I’m trying to implement some type of a mixture model with multiple components, thus want to use sampling over a categorical variable to devide the data points into clusters randomly. However, when I try to sample `dist.Categorial`

through MCMC, even without any observations, the sample sizes suddenly changes on the second sample. The following code reproduces it.

What am I doing wrong? Should I approach this differently?

Note that just using `dist.Categorical`

outside any model works fine.

```
import pyro
import pyro.distributions as dist
from pyro.infer.mcmc.api import HMC, MCMC, NUTS
def model():
classes = pyro.sample("classes",dist.Bernoulli(torch.tensor([[0.3,0.7], [0.3,0.7],[0.3,0.7], [0.3,0.7],[0.3,0.7], [0.3,0.7],[0.3,0.7], [0.3,0.7]])))
print(classes.shape)
mcmc = MCMC(HMC(model, target_accept_prob=0.8),
num_samples=10,
warmup_steps=10,
num_chains=1)
mcmc.run()
```