code: def test_return_attention_mask(self):
      speech_inputs = [
          floats_list((1, x))[0] for x in range(800, 1400, 200)
      ]
  
      # default case -> no attention_mask is returned
      tokenizer = self.get_tokenizer()
      processed = tokenizer(speech_inputs)
      self.assertNotIn("attention_mask", processed)
  
      # wav2vec2-lv60 -> return attention_mask
      tokenizer = self.get_tokenizer(return_attention_mask=True)
      processed = tokenizer(speech_inputs, padding="longest")
  
      self.assertIn("attention_mask", processed)
      self.assertListEqual(
          list(processed.attention_mask.shape),
          list(processed.input_values.shape),
      )
      self.assertListEqual(
          processed.attention_mask.sum(-1).tolist(), [800, 1000, 1200]
      )
  
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 13 for calling the Wav2Vec2TokenizerTest obj's underlying member method code with example usage: obj.test_return_attention_mask() without return types