code: def test_classification(df_normal_dist):
      transformer = DecisionTreeDiscretiser(
          cv=3,
          scoring="roc_auc",
          variables=None,
          param_grid={"max_depth": [1, 2, 3, 4]},
          regression=False,
          random_state=0,
      )
      np.random.seed(0)
      y = pd.Series(np.random.binomial(1, 0.7, 100))
      X = transformer.fit_transform(df_normal_dist, y)
      X_t = [1.0, 0.71, 0.93, 0.0]
  
      # init params
      assert transformer.cv == 3
      assert transformer.variables is None
      assert transformer.scoring == "roc_auc"
      assert transformer.regression is False
      # fit params
      assert transformer.variables_ == ["var"]
      assert transformer.n_features_in_ == 1
      # transform params
      assert all(
          x for x in np.round(X["var"].unique(), 2) if x not in X_t
      )
      assert np.round(transformer.scores_dict_["var"], 3) == np.round(
          0.717391304347826, 3
      )
  
file_path: feature_engine/tests/test_discretisation/test_decision_tree_discretiser.py
imports: import numpy
  import pandas
  import pytest
  from sklearn.exceptions import NotFittedError
  from feature_engine.discretisation import DecisionTreeDiscretiser, EqualWidthDiscretiser
label_desc: Use image node_id 1 for calling a global function with example usage: test_classification(df_normal_dist) without return types