*   `tf.math`

    * `tf.nn.top_k` now supports specifying the output index type via parameter `index_type`.  Supported types are `tf.int16`, `tf.int32` (default), and `tf.int64`.

* 🐛 Fix `jsonable_encoder` alters `json_encoders` of Pydantic v1 objects. PR [#4972](https://github.com/fastapi/fastapi/pull/4972) by [@aboubacs](https://github.com/aboubacs).

v.points[0].FromP3(q) tmpP3 := Point{} tmpP1xP1 := projP1xP1{} for i := 0; i < 7; i++ { // Compute (i+1)*Q as Q + i*Q and convert to a projCached // This is needlessly complicated because the API has explicit // receivers instead of creating stack objects and relying on RVO v.points[i+1].FromP3(tmpP3.fromP1xP1(tmpP1xP1.Add(q, &v.points[i]))) } } // This is not optimised for speed; fixed-base tables should be precomputed. func (v *affineLookupTable) FromP3(q *Point) { // Goal: v.points[i] = (i+1)*Q,...

v.points[0].FromP3(q) tmpP3 := Point{} tmpP1xP1 := projP1xP1{} for i := 0; i < 7; i++ { // Compute (i+1)*Q as Q + i*Q and convert to a projCached // This is needlessly complicated because the API has explicit // receivers instead of creating stack objects and relying on RVO v.points[i+1].FromP3(tmpP3.fromP1xP1(tmpP1xP1.Add(q, &v.points[i]))) } } // This is not optimised for speed; fixed-base tables should be precomputed. func (v *affineLookupTable) FromP3(q *Point) { // Goal: v.points[i] = (i+1)*Q,...