```

**Important**: Return types or response models are what filter data ensuring no sensitive information is exposed. And they are used to serialize data with Pydantic (in Rust), this is the main idea that can increase response performance.

The return type doesn't have to be a Pydantic model, it could be a different type, like a list of integers, or a dict, etc.

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.model.building;

/**
 * Caches auxiliary data used during model building like already processed raw/effective models. The data in the cache
 * is meant for exclusive consumption by the model builder and is opaque to the cache implementation. The cache key is

import binascii
from base64 import b64decode
from typing import Annotated

from annotated_doc import Doc
from fastapi.exceptions import HTTPException
from fastapi.openapi.models import HTTPBase as HTTPBaseModel
from fastapi.openapi.models import HTTPBearer as HTTPBearerModel
from fastapi.security.base import SecurityBase
from fastapi.security.utils import get_authorization_scheme_param
from pydantic import BaseModel
from starlette.requests import Request

    return {"message": result}
```

## SQLModel for SQL databases

When working with SQL databases, prefer using SQLModel as it is integrated with Pydantic and will allow declaring data validation with the same models.

Prefer it over SQLAlchemy.

## HTTPX

Use HTTPX for handling HTTP communication (e.g. with other APIs). It support sync and async usage.

**FastAPI** ile bir *path operation* oluşturduğunuzda, normalde ondan herhangi bir veri döndürebilirsiniz: bir `dict`, bir `list`, bir Pydantic model, bir veritabanı modeli vb.

Bir [Response Model](../tutorial/response-model.md) deklare ederseniz, FastAPI veriyi Pydantic kullanarak JSON'a serialize etmek için bunu kullanır.

 */
package org.apache.maven.model.composition;

import javax.inject.Named;
import javax.inject.Singleton;

import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

import org.apache.maven.model.Dependency;
import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.Model;
import org.apache.maven.model.building.ModelBuildingRequest;

  script.setAttribute("data-modal-title-color", "#FFFFFF");
  script.setAttribute("data-modal-title-font-family", "Roboto, sans-serif");
  script.setAttribute("data-modal-example-questions", "How to define a route?,How to validate models?,How to handle responses?,How to deploy FastAPI?");

이것이 어떻게 동작하는지, 그리고 필요하다면 어떻게 변경할 수 있는지 살펴보겠습니다.

## 입력과 출력을 위한 Pydantic 모델 { #pydantic-models-for-input-and-output }

예를 들어, 다음처럼 기본값이 있는 Pydantic 모델이 있다고 해보겠습니다:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### 입력용 모델 { #model-for-input }

이 모델을 다음처럼 입력으로 사용하면:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

---> 100%
```

</div>

## Создание приложения с единственной моделью { #create-the-app-with-a-single-model }

Сначала мы создадим самую простую первую версию приложения с одной моделью **SQLModel**.

Позже мы улучшим его, повысив безопасность и универсальность, добавив **несколько моделей**. 🤓

### Создание моделей { #create-models }

Импортируйте `SQLModel` и создайте модель базы данных:

}

func BenchmarkMutexWorkSlack(b *testing.B) {
	benchmarkMutex(b, true, true)
}

func BenchmarkMutexNoSpin(b *testing.B) {
	b.ResetTimer()
	b.ReportAllocs()

	// This benchmark models a situation where spinning in the mutex should be
	// non-profitable and allows to confirm that spinning does not do harm.
	// To achieve this we create excess of goroutines most of which do local work.