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: Calculates how many samples to skip based on the call number.
docstr_tok: ["Calculates","how","many","samples","to","skip","based","on","the","call","number","."]
code: def _num_samples_to_skip(self, call_counter):
      """Calculates how many samples to skip based on the call number."""
      # If `self.num_burnin_steps` is statically known to be 0,
      # `self.num_steps_between_results` will be returned outright.
      num_burnin_steps = ps.convert_to_shape_tensor(
          self.num_burnin_steps, dtype_hint=tf.int32
      )
      num_burnin_steps_ = tf.get_static_value(num_burnin_steps)
      if num_burnin_steps_ == 0:
          return self.num_steps_between_results
      else:
          return tf.where(
              tf.equal(call_counter, 0), num_burnin_steps, 0
          ) + tf.convert_to_tensor(
              self.num_steps_between_results, dtype_hint=tf.int32
          )
  
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 2 for calling the SampleDiscardingKernel obj's underlying member method code with example usage: obj._num_samples_to_skip(call_counter) and returns: self, unknown