code: def posterior_guide(
      y_dict, design, observation_labels, target_labels
  ):
      y = torch.cat(list(y_dict.values()), dim=-1)
      a, b = pyro.param("a", torch.tensor(0.0)), pyro.param(
          "b", torch.tensor(0.0)
      )
      pyro.sample("theta", dist.Bernoulli(logits=a + b * y))
  
imports: from contextlib import ExitStack
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.optim
  from pyro.contrib.oed.eig import donsker_varadhan_eig, lfire_eig, marginal_eig, marginal_likelihood_eig, nmc_eig, posterior_eig, vnmc_eig
  from pyro.contrib.util import iter_plates_to_shape
  from tests.common import assert_equal
label_desc: Use image node_id 4 for calling a global function with example usage: posterior_guide(y_dict, design, observation_labels, target_labels) without return types