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;

# Declarar Ejemplos de Request { #declare-request-example-data }

Puedes declarar ejemplos de los datos que tu aplicación puede recibir.

Aquí tienes varias formas de hacerlo.

## Datos extra de JSON Schema en modelos de Pydantic { #extra-json-schema-data-in-pydantic-models }

Puedes declarar `examples` para un modelo de Pydantic que se añadirá al JSON Schema generado.

//// tab | Pydantic v2

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;

@Named
@Singleton
@Deprecated(since = "4.0.0")
public class DefaultReportConfigurationExpander implements ReportConfigurationExpander {

    @Override
    public void expandPluginConfiguration(Model model, ModelBuildingRequest request, ModelProblemCollector problems) {
        Reporting reporting = model.getReporting();

        if (reporting != null) {
            for (ReportPlugin reportPlugin : reporting.getPlugins()) {

        MavenExecutionRequest request = new DefaultMavenExecutionRequest()
                .setSystemProperties(properties)
                .setGoals(Collections.emptyList())
                .setBaseDirectory(new File(""))
                .setLocalRepository(repo);

        return new MavenSession(container, request, new DefaultMavenExecutionResult(), Collections.emptyList());
    }

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;

{* ../../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