code: def test_custom_fns_with_same_graph(self):
      def fn():
          class MyFn1(torch.autograd.Function):
              @staticmethod
              def forward(ctx, x):
                  return x
  
              @staticmethod
              def backward(ctx, gO):
                  return gO
  
          # same as MyFn1, but different autograd function id
          # should not be using same graph as MyFn1
          class MyFn2(torch.autograd.Function):
              @staticmethod
              def forward(ctx, x):
                  return x
  
              @staticmethod
              def backward(ctx, gO):
                  return gO
  
          for myfn in [MyFn1, MyFn2, MyFn1, MyFn2]:
              x = torch.arange(0.0, 10, requires_grad=True)
              out = myfn.apply(x)
              loss = out.sum()
              loss.backward()
              yield x.grad
  
      self.check_output_and_recompiles(fn, 2)
  
imports: import functools
  import re
  import sys
  import unittest
  from importlib.machinery import SourceFileLoader
  from pathlib import Path
  from unittest import mock
  import torch
  import torch.nn
  from torch import _inductor
  from torch._dynamo import compiled_autograd
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import counters
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
label_desc: Use image node_id 24 for calling the TestCompiledAutograd obj's underlying member method code with example usage: obj.test_custom_fns_with_same_graph() and returns: x, gO, x, gO