}

  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;

	}

	return a, err
}

// NewInMemory creates an in-memory allocator.
func NewInMemory(pr net.PortRange) (*PortAllocator, error) {
	return New(pr, func(max int, rangeSpec string, offset int) (allocator.Interface, error) {
		return allocator.NewAllocationMapWithOffset(max, rangeSpec, offset), nil
	})
}

// NewFromSnapshot allocates a PortAllocator and initializes it from a snapshot.

// MetaAllocator maintains a Tree with the ServiceCIDRs containing an IP Allocator
// on the nodes. Since each allocator doesn't stored the IPAddresses because it reads
// them from the informer cache, it is cheap to create and delete IP Allocators.
// MetaAllocator forwards the request to any of the internal allocators that has free
// addresses.

// MetaAllocator implements current allocator interface using
// ServiceCIDR and IPAddress API objects.

	"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 {

  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);

	"k8s.io/kubernetes/pkg/registry/core/service/allocator"
	allocatorstore "k8s.io/kubernetes/pkg/registry/core/service/allocator/storage"
	"k8s.io/kubernetes/pkg/registry/core/service/portallocator"
	"k8s.io/kubernetes/pkg/registry/registrytest"
)

const (
	basePortRange = 30000
	sizePortRange = 2768
)

// Any free page of memory can be split into a set of objects
// of one size class, which are then managed using a free bitmap.
//
// The allocator's data structures are:
//
//	fixalloc: a free-list allocator for fixed-size off-heap objects,
//		used to manage storage used by the allocator.
//	mheap: the malloc heap, managed at page (8192-byte) granularity.
//	mspan: a run of in-use pages managed by the mheap.

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,