code: def _test_plate_in_elbo(
      self, n_superfluous_top, n_superfluous_bottom, n_steps, lr=0.0012
  ):
      pyro.clear_param_store()
      self.data_tensor = torch.zeros(9, 2)
      for _out in range(self.n_outer):
          for _in in range(self.n_inner):
              self.data_tensor[3 * _out + _in, :] = self.data[_out][_in]
      self.data_as_list = [
          self.data_tensor[0:4, :],
          self.data_tensor[4:7, :],
          self.data_tensor[7:9, :],
      ]
  
      def model():
          loc_latent = pyro.sample(
              "loc_latent",
              fakes.NonreparameterizedNormal(
                  self.loc0, torch.pow(self.lam0, -0.5)
              ).to_event(1),
          )
  
          for i in pyro.plate("outer", 3):
              x_i = self.data_as_list[i]
              with pyro.plate("inner_%d" % i, x_i.size(0)):
                  for k in range(n_superfluous_top):
                      z_i_k = pyro.sample(
                          "z_%d_%d" % (i, k),
                          fakes.NonreparameterizedNormal(
                              0, 1
                          ).expand_by([4 - i]),
                      )
                      assert z_i_k.shape == (4 - i,)
                  obs_i = pyro.sample(
                      "obs_%d" % i,
                      dist.Normal(
                          loc_latent, torch.pow(self.lam, -0.5)
                      ).to_event(1),
                      obs=x_i,
                  )
                  assert obs_i.shape == (4 - i, 2)
                  for k in range(
                      n_superfluous_top,
                      n_superfluous_top + n_superfluous_bottom,
                  ):
                      z_i_k = pyro.sample(
                          "z_%d_%d" % (i, k),
                          fakes.NonreparameterizedNormal(
                              0, 1
                          ).expand_by([4 - i]),
                      )
                      assert z_i_k.shape == (4 - i,)
  
      pt_loc_baseline = torch.nn.Linear(1, 1)
      pt_superfluous_baselines = []
      for k in range(n_superfluous_top + n_superfluous_bottom):
          pt_superfluous_baselines.extend(
              [
                  torch.nn.Linear(2, 4),
                  torch.nn.Linear(2, 3),
                  torch.nn.Linear(2, 2),
              ]
          )
  
      def guide():
          loc_q = pyro.param(
              "loc_q", self.analytic_loc_n.expand(2) + 0.094
          )
          log_sig_q = pyro.param(
              "log_sig_q", self.analytic_log_sig_n.expand(2) - 0.07
          )
          sig_q = torch.exp(log_sig_q)
          trivial_baseline = pyro.module(
              "loc_baseline", pt_loc_baseline
          )
          baseline_value = trivial_baseline(torch.ones(1)).squeeze()
          loc_latent = pyro.sample(
              "loc_latent",
              fakes.NonreparameterizedNormal(loc_q, sig_q).to_event(1),
              infer=dict(baseline=dict(baseline_value=baseline_value)),
          )
  
          for i in pyro.plate("outer", 3):
              with pyro.plate("inner_%d" % i, 4 - i):
                  for k in range(
                      n_superfluous_top + n_superfluous_bottom
                  ):
                      z_baseline = pyro.module(
                          "z_baseline_%d_%d" % (i, k),
                          pt_superfluous_baselines[3 * k + i],
                      )
                      baseline_value = z_baseline(loc_latent.detach())
                      mean_i = pyro.param(
                          "mean_%d_%d" % (i, k), 0.5 * torch.ones(4 - i)
                      )
                      z_i_k = pyro.sample(
                          "z_%d_%d" % (i, k),
                          fakes.NonreparameterizedNormal(mean_i, 1),
                          infer=dict(
                              baseline=dict(
                                  baseline_value=baseline_value
                              )
                          ),
                      )
                      assert z_i_k.shape == (4 - i,)
  
      def per_param_callable(param_name):
          if "baseline" in param_name:
              return {"lr": 0.010, "betas": (0.95, 0.999)}
          else:
              return {"lr": lr, "betas": (0.95, 0.999)}
  
      adam = optim.Adam(per_param_callable)
      svi = SVI(model, guide, adam, loss=TraceGraph_ELBO())
  
      for step in range(n_steps):
          svi.step()
  
          loc_error = param_abs_error("loc_q", self.analytic_loc_n)
          log_sig_error = param_abs_error(
              "log_sig_q", self.analytic_log_sig_n
          )
  
          if n_superfluous_top > 0 or n_superfluous_bottom > 0:
              superfluous_errors = []
              for k in range(n_superfluous_top + n_superfluous_bottom):
                  mean_0_error = torch.sum(
                      torch.pow(pyro.param("mean_0_%d" % k), 2.0)
                  )
                  mean_1_error = torch.sum(
                      torch.pow(pyro.param("mean_1_%d" % k), 2.0)
                  )
                  mean_2_error = torch.sum(
                      torch.pow(pyro.param("mean_2_%d" % k), 2.0)
                  )
                  superfluous_error = torch.max(
                      torch.max(mean_0_error, mean_1_error),
                      mean_2_error,
                  )
                  superfluous_errors.append(
                      superfluous_error.detach().cpu().numpy()
                  )
  
          if step % 500 == 0:
              logger.debug(
                  "loc error, log(scale) error:  %.4f, %.4f"
                  % (loc_error, log_sig_error)
              )
              if n_superfluous_top > 0 or n_superfluous_bottom > 0:
                  logger.debug(
                      "superfluous error: %.4f"
                      % np.max(superfluous_errors)
                  )
  
      assert_equal(0.0, loc_error, prec=0.04)
      assert_equal(0.0, log_sig_error, prec=0.05)
      if n_superfluous_top > 0 or n_superfluous_bottom > 0:
          assert_equal(0.0, np.max(superfluous_errors), prec=0.04)
  
imports: import logging
  from unittest import TestCase
  import numpy
  import pytest
  import torch
  from torch import nn
  import pyro
  import pyro.distributions
  import pyro.optim
  from pyro.distributions.testing import fakes
  from pyro.infer import SVI, TraceGraph_ELBO
  from tests.common import assert_equal
label_desc: Use image node_id 4 for calling the RaoBlackwellizationTests obj's underlying member method code with example usage: obj._test_plate_in_elbo(n_superfluous_top, n_superfluous_bottom, n_steps, lr) and returns: dict, dict