text: # from detr.subset_seque import SubsetSequentialSampler
  # from .nvidia_utils import detection_collate
  # from datasets import nvidia_utils
  import argparse
  from copy import deepcopy ## add from Ismail
  import os
  import random
  from statistics import mean, pstdev
  import time
  import numpy as np
  import torch.utils.data as data
  import IPython
  
  from detr.datasets.nvidia_utils import BaseTransform, detection_collate
  from torch.utils.data.sampler import SubsetRandomSampler
  from detr.datasets.nvidia import (
      NVIDIADetection,
  )
  # import pycocotools
  # import submitit
  from detr.datasets.nvidia import build_nvdataset
   
  if __name__ == '__main__':
      # os.fil
      # train_dataset= build_nvdataset(
      #                     # indices=np.load('/home/shuxuang/datasets/id_1_criterion_Max_SSD_num_labels_50000.npy'),
      #                     dataset_root=['/home/shuxuang/datasets/annotation_sql_nvidia', '/home/shuxuang/datasets/frames_nvidia'],
      #                     # batch_size=2,
      #                     # num_workers=2,
      #                     data_config=None,  # <-- this really should be refactored.
      # )
      train_dataset=build_nvdataset(dataset_root=['~/datasets/annotation_sql_nvidia', '~/datasets/frames_nvidia'], 
                                  mode='train')
      # import ipdb; ipdb.set_trace()
      # IPython.embed()
      print(len(train_dataset))
      print(train_dataset[0])
  
  # dazel run //detr/datasets:nvidia  
  # dazel run //detr:debug