code: def __init__(self, input_file, max_predictions_per_seq):
      self.input_file = input_file
      self.max_predictions_per_seq = max_predictions_per_seq
      f = h5py.File(input_file, "r")
      keys = [
          "input_ids",
          "input_mask",
          "segment_ids",
          "masked_lm_positions",
      ]
      self.inputs = [np.asarray(f[key][:]) for key in keys]
      f.close()
  
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 to create a new pretraining_dataset object from inherited base classes: Dataset with example: obj = pretraining_dataset(input_file, max_predictions_per_seq)