code: def save_embedding(self, id2word, file_name):
      embedding = self.u_embeddings.weight.cpu().data.numpy()
      with open(file_name, "w") as f:
          f.write("%d %d\n" % (len(id2word), self.emb_dimension))
          for wid, w in id2word.items():
              e = " ".join(map(lambda x: str(x), embedding[wid]))
              f.write("%s %s\n" % (w, e))
  
file_path: dgl/examples/pytorch/metapath2vec/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import init
label_desc: Use image node_id 3 for calling the SkipGramModel obj's underlying member method code with example usage: obj.save_embedding(id2word, file_name) without return types