### Target language

Translate to Turkish (Türkçe).

Language code: tr.

### Core principle

Don't translate word-by-word. Rewrite naturally in Turkish as if writing the doc from scratch. Preserve meaning, but prioritize fluency over literal accuracy.

### Grammar and tone

- Use instructional Turkish, consistent with existing Turkish docs.
- Use imperative/guide language (e.g. "açalım", "gidin", "kopyalayalım", "bir bakalım").

* loop (as in async loop): цикл событий
* Machine Learning: Машинное обучение
* Deep Learning: Глубокое обучение
* callback hell: callback hell (clarify as `ад обратных вызовов`)
* on the fly: на лету
* scratch the surface: поверхностно ознакомиться
* tip: совет (or `подсказка` depending on context)
* Pydantic model: Pydantic-модель (`модель Pydantic` and `Pydantic модель` are also fine)
* declare: объявить

	return p.ctxt.PosTable.XPos(src.MakePos(p.lex.Base(), uint(p.lineNum), 0))
}

func (p *Parser) Parse() (*obj.Prog, bool) {
	scratch := make([][]lex.Token, 0, 3)
	for {
		word, cond, operands, ok := p.line(scratch)
		if !ok {
			break
		}
		scratch = operands

		if p.pseudo(word, operands) {
			continue
		}
		i, present := p.arch.Instructions[word]
		if present {

## Build a Docker Image for FastAPI { #build-a-docker-image-for-fastapi }

Okay, let's build something now! 🚀

I'll show you how to build a **Docker image** for FastAPI **from scratch**, based on the **official Python** image.

This is what you would want to do in **most cases**, for example:

* Using **Kubernetes** or similar tools
* When running on a **Raspberry Pi**

            + positionFrac * (dataset[positionFloor + 1] - dataset[positionFloor]);
      }
    }
  },

  /**
   * Uses quickselect. When calculating multiple quantiles, each quickselect starts from scratch.
   */
  QUICKSELECT {

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      long numerator = (long) index * (dataset.length - 1);

In the next chapter about [FastAPI in Containers - Docker](docker.md) I'll explain some strategies you could use to handle the other **deployment concepts**.

I'll show you how to **build your own image from scratch** to run a single Uvicorn process. It is a simple process and is probably what you would want to do when using a distributed container management system like **Kubernetes**.

## Recap { #recap }

 * fragile because they assume that only certain methods will be called and because they often
 * implement subtleties of the API improperly.
 *
 * <p><b>Custom implementation</b>: Avoid implementing {@code ListenableFuture} from scratch. If you
 * can't get by with the standard implementations, prefer to derive a new {@code Future} instance
 * with the methods in {@link Futures} or, if necessary, to extend {@link AbstractFuture}.
 *

* type checks: chequeo de tipos
* parse: parse
* parsing: parsing
* marshall: marshall
* library: paquete (do not translate to "biblioteca" or "librería")
* instance: instance (do not translate to "instancia")
* scratch the surface: tocar los conceptos básicos
* string: string
* bug: bug
* docs: documentación (do not translate to "documentos")
* cheat sheet: cheat sheet (do not translate to "chuleta")

            + positionFrac * (dataset[positionFloor + 1] - dataset[positionFloor]);
      }
    }
  },

  /**
   * Uses quickselect. When calculating multiple quantiles, each quickselect starts from scratch.
   */
  QUICKSELECT {

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      long numerator = (long) index * (dataset.length - 1);

 * fragile because they assume that only certain methods will be called and because they often
 * implement subtleties of the API improperly.
 *
 * <p><b>Custom implementation</b>: Avoid implementing {@code ListenableFuture} from scratch. If you
 * can't get by with the standard implementations, prefer to derive a new {@code Future} instance
 * with the methods in {@link Futures} or, if necessary, to extend {@link AbstractFuture}.
 *