class_docstr: Holds a TokenizationDataset for use in a torch DataLoader
  
  The torch DataLoader is different from the DataLoader defined here
  and allows for cpu & gpu parallelism.  Updating output_predictions
  to use this class as a wrapper to a TokenizationDataset means the
  calculation of features can happen in parallel, saving quite a
  bit of time.
class_docstr_tok: ["Holds","a","TokenizationDataset","for","use","in","a","torch","DataLoader","The","torch","DataLoader","is","different","from","the","DataLoader","defined","here","and","allows","for","cpu","&","gpu","parallelism",".","Updating","output_predictions","to","use","this","class","as","a","wrapper","to","a","TokenizationDataset","means","the","calculation","of","features","can","happen","in","parallel",",","saving","quite","a","bit","of","time","."]
code: def __getitem__(self, index):
      return self.dataset.para_to_sentences(self.data[index])
  
imports: from bisect import bisect_right
  from copy import copy
  import numpy
  import random
  import logging
  import re
  import torch
  from torch.utils.data import Dataset
  from .vocab import Vocab
  from stanza.models.common.utils import sort_with_indices, unsort
label_desc: Use image node_id 3 for calling the SortedDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: self