"Unable to extract the trust manager on ${Platform.get()}, " +
              "sslSocketFactory is ${sslSocketFactory.javaClass}",
          )
        this.certificateChainCleaner =
          Platform.get().buildCertificateChainCleaner(x509TrustManagerOrNull!!)
      }

    /**
     * Sets the socket factory and trust manager used to secure HTTPS connections. If unset, the

                alreadyEncoded = true,
                strict = true,
                plusIsSpace = true,
              )
          }
        }

        encodedFragment =
          encodedFragment?.canonicalize(
            encodeSet = FRAGMENT_ENCODE_SET_URI,
            alreadyEncoded = true,
            strict = true,
            unicodeAllowed = true,
          )
      }

        try {
            boolean strict = request.getValidationLevel() >= ModelBuildingRequest.VALIDATION_LEVEL_MAVEN_2_0;
            InputSource source = request.isLocationTracking() ? new InputSource() : null;

            Map<String, Object> options = new HashMap<>();
            options.put(ModelProcessor.IS_STRICT, strict);
            options.put(ModelProcessor.INPUT_SOURCE, source);

			Optional:    true,
			Type:        "duration",
		},
		config.HelpKV{
			Key:         apiListQuorum,
			Description: `set the acceptable quorum expected for list operations e.g. "optimal", "reduced", "disk", "strict", "auto"` + defaultHelpPostfix(apiListQuorum),
			Optional:    true,
			Type:        "string",
		},
		config.HelpKV{
			Key:         apiReplicationPriority,

Ten en cuenta que los dataclasses no pueden hacer todo lo que los modelos de Pydantic pueden hacer.

Así que, podrías necesitar seguir usando modelos de Pydantic.

Pero si tienes un montón de dataclasses por ahí, este es un buen truco para usarlos para potenciar una API web usando FastAPI. 🤓

///

## Dataclasses en `response_model` { #dataclasses-in-response-model }

También puedes usar `dataclasses` en el parámetro `response_model`:

    boolean isResolveDependencies();

    /**
     * Controls the level of validation to perform on processed models. By default, models are validated in strict mode.
     *
     * @param validationLevel The level of validation to perform on processed models, e.g.
     *            {@link org.apache.maven.model.building.ModelBuildingRequest#VALIDATION_LEVEL_STRICT}.

* Fixes issue with cleaning up stale NFS subpath mounts ([#71804](https://github.com/kubernetes/kubernetes/pull/71804), [@msau42](https://github.com/msau42))
* Modify the scheduling result struct and improve logging for successful binding. ([#71926](https://github.com/kubernetes/kubernetes/pull/71926), [@wgliang](https://github.com/wgliang))

Pydantic has an official <a href="https://docs.pydantic.dev/latest/migration/" class="external-link" target="_blank">Migration Guide</a> from v1 to v2.

It also includes what has changed, how validations are now more correct and strict, possible caveats, etc.

You can read it to understand better what has changed.

## Tests { #tests }

    /**
     * Denotes validation as performed by Maven 3.1. This validation level is meant for new projects.
     */
    int VALIDATION_LEVEL_MAVEN_3_1 = 31;

    /**
     * Denotes strict validation as recommended by the current Maven version.
     */
    int VALIDATION_LEVEL_STRICT = VALIDATION_LEVEL_MAVEN_3_0;

    /**

    public static final String ORIGIN = "origin";
    public static final String STRICT_ORIGIN = "strict-origin";
    public static final String ORIGIN_WHEN_CROSS_ORIGIN = "origin-when-cross-origin";
    public static final String STRICT_ORIGIN_WHEN_CROSS_ORIGIN = "strict-origin-when-cross-origin";
    public static final String UNSAFE_URL = "unsafe-url";
  }

  /**