class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
docstr: Evaluate the decision function for the samples in X.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      The input samples.
  
  Returns
  -------
  X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2)
      Returns the decision function of the sample for each class
      in the model.
      If decision_function_shape='ovr', the shape is (n_samples,
      n_classes).
  
  Notes
  -----
  If decision_function_shape='ovo', the function values are proportional
  to the distance of the samples X to the separating hyperplane. If the
  exact distances are required, divide the function values by the norm of
  the weight vector (``coef_``). See also `this question
  <https://stats.stackexchange.com/questions/14876/
  interpreting-distance-from-hyperplane-in-svm>`_ for further details.
  If decision_function_shape='ovr', the decision function is a monotonic
  transformation of ovo decision function.
code: def decision_function(self, X):
      """Evaluate the decision function for the samples in X.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          The input samples.
  
      Returns
      -------
      X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2)
          Returns the decision function of the sample for each class
          in the model.
          If decision_function_shape='ovr', the shape is (n_samples,
          n_classes).
  
      Notes
      -----
      If decision_function_shape='ovo', the function values are proportional
      to the distance of the samples X to the separating hyperplane. If the
      exact distances are required, divide the function values by the norm of
      the weight vector (``coef_``). See also `this question
      <https://stats.stackexchange.com/questions/14876/
      interpreting-distance-from-hyperplane-in-svm>`_ for further details.
      If decision_function_shape='ovr', the decision function is a monotonic
      transformation of ovo decision function.
      """
      dec = self._decision_function(X)
      if (
          self.decision_function_shape == "ovr"
          and len(self.classes_) > 2
      ):
          return _ovr_decision_function(
              dec < 0, -dec, len(self.classes_)
          )
      return dec
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, ClassifierMixin, _fit_context
  from ..exceptions import ConvergenceWarning, NotFittedError
  from ..preprocessing import LabelEncoder
  from ..utils import check_array, check_random_state, column_or_1d, compute_class_weight
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import safe_sparse_dot
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import _ovr_decision_function, check_classification_targets
  from ..utils.validation import _check_large_sparse, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
  from .None import _liblinear
  from .None import _libsvm
  from .None import _libsvm_sparse
label_desc: Use image node_id 3 for calling the BaseSVC obj's underlying member method code with example usage: obj.decision_function(X) and returns: dec, _ovr_decision_function