code: def write_embeddings(filename, vocab, embeddings):
      with open(filename, "wb") as file:
          for i in range(min(len(embeddings), len(vocab))):
              str = vocab.lookup_index(i).encode("utf-8")
              for j in range(len(embeddings[0])):
                  str = str + (" %5f" % (embeddings[i][j])).encode(
                      "utf-8"
                  )
              file.write(str + b"\n")
  
file_path: OpenNMT-py/tools/extract_embeddings.py
imports: import argparse
  import torch
  import onmt
  import onmt.model_builder
  from onmt.models.model_saver import load_checkpoint
  from onmt.utils.parse import ArgumentParser
  import onmt.opts
  from onmt.inputters.inputter import dict_to_vocabs
  from onmt.utils.logging import init_logger, logger
label_desc: Use image node_id 1 for calling a global function with example usage: write_embeddings(filename, vocab, embeddings) without return types