code: def predict_proba(self):
      """Compute probabilities of possible outcomes for samples in X.
  
      The model need to have probability information computed at training
      time: fit with attribute `probability` set to True.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          For kernel="precomputed", the expected shape of X is
          [n_samples_test, n_samples_train]
  
      Returns
      -------
      T : ndarray of shape (n_samples, n_classes)
          Returns the probability of the sample for each class in
          the model. The columns correspond to the classes in sorted
          order, as they appear in the attribute :term:`classes_`.
  
      Notes
      -----
      The probability model is created using cross validation, so
      the results can be slightly different than those obtained by
      predict. Also, it will produce meaningless results on very small
      datasets.
      """
  
      self._check_proba()
      _patching_status = PatchingConditionsChain(
          "sklearn.svm.SVC.predict_proba"
      )
      _dal_ready = _patching_status.and_conditions(
          [
              (
                  getattr(self, "_daal_fit", False),
                  "oneDAL model was not trained.",
              )
          ]
      )
      _patching_status.write_log()
      if _dal_ready:
          algo = self._daal4py_predict_proba
      else:
          algo = self._predict_proba
      return algo
  
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 21 for calling a global function with example usage: predict_proba() and returns: algo