class_docstr: Implements forward filtering / backward sampling for sampling
  from the joint posterior distribution
class_docstr_tok: ["Implements","forward","filtering","\/","backward","sampling","for","sampling","from","the","joint","posterior","distribution"]
code: def __enter__(self):
      self.cache = {}
      return super().__enter__()
  
imports: import queue
  import warnings
  import weakref
  from collections import OrderedDict
  import torch
  from opt_einsum import shared_intermediates
  import pyro
  import pyro.distributions
  import pyro.ops.jit
  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 Dice, is_validation_enabled
  from pyro.ops import packed
  from pyro.ops.contract import contract_tensor_tree, contract_to_tensor
  from pyro.ops.rings import SampleRing
  from pyro.poutine.enum_messenger import EnumMessenger
  from pyro.util import check_traceenum_requirements, ignore_jit_warnings, warn_if_nan
label_desc: Use image node_id 2 for calling the BackwardSampleMessenger obj's underlying member method code with example usage: obj.__enter__() and returns: super