class_docstr: A single training/test example for simple sequence classification.
docstr: Constructs a InputExample.
      Args:
          guid(string): 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(string, optional): The untokenized text of the second sequence. Only must be specified for sequence pair tasks.
          label(string, optional): 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(string): 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(string, optional): The untokenized text of the second sequence. Only must be specified for sequence pair tasks.
              label(string, optional): 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
      self.label = label
  
file_path: DeepKE/src/deepke/name_entity_re/standard/tools/dataset.py
imports: import torch
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)