TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 422, response.text

    data = [{"x": 1, "y": 2}]
    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 422, response.text


def test_tuple_form_valid():
    response = client.post("/tuple-form/", data={"values": ("1", "2")})

Et nous analysons directement ce contenu YAML, puis nous utilisons à nouveau le même modèle Pydantic pour valider le contenu YAML :

```Python hl_lines="26-33"
{!../../docs_src/path_operation_advanced_configuration/tutorial007.py!}
```

/// tip | "Astuce"

Ici, nous réutilisons le même modèle Pydantic.

Mais nous aurions pu tout aussi bien pu le valider d'une autre manière.

Stellen wir uns vor, dass das Laden des Modells **eine ganze Weile dauern** kann, da viele **Daten von der Festplatte** gelesen werden müssen. Sie möchten das also nicht für jeden Request tun.

under the License.
-->

<project>
  <modelVersion>4.0.0</modelVersion>
  <artifactId>aid</artifactId>
  <groupId>gid</groupId>
  <version>0.1</version>
  <packaging>pom</packaging>

  <modules>
    <module> </module>
  </modules>

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,

Sebastián Ramírez <******@****.***> 1728247014 +0200

            @Override
            public Model getRawModel(Path from, String gId, String aId) {
                Model model = findRawModel(from, gId, aId);
                if (model != null) {
                    context.modelByGA.put(new GAKey(gId, aId), new Holder(model));
                    context.modelByPath.put(model.getPomPath(), new Holder(model));
                }
                return model;
            }

            @Override

Sebastián Ramírez <******@****.***> 1728247014 +0200

Sebastián Ramírez <******@****.***> 1728247014 +0200