* initiated by the client (MSIE post requests after successful NTLM SSP
     * authentication). If false and the user has not been authenticated yet
     * the client will be forced to send an authentication (server sends
     * HttpServletResponse.SC_UNAUTHORIZED).
     * @return True if the negotiation is complete, otherwise false
     * @throws IOException if an I/O error occurs

Por ejemplo, como en el modelo `Image` tenemos un campo `url`, podemos declararlo como una instance de `HttpUrl` de Pydantic en lugar de un `str`:

{* ../../docs_src/body_nested_models/tutorial005_py310.py hl[2,8] *}

El string será verificado para ser una URL válida, y documentado en JSON Schema / OpenAPI como tal.

## Atributos con listas de submodelos

También puedes usar modelos Pydantic como subtipos de `list`, `set`, etc.:

	if err != nil {
		return nil, err
	}
	defer done(0, &err)

	return p.storage.ReadParts(ctx, volume, partMetaPaths...)
}

// ReadMultiple will read multiple files and send each files as response.
// Files are read and returned in the given order.
// The resp channel is closed before the call returns.
// Only a canceled context will return an error.

      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the

or networks.

For performance reasons, the default TensorFlow server does not include any
authorization protocol and sends messages unencrypted. It accepts connections
from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

                    logger.info("Sent {} docs (Doc:{process {}ms, send {}ms, size {}}, {})", docList.size(), docList.getProcessingTime(),
                            systemHelper.getCurrentTimeAsLong() - execTime, MemoryUtil.byteCountToDisplaySize(docList.getContentSize()),
                            MemoryUtil.getMemoryUsageLog());
                } else {

### Update a Hero with `HeroUpdate` { #update-a-hero-with-heroupdate }

We can **update a hero**. For this we use an HTTP `PATCH` operation.

And in the code, we get a `dict` with all the data sent by the client, **only the data sent by the client**, excluding any values that would be there just for being the default values. To do it we use `exclude_unset=True`. This is the main trick. 🪄

Imagina que quieres que el parámetro sea `item-query`.

Como en:

```
http://127.0.0.1:8000/items/?item-query=foobaritems
```

Pero `item-query` no es un nombre de variable válido en Python.

Lo más cercano sería `item_query`.

Pero aún necesitas que sea exactamente `item-query`...

Entonces puedes declarar un `alias`, y ese alias será usado para encontrar el valor del parámetro:

    }

    /**
     * Sends a command to the process associated with the given session ID.
     *
     * @param sessionId unique identifier for the process session
     * @param command the command to send to the process
     * @throws JobNotFoundException if no process is found for the given session ID
     * @throws JobProcessingException if there's an error sending the command
     */

          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "Data Sent [{{bucket}}]",
          "refId": "A"
        }
      ],
      "title": "Total Data Sent Rate",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },