code: def __init__(
      self, in_planes, out_planes, num_features, init_weight=True
  ):
      super(ScaleChannelAttention, self).__init__()
      self.avgpool = nn.AdaptiveAvgPool2d(1)
      print(self.avgpool)
      self.fc1 = nn.Conv2d(in_planes, out_planes, 1, bias=False)
      self.bn = nn.BatchNorm2d(out_planes)
      self.fc2 = nn.Conv2d(out_planes, num_features, 1, bias=False)
      if init_weight:
          self._initialize_weights()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ScaleChannelAttention object from inherited base classes: nn with example: obj = ScaleChannelAttention(in_planes, out_planes, num_features, init_weight)