code: def check_structure(model, expected_str, expected_dependencies=None):
      guide = AutoGaussian(model, backend="dense")
      guide()  # initialize
      if expected_dependencies is not None:
          assert guide.dependencies == expected_dependencies
  
      # Inject random noise into all unconstrained parameters.
      for parameter in guide.parameters():
          parameter.data.normal_()
  
      with torch.no_grad():
          # Check flatten & unflatten.
          mvn = guide._dense_get_mvn()
          expected = mvn.sample()
          samples = guide._dense_unflatten(expected)
          actual = guide._dense_flatten(samples)
          assert_equal(actual, expected)
  
          # Check sparsity structure.
          precision = mvn.precision_matrix
          actual = precision.abs().gt(1e-5).long()
          str_to_number = {"?": 1, ".": 0}
          expected = torch.tensor(
              [
                  [str_to_number[c] for c in row if c != " "]
                  for row in expected_str
              ]
          )
          assert (actual == expected).all()
  
imports: from collections import OrderedDict, namedtuple
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.infer import SVI, JitTrace_ELBO, Predictive, Trace_ELBO
  from pyro.infer.autoguide import AutoGaussian, AutoGuideList
  from pyro.infer.autoguide.gaussian import AutoGaussianDense, AutoGaussianFunsor, _break_plates
  from pyro.infer.reparam import LocScaleReparam
  from pyro.optim import ClippedAdam
  from tests.common import assert_close, assert_equal, xfail_if_not_implemented
label_desc: Use image node_id 3 for calling a global function with example usage: check_structure(model, expected_str, expected_dependencies) without return types