code: def META(**kwargs):
      """META class for Meta-modelling thresholder.
  
      Use a trained meta-model to evaluate a non-parametric means
      to threshold scores generated by the decision_scores where outliers
      are set based on the trained meta-model classifier.
  
      Parameters
      ----------
  
      method : {'LIN', 'GNB', 'GNBC', 'GNBM'}, optional (default='GNBM')
          select
  
          - 'LIN':  RidgeCV trained linear classifier meta-model on true labels
          - 'GNB':  Gaussian Naive Bayes trained classifier meta-model on true labels
          - 'GNBC': Gaussian Naive Bayes trained classifier meta-model on best contamination
          - 'GNBM': Gaussian Naive Bayes multivariate trained classifier meta-model
  
      """
  
      from pythresh.thresholds.meta import META as META_thres
  
      return META_thres(**kwargs)
  
label_desc: Use image node_id 19 for calling a global function with example usage: META() and returns: META_thres