code: def train(st, end):
      model.train()
      output = model(train_data, st, end)
      acc_train = paddle.metric.accuracy(
          input=output, label=train_labels[st:end], k=1
      )
      loss_train = criterion(output, train_labels[st:end])
      loss_train.backward()
      optimizer.step()
      optimizer.clear_grad()
  
      return loss_train.item(), acc_train.item()
  
file_path: PGL/examples/chebnetii/ogb/large_train.py
imports: import random
  import argparse
  import uuid
  import numpy
  import paddle
  from paddle.optimizer import Adam
  from model import ChebNetII
  from utils import set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: train(st, end) and returns: loss_train,  acc_train