Map<String, Object> params = new HashMap<>();
        params.put("param", "value");

        // First call with model name
        ComponentUtil.setFessConfig(new FessConfig.SimpleImpl() {
            @Override
            public String getLtrModelName() {
                return "model1";
            }

            @Override
            public int getLtrWindowSize() {
                return 50;

        Objects.requireNonNull(modelId, "modelId cannot null");

        modelIds.add(modelId);

        return this;
    }

    @Override
    public Model getRawModel() {
        return rawModels.get(modelIds.get(0));
    }

    @Override
    public Model getRawModel(String modelId) {
        return rawModels.get(modelId);
    }

     */
    @Deprecated
    public ModelBuildingException(Model model, String modelId, List<ModelProblem> problems) {
        super(toMessage(modelId, problems));

        if (model != null) {
            DefaultModelBuildingResult tmp = new DefaultModelBuildingResult();
            if (modelId == null) {
                modelId = "";
            }
            tmp.addModelId(modelId);
            tmp.setRawModel(modelId, model);

    }

    /**
     * Converts this compat model InputSource to an API model InputSource.
     * This method is used for converting between the compat model and the API model.
     *
     * @return the equivalent API model InputSource
     */
    public org.apache.maven.api.model.InputSource toApiSource() {
        return org.apache.maven.api.model.InputSource.of(modelId, location);
    }

org.apache.maven.model.root.DefaultRootLocator...

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

     *
     * This method will be called on each raw model being loaded,
     * just before validation.
     *
     * @param model the input model
     * @return the transformed model, or the input model if no transformation is needed
     * @throws ModelTransformerException
     */
    @Nonnull
    default Model transformRawModel(@Nonnull Model model) throws ModelTransformerException {
        return model;
    }

    /**

* Récupérez les données stockées.
* Placez ces données dans un modèle Pydantic.
* Générez un `dict` sans valeurs par défaut à partir du modèle d’entrée (en utilisant `exclude_unset`).
    * De cette façon, vous mettez à jour uniquement les valeurs effectivement définies par l’utilisateur, au lieu d’écraser des valeurs déjà stockées par des valeurs par défaut de votre modèle.

It's **not supported** by Pydantic to have a model of Pydantic v2 with its own fields defined as Pydantic v1 models or vice versa.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

E você pode usar qualquer modelo ou dado para os requisitos de segurança (neste caso, um modelo Pydantic `User`).

Mas você não está restrito a usar um modelo de dados, classe ou tipo específico.

Você quer ter apenas um `id` e `email`, sem incluir nenhum `username` no modelo? Claro. Você pode usar essas mesmas ferramentas.