human
Complete the below
@requires_sklearn_0_15
def test_get_coef():

    ''''Test the retrieval of linear coefficients (filters and patterns) from
    simple and pipeline estimators.'
    '''
 Can you provide a the Python code you requested?
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#Here is the Python code you requested.
    from sklearn.base import TransformerMixin, BaseEstimator
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import Ridge, LinearRegression
    lm = LinearModel(Ridge())
    class Clf(BaseEstimator, ):
        def fit(self, X, y):
            return self
    class NoInv(TransformerMixin, ):
        def fit(self, X, y):
            return self
        def transform(self, X):
            return X
    class Inv(NoInv, ):
        def inverse_transform(self, X):
            return X
    (X, y, A) = _make_data(n_samples=2000, n_features=3, n_targets=1)
    good_estimators = [(1, make_pipeline(Inv(), Clf())), (2, make_pipeline(Inv(), Inv(), Clf())), (3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf()))]
    for (expected_n, est) in good_estimators:
        est.fit(X, y)
        assert_true((expected_n == len(_get_inverse_funcs(est))))
    bad_estimators = [Clf(), Inv(), make_pipeline(NoInv(), Clf()), make_pipeline(Inv(), make_pipeline(Inv(), NoInv()), Clf())]
    for est in bad_estimators:
        est.fit(X, y)
        invs = _get_inverse_funcs(est)
        assert_equal(invs, list())
    for clf in (lm, make_pipeline(StandardScaler(), lm)):
        clf.fit(X, y)
        filters = get_coef(clf, 'filters_', False)
        if hasattr(clf, 'steps'):
            coefs = clf.steps[(-1)][(-1)].model.coef_
        else:
            coefs = clf.model.coef_
        assert_array_equal(filters, coefs[0])
        patterns = get_coef(clf, 'patterns_', False)
        assert_true((filters[0] != patterns[0]))
        n_chans = X.shape[1]
        assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans])
    filters_inv = get_coef(clf, 'filters_', True)
    assert_true((filters[0] != filters_inv[0]))
    patterns_inv = get_coef(clf, 'patterns_', True)
    assert_true((patterns[0] != patterns_inv[0]))
    slider = SlidingEstimator(make_pipeline(StandardScaler(), lm))
    X = np.transpose([X, (- X)], [1, 2, 0])
    clfs =...