<td>${f:h(data.inputs)}</td>
                                                    <td>${f:h(data.output)}</td>
                                                </tr>
                                            </c:forEach>
                                            </tbody>
                                        </table>

 * also be resolved to yield the name of the associated Windows account
 * such as <code>Administrators</code> or <code>MYDOM\alice</code>.
 * <p>
 * Consider the following output of <code>examples/SidLookup.java</code>:
 *
 * <pre>
 *        toString: S-1-5-21-4133388617-793952518-2001621813-512
 * toDisplayString: WNET\Domain Admins
 *         getType: 2
 *     getTypeText: Domain group

    delegate.awaitTerminated(timeout, unit);
  }

  /**
   * Returns the name of this service. {@link AbstractIdleService} may include the name in debugging
   * output.
   *
   * @since 14.0
   */
  protected String serviceName() {
    return getClass().getSimpleName();
  }

			delete(entry.RespHeader, filterKey)
		}

		var (
			statusCode      int
			timeToResponse  time.Duration
			timeToFirstByte time.Duration
			outputBytes     int64 = -1 // -1: unknown output bytes
			headerBytes     int64
		)

		tc, ok := r.Context().Value(mcontext.ContextTraceKey).(*mcontext.TraceCtxt)
		if ok {
			statusCode = tc.ResponseRecorder.StatusCode

            throw new GradleException("Unused sha files found: \n" + joinFilenames(shaFiles));
        }

    }

    // This is just a marker output folder to allow this task being up-to-date.
    // The check logic is exception driven so a failed tasks will not be defined
    // by this output but when successful we can safely mark the task as up-to-date.
    @OutputDirectory
    public File getOutputMarker() {

    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);
    if (status.status.ok()) {
      for (int i = 0; i < *num_retvals; ++i) {
        retvals[i] = tensorflow::unwrap(outputs[i]);
        retvals[i]->Ref();
        TFE_DeleteTensorHandle(outputs[i]);
      }
    }

          null
        }
      }
    }

    /**
     * Returns a new unbuffered output stream to write the value at [index]. If the underlying
     * output stream encounters errors when writing to the filesystem, this edit will be aborted
     * when [commit] is called. The returned output stream does not throw IOExceptions.
     */
    fun newSink(index: Int): Sink {
      synchronized(this@DiskLruCache) {

Depois, ele 🤖 pega a primeira tarefa para finalizar (vamos dizer, nosso "arquivo lento" 📝) e continua o que tem que fazer com ela.

Esse "esperar por algo" normalmente se refere a operações <abbr title="Input and Output - Entrada e Saída">I/O</abbr> que são relativamente "lentas" (comparadas à velocidade do processador e da memória RAM), como esperar por:

* dados do cliente para serem enviados através da rede

  private Map<R, V> removeColumn(@Nullable Object column) {
    Map<R, V> output = new LinkedHashMap<>();
    Iterator<Entry<R, Map<C, V>>> iterator = backingMap.entrySet().iterator();
    while (iterator.hasNext()) {
      Entry<R, Map<C, V>> entry = iterator.next();
      V value = entry.getValue().remove(column);
      if (value != null) {
        output.put(entry.getKey(), value);
        if (entry.getValue().isEmpty()) {

            builtBy(localPublish)
        }
    }

    if (signArtifacts) {
        // Otherwise we get
        // ask ':tooling-api:publishGradleDistributionPublicationToRemoteRepository' uses this output of task ':tooling-api:signLocalPublication'
        // without declaring an explicit or implicit dependency. This can lead to incorrect results being produced, depending on what order the tasks are executed.