code: def make_lfire_classifier(n_theta_samples):
      def lfire_classifier(
          design, trace, observation_labels, target_labels
      ):
          y_dict = {
              l: trace.nodes[l]["value"] for l in observation_labels
          }
          y = torch.cat(list(y_dict.values()), dim=-1)
          a, b = pyro.param(
              "a", torch.zeros(n_theta_samples)
          ), pyro.param("b", torch.zeros(n_theta_samples))
  
          return a + b * y
  
      return lfire_classifier
  
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 7 for calling a global function with example usage: make_lfire_classifier(n_theta_samples) and returns: lfire_classifier, unknown