new_field.post_validators = field.post_validators  # type: ignore[attr-defined]
    new_field.parse_json = field.parse_json  # type: ignore[attr-defined]
    new_field.shape = field.shape  # type: ignore[attr-defined]
    new_field.populate_validators()  # type: ignore[attr-defined]
    return new_field


def generate_operation_id_for_path(
    *, name: str, path: str, method: str

This schema definition includes your API paths, the possible parameters they take, etc.

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

In that case, it would mean the JSON attributes, and data types they have, etc.

#### OpenAPI and JSON Schema

* Add a **JSON Schema** for the response, in the OpenAPI *path operation*.
    * This will be used by the **automatic docs**.
    * It will also be used by automatic client code generation tools.

## Pydantic models

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.

* 🌐 Add Russian translation for `docs/ru/docs/tutorial/response-status-code.md`. PR [#9370](https://github.com/tiangolo/fastapi/pull/9370) by [@nadia3373](https://github.com/nadia3373).

### Internal

* 🐛 Fix `flask.escape` warning for internal tests. PR [#9468](https://github.com/tiangolo/fastapi/pull/9468) by [@samuelcolvin](https://github.com/samuelcolvin).

        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="8-11"
    {!> ../../../docs_src/dependencies/tutorial001.py!}
    ```

That's it.

**2 lines**.

And it has the same shape and structure that all your *path operation functions* have.

You can think of it as a *path operation function* without the "decorator" (without the `@app.get("/some-path")`).

And it can return anything you want.

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="105  107-115"
    {!> ../../../docs_src/security/tutorial005.py!}
    ```

## Verify the `username` and data shape

We verify that we get a `username`, and extract the scopes.

    These Pydantic models define more or less a "schema" (a valid data shape).

    So this will help us avoiding confusion while using both.

### Create the Pydantic *models* / schemas

Create all the same Pydantic models as in the SQLAlchemy tutorial:

    sequence_shapes = {
        SHAPE_LIST,
        SHAPE_SET,
        SHAPE_FROZENSET,
        SHAPE_TUPLE,
        SHAPE_SEQUENCE,
        SHAPE_TUPLE_ELLIPSIS,
    }
    sequence_shape_to_type = {
        SHAPE_LIST: list,
        SHAPE_SET: set,
        SHAPE_TUPLE: tuple,
        SHAPE_SEQUENCE: list,
        SHAPE_TUPLE_ELLIPSIS: list,
    }

    @dataclass

<img src="/img/python-types/image06.png">

## Pydantic 모델

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a>은 데이터 검증(Validation)을 위한 파이썬 라이브러리입니다.

당신은 속성들을 포함한 클래스 형태로 "모양(shape)"을 선언할 수 있습니다.

그리고 각 속성은 타입을 가지고 있습니다.

이 클래스를 활용하여서 값을 가지고 있는 인스턴스를 만들게 되면, 필요한 경우에는 적당한 타입으로 변환까지 시키기도 하여 데이터가 포함된 객체를 반환합니다.

그리고 결과 객체에 대해서는 에디터의 도움을 받을 수 있게 됩니다.

Pydantic 공식 문서 예시: