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Complete this python program:
def _minimize_cobyla(fun, x0, args=(), constraints=(), rhobeg=1.0, tol=0.0001, iprint=1, maxiter=1000, disp=False, catol=0.0002, **unknown_options):
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#Here is the Python code you requested.

    """Minimize a scalar function of one or more variables using the
    Constrained Optimization BY Linear Approximation (COBYLA) algorithm.
    Options
    rhobeg : float
    Reasonable initial changes to the variables.
    tol : float
    Final accuracy in the optimization (not precisely guaranteed).
    This is a lower bound on the size of the trust region.
    disp : bool
    Set to True to print convergence messages. If False,
    `verbosity` is ignored as set to 0.
    maxiter : int
    Maximum number of function evaluations.
    catol : float
    Tolerance (absolute) for constraint violations
    """
    _check_unknown_options(unknown_options)
    maxfun = maxiter
    rhoend = tol
    if (not disp):
        iprint = 0
    if isinstance(constraints, dict):
        constraints = (constraints,)
    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 != 'ineq'):
                raise ValueError(("Constraints    of    type    '%s'    not    handled    by    COBYLA." % con['type']))
        if ('fun' not in con):
            raise KeyError(('Constraint    %d    has    no    function    defined.' % ic))
        if ('args' not in con):
            con['args'] = ()
    cons_lengths = []
    for c in constraints:
        f = c['fun'](x0, *c['args'])
        try:
            cons_length = len(f)
        except TypeError:
            cons_length = 1
        cons_lengths.append(cons_length)
    m = sum(cons_lengths)
    def calcfc(x, con):
        f = fun(x, *args)
        i = 0
        for (size, c) in izip(cons_lengths, constraints):
            con[i:(i + size)] = c['fun'](x, *c['args'])
            i += size
        return f
    info = np.zeros(4, np.float64)
    (xopt, info) = _cobyla.minimize(calcfc, m=m, x=np.copy(x0), rhobeg=rhobeg, rhoend=rhoend, iprint=iprint, maxfun=maxfun, dinfo=info)
    if (info[3] > catol):
        info[0] = 4
    return OptimizeResult(x=xopt, status=int(info[0]), success=(info[0] == 1), message={1: 'Optimization    terminated    successfully.', 2: 'Maximum    number    of    function    evaluations    has    been    exceeded.', 3: 'Rounding    errors    are    becoming    damaging    in    COBYLA    subroutine.', 4: 'Did    not    converge    to    a    solution   ...