def train(self, features, labels, **kwargs):
    models = []
    model = None
    for T in self.transforms:
        if (model is not None):
            features = np.array([model.apply(f) for f in features])
        model = T.train(features, labels, **kwargs)
        models.append(model)
    return ctransforms_model(models)
def train(self, features, labels, **kwargs):
    models = []
    model = None
    for T in self.transforms:
        if (model is not None):
            features = np.array([model.apply(f) for f in features])
        model = T.train(features, labels, **kwargs)
        models.append(model)
    return ctransforms_model(models)