code: def test_stable_hmm_shape_error(
      batch_shape, duration, hidden_dim, obs_dim
  ):
      stability = dist.Uniform(0.5, 2).sample(batch_shape)
      init_dist = random_stable(
          batch_shape + (hidden_dim,), stability.unsqueeze(-1)
      ).to_event(1)
      trans_mat = torch.randn(batch_shape + (1, hidden_dim, hidden_dim))
      trans_dist = random_stable(
          batch_shape + (1, hidden_dim),
          stability.unsqueeze(-1).unsqueeze(-1),
      ).to_event(1)
      obs_mat = torch.randn(batch_shape + (1, hidden_dim, obs_dim))
      obs_dist = random_stable(
          batch_shape + (1, obs_dim),
          stability.unsqueeze(-1).unsqueeze(-1),
      ).to_event(1)
      hmm = dist.LinearHMM(
          init_dist, trans_mat, trans_dist, obs_mat, obs_dist
      )
      assert hmm.batch_shape == batch_shape
      assert hmm.event_shape == (1, obs_dim)
  
      def model(data=None):
          with pyro.plate_stack("plates", batch_shape):
              return pyro.sample("x", hmm, obs=data)
  
      data = torch.randn(duration, obs_dim)
      rep = StableReparam()
      with poutine.reparam(
          config={"x": LinearHMMReparam(rep, rep, rep)}
      ):
          with pytest.raises(ValueError):
              model(data)
  
file_path: pyro/tests/infer/reparam/test_hmm.py
imports: import pytest
  import torch
  import pyro
  import pyro.distributions
  from pyro import poutine
  from pyro.infer.reparam import LinearHMMReparam, StableReparam, StudentTReparam, SymmetricStableReparam
  from tests.common import assert_close
  from tests.ops.gaussian import random_mvn
  from .util import check_init_reparam
label_desc: Use image node_id 9 for calling a global function with example usage: test_stable_hmm_shape_error(batch_shape, duration, hidden_dim, obs_dim) and returns: pyro