code: def test_special_characters_in_vocab(self):
      sent = "ʈʰ æ æ̃ ˧ kʰ"
  
      vocab_dict = {
          k: v
          for v, k in enumerate({phoneme for phoneme in sent.split()})
      }
      vocab_file = os.path.join(self.tmpdirname, "vocab_special.json")
  
      with open(vocab_file, "w") as f:
          json.dump(vocab_dict, f)
  
      tokenizer = Wav2Vec2CTCTokenizer(vocab_file)
  
      expected_sent = tokenizer.decode(
          tokenizer(sent).input_ids, spaces_between_special_tokens=True
      )
      self.assertEqual(sent, expected_sent)
  
      tokenizer.save_pretrained(
          os.path.join(self.tmpdirname, "special_tokenizer")
      )
      tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(
          os.path.join(self.tmpdirname, "special_tokenizer")
      )
  
      expected_sent = tokenizer.decode(
          tokenizer(sent).input_ids, spaces_between_special_tokens=True
      )
      self.assertEqual(sent, expected_sent)
  
imports: import inspect
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  import numpy
  from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2Tokenizer
  from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES, Wav2Vec2CTCTokenizerOutput
  from transformers.testing_utils import require_torch, slow
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 8 for calling the Wav2Vec2CTCTokenizerTest obj's underlying member method code with example usage: obj.test_special_characters_in_vocab() without return types