common:mkl --define=build_with_mkl=true --define=enable_mkl=true
common:mkl --define=tensorflow_mkldnn_contraction_kernel=0
common:mkl --define=build_with_openmp=true
common:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
common:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
common:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

            if it should exclude from the output the fields that had the same default
            value, even when they were explicitly set.
            """
        ),
    ] = False,
    exclude_none: Annotated[
        bool,
        Doc(
            """
            Pydantic's `exclude_none` parameter, passed to Pydantic models to define

                """
            ),
        ] = None,
        response_model_by_alias: Annotated[
            bool,
            Doc(
                """
                Configuration passed to Pydantic to define if the response model
                should be serialized by alias when an alias is used.

                Read more about it in the

from starlette.datastructures import UploadFile as StarletteUploadFile


class UploadFile(StarletteUploadFile):
    """
    A file uploaded in a request.

    Define it as a *path operation function* (or dependency) parameter.

    If you are using a regular `def` function, you can use the `upload_file.file`
    attribute to access the raw standard Python file (blocking, not async), useful and

                ],
            ),
        ):
            return item

    # TODO: enable these tests once/if Form(embed=False) is supported
    # TODO: In that case, define if File() should support example/examples too
    # @app.post("/form_example")
    # def form_example(firstname: str = Form(example="John")):
    #     return firstname

    # @app.post("/form_examples")

            """
        ),
    ] = True,
    scope: Annotated[
        Union[Literal["function", "request"], None],
        Doc(
            """
            Mainly for dependencies with `yield`, define when the dependency function
            should start (the code before `yield`) and when it should end (the code
            after `yield`).

    Wenn Sie eine Auffrischung darüber benötigen, wann welche Anwendung sinnvoll ist, lesen Sie den Abschnitt „In Eile?“ in der Dokumentation zu [`async` und `await`](../async.md#in-a-hurry){.internal-link target=_blank}.

9. Diese *Pfadoperation-Funktion* gibt keine Datenklassen zurück (obwohl dies möglich wäre), sondern eine Liste von Dictionarys mit internen Daten.

---

FastAPI ist ein modernes, schnelles (hoch performantes) Webframework zur Erstellung von APIs mit Python auf Basis von Standard-Python-Typhinweisen.

Seine Schlüssel-Merkmale sind:

* **Schnell**: Sehr hohe Performanz, auf Augenhöhe mit **NodeJS** und **Go** (dank Starlette und Pydantic). [Eines der schnellsten verfügbaren Python-Frameworks](#performance).

# Copy from Pydantic v2, compatible with v1
if sys.version_info < (3, 10):
    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias)  # type: ignore[attr-defined]
else:
    WithArgsTypes: tuple[Any, ...] = (
        typing._GenericAlias,  # type: ignore[attr-defined]
        types.GenericAlias,
        types.UnionType,
    )  # pyright: ignore[reportAttributeAccessIssue]

from fastapi import Depends, FastAPI
from fastapi.testclient import TestClient


@pytest.fixture(
    name="module",
    params=[
        "tutorial008_py39",
        # Fails with `NameError: name 'DepA' is not defined`
        pytest.param("tutorial008_an_py39", marks=pytest.mark.xfail),
    ],
)
def get_module(request: pytest.FixtureRequest):
    mod_name = f"docs_src.dependencies.{request.param}"