代码不完整

非常抱歉，可能是因为代码太长了，超过了限制导致不完整。下面是完整的代码示例：

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, VGAE
from torch_geometric.datasets import Planetoid

# 加载Cora数据集
dataset = Planetoid(root='/tmp/Cora', name='Cora')
data = dataset[0]

# 创建VGAE模型
class VGAEModel(torch.nn.Module):
    def __init__(self):
        super(VGAEModel, self).__init__()
        self.conv1 = GCNConv(dataset.num_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)
        self.vgae = VGAE(self.conv1, self.conv2)

    def encode(self, x, edge_index):
        return self.vgae.encode(x, edge_index)

    def reparameterize(self, mu, logvar):
        if self.training:
            std = torch.exp(0.5 * logvar)
            eps = torch.randn_like(std)
            return eps.mul(std).add(mu)
        else:
            return mu

    def decode(self, z, edge_index):
        return self.vgae.decode(z, edge_index)

    def forward(self, x, edge_index):
        z, mu, logvar = self.encode(x, edge_index)
        z = self.reparameterize(mu, logvar)
        return self.decode(z, edge_index), mu, logvar

model = VGAEModel()

# 训练模型
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
def train():
    model.train()
    optimizer.zero_grad()
    recon, mu, logvar = model(data.x, data.edge_index)
    loss = F.binary_cross_entropy(recon, data.adjacency_matrix.to_dense())
    loss = loss + (1 / data.num_nodes) * (-0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()))
    loss.backward()
    optimizer.step()

for epoch in range(200):
    train()
    print('Epoch:', epoch, 'Loss:', loss.item())

# 在测试集上评估模型
model.eval()
z, _, _ = model.encode(data.x, data.edge_index)
pred = model.decode(z, data.edge_index)

# 可以使用重构误差或其他指标来评估模型的性能