It doesn't return a large `str` containing the data in JSON format (as a string). It returns a Python standard data structure (e.g. a `dict`) with values and sub-values that are all compatible with JSON.

/// note

`jsonable_encoder` is actually used by **FastAPI** internally to convert data. But it is useful in many other scenarios.

	Port      string `json:"port"`
	UserAgent string `json:"userAgent"`
}

// Event represents event notification information defined in
// http://docs.aws.amazon.com/AmazonS3/latest/dev/notification-content-structure.html.
type Event struct {
	EventVersion      string            `json:"eventVersion"`
	EventSource       string            `json:"eventSource"`
	AwsRegion         string            `json:"awsRegion"`

  <groupId>inheritance</groupId>
  <artifactId>parent</artifactId>
  <version>11-SNAPSHOT</version>

  <name>Model urls inheritance test parent</name>
  <description>Flat directory structure case: module = ../child-artifact-id + child directory path != child-artifact-id</description>

  <modules>
    <module>../child-artifact-id</module><!-- use child artifact id, even if different from directory -->
  </modules>

- **Flexibility and Control:**
Java serialization offers limited control over the serialization process, such as excluding certain fields, customizing naming conventions, and handling complex data structures more gracefully.

- **Security:**
Java serialization poses security risks, especially related to deserialization vulnerabilities.

- **Version Compatibility:**

import java.util.Map;
import javax.annotation.CheckForNull;

/**
 * A {@link MapIteratorCache} that adds additional caching. In addition to the caching provided by
 * {@link MapIteratorCache}, this structure caches values for the two most recently retrieved keys.
 *
 * @author James Sexton
 */
@ElementTypesAreNonnullByDefault
final class MapRetrievalCache<K, V> extends MapIteratorCache<K, V> {

  </parent>

  <groupId>inheritance</groupId>
  <artifactId>inheritance</artifactId>
  <version>11-SNAPSHOT</version>
  <name>Model urls inheritance test child</name>
  <description>Flat directory structure case: module = ../child-artifact-id + child directory path == child-artifact-id</description>

  <!-- 5 inherited urls with ../${project.artifactId} added to parent -->

  <groupId>inheritance</groupId>
  <artifactId>parent</artifactId>
  <version>11-SNAPSHOT</version>

  <name>Model urls inheritance test parent</name>
  <description>Flat directory structure case: module = ../child-artifact-id + child directory path == child-artifact-id</description>

  <modules>
    <module>../inheritance</module>
  </modules>

  <!-- 5 urls in the pom will be inherited with path added -->

    * Parce que les structures de données de pydantic consistent seulement en une instance de classe que vous définissez; l'auto-complétion, le linting, mypy et votre intuition devrait être largement suffisante pour valider vos données.
* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.

   * code would be responsible for populating a "real" {@code T} (which might still be the value
   * {@code null}!) before returning it to callers. Depending on how the code is structured, a
   * nullness analysis might not understand that the field has been populated. To avoid that problem
   * without having to add {@code @SuppressWarnings}, the code can call this method.
   *

   * code would be responsible for populating a "real" {@code T} (which might still be the value
   * {@code null}!) before returning it to callers. Depending on how the code is structured, a
   * nullness analysis might not understand that the field has been populated. To avoid that problem
   * without having to add {@code @SuppressWarnings}, the code can call this method.
   *