code: def test_triple_type(self):
      DPP = FeatTypeSplit(
          feat_type={
              "A": "numerical",
              "B": "categorical",
              "C": "string",
          }
      )
      cs = DPP.get_hyperparameter_search_space(
          feat_type={
              "A": "numerical",
              "B": "categorical",
              "C": "string",
          },
          dataset_properties={
              "task": 1,
              "sparse": False,
              "multilabel": False,
              "multiclass": False,
              "target_type": "classification",
              "signed": False,
          },
      )
      truth_table = [False] * 3
      for key in cs.get_hyperparameters_dict().keys():
          if "text" in key.split(":")[0]:
              truth_table[0] = True
          elif "categorical" in key.split(":")[0]:
              truth_table[1] = True
          elif "numerical" in key.split(":")[0]:
              truth_table[2] = True
  
      self.assertEqual(sum(truth_table), 3)
      self.assertEqual(
          len(cs),
          self.num_numerical + self.num_categorical + self.num_text,
      )
  
imports: from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import unittest
label_desc: Use image node_id 3 for calling the PreprocessingPipelineFeatTypeTest obj's underlying member method code with example usage: obj.test_triple_type() without return types