code: def test_output_evaluate_grid_hyperparameters_ForecasterAutoregMultiSeries_lags_grid_is_None_with_mocked():
      """
      Test output of _evaluate_grid_hyperparameters in ForecasterAutoregMultiSeries
      when `lags_grid` is `None` with mocked (mocked done in Skforecast v0.5.0),
      should use forecaster.lags as lags_grid.
      """
      forecaster = ForecasterAutoregMultiSeries(
          regressor=Ridge(random_state=123),
          lags=2,
          transformer_series=None,
      )
  
      lags_grid = None
      steps = 3
      n_validation = 12
      param_grid = [{"alpha": 0.01}, {"alpha": 0.1}, {"alpha": 1}]
  
      results = _evaluate_grid_hyperparameters_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=["l1", "l2"],
          exog=None,
          lags_grid=lags_grid,
          refit=False,
          return_best=False,
          verbose=False,
      )
  
      expected_results = pd.DataFrame(
          {
              "levels": [["l1", "l2"], ["l1", "l2"], ["l1", "l2"]],
              "lags": [[1, 2], [1, 2], [1, 2]],
              "params": [{"alpha": 1}, {"alpha": 0.1}, {"alpha": 0.01}],
              "mean_absolute_error": np.array(
                  [
                      0.21077344827205086,
                      0.21078653113227208,
                      0.21078779824759553,
                  ]
              ),
              "alpha": np.array([1.0, 0.1, 0.01]),
          },
          index=pd.Index([2, 1, 0], dtype="int64"),
      )
  
      pd.testing.assert_frame_equal(results, expected_results)
  
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.linear_model import Ridge
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from skforecast.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  from skforecast.model_selection_multiseries.model_selection_multiseries import _evaluate_grid_hyperparameters_multiseries
  from tqdm import tqdm
  from functools import partialmethod
  from .fixtures_model_selection_multiseries import series
label_desc: Use image node_id 6 for calling a global function with example usage: test_output_evaluate_grid_hyperparameters_ForecasterAutoregMultiSeries_lags_grid_is_None_with_mocked() without return types