code: def train():
      model.train()
  
      total_loss = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          model.redraw_projection.redraw_projections()
          out = model(
              data.x,
              data.pe,
              data.edge_index,
              data.edge_attr,
              data.batch,
          )
          loss = (out.squeeze() - data.y).abs().mean()
          loss.backward()
          total_loss += loss.item() * data.num_graphs
          optimizer.step()
      return total_loss / len(train_loader.dataset)
  
file_path: pytorch_geometric/examples/graph_gps.py
imports: import argparse
  import os.path
  from typing import Any, Dict, Optional
  import torch
  from torch.nn import BatchNorm1d, Embedding, Linear, ModuleList, ReLU, Sequential
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  import torch_geometric.transforms
  from torch_geometric.datasets import ZINC
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GINEConv, GPSConv, global_add_pool
  from torch_geometric.nn.attention import PerformerAttention
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown