code: def helper_test_models_accuracy(
      self, model_instance, n, series, past_covariates, min_rmse
  ):
      # for every model, test whether it predicts the target with a minimum r2 score of `min_rmse`
      train_series, test_series = train_test_split(
          series, pd.Timestamp("20010101")
      )
      train_past_covariates, _ = train_test_split(
          past_covariates, pd.Timestamp("20010101")
      )
  
      model_instance.fit(
          series=train_series, past_covariates=train_past_covariates
      )
      prediction = model_instance.predict(
          n=n, past_covariates=past_covariates
      )
      current_rmse = rmse(test_series, prediction)
  
      assert (
          current_rmse <= min_rmse
      ), f"Model was not able to denoise data. A rmse score of {current_rmse} was recorded."
  
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 16 for calling the TestRegressionEnsembleModels obj's underlying member method code with example usage: obj.helper_test_models_accuracy(model_instance, n, series, past_covariates, min_rmse) without return types