code: def validate(args, trainer, task, epoch_itr, subsets):
      """Evaluate the model on the validation set(s) and return the losses."""
  
      if args.fixed_validation_seed is not None:
          # set fixed seed for every validation
          utils.set_torch_seed(args.fixed_validation_seed)
  
      trainer.begin_valid_epoch(epoch_itr.epoch)
      valid_losses = []
      for subset in subsets:
          logger.info('begin validation on "{}" subset'.format(subset))
  
          # Initialize data iterator
          itr = trainer.get_valid_iterator(subset).next_epoch_itr(
              shuffle=False
          )
          if getattr(args, "tpu", False):
              itr = utils.tpu_data_loader(itr)
          progress = progress_bar.progress_bar(
              itr,
              log_format=args.log_format,
              log_interval=args.log_interval,
              epoch=epoch_itr.epoch,
              prefix=f"valid on '{subset}' subset",
              tensorboard_logdir=(
                  args.tensorboard_logdir
                  if distributed_utils.is_master(args)
                  else None
              ),
              default_log_format=(
                  "tqdm" if not args.no_progress_bar else "simple"
              ),
          )
  
          # create a new root metrics aggregator so validation metrics
          # don't pollute other aggregators (e.g., train meters)
          with metrics.aggregate(new_root=True) as agg:
              for sample in progress:
                  trainer.valid_step(sample)
  
          # log validation stats
          stats = get_valid_stats(
              args, trainer, agg.get_smoothed_values()
          )
          progress.print(
              stats, tag=subset, step=trainer.get_num_updates()
          )
  
          valid_losses.append(stats[args.best_checkpoint_metric])
      return valid_losses
  
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 6 for calling a global function with example usage: validate(args, trainer, task, epoch_itr, subsets) and returns: valid_losses