code: def __init__(self, in_channels, out_channels, **kwargs):
      super().__init__()
      self.conv = torch.nn.Conv2d(
          in_channels, out_channels, bias=False, **kwargs
      )
      self.bn = torch.nn.BatchNorm2d(out_channels, eps=0.001)
      self.relu = torch.nn.ReLU()
  
imports: import functools
  import unittest
  import torch
  import torch._dynamo
  import torch._dynamo.test_case
  from torch._dynamo.backends.debugging import ExplainWithBackend
  from torch._dynamo.backends.onnxrt import has_onnxruntime
  from torch._dynamo.backends.tvm import has_tvm
  from torch._dynamo.testing import same
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 1 to create a new Conv_Bn_Relu object from inherited base classes: torch.nn with example: obj = Conv_Bn_Relu(in_channels, out_channels)