class_docstr: A single training/test example for simple sequence classification.
class_docstr_tok: ["A","single","training\/test","example","for","simple","sequence","classification","."]
docstr: Constructs a InputExample.
  
  Args:
      guid: Unique id for the example.
      text_a: string. The untokenized text of the first sequence. For single
      sequence tasks, only this sequence must be specified.
      text_b: (Optional) string. The untokenized text of the second sequence.
      Only must be specified for sequence pair tasks.
      labels: (Optional) [string]. The label of the example. This should be
      specified for train and dev examples, but not for test examples.
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code: def __init__(self, guid, text_a, text_b=None, label=None):
      """Constructs a InputExample.
  
      Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
          sequence tasks, only this sequence must be specified.
          text_b: (Optional) string. The untokenized text of the second sequence.
          Only must be specified for sequence pair tasks.
          labels: (Optional) [string]. The label of the example. This should be
          specified for train and dev examples, but not for test examples.
      """
      self.guid = guid
      self.text_a = text_a
      self.text_b = text_b
      if isinstance(label, list):
          self.label = label
      elif label:
          self.label = str(label)
      else:
          self.label = None
  
imports: import pandas
  import os
  import torch
  from pathlib import Path
  import pickle
  import logging
  import shutil
  from torch.utils.data import Dataset, TensorDataset, DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers import AutoTokenizer
label_desc: Use image node_id 1 to create a new InputExample object from inherited base classes: object with example: obj = InputExample(guid, text_a, text_b, label)