* Проверка **сложных структур**:
    * Использование иерархических моделей Pydantic; `List`, `Dict` и т.п. из модуля `typing` (входит в стандартную библиотеку Python).
    * Валидаторы позволяют четко и легко определять, проверять и документировать сложные схемы данных в виде JSON Schema.

* Valida **estructuras complejas**:
    * Usa modelos jerárquicos de modelos de Pydantic, `typing` de Python,  `List` y `Dict`, etc.
    * Los validadores también permiten que se definan fácil y claramente schemas complejos de datos. Estos son chequeados y documentados como JSON Schema.

- [`KT-56755`](https://youtrack.jetbrains.com/issue/KT-56755) K2: Investigate failures related to line numbers with LT compilation enabled
- [`KT-56139`](https://youtrack.jetbrains.com/issue/KT-56139) K2: consider adding source element for implicit receivers
- [`KT-57489`](https://youtrack.jetbrains.com/issue/KT-57489) K2: Incorrectly generated line numbers in companion object access inside class

* اعتبار سنجی **ساختارهای پیچیده**:
    * استفاده از مدل های سلسله مراتبی Pydantic, `List` و `Dict` کتابخانه `typing` پایتون و غیره.
    * و اعتبارسنج ها اجازه میدهند که طرحواره های داده پیچیده به طور واضح و آسان تعریف، بررسی و بر پایه JSON مستند شوند.

    * Pydantic 데이터 구조는 단순 여러분이 정의한 클래스의 인스턴스이기 때문에, 자동 완성, 린팅, mypy 그리고 여러분의 직관까지 여러분의 검증된 데이터와 올바르게 작동합니다.
* **복잡한 구조**를 검증합니다:
    * 계층적인 Pydantic 모델, 파이썬 `typing`의 `List`와 `Dict`, 그 외를 사용합니다.
    * 그리고 검증자는 복잡한 데이터 스키마를 명확하고 쉽게 정의 및 확인하며 JSON 스키마로 문서화합니다.
    * 여러분은 깊게 **중첩된 JSON** 객체를 가질 수 있으며, 이 객체 모두 검증하고 설명을 붙일 수 있습니다.
* **확장 가능성**:

* Fix a scalability issue where high rates of event writes degraded etcd performance. ([#64539](https://github.com/kubernetes/kubernetes/pull/64539), [@ccding](https://github.com/ccding))
* "kubectl delete" no longer waits for dependent objects to be deleted when removing parent resources ([#65908](https://github.com/kubernetes/kubernetes/pull/65908), [@juanvallejo](https://github.com/juanvallejo))

And we return the scopes as part of the JWT token.

!!! danger
    For simplicity, here we are just adding the scopes received directly to the token.

    But in your application, for security, you should make sure you only add the scopes that the user is actually able to have, or the ones you have predefined.

=== "Python 3.10+"

```

!!! tip
    Here we are creating the models with an `id`.

    We didn't explicitly specify an `id` attribute in the Peewee models, but Peewee adds one automatically.

    We are also adding the magic `owner_id` attribute to `Item`.

### Create a `PeeweeGetterDict` for the Pydantic *models* / schemas

* Improve Azure instance metadata handling by adding caches. ([#70353](https://github.com/kubernetes/kubernetes/pull/70353), [@feiskyer](https://github.com/feiskyer))
* adding cn-northwest-1 for AWS China Ningxia region ([#70155](https://github.com/kubernetes/kubernetes/pull/70155), [@pahud](https://github.com/pahud))

     * to ["a", "b", ""].
     */
    private fun pop() {
      val removed = encodedPathSegments.removeAt(encodedPathSegments.size - 1)

      // Make sure the path ends with a '/' by either adding an empty string or clearing a segment.
      if (removed.isEmpty() && encodedPathSegments.isNotEmpty()) {
        encodedPathSegments[encodedPathSegments.size - 1] = ""
      } else {
        encodedPathSegments.add("")