def METHOD_NAME():
    # Arrange
    train, test = tweet_emotion.load_data(data_format='Dataframe', as_train_test=True)
    full = tweet_emotion.load_data(data_format='Dataframe', as_train_test=False)
    full_ds = tweet_emotion.load_data(data_format='TextData', as_train_test=False)
    preds = tweet_emotion.load_precalculated_predictions(pred_format='predictions', as_train_test=False)
    probas = tweet_emotion.load_precalculated_predictions(pred_format='probabilities', as_train_test=False)
    properties = tweet_emotion.load_properties(as_train_test=False)
    train_props, test_props = tweet_emotion.load_properties(as_train_test=True)
    embeddings = tweet_emotion.load_embeddings(as_train_test=False)
    train_embeddings, test_embeddings = tweet_emotion.load_embeddings(as_train_test=True)
    # Act & Assert
    assert_that(len(train) + len(test), equal_to(len(full)))
    assert_that(train.columns, contains_exactly(*test.columns))
    assert_that(train.columns, contains_exactly(*full.columns))
    assert_that(len(full_ds.text), equal_to(len(full)))
    assert_that(len(full.text), equal_to(len(preds)))
    assert_that([tweet_emotion._LABEL_MAP[x] for x in np.argmax(probas, axis=1)],  # pylint: disable=protected-access
                contains_exactly(*preds))
    assert_that(len(properties), equal_to(len(full)))
    assert_that(len(train_props) + len(test_props), equal_to(len(full)))
    assert_that(len(train_props), equal_to(len(train)))
    assert_that(len(embeddings), equal_to(len(full)))
    assert_that(len(train_embeddings) + len(test_embeddings), equal_to(len(full)))
    assert_that(len(train_embeddings), equal_to(len(train)))
    assert_that(embeddings.shape, contains_exactly(4653, 1536))
    assert_that(train_embeddings.shape, contains_exactly(2675, 1536))
    assert_that(test_embeddings.shape, contains_exactly(1978, 1536))