keras/src/layers/normalization/unit_normalization.py/UnitNormalization/__init__
class UnitNormalization:    def __init__(self, axis=-1, **kwargs):
        super().__init__(**kwargs)
        if isinstance(axis, (list, tuple)):
            self.axis = list(axis)
        elif isinstance(axis, int):
            self.axis = axis
        else:
            raise TypeError(
                "Invalid value for `axis` argument: "
                "expected an int or a list/tuple of ints. "
                f"Received: axis={axis}"
            )
        self.supports_masking = True