code: def predict(self, X):
      """Perform regression on samples in X.
  
      For an one-class model, +1 (inlier) or -1 (outlier) is returned.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          For kernel="precomputed", the expected shape of X is
          (n_samples_test, n_samples_train).
  
      Returns
      -------
      y_pred : array, shape (n_samples,)
      """
      check_is_fitted(self)
      _break_ties = getattr(self, "break_ties", False)
      if _break_ties and self.decision_function_shape == "ovo":
          raise ValueError(
              "break_ties must be False when "
              "decision_function_shape is 'ovo'"
          )
  
      _patching_status = PatchingConditionsChain(
          "sklearn.svm.SVC.predict"
      )
      _dal_ready = _patching_status.and_conditions(
          [
              (not _break_ties, "Breaking ties is not supported."),
              (
                  self.decision_function_shape != "ovr",
                  "'ovr' decision function shape is not supported.",
              ),
              (len(self.classes_) <= 2, "Number of classes > 2."),
          ],
          conditions_merging=any,
      )
      _patching_status.write_log()
      if not _dal_ready:
          y = np.argmax(self.decision_function(X), axis=1)
      else:
          X = self._validate_for_predict(X)
          _dal_ready = _patching_status.and_conditions(
              [
                  (
                      getattr(self, "_daal_fit", False)
                      and hasattr(self, "daal_model_"),
                      "oneDAL model was not trained.",
                  )
              ]
          )
          if _dal_ready:
              y = _daal4py_predict(self, X)
          else:
              predict_func = (
                  self._sparse_predict
                  if self._sparse
                  else self._dense_predict
              )
              y = predict_func(X)
  
      return self.classes_.take(np.asarray(y, dtype=np.intp))
  
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 19 for calling a global function with example usage: predict(X) and returns: self