code: def testWithUntemperedLPGeometricAndStateParts(self):
      inverse_temperatures = tf.convert_to_tensor([1.0, 0.5, 0.25])
  
      # The tolerance on these is not very tight...
      # However, I (langmore) tried running these with num_results much higher,
      # and we get progressively better results...so the algorithm is correct, but
      # the sampling is inefficient (no preconditioning).
      if tf.executing_eagerly():
          num_results = 50
          tol_multiplier = 25
          expected_max_conditional_swap_prob = 0.99
      else:
          num_results = 5000
          tol_multiplier = 30
          expected_max_conditional_swap_prob = 0.9
  
      self.checkAndMakeResultsForTestingUntemperedLogProbFn(
          likelihood_variance=[
              tf.convert_to_tensor([0.25] * 2),
              tf.convert_to_tensor([0.45] * 2),
          ],
          prior_variance=[
              tf.convert_to_tensor([0.5] * 2),
              tf.convert_to_tensor([1.0] * 2),
          ],
          inverse_temperatures=inverse_temperatures,
          num_results=num_results,
          # The temperatures were dispersed geometrically, so expect a reasonable
          # range of conditional swap probs.
          expected_min_conditional_swap_prob=0.2,
          expected_max_conditional_swap_prob=expected_max_conditional_swap_prob,
          tol_multiplier=tol_multiplier,
      )
  
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 17 for calling the REMCTest obj's underlying member method code with example usage: obj.testWithUntemperedLPGeometricAndStateParts() without return types