class_docstr: Dataset for already preprocessed data.
class_docstr_tok: ["Dataset","for","already","preprocessed","data","."]
code: def __getitem__(self, index: int):
      [
          input_ids,
          input_mask,
          segment_ids,
          masked_lm_positions,
          masked_lm_ids,
          next_sentence_labels,
      ] = [input[index].astype(np.int64) for input in self.inputs]
  
      output_mask = np.zeros_like(input_ids)
      output_ids = input_ids.copy()
  
      index = self.max_predictions_per_seq
      padded_mask_indices = (masked_lm_positions == 0).nonzero()
      if len(padded_mask_indices[0]) != 0:
          index = padded_mask_indices[0][0]
  
      output_mask[masked_lm_positions[:index]] = 1.0
      output_ids[masked_lm_positions[:index]] = masked_lm_ids[:index]
  
      # input_mask = np.asarray(input_mask, dtype=np.float32)
      # output_mask = np.asarray(output_mask, dtype=np.float32)
      return (
          input_ids,
          segment_ids,
          input_mask,
          output_ids,
          output_mask,
          next_sentence_labels,
      )
  
imports: import array
  import os
  import pickle
  import random
  from typing import Dict, List, Optional
  import h5py
  import numpy
  from torch.utils.data import DataLoader, DistributedSampler
  from tqdm import tqdm
  from nemo.collections.nlp.data.data_utils.data_preprocessing import find_newlines, load_data_indices
  from nemo.core.classes import Dataset
label_desc: Use image node_id 3 for calling the BertPretrainingPreprocessedDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: input_ids,  segment_ids,  input_mask,  output_ids,  output_mask,  next_sentence_labels