package gorm

import "time"

// Model a basic GoLang struct which includes the following fields: ID, CreatedAt, UpdatedAt, DeletedAt
// It may be embedded into your model or you may build your own model without it
//
//	type User struct {
//	  gorm.Model
//	}
type Model struct {
	ID        uint `gorm:"primarykey"`
	CreatedAt time.Time
	UpdatedAt time.Time
	DeletedAt DeletedAt `gorm:"index"`

因此，**FastAPI** 将会负责过滤掉未在输出模型中声明的所有数据（使用 Pydantic）。

## 在文档中查看

当你查看自动化文档时，你可以检查输入模型和输出模型是否都具有自己的 JSON Schema：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

并且两种模型都将在交互式 API 文档中使用：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## 响应模型编码参数

你的响应模型可以具有默认值，例如：

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}

        <field>
          <name>project</name>
          <version>1.0.0</version>
          <description>Snippets of POM xml files used to supplement the data model.</description>
          <type>DOM</type>
        </field>
      </fields>
    </class>
  </classes>

## ドキュメントを見る

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます。

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

そして、両方のモデルは、対話型のAPIドキュメントに使用されます:

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

{* ../../docs_src/response_model/tutorial004.py hl[11,13,14] *}

따라서 **FastAPI**는 출력 모델에서 선언하지 않은 모든 데이터를 (Pydantic을 사용하여) 필터링합니다.

## 문서에서 보기

자동 생성 문서를 보면 입력 모델과 출력 모델이 각자의 JSON 스키마를 가지고 있음을 확인할 수 있습니다:

<img src="/img/tutorial/response-model/image01.png">

그리고 두 모델 모두 대화형 API 문서에 사용됩니다:

<img src="/img/tutorial/response-model/image02.png">

## 응답 모델 인코딩 매개변수

응답 모델은 아래와 같이 기본값을 가질 수 있습니다:

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}

# Build state model

The Gradle daemon tracks state for various elements. These are arranged in a hierarchy:

```mermaid
  graph TD

  process["build process"]
  
  session["build session"]
  process --> session
  
  build_tree["build tree"]
  session --> build_tree
  
  build1["root build"]
  build_tree --> build1
  
  project1["root project"]
  build1 --> project1
  
  project2["project"]
  build1 --> project2

# Build execution model

At the highest level, Gradle's execution model is quite simple:

Below is the protocol in some more detail:

1. The client looks for a compatible idle daemon. If there isn't one, it starts a new daemon.
2. The client connects to the idle daemon and sends it a request to do some work. If the daemon is no longer running, the client starts again.

org.apache.maven.model.root.DefaultRootLocator...

    return Model2(
        ref=Model1(foo="simple_include model foo", bar="simple_include model bar"),
        baz="simple_include model2 baz",
    )


@app.get(
    "/simple_include_dict",
    response_model=Model2,
    response_model_include={"baz": ..., "ref": {"foo"}},
)
def simple_include_dict():
    return {
        "ref": {
            "foo": "simple_include_dict model foo",

# Extra Models { #extra-models }

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

/// danger