human
Complete the below
def _minimize_lbfgsb(fun, x0, args=(), jac=None, bounds=None, disp=None, maxcor=10, ftol=2.220446049250313e-09, gtol=1e-05, eps=1e-08, maxfun=15000, maxiter=15000, iprint=(-1), callback=None, maxls=20, **unknown_options):

    ''''Minimize a scalar function of one or more variables using the L-BFGS-B
    algorithm.
    Options
    disp : bool
    Set to True to print convergence messages.
    maxcor : int
    The maximum number of variable metric corrections used to
    define the limited memory matrix. (The limited memory BFGS
    method does not store the full hessian but uses this many terms
    in an approximation to it.)
    ftol : float
    The iteration stops when ``(f^k -
    f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol``.
    gtol : float
    The iteration will stop when ``max{|proj g_i | i = 1, ..., n}
    <= gtol`` where ``pg_i`` is the i-th component of the
    projected gradient.
    eps : float
    Step size used for numerical approximation of the jacobian.
    disp : int
    Set to True to print convergence messages.
    maxfun : int
    Maximum number of function evaluations.
    maxiter : int
    Maximum number of iterations.
    maxls : int, optional
    Maximum number of line search steps (per iteration). Default is 20.
    Notes
    The option `ftol` is exposed via the `scipy.optimize.minimize` interface,
    but calling `scipy.optimize.fmin_l_bfgs_b` directly exposes `factr`. The
    relationship between the two is ``ftol = factr * numpy.finfo(float).eps``.
    I.e., `factr` multiplies the default machine floating-point precision to
    arrive at `ftol`.'
    '''

=====
What is a Python code to print convergence messages?
assistant
#Here is Python code to print convergence messages.
    _check_unknown_options(unknown_options)
    m = maxcor
    epsilon = eps
    pgtol = gtol
    factr = (ftol / np.finfo(float).eps)
    x0 = asarray(x0).ravel()
    (n,) = x0.shape
    if (bounds is None):
        bounds = ([(None, None)] * n)
    if (len(bounds) != n):
        raise ValueError('length    of    x0    !=    length    of    bounds')
    bounds = [((None if (l == (- np.inf)) else l), (None if (u == np.inf) else u)) for (l, u) in bounds]
    if (disp is not None):
        if (disp == 0):
            iprint = (-1)
        else:
            iprint = disp
    (n_function_evals, fun) = wrap_function(fun, ())
    if (jac is None):
        def func_and_grad(x):
            f = fun(x, *args)
            g = _approx_fprime_helper(x, fun, epsilon, args=args, f0=f)
            return (f, g)
    else:
        def func_and_grad(x):
            f = fun(x, *args)
            g = jac(x, *args)
            return (f, g)
    nbd = zeros(n, int32)
    low_bnd = zeros(n, float64)
    upper_bnd = zeros(n, float64)
    bounds_map = {(None, None): 0, (1, None): 1, (1, 1): 2, (None, 1): 3}
    for i in range(0, n):
        (l, u) = bounds[i]
        if (l is not None):
            low_bnd[i] = l
            l = 1
        if (u is not None):
            upper_bnd[i] = u
            u = 1
        nbd[i] = bounds_map[(l, u)]
    if (not (maxls > 0)):
        raise ValueError('maxls    must    be    positive.')
    x = array(x0, float64)
    f = array(0.0, float64)
    g = zeros((n,), float64)
    wa = zeros((((((2 * m) * n) + (5 * n)) + ((11 * m) * m)) + (8 * m)), float64)
    iwa = zeros((3 * n), int32)
    task = zeros(1, 'S60')
    csave = zeros(1, 'S60')
    lsave = zeros(4, int32)
    isave = zeros(44, int32)
    dsave = zeros(29, float64)
    task[:] = 'START'
    n_iterations = 0
    while 1:
        _lbfgsb.setulb(m, x, low_bnd, upper_bnd, nbd, f, g, factr, pgtol, wa, iwa, task, iprint, csave, lsave, isave, dsave, maxls)
        task_str = task.tostring()
        if task_str.startswith('FG'):
            (f, g)...