I have a numpyro `model`

that is pretty much a very simple logistic regression. Each data point in my dataset has its own weight, so I want to weight them accordingly in terms of the log-likelihood, so the posterior will look something like

log p(theta|x) \proporto w*log p(x|theta) + log p(theta)

where w is the weight associated with x. I’m trying to use the `scale`

effect handler to do this, i.e.

```
model = scale(model, weights)
```

where weights is a numpy array consist of weights for the data points. But apparently this did not work, as the model did not scale the log-likelihood with the weights at all. Am I doing something wrong?