human
I wrote the below code

    def info(s):
        print s
    predict_probability = 0
    argv = options.split()
    i = 0
    while (i < len(argv)):
        if (argv[i] == '-b'):
            i += 1
            predict_probability = int(argv[i])
        elif (argv[i] == '-q'):
            info = print_null
        else:
            raise ValueError('Wrong    options')
        i += 1
    solver_type = m.param.solver_type
    nr_class = m.get_nr_class()
    nr_feature = m.get_nr_feature()
    is_prob_model = m.is_probability_model()
    bias = m.bias
    if (bias >= 0):
        biasterm = feature_node((nr_feature + 1), bias)
    else:
        biasterm = feature_node((-1), bias)
    pred_labels = []
    pred_values = []
    if predict_probability:
        if (not is_prob_model):
            raise TypeError('probability    output    is    only    supported    for    logistic    regression')
        prob_estimates = (c_double * nr_class)()
        for xi in x:
            (xi, idx) = gen_feature_nodearray(xi, feature_max=nr_feature)
            xi[(-2)] = biasterm
            label = liblinear.predict_probability(m, xi, prob_estimates)
            values = prob_estimates[:nr_class]
            pred_labels += [label]
            pred_values += [values]
    else:
        if (nr_class <= 2):
            nr_classifier = 1
        else:
            nr_classifier = nr_class
        dec_values = (c_double * nr_classifier)()
        for xi in x:
            (xi, idx) = gen_feature_nodearray(xi, feature_max=nr_feature)
            xi[(-2)] = biasterm
            label = liblinear.predict_values(m, xi, dec_values)
            values = dec_values[:nr_classifier]
            pred_labels += [label]
            pred_values += [values]
    if (len(y) == 0):
        y = ([0] * len(x))
    (ACC, MSE, SCC) = evaluations(y, pred_labels)
    l = len(y)
    if (solver_type in [L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL]):
        info(('Mean    squared    error    =    %g    (regression)' % MSE))
        info(('Squared    correlation   ...

What's a good function header?

=====
What is a Python program to print s?
assistant
#Here is Python program to print s
.

def predict(y, x, m, options=''):