class_docstr: Discards samples to perform thinning.
  
  `ThinningKernel` is a composable `TransitionKernel` that thins samples
  returned by its `inner_kernel`. All Transition Kernels wrapping it will only
  see non-discarded samples.
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).
  
  Returns:
    kernel_results: `collections.namedtuple` of `Tensor`s representing
      internal calculations made within this function.
docstr_tok: ["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",")",".","Returns",":","kernel_results",":","`","collections.namedtuple","`","of","`","Tensor","`","s","representing","internal","calculations","made","within","this","function","."]
code: def bootstrap_results(self, init_state):
      """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).
  
      Returns:
        kernel_results: `collections.namedtuple` of `Tensor`s representing
          internal calculations made within this function.
      """
      return self.inner_kernel.bootstrap_results(init_state)
  
file_path: probability/tensorflow_probability/python/experimental/mcmc/thinning_kernel.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import step
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 3 for calling the ThinningKernel obj's underlying member method code with example usage: obj.bootstrap_results(init_state) and returns: self