code: def dv_critic(design, trace, observation_labels, target_labels):
      y_dict = {l: trace.nodes[l]["value"] for l in observation_labels}
      theta_dict = {l: trace.nodes[l]["value"] for l in target_labels}
      x = torch.cat(
          list(theta_dict.values()) + list(y_dict.values()), dim=-1
      )
  
      B = pyro.param("B", torch.zeros(5, 5))
      return rvv(x, rmv(B, x))
  
imports: import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.optim
  from pyro.contrib.oed.eig import donsker_varadhan_eig, laplace_eig, lfire_eig, marginal_eig, marginal_likelihood_eig, nmc_eig, posterior_eig, vnmc_eig
  from pyro.contrib.oed.glmm import known_covariance_linear_model
  from pyro.contrib.oed.glmm.guides import LinearModelLaplaceGuide
  from pyro.contrib.oed.util import linear_model_ground_truth
  from pyro.contrib.util import rmv, rvv
  from pyro.infer import Trace_ELBO
  from tests.common import assert_equal
label_desc: Use image node_id 7 for calling a global function with example usage: dv_critic(design, trace, observation_labels, target_labels) and returns: rvv