code: def test_nuts_kernel_tracks_axis_names(self):
      kernel = nuts.NoUTurnSampler(
          normal.Normal(0, 1).log_prob, step_size=1.9
      )
      self.assertIsNone(kernel.experimental_shard_axis_names)
      kernel = nuts.NoUTurnSampler(
          normal.Normal(0, 1).log_prob,
          step_size=1.9,
          experimental_shard_axis_names=["a"],
      )
      self.assertListEqual(kernel.experimental_shard_axis_names, ["a"])
      kernel = nuts.NoUTurnSampler(
          normal.Normal(0, 1).log_prob, step_size=1.9
      ).experimental_with_shard_axes(["a"])
      self.assertListEqual(kernel.experimental_shard_axis_names, ["a"])
  
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 1 for calling the DistributedNutsTest obj's underlying member method code with example usage: obj.test_nuts_kernel_tracks_axis_names() without return types