},
	},
	// Issue 66869: Don't skip over an EOCDR with a truncated comment.
	// The test file sneakily hides a second EOCDR before the first one;
	// previously we would extract one file ("file") from this archive,
	// while most other tools would reject the file or extract a different one ("FILE").
	{
		Name:  "comment-truncated.zip",
		Error: ErrFormat,
	},
}

func TestReader(t *testing.T) {
	for _, zt := range tests {

runAsUser: 1000 runAsGroup: 1000 resources: requests: memory: 128Mi ## Additional volumes to add to the post-job. extraVolumes: [] # - name: extra-policies # configMap: # name: my-extra-policies-cm ## Additional volumeMounts to add to the custom commands container when ## running the post-job. extraVolumeMounts: [] # - name: extra-policies # mountPath: /mnt/extras/ # Command to run after the main command on exit exitCommand: "" ## Merge jobs postJob: podAnnotations: {} annotations: {} securityContext:...

runAsUser: 1000 runAsGroup: 1000 resources: requests: memory: 128Mi ## Additional volumes to add to the post-job. extraVolumes: [] # - name: extra-policies # configMap: # name: my-extra-policies-cm ## Additional volumeMounts to add to the custom commands container when ## running the post-job. extraVolumeMounts: [] # - name: extra-policies # mountPath: /mnt/extras/ # Command to run after the main command on exit exitCommand: "" ## Merge jobs postJob: podAnnotations: {} annotations: {} securityContext:...

# Modelos Extra { #extra-models }

Continuando con el ejemplo anterior, será común tener más de un modelo relacionado.

Esto es especialmente el caso para los modelos de usuario, porque:

* El **modelo de entrada** necesita poder tener una contraseña.
* El **modelo de salida** no debería tener una contraseña.
* El **modelo de base de datos** probablemente necesitaría tener una contraseña hasheada.

/// danger | Peligro

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

So, we get a Pydantic model from the data in another Pydantic model.

#### Unpacking a `dict` and extra keywords { #unpacking-a-dict-and-extra-keywords }

And then adding the extra keyword argument `hashed_password=hashed_password`, like in:

```Python
UserInDB(**user_in.model_dump(), hashed_password=hashed_password)
```

...ends up being like:

# Zusätzliche Datentypen { #extra-data-types }

Bisher haben Sie gängige Datentypen verwendet, wie zum Beispiel:

* `int`
* `float`
* `str`
* `bool`

Sie können aber auch komplexere Datentypen verwenden.

Und Sie haben immer noch dieselbe Funktionalität wie bisher gesehen:

* Großartige Editor-Unterstützung.
* Datenkonvertierung bei eingehenden <abbr title="Request – Anfrage: Daten, die der Client zum Server sendet">Requests</abbr>.

# Extramodelle { #extra-models }

Im Anschluss an das vorherige Beispiel ist es üblich, mehr als ein zusammenhängendes Modell zu haben.

Dies gilt insbesondere für Benutzermodelle, denn:

* Das **Eingabemodell** muss ein Passwort enthalten können.
* Das **Ausgabemodell** sollte kein Passwort haben.
* Das **Datenbankmodell** müsste wahrscheinlich ein gehashtes Passwort haben.

/// danger | Gefahr

# Дополнительные модели { #extra-models }

В продолжение прошлого примера будет уже обычным делом иметь несколько связанных между собой моделей.

Это особенно применимо в случае моделей пользователя, потому что:

* **Модель для ввода** должна иметь возможность содержать пароль.
* **Модель для вывода** не должна содержать пароль.
* **Модель для базы данных**, возможно, должна содержать хэшированный пароль.

/// danger | Внимание

# Extra Data Types { #extra-data-types }

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.