code: def get_shard(self, index):
      start = time.time()
      if self.dataset_future is None:
          self.data_file = self._get_shard_file(index)
          (
              self.train_dataloader,
              sample_count,
          ) = create_pretraining_dataset(
              input_file=self.data_file,
              max_predictions_per_seq=self.max_predictions_per_seq,
              num_workers=self.num_workers,
              train_batch_size=self.train_micro_batch_size_per_gpu,
              worker_init=self.worker_init,
              data_sampler=self.data_sampler,
          )
      else:
          (
              self.train_dataloader,
              sample_count,
          ) = self.dataset_future.result(timeout=None)
  
      self.logger.info(
          f"Data Loading Completed for Pretraining Data from {self.data_file} with {sample_count} samples took {time.time()-start:.2f}s."
      )
  
      return self.train_dataloader, sample_count
  
imports: import os
  import random
  import time
  from concurrent.futures import ProcessPoolExecutor
  import h5py
  import numpy
  import torch
  import torch.distributed
  from bert_dataset_provider import BertDatasetProviderInterface
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.distributed import DistributedSampler
label_desc: Use image node_id 2 for calling the NvidiaBertDatasetProvider obj's underlying member method code with example usage: obj.get_shard(index) and returns: self,  sample_count