code: def __getitem__(self, index):
      [input_ids, input_mask, segment_ids, masked_lm_labels] = [
          torch.from_numpy(input[index].astype(np.int64))
          if indice < 5
          else torch.from_numpy(
              np.asarray(input[index].astype(np.int64))
          )
          for indice, input in enumerate(self.inputs)
      ]
  
      return [input_ids, input_mask, segment_ids, masked_lm_labels]
  
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 3 for calling the pretraining_dataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: list