code: def testProbSwapNumReplicaNoBatch(self, prob_swap, num_replica):
      fn = replica_exchange_mc.default_swap_proposal_fn(prob_swap)
      num_results = 100
      seeds = samplers.split_seed(test_util.test_seed(), n=num_results)
      swaps = tf.stack(
          [fn(num_replica, seed=seeds[i]) for i in range(num_results)],
          axis=0,
      )
  
      self.assertAllEqual((num_results, num_replica), swaps.shape)
      self.check_swaps_with_no_batch_shape(
          self.evaluate(swaps), prob_swap
      )
  
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 1 for calling the DefaultSwapProposedFnTest obj's underlying member method code with example usage: obj.testProbSwapNumReplicaNoBatch(prob_swap, num_replica) without return types