code: def dataloader_creator(cfg):
      distributed = cfg.distributed
      runner_type = (
          "EpochBasedRunner"
          if "runner" not in cfg
          else cfg.runner["type"]
      )
      dataset = build_dataset(cfg.data.train)
  
      train_dataloader_default_args = dict(
          samples_per_gpu=2,
          workers_per_gpu=0,
          # `num_gpus` will be ignored if distributed
          num_gpus=len(cfg.gpu_ids),
          dist=distributed,
          seed=cfg.seed,
          runner_type=runner_type,
          persistent_workers=False,
      )
  
      train_loader_cfg = {
          **train_dataloader_default_args,
          **cfg.data.get("train_dataloader", {}),
      }
  
      data_loaders = build_dataloader(dataset, **train_loader_cfg)
      return data_loaders
  
file_path: BigDL/python/orca/example/learn/mmcv/faster_rcnn/train.py
imports: import argparse
  import os
  import numpy
  import os.path
  from os.path import exists, join
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.data.file import get_remote_dir_to_local
  from bigdl.orca.learn.pytorch.experimential.mmcv.mmcv_ray_estimator import MMCVRayEstimator
  import mmcv
  from mmcv import Config
  from mmdet.apis import set_random_seed
  from mmdet.datasets import build_dataloader, build_dataset, replace_ImageToTensor
label_desc: Use image node_id 2 for calling a global function with example usage: dataloader_creator(cfg) and returns: data_loaders