code: def _ovr_decision_function(self, predictions, confidences, n_classes):
      n_samples = predictions.shape[0]
      votes = np.zeros((n_samples, n_classes))
      sum_of_confidences = np.zeros((n_samples, n_classes))
  
      k = 0
      for i in range(n_classes):
          for j in range(i + 1, n_classes):
              sum_of_confidences[:, i] -= confidences[:, k]
              sum_of_confidences[:, j] += confidences[:, k]
              votes[predictions[:, k] == 0, i] += 1
              votes[predictions[:, k] == 1, j] += 1
              k += 1
  
      transformed_confidences = sum_of_confidences / (
          3 * (np.abs(sum_of_confidences) + 1)
      )
      return votes + transformed_confidences
  
imports: from abc import ABCMeta, abstractmethod
  from enum import Enum
  from numbers import Number, Real
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import sklearn_check_version
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin, RegressorMixin
  from ..common._policy import _get_policy
  from ..datatypes import from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _column_or_1d, _validate_targets
label_desc: Use image node_id 9 for calling the BaseSVM obj's underlying member method code with example usage: obj._ovr_decision_function(predictions, confidences, n_classes) and returns: unknown