The code below works in pymc3. When I try in version 4, I get a message about using `pm.logp(rv, x)`

instead of `rv.logp(x)`

. So I naively tried:

`obs = pm.logp(weights * pm.Normal.dist(mu=mu, sigma=sd), observed)`

but the sampler doesn’t seem to take this likelihood into account.

The code that works in pymc3:

```
import pymc3 as pm
import numpy as np
observed = np.random.normal(loc=2, scale=3, size=100000)
weights = np.random.uniform(size=100000)
# bump the highly weighted obs, drop the lower weighted ones
# we should see a posterior mu of >2
observed[weights > .5] += .1
observed[weights < .5] -= .1
with pm.Model() as m:
mu = pm.Normal('mu', mu=0, sd=5)
sd = pm.HalfNormal('sd', 6)
obs = pm.Potential('obs', weights * pm.Normal.dist(mu=mu, sd=sd).logp(observed))
with m:
trace = pm.sample(return_inferencedata=True)
posterior = trace.posterior.stack(samples=["chain", "draw"])
np.median(posterior['mu'])
```