After you have tests, then you can upgrade the **FastAPI** version to a more recent one, and make sure that all your code is working correctly by running your tests.

If everything is working, or after you make the necessary changes, and all your tests are passing, then you can pin your `fastapi` to that new recent version.

## About Starlette { #about-starlette }

You shouldn't pin the version of `starlette`.

    subject to change. They can be modified in any way, or even removed, at any
    time. If your code is a library itself (i.e., it is used on the CLASSPATH of
    users outside your own control), you should not use beta APIs unless you
    [repackage] them. **If your code is a library, we strongly recommend using
    the [Guava Beta Checker] to ensure that you do not use any `@Beta` APIs!**

  A more thorough check will be performed on your pull request automatically.
- [ ] You have run the [Core IT][core-its] successfully.

If your pull request is about ~20 lines of code you don't need to sign an
[Individual Contributor License Agreement](https://www.apache.org/licenses/icla.pdf) if you are unsure
please ask on the developers list.

To make clear that you license your contribution under

          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and

/// tip

By the spec, you should return a JSON with an `access_token` and a `token_type`, the same as in this example.

This is something that you have to do yourself in your code, and make sure you use those JSON keys.

It's almost the only thing that you have to remember to do correctly yourself, to be compliant with the specifications.

For the rest, **FastAPI** handles it for you.

///

available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced
variables from your model are also going to be untrusted. That means that if
your code interacts with the filesystem, network, etc. and uses checkpointed

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 { #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`.

To run a single test case, you can use:

    $ mvn test -P integrationTests -Dtest.fess.url="http://localhost:8080" -Dtest.search_engine.url="http://localhost:9201" -Dtest=SearchApiTests

### Translate In Your Language

Fess is internationalized software.

If you want to add labels/messages for your language, please translate properties file and then rename to fess\_\*\_[lang].properties.

You will see something like this:

<img src="/img/tutorial/security/image01.png">

/// check | Authorize button!

You already have a shiny new "Authorize" button.

And your *path operation* has a little lock in the top-right corner that you can click.

///

///

## Run your code with your debugger { #run-your-code-with-your-debugger }

Because you are running the Uvicorn server directly from your code, you can call your Python program (your FastAPI application) directly from the debugger.

---

For example, in Visual Studio Code, you can:

* Go to the "Debug" panel.
* "Add configuration...".
* Select "Python"