code: def test_differentiable_graph_ops_requires_grad(self):
      x = torch.randn(8, 2, dtype=torch.float).requires_grad_()
      y = torch.randn(8, 2, dtype=torch.float)
  
      def t(x: torch.Tensor, y: torch.Tensor, flag: bool):
          o = x + 1.0
          o1 = torch.relu(o)
          o = y + 1.5
          o2 = torch.relu(o)
          o3 = o1 + o2
  
          if flag:
              o = o1 + 1.0
              oo1 = torch.relu(o)
              o = o2 + 2.5
              oo2 = torch.relu(o)
              oo3 = oo1 + oo2
          else:
              o = o1 * 1.0
              oo1 = torch.relu(o)
              o = o2 * 2.0
              oo2 = torch.relu(o)
              oo3 = oo1 + oo2
  
          return o1, o2, o3, oo1, oo2, oo3
  
      with enable_profiling_mode_for_profiling_tests():
          t_jit = torch.jit.script(t)
          jit_o = t_jit(x, y, False)
          jit_o = t_jit(x, y, False)
          o = t(x, y, False)
  
          FileCheck().check("prim::DifferentiableGraph").run(
              t_jit.graph_for(x, y, False)
          )
          # validate the differentiableGraphOps are marking proper requires_grad
          for oo, jit_oo in zip(o, jit_o):
              self.assertEqual(oo.requires_grad, jit_oo.requires_grad)
              self.assertEqual(oo, jit_oo)
          # one more runs to trigger fusion
          jit_o = t_jit(x, y, False)
          for oo, jit_oo in zip(o, jit_o):
              self.assertEqual(oo.dtype, jit_oo.dtype)
              self.assertEqual(oo.requires_grad, jit_oo.requires_grad)
              self.assertEqual(oo, jit_oo)
  
imports: import os
  import sys
  import unittest
  from torch.testing._internal.common_utils import GRAPH_EXECUTOR, ProfilingMode, num_profiled_runs, enable_profiling_mode_for_profiling_tests
  from torch.testing._internal.common_jit import check_against_reference
  import torch
  from torch.testing._internal.jit_utils import JitTestCase, disable_autodiff_subgraph_inlining
  from torch.testing import FileCheck
  from typing import List, Tuple, Optional
label_desc: Use image node_id 9 for calling the TestAutodiffSubgraphSlicing obj's underlying member method code with example usage: obj.test_differentiable_graph_ops_requires_grad() and returns: o1,  o2,  o3,  oo1,  oo2,  oo3