code: def test_simple_linears():
      def sum_grad(parameters):
          return sum(
              [p.grad.sum() for p in parameters if p.grad is not None]
          )
  
      def zero_grad(parameters):
          for p in parameters:
              p.grad = None
  
      inputs = torch.rand(8, 1)
      model = nn.Sequential(
          nn.Linear(1, 2),
          nn.Linear(2, 4),
          nn.Linear(4, 2),
          nn.Linear(2, 1),
      )
  
      # Without Pipe
      outputs = model(inputs)
      loss = outputs.mean()
      loss.backward()
  
      grad_without_pipe = sum_grad(model.parameters())
  
      zero_grad(model.parameters())
  
      # With Pipe
      model = Pipe(model, [2, 2], devices=["cpu", "cpu"], chunks=4)
  
      outputs = model(inputs)
      loss = outputs.mean()
      loss.backward()
  
      grad_with_pipe = sum_grad(model.parameters())
  
      # Both grads should be identical.
      assert torch.allclose(grad_with_pipe, grad_without_pipe)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_simple_linears() and returns: sum