code: def predict_proba(self, features):
          
          if not self.fitted_pipeline_:
              raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.')
          else:
              if not (hasattr(self.fitted_pipeline_, 'predict_proba')):
                  raise RuntimeError('The fitted pipeline does not have the predict_proba() function.')
  
              features = self._check_dataset(features, target=None, sample_weight=None)
  
              return self.fitted_pipeline_.predict_proba(features)
docs: Use the optimized pipeline to estimate the class probabilities for a feature set.
  
          Parameters
          ----------
          features: array-like {n_samples, n_features}
              Feature matrix of the testing set
  
          Returns
          -------
          array-like: {n_samples, n_target}
              The class probabilities of the input samples