code: def test_invoke_make_fx_forward_contrived(self):
      x = torch.tensor([0.5, 0.5], requires_grad=True)
      out = make_fx(_multiply_invoke)(x)
      self.assertEqual(out(x), torch.tensor([0.25, 0.25]))
      actual = normalize_gm(out.print_readable(False))
  
      expected = """\
  class _multiply_invoke(torch.nn.Module):
      def forward(self, grad_1: "f32[2]"):
          trace_wrapped: "f32[2]" = torch__dynamo__trace_wrapped_higher_order_op_self_invoke(grad_1);  grad_1 = None
          assert_1: "f32[2]" = torch__dynamo__trace_wrapped_higher_order_op__assert_meta(trace_wrapped, (2,), (1,), torch.float32);  trace_wrapped = None
          detach: "f32[2]" = torch.ops.aten.detach.default(assert_1);  assert_1 = None
          detach_1: "f32[2]" = torch.ops.aten.detach.default(detach);  detach = None
          detach_2: "f32[2]" = torch.ops.aten.detach.default(detach_1);  detach_1 = None
          detach_3: "f32[2]" = torch.ops.aten.detach.default(detach_2);  detach_2 = None
          return detach_3
  """
      self.assertExpectedInline(actual, expected)
  
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 3 for calling the BackwardHigherOrderOpTests obj's underlying member method code with example usage: obj.test_invoke_make_fx_forward_contrived() without return types