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 this object.
  
  Args:
    inner_kernel: `TransitionKernel` whose `one_step` will generate
      MCMC results.
    num_steps_to_skip: Integer or scalar `Tensor` representing
      the number of chain steps skipped before collecting a result.
    name: Python `str` name prefixed to Ops created by this function.
      Default value: `None` (i.e., "thinning_kernel").
docstr_tok: ["Instantiates","this","object",".","Args",":","inner_kernel",":","`","TransitionKernel","`","whose","`","one_step","`","will","generate","MCMC","results",".","num_steps_to_skip",":","Integer","or","scalar","`","Tensor","`","representing","the","number","of","chain","steps","skipped","before","collecting","a","result",".","name",":","Python","`","str","`","name","prefixed","to","Ops","created","by","this","function",".","Default","value",":","`","None","`","(","i.e.",",","``","thinning_kernel","''",")","."]
code: def __init__(self, inner_kernel, num_steps_to_skip, name=None):
      """Instantiates this object.
  
      Args:
        inner_kernel: `TransitionKernel` whose `one_step` will generate
          MCMC results.
        num_steps_to_skip: Integer or scalar `Tensor` representing
          the number of chain steps skipped before collecting a result.
        name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., "thinning_kernel").
      """
      self._parameters = dict(
          inner_kernel=inner_kernel,
          num_steps_to_skip=num_steps_to_skip,
          name=name or "thinning_kernel",
      )
  
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 1 to create a new ThinningKernel object from inherited base classes: kernel_base with example: obj = ThinningKernel(inner_kernel, num_steps_to_skip, name)