- github.com/docopt/docopt-go: [ee0de3b](https://github.com/docopt/docopt-go/tree/ee0de3b)
- github.com/go-gl/glfw/v3.3/glfw: [12ad95a](https://github.com/go-gl/glfw/v3.3/glfw/tree/12ad95a)
- github.com/go-ini/ini: [v1.9.0](https://github.com/go-ini/ini/tree/v1.9.0)
- github.com/godbus/dbus/v5: [v5.0.3](https://github.com/godbus/dbus/v5/tree/v5.0.3)
- github.com/ianlancetaylor/demangle: [5e5cf60](https://github.com/ianlancetaylor/demangle/tree/5e5cf60)

v.Z.Set(&p.z) v.T2d.Multiply(&p.t, d2) return v } func (v *affineCached) FromP3(p *Point) *affineCached { v.YplusX.Add(&p.y, &p.x) v.YminusX.Subtract(&p.y, &p.x) v.T2d.Multiply(&p.t, d2) var invZ field.Element invZ.Invert(&p.z) v.YplusX.Multiply(&v.YplusX, &invZ) v.YminusX.Multiply(&v.YminusX, &invZ) v.T2d.Multiply(&v.T2d, &invZ) return v } // (Re)addition and subtraction. // Add sets v = p + q, and returns v. func (v *Point) Add(p, q *Point) *Point { checkInitialized(p, q) qCached := new(projCached).FromP3(q)...

v.Z.Set(&p.z) v.T2d.Multiply(&p.t, d2) return v } func (v *affineCached) FromP3(p *Point) *affineCached { v.YplusX.Add(&p.y, &p.x) v.YminusX.Subtract(&p.y, &p.x) v.T2d.Multiply(&p.t, d2) var invZ field.Element invZ.Invert(&p.z) v.YplusX.Multiply(&v.YplusX, &invZ) v.YminusX.Multiply(&v.YminusX, &invZ) v.T2d.Multiply(&v.T2d, &invZ) return v } // (Re)addition and subtraction. // Add sets v = p + q, and returns v. func (v *Point) Add(p, q *Point) *Point { checkInitialized(p, q) qCached := new(projCached).FromP3(q)...

053A          ; mapped                 ; 056A          # 1.1  ARMENIAN CAPITAL LETTER ZHE
053B          ; mapped                 ; 056B          # 1.1  ARMENIAN CAPITAL LETTER INI
053C          ; mapped                 ; 056C          # 1.1  ARMENIAN CAPITAL LETTER LIWN
053D          ; mapped                 ; 056D          # 1.1  ARMENIAN CAPITAL LETTER XEH

    as gradient clipping).
*   Improvements to Keras preprocessing layers:
    *   TextVectorization can now accept a vocabulary list or file as an init
        arg.
    *   Normalization can now accept mean and variance values as init args.
*   In `Attention` and `AdditiveAttention` layers, the `call()` method now
    accepts a `return_attention_scores` argument. When set to True, the layer