code: def prepare_for_training(args, model_args, model_arch):
      args.distributed = False
      if "WORLD_SIZE" in os.environ:
          args.distributed = int(os.environ["WORLD_SIZE"]) > 1
          args.local_rank = int(os.environ["LOCAL_RANK"])
      else:
          args.local_rank = 0
  
      args.gpu = 0
      args.world_size = 1
  
      if args.distributed:
          args.gpu = args.local_rank % torch.cuda.device_count()
          torch.cuda.set_device(args.gpu)
          dist.init_process_group(backend="nccl", init_method="env://")
          args.world_size = torch.distributed.get_world_size()
  
      affinity = set_affinity(args.gpu, mode=args.gpu_affinity)
      print(f"Training process {args.local_rank} affinity: {affinity}")
  
      if args.seed is not None:
          print("Using seed = {}".format(args.seed))
          torch.manual_seed(args.seed + args.local_rank)
          torch.cuda.manual_seed(args.seed + args.local_rank)
          np.random.seed(seed=args.seed + args.local_rank)
          random.seed(args.seed + args.local_rank)
  
          def _worker_init_fn(id):
              # Worker process should inherit its affinity from parent
              affinity = os.sched_getaffinity(0)
              print(
                  f"Process {args.local_rank} Worker {id} set affinity to: {affinity}"
              )
  
              np.random.seed(seed=args.seed + args.local_rank + id)
              random.seed(args.seed + args.local_rank + id)
  
      else:
  
          def _worker_init_fn(id):
              # Worker process should inherit its affinity from parent
              affinity = os.sched_getaffinity(0)
              print(
                  f"Process {args.local_rank} Worker {id} set affinity to: {affinity}"
              )
  
      if args.static_loss_scale != 1.0:
          if not args.amp:
              print(
                  "Warning: if --amp is not used, static_loss_scale will be ignored."
              )
  
      if args.optimizer_batch_size < 0:
          batch_size_multiplier = 1
      else:
          tbs = args.world_size * args.batch_size
          if args.optimizer_batch_size % tbs != 0:
              print(
                  "Warning: simulated batch size {} is not divisible by actual batch size {}".format(
                      args.optimizer_batch_size, tbs
                  )
              )
          batch_size_multiplier = int(args.optimizer_batch_size / tbs)
          print("BSM: {}".format(batch_size_multiplier))
  
      start_epoch = 0
      best_prec1 = 0
      # optionally resume from a checkpoint
      if args.resume is not None:
          if os.path.isfile(args.resume):
              print("=> loading checkpoint '{}'".format(args.resume))
              checkpoint = torch.load(
                  args.resume,
                  map_location=lambda storage, loc: storage.cuda(
                      args.gpu
                  ),
              )
              start_epoch = checkpoint["epoch"]
              best_prec1 = checkpoint["best_prec1"]
              model_state = checkpoint["state_dict"]
              optimizer_state = checkpoint["optimizer"]
              if "state_dict_ema" in checkpoint:
                  model_state_ema = checkpoint["state_dict_ema"]
              print(
                  "=> loaded checkpoint '{}' (epoch {})".format(
                      args.resume, checkpoint["epoch"]
                  )
              )
              if start_epoch >= args.epochs:
                  print(
                      f"Launched training for {args.epochs}, checkpoint already run {start_epoch}"
                  )
                  exit(1)
          else:
              print(
                  "=> no checkpoint found at '{}'".format(args.resume)
              )
              model_state = None
              model_state_ema = None
              optimizer_state = None
      else:
          model_state = None
          model_state_ema = None
          optimizer_state = None
  
      loss = nn.CrossEntropyLoss
      if args.mixup > 0.0:
          loss = lambda: NLLMultiLabelSmooth(args.label_smoothing)
      elif args.label_smoothing > 0.0:
          loss = lambda: LabelSmoothing(args.label_smoothing)
  
      memory_format = (
          torch.channels_last
          if args.memory_format == "nhwc"
          else torch.contiguous_format
      )
      model = model_arch(
          **{
              k: v
              if k != "pretrained"
              else v and (not args.distributed or dist.get_rank() == 0)
              for k, v in model_args.__dict__.items()
          }
      )
  
      image_size = (
          args.image_size
          if args.image_size is not None
          else model.arch.default_image_size
      )
  
      scaler = torch.cuda.amp.GradScaler(
          init_scale=args.static_loss_scale,
          growth_factor=2,
          backoff_factor=0.5,
          growth_interval=100,
          enabled=args.amp,
      )
  
      executor = Executor(
          model,
          loss(),
          cuda=True,
          memory_format=memory_format,
          amp=args.amp,
          scaler=scaler,
          divide_loss=batch_size_multiplier,
          ts_script=args.jit == "script",
      )
  
