You can declare a `response_model`, using the default status code `200` (or a custom one if you need), and then declare additional information for that same response in `responses`, directly in the OpenAPI schema.

**FastAPI** will keep the additional information from `responses`, and combine it with the JSON Schema from your model.

# Cookie Parameter Models { #cookie-parameter-models }

If you have a group of **cookies** that are related, you can create a **Pydantic model** to declare them. 🍪

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

/// tip

{* ../../docs_src/python_types/tutorial004.py hl[2] *}

## Declaring types { #declaring-types }

You just saw the main place to declare type hints. As function parameters.

This is also the main place you would use them with **FastAPI**.

### Simple types { #simple-types }

You can declare all the standard Python types, not only `str`.

You can use, for example:

* `int`
* `float`
* `bool`
* `bytes`

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

Isso vai lhe permitir **reusar o modelo** em **múltiplos lugares** e também declarar validações e metadadados para todos os parâmetros de uma vez. 😎

/// note | Nota

Isso é possível desde a versão `0.115.0` do FastAPI. 🤓

///

## Parâmetros do Cabeçalho com um Modelo Pydantic

## Additional Responses { #additional-responses }

You probably have seen how to declare the `response_model` and `status_code` for a *path operation*.

That defines the metadata about the main response of a *path operation*.

You can also declare additional responses with their models, status codes, etc.

            assertEquals(1, existsExceptions.length, "exists() should declare CIFSException");
            assertEquals(CIFSException.class, existsExceptions[0], "Should declare CIFSException");
            assertEquals(1, getTypeExceptions.length, "getType() should declare CIFSException");
            assertEquals(CIFSException.class, getTypeExceptions[0], "Should declare CIFSException");
        }

        @Test

## 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.

///

## Cookies com Modelos Pydantic

Declare o parâmetro de **cookie** que você precisa em um **modelo Pydantic**, e depois declare o parâmetro como um `Cookie`:

{* ../../docs_src/cookie_param_models/tutorial001_an_py310.py hl[9:12,16] *}

O **FastAPI** irá **extrair** os dados para **cada campo** dos **cookies** recebidos na requisição e lhe fornecer o modelo Pydantic que você definiu.

## Verifique os Documentos

Vous pouvez également déclarer des valeurs [scalaires](https://docs.github.com/fr/graphql/reference/scalars) à recevoir dans le body.