code: def get_properties(dataset_properties=None):
      return {
          "shortname": "KRR",
          "name": "Kernel Ridge Regression",
          "handles_regression": True,
          "handles_classification": False,
          "handles_multiclass": False,
          "handles_multilabel": False,
          "handles_multioutput": True,
          "is_deterministic": True,
          "input": (SPARSE, DENSE, UNSIGNED_DATA, SIGNED_DATA),
          "output": (PREDICTIONS,),
      }
  
file_path: auto-sklearn/examples/80_extending/example_extending_regression.py
imports: from typing import Optional
  from pprint import pprint
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, UniformIntegerHyperparameter, CategoricalHyperparameter
  from ConfigSpace.conditions import EqualsCondition
  import sklearn.metrics
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.regression
  import autosklearn.pipeline.components.regression
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS
  from sklearn.datasets import load_diabetes
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 4 for calling the KernelRidgeRegression obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict