El sistema de inyección de dependencias requiere pre-registrar las dependencias y las dependencias se resuelven en base a los tipos declarados. Por lo tanto, no es posible declarar más de un "componente" que proporcione cierto tipo.

## Parameterized dependencies { #parameterized-dependencies }

All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

The files and form fields will be uploaded as form data and you will receive the files and form fields.

And you can declare some of the files as `bytes` and some as `UploadFile`.

/// warning

You can declare multiple `File` and `Form` parameters in a *path operation*, but you can't also declare `Body` fields that you expect to receive as JSON, as the request will have the body encoded using `multipart/form-data` instead of `application/json`.

We could do better.

We can declare a `UserBase` model that serves as a base for our other models. And then we can make subclasses of that model that inherit its attributes (type declarations, validation, etc).

All the data conversion, validation, documentation, etc. will still work as normally.

///

## Declarar metadatos

Puedes declarar todos los mismos parámetros que para `Query`.

Por ejemplo, para declarar un valor de metadato `title` para el parámetro de path `item_id` puedes escribir:

{* ../../docs_src/path_params_numeric_validations/tutorial001_an_py310.py hl[10] *}

# Declare Request Example Data { #declare-request-example-data }

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

//// tab | Pydantic v2

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

- Sometimes, the scheduler incorrectly placed a pod in the "unschedulable" queue instead of the "backoff" queue. This happened when some plugin previously declared the pod as "unschedulable" and then in a later attempt encounters some other error. Scheduling of that pod then got delayed by up to five minutes, after which periodic flushing moved the pod back into the "active" queue. ([#120334](https://github.com/kubernetes/kubernetes/pull/120334), [@pohly](https://github.com/pohly))...

# Body - Campos

De la misma manera que puedes declarar validaciones adicionales y metadatos en los parámetros de las *path operation function* con `Query`, `Path` y `Body`, puedes declarar validaciones y metadatos dentro de los modelos de Pydantic usando `Field` de Pydantic.

## Importar `Field`

Primero, tienes que importarlo:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}

/// warning | Advertencia

Si vous utilisez des bibliothèques tierces qui nécessitent d'être appelées avec `await`, telles que :

```Python
results = await some_library()
```
Alors, déclarez vos *fonctions de chemins* avec `async def` comme ceci :

```Python hl_lines="2"
@app.get('/')
async def read_results():
    results = await some_library()
    return results
```

/// note

```

///

/// note | Nota

Isto é suportado desde a versão `0.113.0` do FastAPI. 🤓

///

## Modelos Pydantic para Formulários

Você precisa apenas declarar um **modelo Pydantic** com os campos que deseja receber como **campos de formulários**, e então declarar o parâmetro como um `Form`:

{* ../../docs_src/request_form_models/tutorial001_an_py39.py hl[9:11,15] *}