### `tf.keras`:

*   Improvements from the Functional API refactoring:
    *   Functional model construction does not need to maintain a global
        workspace graph, removing memory leaks especially when building many
        models or very large models.
    *   Functional model construction should be ~8-10% faster on average.
    *   Functional models can now contain non-symbolic values in their call

skip over it. The max cap is specified to avoid // an allocation. buf := make([]byte, d.n, rateK256) function := d.function() switch function { case sha3_224, sha3_256, sha3_384, sha3_512: klmd(function, &a, nil, buf) return append(b, a[:d.outputLen]...) case shake_128, shake_256: h := make([]byte, d.outputLen, 64) klmd(function, &a, h, buf) return append(b, h...) default: panic("sha3: unknown function") } } func (d *Digest) read(out []byte) (n int, err error) { if !useSHA3 || d.dsbyte != dsbyteShake...

**several benefits**, one of the main ones is that now the parameters of your functions with `Annotated` would **not be affected** at all.

If you call those functions in **other places in your code**, the actual **default values** will be kept, your editor will help you notice missing **required arguments**, Python will require you to pass required arguments at **runtime**, you will be able to **use the same functions** for different things and with different libraries (e.g. **Typer** will...

skip over it. The max cap is specified to avoid // an allocation. buf := make([]byte, d.n, rateK256) function := d.function() switch function { case sha3_224, sha3_256, sha3_384, sha3_512: klmd(function, &a, nil, buf) return append(b, a[:d.outputLen]...) case shake_128, shake_256: h := make([]byte, d.outputLen, 64) klmd(function, &a, h, buf) return append(b, h...) default: panic("sha3: unknown function") } } func (d *Digest) read(out []byte) (n int, err error) { if !useSHA3 || d.dsbyte != dsbyteShake...