code: def predict_proba(self, X):
      if self.estimator is None:
          raise NotImplementedError()
      decision = self.estimator.decision_function(X)
      return softmax(decision)
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/libsvm_svc.py
imports: from typing import Optional
  import resource
  import sys
  from ConfigSpace.conditions import EqualsCondition, InCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.implementations.util import softmax
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 4 for calling the LibSVM_SVC obj's underlying member method code with example usage: obj.predict_proba(X) and returns: softmax