According to the PyMC3 docs the log posterior can be evaluated at specific points using the following:

```
with pm.Model() as model:
z = pm.Normal('z', mu=0., sigma=5.)
x = pm.Normal('x', mu=z, sigma=1., observed=5.)
model.logp({'z': 2.5})
```

(A related question regarding evaluating the log posterior in PyMC3 can be found here).

When I try this in pymc v4 I get the type error:

`TypeError: unhashable type: 'dict'`

Is there any way of doing this? I have checked the pymc v4 documentation for this versatility but without any examples I find it hard to understand/implement.

If it is possible, a simple working example would be greatly appreciated.

Many thanks,

Harry