code: def denoising_input(self):
      np.random.seed(self.RANDOM_SEED)
  
      ts_periodic = tg.sine_timeseries(length=500)
      ts_gaussian = tg.gaussian_timeseries(length=500)
      ts_random_walk = tg.random_walk_timeseries(length=500)
  
      ts_cov1 = ts_periodic.stack(ts_gaussian)
      ts_cov1 = ts_cov1.pd_dataframe()
      ts_cov1.columns = ["Periodic", "Gaussian"]
      ts_cov1 = TimeSeries.from_dataframe(ts_cov1)
      ts_sum1 = ts_periodic + ts_gaussian
  
      ts_cov2 = ts_sum1.stack(ts_random_walk)
      ts_sum2 = ts_sum1 + ts_random_walk
  
      return ts_sum1, ts_cov1, ts_sum2, ts_cov2
  
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 17 for calling the TestRegressionEnsembleModels obj's underlying member method code with example usage: obj.denoising_input() and returns: ts_sum1,  ts_cov1,  ts_sum2,  ts_cov2