available, along with experimental support for multi worker and Cloud TPUs.
    Check out the
    [guide](https://www.tensorflow.org/beta/guide/distribute_strategy) for more
    details.
*   Functions, not Sessions. The traditional declarative programming model of
    building a graph and executing it via a `tf.Session` is discouraged, and
    replaced with by writing regular Python functions. Using the `tf.function`

* Update [SQL Tutorial](https://fastapi.tiangolo.com/tutorial/sql-databases/#create-a-middleware-to-handle-sessions) to close database sessions even when there are exceptions. PR [#89](https://github.com/tiangolo/fastapi/pull/89) by [@alexiri](https://github.com/alexiri).

Direction{[]byte{'A'}, []byte{'C'}, []byte{'E'}} } func Keys[Hash fips140.Hash](hash func() Hash, d Direction, K, H, sessionID []byte, ivKeyLen, keyLen, macKeyLen int, ) (ivKey, key, macKey []byte) { h := hash() generateKeyMaterial := func(tag []byte, length int) []byte { var key []byte for len(key) < length { h.Reset() h.Write(K) h.Write(H) if len(key) == 0 { h.Write(tag) h.Write(sessionID) } else { h.Write(key) } key = h.Sum(key) } return key[:length] } ivKey = generateKeyMaterial(d.ivTag, ivKeyLen)...