code: def monte_carlo_approximate(state, op, model, guide, approx_vars):
      sample_options = {}
      if state.rng_key is not None and get_backend() == "jax":
          import jax
  
          sample_options["rng_key"], state.rng_key = jax.random.split(
              state.rng_key
          )
  
      sample = guide.sample(
          approx_vars, state.sample_inputs, **sample_options
      )
      if sample is guide:
          return model  # cannot progress
      result = sample + model - guide
  
      return result
  
imports: import functools
  from collections import OrderedDict
  from funsor.cnf import Contraction
  from funsor.delta import Delta
  from funsor.gaussian import Gaussian
  from funsor.integrate import Integrate
  from funsor.interpretations import StatefulInterpretation
  from funsor.tensor import Tensor
  from funsor.terms import Approximate, Funsor, Number, Subs, Unary
  from funsor.util import get_backend
  from .None import ops
label_desc: Use image node_id 2 for calling a global function with example usage: monte_carlo_approximate(state, op, model, guide, approx_vars) and returns: result, model