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 bootstrap_results(self, init_state):
      with tf.name_scope(
          mcmc_util.make_name(self.name, "rwm", "bootstrap_results")
      ):
          if not mcmc_util.is_list_like(init_state):
              init_state = [init_state]
          init_state = [tf.convert_to_tensor(x) for x in init_state]
          init_target_log_prob = self.target_log_prob_fn(
              *init_state
          )  # pylint:disable=not-callable
          return UncalibratedRandomWalkResults(
              log_acceptance_correction=tf.zeros_like(
                  init_target_log_prob
              ),
              target_log_prob=init_target_log_prob,
              # Allow room for one_step's seed.
              seed=samplers.zeros_seed(),
          )
  
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 8 for calling the UncalibratedRandomWalk obj's underlying member method code with example usage: obj.bootstrap_results(init_state) and returns: UncalibratedRandomWalkResults