#include "tensorflow/c/experimental/filesystem/modular_filesystem.h"
#include "tensorflow/cc/framework/gradients.h"
#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope_internal.h"
#include "tensorflow/cc/ops/while_loop.h"
#include "tensorflow/cc/saved_model/loader.h"
#include "tensorflow/core/distributed_runtime/server_lib.h"
#include "tensorflow/core/framework/logging.h"

ofset X and Y.\n  const splitRegex = /\\s*,\\s*|\\s+/;\n  let ops = divider !== -1\n    ? [\n        fragments\n          .slice(0, divider)\n          .concat([fragments[divider].split(splitRegex)[0]]),\n        [fragments[divider].split(splitRegex)[1]].concat(\n          fragments.slice(divider + 1)\n        ),\n      ]\n    : [fragments];\n\n  // Convert the values with units to absolute pixels to allow our computations\n  ops = ops.map((op, index) => {\n    // Most of the units rely on the orientation...

	globalReplicationPool  = once.NewSingleton[ReplicationPool]()
	globalReplicationStats atomic.Pointer[ReplicationStats]
)

// ReplicationPool describes replication pool
type ReplicationPool struct {
	// atomic ops:
	activeWorkers    int32
	activeLrgWorkers int32
	activeMRFWorkers int32

	objLayer    ObjectLayer
	ctx         context.Context
	priority    string
	maxWorkers  int
	maxLWorkers int

- **Cluster Ops**