# Recommended: Configure Docker. (Linux only)
#
#   TF uses hub.docker.com/r/tensorflow/build containers for CI,
#   and scripts on Linux create a persistent container called "tf"
#   which mounts your TensorFlow directory into the container.
#
#   Important: because the container is persistent, you cannot change TFCI
#   variables in between script executions. To forcibly remove the

## Opinions

"_[...] I'm using **FastAPI** a ton these days. [...] I'm actually planning to use it for all of my team's **ML services at Microsoft**. Some of them are getting integrated into the core **Windows** product and some **Office** products._"

<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/fastapi/fastapi/pull/26"><small>(ref)</small></a></div>

O OAuth2 foi projetado para que o backend ou a API pudesse ser independente do servidor que autentica o usuário.

Mas, neste caso, a mesma aplicação **FastAPI** irá lidar com a API e com a autenticação.

Então, vamos rever de um ponto de vista simplificado:

* O usuário digita o `username` e o `password` no frontend e pressiona `Enter`.

### À propos de `**user_in.model_dump()` { #about-user-in-model-dump }

#### La méthode `.model_dump()` de Pydantic { #pydantics-model-dump }

`user_in` est un modèle Pydantic de classe `UserIn`.

Les modèles Pydantic ont une méthode `.model_dump()` qui renvoie un `dict` avec les données du modèle.

Ainsi, si nous créons un objet Pydantic `user_in` comme :

```Python

///

### Support des éditeurs { #editor-support }

Tout le framework a été conçu pour être facile et intuitif à utiliser, toutes les décisions ont été testées sur plusieurs éditeurs avant même de commencer le développement, pour assurer la meilleure expérience de développement.

    for (int i = 0; i < reps; i++) {
      Integer key = keys.get(random.nextInt(nKeys));
      // This range is [-5, 4] - slight negative bias so we often hit zero, which brings the
      // auto-removal of zeroes into play.
      int delta = random.nextInt(10) - 5;
      blah += delta;
      if (delta >= 0) {
        multiset.add(key, delta);
      } else {
        multiset.remove(key, -delta);
      }
    }

That way, you can edit the documentation/source files and see the changes live.

/// tip

Alternatively, you can perform the same steps that scripts does manually.

Go into the language directory, for the main docs in English it's at `docs/en/`:

```console
$ cd docs/en/
```

Then run `mkdocs` in that directory:

```console
$ mkdocs serve --dev-addr 127.0.0.1:8008
```

  static final int SPLITERATOR_CHARACTERISTICS =
      ImmutableCollection.SPLITERATOR_CHARACTERISTICS | Spliterator.DISTINCT;

  /**
   * Returns a {@code Collector} that accumulates the input elements into a new {@code
   * ImmutableSet}. Elements appear in the resulting set in the encounter order of the stream; if
   * the stream contains duplicates (according to {@link Object#equals(Object)}), only the first

*   Pod preemption has been improved.
    *   Now takes PodDisruptionBudget into account. ([#56178](https://github.com/kubernetes/kubernetes/pull/56178),[ @bsalamat](https://github.com/bsalamat))

		Info: dataUsageCacheInfo{
			Name:      dataUsageRoot,
			NextCycle: oldCache.Info.NextCycle,
		},
		Cache: make(map[string]dataUsageEntry, len(oldCache.Cache)),
	}

	// Put all buckets into channel.
	bucketCh := make(chan BucketInfo, len(buckets))

	// Shuffle buckets to ensure total randomness of buckets, being scanned.
	// Otherwise same set of buckets get scanned across erasure sets always.