code: def run_std_bootstrap(ns_run, estimator_list, **kwargs):
      
      bs_values = run_bootstrap_values(ns_run, estimator_list, **kwargs)
      stds = np.zeros(bs_values.shape[0])
      for j, _ in enumerate(stds):
          stds[j] = np.std(bs_values[j, :], ddof=1)
      return stds
docs: Uses bootstrap resampling to calculate an estimate of the
      standard deviation of the distribution of sampling errors (the
      uncertainty on the calculation) for a single nested sampling run.
  
      For more details about bootstrap resampling for estimating sampling
      errors see 'Sampling errors in nested sampling parameter estimation'
      (Higson et al. 2018).
  
      Parameters
      ----------
      ns_run: dict
          Nested sampling run dictionary.
      estimator_list: list of functions for estimating quantities (such as the
          Bayesian evidence or mean of parameters) from nested sampling runs.
          Example functions can be found in estimators.py. Each should have
          arguments: func(ns_run, logw=None)
      kwargs: dict
          kwargs for run_bootstrap_values
  
      Returns
      -------
      output: 1d numpy array
          Sampling error on calculation result for each estimator in
          estimator_list.