code: def test_when_fit_and_predict_polynom_classifier_then_returns_accurate_results():
      def _generate_data():
          X = np.random.normal(0, 1, (1000, 2))
          Y = []
  
          for x in X:
              if x[0] * x[1] > 0:
                  Y.append("Class 0")
              else:
                  Y.append("Class 1")
  
          return X, np.array(Y)
  
      X_training, Y_training = _generate_data()
      X_test, Y_test = _generate_data()
      mdl = create_polynom_logistic_regression_classifier()
      mdl.fit(X_training, Y_training)
  
      assert np.sum(mdl.predict(X_test).reshape(-1) == Y_test) > 950
  
imports: import numpy
  from flaky import flaky
  from dowhy.gcm.ml import create_hist_gradient_boost_classifier, create_polynom_logistic_regression_classifier
  from dowhy.gcm.ml.classification import create_support_vector_classifier
label_desc: Use image node_id 1 for calling a global function with example usage: test_when_fit_and_predict_polynom_classifier_then_returns_accurate_results() and returns: X,  np