code: def loglogs_plot(
      cls,
      loc=None,
      iloc=None,
      show_censors=False,
      censor_styles=None,
      ax=None,
      **kwargs,
  ):
      """
      Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function.
      """
      from matplotlib import pyplot as plt
  
      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 = {}
      if ax is None:
          ax = plt.gca()
      kwargs["ax"] = ax
      set_kwargs_color(kwargs)
      set_kwargs_drawstyle(kwargs)
  
      dataframe_slicer = create_dataframe_slicer(
          iloc, loc, cls.timeline
      )
  
      # plot censors
      colour = kwargs["color"]
  
      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)
          ax.plot(
              np.log(times),
              loglog(v),
              linestyle="None",
              color=colour,
              **cs,
          )
      # plot estimate
      sliced_estimates = dataframe_slicer(
          loglog(cls.survival_function_)
      )
      sliced_estimates["log(timeline)"] = np.log(sliced_estimates.index)
      sliced_estimates.plot(x="log(timeline)", **kwargs)
      ax.set_xlabel("log(timeline)")
      ax.set_ylabel("log(-log(survival_function_))")
  
      return ax
  
imports: import warnings
  from typing import Union, Optional, Iterable
  from scipy import stats
  import pandas
  import numpy
  from lifelines.utils import coalesce, CensoringType, _group_event_table_by_intervals
label_desc: Use image node_id 18 for calling a global function with example usage: loglogs_plot(cls, loc, iloc, show_censors, censor_styles, ax) and returns: ax, np