code: def train(model, optimizer, hetero_graph, train_idx):
      model.train()
      optimizer.zero_grad()
      preds = model(hetero_graph.node_feature, hetero_graph.edge_index)
      loss = model.loss(preds, hetero_graph.node_label, train_idx)
      loss.backward()
      optimizer.step()
      return loss.item()
  
imports: import copy
  import torch
  import argparse
  import deepsnap
  import numpy
  import torch.nn
  import torch.nn.functional
  import torch_geometric.nn
  from sklearn.metrics import f1_score
  from deepsnap.hetero_gnn import forward_op, HeteroSAGEConv
  from deepsnap.hetero_graph import HeteroGraph
  from torch_sparse import SparseTensor, matmul
label_desc: Use image node_id 3 for calling a global function with example usage: train(model, optimizer, hetero_graph, train_idx) and returns: loss