写一段一构图神经网络节点分类代码

以下是一段构建图神经网络节点分类的代码，供参考：

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(dataset.num_node_features, 16)
self.conv2 = GCNConv(16, dataset.num_classes)

def forward(self, data):
x, edge_index = data.x, data.edge_index

x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)

return F.log_softmax(x, dim=1)

# 实例化模型
model = Net()

# 定义优化器和损失函数
optimizer = torch.optim.Adam(model.parameters(), lr=.01)
criterion = torch.nn.CrossEntropyLoss()

# 训练模型
model.train()
for epoch in range(200):
optimizer.zero_grad()
out = model(data)
loss = criterion(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()

# 测试模型
model.eval()
_, pred = model(data).max(dim=1)
correct = float(pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())
acc = correct / data.test_mask.sum().item()
print('Accuracy: {:.4f}'.format(acc))