code: def test_invoke_in_eager(self):
      x = torch.tensor([0.5, 0.5], requires_grad=True)
      y = torch.tensor([0.5, 0.5], requires_grad=True)
  
      def fn(x, y):
          x.register_hook(_multiply_invoke)
          return x * y
  
      out = fn(x, y)
      grad_out = torch.tensor([2.0, 2.0])
      out.backward(grad_out)
      self.assertEqual(x.grad, y * grad_out)
  
imports: import functools
  import torch
  import torch._dynamo.test_case
  import torch._dynamo.testing
  import torch._dynamo.utils
  from torch import _inductor
  from torch._dynamo import compiled_autograd
  from torch._dynamo._trace_wrapped_higher_order_op import trace_wrapped
  from torch._dynamo.testing import normalize_gm
  from torch._dynamo.utils import counters
  from torch.fx.experimental.proxy_tensor import make_fx
label_desc: Use image node_id 1 for calling the BackwardHigherOrderOpTests obj's underlying member method code with example usage: obj.test_invoke_in_eager() and returns: unknown