code: def test_io_dict(self):
      regressors = regression_components._regressors
      for r in regressors:
          if regressors[r] == regression_components.RegressorChoice:
              continue
          props = regressors[r].get_properties()
          self.assertIn("input", props)
          self.assertIn("output", props)
          inp = props["input"]
          output = props["output"]
  
          self.assertIsInstance(inp, tuple)
          self.assertIsInstance(output, tuple)
          for i in inp:
              self.assertIn(
                  i, (SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA)
              )
          self.assertEqual(output, (PREDICTIONS,))
          self.assertIn("handles_regression", props)
          self.assertTrue(props["handles_regression"])
          self.assertIn("handles_classification", props)
          self.assertIn("handles_multiclass", props)
          self.assertIn("handles_multilabel", props)
          self.assertIn("handles_multioutput", props)
          self.assertFalse(props["handles_classification"])
          self.assertFalse(props["handles_multiclass"])
          self.assertFalse(props["handles_multilabel"])
  
imports: import copy
  import itertools
  import resource
  import tempfile
  import numpy
  import sklearn.datasets
  import sklearn.decomposition
  import sklearn.ensemble
  import sklearn.svm
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter
  from joblib import Memory
  from sklearn.base import clone
  from sklearn.utils.validation import check_is_fitted
  import autosklearn.pipeline.components.feature_preprocessing
  import autosklearn.pipeline.components.regression
  from autosklearn.pipeline.components.base import AutoSklearnChoice, AutoSklearnComponent, AutoSklearnPreprocessingAlgorithm, AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SIGNED_DATA, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.regression import SimpleRegressionPipeline
  from autosklearn.pipeline.util import get_dataset
  import unittest
  import unittest.mock
  from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings
label_desc: Use image node_id 1 for calling the SimpleRegressionPipelineTest obj's underlying member method code with example usage: obj.test_io_dict() without return types