code: def test_audio_byteformer() -> None:
      # Make sure it matches the image classification network.
      opts = test_byteformer.get_opts()
  
      byteformer1 = image_byteformer.ByteFormer(opts)
      byteformer2 = audio_byteformer.AudioByteFormer(opts)
  
      # Make their state_dicts match.
      byteformer2.load_state_dict(byteformer1.state_dict())
  
      batch_size, sequence_length = 2, 32
  
      x = torch.randint(0, 128, [batch_size, sequence_length])
  
      assert torch.all(byteformer1(x) == byteformer2({"audio": x}))
  
imports: import torch
  from cvnets.models.audio_classification import audio_byteformer
  from cvnets.models.classification import byteformer
  from tests.models.classification import test_byteformer
label_desc: Use image node_id 1 for calling a global function with example usage: test_audio_byteformer() without return types