code: def decision_function(self, X):
      X = self._validate_for_predict(X)
      n_binary_model = (c_int * 1)()
      thundersvm.get_n_binary_models(
          c_void_p(self.model), n_binary_model
      )
      self.n_binary_model = n_binary_model[0]
      if not (self._impl in ["c_svc", "nu_svc", "one_class"]):
          print("Not support decision_function!")
          return
      if self._sparse:
          dec_func = self._sparse_decision_function(X)
      else:
          dec_func = self._dense_decision_function(X)
      return dec_func
  
imports: from sklearn.base import BaseEstimator
  from sklearn.base import RegressorMixin, ClassifierMixin
  import numpy
  import scipy.sparse
  from sklearn.utils import check_X_y, column_or_1d, check_array
  from sklearn.utils.validation import _num_samples
  from ctypes import *
  from os import path, curdir
  from sys import platform
label_desc: Use image node_id 11 for calling the SvmModel obj's underlying member method code with example usage: obj.decision_function(X) and returns: dec_func