case 2:
                    segmentation = values[1];
                case 1:
                    token = values[0];
                default:
                    break;
                }

                id++;
                final KuromojiItem item = new KuromojiItem(id, token, segmentation, reading, pos);
                if (updater != null) {

    github_repository: str
    token: SecretStr
    debug: bool | None = False
    config: dict[str, LabelSettings] | Literal[""] = default_config


settings = Settings()
if settings.debug:
    logging.basicConfig(level=logging.DEBUG)
else:
    logging.basicConfig(level=logging.INFO)
logging.debug(f"Using config: {settings.model_dump_json()}")
g = Github(settings.token.get_secret_value())

    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: tiangolo/issue-manager@0.6.0
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
          config: >
            {
              "answered": {
                "delay": 864000,

    runs-on: ubuntu-latest
    env:
      ACTIONS_STEP_DEBUG: true
    steps:
    - name: Close Stale Issues
      uses: actions/stale@v9
      with:
        repo-token: ${{ secrets.GITHUB_TOKEN }}
        stale-issue-message: "This issue has been automatically marked as stale because it has been open 360 days with no activity. Remove stale label or comment or this will be closed in 180 days"

	s, err := bufio.NewReader(file).ReadString('\n')
	if err != nil && !errors.Is(err, io.EOF) {
		return nil, err
	}
	statLine := strings.TrimSpace(s)
	for _, token := range strings.Fields(statLine) {
		ui64, err := strconv.ParseUint(token, 10, 64)
		if err != nil {
			return nil, err
		}
		stats = append(stats, ui64)
	}

	return stats, nil

# 현재 사용자 가져오기 { #get-current-user }

이전 장에서 (의존성 주입 시스템을 기반으로 한) 보안 시스템은 *경로 처리 함수*에 `str`로 `token`을 제공했습니다:

{* ../../docs_src/security/tutorial001_an_py310.py hl[12] *}

하지만 이는 여전히 그다지 유용하지 않습니다.

현재 사용자를 제공하도록 해봅시다.

## 사용자 모델 생성하기 { #create-a-user-model }

먼저 Pydantic 사용자 모델을 만들어 봅시다.

Pydantic을 사용해 본문을 선언하는 것과 같은 방식으로, 다른 곳에서도 어디서든 사용할 수 있습니다:

{* ../../docs_src/security/tutorial002_an_py310.py hl[5,12:6] *}

            amazon_s3_base_path=%BRANCH%
      - shell: |
          #!/usr/local/bin/runbld --redirect-stderr
          set -euo pipefail

          set +x
          VAULT_TOKEN=$(vault write -field=token auth/approle/login role_id=$VAULT_ROLE_ID secret_id=$VAULT_SECRET_ID)
          export VAULT_TOKEN
          export data=$(vault read -format=json aws-test/creds/elasticsearch-ci-s3)

from fastapi.testclient import TestClient
from inline_snapshot import snapshot
from pydantic import BaseModel

app = FastAPI()

reusable_oauth2 = OAuth2(
    flows={
        "password": {
            "tokenUrl": "token",
            "scopes": {"read:users": "Read the users", "write:users": "Create users"},
        }
    }
)


class User(BaseModel):
    username: str


# Here we use string annotations to test them

from fastapi.testclient import TestClient
from inline_snapshot import snapshot
from pydantic import BaseModel

app = FastAPI()

reusable_oauth2 = OAuth2(
    flows={
        "password": {
            "tokenUrl": "token",
            "scopes": {"read:users": "Read the users", "write:users": "Create users"},
        }
    },
    description="OAuth2 security scheme",
    auto_error=False,
)


class User(BaseModel):

                It is also useful when you want to have authentication that can be
                provided in one of multiple optional ways (for example, in a query
                parameter or in an HTTP Bearer token).
                """
            ),
        ] = True,
    ):
        super().__init__(
            location=APIKeyIn.query,
            name=name,
            scheme_name=scheme_name,