objects  map[Key]*selectingObject
	refCount int
}

type labelsKey struct {
	key       string
	namespace string
}

type labeledObject struct {
	key    Key
	labels map[string]string
	// labelsKey is a stable serialization of labels for association
	// caching.
	labelsKey labelsKey
}

type labeledObjects struct {
	objects  map[Key]*labeledObject
	refCount int
}

	// Thus Add() is only increasing refCount and generation of a given object.
	// Then Get() is responsible for fetching if needed.
	s.lock.Lock()
	defer s.lock.Unlock()
	item, exists := s.items[key]
	if !exists {
		item = &objectStoreItem{
			refCount: 0,
			data:     &objectData{},
		}
		s.items[key] = item
	}

	item.refCount++
	// This will trigger fetch on the next Get() operation.

	// underlying file descriptor with reference counting.
	RefCountPtr *int32

	// RefCount is the reference count of Sysfd. When a copy of an FD is made,
	// it points to the reference count of the original FD instance.
	RefCount int32

	// Cache for the file type, lazily initialized when Seek is called.
	Filetype uint32

  core::RefCountPtr<Var> variable;
  const Tensor& value = context->input(1);
  // Note: every resource-variable-manipulating op assumes copy-on-write
  // semantics, and creates a copy of the variable's Tensor if its refcount is
  // bigger than 1 when we try to modify it. This means we never need to copy
  // the original tensor for AssignVariableOp; even if there are other live
  // users of it we know none can modify it so this is always safe (even in

#ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_
#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.
class GraphFunction : public AbstractFunction {
 public:

#define TENSORFLOW_C_EAGER_ABSTRACT_FUNCTION_H_

#include "absl/status/statusor.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/platform/intrusive_ptr.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

class FunctionRecord;

// A traced function: this hides the complexity of converting the serialized

#include <set>
#include <utility>
#include <vector>

#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/refcount.h"
#include "tsl/platform/status.h"

namespace tensorflow {

VariableInfo::VariableInfo(
    int index, absl::string_view name, Var* var,
    const std::optional<ManagedStackTrace>& definition_stack_trace)

#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/lib/core/refcount.h"
#include "tensorflow/core/platform/status.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

using PjRtDeviceCompiler =

  // is placed on a regular CPU or GPU device then device_allocator_ is null.
  // The allocator is of unknown provenance; keep it in a shared pointer to
  // set an artificial refcount of one.
  std::shared_ptr<se::DeviceMemoryAllocator> device_allocator_;

  XlaPlatformInfo(const XlaPlatformInfo&) = delete;
  void operator=(const XlaPlatformInfo&) = delete;
};

                              int* num_retvals) {
  TF_RETURN_IF_ERROR(parent_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