code: def test_output(self, get_iris_data):
      ground_truth = np.array(
          [
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
              [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 2, 2, 2, 1, 2, 1, 2, 0, 2, 0],
              [0, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2],
              [2, 2, 2, 2, 1, 1, 1, 1, 2, 0, 2, 2, 2, 1, 1],
              [1, 2, 1, 0, 1, 1, 1, 2, 0, 1, 2, 1, 2, 2, 2],
              [2, 0, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2],
              [2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
              [2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2],
          ]
      )  # ground_truth is generated by manually use MDLP to extract thresholds
  
      discretiser = MDLPSupervisedDiscretiserMethod(
          {
              "min_depth": 0,
              "random_state": 2020,
              "min_split": 1e-3,
              "dtype": int,
          }
      )
  
      discretiser.fit(
          feat_names=["sepal length (cm)"],
          dataframe=get_iris_data,
          target_continuous=False,
          target="target",
      )
  
      output = discretiser.transform(
          get_iris_data[["sepal length (cm)"]]
      ).values
      assert (output.reshape(-1, 15) == ground_truth).all()
  
file_path: causalnex/tests/discretiser/test_mdlp.py
imports: from importlib import reload
  import numpy
  import pytest
  from mock import patch
  from causalnex.discretiser import discretiser_strategy
  from causalnex.discretiser.discretiser_strategy import MDLPSupervisedDiscretiserMethod
label_desc: Use image node_id 1 for calling the TestMDLP obj's underlying member method code with example usage: obj.test_output(get_iris_data) without return types