class_docstr: Generate proposal for the Random Walk Metropolis algorithm.
  
  Warning: this kernel will not result in a chain which converges to the
  `target_log_prob`. To get a convergent MCMC, use
  `tfp.mcmc.RandomWalkMetropolisNormal(...)` or
  `tfp.mcmc.MetropolisHastings(tfp.mcmc.UncalibratedRandomWalk(...))`.
  
  For more details on `UncalibratedRandomWalk`, see
  `RandomWalkMetropolis`.
class_docstr_tok: ["Generate","proposal","for","the","Random","Walk","Metropolis","algorithm",".","Warning",":","this","kernel","will","not","result","in","a","chain","which","converges","to","the","`","target_log_prob","`",".","To","get","a","convergent","MCMC",",","use","`","tfp.mcmc.RandomWalkMetropolisNormal","(","...",")","`","or","`","tfp.mcmc.MetropolisHastings","(","tfp.mcmc.UncalibratedRandomWalk","(","...",")",")","`",".","For","more","details","on","`","UncalibratedRandomWalk","`",",","see","`","RandomWalkMetropolis","`","."]
code: def __init__(
      self,
      target_log_prob_fn,
      new_state_fn=None,
      experimental_shard_axis_names=None,
      name=None,
  ):
      if new_state_fn is None:
          new_state_fn = random_walk_normal_fn()
  
      self._target_log_prob_fn = target_log_prob_fn
      self._name = name
      self._parameters = dict(
          target_log_prob_fn=target_log_prob_fn,
          new_state_fn=new_state_fn,
          experimental_shard_axis_names=experimental_shard_axis_names,
          name=name,
      )
  
imports: import collections
  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.internal import tensorshape_util
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc import metropolis_hastings
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 1 to create a new UncalibratedRandomWalk object from inherited base classes: kernel_base with example: obj = UncalibratedRandomWalk(target_log_prob_fn, new_state_fn, experimental_shard_axis_names, name)