code: def test_zero_mean_unit_variance_normalization(self):
      tokenizer = self.get_tokenizer(do_normalize=True)
      speech_inputs = [
          floats_list((1, x))[0] for x in range(800, 1400, 200)
      ]
      processed = tokenizer(speech_inputs, padding="longest")
      input_values = processed.input_values
  
      def _check_zero_mean_unit_variance(input_vector):
          self.assertTrue(np.abs(np.mean(input_vector)) < 1e-3)
          self.assertTrue(np.abs(np.var(input_vector) - 1) < 1e-3)
  
      _check_zero_mean_unit_variance(input_values[0, :800])
      _check_zero_mean_unit_variance(input_values[1, :1000])
      _check_zero_mean_unit_variance(input_values[2])
  
imports: import inspect
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  import numpy
  from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2Tokenizer
  from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES, Wav2Vec2CTCTokenizerOutput
  from transformers.testing_utils import require_torch, slow
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 12 for calling the Wav2Vec2TokenizerTest obj's underlying member method code with example usage: obj.test_zero_mean_unit_variance_normalization() without return types