code: def create_loaders(
      train_set: VideoDatasetFeatures,
      val_set: VideoDatasetFeatures,
      batch_size: int,
      num_workers: int,
      eval=False,
  ):
      if eval:
          val_loader = data.DataLoader(
              val_set,
              batch_size=batch_size,
              shuffle=False,
              num_workers=num_workers,
              collate_fn=val_set.collate_fn,
              pin_memory=True,
          )
          return val_loader
  
      train_loader = data.DataLoader(
          train_set,
          batch_size=batch_size,
          shuffle=True,
          num_workers=num_workers,
          collate_fn=train_set.collate_fn,
          pin_memory=True,
      )
      val_loader = data.DataLoader(
          val_set,
          batch_size=batch_size,
          shuffle=False,
          num_workers=num_workers,
          collate_fn=val_set.collate_fn,
          pin_memory=True,
      )
      return train_loader, val_loader
  
imports: import json
  from collections import OrderedDict
  from pathlib import Path
  from typing import List
  import re
  import numpy
  import torch
  import torch.utils.data
  from tqdm import tqdm
  from gluoncv.torch.utils import coot_utils
label_desc: Use image node_id 3 for calling a global function with example usage: create_loaders(train_set, val_set, batch_size, num_workers, eval) and returns: train_loader,  val_loader, val_loader