class_docstr: Performs uniform random walks on the given graph
  
  .. seealso::
  
     Related functionality:
  
     - :class:`.UnsupervisedSampler` for transforming random walks into links for unsupervised training of link prediction models
     - Other random walks: :class:`.BiasedRandomWalk`, :class:`.UniformRandomMetaPathWalk`, :class:`.TemporalRandomWalk`.
  
  Args:
      graph (StellarGraph): Graph to traverse
      n (int, optional): Total number of random walks per root node
      length (int, optional): Maximum length of each random walk
      seed (int, optional): Random number generator seed
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code: def _walk(self, rs, start_node, length):
      walk = [start_node]
      current_node = start_node
      for _ in range(length - 1):
          neighbours = self.graph.neighbor_arrays(
              current_node, use_ilocs=True
          )
          if len(neighbours) == 0:
              # dead end, so stop
              break
          else:
              # has neighbours, so pick one to walk to
              current_node = rs.choice(neighbours)
          walk.append(current_node)
  
      return list(self.graph.node_ilocs_to_ids(walk))
  
imports: import numpy
  import warnings
  from collections import defaultdict, deque
  from scipy import stats
  from scipy.special import softmax
  from ..core.schema import GraphSchema
  from ..core.graph import StellarGraph
  from ..core.utils import is_real_iterable
  from ..core.validation import require_integer_in_range, comma_sep
  from ..random import random_state
  from abc import ABC, abstractmethod
label_desc: Use image node_id 3 for calling the UniformRandomWalk obj's underlying member method code with example usage: obj._walk(rs, start_node, length) and returns: list