code: def _daal4py_predict(self, X):
      X_fptype = getFPType(X)
      num_classes = len(self.classes_)
  
      kf = _daal4py_kf(self.kernel, X_fptype, gamma=self._gamma)
  
      svm_predict = daal4py.svm_prediction(
          fptype=X_fptype, method="defaultDense", kernel=kf
      )
      if num_classes == 2:
          alg = svm_predict
      else:
          alg = daal4py.multi_class_classifier_prediction(
              nClasses=num_classes,
              fptype=X_fptype,
              maxIterations=int(
                  self.max_iter if self.max_iter > 0 else 2**30
              ),
              accuracyThreshold=float(self.tol),
              pmethod="voteBased",
              tmethod="oneAgainstOne",
              prediction=svm_predict,
          )
  
      predictionRes = alg.compute(X, self.daal_model_)
  
      res = predictionRes.prediction
      res = res.ravel()
  
      if num_classes == 2:
          # 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.utils import check_random_state, check_X_y
  from sklearn.utils.validation import _check_sample_weight, check_is_fitted
  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) and returns: res