code: def test_output_backtesting_forecaster_multiseries_ForecasterAutoregMultiVariate_refit_fixed_train_size_exog_interval_with_mocked():
      """
      Test output of backtesting_forecaster_multiseries in ForecasterAutoregMultiVariate
      with refit and fixed_train_size with mocked using exog and intervals
      (mocked done in Skforecast v0.7.0).
      """
  
      forecaster = ForecasterAutoregMultiVariate(
          regressor=Ridge(random_state=123),
          level="l1",
          lags=2,
          steps=3,
          transformer_series=None,
      )
  
      steps = 3
      n_validation = 12
  
      (
          metrics_levels,
          backtest_predictions,
      ) = backtesting_forecaster_multiseries(
          forecaster=forecaster,
          series=series,
          steps=steps,
          levels="l1",
          metric="mean_absolute_error",
          initial_train_size=len(series) - n_validation,
          refit=True,
          fixed_train_size=True,
          exog=series["l1"].rename("exog_1"),
          interval=[5, 95],
          n_boot=500,
          random_state=123,
          in_sample_residuals=True,
          verbose=False,
      )
  
      expected_metric = pd.DataFrame(
          {
              "levels": ["l1"],
              "mean_absolute_error": [0.07705832858897509],
          }
      )
      expected_predictions = pd.DataFrame(
          data=np.array(
              [
                  [0.79017158, 0.65165001, 0.92309077],
                  [0.49318335, 0.33877981, 0.59549335],
                  [0.58563228, 0.45036051, 0.72877519],
                  [0.25636363, 0.10743222, 0.37710866],
                  [0.37604542, 0.24241796, 0.52792491],
                  [0.45439247, 0.31741445, 0.6007327],
                  [0.70488052, 0.5596997, 0.84559181],
                  [0.32693583, 0.19870532, 0.47910327],
                  [0.49099895, 0.35617576, 0.63524443],
                  [0.82066806, 0.67917812, 0.95193044],
                  [0.51109877, 0.39178411, 0.66186064],
                  [0.54738032, 0.42195622, 0.69395717],
              ]
          ),
          columns=["l1", "lower_bound", "upper_bound"],
          index=pd.RangeIndex(start=38, stop=50, step=1),
      )
  
      pd.testing.assert_frame_equal(expected_metric, metrics_levels)
      pd.testing.assert_frame_equal(
          expected_predictions, backtest_predictions
      )
  
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.linear_model import Ridge
  from sklearn.metrics import mean_absolute_error
  from skforecast.exceptions import IgnoredArgumentWarning
  from skforecast.ForecasterAutoreg import ForecasterAutoreg
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from skforecast.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  from skforecast.model_selection_multiseries import backtesting_forecaster_multiseries
  from skforecast.model_selection_multiseries import backtesting_forecaster_multivariate
  from .fixtures_model_selection_multiseries import series
label_desc: Use image node_id 26 for calling a global function with example usage: test_output_backtesting_forecaster_multiseries_ForecasterAutoregMultiVariate_refit_fixed_train_size_exog_interval_with_mocked() without return types