class_docstr: A single set of features of data.
  
  Args:
      input_ids: input/token ids
      input_mask: masks out subword tokens
      segment_ids: distinguish one sentence from the other one (if present)
      label_ids: label for the current example
docstr: Initialized InputFeatures.
docstr_tok: ["Initialized","InputFeatures","."]
code: def __init__(
      self,
      input_ids: List[int],
      input_mask: List[int],
      segment_ids: List[int],
      label_id: Union[float, int],
  ):
      """Initialized InputFeatures."""
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.label_id = label_id
  
imports: import os
  import pickle
  from typing import Dict, List, Optional, Union
  import numpy
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.glue_benchmark.data_processors import ColaProcessor, MnliMismatchedProcessor, MnliProcessor, MrpcProcessor, QnliProcessor, QqpProcessor, RteProcessor, Sst2Processor, StsbProcessor, WnliProcessor, XNLIProcessor
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType, RegressionValuesType
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new InputFeatures object from inherited base classes: object with example: obj = InputFeatures(input_ids, input_mask, segment_ids, label_id)