code: def testStepSizeDecreases(self):
      kernel = FakeMHKernel(
          FakeSteppedKernel(step_size=0.1),
          # Mean is just under the target_accept_prob.
          # Log-accept ratio over 0. gets clipped to 0.
          log_accept_ratio=tf.stack(
              [tf.math.log(0.24), tf.math.log(1.0), tf.math.log(100.0)]
          ),
      )
      kernel = sssa.SimpleStepSizeAdaptation(
          kernel,
          num_adaptation_steps=1,
          adaptation_rate=_RATE - 1.0,
          validate_args=True,
      )
  
      kernel_results = kernel.bootstrap_results(tf.zeros(3))
      for _ in range(2):
          _, kernel_results = kernel.one_step(
              tf.zeros(3), kernel_results
          )
  
      step_size = self.evaluate(
          kernel_results.inner_results.accepted_results.step_size,
      )
  
      self.assertAllClose(0.1 / _RATE, step_size)
  
imports: import collections
  import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.experimental.mcmc import sharded
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
label_desc: Use image node_id 3 for calling the SimpleStepSizeAdaptationTest obj's underlying member method code with example usage: obj.testStepSizeDecreases() without return types