docstr: Generates the random walks which will be used as the skip-gram input.
  :return: List of walks. Each walk is a list of nodes.
docstr_tok: ["Generates","the","random","walks","which","will","be","used","as","the","skip-gram","input",".",":","return",":","List","of","walks",".","Each","walk","is","a","list","of","nodes","."]
code: def _generate_walks(self) -> list:
      """
      Generates the random walks which will be used as the skip-gram input.
      :return: List of walks. Each walk is a list of nodes.
      """
  
      flatten = lambda l: [item for sublist in l for item in sublist]
  
      # Split num_walks for each worker
      num_walks_lists = np.array_split(
          range(self.num_walks), self.workers
      )
  
      walk_results = Parallel(
          n_jobs=self.workers,
          temp_folder=self.temp_folder,
          require=self.require,
      )(
          delayed(parallel_generate_walks)(
              self.d_graph,
              self.walk_length,
              len(num_walks),
              idx,
              self.sampling_strategy,
              self.NUM_WALKS_KEY,
              self.WALK_LENGTH_KEY,
              self.NEIGHBORS_KEY,
              self.PROBABILITIES_KEY,
              self.FIRST_TRAVEL_KEY,
              self.quiet,
          )
          for idx, num_walks in enumerate(num_walks_lists, 1)
      )
  
      walks = flatten(walk_results)
  
      return walks
  
file_path: node2vec/node2vec/node2vec.py
imports: import os
  import random
  from collections import defaultdict
  import gensim
  import networkx
  import numpy
  import pkg_resources
  from joblib import Parallel, delayed
  from tqdm.auto import tqdm
  from .parallel import parallel_generate_walks
label_desc: Use image node_id 3 for calling the Node2Vec obj's underlying member method code with example usage: obj._generate_walks() and returns: walks