clusters = append(clusters, c)
	}
	if errs != nil {
		return nil, errs
	}

	// validate the topology has no open edges
	for _, c := range allClusters {
		if _, ok := allClusters[c.PrimaryName()]; !ok {
			errs = multierror.Append(errs, fmt.Errorf("primary %s for %s is not in the topology", c.PrimaryName(), c.Name()))
			continue
		}
		if _, ok := allClusters[c.ConfigName()]; !ok {

    %0 = tf_executor.graph {
      %output, %control = tf_executor.island {
       "tf.TPUReplicateMetadata"() {_xla_compile_device_type = "TPU", _replication_info = "cluster", device = "device", num_replicas = 1, topology = "topology"} : () -> ()
        %index = "tf.opA"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i1>) -> tensor<i32>

		// the node(node3) that don't have the label {"region": "whatever the value is"} (mismatch the topology key) but that have existing pods that match the labelSelector get low score

is the list of keys supported by the driver. When a driver is initialized on a cluster, it provides a set of topology keys that it understands (e.g. \"company.com/zone\", \"company.com/region\"). When a driver is initialized on a node, it provides the same topology keys along with values. Kubelet will expose these topology keys as labels on its own node object. When Kubernetes does topology aware provisioning, it can use this list to determine which labels it should retrieve from the node object and...

            "failed to parse 'topology' attribute to TopologyProto"),
        std::make_tuple(4, 2, TopologyWithMeshShape({0}),
                        std::vector<int64_t>(),
                        "'topology' 'mesh_shape' must be rank 4, got rank 1"),
        std::make_tuple(
            2, 1, TopologyWithMeshShape({2, 0, 1, 2}), std::vector<int64_t>(),
            "'topology' 'mesh_shape' dimension 1 must be positive, got 0"),

      "tf.TPUReplicateMetadata"() {_xla_compile_device_type = "TPU", _replication_info = "cluster", device = "device", num_replicas = 1 : i64, topology = "topology"} : () -> ()
      %0 = "tf.opA"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i1>) -> tensor<i32>

    %control = tf_executor.island wraps "tf.TPUReplicateMetadata"()...

					Labels: map[string]string{
						"app":                           "wle",
						"topology.kubernetes.io/region": "region1",
						"topology.kubernetes.io/zone":   "zone1",
						"topology.istio.io/subzone":     "subzone1",
						"topology.istio.io/network":     "network1",
						"topology.istio.io/cluster":     clusterID,
					},
					Address: "1.1.1.1",
					Network: "network1",

    "tf_device.cluster_func"() {_xla_compile_device_type = "TPU", _replication_info = "cluster0", func = @empty_func, step_marker_location = "STEP_MARK_AT_TOP_LEVEL_WHILE_LOOP", topology = "", device_assignment = [], input_sharding_configuration = [], output_sharding_configuration = [], use_spmd_for_xla_partitioning = false} : () -> ()
    func.return
  }
  func.func @empty_func() {
    func.return
  }

  // When a driver is initialized on a cluster, it provides a set of topology
  // keys that it understands (e.g. "company.com/zone", "company.com/region").
  // When a driver is initialized on a node, it provides the same topology keys
  // along with values. Kubelet will expose these topology keys as labels
  // on its own node object.
  // When Kubernetes does topology aware provisioning, it can use this list to