After that, all of the processing logic is the same.

But because of our changes in `GzipRequest.body`, the request body will be automatically decompressed when it is loaded by **FastAPI** when needed.

## Accessing the request body in an exception handler

/// tip

  }

  @Test
  fun testNoUncompress() {
    val response = response("https://httpbin.org/brotli", "XXXX".encodeUtf8())

    val same = uncompress(response)

    val responseString = same.body.string()
    assertThat(responseString).isEqualTo("XXXX")
  }

  @Test
  fun testFailsUncompress() {
    val response =

* There can be **multiple processes** of the **same program** running at the same time.

If you check out the "task manager" or "system monitor" (or similar tools) in your operating system, you will be able to see many of those processes running.

### Same Schema for Input and Output Models in Docs

And now there will be one single schema for input and output for the model, only `Item`, and it will have `description` as **not required**:

<div class="screenshot">
<img src="/img/tutorial/separate-openapi-schemas/image05.png">
</div>

   *       the same.
   *   <li>The unreserved characters ".", "-", "~", and "_" remain the same.
   *   <li>The general delimiters "@" and ":" remain the same.
   *   <li>The subdelimiters "!", "$", "&amp;", "'", "(", ")", "*", "+", ",", ";", and "=" remain
   *       the same.
   *   <li>The space character " " is converted into %20.

//// tab | Python 3.10+

You can use the same builtin types as generics (with square brackets and types inside):

* `list`
* `tuple`
* `set`
* `dict`

And the same as with Python 3.8, from the `typing` module:

* `Union`
* `Optional` (the same as with Python 3.8)
* ...and others.

This is the same mechanism used when you give permissions while logging in with Facebook, Google, GitHub, etc:

<img src="/img/tutorial/security/image11.png">

## JWT token with scopes

Now, modify the token *path operation* to return the scopes requested.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

But it's still part of the same **FastAPI** application/web API (it's part of the same "Python Package").

You can create the *path operations* for that module using `APIRouter`.

### Import `APIRouter`

You import it and create an "instance" the same way you would with the class `FastAPI`:

```Python hl_lines="1  3" title="app/routers/users.py"

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.