Allocator* allocator = nullptr;
  if (arg == nullptr) {
    allocator = cpu_allocator();
  } else {
    allocator = reinterpret_cast<Allocator*>(arg);
  }
  if (LogMemory::IsEnabled() && data != nullptr) {
    LogMemory::RecordRawDeallocation(
        "TensorFlow C Api", LogMemory::EXTERNAL_TENSOR_ALLOCATION_STEP_ID, data,
        allocator, false);
  }
  allocator->DeallocateRaw(data);

    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status) {
  tensorflow::Device* device = nullptr;
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  status->status = context->FindDeviceFromName(device_name, &device);
  if (!status->status.ok()) {
    deallocator(data, len, deallocator_arg);

func (c *MetaAllocator) AllocateService(service *api.Service, ip net.IP) error {
	allocator, err := c.getAllocator(ip)
	if err != nil {
		return err
	}
	return allocator.AllocateService(service, ip)
}

func (c *MetaAllocator) Allocate(ip net.IP) error {
	allocator, err := c.getAllocator(ip)
	if err != nil {
		return err
	}
	return allocator.Allocate(ip)
}

    // If we are on an XlaDevice, use the underlying XLA platform's allocator
    // directly. We could use the StreamExecutor's allocator which may
    // theoretically be more correct, but XLA returns a nice OOM message in a
    // Status and StreamExecutor does not.
    //
    // Importantly we can't use ctx->device()->GetAllocator() as the allocator
    // (which xla_allocator above uses) as on an XlaDevice, this is a dummy

  }

  Status status;
  if (alloc_attrs.on_host()) {
    *tensor = parsed;
  } else {
    Allocator* allocator;
    {
      mutex_lock lock(mu_);
      allocator = GetAllocatorLocked(alloc_attrs);
    }
    Tensor copy(allocator, parsed.dtype(), parsed.shape());
    TF_RETURN_IF_ERROR(
        device_context->CopyCPUTensorToDeviceSync(&parsed, this, &copy));
    *tensor = copy;

	defer r.svcQueue.ShutDown()
	r.broadcaster.StartRecordingToSink(stopCh)
	defer r.broadcaster.Shutdown()

	klog.Info("Starting ipallocator-repair-controller")
	defer klog.Info("Shutting down ipallocator-repair-controller")

	if !cache.WaitForNamedCacheSync("ipallocator-repair-controller", stopCh, r.ipAddressSynced, r.servicesSynced, r.serviceCIDRSynced) {
		return
	}

      }

      std::shared_ptr<se::DeviceMemoryAllocator> allocator =
          GetAllocator(ctx->device(), GetStream(ctx), platform_info);
      XlaComputationLaunchContext launch_context =
          GetLaunchContext(platform_info, ctx, client, allocator.get());

      const xla::HloInputOutputAliasConfig& input_output_alias =

    const std::map<int, const Tensor*>& resource_vars) {
  se::Stream* stream =
      ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr;
  Allocator* allocator = ctx->device()->GetAllocator({});

  // Computation output should always be a tuple.
  VLOG(2) << "Result tuple shape: " << output.on_host_shape().DebugString();
  VLOG(2) << "Result tuple shape (on device): "

		// Initialize the range allocator.
		allocator, err := NewCIDRRangeAllocator(tCtx, tc.fakeNodeHandler, fakeNodeInformer, tc.allocatorParams, nodeList)
		if err != nil {
			t.Errorf("%v: failed to create CIDRRangeAllocator with error %v", tc.description, err)
			return
		}
		rangeAllocator, ok := allocator.(*rangeAllocator)
		if !ok {

def ExecuteOpWithAllocator : FallbackAsync_Op<"executeop.allocator",
    [Pure, CoreRT_TypedAttributeTrait, TFRT_CostFunctionInterface, TFRT_AttrCostTrait]> {
  let summary = "The Fallback ExecuteOp with custom allocator";
  let description = [{
    Similar to ExecuteOp but takes a custom allocator for allocating output tensors.
  }];

  let arguments = (ins
    TFAllocatorType:$allocator,
    Variadic<TFTensorType>:$args,