}

  private static final class PaddedLock extends ReentrantLock {
    /*
     * Padding from 40 into 64 bytes, same size as cache line. Might be beneficial to add a fourth
     * long here, to minimize chance of interference between consecutive locks, but I couldn't
     * observe any benefit from that.
     */
    long unused1;
    long unused2;
    long unused3;

    PaddedLock() {
      super(false);

Ainda, em ambas as situações, as chances são que o **FastAPI** [ainda será mais rápido](index.md#performance){.internal-link target=_blank} do que (ou ao menos comparável a) seu framework anterior.

### Dependências { #dependencies }

				results.NodeResults = append(results.NodeResults, result)
				resultsMu.Unlock()
			}()
			continue
		}
		wg.Add(1)
		go func() {
			defer wg.Done()
			ctx, cancel := context.WithTimeout(ctx, duration+10*time.Second)
			defer cancel()
			result := perfNetRequest(
				ctx,
				info.DeploymentID,
				adminPathPrefix+adminAPIVersionPrefix+adminAPISiteReplicationNetPerf,
				nil,
			)
			resultsMu.Lock()

       */
      ignoredMembers.add(Converter.class.getMethod("apply", Object.class));
    } catch (NoSuchMethodException shouldBeImpossible) {
      // Fine: If it doesn't exist, then there's no chance that we're going to be asked to test it.
    }

    /*
     * These methods "should" call checkNotNull. However, I'm wary of accidentally introducing

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**.

There's also the chance that for some reason you have a **spike** of usage of your API. Maybe it went viral, or maybe some other services or bots start using it. And you might want to have extra resources to be safe in those cases.

}

public abstract interface class okhttp3/Call : java/lang/Cloneable {
	public abstract fun addEventListener (Lokhttp3/EventListener;)V
	public abstract fun cancel ()V
	public abstract fun clone ()Lokhttp3/Call;
	public abstract fun enqueue (Lokhttp3/Callback;)V
	public abstract fun execute ()Lokhttp3/Response;
	public abstract fun isCanceled ()Z
	public abstract fun isExecuted ()Z

Nesse caso, seria melhor obter **um servidor extra** e executar alguns processos nele para que todos tenham **RAM e tempo de CPU suficientes**.

Também há a chance de que, por algum motivo, você tenha um **pico** de uso da sua API. Talvez ela tenha se tornado viral, ou talvez alguns outros serviços ou bots comecem a usá-la. E você pode querer ter recursos extras para estar seguro nesses casos.

func (t *transitionState) ActiveTasks() int64 {
	return t.activeTasks.Load()
}

// MissedImmediateTasks returns the number of tasks - deferred to scanner due
// to tasks channel being backlogged.
func (t *transitionState) MissedImmediateTasks() int64 {
	return t.missedImmediateTasks.Load()
}

// worker waits for transition tasks
func (t *transitionState) worker(objectAPI ObjectLayer) {

```console
$ pip install "harry==3"
```

</div>

And then you would end up with `harry` version `3` installed in your global Python environment.

And if you try to run `philosophers-stone` again, there's a chance it would **not work** because it needs `harry` version `1`.

```mermaid
flowchart LR
    subgraph global[global env]
        harry-1[<strike>harry v1</strike>]
        style harry-1 fill:#ccc,stroke-dasharray: 5 5

TEST(CAPI, Status) {
  TF_Status* s = TF_NewStatus();
  EXPECT_EQ(TF_OK, TF_GetCode(s));
  EXPECT_EQ(string(), TF_Message(s));
  TF_SetStatus(s, TF_CANCELLED, "cancel");
  EXPECT_EQ(TF_CANCELLED, TF_GetCode(s));
  EXPECT_EQ(string("cancel"), TF_Message(s));
  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);