code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      dilation,
      affine,
      track_running_stats=True,
      use_bn=True,
      name="ReLUConvBN",
  ):
      super(ReLUConvBN, self).__init__()
      self.name = name
      if use_bn:
          self.op = nn.Sequential(
              nn.ReLU(inplace=False),
              nn.Conv2d(
                  in_channels,
                  out_channels,
                  kernel_size,
                  stride=stride,
                  padding=padding,
                  dilation=dilation,
                  bias=not affine,
              ),
              nn.BatchNorm2d(
                  out_channels,
                  affine=affine,
                  track_running_stats=track_running_stats,
              ),
          )
      else:
          self.op = nn.Sequential(
              nn.ReLU(inplace=False),
              nn.Conv2d(
                  in_channels,
                  out_channels,
                  kernel_size,
                  stride=stride,
                  padding=padding,
                  dilation=dilation,
                  bias=not affine,
              ),
          )
  
imports: import torch.nn
label_desc: Use image node_id 1 to create a new ReLUConvBN object from inherited base classes: nn with example: obj = ReLUConvBN(in_channels, out_channels, kernel_size, stride, padding, dilation, affine, track_running_stats, use_bn, name)