No posterior samples when using categorical distribution [bug]

[joshuaspear]

Issue Description

I have developed which depends on categorical distributions however, I am unable to obtain posterior samples. When running the code define in "Code snippet", I would expect an output of the form:
{"oil":[torch.tensor(0),...]}

Environment

For any bugs, please provide the following:

• OS and python version: GoogleCollab, Python 3.10
• PyTorch version, or if relevant, output of pip freeze.: 2.2.1
• Pyro version: output of python -c 'import pyro; print pyro.__version__': 1.9.0

Code Snippet

class Model:

    def __init__(self):
        pass

    def __call__(self, oil_pr):
        oil = pyro.sample("oil", dist.Categorical(oil_pr)) # 0,1,2
        seis_dist = torch.tensor(
            [
              [0.1,0.3,0.6], # 0,1,2|0
              [0.3,0.4,0.3], # 0,1,2|1
              [0.5,0.4,0.1]  # 0,1,2|2
            ]
            )
        seis = pyro.sample("seis", dist.Categorical(seis_dist[oil,:]))
        return seis

pyro.clear_param_store()
model = Model()
conditioned_seis = pyro.condition(
    belief_model, data={"seis": torch.tensor(1.0)})
hmc_kernel = HMC(conditioned_seis, step_size=0.9, num_steps=4)
posterior = MCMC(
    hmc_kernel,
    num_samples=10,
    warmup_steps=50,
    )
posterior.run(torch.tensor([1/3,1/3,1/3]))
samples = posterior.get_samples()
samples