code: def CLUST(**kwargs):
      """CLUST class for clustering type thresholders.
  
      Use the clustering methods to evaluate a non-parametric means to
      threshold scores generated by the decision_scores where outliers
      are set to any value not labelled as part of the main cluster.
  
      Parameters
      ----------
  
      method : {'agg', 'birch', 'bang', 'bgm', 'bsas', 'dbscan', 'ema', 'kmeans', 'mbsas', 'mshift', 'optics', 'somsc', 'spec', 'xmeans'}, optional (default='spec')
           Clustering method
  
           - 'agg':    Agglomerative
           - 'birch':  Balanced Iterative Reducing and Clustering using Hierarchies
           - 'bang':   BANG
           - 'bgm':    Bayesian Gaussian Mixture
           - 'bsas':   Basic Sequential Algorithmic Scheme
           - 'dbscan': Density-based spatial clustering of applications with noise
           - 'ema':    Expectation-Maximization clustering algorithm for Gaussian Mixture Model
           - 'kmeans': K-means
           - 'mbsas':  Modified Basic Sequential Algorithmic Scheme
           - 'mshift': Mean shift
           - 'optics': Ordering Points To Identify Clustering Structure
           - 'somsc':  Self-organized feature map
           - 'spec':   Clustering to a projection of the normalized Laplacian
           - 'xmeans': X-means
  
      random_state : int, optional (default=1234)
           Random seed for the BayesianGaussianMixture clustering (method='bgm'). Can
           also be set to None.
      """
  
      from pythresh.thresholds.clust import CLUST as CLUST_thres
  
      return CLUST_thres(**kwargs)
  
label_desc: Use image node_id 5 for calling a global function with example usage: CLUST() and returns: CLUST_thres