You can return a `RedirectResponse` directly:

```Python hl_lines="2  9"
{!../../../docs_src/custom_response/tutorial006.py!}
```

---

Or you can use it in the `response_class` parameter:


```Python hl_lines="2  7  9"
{!../../../docs_src/custom_response/tutorial006b.py!}
```

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

For example, we can define an `Image` model:

=== "Python 3.10+"

    ```Python hl_lines="7-9"

#### Application's calling thread

The application-layer must block on writing I/O. We can't return from a write until we've pushed its bytes onto the socket. Otherwise, if the write fails we are unable to deliver its IOException to the application. We would have told the application layer that the write succeeded, but it didn't!

The install is organized in 'environments' - each environment consists of a set of components
in different namespaces that are configured to work together. Regardless of 'environment',
workloads can talk with each other and obey the Istio configuration resources, but each environment
can use different Istio versions and different configuration defaults.

`istioctl kube-inject` or the automatic sidecar injector are used to select the environment.

This is of course not the frontend for the final users, but it's a great automatic tool to document interactively all your API.

It can be used by the frontend team (that can also be yourself).

It can be used by third party applications and systems.

And it can also be used by yourself, to debug, check and test the same application.

## The `password` flow

Now let's go back a bit and understand what is all that.

There are several parameters that you can pass to your *path operation decorator* to configure it.

!!! warning
    Notice that these parameters are passed directly to the *path operation decorator*, not to your *path operation function*.

## Response Status Code

You can define the (HTTP) `status_code` to be used in the response of your *path operation*.

You can pass directly the `int` code, like `404`.

object browser built into MinIO Server. Point a web browser running on the host machine to <http://127.0.0.1:9000> and log in with the
root credentials. You can use the Browser to create buckets, upload objects, and browse the contents of the MinIO server.

# Response Cookies

## Use a `Response` parameter

You can declare a parameter of type `Response` in your *path operation function*.

And then you can set cookies in that *temporal* response object.

```Python hl_lines="1  8-9"
{!../../../docs_src/response_cookies/tutorial002.py!}
```

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

  scheme: http
  static_configs:
  - targets: ['localhost:9000']
```

##### Node (optional)

Optionally you can also collect per node metrics. This needs to be done on a per server instance.
The scrape configurations should use all the servers under `targets` so that graphing systems like
grafana can visualize them for all the nodes

```yaml
scrape_configs:
- job_name: minio-job
  metrics_path: /minio/v2/metrics/node

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized