用pytorch,使用VGG块与残差结构搭建一个网络模型,仅给出网络模型

好的，下面是一个使用 PyTorch 搭建的网络模型，同时包含 VGG 块和残差结构：

import torch
import torch.nn as nn

class VGGBlock(nn.Module):
    def __init__(self, in_channels, middle_channels, out_channels, activation=nn.ReLU):
        super(VGGBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(middle_channels)
        self.activation = activation()
        self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        
    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.activation(out)
        out = self.conv2(out)
        out = self.bn2(out)
        return out

class ResidualBlock(nn.Module):
    def __init__(self, channels, activation=nn.ReLU):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.activation = activation()
        self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)
        
    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.activation(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += residual
        return out

class Network(nn.Module):
    def __init__(self, num_classes=10):
        super(Network, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.activation = nn.ReLU()
        self.vgg_block1 = VGGBlock(64, 128, 128)
        self.vgg_block2 = VGGBlock(128, 256, 256)
        self.residual_block = ResidualBlock(256)
        self.fc = nn.Linear(256 * 8 * 8, num_classes)
        
    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.activation(out)
        out = self.