code: def __init__(
      self, channel, reduction=2, is_bn=True, is_cse=True, is_sse=True
  ):
      super(SELayer, self).__init__()
      self.is_cse = is_cse
      self.is_sse = is_sse
      self.is_bn = is_bn
  
      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(),
      )
  
      self.sse1 = nn.Conv2d(channel, 1, 1)
      self.sse2 = nn.Sigmoid()
      self.bn = nn.BatchNorm2d(channel)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import init
label_desc: Use image node_id 1 to create a new SELayer object from inherited base classes: nn with example: obj = SELayer(channel, reduction, is_bn, is_cse, is_sse)