class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
code: def _check_proba(self):
      if not self.probability:
          raise AttributeError(
              "predict_proba is not available when "
              " probability=False"
          )
      if self._impl not in ("c_svc", "nu_svc"):
          raise AttributeError(
              "predict_proba only implemented for SVC" " and NuSVC"
          )
  
imports: from __future__ import print_function
  import numpy
  import scipy.sparse
  import warnings
  from abc import ABCMeta, abstractmethod
  from .None import libsvm, liblinear
  from .None import libsvm_sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..preprocessing import LabelEncoder
  from ..utils.multiclass import _ovr_decision_function
  from ..utils import check_array, check_consistent_length, check_random_state
  from ..utils import column_or_1d, check_X_y
  from ..utils import compute_class_weight, deprecated
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..externals import six
  from ..exceptions import ChangedBehaviorWarning
  from ..exceptions import ConvergenceWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 5 for calling the BaseSVC obj's underlying member method code with example usage: obj._check_proba() without return types