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 `HamiltonianMonteCarlo(...)`
  or `MetropolisHastings(UncalibratedHamiltonianMonteCarlo(...))`.
  
  For more details on `UncalibratedHamiltonianMonteCarlo`, see
  `HamiltonianMonteCarlo`.
class_docstr_tok: ["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","`","HamiltonianMonteCarlo","(","...",")","`","or","`","MetropolisHastings","(","UncalibratedHamiltonianMonteCarlo","(","...",")",")","`",".","For","more","details","on","`","UncalibratedHamiltonianMonteCarlo","`",",","see","`","HamiltonianMonteCarlo","`","."]
code: def is_calibrated(self):
      return False
  
imports: import collections
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.mcmc import kernel
  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
  from tensorflow.python.util import deprecation
label_desc: Use image node_id 8 for calling the UncalibratedHamiltonianMonteCarlo obj's underlying member method code with example usage: obj.is_calibrated() and returns: False