code: def __getitem__(self, index):
      [
          input_ids,
          input_mask,
          segment_ids,
          masked_lm_positions,
          masked_lm_ids,
          next_sentence_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)
      ]
  
      masked_lm_labels = (
          torch.ones(input_ids.shape, dtype=torch.long) * -1
      )
      index = self.max_predictions_per_seq
      # store number of  masked tokens in index
      padded_mask_indices = (masked_lm_positions == 0).nonzero()
      if len(padded_mask_indices) != 0:
          index = padded_mask_indices[0].item()
      masked_lm_labels[masked_lm_positions[:index]] = masked_lm_ids[
          :index
      ]
  
      return [
          map_to_torch([BatchType.PRETRAIN_BATCH]),
          input_ids,
          input_mask,
          segment_ids,
          next_sentence_labels,
          masked_lm_labels,
      ]
  
imports: import os
  import random
  import h5py
  import logging
  import json
  from concurrent.futures import ProcessPoolExecutor
  import numpy
  import torch
  import torch.distributed
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.sampler import RandomSampler
  from torch.utils.data.distributed import DistributedSampler
  from bert_dataset_provider import BertDatasetProviderInterface
  from turing.dataset import BatchType, map_to_torch
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