format: int32
                type: integer
              nodeSelectors:
                description: NodeSelectors allows to limit the nodes to announce as
                  next hops for the LoadBalancer IP. When empty, all the nodes having  are
                  announced as next hops.
                items:
                  description: A label selector is a label query over a set of resources.

        this.dependenciesMayChange = component.getModule().isVirtualPlatform();
    }

    // the enqueue and dequeue methods are used for performance reasons
    // in order to avoid tracking the set of enqueued nodes
    boolean enqueue() {
        if (queued) {
            return false;
        }
        queued = true;
        return true;
    }

    NodeState dequeue() {
        queued = false;

     * be read without locking. Next fields of nodes are immutable (final). All list additions are
     * performed at the front of each bin. This makes it easy to check changes, and also fast to
     * traverse. When nodes would otherwise be changed, new nodes are created to replace them. This

		"res1": map[string]pluginapi.Device{
			"dev1": {
				ID: "dev1",
				Topology: &pluginapi.TopologyInfo{
					Nodes: []*pluginapi.NUMANode{
						{
							ID: 1,
						},
					},
				},
			},
			"dev2": {
				ID: "dev2",
				Topology: &pluginapi.TopologyInfo{
					Nodes: []*pluginapi.NUMANode{
						{
							ID: 1,
						},
						{
							ID: 2,
						},
					},
				},

     * be read without locking. Next fields of nodes are immutable (final). All list additions are
     * performed at the front of each bin. This makes it easy to check changes, and also fast to
     * traverse. When nodes would otherwise be changed, new nodes are created to replace them. This

// The cluster will be wrapped in a `tf_device.cluster` first and then by a
// replicate.
// TPUPartitionedInputV2 nodes would be inside the replicate but outside the
// cluster.
// TPUReplicatedInput and TPUReplicatedOutput nodes will be replaced by the
// replicate operands and results.
// CHECK-LABEL: func @replication_with_model_parallelism

  TF_DeleteBuffer(graph_def);

  // Can add nodes to the imported graph without trouble.
  Add(feed, scalar, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_DeleteGraph(graph);
  TF_DeleteStatus(s);
}

TEST(CAPI, ImportGraphDef_WithReturnOutputs) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Create a graph with two nodes: x and 3
  Placeholder(graph, s);

  }
  func->setAttr(
      kTfSavedModelExportedNamesAttr,
      mlir::ArrayAttr::get(
          context, {mlir::StringAttr::get(context, signature->signature_key)}));
}

// There are control nodes at each end of each control edge. For each of them,
// we store the source vertices of the incoming edges (if any) and the control
// node's output token. To improve testability, we use an ordered set for the
// source vertices.

- Improved scheduling performance when many nodes, and prefilter returns 1-2 nodes (e.g. daemonset)
  

			// the range operator is only allowed at the top of a for statement
			p.errorExpected(x.Pos(), "expression")
			x = &ast.BadExpr{x.Pos(), x.End()}
		}
	case *ast.BinaryExpr:
	default:
		// all other nodes are not proper expressions
		p.errorExpected(x.Pos(), "expression")
		x = &ast.BadExpr{x.Pos(), x.End()}
	}
	return x
}

// isTypeName reports whether x is a (qualified) TypeName.