/// note | 技術詳細

レスポンスモデルは、関数の戻り値のアノテーションではなく、このパラメータで宣言されています。なぜなら、パス関数は実際にはそのレスポンスモデルを返すのではなく、`dict`やデータベースオブジェクト、あるいは他のモデルを返し、`response_model`を使用してフィールドの制限やシリアライズを行うからです。

///

## 同じ入力データの返却

ここでは`UserIn`モデルを宣言しています。それには平文のパスワードが含まれています:

{* ../../docs_src/response_model/tutorial002.py hl[9,11] *}

そして、このモデルを使用して入力を宣言し、同じモデルを使って出力を宣言しています:

{* ../../docs_src/response_model/tutorial002.py hl[17,18] *}

	if err == nil {
		c.Fatalf("user account was not disabled!")
	}

	// 5. Check that user can be deleted and verify it.
	err = s.adm.RemoveUser(ctx, accessKey)
	if err != nil {
		c.Fatalf("user could not be deleted: %v", err)
	}
	usersMap, err = s.adm.ListUsers(ctx)
	if err != nil {
		c.Fatalf("error listing: %v", err)
	}
	_, ok = usersMap[accessKey]
	if ok {
		c.Fatalf("user not deleted: %s", accessKey)
	}

                 * for the corresponding scope and language. This rule exists because
                 * Maven provides default values for those elements which may conflict
                 * with user's configuration.
                 */
                if (!hasScript) {
                    project.addScriptSourceRoot(build.getScriptSourceDirectory());
                }
                if (!hasMain) {

            elements are not used for inferring the path (they are still used as compiler options however).
            It means that for scripts and resources, the files below the path specified by {@code directory}
            are copied to the path specified by {@code targetPath} with the exact same directory structure.

```Python
def get_current_user(token: str):
    # authenticate user
    return User()


@app.get("/items/")
def read_items(user: User = Depends(get_current_user)):
    ...


@app.post("/items/")
def create_item(*, user: User = Depends(get_current_user), item: Item):
    ...


@app.get("/items/{item_id}")
def read_item(*, user: User = Depends(get_current_user), item_id: int):
    ...

    *   New `tf.data.Dataset.reduce()` API allows users to reduce a finite
        dataset to a single element using a user-provided reduce function.
    *   New `tf.data.Dataset.window()` API allows users to create finite windows
        of input dataset; when combined with the `tf.data.Dataset.reduce()` API,
        this allows users to implement customized batching.

doubtful cases shall be resolved in favor of coverage.  For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product.  A product
is a consumer product regardless of whether the product has substantial