code: def train(cfg):
      """
      Train a video model for many epochs on train set and evaluate it on val set.
      Args:
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
      """
      # Set up environment.
      du.init_distributed_training(cfg)
      # Set random seed from configs.
      np.random.seed(cfg.RNG_SEED)
      torch.manual_seed(cfg.RNG_SEED)
  
      # Setup logging format.
      logging.setup_logging(cfg.OUTPUT_DIR)
  
      # Init multigrid.
      multigrid = None
      if cfg.MULTIGRID.LONG_CYCLE or cfg.MULTIGRID.SHORT_CYCLE:
          multigrid = MultigridSchedule()
          cfg = multigrid.init_multigrid(cfg)
          if cfg.MULTIGRID.LONG_CYCLE:
              cfg, _ = multigrid.update_long_cycle(cfg, cur_epoch=0)
      # Print config.
      logger.info("Train with config:")
      logger.info(pprint.pformat(cfg))
  
      # Build the video model and print model statistics.
      model = build_model(cfg)
      flops, params = 0.0, 0.0
      if du.is_master_proc() and cfg.LOG_MODEL_INFO:
          flops, params = misc.log_model_info(
              model, cfg, use_train_input=True
          )
  
      # Construct the optimizer.
      optimizer = optim.construct_optimizer(model, cfg)
      # Create a GradScaler for mixed precision training
      scaler = torch.cuda.amp.GradScaler(
          enabled=cfg.TRAIN.MIXED_PRECISION
      )
  
      # Load a checkpoint to resume training if applicable.
      if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(cfg.OUTPUT_DIR):
          logger.info("Load from last checkpoint.")
          last_checkpoint = cu.get_last_checkpoint(
              cfg.OUTPUT_DIR, task=cfg.TASK
          )
          if last_checkpoint is not None:
              checkpoint_epoch = cu.load_checkpoint(
                  last_checkpoint,
                  model,
                  cfg.NUM_GPUS > 1,
                  optimizer,
                  scaler if cfg.TRAIN.MIXED_PRECISION else None,
              )
              start_epoch = checkpoint_epoch + 1
          elif "ssl_eval" in cfg.TASK:
              last_checkpoint = cu.get_last_checkpoint(
                  cfg.OUTPUT_DIR, task="ssl"
              )
              checkpoint_epoch = cu.load_checkpoint(
                  last_checkpoint,
                  model,
                  cfg.NUM_GPUS > 1,
                  optimizer,
                  scaler if cfg.TRAIN.MIXED_PRECISION else None,
                  epoch_reset=True,
                  clear_name_pattern=cfg.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN,
              )
              start_epoch = checkpoint_epoch + 1
          else:
              start_epoch = 0
      elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "":
          logger.info("Load from given checkpoint file.")
          checkpoint_epoch = cu.load_checkpoint(
              cfg.TRAIN.CHECKPOINT_FILE_PATH,
              model,
              cfg.NUM_GPUS > 1,
              optimizer,
              scaler if cfg.TRAIN.MIXED_PRECISION else None,
              inflation=cfg.TRAIN.CHECKPOINT_INFLATE,
              convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
              epoch_reset=cfg.TRAIN.CHECKPOINT_EPOCH_RESET,
              clear_name_pattern=cfg.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN,
              image_init=cfg.TRAIN.CHECKPOINT_IN_INIT,
          )
          start_epoch = checkpoint_epoch + 1
      else:
          start_epoch = 0
  
      # Create the video train and val loaders.
      train_loader = loader.construct_loader(cfg, "train")
      val_loader = loader.construct_loader(cfg, "val")
      precise_bn_loader = (
          loader.construct_loader(cfg, "train", is_precise_bn=True)
          if cfg.BN.USE_PRECISE_STATS
          else None
      )
  
      if (
          cfg.TASK == "ssl"
          and cfg.MODEL.MODEL_NAME == "ContrastiveModel"
          and cfg.CONTRASTIVE.KNN_ON
      ):
          if hasattr(model, "module"):
              model.module.init_knn_labels(train_loader)
          else:
              model.init_knn_labels(train_loader)
  
      # Create meters.
      if cfg.DETECTION.ENABLE:
          train_meter = AVAMeter(len(train_loader), cfg, mode="train")
          val_meter = AVAMeter(len(val_loader), cfg, mode="val")
      else:
          train_meter = TrainMeter(len(train_loader), cfg)
          val_meter = ValMeter(len(val_loader), cfg)
  
      # set up writer for logging to Tensorboard format.
      if cfg.TENSORBOARD.ENABLE and du.is_master_proc(
          cfg.NUM_GPUS * cfg.NUM_SHARDS
      ):
          writer = tb.TensorboardWriter(cfg)
      else:
          writer = None
  
      # Perform the training loop.
      logger.info("Start epoch: {}".format(start_epoch + 1))
  
      epoch_timer = EpochTimer()
      for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH):
          if cur_epoch > 0 and cfg.DATA.LOADER_CHUNK_SIZE > 0:
              num_chunks = math.ceil(
                  cfg.DATA.LOADER_CHUNK_OVERALL_SIZE
                  / cfg.DATA.LOADER_CHUNK_SIZE
              )
              skip_rows = (
                  (cur_epoch) % num_chunks * cfg.DATA.LOADER_CHUNK_SIZE
              )
              logger.info(
                  f"=================+++ num_chunks {num_chunks} skip_rows {skip_rows}"
              )
              cfg.DATA.SKIP_ROWS = skip_rows
              logger.info(f"|===========| skip_rows {skip_rows}")
              train_loader = loader.construct_loader(cfg, "train")
              loader.shuffle_dataset(train_loader, cur_epoch)
  
          if cfg.MULTIGRID.LONG_CYCLE:
              cfg, changed = multigrid.update_long_cycle(cfg, cur_epoch)
              if changed:
                  (
                      model,
                      optimizer,
                      train_loader,
                      val_loader,
                      precise_bn_loader,
                      train_meter,
                      val_meter,
                  ) = build_trainer(cfg)
  
