The main logic with ADRs is to describe (architectural) decisions made:

* To provide best practices and solutions we (as the *build tool* team) want to promote.
* To avoid asking the same thing multiple times during code review.
* To explain *rejected solutions*, for now, and future development, in case they are proposed again.

ADRs can be written by any team.

- Promote DRAPrioritizedList to GA ([#136924](https://github.com/kubernetes/kubernetes/pull/136924), [@troychiu](https://github.com/troychiu)) [SIG Apps, Architecture, Autoscaling, CLI, Cloud Provider, Cluster Lifecycle, Network, Node, Release, Scheduling, Storage and Testing]

- Promote InPlacePodVerticalScaling to GA. ([#134949](https://github.com/kubernetes/kubernetes/pull/134949), [@natasha41575](https://github.com/natasha41575)) [SIG API Machinery, Node and Scheduling]
- Promote Relaxed validation for Services names to beta (enabled by default)
  
  Promote `RelaxedServiceNameValidation` feature to beta (enabled by default)

- Promote MutableCSINodeAllocatableCount to Beta. ([#132429](https://github.com/kubernetes/kubernetes/pull/132429), [@torredil](https://github.com/torredil)) [SIG Storage]
- Promoted feature-gate `VolumeAttributesClass` to GA

- Promoted kubectl `--subresource` flag to stable. ([#130238](https://github.com/kubernetes/kubernetes/pull/130238), [@soltysh](https://github.com/soltysh))
- Promoted the `CRDValidationRatcheting` feature gate to GA in 1.33 ([#130013](https://github.com/kubernetes/kubernetes/pull/130013), [@yongruilin](https://github.com/yongruilin)) [SIG API Machinery]

Translation pull requests are made by LLMs guided with prompts designed by the FastAPI team together with the community of native speakers for each supported language.

#### LLM Prompt per Language

Each language has a directory: [https://github.com/fastapi/fastapi/tree/master/docs](https://github.com/fastapi/fastapi/tree/master/docs), in it you can see a file `llm-prompt.md` with the prompt specific for that language.

                        const promptStart = line.indexOf(promptLiteralStart);
                        if (promptStart === -1) {
                            console.error("Custom prompt found but no end delimiter", line)
                        }
                        const prompt = line.slice(0, promptStart).replace(customPromptLiteralStart, "")
                        let value = line.slice(promptStart + promptLiteralStart.length);

        includes:
        `tf.data.Dataset.from_tensor_slices`,`tf.data.Dataset.shuffle`,
        `tf.data.Dataset.batch`, `tf.data.Dataset.shard`, `tf.data.Dataset.map`,
        and `tf.data.Dataset.range`.
    *   Promote `tf.data.Options.experimental_deterministic` API to
        `tf.data.Options.deterministic` and deprecate the experimental endpoint.
    *   Move autotuning options

Os testes adicionados aqui serão vistos por todos os designers dos prompts específicos de idioma.

Use da seguinte forma:

* Tenha um prompt específico do idioma – `docs/{language code}/llm-prompt.md`.

    }

    /**
     * Resolves the {{languageInstruction}} placeholder in a prompt.
     *
     * @param prompt the prompt template
     * @return the prompt with language instruction resolved
     */
    protected String resolveLanguageInstruction(final String prompt) {
        if (prompt == null) {
            return null;
        }