code: def simulated_v_estimated_qq_plots(
      model,
      learner,
      show=True,
  ):
      fig, ax_list = plt.subplots(2, 1, figsize=(10, 6))
      timestamps = model.timestamps
      end_time = model.end_time
      learner.qq_plots(events=timestamps, end_time=end_time, ax=ax_list)
      model.store_compensator_values()
      qq_plots(model, ax=ax_list)
  
      # Enhance plot
      for ax in ax_list:
          # Set labels to both plots
          ax.lines[0].set_label("estimated")
          ax.lines[2].set_label("original")
  
          # Change original intensity style
          ax.lines[2].set_alpha(0.6)
          ax.lines[3].set_alpha(0.6)
  
          ax.lines[2].set_markerfacecolor("orange")
          ax.lines[2].set_markeredgecolor("orange")
  
          ax.legend()
  
      if show:
          fig.tight_layout()
          plt.show()
      return fig
  
imports: import matplotlib.pyplot
  from tick.hawkes import SimuHawkesSumExpKernels, HawkesSumExpKern
  from tick.hawkes import SimuHawkesExpKernels
  from tick.hawkes import HawkesExpKern
  from tick.plot import plot_point_process, qq_plots
label_desc: Use image node_id 1 for calling a global function with example usage: simulated_v_estimated_qq_plots(model, learner, show) and returns: fig