code: def CPD(**kwargs):
      """CPD class for Change Point Detection thresholder.
  
      Use change point detection to find a non-parametric means
      to threshold scores generated by the decision_scores where outliers
      are set to any value beyond the detected change point.
  
      Parameters
      ----------
  
      method : {'Dynp', 'KernelCPD', 'Binseg', 'BottomUp'}, optional (default='Dynp')
           Method for change point detection
  
           - 'Dynp':      Dynamic programming (optimal minimum sum of errors per partition)
           - 'KernelCPD': RBF kernel function (optimal minimum sum of errors per partition)
           - 'Binseg':    Binary segmentation
           - 'BottomUp':  Bottom-up segmentation
  
      transform : {'cdf', 'kde'}, optional (default='cdf')
           Data transformation method prior to fit
  
           - 'cdf': Use the cumulative distribution function
           - 'kde': Use the kernel density estimation
  
      """
  
      from pythresh.thresholds.cpd import CPD as CPD_thres
  
      return CPD_thres(**kwargs)
  
label_desc: Use image node_id 6 for calling a global function with example usage: CPD() and returns: CPD_thres