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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def plot_violin( data, var_names=None, quartiles=True, credible_interval=0.94, shade=0.35, bw=4.5, sharey=True, figsize=None, textsize=None, ax=None, kwargs_shade=None, ):
 """Plot posterior of traces as violin plot. Notes ----- If multiple chains are provided for a variable they will be combined Parameters data : obj Any object that can be converted to an az.InferenceData object Refer to documentation of az.convert_to_dataset for details var_names: list, optional List of variables to plot (defaults to None, which results in all variables plotted) quartiles : bool, optional Flag for plotting the interquartile range, in addition to the credible_interval*100% intervals. Defaults to True credible_interval : float, optional Credible intervals. Defaults to 0.94. shade : float Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1 (opaque). Defaults to 0 bw : float Bandwidth scaling factor. Should be larger than 0. The higher this number the smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule of thumb (the default rule used by SciPy). figsize : tuple Figure size. If None it will be defined automatically. textsize: int Text size of the point_estimates, axis ticks, and HPD. If None it will be autoscaled based on figsize. sharey : bool Defaults to True, violinplots share a common y-axis scale. ax : matplotlib axes kwargs_shade : dicts, optional Additional keywords passed to `fill_between`, or `barh` to control the shade Returns ------- ax : matplotlib axes """

    data = convert_to_dataset(data, group="posterior")
    var_names = _var_names(var_names, data)

    plotters = list(xarray_var_iter(data, var_names=var_names, combined=True))

    if kwargs_shade is None:
        kwargs_shade = {}

    (figsize, ax_labelsize, _, xt_labelsize, linewidth, _) = _scale_fig_size(
        figsize, textsize, 1, len(plotters)
    )
    ax_labelsize *= 2

    if ax is None:
        fig, ax = plt.subplots(
            1, len(plotters), figsize=figsize, sharey=sharey, constrained_layout=True
        )

    else:
        fig = ax.figure

    ax = np.atleast_1d(ax)

    for axind, (var_name, selection, x) in enumerate(plotters):
        val = x.flatten()
        if val[0].dtype.kind == "i":
            cat_hist(val, shade, ax[axind], **kwargs_shade)
        else:
            _violinplot(val, shade, bw, ax[axind], **kwargs_shade)

        per = np.percentile(val, [25, 75, 50])
        hpd_intervals = hpd(val, credible_interval)

        if quartiles:
            ax[axind].plot([0, 0], per[:2], lw=linewidth * 3, color="k", solid_capstyle="round")
        ax[axind].plot([0, 0], hpd_intervals, lw=linewidth, color="k", solid_capstyle="round")
        ax[axind].plot(0, per[-1], "wo", ms=linewidth * 1.5)

        ax[axind].set_xlabel(make_label(var_name, selection), fontsize=ax_labelsize)
        ax[axind].set_xticks([])
        ax[axind].tick_params(labelsize=xt_labelsize)
        ax[axind].grid(None, axis="x")

    if sharey:
        fig.subplots_adjust(wspace=0)
    else:
        fig.tight_layout()

    return ax