code: def _compute_mu_star_confidence(
      elementary_effects: np.ndarray,
      num_vars: int,
      num_resamples: int,
      conf_level: float,
  ) -> np.ndarray:
      """Computes the confidence intervals for the mu_star variable.
  
      Uses bootstrapping where the elementary effects are resampled with
      replacement to produce a histogram of resampled mu_star metrics.
      This resample is used to produce a confidence interval.
  
      Parameters
      ----------
      elementary_effects : np.array
          Elementary effects for each parameter
      num_vars: int
          Number of problem's variables
      num_resamples: int
          Number of resamples
      conf_level: float
          Confidence level
  
      Returns
      -------
      mu_star_conf: np.ndarray
          Confidence intervals for the mu_star variable
      """
      if not 0 < conf_level < 1:
          raise ValueError("Confidence level must be between 0-1.")
  
      mu_star_conf = []
      for j in range(num_vars):
          ee = elementary_effects[j, :]
          resample_index = np.random.randint(
              len(ee), size=(num_resamples, len(ee))
          )
          ee_resampled = ee[resample_index]
  
          # Compute average of the absolute values over each of the resamples
          mu_star_resampled = np.average(np.abs(ee_resampled), axis=1)
  
          mu_star_conf.append(
              norm.ppf(0.5 + conf_level / 2)
              * mu_star_resampled.std(ddof=1)
          )
  
      mu_star_conf = np.asarray(mu_star_conf)
  
      return mu_star_conf
  
imports: from typing import Dict, List
  import numpy
  from scipy.stats import norm
  from .None import common_args
  from ..util import read_param_file, compute_groups_matrix, ResultDict, _define_problem_with_groups, _compute_delta, _check_groups
label_desc: Use image node_id 12 for calling a global function with example usage: _compute_mu_star_confidence(elementary_effects, num_vars, num_resamples, conf_level) and returns: mu_star_conf