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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 prais(pmat):
 """ Prais conditional mobility measure. Parameters pmat : matrix (k, k), Markov probability transition matrix. Returns ------- pr : matrix (1, k), conditional mobility measures for each of the k classes. Notes ----- Prais' conditional mobility measure for a class is defined as: .. math:: pr_i = 1 - p_{i,i} Examples -------- array([[729., 71., 1., 0., 0.], [ 72., 567., 80., 3., 0.], [ 0., 81., 631., 86., 2.], [ 0., 3., 86., 573., 56.], [ 0., 0., 1., 57., 741.]]) array([[0.91011236, 0.0886392 , 0.00124844, 0. , 0. ], [0.09972299, 0.78531856, 0.11080332, 0.00415512, 0. ], [0. , 0.10125 , 0.78875 , 0.1075 , 0.0025 ], [0. , 0.00417827, 0.11977716, 0.79805014, 0.07799443], [0. , 0. , 0.00125156, 0.07133917, 0.92740926]]) array([0.08988764, 0.21468144, 0.21125 , 0.20194986, 0.07259074]) """

    pmat = np.array(pmat)
    pr = 1 - np.diag(pmat)
    return pr