Most source code in Gradle is arranged by which part(s) of the build state model it acts on.
This affects the lifecycle of the code and the set of services available for dependency injection.
When working on some source code it is important to be aware of the model it acts on.  

    ```

!!! info
    In Pydantic version 1 the method to get the JSON Schema for a model was called `Item.schema()`, in Pydantic version 2, the method is called `Item.model_json_schema()`.

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

    *   Metrics update and collection logic in default `Model.train_step()` is
        now customizable via overriding `Model.compute_metrics()`.
    *   Losses computation logic in default `Model.train_step()` is now
        customizable via overriding `Model.compute_loss()`.
    *   `jit_compile` added to `Model.compile()` on an opt-in basis to compile
        the model's training step with [XLA](https://www.tensorflow.org/xla).

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

The dataclass will be automatically converted to a Pydantic dataclass.

You could load it at the top level of the module/file, but that would also mean that it would **load the model** even if you are just running a simple automated test, then that test would be **slow** because it would have to wait for the model to load before being able to run an independent part of the code.

## Réponse supplémentaire avec `model`

Vous pouvez ajouter à votre décorateur de *paramètre de chemin* un paramètre `responses`.

Il prend comme valeur un `dict` dont les clés sont des codes HTTP pour chaque réponse, comme `200`, et la valeur de ces clés sont d'autres `dict` avec des informations pour chacun d'eux.

Chacun de ces `dict` de réponse peut avoir une clé `model`, contenant un modèle Pydantic, tout comme `response_model`.

 -----
 Introduction
 -----
 Guillaume Nodet
 -----
 2024-04-04
 -----

Maven 4 API - Repository Metadata Model

 This is the immutable model for Repository Metadata in <<<org.apache.maven.api.metadata>>> package.

 The following are generated from this model:

Тим самим способом ця база даних не прийматиме об'єкт типу Pydantic model (об'єкт з атрибутами), а лише `dict`.

Ви можете використовувати `jsonable_encoder` для цього.

Вона приймає об'єкт, такий як Pydantic model, і повертає його версію, сумісну з JSON:

=== "Python 3.10+"

    ```Python hl_lines="4  21"

```Python
id = data.id
```

And with this, the Pydantic *model* is compatible with ORMs, and you can just declare it in the `response_model` argument in your *path operations*.

You will be able to return a database model and it will read the data from it.

#### Technical Details about ORM mode

SQLAlchemy and many others are by default "lazy loading".

    ```

That extra info will be added as-is to the output **JSON Schema** for that model, and it will be used in the API docs.

=== "Pydantic v2"

    In Pydantic version 2, you would use the attribute `model_config`, that takes a `dict` as described in <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic's docs: Model Config</a>.