code: def plot_roc_curve(self, on, bootstrap_samples=100, ax=None, **kwargs):
          
          plot_col, df = self.as_dataframe(on, return_cols=True, **kwargs)
          df = filter_not_null(df, "benefit")
          df = filter_not_null(df, plot_col)
          df.benefit = df.benefit.astype(bool)
          return roc_curve_plot(df, plot_col, "benefit", bootstrap_samples, ax=ax)
docs: Plot an ROC curve for benefit and a given variable
  
          Parameters
          ----------
          on : str or function or list or dict
              See `cohort.load.as_dataframe`
          bootstrap_samples : int, optional
              Number of boostrap samples to use to compute the AUC
          ax : Axes, default None
              Axes to plot on
  
          Returns
          -------
          (mean_auc_score, plot): (float, matplotlib plot)
              Returns the average AUC for the given predictor over `bootstrap_samples`
              and the associated ROC curve