If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

        },
        "baz": "simple_include_dict model2 baz",
    }


@app.get(
    "/simple_exclude",
    response_model=Model2,
    response_model_exclude={"ref": {"bar"}},
)
def simple_exclude():
    return Model2(
        ref=Model1(foo="simple_exclude model foo", bar="simple_exclude model bar"),
        baz="simple_exclude model2 baz",
    )


@app.get(
    "/simple_exclude_dict",

This kind of asynchronicity is what made NodeJS popular (even though NodeJS is not parallel) and that's the strength of Go as a programming language.

And that's the same level of performance you get with **FastAPI**.

        return name


async def get_model_c() -> ModelC:
    return ModelC(username="test-user", password="test-password")


@app.get("/model/{name}", response_model=ModelA)
async def get_model_a(name: str, model_c=Depends(get_model_c)):

            return name

    async def get_model_c() -> ModelC:
        return ModelC(username="test-user", password="test-password")

    @app.get("/model/{name}", response_model=ModelA)
    async def get_model_a(name: str, model_c=Depends(get_model_c)):
        return {"name": name, "description": "model-a-desc", "foo": model_c}

    client = TestClient(app)
    return client


@needs_pydanticv2

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

app = FastAPI()


class Model(BaseModel):
    pass


class Model2(BaseModel):
    a: Model


class Model3(BaseModel):
    c: Model
    d: Model2


@app.get("/", response_model=Model3)
def f():
    return {"c": {}, "d": {"a": {}}}


client = TestClient(app)


def test_get_api_route():

        response_fields = {}
        for additional_status_code, response in self.responses.items():
            assert isinstance(response, dict), "An additional response must be a dict"
            model = response.get("model")
            if model:
                assert is_body_allowed_for_status_code(
                    additional_status_code
                ), f"Status code {additional_status_code} must not have a response body"

 * string is not human readable, but can be used with classes like [InetAddress] to establish
 * connections.
 *
 * ## Why another URL model?
 *
 * Java includes both [java.net.URL][URL] and [java.net.URI][URI]. We offer a new URL
 * model to address problems that the others don't.
 *
 * ### Different URLs should be different
 *

    return client


@needs_pydanticv1
def test_filter_sub_model(client: TestClient):
    response = client.get("/model/modelA")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "name": "modelA",
        "description": "model-a-desc",
        "model_b": {"username": "test-user"},
    }


@needs_pydanticv1
def test_validator_is_cloned(client: TestClient):

Ce qui améliore le support pour les modèles Pydantic avec :

* de l'auto-complétion
* des vérifications de type
* du "refactoring" (ou remaniement de code)
* de la recherche
* de l'inspection

///

## Utilisez le modèle

Dans la fonction, vous pouvez accéder à tous les attributs de l'objet du modèle directement :

{* ../../docs_src/body/tutorial002.py hl[21] *}