https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats
Here are the affected tests:
EOF
    while read dep; do
      echo "For dependency $dep:"
      # For every missing dependency, find the tests which directly depend on
      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.

# ==============================================================================

# Need a newer version of patchelf as the installed version is buggy in 20.04
# so get patchelf source from 22.04 ie 'jammy' and build it to avoid dependency
# problems that would occur with a binary package

mkdir -p /patchelf
cd /patchelf
echo deb-src http://ports.ubuntu.com/ubuntu-ports/ jammy universe>>/etc/apt/sources.list
apt-get update

        with:
          python-version: "3.11"
      - name: Setup uv
        uses: astral-sh/setup-uv@v3
        with:
          version: "0.4.15"
          enable-cache: true
          cache-dependency-glob: |
            requirements**.txt
            pyproject.toml
      - name: Install GitHub Actions dependencies
        run: uv pip install -r requirements-github-actions.txt
      - name: Deploy Docs Status Pending

# Request Parameters

Here's the reference information for the request parameters.

These are the special functions that you can put in *path operation function* parameters or dependency functions with `Annotated` to get data from the request.

It includes:

* `Query()`
* `Path()`
* `Body()`
* `Cookie()`
* `Header()`
* `Form()`
* `File()`

You can import them all directly from `fastapi`:

```python

### Dependency injection

FastAPI'ın inanılmaz derecede kullanımı kolay, fakat inanılmaz derecede güçlü <abbr title='"components", "resources", "services", "providers" olarak da bilinen'><strong>Dependency Injection </strong></abbr> sistemi var.

* Dependencylerin bile dependencies'i olabiliyor, FastAPI bunun  için **graph of "dependency"** yaratıyor.

### Einbringen von Abhängigkeiten (Dependency Injection)

FastAPI enthält ein extrem einfach zu verwendendes, aber extrem mächtiges <abbr title='Dependency Injection – Einbringen von Abhängigkeiten: Auch bekannt als Komponenten, Resourcen, Dienste, Dienstanbieter'><strong>Dependency Injection</strong></abbr> System.

Utiliser `BackgroundTasks` fonctionne aussi avec le système d'injection de dépendances. Vous pouvez déclarer un paramètre de type `BackgroundTasks` à différents niveaux : dans une *fonction de chemin*, dans une dépendance, dans une sous-dépendance...

**FastAPI** sait quoi faire dans chaque cas et comment réutiliser le même objet, afin que tous les paramètres de type `BackgroundTasks` soient fusionnés et que les tâches soient exécutées en arrière-plan :

Es erreicht etwas Ähnliches wie Flask-apispec.

Es verfügt über ein integriertes Dependency Injection System, welches von Angular 2 inspiriert ist. Erfordert ein Vorab-Registrieren der „Injectables“ (wie alle anderen Dependency Injection Systeme, welche ich kenne), sodass der Code ausschweifender wird und es mehr Codeverdoppelung gibt.

        with:
          python-version: '3.9'
      - name: Setup uv
        uses: astral-sh/setup-uv@v3
        with:
          version: "0.4.15"
          enable-cache: true
          cache-dependency-glob: |
            requirements**.txt
            pyproject.toml
      - run: uv pip install -r requirements-github-actions.txt
      - uses: actions/download-artifact@v4
        with:
          name: coverage-html

        with:
          python-version: "3.11"
      - name: Setup uv
        uses: astral-sh/setup-uv@v3
        with:
          version: "0.4.15"
          enable-cache: true
          cache-dependency-glob: |
            requirements**.txt
            pyproject.toml
      # Allow debugging with tmate
      - name: Setup tmate session
        uses: mxschmitt/action-tmate@v3