code: def generate_data_loader():
      if args.dataset in [Config.c10, Config.c100, Config.imgNet]:
          train_loader, val_loader, class_num = dataset.get_dataloader(
              train_batch_size=args.batch_size,
              test_batch_size=args.batch_size,
              dataset=args.dataset,
              num_workers=1,
              datadir=os.path.join(args.base_dir, "data"),
          )
          test_loader = val_loader
      else:
          train_loader, val_loader, test_loader = libsvm_dataloader(
              args=args,
              data_dir=os.path.join(
                  args.base_dir, "data", "structure_data", args.dataset
              ),
              nfield=args.nfield,
              batch_size=args.batch_size,
          )
          class_num = args.num_labels
  
      return train_loader, val_loader, test_loader, class_num
  
file_path: singa/examples/model_selection/TRAILS-Database-Native-Model-Selection/internal/ml/model_selection/main.py
imports: import calendar
  import os
  import time
  from src.common.constant import Config
  from src.dataset_utils.structure_data_loader import libsvm_dataloader
  from exps.shared_args import parse_arguments
label_desc: Use image node_id 1 for calling a global function with example usage: generate_data_loader() and returns: train_loader,  val_loader,  test_loader,  class_num