code: def test_wav_collate() -> None:
      C, N = 1, 16000
      batch = [
          {
              "samples": {"audio": torch.rand([C, N])},
              "metadata": {"audio_fps": 16000},
          },
          {
              "samples": {"audio": torch.rand([C, N])},
              "metadata": {"audio_fps": 16000},
          },
      ]
  
      padding_index = -1
      opts = argparse.Namespace()
      setattr(opts, "audio_augmentation.torchaudio_save.enable", True)
      setattr(
          opts,
          "audio_augmentation.torchaudio_save.encoding_dtype",
          "float32",
      )
      setattr(opts, "audio_augmentation.torchaudio_save.format", "mp3")
      setattr(
          opts,
          "model.classification.byteformer.padding_index",
          padding_index,
      )
  
      collated_batch = (
          byteformer_collate_functions.byteformer_audio_collate_fn(
              batch, opts
          )
      )
  
      assert list(collated_batch.keys()) == ["samples"]
  
imports: import argparse
  import torch
  from data.collate_fns import byteformer_collate_functions
label_desc: Use image node_id 2 for calling a global function with example usage: test_wav_collate() without return types