帮我实现VGG网络模型，添加注释谢谢

好的，下面是使用PyTorch实现的VGG网络模型，这里使用了VGG16的结构，并加上了注释。

import torch
import torch.nn as nn

class VGG16(nn.Module):
    def __init__(self, num_classes=1000):
        super(VGG16, self).__init__()
        # 定义卷积层1，输入通道数为3，输出通道数为64，卷积核大小为3x3
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        # 定义卷积层2，输入通道数为64，输出通道数为64，卷积核大小为3x3
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        # 定义池化层1，池化核大小为2x2
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 定义卷积层3，输入通道数为64，输出通道数为128，卷积核大小为3x3
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        # 定义卷积层4，输入通道数为128，输出通道数为128，卷积核大小为3x3
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        # 定义池化层2，池化核大小为2x2
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 定义卷积层5，输入通道数为128，输出通道数为256，卷积核大小为3x3
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        # 定义卷积层6，输入通道数为256，输出通道数为256，卷积核大小为3x3
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        # 定义卷积层7，输入通道数为256，输出通道数为256，卷积核大小为3x3
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        # 定义池化层3，池化核大小为2x2
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 定义卷积层8，输入通道数为256，输出通道数为512，卷积核大小为3x3
        self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        # 定义卷积层9，输入通道数为512，输出通道数为512，卷积核大小为3x3
        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        # 定义卷积层10，输入通道数为512，输出通道数为512，卷积核大小为3x3
        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        # 定义池化层4，池化核大小为2x2
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 定义卷积层11，输入通道数为512，输出通道数为512，卷积核大小为3x3
        self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        # 定义卷积层12，输入通道数为512，输出通道数为512，卷积核大小为3x3
        self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        # 定义卷积层13，输入通道数为512，输出通道数为512，卷积核大小为3x3
        self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        # 定义池化层5，池化核大小为2x2
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 定义全连接层1，输入节点数为7x7x512，输出节点数为4096
        self.fc1 = nn.Linear(7 * 7 * 512, 4096)
        # 定义全连接层2，输入节点数为4096，输出节点数为4096
        self.fc2 = nn.Linear(4096, 4096)
        # 定义全连接层3，输入节点数为4096，输出节点数为num_classes
        self.fc3 = nn.Linear(4096, num_classes)

    def forward(self, x):
        # 进行卷积层1-2