%0:2 = tf_executor.graph {
    %control = tf_executor.island() wraps "tf.TPUReplicateMetadata"() {_tpu_replicate = "cluster", device = "/device:TPU:0", num_replicas = 2, topology = "topology"} : () -> ()
    %ri, %c0 = tf_executor.island wraps "tf.TPUReplicatedInput"(%arg0, %arg1) {index = 1 : i64, is_mirrored_variable = false, is_packed = false} : (tensor<i32>, tensor<i32>) -> tensor<i32>

			expected         []*endpoint.LocalityLbEndpoints
		}{
			{
				name:             "match none label",
				failoverPriority: []string{"topology.istio.io/network", "topology.istio.io/cluster"},
				proxyLabels: map[string]string{
					"topology.istio.io/network": "test",
					"topology.istio.io/cluster": "test",
				},
				expected: []*endpoint.LocalityLbEndpoints{
					{
						Locality: &core.Locality{

			}
			activePods = append(activePods, &pod)
		}

		m := manager{
			policy: &staticPolicy{
				topology: topology,
			},
			state: &mockState{
				assignments:   tc.assignments,
				defaultCPUSet: tc.defaultCPUSet,
			},
			topology:          topology,
			activePods:        func() []*v1.Pod { return activePods },
			podStatusProvider: mockPodStatusProvider{},

		// - Add. An unschedulable Pod may fail due to violating topology spread constraints,
		// adding an assigned Pod may make it schedulable.
		// - Update. Updating on an existing Pod's labels (e.g., removal) may make
		// an unschedulable Pod schedulable.
		// - Delete. An unschedulable Pod may fail due to violating an existing Pod's topology spread constraints,
		// deleting an existing Pod may make it schedulable.

		{
			ResourceName: "resource",
			DeviceIds:    []string{"dev0", "dev1"},
			Topology:     &podresourcesapi.TopologyInfo{Nodes: []*podresourcesapi.NUMANode{{ID: numaID}}},
		},
	}

	cpus := []int64{12, 23, 30}

	memory := []*podresourcesapi.ContainerMemory{
		{
			MemoryType: "memory",
			Size_:      1073741824,
			Topology:   &podresourcesapi.TopologyInfo{Nodes: []*podresourcesapi.NUMANode{{ID: numaID}}},
		},
		{

      auto& task_and_device = topology(x, y, z, core);
      if (task_and_device.task != -1)
        return DuplicateCoordinateErrorMsg(kTopologyAttr, x, y, z, core);

      task_and_device = {task, device};
    }
  }

  return topology;
}

// Determine execution devices when topology and device assignment are defined.
// With a topology device coordinate to task and device mapping, device

    %1 = "tf.UnsupportedOp"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
    tf_device.return %2 : tensor<i32>
  }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> tensor<i32>
  func.return %0 : tensor<i32>
}

// -----

func.func @unsupported_op_soft_placement_false() -> tensor<i32> {
  %0 = "tf_device.cluster"() ({

// topology information.
func updateTopologyLabels(topology map[string]string) nodeUpdateFunc {
	return func(node *v1.Node) (*v1.Node, bool, error) {
		if len(topology) == 0 {
			return node, false, nil
		}

		for k, v := range topology {
			if curVal, exists := node.Labels[k]; exists && curVal != v {

      // Metadata op for cluster `cluster` with 1 replica, 1 core per replica and
      // with topology `<topology>`.
      "tf.TPUReplicateMetadata"() {_xla_compile_device_type = "TPU", _replication_info = "cluster", num_relicas = 1, num_cores_per_replica = 1, topology = "<topology>", device_assignment = [], padding_map = []} : () -> ()
      %replicated_input = "tf.TPUReplicatedInput"(%arg0) : (tensor<i32>) -> tensor<i32>

						DeprecatedTopology: map[string]string{corev1.LabelHostname: "node-1"},
					},
				},
			},
		},
		{
			name:      "v1 request, updated endpoints with topology node names + other topology fields",
			v1Request: true,
			originalEPS: &discovery.EndpointSlice{
				Endpoints: []discovery.Endpoint{
					{
						Hostname:           ptr.To("hostname-1"),