python-version: "3.13"
            coverage: coverage
            codspeed: codspeed
          - os: ubuntu-latest
            python-version: "3.14"
            coverage: coverage
      fail-fast: false
    runs-on: ${{ matrix.os }}
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v6

    newArchives.from(currentDistroJars)
    oldArchives.from(baselineJars)

    // binary breaking change checking setup
    onlyModified = false
    failOnModification = false // we rely on the custom report to fail or not
    ignoreMissingClasses = true // because of a missing scala.runtime.AbstractFunction0 class

    BinaryCompatibilityHelper.setupJApiCmpRichReportRules(
        delegate,
        project,

                        newClasspath.from(v2.configurations.named("runtimeClasspath"), v2Jar)

                        onlyModified.set(false)
                        failOnModification.set(false) // we rely on the rich report to fail

                        txtOutputFile.set(file("build/japi-report.txt"))

                        BinaryCompatibilityHelper.setupJApiCmpRichReportRules(
                            this,

# CROSS-COMPILE ARM64 PYCPP
common:cross_compile_linux_arm64_pycpp_test --config=linux_arm64_pycpp_test
# Tests that fail only when cross-compiled
common:cross_compile_linux_arm64_pycpp_test -//tensorflow/compiler/mlir/quantization/stablehlo:convert_tf_quant_to_mhlo_int_test
# MACOS ARM64 PYCPP

In einigen Fällen müssen Sie möglicherweise immer noch Pydantics Version von `dataclasses` verwenden. Zum Beispiel, wenn Sie Fehler in der automatisch generierten API-Dokumentation haben.

In diesem Fall können Sie einfach die Standard-`dataclasses` durch `pydantic.dataclasses` ersetzen, was einen direkten Ersatz darstellt:

{* ../../docs_src/dataclasses_/tutorial003_py310.py hl[1,4,7:10,13:16,22:24,27] *}

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}"
    mod = importlib.import_module(mod_name)

# GraphQL { #graphql }

Como **FastAPI** se basa en el estándar **ASGI**, es muy fácil integrar cualquier paquete de **GraphQL** que también sea compatible con ASGI.

Puedes combinar las *path operations* normales de FastAPI con GraphQL en la misma aplicación.

/// tip | Consejo

**GraphQL** resuelve algunos casos de uso muy específicos.

Tiene **ventajas** y **desventajas** en comparación con las **APIs web** comunes.

# GraphQL { #graphql }

Como o **FastAPI** é baseado no padrão **ASGI**, é muito fácil integrar qualquer biblioteca **GraphQL** também compatível com ASGI.

Você pode combinar *operações de rota* normais do FastAPI com GraphQL na mesma aplicação.

/// tip | Dica

**GraphQL** resolve alguns casos de uso muito específicos.

Ele tem **vantagens** e **desvantagens** quando comparado a **web APIs** comuns.

    * Da der `q`-Parameter mit `= None` deklariert ist, ist er optional.
    * Ohne das `None` wäre er erforderlich (wie der Body im Fall von `PUT`).
* Bei `PUT`-Requests an `/items/{item_id}` den Body als JSON lesen:
    * Prüfen, ob er ein erforderliches Attribut `name` hat, das ein `str` sein muss.

**This version of FastAPI still supports Pydantic v1**. And although Pydantic v1 will be deprecated at some point, it will still be supported for a while.

This means that you can install the new Pydantic v2, and if something fails, you can install Pydantic v1 while you fix any problems you might have, but having the latest FastAPI.

There are **tests for both Pydantic v1 and v2**, and test **coverage** is kept at **100%**.

### Changes