TensorFlow models, please demonstrate an exploit against one of Alphabet's
    models in <https://tfhub.dev/>
  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue
    violates the use cases and security assumptions defined in the threat model.

```

</div>

/// note | Remarque

La commande `uvicorn main:app` fait référence à :

* `main` : le fichier `main.py` (le « module » Python).
* `app` : l'objet créé dans `main.py` avec la ligne `app = FastAPI()`.

C'est équivalent à :

```Python
from main import app
```

///

    default ProducedArtifact getPomArtifact() {
        return getArtifacts().get(0);
    }

    /**
     * {@return the project main artifact}, which is the artifact produced by this project build, if applicable.
     * This artifact MAY be absent if the project is actually not producing any main artifact (i.e. "pom" packaging).
     *
     * @see #getPackaging()
     * @see org.apache.maven.api.services.ArtifactManager#getPath(Artifact)

Consider installing the [Develocity IntelliJ plugin](https://plugins.jetbrains.com/plugin/27471-develocity) to bring real-time Gradle build analysis directly into your IDE. 
It helps developers optimize performance and gain deeper insights into their builds.

## Making your change

### Code change guidelines

Code contributions should follow these guidelines to maximize the chance of them being accepted:

- find accounts whose group memberships have changed; access policies available to a credential are updated to reflect the change, i.e. they will lose any privileges associated with a group they are removed from, and gain any privileges associated with a group they are added to.

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

 * is less error-prone and more readable than code using {@code ReentrantLock}, without significant
 * performance loss. {@code Monitor} even has the potential for performance gain by optimizing the
 * evaluation and signaling of conditions. Signaling is entirely <a
 * href="http://en.wikipedia.org/wiki/Monitor_(synchronization)#Implicit_signaling">implicit</a>. By

					dstOpts.WantChecksum = nil
				}
			} else {
				// S3: All copied objects without checksums and specified destination checksum algorithms
				// automatically gain a CRC-64NVME checksum algorithm.
				dstOpts.WantServerSideChecksumType = hash.ChecksumCRC64NVME
				srcInfo.Reader.AddServerSideChecksumHasher(dstOpts.WantServerSideChecksumType)
				dstOpts.WantChecksum = nil
			}

Si vous venez d'un autre framework asynchrone qui ne fonctionne pas comme de la façon décrite ci-dessus et que vous êtes habitué à définir des *fonctions de chemin d'accès* basiques et purement calculatoires avec un simple `def` pour un faible gain de performance (environ 100 nanosecondes), veuillez noter que dans **FastAPI**, l'effet serait plutôt contraire. Dans ces cas-là, il vaut mieux utiliser `async def` à moins que votre *fonction de chemin d'accès* utilise du code qui effectue des opérations...

    of other integer types) in `tf.constant`.
*   Make the `gain` argument of convolutional orthogonal initializers
    (`convolutional_delta_orthogonal`, `convolutional_orthogonal_1D`,
    `convolutional_orthogonal_2D`, `convolutional_orthogonal_3D`) have
    consistent behavior with the `tf.initializers.orthogonal` initializer, i.e.
    scale the output l2-norm by `gain` and NOT by `sqrt(gain)`. (Note that these