code: def test_pretrained_checkpoints_are_set_correctly(self):
      # this test makes sure that models that are using
      # group norm don't have their tokenizer return the
      # attention_mask
      for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          config = Wav2Vec2Config.from_pretrained(model_id)
          tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_id)
  
          # only "layer" feature extraction norm should make use of
          # attention_mask
          self.assertEqual(
              tokenizer.return_attention_mask,
              config.feat_extract_norm == "layer",
          )
  
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 14 for calling the Wav2Vec2TokenizerTest obj's underlying member method code with example usage: obj.test_pretrained_checkpoints_are_set_correctly() without return types