code: def test_predict_with_target(self):
      series_long = self.combined
      series_short = series_long[:25]
  
      # train with a single series
      ensemble_model = self.get_global_ensemble_model()
      ensemble_model.fit(series_short, past_covariates=series_long)
      # predict after end of train series
      preds = ensemble_model.predict(n=5, past_covariates=series_long)
      assert isinstance(preds, TimeSeries)
      # predict a new target series
      preds = ensemble_model.predict(
          n=5, series=series_long, past_covariates=series_long
      )
      assert isinstance(preds, TimeSeries)
      # predict multiple target series
      preds = ensemble_model.predict(
          n=5,
          series=[series_long] * 2,
          past_covariates=[series_long] * 2,
      )
      assert isinstance(preds, list) and len(preds) == 2
      # predict single target series in list
      preds = ensemble_model.predict(
          n=5, series=[series_long], past_covariates=[series_long]
      )
      assert isinstance(preds, list) and len(preds) == 1
  
      # train with multiple series
      ensemble_model = self.get_global_ensemble_model()
      ensemble_model.fit(
          [series_short] * 2, past_covariates=[series_long] * 2
      )
      with pytest.raises(ValueError):
          # predict without passing series should raise an error
          ensemble_model.predict(n=5, past_covariates=series_long)
      # predict a new target series
      preds = ensemble_model.predict(
          n=5, series=series_long, past_covariates=series_long
      )
      assert isinstance(preds, TimeSeries)
      # predict multiple target series
      preds = ensemble_model.predict(
          n=5,
          series=[series_long] * 2,
          past_covariates=[series_long] * 2,
      )
      assert isinstance(preds, list) and len(preds) == 2
      # predict single target series in list
      preds = ensemble_model.predict(
          n=5, series=[series_long], past_covariates=[series_long]
      )
      assert isinstance(preds, list) and len(preds) == 1
  
imports: from typing import List, Union
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from darts import TimeSeries
  from darts.logging import get_logger
  from darts.metrics import mape, rmse
  from darts.models import LinearRegressionModel, NaiveDrift, NaiveSeasonal, RandomForest, RegressionEnsembleModel, RegressionModel, Theta
  from darts.tests.conftest import tfm_kwargs
  from darts.tests.models.forecasting.test_ensemble_models import _make_ts
  from darts.tests.models.forecasting.test_regression_models import train_test_split
  from darts.utils import timeseries_generation
label_desc: Use image node_id 15 for calling the TestRegressionEnsembleModels obj's underlying member method code with example usage: obj.test_predict_with_target() without return types