code: def marginal_guide(design, observation_labels, target_labels):
      mu = pyro.param("mu", torch.zeros(3))
      scale_tril = pyro.param(
          "scale_tril",
          torch.eye(3),
          constraint=torch.distributions.constraints.lower_cholesky,
      )
      pyro.sample(
          "y", dist.MultivariateNormal(mu, scale_tril=scale_tril)
      )
  
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 4 for calling a global function with example usage: marginal_guide(design, observation_labels, target_labels) without return types