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 a new kernel state with no calls.
  
  Args:
    init_state: `Tensor` or Python `list` of `Tensor`s representing the
      state(s) of the Markov chain(s).
    inner_results: Optional results tuple for the inner kernel.  Will be
      re-bootstrapped if omitted.
  
  Returns:
    kernel_results: `collections.namedtuple` of `Tensor`s representing
      internal calculations made within this function.
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code: def bootstrap_results(self, init_state, inner_results=None):
      """Instantiates a new kernel state with no calls.
  
      Args:
        init_state: `Tensor` or Python `list` of `Tensor`s representing the
          state(s) of the Markov chain(s).
        inner_results: Optional results tuple for the inner kernel.  Will be
          re-bootstrapped if omitted.
  
      Returns:
        kernel_results: `collections.namedtuple` of `Tensor`s representing
          internal calculations made within this function.
      """
      with tf.name_scope(
          mcmc_util.make_name(
              self.name, "sample_discarding_kernel", "bootstrap_results"
          )
      ):
          if inner_results is None:
              inner_results = self.inner_kernel.bootstrap_results(
                  init_state
              )
          return SampleDiscardingKernelResults(
              tf.zeros((), dtype=tf.int32), inner_results
          )
  
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 4 for calling the SampleDiscardingKernel obj's underlying member method code with example usage: obj.bootstrap_results(init_state, inner_results) and returns: SampleDiscardingKernelResults