code: def dv_critic(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)
      theta_dict = {l: trace.nodes[l]["value"] for l in target_labels}
      theta = torch.cat(list(theta_dict.values()), dim=-1)
  
      w_y = pyro.param("w_y", torch.tensor(0.0))
      w_theta = pyro.param("w_theta", torch.tensor(0.0))
      w_ytheta = pyro.param("w_ytheta", torch.tensor(0.0))
  
      return y * w_y + theta * w_theta + y * theta * w_ytheta
  
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 8 for calling a global function with example usage: dv_critic(design, trace, observation_labels, target_labels) and returns: unknown