keras/src/layers/normalization/layer_normalization.py/LayerNormalization/__init__
class LayerNormalization:    def __init__(
        self,
        axis=-1,
        epsilon=1e-3,
        center=True,
        scale=True,
        rms_scaling=False,
        beta_initializer="zeros",
        gamma_initializer="ones",
        beta_regularizer=None,
        gamma_regularizer=None,
        beta_constraint=None,
        gamma_constraint=None,
        **kwargs
    ):
        super().__init__(**kwargs)
        if isinstance(axis, (list, tuple)):
            self.axis = list(axis)
        elif isinstance(axis, int):
            self.axis = axis
        else:
            raise TypeError(
                "Expected an int or a list/tuple of ints for the "
                "argument 'axis', but received: %r" % axis
            )

        self.epsilon = epsilon
        self.center = center
        self.scale = scale
        self.rms_scaling = rms_scaling
        self.beta_initializer = initializers.get(beta_initializer)
        self.gamma_initializer = initializers.get(gamma_initializer)
        self.beta_regularizer = regularizers.get(beta_regularizer)
        self.gamma_regularizer = regularizers.get(gamma_regularizer)
        self.beta_constraint = constraints.get(beta_constraint)
        self.gamma_constraint = constraints.get(gamma_constraint)

        self.supports_masking = True
        self.autocast = False