code: def predict(self, x_test):
          
          if self.model:
              lengths = map(len, x_test)
              x_test = self.p.transform(x_test)
              y_pred = self.model.predict(x_test)
              y_pred = self.p.inverse_transform(y_pred, lengths)
              return y_pred 
          else:
              raise OSError('Could not find a model. Call load(dir_path).')
docs: Returns the prediction of the model on the given test data.
  
          Args:
              x_test : array-like, shape = (n_samples, sent_length)
              Test samples.
  
          Returns:
              y_pred : array-like, shape = (n_smaples, sent_length)
              Prediction labels for x.