message: DOCFIX:  hmc.sample_chain kwarg num_steps_between_results docstring seemed to
  indicate a different type of thinning than what is actually going on.
diff: @@ -109,10 +109,13 @@ def sample_chain(
  Note: `target_log_prob_fn` is called exactly twice.
  - Only one out of every `num_steps_between_samples + 1` steps is included in the
  - returned results. This "thinning" comes at a cost of reduced statistical
  - power, while reducing memory requirements and autocorrelation. For more
  - discussion see [1].
  + Since HMC states are correlated, it is sometimes desirable to produce
  + additional intermediate states, and then discard them, ending up with a set of
  + states with decreased autocorrelation. See [1]. Such "thinning" is made
  + possible by setting `num_steps_between_results > 0`. The chain then takes
  + `num_steps_between_results` extra steps between the steps that make it into
  + the results. The extra steps are never materialized (in calls to `sess.run`),
  + and thus do not increase memory requirements.
  [1]: "Statistically efficient thinning of a Markov chain sampler."
  Art B. Owen. April 2017.
  @@ -225,10 +228,8 @@ def sample_chain(
  Default value: 0 (i.e., no burn-in).
  num_steps_between_results: Integer number of chain steps between collecting
  a result. Only one out of every `num_steps_between_samples + 1` steps is
  - included in the returned results. This "thinning" comes at a cost of
  - reduced statistical power, while reducing memory requirements and
  - autocorrelation. For more discussion see [1].
  - Default value: 0 (i.e., no subsampling).
  + included in the returned results. The number of returned chain states is
  + still equal to `num_results`. Default value: 0 (i.e., no thinning).
  seed: Python integer to seed the random number generator.
  current_target_log_prob: (Optional) `Tensor` representing the value of
  `target_log_prob_fn` at the `current_state`. The only reason to specify
  