How to use the implicit.nearest_neighbours.BM25Recommender function in implicit

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)

    # train the model
    log.debug("training model %s", model_name)
    start = time.time()
    model.fit(ratings)
    log.debug("trained model '%s' in %s", model_name, time.time() - start)
    log.debug("calculating top movies")

    user_count = np.ediff1d(ratings.indptr)
    to_generate = sorted(np.arange(len(titles)), key=lambda x: -user_count[x])

    log.debug("calculating similar movies")
    with tqdm.tqdm(total=len(to_generate)) as progress:

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":
        params = {'factors': 63}
    elif model_name == "lmf":

def __init__(self, K=20, K1=1.2, B=.75, num_threads=0):
        super(BM25Recommender, self).__init__(K, num_threads)
        self.K1 = K1
        self.B = B