code: def train_one_epoch(
      epoch,
      model,
      loader,
      optimizer,
      loss_fn,
      args,
      device=torch.device("cuda"),
      lr_scheduler=None,
      saver=None,
      output_dir=None,
      amp_autocast=suppress,
      loss_scaler=None,
      model_ema=None,
      mixup_fn=None,
  ):
      if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
          if args.prefetcher and loader.mixup_enabled:
              loader.mixup_enabled = False
          elif mixup_fn is not None:
              mixup_fn.mixup_enabled = False
  
      second_order = (
          hasattr(optimizer, "is_second_order")
          and optimizer.is_second_order
      )
      has_no_sync = hasattr(model, "no_sync")
      update_time_m = utils.AverageMeter()
      data_time_m = utils.AverageMeter()
      losses_m = utils.AverageMeter()
  
      model.train()
  
      accum_steps = args.grad_accum_steps
      last_accum_steps = len(loader) % accum_steps
      updates_per_epoch = (len(loader) + accum_steps - 1) // accum_steps
      num_updates = epoch * updates_per_epoch
      last_batch_idx = len(loader) - 1
      last_batch_idx_to_accum = len(loader) - last_accum_steps
  
      data_start_time = update_start_time = time.time()
      optimizer.zero_grad()
      update_sample_count = 0
      for batch_idx, (input, target) in enumerate(loader):
          last_batch = batch_idx == last_batch_idx
          need_update = last_batch or (batch_idx + 1) % accum_steps == 0
          update_idx = batch_idx // accum_steps
          if batch_idx >= last_batch_idx_to_accum:
              accum_steps = last_accum_steps
  
          if not args.prefetcher:
              input, target = input.to(device), target.to(device)
              if mixup_fn is not None:
                  input, target = mixup_fn(input, target)
          if args.channels_last:
              input = input.contiguous(
                  memory_format=torch.channels_last
              )
  
          # multiply by accum steps to get equivalent for full update
          data_time_m.update(
              accum_steps * (time.time() - data_start_time)
          )
  
          def _forward():
              with amp_autocast():
                  output = model(input)
                  loss = loss_fn(output, target)
              if accum_steps > 1:
                  loss /= accum_steps
              return loss
  
          def _backward(_loss):
              if loss_scaler is not None:
                  loss_scaler(
                      _loss,
                      optimizer,
                      clip_grad=args.clip_grad,
                      clip_mode=args.clip_mode,
                      parameters=model_parameters(
                          model, exclude_head="agc" in args.clip_mode
                      ),
                      create_graph=second_order,
                      need_update=need_update,
                  )
              else:
                  _loss.backward(create_graph=second_order)
                  if need_update:
                      if args.clip_grad is not None:
                          utils.dispatch_clip_grad(
                              model_parameters(
                                  model,
                                  exclude_head="agc" in args.clip_mode,
                              ),
                              value=args.clip_grad,
                              mode=args.clip_mode,
                          )
                      optimizer.step()
  
          if has_no_sync and not need_update:
              with model.no_sync():
                  loss = _forward()
                  _backward(loss)
          else:
              loss = _forward()
              _backward(loss)
  
          if not args.distributed:
              losses_m.update(loss.item() * accum_steps, input.size(0))
          update_sample_count += input.size(0)
  
          if not need_update:
              data_start_time = time.time()
              continue
  
          num_updates += 1
          optimizer.zero_grad()
          if model_ema is not None:
              model_ema.update(model)
  
          if args.synchronize_step and device.type == "cuda":
              torch.cuda.synchronize()
          time_now = time.time()
          update_time_m.update(time.time() - update_start_time)
          update_start_time = time_now
  
          if update_idx % args.log_interval == 0:
              lrl = [
                  param_group["lr"]
                  for param_group in optimizer.param_groups
              ]
              lr = sum(lrl) / len(lrl)
  
              if args.distributed:
                  reduced_loss = utils.reduce_tensor(
                      loss.data, args.world_size
                  )
                  losses_m.update(
                      reduced_loss.item() * accum_steps, input.size(0)
                  )
                  update_sample_count *= args.world_size
  
              if utils.is_primary(args):
                  _logger.info(
                      f"Train: {epoch} [{update_idx:>4d}/{updates_per_epoch} "
                      f"({100. * update_idx / (updates_per_epoch - 1):>3.0f}%)]  "
                      f"Loss: {losses_m.val:#.3g} ({losses_m.avg:#.3g})  "
                      f"Time: {update_time_m.val:.3f}s, {update_sample_count / update_time_m.val:>7.2f}/s  "
                      f"({update_time_m.avg:.3f}s, {update_sample_count / update_time_m.avg:>7.2f}/s)  "
                      f"LR: {lr:.3e}  "
                      f"Data: {data_time_m.val:.3f} ({data_time_m.avg:.3f})"
                  )
  
                  if args.save_images and output_dir:
                      torchvision.utils.save_image(
                          input,
                          os.path.join(
                              output_dir,
                              "train-batch-%d.jpg" % batch_idx,
                          ),
                          padding=0,
                          normalize=True,
                      )
  
          if (
              saver is not None
              and args.recovery_interval
              and ((update_idx + 1) % args.recovery_interval == 0)
          ):
              saver.save_recovery(epoch, batch_idx=update_idx)
  
          if lr_scheduler is not None:
              lr_scheduler.step_update(
                  num_updates=num_updates, metric=losses_m.avg
              )
  
          update_sample_count = 0
          data_start_time = time.time()
          # end for
  
      if hasattr(optimizer, "sync_lookahead"):
          optimizer.sync_lookahead()
  
      return OrderedDict([("loss", losses_m.avg)])
  
imports: import argparse
  import json
  import logging
  import os
  import time
  from collections import OrderedDict
  from contextlib import suppress
  from datetime import datetime
  from functools import partial
  import torch
  import torch.nn
  import torchvision.utils
  import yaml
  from torch.nn.parallel import DistributedDataParallel
  from timm import utils
  from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
  from timm.layers import convert_splitbn_model, convert_sync_batchnorm, set_fast_norm
  from timm.loss import JsdCrossEntropy, SoftTargetCrossEntropy, BinaryCrossEntropy, LabelSmoothingCrossEntropy
  from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint, model_parameters
  from timm.optim import create_optimizer_v2, optimizer_kwargs
  from timm.scheduler import create_scheduler_v2, scheduler_kwargs
  from timm.utils import ApexScaler, NativeScaler
label_desc: Use image node_id 3 for calling a global function with example usage: train_one_epoch(epoch, model, loader, optimizer, loss_fn, args, device, lr_scheduler, saver, output_dir, amp_autocast, loss_scaler, model_ema, mixup_fn) and returns: OrderedDict, loss