code: def DSN(**kwargs):
      """DSN class for Distance Shift from Normal thresholder.
  
      Use the distance shift from normal to evaluate a non-parametric means
      to threshold scores generated by the decision_scores where outliers
      are set to any value beyond the distance calculated by the selected
      metric.
  
      Parameters
      ----------
  
      metric : {'JS', 'WS', 'ENG', 'BHT', 'HLL', 'HI', 'LK', 'LP', 'MAH', 'TMT', 'RES', 'KS', 'INT', 'MMD'}, optional (default='MAH')
           Metric to use for distance computation
  
           - 'JS':  Jensen-Shannon distance
           - 'WS':  Wasserstein or Earth Movers distance
           - 'ENG': Energy distance
           - 'BHT': Bhattacharyya distance
           - 'HLL': Hellinger distance
           - 'HI':  Histogram intersection distance
           - 'LK':  Lukaszyk-Karmowski metric for normal distributions
           - 'LP':  Levy-Prokhorov metric
           - 'MAH': Mahalanobis distance
           - 'TMT': Tanimoto distance
           - 'RES': Studentized residual distance
           - 'KS':  Kolmogorov-Smirnov distance
           - 'INT': Weighted spline interpolated distance
           - 'MMD': Maximum Mean Discrepancy distance
  
      random_state : int, optional (default=1234)
           Random seed for the normal distribution. Can also be set to None.
  
      """
  
      from pythresh.thresholds.dsn import DSN as DSN_thres
  
      return DSN_thres(**kwargs)
  
label_desc: Use image node_id 8 for calling a global function with example usage: DSN() and returns: DSN_thres