code: def test_specifying_distribution_type(
      self, event_shape, base_distribution_cls, is_stateless=JAX_MODE
  ):
      init_seed, sample_seed = samplers.split_seed(
          test_util.test_seed(sampler_type="stateless"), n=2
      )
      surrogate_posterior = self._initialize_surrogate(
          "build_factored_surrogate_posterior",
          is_stateless=is_stateless,
          seed=init_seed,
          event_shape=event_shape,
          base_distribution_cls=base_distribution_cls,
          validate_args=True,
      )
  
      # Test that the surrogate uses the expected distribution types.
      if tf.nest.is_nested(surrogate_posterior.event_shape):
          ds, _ = surrogate_posterior.sample_distributions(
              seed=sample_seed
          )
      else:
          ds = [surrogate_posterior]
      for cls, d in zip(
          nest_util.broadcast_structure(ds, base_distribution_cls), ds
      ):
          d = _as_concrete_instance(d)
          while isinstance(d, independent.Independent):
              d = _as_concrete_instance(d.distribution)
          self.assertIsInstance(d, cls)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import fill_triangular
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import joint_map
  from tensorflow_probability.python.bijectors import masked_autoregressive
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  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 laplace
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.experimental.vi import surrogate_posteriors
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.vi import optimization
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling the FactoredSurrogatePosterior obj's underlying member method code with example usage: obj.test_specifying_distribution_type(event_shape, base_distribution_cls, is_stateless) without return types