code: def test_10_ts_stacked_ensemble(self):
      from lightwood.ensemble.ts_stacked_ensemble import (
          TsStackedEnsemble,
      )
  
      data = pd.read_csv("tests/data/arrivals.csv")
      data = data[data["Country"] == "UK"]
      train_df, test_df = self.split_arrivals(data, grouped=False)
      target = "Traffic"
      order_by = "T"
      horizon = 2
      json_ai = json_ai_from_problem(
          data,
          ProblemDefinition.from_dict(
              {
                  "target": target,
                  "timeseries_settings": {
                      "horizon": horizon,
                      "order_by": order_by,
                      "window": 5,
                  },
              }
          ),
      )
      json_ai.model["module"] = "TsStackedEnsemble"
      pred = predictor_from_json_ai(json_ai)
      pred.learn(train_df)
      preds = pred.predict(test_df[0:10])
      self.assertTrue(isinstance(pred.ensemble, TsStackedEnsemble))
      self.check_ts_prediction_df(preds, horizon, [order_by])
  
      # test weight bypassing
      pred.ensemble.set_weights(
          [0 for _ in range(len(pred.ensemble.mixers))]
      )
      self.assertEqual(
          0,
          sum(
              [
                  sum(row)
                  for row in pred.predict(test_df[0:10])[
                      "prediction"
                  ].tolist()
              ]
          ),
      )
  
imports: import random
  from datetime import datetime
  from datetime import timedelta
  import unittest
  import numpy
  import pandas
  from typing import List
  from scipy import signal
  from sklearn.metrics import r2_score
  from lightwood.api.types import ProblemDefinition
  from tests.utils.timing import train_and_check_time_aim
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.statsforecast import StatsForecastAutoARIMA
  from dataprep_ml.splitters import stratify
  from lightwood.api.high_level import json_ai_from_problem, code_from_json_ai, predictor_from_code, predictor_from_problem, predictor_from_json_ai
  from lightwood.mixer.sktime import SkTime
label_desc: Use image node_id 14 for calling the TestTimeseries obj's underlying member method code with example usage: obj.test_10_ts_stacked_ensemble() without return types