class_docstr: An empirical distribution function produced by `scipy.stats.ecdf`
  
  Attributes
  ----------
  quantiles : ndarray
      The unique values of the sample from which the
      `EmpiricalDistributionFunction` was estimated.
  probabilities : ndarray
      The point estimates of the cumulative distribution function (CDF) or
      its complement, the survival function (SF), corresponding with
      `quantiles`.
code: def _loglog_ci(self, confidence_level):
      sf, d, n = self._sf, self._d, self._n
  
      with np.errstate(divide="ignore", invalid="ignore"):
          var = 1 / np.log(sf) ** 2 * np.cumsum(d / (n * (n - d)))
  
      se = np.sqrt(var)
      z = special.ndtri(1 / 2 + confidence_level / 2)
  
      with np.errstate(divide="ignore"):
          lnl_points = np.log(-np.log(sf))
  
      z_se = z * se
      low = np.exp(-np.exp(lnl_points + z_se))
      high = np.exp(-np.exp(lnl_points - z_se))
      if self._kind == "cdf":
          low, high = 1 - high, 1 - low
  
      return low, high
  
imports: from __future__ import annotations
  from dataclasses import dataclass, field
  from typing import TYPE_CHECKING
  import warnings
  import numpy
  from scipy import special, interpolate, stats
  from scipy.stats._censored_data import CensoredData
  from scipy.stats._common import ConfidenceInterval
label_desc: Use image node_id 6 for calling the EmpiricalDistributionFunction obj's underlying member method code with example usage: obj._loglog_ci(confidence_level) and returns: low,  high