*   tf.optimizers => tf.keras.optimizers
    *   Add tf.keras.layers.AbstractRNNCell as the preferred implementation of
        RNN cell for TF v2. User can use it to implement RNN cell with custom
        behavior.
    *   Adding `clear_losses` API to be able to clear losses at the end of
        forward pass in a custom training loop in eager.

## Dependencies

### Added
_Nothing has changed._

### Changed
- github.com/google/cel-go: [v0.12.6 → v0.12.7](https://github.com/google/cel-go/compare/v0.12.6...v0.12.7)
- go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp: v0.35.0 → v0.35.1

### Removed
_Nothing has changed._



# v1.26.8

En lugar de eso, al ser un sistema "asíncrono", una vez terminado, la tarea puede esperar un poco en la cola (algunos microsegundos) para que la computadora / programa termine lo que salió a hacer, y luego regrese para tomar los resultados y continuar trabajando con ellos.

Uma das principais funcionalidades necessárias em sistemas de API é a "<abbr title="também chamado marshalling, conversão">serialização</abbr>" de dados, que é pegar dados do código (Python) e convertê-los em algo que possa ser enviado pela rede. Por exemplo, converter um objeto contendo dados de um banco de dados em um objeto JSON. Converter objetos `datetime` em strings, etc.

block !important;\n}\n\n.d-grid {\n  display: grid !important;\n}\n\n.d-inline-grid {\n  display: inline-grid !important;\n}\n\n.d-table {\n  display: table !important;\n}\n\n.d-table-row {\n  display: table-row !important;\n}\n\n.d-table-cell {\n  display: table-cell !important;\n}\n\n.d-flex {\n  display: flex !important;\n}\n\n.d-inline-flex {\n  display: inline-flex !important;\n}\n\n.d-none {\n  display: none !important;\n}\n\n.shadow {\n  box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15) !important;\n}\n\n.shadow-sm...

- github.com/google/cadvisor: [v0.48.1 → v0.49.0](https://github.com/google/cadvisor/compare/v0.48.1...v0.49.0)
- github.com/google/cel-go: [v0.17.7 → v0.17.8](https://github.com/google/cel-go/compare/v0.17.7...v0.17.8)
- github.com/onsi/ginkgo/v2: [v2.13.0 → v2.15.0](https://github.com/onsi/ginkgo/compare/v2.13.0...v2.15.0)

- github.com/go-logr/zapr: [v1.2.0](https://github.com/go-logr/zapr/tree/v1.2.0)
- github.com/google/cel-go: [v0.9.0](https://github.com/google/cel-go/tree/v0.9.0)
- github.com/google/cel-spec: [v0.6.0](https://github.com/google/cel-spec/tree/v0.6.0)
- github.com/google/martian/v3: [v3.1.0](https://github.com/google/martian/v3/tree/v3.1.0)

colw??in&byr?diws,sark,?le?o&nas?tsylaib??rob&el?lam??s&als?jazel?nadg,puls?rowezrp???l&colw?e&r?vart??i&am?m???m&o&c?dar?n?tyb??s&g?iruot??t!a???n&a&gaz?nzop,?i&bul?cezczs?lbul,molow?nok?zd&eb?obeiws???u&leiw?rot,?y&tzslo?z&rtek?seic????o&c,fni?k&celo?sleib,zdolk??lkan?n&leim?pek?t&uk?yzczs??z&copo?eing?rowaj???rga?t&nokd,ua??w&ejarg?ogarm???p&e&eb,lks!emoh,??klwwortso?ohs!-ecremmoce,daerpsym,??romophcaz?s&klofc,os??t&aiwop?en?opos,ra,sezc??ude?v&irp?og!.&a&io?p?s!w???bni&p?w??ci?dtiw?e&ko?ss&p...

    public Map<R, V> column(@ParametricNullness C columnKey) {
      synchronized (mutex) {
        return map(delegate().column(columnKey), mutex);
      }
    }

    @Override
    public Set<Cell<R, C, V>> cellSet() {
      synchronized (mutex) {
        return set(delegate().cellSet(), mutex);
      }
    }

    @Override
    public Set<R> rowKeySet() {
      synchronized (mutex) {

    public Map<R, V> column(@ParametricNullness C columnKey) {
      synchronized (mutex) {
        return map(delegate().column(columnKey), mutex);
      }
    }

    @Override
    public Set<Cell<R, C, V>> cellSet() {
      synchronized (mutex) {
        return set(delegate().cellSet(), mutex);
      }
    }

    @Override
    public Set<R> rowKeySet() {
      synchronized (mutex) {