code: def testBasicOperation(self):
      kernel = TestTransitionKernel()
      samples, kernel_results = sample_chain(
          num_results=2,
          current_state=0,
          kernel=kernel,
          seed=test_util.test_seed(),
      )
  
      self.assertAllClose([2], tensorshape_util.as_list(samples.shape))
      self.assertAllClose(
          [2], tensorshape_util.as_list(kernel_results.counter_1.shape)
      )
      self.assertAllClose(
          [2], tensorshape_util.as_list(kernel_results.counter_2.shape)
      )
  
      samples, kernel_results = self.evaluate([samples, kernel_results])
      self.assertAllClose([1, 2], samples)
      self.assertAllClose([1, 2], kernel_results.counter_1)
      self.assertAllClose([2, 4], kernel_results.counter_2)
  
imports: import collections
  import warnings
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.mcmc import diagonal_mass_matrix_adaptation
  from tensorflow_probability.python.experimental.mcmc import preconditioned_hmc
  from tensorflow_probability.python.experimental.stats import sample_stats
  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
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc import nuts
  from tensorflow_probability.python.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
  from tensorflow_probability.python.mcmc import transformed_kernel
  from tensorflow_probability.python.mcmc.sample import sample_chain
  from tensorflow_probability.python.random.random_ops import rayleigh
label_desc: Use image node_id 3 for calling the SampleChainTest obj's underlying member method code with example usage: obj.testBasicOperation() without return types