code: def plot_loglogs(cls, loc=None, iloc=None, show_censors=False, censor_styles=None, **kwargs):
      
  
      def loglog(s):
          return np.log(-np.log(s))
  
      if (loc is not None) and (iloc is not None):
          raise ValueError("Cannot set both loc and iloc in call to .plot().")
  
      if censor_styles is None:
          censor_styles = {}
  
      set_kwargs_ax(kwargs)
      set_kwargs_color(kwargs)
      set_kwargs_drawstyle(kwargs)
      kwargs["logx"] = True
  
      dataframe_slicer = create_dataframe_slicer(iloc, loc)
  
      # plot censors
      ax = kwargs["ax"]
      colour = kwargs["c"]
  
      if show_censors and cls.event_table["censored"].sum() > 0:
          cs = {"marker": "+", "ms": 12, "mew": 1}
          cs.update(censor_styles)
          times = dataframe_slicer(cls.event_table.loc[(cls.event_table["censored"] > 0)]).index.values.astype(float)
          v = cls.predict(times)
          # don't log times, as Pandas will take care of all log-scaling later.
          ax.plot(times, loglog(v), linestyle="None", color=colour, **cs)
  
      # plot estimate
      dataframe_slicer(loglog(cls.survival_function_)).plot(**kwargs)
      ax.set_xlabel("log(timeline)")
      ax.set_ylabel("log(-log(survival_function_))")
      return ax
docs: Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function.