* Le support des **WebSockets**.
* Le support de **GraphQL**.
* Les <abbr title="En anglais: In-process background tasks">tâches d'arrière-plan.</abbr>
* Des évènements de démarrages et d'arrêt.
* Un client de test basé sur `request`

}

TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name) {
  TF_Operation* op;
  BinaryOpHelper("Mul", l, r, graph, s, name, &op, "", true);
  return op;
}

TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,
                  const char* name) {

    **FastAPI** no impone ningún significado específico.

    La información que está presentada aquí es una guía, no un requerimiento.

    Por ejemplo, cuando usas GraphQL normalmente realizas todas las acciones usando únicamente operaciones `POST`.

### Paso 4: define la **función de la operación de path**

Esta es nuestra  "**función de la operación de path**":

* **path**: es `/`.

    **FastAPI** n'impose pas de sens spécifique à chacune d'elle.

    Les informations qui sont présentées ici forment une directive générale, pas des obligations.

    Par exemple, quand l'on utilise **GraphQL**, toutes les actions sont effectuées en utilisant uniquement des opérations `POST`.

### Étape 4 : définir la **fonction de chemin**.

Voici notre "**fonction de chemin**" (ou fonction d'opération de chemin) :

    }
    tf_executor.fetch %island#0, %island#1 : tensor<*xi32>, tensor<*xi32>
  }
  func.return %graph#0, %graph#1 : tensor<*xi32>, tensor<*xi32>
}

// CHECK-LABEL: func @multiple_return
// CHECK:   %[[GRAPH:.*]]:2 = tf_executor.graph {
// CHECK:     %[[ADD1:.*]], %[[ADD1_control:.*]] = tf_executor.island wraps "tf.Add"(%arg0, %arg1)

	return true
}

// disqualify records why the dependencies of m cannot be included in the module
// graph if reached from a part of the graph with the given pruning.
//
// Since the pruned graph is a subgraph of the unpruned graph, disqualifying a
// module from a pruned part of the graph also disqualifies it in the unpruned
// parts.

import org.gradle.internal.serialize.graph.readEnum
import org.gradle.internal.serialize.graph.readNonNull
import org.gradle.internal.serialize.graph.readPropertyValue
import org.gradle.internal.serialize.graph.withDebugFrame
import org.gradle.internal.serialize.graph.withIsolate
import org.gradle.internal.serialize.graph.withPropertyTrace
import org.gradle.internal.serialize.graph.writeCollection
import org.gradle.internal.serialize.graph.writeEnum

    /**
     *  null in dirty graph, actual
     *  scope for conflict-resolved graph
     */
    ArtifactScopeEnum scope;

    // ------------------------------------------------------------------------
    /**
     * init graph
     */
    public MetadataGraph(int nVertices) {
        init(nVertices, 2 * nVertices);
    }

    public MetadataGraph(int nVertices, int nEdges) {

	// The data structure of each (provided or inferred) type represents a graph, where
	// each node corresponds to a type and each (directed) vertex points to a component
	// type. The substitution process described above repeatedly replaces type parameter
	// nodes in these graphs with the graphs of the types the type parameters stand for,
	// which creates a new (possibly bigger) graph for each type.

Value BacktrackAnalysis::BacktrackValue(Value value) {
  while (Operation* op = value.getDefiningOp()) {
    int res_index = mlir::cast<OpResult>(value).getResultNumber();
    if (auto graph = dyn_cast<tf_executor::GraphOp>(op)) {
      value = graph.GetFetch().getOperand(res_index);
    } else if (auto island = dyn_cast<tf_executor::IslandOp>(op)) {
      // Control output is generated by the IslandOp, not the yield in