code: def testNoBatchOneReplica(self):
      # There is only one replica, so in all cases swaps = [0].
      for swap_frequency in [0.0, 0.5, 1.0]:
          fn = replica_exchange_mc.even_odd_swap_proposal_fn(
              swap_frequency=swap_frequency
          )
          for step_count in range(5):
              swaps = fn(num_replica=1, step_count=step_count)
              self.assertAllEqual((1,), swaps.shape)
              swaps_ = self.evaluate(swaps)
              self.assertAllEqual(swaps_, [0])
  
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 2 for calling the EvenOddSwapProposedFnTest obj's underlying member method code with example usage: obj.testNoBatchOneReplica() without return types