purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production,...

Tout d'abord il faut "épingler" la version de **FastAPI** que vous utilisez à la dernière version dont vous savez
qu'elle fonctionne correctement pour votre application.

Par exemple, disons que vous utilisez la version `0.45.0` dans votre application.

Si vous utilisez un fichier `requirements.txt`, vous pouvez spécifier la version avec :

```txt
fastapi==0.45.0
```

ce qui signifierait que vous utiliseriez exactement la version `0.45.0`.

* do we need a sort of bus for application data
* do we need a dictionary for our applications like Apple does. We could easily hook into this and this is the model we need to follow. Following the model of apple applications the dictionary is what the REST URI should attach to. Not creating custom logic in the resource code.
* how many of our REST services do not map directory to a method in the application interface?

- an example of adding js

## API Request Parameters

### Token

The OAuth 2.0 access token that is provided by the identity provider. Application must get this token by authenticating the application using client credential grants before the application makes an AssumeRoleWithClientGrants call.

| Params               | Value                                          |

And the `items_per_user` would keep its default value of `50`.

## Settings in another module { #settings-in-another-module }

You could put those settings in another module file as you saw in [Bigger Applications - Multiple Files](../tutorial/bigger-applications.md){.internal-link target=_blank}.

For example, you could have a file `config.py` with:

{* ../../docs_src/settings/app01_py39/config.py *}

And then use it in a file `main.py`:

///

## Separating tests { #separating-tests }

In a real application, you probably would have your tests in a different file.

And your **FastAPI** application might also be composed of several files/modules, etc.

    tags=["admin"],
    dependencies=[Depends(get_token_header)],
    responses={418: {"description": "I'm a teapot"}},
)


@app.get("/")
async def root():

1. **Import your GSA configuration** using the method described above
2. **Update client applications** to use Fess's JSON Search API (see [API Reference](#api-reference) section)
3. **Gradually migrate** from GSA query format to Fess's native API

## Exemplo { #example }

Para um exemplos simples, vamos considerar uma estrutura de arquivos semelhante ao descrito em [Bigger Applications](../tutorial/bigger-applications.md){.internal-link target=_blank} e [Testing](../tutorial/testing.md){.internal-link target=_blank}:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
│   └── test_main.py
```

# Reference

Here's the reference or code API, the classes, functions, parameters, attributes, and
all the FastAPI parts you can use in your applications.

If you want to **learn FastAPI** you are much better off reading the