code: def monte_carlo_entropy(
      model, design, target_labels, num_prior_samples=1000
  ):
      """Computes a Monte Carlo estimate of the entropy of `model` assuming that each of sites in `target_labels` is
      independent and the entropy is to be computed for that subset of sites only.
      """
  
      if isinstance(target_labels, str):
          target_labels = [target_labels]
  
      expanded_design = lexpand(design, num_prior_samples)
      trace = pyro.poutine.trace(model).get_trace(expanded_design)
      trace.compute_log_prob()
      lp = sum(trace.nodes[l]["log_prob"] for l in target_labels)
      return -lp.sum(0) / num_prior_samples
  
imports: import math
  import warnings
  import torch
  import pyro
  from pyro import poutine
  from pyro.contrib.oed.search import Search
  from pyro.contrib.util import lexpand
  from pyro.infer import SVI, EmpiricalMarginal, Importance
  from pyro.infer.autoguide.utils import mean_field_entropy
  from pyro.util import torch_isinf, torch_isnan
label_desc: Use image node_id 15 for calling a global function with example usage: monte_carlo_entropy(model, design, target_labels, num_prior_samples) and returns: unknown