code: def test_generate_noise(self):
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_dict
      )
      speech_inputs = [
          floats_list((1, x))[0] for x in range(800, 1400, 200)
      ]
  
      features = feature_extractor(speech_inputs, return_noise=True)
      input_features = features.input_features
      noise_features = features.noise_sequence
  
      for spectrogram, noise in zip(input_features, noise_features):
          self.assertEqual(spectrogram.shape[0], noise.shape[0])
  
imports: import itertools
  import os
  import random
  import tempfile
  import unittest
  import numpy
  from datasets import Audio, load_dataset
  from transformers import UnivNetFeatureExtractor
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, slow
  from transformers.utils.import_utils import is_torch_available
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 6 for calling the UnivNetFeatureExtractionTest obj's underlying member method code with example usage: obj.test_generate_noise() without return types