写一段基于pytorch的条件生成对抗神经网络用于生成新数据的代码

好的，以下是一段基于 PyTorch 的条件生成对抗神经网络的示例代码，用于生成新数据：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self, input_size, num_classes, output_size):
        super(Generator, self).__init__()
        self.input_size = input_size
        self.num_classes = num_classes
        self.output_size = output_size
        
        # 定义输入层
        self.input_layer = nn.Linear(input_size + num_classes, 128)
        # 定义隐藏层
        self.hidden_layer = nn.Linear(128, 256)
        # 定义输出层
        self.output_layer = nn.Linear(256, output_size)
        
    def forward(self, input, labels):
        # 将输入和标签连接起来
        x = torch.cat([input, labels], 1)
        # 传播输入层
        x = nn.functional.leaky_relu(self.input_layer(x), 0.2)
        # 传播隐藏层
        x = nn.functional.leaky_relu(self.hidden_layer(x), 0.2)
        # 传播输出层
        x = nn.functional.tanh(self.output_layer(x))
        return x

# 定义判别器网络
class Discriminator(nn.Module):
    def __init__(self, input_size, num_classes):
        super(Discriminator, self).__init__()
        self.input_size = input_size
        self.num_classes = num_classes
        
        # 定义输入层
        self.input_layer = nn.Linear(input_size + num_classes, 256)
        # 定义隐藏层
        self.hidden_layer = nn.Linear(256, 128)
        # 定义输出层
        self.output_layer = nn.Linear(128, 1)
        
    def forward(self, input, labels):
        # 将输入和标签连接起来
        x = torch.cat([input, labels], 1)
        # 传播输入层
        x = nn.functional.leaky_relu(self.input_layer(x), 0.2)
        # 传播隐藏层
        x = nn.functional.leaky_relu(self.hidden_layer(x), 0.2)
        # 传播输出层
        x = nn.functional.sigmoid(self.output_layer(x))
        return x

# 定义训练函数
def train(num_epochs, data_loader, generator, discriminator, g_optimizer, d_optimizer, criterion):
    for epoch in range(num_epochs):
        for i, (real_data, labels) in enumerate(data_loader):
            # 训练判别器
            discriminator.zero_grad()
            real_data = real_data.view(real_data.size(0), -1)
            labels = labels.view(labels.size(0), -1)
            real_pred = discriminator(real_data, labels)
            real_loss = criterion(real_pred, torch.ones(real_pred.size()))
            real_loss.backward()
            
            noise = torch.randn(real_data.size(0), generator.input_size)
            fake_labels = torch.randint(0, 10, (real_data.size(0), 1))
            fake_data = generator(noise, fake_labels).detach()
            fake_pred = discriminator(fake_data, fake_labels)
            fake_loss = criterion(fake_pred, torch.zeros(fake_pred.size()))
            fake_loss.backward()
            
            d_optimizer.step()
            
            # 训练生成器
            generator.zero_grad()
            noise = torch