custom estimator with optimizer v2, you have to scale losses. We have
        new utilities to help scale losses `tf.nn.compute_average_loss`,
        `tf.nn.scale_regularization_loss`.

*   `tf.keras`:

    *   Premade models (including Linear and WideDeep) have been introduced for
        the purpose of replacing Premade estimators.
    *   Model saving changes

This change also meant that if you returned a `StreamingResponse`, the exit code of the dependency with `yield` would have been already run.

For example, if you had a database session in a dependency with `yield`, the `StreamingResponse` would not be able to use that session while streaming data because the session would have already been closed in the exit code after `yield`.

uintptr(unsafe.Pointer(&y[0])) <= uintptr(unsafe.Pointer(&x[len(x)-1])) } // InexactOverlap reports whether x and y share memory at any non-corresponding // index. The memory beyond the slice length is ignored. Note that x and y can // have different lengths and still not have any inexact overlap. // // InexactOverlap can be used to implement the requirements of the crypto/cipher // AEAD, Block, BlockMode and Stream interfaces. func InexactOverlap(x, y []byte) bool { if len(x) == 0 || len(y) == 0 || &x[0]...

uintptr(unsafe.Pointer(&y[0])) <= uintptr(unsafe.Pointer(&x[len(x)-1])) } // InexactOverlap reports whether x and y share memory at any non-corresponding // index. The memory beyond the slice length is ignored. Note that x and y can // have different lengths and still not have any inexact overlap. // // InexactOverlap can be used to implement the requirements of the crypto/cipher // AEAD, Block, BlockMode and Stream interfaces. func InexactOverlap(x, y []byte) bool { if len(x) == 0 || len(y) == 0 || &x[0]...