code: def test_get_coef_inverse_transform(inverse, Scale, kwargs):
      """Test get_coef with and without inverse_transform."""
      from sklearn.linear_model import Ridge
      from sklearn.pipeline import make_pipeline
  
      lm_regression = LinearModel(Ridge())
      X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1)
      # Check with search_light and combination of preprocessing ending with sl:
      # slider = SlidingEstimator(make_pipeline(StandardScaler(), lm_regression))
      # XXX : line above should work but does not as only last step is
      # used in get_coef ...
      slider = SlidingEstimator(make_pipeline(lm_regression))
      X = np.transpose(
          [X, -X], [1, 2, 0]
      )  # invert X across 2 time samples
      clf = make_pipeline(Scale(**kwargs), slider)
      clf.fit(X, y)
      patterns = get_coef(clf, "patterns_", inverse)
      filters = get_coef(clf, "filters_", inverse)
      assert_array_equal(filters.shape, patterns.shape, X.shape[1:])
      # the two time samples get inverted patterns
      assert_equal(patterns[0, 0], -patterns[0, 1])
      for t in [0, 1]:
          filters_t = get_coef(
              clf.named_steps["slidingestimator"].estimators_[t],
              "filters_",
              False,
          )
          if Scale is _Noop:
              assert_array_equal(filters_t, filters[:, t])
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less, assert_equal
  from mne import EpochsArray, create_info
  from mne.decoding import GeneralizingEstimator, Scaler, TransformerMixin, Vectorizer
  from mne.decoding.base import BaseEstimator, LinearModel, _get_inverse_funcs, cross_val_multiscore, get_coef
  from mne.decoding.search_light import SlidingEstimator
label_desc: Use image node_id 3 for calling a global function with example usage: test_get_coef_inverse_transform(inverse, Scale, kwargs) without return types