//                                                                       Login Execute
    //                                                                      ==============

    /**
     * Main SSO authentication endpoint.
     *
     * This method handles the primary SSO authentication flow. It checks if a user
     * is already logged in, attempts SSO authentication, and handles various

# the `VERSION` parameter
  # Download binary libstdc++ 4.8 shared library release
wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 "http://old-releases.ubuntu.com/ubuntu/pool/main/g/gcc-4.8/libstdc++6_4.8.1-10ubuntu8_arm64.deb" && \
    unar "libstdc++6_4.8.1-10ubuntu8_arm64.deb" && \
    tar -C "${TARGET}" -xvzf "libstdc++6_4.8.1-10ubuntu8_arm64/data.tar.gz" "./usr/lib/aarch64-linux-gnu/libstdc++.so.6.0.18"  && \

   * if Fire.run is called by delegate.addListener since addListener is called after the assignment
   * to timer, and importantly this is the main situation in which we need to be able to see the
   * write.
   *
   * 2. visibility of the writes to an afterDone() call triggered by cancel():
   *

              @Override
              public void run() {
                notifyStarted();
                // We need to wait for the main thread to leave the ServiceManager.startAsync call
                // to
                // ensure that the thread running the failure callbacks is not the main thread.
                Uninterruptibles.awaitUninterruptibly(afterStarted);
                notifyFailed(new Exception("boom"));
              }

**FastAPI**では、これを**OAuth2**を使用して構築できます。

ですが、ちょっとした必要な情報を探すために、長い仕様のすべてを読む必要はありません。

**FastAPI**が提供するツールを使って、セキュリティを制御してみましょう。

## どう見えるか

まずはこのコードを使って、どう動くか観察します。その後で、何が起こっているのか理解しましょう。

## `main.py`を作成

`main.py`に、下記の例をコピーします:

{* ../../docs_src/security/tutorial001.py *}

## 実行

/// info | 情報

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[66:73] hl[71] *}

### Ejecutar la App

Puedes ejecutar la aplicación:

<div class="termy">

```console
$ fastapi dev main.py

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

</div>

    }

    @Override
    public int hashCode() {
        return items.hashCode();
    }

    // CHECKSTYLE_OFF: LineLength

    /**
     * Main to test version parsing and comparison.
     * <p>
     * To check how "1.2.7" compares to "1.2-SNAPSHOT", for example, you can issue

***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.

### Contributing code

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

The main tasks that you can do right now are:

* [Help others with questions in GitHub](#help-others-with-questions-in-github){.internal-link target=_blank} (see the section above).
* [Review Pull Requests](#review-pull-requests){.internal-link target=_blank} (see the section above).

Those two tasks are what **consume time the most**. That's the main work of maintaining FastAPI.