When translating sentences, avoid unnecessary repetition of words or phrases that are implied in context.
- Merge repeated words naturally while keeping the meaning.
- Do **not** introduce extra words to replace repeated phrases unnecessarily.
- Keep translations fluent and concise, but maintain the original meaning.

**Example:**

Source:
Let's see how that works and how to change it if you need to do that.

Avoid translating literally as:

* production (meaning production software or environment): продакшн (do not change the ending, for example, translate `in production` as `в продакшн` (not `в продакшене`))
* completion (meaning code auto-completion): автозавершение
* editor (meaning component of IDE): редактор кода
* adopt (meaning start to use): использовать (or `начать использовать`)
* headers (meaning HTTP-headers): HTTP-заголовки

    [Environment]::SetEnvironmentVariable('PATH', $env:PATH, 'Machine');

# Disable signature checking on pacman because we cannot initialize the keyring.
RUN Add-Content -Path C:\tools\msys64\etc\pacman.d\mirrorlist.mingw32 -Value 'SigLevel = Never'
RUN Add-Content -Path C:\tools\msys64\etc\pacman.d\mirrorlist.mingw64 -Value 'SigLevel = Never'

Lets start the lambda handler.

```
python lambda_handler.py
 * Serving Flask app 'webhook'
 * Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:5000
Press CTRL+C to quit
```

```bash
gpg --verify plugin-publish-plugin-2.0.0.jar.asc plugin-publish-plugin-2.0.0.jar
```

If you see a warning message like `gpg: WARNING: This key is not certified with a trusted signature!`, you can locally sign the Gradle key after importing it.
This tells your GPG installation that you trust this key and will prevent the warning from appearing again.

To do this, run the following command:

```bash

- Mozilla Developer Network: Мережа Розробників Mozilla
- tutorial: навчальний посібник
- advanced user guide: просунутий посібник користувача
- deep learning: глибоке навчання
- machine learning: машинне навчання
- dependency injection: впровадження залежностей
- digest (HTTP): дайджест
- basic authentication (HTTP): базова автентифікація
- JSON schema: Схема JSON
- password flow: потік паролю

## Kullanım Senaryosu { #use-case }

Önce bir **kullanım senaryosu** örneğiyle başlayalım, sonra bunu bununla nasıl çözeceğimize bakalım.

Request’leri işlemek için kullanmak istediğiniz bazı **machine learning modelleriniz** olduğunu hayal edelim. 🤖

* 100% test coverage: cobertura de tests del 100%
* back and forth: de un lado a otro
* I/O (as in "input and output"): I/O (do not translate to "E/S")
* Machine Learning: Machine Learning (do not translate to "Aprendizaje Automático")
* Deep Learning: Deep Learning (do not translate to "Aprendizaje Profundo")
* callback hell: callback hell (do not translate to "infierno de callbacks")
* tip: Consejo (do not translate to "tip")

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar requests. 🤖