class_docstr: Runs one step of Uncalibrated Hamiltonian Monte Carlo.
  
  Warning: this kernel will not result in a chain which converges to the
  `target_log_prob`. To get a convergent MCMC, use
  `PreconditionedHamiltonianMonteCarlo(...)` or
  `MetropolisHastings(UncalibratedPreconditionedHamiltonianMonteCarlo(...))`.
  
  For more details on `UncalibratedPreconditionedHamiltonianMonteCarlo`, see
  `PreconditionedHamiltonianMonteCarlo`.
code: def momentum_distribution(self):
      return self._parameters["momentum_distribution"]
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import preconditioning_utils
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import metropolis_hastings
  from tensorflow_probability.python.mcmc.internal import leapfrog_integrator
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 2 for calling the UncalibratedPreconditionedHamiltonianMonteCarlo obj's underlying member method code with example usage: obj.momentum_distribution() and returns: self