在基本结构上，变分自编码器的代码基本上不会改变。唯一需要修改的是数据集相关的代码部分，包括数据的读入、预处理、批量处理等。以下是使用PyTorch框架实现变分自编码器的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class VAE(nn.Module):
    def __init__(self, input_dim, latent_dim):
        super(VAE, self).__init__()

        self.input_dim = input_dim
        self.latent_dim = latent_dim

        # Encode
        self.fc1 = nn.Linear(input_dim, 512)
        self.fc2_mean = nn.Linear(512, latent_dim)
        self.fc2_log_var = nn.Linear(512, latent_dim)

        # Decode
        self.fc3 = nn.Linear(latent_dim, 512)
        self.fc4 = nn.Linear(512, input_dim)

    def encode(self, x):
        x = F.relu(self.fc1(x))
        mean = self.fc2_mean(x)
        log_var = self.fc2_log_var(x)
        return mean, log_var

    def reparameterize(self, mean, log_var):
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        return eps * std + mean

    def decode(self, z):
        z = F.relu(self.fc3(z))
        reconstruction = torch.sigmoid(self.fc4(z))
        return reconstruction

    def forward(self, x):
        mean, log_var = self.encode(x)
        z = self.reparameterize(mean, log_var)
        reconstruction = self.decode(z)
        return reconstruction, mean, log_var

# Load data
train_data = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('./data',
    train=True,
    download=True,
    transform=torchvision.transforms.Compose([
        torchvision.transforms.ToTensor()
    ])),
    batch_size=128,
    shuffle=True
)

# Train
model = VAE(input_dim=28*28, latent_dim=10)
optimizer = torch.optim.Adam(model.parameters(), lr=1