code: def test_torch_checkpoint_wrapper(device: str):
      device_ = torch.device(device)
  
      checkpoint_wrapper: TorchCheckpointWrapper = CheckpointWrapper.from_params(  # type: ignore[assignment]
          Params({"type": "torch"})
      )
      module = BasicModule(checkpoint_wrapper).to(device_)
      optim = torch.optim.Adam(module.parameters(), lr=0.0001)
  
      # Test forward pass
      module.train()
      # Shape (batch_size = 2, 3)
      x = torch.randn(2, 3).to(device_)
      loss = module(x).sum()
      assert (
          forward_calls == 1
      ), f"wrong number of forward calls: {forward_calls}"
  
      # Test backward pass + optimizer step.
      loss.backward()
      assert (
          forward_calls == 2
      ), f"wrong number of forward calls: {forward_calls}"
      for param in module.parameters():
          assert param.grad is not None
      optim.step()
      optim.zero_grad(set_to_none=True)
  
      # Now test forward pass in eval mode.
      module.eval()
      x = torch.randn(2, 3).to(device_)
      module(x)
  
imports: import torch
  import torch.nn
  import pytest
  from allennlp.common.params import Params
  from allennlp.common.testing import multi_device
  from allennlp.nn.checkpoint.checkpoint_wrapper import CheckpointWrapper, TorchCheckpointWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: test_torch_checkpoint_wrapper(device) without return types