class_docstr: A single set of features of data.
class_docstr_tok: ["A","single","set","of","features","of","data","."]
code: def __init__(
      self,
      unique_id,
      example_index,
      doc_span_index,
      tokens,
      token_to_orig_map,
      token_is_max_context,
      input_ids,
      input_mask,
      segment_ids,
      cls_index,
      p_mask,
      paragraph_len,
      start_position=None,
      end_position=None,
      is_impossible=None,
  ):
      self.unique_id = unique_id
      self.example_index = example_index
      self.doc_span_index = doc_span_index
      self.tokens = tokens
      self.token_to_orig_map = token_to_orig_map
      self.token_is_max_context = token_is_max_context
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.cls_index = cls_index
      self.p_mask = p_mask
      self.paragraph_len = paragraph_len
      self.start_position = start_position
      self.end_position = end_position
      self.is_impossible = is_impossible
  
imports: import json
  import os
  import torch
  from pathlib import Path
  import pickle
  import logging
  import collections
  from io import open
  import shutil
  from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
  from transformers import WEIGHTS_NAME, BertTokenizer, XLNetTokenizer, DistilBertTokenizer
label_desc: Use image node_id 1 to create a new InputFeatures object from inherited base classes: object with example: obj = InputFeatures(unique_id, example_index, doc_span_index, tokens, token_to_orig_map, token_is_max_context, input_ids, input_mask, segment_ids, cls_index, p_mask, paragraph_len, start_position, end_position, is_impossible)