}

func TestResourceConfigForPodWithCustomCPUCFSQuotaPeriod(t *testing.T) {
	defaultQuotaPeriod := uint64(100 * time.Millisecond / time.Microsecond) // in microseconds
	tunedQuotaPeriod := uint64(5 * time.Millisecond / time.Microsecond)     // in microseconds
	tunedQuota := int64(1 * time.Millisecond / time.Microsecond)

	featuregatetesting.SetFeatureGateDuringTest(t, utilfeature.DefaultFeatureGate, pkgfeatures.CPUCFSQuotaPeriod, true)

}  // namespace

// Counter that records how long it took to execute the checkpoint sharding
// callback in microseconds.
auto* sharding_callback_duration = monitoring::Counter<0>::New(
    "/tensorflow/core/checkpoint/sharding/callback_duration",
    "Sharding callback execution duration in microseconds.");

// Counter that records how many checkpoint shard files were written during
// saving.

  }

  public void testTryAcquire_overflow() {
    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertTrue(limiter.tryAcquire(0, MICROSECONDS));
    stopwatch.sleepMillis(100);
    assertTrue(limiter.tryAcquire(Long.MAX_VALUE, MICROSECONDS));
  }

  public void testTryAcquire_negative() {
    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertTrue(limiter.tryAcquire(5, 0, SECONDS));

  }

  public void testTryAcquire_overflow() {
    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertTrue(limiter.tryAcquire(0, MICROSECONDS));
    stopwatch.sleepMillis(100);
    assertTrue(limiter.tryAcquire(Long.MAX_VALUE, MICROSECONDS));
  }

  public void testTryAcquire_negative() {
    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertTrue(limiter.tryAcquire(5, 0, SECONDS));

	// Output: I've got 4.5 hours of work left.
}

func ExampleDuration_Microseconds() {
	u, _ := time.ParseDuration("1s")
	fmt.Printf("One second is %d microseconds.\n", u.Microseconds())
	// Output:
	// One second is 1000000 microseconds.
}

func ExampleDuration_Milliseconds() {
	u, _ := time.ParseDuration("1s")
	fmt.Printf("One second is %d milliseconds.\n", u.Milliseconds())
	// Output:

    "/tensorflow/cc/saved_model/load_latency",
    "Latency in microseconds for SavedModels that were successfully loaded.",
    "model_path");
auto* load_latency_by_stage = monitoring::Sampler<2>::New(
    {
        "/tensorflow/cc/saved_model/load_latency_by_stage",  // metric name
        "Distribution of wall time spent (in microseconds) in each stage "
        "(restore graph from disk, run init graph op, etc) when loading the "

  private var nextQueueName = 10000
  private var coordinatorWaiting = false
  private var coordinatorWakeUpAt = 0L

  /**
   * When we need a new thread to run tasks, we call [Backend.execute]. A few microseconds later we
   * expect a newly-started thread to call [Runnable.run]. We shouldn't request new threads until
   * the already-requested ones are in service, otherwise we might create more threads than we need.
   *

			enforceCPULimits:  cm.EnforceCPULimits,
			// cpuCFSQuotaPeriod is in microseconds. NodeConfig.CPUCFSQuotaPeriod is time.Duration (measured in nano seconds).
			// Convert (cm.CPUCFSQuotaPeriod) [nanoseconds] / time.Microsecond (1000) to get cpuCFSQuotaPeriod in microseconds.
			cpuCFSQuotaPeriod: uint64(cm.CPUCFSQuotaPeriod / time.Microsecond),
		}
	}
	return &podContainerManagerNoop{
		cgroupRoot: cm.cgroupRoot,
	}
}

			// kubeGenericRuntimeManager.cpuCFSQuotaPeriod is provided in time.Duration,
			// but we need to convert it to number of microseconds which is used by kernel.
			cpuPeriod = int64(m.cpuCFSQuotaPeriod.Duration / time.Microsecond)
		}
		cpuQuota := milliCPUToQuota(cpuLimit.MilliValue(), cpuPeriod)
		resources.CpuQuota = cpuQuota
		resources.CpuPeriod = cpuPeriod
	}

Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.