researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

# Contributing guidelines

## Pull Request Checklist

Before sending your pull requests, make sure you do the following:

-   Read the [contributing guidelines](CONTRIBUTING.md).
-   Read the [Code of Conduct](CODE_OF_CONDUCT.md).
-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the

    static final InetAddress[] NBNS = Config.getInetAddressArray("jcifs.smb1.netbios.wins", ",", new InetAddress[0]);

    /* Construct the shared static client object that will
     * conduct all encoding and decoding of NetBIOS name service
     * messages as well as socket IO in a synchronized fashon.
     */

    private static final NameServiceClient CLIENT = new NameServiceClient();

Cette combinaison de Flask, Flask-apispec avec Marshmallow et Webargs était ma stack backend préférée jusqu'à la création de **FastAPI**.

Son utilisation a conduit à la création de plusieurs générateurs Flask full-stack. Ce sont les principales stacks que
j'ai (ainsi que plusieurs équipes externes) utilisées jusqu'à présent :