code: def predict(self, X):
      if self.estimator is None:
          raise NotImplementedError
      if self.scaler is None:
          raise NotImplementedError
      y_pred = self.estimator.predict(X)
  
      inverse = self.scaler.inverse_transform(y_pred)
  
      # Flatten: [[0], [0], [0]] -> [0, 0, 0]
      if inverse.ndim == 2 and inverse.shape[1] == 1:
          inverse = inverse.flatten()
  
      return inverse
  
file_path: auto-sklearn/autosklearn/pipeline/components/regression/libsvm_svr.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 AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 3 for calling the LibSVM_SVR obj's underlying member method code with example usage: obj.predict(X) and returns: inverse