code: def test_gscv_backends(backend_set):
      """Test ForecastingGridSearchCV."""
      backend = backend_set["backend"]
      backend_params = backend_set["backend_params"]
  
      y, X = load_longley()
  
      gscv = ForecastingGridSearchCV(
          PIPE,
          param_grid=PIPE_GRID,
          cv=CVs[0],
          scoring=TEST_METRICS[0],
          error_score=ERROR_SCORES[0],
          backend=backend,
          backend_params=backend_params,
      )
      gscv.fit(y, X)
  
imports: import numpy
  import pytest
  from sklearn.model_selection import ParameterGrid, ParameterSampler
  from sktime.datasets import load_airline, load_longley
  from sktime.forecasting.arima import ARIMA
  from sktime.forecasting.compose import TransformedTargetForecaster
  from sktime.forecasting.exp_smoothing import ExponentialSmoothing
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.model_selection import ForecastingGridSearchCV, ForecastingRandomizedSearchCV, ForecastingSkoptSearchCV
  from sktime.forecasting.model_selection._tune import BaseGridSearch
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.tests._config import TEST_N_ITERS, TEST_OOS_FHS, TEST_RANDOM_SEEDS, TEST_WINDOW_LENGTHS_INT
  from sktime.forecasting.trend import PolynomialTrendForecaster
  from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError, MeanSquaredError
  from sktime.performance_metrics.forecasting.probabilistic import CRPS, PinballLoss
  from sktime.split import SingleWindowSplitter, SlidingWindowSplitter
  from sktime.tests.test_switch import run_test_for_class
  from sktime.transformations.series.detrend import Detrender
  from sktime.transformations.series.impute import Imputer
  from sktime.utils._testing.hierarchical import _make_hierarchical
  from sktime.utils.parallel import _get_parallel_test_fixtures
label_desc: Use image node_id 11 for calling a global function with example usage: test_gscv_backends(backend_set) without return types