#include "tensorflow/core/framework/op_requires.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/refcount.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/statusor.h"
#include "tensorflow/core/tfrt/common/pjrt_util.h"
#include "tsl/platform/errors.h"

namespace tensorflow {
namespace {

	Flags   int32
	Index   uint16
	_       [2]byte
}

type IfmaMsghdr2 struct {
	Msglen   uint16
	Version  uint8
	Type     uint8
	Addrs    int32
	Flags    int32
	Index    uint16
	Refcount int32
}

type RtMsghdr struct {
	Msglen  uint16
	Version uint8
	Type    uint8
	Index   uint16
	Flags   int32
	Addrs   int32
	Pid     int32
	Seq     int32
	Errno   int32

#include "tensorflow/core/platform/abi.h"
#endif  // IS_MOBILE_PLATFORM

#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/refcount.h"

using tensorflow::AllocatorAttributes;
using tensorflow::mutex_lock;
using tensorflow::ResourceBase;
using tensorflow::Status;
using tensorflow::Tensor;
using tensorflow::TF_TensorFromTensor;

	key := objectKey{namespace: namespace, name: name}

	// AddReference is called from RegisterPod thus it needs to be efficient.
	// Thus, it is only increasing refCount and in case of first registration
	// of a given object it starts corresponding reflector.
	// It's responsibility of the first Get operation to wait until the
	// reflector propagated the store.
	c.lock.Lock()

#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/statusor.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

               const GradientRegistry& registry) {
  TF_RETURN_IF_ERROR(op_->Execute(retvals, num_retvals));
  for (int i = 0; i < *num_retvals; i++) {
    // TODO(srbs): Manage refcount of ForwardOperation's inputs/outputs.
    forward_op_->outputs.push_back(retvals[i]);
  }
  // TODO(b/166669239): This is needed to support AttrBuilder::Get for string

var devApplierMap = make(map[string]common.LinuxVolumeQuotaApplier)

// Directory -> applier
var dirApplierMap = make(map[string]common.LinuxVolumeQuotaApplier)
var dirApplierLock sync.RWMutex

// Pod -> refcount
var podDirCountMap = make(map[types.UID]int)

// ID -> size
var quotaSizeMap = make(map[common.QuotaID]int64)
var quotaLock sync.RWMutex

var supportsQuotasMap = make(map[string]bool)

	edgeCount := g.graph.Degree(n)
	if edgeCount < g.destinationEdgeThreshold {
		delete(g.destinationEdgeIndex, n.ID())
		return
	}

	// decrement the nodeID->destinationID refcount in the index, if the index exists
	index := g.destinationEdgeIndex[n.ID()]
	if index == nil {
		return
	}
	if destinationEdge, ok := e.(*destinationEdge); ok {
		index.decrement(destinationEdge.DestinationID())

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/refcount.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace mlir {
namespace TF {
using tensorflow::AbstractFunction;
using tensorflow::AbstractOperation;

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/tfrt/common/create_pjrt_client_util.h"
#include "tensorflow/core/tfrt/common/pjrt_util.h"
#include "tsl/framework/allocator.h"
#include "tsl/framework/device_id_utils.h"