/// check | **FastAPI** lo usa para

Manejar toda la validación de datos, serialización de datos y documentación automática de modelos (basada en JSON Schema).

**FastAPI** luego toma esos datos JSON Schema y los coloca en OpenAPI, aparte de todas las otras cosas que hace.

///

### <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a>

		u, err := url.Parse(apiEndpoint)
		if err != nil {
			continue
		}
		if host == "" && isIPv6(u.Hostname()) {
			// Skip all IPv6 endpoints
			continue
		}
		if u.Port() == "80" && u.Scheme == "http" || u.Port() == "443" && u.Scheme == "https" {
			u.Host = u.Hostname()
		}
		newAPIEndpoints[i] = u.String()
	}
	return newAPIEndpoints
}

	if err != nil {
		log.Fatalf("Error parsing sts endpoint: %v", err)
	}

	opts := &minio.Options{
		Creds:  li,
		Secure: stsEndpointURL.Scheme == "https",
	}

	mopts := &madmin.Options{
		Creds:  li,
		Secure: stsEndpointURL.Scheme == "https",
	}

	v, err := li.Get()
	if err != nil {
		log.Fatalf("Error retrieving STS credentials: %v", err)
	}

	if displayCreds {

            // sitemaps.xml
            buf = new StringBuilder();
            buf.append("<?xml version=\"1.0\" encoding=\"UTF-8\"?>").append('\n');
            buf.append("<urlset ").append("xmlns=\"http://www.sitemaps.org/schemas/sitemap/0.9\">").append('\n');
            buf.append("<url>").append('\n');
            buf.append("<loc>http://localhost:7070/index.html</loc>").append('\n');

You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures { #dataclasses-in-nested-data-structures }

///

Das wird:

* Diesen Statuscode mit der Response zurücksenden.
* Ihn als solchen im OpenAPI-Schema dokumentieren (und somit in den Benutzeroberflächen):

<img src="/img/tutorial/response-status-code/image01.png">

/// note | Hinweis

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

Die Datenklasse wird automatisch in eine Pydantic-Datenklasse konvertiert.

Auf diese Weise wird deren Schema in der Benutzeroberfläche der API-Dokumentation angezeigt:

<img src="/img/tutorial/dataclasses/image01.png">

## Datenklassen in verschachtelten Datenstrukturen

package schema

import (
	"context"
	"fmt"
	"reflect"
	"regexp"
	"strings"

	"gorm.io/gorm/clause"
	"gorm.io/gorm/utils"
)

var embeddedCacheKey = "embedded_cache_store"

func ParseTagSetting(str string, sep string) map[string]string {
	settings := map[string]string{}
	names := strings.Split(str, sep)

	for i := 0; i < len(names); i++ {
		j := i
		if len(names[j]) > 0 {
			for {

    const val RESPONSE_STATUS_UTF8 = ":status"
    const val TARGET_METHOD_UTF8 = ":method"
    const val TARGET_PATH_UTF8 = ":path"
    const val TARGET_SCHEME_UTF8 = ":scheme"
    const val TARGET_AUTHORITY_UTF8 = ":authority"

    @JvmField val RESPONSE_STATUS: ByteString = RESPONSE_STATUS_UTF8.encodeUtf8()

    @JvmField val TARGET_METHOD: ByteString = TARGET_METHOD_UTF8.encodeUtf8()

      doExpectCollects(expectedResult, list);
    }
    return this;
  }

  private void doExpectCollects(R expectedResult, List<T> inputs) {
    for (CollectStrategy scheme : CollectStrategy.values()) {
      A finalAccum = scheme.result(collector, inputs);
      if (collector.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
        @SuppressWarnings("unchecked") // `R` and `A` match for an `IDENTITY_FINISH`