code: def test_zero_mean_unit_variance_normalization(self):
      feat_extract = self.feature_extraction_class(
          **self.feat_extract_tester.prepare_feat_extract_dict()
      )
      lengths = range(800, 1400, 200)
      speech_inputs = [floats_list((1, x))[0] for x in lengths]
  
      paddings = ["longest", "max_length", "do_not_pad"]
      max_lengths = [None, 1600, None]
  
      for max_length, padding in zip(max_lengths, paddings):
          processed = feat_extract(
              speech_inputs, max_length=max_length, padding=padding
          )
          input_values = processed.input_values
  
          self._check_zero_mean_unit_variance(input_values[0][:800])
          self._check_zero_mean_unit_variance(input_values[1][:1000])
          self._check_zero_mean_unit_variance(input_values[2][:1200])
  
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 5 for calling the Wav2Vec2FeatureExtractionTest obj's underlying member method code with example usage: obj.test_zero_mean_unit_variance_normalization() without return types