                  # Load checkpoint.
                  if cu.has_checkpoint(cfg.OUTPUT_DIR):
                      last_checkpoint = cu.get_last_checkpoint(
                          cfg.OUTPUT_DIR, task=cfg.TASK
                      )
                      assert (
                          "{:05d}.pyth".format(cur_epoch)
                          in last_checkpoint
                      )
                  else:
                      last_checkpoint = cfg.TRAIN.CHECKPOINT_FILE_PATH
                  logger.info("Load from {}".format(last_checkpoint))
                  cu.load_checkpoint(
                      last_checkpoint,
                      model,
                      cfg.NUM_GPUS > 1,
                      optimizer,
                  )
  
          # Shuffle the dataset.
          loader.shuffle_dataset(train_loader, cur_epoch)
          if hasattr(train_loader.dataset, "_set_epoch_num"):
              train_loader.dataset._set_epoch_num(cur_epoch)
          # Train for one epoch.
          epoch_timer.epoch_tic()
          train_epoch(
              train_loader,
              model,
              optimizer,
              scaler,
              train_meter,
              cur_epoch,
              cfg,
              writer,
          )
          epoch_timer.epoch_toc()
          logger.info(
              f"Epoch {cur_epoch} takes {epoch_timer.last_epoch_time():.2f}s. Epochs "
              f"from {start_epoch} to {cur_epoch} take "
              f"{epoch_timer.avg_epoch_time():.2f}s in average and "
              f"{epoch_timer.median_epoch_time():.2f}s in median."
          )
          logger.info(
              f"For epoch {cur_epoch}, each iteraction takes "
              f"{epoch_timer.last_epoch_time()/len(train_loader):.2f}s in average. "
              f"From epoch {start_epoch} to {cur_epoch}, each iteraction takes "
              f"{epoch_timer.avg_epoch_time()/len(train_loader):.2f}s in average."
          )
  
          is_checkp_epoch = (
              cu.is_checkpoint_epoch(
                  cfg,
                  cur_epoch,
                  None if multigrid is None else multigrid.schedule,
              )
              or cur_epoch == cfg.SOLVER.MAX_EPOCH - 1
          )
          is_eval_epoch = (
              misc.is_eval_epoch(
                  cfg,
                  cur_epoch,
                  None if multigrid is None else multigrid.schedule,
              )
              and not cfg.MASK.ENABLE
          )
  
          # Compute precise BN stats.
          if (
              (is_checkp_epoch or is_eval_epoch)
              and cfg.BN.USE_PRECISE_STATS
              and len(get_bn_modules(model)) > 0
          ):
              calculate_and_update_precise_bn(
                  precise_bn_loader,
                  model,
                  min(
                      cfg.BN.NUM_BATCHES_PRECISE, len(precise_bn_loader)
                  ),
                  cfg.NUM_GPUS > 0,
              )
          _ = misc.aggregate_sub_bn_stats(model)
  
          # Save a checkpoint.
          if is_checkp_epoch:
              cu.save_checkpoint(
                  cfg.OUTPUT_DIR,
                  model,
                  optimizer,
                  cur_epoch,
                  cfg,
                  scaler if cfg.TRAIN.MIXED_PRECISION else None,
              )
          # Evaluate the model on validation set.
          if is_eval_epoch:
              eval_epoch(
                  val_loader,
                  model,
                  val_meter,
                  cur_epoch,
                  cfg,
                  train_loader,
                  writer,
              )
      if (
          start_epoch == cfg.SOLVER.MAX_EPOCH and not cfg.MASK.ENABLE
      ):  # final checkpoint load
          eval_epoch(
              val_loader,
              model,
              val_meter,
              start_epoch,
              cfg,
              train_loader,
              writer,
          )
      if writer is not None:
          writer.close()
      result_string = (
          "_p{:.2f}_f{:.2f} _t{:.2f}_m{:.2f} _a{:.2f} Top5 Acc: {:.2f} MEM: {:.2f} f: {:.4f}"
          "".format(
              params / 1e6,
              flops,
              epoch_timer.median_epoch_time() / 60.0
              if len(epoch_timer.epoch_times)
              else 0.0,
              misc.gpu_mem_usage(),
              100 - val_meter.min_top1_err,
              100 - val_meter.min_top5_err,
              misc.gpu_mem_usage(),
              flops,
          )
      )
      logger.info("training done: {}".format(result_string))
  
      return result_string
  
imports: import math
  import numpy
  import pprint
  import torch
  from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats
  import slowfast.models.losses
  import slowfast.models.optimizer
  import slowfast.utils.checkpoint
  import slowfast.utils.distributed
  import slowfast.utils.logging
  import slowfast.utils.metrics
  import slowfast.utils.misc
  import slowfast.visualization.tensorboard_vis
  from slowfast.datasets import loader
  from slowfast.datasets.mixup import MixUp
  from slowfast.models import build_model
  from slowfast.models.contrastive import contrastive_forward, contrastive_parameter_surgery
  from slowfast.utils.meters import AVAMeter, EpochTimer, TrainMeter, ValMeter
  from slowfast.utils.multigrid import MultigridSchedule
label_desc: Use image node_id 5 for calling a global function with example usage: train(cfg) and returns: result_string