def METHOD_NAME(results_base_path, tasks_base_path):
    corpus = flair.datasets.UD_ENGLISH().downsample(0.01)
    tag_dictionary = corpus.make_label_dictionary("pos")
    tagger: SequenceTagger = SequenceTagger(
        hidden_size=64,
        embeddings=turian_embeddings,
        tag_dictionary=tag_dictionary,
        tag_type="pos",
        use_crf=False,
    )
    # initialize trainer
    trainer: ModelTrainer = ModelTrainer(tagger, corpus)
    trainer.train(
        results_base_path,
        learning_rate=0.1,
        mini_batch_size=32,
        max_epochs=2,
        shuffle=False,
    )
    del trainer, tagger, tag_dictionary, corpus
    loaded_model: SequenceTagger = SequenceTagger.load(results_base_path / "final-model.pt")
    sentence = Sentence("I love Berlin")
    sentence_empty = Sentence("       ")
    loaded_model.predict(sentence)
    loaded_model.predict([sentence, sentence_empty])
    loaded_model.predict([sentence_empty])
    del loaded_model