code: def test_base_model_classification(self):
      (
          x_train,
          x_test,
          y_train,
          y_test,
          feature_names,
          classes,
      ) = create_iris_data()
      trained_model = self.create_sklearn_random_forest_classifier(
          x_train, y_train
      )
  
      diceml_model = dice_ml.Model(
          model=trained_model, backend="sklearn"
      )
      diceml_model.transformer.initialize_transform_func()
  
      assert diceml_model is not None
  
      prediction_probabilities = diceml_model.get_output(x_test)
      assert prediction_probabilities.shape[0] == x_test.shape[0]
      assert prediction_probabilities.shape[1] == len(classes)
  
      predictions = diceml_model.get_output(
          x_test, model_score=False
      ).reshape(-1, 1)
      assert predictions.shape[0] == x_test.shape[0]
      assert predictions.shape[1] == 1
      assert np.all(np.unique(predictions) == np.unique(y_test))
  
      with pytest.raises(NotImplementedError):
          diceml_model.get_gradient()
  
      assert diceml_model.get_num_output_nodes2(x_test) == len(classes)
  
file_path: DiCE/tests/test_model_interface/test_base_model.py
imports: import numpy
  import pytest
  from rai_test_utils.datasets.tabular import create_housing_data, create_iris_data
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import dice_ml
  from dice_ml.utils.exception import SystemException
label_desc: Use image node_id 2 for calling the TestModelClassification obj's underlying member method code with example usage: obj.test_base_model_classification() without return types