code: def test_batch_decode_outputs(self):
      # test batch decode with model, feature-extractor outputs(beatsteps, extrapolated_beatstep)
  
      # Please note that this test tests the accuracy of the outputs of the tokenizer's `batch_decode` method.
  
      model_output = torch.tensor(
          [
              [134, 133, 74, 135, 77, 82, 84, 136, 132, 74, 77, 82, 84],
              [
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  1.0,
              ],
          ]
      )
      input_features = BatchEncoding(
          {
              "beatsteps": torch.tensor(
                  [[0.0697, 0.1103, 0.1509, 0.1916]]
              ),
              "extrapolated_beatstep": torch.tensor(
                  [[0.0000, 0.0406, 0.0813, 0.1219]]
              ),
          }
      )
  
      output = self.tokenizer.batch_decode(
          token_ids=model_output,
          feature_extractor_output=input_features,
      )
  
      # check outputs
      self.assertEqual(len(output["notes"]), 4)
  
      predicted_start_timings, predicted_end_timings = [], []
      for i in output["notes"]:
          predicted_start_timings.append(i.start)
          predicted_end_timings.append(i.end)
  
      # Checking note start timings
      expected_start_timings = torch.tensor(
          [
              0.069700,
              0.110300,
              0.110300,
              0.110300,
          ]
      )
      predicted_start_timings = torch.tensor(predicted_start_timings)
  
      self.assertTrue(
          torch.allclose(
              expected_start_timings, predicted_start_timings, atol=1e-4
          )
      )
  
      # Checking note end timings
      expected_end_timings = torch.tensor(
          [
              0.191600,
              0.191600,
              0.191600,
              0.191600,
          ]
      )
      predicted_end_timings = torch.tensor(predicted_end_timings)
  
      self.assertTrue(
          torch.allclose(
              expected_end_timings, predicted_end_timings, atol=1e-4
          )
      )
  
imports: import os
  import pickle
  import shutil
  import tempfile
  import unittest
  from transformers.feature_extraction_utils import BatchFeature
  from transformers.testing_utils import is_pretty_midi_available, is_torch_available, require_pretty_midi, require_torch
  from transformers.tokenization_utils import BatchEncoding
label_desc: Use image node_id 5 for calling the Pop2PianoTokenizerTest obj's underlying member method code with example usage: obj.test_batch_decode_outputs() without return types