code: def test_filter_train_X_y_for_step_output_when_lags_3_steps_2_exog_is_None_for_step_1():
      """
      Test output of filter_train_X_y_for_step when regressor is LinearRegression,
      lags is 3 and steps is 2 for step 1.
      """
      series = pd.DataFrame(
          {
              "l1": pd.Series(np.arange(10), dtype=float),
              "l2": pd.Series(np.arange(100, 110), dtype=float),
          }
      )
  
      forecaster = ForecasterAutoregMultiVariate(
          LinearRegression(),
          level="l1",
          lags=3,
          steps=2,
          transformer_series=None,
      )
      X_train, y_train = forecaster.create_train_X_y(series=series)
      results = forecaster.filter_train_X_y_for_step(
          step=1, X_train=X_train, y_train=y_train
      )
  
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [2.0, 1.0, 0.0, 102.0, 101.0, 100.0],
                      [3.0, 2.0, 1.0, 103.0, 102.0, 101.0],
                      [4.0, 3.0, 2.0, 104.0, 103.0, 102.0],
                      [5.0, 4.0, 3.0, 105.0, 104.0, 103.0],
                      [6.0, 5.0, 4.0, 106.0, 105.0, 104.0],
                      [7.0, 6.0, 5.0, 107.0, 106.0, 105.0],
                  ],
                  dtype=float,
              ),
              index=pd.RangeIndex(start=3, stop=9, step=1),
              columns=[
                  "l1_lag_1",
                  "l1_lag_2",
                  "l1_lag_3",
                  "l2_lag_1",
                  "l2_lag_2",
                  "l2_lag_3",
              ],
          ),
          pd.Series(
              data=np.array(
                  [3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=float
              ),
              index=pd.RangeIndex(start=3, stop=9, step=1),
              name="l1_step_1",
          ),
      )
  
      pd.testing.assert_frame_equal(results[0], expected[0])
      pd.testing.assert_series_equal(results[1], expected[1])
  
file_path: skforecast/skforecast/ForecasterAutoregMultiVariate/tests/test_filter_train_X_y_for_step.py
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 2 for calling a global function with example usage: test_filter_train_X_y_for_step_output_when_lags_3_steps_2_exog_is_None_for_step_1() without return types