It doesn't mean "`one_person` is the **class** called `Person`".

## Pydantic models { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/) 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.

"optimum","part","pattern","placeholder","playsinline","popover","popovertarget","popovertargetaction","poster","preload","pubdate","radiogroup","readonly","rel","required","rev","reversed","role","rows","rowspan","spellcheck","scope","selected","shape","size","sizes","slot","span","srclang","start","src","srcset","step","style","summary","tabindex","title","translate","type","usemap","valign","value","width","wrap","xmlns","slot"]),P=i(["accent-height","accumulate","additive","alignment-baselin...

It is just a function that can take all the same parameters that a *path operation function* can take:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[8:9] *}

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.

And Swagger UI has supported this particular `examples` field for a while. So, you can use it to **show** different **examples in the docs UI**.

The shape of this OpenAPI-specific field `examples` is a `dict` with **multiple examples** (instead of a `list`), each with extra information that will be added to **OpenAPI** too.

   *     </ul>
   */
  @Deprecated
  public static HashFunction sha1() {
    return Sha1Holder.SHA_1;
  }

  private static final class Sha1Holder {
    static final HashFunction SHA_1 = new MessageDigestHashFunction("SHA-1", "Hashing.sha1()");
  }

  /** Returns a hash function implementing the SHA-256 algorithm (256 hash bits). */

"`one_person`, `Person` adlı **class**'tır" anlamına gelmez.

## Pydantic modelleri { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/), data validation yapmak için bir Python kütüphanesidir.

Verinin "shape"'ini attribute'lara sahip sınıflar olarak tanımlarsınız.

Ve her attribute'un bir tipi vardır.

{* ../../docs_src/security/tutorial005_an_py310.py hl[106,108:116] *}

## Den `username` und das Format der Daten überprüfen { #verify-the-username-and-data-shape }

Wir verifizieren, dass wir einen `username` erhalten, und extrahieren die Scopes.

{* ../../docs_src/security/tutorial005_an_py310.py hl[106,108:116] *}

## `username` ve veri şeklinin doğrulanması { #verify-the-username-and-data-shape }

Bir `username` aldığımızı doğruluyoruz ve scope'ları çıkarıyoruz.

{* ../../docs_src/security/tutorial005_an_py310.py hl[106,108:116] *}

## Проверка `username` и формата данных { #verify-the-username-and-data-shape }

Мы проверяем, что получили `username`, и извлекаем scopes.

  }

 private:
  TFE_Context* context_;
  TFE_CustomDevice device_;
  void* info_;
  string name_;
};

// An adapter which wraps the shape/data produced by C custom devices and uses
// it to implement custom device methods.
class CAPICustomDeviceTensorHandle
    : public tensorflow::CustomDeviceTensorHandle {
 public: