code: def test_pretrained_checkpoints_are_set_correctly(self):
      # this test makes sure that models that are using
      # group norm don't have their feature extractor return the
      # attention_mask
      for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          config = Wav2Vec2Config.from_pretrained(model_id)
          feat_extract = Wav2Vec2FeatureExtractor.from_pretrained(
              model_id
          )
  
          # only "layer" feature extraction norm should make use of
          # attention_mask
          self.assertEqual(
              feat_extract.return_attention_mask,
              config.feat_extract_norm == "layer",
          )
  
imports: import itertools
  import random
  import unittest
  import numpy
  from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2Config, Wav2Vec2FeatureExtractor
  from transformers.testing_utils import require_torch, slow
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 9 for calling the Wav2Vec2FeatureExtractionTest obj's underlying member method code with example usage: obj.test_pretrained_checkpoints_are_set_correctly() without return types