"title": "Q",
                            },
                            "name": "q",
                            "in": "query",
                        },
                    ],
                    "requestBody": {
                        "content": {
                            "application/json": {

    logging.info(f"Using config: {settings.model_dump_json()}")
    g = Github(settings.github_token.get_secret_value(), per_page=100)
    r = g.get_repo(settings.github_repository)
    repos = g.search_repositories(query="topic:fastapi")
    repos_list = list(repos)
    final_repos: list[Repo] = []
    for repo in repos_list[:100]:
        if repo.full_name == settings.github_repository:
            continue
        final_repos.append(

                                "title": "Img",
                            },
                            "name": "img",
                            "in": "query",
                        },
                    ],
                }
            }
        },
        "components": {
            "schemas": {
                "Item": {
                    "title": "Item",

	b.ReportAllocs()
	// the actual benchmark for PutObject starts here. Reset the benchmark timer.
	b.ResetTimer()

	b.RunParallel(func(pb *testing.PB) {
		for pb.Next() {
			req.URL.Query().Get("uploadId")
		}
	})

	// Benchmark ends here. Stop timer.
	b.StopTimer()

The daemon never runs any user code in the background.

There are several different types of requests, such as a request to run a set of tasks, or to query a tooling model, or to stop.
Some requests will require that the build is configured and maybe some work executed, and other requests might not.

See [Build execution model](build-execution-model.md) for more details.

{* ../../docs_src/background_tasks/tutorial002_an_py310.py hl[13,15,22,25] *}

В этом примере сообщения будут записаны в файл `log.txt` после отправки ответа.

Если в запросе была строка запроса (query), она будет записана в лог фоновой задачей.

Затем другая фоновая задача, созданная в функции‑обработчике пути, запишет сообщение, используя path‑параметр `email`.

## Технические детали { #technical-details }

이를 통해 **여러 위치**에서 **모델을 재사용** 할 수 있고 모든 매개변수에 대한 유효성 검사 및 메타데이터를 한 번에 선언할 수도 있습니다. 😍

/// note | 참고

이 기능은 FastAPI 버전 `0.115.0` 이후부터 지원됩니다. 🤓

///

/// tip | 팁

동일한 기술이 `Query`, `Cookie`, 그리고 `Header`에 적용됩니다. 😎

///

## Pydantic 모델을 사용한 쿠키

**Pydantic 모델**에 필요한 **쿠키** 매개변수를 선언한 다음, 해당 매개변수를 `Cookie`로 선언합니다:

{* ../../docs_src/cookie_param_models/tutorial001_an_py310.py hl[9:12,16] *}

        "multi": false,
        "name": "scrape_jobs",
        "options": [],
        "query": {
          "query": "label_values(job)",
          "refId": "StandardVariableQuery"
        },
        "refresh": 1,
        "regex": "",
        "skipUrlSync": false,
        "sort": 0,
        "type": "query"
      }
    ]
  },
  "time": {
    "from": "now-1h",
    "to": "now"

OpenAPI define los siguientes esquemas de seguridad:

* `apiKey`: una clave específica de la aplicación que puede provenir de:
  * Un parámetro de query.
  * Un header.
  * Una cookie.
* `http`: sistemas de autenticación HTTP estándar, incluyendo:
  * `bearer`: un header `Authorization` con un valor de `Bearer ` más un token. Esto se hereda de OAuth2.

		for k, v := range rm {
			resultsMap[k] = append(resultsMap[k], v...)
		}
		results = append(results, rs...)
	}
	return resultsMap, results
}

// ToQueryValues to query values
func ToQueryValues(table string, foreignKeys []string, foreignValues [][]interface{}) (interface{}, []interface{}) {
	queryValues := make([]interface{}, len(foreignValues))
	if len(foreignKeys) == 1 {