code: def test_accepts_one_model(self):
      regr1 = LinearRegression()
      regr2 = RandomForest(lags_future_covariates=[0])
  
      model0 = RegressionEnsembleModel([self.get_local_models()[0]], 10)
      model1 = RegressionEnsembleModel(
          [self.get_local_models()[0]], 10, regr1
      )
      model2 = RegressionEnsembleModel(
          [self.get_local_models()[0]], 10, regr2
      )
  
      models = [model0, model1, model2]
      for model in models:
          model.fit(series=self.combined)
          model.predict(10)
  
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 5 for calling the TestRegressionEnsembleModels obj's underlying member method code with example usage: obj.test_accepts_one_model() without return types