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)
	}

if "getsitepackages" in dir(_site):
  _site_packages_dirs += _site.getsitepackages()

for _scheme in sysconfig.get_scheme_names():
  for _name in ["purelib", "platlib"]:
    _site_packages_dirs += [sysconfig.get_path(_name, _scheme)]

_site_packages_dirs = list(set(_site_packages_dirs))

# Find the location of this exact file.
_current_file_location = _inspect.getfile(_inspect.currentframe())

    "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}

- 2 つの対話的なドキュメントのWebインターフェイスを直接提供します。

---

まだ表面的な部分に触れただけですが、もう全ての仕組みは分かっているはずです。

以下の行を変更してみてください:

```Python
    return {"item_name": item.name, "item_id": item_id}
```

...以下を:

```Python
        ... "item_name": item.name ...
```

...以下のように:

```Python
        ... "item_price": item.price ...
```

...そして、エディタが属性を自動補完し、そのタイプを知る方法を確認してください。:

<div class="wrapper">
    <jsp:include page="/WEB-INF/view/common/admin/header.jsp"></jsp:include>
    <jsp:include page="/WEB-INF/view/common/admin/sidebar.jsp">
        <jsp:param name="menuCategoryType" value="system"/>
        <jsp:param name="menuType" value="dict"/>
    </jsp:include>
    <div class="content-wrapper">
        <div class="content-header">
            <div class="container-fluid">