code: def run_estimators(ns_run, estimator_list, simulate=False):
      
      logw = get_logw(ns_run, simulate=simulate)
      output = np.zeros(len(estimator_list))
      for i, est in enumerate(estimator_list):
          output[i] = est(ns_run, logw=logw)
      return output
docs: Calculates values of list of quantities (such as the Bayesian evidence
      or mean of parameters) for a single nested sampling run.
  
      Parameters
      ----------
      ns_run: dict
          Nested sampling run dict (see data_processing module docstring for more
          details).
      estimator_list: list of functions for estimating quantities from nested
          sampling runs. Example functions can be found in estimators.py. Each
          should have arguments: func(ns_run, logw=None).
      simulate: bool, optional
          See get_logw docstring.
  
      Returns
      -------
      output: 1d numpy array
          Calculation result for each estimator in estimator_list.