class_docstr: A single set of features of data.
code: def __init__(
      self, input_ids, char_ids, shape_ids, mask=None, label_id=None
  ):
      self.input_ids = input_ids
      self.char_ids = char_ids
      self.shape_ids = shape_ids
      self.mask = mask
      self.label_id = label_id
  
imports: import io
  import logging
  import os
  import pickle
  from typing import List
  import numpy
  import torch
  import torch.optim
  from torch.nn import CrossEntropyLoss
  from torch.nn import functional
  from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
  from tqdm import tqdm, trange
  from nlp_architect.data.sequential_tagging import TokenClsInputExample
  from nlp_architect.models import TrainableModel
  from nlp_architect.nn.torch.layers import CRF
  from nlp_architect.nn.torch.distillation import TeacherStudentDistill
  from nlp_architect.nn.torch.modules.embedders import IDCNN
  from nlp_architect.utils.metrics import tagging
  from nlp_architect.utils.text import Vocabulary, char_to_id
label_desc: Use image node_id 1 to create a new InputFeatures object from inherited base classes: object with example: obj = InputFeatures(input_ids, char_ids, shape_ids, mask, label_id)