class_docstr: A raw input example consisting of segments of text,
  a label for classification task or a target sequence of generation task.
  Other desired information can be passed via meta.
  
  Args:
      guid (:obj:`str`, optional): A unique identifier of the example.
      text_a (:obj:`str`, optional): The placeholder for sequence of text.
      text_b (:obj:`str`, optional): A secend sequence of text, which is not always necessary.
      label (:obj:`int`, optional): The label id of the example in classification task.
      tgt_text (:obj:`Union[str,List[str]]`, optional):  The target sequence of the example in a generation task..
      meta (:obj:`Dict`, optional): An optional dictionary to store arbitrary extra information for the example.
code: def __init__(
      self,
      guid=None,
      text_a="",
      text_b="",
      label=None,
      meta: Optional[Dict] = None,
      tgt_text: Optional[Union[str, List[str]]] = None,
  ):
      self.guid = guid
      self.text_a = text_a
      self.text_b = text_b
      self.label = label
      self.meta = meta if meta else {}
      self.tgt_text = tgt_text
  
imports: import copy
  import json
  import pickle
  from typing import *
  import torch
  from torch.utils.data._utils.collate import default_collate
  from openprompt.utils.logging import logger
  from typing import Union
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, meta, tgt_text)