code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      dilation,
      relu=True,
  ):
      super(_ConvBatchNormReLU, self).__init__()
      self.add_module(
          "conv",
          nn.Conv2d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              stride=stride,
              padding=padding,
              dilation=dilation,
              bias=False,
          ),
      )
      self.add_module(
          "bn",
          nn.BatchNorm2d(
              num_features=out_channels,
              eps=1e-5,
              momentum=0.999,
              affine=True,
          ),
      )
  
      if relu:
          self.add_module("relu", nn.ReLU())
  
file_path: pywick/pywick/models/segmentation/testnets/resnet.py
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new _ConvBatchNormReLU object from inherited base classes: nn with example: obj = _ConvBatchNormReLU(in_channels, out_channels, kernel_size, stride, padding, dilation, relu)