能不能给我以pytorch为环境写一段详细的条件生成对抗网络的示例程序，要求输入和条件都是一维张量，生成的输出也是一维张量

可以的，以下是一个简单的条件生成对抗网络的示例程序：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义生成器
class Generator(nn.Module):
    def __init__(self, input_dim, output_dim, condition_dim):
        super(Generator, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.condition_dim = condition_dim

        self.fc1 = nn.Linear(input_dim + condition_dim, 128)
        self.fc2 = nn.Linear(128, output_dim)

    def forward(self, z, c):
        x = torch.cat([z, c], dim=1)
        x = nn.functional.relu(self.fc1(x))
        x = torch.tanh(self.fc2(x))
        return x

# 定义判别器
class Discriminator(nn.Module):
    def __init__(self, input_dim, condition_dim):
        super(Discriminator, self).__init__()
        self.input_dim = input_dim
        self.condition_dim = condition_dim

        self.fc1 = nn.Linear(input_dim + condition_dim, 128)
        self.fc2 = nn.Linear(128, 1)

    def forward(self, x, c):
        x = torch.cat([x, c], dim=1)
        x = nn.functional.relu(self.fc1(x))
        x = torch.sigmoid(self.fc2(x))
        return x

# 定义训练函数
def train(generator, discriminator, dataloader, num_epochs, lr):
    criterion = nn.BCELoss()
    g_optimizer = optim.Adam(generator.parameters(), lr=lr)
    d_optimizer = optim.Adam(discriminator.parameters(), lr=lr)

    for epoch in range(num_epochs):
        for i, (real_data, condition) in enumerate(dataloader):
            batch_size = real_data.size(0)
            real_label = torch.ones(batch_size, 1)
            fake_label = torch.zeros(batch_size, 1)

            # 训练判别器
            d_optimizer.zero_grad()
            real_output = discriminator(real_data, condition)
            real_loss = criterion(real_output, real_label)
            real_loss.backward()

            z = torch.randn(batch_size, generator.input_dim)
            fake_data = generator(z, condition)
            fake_output = discriminator(fake_data.detach(), condition)
            fake_loss = criterion(fake_output, fake_label)
            fake_loss.backward()

            d_optimizer.step()

            # 训练生成器
            g_optimizer.zero_grad()
            fake_output = discriminator(fake_data, condition)
            g_loss = criterion(fake_output, real_label)
            g_loss.backward()
            g_optimizer.step()

            if i % 100 == 0:
                print('Epoch [{}/{}], Step [{}/{}], D_loss: {:.4f}, G_loss: {:.4f}'
                      .format(epoch, num_epochs, i, len(dataloader), real_loss.item() + fake_loss.item(), g_loss.item()))

# 定义数据集
class MyDataset(torch.utils.data.Dataset):
    def __init__(self, data, condition):
        self.data = data
        self.condition = condition

    def __getitem__(self, index):
        return self.data[index], self.condition[index]

    def __len__(self):
        return len(self.data)

# 定义训练数据
data = np.random.randn(1000, 1)
condition = np.random.randn(1000, 1)
train_dataset = MyDataset(torch.from_numpy(data).float(), torch.from_numpy(condition).float())
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# 定义模型和训练
input_dim = 10
output_dim = 1
condition_dim = 1
generator = Generator(input_dim, output_dim, condition_dim)
discriminator = Discriminator(output_dim, condition_dim)
train(generator, discriminator, train_dataloader, num_epochs=100, lr=0.001)

# 测试生成器
z = torch.randn(10, input_dim)
c = torch.randn(10, condition_dim)
fake_data = generator(z, c)
print(fake_data)

这个程序实现了一个简单的条件生成对抗网络，输入和条件都是一维张量，生成的输出也是一维张量。其中，生成器和判别器都是简单的全连接神经网络，训练函数使用了交替训练的方法。