class_docstr: Appropriately discards samples to conduct thinning and burn-in.
  
  `SampleDiscardingKernel` is a composable `TransitionKernel` that
  applies thinning and burn-in to samples returned by its
  `inner_kernel`. All Transition Kernels wrapping it will only
  see non-discarded samples.
  
  The burn-in step conducts both burn-in and one step of thinning.
  In other words, the first call to `one_step` will skip
  `num_burnin_steps + num_steps_between_results` samples. All
  subsequent calls skip only `num_steps_between_results` samples.
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docstr: Instantiates this object.
  
  Args:
    inner_kernel: `TransitionKernel` whose `one_step` will generate
      MCMC results.
    num_burnin_steps: Integer or scalar `Tensor` representing the number
      of chain steps to take before starting to collect results.
      Defaults to 0 (i.e., no burn-in).
    num_steps_between_results: Integer or scalar `Tensor` representing
      the number of chain steps between collecting a result. Only one out
      of every `num_steps_between_samples + 1` steps is included in the
      returned results. Defaults to 0 (i.e., no thinning).
    name: Python `str` name prefixed to Ops created by this function.
      Default value: `None` (i.e., "sample_discarding_kernel").
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code: def __init__(
      self,
      inner_kernel,
      num_burnin_steps=0,
      num_steps_between_results=0,
      name=None,
  ):
      """Instantiates this object.
  
      Args:
        inner_kernel: `TransitionKernel` whose `one_step` will generate
          MCMC results.
        num_burnin_steps: Integer or scalar `Tensor` representing the number
          of chain steps to take before starting to collect results.
          Defaults to 0 (i.e., no burn-in).
        num_steps_between_results: Integer or scalar `Tensor` representing
          the number of chain steps between collecting a result. Only one out
          of every `num_steps_between_samples + 1` steps is included in the
          returned results. Defaults to 0 (i.e., no thinning).
        name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., "sample_discarding_kernel").
      """
      self._parameters = dict(
          inner_kernel=inner_kernel,
          num_burnin_steps=num_burnin_steps,
          num_steps_between_results=num_steps_between_results,
          name=name or "sample_discarding_kernel",
      )
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import step
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 1 to create a new SampleDiscardingKernel object from inherited base classes: kernel_base with example: obj = SampleDiscardingKernel(inner_kernel, num_burnin_steps, num_steps_between_results, name)