code: def plot_cumulative_density(self, **kwargs):
          
          return _plot_estimate(
              self,
              estimate=self.cumulative_density_,
              confidence_intervals=self.confidence_interval_cumulative_density_,
              **kwargs
          )
docs: Plots a pretty figure of {0}.{1}
  
          Matplotlib plot arguments can be passed in inside the kwargs, plus
  
          Parameters
          -----------
          show_censors: bool
              place markers at censorship events. Default: False
          censor_styles: bool
              If show_censors, this dictionary will be passed into the plot call.
          ci_alpha: bool
              the transparency level of the confidence interval. Default: 0.3
          ci_force_lines: bool
              force the confidence intervals to be line plots (versus default shaded areas). Default: False
          ci_show: bool
              show confidence intervals. Default: True
          ci_legend: bool
              if ci_force_lines is True, this is a boolean flag to add the lines' labels to the legend. Default: False
          at_risk_counts: bool
              show group sizes at time points. See function ``add_at_risk_counts`` for details. Default: False
          loc: slice
              specify a time-based subsection of the curves to plot, ex:
  
              >>> model.plot(loc=slice(0.,10.))
  
              will plot the time values between t=0. and t=10.
          iloc: slice
              specify a location-based subsection of the curves to plot, ex:
  
              >>> model.plot(iloc=slice(0,10))
  
              will plot the first 10 time points.
          invert_y_axis: bool
              boolean to invert the y-axis, useful to show cumulative graphs instead of survival graphs. (Deprecated, use ``plot_cumulative_density()``)
  
          Returns
          -------
          ax:
              a pyplot axis object