return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    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):

## Other models { #other-models }

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

/**
 * A test which demonstrates maven's recursive inheritance where
 * a distinct value is taken from each parent contributing to
 * the final model of the project being assembled. There is no
 * overriding going on amongst the models being used in this test:
 * each model in the lineage is providing a value that is not present
 * anywhere else in the lineage. We are just making sure that values

Es verbessert die Editor-Unterstützung für Pydantic-Modelle, mit:

* Code-Vervollständigung
* Typüberprüfungen
* Refaktorisierung
* Suche
* Inspektionen

///

## Das Modell verwenden { #use-the-model }

Innerhalb der Funktion können Sie alle Attribute des Modellobjekts direkt verwenden:

{* ../../docs_src/body/tutorial002_py310.py *}

    - tutorial/query-param-models.md
    - tutorial/body-multiple-params.md
    - tutorial/body-fields.md
    - tutorial/body-nested-models.md
    - tutorial/schema-extra-example.md
    - tutorial/extra-data-types.md
    - tutorial/cookie-params.md
    - tutorial/header-params.md
    - tutorial/cookie-param-models.md
    - tutorial/header-param-models.md
    - tutorial/response-model.md

import binascii
from base64 import b64decode
from typing import Annotated, Optional

from annotated_doc import Doc
from fastapi.exceptions import HTTPException
from fastapi.openapi.models import HTTPBase as HTTPBaseModel
from fastapi.openapi.models import HTTPBearer as HTTPBearerModel
from fastapi.security.base import SecurityBase
from fastapi.security.utils import get_authorization_scheme_param
from pydantic import BaseModel

/**
 * Strategy for applying Maven inference optimizations.
 * For 4.0.0 models: applies limited inference (parent-related only).
 * For 4.1.0+ models: applies full inference optimizations.
 * Removes redundant information that can be inferred by Maven during model building.
 */
@Named
@Singleton
@Priority(30)
public class InferenceStrategy extends AbstractUpgradeStrategy {

* el evento de lifespan

* el manejador
* el manejador de eventos
* el manejador de excepciones
* manejar

* el modelo
* el modelo de Pydantic
* el modelo de datos
* el modelo de base de datos
* el modelo de formulario
* el objeto del modelo

* la clase
* la clase base
* la clase padre
* la subclase
* la clase hija
* la clase hermana
* el método de clase

    "functionalTests": true,
    "crossVersionTests": false
  },
  {
    "name": "model-core",
    "path": "platforms/core-configuration/model-core",
    "unitTests": true,
    "functionalTests": true,
    "crossVersionTests": false
  },
  {
    "name": "model-groovy",
    "path": "platforms/core-configuration/model-groovy",
    "unitTests": true,
    "functionalTests": true,
    "crossVersionTests": false

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,