second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` signifie:

    Utilise les clés et valeurs du dictionnaire `second_user_data` directement comme des arguments clé-valeur. C'est équivalent à: `User(id=4, name="Mary", joined="2018-11-30")`

### Support d'éditeurs

[[tasks_parallel_worker]]
= Developing Parallel Tasks

Gradle provides an API that can split tasks into sections that can be executed in parallel.

image::writing-tasks-5.png[]

This allows Gradle to fully utilize the resources available and complete builds faster.

[[worker_api]]
== The Worker API

Dans les versions précédentes de Python, vous auriez utilisé des *threads* ou <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>.  Mais le code aurait été bien plus difficile à comprendre, débugger, et concevoir.

	VariableVarName                  = "variables"
)

// BuildRequestType generates a DeclType for AdmissionRequest. This may be replaced with a utility that
// converts the native type definition to apiservercel.DeclType once such a utility becomes available.
// The 'uid' field is omitted since it is not needed for in-process admission review.

En versiones anteriores de Python, podrías haber utilizado <abbr title="En español: hilos.">threads</abbr> o <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>. Pero el código es mucho más complejo de entender, depurar y desarrollar.

prehension tool. Based on the concept of a project object model: buil ds, dependency management, documentation creation, site publication, and distribution publication are all controlled from the declarative file. Maven can be extended by plugins to utilise a number of other d evelopment tools for reporting or the build process. Specification-Vendor: Apache Software Foundation Implementation-Vendor: Apache Software Foundation Implementation-Title: maven-plugin-api Implementation-Version: 2.0 org/apa...

      </replaceConfiguration>

    Given a const min, max, num_bits and narrow_range attribute, applies the
    same uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_args op. See the fakeQuantAttrsToType() utility
    method and the quant-convert-simulated-quantization pass for further details.
  }];

  let arguments = (ins
    F32Tensor:$inputs,
    F32Attr:$min,
    F32Attr:$max,

    Given a const min, max, num_bits and narrow_range attribute, applies the
    same uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_args op. See the fakeQuantAttrsToType() utility
    method and the quant-convert-simulated-quantization pass for further details.
  }];

  let arguments = (ins
    F32Tensor:$inputs,
    F32Attr:$min,
    F32Attr:$max,

    And be compatible with all of them at the same time.

Create a utility function to hash a password coming from the user.

And another utility to verify if a received password matches the hash stored.

And another one to authenticate and return a user.

=== "Python 3.10+"