* **Editor support**.
* **Type checks**.

...and **FastAPI** uses the same declarations to:

* **Define requirements**: from request path parameters, query parameters, headers, bodies, dependencies, etc.
* **Convert data**: from the request to the required type.
* **Validate data**: coming from each request:
    * Generating **automatic errors** returned to the client when the data is invalid.
* **Document** the API using OpenAPI:

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

**FastAPI** **extraerá** los datos para **cada campo** de los **parámetros query** en el request y te proporcionará el modelo de Pydantic que definiste.

## Revisa la Documentación { #check-the-docs }

Puedes ver los parámetros query en la UI de documentación en `/docs`:

<div class="screenshot">

events {
    worker_connections  4096;
}

http {
    include       /etc/nginx/mime.types;
    default_type  application/octet-stream;

    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_user_agent" "$http_x_forwarded_for"';

    access_log  /var/log/nginx/access.log  main;
    sendfile        on;

#       ...
#       ./any.sh
#     or
#       export TF_ANY_EXTRA_ENV=ci/official/envs/local_rbe
#       ./any.sh
#       ...
set -exo pipefail
cd "$(dirname "$0")/../../"  # tensorflow/
# Any request that includes "nightly_upload" should just use the
# local multi-cache (public read-only cache + disk cache) instead.
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"

// Identity represents access key who caused the event.
type Identity struct {
	Type        string `json:"type"`
	PrincipalID string `json:"principalId"`
	AccessKeyID string `json:"accessKeyId"`
}

// UserRequest user request headers
type UserRequest struct {
	URL     string      `json:"url"`
	Headers http.Header `json:"headers"`
}

// GetObjectContext provides the necessary details to perform

That would generate a `dict` with only the data that was set when creating the `item` model, excluding default values.

Then you can use this to generate a `dict` with only the data that was set (sent in the request), omitting default values:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Using Pydantic's `update` parameter { #using-pydantics-update-parameter }

		Err:     "failed to decrypt ciphertext",
	}

	// ErrNotSupported is an error returned by the KMS when the requested
	// functionality is not supported by the KMS service.
	ErrNotSupported = Error{
		Code:    http.StatusNotImplemented,
		APICode: "kms:NotSupported",
		Err:     "requested functionality is not supported",
	}
)

// Error is a KMS error that can be translated into an S3 API error.
//

* insensible a mayúsculas/minúsculas

* servir la aplicación
* servir la página

* la app
* la aplicación

* la request
* la response
* la response de error

* la path operation
* el decorador de path operation
* la path operation function

* el body
* el request body
* el response body
* el body JSON
* el body del formulario
* el body de archivo
* el cuerpo de la función

        pytest.param("tutorial005_an_py39"),
        pytest.param("tutorial005_an_py310", marks=needs_py310),
    ],
)
def get_mod(request: pytest.FixtureRequest):
    mod = importlib.import_module(f"docs_src.security.{request.param}")

    return mod


def get_access_token(
    *, username="johndoe", password="secret", scope=None, client: TestClient
):

        map sigbuild-r2.17-clang-python3.11 2.17-python3.11
        map sigbuild-r2.17-clang-python3.12 2.17-python3.12
    - name: Create Pull Request with changes
      uses: peter-evans/create-pull-request@84ae59a2cdc2258d6fa0732dd66352dddae2a412 # v7.0.9
      with:
        title: Update the RBE images to the latest container versions