code: def train():
      model.train()
      optimizer.zero_grad()
      out = model(x_train)
      loss = criterion(out, y_train.view(-1))
      loss.backward()
      optimizer.step()
      return float(loss)
  
imports: import os.path
  import torch
  from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
  import torch_geometric.transforms
  from torch_geometric.nn import MLP, CorrectAndSmooth
  from torch_geometric.typing import WITH_TORCH_SPARSE
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float