name: Label Approved

on:
  schedule:
    - cron: "0 12 * * *"
  workflow_dispatch:

permissions:
  pull-requests: write

jobs:
  label-approved:
    if: github.repository_owner == 'fastapi'
    runs-on: ubuntu-latest
    steps:
    - name: Dump GitHub context
      env:
        GITHUB_CONTEXT: ${{ toJson(github) }}
      run: echo "$GITHUB_CONTEXT"
    - uses: actions/checkout@v6
    - name: Set up Python

Once a native speaker comes, reviews the PR, and approves it, the GitHub Action will come and remove the `awaiting-review` label, and add the `approved-1` label.

This way, we can notice when there are new translations ready, because they have the `approved-1` label.

## Merge Translation PRs

Translations are generated automatically with LLMs and scripts.

			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>
		<Logger name="org.dbflute" additivity="false" level="${log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>
		<Logger name="org.lastaflute" additivity="false" level="${log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>

			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>
		<Logger name="org.dbflute" additivity="false" level="${log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>
		<Logger name="org.lastaflute" additivity="false" level="${log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>

	</Appenders>

	<Loggers>
		<Logger name="org.codelibs.fess" additivity="false" level="${log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>
		<Root level="${root.log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Root>
	</Loggers>

	</Appenders>

	<Loggers>
		<Logger name="org.codelibs.fess" additivity="false" level="${log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Logger>
		<Root level="${root.log.level}">
			<AppenderRef ref="AppFile" />
			<AppenderRef ref="LogNotification" />
		</Root>
	</Loggers>

{* ../../docs_src/path_params/tutorial002_py310.py hl[7] *}

Ici, `item_id` est déclaré comme `int`.

/// check | Vérifications

Cela vous apporte la prise en charge par l'éditeur dans votre fonction, avec vérifications d'erreurs, autocomplétion, etc.

///

## <dfn title="également appelé : sérialisation, parsing, marshalling">Conversion</dfn> de données { #data-conversion }

* **Embrace modern AI coding practices**: Leave everything up to an LLM to decide. The model knows best. Always.
* **No code reviews**: There's no code to review. No PRs to approve. No comments to address. Embrace vibe coding fully, replace the theater of approving and merging vibe coded PRs that no one looks at with full proper vibes only.

/// tip

errors.failed_to_download_elevate_file = Impossibile scaricare il file delle parole elevate.
errors.failed_to_upload_elevate_file = Impossibile caricare il file delle parole elevate.
errors.failed_to_download_badword_file = Impossibile scaricare il file delle parole proibite.
errors.failed_to_upload_badword_file = Impossibile caricare il file delle parole proibite.
errors.failed_to_download_mapping_file = Impossibile scaricare il file di mappatura.

* **Starlette** :
    * A les secondes meilleures performances après Uvicorn. En réalité, Starlette utilise Uvicorn. De ce fait, il ne peut qu’être plus « lent » qu'Uvicorn car il requiert l'exécution de plus de code.
    * Cependant, il apporte les outils pour construire une application web simple, avec un routage basé sur des chemins, etc.
    * Si on compare Starlette, il faut le comparer à d'autres frameworks web (ou microframeworks) comme Sanic, Flask, Django, etc.