class_docstr: A Donsker-Varadhan `T` family based on a guide family via
  the relation `T = log p(theta) - log q(theta | y, d)`
class_docstr_tok: ["A","Donsker-Varadhan","`","T","`","family","based","on","a","guide","family","via","the","relation","`","T","=","log","p","(","theta",")","-","log","q","(","theta","|","y",",","d",")","`"]
code: def forward(self, design, trace, observation_labels, target_labels):
      trace.compute_log_prob()
      prior_lp = sum(trace.nodes[l]["log_prob"] for l in 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}
  
      conditional_guide = pyro.condition(self.guide, data=theta_dict)
      cond_trace = poutine.trace(conditional_guide).get_trace(
          y_dict, design, observation_labels, target_labels
      )
      cond_trace.compute_log_prob()
  
      posterior_lp = sum(
          cond_trace.nodes[l]["log_prob"] for l in target_labels
      )
  
      return posterior_lp - prior_lp
  
imports: from contextlib import ExitStack
  import torch
  from torch import nn
  import pyro
  import pyro.distributions
  from pyro import poutine
  from pyro.contrib.util import iter_plates_to_shape, lexpand, rmv, rtril, rvv, tensor_to_dict
  from pyro.ops.linalg import rinverse
label_desc: Use image node_id 2 for calling the GuideDV obj's underlying member method code with example usage: obj.forward(design, trace, observation_labels, target_labels) and returns: unknown