code: def test_auto_transform(auto_class):
      n_steps = 3500
  
      def model():
          pyro.sample("x", dist.LogNormal(0.2, 0.7))
  
      if auto_class is AutoLowRankMultivariateNormal:
          guide = auto_class(model, rank=1)
      else:
          guide = auto_class(model)
      adam = optim.Adam({"lr": 0.001, "betas": (0.90, 0.999)})
      svi = SVI(model, guide, adam, loss=Trace_ELBO())
  
      for k in range(n_steps):
          loss = svi.step()
          assert np.isfinite(loss), loss
  
      if auto_class is AutoLaplaceApproximation:
          guide = guide.laplace_approximation()
  
      loc, scale = guide._loc_scale()
      assert_equal(
          loc.detach(),
          torch.tensor([0.2]),
          prec=0.04,
          msg="guide mean off",
      )
      assert_equal(
          scale.detach(),
          torch.tensor([0.7]),
          prec=0.04,
          msg="guide covariance off",
      )
  
imports: import logging
  from functools import partial
  import numpy
  import pytest
  import torch
  from torch.distributions import biject_to, constraints
  import pyro
  import pyro.distributions
  import pyro.optim
  from pyro.distributions.transforms import block_autoregressive, iterated
  from pyro.infer import SVI, Trace_ELBO, TraceMeanField_ELBO
  from pyro.infer.autoguide import AutoDiagonalNormal, AutoIAFNormal, AutoLaplaceApproximation, AutoLowRankMultivariateNormal, AutoMultivariateNormal
  from pyro.infer.autoguide.guides import AutoNormalizingFlow
  from tests.common import assert_equal
  from tests.integration_tests.test_conjugate_gaussian_models import GaussianChain
label_desc: Use image node_id 2 for calling a global function with example usage: test_auto_transform(auto_class) without return types