code: def OCSVM(**kwargs):
      """OCSVM class for One-Class Support Vector Machine thresholder.
  
      Use a one-class svm to evaluate a non-parametric means
      to threshold scores generated by the decision_scores where outliers
      are determined by the one-class svm using a polynomial kernel
      with the polynomial degree either set or determined by regression
      internally.
  
      Parameters
      ----------
  
      model : {'poly', 'sgd'}, optional (default='sgd')
          OCSVM model to apply
  
          - 'poly':  Use a polynomial kernel with a regular OCSVM
          - 'sgd':   Used the Additive Chi2 kernel approximation with a SGDOneClassSVM
  
      degree : int, optional (default='auto')
          Polynomial degree to use for the one-class svm.
          Default 'auto' finds the optimal degree with linear regression
  
      gamma : float, optional (default='auto')
          Kernel coefficient for polynomial fit for the one-class svm.
          Default 'auto' uses 1 / n_features
  
      criterion : {'aic', 'bic'}, optional (default='bic')
          regression performance metric. AIC is the Akaike Information Criterion,
          and BIC is the Bayesian Information Criterion. This only applies
          when degree is set to 'auto'
  
      nu : float, optional (default='auto')
          An upper bound on the fraction of training errors and a lower bound
          of the fraction of support vectors. Default 'auto' sets nu as the ratio
          between the any point that is less than or equal to the median plus
          the absolute difference between the mean and geometric mean over the
          the number of points in the entire dataset
  
      tol : float, optional (default=1e-3)
          The stopping criterion for the one-class svm
  
      random_state : int, optional (default=1234)
          Random seed for the SVM's data sampling. Can also be set to None.
  
      """
  
      from pythresh.thresholds.ocsvm import OCSVM as OCSVM_thres
  
      return OCSVM_thres(**kwargs)
  
label_desc: Use image node_id 22 for calling a global function with example usage: OCSVM() and returns: OCSVM_thres