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 one_step(self, current_state, previous_kernel_results, seed=None):
      if seed is not None and not self._accepts_seed:
          raise TypeError("seed arg not accepted")
      new_state = random_ops.rayleigh(self._shape, seed=seed)
      return new_state, TestTransitionKernelResults(
          counter_1=previous_kernel_results.counter_1 + 1,
          counter_2=previous_kernel_results.counter_2 + 2,
      )
  
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 2 for calling the RandomTransitionKernel obj's underlying member method code with example usage: obj.one_step(current_state, previous_kernel_results, seed) and returns: new_state,  TestTransitionKernelResults