In this example for using Minibatch with ADVI, it is emphasized that `total_size`

needs to be set in order to properly calibrate the full likelihood. This is fine when the observable is a standard RandomVariable, which has `observed`

and `total_size`

parameters. However, it isn’t clear to me how this particular correction is to be done when using `pm.Potential`

to weight observations.

For example, I have a gamma regression with weighted observations (weights `W`

and observations `Y`

), where I use

`y = pm.Potential('y_logp_weighted', W*pm.Gamma.dist(mu=mu, sd=sigma).logp(Y))`

**Can anyone provide guidance for how to deal with this situation?**