class_docstr: 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,
      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.start_position = start_position
      self.end_position = end_position
      self.is_impossible = is_impossible
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import json
  import math
  import os
  import random
  import mesh_tensorflow
  import mesh_tensorflow.bert.bert
  import mesh_tensorflow.bert.optimization
  import mesh_tensorflow.bert.tokenization
  import six
  from six.moves import range
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
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, start_position, end_position, is_impossible)