### 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}.
 *

            + 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}.
 *

{* ../../docs_src/python_types/tutorial001_py310.py hl[2] *}

### Edit it { #edit-it }

It's a very simple program.

But now imagine that you were writing it from scratch.

At some point you would have started the definition of the function, you had the parameters ready...

But then you have to call "that method that converts the first letter to upper case".