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 bootstrap_results(self, init_state):
      with tf.name_scope(
          mcmc_util.make_name(self.name, "phmc", "bootstrap_results")
      ):
          result = super(
              UncalibratedPreconditionedHamiltonianMonteCarlo, self
          ).bootstrap_results(init_state)
  
          state_parts, _ = mcmc_util.prepare_state_parts(
              init_state, name="current_state"
          )
          target_log_prob = result.target_log_prob
          if (
              not self._store_parameters_in_results
              or self.momentum_distribution is None
          ):
              momentum_distribution = pu.make_momentum_distribution(
                  state_parts,
                  ps.shape(target_log_prob),
                  shard_axis_names=self.experimental_shard_axis_names,
              )
          else:
              momentum_distribution = (
                  pu.maybe_make_list_and_batch_broadcast(
                      self.momentum_distribution,
                      ps.shape(target_log_prob),
                  )
              )
          result = UncalibratedPreconditionedHamiltonianMonteCarloKernelResults(
              **result._asdict(),  # pylint: disable=protected-access
              momentum_distribution=momentum_distribution,
          )
      return result
  
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 4 for calling the UncalibratedPreconditionedHamiltonianMonteCarlo obj's underlying member method code with example usage: obj.bootstrap_results(init_state) and returns: result