code: def __init__(self, in_channels, out_channels):
      super(reduction, self).__init__()
      self.residual = nn.Sequential(
          nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
          nn.Conv2d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=1,
              stride=1,
              padding=0,
              bias=False,
          ),
      )
  
      self.conv_a = ReLUConvBN(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=3,
          stride=2,
          padding=1,
          dilation=1,
          affine=True,
          track_running_stats=True,
      )
      self.conv_b = ReLUConvBN(
          in_channels=out_channels,
          out_channels=out_channels,
          kernel_size=3,
          stride=1,
          padding=1,
          dilation=1,
          affine=True,
          track_running_stats=True,
      )
  
imports: import torch.nn
label_desc: Use image node_id 1 to create a new reduction object from inherited base classes: nn with example: obj = reduction(in_channels, out_channels)