human
Complete the below
def _minimize_slsqp(func, x0, args=(), jac=None, bounds=None, constraints=(), maxiter=100, ftol=1e-06, iprint=1, disp=False, eps=_epsilon, callback=None, **unknown_options):

    ''''Minimize a scalar function of one or more variables using Sequential
    Least SQuares Programming (SLSQP).
    Options
    ftol : float
    Precision goal for the value of f in the stopping criterion.
    eps : float
    Step size used for numerical approximation of the jacobian.
    disp : bool
    Set to True to print convergence messages. If False,
    `verbosity` is ignored and set to 0.
    maxiter : int
    Maximum number of iterations.'
    '''

Can you provide a Python code to print convergence messages?
assistant
#Here is Python code to print convergence messages.
    _check_unknown_options(unknown_options)
    fprime = jac
    iter = maxiter
    acc = ftol
    epsilon = eps
    if (not disp):
        iprint = 0
    if isinstance(constraints, dict):
        constraints = (constraints,)
    cons = {'eq': (), 'ineq': ()}
    for (ic, con) in enumerate(constraints):
        try:
            ctype = con['type'].lower()
        except KeyError:
            raise KeyError(('Constraint    %d    has    no    type    defined.' % ic))
        except TypeError:
            raise TypeError('Constraints    must    be    defined    using    a    dictionary.')
        except AttributeError:
            raise TypeError("Constraint's    type    must    be    a    string.")
        else:
            if (ctype not in ['eq', 'ineq']):
                raise ValueError(("Unknown    constraint    type    '%s'." % con['type']))
        if ('fun' not in con):
            raise ValueError(('Constraint    %d    has    no    function    defined.' % ic))
        cjac = con.get('jac')
        if (cjac is None):
            def cjac_factory(fun):
                def cjac(x, *args):
                    return approx_jacobian(x, fun, epsilon, *args)
                return cjac
            cjac = cjac_factory(con['fun'])
        cons[ctype] += ({'fun': con['fun'], 'jac': cjac, 'args': con.get('args', ())},)
    exit_modes = {(-1): 'Gradient    evaluation    required    (g    &    a)', 0: 'Optimization    terminated    successfully.', 1: 'Function    evaluation    required    (f    &    c)', 2: 'More    equality    constraints    than    independent    variables', 3: 'More    than    3*n    iterations    in    LSQ    subproblem', 4: 'Inequality    constraints    incompatible', 5: 'Singular    matrix    E    in    LSQ    subproblem', 6: 'Singular    matrix    C    in    LSQ    subproblem', 7: 'Rank-deficient    equality    constraint    subproblem    HFTI', 8: 'Positive    directional    derivative    for    linesearch', 9: 'Iteration    limit    exceeded'}
    (feval, func) = wrap_function(func, args)
 ...