///

This is of course not the frontend for the final users, but it's a great automatic tool to document interactively all your API.

It can be used by the frontend team (that can also be yourself).

It can be used by third party applications and systems.

And it can also be used by yourself, to debug, check and test the same application.

## The `password` flow

 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */
package jcifs.smb;


import org.bouncycastle.util.encoders.Hex;


/**
 * Authenticator directly specifing the user's NT hash
 * 
 * @author mbechler
 *
 */
public class NtlmNtHashAuthenticator extends NtlmPasswordAuthenticator {

    private static final long serialVersionUID = 4328214169536360351L;

            echo " OK"
        fi

        # Create fess user if not existing
        if ! id $FESS_USER > /dev/null 2>&1 ; then
            echo -n "Creating $FESS_USER user..."
            useradd --system \
                    -M \
                    --gid "$FESS_GROUP" \
                    --shell /sbin/nologin \
                    --comment "fess user" \
                    -d "$FESS_USER_HOME"  \

## 获取用户

`get_current_user` 使用创建的（伪）工具函数，该函数接收 `str` 类型的令牌，并返回 Pydantic 的 `User` 模型：

```Python hl_lines="19-22  26-27"
{!../../docs_src/security/tutorial002.py!}
```

## 注入当前用户

在*路径操作* 的 `Depends` 中使用 `get_current_user`：

```Python hl_lines="31"
{!../../docs_src/security/tutorial002.py!}
```

注意，此处把 `current_user` 的类型声明为 Pydantic 的 `User` 模型。

这有助于在函数内部使用代码补全和类型检查。

/// tip | "提示"

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


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

Sau đó có thể được sử dụng:

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

second_user_data = {
    "id": 4,
    "name": "Mary",

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


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

可以像这样来使用：

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

second_user_data = {
    "id": 4,
    "name": "Mary",

这会需要一些“等待时间”。

但是由于 SQLAlchemy 不具有`await`直接使用的兼容性，因此类似于：

```Python
user = await db.query(User).first()
```

...相反，我们可以使用：

```Python
user = db.query(User).first()
```

然后我们应该声明*路径操作函数*和不带 的依赖关系`async def`，只需使用普通的`def`，如下：

```Python hl_lines="2"
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):

///

## Erros das dependências e valores de retorno

Você pode utilizar as mesmas *funções* de dependências que você usaria normalmente.

### Requisitos de Dependências

Dependências podem declarar requisitos de requisições (como cabeçalhos) ou outras subdependências:

//// tab | Python 3.9+

```Python hl_lines="8  13"

/// note | "기술 세부사항"

응답 모델은 함수의 타입 어노테이션 대신 이 매개변수로 선언하는데, 경로 함수가 실제 응답 모델을 반환하지 않고 `dict`, 데이터베이스 객체나 기타 다른 모델을 `response_model`을 사용하여 필드 제한과 직렬화를 수행하고 반환할 수 있기 때문입니다

///

## 동일한 입력 데이터 반환

여기서 우리는 평문 비밀번호를 포함하는 `UserIn` 모델을 선언합니다:

```Python hl_lines="9  11"
{!../../docs_src/response_model/tutorial002.py!}
```

그리고 이 모델을 사용하여 입력을 선언하고 같은 모델로 출력을 선언합니다:

```Python hl_lines="17-18"

 * up the build process by avoiding unnecessary downloads.</p>
 *
 * <p>By default, the local repository is located in the {@code .m2/repository}
 * directory within the user's home directory ({@code ~/.m2/repository} on
 * Unix-like systems or {@code C:\Users\YourName\.m2\repository} on Windows).
 * The location of the local repository can be customized in the
 * {@code settings.xml} file.</p>
 *
 * @since 4.0.0
 * @see Repository