code: def FILTER(**kwargs):
      """FILTER class for Filtering based thresholders.
  
      Use the filtering based methods to evaluate a non-parametric means
      to threshold scores generated by the decision_scores where outliers
      are set to any value beyond the maximum filter value.
      See :cite:`hashemi2019filter` for details.
  
      Parameters
      ----------
  
      method : {'gaussian', 'savgol', 'hilbert', 'wiener', 'medfilt', 'decimate','detrend', 'resample'}, optional (default='savgol')
           Method to filter the scores
  
           - 'gaussian': use a gaussian based filter
           - 'savgol':   use the savgol based filter
           - 'hilbert':  use the hilbert based filter
           - 'wiener':   use the wiener based filter
           - 'medfilt:   use a median based filter
           - 'decimate': use a decimate based filter
           - 'detrend':  use a detrend based filter
           - 'resample': use a resampling based filter
  
  
      sigma : int, optional (default='auto')
           Variable specific to each filter type, default sets sigma to len(scores)*np.std(scores)
  
           - 'gaussian': standard deviation for Gaussian kernel
           - 'savgol':   savgol filter window size
           - 'hilbert':  number of Fourier components
           - 'medfilt:   kernel size
           - 'decimate': downsampling factor
           - 'detrend':  number of break points
           - 'resample': resampling window size
      """
  
      from pythresh.thresholds.filter import FILTER as FILTER_thres
  
      return FILTER_thres(**kwargs)
  
label_desc: Use image node_id 11 for calling a global function with example usage: FILTER() and returns: FILTER_thres