}
    add(FirErrors.NULLABLE_SUPERTYPE) { firDiagnostic ->
        NullableSupertypeImpl(
            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }
    add(FirErrors.MANY_CLASSES_IN_SUPERTYPE_LIST) { firDiagnostic ->
        ManyClassesInSupertypeListImpl(
            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }

Vous faites cela avec les types Python standard modernes.

Vous n'avez pas à apprendre une nouvelle syntaxe, les méthodes ou les classes d'une bibliothèque spécifique, etc.

Juste du **Python** standard.

Par exemple, pour un `int`:

```Python
item_id: int
```

ou pour un modèle `Item` plus complexe :

```Python

### Recap

In summary, you declare **once** the types of parameters, body, etc. as function parameters.

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

Just standard **Python**.

For example, for an `int`:

```Python
item_id: int
```

or for a more complex `Item` model:

```Python
item: Item
```

/.fleet/ "Kotlin Build Infrastructure"

/.space/ "Kotlin Build Infrastructure"

/analysis/ "Kotlin IDE Analysis Core"
/analysis/light-classes-base/ Dmitry.Gridin
/analysis/symbol-light-classes/ Dmitry.Gridin
/analysis/analysis-api-fir/src/org/jetbrains/kotlin/analysis/api/fir/diagnostics/KtFirDiagnostics.kt "Kotlin Compiler Core"

comply.


                   GNU LESSER GENERAL PUBLIC LICENSE
                       Version 3, 29 June 2007

 Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.


  This version of the GNU Lesser General Public License incorporates

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

Resumindo, você declara **uma vez** os tipos dos parâmetros, corpo etc. como parâmetros de função.

Você faz com tipos padrão do Python moderno.

Você não terá que aprender uma nova sintaxe, métodos ou classes de uma biblioteca específica etc.

Apenas **Python** padrão.

Por exemplo, para um `int`:

```Python
item_id: int
```

ou para um modelo mais complexo, `Item`:

```Python

*   Added `tf.keras.__internal__.KerasTensor`,`tf.keras.__internal__.SparseKerasTensor`, and `tf.keras.__internal__.RaggedKerasTensor` classes. You can use these classes to do instance type checking and type annotations for layer/model inputs and outputs.

        /**
         * A scope containing both non-static and static members. A smart combined scope (as opposed to a naive combination of [KtScope]s
         * with [getCompositeScope]) avoids duplicate inner classes, as they are contained in non-static and static scopes.
         *
         * A proper combined declared member scope kind also makes it easier to cache combined scopes directly (if needed).
         */
        COMBINED,

    detail at that link.

4.  Serialized forms of ALL objects are subject to change unless noted
    otherwise. Do not persist these and assume they can be read by a future
    version of the library.

5.  Our classes are not designed to protect against a malicious caller. You
    should not use them for communication between trusted and untrusted code.

6.  For the mainline flavor, we test the libraries using OpenJDK 8, 11, and 17