code: def test_only_future_covariates(self, mode):
      model = RegressionModel(
          lags_future_covariates=[-2], multi_models=mode
      )
  
      target_series = tg.linear_timeseries(
          start_value=0, end_value=49, length=50
      )
      covariates = tg.linear_timeseries(
          start_value=100, end_value=149, length=50
      )
      covariates = covariates.stack(
          tg.linear_timeseries(
              start_value=400, end_value=449, length=50
          )
      )
  
      target_train, target_test = target_series.split_after(0.7)
      covariates_train, covariates_test = covariates.split_after(0.7)
      model.fit(
          series=[target_train, target_train + 0.5],
          future_covariates=[covariates_train, covariates_train + 0.5],
      )
  
      predictions = model.predict(
          10,
          series=[target_train, target_train + 0.5],
          future_covariates=[covariates, covariates + 0.5],
      )
  
      assert (
          len(predictions[0]) == 10
      ), f"Found {len(predictions[0])} instead"
  
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 19 for calling the TestRegressionModels obj's underlying member method code with example usage: obj.test_only_future_covariates(mode) without return types