class_docstr: Fake Transition Kernel.
class_docstr_tok: ["Fake","Transition","Kernel","."]
code: def __init__(
      self, target_log_prob_fn, is_calibrated=True, step_size=10
  ):
      self._parameters = dict(
          target_log_prob_fn=target_log_prob_fn,
          is_calibrated=is_calibrated,
          step_size=step_size,
      )
  
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc import langevin
  from tensorflow_probability.python.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
  from tensorflow_probability.python.mcmc import transformed_kernel
label_desc: Use image node_id 1 to create a new FakeInnerKernel object from inherited base classes: kernel_lib with example: obj = FakeInnerKernel(target_log_prob_fn, is_calibrated, step_size)