code: def test_extend_vectors_1(self):
      vectors_cache_dir = ".cache"
      if os.path.exists(vectors_cache_dir):
          shutil.rmtree(vectors_cache_dir)
  
      pathdir = os.path.abspath(
          os.path.join(test_dir_path, "test_datasets")
      )
      filename = "fasttext_sample.vec"
      file = os.path.join(pathdir, filename)
      url_base = urljoin("file:", pathname2url(file))
      vecs = Vectors(
          name=filename, cache=vectors_cache_dir, url=url_base
      )
      self.assertIsInstance(vecs, Vectors)
  
      vec_data = MatchingField._get_vector_data(vecs, vectors_cache_dir)
      v = MatchingVocab(Counter())
      v.vectors = torch.Tensor(1, vec_data[0].dim)
      v.unk_init = torch.Tensor.zero_
      tokens = {"hello", "world"}
      v.extend_vectors(tokens, vec_data)
      self.assertEqual(len(v.itos), 4)
      self.assertEqual(v.vectors.size(), torch.Size([4, 300]))
      self.assertEqual(list(v.vectors[2][0:10]), [0.0] * 10)
      self.assertEqual(list(v.vectors[3][0:10]), [0.0] * 10)
  
      if os.path.exists(vectors_cache_dir):
          shutil.rmtree(vectors_cache_dir)
  
imports: import os
  import shutil
  import unittest
  from collections import Counter
  from test import test_dir_path
  from nose.tools import *
  import torch
  from deepmatcher.data.dataset import MatchingDataset
  from deepmatcher.data.field import FastText, FastTextBinary, MatchingField, MatchingVocab, reset_vector_cache
  from torchtext.vocab import Vectors
  from urllib.parse import urljoin
  from urllib.request import pathname2url
label_desc: Use image node_id 1 for calling the ClassMatchingVocabTestCases obj's underlying member method code with example usage: obj.test_extend_vectors_1() without return types