class_docstr: Ensemble of forecasters.
  
  Overview: Input one series of length `n` and EnsembleForecaster performs
  fitting and prediction for each estimator passed in `forecasters`. It then
  applies `aggfunc` aggregation function by row to the predictions dataframe
  and returns final prediction - one series.
  
  Parameters
  ----------
  forecasters : list of (str, estimator) tuples
      Estimators to apply to the input series.
  n_jobs : int or None, optional, default=None
      The number of jobs to run in parallel for fit. None means 1 unless
      in a joblib.parallel_backend context.
      -1 means using all processors.
  aggfunc : str, {'mean', 'median', 'min', 'max'}, default='mean'
      The function to aggregate prediction from individual forecasters.
  weights : list of floats
      Weights to apply in aggregation.
  
  See Also
  --------
  AutoEnsembleForecaster
  
  Examples
  --------
  >>> from sktime.forecasting.compose import EnsembleForecaster
  >>> from sktime.forecasting.naive import NaiveForecaster
  >>> from sktime.forecasting.trend import PolynomialTrendForecaster
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> forecasters = [
  ...     ("trend", PolynomialTrendForecaster()),
  ...     ("naive", NaiveForecaster()),
  ... ]
  >>> forecaster = EnsembleForecaster(forecasters=forecasters, weights=[4, 10])
  >>> forecaster.fit(y=y, fh=[1,2,3])
  EnsembleForecaster(...)
  >>> y_pred = forecaster.predict()
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docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
  
  
  Returns
  -------
  params : dict or list of dict
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default=","''","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
  
  
      Returns
      -------
      params : dict or list of dict
      """
      from sktime.forecasting.compose._reduce import (
          DirectReductionForecaster,
      )
      from sktime.forecasting.naive import NaiveForecaster
  
      # univariate case
      FORECASTER = NaiveForecaster()
      params = [
          {"forecasters": [("f1", FORECASTER), ("f2", FORECASTER)]}
      ]
  
      # test multivariate case, i.e., ensembling multiple variables at same time
      FORECASTER = DirectReductionForecaster.create_test_instance()
      params = params + [
          {"forecasters": [("f1", FORECASTER), ("f2", FORECASTER)]}
      ]
  
      return params
  
imports: import numpy
  import pandas
  from scipy.stats import gmean
  from sklearn.pipeline import Pipeline
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.base._meta import _HeterogenousEnsembleForecaster
  from sktime.split import temporal_train_test_split
  from sktime.utils.stats import _weighted_geometric_mean, _weighted_max, _weighted_median, _weighted_min
  from sktime.utils.validation.forecasting import check_regressor
label_desc: Use image node_id 4 for calling the EnsembleForecaster obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params