class_docstr: Outputs a random next state following a Rayleigh distribution.
class_docstr_tok: ["Outputs","a","random","next","state","following","a","Rayleigh","distribution","."]
code: def __init__(self, shape=(), is_calibrated=True, accepts_seed=True):
      self._shape = shape
      self._is_calibrated = is_calibrated
      self._accepts_seed = accepts_seed
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import reducer
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.random import random_ops
label_desc: Use image node_id 1 to create a new RandomTransitionKernel object from inherited base classes: kernel with example: obj = RandomTransitionKernel(shape, is_calibrated, accepts_seed)