@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
@pytest.mark.parametrize("missing_value_test", [np.nan, 1])
def test_imputation_adds_missing_indicator_if_add_indicator_is_true(
    imputer, missing_value_test
):
    """Check that missing indicator always exists when add_indicator=True.

    Non-regression test for gh-26590.
    """
    X_train = np.array([[0, np.NaN], [1, 2]])

    # Test data without missing values that can be set to np.NaN or 1.
    X_test = np.array([[0, missing_value_test], [1, 2]])

    imputer.set_params(add_indicator=True)
    imputer.fit(X_train)

    X_test_imputed_with_indicator = imputer.transform(X_test)
    assert X_test_imputed_with_indicator.shape == (2, 3)

    imputer.set_params(add_indicator=False)
    imputer.fit(X_train)
    X_test_imputed_without_indicator = imputer.transform(X_test)
    assert X_test_imputed_without_indicator.shape == (2, 2)

    assert_allclose(
        X_test_imputed_with_indicator[:, :-1], X_test_imputed_without_indicator
    )
    if np.isnan(missing_value_test):
        expected_missing_indicator = [1, 0]
    else:
        expected_missing_indicator = [0, 0]

    assert_allclose(X_test_imputed_with_indicator[:, -1], expected_missing_indicator)