code: def _daal4py_predict(self, X, is_decision_function=False):
      X_fptype = getFPType(X)
      num_classes = len(self.classes_)
  
      kf = _daal4py_kf(
          self.kernel,
          X_fptype,
          gamma=self._gamma,
          is_sparse=sp.isspmatrix(X),
      )
  
      svm_predict = daal4py.svm_prediction(
          fptype=X_fptype, method="defaultDense", kernel=kf
      )
      if num_classes == 2:
          alg = svm_predict
      else:
          result_to_compute = (
              "computeDecisionFunction"
              if is_decision_function
              else "computeClassLabels"
          )
          alg = daal4py.multi_class_classifier_prediction(
              nClasses=num_classes,
              fptype=X_fptype,
              pmethod="voteBased",
              tmethod="oneAgainstOne",
              resultsToEvaluate=result_to_compute,
              prediction=svm_predict,
          )
  
      predictionRes = alg.compute(X, self.daal_model_)
      if not is_decision_function or num_classes == 2:
          res = predictionRes.prediction
          res = res.ravel()
      else:
          res = -predictionRes.decisionFunction
  
      if num_classes == 2 and not is_decision_function:
          # Convert from Intel(R) oneAPI Data Analytics Library format back to
          # original classes
          np.greater(res, 0, out=res)
  
      return res
  
imports: from __future__ import print_function
  import warnings
  import numpy
  import sklearn.svm._base
  import sklearn.svm._classes
  from scipy import sparse
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.exceptions import NotFittedError
  from sklearn.model_selection import StratifiedKFold
  from sklearn.utils import check_random_state
  from sklearn.utils.multiclass import _ovr_decision_function
  from sklearn.utils.validation import _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
  from sklearn import __version__
  import daal4py
  from .._utils import PatchingConditionsChain, getFPType, make2d, sklearn_check_version
label_desc: Use image node_id 18 for calling a global function with example usage: _daal4py_predict(X, is_decision_function) and returns: res