code: def testKernelResultsHaveCorrectShapeWhenMultipleStatesAndBatchDims(
      self, state_includes_replicas
  ):
      def target_log_prob(x, y):
          xy = tf.concat([x, y], axis=-1)
          return -0.5 * tf.reduce_sum(xy**2, axis=-1)
  
      def make_kernel_fn(target_log_prob_fn):
          return random_walk_metropolis.RandomWalkMetropolis(
              target_log_prob_fn=target_log_prob_fn,
              new_state_fn=random_walk_metropolis.random_walk_normal_fn(
                  scale=[0.3, 0.1]
              ),
          )
  
      inverse_temperatures = [1.0, 0.5, 0.25, 0.1]
      remc = replica_exchange_mc.ReplicaExchangeMC(
          target_log_prob_fn=target_log_prob,
          inverse_temperatures=inverse_temperatures,
          state_includes_replicas=state_includes_replicas,
          make_kernel_fn=make_kernel_fn,
      )
  
      num_results = 6
      n_batch = 5
      n_events = 3
      n_states = 2  # Set by target_log_prob.
      num_replica = len(inverse_temperatures)
  
      if state_includes_replicas:
          current_state = [
              tf.zeros((num_replica, n_batch, n_events))
          ] * n_states
      else:
          current_state = [tf.zeros((n_batch, n_events))] * n_states
  
      states, kernel_results = sample.sample_chain(
          num_results=num_results,
          current_state=current_state,
          kernel=remc,
          num_burnin_steps=2,
          trace_fn=lambda _, results: results,
          seed=test_util.test_seed(),
      )
  
      self.assertLen(states, n_states)
  
      if state_includes_replicas:
          state_shape = (num_results, num_replica, n_batch, n_events)
      else:
          state_shape = (num_results, n_batch, n_events)
  
      self.assertAllEqual(state_shape, states[0].shape)
      self.assertAllEqual(state_shape, states[1].shape)
  
      kr_ = self.evaluate(kernel_results)
  
      # Boring checks of existence/shape.
      self.assertEqual(
          (num_results, num_replica, n_batch, n_states, n_events),
          tf.stack(kr_.post_swap_replica_states, axis=-2).shape,
      )
  
      self.assertEqual(
          (num_results, num_replica, n_batch),
          kr_.pre_swap_replica_results.log_accept_ratio.shape,
      )
  
      self.assertEqual(
          (num_results, num_replica, n_batch),
          kr_.post_swap_replica_results.log_accept_ratio.shape,
      )
  
      self.assertEqual(
          (num_results, num_replica, num_replica, n_batch),
          kr_.is_swap_proposed.shape,
      )
      self.assertEqual(
          (num_results, num_replica, num_replica, n_batch),
          kr_.is_swap_accepted.shape,
      )
  
      self.assertEqual(
          (num_results, num_replica - 1, n_batch),
          kr_.is_swap_proposed_adjacent.shape,
      )
      self.assertEqual(
          (num_results, num_replica - 1, n_batch),
          kr_.is_swap_accepted_adjacent.shape,
      )
  
      self.assertEqual(
          (num_results, num_replica),
          tf.stack(kr_.inverse_temperatures, axis=1).shape,
      )
  
      self.assertEqual(
          (num_results, num_replica, n_batch), kr_.swaps.shape
      )
  
imports: from absl import logging
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_combinations
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import unnest
  from tensorflow_probability.python.mcmc import diagnostic
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import replica_exchange_mc
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 10 for calling the REMCTest obj's underlying member method code with example usage: obj.testKernelResultsHaveCorrectShapeWhenMultipleStatesAndBatchDims(state_includes_replicas) and returns: unknown, random_walk_metropolis