code: def test_mp3_compresssion(art_warning, audio_batch, channels_first):
      try:
          test_input, test_output, sample_rate = audio_batch
          mp3compression = Mp3CompressionPyTorch(
              sample_rate=sample_rate, channels_first=channels_first
          )
  
          assert_array_equal(mp3compression(test_input)[0], test_output)
  
          import torch
  
          test_input_tensor = torch.from_numpy(test_input.astype(float))
          test_input_tensor.requires_grad = True
          loss = mp3compression.forward(
              x=test_input_tensor, y=test_output
          )[0].sum()
          loss.backward()
  
          assert test_input_tensor.grad is not None
          if channels_first:
              assert (
                  test_input_tensor.grad.detach().cpu().numpy().shape
                  == (2, test_input.shape[1], 44100)
              )
          else:
              assert (
                  test_input_tensor.grad.detach().cpu().numpy().shape
                  == (2, 44100, test_input.shape[2])
              )
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/defences/preprocessor/test_mp3_compression_pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from art.defences.preprocessor import Mp3CompressionPyTorch
  from tests.utils import ARTTestException
label_desc: Use image node_id 4 for calling a global function with example usage: test_mp3_compresssion(art_warning, audio_batch, channels_first) without return types