## Die Dokumentation testen { #check-the-docs }

Sie können die erforderlichen Header in der Dokumentationsoberfläche unter `/docs` sehen:

<div class="screenshot">
<img src="/img/tutorial/header-param-models/image01.png">
</div>

## Zusätzliche Header verbieten { #forbid-extra-headers }

## Check the Docs { #check-the-docs }

You can see the required headers in the docs UI at `/docs`:

<div class="screenshot">
<img src="/img/tutorial/header-param-models/image01.png">
</div>

## Forbid Extra Headers { #forbid-extra-headers }

In some special use cases (probably not very common), you might want to **restrict** the headers that you want to receive.

**FastAPI**는 요청의 **쿼리 매개변수**에서 **각 필드**의 데이터를 **추출**해 정의한 Pydantic 모델로 제공합니다.

## 문서 확인하기

`/docs` 경로의 API 문서에서 매개변수를 확인할 수 있습니다.

<div class="screenshot">
<img src="/img/tutorial/query-param-models/image01.png">
</div>

## 추가 쿼리 매개변수 금지

몇몇의 특이한 경우에 (흔치 않지만), 허용할 쿼리 매개변수를 **제한**해야할 수 있습니다.

Pydantic 모델 설정에서 `extra` 필드를 `forbid` 로 설정할 수 있습니다.

{* ../../docs_src/query_param_models/tutorial002_an_py310.py hl[10] *}

import jakarta.servlet.ServletContext;

/**
 * Job class for generating thumbnails for documents in the search engine.
 * This job executes the ThumbnailGenerator process as a separate JVM process
 * to create thumbnail images for supported document types.
 */
public class GenerateThumbnailJob extends ExecJob {
    /** Logger for this class. */
    static final Logger logger = LogManager.getLogger(GenerateThumbnailJob.class);

errors.design_jsp_file_does_not_exist = O arquivo JSP não existe.
errors.design_file_name_is_not_found = O nome do arquivo não foi especificado.
errors.failed_to_write_design_image_file = Não foi possível carregar o arquivo de imagem.
errors.failed_to_update_jsp_file = Não foi possível atualizar o arquivo JSP.
errors.design_file_name_is_invalid = O nome do arquivo é inválido.
errors.design_file_is_unsupported_type = Este tipo de arquivo não é suportado.

running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

    buffer.writeUtf8CodePoint(0x1a)
    buffer.writeUtf8CodePoint(0x0a)
    server.enqueue(
      MockResponse
        .Builder()
        .body(buffer)
        .setHeader("Content-Type", "image/png; charset=utf-8")
        .build(),
    )
    val response = client.newCall(request().build()).execute()
    response.body.close()
    applicationLogs
      .assertLogEqual("--> GET $url")

Вы увидите, что в документации есть обычные операции пути, а также появились вебхуки:

    )
  }

  @Test fun testValidParse() {
    assertMediaType("text/plain")
    assertMediaType("application/atom+xml; charset=utf-8")
    assertMediaType("application/atom+xml; a=1; a=2; b=3")
    assertMediaType("image/gif; foo=bar")
    assertMediaType("text/plain; a=1")
    assertMediaType("text/plain; a=1; a=2; b=3")
    assertMediaType("text/plain; charset=utf-16")
    assertMediaType("text/plain; \t \n \r a=b")

![Interceptors Diagram](../assets/images/******@****.***)

### Application Interceptors

Interceptors are registered as either _application_ or _network_ interceptors. We'll use the `LoggingInterceptor` defined above to show the difference.