It is just a function that can take all the same parameters that a *path operation function* can take:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[8:9] *}

That's it.

**2 lines**.

And it has the same shape and structure that all your *path operation functions* have.

You can think of it as a *path operation function* without the "decorator" (without the `@app.get("/some-path")`).

And it can return anything you want.

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 }

            EndpointPair.ordered(1, 1),
            EndpointPair.ordered(1, 3),
            EndpointPair.ordered(1, 2))
        .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 populateStarShapedGraph() {
    putEdge(2, 1);

And Swagger UI has supported this particular `examples` field for a while. So, you can use it to **show** different **examples in the docs UI**.

The shape of this OpenAPI-specific field `examples` is a `dict` with **multiple examples** (instead of a `list`), each with extra information that will be added to **OpenAPI** too.

                                                           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,

<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:

{* ../../docs_src/dependencies/tutorial007.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.

* 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.

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] *}

## Pydantic models { #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.