code: def test_objectmapper(self):
      df = pdml.ModelFrame([])
  
      # Splitter Classes
      self.assertIs(df.model_selection.KFold, ms.KFold)
      self.assertIs(df.model_selection.GroupKFold, ms.GroupKFold)
      self.assertIs(
          df.model_selection.StratifiedKFold, ms.StratifiedKFold
      )
  
      self.assertIs(
          df.model_selection.LeaveOneGroupOut, ms.LeaveOneGroupOut
      )
      self.assertIs(
          df.model_selection.LeavePGroupsOut, ms.LeavePGroupsOut
      )
      self.assertIs(df.model_selection.LeaveOneOut, ms.LeaveOneOut)
      self.assertIs(df.model_selection.LeavePOut, ms.LeavePOut)
  
      self.assertIs(df.model_selection.ShuffleSplit, ms.ShuffleSplit)
      self.assertIs(
          df.model_selection.GroupShuffleSplit, ms.GroupShuffleSplit
      )
      # self.assertIs(df.model_selection.StratifiedShuffleSplit,
      #               ms.StratifiedShuffleSplit)
      self.assertIs(
          df.model_selection.PredefinedSplit, ms.PredefinedSplit
      )
      self.assertIs(
          df.model_selection.TimeSeriesSplit, ms.TimeSeriesSplit
      )
  
      # Splitter Functions
  
      # Hyper-parameter optimizers
      self.assertIs(df.model_selection.GridSearchCV, ms.GridSearchCV)
      self.assertIs(
          df.model_selection.RandomizedSearchCV, ms.RandomizedSearchCV
      )
      self.assertIs(df.model_selection.ParameterGrid, ms.ParameterGrid)
      self.assertIs(
          df.model_selection.ParameterSampler, ms.ParameterSampler
      )
  
imports: import sklearn.datasets
  import sklearn.svm
  import sklearn.naive_bayes
  import numpy
  import pandas
  import pandas_ml
  import pandas_ml.util.testing
label_desc: Use image node_id 1 for calling the TestModelSelection obj's underlying member method code with example usage: obj.test_objectmapper() without return types