rateLimiter.acquire(2); // R0.50, to repay previous (now the rate takes effect)
    rateLimiter.acquire(4); // R1.00, to repay previous
    rateLimiter.acquire(1); // R2.00, to repay previous
    assertEvents("R0.00", "R1.00", "R1.00", "R0.50", "R1.00", "R2.00");
  }

  public void testTryAcquire_noWaitAllowed() {
    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertTrue(limiter.tryAcquire(0, SECONDS));

    rateLimiter.acquire(2); // R0.50, to repay previous (now the rate takes effect)
    rateLimiter.acquire(4); // R1.00, to repay previous
    rateLimiter.acquire(1); // R2.00, to repay previous
    assertEvents("R0.00", "R1.00", "R1.00", "R0.50", "R1.00", "R2.00");
  }

  public void testTryAcquire_noWaitAllowed() {
    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertTrue(limiter.tryAcquire(0, SECONDS));

      # on that branch for a workflow.yml file, and we'd have to cherry-pick
      # this file into those branches.
      release_branch:
        description: 'Release branch name (e.g. r2.9)'
        required: true
        type: string
      git_commit:
        description: 'Git commit to cherry-pick'
        required: true
        type: string

permissions:
  contents: read

jobs:

apply fixes to bugs or security vulnerabilities:

*   Clone the TensorFlow repository and switch to the appropriate branch for
    your desired version—for example, `r2.8` for version 2.8.
*   Apply the desired changes (i.e., cherry-pick them) and resolve any code
    conflicts.
*   Run TensorFlow tests and ensure they pass.

    val executor = Executors.newCachedThreadPool(threadFactory("HttpOverHttp2Test"))
    val countDownLatch = CountDownLatch(2)
    executor.execute(AsyncRequest("/r1", countDownLatch))
    executor.execute(AsyncRequest("/r2", countDownLatch))
    countDownLatch.await()
    assertThat(server.takeRequest().exchangeIndex).isEqualTo(0)
    assertThat(server.takeRequest().exchangeIndex).isEqualTo(1)
  }

  @Test

        [`experimental_io_device`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/saved_model/LoadOptions?hl=en)
        as arg with default value `None` to choose the I/O device for loading
        models and weights.
    *   Update `tf.saved_model.SaveOptions` with
        [`experimental_io_device`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/saved_model/SaveOptions?hl=en)

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