code: def stable_model():
      zero = torch.zeros(2)
      a = pyro.sample("a", dist.Normal(0, 1))
      b = pyro.sample("b", dist.LogNormal(0, 1))
      c = pyro.sample("c", dist.Stable(1.5, 0.0, b, a))
      d = pyro.sample("d", dist.Stable(1.5, 0.0, b, 0.0), obs=a)
      e = pyro.sample("e", dist.Stable(1.5, 0.1, b, a))
      f = pyro.sample("f", dist.Stable(1.5, 0.1, b, 0.0), obs=a)
      g = pyro.sample("g", dist.Stable(1.5, zero, b, a).to_event(1))
      h = pyro.sample(
          "h", dist.Stable(1.5, zero, b, 0).to_event(1), obs=a
      )
      i = pyro.sample(
          "i",
          dist.TransformedDistribution(
              dist.Stable(1.5, 0, b, a), dist.transforms.ExpTransform()
          ),
      )
      j = pyro.sample(
          "j",
          dist.TransformedDistribution(
              dist.Stable(1.5, 0, b, a), dist.transforms.ExpTransform()
          ),
          obs=a.exp(),
      )
      k = pyro.sample(
          "k",
          dist.TransformedDistribution(
              dist.Stable(1.5, zero, b, a),
              dist.transforms.ExpTransform(),
          ).to_event(1),
      )
      l = pyro.sample(
          "l",
          dist.TransformedDistribution(
              dist.Stable(1.5, zero, b, a),
              dist.transforms.ExpTransform(),
          ).to_event(1),
          obs=a.exp() + zero,
      )
      return a, b, c, d, e, f, g, h, i, j, k, l
  
imports: import pytest
  import torch
  import pyro
  import pyro.distributions
  from pyro import poutine
  from pyro.infer import SVI, Predictive, Trace_ELBO
  from pyro.infer.autoguide import AutoNormal
  from pyro.infer.reparam import AutoReparam, MinimalReparam
  from pyro.optim import Adam
label_desc: Use image node_id 5 for calling a global function with example usage: stable_model() and returns: a,  b,  c,  d,  e,  f,  g,  h,  i,  j,  k,  l