code: def test_train_pytorch_dtype(dtype, device, model, conf):
      if device == "cuda" and not torch.cuda.is_available():
          pytest.skip("no cuda device is available")
      if device == "cpu" and dtype == "float16":
          pytest.skip(
              "cpu float16 implementation is not available in pytorch yet"
          )
  
      idim = 10
      odim = 10
      model = dynamic_import_asr(model, "pytorch").build(
          idim, odim, **conf
      )
      dtype = getattr(torch, dtype)
      device = torch.device(device)
      model.to(dtype=dtype, device=device)
  
      x = torch.rand(2, 10, idim, dtype=dtype, device=device)
      ilens = torch.tensor([10, 7], device=device)
      y = torch.randint(1, odim, (2, 3), device=device)
      opt = torch.optim.Adam(model.parameters())
      loss = model(x, ilens, y)
      assert loss.dtype == dtype
      model.zero_grad()
      loss.backward()
      assert any(p.grad is not None for p in model.parameters())
      opt.step()
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_train_pytorch_dtype(dtype, device, model, conf) without return types