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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 superMDS(X0, N, d, **kwargs):
 """ Find the set of points from an edge kernel. """

    Om = kwargs.get('Om', None)
    dm = kwargs.get('dm', None)
    if Om is not None and dm is not None:
        KE = kwargs.get('KE', None)
        if KE is not None:
            print('superMDS: KE and Om, dm given. Continuing with Om, dm')
        factor, u = eigendecomp(Om, d)
        uhat = u[:, :d]
        lambdahat = np.diag(factor[:d])
        diag_dm = np.diag(dm)
        Vhat = np.dot(diag_dm, np.dot(uhat, lambdahat))
    elif Om is None or dm is None:
        KE = kwargs.get('KE', None)
        if KE is None:
            raise NameError('Either KE or Om and dm have to be given.')
        factor, u = eigendecomp(KE, d)
        lambda_ = np.diag(factor)
        Vhat = np.dot(u, lambda_)[:, :d]

    C_inv = -np.eye(N)
    C_inv[0, 0] = 1.0
    C_inv[:, 0] = 1.0
    b = np.zeros((C_inv.shape[1], d))
    b[0, :] = X0
    b[1:, :] = Vhat[:N - 1, :]
    Xhat = np.dot(C_inv, b)
    return Xhat, Vhat