<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE components PUBLIC "-//DBFLUTE//DTD LastaDi 1.0//EN"
	"http://dbflute.org/meta/lastadi10.dtd">
<components namespace="fessCrawler">
	<include path="crawler/container.xml" />

	<component name="robotsTxtHelper" class="org.codelibs.fess.crawler.helper.RobotsTxtHelper"
		instance="prototype">
	</component>

상기와 같은 방식으로 "호출(실행)" 할 수 있다면 "호출 가능"이 됩니다.

## 의존성으로서의 클래스

파이썬 클래스의 인스턴스를 생성하기 위해 사용하는 것과 동일한 문법을 사용한다는 걸 알 수 있습니다.

예를 들어:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

이 경우에 `fluffy`는 클래스 `Cat`의 인스턴스입니다. 그리고 우리는 `fluffy`를 만들기 위해서 `Cat`을 "호출"했습니다.

따라서, 파이썬 클래스는 **호출 가능**합니다.

그래서 **FastAPI**에서는 파이썬 클래스를 의존성으로 사용할 수 있습니다.

    return user_id


# Een Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Vervolgens kan je het op deze manier gebruiken:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

    private static final Logger log = LoggerFactory.getLogger(KerberosTest.class);


    /**
     * @param properties
     */
    public KerberosTest ( String name, Map<String, String> properties ) {
        super(name, properties);
    }


    @Parameters ( name = "{0}" )
    public static Collection<Object> configs () {
        return getConfigs("smb1", "smb2", "smb30", "smb31", "forceSpnegoIntegrity");
    }

// Creates Object layer setup ( MakeBucket ) and then runs the PutObject benchmark.
func runPutObjectBenchmark(b *testing.B, obj ObjectLayer, objSize int) {
	var err error
	// obtains random bucket name.
	bucket := getRandomBucketName()
	// create bucket.
	err = obj.MakeBucket(context.Background(), bucket, MakeBucketOptions{})
	if err != nil {
		b.Fatal(err)
	}

    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }
    ],
    "access_right": "open",

name := """fess-suggest-test"""

version := "1.0-SNAPSHOT"

lazy val root = (project in file(".")).enablePlugins(PlayJava)

resolvers += Resolver.url("codelibs", url("http://maven.codelibs.org/"))(Resolver.ivyStylePatterns)

resolvers += (
  "Local Maven Repository" at "file://"+Path.userHome.absolutePath+"/.m2/repository"
  )

scalaVersion := "2.11.1"

libraryDependencies ++= Seq(
  javaJdbc,
  javaEbean,
  cache,
  javaWs

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item = Body(embed=True)):
    results = {"item_id": item_id, "item": item}

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: list[str] = []


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}