code: def test_bijector_for_autobatched_model(
      self, pin_batch_shape, sample_shape
  ):
      if not tf1.control_flow_v2_enabled():
          self.skipTest("b/183994961")
  
      @jdab.JointDistributionCoroutineAutoBatched
      def model():
          a = yield normal.Normal(0.0, 1.0, name="a")
          yield uniform.Uniform(
              low=0.0, high=tf.exp(a * tf.ones([2])), name="b"
          )
  
      pinned = jdp.JointDistributionPinned(
          model,
          a=tf.random.stateless_normal(
              pin_batch_shape,
              seed=test_util.test_seed(sampler_type="stateless"),
          ),
      )
      ys = self.evaluate(
          pinned.sample_unpinned(
              sample_shape,
              seed=test_util.test_seed(sampler_type="stateless"),
          )
      )
      bij = pinned.experimental_default_event_space_bijector()
      self.assertAllCloseNested(
          ys,
          bij.forward(
              tf.nest.map_structure(
                  tf.identity, bij.inverse(ys)  # Bypass bijector cache.
              )
          ),
      )
  
imports: import collections
  import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  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 multinomial
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.distributions import increment_log_prob
  from tensorflow_probability.python.experimental.distributions import joint_distribution_pinned
  from tensorflow_probability.python.internal import structural_tuple
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 12 for calling the JointDistributionPinnedTest obj's underlying member method code with example usage: obj.test_bijector_for_autobatched_model(pin_batch_shape, sample_shape) without return types