code: def test_amortized_smoke(init_fn):
      rank = 2
  
      @easy_guide(model)
      def guide(self, batch, subsample, full_size):
          num_time_steps, batch_size = batch.shape
          self.map_estimate("drift")
  
          group = self.group(match="state_[0-9]*")
          cov_diag = pyro.param(
              "state_cov_diag",
              lambda: torch.full(group.event_shape, 0.01),
              constraint=constraints.positive,
          )
          cov_factor = pyro.param(
              "state_cov_factor",
              lambda: torch.randn(group.event_shape + (rank,)) * 0.01,
          )
  
          if not hasattr(self, "nn"):
              self.nn = torch.nn.Linear(
                  group.event_shape.numel(), group.event_shape.numel()
              )
              self.nn.weight.data.fill_(1.0 / num_time_steps)
              self.nn.bias.data.fill_(-0.5)
          pyro.module("state_nn", self.nn)
          with self.plate("data", full_size, subsample=subsample):
              loc = self.nn(batch.t())
              group.sample(
                  "states",
                  dist.LowRankMultivariateNormal(
                      loc, cov_factor, cov_diag
                  ),
              )
  
      if init_fn is not None:
          guide.init = init_fn
  
      check_guide(guide)
  
imports: import io
  import warnings
  import pytest
  import torch
  from torch.distributions import constraints
  import pyro
  import pyro.distributions
  from pyro.contrib.easyguide import EasyGuide, easy_guide
  from pyro.infer import SVI, Trace_ELBO
  from pyro.infer.autoguide.initialization import init_to_mean, init_to_median
  from pyro.optim import Adam
  from pyro.util import ignore_jit_warnings
label_desc: Use image node_id 6 for calling a global function with example usage: test_amortized_smoke(init_fn) without return types