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 __init__(
      self,
      target_log_prob_fn,
      step_size,
      num_leapfrog_steps,
      momentum_distribution=None,
      state_gradients_are_stopped=False,
      store_parameters_in_results=False,
      experimental_shard_axis_names=None,
      name=None,
  ):
      super(
          UncalibratedPreconditionedHamiltonianMonteCarlo, self
      ).__init__(
          target_log_prob_fn,
          step_size,
          num_leapfrog_steps,
          state_gradients_are_stopped=state_gradients_are_stopped,
          store_parameters_in_results=store_parameters_in_results,
          experimental_shard_axis_names=experimental_shard_axis_names,
          name=name,
      )
      self._parameters["momentum_distribution"] = momentum_distribution
      self._parameters.pop("seed", None)
  
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 1 to create a new UncalibratedPreconditionedHamiltonianMonteCarlo object from inherited base classes: hmc with example: obj = UncalibratedPreconditionedHamiltonianMonteCarlo(target_log_prob_fn, step_size, num_leapfrog_steps, momentum_distribution, state_gradients_are_stopped, store_parameters_in_results, experimental_shard_axis_names, name)