class_docstr: A set of functions used to augment data.
  
  Typical usage: ::
      >>> import nltk
      >>> nltk.download('punkt')
      >>> nltk.download('wordnet')
      >>> nltk.download('averaged_perceptron_tagger')
      >>> from textaugment import Wordnet
      >>> t = Wordnet(v=True,n=True,p=0.5)
      >>> t.augment('I love school')
      i adore school
docstr: Data augmentation for text. Generate new dataset based on verb/nouns synonyms.
  
  :type data: str
  :param data: sentence used for data augmentation 
  :rtype:   str
  :return:  The augmented data
  :type lang: str
  :param lang: choose lang
  :type top_n: int
  :param top_n: top_n of synonyms to randomly choose from
  
  :rtype:   str
  :return:  The augmented data
code: def augment(self, data, lang="eng", top_n=10):
      """
      Data augmentation for text. Generate new dataset based on verb/nouns synonyms.
  
      :type data: str
      :param data: sentence used for data augmentation
      :rtype:   str
      :return:  The augmented data
      :type lang: str
      :param lang: choose lang
      :type top_n: int
      :param top_n: top_n of synonyms to randomly choose from
  
      :rtype:   str
      :return:  The augmented data
      """
      # Error handling
      if type(data) is not str:
          raise TypeError("Only strings are supported")
      if type(lang) is not str:
          raise TypeError("Only strings are supported")
      if type(top_n) is not int:
          raise TypeError("Only integers are supported")
  
      data = self.replace(data, lang, top_n)
      return data
  
file_path: textaugment/textaugment/wordnet.py
imports: import numpy
  import nltk
  from itertools import chain
  from nltk.corpus import wordnet
label_desc: Use image node_id 4 for calling the Wordnet obj's underlying member method code with example usage: obj.augment(data, lang, top_n) and returns: data