code: def test_get_dependencies(grad_enabled):
      def model(data):
          a = pyro.sample("a", dist.Normal(0, 1))
          b = pyro.sample("b", NonreparameterizedNormal(a, 0))
          c = pyro.sample("c", dist.Normal(b, 1))
          d = pyro.sample("d", dist.Normal(a, c.exp()))
  
          e = pyro.sample("e", dist.Normal(0, 1))
          f = pyro.sample("f", dist.Normal(0, 1))
          g = pyro.sample(
              "g", dist.Bernoulli(logits=e + f), obs=torch.tensor(0.0)
          )
  
          with pyro.plate("p", len(data)):
              d_ = d.detach()  # this results in a known failure
              h = pyro.sample("h", dist.Normal(c, d_.exp()))
              i = pyro.deterministic("i", h + 1)
              j = pyro.sample("j", dist.Delta(h + 1), obs=h + 1)
              k = pyro.sample("k", dist.Normal(a, j.exp()), obs=data)
  
          return [a, b, c, d, e, f, g, h, i, j, k]
  
      data = torch.randn(3)
      with torch.set_grad_enabled(grad_enabled):
          actual = get_dependencies(model, (data,))
      _ = set()
      expected = {
          "prior_dependencies": {
              "a": {"a": _},
              "b": {"b": _, "a": _},
              "c": {"c": _, "b": _},
              "d": {"d": _, "c": _, "a": _},
              "e": {"e": _},
              "f": {"f": _},
              "g": {"g": _, "e": _, "f": _},
              "h": {"h": _, "c": _, "d": _},
              "k": {"k": _, "a": _, "h": _},
          },
          "posterior_dependencies": {
              "a": {"a": _, "b": _, "c": _, "d": _, "h": _, "k": _},
              "b": {"b": _, "c": _},
              "c": {"c": _, "d": _, "h": _},
              "d": {"d": _, "h": _},
              "e": {"e": _, "g": _, "f": _},
              "f": {"f": _, "g": _},
              "h": {"h": _, "k": _},
          },
      }
      assert actual == expected
  
imports: import pytest
  import torch
  import pyro
  import pyro.distributions
  from pyro.distributions.testing.fakes import NonreparameterizedNormal
  from pyro.infer.inspect import _deep_merge, get_dependencies, get_model_relations
label_desc: Use image node_id 1 for calling a global function with example usage: test_get_dependencies(grad_enabled) and returns: list