How to use the probscale.probplot function in probscale

fig, axes = plt.subplots(*shape, sharex=True, sharey=True, figsize=figsize)

    scatter_kws = dict(label="", marker=None, linestyle="-")
    common_opts = dict(plottype="qq", problabel="", datalabel="")

    for ax, (label, series) in zip(axes.flat, y_hat.items()):

        scatter_kws["label"] = "original"
        fig = probscale.probplot(x, ax=ax, scatter_kws=scatter_kws, **common_opts)

        scatter_kws["label"] = "target"
        fig = probscale.probplot(y, ax=ax, scatter_kws=scatter_kws, **common_opts)

        scatter_kws["label"] = "corrected"
        fig = probscale.probplot(series, ax=ax, scatter_kws=scatter_kws, **common_opts)
        ax.set_title(label)
        ax.legend()

    [ax.set_xlabel("Standard Normal Quantiles") for ax in axes[-1]]
    [ax.set_ylabel("Temperature [C]") for ax in axes[:, 0]]
    [fig.delaxes(ax) for ax in axes.flat[len(y_hat.keys()) :]]
    fig.tight_layout()

    return fig

def prob_plots(x, y, y_hat, shape=(2, 2), figsize=(8, 8)):

    fig, axes = plt.subplots(*shape, sharex=True, sharey=True, figsize=figsize)

    scatter_kws = dict(label="", marker=None, linestyle="-")
    common_opts = dict(plottype="qq", problabel="", datalabel="")

    for ax, (label, series) in zip(axes.flat, y_hat.items()):

        scatter_kws["label"] = "original"
        fig = probscale.probplot(x, ax=ax, scatter_kws=scatter_kws, **common_opts)

        scatter_kws["label"] = "target"
        fig = probscale.probplot(y, ax=ax, scatter_kws=scatter_kws, **common_opts)

        scatter_kws["label"] = "corrected"
        fig = probscale.probplot(series, ax=ax, scatter_kws=scatter_kws, **common_opts)
        ax.set_title(label)
        ax.legend()

    [ax.set_xlabel("Standard Normal Quantiles") for ax in axes[-1]]
    [ax.set_ylabel("Temperature [C]") for ax in axes[:, 0]]
    [fig.delaxes(ax) for ax in axes.flat[len(y_hat.keys()) :]]
    fig.tight_layout()

    return fig

def prob_plots(x, y, y_hat, shape=(2, 2), figsize=(8, 8)):

    fig, axes = plt.subplots(*shape, sharex=True, sharey=True, figsize=figsize)

    scatter_kws = dict(label="", marker=None, linestyle="-")
    common_opts = dict(plottype="qq", problabel="", datalabel="")

    for ax, (label, series) in zip(axes.flat, y_hat.items()):

        scatter_kws["label"] = "original"
        fig = probscale.probplot(x, ax=ax, scatter_kws=scatter_kws, **common_opts)

        scatter_kws["label"] = "target"
        fig = probscale.probplot(y, ax=ax, scatter_kws=scatter_kws, **common_opts)

        scatter_kws["label"] = "corrected"
        fig = probscale.probplot(series, ax=ax, scatter_kws=scatter_kws, **common_opts)
        ax.set_title(label)
        ax.legend()

    [ax.set_xlabel("Standard Normal Quantiles") for ax in axes[-1]]
    [ax.set_ylabel("Temperature [C]") for ax in axes[:, 0]]
    [fig.delaxes(ax) for ax in axes.flat[len(y_hat.keys()) :]]
    fig.tight_layout()

    return fig