code: def test_real_music(self):
      if (
          is_librosa_available()
          and is_scipy_available()
          and is_essentia_available()
          and is_torch_available()
      ):
          from transformers import (
              Pop2PianoFeatureExtractor,
              Pop2PianoTokenizer,
          )
  
          model = Pop2PianoForConditionalGeneration.from_pretrained(
              "sweetcocoa/pop2piano"
          )
          model.eval()
          feature_extractor = Pop2PianoFeatureExtractor.from_pretrained(
              "sweetcocoa/pop2piano"
          )
          tokenizer = Pop2PianoTokenizer.from_pretrained(
              "sweetcocoa/pop2piano"
          )
          ds = load_dataset("sweetcocoa/pop2piano_ci", split="test")
  
          output_fe = feature_extractor(
              ds["audio"][0]["array"],
              sampling_rate=ds["audio"][0]["sampling_rate"],
              return_tensors="pt",
          )
          output_model = model.generate(
              input_features=output_fe["input_features"],
              composer="composer1",
          )
          output_tokenizer = tokenizer.batch_decode(
              token_ids=output_model, feature_extractor_output=output_fe
          )
          pretty_midi_object = output_tokenizer["pretty_midi_objects"][
              0
          ]
  
          # Checking if no of notes are same
          self.assertEqual(
              len(pretty_midi_object.instruments[0].notes), 59
          )
          predicted_timings = []
          for i in pretty_midi_object.instruments[0].notes:
              predicted_timings.append(i.start)
  
          # Checking note start timings(first 6)
          EXPECTED_START_TIMINGS = [
              0.4876190423965454,
              0.7314285635948181,
              0.9752380847930908,
              1.4396371841430664,
              1.6718367338180542,
              1.904036283493042,
          ]
  
          np.allclose(EXPECTED_START_TIMINGS, predicted_timings[:6])
  
          # Checking note end timings(last 6)
          EXPECTED_END_TIMINGS = [
              12.341403007507324,
              12.567797183990479,
              12.567797183990479,
              12.567797183990479,
              12.794191360473633,
              12.794191360473633,
          ]
  
          np.allclose(EXPECTED_END_TIMINGS, predicted_timings[-6:])
  
imports: import copy
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  from transformers import Pop2PianoConfig
  from transformers.feature_extraction_utils import BatchFeature
  from transformers.testing_utils import require_essentia, require_librosa, require_onnx, require_scipy, require_torch, slow, torch_device
  from transformers.utils import is_essentia_available, is_librosa_available, is_scipy_available, is_torch_available
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the Pop2PianoModelIntegrationTests obj's underlying member method code with example usage: obj.test_real_music() without return types