code: def main(args, model_args, model_arch):
      exp_start_time = time.time()
  
      (
          trainer,
          lr_policy,
          train_loader,
          train_loader_len,
          val_loader,
          logger,
          start_epoch,
          best_prec1,
      ) = prepare_for_training(args, model_args, model_arch)
  
      train_loop(
          trainer,
          lr_policy,
          train_loader,
          train_loader_len,
          val_loader,
          logger,
          start_epoch=start_epoch,
          end_epoch=min((start_epoch + args.run_epochs), args.epochs)
          if args.run_epochs != -1
          else args.epochs,
          early_stopping_patience=args.early_stopping_patience,
          best_prec1=best_prec1,
          prof=args.prof,
          skip_training=args.evaluate,
          skip_validation=args.training_only,
          save_checkpoints=args.save_checkpoints and not args.evaluate,
          checkpoint_dir=args.workspace,
          checkpoint_filename=args.checkpoint_filename,
          keep_last_n_checkpoints=args.gather_checkpoints,
          topk=args.topk,
      )
      exp_duration = time.time() - exp_start_time
      if (
          not torch.distributed.is_initialized()
          or torch.distributed.get_rank() == 0
      ):
          logger.end()
      print("Experiment ended")
  
imports: import os
  import argparse
  import random
  from copy import deepcopy
  import torch.backends.cudnn
  import torch.distributed
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import image_classification.logger
  from image_classification.smoothing import LabelSmoothing
  from image_classification.mixup import NLLMultiLabelSmooth, MixUpWrapper
  from image_classification.dataloaders import *
  from image_classification.training import *
  from image_classification.utils import *
  from image_classification.models import efficientnet_b0
  from image_classification.optimizers import get_optimizer, lr_cosine_policy, lr_linear_policy, lr_step_policy
  from image_classification.gpu_affinity import set_affinity, AffinityMode
  import dllogger
label_desc: Use image node_id 4 for calling a global function with example usage: main(args, model_args, model_arch) without return types