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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_rebit_prior(prior, rebit_axes=REBIT_AXES, n_samples=2000, true_state=None, true_size=250, force_mean=None, legend=True, mean_color_index=2 ):
 """ Plots rebit states drawn from a given prior. :param qinfer.tomography.DensityOperatorDistribution prior: Distribution over rebit states to plot. :param list rebit_axes: List containing indices for the :math:`x` and :math:`z` axes. :param int n_samples: Number of samples to draw from the prior. :param np.ndarray true_state: State to be plotted as a "true" state for comparison. """

    pallette = plt.rcParams['axes.color_cycle']

    plot_rebit_modelparams(prior.sample(n_samples),
        c=pallette[0],
        label='Prior',
        rebit_axes=rebit_axes
    )

    if true_state is not None:
        plot_rebit_modelparams(true_state,
            c=pallette[1],
            label='True', marker='*', s=true_size,
            rebit_axes=rebit_axes
        )

    if hasattr(prior, '_mean') or force_mean is not None:
        mean = force_mean if force_mean is not None else prior._mean
        plot_rebit_modelparams(
            prior._basis.state_to_modelparams(mean)[None, :],
            edgecolors=pallette[mean_color_index], s=250, facecolors='none', linewidth=3,
            label='Mean',
            rebit_axes=rebit_axes
        )

    plot_decorate_rebits(prior.basis,
        rebit_axes=rebit_axes
    )
    if legend:
        plt.legend(loc='lower left', ncol=3, scatterpoints=1)