class_docstr: Augments words by using the transformation methods provided by CheckList
  INV testing, which combines:
  
  - Name Replacement
  - Location Replacement
  - Number Alteration
  - Contraction/Extension
  
  "Beyond Accuracy: Behavioral Testing of NLP models with CheckList" (Ribeiro et al., 2020)
  https://arxiv.org/abs/2005.04118
class_docstr_tok: ["Augments","words","by","using","the","transformation","methods","provided","by","CheckList","INV","testing",",","which","combines",":","-","Name","Replacement","-","Location","Replacement","-","Number","Alteration","-","Contraction\/Extension","''","Beyond","Accuracy",":","Behavioral","Testing","of","NLP","models","with","CheckList","''","(","Ribeiro","et","al.",",","2020",")","https",":","\/\/arxiv.org\/abs\/2005.04118"]
code: def __init__(self, **kwargs):
      from textattack.transformations import (
          CompositeTransformation,
          WordSwapChangeLocation,
          WordSwapChangeName,
          WordSwapChangeNumber,
          WordSwapContract,
          WordSwapExtend,
      )
  
      transformation = CompositeTransformation(
          [
              WordSwapChangeNumber(),
              WordSwapChangeLocation(),
              WordSwapChangeName(),
              WordSwapExtend(),
              WordSwapContract(),
          ]
      )
  
      constraints = [DEFAULT_CONSTRAINTS[0]]
  
      super().__init__(
          transformation, constraints=constraints, **kwargs
      )
  
imports: import random
  from textattack.constraints.pre_transformation import RepeatModification, StopwordModification
  from textattack.constraints.semantics.sentence_encoders import UniversalSentenceEncoder
  from .None import Augmenter
label_desc: Use image node_id 1 to create a new CheckListAugmenter object from inherited base classes: Augmenter with example: obj = CheckListAugmenter()