code: def train(args, trainer, task, epoch_itr):
      """Train the model for one epoch and return validation losses."""
      # Initialize data iterator
      itr = epoch_itr.next_epoch_itr(
          fix_batches_to_gpus=args.fix_batches_to_gpus,
          shuffle=(epoch_itr.next_epoch_idx > args.curriculum),
      )
      update_freq = (
          args.update_freq[epoch_itr.epoch - 1]
          if epoch_itr.epoch <= len(args.update_freq)
          else args.update_freq[-1]
      )
      itr = iterators.GroupedIterator(itr, update_freq)
      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,
          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"
          ),
      )
  
      trainer.begin_epoch(epoch_itr.epoch)
  
      valid_losses = [None]
      valid_subsets = args.valid_subset.split(",")
      should_stop = False
      num_updates = trainer.get_num_updates()
      for i, samples in enumerate(progress):
          with metrics.aggregate(
              "train_inner"
          ), torch.autograd.profiler.record_function(
              "train_step-%d" % i
          ):
              log_output = trainer.train_step(samples)
  
          if log_output is not None:  # not OOM, overflow, ...
              # log mid-epoch stats
              num_updates = trainer.get_num_updates()
              if num_updates % args.log_interval == 0:
                  stats = get_training_stats(
                      metrics.get_smoothed_values("train_inner")
                  )
                  progress.log(
                      stats, tag="train_inner", step=num_updates
                  )
  
                  # reset mid-epoch stats after each log interval
                  # the end-of-epoch stats will still be preserved
                  metrics.reset_meters("train_inner")
  
          end_of_epoch = not itr.has_next()
          valid_losses, should_stop = validate_and_save(
              args,
              trainer,
              task,
              epoch_itr,
              valid_subsets,
              end_of_epoch,
          )
  
          if should_stop:
              break
  
      # log end-of-epoch stats
      logger.info(
          "end of epoch {} (average epoch stats below)".format(
              epoch_itr.epoch
          )
      )
      stats = get_training_stats(metrics.get_smoothed_values("train"))
      progress.print(stats, tag="train", step=num_updates)
  
      # reset epoch-level meters
      metrics.reset_meters("train")
      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 3 for calling a global function with example usage: train(args, trainer, task, epoch_itr) and returns: valid_losses,  should_stop