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,
      sub_idx,
      obj_idx,
  ):
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.label_id = label_id
      self.sub_idx = sub_idx
      self.obj_idx = obj_idx
  
imports: import argparse
  import logging
  import os
  import random
  import time
  import json
  import sys
  import numpy
  import torch
  from torch.utils.data import DataLoader, TensorDataset
  from collections import Counter
  from torch.nn import CrossEntropyLoss
  from transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
  from transformers import AutoTokenizer
  from transformers import AdamW, get_linear_schedule_with_warmup
  from models.const import *
  from models.data_structures import *
  from models.entityModels import *
  from models.entityUtils import *
  from models.relationModels import *
  from models.relationUtils import *
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, sub_idx, obj_idx)