Hi everyone,

I am following the great book Think Bayes and I got to the chapter introducing pymc for MCMC. This code is the last example in the chapter:

```
with pm.Model() as model9:
p0 = pm.Beta('p0', alpha=1, beta=1)
p1 = pm.Beta('p1', alpha=1, beta=1)
N = pm.DiscreteUniform('N', num_seen, 350)
q0 = 1-p0
q1 = 1-p1
ps = [q0*q1, q0*p1, p0*q1, p0*p1]
k00 = N - num_seen
data = pm.math.stack((k00, k01, k10, k11))
y = pm.Multinomial('y', n=N, p=ps, observed=data)
```

and according to the book, it should produce the following warning:

```
Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.
/home/downey/miniconda3/envs/ThinkBayes2/lib/python3.10/site-packages/pymc/backends/arviz.py:65: UserWarning: Could not extract data from symbolic observation y
warnings.warn(f"Could not extract data from symbolic observation {obs}")
The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details
The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details
```

but in many runs I always get the following

```
/usr/local/lib/python3.10/dist-packages/pymc/backends/arviz.py:60: UserWarning: Could not extract data from symbolic observation y
warnings.warn(f"Could not extract data from symbolic observation {obs}")
```

and the distribution forms are quite different from the expected.

Link to the chapter: MCMC — Think Bayes

Package versions:

pymc: 5.10.4

arviz: 0.18.0

pytensor: 2.18.6