code: def validate_and_save(
      cfg: DictConfig,
      trainer: Trainer,
      task: tasks.FairseqTask,
      epoch_itr,
      valid_subsets: List[str],
      end_of_epoch: bool,
  ) -> Tuple[List[Optional[float]], bool]:
      num_updates = trainer.get_num_updates()
      max_update = cfg.optimization.max_update or math.inf
  
      # Stopping conditions (and an additional one based on validation loss later
      # on)
      should_stop = False
      if num_updates >= max_update:
          should_stop = True
          logger.info(
              f"Stopping training due to "
              f"num_updates: {num_updates} >= max_update: {max_update}"
          )
  
      training_time_hours = trainer.cumulative_training_time() / (
          60 * 60
      )
      if (
          cfg.optimization.stop_time_hours > 0
          and training_time_hours > cfg.optimization.stop_time_hours
      ):
          should_stop = True
          logger.info(
              f"Stopping training due to "
              f"cumulative_training_time: {training_time_hours} > "
              f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
          )
  
      do_save = (
          (
              end_of_epoch
              and epoch_itr.epoch % cfg.checkpoint.save_interval == 0
          )
          or should_stop
          or (
              cfg.checkpoint.save_interval_updates > 0
              and num_updates > 0
              and num_updates % cfg.checkpoint.save_interval_updates
              == 0
              and num_updates >= cfg.dataset.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 % cfg.dataset.validate_interval
                  == 0
              )
              or should_stop
              or (
                  cfg.dataset.validate_interval_updates > 0
                  and num_updates > 0
                  and num_updates
                  % cfg.dataset.validate_interval_updates
                  == 0
              )
          )
          and not cfg.dataset.disable_validation
          and num_updates >= cfg.dataset.validate_after_updates
      )
  
      # Validate
      valid_losses = [None]
      if do_validate:
          valid_losses = validate(
              cfg, trainer, task, epoch_itr, valid_subsets
          )
  
      should_stop |= should_stop_early(cfg, valid_losses[0])
  
      # Save checkpoint
      if do_save or should_stop:
          cp_path = checkpoint_utils.save_checkpoint(
              cfg.checkpoint, trainer, epoch_itr, valid_losses[0]
          )
          if cp_path is not None and hasattr(task, "post_save"):
              task.post_save(cp_path, num_updates)
  
      return valid_losses, should_stop
  
imports: import argparse
  import logging
  import math
  import os
  import sys
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import numpy
  import torch
  from omegaconf import DictConfig, OmegaConf
  from fairseq import checkpoint_utils, options, quantization_utils, tasks, utils
  from fairseq.data import data_utils, iterators
  from fairseq.data.plasma_utils import PlasmaStore
  from fairseq.dataclass.configs import FairseqConfig
  from fairseq.dataclass.initialize import add_defaults
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap
  from fairseq.distributed import utils
  from fairseq.file_io import PathManager
  from fairseq.logging import meters, metrics, progress_bar
  from fairseq.model_parallel.megatron_trainer import MegatronTrainer
  from fairseq.trainer import Trainer
label_desc: Use image node_id 5 for calling a global function with example usage: validate_and_save(cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch) and returns: valid_losses,  should_stop