def METHOD_NAME(nn_data, local_connectivity=1.0):
    knn_indices, knn_dists, _ = nearest_neighbors(
        nn_data, 10, "euclidean", {}, False, np.random
    )
    sigmas, rhos = smooth_knn_dist(
        knn_dists, 10.0, local_connectivity=local_connectivity
    )
    shifted_dists = knn_dists - rhos[:, np.newaxis]
    shifted_dists[shifted_dists < 0.0] = 0.0
    vals = np.exp(-(shifted_dists / sigmas[:, np.newaxis]))
    norms = np.sum(vals, axis=1)
    return norms