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.

            return Collections.emptyList();
        }
    }

    /**
     * Escapes special characters in the value for use in Fess queries.
     *
     * @param value the value to escape
     * @return the escaped value
     */
    protected String escapeQueryValue(final String value) {
        if (value == null) {
            return "";
        }

	//	return append(b, make([]byte, n)...)
	// This avoids unnecessary zero-ing of the first len(b) bytes of the
	// allocated slice, but this pattern causes b to escape onto the heap.
	//
	// Instead use the append-make pattern with a nil slice to ensure that
	// we allocate buffers rounded up to the closest size class.
	c := len(b) + n // ensure enough space for n elements

 = 1000\nconst TRANSITION_END = 'transitionend'\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`)\n  }\n\n  return selector\n}\n\n// Shout-out Angus Croll (ht...

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

## Verifique o `username` e o formato dos dados { #verify-the-username-and-data-shape }

Nós verificamos que nós obtemos um `username`, e extraímos os escopos.

) -> list[str]:
    """
    Update each code block in `text` with the corresponding code block from
    `original_code_blocks` with comments taken from `code_blocks`.

    Raises ValueError if the number, language, or shape of code blocks do not match.
    """

    if len(code_blocks) != len(original_code_blocks):
        raise ValueError(
            "Number of code blocks does not match the number in the original document "

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

## Verificar el `username` y la forma de los datos { #verify-the-username-and-data-shape }

Verificamos que obtenemos un `username`, y extraemos los scopes.

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] *}

"`one_person`은 `Person`이라는 **클래스**다"라는 뜻이 아닙니다.

## Pydantic 모델 { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/)은 데이터 검증을 수행하는 파이썬 라이브러리입니다.

속성을 가진 클래스 형태로 데이터의 "모양(shape)"을 선언합니다.

그리고 각 속성은 타입을 가집니다.

그 다음 그 클래스의 인스턴스를 몇 가지 값으로 생성하면, 값들을 검증하고, (그런 경우라면) 적절한 타입으로 변환한 뒤, 모든 데이터를 가진 객체를 제공합니다.

그리고 그 결과 객체에 대해 에디터의 모든 지원을 받을 수 있습니다.

Pydantic 공식 문서의 예시: