def METHOD_NAME(model, dataset):
    from river import utils
    params = model._get_params()
    seeded_params = seed_params(params, seed=42)
    A = model.clone(seeded_params)
    B = model.clone(seeded_params)
    for x, y in dataset:
        if utils.inspect.isanomalydetector(model):
            assert A.score_one(x) == B.score_one(x)
        else:
            assert A.predict_one(x) == B.predict_one(x)
        if model._supervised:
            A.learn_one(x, y)
            B.learn_one(x, y)
        else:
            A.learn_one(x)
            B.learn_one(x)