code: def make_transform_then_adapt_kernel(bijector):
      trans_kernel = transformed_kernel.TransformedTransitionKernel(
          inner_kernel=FakeInnerKernel(
              target_log_prob_fn=fake_target_log_prob
          ),
          bijector=bijector,
      )
      return sssa.SimpleStepSizeAdaptation(
          inner_kernel=trans_kernel, num_adaptation_steps=9
      )
  
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 2 for calling a global function with example usage: make_transform_then_adapt_kernel(bijector) and returns: sssa