code: def validate(st, end):
      model.eval()
      output = model(valid_data, st, end)
      loss_val = criterion(output, valid_labels[st:end])
      acc_val = paddle.metric.accuracy(
          input=output, label=valid_labels[st:end], k=1
      )
      return loss_val.item(), acc_val.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 3 for calling a global function with example usage: validate(st, end) and returns: loss_val,  acc_val