val endsFirst = buildRequestThread(secondRequest, secondResponseCode)
    endsFirst.start()
    endsFirst.join()
    // First response is still waiting.
    assertThat(firstResponseCode.get()).isEqualTo(0)
    // Second response is done.
    assertThat(secondResponseCode.get()).isEqualTo(200)
    latch.countDown()
    startsFirst.join()
    // And now it's done!
    assertThat(firstResponseCode.get()).isEqualTo(200)
    // (Still done).

    testRule.beforeEach(extensionContext)

    val thread =
      object : Thread() {
        override fun run() {
          throw RuntimeException("boom!")
        }
      }
    thread.start()
    thread.join()

    assertFailsWith<AssertionError> {
      testRule.afterEach(extensionContext)
    }.also { expected ->
      assertThat(expected).hasMessage("uncaught exception thrown during test")

* Have a look at the [Samples](https://docs.gradle.org/current/samples/index.html)
* Checkout the [Community Resources](https://gradle.org/resources/) as well
* Join our [Slack Channel](https://gradle.com/slack-invite)


## Contributing

Contributing
------------

If you are interested in the development of Maven, please consult the
documentation first and afterward you are welcome to join the developers
mailing list to ask questions or discuss new ideas/features/bugs etc.

Take a look into the [contribution guidelines](CONTRIBUTING.md).

License
-------

ls build_output
```

## Contribution & Maintenance

The TensorFlow team does not yet have guidelines in place for contributing to
this directory. We are working on it. Please join a TF SIG Build meeting (see:
bit.ly/tf-sig-build-notes) if you'd like to discuss the future of contributions.

### Brief System Overview

The top-level scripts and utility scripts should be fairly well-documented. Here

Os modelos Pydantic possuem um método `.dict()` que retorna um `dict` com os dados do modelo.

Então, se criarmos um objeto Pydantic `user_in` como:

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

e depois chamarmos:

```Python
user_dict = user_in.dict()
```

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

& ⤴️ 👥 🤙:

```Python
user_dict = user_in.dict()
```

👥 🔜 ✔️ `dict` ⏮️ 💽 🔢 `user_dict` (⚫️ `dict` ↩️ Pydantic 🏷 🎚).

& 🚥 👥 🤙:

```Python
print(user_dict)
```

👥 🔜 🤚 🐍 `dict` ⏮️:

```Python
{
    'username': 'john',
    'password': 'secret',

      <organization>ASF</organization>
      <roles>
        <role>PMC Member</role>
      </roles>
      <timezone>+1</timezone>
    </developer>
    <developer>
      <id>jdcasey</id>
      <name>John Casey</name>
      <email>******@****.***</email>
      <organization>ASF</organization>
      <roles>
        <role>PMC Member</role>
      </roles>
      <timezone>-5</timezone>
    </developer>

Pydantic models have a `.dict()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

and then we call:

```Python
user_dict = user_in.dict()
```

<img src="/img/tutorial/security/image08.png">

🤙 🔗 `/users/me/`, 👆 🔜 🤚 📨:

```JSON
{
  "username": "johndoe",
  "email": "******@****.***",
  "full_name": "John Doe",
  "disabled": false
}
```

<img src="/img/tutorial/security/image09.png">

🚥 👆 📂 👩‍💻 🧰, 👆 💪 👀 ❔ 📊 📨 🕴 🔌 🤝, 🔐 🕴 📨 🥇 📨 🔓 👩‍💻 &amp; 🤚 👈 🔐 🤝, ✋️ 🚫 ⏮️: