docstr: Neals funnel with large step size.
code: def testDivergence(self):
      """Neals funnel with large step size."""
      strm = test_util.test_seed_stream()
      neals_funnel = jds.JointDistributionSequential(
          [
              normal.Normal(loc=0.0, scale=3.0),  # b0
              lambda y: sample_dist_lib.Sample(  # pylint: disable=g-long-lambda
                  normal.Normal(loc=0.0, scale=tf.math.exp(y / 2)),
                  sample_shape=9,
              ),
          ],
          validate_args=True,
      )
  
      @tf.function(autograph=False)
      def run_chain_and_get_divergence():
          nchains = 5
          init_states = neals_funnel.sample(nchains, seed=strm())
          _, has_divergence = sample.sample_chain(
              num_results=100,
              kernel=nuts.NoUTurnSampler(
                  target_log_prob_fn=lambda *args: neals_funnel.log_prob(
                      args
                  ),
                  step_size=[1.0, 1.0],
              ),
              current_state=init_states,
              trace_fn=lambda _, pkr: pkr.has_divergence,
              seed=strm(),
          )
          return tf.reduce_sum(tf.cast(has_divergence, dtype=tf.int32))
  
      divergence_count = self.evaluate(run_chain_and_get_divergence())
  
      # Test that we observe a fair among of divergence.
      self.assertAllGreater(divergence_count, 100)
  
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 identity
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  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 sample
  from tensorflow_probability.python.distributions import sigmoid_beta
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.mcmc import diagnostic
  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.stats import sample_stats
label_desc: Use image node_id 12 for calling the NutsTest obj's underlying member method code with example usage: obj.testDivergence() and returns: tf