def METHOD_NAME():
    class Model(Module):
        def test(self, x: Tensor):
            z0 = x.reshape([2, 5, 2])
            z1 = x.permute_dims([2, 1, 0])
            z2 = x.repeat(2, axis=1)
            return (z0, z1, z2)
    # fmt: off
    @R.function
    def test(x: R.Tensor((2, 1, 10), dtype="float32"), _io: R.Object) -> R.Tuple(R.Tuple(R.Tensor((2, 5, 2), dtype="float32"), R.Tensor((10, 1, 2), dtype="float32"), R.Tensor((2, 2, 10), dtype="float32")), R.Tuple(R.Object)):
        R.func_attr({"num_input": 2})
        with R.dataflow():
            reshape: R.Tensor((2, 5, 2), dtype="float32") = R.reshape(x, R.shape([2, 5, 2]))
            permute_dims: R.Tensor((10, 1, 2), dtype="float32") = R.permute_dims(x, axes=[2, 1, 0])
            repeat: R.Tensor((2, 2, 10), dtype="float32") = R.repeat(x, repeats=2, axis=1)
            gv1: R.Tuple(R.Tuple(R.Tensor((2, 5, 2), dtype="float32"), R.Tensor((10, 1, 2), dtype="float32"), R.Tensor((2, 2, 10), dtype="float32")), R.Tuple(R.Object)) = (reshape, permute_dims, repeat), (_io,)
            R.output(gv1)
        return gv1
    # fmt: on
    m = Model()
    irmodule, _ = m.export_tvm(spec={"test": {"x": spec.Tensor([2, 1, 10], "float32")}}, debug=True)
    tvm.ir.assert_structural_equal(irmodule["test"], test)