import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient

from ...utils import needs_py310


@pytest.fixture(name="client")
def get_client():
    from docs_src.schema_extra_example.tutorial004_py310 import app

    client = TestClient(app)
    return client


# Test required and embedded body parameters with no bodies sent
@needs_py310
def test_post_body_example(client: TestClient):

  private void enqueueStoppingEvent(final State from) {
    if (from == State.STARTING) {
      listeners.enqueue(STOPPING_FROM_STARTING_EVENT);
    } else if (from == State.RUNNING) {
      listeners.enqueue(STOPPING_FROM_RUNNING_EVENT);
    } else {
      throw new AssertionError();
    }
  }

  private void enqueueTerminatedEvent(final State from) {
    switch (from) {
      case NEW:

import pytest
from dirty_equals import IsDict, IsOneOf
from fastapi.testclient import TestClient

from ...utils import needs_py39


@pytest.fixture(name="client")
def get_client():
    from docs_src.security.tutorial005_an_py39 import app

    client = TestClient(app)
    return client


def get_access_token(
    *, username="johndoe", password="secret", scope=None, client: TestClient
):

from fastapi import Depends, FastAPI, Security
from fastapi.security import APIKeyHeader
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()

api_key = APIKeyHeader(name="key", description="An API Key Header")


class User(BaseModel):
    username: str


def get_current_user(oauth_header: str = Security(api_key)):
    user = User(username=oauth_header)
    return user

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

import time
from typing import Callable

from fastapi import APIRouter, FastAPI, Request, Response
from fastapi.routing import APIRoute


class TimedRoute(APIRoute):
    def get_route_handler(self) -> Callable:
        original_route_handler = super().get_route_handler()

        async def custom_route_handler(request: Request) -> Response:
            before = time.time()
            response: Response = await original_route_handler(request)

from pathlib import Path
from typing import Optional

from fastapi import APIRouter, FastAPI, File, UploadFile
from fastapi.exceptions import HTTPException
from fastapi.testclient import TestClient

app = FastAPI()

router = APIRouter()


class ContentSizeLimitMiddleware:
    """Content size limiting middleware for ASGI applications
    Args:
      app (ASGI application): ASGI application

from typing import Optional, Union

from dirty_equals import IsDict
from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: Optional[str] = None


class ExtendedItem(Item):
    age: int


@app.post("/items/")
def save_union_different_body(item: Union[ExtendedItem, Item]):
    return {"item": item}

        // then relative path calculation will fail during build from disk but success when calculated from repo
        try {
            // build from disk expected to fail
            testInheritance("tricky-flat-artifactId-urls", false);
            // fail( "should have failed since module reference == artifactId != directory name" );
        } catch (AssertionError afe) {

```

////

With this, we get tooling support, from editors and mypy as this code is correct in terms of types, but we also get the data filtering from FastAPI.

How does this work? Let's check that out. 🤓

### Type Annotations and Tooling

First let's see how editors, mypy and other tools would see this.