class_docstr: A set of functions used to augment data.
  
  Typical usage: :: 
      >>> from textaugment import Word2vec
      >>> t = Word2vec(model='path/to/gensim/model'or 'gensim model itself')
      >>> t.augment('I love school', top_n=10)
      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/word2vec.py
imports: import gensim
  import numpy
  import random
label_desc: Use image node_id 2 for calling the Word2vec obj's underlying member method code with example usage: obj.geometric(data) and returns: data