standard storage class. ## If the PV uses a different storage class, specify that here. storageClass: "" VolumeName: "" accessMode: ReadWriteOnce size: 500Gi ## If subPath is set mount a sub folder of a volume instead of the root of the volume. ## This is especially handy for volume plugins that don't natively support sub mounting (like glusterfs). ## subPath: "" ## Expose the MinIO service to be accessed from outside the cluster (LoadBalancer service). ## or access it from within the cluster (ClusterIP...

## Responses disponibles { #available-responses }

Aquí hay algunos de los responses disponibles.

Ten en cuenta que puedes usar `Response` para devolver cualquier otra cosa, o incluso crear una sub-clase personalizada.

/// note | Nota Técnica

También podrías usar `from starlette.responses import HTMLResponse`.

<div class="termy">

```console
$ pip install "fastapi[all]"
---> 100%
```

</div>

### Create the `Settings` object { #create-the-settings-object }

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

## Starlette features { #starlette-features }

**FastAPI** is fully compatible with (and based on) [**Starlette**](https://www.starlette.dev/). So, any additional Starlette code you have, will also work.

`FastAPI` is actually a sub-class of `Starlette`. So, if you already know or use Starlette, most of the functionality will work the same way.

With **FastAPI** you get all of **Starlette**'s features (as FastAPI is just Starlette on steroids):

**FastAPI** é totalmente compatível com (e baseado no) [**Starlette**](https://www.starlette.dev/). Então, qualquer código adicional Starlette que você tiver, também funcionará.

`FastAPI` é na verdade uma sub-classe do `Starlette`. Então, se você já conhece ou usa Starlette, a maioria das funcionalidades se comportará da mesma forma.

```console
$ pip install "fastapi[all]"
---> 100%
```

</div>

### `Settings` nesnesini oluşturun { #create-the-settings-object }

Pydantic'ten `BaseSettings` import edin ve bir alt sınıf (sub-class) oluşturun; tıpkı bir Pydantic model'inde olduğu gibi.

Pydantic model'lerinde olduğu gibi, type annotation'larla (ve gerekirse default değerlerle) class attribute'ları tanımlarsınız.

<div class="termy">

```console
$ pip install "fastapi[all]"
---> 100%
```

</div>

### Crear el objeto `Settings` { #create-the-settings-object }

Importa `BaseSettings` de Pydantic y crea una sub-clase, muy similar a un modelo de Pydantic.

De la misma forma que con los modelos de Pydantic, declaras atributos de clase con anotaciones de tipos, y posiblemente, valores por defecto.

    }

    @Test
    @DisplayName("getInput/getOutput return cached streams when open; throw when closed")
    void getInputOutput_caching_and_closed() throws Exception {
        // Arrange: minimal stubs so stream construction works
        SmbPipeHandleImpl handle = newHandleWithBasicStubs(0, "\\\\pipe\\\\foo");
        when(pipe.ensureTreeConnected()).thenReturn(tree);
        when(tree.acquire()).thenReturn(tree);

                    configureVMWrapperTasks(vmProject, entry.getValue(), depsTasks, wrapperTask -> {
                        vmLifecycleTask.configure(t -> t.dependsOn(wrapperTask));

                        // Only VM sub-projects that are specifically opted-in to testing Docker should
                        // have the Docker task added as a dependency. Although we control whether Docker

### Sous-dépendances { #sub-dependencies }