```

means:

* Starting in the same package that this module (the file `app/main.py`) lives in (the directory `app/`)...
* look for the subpackage `routers` (the directory at `app/routers/`)...
* and from it, import the submodule `items` (the file at `app/routers/items.py`) and `users` (the file at `app/routers/users.py`)...

But documentation is still missing. Then APISpec was created.

It is a plug-in for many frameworks (and there's a plug-in for Starlette too).

The way it works is that you write the definition of the schema using YAML format inside the docstring of each function handling a route.

And it generates OpenAPI schemas.

That's how it works in Flask, Starlette, Responder, etc.

At some point you would have started the definition of the function, you had the parameters ready...

But then you have to call "that method that converts the first letter to upper case".

Was it `upper`? Was it `uppercase`? `first_uppercase`? `capitalize`?

Then, you try with the old programmer's friend, editor autocompletion.

You type the first parameter of the function, `first_name`, then a dot (`.`) and then hit `Ctrl+Space` to trigger the completion.

Many of the tutorials have blocks of code.

In most of the cases, these blocks of code are actual complete applications that can be run as is.

In fact, those blocks of code are not written inside the Markdown, they are Python files in the `./docs_src/` directory.

And those Python files are included/injected in the documentation when generating the site.

### Docs for tests

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs (by Swagger UI).

And that same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Generate a TypeScript Client

Now that we have the app with the models, we can generate the client code for the frontend.

```

</div>

And copy the output to the variable `SECRET_KEY` (don't use the one in the example).

Create a variable `ALGORITHM` with the algorithm used to sign the JWT token and set it to `"HS256"`.

Create a variable for the expiration of the token.

Define a Pydantic Model that will be used in the token endpoint for the response.

Create a utility function to generate a new access token.

### Tests

* Help me check that the PR has **tests**.

* Check that the tests **fail** before the PR. 🚨

* Then check that the tests **pass** after the PR. ✅

* Many PRs don't have tests, you can **remind** them to add tests, or you can even **suggest** some tests yourself. That's one of the things that consume most time and you can help a lot with that.

We want FastAPI to keep **filtering** the data using the response model.

In the previous example, because the classes were different, we had to use the `response_model` parameter. But that also means that we don't get the support from the editor and tools checking the function return type.

This Manager Process would probably be the one listening on the **port** in the IP. And it would transmit all the communication to the worker processes.

Those worker processes would be the ones running your application, they would perform the main computations to receive a **request** and return a **response**, and they would load anything you put in variables in RAM.

* the data from the client to be sent through the network
* the data sent by your program to be received by the client through the network
* the contents of a file in the disk to be read by the system and given to your program
* the contents your program gave to the system to be written to disk
* a remote API operation