code: def QMCD(**kwargs):
      """QMCD class for Quasi-Monte Carlo Discreprancy thresholder.
  
      Use the quasi-Monte Carlo discreprancy to evaluate a non-parametric means
      to threshold scores generated by the decision_scores where outliers
      are set to any value beyond and percentile or quantile of one minus the
      descreperancy (Note** A discrepancy quantifies the distance between the
      continuous uniform distribution on a hypercube and the discrete uniform
      distribution on distinct sample points).
  
      Parameters
      ----------
  
      method : {'CD', 'WD', 'MD', 'L2-star'}, optional (default='WD')
           Type of discrepancy
  
           - 'CD':      Centered Discrepancy
           - 'WD':      Wrap-around Discrepancy
           - 'MD':      Mix between CD/WD
           - 'L2-star': L2-star discrepancy
  
      lim : {'Q', 'P'}, optional (default='P')
           Filtering method to threshold scores using 1 - discrepancy
  
           - 'Q': Use quntile limiting
           - 'P': Use percentile limiting
      """
  
      from pythresh.thresholds.qmcd import QMCD as QMCD_thres
  
      return QMCD_thres(**kwargs)
  
label_desc: Use image node_id 23 for calling a global function with example usage: QMCD() and returns: QMCD_thres