class_docstr: A trace-based implementation of Tensor Monte Carlo [1]
  by way of Tensor Variable Elimination [2] that supports:
  - local parallel sampling over any sample site in the model or guide
  - exhaustive enumeration over any sample site in the model or guide
  
  To take multiple samples, mark the site with
  ``infer={'enumerate': 'parallel', 'num_samples': N}``.
  To configure all sites in a model or guide at once,
  use :func:`~pyro.infer.enum.config_enumerate` .
  To enumerate or sample a sample site in the ``model``,
  mark the site and ensure the site does not appear in the ``guide``.
  
  This assumes restricted dependency structure on the model and guide:
  variables outside of an :class:`~pyro.plate` can never depend on
  variables inside that :class:`~pyro.plate` .
  
  References
  
  [1] `Tensor Monte Carlo: Particle Methods for the GPU Era`,
      Laurence Aitchison (2018)
  
  [2] `Tensor Variable Elimination for Plated Factor Graphs`,
      Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Justin Chiu, Neeraj Pradhan,
      Alexander Rush, Noah Goodman (2019)
code: def loss(self, model, guide, *args, **kwargs):
      with torch.no_grad():
          loss = self.differentiable_loss(model, guide, *args, **kwargs)
          if is_identically_zero(loss) or not loss.requires_grad:
              return torch_item(loss)
          return loss.item()
  
imports: import queue
  import warnings
  import torch
  import pyro.poutine
  from pyro.distributions.util import is_identically_zero
  from pyro.infer.elbo import ELBO
  from pyro.infer.enum import get_importance_trace, iter_discrete_escape, iter_discrete_extend
  from pyro.infer.util import compute_site_dice_factor, is_validation_enabled, torch_item
  from pyro.ops import packed
  from pyro.ops.contract import einsum
  from pyro.poutine.enum_messenger import EnumMessenger
  from pyro.util import check_traceenum_requirements, warn_if_nan
label_desc: Use image node_id 4 for calling the TraceTMC_ELBO obj's underlying member method code with example usage: obj.loss(model, guide) and returns: loss, torch_item