code: def test_invoke_in_pt2_compiled_autograd_graph_breaks(self):
      def _graph_breaking_fn(x):
          print("Boo!")
          return _multiply(x)
  
      def _graph_break_invoke(grad):
          return trace_wrapped(grad, fn=_graph_breaking_fn)
  
      def compiler_fn(gm):
          return torch.compile(
              gm, backend="inductor", fullgraph=True, dynamic=True
          )
  
      for backend in ["eager", "aot_eager", "inductor"]:
          torch._dynamo.reset()
          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(_graph_break_invoke)
              return x + y
  
          fn = torch._dynamo.optimize(backend, nopython=True)(fn)
          out = fn(x, y)
          grad_out = torch.tensor([2.0, 2.0])
          with self.assertRaisesRegex(
              torch._dynamo.exc.Unsupported,
              "print",
          ):
              with compiled_autograd.enable(compiler_fn):
                  out.backward(grad_out)
  
          graph = None
  
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 7 for calling the BackwardHigherOrderOpTests obj's underlying member method code with example usage: obj.test_invoke_in_pt2_compiled_autograd_graph_breaks() and returns: _multiply, trace_wrapped, torch, unknown