code: def test_sample_distributions_not_composite_tensor_raises_error(self):
      def coroutine_model():
          yield transformed_distribution.TransformedDistribution(
              normal.Normal(0.0, 1.0),
              bijector_test_util.NonCompositeTensorExp(),
              name="td",
          )
  
      joint = jdab.JointDistributionCoroutineAutoBatched(
          coroutine_model
      )
  
      # Sampling with trivial sample shape avoids the vmap codepath.
      ds, _ = joint.sample_distributions([], seed=test_util.test_seed())
      self.assertIsInstance(
          ds[0],
          # Non-CompositeTensor version of TransformedDistribution.
          transformed_distribution._TransformedDistribution,
      )
  
      with self.assertRaisesRegex(
          TypeError,
          r"Some component distribution\(s\) cannot be returned",
      ):
          joint.sample_distributions([4, 3], seed=test_util.test_seed())
  
imports: import collections
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 9 for calling the JointDistributionAutoBatchedTest obj's underlying member method code with example usage: obj.test_sample_distributions_not_composite_tensor_raises_error() without return types