Hi,

I was trying to implement gluten sensitivity example from Think bayes 2. See here for details. I think it should be implemented using `CustomDist`

like this

```
def my_dist(n_sensitive: TensorVariable, size: TensorVariable) -> TensorVariable:
n_insensitive = 35-n_sensitive
n1 = pm.Binomial.dist(n=n_sensitive,p=0.95,size=size)
n2 = pm.Binomial.dist(n=n_insensitive,p=0.4,size=size)
return n1+n2
with pm.Model() as model:
sensitive = pm.DiscreteUniform(name="n_sensitive",lower=0,upper=35)
total = pm.CustomDist("total",sensitive,dist=my_dist,observed=12)
prior = pm.sample_prior_predictive()
posterior = pm.sample()
```

but that fails:

`The logprob terms of the following value variables could not be derived: {TensorConstant(TensorType(int64, shape=()), data=array(12))}`

Returning only `n1`

or `n2`

works or if I comment last line. “Works” means that no error given.

Any ideas how to proceed?

br,

H