lhs_device_attr.getValue() == rhs_device_attr.getValue());
  };

  // Check if tf_executor.NextIteration.Source/tf_executor.NextIteration.Sink
  // pair has matching devices or no devices.
  if (auto source = llvm::dyn_cast<tf_executor::NextIterationSourceOp>(op)) {
    return ops_have_same_device(source, source.GetSink());
  } else if (auto sink = llvm::dyn_cast<tf_executor::NextIterationSinkOp>(op)) {

  if (execute_arg_to_outer_args.empty()) return false;

  // Extract the replicated devices.
  auto devices_attr = replicate.getDevices();
  if (!devices_attr) return false;

  auto device_map = devices_attr.value();
  llvm::SmallDenseMap<llvm::StringRef, llvm::SmallVector<StringRef, 4>> devices;
  devices.reserve(device_map.size());

  for (auto it : device_map) {
    auto device_alias = it.getName().strref();

    // If padded_shape_fn is empty, a default implementation that returns
    // the logical on-device shape without padding is used.
    PaddedShapeFn padded_shape_fn;

    // Set of devices to use. This controls which of the devices on the given
    // platform will have resources allocated. For GPUs this will be
    // filled from visible_gpu_devices list from session configuration.
    std::optional<std::set<int>> allowed_devices;

  return tensorflow::WrapOpInLaunch(builder, compile_op.getLoc(), compile_op,
                                    compilation_device);
}

// Assigns explicit devices to replicate op. An aliased device is created per
// core, and all replica devices per core are grouped together.
void AssignDevicesToReplicate(
    tf_device::ReplicateOp replicate,
    llvm::ArrayRef<llvm::SmallVector<tensorflow::TPUDeviceAndHost, 8>>

  auto& side_effect_analysis = getAnalysis<mlir::TF::SideEffectAnalysis>();
  // Get runtime devices information from the closest parent module.
  auto module = getOperation();
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(module, &devices)))
    return signalPassFailure();

  OpBuilder builder(&getContext());
  llvm::SmallVector<mlir::tf_device::ClusterOp, 4> clusters;

    Builder* builder, OperationState* state, int n,
    std::optional<DictionaryAttr> devices,
    llvm::ArrayRef<std::pair<ValueRange, Type>> replicated_inputs,
    ValueRange packed_inputs, TypeRange replica_output_types) {
  DCHECK_GE(n, 2);
  state->addAttribute("n", builder->getI32IntegerAttr(n));

  if (devices.has_value()) state->addAttribute("devices", devices.value());

  Region* region = state->addRegion();

      TF_NewStatus(), TF_DeleteStatus);
  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status.get());
  CHECK_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  const int num_devices = TF_DeviceListCount(devices);
  for (int i = 0; i < num_devices; ++i) {
    const string dev_type(TF_DeviceListType(devices, i, status.get()));

                                                   size_t proto_len,
                                                   TF_Status* status);

// TODO(b/166642410): It would be nice, for custom devices and for other users,
// to have a non-string representation of devices (TF_Device) extracted from
// tensors/ops/etc. and usable in APIs like OpSetDevice/ResetOp/etc.

#define TFE_CUSTOM_DEVICE_VERSION 4

		expectedResult []kubecontainer.CDIDevice
	}{
		{
			description: "empty CDI devices",
			claimInfo: &ClaimInfo{
				ClaimInfoState: state.ClaimInfoState{
					CDIDevices: map[string][]string{},
				},
			},
		},
		{
			description: "nil CDI devices",
			claimInfo:   &ClaimInfo{},
		},
		{
			description: "valid CDI devices",
			claimInfo: &ClaimInfo{
				ClaimInfoState: state.ClaimInfoState{

    // Once the rollout to GPU is complete, this flag can be deprecated.
    bool enabled_for_gpu_;

   private:
    // Devices for which using Device API (PjRt) is allowed in the XlaLaunch op.
    // This can only be modified programmatically.
    absl::flat_hash_set<std::string> xla_launch_allowed_devices_;
    // Devices for which using Device API (PjRt) is allowed in the
    // XlaCompileOnDemand op. This can only be modified programmatically.