{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

В данном примере она преобразует Pydantic модель в `dict`, а `datetime` - в `str`.

Результатом её вызова является объект, который может быть закодирован с помощью функции из стандартной библиотеки Python – <a href="https://docs.python.org/3/library/json.html#json.dumps" class="external-link" target="_blank">`json.dumps()`</a>.

	echo "BUG: Expected no of users: 2 Found: ${USER_COUNT}"
	exit 1
fi
./mc admin user svcacct list myminio "uid=bobfisher,ou=people,ou=hwengg,dc=min,dc=io" --json
SVCACCT_COUNT_1=$(./mc admin user svcacct list myminio "uid=bobfisher,ou=people,ou=hwengg,dc=min,dc=io" --json | jq '.accessKey' | wc -l)
if [ "${SVCACCT_COUNT_1}" -ne 2 ]; then

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

In diesem Beispiel wird das Pydantic-Modell in ein `dict`, und das `datetime`-Objekt in ein `str` konvertiert.

Das Resultat dieses Aufrufs ist etwas, das mit Pythons Standard-<a href="https://docs.python.org/3/library/json.html#json.dumps" class="external-link" target="_blank">`json.dumps()`</a> kodiert werden kann.

	}

	data, err := json.Marshal(bucketPolicy)
	if err != nil {
		// This should not happen because bucketPolicy is valid to convert to JSON data.
		return nil, err
	}

	var policyInfo miniogopolicy.BucketAccessPolicy
	json := jsoniter.ConfigCompatibleWithStandardLibrary
	if err = json.Unmarshal(data, &policyInfo); err != nil {

type formatMetaV1 struct {
	// Version of the format config.
	Version string `json:"version"`
	// Format indicates the backend format type, supports two values 'xl' and 'fs'.
	Format string `json:"format"`
	// ID is the identifier for the minio deployment
	ID string `json:"id"`

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

En este ejemplo, convertiría el modelo de Pydantic a un `dict`, y el `datetime` a un `str`.

El resultado de llamarlo es algo que puede ser codificado con la función estándar de Python <a href="https://docs.python.org/3/library/json.html#json.dumps" class="external-link" target="_blank">`json.dumps()`</a>.

    public HtmlResponse index() {
        throw new UnsupportedOperationException();
    }

    /**
     * Starts a scheduled job by ID.
     *
     * @param id the ID of the scheduled job to start
     * @return JSON response indicating success or failure
     */
    // PUT /api/admin/scheduler/{id}/start
    @Execute(urlPattern = "{}/@word")
    public JsonResponse<ApiResult> put$start(final String id) {

:

{* ../../docs_src/body_multiple_params/tutorial005.py hl[17] *}

👉 💼 **FastAPI** 🔜 ⌛ 💪 💖:

```JSON hl_lines="2"
{
    "item": {
        "name": "Foo",
        "description": "The pretender",
        "price": 42.0,
        "tax": 3.2
    }
}
```

↩️:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.mylasta.direction.sponsor;

import org.lastaflute.core.json.JsonMappingOption;
import org.lastaflute.core.json.JsonMappingOption.JsonFieldNaming;
import org.lastaflute.core.json.JsonResourceProvider;

public class FessJsonResourceProvider implements JsonResourceProvider {
    @Override
    public boolean isNullsSuppressed() {

## Attribute mit Listen von Kindmodellen

Sie können Pydantic-Modelle auch als Typen innerhalb von `list`, `set`, usw. verwenden:

{* ../../docs_src/body_nested_models/tutorial006_py310.py hl[18] *}

Das wird einen JSON-Body erwarten (konvertieren, validieren, dokumentieren), wie:

```JSON hl_lines="11"
{
    "name": "Foo",