/**
 * 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 {

## Zusätzliche JSON-Schemadaten in Pydantic-Modellen { #extra-json-schema-data-in-pydantic-models }

Sie können `examples` („Beispiele“) für ein Pydantic-Modell deklarieren, welche dem generierten JSON-Schema hinzugefügt werden.

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

Diese zusätzlichen Informationen werden unverändert zum für dieses Modell ausgegebenen **JSON-Schema** hinzugefügt und in der API-Dokumentation verwendet.

* 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

import org.apache.maven.api.di.Singleton;
import org.apache.maven.api.model.Build;
import org.apache.maven.api.model.Model;
import org.apache.maven.api.model.Parent;
import org.apache.maven.api.model.Plugin;
import org.apache.maven.api.model.PluginManagement;
import org.apache.maven.api.model.Repository;
import org.apache.maven.api.model.RepositoryPolicy;
import org.apache.maven.api.services.ModelBuilder;

    hashed_password = user_dict["hashed_password"],
)
```

/// info | Informação

Para uma explicação mais completa de `**user_dict`, verifique [a documentação para **Extra Models**](../extra-models.md#about-user-in-dict){.internal-link target=_blank}.

///

## Retorne o token { #return-the-token }

A resposta do endpoint `token` deve ser um objeto JSON.

                if annotation_is_pydantic_v1(model):
                    raise PydanticV1NotSupportedError(
                        "pydantic.v1 models are no longer supported by FastAPI."
                        f" In responses={{}}, please update {model}."
                    )
                response_field = create_model_field(
                    name=response_name, type_=model, mode="serialization"
                )

from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":

and when not to use hyphens in words (written in first person, which is you)

In der Regel versuche ich so weit wie möglich Worte zusammenzuschreiben, also ohne Bindestrich, es sei denn, es ist Konkretesding-Klassevondingen, etwa «Pydantic-Modell» (aber: «Datenbankmodell»), «Python-Modul» (aber: «Standardmodul»). Ich setze auch einen Bindestrich, wenn er die gleichen Buchstaben verbindet, etwa «Enum-Member», «Cloud-Dienst», «Template-Engine». Oder wenn das Wort sonst einfach zu lang wird,...

from typing import Optional, Union

import pytest
from fastapi.openapi.models import Schema, SchemaType


@pytest.mark.parametrize(
    "type_value",
    [
        "array",
        ["string", "null"],
        None,
    ],
)
def test_allowed_schema_type(
    type_value: Optional[Union[SchemaType, list[SchemaType]]],
) -> None:
    """Test that Schema accepts SchemaType, List[SchemaType] and None for type field."""

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.