code: def big_model_guide(
      include_obs=True,
      include_single=False,
      include_inner_1=False,
      flip_c23=False,
      include_triple=False,
      include_z1=False,
  ):
      p0 = torch.tensor(math.exp(-0.20), requires_grad=True)
      p1 = torch.tensor(math.exp(-0.33), requires_grad=True)
      p2 = torch.tensor(math.exp(-0.70), requires_grad=True)
      if include_triple:
          with pyro.plate("plate_triple1", 6) as ind_triple1:
              with pyro.plate("plate_triple2", 7) as ind_triple2:
                  if include_z1:
                      pyro.sample(
                          "z1",
                          dist.Bernoulli(p2).expand_by(
                              [len(ind_triple2), len(ind_triple1)]
                          ),
                      )
                  with pyro.plate("plate_triple3", 9) as ind_triple3:
                      pyro.sample(
                          "z0",
                          dist.Bernoulli(p2).expand_by(
                              [
                                  len(ind_triple3),
                                  len(ind_triple2),
                                  len(ind_triple1),
                              ]
                          ),
                      )
      pyro.sample("a1", dist.Bernoulli(p0))
      if include_single:
          with pyro.plate("plate_single", 5) as ind_single:
              b0 = pyro.sample(
                  "b0", dist.Bernoulli(p0).expand_by([len(ind_single)])
              )
              assert b0.shape == (5,)
      with pyro.plate("plate_outer", 2) as ind_outer:
          pyro.sample(
              "b1", dist.Bernoulli(p0).expand_by([len(ind_outer)])
          )
          if include_inner_1:
              with pyro.plate("plate_inner_1", 3) as ind_inner:
                  pyro.sample(
                      "c1",
                      dist.Bernoulli(p1).expand_by(
                          [len(ind_inner), len(ind_outer)]
                      ),
                  )
                  if flip_c23 and not include_obs:
                      pyro.sample(
                          "c3",
                          dist.Bernoulli(p0).expand_by(
                              [len(ind_inner), len(ind_outer)]
                          ),
                      )
                      pyro.sample(
                          "c2",
                          dist.Bernoulli(p1).expand_by(
                              [len(ind_inner), len(ind_outer)]
                          ),
                      )
                  else:
                      pyro.sample(
                          "c2",
                          dist.Bernoulli(p0).expand_by(
                              [len(ind_inner), len(ind_outer)]
                          ),
                      )
                      pyro.sample(
                          "c3",
                          dist.Bernoulli(p2).expand_by(
                              [len(ind_inner), len(ind_outer)]
                          ),
                      )
          with pyro.plate("plate_inner_2", 4) as ind_inner:
              pyro.sample(
                  "d1",
                  dist.Bernoulli(p0).expand_by(
                      [len(ind_inner), len(ind_outer)]
                  ),
              )
              d2 = pyro.sample(
                  "d2",
                  dist.Bernoulli(p2).expand_by(
                      [len(ind_inner), len(ind_outer)]
                  ),
              )
              assert d2.shape == (4, 2)
              if include_obs:
                  pyro.sample(
                      "obs",
                      dist.Bernoulli(p0).expand_by(
                          [len(ind_inner), len(ind_outer)]
                      ),
                      obs=torch.ones(d2.size()),
                  )
  
imports: import math
  from collections import defaultdict
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.infer.tracegraph_elbo import TrackNonReparam, _compute_downstream_costs
  from pyro.infer.util import MultiFrameTensor, get_plate_stacks
  from pyro.ops.provenance import get_provenance
  from pyro.poutine.util import prune_subsample_sites
  from tests.common import assert_equal
label_desc: Use image node_id 3 for calling a global function with example usage: big_model_guide(include_obs, include_single, include_inner_1, flip_c23, include_triple, include_z1) without return types