code: def test_pre_backward_hook(temp_files):
      """Test FSDP with a model that triggers a pre_backward hook bug."""
  
      result = dist_init(
          rank=0,
          world_size=1,
          filename=temp_files[0],
          filename_rpc=temp_files[1],
      )
      assert result, "Dist init failed"
  
      class Model(Module):
          def __init__(self):
              super().__init__()
              self.l1 = Linear(4, 4).cuda()
              self.l2 = FSDP(Linear(4, 4).cuda())
              self.l3 = Linear(4, 4).cuda()
  
          def forward(self, x):
              x = self.l1(x)
              x = self.l2(x)
              inner_result = x
              x = self.l3(x)
              return x, inner_result
  
          def assert_and_clear_grad(self):
              for p in self.parameters():
                  assert p.shape in [
                      (4, 4),
                      (4,),
                      (4 * 4 + 4,),
                  ], p.shape
                  assert p.grad is not None
                  p.grad = None
  
      model = FSDP(Model(), flatten_parameters=False).cuda()
      in_data = torch.rand(1, 4).cuda()
      for _ in range(3):
          out, _ = model(in_data)
          out.sum().backward()
          model.assert_and_clear_grad()
  
      teardown()
  
imports: import pytest
  import torch
  from torch.nn import Linear, Module
  from fairscale.fair_dev.testing.testing import dist_init, skip_if_no_cuda, teardown, temp_files_ctx
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 2 for calling a global function with example usage: test_pre_backward_hook(temp_files) and returns: x,  inner_result