## 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"

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.

  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

    You can probably skip this part.

There's an alternative way to define this logic to be executed during *startup* and during *shutdown*.

You can define event handlers (functions) that need to be executed before the application starts up, or when the application is shutting down.

These functions can be declared with `async def` or normal `def`.

### `startup` event

These are the exceptions that you can raise to show errors to the client.

When you raise an exception, as would happen with normal Python, the rest of the execution is aborted. This way you can raise these exceptions from anywhere in the code to abort a request and show the error to the client.

You can use:

* `HTTPException`
* `WebSocketException`

These exceptions can be imported directly from `fastapi`:

```python

// And then call the program again
$ python main.py

// Now it can read the environment variable

Hello Wade Wilson from Python
```

</div>

As environment variables can be set outside of the code, but can be read by the code, and don't have to be stored (committed to `git`) with the rest of the files, it's common to use them for configurations or settings.

# Response Model - Return Type

You can declare the type used for the response by annotating the *path operation function* **return type**.

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

=== "Python 3.10+"

    ```Python hl_lines="16  21"
    {!> ../../../docs_src/response_model/tutorial001_01_py310.py!}
    ```

		case objectlock.RetCompliance:
			// In compliance mode, a protected object version can't be overwritten
			// or deleted by any user, including the root user in your AWS account.
			// When an object is locked in compliance mode, its retention mode can't
			// be changed, and its retention period can't be shortened. Compliance mode
			// ensures that an object version can't be overwritten or deleted for the
			// duration of the retention period.

# `Request` class

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip

Here is the reference for it and its parameters.

You can import it directly from `fastapi`:

```python
from fastapi import Depends
```

::: fastapi.Depends

## `Security()`

For many scenarios, you can handle security (authorization, authentication, etc.) with dependencies, using `Depends()`.

But when you want to also declare OAuth2 scopes, you can use `Security()` instead of `Depends()`.