`tf.mixed_precision. experimental.LossScale` in the constructor, and no
        longer has a `LossScale` associated with it. Instead, `Model.compile`
        will automatically wrap the optimizer with a `LossScaleOptimizer` using
        dynamic loss scaling if `Policy.name` is "mixed_float16".
    *   `tf.keras.mixed_precision.LossScaleOptimizer`'s constructor takes in

if p.x == (field.Element{}) && p.y == (field.Element{}) { panic("edwards25519: use of uninitialized Point") } } } type projCached struct { YplusX, YminusX, Z, T2d field.Element } type affineCached struct { YplusX, YminusX, T2d field.Element } // Constructors. func (v *projP2) Zero() *projP2 { v.X.Zero() v.Y.One() v.Z.One() return v } // identity is the point at infinity. var identity, _ = new(Point).SetBytes([]byte{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...

if p.x == (field.Element{}) && p.y == (field.Element{}) { panic("edwards25519: use of uninitialized Point") } } } type projCached struct { YplusX, YminusX, Z, T2d field.Element } type affineCached struct { YplusX, YminusX, T2d field.Element } // Constructors. func (v *projP2) Zero() *projP2 { v.X.Zero() v.Y.One() v.Z.One() return v } // identity is the point at infinity. var identity, _ = new(Point).SetBytes([]byte{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...