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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code: def is_calibrated(self):
      return self.inner_kernel.is_calibrated
  
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 5 for calling the SampleDiscardingKernel obj's underlying member method code with example usage: obj.is_calibrated() and returns: self