code: def __next__(self):
      if self.tput_timer:
          self.tput_timer.start()
      if self.curriculum_learning_enabled:
          data = next(self.data_iterator)
          if self.post_process_func is not None:
              data = self.post_process_func(
                  data, self.data_sampler.state_dict()
              )
          return data
      else:
          return next(self.data)
  
file_path: DeepSpeed/deepspeed/runtime/dataloader.py
imports: from torch.utils.data import DataLoader, RandomSampler
  from torch.utils.data.distributed import DistributedSampler
  from deepspeed.accelerator import get_accelerator
  from deepspeed.runtime.data_pipeline.data_sampling.data_sampler import DeepSpeedDataSampler
  from deepspeed.runtime.data_pipeline.constants import CURRICULUM_LEARNING, DATA_EFFICIENCY, DATA_SAMPLING_NUM_WORKERS
  from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, DATA_PARALLEL_GROUP, GLOBAL_RANK
label_desc: Use image node_id 4 for calling the DeepSpeedDataLoader obj's underlying member method code with example usage: obj.__next__() and returns: data, next