// WithSharedResponse indicates it is unsafe to reuse the response
// when it has been returned on a handler.
// This will disable automatic response recycling/pooling.
// Typically this is used when the response sharing part of its data structure.
func (h *SingleHandler[Req, Resp]) WithSharedResponse() *SingleHandler[Req, Resp] {
	h.sharedResp = true
	return h
}

// NewResponse creates a new response.

EXAMPLES:
  1. Start MinIO server on "/home/shared" directory.
     {{.Prompt}} {{.HelpName}} /home/shared

  2. Start single node server with 64 local drives "/mnt/data1" to "/mnt/data64".
     {{.Prompt}} {{.HelpName}} /mnt/data{1...64}

  3. Start distributed MinIO server on an 32 node setup with 32 drives each, run following command on all the nodes
     {{.Prompt}} {{.HelpName}} http://node{1...32}.example.com/mnt/export{1...32}

Dies und die einfache Tatsache, dass Python die Hauptsprache für **Data Science**, maschinelles Lernen und insbesondere Deep Learning ist, machen FastAPI zu einem sehr passenden Werkzeug für Web-APIs und Anwendungen für Data Science / maschinelles Lernen (neben vielen anderen).

 * **Security** of the connection. This includes verification of the remote webserver with certificates and the privacy of data exchanged with strong ciphers.

```

But possibly the most important part of the callback is making sure that your API user (the external developer) implements the *external API* correctly, according to the data that *your API* is going to send in the request body of the callback, etc.

So, what we will do next is add the code to document how that *external API* should look like to receive the callback from *your API*.

* Para corrigir um erro de digitação que você encontrou na documentação.
* Para compartilhar um artigo, video, ou podcast criados por você sobre o FastAPI <a href="https://github.com/fastapi/fastapi/edit/master/docs/en/data/external_links.yml" class="external-link" target="_blank">editando este arquivo</a>.
    * Não se esqueça de adicionar o link no começo da seção correspondente.

Cada atributo tiene un tipo.

Luego creas un instance de esa clase con algunos valores y Pydantic validará los valores, los convertirá al tipo apropiado (si ese es el caso) y te dará un objeto con todos los datos.

Y obtienes todo el soporte del editor con el objeto resultante.

Tomado de la documentación oficial de Pydantic:

```Python
{!../../docs_src/python_types/tutorial010.py!}
```

```Python hl_lines="7"
{!../../docs_src/path_params/tutorial002.py!}
```

En este caso, `item_id` es declarado como un `int`.

/// check | Revisa

Esto te dará soporte en el editor dentro de tu función, con chequeo de errores, auto-completado, etc.

///

## <abbr title="también conocido en inglés como: serialization, parsing, marshalling">Conversión</abbr> de datos

Tente enviar um item inválido como este:

```JSON
{
  "title": "towel",
  "size": "XL"
}
```

Você receberá uma *response* informando-o de que a data é inválida, e contendo o *body* recebido:

```JSON hl_lines="12-15"
{
  "detail": [
    {
      "loc": [
        "body",
        "size"
      ],
      "msg": "value is not a valid integer",

			defaultValueFieldsHavingValue := map[*schema.Field][]interface{}{}
			for i := 0; i < rValLen; i++ {
				rv := reflect.Indirect(stmt.ReflectValue.Index(i))
				if !rv.IsValid() {
					stmt.AddError(fmt.Errorf("slice data #%v is invalid: %w", i, gorm.ErrInvalidData))
					return
				}

				values.Values[i] = make([]interface{}, len(values.Columns))
				for idx, column := range values.Columns {