code: def __init__(self, in_channels, out_channels, bias=False, **kwargs):
      super().__init__()
      self.conv = torch.nn.Conv2d(
          in_channels, out_channels, bias=bias, **kwargs
      )
      self.bn = torch.nn.BatchNorm2d(
          out_channels, eps=0.001, dtype=torch.float
      )
  
imports: import contextlib
  import functools
  import importlib
  import itertools
  import os
  import sys
  import unittest
  import weakref
  import torch
  from torch import nn
  from torch._inductor import config
  from torch._inductor.utils import override_lowering, run_and_get_code
  from torch.testing import FileCheck
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, TEST_WITH_ASAN, TEST_WITH_ROCM, TestCase
  from inductor.test_torchinductor import check_model, check_model_cuda, copy_tests
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
label_desc: Use image node_id 1 to create a new ConvBN object from inherited base classes: torch.nn with example: obj = ConvBN(in_channels, out_channels, bias)