changed to not return an error when the specified target can be detected as not a mount point. On Linux, the behavior of detecting a mount point depends on `umount` command is validated when the mounter is created. Additionally, mount point checks will be skipped in CleanupMountPoint/CleanupMountWithForce if the mounter's Unmount having the changed behavior of not returning error when target is not a mount point. ([#109676](https://github.com/kubernetes/kubernetes/pull/109676), [@cartermckinnon](h...

- `kubectl rollout restart` now works for DaemonSets and StatefulSets. ([#77423](https://github.com/kubernetes/kubernetes/pull/77423), [@apelisse](https://github.com/apelisse))
- `kubectl get --watch=true` now prints custom resource definitions with custom print columns. ([#76161](https://github.com/kubernetes/kubernetes/pull/76161), [@liggitt](https://github.com/liggitt))

		deleteObjectsReq.Objects[i].ObjectName = trimLeadingSlash(deleteObjectsReq.Objects[i].ObjectName)
		objects[i] = deleteObjectsReq.Objects[i].ObjectV
	}

	// Make sure to update context to print ObjectNames for multi objects.
	ctx = updateReqContext(ctx, objects...)

	// Call checkRequestAuthType to populate ReqInfo.AccessKey before GetBucketInfo()

Zum Beispiel könnten Sie eine Datei `main.py` haben mit:

```Python hl_lines="3"
import os

name = os.getenv("MY_NAME", "World")
print(f"Hello {name} from Python")
```

/// tip | Tipp

Das zweite Argument von [`os.getenv()`](https://docs.python.org/3.8/library/os.html#os.getenv) ist der Defaultwert, der zurückgegeben wird.

```

就能以如下方式调用：

```Python
user_dict = user_in.model_dump()
```

现在，变量 `user_dict` 中的是包含数据的 `dict`（它是 `dict`，不是 Pydantic 模型对象）。

以如下方式调用：

```Python
print(user_dict)
```

输出的就是 Python `dict`：

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

- Unreserve extension point for scheduler plugins is merged into Reserve extension point ([#92200](https://github.com/kubernetes/kubernetes/pull/92200), [@adtac](https://github.com/adtac)) [SIG Scheduling and Testing]

user_dict = user_in.model_dump()
```

artık `user_dict` değişkeninde modelin verilerini içeren bir `dict` vardır (Pydantic model nesnesi yerine bir `dict` elde etmiş oluruz).

Ve eğer şunu çağırırsak:

```Python
print(user_dict)
```

şöyle bir Python `dict` elde ederiz:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

Por exemplo, você poderia ter um arquivo `main.py` com:

```Python hl_lines="3"
import os

name = os.getenv("MY_NAME", "World")
print(f"Hello {name} from Python")
```

/// tip | Dica

O segundo argumento para [`os.getenv()`](https://docs.python.org/3.8/library/os.html#os.getenv) é o valor padrão a ser retornado.

```Python
user_dict = user_in.model_dump()
```

то теперь у нас есть `dict` с данными в переменной `user_dict` (это `dict` вместо объекта Pydantic-модели).

И если мы вызовем:

```Python
print(user_dict)
```

мы получим Python `dict` с:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

```Python
user_dict = user_in.model_dump()
```

agora temos um `dict` com os dados na variável `user_dict` (é um `dict` em vez de um objeto de modelo Pydantic).

E se chamarmos:

```Python
print(user_dict)
```

teríamos um `dict` Python com:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```