Estes tipos que possuem tipos internos são chamados de tipos "**genéricos**". E é possível declará-los mesmo com os seus tipos internos.

Para declarar esses tipos e os tipos internos, você pode usar o módulo Python padrão `typing`. Ele existe especificamente para suportar esses type hints.

### `Depends` importieren { #import-depends }

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[3] *}

### Die Abhängigkeit im <abbr title="Das Abhängige, der Verwender der Abhängigkeit">„Dependant“</abbr> deklarieren { #declare-the-dependency-in-the-dependant }

So wie auch `Body`, `Query`, usw., verwenden Sie `Depends` mit den Parametern Ihrer *Pfadoperation-Funktion*:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[13,18] *}

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

///

{* ../../docs_src/security/tutorial005_an_py310.py hl[157] *}

## Объявление scopes в *обработчиках путей* и зависимостях { #declare-scopes-in-path-operations-and-dependencies }

Теперь объявим, что операция пути для `/users/me/items/` требует scope `items`.

Для этого импортируем и используем `Security` из `fastapi`.

如果你需要2分钟来学习如何使用 Python 类型（即使你不使用 FastAPI ），看看这个简短的教程：[Python Types](python-types.md){.internal-link target=_blank}。

编写带有类型标注的标准 Python：

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

# Beispiel-Request-Daten deklarieren { #declare-request-example-data }

Sie können Beispiele für die Daten deklarieren, die Ihre App empfangen kann.

Hier sind mehrere Möglichkeiten, das zu tun.

## Zusätzliche JSON-Schemadaten in Pydantic-Modellen { #extra-json-schema-data-in-pydantic-models }

Sie können `examples` („Beispiele“) für ein Pydantic-Modell deklarieren, welche dem generierten JSON-Schema hinzugefügt werden.

</div>

### Create the `Settings` object { #create-the-settings-object }

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

  We'll examine the different parts of maven-core on a case-by-case
  basis, below:

*** DefaultLifecycleExecutor

  When binding goals to the project's configured lifecycle, each mojo
  descriptor should declare whether it requires online/offline status.
  This value should be a java.lang.Boolean, so it can implement 3VL
  (three value logic: yes, no, don't-care). The requiresOnline

Você escreve Python padrão com tipos:

```Python
from datetime import date

from pydantic import BaseModel

# Declare uma variável como str
# e obtenha suporte do editor dentro da função
def main(user_id: str):
    return user_id


# Um modelo do Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date

    </server>
    -->
  </servers>

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