code: def test_cepstral_mean_and_variance_normalization_trunc_longest(self):
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_tester.prepare_feat_extract_dict()
      )
      speech_inputs = [
          floats_list((1, x))[0] for x in range(8000, 14000, 2000)
      ]
      inputs = feature_extractor(
          speech_inputs,
          padding="longest",
          max_length=4,
          truncation=True,
          return_tensors="np",
          return_attention_mask=True,
      )
      input_features = inputs.input_features
      attention_mask = inputs.attention_mask
      fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
  
      self._check_zero_mean_unit_variance(
          input_features[0, : fbank_feat_lengths[0]]
      )
      self._check_zero_mean_unit_variance(
          input_features[1, : fbank_feat_lengths[1]]
      )
      self._check_zero_mean_unit_variance(input_features[2])
  
      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape, (3, 4, 24))
  
      speech_inputs = [
          floats_list((1, x))[0] for x in range(8000, 14000, 2000)
      ]
      inputs = feature_extractor(
          speech_inputs,
          padding="longest",
          max_length=16,
          truncation=True,
          return_tensors="np",
          return_attention_mask=True,
      )
      input_features = inputs.input_features
      attention_mask = inputs.attention_mask
      fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
  
      self._check_zero_mean_unit_variance(
          input_features[0, : fbank_feat_lengths[0]]
      )
      self._check_zero_mean_unit_variance(
          input_features[1, : fbank_feat_lengths[1]]
      )
      self._check_zero_mean_unit_variance(input_features[2])
  
      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape, (3, 16, 24))
  
imports: import itertools
  import random
  import unittest
  import numpy
  from transformers import is_speech_available
  from transformers.testing_utils import require_torch, require_torchaudio
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 7 for calling the MCTCTFeatureExtractionTest obj's underlying member method code with example usage: obj.test_cepstral_mean_and_variance_normalization_trunc_longest() without return types