netns1 := p.UpsertPodCacheWithNetns(string(pod.UID), wl)
	if !reflect.DeepEqual(netns1, ns) {
		t.Fatalf("Expected the same Netns for the same uid, got %v and %v", netns1, ns)
	}

	ns2 := newFakeNsInode(inc(), 1)
	wl2 := WorkloadInfo{
		Workload: podToWorkload(pod),
		Netns:    ns2,
	}
	// when using same uid, the original netns should be returned
	netns2 := p.UpsertPodCacheWithNetns(string(pod.UID), wl2)
	if netns2 != ns {

And these same full-stack generators were the base of the [**FastAPI** Project Generators](project-generation.md){.internal-link target=_blank}.

!!! info
    Flask-apispec was created by the same Marshmallow developers.

!!! check "Inspired **FastAPI** to"
    Generate the OpenAPI schema automatically, from the same code that defines serialization and validation.

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"

    * So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would if you didn't use it (as you would have to implement it all in your code).

```

### Testing file

Then you could have a file `test_main.py` with your tests. It could live on the same Python package (the same directory with a `__init__.py` file):

``` hl_lines="5"
.
├── app
│   ├── __init__.py
│   ├── main.py
│   └── test_main.py
```

Because this file is in the same package, you can use relative imports to import the object `app` from the `main` module (`main.py`):

```Python hl_lines="3"

    {!> ../../../docs_src/response_model/tutorial002.py!}
    ```

Now, whenever a browser is creating a user with a password, the API will return the same password in the response.

In this case, it might not be a problem, because it's the same user sending the password.

But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

!!! danger

* 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.

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.

This will be especially useful when you use it in a **large code base** where you use **the same dependencies** over and over again in **many *path operations***.

## To `async` or not to `async`

As dependencies will also be called by **FastAPI** (the same as your *path operation functions*), the same rules apply while defining your functions.

## Password hashing

"Hashing" means converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

Whenever you pass exactly the same content (exactly the same password) you get exactly the same gibberish.

But you cannot convert from the gibberish back to the password.

### Why use password hashing