`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

## GraphQL with Strawberry

If you need or want to work with **GraphQL**, <a href="https://strawberry.rocks/" class="external-link" target="_blank">**Strawberry**</a> is the **recommended** library as it has the design closest to **FastAPI's** design, it's all based on **type annotations**.

Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.

## GraphQL mit Strawberry

Wenn Sie mit **GraphQL** arbeiten möchten oder müssen, ist <a href="https://strawberry.rocks/" class="external-link" target="_blank">**Strawberry**</a> die **empfohlene** Bibliothek, da deren Design dem Design von **FastAPI** am nächsten kommt und alles auf **Typannotationen** basiert.

## GraphQL com Strawberry

Se você precisar ou quiser trabalhar com **GraphQL**, <a href="https://strawberry.rocks/" class="external-link" target="_blank">**Strawberry**</a> é a biblioteca **recomendada** pois tem o design mais próximo ao design do **FastAPI**, ela é toda baseada em **type annotations**.

```

Whenever you need the client to pass information in the request and you don't know how to, you can search (Google) how to do it in `httpx`, or even how to do it with `requests`, as HTTPX's design is based on Requests' design.

Then you just do the same in your tests.

E.g.:

* To pass a *path* or *query* parameter, add it to the URL itself.

Você também poderá receber verificações de erros para operações de tipos incorretas:

<img src="/img/tutorial/body/image04.png">

Isso não é por acaso, todo o framework foi construído em volta deste design.

E foi imensamente testado na fase de design, antes de qualquer implementação, para garantir que funcionaria para todos os editores de texto.

Houveram mudanças no próprio Pydantic para que isso fosse possível.

```

Sempre que você precisar que o cliente passe informações na requisição e não souber como, você pode pesquisar (no Google) como fazer isso no `httpx`, ou até mesmo como fazer isso com `requests`, já que o design do HTTPX é baseado no design do Requests.

Depois é só fazer o mesmo nos seus testes.

Por exemplo:

* Para passar um parâmetro *path* ou *query*, adicione-o à própria URL.

request, and "write" parts to the response. Because of this design, it is not possible to declare request parameters and bodies with standard Python type hints as function parameters.

So, data validation, serialization, and documentation, have to be done in code, not automatically. Or they have to be implemented as a framework on top of Falcon, like Hug. This same distinction happens in other frameworks that are inspired by Falcon's design, of having one request object and one response object...

import org.codehaus.plexus.logging.AbstractLogEnabled;

/**
 * Describes a version transformation during artifact resolution.
 *
 * TODO try and refactor to remove abstract methods - not particular happy about current design
 */
@Deprecated
public abstract class AbstractVersionTransformation extends AbstractLogEnabled implements ArtifactTransformation {
    @Inject
    protected RepositoryMetadataManager repositoryMetadataManager;

You also get error checks for incorrect type operations:

<img src="/img/tutorial/body/image04.png">

This is not by chance, the whole framework was built around that design.

And it was thoroughly tested at the design phase, before any implementation, to ensure it would work with all the editors.

There were even some changes to Pydantic itself to support this.