code: def test_consistent_data_passing_to_component_estimators_in_fit_and_predict(
      estimator, window_length, strategy
  ):
      """Test consistent data passing to component estimators in fit and predict.
  
      We generate data that represents time points in its values, i.e. an array of values
      that increase in unit steps for each time point.
      """
      n_variables = 3
      n_timepoints = 10
      y, X = _make_y_X(n_timepoints, n_variables)
      y_train, y_test, X_train, X_test = temporal_train_test_split(
          y, X, fh=FH
      )
  
      forecaster = make_reduction(
          estimator, strategy=strategy, window_length=window_length
      )
      forecaster.fit(y_train, X_train, fh=FH)
      forecaster.predict(X=X_test)
  
      # Get recorded data.
      if strategy == "direct":
          estimator_ = forecaster.estimators_[0]
      else:
          estimator_ = forecaster.estimator_
  
      X_fit = estimator_.X_fit
      y_fit = estimator_.y_fit
      X_pred = estimator_.X_pred
  
      # Format feature data into 3d array if the data is not in that format already.
      X_fit = X_fit.reshape(X_fit.shape[0], n_variables, -1)
      X_pred = X_pred.reshape(X_pred.shape[0], n_variables, -1)
  
      # Format target data into 2d array.
      y_fit = y_fit.reshape(y_fit.shape[0], -1)
  
      # Check that both fit and predict data have unit steps between them.
      assert np.allclose(np.diff(X_fit), 1)
      assert np.allclose(np.diff(X_pred), 1)
  
      # Check that predict data is a step ahead from last row in fit data.
      np.testing.assert_array_equal(X_pred, X_fit[[-1]] + 1)
  
      # Check that y values are further ahead than X values.
      assert np.all(X_fit < y_fit[:, np.newaxis, :])
  
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 9 for calling a global function with example usage: test_consistent_data_passing_to_component_estimators_in_fit_and_predict(estimator, window_length, strategy) without return types