*   `tf.raw_ops.Bucketize` op on CPU.
        *   `tf.where` op for data types
            `tf.int32`/`tf.uint32`/`tf.int8`/`tf.uint8`/`tf.int64`.
        *   `tf.random.normal` op for output data type `tf.float32` on CPU.
        *   `tf.random.uniform` op for output data type `tf.float32` on CPU.
        *   `tf.random.categorical` op for output data type `tf.int64` on CPU.

*   `tensorflow.experimental.tensorrt`:

* 🔨 Add MkDocs hook that renames sections based on the first index file. PR [#9737](https://github.com/tiangolo/fastapi/pull/9737) by [@tiangolo](https://github.com/tiangolo).

time by turning it into a mask. type choice uint func not(c choice) choice { return 1 ^ c } const yes = choice(1) const no = choice(0) // ctMask is all 1s if on is yes, and all 0s otherwise. func ctMask(on choice) uint { return -uint(on) } // ctEq returns 1 if x == y, and 0 otherwise. The execution time of this // function does not depend on its inputs. func ctEq(x, y uint) choice { // If x != y, then either x - y or y - x will generate a carry. _, c1 := bits.Sub(x, y, 0) _, c2 := bits.Sub(y, x, 0)...

time by turning it into a mask. type choice uint func not(c choice) choice { return 1 ^ c } const yes = choice(1) const no = choice(0) // ctMask is all 1s if on is yes, and all 0s otherwise. func ctMask(on choice) uint { return -uint(on) } // ctEq returns 1 if x == y, and 0 otherwise. The execution time of this // function does not depend on its inputs. func ctEq(x, y uint) choice { // If x != y, then either x - y or y - x will generate a carry. _, c1 := bits.Sub(x, y, 0) _, c2 := bits.Sub(y, x, 0)...