code: def test_conformal_with_gscv():
      """With ForecastingGridSearchCV and parameter plugin."""
      from sktime.forecasting.model_selection import (
          ForecastingGridSearchCV,
      )
      from sktime.param_est.plugin import PluginParamsForecaster
      from sktime.split import ExpandingWindowSplitter
  
      y = load_airline()
  
      # part 1 = grid search
      cv = ExpandingWindowSplitter(fh=[1, 2, 3])
      forecaster = NaiveForecaster()
      param_grid = {"strategy": ["last", "mean", "drift"]}
      gscv = ForecastingGridSearchCV(
          forecaster=forecaster,
          param_grid=param_grid,
          cv=cv,
      )
  
      # part 2 = plug in results of grid search into conformal intervals estimator
      conformal_with_fallback = ConformalIntervals(NaiveForecaster())
      gscv_with_conformal = PluginParamsForecaster(
          gscv,
          conformal_with_fallback,
          params={"forecaster": "best_forecaster"},
      )
  
      gscv_with_conformal.fit(y, fh=[1, 2, 3])
  
      y_pred_quantiles = gscv_with_conformal.predict_quantiles()
  
      assert check_is_mtype(
          y_pred_quantiles,
          "pred_quantiles",
          "Proba",
          msg_return_dict="list",
      )
  
file_path: sktime/sktime/forecasting/tests/test_conformal.py
imports: import pytest
  from sktime.datasets import load_airline
  from sktime.datatypes import check_is_mtype
  from sktime.forecasting.conformal import ConformalIntervals
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.tests.test_switch import run_test_for_class
label_desc: Use image node_id 2 for calling a global function with example usage: test_conformal_with_gscv() without return types