How to use the lightgbm.basic.LightGBMError function in lightgbm

bst = lgb.Booster(params, model_file="model.txt")
        os.remove("model.txt")
        pred_from_model_file = bst.predict(X_test)
        # we need to check the consistency of model file here, so test for exact equal
        np.testing.assert_array_equal(pred_from_matr, pred_from_model_file)

        # check early stopping is working. Make it stop very early, so the scores should be very close to zero
        pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5}
        pred_early_stopping = bst.predict(X_test, **pred_parameter)
        # scores likely to be different, but prediction should still be the same
        np.testing.assert_array_equal(np.sign(pred_from_matr), np.sign(pred_early_stopping))

        # test that shape is checked during prediction
        bad_X_test = X_test[:, 1:]
        bad_shape_error_msg = "The number of features in data*"
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, bad_X_test)
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, sparse.csr_matrix(bad_X_test))
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, sparse.csc_matrix(bad_X_test))
        with open(tname, "w+b") as f:
            dump_svmlight_file(bad_X_test, y_test, f)
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, tname)
        with open(tname, "w+b") as f:
            dump_svmlight_file(X_test, y_test, f, zero_based=False)
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, tname)
        os.remove(tname)

np.testing.assert_array_equal(pred_from_matr, pred_from_model_file)

        # check early stopping is working. Make it stop very early, so the scores should be very close to zero
        pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5}
        pred_early_stopping = bst.predict(X_test, **pred_parameter)
        # scores likely to be different, but prediction should still be the same
        np.testing.assert_array_equal(np.sign(pred_from_matr), np.sign(pred_early_stopping))

        # test that shape is checked during prediction
        bad_X_test = X_test[:, 1:]
        bad_shape_error_msg = "The number of features in data*"
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, bad_X_test)
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, sparse.csr_matrix(bad_X_test))
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, sparse.csc_matrix(bad_X_test))
        with open(tname, "w+b") as f:
            dump_svmlight_file(bad_X_test, y_test, f)
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, tname)
        with open(tname, "w+b") as f:
            dump_svmlight_file(X_test, y_test, f, zero_based=False)
        np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
                                       bst.predict, tname)
        os.remove(tname)

np.testing.assert_array_equal(hist_idx, hist_name)
        np.testing.assert_allclose(bins_idx, bins_name)
        # test bins string type
        if np.__version__ > '1.11.0':
            hist_vals, bin_edges = gbm.get_split_value_histogram(0, bins='auto')
            hist = gbm.get_split_value_histogram(0, bins='auto', xgboost_style=True)
            if lgb.compat.PANDAS_INSTALLED:
                mask = hist_vals > 0
                np.testing.assert_array_equal(hist_vals[mask], hist['Count'].values)
                np.testing.assert_allclose(bin_edges[1:][mask], hist['SplitValue'].values)
            else:
                mask = hist_vals > 0
                np.testing.assert_array_equal(hist_vals[mask], hist[:, 1])
                np.testing.assert_allclose(bin_edges[1:][mask], hist[:, 0])
        # test histogram is disabled for categorical features
        self.assertRaises(lgb.basic.LightGBMError, gbm.get_split_value_histogram, 2)

self.assertRaises(lgb.basic.LightGBMError, get_cv_result,
                          params_class_3_verbose)
        # no metric with non-default num_class for custom objective
        res = get_cv_result(params_class_3_verbose, fobj=dummy_obj)
        self.assertEqual(len(res), 0)
        for metric_multi_alias in obj_multi_aliases + ['multi_logloss']:
            # multiclass metric alias for custom objective
            res = get_cv_result(params_class_3_verbose, metrics=metric_multi_alias, fobj=dummy_obj)
            self.assertEqual(len(res), 2)
            self.assertIn('multi_logloss-mean', res)
        # multiclass metric for custom objective
        res = get_cv_result(params_class_3_verbose, metrics='multi_error', fobj=dummy_obj)
        self.assertEqual(len(res), 2)
        self.assertIn('multi_error-mean', res)
        # binary metric with non-default num_class for custom objective
        self.assertRaises(lgb.basic.LightGBMError, get_cv_result,
                          params_class_3_verbose, metrics='binary_error', fobj=dummy_obj)