code: def forward(self, x):
      # this conv-bn pair can use efficient_conv_bn_eval
      x = self.bn1(self.conv1(input=x))
      # this conv-bn pair cannot use efficient_conv_bn_eval feature
      # just for the second forward of the `self.conv2`
      x = self.bn2(input=self.conv2(self.conv2(x)))
      # this conv-bn pair can use efficient_conv_bn_eval feature
      # just for the first forward of the `self.bn3`
      # test for multiple users of one computation node
      x = self.bn3(input=self.conv3(input=x))
      x = self.bn3(x) + x
      return x
  
imports: import copy
  import importlib
  import itertools
  import os
  import sys
  import unittest
  import torch
  from torch import nn
  from torch._dynamo.test_case import TestCase
  from torch._dynamo.utils import counters
  from torch._inductor import config
  from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, TEST_WITH_ASAN
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
  from inductor.test_torchinductor import copy_tests
label_desc: Use image node_id 2 for calling the MultiUserConvOp obj's underlying member method code with example usage: obj.forward(x) and returns: x