code: def _test_configurations(
      self,
      configurations_space,
      make_sparse=False,
      data=None,
      dataset_properties=None,
  ):
      # Use a limit of ~4GiB
      limit = 3072 * 1024 * 1024
      resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
  
      configurations_space.seed(1)
  
      for i in range(10):
          config = configurations_space.sample_configuration()
          config._populate_values()
  
          # Restrict configurations which could take too long on travis-ci
          restrictions = {
              "regressor:adaboost:n_estimators": 50,
              "regressor:adaboost:max_depth": 1,
              "feature_preprocessor:kernel_pca:n_components": 10,
              "feature_preprocessor:kitchen_sinks:n_components": 50,
              "regressor:libsvm_svc:degree": 2,
              "regressor:libsvm_svr:degree": 2,
              "regressor:libsvm_svr:C": 1.0,
              "feature_preprocessor:truncatedSVD:target_dim": 10,
              "feature_preprocessor:polynomial:degree": 2,
              "regressor:lda:n_components": 10,
          }
  
          for restrict_parameter in restrictions:
              restrict_to = restrictions[restrict_parameter]
              if (
                  restrict_parameter in config
                  and config[restrict_parameter] is not None
              ):
                  config._values[restrict_parameter] = restrict_to
  
          if data is None:
              X_train, Y_train, X_test, Y_test = get_dataset(
                  dataset="boston",
                  make_sparse=make_sparse,
                  add_NaNs=True,
              )
          else:
              X_train = data["X_train"].copy()
              Y_train = data["Y_train"].copy()
              X_test = data["X_test"].copy()
              data["Y_test"].copy()
  
          cls = SimpleRegressionPipeline(
              random_state=1, dataset_properties=dataset_properties
          )
          cls.set_hyperparameters(config)
  
          # First make sure that for this configuration, setting the parameters
          # does not mistakenly set the estimator as fitted
          for name, step in cls.named_steps.items():
              with self.assertRaisesRegex(
                  sklearn.exceptions.NotFittedError,
                  "instance is not fitted yet",
              ):
                  check_is_fitted(step)
  
          try:
              with ignore_warnings(regressor_warnings):
                  cls.fit(X_train, Y_train)
  
              # After fit, all components should be tagged as fitted
              # by sklearn. Check is fitted raises an exception if that
              # is not the case
              try:
                  for name, step in cls.named_steps.items():
                      check_is_fitted(step)
              except sklearn.exceptions.NotFittedError:
                  self.fail(
                      "config={} raised NotFittedError unexpectedly!".format(
                          config
                      )
                  )
  
              cls.predict(X_test)
          except MemoryError:
              continue
          except np.linalg.LinAlgError:
              continue
          except ValueError as e:
              if (
                  "Floating-point under-/overflow occurred at epoch"
                  in e.args[0]
              ):
                  continue
              elif "removed all features" in e.args[0]:
                  continue
              elif "all features are discarded" in e.args[0]:
                  continue
              elif "Numerical problems in QDA" in e.args[0]:
                  continue
              elif "Bug in scikit-learn" in e.args[0]:
                  continue
              elif (
                  "The condensed distance matrix must contain only finite "
                  "values." in e.args[0]
              ):
                  continue
              elif (
                  "zero-size array to reduction operation maximum which has no "
                  "identity" in e.args[0]
              ):
                  continue
              else:
                  e.args += (f"config={config}",)
                  raise e
  
          except RuntimeWarning as e:
              if "invalid value encountered in sqrt" in e.args[0]:
                  continue
              elif "divide by zero encountered in" in e.args[0]:
                  continue
              elif "invalid value encountered in divide" in e.args[0]:
                  continue
              elif (
                  "invalid value encountered in true_divide"
                  in e.args[0]
              ):
                  continue
              elif "invalid value encountered in multiply" in e.args[0]:
                  continue
              else:
                  e.args += (f"config={config}",)
                  raise e
  
          except UserWarning as e:
              if "FastICA did not converge" in e.args[0]:
                  continue
              else:
                  e.args += (f"config={config}",)
                  raise e
  
          except Exception as e:
              if (
                  "Multiple input features cannot have the same target value"
                  in e.args[0]
              ):
                  continue
              else:
                  e.args += (f"config={config}",)
                  raise e
  
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 8 for calling the SimpleRegressionPipelineTest obj's underlying member method code with example usage: obj._test_configurations(configurations_space, make_sparse, data, dataset_properties) without return types