class_docstr: Construct convolution-based channel-wise SELayer.
class_docstr_tok: ["Construct","convolution-based","channel-wise","SELayer","."]
code: def __init__(self, channel, reduction=16):
      super(SELayerCoC, self).__init__(channel, reduction)
      self.conv1 = nn.Conv3d(
          in_channels=self.channel,
          out_channels=self.channel // self.reduction,
          kernel_size=1,
          bias=False,
      )
      self.bn1 = nn.BatchNorm3d(
          num_features=self.channel // self.reduction
      )
      self.avg_pool = nn.AdaptiveAvgPool3d(1)
      self.sigmoid = nn.Sigmoid()
      self.conv2 = nn.Conv3d(
          in_channels=self.channel // self.reduction,
          out_channels=self.channel,
          kernel_size=1,
          bias=False,
      )
      self.bn2 = nn.BatchNorm3d(num_features=self.channel)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new SELayerCoC object from inherited base classes: SELayer with example: obj = SELayerCoC(channel, reduction)