code: def validate_and_save(
      args, trainer, task, epoch_itr, valid_subsets, end_of_epoch
  ):
      num_updates = trainer.get_num_updates()
      max_update = args.max_update or math.inf
      do_save = (
          (end_of_epoch and epoch_itr.epoch % args.save_interval == 0)
          or num_updates >= max_update
          or (
              args.save_interval_updates > 0
              and num_updates > 0
              and num_updates % args.save_interval_updates == 0
              and num_updates >= args.validate_after_updates
          )
      )
      do_validate = (
          (
              not end_of_epoch and do_save
          )  # validate during mid-epoch saves
          or (
              end_of_epoch
              and epoch_itr.epoch % args.validate_interval == 0
          )
          or num_updates >= max_update
          or (
              args.validate_interval_updates > 0
              and num_updates > 0
              and num_updates % args.validate_interval_updates == 0
          )
      ) and not args.disable_validation
  
      # Validate
      valid_losses = [None]
      if do_validate:
          valid_losses = validate(
              args, trainer, task, epoch_itr, valid_subsets
          )
  
      # Stopping conditions
      should_stop = (
          should_stop_early(args, valid_losses[0])
          or num_updates >= max_update
          or (
              args.stop_time_hours > 0
              and trainer.cumulative_training_time() / (60 * 60)
              > args.stop_time_hours
          )
      )
  
      # Save checkpoint
      if do_save or should_stop:
          logger.info("begin save checkpoint")
          checkpoint_utils.save_checkpoint(
              args, trainer, epoch_itr, valid_losses[0]
          )
  
      return valid_losses, should_stop
  
imports: import argparse
  import logging
  import math
  import os
  import random
  import sys
  import numpy
  import torch
  from fairseq import checkpoint_utils, distributed_utils, options, quantization_utils, tasks, utils
  from fairseq.data import iterators
  from fairseq.logging import meters, metrics, progress_bar
  from fairseq.model_parallel.megatron_trainer import MegatronTrainer
  from .ls_fs_gcq_trainer import LSTrainer
label_desc: Use image node_id 4 for calling a global function with example usage: validate_and_save(args, trainer, task, epoch_itr, valid_subsets, end_of_epoch) and returns: valid_losses,  should_stop