#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA
#     tpu:          Build TF with TPU support
#     cuda:         Build with CUDA support.
#     cuda_clang    Build with CUDA Clang support.
#     rocm:         Build with AMD GPU support (rocm)
#     mkl:          Enable full mkl support.
#     nogcp:        Disable GCS support.
#     nonccl:       Disable nccl support.
#
#

 *  New: APIs to iterate and selectively clear the response cache.
 *  New: Support for SOCKS proxies.
 *  New: Support for `TLS_FALLBACK_SCSV`.
 *  New: Update HTTP/2 support to `h2-16` and `hpack-10`.
 *  New: APIs to prevent retrying non-idempotent requests.
 *  Fix: Drop NPN support. Going forward we support ALPN only.
 *  Fix: The hostname verifier is now strict. This is consistent with the hostname

This platform does not provide special support for a particular language or ecosystem.

### JVM platform

This is a platform that builds on the core and software platforms to add support for developing software that runs on the JVM.
This includes software that is implemented using Java, Kotlin or some other JVM language.

This platform provides specific support for Java, Groovy and Scala, and includes the foojay toolchain plugin.

            **BUG!** This issue should not be marked stale by the "stale" workflow.
            Please report it to @gradle/bt-support team
          days-before-issue-close: -1
          close-issue-message: >
            **BUG!** This issue should not be closed by the "stale" workflow.
            Please report it to @gradle/bt-support team
          close-issue-reason: not_planned

   *
   * <p>The returned view supports the {@link #add} operation if this {@code RangeSet} supports
   * {@link #remove}, and vice versa.
   */
  RangeSet<C> complement();

  /**
   * Returns a view of the intersection of this {@code RangeSet} with the specified range.
   *
   * <p>The returned view supports all optional operations supported by this {@code RangeSet}, with

* `tf.lite`
    * Added support for `stablehlo.gather`.
    * Added support for `stablehlo.add`.
    * Added support for `stablehlo.multiply`.
    * Added support for `stablehlo.maximum`.
    * Added support for `stablehlo.minimum`.
    * Added boolean parameter support for `tfl.gather_nd`.
    * C API:
        * New API functions:
            * `tensorflow/lite/c/c_api_experimental.h`:

# Introduction aux Types Python

Python supporte des annotations de type (ou *type hints*) optionnelles.

Ces annotations de type constituent une syntaxe spéciale qui permet de déclarer le <abbr title="par exemple : str, int, float, bool">type</abbr> d'une variable.

En déclarant les types de vos variables, cela permet aux différents outils comme les éditeurs de texte d'offrir un meilleur support.

/// info

`**second_user_data` means:

Pass the keys and values of the `second_user_data` dict directly as key-value arguments, equivalent to: `User(id=4, name="Mary", joined="2018-11-30")`

///

### Editor support { #editor-support }

All the framework was designed to be easy and intuitive to use, all the decisions were tested on multiple editors even before starting development, to ensure the best development experience.

/// check | Inspired **FastAPI** to

Define extra validations for data types using the "default" value of model attributes. This improves editor support, and it was not available in Pydantic before.

This actually inspired updating parts of Pydantic, to support the same validation declaration style (all this functionality is now already available in Pydantic).

///

<img src="/img/tutorial/body/image01.png">

And will also be used in the API docs inside each *path operation* that needs them:

<img src="/img/tutorial/body/image02.png">

## Editor support { #editor-support }

In your editor, inside your function you will get type hints and completion everywhere (this wouldn't happen if you received a `dict` instead of a Pydantic model):

<img src="/img/tutorial/body/image03.png">