How to use the fasttext.skipgram function in fasttext

print("Training complete")

    elif option == 1:
        print("Training a Gensim FastText model")
        model = FastText(sentences=sentences, size = embed_dim, workers = workers, window = window)
        print("Training complete")

    elif option == 2:
        print("Training a Fasttext model from Facebook Research")
        y_train = ["__label__positive" if i==1 else "__label__negative" for i in y_train]

        with open("imdb_train.txt","w") as text_file:
            for i in range(len(sentences)):
                print(sentences[i],y_train[i],file = text_file)

        model = fasttext.skipgram("imdb_train.txt","model_ft_2018_imdb",dim = embed_dim)
        print("Training complete")

    return model

"""
        if model_filename is None:
            if model_type == 'skipgram':
                model_filename = FAST_TEXT_SKIPGRAM_MODEL_FILENAME
            elif model_type == 'cbow':
                model_filename = FAST_TEXT_CBOW_MODEL_FILENAME

        full_model_filename = self.full_filename(model_filename)
        full_input_filename = self.full_filename(input_filename)

        if model_type == 'skipgram':
            self.logger.info(
                'Training fasttext skipgram model on {} to {}'.format(
                    full_input_filename, full_model_filename))
            self.model = fasttext.skipgram(
                full_input_filename, full_model_filename)
        elif model_type == 'cbow':
            self.logger.info(
                'Training fasttext cbow model on {} to {}'.format(
                    full_input_filename, full_model_filename))
            self.model = fasttext.cbow(
                full_input_filename, full_model_filename)
        else:
            raise ValueError('Wrong argument to model_type')

        # Invalidate computed normalized matrix
        self._normalized_matrix = None

def train(self):
        # silent设为False, 训练过程会打印日志信息
        return fasttext.skipgram(self.train_file, self.model_path, silent=False)

def train(inp = "wiki.he.text",out_model = "wiki.he.fasttext.model",
          alg = "CBOW"):

    start = time.time()

    if alg == "skipgram":
        # Skipgram model
        model = fasttext.skipgram(inp, out_model)
        print(model.words) # list of words in dictionary
    else:
        # CBOW model
        model = fasttext.cbow(inp, out_model)
        print(model.words) # list of words in dictionary

    print(time.time()-start)
          
    model.save(out_model)