code: 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
      ):
      
      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)
docs: 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.