text: import os.path as osp
  
  import torch
  import torch.nn.functional as F
  from torch_geometric.datasets import PPI
  from torch_geometric.data import DataLoader
  from torch_geometric.nn import GATConv
  from sklearn import metrics
  
  path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'PPI')
  train_dataset = PPI(path, split='train')
  val_dataset = PPI(path, split='test')
  test_dataset = PPI(path, split='test')
  train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
  val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
  test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False)
  
  
  class Net(torch.nn.Module):
      def __init__(self):
          super(Net, self).__init__()
          self.conv1 = GATConv(train_dataset.num_features, 256, heads=4)
          self.lin1 = torch.nn.Linear(train_dataset.num_features, 4 * 256)
          self.conv2 = GATConv(4 * 256, 256, heads=4)
          self.lin2 = torch.nn.Linear(4 * 256, 4 * 256)
          self.conv3 = GATConv(
              4 * 256, train_dataset.num_classes, heads=6, concat=False)
          self.lin3 = torch.nn.Linear(4 * 256, train_dataset.num_classes)
  
      def forward(self, x, edge_index):
          x = F.elu(self.conv1(x, edge_index) + self.lin1(x))
          x = F.elu(self.conv2(x, edge_index) + self.lin2(x))
          x = self.conv3(x, edge_index) + self.lin3(x)
          return x
  
  
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  model = Net().to(device)
  loss_op = torch.nn.BCEWithLogitsLoss()
  optimizer = torch.optim.Adam(model.parameters(), lr=0.005)
  
  
  def train():
      model.train()
  
      total_loss = 0
      for data in train_loader:
          num_graphs = data.num_graphs
          data.batch = None
          data = data.to(device)
          optimizer.zero_grad()
          loss = loss_op(model(data.x, data.edge_index), data.y)
          total_loss += loss.item() * num_graphs
          loss.backward()
          optimizer.step()
      return total_loss / len(train_loader.dataset)
  
  
  def test(loader):
      model.eval()
  
      total_micro_f1 = 0
      for data in loader:
          with torch.no_grad():
              out = model(data.x.to(device), data.edge_index.to(device))
          pred = (out > 0).float().cpu()
          micro_f1 = metrics.f1_score(data.y, pred, average='micro')
          total_micro_f1 += micro_f1 * data.num_graphs
      return total_micro_f1 / len(loader.dataset)
  
  
  for epoch in range(1, 101):
      loss = train()
      acc = test(val_loader)
      print('Epoch: {:02d}, Loss: {:.4f}, Acc: {:.4f}'.format(epoch, loss, acc))
  