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:

- 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]

bits, which guarantees // that all carries are at most 111 - 51 = 60 bits, which fits in a uint64. // // r0 = a0×b0 + 19×(a1×b4 + a2×b3 + a3×b2 + a4×b1) // r0 < 2⁵²×2⁵² + 19×(2⁵²×2⁵² + 2⁵²×2⁵² + 2⁵²×2⁵² + 2⁵²×2⁵²) // r0 < (1 + 19 × 4) × 2⁵² × 2⁵² // r0 < 2⁷ × 2⁵² × 2⁵² // r0 < 2¹¹¹ // // Moreover, the top coefficient (r4) is at most 107 bits, so c4 is at most // 56 bits, and c4 * 19 is at most 61 bits, which again fits in a uint64 and // allows us to easily apply the reduction identity. // // r4 =...

        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`
        boundaries.

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bits, which guarantees // that all carries are at most 111 - 51 = 60 bits, which fits in a uint64. // // r0 = a0×b0 + 19×(a1×b4 + a2×b3 + a3×b2 + a4×b1) // r0 < 2⁵²×2⁵² + 19×(2⁵²×2⁵² + 2⁵²×2⁵² + 2⁵²×2⁵² + 2⁵²×2⁵²) // r0 < (1 + 19 × 4) × 2⁵² × 2⁵² // r0 < 2⁷ × 2⁵² × 2⁵² // r0 < 2¹¹¹ // // Moreover, the top coefficient (r4) is at most 107 bits, so c4 is at most // 56 bits, and c4 * 19 is at most 61 bits, which again fits in a uint64 and // allows us to easily apply the reduction identity. // // r4 =...