content: import numpy as np
  from sklearn.preprocessing import FunctionTransformer
  
  from ._transformer import SklearnTransformer
  
  
  class Noise(SklearnTransformer):
      """
      This transformer adds gaussian noise to an embeddingset.
  
      Arguments:
          sigma: the amount of gaussian noise to add
          seed: seed value for random number generator
  
      Usage:
  
      ```python
      from whatlies.language import SpacyLanguage
      from whatlies.transformers import Noise
  
      words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
               "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
               "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
               "person", "family", "brother", "sister"]
  
      lang = SpacyLanguage("en_core_web_md")
      emb = lang[words]
  
      emb.transform(Noise(3))
      ```
      """
  
      def __init__(self, sigma=0.1, seed=42, **kwargs):
          np.random.seed(seed)
          super().__init__(
              FunctionTransformer,
              f"noise_{sigma}",
              func=lambda X: X + np.random.normal(0, sigma, X.shape),
              **kwargs,
          )
  