endpoint.
    *   Promoting `tf.data.experimental.RandomDataset` API to
        `tf.data.Dataset.random` and deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.scan` API to `tf.data.Dataset.scan` and
        deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.snapshot` API to
        `tf.data.Dataset.shapshot` and deprecating the experimental endpoint.

- The Kubelet's `--experimental-allocatable-ignore-eviction` option is now marked as deprecated. ([#91578](https://github.com/kubernetes/kubernetes/pull/91578), [@knabben](https://github.com/knabben)) [SIG Node]

square-and-double // chain log2(_W * n) long. Turns out the fastest thing is to start out with // doublings, and switch to square-and-double once the exponent is large // enough to justify the cost of the multiplications. // The threshold is selected experimentally as a linear function of n. threshold := n / 4 // We calculate how many of the most-significant bits of the exponent we can // compute before crossing the threshold, and we do it with doublings. i := bits.UintSize for logR>>i <= threshold { i--...

square-and-double // chain log2(_W * n) long. Turns out the fastest thing is to start out with // doublings, and switch to square-and-double once the exponent is large // enough to justify the cost of the multiplications. // The threshold is selected experimentally as a linear function of n. threshold := n / 4 // We calculate how many of the most-significant bits of the exponent we can // compute before crossing the threshold, and we do it with doublings. i := bits.UintSize for logR>>i <= threshold { i--...