code: def __init__(self, channel, reduction=16):
      if channel <= reduction:
          raise AssertionError(
              "Make sure your input channel bigger than reduction which equals to {}".format(
                  reduction
              )
          )
      super(SELayer, self).__init__()
      self.avg_pool = nn.AdaptiveAvgPool2d(1)
      self.fc = nn.Sequential(
          nn.Linear(channel, channel // reduction),
          nn.ReLU(inplace=True),
          nn.Linear(channel // reduction, channel),
          nn.Sigmoid(),
      )
  
imports: from torch import nn
label_desc: Use image node_id 1 to create a new SELayer object from inherited base classes: nn with example: obj = SELayer(channel, reduction)