code: def __init__(
      self,
      inner_kernel,
      log_accept_ratio,
      store_parameters_in_results=False,
  ):
      self.inner_kernel = inner_kernel
      self.parameters = dict(
          inner_kernel=inner_kernel,
          log_accept_ratio=log_accept_ratio,
          store_parameters_in_results=store_parameters_in_results,
      )
  
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 1 to create a new FakeMHKernel object from inherited base classes: kernel_lib with example: obj = FakeMHKernel(inner_kernel, log_accept_ratio, store_parameters_in_results)