code: def train(epoch):
      model.train()
  
      pbar = tqdm(total=node_type.size(0))
      pbar.set_description(f"Epoch {epoch:02d}")
  
      total_loss = total_examples = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          out = model(
              x_dict,
              data.edge_index,
              data.edge_attr,
              data.node_type,
              data.local_node_idx,
          )
          out = out[data.train_mask]
          y = data.y[data.train_mask].squeeze()
          loss = F.nll_loss(out, y)
          loss.backward()
          optimizer.step()
  
          num_examples = data.train_mask.sum().item()
          total_loss += loss.item() * num_examples
          total_examples += num_examples
          pbar.update(data.node_type.size(0))
  
      pbar.close()
  
      return total_loss / total_examples
  
imports: from copy import copy
  import argparse
  from tqdm.auto import tqdm
  import torch
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, ParameterDict, Parameter
  from torch_sparse import SparseTensor
  from torch_geometric.utils import to_undirected
  from torch_geometric.data import Data, ClusterData, ClusterLoader
  from torch_geometric.utils.hetero import group_hetero_graph
  from torch_geometric.nn import MessagePassing
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown