from typing import Any


def get_body_model_name(openapi: dict[str, Any], path: str) -> str:
    body = openapi["paths"][path]["post"]["requestBody"]
    body_schema = body["content"]["multipart/form-data"]["schema"]

import importlib

import pytest
from fastapi.testclient import TestClient


@pytest.fixture(
    name="client",
    params=[
        pytest.param("tutorial004_py39"),
    ],
)
def get_client(request: pytest.FixtureRequest):
    mod = importlib.import_module(f"docs_src.extra_models.{request.param}")

    client = TestClient(mod.app)
    return client


def test_get_items(client: TestClient):

```{ .dockerfile .annotate }
# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)

from fastapi import FastAPI, File, UploadFile

app = FastAPI()


@app.post("/files/")
async def create_file(file: bytes = File()):
    return {"file_size": len(file)}


@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile):

from fastapi import FastAPI, Request

app = FastAPI()


def magic_data_reader(raw_body: bytes):
    return {
        "size": len(raw_body),
        "content": {
            "name": "Maaaagic",
            "price": 42,
            "description": "Just kiddin', no magic here. ✨",
        },
    }


@app.post(
    "/items/",
    openapi_extra={
        "requestBody": {
            "content": {

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: str | None = Query(default=None, alias="item-query")):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

import runpy
from unittest.mock import patch

import pytest


@pytest.mark.parametrize(
    "module_name",
    [
        "tutorial001_py39",
        "tutorial002_py39",
    ],
)
def test_run_module(module_name: str):
    with patch("builtins.print") as mock_print:
        runpy.run_module(f"docs_src.python_types.{module_name}", run_name="__main__")

fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.
# This aims to maintain TF compatibility with NumPy 1.x until 2025 b/361369076.
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  # Uninstall tf nightly wheel built with numpy1.
  "$python" -m pip uninstall -y tf_nightly_numpy1
  # Install tf nightly cpu wheel built with numpy2.x from PyPI in numpy1.x env.
  "$python" -m pip install tf-nightly-cpu

from fastapi import FastAPI, Path

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(*, item_id: int = Path(title="The ID of the item to get"), q: str):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})

Deklarieren Sie den Body mit Standard-Python-Typen, dank Pydantic.

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")