extra = "forbid"

    limit: int = Field(100, gt=0, le=100)
    offset: int = Field(0, ge=0)
    order_by: Literal["created_at", "updated_at"] = "created_at"
    tags: list[str] = []


@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):

elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.
# Import compat before trying to import summary from tensorboard, so that
# reexport_tf_summary can get compat from sys.modules. Only needed if using
# lazy loading.
_current_module.compat.v2  # pylint: disable=pointless-statement

# Load tensorflow-io-gcs-filesystem if enabled

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: str | None = Query(default=None, max_length=50)):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

from pydantic import BaseModel

app = FastAPI()


class Cookies(BaseModel):
    session_id: str
    fatebook_tracker: str | None = None
    googall_tracker: str | None = None


@app.get("/items/")
async def read_items(cookies: Cookies = Cookie()):

from fastapi import FastAPI, Query
from typing_extensions import Annotated

app = FastAPI()


@app.get("/items/")
async def read_items(q: Annotated[str, Query(min_length=3)]):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

from typing import Annotated, Union

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    hidden_query: Annotated[Union[str, None], Query(include_in_schema=False)] = None,
):
    if hidden_query:
        return {"hidden_query": hidden_query}
    else:

from typing import Annotated, Union

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Annotated[
        Union[str, None],
        Query(
            title="Query string",
            description="Query string for the items to search in the database that have a good match",
            min_length=3,
        ),
    ] = None,
):

    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)

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from pydantic import BaseModel


class Item(BaseModel):
    id: str
    value: str


class Message(BaseModel):
    message: str


app = FastAPI()


@app.get("/items/{item_id}", response_model=Item, responses={404: {"model": Message}})
async def read_item(item_id: str):
    if item_id == "foo":
        return {"id": "foo", "value": "there goes my hero"}