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"() ({

     // CHECK-NEXT: "tf.A"
     // CHECK: device_assignment =  [], num_cores_per_replica = 1 : i64, topology =  ""
    "tf_device.cluster"() ({
      "tf.A"() : () -> ()
      "tf.B"() {_xla_outside_compilation = "cluster1"} : () -> ()
      "tf.C"() : () -> ()
      tf_device.return
    }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> ()
    func.return
  }

// 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>

  // endpoint.
  // +optional
  optional .k8s.io.api.core.v1.ObjectReference targetRef = 4;

  // topology contains arbitrary topology information associated with the
  // endpoint. These key/value pairs must conform with the label format.
  // https://kubernetes.io/docs/concepts/overview/working-with-objects/labels
  // Topology may include a maximum of 16 key/value pairs. This includes, but
  // is not limited to the following well known keys:

	TargetRef *v1.ObjectReference `json:"targetRef,omitempty" protobuf:"bytes,4,opt,name=targetRef"`

	// topology contains arbitrary topology information associated with the
	// endpoint. These key/value pairs must conform with the label format.
	// https://kubernetes.io/docs/concepts/overview/working-with-objects/labels
	// Topology may include a maximum of 16 key/value pairs. This includes, but
	// is not limited to the following well known keys:

    func.return
  }
  func.func @empty_func() {
    func.return
  }
}

// -----

// Tests `tf_device.cluster_func` with missing `topology` attribute.

	if !alphaOptions.Has(option) && !betaOptions.Has(option) && !stableOptions.Has(option) {
		return fmt.Errorf("unknown Topology Manager Policy option: %q", option)
	}

	if alphaOptions.Has(option) && !utilfeature.DefaultFeatureGate.Enabled(kubefeatures.TopologyManagerPolicyAlphaOptions) {
		return fmt.Errorf("Topology Manager Policy Alpha-level Options not enabled, but option %q provided", option)
	}

  %0 = "tf_device.cluster_func"(%arg0) {_producer_name = "UNKNOWN", func = @_func, input_sharding_configuration = [""], num_cores_per_replica = 1 : i64, device_assignment = [], topology = "", output_sharding_configuration = [""], step_marker_location = "STEP_MARK_AT_ENTRY", use_spmd_for_xla_partitioning = false, use_tpu = true} : (tensor<1x3xf32>) -> (tensor<1x3xf32>)
  return %0 : tensor<1x3xf32>
}