class Residual(nn.Module):     def __init__(self,in_channels,out_channels,use_1x1conv=False,stride=1):         super(Residual,self).__init__()         self.conv1=nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1,stride=stride)                 self.conv2=nn.Conv2d(out_channels,out_channels,kernel_size=3,padding=1)         if use_1x1conv:             self.conv3=nn.Conv2d(in_channels,out_channels,kernel_size=1,stride=stride)         else:             self.conv3=None         self.bn1=nn.BatchNorm2d(out_channels)         self.bn2=nn.BatchNorm2d(out_channels)         self.avg_pool=nn.AdaptiveAvgPool2d(1)         self.fc=nn.Sequential(nn.Linear(out_channels,out_channels,bias=False),                               nn.ReLU(inplace=True),                               nn.Linear(out_channels,out_channels,bias=False),                               nn.Sigmoid())
    def forward(self,X):                 Y=F.relu(self.bn1(self.conv1(X)))         Y=self.bn2(self.conv2(Y))         if self.conv3:             X=self.conv3(X)         b,c,_,_=Y.size()         y=self.avg_pool(Y).view(b,c)         y=self.fc(y).view(b,c,1,1)         Y=y.expand_as(Y)*Y         return F.relu(Y+X)