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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 _centroids(n_clusters: int, points: List[List[float]]) -> List[List[float]]: """ Return n_clusters centroids of points """


    k_means = KMeans(n_clusters=n_clusters)
    k_means.fit(points)

    closest, _ = pairwise_distances_argmin_min(k_means.cluster_centers_, points)

    return list(map(list, np.array(points)[closest.tolist()]))