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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 all_points_core_distance(distance_matrix, d=2.0):
 """ Compute the all-points-core-distance for all the points of a cluster. Parameters distance_matrix : array (cluster_size, cluster_size) The pairwise distance matrix between points in the cluster. d : integer The dimension of the data set, which is used in the computation of the all-point-core-distance as per the paper. Returns ------- core_distances : array (cluster_size,) The all-points-core-distance of each point in the cluster References Moulavi, D., Jaskowiak, P.A., Campello, R.J., Zimek, A. and Sander, J., 2014. Density-Based Clustering Validation. In SDM (pp. 839-847). """

    distance_matrix[distance_matrix != 0] = (1.0 / distance_matrix[
        distance_matrix != 0]) ** d
    result = distance_matrix.sum(axis=1)
    result /= distance_matrix.shape[0] - 1
    result **= (-1.0 / d)

    return result