NewGeneratorPoint returns a new Point set to the canonical generator. func NewGeneratorPoint() *Point { return new(Point).Set(generator) } func (v *projCached) Zero() *projCached { v.YplusX.One() v.YminusX.One() v.Z.One() v.T2d.Zero() return v } func (v *affineCached) Zero() *affineCached { v.YplusX.One() v.YminusX.One() v.T2d.Zero() return v } // Assignments. // Set sets v = u, and returns v. func (v *Point) Set(u *Point) *Point { *v = *u return v } // Encoding. // Bytes returns the canonical 32-byte...

NewGeneratorPoint returns a new Point set to the canonical generator. func NewGeneratorPoint() *Point { return new(Point).Set(generator) } func (v *projCached) Zero() *projCached { v.YplusX.One() v.YminusX.One() v.Z.One() v.T2d.Zero() return v } func (v *affineCached) Zero() *affineCached { v.YplusX.One() v.YminusX.One() v.T2d.Zero() return v } // Assignments. // Set sets v = u, and returns v. func (v *Point) Set(u *Point) *Point { *v = *u return v } // Encoding. // Bytes returns the canonical 32-byte...

        policy with `tf.data.experimental.service.ShardingPolicy` enum. It can
        be one of `OFF` (equivalent to today's `"parallel_epochs"` mode),
        `DYNAMIC` (equivalent to today's `"distributed_epoch"` mode), or one of
        the static sharding policies: `FILE`, `DATA`, `FILE_OR_DATA`, or `HINT`
        (corresponding to values of `tf.data.experimental.AutoShardPolicy`).

in it, e.g. in a `List[Item]`), the Pydantic model class(es) will be a different one (the "cloned" one). So, an object that is a subclass won't simply pass the validation and returned as-is, because it is no longer a sub-class of the cloned `response_model`. Instead, a new Pydantic model object will be created with the contents of the returned object. So, it will be a new object (made with the data from the returned one), and will be filtered by the cloned `response_model`, containing only the declared...