def METHOD_NAME():
    def column_val_1(data):
        """Checks column mean equal to 1."""
        return (data.mean() == 1, {})
    def column_val_2(data):
        """Checks column mean equal to 1.5."""
        return (data.mean() == 1.5, {})
    df = DataFrame(
        {
            "foo": [1, 2],
            "bar": [1, 1],
        }
    )
    aggregate_val = MultiAggregateConstraintWithMetadata(
        "Confirms column means equal to 1.",
        dict([("bar", [column_val_1, column_val_2]), ("foo", [column_val_1, column_val_2])]),
        ConstraintWithMetadataException,
        raise_or_typecheck=False,
    )
    val = aggregate_val.validate(df).metadata[CONSTRAINT_METADATA_KEY].data
    assert val["expected"] == {
        "bar": {"column_val_2": "Checks column mean equal to 1.5."},
        "foo": {"column_val_1": "Checks column mean equal to 1."},
    }
    assert val["offending"] == {
        "bar": {"column_val_2": "a violation"},
        "foo": {"column_val_1": "a violation"},
    }