code: def marginal_guide(design, observation_labels, target_labels):
      logit_p = pyro.param("logit_p", torch.tensor(0.0))
      pyro.sample("y", dist.Bernoulli(logits=logit_p))
  
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 5 for calling a global function with example usage: marginal_guide(design, observation_labels, target_labels) without return types