How to use the flair.embeddings.DocumentRNNEmbeddings function in flair

def init(tasks_base_path) -> Tuple[TaggedCorpus, TextRegressor, ModelTrainer]:
    corpus = NLPTaskDataFetcher.load_corpus(NLPTask.REGRESSION, tasks_base_path)

    glove_embedding: WordEmbeddings = WordEmbeddings("glove")
    document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
        [glove_embedding], 128, 1, False, 64, False, False
    )

    model = TextRegressor(document_embeddings)

    trainer = ModelTrainer(model, corpus)

    return corpus, model, trainer

def test_train_load_use_classifier(results_base_path, tasks_base_path):
    corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
    label_dict = corpus.make_label_dictionary()

    word_embedding: WordEmbeddings = WordEmbeddings("turian")
    document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
        [word_embedding], 128, 1, False, 64, False, False
    )

    model: TextClassifier = TextClassifier(document_embeddings, label_dict, False)

    trainer = ModelTrainer(model, corpus)
    trainer.train(results_base_path, max_epochs=2, shuffle=False)

    sentence = Sentence("Berlin is a really nice city.")

    for s in model.predict(sentence):
        for l in s.labels:
            assert l.value is not None
            assert 0.0 <= l.score <= 1.0
            assert type(l.score) is float

def test_train_load_use_classifier_with_prob(results_base_path, tasks_base_path):
    corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
    label_dict = corpus.make_label_dictionary()

    word_embedding: WordEmbeddings = WordEmbeddings("turian")
    document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
        [word_embedding], 128, 1, False, 64, False, False
    )

    model: TextClassifier = TextClassifier(document_embeddings, label_dict, False)

    trainer = ModelTrainer(model, corpus)
    trainer.train(results_base_path, max_epochs=2, shuffle=False)

    sentence = Sentence("Berlin is a really nice city.")

    for s in model.predict(sentence, multi_class_prob=True):
        for l in s.labels:
            assert l.value is not None
            assert 0.0 <= l.score <= 1.0
            assert type(l.score) is float

def test_train_resume_text_classification_training(results_base_path, tasks_base_path):
    corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
    label_dict = corpus.make_label_dictionary()

    embeddings: TokenEmbeddings = FlairEmbeddings("news-forward-fast")
    document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
        [embeddings], 128, 1, False
    )

    model = TextClassifier(document_embeddings, label_dict, False)

    trainer = ModelTrainer(model, corpus)
    trainer.train(results_base_path, max_epochs=2, shuffle=False, checkpoint=True)

    trainer = ModelTrainer.load_checkpoint(results_base_path / "checkpoint.pt", corpus)
    trainer.train(results_base_path, max_epochs=2, shuffle=False, checkpoint=True)

    # clean up results directory
    shutil.rmtree(results_base_path)

file_path,
        train_file=train,
        dev_file=dev,
        test_file=test,
    )
    # Create label dictionary from provided labels in data
    label_dict = corpus.make_label_dictionary()

    # Stack Flair string-embeddings with optional embeddings
    word_embeddings = list(filter(None, [
        stacked_embedding,
        FlairEmbeddings('news-forward'),
        FlairEmbeddings('news-backward'),
    ]))
    # Initialize document embedding by passing list of word embeddings
    document_embeddings = DocumentRNNEmbeddings(
        word_embeddings,
        hidden_size=512,
        reproject_words=True,
        reproject_words_dimension=256,
    )
    # Define classifier
    classifier = TextClassifier(
        document_embeddings,
        label_dictionary=label_dict,
        multi_label=False
    )

    if not checkpoint:
        trainer = ModelTrainer(classifier, corpus)
    else:
        # If checkpoint file is defined, resume training