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: Used to generate Geometric distribution.
  
  :type data: list
  :param data: Input data
  :rtype:   ndarray or scalar
  :return:  Drawn samples from the parameterized Geometric distribution.
code: def geometric(self, data):
      """
      Used to generate Geometric distribution.
  
      :type data: list
      :param data: Input data
      :rtype:   ndarray or scalar
      :return:  Drawn samples from the parameterized Geometric distribution.
      """
  
      data = np.array(data)
      first_trial = (
          np.random.geometric(p=self.p, size=data.shape[0]) == 1
      )  # Capture success after first trial
      return data[first_trial]
  
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 2 for calling the Wordnet obj's underlying member method code with example usage: obj.geometric(data) and returns: data