def METHOD_NAME():
    time_dim = Dim(Tensor("time", [batch_dim], dtype="int32"))
    in_dim = Dim(7, name="in")
    extern_data = TensorDict(
        {
            "data": Tensor("data", [batch_dim, time_dim, in_dim], dtype="float32", feature_dim=in_dim),
        }
    )
    # noinspection PyShadowingNames
    def _forward_step(*, model: rf.Module, extern_data: TensorDict):
        model  # noqa  # unused
        data = extern_data["data"]
        _, out = rf.while_loop(
            cond=lambda s: s[0] < 2, body=lambda s: (s[0] + 1, s[1] * 0.9), initial=(rf.constant(0, dims=()), data)
        )
        assert out.control_flow_ctx is None
        out.mark_as_default_output(shape=(batch_dim, time_dim, in_dim))
    run_model(extern_data, lambda *, epoch, step: rf.Module(), _forward_step)