code: def test_nested_joint_distribution(self):
      def model():
          x = yield normal.Normal(loc=-2.0, scale=1.0)
          yield jdab.JointDistributionSequentialAutoBatched(
              [
                  uniform.Uniform(
                      low=1.0 - tf.exp(x),
                      high=2.0 + tf.exp(x) + tf.nn.softplus(x),
                  ),
                  lambda v: exponential.Exponential(
                      v
                  ),  # pylint: disable=unnecessary-lambda
              ]
          )
  
      dist = jdab.JointDistributionCoroutineAutoBatched(model)
      b = distribution_bijectors.make_distribution_bijector(dist)
      self.assertDistributionIsApproximatelyStandardNormal(
          invert.Invert(b)(dist), rtol=1e-4
      )
  
imports: from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import markov_chain
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.bijectors import distribution_bijectors
  from tensorflow_probability.python.experimental.vi import automatic_structured_vi
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.mcmc import dual_averaging_step_size_adaptation
  from tensorflow_probability.python.mcmc import nuts
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import transformed_kernel
  from tensorflow_probability.python.vi import optimization
label_desc: Use image node_id 6 for calling the DistributionBijectorsTest obj's underlying member method code with example usage: obj.test_nested_joint_distribution() without return types