = x / y
            z3 = x.maximum(y)
            z4 = x.minimum(y)
            return (z0, z1, z2, z3, z4)
    # fmt: off
    @R.function
    def test(x: R.Tensor((1, 10), dtype="float32"), y: R.Tensor((2, 1), dtype="float32"), _io: R.Object) -> R.Tuple(R.Tuple(R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32")), R.Tuple(R.Object)):
        R.func_attr({"num_input": 3})
        with R.dataflow():
            add: R.Tensor((2, 10), dtype="float32") = R.add(x, y)
            mul: R.Tensor((2, 10), dtype="float32") = R.multiply(x, y)
            divide: R.Tensor((2, 10), dtype="float32") = R.divide(x, y)
            maximum: R.Tensor((2, 10), dtype="float32") = R.maximum(x, y)
            minimum: R.Tensor((2, 10), dtype="float32") = R.minimum(x, y)
            gv1: R.Tuple(R.Tuple(R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32"), R.Tensor((2, 10), dtype="float32")), R.Tuple(R.Object)) = (add, 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"), "y": spec.Tensor([2, 1], "float32")}},
        debug=True,
    )
    tvm.ir.assert_structural_equal(irmodule["test"], test)