/**
   * Returns an estimate of the number of threads waiting to enter this monitor. The value is only
   * an estimate because the number of threads may change dynamically while this method traverses
   * internal data structures. This method is designed for use in monitoring of the system state,
   * not for synchronization control.
   */
  public int getQueueLength() {
    return lock.getQueueLength();
  }

  /**

   * exceeded.
   *
   * The page replacement algorithm's data structures are kept casually consistent with the map. The
   * ordering of writes to a segment is sequentially consistent. An update to the map and recording
   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The

該 dataclass 會自動轉換為 Pydantic 的 dataclass。

如此一來，其結構描述（schema）會顯示在 API 文件介面中：

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

## 巢狀資料結構中的 Dataclasses { #dataclasses-in-nested-data-structures }

你也可以將 `dataclasses` 與其他型別註記結合，建立巢狀的資料結構。

在某些情況下，你可能仍需要使用 Pydantic 版本的 `dataclasses`。例如，當自動產生的 API 文件出現錯誤時。

這種情況下，你可以把標準的 `dataclasses` 直接換成 `pydantic.dataclasses`，它是可直接替換（drop-in replacement）的：

dataclass는 자동으로 Pydantic dataclass로 변환됩니다.

이렇게 하면 해당 스키마가 API docs 사용자 인터페이스에 표시됩니다:

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

## 중첩 데이터 구조에서 Dataclasses 사용하기 { #dataclasses-in-nested-data-structures }

`dataclasses`를 다른 타입 애너테이션과 조합해 중첩 데이터 구조를 만들 수도 있습니다.

일부 경우에는 Pydantic 버전의 `dataclasses`를 사용해야 할 수도 있습니다. 예를 들어 자동 생성된 API 문서에서 오류가 발생하는 경우입니다.

그런 경우 표준 `dataclasses`를 드롭인 대체재인 `pydantic.dataclasses`로 간단히 바꾸면 됩니다:

dataclass は自動的に Pydantic の dataclass に変換されます。

このため、そのスキーマは API ドキュメントの UI に表示されます:

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

## ネストしたデータ構造での dataclasses { #dataclasses-in-nested-data-structures }

`dataclasses` を他の型注釈と組み合わせて、ネストしたデータ構造を作成できます。

場合によっては、自動生成された API ドキュメントでエラーが発生するなどの理由で、Pydantic 版の `dataclasses` を使う必要があるかもしれません。

その場合は、標準の `dataclasses` を `pydantic.dataclasses` に置き換えるだけで済みます。これはドロップイン置換です:

- kubeadm: JoinConfiguration now houses the discovery options in a nested Discovery structure, which in turn has a couple of other nested structures to house more specific options (BootstrapTokenDiscovery and FileDiscovery) ([#67763](https://github.com/kubernetes/kubernetes/pull/67763), [@rosti](https://github.com/rosti))

	jweak

3. The EGLDisplay and EGLConfig types from the EGL API.

These types are uintptr on the Go side because they would otherwise
confuse the Go garbage collector; they are sometimes not really
pointers but data structures encoded in a pointer type. All operations
on these types must happen in C. The proper constant to initialize an
empty such reference is 0, not nil.

These special cases were introduced in Go 1.10. For auto-updating code

        return extension;
    }

    protected Object getExclusionKey(Exclusion exclusion) {
        return exclusion;
    }

    /**
     * Use to compute keys for data structures
     * @param <T>
     */
    private interface KeyComputer<T> {
        Object key(T t);
    }

    /**
     * Remapping function
     * @param <T>
     */

// PutObjectExtractHandler - PUT Object extract is an extended API
// based off from AWS Snowball feature to auto extract compressed
// stream will be extracted in the same directory it is stored in
// and the folder structures will be built out accordingly.
func (api objectAPIHandlers) PutObjectExtractHandler(w http.ResponseWriter, r *http.Request) {
	ctx := newContext(r, w, "PutObjectExtract")

        TensorList operations.
    *   `tf.custom_gradient` can now be applied to functions that accept nested
        structures of `tensors` as inputs (instead of just a list of tensors).
        Note that Python structures such as tuples and lists now won't be
        treated as tensors, so if you still want them to be treated that way,
        you need to wrap them with `tf.convert_to_tensor`.