code: def run_torchaudio(self, audio_classifier):
      import datasets
  
      # test with a local file
      dataset = datasets.load_dataset(
          "hf-internal-testing/librispeech_asr_dummy",
          "clean",
          split="validation",
      )
      audio = dataset[0]["audio"]["array"]
      output = audio_classifier(audio)
      self.assertEqual(
          output,
          [
              {"score": ANY(float), "label": ANY(str)},
              {"score": ANY(float), "label": ANY(str)},
          ],
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/pipelines/test_pipelines_audio_classification.py
imports: import unittest
  import numpy
  from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  from transformers.pipelines import AudioClassificationPipeline, pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torchaudio, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 3 for calling the AudioClassificationPipelineTests obj's underlying member method code with example usage: obj.run_torchaudio(audio_classifier) without return types