class_docstr: Creates dataset to use during inference for token classification tasks with a pretrained model.
  For dataset to use during training with labels, see BertTokenClassificationDataset.
code: def __getitem__(self, idx):
      return (
          np.array(self.all_input_ids[idx]),
          np.array(self.all_segment_ids[idx]),
          np.array(self.all_input_mask[idx], dtype=np.longlong),
          np.array(self.all_subtokens_mask[idx]),
      )
  
imports: import os
  import pickle
  import tempfile
  import time
  from typing import Dict, List, Optional
  import numpy
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.data_utils.data_preprocessing import get_stats
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import ChannelType, LabelsType, MaskType, NeuralType
  from nemo.utils import logging
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 4 for calling the BertTokenClassificationInferDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: np,  np,  np,  np