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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 s_boot(Ss, ipar=0, nb=1000):
 """ Returns bootstrap parameters for S data Parameters __________ ipar : if True, do a parametric bootstrap nb : number of bootstraps Returns ________ Tmean : average eigenvalues Vmean : average eigvectors Taus : bootstrapped eigenvalues Vs : bootstrapped eigenvectors """

    #npts = len(Ss)
    Ss = np.array(Ss)
    npts = Ss.shape[0]
# get average s for whole dataset
    nf, Sigma, avs = sbar(Ss)
    Tmean, Vmean = doseigs(avs)  # get eigenvectors of mean tensor
#
# now do bootstrap to collect Vs and taus of bootstrap means
#
    Taus, Vs = [], []  # number of bootstraps, list of bootstrap taus and eigenvectors
#
    for k in range(int(float(nb))):  # repeat nb times
        #        if k%50==0:print k,' out of ',nb
        # get a pseudosample - if ipar=1, do a parametric bootstrap
        BSs = apseudo(Ss, ipar, Sigma)
        nf, sigma, avbs = sbar(BSs)  # get bootstrap mean s
        tau, Vdirs = doseigs(avbs)  # get bootstrap eigenparameters
        Taus.append(tau)
        Vs.append(Vdirs)
    return Tmean, Vmean, Taus, Vs