code: def test_attention_mask(self):
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_tester.prepare_feat_extract_dict()
      )
      speech_input1 = np.zeros([1_000_000], dtype=np.float32)
      speech_input2 = np.random.randint(
          low=0, high=10, size=500_000
      ).astype(np.float32)
      input_features = feature_extractor(
          [speech_input1, speech_input2],
          sampling_rate=[44_100, 16_000],
          return_tensors="np",
          return_attention_mask=True,
      )
  
      self.assertTrue(hasattr(input_features, "attention_mask"))
  
      # check shapes
      self.assertTrue(input_features["attention_mask"].ndim == 2)
      self.assertEqual(
          input_features["attention_mask_beatsteps"].shape[0], 2
      )
      self.assertEqual(
          input_features["attention_mask_extrapolated_beatstep"].shape[
              0
          ],
          2,
      )
  
      # check if they are any values except 0 and 1
      self.assertTrue(np.max(input_features["attention_mask"]) == 1)
      self.assertTrue(
          np.max(input_features["attention_mask_beatsteps"]) == 1
      )
      self.assertTrue(
          np.max(input_features["attention_mask_extrapolated_beatstep"])
          == 1
      )
  
      self.assertTrue(np.min(input_features["attention_mask"]) == 0)
      self.assertTrue(
          np.min(input_features["attention_mask_beatsteps"]) == 0
      )
      self.assertTrue(
          np.min(input_features["attention_mask_extrapolated_beatstep"])
          == 0
      )
  
imports: import os
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  from transformers.testing_utils import check_json_file_has_correct_format, require_essentia, require_librosa, require_scipy, require_tf, require_torch
  from transformers.utils.import_utils import is_essentia_available, is_librosa_available, is_scipy_available, is_torch_available
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 6 for calling the Pop2PianoFeatureExtractionTest obj's underlying member method code with example usage: obj.test_attention_mask() without return types