politely:  `Did you speak?'

  `Not I!' said the Lory hastily.

  `I thought you did,' said the Mouse.  `--I proceed.  "Edwin and
Morcar, the earls of Mercia and Northumbria, declared for him:
and even Stigand, the patriotic archbishop of Canterbury, found
it advisable--"'

  `Found WHAT?' said the Duck.

  `Found IT,' the Mouse replied rather crossly:  `of course you
know what "it" means.'

## Crear un modelo de usuario { #create-a-user-model }

Primero, vamos a crear un modelo de usuario con Pydantic.

De la misma manera que usamos Pydantic para declarar cuerpos, podemos usarlo en cualquier otra parte:

{* ../../docs_src/security/tutorial002_an_py310.py hl[5,12:6] *}

## Crear una dependencia `get_current_user` { #create-a-get-current-user-dependency }

# Body - Fields { #body-fields }

The same way you can declare additional validation and metadata in *path operation function* parameters with `Query`, `Path` and `Body`, you can declare validation and metadata inside of Pydantic models using Pydantic's `Field`.

## Import `Field` { #import-field }

First, you have to import it:

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


/// warning

Dessa forma, você diz ao editor que está retornando qualquer coisa intencionalmente. Mas o FastAPI ainda fará a documentação de dados, validação, filtragem, etc. com o `response_model`.

///

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

This will make `tags` be a list, although it doesn't declare the type of the elements of the list.

## List fields with type parameter { #list-fields-with-type-parameter }

But Python has a specific way to declare lists with internal types, or "type parameters":

### Declare a `list` with a type parameter { #declare-a-list-with-a-type-parameter }

To declare types that have type parameters (internal types), like `list`, `dict`, `tuple`,

## Import `Header` { #import-header }

First import `Header`:

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

## Declare `Header` parameters { #declare-header-parameters }

Then declare the header parameters using the same structure as with `Path`, `Query` and `Cookie`.

You can define the default value as well as all the extra validation or annotation parameters:

## Import `Cookie` { #import-cookie }

First import `Cookie`:

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

## Declare `Cookie` parameters { #declare-cookie-parameters }

Then declare the cookie parameters using the same structure as with `Path` and `Query`.

You can define the default value as well as all the extra validation or annotation parameters:

- Fixed a situation when, 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))...

You can declare multiple path parameters and query parameters at the same time, **FastAPI** knows which is which.

And you don't have to declare them in any specific order.

They will be detected by name:

{* ../../docs_src/query_params/tutorial004_py310.py hl[6,8] *}

## Required query parameters { #required-query-parameters }

        val distributionRuntimeOnly = bucket("${prefix}TestDistributionRuntimeOnly", "Declare the distribution that is required to run tests")
        val localRepository = bucket("${prefix}TestLocalRepository", "Declare a local repository required as input data for the tests (e.g. :tooling-api)")