class_docstr: A single set of features of data.
class_docstr_tok: ["A","single","set","of","features","of","data","."]
code: def __init__(
      self,
      input_ids,
      input_mask,
      segment_ids,
      label_id=None,
      valid_ids=None,
  ):
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.label_id = label_id
      self.valid_ids = valid_ids
  
imports: import io
  import logging
  import os
  from typing import List, Union
  import torch
  from torch.utils.data import DataLoader
  from tqdm import tqdm, trange
  from transformers import AdamW, BertConfig, BertTokenizer, RobertaConfig, RobertaTokenizer, XLMConfig, XLMTokenizer, XLNetConfig, XLNetTokenizer, get_linear_schedule_with_warmup
  from nlp_architect.models import TrainableModel
  from nlp_architect.models.transformers.quantized_bert import QuantizedBertConfig
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, valid_ids)