}

  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 =

    std::optional<std::set<int>> allowed_devices;
  };

  // Creates a new XLA Device.
  XlaDevice(const SessionOptions& session_options, const Options& options);
  ~XlaDevice() override;

  Allocator* GetAllocator(AllocatorAttributes attr) override
      TF_LOCKS_EXCLUDED(mu_);
  void Compute(OpKernel* op_kernel, OpKernelContext* context) override;
  void ComputeAsync(AsyncOpKernel* op_kernel, OpKernelContext* context,

    TF_CHECK_OK(device_->TryGetDeviceContext(&device_context_));

    // Get the host allocator.
    AllocatorAttributes host_alloc_attr;
    host_alloc_attr.set_on_host(true);
    host_allocator_ = device_->GetAllocator(host_alloc_attr);

    // Get the device allocator. This should give us an AsyncValueAllocator.
    AllocatorAttributes device_alloc_attr;
    device_alloc_attr.set_on_host(false);

  std::shared_ptr<se::DeviceMemoryAllocator> allocator_ptr =
      GetAllocator(ctx->device(), stream, platform_info_);
  se::DeviceMemoryAllocator* allocator = allocator_ptr.get();
  XlaComputationLaunchContext launch_context(
      client, allocator, client->default_device_ordinal(),
      /*allocate_xla_tensors=*/platform_info_.xla_device_metadata() != nullptr,

    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): "

	"k8s.io/klog/v2"
)

// CIDRAllocatorType is the type of the allocator to use.
type CIDRAllocatorType string

const (
	// RangeAllocatorType is the allocator that uses an internal CIDR
	// range allocator to do node CIDR range allocations.
	RangeAllocatorType CIDRAllocatorType = "RangeAllocator"
	// CloudAllocatorType is the allocator that uses cloud platform
	// support to do node CIDR range allocations.

  return new PJRT_Buffer{std::move(*buffer), c_api_client->pjrt_c_client()};
}

TEST(TensorPjRtBufferUtilTest, GetPjRtCBufferFromTensorNoBuffer) {
  auto allocator = std::make_unique<AsyncValueAllocator>();
  tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});

  EXPECT_THAT(
      GetPjRtCBufferFromTensor(&tensor),
      StatusIs(error::INTERNAL, HasSubstr(absl::StrCat(

		// 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 {