content: import numpy as np
  
  from ._transformer import Transformer
  from whatlies import Embedding, EmbeddingSet
  
  
  class AddRandom(Transformer):
      """
      This transformer adds random embeddings to the embeddingset.
  
      Arguments:
          n: the number of random vectors to add
  
      Usage:
  
      ```python
      from whatlies.language import SpacyLanguage
      from whatlies.transformers import AddRandom
  
      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(AddRandom(3)).plot_interactive_matrix('rand_0', 'rand_1', 'rand_2')
      ```
      """
  
      def __init__(self, n=1, sigma=0.1, seed=42):
          super().__init__()
          self.n = n
          self.sigma = sigma
          self.seed = seed
  
      def fit(self, embset):
          self.is_fitted = True
          return self
  
      def transform(self, embset):
          names, X = embset.to_names_X()
          np.random.seed(self.seed)
          orig_dict = embset.embeddings.copy()
          new_dict = {
              f"rand_{k}": Embedding(
                  f"rand_{k}", np.random.normal(0, self.sigma, X.shape[1])
              )
              for k in range(self.n)
          }
          return EmbeddingSet({**orig_dict, **new_dict})
  