//   system scheduling and as such we could either miss our deadline, or unpark() could be delayed
  //   so that it looks like we timed out even though we didn't. For comparison FutureTask respects
  //   completion preferably and AQS is non-deterministic (depends on where in the queue the waiter
  //   is). If we wanted to be strict about it, we could store the unpark() time in the Waiter node

  //   system scheduling and as such we could either miss our deadline, or unpark() could be delayed
  //   so that it looks like we timed out even though we didn't. For comparison FutureTask respects
  //   completion preferably and AQS is non-deterministic (depends on where in the queue the waiter
  //   is). If we wanted to be strict about it, we could store the unpark() time in the Waiter node

         * protect ourselves from this, we park ourselves and tell our interrupter that we did so.
         */
        if (state == PARKED || compareAndSet(state, PARKED)) {
          // Interrupting Cow Says:
          //  ______
          // < Park >
          //  ------
          //        \   ^__^
          //         \  (oo)\_______

    waiter1.interrupt();

    waiter1.join();
    waiter2.awaitWaiting(); // should still be blocked

    LockSupport.unpark(waiter2); // spurious wakeup
    waiter2.awaitWaiting(); // should eventually re-park

    future.set(null);
    waiter2.join();
  }

  public void testRemoveWaiter_polling() throws Exception {

    waiter1.interrupt();

    waiter1.join();
    waiter2.awaitWaiting(); // should still be blocked

    LockSupport.unpark(waiter2); // spurious wakeup
    waiter2.awaitWaiting(); // should eventually re-park

    future.set(null);
    waiter2.join();
  }

  public void testRemoveWaiter_polling() throws Exception {

```
spark.hadoop.fs.s3a.access.key minio
spark.hadoop.fs.s3a.secret.key minio123
spark.hadoop.fs.s3a.path.style.access true
spark.hadoop.fs.s3a.block.size 512M
spark.hadoop.fs.s3a.buffer.dir ${hadoop.tmp.dir}/s3a
spark.hadoop.fs.s3a.committer.magic.enabled false
spark.hadoop.fs.s3a.committer.name directory
spark.hadoop.fs.s3a.committer.staging.abort.pending.uploads true
spark.hadoop.fs.s3a.committer.staging.conflict-mode append

      </plugin>
      <plugin>
        <artifactId>maven-dependency-plugin</artifactId>
        <executions>
          <execution>
            <id>unpack-failureaccess-sources</id>
            <phase>generate-resources</phase>
            <goals><goal>unpack-dependencies</goal></goals>
            <configuration>
              <includeArtifactIds>failureaccess</includeArtifactIds>
              <classifier>sources</classifier>

      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-dependency-plugin</artifactId>
        <executions>
          <execution>
            <id>unpack-jline-native</id>
            <goals>
              <goal>unpack-dependencies</goal>
            </goals>
            <configuration>
              <includeArtifactIds>jline-native</includeArtifactIds>

    'full_name': None,
}
```

#### Unpacking a `dict` { #unpacking-a-dict }

If we take a `dict` like `user_dict` and pass it to a function (or class) with `**user_dict`, Python will "unpack" it. It will pass the keys and values of the `user_dict` directly as key-value arguments.

So, continuing with the `user_dict` from above, writing:

```Python
UserInDB(**user_dict)
```

    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

#### `dict` 언패킹(Unpacking)

`user_dict`와 같은 `dict`를 함수(또는 클래스)에 `**user_dict`로 전달하면, Python은 이를 "언팩(unpack)"합니다. 이 과정에서 `user_dict`의 키와 값을 각각 키-값 인자로 직접 전달합니다.

따라서, 위에서 생성한 `user_dict`를 사용하여 다음과 같이 작성하면:

```Python
UserInDB(**user_dict)
```

다음과 같은 결과를 생성합니다:

```Python
UserInDB(
    username="john",