## importe `Header`

Primeiro importe `Header`:

{* ../../docs_src/header_params/tutorial001_py310.py hl[1] *}

## Declare parâmetros de `Header`

Então declare os paramêtros de cabeçalho usando a mesma estrutura que em `Path`, `Query` e `Cookie`.

O primeiro valor é o valor padrão, você pode passar todas as validações adicionais ou parâmetros de anotação:

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.

///

/// 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] *}

            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

/// note | Nota

Isso é suportado desde o FastAPI versão `0.115.0`. 🤓

///

## Parâmetros de Consulta com um Modelo Pydantic

Declare os **parâmetros de consulta** que você precisa em um **modelo Pydantic**, e então declare o parâmetro como `Query`:

{* ../../docs_src/query_param_models/tutorial001_an_py310.py hl[9:13,17] *}

# 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] *}

/// tip

If you have strict type checks in your editor, mypy, etc, you can declare the function return type as `Any`.

That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the `response_model`.

///

### `response_model` Priority { #response-model-priority }

# Response Headers { #response-headers }

## Use a `Response` parameter { #use-a-response-parameter }

You can declare a parameter of type `Response` in your *path operation function* (as you can do for cookies).

And then you can set headers in that *temporal* response object.

{* ../../docs_src/response_headers/tutorial002.py hl[1, 7:8] *}

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

# Response Cookies { #response-cookies }

## Use a `Response` parameter { #use-a-response-parameter }

You can declare a parameter of type `Response` in your *path operation function*.

And then you can set cookies in that *temporal* response object.

{* ../../docs_src/response_cookies/tutorial002.py hl[1, 8:9] *}

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

///

### Import `Depends` { #import-depends }

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[3] *}

### Declare the dependency, in the "dependant" { #declare-the-dependency-in-the-dependant }

The same way you use `Body`, `Query`, etc. with your *path operation function* parameters, use `Depends` with a new parameter: