I am using pretrained ResNet 50, I am performing finetuning, My code is as follows:
class ResNet50(nn.Module):
    def __init__(self,num_classes,loss={'xent'},**kwargs):
        super(ResNet50,self).__init__()
        self.loss = loss
        resnet50 = torchvision.models.resnet50(pretrained=True)
        num_ftrs = resnet50.fc.in_features
        self.base= nn.Sequential(*list(resnet50.children())[:-2])
        self.fc1 = nn.Linear(2048,num_classes)
        self.fc2 = nn.Linear(2048, num_classes)


    def forward(self,x):
        x = self.base(x)
        x = F.avg_pool2d(x,x.size()[2:])
        f = x.view(x.size(0),-1)
        if not self.training:
            return f
        y = self.classifier(f)


I want to add multiple fc layers in parallel after ResNet

How can I add multiple FC layers in parallel ?,