class_docstr: A transition kernel to control warmup adaptation.
  
  This assumes we do something proportional to
  
  fast window: 75 steps
  slow window: 25 steps
  slow window: 50 steps
  slow window: 100 steps
  slow window: 200 steps
  fast window: 50 steps
  
  Which is a total of 500 steps.
  
  We will adapt step size during both fast and slow windows. Mass matrix is
  only adapted during the slow windows.
code: def is_calibrated(self):
      return self.inner_kernel.is_calibrated
  
imports: import collections
  import functools
  import warnings
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import joint_map
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import restructure
  from tensorflow_probability.python.experimental.mcmc import diagonal_mass_matrix_adaptation
  from tensorflow_probability.python.experimental.mcmc import initialization
  from tensorflow_probability.python.experimental.mcmc import preconditioned_hmc
  from tensorflow_probability.python.experimental.mcmc import preconditioned_nuts
  from tensorflow_probability.python.experimental.mcmc import preconditioning_utils
  from tensorflow_probability.python.experimental.mcmc import sharded
  from tensorflow_probability.python.experimental.stats import sample_stats
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import unnest
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.mcmc import dual_averaging_step_size_adaptation
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc.internal import util
  from tensorflow.python.ops import control_flow_util
  from tensorflow.python.util import nest
label_desc: Use image node_id 8 for calling the WindowedAdaptation obj's underlying member method code with example usage: obj.is_calibrated() and returns: self