code: def test_markov_chain(self):
      d = markov_chain.MarkovChain(
          initial_state_prior=uniform.Uniform(low=0.0, high=1.0),
          transition_fn=lambda _, x: uniform.Uniform(  # pylint:disable=g-long-lambda
              low=0.0, high=tf.nn.softplus(x)
          ),
          num_steps=3,
      )
      b = distribution_bijectors.make_distribution_bijector(d)
      self.assertDistributionIsApproximatelyStandardNormal(
          invert.Invert(b)(d), 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 4 for calling the DistributionBijectorsTest obj's underlying member method code with example usage: obj.test_markov_chain() without return types