code: def test_iris_quartiles_resolve_ties_chi_squared_1():
      oner = OneRClassifier(resolve_ties="chi-squared")
      oner.fit(Xd_train, y_train)
      assert oner.feature_idx_ == 2
      assert oner.prediction_dict_["total error"] == 16
      assert round(oner.score(Xd_train, y_train), 4) == 0.8571
      assert round(oner.score(Xd_test, y_test), 4) == 0.8421
      np.testing.assert_almost_equal(oner.p_value_, 0.0, decimal=7)
  
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 3 for calling a global function with example usage: test_iris_quartiles_resolve_ties_chi_squared_1() without return types