code: def train(
      cfg: DictConfig,
      trainer: Trainer,
      task: tasks.FairseqTask,
      epoch_itr,
  ) -> Tuple[List[Optional[float]], bool]:
      """Train the model for one epoch and return validation losses."""
      # Initialize data iterator
      itr = epoch_itr.next_epoch_itr(
          fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
          shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
      )
      update_freq = (
          cfg.optimization.update_freq[epoch_itr.epoch - 1]
          if epoch_itr.epoch <= len(cfg.optimization.update_freq)
          else cfg.optimization.update_freq[-1]
      )
      itr = iterators.GroupedIterator(
          itr,
          update_freq,
          skip_remainder_batch=cfg.optimization.skip_remainder_batch,
      )
      if cfg.common.tpu:
          itr = utils.tpu_data_loader(itr)
      progress = progress_bar.progress_bar(
          itr,
          log_format=cfg.common.log_format,
          log_file=cfg.common.log_file,
          log_interval=cfg.common.log_interval,
          epoch=epoch_itr.epoch,
          aim_repo=(
              cfg.common.aim_repo
              if distributed_utils.is_master(cfg.distributed_training)
              else None
          ),
          aim_run_hash=(
              cfg.common.aim_run_hash
              if distributed_utils.is_master(cfg.distributed_training)
              else None
          ),
          aim_param_checkpoint_dir=cfg.checkpoint.save_dir,
          tensorboard_logdir=(
              cfg.common.tensorboard_logdir
              if distributed_utils.is_master(cfg.distributed_training)
              else None
          ),
          default_log_format=(
              "tqdm" if not cfg.common.no_progress_bar else "simple"
          ),
          wandb_project=(
              cfg.common.wandb_project
              if distributed_utils.is_master(cfg.distributed_training)
              else None
          ),
          wandb_run_name=os.environ.get(
              "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
          ),
          azureml_logging=(
              cfg.common.azureml_logging
              if distributed_utils.is_master(cfg.distributed_training)
              else False
          ),
      )
      progress.update_config(_flatten_config(cfg))
  
      trainer.begin_epoch(epoch_itr.epoch)
  
      valid_subsets = cfg.dataset.valid_subset.split(",")
      should_stop = False
      num_updates = trainer.get_num_updates()
      logger.info("Start iterating over samples")
      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 % cfg.common.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(
              cfg, 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 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 3 for calling a global function with example usage: train(cfg, trainer, task, epoch_itr) and returns: valid_losses,  should_stop