labels: [ "a:chore", "to-triage" ]
assignees: [ ]
body:
  - type: dropdown
    id: issue-type
    attributes:
      label: Issue type
      options:
        - Polishing
        - Deprecation
        - Refactoring
        - Other
    validations:
      required: true
  - type: textarea
    id: description
    attributes:
      label: Problem description
      description: |

Ce qui améliore le support pour les modèles Pydantic avec :

* de l'auto-complétion
* des vérifications de type
* du "refactoring" (ou remaniement de code)
* de la recherche
* de l'inspection

///

## Utilisez le modèle

Dans la fonction, vous pouvez accéder à tous les attributs de l'objet du modèle directement :

It improves editor support for Pydantic models, with:

* auto-completion
* type checks
* refactoring
* searching
* inspections

///

## Use the model { #use-the-model }

Inside of the function, you can access all the attributes of the model object directly:

   * characters).
   *
   * @param c the character to escape
   * @return the replacement characters, or {@code null} if no escaping was required
   */
  // TODO(dbeaumont,cpovirk): Rename this something better once refactoring done
  protected abstract char @Nullable [] escapeUnsafe(char c);

      }

      return mapGenerator.create(entries.toArray()).keySet();
    }

    @Override
    public K[] createArray(int length) {
      // TODO: with appropriate refactoring of OneSizeGenerator, we can perhaps
      // tidy this up and get rid of the casts here and in
      // MapValueCollectionGenerator.

    * Some refactoring of error/status messages and functions to align with new approach.
* This PR will leverage subtests on the existing table tests for the scheduler units. ([#63661](https://github.com/kubernetes/kubernetes/pull/63661), [@xchapter7x](https://github.com/xchapter7x))
    * Some refactoring of error/status messages and functions to align with new approach.

modifier, use it as base\n        ...(Popper.Defaults.modifiers[name] || {}),\n        // If there are custom options, override and merge with default ones\n        ...(options.modifiers ? options.modifiers[name] : {}),\n      };\n    });\n\n    // Refactoring modifiers' list (Object => Array)\n    this.modifiers = Object.keys(this.options.modifiers)\n      .map(name => ({\n        name,\n        ...this.options.modifiers[name],\n      }))\n      // sort the modifiers by order\n      .sort((a, b) => a.order...

### SIG CLI

SIG CLI's main focus this release was on refactoring `kubectl` internals to improve composability, readability and testability of `kubectl` commands. Those refactors will allow the team to extract a mechanism for extensibility of kubectl -- that is, plugins -- in the next releases.

    on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for
    NVIDIA Ampere based GPUs and is enabled by default.

*   A major refactoring of the internals of the Keras Functional API has been
    completed, that should improve the reliability, stability, and performance
    of constructing Functional models.

*   Keras mixed precision API

work, being done in conjunction with SIG Cloud Provider, is focusing on moving internal APIs that the OpenStack (and other providers) depends upon to staging to guarantee API stability. This work also included abstracting the in-tree Cinder API and refactoring code to the external Cinder provider to remove additional Cinder volume provider code.

Additional work was also done to implement an OpenStack driver for the Cluster API effort lead by SIG Cluster Lifecycle. For the external Cloud-Provider-OpenStack...