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","`","."]
docstr: Return `dict` of ``__init__`` arguments and their values.
docstr_tok: ["Return","`","dict","`","of","``","__init__","``","arguments","and","their","values","."]
code: def parameters(self):
      """Return `dict` of ``__init__`` arguments and their values."""
      return self._parameters
  
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 5 for calling the UncalibratedRandomWalk obj's underlying member method code with example usage: obj.parameters() and returns: self