编写python代码，使用PGD方法对图卷积神经网络进行攻击

可以使用以下Python代码对图卷积神经网络进行攻击：```
import torch
import torch.nn as nn
import torch.optim as optim # 定义PGD攻击函数
def pgd_attack(model, data, target, eps, alpha):
# 计算模型输出
output = model(data)
# 计算损失
loss = nn.CrossEntropyLoss()(output, target)
# 计算梯度
grad = torch.autograd.grad(loss, data)[0]
# 更新数据
data = data + alpha * torch.sign(grad)
data = torch.max(torch.min(data, data + eps), data - eps)
return data# 定义优化器
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
# 训练模型
for epoch in range(num_epochs):
# 进行PGD攻击
data = pgd_attack(model, data, target, eps, alpha)
# 计算损失
output = model(data)
loss = nn.CrossEntropyLoss()(output, target)
# 反向传播
optimizer.zero_grad()
loss.backward()
optimizer.step()