code: def train(
      self, embed_size=128, window_size=5, workers=3, iter=5, **kwargs
  ):
      kwargs["sentences"] = self.sentences
      kwargs["min_count"] = kwargs.get("min_count", 0)
      kwargs["vector_size"] = embed_size
      kwargs["sg"] = 1
      kwargs["hs"] = 0  # node2vec not use Hierarchical Softmax
      kwargs["workers"] = workers
      kwargs["window"] = window_size
      kwargs["epochs"] = iter
  
      print("Learning embedding vectors...")
      model = Word2Vec(**kwargs)
      print("Learning embedding vectors done!")
  
      self.w2v_model = model
  
      return model
  
imports: from gensim.models import Word2Vec
  from ..walker import RandomWalker
label_desc: Use image node_id 2 for calling the Node2Vec obj's underlying member method code with example usage: obj.train(embed_size, window_size, workers, iter) and returns: model