labels.plugin_name=Nome
labels.plugin_version=Versão
labels.plugin_delete=Excluir
labels.plugin_install=Instalar
labels.plugin_install_title=Instalar plugin
labels.plugin_jar_file=Arquivo Jar
labels.plugin_local_install=Local
labels.plugin_remote_install=Remoto
labels.crud_button_install=Instalar
labels.storage_configuration=Armazenamento
labels.storage_name=Nome
labels.storage_size=Tamanho

`Policy` optionally took in a `tf.compat.v1.mixed_precision.LossScale` in the constructor, which defaulted to a dynamic loss scale for the `"mixed_float16"` policy and no loss scale for other policies. In `Model.compile`, if the model's policy had a loss scale, the optimizer would be wrapped with a `LossScaleOptimizer`. With the non-experimental `Policy`, there is no loss scale associated with the `Policy`, and `Model.compile` wraps the optimizer with a `LossScaleOptimizer` if and only if the policy...

labels.plugin_version=Version
labels.plugin_delete=Supprimer
labels.plugin_install=Installer
labels.plugin_install_title=Installer le plugin
labels.plugin_jar_file=Fichier Jar
labels.plugin_local_install=Local
labels.plugin_remote_install=Distant
labels.crud_button_install=Installer
labels.storage_configuration=Stockage
labels.storage_name=Nom
labels.storage_size=Taille

labels.plugin_name=Nombre
labels.plugin_version=Versión
labels.plugin_delete=Eliminar
labels.plugin_install=Instalar
labels.plugin_install_title=Instalar plugin
labels.plugin_jar_file=Archivo Jar
labels.plugin_local_install=Local
labels.plugin_remote_install=Remoto
labels.crud_button_install=Instalar
labels.storage_configuration=Almacenamiento
labels.storage_name=Nombre
labels.storage_size=Tamaño

      'HERMETIC_CUDA_COMPUTE_CAPABILITIES',
      hermetic_cuda_compute_capabilities,
  )


def set_cuda_local_path(environ_cp, dist_name, env_var):
  ask_path = (
      'Please specify the local {} path you want to use '
      'or leave empty to use the default version. '
  ).format(dist_name)
  local_path = get_from_env_or_user_or_default(
      environ_cp, env_var, ask_path, None
  )
  if local_path:

Ele tinha validação de dados automática, serialização de dados e geração de _schema_ OpenAPI baseado nos mesmos _type hints_ em vários locais.

labels.plugin_name=Name
labels.plugin_version=Version
labels.plugin_delete=Delete
labels.plugin_install=Install
labels.plugin_install_title=Install Plugin
labels.plugin_jar_file=Jar File
labels.plugin_local_install=Local
labels.plugin_remote_install=Remote
labels.crud_button_install=Install
labels.storage_configuration=Storage
labels.storage_name=Name
labels.storage_size=Size
labels.storage_last_modified=Last Modified

	return notifyEndpoint(ctx, ri, r.Flags.Notify.Endpoint, r.Flags.Notify.Token)
}

// ReplicateFromSource - this is not implemented yet where source is 'remote' and target is local.
func (r *BatchJobReplicateV1) ReplicateFromSource(ctx context.Context, api ObjectLayer, core *minio.Core, srcObjInfo ObjectInfo, retry bool) error {
	srcBucket := r.Source.Bucket
	tgtBucket := r.Target.Bucket

Então, nesse caso, poderia ser mais simples ter **um único contêiner** com **múltiplos processos**, e uma ferramenta local (por exemplo, um exportador do Prometheus) no mesmo contêiner coletando métricas do Prometheus para todos os processos internos e expor essas métricas no único contêiner.

---

				return
			}
			mw.Flush()
		}
	}()
	err = s.getStorage().ReadMultiple(r.Context(), req, responses)
	wg.Wait()
	rw.CloseWithError(err)
}

// globalLocalSetDrives is used for local drive as well as remote REST
// API caller for other nodes to talk to this node.
//
// Any updates to this must be serialized via globalLocalDrivesMu (locker)
var globalLocalSetDrives [][][]StorageAPI