code: def posterior_replay(model, posterior_samples, *args, **kwargs):
      r"""
      Given a model and samples from the posterior (potentially with conjugate sites
      collapsed), return a `dict` of samples from the posterior with conjugate sites
      uncollapsed. Note that this can also be used to generate samples from the
      posterior predictive distribution.
  
      :param model: Python callable.
      :param dict posterior_samples: posterior samples keyed by site name.
      :param args: arguments to `model`.
      :param kwargs: keyword arguments to `model`.
      :return: `dict` of samples from the posterior.
      """
      posterior_samples = posterior_samples.copy()
      num_samples = kwargs.pop("num_samples", None)
      assert (
          posterior_samples or num_samples
      ), "`num_samples` must be provided if `posterior_samples` is empty."
      if num_samples is None:
          num_samples = list(posterior_samples.values())[0].shape[0]
  
      return_samples = defaultdict(list)
      for i in range(num_samples):
          conditioned_nodes = {
              k: v[i] for k, v in posterior_samples.items()
          }
          collapsed_trace = poutine.trace(
              poutine.condition(
                  collapse_conjugate(model), conditioned_nodes
              )
          ).get_trace(*args, **kwargs)
          trace = poutine.trace(
              uncollapse_conjugate(model, collapsed_trace)
          ).get_trace(*args, **kwargs)
          for name, site in trace.iter_stochastic_nodes():
              if not site_is_subsample(site):
                  return_samples[name].append(site["value"])
  
      return {k: torch.stack(v) for k, v in return_samples.items()}
  
imports: from collections import defaultdict
  import torch
  import pyro.distributions
  from pyro import poutine
  from pyro.distributions.util import sum_leftmost
  from pyro.poutine.messenger import Messenger
  from pyro.poutine.util import site_is_subsample
label_desc: Use image node_id 7 for calling a global function with example usage: posterior_replay(model, posterior_samples) and returns: unknown