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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _clone_and_score_clusterer(clf, X, n_clusters):
 """Clones and scores clusterer instance. Args: clf: Clusterer instance that implements ``fit``,``fit_predict``, and ``score`` methods, and an ``n_clusters`` hyperparameter. e.g. :class:`sklearn.cluster.KMeans` instance X (array-like, shape (n_samples, n_features)):
 Data to cluster, where n_samples is the number of samples and n_features is the number of features. n_clusters (int):
 Number of clusters Returns: score: Score of clusters time: Number of seconds it took to fit cluster """

    start = time.time()
    clf = clone(clf)
    setattr(clf, 'n_clusters', n_clusters)
    return clf.fit(X).score(X), time.time() - start