code: def test_multipart_bijector(self):
      seed_stream = test_util.test_seed_stream()
  
      prior = jds.JointDistributionSequential(
          [
              gamma.Gamma(1.0, 1.0),
              lambda scale: uniform.Uniform(0.0, scale),
              lambda concentration: cholesky_lkj.CholeskyLKJ(
                  4, concentration
              ),
          ],
          validate_args=True,
      )
      likelihood = lambda corr: mvn_tril.MultivariateNormalTriL(
          scale_tril=corr
      )
      obs = self.evaluate(
          likelihood(prior.sample(seed=seed_stream())[-1]).sample(
              seed=seed_stream()
          )
      )
  
      bij = prior.experimental_default_event_space_bijector()
  
      def target_log_prob(s, conc, corr):
          return prior.log_prob(s, conc, corr) + likelihood(
              corr
          ).log_prob(obs)
  
      kernel = hmc.HamiltonianMonteCarlo(
          target_log_prob, num_leapfrog_steps=3, step_size=0.5
      )
      kernel = transformed_kernel.TransformedTransitionKernel(
          kernel, bij
      )
  
      init = self.evaluate(
          tuple(
              tf.random.uniform(s, -2.0, 2.0, seed=seed_stream())
              for s in bij.inverse_event_shape(prior.event_shape)
          )
      )
      state = bij.forward(init)
      kr = kernel.bootstrap_results(state)
      next_state, next_kr = kernel.one_step(
          state, kr, seed=seed_stream()
      )
      self.evaluate((state, kr, next_state, next_kr))
      expected = target_log_prob(*state) - bij.inverse_log_det_jacobian(
          state, [0, 0, 2]
      )
      actual = kernel._inner_kernel.target_log_prob_fn(
          *init
      )  # pylint: disable=protected-access
      self.assertAllClose(expected, actual)
  
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc import langevin
  from tensorflow_probability.python.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
  from tensorflow_probability.python.mcmc import transformed_kernel
label_desc: Use image node_id 15 for calling the TransformedTransitionKernelTest obj's underlying member method code with example usage: obj.test_multipart_bijector() and returns: unknown