class_docstr: Transforms an input by replacing its words with antonyms provided by
  WordNet. Download nltk_data before running.
  
  Implement follow by Stress Test Evaluation for Natural Language Inference
  For the correctness of trasformation we swap the word has
  best_sense(Wordnet) to its antonym
  
  https://www.aclweb.org/anthology/C18-1198/
  
  Example::
  
      {
          sentence1: I hate this book.
          sentence2: This book is my favorite.
          y: 0
      }
docstr: Transform data sample to a list of Sample.
  
  :param ~SMSample sample: Data sample for augmentation
  :param int n: Default is 1. MAX number of unique augmented output
  :param **kwargs:
  :return: Augmented data
code: def transform(self, sample, n=1, **kwargs):
      r"""
      Transform data sample to a list of Sample.
  
      :param ~SMSample sample: Data sample for augmentation
      :param int n: Default is 1. MAX number of unique augmented output
      :param **kwargs:
      :return: Augmented data
  
      """
      transform_results = self._transform(sample, **kwargs)
  
      if transform_results:
          return [
              data for data in transform_results if not data.is_origin
          ]
      else:
          return []
  
file_path: textflint/textflint/generation/transformation/SM/swap_word.py
imports: from nltk.wsd import lesk
  from ...transformation import Transformation
  from ....common.settings import BLACK_LIST_WORD
label_desc: Use image node_id 3 for calling the SwapWord obj's underlying member method code with example usage: obj.transform(sample, n) and returns: unknown, list