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,

  }

 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:

* 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.

<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:

```Python hl_lines="3  5"
{!../../docs_src/dependencies/tutorial007.py!}
```

## 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.

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

## Verify the `username` and data shape

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

      gtl::ArraySlice<AbstractTensorHandle*> 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.

## 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.

For example, if you declare a parameter of type `Hero`, it will be read from the **JSON body**.

The same way, you can declare it as the function's **return type**, and then the shape of the data will show up in the automatic API docs UI.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[40:45] hl[40:45] *}

</details>

                or is_scalar_sequence_field(field)
                or (
                    lenient_issubclass(field.type_, BaseModel)
                    # For Pydantic v1
                    and getattr(field, "shape", 1) == 1
                )
            )

    return ParamDetails(type_annotation=type_annotation, depends=depends, field=field)


def add_param_to_fields(*, field: ModelField, dependant: Dependant) -> None: