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.
docstr: Returns definitions of module output ports.
                 
docstr_tok: ["Returns","definitions","of","module","output","ports","."]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      """Returns definitions of module output ports."""
      return {
          "input_ids": NeuralType(("B", "T"), ChannelType()),
          "segment_ids": NeuralType(("B", "T"), ChannelType()),
          "input_mask": NeuralType(("B", "T"), MaskType()),
          "subtokens_mask": NeuralType(("B", "T"), MaskType()),
      }
  
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 1 for calling the BertTokenClassificationInferDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict