如何在Pytorch中访问CNN中卷积层的权重和L2范数？

import torch
import torch.nn as nn

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc = nn.Linear(32 * 32 * 32, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

model = CNN()

conv1_weights = model.conv1.weight
conv2_weights = model.conv2.weight

conv1_l2_norm = torch.norm(conv1_weights, p=2)
conv2_l2_norm = torch.norm(conv2_weights, p=2)