code: def test_thinning_and_burnin(self):
      fake_kernel = test_fixtures.TestTransitionKernel()
      fake_reducer = test_fixtures.NaiveMeanReducer()
      reduction_rslt, last_sample, kr = sample_fold(
          num_steps=5,
          current_state=0.0,
          kernel=fake_kernel,
          reducer=fake_reducer,
          num_burnin_steps=10,
          num_steps_between_results=1,
          seed=test_util.test_seed(),
      )
      reduction_rslt, last_sample, kernel_results = self.evaluate(
          [reduction_rslt, last_sample, kr]
      )
      self.assertEqual(16, reduction_rslt)
      self.assertEqual(20, last_sample)
      self.assertEqual(20, kernel_results.counter_1)
      self.assertEqual(40, kernel_results.counter_2)
  
imports: import collections
  import warnings
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.mcmc import covariance_reducer
  from tensorflow_probability.python.experimental.mcmc import expectations_reducer
  from tensorflow_probability.python.experimental.mcmc.internal import test_fixtures
  from tensorflow_probability.python.experimental.mcmc.sample_fold import sample_chain_with_burnin
  from tensorflow_probability.python.experimental.mcmc.sample_fold import sample_fold
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
label_desc: Use image node_id 9 for calling the SampleFoldTest obj's underlying member method code with example usage: obj.test_thinning_and_burnin() without return types