code: def tokenize(self, vector_list):
          '''
          Tokenize vector.
  
          Args:
              vector_list:    The list of vector of one token.
          
          Returns:
              token
          '''
          if self.computable_distance is None:
              self.computable_distance = EuclidDistance()
          vector_arr = np.array(vector_list)
          distance_arr = np.empty_like(vector_arr)
          feature_arr = self.__dbm.get_feature_point(layer_number=0)
          key_arr = np.empty(vector_arr.shape[0], dtype=int)
          for i in range(vector_arr.shape[0]):
              distance_arr = self.computable_distance.compute(
                  np.expand_dims(vector_arr[i], axis=0).repeat(feature_arr.shape[0], axis=0),
                  feature_arr
              )
              key_arr[i] = distance_arr.argmin(axis=0)
          return self.token_arr[key_arrf tokenize(self, vector_list):
          '''
          Tokenize vector.
  
          Args:
              vector_list:    The list of vector of one token.
          
          Returns:
              token
          '''
          if self.computable_distance is None:
              self.computable_distance = EuclidDistance()
          vector_arr = np.array(vector_list)
          distance_arr = np.empty_like(vector_arr)
          feature_arr = self.__dbm.get_feature_point(layer_number=0)
          key_arr = np.empty(vector_arr.shape[0], dtype=int)
          for i in range(vector_arr.shape[0]):
              distance_arr = self.computable_distance.compute(
                  np.expand_dims(vector_arr[i], axis=0).repeat(feature_arr.shape[0], axis=0),
                  feature_arr
              )
              key_arr[i] = distance_arr.argmin(axis=0)
          return self.token_arr[key_arr]
docs: Tokenize vector.
  
          Args:
              vector_list:    The list of vector of one token.
          
          Returns:
              token