"`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:

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

## Перевірте `username` і структуру даних { #verify-the-username-and-data-shape }

Ми перевіряємо, що отримали `username`, і видобуваємо scopes.

                                                           TF_DeleteStatus);

  TF_Tensor* tensor_shape = Int32Tensor({37, 1});
  TF_Operation* shape = Const(tensor_shape, func_graph.get(), s.get(), "shape");
  TF_Operation* random =
      RandomUniform(shape, TF_FLOAT, func_graph.get(), s.get());

  TF_Output outputs[] = {{random, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name,

{* ../../docs_src/dependencies/tutorial007_py310.py hl[3,5] *}

## Sub-dependencies with `yield` { #sub-dependencies-with-yield }

You can have sub-dependencies and "trees" of sub-dependencies of any size and shape, and any or all of them can use `yield`.

**FastAPI** will make sure that the "exit code" in each dependency with `yield` is run in the correct order.

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

#### Data "schema" { #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 { #openapi-and-json-schema }

      absl::Span<AbstractTensorHandle* const> output_gradients,
      absl::Span<AbstractTensorHandle*> result) const override;

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  absl::Status BuildOnesLike(const TapeTensor& t,
                             AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.