class_docstr: Unit test class for testing scikit-learn converter.
code: def test_conversion(self):
      output_name = "target"
      spec = skl_converter(
          self.scikit_model, "data", "target"
      ).get_spec()
      self.assertIsNotNone(spec)
  
      # Test the model class
      self.assertIsNotNone(spec.description)
      self.assertIsNotNone(spec.treeEnsembleClassifier)
  
      # Test the interface class
      self.assertEqual(spec.description.predictedFeatureName, "target")
  
      # Test the inputs and outputs
      self.assertEqual(len(spec.description.output), 2)
      self.assertEqual(spec.description.output[0].name, "target")
      self.assertEqual(
          spec.description.output[0].type.WhichOneof("Type"),
          "int64Type",
      )
      self.assertEqual(spec.description.input[0].name, "data")
      self.assertEqual(
          spec.description.input[0].type.WhichOneof("Type"),
          "multiArrayType",
      )
  
      tr = spec.treeEnsembleClassifier.treeEnsemble
      self.assertIsNotNone(tr)
      self.assertEqual(
          len(tr.nodes), self.scikit_model.tree_.node_count
      )
  
file_path: coremltools/coremltools/test/xgboost_tests/test_decision_tree_classifier.py
imports: import unittest
  from coremltools._deps import _HAS_SKLEARN, _HAS_XGBOOST
label_desc: Use image node_id 2 for calling the DecisionTreeMultiClassClassifierScikitTest obj's underlying member method code with example usage: obj.test_conversion() without return types