code: def test_output_grid_search_forecaster_multiseries_ForecasterAutoregMultiSeries_with_mocked():
      """
      Test output of grid_search_forecaster_multiseries in ForecasterAutoregMultiSeries
      with mocked (mocked done in Skforecast v0.5.0)
      """
      forecaster = ForecasterAutoregMultiSeries(
          regressor=Ridge(random_state=123),
          lags=2,
          transformer_series=None,
      )
  
      steps = 3
      n_validation = 12
      lags_grid = [2, 4]
      param_grid = {"alpha": [0.01, 0.1, 1]}
  
      results = grid_search_forecaster_multiseries(
          forecaster=forecaster,
          series=series,
          param_grid=param_grid,
          steps=steps,
          metric="mean_absolute_error",
          initial_train_size=len(series) - n_validation,
          fixed_train_size=False,
          levels=None,
          exog=None,
          lags_grid=lags_grid,
          refit=False,
          return_best=False,
          verbose=True,
      )
  
      expected_results = pd.DataFrame(
          {
              "levels": [
                  ["l1", "l2"],
                  ["l1", "l2"],
                  ["l1", "l2"],
                  ["l1", "l2"],
                  ["l1", "l2"],
                  ["l1", "l2"],
              ],
              "lags": [
                  [1, 2, 3, 4],
                  [1, 2, 3, 4],
                  [1, 2, 3, 4],
                  [1, 2],
                  [1, 2],
                  [1, 2],
              ],
              "params": [
                  {"alpha": 0.01},
                  {"alpha": 0.1},
                  {"alpha": 1},
                  {"alpha": 1},
                  {"alpha": 0.1},
                  {"alpha": 0.01},
              ],
              "mean_absolute_error": np.array(
                  [
                      0.20968100463227382,
                      0.20969259779858337,
                      0.20977945312386406,
                      0.21077344827205086,
                      0.21078653113227208,
                      0.21078779824759553,
                  ]
              ),
              "alpha": np.array([0.01, 0.1, 1.0, 1.0, 0.1, 0.01]),
          },
          index=pd.Index([3, 4, 5, 2, 1, 0], dtype="int64"),
      )
  
      pd.testing.assert_frame_equal(results, expected_results)
  
file_path: skforecast/skforecast/model_selection_multiseries/tests/test_grid_search_forecaster_multiseries.py
imports: import pytest
  import numpy
  import pandas
  from sklearn.linear_model import Ridge
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from skforecast.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  from skforecast.model_selection_multiseries import grid_search_forecaster_multiseries
  from skforecast.model_selection_multiseries import grid_search_forecaster_multivariate
  from tqdm import tqdm
  from functools import partialmethod
  from .fixtures_model_selection_multiseries import series
label_desc: Use image node_id 2 for calling a global function with example usage: test_output_grid_search_forecaster_multiseries_ForecasterAutoregMultiSeries_with_mocked() without return types