code: def forward(self, x):
      b, c, _, _ = x.size()
      y = self.avg_pool(x).view(b, c)
      y = self.fc(y).view(b, c, 1, 1)
      return y
  
imports: from torch import nn
  from torch.nn import functional
  import torch
  from torchvision import models
  from torchvision.models import resnet34, resnet101, resnet50, resnet152
  import torchvision
label_desc: Use image node_id 2 for calling the ChannelAttentionGate obj's underlying member method code with example usage: obj.forward(x) and returns: y