text: try:
      from gensim.models import KeyedVectors
  except ImportError:
      # No installation required if not using this function
      pass
  
  from nlpaug.model.word_embs import WordEmbeddings
  
  
  class Fasttext(WordEmbeddings):
      # https://arxiv.org/pdf/1712.09405.pdf,
      def __init__(self, top_k=100, skip_check=False):
          super().__init__(top_k, skip_check)
  
          try:
              from gensim.models import KeyedVectors
          except ModuleNotFoundError:
              raise ModuleNotFoundError('Missed gensim library. Install transfomers by `pip install gensim`')
  
          self.model = None
          self.words = []
  
      def read(self, file_path, max_num_vector=None):
          self.model = KeyedVectors.load_word2vec_format(file_path, limit=max_num_vector)
          super()._read()
  