code: def create_pretraining_dataset(
      input_file,
      max_predictions_per_seq,
      num_workers,
      train_batch_size,
      worker_init,
      data_sampler,
  ):
      train_data = pretraining_dataset(
          input_file=input_file,
          max_predictions_per_seq=max_predictions_per_seq,
      )
      train_dataloader = DataLoader(
          train_data,
          sampler=data_sampler(train_data),
          batch_size=train_batch_size,
          num_workers=num_workers,
          worker_init_fn=worker_init,
          pin_memory=True,
      )
      return train_dataloader, len(train_data)
  
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 1 for calling a global function with example usage: create_pretraining_dataset(input_file, max_predictions_per_seq, num_workers, train_batch_size, worker_init, data_sampler) and returns: train_dataloader,  len