code: def main_worker(cfg):
      # create tensorboard and logs
      if cfg.DDP_CONFIG.GPU_WORLD_RANK == 0:
          tb_logdir = build_log_dir(cfg)
          writer = SummaryWriter(log_dir=tb_logdir)
      else:
          writer = None
      cfg.freeze()
      logger = get_logger(tb_logdir, "trainer", log_file=True)
  
      # create model
      model = get_model(cfg)
      model = deploy_model(model, cfg)
  
      # create dataset and dataloader
      data_path_dict = create_dataloader_path(
          cfg.CONFIG.COOT_DATA.DATA_PATH,
          cfg.CONFIG.COOT_DATA.DATASET_NAME,
          video_feature_name=cfg.CONFIG.COOT_DATA.FEATURE,
      )
  
      train_set, val_set = create_datasets(
          data_path_dict,
          cfg,
          cfg.CONFIG.COOT_DATA.VIDEO_PRELOAD,
          cfg.CONFIG.COOT_DATA.TEXT_PRELOAD,
      )
      train_loader, val_loader = create_loaders(
          train_set,
          val_set,
          cfg.CONFIG.TRAIN.BATCH_SIZE,
          cfg.CONFIG.DATA.NUM_WORKERS,
      )
      optimizer = RAdam(
          model.get_params(),
          lr=cfg.CONFIG.TRAIN.LR,
          betas=(
              cfg.CONFIG.TRAIN.MOMENTUM,
              cfg.CONFIG.TRAIN.ADAM_BETA2,
          ),
          eps=cfg.CONFIG.TRAIN.ADAM_EPS,
          weight_decay=cfg.CONFIG.TRAIN.W_DECAY,
      )
  
      if cfg.CONFIG.MODEL.LOAD:
          model, _ = load_model(model, optimizer, cfg, load_fc=True)
  
      if cfg.CONFIG.TRAIN.LR_POLICY == "Step":
          scheduler = torch.optim.lr_scheduler.MultiStepLR(
              optimizer,
              milestones=cfg.CONFIG.TRAIN.LR_MILESTONE,
              gamma=cfg.CONFIG.TRAIN.STEP,
          )
  
      elif cfg.CONFIG.TRAIN.LR_POLICY == "Cosine":
          scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
              optimizer,
              T_max=cfg.CONFIG.TRAIN.EPOCH_NUM
              - cfg.CONFIG.TRAIN.WARMUP_EPOCHS,
              eta_min=0,
              last_epoch=cfg.CONFIG.TRAIN.RESUME_EPOCH,
          )
  
      elif cfg.CONFIG.TRAIN.LR_POLICY == "LR_Warmup":
          scheduler = ReduceLROnPlateauWarmup(
              optimizer,
              cfg.CONFIG.TRAIN.WARMUP_EPOCHS,
              mode="max",
              patience=cfg.CONFIG.TRAIN.PATIENCE,
              cooldown=cfg.CONFIG.TRAIN.COOLDOWN,
          )
  
      else:
          print(
              "Learning rate schedule %s is not supported yet. Please use Step or Cosine."
          )
  
      criterion_cycleconsistency = CycleConsistencyCootLoss(
          num_samples=1, use_cuda=True
      )
      criterion_alignment = MaxMarginRankingLoss(use_cuda=True)
  
      base_iter = 0
      det_best_field_best = 0
      for epoch in range(cfg.CONFIG.TRAIN.EPOCH_NUM):
          ## ======== Training step ===============
          base_iter = train_coot(
              cfg,
              base_iter,
              model,
              train_loader,
              epoch,
              criterion_alignment,
              criterion_cycleconsistency,
              optimizer,
              writer,
              logger,
          )
  
          ## ======= Validation step ================
          if (
              epoch % cfg.CONFIG.VAL.FREQ == 0
              or epoch == cfg.CONFIG.TRAIN.EPOCH_NUM - 1
          ):
              vid_metrics, clip_metrics = validate_coot(
                  cfg,
                  model,
                  val_loader,
                  epoch,
                  criterion_alignment,
                  criterion_cycleconsistency,
                  writer,
                  logger,
                  True,
              )
  
          # Check if the performance of model is improving
          logger.info(
              "---------- Validating epoch {} ----------".format(epoch)
          )
          c2s_res, s2c_res, clip_best_at_1 = None, None, None
          if clip_metrics is not None:
              c2s_res, s2c_res, clip_best_at_1 = clip_metrics
  
          # find field which determines is_best
          det_best_field_current = clip_best_at_1
  
          # check if best
          is_best = compare_metrics(
              det_best_field_current, det_best_field_best
          )
          if is_best:
              det_best_field_best = det_best_field_current
              best_epoch = epoch
  
          # step lr scheduler
          scheduler.step_rop(det_best_field_current, True)
          logger.info(
              f"ROP: model improved: {is_best}, "
              f"value {det_best_field_current:.3f},"
              f"new LR: {optimizer.param_groups[0]['lr']:5.3e}"
          )
  
          if epoch % cfg.CONFIG.LOG.SAVE_FREQ == 0:
              if (
                  cfg.DDP_CONFIG.GPU_WORLD_RANK == 0
                  or cfg.DDP_CONFIG.DISTRIBUTED == False
              ):
                  model.save_model(optimizer, epoch, cfg)
  
          # check if model did not improve for too long
          term_after = 15
          if epoch - best_epoch > term_after:
              logger.info(
                  f"NO improvements for {term_after} epochs (current "
                  f"{epoch} best {best_epoch}) STOP training."
              )
              break
  
      if writer is not None:
          writer.close()
      if logger is not None:
          close_logger(logger)
  
file_path: gluon-cv/scripts/vision-language/video-language/coot/train_pytorch.py
imports: import os
  import argparse
  import torch
  import torch.nn
  import torch.distributed
  import torch.optim
  from tensorboardX import SummaryWriter
  from gluoncv.torch.model_zoo import get_model
  from gluoncv.torch.data import create_datasets, create_loaders
  from gluoncv.torch.utils.model_utils import deploy_model, load_model, save_model
  from gluoncv.torch.utils.task_utils import train_coot, validate_coot
  from gluoncv.torch.engine.config import get_cfg_defaults
  from gluoncv.torch.engine.launch import spawn_workers
  from gluoncv.torch.utils.utils import build_log_dir
  from gluoncv.torch.utils.lr_policy import ReduceLROnPlateau, ReduceLROnPlateauWarmup
  from gluoncv.torch.utils.optimizer import RAdam
  from gluoncv.torch.utils.loss import MaxMarginRankingLoss, CycleConsistencyCootLoss
  from gluoncv.torch.utils.coot_utils import get_logger, close_logger, compare_metrics, create_dataloader_path
label_desc: Use image node_id 1 for calling a global function with example usage: main_worker(cfg) without return types