human
I wrote the below code

    def create_axes_grid(figsize, traces):
        l_trace = len(traces)
        if (l_trace == 1):
            (fig, ax) = plt.subplots(figsize=figsize)
        else:
            n = np.ceil((l_trace / 2.0)).astype(int)
            if (figsize is None):
                figsize = (12, (n * 2.5))
            (fig, ax) = plt.subplots(n, 2, figsize=figsize)
            ax = ax.reshape((2 * n))
            if ((l_trace % 2) == 1):
                ax[(-1)].set_axis_off()
                ax = ax[:(-1)]
        return (fig, ax)
    def get_trace_dict(tr, varnames):
        traces = OrderedDict()
        for v in varnames:
            vals = tr.get_values(v, combine=True, squeeze=True)
            if (vals.ndim > 1):
                vals_flat = vals.reshape(vals.shape[0], (-1)).T
                for (i, vi) in enumerate(vals_flat):
                    traces['_'.join([v, str(i)])] = vi
            else:
                traces[v] = vals
        return traces
    if isinstance(trace, np.ndarray):
        if (figsize is None):
            figsize = (6, 2)
        if (ax is None):
            (fig, ax) = plt.subplots(figsize=figsize)
        plot_posterior_op(transform(trace), ax=ax, kde_plot=kde_plot, point_estimate=point_estimate, round_to=round_to, alpha_level=alpha_level, ref_val=ref_val, rope=rope, text_size=text_size, **kwargs)
    else:
        if (varnames is None):
            varnames = get_default_varnames(trace.varnames, plot_transformed)
        trace_dict = get_trace_dict(trace, varnames)
        if (ax is None):
            (fig, ax) = create_axes_grid(figsize, trace_dict)
        var_num = len(trace_dict)
        if (ref_val is None):
            ref_val = ([None] * var_num)
        elif np.isscalar(ref_val):
            ref_val = [ref_val for _ in range(var_num)]
        if (rope is None):
            rope = ([None] * var_num)
        elif (np.ndim(rope) == 1):
            rope = ([rope] * var_num)
        for (idx, (a, v)) in enumerate(zip(np.atleast_1d(ax), trace_dict)):
            tr_values =...

What's a good function header?

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

def plot_posterior(trace, varnames=None, transform=identity_transform, figsize=None, text_size=16, alpha_level=0.05, round_to=3, point_estimate='mean', rope=None, ref_val=None, kde_plot=False, plot_transformed=False, ax=None, **kwargs):