The key updated docs are:

* Python Types Intro:
    * [Type Hints with Metadata Annotations](https://fastapi.tiangolo.com/python-types/#type-hints-with-metadata-annotations).
* Tutorial:

    String QUERY_COLLAPSE_INNER_HITS_NAME = "query.collapse.inner.hits.name";

    /** The key of the configuration. e.g. 0 */
    String QUERY_COLLAPSE_INNER_HITS_SIZE = "query.collapse.inner.hits.size";

    /** The key of the configuration. e.g.  */
    String QUERY_COLLAPSE_INNER_HITS_SORTS = "query.collapse.inner.hits.sorts";

    /** The key of the configuration. e.g.  */

        inner spec. This can be used to extract the structure of nested
        datasets.
    *   Add `tf.data.experimental.AutoShardingPolicy.HINT` which can be used to
        provide hints to tf.distribute-based auto-sharding as to where in the
        input pipeline to insert sharding transformations.
    *   Make tf.data.Options persistent across `tf.function` and `GraphDef`

- Using NUMA nodes instead of sockets for CPU manager hints. ([#99276](https://github.com/kubernetes/kubernetes/pull/99276), [@iwankgb](https://github.com/iwankgb)) [SIG Node]

for details. case 1024 / _W: const n = 1024 / _W // compiler hint T := make([]uint, n*2) for i := 0; i < n; i++ { T[n+i] = addMulVVW1024(&T[i], &xLimbs[0], yLimbs[i]) } return x.Mod(&Nat{limbs: T}, m) case 1536 / _W: const n = 1536 / _W // compiler hint T := make([]uint, n*2) for i := 0; i < n; i++ { T[n+i] = addMulVVW1536(&T[i], &xLimbs[0], yLimbs[i]) } return x.Mod(&Nat{limbs: T}, m) case 2048 / _W: const n = 2048 / _W // compiler hint T := make([]uint, n*2) for i := 0; i < n; i++ { T[n+i] = addMulVVW2048(&T[i],...