class_docstr: Runs one step of the No U-Turn Sampler.
  
  The No U-Turn Sampler (NUTS) is an adaptive variant of the Hamiltonian Monte
  Carlo (HMC) method for MCMC. NUTS adapts the distance traveled in response to
  the curvature of the target density. Conceptually, one proposal consists of
  reversibly evolving a trajectory through the sample space, continuing until
  that trajectory turns back on itself (hence the name, 'No U-Turn'). This class
  implements one random NUTS step from a given `current_state`.
  Mathematical details and derivations can be found in
  [Hoffman, Gelman (2011)][1] and [Betancourt (2018)][2].
  
  The `one_step` function can update multiple chains in parallel. It assumes
  that a prefix of leftmost dimensions of `current_state` index independent
  chain states (and are therefore updated independently).  The output of
  `target_log_prob_fn(*current_state)` should sum log-probabilities across all
  event dimensions.  Slices along the rightmost dimensions may have different
  target distributions; for example, `current_state[0][0, ...]` could have a
  different target distribution from `current_state[0][1, ...]`.  These
  semantics are governed by `target_log_prob_fn(*current_state)`. (The number of
  independent chains is `tf.size(target_log_prob_fn(*current_state))`.)
  
  #### References
  
  [1]: Matthew D. Hoffman, Andrew Gelman.  The No-U-Turn Sampler: Adaptively
  Setting Path Lengths in Hamiltonian Monte Carlo.  2011.
  https://arxiv.org/pdf/1111.4246.pdf.
  
  [2]: Michael Betancourt. A Conceptual Introduction to Hamiltonian Monte Carlo.
  _arXiv preprint arXiv:1701.02434_, 2018. https://arxiv.org/abs/1701.02434
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code: def write_instruction(self):
      return self._write_instruction
  
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import preconditioning_utils
  from tensorflow_probability.python.internal import broadcast_util
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math.generic import log_add_exp
  from tensorflow_probability.python.mcmc.internal import leapfrog_integrator
  from tensorflow_probability.python.mcmc.internal import util
  from tensorflow_probability.python.mcmc.kernel import TransitionKernel
label_desc: Use image node_id 9 for calling the PreconditionedNoUTurnSampler obj's underlying member method code with example usage: obj.write_instruction() and returns: self