* "expected arrival time of the next request" is actually in the past, then the difference (now -
   * past) is the amount of time that the RateLimiter was formally unused, and it is that amount of
   * time which we translate to storedPermits. (We increase storedPermits with the amount of permits
   * that would have been produced in that idle time). So, if rate == 1 permit per second, and

    * Make sure you add your link to the start of the corresponding section.
* To help [translate the documentation](contributing.md#translations) to your language.
    * You can also help to review the translations created by others.
* To propose new documentation sections.
* To fix an existing issue/bug.

// UTF-8-encoded rune as a token. The sequence of runes returned is
// equivalent to that from a range loop over the input as a string, which
// means that erroneous UTF-8 encodings translate to U+FFFD = "\xef\xbf\xbd".
// Because of the Scan interface, this makes it impossible for the client to
// distinguish correctly encoded replacement runes from encoding errors.

* Bu dokümanın hedeflediğiniz dile sıfırdan yeni bir çevirisini yapın (örneğin `translate.py` içindeki `translate-page` komutu). Bu, çeviriyi `docs/{language code}/docs/_llm-test.md` altında oluşturur.
* Çeviride her şeyin yolunda olup olmadığını kontrol edin.
* Gerekirse dile özel prompt'u, genel prompt'u veya İngilizce dokümanı iyileştirin.
* Ardından çeviride kalan sorunları elle düzeltin; böylece iyi bir çeviri elde edin.

load","pubdate","radiogroup","readonly","rel","required","rev","reversed","role","rows","rowspan","spellcheck","scope","selected","shape","size","sizes","slot","span","srclang","start","src","srcset","step","style","summary","tabindex","title","translate","type","usemap","valign","value","width","wrap","xmlns","slot"]),P=i(["accent-height","accumulate","additive","alignment-baseline","amplitude","ascent","attributename","attributetype","azimuth","basefrequency","baseline-shift","begin","bias","b...

w/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,t...

* Tenha um prompt específico do idioma – `docs/{language code}/llm-prompt.md`.
* Faça uma tradução nova deste documento para o seu idioma de destino (veja, por exemplo, o comando `translate-page` do `translate.py`). Isso criará a tradução em `docs/{language code}/docs/_llm-test.md`.
* Verifique se está tudo certo na tradução.
* Se necessário, melhore seu prompt específico do idioma, o prompt geral ou o documento em inglês.

,"۱","۲","۳","۴","۵","۶","۷","۸","۹","۰","numbersPast","numbersFuture","translate$2","monthsRegex$7","monthsParse$6","monthsShortWithDots","monthsShortWithoutDots","processRelativeTime$4","processRelativeTime$5","symbolMap$7","numberMap$6","૧","૨","૩","૪","૫","૬","૭","૮","૯","૦","symbolMap$8","numberMap$7","१","२","३","४","५","६","७","८","९","०","monthsParse$7","translate$3","weekEndings","translate$4","plural$2","translate$5","eraYearOrdinalRegex","$0","$1","$2","suffixes$1","40","symbolMap$9",...

本文件用來測試用於翻譯文件的 <abbr title="Large Language Model - 大型語言模型">LLM</abbr> 是否理解 `scripts/translate.py` 中的 `general_prompt`，以及 `docs/{language code}/llm-prompt.md` 中的語言特定提示。語言特定提示會附加在 `general_prompt` 後面。

此處新增的測試會提供給所有語言特定提示的設計者參考。

使用方式：

* 準備語言特定提示 - `docs/{language code}/llm-prompt.md`。
* 針對本文件做一次全新的翻譯為你想要的目標語言（例如使用 `translate.py` 的 `translate-page` 指令）。這會在 `docs/{language code}/docs/_llm-test.md` 產生翻譯檔。
* 檢查翻譯是否正確。

	// Parts in FileInfo should be in sorted order by part number.
	sort.Slice(fi.Parts, func(i, j int) bool { return fi.Parts[i].Number < fi.Parts[j].Number })
}

// ObjectToPartOffset - translate offset of an object to offset of its individual part.
func (fi FileInfo) ObjectToPartOffset(ctx context.Context, offset int64) (partIndex int, partOffset int64, err error) {
	if offset == 0 {