human
Complete the below
def find_MAP(start=None, vars=None, fmin=None, return_raw=False, model=None, live_disp=False, callback=None, *args, **kwargs):

    ''''Sets state to the local maximum a posteriori point given a model.
    Current default of fmin_Hessian does not deal well with optimizing close
    to sharp edges, especially if they are the minimum.
    Parameters
    start : `dict` of parameter values (Defaults to `model.test_point`)
    vars : list
    List of variables to set to MAP point (Defaults to all continuous).
    fmin : function
    Optimization algorithm (Defaults to `scipy.optimize.fmin_bfgs` unless
    discrete variables are specified in `vars`, then
    `scipy.optimize.fmin_powell` which will perform better).
    return_raw : Bool
    Whether to return extra value returned by fmin (Defaults to `False`)
    model : Model (optional if in `with` context)
    live_disp : Bool
    Display table tracking optimization progress when run from within
    an IPython notebook.
    callback : callable
    Callback function to pass to scipy optimization routine.  Overrides
    live_disp if callback is given.
    *args, **kwargs
    Extra args passed to fmin'
    '''
assistant
#Here is the Python code you requested.
    model = modelcontext(model)
    if (start is None):
        start = model.test_point
    else:
        update_start_vals(start, model.test_point, model)
    if (not set(start.keys()).issubset(model.named_vars.keys())):
        extra_keys = ',    '.join((set(start.keys()) - set(model.named_vars.keys())))
        valid_keys = ',    '.join(model.named_vars.keys())
        raise KeyError('Some    start    parameters    do    not    appear    in    the    model!\nValid    keys    are:    {},    but    {}    was    supplied'.format(valid_keys, extra_keys))
    if (vars is None):
        vars = model.cont_vars
    vars = inputvars(vars)
    disc_vars = list(typefilter(vars, discrete_types))
    try:
        model.fastdlogp(vars)
        gradient_avail = True
    except AttributeError:
        gradient_avail = False
    if (disc_vars or (not gradient_avail)):
        pm._log.warning((((('Warning:    gradient    not    available.' + '(E.g.    vars    contains    discrete    variables).    MAP    ') + 'estimates    may    not    be    accurate    for    the    default    ') + 'parameters.    Defaulting    to    non-gradient    minimization    ') + 'fmin_powell.'))
        fmin = optimize.fmin_powell
    if (fmin is None):
        if disc_vars:
            fmin = optimize.fmin_powell
        else:
            fmin = optimize.fmin_bfgs
    allinmodel(vars, model)
    start = Point(start, model=model)
    bij = DictToArrayBijection(ArrayOrdering(vars), start)
    logp = bij.mapf(model.fastlogp)
    def logp_o(point):
        return nan_to_high((- logp(point)))
    if ('fprime' in getargspec(fmin).args):
        dlogp = bij.mapf(model.fastdlogp(vars))
        def grad_logp_o(point):
            return nan_to_num((- dlogp(point)))
        if (live_disp and (callback is None)):
            callback = Monitor(bij, logp_o, model, grad_logp_o)
        r = fmin(logp_o, bij.map(start), fprime=grad_logp_o, callback=callback, *args, **kwargs)
        compute_gradient = True
    else:
        if (live_disp and (callback is...