code: def train_loop(
      trainer: Trainer,
      lr_scheduler,
      train_loader,
      train_loader_len,
      val_loader,
      logger,
      best_prec1=0,
      start_epoch=0,
      end_epoch=0,
      early_stopping_patience=-1,
      prof=-1,
      skip_training=False,
      skip_validation=False,
      save_checkpoints=True,
      checkpoint_dir="./",
      checkpoint_filename="checkpoint.pth.tar",
      keep_last_n_checkpoints=0,
      topk=5,
  ):
      checkpointer = utils.Checkpointer(
          last_filename=checkpoint_filename,
          checkpoint_dir=checkpoint_dir,
          keep_last_n=keep_last_n_checkpoints,
      )
      train_metrics = TrainingMetrics(logger)
      val_metrics = {
          k: ValidationMetrics(logger, k, topk)
          for k in trainer.validation_steps().keys()
      }
      training_step = trainer.train_step
  
      prec1 = -1
  
      if early_stopping_patience > 0:
          epochs_since_improvement = 0
  
      print(f"RUNNING EPOCHS FROM {start_epoch} TO {end_epoch}")
      with utils.TimeoutHandler() as timeout_handler:
          interrupted = False
          for epoch in range(start_epoch, end_epoch):
              if logger is not None:
                  logger.start_epoch()
              if not skip_training:
                  if logger is not None:
                      data_iter = logger.iteration_generator_wrapper(
                          train_loader, mode="train"
                      )
                  else:
                      data_iter = train_loader
  
                  trainer.train()
                  interrupted = train(
                      training_step,
                      data_iter,
                      lambda i: lr_scheduler(
                          trainer.optimizer, i, epoch
                      ),
                      trainer.executor.scaler.get_scale,
                      train_metrics.log,
                      timeout_handler,
                      prof=prof,
                      step=epoch * train_loader_len,
                  )
  
              if not skip_validation:
                  trainer.eval()
                  for k, infer_fn in trainer.validation_steps().items():
                      if logger is not None:
                          data_iter = (
                              logger.iteration_generator_wrapper(
                                  val_loader, mode="val"
                              )
                          )
                      else:
                          data_iter = val_loader
  
                      step_prec1, _ = validate(
                          infer_fn,
                          data_iter,
                          val_metrics[k].log,
                          prof=prof,
                          topk=topk,
                      )
  
                      if k == "val":
                          prec1 = step_prec1
  
                  if prec1 > best_prec1:
                      is_best = True
                      best_prec1 = prec1
                  else:
                      is_best = False
              else:
                  is_best = False
                  best_prec1 = 0
  
              if logger is not None:
                  logger.end_epoch()
  
              if save_checkpoints and (
                  not torch.distributed.is_initialized()
                  or torch.distributed.get_rank() == 0
              ):
                  checkpoint_state = {
                      "epoch": epoch + 1,
                      "best_prec1": best_prec1,
                      **trainer.state_dict(),
                  }
                  checkpointer.save_checkpoint(
                      checkpoint_state,
                      is_best,
                      filename=f"checkpoint_{epoch:04}.pth.tar",
                  )
  
              if early_stopping_patience > 0:
                  if not is_best:
                      epochs_since_improvement += 1
                  else:
                      epochs_since_improvement = 0
                  if (
                      epochs_since_improvement
                      >= early_stopping_patience
                  ):
                      break
              if interrupted:
                  break
  
imports: import time
  from copy import deepcopy
  from functools import wraps
  from typing import Callable, Dict, Optional, Tuple
  import torch
  import torch.nn
  from torch.cuda.amp import autocast
  from torch.nn.parallel import DistributedDataParallel
  from .None import logger
  from .None import utils
  from .logger import TrainingMetrics, ValidationMetrics
  from .models.common import EMA
label_desc: Use image node_id 3 for calling a global function with example usage: train_loop(trainer, lr_scheduler, train_loader, train_loader_len, val_loader, logger, best_prec1, start_epoch, end_epoch, early_stopping_patience, prof, skip_training, skip_validation, save_checkpoints, checkpoint_dir, checkpoint_filename, keep_last_n_checkpoints, topk) without return types