code: def test_direct_vs_recursive():
      """Test reduction forecasters.
  
      Test reduction forecasters by making prediction on airline dataset using linear
      estimators. Wenn windows_identical = False, all observations should be considered
      (see documentation in make_reduction function), so results for direct and recursive
      forecasting should match for the first forecasting horizon. With the
      windows_identical
      """
      y = load_airline()
      y_train, y_test = temporal_train_test_split(y, test_size=24)
      fh = ForecastingHorizon(y_test.index, is_relative=False)
      forecaster_dir_max = DirectTabularRegressionForecaster(
          LinearRegression(), windows_identical=False
      )
      forecaster_dir_spec = DirectTabularRegressionForecaster(
          LinearRegression(), windows_identical=True
      )
      forecaster_rec_max = RecursiveTabularRegressionForecaster(
          LinearRegression()
      )
      forecaster_rec_spec = RecursiveTabularRegressionForecaster(
          LinearRegression()
      )
  
      pred_dir_max = forecaster_dir_max.fit(y_train, fh=fh).predict(fh)
      pred_dir_spec = forecaster_dir_spec.fit(y_train, fh=fh).predict(
          fh
      )
      pred_rec_max = forecaster_rec_max.fit(y_train, fh=fh).predict(fh)
      pred_rec_spec = forecaster_rec_spec.fit(y_train, fh=fh).predict(
          fh
      )
  
      assert pred_dir_max.head(1).equals(pred_rec_max.head(1))
      assert pred_dir_max.head(1).equals(pred_rec_spec.head(1))
      assert not pred_dir_max.head(1).equals(pred_dir_spec.head(1))
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.dummy import DummyRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sktime.datasets import load_airline
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.compose import DirectTabularRegressionForecaster, DirectTimeSeriesRegressionForecaster, DirRecTabularRegressionForecaster, DirRecTimeSeriesRegressionForecaster, MultioutputTabularRegressionForecaster, MultioutputTimeSeriesRegressionForecaster, RecursiveTabularRegressionForecaster, RecursiveTimeSeriesRegressionForecaster, make_reduction
  from sktime.forecasting.compose._reduce import _sliding_window_transform
  from sktime.forecasting.tests._config import TEST_OOS_FHS, TEST_WINDOW_LENGTHS_INT
  from sktime.performance_metrics.forecasting import mean_absolute_percentage_error
  from sktime.regression.base import BaseRegressor
  from sktime.regression.interval_based import TimeSeriesForestRegressor
  from sktime.split import SlidingWindowSplitter, temporal_train_test_split
  from sktime.split.tests.test_split import _get_windows
  from sktime.transformations.panel.reduce import Tabularizer
  from sktime.utils._testing.forecasting import make_forecasting_problem
  from sktime.utils.validation.forecasting import check_fh
label_desc: Use image node_id 16 for calling a global function with example usage: test_direct_vs_recursive() without return types