}
		pre = cur
	}
	return true
}

// alignSparseEntries mutates src and returns dst where each fragment's
// starting offset is aligned up to the nearest block edge, and each
// ending offset is aligned down to the nearest block edge.
//
// Even though the Go tar Reader and the BSD tar utility can handle entries
// with arbitrary offsets and lengths, the GNU tar utility can only handle

	// This matches the limit used by libarchive.
	maxSpecialFileSize = 1 << 20
)

// blockPadding computes the number of bytes needed to pad offset up to the
// nearest block edge where 0 <= n < blockSize.
func blockPadding(offset int64) (n int64) {
	return -offset & (blockSize - 1)
}

var zeroBlock block

type block [blockSize]byte

// Convert block to any number of formats.

Und Sie werden beispielsweise wahrscheinlich feststellen, dass mehrere Prozesse dasselbe Browserprogramm ausführen (Firefox, Chrome, Edge, usw.). Normalerweise führen diese einen Prozess pro Browsertab sowie einige andere zusätzliche Prozesse aus.

<img class="shadow" src="/img/deployment/concepts/image01.png">

---

And, for example, you will probably see that there are multiple processes running the same browser program (Firefox, Chrome, Edge, etc). They normally run one process per tab, plus some other extra processes.

<img class="shadow" src="/img/deployment/concepts/image01.png">

---

  /** Settings we communicate to the peer. */
  val okHttpSettings =
    Settings().apply {
      // Flow control was designed more for servers, or proxies than edge clients. If we are a client,
      // set the flow control window to 16MiB.  This avoids thrashing window updates every 64KiB, yet
      // small enough to avoid blowing up the heap.
      if (builder.client) {

    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.

* 您计算机上运行的每个应用程序背后都有一些进程，每个正在运行的程序，每个窗口等。并且通常在计算机打开时**同时**运行许多进程。
* **同一程序**可以有**多个进程**同时运行。

如果您检查操作系统中的“任务管理器”或“系统监视器”（或类似工具），您将能够看到许多正在运行的进程。

例如，您可能会看到有多个进程运行同一个浏览器程序（Firefox、Chrome、Edge 等）。 他们通常每个tab运行一个进程，再加上一些其他额外的进程。

<img class="shadow" src="/img/deployment/concepts/image01.png">

---

现在我们知道了术语“进程”和“程序”之间的区别，让我们继续讨论部署。

## 启动时运行

Y, por ejemplo, probablemente verás que hay múltiples procesos ejecutando el mismo programa del navegador (Firefox, Chrome, Edge, etc.). Normalmente ejecutan un proceso por pestaña, además de algunos otros procesos extra.

<img class="shadow" src="/img/deployment/concepts/image01.png">

---

E, por exemplo, você provavelmente verá que há vários processos executando o mesmo programa de navegador (Firefox, Chrome, Edge, etc.). Eles normalmente executam um processo por aba, além de alguns outros processos extras.

<img class="shadow" src="/img/deployment/concepts/image01.png">

---

        slf4jLogger.error(MessageUtils.builder().a("  ").strong(resumeBuildHint).toString());
    }

    /**
     * A helper method to determine the value to resume the build with {@code -rf} taking into account the edge case
     *   where multiple modules in the reactor have the same artifactId.
     * <p>
     * {@code -rf :artifactId} will pick up the first module which matches, but when multiple modules in the reactor