%0:8 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<*xf32>) {devices = {TPU_REPLICATED_CORE_0 = ["/device:TPU:0", "/device:TPU:1"]}, n = 2: i32} {
    %1 = "tf.Shape"(%ri) {device = "", T = "tfdtype$DT_FLOAT", out_type = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>
    %2 = "tf.opA"(%1) {device = "TPU_REPLICATED_CORE_0"} : (tensor<?xi32>) -> tensor<*xi32>
    %3 = "tf_device.launch"() ({

    %2:3 = "tf.UnpackHyp"(%arg1) {_tpu_replicate = "cluster_Fn", _xla_outside_compilation = "0", device = "", max_seq_length = 16 : i64} : (tensor<*x!tf_type.string>) -> (tensor<*xi32>, tensor<*xi32>, tensor<*xf32>)
    %3 = "tf.Reshape"(%2#2, %1) {_tpu_replicate = "cluster_Fn", _xla_outside_compilation = "0", device = ""} : (tensor<*xf32>, tensor<*xi32>) -> tensor<*xf32>

// Check that replicate op's 'devices' attribute must consist of dictionary
// with values as list with size equal to 'n' attribute.
func.func @verifier_replicate_n_device_multiple_alias() {
  "tf_device.replicate" () ({
// expected-error@-1 {{'tf_device.replicate' op expects number of devices (2) to be equal to 'n' (3)}}
  ^entry:
    tf_device.return

func (info *ClaimInfo) cdiDevicesAsList() []kubecontainer.CDIDevice {
	var cdiDevices []kubecontainer.CDIDevice
	for _, devices := range info.CDIDevices {
		for _, device := range devices {
			cdiDevices = append(cdiDevices, kubecontainer.CDIDevice{Name: device})
		}
	}
	return cdiDevices
}

// addPodReference adds a pod reference to the claim info.

  // To keep things simple, I've stuck to raw pointers for now.
  //
  // Params:
  //  device - The device string associated with the SavedResource
  //           https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/saved_object_graph.proto#L182
  //           Conceptually, this is the same device used in CapturableResource:

  // CHECK-DAG: %[[COPY1:.*]] = "tf.TPUCopyWithLayout"(%[[ITER0]]#1, %[[LAYOUT1]]) {device = "/device:TPU:0"}
  // CHECK-DAG: %[[COPY2:.*]] = "tf.TPUCopyWithLayout"(%[[ITER1]]#0, %[[LAYOUT0]]) {device = "/device:TPU:1"}
  // CHECK-DAG: %[[COPY3:.*]] = "tf.TPUCopyWithLayout"(%[[ITER1]]#1, %[[LAYOUT1]]) {device = "/device:TPU:1"}

            "Switch a device into 'on-demand' mode, where instead of "
            "autoclustering ops are compiled one by one just-in-time."),

       Flag("tf_xla_enable_xla_devices",
            &device_flags->tf_xla_enable_xla_devices,
            "Generate XLA_* devices, where placing a computation on such a "
            "device"
            "forces compilation by XLA. Deprecated."),

    %1 = "tf.Rank"(%0) {_tpu_replicate = "cluster", device = ""} : (tensor<*xi1>) -> tensor<*xi32>
    %2 = "tf.Range"(%cst_0, %1, %cst_1) {_tpu_replicate = "cluster", _xla_outside_compilation = "0", device = ""} : (tensor<i32>, tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
    %3 = "tf.All"(%0, %2) {_tpu_replicate = "cluster", _xla_outside_compilation = "0", device = "", keep_dims = false} : (tensor<*xi1>, tensor<*xi32>) -> tensor<*xi1>

      %outputs_11, %control_12 = tf_executor.island wraps "tf.Placeholder"() {device = "", dtype = "tfdtype$DT_FLOAT", name = "y", shape = "tfshape$dim { }"} : () -> tensor<0xf32>

  auto node_device = [&](Operation *n) -> StringAttr {
    auto device = n->getAttrOfType<StringAttr>(device_id);
    if (!device || device.getValue().empty()) return caller_device;

    DeviceNameUtils::ParsedName ndef_parsed_device;
    if (!DeviceNameUtils::ParseFullName(device.getValue().str(),
                                        &ndef_parsed_device))
      return device;
    DeviceNameUtils::MergeUnsetDevNames(&ndef_parsed_device,