code: def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
      feat_extract = self.feature_extraction_class(
          **self.feat_extract_tester.prepare_feat_extract_dict()
      )
      audio = self._load_datasamples(1)[0]
      audio = (
          (audio - audio.min()) / (audio.max() - audio.min())
      ) * 65535  # Rescale to [0, 65535] to show issue
      audio = feat_extract.zero_mean_unit_var_norm(
          [audio], attention_mask=None
      )[0]
  
      self.assertTrue(np.all(np.mean(audio) < 1e-3))
      self.assertTrue(np.all(np.abs(np.var(audio) - 1) < 1e-3))
  
imports: import itertools
  import os
  import random
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  from transformers import WhisperFeatureExtractor
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch
  from transformers.utils.import_utils import is_torch_available
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 9 for calling the WhisperFeatureExtractionTest obj's underlying member method code with example usage: obj.test_zero_mean_unit_variance_normalization_trunc_np_longest() without return types