EndpointPair.unordered(1, 1),
            EndpointPair.unordered(1, 3))
        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateTShapedGraph() {
    putEdge(2, 1);

{* ../../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.

                                                           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,

) -> 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 "

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] *}

* 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.

在這個例外中，我們把所需的 scopes（若有）以空白分隔的字串形式（透過 `scope_str`）加入，並將該包含 scopes 的字串放在 `WWW-Authenticate` 標頭中（這是規格的一部分）。

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

## 驗證 `username` 與資料結構 { #verify-the-username-and-data-shape }

我們先確認取得了 `username`，並取出 scopes。

接著用 Pydantic 模型驗證這些資料（捕捉 `ValidationError` 例外），若在讀取 JWT token 或用 Pydantic 驗證資料時出錯，就丟出先前建立的 `HTTPException`。

為此，我們更新了 Pydantic 模型 `TokenData`，加入新屬性 `scopes`。

                                                   TF_Status* status);

// A handle to a tensor on a device.
//
// Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape,
// type etc. Unlike a TF_Tensor, a TFE_TensorHandle may refer to such tensors
// placed in the memory of different devices or remote address spaces.
typedef struct TFE_TensorHandle TFE_TensorHandle;