Harshavardhana <******@****.***> 1696068836 -0700

	}{
		{
			config:      "testdata/config/1.yaml",
			expectedErr: false,
			hash:        "hash:02bf70285dc71f76",
		},
		{
			config:      "testdata/config/2.yaml",
			expectedErr: false,
			hash:        "hash:676d2da00f71f205",
		},
		{
			config:      "testdata/config/invalid.yaml",
			expectedErr: true,
		},
		{
			config:      "testdata/config/invalid-types.yaml",
			expectedErr: true,
		},
		{

    delay: 500ms # least amount of delay between each retry
`
	var job BatchJobRequest
	err := yaml.Unmarshal([]byte(expireYaml), &job)
	if err != nil {
		t.Fatal("Failed to parse batch-job-expire yaml", err)
	}
	if !slices.Equal(job.Expire.Prefix.F(), []string{"myprefix"}) {
		t.Fatal("Failed to parse batch-job-expire yaml")
	}

	multiPrefixExpireYaml := `
expire: # Expire objects that match a condition
  apiVersion: v1

- Added support for a new kubectl output format, `kyaml`. KYAML was a strict subset of YAML and should be accepted by any YAML processor. The formatting of KYAML was halfway between JSON and YAML.  Because it was more explicit than the default YAML style, it was less error-prone. ([#132942](https://github.com/kubernetes/kubernetes/pull/132942), [@thockin](https://github.com/thockin)) [SIG API Machinery,...

{* ../../docs_src/path_operation_advanced_configuration/tutorial007.py hl[17:22,24] *}

Néanmoins, bien que nous n'utilisions pas la fonctionnalité par défaut, nous utilisons toujours un modèle Pydantic pour générer manuellement le schéma JSON pour les données que nous souhaitons recevoir en YAML.

Dann verwenden wir den Request direkt und extrahieren den Body als `bytes`. Das bedeutet, dass FastAPI nicht einmal versucht, den Request-Payload als JSON zu parsen.

Und dann parsen wir in unserem Code diesen YAML-Inhalt direkt und verwenden dann wieder dasselbe Pydantic-Modell, um den YAML-Inhalt zu validieren:

//// tab | Pydantic v2

Luego usamos el request directamente, y extraemos el cuerpo como `bytes`. Esto significa que FastAPI ni siquiera intentará parsear la carga útil del request como JSON.

Y luego en nuestro código, parseamos ese contenido YAML directamente, y nuevamente estamos usando el mismo modelo Pydantic para validar el contenido YAML:

//// tab | Pydantic v2

        OutputStream jsonOutput = SearchEngineUtil.getXContentBuilderOutputStream(callback, XContentType.JSON);
        assertNotNull(jsonOutput);

        // Test with YAML
        OutputStream yamlOutput = SearchEngineUtil.getXContentBuilderOutputStream(callback, XContentType.YAML);
        assertNotNull(yamlOutput);
    }

    public void test_getXContentOutputStream_success() {
        ToXContent xContent = new ToXContent() {

Então utilizamos a requisição diretamente, e extraímos o corpo como `bytes`. Isso significa que o FastAPI não vai sequer tentar analisar o corpo da requisição como JSON.

E então no nosso código, nós analisamos o conteúdo YAML diretamente, e estamos utilizando o mesmo modelo Pydantic para validar o conteúdo YAML:

//// tab | Pydantic v2

             docker-compose -f ${GITHUB_WORKSPACE}/.github/workflows/mint/minio-${mode}.yaml rm || true
          done

          docker-compose -f ${GITHUB_WORKSPACE}/.github/workflows/multipart/docker-compose-site1.yaml rm -s -f || true
          docker-compose -f ${GITHUB_WORKSPACE}/.github/workflows/multipart/docker-compose-site2.yaml rm -s -f || true