code: def test_basic(self):
      def test_conv_bn_eval(test_class, use_bias, module, sync_bn):
          kwargs = (
              {"kernel_size": 3, "stride": 2}
              if module[0] != nn.Linear
              else {}
          )
          mod_eager = test_class(
              module[0],
              module[1],
              use_bias,
              3,
              32,
              self.device,
              **kwargs,
          ).eval()
          # Copy module to test backward
          mod_optimized = copy.deepcopy(mod_eager)
          if sync_bn:
              mod_eager = nn.SyncBatchNorm.convert_sync_batchnorm(
                  mod_eager
              ).eval()
              mod_optimized = nn.SyncBatchNorm.convert_sync_batchnorm(
                  mod_optimized
              ).eval()
          torch._dynamo.reset()
          mod_optimized = torch.compile(mod_optimized)
  
          inps = [4, 3]
          # Conv shape goes from big to small, and ConvTranspose shape goes from small to big
          spatial_d = (
              4
              if issubclass(module[0], nn.modules.conv._ConvTransposeNd)
              else 96
          )
          if module[0] == nn.Conv1d or module[0] == nn.ConvTranspose1d:
              inps += [spatial_d] * 1
          if module[0] == nn.Conv2d or module[0] == nn.ConvTranspose2d:
              inps += [spatial_d] * 2
          if module[0] == nn.Conv3d or module[0] == nn.ConvTranspose3d:
              inps += [spatial_d] * 3
          inp = torch.rand(inps).to(self.device)
  
          original_value = counters["inductor"][
              "efficient_conv_bn_eval"
          ]
  
          optim_eager = torch.optim.SGD(mod_eager.parameters(), lr=1e-3)
          optim_optimized = torch.optim.SGD(
              mod_optimized.parameters(), lr=1e-3
          )
  
          optim_eager.zero_grad()
          optim_optimized.zero_grad()
  
          # test forward
          out_eager = mod_eager(inp)
          out_optimized = mod_optimized(inp)
  
          self.assertEqual(
              out_optimized, out_eager, atol=2e-04, rtol=1e-5
          )
  
          out_eager.mean().backward()
          out_optimized.mean().backward()
  
          optim_eager.step()
          optim_optimized.step()
          # test forward (by testing forward again after one training iteration)
          inp_bw = torch.rand_like(inp)
          out_eager_bw = mod_eager(inp_bw)
          out_optimized_bw = mod_optimized(inp_bw)
  
          self.assertEqual(
              out_eager_bw, out_optimized_bw, atol=2e-04, rtol=1e-5
          )
          current_value = counters["inductor"]["efficient_conv_bn_eval"]
          self.assertEqual(
              current_value - original_value,
              test_class.expected_optimization_count,
          )
  
      conv_bias = [True, False]
      modules = [
          (nn.Linear, nn.BatchNorm1d),
          (nn.Conv1d, nn.BatchNorm1d),
          (nn.Conv2d, nn.BatchNorm2d),
          (nn.Conv3d, nn.BatchNorm3d),
          (nn.ConvTranspose1d, nn.BatchNorm1d),
          (nn.ConvTranspose2d, nn.BatchNorm2d),
          (nn.ConvTranspose3d, nn.BatchNorm3d),
      ]
      test_classes = [ConvOp, MultiUserConvOp]
      sync_bns = [False, True]
      for test_class, use_bias, module, sync_bn in itertools.product(
          test_classes, conv_bias, modules, sync_bns
      ):
          test_conv_bn_eval(test_class, use_bias, module, sync_bn)
  
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 1 for calling the EfficientConvBNEvalTemplate obj's underlying member method code with example usage: obj.test_basic() without return types