var multipleReadyTasks = false

      // Decide what to run. This loop's goal wants to:
      //  * Find out what this thread should do (either run a task or sleep)
      //  * Find out if there's enough work to start another thread.
      eachQueue@ for (queue in readyQueues) {
        val candidate = queue.futureTasks[0]
        val candidateDelay = maxOf(0L, candidate.nextExecuteNanoTime - now)

        when {

      throw AssertionError("Thread ${Thread.currentThread().name} MUST NOT hold lock on $this")
    }
  }

  val logger = Logger.getLogger("TaskFaker." + instance++)

  /** Though this executor service may hold many threads, they are not executed concurrently. */
  private val tasksExecutor = Executors.newCachedThreadPool(threadFactory("TaskFaker"))

  /**

    if (deferredPlans.isNotEmpty()) return deferredPlans.removeFirst() as FakePlan

    if (nextPlanIndex >= plans.size && autoGeneratePlans) addPlan()

    require(nextPlanIndex < plans.size) {
      "not enough plans! call addPlan() or set autoGeneratePlans=true in the test to set this up"
    }
    val result = plans[nextPlanIndex++]
    events += "take plan ${result.id}"

    if (result.yieldBeforePlanReturns) {

  "Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law.  If additional permissions
apply only to part of the Program, that part may be used separately

The ambient CNI agent is the only place where ambient network config and pod redirection machinery happens.

        TensorFlow.

*   `tf.experimental.dtensor`: Added DTensor, an extension to TensorFlow for
    large-scale modeling with minimal changes to user code. You are welcome to
    try it out, though be aware that the DTensor API is experimental and up-to
    backward-incompatible changes. DTensor and Keras integration is published
    under `tf.keras.dtensor` in this release (refer to the `tf.keras` entry).

  * Explicitly opting into behavioral changes can be acceptable. For example, introducing Istio multi-cluster semantics.
* Ensure traffic between mesh workloads is securely encrypted with an Istio identity.
* Be lightweight enough to not limit adoption.
  * This puts a much tighter budget on CPU, memory, latency, and throughput requirements than traditional Istio sidecars.

Ztunnel was not designed to be a feature-rich data plane.

In this case, the original path `/app` would actually be served at `/api/v1/app`.

Even though all your code is written assuming there's just `/app`.

```Python hl_lines="6"
{!../../../docs_src/behind_a_proxy/tutorial001.py!}
```

This information is used to generate documentation for each Gradle version
on [docs.gradle.org](https://docs.gradle.org/).
The documentation is mostly implemented in Asciidoc
though we use GitHub-flavored Markdown for internal documentation too.

You can generate docs by running `./gradlew :docs:docs`.
This will build the whole documentation locally in [platforms/documentation](./platforms/documentation).

In this case, it would be better to get **one extra server** and run some processes on it so that they all have **enough RAM and CPU time**.