How to implement machine learning to convert text into images with sample code?

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.fc = nn.Linear(100, 256)
        self.main = nn.Sequential(
            nn.ConvTranspose2d(256, 128, 5, stride=2, padding=2),
            nn.BatchNorm2d(128),
            nn.ReLU(True),
            nn.ConvTranspose2d(128, 64, 5, stride=2, padding=2),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
            nn.ConvTranspose2d(64, 1, 4, stride=2, padding=1),
            nn.Tanh()
        )

    def forward(self, x):
        x = self.fc(x)
        x = x.view(-1, 256, 1, 1)
        x = self.main(x)
        return x

# 定义判别器网络
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(1, 64, 4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, 4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, 4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(256, 1, 4, stride=1, padding=0),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.main(x)
        return x.view(-1, 1)

# 定义训练函数
def train(generator, discriminator, dataloader, optimizer_G, optimizer_D, device):
    criterion = nn.BCELoss()
    real_label = 1
    fake_label = 0

    for epoch in range(200):
        for i, (data, _) in enumerate(dataloader):
            # 训练判别器
            discriminator.zero_grad()
            real_data = data.to(device)
            batch_size = real_data.size(0)
            label = torch.full((batch_size,), real_label, device=device)
            output = discriminator(real_data).view(-1)
            errD_real = criterion(output, label)
            errD_real.backward()
            D_x = output.mean().item()

            noise = torch.randn(batch_size, 100, device=device)
            fake_data = generator(noise)
            label.fill_(fake_label)
            output = discriminator(fake_data.detach()).view(-1)
            errD_fake = criterion(output, label)
            errD_fake.backward()
            D_G_z1 = output.mean().item()
            errD = errD_real + errD_fake
            optimizer_D.step()

            # 训练生成器
            generator.zero_grad()
            label.fill_(real_label)
            output = discriminator(fake_data).view(-1)
            errG = criterion(output, label)
            errG.backward()
            D_G_z2 = output.mean().item()
            optimizer_G.step()

            if i % 100 == 0:
                print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'
                      % (epoch+1, 200, i, len(dataloader),
                         errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
        # 保存生成的图像
        fake = generator(fixed_noise)
        save_image(fake.detach(), 'generated_images_%03d.png' % epoch, normalize=True)

# 加载MNIST数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
dataset = datasets.MNIST(root='./数据集', train=True, transform=transform, download=True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)

# 定义设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 初始化生成器和判别器
generator = Generator().to(device)
discriminator = Discriminator().to(device)

# 定义优化器
optimizer_G = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_D = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 定义固定噪声用于保存生成的图像
fixed_noise = torch.randn(64, 100, device=device)

# 开始训练
train(generator, discriminator, dataloader, optimizer_G, optimizer_D, device)