* This allows adding extra code after a dependency is done. It can be used, for example, to close database connections.
    * Dependencies with `yield` can be normal or `async`, **FastAPI** will run normal dependencies in a threadpool.
    * They can be combined with normal dependencies.
    * It's possible to have arbitrary trees/levels of dependencies with `yield` and exit steps are handled in the correct order automatically.

are in bit reversed order. var productTable [16]gcmFieldElement // We precompute 16 multiples of H. However, when we do lookups // into this table we'll be using bits from a field element and // therefore the bits will be in the reverse order. So normally one // would expect, say, 4*H to be in index 4 of the table but due to // this bit ordering it will actually be in index 0010 (base 2) = 2. x := gcmFieldElement{ byteorder.BEUint64(H[:8]), byteorder.BEUint64(H[8:]), } productTable[reverseBits(1)]...

are in bit reversed order. var productTable [16]gcmFieldElement // We precompute 16 multiples of H. However, when we do lookups // into this table we'll be using bits from a field element and // therefore the bits will be in the reverse order. So normally one // would expect, say, 4*H to be in index 4 of the table but due to // this bit ordering it will actually be in index 0010 (base 2) = 2. x := gcmFieldElement{ byteorder.BEUint64(H[:8]), byteorder.BEUint64(H[8:]), } productTable[reverseBits(1)]...

        *   `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.