请帮我写出基于PYG搭建的GIN实现图分类的代码

好的，下面是基于PyG搭建的GIN实现图分类的代码。

首先，需要安装PyG和其他必要的库：

!pip install torch
!pip install torch-geometric
!pip install torch-scatter
!pip install torch-sparse
!pip install torch-cluster

接下来，导入必要的库和数据集：

import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GINConv, global_mean_pool

# 加载数据集
dataset = 'Cora'
path = './data'
dataset = Planetoid(path, dataset)

然后，定义GIN模型：

class GIN(torch.nn.Module):
    def __init__(self, num_features, num_classes):
        super(GIN, self).__init__()
        
        # 定义GINConv层
        self.conv1 = GINConv(torch.nn.Sequential(
            torch.nn.Linear(num_features, 64),
            torch.nn.BatchNorm1d(64),
            torch.nn.ReLU(),
            torch.nn.Linear(64, 64),
            torch.nn.BatchNorm1d(64),
            torch.nn.ReLU(),
            torch.nn.Linear(64, 64),
            torch.nn.BatchNorm1d(64),
            torch.nn.ReLU(),
        ), train_eps=True)
        
        self.conv2 = GINConv(torch.nn.Sequential(
            torch.nn.Linear(64, 64),
            torch.nn.BatchNorm1d(64),
            torch.nn.ReLU(),
            torch.nn.Linear(64, 64),
            torch.nn.BatchNorm1d(64),
            torch.nn.ReLU(),
            torch.nn.Linear(64, 64),
            torch.nn.BatchNorm1d(64),
            torch.nn.ReLU(),
        ), train_eps=True)
        
        # 定义全连接层
        self.fc1 = torch.nn.Linear(64, 128)
        self.fc2 = torch.nn.Linear(128, num_classes)
        
    def forward(self, x, edge_index):
        # 第一层GINConv
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        
        # 全局平均池化
        x = global_mean_pool(x, edge_index)
        
        x = F.relu(self.fc1(x))
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.fc2(x)
        
        return F.log_softmax(x, dim=1)

最后，定义训练函数和测试函数：

def train(model, optimizer, data):
    model.train()
    optimizer.zero_grad()
    out = model(data.x, data.edge_index)
    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()
    return loss.item()

def test(model, data):
    model.eval()
    out = model(data.x, data.edge_index)
    pred = out.argmax(dim=1)
    test_correct = pred[data.test_mask] == data.y[data.test_mask]
    test_acc = int(test_correct.sum()) / int(data.test_mask.sum())
    return test_acc

最后，训练和测试模型：

# 定义模型、优化器和超参数
model = GIN(dataset.num_features, dataset.num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
epochs = 200

for epoch in range(1, epochs+1):
    loss = train(model, optimizer, dataset[0])
    acc = test(model, dataset[0])
    print('Epoch {:03d}, Loss: {:.4f}, Test Acc: {:.4f}'.format(epoch, loss, acc))

这样，就完成了基于PyG搭建的GIN实现图分类的代码。