I’m trying to sample from a little model where we want to learn ‘true’ four digit code given some data which are noisy observations:

```
import numpy as np
import pymc as pm
import aesara.tensor as at
true = np.array([1, 2, 3, 4])
observations = true[:, np.newaxis] + np.random.poisson(2, size=(4, 10))
with pm.Model() as model:
code = pm.DiscreteUniform("true", 1, 9, size=(4,))
shuffle = pm.Normal("shuffle", mu=code, sigma=4, size=(4, 10)).round()
observed = pm.Deterministic("observed", at.mod(code + shuffle, 9), observed=observations)
idata = pm.sample()
```

but the `pm.Deterministic`

doesn’t accept observed values and I can’t apply the observations directly to the data because of the `round`

and `mod`

transformations.

Is it possible to learn this sort of model with pymc?