code: def test_evaluate_initial_window():
      """Test evaluate initial window."""
      initial_window = 20
      y = make_forecasting_problem(n_timepoints=30, index_type="int")
      forecaster = NaiveForecaster()
      fh = 1
      cv = SlidingWindowSplitter(fh=fh, initial_window=initial_window)
      scoring = MeanAbsolutePercentageError(symmetric=True)
      out = evaluate(
          forecaster=forecaster,
          y=y,
          cv=cv,
          strategy="update",
          scoring=scoring,
      )
      _check_evaluate_output(out, cv, y, scoring, False)
      assert out.loc[0, "len_train_window"] == initial_window
  
      # check scoring
      actual = out.loc[0, f"test_{scoring.name}"]
      train, test = next(cv.split(y))
      f = forecaster.clone()
      f.fit(y.iloc[train], fh=fh)
      expected = scoring(
          y.iloc[test], f.predict(), y_Train=y.iloc[train]
      )
      np.testing.assert_equal(actual, expected)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LinearRegression
  from sktime.datasets import load_airline, load_longley
  from sktime.exceptions import FitFailedWarning
  from sktime.forecasting.arima import ARIMA, AutoARIMA
  from sktime.forecasting.compose._reduce import DirectReductionForecaster
  from sktime.forecasting.exp_smoothing import ExponentialSmoothing
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.model_evaluation._functions import _check_scores, _get_column_order_and_datatype
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.tests._config import TEST_FHS, TEST_STEP_LENGTHS_INT
  from sktime.performance_metrics.forecasting import MeanAbsoluteError, MeanAbsolutePercentageError, MeanAbsoluteScaledError
  from sktime.performance_metrics.forecasting.probabilistic import CRPS, EmpiricalCoverage, LogLoss, PinballLoss
  from sktime.split import ExpandingWindowSplitter, SlidingWindowSplitter
  from sktime.tests.test_switch import run_test_for_class
  from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
  from sktime.utils._testing.forecasting import make_forecasting_problem
  from sktime.utils._testing.hierarchical import _make_hierarchical
  from sktime.utils._testing.series import _make_series
  from sktime.utils.parallel import _get_parallel_test_fixtures
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 4 for calling a global function with example usage: test_evaluate_initial_window() without return types