code: def _simulate_walks(
      self,
      nodes,
      num_walks,
      walk_length,
  ):
      walks = []
      for _ in range(num_walks):
          random.shuffle(nodes)
          for v in nodes:
              if self.p == 1 and self.q == 1:
                  walks.append(
                      self.deepwalk_walk(
                          walk_length=walk_length, start_node=v
                      )
                  )
              elif self.use_rejection_sampling:
                  walks.append(
                      self.node2vec_walk2(
                          walk_length=walk_length, start_node=v
                      )
                  )
              else:
                  walks.append(
                      self.node2vec_walk(
                          walk_length=walk_length, start_node=v
                      )
                  )
      return walks
  
imports: import itertools
  import math
  import random
  import pandas
  from joblib import Parallel, delayed
  from .alias import alias_sample, create_alias_table
  from .utils import partition_num
label_desc: Use image node_id 6 for calling the RandomWalker obj's underlying member method code with example usage: obj._simulate_walks(nodes, num_walks, walk_length) and returns: walks