code: def precondition_approximate_gaussian(
      state, op, model, guide, approx_vars
  ):
      # Eagerly winnow approx_vars.
      approx_vars = approx_vars.intersection(guide.input_vars)
      if not approx_vars:
          return model
  
      # Determine how much white noise is needed to generate a sample.
      batch_shape = []
      event_numel = 0
      for k, d in guide.inputs.items():
          if d.dtype == "real":
              if Variable(k, d) in approx_vars:
                  event_numel += d.num_elements
          else:
              batch_shape += (d.size,)
      shape = tuple(batch_shape) + (event_numel,)
      name = f"{state.aux_name}_{len(state.sample_inputs)}"
      state.sample_inputs[name] = Reals[shape]
      state.sample_vars.add(Variable(name, Reals[shape]))
  
      # Precondition this factor.
      sample = guide.sample(
          approx_vars, OrderedDict([(name, Reals[shape])])
      )
      assert sample is not guide, "no progress"
      result = sample + model - guide
      return result
  
file_path: funsor/funsor/precondition.py
imports: from collections import OrderedDict
  from .None import ops
  from .cnf import Contraction, GaussianMixture
  from .domains import Reals
  from .gaussian import Gaussian
  from .interpretations import StatefulInterpretation
  from .terms import Approximate, Funsor, Subs, Variable
label_desc: Use image node_id 4 for calling a global function with example usage: precondition_approximate_gaussian(state, op, model, guide, approx_vars) and returns: result, model