code: 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)
  
imports: import argparse
  import os.path
  import time
  import torch
  from sklearn.metrics import f1_score
  from torch_geometric.datasets import PPI
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GATConv
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown