Tenía un sistema de inyección de dependencias. Requería pre-registrar componentes, como otras herramientas discutidas anteriormente. Pero aún así, era una gran funcionalidad.

Ele tinha um sistema de injeção de dependência. Exigia pré-registro dos componentes, como outras ferramentas já discutidas acima. Mas ainda assim era um grande recurso.

	},
	ErrNoSuchUpload: {
		Code:           "NoSuchUpload",
		Description:    "The specified multipart upload does not exist. The upload ID may be invalid, or the upload may have been aborted or completed.",
		HTTPStatusCode: http.StatusNotFound,
	},
	ErrInvalidVersionID: {
		Code:           "InvalidArgument",
		Description:    "Invalid version id specified",
		HTTPStatusCode: http.StatusBadRequest,
	},

      }
    }

    /**
     * Calculates the time at which to next invoke the task.
     *
     * <p>This is guaranteed to be called immediately after the task has completed an iteration and
     * on the same thread as the previous execution of {@link
     * AbstractScheduledService#runOneIteration}.
     *
     * @return a schedule that defines the delay before the next execution.
     */

     * place. For example at the time of this writing the readXxxWireFormat
     * for requests and the writeXxxWireFormat for responses are not implemented
     * and simply return 0. These would need to be completed for a server
     * implementation.
     */

    /**
     * SMB command to create a directory.
     */
    public static final byte SMB_COM_CREATE_DIRECTORY = (byte) 0x00;

    /**

  buf->Unref();
  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(
      std::move(t), device, device, context));
}

// This function will block till the operation that produces `h` has
// completed. This is only valid on local TFE_TensorHandles. Returns the size in
// bytes of the memory pointed to by the device pointer returned above.
size_t TFE_TensorHandleDeviceMemorySize(TFE_TensorHandle* h,

        }
        // re-read ticker now that loading has completed
        long now = map.ticker.read();
        recordRead(e, now);
        return value;
      } finally {
        statsCounter.recordMisses(1);
      }
    }

    @Nullable V compute(
        K key,
        int hash,

					ModTime:   baseTime.Add(30 * time.Minute).UnixNano(),
					Signature: [4]byte{1, 1, 1, 1},
					Type:      ObjectType,
					Flags:     0,
				}},
			},
		},
		// Has a completely different version id
		3: {
			versions: []xlMetaV2ShallowVersion{
				{header: xlMetaV2VersionHeader{
					VersionID: [16]byte{3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},

Cette même syntaxe (ou presque) a aussi été incluse récemment dans les versions modernes de JavaScript (dans les navigateurs et NodeJS).

Mais avant ça, gérer du code asynchrone était bien plus complexe et difficile.

Dans les versions précédentes de Python, vous auriez utilisé des threads ou [Gevent](https://www.gevent.org/).  Mais le code aurait été bien plus difficile à comprendre, débugger, et concevoir.

Los contenedores de Linux se ejecutan utilizando el mismo núcleo de Linux del host (máquina, máquina virtual, servidor en la nube, etc.). Esto significa que son muy ligeros (en comparación con las máquinas virtuales completas que emulan un sistema operativo completo).

De esta forma, los contenedores consumen **pocos recursos**, una cantidad comparable a ejecutar los procesos directamente (una máquina virtual consumiría mucho más).