code: def test_not_enough_covariates(self, config):
      # mode, output_chunk_length, required past_offset, required future_offset
      mode, (
          output_chunk_length,
          req_past_offset,
          req_future_offset,
      ) = config
      target_series = tg.linear_timeseries(
          start_value=0, end_value=100, length=50
      )
      past_covariates = tg.linear_timeseries(
          start_value=100, end_value=200, length=50
      )
      future_covariates = tg.linear_timeseries(
          start_value=200, end_value=300, length=50
      )
  
      model = RegressionModel(
          lags_past_covariates=[-10],
          lags_future_covariates=[-5, 5],
          output_chunk_length=7,
          multi_models=mode,
      )
      model.fit(
          series=target_series,
          past_covariates=past_covariates,
          future_covariates=future_covariates,
          max_samples_per_ts=1,
      )
  
      n = 10
  
      model = RegressionModel(
          lags_past_covariates=[-10],
          lags_future_covariates=[-4, 3],
          output_chunk_length=output_chunk_length,
          multi_models=mode,
      )
      model.fit(
          series=target_series,
          past_covariates=past_covariates,
          future_covariates=future_covariates,
      )
  
      # check that given the required offsets no ValueError is raised
      model.predict(
          n,
          series=target_series[:-25],
          past_covariates=past_covariates[: -25 + req_past_offset],
          future_covariates=future_covariates[
              : -25 + req_future_offset
          ],
      )
      # check that one less past covariate time step causes ValueError
      with pytest.raises(ValueError):
          model.predict(
              n,
              series=target_series[:-25],
              past_covariates=past_covariates[: -26 + req_past_offset],
              future_covariates=future_covariates[
                  : -25 + req_future_offset
              ],
          )
      # check that one less future covariate time step causes ValueError
      with pytest.raises(ValueError):
          model.predict(
              n,
              series=target_series[:-25],
              past_covariates=past_covariates[: -25 + req_past_offset],
              future_covariates=future_covariates[
                  : -26 + req_future_offset
              ],
          )
  
imports: import copy
  import functools
  import itertools
  import math
  from unittest.mock import patch
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  import darts
  from darts import TimeSeries
  from darts.dataprocessing.encoders import FutureCyclicEncoder, PastDatetimeAttributeEncoder
  from darts.logging import get_logger
  from darts.metrics import mae, rmse
  from darts.models import CatBoostModel, LightGBMModel, LinearRegressionModel, NotImportedModule, RandomForest, RegressionModel, XGBModel
  from darts.utils import timeseries_generation
  from darts.utils.multioutput import MultiOutputRegressor
label_desc: Use image node_id 20 for calling the TestRegressionModels obj's underlying member method code with example usage: obj.test_not_enough_covariates(config) without return types