torch搭建网络，模块是否可以重复调用？

参考： https://blog.youkuaiyun.com/rocking_struggling/article/details/108357089

class MyNetk(t.nn.Module):
    def __init__(self):
        super(MyNetk,self).__init__()
        # print("卷积网络")
        self.conv1 = nn.Sequential(
          nn.Conv2d(
            in_channels = 3,  #(, 64, 64, 3)
            out_channels = 16,
            kernel_size = 3,
            stride = 1,
            padding = 1
          ),   ##( , 64, 64, 16)
          nn.ReLU(),
          nn.MaxPool2d(kernel_size = 2)
        )  ##( , 32, 32, 16)
        self.conv2 = nn.Sequential(
          nn.Conv2d(16,32,3,1,1),
          nn.ReLU(),
          nn.MaxPool2d(2)
        )
        self.conv3 = nn.Sequential(
          nn.Conv2d(32,64,3,1,1),
          nn.ReLU(),
          nn.MaxPool2d(2)
        )
        self.conv4 = nn.Sequential(
          nn.Conv2d(64,64,3,1,1),
          nn.BatchNorm2d(64),
          nn.BatchNorm2d(64),
          nn.ReLU(),
        )
    def forward(self, x):
        # print("前向传播")
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv4(x)
        x = self.conv4(x)
        x = self.conv4(x)
        return x
if __name__ == '__main__':
    net=MyNetk()
    print(net)
    for name, param in net.named_parameters():
        print(name)

torch 网络中的模块不可以重复调用，网络的参数的数目在初始化时已经固定住了。如 之定义了一个conv4

但是组件中的其他重复模块是可以创建不同参数的。如conv4中nn.batchnorm2d的模块分别为4.1、4.2