code: def test_timezoneaware_index():
      y = load_solar(api_version=None)
      y_notz = y.copy().tz_localize(None)
  
      assert y.index.tz is not None
      assert y_notz.index.tz is None
  
      window_trafo = WindowSummarizer(
          n_jobs=1, **{"lag_feature": {"lag": [1, 2, 48]}}
      )
      regressor = LinearRegression()
      forecaster = make_reduction(
          estimator=regressor,
          strategy="recursive",
          transformers=[window_trafo],
          window_length=None,
          pooling="global",
      )
  
      # check coefficients
      tzaware = forecaster.clone().fit(y)
      tznaive = forecaster.clone().fit(y_notz)
      tzaware_coef = tzaware.get_fitted_params()["estimator__coef"]
      tznaive_coef = tznaive.get_fitted_params()["estimator__coef"]
  
      np.testing.assert_almost_equal(tzaware_coef, tznaive_coef)
  
      fh = np.arange(1, 97)
      pred_tzaware = tzaware.predict(fh=fh)
      pred_tznaive = tznaive.predict(fh=fh)
  
      msg = (
          "Time-zone of predictions not consistent with training data."
      )
      assert pred_tzaware.index.tz == y.index.tz, msg
      assert pred_tznaive.index.tz == y_notz.index.tz, msg
  
      # These should give us identical predictions
      np.testing.assert_almost_equal(
          pred_tzaware.values, pred_tznaive.values
      )
  
imports: from sktime.tests.test_switch import run_test_for_class
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  import random
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import GradientBoostingRegressor, HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sktime.datasets import load_airline, load_solar
  from sktime.datatypes import get_examples
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.compose import make_reduction
  from sktime.forecasting.compose._reduce import _DirectReducer, _RecursiveReducer
  from sktime.performance_metrics.forecasting import mean_absolute_percentage_error
  from sktime.split import temporal_train_test_split
  from sktime.transformations.series.summarize import WindowSummarizer
  from sktime.utils._testing.hierarchical import _make_hierarchical
label_desc: Use image node_id 8 for calling a global function with example usage: test_timezoneaware_index() without return types