human
What is a Python code to print s given the following:
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
    svm_type = m.get_svm_type()
    is_prob_model = m.is_probability_model()
    nr_class = m.get_nr_class()
    pred_labels = []
    pred_values = []
    if predict_probability:
        if (not is_prob_model):
            raise ValueError('Model    does    not    support    probabiliy    estimates')
        if (svm_type in [NU_SVR, EPSILON_SVR]):
            info(('Prob.    model    for    test    data:    target    value    =    predicted    value    +    z,\nz:    Laplace    distribution    e^(-|z|/sigma)/(2sigma),sigma=%g' % m.get_svr_probability()))
            nr_class = 0
        prob_estimates = (c_double * nr_class)()
        for xi in x:
            (xi, idx) = gen_svm_nodearray(xi, isKernel=(m.param.kernel_type == PRECOMPUTED))
            label = libsvm.svm_predict_probability(m, xi, prob_estimates)
            values = prob_estimates[:nr_class]
            pred_labels += [label]
            pred_values += [values]
    else:
        if is_prob_model:
            info('Model    supports    probability    estimates,    but    disabled    in    predicton.')
        if (svm_type in (ONE_CLASS, EPSILON_SVR, NU_SVC)):
            nr_classifier = 1
        else:
            nr_classifier = ((nr_class * (nr_class - 1)) // 2)
        dec_values = (c_double * nr_classifier)()
        for xi in x:
            (xi, idx) = gen_svm_nodearray(xi, isKernel=(m.param.kernel_type == PRECOMPUTED))
            label = libsvm.svm_predict_values(m, xi, dec_values)
            if (nr_class == 1):
                values = [1]
            else:
                values = dec_values[:nr_classifier]
            pred_labels += [label]
            pred_values += [values]
   ...

What's a good function header?
assistant
#Here is Python code to print s
.

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