code: def normalize_words(self, ord=2, inplace=False):
      
      if ord == 2:
        ord = None # numpy uses this flag to indicate l2.
      vectors = self.vectors.T / np.linalg.norm(self.vectors, ord, axis=1)
      if inplace:
        self.vectors = vectors.T
        return self
      return Embedding(vectors=vectors.T, vocabulary=self.vocabulary)
docs: Normalize embeddings matrix row-wise.
  
      Args:
        ord: normalization order. Possible values {1, 2, 'inf', '-inf'}