Interactions in latent variable models

I am fairly new to SEM in general, and I've been trying to figure out the best approach to modeling interaction effects between latent variables in OpenMx.

[Umbach et al (2017)](http://statistik-jstat.uibk.ac.at/article/view/v077i07) and [Cortina et al (2021)](https://journals.sagepub.com/doi/full/10.1177/1094428119872531?casa_token=71rJuz5kFSUAAAAA%3AHPIfNnTqLHii82DpQpuHFKJ5XaQDCipQ1WsdspuZD6M87_ogmmbhLxIes_0DPPvvI46ulXCC_vbWEQ) both provide an overview of the existing methods for identifying latent interactions.
At least product indicator approaches, such as the constrained approach described by [Kenny & Judd (1984)](https://psycnet.apa.org/record/1984-27738-001) and the unconstrained approach by [Marsh et al. (2004)](https://psycnet.apa.org/buy/2004-17801-001) should be possible to model in OpenMx. Can for example the indicator products be computed as `mxAlgebra` objects? What about distribution analytic approaches such as LMS ([Klein and Moosbrugger, 2000](https://link.springer.com/article/10.1007/BF02296338))?

Any example scripts or tutorials would be appreciated.