How to use the implicit.bpr.BayesianPersonalizedRanking function in implicit

ratings.data[ratings.data < min_rating] = 0
    ratings.eliminate_zeros()
    ratings.data = np.ones(len(ratings.data))

    log.info("read data file in %s", time.time() - start)

    # generate a recommender model based off the input params
    if model_name == "als":
        model = AlternatingLeastSquares()

        # lets weight these models by bm25weight.
        log.debug("weighting matrix by bm25_weight")
        ratings = (bm25_weight(ratings, B=0.9) * 5).tocsr()

    elif model_name == "bpr":
        model = BayesianPersonalizedRanking()

    elif model_name == "lmf":
        model = LogisticMatrixFactorization()

    elif model_name == "tfidf":
        model = TFIDFRecommender()

    elif model_name == "cosine":
        model = CosineRecommender()

    elif model_name == "bm25":
        model = BM25Recommender(B=0.2)

    else:
        raise NotImplementedError("TODO: model %s" % model_name)

def __init__(self, *args, **kwargs):
        """
        Construct an ALS recommender.  The arguments are passed as-is to
        :py:class:`implicit.als.BayesianPersonalizedRanking`.
        """
        from implicit.bpr import BayesianPersonalizedRanking
        super().__init__(BayesianPersonalizedRanking(*args, **kwargs))

from implicit.approximate_als import (AnnoyAlternatingLeastSquares, FaissAlternatingLeastSquares,
                                      NMSLibAlternatingLeastSquares)
from implicit.bpr import BayesianPersonalizedRanking
from implicit.datasets.lastfm import get_lastfm
from implicit.lmf import LogisticMatrixFactorization
from implicit.nearest_neighbours import (BM25Recommender, CosineRecommender,
                                         TFIDFRecommender, bm25_weight)

# maps command line model argument to class name
MODELS = {"als":  AlternatingLeastSquares,
          "nmslib_als": NMSLibAlternatingLeastSquares,
          "annoy_als": AnnoyAlternatingLeastSquares,
          "faiss_als": FaissAlternatingLeastSquares,
          "tfidf": TFIDFRecommender,
          "cosine": CosineRecommender,
          "bpr": BayesianPersonalizedRanking,
          "lmf": LogisticMatrixFactorization,
          "bm25": BM25Recommender}


def get_model(model_name):
    print("getting model %s" % model_name)
    model_class = MODELS.get(model_name)
    if not model_class:
        raise ValueError("Unknown Model '%s'" % model_name)

    # some default params
    if issubclass(model_class, AlternatingLeastSquares):
        params = {'factors': 16, 'dtype': np.float32}
    elif model_name == "bm25":
        params = {'K1': 100, 'B': 0.5}
    elif model_name == "bpr":