{* ../../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.

        netdfs.DfsStorageInfo storageInfo = new netdfs.DfsStorageInfo();
        storageInfo.state = netdfs.DFS_STORAGE_STATE_ONLINE;
        storageInfo.server_name = "server1";
        storageInfo.share_name = "share1";

        ByteArrayOutputStream bos = new ByteArrayOutputStream();
        // Create buffer for encoding - initially allocate some space
        byte[] encodeBuffer = new byte[1024];

                                                           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.

{* ../../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 "

* 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.

在这个异常中，我们包含所需的作用域（如果有的话），以空格分隔的字符串（使用 `scope_str`）。我们把这个包含作用域的字符串放在 `WWW-Authenticate` 响应头中（这是规范要求的一部分）。

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

## 校验 `username` 与数据形状 { #verify-the-username-and-data-shape }

我们校验是否获取到了 `username`，并提取作用域。

然后使用 Pydantic 模型验证这些数据（捕获 `ValidationError` 异常），如果读取 JWT 令牌或用 Pydantic 验证数据时出错，就抛出我们之前创建的 `HTTPException`。

为此，我们给 Pydantic 模型 `TokenData` 添加了一个新属性 `scopes`。