code: def train():
      model.train()
      optimizer.zero_grad()
      out = model(data.edge_index, data.edge_type)
      loss = F.nll_loss(out[data.train_idx], data.train_y)
      loss.backward()
      optimizer.step()
      return float(loss)
  
file_path: pytorch_geometric/examples/rgcn.py
imports: import argparse
  import os.path
  import time
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Entities
  from torch_geometric.nn import FastRGCNConv, RGCNConv
  from torch_geometric.utils import k_hop_subgraph
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float