human
I wrote the below code

    def time_now(*args, **kwds):
        now = datetime.datetime.now()
        return now.strftime('%Y-%m-%d    %H:%M')
    info = OrderedDict()
    info['Model:'] = (lambda x: x.model.__class__.__name__)
    info['Model    Family:'] = (lambda x: x.family.__class.__name__)
    info['Link    Function:'] = (lambda x: x.family.link.__class__.__name__)
    info['Dependent    Variable:'] = (lambda x: x.model.endog_names)
    info['Date:'] = time_now
    info['No.    Observations:'] = (lambda x: ('%#6d' % x.nobs))
    info['Df    Model:'] = (lambda x: ('%#6d' % x.df_model))
    info['Df    Residuals:'] = (lambda x: ('%#6d' % x.df_resid))
    info['Converged:'] = (lambda x: x.mle_retvals['converged'])
    info['No.    Iterations:'] = (lambda x: x.mle_retvals['iterations'])
    info['Method:'] = (lambda x: x.method)
    info['Norm:'] = (lambda x: x.fit_options['norm'])
    info['Scale    Est.:'] = (lambda x: x.fit_options['scale_est'])
    info['Cov.    Type:'] = (lambda x: x.fit_options['cov'])
    info['R-squared:'] = (lambda x: ('%#8.3f' % x.rsquared))
    info['Adj.    R-squared:'] = (lambda x: ('%#8.3f' % x.rsquared_adj))
    info['Pseudo    R-squared:'] = (lambda x: ('%#8.3f' % x.prsquared))
    info['AIC:'] = (lambda x: ('%8.4f' % x.aic))
    info['BIC:'] = (lambda x: ('%8.4f' % x.bic))
    info['Log-Likelihood:'] = (lambda x: ('%#8.5g' % x.llf))
    info['LL-Null:'] = (lambda x: ('%#8.5g' % x.llnull))
    info['LLR    p-value:'] = (lambda x: ('%#8.5g' % x.llr_pvalue))
    info['Deviance:'] = (lambda x: ('%#8.5g' % x.deviance))
    info['Pearson    chi2:'] = (lambda x: ('%#6.3g' % x.pearson_chi2))
    info['F-statistic:'] = (lambda x: ('%#8.4g' % x.fvalue))
    info['Prob    (F-statistic):'] = (lambda x: ('%#6.3g' % x.f_pvalue))
    info['Scale:'] = (lambda x: ('%#8.5g' % x.scale))
    out = OrderedDict()
    for (key, func) in iteritems(info):
        try:
            out[key] = func(results)
        except (AttributeError, KeyError, NotImplementedError):
            pass
    return out

What's a good function header?

=====
Here's a good function header?
assistant
#Here's a good function header.

def summary_model(results):