      # Create data loaders and optimizers as needed
      if args.data_backend == "pytorch":
          args.workers = args.workers * 2
          get_train_loader = get_pytorch_train_loader
          get_val_loader = get_pytorch_val_loader
      elif args.data_backend == "dali":
          get_train_loader = get_dali_train_loader(
              dali_device=args.dali_device
          )
          get_val_loader = get_dali_val_loader()
      elif args.data_backend == "synthetic":
          get_val_loader = get_synthetic_loader
          get_train_loader = get_synthetic_loader
      else:
          print("Bad databackend picked")
          exit(1)
  
      train_loader, train_loader_len = get_train_loader(
          args.data,
          image_size,
          args.batch_size,
          model_args.num_classes,
          args.mixup > 0.0,
          interpolation=args.interpolation,
          augmentation=args.automatic_augmentation,
          start_epoch=start_epoch,
          workers=args.workers,
          _worker_init_fn=_worker_init_fn,
          memory_format=memory_format,
          prefetch_factor=args.prefetch,
      )
      if args.mixup != 0.0:
          train_loader = MixUpWrapper(args.mixup, train_loader)
  
      val_loader, val_loader_len = get_val_loader(
          args.data,
          image_size,
          args.batch_size,
          model_args.num_classes,
          False,
          interpolation=args.interpolation,
          workers=args.workers,
          _worker_init_fn=_worker_init_fn,
          memory_format=memory_format,
          prefetch_factor=args.prefetch,
      )
  
      if (
          not torch.distributed.is_initialized()
          or torch.distributed.get_rank() == 0
      ):
          logger = log.Logger(
              args.print_freq,
              [
                  dllogger.StdOutBackend(
                      dllogger.Verbosity.DEFAULT,
                      step_format=log.format_step,
                  ),
                  dllogger.JSONStreamBackend(
                      dllogger.Verbosity.VERBOSE,
                      os.path.join(args.workspace, args.report_file),
                  ),
              ],
              start_epoch=start_epoch - 1,
          )
  
      else:
          logger = log.Logger(
              args.print_freq, [], start_epoch=start_epoch - 1
          )
  
      logger.log_parameter(
          args.__dict__, verbosity=dllogger.Verbosity.DEFAULT
      )
      logger.log_parameter(
          {f"model.{k}": v for k, v in model_args.__dict__.items()},
          verbosity=dllogger.Verbosity.DEFAULT,
      )
  
      optimizer = get_optimizer(
          list(executor.model.named_parameters()),
          args.lr,
          args=args,
          state=optimizer_state,
      )
  
      if args.lr_schedule == "step":
          lr_policy = lr_step_policy(
              args.lr, [30, 60, 80], 0.1, args.warmup
          )
      elif args.lr_schedule == "cosine":
          lr_policy = lr_cosine_policy(
              args.lr, args.warmup, args.epochs, end_lr=args.end_lr
          )
      elif args.lr_schedule == "linear":
          lr_policy = lr_linear_policy(
              args.lr, args.warmup, args.epochs
          )
  
      if args.distributed:
          executor.distributed(args.gpu)
  
      if model_state is not None:
          executor.model.load_state_dict(model_state)
  
      trainer = Trainer(
          executor,
          optimizer,
          grad_acc_steps=batch_size_multiplier,
          ema=args.use_ema,
      )
  
      if (args.use_ema is not None) and (model_state_ema is not None):
          trainer.ema_executor.model.load_state_dict(model_state_ema)
  
      return (
          trainer,
          lr_policy,
          train_loader,
          train_loader_len,
          val_loader,
          logger,
          start_epoch,
          best_prec1,
      )
  
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 3 for calling a global function with example usage: prepare_for_training(args, model_args, model_arch) and returns: trainer,  lr_policy,  train_loader,  train_loader_len,  val_loader,  logger,  start_epoch,  best_prec1