code: def test_FittedParamExtractor(param_names):
      forecaster = ExponentialSmoothing()
      t = FittedParamExtractor(
          forecaster=forecaster, param_names=param_names
      )
      Xt = t.fit_transform(X_train)
      assert Xt.shape == (
          X_train.shape[0],
          len(t._check_param_names(param_names)),
      )
  
      # check specific value
      forecaster.fit(X_train.iloc[47, 0])
      fitted_param = forecaster.get_fitted_params()[param_names]
      assert Xt.iloc[47, 0] == fitted_param
  
imports: import pytest
  from sktime.datasets import load_gunpoint
  from sktime.forecasting.exp_smoothing import ExponentialSmoothing
  from sktime.transformations.panel.summarize import FittedParamExtractor
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 1 for calling a global function with example usage: test_FittedParamExtractor(param_names) without return types