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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 condest(A, tol=0.1, maxiter=25, symmetric=False):
 r"""Estimates the condition number of A. Parameters A : {dense or sparse matrix} tol : {float} Approximation tolerance, currently not used maxiter: {int} Max number of Arnoldi/Lanczos iterations symmetric : {bool} If symmetric use the far more efficient Lanczos algorithm, Else use Arnoldi Returns ------- Estimate of cond(A) with \|lambda_max\| / \|lambda_min\| through the use of Arnoldi or Lanczos iterations, depending on the symmetric flag Notes ----- The condition number measures how large of a change in the the problems solution is caused by a change in problem's input. Large condition numbers indicate that small perturbations and numerical errors are magnified greatly when solving the system. Examples -------- 2.0 """

    [evect, ev, H, V, breakdown_flag] =\
        _approximate_eigenvalues(A, tol, maxiter, symmetric)

    return np.max([norm(x) for x in ev])/min([norm(x) for x in ev])