code: def build_trainer(cfg):
      """
      Build training model and its associated tools, including optimizer,
      dataloaders and meters.
      Args:
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
      Returns:
          model (nn.Module): training model.
          optimizer (Optimizer): optimizer.
          train_loader (DataLoader): training data loader.
          val_loader (DataLoader): validatoin data loader.
          precise_bn_loader (DataLoader): training data loader for computing
              precise BN.
          train_meter (TrainMeter): tool for measuring training stats.
          val_meter (ValMeter): tool for measuring validation stats.
      """
      # Build the video model and print model statistics.
      model = build_model(cfg)
      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 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
      )
      # Create meters.
      train_meter = TrainMeter(len(train_loader), cfg)
      val_meter = ValMeter(len(val_loader), cfg)
  
      return (
          model,
          optimizer,
          train_loader,
          val_loader,
          precise_bn_loader,
          train_meter,
          val_meter,
      )
  
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 4 for calling a global function with example usage: build_trainer(cfg) and returns: model,  optimizer,  train_loader,  val_loader,  precise_bn_loader,  train_meter,  val_meter