code: def _simulate_walks(
      self,
      nodes,
      num_walks,
      walk_length,
      stay_prob,
      layers_adj,
      layers_accept,
      layers_alias,
      gamma,
  ):
      walks = []
      for _ in range(num_walks):
          random.shuffle(nodes)
          for v in nodes:
              walks.append(
                  self._exec_random_walk(
                      layers_adj,
                      layers_accept,
                      layers_alias,
                      v,
                      walk_length,
                      gamma,
                      stay_prob,
                  )
              )
      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 3 for calling the BiasedWalker obj's underlying member method code with example usage: obj._simulate_walks(nodes, num_walks, walk_length, stay_prob, layers_adj, layers_accept, layers_alias, gamma) and returns: walks