kwargs = {‘batch_size’: 512, ‘num_workers’: 6, ‘persistent_workers’: True} subgraph_loader = NeighborLoader(copy.copy(data), input_nodes=None, num_neighbors=[-1], shuffle=False, **kwargs) for _ in range(n_epochs): model.train() total_loss = total_correct = total_examples = 0 for batch in train_loader: optimizer.zero_grad() y = batch.y[:batch.batch_size] y_hat = model(batch.x, batch.edge_index)[:batch.batch_size] loss = F.cross_entropy(y_hat, y) loss.backward() optimizer.step() total_loss += float(loss) * batch.batch_size total_correct += int((y_hat.argmax(dim=-1) == y).sum()) […]