code: def test_fit_same_residuals_when_residuals_greater_than_1000():
      """
      Test fit return same residuals when residuals len is greater than 1000.
      Testing with two different forecaster. Residuals shouldn't be more than
      1000 values.
      """
      series = pd.DataFrame(
          {
              "1": pd.Series(np.arange(1010)),
              "2": pd.Series(np.arange(1010)),
          }
      )
  
      forecaster = ForecasterAutoregMultiSeries(
          LinearRegression(), lags=3
      )
      forecaster.fit(series=series, store_in_sample_residuals=True)
      results_1 = forecaster.in_sample_residuals
  
      forecaster = ForecasterAutoregMultiSeries(
          LinearRegression(), lags=3
      )
      forecaster.fit(series=series, store_in_sample_residuals=True)
      results_2 = forecaster.in_sample_residuals
  
      assert isinstance(results_1, dict)
      assert np.all(
          isinstance(x, np.ndarray) for x in results_1.values()
      )
      assert isinstance(results_2, dict)
      assert np.all(
          isinstance(x, np.ndarray) for x in results_2.values()
      )
      assert results_1.keys() == results_2.keys()
      assert np.all(len(results_1[k] == 1000) for k in results_1.keys())
      assert np.all(len(results_2[k] == 1000) for k in results_2.keys())
      assert np.all(
          np.all(results_1[k] == results_2[k]) for k in results_2.keys()
      )
  
file_path: skforecast/skforecast/ForecasterAutoregMultiSeries/tests/test_fit.py
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 6 for calling a global function with example usage: test_fit_same_residuals_when_residuals_greater_than_1000() without return types