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.
docstr: Initializes this transition kernel.
  
  Args:
    inner_kernel: `TransitionKernel`-like object.
    num_adaptation_steps: Scalar `int` `Tensor` number of initial steps during
      which to adjust the step size and mass matrix. This may be greater, less
      than, or equal to the number of burnin steps.
    name: Python `str` name prefixed to Ops created by this class. Default:
      'windowed_adaptation'.
docstr_tok: ["Initializes","this","transition","kernel",".","Args",":","inner_kernel",":","`","TransitionKernel","`","-like","object",".","num_adaptation_steps",":","Scalar","`","int","`","`","Tensor","`","number","of","initial","steps","during","which","to","adjust","the","step","size","and","mass","matrix",".","This","may","be","greater",",","less","than",",","or","equal","to","the","number","of","burnin","steps",".","name",":","Python","`","str","`","name","prefixed","to","Ops","created","by","this","class",".","Default",":","'windowed_adaptation","'","."]
code: def __init__(self, inner_kernel, num_adaptation_steps, name=None):
      """Initializes this transition kernel.
  
      Args:
        inner_kernel: `TransitionKernel`-like object.
        num_adaptation_steps: Scalar `int` `Tensor` number of initial steps during
          which to adjust the step size and mass matrix. This may be greater, less
          than, or equal to the number of burnin steps.
        name: Python `str` name prefixed to Ops created by this class. Default:
          'windowed_adaptation'.
      """
      inner_kernel = mcmc_util.enable_store_parameters_in_results(
          inner_kernel
      )
      self._parameters = dict(
          inner_kernel=inner_kernel,
          num_adaptation_steps=num_adaptation_steps,
          name=name,
      )
  
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 1 to create a new WindowedAdaptation object from inherited base classes: kernel_base with example: obj = WindowedAdaptation(inner_kernel, num_adaptation_steps, name)