// +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.ListMeta metadata = 1;

  // items list individual PodDisruptionBudget objects
  repeated PodDisruptionBudget items = 2;
}

// PodDisruptionBudgetSpec is a description of a PodDisruptionBudget.
message PodDisruptionBudgetSpec {

```

////

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

## Nested Models

Each attribute of a Pydantic model has a type.

	// Heal all buckets with all objects
	for _, bucket := range healBuckets {
		if tracker.isHealed(bucket) {
			continue
		}

		var forwardTo string
		// If we resume to the same bucket, forward to last known item.
		b := tracker.getBucket()
		if b == bucket {
			forwardTo = tracker.getObject()
		}
		if b != "" {
			// Reset to where last bucket ended if resuming.
			tracker.resume()
		}
		tracker.setObject("")

* look for the subpackage `routers` (the directory at `app/routers/`)...
* and from it, import the submodule `items` (the file at `app/routers/items.py`) and `users` (the file at `app/routers/users.py`)...

The module `items` will have a variable `router` (`items.router`). This is the same one we created in the file `app/routers/items.py`, it's an `APIRouter` object.

And then we do the same for the module `users`.

# Finally: Run your script of choice.
#   If you've clicked on a test result from our CI (via a dashboard or GitHub link),
#   click to "Invocation Details" and find BUILD_CONFIG, which will contain a
#   "build_file" item that indicates the script used.
ci/official/wheel.sh

# Advanced: Select specific build/test targets with "any.sh".
# TF_ANY_TARGETS=":your/target" TF_ANY_MODE="test" ci/official/any.sh

```Python hl_lines="9  14  20  23  27"
{!> ../../docs_src/body_nested_models/tutorial007.py!}
```

////

/// info | "Информация"

Заметьте, что у объекта `Offer` есть список объектов `Item`, которые, в свою очередь, могут содержать необязательный список объектов `Image`

///

## Тела с чистыми списками элементов

## 깊게 중첩된 모델

단독으로 깊게 중첩된 모델을 정의할 수 있습니다:

```Python hl_lines="9  14  20  23  27"
{!../../docs_src/body_nested_models/tutorial007.py!}
```

/// info | "정보"

`Offer`가 선택사항 `Image` 리스트를 차례로 갖는 `Item` 리스트를 어떻게 가지고 있는지 주목하세요

///

## 순수 리스트의 본문

예상되는 JSON 본문의 최상위 값이 JSON `array`(파이썬 `list`)면, Pydantic 모델에서와 마찬가지로 함수의 매개변수에서 타입을 선언할 수 있습니다:

```Python
images: List[Image]
```

이를 아래처럼:

                    type: array
                    items:
                      type: string
                    example: ["aaa"]
                  related_contents:
                    type: array
                    items:
                      type: string
                    example: []
                  data:
                    type: array
                    items:
                      type: object

But by default, it all **"just works"**.

### Validation

* Validation for most (or all?) Python **data types**, including:
    * JSON objects (`dict`).
    * JSON array (`list`) defining item types.
    * String (`str`) fields, defining min and max lengths.
    * Numbers (`int`, `float`) with min and max values, etc.

* Validation for more exotic types, like:
    * URL.
    * Email.
    * UUID.

Hepsi varsayılan olarak **çalışıyor**.


### Doğrulama

* Neredeyse bütün (ya da hepsi?) Python **data typeları** için doğrulama, kapsadıkları:
    * JSON objeleri (`dict`).
    * JSON array (`list`) item type'ı belirtirken.
    * String (`str`) parametresi, minimum ve maksimum uzunluk gibi sınırlandırmalar yaparken.
    * Numaralar (`int`, `float`) maksimum ve minimum gibi sınırlandırmalar yaparken.