code: def test_iris_quartiles_resolve_ties_chi_squared_2():
      # tests with duplicate column
      oner = OneRClassifier(resolve_ties="chi-squared")
  
      Xd_traintemp = np.zeros(
          (Xd_train.shape[0], Xd_train.shape[1] + 1)
      )
      Xd_traintemp[:, 0] = Xd_train[:, 2]
      Xd_traintemp[:, 1] = Xd_train[:, 0]
      Xd_traintemp[:, 2] = Xd_train[:, 1]
      Xd_traintemp[:, 3] = Xd_train[:, 2]
      Xd_traintemp[:, 4] = Xd_train[:, 3]
  
      oner.fit(Xd_traintemp, y_train)
      assert oner.feature_idx_ == 0
      assert oner.prediction_dict_["total error"] == 16
      assert round(oner.score(Xd_traintemp, y_train), 4) == 0.8571
  
imports: import numpy
  from sklearn.model_selection import train_test_split
  from mlxtend.classifier import OneRClassifier
  from mlxtend.data import iris_data
label_desc: Use image node_id 4 for calling a global function with example usage: test_iris_quartiles_resolve_ties_chi_squared_2() without return types